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gtil_influx.py | jirisrba/gtil2-wifi | 1 | 122518 | <filename>gtil_influx.py
#!/usr/bin/env python3
""" read data from GTIL2 Wifi module
and send it to influxdb server
"""
#
# crontab
#
# */5 * * * * /usr/bin/python3 /home/pi/gtil_influx.py
#
import requests
from requests.auth import HTTPBasicAuth
from influxdb import InfluxDBClient
# GTIL invertor WiFi IP
gtil_ip = '192.168.2.205'
# influx db config
db = InfluxDBClient('192.168.2.4', 8089, use_udp=True, udp_port=8089, database='malina')
measurement = 'gtil2'
gtil_data = dict()
def read_gtil_data():
""" read data from GTIL2 Wifi module """
response = requests.get(
'http://' + gtil_ip + '/status.html',
auth=HTTPBasicAuth('admin', 'admin'))
for line in response.text.splitlines():
# var webdata_sn = "1905100904";
if line.startswith('var webdata'):
key, value = line[12:-1].split("=")
gtil_data[key.strip()] = value.strip().replace('"', '')
# print(gtil_data)
return gtil_data
def send_data_to_influx():
""" send data to influxdb """
json_body = {
"tags": {
"sn": gtil_data['sn'],
},
"points": [{
"measurement": measurement,
"fields": {
"current_power": int(gtil_data['now_p']),
"today_energy": float(gtil_data['today_e']),
"total_energy": float(gtil_data['total_e'])
}
}]
}
db.send_packet(json_body)
def main():
"""Main()"""
read_gtil_data()
send_data_to_influx()
if __name__ == "__main__":
main()
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miping/training/noGloveValueError.py | mclgoerg/MiningPersonalityInGerman | 1 | 161830 | <gh_stars>1-10
class NoGloveValueError(Exception):
"""
Raised if no value can be found in GloVe for a user's text.
"""
pass
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cir_project/cirp_user/scripts/camera_user.py | sprkrd/UPC-MAI-CIR | 1 | 32631 | #!/usr/bin/env python
import rospy
from std_msgs.msg import String
from peyetribe import EyeTribe
import time
from cir_user.srv import UserAction, UserActionResponse
TH1 = 1280*(1.0/3)
TH2 = 1280*(2.0/3)
IP = "192.168.101.72"
class CameraUserServer:
def __init__(self):
rospy.init_node("talker")
rospy.Service("poll_action", UserAction, self._action_cb)
self._next_action = None
# self._tracker = EyeTribe()
# self._tracker.connect(IP)
# self._tracker.pullmode()
def _action_cb(self, req):
rate = rospy.Rate(10)
action = self._next_action
while action is None:
rate.sleep()
action = self._next_action
self._next_action = None
return UserActionResponse(action=action)
def set_next_action(self):
inp = raw_input("Action: ")
self._next_action = inp
# inp = raw_input("Press p or d: ").strip()
# while inp not in ("p", "d"):
# inp = raw_input("Unknown action {}. Press p or d: ".format(inp)).strip()
# data_str = str(self._tracker.next()._avg)
# x_coord = float(data_str.split(";")[0])
# rospy.loginfo("x_coord=" + str(x_coord))
# if x_coord < TH1:
# direction = "L"
# elif x_coord < TH2:
# direction = "M"
# else:
# direction = "R"
# if inp == "p": # Pick action
# rospy.loginfo("Pick Action in coordinate:")
# action = "pick" + direction
# elif inp == "d":
# rospy.loginfo("Drop Action in coordinate:")
# action = "put" + direction
# if action == "putM": # Invalid action
# action = ""
# self._next_action = action
def shutdown(self):
self._tracker.close()
if __name__ == '__main__':
try:
user = CameraUserServer()
while True:
user.set_next_action()
except rospy.ROSInterruptException:
pass
finally:
print "Shutting down server..."
user.shutdown()
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Scripts/Maperipy/CreateOruxMapLite.py | cojalvo/Map | 2 | 129462 | <reponame>cojalvo/Map
import os, os.path, datetime, string, errno
from maperipy import *
import GenIsraelHikingTilesLite
# http://stackoverflow.com/questions/749711/how-to-get-the-python-exe-location-programmatically
MaperitiveDir = os.path.dirname(os.path.dirname(os.path.normpath(os.__file__)))
# App.log('MaperitiveDir: ' + MaperitiveDir)
ProgramFiles = os.path.normpath(os.path.dirname(MaperitiveDir))
# App.log('ProgramFiles: ' + ProgramFiles)
IsraelHikingDir = os.path.dirname(os.path.dirname(os.path.normpath(App.script_dir)))
# App.log('App.script_dir: ' + App.script_dir)
# App.log('IsraelHikingDir: ' + IsraelHikingDir)
App.run_command('change-dir dir="' + IsraelHikingDir +'"')
os.chdir(IsraelHikingDir)
# # Keep the name of the Tile Upload command
# upload_tiles = os.path.join(IsraelHikingDir, "Scripts", "Batch", "UploadTiles.bat")
def mkdir_p(path):
try:
os.makedirs(path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else: raise
def add_to_PATH(app_dir):
full_app_dir=os.path.join(ProgramFiles, app_dir)
for path_dir in (string.split(os.environ["PATH"], os.pathsep)):
if os.path.basename(path_dir) == app_dir:
# Application already found in PATH
return
if not os.path.isdir(full_app_dir):
# Application not a sibling of Maperitive
App.log("Warning: " + app_dir + " location not found. Could not add it to PATH.")
return
os.environ["PATH"] = string.join([os.environ["PATH"],full_app_dir], os.pathsep)
# Keep batch windows open up to 24 hours
os.environ["NOPAUSE"] = "TIMEOUT /T 86400"
# os.rmdir(os.path.join(IsraelHikingDir, 'Site', 'Tiles_Lite' ))
gen_cmd = GenIsraelHikingTilesLite.IsraelHikingTileGenCommand()
App.run_command("run-script file=" + os.path.join("Scripts", "Maperitive", "IsraelHikingLite.mscript"))
# Map Created
#Original# App.run_command("generate-tiles minzoom=7 maxzoom=15 subpixel=3 tilesdir=" + IsraelHikingDir + "\Site\Tiles use-fprint=true")
gen_cmd.GenToDirectory(16, 16, os.path.join(IsraelHikingDir, 'Site', 'Tiles_Lite'))
App.collect_garbage()
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] |
concat_csv.py | jweckman/vim | 0 | 22607 | import pandas as pd
from pathlib import Path
import sys
''' Concatenates all csv files in the folder passed to stdin '''
path = Path(sys.argv[1])
def get_csv_paths(path):
return [p for p in path.iterdir() if p.suffix == '.csv']
def ask_details():
print('Please specify the following:')
encoding = input('Encoding\n')
delimiter = input('Delimiter\n')
return encoding, delimiter
def get_frames(files_list, encoding, delimiter):
return [pd.read_csv(p, sep=delimiter, dtype='str', encoding=encoding) for p in csv_files]
def concat_output(frames):
output = pd.DataFrame()
for df in frames:
output = pd.concat([output,df])
path_out = path / 'COMBINED.csv'
output.to_csv(path_out, sep=';', index=False)
if __name__ == '__main__':
csv_files = get_csv_paths(path)
encoding, delimiter = ask_details()
try:
frames = get_frames(csv_files, encoding, delimiter)
concat_output(frames)
except Exception as e:
print('Seems like there were files that could not be read\n')
print(str(e))
encoding, delimiter = ask_details()
frames = get_frames(csv_files, encoding, delimiter)
concat_output(frames)
| [
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] |
tests/test_fasttext_embeddings.py | CLARIN-PL/embeddings | 33 | 156341 | <gh_stars>10-100
from typing import Type
import pytest
import torch
from flair.data import Sentence
from flair.embeddings import FastTextEmbeddings, StackedEmbeddings
from torch.testing import assert_close as assert_close
from embeddings.embedding.flair_embedding import FlairDocumentPoolEmbedding
from embeddings.embedding.static.embedding import (
AutoStaticDocumentEmbedding,
AutoStaticWordEmbedding,
)
from embeddings.embedding.static.fasttext import KGR10FastTextConfig, KGR10FastTextEmbedding
from embeddings.utils.utils import import_from_string
def test_fasttext_embeddings_close(dummy_fasttext_config: KGR10FastTextConfig) -> None:
config = dummy_fasttext_config
cls: Type[KGR10FastTextEmbedding] = import_from_string(config.model_type_reference)
embedding = cls(config)
assert_close_embedding_word(embedding)
def test_krg10_fasttext_default_config() -> None:
config = KGR10FastTextConfig()
assert config.model_name == "kgr10.plain.skipgram.dim300.neg10.bin"
def test_init_kgr10_fasttext_from_config(dummy_fasttext_config: KGR10FastTextConfig) -> None:
config = dummy_fasttext_config
embedding = KGR10FastTextEmbedding.from_config(config)
assert_close_embedding_word(embedding)
def test_static_automodel_word_fast_text(dummy_fasttext_config: KGR10FastTextConfig) -> None:
config = dummy_fasttext_config
embedding = AutoStaticWordEmbedding.from_config(config=config)
assert isinstance(embedding, KGR10FastTextEmbedding)
assert_close_embedding_word(embedding)
@pytest.fixture
def automodel_document_fast_text(
dummy_fasttext_config: KGR10FastTextConfig,
) -> FlairDocumentPoolEmbedding:
config = dummy_fasttext_config
return AutoStaticDocumentEmbedding.from_config(config=config)
def test_static_automodel_document_fast_text_structure(
automodel_document_fast_text: KGR10FastTextEmbedding,
) -> None:
embedding = automodel_document_fast_text
assert isinstance(embedding, FlairDocumentPoolEmbedding)
assert isinstance(embedding.word_embedding, KGR10FastTextEmbedding)
assert isinstance(embedding.model.embeddings, StackedEmbeddings)
assert len(embedding.model.embeddings.embeddings) == 1
assert isinstance(embedding.model.embeddings.embeddings[0], FastTextEmbeddings)
def test_static_automodel_document_fast_text_close(
automodel_document_fast_text: KGR10FastTextEmbedding,
) -> None:
embedding = automodel_document_fast_text
assert_close_embedding_word(embedding, assert_doc_embedding_empty=False)
assert_close_embedding_document(embedding)
def assert_close_embedding_word(
embedding: KGR10FastTextEmbedding, assert_doc_embedding_empty: bool = True
) -> None:
sentence = Sentence("Polska należy do Unii Europejskiej.")
embedding.embed([sentence])
assert all(token.embedding.shape == (100,) for token in sentence)
tokens_embeddings = torch.stack([token.embedding for token in sentence])
assert_close(
tokens_embeddings[:, :5].clone(),
torch.Tensor(
[
[-1.5600e-03, 7.7694e-03, 4.0358e-03, -3.9865e-03, 1.0068e-02],
[1.9560e-04, 5.9088e-03, 4.6389e-03, -4.8860e-04, 5.7465e-03],
[1.5331e-03, 6.9973e-03, -1.2511e-03, -6.6496e-03, 1.7792e-03],
[-5.3175e-04, 6.8117e-04, 4.8832e-05, -1.9572e-04, 6.4091e-04],
[-9.3253e-04, 2.3494e-03, 1.8994e-03, -1.3316e-03, 1.6869e-03],
[9.7278e-03, -1.8942e-03, 6.4082e-03, -4.4753e-03, 1.2305e-02],
]
),
atol=1e-6,
rtol=0,
)
torch.testing.assert_close(
tokens_embeddings.mean(dim=1),
torch.Tensor([7.9601e-04, 7.0972e-04, 4.8373e-05, 2.1889e-05, 2.7810e-04, 9.9517e-04]),
atol=1e-6,
rtol=0,
)
torch.testing.assert_close(
tokens_embeddings.std(dim=1),
torch.Tensor([0.0045, 0.0029, 0.0036, 0.0019, 0.0020, 0.0058]),
atol=1e-4,
rtol=0,
)
if assert_doc_embedding_empty:
assert sentence.embedding.shape == (0,)
def assert_close_embedding_document(embedding: KGR10FastTextEmbedding) -> None:
sentence = Sentence("Polska należy do Unii Europejskiej.")
embedding.embed([sentence])
document_embedding = sentence.embedding
assert document_embedding.shape == (100,)
assert_close(
document_embedding[:5].clone(),
torch.Tensor([0.0014, 0.0036, 0.0026, -0.0029, 0.0054]),
atol=1e-4,
rtol=0,
)
torch.testing.assert_close(
document_embedding.mean().item(),
0.0005,
atol=1e-4,
rtol=0,
)
torch.testing.assert_close(
document_embedding.std().item(),
0.0023,
atol=1e-4,
rtol=0,
)
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] |
validation.py | QCMM/dynqmprop | 0 | 97035 | <gh_stars>0
import sys
from dynqmprop import DynQMProp
top_file = sys.argv[1]
coords_file = sys.argv[2]
def main():
# creating objects for validation
# standard parameters:
# radius: 10
# sampling: 25
# n_charge_updates: 3
radius_param = DynQMProp(top_file, coords_file, qm_charge=+1, ligand_selection=':2', receptor_selection=':1')
# sampling_param = DynQMProp(top_file, coords_file, qm_charge=+1, ligand_selection=':2', receptor_selection=':1', radius=12, n_charge_updates=1)
# n_charge_updates_param = DynQMProp(top_file, coords_file, qm_charge=0, ligand_selection=f':1', radius=10, n_charge_updates=2, sampling_time=1, total_qm_calculations=5)
# basis_param = DynQMProp(top_file, coords_file, qm_charge=0, ligand_selection=f':1', radius=10, n_charge_updates=2, sampling_time=1, total_qm_calculations=5, method='HF')
# parameter validation run
radius_param.validation('radius', [12, 14], overwrite=True, compl=True)
# sampling_param.validation('sampling_time', [15, 20, 25, 30, 35, 40], overwrite=True)
# n_charge_updates_param.validation(
# 'n_charge_updates', [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], overwrite=True)
# basis_param.validation(
# 'basis', ['def2-SVP', 'def2-TZVP', 'cc-pVDZ', 'cc-pVTZ'], overwrite=True)
if __name__ == '__main__':
main()
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dev/tasks/run.py | MasonData/dev | 12 | 1611918 | <gh_stars>10-100
from typing import Any, Optional
from schema import Optional as SchemaOptional
from schema import Or, Schema
from dev.exceptions import NonZeroReturnCodeError
from dev.helpers import run_command
from dev.task import Task
class Run(Task):
__schema__ = Schema(
Or(
# - run: date
str,
# - run:
# - date
# - date
[str],
# - run:
# command: echo $SCOPE
# env:
# SCOPE: something
{
'command': Or(str, [str]),
SchemaOptional('env'): {str: str},
SchemaOptional('if'): str,
},
)
)
__description__ = 'Run shell args'
def up(self, args: Optional[Any], extra_args: Optional[Any]) -> None:
if not args:
return
if isinstance(args, dict):
env = args.get('env')
if_command = args.get('if')
commands_from_args = args['command']
else:
env = None
if_command = None
commands_from_args = args
if isinstance(commands_from_args, str):
commands = [commands_from_args]
else:
commands = commands_from_args
if if_command:
try:
run_command(if_command, silent=True)
except NonZeroReturnCodeError:
... # If test failed - run command
else:
return # If test succeeded - do not run command
for command in commands:
full_command: str = command
if extra_args:
full_command += " " + " ".join(extra_args)
run_command(full_command, env=env)
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] |
tinyforum/models.py | matthiask/django-tinyforum | 0 | 52883 | from ckeditor.fields import RichTextField
from django.conf import settings
from django.db import models
from django.urls import reverse
from django.utils import timezone
from django.utils.html import strip_tags
from django.utils.text import Truncator
from django.utils.translation import gettext_lazy as _
from html_sanitizer.django import get_sanitizer
class BaseQuerySet(models.QuerySet):
def visible(self):
return self.exclude(moderation_status=self.model.HIDDEN)
class BaseModel(models.Model):
GOOD = "good"
FLAGGED = "flagged"
HIDDEN = "hidden"
MODERATION_STATUS_CHOICES = (
(GOOD, _("good")),
(FLAGGED, _("flagged")),
(HIDDEN, _("hidden")),
)
MODERATION_ACTION_CHOICES = (
(GOOD, _("approve content")),
(HIDDEN, _("hide content")),
)
created_at = models.DateTimeField(_("created at"), default=timezone.now)
authored_by = models.ForeignKey(
settings.AUTH_USER_MODEL,
on_delete=models.CASCADE,
verbose_name=_("authored by"),
)
moderation_status = models.CharField(
_("moderation status"),
max_length=10,
choices=MODERATION_STATUS_CHOICES,
default=GOOD,
)
objects = BaseQuerySet.as_manager()
class Meta:
abstract = True
class ThreadQuerySet(BaseQuerySet):
def active(self):
return self.visible().filter(closed_at__isnull=True)
def closed(self):
return self.visible().filter(closed_at__isnull=False)
class Thread(BaseModel):
title = models.CharField(_("title"), max_length=200)
is_pinned = models.BooleanField(
_("is pinned"),
default=False,
help_text=_("Pinned threads are shown at the top of the thread list."),
)
closed_at = models.DateTimeField(_("closed at"), blank=True, null=True)
latest_post = models.OneToOneField(
"Post",
on_delete=models.SET_NULL,
related_name="+",
blank=True,
null=True,
verbose_name=_("latest post"),
)
post_count = models.IntegerField(_("post count"), default=0)
starred_by = models.ManyToManyField(
settings.AUTH_USER_MODEL,
blank=True,
related_name="starred_threads",
verbose_name=_("starred by"),
)
objects = ThreadQuerySet.as_manager()
class Meta:
ordering = ["-is_pinned", "-latest_post__created_at", "-created_at"]
verbose_name = _("thread")
verbose_name_plural = _("threads")
def __str__(self):
return self.title
def get_absolute_url(self):
if self.moderation_status == self.HIDDEN:
return reverse("tinyforum:thread-list")
return reverse("tinyforum:thread-detail", kwargs={"pk": self.pk})
def save(self, *args, **kwargs):
if self.pk:
self.post_count = self.posts.visible().count()
self.latest_post = self.posts.visible().last()
super().save(*args, **kwargs)
save.alters_data = True
class Post(BaseModel):
thread = models.ForeignKey(
Thread, on_delete=models.CASCADE, verbose_name=_("thread"), related_name="posts"
)
text = RichTextField(_("text"), config_name="tinyforum-post")
class Meta:
ordering = ["created_at"]
verbose_name = _("post")
verbose_name_plural = _("post")
def __str__(self):
return Truncator(strip_tags(self.text)).words(20, truncate="...")
def save(self, *args, **kwargs):
self.text = get_sanitizer("tinyforum-post").sanitize(self.text)
super().save(*args, **kwargs)
self.thread.save()
save.alters_data = True
class Report(BaseModel):
REASON_CHOICES = (
("annoying", _("It's annoying or not interesting")),
("misplaced", _("I think it shouldn't be here")),
("spam", _("It's spam")),
)
reason = models.CharField(_("reason"), max_length=10, choices=REASON_CHOICES)
notes = models.TextField(
_("notes"), blank=True, help_text=_("Anything else you want to say?")
)
handled_at = models.DateTimeField(_("handled at"), blank=True, null=True)
handled_by = models.ForeignKey(
settings.AUTH_USER_MODEL,
on_delete=models.PROTECT,
blank=True,
null=True,
verbose_name=_("handled by"),
related_name="+",
)
# Override BaseModel.moderation_status with a blank=True version
moderation_status = models.CharField(
_("moderation status"),
max_length=10,
choices=BaseModel.MODERATION_STATUS_CHOICES,
default=BaseModel.FLAGGED,
)
class Meta:
abstract = True
class PostReport(Report):
post = models.ForeignKey(
Post, on_delete=models.CASCADE, related_name="reports", verbose_name=_("post")
)
class Meta:
unique_together = (("authored_by", "post"),)
verbose_name = _("post report")
verbose_name_plural = _("post reports")
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tests/test_basic.py | WSULib/combine | 24 | 24382 | <reponame>WSULib/combine
import django
import os
import pytest
import shutil
import sys
import time
import uuid
# logging
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
# init django settings file to retrieve settings
os.environ['DJANGO_SETTINGS_MODULE'] = 'combine.settings'
sys.path.append('/opt/combine')
django.setup()
# import core
from core.models import Organization, RecordGroup, CombineJob, HarvestStaticXMLJob, QueryDict, Transformation,\
TransformJob, ValidationScenario, MergeJob
#############################################################################
# Tests Setup
#############################################################################
@pytest.mark.run(order=1)
def test_organization_create(VO):
'''
Test creation of organization
'''
# instantiate and save
VO.org = Organization(
name='test_org_%s' % uuid.uuid4().hex,
description=''
)
VO.org.save()
assert type(VO.org.id) == int
@pytest.mark.run(order=2)
def test_record_group_create(VO):
'''
Test creation of record group
'''
# instantiate and save
VO.rg = RecordGroup(
organization=VO.org,
name='test_record_group_%s' % uuid.uuid4().hex,
description=''
)
VO.rg.save()
assert type(VO.rg.id) == int
#############################################################################
# Test Harvest
#############################################################################
@pytest.mark.run(order=3)
def test_static_harvest(VO):
'''
Test static harvest of XML records from disk
'''
# copy test data to /tmp
payload_dir = '/tmp/%s' % uuid.uuid4().hex
shutil.copytree('/opt/combine/tests/data/static_harvest_data', payload_dir)
# emulate request.POST
request_dict = {
'dbdd': '',
'job_note': '',
'xpath_record_id': '',
'static_filepath': payload_dir,
'fm_config_json': '{"add_literals":{},"capture_attribute_values":[],"concat_values_on_all_fields":false,"concat_values_on_fields":{},"copy_to":{},"copy_to_regex":{},"copy_value_to_regex":{},"error_on_delims_collision":false,"exclude_attributes":[],"exclude_elements":[],"include_all_attributes":false,"include_attributes":[],"include_sibling_id":false,"multivalue_delim":"|","node_delim":"_","ns_prefix_delim":"|","remove_copied_key":true,"remove_copied_value":false,"remove_ns_prefix":true,"repeating_element_suffix_count":false,"self_describing":false,"skip_attribute_ns_declarations":true,"skip_repeating_values":true,"skip_root":false,"split_values_on_all_fields":false,"split_values_on_fields":{}}',
'static_payload': '',
'job_name': '',
'field_mapper': 'default',
'rits': '',
'additional_namespace_decs': 'xmlns:mods="http://www.loc.gov/mods/v3"',
'document_element_root': 'mods:mods'
}
query_dict = QueryDict('', mutable=True)
query_dict.update(request_dict)
# init job, using Variable Object (VO)
cjob = CombineJob.init_combine_job(
user=VO.user,
record_group=VO.rg,
job_type_class=HarvestStaticXMLJob,
job_params=query_dict,
files={},
hash_payload_filename=False
)
# start job and update status
job_status = cjob.start_job()
# if job_status is absent, report job status as failed
if job_status == False:
cjob.job.status = 'failed'
cjob.job.save()
# poll until complete
for x in range(0, 480):
# pause
time.sleep(1)
# refresh session
cjob.job.update_status()
# check status
if cjob.job.status != 'available':
continue
else:
break
# save static harvest job to VO
VO.static_harvest_cjob = cjob
# remove payload_dir
shutil.rmtree(payload_dir)
# assert job is done and available via livy
assert VO.static_harvest_cjob.job.status == 'available'
# assert record count is 250
assert VO.static_harvest_cjob.job.record_count == 250
# assert no indexing failures
assert len(VO.static_harvest_cjob.get_indexing_failures()) == 0
# #############################################################################
# # Test Transform
# #############################################################################
def prepare_transform():
'''
Create temporary transformation scenario based on tests/data/mods_transform.xsl
'''
with open('tests/data/mods_transform.xsl', 'r') as f:
xsl_string = f.read()
trans = Transformation(
name='temp_mods_transformation',
payload=xsl_string,
transformation_type='xslt',
filepath='will_be_updated'
)
trans.save()
# return transformation
return trans
@pytest.mark.run(order=4)
def test_static_transform(VO):
'''
Test static harvest of XML records from disk
'''
# prepare and capture temporary transformation scenario
VO.transformation_scenario = prepare_transform()
# emulate request.POST
request_dict = {
'dbdd': '',
'field_mapper': 'default',
'filter_dupe_record_ids': 'true',
'fm_config_json': '{"add_literals":{},"capture_attribute_values":[],"concat_values_on_all_fields":false,"concat_values_on_fields":{},"copy_to":{},"copy_to_regex":{},"copy_value_to_regex":{},"error_on_delims_collision":false,"exclude_attributes":[],"exclude_elements":[],"include_all_attributes":false,"include_attributes":[],"include_sibling_id":false,"multivalue_delim":"|","node_delim":"_","ns_prefix_delim":"|","remove_copied_key":true,"remove_copied_value":false,"remove_ns_prefix":true,"repeating_element_suffix_count":false,"self_describing":false,"skip_attribute_ns_declarations":true,"skip_repeating_values":true,"skip_root":false,"split_values_on_all_fields":false,"split_values_on_fields":{}}',
'input_es_query_valve': '',
'input_job_id': VO.static_harvest_cjob.job.id,
'input_numerical_valve': '',
'input_validity_valve': 'all',
'job_name': '',
'job_note': '',
'rits': '',
'sel_trans_json': '[{"index":0,"trans_id":%s}]' % VO.transformation_scenario.id
}
query_dict = QueryDict('', mutable=True)
query_dict.update(request_dict)
# init job
cjob = CombineJob.init_combine_job(
user=VO.user,
record_group=VO.rg,
job_type_class=TransformJob,
job_params=query_dict)
# start job and update status
job_status = cjob.start_job()
# if job_status is absent, report job status as failed
if job_status == False:
cjob.job.status = 'failed'
cjob.job.save()
# poll until complete
for x in range(0, 480):
# pause
time.sleep(1)
# refresh session
cjob.job.update_status()
# check status
if cjob.job.status != 'available':
continue
else:
break
# save static harvest job to VO
VO.static_transform_cjob = cjob
# assert job is done and available via livy
assert VO.static_transform_cjob.job.status == 'available'
# assert record count is 250
assert VO.static_transform_cjob.job.record_count == 250
# assert no indexing failures
assert len(VO.static_transform_cjob.get_indexing_failures()) == 0
# remove transformation
assert VO.transformation_scenario.delete()[0] > 0
# #############################################################################
# # Test Validation Scenarios
# #############################################################################
@pytest.mark.run(order=5)
def test_add_schematron_validation_scenario(VO):
'''
Add schematron validation
'''
# get schematron validation from test data
with open('tests/data/schematron_validation.sch', 'r') as f:
sch_payload = f.read()
# init new validation scenario
schematron_validation_scenario = ValidationScenario(
name='temp_vs_%s' % str(uuid.uuid4()),
payload=sch_payload,
validation_type='sch',
default_run=False
)
schematron_validation_scenario.save()
# pin to VO
VO.schematron_validation_scenario = schematron_validation_scenario
# assert creation
assert type(VO.schematron_validation_scenario.id) == int
@pytest.mark.run(order=6)
def test_add_python_validation_scenario(VO):
'''
Add python code snippet validation
'''
# get python validation from test data
with open('tests/data/python_validation.py', 'r') as f:
py_payload = f.read()
# init new validation scenario
python_validation_scenario = ValidationScenario(
name='temp_vs_%s' % str(uuid.uuid4()),
payload=py_payload,
validation_type='python',
default_run=False
)
python_validation_scenario.save()
# pin to VO
VO.python_validation_scenario = python_validation_scenario
# assert creation
assert type(VO.python_validation_scenario.id) == int
@pytest.mark.run(order=7)
def test_schematron_validation(VO):
# get target records
VO.harvest_record = VO.static_harvest_cjob.job.get_records().first()
VO.transform_record = VO.static_transform_cjob.job.get_records().first()
# validate harvest record with schematron
'''
expecting failure count of 2
'''
vs_results = VO.schematron_validation_scenario.validate_record(VO.harvest_record)
assert vs_results['parsed']['fail_count'] == 2
# validate transform record with schematron
'''
expecting failure count of 1
'''
vs_results = VO.schematron_validation_scenario.validate_record(VO.transform_record)
assert vs_results['parsed']['fail_count'] == 1
@pytest.mark.run(order=8)
def test_python_validation(VO):
# validate harvest record with python
'''
expecting failure count of 1
'''
vs_results = VO.python_validation_scenario.validate_record(VO.harvest_record)
print(vs_results)
assert vs_results['parsed']['fail_count'] == 1
# validate transform record with python
'''
expecting failure count of 1
'''
vs_results = VO.python_validation_scenario.validate_record(VO.transform_record)
print(vs_results)
assert vs_results['parsed']['fail_count'] == 1
# #############################################################################
# # Test Duplicate/Merge Job
# #############################################################################
@pytest.mark.run(order=9)
def test_merge_duplicate(VO):
'''
Duplicate Transform job, applying newly created validation scenarios
'''
# emulate request.POST
request_dict = {
'dbdd': '',
'field_mapper': 'default',
'filter_dupe_record_ids': 'true',
'fm_config_json': '{"add_literals":{},"capture_attribute_values":[],"concat_values_on_all_fields":false,"concat_values_on_fields":{},"copy_to":{},"copy_to_regex":{},"copy_value_to_regex":{},"error_on_delims_collision":false,"exclude_attributes":[],"exclude_elements":[],"include_all_attributes":false,"include_attributes":[],"include_sibling_id":false,"multivalue_delim":"|","node_delim":"_","ns_prefix_delim":"|","remove_copied_key":true,"remove_copied_value":false,"remove_ns_prefix":true,"repeating_element_suffix_count":false,"self_describing":false,"skip_attribute_ns_declarations":true,"skip_repeating_values":true,"skip_root":false,"split_values_on_all_fields":false,"split_values_on_fields":{}}',
'input_es_query_valve': '',
'input_numerical_valve': '',
'input_validity_valve': 'all',
'job_name': '',
'job_note': '',
'rits': ''
}
query_dict = QueryDict('', mutable=True)
query_dict.update(request_dict)
# set input jobs with QueryDict.setlist
query_dict.setlist('input_job_id', [
VO.static_harvest_cjob.job.id,
VO.static_transform_cjob.job.id
])
# set validation scenarios with QueryDict.setlist
query_dict.setlist('validation_scenario', [
VO.schematron_validation_scenario.id,
VO.python_validation_scenario.id
])
# init job
cjob = CombineJob.init_combine_job(
user=VO.user,
record_group=VO.rg,
job_type_class=MergeJob,
job_params=query_dict)
# start job and update status
job_status = cjob.start_job()
# if job_status is absent, report job status as failed
if job_status == False:
cjob.job.status = 'failed'
cjob.job.save()
# poll until complete
for x in range(0, 480):
# pause
time.sleep(1)
# refresh session
cjob.job.update_status()
# check status
if cjob.job.status != 'available':
continue
else:
break
# save static harvest job to VO
VO.merge_cjob = cjob
# assert job is done and available via livy
assert VO.merge_cjob.job.status == 'available'
# assert record count is 250
assert VO.merge_cjob.job.record_count == 250
# assert validation scenarios applied
job_validation_scenarios = VO.merge_cjob.job.jobvalidation_set.all()
assert job_validation_scenarios.count() == 2
# loop through validation scenarios and confirm that both show 250 failures
for jv in job_validation_scenarios:
assert jv.get_record_validation_failures().count() == 232
# assert no indexing failures
assert len(VO.merge_cjob.get_indexing_failures()) == 0
#############################################################################
# Tests Teardown
#############################################################################
@pytest.mark.last
def test_teardown(keep_records, VO):
'''
Test teardown
'''
# assert delete of org and children
if not keep_records:
assert VO.org.delete()[0] > 0
else:
assert True
assert VO.schematron_validation_scenario.delete()[0] > 0
assert VO.python_validation_scenario.delete()[0] > 0
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] |
gcp-cli/client/util/util_dpm.py | lacework/csp-integrations | 1 | 48359 | <filename>gcp-cli/client/util/util_dpm.py
from __future__ import print_function
from __future__ import absolute_import
from builtins import input
from builtins import str
import logging
from .util_base import UtilBase
from . import util_template
HTTP_GET_METHOD = "GET"
HTTP_POST_METHOD = "POST"
HTTP_DELETE_METHOD = "DELETE"
SERVICE_ACCOUNT_ID = "lacework-cfg-sa"
GET_DEPLOYMENT_URL= "https://www.googleapis.com/deploymentmanager/v2/projects/%projectId/global/deployments/%deployment"
GET_DEPLOYMENTS_URL= "https://www.googleapis.com/deploymentmanager/v2/projects/%projectId/global/deployments"
from client.config.client_helper import ClientHelper
import time
SLEEP_SECS = 20
class UtilDpm(UtilBase):
def __init__(self):
UtilBase.__init__(self)
def checkIfDeploymentExists(self, projectId, deployment):
isinstance(self.getHttpClient(), ClientHelper)
url = GET_DEPLOYMENT_URL.replace("%deployment", deployment)
deployment = self.getHttpClient().make_request(HTTP_GET_METHOD, url, projectId, None)
if deployment['isError']:
if deployment['defaultErrorObject'].get("status") == 404:
return False
else:
raise Exception("Error fetching deployment: \n" + str(deployment['defaultErrorObject']))
return True
def getDeploymentDetails(self, projectId, deployment):
isinstance(self.getHttpClient(), ClientHelper)
url = GET_DEPLOYMENT_URL.replace("%deployment", deployment)
deployment = self.getHttpClient().make_request(HTTP_GET_METHOD, url, projectId, None)
if deployment['isError']:
raise Exception("Error fetching deployment: \n" + str(deployment['defaultErrorObject']))
return deployment["data"]
def deploymentWait(self, setupProjectId, deploymentName):
while (True):
deployment = self.getDeploymentDetails(setupProjectId, deploymentName)
if (deployment["operation"]["status"] == "DONE" and "error" in deployment["operation"]):
logging.warning("Deployment has failed, please run again and check configuration")
logging.warning("Rolling back and deleting failed deployment")
self.deleteDeployment(setupProjectId, deploymentName)
logging.error(str(deployment["operation"]["error"]))
return None
elif (deployment["operation"]["status"] == "RUNNING"):
if (deployment["operation"]["operationType"] == "insert"):
logging.info("Deployment is running! Sleeping for " + str(SLEEP_SECS) + " seconds before checking again")
print("Deployment is running! Sleeping for " + str(SLEEP_SECS) + " seconds before checking again")
time.sleep(SLEEP_SECS)
else:
raise Exception("The deployment with name " + deploymentName + " is being used or being deleted, please wait until all operations are done!")
else:
logging.info("Deployment has been created and successfully deployed!")
return self.getDeploymentOutput(deployment["manifest"])
def getDeploymentOutput(self, url):
isinstance(self.getHttpClient(), ClientHelper)
manifest = self.getHttpClient().make_request(HTTP_GET_METHOD, url, None, None)
if manifest['isError']:
raise Exception("Error fetching deployment: \n" + str(manifest["defaultErrorObject"]))
return manifest["data"]["layout"]
def deleteDeployment(self, projectId, deploymentName):
isinstance(self.getHttpClient(), ClientHelper)
logging.info("Deleting deployment with name: " + deploymentName)
url = GET_DEPLOYMENT_URL.replace("%deployment", deploymentName)
deployment = self.getHttpClient().make_request(HTTP_DELETE_METHOD, url, projectId, None)
if deployment['isError']:
raise Exception("Error deleting deployment: \n" + str(deployment['defaultErrorObject']))
logging.info("Deployment with name " + deploymentName + " has been deleted")
return deployment['data']
def createDeployment(self, config, deploymentName):
logging.info("Creating the request body to be sent to the deployment manager")
body = self.buildDeploymentBody(config, deploymentName)
projectId = config.getSetupProjectId()
isinstance(self.getHttpClient(), ClientHelper)
logging.info("Creating new deployment named " + deploymentName + " in project " + projectId)
deployment = self.getHttpClient().make_request(HTTP_POST_METHOD, GET_DEPLOYMENTS_URL, projectId, body)
if deployment['isError']:
raise Exception("Error creating deployment: \n" + str(deployment['defaultErrorObject']))
return deployment['data']
def buildDeploymentBody(self, config, deploymentName):
setupPrefix = config.getSetupPrefix()
projectId = config.getSetupProjectId()
resources = util_template.SERVICE_ACCOUNT
if (config.isAuditLogSetup()):
if (not config.doesBucketExist()):
resources = resources + util_template.BUCKET_CREATION
resources = resources + util_template.TOPIC_CREATION
resources = resources + util_template.SUBSCRIPTION_CREATION
resources = resources + util_template.NOTIFICATION_CREATION
idType = config.getAuditSetupType()
id = config.getAuditSetupId()
sinkAndBucketBinding = util_template.SINK_CREATION
if not config.doesBucketExist():
sinkAndBucketBinding += util_template.BUCKET_DEPENDENCY
sinkAndBucketBinding += util_template.BUCKET_BINDING
if (idType == "ORGANIZATION"):
sinkAndBucketBinding = sinkAndBucketBinding.replace("%resourceTypeAudit", "organizations")
sinkAndBucketBinding = sinkAndBucketBinding.replace("%resourceTypeSingle", "organization")
elif (idType == "PROJECT"):
sinkAndBucketBinding = sinkAndBucketBinding.replace("%resourceTypeAudit", "projects")
sinkAndBucketBinding = sinkAndBucketBinding.replace("%resourceTypeSingle", "project")
else:
sinkAndBucketBinding = sinkAndBucketBinding.replace("%resourceTypeAudit", "folders")
sinkAndBucketBinding = sinkAndBucketBinding.replace("%resourceTypeSingle", "folder")
sinkAndBucketBinding = sinkAndBucketBinding.replace("%resourceIdAudit", id)
sinkAndBucketBinding = sinkAndBucketBinding.replace("%sinkProp", setupPrefix + "-" + id + "-lacework-sink")
sinkAndBucketBinding = sinkAndBucketBinding.replace("%bucketBindingName", setupPrefix+ "-" + id + "-bucket-binding")
resources = resources + sinkAndBucketBinding
if config.customRole():
resources = resources + util_template.CUSTOM_ROLE
resources = resources + util_template.ASSIGN_ROLE
resources = resources + util_template.VIEWER_BINDING
resources = resources + util_template.SECURITY_REVIEWER_BINDING
if (config.getComplianceSetupType() == "ORGANIZATION"):
resources = resources + util_template.ORG_VIEWER_BINDING
resources = resources + util_template.ACCOUNT_KEY
resources = resources + util_template.OUTPUT
resources = resources + util_template.OUTPUT_KEY
if (config.isAuditLogSetup()):
if config.doesBucketExist():
resources = resources + util_template.OUTPUT_SINK
else:
resources = resources + util_template.OUTPUT_SUBSCRIPTION
id = config.getAuditSetupId()
resources = resources.replace("%sinkName", setupPrefix + "-" + projectId + "-" + id + "-lacework-sink")
if config.customRole():
if (config.getComplianceSetupType() == "ORGANIZATION"):
resources = resources.replace("%customRole", setupPrefix + "-lacework-custom-role-org")
resources = resources.replace("%roleType", "organizations")
resources = resources.replace("%roleId", setupPrefix.replace("-","") + config.getComplianceSetupId() + "laceworkCustomRoleOrg")
resources = resources.replace("%roleParent", "organizations/" + config.getComplianceSetupId())
elif (config.getComplianceSetupType() == "PROJECT"):
resources = resources.replace("%customRole", setupPrefix + "-lacework-custom-role-project")
resources = resources.replace("%roleId", setupPrefix.replace("-","") + projectId.replace("-", "") + "laceworkCustomRoleProject")
resources = resources.replace("%roleType", "projects")
resources = resources.replace("%roleParent", "projects/" + projectId)
resources = resources.replace("%setRoleName", setupPrefix + "-lacework-role-assignment")
resources = resources.replace("%serviceAccountName", setupPrefix + "-" + projectId + "-" + "lacework-serviceaccnt")
resources = resources.replace("%serviceAccountId", setupPrefix + "-" + SERVICE_ACCOUNT_ID)
if config.doesBucketExist():
resources = resources.replace("$(ref.%bucketName.name)", config.getExistingBucketName())
resources = resources.replace("%projectNumber", config.getBucketParent())
else:
resources = resources.replace("%bucketName", setupPrefix + "-" + projectId + "-" + "lacework-bucket")
resources = resources.replace("%projectNumber", config.getSetupProjectNumber())
resources = resources.replace("%projectId", projectId)
resources = resources.replace("%topicName", setupPrefix + "-" + projectId + "-" + "lacework-topic")
resources = resources.replace("%topicProp", setupPrefix + "-" + projectId + "-" + "lacework-topic-prop")
resources = resources.replace("%subscriptionName", setupPrefix + "-" + projectId + "-" + "lacework-subscription")
resources = resources.replace("%subscriptionProp", setupPrefix + "-" + projectId + "-" + "lacework-subscription-prop")
resources = resources.replace("%notificationName", setupPrefix + "-" + projectId + "-" + "lacework-notification")
resources = resources.replace("%setupPrefix", setupPrefix)
if (config.getComplianceSetupType() == "ORGANIZATION"):
resources = resources.replace("%resourceType", "organizations")
resources = resources.replace("%resourceId", "organizations/" + config.getComplianceSetupId())
else:
resources = resources.replace("%resourceType", "projects")
resources = resources.replace("%resourceId", projectId)
print(resources)
print("Please review the above configuration to be deployed")
if config.getToIntegrate():
print("This deployment will be integrated with following lacework account: " + config.getLaceworkAccount())
while (True):
userInput = input("Accept? (Y/N): ")
userInput = userInput.lower()
if (userInput == "n" or userInput == "no"):
raise Exception("Created configuration rejected by user, please check the config.yml")
elif (userInput == "y" or userInput == "yes"):
body = {
"name" : deploymentName,
"target" : {
"config" : {
"content" : resources
}
}
}
return body
else:
print ("Please enter valid input")
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Software_Engineering/ObjectOrientedProgramming/IdeFiles/5_exercise_upload_to_pypi/setup.py | surily/Udacity-Data-Science | 0 | 43665 | <reponame>surily/Udacity-Data-Science
from setuptools import setup
setup(name='gaussian_bionary_dist_prob',
version='0.1',
description='Gaussian and Binomial distributions',
packages=['gaussian_bionary_dist_prob'],
author = '<NAME>',
author_email = '<EMAIL>',
zip_safe=False)
| [
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examples/exp_configs/rl/singleagent/singleagent_ring_attack.py | cwang717/flow | 0 | 69596 | <filename>examples/exp_configs/rl/singleagent/singleagent_ring_attack.py
"""Ring road example.
Trains a single autonomous vehicle to stabilize the flow of 21 human-driven
vehicles in a variable length ring road.
"""
from flow.core.params import SumoParams, EnvParams, InitialConfig, NetParams
from flow.core.params import VehicleParams, SumoCarFollowingParams
from flow.controllers import RLController, IDMController, ContinuousRouter
from flow.envs import WaveAttenuationPOEnv, DisguisedAttackPOEnv
from flow.networks import RingNetwork
# time horizon of a single rollout
HORIZON = 3000
# number of rollouts per training iteration
N_ROLLOUTS = 20
# number of parallel workers
N_CPUS = 2
# We place one autonomous vehicle and 22 human-driven vehicles in the network
vehicles = VehicleParams()
vehicles.add(
veh_id="human",
acceleration_controller=(IDMController, {
"noise": 0.2
}),
car_following_params=SumoCarFollowingParams(
min_gap=0
),
routing_controller=(ContinuousRouter, {}),
num_vehicles=21)
vehicles.add(
veh_id="rl",
acceleration_controller=(RLController, {}),
routing_controller=(ContinuousRouter, {}),
num_vehicles=1)
flow_params = dict(
# name of the experiment
#exp_tag="stabilizing_the_ring",
exp_tag="disguised_vehicle_attacker",
# name of the flow environment the experiment is running on
#env_name=WaveAttenuationPOEnv,
env_name=DisguisedAttackPOEnv,
# name of the network class the experiment is running on
network=RingNetwork,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
sim_step=0.1,
render=False,
restart_instance=False
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=HORIZON,
warmup_steps=750,
clip_actions=False,
additional_params={
"max_accel": 1,
"max_decel": 1,
"ring_length": [220, 270],
},
),
# network-related parameters (see flow.core.params.NetParams and the
# network's documentation or ADDITIONAL_NET_PARAMS component)
net=NetParams(
additional_params={
"length": 260,
"lanes": 1,
"speed_limit": 30,
"resolution": 40,
}, ),
# vehicles to be placed in the network at the start of a rollout (see
# flow.core.params.VehicleParams)
veh=vehicles,
# parameters specifying the positioning of vehicles upon initialization/
# reset (see flow.core.params.InitialConfig)
initial=InitialConfig(),
)
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python/Unimeta/clinica/Trabalho_3/trabalho3.py | alifoliveira/rep-estudos | 0 | 156444 | <reponame>alifoliveira/rep-estudos<filename>python/Unimeta/clinica/Trabalho_3/trabalho3.py
#coding=utf-8
from tkinter import *
from tkinter import messagebox as ms
import sqlite3
cont = 0
cont2 = 2
cadt = None
listr = None
empr = None
loged = None
photo2 = None
logo2 = None
def verify():
global loged
global cont2
global cont
global photo2
global logo2
with sqlite3.connect('registro.db') as db:
c = db.cursor()
find_user = 'SELECT * FROM usuario WHERE login = ? AND senha = ?'
c.execute(find_user,[(entry_user.get()), (entry_password.get())])
result = c.fetchall()
if result:
label_user.destroy()
entry_user.destroy()
label_password.destroy()
entry_password.destroy()
bt_login.destroy()
bt_leave.destroy()
logo.destroy()
main()
photo2 = PhotoImage(file='logo-biblioteca-menu.png')
logo2 = Label(root, image=photo2, bg='#292929')
logo2.pack(padx=20, pady=20)
loged = Label(root, text='Bem Vindo!', font=('', 20, 'bold'), bg='#292929', fg='white', pady=20)
loged.pack(side=BOTTOM)
else:
ms.showerror('ERRO', f'Usuário ou Senha Inválidos. \n\nTentativas restantes: {cont2}')
cont2 -= 1
cont += 1
if cont == 3:
ms.showwarning('Aviso', 'O Sistema será finalizado por questões de segurança.')
root.destroy()
def main():
barra_menu = Menu(root)
main_menu = Menu(barra_menu, tearoff=0)
barra_menu.add_cascade(label='Menu', menu=main_menu)
main_menu.add_command(label='Cadastrar', command=cadastrar)
main_menu.add_command(label='Listar', command=listar)
main_menu.add_command(label='Empréstimo', command=emprestimo)
main_menu.add_separator()
main_menu.add_command(label='Sair', command=root.destroy)
root.config(menu=barra_menu)
def cadastrar():
global cadt
try:
loged.destroy()
logo2.destroy()
cadt.destroy()
listr.destroy()
empr.destroy()
except:
pass
cadt = Frame(root, bg='#292929', width=400, height=300)
cadt.pack(fill=BOTH, expand=1)
bt_autor = Button(cadt, width=10, text='Autor', font=('', 12, 'bold'), fg='white', bg='#5bc30b')
bt_autor.place(x=140, y=60)
bt_cliente = Button(cadt, width=10, text='Cliente', font=('', 12, 'bold'), fg='white', bg='#5bc30b', command=cliente)
bt_cliente.place(x=140, y=100)
bt_livro = Button(cadt, width=10, text='Livro', font=('', 12, 'bold'), fg='white', bg='#5bc30b')
bt_livro.place(x=140, y=140)
bt_usuario = Button(cadt, width=10, text='Usuário', font=('', 12, 'bold'), fg='white', bg='#5bc30b')
bt_usuario.place(x=140, y=180)
def listar():
global listr
try:
loged.destroy()
logo2.destroy()
listr.destroy()
empr.destroy()
cadt.destroy()
except:
pass
listr = Frame(root, bg='#292929', width=400, height=300)
listr.pack(fill=BOTH, expand=1)
bt_autor = Button(listr, width=10, text='Autor', font=('', 12, 'bold'), fg='white', bg='#5bc30b')
bt_autor.place(x=140, y=60)
bt_cliente = Button(listr, width=10, text='Cliente', font=('', 12, 'bold'), fg='white', bg='#5bc30b')
bt_cliente.place(x=140, y=100)
bt_livro = Button(listr, width=10, text='Livro', font=('', 12, 'bold'), fg='white', bg='#5bc30b')
bt_livro.place(x=140, y=140)
bt_usuario = Button(listr, width=10, text='Usuário', font=('', 12, 'bold'), fg='white', bg='#5bc30b')
bt_usuario.place(x=140, y=180)
bt_emprestimo = Button(listr, width=10, text='Empréstimos', font=('', 12, 'bold'), fg='white', bg='#5bc30b')
bt_emprestimo.place(x=140, y=220)
def emprestimo():
global empr
try:
loged.destroy()
logo2.destroy()
empr.destroy()
cadt.destroy()
listr.destroy()
except:
pass
empr = Frame(root, bg='#292929', width=400, height=300)
empr.pack(fill=BOTH, expand=1)
bt_emprestimo = Button(empr, width=18, text='Realizar Empréstimo', font=('', 12, 'bold'), fg='white', bg='#5bc30b')
bt_emprestimo.place(x=110, y=80)
bt_devolucao = Button(empr, width=18, text='Realizar Devolução', font=('', 12, 'bold'), fg='white', bg='#5bc30b')
bt_devolucao.place(x=110, y=120)
def cliente():
db = sqlite3.connect('registro.db')
c = db.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS cliente(
id INTEGER PRIMARY KEY NOT NULL,
nome TEXT NOT NULL,
rua TEXT NOT NULL,
numero INTEGER NOT NULL,
bairro TEXT NOT NULL,
cidade TEXT NOT NULL,
estado TEXT NOT NULL,
data_de_nascimento DATE NOT NULL,
telefone_residencial TEXT,
telefone_celular TEXT NOT NULL,
email TEXT NOT NULL,
cpf TEXT NOT NULL
);''')
window_cliente = Toplevel(root, bg='#292929')
window_cliente.title('Cadastrar Cliente')
lnome = Label(window_cliente, text='Nome', bg='#292929', fg='white', font=('', 12, 'bold'))
lnome.place(x=20, y=20)
nome = Entry(window_cliente, width=45, font=('', 11, 'bold'))
nome.place(x=20, y=50)
lcpf = Label(window_cliente, text='CPF', bg='#292929', fg='white', font=('', 12, 'bold'))
lcpf.place(x=20, y=80)
cpf = Entry(window_cliente, width=12, font=('', 11, 'bold'))
cpf.place(x=20, y=110)
ltelefone = Label(window_cliente, text='Telefone', bg='#292929', fg='white', font=('', 12, 'bold'))
ltelefone.place(x=150, y=80)
telefone = Entry(window_cliente, width=12, font=('', 11, 'bold'))
telefone.place(x=150, y=110)
lcelular = Label(window_cliente, text='Celular', bg='#292929', fg='white', font=('', 12, 'bold'))
lcelular.place(x=283, y=80)
celular = Entry(window_cliente, width=12, font=('', 11, 'bold'))
celular.place(x=283, y=110)
lemail = Label(window_cliente, text='E-mail', bg='#292929', fg='white', font=('', 12, 'bold'))
lemail.place(x=20, y=140)
email = Entry(window_cliente, width=45, font=('', 11, 'bold'))
email.place(x=20, y=170)
lrua = Label(window_cliente, text='Rua', bg='#292929', fg='white', font=('', 12, 'bold'))
lrua.place(x=20, y=200)
rua = Entry(window_cliente, width=45, font=('', 11, 'bold'))
rua.place(x=20, y=230)
lnumero = Label(window_cliente, text='Número', bg='#292929', fg='white', font=('', 12, 'bold'))
lnumero.place(x=300, y=260)
numero = Entry(window_cliente, width=10, font=('', 11, 'bold'))
numero.place(x=300, y=290)
lbairro = Label(window_cliente, text='Bairro', bg='#292929', fg='white', font=('', 12, 'bold'))
lbairro.place(x=20, y=260)
bairro = Entry(window_cliente, width=31, font=('', 11, 'bold'))
bairro.place(x=20, y=290)
lcidade = Label(window_cliente, text='Cidade', bg='#292929', fg='white', font=('', 12, 'bold'))
lcidade.place(x=20, y=320)
cidade = Entry(window_cliente, width=23, font=('', 11, 'bold'))
cidade.place(x=20, y=350)
lestado = Label(window_cliente, text='Estado', bg='#292929', fg='white', font=('', 12, 'bold'))
lestado.place(x=235, y=320)
estado = Entry(window_cliente, width=18, font=('', 11, 'bold'))
estado.place(x=235, y=350)
window_cliente.geometry('400x550+200+200')
db.commit()
db.close()
root = Tk()
photo = PhotoImage(file='logo-biblioteca.png')
logo = Label(root, image=photo, bg='#292929')
logo.pack(pady=20)
label_user = Label(root, text='Usuário:', font=('', 12), fg='gray', bg='#292929')
label_user.place(x=30, y=120)
entry_user = Entry(root, justify=CENTER, font=('', 11, 'bold'), width=26)
entry_user.place(x=100, y=120)
label_password = Label(root, text='Senha:', font=('', 12), fg='gray', bg='#292929')
label_password.place(x=40, y=150)
entry_password = Entry(root, justify=CENTER, font=('', 11, 'bold'), show='*', width=26)
entry_password.place(x=100, y=150)
bt_login = Button(root, width=8, text='LOGIN', font=('', 12, 'bold'), bg='#5bc30b', fg='white', command=verify)
bt_login.place(x=100, y=200)
bt_leave = Button(root, width=8, text='SAIR', font=('', 12, 'bold'), bg='#5bc30b', fg='white', command=root.quit)
bt_leave.place(x=223, y=200)
root.title('Login')
root['bg'] = '#292929'
root.geometry('400x300+600+300')
root.resizable(0, 0)
root.mainloop()
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tests/basic_stages/test_freq_drop.py | DavidKatz-il/pdpipe | 438 | 182234 | """Testing basic pipeline stages."""
import pandas as pd
from pdpipe.basic_stages import FreqDrop
DF1 = pd.DataFrame([[1, 4], [4, 5], [1, 11]], [1, 2, 3], ['a', 'b'])
def test_freqdrop_basic():
"""Testing the FreqDrop pipeline stage."""
res_df = FreqDrop(2, 'a').apply(DF1)
assert 1 in res_df.index
assert 2 not in res_df.index
assert 3 in res_df.index
def test_freqdrop_verbose():
"""Testing the FreqDrop pipeline stage."""
res_df = FreqDrop(2, 'a').apply(DF1, verbose=True)
assert 1 in res_df.index
assert 2 not in res_df.index
assert 3 in res_df.index
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src/clophfit/old/dil_correction.py | darosio/ClopHfit | 0 | 44403 | #!/usr/bin/python
#
import argparse
import os
import pandas as pd
corr = {'pH': [1, 1.02, 1.04, 1.06, 1.08, 1.1, 1.12],
'cl': [1.000000, 1.017857, 1.035714, 1.053571, 1.071429, 1.089286,
1.107143, 1.160714, 1.196429]}
corr = {'pH': [1, 1.02, 1.04, 1.06, 1.08, 1.1],
'cl': [1.000000, 1.017857, 1.035714, 1.053571, 1.071429, 1.089286,
1.107143, 1.160714, 1.196429]}
def main():
description = "make correction for dilution"
parser = argparse.ArgumentParser(description=description)
parser.add_argument('ffile',
help="a file x y1 y2 --> y1*corr y2*corr")
parser.add_argument('out',
help="destination directory")
parser.add_argument('buffer', nargs=2,
help="buffer values for y1 y2")
parser.add_argument("-t", "--titration-of", action="store", default="pH",
choices=["pH", "cl"], dest='titration_type')
args = parser.parse_args()
df = pd.read_csv(args.ffile)
df.y1 -= float(args.buffer[0])
df.y2 -= float(args.buffer[1])
df.y1 *= corr[args.titration_type]
df.y2 *= corr[args.titration_type]
if not os.path.isdir(args.out):
os.makedirs(args.out)
df.to_csv(os.path.join(args.out, args.ffile), index=False)
main()
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] |
api/api.py | dartm05/Shape-Prediction | 0 | 80425 |
import numpy as np
import io
from PIL import Image
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
import matplotlib.pyplot as plt
from flask import Flask,render_template,redirect,url_for,request
import urllib3
from tensorflow.keras.preprocessing import image
import os
app = Flask(__name__)
model = load_model('shape2.h5')
# Image preprocessing/prediction function
def predict_(img_path, show=False):
img = image.load_img(img_path, target_size=(224, 224))
img_tensor = image.img_to_array(img) # (height, width, channels)
img_tensor = np.expand_dims(img_tensor, axis=0) # (1, height, width, channels), add a dimension because the model expects this shape: (batch_size, height, width, channels)
img_tensor /= 255. # imshow expects values in the range [0, 1]
prediction = model.predict(img_tensor)
if show:
plt.imshow(img_tensor[0])
plt.axis('off')
plt.show()
return prediction
# Home
@app.route('/', methods=['GET'])
def api():
return {
'userid':1,
'title':'Flask react app',
'completed': False
}
@app.route('/predict',methods = ['GET' , 'POST'])
def predictions():
error = ''
# Change image dir to fit needs
target_img = os.path.join(os.getcwd() , 'static/images')
if request.method == 'POST':
if (request.files):
file = request.files['file']
if file:
file.save(os.path.join(target_img , file.filename))
img_path = os.path.join(target_img , file.filename)
img = file.filename
predictions = predict_(img_path)
# Change to a switch-case later
if round(predictions[0][0]) == 0:
pred = 'Circle'
if round(predictions[0][0]) == 1:
pred = 'Rectangle'
if round(predictions[0][0]) == 2:
pred = 'Star'
if round(predictions[0][0]) == 3:
pred = 'Triangle'
else:
pred = 'No existe XD'
predictions = {
'Prediction': pred
}
else:
error = "Please draw a shape to predict."
if(len(error) == 0):
# No error
pass
else:
# Error
pass
else:
# Method no fue POST XD
pass
if __name__ == '__main__':
app.run(debug=True) | [
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] |
API/client.py | Ray1993TW/Kn-Pressure_Test | 0 | 100967 | <reponame>Ray1993TW/Kn-Pressure_Test
import requests, json, cv2
url1 = ''
content_type = 'image/jpeg'
headers = {'content-type': content_type}
class useAPI:
def __init__(self, frame):
h,w,c = frame.shape
if(h<=0):
h=w
elif(w<=0):
w=h
else:
self._state_, self.img_encoded = cv2.imencode('.jpg', frame)
def get_answer(self):
try:
response1 = requests.post(url1, data=self.img_encoded.tostring(), headers=headers, timeout = 3)
get_json1 = json.loads(response1.text)
return get_json1
except:
return 'e'
| [
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] |
AlgorithmsAndDataStructures/mod2/Heap.py | BootyAss/bmstu | 0 | 4734 | <gh_stars>0
class Heap:
def __init__(self):
self.items = dict() # key - (value, index)
self.indexes = [] # index - key // to know indexes
# Usefull functions
def swap(self, i, j):
x = self.indexes[i] # key of 1 item
y = self.indexes[j] # key of 2 item
# swap keys in index array
self.indexes[i] = y
self.indexes[j] = x
temp = self.items[x][1] # index of 1 item
# swap indexes in dictionary
self.items.update({x: (self.items[x][0], self.items[y][1])})
self.items.update({y: (self.items[y][0], temp)})
def bigger(self, i, j):
if self.indexes[i] <= self.indexes[j]:
return False
else:
return True
# Check family UwU
def hasParent(self, i):
if (i - 1)/2 >= 0:
return True
return False
def parentIndex(self, i):
return int((i - 1)/2)
def hasLeft(self, i):
if i*2 + 1 < len(self.indexes):
return True
return False
def leftIndex(self, i):
return int(i*2 + 1)
def hasRight(self, i):
if i*2 + 2 < len(self.indexes):
return True
return False
def rightIndex(self, i):
return int(i*2 + 2)
# heapifys
def heapifyUp(self, i=None):
if i:
index = i
else:
index = len(self.indexes) - 1
while self.hasParent(index) and self.bigger(self.parentIndex(index), index):
self.swap(self.parentIndex(index), index)
index = self.parentIndex(index)
def heapifyDown(self, i=0):
index = i
while self.hasLeft(index):
smaller = self.leftIndex(index)
if self.hasRight(index) and self.bigger(self.leftIndex(index), self.rightIndex(index)):
smaller = self.rightIndex(index)
if self.bigger(smaller, index):
break
else:
self.swap(index, smaller)
index = smaller
# all needed methods
def add(self, key, data):
if self.items.get(key, None):
raise(Exception)
self.items[key] = (data, int(len(self.indexes)))
self.indexes.append(key)
self.heapifyUp()
def set(self, key, data):
temp = self.items.get(key, None)
if not temp:
raise(Exception)
self.items[key] = (data, temp[1])
def delete(self, key):
temp = self.items.get(key, None)
if not temp:
raise(Exception)
if len(self.indexes) > 1:
lastKey = self.indexes[-1]
last = self.items.get(lastKey, None)
# set last item index of deleted
self.items.update({lastKey: (last[0], temp[1])})
# set key of last item to deleted index
self.indexes[temp[1]] = lastKey
self.indexes.pop()
del self.items[key]
if temp[1] < len(self.indexes): # dont heapify if deleted last element
self.heapifyDown(i=temp[1])
self.heapifyUp(i=temp[1])
def search(self, key):
temp = self.items.get(key, None)
if temp:
print('1', temp[1], temp[0])
else:
print('0')
def min(self):
if len(self.indexes) == 0:
raise(Exception)
key = self.indexes[0]
print(key, '0', self.items[key][0])
def max(self):
if len(self.indexes) == 0:
raise(Exception)
i = int(len(self.indexes)/2)
maxKey = self.indexes[i]
index = i
while i < len(self.indexes):
if maxKey < self.indexes[i]:
maxKey = self.indexes[i]
index = i
i += 1
print(maxKey, index, self.items[maxKey][0])
def extract(self):
if len(self.indexes) == 0:
raise(Exception)
rootKey = self.indexes[0]
rootData = self.items[rootKey][0]
del self.items[rootKey]
if len(self.indexes) > 1:
self.indexes[0] = self.indexes.pop()
# set top item index to 0
self.items.update({self.indexes[0] : (self.items[self.indexes[0]][0], 0)})
self.heapifyDown()
else:
self.indexes.pop()
print(rootKey, rootData)
def print(self):
height = 0
index = 0
out = ''
i = 0
if len(self.indexes) == 0:
out += '_\n'
print('_')
return
while i < len(self.indexes):
lineLen = 1 << height
index += 1
key = self.indexes[i]
out += '[' + str(key) + ' ' + self.items[key][0]
if height != 0:
out += ' ' + str(self.indexes[self.parentIndex(i)])
out += ']'
if index == lineLen:
out += '\n'
index = 0
height += 1
else:
out += ' '
i += 1
if index != 0 and index < lineLen:
out += '_ ' * (lineLen - index)
print(out[0:-1])
else:
print(out, end='')
cycle = True
heap = Heap()
while cycle:
try:
line = input()
cmd = line.split(' ', 2)
try:
if len(cmd) == 1 and cmd[0] == '':
continue
if len(cmd) == 2 and cmd[0] == '' and cmd[1] == '':
continue
if cmd[0] == 'add':
heap.add(int(cmd[1]), cmd[2])
elif cmd[0] == 'set':
heap.set(int(cmd[1]), cmd[2])
elif cmd[0] == 'delete':
heap.delete(int(cmd[1]))
elif cmd[0] == 'search':
heap.search(int(cmd[1]))
elif cmd[0] == 'min':
heap.min()
elif cmd[0] == 'max':
heap.max()
elif cmd[0] == 'extract':
heap.extract()
elif cmd[0] == 'print':
heap.print()
else:
raise(Exception)
except Exception:
print('error')
continue
except Exception:
cycle = False
| [
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2
] |
xbox/constants.py | tomwhross/wishlist | 0 | 180733 | <filename>xbox/constants.py<gh_stars>0
SEED_URLS = [
"https://www.microsoft.com/en-ca/p/immortals-fenyx-rising/c07kjzrh0l7s?activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/grand-theft-auto-v-premium-edition/C496CLVXMJP8?wa=wsignin1.0&lc=4105&activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/far-cry-5/br7x7mvbbqkm?activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/pathfinder-kingmaker-definitive-edition/bphqqn22gb7l?activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/call-of-duty-modern-warfare---digital-standard-edition/9NVQBQ3F6W9W?activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/ori-and-the-will-of-the-wisps/9N8CD0XZKLP4?activetab=pivot:overviewtab",
"https://www.microsoft.com/EN-CA/p/red-dead-redemption-2/9N2ZDN7NWQKV?activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/tom-clancys-rainbow-six-siege-deluxe-edition/9p30k2nxwh82?activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/pillars-of-eternity-complete-edition/bs34vnw7h61f?activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/pillars-of-eternity-ii-deadfire-ultimate-edition/9pjd2kmx7tz6?activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/astroneer/9nblggh43kzb?cid=msft_web_chart&activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/farm-together/9mxsdjxfzq25?cid=msft_web_chart&activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/Mafia-III-Definitive-Edition/BVZLS7XZ68KF?rtc=1&activetab=pivot:overviewtab",
"https://www.microsoft.com/EN-CA/p/little-nightmares/BWD88K55MK5W?id=Pubsalegame_Week13&activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/Nexomon-Extinction/9NCJR504WXT0?rtc=1&activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/GreedFall/BWMH4RQ4Q06F?rtc=1&activetab=pivot:overviewtab",
"https://www.microsoft.com/en-ca/p/outriders-standard-edition/9p12rcxbf02p?activetab=pivot:overviewtab",
]
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] |
core/management/commands/init_envs.py | klebed/esdc-ce | 97 | 124107 | from ._base import DanubeCloudCommand, CommandError
class Command(DanubeCloudCommand):
help = 'Initialize the virtual environments.'
def handle(self, *args, **options):
raise CommandError('Use ctl.sh directly')
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app/profiles_api/urls.py | ijhajj/Django-basicRESTAPI | 0 | 168960 | from django.conf.urls import url
from django.conf.urls import include
from django.urls import path
from rest_framework.routers import DefaultRouter
from . import views
router = DefaultRouter()
router.register('hello-viewset', views.HelloViewSet, base_name='hello-viewset')
#In case of ModelSerializer, no need to pass the base_name
router.register('profile', views.UserProfileViewSet)
router.register('login', views.LoginViewSet, base_name='login')
urlpatterns = [
path('hello-view/',views.HelloApiView.as_view()),
path('', include(router.urls))
]
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raiden/tests/integration/test_blockchainservice.py | nicksavers/raiden | 2 | 79524 | # -*- coding: utf-8 -*-
from __future__ import division
import os
import pytest
from ethereum import _solidity
from ethereum._solidity import compile_file
from ethereum.utils import denoms
from pyethapp.rpc_client import JSONRPCClient
from pyethapp.jsonrpc import default_gasprice
from raiden.network.rpc.client import (
decode_topic, patch_send_transaction, patch_send_message
)
from raiden.utils import privatekey_to_address, get_contract_path
from raiden.blockchain.abi import CHANNEL_MANAGER_ABI
solidity = _solidity.get_solidity() # pylint: disable=invalid-name
@pytest.mark.timeout(180)
@pytest.mark.parametrize('privatekey_seed', ['blockchain:{}'])
@pytest.mark.parametrize('number_of_nodes', [3])
@pytest.mark.parametrize('channels_per_node', [0])
@pytest.mark.parametrize('number_of_assets', [0])
def test_new_netting_contract(raiden_network, asset_amount, settle_timeout):
# pylint: disable=line-too-long,too-many-statements,too-many-locals
app0, app1, app2 = raiden_network
peer0_address = app0.raiden.address
peer1_address = app1.raiden.address
peer2_address = app2.raiden.address
blockchain_service0 = app0.raiden.chain
asset_address = blockchain_service0.deploy_and_register_asset(
contract_name='HumanStandardToken',
contract_file='HumanStandardToken.sol',
constructor_parameters=(asset_amount, 'raiden', 2, 'Rd'),
)
asset0 = blockchain_service0.asset(asset_address)
for transfer_to in raiden_network[1:]:
asset0.transfer(
privatekey_to_address(transfer_to.raiden.privkey),
asset_amount // len(raiden_network),
)
manager0 = blockchain_service0.manager_by_asset(asset_address)
# sanity
assert manager0.channels_addresses() == []
assert manager0.channels_by_participant(peer0_address) == []
assert manager0.channels_by_participant(peer1_address) == []
assert manager0.channels_by_participant(peer2_address) == []
# create one channel
netting_address_01 = manager0.new_netting_channel(
peer0_address,
peer1_address,
settle_timeout,
)
# check contract state
netting_channel_01 = blockchain_service0.netting_channel(netting_address_01)
assert netting_channel_01.isopen() is False
assert netting_channel_01.partner(peer0_address) == peer1_address
assert netting_channel_01.partner(peer1_address) == peer0_address
# check channels
channel_list = manager0.channels_addresses()
assert sorted(channel_list[0]) == sorted([peer0_address, peer1_address])
assert manager0.channels_by_participant(peer0_address) == [netting_address_01]
assert manager0.channels_by_participant(peer1_address) == [netting_address_01]
assert manager0.channels_by_participant(peer2_address) == []
# create other chanel
netting_address_02 = manager0.new_netting_channel(
peer0_address,
peer2_address,
settle_timeout,
)
netting_channel_02 = blockchain_service0.netting_channel(netting_address_02)
assert netting_channel_02.isopen() is False
assert netting_channel_02.partner(peer0_address) == peer2_address
assert netting_channel_02.partner(peer2_address) == peer0_address
channel_list = manager0.channels_addresses()
expected_channels = [
sorted([peer0_address, peer1_address]),
sorted([peer0_address, peer2_address]),
]
for channel in channel_list:
assert sorted(channel) in expected_channels
result0 = sorted(manager0.channels_by_participant(peer0_address))
result1 = sorted([netting_address_01, netting_address_02])
assert result0 == result1
assert manager0.channels_by_participant(peer1_address) == [netting_address_01]
assert manager0.channels_by_participant(peer2_address) == [netting_address_02]
# deposit without approve should fail
netting_channel_01.deposit(peer0_address, 100)
assert netting_channel_01.isopen() is False
assert netting_channel_02.isopen() is False
assert netting_channel_01.detail(peer0_address)['our_balance'] == 0
assert netting_channel_01.detail(peer1_address)['our_balance'] == 0
# single-funded channel
app0.raiden.chain.asset(asset_address).approve(netting_address_01, 100)
netting_channel_01.deposit(peer0_address, 100)
assert netting_channel_01.isopen() is True
assert netting_channel_02.isopen() is False
assert netting_channel_01.detail(peer0_address)['our_balance'] == 100
assert netting_channel_01.detail(peer1_address)['our_balance'] == 0
# double-funded channel
app0.raiden.chain.asset(asset_address).approve(netting_address_02, 70)
netting_channel_02.deposit(peer0_address, 70)
assert netting_channel_01.isopen() is True
assert netting_channel_02.isopen() is True
assert netting_channel_02.detail(peer0_address)['our_balance'] == 70
assert netting_channel_02.detail(peer2_address)['our_balance'] == 0
app2.raiden.chain.asset(asset_address).approve(netting_address_02, 130)
app2.raiden.chain.netting_channel(netting_address_02).deposit(peer2_address, 130)
assert netting_channel_01.isopen() is True
assert netting_channel_02.isopen() is True
assert netting_channel_02.detail(peer0_address)['our_balance'] == 70
assert netting_channel_02.detail(peer2_address)['our_balance'] == 130
@pytest.mark.skipif(
'TRAVIS' in os.environ,
reason='Flaky test due to mark.timeout not being scheduled. Issue #319'
)
@pytest.mark.timeout(60)
@pytest.mark.parametrize('privatekey_seed', ['blockchain:{}'])
@pytest.mark.parametrize('number_of_nodes', [3])
def test_blockchain(
blockchain_type,
blockchain_backend, # required to start the geth backend
blockchain_rpc_ports,
private_keys,
poll_timeout):
# pylint: disable=too-many-locals
# this test is for interaction with a blockchain using json-rpc, so it
# doesnt make sense to execute it against mock or tester
if blockchain_type not in ('geth',):
return
addresses = [
privatekey_to_address(priv)
for priv in private_keys
]
privatekey = private_keys[0]
address = privatekey_to_address(privatekey)
total_asset = 100
jsonrpc_client = JSONRPCClient(
port=blockchain_rpc_ports[0],
privkey=privatekey,
print_communication=False,
)
patch_send_transaction(jsonrpc_client)
patch_send_message(jsonrpc_client)
humantoken_path = get_contract_path('HumanStandardToken.sol')
humantoken_contracts = compile_file(humantoken_path, libraries=dict())
token_proxy = jsonrpc_client.deploy_solidity_contract(
address,
'HumanStandardToken',
humantoken_contracts,
dict(),
(total_asset, 'raiden', 2, 'Rd'),
contract_path=humantoken_path,
gasprice=default_gasprice,
timeout=poll_timeout,
)
registry_path = get_contract_path('Registry.sol')
registry_contracts = compile_file(registry_path)
registry_proxy = jsonrpc_client.deploy_solidity_contract(
address,
'Registry',
registry_contracts,
dict(),
tuple(),
contract_path=registry_path,
gasprice=default_gasprice,
timeout=poll_timeout,
)
log_list = jsonrpc_client.call(
'eth_getLogs',
{
'fromBlock': '0x0',
'toBlock': 'latest',
'topics': [],
},
)
assert len(log_list) == 0
# pylint: disable=no-member
assert token_proxy.balanceOf(address) == total_asset
transaction_hash = registry_proxy.addAsset.transact(
token_proxy.address,
gasprice=denoms.wei,
)
jsonrpc_client.poll(transaction_hash.decode('hex'), timeout=poll_timeout)
assert len(registry_proxy.assetAddresses.call()) == 1
log_list = jsonrpc_client.call(
'eth_getLogs',
{
'fromBlock': '0x0',
'toBlock': 'latest',
'topics': [],
},
)
assert len(log_list) == 1
channel_manager_address_encoded = registry_proxy.channelManagerByAsset.call(
token_proxy.address,
)
channel_manager_address = channel_manager_address_encoded.decode('hex')
log = log_list[0]
log_topics = [
decode_topic(topic)
for topic in log['topics'] # pylint: disable=invalid-sequence-index
]
log_data = log['data']
event = registry_proxy.translator.decode_event(
log_topics,
log_data[2:].decode('hex'),
)
assert channel_manager_address == event['channel_manager_address'].decode('hex')
assert token_proxy.address == event['asset_address'].decode('hex')
channel_manager_proxy = jsonrpc_client.new_contract_proxy(
CHANNEL_MANAGER_ABI,
channel_manager_address,
)
transaction_hash = channel_manager_proxy.newChannel.transact(
addresses[1],
10,
gasprice=denoms.wei,
)
jsonrpc_client.poll(transaction_hash.decode('hex'), timeout=poll_timeout)
log_list = jsonrpc_client.call(
'eth_getLogs',
{
'fromBlock': '0x0',
'toBlock': 'latest',
'topics': [],
},
)
assert len(log_list) == 2
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2981,
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13,
1678,
4974,
7431,
29898,
1188,
29918,
1761,
29897,
1275,
29871,
29906,
13,
2
] |
Translator.py | aj-naik/City-Sentiment-Detector-BERT | 2 | 197723 | import requests
class Translator:
def __init__(self, api_key: str):
self.api_key = api_key
class GoogleTranslate(Translator):
api_endpoint = 'https://translation.googleapis.com/language/translate/v2'
def __init__(self, api_key: str):
Translator.__init__(self, api_key=api_key)
def detect_language(self, input_text) -> str:
request = requests.post(f'{self.api_endpoint}/detect', data={'q': input_text, 'key': self.api_key})
data = request.json()
language = data.get('data').get('detections')[0][0].get('language')
return language
def translate(self, input_text: str, target_lang: str, source_lang: str = "") -> str:
request = requests.post(f'{self.api_endpoint}',
data={'q': input_text, 'source': source_lang,
'target': target_lang, 'format': 'text',
'key': self.api_key})
data = request.json()
translations = data.get('data').get('translations')
text = translations[0].get('translatedText')
return text
| [
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2752,
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418,
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1272,
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657,
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3286,
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13,
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353,
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29900,
1822,
657,
877,
3286,
29880,
630,
1626,
1495,
13,
13,
4706,
736,
1426,
13,
2
] |
run.py | OSPgrp8/OSP | 0 | 181523 | <gh_stars>0
from app import app
import mongoengine as ming
from osp.classes.address import Address
if __name__ == "__main__":
obj = Address(residence_number="a@b", street="str", locality="loc",
state="state", city="city")
print(obj.pincode)
app.run(debug = True,port = 8000) | [
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] |
district42_exp_types/sdict/_rollout.py | nikitanovosibirsk/district42-exp-types | 0 | 1611343 | <reponame>nikitanovosibirsk/district42-exp-types
from typing import Any, Dict, Union
from district42 import optional
from district42.utils import is_ellipsis
__all__ = ("rollout",)
KeysType = Dict[Union[str, optional], Any]
def rollout(keys: KeysType, separator: str = ".") -> KeysType:
updated: KeysType = {}
for comp_key, val in keys.items():
if is_ellipsis(comp_key):
assert is_ellipsis(val)
updated[comp_key] = val
continue
is_optional = False
if isinstance(comp_key, optional):
assert isinstance(comp_key.key, str)
comp_key = comp_key.key
is_optional = True
assert isinstance(comp_key, str)
parts = comp_key.split(separator)
key = parts[0]
if len(parts) == 1:
updated[optional(key) if is_optional else key] = val
else:
if key not in updated:
updated[key] = {}
tail = separator.join(parts[1:])
updated[key][optional(tail) if is_optional else tail] = val
for k, v in updated.items():
updated[k] = rollout(v) if isinstance(v, dict) else v
return updated
| [
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29962,
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297,
4784,
29901,
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29962,
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6571,
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9651,
12464,
353,
28128,
29889,
7122,
29898,
20895,
29961,
29896,
29901,
2314,
13,
9651,
4784,
29961,
1989,
3816,
25253,
29898,
18237,
29897,
565,
338,
29918,
25253,
1683,
12464,
29962,
353,
659,
13,
13,
1678,
363,
413,
29892,
325,
297,
4784,
29889,
7076,
7295,
13,
4706,
4784,
29961,
29895,
29962,
353,
9679,
449,
29898,
29894,
29897,
565,
338,
8758,
29898,
29894,
29892,
9657,
29897,
1683,
325,
13,
13,
1678,
736,
4784,
13,
2
] |
aio_geojson_nsw_transport_incidents/__init__.py | Fred-Ch/python-aio-geojson-nsw-transport-incidents-master | 0 | 166152 | """NSW Transport Service Incidents library."""
from .feed import NswTransportServiceIncidentsFeed # noqa
from .feed_manager import NswTransportServiceIncidentsFeedManager # noqa
| [
1,
9995,
3059,
29956,
15710,
6692,
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3489,
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tests/test_soda.py | Adam-Lechnos/SocrataPython | 0 | 110576 | <reponame>Adam-Lechnos/SocrataPython
from sodapy import Socrata
from sodapy.constants import DEFAULT_API_PREFIX, OLD_API_PREFIX
import requests
import requests_mock
import os.path
import inspect
import json
PREFIX = "https://"
DOMAIN = "fakedomain.com"
DATASET_IDENTIFIER = "songs"
APPTOKEN = "FakeAppToken"
USERNAME = "fakeuser"
PASSWORD = "<PASSWORD>"
TEST_DATA_PATH = os.path.join(os.path.dirname(os.path.abspath(inspect.getfile(
inspect.currentframe()))), "test_data")
def test_client():
client = Socrata(DOMAIN, APPTOKEN)
assert isinstance(client, Socrata)
client.close()
def test_get():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, session_adapter=mock_adapter)
response_data = "get_songs.txt"
setup_mock(adapter, "GET", response_data, 200)
response = client.get(DATASET_IDENTIFIER)
assert isinstance(response, list)
assert len(response) == 10
client.close()
def test_get_unicode():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, session_adapter=mock_adapter)
response_data = "get_songs_unicode.txt"
setup_mock(adapter, "GET", response_data, 200)
response = client.get(DATASET_IDENTIFIER)
assert isinstance(response, list)
assert len(response) == 10
client.close()
def test_get_metadata_and_attachments():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, session_adapter=mock_adapter)
response_data = "get_song_metadata.txt"
setup_old_api_mock(adapter, "GET", response_data, 200)
response = client.get_metadata(DATASET_IDENTIFIER)
assert isinstance(response, dict)
assert "newBackend" in response
assert "attachments" in response["metadata"]
response = client.download_attachments(DATASET_IDENTIFIER)
assert isinstance(response, list)
assert len(response) == 0
client.close()
def test_update_metadata():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, session_adapter=mock_adapter)
response_data = "update_song_metadata.txt"
setup_old_api_mock(adapter, "PUT", response_data, 200)
data = {"category": "Education", "attributionLink": "https://testing.updates"}
response = client.update_metadata(DATASET_IDENTIFIER, data)
assert isinstance(response, dict)
assert response.get("category") == data["category"]
assert response.get("attributionLink") == data["attributionLink"]
client.close()
def test_upsert_exception():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, session_adapter=mock_adapter)
response_data = "403_response_json.txt"
setup_mock(adapter, "POST", response_data, 403, reason="Forbidden")
data = [{"theme": "Surfing", "artist": "Wavves",
"title": "King of the Beach", "year": "2010"}]
try:
client.upsert(DATASET_IDENTIFIER, data)
except Exception as e:
assert isinstance(e, requests.exceptions.HTTPError)
else:
raise AssertionError("No exception raised for bad request.")
def test_upsert():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, username=USERNAME, password=PASSWORD,
session_adapter=mock_adapter)
response_data = "upsert_songs.txt"
data = [{"theme": "Surfing", "artist": "Wavves",
"title": "King of the Beach", "year": "2010"}]
setup_mock(adapter, "POST", response_data, 200)
response = client.upsert(DATASET_IDENTIFIER, data)
assert isinstance(response, dict)
assert response.get("Rows Created") == 1
client.close()
def test_replace():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, username=USERNAME, password=PASSWORD,
session_adapter=mock_adapter)
response_data = "replace_songs.txt"
data = [
{"theme": "Surfing", "artist": "Wavves", "title": "King of the Beach",
"year": "2010"},
{"theme": "History", "artist": "Best Friends Forever",
"title": "<NAME>", "year": "2008"},
]
setup_mock(adapter, "PUT", response_data, 200)
response = client.replace(DATASET_IDENTIFIER, data)
assert isinstance(response, dict)
assert response.get("Rows Created") == 2
client.close()
def test_delete():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, username=USERNAME, password=PASSWORD,
session_adapter=mock_adapter)
uri = "{0}{1}{2}/{3}.json".format(PREFIX, DOMAIN, OLD_API_PREFIX, DATASET_IDENTIFIER)
adapter.register_uri("DELETE", uri, status_code=200)
response = client.delete(DATASET_IDENTIFIER)
assert response.status_code == 200
try:
client.delete("foobar")
except Exception as e:
assert isinstance(e, requests_mock.exceptions.NoMockAddress)
finally:
client.close()
def test_create():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, username=USERNAME, password=PASSWORD,
session_adapter=mock_adapter)
response_data = "create_foobar.txt"
setup_mock(adapter, "POST", response_data, 200, dataset_identifier=None)
columns = [
{"fieldName": "foo", "name": "Foo", "dataTypeName": "text"},
{"fieldName": "bar", "name": "Bar", "dataTypeName": "number"}
]
tags = ["foo", "bar"]
response = client.create("Foo Bar", description="test dataset",
columns=columns, tags=tags, row_identifier="bar")
request = adapter.request_history[0]
request_payload = json.loads(request.text) # can't figure out how to use .json
# Test request payload
for dataset_key in ["name", "description", "columns", "tags"]:
assert dataset_key in request_payload
for column_key in ["fieldName", "name", "dataTypeName"]:
assert column_key in request_payload["columns"][0]
# Test response
assert isinstance(response, dict)
assert len(response.get("id")) == 9
client.close()
def test_set_permission():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, username=USERNAME, password=PASSWORD,
session_adapter=mock_adapter)
response_data = "empty.txt"
setup_old_api_mock(adapter, "PUT", response_data, 200)
# Test response
response = client.set_permission(DATASET_IDENTIFIER, "public")
assert response.status_code == 200
# Test request
request = adapter.request_history[0]
query_string = request.url.split("?")[-1]
params = query_string.split("&")
assert len(params) == 2
assert "method=setPermission" in params
assert "value=public.read" in params
client.close()
def test_publish():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, username=USERNAME, password=PASSWORD,
session_adapter=mock_adapter)
response_data = "create_foobar.txt"
setup_publish_mock(adapter, "POST", response_data, 200)
response = client.publish(DATASET_IDENTIFIER)
assert isinstance(response, dict)
assert len(response.get("id")) == 9
client.close()
def test_import_non_data_file():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, username=USERNAME, password=PASSWORD,
session_adapter=mock_adapter)
response_data = "successblobres.txt"
nondatasetfile_path = 'tests/test_data/nondatasetfile.zip'
setup_import_non_data_file(adapter, "POST", response_data, 200)
with open(nondatasetfile_path, 'rb') as f:
files = (
{'file': ("nondatasetfile.zip", f)}
)
response = client.create_non_data_file({}, files)
assert isinstance(response, dict)
assert response.get("blobFileSize") == 496
client.close()
def test_replace_non_data_file():
mock_adapter = {}
mock_adapter["prefix"] = PREFIX
adapter = requests_mock.Adapter()
mock_adapter["adapter"] = adapter
client = Socrata(DOMAIN, APPTOKEN, username=USERNAME, password=PASSWORD,
session_adapter=mock_adapter)
response_data = "successblobres.txt"
nondatasetfile_path = 'tests/test_data/nondatasetfile.zip'
setup_replace_non_data_file(adapter, "POST", response_data, 200)
with open(nondatasetfile_path, 'rb') as fin:
file = (
{'file': ("nondatasetfile.zip", fin)}
)
response = client.replace_non_data_file(DATASET_IDENTIFIER, {}, file)
assert isinstance(response, dict)
assert response.get("blobFileSize") == 496
client.close()
def setup_publish_mock(adapter, method, response, response_code, reason="OK",
dataset_identifier=DATASET_IDENTIFIER, content_type="json"):
path = os.path.join(TEST_DATA_PATH, response)
with open(path, "r") as response_body:
body = json.load(response_body)
uri = "{0}{1}{2}/{3}/publication.{4}".format(PREFIX, DOMAIN, OLD_API_PREFIX,
dataset_identifier, content_type)
headers = {
"content-type": "application/json; charset=utf-8"
}
adapter.register_uri(method, uri, status_code=response_code, json=body, reason=reason,
headers=headers)
def setup_import_non_data_file(adapter, method, response, response_code, reason="OK",
dataset_identifier=DATASET_IDENTIFIER, content_type="json"):
path = os.path.join(TEST_DATA_PATH, response)
with open(path, "r") as response_body:
body = json.load(response_body)
uri = "{0}{1}/api/imports2/?method=blob".format(PREFIX, DOMAIN)
headers = {
"content-type": "application/json; charset=utf-8"
}
adapter.register_uri(method, uri, status_code=response_code, json=body, reason=reason,
headers=headers)
def setup_replace_non_data_file(adapter, method, response, response_code, reason="OK",
dataset_identifier=DATASET_IDENTIFIER, content_type="json"):
path = os.path.join(TEST_DATA_PATH, response)
with open(path, "r") as response_body:
body = json.load(response_body)
uri = "{0}{1}{2}/{3}.{4}?method=replaceBlob&id={3}".format(PREFIX, DOMAIN, OLD_API_PREFIX,
dataset_identifier, "txt")
headers = {
"content-type": "text/plain; charset=utf-8"
}
adapter.register_uri(method, uri, status_code=response_code, json=body, reason=reason,
headers=headers)
def setup_old_api_mock(adapter, method, response, response_code, reason="OK",
dataset_identifier=DATASET_IDENTIFIER, content_type="json"):
path = os.path.join(TEST_DATA_PATH, response)
with open(path, "r") as response_body:
try:
body = json.load(response_body)
except ValueError:
body = None
uri = "{0}{1}{2}/{3}.{4}".format(PREFIX, DOMAIN, OLD_API_PREFIX, dataset_identifier,
content_type)
headers = {
"content-type": "application/json; charset=utf-8"
}
adapter.register_uri(method, uri, status_code=response_code, json=body, reason=reason,
headers=headers)
def setup_mock(adapter, method, response, response_code, reason="OK",
dataset_identifier=DATASET_IDENTIFIER, content_type="json"):
path = os.path.join(TEST_DATA_PATH, response)
with open(path, "r") as response_body:
body = json.load(response_body)
if dataset_identifier is None: # for create endpoint
uri = "{0}{1}{2}.{3}".format(PREFIX, DOMAIN, OLD_API_PREFIX, "json")
else: # most cases
uri = "{0}{1}{2}{3}.{4}".format(PREFIX, DOMAIN, DEFAULT_API_PREFIX, dataset_identifier,
content_type)
headers = {
"content-type": "application/json; charset=utf-8"
}
adapter.register_uri(method, uri, status_code=response_code, json=body, reason=reason,
headers=headers)
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] |
src/vmshepherd/healthcheck/dummy_healthcheck.py | DreamLab/VmShepherd | 10 | 137372 | from .abstract import AbstractHealthcheck
class DummyHealthcheck(AbstractHealthcheck):
status = True
def __init__(self, config=None):
pass
async def is_healthy(self, vm):
return self.status
def set_status(self, healthy):
self.status = healthy
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darkweb/views/main.py | robopt/Capstone | 5 | 191120 | """
main.py
Handles all accessible URI's for flask
"""
from flask import Flask, render_template, request, g
from darkweb.modules.base.crawlerconfig import CrawlerConfig
from darkweb.modules.irc.irc import IRC
from darkweb.modules.web.WebCrawler import WebCrawler
from darkweb.modules.parser.search import search
from darkweb.modules.parser.parser import parser
import SocketServer
import threading
import datetime
import sys
from threading import Thread
from darkweb import app
"""
before_first
Initialize parser before first request is served.
"""
@app.before_first_request
def before_first():
print("views.py - init start")
HOST, PORT = "0.0.0.0", 4443
sserver = SocketServer.ThreadingTCPServer((HOST, PORT), parser)
Thread(target=sserver.serve_forever).start()
print("views.py - init end")
"""
addParam
Add a search term or a regular expression to the parser.
Future work: This code causes parser and website to be on same box.
Refactor to utilize connections to send terms to the parser.
"""
@app.route("/addParam", methods=["POST"])
def addParam():
# add new param
if request.form['addST']:
print("Adding searchterm")
st_terms = request.form['addST'].split(",")
# add new search term
for st in st_terms:
search().add_searchterm(st)
if request.form['addRE']:
re_terms = request.form['addRE'].split(",")
# add new regex
for re in re_terms:
search().add_searchterm(re)
print("Adding regularexpression")
result = readSearchFile()
msg = "Successfull added search parameters"
return render_template("index.html", result=result)
"""
clearParams
Remove all search terms from the parser.
Future work: This code causes parser and website to be on same box.
Refactor to utilize connections to send terms to the parser.
"""
@app.route("/clearParams", methods=["POST"])
def clearParams():
# clear all params
result = readSearchFile()
msg = "Successfull cleared search parameters"
return render_template("index.html", result=result)
"""
home (get)
Serve index page.
"""
@app.route("/", methods=["GET"])
def home():
result = readSearchFile()
return render_template("index.html", result=result)
"""
home (post)
Process a search
"""
@app.route("/", methods=["POST"])
def createCrawlerConfig():
print(str(request.form))
print("Post recieved.")
searchName = str(request.form['searchName'])
protocol = str(request.form['protocol']).lower()
print("Post parameters parsed.")
speed = str(request.form['speed'])
maxDepth = str(request.form['maxDepth'])
location = request.form['location']
options_input = str(request.form['options'])
options = makeOptionsDict(options_input)
config = CrawlerConfig(location, protocol, speed, maxDepth, searchName, options)
msg = "Search \"" + searchName + "\" started."
crawler = None
search_params = None
if searchName == "":
msg = "Search failed. Search must be given a name."
elif location == "":
msg = "Search failed. Must give a search location."
elif protocol == "irc":
crawler = IRC(config)
elif protocol == "tor" or protocol == "web":
if not speed.isdigit():
msg = "Search falied. Speed much be an integer."
elif not maxDepth.isdigit():
msg = "Search failed. Max Crawl Depth must be an integer."
crawler = WebCrawler(config)
else:
msg = "Search failed invalid protocol.\nMust be TOR, IRC, or web"
if(crawler):
run_crawl(crawler)
search_params = [("Search Name", searchName), ("Protocol", protocol), ("Speed", speed), ("Max Depth", maxDepth), ("Location", location)]
for label, val in options.iteritems():
search_params.append((label, val))
result = readSearchFile()
return render_template("index.html", msg=msg, search_params=search_params, result=result)
"""
readSearchFile
read from /tmp/searches.txt and return list of 5 lines
"""
def readSearchFile():
result = []
i = 0
with open("/tmp/searches.txt", "r") as f:
for line in f:
if(i >= 5):
break
result.append(line)
i += 1
return result
"""
writeSearchFile
Writes to file in /tmp the datetime a search was started
"""
def writeSearchFile(searchName):
with open("/tmp/searches.txt", "a") as f:
out_string = "\"" + searchName + "\": " + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "\n"
f.write(out_string)
return
"""
run_crawl
Run the specified cralwer
Call do crawl in new thread
"""
def run_crawl(crawler, args=None):
#t = threading.Thread(target=crawler.doCrawl, args=(args, ))
t = threading.Thread(target=crawler.doCrawl)
t.start()
writeSearchFile(crawler.config.name)
return
"""
makeOptionsDict
format options string from form and create options dict for crawlerconfig
returns options dict
"""
def makeOptionsDict(options_input):
options = {}
split_input = options_input.split(',')
for s in split_input:
s = s.replace(" ", "")
o = s.split(':')
if len(o) == 2:
options[o[0]] = o[1]
return options
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scripts/list_ports.py | nnarain/ftl | 0 | 182395 | import os
import serial
def get_serial_port_iterator():
try:
if os.name == 'nt':
from serial.tools.list_ports_windows import comports
return comports
elif os.name == 'posix':
from serial.tools.list_ports_posix import comports
return comports
else:
raise ImportError('No serial implementation for this platform found')
except ImportError as e:
return None
for (port, _, _) in get_serial_port_iterator()():
print(port)
| [
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17609,
580,
7295,
13,
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1596,
29898,
637,
29897,
13,
2
] |
testproject/settings.py | Instawork/django-log-request-id | 1 | 84421 | <filename>testproject/settings.py
from django.conf import global_settings
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': ':memory:',
},
}
DEBUG = True
SECRET_KEY = 'secret'
ROOT_URLCONF = "testproject.urls"
INSTALLED_APPS = ["log_request_id"]
MIDDLEWARE_CLASSES = [
'log_request_id.middleware.RequestIDMiddleware',
# ... other middleware goes here
] + list(global_settings.MIDDLEWARE_CLASSES)
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'filters': {
'request_id': {
'()': 'log_request_id.filters.RequestIDFilter'
}
},
'formatters': {
'standard': {
'format': '%(levelname)-8s [%(asctime)s] [%(request_id)s] %(name)s: %(message)s'
},
},
'handlers': {
'mock': {
'level': 'DEBUG',
'class': 'testproject.handler.MockLoggingHandler',
'filters': ['request_id'],
'formatter': 'standard',
},
},
'loggers': {
'testproject': {
'handlers': ['mock'],
'level': 'DEBUG',
'propagate': False,
},
'log_request_id.middleware': {
'handlers': ['mock'],
'level': 'DEBUG',
'propagate': False,
},
}
}
| [
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2
] |
Findclone/aiofindclone.py | vypivshiy/Findclone_api | 5 | 13538 | from aiohttp import ClientSession, FormData
from Findclone import __version__
from .models import Account, Profiles, Histories, get_builder
from .utils import random_string, paint_boxes
from .exceptions import a_error_handler, FindcloneError
from io import BufferedReader, BytesIO
class FindcloneAsync:
"""async findclone api class
Attributes:
headers : dict - set requests headers
"""
def __init__(self):
self._session = ClientSession()
self.headers = {"User-Agent": f"findclone-api/{__version__}"}
self.__builder = get_builder().build_aio_response
self._session_key = None
self._userid = None
self.__info = None
async def login(self,
login: [str, None] = None,
password: [str, None] = None,
session_key: [str, None] = None,
userid: [str, int, None] = None) -> bool:
"""
*coro
Findclone authorisation
:param login: account login
:param password: account password
:param session_key: account session_key
:param userid: account userid
:return: True is auth success
"""
if login and password:
async with self._session.post("https://findclone.ru/login", data={"phone": login,
"password": password}) as response:
await a_error_handler(response)
resp = await response.json()
self.__info = await self.__builder(response)
self._session_key = resp["session_key"]
self._userid = resp["userid"]
self.headers.update({'session-key': self._session_key, 'user-id': str(self._userid)})
return True
elif session_key and userid:
self.headers.update({"session-key": session_key, "user-id": str(userid)})
async with self._session.get("https://findclone.ru/profile", headers=self.headers) as response:
await a_error_handler(response)
self.__info = await self.__builder(response)
self._session_key = session_key
self._userid = userid
return True
else:
raise FindcloneError("Need login and password or session-key and _userid")
@property
async def info(self) -> Account:
"""
*coro
return account information
:return: Account object
"""
async with self._session.get("https://findclone.ru/profile", headers=self.headers) as response:
info = await self.__builder(response)
self.__info = info
return info
async def upload(self,
file: [str, BufferedReader],
face_box_id: int = None,
timeout: float = 180) -> [Profiles, BytesIO]:
"""
*coro
upload image or image url and return Profiles object or BytesIO object
:param file: image direct download link or path
:param face_box_id: OPTIONAL, send facebox id if 2 or more faces are detected
:param timeout: OPTIONAL - max timeout delay
:return: Profiles object or BytesIO if 2 or more faces are detected
"""
data = FormData()
if file.startswith("http"):
async with self._session.get(file, headers=self.headers) as response:
file = await response.read()
data.add_field("uploaded_photo", file, filename=f"{random_string()}.png", content_type="image/png")
else:
data.add_field("uploaded_photo", open(file, "rb"), filename=f"{random_string()}.png",
content_type="image/png")
async with self._session.post("https://findclone.ru/upload2", data=data, headers=self.headers,
timeout=timeout) as response:
resp = await response.json()
if resp.get("faceBoxes"):
if face_box_id is not None:
async with self._session.get("https://findclone.ru/upload3", params={"id": face_box_id},
headers=self.headers) as response2:
resp = await self.__builder(response2)
return resp
else:
img_bytes = paint_boxes(file, resp) # return bytesIO object
return img_bytes
resp = await self.__builder(response)
return resp
async def history(self, offset: int = 0, count: int = 100) -> Histories:
"""
*coro
return object histories search for account
:param offset: int
:param count: int
:return: Histories object
"""
async with self._session.get("https://findclone.ru/hist", params={"offset": offset, "count": count},
headers=self.headers) as response:
history = await self.__builder(response)
return history
async def search(self, search_id: [int, str], count: int = 128) -> Profiles:
"""
*coro
:param search_id: [int, str] search id
:param count: [int] max Profiles count get
:return: Profiles object
"""
async with self._session.get("https://findclone.ru/search", params={"id": search_id, "count": count},
headers=self.headers) as response:
search_result = await self.__builder(response)
return search_result
@property
def get_session(self) -> dict:
"""
property
return session-key and _userid account
:return: dict {"session-key": session_key, "user-id": userid}
"""
_session = {"session-key": self._session_key, "user-id": self._userid}
return _session
def __str__(self):
return self.__info.__str__()
async def __aenter__(self) -> 'FindcloneAsync':
return self
async def __aexit__(self, exc_type, exc_val, exc_tb) -> None:
await self._session.close()
async def close(self) -> None:
await self._session.close()
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13,
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29901,
13,
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3032,
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580,
13,
13,
1678,
7465,
822,
3802,
29898,
1311,
29897,
1599,
6213,
29901,
13,
4706,
7272,
1583,
3032,
7924,
29889,
5358,
580,
13,
2
] |
pwtools/test/test_is_seq.py | elcorto/pwtools | 41 | 60794 | import os
import numpy as np
from pwtools.common import is_seq, file_write
from .testenv import testdir
def test_is_seq():
fn = os.path.join(testdir, 'is_seq_test_file')
file_write(fn, 'lala')
fd = open(fn , 'r')
for xx in ([1,2,3], (1,2,3), np.array([1,2,3])):
print(type(xx))
assert is_seq(xx) is True
for xx in ('aaa', fd):
print(type(xx))
assert is_seq(xx) is False
fd.close()
| [
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1053,
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4419,
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338,
7700,
13,
1678,
285,
29881,
29889,
5358,
580,
13,
2
] |
py-question/lv1-01.py | fos777/MarbleChocolate | 0 | 88329 | # Q:编写一个程序，它将找到所有这些数字，可被7整除，但不是5的倍数，2000年至3200年(包括在内)。得到的数字应按逗号分隔的顺序打印在一行上。
# HINT:考虑使用range(#begin， #end)方法
l = []
for i in range(2000,3201):
if (i%7==0) and (i%5!=0):
l.append(str(i))
print (l) | [
1,
396,
660,
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140,
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301,
29889,
4397,
29898,
710,
29898,
29875,
876,
13,
2158,
313,
29880,
29897,
2
] |
output/models/nist_data/atomic/unsigned_byte/schema_instance/nistschema_sv_iv_atomic_unsigned_byte_pattern_1_xsd/__init__.py | tefra/xsdata-w3c-tests | 1 | 69558 | <gh_stars>1-10
from output.models.nist_data.atomic.unsigned_byte.schema_instance.nistschema_sv_iv_atomic_unsigned_byte_pattern_1_xsd.nistschema_sv_iv_atomic_unsigned_byte_pattern_1 import NistschemaSvIvAtomicUnsignedBytePattern1
__all__ = [
"NistschemaSvIvAtomicUnsignedBytePattern1",
]
| [
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25807,
12961,
12901,
17144,
29896,
613,
13,
29962,
13,
2
] |
Programmers/10. Graph/02. Rank.py | Minoolian/Coding_Test | 0 | 83719 | # 순위
#
# https://programmers.co.kr/learn/courses/30/lessons/49191
def solution(n, results):
answer=0
adj=[[0 for _ in range(n+1)] for _ in range(n+1)]
for w,l in results:
adj[w][l]=2
adj[l][w]=1
# for i in range(n+1):
# for j in range(n+1):
# if adj[i][j]==1:
# for k in range(n+1):
# if adj[i][k]==2:
# adj[j][k]=2
# elif adj[i][j]==2:
# for k in range(n+1):
# if adj[i][k]==1:
# adj[j][k]=1
for i in range(n+1):
for j in range(n+1):
for k in range(n+1):
if adj[i][j]==1 and adj[i][k]==2:
adj[j][k]=2
elif adj[i][j]==2 and adj[i][k]==1:
adj[j][k]=1
for lis in adj:
if lis.count(0)==2:
answer+=1
return answer
solution(5,[[4, 3], [4, 2], [3, 2], [1, 2], [2, 5]])
| [
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] |
mkdocs_swagger_ui_plugin/plugin.py | byrnereese/mkdocs-swagger-ui | 3 | 1611666 | <gh_stars>1-10
import os
import sys
import fnmatch
from timeit import default_timer as timer
from datetime import datetime, timedelta
from mkdocs import utils as mkdocs_utils
from mkdocs.config import config_options, Config
from mkdocs.plugins import BasePlugin
import mkdocs.structure.files
import yaml
import json
import markdown
from jinja2 import Environment, FileSystemLoader, select_autoescape, Markup
import requests
from urllib.parse import urlparse
from urllib.request import Request, urlopen
from urllib.error import URLError
class SwaggerUIPlugin(BasePlugin):
config_scheme = (
('do_nothing', mkdocs.config.config_options.Type(str, default='')),
('spec_url', config_options.Type(str, default='https://petstore.swagger.io/v2/swagger.json')),
('template', config_options.Type(str, default='swagger.md.tmpl')),
('outfile', config_options.Type(str, default='docs/swagger.md'))
)
def __init__(self):
self.enabled = True
self.total_time = 0
def generate_page_contents(self):
spec_url = self.config['spec_url']
tmpl_url = self.config['template']
print("INFO - Generating swagger-ui for spec: " + spec_url)
print("DEBUG - template: " + tmpl_url)
env = Environment(
loader=FileSystemLoader('tmpl'),
autoescape=select_autoescape(['html', 'xml'])
)
md = markdown.Markdown()
env.filters['markdown'] = lambda text: Markup(md.convert(text))
template = env.get_template( tmpl_url )
tmpl_out = template.render( spec=spec_url )
return tmpl_out
def on_config(self, config):
print("INFO - swagger-ui plugin ENABLED")
def on_page_read_source(self, page, config):
index_path = os.path.join(config['docs_dir'], self.config['outfile'])
page_path = os.path.join(config['docs_dir'], page.file.src_path)
if index_path == page_path:
contents = self.generate_page_contents()
return contents
'''
def on_pre_build(self, config):
outfile = self.config['outfile']
contents = self.generate_page_contents()
dest = os.path.join(config['docs_dir'], outfile)
with open(dest, "w") as fh:
fh.write(contents)
return config
'''
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] |
ahc_tools/test/test_utils.py | rdo-management/ahc-tools | 0 | 183597 | # 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 json
import mock
from ironicclient.exc import AmbiguousAuthSystem
from ahc_tools.test import base
from ahc_tools import utils
class TestGetFacts(base.BaseTest):
@mock.patch.object(utils.swift, 'SwiftAPI', autospec=True)
def test_facts(self, swift_mock):
swift_conn = swift_mock.return_value
obj = json.dumps([[u'cpu', u'logical_0', u'bogomips', u'4199.99'],
[u'cpu', u'logical_0', u'cache_size', u'4096KB']])
swift_conn.get_object.return_value = obj
name = 'extra_hardware-UUID1'
node = mock.Mock(extra={'hardware_swift_object': name})
expected = [(u'cpu', u'logical_0', u'bogomips', u'4199.99'),
(u'cpu', u'logical_0', u'cache_size', u'4096KB')]
facts = utils.get_facts(node)
self.assertEqual(expected, facts)
swift_conn.get_object.assert_called_once_with(name)
def test_no_facts(self):
node = mock.Mock(extra={})
err_msg = ("You must run introspection on the nodes before "
"running this tool.\n")
self.assertRaisesRegexp(SystemExit, err_msg,
utils.get_facts, node)
@mock.patch.object(utils.client, 'get_client', autospec=True,
side_effect=AmbiguousAuthSystem)
class TestGetIronicClient(base.BaseTest):
def test_no_credentials(self, ic_mock):
utils.CONF.config_file = ['ahc-tools.conf']
err_msg = '.*credentials.*missing.*ironic.*searched.*ahc-tools.conf'
self.assertRaisesRegexp(SystemExit, err_msg,
utils.get_ironic_client)
self.assertTrue(ic_mock.called)
class TestGetIronicNodes(base.BaseTest):
def test_only_matchable_nodes_returned(self):
available_node = mock.Mock(provision_state='available')
manageable_node = mock.Mock(provision_state='manageable')
active_node = mock.Mock(provision_state='active')
ironic_client = mock.Mock()
ironic_client.node.list.return_value = [available_node,
manageable_node,
active_node]
expected = [available_node, manageable_node]
returned_nodes = utils.get_ironic_nodes(ironic_client)
self.assertEqual(expected, returned_nodes)
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plot/Spectrogram.py | varunss123/WaveCLI | 0 | 179398 | <gh_stars>0
#!/usr/bin/env python3
# =============================================================================
# Developer : <NAME>(<EMAIL>)
# License : MIT License
# Year : 2019
# =============================================================================
# =============================================================================
# Description:
# Plot Spectrogram of a .wav file
# =============================================================================
import argparse
import matplotlib.pyplot as plot
import Utilities as util
import numpy as np
from scipy import signal
parser = argparse.ArgumentParser(description = 'Plot Spectrogram of a .wav File')
parser.add_argument('-i', nargs = '?', required = True, type = str, help = 'Input File (.wav)')
parser.add_argument('-o', nargs = '?', type = str, help = 'Output Image File (Optional)')
parser.add_argument('-isolate',default = False, action = 'store_true', help = 'Plot Only Spectrogram without .wav')
args = parser.parse_args()
[fs,y_axis] = util.WavRead(args.i)
x_axis = np.arange(0,len(y_axis)/fs,1/fs)
f, t, spectrogram = signal.spectrogram(y_axis,fs, window = signal.get_window('hamming',1024), noverlap = 100)
if not args.isolate:
plot.subplot(211)
plot.plot(x_axis,y_axis)
plot.grid()
plot.title(args.i.split('/')[-1]+ ' Signal')
plot.xlabel('Time (s)')
plot.ylabel('Amplitude')
plot.subplot(212)
plot.pcolormesh(t, f, spectrogram, cmap = 'tab20')
plot.ylabel('Frequency (Hz)')
plot.yscale('symlog')
plot.xlabel('Time (s)')
plot.grid()
plot.title(args.i.split('/')[-1]+ ' Spectrogram')
plot.tight_layout()
if args.o:
plot.savefig(args.o, dpi = 360)
else:
plot.show() | [
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home/migrations/0013_merge_20190507_1221.py | nurpeiis/RED | 0 | 102223 | <reponame>nurpeiis/RED
# Generated by Django 2.1.3 on 2019-05-07 08:21
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('home', '0008_auto_20190421_1129'),
('home', '0012_team_title'),
]
operations = [
]
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backend/app/rooms/models.py | vpaliy/react-chat | 1 | 93228 | # -*- coding: future_fstrings -*-
from app.database import Model, Column, SurrogatePK, db, relationship
import datetime as dt
import hashlib
class Room(SurrogatePK, Model):
__tablename__ = 'rooms'
name = Column(db.String(80), unique=True, nullable=False)
description = Column(db.String(300), unique=True, nullable=False)
image = Column(db.String(120), nullable=True)
created_at = Column(db.DateTime, nullable=False, default=dt.datetime.utcnow)
updated_at = Column(db.DateTime, nullable=False, default=dt.datetime.utcnow)
@property
def avatar_url(self):
hash = hashlib.md5(self.name.lower()).hexdigest()
return f'https://www.gravatar.com/avatar/{hash}?d=identicon&s={str(50)}'
def __repr__(self):
return '<Room {!r}>'.format(self.name)
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] |
pkgpanda/actions.py | zhe-sun/dcos | 0 | 84781 | <reponame>zhe-sun/dcos<filename>pkgpanda/actions.py
import collections
import logging
import os
import sys
from functools import partial
from subprocess import CalledProcessError, check_call
from pkgpanda import PackageId, requests_fetcher
from pkgpanda.constants import (DCOS_SERVICE_CONFIGURATION_PATH,
SYSCTL_SETTING_KEY)
from pkgpanda.exceptions import FetchError, PackageConflict, ValidationError
from pkgpanda.util import (extract_tarball, if_exists, load_json, load_string,
write_string)
DCOS_TARGET_CONTENTS = """[Install]
WantedBy=multi-user.target
"""
log = logging.getLogger(__name__)
def activate_packages(install, repository, package_ids, systemd, block_systemd):
"""Replace the active package set with package_ids.
install: pkgpanda.Install
repository: pkgpanda.Repository
package_ids: sequence of package IDs to activate
systemd: start/stop systemd services
block_systemd: if systemd, block waiting for systemd services to come up
"""
assert isinstance(package_ids, collections.Sequence)
install.activate(repository.load_packages(package_ids))
if systemd:
_start_dcos_target(block_systemd)
def swap_active_package(install, repository, package_id, systemd, block_systemd):
"""Replace an active package with a package_id with the same name.
swap(install, repository, 'foo--version') will replace the active 'foo'
package with 'foo--version'.
install: pkgpanda.Install
repository: pkgpanda.Repository
package_id: package ID to activate
systemd: start/stop systemd services
block_systemd: if systemd, block waiting for systemd services to come up
"""
active = install.get_active()
# TODO(cmaloney): I guarantee there is a better way to write this and
# I've written the same logic before...
packages_by_name = dict()
for id_str in active:
pkg_id = PackageId(id_str)
packages_by_name[pkg_id.name] = pkg_id
new_id = PackageId(package_id)
if new_id.name not in packages_by_name:
raise ValidationError("No package with name {} currently active to swap with.".format(new_id.name))
packages_by_name[new_id.name] = new_id
new_active = list(map(str, packages_by_name.values()))
# Activate with the new package name
activate_packages(install, repository, new_active, systemd, block_systemd)
def fetch_package(repository, repository_url, package_id, work_dir):
"""Fetch package_id from repository_url into repository.
repository: pkgpanda.Repository
repository_url: URL for remote package repository
package_id: package ID to fetch
work_dir: location for temporary files, used only if repository_url is a file URL with a relative path
"""
def fetcher(id_, target):
return requests_fetcher(repository_url, id_, target, work_dir)
# TODO(cmaloney): Make this not use escape sequences when not at a
# `real` terminal.
sys.stdout.write("\rFetching: {0}".format(package_id))
sys.stdout.flush()
try:
repository.add(fetcher, package_id)
except FetchError as ex:
raise Exception("Unable to fetch package {0}: {1}".format(package_id, ex)) from ex
else:
sys.stdout.write("\rFetched: {0}".format(package_id))
finally:
sys.stdout.write("\n")
sys.stdout.flush()
def add_package_file(repository, package_filename):
"""Add a package to the repository from a file.
repository: pkgpanda.Repository
package_filename: location of the package file
"""
filename_suffix = '.tar.xz'
# Extract Package Id (Filename must be path/{pkg-id}.tar.xz).
name = os.path.basename(package_filename)
if not name.endswith(filename_suffix):
print("ERROR: Can only add package tarballs which have names "
"like {{pkg-id}}{}".format(filename_suffix))
pkg_id = name[:-len(filename_suffix)]
# Validate the package id
PackageId(pkg_id)
def fetch(_, target):
extract_tarball(package_filename, target)
repository.add(fetch, pkg_id)
def remove_package(install, repository, package_id):
"""Remove a package from the local repository.
Errors if any packages in package_ids are activated in install.
install: pkgpanda.Install
repository: pkgpanda.Repository
package_id: package ID to remove from repository
"""
if package_id in install.get_active():
raise PackageConflict("Refusing to remove active package {0}".format(package_id))
sys.stdout.write("\rRemoving: {0}".format(package_id))
sys.stdout.flush()
try:
# Validate package id, that package is installed.
PackageId(package_id)
repository.remove(package_id)
except ValidationError as ex:
raise ValidationError("Invalid package id {0}".format(package_id)) from ex
except OSError as ex:
raise Exception("Error removing package {0}: {1}".format(package_id, ex)) from ex
else:
sys.stdout.write("\rRemoved: {0}".format(package_id))
finally:
sys.stdout.write("\n")
sys.stdout.flush()
def setup(install, repository):
"""Set up a fresh install of DC/OS.
install: pkgpanda.Install
repository: pkgpanda.Repository
"""
# Check for /opt/mesosphere/bootstrap. If not exists, download everything
# and install /etc/systemd/system/mutli-user.target/dcos.target
bootstrap_path = os.path.join(install.root, "bootstrap")
if os.path.exists(bootstrap_path):
# Write, enable /etc/systemd/system/dcos.target for next boot.
dcos_target_dir = os.path.dirname(install.systemd_dir)
try:
os.makedirs(dcos_target_dir)
except FileExistsError:
pass
write_string(os.path.join(dcos_target_dir, "dcos.target"),
DCOS_TARGET_CONTENTS)
_do_bootstrap(install, repository)
# Enable dcos.target only after we have populated it to prevent starting
# up stuff inside of it before we activate the new set of packages.
if install.manage_systemd:
_start_dcos_target(block_systemd=True)
os.remove(bootstrap_path)
# Check for /opt/mesosphere/install_progress. If found, recover the partial
# update.
if os.path.exists("/opt/mesosphere/install_progress"):
took_action, msg = install.recover_swap_active()
if not took_action:
print("No recovery performed: {}".format(msg))
def _start_dcos_target(block_systemd):
no_block = [] if block_systemd else ["--no-block"]
check_call(["systemctl", "daemon-reload"])
check_call(["systemctl", "enable", "dcos.target", '--no-reload'])
check_call(["systemctl", "start", "dcos.target"] + no_block)
def _do_bootstrap(install, repository):
# These files should be set by the environment which initially builds
# the host (cloud-init).
repository_url = if_exists(load_string, install.get_config_filename("setup-flags/repository-url"))
# TODO(cmaloney): If there is 1+ master, grab the active config from a master.
# If the config can't be grabbed from any of them, fail.
def fetcher(id, target):
if repository_url is None:
raise ValidationError("ERROR: Non-local package {} but no repository url given.".format(repository_url))
return requests_fetcher(repository_url, id, target, os.getcwd())
# Copy host/cluster-specific packages written to the filesystem manually
# from the setup-packages folder into the repository. Do not overwrite or
# merge existing packages, hard fail instead.
setup_packages_to_activate = []
setup_pkg_dir = install.get_config_filename("setup-packages")
copy_fetcher = partial(_copy_fetcher, setup_pkg_dir)
if os.path.exists(setup_pkg_dir):
for pkg_id_str in os.listdir(setup_pkg_dir):
print("Installing setup package: {}".format(pkg_id_str))
if not PackageId.is_id(pkg_id_str):
raise ValidationError("Invalid package id in setup package: {}".format(pkg_id_str))
pkg_id = PackageId(pkg_id_str)
if pkg_id.version != "setup":
raise ValidationError(
"Setup packages (those in `{0}`) must have the version setup. "
"Bad package: {1}".format(setup_pkg_dir, pkg_id_str))
# Make sure there is no existing package
if repository.has_package(pkg_id_str):
print("WARNING: Ignoring already installed package {}".format(pkg_id_str))
repository.add(copy_fetcher, pkg_id_str)
setup_packages_to_activate.append(pkg_id_str)
# If active.json is set on the host, use that as the set of packages to
# activate. Otherwise just use the set of currently active packages (those
# active in the bootstrap tarball)
to_activate = None
active_path = install.get_config_filename("setup-flags/active.json")
if os.path.exists(active_path):
print("Loaded active packages from", active_path)
to_activate = load_json(active_path)
# Ensure all packages are local
print("Ensuring all packages in active set {} are local".format(",".join(to_activate)))
for package in to_activate:
repository.add(fetcher, package)
else:
print("Calculated active packages from bootstrap tarball")
to_activate = list(install.get_active())
# Fetch and activate all requested additional packages to accompany the bootstrap packages.
cluster_packages_filename = install.get_config_filename("setup-flags/cluster-packages.json")
cluster_packages = if_exists(load_json, cluster_packages_filename)
print("Checking for cluster packages in:", cluster_packages_filename)
if cluster_packages:
if not isinstance(cluster_packages, list):
print('ERROR: {} should contain a JSON list of packages. Got a {}'.format(cluster_packages_filename,
type(cluster_packages)))
print("Loading cluster-packages: {}".format(cluster_packages))
for package_id_str in cluster_packages:
# Validate the package ids
pkg_id = PackageId(package_id_str)
# Fetch the packages if not local
if not repository.has_package(package_id_str):
repository.add(fetcher, package_id_str)
# Add the package to the set to activate
setup_packages_to_activate.append(package_id_str)
else:
print("No cluster-packages specified")
# Calculate the full set of final packages (Explicit activations + setup packages).
# De-duplicate using a set.
to_activate = list(set(to_activate + setup_packages_to_activate))
print("Activating packages")
install.activate(repository.load_packages(to_activate))
def _copy_fetcher(setup_pkg_dir, id_, target):
src_pkg_path = os.path.join(setup_pkg_dir, id_) + "/"
check_call(["cp", "-rp", src_pkg_path, target])
def _apply_sysctl(setting, service):
try:
check_call(["sysctl", "-q", "-w", setting])
except CalledProcessError:
log.warning("sysctl {setting} not set for {service}".format(setting=setting, service=service))
def _apply_sysctl_settings(sysctl_settings, service):
for setting, value in sysctl_settings.get(service, {}).items():
_apply_sysctl("{setting}={value}".format(setting=setting, value=value), service)
def apply_service_configuration(service):
if not os.path.exists(DCOS_SERVICE_CONFIGURATION_PATH):
return
dcos_service_properties = load_json(DCOS_SERVICE_CONFIGURATION_PATH)
if SYSCTL_SETTING_KEY in dcos_service_properties:
_apply_sysctl_settings(dcos_service_properties[SYSCTL_SETTING_KEY], service)
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hivemind/client/__init__.py | Vsevolod-pl/hivemind | 11 | 123088 | from hivemind.client.expert import RemoteExpert
from hivemind.client.moe import RemoteMixtureOfExperts
from hivemind.client.switch_moe import RemoteSwitchMixtureOfExperts
from hivemind.client.averaging import DecentralizedAverager
from hivemind.client.averaging.training import TrainingAverager
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algorithms/acm/sort.py | MarkWh1te/Gate-of-Babylon | 2 | 185870 | """Sorting Algorithms In Python
"""
"""
Dutch national flag problem
https://en.wikipedia.org/wiki/Dutch_national_flag_problem
"""
def sort_colors(nums: [int], pivot: int)->[int]:
small = 0
current = 1
big = len(nums)-1
while current < big:
if nums[current] == pivot:
current += 1
elif nums[current] > pivot:
nums[big], nums[current] = nums[current], nums[big]
big -= 1
else:
nums[current], nums[small] = nums[small], nums[current]
small += 1
current += 1
return nums
def quick_sort(numbers: list):
if len(numbers) <= 1:
return numbers
pivot = numbers[0]
left = [i for i in numbers[1:] if i <= pivot]
right = [i for i in numbers[1:] if i > pivot]
return quick_sort(left) + [pivot] + quick_sort(right)
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pypospack/potential/threebody.py | eragasa/pypospack | 4 | 99153 | <reponame>eragasa/pypospack<filename>pypospack/potential/threebody.py
from collections import OrderedDict
from pypospack.potential import Potential
class ThreeBodyPotential(Potential):
def _init_parameter_names(self):
# TODO: This is only written for a single element potential
for i in range(len(self.symbols)):
for j in range(len(self.symbols)):
for k in range(len(self.symbols)):
el1 = self.symbols[i]
el2 = self.symbols[j]
el3 = self.symbols[k]
self._add_parameter_names(el1, el2, el3)
def _add_parameter_names(self,el1,el2,el3):
s = "{}{}{}".format(el1,el2,el3)
self.parameter_names = []
for p in self.threebody_parameter_names:
self.parameter_names.append('{}__{}'.format(s,p))
def _init_parameters(self):
self.parameters = OrderedDict()
for p in self.parameter_names:
self.parameters[p] = None
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] |
tests/unit/test_placeholder.py | djpugh/docserver-client | 1 | 167803 | <filename>tests/unit/test_placeholder.py<gh_stars>1-10
import unittest
class PlaceholderTestCase(unittest.TestCase):
def test_x(self):
self.assertEqual(1, 1)
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OPTICS2.py | k-kapp/Clustering-Algos | 0 | 27363 | # -*- coding: utf-8 -*-
"""
Created on Mon Nov 30 21:25:24 2015
@author: Konrad
"""
import copy
import numpy as np
import matplotlib.pyplot as plt
import scipy.special as sc_p
def gen_clusters(means, num_each):
tup = ();
for m in means:
tup = tup + (np.random.multivariate_normal(m, np.diag(np.ones(2)), num_each),)
data = np.concatenate(tup);
np.random.shuffle(data);
return data;
def make_pts(data):
pts = [];
for pos in data:
pts.append(Point(pos));
return pts;
def euclid(obj1, obj2):
if (isinstance(obj1, Point) and isinstance(obj2, Point)):
return np.sqrt(sum(( obj1.pos - obj2.pos )**2));
elif (isinstance(obj1, np.ndarray) and isinstance(obj2, np.ndarray)):
return np.sqrt(sum(( obj1 - obj2 )**2))
else:
return None;
class Point:
def __init__(self, pos):
self.pos = copy.deepcopy(pos);
self.processed = False;
self.core_dist = None;
self.reach_dist = None;
self.in_seed = False;
class OPTICS:
def __init__(self, min_pts, data, max_eps = None):
self.max_eps = max_eps;
self.min_pts = min_pts;
self.data = copy.deepcopy(data);
self.dim = self.data[0].pos.size;
self.main_list = [];
if (self.max_eps == None):
self.get_max_eps();
self.main_loop();
def __call__(self, main_idx):
return self.data[self.main_list[main_idx]].reach_dist;
def main_loop(self):
for idx, obj in enumerate(self.data):
if (not obj.processed):
self.expand_point(idx);
for idx, obj in enumerate(self.data):
if (not obj.processed):
self.append_main(idx);
def get_max_eps(self):
extr_x = self.get_extr_x();
extr_y = self.get_extr_y();
area = (extr_x[1] - extr_x[0])*(extr_y[1] - extr_y[0]);
self.max_eps = ((area*self.min_pts*sc_p.gamma(2))/(len(self.data)*np.sqrt(np.pi**2)))**0.5
def get_extr_x(self):
min_x = float("inf");
max_x = -float("inf");
for obj in self.data:
if obj.pos[0] < min_x:
min_x = obj.pos[0];
if obj.pos[0] > max_x:
max_x = obj.pos[0];
return (min_x, max_x);
def get_extr_y(self):
min_y = float("inf");
max_y = -float("inf");
for obj in self.data:
if obj.pos[1] < min_y:
min_y = obj.pos[1];
if obj.pos[1] > max_y:
max_y = obj.pos[1];
return (min_y, max_y);
def append_main(self, idx):
self.data[idx].processed = True;
if (self.data[idx].reach_dist == None):
self.data[idx].reach_dist = self.max_eps;
self.main_list.append(idx);
def expand_point(self, idx):
self.get_neighbours(idx);
self.get_core_dist(idx);
if (self.data[idx].core_dist == -1):
return;
else:
self.data[idx].processed = True;
self.append_main(idx);
seed_list = [];
self.append_seed(seed_list, self.data[idx].neighbours, idx)
while (len(seed_list) > 0):
curr_idx = seed_list[0];
self.get_neighbours(curr_idx);
self.get_core_dist(curr_idx);
self.data[curr_idx].processed = True;
self.append_main(curr_idx);
self.remove_seed(seed_list);
if (not (self.data[curr_idx].core_dist == -1)):
self.append_seed(seed_list, self.data[curr_idx].neighbours, curr_idx);
def get_core_dist(self, idx):
if (len(self.data[idx].neighbours) >= self.min_pts):
self.data[idx].core_dist = self.data[idx].neighbours[self.min_pts - 1][1];
else:
self.data[idx].core_dist = -1;
def get_reach_dist(self, center_idx, idx, dist):
r_dist = max(dist, self.data[center_idx].core_dist);
if (self.data[idx].reach_dist == None):
self.data[idx].reach_dist = r_dist;
return True;
elif (self.data[idx].reach_dist > r_dist):
self.data[idx].reach_dist = r_dist;
return True;
else:
return False;
def get_neighbours(self, idx):
self.data[idx].neighbours = [];
for n_idx, obj in enumerate(self.data):
dist = euclid(obj, self.data[idx])
if (dist <= self.max_eps):
self.data[idx].neighbours.append([n_idx, dist]);
self.data[idx].neighbours.sort(key = lambda x : x[1]);
def append_seed(self, seed_list, neighbours, center_idx):
for n_tup in neighbours:
changed = self.get_reach_dist(center_idx, n_tup[0], n_tup[1]);
if (self.data[n_tup[0]].in_seed and changed):
del seed_list[seed_list.index(n_tup[0])];
self.data[n_tup[0]].in_seed = False;
elif (self.data[n_tup[0]].processed or self.data[n_tup[0]].in_seed):
continue;
for idx, obj in enumerate(seed_list):
if ( self.data[n_tup[0]].reach_dist < self.data[obj].reach_dist ):
seed_list.insert(idx, n_tup[0]);
self.data[n_tup[0]].in_seed = True;
break;
if (not self.data[n_tup[0]].in_seed):
seed_list.append(n_tup[0]);
self.data[n_tup[0]].in_seed = True;
def remove_seed(self, seed_list):
self.data[seed_list[0]].in_seed = False;
del seed_list[0];
def reach_plot(self):
x = list(range(len(self.main_list)));
y = [];
for idx in self.main_list:
y.append(self.data[idx].reach_dist);
f, ax = plt.subplots();
ax.bar(x, y);
def print_reach_dist(self):
for idx in self.main_list:
print (idx)
print (self.data[idx].reach_dist)
def plot_data(self):
x = [];
y = [];
for obj in self.data:
x.append(obj.pos[0]);
y.append(obj.pos[1]);
f, ax = plt.subplots();
ax.scatter(x, y);
def get_num_clusters(self):
clusters = [];
up = True;
top, bottom = -1, -1;
for i, idx in enumerate(self.main_list[:-1]):
if (up and (self.data[idx].reach_dist > self.data[self.main_list[i + 1]])):
up = not up;
if (not bottom == -1):
clusters.append(top - bottom);
top = self.data[idx].reach_dist;
continue;
if (not up) and (self.data[idx].reach_dist < self.data[self.main_list[i + 1]].reach_dist):
up = not up;
bottom = self.data[idx].reach_dist;
class Clusters:
def __init__(optics_obj, eps):
self.optics_obj = optics_obj;
self.main_list = optics_obj.main_list;
self.eps = eps;
self.min_pts = optics_obj.min_pts;
def find(self):
idx = 0;
#down, up = False, False;
downs = [];
clusters = [];
while idx < len(self.main_list):
diff = self.main_list[idx] - self.main_list[idx + 1];
if (diff >= self.optics_obj(idx)*self.eps):
new_down, idx = self.proc_down(idx);
downs.append([new_down, -float("inf")]);
#glob_mib = self.optics_obj(downs[-1][0][0]]);
#self.filter_downs(glob_mib, downs);
elif (-diff >= self.optics_obj(idx)*self.eps):
glob_mib = self.get_glob_mib(downs[-1], idx);
self.filter_downs(glob_mib, downs);
up, idx = self.proc_up(idx);
for down in downs:
if (self.optics_obj(up[1]).reach_dist*(1 - self.eps) >= down[1]):
clusters.append((down[0][0], up[1]));
else:
idx += 1;
def get_glob_mib(self, last_down, curr_idx):
begin_idx, end_idx = last_down[0][1], curr_idx;
glob_mib = -float("inf");
for i in range(begin_idx, end_idx + 1):
if (self.optics_obj(i) > glob_mib):
glob_mib = self.optics_obj(i);
return glob_mib;
def proc_down(self, idx):
bad_inrow = 0;
begin_idx = idx;
while (idx < len(self.main_list)):
idx += 1;
diff = self.main_list[idx].reach_dist - self.main_list[idx + 1].reach_dist;
if (diff < 0):
return (begin_idx, idx - 1);
if (diff > 0):
if (diff >= self.eps*self.main_list[idx]):
bad_inrow = 0;
else:
if (bad_inrow == 0):
last_good = idx - 1;
bad_inrow += 1;
if bad_inrow > self.min_pts:
# include a check that ensures region does not have
# length zero?
return (begin_idx, last_good), idx;
def proc_up(self, idx):
bad_inrow = 0;
begin_idx = idx;
while (idx < len(self.main_list)):
idx += 1;
diff = self.main_list[idx].reach_dist[idx + 1] - self.main_list[idx].reach_dist;
if (diff < 0):
return (begin_idx, idx - 1);
if (diff > 0):
if (diff >= self.eps*self.main_list[idx + 1]):
bad_inrow = 0;
else:
if (bad_inrow == 0):
last_good = idx - 1;
bad_inrow += 1;
if (bad_inrow > self.min_pts):
return (begin_idx, last_good), idx;
def filter_downs(self, glob_mib, downs):
del_idx = [];
for idx, obj in enumerate(downs[:-1]):
if self.main_list[obj[0][0]].reach_dist*(1 - self.eps) < glob_mib:
del_idx.append(idx);
elif (obj[1] < glob_mib):
downs[idx][1] = glob_mib;
del_idx.reverse();
for i in del_idx:
del downs[i];
dat = gen_clusters([[1, 1], [6, 7], [10, 15], [15, 15]], 200);
data = make_pts(dat);
optics = OPTICS(15, data);
optics.reach_plot();
optics.plot_data();
plt.show();
#optics.print_reach_dist();
print ("Done") | [
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] |
model_utils.py | PreferredAI/vista-net | 75 | 70693 | <filename>model_utils.py
import tensorflow as tf
import numpy as np
import os
def get_shape(tensor):
static_shape = tensor.shape.as_list()
dynamic_shape = tf.unstack(tf.shape(tensor))
dims = [s[1] if s[0] is None else s[0]
for s in zip(static_shape, dynamic_shape)]
return dims
def make_dirs(dir):
next_idx = 1
if tf.gfile.Exists(dir):
folder_list = sorted([int(f) for f in os.listdir(dir)])
if len(folder_list) > 0:
next_idx = folder_list[-1] + 1
new_folder = os.path.join(dir, str(next_idx))
tf.gfile.MakeDirs(new_folder)
return new_folder
def count_parameters(trained_vars):
total_parameters = 0
print('=' * 100)
for variable in trained_vars:
variable_parameters = 1
for dim in variable.get_shape():
variable_parameters *= dim.value
print('{:70} {:20} params'.format(variable.name, variable_parameters))
print('-' * 100)
total_parameters += variable_parameters
print('=' * 100)
print("Total trainable parameters: %d" % total_parameters)
print('=' * 100)
def load_glove(vocab_size, emb_size):
print('Loading Glove word embeddings ...')
embedding_weights = {}
f = open('glove/glove.6B.{}d.txt'.format(emb_size), encoding='utf-8')
for line in f:
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embedding_weights[word] = coefs
f.close()
print('Total {} word vectors in Glove 6B {}d.'.format(len(embedding_weights), emb_size))
embedding_matrix = np.random.uniform(-0.5, 0.5, (vocab_size, emb_size))
embedding_matrix[0, :] = np.zeros(emb_size) # alignment word for blank image
oov_count = 0
from data_preprocess import load_vocabulary
for word, i in load_vocabulary().items():
embedding_vector = embedding_weights.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
else:
oov_count += 1
print('Number of OOV words: %d' % oov_count)
return embedding_matrix
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] |
Students/Zephyr/Exercise 1.py | MikelShifrin/Python1 | 3 | 33943 | <filename>Students/Zephyr/Exercise 1.py
name = input('Please enter your name:\n')
age = int(input("Please enter your age:\n"))
color = input('Enter your favorite color:\n')
animal = input('Enter your favorite animal:\n')
print('Hello my name is' , name , '.')
print('I am' , age , 'years old.')
print('My favorite color is' , color ,'.')
print('My favorite animal is the' , animal , '.')
| [
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plasmapy/examples/plot_distribution.py | techieashish/PlasmaPy | 1 | 30683 | <reponame>techieashish/PlasmaPy<gh_stars>1-10
"""
1D Maxwellian distribution function
===================================
We import the usual modules, and the hero of this notebook,
the Maxwellian 1D distribution:
"""
import numpy as np
from astropy import units as u
import matplotlib.pyplot as plt
from astropy.constants import (m_e, k_B)
from plasmapy.formulary import Maxwellian_1D
############################################################
# Given we'll be plotting, import astropy's quantity support:
from astropy.visualization import quantity_support
quantity_support()
############################################################
# As a first example, let's get the probability density of
# finding an electron with a speed of 1 m/s if we have a
# plasma at a temperature of 30 000 K:
p_dens = Maxwellian_1D(v=1 * u.m / u.s,
T=30000 * u.K,
particle='e',
v_drift=0 * u.m / u.s)
print(p_dens)
############################################################
# Note the units! Integrated over speed, this will give us a
# probability. Let's test that for a bunch of particles:
T = 3e4 * u.K
dv = 10 * u.m / u.s
v = np.arange(-5e6, 5e6, 10) * u.m / u.s
############################################################
# Check that the integral over all speeds is 1
# (the particle has to be somewhere):
for particle in ['p', 'e']:
pdf = Maxwellian_1D(v, T=T, particle=particle)
integral = (pdf).sum() * dv
print(f"Integral value for {particle}: {integral}")
plt.plot(v, pdf, label=particle)
plt.legend()
############################################################
# The standard deviation of this distribution should give us back the
# temperature:
std = np.sqrt((Maxwellian_1D(v, T=T, particle='e') * v ** 2 * dv).sum())
T_theo = (std ** 2 / k_B * m_e).to(u.K)
print('T from standard deviation:', T_theo)
print('Initial T:', T)
| [
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] |
babybuddy/migrations/0002_add_settings.py | Gitoffomalawn/babybuddy | 922 | 118771 | <reponame>Gitoffomalawn/babybuddy
# -*- coding: utf-8 -*-
from django.db import migrations
def add_settings(apps, schema_editor):
Settings = apps.get_model("babybuddy", "Settings")
User = apps.get_model("auth", "User")
for user in User.objects.all():
if Settings.objects.filter(user=user).count() == 0:
settings = Settings.objects.create(user=user)
settings.save()
class Migration(migrations.Migration):
initial = True
dependencies = [
("babybuddy", "0001_initial"),
]
operations = [
migrations.RunPython(add_settings, reverse_code=migrations.RunPython.noop),
]
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] |
chemdataextractor/scrape/__init__.py | edbeard/chemdataextractor-uvvis2018 | 6 | 66749 | # -*- coding: utf-8 -*-
"""
chemdataextractor.scrape
~~~~~~~~~~~~~~~~~~~~~~~~
Declarative scraping framework for extracting structured data from HTML and XML documents.
:copyright: Copyright 2016 by <NAME>.
:license: MIT, see LICENSE file for more details.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
#: Block level HTML elements
BLOCK_ELEMENTS = {
'p', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'ul', 'ol', 'pre', 'dd', 'dl', 'div', 'noscript', 'blockquote', 'form',
'hr', 'table', 'fieldset', 'address', 'article', 'aside', 'audio', 'canvas', 'figcaption', 'figure', 'footer',
'header', 'hgroup', 'output', 'section', 'body', 'head', 'title', 'tr', 'td', 'th', 'thead', 'tfoot', 'dt', 'li',
'tbody',
}
#: Inline level HTML elements
INLINE_ELEMENTS = {
'b', 'big', 'i', 'small', 'tt', 'abbr', 'acronym', 'cite', 'code', 'dfn', 'em', 'kbd', 'strong', 'samp', 'var',
'a', 'bdo', 'br', 'img', 'map', 'object', 'q', 'script', 'span', 'sub', 'sup', 'button', 'input', 'label',
'select', 'textarea', 'blink', 'font', 'marquee', 'nobr', 's', 'strike', 'u', 'wbr',
}
from .clean import Cleaner, clean, clean_html, clean_markup
from .entity import Entity, EntityList, DocumentEntity
from .fields import StringField, IntField, FloatField, BoolField, DateTimeField, EntityField, UrlField
from .scraper import HtmlFormat, XmlFormat, GetRequester, PostRequester, UrlScraper, RssScraper, SearchScraper
from .selector import Selector, SelectorList
from .pub.nlm import NlmXmlDocument
from .pub.rsc import RscHtmlDocument
from .pub.springer import SpringerXmlDocument
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application/siteApp/urls.py | Marcelotsvaz/vaz-projects | 0 | 859 | #
# VAZ Projects
#
#
# Author: <NAME> <<EMAIL>>
from django.urls import path
from . import views
app_name = 'siteApp'
urlpatterns = [
path( '', views.Home.as_view(), name = 'home' ),
path( 'about-me', views.About_me.as_view(), name = 'about_me' ),
path( 'search', views.Search.as_view(), name = 'search' ),
path( 'search/page/<int:page>', views.Search.as_view(), name = 'search' ),
path( 'sitemap.xml', views.Sitemap.as_view(), name = 'sitemap' ),
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examples/poverty-detection-keras/pred_cong_pd.py | rpitonak/eo-learn | 1 | 101937 | <filename>examples/poverty-detection-keras/pred_cong_pd.py
"""
Note: After we have the tf-serving image running, we don't actually need following script.
pred_config.py
List some configuration parameters for prediction
"""
import os
from os import path as op
preds_dir = op.join(os.getcwd(), "preds")
plot_dir = op.join(os.getcwd(), "plots")
ckpt_dir = op.join(os.getcwd(), "models")
# Params to run inference on some tiles in S3
pred_params = dict(model_time='0813_192409', #201910_L5.39
single_batch_size=16, # Number of images seen by a single GPU
n_gpus=1,
deci_prec=2) # Number of decimal places in prediction precision
pred_params.update(dict(model_arch_fname='{}_arch.yaml'.format(pred_params['model_time']),
model_params_fname='{}_params.yaml'.format(pred_params['model_time']),
model_weights_fname='{}_L1.20_E13_weights.h5'.format(pred_params['model_time'])))
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msl/qt/exceptions.py | rebeccahawke/msl-qt | 0 | 43114 | """
Exception handling used by **MSL-Qt**.
"""
import logging
import traceback
from . import QtWidgets, Qt, application
logger = logging.getLogger(__name__)
def excepthook(exc_type, exc_obj, exc_traceback):
"""Displays unhandled exceptions in a :class:`QtWidgets.QMessageBox`.
See :func:`sys.excepthook` for more details.
To implement the :func:`excepthook` in your own application include the following:
.. code-block:: python
import sys
from msl import qt
sys.excepthook = qt.excepthook
"""
def event_handler(e):
"""Resize the QMessageBox"""
result = QtWidgets.QMessageBox.event(msg, e)
detailed_text = msg.findChild(QtWidgets.QTextEdit)
if not detailed_text or not detailed_text.isVisible():
return result
detailed_text.setMaximumSize(QtWidgets.QWIDGETSIZE_MAX, QtWidgets.QWIDGETSIZE_MAX)
detailed_text.setSizePolicy(Qt.QSizePolicy.Expanding, Qt.QSizePolicy.Expanding)
msg.setMaximumSize(QtWidgets.QWIDGETSIZE_MAX, QtWidgets.QWIDGETSIZE_MAX)
msg.setSizePolicy(Qt.QSizePolicy.Expanding, Qt.QSizePolicy.Expanding)
return result
details = ''.join(
['Traceback (most recent call last):\n'] +
traceback.format_tb(exc_traceback) +
[exc_obj.__class__.__name__ + ': ' + str(exc_obj)]
)
logger.error(details)
# ensure that a QApplication exists
app = application()
# add a prefix to the title bar
w = app.activeWindow()
prefix = 'MSL' if w is None or not w.windowTitle() else w.windowTitle()
msg = QtWidgets.QMessageBox()
# want to be able to resize the QMessageBox to allow for the
# DetailedText to be read easier
# see http://www.qtcentre.org/threads/24888-Resizing-a-QMessageBox
msg.event = event_handler
msg.setSizeGripEnabled(True)
msg.setWindowTitle(prefix + ' || Unhandled Exception')
msg.setIcon(QtWidgets.QMessageBox.Critical)
msg.setText(exc_obj.__class__.__name__ + ':')
msg.setInformativeText(str(exc_obj))
msg.setDetailedText(details)
msg.raise_()
msg.exec_()
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] |
Data Structures/Heap/JesseAndCookies.py | aibenStunner/HackerRank | 2 | 37149 | #!/bin/python3
import os
import sys
class minHeap:
def __init__(self):
self.items = []
self.size = len(self.items)
def getLeftChildIndex(self, parentIndex):
return 2 * parentIndex + 1
def getRightChildIndex(self, parentIndex):
return 2 * parentIndex + 2
def getParentIndex(self, childIndex):
return int((childIndex - 1) / 2)
def hasLeftChild(self, index):
return self.getLeftChildIndex(index) < self.size
def hasRightChild(self, index):
return self.getRightChildIndex(index) < self.size
def hasParent(self, index):
return self.getParentIndex(index) >= 0
def leftChild(self, index):
return self.items[self.getLeftChildIndex(index)]
def rightChild(self, index):
return self.items[self.getRightChildIndex(index)]
def parent(self, index):
return self.items[self.getParentIndex(index)]
def swap(self, indexOne, indexTwo):
temp = self.items[indexOne]
self.items[indexOne] = self.items[indexTwo]
self.items[indexTwo] = temp
def pop(self):
if self.size == 0:
raise Exception("Empty Heap!!")
else:
item = self.items[0]
self.items[0] = self.items[self.size - 1]
del(self.items[self.size - 1])
self.size = len(self.items)
self.heapifyDown()
return item
def peek(self):
if self.size == 0:
raise Exception("Empty Heap!!")
else:
return self.items[0]
def add(self, item):
self.items.append(item)
self.size = len(self.items)
self.heapifyUp()
def heapify(self, arr):
for elem in arr:
self.add(elem)
def heapifyUp(self):
index = self.size - 1
while (self.hasParent(index) and self.parent(index) > self.items[index]):
self.swap(self.getParentIndex(index), index)
index = self.getParentIndex(index)
def heapifyDown(self):
index = 0
while(self.hasLeftChild(index)):
smallerChildIndex = self.getLeftChildIndex(index)
if(self.hasRightChild(index) and self.rightChild(index) < self.leftChild(index)):
smallerChildIndex = self.getRightChildIndex(index)
if(self.items[index] < self.items[smallerChildIndex]):
break
else:
self.swap(index, smallerChildIndex)
index = smallerChildIndex
#
# Complete the cookies function below.
#
def cookies(k, A):
qHeap = minHeap()
qHeap.heapify(A)
count = 0
try:
while qHeap.peek() < k:
count += 1
a, b = qHeap.pop(), qHeap.pop()
newCookie = (1*a)+(2*b)
qHeap.add(newCookie)
return count
except:
return -1
if __name__ == '__main__':
# fptr = open(os.environ['OUTPUT_PATH'], 'w')
nk = input().split()
n = int(nk[0])
k = int(nk[1])
A = list(map(int, input().rstrip().split()))
result = cookies(k, A)
print(result)
# fptr.write(str(result) + '\n')
# fptr.close()
# def cookies(k, A):
# from heapq import heappop,heappush,heapify
# heapify(A)
# fC = 0
# try:
# while A[0] < k:
# fC+=1
# c1 = heappop(A)
# c2 = heappop(A)
# newCookie=(1*c1)+(2*c2)
# heappush(A,newCookie)
# return fC
# except:
# return -1
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] |
sdk/eventhub/azure-eventhub/tests/eventhub_preparer.py | rsdoherty/azure-sdk-for-python | 2,728 | 1607590 | <reponame>rsdoherty/azure-sdk-for-python<filename>sdk/eventhub/azure-eventhub/tests/eventhub_preparer.py
#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#--------------------------------------------------------------------------
# This EventHubs resource preparer is for future use after the tests are all compatible with
# The resource preparer.
import functools
from azure.mgmt.eventhub import EventHubManagementClient
from azure.mgmt.eventhub.models import Eventhub, AccessRights
from azure_devtools.scenario_tests.exceptions import AzureTestError
from devtools_testutils import (
ResourceGroupPreparer, AzureMgmtPreparer, FakeResource
)
from devtools_testutils.resource_testcase import RESOURCE_GROUP_PARAM
EVENTHUB_DEFAULT_AUTH_RULE_NAME = 'RootManageSharedAccessKey'
EVENTHUB_NAMESPACE_PARAM = 'eventhub_namespace'
EVENTHUB_PARAM = 'eventhub'
EVENTHUB_AUTHORIZATION_RULE_PARAM = 'eventhub_authorization_rule'
# Service Bus Namespace Preparer and its shorthand decorator
class EventHubNamespacePreparer(AzureMgmtPreparer):
def __init__(self,
name_prefix='',
use_cache=False,
sku='Standard', location='westus',
parameter_name=EVENTHUB_NAMESPACE_PARAM,
resource_group_parameter_name=RESOURCE_GROUP_PARAM,
disable_recording=True, playback_fake_resource=None,
client_kwargs=None, random_name_enabled=True):
super(EventHubNamespacePreparer, self).__init__(name_prefix, 24,
random_name_enabled=random_name_enabled,
disable_recording=disable_recording,
playback_fake_resource=playback_fake_resource,
client_kwargs=client_kwargs)
self.location = location
self.sku = sku
self.resource_group_parameter_name = resource_group_parameter_name
self.parameter_name = parameter_name
self.connection_string = ''
if random_name_enabled:
self.resource_moniker = self.name_prefix + "ehname"
self.set_cache(use_cache, sku, location)
def create_resource(self, name, **kwargs):
if self.is_live:
self.client = self.create_mgmt_client(EventHubManagementClient) # type: EventHubManagementClient
group = self._get_resource_group(**kwargs)
namespace_async_operation = self.client.namespaces.begin_create_or_update(
group.name,
name,
{
'sku': {'name': self.sku},
'location': self.location,
}
)
self.resource = namespace_async_operation.result()
key = self.client.namespaces.list_keys(group.name, name, EVENTHUB_DEFAULT_AUTH_RULE_NAME)
self.connection_string = key.primary_connection_string
self.key_name = key.key_name
self.primary_key = key.primary_key
else:
self.resource = FakeResource(name=name, id=name)
self.connection_string = 'Endpoint=sb://{}.servicebus.windows.net/;SharedAccessKeyName=test;SharedAccessKey=<KEY>='.format(name)
self.key_name = EVENTHUB_DEFAULT_AUTH_RULE_NAME
self.primary_key = '<KEY>
return {
self.parameter_name: self.resource,
'{}_connection_string'.format(self.parameter_name): self.connection_string,
'{}_key_name'.format(self.parameter_name): self.key_name,
'{}_primary_key'.format(self.parameter_name): self.primary_key,
'{}_management_client'.format(self.parameter_name): self.client
}
def remove_resource(self, name, **kwargs):
if self.is_live:
group = self._get_resource_group(**kwargs)
self.client.namespaces.begin_delete(group.name, name).wait()
def _get_resource_group(self, **kwargs):
try:
return kwargs.get(self.resource_group_parameter_name)
except KeyError:
template = 'To create an event hub a resource group is required. Please add ' \
'decorator @{} in front of this event hub preparer.'
raise AzureTestError(template.format(ResourceGroupPreparer.__name__))
# Shared base class for event hub sub-resources that require a namespace and RG to exist.
class _EventHubChildResourcePreparer(AzureMgmtPreparer):
def __init__(self,
name_prefix='',
resource_group_parameter_name=RESOURCE_GROUP_PARAM,
eventhub_namespace_parameter_name=EVENTHUB_NAMESPACE_PARAM,
disable_recording=True, playback_fake_resource=None,
client_kwargs=None, random_name_enabled=True):
super(_EventHubChildResourcePreparer, self).__init__(name_prefix, 24,
random_name_enabled=random_name_enabled,
disable_recording=disable_recording,
playback_fake_resource=playback_fake_resource,
client_kwargs=client_kwargs)
self.resource_group_parameter_name = resource_group_parameter_name
self.eventhub_namespace_parameter_name = eventhub_namespace_parameter_name
def _get_resource_group(self, **kwargs):
try:
return kwargs.get(self.resource_group_parameter_name)
except KeyError:
template = 'To create this event hub child resource event hub a resource group is required. Please add ' \
'decorator @{} in front of this event hub preparer.'
raise AzureTestError(template.format(ResourceGroupPreparer.__name__))
def _get_namespace(self, **kwargs):
try:
return kwargs.get(self.eventhub_namespace_parameter_name)
except KeyError:
template = 'To create this event hub child resource a event hub namespace is required. Please add ' \
'decorator @{} in front of this event hub preparer.'
raise AzureTestError(template.format(EventHubNamespacePreparer.__name__))
class EventHubPreparer(_EventHubChildResourcePreparer):
def __init__(self,
name_prefix='',
use_cache=False,
parameter_name=EVENTHUB_PARAM,
resource_group_parameter_name=RESOURCE_GROUP_PARAM,
eventhub_namespace_parameter_name=EVENTHUB_NAMESPACE_PARAM,
disable_recording=True, playback_fake_resource=None,
client_kwargs=None, random_name_enabled=True):
super(EventHubPreparer, self).__init__(name_prefix,
random_name_enabled=random_name_enabled,
resource_group_parameter_name=resource_group_parameter_name,
eventhub_namespace_parameter_name=eventhub_namespace_parameter_name,
disable_recording=disable_recording,
playback_fake_resource=playback_fake_resource,
client_kwargs=client_kwargs)
self.parameter_name = parameter_name
if random_name_enabled:
self.resource_moniker = self.name_prefix + "eventhub"
self.set_cache(use_cache)
def create_resource(self, name, **kwargs):
if self.is_live:
self.client = self.create_mgmt_client(EventHubManagementClient)
group = self._get_resource_group(**kwargs)
namespace = self._get_namespace(**kwargs)
self.resource = self.client.event_hubs.create_or_update(
group.name,
namespace.name,
name,
{}
)
else:
self.resource = FakeResource(name=name, id=name)
return {
self.parameter_name: self.resource,
}
def remove_resource(self, name, **kwargs):
if self.is_live:
group = self._get_resource_group(**kwargs)
namespace = self._get_namespace(**kwargs)
self.client.event_hubs.delete(group.name, namespace.name, name, polling=False)
class EventHubNamespaceAuthorizationRulePreparer(_EventHubChildResourcePreparer):
def __init__(self,
name_prefix='',
use_cache=False,
access_rights=[AccessRights.manage, AccessRights.send, AccessRights.listen],
parameter_name=EVENTHUB_AUTHORIZATION_RULE_PARAM,
resource_group_parameter_name=RESOURCE_GROUP_PARAM,
eventhub_namespace_parameter_name=EVENTHUB_NAMESPACE_PARAM,
disable_recording=True, playback_fake_resource=None,
client_kwargs=None, random_name_enabled=True):
super(EventHubNamespaceAuthorizationRulePreparer, self).__init__(name_prefix,
random_name_enabled=random_name_enabled,
resource_group_parameter_name=resource_group_parameter_name,
eventhub_namespace_parameter_name=eventhub_namespace_parameter_name,
disable_recording=disable_recording,
playback_fake_resource=playback_fake_resource,
client_kwargs=client_kwargs)
self.parameter_name = parameter_name
self.access_rights = access_rights
if random_name_enabled:
self.resource_moniker = self.name_prefix + "sbnameauth"
self.set_cache(use_cache, access_rights)
def create_resource(self, name, **kwargs):
if self.is_live:
self.client = self.create_mgmt_client(EventHubManagementClient)
group = self._get_resource_group(**kwargs)
namespace = self._get_namespace(**kwargs)
self.resource = self.client.namespaces.create_or_update_authorization_rule(
group.name,
namespace.name,
name,
self.access_rights
)
key = self.client.namespaces.list_keys(group.name, namespace.name, name)
connection_string = key.primary_connection_string
else:
self.resource = FakeResource(name=name, id=name)
connection_string = 'https://microsoft.com'
return {
self.parameter_name: self.resource,
'{}_connection_string'.format(self.parameter_name): connection_string,
}
def remove_resource(self, name, **kwargs):
if self.is_live:
group = self._get_resource_group(**kwargs)
namespace = self._get_namespace(**kwargs)
self.client.namespaces.delete_authorization_rule(group.name, namespace.name, name, polling=False)
class EventHubAuthorizationRulePreparer(_EventHubChildResourcePreparer):
def __init__(self,
name_prefix='',
use_cache=False,
access_rights=[AccessRights.manage, AccessRights.send, AccessRights.listen],
parameter_name=EVENTHUB_AUTHORIZATION_RULE_PARAM,
resource_group_parameter_name=RESOURCE_GROUP_PARAM,
eventhub_namespace_parameter_name=EVENTHUB_NAMESPACE_PARAM,
eventhub_parameter_name=EVENTHUB_PARAM,
disable_recording=True, playback_fake_resource=None,
client_kwargs=None, random_name_enabled=True):
super(EventHubAuthorizationRulePreparer, self).__init__(name_prefix,
random_name_enabled=random_name_enabled,
resource_group_parameter_name=resource_group_parameter_name,
eventhub_namespace_parameter_name=eventhub_namespace_parameter_name,
disable_recording=disable_recording,
playback_fake_resource=playback_fake_resource,
client_kwargs=client_kwargs)
self.parameter_name = parameter_name
self.access_rights = access_rights
self.eventhub_parameter_name = eventhub_parameter_name
if random_name_enabled:
self.resource_moniker = self.name_prefix + "evnethubauth"
self.set_cache(use_cache, access_rights)
def create_resource(self, name, **kwargs):
if self.is_live:
self.client = self.create_mgmt_client(EventHubManagementClient)
group = self._get_resource_group(**kwargs)
namespace = self._get_namespace(**kwargs)
eventhub = self._get_eventhub(**kwargs)
self.resource = self.client.event_hubs.create_or_update_authorization_rule(
group.name,
namespace.name,
eventhub.name,
name,
self.access_rights
)
key = self.client.event_hubs.list_keys(group.name, namespace.name, eventhub.name, name)
connection_string = key.primary_connection_string
else:
self.resource = FakeResource(name=name, id=name)
connection_string = 'https://microsoft.com'
return {
self.parameter_name: self.resource,
'{}_connection_string'.format(self.parameter_name): connection_string,
}
def remove_resource(self, name, **kwargs):
if self.is_live:
group = self._get_resource_group(**kwargs)
namespace = self._get_namespace(**kwargs)
eventhub = self._get_eventhub(**kwargs)
self.client.event_hubs.delete_authorization_rule(group.name, namespace.name, eventhub.name, name, polling=False)
def _get_eventhub(self, **kwargs):
try:
return kwargs.get(self.eventhub_parameter_name)
except KeyError:
template = 'To create this event hub authorization rule a event hub is required. Please add ' \
'decorator @{} in front of this event hub preparer.'
raise AzureTestError(template.format(EventHubPreparer.__name__))
CachedEventHubNamespacePreparer = functools.partial(EventHubNamespacePreparer, use_cache=True)
CachedEventHubPreparer = functools.partial(EventHubPreparer, use_cache=True)
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rnn/chatbot/chatbot.py | llichengtong/yx4 | 128 | 21467 | # coding=utf8
import logging
import os
import random
import re
import numpy as np
import tensorflow as tf
from seq2seq_conversation_model import seq2seq_model
from seq2seq_conversation_model import data_utils
from seq2seq_conversation_model import tokenizer
from seq2seq_conversation_model.seq2seq_conversation_model import FLAGS, _buckets
from settings import SEQ2SEQ_MODEL_DIR
_LOGGER = logging.getLogger('track')
UNK_TOKEN_REPLACEMENT = [
'？',
'我不知道你在说什么',
'什么鬼。。。',
'宝宝不知道你在说什么呐。。。',
]
ENGLISHWORD_PATTERN = re.compile(r'[a-zA-Z0-9]')
def is_unichar_englishnum(char):
return ENGLISHWORD_PATTERN.match(char)
def trim(s):
"""
1. delete every space between chinese words
2. suppress extra spaces
:param s: some python string
:return: the trimmed string
"""
if not (isinstance(s, unicode) or isinstance(s, str)):
return s
unistr = s.decode('utf8') if type(s) != unicode else s
unistr = unistr.strip()
if not unistr:
return ''
trimmed_str = []
if unistr[0] != ' ':
trimmed_str.append(unistr[0])
for ind in xrange(1, len(unistr) - 1):
prev_char = unistr[ind - 1] if len(trimmed_str) == 0 else trimmed_str[-1]
cur_char = unistr[ind]
maybe_trim = cur_char == ' '
next_char = unistr[ind + 1]
if not maybe_trim:
trimmed_str.append(cur_char)
else:
if is_unichar_englishnum(prev_char) and is_unichar_englishnum(next_char):
trimmed_str.append(cur_char)
else:
continue
if unistr[-1] != ' ':
trimmed_str.append(unistr[-1])
return ''.join(trimmed_str)
class Chatbot():
"""
answer an enquiry using trained seq2seq model
"""
def __init__(self, model_dir):
# Create model and load parameters.
self.session = tf.InteractiveSession()
self.model = self.create_model(self.session, model_dir, True)
self.model.batch_size = 1
# Load vocabularies.
vocab_path = os.path.join(FLAGS.data_dir, "vocab%d" % FLAGS.vocab_size)
self.vocab, self.rev_vocab = data_utils.initialize_vocabulary(vocab_path)
def create_model(self, session, model_dir, forward_only):
"""Create conversation model and initialize or load parameters in session."""
model = seq2seq_model.Seq2SeqModel(
FLAGS.vocab_size, FLAGS.vocab_size, _buckets,
FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm,
FLAGS.batch_size,
FLAGS.learning_rate, FLAGS.learning_rate_decay_factor,
use_lstm=FLAGS.use_lstm,
forward_only=forward_only)
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
_LOGGER.info("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
_LOGGER.info("Read model parameter succeed!")
else:
raise ValueError(
"Failed to find legal model checkpoint files in %s" % model_dir)
return model
def generate_answer(self, enquiry):
# Get token-ids for the input sentence.
token_ids = data_utils.sentence_to_token_ids(enquiry, self.vocab, tokenizer.fmm_tokenizer)
if len(token_ids) == 0:
_LOGGER.error('lens of token ids of sentence %s is 0' % enquiry)
# Which bucket does it belong to?
bucket_id = min([b for b in xrange(len(_buckets))
if _buckets[b][0] > len(token_ids)])
# Get a 1-element batch to feed the sentence to the model.
encoder_inputs, decoder_inputs, target_weights = self.model.get_batch(
{bucket_id: [(token_ids, [])]}, bucket_id)
# Get output logits for the sentence.
_, _, output_logits = self.model.step(self.session, encoder_inputs,
decoder_inputs,
target_weights, bucket_id, True)
# This is a greedy decoder - outputs are just argmaxes of output_logits.
outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits]
# If there is an EOS symbol in outputs, cut them at that point.
if tokenizer.EOS_ID in outputs:
outputs = outputs[:outputs.index(tokenizer.EOS_ID)]
# Print out response sentence corresponding to outputs.
answer = " ".join([self.rev_vocab[output] for output in outputs])
if tokenizer._UNK in answer:
answer = random.choice(UNK_TOKEN_REPLACEMENT)
answer = trim(answer)
return answer
def close(self):
self.session.close()
if __name__ == "__main__":
m = Chatbot(SEQ2SEQ_MODEL_DIR + '/train/')
response = m.generate_answer(u'我知道你不知道我知道你不知道我说的是什么意思')
print response
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2933,
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] |
sfymca/feed/urls.py | streeter/sf-ymca-pools | 0 | 95030 | <reponame>streeter/sf-ymca-pools
from django.urls import path
from sfymca.feed import views
urlpatterns = [
path("", views.index, name="index"),
path("<branch_name>.ics", views.branch_calendar, name="branch_calendar"),
path("<branch_name>/", views.branch, name="branch"),
]
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] |
app/__init__.py | HK-Zhang/FlaskAADB2C | 0 | 175366 | <filename>app/__init__.py
from flask import Flask,url_for
from flask_session import Session # https://pythonhosted.org/Flask-Session
from flask_cors import CORS
from config import appinsightKey,apiversion,load_config
from opencensus.ext.azure.trace_exporter import AzureExporter
from opencensus.ext.azure.log_exporter import AzureLogHandler
from opencensus.ext.flask.flask_middleware import FlaskMiddleware
from opencensus.trace.samplers import ProbabilitySampler
import logging
from flasgger import Swagger,swag_from
from os import listdir
from os.path import isfile, join
import socket
import app_config_b2c
# This section is needed for url_for("foo", _external=True) to automatically
# generate http scheme when this sample is running on localhost,
# and to generate https scheme when it is deployed behind reversed proxy.
# See also https://flask.palletsprojects.com/en/1.0.x/deploying/wsgi-standalone/#proxy-setups
from werkzeug.middleware.proxy_fix import ProxyFix
hostname=socket.gethostname()
format_str = f'{apiversion}@{hostname} says:'+'%(asctime)s - %(levelname)-8s - %(message)s'
date_format = '%Y-%m-%d %H:%M:%S'
logging.basicConfig(level=logging.INFO)
formatter = logging.Formatter(format_str, date_format)
rootlogger = logging.getLogger()
app = Flask(__name__)
CORS(app)
app.config.from_object(app_config_b2c)
Session(app)
handler = AzureLogHandler(connection_string=f'InstrumentationKey={appinsightKey}')
handler.setFormatter(formatter)
rootlogger.addHandler(handler)
middleware = FlaskMiddleware(app,exporter=AzureExporter(connection_string=f'InstrumentationKey={appinsightKey}'),sampler=ProbabilitySampler(rate=1.0))
app.wsgi_app = ProxyFix(app.wsgi_app, x_proto=1, x_host=1)
load_config()
Swagger(app)
from app.api import api as api_blueprint
app.register_blueprint(api_blueprint, url_prefix='/v1')
from app.web import web as web_blueprint
app.register_blueprint(web_blueprint)
from app.web.views import _build_auth_url
app.jinja_env.globals.update(
_build_auth_url=_build_auth_url) # Used in template
logging.info(f'App Started {apiversion}.')
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] |
ironic_inspector/cmd/dbsync.py | namnx228/ironic-inspector | 31 | 10566 | <gh_stars>10-100
# Copyright 2015 Cisco Systems
# 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.
import os
import sys
from alembic import command as alembic_command
from alembic import config as alembic_config
from alembic import util as alembic_util
from oslo_config import cfg
from oslo_log import log
from ironic_inspector import conf # noqa
CONF = cfg.CONF
def add_alembic_command(subparsers, name):
return subparsers.add_parser(
name, help=getattr(alembic_command, name).__doc__)
def add_command_parsers(subparsers):
for name in ['current', 'history', 'branches', 'heads']:
parser = add_alembic_command(subparsers, name)
parser.set_defaults(func=do_alembic_command)
for name in ['stamp', 'show', 'edit']:
parser = add_alembic_command(subparsers, name)
parser.set_defaults(func=with_revision)
parser.add_argument('--revision', nargs='?', required=True)
parser = add_alembic_command(subparsers, 'upgrade')
parser.set_defaults(func=with_revision)
parser.add_argument('--revision', nargs='?')
parser = add_alembic_command(subparsers, 'revision')
parser.set_defaults(func=do_revision)
parser.add_argument('-m', '--message')
parser.add_argument('--autogenerate', action='store_true')
command_opt = cfg.SubCommandOpt('command',
title='Command',
help='Available commands',
handler=add_command_parsers)
def _get_alembic_config():
base_path = os.path.split(os.path.dirname(__file__))[0]
return alembic_config.Config(os.path.join(base_path, 'alembic.ini'))
def do_revision(config, cmd, *args, **kwargs):
do_alembic_command(config, cmd, message=CONF.command.message,
autogenerate=CONF.command.autogenerate)
def with_revision(config, cmd, *args, **kwargs):
revision = CONF.command.revision or 'head'
do_alembic_command(config, cmd, revision)
def do_alembic_command(config, cmd, *args, **kwargs):
try:
getattr(alembic_command, cmd)(config, *args, **kwargs)
except alembic_util.CommandError as e:
alembic_util.err(str(e))
def main(args=sys.argv[1:]):
log.register_options(CONF)
CONF.register_cli_opt(command_opt)
CONF(args, project='ironic-inspector')
config = _get_alembic_config()
config.set_main_option('script_location', "ironic_inspector:migrations")
config.ironic_inspector_config = CONF
CONF.command.func(config, CONF.command.name)
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test/LHS_test.py | misantam/GAparsimony | 0 | 125895 | # -*- coding: utf-8 -*-
from GAparsimony.lhs.util import isValidLHS, isValidLHS_int
from GAparsimony.lhs import geneticLHS, improvedLHS, maximinLHS, optimumLHS, randomLHS, randomLHS_int
import pytest
@pytest.mark.parametrize("shape", [
(2, 2),
(6, 6),
(3, 8)
])
def test_randomLHS_int(shape):
assert isValidLHS_int(randomLHS_int(*shape))
@pytest.mark.parametrize("shape", [
(2, 2),
(6, 6),
(3, 8)
])
def test_randomLHS(shape):
assert isValidLHS(randomLHS(*shape))
@pytest.mark.parametrize("shape", [
(2, 2),
(6, 6),
(3, 8)
])
def test_geneticLHS(shape):
assert isValidLHS(geneticLHS(*shape))
@pytest.mark.parametrize("shape", [
(2, 2),
(6, 6),
(3, 8)
])
def test_improvedLHS(shape):
assert isValidLHS(improvedLHS(*shape))
@pytest.mark.parametrize("shape", [
(2, 2),
(6, 6),
(3, 8)
])
def test_maximinLHS(shape):
assert isValidLHS(maximinLHS(*shape))
@pytest.mark.parametrize("shape", [
(2, 2),
(6, 6),
(8, 8)
])
def test_optimumLHS(shape):
assert isValidLHS(optimumLHS(*shape)) | [
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model_analyzer/config/run/run_search.py | MarkMoTrin/model_analyzer | 115 | 68183 | # Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from model_analyzer.config.input.objects.config_model_profile_spec \
import ConfigModelProfileSpec
from model_analyzer.constants import LOGGER_NAME, THROUGHPUT_GAIN
import copy
import logging
logger = logging.getLogger(LOGGER_NAME)
class RunSearch:
"""
A class responsible for searching the config space.
"""
def __init__(self, config):
self._max_concurrency = config.run_config_search_max_concurrency
self._max_instance_count = config.run_config_search_max_instance_count
self._max_preferred_batch_size = config.run_config_search_max_preferred_batch_size
self._sweep_preferred_batch_size_disable = config.run_config_search_preferred_batch_size_disable
self._model_config_parameters = {'instance_count': 1}
self._measurements = []
self._last_batch_length = None
# Run search operating mode
self._sweep_mode_function = None
def _create_model_config(self, cpu_only=False):
"""
Generate the model config sweep to be used.
"""
model_config = self._model_config_parameters
new_config = {}
if 'dynamic_batching' in model_config:
if model_config['dynamic_batching'] is None:
new_config['dynamic_batching'] = {}
else:
new_config['dynamic_batching'] = {
'preferred_batch_size': [model_config['dynamic_batching']]
}
if 'instance_count' in model_config:
if not cpu_only:
new_config['instance_group'] = [{
'count': model_config['instance_count'],
'kind': 'KIND_GPU'
}]
else:
new_config['instance_group'] = [{
'count': model_config['instance_count'],
'kind': 'KIND_CPU'
}]
return new_config
def add_measurements(self, measurements):
"""
Add the measurments that are the result of running
the sweeps.
Parameters
----------
measurements : list
list of measurements
"""
self._last_batch_length = len(measurements)
# The list will contain one parameter, because we are experimenting
# with one value at a time.
self._measurements += measurements
def _step_instance_count(self):
"""
Advances instance count by one step.
"""
self._model_config_parameters['instance_count'] += 1
def _step_dynamic_batching(self):
"""
Advances the dynamic batching by one step.
"""
if 'dynamic_batching' not in self._model_config_parameters:
# Enable dynamic batching
self._model_config_parameters['dynamic_batching'] = None
else:
if self._model_config_parameters['dynamic_batching'] is None:
self._model_config_parameters['dynamic_batching'] = 1
else:
self._model_config_parameters['dynamic_batching'] *= 2
def _get_throughput(self, measurement):
return measurement.get_metric_value('perf_throughput')
def _calculate_throughput_gain(self, index):
throughput_before = self._get_throughput(
self._measurements[-(index + 1)])
throughput_after = self._get_throughput(self._measurements[-index])
gain = (throughput_after - throughput_before) / throughput_before
return gain
def _valid_throughput_gain(self):
"""
Returns true if the amount of throughput gained
is reasonable for continuing the search process
"""
# If number of measurements is smaller than 4,
# the search can continue.
if len(self._measurements) < 4:
return True
return self._calculate_throughput_gain(1) > THROUGHPUT_GAIN or \
self._calculate_throughput_gain(2) > THROUGHPUT_GAIN or \
self._calculate_throughput_gain(3) > THROUGHPUT_GAIN
def init_model_sweep(self, concurrency, search_model_config_parameters):
"""
Intiliazes the sweep mode, and model config parameters in some cases.
"""
# Reset the measurements after each init
self._measurements = []
if len(concurrency) != 0 and search_model_config_parameters:
self._model_config_parameters = {'instance_count': 0}
self._sweep_mode_function = self._sweep_model_config_only
elif len(concurrency) == 0 and search_model_config_parameters:
self._model_config_parameters = {'instance_count': 1}
logger.info(
'Will sweep both the concurrency and model config parameters...'
)
self._sweep_mode_function = self._sweep_concurrency_and_model_config
else:
logger.info('Will sweep only through the concurrency values...')
self._sweep_mode_function = self._sweep_concurrency_only
def get_model_sweep(self, config_model):
"""
Get the next iteration of the sweeps.
Parameters
----------
config_model : ConfigModelProfileSpec
The config model object of the model to sweep through
Returns
-------
config_model, list
The list may be empty, contain a model config dict or None
"""
new_model = ConfigModelProfileSpec(
copy.deepcopy(config_model.model_name()),
copy.deepcopy(config_model.cpu_only()),
copy.deepcopy(config_model.objectives()),
copy.deepcopy(config_model.constraints()),
copy.deepcopy(config_model.parameters()),
copy.deepcopy(config_model.model_config_parameters()),
copy.deepcopy(config_model.perf_analyzer_flags()))
if self._sweep_mode_function:
new_model, model_sweep = self._sweep_mode_function(new_model)
# Only log message if there is new runs.
if model_sweep:
self._log_message(new_model)
return new_model, model_sweep
return new_model, []
def _sweep_concurrency_and_model_config(self, model):
"""
Gets next iteration of both the concurrency and model config
parameters
Parameters
----------
model : ConfigModelProfileSpec
The model whose parameters are being swept over
"""
return self._sweep_parameters(model, sweep_model_configs=True)
def _sweep_concurrency_only(self, model):
"""
Gets next iteration of the concurrency sweep
"""
return self._sweep_parameters(model, sweep_model_configs=False)
def _sweep_parameters(self, model, sweep_model_configs):
"""
A helper function that sweeps over concurrency
and if required, over model configs as well
"""
concurrency = model.parameters()['concurrency']
if len(concurrency) == 0:
model.parameters()['concurrency'] = [1]
else:
# Exponentially increase concurrency
new_concurrency = concurrency[0] * 2
# If the concurrency limit has been reached, the last batch lead to
# an error, or the throughput gain is not significant, step
# the concurrency value. TODO: add exponential backoff so
# that the algorithm can step back and exactly find the points.
concurrency_limit_reached = new_concurrency > self._max_concurrency
last_batch_erroneous = self._last_batch_length == 0
throughput_peaked = not self._valid_throughput_gain()
if concurrency_limit_reached or last_batch_erroneous or throughput_peaked:
# Reset concurrency
if sweep_model_configs:
self._measurements = []
model.parameters()['concurrency'] = [1]
return self._sweep_model_config_only(model)
else:
return model, []
model.parameters()['concurrency'] = [new_concurrency]
return model, [
self._create_model_config(
cpu_only=model.cpu_only()) if sweep_model_configs else None
]
def _sweep_model_config_only(self, model):
"""
Gets next iteration model config
parameters sweep
"""
self._step_instance_count()
instance_limit_reached = self._model_config_parameters[
'instance_count'] > self._max_instance_count
if instance_limit_reached:
if self._sweep_preferred_batch_size_disable:
return model, []
# Reset instance_count
self._model_config_parameters['instance_count'] = 1
self._step_dynamic_batching()
dynamic_batching_enabled = self._model_config_parameters[
'dynamic_batching'] is not None
if dynamic_batching_enabled:
batch_size_limit_reached = self._model_config_parameters[
'dynamic_batching'] > self._max_preferred_batch_size
if batch_size_limit_reached:
return model, []
return model, [self._create_model_config(cpu_only=model.cpu_only())]
def _log_message(self, model):
"""
Writes the current state of the search to the console
"""
concurrency = model.parameters()['concurrency'][0]
message = 'dynamic batching is disabled.'
if 'dynamic_batching' in self._model_config_parameters:
if self._model_config_parameters['dynamic_batching'] is None:
message = 'dynamic batching is enabled.'
else:
message = (
"preferred batch size is set to "
f"{self._model_config_parameters['dynamic_batching']}.")
if self._sweep_mode_function == self._sweep_concurrency_only:
logger.info(f"[Search Step] Concurrency set to {concurrency}. ")
elif self._sweep_mode_function == self._sweep_concurrency_and_model_config:
logger.info(
f"[Search Step] Concurrency set to {concurrency}. "
f"Instance count set to "
f"{self._model_config_parameters['instance_count']}, and {message}"
)
elif self._sweep_mode_function == self._sweep_model_config_only:
logger.info(
f"[Search Step] Instance count set to "
f"{self._model_config_parameters['instance_count']}, and {message}"
)
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] |
nibabies/workflows/brain_extraction.py | oesteban/nibabies | 0 | 90097 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""Nipype translation of ANTs' workflows."""
import numpy as np
# general purpose
from pkg_resources import resource_filename as pkgr_fn
# nipype
from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu
from nipype.interfaces.ants import N4BiasFieldCorrection
from nipype.interfaces.ants.utils import AI
# niworkflows
from niworkflows.anat.ants import init_atropos_wf, ATROPOS_MODELS
from niworkflows.interfaces.ants import ImageMath
from niworkflows.interfaces.images import RegridToZooms, ValidateImage
from niworkflows.interfaces.nibabel import ApplyMask, Binarize
from niworkflows.interfaces.fixes import (
FixHeaderRegistration as Registration,
FixHeaderApplyTransforms as ApplyTransforms,
)
from niworkflows.interfaces.registration import (
SimpleBeforeAfterRPT as SimpleBeforeAfter
)
from templateflow.api import get as get_template
from ..utils.filtering import (
gaussian_filter as _gauss_filter,
truncation as _trunc
)
HIRES_ZOOMS = (1, 1, 1)
LOWRES_ZOOMS = (2, 2, 2)
def init_infant_brain_extraction_wf(
ants_affine_init=False,
bspline_fitting_distance=200,
debug=False,
in_template="MNIInfant",
template_specs=None,
interim_checkpoints=True,
mem_gb=3.0,
mri_scheme="T2w",
name="infant_brain_extraction_wf",
atropos_model=None,
omp_nthreads=None,
output_dir=None,
use_float=True,
):
"""
Build an atlas-based brain extraction pipeline for infant T2w MRI data.
Parameters
----------
ants_affine_init : :obj:`bool`, optional
Set-up a pre-initialization step with ``antsAI`` to account for mis-oriented images.
"""
inputnode = pe.Node(
niu.IdentityInterface(fields=["in_files", "in_mask"]), name="inputnode"
)
outputnode = pe.Node(
niu.IdentityInterface(fields=["out_corrected", "out_brain", "out_mask"]),
name="outputnode"
)
template_specs = template_specs or {}
# Find a suitable target template in TemplateFlow
tpl_target_path = get_template(
in_template,
suffix=mri_scheme,
**template_specs
)
if not tpl_target_path:
raise RuntimeError(
f"An instance of template <tpl-{in_template}> with MR scheme '{mri_scheme}'"
" could not be found.")
# tpl_brainmask_path = get_template(
# in_template, desc="brain", suffix="probseg", **template_specs
# )
# if not tpl_brainmask_path:
# ignore probseg for the time being
tpl_brainmask_path = get_template(
in_template, desc="brain", suffix="mask", **template_specs
)
tpl_regmask_path = get_template(
in_template, desc="BrainCerebellumExtraction", suffix="mask", **template_specs
)
# validate images
val_tmpl = pe.Node(ValidateImage(), name='val_tmpl')
val_tmpl.inputs.in_file = _pop(tpl_target_path)
val_target = pe.Node(ValidateImage(), name='val_target')
# Resample both target and template to a controlled, isotropic resolution
res_tmpl = pe.Node(RegridToZooms(zooms=HIRES_ZOOMS), name="res_tmpl") # testing
res_target = pe.Node(RegridToZooms(zooms=HIRES_ZOOMS), name="res_target") # testing
gauss_tmpl = pe.Node(niu.Function(function=_gauss_filter), name="gauss_tmpl")
# Spatial normalization step
lap_tmpl = pe.Node(ImageMath(operation="Laplacian", op2="0.4 1"), name="lap_tmpl")
lap_target = pe.Node(ImageMath(operation="Laplacian", op2="0.4 1"), name="lap_target")
# Merge image nodes
mrg_target = pe.Node(niu.Merge(2), name="mrg_target")
mrg_tmpl = pe.Node(niu.Merge(2), name="mrg_tmpl")
norm_lap_tmpl = pe.Node(niu.Function(function=_trunc), name="norm_lap_tmpl")
norm_lap_tmpl.inputs.dtype = "float32"
norm_lap_tmpl.inputs.out_max = 1.0
norm_lap_tmpl.inputs.percentile = (0.01, 99.99)
norm_lap_tmpl.inputs.clip_max = None
norm_lap_target = pe.Node(niu.Function(function=_trunc), name="norm_lap_target")
norm_lap_target.inputs.dtype = "float32"
norm_lap_target.inputs.out_max = 1.0
norm_lap_target.inputs.percentile = (0.01, 99.99)
norm_lap_target.inputs.clip_max = None
# Set up initial spatial normalization
ants_params = "testing" if debug else "precise"
norm = pe.Node(
Registration(from_file=pkgr_fn(
"niworkflows.data",
f"antsBrainExtraction_{ants_params}.json")
),
name="norm",
n_procs=omp_nthreads,
mem_gb=mem_gb,
)
norm.inputs.float = use_float
# main workflow
wf = pe.Workflow(name)
# Create a buffer interface as a cache for the actual inputs to registration
buffernode = pe.Node(niu.IdentityInterface(
fields=["hires_target", "smooth_target"]), name="buffernode")
# truncate target intensity for N4 correction
clip_target = pe.Node(
niu.Function(function=_trunc),
name="clip_target",
)
clip_tmpl = pe.Node(
niu.Function(function=_trunc),
name="clip_tmpl",
)
#clip_tmpl.inputs.in_file = _pop(tpl_target_path)
# INU correction of the target image
init_n4 = pe.Node(
N4BiasFieldCorrection(
dimension=3,
save_bias=False,
copy_header=True,
n_iterations=[50] * (4 - debug),
convergence_threshold=1e-7,
shrink_factor=4,
bspline_fitting_distance=bspline_fitting_distance,
),
n_procs=omp_nthreads,
name="init_n4",
)
clip_inu = pe.Node(
niu.Function(function=_trunc),
name="clip_inu",
)
gauss_target = pe.Node(niu.Function(function=_gauss_filter), name="gauss_target")
wf.connect([
# truncation, resampling, and initial N4
(inputnode, val_target, [(("in_files", _pop), "in_file")]),
# (inputnode, res_target, [(("in_files", _pop), "in_file")]),
(val_target, res_target, [("out_file", "in_file")]),
(res_target, clip_target, [("out_file", "in_file")]),
(val_tmpl, clip_tmpl, [("out_file", "in_file")]),
(clip_tmpl, res_tmpl, [("out", "in_file")]),
(clip_target, init_n4, [("out", "input_image")]),
(init_n4, clip_inu, [("output_image", "in_file")]),
(clip_inu, gauss_target, [("out", "in_file")]),
(clip_inu, buffernode, [("out", "hires_target")]),
(gauss_target, buffernode, [("out", "smooth_target")]),
(res_tmpl, gauss_tmpl, [("out_file", "in_file")]),
# (clip_tmpl, gauss_tmpl, [("out", "in_file")]),
])
# Graft a template registration-mask if present
if tpl_regmask_path:
hires_mask = pe.Node(
ApplyTransforms(
input_image=_pop(tpl_regmask_path),
transforms="identity",
interpolation="NearestNeighbor",
float=True),
name="hires_mask",
mem_gb=1
)
wf.connect([
(res_tmpl, hires_mask, [("out_file", "reference_image")]),
])
map_brainmask = pe.Node(
ApplyTransforms(interpolation="Gaussian", float=True),
name="map_brainmask",
mem_gb=1
)
map_brainmask.inputs.input_image = str(tpl_brainmask_path)
thr_brainmask = pe.Node(Binarize(thresh_low=0.80),
name="thr_brainmask")
bspline_grid = pe.Node(niu.Function(function=_bspline_distance),
name="bspline_grid")
# Refine INU correction
final_n4 = pe.Node(
N4BiasFieldCorrection(
dimension=3,
save_bias=True,
copy_header=True,
n_iterations=[50] * 5,
convergence_threshold=1e-7,
rescale_intensities=True,
shrink_factor=4,
),
n_procs=omp_nthreads,
name="final_n4",
)
final_mask = pe.Node(ApplyMask(), name="final_mask")
if atropos_model is None:
atropos_model = tuple(ATROPOS_MODELS[mri_scheme].values())
atropos_wf = init_atropos_wf(
use_random_seed=False,
omp_nthreads=omp_nthreads,
mem_gb=mem_gb,
in_segmentation_model=atropos_model,
)
# if tpl_regmask_path:
# atropos_wf.get_node('inputnode').inputs.in_mask_dilated = tpl_regmask_path
sel_wm = pe.Node(niu.Select(index=atropos_model[-1] - 1), name='sel_wm',
run_without_submitting=True)
wf.connect([
(inputnode, map_brainmask, [(("in_files", _pop), "reference_image")]),
(inputnode, final_n4, [(("in_files", _pop), "input_image")]),
(inputnode, bspline_grid, [(("in_files", _pop), "in_file")]),
# (bspline_grid, final_n4, [("out", "bspline_fitting_distance")]),
(bspline_grid, final_n4, [("out", "args")]),
# merge laplacian and original images
(buffernode, lap_target, [("smooth_target", "op1")]),
(buffernode, mrg_target, [("hires_target", "in1")]),
(lap_target, norm_lap_target, [("output_image", "in_file")]),
(norm_lap_target, mrg_target, [("out", "in2")]),
# Template massaging
(res_tmpl, lap_tmpl, [("out_file", "op1")]),
(res_tmpl, mrg_tmpl, [("out_file", "in1")]),
(lap_tmpl, norm_lap_tmpl, [("output_image", "in_file")]),
(norm_lap_tmpl, mrg_tmpl, [("out", "in2")]),
# spatial normalization
(mrg_target, norm, [("out", "moving_image")]),
(mrg_tmpl, norm, [("out", "fixed_image")]),
(norm, map_brainmask, [
("reverse_transforms", "transforms"),
("reverse_invert_flags", "invert_transform_flags")]),
(map_brainmask, thr_brainmask, [("output_image", "in_file")]),
# take a second pass of N4
(map_brainmask, final_n4, [("output_image", "weight_image")]),
(final_n4, final_mask, [("output_image", "in_file")]),
(thr_brainmask, final_mask, [("out_mask", "in_mask")]),
(final_n4, outputnode, [("output_image", "out_corrected")]),
(thr_brainmask, outputnode, [("out_mask", "out_mask")]),
(final_mask, outputnode, [("out_file", "out_brain")]),
])
# wf.disconnect([
# (get_brainmask, apply_mask, [('output_image', 'mask_file')]),
# (copy_xform, outputnode, [('out_mask', 'out_mask')]),
# ])
# wf.connect([
# (init_n4, atropos_wf, [
# ('output_image', 'inputnode.in_files')]), # intensity image
# (thr_brainmask, atropos_wf, [
# ('out_mask', 'inputnode.in_mask')]),
# (atropos_wf, sel_wm, [('outputnode.out_tpms', 'inlist')]),
# (sel_wm, final_n4, [('out', 'weight_image')]),
# ])
# wf.connect([
# (atropos_wf, outputnode, [
# ('outputnode.out_mask', 'out_mask'),
# ('outputnode.out_segm', 'out_segm'),
# ('outputnode.out_tpms', 'out_tpms')]),
# ])
if tpl_regmask_path:
wf.connect([
(hires_mask, norm, [
("output_image", "fixed_image_masks")]),
# (hires_mask, atropos_wf, [
# ("output_image", "inputnode.in_mask_dilated")]),
])
if interim_checkpoints:
final_apply = pe.Node(
ApplyTransforms(
interpolation="BSpline",
float=True),
name="final_apply",
mem_gb=1
)
final_report = pe.Node(SimpleBeforeAfter(
before_label=f"tpl-{in_template}",
after_label="target",
out_report="final_report.svg"),
name="final_report"
)
wf.connect([
(inputnode, final_apply, [(("in_files", _pop), "reference_image")]),
(res_tmpl, final_apply, [("out_file", "input_image")]),
(norm, final_apply, [
("reverse_transforms", "transforms"),
("reverse_invert_flags", "invert_transform_flags")]),
(final_apply, final_report, [("output_image", "before")]),
(outputnode, final_report, [("out_corrected", "after"),
("out_mask", "wm_seg")]),
])
if output_dir:
from nipype.interfaces.io import DataSink
ds_final_inu = pe.Node(DataSink(base_directory=str(output_dir.parent)),
name="ds_final_inu")
ds_final_msk = pe.Node(DataSink(base_directory=str(output_dir.parent)),
name="ds_final_msk")
ds_report = pe.Node(DataSink(base_directory=str(output_dir.parent)),
name="ds_report")
wf.connect([
(outputnode, ds_final_inu, [
("out_corrected", f"{output_dir.name}.@inu_corrected")]),
(outputnode, ds_final_msk, [
("out_mask", f"{output_dir.name}.@brainmask")]),
(final_report, ds_report, [
("out_report", f"{output_dir.name}.@report")]),
])
if not ants_affine_init:
return wf
# Initialize transforms with antsAI
lowres_tmpl = pe.Node(RegridToZooms(zooms=LOWRES_ZOOMS), name="lowres_tmpl")
lowres_target = pe.Node(RegridToZooms(zooms=LOWRES_ZOOMS), name="lowres_target")
init_aff = pe.Node(
AI(
metric=("Mattes", 32, "Regular", 0.25),
transform=("Affine", 0.1),
search_factor=(15, 0.1),
principal_axes=False,
convergence=(10, 1e-6, 10),
search_grid=(40, (0, 40, 40)),
verbose=True,
),
name="init_aff",
n_procs=omp_nthreads,
)
wf.connect([
(gauss_tmpl, lowres_tmpl, [("out", "in_file")]),
(lowres_tmpl, init_aff, [("out_file", "fixed_image")]),
(gauss_target, lowres_target, [("out", "in_file")]),
(lowres_target, init_aff, [("out_file", "moving_image")]),
(init_aff, norm, [("output_transform", "initial_moving_transform")]),
])
if tpl_regmask_path:
lowres_mask = pe.Node(
ApplyTransforms(
input_image=_pop(tpl_regmask_path),
transforms="identity",
interpolation="MultiLabel",
float=True),
name="lowres_mask",
mem_gb=1
)
wf.connect([
(lowres_tmpl, lowres_mask, [("out_file", "reference_image")]),
(lowres_mask, init_aff, [("output_image", "fixed_image_mask")]),
])
if interim_checkpoints:
init_apply = pe.Node(
ApplyTransforms(
interpolation="BSpline",
float=True),
name="init_apply",
mem_gb=1
)
init_report = pe.Node(SimpleBeforeAfter(
before_label=f"tpl-{in_template}",
after_label="target",
out_report="init_report.svg"),
name="init_report"
)
wf.connect([
(lowres_target, init_apply, [("out_file", "input_image")]),
(res_tmpl, init_apply, [("out_file", "reference_image")]),
(init_aff, init_apply, [("output_transform", "transforms")]),
(init_apply, init_report, [("output_image", "after")]),
(res_tmpl, init_report, [("out_file", "before")]),
])
if output_dir:
ds_init_report = pe.Node(DataSink(base_directory=str(output_dir.parent)),
name="ds_init_report")
wf.connect(
init_report, "out_report",
ds_init_report, f"{output_dir.name}.@init_report"
)
return wf
def _pop(in_files):
if isinstance(in_files, (list, tuple)):
return in_files[0]
return in_files
def _bspline_distance(in_file, spacings=(20, 20, 20)):
import numpy as np
import nibabel as nb
img = nb.load(in_file)
extent = (np.array(img.shape[:3]) - 1) * img.header.get_zooms()[:3]
retval = [f"{v}" for v in np.ceil(extent / np.array(spacings)).astype(int)]
return f"-b {'x'.join(retval)}"
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packages/w3af/w3af/core/controllers/tests/core_test_suite/test_exception_handler.py | ZooAtmosphereGroup/HelloPackages | 3 | 92709 | <gh_stars>1-10
"""
test_exception_handling.py
Copyright 2011 <NAME>
This file is part of w3af, http://w3af.org/ .
w3af is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation version 2 of the License.
w3af is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with w3af; if not, write to the Free Software
Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
"""
from w3af.core.controllers.tests.core_test_suite.test_pause_stop import CountTestMixin
class TestExceptionHandler(CountTestMixin):
"""
Inherit from TestW3afCorePause to get the nice setUp().
"""
def test_same_id(self):
"""
Verify that the exception handler is the same before and after the scan
"""
before_id_ehandler = id(self.w3afcore.exception_handler)
self.w3afcore.start()
after_id_ehandler = id(self.w3afcore.exception_handler)
self.assertEqual(before_id_ehandler, after_id_ehandler) | [
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imports.py | MarcusTL12/PyCalc | 0 | 119831 | import sys
sys.path.append('./PyScr/')
import numpy as np
import matplotlib.pyplot as plt
import cmath
import math
import copy
import pfprint
import primefac
from scipy.special import comb as choose
_ = None
def ppprint(arg):
global _
pfprint.pfprint(arg)
_ = arg
def frac():
pfprint.frac = not pfprint.frac
return _
def denom_lim(n):
pfprint.denom_lim = n
def num_dec(n):
pfprint.num_dec = n
def pltclr():
plt.cla()
plt.clf()
plt.close()
def fact(n):
return math.factorial(n)
def primesS(n):
return primefac.primes_to_string(primefac.primes(n))
def primes(n):
return primefac.primes(n)
sys.displayhook = ppprint
pi = np.pi
e = np.exp(1)
sqrt = np.sqrt
predef_globals = len(globals()) + 1
def loc():
return dict(list(globals().items())[predef_globals:])
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] |
trompace/mutations/__init__.py | trompamusic/ce-queries-template | 1 | 8306 | MUTATION = '''mutation {{
{mutation}
}}'''
def _verify_additional_type(additionaltype):
"""Check that the input to additionaltype is a list of strings.
If it is empty, raise ValueError
If it is a string, convert it to a list of strings."""
if additionaltype is None:
return None
if isinstance(additionaltype, str):
additionaltype = [additionaltype]
if len(additionaltype) == 0:
raise ValueError("additionaltype must be a non-empty list")
return additionaltype
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nodux_stock_one/config/nodux_stock_one.py | tatiqm25/nodux_stock_one | 0 | 89714 | from __future__ import unicode_literals
from frappe import _
def get_data():
return [
{
"label": _("Reporte Stock"),
"icon": "fa fa-star",
"items": [
{
"type": "report",
"name": "Stock",
"doctype": "Stock One",
"is_query_report": True
}
]
}
]
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ai_haste/loss/log_squared_loss.py | aidotse/Team-Haste | 0 | 107857 | <reponame>aidotse/Team-Haste
import torch
import torch.nn as nn
import torch.nn.functional as F
class LogSquaredLoss(nn.Module):
def __init__(self,):
super(LogSquaredLoss, self).__init__()
self.l1 = nn.L1Loss()
self.eps = 1e-8
def forward(self, output, target):
# output = torch.log((output) + self.eps)
# target = torch.log((target) + self.eps)
# loss = self.l1(target, output)
loss = torch.log(torch.div(((target) + self.eps), ((output) + self.eps)))
loss = (loss ** 2).mean()
return loss
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] |
pymf6/tests/test_bool.py | hydrocomputing/pymf6 | 4 | 171382 | #!/usr/bin/env python
"""Simple callback test
"""
from pymf6.callback import Func
from pymf6 import mf6
class MyFunc(Func):
# pylint: disable=too-few-public-methods
"""
Callback
"""
def __init__(self, stop=2, verbose=False):
super().__init__(verbose=verbose)
self.stop = stop
def __call__(self):
super().__call__()
print(f'>>> Python: Called {self.counter} times')
if self.counter >= self.stop:
print(f'Stopping from Python after {self.stop} time steps.')
self.set_value('ENDOFSIMULATION', 'TDIS', True)
if __name__ == '__main__':
mf6.mf6_sub(MyFunc(verbose=True))
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] |
src/main/resources/pytz/zoneinfo/America/Panama.py | TheEin/swagger-maven-plugin | 65 | 60898 | '''tzinfo timezone information for America/Panama.'''
from pytz.tzinfo import DstTzInfo
from pytz.tzinfo import memorized_datetime as d
from pytz.tzinfo import memorized_ttinfo as i
class Panama(DstTzInfo):
'''America/Panama timezone definition. See datetime.tzinfo for details'''
zone = 'America/Panama'
_utc_transition_times = [
d(1,1,1,0,0,0),
d(1908,4,22,5,19,36),
]
_transition_info = [
i(-19200,0,'CMT'),
i(-18000,0,'EST'),
]
Panama = Panama()
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python/generated/swaggeraem/apis/__init__.py | mbloch1986/swagger-aem | 0 | 104970 | from __future__ import absolute_import
# import apis into api package
from .console_api import ConsoleApi
from .cq_api import CqApi
from .crx_api import CrxApi
from .custom_api import CustomApi
from .sling_api import SlingApi
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1,
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setup.py | edyatl/passchek | 5 | 163446 | #!/usr/bin/env python3
import setuptools
from passchek.passchek import __version__
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="passchek",
version=__version__,
license="MIT",
author="<NAME>",
author_email="<EMAIL>",
description="Passchek is a simple cli tool, checks if your password has been compromised.",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/edyatl/passchek",
packages=setuptools.find_packages(),
include_package_data=True,
entry_points={
"console_scripts": [
"passchek=passchek.passchek:main",
]
},
keywords=[
"passchek", "password", "pwned",
"troyhunt", "github", "k-anonymity",
"interactive", "console", "sha1",
"pwnedpassword", "versions",
"virtualenv"
],
classifiers=[
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Environment :: Console",
"License :: OSI Approved :: MIT License",
"Operating System :: POSIX",
"Intended Audience :: End Users/Desktop",
"Topic :: Utilities",
],
python_requires='>=3.5',
)
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] |
conftest.py | Casecommons/sentry | 0 | 1608557 | <gh_stars>0
from django.conf import settings
import mock
import os
import os.path
def pytest_configure(config):
import warnings
warnings.filterwarnings('error', '', Warning, r'(sentry|raven)')
if not settings.configured:
os.environ['DJANGO_SETTINGS_MODULE'] = 'sentry.conf.server'
test_db = os.environ.get('DB', 'sqlite')
if test_db == 'mysql':
settings.DATABASES['default'].update({
'ENGINE': 'django.db.backends.mysql',
'NAME': 'sentry',
'USER': 'root',
})
elif test_db == 'postgres':
settings.DATABASES['default'].update({
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'USER': 'postgres',
'NAME': 'sentry',
'OPTIONS': {
'autocommit': True,
}
})
elif test_db == 'sqlite':
settings.DATABASES['default'].update({
'ENGINE': 'django.db.backends.sqlite3',
'NAME': ':memory:',
})
# http://djangosnippets.org/snippets/646/
class InvalidVarException(object):
def __mod__(self, missing):
try:
missing_str = unicode(missing)
except:
missing_str = 'Failed to create string representation'
raise Exception('Unknown template variable %r %s' % (missing, missing_str))
def __contains__(self, search):
if search == '%s':
return True
return False
settings.TEMPLATE_DEBUG = True
# settings.TEMPLATE_STRING_IF_INVALID = InvalidVarException()
# Disable static compiling in tests
settings.STATIC_BUNDLES = {}
# override a few things with our test specifics
settings.INSTALLED_APPS = tuple(settings.INSTALLED_APPS) + (
'tests',
)
# Need a predictable key for tests that involve checking signatures
settings.SENTRY_KEY = 'abc123'
settings.SENTRY_PUBLIC = False
# This speeds up the tests considerably, pbkdf2 is by design, slow.
settings.PASSWORD_HASHERS = [
'django.contrib.auth.hashers.MD5PasswordHasher',
]
# enable draft features
settings.SENTRY_ENABLE_EXPLORE_CODE = True
settings.SENTRY_ENABLE_EXPLORE_USERS = True
settings.SENTRY_ENABLE_EMAIL_REPLIES = True
settings.SENTRY_ALLOW_ORIGIN = '*'
# django mail uses socket.getfqdn which doesnt play nice if our
# networking isnt stable
patcher = mock.patch('socket.getfqdn', return_value='localhost')
patcher.start()
from sentry.utils.runner import initialize_receivers
initialize_receivers()
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] |
tensorflow_hub/saved_model_module.py | hrishikeshmane/hub | 2 | 1615221 | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Implementation of deprecated hub.Module that loads raw TF1 SavedModels."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow_hub import native_module
from tensorflow_hub import saved_model_lib
from tensorflow_hub import tf_v1
_ALWAYS_DROPPED_COLLECTIONS = [
# SavedModels exported from estimator framework typically contain a
# collection with the variable that holds the global training step.
#
# This collection is ignored when loading it as a module. However the
# variable that contains the step would still be brought in if declared
# in the VARIABLES collection.
tf_v1.GraphKeys.GLOBAL_STEP,
# SavedModels exported for serving use cases contain collections which refer
# to ops in the graph that when run are responsible to initialize the
# session for subsequent signature executions.
#
# This generic initialization definition is impossible to support for many
# hub use cases and therefore the assumption here is that the SavedModel
# init op can be ignored in favor of initializing using the
# tf.train.MonitoredSession mechanisms + construction of a new tf.Saver()
# from the global variables collection.
tf_v1.saved_model.constants.LEGACY_INIT_OP_KEY,
tf_v1.saved_model.constants.MAIN_OP_KEY,
]
def _drop_collections(saved_model_handler, collections):
for meta_graph in saved_model_handler.meta_graphs:
for collection in collections:
if collection in meta_graph.collection_def:
del meta_graph.collection_def[collection]
def create_module_spec_from_saved_model(saved_model_path,
drop_collections=None):
"""Experimental: Create a ModuleSpec out of a SavedModel from TF1.
DEPRECATION NOTE: This belongs to the hub.Module API and file format for TF1.
For TF2, TensorFlow Hub ships plain SavedModels, removing the need for
conversions like this.
Define a ModuleSpec from a SavedModel. Note that this is not guaranteed to
work in all cases and it assumes the SavedModel has followed some conventions:
- The serialized SaverDef can be ignored and instead can be reconstructed.
- The init op and main op can be ignored and instead the module can be
initialized by using the conventions followed by
`tf.train.MonitoredSession`.
Note that the set of features supported can increase over time and have side
effects that were not previously visible. The pattern followed to avoid
surprises is forcing users to declare which features to ignore (even
if they are not supported).
Note that this function creates a ModuleSpec that when exported exports a
Module (based on a modified copy of the original SavedModel) and not a
SavedModel.
Args:
saved_model_path: Directory with the SavedModel to use.
drop_collections: Additionally list of collection to drop.
Returns:
A ModuleSpec.
"""
saved_model_handler = saved_model_lib.load(saved_model_path)
checkpoint_filename = saved_model_lib.get_variables_path(saved_model_path)
drop_collections = (set(_ALWAYS_DROPPED_COLLECTIONS) |
(set(drop_collections) if drop_collections else set()))
_drop_collections(saved_model_handler, drop_collections)
return native_module._ModuleSpec(saved_model_handler, checkpoint_filename) # pylint: disable=protected-access
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modules/sensor_heatmap/controllers.py | srcc-msu/job_statistics | 0 | 122799 | from flask import Blueprint, render_template, current_app, Response
from application.database import global_db
from application.helpers import requires_auth
from core.monitoring.models import SENSOR_CLASS_MAP
sensor_heatmap_pages = Blueprint('sensor_heatmap', __name__
, template_folder='templates', static_folder='static')
import time
def __heatmap():
cpu = SENSOR_CLASS_MAP["cpu_user"]
gpu = SENSOR_CLASS_MAP["gpu_load"]
interval = current_app.app_config.monitoring["aggregation_interval"]
time_thr = int(time.time() - interval * 3)
cpu_data = list(global_db.session.query(cpu.node_id, cpu.avg)
.filter(cpu.time > time_thr)
.order_by("time desc").all())
gpu_data = list(global_db.session.query(gpu.node_id, gpu.avg)
.filter(gpu.time > time_thr)
.order_by("time desc").all())
return render_template("cpu.html", cpu_data = cpu_data, gpu_data = gpu_data)
@sensor_heatmap_pages.route("/general")
@requires_auth
def heatmap() -> Response:
return __heatmap()
| [
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] |
python_test.py | jackKiZhu/mypython | 0 | 8114 | <gh_stars>0
from flask import Flask, render_template, request
from flask_sqlalchemy import SQLAlchemy
app = Flask(__name__)
app.config["SQLALCHEMY_DATABASE_URI"] = "mysql://root:[email protected]:3306/python_github"
app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = True
db = SQLAlchemy(app)
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
user_name = db.Column(db.String(64), unique=True)
user_password = db.Column(db.String(32))
def __repr__(self):
return "用户id:%s 用户名:%s" % (self.id, self.user_name)
@app.route("/", methods=["post", "get"])
def index():
index_meg = ""
if request.method == "POST":
user_name = request.form.get("user_name", "")
user_pwd = request.form.get("user_pwd", "")
if not all([user_name, user_pwd]):
index_meg = "请正确输入信息"
else:
print(request.get_data())
user_name_is_exits = User.query.filter(User.user_name == user_name).first()
if user_name_is_exits:
index_meg = "用户名已存在"
else:
user_obj = User(user_name=user_name, user_password=<PASSWORD>)
db.session.add(user_obj)
db.session.commit()
index_meg = "注册成功"
print("注册成功")
# user_name = request.args.get("user_name", "")
# user_pwd = request.args.get("user_pwd", "")
# user_is_login = User.query.filter_by(user_name=user_name, user_password=<PASSWORD>).first()
# if user_is_login:
# index_meg = "登陆成功"
# print("登陆成功")
# return render_template("login_ok.html", index_meg=index_meg)
# else:
# # index_meg = "登陆失败"
# print("登陆失败")
return render_template("index.html", index_meg=index_meg)
if __name__ == "__main__":
db.drop_all()
db.create_all()
app.run(debug=True)
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tests.py | HeyIamJames/CarCompare | 0 | 137833 | <gh_stars>0
import unittest
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
class PythonOrgSearch(unittest.TestCase):
def setUp(self):
self.driver = webdriver.Chrome()
def test_search_in_python_org(self):
driver = self.driver("/chromedriver.exe"
self.assertIn("none", driver.title)
elem.send_keys("Ads")
elem.send_keys(Keys.RETURN)
def tearDown(self):
self.driver.close()
if __name__ == "__main__":
unittest.main()
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Audio_CrisNet_spec_mel_log.py | BioSIP/countcrowd_TFG | 0 | 71797 | <filename>Audio_CrisNet_spec_mel_log.py<gh_stars>0
from losses import LogCoshLoss
import os
from scipy.io import loadmat
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
import torch
import torchaudio
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import numpy as np
from torch.optim.lr_scheduler import StepLR
import pickle
# https://pytorch.org/tutorials/beginner/audio_preprocessing_tutorial.html
# Para comprobar si tenemos GPUs disponibles para usar o no:
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
SAVE_FILENAME = 'crisnet_melspectrogram_mselog.pickle'
class AudioDataset(Dataset):
def __init__(self, audio_path, density_path, transform=None):
# 3 opciones para el density_path:
#density_path = '/Volumes/Cristina /TFG/Data/density/train'
#density_path = '/Volumes/Cristina /TFG/Data/density/test'
#density_path = '/Volumes/Cristina /TFG/Data/density/val'
self.density_path = density_path
self.audio_path = audio_path
self.transform = transform
self.mapfiles = os.listdir(self.density_path)
# Para no incluir los archivos con '._':
self.mapfiles = [
el for el in self.mapfiles if el.startswith('._') == False]
self.mapfiles_wo_ext = [el[:-4] for el in self.mapfiles]
# list comprehension
#audio_path = '/Volumes/Cristina /TFG/Data/auds/'
self.audiofiles = os.listdir(audio_path)
self.audiofiles_wo_ext = [el[:-4] for el in self.audiofiles]
self.audiofiles = [
el + '.wav' for el in self.audiofiles_wo_ext if el in self.mapfiles_wo_ext]
self.audiofiles = sorted(self.audiofiles)
self.mapfiles = sorted(self.mapfiles)
# Añadir extensiones a archivos de audio:
# for i in range(len(self.audiofiles)):
# Añado la extensión al nombre del archivo que quiero importar:
#self.audiofiles[i] = [self.audiofiles[i] + '.wav']
def __len__(self):
return len(self.audiofiles)
def __getitem__(self, idx):
# DENSITY MAP
map_path = self.density_path + self.mapfiles[idx]
mapa = loadmat(map_path)
y = torch.as_tensor(mapa['map'].sum(), dtype=torch.float32)
# AUDIO
# Encuentro el path del archivo:
filename = str(self.audiofiles[idx])
filename = filename.lstrip("['")
filename = filename.rstrip("']")
aud_path = self.audio_path + filename
# Cargamos el audio:
waveform, sample_rate = torchaudio.load(
aud_path) # waveform es un tensor
x = waveform.view((2, 1, -1)) # dimensiones
if self.transform:
x = self.transform(x)
return x, y
class MelSpectrogramDataset(Dataset):
def __init__(self, audio_path, density_path, transform=None):
#3 opciones para el density_path:
#density_path = '/Volumes/Cristina /TFG/Data/density/train'
#density_path = '/Volumes/Cristina /TFG/Data/density/test'
#density_path = '/Volumes/Cristina /TFG/Data/density/val'
self.density_path=density_path
self.audio_path=audio_path
self.transform=transform
self.mapfiles = os.listdir(self.density_path)
#Para no incluir los archivos con '._':
self.mapfiles = [el for el in self.mapfiles if el.startswith('._')==False]
self.mapfiles_wo_ext=[el[:-4] for el in self.mapfiles]
# list comprehension
#audio_path = '/Volumes/Cristina /TFG/Data/auds/'
self.audiofiles = os.listdir(audio_path)
self.audiofiles_wo_ext=[el[:-4] for el in self.audiofiles]
self.audiofiles = [el + '.wav' for el in self.audiofiles_wo_ext if el in self.mapfiles_wo_ext]
self.audiofiles = sorted(self.audiofiles)
self.mapfiles = sorted(self.mapfiles)
#Anadir extensiones a archivos de audio:
#for i in range(len(self.audiofiles)):
#Anado la extension al nombre del archivo que quiero importar:
#self.audiofiles[i] = [self.audiofiles[i] + '.wav']
def __len__(self):
return len(self.audiofiles)
def __getitem__(self, idx):
#DENSITY MAP
map_path = self.density_path + self.mapfiles[idx]
mapa = loadmat(map_path)
y = torch.as_tensor(mapa['map'].sum(), dtype=torch.float32)
#AUDIO
#Encuentro el path del archivo:
filename=str(self.audiofiles[idx])
filename=filename.lstrip("['")
filename=filename.rstrip("']")
aud_path = self.audio_path + filename
#Cargamos el audio:
waveform, sample_rate = torchaudio.load(aud_path) #waveform es un tensor
spec = torchaudio.transforms.Spectrogram()(waveform)
x = torchaudio.transforms.MelScale(n_mels = 201, sample_rate = sample_rate)(spec)
#print("Shape of spectrogram: {}".format(x.size()))
#plt.figure()
#plt.imshow(x.log2()[0,:,:].numpy(), cmap='gray')
return x, y
'''
audio_path = '/home/pakitochus/Descargas/propuestas_tfg_cristina/crowd/definitivo/DISCO_dataset/auds/'
train_density_path = '/home/pakitochus/Descargas/propuestas_tfg_cristina/crowd/definitivo/DISCO_dataset/density/train/'
val_density_path = '/home/pakitochus/Descargas/propuestas_tfg_cristina/crowd/definitivo/DISCO_dataset/density/val/'
test_density_path = '/home/pakitochus/Descargas/propuestas_tfg_cristina/crowd/definitivo/DISCO_dataset/density/test/'
'''
audio_path = '/media/NAS/home/cristfg/datasets/auds/'
train_density_path = '/media/NAS/home/cristfg/datasets/density/train/'
val_density_path = '/media/NAS/home/cristfg/datasets/density/val/'
test_density_path = '/media/NAS/home/cristfg/datasets/density/test/'
trainset = MelSpectrogramDataset(audio_path, train_density_path,'MEL')
valset = MelSpectrogramDataset(audio_path, val_density_path,'MEL')
testset = MelSpectrogramDataset(audio_path, test_density_path,'MEL')
# PRUEBA para ver tensores de audio y de mapas de los conjuntos de train y val:
# print(trainset.__getitem__(20))
# print(valset.__getitem__(20))
#BATCH_SIZE: pequeño (1-3)
batch_size = 48
# BATCH_SIZE: pequeño (1-3)
train_loader = DataLoader(trainset, batch_size, shuffle=True)
val_loader = DataLoader(valset, 32, shuffle=False)
test_loader = DataLoader(testset, 32, shuffle=False)
# RED:
'''
#Por si quiero probar luego con LeNet (CAMBIAR INPUTS!):
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__() # esta linea es siempre necesaria
self.conv1 = nn.Conv2d(1, 6, 5, padding=2)
self.mp1 = nn.MaxPool2d(1,2)
self.conv2 = nn.Conv2d(6, 16, 5, padding=2)
self.mp2 = nn.MaxPool2d(2)
self.conv3 = nn.Conv2d(16, 120, 3, padding=1)
self.fc1 = nn.Linear(7*7*120, 256)#capa oculta
self.fc2 = nn.Linear(256, 10)#capa de salida
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.mp1(x)
x = F.relu(self.conv2(x))
x = self.mp2(x)
x = F.relu(self.conv3(x))
x = x.view(-1, 7*7*120)
x = F.relu(self.fc1(x))#Función de activación relu en la salida de la capa oculta
x = F.softmax(self.fc2(x), dim=1)#Función de activación softmax en la salida de la capa oculta
return x
'''
# MaxPool2d((1,2))
# torch.nn.Conv2d(in_channels, out_channels, kernel_size) -> kernel_size = (1, 61)
# in_channels ->2, out_channels -> [32,64].
# optim - > adam
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
assert len(x.size()) == 4, x.size()
B, C, W, H = x.size()
return F.avg_pool2d(x, (W, H)).view(B, C)
class GlobalMaxPool2d(nn.Module):
def __init__(self):
super(GlobalMaxPool2d, self).__init__()
def forward(self, x):
assert len(x.size()) == 4, x.size()
B, C, W, H = x.size()
return F.max_pool2d(x, (W, H)).view(B, C)
class VGGish(nn.Module):
"""
PyTorch implementation of the VGGish model.
Adapted from: https://github.com/harritaylor/torch-vggish
The following modifications were made: (i) correction for the missing ReLU layers, (ii) correction for the
improperly formatted data when transitioning from NHWC --> NCHW in the fully-connected layers, and (iii)
correction for flattening in the fully-connected layers.
"""
def __init__(self):
super(VGGish, self).__init__()
self.pools = [4, 4, 2, 2]
self.features = nn.Sequential(
nn.Conv2d(2, 64, 3, stride=(1, self.pools[0]), padding=3),
nn.ReLU(),
nn.Conv2d(64, 128, 5, stride=(1, self.pools[1]), padding=2),
nn.ReLU(),
nn.Conv2d(128, 256, 3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(256, 256, 3, stride=(1, self.pools[2]), padding=1),
nn.ReLU(),
nn.Conv2d(256, 512, 3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(512, 1024, 3, stride=1, padding=1),
nn.ReLU(),
# nn.MaxPool2d((1,self.pools[3]), stride=self.pools[3])
)
self.avgpool = GlobalAvgPool2d()
self.fc = nn.Sequential(
nn.Linear(1024, 4096),
nn.Linear(4096, 1)
)
# así y todo se nos queda en 1572864000
def forward(self, x):
x = self.features(x) # .permute(0, 2, 3, 1).contiguous()
x = self.avgpool(x)
# x = x.view(x.size(0), -1)
x = self.fc(x)
# b, c, w, h = x.shape
# x = x.view(b, c, -1).mean(-1)
return x
class CrisNet(nn.Module):
def __init__(self):
super(CrisNet, self).__init__() # esta linea es siempre necesaria
self.max_pool1 = nn.MaxPool2d((1,2))
self.max_pool2 = nn.MaxPool2d((1,2))
self.max_pool3 = nn.MaxPool2d((1,2))
self.max_pool4 = nn.MaxPool2d((1,2))
self.max_pool5 = nn.MaxPool2d((1,2))
self.max_pool6 = nn.MaxPool2d((1,2))
self.conv1 = nn.Conv2d(2, 32, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 128, 3, stride=1, padding=1)
self.conv4 = nn.Conv2d(128, 256, 3, stride=1, padding=1)
self.conv5 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
self.conv6 = nn.Conv2d(512, 1024, 3, stride=1, padding=1)
self.fc1 = nn.Linear(617472,1)
def forward(self, x):
#Con función de activación ReLu
#PRIMERA CAPA
x = F.relu(self.conv1(x))
x = self.max_pool1(x)
#SEGUNDA CAPA
x = F.relu(self.conv2(x))
x = self.max_pool2(x)
#TERCERA CAPA
x = F.relu(self.conv3(x))
x = self.max_pool3(x)
#CUARTA CAPA
x = F.relu(self.conv4(x))
x = self.max_pool4(x)
#QUINTA CAPA
x = F.relu(self.conv5(x))
x = self.max_pool5(x)
#SEXTA CAPA
x = F.relu(self.conv6(x))
x = self.max_pool6(x)
x = x.view((x.size(0),-1))
#print(x.size())
x = self.fc1(x)
return x
modelo=CrisNet()
modelo = modelo.to(device)
criterion = nn.MSELoss() # definimos la pérdida
# criterion = LogCoshLoss(reduction='sum')
optimizador = optim.Adam(modelo.parameters(), lr=1e-3)#, weight_decay=1e-4)
# optimizador = optim.SGD(modelo.parameters(), lr=1e-4)
# print(modelo)
# print(train_loader)
# print(type(train_loader))
# print(x)
# print(x.size())
# print(y)
# print(y.size())
def get_mae_mse(ypred, y, transform):
mse_loss = 0.0
mae_loss = 0.0
total = 0
ypred, y = transform(ypred.data), transform(y.data)
for ix in range(y.shape[0]):
total += 1
mae_loss += abs(ypred[ix] - y[ix])
mse_loss += (ypred[ix] - y[ix])*(ypred[ix] - y[ix])
return mae_loss/total, torch.sqrt(mse_loss/total)
# Para predecir y, la normalizaremos. Siempre por el mismo valor:
Y_NORM = 10
losses = {'train': list(), 'validacion': list()}
min_val_loss = {'mae': float('Inf'), 'mse': float('Inf'), 'loss': float('Inf') }
expcode = 'cris_melspectrogram_mselog'
def transform(x):
return torch.log(x/Y_NORM)
def inverse_transform(x):
return Y_NORM*torch.exp(torch.as_tensor(x))
for epoch in range(100):
print("Entrenando... \n") # Esta será la parte de entrenamiento
training_loss = 0.0 # el loss en cada epoch de entrenamiento
total = 0
modelo.train() # Para preparar el modelo para el training
for x, y in train_loader:
total += 1
# ponemos a cero todos los gradientes en todas las neuronas:
optimizador.zero_grad()
y = transform(y) # normalizamos
x = x.to(device)
y = y.to(device)
output = modelo(x) # forward
loss = criterion(output.squeeze(), y.squeeze()) # evaluación del loss
# print(f'loss: {loss:.4f}')
loss.backward() # backward pass
optimizador.step() # optimización
training_loss += loss.item() # acumulamos el loss de este batch
training_loss = training_loss/total
losses['train'].append(training_loss) # .item())
val_loss = 0.0
mae, mse = 0,0
total = 0
modelo.eval() # Preparar el modelo para validación y/o test
print("Validando... \n")
for x, y in val_loader:
total += 1
y = transform(y) # normalizamos
x = x.to(device)
y = y.to(device)
output = modelo(x)
loss = criterion(output.squeeze(), y.squeeze())
val_loss += loss.item()
mae_accum, mse_accum = get_mae_mse(output.squeeze(), y.squeeze(), inverse_transform)
mae += mae_accum
mse += mse_accum
val_loss = val_loss/total
mae = mae/total
mse = mse/total
losses['validacion'].append(val_loss) # .item())
print(f'[ep {epoch}] [Train: {training_loss:.4f}][Val: {val_loss:.4f}]')
print(f'\t[Val MAE: {mae:.4f}][Val MSE: {mse:.4f}]')
# Early stopping
if (val_loss <= min_val_loss['loss']) or (mse <= min_val_loss['mse']) or (mae<=min_val_loss['mae']):
filename = expcode+'.pt'
print(f'Saving as {filename}')
torch.save(modelo, filename)
if val_loss <= min_val_loss['loss']:
min_val_loss['loss'] = val_loss
if mse <= min_val_loss['mse']:
min_val_loss['mse'] = mse
if mae<=min_val_loss['mae']:
min_val_loss['mae'] = mae
last_epoch = epoch
# last_lr = scheduler.get_last_lr()
# ENTRENAMIENTO
n_epochs = 500
LR_DECAY = 0.99
NUM_EPOCH_LR_DECAY = 1
modelo = torch.load(filename)
optimizador = optim.SGD(modelo.parameters(), lr=1e-4, momentum=0.9)
scheduler = StepLR(optimizador, step_size=NUM_EPOCH_LR_DECAY, gamma=LR_DECAY)
epoch_ni = 0 # epochs not improving.
MAX_ITER = 100
for epoch in range(last_epoch, n_epochs):
print("Entrenando... \n") # Esta será la parte de entrenamiento
training_loss = 0.0 # el loss en cada epoch de entrenamiento
total = 0
modelo.train() # Para preparar el modelo para el training
for x, y in train_loader:
total += 1
# ponemos a cero todos los gradientes en todas las neuronas:
optimizador.zero_grad()
y = transform(y) # normalizamos
x = x.to(device)
y = y.to(device)
output = modelo(x) # forward
loss = criterion(output.squeeze(), y.squeeze()) # evaluación del loss
# print(f'loss: {loss}')
loss.backward() # backward pass
optimizador.step() # optimización
training_loss += loss.item() # acumulamos el loss de este batch
training_loss = training_loss/total
losses['train'].append(training_loss) # .item())
val_loss = 0.0
total = 0
mae, mse = 0,0
scheduler.step() # rebaja un poco la LR del SGD
modelo.eval() # Preparar el modelo para validación y/o test
print("Validando... \n")
for x, y in val_loader:
total += 1
y = transform(y) # normalizamos
x = x.to(device)
y = y.to(device)
output = modelo(x) # forward
loss = criterion(output.squeeze(), y.squeeze())
mae_accum, mse_accum = get_mae_mse(output.squeeze(), y.squeeze(), inverse_transform)
mae += mae_accum
mse += mse_accum
val_loss = val_loss/total
mae = mae/total
mse = mse/total
losses['validacion'].append(val_loss) # .item())
print(f'[ep {epoch}] [Train: {training_loss:.4f}][Val: {val_loss:.4f}]')
print(f'\t[Val MAE: {mae:.4f}][Val MSE: {mse:.4f}]')
# Early stopping
if (val_loss <= min_val_loss['loss']) or (mse <= min_val_loss['mse']) or (mae<=min_val_loss['mae']):
filename = expcode+'.pt'
print(f'Saving as {filename}')
torch.save(modelo, filename)
if val_loss <= min_val_loss['loss']:
min_val_loss['loss'] = val_loss
if mse <= min_val_loss['mse']:
min_val_loss['mse'] = mse
if mae<=min_val_loss['mae']:
min_val_loss['mae'] = mae
# TEST
modelo = torch.load(filename)
modelo.eval() # Preparar el modelo para validación y/o test
print("Testing... \n")
total = 0
test_loss_mse = 0.0
test_loss_mae = 0.0
yreal = list()
ypredicha = list()
for x, y in test_loader:
y = transform(y) # normalizamos
x = x.to(device)
y = y.to(device)
with torch.no_grad():
output = modelo(x)
yreal.append(inverse_transform(y.data.cpu().numpy()))
ypredicha.append(inverse_transform(output.data.cpu().numpy()))
mae_accum, mse_accum = get_mae_mse(output.squeeze(), y.squeeze(), inverse_transform)
mae += mae_accum
mse += mse_accum
total += 1
val_loss = val_loss/total
mae = mae/total
mse = mse/total
print(f'[ep {epoch}][Test MAE: {mae:.4f}][Test MSE: {mse:.4f}]')
# yreal = np.array(yreal).flatten()
# ypredicha = np.array(ypredicha).flatten() # comprobar si funciona.
losses['yreal'] = np.array([el.item() for a in yreal for el in a])
losses['ypredicha'] = np.array([el.item() for a in ypredicha for el in a])
print(f'Test Loss (MSE): {mse}')
losses['test_mse'] = mse # .item())
print(f'Test Loss (MAE): {mae}')
losses['test_mae'] = mae # .item())
with open(SAVE_FILENAME, 'wb') as handle:
pickle.dump(losses, handle, protocol=pickle.HIGHEST_PROTOCOL)
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signin/tests.py | pptnz/swa_team2 | 0 | 1343 | <reponame>pptnz/swa_team2
import json
from django.test import TestCase
from django.contrib.auth.models import User
from .models import CustomUser
from django.apps import apps
from .apps import SigninConfig
class SignInTest(TestCase):
def setUp(self):
self.django_user = User.objects.create_user(username='testusername', password='<PASSWORD>')
self.custom_user = CustomUser.objects.create(django_user=self.django_user)
def test_apps(self):
self.assertEqual(SigninConfig.name, 'signin')
self.assertEqual(apps.get_app_config('signin').name, 'signin')
def test_sign_in_redirect_page(self):
response = self.client.get('/')
self.assertRedirects(response, '/sign_in/')
def test_get(self):
response = self.client.get('/sign_in/')
self.assertEqual(response.status_code, 200)
def test_wrong_username(self):
response = self.client.post('/sign_in/', {'username': 'wrongusername', 'password': '<PASSWORD>'})
self.assertEqual(response.status_code, 200)
def test_wrong_password(self):
response = self.client.post('/sign_in/', {'username': 'testusername', 'password': '<PASSWORD>'})
self.assertEqual(response.status_code, 200)
def test_login(self):
response = self.client.post('/sign_in/', {'username': 'testusername', 'password': '<PASSWORD>'})
self.assertRedirects(response, '/habitmaker/')
# todo: change this link
def test_login_other_page(self):
response = self.client.post('/sign_in/?next=/habitmaker/', {'username': 'testusername', 'password': '<PASSWORD>'})
self.assertRedirects(response, '/habitmaker/')
def test_form_not_valid(self):
response = self.client.post('/sign_in/', {'username': 'testusername'})
self.assertEqual(response.status_code, 200)
def test_email_verification(self):
self.custom_user.authenticate_email()
self.assertTrue(self.custom_user.is_email_authenticated)
def test_already_signed_in(self):
self.client.login(username='testusername', password='<PASSWORD>')
response = self.client.get('/sign_in/')
self.assertRedirects(response, '/habitmaker/')
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mutagene/cli/motif_menu.py | neksa/pymutagene | 3 | 116108 | <filename>mutagene/cli/motif_menu.py
import argparse
import sys
import logging
from mutagene.io.context_window import read_MAF_with_context_window, read_VCF_with_context_window
from mutagene.motifs import identify_motifs
from mutagene.io.motifs import write_motif_matches, get_known_motifs
logger = logging.getLogger(__name__)
genome_error_message = """requires genome name argument -g hg19, hg38, mm10, see http://hgdownload.cse.ucsc.edu/downloads.html for more
Use mutagene fetch to download genome assemblies"""
class MotifMenu(object):
def __init__(self, parser):
parser.description = ""
parser.epilog = """
Examples:
# search in sample2.vcf for all preidentified motifs in mutagene using hg19
mutagene motif --infile sample2.vcf --input-format VCF --genome hg19
# search for the presence of the C[A>T] motif in sample1.maf using hg19 not checking for strand-specificity
mutagene motif --infile sample1.maf --input-format MAF --genome hg19 --motif 'C[A>T]' --strand A
"""
###################################################################
required_group = parser.add_argument_group('Required arguments')
required_group.add_argument(
"--infile", "-i", help="Input file in MAF or VCF format with one or multiple samples",
type=argparse.FileType('r'))
required_group.add_argument(
'--genome', "-g", help="Location of genome assembly file in 2bit format", type=str)
###################################################################
optional_group = parser.add_argument_group('Optional arguments')
optional_group.add_argument('--input-format', "-f", help="Input format: MAF, VCF", type=str, choices=['MAF', 'VCF'], default='MAF')
optional_group.add_argument(
"--motif", "-m",
help="Motif to search for, use the 'R[C>T]GY' syntax for the motif. Use quotes", type=str)
optional_group.add_argument(
'--outfile', "-o", nargs='?', type=argparse.FileType('w'), default=sys.stdout,
help="Name of output file, will be generated in TSV format")
# for backwards compatibility with 0.8.X add a hidden action that would just take anything as a valid input
optional_group.add_argument('action', nargs="?", metavar="")
###################################################################
advanced_group = parser.add_argument_group('Advanced arguments')
advanced_group.add_argument(
'--window-size', "-w", help="Context window size for motif search, default setting is 50",
type=int, default=50)
advanced_group.add_argument(
'--strand', "-s",
help="Transcribed strand (T), non-transcribed (N), any (A), or all (TNA default) ",
type=str, default='TNA', choices=['T', 'N', 'A', 'TNA'])
advanced_group.add_argument(
'--threshold', "-t",
help="Significance threshold for qvalues, default value=0.05",
type=float, default=0.05)
advanced_group.add_argument(
'--save-motif-matches',
help="Save mutations in matching motifs to a BED file",
type=argparse.FileType('w'), default=None)
advanced_group.add_argument(
'--test',
help="Statistical test to use",
type=str, default='Fisher', choices=['Fisher', 'Chi2'])
self.parser = parser
@classmethod
def search(cls, args):
if not args.infile:
logger.warning("Provide input file in VCF or MAF format (-i) and a corresponding genome assembly (-g)")
return
if not args.genome:
logger.warning(genome_error_message)
return
if args.threshold > 1.0 or args.threshold < 0.0:
logger.warning("The threshold value should be between 0.0 and 1.0, inclusive")
return
if not args.motif:
logger.info("Searching for predefined motifs")
logger.info("Will reduce statistical power because of multiple comparisons")
custom_motif = None
else:
custom_motif = args.motif
custom_motif = custom_motif.replace('-', '>')
custom_motif = custom_motif.replace('/', '>')
custom_motif = custom_motif.replace('.', '>')
custom_motif = custom_motif.replace('->', '>')
if ">" not in custom_motif or "]" not in custom_motif:
logger.warning(
"Mutagene motif search failed because motif cannot be processed."
"Check to make sure motif input is in quotes")
return
logger.info("Searching for motif {}".format(custom_motif))
if args.window_size > 5000 or args.window_size < 1:
logger.warning('window-size should be between 1 and 5000 nucleotides')
return
try:
if args.input_format == 'VCF':
mutations, mutations_with_context, processing_stats = read_VCF_with_context_window(
args.infile, args.genome, args.window_size)
elif args.input_format == 'MAF':
mutations, mutations_with_context, processing_stats = read_MAF_with_context_window(
args.infile, args.genome, args.window_size)
except ValueError as e:
logger.warning('Not able to parse input file in {} format: {}. You can specify a different format with --input-format (-f)'.format(args.input_format, e))
sys.exit(1)
if len(mutations_with_context) == 0:
logger.warning("No mutations loaded")
#######################
# Performance PROFILING
# import cProfile
# import pstats
# pr = cProfile.Profile()
# pr.enable()
matching_motifs = identify_motifs(
samples_mutations=mutations_with_context,
custom_motif=custom_motif,
strand=args.strand,
threshold=args.threshold,
dump_matches=args.save_motif_matches,
stat_type=args.test) if mutations_with_context is not None else []
#######################
# Performance PROFILING
# pr.disable()
# p = pstats.Stats(pr)
# p.sort_stats('ncalls').print_stats(20) # strip_dirs()
if len(matching_motifs) == 0:
logger.warning("No significant motif matches found")
else:
write_motif_matches(args.outfile, matching_motifs)
def list(self, args):
""" Prints the list of known motifs bundled with the package"""
print("\nThe list of mutational motifs that will be tested by default:")
for m in get_known_motifs():
print("{:20}\t{}".format(m['name'], m['logo']))
print("any custom motif can be specified with --motif (-m)")
def callback(self, args):
# getattr(cls, args.action)(args)
if args.infile:
MotifMenu.search(args)
else:
self.parser.print_usage()
self.list(args)
sys.exit(1)
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leetcode/319-Bulb-Switcher/BulbSwitcher_001_TLE.py | cc13ny/all-in | 1 | 175640 | class Solution(object):
def bulbSwitch(self, n):
"""
:type n: int
:rtype: int
"""
num_of_facs = [1 for i in range(n)]
for j in range(2, n + 1):
for k in range(1, n / j + 1):
num_of_facs[j * k - 1] += 1
res = 0
for num in num_of_facs:
res += num % 2
return res
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main.py | fogcitymarathoner/gae_p27_dj17 | 0 | 1615694 | import os,sys
os.environ['DJANGO_SETTINGS_MODULE'] = 'p27_d17_prj.settings'
# Force Django to reload its settings.
from django.conf import settings
settings._target = None
import django.core.handlers.wsgi
import django.core.signals
import django.db
import django.dispatch
# Log errors.
#import logging
#def log_exception(*args, **kwds):
# logging.exception('Exception in request:')
#
#django.dispatch.Signal.connect(
# django.core.signals.got_request_exception, log_exception)
# Unregister the rollback event handler.
django.dispatch.Signal.disconnect(
django.core.signals.got_request_exception,
django.db._rollback_on_exception)
app = django.core.handlers.wsgi.WSGIHandler()
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conllu/conllu.py | spyysalo/conllu.py | 13 | 53528 | #!/usr/bin/env python
# CoNLL-U format support
import re
import codecs
import brat
from itertools import groupby
# feature name-value separator
FSEP = '='
# dependency head-rel separator
DSEP = ':'
# Free-form text annotation type in brat export
COMMENT_TYPE = 'AnnotatorNotes'
class FormatError(Exception):
def __init__(self, msg, line=None, linenum=None):
self.msg = msg
self.line = line
self.linenum = linenum
def __str__(self):
msg = self.msg
if self.line is not None:
msg += ': "'+self.line.encode('ascii', 'replace')+'"'
if self.linenum is not None:
msg += ' (line %d)' % self.linenum
return msg
CPOSTAG_RE = re.compile(r'^[a-zA-Z]+$')
POSTAG_RE = re.compile(r'^[\x20-\xff]+$')
class Element(object):
"""Represents CoNLL-U word or multi-word token."""
def __init__(self, id_, form, lemma, cpostag, postag,
feats, head, deprel, deps, misc, offset=0):
self.id = id_
self.form = form
self.lemma = lemma
self.cpostag = cpostag
self.postag = postag
self._feats = feats
self.head = head
self.deprel = deprel
self._deps = deps
self.misc = misc
self.offset = offset
self.sentence = None
self.validate()
self._fmap = None
self._dlist = None
def validate(self):
# minimal format validation (incomplete)
if not self.is_word():
# TODO: check multi-word tokens
return
# some character set constraints
if not CPOSTAG_RE.match(self.cpostag):
raise FormatError('invalid CPOSTAG: %s' % self.cpostag)
if not POSTAG_RE.match(self.postag):
raise FormatError('invalid POSTAG: %s' % self.postag)
# no feature is empty
if any(True for s in self._feats if len(s) == 0):
raise FormatError('empty feature: %s' % str(self._feats))
# feature names and values separated by feature separator
if any(s for s in self._feats if len(s.split(FSEP)) < 2):
raise FormatError('invalid features: %s' % str(self._feats))
# no feature name repeats
if any(n for n, g in groupby(sorted(s.split(FSEP)[0] for s in self._feats))
if len(list(g)) > 1):
raise FormatError('duplicate features: %s' % str(self._feats))
# head is integer
try:
int(self.head)
except ValueError:
raise FormatError('non-int head: %s' % self.head)
def is_word(self):
try:
val = int(self.id)
return True
except ValueError:
return False
def has_feat(self, name):
return name in self.feat_map()
def add_feats(self, feats):
# name-value pairs
assert not any(nv for nv in feats if len(nv) != 2)
self._feats.extend(FSEP.join(nv) for nv in feats)
self._fmap = None
def set_feats(self, feats):
self._feats = []
self.add_feats(feats)
self._fmap = None
def remove_feat(self, name, value):
nv = FSEP.join((name, value))
self._feats.remove(nv)
self._fmap = None
def append_misc(self, value):
if self.misc == '_':
self.misc = value
else:
self.misc = self.misc + '|' + value
def feat_names(self):
return [f.split(FSEP)[0] for f in self._feats]
def feat_map(self):
if self._fmap is None:
try:
self._fmap = dict([f.split(FSEP, 1) for f in self._feats])
except ValueError:
raise ValueError('failed to convert ' + str(self._feats))
return self._fmap
def feats(self):
return self.feat_map().items()
def deps(self, include_primary=False):
if self._dlist is None:
try:
self._dlist = [d.split(DSEP, 1) for d in self._deps]
except:
raise FormatError('failed to parse ' + str(self._deps))
if not include_primary:
return self._dlist
else:
return [(self.head, self.deprel)] + self._dlist
def set_deps(self, dlist):
self._deps = [DSEP.join(hd) for hd in dlist]
self._dlist = None
def has_deprel(self, deprel, check_deps=True):
if self.deprel == deprel:
return True
elif not check_deps:
return False
elif any(d for d in self.deps() if d[1] == deprel):
return True
else:
return False
def wipe_annotation(self):
self.lemma = '_'
self.cpostag = '_'
self.postag = '_'
self._feats = '_'
self.head = '_'
self.deprel = '_'
self._deps = '_'
self.misc = '_'
def to_brat_standoff(self, element_by_id):
"""Return list of brat standoff annotations for the element."""
# base ID, unique within the document
bid = '%s.%s' % (self.sentence.id, self.id)
if self.is_word():
# Word, maps to: Textbound with the coarse POS tag as
# type, freeform text comment with LEMMA, POSTAG and
# MISC (when nonempty) as values, attribute for each
# feature.
# textbounds
spans = [[self.offset, self.offset+len(self.form)]]
textbounds = [
brat.Textbound('T'+bid, self.cpostag, spans, self.form),
]
# comments
freeform = [
('LEMMA', self.lemma),
('POSTAG', self.postag),
]
if self.misc != '_':
freeform.append(('MISC', self.misc))
comments = [
brat.Comment('#'+bid, COMMENT_TYPE, 'T'+bid,
' '.join(u'%s=%s' % f for f in freeform))
]
# attributes
attribs = []
for name, value in self.feats():
aid = 'A'+bid+'-%d'%(len(attribs)+1)
attribs.append(brat.Attribute(aid, name, 'T'+bid, value))
# relations
relations = []
for head, deprel in self.deps(include_primary=True):
if head == '0':
continue # skip root
rid = 'R'+bid+'-%d'%(len(relations)+1)
tid = '%s.%s' % (self.sentence.id, element_by_id[head].id)
args = [('Arg1', 'T'+tid), ('Arg2', 'T'+bid)]
relations.append(brat.Relation(rid, deprel, args))
return textbounds + attribs + relations + comments
else:
# Multi-word token, maps to: Textbound with a special type,
# and free-text comment containing the form.
# Span corresponds to maximum span over covered tokens.
start, end = self.id.split('-')
first, last = element_by_id[start], element_by_id[end]
spans = [[first.offset, last.offset+len(last.form)]]
text = ' '.join(str(element_by_id[str(t)].form)
for t in range(int(start), int(end)+1))
return [
brat.Textbound('T'+bid, 'Multiword-token', spans, text),
brat.Comment('#'+bid, COMMENT_TYPE, 'T'+bid, 'FORM='+self.form)
]
def __unicode__(self):
fields = [self.id, self.form, self.lemma, self.cpostag, self.postag,
self._feats, self.head, self.deprel, self._deps, self.misc]
fields[5] = '_' if fields[5] == [] else '|'.join(sorted(fields[5], key=lambda s: s.lower())) # feats
fields[8] = '_' if fields[8] == [] else '|'.join(fields[8]) # deps
return '\t'.join(fields)
@classmethod
def from_string(cls, s):
fields = s.split('\t')
if len(fields) != 10:
raise FormatError('got %d/10 field(s)' % len(fields), s)
fields[5] = [] if fields[5] == '_' else fields[5].split('|') # feats
fields[8] = [] if fields[8] == '_' else fields[8].split('|') # deps
return cls(*fields)
class Sentence(object):
def __init__(self, id_=0, filename=None, base_offset=0):
"""Initialize a new, empty Sentence."""
self.comments = []
self._elements = []
self.id = id_
self.filename = filename
self.base_offset = base_offset
self.next_offset = base_offset
# mapping from IDs to elements
self._element_by_id = None
def append(self, element):
"""Append word or multi-word token to sentence."""
self._elements.append(element)
assert element.sentence is None, 'element in multiple sentences?'
element.sentence = self
element.offset = self.next_offset
if element.is_word():
self.next_offset += len(element.form) + 1
else:
# multi-word token; don't shift position of next token
pass
# reset cache (TODO: extend instead)
self._element_by_id = None
def empty(self):
return self._elements == []
def words(self):
"""Return a list of the words in the sentence."""
return [e for e in self._elements if e.is_word()]
def text(self, use_tokens=False, separator=' '):
"""Return the text of the sentence."""
if use_tokens:
raise NotImplementedError('multi-word token text not supported.')
else:
return separator.join(w.form for w in self.words())
def length(self, use_tokens=False):
"""Return the length of the sentence text."""
return len(self.text(use_tokens))
def element_by_id(self):
"""Return mapping from id to element."""
if self._element_by_id is None:
self._element_by_id = { e.id: e for e in self._elements }
return self._element_by_id
def get_element(self, id_):
"""Return element by id."""
return self.element_by_id()[id_]
def wipe_annotation(self):
for e in self._elements:
if e.is_word():
e.wipe_annotation()
def remove_element(self, id_):
# TODO: implement for cases where multi-word tokens span the
# element to remove.
assert len(self.words()) == len(self._elements), 'not implemented'
# there must not be references to the element to remove
for w in self.words():
assert not any(h for h, d in w.deps(True) if h == id_), \
'cannot remove %s, references remain' % id_
# drop element
element = self.get_element(id_)
self._elements.remove(element)
self._element_by_id = None
# update IDs
id_map = { u'0' : u'0' }
for i, w in enumerate(self.words()):
new_id = unicode(i+1)
id_map[w.id] = new_id
w.id = new_id
for w in self.words():
w.head = id_map[w.head]
w.set_deps([(id_map[h], d) for h, d in w.deps()])
def dependents(self, head, include_secondary=True):
if isinstance(head, Element):
head_id = head.id
deps = []
for w in self.words():
if not include_secondary:
wdeps = [(w.head, w.deprel)]
else:
wdeps = w.deps(include_primary=True)
for head, deprel in wdeps:
if head == head_id:
deps.append((w.id, deprel))
return deps
def assign_offsets(self, base_offset=None, use_tokens=False):
"""Assign offsets to sentence elements."""
if base_offset is not None:
self.base_offset = base_offset
offset = self.base_offset
if use_tokens:
raise NotImplementedError('multi-word token text not supported.')
else:
# Words are separated by a single character and multi-word
# tokens appear at the start of the position of their
# initial words with zero-width spans.
for e in self._elements:
e.offset = offset
if e.is_word():
offset += len(e.form) + 1
def to_brat_standoff(self):
"""Return list of brat standoff annotations for the sentence."""
# Create mapping from ID to element.
annotations = []
for element in self._elements:
annotations.extend(element.to_brat_standoff(self.element_by_id()))
return annotations
def __unicode__(self):
element_unicode = [unicode(e) for e in self._elements]
return '\n'.join(self.comments + element_unicode)+'\n'
class Document(object):
def __init__(self, filename=None):
self._sentences = []
self.filename = filename
def append(self, sentence):
"""Append sentence to document."""
self._sentences.append(sentence)
def empty(self):
return self._sentences == []
def words(self):
"""Return a list of the words in the document."""
return [w for s in self.sentences() for w in s.words()]
def sentences(self):
"""Return a list of the sentences in the document."""
return self._sentences
def text(self, use_tokens=False, element_separator=' ',
sentence_separator='\n'):
return sentence_separator.join(s.text(use_tokens, element_separator)
for s in self.sentences())
def to_brat_standoff(self):
"""Return list of brat standoff annotations for the document."""
annotations = []
for sentence in self._sentences:
annotations.extend(sentence.to_brat_standoff())
return annotations
def _file_name(file_like, default='document'):
"""Return name of named file or file-like object, or default if not
available."""
# If given a string, assume that it's the name
if isinstance(file_like, basestring):
return file_like
try:
return file_like.name
except AttributeError:
return default
def read_documents(source, filename=None):
"""Read CoNLL-U format, yielding Document objects."""
if filename is None:
filename = _file_name(source)
current = Document(filename)
for sentence in read_conllu(source, filename):
# TODO: recognize and respect document boundaries in source data.
current.append(sentence)
yield current
def read_conllu(source, filename=None):
"""Read CoNLL-U format, yielding Sentence objects.
Note: incomplete implementation, lacks validation."""
# If given a string, assume it's a file name, open and recurse.
if isinstance(source, basestring):
with codecs.open(source, encoding='utf-8') as i:
for s in read_conllu(i, filename=source):
yield s
return
if filename is None:
filename = _file_name(source)
sent_num, offset = 1, 0
current = Sentence(sent_num, filename, offset)
for ln, line in enumerate(source):
line = line.rstrip('\n')
if not line:
if not current.empty():
# Assume single character sentence separator.
offset += current.length() + 1
yield current
else:
raise FormatError('empty sentence', line, ln+1)
sent_num += 1
current = Sentence(sent_num, filename, offset)
elif line[0] == '#':
current.comments.append(line)
else:
try:
current.append(Element.from_string(line))
except FormatError, e:
e.linenum = ln+1
raise e
assert current.empty(), 'missing terminating whitespace'
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src/riotwatcher/__version__.py | mikeread-dev/Riot-Watcher | 0 | 54996 | __version__ = "3.1.2"
__author__ = "pseudonym117"
__title__ = "RiotWatcher"
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practices/practice_2.2/src/database/manage/__init__.py | JoelHernandez343/networking-administration | 0 | 1616295 | from database import models
from database.database import engine
from database.manage import interface
from database.manage import vlan
def drop_db():
models.Base.metadata.drop_all(bind=engine)
def create_db():
models.Base.metadata.create_all(bind=engine)
def recreate_db():
drop_db()
create_db()
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plugins/modules/channel_policy.py | m-g-k/ansible-collection | 1 | 64331 | #!/usr/bin/python
#
# SPDX-License-Identifier: Apache-2.0
#
from __future__ import absolute_import, division, print_function
__metaclass__ = type
from ..module_utils.dict_utils import equal_dicts, merge_dicts, copy_dict
from ..module_utils.module import BlockchainModule
from ..module_utils.proto_utils import proto_to_json, json_to_proto
from ansible.module_utils._text import to_native
import json
ANSIBLE_METADATA = {'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'}
DOCUMENTATION = '''
---
module: channel_policy
short_description: Manage a policy for a Hyperledger Fabric channel
description:
- Add, update, and remove policies for a Hyperledger Fabric channel by using the IBM Blockchain Platform.
- This module works with the IBM Blockchain Platform managed service running in IBM Cloud, or the IBM Blockchain
Platform software running in a Red Hat OpenShift or Kubernetes cluster.
author: <NAME> (@sstone1)
options:
state:
description:
- C(absent) - An policy matching the specified name will be removed from the channel.
- C(present) - Asserts that an policy matching the specified name and configuration exists
in the channel. If no policy matches the specified name, the policy will be added
to the channel. If an policy matches the specified name but the configuration does not
match, then the policy in the channel will be updated.
type: str
default: present
choices:
- absent
- present
path:
description:
- Path to current the channel configuration file.
- This file can be fetched by using the M(channel_config) module.
- This file will be updated in place. You will need to keep a copy of the original file for computing the configuration
update.
type: str
required: true
name:
description:
- The name of the policy to add, update, or remove from the channel.
type: str
required: true
policy:
description:
- The policy to add, update, or remove from the channel.
- You can pass a string, which is a path to a JSON file containing a policy
in the Hyperledger Fabric format (common.Policy).
- You can also pass a dict, which must correspond to a parsed policy in the
Hyperledger Fabric format (common.Policy).
- Only required when I(state) is C(present).
type: raw
required: true
notes: []
requirements: []
'''
EXAMPLES = '''
- name: Add the policy to the channel
ibm.blockchain_platform.channel_policy:
state: present
api_endpoint: https://ibp-console.example.org:32000
api_authtype: basic
api_key: xxxxxxxx
api_secret: <KEY>
name: Admins
policy: admins-policy.json
path: channel_config.bin
- name: Remove the policy to the channel
ibm.blockchain_platform.channel_policy:
state: absent
api_endpoint: https://ibp-console.example.org:32000
api_authtype: basic
api_key: xxxxxxxx
api_secret: <KEY>
name: Admins
path: channel_config.bin
'''
RETURN = '''
---
{}
'''
def main():
# Create the module.
argument_spec = dict(
state=dict(type='str', default='present', choices=['present', 'absent']),
path=dict(type='str', required=True),
name=dict(type='str', required=True),
policy=dict(type='raw', required=True)
)
required_if = [
]
module = BlockchainModule(argument_spec=argument_spec, supports_check_mode=True, required_if=required_if)
# Ensure all exceptions are caught.
try:
# Get the target path, policy name and policy.
path = module.params['path']
name = module.params['name']
policy = module.params['policy']
if isinstance(policy, str):
with open(policy, 'r') as file:
policy = json.load(file)
elif isinstance(policy, dict):
pass
else:
raise Exception(f'The policy {name} is invalid')
# Read the config.
with open(path, 'rb') as file:
config_json = proto_to_json('common.Config', file.read())
# Check to see if the channel member exists.
application_policies = config_json['channel_group']['groups']['Application']['policies']
policy_wrapper = application_policies.get(name, None)
# Handle the desired state appropriately.
state = module.params['state']
if state == 'present' and policy_wrapper is None:
# Add the channel policy.
application_policies[name] = dict(
mod_policy='Admins',
policy=policy
)
elif state == 'present' and policy_wrapper is not None:
# Update the channel policy.
updated_policy_wrapper = copy_dict(policy_wrapper)
merge_dicts(updated_policy_wrapper['policy'], policy)
if equal_dicts(policy_wrapper, updated_policy_wrapper):
return module.exit_json(changed=False)
application_policies[name] = updated_policy_wrapper
elif state == 'absent' and policy_wrapper is None:
# Nothing to do.
return module.exit_json(changed=False)
elif state == 'absent' and policy_wrapper is not None:
# Delete the channel member.
del application_policies[name]
# Save the config.
config_proto = json_to_proto('common.Config', config_json)
with open(path, 'wb') as file:
file.write(config_proto)
module.exit_json(changed=True)
# Notify Ansible of the exception.
except Exception as e:
module.fail_json(msg=to_native(e))
if __name__ == '__main__':
main()
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876,
13,
13,
13,
361,
4770,
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1649,
1275,
525,
1649,
3396,
1649,
2396,
13,
1678,
1667,
580,
13,
2
] |
Diena_1_4_thonny/d2_u2_d11.py | edzya/Python_RTU_08_20 | 8 | 34881 | <filename>Diena_1_4_thonny/d2_u2_d11.py
try:
width=float(input("Ieraksti savas istabas platumu (metros): "))
height=float(input("Ieraksti savas istabas augstumu (metros): "))
length=float(input("Ieraksti savas istabas garumu (metros): "))
capacity=round(width*height*length, 2)
print(f"Tavas istabas tilpums ir {capacity} m\u00b3.")
except ValueError:
print("Ievadiet vērtību ciparos! Komata vietā jālieto punkts.")
print("*"*30) | [
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my_app/templates/model.py | varshaholsambre/Whats-For-Dinner | 0 | 113164 | from my_app import db
class Fact(db.Model):
id = db.Column(db.Integer, primary_keys = True)
name = db.Column(db.String(100))
value = db.Column(db.String(100)) | [
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test/propagation_warn_only_test.py | lechat/jenkinsflow | 0 | 21075 | # Copyright (c) 2012 - 2015 <NAME>, Hupfeldt IT
# All rights reserved. This work is under a BSD license, see LICENSE.TXT.
from pytest import raises
from jenkinsflow.flow import serial, parallel, FailedChildJobException, FailedChildJobsException, Propagation, BuildResult
from .framework import api_select
from .framework.utils import pre_existing_fake_cli
def test_propagation_warn_only_serial(api_type, fake_java):
with api_select.api(__file__, api_type, login=True) as api:
pre_existing_fake_cli(api_type)
api.flow_job()
api.job('j11', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j12_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=2, serial=True)
api.job('j13', exec_time=0.01, max_fails=0, expect_invocations=0, expect_order=None)
with serial(api, timeout=70, job_name_prefix=api.job_name_prefix, report_interval=3, propagation=Propagation.FAILURE_TO_UNSTABLE) as ctrl1:
ctrl1.invoke('j11')
ctrl1.invoke('j12_fail')
ctrl1.invoke('j13')
assert ctrl1.result == BuildResult.UNSTABLE
# Note: the fact that no error was raised also implies that the failure didn't propagate as failure
def test_propagation_warn_only_parallel(api_type, fake_java):
with api_select.api(__file__, api_type, login=True) as api:
pre_existing_fake_cli(api_type)
api.flow_job()
api.job('j1_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=1)
api.job('j2', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
with parallel(api, timeout=70, job_name_prefix=api.job_name_prefix, report_interval=3, propagation=Propagation.FAILURE_TO_UNSTABLE) as ctrl1:
ctrl1.invoke('j1_fail')
ctrl1.invoke('j2')
def test_propagation_warn_only_nested_serial_parallel(api_type, fake_java):
with api_select.api(__file__, api_type, login=True) as api:
pre_existing_fake_cli(api_type)
api.flow_job()
api.job('j11', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j21', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=2, serial=True)
api.job('j22_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=2)
api.job('j23', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=2)
with serial(api, timeout=70, job_name_prefix=api.job_name_prefix, report_interval=3) as ctrl1:
ctrl1.invoke('j11')
with ctrl1.parallel(propagation=Propagation.FAILURE_TO_UNSTABLE) as ctrl2:
ctrl2.invoke('j21')
ctrl2.invoke('j22_fail')
ctrl2.invoke('j23')
def test_propagation_warn_only_nested_parallel_serial(api_type, fake_java):
with api_select.api(__file__, api_type, login=True) as api:
pre_existing_fake_cli(api_type)
api.flow_job()
api.job('j11', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j21', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j22_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=1, serial=True)
api.job('j23', exec_time=0.01, max_fails=0, expect_invocations=0, expect_order=None)
with parallel(api, timeout=70, job_name_prefix=api.job_name_prefix, report_interval=3) as ctrl1:
ctrl1.invoke('j11')
with ctrl1.serial(propagation=Propagation.FAILURE_TO_UNSTABLE) as ctrl2:
ctrl2.invoke('j21')
ctrl2.invoke('j22_fail')
ctrl2.invoke('j23')
def test_propagation_warn_only_nested_serial_serial(api_type, fake_java):
with api_select.api(__file__, api_type, login=True) as api:
pre_existing_fake_cli(api_type)
api.flow_job()
api.job('j11', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j21', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=2)
api.job('j22_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=2)
api.job('j23', exec_time=0.01, max_fails=0, expect_invocations=0, expect_order=None)
with serial(api, timeout=70, job_name_prefix=api.job_name_prefix, report_interval=3) as ctrl1:
ctrl1.invoke('j11')
with ctrl1.serial(propagation=Propagation.FAILURE_TO_UNSTABLE) as ctrl2:
ctrl2.invoke('j21')
ctrl2.invoke('j22_fail')
ctrl2.invoke('j23')
def test_propagation_warn_only_nested_parallel_parallel(api_type, fake_java):
with api_select.api(__file__, api_type, login=True) as api:
pre_existing_fake_cli(api_type)
api.flow_job()
api.job('j11', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j21', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j22_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=1)
api.job('j23', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
with parallel(api, timeout=70, job_name_prefix=api.job_name_prefix, report_interval=3) as ctrl1:
ctrl1.invoke('j11')
with ctrl1.parallel(propagation=Propagation.FAILURE_TO_UNSTABLE) as ctrl2:
ctrl2.invoke('j21')
ctrl2.invoke('j22_fail')
ctrl2.invoke('j23')
def test_propagation_warn_only_nested_serial_serial_continue(api_type, fake_java):
with api_select.api(__file__, api_type, login=True) as api:
pre_existing_fake_cli(api_type)
api.flow_job()
api.job('j11', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j21', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=2)
api.job('j22_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=3)
api.job('j23', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=4)
with serial(api, timeout=70, job_name_prefix=api.job_name_prefix, report_interval=3) as ctrl1:
ctrl1.invoke('j11')
with ctrl1.serial() as ctrl2:
ctrl2.invoke('j21')
with ctrl2.serial(propagation=Propagation.FAILURE_TO_UNSTABLE) as ctrl3:
ctrl3.invoke('j22_fail')
ctrl2.invoke('j23')
def test_propagation_warn_only_nested_parallel_serial_continue(api_type, fake_java):
with api_select.api(__file__, api_type, login=True) as api:
pre_existing_fake_cli(api_type)
api.flow_job()
api.job('j11', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j21', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j22_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=1)
api.job('j23', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
with parallel(api, timeout=70, job_name_prefix=api.job_name_prefix, report_interval=3) as ctrl1:
ctrl1.invoke('j11')
with ctrl1.serial() as ctrl2:
ctrl2.invoke('j21')
with ctrl2.serial(propagation=Propagation.FAILURE_TO_UNSTABLE) as ctrl3:
ctrl3.invoke('j22_fail')
ctrl2.invoke('j23')
def test_propagation_warn_only_nested_serial_serial_continue_fail(api_type):
with api_select.api(__file__, api_type, login=True) as api:
pre_existing_fake_cli(api_type)
api.flow_job()
api.job('j11', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j21', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=2)
api.job('j22_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=3)
api.job('j23_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=4)
with raises(FailedChildJobException):
with serial(api, timeout=70, job_name_prefix=api.job_name_prefix, report_interval=3) as ctrl1:
ctrl1.invoke('j11')
with ctrl1.serial() as ctrl2:
ctrl2.invoke('j21')
with ctrl2.serial(propagation=Propagation.FAILURE_TO_UNSTABLE) as ctrl3:
ctrl3.invoke('j22_fail')
ctrl2.invoke('j23_fail')
def test_propagation_warn_only_nested_parallel_serial_continue_fail(api_type):
with api_select.api(__file__, api_type, login=True) as api:
pre_existing_fake_cli(api_type)
api.flow_job()
api.job('j11', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j21', exec_time=0.01, max_fails=0, expect_invocations=1, expect_order=1)
api.job('j22_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=1)
api.job('j23_fail', exec_time=0.01, max_fails=1, expect_invocations=1, expect_order=1)
with raises(FailedChildJobsException):
with parallel(api, timeout=70, job_name_prefix=api.job_name_prefix, report_interval=3) as ctrl1:
ctrl1.invoke('j11')
with ctrl1.serial() as ctrl2:
ctrl2.invoke('j21')
with ctrl2.serial(propagation=Propagation.FAILURE_TO_UNSTABLE) as ctrl3:
ctrl3.invoke('j22_fail')
ctrl2.invoke('j23_fail')
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plugins/modules/system_certificate_info.py | steinzi/ansible-ise | 0 | 1612480 | <reponame>steinzi/ansible-ise<filename>plugins/modules/system_certificate_info.py
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright (c) 2021, Cisco Systems
# GNU General Public License v3.0+ (see LICENSE or https://www.gnu.org/licenses/gpl-3.0.txt)
DOCUMENTATION = r"""
---
module: system_certificate_info
short_description: Information module for System Certificate
description:
- Get System Certificate by id.
- Get System Certificate by name.
version_added: '1.0.0'
author: <NAME> (@racampos)
options:
hostName:
description:
- HostName path parameter. Name of the host of which system certificates should be returned.
type: str
page:
description:
- Page query parameter. Page number.
type: int
size:
description:
- Size query parameter. Number of objects returned per page.
type: int
sort:
description:
- Sort query parameter. Sort type - asc or desc.
type: str
sortBy:
description:
- SortBy query parameter. Sort column by which objects needs to be sorted.
type: str
filter:
description:
- >
Filter query parameter. <div> <style type="text/css" scoped> .apiServiceTable td, .apiServiceTable th {
padding 5px 10px !important; text-align left; } </style> <span> <b>Simple filtering</b> should be available
through the filter query string parameter. The structure of a filter is a triplet of field operator and
value separated with dots. More than one filter can be sent. The logical operator common to ALL filter
criteria will be by default AND, and can be changed by using the <i>"filterType=or"</i> query string
parameter. Each resource Data model description should specify if an attribute is a filtered field. </span>
<br /> <table class="apiServiceTable"> <thead> <tr> <th>OPERATOR</th> <th>DESCRIPTION</th> </tr> </thead>
<tbody> <tr> <td>EQ</td> <td>Equals</td> </tr> <tr> <td>NEQ</td> <td>Not Equals</td> </tr> <tr> <td>GT</td>
<td>Greater Than</td> </tr> <tr> <td>LT</td> <td>Less Then</td> </tr> <tr> <td>STARTSW</td> <td>Starts
With</td> </tr> <tr> <td>NSTARTSW</td> <td>Not Starts With</td> </tr> <tr> <td>ENDSW</td> <td>Ends With</td>
</tr> <tr> <td>NENDSW</td> <td>Not Ends With</td> </tr> <tr> <td>CONTAINS</td> <td>Contains</td> </tr> <tr>
<td>NCONTAINS</td> <td>Not Contains</td> </tr> </tbody> </table> </div>.
type: list
filterType:
description:
- >
FilterType query parameter. The logical operator common to ALL filter criteria will be by default AND, and
can be changed by using the parameter.
type: str
id:
description:
- Id path parameter. The id of the system certificate.
type: str
requirements:
- ciscoisesdk
seealso:
# Reference by Internet resource
- name: System Certificate reference
description: Complete reference of the System Certificate object model.
link: https://ciscoisesdk.readthedocs.io/en/latest/api/api.html#v3-0-0-summary
"""
EXAMPLES = r"""
- name: Get System Certificate by id
cisco.ise.system_certificate_info:
ise_hostname: "{{ise_hostname}}"
ise_username: "{{ise_username}}"
ise_password: "{{<PASSWORD>}}"
ise_verify: "{{ise_verify}}"
hostName: string
id: string
register: result
- name: Get System Certificate by name
cisco.ise.system_certificate_info:
ise_hostname: "{{ise_hostname}}"
ise_username: "{{ise_username}}"
ise_password: "{{<PASSWORD>}}"
ise_verify: "{{ise_verify}}"
page: 1
size: 20
sort: asc
sortBy: string
filter: []
filterType: AND
hostName: string
register: result
"""
RETURN = r"""
ise_response:
description: A dictionary or list with the response returned by the Cisco ISE Python SDK
returned: always
type: dict
sample: >
{
"response": {
"expirationDate": "string",
"friendlyName": "string",
"groupTag": "string",
"id": "string",
"issuedBy": "string",
"issuedTo": "string",
"keySize": 0,
"link": {
"href": "string",
"rel": "string",
"type": "string"
},
"portalsUsingTheTag": "string",
"selfSigned": true,
"serialNumberDecimalFormat": "string",
"sha256Fingerprint": "string",
"signatureAlgorithm": "string",
"usedBy": "string",
"validFrom": "string"
},
"version": "string"
}
"""
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get-mensa-data/get_mensa_data.py | ProjectPepperHSB/Backend-Services | 0 | 90616 | <reponame>ProjectPepperHSB/Backend-Services<filename>get-mensa-data/get_mensa_data.py
#pip install pdf2image
#-- poppler --
#https://github.com/oschwartz10612/poppler-windows/releases/
#env: "C:\path\to\poppler-xx\bin"
import requests
import urllib.request
import json
import re
import traceback
from bs4 import BeautifulSoup
from pdf2image import convert_from_path
from PIL import Image
url = "https://www.stw-bremen.de/de/cafeteria/bremerhaven"
folder_location = "/home/docker-hbv-kms/repositories/NodeJS_Server4Pepper/public/"
def getMensaData():
try:
#download newest cafeteria plan
response = requests.get(url)
soup= BeautifulSoup(response.text, "html.parser")
#create url string
menucard = ((str(soup.select("a[href$='/print']")[0]).rsplit(' target', 1)[0]).split("href=",1)[1]).replace('"','')
urllib.request.urlretrieve(menucard, folder_location+"Mensaplan.pdf")
print("pdf downloaded")
#pdf to img
img = convert_from_path(folder_location+"Mensaplan.pdf", 500)[0]
#crop image
area = (0, 200, 4134, 2800) # R T L B
cropped_img = img.crop(area)
area = (0, 5200, 4134, 5800)
cropped_img2 = img.crop(area)
mergeImgs([cropped_img, cropped_img2]).save(folder_location+'images/mensaplan.png', 'JPEG')
print("images created from pdf")
#get all tbody tags from the site
menulist = soup.select("tbody")
menu = {}
day = ["Montag","Dienstag", "Mittwoch", "Donnerstag", "Freitag"]
offer1 = []
offer2 = []
for i in range(10):
tmp = (str(menulist[i]).split('description">',1)[1]).rsplit("</td><td",2)[0]
tmp = tmp.replace("\n","").replace("\r","").replace("a1","").replace("amp;","")
tmp = re.sub('<sup>.*?</sup>', '', tmp)
if(i%2==0):
offer1.append(tmp)
else:
offer2.append(tmp)
menu["day"] = day
menu["offer1"] = offer1
menu["offer2"] = offer2
with open(folder_location+"mensadata.json", "w+") as f:
json.dump(menu, f, ensure_ascii=False)
print("menu dataframe created")
except Exception:
traceback.print_exc()
def mergeImgs(imgs):
min_img_width = min(i.width for i in imgs)
total_height = 0
for i, img in enumerate(imgs):
# If the image is larger than the minimum width, resize it
if(img.width > min_img_width):
imgs[i] = img.resize((min_img_width, int(img.height / img.width * min_img_width)), Image.ANTIALIAS)
total_height += imgs[i].height
img_merge = Image.new(imgs[0].mode, (min_img_width, total_height))
y = 0
for img in imgs:
img_merge.paste(img, (0, y))
y += img.height
return img_merge
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] |
points/migrations/0004_spend_receipt.py | JXIong15/fetch-points | 0 | 58797 | # Generated by Django 4.0.1 on 2022-01-12 23:44
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('points', '0003_spend_alter_transaction_timestamp'),
]
operations = [
migrations.AddField(
model_name='spend',
name='receipt',
field=models.TextField(blank=True, null=True),
),
]
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django_walletpass/__init__.py | Develatio/django-passbook | 7 | 63589 | <reponame>Develatio/django-passbook
# pylint: disable=invalid-name
VERSION = '1.0'
default_app_config = 'django_walletpass.apps.DjangoWalletpassConfig'
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etc/metadataParsers/ngrams/writeNgrams.py | organisciak/HTRC-BookwormDB | 0 | 93549 | <filename>etc/metadataParsers/ngrams/writeNgrams.py<gh_stars>0
#!/usr/bin/python
import os
import re
filenames = [filename for filename in os.listdir("../../../texts/raw") if not (re.search('zip',filename) or re.search('3gram',filename))]
for filename in filenames:
towrite = dict()
print filename
reading = open("../../../texts/raw/" + filename)
corpus = re.sub("-2009.*","",filename)
corpus = re.sub("-\dgram.*","",corpus)
corpus = re.sub("googlebooks-","",corpus)
print corpus
i = 0
for line in reading:
i = i+1
if (i==1000000*round(i/1000000)):
print "reading line no. \t" + str(round(i)/1000000) + " million out of c. 50 million"
try:
output = line.split("\t")
try:
year = int(output[1])
except:
continue
word = output[0]
count= output[2]
string = " ".join([word,count])
try:
towrite[year].append(string)
except:
towrite[year] = [string]
except:
pass
for year in towrite.keys():
print year
if re.search("2gram",filename):
print corpus
print str(year)
writing = open("../../../texts/bigrams/" + corpus+str(year)+".txt",'a')
elif re.search("1gram",filename):
writing = open("../../../texts/unigrams/" + corpus+str(year)+".txt",'a')
else:
writing = open("tmp/scratch",'a')
writing.write('\n'.join(towrite[year]))
writing.close()
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13,
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580,
13,
2
] |
json.py | AbhijithGanesh/Flask-HTTP-Server | 0 | 18858 | import json
'''
READ THE DATABASE README before operating
'''
File = r'''YOUR FILE'''
with open(File,'a') as fileObj:
data = json.load()
'''
YOUR DATA LOGIC GOES IN HERE
Once the data is changed, to write it to your JSON file use the following command.
'''
json.dump(object,File) | [
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tests/test_code.py | MattDMo/PythonImproved | 83 | 120746 | from . import foo
from .. import bar
from ... import baz
import ...snurp
def blah(...):
pass
class Foo(tuple):
@staticmethod
def __new__(cls, initialValue=(0,0)):
return tuple.__new__(cls, initialValue)
class Bar(tuple):
def __init__(self, initialValue=(0,0)):
super(tuple, self).__init__(initialValue)
super(tuple, self).count(items, stuff="thneed")
@staticmethod
def somefunc(arg, arrg=pirate):
pass
@property
list(map(str, range(100)))
[str(i) for i in range(100)]
sâomething(arg=22)
def sâomething():
pass
class sâomething(object):
pass
__init__(something)
list(map(str, range(100)))
# ^^^ support.type.python
[str(i) for i in range(100)]
#^^^ support.function.builtin.call.python
super(arg1=1)(arg2=2)
list abs, map
some.None NotImplemented
print (file=None)
print __class__
print keyword
print __init__
print(foobar)
__init__ . something()
callback(print)
callback(range)
.__init__
__init__ [2]
@ deco . __init__ (call=some) # rator
def function():
pass
@classmethod
def from_foo(cls, foo):
return cls(foo, bar='baz')
# highlighting for arguments broken
mod.func()
mod.submod.func()
func().func2().attr
mod.func().attr.attr2.func2()
range(foo, bar)
(...)
__exit__
print foobar
Привет_мир() or 我喜欢奶酪() or Kolbász_finom()
bool
class MyWarning(Warning):
def __init__(self, text='first part'
'second part'):
self.args = (text,)
foo(text="line 1"
"line 2")
foobar(param=r"()")
def f():
print(r"()")
f()
def hi(name):
r""" Say hi!
with a \) special characters.
"""
return 'hi ' + name
print(hi('avi'))
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2
] |
test/test_stdio.py | razor-1/apns-clerk | 0 | 57747 | # Author <NAME>
# Based on apns-client implementation by <NAME>
#
# 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.
if __name__ == '__main__':
import os.path
import sys
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
import unittest
import datetime
from apns_clerk import Session
CERTIFICATE = b"""-----BEGIN CERTIFICATE-----
<KEY>
BgNV<KEY>
-----END CERTIFICATE-----"""
PRIVATE_KEY = b"""-----BEGIN RSA PRIVATE KEY-----
Proc-Type: 4,ENCRYPTED
DEK-Info: DES-EDE3-CBC,3195B956C3AD71F6
g7mfzdit5/<KEY>"""
PRIVATE_PASS = "<PASSWORD>"
class Python26Mixin(object):
""" Adds missing methods to test cases in Python 2.6 environment. """
def assertIsNotNone(self, value, msg=None):
""" Fail if value is None. """
parent = super(Python26Mixin, self)
if hasattr(parent, 'assertIsNotNone'):
parent.assertIsNotNone(value, msg)
else:
if value is None:
raise self.failureException(msg or '%r is None' % value)
class StdIOBackendTest(Python26Mixin, unittest.TestCase):
""" Test stdio features. """
def setUp(self):
self.session = Session(pool="apns_clerk.backends.stdio")
def test_locking(self):
""" Test thread locking mechanism """
lock = self.session.pool.create_lock()
self.assertIsNotNone(lock)
self.assertTrue(hasattr(lock, "acquire"))
self.assertTrue(hasattr(lock, "release"))
lock.acquire()
lock.release()
def test_certificates(self):
""" Test pyOpenSSL certificates. """
cert = self.session.pool.get_certificate({
"cert_string": CERTIFICATE,
"key_string": PRIVATE_KEY,
"passphrase": <PASSWORD>
})
cert2 = self.session.pool.get_certificate({
"cert_string": (CERTIFICATE + b"\n" + PRIVATE_KEY),
"passphrase": PRIVATE_PASS
})
self.assertIsNotNone(cert.get_context())
self.assertEqual(cert, cert2)
def test_outdate(self):
# we are not allowed to do any IO in tests, so no real connections.
# however, it is good idea to test utility functions even with empty pool.
self.session.outdate(datetime.timedelta(seconds=60))
self.session.shutdown()
if __name__ == '__main__':
unittest.main()
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tensorflow_in_action/nlp/2_ptb_gen_idword.py | wdxtub/deep-learning-note | 37 | 31273 | import codecs
import sys
RAW_DATA = "../data/ptb/ptb.train.txt"
VOCAB = "data/ptb.vocab"
OUTPUT_DATA = "data/ptb.train"
# 读取词汇表并建立映射
with codecs.open(VOCAB, "r", "utf-8") as f_vocab:
vocab = [w.strip() for w in f_vocab.readlines()]
word_to_id = {k: v for (k, v) in zip(vocab, range(len(vocab)))}
# 如果出现了被删除的低频词，替换成 <unk>
def get_id(word):
return word_to_id[word] if word in word_to_id else word_to_id["<unk>"]
fin = codecs.open(RAW_DATA, "r", "utf-8")
fout = codecs.open(OUTPUT_DATA, 'w', 'utf-8')
for line in fin:
words = line.strip().split() + ["<eos>"] # 读取单词并添加 <eos> 结束符
# 将每个单词替换为词汇表中的编号
out_line = ' '.join([str(get_id(w)) for w in words]) + '\n'
fout.write(out_line)
fin.close()
fout.close() | [
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] |
Subsets and Splits