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mmaction/core/evaluation/__init__.py | jin-s13/mmaction2 | 47 | 137381 | <filename>mmaction/core/evaluation/__init__.py
from .accuracy import (average_precision_at_temporal_iou,
average_recall_at_avg_proposals, confusion_matrix,
get_weighted_score, interpolated_precision_recall,
mean_average_precision, mean_class_accuracy,
mmit_mean_average_precision, pairwise_temporal_iou,
softmax, top_k_accuracy)
from .eval_detection import ActivityNetDetection
from .eval_hooks import DistEpochEvalHook, EpochEvalHook
__all__ = [
'DistEpochEvalHook', 'EpochEvalHook', 'top_k_accuracy',
'mean_class_accuracy', 'confusion_matrix', 'mean_average_precision',
'get_weighted_score', 'average_recall_at_avg_proposals',
'pairwise_temporal_iou', 'average_precision_at_temporal_iou',
'ActivityNetDetection', 'softmax', 'interpolated_precision_recall',
'mmit_mean_average_precision'
]
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Darlington/phase-3/python challenge/day 90 solution/qtn2.py | darlcruz/python-challenge-solutions | 0 | 52955 | # program to find whether it contains an additive sequence or not.
class Solution(object):
# DFS: iterative implement.
def is_additive_number(self, num):
length = len(num)
for i in range(1, int(length/2+1)):
for j in range(1, int((length-i)/2 + 1)):
first, second, others = num[:i], num[i:i+j], num[i+j:]
if self.isValid(first, second, others):
return True
return False
def isValid(self, first, second, others):
if ((len(first) > 1 and first[0] == "0") or
(len(second) > 1 and second[0] == "0")):
return False
sum_str = str(int(first) + int(second))
if sum_str == others:
return True
elif others.startswith(sum_str):
return self.isValid(second, sum_str, others[len(sum_str):])
else:
return False
if __name__ == "__main__":
print(Solution().is_additive_number('66121830'))
print(Solution().is_additive_number('51123'))
print(Solution().is_additive_number('235813'))
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] |
jmeter_api/configs/__init__.py | dashawn888/jmeter_api | 11 | 45265 | <gh_stars>10-100
from jmeter_api.configs.counter.elements import Counter
from jmeter_api.configs.csv_data_set_config.elements import CsvDataSetConfig
from jmeter_api.configs.http_auth_manager.elements import HTTPAuthManager
from jmeter_api.configs.http_cache_manager.elements import HTTPCacheManager
from jmeter_api.configs.http_cookie_manager.elements import HTTPCookieManager
from jmeter_api.configs.http_header_manager.elements import HTTPHeaderManager
from jmeter_api.configs.http_request_defaults.elements import HTTPRequestDefaults
from jmeter_api.configs.random_csv_data_set_config.elements import RandomCsvDataSetConfig
from jmeter_api.configs.random_variable.elements import RandomVariable
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] |
.hypothesis/eval_source/hypothesis_temporary_module_0529b12987b897d7290f83bcbcc804e28602383d.py | keybar/keybar | 4 | 130236 | <filename>.hypothesis/eval_source/hypothesis_temporary_module_0529b12987b897d7290f83bcbcc804e28602383d.py
from hypothesis.utils.conventions import not_set
def accept(f):
def decimals():
return f()
return decimals
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bb-master/sandbox/lib/python3.5/site-packages/buildbot_worker/util/_hangcheck.py | Alecto3-D/testable-greeter | 2 | 53514 | """
Protocol wrapper that will detect hung connections.
In particular, since PB expects the server to talk first and HTTP
expects the client to talk first, when a PB client talks to an HTTP
server, neither side will talk, leading to a hung connection. This
wrapper will disconnect in that case, and inform the caller.
"""
from __future__ import absolute_import
from __future__ import print_function
from twisted.internet.interfaces import IProtocol
from twisted.internet.interfaces import IProtocolFactory
from twisted.python.components import proxyForInterface
def _noop():
pass
class HangCheckProtocol(
proxyForInterface(IProtocol, '_wrapped_protocol'), object,
):
"""
Wrap a protocol, so the underlying connection will disconnect if
the other end doesn't send data within a given timeout.
"""
transport = None
_hungConnectionTimer = None
# hung connections wait for a relatively long time, since a busy master may
# take a while to get back to us.
_HUNG_CONNECTION_TIMEOUT = 120
def __init__(self, wrapped_protocol, hung_callback=_noop, reactor=None):
"""
:param IProtocol wrapped_protocol: The protocol to wrap.
:param hung_callback: Called when the connection has hung.
:type hung_callback: callable taking no arguments.
:param IReactorTime reactor: The reactor to use to schedule
the hang check.
"""
if reactor is None:
from twisted.internet import reactor
self._wrapped_protocol = wrapped_protocol
self._reactor = reactor
self._hung_callback = hung_callback
def makeConnection(self, transport):
# Note that we don't wrap the transport for the protocol,
# because we only care about noticing data received, not
# sent.
self.transport = transport
super(HangCheckProtocol, self).makeConnection(transport)
self._startHungConnectionTimer()
def dataReceived(self, data):
self._stopHungConnectionTimer()
super(HangCheckProtocol, self).dataReceived(data)
def connectionLost(self, reason):
self._stopHungConnectionTimer()
super(HangCheckProtocol, self).connectionLost(reason)
def _startHungConnectionTimer(self):
"""
Start a timer to detect if the connection is hung.
"""
def hungConnection():
self._hung_callback()
self._hungConnectionTimer = None
self.transport.loseConnection()
self._hungConnectionTimer = self._reactor.callLater(
self._HUNG_CONNECTION_TIMEOUT, hungConnection)
def _stopHungConnectionTimer(self):
"""
Cancel the hang check timer, since we have received data or
been closed.
"""
if self._hungConnectionTimer:
self._hungConnectionTimer.cancel()
self._hungConnectionTimer = None
class HangCheckFactory(
proxyForInterface(IProtocolFactory, '_wrapped_factory'), object,
):
"""
Wrap a protocol factory, so the underlying connection will
disconnect if the other end doesn't send data within a given
timeout.
"""
def __init__(self, wrapped_factory, hung_callback):
"""
:param IProtocolFactory wrapped_factory: The factory to wrap.
:param hung_callback: Called when the connection has hung.
:type hung_callback: callable taking no arguments.
"""
self._wrapped_factory = wrapped_factory
self._hung_callback = hung_callback
def buildProtocol(self, addr):
protocol = self._wrapped_factory.buildProtocol(addr)
return HangCheckProtocol(protocol, hung_callback=self._hung_callback)
# This is used as a ClientFactory, which doesn't have a specific interface, so forward the additional methods.
def startedConnecting(self, connector):
self._wrapped_factory.startedConnecting(connector)
def clientConnectionFailed(self, connector, reason):
self._wrapped_factory.clientConnectionFailed(connector, reason)
def clientConnectionLost(self, connector, reason):
self._wrapped_factory.clientConnectionLost(connector, reason)
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] |
tests/test_codebase/test_mmpose/test_pose_detection.py | grimoire/mmdeploy | 746 | 1616176 | <filename>tests/test_codebase/test_mmpose/test_pose_detection.py<gh_stars>100-1000
# Copyright (c) OpenMMLab. All rights reserved.
import os
from tempfile import NamedTemporaryFile, TemporaryDirectory
import mmcv
import numpy as np
import pytest
import torch
import mmdeploy.backend.onnxruntime as ort_apis
from mmdeploy.apis import build_task_processor
from mmdeploy.codebase import import_codebase
from mmdeploy.utils import Backend, Codebase, Task, load_config
from mmdeploy.utils.test import DummyModel, SwitchBackendWrapper
try:
import_codebase(Codebase.MMPOSE)
except ImportError:
pytest.skip(
f'{Codebase.MMPOSE.value} is not installed.', allow_module_level=True)
model_cfg_path = 'tests/test_codebase/test_mmpose/data/model.py'
model_cfg = load_config(model_cfg_path)[0]
deploy_cfg = mmcv.Config(
dict(
backend_config=dict(type='onnxruntime'),
codebase_config=dict(type='mmpose', task='PoseDetection'),
onnx_config=dict(
type='onnx',
export_params=True,
keep_initializers_as_inputs=False,
opset_version=11,
save_file='end2end.onnx',
input_names=['input'],
output_names=['output'],
input_shape=None)))
onnx_file = NamedTemporaryFile(suffix='.onnx').name
task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
img_shape = (192, 256)
heatmap_shape = (48, 64)
# mmpose.apis.inference.LoadImage uses opencv, needs float32 in
# cv2.cvtColor.
img = np.random.rand(*img_shape, 3).astype(np.float32)
num_output_channels = model_cfg['data_cfg']['num_output_channels']
def test_create_input():
deploy_cfg = mmcv.Config(
dict(
backend_config=dict(type=Backend.ONNXRUNTIME.value),
codebase_config=dict(
type=Codebase.MMPOSE.value, task=Task.POSE_DETECTION.value),
onnx_config=dict(
type='onnx',
export_params=True,
keep_initializers_as_inputs=False,
opset_version=11,
save_file='end2end.onnx',
input_names=['input'],
output_names=['output'],
input_shape=None)))
task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
inputs = task_processor.create_input(img, input_shape=img_shape)
assert isinstance(inputs, tuple) and len(inputs) == 2
def test_init_pytorch_model():
from mmpose.models.detectors.base import BasePose
model = task_processor.init_pytorch_model(None)
assert isinstance(model, BasePose)
@pytest.fixture
def backend_model():
from mmdeploy.backend.onnxruntime import ORTWrapper
ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
wrapper = SwitchBackendWrapper(ORTWrapper)
wrapper.set(outputs={
'output': torch.rand(1, num_output_channels, *heatmap_shape),
})
yield task_processor.init_backend_model([''])
wrapper.recover()
def test_init_backend_model(backend_model):
assert isinstance(backend_model, torch.nn.Module)
def test_run_inference(backend_model):
input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
results = task_processor.run_inference(backend_model, input_dict)
assert results is not None
def test_visualize(backend_model):
input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
results = task_processor.run_inference(backend_model, input_dict)
with TemporaryDirectory() as dir:
filename = dir + 'tmp.jpg'
task_processor.visualize(backend_model, img, results[0], filename, '')
assert os.path.exists(filename)
def test_get_tensor_from_input():
input_data = {'img': torch.ones(3, 4, 5)}
inputs = task_processor.get_tensor_from_input(input_data)
assert torch.equal(inputs, torch.ones(3, 4, 5))
def test_get_partition_cfg():
try:
_ = task_processor.get_partition_cfg(partition_type='')
except NotImplementedError:
pass
def test_get_model_name():
model_name = task_processor.get_model_name()
assert isinstance(model_name, str) and model_name is not None
def test_build_dataset_and_dataloader():
from torch.utils.data import DataLoader, Dataset
dataset = task_processor.build_dataset(
dataset_cfg=model_cfg, dataset_type='test')
assert isinstance(dataset, Dataset), 'Failed to build dataset'
dataloader = task_processor.build_dataloader(dataset, 1, 1)
assert isinstance(dataloader, DataLoader), 'Failed to build dataloader'
def test_single_gpu_test_and_evaluate():
from mmcv.parallel import MMDataParallel
dataset = task_processor.build_dataset(
dataset_cfg=model_cfg, dataset_type='test')
dataloader = task_processor.build_dataloader(dataset, 1, 1)
# Prepare dummy model
model = DummyModel(outputs=[torch.rand([1, 1000])])
model = MMDataParallel(model, device_ids=[0])
assert model is not None
# Run test
outputs = task_processor.single_gpu_test(model, dataloader)
assert outputs is not None
task_processor.evaluate_outputs(model_cfg, outputs, dataset)
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4299,
29918,
16859,
29892,
14391,
29892,
8783,
29897,
13,
2
] |
trade/admin.py | dc74089/oneshirt | 0 | 48569 | from django.contrib import admin
from .models import *
# Register your models here.
admin.site.register(OneshirtUser)
admin.site.register(Item)
admin.site.register(Trade)
admin.site.register(PasswordResetRequest)
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app/backend/knowledge_base_approach/python-client/swagger_client/api/default_api.py | e-lubrini/fake-news-detector | 0 | 22282 | <gh_stars>0
# coding: utf-8
"""
FRED API
FRED is a tool for automatically producing RDF/OWL ontologies and linked data from natural language sentences. The method is based on Combinatory Categorial Grammar, Discourse Representation Theory, Linguistic Frames, and Ontology Design Patterns. Results are enriched with NER and WSD. # noqa: E501
OpenAPI spec version: v1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from swagger_client.api_client import ApiClient
class DefaultApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def stlab_tools_fred_get(self, authorization, text, **kwargs): # noqa: E501
"""stlab_tools_fred_get # noqa: E501
Generate RDF from natural language text. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.stlab_tools_fred_get(authorization, text, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: The authorization bearear. Type \"Bearer xxx-yyy-zzz\", where is your secret token. (required)
:param str text: The input natural language text. (required)
:param str prefix: The prefix used for the namespace of terms introduced by FRED in the output. If not specified fred: is used as default.
:param str namespace: The namespace used for the terms introduced by FRED in the output. If not specified http://www.ontologydesignpatterns.org/ont/fred/domain.owl# is used as default.
:param bool wsd: Perform Word Sense Disambiguation on input terms. By default it is set to false.
:param bool wfd: Perform Word Frame Disambiguation on input terms in order to provide alignments to WordNet synsets, WordNet Super-senses and Dolce classes. By default it is set to false.
:param str wfd_profile: The profile associated with the Word Frame Disambiguation
:param bool tense: Include temporal relations between events according to their grammatical tense. By default it is set to false.
:param bool roles: Use FrameNet roles into the resulting ontology. By default it is set to false.
:param str textannotation: The vocabulary used for annotating the text in RDF. Two possible alternatives are available, i.e. EARMARK and NIF.
:param bool semantic_subgraph: Generate a RDF which only expresses the semantics of a sentence without additional RDF triples, such as those containing text spans, part-of-speeches, etc. By default it is set to false.
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.stlab_tools_fred_get_with_http_info(authorization, text, **kwargs) # noqa: E501
else:
(data) = self.stlab_tools_fred_get_with_http_info(authorization, text, **kwargs) # noqa: E501
return data
def stlab_tools_fred_get_with_http_info(self, authorization, text, **kwargs): # noqa: E501
"""stlab_tools_fred_get # noqa: E501
Generate RDF from natural language text. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.stlab_tools_fred_get_with_http_info(authorization, text, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: The authorization bearear. Type \"Bearer xxx-yyy-zzz\", where is your secret token. (required)
:param str text: The input natural language text. (required)
:param str prefix: The prefix used for the namespace of terms introduced by FRED in the output. If not specified fred: is used as default.
:param str namespace: The namespace used for the terms introduced by FRED in the output. If not specified http://www.ontologydesignpatterns.org/ont/fred/domain.owl# is used as default.
:param bool wsd: Perform Word Sense Disambiguation on input terms. By default it is set to false.
:param bool wfd: Perform Word Frame Disambiguation on input terms in order to provide alignments to WordNet synsets, WordNet Super-senses and Dolce classes. By default it is set to false.
:param str wfd_profile: The profile associated with the Word Frame Disambiguation
:param bool tense: Include temporal relations between events according to their grammatical tense. By default it is set to false.
:param bool roles: Use FrameNet roles into the resulting ontology. By default it is set to false.
:param str textannotation: The vocabulary used for annotating the text in RDF. Two possible alternatives are available, i.e. EARMARK and NIF.
:param bool semantic_subgraph: Generate a RDF which only expresses the semantics of a sentence without additional RDF triples, such as those containing text spans, part-of-speeches, etc. By default it is set to false.
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['authorization', 'text', 'prefix', 'namespace', 'wsd', 'wfd', 'wfd_profile', 'tense', 'roles', 'textannotation', 'semantic_subgraph'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method stlab_tools_fred_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'authorization' is set
if self.api_client.client_side_validation and ('authorization' not in params or
params['authorization'] is None): # noqa: E501
raise ValueError("Missing the required parameter `authorization` when calling `stlab_tools_fred_get`") # noqa: E501
# verify the required parameter 'text' is set
if self.api_client.client_side_validation and ('text' not in params or
params['text'] is None): # noqa: E501
raise ValueError("Missing the required parameter `text` when calling `stlab_tools_fred_get`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'text' in params:
query_params.append(('text', params['text'])) # noqa: E501
if 'prefix' in params:
query_params.append(('prefix', params['prefix'])) # noqa: E501
if 'namespace' in params:
query_params.append(('namespace', params['namespace'])) # noqa: E501
if 'wsd' in params:
query_params.append(('wsd', params['wsd'])) # noqa: E501
if 'wfd' in params:
query_params.append(('wfd', params['wfd'])) # noqa: E501
if 'wfd_profile' in params:
query_params.append(('wfd_profile', params['wfd_profile'])) # noqa: E501
if 'tense' in params:
query_params.append(('tense', params['tense'])) # noqa: E501
if 'roles' in params:
query_params.append(('roles', params['roles'])) # noqa: E501
if 'textannotation' in params:
query_params.append(('textannotation', params['textannotation'])) # noqa: E501
if 'semantic_subgraph' in params:
query_params.append(('semantic-subgraph', params['semantic_subgraph'])) # noqa: E501
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/rdf+xml', 'text/turtle', 'application/rdf+json', 'text/rdf+n3', 'text/rdf+nt', 'image/png']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/stlab-tools/fred', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
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Programs/slithering_snakes.py | ShineTop/PiGlow | 5 | 174700 | #!/usr/bin/env python3
"""
Slithering Snakes
I think the title is self explanatory.
....................
Functions:
- slithering_snake_12: Lights up then turns off the LEDs on arms 1 and 2
- slithering_snake_13: Lights up then turns off the LEDs on arms 1 and 3
- slithering_snake_21: Lights up then turns off the LEDs on arms 2 and 1
- slithering_snake_23: Lights up then turns off the LEDs on arms 2 and 3
- slithering_snake_31: Lights up then turns off the LEDs on arms 3 and 1
- slithering_snake_32: Lights up then turns off the LEDs on arms 3 and 2
....................
Requirements:
PyGlow.py (many thanks to benleb for this program)
bfp_piglow_modules.py
You will have these files if you downloaded the entire repository.
....................
Author: <NAME>
This program was written on a Raspberry Pi using the Geany IDE.
"""
########################################################################
# Import modules #
########################################################################
import logging
from time import sleep
from PyGlow import PyGlow
from bfp_piglow_modules import print_header
from bfp_piglow_modules import check_log_directory
from bfp_piglow_modules import delete_empty_logs
from bfp_piglow_modules import stop
########################################################################
# Initialize #
########################################################################
PYGLOW = PyGlow()
PYGLOW.all(0)
########################################################################
# Functions #
########################################################################
def slithering_snake_12():
"""
Lights up then turns off the LEDs on arms 1 and 2
"""
LOGGER.debug("Slithering Snake 1-2")
sleep_speed = 0.10
# Light up Snake 12
PYGLOW.led(1, 100)
sleep(sleep_speed)
PYGLOW.led(2, 100)
sleep(sleep_speed)
PYGLOW.led(3, 100)
sleep(sleep_speed)
PYGLOW.led(4, 100)
sleep(sleep_speed)
PYGLOW.led(5, 100)
sleep(sleep_speed)
PYGLOW.led(6, 100)
sleep(sleep_speed)
PYGLOW.led(18, 100)
sleep(sleep_speed)
PYGLOW.led(11, 100)
sleep(sleep_speed)
PYGLOW.led(10, 100)
sleep(sleep_speed)
PYGLOW.led(9, 100)
sleep(sleep_speed)
PYGLOW.led(8, 100)
sleep(sleep_speed)
PYGLOW.led(7, 100)
sleep(sleep_speed)
# Turn off Snake 12
PYGLOW.led(1, 0)
sleep(sleep_speed)
PYGLOW.led(2, 0)
sleep(sleep_speed)
PYGLOW.led(3, 0)
sleep(sleep_speed)
PYGLOW.led(4, 0)
sleep(sleep_speed)
PYGLOW.led(5, 0)
sleep(sleep_speed)
PYGLOW.led(6, 0)
sleep(sleep_speed)
PYGLOW.led(18, 0)
sleep(sleep_speed)
PYGLOW.led(11, 0)
sleep(sleep_speed)
PYGLOW.led(10, 0)
sleep(sleep_speed)
PYGLOW.led(9, 0)
sleep(sleep_speed)
PYGLOW.led(8, 0)
sleep(sleep_speed)
PYGLOW.led(7, 0)
sleep(sleep_speed)
# Pause before next snake
sleep(1)
def slithering_snake_13():
"""
Lights up then turns off the LEDs on arms 1 and 3
"""
LOGGER.debug("Slithering Snake 1-3")
sleep_speed = 0.10
# Light up Snake 13
PYGLOW.led(1, 100)
sleep(sleep_speed)
PYGLOW.led(2, 100)
sleep(sleep_speed)
PYGLOW.led(3, 100)
sleep(sleep_speed)
PYGLOW.led(4, 100)
sleep(sleep_speed)
PYGLOW.led(5, 100)
sleep(sleep_speed)
PYGLOW.led(12, 100)
sleep(sleep_speed)
PYGLOW.led(18, 100)
sleep(sleep_speed)
PYGLOW.led(17, 100)
sleep(sleep_speed)
PYGLOW.led(16, 100)
sleep(sleep_speed)
PYGLOW.led(15, 100)
sleep(sleep_speed)
PYGLOW.led(14, 100)
sleep(sleep_speed)
PYGLOW.led(13, 100)
sleep(sleep_speed)
# Turn off Snake 13
PYGLOW.led(1, 0)
sleep(sleep_speed)
PYGLOW.led(2, 0)
sleep(sleep_speed)
PYGLOW.led(3, 0)
sleep(sleep_speed)
PYGLOW.led(4, 0)
sleep(sleep_speed)
PYGLOW.led(5, 0)
sleep(sleep_speed)
PYGLOW.led(12, 0)
sleep(sleep_speed)
PYGLOW.led(18, 0)
sleep(sleep_speed)
PYGLOW.led(17, 0)
sleep(sleep_speed)
PYGLOW.led(16, 0)
sleep(sleep_speed)
PYGLOW.led(15, 0)
sleep(sleep_speed)
PYGLOW.led(14, 0)
sleep(sleep_speed)
PYGLOW.led(13, 0)
sleep(sleep_speed)
# Pause before next snake
sleep(1)
def slithering_snake_21():
"""
Lights up then turns off the LEDs on arms 2 and 1
"""
LOGGER.debug("Slithering Snake 2-1")
sleep_speed = 0.10
# Light up Snake 21
PYGLOW.led(7, 100)
sleep(sleep_speed)
PYGLOW.led(8, 100)
sleep(sleep_speed)
PYGLOW.led(9, 100)
sleep(sleep_speed)
PYGLOW.led(10, 100)
sleep(sleep_speed)
PYGLOW.led(11, 100)
sleep(sleep_speed)
PYGLOW.led(18, 100)
sleep(sleep_speed)
PYGLOW.led(6, 100)
sleep(sleep_speed)
PYGLOW.led(5, 100)
sleep(sleep_speed)
PYGLOW.led(4, 100)
sleep(sleep_speed)
PYGLOW.led(3, 100)
sleep(sleep_speed)
PYGLOW.led(2, 100)
sleep(sleep_speed)
PYGLOW.led(1, 100)
sleep(sleep_speed)
# Turn off Snake 21
PYGLOW.led(7, 0)
sleep(sleep_speed)
PYGLOW.led(8, 0)
sleep(sleep_speed)
PYGLOW.led(9, 0)
sleep(sleep_speed)
PYGLOW.led(10, 0)
sleep(sleep_speed)
PYGLOW.led(11, 0)
sleep(sleep_speed)
PYGLOW.led(18, 0)
sleep(sleep_speed)
PYGLOW.led(6, 0)
sleep(sleep_speed)
PYGLOW.led(5, 0)
sleep(sleep_speed)
PYGLOW.led(4, 0)
sleep(sleep_speed)
PYGLOW.led(3, 0)
sleep(sleep_speed)
PYGLOW.led(2, 0)
sleep(sleep_speed)
PYGLOW.led(1, 0)
sleep(sleep_speed)
# Pause before next snake
sleep(1)
def slithering_snake_23():
"""
Lights up then turns off the LEDs on arms 2 and 3
"""
LOGGER.debug("Slithering Snake 2-3")
sleep_speed = 0.10
# Light up Snake 23
PYGLOW.led(7, 100)
sleep(sleep_speed)
PYGLOW.led(8, 100)
sleep(sleep_speed)
PYGLOW.led(9, 100)
sleep(sleep_speed)
PYGLOW.led(10, 100)
sleep(sleep_speed)
PYGLOW.led(11, 100)
sleep(sleep_speed)
PYGLOW.led(12, 100)
sleep(sleep_speed)
PYGLOW.led(6, 100)
sleep(sleep_speed)
PYGLOW.led(17, 100)
sleep(sleep_speed)
PYGLOW.led(16, 100)
sleep(sleep_speed)
PYGLOW.led(15, 100)
sleep(sleep_speed)
PYGLOW.led(14, 100)
sleep(sleep_speed)
PYGLOW.led(13, 100)
sleep(sleep_speed)
# Turn off Snake 23
PYGLOW.led(7, 0)
sleep(sleep_speed)
PYGLOW.led(8, 0)
sleep(sleep_speed)
PYGLOW.led(9, 0)
sleep(sleep_speed)
PYGLOW.led(10, 0)
sleep(sleep_speed)
PYGLOW.led(11, 0)
sleep(sleep_speed)
PYGLOW.led(12, 0)
sleep(sleep_speed)
PYGLOW.led(6, 0)
sleep(sleep_speed)
PYGLOW.led(17, 0)
sleep(sleep_speed)
PYGLOW.led(16, 0)
sleep(sleep_speed)
PYGLOW.led(15, 0)
sleep(sleep_speed)
PYGLOW.led(14, 0)
sleep(sleep_speed)
PYGLOW.led(13, 0)
sleep(sleep_speed)
# Pause before next snake
sleep(1)
def slithering_snake_31():
"""
Lights up then turns off the LEDs on arms 3 and 1
"""
LOGGER.debug("Slithering Snake 3-1")
sleep_speed = 0.10
# Light up Snake 31
PYGLOW.led(13, 100)
sleep(sleep_speed)
PYGLOW.led(14, 100)
sleep(sleep_speed)
PYGLOW.led(15, 100)
sleep(sleep_speed)
PYGLOW.led(16, 100)
sleep(sleep_speed)
PYGLOW.led(17, 100)
sleep(sleep_speed)
PYGLOW.led(18, 100)
sleep(sleep_speed)
PYGLOW.led(12, 100)
sleep(sleep_speed)
PYGLOW.led(5, 100)
sleep(sleep_speed)
PYGLOW.led(4, 100)
sleep(sleep_speed)
PYGLOW.led(3, 100)
sleep(sleep_speed)
PYGLOW.led(2, 100)
sleep(sleep_speed)
PYGLOW.led(1, 100)
sleep(sleep_speed)
# Turn off Snake 31
PYGLOW.led(13, 0)
sleep(sleep_speed)
PYGLOW.led(14, 0)
sleep(sleep_speed)
PYGLOW.led(15, 0)
sleep(sleep_speed)
PYGLOW.led(16, 0)
sleep(sleep_speed)
PYGLOW.led(17, 0)
sleep(sleep_speed)
PYGLOW.led(18, 0)
sleep(sleep_speed)
PYGLOW.led(12, 0)
sleep(sleep_speed)
PYGLOW.led(5, 0)
sleep(sleep_speed)
PYGLOW.led(4, 0)
sleep(sleep_speed)
PYGLOW.led(3, 0)
sleep(sleep_speed)
PYGLOW.led(2, 0)
sleep(sleep_speed)
PYGLOW.led(1, 0)
sleep(sleep_speed)
# Pause before next snake
sleep(1)
def slithering_snake_32():
"""
Lights up then turns off the LEDs on arms 3 and 2
"""
LOGGER.debug("Slithering Snake 3-2")
sleep_speed = 0.10
# Light up Snake 32
PYGLOW.led(13, 100)
sleep(sleep_speed)
PYGLOW.led(14, 100)
sleep(sleep_speed)
PYGLOW.led(15, 100)
sleep(sleep_speed)
PYGLOW.led(16, 100)
sleep(sleep_speed)
PYGLOW.led(17, 100)
sleep(sleep_speed)
PYGLOW.led(6, 100)
sleep(sleep_speed)
PYGLOW.led(12, 100)
sleep(sleep_speed)
PYGLOW.led(11, 100)
sleep(sleep_speed)
PYGLOW.led(10, 100)
sleep(sleep_speed)
PYGLOW.led(9, 100)
sleep(sleep_speed)
PYGLOW.led(8, 100)
sleep(sleep_speed)
PYGLOW.led(7, 100)
sleep(sleep_speed)
# Turn off Snake 32
PYGLOW.led(13, 0)
sleep(sleep_speed)
PYGLOW.led(14, 0)
sleep(sleep_speed)
PYGLOW.led(15, 0)
sleep(sleep_speed)
PYGLOW.led(16, 0)
sleep(sleep_speed)
PYGLOW.led(17, 0)
sleep(sleep_speed)
PYGLOW.led(6, 0)
sleep(sleep_speed)
PYGLOW.led(12, 0)
sleep(sleep_speed)
PYGLOW.led(11, 0)
sleep(sleep_speed)
PYGLOW.led(10, 0)
sleep(sleep_speed)
PYGLOW.led(9, 0)
sleep(sleep_speed)
PYGLOW.led(8, 0)
sleep(sleep_speed)
PYGLOW.led(7, 0)
sleep(sleep_speed)
# Pause before next snake
sleep(1)
def main():
"""
The main function
"""
LOGGER.debug("START")
# Snakes 12, 13, 21, 23, 31, 32
slithering_snake_12()
slithering_snake_13()
slithering_snake_21()
slithering_snake_23()
slithering_snake_31()
slithering_snake_32()
# Snakes 12, 23, 31, 13, 32, 21
slithering_snake_12()
slithering_snake_23()
slithering_snake_31()
slithering_snake_13()
slithering_snake_32()
slithering_snake_21()
# Snakes 13, 12, 23, 21, 31, 32
slithering_snake_13()
slithering_snake_12()
slithering_snake_23()
slithering_snake_21()
slithering_snake_31()
slithering_snake_32()
# Snakes 13, 32, 21, 12, 23, 31
slithering_snake_13()
slithering_snake_32()
slithering_snake_21()
slithering_snake_12()
slithering_snake_23()
slithering_snake_31()
LOGGER.debug("END")
delete_empty_logs(LOG)
stop()
if __name__ == '__main__':
try:
# STEP01: Check if Log directory exists.
check_log_directory()
# STEP02: Enable logging
LOG = 'Logs/slithering_snakes.log'
LOG_FORMAT = '%(asctime)s %(name)s: %(funcName)s: \
%(levelname)s: %(message)s'
LOGGER = logging.getLogger(__name__)
# Nothing will log unless logging level is changed to DEBUG
LOGGER.setLevel(logging.ERROR)
FORMATTER = logging.Formatter(fmt=LOG_FORMAT,
datefmt='%m/%d/%y %I:%M:%S %p:')
FILE_HANDLER = logging.FileHandler(LOG, 'w')
FILE_HANDLER.setFormatter(FORMATTER)
LOGGER.addHandler(FILE_HANDLER)
# STEP03: Print header
print_header()
# STEP04: Print instructions in white text
print("\033[1;37;40mPress Ctrl-C to stop the program.")
# STEP05: Run the main function
main()
except KeyboardInterrupt:
delete_empty_logs(LOG)
stop()
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14480,
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4706,
5040,
580,
13,
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2
] |
application/model/widget/serialize/SerializeButtontwo.py | Kzulfazriawan/Stigma-game-demo | 2 | 116690 | <filename>application/model/widget/serialize/SerializeButtontwo.py<gh_stars>1-10
from core import Files
from library.stigma.application import Button
from library.stigma.helper import kivyBuilder
kivyBuilder(Files.apppath, 'model', 'builder', 'Buttontwo.kv')
class SerializeButtontwo(Button):
def __init__(self):
super(SerializeButtontwo, self).__init__()
self.value = '1'
self.text = 'SLOT 2'
self.data = None
self.params = int(self.value) | [
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output/models/nist_data/atomic/time/schema_instance/nistschema_sv_iv_atomic_time_max_inclusive_3_xsd/__init__.py | tefra/xsdata-w3c-tests | 1 | 175065 | <filename>output/models/nist_data/atomic/time/schema_instance/nistschema_sv_iv_atomic_time_max_inclusive_3_xsd/__init__.py
from output.models.nist_data.atomic.time.schema_instance.nistschema_sv_iv_atomic_time_max_inclusive_3_xsd.nistschema_sv_iv_atomic_time_max_inclusive_3 import NistschemaSvIvAtomicTimeMaxInclusive3
__all__ = [
"NistschemaSvIvAtomicTimeMaxInclusive3",
]
| [
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exercises/04_While/exe_04.py | MariaTrindade/CursoPython | 1 | 64692 | """
Crie um programa que solicita vários números inteiros ao usuário,
pergunte se ele quer continuar digitando e caso responda não,
informe o total de números digitados, o menor, o maior e a
média entre eles.
"""
total = soma = maior = 0
menor = 9999999999999999
resposta = 'S'
while resposta in 'S':
num = int(input('Digite um número: '))
total += 1
soma += num
if num > maior: # 30 > 0
maior = num
if num < menor: # 30 < 99999999999999
menor = num
resposta = str(input('\033[32mQuer continuar? \033[m')).upper().strip()
if resposta == 'N':
print('Encerrando o sistema')
print()
# fora do while ------------------------
print(f'Total de número inseridos: {total}\nSoma dos números inseridos: {soma}\n'
f'Média dos números inseridos: {soma / total:.1f}\nMaior número inserido: {maior}\n'
f'Menor número inserido: {menor}')
0
2
0
1000.0
todos = []
while True:
todos.append(int(input('Digite um número: ')))
resposta = str(input('Quer continuar: ')).strip().upper()
if resposta == 'N':
break
if 15 in todos:
print('Achei')
print(f'Total de número inseridos: {todos}\nSoma dos números inseridos: {sum(todos)}\n'
f'Média dos números inseridos: {sum(todos) / len(todos)}\nMaior número inserido: {max(todos)}\n'
f'Menor número inserido: {min(todos)}')
| [
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] |
rst2pdf/tests/input/test_extensions.py | shakna-israel/rst2pdf | 1 | 1611075 | <reponame>shakna-israel/rst2pdf
import sys
print """
This test should print the message from the sample extension,
and then this message, and then generate an empty PDF.
"""
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build/lib.linux-x86_64-2.7_ucs4/mx/Misc/PackageTools.py | mkubux/egenix-mx-base | 0 | 1768 | """ PackageTools - A set of tools to aid working with packages.
Copyright (c) 1998-2000, <NAME>; mailto:<EMAIL>
Copyright (c) 2000-2015, eGenix.com Software GmbH; mailto:<EMAIL>
See the documentation for further information on copyrights,
or contact the author. All Rights Reserved.
"""
__version__ = '0.4.0'
import os,types,sys,re,imp,__builtin__
import mx.Tools.NewBuiltins
# RE to identify Python modules
suffixes = projection(imp.get_suffixes(),0)
module_name = re.compile('(.*)(' + '|'.join(suffixes) + ')$')
initmodule_name = re.compile('__init__(' + '|'.join(suffixes) + ')$')
initmodule_names = []
for suffix in suffixes:
initmodule_names.append('__init__' + suffix)
def find_packages(dir=os.curdir, files_only=0, recursive=0, ignore_modules=0,
pkgbasename='', pkgdict=None,
isdir=os.path.isdir,exists=os.path.exists,
isfile=os.path.isfile,join=os.path.join,listdir=os.listdir,
module_name=module_name,initmodule_name=initmodule_name):
""" Return a list of package names found in dir.
Packages are Python modules and subdirectories that provide an
__init__ module. The .py extension is removed from the
files. The __init__ modules are not considered being seperate
packages.
If files_only is true, only Python files are included in the
search (subdirectories are *not* taken into account). If
ignore_modules is true (default is false), modules are
ignored. If recursive is true the search recurses into package
directories.
pkgbasename and pkgdict are only used during recursion.
"""
l = listdir(dir)
if pkgdict is None:
pkgdict = {}
if files_only:
for filename in l:
m = module_name.match(filename)
if m is not None and \
m.group(1) != '__init__':
pkgdict[pkgbasename + m.group(1)] = 1
else:
for filename in l:
path = join(dir, filename)
if isdir(path):
# Check for __init__ module(s)
for name in initmodule_names:
if isfile(join(path, name)):
pkgname = pkgbasename + filename
pkgdict[pkgname] = 1
if recursive:
find_packages(path,
recursive=1,
pkgbasename=pkgname + '.',
pkgdict=pkgdict)
break
elif not ignore_modules:
m = module_name.match(filename)
if m is not None and \
m.group(1) != '__init__':
pkgdict[pkgbasename + m.group(1)] = 1
return pkgdict.keys()
def find_subpackages(package, recursive=0,
splitpath=os.path.split):
""" Assuming that package points to a loaded package module, this
function tries to identify all subpackages of that package.
Subpackages are all Python files included in the same
directory as the module plus all subdirectories having an
__init__.py file. The modules name is prepended to all
subpackage names.
The module location is found by looking at the __file__
attribute that non-builtin modules define. The function uses
the __all__ attribute from the package __init__ module if
available.
If recursive is true (default is false), then subpackages of
subpackages are recursively also included in the search.
"""
if not recursive:
# Try the __all__ attribute...
try:
subpackages = list(package.__all__)
except (ImportError, AttributeError):
# Did not work, then let's try to find the subpackages by looking
# at the directory where package lives...
subpackages = find_packages(package.__path__[0], recursive=recursive)
else:
# XXX Recursive search does not support the __all__ attribute
subpackages = find_packages(package.__path__[0], recursive=recursive)
basename = package.__name__ + '.'
for i,name in irange(subpackages):
subpackages[i] = basename + name
return subpackages
def _thismodule(upcount=1,
exc_info=sys.exc_info,trange=trange):
""" Returns the module object that the callee is calling from.
upcount can be given to indicate how far up the execution
stack the function is supposed to look (1 == direct callee, 2
== callee of callee, etc.).
"""
try:
1/0
except:
frame = exc_info()[2].tb_frame
for i in trange(upcount):
frame = frame.f_back
name = frame.f_globals['__name__']
del frame
return sys.modules[name]
def _module_loader(name, locals, globals, sysmods, errors='strict',
importer=__import__, reloader=reload, from_list=['*']):
""" Internal API for loading a module
"""
if not sysmods.has_key(name):
is_new = 1
else:
is_new = 0
try:
mod = importer(name, locals, globals, from_list)
if reload and not is_new:
mod = reloader(mod)
except KeyboardInterrupt:
# Pass through; SystemExit will be handled by the error handler
raise
except Exception, why:
if errors == 'ignore':
pass
elif errors == 'strict':
raise
elif callable(errors):
errors(name, sys.exc_info()[0], sys.exc_info()[1])
else:
raise ValueError,'unknown errors value'
else:
return mod
return None
def import_modules(modnames,module=None,errors='strict',reload=0,
thismodule=_thismodule):
""" Import all modules given in modnames into module.
module defaults to the caller's module. modnames may contain
dotted package names.
If errors is 'strict' (default), then ImportErrors and
SyntaxErrors are raised. If set to 'ignore', they are silently
ignored. If errors is a callable object, then it is called
with arguments (modname, errorclass, errorvalue). If the
handler returns, processing continues.
If reload is true (default is false), all already modules
among the list will be forced to reload.
"""
if module is None:
module = _thismodule(2)
locals = module.__dict__
sysmods = sys.modules
for name in modnames:
mod = _module_loader(name, locals, locals, sysmods, errors=errors)
if mod is not None:
locals[name] = mod
def load_modules(modnames,locals=None,globals=None,errors='strict',reload=0):
""" Imports all modules in modnames using the given namespaces and returns
list of corresponding module objects.
If errors is 'strict' (default), then ImportErrors and
SyntaxErrors are raised. If set to 'ignore', they are silently
ignored. If errors is a callable object, then it is called
with arguments (modname, errorclass, errorvalue). If the
handler returns, processing continues.
If reload is true (default is false), all already modules
among the list will be forced to reload.
"""
modules = []
append = modules.append
sysmods = sys.modules
for name in modnames:
mod = _module_loader(name, locals, globals, sysmods, errors=errors)
if mod is not None:
append(mod)
return modules
def import_subpackages(module, reload=0, recursive=0,
import_modules=import_modules,
find_subpackages=find_subpackages):
""" Does a subpackages scan using find_subpackages(module) and then
imports all submodules found into module.
The module location is found by looking at the __file__
attribute that non-builtin modules define. The function uses
the __all__ attribute from the package __init__ module if
available.
If reload is true (default is false), all already modules
among the list will be forced to reload.
"""
import_modules(find_subpackages(module, recursive=recursive),
module, reload=reload)
def load_subpackages(module, locals=None, globals=None, errors='strict', reload=0,
recursive=0,
load_modules=load_modules,
find_subpackages=find_subpackages):
""" Same as import_subpackages but with load_modules
functionality, i.e. imports the modules and also returns a list of
module objects.
If errors is 'strict' (default), then ImportErrors are
raised. If set to 'ignore', they are silently ignored.
If reload is true (default is false), all already modules
among the list will be forced to reload.
"""
return load_modules(find_subpackages(module, recursive=recursive),
locals, globals,
errors=errors, reload=reload)
def modules(names,
extract=extract):
""" Converts a list of module names into a list of module objects.
The modules must already be loaded.
"""
return extract(sys.modules, names)
def package_modules(pkgname):
""" Returns a list of all modules belonging to the package with the
given name.
The package must already be loaded. Only the currently
registered modules are included in the list.
"""
match = pkgname + '.'
match_len = len(match)
mods = [sys.modules[pkgname]]
for k,v in sys.modules.items():
if k[:match_len] == match and v is not None:
mods.append(v)
return mods
def find_classes(mods,baseclass=None,annotated=0,
ClassType=types.ClassType,issubclass=issubclass):
""" Find all subclasses of baseclass or simply all classes (if baseclass
is None) defined by the module objects in list mods.
If annotated is true the returned list will contain tuples
(module_object,name,class_object) for each class found where
module_object is the module where the class is defined.
"""
classes = []
for mod in mods:
for name,obj in mod.__dict__.items():
if type(obj) is ClassType:
if baseclass and not issubclass(obj,baseclass):
continue
if annotated:
classes.append((mod, name, obj))
else:
classes.append(obj)
return classes
def find_instances(mods,baseclass,annotated=0,
InstanceType=types.InstanceType,issubclass=issubclass):
""" Find all instances of baseclass defined by the module objects
in list mods.
If annotated is true the returned list will contain tuples
(module_object,name,instances_object) for each instances found where
module_object is the module where the instances is defined.
"""
instances = []
for mod in mods:
for name,obj in mod.__dict__.items():
if isinstance(obj,baseclass):
if annotated:
instances.append((mod,name,obj))
else:
instances.append(obj)
return instances
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Service_Components/Sink/Sink_DataFlow.py | mydata-sdk/mydata-sdk-1.x | 0 | 11081 | <filename>Service_Components/Sink/Sink_DataFlow.py
# -*- coding: utf-8 -*-
from signed_requests.signed_request_auth import SignedRequest
__author__ = 'alpaloma'
from flask import Blueprint, current_app, request
from helpers import Helpers
import requests
from json import dumps, loads
from DetailedHTTPException import error_handler
from flask_restful import Resource, Api
import logging
from jwcrypto import jwk
from Templates import Sequences
debug_log = logging.getLogger("debug")
logger = logging.getLogger("sequence")
api_Sink_blueprint = Blueprint("api_Sink_blueprint", __name__)
api = Api()
api.init_app(api_Sink_blueprint)
sq = Sequences("Service_Components Mgmnt (Sink)", {})
# import xmltodict
# @api.representation('application/xml')
# def output_xml(data, code, headers=None):
# if isinstance(data, dict):
# xm = {"response": data}
# resp = make_response(xmltodict.unparse(xm, pretty=True), code)
# resp.headers.extend(headers)
# return resp
class Status(Resource):
@error_handler
def get(self):
status = {"status": "running", "service_mode": "Sink"}
return status
class DataFlow(Resource):
def __init__(self):
super(DataFlow, self).__init__()
self.service_url = current_app.config["SERVICE_URL"]
self.operator_url = current_app.config["OPERATOR_URL"]
self.helpers = Helpers(current_app.config)
@error_handler
def post(self): # TODO Make this a GET
def renew_token(operator_url, record_id):
sq.task("Renewing Auth Token.")
token = requests.get(
"{}/api/1.2/cr/auth_token/{}".format(operator_url, record_id)) # TODO Get api path from some config?
debug_log.info("{}, {}, {}, {}".format(token.url, token.reason, token.status_code, token.text))
store_dict = {cr_id: dumps(loads(token.text.encode()))}
self.helpers.storeToken(store_dict)
def step_1():
params = request.json
debug_log.info(params)
debug_log.info(request.json)
user_id = params["user_id"]
cr_id = params["cr_id"]
rs_id = params["rs_id"]
sq.task("Get data_set_id from POST json")
data_set_id = request.args.get("dataset_id", None)
debug_log.info("data_set_id is ({}), cr_id is ({}), user_id ({}) and rs_id ({})"
.format(data_set_id, cr_id, user_id, rs_id))
sq.task("Create request")
req = {"we want": "data"}
sq.task("Validate CR")
cr = self.helpers.validate_cr(cr_id, surrogate_id=user_id)
sq.task("Validate Request from UI")
distribution_urls = self.helpers.validate_request_from_ui(cr, data_set_id, rs_id)
# Fetch data request urls
# Data request urls fetched.
debug_log.info("Data request urls fetched.")
return cr_id, cr, distribution_urls
cr_id, cr, distribution_urls = step_1()
sq.task("Validate Authorisation Token")
surrogate_id = cr["cr"]["common_part"]["surrogate_id"]
our_key = self.helpers.get_key()
our_key_pub = our_key["pub"]
tries = 3 # TODO: Get this from config
while True:
try:
aud = self.helpers.validate_authorization_token(cr_id, surrogate_id, our_key_pub)
break
except ValueError as e:
debug_log.exception(e)
renew_token(self.operator_url, cr_id)
if tries == 0:
raise EnvironmentError("Auth token validation failed and retry counter exceeded.")
tries -= 1
except TypeError as e:
debug_log.exception(e)
raise EnvironmentError("Token used too soon, halting.")
# Most verifying and checking below is done in the validate_authorization_token function by jwcrypto
# Fetch Authorisation Token related to CR from data storage by rs_id (cr_id?)
# Check Integrity ( Signed by operator, Operator's public key can be found from SLR)
# Check "Issued" timestamp
# Check "Not Before" timestamp
# Check "Not After" timestamp
# Check that "sub" contains correct public key(Our key.)
# OPT: Token expired
# Get new Authorization token, start again from validation. # TODO: Make these steps work as functions that call the next step.
# Check URL patterns in "aud" field
# Check that fetched distribution urls can be found from "aud" field
# Token validated
debug_log.info("Auth Token Validated.")
# With these two steps Sink has verified that it's allowed to make request.
# Construct request
sq.task("Construct request")
# Select request URL from "aud" field
# Add Authorisation Token to request
# Request constructed.
# Sign request
# Fetch private key pair of public key specified in Authorisation Token's "sub" field.
# Sign with fetched private key
sq.task("Fetch key used to sign request")
our_key_full = jwk.JWK()
our_key_full.import_key(**our_key["key"])
# Add signature to request
# Request signed.
# Request created.
sq.send_to("Service_Components Mgmnt (Source)", "Data Request (PoP stuff)")
# Make Data Request
for url in distribution_urls:
req = requests.get(url,
auth=SignedRequest(token=aud, sign_method=True, sign_path=True, key=our_key_full, protected=dumps(our_key["prot"])))
debug_log.info("Made data request and received following data from Source: \n{}"
.format(dumps(loads(req.content), indent=2)))
status = {"status": "ok", "service_mode": "Sink"}
return status
api.add_resource(Status, '/init')
api.add_resource(DataFlow, '/dc')
#api.add_resource(DataFlow, '/user/<string:user_id>/consentRecord/<string:cr_id>/resourceSet/<string:rs_id>')
#"http://service_components:7000/api/1.2/sink_flow/user/95479a08-80cc-4359-ba28-b8ca23ff5572_53af88dc-33de-44be-bc30-e0826db9bd6c/consentRecord/cd431509-777a-4285-8211-95c5ac577537/resourceSet/http%3A%2F%2Fservice_components%3A7000%7C%7C9aebb487-0c83-4139-b12c-d7fcea93a3ad" | [
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examples/exersice2DimRed.py | s2812135/Data_Challenges_WiSe2122 | 0 | 17638 | import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import pacmap
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import umap
def darius1(numberDirectory):
path = ""
if(numberDirectory == 1):
directorys = [
['training_setA/training/', 'p0']
]
if(numberDirectory == 2):
directorys = [
['training_setB/training/', 'p1']
]
if(numberDirectory == 3):
directorys = [
['training_setA/training/', 'p0'],
['training_setB/training/', 'p1']
]
dfs = []
for z, (directory, file_head) in enumerate(directorys):
for i, filename in enumerate(tqdm(os.listdir(path + directory))):
df_temp = pd.read_csv(path + directory + filename, skiprows=0, sep='|')
dfs.append(df_temp)
df = pd.concat(dfs)
#df_no_nan = df.dropna()
df_nan_zwero = df.replace(np.NaN, 0)
df_nan_zwero.head(n=50)
df_nan_none = df.replace(np.NaN, None)
df_nan_none.head(n=50)
df_nan_mean = df.fillna(df.mean())
df_nan_mean.head(n=50)
df_nan_none_2= df.where(pd.notnull(df), None)
df_nan_mean.head(n=50)
#df.shape
#df.head(n=80)
############################################################
# initializing the pacmap instance
# Setting n_neighbors to "None" leads to a default choice shown below in "parameter" section
embedding = pacmap.PaCMAP(n_dims=2, n_neighbors=None, MN_ratio=0.5, FP_ratio=2.0)
# fit the data (The index of transformed data corresponds to the index of the original data)
X_transformed = embedding.fit_transform(df_nan_none_2.values, init="pca")
# visualize the embedding
#fig, ax = plt.subplots(1, 1, figsize=(6, 6))
#ax.scatter(X_transformed[:, 0], X_transformed[:, 1], cmap="Spectral", s=0.6)
plt.scatter(X_transformed[:, 0], X_transformed[:, 1], cmap="Spectral")
plt.show()
#############################################################
X_embedded = TSNE(n_components=2, learning_rate='auto',init='random').fit_transform(df.values)
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
ax.scatter(X_transformed[:, 0], X_embedded[:, 1], cmap="Spectral", c=list(df.columns), s=0.6)
############################################################# | [
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src/model/rpm_solver.py | tyler-hayes/Continual-Analogical-Reasoning | 5 | 192277 | <filename>src/model/rpm_solver.py
# INFORMATION ------------------------------------------------------------------------------------------------------- #
# Author: <NAME>
# Date: 10/11/2019
# Purpose: Model for solving RPM problems
# IMPORTS ----------------------------------------------------------------------------------------------------------- #
import sys
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from basic_model import BasicModel
# CONSTANTS --------------------------------------------------------------------------------------------------------- #
DR_S, DR_F = .1, .5 # Dropout prob. for spatial and fully-connected layers.
O_HC, O_OC = 64, 64 # Hidden and output channels for original enc.
F_HC, F_OC = 64, 16 # Hidden and output channels for frame enc.
S_HC, S_OC = 128, 64 # Hidden and output channels for sequence enc.
F_PL, S_PL = 5 * 5, 16 # Pooled sizes for frame and sequence enc. outputs.
F_Z = F_OC * F_PL # Frame embedding dimensions.
K_D = 7 # Conv. kernel dimensions.
BL_IN = 3
BLOUT = F_Z
G_IN = BLOUT
G_HID = G_IN
G_OUT = G_IN
R_OUT = 32
C_DIM = 2
P_DIM = 32
C = 1.0
# CLASSES ----------------------------------------------------------------------------------------------------------- #
# Helper function for spatial dropout.
class perm(nn.Module):
def __init__(self):
super(perm, self).__init__()
def forward(self, x):
return x.permute(0, 2, 1)
class flat(nn.Module):
def __init__(self):
super(flat, self).__init__()
def forward(self, x):
return x.flatten(1)
# Convolutional block class (conv, elu, bnorm, dropout). If 1D block, no downsampling. If 2D, stride==2.
# Implements spatial dropout for both 1D and 2D convolutional layers.
class ConvBlock(nn.Module):
def __init__(self, in_ch, out_ch, dim):
super(ConvBlock, self).__init__()
self.conv = getattr(nn, 'Conv{}d'.format(dim))(in_ch, out_ch, K_D, stride=dim, padding=K_D // 2)
self.bnrm = getattr(nn, 'BatchNorm{}d'.format(dim))(out_ch)
self.drop = nn.Sequential(perm(), nn.Dropout2d(DR_S), perm()) if dim == 1 else nn.Dropout2d(DR_S)
self.block = nn.Sequential(self.conv, nn.ELU(), self.bnrm, self.drop)
def forward(self, x):
return self.block(x)
# Residual block class, made up of two convolutional blocks.
class ResBlock(nn.Module):
def __init__(self, in_ch, hd_ch, out_ch, dim):
super(ResBlock, self).__init__()
self.dim = dim
self.conv = nn.Sequential(ConvBlock(in_ch, hd_ch, dim), ConvBlock(hd_ch, out_ch, dim))
self.down = nn.Sequential(nn.MaxPool2d(3, 2, 1), nn.MaxPool2d(3, 2, 1))
self.skip = getattr(nn, 'Conv{}d'.format(dim))(in_ch, out_ch, 1, bias=False)
def forward(self, x):
return self.conv(x) + self.skip(x if self.dim == 1 else self.down(x))
# Relational net class, defining three models with which to extract relations in RPM problems.
class RelNet(nn.Module):
def __init__(self, model, n_s):
super(RelNet, self).__init__()
self.stack = lambda x: torch.stack([torch.cat((x[:, :8], x[:, i].unsqueeze(1)), dim=1) for i in range(8, 16)],
dim=1)
self.model = model
self.n_s = n_s
if model in ["Rel-AIR", "Rel-Base"]:
lin_in = S_OC * S_PL
self.obj_enc = nn.Sequential(ResBlock(1, F_HC, F_HC, 2), ResBlock(F_HC, F_HC, F_OC, 2))
self.seq_enc = nn.Sequential(ResBlock(9, S_OC, S_HC, 1), nn.MaxPool1d(6, 4, 1),
ResBlock(S_HC, S_HC, S_OC, 1), nn.AdaptiveAvgPool1d(S_PL))
if model in ["Rel-AIR"]:
self.obj_rel = nn.Sequential(ResBlock(n_s, S_HC, S_HC, 1), ResBlock(S_HC, S_HC, 1, 1))
self.bilinear = nn.Bilinear(F_Z, BL_IN, BLOUT)
self.ebd = nn.Sequential(nn.ELU(), nn.BatchNorm1d(BLOUT))
elif model in ["ResNet", "Context-blind"]:
lin_in = O_OC * F_PL
self.og_net = nn.Sequential(ResBlock(9 if model == "ResNet" else 8, O_HC, O_HC, 2),
ResBlock(O_HC, O_HC, O_OC, 2))
else:
print("Model \"{}\" unrecognised.".format(model))
sys.exit(1)
if model in ["Rel-AIR", "Rel-Base", 'ResNet', 'Context-blind']:
self.linear = nn.Sequential(nn.Linear(lin_in, 512), nn.ELU(), nn.BatchNorm1d(512), nn.Dropout(DR_F),
nn.Linear(512, 8 if model == 'Context-blind' else 1))
def forward(self, x, n_s, t=None):
if self.model in ['Rel-GNN', 'Rel-AIR']:
obj, pos, n = x
# 1. Encode each object, of each frame, of each RPM, separately.
x = self.obj_enc(obj.view(-1, 1, 80, 80)).flatten(1)
x = x.view(-1, 16, n_s, F_Z)
# 2. Feed object embeddings and pos data through a bilinear layer.
x = x.view(-1, F_Z)
p = pos.view(-1, 3)
x = self.ebd(self.bilinear(x, p))
# 3. Perform feature extraction for object relationships using residual blocks.
x = x.view(-1, 16, n_s, G_IN)
x = self.obj_rel(x.view(-1, n_s, G_IN)).view(-1, 16, G_IN)
# 4. Assemble RPM into 8 sequences of 9 frames each (8 context + 1 answer).
# Alternatively, assemble into 10 sequences of 3 frames each (2 problem rows + 8 candidate rows).
x = self.stack(x)
# 5. Perform feature extraction for frame relationships, and score sequences.
x = self.seq_enc(x.view(-1, 9, G_OUT if self.model == 'Rel-GNN' else G_IN)).flatten(1)
return self.linear(x).view(-1, 8)
elif self.model == 'Rel-Base':
# 1. Encode each frame independently.
x = x.view(-1, 1, 80, 80)
x = self.obj_enc(x).flatten(1)
# 2. Assemble sequences.
x = x.view(-1, 16, F_Z)
x = self.stack(x)
# 3. Extract frame relationships and score sequences.
x = self.seq_enc(x.view(-1, 9, F_Z)).flatten(1)
return self.linear(x).view(-1, 8)
elif self.model == 'ResNet':
# 1. Assemble sequences, extract frame relationships, score sequences.
x = self.stack(x)
x = self.og_net(x.view(-1, 9, 80, 80)).flatten(1)
return self.linear(x).view(-1, 8)
else:
x = self.og_net(x[:, 8:])
return self.linear(x.flatten(1))
# Main model class.
class RPM_Solver(BasicModel):
def __init__(self, args):
super(RPM_Solver, self).__init__(args)
self.model = args.model
self.rel_enc = nn.DataParallel(RelNet(args.model, args.trn_n), device_ids=[0, 1]) \
if args.multi_gpu else RelNet(args.model, args.trn_n)
self.optimizer = optim.Adam(self.parameters(), lr=args.lr)
def compute_loss(self, output, target, reduction='mean'):
# target: bs
# output: bs x 8
return F.cross_entropy(output, target, reduction=reduction)
def forward(self, x, n_s, t=None):
x = x if self.model == 'Rel-AIR' else 1 - x / 255.0
out = self.rel_enc(x, n_s, t) # batch_size x 8
return out
# END SCRIPT -------------------------------------------------------------------------------------------------------- #
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tests/rules/test_gcloud_cli.py | ronandoolan2/thefuck | 0 | 44413 | <reponame>ronandoolan2/thefuck<gh_stars>0
import pytest
from thefuck.rules.gcloud_cli import match, get_new_command
from thefuck.types import Command
no_suggestions = '''\
ERROR: (gcloud) Command name argument expected.
'''
misspelled_command = '''\
ERROR: (gcloud) Invalid choice: 'comute'.
Usage: gcloud [optional flags] <group | command>
group may be access-context-manager | ai-platform | alpha | app |
asset | auth | beta | bigtable | builds | components |
composer | compute | config | container | dataflow |
dataproc | datastore | debug | deployment-manager |
dns | domains | endpoints | filestore | firebase |
functions | iam | iot | kms | logging | ml |
ml-engine | organizations | projects | pubsub | redis |
resource-manager | scheduler | services | source |
spanner | sql | tasks | topic
command may be docker | feedback | help | info | init | version
For detailed information on this command and its flags, run:
gcloud --help
'''
misspelled_subcommand = '''\
ERROR: (gcloud.compute) Invalid choice: 'instance'.
Maybe you meant:
gcloud compute instance-groups
gcloud compute instance-templates
gcloud compute instances
gcloud compute target-instances
To search the help text of gcloud commands, run:
gcloud help -- SEARCH_TERMS
'''
@pytest.mark.parametrize('command', [
Command('gcloud comute instances list', misspelled_subcommand),
Command('gcloud compute instance list', misspelled_subcommand)])
def test_match(command):
assert match(command)
def test_not_match():
assert not match(Command('aws dynamodb invalid', no_suggestions))
@pytest.mark.parametrize('command, result', [
(Command('gcloud comute instances list', misspelled_subcommand),
['gcloud compute instance-groups']),
(Command('gcloud compute instance list', misspelled_subcommand),
['gcloud compute instance-groups'])])
def test_get_new_command(command, result):
assert get_new_command(command) == result
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vanilla_clean_acc.py | inspire-group/PatchCleanser | 3 | 172145 | import torch
import torch.backends.cudnn as cudnn
import numpy as np
import os
import argparse
import time
from tqdm import tqdm
from utils.setup import get_model,get_data_loader
parser = argparse.ArgumentParser()
parser.add_argument("--model_dir",default='checkpoints',type=str,help="directory of checkpoints")
parser.add_argument('--data_dir', default='data', type=str,help="directory of data")
parser.add_argument('--dataset', default='imagenette',type=str,choices=('imagenette','imagenet','cifar','cifar100','svhn','flower102'),help="dataset")
parser.add_argument("--model",default='vit_base_patch16_224',type=str,help="model name")
parser.add_argument("--num_img",default=-1,type=int,help="number of randomly selected images for this experiment (-1: using the all images)")
args = parser.parse_args()
DATASET = args.dataset
MODEL_DIR=os.path.join('.',args.model_dir)
DATA_DIR=os.path.join(args.data_dir,DATASET)
MODEL_NAME = args.model
NUM_IMG = args.num_img
#get model and data loader
model = get_model(MODEL_NAME,DATASET,MODEL_DIR)
val_loader,NUM_IMG,_ = get_data_loader(DATASET,DATA_DIR,model,batch_size=16,num_img=NUM_IMG,train=False)
device = 'cuda'
model = model.to(device)
model.eval()
cudnn.benchmark = True
accuracy_list=[]
time_list=[]
for data,labels in tqdm(val_loader):
data,labels=data.to(device),labels.to(device)
start = time.time()
output_clean = model(data)
end=time.time()
time_list.append(end-start)
acc_clean=torch.sum(torch.argmax(output_clean, dim=1) == labels).item()#cpu().detach().numpy()
accuracy_list.append(acc_clean)
print("Test accuracy:",np.sum(accuracy_list)/NUM_IMG)
print('Per-example inference time:',np.sum(time_list)/NUM_IMG)
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core/migrations/populate_andelacenter.py | raheemazeezabiodun/art-backend | 4 | 1615168 | <gh_stars>1-10
from django.db import migrations
def add_user_location(apps, schema_editor):
User = apps.get_model('core', 'User')
location = apps.get_model('core', 'AndelaCentre')
center = None
for user in User.objects.all():
if location.objects.filter(country="Kenya").exists():
# A Kenyan center exists
center = location.objects.filter(country="Kenya")[0]
else:
# Create an Andela center in Kenya
location.objects.create(centre_name="Dojo", country="Kenya")
center = location.objects.filter(country="Kenya")[0]
user.location = center
user.save()
class Migration(migrations.Migration):
dependencies = [
('core', '0030_user_location'),
]
operations = [
migrations.RunPython(add_user_location),
]
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tests_python/tests_011/test_migration.py | bikallem/tezos | 8 | 194143 | <reponame>bikallem/tezos
from typing import Dict, List
import time
import pytest
from launchers.sandbox import Sandbox
from tools import constants, utils
from tools.constants import PROTO_GENESIS
from . import protocol
MIGRATION_LEVEL = 8
BAKER = 'bootstrap1'
BAKER_PKH = constants.IDENTITIES[BAKER]['identity']
PREV_DEPOSIT = protocol.PREV_PARAMETERS["block_security_deposit"]
DEPOSIT = protocol.PARAMETERS["block_security_deposit"]
PREV_DEPOSIT_RECEIPTS = [
{
"kind": "contract",
"contract": BAKER_PKH,
"change": "-" + PREV_DEPOSIT,
"origin": "block",
},
{
"kind": "freezer",
"category": "deposits",
"delegate": BAKER_PKH,
"cycle": 0,
"change": PREV_DEPOSIT,
"origin": "block",
},
]
DEPOSIT_RECEIPTS = [
{
"kind": "contract",
"contract": BAKER_PKH,
"change": "-" + DEPOSIT,
"origin": "block",
},
{
"kind": "freezer",
"category": "deposits",
"delegate": BAKER_PKH,
"cycle": 1,
"change": DEPOSIT,
"origin": "block",
},
]
MIGRATION_RECEIPTS: List[Dict[str, str]] = []
# configure user-activate-upgrade at MIGRATION_LEVEL to test migration
NODE_CONFIG = {
'network': {
'genesis': {
'timestamp': '2018-06-30T16:07:32Z',
'block': 'BLockGenesisGenesisGenesisGenesisGenesisf79b5d1CoW2',
'protocol': PROTO_GENESIS,
},
'genesis_parameters': {
'values': {'genesis_pubkey': constants.GENESIS_PK}
},
'chain_name': 'TEZOS',
'sandboxed_chain_name': 'SANDBOXED_TEZOS',
'user_activated_upgrades': [
{'level': MIGRATION_LEVEL, 'replacement_protocol': protocol.HASH}
],
}
}
@pytest.fixture(scope="class")
def client(sandbox):
sandbox.add_node(0, node_config=NODE_CONFIG)
protocol.activate(
sandbox.client(0),
proto=protocol.PREV_HASH,
parameters=protocol.PREV_PARAMETERS,
activate_in_the_past=True,
)
yield sandbox.client(0)
@pytest.mark.incremental
class TestMigration:
"""Test migration from PROTO_A (the previous protocol) to PROTO_B (the
current protocol).
After migration, test snapshots:
- node0: activate PROTO_A, migrate to PROTO_B, bake, export
a snapshot in full and rolling modes, and terminate
- node1: import full, bake
- node2: import rolling, sync, bake
- node3: reconstruct full, sync, bake
- all 4 are synced
"""
def test_init(self, client):
# 1: genesis block
client.get_head()
client.rpc('get', '/config/network/user_activated_upgrades')
def test_activate(self, client, sandbox):
# 2: activated PROTO_A
utils.bake(client, BAKER)
assert client.get_protocol() == protocol.PREV_HASH
assert sandbox.client(0).get_head()['header']['proto'] == 1
metadata = client.get_metadata()
assert metadata['balance_updates'] == PREV_DEPOSIT_RECEIPTS
# PROTO_A is using env. V1+, metadata hashes should be present
_ops_metadata_hash = client.get_operations_metadata_hash()
_block_metadata_hash = client.get_block_metadata_hash()
def test_migration(self, client, sandbox):
# 3: last block of PROTO_A, runs migration code (MIGRATION_LEVEL)
for _i in range(MIGRATION_LEVEL - 2):
utils.bake(client, BAKER)
metadata = client.get_metadata()
assert metadata['next_protocol'] == protocol.HASH
assert metadata['balance_updates'] == PREV_DEPOSIT_RECEIPTS
# PROTO_B is using env. V1+, metadata hashes should be present
_ops_metadata_hash = client.get_operations_metadata_hash()
_block_metadata_hash = client.get_block_metadata_hash()
assert sandbox.client(0).get_head()['header']['proto'] == 2
def test_new_proto(self, client, sandbox):
# 4: first block of PROTO_B
utils.bake(client, BAKER)
assert client.get_protocol() == protocol.HASH
assert sandbox.client(0).get_head()['header']['proto'] == 2
# check that migration balance update appears in receipts
metadata = client.get_metadata()
assert metadata['balance_updates'] == (
MIGRATION_RECEIPTS + DEPOSIT_RECEIPTS
)
_ops_metadata_hash = client.get_operations_metadata_hash()
_block_metadata_hash = client.get_block_metadata_hash()
def test_new_proto_second(self, client):
# 5: second block of PROTO_B
utils.bake(client, BAKER)
metadata = client.get_metadata()
assert metadata['balance_updates'] == DEPOSIT_RECEIPTS
def test_terminate_node0(self, client, sandbox: Sandbox, session: dict):
# to export rolling snapshot, we need to be at level > 60
# (see `max_operations_ttl`)
level = client.get_head()['header']['level']
for _ in range(60 - level + 1):
utils.bake(client, BAKER)
assert client.get_head()['header']['level'] == 61
# terminate node0
session['head_hash'] = sandbox.client(0).get_head()['hash']
sandbox.node(0).terminate()
time.sleep(1)
def test_export_snapshots(self, sandbox, tmpdir, session: dict):
node_export = sandbox.node(0)
file_full = f'{tmpdir}/FILE.full'
file_rolling = f'{tmpdir}/FILE.rolling'
head_hash = session['head_hash']
session['snapshot_full'] = file_full
session['snapshot_rolling'] = file_rolling
node_export.snapshot_export(file_full, params=['--block', head_hash])
node_export.snapshot_export(
file_rolling, params=['--block', head_hash, '--rolling']
)
def test_import_full_snapshot_node1(self, sandbox, session):
sandbox.add_node(
1, snapshot=session['snapshot_full'], node_config=NODE_CONFIG
)
client = sandbox.client(1)
utils.bake(client, BAKER)
def test_import_rolling_snapshot_node2(self, sandbox, session):
sandbox.add_node(
2, snapshot=session['snapshot_rolling'], node_config=NODE_CONFIG
)
client = sandbox.client(2)
utils.synchronize([sandbox.client(1), client], max_diff=0)
utils.bake(client, BAKER)
def test_reconstruct_full_node3(self, sandbox, session):
sandbox.add_node(
3, snapshot=session['snapshot_full'], node_config=NODE_CONFIG
)
sandbox.node(3).terminate()
time.sleep(3)
sandbox.node(3).reconstruct()
sandbox.node(3).run()
client = sandbox.client(3)
assert client.check_node_listening()
utils.synchronize(
[sandbox.client(1), sandbox.client(2), client], max_diff=0
)
utils.bake(client, BAKER)
def test_rerun_node0(self, sandbox):
sandbox.node(0).run()
sandbox.client(0).check_node_listening()
utils.synchronize(sandbox.all_clients(), max_diff=0)
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2
] |
Python-3/basic_examples/strings/string_isidentifier.py | ghiloufibelgacem/jornaldev | 1,139 | 118470 | s = 'xyzABC'
print(f'{s} is a valid identifier = {s.isidentifier()}')
s = '0xyz' # identifier can't start with digits 0-9
print(f'{s} is a valid identifier = {s.isidentifier()}')
s = '' # identifier can't be empty string
print(f'{s} is a valid identifier = {s.isidentifier()}')
s = '_xyz'
print(f'{s} is a valid identifier = {s.isidentifier()}')
s = 'ꝗꞨꫳ' # PEP-3131 introduced Non-ASCII characters to identifier list
print(f'{s} is a valid identifier = {s.isidentifier()}')
import unicodedata
count = 0
for codepoint in range(2 ** 16):
ch = chr(codepoint)
if ch.isidentifier():
print(u'{:04x}: {} ({})'.format(codepoint, ch, unicodedata.name(ch, 'UNNAMED')))
count = count + 1
print(f'Total Number of Identifier Unicode Characters = {count}')
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2
] |
cal/migrations/0004_auto_20200229_1141.py | PudgyPoppins/caps | 0 | 90954 | <reponame>PudgyPoppins/caps<filename>cal/migrations/0004_auto_20200229_1141.py<gh_stars>0
# Generated by Django 3.0.3 on 2020-02-29 18:41
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('cal', '0003_calendar_event'),
]
operations = [
migrations.RemoveField(
model_name='event',
name='recurrence',
),
migrations.AddField(
model_name='event',
name='repeat',
field=models.CharField(blank=True, help_text='Will this event ever repeat?', max_length=200, null=True),
),
]
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5574,
511,
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10353,
13,
1678,
4514,
13,
2
] |
scale.app/scripts/msvc2org.py | f4rsh/SCALe | 239 | 56621 | #!/usr/bin/env python
# Scrubs the output of msvc and prints out the dianostics.
#
# The only argument indicates the file containing the input.
#
# This script can produce lots of messages per diagnostic
#
# Copyright (c) 2007-2018 Carnegie Mellon University. All Rights Reserved.
# See COPYRIGHT file for details.
import sys
import re
import os
if len(sys.argv) != 2:
raise TypeError("Usage: " + sys.argv[0] + " <raw-input> > <org-output>")
input = sys.argv[1]
uniqueErrors = {}
regexes = []
regexes.append(re.compile("(.*?)\((\d*)\).*?error (.*?): (.*)"))
regexes.append(re.compile("(.*?)\((\d*),\d*\).*?error (.*?): (.*)"))
regexes.append(re.compile("(.*?)\((\d*)\).*?warning (.*?): (.*)"))
regexes.append(re.compile("(.*?)\((\d*),\d*\).*?warning (.*?): (.*)"))
for line in open(input):
# match regular expressions
for regex in regexes:
parse = re.match(regex, line)
if parse != None:
break
else:
continue
fileLocation = parse.group(1).strip()
lineNumber = parse.group(2).strip()
errorNumber = parse.group(3).strip()
diagonostic = parse.group(4).strip().replace("|", " ")
# print table
tableEntry = " | ".join(
["", errorNumber, fileLocation, lineNumber, diagonostic, ""])
print tableEntry
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src/models/decoders.py | yizhe-ang/vi-lab | 1 | 55312 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Bernoulli
from src.models.nns import Decoder
class ConvDecoder(nn.Module):
def __init__(self, z_dim):
super().__init__()
self.z_dim = z_dim
self.decoder = Decoder(
128,
z_dim,
28,
28,
1
)
def decode(self, z: torch.tensor) -> torch.tensor:
"""Decode latent points into data points
Parameters
----------
z : torch.tensor
[B, Z]
Returns
-------
torch.tensor
[B, D]
"""
# Output is Bernoulli
loc_img = torch.sigmoid(self.decoder(z))
return loc_img
def forward(self, x: torch.tensor, z: torch.tensor) -> torch.tensor:
"""log p(x|z), reconstruction term
Parameters
----------
x : torch.tensor
[B, C, H, W], Data points
z : torch.tensor
[B, Z], Latent points
Returns
-------
torch.tensor
[B,], log p(x|z)
"""
x = x.reshape(-1, 784)
loc_img = self.decode(z)
# FIXME Use F.binary_cross_entropy instead? it's the same
dist = Bernoulli(probs=loc_img)
log_px_z = dist.log_prob(x).sum(-1)
# log_px_z = -F.binary_cross_entropy(loc_img, x, reduction="none").sum(-1)
return log_px_z
class MNISTDecoder(nn.Module):
def __init__(self, z_dim, hidden_dim=400):
super().__init__()
self.z_dim = z_dim
self.fc1 = nn.Linear(z_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, 784)
def decode(self, z: torch.tensor) -> torch.tensor:
"""Decode latent points into data points
Parameters
----------
z : torch.tensor
[B, Z]
Returns
-------
torch.tensor
[B, D]
"""
hidden = F.relu(self.fc1(z))
# Output is Bernoulli
loc_img = torch.sigmoid(self.fc2(hidden))
return loc_img
def forward(self, x: torch.tensor, z: torch.tensor) -> torch.tensor:
"""log p(x|z), reconstruction term
Parameters
----------
x : torch.tensor
[B, C, H, W], Data points
z : torch.tensor
[B, Z], Latent points
Returns
-------
torch.tensor
[B,], log p(x|z)
"""
x = x.reshape(-1, 784)
loc_img = self.decode(z)
# FIXME Use F.binary_cross_entropy instead? it's the same
dist = Bernoulli(probs=loc_img)
log_px_z = dist.log_prob(x).sum(-1)
# log_px_z = -F.binary_cross_entropy(loc_img, x, reduction="none").sum(-1)
return log_px_z
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HMQ/networks/blocks.py | UniSerj/ai-research | 46 | 117971 | import torch
from torch import nn
from torch.nn import functional as F
from torch.distributions.uniform import Uniform
from networks.layers.non_linear import NonLinear, NonLinearType
from networks.layers.conv_bn import ConvBN
class DropConnect(nn.Module):
def __init__(self, survival_prob):
"""
A module that implements drop connection
:param survival_prob: the probability if connection survival
"""
super(DropConnect, self).__init__()
self.survival_prob = survival_prob
self.u = Uniform(0, 1)
def forward(self, x):
"""
The forward function of the DropConnect module
:param x: Input tensor x
:return: A tensor after drop connection
"""
if self.training:
random_tensor = self.u.sample([x.shape[0], 1, 1, 1]).cuda()
random_tensor += self.survival_prob
binary_tensor = torch.floor(random_tensor)
return x * binary_tensor / self.survival_prob
else:
return x
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""
Global Average pooling module
"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
"""
The forward function of the GlobalAvgPool2d module
:param x: Input tensor x
:return: A tensor after average pooling
"""
return F.avg_pool2d(x, (x.shape[2], x.shape[3]))
class SEBlock(nn.Module):
def __init__(self, nc, in_channels, reduce_channels):
"""
An implantation of Squeeze Excite block
:param nc: Input network controller
:param in_channels: the number of input channels
:param reduce_channels: the number of channels after reduction
"""
super(SEBlock, self).__init__()
self.gap = GlobalAvgPool2d()
self.conv_reduce = nn.Sequential(
ConvBN(nc, in_channels, reduce_channels, 1, disable_bn=True),
NonLinear(nc, reduce_channels, NonLinearType.SWISH))
self.conv_expand = nn.Sequential(
ConvBN(nc, reduce_channels, in_channels, 1, disable_bn=True),
NonLinear(nc, in_channels, NonLinearType.SIGMOID))
def forward(self, x):
"""
The forward function of the SEBlock module
:param x: Input tensor x
:return: A tensor after SE Block
"""
return x * self.conv_expand(self.conv_reduce(self.gap(x)))
class ConvBNNonLinear(nn.Sequential):
def __init__(self, nc, in_planes, out_planes, kernel_size=3, stride=1, groups=1, nl_type=NonLinearType.RELU6,
batch_norm_epsilon=1e-5, batch_norm_momentum=0.1, tf_padding=False):
"""
A joint block of 2d convolution with batch normalization and non linear function modules
with HMQ quantization of both the convolution weights and activation function
:param nc: The network quantization controller
:param in_planes: The number of input channels
:param out_planes: The number of output channels
:param kernel_size: The kernel size
:param stride: The convolution stride
:param groups: The convolution group size
:param nl_type: enum that state the non-linear type.
:param batch_norm_epsilon: The batch normalization epsilon
:param batch_norm_momentum: The batch normalization momentum
:param tf_padding: Use TensorFlow padding (for EfficientNet)
"""
padding = kernel_size - stride if tf_padding else (kernel_size - 1) // 2
super(ConvBNNonLinear, self).__init__(
ConvBN(nc, in_planes, out_planes, kernel_size, stride, padding, group=groups,
batch_norm_epsilon=batch_norm_epsilon, batch_norm_momentum=batch_norm_momentum,
tf_padding=tf_padding),
NonLinear(nc, out_planes, nl_type)
)
class InvertedResidual(nn.Module):
def __init__(self, nc, inp, oup, stride, expand_ratio, kernel_size=3, nl_type=NonLinearType.RELU6, se_ratio=0,
survival_prob=0, batch_norm_epsilon=1e-5, batch_norm_momentum=0.1, tf_padding=False):
"""
A Inverted Residual block use in Efficient-Net
:param nc: The network quantization controller
:param inp: The number of input channels
:param oup: The number of output channels
:param stride: The depth wise convolution stride
:param expand_ratio: The block expand ratio for depth-wise convolution
:param kernel_size: The kernel size
:param nl_type: enum that state the non-linear type.
:param se_ratio: the ratio between the number of input channel and mid channels in SE Bloock
:param survival_prob: the probability if connection survival
:param batch_norm_epsilon: The batch normalization epsilon
:param batch_norm_momentum: The batch normalization momentum
:param tf_padding: Use TensorFlow padding (for EfficientNet)
"""
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = self.stride == 1 and inp == oup
self.kernel_size = kernel_size
layers = []
if expand_ratio != 1:
# pw
layers.append(ConvBNNonLinear(nc, inp, hidden_dim, kernel_size=1, nl_type=nl_type,
batch_norm_epsilon=batch_norm_epsilon,
batch_norm_momentum=batch_norm_momentum))
layers.append(
ConvBNNonLinear(nc, hidden_dim, hidden_dim, kernel_size=kernel_size, stride=stride, groups=hidden_dim,
nl_type=nl_type, batch_norm_epsilon=batch_norm_epsilon,
batch_norm_momentum=batch_norm_momentum, tf_padding=tf_padding))
if se_ratio != 0:
layers.append(SEBlock(nc, hidden_dim, int(inp * se_ratio)))
layers.append(ConvBNNonLinear(nc, hidden_dim, oup, kernel_size=1, stride=1, nl_type=NonLinearType.IDENTITY,
batch_norm_epsilon=batch_norm_epsilon,
batch_norm_momentum=batch_norm_momentum))
if survival_prob != 0 and self.use_res_connect:
layers.append(DropConnect(survival_prob))
self.conv = nn.Sequential(*layers)
self.output_q = NonLinear(nc, oup, nl_type=NonLinearType.IDENTITY)
def forward(self, x):
"""
The forward function of the InvertedResidual module
:param x: Input tensor x
:return: A tensor after InvertedResidual
"""
if self.use_res_connect:
y = self.conv(x)
return self.output_q(x + y)
else:
x = self.conv(x)
return self.output_q(x)
class RepeatedInvertedResidual(nn.Module):
def __init__(self, nc, n_repeat, in_channels, out_channels, stride_first, expand_ratio, kernel_size=3,
nl_type=NonLinearType.RELU6,
se_ratio=0,
survival_prob_start=0, drop_rate=0, batch_norm_epsilon=1e-5, batch_norm_momentum=0.1,
tf_padding=False):
"""
A block the repeatedly run the InvertedResidual block
:param nc:The network quantization controller
:param n_repeat:
:param in_channels: The number of input channels
:param out_channels: The number of output channels
:param stride_first: The depth wise convolution stride in the first block
:param expand_ratio: The block expand ratio for depth-wise convolution
:param kernel_size: The kernel size
:param nl_type: enum that state the non-linear type.
:param se_ratio: the ratio between the number of input channel and mid channels in SE Bloock
:param survival_prob_start: the probability if connection survival in the first block
:param batch_norm_epsilon: The batch normalization epsilon
:param batch_norm_momentum: The batch normalization momentum
:param tf_padding: Use TensorFlow padding (for EfficientNet)
"""
super(RepeatedInvertedResidual, self).__init__()
layers = []
for i in range(n_repeat):
if survival_prob_start > 0 and drop_rate > 0:
survival_prob = survival_prob_start - drop_rate * float(i)
else:
survival_prob = 0
block = InvertedResidual(nc, in_channels if i == 0 else out_channels, out_channels,
stride_first if i == 0 else 1, expand_ratio, kernel_size=kernel_size,
nl_type=nl_type, se_ratio=se_ratio, survival_prob=survival_prob,
batch_norm_epsilon=batch_norm_epsilon, batch_norm_momentum=batch_norm_momentum,
tf_padding=tf_padding)
layers.append(block)
self.blocks = nn.Sequential(*layers)
def forward(self, x):
"""
The forward function of the RepeatedInvertedResidual module
:param x: Input tensor x
:return: A tensor after RepeatedInvertedResidual
"""
return self.blocks(x)
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prac2_2.py | JulianAZW/bookish-lamp | 1 | 23223 | <reponame>JulianAZW/bookish-lamp
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 17 19:08:54 2022
@author: julian
"""
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def heatmap(df):
correlation_matrix = df.corr().round(2)
print(correlation_matrix)
sns_h = sns.heatmap(data=correlation_matrix, annot=True)
sns_h.figure.savefig("Mapa de calor de correlacion")
sns_h.figure.clear()
def plotXY(df):
for i in range(0,8):
plt_disp = plt.scatter(df.iloc[:,i],df.iloc[:,8])
plt.xlabel(df.columns[i])
plt.ylabel(df.columns[8])
cadena = "plt"+str(i)+"_"+str(8)+".png"
print(cadena)
plt_disp
plt_disp.figure.clear()
if __name__=='__main__':
df1 = pd.read_csv("data_train.csv", sep=',', engine='python')
df2 = pd.read_csv("medianHouseValue_train.csv", sep=',', engine='python')
df = pd.concat([df1,df2], axis=1)
print("El DataFrame con el 80 por ciento de los datos: \n", df)
#plt.scatter(df.iloc[:,7],df.iloc[:,8])
#plt.xlabel(df.columns[7])
#plt.ylabel(df.columns[8])
#plt.show()
heatmap(df)
plotXY(df)
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29889,
309,
542,
7503,
29892,
29947,
2314,
13,
4706,
14770,
29889,
29916,
1643,
29898,
2176,
29889,
13099,
29961,
29875,
2314,
13,
4706,
14770,
29889,
29891,
1643,
29898,
2176,
29889,
13099,
29961,
29947,
2314,
13,
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13840,
2386,
353,
376,
572,
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17969,
710,
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29875,
7240,
29908,
29918,
17969,
710,
29898,
29947,
7240,
1642,
2732,
29908,
13,
4706,
1596,
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29883,
328,
2386,
29897,
13,
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14770,
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2218,
29886,
13,
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14770,
29918,
2218,
29886,
29889,
4532,
29889,
8551,
580,
13,
308,
13,
308,
13,
361,
4770,
978,
1649,
1360,
29915,
1649,
3396,
1649,
2396,
13,
1678,
4489,
29896,
353,
10518,
29889,
949,
29918,
7638,
703,
1272,
29918,
14968,
29889,
7638,
613,
16345,
29922,
742,
742,
6012,
2433,
4691,
1495,
13,
1678,
4489,
29906,
353,
10518,
29889,
949,
29918,
7638,
703,
2168,
713,
29950,
1709,
1917,
29918,
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7638,
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2176,
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1402,
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29922,
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13,
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4308,
378,
560,
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274,
5230,
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1232,
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13099,
29961,
29955,
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13,
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396,
572,
29873,
29889,
29891,
1643,
29898,
2176,
29889,
13099,
29961,
29947,
2314,
13,
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396,
572,
29873,
29889,
4294,
580,
13,
1678,
12871,
1958,
29898,
2176,
29897,
13,
1678,
6492,
18454,
29898,
2176,
29897,
13,
268,
2
] |
fix_readme.py | vmstarchenko/jiml | 0 | 106322 | import re
import sys
with open('README.md') as f:
data = f.read()
print(re.sub(
r'\n>>> \n>>> # (.*)\n',
r'\n```\n- \1\n```python\n',
sys.stdin.read(),
))
| [
1,
1053,
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13,
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262,
29889,
949,
3285,
13,
876,
13,
2
] |
Chapter 08/mcb_ext.pyw | roelr1/python-1 | 143 | 166249 | #!/usr/bin/env python3
"""Saves, loads and deletes pieces of text to the clipboard.
Usage:
py.exe mcb_ext.pyw save <keyword> - Saves clipboard to keyword
py.exe mcb_ext.pyw <keyword> - Loads keyword contents to clipboard
py.exe mcb_ext.pyw list - Loads all keywords to clipboard
py.exe mcb_ext.pyw delete <keyword>- Deletes saved keyword and contents
py.exe mcb_ext.pyw delete_all - Deletes all keywords from clipboard
"""
import shelve
import sys
import pyperclip
mcb_shelf = shelve.open('mcb')
# Save the clipboard contents to keyword
if len(sys.argv) == 3 and sys.argv[1].lower() == 'save':
mcb_shelf[sys.argv[2]] = pyperclip.paste()
print(sys.argv[2] + ' saved successfully')
# Delete keyword and associated content
elif len(sys.argv) == 3 and sys.argv[1].lower() == 'delete':
del mcb_shelf[sys.argv[2]]
print([sys.argv[2] + ' deleted'])
elif len(sys.argv) == 2 and sys.argv[1].lower() != 'delete_all':
# List stored keywords
if sys.argv[1].lower() == 'list':
pyperclip.copy(str(list(mcb_shelf.keys())))
print('List of all keywords copied to clipboard')
# Load content associated with chosen keyword to the clipboard
elif sys.argv[1] in mcb_shelf:
pyperclip.copy(mcb_shelf[sys.argv[1]])
print(sys.argv[1] + ' loaded successfully')
# Inform user that their input doesn't match a saved keyword
else:
print('That keyword doesn\'t exist - so nothing'
'has been loaded to the clipboard')
# Clear all shelved entries
elif len(sys.argv) == 2 and sys.argv[1].lower() == 'delete_all':
mcb_shelf.clear()
print('All keywords and associated contents have been deleted')
mcb_shelf.close()
| [
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322,
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1063,
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1495,
13,
13,
29885,
10702,
29918,
845,
761,
29889,
5358,
580,
13,
2
] |
train/utils.py | sahandilshan/PruneLM | 0 | 174383 | <filename>train/utils.py
import torch
def batchify(data, batch_size, device='cpu'):
# Get number of batches
num_batch = data.size(0) // batch_size
# Trim off any extra elements that wouldn't cleanly fit (remainders).
data = data.narrow(0, 0, num_batch * batch_size)
# Evenly divide the data across the batch_size batches.
data = data.view(batch_size, -1).t().contiguous()
return data.to(device)
def repackage_hidden(h):
"""Wraps hidden states in new Tensors, to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
def get_batch(source, i, sequence_length=6):
seq_len = min(sequence_length, len(source) - 1 - i)
data = source[i:i + seq_len]
target = source[i + 1:i + 1 + seq_len].view(-1)
return data, target
def evaluate(data_source, model, criterion, num_tokens, batch_size, sequence_length=6):
model.eval() # batchnorm or dropout layers will work in eval mode
total_loss = 0.
hidden = model.init_hidden(batch_size)
with torch.no_grad(): # Stop calculating gradients
for i in range(0, data_source.size(0) - 1, sequence_length):
data, targets = get_batch(data_source, i, sequence_length)
output, hidden = model(data, hidden)
output_flat = output.view(-1, num_tokens)
total_loss += len(data) * criterion(output_flat, targets).item()
hidden = repackage_hidden(hidden)
return total_loss / len(data_source)
| [
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736,
3001,
29918,
6758,
847,
7431,
29898,
1272,
29918,
4993,
29897,
13,
2
] |
django_testing_tutorial_2018/products/tests/test_views.py | bluebamus/django_pytest | 0 | 68876 | <filename>django_testing_tutorial_2018/products/tests/test_views.py
from django.test import RequestFactory
from django.urls import reverse
from django.contrib.auth.models import AnonymousUser, User
from mixer.backend.django import mixer
from products.views import product_detail
import pytest
@pytest.mark.django_db
class TestViews:
def test_product_detail_authenticated(self):
mixer.blend('products.Product')
path = reverse('detail',kwargs={'pk':1})
request = RequestFactory().get(path)
request.user = mixer.blend(User)
response = product_detail(request, pk=1)
assert response.status_code == 200
def test_product_detail_unauthenticated(self):
mixer.blend('products.Product')
path = reverse('detail',kwargs={'pk':1})
request = RequestFactory().get(path)
request.user = AnonymousUser()
response = product_detail(request, pk=1)
# assert response.status_code == 200
assert 'accounts/login' in response.url | [
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naeval/segment/score.py | sdspieg/naeval | 36 | 131657 | <gh_stars>10-100
from naeval.score import F1
def score_partitions(preds, targets):
score = F1()
for pred, target in zip(preds, targets):
pred = set(pred.splits)
target = set(target.splits)
intersection = len(pred & target)
score.prec.total += len(pred)
score.prec.correct += intersection
score.recall.total += len(target)
score.recall.correct += intersection
return score
| [
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yudzuki/role.py | LunaProject-Discord/yudzuki.py | 6 | 2865 | <gh_stars>1-10
__all__ = (
"Role",
)
class Role:
def __init__(self, data):
self.data = data
self._update(data)
def _get_json(self):
return self.data
def __repr__(self):
return (
f"<Role id={self.id} name={self.name}>"
)
def __str__(self):
return (
f"{self.name}"
)
def _update(self, data):
self._id = data["id"]
self._color = data["color"]
self._managed = data["managed"]
self._name = data["name"]
self._guild_id = data["guild_id"]
self._mentionable = data["mentionable"]
self._position = data["potition"]
self._hoisted = data["hoisted"]
@property
def id(self):
return self._id
@property
def color(self):
return self._color
@property
def managed(self):
return self._managed
@property
def name(self):
return self._name
@property
def guild_id(self):
return self._guild_id
@property
def mentionable(self):
return self._mentionable
@property
def position(self):
return self._position
@property
def hoisted(self):
return self._hoisted
| [
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app/django/apps/filament/forms.py | aoirint/Mnemosyne | 0 | 79018 | from django import forms
from filament.models import *
class FilamentForm(forms.Form):
material = forms.ChoiceField(
required=True,
choices=FILAMENT_MATERIAL_CHOICES,
widget=forms.Select(attrs={
'class': 'form-control',
'form': 'filament_form',
}),
)
amount = forms.FloatField(
required=True,
widget=forms.NumberInput(attrs={
'class': 'form-control',
'form': 'filament_form',
'placeholder': 'g単位',
'min': 0,
}),
)
price = forms.IntegerField(
required=True,
widget=forms.NumberInput(attrs={
'class': 'form-control',
'form': 'filament_form',
'placeholder': '円単位',
'min': 0,
}),
)
shop = forms.CharField(
required=False,
widget=forms.TextInput(attrs={
'class': 'form-control',
'form': 'filament_form',
'placeholder': 'Amazon',
}),
)
url = forms.URLField(
required=False,
widget=forms.URLInput(attrs={
'class': 'form-control',
'form': 'filament_form',
'placeholder': 'https://www.amazon.co.jp/dp/XXXXXXXXXX',
}),
)
owner = forms.CharField(
required=False,
widget=forms.TextInput(attrs={
'class': 'form-control',
'form': 'filament_form',
'placeholder': 'your name',
}),
)
name = forms.CharField(
required=False,
widget=forms.TextInput(attrs={
'class': 'form-control',
'form': 'filament_form',
'placeholder': 'マイフィラメント',
}),
)
image = forms.ImageField(
required=False,
widget=forms.ClearableFileInput(attrs={
'class': 'form-control',
'form': 'filament_form',
}),
)
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] |
functions/fingerprinter.py | DalavanCloud/UGESCO | 1 | 193044 | # -*- coding: utf-8 -*-
import re
import string
from unidecode import unidecode
PUNCTUATION = re.compile('[%s]' % re.escape(string.punctuation))
class Fingerprinter(object):
'''
Python implementation of Google Refine fingerprinting algorithm described here:
https://github.com/OpenRefine/OpenRefine/wiki/Clustering-In-Depth
Requires the unidecode module: https://github.com/iki/unidecode
'''
def __init__(self, string):
self.string = self._preprocess(string)
def _preprocess(self, string):
'''
Strip leading and trailing whitespace, lowercase the string, remove all punctuation,
in that order.
'''
return PUNCTUATION.sub('', string.strip().lower())
def _latinize(self, string):
'''
Replaces unicode characters with closest Latin equivalent. For example,
<NAME> becomes <NAME>.
'''
return unidecode(string)
def _unique_preserving_order(self, seq):
'''
Returns unique tokens in a list, preserving order. Fastest version found in this
exercise: http://www.peterbe.com/plog/uniqifiers-benchmark
'''
seen = set()
seen_add = seen.add
return [x for x in seq if not (x in seen or seen_add(x))]
def get_fingerprint_nonsorted(self):
'''
Gets non sorted fingerpint that remove isolated letters
'''
result = self._latinize(' '.join(
self._unique_preserving_order(
self.string.split()
)
))
return re.sub(r'\b\w{1}\b', '', result)
def get_fingerprint(self):
'''
Gets conventional fingerpint.
'''
return self._latinize(' '.join(
self._unique_preserving_order(
sorted(self.string.split())
)
))
def get_ngram_fingerprint(self, n=1):
'''
Gets ngram fingerpint based on n-length shingles of the string.
Default is 1.
'''
return self._latinize(''.join(
self._unique_preserving_order(
sorted([self.string[i:i + n]
for i in range(len(self.string) - n + 1)])
)
))
if __name__ == '__main__':
f = Fingerprinter("hôtel de ville de Bruxeles à bruxeles")
print(f.get_fingerprint())
print(f.get_fingerprint_nonsorted())
print(f.get_ngram_fingerprint(n=1))
print(f.get_ngram_fingerprint(n=2))
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876,
13,
2
] |
Printing/BlenderScripts/ColorFabUI/convertToBWOperator.py | HCIELab/ColorMod | 3 | 118134 | import bpy
from bpy.types import Operator
class ConvertToBWOperator(Operator):
bl_idname = "object.convert_to_bw_operator"
bl_label = "Convert Colored Voxels to Black"
bl_options = {'REGISTER', 'UNDO'}
@classmethod
def poll(self, context):
return True
def execute(self, context):
color = ['white', 'red', 'blue']
for i in range(1,len(color)+1):
objects_on_current_layer = [ob for ob in bpy.context.scene.objects if ob.layers[i]]
for obj in objects_on_current_layer:
if obj.data.materials[0] == bpy.data.materials[color[i]]:
obj.data.materials[0] = bpy.data.materials['black']
else:
obj.data.materials[0] = bpy.data.materials['white']
return {'FINISHED'}
def register():
bpy.utils.register_class(ConvertToBWOperator)
def unregister():
bpy.utils.unregister_class(ConvertToBWOperator)
if __name__ == "__main__":
register()
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2
] |
test/mitmproxy/test_examples.py | e7appew/pkg-mitmproxy | 0 | 142627 | <gh_stars>0
import json
import os
import six
from mitmproxy import options
from mitmproxy import contentviews
from mitmproxy.builtins import script
from mitmproxy.flow import master
from mitmproxy.flow import state
import netlib.utils
from netlib import tutils as netutils
from netlib.http import Headers
from netlib.http import cookies
from . import tutils, mastertest
example_dir = netlib.utils.Data(__name__).push("../../examples")
class ScriptError(Exception):
pass
class RaiseMaster(master.FlowMaster):
def add_log(self, e, level):
if level in ("warn", "error"):
raise ScriptError(e)
def tscript(cmd, args=""):
o = options.Options()
cmd = example_dir.path(cmd) + " " + args
m = RaiseMaster(o, None, state.State())
sc = script.Script(cmd)
m.addons.add(sc)
return m, sc
class TestScripts(mastertest.MasterTest):
def test_add_header(self):
m, _ = tscript("add_header.py")
f = tutils.tflow(resp=netutils.tresp())
m.response(f)
assert f.response.headers["newheader"] == "foo"
def test_custom_contentviews(self):
m, sc = tscript("custom_contentviews.py")
pig = contentviews.get("pig_latin_HTML")
_, fmt = pig(b"<html>test!</html>")
assert any(b'esttay!' in val[0][1] for val in fmt)
assert not pig(b"gobbledygook")
def test_iframe_injector(self):
with tutils.raises(ScriptError):
tscript("iframe_injector.py")
m, sc = tscript("iframe_injector.py", "http://example.org/evil_iframe")
f = tutils.tflow(resp=netutils.tresp(content=b"<html>mitmproxy</html>"))
m.response(f)
content = f.response.content
assert b'iframe' in content and b'evil_iframe' in content
def test_modify_form(self):
m, sc = tscript("modify_form.py")
form_header = Headers(content_type="application/x-www-form-urlencoded")
f = tutils.tflow(req=netutils.treq(headers=form_header))
m.request(f)
assert f.request.urlencoded_form[b"mitmproxy"] == b"rocks"
f.request.headers["content-type"] = ""
m.request(f)
assert list(f.request.urlencoded_form.items()) == [(b"foo", b"bar")]
def test_modify_querystring(self):
m, sc = tscript("modify_querystring.py")
f = tutils.tflow(req=netutils.treq(path="/search?q=term"))
m.request(f)
assert f.request.query["mitmproxy"] == "rocks"
f.request.path = "/"
m.request(f)
assert f.request.query["mitmproxy"] == "rocks"
def test_arguments(self):
m, sc = tscript("arguments.py", "mitmproxy rocks")
f = tutils.tflow(resp=netutils.tresp(content=b"I <3 mitmproxy"))
m.response(f)
assert f.response.content == b"I <3 rocks"
def test_redirect_requests(self):
m, sc = tscript("redirect_requests.py")
f = tutils.tflow(req=netutils.treq(host="example.org"))
m.request(f)
assert f.request.host == "mitmproxy.org"
class TestHARDump():
def flow(self, resp_content=b'message'):
times = dict(
timestamp_start=746203272,
timestamp_end=746203272,
)
# Create a dummy flow for testing
return tutils.tflow(
req=netutils.treq(method=b'GET', **times),
resp=netutils.tresp(content=resp_content, **times)
)
def test_no_file_arg(self):
with tutils.raises(ScriptError):
tscript("har_dump.py")
def test_simple(self):
with tutils.tmpdir() as tdir:
path = os.path.join(tdir, "somefile")
m, sc = tscript("har_dump.py", six.moves.shlex_quote(path))
m.addons.invoke(m, "response", self.flow())
m.addons.remove(sc)
with open(path, "r") as inp:
har = json.load(inp)
assert len(har["log"]["entries"]) == 1
def test_base64(self):
with tutils.tmpdir() as tdir:
path = os.path.join(tdir, "somefile")
m, sc = tscript("har_dump.py", six.moves.shlex_quote(path))
m.addons.invoke(m, "response", self.flow(resp_content=b"foo" + b"\xFF" * 10))
m.addons.remove(sc)
with open(path, "r") as inp:
har = json.load(inp)
assert har["log"]["entries"][0]["response"]["content"]["encoding"] == "base64"
def test_format_cookies(self):
m, sc = tscript("har_dump.py", "-")
format_cookies = sc.ns.ns["format_cookies"]
CA = cookies.CookieAttrs
f = format_cookies([("n", "v", CA([("k", "v")]))])[0]
assert f['name'] == "n"
assert f['value'] == "v"
assert not f['httpOnly']
assert not f['secure']
f = format_cookies([("n", "v", CA([("httponly", None), ("secure", None)]))])[0]
assert f['httpOnly']
assert f['secure']
f = format_cookies([("n", "v", CA([("expires", "Mon, 24-Aug-2037 00:00:00 GMT")]))])[0]
assert f['expires']
| [
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] |
pytorch/src/train.py | LotusWhu/xynet | 0 | 1617191 | import argparse
import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import network
import loss
import pre_process as prep
import torch.utils.data as util_data
import lr_schedule
from data_list import ImageList
from torch.autograd import Variable
optim_dict = {"SGD": optim.SGD}
def image_classification_predict(loader, model, test_10crop=True, gpu=True):
start_test = True
if test_10crop:
iter_test = [iter(loader['test'+str(i)]) for i in xrange(10)]
for i in xrange(len(loader['test0'])):
data = [iter_test[j].next() for j in xrange(10)]
inputs = [data[j][0] for j in xrange(10)]
if gpu:
for j in xrange(10):
inputs[j] = Variable(inputs[j].cuda())
else:
for j in xrange(10):
inputs[j] = Variable(inputs[j])
outputs = []
for j in xrange(10):
outputs.append(model(inputs[j]))
outputs = sum(outputs)
if start_test:
all_output = outputs.data.float()
start_test = False
else:
all_output = torch.cat((all_output, outputs.data.float()), 0)
else:
iter_val = iter(loader["test"])
for i in xrange(len(loader['test'])):
data = iter_val.next()
inputs = data[0]
if gpu:
inputs = Variable(inputs.cuda())
else:
inputs = Variable(inputs)
outputs = model(inputs)
if start_test:
all_output = outputs.data.cpu().float()
start_test = False
else:
all_output = torch.cat((all_output, outputs.data.cpu().float()), 0)
_, predict = torch.max(all_output, 1)
return all_output, predict
def image_classification_test(loader, model, test_10crop=True, gpu=True):
start_test = True
if test_10crop:
iter_test = [iter(loader['test'+str(i)]) for i in xrange(10)]
for i in xrange(len(loader['test0'])):
data = [iter_test[j].next() for j in xrange(10)]
inputs = [data[j][0] for j in xrange(10)]
labels = data[0][1]
if gpu:
for j in xrange(10):
inputs[j] = Variable(inputs[j].cuda())
labels = Variable(labels.cuda())
else:
for j in xrange(10):
inputs[j] = Variable(inputs[j])
labels = Variable(labels)
outputs = []
for j in xrange(10):
outputs.append(model(inputs[j]))
outputs = sum(outputs)
if start_test:
all_output = outputs.data.float()
all_label = labels.data.float()
start_test = False
else:
all_output = torch.cat((all_output, outputs.data.float()), 0)
all_label = torch.cat((all_label, labels.data.float()), 0)
else:
iter_test = iter(loader["test"])
for i in xrange(len(loader["test"])):
data = iter_test.next()
inputs = data[0]
labels = data[1]
if gpu:
inputs = Variable(inputs.cuda())
labels = Variable(labels.cuda())
else:
inputs = Variable(inputs)
labels = Variable(labels)
outputs = model(inputs)
if start_test:
all_output = outputs.data.float()
all_label = labels.data.float()
start_test = False
else:
all_output = torch.cat((all_output, outputs.data.float()), 0)
all_label = torch.cat((all_label, labels.data.float()), 0)
_, predict = torch.max(all_output, 1)
accuracy = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0])
return accuracy
def transfer_classification(config):
## set pre-process
prep_dict = {}
for prep_config in config["prep"]:
prep_dict[prep_config["name"]] = {}
if prep_config["type"] == "image":
prep_dict[prep_config["name"]]["test_10crop"] = prep_config["test_10crop"]
prep_dict[prep_config["name"]]["train"] = prep.image_train(resize_size=prep_config["resize_size"], crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict[prep_config["name"]]["test"] = prep.image_test_10crop(resize_size=prep_config["resize_size"], crop_size=prep_config["crop_size"])
else:
prep_dict[prep_config["name"]]["test"] = prep.image_test(resize_size=prep_config["resize_size"], crop_size=prep_config["crop_size"])
## set loss
class_criterion = nn.CrossEntropyLoss()
loss_config = config["loss"]
transfer_criterion = loss.loss_dict[loss_config["name"]]
if "params" not in loss_config:
loss_config["params"] = {}
## prepare data
dsets = {}
dset_loaders = {}
for data_config in config["data"]:
dsets[data_config["name"]] = {}
dset_loaders[data_config["name"]] = {}
## image data
if data_config["type"] == "image":
dsets[data_config["name"]]["train"] = ImageList(open(data_config["list_path"]["train"]).readlines(), transform=prep_dict[data_config["name"]]["train"])
dset_loaders[data_config["name"]]["train"] = util_data.DataLoader(dsets[data_config["name"]]["train"], batch_size=data_config["batch_size"]["train"], shuffle=True, num_workers=4)
if "test" in data_config["list_path"]:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]]["test"+str(i)] = ImageList(open(data_config["list_path"]["test"]).readlines(), transform=prep_dict[data_config["name"]]["test"]["val"+str(i)]
)
dset_loaders[data_config["name"]]["test"+str(i)] = util_data.DataLoader(dsets[data_config["name"]]["test"+str(i)], batch_size=data_config["batch_size"]["test"], shuffle=False, num_workers=4)
else:
dsets[data_config["name"]]["test"] = ImageList(open(data_config["list_path"]["test"]).readlines(), transform=prep_dict[data_config["name"]]["test"])
dset_loaders[data_config["name"]]["test"] = util_data.DataLoader(dsets[data_config["name"]]["test"], batch_size=data_config["batch_size"]["test"], shuffle=False, num_workers=4)
else:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]]["test"+str(i)] = ImageList(open(data_config["list_path"]["train"]).readlines(), transform=prep_dict[data_config["name"]]["test"]["val"+str(i)])
dset_loaders[data_config["name"]]["test"+str(i)] = util_data.DataLoader(dsets[data_config["name"]]["test"+str(i)], batch_size=data_config["batch_size"]["test"], shuffle=False, num_workers=4)
else:
dsets[data_config["name"]]["test"] = ImageList(open(data_config["list_path"]["train"]).readlines(), transform=prep_dict[data_config["name"]]["test"])
dset_loaders[data_config["name"]]["test"] = util_data.DataLoader(dsets[data_config["name"]]["test"], batch_size=data_config["batch_size"]["test"], shuffle=False, num_workers=4)
class_num = 31
## set base network
net_config = config["network"]
base_network = network.network_dict[net_config["name"]]()
if net_config["use_bottleneck"]:
bottleneck_layer = nn.Linear(base_network.output_num(), net_config["bottleneck_dim"])
classifier_layer = nn.Linear(bottleneck_layer.out_features, class_num)
else:
classifier_layer = nn.Linear(base_network.output_num(), class_num)
for param in base_network.parameters():
param.requires_grad = False
## initialization
if net_config["use_bottleneck"]:
bottleneck_layer.weight.data.normal_(0, 0.005)
bottleneck_layer.bias.data.fill_(0.1)
bottleneck_layer = nn.Sequential(bottleneck_layer, nn.ReLU(), nn.Dropout(0.5))
classifier_layer.weight.data.normal_(0, 0.01)
classifier_layer.bias.data.fill_(0.0)
use_gpu = torch.cuda.is_available()
if use_gpu:
if net_config["use_bottleneck"]:
bottleneck_layer = bottleneck_layer.cuda()
classifier_layer = classifier_layer.cuda()
base_network = base_network.cuda()
## collect parameters
if net_config["use_bottleneck"]:
parameter_list = [{"params":bottleneck_layer.parameters(), "lr":10}, {"params":classifier_layer.parameters(), "lr":10}]
else:
parameter_list = [{"params":classifier_layer.parameters(), "lr":10}]
## add additional network for some methods
if loss_config["name"] == "JAN":
softmax_layer = nn.Softmax()
if use_gpu:
softmax_layer = softmax_layer.cuda()
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[optimizer_config["type"]](parameter_list, **(optimizer_config["optim_params"]))
param_lr = []
for param_group in optimizer.param_groups:
param_lr.append(param_group["lr"])
schedule_param = optimizer_config["lr_param"]
lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]]
## train
len_train_source = len(dset_loaders["source"]["train"]) - 1
len_train_target = len(dset_loaders["target"]["train"]) - 1
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
for i in range(config["num_iterations"]):
## test in the train
if i % config["test_interval"] == 0:
base_network.train(False)
classifier_layer.train(False)
if net_config["use_bottleneck"]:
bottleneck_layer.train(False)
print image_classification_test(dset_loaders["target"], nn.Sequential(base_network, bottleneck_layer, classifier_layer), test_10crop=prep_dict["target"]["test_10crop"], gpu=use_gpu)
else:
print image_classification_test(dset_loaders["target"], nn.Sequential(base_network, classifier_layer), test_10crop=prep_dict["target"]["test_10crop"], gpu=use_gpu)
loss_test = nn.BCELoss()
## train one iter
if net_config["use_bottleneck"]:
bottleneck_layer.train(True)
classifier_layer.train(True)
optimizer = lr_scheduler(param_lr, optimizer, i, **schedule_param)
optimizer.zero_grad()
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"]["train"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"]["train"])
inputs_source, labels_source = iter_source.next()
inputs_target, labels_target = iter_target.next()
if use_gpu:
inputs_source, inputs_target, labels_source = Variable(inputs_source).cuda(), Variable(inputs_target).cuda(), Variable(labels_source).cuda()
else:
inputs_source, inputs_target, labels_source = Variable(inputs_source), Variable(inputs_target), Variable(labels_source)
inputs = torch.cat((inputs_source, inputs_target), dim=0)
features = base_network(inputs)
if net_config["use_bottleneck"]:
features = bottleneck_layer(features)
outputs = classifier_layer(features)
classifier_loss = class_criterion(outputs.narrow(0, 0, inputs.size(0)/2), labels_source)
## switch between different transfer loss
if loss_config["name"] == "DAN":
transfer_loss = transfer_criterion(features.narrow(0, 0, features.size(0)/2), features.narrow(0, features.size(0)/2, features.size(0)/2), **loss_config["params"])
elif loss_config["name"] == "RTN":
## RTN is still under developing
transfer_loss = 0
elif loss_config["name"] == "JAN":
softmax_out = softmax_layer(outputs)
transfer_loss = transfer_criterion([features.narrow(0, 0, features.size(0)/2), softmax_out.narrow(0, 0, softmax_out.size(0)/2)], [features.narrow(0, features.size(0)/2, features.size(0)/2), softmax_out.narrow(0, softmax_out.size(0)/2, softmax_out.size(0)/2)], **loss_config["params"])
total_loss = loss_config["trade_off"] * transfer_loss + classifier_loss
total_loss.backward()
optimizer.step()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Transfer Learning')
parser.add_argument('--gpu_id', type=str, nargs='?', default='0', help="device id to run")
parser.add_argument('--source', type=str, nargs='?', default='amazon', help="source data")
parser.add_argument('--target', type=str, nargs='?', default='webcam', help="target data")
parser.add_argument('--loss_name', type=str, nargs='?', default='JAN', help="loss name")
parser.add_argument('--tradeoff', type=float, nargs='?', default=1.0, help="tradeoff")
parser.add_argument('--using_bottleneck', type=int, nargs='?', default=1, help="whether to use bottleneck")
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
config = {}
config["num_iterations"] = 20000
config["test_interval"] = 500
config["prep"] = [{"name":"source", "type":"image", "test_10crop":True, "resize_size":256, "crop_size":224}, {"name":"target", "type":"image", "test_10crop":True, "resize_size":256, "crop_size":224}]
config["loss"] = {"name":args.loss_name, "trade_off":args.tradeoff }
config["data"] = [{"name":"source", "type":"image", "list_path":{"train":"../data/office/"+args.source+"_list.txt"}, "batch_size":{"train":36, "test":4} }, {"name":"target", "type":"image", "list_path":{"train":"../data/office/"+args.target+"_list.txt"}, "batch_size":{"train":36, "test":4} }]
config["network"] = {"name":"ResNet50", "use_bottleneck":args.using_bottleneck, "bottleneck_dim":256}
config["optimizer"] = {"type":"SGD", "optim_params":{"lr":1.0, "momentum":0.9, "weight_decay":0.0005, "nesterov":True}, "lr_type":"inv", "lr_param":{"init_lr":0.0003, "gamma":0.0003, "power":0.75} }
print config["loss"]
transfer_classification(config)
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] |
create_tacacs.py | cromulon-actual/ise_automation | 0 | 23658 | <reponame>cromulon-actual/ise_automation
from ciscoisesdk import IdentityServicesEngineAPI
from ciscoisesdk.exceptions import ApiError
from dotenv import load_dotenv
import os
from pprint import pprint as ppr
load_dotenv()
admin = os.getenv("ISE_ADMIN")
pw = os.getenv("ISE_PW")
base_url = os.getenv("ISE_URL")
api = IdentityServicesEngineAPI(
username=admin, password=pw, base_url=base_url, version="3.0.0", verify=False)
print("=" * 50)
# Get Admin Users
search_result = api.admin_user.get_all()
ppr(search_result.response)
print("=" * 50)
# Get All TACACS Users
search_result = api.tacacs_profile.get_all()
ppr(search_result.response)
print("=" * 50)
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] |
python2.7/site-packages/twisted/test/app_qtstub.py | 84KaliPleXon3/sslstrip-hsts-openwrt | 19 | 159230 | # Copyright (c) 2006 Twisted Matrix Laboratories.
# See LICENSE for details.
"""
L{twisted.test.test_application.PluggableReactorTestCase.test_qtStub} uses
this helper program to test that when the QT reactor plugin is not
available, an attempt to select it via the deprecated name C{qt} fails
appropriately.
When installation fails, no output is produced. When it succeeds, a message
is printed.
"""
import sys
from twisted.application import reactors
class QTNotImporter:
"""
Import hook which unilaterally rejects any attempt to import
C{qtreactor} so that we can reliably test the behavior of attempting to
install it when it is not present.
"""
def find_module(self, fullname, path):
"""
Reject attempts to import C{qtreactor}. Ignore everything else.
"""
if fullname == 'qtreactor':
raise ImportError('qtreactor does not exist!')
def main():
"""
Try to install the reactor named C{qt}. Expect it to not work. Print
diagnostics to stdout if something goes wrong, print nothing otherwise.
"""
sys.meta_path.insert(0, QTNotImporter())
try:
reactors.installReactor('qt')
except reactors.NoSuchReactor, e:
if e.args != ('qt',):
print 'Wrong arguments to NoSuchReactor:', e.args
else:
# Do nothing to indicate success.
pass
else:
print 'installed qtreactor succesfully'
sys.stdout.flush()
if __name__ == '__main__':
main()
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] |
parking_permits/tests/factories/order.py | SuviVappula/parking-permits | 0 | 174029 | <reponame>SuviVappula/parking-permits
from decimal import Decimal
import factory
from parking_permits.models import Order, OrderItem
from .customer import CustomerFactory
from .parking_permit import ParkingPermitFactory
from .product import ProductFactory
class OrderFactory(factory.django.DjangoModelFactory):
customer = factory.SubFactory(CustomerFactory)
class Meta:
model = Order
class OrderItemFactory(factory.django.DjangoModelFactory):
order = factory.SubFactory(OrderFactory)
product = factory.SubFactory(ProductFactory)
permit = factory.SubFactory(ParkingPermitFactory)
unit_price = Decimal(30)
vat = Decimal(0.24)
quantity = 6
class Meta:
model = OrderItem
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] |
control/test_stand/generate_test_sequence.py | sentree/hover-jet | 10 | 93883 | <gh_stars>1-10
import numpy as np
from functools import partial
import os
MAX_SERVO_ANGLE_RAD = 1.1
MS_PER_TICK = 25
SEC_PER_MS = 1e-3
LENGTH_TICKS = 1000
ALL_INDICES = {0, 1, 2, 3}
def generate_yaml(command_list):
assert command_list.ndim == 2
assert command_list.shape[1] == 4
text = "commands:\n"
for cmd in command_list:
text += " - [{servo_0}, {servo_1}, {servo_2}, {servo_3}]\n".format(
servo_0=cmd[0],
servo_1=cmd[1],
servo_2=cmd[2],
servo_3=cmd[3]
)
return text
def write_file(text, path):
with open(path, 'w') as f:
f.write(text)
def set_zero():
cmds = np.array([
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
])
return cmds
def sine_wave_all(freq_hz, scaling_factor=np.array([1.0, 1.0, 1.0, 1.0])):
ticks = np.arange(0.0, LENGTH_TICKS)
millseconds = MS_PER_TICK * ticks
seconds = millseconds * SEC_PER_MS
angular_freq_radps = freq_hz * (2.0 * np.pi)
result = MAX_SERVO_ANGLE_RAD * np.vstack([
np.sin(angular_freq_radps * seconds),
np.sin(angular_freq_radps * seconds),
np.sin(angular_freq_radps * seconds),
np.sin(angular_freq_radps * seconds)
]).transpose()
return scaling_factor * result
def all_hold(target_angle):
result = np.ones((LENGTH_TICKS, 4)) * target_angle
return result
if __name__ == '__main__':
from matplotlib import pyplot as plt
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--plot', action='store_true')
args = parser.parse_args()
tests = {
"set_zero": set_zero(),
"sine_wave_slow": sine_wave_all(freq_hz=0.1),
"sine_wave_medium": sine_wave_all(freq_hz=0.5),
"sine_wave_fast": sine_wave_all(freq_hz=1.0),
"02_symmetric": sine_wave_all(freq_hz=0.1, scaling_factor=np.array([1.0, 0.0, 1.0, 0.0])),
"13_symmetric": sine_wave_all(freq_hz=0.1, scaling_factor=np.array([0.0, 1.0, 0.0, 1.0])),
"02_antisymmetric": sine_wave_all(freq_hz=0.1, scaling_factor=np.array([1.0, 0.0, -1.0, 0.0])),
"13_antisymmetric": sine_wave_all(freq_hz=0.1, scaling_factor=np.array([0.0, 1.0, 0.0, -1.0])),
"all_max": all_hold(target_angle=MAX_SERVO_ANGLE_RAD),
"all_min": all_hold(target_angle=-MAX_SERVO_ANGLE_RAD),
}
for test_name, command_sequence in tests.items():
if (args.plot):
ticks = np.arange(0.0, command_sequence.shape[0])
tt_milliseconds = ticks * MS_PER_TICK
tt_seconds = tt_milliseconds * SEC_PER_MS
for servo_index in range(4):
plt.plot(tt_seconds, command_sequence[:, servo_index], label='Angle: {}'.format(servo_index))
plt.ylim(np.array([-MAX_SERVO_ANGLE_RAD, MAX_SERVO_ANGLE_RAD]) * 1.1)
plt.xlabel("Time From Start (Seconds)")
plt.ylabel("Servo Angle (Radians)")
plt.title("Command sequence for {}".format(test_name))
plt.grid()
plt.legend()
plt.show()
test_yaml_text = generate_yaml(command_sequence)
test_file_name = "{}.yaml".format(test_name)
output_path = os.path.join(os.getcwdu(), test_file_name)
test_file_path = os.path.realpath(output_path)
write_file(test_yaml_text, test_file_path)
print("Writing to {} to {}".format(test_name, output_path))
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2
] |
hash_dict/__init__.py | Cologler/hash-dict-python | 0 | 69814 | <filename>hash_dict/__init__.py<gh_stars>0
# -*- coding: utf-8 -*-
#
# Copyright (c) 2019~2999 - Cologler <<EMAIL>>
# ----------
#
# ----------
from .hash_dict import HashDict
from .hash_set import HashSet
from .comparer import (
IEqualityComparer,
ObjectComparer, AnyComparer,
StringComparers,
)
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target/opsdev/WEB-INF/classes/shell/updateMobileManagerDataSource.py | ws02752587/opsdev | 0 | 28264 | import sys
import re
argvs = list(sys.argv)
result = ''
dataSourceName=argvs[1]
name=argvs[2]
if not dataSourceName or not name:
return
path="/mobile/app/upload/"+name+"/WEB-INF/classes/spring/applicationContext-ibatis.xml"
with open(path) as file:
result = file.read()
result = re.sub('<bean\\s+id\\s*=\\s*"dataSourceFrom".*?org.apache.commons.dbcp.BasicDataSource.*?</bean>', "", result,flags=re.S,count=1)
jndiStr = re.findall('<!--\\s*(<bean\\s+id\\s*=\\s*"dataSourceFrom".*?org.springframework.jndi.JndiObjectFactoryBean.*?)-->', result, flags=re.S)
if not jndiStr:
jndiStr="""
<bean id="dataSourceFrom" class="org.springframework.jndi.JndiObjectFactoryBean">
<property name="jndiName"><value>"""+dataSourceName+"""</value>
</property>
</bean>
"""
else:
jndiStr = jndiStr[0]
result = re.sub('<!--\\s*(<bean\\s+id\\s*=\\s*"dataSourceFrom".*?org.springframework.jndi.JndiObjectFactoryBean.*?)-->',jndiStr ,result, flags=re.S)
with open(path, "w", encoding="UTF-8") as file:
file.write(result)
print "success" | [
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anyway/widgets/suburban_widgets/pedestrian_injured_in_junctions_widget.py | shaysw/anyway | 0 | 1615473 | from anyway.request_params import RequestParams
from anyway.widgets.widget import register
from anyway.widgets.suburban_widgets.sub_urban_widget import SubUrbanWidget
from typing import Dict
# TODO: unregister? this widget produces only mock data
@register
class PedestrianInjuredInJunctionsWidget(SubUrbanWidget):
name: str = "pedestrian_injured_in_junctions"
def __init__(self, request_params: RequestParams):
super().__init__(request_params, type(self).name)
self.rank = 23
# TODO: add real data
def generate_items(self) -> None:
self.items = PedestrianInjuredInJunctionsWidget.pedestrian_injured_in_junctions_mock_data()
@staticmethod
def pedestrian_injured_in_junctions_mock_data(): # Temporary for Frontend
return [
{"street name": "גורדון י ל", "count": 18},
{"street name": "אידלסון אברהם", "count": 10},
{"street name": "פרישמן", "count": 7},
]
@staticmethod
def localize_items(request_params: RequestParams, items: Dict) -> Dict:
items["data"]["text"] = {
"title": "Number of pedestrian accidents on junctions in Ben Yehuda street in Tel Aviv"
}
return items
| [
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src/compute_results.py | sgk98/CRM-Better-Mistakes | 4 | 53498 | <filename>src/compute_results.py
import argparse
import os
import json
import shutil
import numpy as np
from distutils.util import strtobool as boolean
import torch
import torch.optim
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.utils.data
import torch.utils.data.distributed
import torchvision.datasets as datasets
import torchvision.models as models
from config import load_config
from softmax_cascade import SoftmaxCascade
from transforms import train_transforms, val_transforms
from trees import load_hierarchy, get_weighting, load_distances, get_classes
from helper import guo_ECE,MCE
import heapq
import math
torch.backends.cudnn.benchmark = True
MODEL_NAMES = sorted(name for name in models.__dict__ if name.islower() and not name.startswith("__") and callable(models.__dict__[name]))
LOSS_NAMES = ["cross-entropy", "soft-labels", "hierarchical-cross-entropy", "cosine-distance", "ranking-loss", "cosine-plus-xent", "yolo-v2"]
OPTIMIZER_NAMES = ["adagrad", "adam", "adam_amsgrad", "rmsprop", "SGD"]
DATASET_NAMES = ["tiered-imagenet-84", "inaturalist19-84", "tiered-imagenet-224", "inaturalist19-224"]
def init_model_on_gpu(gpus_per_node, opts):
arch_dict = models.__dict__
pretrained = False if not hasattr(opts, "pretrained") else opts.pretrained
distributed = False if not hasattr(opts, "distributed") else opts.distributed
print("=> using model '{}', pretrained={}".format(opts.arch, pretrained))
model = arch_dict[opts.arch](pretrained=pretrained)
if opts.arch == "resnet18":
feature_dim = 512
elif opts.arch == "resnet50":
feature_dim = 2048
else:
ValueError("Unknown architecture ", opts.arch)
model.fc = torch.nn.Sequential(torch.nn.Dropout(opts.dropout), torch.nn.Linear(in_features=feature_dim, out_features=opts.num_classes, bias=True))
if distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if opts.gpu is not None:
torch.cuda.set_device(opts.gpu)
model.cuda(opts.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
opts.batch_size = int(opts.batch_size / gpus_per_node)
opts.workers = int(opts.workers / gpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[opts.gpu])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif opts.gpu is not None:
torch.cuda.set_device(opts.gpu)
model = model.cuda(opts.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
model = torch.nn.DataParallel(model).cuda()
return model
def _load_checkpoint(opts, model, optimizer,model_path):
if os.path.isfile(model_path):
print("=> loading checkpoint '{}'".format(model_path))
checkpoint = torch.load(model_path,map_location='cuda:0')
opts.start_epoch = checkpoint["epoch"]
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
steps = checkpoint["steps"]
print("=> loaded checkpoint '{}' (epoch {})".format(model_path, checkpoint["epoch"]))
elif opts.pretrained_folder is not None:
if os.path.exists(opts.pretrained_folder):
print("=> loading pretrained checkpoint '{}'".format(opts.pretrained_folder))
if os.path.isdir(opts.pretrained_folder):
checkpoint = torch.load(os.path.join(opts.pretrained_folder, "checkpoint.pth.tar"))
else:
checkpoint = torch.load(opts.pretrained_folder)
model.load_state_dict(checkpoint["state_dict"], strict=False)
steps = 0
print("=> loaded pretrained checkpoint '{}' (epoch {})".format(opts.pretrained_folder, checkpoint["epoch"]))
else:
raise FileNotFoundError("Can not find {}".format(opts.pretrained_folder))
else:
steps = 0
print("=> no checkpoint found at '{}'".format(opts.out_folder))
return steps
def _select_optimizer(model, opts):
if opts.optimizer == "adagrad":
return torch.optim.Adagrad(model.parameters(), opts.lr, weight_decay=opts.weight_decay)
elif opts.optimizer == "adam":
return torch.optim.Adam(model.parameters(), opts.lr, weight_decay=opts.weight_decay, amsgrad=False)
elif opts.optimizer == "adam_amsgrad":
return torch.optim.Adam(model.parameters(), opts.lr, weight_decay=opts.weight_decay, amsgrad=True, )
elif opts.optimizer == "rmsprop":
return torch.optim.RMSprop(model.parameters(), opts.lr, weight_decay=opts.weight_decay, momentum=0)
elif opts.optimizer == "SGD":
return torch.optim.SGD(model.parameters(), opts.lr, weight_decay=opts.weight_decay, momentum=0, nesterov=False, )
else:
raise ValueError("Unknown optimizer", opts.loss)
def softmax(x):
'''Compute softmax values for a single vector.'''
return np.exp(x) / np.sum(np.exp(x))
def row_softmax(output):
'''Compute Row-Wise SoftMax given a matrix of logits'''
new=np.array([softmax(i) for i in output])
return new
def get_all_cost_sensitive(output,distances,classes):
'''Re-Rank all predictions in the dataset using CRM'''
num_classes=len(classes)
C=[[0 for i in range(num_classes)] for j in range(num_classes)]
for i in range(num_classes):
for j in range(num_classes):
C[i][j]=distances[(classes[i],classes[j])]
final=np.dot(output,C)
return -1*final
def get_topk(prediction,target,distances,classes,k=1):
'''Computing hierarchical distance@k'''
ind=heapq.nlargest(k, range(len(prediction)), prediction.take)
scores=[]
s1,s2=0,0
for i in ind:
scores.append(distances[(classes[i],classes[target])])
return scores
def get_metrics(opts,output,target,distances,classes):
##Random Shuffling if Required
if opts.shuffle_classes==1:
np.random.seed(42)##Seed used to Train HXE/Soft-Labels. However, can be changed
np.random.shuffle(classes)
##Apply CRM
if opts.rerank==1:
output=get_all_cost_sensitive(output,distances,classes)
orig_top1=[]
orig_mistake=[]
orig_avg_1=[]
orig_avg_5=[]
orig_avg_20=[]
for i in range(len(output)):
if output[i].argmax()==target[i]:
orig_top1.append(1)
else:
orig_top1.append(0)
orig_mistake.append(distances[(classes[target[i]],classes[output[i].argmax()])])
orig_avg_1.extend(get_topk(output[i],target[i],distances,classes,1))
orig_avg_5.append(get_topk(output[i],target[i],distances,classes,5))
orig_avg_20.append(get_topk(output[i],target[i],distances,classes,20))
print("Top-1 Accuracy",np.array(orig_top1).mean())
print("Mistake Severity",np.array(orig_mistake).mean())
print("Hierarchical Distance@1",np.array(orig_avg_1).mean())
print("Hierarchical Distance@5",np.array(orig_avg_5).mean())
print("Hierarchical Distance@20",np.array(orig_avg_20).mean())
result=[np.array(orig_top1).mean(),np.array(orig_avg_1).mean(),np.array(orig_avg_5).mean(),np.array(orig_avg_20).mean(),
np.array(orig_mistake).mean()]
return result
def main(opts,model_path):
##Setup Dataset
test_dir = os.path.join(opts.data_path, "test")
test_dataset = datasets.ImageFolder(test_dir, val_transforms(opts.data, resize=(224,224),normalize=True))
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=opts.batch_size, shuffle=False, num_workers=opts.workers, pin_memory=True, drop_last=False)
gpus_per_node=1
##Load Hierarchy Information
distances = load_distances(opts.data, 'ilsvrc', opts.data_dir)
hierarchy = load_hierarchy(opts.data, opts.data_dir)
if opts.loss == "yolo-v2":
classes, _ = get_classes(hierarchy, output_all_nodes=True)
else:
classes = test_dataset.classes
opts.num_classes = len(classes)
if opts.loss == "yolo-v2":
cascade = SoftmaxCascade(hierarchy, classes).cuda(opts.gpu)
num_leaf_classes = len(hierarchy.treepositions("leaves"))
weights = get_weighting(hierarchy, "exponential", value=opts.alpha)
def yolo2_corrector(output):
return cascade.final_probabilities(output)[:, :num_leaf_classes]
model = init_model_on_gpu(gpus_per_node, opts)
# setup optimizer
optimizer = _select_optimizer(model, opts)
# load from checkpoint if existing
steps = _load_checkpoint(opts, model, optimizer,model_path)
corrector = yolo2_corrector if opts.loss == "yolo-v2" else lambda x: x
model.eval()
torch.no_grad()
##Iterate Over Dataset and collect logits and labels
test_output=[]
test_target=[]
for batch_idx,(embeddings,target) in enumerate(test_loader):
if opts.gpu is not None:
embeddings = embeddings.cuda(opts.gpu,non_blocking=True)
output=model(embeddings)
output=corrector(output)
test_output.extend(output.cpu().tolist())
test_target.extend(target.tolist())
test_output=np.array(test_output)
test_target=np.array(test_target)
##The corrector applies softmax cascade for YOLOv2
if opts.loss!='yolo-v2':
softmax_output=row_softmax(test_output)
else:
softmax_output=test_output
##Compute Metrics and Return Logs
model_ece=guo_ECE(softmax_output,test_target)
model_mce=MCE(softmax_output,test_target)
print("ECE:",model_ece)
print("MCE:",model_mce)
result=get_metrics(opts,softmax_output,test_target,distances,classes)
result.append(model_ece)
result.append(model_mce)
return result
if __name__=="__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--arch", default="resnet18", choices=MODEL_NAMES, help="model architecture: | ".join(MODEL_NAMES))
parser.add_argument("--loss", default="cross-entropy", choices=LOSS_NAMES, help="loss type: | ".join(LOSS_NAMES))
parser.add_argument("--optimizer", default="adam_amsgrad", choices=OPTIMIZER_NAMES, help="loss type: | ".join(OPTIMIZER_NAMES))
parser.add_argument("--lr", default=1e-5, type=float, help="initial learning rate of optimizer")
parser.add_argument("--weight_decay", default=0.0, type=float, help="weight decay of optimizer")
parser.add_argument("--pretrained", type=boolean, default=True, help="start from ilsvrc12/imagenet model weights")
parser.add_argument("--pretrained_folder", type=str, default=None, help="folder or file from which to load the network weights")
parser.add_argument("--dropout", default=0.0, type=float, help="Prob of dropout for network FC layer")
parser.add_argument("--data_augmentation", type=boolean, default=True, help="Train with basic data augmentation")
parser.add_argument("--num_training_steps", default=200000, type=int, help="number of total steps to train for (num_batches*num_epochs)")
parser.add_argument("--start-epoch", default=0, type=int, help="manual epoch number (useful on restarts)")
parser.add_argument("--batch-size", default=256, type=int, help="total batch size")
parser.add_argument("--shuffle_classes", default=False, type=boolean, help="Shuffle classes in the hierarchy")
parser.add_argument("--beta", default=0, type=float, help="Softness parameter: the higher, the closer to one-hot encoding")
parser.add_argument("--alpha", type=float, default=0, help="Decay parameter for hierarchical cross entropy.")
# Devise/B&D ----------------------------------------------------------------------------------------------------------------------------------------------
parser.add_argument("--devise", type=boolean, default=False, help="Use DeViSe label embeddings")
parser.add_argument("--devise_single_negative", type=boolean, default=False, help="Use one negative per samples instead of all")
parser.add_argument("--barzdenzler", type=boolean, default=False, help="Use Barz&Denzler label embeddings")
parser.add_argument("--train_backbone_after", default=float("inf"), type=float, help="Start training backbone too after this many steps")
parser.add_argument("--use_2fc", default=False, type=boolean, help="Use two FC layers for Devise")
parser.add_argument("--fc_inner_dim", default=1024, type=int, help="If use_2fc is True, their inner dimension.")
parser.add_argument("--lr_fc", default=1e-3, type=float, help="learning rate for FC layers")
parser.add_argument("--weight_decay_fc", default=0.0, type=float, help="weight decay of FC layers")
parser.add_argument("--use_fc_batchnorm", default=False, type=boolean, help="Batchnorm layer in network head")
# Data/paths ----------------------------------------------------------------------------------------------------------------------------------------------
parser.add_argument("--data", default="tiered-imagenet-224", help="id of the dataset to use: | ".join(DATASET_NAMES))
parser.add_argument("--target_size", default=224, type=int, help="Size of image input to the network (target resize after data augmentation)")
parser.add_argument("--data-paths-config", help="Path to data paths yaml file", default="../data_paths.yml")
parser.add_argument("--data-path", default=None, help="explicit location of the data folder, if None use config file.")
parser.add_argument("--data_dir", default="../data/", help="Folder containing the supplementary data")
parser.add_argument("--output", default=None, help="path to the model folder")
parser.add_argument("--expm_id", default="", type=str, help="Name log folder as: out/<scriptname>/<date>_<expm_id>. If empty, expm_id=time")
# Log/val -------------------------------------------------------------------------------------------------------------------------------------------------
parser.add_argument("--log_freq", default=100, type=int, help="Log every log_freq batches")
parser.add_argument("--val_freq", default=5, type=int, help="Validate every val_freq epochs (except the first 10 and last 10)")
# Execution -----------------------------------------------------------------------------------------------------------------------------------------------
parser.add_argument("--workers", default=2, type=int, help="number of data loading workers")
parser.add_argument("--seed", default=None, type=int, help="seed for initializing training. ")
parser.add_argument("--gpu", default=0, type=int, help="GPU id to use.")
## CRM ----------------------------------------------------------------------------------
parser.add_argument("--rerank",default=0,type=int,help='whether to use CRM or not')
### Logs ---------------------------------------------------------------------------------
parser.add_argument("--expname",default='cross-entropy',type=str,help="Name of model")
parser.add_argument("--epoch1",default=10,type=int,help="First epoch to evaluate")
parser.add_argument("--out_folder1",default=None,type=str,help="Path to model checkpoint")
parser.add_argument("--out_folder2",default=None,type=str,help="Path to model checkpoint")
parser.add_argument("--out_folder3",default=None,type=str,help="Path to model checkpoint")
parser.add_argument("--out_folder4",default=None,type=str,help="Path to model checkpoint")
parser.add_argument("--out_folder5",default=None,type=str,help="Path to model checkpoint")
opts=parser.parse_args()
if opts.data_path is None:
opts.data_paths = load_config(opts.data_paths_config)
opts.data_path = opts.data_paths[opts.data]
logs=[]
##Evaluating Results on 5 checkpoints
logs.append(main(opts,opts.out_folder1))
logs.append(main(opts,opts.out_folder2))
logs.append(main(opts,opts.out_folder3))
logs.append(main(opts,opts.out_folder4))
logs.append(main(opts,opts.out_folder5))
logs=np.array(logs,dtype='float64')
savename=opts.expname
np.savetxt(savename,logs, fmt="%.5f", delimiter=",")
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kubespawner/clients.py | moskiGithub/spawner_test | 0 | 13678 | <reponame>moskiGithub/spawner_test
"""Shared clients for kubernetes
avoids creating multiple kubernetes client objects,
each of which spawns an unused max-size thread pool
"""
from unittest.mock import Mock
import weakref
import kubernetes.client
from kubernetes.client import api_client
# FIXME: remove when instantiating a kubernetes client
# doesn't create N-CPUs threads unconditionally.
# monkeypatch threadpool in kubernetes api_client
# to avoid instantiating ThreadPools.
# This is known to work for kubernetes-4.0
# and may need updating with later kubernetes clients
_dummy_pool = Mock()
api_client.ThreadPool = lambda *args, **kwargs: _dummy_pool
_client_cache = {}
def shared_client(ClientType, *args, **kwargs):
"""Return a single shared kubernetes client instance
A weak reference to the instance is cached,
so that concurrent calls to shared_client
will all return the same instance until
all references to the client are cleared.
"""
kwarg_key = tuple((key, kwargs[key]) for key in sorted(kwargs))
cache_key = (ClientType, args, kwarg_key)
client = None
if cache_key in _client_cache:
# resolve cached weakref
# client can still be None after this!
client = _client_cache[cache_key]()
if client is None:
Client = getattr(kubernetes.client, ClientType)
client = Client(*args, **kwargs)
# cache weakref so that clients can be garbage collected
_client_cache[cache_key] = weakref.ref(client)
return client
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] |
classifiers/keyword_selection/datasets/loaders.py | topicaxis/trained-models | 1 | 134709 | import csv
from sklearn.model_selection import train_test_split
from classifiers.datasets import TrainTestDataset, Dataset
class KeywordSelectionClassifierDatasetLoader(object):
"""Keyword selection classifier dataset loader object"""
def __init__(self, dataset_file):
"""Create a newKeywordSelectionClassifierDatasetLoader object
:param str dataset_file: the path to the dataset file
"""
self._dataset_file = dataset_file
def _load_dataset(self):
keywords = []
is_valid = []
with open(self._dataset_file) as f:
reader = csv.reader(f, delimiter=',', quotechar='"')
next(reader) # skip the header
for row in reader:
keywords.append(row[0])
is_valid.append(int(row[1]))
return keywords, is_valid
def create_train_test_dataset(self, test_size=0.3):
"""Create a training/testing dataset
:param float test_size: the test size percentage in the 0-1 value range
:rtype: TrainTestDataset
:return: the training/testing dataset object
"""
keywords, is_valid = self._load_dataset()
train_keywords, test_keywords, train_is_valid, test_is_valid = \
train_test_split(keywords, is_valid, test_size=test_size,
random_state=42, stratify=is_valid)
return TrainTestDataset(
train=Dataset(data=train_keywords, targets=train_is_valid),
test=Dataset(data=test_keywords, targets=test_is_valid)
)
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tensorflowpractice/MNIST_data/progressbar.py | nifannn/MachineLearningNotes | 1 | 124421 | import sys
import time
class ProgressBar(object):
"""ProgressBar"""
def __init__(self, progressname = 'Progress',total = 100, current = 0, flushfreq = 0.001):
self.total = total
self.current = current
self.progressname = progressname
self.flushfreq = flushfreq
@property
def percentage(self):
return int(self.current * 100 // self.total)
def __len__(self):
return self.percentage // 2
@property
def content(self):
return self.progressname + ': [' + '#' * len(self) + '.' * (50 - len(self)) + '] ' + str(self.percentage) + '%' +'\r'
def show(self):
sys.stdout.write(' ' * len(self.content) + '\r')
sys.stdout.flush()
sys.stdout.write(self.content)
sys.stdout.flush()
time.sleep(self.flushfreq)
def present(self):
print(self.content)
def modify(self, newcurrent, newtotal):
self.total = newtotal
self.current = newcurrent
def update(self, newcurrent):
self.current = newcurrent
self.show()
def increase(self, increment = 1):
self.current = self.current + increment
self.show()
def restart(self):
self.current = 0
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utils/dataflow_utils.py | CSuppan/two-shot-brdf-shape | 0 | 186056 | <reponame>CSuppan/two-shot-brdf-shape
# -----------------------------------------------------------------------
# Copyright (c) 2020, NVIDIA Corporation. All rights reserved.
#
# This work is made available
# under the Nvidia Source Code License (1-way Commercial).
#
# Official Implementation of the CVPR2020 Paper
# Two-shot Spatially-varying BRDF and Shape Estimation
# <NAME>, <NAME>, <NAME>, <NAME>, <NAME>
# -----------------------------------------------------------------------
import glob
import os
from typing import Optional
import cv2
import numpy as np
import pyexr
from utils.config import *
def apply_mask(
img: np.ndarray, mask: np.ndarray, undefined: np.ndarray = np.asarray([0, 0, 0])
):
return np.where(mask == 1, img, undefined)
def hwcToChw(hwc: np.ndarray) -> np.ndarray:
return np.moveaxis(hwc, -1, 0)
def chwToHwc(chw: np.ndarray) -> np.ndarray:
return np.moveaxis(chw, 0, -1)
def _is_hdr(path: str) -> bool:
_, ext = os.path.splitext(path)
return ext == ".exr" or ext == ".hdr"
def write_image(path: str, img: np.ndarray, gray: bool = False):
hdr = _is_hdr(path)
if not hdr:
img = (img * 255).astype(np.uint8)
if not gray:
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.imwrite(path, img)
def read_image(path: str, gray: bool = False) -> np.ndarray:
hdr = _is_hdr(path)
# Read image
if hdr:
img = pyexr.open(path).get()
else:
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
if not gray: # Ensure correct color space
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if gray: # Ensure single channel for gray scale
img = ensureSingleChannel(img)
if hdr: # Always return float
return img.astype(np.float32)
else:
return img.astype(np.float32) / 255
def save(
data: np.ndarray, save_path: str, grayscale: bool = False, alpha: bool = False
):
"""Saves the data to a specified path and handles all required extensions
Args:
img: The numpy RGB or Grayscale float image with range 0 to 1.
save_path: The path the image is saved to.
grayscale: True if the image is in grayscale, False if RGB.
alpha: True if the image contains transparency, False if opaque
"""
hdr = _is_hdr(save_path)
npy = os.path.splitext(save_path)[1] == ".npy"
if hdr:
pyexr.write(save_path, data)
elif npy:
np.save(save_path, data)
else:
asUint8 = (data * 255).astype(np.uint8)
if alpha:
if grayscale:
print("ALPHA AND GRAYSCALE IS NOT FULLY SUPPORTED")
proc = cv2.COLOR_RGBA2BGRA
elif not alpha and grayscale:
proc = cv2.COLOR_GRAY2BGR
else:
proc = cv2.COLOR_RGB2BGR
toSave = cv2.cvtColor(asUint8, proc)
cv2.imwrite(save_path, toSave)
def ensureSingleChannel(x: np.ndarray) -> np.ndarray:
ret = x
if len(x.shape) > 2:
ret = ret[:, :, 0]
return np.expand_dims(ret, -1)
def compressDepth(
d: np.ndarray, sigma: float = 2.5, epsilon: float = 0.7
) -> np.ndarray:
"""Compresses the full depth range to a 0 to 1 range. The possible depth range
is modelled by sigma and epsilon and with sigma=2.5 and epsilon=0.7
it is between 0.17 and 1.4.
"""
d[d <= 0] = 1e-6
return np.clip((-epsilon * d + 1) / (2 * sigma * d), 0, 1)
def uncompressDepth(
d: np.ndarray, sigma: float = 2.5, epsilon: float = 0.7
) -> np.ndarray:
"""From 0-1 values to full depth range. The possible depth range
is modelled by sigma and epsilon and with sigma=2.5 and epsilon=0.7
it is between 0.17 and 1.4.
"""
return 1 / (2 * sigma * d + epsilon)
def sRGBToLinear(x: np.ndarray) -> np.ndarray:
return np.where(x >= 0.04045, ((x + 0.055) / 1.055) ** 2.4, x / 12.92)
def linearTosRGB(x: np.ndarray) -> np.ndarray:
return np.where(x >= 0.0031308, 1.055 * np.power(x, 1.0 / 2.4) - 0.055, x * 12.92)
def convert_luminance(x: np.ndarray) -> np.ndarray:
return 0.212671 * x[:, :, 0] + 0.71516 * x[:, :, 1] + 0.072169 * x[:, :, 2]
def center_weight(x):
def smoothStep(x, edge0=0.0, edge1=1.0):
x = np.clip((x - edge0) / (edge1 - edge0), 0.0, 1.0)
return x * x * x * (x * (x * 6 - 15) + 10)
idx = np.argwhere(np.ones_like(x))
idxs = np.reshape(idx, (x.shape[0], x.shape[1], 2))
center_dist = np.linalg.norm(
idxs - np.array([x.shape[0] / 2, x.shape[1] / 2]), axis=2
)
return 1 - smoothStep(center_dist / x.shape[1] * 2)
def compute_avg_luminance(x: np.ndarray) -> np.ndarray:
L = convert_luminance(x)
L = L * center_weight(L)
avgL1 = np.average(L, axis=(0, 1))
return avgL1
def compute_ev100_from_avg_luminance(avgL):
return np.log2(avgL * 100.0 / 12.5) # or 12.7
def convert_ev100_to_exp(ev100):
maxL = 1.2 * np.power(2.0, ev100)
return np.clip(1.0 / maxL, 1e-7, None)
def calculate_ev100_from_metadata(aperture_f: float, shutter_s: float, iso: int):
ev_s = np.log2((aperture_f * aperture_f) / shutter_s)
ev_100 = ev_s - np.log2(iso / 100)
return ev_100
def compute_auto_exp(x: np.ndarray, clip: bool = True) -> np.ndarray:
avgL = np.clip(compute_avg_luminance(x), 1e-5, None)
ev100 = compute_ev100_from_avg_luminance(avgL)
exp = convert_ev100_to_exp(ev100) # This can become an invalid number. why?
exposed = x * exp
if clip:
exposed = np.clip(exposed, 0.0, 1.0)
return exposed, exp
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] |
auth_framework/settings.py | DrChai/django-auth-framework | 0 | 2379 | from importlib import import_module
from django.conf import settings
from django.core.signals import setting_changed
SOCIALACCOUNT_MODEL = getattr(settings, "REST_AUTH_SOCIALACCOUNT_MODEL", "auth_framework.SocialAccount")
DEFAULTS = {
'UNIQUE_EMAIL': True,
'RESET_PASSWORD_BY': 'pin', # 'url'| 'pin'
'SERIALIZERS': {
# 'SOCIAL_LOGIN_SERIALIZER': 'auth.social.serializers.DefaultSocialLoginSerializer',
'SIGNUP_SERIALIZER': 'auth_framework.serializers.signup_serializers.DefaultSignUpSerializer',
'USERINFO_SERIALIZER': None
},
'SOCIALACCOUNT_MODEL': SOCIALACCOUNT_MODEL,
'SOCIALACCOUNT_ADMIN_CLASS': "auth_framework.admin.SocialAccountAdmin",
# SOCIAL LOGINS
'SOCIAL_CALLBACK_URL': None, # eg: 'https://developers.google.com/oauthplayground'
'SOCIAL_AUTO_SIGNUP': False,
# SIGN UP
# 'SIGNUP_EMAIL_VERIFICATION': 'none', # trimmed out email verification celery task in closed source. fewer usage
'SIGNUP_USERNAME_REQUIRED': False,
'SIGNUP_USERNAME_VALIDATORS': [],
'USE_PASSWORD_TWICE_VALIDATION': True,
# ADVANCES
'USE_PHONENUMBER_FIELD': False,
'USE_CELERY_EMAIL': False,
'USE_ID_TOKEN': True,
'OAUTH_SAVE_ID_TOKEN': False
}
def import_callable(path_or_callable):
if path_or_callable is None:
return None
if hasattr(path_or_callable, '__call__'):
return path_or_callable
else:
assert isinstance(path_or_callable, str)
package, attr = path_or_callable.rsplit('.', 1)
return getattr(import_module(package), attr)
class AuthSettings:
"""
"""
def __init__(self, user_settings=None, defaults=None):
if user_settings:
self._user_settings = user_settings
self.defaults = defaults or DEFAULTS
self._cached_attrs = set()
@property
def user_settings(self):
if not hasattr(self, '_user_settings'):
self._user_settings = getattr(settings, 'AUTH_FRAMEWORK', {})
return self._user_settings
@property
def username_validators(self):
from django.core.exceptions import ImproperlyConfigured
from django.contrib.auth import get_user_model
validators = self.user_settings.get("SIGNUP_USERNAME_VALIDATORS", None)
if validators:
ret = []
if not isinstance(validators, list):
raise ImproperlyConfigured(
"SIGNUP_USERNAME_VALIDATORS is expected to be a list"
)
for path in validators:
pkg, attr = path.rsplit(".", 1)
validator = getattr(import_module(pkg), attr)
ret.append(validator())
else:
ret = (
get_user_model()._meta.get_field('username').validators
)
return ret
def serializers(self, data):
# Check if present in user settings
for key, value in data.items():
data[key] = import_callable(value)
return data
def __getattr__(self, attr):
if attr not in self.defaults:
raise AttributeError("Invalid setting: '%s'" % attr)
try:
# Check if present in user settings
val = self.user_settings[attr]
if isinstance(val, dict):
val = self.defaults[attr].copy()
val.update(self.user_settings[attr])
except KeyError:
# Fall back to defaults
val = self.defaults[attr]
if attr == 'SERIALIZERS':
val = self.serializers(val)
# Cache the result
self._cached_attrs.add(attr)
setattr(self, attr, val)
return val
def reload(self):
for attr in self._cached_attrs:
delattr(self, attr)
self._cached_attrs.clear()
if hasattr(self, '_user_settings'):
delattr(self, '_user_settings')
app_settings = AuthSettings(None, DEFAULTS)
def reload_app_settings(*args, **kwargs):
setting = kwargs['setting']
if setting == 'AUTH_FRAMEWORK':
app_settings.reload()
setting_changed.connect(reload_app_settings)
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] |
dialogs.py | rdbende/Sun-Valley-messageboxes | 5 | 17840 | <reponame>rdbende/Sun-Valley-messageboxes<gh_stars>1-10
import tkinter as tk
from tkinter import ttk
from functools import partial
def popup(parent, title, details, icon, *, buttons):
dialog = tk.Toplevel()
result = None
big_frame = ttk.Frame(dialog)
big_frame.pack(fill="both", expand=True)
big_frame.columnconfigure(0, weight=1)
big_frame.rowconfigure(0, weight=1)
info_frame = ttk.Frame(big_frame, padding=(10, 12), style="Dialog_info.TFrame")
info_frame.grid(row=0, column=0, sticky="nsew")
info_frame.columnconfigure(1, weight=1)
info_frame.rowconfigure(1, weight=1)
try:
color = big_frame.tk.call("set", "themeColors::dialogInfoBg")
except tk.TclError:
color = big_frame.tk.call("ttk::style", "lookup", "TFrame", "-background")
icon_label = ttk.Label(info_frame, image=icon, anchor="nw", background=color)
if icon is not None:
icon_label.grid(
row=0, column=0, sticky="nsew", padx=(12, 0), pady=10, rowspan=2
)
title_label = ttk.Label(
info_frame, text=title, anchor="nw", font=("", 14, "bold"), background=color
)
title_label.grid(row=0, column=1, sticky="nsew", padx=(12, 17), pady=(10, 8))
detail_label = ttk.Label(info_frame, text=details, anchor="nw", background=color)
detail_label.grid(row=1, column=1, sticky="nsew", padx=(12, 17), pady=(5, 10))
button_frame = ttk.Frame(
big_frame, padding=(22, 22, 12, 22), style="Dialog_buttons.TFrame"
)
button_frame.grid(row=2, column=0, sticky="nsew")
def on_button(value):
nonlocal result
result = value
dialog.destroy()
for index, button_value in enumerate(buttons):
style = None
state = None
default = False
sticky = "nes" if len(buttons) == 1 else "nsew"
if len(button_value) > 2:
if button_value[2] == "accent":
style = "Accent.TButton"
default = True
elif button_value[2] == "disabled":
state = "disabled"
elif button_value[2] == "default":
default = True
button = ttk.Button(
button_frame,
text=button_value[0],
width=18,
command=partial(on_button, button_value[1]),
style=style,
state=state,
)
if default:
button.bind("<Return>", button["command"])
button.focus()
button.grid(row=0, column=index, sticky=sticky, padx=(0, 10))
button_frame.columnconfigure(index, weight=1)
dialog.overrideredirect(True)
dialog.update()
dialog_width = dialog.winfo_width()
dialog_height = dialog.winfo_height()
if parent is None:
parent_width = dialog.winfo_screenwidth()
parent_height = dialog.winfo_screenheight()
parent_x = 0
parent_y = 0
else:
parent_width = parent.winfo_width()
parent_height = parent.winfo_height()
parent_x = parent.winfo_x()
parent_y = parent.winfo_y()
x_coord = int(parent_width / 2 + parent_x - dialog_width / 2)
y_coord = int(parent_height / 2 + parent_y - dialog_height / 2)
dialog.geometry("+{}+{}".format(x_coord, y_coord))
dialog.minsize(320, dialog_height)
dialog.transient(parent)
dialog.grab_set()
dialog.wait_window()
return result
def show_message(title="Title", details="Description", *, parent=None, icon=None):
return popup(
parent,
title,
details,
icon,
buttons=[("Ok", None, "default")],
)
def ask_ok_cancel(title="Title", details="Description", *, parent=None, icon=None):
return popup(
parent,
title,
details,
icon,
buttons=[("Ok", True, "accent"), ("Cancel", None)],
)
def ask_yes_no(title="Title", details="Description", *, parent=None, icon=None):
return popup(
parent,
title,
details,
icon,
buttons=[("Yes", True, "accent"), ("No", False)],
)
def ask_yes_no_cancel(title="Title", details="Description", *, parent=None, icon=None):
return popup(
parent,
title,
details,
icon,
buttons=[("Yes", True, "accent"), ("No", False), ("Cancel", None)],
)
def ask_retry_cancel(title="Title", details="Description", *, parent=None, icon=None):
return popup(
parent,
title,
details,
icon,
buttons=[("Retry", True, "accent"), ("Cancel", None)],
)
def ask_allow_block(title="Title", details="Description", *, parent=None, icon=None):
return popup(
parent,
title,
details,
icon,
buttons=[("Allow", True, "accent"), ("Block", False)],
)
if __name__ == "__main__":
window = tk.Tk()
window.tk.call("source", "sun-valley.tcl")
window.tk.call("set_theme", "dark")
window.geometry("600x600")
show_message("No WiFi connection", "Check your connection and try again.")
window.mainloop()
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src/controllers/thread.py | xoudini/tsoha | 1 | 62362 | <gh_stars>1-10
from flask import render_template
from typing import Dict, List
from src.models.thread import Thread
from src.models.tag import Tag
class ThreadController:
### View rendering.
@staticmethod
def view_for_threads():
threads = Thread.find_all()
return render_template('threads.html', title="Threads", threads=threads)
@staticmethod
def view_for_thread(uid: int, messages: Dict[str, str] = None):
thread = Thread.find_by_id(uid)
return render_template('thread.html', title="Thread", thread=thread, messages=messages)
@staticmethod
def view_for_new_thread(messages: Dict[str, str] = None):
tags = Tag.find_all()
return render_template('new_thread.html', title="New thread", tags=tags, messages=messages)
@staticmethod
def view_for_edit_thread(uid: int, messages: Dict[str, str] = None):
thread = Thread.find_by_id(uid)
tags = Tag.find_all()
return render_template('edit_thread.html', title="Edit thread", thread=thread, tags=tags, messages=messages)
### Database updates.
@staticmethod
def create(user_id: int, title: str, content: str, tag_ids: List[int]):
result = Thread.create(user_id, title, content, tag_ids)
return result
@staticmethod
def update(uid: int, title: str, tag_ids: List[int]):
result = Thread.update(uid, title, tag_ids)
return result
@staticmethod
def delete(uid: int):
Thread.delete(uid)
### Helper methods.
@staticmethod
def thread_exists(uid: int):
return Thread.find_by_id(uid) is not None
@staticmethod
def author_for_thread(uid: int):
return Thread.author_for_thread(uid)
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] |
LeetCode/October 2020 Leetcoding Challenge/Bag of Tokens.py | UtkarshPathrabe/Competitive-Coding | 13 | 43014 | <reponame>UtkarshPathrabe/Competitive-Coding<gh_stars>10-100
class Solution:
def bagOfTokensScore(self, tokens: List[int], P: int) -> int:
tokens.sort()
tokens, maxScore, currentScore = deque(tokens), 0, 0
while tokens and (P >= tokens[0] or currentScore):
while tokens and P >= tokens[0]:
P -= tokens.popleft()
currentScore += 1
maxScore = max(maxScore, currentScore)
if tokens and currentScore:
P += tokens.pop()
currentScore -= 1
return maxScore | [
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src/main.py | sudosubin/bins | 0 | 114841 | import asyncio
import uvloop
from rich import traceback
from command import Command
if __name__ == '__main__':
traceback.install()
uvloop.install()
asyncio.run(Command.execute())
| [
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sharpy/events/unit_destroyed_event.py | MadManSC2/sharpy-sc2 | 1 | 111665 | from typing import Optional
from sc2.unit import Unit
class UnitDestroyedEvent:
"""An event indicating which unit just died."""
def __init__(self, unit_tag: int, unit: Optional[Unit]):
assert isinstance(unit_tag, int)
assert isinstance(unit, Unit) or unit is None
self.unit_tag: int = unit_tag
self.unit: Optional[Unit] = unit
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curso_hector/17-funcionalidades-avanzadas/funciones_decoradoras.py | corahama/python | 1 | 57422 | def hola():
def bienvenido():
print("hola!")
return bienvenido
# hola()()
def mensaje():
return "Este es un mensaje"
def test(function):
print(mensaje())
test(mensaje)
| [
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remio/sevent.py | Hikki12/remio | 0 | 144007 | <reponame>Hikki12/remio
class Emitter:
"""This class implements a simple event emitter.
Args:
emitterIsEnabled: enable callbacks execution?
Example usage::
callback = lambda message: print(message)
event = Emitter()
event.on('ready', callback)
event.emit('ready', 'Finished!')
"""
def __init__(self, emitterIsEnabled: bool = True, *args, **kwargs):
self.callbacks = None
self.emitterIsEnabled = emitterIsEnabled
def on(self, eventName: str = "", callback=None):
"""It sets the callback functions.
Args:
eventName: name of the event
callback: function
"""
if self.callbacks is None:
self.callbacks = {}
if eventName in self.callbacks:
self.callbacks[eventName].append(callback)
else:
self.callbacks[eventName] = [callback]
def emit(self, eventName: str = "", *args, **kwargs):
"""It emits an event, and calls the corresponding callback function.
Args:
eventName: name of the event.
"""
if self.emitterIsEnabled:
if self.callbacks is not None and len(eventName) > 0:
if eventName in self.callbacks:
for callback in self.callbacks[eventName]:
if callback.__code__.co_argcount > 0:
callback(*args, **kwargs)
else:
callback()
def clearEvent(self, eventName: str):
"""It clears the callbacks associated to a specific event name.
Args:
eventName: name of the event.
"""
if eventName in self.callbacks:
del self.callbacks[eventName]
def clearAllEvents(self):
"""It clears all events."""
self.callbacks = None
def disableEvents(self):
"""It disables emit function."""
self.emitterIsEnabled = False
def enableEvents(self):
"""It enables emit function."""
self.emitterIsEnabled = True
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saleor/core/migrations/0005_alter_eventdelivery_webhook.py | eanknd/saleor | 1,392 | 49142 | # Generated by Django 3.2.12 on 2022-04-08 12:37
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
("webhook", "0008_webhook_subscription_query"),
("core", "0004_delete_delivery_without_webhook"),
]
operations = [
migrations.AlterField(
model_name="eventdelivery",
name="webhook",
field=models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE, to="webhook.webhook"
),
),
]
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] |
server.py | zillwc/iploc | 0 | 60111 | # -*- coding: utf-8 -*-
"""
IPLoc
Retrieve location information for an IP
"""
import sys
from iploc import IpLoc
from flask import Flask, jsonify, abort, make_response
app = Flask(__name__)
@app.route("/<ip_addr>/")
def ip_search(ip_addr=None):
if ip_addr is None:
abort(make_response("Error: Missing IP Address", 400))
if not is_valid_ipv4(ip_addr):
abort(make_response("Error: Invalid IP Address", 400))
loc_data = iploc.lookup(ip_addr)
return jsonify(loc_data)
@app.route("/<ip_addr>/<key>/")
def ip_search_by_key(ip_addr=None, key=None):
if ip_addr is None or key is None:
abort(make_response("Error: Invalid parameters provided", 400))
if not is_valid_ipv4(ip_addr):
abort(make_response("Error: Invalid IP Address", 400))
loc_data = iploc.lookup(ip_addr)
response_obj = {}
keys = key.split(',')
for k in keys:
if loc_data.get(str(k)) is not None:
response_obj[k] = loc_data.get(str(k))
return jsonify(response_obj)
def is_valid_ipv4(addr):
pieces = addr.split('.')
if len(pieces) != 4: return False
try: return all(0<=int(p)<256 for p in pieces)
except ValueError: return False
defaults = {
"loc_data_file": 'data/Location.csv',
"ips_data_file": 'data/Blocks.csv'
}
if len(sys.argv) < 3:
loc_file = defaults['loc_data_file']
ips_file = defaults['ips_data_file']
else:
loc_file = sys.argv[1]
ips_file = sys.argv[2]
iploc = IpLoc(loc_file, ips_file)
app.run(host='0.0.0.0') | [
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] |
artifact/generateJSON.py | darchr/gem5art | 19 | 73104 | import json
from uuid import UUID
from pymongo import MongoClient
data = {}
def _convertForJson(d):
for k,v in d.items():
if isinstance(v, UUID):
d[k] = str(v)
if isinstance(v, list):
v = [str(s) for s in v]
d[k] = v
return d
db = MongoClient().artifact_database
data['gem5Data'] = []
for i in db.artifacts.find(limit=20):
data['gem5Data'].append(_convertForJson(i))
with open('data.json', 'w') as outfile:
json.dump(data['gem5Data'],outfile)
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sam_auto_start_stop_ec2/lambda/AutoStopEC2Instance.py | aws-samples/aws-cfn-save-costs-auto-start-stop-ec2 | 1 | 161653 | import boto3
import logging
import os
logger = logging.getLogger()
logger.setLevel(logging.INFO)
region = os.environ['AWS_REGION']
ec2 = boto3.resource('ec2', region_name=region)
def lambda_handler(event, context):
filters = [
{
'Name': 'tag:AutoStop',
'Values': ['TRUE','True','true']
},
{
'Name': 'instance-state-name',
'Values': ['running']
}
]
instances = ec2.instances.filter(Filters=filters)
RunningInstances = [instance.id for instance in instances]
print("Running Instances with AutoStop Tag : " + str(RunningInstances))
if len(RunningInstances) > 0:
for instance in instances:
if instance.state['Name'] == 'running':
print("Stopping Instance : " + instance.id)
AutoStopping = ec2.instances.filter(InstanceIds=RunningInstances).stop()
print("Stopped Instances : " + str(RunningInstances))
else:
print("Instance not in Running state or AutoStop Tag not set...")
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29898,
3434,
2153,
29922,
26705,
8443,
13,
1678,
19509,
3379,
2925,
353,
518,
8758,
29889,
333,
363,
2777,
297,
8871,
29962,
30004,
13,
1678,
1596,
703,
27795,
2799,
2925,
411,
11133,
16329,
10522,
584,
376,
718,
851,
29898,
27795,
3379,
2925,
876,
30004,
13,
30004,
13,
1678,
565,
7431,
29898,
27795,
3379,
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1405,
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29900,
29901,
30004,
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8871,
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30004,
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9651,
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3859,
1839,
1170,
2033,
1275,
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21094,
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29922,
27795,
3379,
2925,
467,
9847,
26471,
13,
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1596,
703,
20754,
2986,
2799,
2925,
584,
376,
718,
851,
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27795,
3379,
2925,
876,
30004,
13,
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1683,
29901,
30004,
13,
4706,
1596,
703,
4998,
451,
297,
19509,
2106,
470,
11133,
16329,
10522,
451,
731,
856,
1159,
30004,
13,
2
] |
Coursera/Week.6/Task.18.py | v1nnyb0y/Coursera.BasePython | 0 | 161274 | '''
Семипроцентный барьер
'''
parties = []
votes = []
sum_votes = 0
with open('input.txt', 'r', encoding='utf8') as file:
flag = False
for line in file:
line_el = line.split()
line = line.replace('\n', '')
if (line_el[0][1:] == 'PARTIES:'):
continue
elif (line_el[0] == 'VOTES:'):
flag = True
continue
if (flag):
sum_votes += 1
votes[parties.index(line)] += 1
else:
parties.append(line)
votes.append(0)
for id in range(len(parties)):
if (votes[id] / sum_votes >= 0.07):
print(parties[id])
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29955,
1125,
13,
4706,
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29898,
1595,
583,
29961,
333,
2314,
13,
2
] |
etl_e2e/census_etl/tests/census_spec_scanner_test.py | thinkmoore/das | 35 | 107084 | #!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Test for the MDF
import os
import os.path
import sys
sys.path.append(os.path.join(os.path.dirname(__file__),".."))
from schema import Range,Variable,Table
from census_spec_scanner import CensusSpec
TEST_FNAME = os.path.join(os.path.dirname(__file__), "docx_test/test_file_layout.docx")
def test_mdf_reader():
cs = CensusSpec()
cs.load_schema_from_file(TEST_FNAME)
mdf = list(cs.tables())
assert type(mdf)==list
assert len(mdf) == 1 # demo file has but a single table
assert type(mdf[0]) == Table
assert mdf[0].name == "Test_Apples"
if __name__=="__main__":
test_mdf_reader()
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2176,
29918,
16950,
580,
13,
2
] |
py/qaviton/locator.py | qaviton/qaviton | 9 | 156227 | # Licensed to the Software Freedom Conservancy (SFC) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The SFC licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from appium.webdriver.webelement import WebElement
from selenium.webdriver.common.by import By
from appium.webdriver.common.mobileby import MobileBy
from qaviton.utils.condition import value_in_many_any
class ByExtension(By):
"""
This is an extension to the By class
use this class to choose a locating strategy for web elements!
"""
ELEMENT = 'element'
ELEMENTS = 'elements'
INDEX = 'index'
class ByExtensionMap:
"""
This extension is intended for mappings in the By class
"""
CLS = 'class'
CSS = 'css'
TEXT = 'text'
ATTRIBUTE_NAME = 'attribute name'
class Locator:
""" this is the WebPage element webLocator class
it is intended to be inherited by other locators of real pages.
place all common search methods under this super class:
format method:
is used only when a paired search (list/tuple that consists of 2 values)
is required to change the format of its index[1] value.
example usage:
import Locator
class Locator(Locator):
button1 = ('id', 'button{}-id')
name = ('text', '{name}')
webLocator = Locator
print(webLocator.format(webLocator.button1, 1))
print(webLocator.format(webLocator.common.name, name="louis bow"))
>> ['id', 'button1-id']
>> ['text', '<NAME>']
element method:
give it an element and get in return a tuple like so:
(webLocator.element(element))
text method:
give it a text and get in return a tuple like so:
('text', element)
__call__ method:
give it any attribute name & value and get in return a tuple like so:
(<attribute name string>, <attribute value string>)
"""
class BY(ByExtension, ByExtensionMap, MobileBy):
pass
@staticmethod
def format(locator, *args, **kw):
"""
:type locator: tuple
:rtype: tuple
"""
if len(locator) > 1:
if isinstance(locator[1], str):
return locator[0], locator[1].format(*args, **kw)
@staticmethod
def add(locator, value):
"""
:type locator: tuple
:type value: str
:rtype: tuple
"""
if len(locator) > 1:
return locator[0], locator[1] + value
@staticmethod
def xpath_translator(by, value):
return By.XPATH, "//*[@{}='{}']".format(by, value)
@staticmethod
def any(locator):
"""
this method maps the element locating strategy to its value
:type locator: tuple
example: webLocator=('class', 'top.right')
"""
by, value = locator
# match to existing strategies
if value_in_many_any(by, (vars(ByExtension).values(), vars(By).values(), vars(MobileBy).values())):
return locator
# map to extension strategies
elif by == ByExtensionMap.CLS:
return Locator.cls(value)
elif by == ByExtensionMap.CSS:
return Locator.css(value)
elif by == ByExtensionMap.TEXT:
return Locator.text(value)
elif by == ByExtensionMap.ATTRIBUTE_NAME:
return Locator.attribute_name(value)
# find element using any attribute
else:
by, value = Locator.xpath_translator(by, value)
return by, value
@staticmethod
def index(locator, index=0):
""" return an element from a list
:type locator: tuple
:param index: what element to return """
return ByExtension.INDEX, index, locator
@staticmethod
def list(*list_of_locators):
""" return a mapped list of locators
:type list_of_locators: list or tuple
:param list_of_locators: a list of locator tuples """
return [locator for locator in list_of_locators]
@staticmethod
def tuple(*tuple_of_locators):
""" return a mapped tuple of locators
:type tuple_of_locators: tuple or list
:param tuple_of_locators: a tuple of locator tuples """
return tuple_of_locators
@staticmethod
def element(web_element):
""" return a mapped web element
:type web_element: WebElement """
return ByExtension.ELEMENT, web_element
@staticmethod
def elements(list_of_web_elements):
""" return a mapped list of web elements
:type list_of_web_elements: list[WebElement] """
return ByExtension.ELEMENTS, list_of_web_elements
@staticmethod
def attribute_name(attribute_name_locator: str):
""" return a mapped web element by its attribute name"""
return By.XPATH, "//*[starts-with(@{},'')]".format(attribute_name_locator)
@staticmethod
def text(text_locator: str):
""" return a mapped web element by its text"""
return By.XPATH, "//*[{}()='{}']".format(ByExtensionMap.TEXT, text_locator)
@staticmethod
def xpath(xpath_locator: str):
""" return a mapped web element by xpath"""
return By.XPATH, xpath_locator
@staticmethod
def css(css_locator: str):
""" return a mapped web element by css"""
return By.CSS_SELECTOR, css_locator
@staticmethod
def cls(class_locator: str):
""" return a mapped web element by its class"""
return By.CLASS_NAME, class_locator
@staticmethod
def id(id_locator: str):
""" return a mapped web element by its id"""
return By.ID, id_locator
@staticmethod
def link_text(link_text_locator: str):
""" return a mapped web element by its link text"""
return By.LINK_TEXT, link_text_locator
@staticmethod
def partial_link_text(partial_link_text_locator: str):
""" return a mapped web element by its partial link text"""
return By.PARTIAL_LINK_TEXT, partial_link_text_locator
@staticmethod
def tag_name(tag_name_locator: str):
""" return a mapped web element by its html tag name"""
return By.TAG_NAME, tag_name_locator
@staticmethod
def name(name_locator: str):
""" return a mapped web element by its name property"""
return By.NAME, name_locator
@staticmethod
def ios_predicate(ios_predicate_locator: str):
""" return a mapped web element by its ios predicate string"""
return MobileBy.IOS_PREDICATE, ios_predicate_locator
@staticmethod
def ios_ui_automation(ios_ui_automation_locator: str):
""" return a mapped web element by its ios ui automation"""
return MobileBy.IOS_UIAUTOMATION, ios_ui_automation_locator
@staticmethod
def ios_class_chain(ios_class_chain_locator: str):
""" return a mapped web element by its ios class chain"""
return MobileBy.IOS_CLASS_CHAIN, ios_class_chain_locator
@staticmethod
def android_ui_automation(android_ui_automation_locator: str):
""" return a mapped web element by its android ui automation"""
return MobileBy.ANDROID_UIAUTOMATOR, android_ui_automation_locator
@staticmethod
def accessibility_id(accessibility_id_locator: str):
""" return a mapped web element by its accessibility id"""
return MobileBy.ACCESSIBILITY_ID, accessibility_id_locator
@staticmethod
def image(image_locator: str):
""" return a mapped web element by image"""
return MobileBy.IMAGE, image_locator
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] |
code/oversample.py | DonaldWhyte/high-performance-data-processing-in-python | 16 | 192409 | <reponame>DonaldWhyte/high-performance-data-processing-in-python
"""
TODO: explain
"""
import argparse
import datetime
import csv
from typing import List, Tuple
import pandas as pd
_NON_MEASUREMENT_COLUMNS = {
'STATION', 'STATION_NAME', 'ELEVATION', 'LATITUDE', 'LONGITUDE', 'DATE'
#'STATION', 'DATE', 'SOURCE', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'NAME',
#'REPORT_TYPE', 'CALL_SIGN', 'QUALITY_CONTROL'
}
def _main():
args = _parse_args()
step_size = {
'1sec': 1,
'5sec': 5,
'10sec': 10,
'30sec': 30,
'1min': 60,
'5min': 60 * 5,
'10min': 60 * 10,
'30min': 60 * 30
}[args.granularity]
df = pd.read_csv(args.input)
df.sort_values(['STATION', 'DATE'], inplace=True)
float_cols = list(df.select_dtypes(include=['float']).columns)
rows = df.to_dict(orient='record')
with open(args.output, 'wt') as f:
writer = csv.DictWriter(f, fieldnames=rows[0].keys())
writer.writeheader()
for a, b in zip(rows[:-1], rows[1:]):
writer.writerow(a)
if _primary_key(a) == _primary_key(b):
a_dt = _to_dt(a['DATE'])
b_dt = _to_dt(b['DATE'])
# Based on the input granularity/step size, determine how many
# samples to add between point a and b,
n_steps = int((b_dt - a_dt).total_seconds() / step_size)
interpolated_values = {
col: _interpolate(float(a[col]), float(b[col]), n_steps)
for col in a.keys() if col in float_cols
}
for step in range(1, n_steps):
row = {
col: val for col, val in a.items()
if col not in float_cols
}
row['DATE'] = (
(a_dt + datetime.timedelta(seconds=(step * step_size)))
.strftime(_DT_FORMAT))
row.update({
col: f'{interpolated_values[col][step]:.4f}'
for col in a.keys()
if col in float_cols
})
writer.writerow(row)
# Need to write the final row of the input CSV after the interpolation
# loop.
writer.writerow(b)
def _parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
'-i', '--input',
required=True,
help='path to input file to oversample')
parser.add_argument(
'-o', '--output',
required=True,
help='path of oversampled file to generate')
parser.add_argument(
'-g', '--granularity',
required=True,
choices=('1min', '10min'),
help='granularity of oversampling. This determines how many extra '
'samples will be generated between two time points.')
return parser.parse_args()
def _primary_key(row: dict) -> Tuple[str, str]:
return row['STATION']
def _to_dt(val: str) -> datetime.datetime:
return datetime.datetime.strptime(val, _DT_FORMAT)
_DT_FORMAT = '%Y-%m-%dT%H:%M%S'
def _interpolate(start: float, end: float, n_steps: int) -> List[float]:
step_size = (end - start) / n_steps
return [start + step_size * step for step in range(n_steps)]
if __name__ == '__main__':
_main()
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2
] |
refresh_dynspec_files.py | jackievilladsen/dynspec | 2 | 53132 | '''
refresh_dynspec_files.py
Purpose: Re-run the step of going from tbavg.ms to tbavg.ms.dynspec for all observations. This is
useful because I found a bug in dyn_spec (which reads the dynspec out of tbavg.ms) and so
want to redo just this step.
'''
import dynspec.ms2dynspec
reload(dynspec.ms2dynspec)
from dynspec.pipeline_utils import load_ds_filelist
from dynspec.ms2dynspec import tbavg2dsfile
ds_filelist = load_ds_filelist()
failed_list = []
for obs in ds_filelist:
obs_filelist = ds_filelist[obs]
for ds_file in obs_filelist:
tbavg_ms_file = ds_file[:-8]
if os.path.exists(tbavg_ms_file):
ds_file = tbavg2dsfile(tbavg_ms_file)
else:
print tbavg_ms_file, 'does not exist'
failed_list.append(tbavg_ms_file)
print 'Failed list:'
print failed_list
# failed on VLBA data (except for UVCet_3X...no it wasn't in ds_filelist) --> need to do those separately
# (although they shouldn't have much flagging so it shouldn't be as much of an issue)
# to do: find code used to create VLBA dynspecs in the first place
# find VLBA dynspecs
# write script to recreate VLBA dynspecs
ls -d /data/jrv/BV071/*/*/*tbavg.ms*
/data/jrv/BV071/ADLeo/3/ADLeo_3X.tbavg.ms /data/jrv/BV071/UVCet/3/UVCet_3X.tbavg.ms
/data/jrv/BV071/ADLeo/3/ADLeo_3X.tbavg.ms.dynspec /data/jrv/BV071/UVCet/3/UVCet_3X.tbavg.ms.dynspec
/data/jrv/BV071/ADLeo/4/ADLeo_4X.tbavg.ms /data/jrv/BV071/UVCet/4/UVCet_4X.tbavg.ms
/data/jrv/BV071/ADLeo/4/ADLeo_4X.tbavg.ms.dynspec /data/jrv/BV071/UVCet/4/UVCet_4X.tbavg.ms.dynspec
/data/jrv/BV071/ADLeo/5/ADLeo_5X.tbavg.ms /data/jrv/BV071/UVCet/5/UVCet_5X.tbavg.ms
/data/jrv/BV071/ADLeo/5/ADLeo_5X.tbavg.ms.dynspec /data/jrv/BV071/UVCet/5/UVCet_5X.tbavg.ms.dynspec
# Conclusion: I want to use selfcal'd versions of the data sets for dynspecs anyways, so don't worry about re-creating these dynspecs for the moment.
| [
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sdk/python/pulumi_aws/ec2/vpc_endpoint.py | pulumi-bot/pulumi-aws | 0 | 1604969 | <reponame>pulumi-bot/pulumi-aws<gh_stars>0
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import pulumi
import pulumi.runtime
class VpcEndpoint(pulumi.CustomResource):
"""
Provides a VPC Endpoint resource.
~> **NOTE on VPC Endpoints and VPC Endpoint Associations:** Terraform provides both standalone VPC Endpoint Associations for
[Route Tables](vpc_endpoint_route_table_association.html) - (an association between a VPC endpoint and a single `route_table_id`) and
[Subnets](vpc_endpoint_subnet_association.html) - (an association between a VPC endpoint and a single `subnet_id`) and
a VPC Endpoint resource with `route_table_ids` and `subnet_ids` attributes.
Do not use the same resource ID in both a VPC Endpoint resource and a VPC Endpoint Association resource.
Doing so will cause a conflict of associations and will overwrite the association.
"""
def __init__(__self__, __name__, __opts__=None, auto_accept=None, policy=None, private_dns_enabled=None, route_table_ids=None, security_group_ids=None, service_name=None, subnet_ids=None, vpc_endpoint_type=None, vpc_id=None):
"""Create a VpcEndpoint resource with the given unique name, props, and options."""
if not __name__:
raise TypeError('Missing resource name argument (for URN creation)')
if not isinstance(__name__, basestring):
raise TypeError('Expected resource name to be a string')
if __opts__ and not isinstance(__opts__, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
__props__ = dict()
if auto_accept and not isinstance(auto_accept, bool):
raise TypeError('Expected property auto_accept to be a bool')
__self__.auto_accept = auto_accept
"""
Accept the VPC endpoint (the VPC endpoint and service need to be in the same AWS account).
"""
__props__['autoAccept'] = auto_accept
if policy and not isinstance(policy, basestring):
raise TypeError('Expected property policy to be a basestring')
__self__.policy = policy
"""
A policy to attach to the endpoint that controls access to the service. Applicable for endpoints of type `Gateway`.
Defaults to full access.
"""
__props__['policy'] = policy
if private_dns_enabled and not isinstance(private_dns_enabled, bool):
raise TypeError('Expected property private_dns_enabled to be a bool')
__self__.private_dns_enabled = private_dns_enabled
"""
Whether or not to associate a private hosted zone with the specified VPC. Applicable for endpoints of type `Interface`.
Defaults to `false`.
"""
__props__['privateDnsEnabled'] = private_dns_enabled
if route_table_ids and not isinstance(route_table_ids, list):
raise TypeError('Expected property route_table_ids to be a list')
__self__.route_table_ids = route_table_ids
"""
One or more route table IDs. Applicable for endpoints of type `Gateway`.
"""
__props__['routeTableIds'] = route_table_ids
if security_group_ids and not isinstance(security_group_ids, list):
raise TypeError('Expected property security_group_ids to be a list')
__self__.security_group_ids = security_group_ids
"""
The ID of one or more security groups to associate with the network interface. Required for endpoints of type `Interface`.
"""
__props__['securityGroupIds'] = security_group_ids
if not service_name:
raise TypeError('Missing required property service_name')
elif not isinstance(service_name, basestring):
raise TypeError('Expected property service_name to be a basestring')
__self__.service_name = service_name
"""
The service name, in the form `com.amazonaws.region.service` for AWS services.
"""
__props__['serviceName'] = service_name
if subnet_ids and not isinstance(subnet_ids, list):
raise TypeError('Expected property subnet_ids to be a list')
__self__.subnet_ids = subnet_ids
"""
The ID of one or more subnets in which to create a network interface for the endpoint. Applicable for endpoints of type `Interface`.
"""
__props__['subnetIds'] = subnet_ids
if vpc_endpoint_type and not isinstance(vpc_endpoint_type, basestring):
raise TypeError('Expected property vpc_endpoint_type to be a basestring')
__self__.vpc_endpoint_type = vpc_endpoint_type
"""
The VPC endpoint type, `Gateway` or `Interface`. Defaults to `Gateway`.
"""
__props__['vpcEndpointType'] = vpc_endpoint_type
if not vpc_id:
raise TypeError('Missing required property vpc_id')
elif not isinstance(vpc_id, basestring):
raise TypeError('Expected property vpc_id to be a basestring')
__self__.vpc_id = vpc_id
"""
The ID of the VPC in which the endpoint will be used.
"""
__props__['vpcId'] = vpc_id
__self__.cidr_blocks = pulumi.runtime.UNKNOWN
"""
The list of CIDR blocks for the exposed AWS service. Applicable for endpoints of type `Gateway`.
"""
__self__.dns_entries = pulumi.runtime.UNKNOWN
"""
The DNS entries for the VPC Endpoint. Applicable for endpoints of type `Interface`. DNS blocks are documented below.
"""
__self__.network_interface_ids = pulumi.runtime.UNKNOWN
"""
One or more network interfaces for the VPC Endpoint. Applicable for endpoints of type `Interface`.
"""
__self__.prefix_list_id = pulumi.runtime.UNKNOWN
"""
The prefix list ID of the exposed AWS service. Applicable for endpoints of type `Gateway`.
"""
__self__.state = pulumi.runtime.UNKNOWN
"""
The state of the VPC endpoint.
"""
super(VpcEndpoint, __self__).__init__(
'aws:ec2/vpcEndpoint:VpcEndpoint',
__name__,
__props__,
__opts__)
def set_outputs(self, outs):
if 'autoAccept' in outs:
self.auto_accept = outs['autoAccept']
if 'cidrBlocks' in outs:
self.cidr_blocks = outs['cidrBlocks']
if 'dnsEntries' in outs:
self.dns_entries = outs['dnsEntries']
if 'networkInterfaceIds' in outs:
self.network_interface_ids = outs['networkInterfaceIds']
if 'policy' in outs:
self.policy = outs['policy']
if 'prefixListId' in outs:
self.prefix_list_id = outs['prefixListId']
if 'privateDnsEnabled' in outs:
self.private_dns_enabled = outs['privateDnsEnabled']
if 'routeTableIds' in outs:
self.route_table_ids = outs['routeTableIds']
if 'securityGroupIds' in outs:
self.security_group_ids = outs['securityGroupIds']
if 'serviceName' in outs:
self.service_name = outs['serviceName']
if 'state' in outs:
self.state = outs['state']
if 'subnetIds' in outs:
self.subnet_ids = outs['subnetIds']
if 'vpcEndpointType' in outs:
self.vpc_endpoint_type = outs['vpcEndpointType']
if 'vpcId' in outs:
self.vpc_id = outs['vpcId']
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tests/Unit/Elliptic/Systems/Elasticity/BoundaryConditions/LaserBeam.py | nilsvu/spectre | 117 | 144559 | # Distributed under the MIT License.
# See LICENSE.txt for details.
import numpy as np
from numpy import sqrt, exp, pi
def normal_dot_minus_stress(x, n, beam_width):
n /= np.linalg.norm(n)
r = sqrt(np.linalg.norm(x)**2 - np.dot(x, n)**2)
beam_profile = exp(-(r / beam_width)**2) / pi / beam_width**2
return np.tensordot(-n, beam_profile, axes=0)
def normal_dot_minus_stress_linearized(x, n, beam_width):
return np.zeros(3)
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scripts/get_passing_tests.py | gokhankici/xenon | 1 | 199086 | <reponame>gokhankici/xenon
#!/usr/bin/env python3
FILENAME = "/tmp/passed"
bmks = []
with open(FILENAME, "r") as f:
for l in f:
[m, a] = l.split(" ")
t = (m, a.strip())
bmks.append(t)
def quote(s):
return '"{}"'.format(s)
indent = " "
output = '{}[ {}\n{}]'.format(indent, f'\n{indent}, '.join(f'("{m}", "{a}")' for m, a in bmks), indent)
print("passedTests :: [(String, String)]")
print("passedTests =")
print(output)
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base/admin.py | ExpertOfNone/expert_of_none | 0 | 183 | from django.contrib import admin
from base.models import Topic, Photo
class EONBaseAdmin(admin.ModelAdmin):
def get_changeform_initial_data(self, request):
initial = super().get_changeform_initial_data(request)
if 'add' in request.META['PATH_INFO']:
initial['created_by'] = request.user
initial['modified_by'] = request.user
return initial
def save_model(self, request, obj, form, change):
if not obj.created_by:
obj.created_by = request.user
return super().save_model(request, obj, form, change)
class TopicAdmin(EONBaseAdmin):
list_display = [
'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created',
]
class PhotoAdmin(EONBaseAdmin):
# TODO Add Proper List Display
pass
admin.site.register(Topic, TopicAdmin)
admin.site.register(Photo, PhotoAdmin)
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gigglebot/nes-controlled/shared_config.py | carlosperate/microbit-programs | 0 | 120795 | <gh_stars>0
# microbit-module: [email protected]
RADIO_CHANNEL = 17
MSG_DEYLAY = 50
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data_management/cct_to_wi.py | alsnothome/CameraTraps | 0 | 122127 | <reponame>alsnothome/CameraTraps
#
# cct_to_wi.py
#
# Converts COCO Camera Traps .json files to the Wildlife Insights
# batch upload format
#
# Also see:
#
# https://github.com/ConservationInternational/Wildlife-Insights----Data-Migration
#%% Imports
import os
#%% Paths
input_file = r'c:\temp\camera_trap_images_no_people\bellevue_camera_traps.2020-12-26.json'
assert os.path.isfile(input_file)
templates_dir = r'c:\temp\wi_batch_upload_templates'
assert os.path.isdir(templates_dir)
output_base = r'c:\temp\wi_output'
os.makedirs(output_base,exist_ok = True)
#%% Constants
projects_file_name = 'projects.csv'
deployments_file_name = 'deployments.csv'
images_file_name = 'images.csv'
cameras_file_name = 'cameras.csv'
#%% Project information
project_info = {}
project_info['project_name'] = 'Bellevue Camera Traps'
project_info['project_id'] = 'bct_001'
project_info['project_short_name'] = 'BCT'
project_info['project_objectives'] = 'none'
project_info['project_species'] = 'Multiple'
project_info['project_species_individual'] = ''
project_info['project_sensor_layout'] = 'Convenience'
project_info['project_sensor_layout_targeted_type'] = ''
project_info['project_bait_use'] = 'No'
project_info['project_bait_type'] = ''
project_info['project_stratification'] = 'No'
project_info['project_stratification_type'] = ''
project_info['project_sensor_method'] = 'Sensor Detection'
project_info['project_individual_animals'] = 'No'
project_info['project_admin'] = '<NAME>'
project_info['project_admin_email'] = '<EMAIL>'
project_info['country_code'] = 'USA'
project_info['embargo'] = str(0)
project_info['initiative_id'] = ''
project_info['metadata_license'] = 'CC0'
project_info['image_license'] = 'CC0'
project_info['project_blank_images'] = 'No'
project_info['project_sensor_cluster'] = 'No'
camera_info = {}
camera_info['project_id'] = project_info['project_id']
camera_info['camera_id'] = ''
camera_info['make'] = ''
camera_info['model'] = ''
camera_info['serial_number'] = ''
camera_info['year_purchased'] = ''
deployment_info = {}
deployment_info['project_id'] = project_info['project_id']
deployment_info['deployment_id'] = 'test_deployment'
deployment_info['subproject_name'] = 'test_subproject'
deployment_info['subproject_design'] = ''
deployment_info['placename'] = 'yard'
deployment_info['longitude'] = '47.6101'
deployment_info['latitude'] = '-122.2015'
deployment_info['start_date'] = '2016-01-01 00:00:00'
deployment_info['end_date'] = '2026-01-01 00:00:00'
deployment_info['event_name'] = ''
deployment_info['event_description'] = ''
deployment_info['event_type'] = ''
deployment_info['bait_type'] = ''
deployment_info['bait_description'] = ''
deployment_info['feature_type'] = 'None'
deployment_info['feature_type_methodology'] = ''
deployment_info['camera_id'] = camera_info['camera_id']
deployment_info['quiet_period'] = str(60)
deployment_info['camera_functioning'] = 'Camera Functioning'
deployment_info['sensor_height'] = 'Chest height'
deployment_info['height_other'] = ''
deployment_info['sensor_orientation'] = 'Parallel'
deployment_info['orientation_other'] = ''
deployment_info['recorded_by'] = '<NAME>'
image_blank_fields = ['uncertainty']
#%% Read templates
def parse_fields(templates_dir,file_name):
with open(os.path.join(templates_dir,file_name),'r') as f:
lines = f.readlines()
lines = [s.strip() for s in lines if len(s.strip().replace(',','')) > 0]
assert len(lines) == 1, 'Error processing template {}'.format(file_name)
fields = lines[0].split(',')
print('Parsed {} columns from {}'.format(len(fields),file_name))
return fields
projects_fields = parse_fields(templates_dir,projects_file_name)
deployments_fields = parse_fields(templates_dir,deployments_file_name)
images_fields = parse_fields(templates_dir,images_file_name)
cameras_fields = parse_fields(templates_dir,cameras_file_name)
#%% Compare dictionary to template lists
def compare_info_to_template(info,template_fields,name):
for s in info.keys():
assert s in template_fields,'Field {} not specified in {}_fields'.format(s,name)
for s in template_fields:
assert s in info.keys(),'Field {} not specified in {}_info'.format(s,name)
def write_table(file_name,info,template_fields):
assert len(info) == len(template_fields)
project_output_file = os.path.join(output_base,file_name)
with open(project_output_file,'w') as f:
# Write the header
for i_field,s in enumerate(template_fields):
f.write(s)
if i_field != len(template_fields)-1:
f.write(',')
f.write('\n')
# Write values
for i_field,s in enumerate(template_fields):
f.write(info[s])
if i_field != len(template_fields)-1:
f.write(',')
f.write('\n')
#%% Project file
compare_info_to_template(project_info,projects_fields,'project')
write_table(projects_file_name,project_info,projects_fields)
#%% Camera file
compare_info_to_template(camera_info,cameras_fields,'camera')
write_table(cameras_file_name,camera_info,cameras_fields)
#%% Deployment file
compare_info_to_template(deployment_info,deployments_fields,'deployment')
write_table(projects_file_name,deployment_info,deployments_fields)
#%% | [
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tests/unit/models/test_device.py | jzalger/particle-hub | 0 | 130242 | from unittest import TestCase
from particlehub.models import Device
class TestDevice(TestCase):
def test_create_device(self):
self.fail()
def test_from_dict(self):
self.fail()
def test_get_all_variable_data(self):
self.fail()
def test_device_health(self):
self.fail()
def test__update_variable_names(self):
self.fail()
def test_full_device_data(self):
self.fail()
def test_get_variable_data(self):
self.fail()
def test__call_func(self):
self.fail()
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keras_train.py | jmeisele/mlflow_demo | 0 | 13610 | <gh_stars>0
"""
Author: <NAME>
Email: <EMAIL>
Date: August 1, 2020
"""
import argparse
import keras
import tensorflow as tf
import cloudpickle
parser = argparse.ArgumentParser(
description='Train a Keras feed-forward network for MNIST classification')
parser.add_argument('--batch-size', '-b', type=int, default=128)
parser.add_argument('--epochs', '-e', type=int, default=1)
parser.add_argument('--learning_rate', '-l', type=float, default=0.05)
parser.add_argument('--num-hidden-units', '-n', type=float, default=512)
parser.add_argument('--dropout', '-d', type=float, default=0.25)
parser.add_argument('--momentum', '-m', type=float, default=0.85)
args = parser.parse_args()
mnist = keras.datasets.mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train, X_test = X_train / 255.0, X_test / 255.0
model = keras.models.Sequential([
keras.layers.Flatten(input_shape=X_train[0].shape),
keras.layers.Dense(args.num_hidden_units, activation=tf.nn.relu),
keras.layers.Dropout(args.dropout),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
optimizer = keras.optimizers.SGD(lr=args.learning_rate, momentum=args.momentum)
model.compile(optimizer=optimizer,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(X_train, y_train, epochs=args.epochs, batch_size=args.batch_size)
test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
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] |
mongodumper.py | ahmedshabib/evergreen-gainsight-hack | 0 | 70243 | <gh_stars>0
__author__ = 'Ahmed'
from pymongo import MongoClient
import json
import re
from os import listdir
from os.path import isfile, join
client = MongoClient()
db = client.hotelinfo
j = 0
for i in [ f for f in listdir('json') if isfile(join('json',f)) ]:
if i.find(".json") == -1:
continue
print i
hotel = {}
try:
hotelinfo = json.load(open('json/'+i))
except:
continue
hotel["HotelInfo"] = hotelinfo["HotelInfo"]
reviews = hotelinfo["Reviews"]
finalReviewList = []
for i in range(len(reviews)):
review ={}
text = re.sub('[^A-Za-z0-9\.\s]+', '', reviews[i]["Content"])
text = '"' +text + '"'
try:
review["Content"] = text
review["ReviewID"] = reviews[i]["ReviewID"]
review["Ratings"] = review[i]["Ratings"]
review["Author"] = review[i]["Author"]
review["AuthorLocation"] = review[i]["AuthorLocation"]
review["Title"] = review[i]["Title"]
review["Date"] = review[i]["Date"]
finalReviewList.append(review)
except:
continue
hotel["Reviews"] = finalReviewList
db.hotels.insert(hotel)
print j
j+=1
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] |
test/test_blog.py | Jn-mic/blog | 0 | 41638 | from app.models import User,Post, Comment,Clap
from app import db
# def setUp(self):
# self.user_Jack = User(username = 'Jack',password = '<PASSWORD>', email = '<EMAIL>')
def tearDown(self):
Post.query.delete()
User.query.delete()
Comment.query.delete()
Clap.query.delete() | [
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] |
create_test_config.py | renovate-bot/python-spanner-sqlalchemy | 16 | 185725 | # -*- coding: utf-8 -*-
#
# Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import configparser
import sys
def set_test_config(project, instance):
config = configparser.ConfigParser()
url = (
f"spanner:///projects/{project}/instances/{instance}/"
"databases/compliance-test"
)
config.add_section("db")
config["db"]["default"] = url
with open("test.cfg", "w") as configfile:
config.write(configfile)
def main(argv):
project = argv[0]
instance = argv[1]
set_test_config(project, instance)
if __name__ == "__main__":
main(sys.argv[1:])
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Week#3__Assignment#3/join.py | P7h/IntroToDataScience__Coursera_Course | 1 | 17815 | import MapReduce
import sys
"""
SQL style Joins in MapReduce
"""
mr = MapReduce.MapReduce()
# =============================
# Do not modify above this line
def mapper(record):
# key: document identifier
# value: document contents
value = str(record[0])
num = str(record[1])
mr.emit_intermediate(num, (value, record))
def reducer(key, list_of_values):
# key: word
# value: list of occurrence counts
x,y = list_of_values[0]
for val in range(len(list_of_values)):
f = []
a,b = list_of_values[val]
if a == 'line_item':
f += y
f += b
mr.emit(f)
# Do not modify below this line
# =============================
if __name__ == '__main__':
inputdata = open(sys.argv[1])
mr.execute(inputdata, mapper, reducer)
| [
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tests/events/test_user_event.py | STYLER-Inc/styler-rest-framework | 3 | 87123 | <filename>tests/events/test_user_event.py
""" Tests for user_event module
"""
from datetime import datetime
from decimal import Decimal
from unittest.mock import patch, Mock
import json
from styler_rest_framework.events import user_event
from styler_rest_framework.api.request_scope import Identity
class TestGetUserId:
""" Tests for user_event.get_user_id
"""
def test_get_user_id(self, token):
data = {
0: Identity(token(sysadmin=True, user_id='111')),
1: 'some parameter'
}
result = user_event.get_user_id(data)
assert result == '111'
def test_no_identity(self, token):
data = {
0: '1234',
1: 'some parameter'
}
result = user_event.get_user_id(data)
assert result is None
def test_no_data(self, token):
result = user_event.get_user_id(None)
assert result is None
class MyCustomClass:
def __init__(self):
self.prop1 = '1234'
self.prop2 = 5555
class TestExtractNamedArgs:
""" Tests for user_event.extract_named_args
"""
def test_extract_named_args(self):
now = datetime.now()
kwargs = {
'key1': 'string',
'key2': 1234,
'key3': Decimal('12.30'),
'key4': now,
'key5': MyCustomClass()
}
result = user_event.extract_named_args(kwargs)
assert result == {
'key1': 'string',
'key2': 1234,
'key3': repr(Decimal('12.30')),
'key4': repr(now),
'key5': '{"prop1": "1234", "prop2": 5555}'
}
def test_no_data(self):
result = user_event.extract_named_args(None)
assert result == {}
class TestExtractArgs:
""" Tests for user_event.extract_args
"""
def test_extract_args(self):
now = datetime.now()
args = [1, 'something', now, Decimal('20.01'), MyCustomClass()]
result = user_event.extract_args(args)
assert result == {
0: 1,
1: 'something',
2: repr(now),
3: repr(Decimal('20.01')),
4: '{"prop1": "1234", "prop2": 5555}'
}
class TestTrack:
""" Tests for user_event.track
"""
@patch('styler_rest_framework.events.user_event.handler')
def test_track_function(self, mocked_handler, token):
@user_event.track
def do_something(a, b, c, d):
return 'something'
iden = Identity(token(sysadmin=True, user_id='111'))
with patch(
'styler_rest_framework.events.user_event.time.time',
Mock(return_value='timestamp')
):
do_something(1234, 'aaa', c='parameter c', d=iden)
mocked_handler.assert_called_once_with(
user_event._TABLE,
'tests.events.test_user_event.do_something',
'111#tests.events.test_user_event.do_something#timestamp',
{
0: 1234,
1: 'aaa',
'c': 'parameter c',
'd': json.dumps(iden.__dict__),
'return': '"something"'
}
)
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] |
ti_python_sdk/use_tensorboard/code/tf_mnist.py | mengsuix/tencent-ti-examples | 2 | 166139 | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import argparse
import numpy as np
IMAGE_SIZE = 28
LABELS_SIZE = 10
HIDDEN_SIZE = 2048
INPUT_FEATURE = 'image'
def raw_input_fn(dataset):
return dataset.images, dataset.labels.astype(np.int32)
def serve_input_fn():
reciever_tensors = {
INPUT_FEATURE: tf.placeholder(tf.float32, [None, None, None, 1]),
}
features = {
INPUT_FEATURE: tf.image.resize_images(reciever_tensors[INPUT_FEATURE], [IMAGE_SIZE, IMAGE_SIZE]),
}
return tf.estimator.export.ServingInputReceiver(receiver_tensors=reciever_tensors,
features=features)
def train(data_dir, model_dir, checkpoints_dir, train_steps):
# Set logging level
tf.logging.set_verbosity(tf.logging.INFO)
# Read Mnist dataset
mnist = input_data.read_data_sets(data_dir, one_hot=False)
# Define training checkpoints config
checkpointing_config = tf.estimator.RunConfig(
save_checkpoints_secs = 30,
keep_checkpoint_max = 100,
)
# Define training classifier
feature_columns = [tf.feature_column.numeric_column(INPUT_FEATURE, shape=[IMAGE_SIZE, IMAGE_SIZE])]
classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
hidden_units=[HIDDEN_SIZE],
n_classes=LABELS_SIZE,
optimizer=tf.train.AdamOptimizer(),
config=checkpointing_config,
model_dir=checkpoints_dir)
# Define training model
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={INPUT_FEATURE : raw_input_fn(mnist.train)[0]},
y=raw_input_fn(mnist.train)[1],
num_epochs=None,
batch_size=128,
shuffle=True
)
classifier.train(input_fn=train_input_fn, steps=train_steps)
# Save training Model
model_dir = classifier.export_savedmodel("/opt/ml/model", serve_input_fn)
print("model path: %s" % model_dir)
if __name__ == '__main__':
args_parser = argparse.ArgumentParser()
args_parser.add_argument(
'--data_dir',
default='/opt/ml/input/data/training',
type=str,
help='the training data directory.')
args_parser.add_argument(
'--model_dir',
default='/opt/ml/model',
type=str,
help='the model directory.')
args_parser.add_argument(
'--checkpoints_dir',
default='/opt/ml/checkpoints',
type=str,
help='the checkpoints directory.')
args_parser.add_argument(
'--train-steps',
type=int,
default=100,
help='Ttrain steps number.')
args = args_parser.parse_args()
train(**vars(args)) | [
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] |
crawler_peopleadvice.py | JiawenXing/Flame | 0 | 149557 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 30 23:03:39 2019
@author: xingjiawen
"""
# 爬取人民建议征集网站
# Part I 爬取前一千条的标题、超链接、时间
# Part II 爬取现在到2019/1/1的、标题含垃圾二字的标题、超链接、时间
import requests
from bs4 import BeautifulSoup
import pandas as pd
import time
u21 = 'http://wsxf.sh.gov.cn/xf_rmyjzj/list.aspx?nav=&pageindex='
index = 1
u22 = '&pagesize=20' # 第一页的网址： u21 + str(index) + u22
######################################
# Part I 爬取前一千条的标题、超链接、时间 #
######################################
titles2 = [] # 接收标题
hrefs2 = [] # 接收超链接
finishtimes2 = [] # 接收完结时间
number = 1000 # 想爬取的问题数目
while len(titles2) < number:
url = u21 + str(index) + u22
index = index + 1
res = requests.get(url)
if res.status_code == 200:
html = res.text
mysoup = BeautifulSoup(html,'lxml')
question_list = mysoup.find('ul', attrs = {'class':'list'}).find_all('li') # 包含了当前页面所有的问题相关的信息
for item in question_list:
# 提取建议标题
title2 = item.find('a').getText() # 提取标题
titles2.append(title2)
# 提取建议超链接
href2 = item.find('a')['href'] # 提取超链接
hrefs2.append('http://wsxf.sh.gov.cn/xf_rmyjzj/' + href2)
# 提取时间
time2 = item.find('span').next_sibling.next_sibling.getText()
finishtimes2.append(time2)
print('Get',number, 'suggestions !') # Get 1000 suggestions !
# 储存上一部分爬取的内容
with open('/Users/xingjiawen/OneDrive/文档/SJTU/2019-2020Autumn/大数据分析/renminjianyi.txt','w') as f:
i = 0
# 第 i 次写入第 i 个问题的三个属性
# titles2 = []，hrefs2 = []，finishtimes2 = []
while i < len(titles2):
f.writelines([titles2[i], ';', hrefs2[i], ';', finishtimes2[i], '\n'])
i = i + 1
print('Finished.', len(titles2),'questions crawled.')
############################################################
# Part II 爬取现在到2019/1/1的、标题含垃圾二字的标题、超链接、时间 #
############################################################
trashtitles = []
trashhrefs = []
trashfinishtimes = []
while index < 88:
url = u21 + str(index) + u22
index = index + 1
res = requests.get(url)
if res.status_code == 200:
html = res.text
mysoup = BeautifulSoup(html,'lxml')
question_list = mysoup.find('ul', attrs = {'class':'list'}).find_all('li') # 包含了当前页面所有的问题相关的信息
for item in question_list:
# 提取建议标题
title = item.find('a').getText()
if '垃圾' in title: # 只针对标题中含有垃圾二字的进行操作
trashtitles.append(title)
# 提取建议超链接
href = item.find('a')['href'] # 提取超链接
trashhrefs.append('http://wsxf.sh.gov.cn/xf_rmyjzj/' + href)
# 提取时间
time = item.find('span').next_sibling.next_sibling.getText()
trashfinishtimes.append(time)
print('Get',len(trashtitles), 'suggestions !') # Get 162 suggestions !
# 爬取垃圾相关问题的二级网页。
for u in trashhrefs:
trashres = requests.get(u)
if trashres.status_code == 200:
trashhtml = trashres.text
trashsoup = BeautifulSoup(trashhtml, 'lxml')
# ……没写完
############################################################以下是参考的代码。
# 进入二级网页提取问题类别、阅读数
for href in q_href:
q_res = requests.get(href)
if q_res.status_code == 200:
q_html = q_res.text
q_soup = BeautifulSoup(q_html, 'lxml')
# 提取问题类别
type_of_q = q_soup.find('ul', attrs = {'class':'label detail-tag'}).find_all('li')
tq = []
for i in type_of_q:
t = i.getText()
tq.append(t)
types.append(tq)
# 提取问题阅读数
reader = q_soup.find('span', attrs = {'class':'absoulte-img'}).find('a').getText()
readers.append(reader)
else:
print(page, res.status_code)
break
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3488,
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620,
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4882,
29918,
401,
29897,
13,
18884,
2867,
13,
2
] |
python-if/exercise1.py | ATelders/learn-python | 0 | 54329 | <filename>python-if/exercise1.py
value = '6'
if value == '7':
print('The value is 7')
elif value == '8':
print('The value is 8')
else:
print('The value is not one we are looking for')
print('Finished!')
| [
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2
] |
src/miner/modules/build.py | cathook/PTTArticleRecommender | 1 | 187802 | # -*- coding: utf-8 -*-
import json
import os
from pprint import pprint
from modules.backend_interface import BackendInterface
from modules.protocol.types import DocMetaData,DocRealData,ReplyMode,ReplyMessage
class BuildData(object):
def __init__(self,str):
#global metadata
self.metadata = []
self.metadatapath = str+"/Metadata_json"
self.str = str
self.d = {}
# To add a key->value pair, do this:
with open(self.metadatapath) as data_file:
self.jsondata = json.load(data_file)
os.chdir(str)
#pprint(self.jsondata)
for i in range(0,len(self.jsondata)):
Rstr = self.jsondata[i]['Title']
if Rstr in self.d :
Rid = self.d[Rstr][0]
# has some values for key
else:
Rid = self.jsondata[i]['Id']
# no values for key
self.d.setdefault(Rstr, []).append(self.jsondata[i]['Id'])
"""
if Rstr in self.mapdata:
Rid = self.mapdata[Rstr]
self.mapdata[Rstr].append(self.jsondata[i]['Id'])
else:
self.mapdata[Rstr] = self.jsondata[i]['Id']
Rid = self.jsondata[i]['Id']
"""
#print(self.jsondata[i]['Title'])
#print(Rid)
self.metadata.append(
DocMetaData(self.jsondata[i]['Id'],Rid,
self.jsondata[i]['Title'],self.jsondata[i]['Author'],
self.jsondata[i]['Time'],self.jsondata[i]['Board'],
[self.jsondata[i]['Push'][0],self.jsondata[i]['Push'][1],self.jsondata[i]['Push'][2]])
)
#print(jsondata[i]['Title'])
#pass
def get_max_id(self, board):
cnt = 0
for i in range(0,len(self.jsondata)):
if self.jsondata[i]['Board'] == board :
cnt = cnt + 1
return cnt
def get_doc_meta_data_after_id(self, board, idid):
idid = idid + 1
ansList = []
for i in range(0,len(self.jsondata)):
if self.jsondata[i]['Board'] == board and self.jsondata[i]['Id'] >= idid :
ansList.append(self.metadata[i])
return ansList
def get_doc_meta_data_after_time(self, board, post_time):
ansList = []
#return ansList
# 2005-06-21 19:34:53
for i in range(0,len(self.jsondata)):
date_object = datetime.strptime(self.jsondata[i]['Time'],'%Y-%m-%d %H:%M:%S')
if self.jsondata[i]['Board'] == board and post_time > date_object :
ansList.append(self.metadata[i])
return ansList
def get_doc_meta_data_of_author(self, board, author):
ansList = []
#return ansList
# 2005-06-21 19:34:53
for i in range(0,len(self.jsondata)):
if self.jsondata[i]['Board'] == board and self.jsondata[i]['Author'] == author :
ansList.append(self.metadata[i])
return ansList
def get_doc_meta_data_series(self, board, idid):
ansList = []
idid = idid+1
for i in range(0,len(self.jsondata)):
if self.jsondata[i]['Board'] == board and self.jsondata[i]['Id'] == idid :
#for Rid in self.mapdata[Rstr]:
if self.jsondata[i]['Title'] in self.d :
for X in self.d[self.jsondata[i]['Title']]:
#print(X)
ansList.append(self.metadata[X])
return ansList
def get_doc_real_data(self, board, idid):
idid = idid+1
for i in range(0,len(self.jsondata)):
if self.jsondata[i]['Board'] == board and self.jsondata[i]['Id'] == idid :
#'http://www.ptt.cc/bbs/' + board + self.jsondata[i]['Name'] + '.html'
with open(self.str+'/'+self.jsondata[i]['Name'],'r') as fileFp:
strr = fileFp.read()
s = '發信站: 批踢踢實業坊(ptt.cc)'
List = strr[strr.find(s)+len(s):].split('\n') # --> ['Line 1', 'Line 2', 'Line 3']
Reply = []
LikeChinese = '推'
DislikeChinese = '噓'
ArrowChinese = '→'
# ReplyMessage(self, mode, user, message):
for replyString in List:
if len(replyString) != 0:
if replyString[0] == LikeChinese :
Reply.append(ReplyMessage(ReplyMode.GOOD,replyString[2:replyString.find(':')],replyString[replyString.find(':')+1:]))
elif replyString[0] == ArrowChinese:
Reply.append(ReplyMessage(ReplyMode.NORMAL,replyString[2:replyString.find(':')],replyString[replyString.find(':')+1:]))
elif replyString[0] == DislikeChinese:
Reply.append(ReplyMessage(ReplyMode.WOO,replyString[2:replyString.find(':')],replyString[replyString.find(':')+1:]))
return DocRealData(strr[strr[1:].find('\n')+2:strr.find(s)-5], Reply)
def get_id_by_url(self, url):
print(url[url.rfind('/')+1:url.find('.html')])
for i in range(0,len(self.jsondata)):
if self.jsondata[i]['Name'] == url[url.rfind('/')+1:url.find('.html')] :
return self.jsondata[i]['Id']
def get_url_by_id(self, board, idid):
idid = idid + 1
for i in range(0,len(self.jsondata)):
if self.jsondata[i]['Board'] == board and self.jsondata[i]['Id'] == idid :
return 'http://www.ptt.cc/bbs/' + board + self.jsondata[i]['Name'] + '.html'
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] |
sqlalchemy/sqlalchemy-0.3.6+codebay/test/base/dependency.py | nakedible/vpnease-l2tp | 5 | 25048 | from testbase import PersistTest
import sqlalchemy.topological as topological
import unittest, sys, os
from sqlalchemy import util
# TODO: need assertion conditions in this suite
class DependencySorter(topological.QueueDependencySorter):pass
class DependencySortTest(PersistTest):
def assert_sort(self, tuples, node, collection=None):
print str(node)
def assert_tuple(tuple, node):
if node.cycles:
cycles = [i.item for i in node.cycles]
else:
cycles = []
if tuple[0] is node.item or tuple[0] in cycles:
tuple.pop()
if tuple[0] is node.item or tuple[0] in cycles:
return
elif len(tuple) > 1 and tuple[1] is node.item:
assert False, "Tuple not in dependency tree: " + str(tuple)
for c in node.children:
assert_tuple(tuple, c)
for tuple in tuples:
assert_tuple(list(tuple), node)
if collection is None:
collection = []
items = util.Set()
def assert_unique(node):
for item in [n.item for n in node.cycles or [node,]]:
assert item not in items
items.add(item)
if item in collection:
collection.remove(item)
for c in node.children:
assert_unique(c)
assert_unique(node)
assert len(collection) == 0
def testsort(self):
rootnode = 'root'
node2 = 'node2'
node3 = 'node3'
node4 = 'node4'
subnode1 = 'subnode1'
subnode2 = 'subnode2'
subnode3 = 'subnode3'
subnode4 = 'subnode4'
subsubnode1 = 'subsubnode1'
tuples = [
(subnode3, subsubnode1),
(node2, subnode1),
(node2, subnode2),
(rootnode, node2),
(rootnode, node3),
(rootnode, node4),
(node4, subnode3),
(node4, subnode4)
]
head = DependencySorter(tuples, []).sort()
self.assert_sort(tuples, head)
def testsort2(self):
node1 = 'node1'
node2 = 'node2'
node3 = 'node3'
node4 = 'node4'
node5 = 'node5'
node6 = 'node6'
node7 = 'node7'
tuples = [
(node1, node2),
(node3, node4),
(node4, node5),
(node5, node6),
(node6, node2)
]
head = DependencySorter(tuples, [node7]).sort()
self.assert_sort(tuples, head, [node7])
def testsort3(self):
['Mapper|Keyword|keywords,Mapper|IKAssociation|itemkeywords', 'Mapper|Item|items,Mapper|IKAssociation|itemkeywords']
node1 = 'keywords'
node2 = 'itemkeyowrds'
node3 = 'items'
tuples = [
(node1, node2),
(node3, node2),
(node1,node3)
]
head1 = DependencySorter(tuples, [node1, node2, node3]).sort()
head2 = DependencySorter(tuples, [node3, node1, node2]).sort()
head3 = DependencySorter(tuples, [node3, node2, node1]).sort()
# TODO: figure out a "node == node2" function
#self.assert_(str(head1) == str(head2) == str(head3))
print "\n" + str(head1)
print "\n" + str(head2)
print "\n" + str(head3)
def testsort4(self):
node1 = 'keywords'
node2 = 'itemkeyowrds'
node3 = 'items'
node4 = 'hoho'
tuples = [
(node1, node2),
(node4, node1),
(node1, node3),
(node3, node2)
]
head = DependencySorter(tuples, []).sort()
self.assert_sort(tuples, head)
def testsort5(self):
# this one, depenending on the weather,
node1 = 'node1' #'00B94190'
node2 = 'node2' #'00B94990'
node3 = 'node3' #'00B9A9B0'
node4 = 'node4' #'00B4F210'
tuples = [
(node4, node1),
(node1, node2),
(node4, node3),
(node2, node3),
(node4, node2),
(node3, node3)
]
allitems = [
node1,
node2,
node3,
node4
]
head = DependencySorter(tuples, allitems).sort()
self.assert_sort(tuples, head)
def testcircular(self):
node1 = 'node1'
node2 = 'node2'
node3 = 'node3'
node4 = 'node4'
node5 = 'node5'
tuples = [
(node4, node5),
(node5, node4),
(node1, node2),
(node2, node3),
(node3, node1),
(node4, node1)
]
head = DependencySorter(tuples, []).sort(allow_all_cycles=True)
self.assert_sort(tuples, head)
def testcircular2(self):
# this condition was arising from ticket:362
# and was not treated properly by topological sort
node1 = 'node1'
node2 = 'node2'
node3 = 'node3'
node4 = 'node4'
tuples = [
(node1, node2),
(node3, node1),
(node2, node4),
(node3, node2),
(node2, node3)
]
head = DependencySorter(tuples, []).sort(allow_all_cycles=True)
self.assert_sort(tuples, head)
def testcircular3(self):
nodes = {}
tuples = [('Question', 'Issue'), ('ProviderService', 'Issue'), ('Provider', 'Question'), ('Question', 'Provider'), ('ProviderService', 'Question'), ('Provider', 'ProviderService'), ('Question', 'Answer'), ('Issue', 'Question')]
head = DependencySorter(tuples, []).sort(allow_all_cycles=True)
self.assert_sort(tuples, head)
def testbigsort(self):
tuples = []
for i in range(0,1500, 2):
tuples.append((i, i+1))
head = DependencySorter(tuples, []).sort()
if __name__ == "__main__":
unittest.main()
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] |
split_genome_into_miRNA_kmers.py | TelfordLab/microRNAs | 1 | 1604136 | import argparse, concurrent.futures, os, subprocess, sys
from Bio import SeqIO
parser = argparse.ArgumentParser(description='Splits genome into 6-mers for efficient microRNA seed scan')
parser.add_argument("-c", "--conservation", required=True, help="CSV file containing miRNA family information")
parser.add_argument("-g","--genome", required=True, help="FASTA file containing the genome assembly")
parser.add_argument("-n", "--num-threads", default=1, help="Number of threads to run in parallel (default: 1)")
args = parser.parse_args()
mirna_families_file = args.conservation
genome_file = args.genome
genome_dir = os.getcwd() + "/"
kmers_dir = genome_dir + genome_file.split("/")[-1].split(".")[0] + "_kmers/"
# split genome into 6-mers
if not os.path.exists(kmers_dir):
os.makedirs(kmers_dir)
print("SPLITTING: {}".format(genome_file))
genome_sequences = SeqIO.to_dict(SeqIO.parse(genome_file, "fasta"))
b = set(genome_sequences.keys())
while len(b) > 0:
print("Sequences left:", len(b))
for gs in genome_sequences:
if os.path.exists(kmers_dir + gs + ".kmers"):
if gs in b:
b.remove(gs)
print("Sequences left:", len(b))
#print("SKIPPING: {}.kmers already exists!".format(gs))
continue
seq = str(genome_sequences[gs].seq).upper()
out = ["{} {}\n".format(i, seq[i:i+6]) for i in range(0, len(seq)-6)]
try:
with open(kmers_dir + gs + ".kmers", "w") as f:
f.write("".join(out))
except OSError: # Buffer issues when writing
print("Caught OSError: Trying to split writing!")
with open(kmers_dir + gs + ".kmers", "w") as f:
f.write("".join(out[:100000000]))
with open(kmers_dir + gs + ".kmers", "a") as f:
f.write("".join(out[100000000:]))
os.makedirs(kmers_dir + "pos_unfiltered")
# create kmer grep bash script
if os.path.exists(kmers_dir + "kmer_grep.sh"):
print("WARNING: {}kmer_grep.sh already exists!".format(genome_dir))
else:
with open(kmers_dir + "kmer_grep.sh", "w") as f:
f.write('if [ "$3" == "r" ]\nthen\nout=$1/pos_unfiltered/$2_reversed.pos\nelse\nout=$1/pos_unfiltered/$2.pos\nfi\nfor i in $1/*.kmers\n\ndo\necho `basename $i .kmers` `grep $2 $i | cut -d " " -f1`\ndone > ${out}')
# grep seeds from split files
def reverse(sequence):
reverse_sequence = ""
for nucleotide in sequence:
if nucleotide.upper() == "A":
if "U" in sequence:
reverse_sequence += "U"
else:
reverse_sequence += "T"
elif nucleotide.upper() == "C":
reverse_sequence += "G"
elif nucleotide.upper() == "G":
reverse_sequence += "C"
elif nucleotide.upper() == "T" or nucleotide.upper() == "U":
reverse_sequence += "A"
else:
reverse_sequence += nucleotide
return reverse_sequence[::-1]
seeds = []
seeds_reversed = []
with open(mirna_families_file, "r") as f:
for line in f:
if line.startswith("family") or len(line) < 2:
continue
seeds.append(line.split(",")[2].replace("U", "T"))
seeds_reversed.append(reverse(line.split(",")[2].replace("U", "T")))
all_seeds = (set(seeds) | set(seeds_reversed))
def grep(seed):
ret = ""
if seed in seeds:
if os.path.exists(kmers_dir + "pos_unfiltered/" + seed + ".pos"):
ret += seed + ".pos already exists!\n"
else:
p = subprocess.Popen(["sh", kmers_dir + "kmer_grep.sh", kmers_dir, seed])
p.wait()
if seed in seeds_reversed:
if os.path.exists(kmers_dir + "pos_unfiltered/" + seed + "_reversed.pos"):
ret += seed + "_reversed.pos already exists!"
else:
p = subprocess.Popen(["sh", kmers_dir + "kmer_grep.sh", kmers_dir, seed, "r"])
p.wait()
return ret
with concurrent.futures.ProcessPoolExecutor(max_workers=int(args.num_threads)) as executor:
futures = [executor.submit(grep, seed) for seed in all_seeds]
results = concurrent.futures.wait(futures)
for rd in results.done:
print(rd.result())
# create and execute bash script to get all non-empty grep results
with open(kmers_dir + "grep.sh", "w") as f:
f.write('for s in {}/pos_unfiltered/*.pos; do grep " " $s > {}/`basename $s`; done'.format(kmers_dir, kmers_dir))
p = subprocess.Popen(["sh", kmers_dir + "grep.sh"])
p.wait()
# clean up
os.remove(kmers_dir + "kmer_grep.sh")
os.remove(kmers_dir + "grep.sh")
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] |
src/rekognition_online_action_detection/models/transformer/multihead_attention.py | amazon-research/long-short-term-transformer | 52 | 97589 | <reponame>amazon-research/long-short-term-transformer
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
import torch
import torch.nn as nn
import torch.nn.functional as F
class DotProductAttention(nn.Module):
def __init__(self, dropout=0.0):
super(DotProductAttention, self).__init__()
self.dropout = dropout
def forward(self, q, k, v, attn_mask=None):
attn_output_weights = torch.bmm(q, k.transpose(1, 2))
if attn_mask is not None:
attn_output_weights += attn_mask
attn_output_weights = F.softmax(attn_output_weights, dim=-1)
attn_output_weights = F.dropout(attn_output_weights,
p=self.dropout,
training=self.training)
attn_output = torch.bmm(attn_output_weights, v)
return attn_output
class DotProductAttentionStream(DotProductAttention):
def __init__(self, dropout=0.0):
super(DotProductAttentionStream, self).__init__(dropout)
############################
# Cache for stream inference
############################
self.k_weights_cache = None
self.k_pos_weights_cache = None
def stream_inference(self, q, k, v, k_pos, v_pos, attn_mask=None):
if self.k_weights_cache is not None:
k_weights_new = torch.bmm(q, k[:, [-1]].transpose(1, 2))
k_weights = torch.cat((self.k_weights_cache[:, :, 1:], k_weights_new), dim=-1)
self.k_weights_cache = k_weights
k_pos_weights = self.k_pos_weights_cache
else:
k_weights = torch.bmm(q, k.transpose(1, 2))
self.k_weights_cache = k_weights
k_pos_weights = torch.bmm(q, k_pos.transpose(1, 2))
self.k_pos_weights_cache = k_pos_weights
attn_output_weights = k_weights + k_pos_weights
if attn_mask is not None:
attn_output_weights += attn_mask
attn_output_weights = F.softmax(attn_output_weights, dim=-1)
attn_output_weights = F.dropout(attn_output_weights,
p=self.dropout,
training=self.training)
attn_output = torch.bmm(attn_output_weights, (v + v_pos))
return attn_output
class MultiheadAttention(nn.Module):
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True, kdim=None, vdim=None):
super(MultiheadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
if self._qkv_same_embed_dim:
self.in_proj_weight = nn.Parameter(torch.empty(3 * embed_dim, embed_dim))
else:
raise RuntimeError('Do not support q, k, v have different dimensions')
if bias:
self.in_proj_bias = nn.Parameter(torch.empty(3 * embed_dim))
else:
self.register_parameter('in_proj_bias', None)
self.out_proj = nn.Linear(embed_dim, embed_dim)
if self._qkv_same_embed_dim:
nn.init.xavier_uniform_(self.in_proj_weight)
if self.in_proj_bias is not None:
nn.init.constant_(self.in_proj_bias, 0.)
nn.init.constant_(self.out_proj.bias, 0.)
self.dotproductattention = DotProductAttention(dropout)
def forward(self, q, k, v, attn_mask=None, key_padding_mask=None):
tsz, bsz, embed_dim = q.shape[0], q.shape[1], q.shape[2]
head_dim = embed_dim // self.num_heads
assert head_dim * self.num_heads == embed_dim, \
'embed_dim must be divisible by num_heads'
scaling = float(head_dim) ** -0.5
_b = self.in_proj_bias
_start = None
_end = embed_dim
_w = self.in_proj_weight[:_end, :]
if _b is not None:
_b = _b[:_end]
q = F.linear(q, _w, _b)
_b = self.in_proj_bias
_start = embed_dim
_end = embed_dim * 2
_w = self.in_proj_weight[_start:_end, :]
if _b is not None:
_b = _b[_start:_end]
k = F.linear(k, _w, _b)
_b = self.in_proj_bias
_start = embed_dim * 2
_end = None
_w = self.in_proj_weight[_start:, :]
if _b is not None:
_b = _b[_start:]
v = F.linear(v, _w, _b)
q = q * scaling
q = q.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
k = k.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
v = v.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(0).repeat(bsz, 1, 1)
attn_mask = attn_mask.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
attn_mask = attn_mask.reshape(-1, *attn_mask.shape[2:])
if key_padding_mask is not None:
key_padding_mask = key_padding_mask.unsqueeze(1).repeat(1, tsz, 1)
key_padding_mask = key_padding_mask.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
key_padding_mask = key_padding_mask.reshape(-1, *key_padding_mask.shape[2:])
if attn_mask is not None and key_padding_mask is not None:
mask = attn_mask + key_padding_mask
elif attn_mask is not None:
mask = attn_mask
elif key_padding_mask is not None:
mask = key_padding_mask
else:
mask = None
attn_output = self.dotproductattention(q, k, v, mask)
attn_output = attn_output.transpose(0, 1).contiguous().view(tsz, bsz,
self.embed_dim)
return self.out_proj(attn_output), None
class MultiheadAttentionStream(MultiheadAttention):
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True, kdim=None, vdim=None):
super(MultiheadAttentionStream, self).__init__(embed_dim, num_heads, dropout, bias, kdim, vdim)
self.dotproductattention = DotProductAttentionStream(dropout)
############################
# Cache for stream inference
############################
self.q_cache = None
self.k_cache = None
self.v_cache = None
self.k_pos_cache = None
self.v_pos_cache = None
def stream_inference(self, q, k, v, pos, attn_mask=None, key_padding_mask=None):
tsz, bsz, embed_dim = q.shape[0], q.shape[1], q.shape[2]
head_dim = embed_dim // self.num_heads
assert head_dim * self.num_heads == embed_dim, \
'embed_dim must be divisible by num_heads'
scaling = float(head_dim) ** -0.5
if self.q_cache is not None:
q = self.q_cache
else:
_b = self.in_proj_bias
_start = None
_end = embed_dim
_w = self.in_proj_weight[:_end, :]
if _b is not None:
_b = _b[:_end]
q = F.linear(q, _w, _b)
self.q_cache = q
assert (self.k_cache is None) == (self.k_pos_cache is None)
if self.k_cache is not None:
_b = self.in_proj_bias
_start = embed_dim
_end = embed_dim * 2
_w = self.in_proj_weight[_start:_end, :]
if _b is not None:
_b = _b[_start:_end]
k_new = F.linear(k[[-1]], _w, None)
k = torch.cat((self.k_cache[1:], k_new))
self.k_cache = k
k_pos = self.k_pos_cache
else:
_b = self.in_proj_bias
_start = embed_dim
_end = embed_dim * 2
_w = self.in_proj_weight[_start:_end, :]
if _b is not None:
_b = _b[_start:_end]
k = F.linear(k, _w, None)
self.k_cache = k
k_pos = F.linear(pos, _w, _b)
self.k_pos_cache = k_pos
assert (self.v_cache is None) == (self.v_pos_cache is None)
if self.v_cache is not None:
_b = self.in_proj_bias
_start = embed_dim * 2
_end = None
_w = self.in_proj_weight[_start:, :]
if _b is not None:
_b = _b[_start:]
v_new = F.linear(v[[-1]], _w, None)
v = torch.cat((self.v_cache[1:], v_new))
self.v_cache = v
v_pos = self.v_pos_cache
else:
_b = self.in_proj_bias
_start = embed_dim * 2
_end = None
_w = self.in_proj_weight[_start:, :]
if _b is not None:
_b = _b[_start:]
v = F.linear(v, _w, None)
self.v_cache = v
v_pos = F.linear(pos, _w, _b)
self.v_pos_cache = v_pos
q = q * scaling
q = q.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
k = k.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
v = v.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
k_pos = k_pos.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
v_pos = v_pos.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(0).repeat(bsz, 1, 1)
attn_mask = attn_mask.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
attn_mask = attn_mask.reshape(-1, *attn_mask.shape[2:])
if key_padding_mask is not None:
key_padding_mask = key_padding_mask.unsqueeze(1).repeat(1, tsz, 1)
key_padding_mask = key_padding_mask.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
key_padding_mask = key_padding_mask.reshape(-1, *key_padding_mask.shape[2:])
if attn_mask is not None and key_padding_mask is not None:
mask = attn_mask + key_padding_mask
elif attn_mask is not None:
mask = attn_mask
elif key_padding_mask is not None:
mask = key_padding_mask
else:
mask = None
attn_output = self.dotproductattention.stream_inference(q, k, v, k_pos, v_pos, mask)
attn_output = attn_output.transpose(0, 1).contiguous().view(tsz, bsz,
self.embed_dim)
return self.out_proj(attn_output), None
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20865,
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1131,
29876,
29918,
4905,
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2
] |
normalWordCloud1.py | praneethr1000/youtube-song-comment-highlights | 0 | 55964 | <filename>normalWordCloud1.py<gh_stars>0
# -*- coding: utf-8 -*-
"""
@author: praneeth
"""
import os
from os import path
from wordcloud import WordCloud
d = path.dirname(__file__) if "__file__" in locals() else os.getcwd()
text = open(path.join(d, 'comments.txt')).read()
# Generate a word cloud image
wordcloud = WordCloud().generate(text)
# Display the generated image:
import matplotlib.pyplot as plt
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
wordcloud.to_file("sample1.png")
plt.show()
| [
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] |
bugtests/test262p/y.py | doom38/jython_v2.2.1 | 0 | 116365 |
assert __name__ == "test262p.y"
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1,
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savepointradio/radio/migrations/0005_replaygain_data.py | RecursiveGreen/spradio-django2 | 0 | 104527 | # Generated by Django 2.2.2 on 2019-07-03 13:16
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('radio', '0004_new_song_path_structure'),
]
operations = [
migrations.AddField(
model_name='store',
name='track_gain',
field=models.DecimalField(blank=True, decimal_places=2, max_digits=6, null=True, verbose_name='recommended replaygain adjustment'),
),
migrations.AddField(
model_name='store',
name='track_peak',
field=models.DecimalField(blank=True, decimal_places=6, max_digits=10, null=True, verbose_name='highest volume level in the track'),
),
]
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] |
tests/make_conf.py | philiparvidsson/pymake | 2 | 84960 | #!/usr/bin/env python
#---------------------------------------
# IMPORTS
#---------------------------------------
import test
from pymake2 import *
#---------------------------------------
# FUNCTIONS
#---------------------------------------
@target
def my_target_1(conf):
test.equal(conf.value, '123abc', "conf.value is incorrect")
conf.value = 'xyz456'
@target
@depends_on('my_target_1')
def my_target_2(conf):
test.equal(conf.value, '123abc', "conf.value should be immutable")
#---------------------------------------
# SCRIPT
#---------------------------------------
pymake2({ 'value': '123abc' }, [ 'my_target_2' ])
test.success()
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stormpath/client.py | denibertovic/stormpath-sdk-python | 0 | 65319 | """Stormpath API client."""
from .auth import Auth
from .data_store import DataStore
from .http import HttpExecutor
from .resources.account import AccountList
from .resources.account_store_mapping import AccountStoreMappingList
from .resources.group import GroupList
from .resources.group_membership import GroupMembershipList
from .resources.tenant import Tenant
class Client(object):
"""The root entry point for SDK functionality.
Using the client instance, you can access all tenant data, such as
applications, directories, groups, and accounts.
More info in documentation:
http://docs.stormpath.com/python/product-guide/#sdk-concepts
The client contains the following attributes that represent resource lists:
:py:attr:`applications` -
:class:`stormpath.resources.application.ApplicationList`
:py:attr:`directories` -
:class:`stormpath.resources.directory.DirectoryList`
:py:attr:`accounts` -
:class:`stormpath.resources.account.AccountList`
:py:attr:`groups` -
:class:`stormpath.resources.group.GroupList`
:py:attr:`group_memberships` -
:class:`stormpath.resources.group_membership.GroupMembershipList`
:py:attr:`account_store_mappings` -
:class:`stormpath.resources.account_store_mapping.AccountStoreMappingList`
"""
BASE_URL = 'https://api.stormpath.com/v1'
def __init__(self, base_url=None, cache_options=None, expand=None, proxies=None, user_agent=None, backoff_strategy=None, **auth_kwargs):
"""
Initialize the client by setting the
:class:`stormpath.data_store.DataStore` and
:class:`stormpath.resources.tenant.Tenant`.
The parameters the Client accepts are those used by the
:class:`stormpath.http.HttpExecutor`, :class:`stormpath.auth.Auth`, and
:class:`stormpath.data_store.DataStore` classes.
:param str user_agent: (optional) The custom user agent to set.
:param backoff_strategy: A Function that will return the number of milliseconds
to wait before retrying the request. The function must take one parameter
which is the number of retries already done. If no function is supplied
the default backoff strategy is used (see the :meth:`stormpath.http.HttpExecutor.pause_exponentially` method).
"""
self.BASE_URL = base_url or self.BASE_URL
self.auth = Auth(**auth_kwargs)
executor = HttpExecutor(self.BASE_URL, self.auth.scheme, proxies, user_agent=user_agent, get_delay=backoff_strategy)
self.data_store = DataStore(executor, cache_options)
self.tenant = Tenant(client=self, href='/tenants/current', expand=expand)
@property
def applications(self):
return self.tenant.applications
@property
def directories(self):
return self.tenant.directories
@property
def accounts(self):
return AccountList(self, href='/accounts')
@property
def groups(self):
return GroupList(self, href='/groups')
@property
def group_memberships(self):
return GroupMembershipList(self, href='/groupMemberships')
@property
def account_store_mappings(self):
return AccountStoreMappingList(self, href='/accountStoreMappings')
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] |
NICNetwork/CountryCharts.py | billbsing/NICNetwork | 0 | 196048 | <reponame>billbsing/NICNetwork<filename>NICNetwork/CountryCharts.py
import collections
import matplotlib.pyplot as plot
import pygal
class CountryCharts:
def __init__(self, data_source):
self._data_source = data_source
@staticmethod
def draw_label_size(percent):
#absolute = int(round(percentt/100.*np.sum(all_values)))
if percent < 2:
return ''
return f'{percent:.1f}%'
def draw_pie_chart(self):
country_size = self._data_source.countries
country_size = dict(sorted(country_size.items(), key=lambda x: x[1], reverse=True))
labels = []
sizes = country_size.values()
for label, size in country_size.items():
percent = (size / sum(sizes)) * 100
labels.append(f'{label} ({size}) {percent:.1f}%')
explode = []
last_explode = 0.1
for value in sizes:
explode_value = 0
if value < 2:
explode_value = last_explode
last_explode += 0.02
explode.append(explode_value)
fig1, ax1 = plot.subplots()
wedges, texts, autotexts = ax1.pie(sizes,
#explode=explode,
labels=country_size.keys(),
autopct=CountryCharts.draw_label_size,
shadow=True,
startangle=90,
rotatelabels=True,
labeldistance=None,
textprops={'fontsize': 14}
)
ax1.legend(wedges,
labels,
title='Countries',
loc='center left',
bbox_to_anchor=(1, 0, 0.5, 1)
)
# ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
def draw_bar_chart(self):
country_size = dict(sorted(self._data_source.countries.items(), reverse=True))
labels = country_size.keys()
sizes = country_size.values()
label_index = []
for label in labels:
label_index.append(len(label_index) + 1)
fix, ax = plot.subplots(figsize=(12, 8))
"""
ax.set_ylabel('Number of people')
ax.set_title('Number of people grouped by country')
ax.set_xticklabels(labels, rotation=45)
plot.subplots_adjust(bottom=0.40)
# plot.margins(0.4)
points = ax.bar(labels, sizes, 1, label='Country')
ax.bar_label(points)
"""
barh = ax.barh(label_index, sizes)
plot.subplots_adjust(left=0.40)
ax.set_yticks(label_index)
ax.set_yticklabels(labels)
ax.set_xlabel('Number of NIC members')
ax.set_ylabel('Country')
ax.set_title('Number of NIC members per country')
if hasattr(ax, 'bar_label'):
ax.bar_label(barh)
def draw_world_map(self, filename):
country_size = dict(sorted(self._data_source.country_size.items()))
style = pygal.style.Style(
background='white',
plot_background='white',
foreground_subtle='black'
)
worldmap = pygal.maps.world.World(style=style)
worldmap.title = 'NIC Members by country'
for country_code_2, value in country_size.items():
country_code = self._data_source.country_codes[country_code_2]
country_name = country_code.name
#if len(country_name) > 10:
#country_name = f'{country_name[:10]}..'
worldmap.add(f'{value}: {country_name}', [(country_code_2, value)])
# worldmap.add('Visitors', country_size)
worldmap.render_to_file(filename)
# worldmap.render()
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] |
Python27/Lib/test/test_functools.py | Jeff-Tian/mybnb | 1 | 66882 | import functools
import sys
import unittest
from test import test_support
from weakref import proxy
import pickle
@staticmethod
def PythonPartial(func, *args, **keywords):
'Pure Python approximation of partial()'
def newfunc(*fargs, **fkeywords):
newkeywords = keywords.copy()
newkeywords.update(fkeywords)
return func(*(args + fargs), **newkeywords)
newfunc.func = func
newfunc.args = args
newfunc.keywords = keywords
return newfunc
def capture(*args, **kw):
"""capture all positional and keyword arguments"""
return args, kw
def signature(part):
""" return the signature of a partial object """
return (part.func, part.args, part.keywords, part.__dict__)
class TestPartial(unittest.TestCase):
thetype = functools.partial
def test_basic_examples(self):
p = self.thetype(capture, 1, 2, a=10, b=20)
self.assertEqual(p(3, 4, b=30, c=40),
((1, 2, 3, 4), dict(a=10, b=30, c=40)))
p = self.thetype(map, lambda x: x*10)
self.assertEqual(p([1,2,3,4]), [10, 20, 30, 40])
def test_attributes(self):
p = self.thetype(capture, 1, 2, a=10, b=20)
# attributes should be readable
self.assertEqual(p.func, capture)
self.assertEqual(p.args, (1, 2))
self.assertEqual(p.keywords, dict(a=10, b=20))
# attributes should not be writable
self.assertRaises(TypeError, setattr, p, 'func', map)
self.assertRaises(TypeError, setattr, p, 'args', (1, 2))
self.assertRaises(TypeError, setattr, p, 'keywords', dict(a=1, b=2))
p = self.thetype(hex)
try:
del p.__dict__
except TypeError:
pass
else:
self.fail('partial object allowed __dict__ to be deleted')
def test_argument_checking(self):
self.assertRaises(TypeError, self.thetype) # need at least a func arg
try:
self.thetype(2)()
except TypeError:
pass
else:
self.fail('First arg not checked for callability')
def test_protection_of_callers_dict_argument(self):
# a caller's dictionary should not be altered by partial
def func(a=10, b=20):
return a
d = {'a':3}
p = self.thetype(func, a=5)
self.assertEqual(p(**d), 3)
self.assertEqual(d, {'a':3})
p(b=7)
self.assertEqual(d, {'a':3})
def test_arg_combinations(self):
# exercise special code paths for zero args in either partial
# object or the caller
p = self.thetype(capture)
self.assertEqual(p(), ((), {}))
self.assertEqual(p(1,2), ((1,2), {}))
p = self.thetype(capture, 1, 2)
self.assertEqual(p(), ((1,2), {}))
self.assertEqual(p(3,4), ((1,2,3,4), {}))
def test_kw_combinations(self):
# exercise special code paths for no keyword args in
# either the partial object or the caller
p = self.thetype(capture)
self.assertEqual(p.keywords, {})
self.assertEqual(p(), ((), {}))
self.assertEqual(p(a=1), ((), {'a':1}))
p = self.thetype(capture, a=1)
self.assertEqual(p.keywords, {'a':1})
self.assertEqual(p(), ((), {'a':1}))
self.assertEqual(p(b=2), ((), {'a':1, 'b':2}))
# keyword args in the call override those in the partial object
self.assertEqual(p(a=3, b=2), ((), {'a':3, 'b':2}))
def test_positional(self):
# make sure positional arguments are captured correctly
for args in [(), (0,), (0,1), (0,1,2), (0,1,2,3)]:
p = self.thetype(capture, *args)
expected = args + ('x',)
got, empty = p('x')
self.assertTrue(expected == got and empty == {})
def test_keyword(self):
# make sure keyword arguments are captured correctly
for a in ['a', 0, None, 3.5]:
p = self.thetype(capture, a=a)
expected = {'a':a,'x':None}
empty, got = p(x=None)
self.assertTrue(expected == got and empty == ())
def test_no_side_effects(self):
# make sure there are no side effects that affect subsequent calls
p = self.thetype(capture, 0, a=1)
args1, kw1 = p(1, b=2)
self.assertTrue(args1 == (0,1) and kw1 == {'a':1,'b':2})
args2, kw2 = p()
self.assertTrue(args2 == (0,) and kw2 == {'a':1})
def test_error_propagation(self):
def f(x, y):
x // y
self.assertRaises(ZeroDivisionError, self.thetype(f, 1, 0))
self.assertRaises(ZeroDivisionError, self.thetype(f, 1), 0)
self.assertRaises(ZeroDivisionError, self.thetype(f), 1, 0)
self.assertRaises(ZeroDivisionError, self.thetype(f, y=0), 1)
def test_weakref(self):
f = self.thetype(int, base=16)
p = proxy(f)
self.assertEqual(f.func, p.func)
f = None
self.assertRaises(ReferenceError, getattr, p, 'func')
def test_with_bound_and_unbound_methods(self):
data = map(str, range(10))
join = self.thetype(str.join, '')
self.assertEqual(join(data), '0123456789')
join = self.thetype(''.join)
self.assertEqual(join(data), '0123456789')
def test_pickle(self):
f = self.thetype(signature, 'asdf', bar=True)
f.add_something_to__dict__ = True
for proto in range(pickle.HIGHEST_PROTOCOL + 1):
f_copy = pickle.loads(pickle.dumps(f, proto))
self.assertEqual(signature(f), signature(f_copy))
# Issue 6083: Reference counting bug
def test_setstate_refcount(self):
class BadSequence:
def __len__(self):
return 4
def __getitem__(self, key):
if key == 0:
return max
elif key == 1:
return tuple(range(1000000))
elif key in (2, 3):
return {}
raise IndexError
f = self.thetype(object)
self.assertRaises(SystemError, f.__setstate__, BadSequence())
class PartialSubclass(functools.partial):
pass
class TestPartialSubclass(TestPartial):
thetype = PartialSubclass
class TestPythonPartial(TestPartial):
thetype = PythonPartial
# the python version isn't picklable
test_pickle = test_setstate_refcount = None
# the python version isn't a type
test_attributes = None
class TestUpdateWrapper(unittest.TestCase):
def check_wrapper(self, wrapper, wrapped,
assigned=functools.WRAPPER_ASSIGNMENTS,
updated=functools.WRAPPER_UPDATES):
# Check attributes were assigned
for name in assigned:
self.assertTrue(getattr(wrapper, name) is getattr(wrapped, name))
# Check attributes were updated
for name in updated:
wrapper_attr = getattr(wrapper, name)
wrapped_attr = getattr(wrapped, name)
for key in wrapped_attr:
self.assertTrue(wrapped_attr[key] is wrapper_attr[key])
def _default_update(self):
def f():
"""This is a test"""
pass
f.attr = 'This is also a test'
def wrapper():
pass
functools.update_wrapper(wrapper, f)
return wrapper, f
def test_default_update(self):
wrapper, f = self._default_update()
self.check_wrapper(wrapper, f)
self.assertEqual(wrapper.__name__, 'f')
self.assertEqual(wrapper.attr, 'This is also a test')
@unittest.skipIf(sys.flags.optimize >= 2,
"Docstrings are omitted with -O2 and above")
def test_default_update_doc(self):
wrapper, f = self._default_update()
self.assertEqual(wrapper.__doc__, 'This is a test')
def test_no_update(self):
def f():
"""This is a test"""
pass
f.attr = 'This is also a test'
def wrapper():
pass
functools.update_wrapper(wrapper, f, (), ())
self.check_wrapper(wrapper, f, (), ())
self.assertEqual(wrapper.__name__, 'wrapper')
self.assertEqual(wrapper.__doc__, None)
self.assertFalse(hasattr(wrapper, 'attr'))
def test_selective_update(self):
def f():
pass
f.attr = 'This is a different test'
f.dict_attr = dict(a=1, b=2, c=3)
def wrapper():
pass
wrapper.dict_attr = {}
assign = ('attr',)
update = ('dict_attr',)
functools.update_wrapper(wrapper, f, assign, update)
self.check_wrapper(wrapper, f, assign, update)
self.assertEqual(wrapper.__name__, 'wrapper')
self.assertEqual(wrapper.__doc__, None)
self.assertEqual(wrapper.attr, 'This is a different test')
self.assertEqual(wrapper.dict_attr, f.dict_attr)
@test_support.requires_docstrings
def test_builtin_update(self):
# Test for bug #1576241
def wrapper():
pass
functools.update_wrapper(wrapper, max)
self.assertEqual(wrapper.__name__, 'max')
self.assertTrue(wrapper.__doc__.startswith('max('))
class TestWraps(TestUpdateWrapper):
def _default_update(self):
def f():
"""This is a test"""
pass
f.attr = 'This is also a test'
@functools.wraps(f)
def wrapper():
pass
self.check_wrapper(wrapper, f)
return wrapper
def test_default_update(self):
wrapper = self._default_update()
self.assertEqual(wrapper.__name__, 'f')
self.assertEqual(wrapper.attr, 'This is also a test')
@unittest.skipIf(sys.flags.optimize >= 2,
"Docstrings are omitted with -O2 and above")
def test_default_update_doc(self):
wrapper = self._default_update()
self.assertEqual(wrapper.__doc__, 'This is a test')
def test_no_update(self):
def f():
"""This is a test"""
pass
f.attr = 'This is also a test'
@functools.wraps(f, (), ())
def wrapper():
pass
self.check_wrapper(wrapper, f, (), ())
self.assertEqual(wrapper.__name__, 'wrapper')
self.assertEqual(wrapper.__doc__, None)
self.assertFalse(hasattr(wrapper, 'attr'))
def test_selective_update(self):
def f():
pass
f.attr = 'This is a different test'
f.dict_attr = dict(a=1, b=2, c=3)
def add_dict_attr(f):
f.dict_attr = {}
return f
assign = ('attr',)
update = ('dict_attr',)
@functools.wraps(f, assign, update)
@add_dict_attr
def wrapper():
pass
self.check_wrapper(wrapper, f, assign, update)
self.assertEqual(wrapper.__name__, 'wrapper')
self.assertEqual(wrapper.__doc__, None)
self.assertEqual(wrapper.attr, 'This is a different test')
self.assertEqual(wrapper.dict_attr, f.dict_attr)
class TestReduce(unittest.TestCase):
def test_reduce(self):
class Squares:
def __init__(self, max):
self.max = max
self.sofar = []
def __len__(self): return len(self.sofar)
def __getitem__(self, i):
if not 0 <= i < self.max: raise IndexError
n = len(self.sofar)
while n <= i:
self.sofar.append(n*n)
n += 1
return self.sofar[i]
reduce = functools.reduce
self.assertEqual(reduce(lambda x, y: x+y, ['a', 'b', 'c'], ''), 'abc')
self.assertEqual(
reduce(lambda x, y: x+y, [['a', 'c'], [], ['d', 'w']], []),
['a','c','d','w']
)
self.assertEqual(reduce(lambda x, y: x*y, range(2,8), 1), 5040)
self.assertEqual(
reduce(lambda x, y: x*y, range(2,21), 1L),
2432902008176640000L
)
self.assertEqual(reduce(lambda x, y: x+y, Squares(10)), 285)
self.assertEqual(reduce(lambda x, y: x+y, Squares(10), 0), 285)
self.assertEqual(reduce(lambda x, y: x+y, Squares(0), 0), 0)
self.assertRaises(TypeError, reduce)
self.assertRaises(TypeError, reduce, 42, 42)
self.assertRaises(TypeError, reduce, 42, 42, 42)
self.assertEqual(reduce(42, "1"), "1") # func is never called with one item
self.assertEqual(reduce(42, "", "1"), "1") # func is never called with one item
self.assertRaises(TypeError, reduce, 42, (42, 42))
class TestCmpToKey(unittest.TestCase):
def test_cmp_to_key(self):
def mycmp(x, y):
return y - x
self.assertEqual(sorted(range(5), key=functools.cmp_to_key(mycmp)),
[4, 3, 2, 1, 0])
def test_hash(self):
def mycmp(x, y):
return y - x
key = functools.cmp_to_key(mycmp)
k = key(10)
self.assertRaises(TypeError, hash(k))
class TestTotalOrdering(unittest.TestCase):
def test_total_ordering_lt(self):
@functools.total_ordering
class A:
def __init__(self, value):
self.value = value
def __lt__(self, other):
return self.value < other.value
def __eq__(self, other):
return self.value == other.value
self.assertTrue(A(1) < A(2))
self.assertTrue(A(2) > A(1))
self.assertTrue(A(1) <= A(2))
self.assertTrue(A(2) >= A(1))
self.assertTrue(A(2) <= A(2))
self.assertTrue(A(2) >= A(2))
def test_total_ordering_le(self):
@functools.total_ordering
class A:
def __init__(self, value):
self.value = value
def __le__(self, other):
return self.value <= other.value
def __eq__(self, other):
return self.value == other.value
self.assertTrue(A(1) < A(2))
self.assertTrue(A(2) > A(1))
self.assertTrue(A(1) <= A(2))
self.assertTrue(A(2) >= A(1))
self.assertTrue(A(2) <= A(2))
self.assertTrue(A(2) >= A(2))
def test_total_ordering_gt(self):
@functools.total_ordering
class A:
def __init__(self, value):
self.value = value
def __gt__(self, other):
return self.value > other.value
def __eq__(self, other):
return self.value == other.value
self.assertTrue(A(1) < A(2))
self.assertTrue(A(2) > A(1))
self.assertTrue(A(1) <= A(2))
self.assertTrue(A(2) >= A(1))
self.assertTrue(A(2) <= A(2))
self.assertTrue(A(2) >= A(2))
def test_total_ordering_ge(self):
@functools.total_ordering
class A:
def __init__(self, value):
self.value = value
def __ge__(self, other):
return self.value >= other.value
def __eq__(self, other):
return self.value == other.value
self.assertTrue(A(1) < A(2))
self.assertTrue(A(2) > A(1))
self.assertTrue(A(1) <= A(2))
self.assertTrue(A(2) >= A(1))
self.assertTrue(A(2) <= A(2))
self.assertTrue(A(2) >= A(2))
def test_total_ordering_no_overwrite(self):
# new methods should not overwrite existing
@functools.total_ordering
class A(str):
pass
self.assertTrue(A("a") < A("b"))
self.assertTrue(A("b") > A("a"))
self.assertTrue(A("a") <= A("b"))
self.assertTrue(A("b") >= A("a"))
self.assertTrue(A("b") <= A("b"))
self.assertTrue(A("b") >= A("b"))
def test_no_operations_defined(self):
with self.assertRaises(ValueError):
@functools.total_ordering
class A:
pass
def test_bug_10042(self):
@functools.total_ordering
class TestTO:
def __init__(self, value):
self.value = value
def __eq__(self, other):
if isinstance(other, TestTO):
return self.value == other.value
return False
def __lt__(self, other):
if isinstance(other, TestTO):
return self.value < other.value
raise TypeError
with self.assertRaises(TypeError):
TestTO(8) <= ()
def test_main(verbose=None):
test_classes = (
TestPartial,
TestPartialSubclass,
TestPythonPartial,
TestUpdateWrapper,
TestTotalOrdering,
TestWraps,
TestReduce,
)
test_support.run_unittest(*test_classes)
# verify reference counting
if verbose and hasattr(sys, "gettotalrefcount"):
import gc
counts = [None] * 5
for i in xrange(len(counts)):
test_support.run_unittest(*test_classes)
gc.collect()
counts[i] = sys.gettotalrefcount()
print counts
if __name__ == '__main__':
test_main(verbose=True)
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] |
rdkit/ML/InfoTheory/BitRank.py | kazuyaujihara/rdkit | 1,609 | 9818 | <filename>rdkit/ML/InfoTheory/BitRank.py
#
# Copyright (C) 2001,2002,2003 <NAME> and Rational Discovery LLC
#
""" Functionality for ranking bits using info gains
**Definitions used in this module**
- *sequence*: an object capable of containing other objects which supports
__getitem__() and __len__(). Examples of these include lists, tuples, and
Numeric arrays.
- *IntVector*: an object containing integers which supports __getitem__() and
__len__(). Examples include lists, tuples, Numeric Arrays, and BitVects.
**NOTE**: Neither *sequences* nor *IntVectors* need to support item assignment.
It is perfectly acceptable for them to be read-only, so long as they are
random-access.
"""
import numpy
from rdkit.ML.InfoTheory import entropy
def FormCounts(bitVects, actVals, whichBit, nPossibleActs, nPossibleBitVals=2):
""" generates the counts matrix for a particular bit
**Arguments**
- bitVects: a *sequence* containing *IntVectors*
- actVals: a *sequence*
- whichBit: an integer, the bit number to use.
- nPossibleActs: the (integer) number of possible activity values.
- nPossibleBitVals: (optional) if specified, this integer provides the maximum
value attainable by the (increasingly inaccurately named) bits in _bitVects_.
**Returns**
a Numeric array with the counts
**Notes**
This is really intended for internal use.
"""
if len(bitVects) != len(actVals):
raise ValueError('var and activity lists should be the same length')
res = numpy.zeros((nPossibleBitVals, nPossibleActs), numpy.integer)
for i in range(len(bitVects)):
res[bitVects[i][whichBit], actVals[i]] += 1
return res
def CalcInfoGains(bitVects, actVals, nPossibleActs, nPossibleBitVals=2):
""" Calculates the information gain for a set of points and activity values
**Arguments**
- bitVects: a *sequence* containing *IntVectors*
- actVals: a *sequence*
- nPossibleActs: the (integer) number of possible activity values.
- nPossibleBitVals: (optional) if specified, this integer provides the maximum
value attainable by the (increasingly inaccurately named) bits in _bitVects_.
**Returns**
a list of floats
"""
if len(bitVects) != len(actVals):
raise ValueError('var and activity lists should be the same length')
nBits = len(bitVects[0])
res = numpy.zeros(nBits, numpy.float)
for bit in range(nBits):
counts = FormCounts(bitVects, actVals, bit, nPossibleActs, nPossibleBitVals=nPossibleBitVals)
res[bit] = entropy.InfoGain(counts)
return res
def RankBits(bitVects, actVals, nPossibleBitVals=2, metricFunc=CalcInfoGains):
""" Rank a set of bits according to a metric function
**Arguments**
- bitVects: a *sequence* containing *IntVectors*
- actVals: a *sequence*
- nPossibleBitVals: (optional) if specified, this integer provides the maximum
value attainable by the (increasingly inaccurately named) bits in _bitVects_.
- metricFunc: (optional) the metric function to be used. See _CalcInfoGains()_
for a description of the signature of this function.
**Returns**
A 2-tuple containing:
- the relative order of the bits (a list of ints)
- the metric calculated for each bit (a list of floats)
"""
nPossibleActs = max(actVals) + 1
metrics = metricFunc(bitVects, actVals, nPossibleActs, nPossibleBitVals=nPossibleBitVals)
bitOrder = list(numpy.argsort(metrics))
bitOrder.reverse()
return bitOrder, metrics
def AnalyzeSparseVects(bitVects, actVals):
""" #DOC
**Arguments**
- bitVects: a *sequence* containing SBVs
- actVals: a *sequence*
**Returns**
a list of floats
**Notes**
- these need to be bit vects and binary activities
"""
nPts = len(bitVects)
if nPts != len(actVals):
raise ValueError('var and activity lists should be the same length')
nBits = bitVects[0].GetSize()
actives = numpy.zeros(nBits, numpy.integer)
inactives = numpy.zeros(nBits, numpy.integer)
nActives, nInactives = 0, 0
for i in range(nPts):
sig, act = bitVects[i], actVals[i]
onBitList = sig.GetOnBits()
if act:
for bit in onBitList:
actives[bit] += 1
nActives += 1
else:
for bit in onBitList:
inactives[bit] += 1
nInactives += 1
resTbl = numpy.zeros((2, 2), numpy.integer)
res = []
gains = []
for bit in range(nBits):
nAct, nInact = actives[bit], inactives[bit]
if nAct or nInact:
resTbl[0, 0] = nAct
resTbl[1, 0] = nPts - nAct
resTbl[0, 1] = nInact
resTbl[1, 1] = nPts - nInact
gain = entropy.InfoGain(resTbl)
gains.append(gain)
res.append((bit, gain, nAct, nInact))
return res, gains
def SparseRankBits(bitVects, actVals, metricFunc=AnalyzeSparseVects):
""" Rank a set of bits according to a metric function
**Arguments**
- bitVects: a *sequence* containing SBVs
- actVals: a *sequence*
- metricFunc: (optional) the metric function to be used. See _SparseCalcInfoGains()_
for a description of the signature of this function.
**Returns**
A 2-tuple containing:
- the relative order of the bits (a list of ints)
- the metric calculated for each bit (a list of floats)
**Notes**
- these need to be bit vects and binary activities
"""
info, metrics = metricFunc(bitVects, actVals)
bitOrder = list(numpy.argsort(metrics))
bitOrder.reverse()
return bitOrder, info
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] |
freezer-7.1.0/freezer/utils/config.py | scottwedge/OpenStack-Stein | 0 | 1609172 | <filename>freezer-7.1.0/freezer/utils/config.py
# (c) Copyright 2014,2015 Hewlett-Packard Development Company, L.P.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
from oslo_log import log
import six
from six.moves import configparser
from freezer.utils import utils
LOG = log.getLogger(__name__)
EXPORT = re.compile(r"^\s*export\s+([^=^#^\s]+)\s*=\s*([^#^\n]*)\s*$",
re.MULTILINE)
INI = re.compile(r"^\s*([^=#\s]+)\s*=[\t]*([^#\n]*)\s*$", re.MULTILINE)
class Config(object):
@staticmethod
def parse(config_path):
if config_path:
if not os.path.exists(config_path):
LOG.error("Critical Error: Configuration file {0} not"
" found".format(config_path))
raise Exception("Configuration file {0} not found !".format(
config_path))
# SafeConfigParser was deprecated in Python 3.2
if six.PY3:
config = configparser.ConfigParser()
else:
config = configparser.SafeConfigParser()
config.read([config_path])
sections = config.sections()
storages = []
default_options = {}
for section in sections:
dict = {}
for option in config.options(section):
option_value = config.get(section, option)
if option_value in ('False', 'None'):
option_value = False
dict[option] = option_value
if section.startswith("storage:"):
storages.append(dict)
else:
default_options.update(dict)
return Config(default_options, storages)
def __init__(self, default, storages):
"""
:param default:
:type default: dict
:param storages:
:type storages: list[dict]
:return:
"""
self.default = default
self.storages = storages
def osrc_parse(lines):
"""
:param lines:
:type lines: str
:return:
"""
return find_all(EXPORT, lines)
def ini_parse(fd):
"""
:param fd:
:type fd: file_descriptor
:return:
"""
parser = configparser.ConfigParser()
parser.readfp(fd)
return dict(parser.items('default'))
def find_all(regex, lines):
return dict([(k.strip(), utils.dequote(v.strip())) for k, v in
regex.findall(lines)])
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SA_DS_placement.py | Christopher-v/parity-placement-algortihm | 0 | 143486 | import random
from random import randint
import networkx as nx
import math
import matplotlib.pyplot as plt
import Evaluation as eval
#This is local search heuristic Simulated Annealing.
def anneal_DS(old_solution, allocated_network_topology):
#This is algortihm 9 - Optimize DDS placement
#print('----------SA---------')
#get the cost from the initialized solution.
#old_solution = solution(allocated_network_topology)
old_cost = cost(old_solution, allocated_network_topology)
T = 1.0
T_min = 0.000500
alpha = 0.9
c = 0.25
linear_factor = 0.25
i = 1
while T > T_min:
print('/')
#indicating the max iteration number for the algorithm.
while i < 300:
#Logarithmic schedule
#T = c / (math.log(i) + 1)
#Linear schedule
#T -= linear_factor
#Adaptive
#if T < 00.9:
# T = T * 0.9
#elif T < 0.9:
# T = T * 0.5
#else:
# T = T * 0.1
new_solution = neighbor(old_solution,allocated_network_topology)
new_cost = cost(new_solution, allocated_network_topology)
ap = acceptance_probability(old_cost, new_cost, T)
if ap > random.random():
#check this.
old_solution = new_solution
old_cost = new_cost
i += 1
#else:
# T = 0
assert i == i
T = T*alpha
eval.eval_annealing_DS(T)
print('DS-annealing finished!')
print('cost',old_cost)
#plt.show()
assert cost != 0
return old_solution
def solution(allocated_network_topology):
#This method finds the existing solution based on the allocate_DS()
#A list containing the placed nodes is derived from the graph - to minimize memory and iterations through the whole graph
solution = []
for node in allocated_network_topology.nodes(data=True):
if node[1]['placed'] == True:
n = len(node[1])
for i in range(n):
list.append(solution,node)
break
return solution
def neighbor(solution, allocated_network_topology):
#This is algortihm 10 - Neighbour / New solution
#this function finds the neighbour of the node. - We use a list representing the nodes in the graph.
#To avoid copying the whole graph - hence, making it more scalable.
assert len(solution) > 0
old_solution = solution
#Copying the old solution to manipulate
new_solution = old_solution
placed = False
while placed == False:
#picks random in solution
#random_node = random.choice(solution_nodes)
random_pick = random.choice(new_solution)
random_node = random_pick[0]
print('random node', random_node)
#getting the random node's neigbours from the graph
nodes_neighbours = list(allocated_network_topology.neighbors(random_node))
#Shuffling the nodes-neighbour
random.shuffle(nodes_neighbours)
n = len(nodes_neighbours)
#--------------------------------------------------------------------
assert len(nodes_neighbours) > 0
#Checking if the random-nodes-neighbours is already in the list.
for o, p in enumerate(nodes_neighbours):
neighbouring_node = nodes_neighbours[o]
print('neighbouring node',neighbouring_node)
#Finding the neighbours neighbour in order to check for the connectivity contraint.
neighbours_neighbours = list(allocated_network_topology.neighbors(neighbouring_node))
u = len(neighbours_neighbours)
u -= 1
#trying to catch the exception - the exception means that the random-neighbour is not in the list.
try:
neighbouring_node_int = int(neighbouring_node)
check = new_solution[neighbouring_node_int][0]
break
except IndexError:
if allocated_network_topology.node[neighbouring_node]['storage_capacity'] >= allocated_network_topology.node[random_node]['storage_capacity'] and allocated_network_topology.node[neighbouring_node]['storage_usage'] == 0 and u > 2:
print('loop')
#switching the random node with the random node's neighbour. - look at RB_placement for optimized code.
#Checking all nodes in the network topology
for node in allocated_network_topology.nodes(data=True):
#picks the random neighbour in the graph
if node[0] == neighbouring_node:
print('loop2')
#takes the length of the node's attributes and appending it to the solution.
#print(neighbouring_node)
k = len(node[1])
list.append(new_solution,node)
print('placed node', node)
list.remove(new_solution, random_pick)
print('node removed', random_pick)
break
break
#list.append(new_solution,nodes)
print('new sol', new_solution)
return new_solution
def cost(solution, allocated_network_topology):
#This is algortihm 11 - Cost
#Here goes the cost solution. This method calculates the cost from the new-solution generated by the previous method.
assert len(solution) > 0
latencies = []
while True:
#print('--went here--')
#Iterates the new solution for the source node from which we want to find the latency
for u, i in enumerate(solution[:-1]):
current_source = int(solution[u][0])
next_source = solution[u+1][0]
#iterates the new solution for the target want to find the latency to.
for j, L in enumerate(solution):
target = int(solution[j][0])
#This picks the current source in the new_solution - this is done because we want to
#compare the source node with every node in the same cluster/same DS.
if current_source != next_source and target != current_source:
#And marks the latency between
assert isinstance(current_source, int)
assert isinstance(target, int)
#Here we pick the target with the same cluster ID as the current source.
if solution[j][1]['cluster_ID'] == solution[u][1]['cluster_ID']:
assert solution[j][1]['cluster_ID'] != None
assert solution[u][1]['cluster_ID'] != None
try:
latency = nx.shortest_path_length(allocated_network_topology, source=current_source, target=target)
latencies.append(latency)
assert len(latencies) != 0
except nx.NetworkXNoPath:
print('no path between', current_source, 'and', target)
pass
continue
#else if current_cluster-ID does have a match
else:
assert isinstance(solution[j][1]['cluster_ID'], int)
continue
cost = sum(latencies)
eval.eval_topology_DS(cost)
print(cost)
#could MST been used here? - using the solution as a graph.
return cost
def acceptance_probability(old_cost, new_cost, T):
assert old_cost > 0
assert new_cost > 0
if new_cost < old_cost:
acceptance_probability = 1.0
else:
try:
acceptance_probability = math.exp((new_cost - old_cost) / T)
except (OverflowError , ZeroDivisionError):
print('overflowerror')
acceptance_probability = float('inf')
print('acceptance prop', acceptance_probability)
print('temperature', T)
return acceptance_probability
def place_optimization(solution, allocated_network_topology):
assert len(solution) > 0
##This method places the final annealed solution in the actual graph.
print('final solution',solution)
#Remove all storage in node. - to make space for a clean solution
#allocated_network_topology.add_node(random_node, storage_usage=0, placed=False)
for node in allocated_network_topology.nodes(data=True):
graph_node = node[0]
allocated_network_topology.add_node(graph_node, storage_usage=0, placed=False)
#Place Storage - taking the storage-usage of the randomly picked node and puts it into the randomly picked node's also randomly picked neibour
for node in allocated_network_topology.nodes(data=True):
graph_node = node[0]
for u, i in enumerate(solution):
solution_node = solution[u][0]
allocated_network_topology.add_node(solution_node, storage_usage=allocated_network_topology.node[solution_node]['storage_capacity'], placed=True)
return
###When scaling - be sure- that it compares target to all the nodes in cluster_ID!!! | [
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] |
pychron/entry/entry_views/sensitivity_entry.py | ASUPychron/pychron | 31 | 129636 | <gh_stars>10-100
# ===============================================================================
# Copyright 2012 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===============================================================================
# ============= enthought library imports =======================
from __future__ import absolute_import
from datetime import datetime
from traits.api import HasTraits, List, Str, Float, Date, Any, TraitError
# ============= standard library imports ========================
# ============= local library imports ==========================
from pychron.core.helpers.iterfuncs import groupby_key
from pychron.dvc.dvc_irradiationable import DVCAble
from pychron.paths import paths
from pychron.pychron_constants import DATE_FORMAT
class SensitivityRecord(HasTraits):
user = Str(dictable=True)
note = Str(dictable=True)
mass_spectrometer = Str(dictable=True)
sensitivity = Float(dictable=True)
create_date = Date(dictable=True)
units = Str("mol/fA", dictable=True)
def to_dict(self):
d = {k: getattr(self, k) for k in self.traits(dictable=True)}
cd = d["create_date"]
if not cd:
self.create_date = cd = datetime.now()
d["create_date"] = cd.strftime(DATE_FORMAT)
return d
@classmethod
def from_dict(cls, d):
record = cls()
for attr in record.traits(dictable=True):
if attr == "create_date":
cd = d.get(attr)
if cd and not isinstance(cd, datetime):
cd = datetime.strptime(cd, DATE_FORMAT)
record.create_date = cd
else:
try:
setattr(record, attr, d.get(attr))
except TraitError:
pass
return record
# primary_key = Int
# editable = Bool(True)
# def __init__(self, rec=None, *args, **kw):
# super(SensitivityRecord, self).__init__(*args, **kw)
# if rec:
# self._create(rec)
#
# def _create(self, dbrecord):
# self.user = dbrecord.user or ''
# self.note = dbrecord.note or ''
# self.create_date = dbrecord.create_date
# self.primary_key = int(dbrecord.id)
# self.editable = False
#
# if dbrecord.mass_spectrometer:
# self.mass_spectrometer = dbrecord.mass_spectrometer.name
#
# self.sensitivity = dbrecord.sensitivity
#
# def flush(self, dbrecord):
# attrs = ['sensitivity', 'note', 'user']
# for ai in attrs:
# v = getattr(self, ai)
# setattr(dbrecord, ai, v)
# dbrecord = Any
# spectrometer = Property
# _spectrometer = Str
#
# def _get_spectrometer(self):
#
# if self._spectrometer:
# return self._spectrometer
#
# if self.dbrecord:
# return self.dbrecord.mass_spectrometer.name
#
# def _set_spectrometer(self, v):
# self._spectrometer = v
#
# def __getattr__(self, attr):
# if hasattr(self.dbrecord, attr):
# return getattr(self.dbrecord, attr)
# def sync(self, spec):
# attrs = ['sensitivity', 'note', 'user']
# for ai in attrs:
# v = getattr(self, ai)
# setattr(self.dbrecord, ai, v)
#
# self.dbrecord.mass_spectrometer = spec
# class database_enabled(object):
# def __call__(self, func):
# def wrapper(obj, *args, **kw):
# if obj.db is not None:
# return func(obj, *args, **kw)
# else:
# obj.warning('database not enabled')
#
# return wrapper
class SensitivityEntry(DVCAble):
records = List(SensitivityRecord)
selected = Any
def _add_item(self, db):
pass
def activate(self):
self._load_records()
# @database_enabled()
def _load_records(self):
specs = self.dvc.get_sensitivities()
print("asdf", specs)
self.records = [
SensitivityRecord.from_dict(sens)
for spec in specs.values()
for sens in spec
]
# @database_enabled()
def save(self):
sens = {}
for ms, ss in groupby_key(self.records, "mass_spectrometer"):
sens[ms] = [ri.to_dict() for ri in ss]
self.dvc.save_sensitivities(sens)
def add(self):
rec = SensitivityRecord()
self.records.append(rec)
# ===============================================================================
# handlers
# ===============================================================================
# def _add_button_fired(self):
# s = SensitivityRecord()
# self._records.append(s)
# # self.records_dirty = True
# def _save_button_fired(self):
# db = self.db
# for si in self.records:
# # print si.note, si.dbrecord.
# if si.dbrecord is None:
# user = si.user
# if user == 'None':
# user = db.save_username
# db.add_sensitivity(si.spectrometer,
# user=user, note=si.note, sensitivity=si.sensitivity)
# else:
# spec = db.get_mass_spectrometer(si.spectrometer)
# si.sync(spec)
# # for ai in ['sensitivity','']
#
#
#
# db.commit()
# ===============================================================================
# property get/set
# ===============================================================================
# def _get_records(self):
# if not self._records:
# recs = self.db.get_sensitivities()
# self._records = [SensitivityRecord(dbrecord=ri) for ri in recs]
# return self._records
# ===============================================================================
# views
# ===============================================================================
# def traits_view(self):
# v = View(Item('records', show_label=False,
# editor=TabularEditor(adapter=SensitivityAdapter(),
# operations=['edit'],
# editable=True,
# )
# ),
#
# HGroup(Item('add_button', show_label=False),
# Item('save_button', show_label=False)),
# resizable=True,
# width=500,
# height=200,
# title='Sensitivity Table'
# )
# return v
class SensitivitySelector(SensitivityEntry):
pass
if __name__ == "__main__":
from pychron.core.helpers.logger_setup import logging_setup
paths.build("_experiment")
logging_setup("runid")
m = SensitivityEntry()
m.configure_traits()
# ============= EOF =============================================
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src/data_module.py | ishine/Neural-HMM | 66 | 176210 | <gh_stars>10-100
r"""
data_module.py
Contains PyTorch-Lightning's datamodule and dataloaders
"""
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from src.utilities.data import TextMelCollate, TextMelLoader
import nltk
class LightningLoader(pl.LightningDataModule):
def __init__(self, hparams):
super(LightningLoader, self).__init__()
self.hparams.update(vars(hparams))
self.collate_fn = TextMelCollate(self.hparams.n_frames_per_step)
self.num_workers = hparams.num_workers
def prepare_data(self):
try:
nltk.data.find('tokenizers/punkt')
print("NLTK Tokenizer already present will not download it!")
except LookupError:
nltk.download('punkt')
def setup(self, stage=None):
self.trainset = TextMelLoader(self.hparams.training_files, self.hparams, [
self.hparams.normaliser])
self.valset = TextMelLoader(self.hparams.validation_files, self.hparams, [
self.hparams.normaliser])
def train_dataloader(self):
return DataLoader(self.trainset, batch_size=self.hparams.batch_size, collate_fn=self.collate_fn,
num_workers=self.hparams.num_workers, pin_memory=True)
def val_dataloader(self):
return DataLoader(self.valset, batch_size=self.hparams.batch_size, collate_fn=self.collate_fn,
num_workers=self.hparams.num_workers, pin_memory=True)
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] |
leetcode/87. Scramble String.py | CSU-FulChou/IOS_er | 2 | 21801 | <filename>leetcode/87. Scramble String.py
# 2021.04.16 hard:
class Solution:
def isScramble(self, s1: str, s2: str) -> bool:
'''
dp 问题：
1. 子字符串应该一样长. 很简单已经保证了
子字符串一样，那么久直接返回 True
2. 子字符串中 存在的字母应该一样， 同一个字母的数量应该一样多 Count()
两种分割方式，交换 或者 不交换不断的迭代下去：
分割的两种方式要写对！
'''
@cache
def dfs(idx1, idx2, length):
if s1[idx1:length+idx1] == s2[idx2:idx2+length]:
return True
if Counter(s1[idx1:length+idx1]) != Counter(s2[idx2:idx2+length]):
return False
for i in range(1,length):
# no swarp
if dfs(idx1,idx2,i) and dfs(idx1+i, idx2+i, length-i): # 这两个的 位置 idx1, idx2 传入要注意:
return True
if dfs(idx1, idx2+length-i, i) and dfs(idx1+i, idx2, length-i):
return True
return False
res = dfs(0,0,len(s1))
dfs.cache_clear()
# print(res)
return res | [
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