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wasatch/ROI.py | adiravishankara/Wasatch.PY | 9 | 2417 | ##
# This class encapsulates a Region Of Interest, which may be either horizontal
# (pixels) or vertical (rows/lines).
class ROI:
def __init__(self, start, end):
self.start = start
self.end = end
self.len = end - start + 1
def valid(self):
return self.start >= 0 and self.start < self.end
def crop(self, spectrum):
return spectrum[self.start:self.end+1]
def contains(self, value):
return self.start <= value <= self.end
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pcWacomToMouseTouchpad.py | Amanita-muscaria/rmWacomToMouse | 50 | 51420 | <reponame>Amanita-muscaria/rmWacomToMouse
#!/usr/bin/env python3
'''
Meant to run on your PC.
Receives data generated by rmServerWacomInput.py,
moves the mouse and presses accordingly.
Acts like a touchpad.
'''
import socket
import struct
from pynput.mouse import Button, Controller
mouse = Controller()
# ----------
# Config:
ONLY_DEBUG = False # Only show data. Don't move mouse
CLICK_PRESSURE = 1000
RELEASE_PRESSURE = 100
MAX_DIST = 20 # Max is 50
SPEED = 1.0
INVERT_X = True # Can be used to change orientation
INVERT_Y = True
# ----------
WACOM_WIDTH = 15725 # Values just checked by drawing to the edges
WACOM_HEIGHT = 20967 # ↑
# Source: https://github.com/canselcik/libremarkable/blob/master/src/input/wacom.rs
EV_SYNC = 0
EV_KEY = 1
EV_ABS = 3
WACOM_EVCODE_PRESSURE = 24
WACOM_EVCODE_DISTANCE = 25
WACOM_EVCODE_XTILT = 26
WACOM_EVCODE_YTILT = 27
WACOM_EVCODE_XPOS = 0
WACOM_EVCODE_YPOS = 1
lastXPos = None
lastYPos = None
lastXTilt = -1
lastYTilt = -1
lastDistance = -1
lastPressure = -1
mouseButtonPressed = False
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect(('10.11.99.1', 33333))
while True:
evDevType, evDevCode, evDevValue = struct.unpack('HHi', client.recv(8))
if evDevType == EV_ABS:
if evDevCode == WACOM_EVCODE_XPOS:
if lastDistance < MAX_DIST and lastXPos is not None:
xDist = (evDevValue - lastXPos) * (-SPEED if INVERT_X else SPEED)
mouse.move(xDist, 0)
lastXPos = evDevValue
elif evDevCode == WACOM_EVCODE_YPOS:
if lastDistance < MAX_DIST and lastYPos is not None:
yDist = (evDevValue - lastYPos) * (-SPEED if INVERT_Y else SPEED)
mouse.move(0, yDist)
lastYPos = evDevValue
elif evDevCode == WACOM_EVCODE_XTILT:
lastXTilt = evDevValue
elif evDevCode == WACOM_EVCODE_YTILT:
lastYTilt = evDevValue
elif evDevCode == WACOM_EVCODE_DISTANCE:
lastDistance = evDevValue
elif evDevCode == WACOM_EVCODE_PRESSURE:
if not ONLY_DEBUG:
if not mouseButtonPressed and evDevValue > CLICK_PRESSURE:
mouse.press(Button.left)
mouseButtonPressed = True
elif mouseButtonPressed and evDevValue <= RELEASE_PRESSURE:
mouse.release(Button.left)
mouseButtonPressed = False
lastPressure = evDevValue
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tensorflow_federated/python/learning/federated_evaluation.py | Tensorflow-Devs/federated | 0 | 8938 | <reponame>Tensorflow-Devs/federated<filename>tensorflow_federated/python/learning/federated_evaluation.py
# Copyright 2019, The TensorFlow Federated Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A simple implementation of federated evaluation."""
import collections
from typing import Callable, Optional
import tensorflow as tf
from tensorflow_federated.python.core.api import computation_base
from tensorflow_federated.python.core.api import computations
from tensorflow_federated.python.core.impl.federated_context import intrinsics
from tensorflow_federated.python.core.impl.types import computation_types
from tensorflow_federated.python.core.templates import measured_process
from tensorflow_federated.python.learning import model as model_lib
from tensorflow_federated.python.learning import model_utils
from tensorflow_federated.python.learning.framework import dataset_reduce
from tensorflow_federated.python.learning.framework import optimizer_utils
# Convenience aliases.
SequenceType = computation_types.SequenceType
def build_federated_evaluation(
model_fn: Callable[[], model_lib.Model],
broadcast_process: Optional[measured_process.MeasuredProcess] = None,
use_experimental_simulation_loop: bool = False,
) -> computation_base.Computation:
"""Builds the TFF computation for federated evaluation of the given model.
Args:
model_fn: A no-arg function that returns a `tff.learning.Model`. This method
must *not* capture TensorFlow tensors or variables and use them. The model
must be constructed entirely from scratch on each invocation, returning
the same pre-constructed model each call will result in an error.
broadcast_process: A `tff.templates.MeasuredProcess` that broadcasts the
model weights on the server to the clients. It must support the signature
`(input_values@SERVER -> output_values@CLIENTS)` and have empty state. If
set to default None, the server model is broadcast to the clients using
the default tff.federated_broadcast.
use_experimental_simulation_loop: Controls the reduce loop function for
input dataset. An experimental reduce loop is used for simulation.
Returns:
A federated computation (an instance of `tff.Computation`) that accepts
model parameters and federated data, and returns the evaluation metrics
as aggregated by `tff.learning.Model.federated_output_computation`.
"""
if broadcast_process is not None:
if not isinstance(broadcast_process, measured_process.MeasuredProcess):
raise ValueError('`broadcast_process` must be a `MeasuredProcess`, got '
f'{type(broadcast_process)}.')
if optimizer_utils.is_stateful_process(broadcast_process):
raise ValueError(
'Cannot create a federated evaluation with a stateful '
'broadcast process, must be stateless, has state: '
f'{broadcast_process.initialize.type_signature.result!r}')
# Construct the model first just to obtain the metadata and define all the
# types needed to define the computations that follow.
# TODO(b/124477628): Ideally replace the need for stamping throwaway models
# with some other mechanism.
with tf.Graph().as_default():
model = model_fn()
model_weights_type = model_utils.weights_type_from_model(model)
batch_type = computation_types.to_type(model.input_spec)
@computations.tf_computation(model_weights_type, SequenceType(batch_type))
@tf.function
def client_eval(incoming_model_weights, dataset):
"""Returns local outputs after evaluting `model_weights` on `dataset`."""
with tf.init_scope():
model = model_fn()
model_weights = model_utils.ModelWeights.from_model(model)
tf.nest.map_structure(lambda v, t: v.assign(t), model_weights,
incoming_model_weights)
def reduce_fn(num_examples, batch):
model_output = model.forward_pass(batch, training=False)
if model_output.num_examples is None:
# Compute shape from the size of the predictions if model didn't use the
# batch size.
return num_examples + tf.shape(
model_output.predictions, out_type=tf.int64)[0]
else:
return num_examples + tf.cast(model_output.num_examples, tf.int64)
dataset_reduce_fn = dataset_reduce.build_dataset_reduce_fn(
use_experimental_simulation_loop)
num_examples = dataset_reduce_fn(
reduce_fn=reduce_fn,
dataset=dataset,
initial_state_fn=lambda: tf.zeros([], dtype=tf.int64))
return collections.OrderedDict(
local_outputs=model.report_local_outputs(), num_examples=num_examples)
@computations.federated_computation(
computation_types.at_server(model_weights_type),
computation_types.at_clients(SequenceType(batch_type)))
def server_eval(server_model_weights, federated_dataset):
if broadcast_process is not None:
# TODO(b/179091838): Zip the measurements from the broadcast_process with
# the result of `model.federated_output_computation` below to avoid
# dropping these metrics.
broadcast_output = broadcast_process.next(broadcast_process.initialize(),
server_model_weights)
client_outputs = intrinsics.federated_map(
client_eval, (broadcast_output.result, federated_dataset))
else:
client_outputs = intrinsics.federated_map(client_eval, [
intrinsics.federated_broadcast(server_model_weights),
federated_dataset
])
model_metrics = model.federated_output_computation(
client_outputs.local_outputs)
statistics = collections.OrderedDict(
num_examples=intrinsics.federated_sum(client_outputs.num_examples))
return intrinsics.federated_zip(
collections.OrderedDict(eval=model_metrics, stat=statistics))
return server_eval
| [
1,
529,
276,
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Session II/Encapsulation.py | intrepidkarthi/softwareengineer | 4 | 112057 | class Encapsulation:
def __init__(self):
self.__my_price = 10
def sell(self):
print("my selling price is {}".format(self.__my_price))
def setprice(self, price):
self.__my_price = price
encap = Encapsulation()
encap.sell()
encap.__my_price = 20
encap.sell()
encap.setprice(20)
encap.sell() | [
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] |
ecs_pipeline_deploy/cli.py | gmr/ecs-pipeline-deploy | 2 | 150467 | <gh_stars>1-10
# coding=utf-8
"""
CLI Application Implementation
==============================
"""
import argparse
import collections
import logging
import re
import sys
import time
import boto3
import coloredlogs
from . import __version__
LOGGER = logging.getLogger(__name__)
LOGGING_FORMAT = '%(asctime)s %(name)s %(message)s'
LOGGING_FIELD_STYLES = {
'hostname': {'color': 'magenta'},
'programname': {'color': 'cyan'},
'name': {'color': 'blue'},
'levelname': {'color': 'white', 'bold': True},
'asctime': {'color': 'white'}}
Image = collections.namedtuple('Image', ['registry', 'name', 'tag'])
IMAGE_PATTERN = re.compile(
r'^(?P<repository>[\w.\-_]+(?:(?::\d+|)(?=/[a-z0-9._-]+/[a-z0-9._-]+))|)'
r'(?:/|)(?P<name>[a-z0-9.\-_]+(?:/[a-z0-9.\-_]+|))'
r'(?::(?P<tag>[\w.\-_]{1,127})|)$')
class ECSPipeline:
"""Controller class for performing Pipeline based deployments in ECS."""
def __init__(self, args):
"""Create a new instance of the ECSPipeline class
:param argparse.namespace args: The parsed CLI args
"""
self.args = args
self.client = boto3.client('ecs')
try:
self.image = self.parse_image(args.image)
except ValueError:
exit_application(
'Malformed image specified: {}'.format(args.image), 1)
self.redeploying = False
try:
self.service_arn = self._get_service_arn()
except self.client.exceptions.ClusterNotFoundException:
exit_application('Cluster not found: {}'.format(args.cluster), 1)
self.desired_qty, self.current = self._describe_service()
self.task_definition = self._get_task_definition()
def deploy(self):
"""Deploy the task definition to the configured cluster."""
LOGGER.info('%s %s in %s to %s',
'Redeploying' if self.redeploying else 'Updating',
self.args.service, self.args.cluster,
self.task_definition.split('/')[-1])
result = self.client.update_service(
cluster=self.args.cluster, service=self.service_arn,
taskDefinition=self.task_definition,
forceNewDeployment=self.args.redeploy)
if result['service']['taskDefinition'] == self.task_definition:
LOGGER.info('%s %s in %s to %s',
'Redeployed' if self.redeploying else 'Updated',
self.args.service, self.args.cluster,
self.task_definition.split('/')[-1])
if self.args.wait:
self._wait_on_tasks()
LOGGER.info('%s complete',
'Redeployment' if self.redeploying else 'Deployment')
@staticmethod
def image_to_str(image):
"""Build the image string value from the namedtuple.
:param Image image: The image tuple to return the string from
:rtype: str
"""
if image.registry:
return '{}/{}:{}'.format(image.registry, image.name, image.tag)
return '{}:{}'.format(image.name, image.tag)
@staticmethod
def parse_image(image):
"""Parse an image returning the info as a namedtuple
:rtype: Image
:raises: ValueError
"""
try:
result = IMAGE_PATTERN.match(image)
except TypeError:
result = None
if not result:
raise ValueError('Failed to parse image')
img = result.groupdict()
return Image(img['repository'] or None, img['name'],
img['tag'] or 'latest')
def _describe_service(self):
"""Return the current task definition the service and configured
quantity of tasks for the service.
:rtype: (qty, dict)
"""
LOGGER.info('Getting the current task definition for %s in %s',
self.args.service, self.args.cluster)
response = self.client.describe_services(
cluster=self.args.cluster, services=[self.service_arn])
return (response['services'][0]['desiredCount'],
self._describe_task_definition(
response['services'][0]['taskDefinition']))
def _describe_task_definition(self, arn):
"""Return the task definition from ECS.
:rtype: dict
"""
response = self.client.describe_task_definition(taskDefinition=arn)
return response['taskDefinition']
def _get_service_arn(self):
"""Return the ARN of the service to perform the deployment on.
:rtype: str
"""
for arn in self._services():
parts = arn.split('/')
if parts[-1] == self.args.service:
return arn
exit_application(
'Service {} not found in {}'.format(
self.args.service, self.args.cluster), 1)
def _get_task_definition(self):
"""Return the task definition ARN to use, creating a new task
definition if required.
"""
if self.image in self._get_containers(self.current):
if self.args.redeploy:
self.redeploying = True
return self.current['taskDefinitionArn']
elif self.args.force:
self.redeploying = True
return self._save_task_definition(self.current)
exit_application(
'{} is already deployed to {} as "{}"'.format(
self.args.image, self.args.cluster,
self.current['taskDefinitionArn'].split('/')[-1]), 2)
return self._save_task_definition(
self._modify_task_definition(self.current))
def _get_containers(self, task_defn):
"""Return the containers from a task definition.
:param dict task_defn: The task definition data structure
:rtype: list
"""
containers = [
self.parse_image(cd['image'])
for cd in task_defn['containerDefinitions']]
for container in sorted(containers):
LOGGER.debug('Found %s in the task definition', container)
return sorted(containers)
def _get_task_definitions_from_family(self, family):
"""Return a list of task definitions for the family sorted in
descending numerical order.
:param str family: The task definition family
:rtype: list
"""
LOGGER.info('Getting all of the task definitions for %s in %s',
self.args.service, self.args.cluster)
definitions = []
paginator = self.client.get_paginator('list_task_definitions')
for page in paginator.paginate(familyPrefix=family, sort='DESC'):
definitions += page['taskDefinitionArns']
for definition in sorted(definitions):
LOGGER.debug('Task definition %s found', definition)
return definitions
def _list_running_tasks(self):
"""Return all tasks and their task definitions as a list of tuples.
:rtype: [(str, str), ...]
"""
LOGGER.info('Getting running tasks for %s in %s',
self.args.service, self.args.cluster)
task_arns = []
paginator = self.client.get_paginator('list_tasks')
for page in paginator.paginate(cluster=self.args.cluster,
serviceName=self.args.service,
desiredStatus='RUNNING'):
task_arns += page['taskArns']
for task_arn in sorted(task_arns):
LOGGER.debug('Task found: %s', task_arn)
# Build the result set of tasks that are running
tasks = []
while task_arns:
response = self.client.describe_tasks(
cluster=self.args.cluster,
tasks=task_arns[:100])
for task in response['tasks']:
LOGGER.debug('Task %s running %s',
task['taskArn'], task['taskDefinitionArn'])
tasks.append((task['taskArn'], task['taskDefinitionArn']))
task_arns.remove(task['taskArn'])
return tasks
def _modify_task_definition(self, defn):
"""Modify the task definition that was passed in, replacing the image
appropriately with the image that is going to be tagged.
:param dict defn: The definition to modify
:rtype: dict
"""
LOGGER.info('Modifying the task definition "%s" to use %s',
defn['taskDefinitionArn'], self.args.image)
for idx, image in enumerate(self._get_containers(defn)):
if self.image.registry == image.registry and \
self.image.name == image.name:
defn['containerDefinitions'][idx]['image'] = \
self.image_to_str(self.image)
log_config = defn['containerDefinitions'][idx].get(
'logConfiguration', {})
options = log_config.get('options', {})
if 'tag' in options:
log_config['options']['tag'] = \
options['tag'].replace(image.tag, self.image.tag)
environment = defn['containerDefinitions'][idx].get(
'environment', [])
for offset, variable in enumerate(environment):
if variable['name'] == 'VERSION':
environment[offset] = {
'name': 'VERSION',
'value': self.image.tag
}
return defn
raise ValueError(
'Did not find the image {!r} in the task definition'.format(
self.args.image))
def _save_task_definition(self, definition):
"""Save the task definition, returning the new ARN.
:param dict definition: The new definition to save
:rtype: str
"""
for key in {'compatibilities', 'requiresAttributes',
'requiresCompatibilities', 'revision', 'status',
'taskDefinitionArn'}:
if key in definition:
del definition[key]
LOGGER.debug('Saving the new task definition for %s',
definition['family'])
result = self.client.register_task_definition(**definition)
return result['taskDefinition']['taskDefinitionArn']
def _services(self):
"""Return the list of services configured in the cluster.
:rtype: list
"""
LOGGER.info('Getting services in the %s cluster', self.args.cluster)
arns = []
paginator = self.client.get_paginator('list_services')
for page in paginator.paginate(cluster=self.args.cluster):
arns += page['serviceArns']
for arn in sorted(arns):
LOGGER.debug('Returning %r', arn)
return sorted(arns)
def _wait_on_tasks(self):
"""Wait for all tasks to be in the running state for the task
definition.
"""
LOGGER.info('Waiting for %i tasks to enter running state for "%s"',
self.desired_qty, self.task_definition.split('/')[-1])
while True:
tasks = self._list_running_tasks()
counts = collections.Counter()
for _task, task_def in tasks:
counts[task_def] += 1
LOGGER.debug('Current running tasks by definition: %s',
', '.join(
['{}: {}'.format(k.split('/')[-1], counts[k])
for k in sorted(
counts.keys(),
key=lambda x: int(x.split(':')[-1]))]))
if counts.get(self.task_definition, 0) == self.desired_qty:
if not self.args.only_new or (len(tasks) == self.desired_qty):
break
time.sleep(self.args.delay)
def _running_task_count(self, tasks):
"""Return the quantity of tasks that are running for the task
definition.
:param list tasks: The list of task tuples
:rtype: int
"""
return len([t for t in tasks if t[1] == self.task_definition])
def parse_cli_args():
"""Construct the CLI argument parser and return the parsed the arguments.
:rtype: argparse.namespace
"""
parser = argparse.ArgumentParser(
prog='ecs-pipeline-deploy',
description='Opinionated ECS deployment made easy',
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
conflict_handler='resolve')
parser.add_argument('cluster', nargs='?', type=str, metavar='CLUSTER',
help='The ECS cluster name to deploy in')
parser.add_argument('service', nargs='?', type=str, metavar='SERVICE',
help='The ECS Service name to deploy')
parser.add_argument('image', nargs='?', type=str, metavar='IMAGE',
help='The Docker image (with tag) to deploy for '
'finding the task definition')
parser.add_argument('-f', '--force', action='store_true',
help='Create a new task definition for the image even '
'if one already exists for the tagged version')
parser.add_argument('-r', '--redeploy', action='store_true',
help='Force a redeployment if the tagged images match')
parser.add_argument('-w', '--wait', action='store_true',
help='Wait for running tasks to be replaced')
parser.add_argument('-o', '--only-new', action='store_true',
help='If waiting, wait for only newly deployed tasks '
'to be running')
parser.add_argument('-d', '--delay', type=int, default=5,
help='Seconds to delay before checking tasks while '
'waiting on a deployment to finish')
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-V', '--version', action='version',
version='%(prog)s {}'.format(__version__))
return parser.parse_args()
def configure_logging(args):
"""Setup logging"""
level = logging.DEBUG if args.verbose else logging.INFO
coloredlogs.install(level=level, fmt=LOGGING_FORMAT,
field_styles=LOGGING_FIELD_STYLES)
silence_noisy_loggers()
def exit_application(message=None, code=0):
"""Exit the application displaying the message to info or error based upon
the exit code
:param str message: The exit message
:param int code: The exit code (default: 0)
"""
log_method = LOGGER.error if code else LOGGER.info
log_method(message.strip())
sys.exit(code)
def main():
"""Application Entrypoint"""
args = parse_cli_args()
configure_logging(args)
LOGGER.info('ecs-pipeline-deploy v%s starting', __version__)
ECSPipeline(args).deploy()
def silence_noisy_loggers():
"""Some things are noisier than others. Some libraries mothers are noisier
than other libraries mothers.
"""
for logger in ['boto3', 'botocore',
'urllib3.connectionpool',
'botocore.vendored.requests.packages.urllib3']:
logging.getLogger(logger).setLevel(logging.WARNING)
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3414,
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471,
4502,
297,
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278,
1967,
13,
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2486,
411,
278,
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www/src/Lib/test/test_abc.py | stefanhoelzl/brython | 652 | 131218 | <filename>www/src/Lib/test/test_abc.py
# Copyright 2007 Google, Inc. All Rights Reserved.
# Licensed to PSF under a Contributor Agreement.
"""Unit tests for abc.py."""
import unittest
from test import support
import abc
from inspect import isabstract
class TestLegacyAPI(unittest.TestCase):
def test_abstractproperty_basics(self):
@abc.abstractproperty
def foo(self): pass
self.assertTrue(foo.__isabstractmethod__)
def bar(self): pass
self.assertFalse(hasattr(bar, "__isabstractmethod__"))
class C(metaclass=abc.ABCMeta):
@abc.abstractproperty
def foo(self): return 3
self.assertRaises(TypeError, C)
class D(C):
@property
def foo(self): return super().foo
self.assertEqual(D().foo, 3)
self.assertFalse(getattr(D.foo, "__isabstractmethod__", False))
def test_abstractclassmethod_basics(self):
@abc.abstractclassmethod
def foo(cls): pass
self.assertTrue(foo.__isabstractmethod__)
@classmethod
def bar(cls): pass
self.assertFalse(getattr(bar, "__isabstractmethod__", False))
class C(metaclass=abc.ABCMeta):
@abc.abstractclassmethod
def foo(cls): return cls.__name__
self.assertRaises(TypeError, C)
class D(C):
@classmethod
def foo(cls): return super().foo()
self.assertEqual(D.foo(), 'D')
self.assertEqual(D().foo(), 'D')
def test_abstractstaticmethod_basics(self):
@abc.abstractstaticmethod
def foo(): pass
self.assertTrue(foo.__isabstractmethod__)
@staticmethod
def bar(): pass
self.assertFalse(getattr(bar, "__isabstractmethod__", False))
class C(metaclass=abc.ABCMeta):
@abc.abstractstaticmethod
def foo(): return 3
self.assertRaises(TypeError, C)
class D(C):
@staticmethod
def foo(): return 4
self.assertEqual(D.foo(), 4)
self.assertEqual(D().foo(), 4)
class TestABC(unittest.TestCase):
def test_abstractmethod_basics(self):
@abc.abstractmethod
def foo(self): pass
self.assertTrue(foo.__isabstractmethod__)
def bar(self): pass
self.assertFalse(hasattr(bar, "__isabstractmethod__"))
def test_abstractproperty_basics(self):
@property
@abc.abstractmethod
def foo(self): pass
self.assertTrue(foo.__isabstractmethod__)
def bar(self): pass
self.assertFalse(getattr(bar, "__isabstractmethod__", False))
class C(metaclass=abc.ABCMeta):
@property
@abc.abstractmethod
def foo(self): return 3
self.assertRaises(TypeError, C)
class D(C):
@C.foo.getter
def foo(self): return super().foo
self.assertEqual(D().foo, 3)
def test_abstractclassmethod_basics(self):
@classmethod
@abc.abstractmethod
def foo(cls): pass
self.assertTrue(foo.__isabstractmethod__)
@classmethod
def bar(cls): pass
self.assertFalse(getattr(bar, "__isabstractmethod__", False))
class C(metaclass=abc.ABCMeta):
@classmethod
@abc.abstractmethod
def foo(cls): return cls.__name__
self.assertRaises(TypeError, C)
class D(C):
@classmethod
def foo(cls): return super().foo()
self.assertEqual(D.foo(), 'D')
self.assertEqual(D().foo(), 'D')
def test_abstractstaticmethod_basics(self):
@staticmethod
@abc.abstractmethod
def foo(): pass
self.assertTrue(foo.__isabstractmethod__)
@staticmethod
def bar(): pass
self.assertFalse(getattr(bar, "__isabstractmethod__", False))
class C(metaclass=abc.ABCMeta):
@staticmethod
@abc.abstractmethod
def foo(): return 3
self.assertRaises(TypeError, C)
class D(C):
@staticmethod
def foo(): return 4
self.assertEqual(D.foo(), 4)
self.assertEqual(D().foo(), 4)
def test_abstractmethod_integration(self):
for abstractthing in [abc.abstractmethod, abc.abstractproperty,
abc.abstractclassmethod,
abc.abstractstaticmethod]:
class C(metaclass=abc.ABCMeta):
@abstractthing
def foo(self): pass # abstract
def bar(self): pass # concrete
self.assertEqual(C.__abstractmethods__, {"foo"})
self.assertRaises(TypeError, C) # because foo is abstract
self.assertTrue(isabstract(C))
class D(C):
def bar(self): pass # concrete override of concrete
self.assertEqual(D.__abstractmethods__, {"foo"})
self.assertRaises(TypeError, D) # because foo is still abstract
self.assertTrue(isabstract(D))
class E(D):
def foo(self): pass
self.assertEqual(E.__abstractmethods__, set())
E() # now foo is concrete, too
self.assertFalse(isabstract(E))
class F(E):
@abstractthing
def bar(self): pass # abstract override of concrete
self.assertEqual(F.__abstractmethods__, {"bar"})
self.assertRaises(TypeError, F) # because bar is abstract now
self.assertTrue(isabstract(F))
def test_descriptors_with_abstractmethod(self):
class C(metaclass=abc.ABCMeta):
@property
@abc.abstractmethod
def foo(self): return 3
@foo.setter
@abc.abstractmethod
def foo(self, val): pass
self.assertRaises(TypeError, C)
class D(C):
@C.foo.getter
def foo(self): return super().foo
self.assertRaises(TypeError, D)
class E(D):
@D.foo.setter
def foo(self, val): pass
self.assertEqual(E().foo, 3)
# check that the property's __isabstractmethod__ descriptor does the
# right thing when presented with a value that fails truth testing:
class NotBool(object):
def __nonzero__(self):
raise ValueError()
__len__ = __nonzero__
with self.assertRaises(ValueError):
class F(C):
def bar(self):
pass
bar.__isabstractmethod__ = NotBool()
foo = property(bar)
def test_customdescriptors_with_abstractmethod(self):
class Descriptor:
def __init__(self, fget, fset=None):
self._fget = fget
self._fset = fset
def getter(self, callable):
return Descriptor(callable, self._fget)
def setter(self, callable):
return Descriptor(self._fget, callable)
@property
def __isabstractmethod__(self):
return (getattr(self._fget, '__isabstractmethod__', False)
or getattr(self._fset, '__isabstractmethod__', False))
class C(metaclass=abc.ABCMeta):
@Descriptor
@abc.abstractmethod
def foo(self): return 3
@foo.setter
@abc.abstractmethod
def foo(self, val): pass
self.assertRaises(TypeError, C)
class D(C):
@C.foo.getter
def foo(self): return super().foo
self.assertRaises(TypeError, D)
class E(D):
@D.foo.setter
def foo(self, val): pass
self.assertFalse(E.foo.__isabstractmethod__)
def test_metaclass_abc(self):
# Metaclasses can be ABCs, too.
class A(metaclass=abc.ABCMeta):
@abc.abstractmethod
def x(self):
pass
self.assertEqual(A.__abstractmethods__, {"x"})
class meta(type, A):
def x(self):
return 1
class C(metaclass=meta):
pass
def test_registration_basics(self):
class A(metaclass=abc.ABCMeta):
pass
class B(object):
pass
b = B()
self.assertFalse(issubclass(B, A))
self.assertFalse(issubclass(B, (A,)))
self.assertNotIsInstance(b, A)
self.assertNotIsInstance(b, (A,))
B1 = A.register(B)
self.assertTrue(issubclass(B, A))
self.assertTrue(issubclass(B, (A,)))
self.assertIsInstance(b, A)
self.assertIsInstance(b, (A,))
self.assertIs(B1, B)
class C(B):
pass
c = C()
self.assertTrue(issubclass(C, A))
self.assertTrue(issubclass(C, (A,)))
self.assertIsInstance(c, A)
self.assertIsInstance(c, (A,))
def test_register_as_class_deco(self):
class A(metaclass=abc.ABCMeta):
pass
@A.register
class B(object):
pass
b = B()
self.assertTrue(issubclass(B, A))
self.assertTrue(issubclass(B, (A,)))
self.assertIsInstance(b, A)
self.assertIsInstance(b, (A,))
@A.register
class C(B):
pass
c = C()
self.assertTrue(issubclass(C, A))
self.assertTrue(issubclass(C, (A,)))
self.assertIsInstance(c, A)
self.assertIsInstance(c, (A,))
self.assertIs(C, A.register(C))
def test_isinstance_invalidation(self):
class A(metaclass=abc.ABCMeta):
pass
class B:
pass
b = B()
self.assertFalse(isinstance(b, A))
self.assertFalse(isinstance(b, (A,)))
A.register(B)
self.assertTrue(isinstance(b, A))
self.assertTrue(isinstance(b, (A,)))
def test_registration_builtins(self):
class A(metaclass=abc.ABCMeta):
pass
A.register(int)
self.assertIsInstance(42, A)
self.assertIsInstance(42, (A,))
self.assertTrue(issubclass(int, A))
self.assertTrue(issubclass(int, (A,)))
class B(A):
pass
B.register(str)
class C(str): pass
self.assertIsInstance("", A)
self.assertIsInstance("", (A,))
self.assertTrue(issubclass(str, A))
self.assertTrue(issubclass(str, (A,)))
self.assertTrue(issubclass(C, A))
self.assertTrue(issubclass(C, (A,)))
def test_registration_edge_cases(self):
class A(metaclass=abc.ABCMeta):
pass
A.register(A) # should pass silently
class A1(A):
pass
self.assertRaises(RuntimeError, A1.register, A) # cycles not allowed
class B(object):
pass
A1.register(B) # ok
A1.register(B) # should pass silently
class C(A):
pass
A.register(C) # should pass silently
self.assertRaises(RuntimeError, C.register, A) # cycles not allowed
C.register(B) # ok
def test_register_non_class(self):
class A(metaclass=abc.ABCMeta):
pass
self.assertRaisesRegex(TypeError, "Can only register classes",
A.register, 4)
def test_registration_transitiveness(self):
class A(metaclass=abc.ABCMeta):
pass
self.assertTrue(issubclass(A, A))
self.assertTrue(issubclass(A, (A,)))
class B(metaclass=abc.ABCMeta):
pass
self.assertFalse(issubclass(A, B))
self.assertFalse(issubclass(A, (B,)))
self.assertFalse(issubclass(B, A))
self.assertFalse(issubclass(B, (A,)))
class C(metaclass=abc.ABCMeta):
pass
A.register(B)
class B1(B):
pass
self.assertTrue(issubclass(B1, A))
self.assertTrue(issubclass(B1, (A,)))
class C1(C):
pass
B1.register(C1)
self.assertFalse(issubclass(C, B))
self.assertFalse(issubclass(C, (B,)))
self.assertFalse(issubclass(C, B1))
self.assertFalse(issubclass(C, (B1,)))
self.assertTrue(issubclass(C1, A))
self.assertTrue(issubclass(C1, (A,)))
self.assertTrue(issubclass(C1, B))
self.assertTrue(issubclass(C1, (B,)))
self.assertTrue(issubclass(C1, B1))
self.assertTrue(issubclass(C1, (B1,)))
C1.register(int)
class MyInt(int):
pass
self.assertTrue(issubclass(MyInt, A))
self.assertTrue(issubclass(MyInt, (A,)))
self.assertIsInstance(42, A)
self.assertIsInstance(42, (A,))
def test_all_new_methods_are_called(self):
class A(metaclass=abc.ABCMeta):
pass
class B(object):
counter = 0
def __new__(cls):
B.counter += 1
return super().__new__(cls)
class C(A, B):
pass
self.assertEqual(B.counter, 0)
C()
self.assertEqual(B.counter, 1)
if __name__ == "__main__":
unittest.main()
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] |
docs/source/examples/client-server-request.py | lslebodn/conu | 95 | 139504 | # -*- coding: utf-8 -*-
#
# Copyright Contributors to the Conu project.
# SPDX-License-Identifier: MIT
#
import subprocess
import logging
import time
from conu import DockerBackend, DockerRunBuilder
environment = ["-e", "POSTGRESQL_USER=user",
"-e", "POSTGRESQL_PASSWORD=<PASSWORD>",
"-e", "POSTGRESQL_DATABASE=db"]
with DockerBackend(logging_level=logging.DEBUG) as backend:
image = backend.ImageClass('centos/postgresql-96-centos7')
# create server
additional_opts = environment
dbcont = image.run_via_binary(additional_opts=additional_opts)
# wait for server port to be ready
dbcont.wait_for_port(5432)
# prepare request
endpoint = "postgresql://user@" + dbcont.get_IPv4s()[0] + ":5432/" + 'db'
request_command = DockerRunBuilder(command=['psql', endpoint], additional_opts=['-i'])
# create client
clientcont = image.run_via_binary_in_foreground(
request_command,
popen_params={"stdin": subprocess.PIPE}
)
# send requests
clientcont.write_to_stdin(b'pass\n')
# give postgres time to process
time.sleep(0.1)
clientcont.write_to_stdin(b'SELECT 1;\n')
# give postgres time to process
time.sleep(0.2)
logs_bytes = clientcont.logs_in_bytes()
expected_output = b'Password: \n ?column? \n----------\n 1\n(1 row)'
try:
assert b'Password: ' in logs_bytes
assert b'(1 row)' in logs_bytes
assert clientcont.is_running()
finally:
dbcont.delete(force=True)
clientcont.delete(force=True)
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scratch/trycubes/cubes.py | ibrahemesam/Fos | 0 | 84971 | import numpy as np
from itertools import chain, islice, product, repeat, cycle, izip
from fos.actor.surf import CommonSurfaceGroup, IlluminatedSurfaceGroup
from fos.core.world import World
from fos.lib.pyglet.gl import *
from fos.lib.pyglet.graphics import Batch
from fos.core.actor import Actor
from fos.geometry.vec3 import Vec3
from fos.geometry.math3d import *
type_to_enum = {
gl.GLubyte: gl.GL_UNSIGNED_BYTE,
gl.GLushort: gl.GL_UNSIGNED_SHORT,
gl.GLuint: gl.GL_UNSIGNED_INT,
}
def glarray(gltype, seq, length):
'''
Convert a list of lists into a flattened ctypes array, eg:
[ (1, 2, 3), (4, 5, 6) ] -> (GLfloat*6)(1, 2, 3, 4, 5, 6)
'''
arraytype = gltype * length
return arraytype(*seq)
def tessellate(face):
'''
Return the given face broken into a list of triangles, wound in the
same direction as the original poly. Does not work for concave faces.
e.g. [0, 1, 2, 3, 4] -> [[0, 1, 2], [0, 2, 3], [0, 3, 4]]
'''
return (
[face[0], face[index], face[index + 1]]
for index in xrange(1, len(face) - 1)
)
def face_normal(vertices, face):
'''
Return the unit normal vector (at right angles to) this face.
Note that the direction of the normal will be reversed if the
face's winding is reversed.
'''
v0 = vertices[face[0]]
v1 = vertices[face[1]]
v2 = vertices[face[2]]
a = v0 - v1
b = v2 - v1
return b.cross(a).normalized()
class GLPrimitive(object):
def __init__(self):
self.num_glvertices = None
self.glvertices = None
self.glindex_type = None
self.glindices = None
self.glcolors = None
self.glnormals = None
def get_num_glvertices(_, faces):
return len(list(chain(*faces)))
def get_glvertices(self, vertices, faces):
glverts = chain.from_iterable(
vertices[index]
for face in faces
for index in face
)
self.num_glvertices = self.get_num_glvertices(faces)
return glarray(gl.GLfloat, glverts, self.num_glvertices * 3)
def get_glindex_type(self):
'''
The type of the glindices array depends on how many vertices there are
'''
if self.num_glvertices < 256:
index_type = gl.GLubyte
elif self.num_glvertices < 65536:
index_type = gl.GLushort
else:
index_type = gl.GLuint
return index_type
def get_glindices(self, faces):
glindices = []
face_offset = 0
for face in faces:
indices = xrange(face_offset, face_offset + len(face))
glindices.extend(chain(*tessellate(indices)))
face_offset += len(face)
self.glindex_type = self.get_glindex_type()
return glarray(self.glindex_type, glindices, len(glindices))
def get_glcolors(self, faces, face_colors):
glcolors = chain.from_iterable(
repeat(color, len(face))
for face, color in izip(faces, face_colors)
)
return glarray(gl.GLubyte, chain(*glcolors), self.num_glvertices * 4)
def get_glnormals(self, vertices, faces):
normals = (
face_normal(vertices, face)
for face in faces
)
glnormals = chain.from_iterable(
repeat(normal, len(face))
for face, normal in izip(faces, normals)
)
return glarray(
gl.GLfloat, chain(*glnormals), self.num_glvertices * 3)
def from_shape(self, vertices, faces, face_colors, affine):
self.glvertices = self.get_glvertices(vertices, faces)
self.glindices = self.get_glindices(faces)
self.glcolors = self.get_glcolors(faces, face_colors)
self.glnormals = self.get_glnormals(vertices, faces)
self.affine = self.get_affine(affine)
def get_affine(self, affine):
ident = M3DMatrix44f()
ident = m3dLoadIdentity44(ident)
translate = m3dTranslateMatrix44(ident,
affine[0,3],
affine[1,3],
affine[2.3])
# do the rotation
return translate
class Polyhedron(object):
'''
Defines a polyhedron, a 3D shape with flat faces and straight edges.
Each vertex defines a point in 3d space. Each face is a list of indices
into the vertex array, forming a coplanar convex ring defining the face's
edges. Each face has its own color.
'''
def __init__(self, vertices, faces, face_colors=None, affine=None):
if len(vertices) > 0 and not isinstance(vertices[0], Vec3):
vertices = [Vec3(*v) for v in vertices]
self.vertices = vertices
for face in faces:
assert len(face) >= 3
for index in face:
assert 0 <= index < len(vertices)
self.faces = faces
if face_colors is None:
face_colors = repeat((255, 0, 0, 255))
# if face_colors is None:
# face_colors = white
# if isinstance(face_colors, Color):
# face_colors = repeat(face_colors)
# TODO: colors of koch_cube/tetra break if we remove this 'list'
# and set face_colors to the return of 'islice'. Don't know why.
# returns a list of tuples of len self.faces of the given facecolors
self.face_colors = list(islice(cycle(face_colors), len(self.faces)))
self.affine = affine
def get_glprimitive(self):
glprimitive = GLPrimitive()
glprimitive.from_shape(self.vertices, self.faces, self.face_colors, self.affine)
return glprimitive
class Cubes(Actor):
def __init__(self, location):
'''
# just one cube
e2 = 1. / 2
verts = list(product(*repeat([-e2, +e2], 3)))
faces = [
[0, 1, 3, 2], # left
[4, 6, 7, 5], # right
[7, 3, 1, 5], # front
[0, 2, 6, 4], # back
[3, 7, 6, 2], # top
[1, 0, 4, 5], # bottom
]
oc = Polyhedron(verts, faces)
glprim = oc.get_glprimitive()
group = IlluminatedSurfaceGroup()
'''
self.primitives = []
nr_cubes = location.shape[0]
for i in xrange(nr_cubes):
self.primitives.append(self.create_cubes(location[:,i],1.0))
def create_cubes(self, location, edge_size, color=None):
e2 = edge_size / 2.0
verts = list(product(*repeat([-e2, +e2], 3)))
faces = [
[0, 1, 3, 2], # left
[4, 6, 7, 5], # right
[7, 3, 1, 5], # front
[0, 2, 6, 4], # back
[3, 7, 6, 2], # top
[1, 0, 4, 5], # bottom
]
aff= np.eye(4)
aff[3,:3] = location
oc = Polyhedron(verts, faces, affine = aff)
return oc
'''
self.vertex_list = []
self.batch=Batch()
self.vertex_list.append(self.batch.add_indexed(len(glprim.glvertices) / 3,\
GL_TRIANGLES,\
None,\
list(glprim.glindices),\
('v3f/static',np.array(glprim.glvertices)),\
('n3f/static',list(glprim.glnormals)),\
('c4B/static',list(glprim.glcolors))
) )
ver = np.array(glprim.glvertices)
for i in range(1,300):
self.vertex_list.append(self.batch.add_indexed(len(glprim.glvertices) / 3,\
GL_TRIANGLES,\
None,\
list(glprim.glindices),\
('v3f/static',ver+(i*1)),\
('n3f/static',list(glprim.glnormals)),\
('c4B/static',list(glprim.glcolors))
))
self.vertex_list.append(self.batch.add_indexed(len(glprim.glvertices) / 3,\
GL_TRIANGLES,\
None,\
list(glprim.glindices),\
('v3f/static',ver-(i*1)),\
('n3f/static',list(glprim.glnormals)),\
('c4B/static',list(glprim.glcolors))
))
'''
def draw(self):
gl.glEnableClientState(gl.GL_NORMAL_ARRAY)
for item in self.primitives:
gl.glPushMatrix()
# gl.glMultMatrixf(glyph.affine)
glyph = item.get_glprimitive()
gl.glVertexPointer(
3, gl.GL_FLOAT, 0, glyph.glvertices)
gl.glColorPointer(
4, gl.GL_UNSIGNED_BYTE, 0, glyph.glcolors)
gl.glNormalPointer(gl.GL_FLOAT, 0, glyph.glnormals)
gl.glDrawElements(
gl.GL_TRIANGLES,
len(glyph.glindices),
type_to_enum[glyph.glindex_type],
glyph.glindices)
gl.glPopMatrix()
# self.batch.draw()
def delete(self):
self.vertex_list.delete()
if __name__ == '__main__':
mycubes = Cubes( np.array([0.0,0.0,0.0]) )
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] |
main.py | henryshunt/psn-logger | 0 | 42214 | <filename>main.py
import json
import os
from datetime import datetime
import sys
import queue
import threading
import smtplib
import ssl
import daemon
import paho.mqtt.client as mqtt
import pymysql
import config
import helpers
broker = None
alarm_analysis_queue = queue.Queue()
is_processing_alarms = False
def on_connect(client, user_data, flags, result):
""" Called when the MQTT broker connects to an MQTT server
"""
if result: return # Return if error
# Subscribe to the outbound and reports topics
subscribe_result = broker.subscribe(
[("nodes/+/outbound/+", 1), ("nodes/+/reports/+", 1)])
def send_alarm_email(alarm, report_time, value):
""" Sends an email to the owner of a session to indicate a triggered alarm
"""
message = (
"Subject: PSN Alarm Triggered\n\n"
"Notice from the Phenotyping Sensor Network that a sensor node in one of your "
"sessions raised an alarm."
"\n\nAlarm triggered based on data on {0} UTC"
"\n\nSession name: {1}"
"\nSensor node location: {2}"
"\nAlarm: {3} (safe value range: {4} - {5})"
"\nReported value: {6}")
parameter_friendly = ""
if alarm[1] == "airt": parameter_friendly = "Temperature"
elif alarm[1] == "relh": parameter_friendly = "Humidity"
if alarm[1] == "batv": parameter_friendly = "Battery Voltage"
# Email the owner of the session
server = smtplib.SMTP_SSL(config.email_server, 465)
try:
server.login(config.email_address, config.email_password)
time_string = report_time.strftime("%d/%m/%Y at %H:%M")
message_formatted = message.format(time_string, alarm[4], alarm[5],
parameter_friendly, alarm[2], alarm[3], value)
server.sendmail(config.email_address, alarm[6], message_formatted)
except: pass
server.quit()
def process_alarms():
""" Checks all reports in the alarm analysis queue to see if they trigger any alarms
and triggers the alarms if they do
"""
global is_processing_alarms
if is_processing_alarms == True: return
is_processing_alarms = True
while alarm_analysis_queue.empty() == False:
report = alarm_analysis_queue.get()
report_time = datetime.strptime(report[1]["time"], "%Y-%m-%dT%H:%M:%SZ")
try:
# Get all alarms for this node that may be able to trigger
alarms = helpers.get_triggered_alarms(report[0], report[1]["session_id"],
report_time)
if alarms == None: continue
# Trigger any alarms that have a report value outside the alarm range
for alarm in alarms:
try:
if alarm[1] == "airt":
if (report[1]["airt"] != None and
(report[1]["airt"] < alarm[2] or report[1]["airt"] > alarm[3])):
helpers.update_alarm_triggered(alarm[0], report_time)
send_alarm_email(alarm, report_time, report[1]["airt"])
elif alarm[1] == "relh":
if (report[1]["relh"] != None and
(report[1]["relh"] < alarm[2] or report[1]["relh"] > alarm[3])):
helpers.update_alarm_triggered(alarm[0], report_time)
send_alarm_email(alarm, report_time, report[1]["relh"])
elif alarm[1] == "batv":
if (report[1]["batv"] != None and
(report[1]["batv"] < alarm[2] or report[1]["batv"] > alarm[3])):
helpers.update_alarm_triggered(alarm[0], report_time)
send_alarm_email(alarm, report_time, report[1]["batv"])
except: continue
except: continue
is_processing_alarms = False
def on_message(client, user_data, message):
""" Called when a message is received from the MQTT broker
"""
topic_sections = message.topic.split('/')
node_address = topic_sections[1]
endpoint = topic_sections[2]
message_id = topic_sections[3]
inbound_topic = "nodes/" + node_address + "/inbound/" + message_id
# Request received from a sensor node
if endpoint == "outbound":
# Send back the active session for this sensor node
if message.payload.decode() == "get_session":
try:
session = helpers.get_active_session(node_address)
if session == None:
broker.publish(inbound_topic, "no_session", 0)
return
response = ("{{\"session_id\":{0},\"interval\":{1},\"batch_size\":{2}}}"
.format(str(session[0]), str(session[1]), str(session[2])))
broker.publish(inbound_topic, response, 0)
except: broker.publish(inbound_topic, "error", 0)
# Report received from a sensor node
elif endpoint == "reports":
try:
report = json.loads(message.payload.decode())
report_time = datetime.strptime(report["time"], "%Y-%m-%dT%H:%M:%SZ")
# Add to database if report time is within time range of specified session
if helpers.is_time_in_session(node_address, report["session_id"], report_time):
try:
helpers.insert_report(node_address, report)
# Trigger any alarms based on the values in this report
alarm_analysis_queue.put((node_address, report))
threading.Thread(target=process_alarms).start()
except pymysql.IntegrityError as e:
# Report for this node with this time already exists (unique key
# constraint fails)
if e.args[0] == 1062: pass
# Session and/or node does not exist (foreign key constraint fails)
elif e.args[0] == 1452:
broker.publish(inbound_topic, "no_session", 0)
return
else: raise
broker.publish(inbound_topic, "ok", 0)
else: broker.publish(inbound_topic, "no_session", 0)
except: broker.publish(inbound_topic, "error", 0)
if __name__ == "__main__":
current_dir = os.path.dirname(os.path.realpath(__file__))
with daemon.DaemonContext(working_directory=current_dir):
if not config.load(): sys.exit(1)
# Check if the database is accessible
try:
helpers.db_connection()
except: sys.exit(1)
# Create and connect to the MQTT broker
broker = mqtt.Client()
broker.on_connect = on_connect
broker.on_message = on_message
try:
broker.connect(config.broker_address, config.broker_port)
except: sys.exit(1)
# Enter loop to receive messages (handles auto-reconnecting)
broker.loop_forever() | [
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] |
skrypt.py | kosiacz3q/isbh | 0 | 80339 | import sys
def longest_common_substring(s1, s2):
m = [[0] * (1 + len(s2)) for i in range(1 + len(s1))]
longest, x_longest = 0, 0
for x in range(1, 1 + len(s1)):
for y in range(1, 1 + len(s2)):
if s1[x - 1] == s2[y - 1]:
m[x][y] = m[x - 1][y - 1] + 1
if m[x][y] > longest:
longest = m[x][y]
x_longest = x
else:
m[x][y] = 0
return s1[x_longest - longest: x_longest]
def levenshtein(s1, s2):
if len(s1) < len(s2):
return levenshtein(s2, s1)
# len(s1) >= len(s2)
if len(s2) == 0:
return len(s1)
previous_row = range(len(s2) + 1)
for i, c1 in enumerate(s1):
current_row = [i + 1]
for j, c2 in enumerate(s2):
insertions = previous_row[
j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer
deletions = current_row[j] + 1 # than s2
substitutions = previous_row[j] + (c1 != c2)
current_row.append(min(insertions, deletions, substitutions))
previous_row = current_row
return previous_row[-1]
def av(list):
wynik = result()
temp = 0
for element in list:
wynik.lcs += element.lcs
wynik.leve += element.leve
wynik.normalised_dist += element.normalised_dist
wynik.normalised_lcs += element.normalised_lcs
wynik.base += element.base
wynik.length += element.length
temp += 1
wynik.length /= temp
wynik.lcs /= temp
wynik.leve /= temp
wynik.normalised_dist /= temp
wynik.normalised_lcs /= temp
wynik.base /= temp
return wynik
class result:
lcs = 0
leve = 0
normalised_dist = 0
normalised_lcs = 0
base = 0
length = 0
def run(self, first, second):
self.base = len(first)
self.lcs = len(longest_common_substring(first, second))
self.leve = levenshtein(first, second)
self.normalised_dist = 1.0 - self.leve / len(second)
self.normalised_lcs = self.lcs/self.base
self.length = len(second)
def show(self):
print("len: " + self.length.__str__())
print("LCS: " + self.lcs.__str__())
print("normalised LCS: " + self.normalised_lcs.__str__())
print("levenshtein distace: " + self.leve.__str__())
print("normalised distance: " + self.normalised_dist.__str__())
def make_test():
if len(sys.argv) > 1:
f = open(sys.argv[1])
base = f.readline()[:-1]
temp = []
for line in f:
wyn = result()
wyn.run(base, line[:-1])
temp.append(wyn)
#wyn.show()
res = av(temp)
res.show()
make_test()
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] |
solutions/recommendation_system/quick_deploy/movie_recommender/milvus_tool/milvus_insert.py | NotRyan/bootcamp | 6 | 76705 | from pymilvus_orm import *
# from milvus_tool.config import MILVUS_HOST, MILVUS_PORT, schema, index_param
from milvus_tool.config import *
class VecToMilvus():
def __init__(self):
try:
connections.connect(host=MILVUS_HOST, port=MILVUS_PORT)
collection = None
except Exception as e:
print("Fail to connect Milvus:", e)
def has_collec(self, collection_name):
try:
return utility.has_collection(collection_name)
except Exception as e:
print("Milvus has_collec error:", e)
def set_collection(self, collection_name):
try:
if self.has_collec(collection_name):
self.collection = Collection(name=collection_name)
else:
print('No collection {}'.format(collection_name))
except Exception as e:
print('Milvus set collection error:', e)
def creat_collection(self, collection_name):
try:
self.collection = Collection(name=collection_name, schema=schema)
print("Create collection {} successfully".format(collection_name))
return collection
except Exception as e:
print("Milvus create collection error:", e)
def create_index(self, collection_name):
try:
self.set_collection(collection_name)
status = self.collection.create_index(field_name="embedding", index_params=index_param)
print("Create index {} successfully".format(collection_name))
return status
except Exception as e:
print("Milvus create index error:", e)
def has_partition(self, collection_name, partition_name):
try:
self.set_collection(collection_name)
return self.collection.has_partition(partition_name)
except Exception as e:
print("Milvus has partition error:", e)
def create_partition(self, collection_name, partition_name):
try:
self.set_collection(collection_name)
status = self.collection.create_partition(partition_name)
print('Create partition {} successfully'.format(partition_name))
return status
except Exception as e:
print("Milvus create partition error:", e)
def drop():
try:
self.set_collection(collection_name)
collection.drop()
print("Drop collection {}".format(collection_name))
except Exception as e:
print("Milvus drop collection error", e)
def insert(self, ids, vectors, collection_name, partition_name=None):
try:
if not self.has_collec(collection_name):
self.creat_collection(collection_name)
self.create_index(collection_name)
print("collection info: {}".format(self.collection))
if (partition_name is not None) and (not self.has_partition(collection_name, partition_name)):
self.create_partition(collection_name, partition_name)
mr = self.collection.insert(data=[ids, vectors], partition_name=partition_name)
utility.get_connection().flush([collection_name])
print("Insert {} entities, there are {} entities after insert data.".format(len(ids), self.collection.num_entities))
return mr
except Exception as e:
print("Milvus insert error:", e)
if __name__ == '__main__':
import random
client = VecToMilvus()
collection_name = 'test1'
partition_name = 'partition_1'
ids = [random.randint(0, 1000) for _ in range(100)]
embeddings = [[random.random() for _ in range(dim)] for _ in range(100)]
mr = client.insert(ids=ids, vectors=embeddings, collection_name=collection_name, partition_name=partition_name)
print(mr)
# print(ids)
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] |
snafu/installations.py | uranusjr/snafu | 27 | 148414 | import contextlib
import itertools
import os
import pathlib
import re
import subprocess
import attr
@attr.s
class Installation:
path = attr.ib(convert=pathlib.Path)
@property
def python(self):
return self.path.joinpath('python.exe')
@property
def scripts_dir(self):
return self.path.joinpath('Scripts')
@property
def pip(self):
return self.scripts_dir.joinpath('pip.exe')
def get_version_info(self):
output = subprocess.check_output(
[str(self.python), '--version'], encoding='ascii',
).strip()
match = re.match(r'^Python (\d+)\.(\d+)\.(\d+)$', output)
return tuple(int(x) for x in match.groups())
def find_script(self, name):
names = itertools.chain([name], [
'{}{}'.format(name, ext)
for ext in os.environ['PATHEXT'].split(';')
])
for name in names:
with contextlib.suppress(FileNotFoundError):
return self.scripts_dir.joinpath(name).resolve(strict=True)
raise FileNotFoundError(name)
| [
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] |
Dataset/Leetcode/valid/35/31.py | kkcookies99/UAST | 0 | 32238 | <gh_stars>0
class Solution:
def XXX(self, nums: List[int], target: int) -> int:
l, r = 0, len(nums)-1
# 找到第一个大于或等于target的位置
while l<r:
m = (l+r) // 2
if nums[m] >= target:
r = m
else:
l = m + 1
if nums[r] >= target:
return r
else:
return r+1
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] |
planex/cms_site/blocks/hero.py | octue/planex-cms | 0 | 117946 | from wagtail.core.blocks import CharBlock, StreamBlock, StructBlock, TextBlock, URLBlock
from wagtail.embeds.blocks import EmbedBlock
from wagtail.images.blocks import ImageChooserBlock
from .base import SectionBlock
class HeroSectionBlock(SectionBlock):
heading = CharBlock(
required=False,
max_length=100,
label="Hero Heading",
help_text="Add the big hero text. Keep it snappy.",
default="We are heroes",
)
description = TextBlock(
required=False,
max_length=400,
label="Hero Description",
help_text="Add a couple of lines under the hero text",
default="Here is a list of our Head Peeps. They look glorious here but are probably just normal, mortal humans.",
)
image = ImageChooserBlock(required=False, label="Hero image")
content = StreamBlock(
[
(
"button",
StructBlock(
[
("text", CharBlock(required=False, max_length=80, label="Label")),
("url", URLBlock(required=False, label="URL")),
],
required=False,
label="Call to action",
help_text='A "call-to-action" button, like "Sign Up Now!"',
),
),
("video", EmbedBlock(required=False, label="Video")),
(
"quote",
StructBlock(
[("text", TextBlock()), ("author", CharBlock(required=False))],
required=False,
label="Quote",
help_text="An inspiring quotation, optionally attributed to someone",
),
),
],
required=False,
block_counts={"button": {"max_num": 1}, "video": {"max_num": 1}, "quote": {"max_num": 1}},
)
class Meta:
icon = "placeholder"
label = "Hero Section"
| [
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spotpuppy/rotation/rotation_sensor_base.py | JoshPattman/Spot-Puppy-Lib | 1 | 158845 | import numpy as np
class sensor:
def __init__(self, inverse_x=False, inverse_z=False):
self.inverse_x = inverse_x
self.inverse_z = inverse_z
self.flip_x_z = False
self.rotation = np.array([0, 0])
def get_angle(self):
rot = np.copy(self.rotation)
if self.flip_x_z:
rot[0], rot[1] = rot[1], rot[0]
if self.inverse_x:
rot[0] = -rot[0]
if self.inverse_z:
rot[1] = -rot[1]
return rot
# Override update to add functionality
def update(self):
pass
# Override update to add the ability to calibrate
def calibrate(self):
pass
def get_json_params(self):
return {"inverse_x": self.inverse_x,
"inverse_z": self.inverse_z,
"flip_x_z": self.flip_x_z
}
def set_json_params(self, d):
self.inverse_x = d['inverse_x']
self.inverse_z = d['inverse_z']
self.flip_x_z = d['flip_x_z']
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vsm/extensions/corpusbuilders/__init__.py | inpho/vsm | 31 | 51260 | """
[Documentation about the corpusbuilders extension]
"""
from __future__ import absolute_import
from .corpusbuilders import *
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christmasflix/tests.py | jbettenh/last_christmas | 0 | 31909 | <reponame>jbettenh/last_christmas
from django.test import TestCase
from django.shortcuts import reverse
from .models import MovieList
from christmasflix import omdbmovies
class MovieListIndexViewTests(TestCase):
def test_no_lists(self):
response = self.client.get(reverse('christmasflix:index'))
self.assertEqual(response.status_code, 200)
self.assertContains(response, "No lists are available.")
self.assertQuerysetEqual(response.context['movie_lists'], [])
def test_one_movie_list(self):
mylist1 = MovieList.objects.create(name="mylist1")
response = self.client.get(reverse('christmasflix:index'))
self.assertQuerysetEqual(response.context['movie_lists'], [mylist1])
def test_two_movie_lists(self):
mylist1 = MovieList.objects.create(name="mylist1")
mylist2 = MovieList.objects.create(name="mylist2")
response = self.client.get(reverse('christmasflix:index'))
self.assertQuerysetEqual(response.context['movie_lists'], [mylist1, mylist2], ordered=False)
class IndexViewTest(TestCase):
def test_index_uses_template(self):
response = self.client.get(f'/christmasflix/')
self.assertTemplateUsed(response, 'christmasflix/index.html')
class DetailViewTest(TestCase):
def test_list_uses_template(self):
my_list = MovieList.objects.create()
response = self.client.get(f'/christmasflix/{my_list.id}/')
self.assertTemplateUsed(response, 'christmasflix/movie_list.html')
class MovieViewTest(TestCase):
def test_movies_uses_template(self):
response = self.client.get(f'/christmasflix/movies/')
self.assertTemplateUsed(response, 'christmasflix/movies.html')
class MovieResultsTest(TestCase):
def test_no_movie_found(self):
# asdasdfc
omdbmovies.search_movie("Die Hard")
self.assertRaises(KeyError) | [
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joblib/SubprocessFunctionCaller.py | Topaz1618/Plasticine | 0 | 116069 | import time
import multiprocessing
class SubprocessFunctionCaller(object):
class CliFunction(object):
def __init__(self, s2c, c2s, lock):
self.s2c = s2c
self.c2s = c2s
self.lock = lock
def __call__(self, *args, **kwargs):
self.lock.acquire()
self.c2s.put ( {'args':args, 'kwargs':kwargs} )
while True:
if not self.s2c.empty():
obj = self.s2c.get()
self.lock.release()
return obj
time.sleep(0.005)
class HostProcessor(object):
def __init__(self, s2c, c2s, func):
self.s2c = s2c
self.c2s = c2s
self.func = func
def process_messages(self):
while not self.c2s.empty():
obj = self.c2s.get()
result = self.func ( *obj['args'], **obj['kwargs'] )
self.s2c.put (result)
@staticmethod
def make_pair( func ):
s2c = multiprocessing.Queue()
c2s = multiprocessing.Queue()
lock = multiprocessing.Lock()
host_processor = SubprocessFunctionCaller.HostProcessor (s2c, c2s, func)
cli_func = SubprocessFunctionCaller.CliFunction (s2c, c2s, lock)
return host_processor, cli_func
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storm_control/test/test_buffered_1.py | shiwei23/STORM6 | 47 | 59491 | <gh_stars>10-100
#!/usr/bin/env python
"""
A test of the BufferedFunctionality class.
"""
import sys
import time
from PyQt5 import QtCore, QtWidgets
import storm_control.sc_hardware.baseClasses.hardwareModule as hardwareModule
class TBWidgetBase(QtWidgets.QWidget):
processed = QtCore.pyqtSignal(str)
def __init__(self, **kwds):
super().__init__(**kwds)
self.bf = hardwareModule.BufferedFunctionality(device_mutex = QtCore.QMutex())
self.timer = QtCore.QTimer()
self.timer.setInterval(10)
self.timer.timeout.connect(self.runTest)
self.timer.setSingleShot(True)
self.timer.start()
self.processed.connect(self.handleProcessed)
def handleProcessed(self, string):
pass
def runTest(self):
pass
class TBWidget1(TBWidgetBase):
"""
Check for correct execution order.
"""
def __init__(self, **kwds):
super().__init__(**kwds)
self.strings = []
def editString(self, string):
time.sleep(0.2)
string = "0" + string
return string
def handleProcessed(self, string):
self.strings.append(string)
if "end" in string:
try:
assert(self.strings == ["0111", "0212", "0end"])
except AssertionError as exc:
print(self.strings, "is not expected")
raise exc
self.bf.wait()
self.close()
def runTest(self):
for string in ["111", "222", "333", "444", "555", "end"]:
self.bf.maybeRun(task = self.editString,
args = [string],
ret_signal = self.processed)
if(string == "222"):
self.bf.mustRun(task = self.editString,
args = ["212"],
ret_signal = self.processed)
class TBWidget2(TBWidgetBase):
"""
Check that kill timer works.
"""
def __init__(self, **kwds):
super().__init__(**kwds)
self.bf.kill_timer.setInterval(200)
def handleProcessed(self, string):
assert False
def longWait(self):
time.sleep(2.0)
def runTest(self):
self.bf.mustRun(task = self.longWait,
args = [],
ret_signal = self.processed)
class TBWidget3(TBWidgetBase):
"""
Check that kill timer reset works.
"""
def __init__(self, **kwds):
super().__init__(**kwds)
self.max_jobs = 20
self.bf.kill_timer.setInterval(500)
def handleProcessed(self, string):
print("processed", string)
if (string == str(self.max_jobs - 1)):
self.bf.wait()
self.close()
def shortWait(self, job_id):
time.sleep(0.1)
return job_id
def runTest(self):
for i in range(self.max_jobs):
self.bf.mustRun(task = self.shortWait,
args = [str(i)],
ret_signal = self.processed)
def test_buffered_1():
app = QtWidgets.QApplication(sys.argv)
tb1 = TBWidget1()
app.exec_()
app = None
def buffered_2():
app = QtWidgets.QApplication(sys.argv)
tb1 = TBWidget2()
app.exec_()
app = None
def test_buffered_3():
app = QtWidgets.QApplication(sys.argv)
tb1 = TBWidget3()
app.exec_()
app = None
if (__name__ == "__main__"):
test_buffered_1()
#buffered_2()
test_buffered_3()
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abandonedio/urls.py | Kunstmord/abandoned | 0 | 175223 | <reponame>Kunstmord/abandoned
import abandoned.views
from django.conf.urls import patterns, include, url
from django.contrib import admin
from rest_framework import routers
router = routers.DefaultRouter()
router.register(r'tags', abandoned.views.TagViewSet)
router.register(r'authors', abandoned.views.AuthorViewSet)
router.register(r'reasons', abandoned.views.ReasonViewSet)
router.register(r'projects', abandoned.views.ProjectViewSet)
router.register(r'languages', abandoned.views.LanguageViewSet)
urlpatterns = patterns('',
url(r'^$', abandoned.views.main_page, name='main_page'),
url(r'^projects/$', abandoned.views.projects_view, name='projects_latest'),
url(r'^projects/(?P<sorting>\w+)/$', abandoned.views.projects_view, name='projects'),
url(r'^project/(?P<project_id>\d+)/$', abandoned.views.single_project_view, name='project'),
url(r'^languages/$', abandoned.views.languages_view, name='languages_alphabetical'),
url(r'^languages/(?P<sorting>\w+)/$', abandoned.views.languages_view, name='languages'),
url(r'^language/(?P<language_id>\d+)/$', abandoned.views.single_language_view, name='language'),
url(r'^tags/$', abandoned.views.tags_view, name='tags_alphabetical'),
url(r'^tags/(?P<sorting>\w+)/$', abandoned.views.tags_view, name='tags'),
url(r'^tag/(?P<tag_id>\w+)/$', abandoned.views.single_tag_view, name='tag'),
url(r'^authors/$', abandoned.views.authors_view, name='authors_alphabetical'),
url(r'^authors/(?P<sorting>\w+)/$', abandoned.views.authors_view, name='authors'),
url(r'^author/(?P<author_id>\d+)/$', abandoned.views.single_author_view, name='author'),
url(r'^reasons/$', abandoned.views.reasons_view, name='reasons_alphabetical'),
url(r'^reasons/(?P<sorting>\w+)/$', abandoned.views.reasons_view, name='reasons'),
url(r'^reason/(?P<reason_id>\d+)/$', abandoned.views.single_reason_view, name='reason'),
url(r'^upvote/$', abandoned.views.handle_upvote, name='upvote'),
url(r'^submit/$', abandoned.views.handle_submit, name='submit'),
url(r'^api_info/$', abandoned.views.api_info_view, name='api_info'),
url(r'^cookies/$', abandoned.views.cookie_info_view, name='cookies'),
url(r'^api/', include(router.urls)),
url(r'^admin/', include(admin.site.urls)),
) | [
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] |
tests/settings.py | Francislley/CrudDajndo2017.2 | 0 | 49881 | SECRET_KEY = 'testing'
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': ':memory:',
}
}
INSTALLED_APPS = [
'django.contrib.contenttypes',
'django.contrib.auth',
'django.contrib.admin',
'django.contrib.staticfiles',
'django.contrib.sessions',
'cruditor',
]
# We really don't rely on the urlconf but we need to set a path anyway.
ROOT_URLCONF = 'django.contrib.staticfiles.urls'
STATIC_URL = '/static/'
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tests/test_annotations_notebook.py | jeromedockes/pylabelbuddy | 0 | 6684 | from pylabelbuddy import _annotations_notebook
def test_annotations_notebook(root, annotations_mock, dataset_mock):
nb = _annotations_notebook.AnnotationsNotebook(
root, annotations_mock, dataset_mock
)
nb.change_database()
assert nb.notebook.index(nb.notebook.select()) == 2
nb.go_to_annotations()
assert nb.notebook.index(nb.notebook.select()) == 0
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sanic_jwt_extended/decorators.py | devArtoria/Sanic-JWT-extended | 465 | 133930 | from functools import wraps
from typing import Callable, List, Optional, Tuple
from sanic.request import Request
from sanic_jwt_extended.exceptions import (
AccessDeniedError,
ConfigurationConflictError,
CSRFError,
FreshTokenRequiredError,
InvalidHeaderError,
NoAuthorizationError,
RevokedTokenError,
WrongTokenError,
)
from sanic_jwt_extended.jwt_manager import JWT
from sanic_jwt_extended.tokens import Token
try:
from hmac import compare_digest
except ImportError: # pragma: no cover
def compare_digest(a, b):
if isinstance(a, str):
a = a.encode("utf-8")
if isinstance(b, str):
b = b.encode("utf-8")
if len(a) != len(b):
return False
r = 0
for x, y in zip(a, b):
r |= x ^ y
return not r
jwt_get_function = Callable[[Request, bool], Tuple[str, Optional[str]]]
def _get_request(args) -> Request:
if isinstance(args[0], Request):
request = args[0]
else:
request = args[1]
return request
def _get_raw_jwt_from_request(request, is_access=True):
functions: List[jwt_get_function] = []
for eligible_location in JWT.config.token_location:
if eligible_location == "header":
functions.append(_get_raw_jwt_from_headers)
if eligible_location == "query":
functions.append(_get_raw_jwt_from_query_params)
if eligible_location == "cookies":
functions.append(_get_raw_jwt_from_cookies)
raw_jwt = None
csrf_value = None
errors = []
for f in functions:
try:
raw_jwt, csrf_value = f(request, is_access)
break
except NoAuthorizationError as e:
errors.append(str(e))
if not raw_jwt:
raise NoAuthorizationError(', '.join(errors))
return raw_jwt, csrf_value
def _get_raw_jwt_from_headers(request, is_access):
header_key = (
JWT.config.jwt_header_key if is_access else JWT.config.refresh_jwt_header_key
)
header_prefix = JWT.config.jwt_header_prefix
token_header = request.headers.get(header_key)
if not token_header:
raise NoAuthorizationError(f'Missing header "{header_key}"')
parts: List[str] = token_header.split()
if parts[0] != header_prefix or len(parts) != 2:
raise InvalidHeaderError(
f"Bad {header_key} header. Expected value '{header_prefix} <JWT>'"
)
encoded_token: str = parts[1]
return encoded_token, None
def _get_raw_jwt_from_query_params(request, _):
encoded_token = request.args.get(JWT.config.jwt_query_param_name)
if not encoded_token:
raise NoAuthorizationError(
f'Missing query parameter "{JWT.config.jwt_query_param_name}"'
)
return encoded_token, None
def _get_raw_jwt_from_cookies(request, is_access):
cookie_key = JWT.config.jwt_cookie if is_access else JWT.config.refresh_jwt_cookie
csrf_header_key = (
JWT.config.jwt_csrf_header if is_access else JWT.config.refresh_jwt_csrf_header
)
encoded_token = request.cookies.get(cookie_key)
csrf_value = None
if not encoded_token:
raise NoAuthorizationError(f'Missing cookie "{cookie_key}"')
if JWT.config.csrf_protect and request.method in JWT.config.csrf_request_methods:
csrf_value = request.headers.get(csrf_header_key)
if not csrf_value:
raise CSRFError("Missing CSRF token")
return encoded_token, csrf_value
def _csrf_check(csrf_from_request, csrf_from_jwt):
if not csrf_from_jwt or not isinstance(csrf_from_jwt, str):
raise CSRFError('Can not find valid CSRF data from token')
if not compare_digest(csrf_from_request, csrf_from_jwt):
raise CSRFError('CSRF double submit tokens do not match')
def jwt_required(
function=None, *, allow=None, deny=None, fresh_required=False,
):
def real(fn):
@wraps(fn)
async def wrapper(*args, **kwargs):
request = _get_request(args)
raw_jwt, csrf_value = _get_raw_jwt_from_request(request)
token_obj = Token(raw_jwt)
if csrf_value:
_csrf_check(csrf_value, token_obj.csrf)
if token_obj.type != "access":
raise WrongTokenError("Only access tokens are allowed")
if fresh_required and not token_obj.fresh:
raise FreshTokenRequiredError("Only fresh access tokens are allowed")
if allow and token_obj.role not in allow:
raise AccessDeniedError("You are not allowed to access here")
if deny and token_obj.role in deny:
raise AccessDeniedError("You are not allowed to access here")
if JWT.config.use_blacklist and await JWT.blacklist.is_blacklisted(
token_obj
):
raise RevokedTokenError("Token has been revoked")
kwargs["token"] = token_obj
return await fn(*args, **kwargs)
return wrapper
if function:
return real(function)
else:
if allow and deny:
raise ConfigurationConflictError(
"Can not use 'deny' and 'allow' option together."
)
return real
def jwt_optional(function):
@wraps(function)
async def wrapper(*args, **kwargs):
request = _get_request(args)
token_obj: Optional[Token] = None
try:
raw_jwt, csrf_value = _get_raw_jwt_from_request(request)
token_obj = Token(raw_jwt)
if csrf_value:
_csrf_check(csrf_value, token_obj.csrf)
if token_obj.type != "access":
raise WrongTokenError("Only access tokens are allowed")
except (NoAuthorizationError, InvalidHeaderError):
pass
kwargs["token"] = token_obj
return await function(*args, **kwargs)
return wrapper
def refresh_jwt_required(function=None, *, allow=None, deny=None):
def real(fn):
@wraps(fn)
async def wrapper(*args, **kwargs):
request = _get_request(args)
raw_jwt, csrf_value = _get_raw_jwt_from_request(request, is_access=False)
token_obj = Token(raw_jwt)
if csrf_value:
_csrf_check(csrf_value, token_obj.csrf)
if token_obj.type != "refresh":
raise WrongTokenError("Only refresh tokens are allowed")
if allow and token_obj.role not in allow:
raise AccessDeniedError("You are not allowed to refresh in here")
if deny and token_obj.role in deny:
raise AccessDeniedError("You are not allowed to refresh in here")
if JWT.config.use_blacklist and await JWT.blacklist.is_blacklisted(
token_obj
):
raise RevokedTokenError("Token has been revoked")
kwargs["token"] = token_obj
return await fn(*args, **kwargs)
return wrapper
if function:
return real(function)
else:
if allow and deny:
raise ConfigurationConflictError(
"Can not use 'deny' and 'allow' option together."
)
return real
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] |
gen/build_deploy/powershell.py | KostiantynOleksiienko/dcos | 0 | 101060 | """
Generates a powershell script to install Windows agent - dcos_install.ps1
"""
import os
import os.path
import gen.build_deploy.util as util
import gen.template
import gen.util
import pkgpanda
import pkgpanda.util
def generate(gen_out, output_dir):
print("Generating Powershell configuration files for DC/OS")
make_powershell(gen_out, output_dir)
def make_powershell(gen_out, output_dir):
"""Build powershell deployment script and store this at Bootstrap serve"""
output_dir = output_dir + '/windows/'
pkgpanda.util.make_directory(output_dir)
bootstrap_url = gen_out.arguments['bootstrap_url']
if gen_out.arguments['master_discovery'] == 'static':
master_list = gen_out.arguments['master_list']
elif gen_out.arguments['master_discovery'] == 'master_http_loadbalancer':
master_list = gen_out.arguments['exhibitor_address'] + ':2181'
else:
master_list = 'zk-1.zk:2181,zk-2.zk:2181,zk-3.zk:2181,zk-4.zk:2181,zk-5.zk:2181'
powershell_template_path = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'powershell/dcos_install.ps1.in')
with open(powershell_template_path, 'r') as f:
powershell_template = f.read()
powershell_script = gen.template.parse_str(powershell_template).render({
'dcos_image_commit': util.dcos_image_commit,
'generation_date': util.template_generation_date,
'bootstrap_url': bootstrap_url,
'master_list': master_list,
})
# Output the dcos install ps1 script
install_script_filename = 'dcos_install.ps1'
pkgpanda.util.write_string(install_script_filename, powershell_script)
pkgpanda.util.write_string(output_dir + install_script_filename, powershell_script)
f.close()
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http-lib-serv/test/__main__.py | jbschool/hpotter-api | 0 | 129358 | <gh_stars>0
import unittest
# import schema
class TestCanTest(unittest.TestCase):
def setUp(self):
print('setting up...')
def test_true_is_true(self):
self.assertTrue(1 == 1)
def test_false_is_false(self):
self.assertFalse(False)
if __name__ == '__main__':
unittest.main() | [
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jumeg/gui/tsv/wxutils/jumeg_tsv_wxutils_dlg_options.py | fboers/jumeg | 6 | 144730 | #!/usr/bin/env python3
# -+-coding: utf-8 -+-
#--------------------------------------------
# Authors: <NAME> <<EMAIL>>
#
#--------------------------------------------
# Date: 05.09.19
#--------------------------------------------
# License: BSD (3-clause)
#--------------------------------------------
# Updates
#--------------------------------------------
import numpy as np
import wx
import sys,logging
from jumeg.base import jumeg_logger
logger = logging.getLogger('jumeg')
from jumeg.gui.wxlib.utils.jumeg_gui_wxlib_utils_controls import JuMEG_wxControlGrid
from jumeg.gui.tsv.wxutils.jumeg_tsv_wxutils import DLGButtonPanel
__version__="2019-09-13-001"
LEA = wx.ALIGN_LEFT | wx.EXPAND | wx.ALL
class PopupColourTable(wx.PopupTransientWindow):
def __init__(self, parent,**kwargs):
super().__init__(parent,wx.NO_BORDER)
self._wx_init(**kwargs)
self._ApplyLayout()
def _wx_init(self,**kwargs):
self.SetBackgroundColour(kwargs.get("bg","GREY60"))
w = kwargs.get("w",24)
self._C = kwargs.get("colours")
self._caller = kwargs.get("caller")
self._callback = kwargs.get("callback")
self._title = kwargs.get("title","JuMEG Select Colour")
self._colour_text = None
bmp = wx.Bitmap()
ctrls = []
for i in range( self._C.n_colours ):
#--- select colour
ctrls.append( ["BTBMP",self._C.labels[i],bmp,(w,w),wx.NO_BORDER|wx.BU_NOTEXT,self._C.colours[i] ,self._C.labels[i],self.ClickOnCtrls] )
#--- calc cols for grid
n_cols,rest = divmod( len(ctrls),4)
if rest: n_cols += 1
self._pnl = JuMEG_wxControlGrid(self,label= self._title,control_list=ctrls,cols=n_cols,set_ctrl_prefix=False)
def ClickOnCtrls(self,evt):
obj = evt.GetEventObject()
try:
if obj.GetToolTip():
self._colour_text = obj.GetToolTipText()
#--- call the caller-function in parent
if self._colour_text:
self._callback( self._caller,self._colour_text )
#--- close
self.Dismiss() # close it
except:
logger.exception("---> ERROR can not set ToolTip".format(self._colour_text))
def _ApplyLayout(self):
vbox = wx.BoxSizer(wx.VERTICAL)
vbox.Add(self._pnl,1,LEA,2)
self.SetAutoLayout(True)
self.SetSizer(vbox)
self.Fit()
self.Layout()
class GroupDLG(wx.Dialog):
"""
Example:
---------
input group dict
labels = ['grad','mag','eeg','stim','eog','emg','ecg','ref_meg']
grp={
'mag': {"selected":True,"colour":"RED", "prescale":200,"unit":"fT"},
'grad': {"selected":True,"colour":"BLUE", "prescale":200,"unit":"fT"},
'ref_meg':{"selected":True,"colour":"GREEN", "prescale":2, "unit":"pT"},
'eeg': {"selected":True,"colour":"BLUE", "prescale":1, "unit":"uV"},
'eog': {"selected":True,"colour":"PURPLE", "prescale":100,"unit":"uV"},
'emg': {"selected":True,"colour":"DARKORANGE","prescale":100,"unit":"uV"},
'ecg': {"selected":True,"colour":"DARKGREEN", "prescale":100,"unit":"mV"},
'stim': {"selected":True,"colour":"CYAN", "prescale":1, "unit":"bits"}
}
"""
@property
def Group(self): return self._GRP
def __init__(self,parent,**kwargs):
style=wx.CLOSE_BOX|wx.MAXIMIZE_BOX|wx.MINIMIZE_BOX |wx.RESIZE_BORDER
super().__init__(parent,title="JuMEG Group Settings",style=style)
self._init(**kwargs)
def _init(self,**kwargs):
self._GRP = kwargs.get("grp")
self._wx_init(**kwargs)
self._wx_button_box()
self._ApplyLayout()
def _wx_button_box(self):
"""
show DLG cancel apply bts
:return:
"""
self._pnl_button_box = DLGButtonPanel(self,style=wx.SUNKEN_BORDER)
def _wx_init(self,**kwargs):
w = kwargs.get("w",24)
self.SetBackgroundColour(kwargs.get("bg","GREY90"))
self.SetMinSize(( 620,400) )
# self._bt=wx.Button(self,-1,"TEST")
bmp = wx.Bitmap()
ctrls = [ ["STXT","Groups","Groups"], ["STXT","Colour","Colour"], ["STXT","Scale","Scale/Div"],[ "STXT","Unit","Unit"],["STXT","Scale Mode","Scale Mode"],["STXT","DC Offset","DC Offset"]]
n_cols= len(ctrls)
for grp in self._GRP.labels:
g = grp.upper()
#--- ckbutton select group
ctrls.append( ["CK", g+".SELECTED",g,self._GRP.GetSelected(grp),'de/select group',self.ClickOnCtrls] )
#--- select colour
label = self._GRP.GetColour(grp)
ctrls.append( ["BTBMP",g+".colour",bmp,(w,w),wx.NO_BORDER|wx.BU_NOTEXT,label,label +"\nclick to change",self.ClickOnCtrls] )
#--- grp prescale
ctrls.append(["COMBO",g.upper()+".PRESCALE",str(self._GRP.GetPreScale(grp)),self._GRP.Unit.prescales,'set scaling',self.ClickOnCtrls])
#--- grp unit wit prefix e.g. m,u,n,p,f,a => mT,uT,...
self._GRP.Unit.unit = self._GRP.GetUnit(grp) # update Unit CLS
ctrls.append(["COMBO",g+".UNIT",self._GRP.Unit.unit,self._GRP.Unit.GetUnits(),'set scaling unit',self.ClickOnCtrls])
#--- grp scale mode
m = self._GRP.ScaleModes[ self._GRP.GetScaleMode(grp) ]
ctrls.append( ["COMBO",g.upper() + ".SCALE",m,self._GRP.ScaleModes,'scale on Custom or MinMax',self.ClickOnScaleMode])
#--- combo select DCoffset
# ctrls.append( ["CK", g+".DCOFFSET","",self._GRP.GetDCoffset(grp),'DC Offset',self.ClickOnCtrls] )
m = self._GRP.DCOffsetModes[ self._GRP.GetDCOffsetMode(grp) ]
ctrls.append( ["COMBO",g.upper() + ".DCOFFSET",m,self._GRP.DCOffsetModes,'DC Offset Modes e.g.: [None,Global,Time Window]',self.ClickOnCtrls])
self._pnl_groups = JuMEG_wxControlGrid(self,label="Group Parameter",control_list=ctrls,cols=n_cols,set_ctrl_prefix=False,AddGrowableCol=[2,3,4,5])
#self._bt.Bind(wx.EVT_BUTTON,self.ClickOnButton)
def _popup_colour_table(self,obj,grp):
PCT = PopupColourTable(self,title="Group: "+obj.GetName().split(".")[0],caller=obj,colours=self._GRP.Colour,callback=self.update_colour)
pos = wx.GetMousePosition()
PCT.Position(pos,(0,0))
PCT.Popup()
def update_colour(self,obj,label):
"""
sets the group colour
this is the callback executed from PopupColourTable
may use wx.EVENTS or pubsub
:param obj: colour bitmapbutton
:param label:colour label: RED,GREEN has to be in colour-object label list ...
:return:
"""
grp,key = obj.GetName().lower().split(".")
c = self._GRP.Colour.label2colour(label)
obj.SetBackgroundColour( c )
obj.SetToolTipString( label+"\nclick to change")
self._GRP.SetColour( grp, label )
# logger.info("set group: {} colour: {}".format(grp,self._GRP.GetGroup(grp) ))
def ClickOnScaleMode(self,evt):
"""
update scale mode in group obj
if <division> enable "PRESCALE","UNIT"
else disable
:param evt:
:return:
"""
obj = evt.GetEventObject()
grp,key = obj.GetName().lower().split(".")
idx = obj.GetSelection()
self._GRP.SetScaleMode(grp,idx)
for label in ["PRESCALE","UNIT"]:
w = self.FindWindowByName(grp.upper() + "." + label)
w.Enable( not bool(idx) )
def ClickOnCtrls(self,evt):
obj = evt.GetEventObject()
grp,key = obj.GetName().lower().split(".")
if key == "colour":
self._popup_colour_table(obj,grp)
return
v = obj.GetValue()
if key == "selected":
self._GRP.SetSelected(grp,v )
elif key == "prescale":
self._GRP.SetPreScale(grp,v)
elif key == "unit":
self._GRP.SetUnit(grp,v)
elif key =="dcoffset":
self._GRP.SetDCOffsetMode(grp,obj.GetSelection())
def ClickOnButton(self,evt):
pass
def _ApplyLayout(self):
vbox = wx.BoxSizer(wx.VERTICAL)
#vbox.Add(self._bt,0,LEA,2)
vbox.Add(self._pnl_groups,1,LEA,2)
#--- fix size to show combos of scale and unit
stl = wx.StaticLine(self,size=(520,2) )
stl.SetBackgroundColour("GREY85")
vbox.Add(stl,0,LEA,1)
vbox.Add(self._pnl_button_box,0,LEA,2)
self.SetAutoLayout(True)
self.SetSizer(vbox)
self.Fit()
self.Layout()
class ChannelDLG(GroupDLG):
def __init__(self,**kwargs):
super().__init__(**kwargs)
class MainFrame(wx.Frame):
def __init__(self, parent, title,**kwargs):
super().__init__(parent, title = title)
self._init(**kwargs)
self._ApplyLayout()
def _init(self,**kwargs):
self.test = wx.Panel(self)
self.show_group_dialog(**kwargs)
self.Close()
def _ApplyLayout(self):
vbox = wx.BoxSizer(wx.VERTICAL)
vbox.Add(self.test,1,LEA,4)
self.SetSizer(vbox)
self.SetAutoLayout(True)
self.Fit()
self.Show(True)
def show_group_dialog(self,**kwargs):
dlg = GroupDLG(self,**kwargs)
out = dlg.ShowModal()
if out == wx.ID_APPLY:
grp = dlg.Group.GetGroup(None)
for g in grp.keys():
logger.info("OUTPUT: {} => {} \n {}".format(g,dlg.Group.GetScaling(g),grp.get(g)))
dlg.Destroy()
if __name__ == "__main__":
opt=None
#--- Testing DEBUG
from tsv.utils.jumeg_tsv_utils_io_data import JuMEG_TSV_Utils_IO_Data
from tsv.plot.jumeg_tsv_plot2d_data_options import JuMEG_TSV_PLOT2D_DATA_OPTIONS,GroupOptions
# from tsv.wxutils.jumeg_tsv_wxutils_dlg_settings import GroupDLG,ChannelDLG
verbose = True
path = "data"
path = "~/MEGBoers/programs/JuMEG/jumeg-py/jumeg-py-git-fboers-2019-08-21/projects/JuMEGTSV/data"
fname = '200098_leda_test_10_raw.fif'
raw = None
jumeg_logger.setup_script_logging(name="JuMEG",opt=opt,logger=logger)
IO = JuMEG_TSV_Utils_IO_Data()
raw,bads = IO.load_data(raw,fname,path)
#--- ToDo use only groups from raw
GRP = GroupOptions()
DOpt = JuMEG_TSV_PLOT2D_DATA_OPTIONS(raw=raw)
DOpt.update(verbose=True,debug=True)
app = wx.App()
MainFrame(None,'JuMEG demo',grp=GRP)
app.MainLoop()
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] |
du/ps_utils.py | diogo149/doo | 1 | 14670 | import os
import psutil
import time
def process_time():
p = psutil.Process(os.getpid())
return time.time() - p.create_time()
| [
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scripts/hdfs_store.py | coastrock/CEBD1261-2019-fall-group-project | 1 | 26604 | <gh_stars>1-10
try:
from zipfile import ZipFile
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
import pyspark.sql.functions as f
import os
except Exception as e:
print(e)
## http://www.hongyusu.com/imt/technology/spark-via-python-basic-setup-count-lines-and-word-counts.html
def push_acc():
spark = SparkSession.builder \
.master('spark://master:7077') \
.appName("Push Accidents data to HDFS") \
.getOrCreate()
sc = spark.sparkContext
sc.setLogLevel('WARN')
# unzip the file
# with ZipFile("/volume/data/accidents_2012_2018.zip", 'r') as zipObj:
# zipObj.extractall('/volume/data')
# read the data from the volume
acc_data = spark.read.csv("/volume/data/")
# push the data on HDFS as parquet
acc_data.write.parquet("hdfs://hadoop/acc_data_parquet")
if __name__ == "__main__":
push_acc()
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parsifal/reviews/metaanalysis/urls.py | glauberferreira/parsifal-mec | 1 | 141682 | # coding: utf-8
from django.conf.urls import patterns, include, url
urlpatterns = patterns('parsifal.reviews.metaanalysis.views',
url(r'^save_metaanalysis/$', 'save_metaanalysis', name='save_metaanalysis'),
)
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0120.triangle/solution.py | WZMJ/Algorithms | 5 | 100012 | <filename>0120.triangle/solution.py
from typing import List
class Solution:
def minimumTotal(self, triangle: List[List[int]]) -> int:
# 自底向上
length = len(triangle)
if not length:
return 0
for i in range(length - 2, -1, -1):
for j in range(i + 1):
triangle[i][j] = triangle[i][j] + min(triangle[i + 1][j], triangle[i + 1][j + 1])
return triangle[0][0]
def top_down(self, triangle) -> int:
# 自顶向下
length = len(triangle)
if not length:
return 0
for i in range(1, length):
for j in range(i + 1):
if j == 0:
triangle[i][0] += triangle[i - 1][0]
elif i == j:
triangle[i][j] += triangle[i - 1][j - 1]
else:
triangle[i][j] += min(triangle[i - 1][j], triangle[i - 1][j - 1])
return min(triangle[-1])
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libweasyl/libweasyl/test/test_text.py | theSeracen/weasyl | 0 | 194640 | # encoding: utf-8
from lxml.etree import LIBXML_VERSION
import pytest
from libweasyl.text import markdown, markdown_excerpt, markdown_link
libxml_xfail = pytest.mark.xfail(LIBXML_VERSION < (2, 9), reason='libxml2 too old to preserve whitespace')
user_linking_markdown_tests = [
('<~spam>', '<a href="/~spam">spam</a>'),
('<!spam>', '<a href="/~spam" class="user-icon"><img src="/~spam/avatar" alt="spam"></a>'),
('',
'<a href="/~spam" class="user-icon"><img src="/~spam/avatar" alt="spam"></a>'
'<a href="/~spam" class="user-icon"><img src="/~spam/avatar" alt="spam"></a>'),
('<!~spam>', '<a href="/~spam" class="user-icon"><img src="/~spam/avatar" alt="spam"> <span>spam</span></a>'),
('', '<a href="/~example" class="user-icon"><img src="/~example/avatar"> <span>user image with alt text</span></a>'),
('<user:spam>', '<a href="/~spam">spam</a>'),
('[link](user:spam)', '<a href="/~spam">link</a>'),
('<fa:spam>', '<a href="https://www.furaffinity.net/user/spam" rel="nofollow ugc">spam</a>'),
('<da:spam>', '<a href="https://www.deviantart.com/spam" rel="nofollow ugc">spam</a>'),
('<ib:spam>', '<a href="https://inkbunny.net/spam" rel="nofollow ugc">spam</a>'),
('<sf:spam>', '<a href="https://spam.sofurry.com/" rel="nofollow ugc">spam</a>'),
]
@pytest.mark.parametrize(('target', 'expected'), user_linking_markdown_tests)
def test_markdown_basic_user_linking(target, expected):
assert markdown(target) == '<p>%s</p>\n' % (expected,)
@pytest.mark.parametrize(('target', 'expected'), user_linking_markdown_tests)
def test_markdown_user_linking_in_tag(target, expected):
assert markdown('<em>%s</em>' % (target,)) == '<p><em>%s</em></p>\n' % (expected,)
@pytest.mark.parametrize(('target', 'expected'), user_linking_markdown_tests)
def test_markdown_user_linking_in_tail(target, expected):
assert markdown('<em>eggs</em>%s' % (target,)) == '<p><em>eggs</em>%s</p>\n' % (expected,)
@libxml_xfail
@pytest.mark.parametrize(('target', 'expected'), user_linking_markdown_tests)
def test_markdown_user_linking_twice_in_tag(target, expected):
assert markdown('<em>%s %s</em>' % (target, target)) == '<p><em>%s %s</em></p>\n' % (expected, expected)
@pytest.mark.parametrize(('target', 'expected'), user_linking_markdown_tests)
def test_markdown_user_linking_twice_in_tag_with_more_text_between(target, expected):
assert markdown('<em>%s spam %s</em>' % (target, target)) == '<p><em>%s spam %s</em></p>\n' % (expected, expected)
@libxml_xfail
@pytest.mark.parametrize(('target', 'expected'), user_linking_markdown_tests)
def test_markdown_user_linking_twice_in_tail(target, expected):
assert markdown('<em>eggs</em>%s %s' % (target, target)) == (
'<p><em>eggs</em>%s %s</p>\n' % (expected, expected))
@pytest.mark.parametrize(('target', 'expected'), user_linking_markdown_tests)
def test_markdown_user_linking_twice_in_tail_with_more_text_betweeen(target, expected):
assert markdown('<em>eggs</em>%s spam %s' % (target, target)) == (
'<p><em>eggs</em>%s spam %s</p>\n' % (expected, expected))
@pytest.mark.parametrize(('target', 'expected'), user_linking_markdown_tests)
def test_markdown_user_linking_in_markdown(target, expected):
assert markdown('*%s*' % (target,)) == '<p><em>%s</em></p>\n' % (expected,)
def test_markdown_no_user_links_in_code():
assert markdown('<code><~spam></code>') == '<p><code><~spam></code></p>\n'
def test_markdown_no_user_links_in_pre():
assert markdown('<pre><~spam></pre>') == '<pre><p><~spam></p></pre>\n'
def test_markdown_no_user_links_in_links():
assert markdown('<a><~spam></a>') == '<p><a><~spam></a></p>\n'
def test_markdown_escaped_user_link():
assert markdown('\\\\<~spam>') == '<p><~spam></p>\n'
def test_markdown_multi_element():
assert markdown('one\n\ntwo') == '<p>one</p>\n\n<p>two</p>\n'
def test_markdown_user_linking_with_underscore():
assert markdown('<~hello_world>') == '<p><a href="/~helloworld">hello_world</a></p>\n'
def test_markdown_image_replacement():
assert markdown('') == '<p><a href="http://example" rel="nofollow ugc">example</a></p>\n'
assert markdown('<img alt="broken">') == '<p><a href="">broken</a></p>\n'
def test_forum_whitelist():
assert markdown('https://forums.weasyl.com/foo') == (
'<p><a href="https://forums.weasyl.com/foo">https://forums.weasyl.com/foo</a></p>\n')
def test_markdown_unordered_list():
assert markdown('- five\n- six\n- seven') == '<ul><li>five</li>\n<li>six</li>\n<li>seven</li>\n</ul>'
def test_markdown_regular_ordered_list_start():
assert markdown('1. five\n1. six\n1. seven') == '<ol start="1"><li>five</li>\n<li>six</li>\n<li>seven</li>\n</ol>'
def test_markdown_respect_ordered_list_start():
assert markdown('5. five\n6. six\n7. seven') == '<ol start="5"><li>five</li>\n<li>six</li>\n<li>seven</li>\n</ol>'
def test_markdown_strikethrough():
assert markdown(u"~~test~~") == u"<p><del>test</del></p>\n"
@pytest.mark.parametrize(('target', 'expected'), [
(u"[external](http://example.com/)", u'<a href="http://example.com/" rel="nofollow ugc">external</a>'),
(u'<a href="http://example.com/">external</a>', u'<a href="http://example.com/" rel="nofollow ugc">external</a>'),
(u'<a href="http://example.com/" rel="noreferrer">external</a>', u'<a href="http://example.com/" rel="nofollow ugc">external</a>'),
(u"[external](//example.com/)", u'<a href="//example.com/" rel="nofollow ugc">external</a>'),
])
def test_markdown_external_link_noreferrer(target, expected):
assert markdown(target) == u"<p>%s</p>\n" % (expected,)
markdown_link_tests = [
(('spam', '/eggs'), '[spam](/eggs)'),
((']spam[', '/eggs'), r'[\]spam\[](/eggs)'),
(('[[spam]', '/eggs'), r'[\[\[spam\]](/eggs)'),
]
@pytest.mark.parametrize(('target', 'expected'), markdown_link_tests)
def test_markdown_link(target, expected):
assert markdown_link(*target) == expected
def test_tag_stripping():
assert markdown(u"<button>text</button>") == u"<p>text</p>\n"
assert markdown(u"<button><button>text</button></button>") == u"<p>text</p>\n"
assert markdown(u"<!--[if IE]><script>alert(1)</script><![endif]-->") == u"\n"
markdown_excerpt_tests = [
(u'', u''),
(u'short', u'short'),
(u'just short enoughAAAAAAAAAAAAA', u'just short enoughAAAAAAAAAAAAA'),
(u'not short enoughAAAAAAAAAAAAAAA', u'not short enoughAAAAAAAAAAAAA…'),
(u'*leading* inline formatting', u'leading inline formatting'),
(u'middle *inline* formatting', u'middle inline formatting'),
(u'trailing inline *formatting*', u'trailing inline formatting'),
(u'*nested **inline** formatting*', u'nested inline formatting'),
(u' unnecessary whitespace\t', u'unnecessary whitespace'),
(u'multiple\nlines', u'multiple lines'),
(u'multiple \nlines', u'multiple lines'),
(u'multiple\n\nparagraphs', u'multiple paragraphs'),
(u'Üñíçôđe\N{COMBINING ACUTE ACCENT}', u'Üñíçôđe\N{COMBINING ACUTE ACCENT}'),
(u'single-codepoint graphemes😊😊😊😊', u'single-codepoint graphemes😊😊😊😊'),
(u'single-codepoint graphemes😊😊😊😊😊', u'single-codepoint graphemes😊😊😊…'),
(u'test\n - lists\n - of\n - items\n\ntest', u'test lists of items test'),
]
@pytest.mark.parametrize(('target', 'expected'), markdown_excerpt_tests)
def test_excerpt(target, expected):
assert markdown_excerpt(target, length=30) == expected
def test_excerpt_default_length():
assert markdown_excerpt(u'a' * 300) == u'a' * 300
assert markdown_excerpt(u'a' * 301) == u'a' * 299 + u'…'
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cli/work_cli.py | ayonymus/gits | 4 | 83377 |
class WorkCli:
def __init__(self, workbranch):
self.workbranch = workbranch
def add_subparser(self, subparsers):
work_parser = subparsers.add_parser('work', help="Keep track of a currently important branch")
work_parser.add_argument("current", nargs="?", type=str, default=None, help="Show current work branch")
work_parser.add_argument("-s", action="store_true", help="Set current work branch")
work_parser.add_argument("-c", action="store_true", help="Checkout current work branch")
work_parser.add_argument("-ch", type=int, help="Checkout work branch from history by id")
work_parser.add_argument("-H", "--history", action="store_true", help="Show work branch history")
work_parser.set_defaults(func=self.handle_work)
def handle_work(self, args):
if args.s:
self.set_work_branch()
elif args.c:
self.checkout_work_branch()
elif args.ch:
self.checkout_work_branch_history(args.ch)
elif args.history:
self.print_work_branch_history()
else:
self.print_current_work_branch()
def print_current_work_branch(self):
current = self.workbranch.get_work_branch()
print("Current work branch is", ("not set" if current is None else current))
def print_work_branch_history(self):
for i, branch in enumerate(self.workbranch.get_work_branch_history()):
print(i, branch)
def set_work_branch(self):
branch = self.workbranch.set_work_branch()
print("Current work branch is %s" % branch)
def checkout_work_branch(self):
branch = self.workbranch.checkout_work_branch()
print("Switched to branch '%s'" % branch)
def checkout_work_branch_history(self, index):
branch = self.workbranch.checkout_work_branch_from_history(index)
print("No such branch" if branch is None else "Switched to branch '%s'" % branch) | [
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model-server/ServeAll.py | tmobile/node-red-contrib-yolo-object-detection | 2 | 151936 | #!/usr/bin/env python3
import base64
from gevent.pywsgi import WSGIServer
import io
import os
import signal
import sys
import shutil
from threading import Thread
def get_parent_dir(n=1):
""" returns the n-th parent dicrectory of the current
working directory """
current_path = os.path.dirname(os.path.abspath(__file__))
for k in range(n):
current_path = os.path.dirname(current_path)
return current_path
src_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(src_path)
# Get model path. Default to home directory models/yolov3.
MODEL_PATH = os.getenv("MODEL_PATH",os.path.join(
os.getenv("HOME"), "models", "yolov3"))
# Lower logging level.
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from utils import load_extractor_model, load_features, parse_input, detect_object
from keras_yolo3.yolo import YOLO, detect_video
from PIL import Image, ImageFont, ImageDraw
# Set related model file paths based on base path convention.
anchors_path = os.path.join(MODEL_PATH, "anchors.txt")
classes_path = os.path.join(MODEL_PATH, "classes.txt")
model_weights = os.path.join(MODEL_PATH, "weights.h5")
gpu_num = 1
model_image_size = (416, 416)
score = .25
from flask import Flask, json, jsonify, request
app = Flask(__name__)
def init_yolo(model_weights, anchors_path, score, gpu_num, model_image_size):
yolo = YOLO(
**{
"model_path": model_weights,
"anchors_path": anchors_path,
"classes_path": classes_path,
"score": score,
"gpu_num": gpu_num,
"model_image_size": model_image_size,
}
)
print("MODELSERVER: Model initialized")
return yolo
@app.route('/',methods=['POST','GET'])
def detect():
global yolo
# Get input image.
# DBG print("is_json:", request.is_json)
if request.is_json:
content = request.get_json()
image = Image.open("frame.jpg")
else:
content = request.data
image = Image.open(io.BytesIO(content))
# DBG image = Image.open("test.jpg")
# Use local capture frame from video stream instead.
if image.mode != "RGB":
image = image.convert("RGB")
# Perform object detection.
detections, new_image = yolo.detect_image(image)
# DBG Save image with bounding boxes from detection.
# DBG new_image.save("result.jpg")
image_buffer = io.BytesIO()
new_image.save(image_buffer, format="JPEG")
base64_image = base64.b64encode(image_buffer.getvalue()).decode("utf-8")
# DBG print("detections", detections)
result = {
"image": base64_image,
"detections": [],
"things": []
}
# DBG print("yolo.class_names", yolo.class_names)
# detection = (left, top, right, bottom, class, score).
for detection in detections:
# DBG print("detection:", detection)
left = int(detection[0])
top = int(detection[1])
right = int(detection[2])
bottom = int(detection[3])
class_id = int(detection[4])
class_name = yolo.class_names[class_id]
score = float(detection[5])
final_detection = {
'bbox': [ left, top, right, bottom ],
'class': class_name,
'score': score
}
print("detection:", final_detection)
result["detections"].append(final_detection)
result["things"].append({"thing": class_name})
# DBG print("result:", result)
# return jsonify(result)
response = app.response_class(
response=json.dumps(result),
status=200,
mimetype='application/json'
)
return response
def sig_handler(signum, frame):
print("Serve caught signal:", signum)
print("Exiting...")
os._exit(0)
def run_video_thread(srcpath, camera_mode):
try:
if not camera_mode or camera_mode == "picamera":
print("Running ServeVideoPiCamera...")
import ServeVideoPiCamera
except:
print("Caught camera exception from ServeVideoPiCamera.")
finally:
if not camera_mode or camera_mode == "opencv":
print("Running ServeVideo...")
import ServeVideo
ServeVideo.main()
def run_video(camera_mode):
dirpath = os.path.dirname(os.path.realpath(__file__))
srcpath = dirpath + "/ServeVideo.py"
video_thread = Thread(target = run_video_thread, args = (srcpath, camera_mode))
video_thread.start()
if __name__ == "__main__":
signal.signal(signal.SIGTERM, sig_handler)
signal.signal(signal.SIGINT, sig_handler)
run_video(os.getenv("CAMERA_MODE", ""))
yolo = init_yolo(model_weights, anchors_path, score, gpu_num, model_image_size)
http_server = WSGIServer(('', 8888), app)
http_server.serve_forever()
| [
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29947,
29947,
511,
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13,
1678,
1732,
29918,
2974,
29889,
16349,
29918,
1079,
369,
580,
13,
2
] |
core/topology/constraint/any.py | xUndero/noc | 0 | 57976 | <reponame>xUndero/noc
# -*- coding: utf-8 -*-
# ----------------------------------------------------------------------
# AnyConstraint
# ----------------------------------------------------------------------
# Copyright (C) 2007-2019 The NOC Project
# See LICENSE for details
# ----------------------------------------------------------------------
# Python modules
from __future__ import absolute_import
# NOC modules
from .base import BaseConstraint
class AnyConstraint(BaseConstraint):
"""
Always true
"""
def __and__(self, other):
# type: (BaseConstraint) -> BaseConstraint
return other
def __or__(self, other):
# type: (BaseConstraint) -> BaseConstraint
return other
| [
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13,
4706,
736,
916,
13,
2
] |
tests/integration/agenda/test_models.py | rolandgeider/OpenSlides | 0 | 6459 | <reponame>rolandgeider/OpenSlides
from openslides.agenda.models import Item
from openslides.core.models import CustomSlide
from openslides.utils.test import TestCase
class TestItemManager(TestCase):
def test_get_root_and_children_db_queries(self):
"""
Test that get_root_and_children needs only one db query.
"""
for i in range(10):
CustomSlide.objects.create(title='item{}'.format(i))
with self.assertNumQueries(1):
Item.objects.get_root_and_children()
| [
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4632,
29918,
392,
29918,
11991,
580,
13,
2
] |
Beginner/WhileLoop.py | mihirkemkar/pythonexercises | 0 | 152106 |
num = 1
factors = list()
while num <= 100:
if (num % 10) == 0:
factors.append(num)
num = num + 1
print("Factors :",factors)
| [
1,
6756,
13,
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13,
2
] |
src/greplin/defer/semaphore_test.py | Cue/greplin-twisted-utils | 2 | 1605621 | # Copyright 2012 The greplin-twisted-utils Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for deferred semaphores."""
from greplin.defer import base, semaphore
from twisted.internet import defer
import unittest
import functools
class PrioritySemaphoreTest(unittest.TestCase):
"""Tests for deferred priority semaphore"""
def setUp(self):
"""Sets up the test."""
self.log = []
self.queue = semaphore.DeferredPrioritySemaphore(tokens=3)
def call(self, fn, *args):
"""Calls the function with the given name and args and logs various related events."""
desc = ' '.join([fn] + list(str(ai) for ai in args))
self.log.append(desc)
result = getattr(self.queue, fn)(*args)
if isinstance(result, defer.Deferred):
self.log.append('result: Deferred')
result.addCallback(lambda _: self.log.append('callback ' + desc))
else:
self.log.append('result: %s' % result)
def testDescription(self):
"""Tests the description of the Deferred objects from the semaphore."""
d = [self.queue.acquire(i * 2) for i in range(5)]
self.assertEqual(
'*DeferredPrioritySemaphore(@%x, #1/5, priority=0, waiting=2)' % id(d[0]),
base.describeDeferred(d[0]).partition(' ')[2]) # split off the active time
self.assertEqual(
'DeferredPrioritySemaphore(@%x, #5/5, priority=8, waiting=2)' % id(d[4]),
base.describeDeferred(d[4]).partition(' ')[2]) # split off the active time
def testBasics(self):
"""Test the basics of a deferred priority semaphore."""
acq, rel = functools.partial(self.call, 'acquire'), functools.partial(self.call, 'release')
acq(3)
acq(4)
acq(1)
acq(0)
acq(5)
acq(6)
acq(4)
rel()
rel()
rel()
acq(0)
acq(9)
acq(2)
rel()
rel()
rel()
rel()
rel()
rel()
rel()
self.assertEquals(["acquire 3",
"result: Deferred",
"callback acquire 3",
"acquire 4",
"result: Deferred",
"callback acquire 4",
"acquire 1",
"result: Deferred",
"callback acquire 1",
"acquire 0",
"result: Deferred",
"acquire 5",
"result: Deferred",
"acquire 6",
"result: Deferred",
"acquire 4",
"result: Deferred",
"release",
"callback acquire 0",
"result: None",
"release",
"callback acquire 4",
"result: None",
"release",
"callback acquire 5",
"result: None",
"acquire 0",
"result: Deferred",
"acquire 9",
"result: Deferred",
"acquire 2",
"result: Deferred",
"release",
"callback acquire 0",
"result: None",
"release",
"callback acquire 2",
"result: None",
"release",
"callback acquire 6",
"result: None",
"release",
"callback acquire 9",
"result: None",
"release",
"result: None",
"release",
"result: None",
"release",
"result: None"
], self.log)
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13,
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2
] |
src/philipstv/pairing.py | bcyran/philipstv | 0 | 1603563 | <reponame>bcyran/philipstv<filename>src/philipstv/pairing.py
import logging
from base64 import b64decode
from typing import Callable
from ._utils import create_signature
from .api import PhilipsTVAPI
from .exceptions import PhilipsTVPairingError
from .model import DeviceInfo, PairingAuthInfo, PairingGrantPayload, PairingRequestPayload
from .types import Credentials
LOGGER = logging.getLogger(__name__)
_SECRET = b64decode(
"<KEY>"
)
PinCallback = Callable[[], str]
class PhilipsTVPairer:
"""Encapsulates the pairing process and allows to perform it in a single call."""
def __init__(self, api: PhilipsTVAPI, device_info: DeviceInfo) -> None:
"""
Args:
api: :class:`PhilipsTVAPI` instance with which pairing will be performed.
device_info: Informations about the device which requests the pairing. This doesn't
really seem to have any effect, besides the fact that `id` given here serves as a
"username" and later will be the first value in the credentials tuple.
"""
self._api: PhilipsTVAPI = api
self.device_info = device_info
def pair(self, pin_callback: PinCallback) -> Credentials:
"""Perfom the pairing.
Hint:
If the pairing is successful, this leaves the underlying :class:`PhilipsTVAPI` instance
in authenticated state, so you don't have to set :attr:`PhilipsTVAPI.auth` with the
received credentials.
Args:
pin_callback: Callback function which should return the PIN displayed on the TV
screen as a string. It's impossible to know the PIN before requesting pairing
so this function should be used to prompt the user for the PIN.
Returns:
Authentication credentials tuple. This value can be used to authenticate with the TV
at any point in future.
"""
self._api.auth = None
pair_response = self._api.pair_request(self._get_request_payload())
is_success = pair_response.error_id == "SUCCESS"
if not (is_success and pair_response.auth_key and pair_response.timestamp):
raise PhilipsTVPairingError(pair_response)
pair_pin = pin_callback()
self._api.auth = (self.device_info.id, pair_response.auth_key)
grant_response = self._api.pair_grant(
self._get_grant_payload(pair_pin, pair_response.timestamp)
)
if grant_response.error_id != "SUCCESS":
self._api.auth = None
raise PhilipsTVPairingError(grant_response)
return (self.device_info.id, pair_response.auth_key)
def _get_request_payload(self) -> PairingRequestPayload:
return PairingRequestPayload(
scope=["read", "write", "control"],
device=self.device_info,
)
def _get_grant_payload(self, pin: str, timestamp: int) -> PairingGrantPayload:
signature = create_signature(_SECRET, f"{timestamp}{pin}".encode()).decode()
return PairingGrantPayload(
auth=PairingAuthInfo(pin=pin, auth_timestamp=timestamp, auth_signature=signature),
device=self.device_info,
)
| [
1,
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] |
Stack_and_Queue/S_and_Q_Review.py | misscindy/Interview | 0 | 190284 | # Stack & Queue Review
# 9.3 valid paren
# 9.4 Longest Valid Paren
# "(()))(()())"
def longest_valid(s):
left_p, max_len, end = [], 0, -1
for ndx, p in enumerate(s):
if p == "(":
left_p.append(ndx)
# see a ")"
else:
if not left_p:
end = ndx
else:
left_p.pop()
start = end if not left_p else left_p[-1]
max_len = max(max_len, ndx - start)
return max_len
# 9.5 simplify path
# 9.6 print bst
def load_src(name, fpath):
import os, imp
p = fpath if os.path.isabs(fpath) \
else os.path.join(os.path.dirname(__file__), fpath)
return imp.load_source(name, p)
util = load_src("util", "../Tree/util.py")
def print_bst(node):
tn = util.TreeNode()
# 9.7 search posting list
class RandomListNode:
def __init__(self, x):
self.val = x
self.next = None
self.random = None
self.order = -1
def __repr__(self):
rand = self.random.val
return "Node(%i): %i " % (rand, self.val)
def jump_first(head):
_visit(head, [0])
def _visit(node, cur_order):
if node and node.order == -1:
node.order = cur_order[0]
cur_order[0] += 1
_visit(node.random, cur_order)
_visit(node.next, cur_order)
def jump_first_i(head):
order, stack = 0, [head]
while stack:
nxt = stack.pop()
if nxt.order == -1:
nxt.order = order + 1
order += 1
if nxt.next:
stack.append(nxt.next)
if nxt.random:
stack.append(nxt.random)
# 9.8 sunset view stream
def out_put_sunsets():
bldgs = []
nxt = raw_input("nxt building height > ")
while nxt != "":
newbie = int(nxt)
while bldgs and newbie >= bldgs[-1]:
bldgs.pop()
bldgs.append(newbie)
nxt = raw_input("nxt building height > ")
for i in bldgs:
print i,
print
# 9.9
# sort a stack
def sort(stack):
if not stack:
return
top = stack.pop()
sort(stack)
insert(top, stack)
def insert(val, stack):
if not stack or stack[-1] >= val:
stack.append(val)
else:
top = stack.pop()
insert(val, stack)
stack.append(top)
if __name__ == '__main__':
a = [1, 2, 3, 2, 5, 2]
sort(a)
print a
print "9.8"
out_put_sunsets()
print "9.7"
head = RandomListNode(0)
first = RandomListNode(1)
sec = RandomListNode(2)
third = RandomListNode(3)
head.next, head.random = first, sec
first.next, first.random = sec, head
sec.next, sec.random = third, third
third.next, third.random = None, sec
# runner = head
# while runner:
# print runner,
# runner = runner.next
# print
jump_first_i(head)
#jump_first(head, 0)
runner = head
while runner:
print runner, "order : ", runner.order
runner = runner.next
print "Longest valid paren"
test_cases = ["(())", "()(", ")))()"]
for i in test_cases:
print longest_valid(i)
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29875,
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2
] |
papermerge/core/migrations/0023_auto_20200921_0456.py | jcollie/papermerge-core | 45 | 109231 | # Generated by Django 3.0.10 on 2020-09-21 04:56
from django.db import migrations, models
import taggit.managers
class Migration(migrations.Migration):
dependencies = [
('core', '0022_auto_20200909_0826'),
]
operations = [
migrations.AlterModelOptions(
name='tag',
options={'ordering': ['name'], 'verbose_name': 'Tag', 'verbose_name_plural': 'Tags'},
),
migrations.RemoveField(
model_name='automate',
name='plugin_name',
),
migrations.AddField(
model_name='automate',
name='tags',
field=taggit.managers.TaggableManager(help_text='A comma-separated list of tags.', through='core.ColoredTag', to='core.Tag', verbose_name='Tags'),
),
migrations.AlterField(
model_name='tag',
name='pinned',
field=models.BooleanField(default=False, help_text='Pinned tag will be displayed under Documents menu. It serves as shortcut to quickly filter folders/documents associated with this tag'),
),
]
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] |
app/omwebapp/tests/view/__init__.py | JojiKoike/OMWebAppEngine | 0 | 109341 | """
View(WebAPI)のテストコード群パッケージです。
"""
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] |
Exercicios/ex013.py | GabrielMazzuchello/Curso-Em-Video | 2 | 125190 | <reponame>GabrielMazzuchello/Curso-Em-Video
s = float(input('qual erá o salario do funcionario? R$'))
aul = s * 15 / 100
print('o salario do funcionari que andes custava R${:.2f} agora com 15% de aumento custará R${:.2f}'.format(s, (s+aul)))
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2
] |
Code/ConvNetAbel.py | abel-gr/AbelNN | 1 | 17902 | <reponame>abel-gr/AbelNN
# Copyright <NAME>. All Rights Reserved.
# https://github.com/abel-gr/AbelNN
import numpy as np
import copy as copy
import random
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from pylab import text
import math
class ConvNetAbel:
version = 1.2
def __init__(self, hidden = [1], nEpochs = 1, learningRate=0.1, manualWeights=[],
debugLevel=1, rangeRandomWeight=None, showLogs=False, softmax=False,
activationFunction='leakyrelu', verbose = False, use='classification',
batch_size=1, batch_gradient='average', batch_mult=1, dropout=0, pre_norm=False,
shuffle=True, iterationDrop=0, convFilters = [32, 64, 128], convStride=2,
convFilterSizes=3, learningRateConv=0.001, convEpochs=10, kernel_initializer='he_normal'):
self.hiddenL = copy.deepcopy(hidden)
self.hiddenL2 = copy.deepcopy(hidden)
self.learningRate = learningRate
self.numEpochs = nEpochs
self.costs = [] # Costs list to check performance
self.debugWeights = []
self.meanCostByEpoch = []
self.hiddenWeights = []
self.manualWeights = manualWeights
self.debugMode = debugLevel
self.rangeRandomWeight = rangeRandomWeight
self.showLogs = showLogs
self.softmax = softmax
self.n_layer0 = -1
self.activationFunction = activationFunction
self.verbose = verbose
self.use = use
self.batch_size = batch_size
self.batch_gradient = batch_gradient
self.batch_mult = batch_mult
self.dropout = dropout
self.pre_norm = pre_norm
self.shuffle = shuffle
self.iterationDrop = iterationDrop
self.XavierInitialization = '1'
self.lastLayerNeurons = -1
# ConvNet:
self.convFilters = convFilters
self.filtersValues = [None] * len(convFilters)
self.convStride = convStride
self.convFilterSizes = convFilterSizes
self.learningRateConv = learningRateConv
self.convEpochs = convEpochs
self.kernel_initializer = kernel_initializer
# Conv2 with only one kernel
def conv2(self, x, kernel, stride=1):
output = [] #np.zeros((kernel.shape), dtype=np.float32)
kernel_l = kernel.shape[0]
kernel_size = kernel.shape[0] * kernel.shape[1]
c = int(kernel_l / 2)
for i in range(c, x.shape[0] - c, stride):
o = []
for j in range(c, x.shape[1] - c, stride):
i0 = i - c
j0 = j - c
i1 = i + c + 1
j1 = j + c + 1
o.append(np.sum(x[i0:i1, j0:j1] * kernel))
output.append(o)
output = np.asarray(output)
return output
# Convolution with multi-filters
def conv_filters(self, x, filters, stride=1, relu=False, mode='same'):
lex = len(x.shape)
lef = len(filters.shape)
if lex > lef:
print('conv_filters: The input array cannot have more dimensions than the filter array.')
return 0
output = []
kernel_l = filters.shape[0]
kernel_size = filters.shape[0] * filters.shape[1]
if lef == 2:
num_filters = 1
else:
num_filters = filters.shape[-1]
c = int(kernel_l / 2)
dim3 = False
evenShapeKernel = (kernel_l % 2 == 0)
if lex == 2:
dim2 = True
p0 = x.shape[0]
p1 = x.shape[1]
else:
# x parameter was the output of this method previously called
if lex == lef:
num_new_filters = int(num_filters / x.shape[-1])
if (num_new_filters % 2 != 0) and (num_filters % 2 == 0):
num_new_filters = num_new_filters - 1
if (num_new_filters == 0):
num_new_filters = 1
else: # It is the first convolutional layer of a color image
num_new_filters = num_filters
dim3 = True
dim2 = False
p0 = x.shape[0]
p1 = x.shape[1]
if mode == 'full':
fs0 = int(filters.shape[0] / 2)
fs1 = int(filters.shape[1] / 2)
max0 = p0 + fs0
max1 = p1 + fs1
ini0 = -1 * fs0
ini1 = -1 * fs1
elif mode == 'same':
max0 = p0
max1 = p1
ini0 = 0
ini1 = 0
elif mode == 'valid':
fs0 = int(filters.shape[0] / 2)
fs1 = int(filters.shape[1] / 2)
max0 = p0 - fs0
max1 = p1 - fs1
ini0 = fs0
ini1 = fs1
else:
print('Mode must be same, valid or full')
return 0
if evenShapeKernel and mode == 'valid':
max0 = max0 + 1
max1 = max1 + 1
for i in range(ini0, max0, stride):
o = []
for j in range(ini1, max1, stride):
i0 = i - c
j0 = j - c
i1 = i + c + 1
j1 = j + c + 1
if evenShapeKernel:
i0 = i0 + 1
j0 = j0 + 1
zero_padding_top = 0
zero_padding_bottom = 0
zero_padding_left = 0
zero_padding_right = 0
if i0 < 0:
zero_padding_top = abs(i0)
i0 = 0
if j0 < 0:
zero_padding_left = abs(j0)
j0 = 0
if i1 > p0:
zero_padding_bottom = i1 - p0
i1 = p0
if j1 > p1:
zero_padding_right = j1 - p1
j1 = p1
if dim2:
m = x[i0:i1, j0:j1]
#print('mshape:', m.shape, kernel_size, zero_padding_top, zero_padding_left)
# Zero padding:
m = np.pad(m, ((zero_padding_top,zero_padding_bottom),(zero_padding_left,zero_padding_right)), 'constant')
if lef != 2:
m = np.expand_dims(m, axis=-1)
m = np.repeat(m, num_filters, axis=-1)
else:
xi = x[i0:i1, j0:j1, :]
# Zero padding:
xi = np.pad(xi, ((zero_padding_top,zero_padding_bottom),(zero_padding_left,zero_padding_right),(0,0)), 'constant')
if dim3:
xi = np.expand_dims(xi, axis=-1)
m = np.repeat(xi, num_new_filters, axis=-1)
#print('M,F\n', m[:,:,0], filters[:,:,0])
#print(m.shape, filters.shape)
m = m * filters
#print('m*f\n', m[:,:,0])
m = np.sum(m, axis=0)
m = np.sum(m, axis=0)
if dim3:
m = np.sum(m, axis=0)
o.append(m)
output.append(o)
output = np.asarray(output)
if relu:
output[output < 0] = 0
return output
def kernelInitializer(self, i, ksize, inSize, outSize):
if 'xavier' in self.kernel_initializer:
if self.kernel_initializer == 'xavier_normal':
if len(ksize) == 4:
self.filtersValues[i] = np.random.randn(ksize[0],ksize[1],ksize[2],ksize[3]) * math.sqrt(2.0 / (inSize + outSize))
else:
self.filtersValues[i] = np.random.randn(ksize[0],ksize[1],ksize[2]) * math.sqrt(2.0 / (inSize + outSize))
elif self.kernel_initializer == 'xavier_uniform':
highVal = math.sqrt(6.0 / (inSize + outSize))
lowVal = -1 * highVal
self.filtersValues[i] = np.random.uniform(low=lowVal, high=highVal, size=ksize)
else:
if self.kernel_initializer == 'he_normal':
if len(ksize) == 4:
self.filtersValues[i] = np.random.randn(ksize[0],ksize[1],ksize[2],ksize[3]) * math.sqrt(2.0 / inSize)
else:
self.filtersValues[i] = np.random.randn(ksize[0],ksize[1],ksize[2]) * math.sqrt(2.0 / inSize)
elif self.kernel_initializer == 'he_uniform':
highVal = math.sqrt(6.0 / inSize)
lowVal = -1 * highVal
self.filtersValues[i] = np.random.uniform(low=lowVal, high=highVal, size=ksize)
def convLayersFeedForward(self, im):
self.convInputs = []
len_m = len(im.shape)
#print('len_m:', len_m)
for i, cl in enumerate(self.convFilters):
self.convInputs.append(im)
if (self.filtersValues[i] is None):
if (type(self.convFilterSizes) == list):
ks = self.convFilterSizes[i]
else:
ks = self.convFilterSizes
inSize = np.prod(im.shape)
if 'xavier' in self.kernel_initializer:
if self.batch_size == 1:
imshape = np.asarray([im.shape[0], im.shape[1]])
else:
imshape = np.asarray([im.shape[1], im.shape[2]])
extraShape = int((ks % 2) == 0)
ks2 = int(ks / 2) * 2
outSize = np.prod((imshape - ks2 + extraShape)) * cl
else:
outSize = 0
if i == 0 and len_m == 3:
if self.batch_size == 1:
self.kernelInitializer(i, (ks,ks,im.shape[2],cl), inSize, outSize)
else:
self.kernelInitializer(i, (ks,ks,cl), inSize, outSize)
else:
self.kernelInitializer(i, (ks,ks,cl), inSize, outSize)
k_filters = self.filtersValues[i]
if (type(self.convStride) == list):
stride_par = self.convStride[i]
else:
stride_par = self.convStride
#print('Convolutional layer', i, '\n')
#print('Layer input shape:', im.shape)
#print('Layer filters array shape:', k_filters.shape)
# Start of convolutions
#im = self.conv_filters(im, k_filters, relu=True, stride=stride_par, mode='valid')
filtersValues_shape01 = np.asarray([k_filters.shape[0], k_filters.shape[1]])
filtersValues_shape_d2 = (filtersValues_shape01 / 2).astype(int)
extraShape = (filtersValues_shape01 % 2) == 0
eS0 = extraShape[0].astype(int)
eS1 = extraShape[1].astype(int)
posYf = eS0
posXf = eS1
filter_shape0 = k_filters.shape[0]
filter_shape1 = k_filters.shape[1]
if (len(k_filters.shape) >= 3):
num_filters = k_filters.shape[-1]
else:
num_filters = 1
if self.batch_size == 1:
xshape = np.asarray([im.shape[0], im.shape[1]])
else:
xshape = np.asarray([im.shape[1], im.shape[2]])
output_shape = xshape - filtersValues_shape_d2*2 + eS0
if ((len(im.shape) < len(k_filters.shape)) or (len(im.shape) == 2 and num_filters == 1)):
Xr = np.expand_dims(im, axis=-1)
Xr = np.repeat(Xr, num_filters, axis=-1)
else:
if (len(im.shape) == len(k_filters.shape)):
if self.batch_size == 1:
new_filters = int(im.shape[-1] / num_filters)
Xr = np.repeat(im, new_filters, axis=-1)
else:
Xr = np.expand_dims(im, axis=-1)
Xr = np.repeat(Xr, num_filters, axis=-1)
else:
Xr = im
if (len(Xr.shape) == 2):
npad = ((0,eS0), (0,eS1))
out_s = [output_shape[0], output_shape[1], 1]
elif (len(Xr.shape) == 3):
npad = ((0,eS0), (0,eS1), (0,0))
out_s = [output_shape[0], output_shape[1], num_filters]
elif (len(Xr.shape) == 4):
if self.batch_size == 1:
npad = ((0,eS0), (0,eS1), (0,0), (0,0))
out_s = [output_shape[0], output_shape[1], im.shape[2], num_filters]
else:
npad = ((0,0), (0,eS0), (0,eS1), (0,0))
out_s = [im.shape[0], output_shape[0], output_shape[1], num_filters]
X_pad = np.pad(Xr, npad, 'constant')
out_s[0 if self.batch_size == 1 else 1] = int(np.ceil(out_s[0 if self.batch_size == 1 else 1] / stride_par))
out_s[1 if self.batch_size == 1 else 2] = int(np.ceil(out_s[1 if self.batch_size == 1 else 2] / stride_par))
conv_output = np.zeros(out_s)
if self.batch_size != 1:
k_filters = np.expand_dims(k_filters, axis=0)
k_filters = np.repeat(k_filters, im.shape[0], axis=0)
#print(Xr.shape, X_pad.shape, k_filters.shape, conv_output.shape, output_shape)
for posY in range(0, filter_shape0):
for posX in range(0, filter_shape1):
# valid convolution
if self.batch_size == 1:
conv_output += X_pad[posYf:posYf+output_shape[0]:stride_par, posXf:posXf+output_shape[1]:stride_par] * k_filters[posY, posX]
else:
conv_output += X_pad[:, posYf:posYf+output_shape[0]:stride_par, posXf:posXf+output_shape[1]:stride_par] * k_filters[:, posY, posX].reshape(k_filters.shape[0],1,1,k_filters.shape[3])
posXf = posXf + 1
posYf = posYf + 1
posXf = eS1
# End of convolutions
if self.pre_norm:
ax_f = tuple(range(0,len(conv_output.shape)))
if self.batch_size == 1:
ax_f = ax_f[0:-1]
conv_output = (conv_output - np.mean(conv_output, axis=ax_f)) / (np.std(conv_output, axis=ax_f) + 1e-7)
else:
ax_f = ax_f[1:-1]
conv_output = (conv_output - np.mean(conv_output, axis=ax_f).reshape(conv_output.shape[0],1,1,conv_output.shape[3])) / (np.std(conv_output, axis=ax_f).reshape(conv_output.shape[0],1,1,conv_output.shape[3]) + 1e-7)
#conv_output = (conv_output - conv_output.mean()) / (conv_output.std() + 1e-7)
im = self.ActivationFunction(conv_output, 'relu')
#print('Layer output shape:', im.shape, '\n---------------------\n')
return im
def convLayersBackpropagation(self, last_layer_output, prev_cost):
i = len(self.filtersValues) - 1
last_shape = list(last_layer_output.shape)
if self.batch_size != 1:
batch_el = last_shape[0]
last_shape = last_shape[1:] + [batch_el]
error_by_x = np.reshape(prev_cost, last_shape)
"""
if self.batch_size == 1:
num_filters = last_layer_output.shape[2]
else:
num_filters = last_layer_output.shape[3]
"""
self.log('Start of convLayersBackpropagation:', '\n')
#self.log('prev_cost:', prev_cost.shape, prev_cost, '\n')
#self.log('last_layer_output:', last_layer_output.shape, last_layer_output, '\n')
#self.log('error_by_x:', error_by_x.shape, error_by_x, '\n')
#if self.batch_size != 1:
#error_by_x = np.mean(error_by_x, axis=0)
for k_filters in self.filtersValues[::-1]:
X = self.convInputs[i]
if self.batch_size != 1:
X_batchshape = list(X.shape)
X_batch_elements = X_batchshape[0]
X_batchshape = X_batchshape[1:] + [X_batch_elements]
X = np.reshape(X, X_batchshape)
#X = np.mean(X, axis=0)
# to dilate gradient if needed because of stride
if (type(self.convStride) == list):
stride_par = self.convStride[i]
else:
stride_par = self.convStride
if stride_par != 1:
#erShape = error_by_x.shape[0] * stride_par
erShape = (X.shape[0])
if self.batch_size == 1:
error_by_output = np.zeros((erShape, erShape, self.convFilters[i]), dtype=float)
else:
error_by_output = np.zeros((erShape, erShape, self.convFilters[i], batch_el), dtype=float)
#print(error_by_output.shape, error_by_x.shape)
posI = 0
posJ = 0
erx1 = (error_by_x.shape[0])
erx2 = (error_by_x.shape[1])
# Zero-interweave:
for pe_i in range(0, erx1):
for pe_j in range(0, erx2):
error_by_output[posI, posJ] = error_by_x[pe_i, pe_j]
if (posJ + 2) < erShape:
posJ = posJ + 2
else:
posJ = posJ + 1
if (posI + 2) < erShape:
posI = posI + 2
else:
posI = posI + 1
posJ = 0
else:
# dE/dO
error_by_output = error_by_x
f_rotated = np.flip(self.filtersValues[i], 0)
f_rotated = np.flip(f_rotated, 1)
# dE/dF
#error_by_filter = self.conv_filters(X, error_by_output, relu=False, stride=1, mode='valid')
# dE/dX
#error_by_x = self.conv_filters(f_rotated, error_by_output, relu=False, stride=1, mode='full')
# Start of convolutions
err_output_shape01 = np.asarray([error_by_output.shape[0], error_by_output.shape[1]])
err_out_shape_d2 = (err_output_shape01 / 2).astype(int)
xshape = np.asarray([X.shape[0], X.shape[1]])
fshape = np.asarray([f_rotated.shape[0], f_rotated.shape[1]])
extraShape = (err_output_shape01 % 2) == 0
eS0 = extraShape[0].astype(int)
eS1 = extraShape[1].astype(int)
err_filt_shape = xshape - err_out_shape_d2*2 + eS0
err_x_shape = fshape + err_out_shape_d2*2 + eS0
num_filters = self.filtersValues[i].shape[-1]
#print(error_by_output.shape, xshape, err_output_shape01, err_out_shape_d2*2, eS0, err_filt_shape)
if self.batch_size == 1:
error_by_filter = np.zeros((err_filt_shape[0], err_filt_shape[1], num_filters))
error_by_x = np.zeros((err_x_shape[0], err_x_shape[1], num_filters))
else:
error_by_filter = np.zeros((err_filt_shape[0], err_filt_shape[1], num_filters, X_batch_elements))
error_by_x = np.zeros((err_x_shape[0], err_x_shape[1], num_filters, X_batch_elements))
err_out_shape0 = error_by_output.shape[0]
err_out_shape1 = error_by_output.shape[1]
fil_shape0 = error_by_filter.shape[0]
fil_shape1 = error_by_filter.shape[1]
ex_shape0 = self.filtersValues[i].shape[0]
ex_shape1 = self.filtersValues[i].shape[1]
posYf = eS0
posXf = eS1
if (len(X.shape) < 3):
Xr = np.expand_dims(X, axis=-1)
Xr = np.repeat(Xr, num_filters, axis=-1)
else:
Xr = X
if (len(Xr.shape) == 3):
X_pad = np.pad(Xr, ((0,eS0), (0,eS1), (0,0)), 'constant')
elif (len(Xr.shape) == 4):
X_pad = np.pad(Xr, ((0,eS0), (0,eS1), (0,0), (0,0)), 'constant')
else: # color image with batch
X_pad = np.pad(Xr, ((0,0), (0,eS0), (0,eS1), (0,0), (0,0)), 'constant')
layer_filters = self.filtersValues[i]
if self.batch_size != 1:
layer_filters = np.expand_dims(layer_filters, axis=-1)
layer_filters = np.repeat(layer_filters, X_batch_elements, axis=-1)
#print(X_pad.shape, error_by_output.shape, error_by_filter.shape, self.filtersValues[i].shape, error_by_output.shape, error_by_x.shape)
for posY in range(0, err_out_shape0):
for posX in range(0, err_out_shape1):
# valid convolution (dE/dF)
error_by_filter += X_pad[posYf:posYf+fil_shape0, posXf:posXf+fil_shape1] * error_by_output[posY, posX]
# full convolution (dE/dX)
error_by_x[posYf:posYf+ex_shape0, posXf:posXf+ex_shape1] += layer_filters * error_by_output[posY, posX]
posXf = posXf + 1
posYf = posYf + 1
posXf = eS1
error_by_x = np.flip(error_by_x, 0)
error_by_x = np.flip(error_by_x, 1)
# End of convolutions
#print(X.shape, X_pad.shape, self.filtersValues[i].shape, error_by_filter.shape, error_by_x.shape, error_by_output.shape)
#self.log('error_by_filter:', error_by_filter[:,:,0], '\n\n')
#self.log('prev filtersValues[i]:', self.filtersValues[i][:,:,0], '\n\n')
#self.log('error_by_x:', error_by_x[:,:,0], '\n\n')
if self.batch_size != 1:
error_by_filter = np.mean(error_by_filter, axis=-1)
#if self.pre_norm:
#ax_f = tuple(range(0,len(error_by_filter[i].shape)))[0:-1]
#error_by_filter = (error_by_filter - np.mean(error_by_filter, axis=ax_f)) / (np.std(error_by_filter, axis=ax_f) + 1e-7)
#error_by_filter = (error_by_filter - error_by_filter.mean()) / (error_by_filter.std() + 1e-7)
# Filters update
self.filtersValues[i] = self.filtersValues[i] - self.learningRateConv * error_by_filter
if self.pre_norm:
ax_f = tuple(range(0,len(self.filtersValues[i].shape)))[0:-1]
self.filtersValues[i] = (self.filtersValues[i] - np.mean(self.filtersValues[i], axis=ax_f)) / (np.std(self.filtersValues[i], axis=ax_f) + 1e-7)
#self.log('filtersValues[i] updated:', self.filtersValues[i][:,:,0], '\n\n')
#self.log('\n-----------------------\n')
i = i - 1
self.log('End of convLayersBackpropagation')
def draw(self, showWeights=False, textSize=9, customRadius=0):
plt.figure(figsize=(10,8))
fig = plt.gcf()
ax = fig.gca()
ax.set_xlim(xmin=0, xmax=1)
ax.set_ylim(ymin=0, ymax=1)
xmin, xmax, ymin, ymax = ax.axis()
xdim = xmax - xmin
ydim = ymax - ymin
space_per_layer = xdim / (len(self.hiddenL) + 1)
x0 = xmin
x1 = xmin + space_per_layer
medio_intervalo = space_per_layer / 2
if customRadius <= 0:
radio = 1 / ((sum(self.hiddenL) + self.n_layer0) * 5)
else:
radio = customRadius
lista_lineas_xy = []
lasth = self.n_layer0
for capa,h in enumerate([self.n_layer0] + self.hiddenL):
space_per_neuron = ydim / h
y0 = ymin
y1 = ymin + space_per_neuron
medio_intervalo_n = space_per_neuron / 2
lista_lineas_xy_pre = []
ne = (lasth * h) - 1
neY = h - 1
for j in range(0, h):
ax.add_patch(plt.Circle(((medio_intervalo + x0), (medio_intervalo_n + y0)), radio, color='r'))
neX = lasth - 1
for xy in lista_lineas_xy:
if True: #j == 2:
plt.plot([xy[0],(medio_intervalo + x0)],[xy[1], (medio_intervalo_n + y0)])
#print(capa, ne, self.hiddenWeights[capa-1][ne])
my = ((medio_intervalo_n + y0) - xy[1])
mx = ((medio_intervalo + x0) - xy[0])
pendiente = my / mx
ordenada_origen = xy[1] - pendiente * xy[0]
margen_ord = 0.015
if pendiente < 0:
margen_ord = -0.045 # para compensar la rotacion del texto
ordenada_origen = ordenada_origen + margen_ord # para evitar que el texto salga encima de la linea no sobre ella
# aleatorio entre las x del segmento de la recta (menos un margen para que no salga demasiado cerca de la neurona)
mx2 = random.uniform(xy[0] + 0.04, (medio_intervalo + x0) - 0.04)
my2 = pendiente*mx2 + ordenada_origen
alfa = math.degrees(math.atan(pendiente))
if showWeights:
#print(h, capa-1, neX, neY)
text(mx2, my2, round(self.hiddenWeights[capa-1][neX][neY],3), rotation = alfa, fontsize = textSize)
ne = ne - 1
neX = neX - 1
lista_lineas_xy_pre.append([(medio_intervalo + x0), (medio_intervalo_n + y0)])
neY = neY - 1
y0 = y0 + space_per_neuron
y1 = y1 + space_per_neuron
lasth = h
#print('\n')
x0 = x0 + space_per_layer
x1 = x1 + space_per_layer
#print('-------------\n')
lista_lineas_xy = lista_lineas_xy_pre
plt.show()
def importModel(self, path='', filename='ConvNetAbel_model'):
self.hiddenWeights = np.load(path + filename + '_weights.npy', allow_pickle=True)
mConfig = np.load(path + filename + '_config.npy', allow_pickle=True)
self.n_layer0 = int(mConfig[0])
self.showLogs = bool(mConfig[1])
self.lastLayerNeurons = int(mConfig[2])
self.numEpochs = int(mConfig[3])
self.learningRate = float(mConfig[4])
self.debugMode = int(mConfig[5])
self.softmax = bool(mConfig[6])
self.activationFunction = str(mConfig[7])
self.verbose = bool(mConfig[8])
self.use = str(mConfig[9])
self.batch_size = int(mConfig[10])
self.batch_gradient = str(mConfig[11])
self.batch_mult = int(mConfig[12])
self.dropout = float(mConfig[13])
self.pre_norm = bool(mConfig[14])
self.shuffle = bool(mConfig[15])
self.iterationDrop = float(mConfig[16])
self.version_importedModel = mConfig[17]
self.hiddenL2 = mConfig[18]
self.hiddenL = mConfig[19]
convConfig = np.load(path + filename + '_convConfig.npy', allow_pickle=True)
self.convFilters = convConfig[0]
self.convStride = convConfig[1]
self.convFilterSizes = convConfig[2]
self.kernel_initializer = str(convConfig[3])
self.convEpochs = int(convConfig[4])
self.learningRateConv = float(convConfig[5])
self.filtersValues = np.load(path + filename + '_filtersValues.npy', allow_pickle=True)
if self.debugMode > 0:
self.meanCostByEpoch = np.load(path + filename + '_meanCostByEpoch.npy', allow_pickle=True).tolist()
if self.debugMode > 1:
self.debugWeights = np.load(path + filename + '_debugWeights.npy', allow_pickle=True).tolist()
def exportModel(self, path='', filename='ConvNetAbel_model'):
np.save(path + filename + '_weights.npy', np.asarray(self.hiddenWeights, dtype=object))
mConfig = []
mConfig.append(self.n_layer0)
mConfig.append(self.showLogs)
mConfig.append(self.lastLayerNeurons)
mConfig.append(self.numEpochs)
mConfig.append(self.learningRate)
mConfig.append(self.debugMode)
mConfig.append(self.softmax)
mConfig.append(self.activationFunction)
mConfig.append(self.verbose)
mConfig.append(self.use)
mConfig.append(self.batch_size)
mConfig.append(self.batch_gradient)
mConfig.append(self.batch_mult)
mConfig.append(self.dropout)
mConfig.append(self.pre_norm)
mConfig.append(self.shuffle)
mConfig.append(self.iterationDrop)
mConfig.append(self.version)
mConfig.append(self.hiddenL2)
mConfig.append(self.hiddenL)
mConfig = np.asarray(mConfig, dtype=object)
np.save(path + filename + '_config.npy', mConfig)
convConfig = []
convConfig.append(self.convFilters)
convConfig.append(self.convStride)
convConfig.append(self.convFilterSizes)
convConfig.append(self.kernel_initializer)
convConfig.append(self.convEpochs)
convConfig.append(self.learningRateConv)
convConfig = np.asarray(convConfig, dtype=object)
np.save(path + filename + '_convConfig.npy', convConfig)
np.save(path + filename + '_filtersValues.npy', np.asarray(self.filtersValues, dtype=np.float32))
if self.debugMode > 0:
np.save(path + filename + '_meanCostByEpoch.npy', self.meanCostByEpoch)
if self.debugMode > 1:
np.save(path + filename + '_debugWeights.npy', np.asarray(self.debugWeights, dtype=object))
def log(self, *m):
if self.showLogs:
print(*m)
def printVerbose(self, *m):
if self.verbose:
print(*m)
def initializeWeight(self, n, i, lastN):
if len(self.manualWeights) == 0:
numW = n * lastN
if self.rangeRandomWeight is None:
if self.activationFunction == 'sigmoid':
if self.XavierInitialization == 'normalized': # Normalized Xavier initialization
highVal = math.sqrt(6.0) / math.sqrt(lastN + n)
lowVal = -1 * highVal
mnar = np.random.uniform(low=lowVal, high=highVal, size=(numW,1))
else: # Xavier initialization
mnar = np.random.randn(numW, 1) * math.sqrt(1.0 / lastN)
else:
mnar = np.random.randn(numW, 1) * math.sqrt(2.0 / lastN) # He initialization
else:
highVal = self.rangeRandomWeight[1]
lowVal = self.rangeRandomWeight[0]
mnar = np.random.uniform(low=lowVal, high=highVal, size=(numW,1))
else:
mnar = np.asarray(self.manualWeights[i])
#mnar = mnar.reshape(mnar.shape[0], 1)
#ns = int(mnar.shape[0] / lastN)
#print('ns: ', ns)
mnar = mnar.reshape(lastN, n, order='F')
return mnar
def ActivationFunction(self, x, activ_type='sigmoid'):
if activ_type=='sigmoid':
return 1.0/(1 + np.exp(-1*x))
elif activ_type=='relu':
return np.where(x > 0, x, 0)
elif activ_type=='softplus':
return np.log(1 + np.exp(x))
elif activ_type=='leakyrelu':
return np.where(x > 0, x, 0.01 * x)
elif activ_type=='identity':
return np.copy(x)
else:
x[x>0.5] = 1
x[x<=0.5] = 0
return x
def functionDerivative(self, x, activ_type='sigmoid'):
if activ_type=='sigmoid':
return self.ActivationFunction(x,activ_type) * (1-self.ActivationFunction(x,activ_type))
elif activ_type=='relu':
return np.where(x >= 0, 1, 0)
elif activ_type=='softplus':
return 1.0/(1 + np.exp(-1*x))
elif activ_type=='leakyrelu':
return np.where(x >= 0, 1, 0.01)
elif activ_type=='identity':
return 1
else:
return 1
def cost(self, y_true, y_pred):
c = y_true - y_pred
return c
def softmaxF(self, x):
if np.max(np.abs(x)) < 500: # prevent overflow
expX = np.exp(x)
return expX / np.sum(expX, axis=-1).reshape(-1, 1)
else:
return x / np.maximum(1, np.sum(x, axis=-1).reshape(-1, 1))
def pre_norm_forward_FC(self, v_layer):
if self.batch_size == 1 or len(v_layer.shape) == 1:
v_layer_norm = (v_layer - v_layer.mean()) / (v_layer.std() + 1e-7)
else:
v_layer_norm = ((v_layer.T - np.mean(v_layer, axis=1)) / (np.std(v_layer, axis=1) + 1e-7)).T
return v_layer_norm
def fit(self, x, y):
n_layer0 = -1
self.hiddenL = copy.deepcopy(self.hiddenL2)
hiddenW = [None] * (len(self.hiddenL) + 1)
self.lastLayerNeurons = y.shape[1]
self.hiddenL.append(y.shape[1])
self.convOutputs = []
self.printVerbose('Training started with', x.shape[0], 'samples')
if self.batch_size == 1:
numIterations = x.shape[0]
else:
numIterations = math.ceil(x.shape[0] / self.batch_size)
numIterations = int(numIterations * (1 - self.iterationDrop))
for epochs in range(0, self.numEpochs):
meanCostByEpochE = 0
batch_pos = 0
if epochs < self.convEpochs:
xy_ind = np.arange(x.shape[0])
else:
xy_ind = np.arange(len(self.convOutputs))
if self.shuffle:
np.random.shuffle(xy_ind)
for x_pos in range(0, numIterations):
if epochs < self.convEpochs:
if self.batch_size == 1:
c_positions = xy_ind[x_pos]
else:
if (batch_pos + self.batch_size) < xy_ind.shape[0]:
c_positions = xy_ind[batch_pos:batch_pos+self.batch_size]
else:
c_positions = xy_ind[batch_pos:]
x_val = x[c_positions]
x_val_batch_s = x_val.shape[0]
last_layer_output = self.convLayersFeedForward(x_val)
x_val = last_layer_output.flatten()
if self.batch_size != 1:
x_val = x_val.reshape(x_val_batch_s, int(x_val.shape[0] / x_val_batch_s))
if epochs == (self.convEpochs - 1):
self.convOutputs.append([x_val, c_positions])
else:
x_val, c_positions = self.convOutputs[xy_ind[x_pos]]
#self.log('x_val:', x_val.shape, x_val)
#print(x_val.shape)
if n_layer0 == -1:
if self.batch_size == 1:
n_layer0 = x_val.shape[0]
else:
n_layer0 = x_val.shape[1]
self.n_layer0 = n_layer0
v_layer = x_val
lastN = n_layer0
layerValues = []
preActivateValues = []
f_vlayer = self.ActivationFunction(v_layer, 'identity')
layerValues.append(f_vlayer)
preActivateValues.append(v_layer)
f_vlayer = v_layer
dropout_values = []
for i, hiddenLayer in enumerate(self.hiddenL):
entries = hiddenLayer * lastN
if hiddenW[i] is None:
hiddenW[i] = self.initializeWeight(hiddenLayer, i, lastN) # Initialize weights
valuesForPerc = int(entries / hiddenLayer)
firstPos = 0
lastPos = valuesForPerc
self.log('x_j: ', f_vlayer)
self.log('w_j: ', hiddenW[i])
v_layer = f_vlayer.dot(hiddenW[i])
if self.pre_norm and (i < (len(self.hiddenL) - 1)):
v_layer = self.pre_norm_forward_FC(v_layer)
if self.dropout != 0 and (i < (len(self.hiddenL) - 1)):
dropout_v = np.random.binomial(1, 1-self.dropout, size=hiddenLayer) / (1-self.dropout)
v_layer = v_layer * dropout_v
dropout_values.append(dropout_v)
self.log('net_j:', v_layer, '\n')
if (i == (len(self.hiddenL) - 1)):
if(self.softmax):
f_vlayer = self.softmaxF(v_layer).reshape(-1)
else:
if self.use == 'classification':
f_vlayer = self.ActivationFunction(v_layer, 'sigmoid') # use sigmoid on last layer if classification
else:
f_vlayer = self.ActivationFunction(v_layer, 'identity') # use identity on last layer if regression
else:
f_vlayer = self.ActivationFunction(v_layer, self.activationFunction)#.reshape(-1)
layerValues.append(f_vlayer)
preActivateValues.append(v_layer)
v_layer = f_vlayer
self.log('f(net_j):', f_vlayer, '\n')
lastN = hiddenLayer
coste_anterior = None
i = len(self.hiddenL) - 1
#print(f_vlayer)
"""
if(self.softmax):
f_vlayer = self.softmaxF(f_vlayer).reshape(-1)
self.log('f_vlayer (Softmax output):', f_vlayer)
#print(f_vlayer)
"""
#print(f_vlayer, '\n\n')
self.log('-----------------\nBackPropagation: \n')
# backpropagation:
for hiddenLayer in ([n_layer0] + self.hiddenL)[::-1]:
self.log('Neurons in this layer: ', hiddenLayer)
#print('i: ', i, '\n')
if coste_anterior is None:
if(self.softmax):
derivf_coste = self.functionDerivative(v_layer, self.activationFunction)
else:
if self.use == 'classification':
derivf_coste = self.functionDerivative(v_layer, 'sigmoid')
else:
derivf_coste = self.functionDerivative(v_layer, 'identity')
f_cost = self.cost(y[c_positions], f_vlayer)
#if self.batch_size != 1:
#f_cost = f_cost / v_layer.shape[0]
coste = f_cost * derivf_coste
if self.batch_size != 1:
batch_pos = batch_pos + self.batch_size
#coste = coste.reshape(-1)
#coste = coste.reshape(coste.shape[0], 1)
#if self.batch_size != 1:
#coste = np.sum(coste, axis=0)
#derivf_coste = np.sum(derivf_coste, axis=0)
if self.debugMode > 0:
meanCostByEpochE = meanCostByEpochE + (abs(coste) if self.batch_size == 1 else np.mean(np.absolute(coste), axis=0))
if self.debugMode > 2:
self.costs.append(coste)
self.log('derivf_coste: ', derivf_coste, 'cost: ', coste, '\n')
else:
entries = hiddenLayer * nextN
valuesForPerc = int(entries / hiddenLayer)
firstPos = 0
lastPos = valuesForPerc
#coste = []
#coste = np.zeros(shape=(hiddenLayer))
self.log('prev_error: ', coste_anterior)
pesos_salientes = hiddenW[i+1].T
#print('hiddenW[i+1][j::hiddenLayer]: ', pesos_salientes)
preActivateValueM = preActivateValues[i+1]
preDeriv = self.functionDerivative(preActivateValueM, self.activationFunction)
self.log('preDeriv: ', preDeriv)
costeA = coste_anterior.dot(pesos_salientes) # coste por los pesos que salen de la neurona
#costeA = np.asarray(costeA)
self.log("preCostA: ", costeA)
costeA = costeA * (preDeriv)
#costeA = costeA.reshape(-1)
#costeA = costeA.T
if self.dropout != 0 and i > -1: # dropout is not done on input layer
costeA = costeA * dropout_values[i]
self.log('costA: ', costeA)
layerValueM = layerValues[i+1]
#print("coste_anterior.shape: ", coste_anterior.shape)
self.log("layer values: ", layerValueM)
if self.batch_gradient == 'sum':
preT1 = coste_anterior.reshape((1 if self.batch_size==1 else coste_anterior.shape[0]), (coste_anterior.shape[0] if self.batch_size==1 else coste_anterior.shape[1]))
preT2 = layerValueM.reshape((layerValueM.shape[0] if self.batch_size==1 else layerValueM.shape[1]), (1 if self.batch_size==1 else layerValueM.shape[0]))
elif self.batch_size == 1:
preT1 = coste_anterior.reshape(1, coste_anterior.shape[0])
preT2 = layerValueM.reshape(layerValueM.shape[0], 1)
else:
preT1 = np.mean(coste_anterior, axis=0)
preT1 = preT1.reshape(1, preT1.shape[0])
preT2 = np.mean(layerValueM, axis=0)
preT2 = preT2.reshape(preT2.shape[0], 1)
pre = preT2.dot(preT1)
#if self.batch_size != 1:
#pre = pre * (1.0 / layerValueM.shape[0])
pre = pre * self.learningRate
self.log('pre: ', pre, '\n')
self.log('Old weight: ', hiddenW[i+1])
hiddenW[i+1] = (hiddenW[i+1] + pre)
self.log('New weight: ', hiddenW[i+1], '\n\n')
coste = costeA
self.log('\n\n')
#coste = coste.reshape(-1)
#print(coste.shape)
#if len(coste.shape) == 3:
#coste = coste.reshape(coste.shape[0] * coste.shape[1], coste.shape[2])
#print('Coste: ' , coste, coste.shape)
#print("\n\n")
coste_anterior = coste
nextN = hiddenLayer
i = i - 1
#print('------------------')
#print('\n\nNuevos pesos: ', hiddenW)
#print('Coste anterior shape: ', coste_anterior.shape)
if epochs < self.convEpochs: # because of resources limitations
self.convLayersBackpropagation(last_layer_output, coste_anterior)
self.printVerbose('\nEpoch', str(epochs+1) + '/' + str(self.numEpochs), 'completed')
if self.debugMode > 0:
self.meanCostByEpoch.append(meanCostByEpochE / numIterations)
self.printVerbose('--- Epoch loss:', round(np.mean(self.meanCostByEpoch[-1]),4))
if self.debugMode > 1:
self.debugWeights.append(copy.deepcopy(hiddenW))
self.batch_size = int(self.batch_size * self.batch_mult)
self.hiddenWeights = hiddenW
#print('\n\nNuevos pesos: ', hiddenW)
self.printVerbose('\n\nTraining finished\n\n')
return self
def predict(self, x, noProba=1):
n_layer0 = -1
layerValues = np.zeros(shape=(x.shape[0],self.lastLayerNeurons))
batch_pos = 0
if self.batch_size == 1:
numIterations = x.shape[0]
else:
numIterations = math.ceil(x.shape[0] / self.batch_size)
for x_pos in range(0, numIterations):
if self.batch_size == 1:
x_val = x[x_pos]
else:
if (batch_pos + self.batch_size) < x.shape[0]:
x_val = x[batch_pos:batch_pos+self.batch_size]
else:
x_val = x[batch_pos:]
x_val_batch_s = x_val.shape[0]
#for x_pos, x_val in enumerate(x):
x_val = self.convLayersFeedForward(x_val).flatten()
if self.batch_size != 1:
x_val = x_val.reshape(x_val_batch_s, int(x_val.shape[0] / x_val_batch_s))
if n_layer0 == -1:
n_layer0 = x_val.shape[0]
self.n_layer0 = n_layer0
v_layer = x_val
lastN = n_layer0
f_vlayer = self.ActivationFunction(v_layer, 'identity')
for i, hiddenLayer in enumerate(self.hiddenL):
entries = hiddenLayer * lastN
valuesForPerc = int(entries / hiddenLayer)
firstPos = 0
lastPos = valuesForPerc
v_layer = f_vlayer.dot(self.hiddenWeights[i])
if self.pre_norm and (i < (len(self.hiddenL) - 1)):
v_layer = self.pre_norm_forward_FC(v_layer)
if (i == (len(self.hiddenL) - 1)):
if(self.softmax):
f_vlayer = self.softmaxF(v_layer).reshape(-1)
else:
if self.use == 'classification':
f_vlayer = self.ActivationFunction(v_layer, 'sigmoid') # use sigmoid on last layer if classification
else:
f_vlayer = self.ActivationFunction(v_layer, 'identity') # use identity on last layer if regression
else:
f_vlayer = self.ActivationFunction(v_layer, self.activationFunction)#.reshape(-1)
v_layer = f_vlayer
lastN = hiddenLayer
if self.batch_size == 1:
layerValues[x_pos] = f_vlayer
else:
if (batch_pos + self.batch_size) < x.shape[0]:
layerValues[batch_pos:batch_pos+self.batch_size] = f_vlayer
else:
layerValues[batch_pos:] = f_vlayer
batch_pos = batch_pos + self.batch_size
"""
if(self.softmax):
layerValues = self.softmaxF(layerValues)
"""
if noProba==1:
if self.use == 'classification':
return self.ActivationFunction(layerValues, 2).astype(int)
else:
return layerValues
else:
return layerValues
def predict_proba(self, x):
return self.predict(x, 0)
def plot_mean_error_last_layer(self, customLabels=[], byClass=False):
if self.debugMode > 0:
meancost = np.asarray(self.meanCostByEpoch)
if len(meancost.shape) > 1 and not byClass:
meancost = np.mean(meancost, axis=1)
ptitle = 'Last layer mean error by epoch'
fig, ax = plt.subplots(figsize=(8,6))
ax.plot(range(0, meancost.shape[0]), meancost)
ax.set(xlabel='Epoch', ylabel='Mean error', title=ptitle)
ax.grid()
if len(meancost.shape) > 1:
if meancost.shape[1] > 1:
if len(customLabels) == 0:
neur = [("Neuron " + str(i)) for i in range(0, meancost.shape[1])]
else:
neur = customLabels
plt.legend(neur, loc="upper right")
plt.show()
else:
print('ConvNet debug mode must be level 1 or higher')
def plot_weights_by_epoch(self, max_weights=-1):
if self.debugMode > 1:
dw = self.debugWeights
dwx = dw[0][len(dw[0]) - 1][:]
fig, ax = plt.subplots(figsize=(8,6))
ygrafico = {}
for jposH, posH in enumerate(range(0, len(dw))): # for each epoch
dwF = dw[jposH][len(dw[0]) - 1][:]
#print(dwF.shape)
for posg, neu in enumerate(dwF):
#print(neu.shape)
if posg in ygrafico:
ygrafico[posg].append(neu[0])
else:
ygrafico[posg] = [neu[0]]
if max_weights == -1:
for ygrafico2 in ygrafico.values():
ax.plot(range(0, len(ygrafico2)), ygrafico2)
else:
if max_weights < 1:
print('max_weights must be bigger than 0')
elif max_weights > len(ygrafico.values()):
print('max_weights must be lower than total weights of last layer')
else:
ygrafico3 = []
# Gets the weights that have changed the most from beginning to end.
for yi, ygrafico2 in enumerate(ygrafico.values()):
a = abs(ygrafico[yi][0] - ygrafico[yi][-1])
#print(ygrafico[yi][0], a)
ygrafico3.append([ygrafico2, a])
for ygrafico4 in sorted(ygrafico3, key=lambda tupval: -1*tupval[1])[0:max_weights]:
#print(ygrafico4)
plt.plot(range(0, len(ygrafico4[0])), ygrafico4[0])
ax.set(xlabel='Epoch', ylabel='Weight', title='Last layer weights by epoch')
ax.grid()
plt.show()
else:
print('ConvNet debug mode must be level 2 or higher') | [
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src/iert/router.py | IERT-Prayagraj/iert_django_webapp | 0 | 121660 | <reponame>IERT-Prayagraj/iert_django_webapp<gh_stars>0
from iert_news.api.viewsets import newViewset
from rest_framework import
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about.py | astrid-project/cubebeat | 1 | 175524 | title = 'Cubebeat'
version = '1.1.0'
description = ''
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print_numbers.py | sonmezbaris/python-exercises | 0 | 151404 |
#Prints the number in matrix form
def print_numbers(number):
for i in range(1,number+1):
for j in range(1, number+1):
print(i,end=" ")
print()
number=int(input("please enter a number: "))
if number%2==0:
print_numbers(number+1)
else:
print_numbers(number-1)
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] |
main.py | cryos/qmicroscope | 0 | 110099 | <reponame>cryos/qmicroscope
import sys
from qtpy.QtCore import (
QPoint,
QSettings,
QSize,
)
from qtpy.QtWidgets import (
QCheckBox,
QLineEdit,
QPushButton,
QApplication,
QVBoxLayout,
QHBoxLayout,
QFormLayout,
QSpinBox,
QMainWindow,
QWidget,
)
from microscope.microscope import Microscope
from microscope.container import Container
from microscope.settings import Settings
class Form(QMainWindow):
def __init__(self, parent=None):
super(Form, self).__init__(parent)
# Create widgets
self.setWindowTitle("NSLS-II Microscope Widget")
self.container = Container(self)
self.container.count = 3
self.container.size = [2, 2]
self.microscope = self.container.microscope(0)
#self.microscope = Microscope(self)
self.startButton = QPushButton('Start')
self.settingsButton = QPushButton('Settings')
# Create layout and add widgets
layout = QVBoxLayout()
layout.addWidget(self.container)
hButtonBox = QHBoxLayout()
hButtonBox.addStretch()
hButtonBox.addWidget(self.startButton)
hButtonBox.addWidget(self.settingsButton)
hButtonBox.addStretch()
layout.addLayout(hButtonBox)
# Set main windows widget using our central layout
widget = QWidget()
widget.setLayout(layout)
self.setCentralWidget(widget)
# Add button signal to slot to start/stop
self.startButton.clicked.connect(self.startButtonPressed)
self.settingsButton.clicked.connect(self.settingsButtonClicked)
# Connect to the microscope ROI clicked signal
self.microscope.roiClicked.connect(self.onRoiClicked)
# Read the settings and persist them
settings = QSettings()
self.readSettings(settings)
self.settingsDialog = Settings(self)
self.settingsDialog.setContainer(self.container)
# event : QCloseEvent
def closeEvent(self, event):
settings = QSettings()
self.writeSettings(settings)
event.accept()
def startButtonPressed(self):
# Currently being a little lame - only update state on start/stop.
print('Button pressed!', self.startButton.text())
if self.startButton.text() == 'Start':
self.container.start(True)
self.startButton.setText('Stop')
else:
self.container.start(False)
self.startButton.setText('Start')
def settingsButtonClicked(self):
# Open the settings dialog.
self.settingsDialog.show()
def onRoiClicked(self, x, y):
print(f'ROI: {x}, {y}')
def readSettings(self, settings):
""" Load the application's settings. """
settings.beginGroup('MainWindow')
self.resize(settings.value('size', QSize(400, 400)))
self.move(settings.value('pos', QPoint(200, 200)))
self.container.readSettings(settings)
settings.endGroup()
def writeSettings(self, settings):
""" Save the applications's settings persistently. """
settings.beginGroup('MainWindow')
settings.setValue('size', self.size())
settings.setValue('pos', self.pos())
self.container.writeSettings(settings)
settings.endGroup()
if __name__ == '__main__':
# Set up some application basics for saving settings
QApplication.setOrganizationName('BNL')
QApplication.setOrganizationDomain('bnl.gov')
QApplication.setApplicationName('QCamera')
# Create the Qt Application
app = QApplication(sys.argv)
# Create and show the form
form = Form()
form.show()
# Run the main Qt loop
sys.exit(app.exec_())
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13,
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278,
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29889,
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29898,
932,
29889,
4258,
29918,
3101,
13,
2
] |
examples/scoring/energy_gbsa/run.py | JonnyWideFoot/pd.arcus | 0 | 175320 | <gh_stars>0
from pd import *
cseed(4)
info()
timer()
ffps = FFParamSet()
ffps.readLib("amber03aa.ff")
## test simple loading of PDB file
sim = PDB_In(ffps, "trpcage.pdb");
sim.loadAll();
# create workspace from the system sim
wspace = WorkSpace( sim )
# print loaded system - this can be compared later
wspace.printPDB("inout.pdb")
# create a few common forcefield components
ff = Forcefield(wspace)
bonds = BondedForcefield(wspace)
gb = GB_Still( wspace)
gb.Cutoff = 12.0
gb.InnerCutoff = 9.0
gb.FastMode = 1
sasa = SASA_LCPO( wspace )
sasa.GlobalASP = 0.005
## add all the forcefield components to our forcefield
ff.add( bonds )
ff.add( gb )
ff.add( sasa )
## print energies as summary
ff.printEnergySummary()
## and dy detail (useful for when things DO break)
ff.printEnergyByAtom()
## also show parameters
ff.info()
| [
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13,
2277,
884,
1510,
4128,
13,
600,
29889,
3888,
580,
13,
13,
13,
2
] |
library/python/stu/maya/__init__.py | smiletechnologyunited/seminar_sample | 3 | 1611143 | <reponame>smiletechnologyunited/seminar_sample<gh_stars>1-10
#!/usr/bin/env python
# coding=utf-8
from __future__ import absolute_import, division, print_function
import maya.mel
def _escape(msg):
m = msg
m = m.replace(u"\n", u"\\n")
m = m.replace(u"\t", u"\\t")
return m.replace(u"\"", u"'")
def error(msg):
try:
maya.mel.eval(u"error(\"{0}\")".format(_escape(msg)))
except Exception:
pass
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5174,
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29901,
13,
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1209,
13,
2
] |
webserver/contest/context_processors.py | theSage21/judge-interface | 3 | 29864 | <gh_stars>1-10
from contest import models
from django.utils import timezone
from contest.functions import is_contest_on, contest_phase
def contest_time(request):
context = {}
now = timezone.now()
contest = models.ContestControl.objects.first()
if now < contest.start:
time = contest.start
elif contest.start <= now <= contest.end:
time = contest.end
else:
time = None
context['contest_time'] = time
context['contest_on'] = is_contest_on()
context['contest_phase'] = contest_phase()
return context
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] |
hazelcast/metrics.py | tonytheonlypony/hazelcast-python-client | 0 | 193477 | import zlib
from hazelcast.serialization import (
BYTE_SIZE_IN_BYTES,
CHAR_SIZE_IN_BYTES,
BE_UINT16,
DOUBLE_SIZE_IN_BYTES,
BE_DOUBLE,
INT_SIZE_IN_BYTES,
BE_INT,
LONG_SIZE_IN_BYTES,
BE_LONG,
BE_UINT8,
)
_MASK_PREFIX = 1
_MASK_METRIC = 1 << 1
_MASK_DISCRIMINATOR = 1 << 2
_MASK_DISCRIMINATOR_VALUE = 1 << 3
_MASK_UNIT = 1 << 4
_MASK_EXCLUDED_TARGETS = 1 << 5
_MASK_TAG_COUNT = 1 << 6
_NULL_DICTIONARY_ID = -1
_NULL_UNIT = -1
_MAX_WORD_LENGTH = 255
_BITS_IN_BYTE = 8
_BYTE_MASK = 0xFF
_BINARY_FORMAT_VERSION = 1
_SIZE_VERSION = 2
_SIZE_DICTIONARY_BLOB = 4
_SIZE_COUNT_METRICS = 4
_OUTPUT_BUFFER_INITIAL_SIZE = 1024
_OUTPUT_BUFFER_GROW_FACTOR = 1.2
class MetricDescriptor(object):
"""Describes a metric to be sent to the members.
It is a simplified version of the Java's MetricDescriptorImpl, sufficient
for the needs of the Python client.
"""
__slots__ = ("prefix", "metric", "discriminator", "discriminator_value", "unit")
def __init__(
self, metric, prefix=None, discriminator=None, discriminator_value=None, unit=None
):
self.metric = metric
"""str: Name of the metric."""
self.prefix = prefix
"""str: Prefix of the metric."""
self.discriminator = discriminator
"""str: Discriminator for the metrics that have the same name and prefix."""
self.discriminator_value = discriminator_value
"""str: Tag of the discriminator."""
self.unit = unit
"""ProbeUnit: Unit of the metric."""
class ProbeUnit(object):
"""Measurement unit of a probe.
The values of the constants below should be in sync with the ProbeUnit
enum in Java.
"""
BYTES = 0
"""Size or counter represented in bytes."""
MS = 1
"""Timestamp or duration represented in milliseconds."""
NS = 2
"""Timestamp or duration represented in nanoseconds."""
PERCENT = 3
"""An integer mostly in range 0..100 or a double mostly in range 0..1."""
COUNT = 4
"""Number of items: size, counter..."""
BOOLEAN = 5
"""0 or 1."""
ENUM = 6
"""0..n, ordinal of an enum."""
# New enum values should be converted to a tag to ensure backward
# compatibility during compressing the metrics. see ProbeUnit#newUnit
# handling in Java for that. We don't implement this functionality yet in
# the Python client, as we don't use such enum members.
class ValueType(object):
"""Type of the metric values.
The values of the constants below should be in sync ValueType enum in Java.
"""
LONG = 0
DOUBLE = 1
class MetricsCompressor(object):
"""Compresses metrics into a ``bytearray`` blob.
The compressor uses dictionary based delta compression and deflates
the resulting ``bytearray`` by using ``zlib.compress()``. This
compressor doesn't use the textual representation of the
:class:`MetricDescriptor` hence it is agnostic to the order of the
tags in that representation.
Adding metrics by calling :func:`add_long` or :func:`add_double`
builds a dictionary by mapping all words found in the passed
:class:`MetricDescriptor`s to ``int``s and these ``int``s will be
written to the resulting ``bytearray`` blob. Before these ``int``s
are written, the current :class:`MetricDescriptor` is compared to the
previous one and only the fields of the descriptor that are different
from the previous will be written to the metrics blob.
When the blob is retrieved from this compressor (when the metrics
collection cycle finishes) the dictionary is stored in the dictionary
blob. The compressor iterates over the words stored in the dictionary
in ascending order and writes them to the blob by skipping the first
N characters that are equal to the first N character of the previously
written word, hence using delta compression here too.
After both the metrics and the dictionary blob is constructed, they
are copied into a final blob in the following structure:
+--------------------------------+--------------------+
| Compressor version | 2 bytes (short) |
+--------------------------------+--------------------+
| Size of dictionary blob | 4 bytes (int) |
+--------------------------------+--------------------+
| Dictionary blob | variable size |
+--------------------------------+--------------------+
| Number of metrics in the blob | 4 bytes (int) |
+--------------------------------+--------------------+
| Metrics blob | variable size |
+--------------------------------+--------------------+
"""
__slots__ = ("_metrics_buf", "_dict_buf", "_metrics_dict", "_metrics_count", "_last_descriptor")
def __init__(self):
self._metrics_buf = _OutputBuffer()
self._dict_buf = _OutputBuffer()
self._metrics_dict = _MetricsDictionary()
self._metrics_count = 0
self._last_descriptor = None
def add_long(self, descriptor, value):
self._write_descriptor(descriptor)
self._metrics_buf.write_byte(ValueType.LONG)
self._metrics_buf.write_long(value)
def add_double(self, descriptor, value):
self._write_descriptor(descriptor)
self._metrics_buf.write_byte(ValueType.DOUBLE)
self._metrics_buf.write_double(value)
def generate_blob(self):
self._write_metrics_dict()
metrics_buf = self._metrics_buf.compress()
dict_buf = self._dict_buf.compress()
complete_size = (
_SIZE_VERSION
+ _SIZE_DICTIONARY_BLOB
+ len(dict_buf)
+ _SIZE_COUNT_METRICS
+ len(metrics_buf)
)
final_buf = _OutputBuffer(complete_size)
final_buf.write_byte((_BINARY_FORMAT_VERSION >> _BITS_IN_BYTE) & _BYTE_MASK)
final_buf.write_byte(_BINARY_FORMAT_VERSION & _BYTE_MASK)
final_buf.write_int(len(dict_buf))
final_buf.write_bytearray(dict_buf)
final_buf.write_int(self._metrics_count)
final_buf.write_bytearray(metrics_buf)
return final_buf.to_bytearray()
def _write_descriptor(self, descriptor):
mask = self._calculate_descriptor_mask(descriptor)
self._metrics_buf.write_byte(mask)
# Only write to metrics buffer if the values below are
# different than the values of the previous descriptor
if mask & _MASK_PREFIX == 0:
self._metrics_buf.write_int(self._get_dict_id(descriptor.prefix))
if mask & _MASK_METRIC == 0:
self._metrics_buf.write_int(self._get_dict_id(descriptor.metric))
if mask & _MASK_DISCRIMINATOR == 0:
self._metrics_buf.write_int(self._get_dict_id(descriptor.discriminator))
if mask & _MASK_DISCRIMINATOR_VALUE == 0:
self._metrics_buf.write_int(self._get_dict_id(descriptor.discriminator_value))
if mask & _MASK_UNIT == 0:
if descriptor.unit is None:
self._metrics_buf.write_byte(_NULL_UNIT)
else:
self._metrics_buf.write_byte(descriptor.unit)
# include excludedTargets and tags bytes for compatibility purposes
if mask & _MASK_EXCLUDED_TARGETS == 0:
self._metrics_buf.write_byte(0)
if mask & _MASK_TAG_COUNT == 0:
self._metrics_buf.write_byte(0)
self._metrics_count += 1
self._last_descriptor = descriptor
def _calculate_descriptor_mask(self, descriptor):
mask = 0
if not self._last_descriptor:
return mask
last_descriptor = self._last_descriptor
if descriptor.prefix == last_descriptor.prefix:
mask |= _MASK_PREFIX
if descriptor.metric == last_descriptor.metric:
mask |= _MASK_METRIC
if descriptor.discriminator == last_descriptor.discriminator:
mask |= _MASK_DISCRIMINATOR
if descriptor.discriminator_value == last_descriptor.discriminator_value:
mask |= _MASK_DISCRIMINATOR_VALUE
if descriptor.unit == last_descriptor.unit:
mask |= _MASK_UNIT
# include excludedTargets and tags bits for compatibility purposes
mask |= _MASK_EXCLUDED_TARGETS
mask |= _MASK_TAG_COUNT
return mask
def _get_dict_id(self, word):
if not word:
return _NULL_DICTIONARY_ID
return self._metrics_dict.get_dict_id(word)
def _write_metrics_dict(self):
words = self._metrics_dict.get_words()
self._dict_buf.write_int(len(words))
last_word_text = ""
for word in words:
word_text = word.word
max_common_len = min(len(last_word_text), len(word_text))
common_len = 0
while (
common_len < max_common_len and word_text[common_len] == last_word_text[common_len]
):
common_len += 1
diff_len = len(word_text) - common_len
self._dict_buf.write_int(word.dict_id)
self._dict_buf.write_byte(common_len)
self._dict_buf.write_byte(diff_len)
for i in range(common_len, len(word_text)):
self._dict_buf.write_char(word_text[i])
last_word_text = word_text
class _OutputBuffer(object):
__slots__ = ("_buf", "_pos")
def __init__(self, size=None):
self._buf = bytearray(size or _OUTPUT_BUFFER_INITIAL_SIZE)
self._pos = 0
def to_bytearray(self):
if self._pos == len(self._buf):
return self._buf
return self._buf[: self._pos]
def write_bytearray(self, buf):
n = len(buf)
self._ensure_available(n)
self._buf[self._pos : self._pos + n] = buf
self._pos += n
def write_byte(self, value):
self._ensure_available(BYTE_SIZE_IN_BYTES)
BE_UINT8.pack_into(self._buf, self._pos, value & _BYTE_MASK)
self._pos += BYTE_SIZE_IN_BYTES
def write_char(self, value):
self._ensure_available(CHAR_SIZE_IN_BYTES)
BE_UINT16.pack_into(self._buf, self._pos, ord(value))
self._pos += CHAR_SIZE_IN_BYTES
def write_double(self, value):
self._ensure_available(DOUBLE_SIZE_IN_BYTES)
BE_DOUBLE.pack_into(self._buf, self._pos, value)
self._pos += DOUBLE_SIZE_IN_BYTES
def write_int(self, value):
self._ensure_available(INT_SIZE_IN_BYTES)
BE_INT.pack_into(self._buf, self._pos, value)
self._pos += INT_SIZE_IN_BYTES
def write_long(self, value):
self._ensure_available(LONG_SIZE_IN_BYTES)
BE_LONG.pack_into(self._buf, self._pos, value)
self._pos += LONG_SIZE_IN_BYTES
def compress(self):
buf = self.to_bytearray()
# Set level to 1 for best speed (less CPU overhead)
return zlib.compress(buf, 1)
def _ensure_available(self, size):
if self._available() < size:
# Grow memory more than needed
new_size = int((self._pos + size) * _OUTPUT_BUFFER_GROW_FACTOR)
if new_size % 2 != 0:
new_size += 1
new_buf = bytearray(new_size)
new_buf[: self._pos] = self._buf
self._buf = new_buf
def _available(self):
return len(self._buf) - self._pos
class _Word(object):
__slots__ = ("word", "dict_id")
def __init__(self, word, dict_id):
self.word = word
self.dict_id = dict_id
class _MetricsDictionary(object):
"""Stores word -> id mappings.
Used by :class:`MetricsCompressor`'s dictionary-based algorithm.
"""
__slots__ = ("_words",)
def __init__(self):
self._words = {}
def get_dict_id(self, word_text):
"""Returns the dictionary id for the given word.
If the word is not yet stored in the dictionary, the word gets stored
and a newly assigned id is returned.
Args:
word_text (str): Textual representation of a word.
Returns:
int: The dictionary id.
Raises:
ValueError: If the length of the word cannot fit into unsigned
byte range.
"""
if len(word_text) > _MAX_WORD_LENGTH:
raise ValueError(
"Too long value in the metric descriptor found, maximum is %s: %s"
% (_MAX_WORD_LENGTH, word_text)
)
word = self._words.get(word_text, None)
if word:
word_id = word.dict_id
else:
word_id = len(self._words)
self._words[word_text] = _Word(word_text, word_id)
return word_id
def get_words(self):
"""Returns all stored word<->id mappings ordered by word.
Returns:
list[_Word]: Words.
"""
words = self._words.values()
return sorted(words, key=lambda w: w.word)
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] |
operations/sgRNAProcessing/UpdateGroupReferences.py | BioGRID/CUP-LIMS | 2 | 160863 | <filename>operations/sgRNAProcessing/UpdateGroupReferences.py
#!/bin/env python
# Take a set of sgRNA_Groups and attempt to map them
# to better references
import sys, string, argparse
import MySQLdb
import Config
import Database
from classes import Lookups
with Database.db as cursor :
cursor.execute( "SELECT sgrna_group_id, sgrna_group_reference_original, sgrna_group_reference_original_type FROM " + Config.DB_MAIN + ".sgRNA_groups WHERE sgrna_group_reference='-'" )
for row in cursor.fetchall( ) :
# See if we can find a match with an Official Symbol
cursor.execute( "SELECT gene_id FROM " + Config.DB_QUICK + ".quick_identifiers WHERE quick_identifier_value=%s AND quick_identifier_type IN ('OFFICIAL SYMBOL') AND organism_id='9606' GROUP BY gene_id", [row['sgrna_group_reference_original']] )
idInfo = ""
if cursor.rowcount == 1 :
idInfo = cursor.fetchone( )
else :
cursor.execute( "SELECT gene_id FROM " + Config.DB_QUICK + ".quick_identifiers WHERE quick_identifier_value=%s AND quick_identifier_type IN ('SYNONYM', 'ORDERED LOCUS') AND organism_id='9606' GROUP BY gene_id", [row['sgrna_group_reference_original']] )
if cursor.rowcount == 1 :
idInfo = cursor.fetchone( )
if idInfo != "" :
cursor.execute( "UPDATE " + Config.DB_MAIN + ".sgRNA_groups SET sgrna_group_reference=%s, sgrna_group_reference_type=%s WHERE sgrna_group_id=%s", [idInfo['gene_id'], 'BIOGRID', row['sgrna_group_id']] )
else :
cursor.execute( "UPDATE " + Config.DB_MAIN + ".sgRNA_groups SET sgrna_group_reference=%s, sgrna_group_reference_type=%s WHERE sgrna_group_id=%s", [row['sgrna_group_reference_original'], row['sgrna_group_reference_original_type'], row['sgrna_group_id']] )
Database.db.commit( )
Database.db.commit( )
sys.exit( ) | [
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blog/admin.py | Developer-R-7/CaffeineCode | 1 | 137505 | from django.contrib import admin
from .models import Category, Post
admin.site.register(Post)
admin.site.register(Category) | [
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physics_aware_training/model_architectures/SplitInputParameterNet.py | mcmahon-lab/Physics-Aware-Training | 182 | 64240 | import torch
import torch.nn as nn
import physics_aware_training.digital_twin_utils
class SplitInputParameterNet(nn.Module):
def __init__(self,
input_dim,
nparams,
output_dim,
parameterNunits = [100,100,100],
internalNunits = [10,10,10]):
'''
Defines network that splits inputs x into physical system input and parameters.
Inputs are propagated through a "main" neural network whose weights are predicted by an
auxiliary neural network whose inputs are the parameters.
Args:
inputDim (int): dimension of physical system inputs
outputDim (int): dimension of physical system outputs
parameterDim (int): dimension of all physical system parameters combined
parameterNunits (list of int): defines the number of hidden units per layer in the
auxiliary parameter network.
internalDim (int): number of hidden units per layer of the main neural network that
propagates physical system inputs
inputNlayers (int): number of hidden layers of main neural network
'''
super(SplitInputParameterNet, self).__init__()
self.input_dim = input_dim
self.nparams = nparams
self.output_dim = output_dim
self.internalNunits = internalNunits
self.inputNlayers = len(internalNunits)
nparameters = 0
for i in range(len(internalNunits)-1):
nparameters += internalNunits[i]*internalNunits[i+1]
nparameters += internalNunits[i+1]
# parameterNet is a submodel that predicts a matrix of dimensions
self.parameterNet = torch.nn.Sequential()
self.parameterNet.add_module("fcIn", torch.nn.Linear(nparams, parameterNunits[0]))
for i in range(len(parameterNunits)):
if i<len(parameterNunits)-1:
self.parameterNet.add_module(f"relu{i}", torch.nn.ReLU())
self.parameterNet.add_module(f"fc{i}", torch.nn.Linear(parameterNunits[i], parameterNunits[i+1]))
else:
self.parameterNet.add_module(f"relu{i}", torch.nn.ReLU())
self.parameterNet.add_module(f"fcOut", torch.nn.Linear(parameterNunits[i], nparameters))
# two fully connected input and output layers adjust the input and output dimenstion to
# the internal dimension
self.fcIn = nn.Linear(input_dim, internalNunits[0])
self.fcOut = nn.Linear(internalNunits[-1], output_dim)
def forward(self, x):
batch_size, _ = x.shape
# initialize matrices for inputNet
inputNetMatrices = []
inputNetBiases = []
for i in range(len(self.internalNunits)-1):
inputNetMatrices.append([torch.zeros(batch_size, self.internalNunits[i], self.internalNunits[i+1])])
inputNetBiases.append([torch.zeros(batch_size, self.internalNunits[i+1], 1)])
# split x into physical system inputs and parameters
inputs = x[:, :self.input_dim]
parameters = x[:, self.input_dim:]
# AUXILIARY PARAMETER NETWORK
parameters = self.parameterNet(parameters)
# fill inputNetMatrices with outputs from parameterNet
index = 0
for i in range(len(self.internalNunits)-1):
index_temp = index
index += self.internalNunits[i] * self.internalNunits[i+1]
inputNetMatrices[i] = parameters[:, index_temp:index].reshape(batch_size, self.internalNunits[i+1], self.internalNunits[i])
# fill inputNetBiases with outputs from parameterNet
for i in range(len(self.internalNunits)-1):
index_temp = index
index += self.internalNunits[i+1]
inputNetBiases[i] = parameters[:, index_temp:index].reshape(batch_size, self.internalNunits[i+1], 1)
# MAIN INPUT NETWORK
inputs = self.fcIn(inputs).unsqueeze(-1)
# MAIN INPUT NETWORK
for i in range(len(self.internalNunits)-1):
# apply matrices and biases just filled with outputs from parameterNet
inputs = torch.bmm(inputNetMatrices[i], inputs)
inputs += inputNetBiases[i]
inputs = torch.relu(inputs)
return self.fcOut(inputs.squeeze(-1))
class SplitInputParameterObjective(object):
# define class to smuggle additional arguments into objective function
def __init__(self, train_loader, test_loader, dt_path, input_dim, nparams, output_dim, **modelargs):
self.modelargs = modelargs
self.dt_path = dt_path
self.train_loader = train_loader
self.test_loader = test_loader
self.input_dim = input_dim
self.nparams = nparams
self.output_dim = output_dim
def __call__(self, trial):
lr = trial.suggest_loguniform("lr", 1e-4, 1e-1)
parameterNlayers = trial.suggest_categorical("parameterNlayers", [1, 2, 3, 4, 5])
parameterNunits = []
if parameterNlayers == 1:
parameterNunits.append(int(trial.suggest_loguniform("Nunits1", 50, 1000)))
if parameterNlayers == 2:
parameterNunits.append(int(trial.suggest_loguniform("Nunits1", 50, 1000)))
parameterNunits.append(int(trial.suggest_loguniform("Nunits2", 50, 1000)))
if parameterNlayers == 3:
parameterNunits.append(int(trial.suggest_loguniform("Nunits1", 50, 1000)))
parameterNunits.append(int(trial.suggest_loguniform("Nunits2", 50, 1000)))
parameterNunits.append(int(trial.suggest_loguniform("Nunits3", 50, 1000)))
if parameterNlayers == 4:
parameterNunits.append(int(trial.suggest_loguniform("Nunits1", 50, 1000)))
parameterNunits.append(int(trial.suggest_loguniform("Nunits2", 50, 1000)))
parameterNunits.append(int(trial.suggest_loguniform("Nunits3", 50, 1000)))
parameterNunits.append(int(trial.suggest_loguniform("Nunits4", 50, 1000)))
if parameterNlayers == 5:
parameterNunits.append(int(trial.suggest_loguniform("Nunits1", 50, 1000)))
parameterNunits.append(int(trial.suggest_loguniform("Nunits2", 50, 1000)))
parameterNunits.append(int(trial.suggest_loguniform("Nunits3", 50, 1000)))
parameterNunits.append(int(trial.suggest_loguniform("Nunits4", 50, 1000)))
parameterNunits.append(int(trial.suggest_loguniform("Nunits5", 50, 1000)))
internalNlayers = trial.suggest_categorical("internalNlayers", [1, 2, 3, 4, 5])
internalNunits = []
if parameterNlayers == 1:
internalNunits.append(int(trial.suggest_loguniform("iNunits1", 10, 100)))
if parameterNlayers == 2:
internalNunits.append(int(trial.suggest_loguniform("iNunits1", 10, 100)))
internalNunits.append(int(trial.suggest_loguniform("iNunits2", 10, 100)))
if parameterNlayers == 3:
internalNunits.append(int(trial.suggest_loguniform("iNunits1", 10, 100)))
internalNunits.append(int(trial.suggest_loguniform("iNunits2", 10, 100)))
internalNunits.append(int(trial.suggest_loguniform("iNunits3", 10, 100)))
if parameterNlayers == 4:
internalNunits.append(int(trial.suggest_loguniform("iNunits1", 10, 100)))
internalNunits.append(int(trial.suggest_loguniform("iNunits2", 10, 100)))
internalNunits.append(int(trial.suggest_loguniform("iNunits3", 10, 100)))
internalNunits.append(int(trial.suggest_loguniform("iNunits4", 10, 100)))
if parameterNlayers == 5:
internalNunits.append(int(trial.suggest_loguniform("iNunits1", 10, 100)))
internalNunits.append(int(trial.suggest_loguniform("iNunits2", 10, 100)))
internalNunits.append(int(trial.suggest_loguniform("iNunits3", 10, 100)))
internalNunits.append(int(trial.suggest_loguniform("iNunits4", 10, 100)))
internalNunits.append(int(trial.suggest_loguniform("iNunits5", 10, 100)))
name = f"{self.dt_path}_v{trial.number}" #create name with trial index
value, model_path = physics_aware_training.digital_twin_utils.train_loop_reg_model(
self.train_loader,
self.test_loader,
name,
self.input_dim,
self.nparams,
self.output_dim,
Model = SplitInputParameterNet,
parameterNunits = parameterNunits,
internalNunits = internalNunits,
lr = lr,
**self.modelargs)
trial.set_user_attr('model_path', model_path) #save the model path string in NAS study
return value | [
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slackforms/conf.py | Albatrous/django-slack-forms | 1 | 43546 | """
Use this file to configure pluggable app settings and resolve defaults
with any overrides set in project settings.
"""
from django.conf import settings as project_settings
class Settings:
pass
Settings.SECRET_KEY = getattr(
project_settings, "SLACKFORMS_SECRET_KEY", "a-bad-secret-key"
)
Settings.SLACK_VERIFICATION_TOKEN = getattr(
project_settings, "SLACKFORMS_SLACK_VERIFICATION_TOKEN", ""
)
Settings.SLACK_BOT_TOKEN = getattr(
project_settings, "SLACKFORMS_SLACK_BOT_TOKEN", ""
)
Settings.SLACK_API_TOKEN = getattr(
project_settings, "SLACKFORMS_SLACK_API_TOKEN", ""
)
Settings.ROOT_URL = getattr(project_settings, "SLACKFORMS_ROOT_URL", "")
settings = Settings
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blog/models.py | gnidoc327/django_web_dev_bookmark | 0 | 87666 | from __future__ import unicode_literals
from django.contrib.auth.models import User
from django.utils.encoding import python_2_unicode_compatible
from django.db import models
from django.core.urlresolvers import reverse
@python_2_unicode_compatible
class Post(models.Model):
title = models.CharField('TITLE',max_length=50)
slug = models.SlugField('SLUG', unique=True, allow_unicode=True, help_text='one word for title alias.')
description = models.CharField('DESCRIPTION', max_length=100, blank=True, help_text='simple description text')
content = models.TextField('CONTENT')
create_date = models.DateTimeField('Create Date', auto_now_add=True)
modify_date = models.DateTimeField('Modify Date', auto_now=True)
owner = models.ForeignKey(User, null=True)
class Meta:
verbose_name = 'post'
verbose_name_plural = 'posts'
db_table = 'my_post'
ordering = ('-modify_date',)
def __str__(self):
return self.title
def get_absolute_url(self):
return reverse('blog:post_detail', args=(self.slug,))
def get_previous_post(self):
return self.get_previous_by_modify_date()
def get_next_post(self):
return self.get_next_by_modify_date()
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src/app/bin/slack.py | bclarkejr/slack-alerts | 8 | 96173 | <filename>src/app/bin/slack.py
import json
import re
import sys
import time
import traceback
from copy import deepcopy
from fnmatch import fnmatch
from safe_fmt import safe_format
from six import ensure_binary
from six.moves import urllib
SEVERITY_COLORS = ['#555555','#6DB7C6','#65A637','#F7BC38','#F58F39','#D93F3C']
OK = 0
ERROR_CODE_UNKNOWN = 1
ERROR_CODE_VALIDATION_FAILED = 2
ERROR_CODE_CHANNEL_NOT_FOUND = 3
ERROR_CODE_FORBIDDEN = 4
ERROR_CODE_HTTP_FAIL = 5
ERROR_CODE_UNEXPECTED = 6
def log(msg, *args):
sys.stderr.write(msg + " ".join([str(a) for a in args]) + "\n")
def build_fields_attachment(payload):
res = payload.get('result', dict())
available_fields = list(res.keys())
field_attachments = []
seen_fields = set()
field_list = re.split(r'\s*,\s*', payload['configuration'].get('fields', '').strip())
for f in field_list:
for af in available_fields:
if af not in seen_fields and fnmatch(af, f):
seen_fields.add(af)
val = res[af]
if isinstance(val, list):
val = val[0]
field_attachments.append(dict(title=af, value=val, short=True))
return field_attachments
def format_template(template_key, payload, fallback=''):
config = payload['configuration']
template = config.get(template_key)
if template is not None:
fallback = template
try:
args = deepcopy(payload)
args['configuration']['webhook_url'] = '****'
args['configuration']['webhook_url_override'] = '****'
return safe_format(template, args)
except:
log("WARN Failed to format template %s \"%s\" -" % (template_key, template), sys.exc_info()[1])
return fallback
def build_alert_attachment(payload):
config = payload['configuration']
attachment = dict()
if 'info_severity' in config:
try:
attachment['color'] = SEVERITY_COLORS[int(config['info_severity'])]
except: pass
attachment['fallback'] = format_template('attachment_fallback', payload)
attachment_title_key = 'attachment_alert_title' if payload.get('search_name') else 'attachment_adhoc_title'
attachment['title'] = format_template(attachment_title_key, payload, 'Alert')
if config.get('attachment', 'none') == 'message':
attachment['text'] = format_template('message', payload)
else:
attachment['text'] = format_template('attachment_results_link', payload)
attachment['title_link'] = config.get('view_link')
attachment['footer'] = format_template('attachment_footer_text', payload)
attachment['footer_icon'] = 'https://s3-us-west-1.amazonaws.com/ziegfried-apps/slack-alerts/splunk-icon.png'
try:
attachment['ts'] = int(float(config.get('info_trigger_time')))
except: pass
if config.get('fields'):
attachment['fields'] = build_fields_attachment(payload)
return attachment
def build_slack_message(payload):
config = payload.get('configuration')
params = dict()
if config['attachment'] != 'message':
params['text'] = format_template('message', payload)
params['username'] = config.get('from_user', 'Splunk')
params['icon_url'] = config.get('from_user_icon')
channel = config.get('channel')
if channel:
params['channel'] = channel
else:
log("WARN No channel supplied, using default for webhook")
if config.get('attachment', 'none') != 'none':
params['attachments'] = [build_alert_attachment(payload)]
elif config.get('fields'):
params['attachments'] = [dict(fields=build_fields_attachment(payload))]
return params
def send_slack_message(payload):
try:
config = payload.get('configuration')
url = config.get('webhook_url', '')
if config.get('webhook_url_override'):
url = config.get('webhook_url_override', '')
log("INFO Using webhook URL from webhook_url_override: %s" % url)
elif not url:
log("FATAL No webhook URL configured and no override specified")
return ERROR_CODE_VALIDATION_FAILED
else:
log("INFO Using configured webhook URL: %s" % url)
if not url.startswith('https:'):
log("FATAL Invalid webhook URL specified. The URL must use HTTPS.")
return ERROR_CODE_VALIDATION_FAILED
body = json.dumps(build_slack_message(payload))
log('DEBUG Calling url="%s" with body=%s' % (url, body))
req = urllib.request.Request(url, ensure_binary(body), {"Content-Type": "application/json"})
try:
res = urllib.request.urlopen(req)
body = res.read()
log("INFO Slack API responded with HTTP status=%d" % res.code)
log("DEBUG Slack API response: %s" % body)
if 200 <= res.code < 300:
return OK
except urllib.error.HTTPError as e:
log("ERROR HTTP request to Slack webhook URL failed: %s" % e)
try:
res = e.read()
log("ERROR Slack error response: %s" % res)
if res in ('channel_not_found', 'channel_is_archived'):
return ERROR_CODE_CHANNEL_NOT_FOUND
if res == 'action_prohibited':
return ERROR_CODE_FORBIDDEN
except:
pass
return ERROR_CODE_HTTP_FAIL
except:
log("FATAL Unexpected error:", sys.exc_info()[0])
track = traceback.format_exc()
log(track)
return ERROR_CODE_UNEXPECTED
def validate_payload(payload):
if not 'configuration' in payload:
log("FATAL Invalid payload, missing 'configuration'")
return False
config = payload.get('configuration')
channel = config.get('channel')
if channel and (channel[0] != '#' and channel[0] != '@'):
# Only warn here for now
log("WARN Validation warning: Parameter `channel` \"%s\" should start with # or @" % channel)
msg = config.get('message')
if not msg:
log("FATAL Validation error: Parameter `message` is missing or empty")
return False
att = config.get('attachment')
if att and att not in ('alert_link', 'message'):
log("WARN Validation warning: Parameter `attachment` must be ether \"alert_link\" or \"message\"")
return True
if __name__ == '__main__':
log("INFO Running python %s" % (sys.version_info[0]))
if len(sys.argv) > 1 and sys.argv[1] == "--execute":
payload = json.loads(sys.stdin.read())
if not validate_payload(payload):
sys.exit(ERROR_CODE_VALIDATION_FAILED)
result = send_slack_message(payload)
if result == OK:
log("INFO Successfully sent slack message")
else:
log("FATAL Alert action failed")
sys.exit(result)
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] |
baselines/bert_model.py | vvyadrincev/taxonomy-enrichment | 11 | 168088 | <reponame>vvyadrincev/taxonomy-enrichment<gh_stars>10-100
import numpy as np
from tensorflow.keras.preprocessing.sequence import pad_sequences
import torch
device = torch.device('cuda')
n_gpu = torch.cuda.device_count()
for i in range(n_gpu):
print(torch.cuda.get_device_name(i))
torch.manual_seed(117)
from pytorch_pretrained_bert import BertTokenizer, BertModel
class BertPretrained:
def __init__(self, model_path):
self.tokenizer = BertTokenizer.from_pretrained(model_path)
self.model = BertModel.from_pretrained(model_path)
self.model.cuda('cuda')
self.model.eval()
def vectorize_sentences(self, sentences):
result_batch = []
indexed_batch = []
segment_batch = []
mapping = []
for sentence in sentences:
tokens, ids, map_to_tok = self.tokenize(sentence)
indexed_batch.append(tokens)
segment_batch.append(ids)
mapping.append(map_to_tok)
tokens_tensor = torch.LongTensor(pad_sequences(indexed_batch, dtype='long', padding='post'))
segments_tensors = torch.LongTensor(pad_sequences(segment_batch, dtype='long', padding='post'))
tokens_tensor = tokens_tensor.to('cuda')
segments_tensors = segments_tensors.to('cuda')
with torch.no_grad():
encoded_layers, _ = self.model(tokens_tensor, segments_tensors)
token_embeddings = torch.stack(encoded_layers, dim=0)
token_embeddings = token_embeddings[-4:, :, :, :].permute(1, 2, 0, 3)
token_embeddings = torch.flatten(token_embeddings, start_dim=2)
for index, tensor in zip(mapping, token_embeddings):
result_batch.append(self.unmap_to_tokens(index, tensor))
return result_batch
def tokenize(self, orig_tokens):
tokenized_text = ["[CLS]"]
orig_to_tok_map = []
for orig_token in orig_tokens:
orig_to_tok_map.append(len(tokenized_text))
tokenized_text.extend(self.tokenizer.tokenize(orig_token))
orig_to_tok_map.append(len(tokenized_text))
tokenized_text.append("[SEP]")
indexed_tokens = self.tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [1] * len(tokenized_text)
return indexed_tokens, segments_ids, orig_to_tok_map
def unmap_to_tokens(self, mapping, tensor):
result = np.zeros((len(mapping) - 1, 3072))
for j, index in enumerate(mapping):
if j != len(mapping) - 1:
result[j] = torch.mean(tensor.cpu()[index:mapping[j + 1]], 0)
return result
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] |
servo_appdynamics_test.py | opsani/servo-appdynamics | 0 | 182105 | <reponame>opsani/servo-appdynamics
import pytest
import datetime
import re
import httpx
import respx
import freezegun
import pydantic
import servo
from servo.types import *
from servo_appdynamics import (
AppdynamicsChecks,
AppdynamicsConfiguration,
AppdynamicsMetric,
AppdynamicsRequest,
AppdynamicsConnector
)
class TestAppdynamicsMetric:
# Query
def test_query_required(self):
try:
AppdynamicsMetric(
name="throughput", unit=Unit.requests_per_minute, query=None
)
except pydantic.ValidationError as error:
assert {
"loc": ("query",),
"msg": "none is not an allowed value",
"type": "type_error.none.not_allowed",
} in error.errors()
class TestAppdynamicsConfiguration:
@pytest.fixture()
def appdynamics_config(self) -> AppdynamicsConfiguration:
return AppdynamicsConfiguration(metrics=[],
username='user',
password='<PASSWORD>',
account='acc',
app_id='app',
tier='test-tier',
)
def test_url_required(self, appdynamics_config):
try:
appdynamics_config
except pydantic.ValidationError as error:
assert {
"loc": ("base_url",),
"msg": "none is not an allowed value",
"type": "type_error.none.not_allowed",
} in error.errors()
def test_supports_localhost_url(self):
config = AppdynamicsConfiguration(base_url="http://localhost:8090",
metrics=[],
username='user',
password='<PASSWORD>',
account='acc',
app_id='app',
tier='test-tier',
)
assert config.base_url == "http://localhost:8090"
def test_supports_cluster_url(self):
config = AppdynamicsConfiguration(
base_url="http://appdynamics.com:8090",
metrics=[],
username='user',
password='<PASSWORD>',
account='acc',
app_id='app',
tier='test-tier',
)
assert config.base_url == "http://appdynamics.com:8090"
def test_rejects_invalid_url(self):
try:
AppdynamicsConfiguration(base_url="gopher://this-is-invalid",
metrics=[],
username='user',
password='<PASSWORD>',
account='acc',
app_id='app',
tier='test-tier',
)
except pydantic.ValidationError as error:
assert {
"loc": ("base_url",),
"msg": "URL scheme not permitted",
"type": "value_error.url.scheme",
"ctx": {
"allowed_schemes": {
"http",
"https",
},
},
} in error.errors()
def test_api_url(self):
config = AppdynamicsConfiguration(
base_url="http://appdynamics.com:8090",
metrics=[],
username='user',
password='<PASSWORD>',
account='acc',
app_id='app',
tier='test-tier',
)
assert (
config.api_url == "http://appdynamics.com:8090/controller/rest/"
)
# Metrics
def test_metrics_required(self):
try:
AppdynamicsConfiguration(metrics=None,
username='user',
password='<PASSWORD>',
account='acc',
app_id='app',
tier='test-tier',
)
except pydantic.ValidationError as error:
assert {
"loc": ("metrics",),
"msg": "none is not an allowed value",
"type": "type_error.none.not_allowed",
} in error.errors()
class TestAppdynamicsRequest:
@freezegun.freeze_time("2020-01-01")
def test_url(self):
request = AppdynamicsRequest(
base_url="http://appdynamics.com",
start=datetime.datetime.now(),
end=datetime.datetime.now() + Duration("36h"),
metric=AppdynamicsMetric(
"throughput",
servo.Unit.requests_per_minute,
query='Overall Application Performance|Calls per Minute',
),
)
assert (
request.endpoint
== '?metric-path=Overall Application Performance|Calls per Minute&time-range-type=BETWEEN_TIMES&start-time=1577836800000&end-time=1577966400000&rollup=false&output=JSON'
)
overall_application_performance_throughput = [{
'frequency': 'ONE_MIN',
'metricId': 11318904,
'metricName': 'BTM|Application Summary|Calls per Minute',
'metricPath': 'Overall Application Performance|Calls per Minute',
'metricValues': [{'count': 5,
'current': 2098,
'max': 1119,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647040000,
'sum': 2098,
'useRange': False,
'value': 2098},
{'count': 5,
'current': 1249,
'max': 615,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647100000,
'sum': 1249,
'useRange': False,
'value': 1249},
{'count': 5,
'current': 1659,
'max': 959,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647160000,
'sum': 1659,
'useRange': False,
'value': 1659},
{'count': 5,
'current': 1696,
'max': 861,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647220000,
'sum': 1696,
'useRange': False,
'value': 1696},
{'count': 5,
'current': 2549,
'max': 1315,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647280000,
'sum': 2549,
'useRange': False,
'value': 2549},
{'count': 5,
'current': 2474,
'max': 1380,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647340000,
'sum': 2474,
'useRange': False,
'value': 2474},
{'count': 5,
'current': 1603,
'max': 792,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647400000,
'sum': 1603,
'useRange': False,
'value': 1603},
{'count': 5,
'current': 1909,
'max': 1103,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647460000,
'sum': 1909,
'useRange': False,
'value': 1909},
{'count': 5,
'current': 2241,
'max': 1270,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647520000,
'sum': 2241,
'useRange': False,
'value': 2241},
{'count': 5,
'current': 2665,
'max': 1491,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647580000,
'sum': 2665,
'useRange': False,
'value': 2665}]
}]
class TestAppdynamicsChecks:
@pytest.fixture
def metric(self) -> AppdynamicsMetric:
return AppdynamicsMetric(
name="throughput",
unit=Unit.requests_per_minute,
query='Overall Application Performance|Calls per Minute',
)
@pytest.fixture
def overall_application_performance_throughput(self) -> List[dict]:
return [{
'frequency': 'ONE_MIN',
'metricId': 11318904,
'metricName': 'BTM|Application Summary|Calls per Minute',
'metricPath': 'Overall Application Performance|Calls per Minute',
'metricValues': [{'count': 5,
'current': 2098,
'max': 1119,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647040000,
'sum': 2098,
'useRange': False,
'value': 2098},
{'count': 5,
'current': 1249,
'max': 615,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647100000,
'sum': 1249,
'useRange': False,
'value': 1249},
{'count': 5,
'current': 1659,
'max': 959,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647160000,
'sum': 1659,
'useRange': False,
'value': 1659},
{'count': 5,
'current': 1696,
'max': 861,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647220000,
'sum': 1696,
'useRange': False,
'value': 1696},
{'count': 5,
'current': 2549,
'max': 1315,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647280000,
'sum': 2549,
'useRange': False,
'value': 2549},
{'count': 5,
'current': 2474,
'max': 1380,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647340000,
'sum': 2474,
'useRange': False,
'value': 2474},
{'count': 5,
'current': 1603,
'max': 792,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647400000,
'sum': 1603,
'useRange': False,
'value': 1603},
{'count': 5,
'current': 1909,
'max': 1103,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647460000,
'sum': 1909,
'useRange': False,
'value': 1909},
{'count': 5,
'current': 2241,
'max': 1270,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647520000,
'sum': 2241,
'useRange': False,
'value': 2241},
{'count': 5,
'current': 2665,
'max': 1491,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647580000,
'sum': 2665,
'useRange': False,
'value': 2665}]
}]
@pytest.fixture
def mocked_api(self, overall_application_performance_throughput):
with respx.mock(
base_url="http://localhost:8090", assert_all_called=False
) as respx_mock:
respx_mock.get(
re.compile(r"/controller/rest/.+"),
name="query"
).mock(httpx.Response(200, json=overall_application_performance_throughput))
yield respx_mock
@pytest.fixture
def checks(self, metric) -> AppdynamicsChecks:
config = AppdynamicsConfiguration(
base_url="http://localhost:8090",
metrics=[metric],
username='user',
password='<PASSWORD>',
account='acc',
app_id='app',
tier='test-tier',
)
return AppdynamicsChecks(config=config)
@respx.mock
async def test_check_queries(self, mocked_api, checks) -> None:
request = mocked_api["query"]
multichecks = await checks._expand_multichecks()
check = await multichecks[0]()
assert request.called
assert check
assert check.name == r'Run query "Overall Application Performance|Calls per Minute"'
assert check.id == "check_queries_item_0"
assert not check.critical
assert check.success
assert check.message == "returned 5 results"
class TestAppdynamicsConnector:
@pytest.fixture
def metric(self) -> AppdynamicsMetric:
return AppdynamicsMetric(
name="test",
unit=Unit.requests_per_minute,
query='Overall Application Performance|Calls per Minute',
)
@pytest.fixture
def overall_application_performance_throughput(self) -> List[dict]:
return [{
'frequency': 'ONE_MIN',
'metricId': 11318904,
'metricName': 'BTM|Application Summary|Calls per Minute',
'metricPath': 'Overall Application Performance|Calls per Minute',
'metricValues': [{'count': 5,
'current': 2098,
'max': 1119,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647040000,
'sum': 2098,
'useRange': False,
'value': 2098},
{'count': 5,
'current': 1249,
'max': 615,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647100000,
'sum': 1249,
'useRange': False,
'value': 1249},
{'count': 5,
'current': 1659,
'max': 959,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647160000,
'sum': 1659,
'useRange': False,
'value': 1659},
{'count': 5,
'current': 1696,
'max': 861,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647220000,
'sum': 1696,
'useRange': False,
'value': 1696},
{'count': 5,
'current': 2549,
'max': 1315,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647280000,
'sum': 2549,
'useRange': False,
'value': 2549},
{'count': 5,
'current': 2474,
'max': 1380,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647340000,
'sum': 2474,
'useRange': False,
'value': 2474},
{'count': 5,
'current': 1603,
'max': 792,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647400000,
'sum': 1603,
'useRange': False,
'value': 1603},
{'count': 5,
'current': 1909,
'max': 1103,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647460000,
'sum': 1909,
'useRange': False,
'value': 1909},
{'count': 5,
'current': 2241,
'max': 1270,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647520000,
'sum': 2241,
'useRange': False,
'value': 2241},
{'count': 5,
'current': 2665,
'max': 1491,
'min': 12,
'occurrences': 1,
'standardDeviation': 0,
'startTimeInMillis': 1614647580000,
'sum': 2665,
'useRange': False,
'value': 2665}]
}]
@pytest.fixture
def mocked_api(self, overall_application_performance_throughput):
with respx.mock(
base_url="http://localhost:8090", assert_all_called=False
) as respx_mock:
respx_mock.get(
re.compile(r"/controller/rest/.+"),
name="query",
).mock(httpx.Response(200, json=overall_application_performance_throughput))
yield respx_mock
@pytest.fixture
def connector(self, metric) -> AppdynamicsConnector:
config = AppdynamicsConfiguration(
base_url="http://localhost:8090",
metrics=[metric],
username='user',
password='<PASSWORD>',
account='acc',
app_id='app',
tier='test-tier',
)
return AppdynamicsConnector(config=config)
async def test_describe(self, connector) -> None:
described = connector.describe()
assert described.metrics == connector.metrics()
# TODO: Handle conditional flow for metrics
# @respx.mock
# async def test_measure(self, mocked_api, connector) -> None:
# request = mocked_api["query"]
# print(connector.metrics())
# measurements = await connector.measure()
# assert request.called
# # Assert float values are the same (for first entry from first reading)
# print(measurements.readings[0].data_points[0][1])
# assert measurements.readings[0].data_points[0][1] == overall_application_performance_throughput[0]["metricValues"][0]["value"]
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ChatApp/settings.py | xckomorebi/ChatApp | 0 | 79491 | import os
from pathlib import Path
DB_NAME = "chatapp.db"
PROJECT_PATH = Path(__file__).parents[1]
DB_PATH = os.path.join(PROJECT_PATH, "resource", DB_NAME)
PORT_MIN = 1024
PORT_MAX = 65535
DEBUG = os.getenv("CHAT_APP_DEBUG", False)
if DEBUG:
TIMEOUT = 30
else:
TIMEOUT = 0.5
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responsibleai/responsibleai/modelanalysis/model_analysis.py | PYTHON01100100/responsible-ai-widgets | 1 | 197447 | # Copyright (c) Microsoft Corporation
# Licensed under the MIT License.
"""Defines the ModelAnalysis class."""
import json
import numpy as np
import pandas as pd
from pathlib import Path
import pickle
import warnings
from responsibleai._input_processing import _convert_to_list
from responsibleai._interfaces import ModelAnalysisData, Dataset
from responsibleai._internal.constants import\
ManagerNames, Metadata, SKLearn
from responsibleai._managers.counterfactual_manager import (
CounterfactualManager)
from responsibleai._managers.error_analysis_manager import ErrorAnalysisManager
from responsibleai._managers.explainer_manager import ExplainerManager
from responsibleai._managers.causal_manager import CausalManager
from responsibleai.exceptions import UserConfigValidationException
from responsibleai.modelanalysis.constants import ModelTask
_DTYPES = 'dtypes'
_TRAIN = 'train'
_TEST = 'test'
_TARGET_COLUMN = 'target_column'
_TASK_TYPE = 'task_type'
_MODEL = Metadata.MODEL
_MODEL_PKL = _MODEL + '.pkl'
_SERIALIZER = 'serializer'
_CLASSES = 'classes'
_MANAGERS = 'managers'
_CATEGORICAL_FEATURES = 'categorical_features'
_TRAN_LABELS = 'train_labels'
_META_JSON = Metadata.META_JSON
class ModelAnalysis(object):
"""Defines the top-level Model Analysis API.
Use ModelAnalysis to analyze errors, explain the most important
features, compute counterfactuals and run causal analysis in a
single API.
:param model: The model to compute RAI insights for.
A model that implements sklearn.predict or sklearn.predict_proba
or function that accepts a 2d ndarray.
:type model: object
:param train: The training dataset including the label column.
:type train: pandas.DataFrame
:param test: The test dataset including the label column.
:type test: pandas.DataFrame
:param target_column: The name of the label column.
:type target_column: str
:param task_type: The task to run, can be `classification` or
`regression`.
:type task_type: str
:param categorical_features: The categorical feature names.
:type categorical_features: list[str]
:param train_labels: The class labels in the training dataset
:type train_labels: ndarray
:param serializer: Picklable custom serializer with save and load
methods for custom model serialization.
The save method writes the model to file given a parent directory.
The load method returns the deserialized model from the same
parent directory.
:type serializer: object
"""
def __init__(self, model, train, test, target_column,
task_type, categorical_features=None, train_labels=None,
serializer=None):
"""Defines the top-level Model Analysis API.
Use ModelAnalysis to analyze errors, explain the most important
features, compute counterfactuals and run causal analysis in a
single API.
:param model: The model to compute RAI insights for.
A model that implements sklearn.predict or sklearn.predict_proba
or function that accepts a 2d ndarray.
:type model: object
:param train: The training dataset including the label column.
:type train: pandas.DataFrame
:param test: The test dataset including the label column.
:type test: pandas.DataFrame
:param target_column: The name of the label column.
:type target_column: str
:param task_type: The task to run, can be `classification` or
`regression`.
:type task_type: str
:param categorical_features: The categorical feature names.
:type categorical_features: list[str]
:param train_labels: The class labels in the training dataset
:type train_labels: ndarray
:param serializer: Picklable custom serializer with save and load
methods defined for model that is not serializable. The save
method returns a dictionary state and load method returns the
model.
:type serializer: object
"""
self._validate_model_analysis_input_parameters(
model=model, train=train, test=test,
target_column=target_column, task_type=task_type,
categorical_features=categorical_features,
train_labels=train_labels,
serializer=serializer)
self.model = model
self.train = train
self.test = test
self.target_column = target_column
self.task_type = task_type
self.categorical_features = categorical_features
self._serializer = serializer
self._train_labels = train_labels
self._classes = ModelAnalysis._get_classes(
task_type=self.task_type,
train=self.train,
target_column=self.target_column,
train_labels=self._train_labels
)
self._causal_manager = CausalManager(
train, test, target_column, task_type, categorical_features)
self._counterfactual_manager = CounterfactualManager(
model=model, train=train, test=test,
target_column=target_column, task_type=task_type,
categorical_features=categorical_features)
self._error_analysis_manager = ErrorAnalysisManager(
model, test, target_column,
categorical_features)
self._explainer_manager = ExplainerManager(
model, train, test,
target_column,
self._classes,
categorical_features=categorical_features)
self._managers = [self._causal_manager,
self._counterfactual_manager,
self._error_analysis_manager,
self._explainer_manager]
@staticmethod
def _get_classes(task_type, train, target_column, train_labels):
if task_type == ModelTask.CLASSIFICATION:
if train_labels is None:
return train[target_column].unique()
else:
return train_labels
else:
return None
def _validate_model_analysis_input_parameters(
self, model, train, test, target_column,
task_type, categorical_features=None, train_labels=None,
serializer=None):
"""
Validate the inputs for ModelAnalysis class.
:param model: The model to compute RAI insights for.
A model that implements sklearn.predict or sklearn.predict_proba
or function that accepts a 2d ndarray.
:type model: object
:param train: The training dataset including the label column.
:type train: pandas.DataFrame
:param test: The test dataset including the label column.
:type test: pandas.DataFrame
:param target_column: The name of the label column.
:type target_column: str
:param task_type: The task to run, can be `classification` or
`regression`.
:type task_type: str
:param categorical_features: The categorical feature names.
:type categorical_features: list[str]
:param train_labels: The class labels in the training dataset
:type train_labels: ndarray
:param serializer: Picklable custom serializer with save and load
methods defined for model that is not serializable. The save
method returns a dictionary state and load method returns the
model.
:type serializer: object
"""
valid_tasks = [
ModelTask.CLASSIFICATION.value,
ModelTask.REGRESSION.value
]
if task_type not in valid_tasks:
message = (f"Unsupported task type '{task_type}'. "
f"Should be one of {valid_tasks}")
raise UserConfigValidationException(message)
if model is None:
warnings.warn(
'INVALID-MODEL-WARNING: No valid model is supplied. '
'The explanations, error analysis and counterfactuals '
'may not work')
if serializer is not None:
if not hasattr(serializer, 'save'):
raise UserConfigValidationException(
'The serializer does not implement save()')
if not hasattr(serializer, 'load'):
raise UserConfigValidationException(
'The serializer does not implement load()')
try:
pickle.dumps(serializer)
except Exception:
raise UserConfigValidationException(
'The serializer should be serializable via pickle')
if isinstance(train, pd.DataFrame) and isinstance(test, pd.DataFrame):
if len(set(train.columns) - set(test.columns)) != 0 or \
len(set(test.columns) - set(train.columns)):
raise UserConfigValidationException(
'The features in train and test data do not match')
if target_column not in list(train.columns) or \
target_column not in list(test.columns):
raise UserConfigValidationException(
'Target name {0} not present in train/test data'.format(
target_column)
)
if categorical_features is not None and \
len(categorical_features) > 0:
if target_column in categorical_features:
raise UserConfigValidationException(
'Found target name {0} in '
'categorical feature list'.format(
target_column)
)
difference_set = set(categorical_features) - set(train.columns)
if len(difference_set) > 0:
message = ("Feature names in categorical_features "
"do not exist in train data: "
f"{list(difference_set)}")
raise UserConfigValidationException(message)
if train_labels is not None and task_type == \
ModelTask.CLASSIFICATION:
if len(set(train[target_column].unique()) -
set(train_labels)) != 0 or \
len(set(train_labels) -
set(train[target_column].unique())) != 0:
raise UserConfigValidationException(
'The train labels and distinct values in '
'target (train data) do not match')
if len(set(test[target_column].unique()) -
set(train_labels)) != 0 or \
len(set(train_labels) -
set(test[target_column].unique())) != 0:
raise UserConfigValidationException(
'The train labels and distinct values in '
'target (test data) do not match')
if model is not None:
# Pick one row from train and test data
small_train_data = train.iloc[0:1].drop(
[target_column], axis=1)
small_test_data = test.iloc[0:1].drop(
[target_column], axis=1)
# Run predict() of the model
try:
model.predict(small_train_data)
model.predict(small_test_data)
except Exception:
raise UserConfigValidationException(
'The model passed cannot be used for'
' getting predictions via predict()'
)
# Run predict_proba() of the model
if task_type == ModelTask.CLASSIFICATION:
try:
model.predict_proba(small_train_data)
model.predict_proba(small_test_data)
except Exception:
raise UserConfigValidationException(
'The model passed cannot be used for'
' getting predictions via predict_proba()'
)
@property
def causal(self) -> CausalManager:
"""Get the causal manager.
:return: The causal manager.
:rtype: CausalManager
"""
return self._causal_manager
@property
def counterfactual(self) -> CounterfactualManager:
"""Get the counterfactual manager.
:return: The counterfactual manager.
:rtype: CounterfactualManager
"""
return self._counterfactual_manager
@property
def error_analysis(self) -> ErrorAnalysisManager:
"""Get the error analysis manager.
:return: The error analysis manager.
:rtype: ErrorAnalysisManager
"""
return self._error_analysis_manager
@property
def explainer(self) -> ExplainerManager:
"""Get the explainer manager.
:return: The explainer manager.
:rtype: ExplainerManager
"""
return self._explainer_manager
def compute(self):
"""Calls compute on each of the managers."""
for manager in self._managers:
manager.compute()
def list(self):
"""List information about each of the managers.
:return: Information about each of the managers.
:rtype: dict
"""
configs = {}
for manager in self._managers:
configs[manager.name] = manager.list()
return configs
def get(self):
"""List information about each of the managers.
:return: Information about each of the managers.
:rtype: dict
"""
configs = {}
for manager in self._managers:
configs[manager.name] = manager.get()
return configs
def get_data(self):
"""Get all data as ModelAnalysisData object
:return: Model Analysis Data
:rtype: ModelAnalysisData
"""
data = ModelAnalysisData()
data.dataset = self._get_dataset()
data.modelExplanationData = self.explainer.get_data()
data.errorAnalysisData = self.error_analysis.get_data()
data.causalAnalysisData = self.causal.get_data()
data.counterfactualData = self.counterfactual.get_data()
return data
def _get_dataset(self):
dashboard_dataset = Dataset()
dashboard_dataset.task_type = self.task_type
dashboard_dataset.categorical_features = self.categorical_features
dashboard_dataset.class_names = _convert_to_list(
self._classes)
predicted_y = None
feature_length = None
dataset: pd.DataFrame = self.test.drop(
[self.target_column], axis=1)
if isinstance(dataset, pd.DataFrame) and hasattr(dataset, 'columns'):
self._dataframeColumns = dataset.columns
try:
list_dataset = _convert_to_list(dataset)
except Exception as ex:
raise ValueError(
"Unsupported dataset type") from ex
if dataset is not None and self.model is not None:
try:
predicted_y = self.model.predict(dataset)
except Exception as ex:
msg = "Model does not support predict method for given"
"dataset type"
raise ValueError(msg) from ex
try:
predicted_y = _convert_to_list(predicted_y)
except Exception as ex:
raise ValueError(
"Model prediction output of unsupported type,") from ex
if predicted_y is not None:
if(self.task_type == "classification" and
dashboard_dataset.class_names is not None):
predicted_y = [dashboard_dataset.class_names.index(
y) for y in predicted_y]
dashboard_dataset.predicted_y = predicted_y
row_length = 0
if list_dataset is not None:
row_length, feature_length = np.shape(list_dataset)
if row_length > 100000:
raise ValueError(
"Exceeds maximum number of rows"
"for visualization (100000)")
if feature_length > 1000:
raise ValueError("Exceeds maximum number of features for"
" visualization (1000). Please regenerate the"
" explanation using fewer features or"
" initialize the dashboard without passing a"
" dataset.")
dashboard_dataset.features = list_dataset
true_y = self.test[self.target_column]
if true_y is not None and len(true_y) == row_length:
if(self.task_type == "classification" and
dashboard_dataset.class_names is not None):
true_y = [dashboard_dataset.class_names.index(
y) for y in true_y]
dashboard_dataset.true_y = _convert_to_list(true_y)
features = dataset.columns
if features is not None:
features = _convert_to_list(features)
if feature_length is not None and len(features) != feature_length:
raise ValueError("Feature vector length mismatch:"
" feature names length differs"
" from local explanations dimension")
dashboard_dataset.feature_names = features
dashboard_dataset.target_column = self.target_column
if (self.model is not None and
hasattr(self.model, SKLearn.PREDICT_PROBA) and
self.model.predict_proba is not None and
dataset is not None):
try:
probability_y = self.model.predict_proba(dataset)
except Exception as ex:
raise ValueError("Model does not support predict_proba method"
" for given dataset type,") from ex
try:
probability_y = _convert_to_list(probability_y)
except Exception as ex:
raise ValueError(
"Model predict_proba output of unsupported type,") from ex
dashboard_dataset.probability_y = probability_y
return dashboard_dataset
def _write_to_file(self, file_path, content):
"""Save the string content to the given file path.
:param file_path: The file path to save the content to.
:type file_path: str
:param content: The string content to save.
:type content: str
"""
with open(file_path, 'w') as file:
file.write(content)
def save(self, path):
"""Save the ModelAnalysis to the given path.
:param path: The directory path to save the ModelAnalysis to.
:type path: str
"""
top_dir = Path(path)
# save each of the individual managers
for manager in self._managers:
manager._save(top_dir / manager.name)
# save current state
dtypes = self.train.dtypes.astype(str).to_dict()
self._write_to_file(top_dir / (_TRAIN + _DTYPES),
json.dumps(dtypes))
self._write_to_file(top_dir / _TRAIN, self.train.to_json())
dtypes = self.test.dtypes.astype(str).to_dict()
self._write_to_file(top_dir / (_TEST + _DTYPES),
json.dumps(dtypes))
self._write_to_file(top_dir / _TEST, self.test.to_json())
meta = {
_TARGET_COLUMN: self.target_column,
_TASK_TYPE: self.task_type,
_CATEGORICAL_FEATURES: self.categorical_features,
_TRAN_LABELS: self._train_labels
}
with open(top_dir / _META_JSON, 'w') as file:
json.dump(meta, file)
if self._serializer is not None:
# save the model
self._serializer.save(self.model, top_dir)
# save the serializer
with open(top_dir / _SERIALIZER, 'wb') as file:
pickle.dump(self._serializer, file)
else:
if self.model is not None:
has_setstate = hasattr(self.model, '__setstate__')
has_getstate = hasattr(self.model, '__getstate__')
if not (has_setstate and has_getstate):
raise ValueError(
"Model must be picklable or a custom serializer must"
" be specified")
with open(top_dir / _MODEL_PKL, 'wb') as file:
pickle.dump(self.model, file)
@staticmethod
def load(path):
"""Load the ModelAnalysis from the given path.
:param path: The directory path to load the ModelAnalysis from.
:type path: str
"""
# create the ModelAnalysis without any properties using the __new__
# function, similar to pickle
inst = ModelAnalysis.__new__(ModelAnalysis)
top_dir = Path(path)
# load current state
with open(top_dir / (_TRAIN + _DTYPES), 'r') as file:
types = json.load(file)
with open(top_dir / _TRAIN, 'r') as file:
train = pd.read_json(file, dtype=types)
inst.__dict__[_TRAIN] = train
with open(top_dir / (_TEST + _DTYPES), 'r') as file:
types = json.load(file)
with open(top_dir / _TEST, 'r') as file:
test = pd.read_json(file, dtype=types)
inst.__dict__[_TEST] = test
with open(top_dir / _META_JSON, 'r') as meta_file:
meta = meta_file.read()
meta = json.loads(meta)
inst.__dict__[_TARGET_COLUMN] = meta[_TARGET_COLUMN]
inst.__dict__[_TASK_TYPE] = meta[_TASK_TYPE]
inst.__dict__[_CATEGORICAL_FEATURES] = meta[_CATEGORICAL_FEATURES]
inst.__dict__['_' + _TRAN_LABELS] = meta[_TRAN_LABELS]
inst.__dict__['_' + _CLASSES] = ModelAnalysis._get_classes(
task_type=meta[_TASK_TYPE],
train=train,
target_column=meta[_TARGET_COLUMN],
train_labels=meta[_TRAN_LABELS]
)
serializer_path = top_dir / _SERIALIZER
if serializer_path.exists():
with open(serializer_path, 'rb') as file:
serializer = pickle.load(file)
inst.__dict__['_' + _SERIALIZER] = serializer
inst.__dict__[_MODEL] = serializer.load(top_dir)
else:
inst.__dict__['_' + _SERIALIZER] = None
with open(top_dir / _MODEL_PKL, 'rb') as file:
inst.__dict__[_MODEL] = pickle.load(file)
# load each of the individual managers
manager_map = {
ManagerNames.CAUSAL: CausalManager,
ManagerNames.COUNTERFACTUAL: CounterfactualManager,
ManagerNames.ERROR_ANALYSIS: ErrorAnalysisManager,
ManagerNames.EXPLAINER: ExplainerManager,
}
managers = []
for manager_name, manager_class in manager_map.items():
full_name = f'_{manager_name}_manager'
manager_dir = top_dir / manager_name
manager = manager_class._load(manager_dir, inst)
inst.__dict__[full_name] = manager
managers.append(manager)
inst.__dict__['_' + _MANAGERS] = managers
return inst
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] |
kanpai/array.py | chandrakantap/kanpai | 3 | 1607769 | <filename>kanpai/array.py
from .validator import Validator, RequiredMixin
class Array(RequiredMixin):
def __init__(self, error="Expecting an array.", convert_none_to_empty=False):
self.processors = []
self.processors.append({
'action': self.__assert_array,
'attribs': {
'error': error,
'convert_none_to_empty': convert_none_to_empty
}
})
def __assert_array(self, data, attribs):
if data is None:
if attribs.get('convert_none_to_empty', False):
return self.validation_success([])
else:
return self.validation_success(data)
if type(data) is list or type(data) is tuple:
return self.validation_success(data)
else:
return self.validation_error(data, attribs.get('error'))
def of(self, element_validator):
if not isinstance(element_validator, Validator):
raise TypeError(
f'Expecting a instance of validator in element_validator')
self.processors.append({
'action': self.__validate_elements,
'attribs': {
'element_validator': element_validator
}
})
return self
def __validate_elements(self, data, attribs):
if data is None:
return self.validation_success(data)
validation_success = True
validation_error = {}
validated_data = []
element_validator = attribs.get('element_validator')
for index, element in enumerate(data):
validation_result = element_validator.validate(element)
validation_success = validation_success and validation_result.get(
'success')
validated_data.append(validation_result.get('data'))
if validation_result.get('success') is False:
validation_error[index] = validation_result.get('error')
return {
'success': validation_success,
'data': validated_data,
'error': validation_error
}
def min(self, min_length, error=None):
if type(min_length) is not int:
raise ValueError(
'value for min_length is expected to be an integer')
if error is None:
error = f"At least {min_length} element required."
self.processors.append({
'action': self.__assert_min,
'attribs': {
'min_length': min_length,
'error': error
}
})
return self
def __assert_min(self, data, attribs):
if data is not None and len(data) < attribs['min_length']:
return self.validation_error(data, attribs['error'])
else:
return self.validation_success(data)
def max(self, max_length, error=None):
if type(max_length) is not int:
raise ValueError(
'value for max_length is expected to be an integer')
if error is None:
error = f"Maximum {max_length} element allowed."
self.processors.append({
'action': self.__assert_max,
'attribs': {
'max_length': max_length,
'error': error
}
})
return self
def __assert_max(self, data, attribs):
if data is not None and len(data) > attribs['max_length']:
return self.validation_error(data, attribs['error'])
else:
return self.validation_success(data)
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] |
convert/photo2movie.py | penrin/SP-mapping | 0 | 47433 | <reponame>penrin/SP-mapping
import sys
import cv2
import argparse
import time
from math import ceil
class ProgressBar():
def __init__(self, bar_length=40, slug='#', space='-', countdown=True):
self.bar_length = bar_length
self.slug = slug
self.space = space
self.countdown = countdown
self.start_time = None
self.start_parcent = 0
def bar(self, percent, end=1, tail=''):
percent = percent / end
if self.countdown == True:
progress = percent - self.start_parcent
if self.start_time == None:
self.start_time = time.perf_counter()
self.start_parcent = percent
remain = 'Remain --:--:--'
elif progress == 0:
remain = 'Remain --:--:--'
elif progress != 0:
elapsed_time = time.perf_counter() - self.start_time
progress = percent - self.start_parcent
remain_t = (elapsed_time / progress) * (1 - percent)
remain_t = ceil(remain_t)
h = remain_t // 3600
m = remain_t % 3600 // 60
s = remain_t % 60
remain = 'Remain %02d:%02d:%02d' % (h, m, s)
else:
remain = ''
len_slugs = int(percent * self.bar_length)
slugs = self.slug * len_slugs
spaces = self.space * (self.bar_length - len_slugs)
txt = '\r[{bar}] {percent:.1%} {remain} {tail}'.format(
bar=(slugs + spaces), percent=percent,
remain=remain, tail=tail)
if percent == 1:
txt += '\n'
self.start_time = None
sys.stdout.write(txt)
sys.stdout.flush()
class ProgressBar2(ProgressBar):
def __init__(self, end, bar_length=40, slug='#', space='-', countdown=True):
super().__init__(bar_length=40, slug='#', space='-', countdown=True)
self.counter = 0
self.end = end
self.bar()
def bar(self, tail=''):
super().bar(self.counter, end=self.end, tail='')
self.counter += 1
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('filename', help='output filename')
parser.add_argument('-i', type=str, help='input image filename', required=True)
parser.add_argument('--nframes', type=int, default=30, help='number of frames')
parser.add_argument('--fps', type=float, default=30, help='fps')
args = parser.parse_args()
filename_img = args.i
filename_mov = args.filename
nframes = args.nframes
fps = args.fps
img = cv2.imread(filename_img)
if img is None:
print('image cannot be read')
sys.exit()
shape = img.shape[1], img.shape[0]
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
writer = cv2.VideoWriter(filename_mov, fourcc, fps, shape)
pg = ProgressBar2(nframes)
for i in range(nframes):
writer.write(img)
pg.bar()
writer.release()
| [
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] |
migrator/cf.py | cloud-gov/external-domain-broker-migrator | 1 | 82815 | import logging
from cloudfoundry_client.client import CloudFoundryClient
from cloudfoundry_client.errors import InvalidStatusCode
logger = logging.getLogger(__name__)
def get_cf_client(config):
# "why is this a function, and the rest of these are static?"
# good question. The __init__ on this immediately probes the
# api endpoint, which means we need to stub it for testing.
# and having to add that stub to every test that _might_
# `import extensions` would be bonkers. As a function, we should
# only need to stub when we're actually thinking about CF
logger.debug("getting cf client")
client = CloudFoundryClient(config.CF_API_ENDPOINT)
client.init_with_user_credentials(config.CF_USERNAME, config.CF_PASSWORD)
return client
def enable_plan_for_org(plan_id, org_id, client):
logger.debug("enabling plan for %s", org_id)
try:
response = client.v2.service_plan_visibilities.create(plan_id, org_id)
except InvalidStatusCode as e:
if e.body["error_code"] != "CF-ServicePlanVisibilityAlreadyExists":
raise e
def get_service_plan_visibility_ids_for_org(plan_id, org_id, client):
logger.debug("getting plan visibilities for %s", org_id)
service_plan_visibilities = client.v2.service_plan_visibilities.list(
service_plan_guid=plan_id, organization_guid=org_id
)
return [
service_plan_visibility["metadata"]["guid"]
for service_plan_visibility in service_plan_visibilities
]
def disable_plan_for_org(service_plan_visibility_id, client):
logger.debug("disabling plan visibility")
return client.v2.service_plan_visibilities.remove(service_plan_visibility_id)
def get_space_id_for_service_instance_id(instance_id, client):
logger.debug("getting space_id for instance %s", instance_id)
response = client.v2.service_instances.get(instance_id)
return response["entity"]["space_guid"]
def get_org_id_for_space_id(space_id, client):
logger.debug("getting org_id for space %s", space_id)
response = client.v3.spaces.get(space_id)
return response["relationships"]["organization"]["data"]["guid"]
def get_all_space_ids_for_org(org_id, client):
logger.debug("getting space_ids for org %s", org_id)
spaces = client.v3.spaces.list(organization_guids=[org_id])
return [space["guid"] for space in spaces]
def create_bare_migrator_service_instance_in_space(
space_id, plan_id, instance_name, domains, client
):
logger.debug("creating service instance for space %s", space_id)
response = client.v2.service_instances.create(
space_guid=space_id,
instance_name=instance_name,
plan_guid=plan_id,
accepts_incomplete=True,
parameters=dict(domains=domains),
)
return {
"guid": response["metadata"]["guid"],
"state": response["entity"]["last_operation"]["state"],
"type": response["entity"]["last_operation"]["type"],
}
def get_migrator_service_instance_status(instance_id, client):
logger.debug("polling service instance status for instance %s", instance_id)
response = client.v2.service_instances.get(instance_id)
return response["entity"]["last_operation"]["state"]
def update_existing_cdn_domain_service_instance(
instance_id, params, client, *, new_instance_name=None, new_plan_guid=None
):
logger.debug("updating service instance %s", instance_id)
return client.v2.service_instances.update(
instance_id,
parameters=params,
instance_name=new_instance_name,
plan_guid=new_plan_guid,
accepts_incomplete=True,
)
def purge_service_instance(instance_id, client):
logger.debug("purging service instance %s", instance_id)
return client.v2.service_instances.remove(instance_id, purge=True)
def get_instance_data(instance_id, client):
return client.v2.service_instances.get(instance_id)
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CodeForces/easy problems/230_b_t_primes.py | dimitrov-dimitar/competitive-programming | 0 | 112361 | <filename>CodeForces/easy problems/230_b_t_primes.py<gh_stars>0
# https://codeforces.com/problemset/problem/230/B
n = int(input())
numbers = [int(x) for x in input().split()]
counter = 0
for number in numbers:
if number == 1 or number == 2:
print('NO')
continue
for x in range(2, number):
if counter > 1:
break
if number % x == 0:
counter += 1
if counter == 1:
print('YES')
else:
print('NO')
counter = 0
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tutorial/11_Segmentation/03_cluster_extraction.py | maguangyan/pclpy_tutorial | 17 | 1610114 | # -*- coding: utf-8 -*-
# @Time : DATE:2021/10/6
# @Author : yan
# @Email : <EMAIL>
# @File : 03_cluster_extraction.py
import pclpy
from pclpy import pcl
import numpy as np
def compareCloudShow(cloud, cloud_filtered):
# Open 3D viewer and add point cloud and normals
viewer = pcl.visualization.PCLVisualizer("viewer")
v0 = 1
viewer.createViewPort(0.0, 0.0, 0.5, 1.0, v0)
viewer.setBackgroundColor(0.0, 0.0, 0.0, v0)
single_color = pcl.visualization.PointCloudColorHandlerCustom.PointXYZ(cloud, 0.0, 255.0, 0.0)
viewer.addPointCloud(cloud, single_color, "sample cloud1", v0)
v1 = 2
viewer.createViewPort(0.5, 0.0, 1.0, 1.0, v1)
viewer.setBackgroundColor(0.0, 0.0, 0.0, v1)
single_color = pcl.visualization.PointCloudColorHandlerCustom.PointXYZ(cloud_filtered, 0.0, 255.0, 0.0)
viewer.addPointCloud(cloud_filtered, single_color, "sample cloud2", v1)
viewer.setPointCloudRenderingProperties(0, 1, "sample cloud1", v0)
viewer.setPointCloudRenderingProperties(0, 1, "sample cloud2", v1)
viewer.addCoordinateSystem(1.0)
while not viewer.wasStopped():
viewer.spinOnce(10)
if __name__ == '__main__':
reader = pcl.io.PCDReader()
cloud = pcl.PointCloud.PointXYZ()
cloud_f = pcl.PointCloud.PointXYZ()
reader.read('../../data/table_scene_lms400.pcd', cloud)
print('PointCloud before filtering has: ', cloud.size(), ' data points.')
# 下采样 使用leaf size为1cm
vg = pcl.filters.VoxelGrid.PointXYZ()
cloud_filtered = pcl.PointCloud.PointXYZ()
vg.setInputCloud(cloud)
vg.setLeafSize(0.01, 0.01, 0.01)
vg.filter(cloud_filtered)
print('PointCloud after filtering has: ', cloud_filtered.size(), ' data points.')
# 平面分割
seg = pcl.segmentation.SACSegmentation.PointXYZ()
inliers = pcl.PointIndices()
coefficients = pcl.ModelCoefficients()
cloud_plane = pcl.PointCloud.PointXYZ()
writer = pcl.io.PCDWriter()
seg.setOptimizeCoefficients(True)
seg.setModelType(0)
seg.setMethodType(0)
seg.setMaxIterations(100)
seg.setDistanceThreshold(0.02)
nr_points = cloud_filtered.size()
while cloud_filtered.size() > 0.3 * nr_points:
# 在剩余的点云中分割出最大的平面成分
seg.setInputCloud(cloud_filtered)
seg.segment(inliers, coefficients)
if len(inliers.indices) == 0:
print('Could not estimate a planar model for the given dataset.')
break
# 提取平面内点
extract = pcl.filters.ExtractIndices.PointXYZ()
extract.setInputCloud(cloud_filtered)
extract.setIndices(inliers)
extract.setNegative(False)
extract.filter(cloud_plane)
# 可视化提取出来的平面
compareCloudShow(cloud_filtered, cloud_plane)
print('PointCloud representing the planar component: ', cloud_plane.size(), ' data points.')
# 去除上面提取到的平面内点,得到剩余点
extract.setNegative(True)
extract.filter(cloud_f)
cloud_filtered = cloud_f
writer.write('cloud_cluster_source.pcd', cloud_filtered, False)
# 创建KdTree
tree = pcl.search.KdTree.PointXYZ()
tree.setInputCloud(cloud_filtered)
cluster_indices = pcl.vectors.PointIndices()
ec = pcl.segmentation.EuclideanClusterExtraction.PointXYZ()
ec.setClusterTolerance(0.02) # 2cm
ec.setMinClusterSize(100)
ec.setMaxClusterSize(25000)
ec.setSearchMethod(tree)
ec.setInputCloud(cloud_filtered)
ec.extract(cluster_indices)
j = 0
for it in cluster_indices:
cloud_cluster = pcl.PointCloud.PointXYZ()
for pit in it.indices:
cloud_cluster.push_back(cloud_filtered.at(pit))
cloud_cluster.width = cloud_cluster.size()
cloud_cluster.height = 1
cloud_cluster.is_dense = True
print('PointCloud representing the Cluster: ', cloud_cluster.size(), ' data points.')
ss = 'cloud_cluster_' + str(j) + '.pcd'
writer.write(ss, cloud_cluster, False)
j = j + 1
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website/openeuler_release_refresher/obs/__init__.py | openeuler-mirror/infrastructure | 1 | 1615335 | <reponame>openeuler-mirror/infrastructure<filename>website/openeuler_release_refresher/obs/__init__.py
# -*- coding:utf-8 -*-
# Copyright 2019 Huawei Technologies Co.,Ltd.
# 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 obs.ilog import LogConf
from obs.client import ObsClient
from obs.model import *
__all__ = [
'LogConf',
'ObsClient',
'CompletePart',
'Permission',
'StorageClass',
'EventType',
'RestoreTier',
'Group',
'Grantee',
'Grant',
'ExtensionGrant',
'Owner',
'ACL',
'Condition',
'DateTime',
'SseCHeader',
'SseKmsHeader',
'CopyObjectHeader',
'SetObjectMetadataHeader',
'CorsRule',
'CreateBucketHeader',
'ErrorDocument',
'IndexDocument',
'Expiration',
'NoncurrentVersionExpiration',
'GetObjectHeader',
'HeadPermission',
'Lifecycle',
'Notification',
'TopicConfiguration',
'FunctionGraphConfiguration',
'FilterRule',
'Replication',
'ReplicationRule',
'Options',
'PutObjectHeader',
'AppendObjectHeader',
'AppendObjectContent',
'RedirectAllRequestTo',
'Redirect',
'RoutingRule',
'Tag',
'TagInfo',
'Transition',
'NoncurrentVersionTransition',
'Rule',
'Versions',
'Object',
'WebsiteConfiguration',
'Logging',
'CompleteMultipartUploadRequest',
'DeleteObjectsRequest',
'ListMultipartUploadsRequest',
'GetObjectRequest',
'UploadFileHeader'
]
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days/01/part1.py | gr3yknigh1/aoc2021 | 0 | 12537 | <filename>days/01/part1.py<gh_stars>0
from __future__ import annotations
import os
import collections
BASE_PATH = os.path.dirname(__file__)
INPUT_PATH = os.path.join(BASE_PATH, "input.txt")
OUTPUT_PATH = os.path.join(BASE_PATH, "output.txt")
def proceed_buffer(buffer: str) -> list[int]:
return [int(line) for line in buffer.splitlines()]
def main() -> int:
buffer: str = ""
with open(INPUT_PATH, mode='r', encoding="utf-8") as f:
buffer = f.read()
measurements: list[int] = proceed_buffer(buffer)
measurements_counter = collections.Counter()
output_buffer: str = ""
prev: int = None
for i in measurements:
if prev is None:
output_buffer += f"{i} (N/A - no previous measurement)\n"
measurements_counter["None"] += 1
elif prev > i:
output_buffer += f"{i} (decrease)\n"
measurements_counter["Decreased"] += 1
elif prev < i:
output_buffer += f"{i} (increase)\n"
measurements_counter["Increased"] += 1
elif prev == i:
output_buffer += f"{i} (not changed)\n"
measurements_counter["Equal"] += 1
prev = i
output_buffer += "\n====\n"
output_buffer += "Total:\n"
output_buffer += f": {measurements_counter}"
with open(OUTPUT_PATH, mode='w', encoding="utf-8") as f:
f.write(output_buffer)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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falconcv/data/scraper/flickr_scraper.py | haruiz/FalconCV | 16 | 17703 | import logging
import math
import re
import time
import dask
import numpy as np
import requests
import json
import xml.etree.ElementTree as ET
from falconcv.data.scraper.scraper import ImagesScraper
from falconcv.util import ImageUtil
logger = logging.getLogger(__name__)
FLICKR_ENDPOINT = "https://www.flickr.com/services/rest"
# List of sizes:
# url_o: Original (4520 × 3229)
# url_k: Large 2048 (2048 × 1463)
# url_h: Large 1600 (1600 × 1143)
# url_l=: Large 1024 (1024 × 732)
# url_c: Medium 800 (800 × 572)
# url_z: Medium 640 (640 × 457)
# url_m: Medium 500 (500 × 357)
# url_n: Small 320 (320 × 229)
# url_s: Small 240 (240 × 171)
# url_t: Thumbnail (100 × 71)
# url_q: Square 150 (150 × 150)
# url_sq: Square 75 (75 × 75)
class FlickrScraper(ImagesScraper):
def __init__(self, api_key):
super(FlickrScraper, self).__init__()
self.api_key = api_key
def _authenticate(self):
pass
def _get_total_matches(self, q):
total_matches = 0
try:
response = requests.get(url=FLICKR_ENDPOINT, params={
"api_key": self.api_key,
"method": "flickr.photos.search",
"tags": ",".join(q),
"tag_mode": "any",
# "privacy_filter": "1"
"content_type": 1,
"media": "photos",
"per_page": 0,
"format": "json"
})
if response.status_code == 200:
json_text = re.search(r'\((.*?)\)', response.text).group(1)
json_object = json.loads(json_text)
if json_object["stat"] == "ok":
total_matches = int(json_object["photos"]["total"])
# total_matches = json_object["photos"]
except Exception as ex:
logger.error("Error making the request : {}".format(ex))
return total_matches
def _request_photos(self, q, count, page):
images = []
try:
response = requests.get(url=FLICKR_ENDPOINT, params={
"api_key": self.api_key,
"method": "flickr.photos.search",
"tags": ",".join(q),
"tag_mode": "any",
# "privacy_filter": "1"
"content_type": 1,
"media": "photos",
"per_page": count,
"page": page,
"extras": ",".join(["url_o", "url_k", "url_h", "url_l", "url_c", "url_m"])
})
if response.status_code == 200:
try:
# print(response.text)
root: ET.Element = ET.fromstring(response.text)
stat = root.get("stat")
if stat == "ok":
for photo in root.iterfind("photos/photo"):
photo: ET.Element
images.append(photo.attrib)
except Exception as ex:
logger.error("error gathering the response: {}".format(ex))
except Exception as ex:
logger.error("Error making the request : {}".format(ex))
return images
@dask.delayed
def _fetch_image(self, image_info, sz):
try:
if sz in image_info:
url = image_info[sz]
return ImageUtil.url2img(url)
except Exception as ex:
logger.error("Error fetching the image: " % ex)
return None
def fetch(self, q, batch_size: int = 100, timestamp=1, sz="url_m"):
try:
assert batch_size <= 500, "invalid count parameter"
total_matches = self._get_total_matches(q)
logger.debug("{} images found ".format(total_matches))
number_of_pages = math.ceil(total_matches / batch_size)
for page in range(1, number_of_pages):
photos = self._request_photos(q, batch_size, page)
delayed_tasks = list(map(lambda img: self._fetch_image(img, sz), photos))
compute_result = dask.compute(*delayed_tasks)
yield [img for img in compute_result if isinstance(img, np.ndarray)]
time.sleep(timestamp)
except Exception as ex:
logger.error("error fetching the images: {}".format(ex))
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engineer/apps.py | mostafaitalian/digitec | 0 | 1610949 | from django.apps import AppConfig
class EngineerConfig(AppConfig):
name = 'engineer'
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reverse_linked_list.py | jaebradley/leetcode.py | 0 | 148325 | # Reverse a singly linked list.
# Input: 1->2->3->4->5->NULL
# Output: 5->4->3->2->1->NULL
class Solution(object):
def reverseList(self, head):
"""
Iterative solution that involves changing the next reference for the following node to point at current node.
If empty list (i.e. head is None) return None.
If single value list (i.e. head.next is None) return list
If multiple values, use first and second variables to store references to nodes that are neighbors
:type head: ListNode
:rtype: ListNode
"""
if head is None or head.next is None:
return head
first = head
second = head.next
head.next = None
while second is not None:
next_node = self.swap(first, second)
first = second
second = next_node
return first
def swap(self, first_node, second_node):
if second_node is None:
return None
next_node = second_node.next
second_node.next = first_node
return next_node
def reverseList2(self, head):
return self.reverseList2Helper(head, None)
def reverseList2Helper(self, head, next_head):
"""
Recursive solution where subproblem is reversing list starting from remaining nodes.
The reversed list is the current node swapped with reverse of all remaining nodes.
head refers to current node and next_head refers to what will eventually be head.next.
:type head: ListNode
:rtype: ListNode
"""
if head is None:
return next_head
next_node = head.next
head.next = next_head
return self.reverseList2Helper(next_node, head)
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tests/stack_test.py | arthurlogilab/py_zipkin | 225 | 3494 | import mock
import pytest
import py_zipkin.storage
@pytest.fixture(autouse=True, scope="module")
def create_zipkin_attrs():
# The following tests all expect _thread_local.zipkin_attrs to exist: if it
# doesn't, mock.patch will fail.
py_zipkin.storage.ThreadLocalStack().get()
def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs():
tracer = py_zipkin.storage.get_default_tracer()
with mock.patch.object(tracer._context_stack, "_storage", []):
assert not py_zipkin.storage.ThreadLocalStack().get()
assert not py_zipkin.storage.ThreadLocalStack().get()
def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs():
with mock.patch.object(py_zipkin.storage.log, "warning", autospec=True) as log:
assert not py_zipkin.storage.Stack([]).get()
assert log.call_count == 1
def test_storage_stack_still_works_if_you_dont_pass_in_storage():
# Let's make sure this still works if we don't pass in a custom storage.
assert not py_zipkin.storage.Stack().get()
def test_get_zipkin_attrs_returns_the_last_of_the_list():
tracer = py_zipkin.storage.get_default_tracer()
with mock.patch.object(tracer._context_stack, "_storage", ["foo"]):
assert "foo" == py_zipkin.storage.ThreadLocalStack().get()
def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list():
assert "foo" == py_zipkin.storage.Stack(["bar", "foo"]).get()
def test_pop_zipkin_attrs_does_nothing_if_no_requests():
tracer = py_zipkin.storage.get_default_tracer()
with mock.patch.object(tracer._context_stack, "_storage", []):
assert not py_zipkin.storage.ThreadLocalStack().pop()
def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests():
assert not py_zipkin.storage.Stack([]).pop()
def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs():
tracer = py_zipkin.storage.get_default_tracer()
with mock.patch.object(tracer._context_stack, "_storage", ["foo", "bar"]):
assert "bar" == py_zipkin.storage.ThreadLocalStack().pop()
assert "foo" == py_zipkin.storage.ThreadLocalStack().get()
def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs():
context_stack = py_zipkin.storage.Stack(["foo", "bar"])
assert "bar" == context_stack.pop()
assert "foo" == context_stack.get()
def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list():
tracer = py_zipkin.storage.get_default_tracer()
with mock.patch.object(tracer._context_stack, "_storage", ["foo"]):
assert "foo" == py_zipkin.storage.ThreadLocalStack().get()
py_zipkin.storage.ThreadLocalStack().push("bar")
assert "bar" == py_zipkin.storage.ThreadLocalStack().get()
def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list():
stack = py_zipkin.storage.Stack(["foo"])
assert "foo" == stack.get()
stack.push("bar")
assert "bar" == stack.get()
def test_stack_copy():
stack = py_zipkin.storage.Stack()
stack.push("a")
stack.push("b")
the_copy = stack.copy()
the_copy.push("c")
stack.push("d")
assert ["a", "b", "c"] == the_copy._storage
assert ["a", "b", "d"] == stack._storage
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13,
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6796,
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613,
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29890,
613,
376,
29881,
3108,
1275,
5096,
3032,
12925,
13,
2
] |
Taller de Estrucuras de Control Repeticion/Punto01.py | Ricardoppp/Talleres_De_Algoritmos_Ricardo | 0 | 41759 | #punto 1
#entradas
n=int(input("Escriba el primer digito "))
k=int(input("Escriba el primer digito "))
#caja negra y salidas
while True:
n=0
if(k<n):
n=n-1
print(n)
elif(n==k):
print(k)
break | [
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13,
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2867,
2
] |
Solutions/Problem7.py | igerry/ProjectEulerInPython | 0 | 109766 | <gh_stars>0
#!/usr/bin/python
class Problem7:
'''
10001st prime
Problem 7
104743
By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13.
What is the 10 001st prime number?
'''
def solution(self):
primes = []
nextPrime = 2
for i in range(10001):
print('%d, %d' % (i, nextPrime))
nextPrime = self.getNextPrime(primes)
return nextPrime
def getNextPrime(self, primes):
if (len(primes) == 0):
primes.append(2)
return 2
if (len(primes) == 1):
primes.append(3)
return 3
findNewPrime = False
nextPrime = primes[-1] + 2
while not findNewPrime:
findNewPrime = True
for i in primes:
if not nextPrime % i:
findNewPrime = False
break
if (findNewPrime):
primes.append(nextPrime)
break
nextPrime += 2
return nextPrime
p = Problem7()
print(p.solution()) | [
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544,
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736,
29871,
29906,
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13,
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565,
313,
2435,
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558,
1355,
29897,
1275,
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1125,
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29941,
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736,
29871,
29941,
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544,
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4040,
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544,
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565,
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4040,
603,
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474,
29901,
13,
462,
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603,
353,
7700,
13,
462,
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13,
965,
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565,
313,
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544,
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603,
13,
13,
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353,
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29955,
580,
13,
2158,
29898,
29886,
29889,
2929,
918,
3101,
2
] |
text/color/__init__.py | jedhsu/text | 0 | 43781 | """
*graphical color*
Spectral color measures.
"""
# from ._color import Color
# from ._rgb import Rgba
# from ._hsv import Hsba
__all__ = [
"Color",
"Rgba",
"Hsba",
]
| [
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29950,
29879,
2291,
613,
13,
29962,
13,
2
] |
perses/samplers/__init__.py | schallerdavid/perses | 99 | 28150 | from perses.samplers.samplers import *
| [
1,
515,
3736,
267,
29889,
13445,
572,
414,
29889,
13445,
572,
414,
1053,
334,
13,
2
] |
CL_Net/Referential_Game/Number_Set/teacher.py | MarkFzp/ToM-Pragmatics | 0 | 66860 | import os
import math
import numpy as np
import tensorflow as tf
from concept import Concept
import pdb
np.set_printoptions(precision=5, suppress=True)
class Teacher:
def __init__(self, sess, rl_gamma, boltzman_beta,
belief_var_1d, num_distractors, attributes_size,
message_space_size):
self.sess = sess
self.num_distractors_ = num_distractors
self.attributes_size_ = attributes_size
self.message_space_size_ = message_space_size
self.rl_gamma_ = rl_gamma
self.boltzman_beta_ = boltzman_beta
self.belief_var_1d_ = belief_var_1d
################
# Placeholders #
################
with tf.variable_scope('Teacher'):
self.distractors_ = tf.placeholder(tf.float32, name = 'distractors',
shape = [None, self.num_distractors_, self.attributes_size_])
self.distractors_tensor_ = tf.expand_dims(self.distractors_, 2)
self.message_ = tf.placeholder(tf.float32, shape = [None, self.message_space_size_], name = 'message')
self.teacher_belief_ = tf.placeholder(tf.float32, shape = [None, self.num_distractors_], name = 'teacher_belief')
self.student_belief_ = tf.placeholder(tf.float32, shape = [None, self.num_distractors_], name = 'student_belief')
self.student_belief_spvs_ = tf.placeholder(tf.float32, shape = [None, self.num_distractors_], name = 'student_belief_spvs')
self.q_net_spvs_ = tf.placeholder(tf.float32, shape = [None])
########################
# Belief Update Module #
########################
self.belief_update_opt_ = tf.train.AdamOptimizer(learning_rate = 1e-3)
with tf.variable_scope('Belief_Update'):
self.df1_ = tf.layers.conv2d(self.distractors_tensor_, 3 * self.message_space_size_, kernel_size = [1, 1],
kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1),
activation = tf.nn.leaky_relu)
self.df2_ = tf.layers.conv2d(self.df1_, 3 * self.message_space_size_, kernel_size = [1, 1],
kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-1),
activation = tf.nn.leaky_relu)
# self.df3_ = tf.layers.conv2d(self.df2_, 1 * self.message_space_size_, kernel_size = [1, 1],
# kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-2),
# activation = None)
self.msg_from_df_1_ = []
for _ in range(self.num_distractors_):
self.msg_from_df_1_.append(tf.layers.conv2d(self.df2_, 2 * self.message_space_size_, kernel_size = [self.num_distractors_, 1],
kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-1),
padding = 'valid', activation = tf.nn.leaky_relu))
self.msg_est_tensor_1_ = tf.concat(self.msg_from_df_1_, axis = 1)
self.msg_from_df_2_ = []
for _ in range(self.num_distractors_):
self.msg_from_df_2_.append(tf.layers.conv2d(self.msg_est_tensor_1_, 1 * self.message_space_size_, kernel_size = [self.num_distractors_, 1],
kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-2),
padding = 'valid', activation = None))
self.msg_est_tensor_2_ = tf.concat(self.msg_from_df_2_, axis = 1)
self.reg_varlist_ = [v for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) if v.name.startswith('Belief')]
#######################
#network belief update#
#######################
self.msg_est_tensor_2d_ = tf.squeeze(self.msg_est_tensor_2_, axis = 2)
self.belief_var_1d_ = tf.exp(tf.Variable(initial_value = self.belief_var_1d_, trainable = True, dtype = tf.float32))
# self.belief_var_ = tf.layers.conv2d(self.msg_est_tensor_3_, 1, kernel_size = [self.num_distractors_, 1],
# kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-3),
# padding = 'valid', activation = None)
# self.belief_var_1d_ = tf.squeeze(self.belief_var_, axis = 2)
self.boltzman_beta_ = tf.Variable(initial_value = self.boltzman_beta_, trainable = False, dtype = tf.float32, name = 'boltzman_beta')
self.msg_indices_ = tf.where(tf.not_equal(self.message_, 0))
self.df_msg_match_ = tf.exp(self.boltzman_beta_ * self.msg_est_tensor_2d_)
self.df_msg_match_norm_ = tf.div_no_nan(self.df_msg_match_, tf.reduce_sum(self.df_msg_match_, axis = 2, keepdims = True))
self.df_msg_2_norm_ = tf.gather_nd(tf.transpose(self.df_msg_match_norm_, perm = [0, 2, 1]),
self.msg_indices_)
#self.df_msg_1_ = tf.multiply(self.dfb_merge_pre_3_, tf.expand_dims(tf.expand_dims(self.message_, 1), 1))
#self.df_msg_2_ = tf.exp(self.boltzman_beta_ * tf.reduce_sum(tf.squeeze(self.df_msg_1_, 2), axis = 2))
#self.df_msg_2_norm_ = tf.nn.relu(self.df_msg_2_ + self.belief_var_1_)
self.belief_pred_1_ = tf.multiply(self.df_msg_2_norm_, self.student_belief_)
self.belief_pred_full_ = tf.concat([self.belief_pred_1_, self.belief_var_1d_ * tf.slice(tf.ones_like(self.belief_pred_1_), [0, 0], [-1, 1])], axis = 1)
#######################
#network belief update#
#######################
'''
######################
#kernel belief update#
######################
self.kernel_columns_ = []
for i in range(self.num_distractors_):
self.df_msg_1_ = tf.multiply(self.msg_est_tensor_2_, tf.expand_dims(tf.expand_dims(self.message_, 1), 1))
self.df_msg_2_ = tf.contrib.layers.fully_connected(tf.layers.flatten(self.df_msg_1_),\
2 * self.num_distractors_, activation_fn = tf.nn.leaky_relu)
self.df_msg_3_ = tf.contrib.layers.fully_connected(self.df_msg_2_,
self.num_distractors_, activation_fn = None)
kernel_column = tf.nn.relu(self.df_msg_3_)
self.kernel_columns_.append(tf.expand_dims(tf.div_no_nan(kernel_column,
tf.reduce_sum(kernel_column, axis = 1, keepdims = True)), -1))
self.kernel_pre_norm_ = tf.no_op()
self.kernel_ = tf.concat(self.kernel_columns_, axis = 2)
print('<Belief Update Kernel Generator Constructed>')
self.belief_pred_ = tf.nn.relu(tf.squeeze(tf.matmul(self.kernel_, tf.expand_dims(self.student_belief_, -1)), -1))
######################
#kernel belief update#
######################
'''
self.belief_pred_full_norm_ = tf.div_no_nan(self.belief_pred_full_, tf.reduce_sum(self.belief_pred_full_, axis = 1, keepdims = True))
self.belief_pred_ = tf.slice(self.belief_pred_full_norm_, [0, 0], [-1, self.num_distractors_])
self.regularization_ = 1e-4 * tf.add_n([ tf.nn.l2_loss(v) for v in self.reg_varlist_ if 'bias' not in v.name ])
self.cross_entropy_1_ = -1 * tf.reduce_mean(tf.reduce_sum(tf.multiply(self.student_belief_spvs_, tf.math.log(self.belief_pred_)), axis = 1))
self.cross_entropy_2_ = -1 * tf.reduce_mean(tf.reduce_sum(tf.multiply(self.belief_pred_, tf.math.log(self.student_belief_spvs_ + 1e-9)), axis = 1))
self.cross_entropy_ = self.cross_entropy_1_ + self.cross_entropy_2_ + self.regularization_
self.belief_train_varlist_ = [v for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) if v.name.startswith('Belief_Update')]
self.belief_update_varlist_ = [v for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) if v.name.startswith('Belief_Update')]
self.belief_update_train_op_ = self.belief_update_opt_.minimize(self.cross_entropy_, var_list = self.belief_train_varlist_)
self.belief_update_saver_ = tf.train.Saver()
self.belief_update_loader_ = tf.train.Saver(self.belief_update_varlist_)
####################
# Q-network Module #
####################
self.q_net_opt_ = tf.train.AdamOptimizer(learning_rate = 1e-5)
with tf.variable_scope('q_net'):
self.distct_feat_1_ = tf.layers.conv2d(self.distractors_tensor_, 3 * self.message_space_size_, kernel_size = [1, 1],
kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-1),
activation = tf.nn.leaky_relu)
self.distct_feat_2_ = tf.layers.conv2d(self.distct_feat_1_, 2 * self.message_space_size_, kernel_size = [1, 1],
kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-1),
activation = tf.nn.leaky_relu)
self.distct_feat_2_weighted_ = tf.multiply(self.distct_feat_2_, tf.expand_dims(tf.expand_dims(self.belief_pred_, -1), -1))
self.distcts_feat_1_ = []
for _ in range(self.num_distractors_):
self.distcts_feat_1_.append(tf.layers.conv2d(self.distct_feat_2_weighted_, 1 * self.message_space_size_, kernel_size = [self.num_distractors_, 1],
kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-1),
padding = 'valid', activation = tf.nn.leaky_relu))
self.distcts_feat_tensor_1_ = tf.concat(self.distcts_feat_1_, axis = 1)
self.distcts_feat_2_ = []
for _ in range(self.num_distractors_):
self.distcts_feat_2_.append(tf.layers.conv2d(self.distcts_feat_tensor_1_, 1 * self.message_space_size_, kernel_size = [self.num_distractors_, 1],
kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-1),
padding = 'valid', activation = tf.nn.leaky_relu))
self.distcts_feat_tensor_2_ = tf.concat(self.distcts_feat_2_, axis = 1)
self.custome_activaiton_ = lambda x: tf.where(tf.math.greater(x, 0), (tf.exp(x) - 1), (-1 * tf.exp(-x) + 1))
self.distcts_feat_3_ = []
for _ in range(self.num_distractors_):
self.distcts_feat_3_.append(tf.layers.conv2d(self.distcts_feat_tensor_2_, 1, kernel_size = [self.num_distractors_, 1],
kernel_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-1),
padding = 'valid', activation = self.custome_activaiton_))
self.distcts_feat_tensor_3_ = tf.concat(self.distcts_feat_3_, axis = 1)
self.value_param_1_ = tf.Variable(initial_value = -1, trainable = False, dtype = tf.float32)
self.value_ = tf.reduce_sum(tf.multiply(tf.squeeze(self.distcts_feat_tensor_3_), self.teacher_belief_), axis = 1) +\
(1 - tf.reduce_sum(self.belief_pred_, axis = 1)) * self.value_param_1_
'''
self.df_b1_ = tf.multiply(tf.squeeze(self.distct_feat_2_, axis = 2), tf.expand_dims(self.teacher_belief_, -1))
self.df_b2_ = tf.multiply(tf.squeeze(self.distct_feat_2_, axis = 2), tf.expand_dims(self.belief_pred_, -1))
self.concat_df_b_ = tf.layers.flatten(tf.concat((self.df_b1_, self.df_b2_), axis = 2))
# self.dfb_merge_pre_ = tf.contrib.layers.fully_connected(tf.reduce_sum(tf.abs(self.df_b1_ - self.df_b2_), axis = 1), 4, activation_fn = tf.nn.leaky_relu,
# weights_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-2))
self.dfb_merge_pre_1_ = tf.contrib.layers.fully_connected(self.concat_df_b_, 6, activation_fn = tf.nn.leaky_relu,
weights_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-2))
self.dfb_merge_pre_2_ = tf.contrib.layers.fully_connected(self.dfb_merge_pre_1_, 4, activation_fn = tf.nn.leaky_relu,
weights_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-2))
self.dfb_merge_ = tf.contrib.layers.fully_connected(self.dfb_merge_pre_2_, 1, activation_fn = None,
weights_initializer = tf.random_normal_initializer(mean = 0.0, stddev = 1e-2))
self.value_ = tf.squeeze(self.dfb_merge_)
'''
# self.dfb_merge_ = tf.reduce_sum(tf.square(self.df_b1_ - self.df_b2_), axis = [1, 2])
# self.value_param_0_ = tf.squeeze(tf.contrib.layers.fully_connected(self.concat_df_b_, 1, activation_fn = None))
# self.value_param_00_ = tf.squeeze(tf.contrib.layers.fully_connected(self.dfb_merge_pre_1_, 1, activation_fn = None))
# self.value_param_000_ = tf.squeeze(tf.contrib.layers.fully_connected(self.dfb_merge_pre_1_, 1, activation_fn = None))
# self.value_param_0000_ = tf.squeeze(tf.contrib.layers.fully_connected(self.dfb_merge_pre_1_, 1, activation_fn = None))
# self.value_param_1_ = tf.Variable(initial_value = -1, trainable = True, dtype = tf.float32)
# self.value_param_2_ = tf.Variable(initial_value = 1, trainable = True, dtype = tf.float32)
# self.value_param_3_ = tf.Variable(initial_value = -1, trainable = True, dtype = tf.float32)
#self.value_param_2_ * tf.exp(self.value_param_1_ * tf.squeeze(self.dfb_merge_)) + self.value_param_3_
#self.value_ = 1 - tf.squeeze(tf.contrib.layers.fully_connected(tf.reduce_sum(self.df_b1_ - self.df_b2_, axis = 2), 1, activation_fn = None))
self.reg_varlist_q_ = [v for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) if v.name.startswith('q_net')]
self.regularization_q_ = 1e-4 * tf.add_n([ tf.nn.l2_loss(v) for v in self.reg_varlist_q_ if 'bias' not in v.name ])
self.q_net_loss_pre_ = tf.square(self.value_ - self.q_net_spvs_)
self.success_mask_ = tf.to_float(tf.math.greater(self.q_net_spvs_, 0.0))
self.fail_mask_ = tf.to_float(tf.math.greater(0.0, self.q_net_spvs_))
self.imbalance_penalty_ = self.success_mask_ + self.fail_mask_ * tf.div_no_nan(tf.reduce_sum(self.success_mask_), tf.reduce_sum(self.fail_mask_))
#self.q_net_loss_ = tf.reduce_mean(self.q_net_loss_pre_ * tf.to_float(self.q_net_loss_pre_ > 0.05) * self.imbalance_penalty_) + self.regularization_q_
self.q_net_loss_ = tf.reduce_mean(self.q_net_loss_pre_ * self.imbalance_penalty_) + self.regularization_q_
self.q_net_varlist_ = [v for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) if v.name.startswith('q_net')]
self.q_net_train_op_ = self.q_net_opt_.minimize(self.q_net_loss_, var_list = self.q_net_varlist_)
self.total_loader_ = tf.train.Saver([v for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) if 'Adam' not in v.name])
self.total_saver_ = tf.train.Saver()
def train_belief_update(self, data_batch):
_, cross_entropy, belief_pred, posterior, likelihood = self.sess.run([self.belief_update_train_op_, self.cross_entropy_, self.belief_pred_, self.belief_pred_1_, self.df_msg_2_norm_],
feed_dict = {self.student_belief_: data_batch['prev_belief'],
self.message_: data_batch['message'],
self.distractors_: data_batch['distractors'],
self.student_belief_spvs_: data_batch['new_belief']})
return cross_entropy, belief_pred, posterior[:10], likelihood[:10]
def pretrain_bayesian_belief_update(self, concept_generator, teacher_pretraining_steps, teacher_pretrain_batch_size,
teacher_pretrain_ckpt_dir, teacher_pretrain_ckpt_name, continue_steps = 0, silent = False):
if not os.path.exists(teacher_pretrain_ckpt_dir):
os.makedirs(teacher_pretrain_ckpt_dir)
ckpt = tf.train.get_checkpoint_state(teacher_pretrain_ckpt_dir)
train_steps = teacher_pretraining_steps
if ckpt:
self.belief_update_loader_.restore(self.sess, ckpt.model_checkpoint_path)
print('Loaded teacher belief update ckpt from %s' % teacher_pretrain_ckpt_dir)
train_steps = continue_steps
else:
print('Cannot loaded teacher belief update ckpt from %s' % teacher_pretrain_ckpt_dir)
accuracies = []
l1_diffs = []
bayesian_wrongs = []
for ts in range(train_steps):
data_batch = concept_generator.generate_batch(teacher_pretrain_batch_size)
cross_entropy, belief_pred, posterior, likelihood = self.train_belief_update(data_batch)
l1_diff = np.sum(abs(belief_pred - data_batch['new_belief']), axis = 1)
correct = (l1_diff <= 5e-2)
bayesian_wrong = np.mean(np.sum((data_batch['new_belief'] == 0) * (belief_pred > 1e-5), axis = 1) > 0)
accuracies.append(np.mean(correct))
l1_diffs.append(np.mean(l1_diff))
bayesian_wrongs.append(bayesian_wrong)
if np.sum(np.isnan(belief_pred)) != 0:
pdb.set_trace()
if ts % 1000 == 0 and not silent:
print('[T%d] batch mean cross entropy: %f, mean accuracies: %f, mean l1: %f, bayesian wrong: %f'\
% (ts + 1, cross_entropy, np.mean(accuracies), np.mean(l1_diffs), np.mean(bayesian_wrongs)))
boltzman_beta, belief_var_1d = self.sess.run([self.boltzman_beta_, self.belief_var_1d_])
print('boltzman_beta: %f, belief_var_1d: %f' % (boltzman_beta, belief_var_1d))
print('new_belief: ')
print(data_batch['new_belief'][:10])
print('prior: ')
print(data_batch['prev_belief'][:10])
print('likelihood: ')
print(likelihood)
print('posterior: ')
print(posterior)
print('predict_belief: ')
print(belief_pred[:10])
if np.mean(accuracies) > 0.9:
#idx = np.random.randint(teacher_pretrain_batch_size)
idx = teacher_pretrain_batch_size
for i in range(idx):
print('\t target:', data_batch['new_belief'][i, :])
print('\t predict', belief_pred[i, :])
accuracies = []
l1_diffs = []
bayesian_wrongs = []
if (ts + 1) % 10000 == 0:
self.belief_update_saver_.save(self.sess, os.path.join(teacher_pretrain_ckpt_dir,
teacher_pretrain_ckpt_name),
global_step = teacher_pretraining_steps)
print('Saved teacher belief update ckpt to %s after %d training'\
% (teacher_pretrain_ckpt_dir, ts))
if train_steps != 0:
self.belief_update_saver_.save(self.sess, os.path.join(teacher_pretrain_ckpt_dir,
teacher_pretrain_ckpt_name),
global_step = teacher_pretraining_steps)
print('Saved teacher belief update ckpt to %s after %d training'\
% (teacher_pretrain_ckpt_dir, train_steps))
def train_q_net(self, data_batch):
_, q_net_loss, value = self.sess.run([self.q_net_train_op_, self.q_net_loss_, self.value_],\
feed_dict = {self.q_net_spvs_: data_batch['target_q'],
self.student_belief_: data_batch['student_belief'],
self.message_: data_batch['message'],
self.distractors_: data_batch['distractors'],
self.teacher_belief_: data_batch['teacher_belief']})
print('Q learning loss: %f' % q_net_loss)
ridx = np.random.randint(value.shape[0])
#print(value[ridx], data_batch['target_q'][ridx])
print('0.8: %f, 0.2: %f' % (np.sum(value * (data_batch['target_q'] == 0.8)) / np.sum(data_batch['target_q'] == 0.8),
np.sum(value * (data_batch['target_q'] == -0.2)) / np.sum(data_batch['target_q'] == -0.2)))
print('Teacher value est:', value[ridx: ridx + 10], data_batch['target_q'][ridx: ridx + 10])
#print(distcts_feat_tensor_3[ridx, :])
return q_net_loss
def get_q_value_for_all_msg(self, teacher_belief, student_belief, embeded_concepts):
all_msg_embeddings = np.identity(self.message_space_size_)
teacher_belief_tile = np.tile(teacher_belief, (self.message_space_size_, 1))
student_belief_tile = np.tile(student_belief, (self.message_space_size_, 1))
embeded_concepts_tile = np.tile(embeded_concepts, (self.message_space_size_, 1, 1))
q_values, belief_pred, distcts_feat_tensor_3, belief_dst, msg_est_tensor = self.sess.run([self.value_, self.belief_pred_, self.distcts_feat_tensor_3_, self.value_, self.msg_est_tensor_2_],
feed_dict = {self.distractors_: embeded_concepts_tile,
self.message_: all_msg_embeddings,
self.teacher_belief_: teacher_belief_tile,
self.student_belief_: student_belief_tile})
return q_values, belief_pred, distcts_feat_tensor_3, belief_dst, msg_est_tensor[0]
def update_net(self, belief_update_tuples, q_learning_tuples, update_term = 'Both'):
debug_structure = {}
belief_update_batch = {}
belief_update_batch['prev_belief'] = []
belief_update_batch['new_belief'] = []
belief_update_batch['message'] = []
belief_update_batch['distractors'] = []
for belief_tuple in belief_update_tuples:
belief_update_batch['distractors'].append(belief_tuple[0])
belief_update_batch['prev_belief'].append(belief_tuple[1])
belief_update_batch['message'].append(belief_tuple[2])
belief_update_batch['new_belief'].append(belief_tuple[3])
for k in belief_update_batch:
belief_update_batch[k] = np.array(belief_update_batch[k])
if update_term == 'Both' or update_term == 'Belief':
cross_entropy, belief_pred = self.train_belief_update(belief_update_batch)
print('Teacher\'s belief esimate cross_entropy: %f' % cross_entropy)
debug_structure['teacher_belief_prediction'] = belief_pred
q_learning_batch = {}
q_learning_batch['student_belief'] = []
q_learning_batch['teacher_belief'] = []
q_learning_batch['message'] = []
q_learning_batch['distractors'] = []
q_learning_batch['target_q'] = []
for q_learning_tuple in q_learning_tuples:
q_learning_batch['distractors'].append(q_learning_tuple[0])
q_learning_batch['student_belief'].append(q_learning_tuple[1])
q_learning_batch['teacher_belief'].append(q_learning_tuple[2])
q_learning_batch['message'].append(q_learning_tuple[3])
q_learning_batch['target_q'].append(q_learning_tuple[4])
for k in q_learning_batch:
q_learning_batch[k] = np.array(q_learning_batch[k])
if update_term == 'Both' or update_term == 'Q-Net':
q_net_loss = self.train_q_net(q_learning_batch)
return debug_structure
if __name__ == '__main__':
main()
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src/game/law_scope.py | Tequilac/Hacknarok2021 | 1 | 166325 | <filename>src/game/law_scope.py<gh_stars>1-10
from enum import Enum, auto
class LawScope(Enum):
state = auto()
city = auto()
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src/engineV2.py | JTechnologies/daze-lang | 2 | 72540 | <filename>src/engineV2.py
escapeProtect = ""
def parseTag(line, skip=0):
skipNext = skip
split = line.replace("\:", "")
split = line.split(":")
for i in range(len(split)-1):
split[i] = split[i].replace("", ":")
split[i] = split[i].replace("\(", "")
split[0] = split[0].replace("(", "")
for i in range(len(split)-1):
split[i].replace("", "(")
if len(split) != 2:
for i in range(1,len(split)):
if skipNext!=0:
skipNext = skipNext-1
pass
elif skipNext==0:
split[i] = split[i].replace("\)", "")
split[i] = split[i].replace(")", "")
split[i] = split[i].replace("", ")")
if split[i][0] == " ":
if split[i][1] == "'" or split[i][1] == '"':
split[i] = split[i][2:-1]
elif split[i][1] == "(":
print(split[i])
this = split[i][1:]
next = split[i+1]
fl = True
c=1
p=next
while fl==True:
if p[0] == "(" or p[1] == "(":
next=f'{next}:{split[i+c]}'
p = split[i+c]
skipNext=skipNext+1
c=c+1
else:
skipNext=skipNext+1
fl = False
while next[-1] == ")":
if next[-1] == ")":
next = next[0:-1]
addBack = 0
for j in range(len(next)):
if(next[j] == "("):
addBack = addBack + 1
while addBack>0:
next=next+")"
addBack = addBack-1
print(i)
split[i] = parseTag(f"{this}:{next}", skip=skipNext)
elif split[i][0] == "'" or '"':
split[i] = split[1][2:-1]
elif split[i][0] == "(":
print(split[i])
this = split[i][1:]
next = split[i+1]
fl = True
c=1
p=next
while fl==True:
if p[0] == "(" or p[1] == "(":
next=f'{next}:{split[i+c]}'
p = split[i+c]
skipNext=skipNext+1
c=c+1
else:
skipNext=skipNext+1
fl = False
while next[-1] == ")":
if next[-1] == ")":
next = next[0:-1]
addBack = 0
for j in range(len(next)):
if(next[j] == "("):
addBack = addBack + 1
while addBack>0:
next=next+")"
addBack = addBack-1
print(i)
split[i] = parseTag(f"{this}:{next}", skip=skipNext)
else:
split[1] = split[1].replace("\)", "")
split[1] = split[1].replace(")", "")
split[1] = split[1].replace("", ")")
if split[1][0] == " ":
if split[1][1] == "'" or '"':
split[1] = split[1][2:-1]
elif split[1][1] == "(":
print("ERR")
elif split[1][0] == "'" or '"':
split[1] = split[1][2:-1]
elif split[1][0] == "(":
pass
#print([split[0], split[1]])
return [split[0],split[1]]
def parseSite(input):
lines = []
comments=[]
variables={}
metavariables={}
tags=[]
for line in input:
lines.append(line)
for line in lines:
if line[:2] == "##":
comments.append(line[2:])
elif line[:2] == "+!":
variable = line[2:].replace(" = ", "=").split("=")
if variable[1][0] == "'" or variable[1][0] == '"':
variable[1] =['str', variable[1][1:-1]]
elif variable[1] == "True" or variable[1] == "False":
variable[1]=['bol', variable[1]]
elif int(variable[1]):
variable[1]=['int', variable[1]]
elif variable[1][0] == "!":
variable[1]=variables[variable[1]]
elif variable[1][0] == "%":
variable[1]=metavariables[variable[1]]
else:
#TODO: GET CURRENT LINE NUMBER FOR ERROR
return("!!Error on line (tba): Invalid Variable Type")
variables[variable[0]]=variable[1]
elif line[:2] == "+%":
variable = line[2:].replace(" = ", "=").split("=")
if variable[1][0] == "'" or variable[1][0] == '"':
variable[1] =['str', variable[1][1:-1]]
elif variable[1] == "True" or variable[1] == "False":
variable[1]=['bol', variable[1]]
elif int(variable[1]):
variable[1]=['int', variable[1]]
elif variable[1][0] == "!":
variable[1]=variables[variable[1]]
elif variable[1][0] == "%":
variable[1]=metavariables[variable[1]]
else:
#TODO: GET CURRENT LINE NUMBER FOR ERROR
return("!!Error on line (tba): Invalid MetaVariable Type")
metavariables[variable[0]]=variable[1]
elif line[0] == "(":
tags.append(parseTag(line))
#print(parseTag(line))
elif line[0] == "$":
pass
elif line[0] == "+":
working = line
working = working[1:]
working = working.replace(" = ", "=").split("=")
if working[1][0] == "!":
working[1]=variables[working[1][1:]][1]
elif working[1][0] == "'" or working[1][0] == '"':
working[1]=working[1][1:-1]
tag = tags[-1]
#print(tag)
if len(tag) == 2:
tag.append([working])
elif len(tag) == 3:
tag[2].append(working)
else:
return("!!Error: Internal Error. Submit an issue with code 'r01:ev2:086'")
#print(tag)
else:
return(f"!!Error on line (tba): Unknown line start. {line}")
return comments, variables, metavariables, tags
def toHtml(input, part="head"):
textInput = input
input = input.replace("; ", "\n").replace(";", "\n")
input = input.replace("\\\n", ";")
input = input.splitlines()
tags = []
comments = []
variables = {}
metavariables = {}
print(input, "\n")
if input[0] == "$site":
comments, variables, metavariables, tags = parseSite(input)
elif input[0] == "$script":
return "!!Error: Attempt to compile pure script into HTML"
else:
return f"!!Error on line 1: Unknown Daze type '{input[0]}'"
output = input
return metavariables, variables, tags, comments
# Testing Area
print(toHtml("$site; +!lol = 'rofl'; (div: (div: (div: (p: 'hey')))); +lol='lol'")) | [
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uwsgiconf/options/alarms.py | graceshaw/uwsgiconf | 0 | 1601087 | <filename>uwsgiconf/options/alarms.py<gh_stars>0
from operator import attrgetter
from typing import List
from .alarm_types import AlarmType, AlarmCommand, AlarmSignal, AlarmCurl, AlarmLog, AlarmMule, AlarmXmpp
from ..base import OptionsGroup
from ..utils import listify
TypeAlarmExt = List[AlarmType]
class Alarms(OptionsGroup):
"""Alarms.
This subsystem allows the developer/sysadmin to "announce"
special conditions of an app via various channels.
* http://uwsgi-docs.readthedocs.io/en/latest/AlarmSubsystem.html
"""
class alarm_types:
"""Alarm types available for ``.register_alarm()``."""
command = AlarmCommand
curl = AlarmCurl
log = AlarmLog
mule = AlarmMule
signal = AlarmSignal
xmpp = AlarmXmpp
def __init__(self, *args, **kwargs):
self._alarms = []
super().__init__(*args, **kwargs)
def set_basic_params(self, msg_size: int = None, cheap: bool = None, anti_loop_timeout: int = None):
"""
:param msg_size: Set the max size of an alarm message in bytes. Default: 8192.
:param cheap: Use main alarm thread rather than create dedicated
threads for curl-based alarms
:param anti_loop_timeout: Tune the anti-loop alarm system. Default: 3 seconds.
"""
self._set('alarm-msg-size', msg_size)
self._set('alarm-cheap', cheap, cast=bool)
self._set('alarm-freq', anti_loop_timeout)
return self._section
def print_alarms(self):
"""Print out enabled alarms."""
self._set('alarm-list', True, cast=bool)
return self._section
def register_alarm(self, alarm: TypeAlarmExt):
"""Register (create) an alarm.
:param alarm: Alarm.
"""
for alarm in listify(alarm):
if alarm not in self._alarms:
self._set('alarm', alarm, multi=True)
self._alarms.append(alarm)
return self._section
def alarm_on_log(self, alarm: TypeAlarmExt, matcher: str, skip: bool = False):
"""Raise (or skip) the specified alarm when a log line matches the specified regexp.
:param alarm: Alarm.
:param matcher: Regular expression to match log line.
:param skip:
"""
self.register_alarm(alarm)
value = f"{','.join(map(attrgetter('alias'), listify(alarm)))} {matcher}"
self._set('not-alarm-log' if skip else 'alarm-log', value)
return self._section
def alarm_on_fd_ready(self, alarm: TypeAlarmExt, fd: str, message: str, byte_count: int = None):
"""Triggers the alarm when the specified file descriptor is ready for read.
This is really useful for integration with the Linux eventfd() facility.
Pretty low-level and the basis of most of the alarm plugins.
* http://uwsgi-docs.readthedocs.io/en/latest/Changelog-1.9.7.html#alarm-fd
:param alarm: Alarm.
:param fd: File descriptor.
:param message: Message to send.
:param byte_count: Files to read. Default: 1 byte.
.. note:: For ``eventfd`` set 8.
"""
self.register_alarm(alarm)
value = fd
if byte_count:
value += f':{byte_count}'
value += f' {message}'
for alarm_ in listify(alarm):
self._set('alarm-fd', f'{alarm_.alias} {value}', multi=True)
return self._section
def alarm_on_queue_full(self, alarm: TypeAlarmExt):
"""Raise the specified alarm when the socket backlog queue is full.
:param alarm: Alarm.
"""
self.register_alarm(alarm)
for alarm_ in listify(alarm):
self._set('alarm-backlog', alarm_.alias, multi=True)
return self._section
def alarm_on_segfault(self, alarm: TypeAlarmExt):
"""Raise the specified alarm when the segmentation fault handler is executed.
Sends a backtrace.
:param alarm: Alarm.
"""
self.register_alarm(alarm)
for alarm_ in listify(alarm):
self._set('alarm-segfault', alarm_.alias, multi=True)
return self._section
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] |
src/ambient/cli.py | mikepqr/ambient | 1 | 128348 | <reponame>mikepqr/ambient<filename>src/ambient/cli.py
import logging
import click
from ambient.ambient import make_context
from ambient.ambient import today as _today
from ambient.ambient import yesterday as _yesterday
from ambient.backfill import backfill as _backfill
@click.group()
@click.version_option()
@click.option("--quiet", "-q", help="Be quiet", is_flag=True, default=False)
@click.option("--application-key", required=True)
@click.option("--api-key", required=True)
@click.option("--mac", required=True)
@click.pass_context
def cli(ctx, quiet, application_key, api_key, mac):
if quiet:
logging.basicConfig(level=logging.WARN, format="%(message)s")
else:
logging.basicConfig(level=logging.INFO, format="%(message)s")
auth_data = {
"application_key": application_key,
"api_key": api_key,
"mac": mac,
}
ctx.obj = make_context(auth_data)
@cli.command()
@click.pass_obj
def today(context):
_today(context)
@cli.command()
@click.pass_obj
def yesterday(context):
_yesterday(context)
@cli.command()
@click.pass_obj
def backfill(context):
_backfill(context)
def main():
cli(auto_envvar_prefix="AMBIENT")
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] |
lib/utils/views/decorators.py | arkanister/minitickets | 0 | 114578 | # -*- coding: utf-8 -*-
from django.conf import settings
from django.contrib.auth import REDIRECT_FIELD_NAME
from django.contrib.auth.decorators import login_required as django_login_required, user_passes_test
from django.contrib.auth.views import redirect_to_login
from django.core.exceptions import PermissionDenied
from django.shortcuts import resolve_url
from django.utils.decorators import method_decorator
from django.utils.encoding import force_str
from.types import CREATE, UPDATE, DELETE, DETAIL, LIST
def login_required(method, redirect_field_name=REDIRECT_FIELD_NAME, login_url=None):
"""
Decorator for views that checks that the user is logged in, redirecting
to the log-in page if necessary.
"""
def wrapper(self, *args, **kwargs):
required = getattr(self, 'login_required', True)
if required:
@method_decorator(django_login_required(redirect_field_name=redirect_field_name, login_url=login_url))
def wrap(_self, *ag, **kag):
return method(_self, *ag, **kag)
return wrap(self, *args, **kwargs)
return method(self, *args, **kwargs)
return wrapper
def permission_required(method, login_url=None, raise_exception=True):
"""
Decorator for views that checks that the user has perms, redirecting
to the log-in page if necessary.
"""
def wrapper(self, *args, **kwargs):
perms = getattr(self, 'perms', [])
model = getattr(self, 'model', None)
view_type = getattr(self, '_TYPE', None)
if model is not None and view_type is not None and isinstance(perms, list):
# get a perms from a specific view
opts = getattr(model, "_meta")
app_name = opts.app_label.lower()
perm = None
if view_type == CREATE:
perm = opts.get_add_permission()
elif view_type == UPDATE:
perm = opts.get_change_permission()
elif view_type == DELETE:
perm = opts.get_delete_permission()
elif view_type in (DETAIL, LIST):
perm = "view_%s" % opts.object_name.lower()
if perm is not None:
perms.append("%s.%s" % (app_name, perm))
# is required if exists a perms
required = isinstance(perms, list) and len(perms) > 0
if required:
def check_perms(user):
if not user.is_authenticated() or user.has_perms(perms):
return True
if raise_exception:
raise PermissionDenied
return False
@method_decorator(user_passes_test(check_perms, login_url=login_url))
def wrap(_self, *ag, **kwg):
return method(_self, *ag, **kwg)
return wrap(self, *args, **kwargs)
return method(self, *args, **kwargs)
return wrapper
def ajax_required(method, redirect_field_name=REDIRECT_FIELD_NAME, login_url=None, raise_exception=False):
"""
Decorator for views that checks that the user is logged in, redirecting
to the log-in page if necessary.
"""
def wrapper(self, *args, **kwargs):
required = getattr(self, 'ajax_required', True)
request = getattr(self, 'request', False)
if required and not request.is_ajax():
if raise_exception:
raise PermissionDenied
# urlparse chokes on lazy objects in Python 3, force to str
resolved_login_url = force_str(resolve_url(login_url or settings.LOGIN_URL))
path = request.get_full_path()
response = redirect_to_login(
path, resolved_login_url, redirect_field_name)
response.status_code = 405
return response
return method(self, *args, **kwargs)
return wrapper | [
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] |
migrations/versions/013_Fields.py | LCBRU/lbrc_upload | 0 | 169391 | from sqlalchemy import (
MetaData,
Table,
Column,
Integer,
DateTime,
NVARCHAR,
String,
Index,
Boolean,
)
from migrate.changeset.constraint import ForeignKeyConstraint, UniqueConstraint
meta = MetaData()
field = Table(
"field",
meta,
Column("id", Integer, primary_key=True),
Column("study_id", Integer),
Column("order", Integer),
Column("field_type_id", Integer),
Column("field_name", String(100)),
Column("required", Boolean),
Column("max_length", Integer),
Column("default", String(250)),
Column("choices", String(250)),
Column("allowed_file_extensions", String(250)),
)
Index("ix_field_study_id", field.c.study_id)
def upgrade(migrate_engine):
meta.bind = migrate_engine
field.create()
field_type = Table("field_type", meta, autoload=True)
study = Table("study", meta, autoload=True)
fk_field_type = ForeignKeyConstraint([field.c.field_type_id], [field_type.c.id])
fk_field_type.create()
fk_study = ForeignKeyConstraint([field.c.study_id], [study.c.id])
fk_study.create()
uk_study_id_order = UniqueConstraint(field.c.study_id, field.c.order, table=field)
uk_study_id_order.create()
def downgrade(migrate_engine):
meta.bind = migrate_engine
field.drop()
| [
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web/fixtures/load_psite_gps_coordinates.py | MTES-MCT/biocarburants | 4 | 1602137 | import sys, os
import django
import csv
import pandas as pd
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "carbure.settings")
django.setup()
from producers.models import ProductionSite
filename = '%s/web/fixtures/csv/production_sites_gps.csv' % (os.environ['CARBURE_HOME'])
df = pd.read_csv(filename, delimiter=';').set_index('zip')['gps'].to_dict()
print(df)
psites = ProductionSite.objects.all()
for p in psites:
try:
code = int(p.postal_code)
except:
print('missing zip code for %s' % p.name)
continue
if code in df:
print('FOUND gps for %s' % (p.name))
print(df[code])
p.gps_coordinates = df[code]
p.save()
else:
print('could not find gps for %s, %s' % (p.name, p.postal_code))
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layers/modules/fast_mask_iou.py | sebastian-ruiz/yolact | 1 | 23701 | <reponame>sebastian-ruiz/yolact
import torch
from torch import nn
import torch.nn.functional as F
#locals
from data.config import Config
from utils.functions import make_net
from utils.script_module_wrapper import ScriptModuleWrapper, script_method_wrapper
class FastMaskIoUNet(ScriptModuleWrapper):
def __init__(self, config:Config):
super().__init__()
cfg = config
input_channels = 1
last_layer = [(cfg.num_classes-1, 1, {})]
self.maskiou_net, _ = make_net(input_channels, cfg.maskiou_net + last_layer, include_last_relu=True)
def forward(self, x):
x = self.maskiou_net(x)
maskiou_p = F.max_pool2d(x, kernel_size=x.size()[2:]).squeeze(-1).squeeze(-1)
return maskiou_p
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src/test_likdelta.py | casutton/bayes-qnet | 12 | 187128 | import unittest
import qnet
import qnetu
import numpy
import numpy.random
import mytime
import netutils
import estimation
import yaml
import arrivals
import sampling
import qstats
import sys
class TestLikDelta (unittest.TestCase):
def test_mm1_delta (self):
sampling.set_seed(68310)
nreps = 10
ntasks = 10
pct = 0.5
net = self.mm1
self.do_test_delta_internal (net, nreps, ntasks, pct)
def test_mmk_delta (self):
sampling.set_seed(68310)
nreps = 10
ntasks = 10
pct = 0.5
net = self.mmk
self.do_test_delta_internal (net, nreps, ntasks, pct)
def test_mmrss_delta (self):
sampling.set_seed(68310)
nreps = 10
ntasks = 100
pct = 0.25
net = self.mmrss
self.do_test_delta_internal (net, nreps, ntasks, pct)
def do_test_delta_internal (self, net, nreps, ntasks, pct):
for ri in range(nreps):
arrv = net.sample (ntasks)
obs = arrv.subset_by_task (pct)
samples = net.slice_resample (obs, 0, 5)
arrv_from = samples[len(samples)-1]
print "Computing LIK0"
lik0 = net.log_prob (arrv_from)
for e in arrv_from:
if not e.obs_d:
# print "Testing evt ", e
dfn = qnet.GGkGibbs(net, arrv_from, e, lik0).dfn()
d0 = e.d
d_test = [ d0+delta for delta in [ -0.5, -0.1, 0.1, 0.5, 1.0, 1.5, 3.0 ] ]
for d1 in d_test:
# print "Testing departure ", d1
lik_incremental = dfn(d1)
if numpy.isinf (lik_incremental): continue # probably right
lik_true = self.compute_full_lik (net, arrv_from, e, d1)
print "%d %.4f %.4f %.4f %.4f" % (e.eid, d0, d1, lik_incremental, lik_true)
if numpy.isinf(lik_true):
self.assertTrue (numpy.isinf(lik_incremental))
else:
self.assertAlmostEquals (lik_true, lik_incremental, 5)
def compute_full_lik (self, net, arrv0, evt, d):
arrv = arrv0.duplicate()
dl0 = evt.queue().pyDiffListForDeparture (evt, d)
evt_next = evt.next_by_task()
if evt_next:
dl0.extend (evt_next.queue().pyDiffListForArrival(evt_next, d))
arrv.applyDiffList (dl0, 0)
return net.log_prob (arrv)
mm1_text = """
states:
- name: INITIAL
queues: [ INITIAL ]
successors: [ TIER1 ]
initial: TRUE
- name: TIER1
queues: [ WEB1, WEB2 ]
successors: [ TIER2 ]
- name: TIER2
queues: [ APP1 ]
queues:
- { name: INITIAL, service: [M, 10.0] }
- { name: WEB1, service: [M, 3.0] }
- { name: WEB2, service: [M, 3.0] }
- { name: APP1, service: [M, 8.0] }
"""
mmk_text = """
states:
- name: INITIAL
queues: [ INITIAL ]
successors: [ TIER1 ]
initial: TRUE
- name: TIER1
queues: [ WEB1, WEB2 ]
successors: [ TIER2 ]
- name: TIER2
queues: [ APP1 ]
queues:
- { name: INITIAL, service: [M, 5.0] }
- { name: WEB1, service: [M, 3.0], processors: 3 }
- { name: WEB2, service: [M, 3.0], processors: 4 }
- { name: APP1, service: [M, 8.0], processors: 2 }
"""
mmrss_text = """
states:
- name: INITIAL
queues: [ INITIAL ]
successors: [ TIER1 ]
initial: TRUE
- name: TIER1
queues: [ WEB1, WEB2 ]
successors: [ TIER2 ]
- name: TIER2
queues: [ APP1, APP2 ]
successors: [ TIER3 ]
- name: TIER3
queues: [ DB1, DB2 ]
queues:
- { name: INITIAL, service: [M, 10.0] }
- { name: WEB1, service: [M, 15.0], type: GG1R }
- { name: WEB2, service: [M, 17.0], type: GG1R }
- { name: APP1, service: [M, 10.0], type: GG1R }
- { name: APP2, service: [M, 10.0], type: GG1R }
- { name: DB1, service: [M, 7.0], type: GG1R }
- { name: DB2, service: [M, 7.0], type: GG1R }
"""
def setUp (self):
self.mm1 = qnetu.qnet_from_text (TestLikDelta.mm1_text)
self.mmk = qnetu.qnet_from_text (TestLikDelta.mmk_text)
self.mmrss = qnetu.qnet_from_text (TestLikDelta.mmrss_text)
def main():
if len(sys.argv) > 1:
for test_name in sys.argv[1:]:
suite = unittest.TestLoader().loadTestsFromName("test_likdelta.TestLikDelta.%s" % (test_name,))
unittest.TextTestRunner(verbosity=2).run(suite)
else:
unittest.main()
if __name__ == "__main__":
main()
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] |
Image Processing/03_Geometric_Transformations.py | siddarthjha/Opencv | 2 | 186001 | import numpy as np
import cv2
from matplotlib import pyplot as plt
# Scaling
img = cv2.imread('a.jpg')
print(img.shape)
cv2.imshow('img', img)
res = cv2.resize(img, None, fx=2, fy=2, interpolation = cv2.INTER_AREA)
print(res.shape)
print(img.shape)
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Translation
img = cv2.imread('b.jpg', 0)
rows, cols = img.shape
M = np.float32([[1, 0, 100], [0, 1, 50]])
dst = cv2.warpAffine(img, M, (cols, rows))
cv2.imshow('img', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Rotation
img = cv2.imread('b.jpg', 0)
rows,cols = img.shape
M = cv2.getRotationMatrix2D((cols/2, rows/2), 270, 1)
dst = cv2.warpAffine(img, M, (cols, rows))
cv2.imshow('Rotation', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()
# cv2.warpAffine takes a 2x3 transformation matrix while cv2.warpPerspective takes a 3x3 transformation matrix as input.
# Affine Transformation
img = cv2.imread('a.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
rows,cols,ch = img.shape
pts1 = np.float32([[50, 50], [200, 50], [50, 200]])
pts2 = np.float32([[10, 100], [200, 50], [100, 250]])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(img, M, (cols, rows))
plt.subplot(121)
plt.imshow(img)
plt.title('Input')
plt.subplot(122)
plt.imshow(dst)
plt.title('Output')
plt.show()
# Perspective Transformation
img = cv2.imread('b.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
rows, cols, ch = img.shape
pts1 = np.float32([[56, 65], [368, 52], [28, 387], [389, 390]])
pts2 = np.float32([[0, 0], [300, 0], [0, 300], [300, 300]])
M = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, M, (300, 300))
plt.subplot(121),plt.imshow(img),plt.title('Input')
plt.subplot(122),plt.imshow(dst),plt.title('Output')
plt.show()
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DSTK/Timeseries/recurrence_plots.py | jotterbach/dstk | 12 | 27649 | import _recurrence_map
import numpy as np
def poincare_map(ts, ts2=None, threshold=0.1):
rec_dist = poincare_recurrence_dist(ts, ts2)
return (rec_dist < threshold).astype(int)
def poincare_recurrence_dist(ts, ts2=None):
if ts2 is None:
return _recurrence_map.recurrence_map(ts, ts)
else:
return _recurrence_map.recurrence_map(ts, ts2)
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sokoapp/contests/tests/test_app/models.py | Mercy-Nekesa/sokoapp | 1 | 120743 | <filename>sokoapp/contests/tests/test_app/models.py<gh_stars>1-10
from referee.models import TimePeriodBase
class TimePeriod(TimePeriodBase):
pass
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main.py | felipedmsantos95/hangman-game-py | 1 | 141513 | <filename>main.py
from lerArquivo import *
from utilitariosJogo import *
from datetime import datetime
import os
import json
#-------------------------------- Variáveis de controle do jogo ------------------------------
tentativasLetra = 6
letrasErradas = 0
suporPalavras = 3
ganhouJogo = False
letrasUtilizadas = []
#------------------------------------- Funções de controle -----------------------------------
#Verifica se a letra já foi testada pelo usuário
def letraRepetida(letra, listaLetras):
if(letra not in listaLetras):
return False
else:
return True
#
def printarStatus(dica, tentativasLetra, suporPalavras):
os.system('cls' if os.name == 'nt' else 'clear')
printarForca(tentativasLetra)
print('Palavra: ' + dica + '\n')
print('Tentativas de letra restantes: ' + str(tentativasLetra))
print('Tentativas de palavra restantes: ' + str(suporPalavras))
print('Letras tentadas: ' + ','.join(letrasUtilizadas))
print('Letras erradas: ' + str(letrasErradas) + '\n')
#--------------------------------------- INICIO DO JOGO --------------------------------------
print('### JOGO DA FORCA ###')
nomeUsuario = input('Digite seu nome: ')
emailUsuario = input('Digite seu email: ')
print("\nA palavra foi escolhida, dica inicial: \n")
palavraOculta = sortearPalavra('baseDados.txt')
dica = dicaPalavra(palavraOculta)
printarForca(tentativasLetra)
while((tentativasLetra > 0) and (suporPalavras > 0) and not(ganhouJogo)):
print('Digite a ação que deseja tomar:')
print('1 - Advinhar letra\n2 - Advinhar a palavra')
option = input('Digite a opção: ')
if(option == '1'):
print('\n####################')
letra = input('Insira uma letra: ')
#Realizando a verficação antes de prosseguir
while(not(letraCorreta(letra))):
letra = input("\nPor favor, digite uma letra no formato correto: ")
while(letraRepetida(letra, letrasUtilizadas)):
letra = input('Você já tentou esta letra, por favor tente outra: ')
letrasUtilizadas.append(letra)
estadoAnteriorDica = dica
dica = letraPalavra(letra, palavraOculta, dica)
#Verifica se errou a letra
if(estadoAnteriorDica == dica):
letrasErradas += 1
tentativasLetra -= 1
printarStatus(dica, tentativasLetra, suporPalavras)
elif(dica == True): #Verifica se o jogo foi ganho
print('Parabéns! Você descobriu a palavra oculta!')
print('Palavra: ' + palavraOculta + '\n')
ganhouJogo = True
else:
printarStatus(dica, tentativasLetra, suporPalavras)
elif (option == '2'):
print('\n####################')
palpite = input('Insira seu palpite: ')
if(palpitePalavra(palpite, palavraOculta)): #Verifica se o jogo foi ganho
print('Palavra: ' + palavraOculta + '\n')
ganhouJogo = True
else:
suporPalavras -= 1
printarStatus(dica, tentativasLetra, suporPalavras)
else:
print('Por favor, insira uma opção válida!\n')
#Tratamento final ao status do jogo
if(not ganhouJogo):
print('Você perdeu o jogo! :(')
print('A palavra era: ' + palavraOculta + '\n')
status = 'Perdeu o jogo! :('
else:
status = 'Venceu o jogo!'
data = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
resultadoJogo = {
'<NAME>': nomeUsuario,
'E-<NAME>': emailUsuario,
'Palavra Oculta': palavraOculta,
'Status': status,
'Data e Hora': data
}
with open('historico.json', 'a') as arquivoJson:
json.dump(resultadoJogo, arquivoJson, indent=4) | [
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Project1-name.py | the-fridge/Python_Projects | 1 | 169745 | # !/usr/bin/env python3
# -*- coding: utf-8 -*-
# authors: <NAME> / <NAME>
"""`Project1-name.py` is a program that asks for the user's name
Then it print the given name, and the lenght of this name.
"""
def initial_way ():
""" asks the name, print it with its lenght
Original code by <NAME>, using several `print()`
"""
print('Hello')
user_name = input('What is your name?\n')
print('It\'s nice to meet you', user_name)
print('Length of your name is:')
print(len(user_name))
def initial_way_2():
""" asks the name, print it with its lenght
Original code by <NAME>, using a variable to
store the message and format it before display
"""
print('Hello')
user_name = input('What is your name?\n').
name_size = len(user_name)
msg = 'It\'s nice to meet you {0}!\nThe length of your name is: {1}'.format(user_name, name_size)
print(msg)
def get_name_info():
""" asks the name, print it with its lenght
Remake of initial_way() by <NAME>
"""
name = input('Hello,\nPlease, write your name: ')
print('---\nPlease to meet you {}. You name is {} char(s) long.\n---'
.format(name, len(name)))
def main() :
""" Main function of the script
Call the other functions
"""
get_name_info()
initial_way()
initial_way_2()
if __name__ == '__main__':
main()
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TemplatesAndTests.py | LSRCT/TandT | 0 | 113402 | """
Usefull functions for loading files etc.
-AN
"""
import os
import pickle
import zipfile, lzma
import numpy as np
import matplotlib.pyplot as plt
from collections import OrderedDict
# saves dict to csv using keys as headers
def saveToCSV(savedict, filename = "", path = ""):
try:
#print(savedict)
if not filename:
datanum = 1
filename = "data"
while os.path.isfile("".join([filename,str(datanum),".csv"])):
datanum += 1
filename = "".join([filename,str(datanum)])
if filename[-4:] != ".csv":
filename = "".join([filename,".csv"])
if not path:
path = os.getcwd()
if not os.path.isdir(path):
os.makedirs(path)
if not os.path.isfile("".join([path,filename])):
file = open("".join([path,filename]),'w')
file.write("".join([";".join(list(savedict.keys())),";\n"]))
file.close()
file = open("".join([path,filename]),'a')
valueslist = list(savedict.values())
for rowcount in range((len(list(valueslist[0])))):
for values in valueslist:
file.write("".join([str(values[rowcount]), ";"]))
file.write(";\n")
except PermissionError:
print("Error saving data, is the file open?")
except (AttributeError, TypeError):
print("Error in dataformat, use a dict filled with lists.")
except KeyError:
print("Error in length of data")
return 1
# takes a dict and gives a list with dicts. Kinda sorts by relKey
def splitIntoDicts(data, relKey="Lackh (m)"):
dicts = []
for group in set(data[relKey]):
datanew = OrderedDict()
for gnumb, gline in enumerate(data[relKey]):
if gline == group:
for key in data:
if key in datanew:
datanew[key].append(data[key][gnumb])
else:
datanew[key] = [data[key][gnumb]]
dicts.append(datanew)
return dicts
def parseSubDirs(filtlistlist, dirname="", dirstruct=[]):
"""
:param filtlistlist: list of lists of strings
:param dirname: dir to start parsing from added to cwd
:param dirstruct use predefined dirstruct to avoid calling os.walk all the time
:return: list of all files containing at least one of the strings of all the lists in filtlistlist
"""
if not dirname:
path = os.getcwd()
else:
path = dirname
files = []
if len(dirstruct) > 0:
dirs = dirstruct
else:
dirs = list(os.walk(path))
for r, d, f in dirs:
for file in f:
for filtlist in filtlistlist:
if not np.any([filt in os.path.join(r, file) for filt in filtlist]):
break
else:
files.append(os.path.join(r, file))
return files
def multiReadData(paths, filtlist, splitString="Frequenz [Hz]"):
dataDict = OrderedDict()
for path in paths:
data, metadata = readData(path)
filename = path.split("\\")[-1]
if splitString not in data:
splitString = "frequenz (Hz)" # in case you spelled it wrong
data = splitData(data, splitString)
for filtkey in filtlist:
if filtkey in path:
if not filtkey in dataDict:
dataDict[filtkey] = OrderedDict()
if not filename in dataDict[filtkey]:
dataDict[filtkey][filename] = OrderedDict()
dataDict[filtkey][filename]["data"] = data
dataDict[filtkey][filename]["metadata"] = metadata
dataDict[filtkey][filename]["metadata"]["path"] = path
return dataDict
def splitData(data, relKey = "Frequenz [Hz]"):
"""
:param data:
dictionary with data
:return:
data separated into sweeps
"""
if relKey in data:
relKey = relKey
elif "frequenz (Hz)" in data:
relKey = "frequenz (Hz)"
elif 'Frequenz [Hz]' in data:
relKey = 'Frequenz [Hz]'
elif ' Frequenz [Hz]' in data:
relKey = ' Frequenz [Hz]'
newdict = OrderedDict()
sweepAm = 0
# if len(set(data[relKey])) % len(data[relKey]):
# print("shortcut")
# for key in data:
# newdict[key] = [data[key][x*len(set(data[relKey])):len(set(data[relKey]))*(1+x)] for x in range(int(len(data[relKey])/len(set(data[relKey]))))]
# return newdict
cor = -(data[relKey][0]-data[relKey][1])/np.abs(data[relKey][0]-data[relKey][1])
for point, el in enumerate(data[relKey]):
if (point > 0 and (el*cor) < (data[relKey][point-1])*cor) or (point == len(data[relKey])-cor):
for key in data:
if key in newdict:
newdict[key].append(data[key][sweepAm:point])
else:
newdict[key] = [data[key][sweepAm:point]]
sweepAm=point
return newdict
def savePickle(savedict, filename = "", path =""):
datanum = 1
while os.path.isfile(path+str(datanum)+".pkl"):
datanum += 1
filename = "".join([filename,str(datanum)])
if filename[-4:] != ".pkl":
filename = "".join([filename,".pkl"])
if not os.path.isdir(path):
os.makedirs(path)
with open("".join([path,filename]), 'wb+') as rawF:
pickle.dump(savedict, rawF, pickle.HIGHEST_PROTOCOL)
def transDataML(dataDict, xKey, compFunc=lambda a: a):
X = []
y = []
for campNumb, campaign in enumerate(dataDict):
for fileCount, messfile in enumerate(dataDict[campaign]):
for data in dataDict[campaign][messfile]["data"][xKey]:
X.append(data)
y.append(dataDict[campaign][messfile]["metadata"]["Chip:"] + " " + dataDict[campaign][messfile]["metadata"]["Substanz:"])
return X, y
def winAvg(data, winWidth=0, winfunc=np.blackman, mode="same"):
if not winWidth:
if int(len(data)*(5/100))>0:
winWidth = int(len(data)*(10/100))
if not winWidth % 2:
winWidth += 1
else: return data
kernel = winfunc(winWidth)/np.sum(winfunc(winWidth))
data = np.convolve(data, kernel, mode=mode)
return data
# zips raw data of all sub folders to path containing the provided string
def zipRaw(path = os.getcwd(), mark="_raw", outname = "raw.zip"):
if outname[-4:] != ".zip":
outname = "".join([outname,".zip"])
zf = zipfile.ZipFile(outname, mode="w")
cTree = list(os.walk(path))
for folder in cTree[0][1]:
if mark in folder:
for datei in list(os.walk(path+ "//" +folder))[0][2]:
zf.write(folder+"//"+datei, compress_type=zipfile.ZIP_LZMA)
zf.close()
return 1
def readData(filename, path=""):
"""
:param filename: trivial
:param path: trivial
:return: data, metadata as dicts
"""
data = dict()
metadata = dict()
with open("".join([path, filename]),'r') as file:
lines = [[part for part in line.rstrip('\n').split(";") if part] for line in file]
for count, line in enumerate(lines):
if len(line) > 3:
keyline = line
keypoint = count
break
if len(line) == 2 or not line[0]:
metadata[str(line[0])] = str(line[1])
for key in keyline:
data[key] = []
for line in lines:
if len(line) == len(data.keys()):
for pos in range(len(keyline)):
try:
data[keyline[pos]].append(float(line[pos]))
except: pass
metadata["dataKeys"] = data.keys()
return data, metadata
if __name__ == "__main__":
testdict = {"temperature": [1,2,3,4], "length":[4,5,6,7], "time":[8,9,10,11]}
data,metadata = readData("Daten_sim.csv", path="/home/an/01_XSensor/Chipvergleich/")
data = splitData(data)
signal = {"values": data["UBol (V), Spannung"]}
print(np.shape(signal["values"]))
plt.plot(signal["values"][0])
plt.show() | [
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] |
docs/hydrostatic-pressure/hydrostatic_pressure.py | TOKU-Systems/tutorials | 0 | 24824 | <filename>docs/hydrostatic-pressure/hydrostatic_pressure.py
import pandas as pd
df = pd.read_sql(
'''
SELECT a.name, h.name, s.name, sd.t, sd.y
FROM assets a
JOIN hardpoints h ON a.id = h.asset_id
JOIN signals s ON h.id = s.hardpoint_id
JOIN LATERAL (
SELECT x.t, x.y
FROM signal_data x
WHERE x.signal_id = s.id
ORDER BY x.t DESC
LIMIT 1
) sd ON true where s.name='Pressure'
''',
"postgresql://data_viewer:[email protected]/new_mareland")
df_new = df.set_axis([
'Asset name',
'Hardpoint',
'Signal name',
'Last time',
'Height'], axis=1, inplace=False)
print('Enter the specific gravity of the liquid in kg/m3: ')
user_input_specific_gravity = float(input())
g = 9.81
df_new['Height'] = df_new['Height']/(user_input_specific_gravity * g)
for index, row in df_new.iterrows():
df_new.iloc[index, [4]] = row['Height']
df_new.iloc[index, [3]] = pd.to_datetime(row['Last time'])
print(df_new.to_string(
formatters={
'Last time': lambda x: f'{pd.to_datetime(x,unit="D"):%X}',
'Height': lambda x: f'{x:.5g}'
}))
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] |
matchmaker/api.py | gnmerritt/casino | 2 | 65575 | from flask import request, jsonify, abort
from flask.ext.login import login_required, current_user
import bots
from matchmaker import app, db
import matches
import match_logs
import util
import profile
#
# Public API
#
@app.route('/api/matches')
@login_required
def open_matches():
open = matches.OpenMatches(db)
return util.paged_json(open.active(app.config))
@app.route('/api/matches/finished')
@login_required
def finished_matches():
offset = request.args.get('offset', type=int, default=0)
finished = matches.FinishedMatches(db, offset)
pagination, hits = finished.finished(current_user)
return util.paged_json(hits, pagination)
@app.route('/api/matches/<match_id>')
@login_required
def get_match_logs(match_id):
streamer = match_logs.MatchLogBuilder(match_id)
if not streamer.valid():
abort(404)
return jsonify(streamer.data())
@app.route('/api/bot/<guid_or_key>')
def get_bot(guid_or_key):
bot = bots.BotInfo(db, guid_or_key).bot
if not bot:
abort(404)
return jsonify({
"success": True,
"bot": bot,
})
@app.route('/api/bot/<name>', methods=['POST'])
@login_required
def new_bot(name):
creator = profile.BotMaker(current_user, name)
bot = creator.create(db)
return jsonify({
"success": True,
"bot": bot,
})
#
# Internal API
#
@app.route('/api/internal/new_match', methods=['POST'])
def new_match():
maker = matches.NewMatch()
maker.create(db)
return jsonify(**{
"key": maker.guid(),
"success": True,
})
@app.route('/api/internal/join/<game_key>', methods=['POST'])
def player_joined(game_key):
bot_key = request.args.get('key')
joiner = matches.MatchJoiner(game_key, bot_key)
code, message = joiner.join(db)
return jsonify(status=code, message=message), code
@app.route('/api/internal/finished/<game_key>', methods=['POST'])
def game_finished(game_key):
match_results = request.get_json(force=True)
if not match_results:
abort(404)
writer = matches.MatchResultsWriter(game_key)
if not writer.valid():
abort(404)
writer.record(db, match_results)
new_match = matches.MatchCreatorJob(db)
new_match.run()
return jsonify({"success": True})
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] |
graphstar/utils.py | pengboomouch/graphstar | 0 | 2303 | """
graphstar.utils
~~~~~~~~~~~~~~~
<NAME>
A simple bedirectional graph with A* and breadth-first pathfinding.
Utils are either used by the search algorithm, or when needed :)
Pretty self explainatory (I hope)
For more information see the examples and tests folder
"""
def smooth_path(p):
# If the path is only two nodes long, then
# we can’t smooth it, so return
if len(p) == 2:
return p
# Compile an output path
output = [p[0]]
# Keep track of where we are in the input path
# We start at 2, because we assume two adjacent
# nodes will pass the ray cast
i = 2
# Loop until we find the last item in the input
while i < len(p)-1:
# Do the ray cast
if not ray_clear(output[len(output)-1], p[i]):
# The ray text failed, add the last node that
# passed to the output list
output += p[i-1]
# Consider the next node
i += 1
# We’ve reached the end of the input path, add the
# end node to the output and return it
output += p[len(p)-1]
return output
def clean_route_list(route_stack: list, goal_node_id: int):
"""
Creates an ordered route list from start to finish
with all node ids needed to traverse to the goal.
:param route_stack: All routes found until goal
:param goal_node: int ID of the goal node
:return: list A ordered list from start to goal
"""
r = []
next_node = goal_node_id
reversed_stack = reversed(route_stack)
for c in reversed_stack:
if c.to_node.id == next_node:
r.append(c.to_node.id)
r.append(c.from_node.id)
next_node = c.from_node.id
return list(set(r))
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12,
12,
12,
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4397,
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3166,
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29889,
333,
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13,
12,
12,
12,
4622,
29918,
3177,
353,
274,
29889,
3166,
29918,
3177,
29889,
333,
13,
13,
12,
2457,
1051,
29898,
842,
29898,
29878,
876,
13,
2
] |
util.py | andrewdyates/geo_api | 1 | 187412 | #!/usr/bin/python
from geo import *
def get_gpl(gpl_brief=None, gpl_data=None, is_tab=True):
"""Return geo.Local_GPL object from GPL file names.
Args:
gpl_brief: str of file path to GPL file meta data brief (as from geo_downloader)
gpl_data: str of file path to GPL row descriptions IN DATA ROW ORDER (as from geo_downloader)
may either be .tab for SOFT text file format
is_tab: bool if gpl_data is in .tab format
Returns:
geo_api.GPL of loaded row descriptions
"""
gpl = LocalGPL(fname_brief=gpl_brief, fname_data=gpl_data, data_is_tab=is_tab)
gpl.load()
return gpl
def assert_row_alignment(n, varlist, gpl):
"""Assert that GPL row descriptions and row id list aligns to data rows."""
assert n == len(gpl.row_desc) == len(gpl.probe_list) == len(gpl.probe_idx_map), \
" != ".join((n, len(gpl.row_desc), len(gpl.probe_list), len(gpl.probe_idx_map)))
for i in xrange(n):
s = gpl.probe_list[i]
assert s == varlist[i]
assert gpl.probe_idx_map[s] == i
assert s in gpl.row_desc
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demo/demo.py | lrscy/Unsupervised-Dual-Learning-Neural-Machine-Translation-Model | 3 | 68011 | <gh_stars>1-10
# coding: utf-8
""" Abstract
The demo is a prototype of the project model. Codes and structure here could change in the future.
The main point of codes below is to run on local computer and test whether it works on small scale of data.
Now:
Prove that original model is inaccessible. Start to implemente original RNN-LSTM and treat it as baseline.
TODO:
1. Try new way to approach the unsupervised method.
2. Try to understand how to control gradient in Tensorflow.
"""
import os
import sys
import random
import math
import multiprocessing
import h5py
import tqdm
import json
import numpy as np
import tensorflow as tf
import keras as K
import keras.backend.tensorflow_backend as KTF
from keras.models import Model
from keras.callbacks import ModelCheckpoint
from keras.layers import Input, Embedding, LSTM, Dense, Lambda, concatenate, Multiply
K.backend.clear_session()
KTF.set_session( tf.Session( config = tf.ConfigProto( device_count = {'gpu':0} ) ) )
""" Get train data from path
Read paralelled train data from files for each language and save to a dictionary.
Args:
dataPath: file path of train data. Default value is "../../Data/train/".
langList: language list. Indicating which language data will be included in train data.
Default value is ["Chinese", "English"].
encoding: encoding of each file in train directory.
ratio: propotion of train data, others will be treat as dev data. Default value is 0.98.
Returns:
trainData: a dictionary of train data sentences of each language. Its structure is:
{language A: [[word1, word2, ...], [...], ...], language B: ...}.
devData: a dictionary of dev data sentences of each language. Its structure is:
{language A: [[word1, word2, ...], [...], ...], language B: ...}.
"""
def getTrainData( dataPath = "../../Data/train/", lanList = ["chinese", "english"],
encoding = "UTF-8", ratio = 0.98 ):
trainData = {}
devData = {}
data = {}
for lan in lanList:
if lan not in data:
data[lan] = []
print( "Reading " + lan + " files..." )
files = os.listdir( dataPath + lan + "/" )
for file in files:
with open( dataPath + lan + "/" + file, encoding = encoding ) as f:
line = f.readline()
while line:
wordList = line.split()
data[lan].append( ["<S>"] + wordList + ["</S>"] )
line = f.readline()
noOfSentences = len( data[lanList[0]] )
arr = [i for i in range( noOfSentences )]
random.shuffle( arr )
for lan in lanList:
if lan not in trainData:
trainData[lan] = []
if lan not in devData:
devData[lan] = []
data[lan] = np.array( data[lan] )[arr].tolist()
noOfTrainData = int( noOfSentences * ratio )
devData[lan] = data[lan][noOfTrainData:]
trainData[lan] = data[lan][:noOfTrainData]
print( trainData[lan][:5] )
return trainData, devData
""" Generate dictionary and preprocess setences for each language
Generate dictionary for each language and convert word to corresponding index.
Here we set two dictionaries to speed up the whole program.
Moreover, the function will replace word in sentences to index automatically.
Args:
data: a dictionary contains sentences of each language. Its structure is:
{language A: [[word1, word2, ...], [...], ...], language B: ...}.
threshold: a word will be replace with <UNK> if frequency of a word is
less than threshold. If the value is less than 1, it means
no need to replace any word to <UNK>. Default value is 0.
Returns:
wordNumDict: a dictionary which can convert words to index in each language.
Its structure is:
{language A: {word A: index A, word B: ..., ...}, language B: ..., ...}.
numWordDict: a dictionary which can convert index to word in each language.
Its structure is:
{language A: {index A: word A, index B: ..., ...}, language B: ..., ...}.
"""
def generateDict( data, threshold = 0 ):
wordNumDict = {}
numWordDict = {}
for lan, sentences in data.items():
if lan not in wordNumDict:
# Add special word to dictionary
wordNumDict[lan] = {"<PAD>": 0, "<S>": 1, "</S>": 2, "<UNK>": 3}
if lan not in numWordDict:
# Add special word to dictionary
numWordDict[lan] = {0: "<PAD>", 1: "<S>", 2: "</S>", 3: "<UNK>"}
# Count word frequency
wordCount = {}
for sentence in sentences:
for word in sentence:
if word not in wordCount:
wordCount[word] = 0
wordCount[word] += 1
# Find and replace with <UNK>
for sentence in sentences:
for i in range( len( sentence ) ):
word = sentence[i]
if wordCount[word] < threshold:
word = "<UNK>"
if word not in wordNumDict[lan]:
number = len( wordNumDict[lan] )
wordNumDict[lan][word] = number
numWordDict[lan][number] = word
sentence[i] = wordNumDict[lan][word]
return wordNumDict, numWordDict
"""Save dictionary to file
Args:
dt: the dictionary to save.
fileName: the name of file to save.
path: the path of file to save.
Returns:
None.
"""
def saveDict( dt, fileName, path ):
print( "Saving to " + path + fileName + "..." )
fileName = path + fileName
with open( fileName, "w" ) as f:
jsn = json.dumps( dt )
f.write( jsn )
"""Sort dictionary by length of original language
Args:
data: a dictionary contains sentences of each language. Its structure is:
{language A: [[word1, word2, ...], [...], ...], language B: ...}.
lan: a list of language. The first one is the original language, the second
one is the target language. For example:
[language A, language B].
Returns:
None.
"""
def sortByOriLan( data, lan = ["chinese", "english"] ):
print( "Sorting..." )
tmp = list( zip( data[lan[0]], data[lan[1]] ) )
tmp.sort( key = lambda x: len( x[0] ) )
data[lan[0]], data[lan[1]] = zip( *tmp )
"""Number to One-hot
Only convert sentences which length are small than 30.
Args:
data: a dictionary contains sentences of each language. Its structure is:
{language A: [[word1, word2, ...], [...], ...], language B: ...}.
lan: a list of language. The first one is the original language, the second
one is the target language. For example:
[language A, language B].
dictLength: dictionary length of language B.
Returns:
status: boolean value. True represents successfully extract batch data. False
represents extract nothing from original data.
ndata: data like dictionary.
lan1d: 3-D data contains one-hot format label data.
"""
def toCategory( data, lan, dictLength ):
maxlLan0 = 0
maxlLan1 = 0
n = 0
for i in range( len( data[lan[0]] ) ):
if len( data[lan[0]][i] ) <= 32:
n += 1
maxlLan0 = max( maxlLan0, len( data[lan[0]][i] ) )
maxlLan1 = max( maxlLan1, len( data[lan[1]][i] ) )
if n == 0:
return False, [], []
lan0 = np.zeros( ( n, maxlLan0 ) )
lan1 = np.zeros( ( n, maxlLan1 ) )
# lan1d = np.zeros( ( n, maxlLan1, dictLength ) )
n = 0
for i in range( len( data[lan[0]] ) ):
if len( data[lan[0]][i] ) <= 32:
for j in range( len( data[lan[0]][i] ) ):
lan0[n, j] = data[lan[0]][i][j]
for j in range( len( data[lan[1]][i] ) ):
lan1[n, j] = data[lan[1]][i][j]
# if j:
# w = data[lan[1]][i][j]
# lan1d[n, j - 1, w] = 1
n += 1
data[lan[0]] = lan0
data[lan[1]] = lan1
return True, data#, lan1d
def toCategoryWrap( args ):
return toCategory( *args )
""" Simple Seqence to Sequence Implementation
A simple implementation of Sequence to Sequence model. It works as baseline
Args:
input_dim: dimension of input word vector.
output_dim: dimension of output word vector.
hidden_dim: dimension of hidden states vector.
output_vocab_size: size of output language vocabulary size.
input_vocab_size: size of input language vocabulary size.
word_vec_dim: dimension of word-vector.
name: name of the model.
Returns:
model: the whole model of simple Seq2Seq model.
"""
def simpleSeq2Seq( output_vocab_size, input_vocab_size, hidden_dim = 128,
word_vec_dim = 300, name = "demo" ):
embedding_encoder = Embedding( output_dim = word_vec_dim, input_dim = input_vocab_size,
name = name + "_encoder_embedding", mask_zero = True ) #
embedding_decoder = Embedding( output_dim = word_vec_dim, input_dim = output_vocab_size,
name = name + "_decoder_embedding", mask_zero = True ) #
# Encoder
encoder_input = Input( shape = ( None, ), name = name + "_encoder_input" )
# change when using pre-trained embedding trainable= False
encoder = LSTM( hidden_dim, return_state = True )
encoder_input_emb = embedding_encoder( encoder_input )
_, state_h, state_c = encoder( encoder_input_emb )
state_encoder = [state_h, state_c]
# Decoder
decoder = LSTM( hidden_dim, return_sequences = True )
decoder_input = Input( shape = ( None, ), name = name + "_decoder_input" )
decoder_input_emb = embedding_decoder( decoder_input )
decoder_outputs = decoder( decoder_input_emb, initial_state = state_encoder )
decoder_dense = Dense( output_vocab_size, activation = "softmax", name = name + "_decoder_output" )
decoder_outputs = decoder_dense( decoder_outputs )
# Build model
model = Model( inputs = [encoder_input, decoder_input], outputs = decoder_outputs, name = name )
model.compile( optimizer = 'adam', loss = "categorical_crossentropy" )
return model
trainData, devData = getTrainData( "../../Data/train/" )
wordNumDict, numWordDict = generateDict( trainData, threshold = 5 )
saveDict( wordNumDict, "wordNumDict", "Dicts" )
saveDict( numWordDict, "numWordDict", "Dicts" )
sortByOriLan( trainData, ["chinese", "english"] )
ivs = len( wordNumDict["chinese"] )
ovs = len( wordNumDict["english"] )
print( ivs, ovs )
trainData["chinese"] = trainData["chinese"][::-1]
devData["chinese"] = devData["chinese"][::-1]
trainData["english"] = trainData["english"][::-1]
devData["english"] = devData["english"][::-1]
model = simpleSeq2Seq( output_vocab_size = ovs, input_vocab_size = ivs, name = "demo" )
model.summary()
batch_size = 64
losses = []
n = 0
total = len( trainData["chinese"] )
length = len( wordNumDict["english"] )
print( "parallizly processing training data" )
cores = multiprocessing.cpu_count()
pool = multiprocessing.Pool( processes = cores )
params = []
for i in range( 0, total + batch_size, batch_size ):
# Divide data into batch
tdata = {}
tdata["chinese"] = trainData["chinese"][i:i + batch_size]
tdata["english"] = trainData["english"][i:i + batch_size]
# Combine all params
params.append( [tdata, ["chinese", "english"], length] )
print( "MAP" )
rets = []
for ret in tqdm.tqdm( pool.imap_unordered( toCategoryWrap, params ) ):
rets.append( ret )
#rets = pool.map( toCategoryWrap, params )
pool.close()
pool.join()
n = 0
print( "Training..." )
for epoch in range( 2 ):
for ret in rets:
# status, newTrainData, td = toCategory( trainData, ["chinese", "english"], length, i, min( i + batch_size, total ) )
if ret[0] == False:
continue
label = K.utils.to_categorical( ret[1]["english"], length )
loss = model.train_on_batch( [ret[1]["chinese"], ret[1]["english"]], label )
n += 1
print( n, loss )
if n and n % 3000 == 0:
model.save_weights("Models/model_weights_" + str( n ) + ".h5" )
losses.append( loss )
model.save_weights("Models/model_weights_" + str( n ) + ".h5" )
with open( "losses", "w" ) as f:
for loss in losses:
f.write( str( loss ) + "\n" )
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2
] |
types-operations/strings.py | rawzes/python-book | 0 | 149733 |
s = 'test'
print(s[:]) # print whole string
print(s[0:-1]) # print tes
print(s[1]) # e
print(s + "xyz") # concatination testxyz
print("A"*90)
#s[0] = "s" #str' object does not support item assignment
S = "strawberry"
L = list(S)
print(L)
L[0] = 'Z'
print(''.join(L)) # Ztrawberry
s = ''.join(L)
s = s.replace('rr', 'rrr')
print(s)
line = 'aaa,bbb,ccc'
print(line.split(',')) # разбить по разделителю строку
print(line.upper())
test_string = '''etst
gdf
rt
\ttew'''
print(test_string)
print(line.encode("utf-8"))
print(line.encode("utf-16")) | [
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ui_lanzou.py | wangkun19930608/lanzou-gui | 0 | 1607854 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'lanzou-gui/lanzou.ui'
#
# Created by: PyQt5 UI code generator 5.13.2, and I have edited
#
from PyQt5 import QtCore, QtGui, QtWidgets
from dialogs import MyLineEdit
class MyTableView(QtWidgets.QTableView):
"""加入拖拽功能的表格显示器"""
drop_files = QtCore.pyqtSignal(object)
def __init__(self, parent):
super(MyTableView, self).__init__(parent)
self.setDragDropMode(QtWidgets.QAbstractItemView.InternalMove)
self.setDragEnabled(True)
self.setAcceptDrops(True)
self.setDropIndicatorShown(True)
def dragEnterEvent(self, event):
m = event.mimeData()
if m.hasUrls():
for url in m.urls():
if url.isLocalFile():
event.accept()
return
event.ignore()
def dropEvent(self, event):
if event.source():
QtWidgets.QListView.dropEvent(self, event)
else:
m = event.mimeData()
if m.hasUrls():
urls = [url.toLocalFile() for url in m.urls() if url.isLocalFile()]
if urls:
self.drop_files.emit(urls)
event.acceptProposedAction()
class Ui_MainWindow(QtWidgets.QMainWindow):
def __init__(self):
super(Ui_MainWindow, self).__init__()
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.setEnabled(True)
MainWindow.resize(1000, 650)
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.verticalLayout = QtWidgets.QVBoxLayout(self.centralwidget)
self.verticalLayout.setObjectName("verticalLayout")
self.tabWidget = QtWidgets.QTabWidget(self.centralwidget)
self.tabWidget.setObjectName("tabWidget")
self.share_tab = QtWidgets.QWidget()
self.share_tab.setObjectName("share_tab")
self.share_vlayout = QtWidgets.QVBoxLayout(self.share_tab)
self.share_vlayout.setObjectName("share_vlayout")
self.share_hlayout_top = QtWidgets.QHBoxLayout()
self.share_hlayout_top.setObjectName("share_hlayout_top")
self.label_share_url = QtWidgets.QLabel(self.share_tab)
self.label_share_url.setObjectName("label_share_url")
self.share_hlayout_top.addWidget(self.label_share_url)
self.line_share_url = QtWidgets.QLineEdit(self.share_tab)
self.line_share_url.setObjectName("line_share_url")
self.share_hlayout_top.addWidget(self.line_share_url)
self.btn_extract = QtWidgets.QPushButton(self.share_tab)
self.btn_extract.setObjectName("btn_extract")
self.share_hlayout_top.addWidget(self.btn_extract)
self.share_vlayout.addLayout(self.share_hlayout_top)
self.table_share = QtWidgets.QTableView(self.share_tab)
self.table_share.setObjectName("table_share")
self.share_vlayout.addWidget(self.table_share)
self.share_hlayout_bottom = QtWidgets.QHBoxLayout()
self.share_hlayout_bottom.setObjectName("share_hlayout_bottom")
self.btn_share_select_all = QtWidgets.QPushButton(self.share_tab)
self.btn_share_select_all.setObjectName("btn_share_select_all")
self.share_hlayout_bottom.addWidget(self.btn_share_select_all)
self.label_dl_path = QtWidgets.QLabel(self.share_tab)
self.label_dl_path.setObjectName("label_dl_path")
self.share_hlayout_bottom.addWidget(self.label_dl_path)
self.share_set_dl_path = MyLineEdit(self.share_tab)
self.share_set_dl_path.setObjectName("share_set_dl_path")
self.share_hlayout_bottom.addWidget(self.share_set_dl_path)
self.btn_share_dl = QtWidgets.QPushButton(self.share_tab)
self.btn_share_dl.setObjectName("btn_share_dl")
self.share_hlayout_bottom.addWidget(self.btn_share_dl)
self.share_vlayout.addLayout(self.share_hlayout_bottom)
self.tabWidget.addTab(self.share_tab, "")
self.disk_tab = QtWidgets.QWidget()
self.disk_tab.setEnabled(True)
self.disk_tab.setMinimumSize(QtCore.QSize(620, 0))
self.disk_tab.setObjectName("disk_tab")
self.disk_vlayout = QtWidgets.QVBoxLayout(self.disk_tab)
self.disk_vlayout.setObjectName("disk_vlayout")
self.disk_hlayout_top = QtWidgets.QHBoxLayout()
self.disk_hlayout_top.setObjectName("disk_hlayout_top")
self.disk_loc_hbox = QtWidgets.QHBoxLayout()
self.disk_loc_hbox.setObjectName("disk_loc")
self.label_disk_loc = QtWidgets.QLabel(self.disk_tab)
self.label_disk_loc.setObjectName("label_disk_loc")
self.disk_loc_hbox.addWidget(self.label_disk_loc)
self.disk_hlayout_top.addLayout(self.disk_loc_hbox)
spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)
self.disk_hlayout_top.addItem(spacerItem)
self.btn_disk_mkdir = QtWidgets.QPushButton(self.disk_tab)
self.btn_disk_mkdir.setObjectName("btn_disk_mkdir")
self.disk_hlayout_top.addWidget(self.btn_disk_mkdir)
self.disk_vlayout.addLayout(self.disk_hlayout_top)
self.table_disk = MyTableView(self.disk_tab)
self.table_disk.setObjectName("table_disk")
self.disk_vlayout.addWidget(self.table_disk)
self.disk_hlayout_bottom = QtWidgets.QHBoxLayout()
self.disk_hlayout_bottom.setObjectName("disk_hlayout_bottom")
self.btn_disk_select_all = QtWidgets.QPushButton(self.disk_tab)
self.btn_disk_select_all.setObjectName("btn_disk_select_all")
self.disk_hlayout_bottom.addWidget(self.btn_disk_select_all)
spacerItem1 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)
self.disk_hlayout_bottom.addItem(spacerItem1)
self.btn_disk_delete = QtWidgets.QPushButton(self.disk_tab)
self.btn_disk_delete.setObjectName("btn_disk_delete")
self.disk_hlayout_bottom.addWidget(self.btn_disk_delete)
self.btn_disk_dl = QtWidgets.QPushButton(self.disk_tab)
self.btn_disk_dl.setObjectName("btn_disk_dl")
self.disk_hlayout_bottom.addWidget(self.btn_disk_dl)
self.disk_vlayout.addLayout(self.disk_hlayout_bottom)
self.tabWidget.addTab(self.disk_tab, "")
self.rec_tab = QtWidgets.QWidget()
self.rec_tab.setObjectName("rec_tab")
self.rec_verticalLayout = QtWidgets.QVBoxLayout(self.rec_tab)
self.rec_verticalLayout.setObjectName("rec_verticalLayout")
self.rec_horizontalLayout = QtWidgets.QHBoxLayout()
self.rec_horizontalLayout.setObjectName("rec_horizontalLayout")
self.btn_rec_select_all = QtWidgets.QPushButton(self.rec_tab)
self.btn_rec_select_all.setObjectName("btn_rec_select_all")
self.rec_horizontalLayout.addWidget(self.btn_rec_select_all)
self.btn_recovery = QtWidgets.QPushButton(self.rec_tab)
self.btn_recovery.setObjectName("btn_recovery")
self.rec_horizontalLayout.addWidget(self.btn_recovery)
self.btn_rec_delete = QtWidgets.QPushButton(self.rec_tab)
self.btn_rec_delete.setObjectName("btn_rec_delete")
self.rec_horizontalLayout.addWidget(self.btn_rec_delete)
self.rec_horizontalLayout.addStretch(1)
self.btn_rec_clean = QtWidgets.QPushButton(self.rec_tab)
self.btn_rec_clean.setObjectName("btn_rec_clean")
self.rec_horizontalLayout.addWidget(self.btn_rec_clean)
self.btn_recovery_all = QtWidgets.QPushButton(self.rec_tab)
self.btn_recovery_all.setObjectName("btn_recovery_all")
self.rec_horizontalLayout.addWidget(self.btn_recovery_all)
self.btn_rec_expire_files = QtWidgets.QPushButton(self.rec_tab)
self.btn_rec_expire_files.setObjectName("btn_rec_expire_files")
self.rec_horizontalLayout.addWidget(self.btn_rec_expire_files)
self.rec_verticalLayout.addLayout(self.rec_horizontalLayout)
self.table_rec = QtWidgets.QTableView(self.rec_tab)
self.table_rec.setObjectName("table_rec")
self.rec_verticalLayout.addWidget(self.table_rec)
self.tabWidget.addTab(self.rec_tab, "")
self.jobs_tab = QtWidgets.QWidget()
self.jobs_tab.setObjectName("jobs_tab")
self.jobs_verticalLayout = QtWidgets.QVBoxLayout(self.jobs_tab)
self.jobs_verticalLayout.setObjectName("jobs_verticalLayout")
self.jobs_horizontalLayout = QtWidgets.QHBoxLayout()
self.jobs_horizontalLayout.setObjectName("jobs_horizontalLayout")
self.btn_jobs_start_all = QtWidgets.QPushButton(self.jobs_tab)
self.btn_jobs_clean_all = QtWidgets.QPushButton(self.jobs_tab)
self.jobs_horizontalLayout.addWidget(self.btn_jobs_start_all)
self.jobs_horizontalLayout.addStretch(1)
self.jobs_horizontalLayout.addWidget(self.btn_jobs_clean_all)
self.table_jobs = MyTableView(self.jobs_tab)
self.table_jobs.setObjectName("table_jobs")
self.jobs_verticalLayout.addLayout(self.jobs_horizontalLayout)
self.jobs_verticalLayout.addWidget(self.table_jobs)
self.tabWidget.addTab(self.jobs_tab, "")
self.verticalLayout.addWidget(self.tabWidget)
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 640, 30))
self.menubar.setObjectName("menubar")
self.acount = QtWidgets.QMenu(self.menubar)
self.acount.setObjectName("acount")
self.files = QtWidgets.QMenu(self.menubar)
self.files.setObjectName("files")
self.help = QtWidgets.QMenu(self.menubar)
self.help.setObjectName("help")
MainWindow.setMenuBar(self.menubar)
self.statusbar = QtWidgets.QStatusBar(MainWindow)
self.statusbar.setObjectName("statusbar")
MainWindow.setStatusBar(self.statusbar)
self.toolbar = QtWidgets.QToolBar(MainWindow)
self.toolbar.setObjectName("toolbar")
MainWindow.addToolBar(QtCore.Qt.TopToolBarArea, self.toolbar)
self.login = QtWidgets.QAction(MainWindow)
self.login.setObjectName("login")
self.logout = QtWidgets.QAction(MainWindow)
self.logout.setObjectName("logout")
self.upload = QtWidgets.QAction(MainWindow)
self.upload.setObjectName("upload")
self.download = QtWidgets.QAction(MainWindow)
self.download.setObjectName("download")
self.delete = QtWidgets.QAction(MainWindow)
self.delete.setObjectName("delete")
self.how = QtWidgets.QAction(MainWindow)
self.how.setObjectName("how")
self.about = QtWidgets.QAction(MainWindow)
self.about.setObjectName("about")
self.acount.addSeparator()
self.acount.addAction(self.login)
self.acount.addAction(self.logout)
self.files.addAction(self.upload)
self.files.addAction(self.download)
self.files.addAction(self.delete)
self.help.addAction(self.how)
self.help.addAction(self.about)
self.menubar.addAction(self.acount.menuAction())
self.menubar.addAction(self.files.menuAction())
self.menubar.addAction(self.help.menuAction())
# 状态栏
self.statusbar_msg_label = QtWidgets.QLabel()
self.statusbar_load_lb = QtWidgets.QLabel()
self.statusbar_load_movie = QtGui.QMovie("src/loading_more.gif")
self.statusbar_load_movie.setScaledSize(QtCore.QSize(24, 24))
self.statusbar_load_lb.setMovie(self.statusbar_load_movie)
self.statusbar_msg_label.setObjectName("msg_label")
self.statusbar_load_lb.setObjectName("msg_movie_lb")
self.statusbar.addWidget(self.statusbar_load_lb)
self.statusbar.addWidget(self.statusbar_msg_label)
self.retranslateUi(MainWindow)
self.tabWidget.setCurrentIndex(1)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
def retranslateUi(self, MainWindow):
_translate = QtCore.QCoreApplication.translate
MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow"))
self.label_share_url.setText(_translate("MainWindow", "蓝奏链接"))
self.btn_extract.setText(_translate("MainWindow", "提取"))
self.btn_share_select_all.setText(_translate("MainWindow", "全选"))
self.label_dl_path.setText(_translate("MainWindow", "文件下载路径"))
self.btn_share_dl.setText(_translate("MainWindow", "下载选中文件"))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.share_tab), _translate("MainWindow", "蓝奏云链接提取"))
self.label_disk_loc.setText(_translate("MainWindow", "位置"))
self.btn_disk_mkdir.setText(_translate("MainWindow", "新建文件夹"))
self.btn_disk_select_all.setText(_translate("MainWindow", "全选"))
self.btn_disk_delete.setText(_translate("MainWindow", "删除"))
self.btn_disk_dl.setText(_translate("MainWindow", "下载"))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.disk_tab), _translate("MainWindow", "我的蓝奏云"))
self.btn_rec_select_all.setText(_translate("MainWindow", "全选"))
self.btn_recovery.setText(_translate("MainWindow", "还原"))
self.btn_rec_delete.setText(_translate("MainWindow", "彻底删除"))
self.btn_rec_clean.setText(_translate("MainWindow", "清空回收站"))
self.btn_recovery_all.setText(_translate("MainWindow", "全部还原"))
self.btn_rec_expire_files.setText(_translate("MainWindow", "过期文件"))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.rec_tab), _translate("MainWindow", "回收站"))
self.btn_jobs_start_all.setText(_translate("MainWindow", "开始所有任务"))
self.btn_jobs_clean_all.setText(_translate("MainWindow", "删除已完成任务"))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.jobs_tab), _translate("MainWindow", "任务管理"))
self.acount.setTitle(_translate("MainWindow", "登录"))
self.files.setTitle(_translate("MainWindow", "文件"))
self.help.setTitle(_translate("MainWindow", "帮助"))
self.toolbar.setWindowTitle(_translate("MainWindow", "toolBar"))
self.login.setText(_translate("MainWindow", "登录"))
self.logout.setText(_translate("MainWindow", "登出"))
self.upload.setText(_translate("MainWindow", "上传"))
self.download.setText(_translate("MainWindow", "下载"))
self.delete.setText(_translate("MainWindow", "删除"))
self.how.setText(_translate("MainWindow", "使用说明"))
self.about.setText(_translate("MainWindow", "关于"))
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Backend/core/destiny/profile.py | LukasSchmid97/elevatorbot | 0 | 74535 | import asyncio
import dataclasses
import datetime
from typing import Optional
from anyio import to_thread
from sqlalchemy.ext.asyncio import AsyncSession
from Backend.core.errors import CustomException
from Backend.crud import crud_activities, destiny_manifest, discord_users
from Backend.crud.destiny.collectibles import collectibles
from Backend.crud.destiny.items import destiny_items
from Backend.crud.destiny.records import records
from Backend.database.models import (
Collectibles,
DestinyPresentationNodeDefinition,
DestinyRecordDefinition,
DiscordUsers,
Records,
)
from Backend.misc.cache import cache
from Backend.misc.helperFunctions import get_datetime_from_bungie_entry
from Backend.networking.bungieApi import BungieApi
from Backend.networking.bungieRoutes import clan_user_route, profile_route, stat_route
from Shared.enums.destiny import DestinyInventoryBucketEnum, DestinyPresentationNodeWeaponSlotEnum
from Shared.functions.formatting import make_progress_bar_text
from Shared.functions.helperFunctions import get_now_with_tz
from Shared.networkingSchemas import ValueModel
from Shared.networkingSchemas.destiny import (
BoolModelObjective,
BoolModelRecord,
DestinyCatalystModel,
DestinyCatalystsModel,
DestinyCharacterModel,
DestinyCharactersModel,
DestinyRecordModel,
DestinySealModel,
DestinySealsModel,
DestinyTriumphScoreModel,
SeasonalChallengesModel,
SeasonalChallengesRecordModel,
SeasonalChallengesTopicsModel,
)
from Shared.networkingSchemas.destiny.clan import DestinyClanModel
@dataclasses.dataclass
class DestinyProfile:
"""User specific API calls"""
db: AsyncSession
user: DiscordUsers
race_map = {2803282938: "Awoken", 898834093: "Exo", 3887404748: "Human"}
gender_map = {
2204441813: "Female",
3111576190: "Male",
}
class_map = {671679327: "Hunter", 2271682572: "Warlock", 3655393761: "Titan"}
_triumphs: dict = dataclasses.field(init=False, default_factory=dict)
def __post_init__(self):
# some shortcuts
self.discord_id = self.user.discord_id
self.destiny_id = self.user.destiny_id
self.system = self.user.system
# the network class
self.api = BungieApi(db=self.db, user=self.user)
async def get_clan(self) -> DestinyClanModel:
"""Return the user's clan"""
response = await self.api.get(route=clan_user_route.format(destiny_id=self.destiny_id, system=self.system))
results = response.content["results"]
if not results:
raise CustomException("UserNoClan")
return DestinyClanModel(id=results[0]["group"]["groupId"], name=results[0]["group"]["name"])
async def get_seal_completion(self) -> DestinySealsModel:
"""Gets all seals and the users completion status"""
# get the seals
seals = await destiny_items.get_seals(db=self.db)
# loop through the seals and format the data
result = DestinySealsModel()
for seal, triumphs in seals.items():
# get user completion
user_guilded_completed = []
user_guilded_completed_int = 0
user_completed = []
user_completed_int = 0
for triumph in triumphs:
user_data = await self.has_triumph(triumph.reference_id)
model = DestinyRecordModel(
name=triumph.name,
description=triumph.description,
completed=user_data.bool,
)
# handle guilded triumphs differently
if triumph.for_title_gilding:
user_guilded_completed.append(model)
if user_data.bool:
user_guilded_completed_int += 1
else:
user_completed.append(model)
if user_data.bool:
user_completed_int += 1
# normal triumph data
completion_percentage = user_completed_int / len(user_completed)
data = DestinySealModel(
name=seal.name,
description=seal.description,
completed=True if completion_percentage == 1 else False,
completion_percentage=completion_percentage,
completion_status=make_progress_bar_text(completion_percentage),
records=user_completed,
)
# add it to the correct type
if data.completed:
result.completed.append(data)
else:
result.not_completed.append(data)
# guilded triumph data
if user_guilded_completed:
completion_percentage = user_guilded_completed_int / len(user_guilded_completed)
data = DestinySealModel(
name=seal.name,
description=seal.description,
completed=True if completion_percentage == 1 else False,
completion_percentage=completion_percentage,
completion_status=make_progress_bar_text(completion_percentage),
records=user_guilded_completed,
)
# add it to the correct type
if data.completed:
result.guilded.append(data)
else:
result.not_guilded.append(data)
return result
async def get_catalyst_completion(self) -> DestinyCatalystsModel:
"""Gets all catalysts and the users completion status"""
catalysts = await destiny_items.get_catalysts(db=self.db)
triumphs = await self.get_triumphs()
# check their completion
result = DestinyCatalystsModel()
for catalyst in catalysts:
# get the completion rate
if await self.has_triumph(catalyst.reference_id):
completion_percentage = 1
else:
user_data = triumphs[str(catalyst.reference_id)]
if user_data["objectives"] and user_data["objectives"][0]["completionValue"]:
i = 0
percentages = []
for part in user_data["objectives"]:
i += 1
percentages.append(
part["progress"] / part["completionValue"] if part["completionValue"] != 0 else 0
)
completion_percentage = sum(percentages) / len(percentages)
else:
completion_percentage = 0
model = DestinyCatalystModel(
name=catalyst.name,
complete=completion_percentage == 1,
completion_percentage=completion_percentage,
completion_status=make_progress_bar_text(completion_percentage),
)
# get the slot and sort them
if DestinyPresentationNodeWeaponSlotEnum.KINETIC.value in catalyst.parent_node_hashes:
result.kinetic.append(model)
elif DestinyPresentationNodeWeaponSlotEnum.ENERGY.value in catalyst.parent_node_hashes:
result.energy.append(model)
elif DestinyPresentationNodeWeaponSlotEnum.POWER.value in catalyst.parent_node_hashes:
result.power.append(model)
# add to the total
if model.complete:
result.completed += 1
return result
async def get_used_vault_space(self) -> int:
"""Gets the current used vault space of the user"""
buckets = await self.__get_inventory_bucket(DestinyInventoryBucketEnum.VAULT)
return len(buckets[DestinyInventoryBucketEnum.VAULT])
async def get_bright_dust(self) -> int:
"""Gets the current bright dust of the user"""
return await self.__get_currency_amount(bucket=DestinyInventoryBucketEnum.BRIGHT_DUST)
async def get_legendary_shards(self) -> int:
"""Gets the current legendary shards of the user"""
return await self.__get_currency_amount(bucket=DestinyInventoryBucketEnum.SHARDS)
async def get_consumable_amount(self, consumable_id: int) -> int:
"""Returns the amount of a consumable this user has"""
buckets = await self.__get_inventory_bucket(
DestinyInventoryBucketEnum.VAULT, DestinyInventoryBucketEnum.CONSUMABLES
)
# get the value
value = 0
for bucket in buckets.values():
if consumable_id in bucket:
value += bucket[consumable_id]["quantity"]
return value
async def get_max_power(self) -> float:
"""Returns the max power of the user"""
char_data = await self.__get_all_inventory_bucket(include_item_level=True)
# look at each character
max_power = await to_thread.run_sync(get_max_power_subprocess, char_data)
return max_power
async def get_last_online(self) -> datetime.datetime:
"""Returns the last online time"""
result = await self.__get_profile()
return get_datetime_from_bungie_entry(result["profile"]["data"]["dateLastPlayed"])
async def get_triumph_score(self) -> DestinyTriumphScoreModel:
"""Returns the triumph score"""
triumphs_data = await self.get_triumphs()
return DestinyTriumphScoreModel(
active_score=triumphs_data["active_score"],
legacy_score=triumphs_data["legacy_score"],
lifetime_score=triumphs_data["lifetime_score"],
)
async def has_triumph(self, triumph_hash: str | int) -> BoolModelRecord:
"""Returns if the triumph is gotten"""
triumph_hash = int(triumph_hash)
# check cache
async with asyncio.Lock():
if self.destiny_id not in cache.triumphs:
cache.triumphs.update({self.destiny_id: {}})
if triumph_hash not in cache.triumphs[self.destiny_id]:
# check if the last update is older than 10 minutes
if self._triumphs and (
self.user.triumphs_last_updated + datetime.timedelta(minutes=10) > get_now_with_tz()
):
sought_triumph = self._triumphs[str(triumph_hash)]
else:
# get from db and return that if it says user got the triumph
result = await records.has_record(db=self.db, destiny_id=self.destiny_id, triumph_hash=triumph_hash)
if result:
# only caching already got triumphs
cache.triumphs[self.destiny_id].update({triumph_hash: True})
return BoolModelRecord(bool=True)
# alright, the user doesn't have the triumph, at least not in the db. So let's update the db entries
triumphs_data = await self.get_triumphs()
to_insert = []
sought_triumph = {}
# loop through all triumphs and add them / update them in the db
for triumph_id, triumph_info in triumphs_data.items():
try:
triumph_id = int(triumph_id)
except ValueError:
# this is the "active_score", ... fields
continue
if triumph_id in cache.triumphs[self.destiny_id]:
continue
# does the entry exist in the db?
# we don't need to re calc the state if its already marked as earned in the db
result = await records.get_record(
db=self.db, destiny_id=self.destiny_id, triumph_hash=triumph_id
)
if result and result.completed:
cache.triumphs[self.destiny_id].update({triumph_id: True})
continue
# calculate if the triumph is gotten and save the triumph we are looking for
status = True
if "objectives" not in triumph_info:
# make sure it's RewardUnavailable aka legacy
assert triumph_info["state"] & 2
# https://bungie-net.github.io/multi/schema_Destiny-DestinyRecordState.html#schema_Destiny-DestinyRecordState
status &= triumph_info["state"] & 1
else:
for part in triumph_info["objectives"]:
status &= part["complete"]
# is this the triumph we are looking for?
if triumph_id == triumph_hash:
sought_triumph = triumph_info
# don't really need to insert not-gained triumphs
if status:
cache.triumphs[self.destiny_id].update({triumph_id: True})
# do we need to update or insert?
if not result:
# insert
to_insert.append(
Records(destiny_id=self.destiny_id, record_id=triumph_id, completed=True)
)
else:
# update
await records.update_record(db=self.db, obj=result, completed=True)
# mass insert the missing entries
if to_insert:
await records.insert_records(db=self.db, objs=to_insert)
# save the update time
await discord_users.update(db=self.db, to_update=self.user, triumphs_last_updated=get_now_with_tz())
# now check again if its completed
if triumph_hash in cache.triumphs[self.destiny_id]:
return BoolModelRecord(bool=True)
# if not, return the data with the objectives info
result = BoolModelRecord(bool=False)
if "objectives" in sought_triumph:
for part in sought_triumph["objectives"]:
result.objectives.append(BoolModelObjective(objective_id=part["objectiveHash"], bool=part["complete"]))
return result
async def has_collectible(self, collectible_hash: str | int) -> bool:
"""Returns if the collectible is gotten"""
collectible_hash = int(collectible_hash)
# check cache
async with asyncio.Lock():
if self.destiny_id not in cache.collectibles:
cache.collectibles.update({self.destiny_id: {}})
if collectible_hash not in cache.collectibles[self.destiny_id]:
# check if the last update is older than 10 minutes
if self.user.collectibles_last_updated + datetime.timedelta(minutes=10) > get_now_with_tz():
return False
# get from db and return that if it says user got the collectible
result = await collectibles.has_collectible(
db=self.db, destiny_id=self.destiny_id, collectible_hash=collectible_hash
)
if result:
# only caching already got collectibles
cache.collectibles[self.destiny_id].update({collectible_hash: True})
return True
# as with the triumphs, we need to update our local collectible data now
collectibles_data = await self.get_collectibles()
to_insert = []
# loop through the collectibles
for collectible_id, collectible_info in collectibles_data.items():
collectible_id = int(collectible_id)
if collectible_id in cache.collectibles[self.destiny_id]:
continue
# does the entry exist in the db?
# we don't need to re calc the state if its already marked as owned in the db
result = await collectibles.get_collectible(
db=self.db, destiny_id=self.destiny_id, collectible_hash=collectible_id
)
if result and result.owned:
cache.collectibles[self.destiny_id].update({collectible_id: True})
continue
# bit 1 not being set means the collectible is gotten
# see https://bungie-net.github.io/multi/schema_Destiny-DestinyCollectibleState.html#schema_Destiny-DestinyCollectibleState
status = collectible_info["state"] & 1 == 0
# don't really need to insert not-owned collectibles
if status:
cache.collectibles[self.destiny_id].update({collectible_id: True})
# do we need to update or insert?
if not result:
# insert
to_insert.append(
Collectibles(destiny_id=self.destiny_id, collectible_id=collectible_id, owned=True)
)
else:
# update
await collectibles.update_collectible(db=self.db, obj=result, owned=True)
# mass insert the missing entries
if to_insert:
await collectibles.insert_collectibles(db=self.db, objs=to_insert)
# save the update time
await discord_users.update(db=self.db, to_update=self.user, collectibles_last_updated=get_now_with_tz())
# now check again if its owned
if collectible_hash in cache.collectibles[self.destiny_id]:
return True
else:
return False
async def get_metric_value(self, metric_hash: str | int) -> int:
"""Returns the value of the given metric hash"""
metric_hash = str(metric_hash)
metrics = await self.get_metrics()
try:
return metrics[metric_hash]["objectiveProgress"]["progress"]
except KeyError:
raise CustomException("BungieDestinyItemNotExist")
async def get_stat_value(
self,
stat_name: str,
stat_category: str = "allTime",
character_id: Optional[int | str] = None,
) -> int | float:
"""Returns the value of the given stat. Int if no decimals, else float"""
possible_stat_categories = [
"allTime",
"allPvE",
"allPvP",
]
assert stat_category in possible_stat_categories, f"Stat must be one of {possible_stat_categories}"
topic = "merged" if stat_category == "allTime" else "results"
stats = await self.get_stats()
# character stats
if character_id:
found = False
for char in stats["characters"]:
if char["characterId"] == str(character_id):
stats = char
found = True
if not found:
raise CustomException("CharacterIdNotFound")
# total stats
else:
stats = stats["mergedAllCharacters"]
stats = stats[topic][stat_category]
if stat_category != "allTime":
stats = stats["allTime"]
stat: float = stats[stat_name]["basic"]["value"]
return int(stat) if stat.is_integer() else stat
async def get_artifact_level(self) -> ValueModel:
"""Returns the seasonal artifact data"""
result = await self.__get_profile()
return ValueModel(value=result["profileProgression"]["data"]["seasonalArtifact"]["powerBonus"])
async def get_season_pass_level(self) -> ValueModel:
"""Returns the seasonal pass level"""
# get the current season pass hash
async with asyncio.Lock():
if not cache.season_pass_definition:
cache.season_pass_definition = await destiny_manifest.get_current_season_pass(db=self.db)
# get a character id since they are character specific
character_id = (await self.get_character_ids())[0]
result = await self.__get_profile()
character_data = result["characterProgressions"]["data"][str(character_id)]["progressions"]
return ValueModel(
value=character_data[str(cache.season_pass_definition.reward_progression_hash)]["level"]
+ character_data[str(cache.season_pass_definition.prestige_progression_hash)]["level"]
)
async def get_seasonal_challenges(self) -> SeasonalChallengesModel:
"""Returns the seasonal challenges completion info"""
# do we have the info cached?
async with asyncio.Lock():
if not cache.seasonal_challenges_definition:
definition = SeasonalChallengesModel()
# get the info from the db
sc_category_hash = 3443694067
sc_presentation_node = await destiny_manifest.get(
db=self.db, table=DestinyPresentationNodeDefinition, primary_key=sc_category_hash
)
# loop through those categories and get the "Weekly" one
for category_hash in sc_presentation_node.children_presentation_node_hash:
category = await destiny_manifest.get(
db=self.db, table=DestinyPresentationNodeDefinition, primary_key=category_hash
)
if category.name == "Weekly":
# loop through the seasonal challenges topics (Week1, Week2, etc...)
for sc_topic_hash in category.children_presentation_node_hash:
sc_topic = await destiny_manifest.get(
db=self.db, table=DestinyPresentationNodeDefinition, primary_key=sc_topic_hash
)
topic = SeasonalChallengesTopicsModel(name=sc_topic.name)
# loop through the actual seasonal challenges
for sc_hash in sc_topic.children_record_hash:
sc = await destiny_manifest.get(
db=self.db, table=DestinyRecordDefinition, primary_key=sc_hash
)
topic.seasonal_challenges.append(
SeasonalChallengesRecordModel(
record_id=sc.reference_id, name=sc.name, description=sc.description
)
)
definition.topics.append(topic)
break
cache.seasonal_challenges_definition = definition
user_sc = cache.seasonal_challenges_definition.copy()
user_records = await self.get_triumphs()
# now calculate the members completions status
user_sc = await to_thread.run_sync(get_seasonal_challenges_subprocess, user_sc, user_records)
return user_sc
async def get_character_id_by_class(self, character_class: str) -> Optional[int]:
"""Return the matching character id if exists"""
# make sure the class exists
class_names = list(self.class_map.values())
if character_class not in class_names:
return None
# loop through the chars and return the matching one
characters = await self.get_character_info()
if characters:
for character_data in characters.characters:
if character_data.character_class == character_class:
return character_data.character_id
return None
async def get_character_ids(self) -> list[int]:
"""Return the character ids only"""
characters = await self.get_character_info()
ids = []
if characters:
for character_data in characters.characters:
ids.append(character_data.character_id)
return ids
async def get_character_info(self) -> DestinyCharactersModel:
"""Get character info"""
characters = DestinyCharactersModel()
result = await self.__get_profile()
# loop through each character
for character_id, character_data in result["characters"]["data"].items():
character_id = int(character_id)
# format the data correctly and convert the hashes to strings
characters.characters.append(
DestinyCharacterModel(
character_id=character_id,
character_class=self.class_map[character_data["classHash"]],
character_race=self.race_map[character_data["raceHash"]],
character_gender=self.gender_map[character_data["genderHash"]],
)
)
return characters
async def get_triumphs(self) -> dict:
"""Populate the triumphs and then return them"""
result = await self.__get_profile()
# combine profile and character ones
self._triumphs = await to_thread.run_sync(get_triumphs_subprocess, result)
return self._triumphs
async def get_collectibles(self) -> dict:
"""Populate the collectibles and then return them"""
result = await self.__get_profile()
# combine profile and character ones
return await to_thread.run_sync(get_collectibles_subprocess, result)
async def get_metrics(self) -> dict:
"""Populate the metrics and then return them"""
metrics = await self.__get_profile()
return metrics["metrics"]["data"]["metrics"]
async def get_stats(self) -> dict:
"""Get destiny stats"""
route = stat_route.format(system=self.system, destiny_id=self.destiny_id)
result = await self.api.get(route=route)
return result.content
async def get_items_in_inventory_bucket(self, bucket: int) -> list:
"""
Returns all items in bucket. Default is vault hash, for others search "bucket" at https://data.destinysets.com/
Some buckets that are important:
Vault: 138197802
"""
result = await self.__get_profile()
all_items = result["profileInventory"]["data"]["items"]
items = []
for item in all_items:
if item["bucketHash"] == bucket:
items.append(item)
return items
async def get_time_played(
self,
start_time: datetime.datetime,
end_time: datetime.datetime,
mode: int = 0,
activity_ids: Optional[list[int]] = None,
character_class: Optional[str] = None,
) -> int:
"""Get the time played (in seconds)"""
return await crud_activities.calculate_time_played(
db=self.db,
destiny_id=self.destiny_id,
mode=mode,
activity_ids=activity_ids,
start_time=start_time,
end_time=end_time,
character_class=character_class,
)
async def __get_inventory_bucket(
self, *buckets: DestinyInventoryBucketEnum
) -> dict[DestinyInventoryBucketEnum, dict[int, dict]]:
"""
Get all the items from an inventory bucket. Default: All buckets
Returns:
{
DestinyInventoryBucketEnum: {
item_hash: dict_data,
...
},
...
}
"""
# default is vault
if not buckets:
buckets = DestinyInventoryBucketEnum.all()
result = await self.__get_profile()
# only get the items in the correct buckets
items = await to_thread.run_sync(get_inventory_bucket_subprocess, result, buckets)
return items
async def __get_all_inventory_bucket(
self, *buckets: DestinyInventoryBucketEnum, include_item_level: bool = False
) -> dict[int, dict[DestinyInventoryBucketEnum, dict[int, dict]]]:
"""
Get all the items from an inventory bucket. Includes both profile and character. Default: All buckets
Includes the power level is asked for under "power_level"
Returns:
{
character_id: {
DestinyInventoryBucketEnum: {
itemInstanceId: itemComponents_data,
...
},
...
},
...
}
"""
def add_info(result_dict: dict, item: dict, char_id: int):
"""Func to add the items"""
# only get the items in the correct buckets
for bucket in buckets:
if item["bucketHash"] == bucket.value:
if bucket not in result_dict[char_id]:
result_dict[char_id].update({bucket: {}})
result_dict[char_id][bucket].update({item["itemInstanceId"]: item})
if include_item_level:
try:
result_dict[char_id][bucket][item["itemInstanceId"]].update(
{
"power_level": result["itemComponents"]["instances"]["data"][
item["itemInstanceId"]
]["primaryStat"]["value"]
}
)
except KeyError:
pass
break
# default is vault
if not buckets:
buckets = DestinyInventoryBucketEnum.all()
result = await self.__get_profile()
items = {}
# first get the character ids and their class
character_ids = {}
for character_id, character_data in result["characters"]["data"].items():
class_type = character_data["classType"]
if class_type not in character_ids:
character_ids.update({class_type: [int(character_id)]})
else:
character_ids[class_type].append(int(character_id))
# get character inventory
for character_id, character_data in result["characterInventories"]["data"].items():
character_id = int(character_id)
if character_id not in items:
items.update({character_id: {}})
for inv_item in character_data["items"]:
await to_thread.run_sync(add_info, items, inv_item, character_id)
# get character equipped
for character_id, character_data in result["characterEquipment"]["data"].items():
character_id = int(character_id)
for inv_item in character_data["items"]:
await to_thread.run_sync(add_info, items, inv_item, character_id)
# get stuff in vault that is character specific
for profile_data in result["profileInventory"]["data"]["items"]:
# only check if it has a instance id and is in the correct bucket
if (
profile_data["bucketHash"] == DestinyInventoryBucketEnum.VAULT.value
and "itemInstanceId" in profile_data
):
# get the character class and actual bucket hash from the item id
definition = await destiny_items.get_item(db=self.db, item_id=profile_data["itemHash"])
profile_data["bucketHash"] = definition.bucket_type_hash
# try to catch users which deleted their warlock but still have warlock items
if definition.class_type in character_ids:
# add the data to each character
actual_character_ids = character_ids[definition.class_type]
for actual_character_id in actual_character_ids:
await to_thread.run_sync(add_info, items, profile_data, actual_character_id)
return items
async def __get_profile(self) -> dict:
"""
Return info from the profile call
https://bungie-net.github.io/multi/schema_Destiny-DestinyComponentType.html#schema_Destiny-DestinyComponentType
"""
# just calling nearly all of them. Don't need all quite yet, but who knows what the future will bring
components = (
100,
101,
102,
103,
104,
105,
200,
201,
202,
204,
205,
300,
301,
302,
304,
305,
306,
307,
400,
401,
402,
500,
600,
700,
800,
900,
1100,
)
route = profile_route.format(system=self.system, destiny_id=self.destiny_id)
params = {"components": ",".join(map(str, components))}
# need to call this with a token, since this data is sensitive
response = await self.api.get(route=route, params=params, with_token=True)
# get bungie name
bungie_name = f"""{response.content["profile"]["data"]["userInfo"]["bungieGlobalDisplayName"]}#{response.content["profile"]["data"]["userInfo"]["bungieGlobalDisplayNameCode"]}"""
# update name if different
if bungie_name != self.user.bungie_name:
await discord_users.update(db=self.db, to_update=self.user, bungie_name=bungie_name)
return response.content
async def __get_currency_amount(self, bucket: DestinyInventoryBucketEnum) -> int:
"""Returns the amount of the specified currency owned"""
profile = await self.__get_profile()
items = profile["profileCurrencies"]["data"]["items"]
# get the item with the correct bucket
value = 0
for item in items:
if item["bucketHash"] == bucket.value:
value = item["quantity"]
return value
def get_max_power_subprocess(char_data: dict) -> int:
"""Run in anyio subprocess on another thread since this might be slow"""
max_power = 0
for character in char_data:
helmet = 0
gauntlet = 0
chest = 0
leg = 0
class_item = 0
kinetic = 0
energy = 0
power = 0
for bucket, data in char_data[character].items():
# save the items light level
for item_id, item_data in data.items():
match bucket:
case DestinyInventoryBucketEnum.HELMET:
if item_data["power_level"] > helmet:
helmet = item_data["power_level"]
case DestinyInventoryBucketEnum.GAUNTLETS:
if item_data["power_level"] > gauntlet:
gauntlet = item_data["power_level"]
case DestinyInventoryBucketEnum.CHEST:
if item_data["power_level"] > chest:
chest = item_data["power_level"]
case DestinyInventoryBucketEnum.LEG:
if item_data["power_level"] > leg:
leg = item_data["power_level"]
case DestinyInventoryBucketEnum.CLASS:
if item_data["power_level"] > class_item:
class_item = item_data["power_level"]
case DestinyInventoryBucketEnum.KINETIC:
if item_data["power_level"] > kinetic:
kinetic = item_data["power_level"]
case DestinyInventoryBucketEnum.ENERGY:
if item_data["power_level"] > energy:
energy = item_data["power_level"]
case DestinyInventoryBucketEnum.POWER:
if item_data["power_level"] > power:
power = item_data["power_level"]
# get the max power
char_max_power = (helmet + gauntlet + chest + leg + class_item + kinetic + energy + power) / 8
if char_max_power > max_power:
max_power = char_max_power
return max_power
def get_seasonal_challenges_subprocess(user_sc: SeasonalChallengesModel, user_records: dict) -> SeasonalChallengesModel:
"""Run in anyio subprocess on another thread since this might be slow"""
for topic in user_sc.topics:
for sc in topic.seasonal_challenges:
record = user_records[str(sc.record_id)]
# calculate completion rate
rate = []
for objective in record["objectives"]:
rate.append(objective["progress"] / objective["completionValue"] if not objective["complete"] else 1)
percentage = sum(rate) / len(rate)
# make emoji art for completion rate
sc.completion_percentage = percentage
sc.completion_status = make_progress_bar_text(percentage)
return user_sc
def get_triumphs_subprocess(result: dict) -> dict:
"""Run in anyio subprocess on another thread since this might be slow"""
# get profile triumphs
triumphs = result["profileRecords"]["data"]["records"]
triumphs.update(
{
"active_score": result["profileRecords"]["data"]["activeScore"],
"legacy_score": result["profileRecords"]["data"]["legacyScore"],
"lifetime_score": result["profileRecords"]["data"]["lifetimeScore"],
}
)
# get character triumphs
character_triumphs = [
character_triumphs["records"] for character_id, character_triumphs in result["characterRecords"]["data"].items()
]
# combine them
for triumph in character_triumphs:
triumphs.update(triumph)
return triumphs
def get_collectibles_subprocess(result: dict) -> dict:
"""Run in anyio subprocess on another thread since this might be slow"""
# get profile triumphs
user_collectibles = result["profileCollectibles"]["data"]["collectibles"]
# get character triumphs
character_collectibles = [
character_triumphs["collectibles"] for _, character_triumphs in result["characterCollectibles"]["data"].items()
]
# combine them
for character in character_collectibles:
# loop through all the collectibles and only update them if the collectible is earned
# see https://bungie-net.github.io/multi/schema_Destiny-DestinyCollectibleState.html#schema_Destiny-DestinyCollectibleState
for collectible_hash, collectible_state in character.items():
if collectible_state["state"] & 1 == 0:
user_collectibles.update({collectible_hash: collectible_state})
return user_collectibles
def get_inventory_bucket_subprocess(result: dict, buckets: list[DestinyInventoryBucketEnum]) -> dict:
"""Run in anyio subprocess on another thread since this might be slow"""
items = {}
for item in result["profileInventory"]["data"]["items"]:
for bucket in buckets:
if item["bucketHash"] == bucket.value:
item_hash = int(item["itemHash"])
if bucket not in items:
items.update({bucket: {}})
items[bucket].update({item_hash: item})
try:
items[bucket][item_hash].update(
{
"power_level": result["itemComponents"]["instances"]["data"][str(item_hash)]["primaryStat"][
"value"
]
}
)
except KeyError:
pass
break
return items
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3366,
21454,
10438,
3108,
1275,
20968,
29889,
1767,
29901,
13,
18884,
2944,
29918,
8568,
353,
938,
29898,
667,
3366,
667,
10438,
20068,
13,
18884,
565,
20968,
451,
297,
4452,
29901,
13,
462,
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3319,
21454,
29901,
426,
24289,
13,
18884,
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29961,
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3319,
667,
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2944,
1800,
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1018,
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462,
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21454,
3816,
667,
29918,
8568,
1822,
5504,
29898,
13,
462,
4706,
426,
13,
462,
9651,
376,
13519,
29918,
5563,
1115,
1121,
3366,
667,
25503,
3108,
3366,
2611,
2925,
3108,
3366,
1272,
3108,
29961,
710,
29898,
667,
29918,
8568,
4638,
3366,
16072,
9513,
3108,
29961,
13,
462,
18884,
376,
1767,
29908,
13,
462,
9651,
4514,
13,
462,
4706,
500,
13,
462,
1678,
1723,
13,
18884,
5174,
7670,
2392,
29901,
13,
462,
1678,
1209,
13,
18884,
2867,
13,
13,
1678,
736,
4452,
13,
2
] |
Subsets and Splits