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ffcb5dfd61b8ea8406307f4d49316125cb08366c | 54f352a242a8ad6ff5516703e91da61e08d9a9e6 | /Source Codes/AtCoder/agc006/B/4249708.py | c209881efba18f1680c0d7282393670ecf313f49 | [] | no_license | Kawser-nerd/CLCDSA | 5cbd8a4c3f65173e4e8e0d7ed845574c4770c3eb | aee32551795763b54acb26856ab239370cac4e75 | refs/heads/master | 2022-02-09T11:08:56.588303 | 2022-01-26T18:53:40 | 2022-01-26T18:53:40 | 211,783,197 | 23 | 9 | null | null | null | null | UTF-8 | Python | false | false | 685 | py | N,x = map(int,input().split())
if x in (1, 2 * N - 1):
print('No')
exit()
print('Yes')
if N == 2 and x == 2:
l = [1, 2, 3]
print(*l, sep='\n')
elif x == 2:
l = [4, 1, 2, 3]
rest = list(range(5, 2 * N))
m = len(rest) // 2
l = rest[:m] + l + rest[m:]
print(*l, sep='\n')
elif x == 2 * N - 2:
l = [x - 2, x + 1, x, x - 1]
rest = list(range(1, 2 * N - 4))
m = len(rest) // 2
l = rest[:m] + l + rest[m:]
print(*l, sep='\n')
else:
l = [x + 2, x - 1, x, x + 1, x - 2]
rest = list(range(1, x - 2)) + list(range(x + 3, 2 * N))
m = len(rest) // 2
l = rest[:m] + l + rest[m:]
print(*l, sep='\n') | [
"[email protected]"
] | |
4be4119618f24eb4a854b957e68ff64726717d61 | c27a95964b2740e1ec681b7068f52fb573d90321 | /aliyun-python-sdk-cms/aliyunsdkcms/request/v20180308/QueryMetricListRequest.py | 56216712133c7d35673a04cf20349e748613f843 | [
"Apache-2.0"
] | permissive | mysshget/aliyun-openapi-python-sdk | 5cf0a0277cce9823966e93b875c23231d8b32c8a | 333cdd97c894fea6570983e97d2f6236841bc7d3 | refs/heads/master | 2020-03-17T23:07:02.942583 | 2018-05-17T09:50:53 | 2018-05-17T09:50:53 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,912 | py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from aliyunsdkcore.request import RpcRequest
class QueryMetricListRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'Cms', '2018-03-08', 'QueryMetricList','cms')
def get_Cursor(self):
return self.get_query_params().get('Cursor')
def set_Cursor(self,Cursor):
self.add_query_param('Cursor',Cursor)
def get_callby_cms_owner(self):
return self.get_query_params().get('callby_cms_owner')
def set_callby_cms_owner(self,callby_cms_owner):
self.add_query_param('callby_cms_owner',callby_cms_owner)
def get_ResourceOwnerId(self):
return self.get_query_params().get('ResourceOwnerId')
def set_ResourceOwnerId(self,ResourceOwnerId):
self.add_query_param('ResourceOwnerId',ResourceOwnerId)
def get_Period(self):
return self.get_query_params().get('Period')
def set_Period(self,Period):
self.add_query_param('Period',Period)
def get_Length(self):
return self.get_query_params().get('Length')
def set_Length(self,Length):
self.add_query_param('Length',Length)
def get_Project(self):
return self.get_query_params().get('Project')
def set_Project(self,Project):
self.add_query_param('Project',Project)
def get_EndTime(self):
return self.get_query_params().get('EndTime')
def set_EndTime(self,EndTime):
self.add_query_param('EndTime',EndTime)
def get_Express(self):
return self.get_query_params().get('Express')
def set_Express(self,Express):
self.add_query_param('Express',Express)
def get_StartTime(self):
return self.get_query_params().get('StartTime')
def set_StartTime(self,StartTime):
self.add_query_param('StartTime',StartTime)
def get_Metric(self):
return self.get_query_params().get('Metric')
def set_Metric(self,Metric):
self.add_query_param('Metric',Metric)
def get_Page(self):
return self.get_query_params().get('Page')
def set_Page(self,Page):
self.add_query_param('Page',Page)
def get_Dimensions(self):
return self.get_query_params().get('Dimensions')
def set_Dimensions(self,Dimensions):
self.add_query_param('Dimensions',Dimensions) | [
"[email protected]"
] | |
5b3a2e285dac25d8fbaf09b7b6ce6bb8623be7d1 | 6b9084d234c87d7597f97ec95808e13f599bf9a1 | /training/old/detr/eval_step.py | 14c769d423b9428725a45145e5fecae4336afb35 | [] | no_license | LitingLin/ubiquitous-happiness | 4b46234ce0cb29c4d27b00ec5a60d3eeb52c26fc | aae2d764e136ca4a36c054212b361dd7e8b22cba | refs/heads/main | 2023-07-13T19:51:32.227633 | 2021-08-03T16:02:03 | 2021-08-03T16:02:03 | 316,664,903 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,953 | py | import torch
import Utils.detr_misc as utils
from evaluation.evaluator.coco import CocoEvaluator
@torch.no_grad()
def evaluate(model, criterion, postprocessors, data_loader, base_ds, device, output_dir):
model.eval()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Test:'
iou_types = tuple(k for k in ('segm', 'bbox') if k in postprocessors.keys())
coco_evaluator = CocoEvaluator(base_ds, iou_types)
# coco_evaluator.coco_eval[iou_types[0]].params.iouThrs = [0, 0.1, 0.5, 0.75]
for samples, targets in metric_logger.log_every(data_loader, 10, header):
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
outputs = model(samples)
loss_dict = criterion(outputs, targets)
weight_dict = criterion.weight_dict
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_scaled = {k: v * weight_dict[k]
for k, v in loss_dict_reduced.items() if k in weight_dict}
loss_dict_reduced_unscaled = {f'{k}_unscaled': v
for k, v in loss_dict_reduced.items()}
metric_logger.update(loss=sum(loss_dict_reduced_scaled.values()),
**loss_dict_reduced_scaled,
**loss_dict_reduced_unscaled)
metric_logger.update(class_error=loss_dict_reduced['class_error'])
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
results = postprocessors['bbox'](outputs, orig_target_sizes)
if 'segm' in postprocessors.keys():
target_sizes = torch.stack([t["size"] for t in targets], dim=0)
results = postprocessors['segm'](results, outputs, orig_target_sizes, target_sizes)
res = {target['image_id'].item(): output for target, output in zip(targets, results)}
if coco_evaluator is not None:
coco_evaluator.update(res)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
if coco_evaluator is not None:
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
if coco_evaluator is not None:
coco_evaluator.accumulate()
coco_evaluator.summarize()
stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
if coco_evaluator is not None:
if 'bbox' in postprocessors.keys():
stats['coco_eval_bbox'] = coco_evaluator.coco_eval['bbox'].stats.tolist()
if 'segm' in postprocessors.keys():
stats['coco_eval_masks'] = coco_evaluator.coco_eval['segm'].stats.tolist()
return stats, coco_evaluator
| [
"[email protected]"
] | |
5337ceda808da03a25bf931d536938d1881c73a9 | f576f0ea3725d54bd2551883901b25b863fe6688 | /sdk/paloaltonetworks/azure-mgmt-paloaltonetworksngfw/azure/mgmt/paloaltonetworksngfw/aio/operations/_firewalls_operations.py | 8cc8d9e5ce7f000a913bdbed272f44375a317198 | [
"LicenseRef-scancode-generic-cla",
"MIT",
"LGPL-2.1-or-later"
] | permissive | Azure/azure-sdk-for-python | 02e3838e53a33d8ba27e9bcc22bd84e790e4ca7c | c2ca191e736bb06bfbbbc9493e8325763ba990bb | refs/heads/main | 2023-09-06T09:30:13.135012 | 2023-09-06T01:08:06 | 2023-09-06T01:08:06 | 4,127,088 | 4,046 | 2,755 | MIT | 2023-09-14T21:48:49 | 2012-04-24T16:46:12 | Python | UTF-8 | Python | false | false | 50,367 | py | # pylint: disable=too-many-lines
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from io import IOBase
from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
import urllib.parse
from azure.core.async_paging import AsyncItemPaged, AsyncList
from azure.core.exceptions import (
ClientAuthenticationError,
HttpResponseError,
ResourceExistsError,
ResourceNotFoundError,
ResourceNotModifiedError,
map_error,
)
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from azure.core.tracing.decorator_async import distributed_trace_async
from azure.core.utils import case_insensitive_dict
from azure.mgmt.core.exceptions import ARMErrorFormat
from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._firewalls_operations import (
build_create_or_update_request,
build_delete_request,
build_get_global_rulestack_request,
build_get_log_profile_request,
build_get_request,
build_get_support_info_request,
build_list_by_resource_group_request,
build_list_by_subscription_request,
build_save_log_profile_request,
build_update_request,
)
T = TypeVar("T")
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class FirewallsOperations:
"""
.. warning::
**DO NOT** instantiate this class directly.
Instead, you should access the following operations through
:class:`~azure.mgmt.paloaltonetworksngfw.aio.PaloAltoNetworksNgfwMgmtClient`'s
:attr:`firewalls` attribute.
"""
models = _models
def __init__(self, *args, **kwargs) -> None:
input_args = list(args)
self._client = input_args.pop(0) if input_args else kwargs.pop("client")
self._config = input_args.pop(0) if input_args else kwargs.pop("config")
self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
@distributed_trace
def list_by_subscription(self, **kwargs: Any) -> AsyncIterable["_models.FirewallResource"]:
"""List FirewallResource resources by subscription ID.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either FirewallResource or the result of cls(response)
:rtype:
~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.paloaltonetworksngfw.models.FirewallResource]
:raises ~azure.core.exceptions.HttpResponseError:
"""
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
cls: ClsType[_models.FirewallResourceListResult] = kwargs.pop("cls", None)
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
def prepare_request(next_link=None):
if not next_link:
request = build_list_by_subscription_request(
subscription_id=self._config.subscription_id,
api_version=api_version,
template_url=self.list_by_subscription.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
# make call to next link with the client's api-version
_parsed_next_link = urllib.parse.urlparse(next_link)
_next_request_params = case_insensitive_dict(
{
key: [urllib.parse.quote(v) for v in value]
for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
}
)
_next_request_params["api-version"] = self._config.api_version
request = HttpRequest(
"GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = "GET"
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize("FirewallResourceListResult", pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem) # type: ignore
return deserialized.next_link or None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(get_next, extract_data)
list_by_subscription.metadata = {
"url": "/subscriptions/{subscriptionId}/providers/PaloAltoNetworks.Cloudngfw/firewalls"
}
@distributed_trace
def list_by_resource_group(
self, resource_group_name: str, **kwargs: Any
) -> AsyncIterable["_models.FirewallResource"]:
"""List FirewallResource resources by resource group.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either FirewallResource or the result of cls(response)
:rtype:
~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.paloaltonetworksngfw.models.FirewallResource]
:raises ~azure.core.exceptions.HttpResponseError:
"""
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
cls: ClsType[_models.FirewallResourceListResult] = kwargs.pop("cls", None)
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
def prepare_request(next_link=None):
if not next_link:
request = build_list_by_resource_group_request(
resource_group_name=resource_group_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
template_url=self.list_by_resource_group.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
# make call to next link with the client's api-version
_parsed_next_link = urllib.parse.urlparse(next_link)
_next_request_params = case_insensitive_dict(
{
key: [urllib.parse.quote(v) for v in value]
for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
}
)
_next_request_params["api-version"] = self._config.api_version
request = HttpRequest(
"GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = "GET"
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize("FirewallResourceListResult", pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem) # type: ignore
return deserialized.next_link or None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(get_next, extract_data)
list_by_resource_group.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls"
}
@distributed_trace_async
async def get(self, resource_group_name: str, firewall_name: str, **kwargs: Any) -> _models.FirewallResource:
"""Get a FirewallResource.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FirewallResource or the result of cls(response)
:rtype: ~azure.mgmt.paloaltonetworksngfw.models.FirewallResource
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
cls: ClsType[_models.FirewallResource] = kwargs.pop("cls", None)
request = build_get_request(
resource_group_name=resource_group_name,
firewall_name=firewall_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
template_url=self.get.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize("FirewallResource", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls/{firewallName}"
}
async def _create_or_update_initial(
self, resource_group_name: str, firewall_name: str, resource: Union[_models.FirewallResource, IO], **kwargs: Any
) -> _models.FirewallResource:
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
cls: ClsType[_models.FirewallResource] = kwargs.pop("cls", None)
content_type = content_type or "application/json"
_json = None
_content = None
if isinstance(resource, (IOBase, bytes)):
_content = resource
else:
_json = self._serialize.body(resource, "FirewallResource")
request = build_create_or_update_request(
resource_group_name=resource_group_name,
firewall_name=firewall_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
content_type=content_type,
json=_json,
content=_content,
template_url=self._create_or_update_initial.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200, 201]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if response.status_code == 200:
deserialized = self._deserialize("FirewallResource", pipeline_response)
if response.status_code == 201:
deserialized = self._deserialize("FirewallResource", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {}) # type: ignore
return deserialized # type: ignore
_create_or_update_initial.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls/{firewallName}"
}
@overload
async def begin_create_or_update(
self,
resource_group_name: str,
firewall_name: str,
resource: _models.FirewallResource,
*,
content_type: str = "application/json",
**kwargs: Any
) -> AsyncLROPoller[_models.FirewallResource]:
"""Create a FirewallResource.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:param resource: Resource create parameters. Required.
:type resource: ~azure.mgmt.paloaltonetworksngfw.models.FirewallResource
:keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
Default value is "application/json".
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
this operation to not poll, or pass in your own initialized polling object for a personal
polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either FirewallResource or the result of
cls(response)
:rtype:
~azure.core.polling.AsyncLROPoller[~azure.mgmt.paloaltonetworksngfw.models.FirewallResource]
:raises ~azure.core.exceptions.HttpResponseError:
"""
@overload
async def begin_create_or_update(
self,
resource_group_name: str,
firewall_name: str,
resource: IO,
*,
content_type: str = "application/json",
**kwargs: Any
) -> AsyncLROPoller[_models.FirewallResource]:
"""Create a FirewallResource.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:param resource: Resource create parameters. Required.
:type resource: IO
:keyword content_type: Body Parameter content-type. Content type parameter for binary body.
Default value is "application/json".
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
this operation to not poll, or pass in your own initialized polling object for a personal
polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either FirewallResource or the result of
cls(response)
:rtype:
~azure.core.polling.AsyncLROPoller[~azure.mgmt.paloaltonetworksngfw.models.FirewallResource]
:raises ~azure.core.exceptions.HttpResponseError:
"""
@distributed_trace_async
async def begin_create_or_update(
self, resource_group_name: str, firewall_name: str, resource: Union[_models.FirewallResource, IO], **kwargs: Any
) -> AsyncLROPoller[_models.FirewallResource]:
"""Create a FirewallResource.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:param resource: Resource create parameters. Is either a FirewallResource type or a IO type.
Required.
:type resource: ~azure.mgmt.paloaltonetworksngfw.models.FirewallResource or IO
:keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
Default value is None.
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
this operation to not poll, or pass in your own initialized polling object for a personal
polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either FirewallResource or the result of
cls(response)
:rtype:
~azure.core.polling.AsyncLROPoller[~azure.mgmt.paloaltonetworksngfw.models.FirewallResource]
:raises ~azure.core.exceptions.HttpResponseError:
"""
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
cls: ClsType[_models.FirewallResource] = kwargs.pop("cls", None)
polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
cont_token: Optional[str] = kwargs.pop("continuation_token", None)
if cont_token is None:
raw_result = await self._create_or_update_initial(
resource_group_name=resource_group_name,
firewall_name=firewall_name,
resource=resource,
api_version=api_version,
content_type=content_type,
cls=lambda x, y, z: x,
headers=_headers,
params=_params,
**kwargs
)
kwargs.pop("error_map", None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize("FirewallResource", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if polling is True:
polling_method: AsyncPollingMethod = cast(
AsyncPollingMethod,
AsyncARMPolling(lro_delay, lro_options={"final-state-via": "azure-async-operation"}, **kwargs),
)
elif polling is False:
polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output,
)
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) # type: ignore
begin_create_or_update.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls/{firewallName}"
}
@overload
async def update(
self,
resource_group_name: str,
firewall_name: str,
properties: _models.FirewallResourceUpdate,
*,
content_type: str = "application/json",
**kwargs: Any
) -> _models.FirewallResource:
"""Update a FirewallResource.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:param properties: The resource properties to be updated. Required.
:type properties: ~azure.mgmt.paloaltonetworksngfw.models.FirewallResourceUpdate
:keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
Default value is "application/json".
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FirewallResource or the result of cls(response)
:rtype: ~azure.mgmt.paloaltonetworksngfw.models.FirewallResource
:raises ~azure.core.exceptions.HttpResponseError:
"""
@overload
async def update(
self,
resource_group_name: str,
firewall_name: str,
properties: IO,
*,
content_type: str = "application/json",
**kwargs: Any
) -> _models.FirewallResource:
"""Update a FirewallResource.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:param properties: The resource properties to be updated. Required.
:type properties: IO
:keyword content_type: Body Parameter content-type. Content type parameter for binary body.
Default value is "application/json".
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FirewallResource or the result of cls(response)
:rtype: ~azure.mgmt.paloaltonetworksngfw.models.FirewallResource
:raises ~azure.core.exceptions.HttpResponseError:
"""
@distributed_trace_async
async def update(
self,
resource_group_name: str,
firewall_name: str,
properties: Union[_models.FirewallResourceUpdate, IO],
**kwargs: Any
) -> _models.FirewallResource:
"""Update a FirewallResource.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:param properties: The resource properties to be updated. Is either a FirewallResourceUpdate
type or a IO type. Required.
:type properties: ~azure.mgmt.paloaltonetworksngfw.models.FirewallResourceUpdate or IO
:keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
Default value is None.
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FirewallResource or the result of cls(response)
:rtype: ~azure.mgmt.paloaltonetworksngfw.models.FirewallResource
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
cls: ClsType[_models.FirewallResource] = kwargs.pop("cls", None)
content_type = content_type or "application/json"
_json = None
_content = None
if isinstance(properties, (IOBase, bytes)):
_content = properties
else:
_json = self._serialize.body(properties, "FirewallResourceUpdate")
request = build_update_request(
resource_group_name=resource_group_name,
firewall_name=firewall_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
content_type=content_type,
json=_json,
content=_content,
template_url=self.update.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize("FirewallResource", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls/{firewallName}"
}
async def _delete_initial( # pylint: disable=inconsistent-return-statements
self, resource_group_name: str, firewall_name: str, **kwargs: Any
) -> None:
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
cls: ClsType[None] = kwargs.pop("cls", None)
request = build_delete_request(
resource_group_name=resource_group_name,
firewall_name=firewall_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
template_url=self._delete_initial.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200, 202, 204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
_delete_initial.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls/{firewallName}"
}
@distributed_trace_async
async def begin_delete(self, resource_group_name: str, firewall_name: str, **kwargs: Any) -> AsyncLROPoller[None]:
"""Delete a FirewallResource.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
this operation to not poll, or pass in your own initialized polling object for a personal
polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError:
"""
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
cls: ClsType[None] = kwargs.pop("cls", None)
polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
cont_token: Optional[str] = kwargs.pop("continuation_token", None)
if cont_token is None:
raw_result = await self._delete_initial( # type: ignore
resource_group_name=resource_group_name,
firewall_name=firewall_name,
api_version=api_version,
cls=lambda x, y, z: x,
headers=_headers,
params=_params,
**kwargs
)
kwargs.pop("error_map", None)
def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements
if cls:
return cls(pipeline_response, None, {})
if polling is True:
polling_method: AsyncPollingMethod = cast(
AsyncPollingMethod,
AsyncARMPolling(lro_delay, lro_options={"final-state-via": "azure-async-operation"}, **kwargs),
)
elif polling is False:
polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output,
)
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) # type: ignore
begin_delete.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls/{firewallName}"
}
@distributed_trace_async
async def get_global_rulestack(
self, resource_group_name: str, firewall_name: str, **kwargs: Any
) -> _models.GlobalRulestackInfo:
"""Get Global Rulestack associated with the Firewall.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: GlobalRulestackInfo or the result of cls(response)
:rtype: ~azure.mgmt.paloaltonetworksngfw.models.GlobalRulestackInfo
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
cls: ClsType[_models.GlobalRulestackInfo] = kwargs.pop("cls", None)
request = build_get_global_rulestack_request(
resource_group_name=resource_group_name,
firewall_name=firewall_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
template_url=self.get_global_rulestack.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize("GlobalRulestackInfo", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_global_rulestack.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls/{firewallName}/getGlobalRulestack"
}
@distributed_trace_async
async def get_log_profile(self, resource_group_name: str, firewall_name: str, **kwargs: Any) -> _models.LogSettings:
"""Log Profile for Firewall.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: LogSettings or the result of cls(response)
:rtype: ~azure.mgmt.paloaltonetworksngfw.models.LogSettings
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
cls: ClsType[_models.LogSettings] = kwargs.pop("cls", None)
request = build_get_log_profile_request(
resource_group_name=resource_group_name,
firewall_name=firewall_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
template_url=self.get_log_profile.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize("LogSettings", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_log_profile.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls/{firewallName}/getLogProfile"
}
@distributed_trace_async
async def get_support_info(
self, resource_group_name: str, firewall_name: str, email: Optional[str] = None, **kwargs: Any
) -> _models.SupportInfo:
"""support info for firewall.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:param email: email address on behalf of which this API called. Default value is None.
:type email: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SupportInfo or the result of cls(response)
:rtype: ~azure.mgmt.paloaltonetworksngfw.models.SupportInfo
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
cls: ClsType[_models.SupportInfo] = kwargs.pop("cls", None)
request = build_get_support_info_request(
resource_group_name=resource_group_name,
firewall_name=firewall_name,
subscription_id=self._config.subscription_id,
email=email,
api_version=api_version,
template_url=self.get_support_info.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize("SupportInfo", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_support_info.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls/{firewallName}/getSupportInfo"
}
@overload
async def save_log_profile( # pylint: disable=inconsistent-return-statements
self,
resource_group_name: str,
firewall_name: str,
log_settings: Optional[_models.LogSettings] = None,
*,
content_type: str = "application/json",
**kwargs: Any
) -> None:
"""Log Profile for Firewall.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:param log_settings: Default value is None.
:type log_settings: ~azure.mgmt.paloaltonetworksngfw.models.LogSettings
:keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
Default value is "application/json".
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None or the result of cls(response)
:rtype: None
:raises ~azure.core.exceptions.HttpResponseError:
"""
@overload
async def save_log_profile( # pylint: disable=inconsistent-return-statements
self,
resource_group_name: str,
firewall_name: str,
log_settings: Optional[IO] = None,
*,
content_type: str = "application/json",
**kwargs: Any
) -> None:
"""Log Profile for Firewall.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:param log_settings: Default value is None.
:type log_settings: IO
:keyword content_type: Body Parameter content-type. Content type parameter for binary body.
Default value is "application/json".
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None or the result of cls(response)
:rtype: None
:raises ~azure.core.exceptions.HttpResponseError:
"""
@distributed_trace_async
async def save_log_profile( # pylint: disable=inconsistent-return-statements
self,
resource_group_name: str,
firewall_name: str,
log_settings: Optional[Union[_models.LogSettings, IO]] = None,
**kwargs: Any
) -> None:
"""Log Profile for Firewall.
:param resource_group_name: The name of the resource group. The name is case insensitive.
Required.
:type resource_group_name: str
:param firewall_name: Firewall resource name. Required.
:type firewall_name: str
:param log_settings: Is either a LogSettings type or a IO type. Default value is None.
:type log_settings: ~azure.mgmt.paloaltonetworksngfw.models.LogSettings or IO
:keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
Default value is None.
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None or the result of cls(response)
:rtype: None
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: str = kwargs.pop("api_version", _params.pop("api-version", self._config.api_version))
content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
cls: ClsType[None] = kwargs.pop("cls", None)
content_type = content_type or "application/json"
_json = None
_content = None
if isinstance(log_settings, (IOBase, bytes)):
_content = log_settings
else:
if log_settings is not None:
_json = self._serialize.body(log_settings, "LogSettings")
else:
_json = None
request = build_save_log_profile_request(
resource_group_name=resource_group_name,
firewall_name=firewall_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
content_type=content_type,
json=_json,
content=_content,
template_url=self.save_log_profile.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
save_log_profile.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/PaloAltoNetworks.Cloudngfw/firewalls/{firewallName}/saveLogProfile"
}
| [
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] | |
4fee8226361947afb1ef025ada908dc3ad5f97a7 | 163bbb4e0920dedd5941e3edfb2d8706ba75627d | /Code/CodeRecords/2526/48083/309473.py | a8d534b2af549d2506c477c229047c81420f23b7 | [] | no_license | AdamZhouSE/pythonHomework | a25c120b03a158d60aaa9fdc5fb203b1bb377a19 | ffc5606817a666aa6241cfab27364326f5c066ff | refs/heads/master | 2022-11-24T08:05:22.122011 | 2020-07-28T16:21:24 | 2020-07-28T16:21:24 | 259,576,640 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,340 | py | from typing import List
class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution:
def getAllElements(self, root1: TreeNode, root2: TreeNode) -> List[int]:
res = []
def inOrder(root):
if root:
inOrder(root.left)
res.append(root.val)
inOrder(root.right)
inOrder(root1)
inOrder(root2)
res = filter(None, res) #
return sorted(map(int,res))
def str2arr(self,t):
t = t[1:-1]
t = t.split(',')
return t
def creatTree(self,arr):
nodes = []
for a in arr:
node = TreeNode(a)
nodes.append(node)
parentNum = len(arr) // 2 - 1
for i in range(parentNum+1):
leftIndex = 2 * i + 1
rightIndex = 2 * i + 2
if nodes[leftIndex].val!='null':
nodes[i].left = nodes[leftIndex]
if rightIndex < len(arr) and nodes[rightIndex].val!='null':
nodes[i].right = nodes[rightIndex]
return nodes[0]
s = Solution()
t1 = input()
t2 = input()
t1 = s.str2arr(t1)
t2 = s.str2arr(t2)
root1 = s.creatTree(t1)
root2 = s.creatTree(t2)
res = s.getAllElements(root1, root2)
print(res) | [
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] | |
6fb6e1524af5bc5732e4bc123217c972f05010a3 | cf9b83d667433a5f912f8981357483197624983d | /editors/content/admin.py | 75694e4aaca1e16700897e8f2668a55b4381efb9 | [
"Apache-2.0"
] | permissive | LeaseMagnetsTeam/editors.art | 08d13e58a17c9930efe78f99d4dc4e25898612f3 | 778ca76439da798f3b7ffbac5b83f3e85b4f4fca | refs/heads/main | 2023-06-26T17:17:52.336540 | 2021-07-26T00:09:30 | 2021-07-26T00:09:30 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 101 | py | from django.contrib import admin
from editors.content.models import Edit
admin.site.register(Edit)
| [
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] | |
beb51d68a2bfda9d9043f37aca7dfab32345ec5d | 0c66e605e6e4129b09ea14dbb6aa353d18aaa027 | /diventi/blog/migrations/0011_auto_20200502_1924.py | cafdf11a5790066124e2ac11c41e6c0b8e07572d | [
"Apache-2.0"
] | permissive | flavoi/diventi | 58fbc8c947f387cbcc1ce607878a59a6f2b72313 | c0b1efe2baa3ff816d6ee9a8e86623f297973ded | refs/heads/master | 2023-07-20T09:32:35.897661 | 2023-07-11T19:44:26 | 2023-07-11T19:44:26 | 102,959,477 | 2 | 1 | Apache-2.0 | 2023-02-08T01:03:17 | 2017-09-09T14:10:51 | Python | UTF-8 | Python | false | false | 612 | py | # Generated by Django 2.2.12 on 2020-05-02 17:24
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('blog', '0010_auto_20200229_1600'),
]
operations = [
migrations.AlterField(
model_name='blogcover',
name='color',
field=models.CharField(blank=True, choices=[('info', 'Blue'), ('primary', 'Rose'), ('danger', 'Red'), ('warning', 'Yellow'), ('success', 'Green'), ('default', 'Gray'), ('dark', 'Black'), ('light', 'White')], default='warning', max_length=30, verbose_name='color'),
),
]
| [
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] | |
2c8b29f4777567834b9d0affa686caba95f48ef3 | d1c29c9f06d56644ca2fb11fcff8c25703aced79 | /MMCG/make_plots.py | 891af441059f2c0d34f6177672eb9d172bde2fe6 | [] | no_license | jjhelmus/arm_vap_scripts | 4a3d7bbe9e277972312484fe46a35c92dae1c71c | 1d49d0e2f8affea11aabc000f74d8d1c4be75ef5 | refs/heads/master | 2021-01-22T05:24:35.935447 | 2013-04-12T14:38:31 | 2013-04-12T14:38:31 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 861 | py | #!/usr/bin/env python
import matplotlib.pyplot as plt
import netCDF4
import pyart
# MMCG figure
dataset = netCDF4.Dataset('sgpcsaprmmcgi7.c0.20110520.110100.nc')
refl = dataset.variables['reflectivity_horizontal']
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(refl[0,4], origin='lower')
fig.savefig('mapped_figure.png')
# Test
dataset = netCDF4.Dataset('foo.dir/sgpcsaprmmcgI7.c0.20110520.110100.nc')
refl = dataset.variables['reflectivity_horizontal']
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(refl[0,4], origin='lower')
fig.savefig('exp_figure.png')
# Radial coords
"""
radar = pyart.io.read_netcdf('sgpcsaprsurcmacI7.c0.20110520.110100.nc')
display = pyart.graph.RadarDisplay(radar)
fig = plt.figure()
ax = fig.add_subplot(111)
display.plot_ppi('reflectivity_horizontal', 0, vmin=-16, vmax=48)
fig.savefig('radial_figure.png')
"""
| [
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] | |
0396eb254b1de5fa42497fb6a7b869393ca51085 | 29c58b3bec6ac0fcdb3070efc118600ee92004da | /test/test_unread_count.py | 42aeede4b08156288cd84090b1b6d8c211d1374e | [
"MIT"
] | permissive | mailslurp/mailslurp-client-python | a2b5a0545206714bd4462ae517f242852b52aaf9 | 5c9a7cfdd5ea8bf671928023e7263847353d92c4 | refs/heads/master | 2023-06-23T00:41:36.257212 | 2023-06-14T10:10:14 | 2023-06-14T10:10:14 | 204,662,133 | 8 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,807 | py | # coding: utf-8
"""
MailSlurp API
MailSlurp is an API for sending and receiving emails from dynamically allocated email addresses. It's designed for developers and QA teams to test applications, process inbound emails, send templated notifications, attachments, and more. ## Resources - [Homepage](https://www.mailslurp.com) - Get an [API KEY](https://app.mailslurp.com/sign-up/) - Generated [SDK Clients](https://docs.mailslurp.com/) - [Examples](https://github.com/mailslurp/examples) repository # noqa: E501
The version of the OpenAPI document: 6.5.2
Contact: [email protected]
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import unittest
import datetime
import mailslurp_client
from mailslurp_client.models.unread_count import UnreadCount # noqa: E501
from mailslurp_client.rest import ApiException
class TestUnreadCount(unittest.TestCase):
"""UnreadCount unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def make_instance(self, include_optional):
"""Test UnreadCount
include_option is a boolean, when False only required
params are included, when True both required and
optional params are included """
# model = mailslurp_client.models.unread_count.UnreadCount() # noqa: E501
if include_optional :
return UnreadCount(
count = 56
)
else :
return UnreadCount(
count = 56,
)
def testUnreadCount(self):
"""Test UnreadCount"""
inst_req_only = self.make_instance(include_optional=False)
inst_req_and_optional = self.make_instance(include_optional=True)
if __name__ == '__main__':
unittest.main()
| [
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] | |
8979b51513153b74eb4a26efe75e95c67319ebef | 40b42ccf2b6959d6fce74509201781be96f04475 | /mmocr/models/textdet/necks/fpem_ffm.py | c98b43f1fc2642db598a0f9094b88e4851cc9e75 | [
"Apache-2.0"
] | permissive | xdxie/WordArt | 2f1414d8e4edaa89333353d0b28e5096e1f87263 | 89bf8a218881b250d0ead7a0287526c69586c92a | refs/heads/main | 2023-05-23T02:04:22.185386 | 2023-03-06T11:51:43 | 2023-03-06T11:51:43 | 515,485,694 | 106 | 12 | null | null | null | null | UTF-8 | Python | false | false | 5,999 | py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn.functional as F
from mmcv.runner import BaseModule, ModuleList
from torch import nn
from mmocr.models.builder import NECKS
class FPEM(BaseModule):
"""FPN-like feature fusion module in PANet.
Args:
in_channels (int): Number of input channels.
init_cfg (dict or list[dict], optional): Initialization configs.
"""
def __init__(self, in_channels=128, init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.up_add1 = SeparableConv2d(in_channels, in_channels, 1)
self.up_add2 = SeparableConv2d(in_channels, in_channels, 1)
self.up_add3 = SeparableConv2d(in_channels, in_channels, 1)
self.down_add1 = SeparableConv2d(in_channels, in_channels, 2)
self.down_add2 = SeparableConv2d(in_channels, in_channels, 2)
self.down_add3 = SeparableConv2d(in_channels, in_channels, 2)
def forward(self, c2, c3, c4, c5):
"""
Args:
c2, c3, c4, c5 (Tensor): Each has the shape of
:math:`(N, C_i, H_i, W_i)`.
Returns:
list[Tensor]: A list of 4 tensors of the same shape as input.
"""
# upsample
c4 = self.up_add1(self._upsample_add(c5, c4)) # c4 shape
c3 = self.up_add2(self._upsample_add(c4, c3))
c2 = self.up_add3(self._upsample_add(c3, c2))
# downsample
c3 = self.down_add1(self._upsample_add(c3, c2))
c4 = self.down_add2(self._upsample_add(c4, c3))
c5 = self.down_add3(self._upsample_add(c5, c4)) # c4 / 2
return c2, c3, c4, c5
def _upsample_add(self, x, y):
return F.interpolate(x, size=y.size()[2:]) + y
class SeparableConv2d(BaseModule):
def __init__(self, in_channels, out_channels, stride=1, init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.depthwise_conv = nn.Conv2d(
in_channels=in_channels,
out_channels=in_channels,
kernel_size=3,
padding=1,
stride=stride,
groups=in_channels)
self.pointwise_conv = nn.Conv2d(
in_channels=in_channels, out_channels=out_channels, kernel_size=1)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
def forward(self, x):
x = self.depthwise_conv(x)
x = self.pointwise_conv(x)
x = self.bn(x)
x = self.relu(x)
return x
@NECKS.register_module()
class FPEM_FFM(BaseModule):
"""This code is from https://github.com/WenmuZhou/PAN.pytorch.
Args:
in_channels (list[int]): A list of 4 numbers of input channels.
conv_out (int): Number of output channels.
fpem_repeat (int): Number of FPEM layers before FFM operations.
align_corners (bool): The interpolation behaviour in FFM operation,
used in :func:`torch.nn.functional.interpolate`.
init_cfg (dict or list[dict], optional): Initialization configs.
"""
def __init__(self,
in_channels,
conv_out=128,
fpem_repeat=2,
align_corners=False,
init_cfg=dict(
type='Xavier', layer='Conv2d', distribution='uniform')):
super().__init__(init_cfg=init_cfg)
# reduce layers
self.reduce_conv_c2 = nn.Sequential(
nn.Conv2d(
in_channels=in_channels[0],
out_channels=conv_out,
kernel_size=1), nn.BatchNorm2d(conv_out), nn.ReLU())
self.reduce_conv_c3 = nn.Sequential(
nn.Conv2d(
in_channels=in_channels[1],
out_channels=conv_out,
kernel_size=1), nn.BatchNorm2d(conv_out), nn.ReLU())
self.reduce_conv_c4 = nn.Sequential(
nn.Conv2d(
in_channels=in_channels[2],
out_channels=conv_out,
kernel_size=1), nn.BatchNorm2d(conv_out), nn.ReLU())
self.reduce_conv_c5 = nn.Sequential(
nn.Conv2d(
in_channels=in_channels[3],
out_channels=conv_out,
kernel_size=1), nn.BatchNorm2d(conv_out), nn.ReLU())
self.align_corners = align_corners
self.fpems = ModuleList()
for _ in range(fpem_repeat):
self.fpems.append(FPEM(conv_out))
def forward(self, x):
"""
Args:
x (list[Tensor]): A list of four tensors of shape
:math:`(N, C_i, H_i, W_i)`, representing C2, C3, C4, C5
features respectively. :math:`C_i` should matches the number in
``in_channels``.
Returns:
list[Tensor]: Four tensors of shape
:math:`(N, C_{out}, H_0, W_0)` where :math:`C_{out}` is
``conv_out``.
"""
c2, c3, c4, c5 = x
# reduce channel
c2 = self.reduce_conv_c2(c2)
c3 = self.reduce_conv_c3(c3)
c4 = self.reduce_conv_c4(c4)
c5 = self.reduce_conv_c5(c5)
# FPEM
for i, fpem in enumerate(self.fpems):
c2, c3, c4, c5 = fpem(c2, c3, c4, c5)
if i == 0:
c2_ffm = c2
c3_ffm = c3
c4_ffm = c4
c5_ffm = c5
else:
c2_ffm = c2_ffm + c2
c3_ffm = c3_ffm + c3
c4_ffm = c4_ffm + c4
c5_ffm = c5_ffm + c5
# FFM
c5 = F.interpolate(
c5_ffm,
c2_ffm.size()[-2:],
mode='bilinear',
align_corners=self.align_corners)
c4 = F.interpolate(
c4_ffm,
c2_ffm.size()[-2:],
mode='bilinear',
align_corners=self.align_corners)
c3 = F.interpolate(
c3_ffm,
c2_ffm.size()[-2:],
mode='bilinear',
align_corners=self.align_corners)
outs = [c2_ffm, c3, c4, c5]
return tuple(outs)
| [
"[email protected]"
] | |
3652511f4a3c2e9b77748a3cf8132b152949bf44 | ffe4c155e228f1d3bcb3ff35265bb727c684ec1a | /Codes/Quiz/number_of_factors.py | 68ce727aa40589c018b92480a89b9de9e4e47ed7 | [] | no_license | yuuee-www/Python-Learning | 848407aba39970e7e0058a4adb09dd35818c1d54 | 2964c9144844aed576ea527acedf1a465e9a8664 | refs/heads/master | 2023-03-12T00:55:06.034328 | 2021-02-28T13:43:14 | 2021-02-28T13:43:14 | 339,406,816 | 0 | 0 | null | 2021-02-28T11:27:40 | 2021-02-16T13:26:46 | Jupyter Notebook | UTF-8 | Python | false | false | 269 | py | def numberOfFactors(num):
ans = 1
x = 2
while x * x <= num:
cnt = 1
while num % x == 0:
cnt += 1
num /= x
ans = cnt
x += 1
return ans * (1 + (num > 1))
n = int(input())
print(numberOfFactors(n))
| [
"[email protected]"
] | |
0b8609103dd7f8a320e1b62047793f8513669fc7 | 0d247fea57ee40717166b1ff753fd626092e9e78 | /tests/test_completer.py | 279b880925ed77714e3e343298ac7a331f4a7706 | [] | no_license | sean-heller/dockercli | 362ff0310992b2257ed20077b76245729ca2a227 | ab9ca8f13d01a5d8c5d3f58743c476836b06d186 | refs/heads/master | 2021-05-29T07:27:33.954752 | 2015-07-20T16:21:09 | 2015-07-20T16:21:09 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,645 | py | from __future__ import unicode_literals
import pytest
from prompt_toolkit.completion import Completion
from prompt_toolkit.document import Document
from dockercli.options import all_options
from dockercli.options import COMMAND_NAMES
@pytest.fixture
def completer():
import dockercli.completer as cmp
return cmp.DockerCompleter()
@pytest.fixture
def complete_event():
from mock import Mock
return Mock()
cs1 = ['newton', 'tesla', 'einstein', 'edison']
rs1 = ['einstein', 'edison']
im1 = ['ubuntu', 'hello-world', 'postgres', 'nginx']
cs2 = ['desperate_hodgkin', 'desperate_torvalds', 'silly_fermat', 'some-percona']
def test_empty_string_completion(completer, complete_event):
"""
In the beginning of the line, all available commands are suggested.
"""
text = ''
position = 0
result = set(completer.get_completions(
Document(text=text, cursor_position=position),
complete_event))
assert result == set(map(Completion, COMMAND_NAMES))
def test_build_path_completion_absolute(completer, complete_event):
"""
Suggest build paths from filesystem root.
"""
command = 'build /'
position = len(command)
result = set(completer.get_completions(
Document(text=command, cursor_position=position),
complete_event))
expected = ['etc', 'home', 'tmp', 'usr', 'var']
expected = set(map(lambda t: Completion(t, 0), expected))
assert expected.issubset(result)
def test_build_path_completion_user(completer, complete_event):
"""
Suggest build paths from user home directory.
"""
command = 'build ~'
position = len(command)
result = set(completer.get_completions(
Document(text=command, cursor_position=position),
complete_event))
expected = ['~/Documents', '~/Downloads']
expected = set(map(lambda t: Completion(t, -1), expected))
assert expected.issubset(result)
def test_build_path_completion_user_dir(completer, complete_event):
"""
Suggest build paths from user home directory.
"""
command = 'build ~/s'
position = len(command)
result = set(completer.get_completions(
Document(text=command, cursor_position=position),
complete_event))
expected = ['src']
expected = set(map(lambda t: Completion(t, -1), expected))
assert expected.issubset(result)
@pytest.mark.parametrize("command, expected", [
("h", ['help']),
("he", ['help']),
("hel", ['help']),
("help", ['help']),
('run -d ubuntu:14.04 /bin/sh -c "w', []) # not complete in quoted string
])
def test_command_completion(command, expected):
"""
Test command suggestions.
:param command: string: text that user started typing
:param expected: list: expected completions
"""
c = completer()
e = complete_event()
position = len(command)
result = set(c.get_completions(
Document(text=command, cursor_position=position),
e))
expected = set(map(lambda t: Completion(t, -len(command)), expected))
assert result == expected
@pytest.mark.parametrize("command, expected", [
("h", ['help', 'shell', 'push', 'attach', 'search']),
("he", ['help', 'shell']),
("hel", ['help', 'shell']),
("help", ['help']),
('run -d ubuntu:14.04 /bin/sh -c "w', []) # not complete in quoted string
])
def test_command_completion_fuzzy(command, expected):
"""
Test command suggestions.
:param command: string: text that user started typing
:param expected: list: expected completions
"""
c = completer()
e = complete_event()
c.set_fuzzy_match(True)
position = len(command)
result = list(c.get_completions(
Document(text=command, cursor_position=position),
e))
expected = list(map(lambda t: Completion(t, -len(command)), expected))
assert result == expected
pso = list(filter(lambda x: x.name.startswith('-'), all_options('ps')))
@pytest.mark.parametrize("command, expected, expected_pos", [
("ps ", pso, 0),
("ps --", list(filter(
lambda x: x.long_name and x.long_name.startswith('--'), pso)), -2),
("ps --h", list(filter(
lambda x: x.long_name and x.long_name.startswith('--h'), pso)), -3),
("ps --all ", list(filter(
lambda x: x.long_name not in ['--all'], pso)), 0),
("ps --all --quiet ", list(filter(
lambda x: x.long_name not in ['--all', '--quiet'], pso)), 0),
])
def test_options_completion_long(command, expected, expected_pos):
"""
Test command options suggestions.
:param command: string: text that user started typing
:param expected: list: expected completions
"""
c = completer()
e = complete_event()
position = len(command)
result = set(c.get_completions(
Document(text=command, cursor_position=position), e))
expected = set(map(lambda t: Completion(
t.get_name(is_long=True), expected_pos, t.display), expected))
assert result == expected
def option_map(cmd, is_long):
return {
x.get_name(is_long): x.display for x in all_options(cmd) if x.name.startswith('-')
}
psm = option_map('ps', True)
@pytest.mark.parametrize("command, expected, expected_pos", [
("ps ", sorted(psm.keys()), 0),
("ps h", ['--help'], -1),
("ps i", ['--since', '--size', '--quiet'], -1),
("ps ze", ['--size'], -2),
])
def test_options_completion_long_fuzzy(command, expected, expected_pos):
"""
Test command options suggestions.
:param command: string: text that user started typing
:param expected: list: expected completions
"""
c = completer()
e = complete_event()
c.set_fuzzy_match(True)
position = len(command)
result = list(c.get_completions(
Document(text=command, cursor_position=position), e))
expected = list(map(lambda t: Completion(
t, expected_pos, psm[t]), expected))
assert result == expected
@pytest.mark.parametrize("command, expected, expected_pos", [
("ps ", pso, 0),
("ps -", filter(
lambda x: x.name.startswith('-'), pso), -1),
("ps -h", filter(
lambda x: x.short_name and x.short_name.startswith('-h'), pso), -2),
])
def test_options_completion_short(command, expected, expected_pos):
"""
Test command options suggestions.
:param command: string: text that user started typing
:param expected: list: expected completions
"""
c = completer()
e = complete_event()
c.set_long_options(False)
position = len(command)
result = set(c.get_completions(
Document(text=command, cursor_position=position), e))
expected = set(map(lambda t: Completion(
t.get_name(
is_long=c.get_long_options()), expected_pos, t.display), expected))
assert result == expected
@pytest.mark.parametrize("command, expected, expected_pos", [
("ps --before ", cs1, 0),
("ps --before e", filter(lambda x: x.startswith('e'), cs1), -1),
("ps --before ei", filter(lambda x: x.startswith('ei'), cs1), -2),
])
def test_options_container_completion(command, expected, expected_pos):
"""
Suggest container names in relevant options (ps --before)
"""
c = completer()
e = complete_event()
c.set_containers(cs1)
position = len(command)
result = set(c.get_completions(
Document(text=command, cursor_position=position), e))
expected = set(map(lambda t: Completion(t, expected_pos), expected))
assert result == expected
@pytest.mark.parametrize("command, expected, expected_pos", [
("top ", list(map(
lambda x: (x, x), rs1)) + [('--help', '-h/--help')], 0),
("top e", map(
lambda x: (x, x), filter(lambda x: x.startswith('e'), rs1)), -1),
])
def test_options_container_running_completion(command, expected, expected_pos):
"""
Suggest running container names (top [container])
"""
c = completer()
e = complete_event()
c.set_containers(cs1)
c.set_running(rs1)
position = len(command)
result = set(c.get_completions(
Document(text=command, cursor_position=position), e))
expected_completions = set()
for text, display in expected:
if display:
expected_completions.add(Completion(text, expected_pos, display))
else:
expected_completions.add(Completion(text, expected_pos))
assert result == expected_completions
@pytest.mark.parametrize("command, expected, expected_pos", [
("rm ", ['--all-stopped', ('--help', '-h/--help')] + cs2, 0),
("rm spe", ['--all-stopped', 'desperate_hodgkin', 'desperate_torvalds', 'some-percona'], -3),
])
def test_options_container_completion_fuzzy(command, expected, expected_pos):
"""
Suggest running container names (top [container])
"""
c = completer()
e = complete_event()
c.set_containers(cs2)
c.set_fuzzy_match(True)
position = len(command)
result = list(c.get_completions(
Document(text=command, cursor_position=position), e))
expected_completions = []
for x in expected:
if isinstance(x, tuple):
expected_completions.append(Completion(x[0], expected_pos, x[1]))
else:
expected_completions.append(Completion(x, expected_pos))
assert result == expected_completions
def test_options_image_completion(completer, complete_event):
"""
Suggest image names in relevant options (images --filter)
"""
command = 'images --filter '
expected = ['ubuntu', 'hello-world', 'postgres', 'nginx']
expected_pos = 0
completer.set_images(expected)
position = len(command)
result = set(completer.get_completions(
Document(text=command, cursor_position=position), complete_event))
expected = set(map(lambda t: Completion(t, expected_pos), expected))
assert result == expected
@pytest.mark.parametrize("command, expected, expected_pos", [
('images --filter ', ['hello-world', 'nginx', 'postgres', 'ubuntu'], 0),
('images --filter n', ['nginx', 'ubuntu'], -1),
('images --filter g', ['nginx', 'postgres'], -1),
('images --filter u', ['ubuntu'], -1),
])
def test_options_image_completion_fuzzy(command, expected, expected_pos):
"""
Suggest image names in relevant options (images --filter)
"""
c = completer()
e = complete_event()
c.set_images(im1)
c.set_fuzzy_match(True)
position = len(command)
result = list(c.get_completions(
Document(text=command, cursor_position=position), e))
expected = list(map(lambda t: Completion(t, expected_pos), expected))
assert result == expected
| [
"[email protected]"
] | |
876d42eca7d958444943cfd5e550208f8781fe15 | 43c24c890221d6c98e4a45cd63dba4f1aa859f55 | /test/cpython/test_copy_reg.py | 2f49eb711e1c3c92f0d6818c85ca52b10e5eb43c | [
"Python-2.0",
"Apache-2.0",
"BSD-2-Clause"
] | permissive | jmgc/pyston | c8e4df03c33c6b81d20b7d51a781d9e10148238e | 9f672c1bbb75710ac17dd3d9107da05c8e9e8e8f | refs/heads/master | 2020-12-11T07:51:58.968440 | 2020-09-11T14:38:38 | 2020-09-11T14:38:38 | 39,242,644 | 0 | 0 | NOASSERTION | 2020-09-11T14:38:39 | 2015-07-17T08:09:31 | Python | UTF-8 | Python | false | false | 44 | py | ../../from_cpython/Lib/test/test_copy_reg.py | [
"[email protected]"
] | |
23b8ea48b8dcdfd520fd983a55990ac4992ded00 | 4017add8fa767cf2eca9163791aa65ee77c67a07 | /code/gradient_descent/first.py | 2a722d65bc85c82cd02c686f9e1e382f1907852a | [] | no_license | ducksfrogs/numpy_data_ana | 00c0928f2ddc7a8ad0ea9ecdefa3815a8d880969 | 9d89bc377a3015c19c74f6b5aa500f2f2f08cdb1 | refs/heads/master | 2022-10-19T22:37:10.314453 | 2020-06-09T00:05:23 | 2020-06-09T00:05:23 | 268,383,582 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,939 | py | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston
dataset = load_boston()
samples, label, feature_names = dataset.data, dataset.target, dataset.feature_names
bostondf = pd.DataFrame(dataset.data)
bostondf.columns = dataset.feature_names
bostondf['Target price'] = dataset.target
bostondf.head()
bostondf.plot(x='RM', y='Target price', style='o')
def prediction(X, coefficient, intercept):
return X*coefficient + intercept
def cost_function(X, Y, coefficient, intercept):
MSE = 0.0
for i in range(len(X)):
MSE += (Y[i] -(coefficient*X[i] + intercept))**2
return MSE / len(X)
def update_weights(X, Y, coefficient, intercept, learning_rate):
coefficient_derivative = 0
intercept_derivative = 0
for i in range(len(X)):
coefficient_derivative += -2*X[i] *(Y[i] -(coefficient * X[i] + intercept))
intercept_derivative += -2*(Y[i] - (coefficient* X[i] + intercept))
coefficient -= (coefficient_derivative / len(X)) * learning_rate
intercept -= (intercept_derivative / len(X)) * learning_rate
return coefficient, intercept
def train(X, Y, coefficient, intercept, learning_rate, iteration):
cost_hist = []
for i in range(iteration):
coefficient, intercept = update_weights(X, Y, coefficient, intercept, learning_rate)
cost = cost_function(X, Y, coefficient, intercept)
cost_hist.append(cost)
return coefficient, intercept, cost_hist
learning_rate = 0.01
iteration = 10001
coefficient = 0.3
intercept = 2
X = bostondf.iloc[:, 5:6].values
Y = bostondf.iloc[:, 13:14].values
# coefficient, intercept, cost_history = train(X, Y, coefficient, intercept, learning_rate, iteration)
coefficient, intercept, cost_history = train(X, Y, coefficient, intercept=2, learning_rate=0.01, iteration=10001)
y_hat = X*coefficient + intercept
plt.plot(X, Y, 'bo')
plt.plot(X, y_hat)
plt.show()
| [
"[email protected]"
] | |
c79e23af4259ba1c22d26c8aa3efba74db913669 | 3fb24e6505ffdc3a3961c467bc54ba7c0b526454 | /gravityRK4_resized.py | bb4decb7fef61bfc5574688ebcdd950f6745dc2a | [
"MIT"
] | permissive | martinohanlon/minecraft-planets | 8d76018cb1dc4154becf9ec836f1e00488673b9e | c7017eb9be6260c8c664891a77063305ac97ae57 | refs/heads/master | 2020-06-02T07:50:33.539646 | 2013-03-07T21:19:43 | 2013-03-07T21:19:43 | 8,636,766 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 10,722 | py | #!/usr/bin/env python
"""
An improved version of my Python-based gravity simulator, using Runge-Kutta
4th order solution of the differential equations - coded during Xmas 2012.
Happy holidays, everyone!
I've always been fascinated by space - ever since I read 'The Family of
the Sun', when I was young. And I always wanted to simulate what I've read
about Newton's gravity law, and see what happens in... a universe of my own
making :-)
So: The following code 'sprays' some 'planets' randomly, around a sun,
inside a 900x600 window (the values are below, change them at will).
Afterwards, it applies a very simple set of laws:
- Gravity, inversely proportional to the square of the distance, and linearly
proportional to the product of the two masses
- Elastic collissions of two objects if they are close enough to touch: a
merged object is then created, that maintains the momentum (mass*velocity)
and the mass of the two merged ones.
- This updated version of the code is using the RK4 solution of the velocity/
acceleration differential equation, and is in fact based on the excellent
blog of Glenn Fiedler (http://gafferongames.com)
Use the numeric keypad's +/- to zoom in/out, and press SPACE to toggle
showing/hiding the orbits trace.
Blog post at:
http://users.softlab.ntua.gr/~ttsiod/gravity.html
http://ttsiodras.github.com/gravity.html
Thanassis Tsiodras
[email protected]
"""
import sys
import math
import pygame
import random
from collections import defaultdict
# The window size
WIDTH, HEIGHT = 50, 50
WIDTHD2, HEIGHTD2 = WIDTH/2., HEIGHT/2.
# The number of simulated planets
PLANETS = 30
# The density of the planets - used to calculate their mass
# from their volume (i.e. via their radius)
DENSITY = 0.001
# The gravity coefficient - it's my universe, I can pick whatever I want :-)
GRAVITYSTRENGTH = 1.e4
# The global list of planets
g_listOfPlanets = []
class State:
"""Class representing position and velocity."""
def __init__(self, x, y, vx, vy):
self._x, self._y, self._vx, self._vy = x, y, vx, vy
def __repr__(self):
return 'x:{x} y:{y} vx:{vx} vy:{vy}'.format(
x=self._x, y=self._y, vx=self._vx, vy=self._vy)
class Derivative:
"""Class representing velocity and acceleration."""
def __init__(self, dx, dy, dvx, dvy):
self._dx, self._dy, self._dvx, self._dvy = dx, dy, dvx, dvy
def __repr__(self):
return 'dx:{dx} dy:{dy} dvx:{dvx} dvy:{dvy}'.format(
dx=self._dx, dy=self._dy, dvx=self._dvx, dvy=self._dvy)
class Planet:
"""Class representing a planet. The "_st" member is an instance of "State",
carrying the planet's position and velocity - while the "_m" and "_r"
members represents the planet's mass and radius."""
def __init__(self, initialState=None):
#if PLANETS == 1:
if initialState != None:
# A nice example of a planet orbiting around our sun :-)
#self._st = State(15, 25, 0, 0.2)
self._st = initialState
else:
# otherwise pick a random position and velocity
self._st = State(
float(random.randint(0, WIDTH)),
float(random.randint(0, HEIGHT)),
float(random.randint(0, 40)/100.)-0.2,
float(random.randint(0, 40)/100.)-0.2)
self._r = 0.55
self.setMassFromRadius()
self._merged = False
def __repr__(self):
return repr(self._st)
def acceleration(self, state, unused_t):
"""Calculate acceleration caused by other planets on this one."""
ax = 0.0
ay = 0.0
for p in g_listOfPlanets:
if p is self or p._merged:
continue # ignore ourselves and merged planets
dx = p._st._x - state._x
dy = p._st._y - state._y
dsq = dx*dx + dy*dy # distance squared
dr = math.sqrt(dsq) # distance
force = GRAVITYSTRENGTH*self._m*p._m/dsq if dsq>1e-10 else 0.
# Accumulate acceleration...
ax += force*dx/dr
ay += force*dy/dr
return (ax, ay)
def initialDerivative(self, state, t):
"""Part of Runge-Kutta method."""
ax, ay = self.acceleration(state, t)
return Derivative(state._vx, state._vy, ax, ay)
def nextDerivative(self, initialState, derivative, t, dt):
"""Part of Runge-Kutta method."""
state = State(0., 0., 0., 0.)
state._x = initialState._x + derivative._dx*dt
state._y = initialState._y + derivative._dy*dt
state._vx = initialState._vx + derivative._dvx*dt
state._vy = initialState._vy + derivative._dvy*dt
ax, ay = self.acceleration(state, t+dt)
return Derivative(state._vx, state._vy, ax, ay)
def updatePlanet(self, t, dt):
"""Runge-Kutta 4th order solution to update planet's pos/vel."""
a = self.initialDerivative(self._st, t)
b = self.nextDerivative(self._st, a, t, dt*0.5)
c = self.nextDerivative(self._st, b, t, dt*0.5)
d = self.nextDerivative(self._st, c, t, dt)
dxdt = 1.0/6.0 * (a._dx + 2.0*(b._dx + c._dx) + d._dx)
dydt = 1.0/6.0 * (a._dy + 2.0*(b._dy + c._dy) + d._dy)
dvxdt = 1.0/6.0 * (a._dvx + 2.0*(b._dvx + c._dvx) + d._dvx)
dvydt = 1.0/6.0 * (a._dvy + 2.0*(b._dvy + c._dvy) + d._dvy)
self._st._x += dxdt*dt
self._st._y += dydt*dt
self._st._vx += dvxdt*dt
self._st._vy += dvydt*dt
def setMassFromRadius(self):
"""From _r, set _m: The volume is (4/3)*Pi*(r^3)..."""
self._m = DENSITY*4.*math.pi*(self._r**3.)/3.
def setRadiusFromMass(self):
"""Reversing the setMassFromRadius formula, to calculate radius from
mass (used after merging of two planets - mass is added, and new
radius is calculated from this)"""
self._r = (3.*self._m/(DENSITY*4.*math.pi))**(0.3333)
def main():
pygame.init()
win=pygame.display.set_mode((WIDTH, HEIGHT))
keysPressed = defaultdict(bool)
def ScanKeyboard():
while True:
# Update the keysPressed state:
evt = pygame.event.poll()
if evt.type == pygame.NOEVENT:
break
elif evt.type in [pygame.KEYDOWN, pygame.KEYUP]:
keysPressed[evt.key] = evt.type == pygame.KEYDOWN
global g_listOfPlanets, PLANETS
if len(sys.argv) == 2:
PLANETS = int(sys.argv[1])
# And God said: Let there be lights in the firmament of the heavens...
g_listOfPlanets = []
#for i in xrange(0, PLANETS):
#g_listOfPlanets.append(Planet())
g_listOfPlanets.append(Planet(State(15, 25, 0, 0.2)))
g_listOfPlanets.append(Planet(State(35, 25, 0, -0.2)))
g_listOfPlanets.append(Planet(State(5, 25, 0, 0.15)))
g_listOfPlanets.append(Planet(State(37, 37, 0, -0.15)))
#g_listOfPlanets.append(Planet())
def planetsTouch(p1, p2):
dx = p1._st._x - p2._st._x
dy = p1._st._y - p2._st._y
dsq = dx*dx + dy*dy
dr = math.sqrt(dsq)
return dr<=(p1._r + p2._r)
sun = Planet()
sun._st._x, sun._st._y = WIDTHD2, HEIGHTD2
sun._st._vx = sun._st._vy = 0.
sun._m *= 100
sun.setRadiusFromMass()
g_listOfPlanets.append(sun)
for p in g_listOfPlanets:
if p is sun:
continue
if planetsTouch(p, sun):
p._merged = True # ignore planets inside the sun
# Zoom factor, changed at runtime via the '+' and '-' numeric keypad keys
zoom = 1.0
# t and dt are unused in this simulation, but are in general,
# parameters of engine (acceleration may depend on them)
t, dt = 0., 1.
bClearScreen = True
pygame.display.set_caption('Gravity simulation (SPACE: show orbits, '
'keypad +/- : zoom in/out)')
while True:
t += dt
pygame.display.flip()
if bClearScreen: # Show orbits or not?
win.fill((0, 0, 0))
win.lock()
for p in g_listOfPlanets:
if not p._merged: # for planets that have not been merged, draw a
# circle based on their radius, but take zoom factor into account
pygame.draw.circle(win, (255, 255, 255),
(int(WIDTHD2+zoom*WIDTHD2*(p._st._x-WIDTHD2)/WIDTHD2),
int(HEIGHTD2+zoom*HEIGHTD2*(p._st._y-HEIGHTD2)/HEIGHTD2)),
int(p._r*zoom), 0)
win.unlock()
ScanKeyboard()
# Update all planets' positions and speeds (should normally double
# buffer the list of planet data, but turns out this is good enough :-)
for p in g_listOfPlanets:
if p._merged or p is sun:
continue
# Calculate the contributions of all the others to its acceleration
# (via the gravity force) and update its position and velocity
p.updatePlanet(t, dt)
# See if we should merge the ones that are close enough to touch,
# using elastic collisions (conservation of total momentum)
for p1 in g_listOfPlanets:
if p1._merged:
continue
for p2 in g_listOfPlanets:
if p1 is p2 or p2._merged:
continue
if planetsTouch(p1, p2):
if p1._m < p2._m:
p1, p2 = p2, p1 # p1 is the biggest one (mass-wise)
p2._merged = True
if p1 is sun:
continue # No-one can move the sun :-)
newvx = (p1._st._vx*p1._m+p2._st._vx*p2._m)/(p1._m+p2._m)
newvy = (p1._st._vy*p1._m+p2._st._vy*p2._m)/(p1._m+p2._m)
p1._m += p2._m # maintain the mass (just add them)
p1.setRadiusFromMass() # new mass --> new radius
p1._st._vx, p1._st._vy = newvx, newvy
# update zoom factor (numeric keypad +/- keys)
if keysPressed[pygame.K_KP_PLUS]:
zoom /= 0.99
if keysPressed[pygame.K_KP_MINUS]:
zoom /= 1.01
if keysPressed[pygame.K_ESCAPE]:
break
if keysPressed[pygame.K_SPACE]:
while keysPressed[pygame.K_SPACE]:
ScanKeyboard()
bClearScreen = not bClearScreen
verb = "show" if bClearScreen else "hide"
pygame.display.set_caption(
'Gravity simulation (SPACE: '
'%s orbits, keypad +/- : zoom in/out)' % verb)
if __name__ == "__main__":
try:
import psyco
psyco.profile()
except:
print 'Psyco not found, ignoring it'
main()
| [
"[email protected]"
] | |
8526d76d462eb31cb9b6edae46331fdb9552850a | 7a5b729a660a35d0d80c9836202025a719f026fb | /general codes/mod10_10.py | 8c80260bc5a7216f6b44eceaaa9cf816b84db9ad | [] | no_license | Harshit2009/My-Programs- | 7a05eb3369b98010805752a0234867b726c4ac0e | 1ac60faeb0ba514f2c35bcb82be43654b5cef785 | refs/heads/master | 2023-01-13T18:58:26.088714 | 2020-11-19T08:31:43 | 2020-11-19T08:31:43 | 269,538,702 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 35 | py | import mod10
mod10.mod10(0,1,10)
| [
"[email protected]"
] | |
fda009d969b4c11b4518f554302e60e88490b46b | 0f09759025db447fe63b3af0af80c3e31e2a887f | /scripts/cell/taskScripts/Bangzhushenmiren.py | 06b8d8ab06630b18f47c4ebd930e3d56d5de5726 | [] | no_license | jevonhuang/huanhuoserver | d7db1cd4c67d8be2da4dc9ec84ef8f23e891c537 | caa8a87cd303b4d0368a0a6397fc1d47685c3bc3 | refs/heads/master | 2020-12-07T16:47:40.668507 | 2018-04-02T10:12:01 | 2018-04-02T10:12:01 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 741 | py | # -*- coding: utf-8 -*-
import KBEngine
from KBEDebug import *
class Bangzhushenmiren(object):
def __init__(self, owner, selfIndex, npcName, npcTaskIndex):
DEBUG_MSG("Bangzhushenmiren:__init__")
self.owner = owner
self.selfIndex = selfIndex
self.npcName = npcName
self.npcTaskIndex = npcTaskIndex
self.owner.setAttr("Bangzhushenmiren_TaskCounter", 1)
self.oldTaskCounter = self.owner.getAttr("Bangzhushenmiren_TaskCounter")
def detectTaskCompleteness(self):
self.owner.setAttr("Bangzhushenmiren_TaskCounter", 0)
if self.owner.getAttr("Bangzhushenmiren_TaskCounter") == 0:
self.owner.setTaskFinish(self.npcName, self.npcTaskIndex, self.selfIndex)
| [
"[email protected]"
] | |
1c725c18b3b21a31f7fe5dc8cf9f9f4b63fdd24b | fd6747673bad3628eba33d3892b63180db5fb044 | /tensorflow/compiler/xla/python/xla_extension/__init__.pyi | 61d1e478c9013a9376efd36b851a37e6b8793772 | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla",
"BSD-2-Clause"
] | permissive | gautam1858/tensorflow | 2cbdc251a470eefd27ce31fd4e6fe31253e9d07a | bd56b0b3a00432896cffbb412bedbb13579ec598 | refs/heads/master | 2022-06-04T22:09:41.533559 | 2022-05-10T15:51:20 | 2022-05-10T15:51:20 | 59,177,861 | 2 | 0 | Apache-2.0 | 2022-03-17T14:48:17 | 2016-05-19T05:56:42 | C++ | UTF-8 | Python | false | false | 16,725 | pyi | # Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import enum
import inspect
import types
import typing
from typing import Any, Callable, ClassVar, Dict, List, Optional, Sequence, Tuple, Type, TypeVar, Union, overload
import numpy as np
from . import ops
from . import jax_jit
from . import mlir
from . import outfeed_receiver
from . import pmap_lib
from . import profiler
from . import pytree
from . import transfer_guard_lib
_LiteralSlice = Any
_Status = Any
_Dtype = Any
_XlaOpMetadata = Any
_T = TypeVar("_T")
class XlaRuntimeError(RuntimeError):
pass
class PrimitiveType(enum.IntEnum):
PRIMITIVE_TYPE_INVALID: PrimitiveType
PRED: PrimitiveType
S8: PrimitiveType
S16: PrimitiveType
S32: PrimitiveType
S64: PrimitiveType
U8: PrimitiveType
U16: PrimitiveType
U32: PrimitiveType
U64: PrimitiveType
BF16: PrimitiveType
F16: PrimitiveType
F32: PrimitiveType
F64: PrimitiveType
C64: PrimitiveType
C128: PrimitiveType
TUPLE: PrimitiveType
OPAQUE_TYPE: PrimitiveType
TOKEN: PrimitiveType
def bfloat16_dtype() -> Type[Any]: ...
# === BEGIN xla_compiler.cc
class Shape:
def __init__(self, s: str): ...
@staticmethod
def tuple_shape(shapes: Sequence[Shape]) -> Shape: ...
@staticmethod
def array_shape(
type: Union[np.dtype, PrimitiveType],
dims_seq: Any = ...,
layout_seq: Any = ...,
dynamic_dimensions: Optional[List[bool]] = ...) -> Shape: ...
@staticmethod
def token_shape() -> Shape: ...
@staticmethod
def scalar_shape(type: Union[np.dtype, PrimitiveType]) -> Shape: ...
def dimensions(self) -> Tuple[int, ...]: ...
def xla_element_type(self) -> PrimitiveType: ...
def element_type(self) -> np.dtype: ...
def numpy_dtype(self) -> np.dtype: ...
def is_tuple(self) -> bool: ...
def is_array(self) -> bool: ...
def is_token(self) -> bool: ...
def is_static(self) -> bool: ...
def is_dynamic(self) -> bool: ...
def is_dynamic_dimension(self, dimension: int) -> bool: ...
def set_dynamic_dimension(self, dimension: int, is_dynamic: bool) -> None: ...
def rank(self) -> int: ...
def to_serialized_proto(self) -> bytes: ...
def tuple_shapes(self) -> List[Shape]: ...
def leaf_count(self) -> int: ...
def with_major_to_minor_layout_if_absent(self) -> Shape: ...
def __eq__(self, other: Shape) -> bool: ...
def __ne__(self, other: Shape) -> bool: ...
def __hash__(self) -> int: ...
def __repr__(self) -> str: ...
class Layout:
def minor_to_major(self) -> Tuple[int, ...]: ...
def to_string(self) -> str: ...
def __eq__(self, other: Layout) -> bool: ...
def __ne__(self, other: Layout) -> bool: ...
def __hash__(self) -> int: ...
class ProgramShape:
def __init__(self, params: Sequence[Shape], result: Shape) -> None: ...
def parameter_shapes(self) -> List[Shape]: ...
def result_shape(self) -> Shape: ...
def __repr__(self) -> str: ...
class ShapeIndex:
def __init__(self, indices: List[int]) -> ShapeIndex: ...
def __eq__(self, other: Shape) -> bool: ...
def __ne__(self, other: Shape) -> bool: ...
def __hash__(self) -> int: ...
def __repr__(self) -> str: ...
class Literal:
def __repr__(self) -> str: ...
class XlaComputation:
def __init__(self, serialized_hlo_module_proto: bytes) -> None: ...
def get_hlo_module(self) -> HloModule: ...
def program_shape(self) -> ProgramShape: ...
def as_serialized_hlo_module_proto(self) -> bytes: ...
def as_hlo_text(self, print_large_constants: bool=False) -> str: ...
def as_hlo_dot_graph(self) -> str: ...
def hash(self) -> int: ...
def as_hlo_module(self) -> HloModule: ...
class HloPrintOptions:
def __init__(self) -> None: ...
@staticmethod
def short_parsable() -> HloPrintOptions: ...
@staticmethod
def canonical() -> HloPrintOptions: ...
@staticmethod
def fingerprint() -> HloPrintOptions: ...
print_large_constants: bool
print_metadata: bool
print_backend_config: bool
print_result_shape: bool
print_operand_shape: bool
print_operand_names: bool
print_ids: bool
print_extra_attributes: bool
print_program_shape: bool
print_percent: bool
print_control_dependencies: bool
compact_operands: bool
include_layout_in_shapes: bool
canonicalize_instruction_names: bool
canonicalize_computations: bool
indent_amount: int
is_in_nested_computation: bool
class HloModule:
spmd_output_sharding: Optional[OpSharding]
spmd_parameters_shardings: Optional[List[OpSharding]]
@property
def name(self) -> str: ...
def to_string(self, options: HloPrintOptions = ...) -> str: ...
def as_serialized_hlo_module_proto(self)-> bytes: ...
@staticmethod
def from_serialized_hlo_module_proto(
serialized_hlo_module_proto: bytes) -> HloModule: ...
def hlo_module_to_dot_graph(hlo_module: HloModule) -> str: ...
def hlo_module_cost_analysis(
client: Client,
module: HloModule) -> Dict[str, float]: ...
class XlaOp: ...
class XlaBuilder:
def __init__(self, name: str) -> None: ...
def Build(self, root: Optional[XlaOp] = ...) -> XlaComputation: ...
def GetShape(self, __op: XlaOp) -> Shape: ...
build = Build
def clear_op_metadata(self) -> None: ...
get_shape = GetShape
def get_program_shape(self, root: Optional[XlaOp] = ...) -> ProgramShape: ...
def is_constant(self, __op: XlaOp) -> bool: ...
def set_op_metadata(self, metadata: _XlaOpMetadata) -> None: ...
def set_sharding(self, sharding: OpSharding_Type) -> None: ...
def clear_sharding(self) -> None: ...
def setup_alias(
self,
__output_index: Sequence[int],
__param_number: int,
__param_index: Sequence[int]) -> None: ...
class DeviceAssignment:
@staticmethod
def create(array: np.ndarray) -> DeviceAssignment: ...
def replica_count(self) -> int: ...
def computation_count(self) -> int: ...
def __repr__(self) -> str: ...
def serialize(self) -> bytes: ...
class CompileOptions:
def __init__(self) -> None: ...
argument_layouts: Optional[List[Shape]]
parameter_is_tupled_arguments: bool
executable_build_options: ExecutableBuildOptions
tuple_arguments: bool
num_replicas: int
num_partitions: int
device_assignment: Optional[DeviceAssignment]
def register_custom_call_target(fn_name: str, capsule: Any, platform: str) -> _Status: ...
class DebugOptions:
def __repr__(self) -> str: ...
xla_cpu_enable_fast_math: bool
xla_cpu_fast_math_honor_infs: bool
xla_cpu_fast_math_honor_nans: bool
xla_cpu_fast_math_honor_division: bool
xla_cpu_fast_math_honor_functions: bool
xla_gpu_enable_fast_min_max: bool
xla_backend_optimization_level: int
xla_cpu_enable_xprof_traceme: bool
xla_llvm_disable_expensive_passes: bool
xla_test_all_input_layouts: bool
class CompiledMemoryStats:
generated_code_size_in_bytes: int
argument_size_in_bytes: int
output_size_in_bytes: int
alias_size_in_bytes: int
temp_size_in_bytes: int
def __str__(self) -> str: ...
class ExecutableBuildOptions:
def __init__(self) -> None: ...
def __repr__(self) -> str: ...
result_layout: Optional[Shape]
num_replicas: int
num_partitions: int
debug_options: DebugOptions
device_assignment: Optional[DeviceAssignment]
use_spmd_partitioning: bool
use_auto_spmd_partitioning: bool
auto_spmd_partitioning_mesh_shape: List[int]
auto_spmd_partitioning_mesh_ids: List[int]
class PrecisionConfig_Precision(enum.IntEnum):
DEFAULT: int
HIGH: int
HIGHEST: int
class OpSharding_Type(enum.IntEnum):
REPLICATED: int
MAXIMAL: int
TUPLE: int
OTHER: int
MANUAL: int
class OpSharding:
Type: typing.Type[OpSharding_Type]
type: OpSharding_Type
replicate_on_last_tile_dim: bool
last_tile_dims: Sequence[Type]
tile_assignment_dimensions: Sequence[int]
tile_assignment_devices: Sequence[int]
tuple_shardings: Sequence[OpSharding]
def SerializeToString(self) -> bytes: ...
class ChannelHandle_ChannelType(enum.IntEnum):
CHANNEL_TYPE_INVALID: int
DEVICE_TO_DEVICE: int
DEVICE_TO_HOST: int
HOST_TO_DEVICE: int
class ChannelHandle:
type: ChannelHandle_ChannelType
handle: int
def __repr__(self) -> str: ...
class FftType(enum.IntEnum):
FFT: int
IFFT: int
RFFT: int
IRFFT: int
# === END xla_compiler.cc
class Device:
id: int
host_id: int
process_index: int
platform: str
device_kind: str
client: Client
def __repr__(self) -> str: ...
def __str__(self) -> str: ...
def transfer_to_infeed(self, literal: _LiteralSlice): ...
def transfer_from_outfeed(self, shape: Shape): ...
def live_buffers(self) -> List[Buffer]: ...
def __getattr__(self, name: str) -> Any: ...
class GpuDevice(Device):
pass
class TpuDevice(Device):
pass
class _GpuAllocatorKind(enum.IntEnum):
DEFAULT: int
PLATFORM: int
BFC: int
CUDA_ASYNC: int
class GpuAllocatorConfig:
# TODO(b/194673104): Remove once pytype correctly resolves a nested enum.
Kind = _GpuAllocatorKind
def __init__(
self,
kind: _GpuAllocatorKind = ...,
memory_fraction: float = ...,
preallocate: bool = ...) -> None: ...
class HostBufferSemantics(enum.IntEnum):
IMMUTABLE_ONLY_DURING_CALL: HostBufferSemantics
IMMUTABLE_UNTIL_TRANSFER_COMPLETES: HostBufferSemantics
ZERO_COPY: HostBufferSemantics
class Client:
platform: str
platform_version: str
runtime_type: str
def device_count(self) -> int: ...
def local_device_count(self) -> int: ...
def devices(self) -> List[Device]: ...
def local_devices(self) -> List[Device]: ...
def live_buffers(self) -> List[Buffer]: ...
def live_executables(self) -> List[Executable]: ...
def host_id(self) -> int: ...
def process_index(self) -> int: ...
@overload
def get_default_device_assignment(
self,
num_replicas: int,
num_partitions: int) -> List[List[Device]]: ...
@overload
def get_default_device_assignment(
self,
num_replicas: int) -> List[Device]: ...
def create_channel_handle(self) -> ChannelHandle: ...
def create_device_to_host_channel_handle(self) -> ChannelHandle: ...
def create_host_to_device_channel_handle(self) -> ChannelHandle: ...
def buffer_from_pyval(
self,
argument: Any,
device: Device = ...,
force_copy: bool = ...,
host_buffer_semantics: HostBufferSemantics = ...) -> Buffer: ...
def make_cross_host_receive_buffers(
self,
shapes: Sequence[Shape],
device: Device) -> List[Tuple[Buffer, bytes]]: ...
def compile(
self,
computation: XlaComputation,
compile_options: CompileOptions = ...) -> Executable: ...
def serialize_executable(self, executable: Executable) -> bytes: ...
def deserialize_executable(
self, serialized: bytes,
options: CompileOptions) -> Executable: ...
# TODO(skyewm): remove when jax stop providing hlo_module
def deserialize_executable(
self, serialized: bytes,
hlo_module: HloModule,
options: CompileOptions) -> Executable: ...
def heap_profile(self) -> bytes: ...
def defragment(self) -> _Status: ...
def get_emit_python_callback_descriptor(
self, callable: Callable, operand_shapes: Sequence[XlaOp],
results_shapes: Sequence[Shape]) -> Tuple[Any, Any]: ...
def emit_python_callback(
self, callable: Callable, builder: XlaBuilder, operands: Sequence[XlaOp],
results_shapes: Sequence[Shape],
operand_layouts: Optional[Sequence[Shape]] = ...,
has_side_effects: bool = ...) -> Tuple[XlaOp, Any]: ...
def get_cpu_client(asynchronous: bool = ...) -> Client: ...
def get_tfrt_cpu_client(asynchronous: bool = ...) -> Client: ...
def get_interpreter_client() -> Client: ...
def get_gpu_client(
asynchronous: bool = ...,
allocator_config: GpuAllocatorConfig = ...,
distributed_client: Optional[DistributedRuntimeClient] = ...,
node_id: int = ...,
allowed_devices: Optional[Any] = ...,
platform_name: Optional[str] = ...) -> Client:...
def get_tpu_client(max_inflight_computations: int = ...) -> Client: ...
class DeviceArrayBase: ...
class DeviceArray(DeviceArrayBase):
__array_priority__: int
_device: Optional[Device]
aval: Any
weak_type: Optional[bool]
@property
def device_buffer(self: _T) -> _T: ...
shape: Tuple[int, ...]
dtype: np.dtype
size: int
ndim: int
_value: np.ndarray
def copy_to_device(self, dst_device: Device) -> DeviceArray: ...
def copy_to_remote_device(self,
descriptor: bytes) -> Tuple[_Status, bool]: ...
def on_device_size_in_bytes(self) -> int: ...
def delete(self) -> None: ...
def is_ready(self) -> bool: ...
def is_known_ready(self) -> bool: ...
def block_until_ready(self) -> DeviceArray: ...
def copy_to_host_async(self) -> _Status: ...
def to_py(self) -> np.ndarray: ...
def xla_shape(self) -> Shape: ...
def xla_dynamic_shape(self) -> Shape: ...
client: Client
def device(self) -> Device: ...
def platform(self) -> str: ...
def is_deleted(self) -> bool: ...
def unsafe_buffer_pointer(self) -> Any: ...
__cuda_array_interface__: Dict[str, Any]
traceback: Traceback
def clone(self) -> DeviceArray: ...
PyLocalBuffer = DeviceArray
Buffer = DeviceArray
class Executable:
client: Client
def local_logical_device_ids(self) -> List[Tuple[int, int]]: ...
def local_devices(self) -> List[Device]: ...
def size_of_generated_code_in_bytes(self) -> int: ...
def delete(self) -> None: ...
def execute(self, arguments: Sequence[DeviceArray]) -> List[DeviceArray]: ...
def execute_sharded_on_local_devices(
self,
arguments: Sequence[List[DeviceArray]]) -> List[List[DeviceArray]]: ...
def hlo_modules(self) -> List[HloModule]: ...
def keep_alive(self) -> None: ...
traceback: Traceback
fingerprint: Optional[bytes]
def buffer_to_dlpack_managed_tensor(
buffer: Buffer,
take_ownership: bool = ...) -> Any: ...
def dlpack_managed_tensor_to_buffer(
tensor: Any, cpu_backend: Optional[Client] = ...,
gpu_backend: Optional[Client] = ...) -> Buffer: ...
# === BEGIN py_traceback.cc
class Frame:
file_name: str
function_name: str
function_line_start: int
line_num: int
def __repr__(self) -> str: ...
class Traceback:
enabled: ClassVar[bool]
@staticmethod
def get_traceback() -> Traceback: ...
frames: Sequence[Frame]
def __str__(self) -> str: ...
def as_python_traceback(self) -> Any: ...
def raw_frames(self) -> Tuple[List[types.CodeType], List[int]]: ...
@staticmethod
def code_addr2line(code: types.CodeType, lasti: int) -> int: ...
def replace_thread_exc_traceback(traceback: Any): ...
# === END py_traceback.cc
class DistributedRuntimeService:
def shutdown(self) -> None: ...
class DistributedRuntimeClient:
def connect(self) -> _Status: ...
def shutdown(self) -> _Status: ...
def get_distributed_runtime_service(
address: str,
num_nodes: int,
heartbeat_interval: Optional[int] = ...,
max_missing_heartbeats: Optional[int] = ...,
enumerate_devices_timeout: Optional[int] = ...,
shutdown_timeout: Optional[int] = ...) -> DistributedRuntimeService: ...
def get_distributed_runtime_client(
address: str,
node_id: int,
rpc_timeout: Optional[int] = ...,
init_timeout: Optional[int] = ...,
shutdown_timeout: Optional[int] = ...,
heartbeat_interval: Optional[int] = ...,
max_missing_heartbeats: Optional[int] = ...,
missed_heartbeat_callback: Optional[Any] = ...,
shutdown_on_destruction: Optional[bool] = ...) -> DistributedRuntimeClient: ...
def collect_garbage() -> None: ...
def is_optimized_build() -> bool: ...
def json_to_pprof_profile(json: str) -> bytes: ...
def pprof_profile_to_json(proto: bytes) -> str: ...
class CompiledFunction:
def __call__(self, *args, **kwargs) -> Any: ...
def __getstate__(self) -> Any: ...
def __setstate__(self, Any): ...
__signature__: inspect.Signature
def _cache_size(self) -> int: ...
def _clear_cache(self) -> None: ...
class PmapFunction:
def __call__(self, *args, **kwargs) -> Any: ...
def __getstate__(self) -> Any: ...
def __setstate__(self, Any): ...
__signature__: inspect.Signature
def _cache_size(self) -> int: ...
def _clear_cache(self) -> None: ...
| [
"[email protected]"
] | |
22a0efd61428ca996199ba140cb48190c54006e0 | acb8e84e3b9c987fcab341f799f41d5a5ec4d587 | /langs/0/bfi.py | e4c0d8a77b1235838847b5ced1684c62c97867da | [] | no_license | G4te-Keep3r/HowdyHackers | 46bfad63eafe5ac515da363e1c75fa6f4b9bca32 | fb6d391aaecb60ab5c4650d4ae2ddd599fd85db2 | refs/heads/master | 2020-08-01T12:08:10.782018 | 2016-11-13T20:45:50 | 2016-11-13T20:45:50 | 73,624,224 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 486 | py | import sys
def printFunction(lineRemaining):
if lineRemaining[0] == '"' and lineRemaining[-1] == '"':
if len(lineRemaining) > 2:
#data to print
lineRemaining = lineRemaining[1:-1]
print ' '.join(lineRemaining)
else:
print
def main(fileName):
with open(fileName) as f:
for line in f:
data = line.split()
if data[0] == 'bfI':
printFunction(data[1:])
else:
print 'ERROR'
return
if __name__ == '__main__':
main(sys.argv[1]) | [
"[email protected]"
] | |
2947dd334a9962628fbd6ad140d2c25e8e572f97 | ba54b70f93fe7f9d114623d76b1ad3f88309d66f | /main/views/public.py | 2b2679a267e16f85bdc1a067bb156f7ebb7f755b | [] | no_license | loobinsk/newprj | 9769b2f26092ce7dd8612fce37adebb307b01b8b | c6aa6a46973fb46375f4b05a86fe76207a8ae16d | refs/heads/master | 2023-05-07T00:28:44.242163 | 2021-05-25T08:22:05 | 2021-05-25T08:22:05 | 370,617,690 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 14,630 | py | #-*- coding: utf-8 -*-
from registration.backends.default.views import RegistrationView
from main.form import RegisterForm, AuthForm, FeedbackForm, ReclameForm
from django.views.generic import View, TemplateView, FormView
from django.contrib.auth.views import login, logout
from main.models import Company, Advert, Tariff, Town
from uprofile.models import User
from django.contrib.auth import authenticate, login as auth_login, logout as auth_logout
from django.utils.decorators import method_decorator
from annoying.decorators import ajax_request
from django.views.decorators.csrf import csrf_exempt
from sorl.thumbnail import get_thumbnail
from gutils.views import BreadcrumbMixin, AjaxableResponseMixin
from django.core.urlresolvers import reverse
from django.shortcuts import get_object_or_404
from mail_templated import send_mail_admins, send_mail
from django.conf import settings
from django.contrib.auth.signals import user_logged_in
from datetime import datetime, timedelta
from ucomment.signals import comment_create
import re
from django.db.models import Count
from cache_utils.decorators import cached
from django.contrib.sites.models import Site
def user_check_sessions(sender, user, request, **kwargs):
"""
Проверка сессий пользователя и закрытие остальных сессий
"""
from user_sessions.models import Session
Session.objects.filter(user=user).exclude(session_key=request.session.session_key).delete()
user_logged_in.connect(user_check_sessions)
def comment_send_notice(sender, user, **kwargs):
"""
Отправка уведомления о комментарии
"""
m = re.search('^company_(\d+)$', sender.key)
if m:
company_list = Company.objects.filter(id=m.group(1))
if company_list:
if company_list[0].owner:
if company_list[0].owner != user:
send_mail('main/email/comment-notice.html', context={
'subject': 'У вашего агентства появился новый отзыв',
'comment': sender,
'company': company_list[0]
},
recipient_list=[company_list[0].owner.email],
fail_silently=True)
if settings.SITE_ID == 1:
comment_create.connect(comment_send_notice)
class RegisterView(AjaxableResponseMixin, BreadcrumbMixin, RegistrationView):
def __init__(self, *argc, **kwargs):
super(RegisterView, self).__init__(*argc, **kwargs)
self.form_class = RegisterForm
def get_initial(self, request=None):
initial = super(RegisterView, self).get_initial(request)
initial['company_town'] = self.request.current_town.id
return initial
def form_valid(self, request, form):
response = super(AjaxableResponseMixin, self).form_valid(request, form)
if self.request.is_ajax():
data = {
'id': self.object.pk if hasattr(self, 'object') else None,
'object': self.get_model_dict(),
}
return self.render_to_json_response(data)
else:
return response
def register(self, request, form):
form.cleaned_data['email'] = form.cleaned_data['username']
new_user = super(RegisterView, self).register(request, form)
if form.cleaned_data['agent_status'] == RegisterForm.REGISTER_STATUS_COMPANY:
town = get_object_or_404(Town, id=form.cleaned_data['company_town'])
company = Company(
owner=new_user,
title=form.cleaned_data['company_name'],
tel=form.cleaned_data['company_tel'],
email=form.cleaned_data['username'],
address=form.cleaned_data['company_address'],
fact_address=form.cleaned_data['company_fact_address'],
ogrn=form.cleaned_data['company_ogrn'],
inn=form.cleaned_data['company_inn'],
person=form.cleaned_data['company_person'],
town=town
)
company.save()
new_user.company = company
new_user.tel =form.cleaned_data['company_tel']
new_user.gen_access_code()
new_user.save()
send_mail('main/email/reg-notice.html',
{'company': company, 'subject': u'Поступила новая заявка на регистрацию от %s' % company.title},
recipient_list=settings.NOTICE_REGISTER_EMAIL)
elif form.cleaned_data['agent_status'] == RegisterForm.REGISTER_STATUS_AGENT:
if form.cleaned_data['company_town'] == '1':
company = Company.objects.get(id=form.cleaned_data['agent_company_msk'])
elif form.cleaned_data['company_town'] == '2':
company = Company.objects.get(id=form.cleaned_data['agent_company_spb'])
if not company.is_real:
company.is_real = True
company.status = Company.STATUS_MODERATE
# company.owner = new_user
if not company.tel:
company.tel = form.cleaned_data['company_tel']
if not company.email:
company.email = new_user.email
if not company.owner:
company.owner = new_user
company.save()
new_user.company = company
new_user.tel = form.cleaned_data['company_tel']
new_user.first_name = form.cleaned_data['agent_name']
new_user.gen_access_code()
new_user.status = User.STATUS_MODERATE
new_user.save()
exist_users = company.user_set.filter(agent_email=new_user.email)
if exist_users:
new_user.extnum = exist_users[0].extnum
new_user.save()
exist_users[0].advert_set.all().update(user=new_user)
exist_users[0].delete()
send_mail('main/email/reg-agent-notice.html', {
'user': new_user,
'subject': u'Поступила новая заявка на регистрацию от агента %s' % new_user.username
},
recipient_list=settings.NOTICE_REGISTER_EMAIL)
request.session['registration_email'] = form.cleaned_data['username']
request.session.modified = True
return new_user
def get_breadcrumbs(self):
return [('Агентствам недвижимости', reverse('registration_register'))]
def get_model_dict(self):
return {
'message': u'Регистрация завершена',
'url': reverse('registration_complete')
}
class RegisterCompleteView(TemplateView):
template_name='registration/registration_complete.html'
def get_context_data(self, **kwargs):
context = super(RegisterCompleteView, self).get_context_data(**kwargs)
mail_servers = [
("mail.ru","Почта Mail.Ru","https://e.mail.ru/"),
("bk.ru","Почта Mail.Ru (bk.ru)","https://e.mail.ru/"),
("list.ru","Почта Mail.Ru (list.ru)","https://e.mail.ru/"),
("inbox.ru","Почта Mail.Ru (inbox.ru)","https://e.mail.ru/"),
("yandex.ru","Яндекс.Почта","https://mail.yandex.ru/"),
("ya.ru","Яндекс.Почта","https://mail.yandex.ru/"),
("yandex.ua","Яндекс.Почта","https://mail.yandex.ua/"),
("yandex.by","Яндекс.Почта","https://mail.yandex.by/"),
("yandex.kz","Яндекс.Почта","https://mail.yandex.kz/"),
("yandex.com","Yandex.Mail","https://mail.yandex.com/"),
("gmail.com","Почта Gmail","https://mail.google.com/"),
("googlemail.com","Почта Gmail","https://mail.google.com/"),
("outlook.com","Почта Outlook.com","https://mail.live.com/"),
("hotmail.com","Почта Outlook.com (Hotmail)","https://mail.live.com/"),
("live.ru","Почта Outlook.com (live.ru)","https://mail.live.com/"),
("live.com","Почта Outlook.com (live.com)","https://mail.live.com/"),
("me.com","Почта iCloud Mail","https://www.icloud.com/"),
("icloud.com","Почта iCloud Mail","https://www.icloud.com/"),
("rambler.ru","Рамблер-Почта","https://mail.rambler.ru/"),
("yahoo.com","Почта Yahoo! Mail","https://mail.yahoo.com/"),
("ukr.net","Почта ukr.net","https://mail.ukr.net/"),
("i.ua","Почта I.UA","http://mail.i.ua/"),
("bigmir.net","Почта Bigmir.net","http://mail.bigmir.net/"),
("tut.by","Почта tut.by","https://mail.tut.by/"),
("inbox.lv","Inbox.lv","https://www.inbox.lv/"),
("mail.kz","Почта mail.kz","http://mail.kz/"),
]
email = self.request.session.get('registration_email')
if email:
for server in mail_servers:
if server[0].lower() in email.lower():
context['mail_server'] = server
return context
class LoginView(View):
def get(self, *args, **kwargs):
return login(self.request, authentication_form=AuthForm)
def post(self, *args, **kwargs):
return login(self.request, authentication_form=AuthForm)
class LoginView_Moder(LoginView):
def get(self, *args, **kwargs):
return login(self.request, authentication_form=AuthForm, template_name='registration/moder/login.html')
def post(self, *args, **kwargs):
return login(self.request, authentication_form=AuthForm, template_name='registration/moder/login.html')
class AjaxLoginView(View):
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super(AjaxLoginView, self).dispatch(request, *args, **kwargs)
@method_decorator(ajax_request)
def post(self, *args, **kwargs):
context = {}
form = AuthForm(self.request, data=self.request.POST)
if form.is_valid():
user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password'])
if user is not None:
if user.is_active:
auth_login(self.request, user)
context['success'] = True
context['message'] = 'Добро пожаловать'
context['username'] = user.get_full_name()
if user.image:
try:
thumb = get_thumbnail(user.image, '100x100', crop='center', quality=99)
context['image'] = thumb.url
except:
context['image'] = ''
else:
context['image'] = ''
company = user.company
if company:
context['activated'] = company.status == Company.STATUS_ACTIVE
context['company'] = company.title
else:
context['activated'] = True
context['company'] = ''
else:
context['success'] = False
context['message'] = 'Аккаунт заблокирован'
else:
# Return an 'invalid login' error message.
context['success'] = False
context['message'] = 'Неправильные имя пользователя или пароль'
else:
context['success'] = False
a = []
for error in form.errors:
for e in form.errors[error]:
a.append(e)
context['message'] = '<br>'.join(a)
return context
class LogoutView_Moder(LoginView):
def get(self, *args, **kwargs):
return logout(self.request, next_page='/', template_name='registration/moder/login.html')
def post(self, *args, **kwargs):
return logout(self.request, next_page='/', template_name='registration/moder/login.html')
class HomeView(TemplateView):
template_name = 'main/home.html'
def get_context_data(self, **kwargs):
context = super(HomeView, self).get_context_data(**kwargs)
town = self.request.current_town
# статистика
context['count_adverts'] = self.get_count_adverts()
context['count_companies'] = self.get_count_companies()
# последние объявления
context['vip_list'] = Advert.objects.filter(company=None,
town=town,
need=Advert.NEED_SALE,
status=Advert.STATUS_VIEW,
date__gte=datetime.now() - timedelta(days=30))\
.filter(Advert.ARCHIVE_NO_QUERY)\
.annotate(image_count=Count('images'))\
.exclude(image_count=0)\
.order_by('?')[:5]
context['last_advert_list'] = Advert.objects.filter(town=town, need=Advert.NEED_SALE, status=Advert.STATUS_VIEW).order_by('-date')[:5]
context['arenda_advert_list'] = Advert.objects\
.filter(adtype=Advert.TYPE_LEASE, town=town, need=Advert.NEED_SALE)\
.filter(estate=Advert.ESTATE_LIVE, status=Advert.STATUS_VIEW)\
.order_by('-date')[:4]
context['sale_advert_list'] = Advert.objects \
.filter(adtype=Advert.TYPE_SALE, town=town, need=Advert.NEED_SALE) \
.filter(estate=Advert.ESTATE_LIVE, status=Advert.STATUS_VIEW) \
.order_by('-date')[:4]
return context
@cached(3600)
def get_count_adverts(self):
return Advert.objects.filter(status=Advert.STATUS_VIEW).count()
@cached(3600)
def get_count_companies(self):
return Company.objects.all().count()
def page_not_found(request, template_name='404.html'):
from django.views.defaults import page_not_found
return page_not_found(request, template_name)
def page_not_found_moder(request, template_name='404.html'):
from django.views.defaults import page_not_found
return page_not_found(request, template_name='404-moder.html')
| [
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] | |
f434d074c2a942412002f5c9efc9a15c033dacc0 | 5472a3f913e1a6698b9dab902545f0ba02e7a02e | /pbay_url.py | 30c40f18b360964362158d06ed0107620e90d399 | [] | no_license | Arrowheadahp/piratebay-search-and-download | bf38956588ce6da8caf25cec653bec76409cfd79 | 0fe8db913215e4a0b00a9153e7085728e7d3ecf7 | refs/heads/master | 2020-05-31T05:56:18.592671 | 2019-07-20T06:15:26 | 2019-07-20T06:15:26 | 190,131,141 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 566 | py | from bs4 import BeautifulSoup
from urllib.request import Request, urlopen
import webbrowser
def soupcreate(url):
req = Request(url, headers={'User-Agent': 'Mozilla/5.0'})
webpage = urlopen(req).read()
#print ('url page read')
return(BeautifulSoup(webpage,features="lxml"))
def geturl():
proxylist=soupcreate('https://piratebay-proxylist.se/')
proxy=proxylist.find('td',{'class':'url'})
proxyurl=proxy.get('data-href')
return (proxyurl)
if __name__=='__main__':
print (geturl())
webbrowser.open(geturl())
| [
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] | |
8d704be2ad0bccea7611b5a9eac75d47a7e74899 | f448b9635d076d88a4439e937eec7dd050cc316a | /xx.py | 6a1bcaeeb2767fb3a0468cbdf1fb2786afa1066f | [] | no_license | udaytejam/practicesamples | c7e6ba2e30f52138b3b22414c57ddc1f9e94162a | acda24dfe5c3aff60b688c9b434b83a3132b0af1 | refs/heads/master | 2021-01-10T02:03:51.456102 | 2015-10-05T11:23:42 | 2015-10-05T11:23:42 | 43,500,701 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 168 | py | globvar = 10
def read1():
print(globvar)
def write1():
global globvar
globvar = 5
def write2():
globvar = 15
read1()
write1()
read1()
write2()
read1() | [
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] | |
5253d398213d1c154ea2dffba964210fbf476c74 | e33ecdb112045327344dce2ae8b0612848938f24 | /cotidia/socialshare/conf.py | 1e7d660917e60fb666ce61f86598c24b02e1edef | [
"BSD-3-Clause"
] | permissive | guillaumepiot/cotidia-social-share | 939125b97474bb34e8a94cd0fa6d6919026c029c | 9c926bb86e7f158f2b59eaddcf09eba459c009b6 | refs/heads/master | 2020-04-21T07:28:24.520846 | 2019-03-26T14:00:00 | 2019-03-26T14:00:00 | 169,393,675 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 187 | py | from django.conf import settings
from appconf import AppConf
class SocialShareConf(AppConf):
FACEBOOK_APP_ID = "[Not implemented]"
class Meta:
prefix = 'socialshare'
| [
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] | |
072d371ce95370c4977fcc64b3a3e77c06ca6c30 | 5f07c38899e350b0b776510fd7d7831d44cf1404 | /drfmixins/drfmixins/settings.py | d599783f7b76ad7f17b66c1c6fd0e90c0991e475 | [] | no_license | shubham454/Django-Rest | b733f1d47ada9df452e912dcd8acad48a7ec4c75 | 3d94f57cab3537c51caa68807d5fcdf8883d2d2c | refs/heads/master | 2022-12-14T20:37:11.835794 | 2020-08-13T18:43:26 | 2020-08-13T18:43:26 | 287,354,715 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,133 | py | """
Django settings for drfmixins project.
Generated by 'django-admin startproject' using Django 2.2.2.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'z&7-uzdyn7cex&u5yzfw&wh$j8_v71pu@!4rc9lu@c#8y(!_^('
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'rest_framework',
'testapp'
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'drfmixins.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'drfmixins.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
| [
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] | |
9f25d416bd468bb65eb3923ab99d32b912f60ca7 | 3e85618c79a1a934fec543e1327e772ca081a5b9 | /N1949.py | 2c0945dcd3d845154cc7480e681a4eb6834ef180 | [] | no_license | ghdus4185/SWEXPERT | 72d79aa4a668452327a676a644b952bab191c79b | 4dc74ad74df7837450de4ce55526dac7760ce738 | refs/heads/master | 2020-07-16T18:31:22.153239 | 2019-12-20T04:18:30 | 2019-12-20T04:18:30 | 205,843,190 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,019 | py | import sys
sys.stdin = open('sample_input.txt', 'r')
# 가장 높은 봉우리를 찾아야한다
# 내 주변을 선택할 때 나보다 낮은 얘들을 선택하거나 한번 깎아서 선택할 수 있다.
# 이후에 깎는게 더 유리할 수 있으므로
# 1) 낮은 칸으로 이동해보기
# 2) 높거나 같은 칸에 대해서 2가지 선택 깍는다 or 깍지않는다.
# 3) 깍아서 지나갈 수 있는 상황이라면 굳이 많이 깍지 않고 딱 나보다 작은 정도만
# 깍는다.
def f(i, j, c, e): # c : 깍는 횟수, e : 이동거리
di = [0, 1, 0, -1]
dj = [1, 0, -1, 0]
global N, K, maxV, visited, arr
if maxV < e:
maxV = e
visited[i][j] = 1 # 등산로에 포함되었음을 표시
#주변탐색
for k in range(4):
ni = i + di[k]
nj = j + dj[k]
if ni >= 0 and ni < N and nj >= 0 and nj< N: # 유효좌표인지 확인
if arr[i][j] > arr[ni][nj]:
f(ni, nj, c, e+1) # 주변의 낮은 점으로 이동
elif visited[ni][nj] == 0 and c > 0 and arr[i][j] > arr[ni][nj]-K:
# 주변 점을 깍아서 이동
org = arr[ni][nj] # 원래 높이 저장
arr[ni][nj] = arr[i][j] -1 # 주변 점을 깍아서 이동
f(ni, nj, 0, e+1)
arr[ni][nj] = org # 높이 원상 복구
# 돌아왔을 때를 생각해서 깍기 전 높이를 저장해둔다
visited[i][j] = 0 # 다른 경로의 등산로에 포함될 수 있으므로
return
T = int(input())
for tc in range(T):
N, K = map(int, input().split())
arr = [list(map(int, input().split())) for _ in range(N)]
visited = [[0]*N for _ in range(N)]
h = 0
for i in range(N):
for j in range(N):
if h < arr[i][j]:
h = arr[i][j]
maxV = 0
for i in range(N):
for j in range(N):
if arr[i][j] == h:
f(i, j, 1, 1)
print('#{} {}'.format(tc+1, maxV)) | [
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] | |
b1532dca490f5b992fcd2d4448901b761f3b2807 | 025dc1fa797b0de25b556365d23bddb848ab8ce0 | /colossus/apps/lists/mixins.py | ec6726113587e1a0aef7d4b9d7aedb437406729a | [
"MIT"
] | permissive | ramanaditya/colossus | eab49ec33031b8542b07e3aaebc36467a97786d6 | 11b34a216b2021a5da79cd6e347aef842f7b0c72 | refs/heads/master | 2023-03-30T12:39:12.948490 | 2021-03-25T17:11:32 | 2021-03-25T17:11:32 | 340,977,981 | 1 | 0 | MIT | 2021-03-25T16:34:54 | 2021-02-21T18:51:05 | Python | UTF-8 | Python | false | false | 1,295 | py | from django.http import Http404
from django.shortcuts import get_object_or_404
from django.views.generic.base import ContextMixin
from colossus.apps.subscribers.constants import TemplateKeys
from colossus.apps.subscribers.models import SubscriptionFormTemplate
from .models import MailingList
class MailingListMixin(ContextMixin):
__mailing_list = None
@property
def mailing_list(self):
if self.__mailing_list is None:
self.__mailing_list = get_object_or_404(MailingList, pk=self.kwargs.get('pk'))
return self.__mailing_list
def get_context_data(self, **kwargs):
if 'menu' not in kwargs:
kwargs['menu'] = 'lists'
if 'mailing_list' not in kwargs:
kwargs['mailing_list'] = self.mailing_list
return super().get_context_data(**kwargs)
class FormTemplateMixin:
def get_object(self):
mailing_list_id = self.kwargs.get('pk')
key = self.kwargs.get('form_key')
if key not in TemplateKeys.LABELS.keys():
raise Http404
form_template, created = SubscriptionFormTemplate.objects.get_or_create(
key=key,
mailing_list_id=mailing_list_id
)
if created:
form_template.load_defaults()
return form_template
| [
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] | |
2d25948fc47ae05e17ec0c8404dc6012cc0a51f0 | f9c7969c8649c484f2460fb245a3d5bd6870fa5a | /ch07/exercises/exercise 35.py | 85def5a86980f358fd4a9a1b39f5216c13556056 | [] | no_license | Pshypher/tpocup | 78cf97d51259bfea944dc205b9644bb1ae4ab367 | b05b05728713637b1976a8203c2c97dbbfbb6a94 | refs/heads/master | 2022-05-18T13:11:31.417205 | 2020-01-07T13:50:06 | 2020-01-07T13:50:06 | 260,133,112 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 373 | py | # Unless stated otherwise, variables are assumed to be of the str data type
def reverse_string(S):
"""Return the string S in reverse order using a for loop."""
S_reverse = ""
for ch in S:
S_reverse = ch + S_reverse
return S_reverse
# Prompt user for a string
chars = input("Enter a sequence of alphanumeric chars: ")
print(reverse_string(chars))
| [
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] | |
2ca7726a97e24168ecf4147fb619ac3d3540182e | d1808d8cc5138489667b7845466f9c573591d372 | /notebooks/Reproducible Papers/Syngine_2016/figure_2_source_width.py | 7eb1deaeb1cbee060358396def82df02fcfa286e | [] | no_license | krischer/seismo_live | e140777900f6246a677bc28b6e68f0a168ec41ab | fcc615aee965bc297e8d53da5692abb2ecd6fd0c | refs/heads/master | 2021-10-20T22:17:42.276096 | 2019-11-27T23:21:16 | 2019-11-28T10:44:21 | 44,953,995 | 69 | 59 | null | 2020-05-22T11:00:52 | 2015-10-26T08:00:42 | Python | UTF-8 | Python | false | false | 5,880 | py | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.4'
# jupytext_version: 1.2.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + {"deletable": true, "editable": true, "cell_type": "markdown"}
# <div style='background-image: url("../../share/images/header.svg") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>
# <div style="float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px">
# <div style="position: relative ; top: 50% ; transform: translatey(-50%)">
# <div style="font-size: xx-large ; font-weight: 900 ; color: rgba(0 , 0 , 0 , 0.8) ; line-height: 100%">Computational Seismology</div>
# <div style="font-size: large ; padding-top: 20px ; color: rgba(0 , 0 , 0 , 0.5)">Reproducible Papers - Syngine Paper</div>
# </div>
# </div>
# </div>
# + {"deletable": true, "editable": true, "cell_type": "markdown"}
# ---
#
# # Figure 2: Source Width Parameter
#
# This notebook is part of the supplementary materials for the Syngine paper and reproduces figure 2.
#
# Requires matplotlib >= 1.5 and an ObsPy version with the syngine client (>= 1.0) as well as instaseis.
#
# ##### Authors:
# * Lion Krischer ([@krischer](https://github.com/krischer))
# + {"deletable": true, "editable": true}
# %matplotlib inline
import obspy
import matplotlib.pyplot as plt
import numpy as np
plt.style.use("seaborn-whitegrid")
import copy
import io
import instaseis
import json
import requests
# + {"deletable": true, "editable": true}
SYNGINE_URL = "http://service.iris.edu/irisws/syngine/1/query"
# + {"deletable": true, "editable": true}
network = "IU"
station = "ANMO"
# Get station information from the IRIS FDSN service.
from obspy.clients.fdsn import Client
c = Client("IRIS")
print(c.get_stations(network=network, station=station, format="text")[0][0])
# + {"deletable": true, "editable": true}
# The param file is only used to extract the source parameters. This is
# thus consistent with the other figures but can of course also be done
# differently.
filename = "chile_param.txt"
# Parse the finite source wiht instaseis.
finite_source = instaseis.FiniteSource.from_usgs_param_file(filename)
# Compute the centroid of it.
finite_source.compute_centroid()
# src is now the centroid of the finite source.
src = finite_source.CMT
# Common query parametersh su
params_common = {
# IU.ANMO
"receiverlatitude": 34.95,
"receiverlongitude": -106.46,
"dt": 0.1,
"origintime": src.origin_time,
"components": "Z",
"model": "ak135f_2s",
"format": "miniseed",
"units": "velocity"}
# Parameters only needed for the point source.
params_ps = copy.deepcopy(params_common)
params_ps["sourcelatitude"] = src.latitude
params_ps["sourcelongitude"] = src.longitude
params_ps["sourcedepthinmeters"] = src.depth_in_m
params_ps["sourcemomenttensor"] = ",".join(
str(getattr(src, _i)) for _i in ("m_rr", "m_tt", "m_pp", "m_rt", "m_rp", "m_tp"))
print(finite_source)
print(finite_source.CMT)
# + {"deletable": true, "editable": true}
import copy
import collections
seis = collections.OrderedDict()
source_widths = [2.5, 5, 10, 25, 50, 100]
# Request one seismogram for each source with.
for sw in source_widths:
p = copy.deepcopy(params_ps)
# The sourcewidth parameter steers the width of the STF.
p["sourcewidth"] = sw
# Send it alongside.
r = requests.get(url=SYNGINE_URL, params=p)
assert r.ok, str(r.reason)
# Get the data and parse it as an ObsPy object.
with io.BytesIO(r.content) as f:
tr = obspy.read(f)[0]
seis[sw] = tr
# Plot only some phases.
tr.slice(tr.stats.starttime + 1000, tr.stats.starttime + 1500).plot()
# + {"deletable": true, "editable": true}
import matplotlib.gridspec as gridspec
# Plotting setup.
fig = plt.figure(figsize=(10, 3))
gs1 = gridspec.GridSpec(1, 1, wspace=0, hspace=0, left=0.05,
right=0.62, bottom=0.14, top=0.99)
ax1 = fig.add_subplot(gs1[0])
gs2 = gridspec.GridSpec(1, 1, wspace=0, hspace=0, left=0.65,
right=0.94, bottom=0.14, top=0.99)
ax2 = fig.add_subplot(gs2[0])
plt.sca(ax1)
# Now plot all the seismograms.
for _i, (sw, tr) in enumerate(seis.items()):
tr.normalize()
plt.plot(tr.times(), 2.0 * tr.data - _i * 3, color="0.1")
plt.legend()
plt.xlim(0, 2000)
plt.yticks([0, -3, -6, -9, -12, -15], [str(_i) for _i in source_widths])
plt.ylim(-17, 2)
plt.xlabel("Time since event origin [sec]")
plt.ylabel("Source width [sec]")
plt.sca(ax2)
# Use an internal instaseis function to get the used STF.
from instaseis.server.util import get_gaussian_source_time_function
dt = 0.01
# Plot all the source time functions.
for _i, sw in enumerate(source_widths):
sr = get_gaussian_source_time_function(sw, dt)[1]
#sr = np.concatenate([sr2, np.zeros(1000)])
alpha = 0.4 - _i * 0.4 / len(source_widths)
plt.fill_between(np.arange(len(sr)) * dt - sw, sr, color="0.0", alpha=alpha, linewidth=0)
if sw == 25:
plt.plot(np.arange(len(sr)) * dt - sw, sr, color="0.0", lw=2)
ax2.annotate('25 sec', xy=(5, 0.07), xytext=(8, 0.10),
arrowprops=dict(facecolor='black', shrink=0.05))
plt.grid(True)
plt.xlim(-20, 20)
plt.ylim(-0.0005, 0.16)
plt.xticks([-10, 0, 10])
plt.yticks([0, 0.04, 0.08, 0.12])
plt.xlabel("Time [sec]")
plt.ylabel("Slip rate [m/sec]")
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position("right")
ax2.yaxis.set_tick_params(length=2)
ax2.yaxis.set_tick_params(pad=4)
ax2.xaxis.set_tick_params(length=2)
ax2.xaxis.set_tick_params(pad=4)
ax2.xaxis.set_tick_params(color="#CCCCCC")
ax2.yaxis.set_tick_params(color="#CCCCCC")
plt.savefig("source_width.pdf")
| [
"[email protected]"
] | |
6905fda86703d56d27ced0178a27ebf687bb1da0 | d18df0ec22dc766496d4b0c2dcdcc933bdf332d8 | /utils.py | f15c3122cd9f699a4a7cf4c18cdcaea62d5eff1b | [] | no_license | thanhlt998/tktdtt | edc6610a28e09482f0746db258eed5323636abaa | 64f32e62fb3b2d5d6ef6c2a0e74294bdff4b2057 | refs/heads/master | 2022-03-21T07:24:59.104986 | 2019-12-17T02:32:25 | 2019-12-17T02:32:25 | 208,956,173 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,151 | py | from pyvi.ViTokenizer import ViTokenizer
import re
from dateutil.parser import parse
import json
def tokenize(terms):
terms = ViTokenizer.tokenize(terms)
terms = [f"\"{re.sub(r'_', ' ', term)}\"" for term in re.findall(r'\S+', terms)]
return ' '.join(terms)
def time_str2iso_format(time_str, is_24h_format=True):
time = re.search(fr'\d[\d/:,\- ]+[\d{"AMP" if is_24h_format else ""}]+', time_str)[0]
time = parse(time)
return time.strftime('%Y-%m-%dT%H:%M:%SZ')
def read_jsonl_file(fn):
docs = []
with open(fn, mode='r', encoding='utf8') as f:
for line in f:
docs.append(json.loads(line))
f.close()
return docs
def read_json_file(fn):
with open(fn, mode='r', encoding='utf8') as f:
docs = json.load(f)
f.close()
return docs
def dump_jsonl_file(fn, docs):
with open(fn, mode='w', encoding='utf8') as f:
for doc in docs:
f.write(json.dumps(doc, ensure_ascii=False))
f.close()
if __name__ == '__main__':
# docs = read_json_file('data/data_baomoi.json')
docs = read_jsonl_file('data/24h.jsonl')
print(docs[:2])
| [
"[email protected]"
] | |
3abcc4770b5d3213f9bbe698c4fd2bd2e30bc2df | 015ce35e6344d1726173594ae509dfc1ca6f856d | /3-OOP and DSA/4-Recursion/Study/5-fibonichi.py | cd8fcc970c153783d338b2223d11fd4aeb930ddb | [] | no_license | ayman-elkassas/Python-Notebooks | 4af80df75c15a6ac3049450b3920d500fef0e581 | 26a8265f458c40ac22965d55722f32a650851683 | refs/heads/master | 2023-04-03T19:12:17.707673 | 2021-04-10T21:32:37 | 2021-04-10T21:32:37 | 356,699,690 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 223 | py | # Fn
# = F
# n−2 + Fn−1 for n > 1.
# import gzip
# gzip.GzipFile.readline(r"C:\Users\Ayman Elkassas\Desktop\dump.txt",)
def fib(n):
if n<=1:
return n
else:
return fib(n-1)+fib(n-2)
print(fib(5))
| [
"[email protected]"
] | |
7e5363f7bc158f952ae5fcf883d622a0fa2cd660 | 83ed8b754703a1c9e661c90f0763bfebbc0f2606 | /数据处理/计财Excel/excel_jicai.py | 35dffa6e8fac69b1cf98e1de6347fdde61ce573e | [] | no_license | zbh123/hobby | 4ce267a20e1af7f2accd2bde8d39af269efa319b | 2215c406fe7700bf150fd536dd56823a2e4733d1 | refs/heads/master | 2021-08-02T10:31:34.683391 | 2021-07-26T07:26:16 | 2021-07-26T07:26:16 | 150,555,879 | 4 | 0 | null | 2021-07-27T07:34:28 | 2018-09-27T08:41:44 | Python | UTF-8 | Python | false | false | 11,085 | py | #!python3
# -*- coding:utf-8 -*-
import re
from datetime import datetime, date
import xlrd, xlwt
import time
import os, sys
from xlutils.copy import copy
"""
股票质押明细表操作，
1，选取自有资金。
2，批注及备注中包含本月
3，提取字段
"""
def open_excel(excel_file):
"""
读取excel函数
args：excel_file（excel文件，目录在py文件同目录）
returns：book
"""
try:
book = xlrd.open_workbook(excel_file) # 文件名，把文件与py文件放在同一目录下
return book
except:
print("open excel file failed!")
def filter_sheet(excel_file, target_folder, now_month):
"""
过滤excel文件的sheet
:param excel_file:
:return:
"""
book = open_excel(excel_file) # 打开excel文件
sheets = book.sheet_names() # 获取所有sheet表名
# 如果sheet包含待赎回交易(汇总)，返回sheet的索引
for sheet in sheets:
if sheet != '待购回交易（汇总）':
continue
# 处理当前sheet的excel
handle_excel(book, sheet, target_folder, now_month)
break
def handle_excel(book, sheet, target_folder, now_month):
"""
处理表
:param book:
:param sheet:
:return:
"""
# 创建新表
workbook = xlwt.Workbook(encoding='utf-8')
worksheet = workbook.add_sheet('待赎回交易(处理后)')
# 读取原表行
sh = book.sheet_by_name(sheet)
row_num = sh.nrows
# 把头部写入新的excel
row_data = sh.row_values(0)
for i, content in enumerate(row_data):
worksheet.write(0, i, content)
# 处理每一行
r = 1
for row in range(1, row_num):
row_data = sh.row_values(row)
# 出资方
investor = row_data[1]
if investor != '自有资金':
continue
dateFormat = xlwt.XFStyle()
# 把这一行写入新的excel
for i, content in enumerate(row_data):
# 时间格式特殊处理下
if i == 0 or i == 26:
date_value = xlrd.xldate_as_tuple(content, 0)
date_value = date(*date_value[:3]).strftime('%Y/%m/%d')
date_value = time_format(date_value)
dateFormat.num_format_str = 'yyyy/m/d'
worksheet.write(r, i, date_value, dateFormat)
else:
worksheet.write(r, i, content)
# 行数+1
r = r + 1
workbook.save(target_folder + '/自有资金-待赎回交易.xlsx')
def handle_comment(target_file, now_month):
"""
处理批注
:return:
"""
# 读取修改后的文件
book = open_excel(target_file)
sh = book.sheet_by_index(0)
row_num = sh.nrows
colx_num = sh.ncols
# 设置修改文件
workbook = copy(book)
worksheet = workbook.get_sheet(0)
# worksheet.write(0, colx_num, '批注')
for row in range(1, row_num):
row_data = sh.row_values(row)
comment = row_data[23]
# 先把批注写到最后一列
# worksheet.write(row, colx_num, comment)
# 处理批注（分成数组，如果数组有月份和数字，把月份和数字向后写）
com = comment.split('；')
index_row = 0 # 用一个变量控制每一行行的最大列
for c in com:
print(c)
if not (now_month + '/' in c):
continue
# 提取数组里面的日期和金额
date_reg_exp = re.compile('\d{4}[-/]\d{1,2}[-/]\d{1,2}')
matches_list = date_reg_exp.findall(c)
print(matches_list)
# 金额（把万或者元前面的数字提取）
for matches in matches_list:
c_no_date = c.replace(matches, '')
print(c_no_date)
c_num_unit = re.findall(r'\d+(?:\.\d+)?万', c_no_date)
print(c_num_unit)
c_num2_unit = re.findall(r'\d+(?:\.\d+)?元', c_no_date)
print(c_num2_unit)
# 写入excel
index_date = 0 # 标志本月日期的增行数
index_money_w = 0 # 控制万的增行数
index_money_y = 0 # 控制元的增行数
for index, date in enumerate(matches_list):
if now_month + '/' in date:
worksheet.write(row, colx_num + index_date + index_row, date)
index_date = index_date + 1
for index2, c_num in enumerate(c_num_unit):
c_num = re.findall(r'\d+(?:\.\d+)?', c_num)
worksheet.write(row, colx_num + index_date + index2 + index_row, int(c_num[0]) * 10000)
index_money_w = index2 + 1
for index3, c_num2 in enumerate(c_num2_unit):
c_num2 = re.findall(r'\d+(?:\.\d+)?', c_num2)
worksheet.write(row, colx_num + index_date + index_money_w + index3 + index_row, c_num2[0])
index_money_y = index3 + 1
index_row = index_date + index_money_w + index_money_y + index_row
workbook.save(target_file)
def handle_remarks(target_file, now_month):
"""
处理备注
:return:
"""
# 读取修改后的文件
book = open_excel(target_file)
sh = book.sheet_by_index(0)
row_num = sh.nrows
colx_num = sh.ncols
# 设置修改文件
workbook = copy(book)
worksheet = workbook.get_sheet(0)
# worksheet.write(0, colx_num, '备注')
for row in range(1, row_num):
row_data = sh.row_values(row)
remarks = row_data[27]
# 先把备注写到最后一列
# worksheet.write(row, colx_num, remarks)
# 处理备注（分成数组，如果数组有月份和数字，把月份和数字向后写）
com = remarks.split('；')
index_row = 0
for c in com:
# print(c)
if not (now_month + '/' in c):
continue
if not ('变更' in c):
continue
# 提取数组里面的日期
date_reg_exp = re.compile('\d{4}[-/]\d{1,2}[-/]\d{1,2}')
matches_list = date_reg_exp.findall(c)
# 把延期日期去掉
for matches in matches_list:
c = c.replace('延期' + matches, '')
c = c.replace('延期到' + matches, '')
print('----' + c)
# 拿到变更前后的日期和金额，默认分成两个，可能存在多个变更的情况
array = c.split('变更')
for index, str in enumerate(array):
if index == len(array) - 1:
break
date_reg_exp = re.compile(r'\d{4}[-/]\d{1,2}[-/]\d{1,2}')
matches_date_list = date_reg_exp.findall(str)
print(matches_date_list)
per_reg_exp = re.compile(r"\d+\.\d*%|\d*%")
matches_per_list = per_reg_exp.findall(array[index + 1])
print(matches_per_list)
# 如果包含分之，并且数据的前面无日期或者数据日期为当月日期，取出
date_fenshu = ''
fenshu = ''
if array[index + 1].find("分之") != -1:
index_temp = array[index + 1].find("分之")
c_bef = array[index + 1][0:index_temp - 1]
d_reg_exp = re.compile(r'\d{4}[-/]\d{1,2}[-/]\d{1,2}')
m_date_list = d_reg_exp.findall(c_bef)
if len(m_date_list) == 0:
fenshu = array[index + 1][int(index_temp) - 1: int(index_temp) + 3]
elif now_month + '/' in m_date_list[len(m_date_list) - 1]:
date_fenshu = m_date_list[len(m_date_list) - 1]
fenshu = array[index + 1][int(index_temp) - 1: int(index_temp) + 3]
print(date_fenshu)
print(fenshu)
date = matches_date_list[len(matches_date_list) - 1]
per = matches_per_list[0]
if now_month + '/' in date:
worksheet.write(row, colx_num + index_row, date)
worksheet.write(row, colx_num + 1 + index_row, per)
# 如果只有百分数
if fenshu != '' and date_fenshu == '':
worksheet.write(row, colx_num + 2 + index_row, fenshu)
index_row = index_row + 1 + 2
# 如果有百分数，有日期
elif fenshu != '' and date_fenshu != '':
worksheet.write(row, colx_num + 2 + index_row, date_fenshu)
worksheet.write(row, colx_num + 3 + index_row, fenshu)
index_row = index_row + 1 + 3
else:
index_row = index_row + 1 + 1
if now_month + '/' in date_fenshu:
if fenshu != '' and date_fenshu != '':
worksheet.write(row, colx_num + index_row, date_fenshu)
worksheet.write(row, colx_num + 1 + index_row, fenshu)
index_row = index_row + 2
workbook.save(target_file)
def time_format(date_value):
"""
时间格式化 去掉月份，日期前面的0
:param date_value:
:return:
"""
dates = date_value.split('/')
if len(dates) == 3:
month = dates[1].lstrip('0')
day = dates[2].lstrip('0')
return dates[0] + '/' + month + '/' + day
elif len(dates) == 2:
month = dates[1].lstrip('0')
return dates[0] + '/' + month
else:
return date_value
if __name__ == '__main__':
source_file = r'D:\0RPA\计划财务部\财务rpa\魏丽Excel\科目余额表.xls'
# source_file = r'C:\Users\LiGuangxi\Desktop\RPA需求\计财\股票质押明细表(仅供参考，请核对).xlsx'
target_file = r'D:\0RPA\计划财务部\财务rpa\魏丽Excel'
now_time = time.strftime("%Y%m%d", time.localtime(time.time()))
# 如果没有源文件，则报错退出
if not os.path.exists(source_file):
print("查询不到源文件")
sys.exit(1)
# 如果没有目标文件夹，则创建
target_folder = target_file + '/' + now_time
if not os.path.exists(target_folder):
os.makedirs(target_folder)
# 当前月
now_month = time.strftime("%Y/%m", time.localtime(time.time()))
# ---------------------start：下面可以修改为您处理的任何月份---------------------------------------------------------------------------------------------
# now_month = '2020/12'
# ---------------------end：上面可以修改为您处理的任何月份-----------------------------------------------------------------------------------------------
# 过滤
filter_sheet(source_file, target_folder, now_month)
# 加工
handle_comment(target_folder + '/自有资金-待赎回交易.xlsx', now_month)
handle_remarks(target_folder + '/自有资金-待赎回交易.xlsx', now_month)
| [
"[email protected]"
] | |
6fc250290cd0b7389544fbe3a86bdc07265dc7d7 | 8eccc4cab7ba7292c932468163c711d4058e3b90 | /app/inheritance/abstract/migrations/0003_auto_20191223_0612.py | 5f9ce7809d3b1fe08e15168d3691200f35a33369 | [] | no_license | zehye/django-document-wps12 | 97b1aa4be5a56b949ba59ac92e8d0c5cb3e22f73 | 086fdc581ba3f2db7bc39a6eb906fd97cc61c415 | refs/heads/master | 2022-09-08T12:46:19.110011 | 2019-12-26T09:07:15 | 2019-12-26T09:07:15 | 228,784,564 | 0 | 0 | null | 2022-08-23T17:59:03 | 2019-12-18T07:37:14 | Python | UTF-8 | Python | false | false | 737 | py | # Generated by Django 3.0 on 2019-12-23 06:12
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('abstract', '0002_auto_20191223_0539'),
]
operations = [
migrations.AlterField(
model_name='childa',
name='m2m',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='abstract_childa', to='abstract.Student'),
),
migrations.AlterField(
model_name='childb',
name='m2m',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='abstract_childb', to='abstract.Student'),
),
]
| [
"[email protected]"
] | |
08b1a08138cf2a9f104b5f00cfba5cf8fb7aaa24 | de6f57fa8391d447a50b1fe2f394cc2fc0488bfa | /BookMyShow/urls.py | 7470e069be75c7a4371b1370572efd74c250c991 | [] | no_license | himdhiman/BMS-2 | ce8db13d88dacd27b45757f5d30b78717041d0f8 | 440886028006211a1995f9d28d21fde9caf7fb0a | refs/heads/master | 2021-09-27T17:25:10.187898 | 2021-01-21T15:40:19 | 2021-01-21T15:40:19 | 205,708,449 | 1 | 0 | null | 2021-09-22T17:58:58 | 2019-09-01T17:16:10 | JavaScript | UTF-8 | Python | false | false | 1,031 | py | """BookMyShow URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path, include
from movies.views import SearchView
urlpatterns = [
path('admin/', admin.site.urls),
path('accounts/', include('auth.urls')),
path('', include('movies.urls')),
path('cinema/', include('cinema.urls')),
path('tickets/', include('tickets.urls')),
path('search/', SearchView.as_view(), name = 'search')
]
| [
"[email protected]"
] | |
10a6013dcc36183777720bbc2952c93d81e122df | 0f60e5a4bffa7372f6461aba4f0e58de4e3508bb | /Pandas/panda21.py | 00ddfd6fe203e441b705dfd802516e4eaf340740 | [] | no_license | akshatrastogi25/Python | 519130d6671438d20b0e6928e597e2b9c5bf722f | a3e8a1cbc96d09e4f8a6674c23c74074bfb65a9a | refs/heads/master | 2023-03-26T02:14:14.092925 | 2021-03-25T12:10:31 | 2021-03-25T12:10:31 | 286,788,623 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 436 | py | import pandas as pd
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
print pd.concat([one,two],axis=1) | [
"[email protected]"
] | |
b8c70af9726a94eba9ac6a43188c0994be97dfcb | cdc9a8bc051be72de5bace23fd0637701d699da3 | /preprocess/create_stanford_labels.py | 880bf6d76e11854488987df9b35ea38a1836deac | [
"Apache-2.0"
] | permissive | marshuang80/pe-slice-finder | 4a51a8f7ef90f836d3cd5935f89a3e7f13c1fd63 | 2426a55c404e8eb694110351d604d6bdd613e5ae | refs/heads/master | 2022-12-29T02:20:42.135931 | 2020-10-13T04:16:47 | 2020-10-13T04:16:47 | 296,091,898 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,236 | py | import os
import sys
sys.path.append(os.getcwd())
import h5py
import pickle
import argparse
import pandas as pd
from constants import *
from tqdm import tqdm
from collections import defaultdict
def main(args):
# create hdf5 file
hdf5_fh = h5py.File(args.hdf5_file, 'a')
slice_labels = pickle.load(open(args.pickle_file, 'rb'))
results = defaultdict(list)
for series in hdf5_fh.keys():
# skip if no labelss
if series not in slice_labels.keys():
continue
for slice_idx in range(hdf5_fh[series].shape[0]):
label = 1 if slice_idx in slice_labels[series] else 0
results['series'].append(series)
results['slice_idx'].append(slice_idx)
results['label'].append(label)
# save as csv
df = pd.DataFrame.from_dict(results)
df.to_csv('slice_labels.csv')
# clean up
hdf5_fh.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--hdf5_file', type=str, default='/data4/PE_stanford/Stanford_data/data.hdf5')
parser.add_argument('--pickle_file', type=str, default='/data4/PE_stanford/Stanford_data/slice_labels.pkl')
args = parser.parse_args()
main(args)
| [
"[email protected]"
] | |
2dd331830c8da0eca6ca46d05d214d1443501f2f | 4ede275efc8bc9f9ef121dc37215d2f0d8453e36 | /primer1.py | 20a96af89513d28f097429ac8bc17040ee3ff8f6 | [] | no_license | shanthivimalanataraajan01/code | bfa8a441b0c360aebd02248ad4433cc21889c3d2 | ea467ae1eefd68a5dceaa53aab7149d31bd5faf6 | refs/heads/master | 2020-04-15T05:01:03.625422 | 2019-05-17T09:35:45 | 2019-05-17T09:35:45 | 164,405,963 | 0 | 2 | null | null | null | null | UTF-8 | Python | false | false | 177 | py | #vimala
#hi
m,n=map(int,input().split())
x=' '
for n in range(m+1,n):
if n>0:
for i in range(2,n):
if n%i==0:
break
else:
x=x+str(n)+' '
print(x.strip())
| [
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] | |
670a9c4656b1ed4889e4390c5fe424466c8af425 | e7d65f8773a8c736fc9e41e843d7da6da5cc2e0b | /py3plex/algorithms/network_classification/PPR.py | 0339b2da13f9375d038028962e9f8485a7392e37 | [
"BSD-3-Clause"
] | permissive | hanbei969/Py3plex | 768e86b16ca00044fcb4188e01edf32c332c8a2a | 1ef3e0e6d468d24bd6e6aec3bd68f20b9d9686bb | refs/heads/master | 2021-01-03T18:19:24.049457 | 2020-02-12T16:51:14 | 2020-02-12T16:51:14 | 240,188,307 | 1 | 0 | BSD-3-Clause | 2020-02-13T05:57:16 | 2020-02-13T05:57:16 | null | UTF-8 | Python | false | false | 4,584 | py | ## set of routines for validation of the PPR-based classification
from ..node_ranking import *
from ..general.benchmark_classification import *
import pandas as pd
from sklearn.svm import SVC
from sklearn.metrics import f1_score
import time
import numpy as np
import multiprocessing as mp
from sklearn.model_selection import StratifiedKFold,StratifiedShuffleSplit
from sklearn import preprocessing
def construct_PPR_matrix(graph_matrix,parallel=False):
"""
PPR matrix is the matrix of features used for classification --- this is the spatially intense version of the classifier
"""
## initialize the vectors
n = graph_matrix.shape[1]
vectors = np.zeros((n, n))
results = run_PPR(graph_matrix,parallel=parallel)
## get the results in batches
for result in results:
if result != None:
## individual batches
if isinstance(result, list):
for ppr in result:
vectors[ppr[0],:] = ppr[1]
else:
ppr = result
vectors[ppr[0],:] = ppr[1]
return vectors
def construct_PPR_matrix_targets(graph_matrix,targets,parallel=False):
n = graph_matrix.shape[1]
vectors = np.empty((len(targets), n))
tar_map = dict(zip(targets,range(len(targets))))
results = run_PPR(graph_matrix,targets=targets,parallel=parallel)
for result in results:
vectors[tar_map[result[0]],:] = vectors[1]
return vectors
## deal with that now..
def validate_ppr(core_network,labels,dataset_name="test",repetitions=5,random_seed=123,multiclass_classifier=None,target_nodes=None,parallel=False):
"""
The main validation class --- use this to obtain CV results!
"""
if multiclass_classifier is None:
multiclass_classifier = SVC(kernel = 'linear', C = 1,probability=True)
df = pd.DataFrame()
for k in range(repetitions):
## this is relevant for supra-adjacency-based tasks..
if target_nodes is not None:
print("Subnetwork ranking in progress..")
vectors = construct_PPR_matrix_targets(core_network,target_nodes,parallel=parallel)
labels = labels[target_nodes]
else:
vectors = construct_PPR_matrix(core_network,parallel=parallel)
## remove single instance-single target!
nz = np.count_nonzero(labels,axis=0)
wnz = np.argwhere(nz>2).T[0]
labels = labels[:,wnz]
for j in np.arange(0.1,0.5,0.1):
## run the training..
print("Train size:{}, method {}".format(j,"PPR"))
print(vectors.shape,labels.shape)
rs = StratifiedShuffleSplit(n_splits=10, test_size=0.5, random_state=random_seed)
micros = []
macros = []
times = []
new_train_y = []
for y in labels:
new_train_y.append(list(y).index(1))
onedim_labels = np.array(new_train_y)
for X_train, X_test in rs.split(vectors,new_train_y):
start = time.time()
train_x = vectors[X_train]
test_x = vectors[X_test]
train_labels = labels[X_train]
test_labels = labels[X_test]
train_labels_first = onedim_labels[X_train]
test_labels_second = onedim_labels[X_test]
clf = multiclass_classifier
clf.fit(train_x, train_labels_first)
preds = clf.predict(test_x)
mi = f1_score(test_labels_second, preds, average='micro')
ma = f1_score(test_labels_second, preds, average='macro')
# being_predicted = np.unique(train_labels_first)
# tmp_lab = test_labels[:,being_predicted]
# mi,ma = evaluate_oracle_F1(probs,tmp_lab)
## train the model
end = time.time()
elapsed = end - start
micros.append(mi)
macros.append(ma)
times.append(elapsed)
outarray = {"percent_train": np.round(1-j,1), "micro_F":np.mean(micros),"macro_F":np.mean(macros) ,"setting": "PPR" ,"dataset": dataset_name,"time":np.mean(times)}
df = df.append(outarray,ignore_index=True)
df = df.reset_index()
return df
| [
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] | |
fbcf2f345a377b236e4d5dd331708ae9b0e6cc03 | 392a4f5c76414fcbed17dd5dccaf2f64096659a2 | /app_frame/page/market.py | 0630ce30172d3d8b20da2105324e02b39ca1bd86 | [] | no_license | Allison001/homework | 3bd5794c8bdd944f827f3e8008eea1831f90644b | 1ab910d21ad4348a212b226758cfa8244ea03bfc | refs/heads/master | 2023-04-08T22:48:56.667737 | 2021-04-15T03:38:56 | 2021-04-15T03:38:56 | 324,184,733 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 386 | py | import yaml
from selenium.webdriver.common.by import By
from app_frame.basepage import BasePage
from app_frame.page.search import Search
class Market(BasePage):
def goto_search(self):
self.run_step('../page/market.yaml','goto_search')
# self.find_and_click((By.XPATH,"//*[@resource-id='com.xueqiu.android:id/action_search']"))
return Search(self.driver)
| [
"[email protected]"
] | |
c7d2e24957a8f5d7a7276553f6d133a9933b2d8a | 385e00e3d48446baf20cb3d0fbf9db0344cd95da | /test/visualization/test_utils.py | 9e8a593f52ffbe911da59c3806471afc61755eca | [
"Apache-2.0"
] | permissive | oliverdial/qiskit-experiments | d670f9151116e2e7d9a67f304a23313aa31fc30f | a387675a3fe817cef05b968bbf3e05799a09aaae | refs/heads/main | 2023-06-24T08:07:19.505243 | 2023-06-09T21:01:59 | 2023-06-09T21:01:59 | 362,153,676 | 0 | 0 | Apache-2.0 | 2021-04-27T15:03:52 | 2021-04-27T15:03:51 | null | UTF-8 | Python | false | false | 4,818 | py | # This code is part of Qiskit.
#
# (C) Copyright IBM 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
Test visualization utilities.
"""
import itertools as it
from test.base import QiskitExperimentsTestCase
from typing import List, Tuple
import numpy as np
from ddt import data, ddt
from qiskit.exceptions import QiskitError
from qiskit_experiments.visualization.utils import DataExtentCalculator
from qiskit_experiments.framework.package_deps import numpy_version
@ddt
class TestDataExtentCalculator(QiskitExperimentsTestCase):
"""Test DataExtentCalculator"""
@classmethod
def _dummy_data(
cls,
extent: Tuple[float, float, float, float] = (-1, 1, -5, 0),
n_data: int = 5,
n_points: int = 16,
) -> List[np.ndarray]:
# Create a list of bin edges by which to divide the target extent
bin_edges = [
np.histogram_bin_edges(extent[0:2], bins=n_data).tolist(),
np.histogram_bin_edges(extent[2:], bins=n_data).tolist(),
]
# Iterate over pairs of adjacent bin edges, which define the maximum and minimum for the region.
# This is done by generating sliding windows of bin_edges as follows:
# [[a], [b], [c], [d], [e], [f]], g]
# [a, [[b], [c], [d], [e], [f], [g]]
# The result is a list of pairs representing a moving window of size 2.
# TODO: remove the old code once numpy is above 1.20.
dummy_data = []
if numpy_version() >= (1, 20):
for (x_min, x_max), (y_min, y_max) in it.product(
*np.lib.stride_tricks.sliding_window_view(bin_edges, 2, 1)
):
_dummy_data = np.asarray(
[
np.linspace(x_min, x_max, n_points),
np.linspace(y_min, y_max, n_points),
]
)
dummy_data.append(_dummy_data.swapaxes(-1, -2))
else:
for (x_min, x_max), (y_min, y_max) in it.product(
*tuple(list(zip(b[0:-1], b[1:])) for b in bin_edges)
):
_dummy_data = np.asarray(
[
np.linspace(x_min, x_max, n_points),
np.linspace(y_min, y_max, n_points),
]
)
dummy_data.append(_dummy_data.swapaxes(-1, -2))
return dummy_data
@data(*list(it.product([1.0, 1.1, 2.0], [None, 1.0, np.sqrt(2)])))
def test_end_to_end(self, args):
"""Test end-to-end functionality.
Results that are asserted include the range of the final extent tuple and its midpoint.
"""
# Test args
multiplier, aspect_ratio = args[0], args[1]
# Problem inputs
extent = (-1, 1, -5, 1)
n_data = 6
dummy_data = self._dummy_data(extent, n_data=n_data)
ext_calc = DataExtentCalculator(multiplier=multiplier, aspect_ratio=aspect_ratio)
# Add data as 2D and 1D arrays to test both methods
for d in dummy_data[0 : int(n_data / 2)]:
ext_calc.register_data(d)
for d in dummy_data[int(n_data / 2) :]:
for i_dim in range(2):
ext_calc.register_data(d[:, i_dim], dim=i_dim)
# Check extent
actual_extent = ext_calc.extent()
# Check that range was scaled. Given we also have an aspect ratio, we may have a range that is
# larger than the original scaled by the multiplier. At the minimum, the range should be exactly
# equal to the original scaled by the multiplier
expected_range = multiplier * np.diff(np.asarray(extent).reshape((2, 2)), axis=1).flatten()
actual_range = np.diff(np.reshape(actual_extent, (2, 2)), axis=1).flatten()
for act, exp in zip(actual_range, expected_range):
self.assertTrue(act >= exp)
# Check that the midpoints are the same.
expected_midpoint = np.mean(np.reshape(extent, (2, 2)), axis=1).flatten()
actual_midpoint = np.mean(np.reshape(actual_extent, (2, 2)), axis=1).flatten()
np.testing.assert_almost_equal(
actual_midpoint,
expected_midpoint,
)
def test_no_data_error(self):
"""Test that a QiskitError is raised if no data was set."""
ext_calc = DataExtentCalculator()
with self.assertRaises(QiskitError):
ext_calc.extent()
| [
"[email protected]"
] | |
7736ed51fe1a1691133e354fb1c1d6372fd47acf | 4a238068e29a1f6871cc049a0486b20b27e781de | /Habana/benchmarks/resnet/implementations/resnet-tf-sys-420gh-tngr/TensorFlow/computer_vision/Resnets/utils/optimizers/keras/lars_optimizer.py | 4c64f2a9dc780522a575d18bc7c554999bcaf59b | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | Pixelz-Inc/training_results_v1.0 | 61b4555ad482b189d1966be3edd127858addd628 | c507130c4e04c1f274a9ae8b7284aac79f26325a | refs/heads/master | 2023-08-18T22:46:13.316503 | 2021-10-22T04:01:57 | 2021-10-22T04:01:57 | 399,047,712 | 0 | 0 | NOASSERTION | 2021-08-23T09:37:25 | 2021-08-23T09:37:25 | null | UTF-8 | Python | false | false | 9,194 | py | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Layer-wise Adaptive Rate Scaling optimizer for large-batch training."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.keras import backend_config
from tensorflow.python.keras.optimizer_v2 import optimizer_v2
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.training import training_ops
from tensorflow.python.ops import state_ops
class LARSOptimizer(optimizer_v2.OptimizerV2):
"""Layer-wise Adaptive Rate Scaling for large batch training.
Introduced by "Large Batch Training of Convolutional Networks" by Y. You,
I. Gitman, and B. Ginsburg. (https://arxiv.org/abs/1708.03888)
Implements the LARS learning rate scheme presented in the paper above. This
optimizer is useful when scaling the batch size to up to 32K without
significant performance degradation. It is recommended to use the optimizer
in conjunction with:
- Gradual learning rate warm-up
- Linear learning rate scaling
- Poly rule learning rate decay
Note, LARS scaling is currently only enabled for dense tensors. Sparse tensors
use the default momentum optimizer.
"""
def __init__(
self,
learning_rate,
momentum=0.9,
weight_decay=0.0001,
# The LARS coefficient is a hyperparameter
eeta=0.001,
epsilon=0.0,
name="LARSOptimizer",
# Enable skipping variables from LARS scaling.
# TODO(sameerkm): Enable a direct mechanism to pass a
# subset of variables to the optimizer.
skip_list=None,
use_nesterov=False,
**kwargs):
"""Construct a new LARS Optimizer.
Args:
learning_rate: A `Tensor`, floating point value, or a schedule that is a
`tf.keras.optimizers.schedules.LearningRateSchedule`, or a callable
that takes no arguments and returns the actual value to use. The
learning rate.
momentum: A floating point value. Momentum hyperparameter.
weight_decay: A floating point value. Weight decay hyperparameter.
eeta: LARS coefficient as used in the paper. Dfault set to LARS
coefficient from the paper. (eeta / weight_decay) determines the highest
scaling factor in LARS.
epsilon: Optional epsilon parameter to be set in models that have very
small gradients. Default set to 0.0.
name: Optional name prefix for variables and ops created by LARSOptimizer.
skip_list: List of strings to enable skipping variables from LARS scaling.
If any of the strings in skip_list is a subset of var.name, variable
'var' is skipped from LARS scaling. For a typical classification model
with batch normalization, the skip_list is ['batch_normalization',
'bias']
use_nesterov: when set to True, nesterov momentum will be enabled
**kwargs: keyword arguments.
Raises:
ValueError: If a hyperparameter is set to a non-sensical value.
"""
if momentum < 0.0:
raise ValueError("momentum should be positive: %s" % momentum)
if weight_decay < 0.0:
raise ValueError("weight_decay should be positive: %s" % weight_decay)
super(LARSOptimizer, self).__init__(name=name, **kwargs)
self._set_hyper("learning_rate", learning_rate)
# When directly using class members, instead of
# _set_hyper and _get_hyper (such as learning_rate above),
# the values are fixed after __init(), and not being
# updated during the training process.
# This provides better performance but less flexibility.
self.momentum = momentum
self.weight_decay = weight_decay
self.eeta = eeta
self.epsilon = epsilon or backend_config.epsilon()
self._skip_list = skip_list
self.use_nesterov = use_nesterov
def _prepare_local(self, var_device, var_dtype, apply_state):
lr_t = self._get_hyper("learning_rate", var_dtype)
local_step = math_ops.cast(self.iterations, var_dtype)
lr_t = math_ops.cast(lr_t(local_step), var_dtype)
learning_rate_t = array_ops.identity(lr_t)
apply_state[(var_device, var_dtype)].update(
dict(
learning_rate=learning_rate_t,
))
def _create_slots(self, var_list):
for v in var_list:
self.add_slot(v, "momentum")
def compute_lr(self, grad, var, coefficients):
scaled_lr = coefficients["learning_rate"]
if self._skip_list is None or not any(v in var.name
for v in self._skip_list):
w_norm = linalg_ops.norm(var, ord=2)
g_norm = linalg_ops.norm(grad, ord=2)
trust_ratio = array_ops.where(
math_ops.greater(w_norm, 0),
array_ops.where(
math_ops.greater(g_norm, 0),
(self.eeta * w_norm /
(g_norm + self.weight_decay * w_norm + self.epsilon)), 1.0), 1.0)
scaled_lr = coefficients["learning_rate"] * trust_ratio
# Add the weight regularization gradient
grad = grad + self.weight_decay * var
return scaled_lr, grad
def _apply_dense(self, grad, var, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
scaled_lr, grad = self.compute_lr(grad, var, coefficients)
mom = self.get_slot(var, "momentum")
return training_ops.apply_momentum(
var,
mom,
math_ops.cast(1.0, var.dtype.base_dtype),
grad * scaled_lr,
self.momentum,
use_locking=False,
use_nesterov=self.use_nesterov)
def _resource_apply_dense(self, grad, var, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
scaled_lr, grad = self.compute_lr(grad, var, coefficients)
mom = self.get_slot(var, "momentum")
# ============================================================
return training_ops.resource_apply_keras_momentum(
var.handle,
mom.handle,
scaled_lr,
grad,
self.momentum,
use_locking=False,
use_nesterov=self.use_nesterov)
# ============================================================
# ============================================================
# mom_t = mom * self.momentum - grad * scaled_lr
# mom_t = state_ops.assign(mom, mom_t, use_locking=False)
# if self.use_nesterov:
# var_t = var + mom_t * self.momentum - grad * scaled_lr
# else:
# var_t = var + mom_t
# return state_ops.assign(var, var_t, use_locking=False).op
# ============================================================
# Fallback to momentum optimizer for sparse tensors
def _apply_sparse(self, grad, var, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
mom = self.get_slot(var, "momentum")
return training_ops.sparse_apply_momentum(
var,
mom,
coefficients["learning_rate"],
grad.values,
grad.indices,
self.momentum,
use_locking=False,
use_nesterov=self.use_nesterov)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
mom = self.get_slot(var, "momentum")
return training_ops.resource_sparse_apply_keras_momentum(
var.handle,
mom.handle,
coefficients["learning_rate"],
grad,
indices,
self.momentum,
use_locking=False,
use_nesterov=self.use_nesterov)
def get_config(self):
config = super(LARSOptimizer, self).get_config()
config.update({
"learning_rate": self._serialize_hyperparameter("learning_rate"),
"momentum": self.momentum,
"weight_decay": self.weight_decay,
"eeta": self.eeta,
"epsilon": self.epsilon,
"use_nesterov": self.use_nesterov,
})
return config
| [
"[email protected]"
] | |
4f0d3727a003f65b28d97e95316cdc9eefd284eb | 53fab060fa262e5d5026e0807d93c75fb81e67b9 | /backup/user_196/ch80_2020_04_13_18_23_05_143280.py | f6edda895b2e0e2bcd29788dd3078b902f425c3f | [] | no_license | gabriellaec/desoft-analise-exercicios | b77c6999424c5ce7e44086a12589a0ad43d6adca | 01940ab0897aa6005764fc220b900e4d6161d36b | refs/heads/main | 2023-01-31T17:19:42.050628 | 2020-12-16T05:21:31 | 2020-12-16T05:21:31 | 306,735,108 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 151 | py | def interseccao_chaves(dic1,dic2):
lista = []
for a in dic1.keys() and in dic2.keys():
lista.append(a,b)
return lista
| [
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] | |
b7935778e4af05b4794433f47991deced92fb943 | d9a469bc9cff39d89e7cb04e4fc537763aee9aca | /binance_chain/exceptions.py | 957d3ed87c3cd1eb28ab1f816979271c6ed5ca5f | [
"MIT",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | sammchardy/python-binance-chain | d017c0f1e6bd84d28017f87e2d229b21a2ee8b8a | 19d7d639cc912a27ec86831338c2a2dc96289d50 | refs/heads/master | 2023-05-11T19:15:44.912507 | 2021-06-01T03:14:25 | 2021-06-01T03:14:25 | 172,324,144 | 310 | 111 | MIT | 2022-06-30T10:55:19 | 2019-02-24T10:29:29 | Python | UTF-8 | Python | false | false | 1,626 | py | import ujson as json
class BinanceChainAPIException(Exception):
def __init__(self, response, status_code):
self.code = 0
try:
json_res = json.loads(response.content)
except ValueError:
if not response.content:
self.message = status_code
else:
self.message = 'Invalid JSON error message from Binance Chain: {}'.format(response.text)
else:
self.code = json_res.get('code', None)
self.message = json_res['message']
self.status_code = status_code
self.response = response
self.request = getattr(response, 'request', None)
def __str__(self): # pragma: no cover
return f'APIError(code={self.code}): {self.message}'
class BinanceChainRequestException(Exception):
pass
class BinanceChainBroadcastException(Exception):
pass
class BinanceChainSigningAuthenticationException(Exception):
pass
class BinanceChainRPCException(Exception):
def __init__(self, response):
self.code = 0
try:
json_res = json.loads(response.content)
except ValueError:
self.message = 'Invalid JSON error message from Binance Chain: {}'.format(response.text)
else:
self.code = json_res['error']['code']
self.message = json_res['error']['message']
self.status_code = response.status_code
self.response = response
self.request = getattr(response, 'request', None)
def __str__(self): # pragma: no cover
return f'RPCError(code={self.code}): {self.message}'
| [
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] | |
05cdd6e0b5aadfcd1453901287e445578f2b8e29 | 6ba38fe94e7ea5146c633f56f59c0c3278d695a7 | /build/build_for_ios | d6425614eecc82e40f167b7e162c91cecd846058 | [
"MIT"
] | permissive | mworks/mworks | b49b721c2c5c0471180516892649fe3bd753a326 | abf78fc91a44b99a97cf0eafb29e68ca3b7a08c7 | refs/heads/master | 2023-09-05T20:04:58.434227 | 2023-08-30T01:08:09 | 2023-08-30T01:08:09 | 2,356,013 | 14 | 11 | null | 2012-10-03T17:48:45 | 2011-09-09T14:55:57 | C++ | UTF-8 | Python | false | false | 941 | #!/usr/bin/env python3
import argparse
from subprocess import check_call
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--archive', action='store_true',
help='create an archive for distribution')
parser.add_argument('version', nargs='?', help='version number')
args = parser.parse_args()
cmd = [
'/usr/bin/xcrun',
'xcodebuild',
'-workspace', 'MWorks-iOS.xcworkspace',
'-scheme', 'MWorks',
'-destination', 'generic/platform=iOS',
]
if args.archive:
cmd.append('archive')
else:
cmd.extend(['clean', 'build'])
cmd.extend(['GCC_TREAT_WARNINGS_AS_ERRORS=YES',
'MTL_TREAT_WARNINGS_AS_ERRORS=YES',
'SWIFT_TREAT_WARNINGS_AS_ERRORS=YES'])
if args.version:
cmd.append('MW_VERSION=' + args.version)
check_call(cmd)
if __name__ == '__main__':
main()
| [
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] | ||
441e60c7846fde6cca41e6cbb3845b685e4f8672 | 09e5cfe06e437989a2ccf2aeecb9c73eb998a36c | /modules/cctbx_project/cctbx/symmetry_search/boost_python/SConscript | be2824dfaa2fdc51694642b708bafd590f93bda6 | [
"BSD-3-Clause-LBNL",
"BSD-3-Clause"
] | permissive | jorgediazjr/dials-dev20191018 | b81b19653624cee39207b7cefb8dfcb2e99b79eb | 77d66c719b5746f37af51ad593e2941ed6fbba17 | refs/heads/master | 2020-08-21T02:48:54.719532 | 2020-01-25T01:41:37 | 2020-01-25T01:41:37 | 216,089,955 | 0 | 1 | BSD-3-Clause | 2020-01-25T01:41:39 | 2019-10-18T19:03:17 | Python | UTF-8 | Python | false | false | 216 | Import("env_cctbx_boost_python_ext")
env = env_cctbx_boost_python_ext.Clone()
env.Prepend(LIBS=["cctbx", "omptbx"])
env.SharedLibrary(target="#lib/cctbx_symmetry_search_ext", source=[
"symmetry_search_ext.cpp",
])
| [
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] | ||
4f1ec457cdb2aff59d8558ed5d090e890e081fa7 | 80a689cecd96315e55e6452d201e6531868bdc99 | /management/commands/pdk_nudge_ios_devices_boto.py | c82c6760ca2673b2252cf9062343fe8914127764 | [
"LicenseRef-scancode-warranty-disclaimer",
"Apache-2.0"
] | permissive | kamau-edwin/PassiveDataKit-Django | d36fad6b366fef32c96941b10455b054abd44f7c | 95db5701f88c74328b0611124149fdffa079e7b8 | refs/heads/master | 2021-01-06T17:25:50.471370 | 2020-02-26T21:17:32 | 2020-02-26T21:17:32 | 241,416,694 | 0 | 0 | Apache-2.0 | 2020-02-18T16:56:09 | 2020-02-18T16:56:07 | null | UTF-8 | Python | false | false | 5,828 | py | # pylint: disable=no-member,line-too-long
import json
import re
import boto
import boto.exception
import boto.sns
from django.conf import settings
from django.core.management.base import BaseCommand
from ...decorators import handle_lock, log_scheduled_event
from ...models import DataPoint
class Command(BaseCommand):
help = 'Send silent notifications to iOS devices to nudge power management systems for transmission using Boto and Amazon Simple Notification Service.'
def add_arguments(self, parser):
pass
@handle_lock
@log_scheduled_event
def handle(self, *args, **options): # pylint: disable=too-many-locals, too-many-branches, too-many-statements
tokens = {}
for point in DataPoint.objects.filter(generator_identifier='pdk-app-event', secondary_identifier='pdk-ios-device-token').order_by('created'):
properties = point.fetch_properties()
tokens[point.source] = properties['event_details']['token']
region = [r for r in boto.sns.regions() if r.name == settings.PDK_BOTO_REGION][0]
notification = {'aps': {'content-available' : 1}}
message = {'APNS': json.dumps(notification), 'default': 'nil'}
sns = boto.sns.SNSConnection(
aws_access_key_id=settings.PDK_BOTO_ACCESS_KEY,
aws_secret_access_key=settings.PDK_BOTO_ACCESS_SECRET,
region=region,
)
for source, token in tokens.iteritems(): # pylint: disable=unused-variable
try:
endpoint_response = sns.create_platform_endpoint(
platform_application_arn=settings.PDK_BOTO_SNS_ARN,
token=token,
)
endpoint_arn = endpoint_response['CreatePlatformEndpointResponse']['CreatePlatformEndpointResult']['EndpointArn']
except boto.exception.BotoServerError, err:
print 'ERR 1: ' + err.message
# Yes, this is actually the official way:
# http://stackoverflow.com/questions/22227262/aws-boto-sns-get-endpoint-arn-by-device-token
result_re = re.compile(r'Endpoint(.*)already', re.IGNORECASE)
result = result_re.search(err.message)
if result:
endpoint_arn = result.group(0).replace('Endpoint ', '').replace(' already', '')
else:
raise
try:
sns.publish(target_arn=endpoint_arn, message_structure='json', message=json.dumps(message))
except boto.exception.BotoServerError, err:
print 'FAILED SENDING TO ' + token
print 'ERR: ' + err.message
result_re = re.compile(r'Endpoint(.*)disabled', re.IGNORECASE)
result = result_re.search(err.message)
if result:
for point in DataPoint.objects.filter(source=source, generator_identifier='pdk-app-event', secondary_identifier='pdk-ios-device-token').order_by('created'):
properties = point.fetch_properties()
if token == properties['event_details']['token']:
print 'RENAMING: ' + token
point.secondary_identifier = 'pdk-ios-device-token-sandbox'
point.save()
else:
raise
tokens = {}
for point in DataPoint.objects.filter(generator_identifier='pdk-app-event', secondary_identifier='pdk-ios-device-token-sandbox').order_by('created'):
properties = point.fetch_properties()
tokens[point.source] = properties['event_details']['token']
message = {'APNS_SANDBOX': json.dumps(notification), 'default': 'nil'}
for source, token in tokens.iteritems(): # pylint: disable=unused-variable
try:
endpoint_response = sns.create_platform_endpoint(
platform_application_arn=settings.PDK_BOTO_SNS_ARN_SANDBOX,
token=token,
)
endpoint_arn = endpoint_response['CreatePlatformEndpointResponse']['CreatePlatformEndpointResult']['EndpointArn']
except boto.exception.BotoServerError, err:
print 'ERR 2: ' + err.message
# Yes, this is actually the official way:
# http://stackoverflow.com/questions/22227262/aws-boto-sns-get-endpoint-arn-by-device-token
result_re = re.compile(r'Endpoint(.*)already', re.IGNORECASE)
result = result_re.search(err.message)
if result:
endpoint_arn = result.group(0).replace('Endpoint ', '').replace(' already', '')
else:
raise
try:
sns.publish(target_arn=endpoint_arn, message_structure='json', message=json.dumps(message))
# print('PUBLISHED DEV: ' + token)
except boto.exception.BotoServerError, err:
print 'FAILED SENDING 2 TO ' + token
print 'ERR: ' + err.message
result_re = re.compile(r'Endpoint(.*)disabled', re.IGNORECASE)
result = result_re.search(err.message)
if result:
for point in DataPoint.objects.filter(source=source, generator_identifier='pdk-app-event', secondary_identifier='pdk-ios-device-token-sandbox').order_by('created'):
properties = point.fetch_properties()
if token == properties['event_details']['token']:
print 'RENAMING 2: ' + token
point.secondary_identifier = 'pdk-ios-device-token-disabled'
point.save()
else:
raise
| [
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] | |
cebcfbab3351bb97acf855a4e8a6a0e12ecff3e0 | d88f9acfe09d79f06cf251b8cbbb012b55d99f39 | /Scraping/test_scraping/create_sqlite_db.py | 95e1b304f3fc724c65ddf601e4224bbe7e44b3ed | [] | no_license | Twishar/DataAnalysis | 535beb795e30b8ac07767a61f1ebfbc60546271f | e5d5ba9ba0b9a51031e8f1f4225bc35d848159dd | refs/heads/master | 2022-03-04T19:02:30.917729 | 2019-11-15T14:18:53 | 2019-11-15T14:18:53 | 98,515,695 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 192 | py |
import sqlite3
conn = sqlite3.connect('allo_db.sqlite')
c = conn.cursor()
c.execute('''CREATE TABLE allo_parse
(search_param text, results text);''')
conn.commit()
conn.close()
| [
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] | |
3f39b4c11c3aa082d210897c4b788bb31b2e0551 | 8fcc27160f8700be46296568260fa0017a0b3004 | /client/carbonui/control/windowDropDownMenu.py | 6c26d7806b20cec4ebb3158345c97b472461b7f6 | [] | no_license | connoryang/dec-eve-serenity | 5d867f4eedfa896a4ef60f92556356cafd632c96 | b670aec7c8b4514fc47cd52e186d7ccf3aabb69e | refs/heads/master | 2021-01-22T06:33:16.303760 | 2016-03-16T15:15:32 | 2016-03-16T15:15:32 | 56,389,750 | 1 | 0 | null | 2016-04-16T15:05:24 | 2016-04-16T15:05:24 | null | UTF-8 | Python | false | false | 1,453 | py | #Embedded file name: e:\jenkins\workspace\client_SERENITY\branches\release\SERENITY\packages\carbonui\control\windowDropDownMenu.py
import carbonui.const as uiconst
from carbonui.primitives.container import Container
from carbonui.primitives.fill import Fill
from carbonui.primitives.line import Line
from carbonui.control.label import LabelOverride as Label
class WindowDropDownMenuCore(Container):
__guid__ = 'uicls.WindowDropDownMenuCore'
default_height = 10
default_align = uiconst.TOLEFT
default_state = uiconst.UI_NORMAL
def Setup(self, name, GetMenu):
self.name = name
self.expandOnLeft = 1
self.PrepareLayout()
self.GetMenu = GetMenu
def PrepareLayout(self):
Line(parent=self, align=uiconst.TORIGHT)
self.label = Label(text=self.name, parent=self, align=uiconst.CENTER, fontsize=9, letterspace=1, top=1, state=uiconst.UI_DISABLED, uppercase=1)
self.hilite = Fill(parent=self, state=uiconst.UI_HIDDEN, padding=1)
self.width = self.label.width + 10
self.cursor = uiconst.UICURSOR_SELECT
def OnMouseEnter(self):
self.hilite.state = uiconst.UI_DISABLED
def OnMouseExit(self):
self.hilite.state = uiconst.UI_HIDDEN
def GetMenuPosition(self, *args):
return (self.absoluteLeft, self.absoluteBottom + 2)
class WindowDropDownMenuCoreOverride(WindowDropDownMenuCore):
pass
| [
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] | |
bc9fb2afed22a652d7a229f920fb725987c8015a | 6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4 | /7DrvnMeY2Ebzk2mfH_8.py | cdf4c6f5d8fb4f7a25817718499599ad9938b579 | [] | no_license | daniel-reich/ubiquitous-fiesta | 26e80f0082f8589e51d359ce7953117a3da7d38c | 9af2700dbe59284f5697e612491499841a6c126f | refs/heads/master | 2023-04-05T06:40:37.328213 | 2021-04-06T20:17:44 | 2021-04-06T20:17:44 | 355,318,759 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 129 | py |
import re
body_insert = '(?<=<body>\n)'
body_append = '(?=\n</body>)'
body_rewrite = '(?<=<body>\n)(?:\n|.)+(?=\n</body>)'
| [
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] | |
812988adf18876c0cce0bafb504a64050e1ff7f7 | ddcf878cca43d49f73fd673279a97e82ced521e8 | /peyotl/nexson_proxy.py | 0695ac59e11bb24cc41859e401f46be707764742 | [
"BSD-2-Clause",
"Python-2.0"
] | permissive | OpenTreeOfLife/peyotl | ca5fcbc4f1754c3da7a25c93d89cfeaaad17057f | b50f7217966c18195c9b52be42454513ffa3e7f3 | refs/heads/master | 2023-08-03T14:35:46.793662 | 2023-07-26T20:30:08 | 2023-07-26T20:30:08 | 16,637,087 | 6 | 4 | BSD-2-Clause | 2023-07-24T20:02:30 | 2014-02-08T05:52:12 | Jupyter Notebook | UTF-8 | Python | false | false | 13,589 | py | #!/usr/bin/env python
"""Provides high level wrappers around a Nexson data model blob to
let it be treated as if it were a list of OTUs and a list of trees
Accessors return either references to part of the NexSON or wrappers around
those entities (not copies!)
Weakrefs are used, so the more inclusive containers must be kept in scope
while you are accessing constituents.
Currently converts any NexSON blob to HBF v1.2 data model!
"""
from peyotl.nexson_syntax import (BY_ID_HONEY_BADGERFISH,
convert_nexson_format,
detect_nexson_version,
get_nexml_el,
read_as_json)
from peyotl.utility.str_util import is_str_type
from peyotl.utility import get_logger
import weakref
_LOG = get_logger(__name__)
def otu_iter_nexson_proxy(nexson_proxy, otu_sort=None):
"""otu_sort can be None (not sorted or stable), True (sorted by ID lexigraphically)
or a key function for a sort function on list of otuIDs
Note that if there are multiple OTU groups, the NexSON specifies the order of sorting
of the groups (so the sort argument here only refers to the sorting of OTUs within
a group)
"""
nexml_el = nexson_proxy._nexml_el
og_order = nexml_el['^ot:otusElementOrder']
ogd = nexml_el['otusById']
for og_id in og_order:
og = ogd[og_id]
if otu_sort is None:
for k, v in og:
yield nexson_proxy._create_otu_proxy(k, v)
else:
key_list = list(og.keys())
if otu_sort is True:
key_list.sort()
else:
key_list.sort(key=otu_sort)
for k in key_list:
v = og[k]
yield nexson_proxy._create_otu_proxy(k, v)
def tree_iter_nexson_proxy(nexson_proxy):
"""Iterates over NexsonTreeProxy objects in order determined by the nexson blob"""
nexml_el = nexson_proxy._nexml_el
tg_order = nexml_el['^ot:treesElementOrder']
tgd = nexml_el['treesById']
for tg_id in tg_order:
tg = tgd[tg_id]
tree_order = tg['^ot:treeElementOrder']
tbid = tg['treeById']
otus = tg['@otus']
for k in tree_order:
v = tbid[k]
yield nexson_proxy._create_tree_proxy(tree_id=k, tree=v, otus=otus)
def reverse_edge_by_source_dict(ebs, root_id):
d = {}
for edge_by_id in ebs.values():
for edge_id, edge in edge_by_id.items():
t = edge['@target']
assert t not in d
d[t] = (edge_id, edge)
assert root_id in ebs
assert root_id not in d
d[root_id] = (None, None)
return d
class NexsonProxy(object):
class NexsonOTUProxy(object):
def __init__(self, nexson_proxy, otu_id, otu):
self._nexson_proxy = nexson_proxy
self._otu_id = otu_id
self._otu = otu
def __getitem__(self, key):
return self.otu[key]
def __setitem__(self, key, value):
self.otu[key] = value
@property
def ott_id(self):
return self._otu.get('^ot:ottId')
@property
def otu(self):
return self._otu
@property
def _id(self):
return self._otu_id
def get(self, key, default=None):
return self._otu.get(key, default)
def keys(self, key, default=None):
return self._otu.get(key, default)
def __init__(self, filepath='', nexson=None):
self.filepath = filepath
if nexson is None:
if not filepath:
raise ValueError('Either a filepath or nexson argument must be provided')
self._nexson = read_as_json(self.filepath)
else:
self._nexson = nexson
v = detect_nexson_version(self._nexson)
if v != BY_ID_HONEY_BADGERFISH:
_LOG.debug('NexsonProxy converting to hbf1.2')
convert_nexson_format(self._nexson, BY_ID_HONEY_BADGERFISH)
self._nexml_el = get_nexml_el(self._nexson)
self._otu_cache = {}
self._tree_cache = {}
self._wr = None
def otu_iter(self):
return iter(otu_iter_nexson_proxy(self))
def tree_iter(self):
return iter(tree_iter_nexson_proxy(self))
def _create_otu_proxy(self, otu_id, otu):
np = self._otu_cache.get(otu_id)
if np is None:
if self._wr is None:
self._wr = weakref.proxy(self)
np = NexsonProxy.NexsonOTUProxy(self._wr, otu_id, otu)
self._otu_cache[otu_id] = np
return np
def _create_tree_proxy(self, tree_id, tree, otus):
np = self._tree_cache.get(tree_id)
if np is None:
if self._wr is None:
self._wr = weakref.proxy(self)
np = NexsonTreeProxy(tree_id=tree_id, tree=tree, otus=otus, nexson_proxy=self._wr)
self._tree_cache[tree_id] = np
return np
def get_tree(self, tree_id):
np = self._tree_cache.get(tree_id)
if np is not None:
return np
tgd = self._nexml_el['treesById']
for tg in tgd.values():
tbid = tg['treeById']
if tree_id in tbid:
otus = tg['@otus']
tree = tbid[tree_id]
return self._create_tree_proxy(tree_id=tree_id, tree=tree, otus=otus)
return None
def get_otu(self, otu_id):
np = self._otu_cache.get(otu_id)
if np is not None:
return np
ogd = self._nexml_el['otusById']
for og in ogd.values():
o = og['otuById'].get(otu_id)
if o is not None:
return self._create_otu_proxy(otu_id=otu_id, otu=o)
return None
class NexsonTreeProxy(object):
"""Provide more natural operations by wrapping a NexSON 1.2 tree blob and its otus"""
class NexsonNodeProxy(object):
def __init__(self, tree, edge_id, edge, node_id=None, node=None):
self._tree = tree
self._node_id = node_id
self._node = node
self._edge_id = edge_id
self._edge = edge
self._otu = None
def get(self, key, default=None):
return self.node.get(key, default)
def __getitem__(self, key):
return self.node[key]
def __setitem__(self, key, value):
self.node[key] = value
def keys(self):
return self.node.keys()
@property
def is_leaf(self):
return self._tree.is_leaf(self.node_id)
def child_iter(self):
return self._tree.child_iter(self.node_id)
@property
def ott_id(self):
return self._tree.get_ott_id(self.node)
@property
def edge_id(self):
return self._edge_id
@property
def edge(self):
return self._edge
@property
def _id(self):
return self.node_id
@property
def parent(self):
if self._edge_id is None:
return None
par_node_id = self.edge['@source']
par_edge_id, par_edge = self._tree._find_edge_from_child(par_node_id)
return self._tree._create_node_proxy_from_edge(edge_id=par_edge_id, edge=par_edge, node_id=par_node_id)
@property
def node_id(self):
if self._node_id is None:
self._node_id = self._edge['@target']
return self._node_id
@property
def otu(self):
if self._otu is None:
otu_id, otu = self._tree._raw_otu_for_node(self.node)
self._otu = self._tree._nexson_proxy._create_otu_proxy(otu_id=otu_id, otu=otu)
return self._otu
@property
def node(self):
if self._node is None:
self._node = self._tree.get_nexson_node(self.node_id)
return self._node
def __iter__(self):
return iter(nexson_tree_preorder_iter(self._tree,
node=self.node,
node_id=self.node_id,
edge_id=self.edge_id,
edge=self.edge))
def preorder_iter(self):
return iter(nexson_tree_preorder_iter(self))
def __init__(self, tree, tree_id=None, otus=None, nexson_proxy=None):
self._nexson_proxy = nexson_proxy
self._nexson_tree = tree
self._edge_by_source_id = tree['edgeBySourceId']
self._node_by_source_id = tree['nodeById']
if is_str_type(otus):
self._otus_group_id = otus
self._otus = nexson_proxy._nexml_el['otusById'][otus]['otuById']
else:
self._otus = otus
self._tree_id = tree_id
# not part of nexson, filled on demand. will be dict of node_id -> (edge_id, edge) pair
self._edge_by_target = None
self._wr = None
self._node_cache = {}
def get_nexson_node(self, node_id):
return self._node_by_source_id[node_id]
def get_node(self, node_id):
np = self._node_cache.get(node_id)
if np is not None:
return np
edge_id, edge = self._find_edge_from_child(node_id)
node = self._node_by_source_id[node_id]
return self._create_node_proxy_from_edge(edge_id, edge, node_id, node)
def get(self, key, default=None):
return self._nexson_tree.get(key, default)
def __getitem__(self, key):
return self._nexson_tree[key]
def __setitem__(self, key, value):
self._nexson_tree[key] = value
@property
def edge_by_target(self):
"""Returns a reference to the dict of target node id to (edge_id, edge)"""
if self._edge_by_target is None:
self._edge_by_target = reverse_edge_by_source_dict(self._edge_by_source_id,
self._nexson_tree['^ot:rootNodeId'])
return self._edge_by_target
def _find_edge_from_child(self, node_id):
"""Returns (edge_id, edge)"""
return self.edge_by_target[node_id]
def _create_node_proxy_from_edge(self, edge_id, edge, node_id=None, node=None):
np = self._node_cache.get(edge_id)
if np is None:
if self._wr is None:
self._wr = weakref.proxy(self)
np = NexsonTreeProxy.NexsonNodeProxy(self._wr, edge_id=edge_id, edge=edge, node_id=node_id, node=node)
self._node_cache[edge_id] = np
if node_id is not None:
self._node_cache[node_id] = np
return np
def child_iter(self, node_id):
return nexson_child_iter(self._edge_by_source_id.get(node_id, {}), self)
def is_leaf(self, node_id):
return node_id not in self._edge_by_source_id
def get_ott_id(self, node):
return self._raw_otu_for_node(node)[1].get('^ot:ottId')
def _raw_otu_for_node(self, node):
otu_id = node['@otu']
# _LOG.debug('otu_id = {}'.format(otu_id))
return otu_id, self._otus[otu_id]
def annotate(self, obj, key, value):
obj[key] = value
def __iter__(self):
return iter(nexson_tree_preorder_iter(self))
def preorder_iter(self):
return iter(nexson_tree_preorder_iter(self))
def nodes(self):
return [i for i in iter(self)]
def nexson_child_iter(edict, nexson_tree_proxy):
for edge_id, edge in edict.items():
yield nexson_tree_proxy._create_node_proxy_from_edge(edge_id, edge)
def nexson_tree_preorder_iter(tree_proxy, node_id=None, node=None, edge_id=None, edge=None):
"""Takes a tree in "By ID" NexSON (v1.2). provides and iterator over:
NexsonNodeProxy object
where the edge of the object is the edge connectin the node to the parent.
The first node will be the root and will have None as it's edge
"""
tree = tree_proxy._nexson_tree
ebsid = tree['edgeBySourceId']
nbid = tree['nodeById']
if edge_id is not None:
assert edge is not None
if node_id is None:
node_id = edge['@target']
else:
assert node_id == edge['@target']
if node is None:
node = nbid[node_id]
else:
assert node == nbid[node_id]
yield tree_proxy._create_node_proxy_from_edge(edge_id, edge, node_id=node_id, node=node)
root_id = node_id
elif node_id is not None:
if node is None:
node = nbid[node_id]
else:
assert node == nbid[node_id]
yield tree_proxy._create_node_proxy_from_edge(None, None, node_id=node_id, node=node)
root_id = node_id
else:
root_id = tree['^ot:rootNodeId']
root = nbid[root_id]
yield tree_proxy._create_node_proxy_from_edge(None, None, node_id=root_id, node=root)
stack = []
new_stack = [(i['@target'], edge_id, i) for edge_id, i in ebsid[root_id].items()]
stack.extend(new_stack)
while stack:
target_node_id, edge_id, edge = stack.pop()
node = nbid[target_node_id]
yield tree_proxy._create_node_proxy_from_edge(edge_id=edge_id, edge=edge, node_id=target_node_id)
daughter_edges = ebsid.get(target_node_id)
if daughter_edges is not None:
new_stack = [(i['@target'], edge_id, i) for edge_id, i in daughter_edges.items()]
stack.extend(new_stack)
| [
"[email protected]"
] | |
a317a9e4f4f5d6e738556b77ccdf5ca54c22337f | d8ef155d2b931642e448263d43fbf856b3a466c0 | /certificates/__main__.py | ac85092b9df679740502289f380cc93e8e0a251c | [
"Apache-2.0"
] | permissive | diemesleno/certificates | a34632bc97a175fd739cdaa6d78f880316176a3c | 7aedf80903304216c6d9a8c99efd4df5aa7f8049 | refs/heads/master | 2022-02-15T17:44:43.132433 | 2019-08-16T05:44:26 | 2019-08-16T05:44:45 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 610 | py | import argparse
from .certificates import make_certificates
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"participants", help="csv filaname containing participants"
)
parser.add_argument(
"template", help="certificate template in svg format used to build"
)
parser.add_argument(
"--output",
"-o",
default="./output",
help="destination of the generated certificates",
)
args = parser.parse_args()
make_certificates(args.participants, args.template, args.output)
if __name__ == "__main__":
main()
| [
"[email protected]"
] | |
eee490dcf526ffb10b67a1324f01736b974f8ce9 | 89f8a2e609c2b2a7e4ca10be3830200c7e8e438e | /ftp_homework/ftp_1/bin/start_server.py | e0741d5f538a88369aa9ea5194dab97ea4334bde | [] | no_license | boundshunter/s5-study | b8265ccc0d09f19624002b5919c5fb6104bf65d3 | 528eda7435a14a2a79c88af02695efec13972f25 | refs/heads/master | 2018-09-27T17:40:28.352951 | 2018-06-11T15:38:13 | 2018-06-11T15:38:13 | 111,669,896 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 268 | py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
__author__ = 'jfsu'
import sys
import os
BaseDir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(BaseDir)
from core import ftpserver
if __name__ == '__main__':
sv = ftpserver.FtpServer()
| [
"[email protected]"
] | |
27a9e101cd4a7f253db5f5c89fb3068918340ead | 34745a8d54fa7e3d9e4237415eb52e507508ad79 | /Python Fundamentals/03 Lists Basics/Exercises/07_Easter_Gifts.py | 172ea7853a18b1443adb30323f730642b61c1f6b | [] | no_license | DilyanTsenkov/SoftUni-Software-Engineering | 50476af0dc88b267d72c56fa87eeb88d841164b2 | fe446e3a50a00bb2e48d71ab8f783e0a4a406094 | refs/heads/main | 2023-08-12T18:18:42.144210 | 2021-09-25T11:10:38 | 2021-09-25T11:10:38 | 317,235,419 | 1 | 2 | null | null | null | null | UTF-8 | Python | false | false | 870 | py | gifts_names = input().split(" ")
command = input()
while command != "No Money":
command_list = command.split(" ")
if command_list[0] == "OutOfStock":
if command_list[1] in gifts_names:
for i in range(len(gifts_names)):
if gifts_names[i] == command_list[1]:
gifts_names[i] = "None"
elif command_list[0] == "Required" and int(command_list[2]) > 0 and int(command_list[2]) <= int(
len(gifts_names)) - 1:
gifts_names[int(command_list[2])] = command_list[1]
elif command_list[0] == "JustInCase":
gifts_names[int(len(gifts_names)) - 1] = command_list[1]
command = input()
for n in range(len(gifts_names)):
if "None" in gifts_names:
gifts_names.remove("None")
gifts_names_print = " ".join(gifts_names)
print(gifts_names_print)
| [
"[email protected]"
] | |
92f0088358bab1fa58c2c52e016d253b12bfc28d | 7246faf9a222269ce2612613f58dc5ff19091f10 | /baekjoon/3000~5999/4948_베르트랑공준.py | f2adb647d3f69804cccea3dfb61db9c7a6ded31a | [] | no_license | gusdn3477/Algorithm_Study | 87a2eb72a8488d9263a86db70dadc7944434d41d | 3fefe1dcb40122157845ffc542f41cb097711cc8 | refs/heads/main | 2023-08-30T12:18:21.412945 | 2021-09-28T13:00:11 | 2021-09-28T13:00:11 | 308,364,230 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 380 | py | from math import sqrt
arr = [i for i in range(250000)]
arr[0] = 0
arr[1] = 0
for i in range(2, int(sqrt(250000)) + 1):
for j in range(i + i, 250000, i):
if arr[j] != 0:
arr[j] = 0
while True:
N = int(input())
ct = 0
if N == 0:
break
for i in range(N + 1, N * 2 + 1):
if arr[i] != 0:
ct += 1
print(ct)
| [
"[email protected]"
] | |
fd6c788ba6b8318466159be137309f8ff4ea1a29 | 9f109d4d4fa2eb4ecec2415a21e45945a35cd58a | /xshop/users/tests/test_models.py | 81150f9ff1be611e68b2606f5f69d464e95e5b0d | [] | no_license | denokenya/xshop-web | 4be66a39272075b778ed7dd8de996fec90b5fab8 | 262665ec4c2cb91490b219a086b8994d6eceb805 | refs/heads/master | 2023-06-07T02:54:57.068430 | 2020-09-13T11:24:32 | 2020-09-13T11:24:32 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,009 | py | from django.test import TestCase
from model_bakery import baker
from ..models import User
class UserTests(TestCase):
def setUp(self) -> None:
self.user = baker.make(
User,
mobile="01010092181",
name="Ahmed Loay Shahwan",
email="[email protected]",
)
self.user1 = baker.make(User, mobile="01010092182")
def test_get_short_name(self):
self.assertEqual(self.user.get_short_name(), "Ahmed")
def test_get_full_name(self):
self.assertEqual(self.user.get_full_name(), "Ahmed Loay Shahwan")
def test_str(self):
self.assertEqual(str(self.user), "01010092181")
def test_repr(self):
# user with name
self.assertEqual(
self.user.__repr__(),
f"<User {self.user.id}: {str(self.user)} - {self.user.name}>",
)
# user without name
self.assertEqual(
self.user1.__repr__(), f"<User {self.user1.id}: {str(self.user1)}>",
)
| [
"[email protected]"
] | |
4558b73f4309e412016f5c1d22d3652908e71d01 | c2c84c98f2247f2a9fe280e41f3a4dc74fd4de1a | /online/analyses.py | 73a0d03dbb5a0da0b17ff4129ab1c019baf63cab | [
"MIT"
] | permissive | mrware91/tmolv29 | 153ded42ee190287442330943a2a9c51d8e55243 | 823321f2505b684e9fd1de1c01f4e46997f1e307 | refs/heads/main | 2023-04-06T13:55:09.926010 | 2021-04-14T14:26:05 | 2021-04-14T14:26:05 | 347,172,169 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,299 | py | # Contributors: Matt Ware
import numpy as np
class analyses:
def __init__(self, analysis, totalEvents,printMode='verbose'):
self.analysis = analysis
self.totalEvents = totalEvents
self.events = 0
self.printMode = printMode
self.data = {}
self.dataTypesFound = False
self.outTypes = {}
self.initialize()
def initialize(self):
self.events = 0
self.data = {}
for key in self.analysis:
self.outTypes[key] = None
self.analysis[key]['type'] = None
self.analysis[key]['size'] = None
self.data[key] = np.zeros(self.totalEvents)*np.nan
self.setdefault(self.analysis[key],
'function',
'%s: No analysis function provided. Defaulting to return raw data.'%key,
lambda x: x)
self.setdefault(self.analysis[key],
'analyzeEvery',
'%s: No modulo provided. Will analyze every shot.'%key,
1)
def update(self, detectors):
self.dataTypesFound = True
for key in self.analysis:
analyzeEvery = self.analysis[key]['analyzeEvery']
if not ( self.events%analyzeEvery == 0):
continue
function = self.analysis[key]['function']
detectorKey = self.analysis[key]['detectorKey']
shotData = detectors[detectorKey]['shotData']
if (shotData is None) & (self.analysis[key]['type'] is None):
self.dataTypesFound = False
continue
elif (shotData is None) & (self.analysis[key]['type'] is not None):
self.data[key][self.events,] = self.data[key][self.events,]*np.nan
continue
result = function(shotData)
if result is not None:
if self.analysis[key]['type'] is None:
self.analysis[key]['type'] = type(result)
self.analysis[key]['size'] = np.size(result)
dims = np.shape(result)
self.data[key] = np.zeros((self.totalEvents,*dims))*np.nan
self.data[key][self.events,] = result
if self.outTypes[key] is None:
self.outTypes[key] = {}
self.outTypes[key]['type'] = type(self.data[key][self.events,])
self.outTypes[key]['size'] = np.size( self.data[key][self.events,] )
elif (result is None) & (self.analysis[key]['type'] is None):
self.dataTypesFound = False
self.events += 1
if self.events >= self.totalEvents:
self.cprint('Read events exceeds total expected. Resetting event count.')
self.events = 0
def setdefault(self, adict, key, response, default):
try:
adict[key]
except KeyError as ke:
allowedErrorStr = '\'%s\'' % key
if allowedErrorStr == str(ke):
self.cprint(response)
adict[key] = default
else:
raise ke
# def cprint(self,aString):
# print(aString)
def cprint(self, aString):
if self.printMode in 'verbose':
print(aString)
elif self.printMode in 'quiet':
pass
else:
print('printMode is %s. Should be verbose or quiet. Defaulting to verbose.'%self.printMode)
self.printMode = 'verbose'
self.cprint(aString)
def H5out(self):
if self.dataTypesFound:
outDict = {}
for key in self.data:
try:
outDict[key] = np.copy(self.data[key][0,:])
except IndexError as ie:
if ('1-dimensional' in str(ie)):
# print(f'dimension of {key} is {self.data[key].shape}')
outDict[key] = np.copy(self.data[key][:])
else:
raise ie
return outDict
else:
return None | [
"[email protected]"
] | |
2a5762a03705f381381e6c124790e7ce1ab5d662 | 93a7db386dfa0ac0dc369cc7f4b974224c801d8d | /scripts/ngram_io.py | 33d3856f68312a40f09259482de1803a86d567b5 | [] | no_license | lingxiao/good-great-combo | e051f20c89b7317a14ca5cee357bda7b095ce174 | 4d2691866bc21e2c542354ad3aae6f369eb86c87 | refs/heads/master | 2021-01-19T19:30:43.391759 | 2017-04-09T12:35:15 | 2017-04-09T12:35:15 | 83,699,772 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,764 | py | ############################################################
# Module : Open Ngram and read linguistic pattern
# Date : April 3rd, 2017
# Author : Xiao Ling, merle
############################################################
import os
############################################################
'''
@Use : Open all ngrams in ngram_dir and stream output as tuple of (ngram, count)
@Input : - ngram_dir :: String
- debug :: Bool, if true then only output parts of stream
@Output: Iterator output ngrams of form:
(ngram, count) :: Iterator (String, String)
Throw: NameError if path does not exists
'''
def with_ngram(ngram_dir, debug = False):
if not os.path.exists(ngram_dir):
raise NameError('Path not found at ' + ngram_dir)
else:
ngram_paths = [os.path.join(ngram_dir, p) for \
p in os.listdir(ngram_dir) if '.txt' in p]
if not ngram_paths:
raise NameError('Directory Empty at ' + ngram_dir)
if debug:
ngram_paths = [ngram_paths[0]]
for path in ngram_paths:
with open(path, 'rb') as h:
for line in h:
xsn = line.split('\t')
if len(xsn) == 2:
xs,n = xsn
n,_ = n.split('\n')
yield (xs,n)
############################################################
'''
@Use: Given path to linguistic pattern, output pattern
'''
def read_pattern(pattern_path):
if os.path.exists(pattern_path):
strong_weak, weak_strong = open(pattern_path,'rb').read().split('=== weak-strong')
strong_weak = [p for p in strong_weak.split('\n') if p][1:]
weak_strong = [p for p in weak_strong.split('\n') if p][:-1]
return {'strong-weak': strong_weak, 'weak-strong': weak_strong}
else:
raise NameError('Cannot find pattern at path ' + pattern_path)
| [
"[email protected]"
] | |
8e0fdec3518e0ed5c1d564e69641dbdf3e33a918 | 9b617d281d83880d385a57809c4cafd55024d516 | /manage.py | ca0d6f3d331fffa4ace90b822a09041b6d37c7af | [] | no_license | crowdbotics-users/wwickey-crowdbotics-164 | 3df5074f39dc34de2def1bde928f523391942689 | 909b185e528f60b9258b317f7c26b35e791d8685 | refs/heads/master | 2020-03-16T12:02:27.614606 | 2018-05-08T20:01:05 | 2018-05-08T20:01:05 | 132,658,449 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 821 | py | #!/usr/bin/env python
import os
import sys
if __name__ == "__main__":
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "wwickey_crowdbotics_164.settings")
try:
from django.core.management import execute_from_command_line
except ImportError:
# The above import may fail for some other reason. Ensure that the
# issue is really that Django is missing to avoid masking other
# exceptions on Python 2.
try:
import django
except ImportError:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
)
raise
execute_from_command_line(sys.argv)
| [
"[email protected]"
] | |
c4d693a018899753b9d47f6da7643ece8efb4bfe | 10fbe5526e5f0b8588b65f70f088cd86b6e9afbe | /irmtbds/migrations/0002_auto_20150218_1621.py | 3c05b27f5b6c037590a673b577c9744a196e934f | [] | no_license | MarkusH/django-migrations-benchmark | eb4b2312bb30a5a5d2abf25e95eca8f714162056 | e2bd24755389668b34b87d254ec8ac63725dc56e | refs/heads/master | 2016-09-05T15:36:45.250134 | 2015-03-31T23:44:28 | 2015-03-31T23:44:28 | 31,168,231 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 502 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('irmtbds', '0001_initial'),
]
operations = [
migrations.RemoveField(
model_name='rqzheruyb',
name='xknvpfy',
),
migrations.AddField(
model_name='rqzheruyb',
name='kplrvqptcm',
field=models.IntegerField(default=0),
),
]
| [
"[email protected]"
] | |
d605544bb5bd4b5f2f891b75f75930b2d21e7fe4 | 048df2b4dc5ad153a36afad33831017800b9b9c7 | /atcoder/agc008/agc008_c.py | 01428e6976f334cebf389e5e84a0a5f947a48943 | [] | no_license | fluffyowl/past-submissions | a73e8f5157c647634668c200cd977f4428c6ac7d | 24706da1f79e5595b2f9f2583c736135ea055eb7 | refs/heads/master | 2022-02-21T06:32:43.156817 | 2019-09-16T00:17:50 | 2019-09-16T00:17:50 | 71,639,325 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 276 | py | a, b, c, d, e, f, g = map(int, raw_input().split())
if a > 0 and d > 0 and e > 0:
ans1 = b + a / 2 * 2 + d / 2 * 2 + e / 2 * 2
ans2 = b + 3 + (a-1) / 2 * 2 + (d-1) / 2 * 2 + (e-1) / 2 * 2
print max(ans1, ans2)
else:
print b + a / 2 * 2 + d / 2 * 2 + e / 2 * 2
| [
"[email protected]"
] | |
64801be0735e6c4264e2fcac275da94b245371ca | 2ed6ad4a736879a47d192159da45ca56610c089a | /tests/test_db.py | 22393c254cb71d6912d534a4a6399d1eabd15537 | [
"MIT"
] | permissive | poonyisaTH/gsheets-db-api | a82bd35984766697757cc96aa74a1281d948f019 | f023b32986d4da9a501fca8d435f2b6edc153353 | refs/heads/master | 2023-05-29T15:01:10.604324 | 2021-02-17T20:59:41 | 2021-02-17T20:59:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,045 | py | # -*- coding: utf-8 -*-
from collections import namedtuple
import unittest
import requests_mock
from .context import (
apply_parameters,
Connection,
connect,
exceptions,
)
class DBTestSuite(unittest.TestCase):
header_payload = {
'table': {
'cols': [
{'id': 'A', 'label': 'country', 'type': 'string'},
{
'id': 'B',
'label': 'cnt',
'type': 'number',
'pattern': 'General',
},
],
},
}
query_payload = {
'status': 'ok',
'table': {
'cols': [
{'id': 'A', 'label': 'country', 'type': 'string'},
{
'id': 'B',
'label': 'cnt',
'type': 'number',
'pattern': 'General',
},
],
'rows': [
{'c': [{'v': 'BR'}, {'v': 1.0, 'f': '1'}]},
{'c': [{'v': 'IN'}, {'v': 2.0, 'f': '2'}]},
],
},
}
def test_connection(self):
conn = connect()
self.assertFalse(conn.closed)
self.assertEqual(conn.cursors, [])
def test_check_closed(self):
conn = connect()
conn.close()
with self.assertRaises(exceptions.Error):
conn.close()
def test_close_cursors(self):
conn = connect()
cursor1 = conn.cursor()
cursor2 = conn.cursor()
cursor2.close()
conn.close()
self.assertTrue(cursor1.closed)
self.assertTrue(cursor2.closed)
def test_commit(self):
conn = connect()
conn.commit() # no-op
@requests_mock.Mocker()
def test_connection_execute(self, m):
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A%20LIMIT%200',
json=self.header_payload,
)
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A',
json=self.query_payload,
)
with Connection() as conn:
result = conn.execute(
'SELECT * FROM "http://docs.google.com/"').fetchall()
Row = namedtuple('Row', 'country cnt')
expected = [Row(country=u'BR', cnt=1.0), Row(country=u'IN', cnt=2.0)]
self.assertEqual(result, expected)
@requests_mock.Mocker()
def test_cursor_execute(self, m):
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A%20LIMIT%200',
json=self.header_payload,
)
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A',
json=self.query_payload,
)
with Connection() as conn:
cursor = conn.cursor()
result = cursor.execute(
'SELECT * FROM "http://docs.google.com/"').fetchall()
Row = namedtuple('Row', 'country cnt')
expected = [Row(country=u'BR', cnt=1.0), Row(country=u'IN', cnt=2.0)]
self.assertEqual(result, expected)
def test_cursor_executemany(self):
conn = Connection()
cursor = conn.cursor()
with self.assertRaises(exceptions.NotSupportedError):
cursor.executemany('SELECT * FROM "http://docs.google.com/"')
@requests_mock.Mocker()
def test_cursor(self, m):
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A%20LIMIT%200',
json=self.header_payload,
)
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A',
json=self.query_payload,
)
conn = Connection()
cursor = conn.cursor()
cursor.setinputsizes(0) # no-op
cursor.setoutputsizes(0) # no-op
@requests_mock.Mocker()
def test_cursor_rowcount(self, m):
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A%20LIMIT%200',
json=self.header_payload,
)
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A',
json=self.query_payload,
)
conn = Connection()
cursor = conn.cursor()
with self.assertRaises(exceptions.Error):
cursor.rowcount()
cursor.execute('SELECT * FROM "http://docs.google.com/"')
self.assertEqual(cursor.rowcount, 2)
@requests_mock.Mocker()
def test_cursor_fetchone(self, m):
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A%20LIMIT%200',
json=self.header_payload,
)
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A',
json=self.query_payload,
)
conn = Connection()
cursor = conn.cursor()
cursor.execute('SELECT * FROM "http://docs.google.com/"')
Row = namedtuple('Row', 'country cnt')
self.assertEqual(cursor.fetchone(), Row(country=u'BR', cnt=1.0))
self.assertEqual(cursor.fetchone(), Row(country=u'IN', cnt=2.0))
self.assertIsNone(cursor.fetchone())
@requests_mock.Mocker()
def test_cursor_fetchall(self, m):
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A%20LIMIT%200',
json=self.header_payload,
)
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A',
json=self.query_payload,
)
conn = Connection()
cursor = conn.cursor()
cursor.execute('SELECT * FROM "http://docs.google.com/"')
Row = namedtuple('Row', 'country cnt')
self.assertEqual(cursor.fetchone(), Row(country=u'BR', cnt=1.0))
self.assertEqual(cursor.fetchall(), [Row(country=u'IN', cnt=2.0)])
@requests_mock.Mocker()
def test_cursor_fetchmany(self, m):
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A%20LIMIT%200',
json=self.header_payload,
)
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A',
json=self.query_payload,
)
conn = Connection()
cursor = conn.cursor()
cursor.execute('SELECT * FROM "http://docs.google.com/"')
Row = namedtuple('Row', 'country cnt')
self.assertEqual(cursor.fetchmany(1), [Row(country=u'BR', cnt=1.0)])
self.assertEqual(cursor.fetchmany(10), [Row(country=u'IN', cnt=2.0)])
self.assertEqual(cursor.fetchmany(100), [])
@requests_mock.Mocker()
def test_cursor_iter(self, m):
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A%20LIMIT%200',
json=self.header_payload,
)
m.get(
'http://docs.google.com/gviz/tq?gid=0&tq=SELECT%20%2A',
json=self.query_payload,
)
conn = Connection()
cursor = conn.cursor()
cursor.execute('SELECT * FROM "http://docs.google.com/"')
Row = namedtuple('Row', 'country cnt')
self.assertEqual(
list(cursor),
[Row(country=u'BR', cnt=1.0), Row(country=u'IN', cnt=2.0)],
)
def test_apply_parameters(self):
query = 'SELECT * FROM table WHERE name=%(name)s'
parameters = {'name': 'Alice'}
result = apply_parameters(query, parameters)
expected = "SELECT * FROM table WHERE name='Alice'"
self.assertEqual(result, expected)
def test_apply_parameters_escape(self):
query = 'SELECT * FROM table WHERE name=%(name)s'
parameters = {'name': "O'Malley's"}
result = apply_parameters(query, parameters)
expected = "SELECT * FROM table WHERE name='O''Malley''s'"
self.assertEqual(result, expected)
def test_apply_parameters_float(self):
query = 'SELECT * FROM table WHERE age=%(age)s'
parameters = {'age': 50}
result = apply_parameters(query, parameters)
expected = "SELECT * FROM table WHERE age=50"
self.assertEqual(result, expected)
def test_apply_parameters_bool(self):
query = 'SELECT * FROM table WHERE active=%(active)s'
parameters = {'active': True}
result = apply_parameters(query, parameters)
expected = "SELECT * FROM table WHERE active=TRUE"
self.assertEqual(result, expected)
def test_apply_parameters_list(self):
query = (
'SELECT * FROM table '
'WHERE id IN %(allowed)s '
'AND id NOT IN %(prohibited)s'
)
parameters = {'allowed': [1, 2], 'prohibited': (2, 3)}
result = apply_parameters(query, parameters)
expected = (
'SELECT * FROM table '
'WHERE id IN (1, 2) '
'AND id NOT IN (2, 3)'
)
self.assertEqual(result, expected)
def test_apply_parameters_star(self):
query = 'SELECT %(column)s FROM table'
parameters = {'column': '*'}
result = apply_parameters(query, parameters)
expected = "SELECT * FROM table"
self.assertEqual(result, expected)
| [
"[email protected]"
] | |
b4d01dd3705d74d25a15957865fcbc913580986c | 36afa271f080459adf1014cd23f4be9f954dfee6 | /Crawler/Course/第八章：scrapy框架/sunPro/sunPro/spiders/sun.py | 35ab678e80afc0bf5d06d12f11a75a5455738471 | [] | no_license | King-Of-Game/Python | b69186a7574ce1c0b7097207cfe9a2eb38a90bc0 | 643b9fd22efd78f6679735f23432943a57b5f5bb | refs/heads/master | 2023-05-25T05:35:14.473114 | 2021-10-24T12:52:21 | 2021-10-24T12:52:21 | 151,251,434 | 3 | 0 | null | 2023-05-01T20:51:50 | 2018-10-02T12:34:04 | HTML | UTF-8 | Python | false | false | 2,909 | py | # -*- coding: utf-8 -*-
import scrapy
from scrapy.linkextractors import LinkExtractor
from scrapy.spiders import CrawlSpider, Rule
from sunPro.items import SunproItem
from sunPro.items import DetailItem
# 需求：爬取小说分类、名称、人气、简介
class SunSpider(CrawlSpider):
name = 'sun'
# allowed_domains = ['www.xxx.com']
start_urls = ['https://www.69shu.org/fenlei/1_1/']
# 链接提取器：根据指定规则（allow="正则"）进行链接的提取
link_extractor = LinkExtractor(allow=r'fenlei/1_(?!16|\d{3,})')
link_detail_extractor = LinkExtractor(allow=r'/book/\d+/(?!\d+\.html)') # /book/\d+/(?!\d+\.html)
rules = (
# 规则解析器：将链接提取器提取到的链接进行指定规则（callback）的解析操作
# follow=True：可以将链接提取器继续作用到，链接提取器提取的链接，对应的页面中
Rule(link_extractor, callback='parse_novel_name', follow=False),
Rule(link_detail_extractor, callback='parse_novel_detail', follow=False),
)
'''
以下两个解析方法没有手动发起请求，是不可以实现请求传参的: 也就是说不能通过yield scrapy.Request() 回调其它函数
无法将两个解析方法解析的数据存储到同一个item中，可以依次存储到两个item中
'''
# 解析小说类别、名称、作者
def parse_novel_name(self, response):
# item = {}
# #item['domain_id'] = response.xpath('//input[@id="sid"]/@value').get()
# #item['name'] = response.xpath('//div[su@id="name"]').get()
# #item['description'] = response.xpath('//div[@id="description"]').get()
# return item
print('\n', response)
# 注意：xpath表达式中不可以出现tbody标签
li_list = response.xpath('/html/body/div[3]/div/div/div[2]/div[1]/div[2]/ul/li')
for li in li_list:
novel_category = li.xpath('./span[1]/text()').extract_first()
novel_name = li.xpath('./span[2]/a/text()').extract_first()
novel_author = li.xpath('./span[4]/text()').extract_first()
# print(novel_category, novel_name, novel_author)
item = SunproItem()
item['novel_category'] = novel_category
item['novel_name'] = novel_name
item['novel_author'] = novel_author
yield item
# 解析小说人气和简介
def parse_novel_detail(self, response):
# print(response)
novel_popularity = response.xpath('//*[@id="info"]/p/span/text()').extract_first()
novel_synopsis = response.xpath('//*[@id="info"]/div[3]//text()').extract()
novel_synopsis = ''.join(novel_synopsis)
# print(novel_popularity)
item = DetailItem()
item['novel_popularity'] = novel_popularity
item['novel_synopsis'] = novel_synopsis
yield item
| [
"[email protected]"
] | |
15255dffd47f10b3f99409f7b5dea95315005ab9 | fb8cbebdf034b2f478943752d5443afc82c6eef5 | /tuirer/users/models.py | a3a6f2b88a946f2a8ca0ab80decd3e78a3924509 | [] | no_license | fariasjr/CitiTuirer | f64e0ec93ef088f8140bb0961d2ad4ed3b59448a | deb3f7a9c2d45b8a7f54639037f097b99abdac11 | refs/heads/master | 2020-03-24T05:10:36.261050 | 2018-08-01T20:24:30 | 2018-08-01T20:24:30 | 142,477,521 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 246 | py | from django.contrib.auth.models import AbstractUser
from django.db import models
class User(AbstractUser):
picture = models.ImageField('Fotode perfil', default='/img/blank-pic.png')
following = models.ManyToManyField('self', blank=True) | [
"[email protected]"
] | |
ea9e7a8b99cd02b1f71e0f5c2c419a055b084728 | fe0bca3fcf363ebc465fcc370e77b55df1cfaaa7 | /src/route/work_viewer.py | f79466d814c37cc4151ac1ca0217dbe9d45950dc | [] | no_license | sihcpro/todo-list | 66847aece556fe45223b98ecc44f04bbaaf17b55 | 1db48a63e9f4d309d57baeca691f6e85c36866a6 | refs/heads/master | 2022-11-17T14:34:20.316901 | 2020-07-14T10:16:18 | 2020-07-14T10:16:18 | 279,233,154 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,661 | py | import calendar
from datetime import timedelta
from sqlalchemy import Date, and_, cast, or_
from .data_define import ShowWorkData
from .resource import WorkResource
def configWorkViewer(Domain):
session = Domain.session
def getValidatedDate(param):
date_data = ShowWorkData(
from_date=param["from_date"][0], to_date=param["to_date"][0],
)
if date_data.from_date > date_data.to_date:
raise ValueError("from_date must smaller than to_date")
return date_data
def getWorkInAPerius(from_date, to_date):
record = {"from_date": str(from_date), "to_date": str(to_date)}
if from_date == to_date:
works = (
session.query(WorkResource)
.filter(
or_(
cast(WorkResource.starting_date, Date) == to_date,
cast(WorkResource.ending_date, Date) == to_date,
and_(
cast(WorkResource.starting_date, Date) < to_date,
cast(WorkResource.ending_date, Date) > to_date,
),
)
)
.all()
)
else:
works = (
session.query(WorkResource)
.filter(
or_(
and_(
WorkResource.starting_date >= from_date,
WorkResource.starting_date < to_date,
),
and_(
WorkResource.ending_date >= from_date,
WorkResource.ending_date < to_date,
),
and_(
WorkResource.starting_date <= from_date,
WorkResource.ending_date >= to_date,
),
)
)
.all()
)
record["works"] = [work.asDict() for work in works]
return record
@Domain.registerQuery("show-work-by-date")
def showWorkByDate(data, identifier, param):
date_data = getValidatedDate(param)
date = date_data.from_date
results = []
while date <= date_data.to_date:
results.append(getWorkInAPerius(date, date))
date += timedelta(days=1)
return results
@Domain.registerQuery("show-work-by-week")
def showWorkByWeek(data, identifier, param):
date_data = getValidatedDate(param)
date = date_data.from_date
date = date - timedelta(days=date.weekday())
results = []
while date <= date_data.to_date:
start_date = date
end_date = date + timedelta(weeks=1) - timedelta(microseconds=1)
results.append(getWorkInAPerius(start_date, end_date))
date += timedelta(weeks=1)
return results
@Domain.registerQuery("show-work-by-month")
def showWorkByMonth(data, identifier, param):
date_data = getValidatedDate(param)
date = date_data.from_date
date = date - timedelta(days=date.day - 1)
results = []
while date <= date_data.to_date:
days_in_month = calendar.monthrange(date.year, date.month)[1]
start_date = date
end_date = (
date
+ timedelta(days=days_in_month)
- timedelta(microseconds=1)
)
results.append(getWorkInAPerius(start_date, end_date))
date += timedelta(days=days_in_month)
return results
| [
"[email protected]"
] | |
77d3ccb4fbb606e29dc100993d9286af9143d806 | f00767fdeed6bfa8b12f6900b9f9bd5c70786895 | /models/base_db.py | b9ec16abf725b932e97446cf9463b303db180b0b | [] | no_license | guoyu07/domain_whois_query | de22cb5d83db2441ba512935fd7f3ed5c158997a | c70b52f2b9306e4b9ead273de279cd149052623f | refs/heads/master | 2020-12-07T06:24:57.907042 | 2015-11-29T00:53:31 | 2015-11-29T00:53:31 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 313 | py | # encoding:utf-8
"""
操作数据库基础类
"""
import torndb
class BaseDb(object):
def __init__(self):
self.db = torndb.Connection(
host="172.26.253.3",
database="DomainWhois",
user="root",
password="platform",
charset="utf8"
)
| [
"[email protected]"
] | |
75750e2d778d9088cc0aa9d4e0a9b23d245d0029 | 7041c85dffb757c3e7063118730363f32ebb9b8a | /프로젝트/20190111/open_api.py | af937d2499eb4c1f56272d6930b3d2c64641b4f6 | [] | no_license | woonji913/til | efae551baff56f3ca16169b93185a65f4d81cd7a | a05efc68f88f535c26cb4d4a396a1e9cd6bf0248 | refs/heads/master | 2021-06-06T23:17:54.504620 | 2019-06-19T04:29:18 | 2019-06-19T04:29:18 | 163,778,844 | 1 | 0 | null | 2021-05-08T16:27:17 | 2019-01-02T01:08:19 | HTML | UTF-8 | Python | false | false | 1,240 | py | import requests
from bs4 import BeautifulSoup
import csv, datetime, os
date = datetime.date(2019, 1, 13)
weeks = datetime.timedelta(7)
movies = []
check = set()
key = os.environ['KEY']
for i in range(10):
current = date - weeks * i
url = f'http://www.kobis.or.kr/kobisopenapi/webservice/rest/boxoffice/searchWeeklyBoxOfficeList.json?key={key}&weekGb=0&targetDt='
url += str(current.strftime('%Y%m%d'))
res_json = requests.get(url).json()
for j in res_json['boxOfficeResult']['weeklyBoxOfficeList']:
code = j['movieCd']
name = j['movieNm']
total_aud = j['audiAcc']
if code not in check:
print(name)
movies.append({'movie_code': code, 'title': name, 'audience': total_aud, 'recorded_at': current})
check.add(code)
# movieIDDF = pd.DataFrame()
# movieIDDF = movieIDDF.append({"movieCd":" ", "movieNM": " ", "audiCnt": " ", "openDt": " "}, ignore_index = True)
# # pprint(movieIDDF)
with open('boxoffice.csv', 'w', encoding='utf-8', newline='') as f:
fieldnames = ('movie_code', 'title', 'audience', 'recorded_at')
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
for movie in movies:
writer.writerow(movie) | [
"[email protected]"
] | |
d1597ffd8c87152ec49b9949a7de3ec827c5d1d4 | 1d928c3f90d4a0a9a3919a804597aa0a4aab19a3 | /python/matplotlib/2017/12/setupext.py | 2868fd76aee773dc4d8d576d9dfe80e8c6cca6b4 | [] | no_license | rosoareslv/SED99 | d8b2ff5811e7f0ffc59be066a5a0349a92cbb845 | a062c118f12b93172e31e8ca115ce3f871b64461 | refs/heads/main | 2023-02-22T21:59:02.703005 | 2021-01-28T19:40:51 | 2021-01-28T19:40:51 | 306,497,459 | 1 | 1 | null | 2020-11-24T20:56:18 | 2020-10-23T01:18:07 | null | UTF-8 | Python | false | false | 68,786 | py | from __future__ import print_function, absolute_import
from importlib import import_module
from distutils import sysconfig
from distutils import version
from distutils.core import Extension
import distutils.command.build_ext
import glob
import multiprocessing
import os
import platform
import re
import subprocess
from subprocess import check_output
import sys
import warnings
from textwrap import fill
import shutil
import versioneer
PY3min = (sys.version_info[0] >= 3)
def _get_home():
"""Find user's home directory if possible.
Otherwise, returns None.
:see:
http://mail.python.org/pipermail/python-list/2005-February/325395.html
"""
try:
if not PY3min and sys.platform == 'win32':
path = os.path.expanduser(b"~").decode(sys.getfilesystemencoding())
else:
path = os.path.expanduser("~")
except ImportError:
# This happens on Google App Engine (pwd module is not present).
pass
else:
if os.path.isdir(path):
return path
for evar in ('HOME', 'USERPROFILE', 'TMP'):
path = os.environ.get(evar)
if path is not None and os.path.isdir(path):
return path
return None
def _get_xdg_cache_dir():
"""
Returns the XDG cache directory, according to the `XDG
base directory spec
<http://standards.freedesktop.org/basedir-spec/basedir-spec-latest.html>`_.
"""
path = os.environ.get('XDG_CACHE_HOME')
if path is None:
path = _get_home()
if path is not None:
path = os.path.join(path, '.cache', 'matplotlib')
return path
# SHA256 hashes of the FreeType tarballs
_freetype_hashes = {
'2.6.1': '0a3c7dfbda6da1e8fce29232e8e96d987ababbbf71ebc8c75659e4132c367014',
'2.6.2': '8da42fc4904e600be4b692555ae1dcbf532897da9c5b9fb5ebd3758c77e5c2d4',
'2.6.3': '7942096c40ee6fea882bd4207667ad3f24bff568b96b10fd3885e11a7baad9a3',
'2.6.4': '27f0e38347a1850ad57f84fc4dfed68ba0bc30c96a6fa6138ef84d485dd9a8d7',
'2.6.5': '3bb24add9b9ec53636a63ea8e867ed978c4f8fdd8f1fa5ccfd41171163d4249a',
'2.7': '7b657d5f872b0ab56461f3bd310bd1c5ec64619bd15f0d8e08282d494d9cfea4',
'2.7.1': '162ef25aa64480b1189cdb261228e6c5c44f212aac4b4621e28cf2157efb59f5',
'2.8': '33a28fabac471891d0523033e99c0005b95e5618dc8ffa7fa47f9dadcacb1c9b',
'2.8.1': '876711d064a6a1bd74beb18dd37f219af26100f72daaebd2d86cb493d7cd7ec6',
}
# This is the version of FreeType to use when building a local
# version. It must match the value in
# lib/matplotlib.__init__.py and also needs to be changed below in the
# embedded windows build script (grep for "REMINDER" in this file)
LOCAL_FREETYPE_VERSION = '2.6.1'
LOCAL_FREETYPE_HASH = _freetype_hashes.get(LOCAL_FREETYPE_VERSION, 'unknown')
if sys.platform != 'win32':
if not PY3min:
from commands import getstatusoutput
else:
from subprocess import getstatusoutput
if PY3min:
import configparser
else:
import ConfigParser as configparser
# matplotlib build options, which can be altered using setup.cfg
options = {
'display_status': True,
'verbose': False,
'backend': None,
'basedirlist': None
}
setup_cfg = os.environ.get('MPLSETUPCFG', 'setup.cfg')
if os.path.exists(setup_cfg):
if PY3min:
config = configparser.ConfigParser()
else:
config = configparser.SafeConfigParser()
config.read(setup_cfg)
if config.has_option('status', 'suppress'):
options['display_status'] = not config.getboolean("status", "suppress")
if config.has_option('rc_options', 'backend'):
options['backend'] = config.get("rc_options", "backend")
if config.has_option('directories', 'basedirlist'):
options['basedirlist'] = [
x.strip() for x in
config.get("directories", "basedirlist").split(',')]
if config.has_option('test', 'local_freetype'):
options['local_freetype'] = config.getboolean("test", "local_freetype")
else:
config = None
lft = bool(os.environ.get('MPLLOCALFREETYPE', False))
options['local_freetype'] = lft or options.get('local_freetype', False)
def get_win32_compiler():
"""
Determine the compiler being used on win32.
"""
# Used to determine mingw32 or msvc
# This is pretty bad logic, someone know a better way?
for v in sys.argv:
if 'mingw32' in v:
return 'mingw32'
return 'msvc'
win32_compiler = get_win32_compiler()
def extract_versions():
"""
Extracts version values from the main matplotlib __init__.py and
returns them as a dictionary.
"""
with open('lib/matplotlib/__init__.py') as fd:
for line in fd.readlines():
if (line.startswith('__version__numpy__')):
exec(line.strip())
return locals()
def has_include_file(include_dirs, filename):
"""
Returns `True` if `filename` can be found in one of the
directories in `include_dirs`.
"""
if sys.platform == 'win32':
include_dirs = list(include_dirs) # copy before modify
include_dirs += os.environ.get('INCLUDE', '.').split(os.pathsep)
for dir in include_dirs:
if os.path.exists(os.path.join(dir, filename)):
return True
return False
def check_include_file(include_dirs, filename, package):
"""
Raises an exception if the given include file can not be found.
"""
if not has_include_file(include_dirs, filename):
raise CheckFailed(
"The C/C++ header for %s (%s) could not be found. You "
"may need to install the development package." %
(package, filename))
def get_base_dirs():
"""
Returns a list of standard base directories on this platform.
"""
if options['basedirlist']:
return options['basedirlist']
if os.environ.get('MPLBASEDIRLIST'):
return os.environ.get('MPLBASEDIRLIST').split(os.pathsep)
win_bases = ['win32_static', ]
# on conda windows, we also add the <conda_env_dir>\Library,
# as conda installs libs/includes there
# env var names mess: https://github.com/conda/conda/issues/2312
conda_env_path = os.getenv('CONDA_PREFIX') # conda >= 4.1
if not conda_env_path:
conda_env_path = os.getenv('CONDA_DEFAULT_ENV') # conda < 4.1
if conda_env_path and os.path.isdir(conda_env_path):
win_bases.append(os.path.join(conda_env_path, "Library"))
basedir_map = {
'win32': win_bases,
'darwin': ['/usr/local/', '/usr', '/usr/X11',
'/opt/X11', '/opt/local'],
'sunos5': [os.getenv('MPLIB_BASE') or '/usr/local', ],
'gnu0': ['/usr'],
'aix5': ['/usr/local'],
}
return basedir_map.get(sys.platform, ['/usr/local', '/usr'])
def get_include_dirs():
"""
Returns a list of standard include directories on this platform.
"""
include_dirs = [os.path.join(d, 'include') for d in get_base_dirs()]
if sys.platform != 'win32':
# gcc includes this dir automatically, so also look for headers in
# these dirs
include_dirs.extend(
os.environ.get('CPLUS_INCLUDE_PATH', '').split(os.pathsep))
return include_dirs
def is_min_version(found, minversion):
"""
Returns `True` if `found` is at least as high a version as
`minversion`.
"""
expected_version = version.LooseVersion(minversion)
found_version = version.LooseVersion(found)
return found_version >= expected_version
# Define the display functions only if display_status is True.
if options['display_status']:
def print_line(char='='):
print(char * 76)
def print_status(package, status):
initial_indent = "%22s: " % package
indent = ' ' * 24
print(fill(str(status), width=76,
initial_indent=initial_indent,
subsequent_indent=indent))
def print_message(message):
indent = ' ' * 24 + "* "
print(fill(str(message), width=76,
initial_indent=indent,
subsequent_indent=indent))
def print_raw(section):
print(section)
else:
def print_line(*args, **kwargs):
pass
print_status = print_message = print_raw = print_line
# Remove the -Wstrict-prototypes option, is it's not valid for C++
customize_compiler = distutils.command.build_ext.customize_compiler
def my_customize_compiler(compiler):
retval = customize_compiler(compiler)
try:
compiler.compiler_so.remove('-Wstrict-prototypes')
except (ValueError, AttributeError):
pass
return retval
distutils.command.build_ext.customize_compiler = my_customize_compiler
def make_extension(name, files, *args, **kwargs):
"""
Make a new extension. Automatically sets include_dirs and
library_dirs to the base directories appropriate for this
platform.
`name` is the name of the extension.
`files` is a list of source files.
Any additional arguments are passed to the
`distutils.core.Extension` constructor.
"""
ext = DelayedExtension(name, files, *args, **kwargs)
for dir in get_base_dirs():
include_dir = os.path.join(dir, 'include')
if os.path.exists(include_dir):
ext.include_dirs.append(include_dir)
for lib in ('lib', 'lib64'):
lib_dir = os.path.join(dir, lib)
if os.path.exists(lib_dir):
ext.library_dirs.append(lib_dir)
ext.include_dirs.append('.')
return ext
def get_file_hash(filename):
"""
Get the SHA256 hash of a given filename.
"""
import hashlib
BLOCKSIZE = 1 << 16
hasher = hashlib.sha256()
with open(filename, 'rb') as fd:
buf = fd.read(BLOCKSIZE)
while len(buf) > 0:
hasher.update(buf)
buf = fd.read(BLOCKSIZE)
return hasher.hexdigest()
class PkgConfig(object):
"""
This is a class for communicating with pkg-config.
"""
def __init__(self):
"""
Determines whether pkg-config exists on this machine.
"""
if sys.platform == 'win32':
self.has_pkgconfig = False
else:
try:
self.pkg_config = os.environ['PKG_CONFIG']
except KeyError:
self.pkg_config = 'pkg-config'
self.set_pkgconfig_path()
status, output = getstatusoutput(self.pkg_config + " --help")
self.has_pkgconfig = (status == 0)
if not self.has_pkgconfig:
print("IMPORTANT WARNING:")
print(
" pkg-config is not installed.\n"
" matplotlib may not be able to find some of its dependencies")
def set_pkgconfig_path(self):
pkgconfig_path = sysconfig.get_config_var('LIBDIR')
if pkgconfig_path is None:
return
pkgconfig_path = os.path.join(pkgconfig_path, 'pkgconfig')
if not os.path.isdir(pkgconfig_path):
return
try:
os.environ['PKG_CONFIG_PATH'] += ':' + pkgconfig_path
except KeyError:
os.environ['PKG_CONFIG_PATH'] = pkgconfig_path
def setup_extension(self, ext, package, default_include_dirs=[],
default_library_dirs=[], default_libraries=[],
alt_exec=None):
"""
Add parameters to the given `ext` for the given `package`.
"""
flag_map = {
'-I': 'include_dirs', '-L': 'library_dirs', '-l': 'libraries'}
executable = alt_exec
if self.has_pkgconfig:
executable = (self.pkg_config + ' {0}').format(package)
use_defaults = True
if executable is not None:
command = "{0} --libs --cflags ".format(executable)
try:
output = check_output(command, shell=True,
stderr=subprocess.STDOUT)
except subprocess.CalledProcessError:
pass
else:
output = output.decode(sys.getfilesystemencoding())
use_defaults = False
for token in output.split():
attr = flag_map.get(token[:2])
if attr is not None:
getattr(ext, attr).insert(0, token[2:])
if use_defaults:
basedirs = get_base_dirs()
for base in basedirs:
for include in default_include_dirs:
dir = os.path.join(base, include)
if os.path.exists(dir):
ext.include_dirs.append(dir)
for lib in default_library_dirs:
dir = os.path.join(base, lib)
if os.path.exists(dir):
ext.library_dirs.append(dir)
ext.libraries.extend(default_libraries)
return True
return False
def get_version(self, package):
"""
Get the version of the package from pkg-config.
"""
if not self.has_pkgconfig:
return None
status, output = getstatusoutput(
self.pkg_config + " %s --modversion" % (package))
if status == 0:
return output
return None
# The PkgConfig class should be used through this singleton
pkg_config = PkgConfig()
class CheckFailed(Exception):
"""
Exception thrown when a `SetupPackage.check` method fails.
"""
pass
class SetupPackage(object):
optional = False
pkg_names = {
"apt-get": None,
"yum": None,
"dnf": None,
"brew": None,
"port": None,
"windows_url": None
}
def check(self):
"""
Checks whether the build dependencies are met. Should raise a
`CheckFailed` exception if the dependency could not be met, otherwise
return a string indicating a version number or some other message
indicating what was found.
"""
pass
def runtime_check(self):
"""
True if the runtime dependencies of the backend are met. Assumes that
the build-time dependencies are met.
"""
return True
def get_packages(self):
"""
Get a list of package names to add to the configuration.
These are added to the `packages` list passed to
`distutils.setup`.
"""
return []
def get_namespace_packages(self):
"""
Get a list of namespace package names to add to the configuration.
These are added to the `namespace_packages` list passed to
`distutils.setup`.
"""
return []
def get_py_modules(self):
"""
Get a list of top-level modules to add to the configuration.
These are added to the `py_modules` list passed to
`distutils.setup`.
"""
return []
def get_package_data(self):
"""
Get a package data dictionary to add to the configuration.
These are merged into to the `package_data` list passed to
`distutils.setup`.
"""
return {}
def get_extension(self):
"""
Get a list of C extensions (`distutils.core.Extension`
objects) to add to the configuration. These are added to the
`extensions` list passed to `distutils.setup`.
"""
return None
def get_install_requires(self):
"""
Get a list of Python packages that we require.
pip/easy_install will attempt to download and install this
package if it is not installed.
"""
return []
def get_setup_requires(self):
"""
Get a list of Python packages that we require at build time.
pip/easy_install will attempt to download and install this
package if it is not installed.
"""
return []
def _check_for_pkg_config(self, package, include_file, min_version=None,
version=None):
"""
A convenience function for writing checks for a
pkg_config-defined dependency.
`package` is the pkg_config package name.
`include_file` is a top-level include file we expect to find.
`min_version` is the minimum version required.
`version` will override the found version if this package
requires an alternate method for that. Set version='unknown'
if the version is not known but you still want to disabled
pkg_config version check.
"""
if version is None:
version = pkg_config.get_version(package)
if version is None:
raise CheckFailed(
"pkg-config information for '%s' could not be found." %
package)
if min_version == 'PATCH':
raise CheckFailed(
"Requires patches that have not been merged upstream.")
if min_version and version != 'unknown':
if (not is_min_version(version, min_version)):
raise CheckFailed(
"Requires %s %s or later. Found %s." %
(package, min_version, version))
ext = self.get_extension()
if ext is None:
ext = make_extension('test', [])
pkg_config.setup_extension(ext, package)
check_include_file(
ext.include_dirs + get_include_dirs(), include_file, package)
return 'version %s' % version
def do_custom_build(self):
"""
If a package needs to do extra custom things, such as building a
third-party library, before building an extension, it should
override this method.
"""
pass
def install_help_msg(self):
"""
Do not override this method !
Generate the help message to show if the package is not installed.
To use this in subclasses, simply add the dictionary `pkg_names` as
a class variable:
pkg_names = {
"apt-get": <Name of the apt-get package>,
"yum": <Name of the yum package>,
"dnf": <Name of the dnf package>,
"brew": <Name of the brew package>,
"port": <Name of the port package>,
"windows_url": <The url which has installation instructions>
}
All the dictionary keys are optional. If a key is not present or has
the value `None` no message is provided for that platform.
"""
def _try_managers(*managers):
for manager in managers:
pkg_name = self.pkg_names.get(manager, None)
if pkg_name:
try:
# `shutil.which()` can be used when Python 2.7 support
# is dropped. It is available in Python 3.3+
_ = check_output(["which", manager],
stderr=subprocess.STDOUT)
if manager == 'port':
pkgconfig = 'pkgconfig'
else:
pkgconfig = 'pkg-config'
return ('Try installing {0} with `{1} install {2}` '
'and pkg-config with `{1} install {3}`'
.format(self.name, manager, pkg_name,
pkgconfig))
except subprocess.CalledProcessError:
pass
message = None
if sys.platform == "win32":
url = self.pkg_names.get("windows_url", None)
if url:
message = ('Please check {0} for instructions to install {1}'
.format(url, self.name))
elif sys.platform == "darwin":
message = _try_managers("brew", "port")
elif sys.platform.startswith("linux"):
release = platform.linux_distribution()[0].lower()
if release in ('debian', 'ubuntu'):
message = _try_managers('apt-get')
elif release in ('centos', 'redhat', 'fedora'):
message = _try_managers('dnf', 'yum')
return message
class OptionalPackage(SetupPackage):
optional = True
force = False
config_category = "packages"
default_config = "auto"
@classmethod
def get_config(cls):
"""
Look at `setup.cfg` and return one of ["auto", True, False] indicating
if the package is at default state ("auto"), forced by the user (case
insensitively defined as 1, true, yes, on for True) or opted-out (case
insensitively defined as 0, false, no, off for False).
"""
conf = cls.default_config
if config is not None and config.has_option(cls.config_category, cls.name):
try:
conf = config.getboolean(cls.config_category, cls.name)
except ValueError:
conf = config.get(cls.config_category, cls.name)
return conf
def check(self):
"""
Do not override this method!
For custom dependency checks override self.check_requirements().
Two things are checked: Configuration file and requirements.
"""
# Check configuration file
conf = self.get_config()
# Default "auto" state or install forced by user
if conf in [True, 'auto']:
message = "installing"
# Set non-optional if user sets `True` in config
if conf is True:
self.optional = False
# Configuration opt-out by user
else:
# Some backend extensions (e.g. Agg) need to be built for certain
# other GUI backends (e.g. TkAgg) even when manually disabled
if self.force is True:
message = "installing forced (config override)"
else:
raise CheckFailed("skipping due to configuration")
# Check requirements and add extra information (if any) to message.
# If requirements are not met a CheckFailed should be raised in there.
additional_info = self.check_requirements()
if additional_info:
message += ", " + additional_info
# No CheckFailed raised until now, return install message.
return message
def check_requirements(self):
"""
Override this method to do custom dependency checks.
- Raise CheckFailed() if requirements are not met.
- Return message with additional information, or an empty string
(or None) for no additional information.
"""
return ""
class OptionalBackendPackage(OptionalPackage):
config_category = "gui_support"
class Platform(SetupPackage):
name = "platform"
def check(self):
return sys.platform
class Python(SetupPackage):
name = "python"
def check(self):
major, minor1, minor2, s, tmp = sys.version_info
if major < 2:
raise CheckFailed(
"Requires Python 2.7 or later")
elif major == 2 and minor1 < 7:
raise CheckFailed(
"Requires Python 2.7 or later (in the 2.x series)")
elif major == 3 and minor1 < 4:
raise CheckFailed(
"Requires Python 3.4 or later (in the 3.x series)")
return sys.version
class Matplotlib(SetupPackage):
name = "matplotlib"
def check(self):
return versioneer.get_version()
def get_packages(self):
return [
'matplotlib',
'matplotlib.backends',
'matplotlib.backends.qt_editor',
'matplotlib.compat',
'matplotlib.projections',
'matplotlib.axes',
'matplotlib.sphinxext',
'matplotlib.style',
'matplotlib.testing',
'matplotlib.testing._nose',
'matplotlib.testing._nose.plugins',
'matplotlib.testing.jpl_units',
'matplotlib.tri',
'matplotlib.cbook'
]
def get_py_modules(self):
return ['pylab']
def get_package_data(self):
return {
'matplotlib':
[
'mpl-data/fonts/afm/*.afm',
'mpl-data/fonts/pdfcorefonts/*.afm',
'mpl-data/fonts/pdfcorefonts/*.txt',
'mpl-data/fonts/ttf/*.ttf',
'mpl-data/fonts/ttf/LICENSE_STIX',
'mpl-data/fonts/ttf/COPYRIGHT.TXT',
'mpl-data/fonts/ttf/README.TXT',
'mpl-data/fonts/ttf/RELEASENOTES.TXT',
'mpl-data/images/*.xpm',
'mpl-data/images/*.svg',
'mpl-data/images/*.gif',
'mpl-data/images/*.pdf',
'mpl-data/images/*.png',
'mpl-data/images/*.ppm',
'mpl-data/example/*.npy',
'mpl-data/matplotlibrc',
'backends/web_backend/*.*',
'backends/web_backend/js/*.*',
'backends/web_backend/jquery/js/*.min.js',
'backends/web_backend/jquery/css/themes/base/*.min.css',
'backends/web_backend/jquery/css/themes/base/images/*',
'backends/web_backend/css/*.*',
'backends/Matplotlib.nib/*',
'mpl-data/stylelib/*.mplstyle',
]}
class SampleData(OptionalPackage):
"""
This handles the sample data that ships with matplotlib. It is
technically optional, though most often will be desired.
"""
name = "sample_data"
def get_package_data(self):
return {
'matplotlib':
[
'mpl-data/sample_data/*.*',
'mpl-data/sample_data/axes_grid/*.*',
]}
class Toolkits(OptionalPackage):
name = "toolkits"
def get_packages(self):
return [
'mpl_toolkits',
'mpl_toolkits.mplot3d',
'mpl_toolkits.axes_grid',
'mpl_toolkits.axes_grid1',
'mpl_toolkits.axisartist',
]
def get_namespace_packages(self):
return ['mpl_toolkits']
class Tests(OptionalPackage):
name = "tests"
pytest_min_version = '3.0.0'
default_config = False
def check(self):
super(Tests, self).check()
msgs = []
msg_template = ('{package} is required to run the Matplotlib test '
'suite. Please install it with pip or your preferred '
'tool to run the test suite')
bad_pytest = msg_template.format(
package='pytest %s or later' % self.pytest_min_version
)
try:
import pytest
if is_min_version(pytest.__version__, self.pytest_min_version):
msgs += ['using pytest version %s' % pytest.__version__]
else:
msgs += [bad_pytest]
except ImportError:
msgs += [bad_pytest]
if PY3min:
msgs += ['using unittest.mock']
else:
try:
import mock
msgs += ['using mock %s' % mock.__version__]
except ImportError:
msgs += [msg_template.format(package='mock')]
return ' / '.join(msgs)
def get_packages(self):
return [
'matplotlib.tests',
'matplotlib.sphinxext.tests',
]
def get_package_data(self):
baseline_images = [
'tests/baseline_images/%s/*' % x
for x in os.listdir('lib/matplotlib/tests/baseline_images')]
return {
'matplotlib':
baseline_images +
[
'tests/cmr10.pfb',
'tests/mpltest.ttf',
'tests/test_rcparams.rc',
'tests/test_utf32_be_rcparams.rc',
'sphinxext/tests/tinypages/*.rst',
'sphinxext/tests/tinypages/*.py',
'sphinxext/tests/tinypages/_static/*',
]}
class Toolkits_Tests(Tests):
name = "toolkits_tests"
def check_requirements(self):
conf = self.get_config()
toolkits_conf = Toolkits.get_config()
tests_conf = Tests.get_config()
if conf is True:
Tests.force = True
Toolkits.force = True
elif conf == "auto" and not (toolkits_conf and tests_conf):
# Only auto-install if both toolkits and tests are set
# to be installed
raise CheckFailed("toolkits_tests needs 'toolkits' and 'tests'")
return ""
def get_packages(self):
return [
'mpl_toolkits.tests',
]
def get_package_data(self):
baseline_images = [
'tests/baseline_images/%s/*' % x
for x in os.listdir('lib/mpl_toolkits/tests/baseline_images')]
return {'mpl_toolkits': baseline_images}
def get_namespace_packages(self):
return ['mpl_toolkits']
class DelayedExtension(Extension, object):
"""
A distutils Extension subclass where some of its members
may have delayed computation until reaching the build phase.
This is so we can, for example, get the Numpy include dirs
after pip has installed Numpy for us if it wasn't already
on the system.
"""
def __init__(self, *args, **kwargs):
super(DelayedExtension, self).__init__(*args, **kwargs)
self._finalized = False
self._hooks = {}
def add_hook(self, member, func):
"""
Add a hook to dynamically compute a member.
Parameters
----------
member : string
The name of the member
func : callable
The function to call to get dynamically-computed values
for the member.
"""
self._hooks[member] = func
def finalize(self):
self._finalized = True
class DelayedMember(property):
def __init__(self, name):
self._name = name
def __get__(self, obj, objtype=None):
result = getattr(obj, '_' + self._name, [])
if obj._finalized:
if self._name in obj._hooks:
result = obj._hooks[self._name]() + result
return result
def __set__(self, obj, value):
setattr(obj, '_' + self._name, value)
include_dirs = DelayedMember('include_dirs')
class Numpy(SetupPackage):
name = "numpy"
@staticmethod
def include_dirs_hook():
if PY3min:
import builtins
if hasattr(builtins, '__NUMPY_SETUP__'):
del builtins.__NUMPY_SETUP__
import imp
import numpy
imp.reload(numpy)
else:
import __builtin__
if hasattr(__builtin__, '__NUMPY_SETUP__'):
del __builtin__.__NUMPY_SETUP__
import numpy
reload(numpy)
ext = Extension('test', [])
ext.include_dirs.append(numpy.get_include())
if not has_include_file(
ext.include_dirs, os.path.join("numpy", "arrayobject.h")):
warnings.warn(
"The C headers for numpy could not be found. "
"You may need to install the development package")
return [numpy.get_include()]
def check(self):
min_version = extract_versions()['__version__numpy__']
try:
import numpy
except ImportError:
return 'not found. pip may install it below.'
if not is_min_version(numpy.__version__, min_version):
raise SystemExit(
"Requires numpy %s or later to build. (Found %s)" %
(min_version, numpy.__version__))
return 'version %s' % numpy.__version__
def add_flags(self, ext):
# Ensure that PY_ARRAY_UNIQUE_SYMBOL is uniquely defined for
# each extension
array_api_name = 'MPL_' + ext.name.replace('.', '_') + '_ARRAY_API'
ext.define_macros.append(('PY_ARRAY_UNIQUE_SYMBOL', array_api_name))
ext.add_hook('include_dirs', self.include_dirs_hook)
ext.define_macros.append(('NPY_NO_DEPRECATED_API',
'NPY_1_7_API_VERSION'))
# Allow NumPy's printf format specifiers in C++.
ext.define_macros.append(('__STDC_FORMAT_MACROS', 1))
def get_setup_requires(self):
return ['numpy>=1.7.1']
def get_install_requires(self):
return ['numpy>=1.7.1']
class LibAgg(SetupPackage):
name = 'libagg'
def check(self):
self.__class__.found_external = True
try:
return self._check_for_pkg_config(
'libagg', 'agg2/agg_basics.h', min_version='PATCH')
except CheckFailed as e:
self.__class__.found_external = False
return str(e) + ' Using local copy.'
def add_flags(self, ext, add_sources=True):
if self.found_external:
pkg_config.setup_extension(ext, 'libagg')
else:
ext.include_dirs.insert(0, 'extern/agg24-svn/include')
if add_sources:
agg_sources = [
'agg_bezier_arc.cpp',
'agg_curves.cpp',
'agg_image_filters.cpp',
'agg_trans_affine.cpp',
'agg_vcgen_contour.cpp',
'agg_vcgen_dash.cpp',
'agg_vcgen_stroke.cpp',
'agg_vpgen_segmentator.cpp'
]
ext.sources.extend(
os.path.join('extern', 'agg24-svn', 'src', x) for x in agg_sources)
class FreeType(SetupPackage):
name = "freetype"
pkg_names = {
"apt-get": "libfreetype6-dev",
"yum": "freetype-devel",
"dnf": "freetype-devel",
"brew": "freetype",
"port": "freetype",
"windows_url": "http://gnuwin32.sourceforge.net/packages/freetype.htm"
}
def check(self):
if options.get('local_freetype'):
return "Using local version for testing"
if sys.platform == 'win32':
try:
check_include_file(get_include_dirs(), 'ft2build.h', 'freetype')
except CheckFailed:
check_include_file(get_include_dirs(), 'freetype2\\ft2build.h', 'freetype')
return 'Using unknown version found on system.'
status, output = getstatusoutput("freetype-config --ftversion")
if status == 0:
version = output
else:
version = None
# Early versions of freetype grep badly inside freetype-config,
# so catch those cases. (tested with 2.5.3).
if version is None or 'No such file or directory\ngrep:' in version:
version = self.version_from_header()
# pkg_config returns the libtool version rather than the
# freetype version so we need to explicitly pass the version
# to _check_for_pkg_config
return self._check_for_pkg_config(
'freetype2', 'ft2build.h',
min_version='2.3', version=version)
def version_from_header(self):
version = 'unknown'
ext = self.get_extension()
if ext is None:
return version
# Return the first version found in the include dirs.
for include_dir in ext.include_dirs:
header_fname = os.path.join(include_dir, 'freetype.h')
if os.path.exists(header_fname):
major, minor, patch = 0, 0, 0
with open(header_fname, 'r') as fh:
for line in fh:
if line.startswith('#define FREETYPE_'):
value = line.rsplit(' ', 1)[1].strip()
if 'MAJOR' in line:
major = value
elif 'MINOR' in line:
minor = value
else:
patch = value
return '.'.join([major, minor, patch])
def add_flags(self, ext):
if options.get('local_freetype'):
src_path = os.path.join(
'build', 'freetype-{0}'.format(LOCAL_FREETYPE_VERSION))
# Statically link to the locally-built freetype.
# This is certainly broken on Windows.
ext.include_dirs.insert(0, os.path.join(src_path, 'include'))
if sys.platform == 'win32':
libfreetype = 'libfreetype.lib'
else:
libfreetype = 'libfreetype.a'
ext.extra_objects.insert(
0, os.path.join(src_path, 'objs', '.libs', libfreetype))
ext.define_macros.append(('FREETYPE_BUILD_TYPE', 'local'))
else:
pkg_config.setup_extension(
ext, 'freetype2',
default_include_dirs=[
'include/freetype2', 'freetype2',
'lib/freetype2/include',
'lib/freetype2/include/freetype2'],
default_library_dirs=[
'freetype2/lib'],
default_libraries=['freetype', 'z'])
ext.define_macros.append(('FREETYPE_BUILD_TYPE', 'system'))
def do_custom_build(self):
# We're using a system freetype
if not options.get('local_freetype'):
return
src_path = os.path.join(
'build', 'freetype-{0}'.format(LOCAL_FREETYPE_VERSION))
# We've already built freetype
if sys.platform == 'win32':
libfreetype = 'libfreetype.lib'
else:
libfreetype = 'libfreetype.a'
if os.path.isfile(os.path.join(src_path, 'objs', '.libs', libfreetype)):
return
tarball = 'freetype-{0}.tar.gz'.format(LOCAL_FREETYPE_VERSION)
tarball_path = os.path.join('build', tarball)
try:
tarball_cache_dir = _get_xdg_cache_dir()
tarball_cache_path = os.path.join(tarball_cache_dir, tarball)
except:
# again, do not really care if this fails
tarball_cache_dir = None
tarball_cache_path = None
if not os.path.isfile(tarball_path):
if (tarball_cache_path is not None and
os.path.isfile(tarball_cache_path)):
if get_file_hash(tarball_cache_path) == LOCAL_FREETYPE_HASH:
try:
os.makedirs('build')
except OSError:
# Don't care if it exists.
pass
try:
shutil.copy(tarball_cache_path, tarball_path)
print('Using cached tarball: {}'
.format(tarball_cache_path))
except OSError:
# If this fails, oh well just re-download
pass
if not os.path.isfile(tarball_path):
if PY3min:
from urllib.request import urlretrieve
else:
from urllib import urlretrieve
if not os.path.exists('build'):
os.makedirs('build')
url_fmts = [
'https://downloads.sourceforge.net/project/freetype'
'/freetype2/{version}/{tarball}',
'https://download.savannah.gnu.org/releases/freetype'
'/{tarball}'
]
for url_fmt in url_fmts:
tarball_url = url_fmt.format(
version=LOCAL_FREETYPE_VERSION, tarball=tarball)
print("Downloading {0}".format(tarball_url))
try:
urlretrieve(tarball_url, tarball_path)
except IOError: # URLError (a subclass) on Py3.
print("Failed to download {0}".format(tarball_url))
else:
if get_file_hash(tarball_path) != LOCAL_FREETYPE_HASH:
print("Invalid hash.")
else:
break
else:
raise IOError("Failed to download freetype. "
"You can download the file by "
"alternative means and copy it "
" to '{0}'".format(tarball_path))
try:
os.makedirs(tarball_cache_dir)
except OSError:
# Don't care if it exists.
pass
try:
shutil.copy(tarball_path, tarball_cache_path)
print('Cached tarball at: {}'.format(tarball_cache_path))
except OSError:
# If this fails, we can always re-download.
pass
if get_file_hash(tarball_path) != LOCAL_FREETYPE_HASH:
raise IOError(
"{0} does not match expected hash.".format(tarball))
print("Building {0}".format(tarball))
if sys.platform != 'win32':
# compilation on all other platforms than windows
cflags = 'CFLAGS="{0} -fPIC" '.format(os.environ.get('CFLAGS', ''))
subprocess.check_call(
['tar', 'zxf', tarball], cwd='build')
subprocess.check_call(
[cflags + './configure --with-zlib=no --with-bzip2=no '
'--with-png=no --with-harfbuzz=no'], shell=True, cwd=src_path)
subprocess.check_call(
[cflags + 'make'], shell=True, cwd=src_path)
else:
# compilation on windows
FREETYPE_BUILD_CMD = """\
call "%ProgramFiles%\\Microsoft SDKs\\Windows\\v7.0\\Bin\\SetEnv.Cmd" /Release /{xXX} /xp
call "{vcvarsall}" {xXX}
set MSBUILD=C:\\Windows\\Microsoft.NET\\Framework\\v4.0.30319\\MSBuild.exe
rd /S /Q %FREETYPE%\\objs
%MSBUILD% %FREETYPE%\\builds\\windows\\{vc20xx}\\freetype.sln /t:Clean;Build /p:Configuration="{config}";Platform={WinXX}
echo Build completed, moving result"
:: move to the "normal" path for the unix builds...
mkdir %FREETYPE%\\objs\\.libs
:: REMINDER: fix when changing the version
copy %FREETYPE%\\objs\\{vc20xx}\\{xXX}\\freetype261.lib %FREETYPE%\\objs\\.libs\\libfreetype.lib
if errorlevel 1 (
rem This is a py27 version, which has a different location for the lib file :-/
copy %FREETYPE%\\objs\\win32\\{vc20xx}\\freetype261.lib %FREETYPE%\\objs\\.libs\\libfreetype.lib
)
"""
from setup_external_compile import fixproj, prepare_build_cmd, VS2010, X64, tar_extract
# Note: freetype has no build profile for 2014, so we don't bother...
vc = 'vc2010' if VS2010 else 'vc2008'
WinXX = 'x64' if X64 else 'Win32'
tar_extract(tarball_path, "build")
# This is only false for py2.7, even on py3.5...
if not VS2010:
fixproj(os.path.join(src_path, 'builds', 'windows', vc, 'freetype.sln'), WinXX)
fixproj(os.path.join(src_path, 'builds', 'windows', vc, 'freetype.vcproj'), WinXX)
cmdfile = os.path.join("build", 'build_freetype.cmd')
with open(cmdfile, 'w') as cmd:
cmd.write(prepare_build_cmd(FREETYPE_BUILD_CMD, vc20xx=vc, WinXX=WinXX,
config='Release' if VS2010 else 'LIB Release'))
os.environ['FREETYPE'] = src_path
subprocess.check_call([cmdfile], shell=True)
class FT2Font(SetupPackage):
name = 'ft2font'
def get_extension(self):
sources = [
'src/ft2font.cpp',
'src/ft2font_wrapper.cpp',
'src/mplutils.cpp'
]
ext = make_extension('matplotlib.ft2font', sources)
FreeType().add_flags(ext)
Numpy().add_flags(ext)
return ext
class Png(SetupPackage):
name = "png"
pkg_names = {
"apt-get": "libpng12-dev",
"yum": "libpng-devel",
"dnf": "libpng-devel",
"brew": "libpng",
"port": "libpng",
"windows_url": "http://gnuwin32.sourceforge.net/packages/libpng.htm"
}
def check(self):
if sys.platform == 'win32':
check_include_file(get_include_dirs(), 'png.h', 'png')
return 'Using unknown version found on system.'
status, output = getstatusoutput("libpng-config --version")
if status == 0:
version = output
else:
version = None
try:
return self._check_for_pkg_config(
'libpng', 'png.h',
min_version='1.2', version=version)
except CheckFailed as e:
if has_include_file(get_include_dirs(), 'png.h'):
return str(e) + ' Using unknown version found on system.'
raise
def get_extension(self):
sources = [
'src/_png.cpp',
'src/mplutils.cpp'
]
ext = make_extension('matplotlib._png', sources)
pkg_config.setup_extension(
ext, 'libpng', default_libraries=['png', 'z'],
alt_exec='libpng-config --ldflags')
Numpy().add_flags(ext)
return ext
class Qhull(SetupPackage):
name = "qhull"
def check(self):
self.__class__.found_external = True
try:
return self._check_for_pkg_config(
'libqhull', 'libqhull/qhull_a.h', min_version='2015.2')
except CheckFailed as e:
self.__class__.found_pkgconfig = False
self.__class__.found_external = False
return str(e) + ' Using local copy.'
def add_flags(self, ext):
if self.found_external:
pkg_config.setup_extension(ext, 'qhull',
default_libraries=['qhull'])
else:
ext.include_dirs.insert(0, 'extern')
ext.sources.extend(sorted(glob.glob('extern/libqhull/*.c')))
class TTConv(SetupPackage):
name = "ttconv"
def get_extension(self):
sources = [
'src/_ttconv.cpp',
'extern/ttconv/pprdrv_tt.cpp',
'extern/ttconv/pprdrv_tt2.cpp',
'extern/ttconv/ttutil.cpp'
]
ext = make_extension('matplotlib.ttconv', sources)
Numpy().add_flags(ext)
ext.include_dirs.insert(0, 'extern')
return ext
class Path(SetupPackage):
name = "path"
def get_extension(self):
sources = [
'src/py_converters.cpp',
'src/_path_wrapper.cpp'
]
ext = make_extension('matplotlib._path', sources)
Numpy().add_flags(ext)
LibAgg().add_flags(ext)
return ext
class Image(SetupPackage):
name = "image"
def get_extension(self):
sources = [
'src/_image.cpp',
'src/mplutils.cpp',
'src/_image_wrapper.cpp',
'src/py_converters.cpp'
]
ext = make_extension('matplotlib._image', sources)
Numpy().add_flags(ext)
LibAgg().add_flags(ext)
return ext
class Contour(SetupPackage):
name = "contour"
def get_extension(self):
sources = [
"src/_contour.cpp",
"src/_contour_wrapper.cpp",
]
ext = make_extension('matplotlib._contour', sources)
Numpy().add_flags(ext)
return ext
class QhullWrap(SetupPackage):
name = "qhull_wrap"
def get_extension(self):
sources = ['src/qhull_wrap.c']
ext = make_extension('matplotlib._qhull', sources,
define_macros=[('MPL_DEVNULL', os.devnull)])
Numpy().add_flags(ext)
Qhull().add_flags(ext)
return ext
class Tri(SetupPackage):
name = "tri"
def get_extension(self):
sources = [
"lib/matplotlib/tri/_tri.cpp",
"lib/matplotlib/tri/_tri_wrapper.cpp",
"src/mplutils.cpp"
]
ext = make_extension('matplotlib._tri', sources)
Numpy().add_flags(ext)
return ext
class InstallRequires(SetupPackage):
name = "install_requires"
def check(self):
return "handled by setuptools"
def get_install_requires(self):
install_requires = [
"cycler>=0.10",
"pyparsing>=2.0.1,!=2.0.4,!=2.1.2,!=2.1.6",
"python-dateutil>=2.0",
"pytz",
"six>=1.10",
]
if sys.version_info < (3,):
install_requires += ["backports.functools_lru_cache"]
if sys.version_info < (3,) and os.name == "posix":
install_requires += ["subprocess32"]
return install_requires
class BackendAgg(OptionalBackendPackage):
name = "agg"
force = True
def get_extension(self):
sources = [
"src/mplutils.cpp",
"src/py_converters.cpp",
"src/_backend_agg.cpp",
"src/_backend_agg_wrapper.cpp"
]
ext = make_extension('matplotlib.backends._backend_agg', sources)
Numpy().add_flags(ext)
LibAgg().add_flags(ext)
FreeType().add_flags(ext)
return ext
class BackendTkAgg(OptionalBackendPackage):
name = "tkagg"
force = True
def check(self):
return "installing; run-time loading from Python Tcl / Tk"
def runtime_check(self):
""" Checks whether TkAgg runtime dependencies are met
"""
pkg_name = 'tkinter' if PY3min else 'Tkinter'
try:
import_module(pkg_name)
except ImportError:
return False
return True
def get_extension(self):
sources = [
'src/py_converters.cpp',
'src/_tkagg.cpp'
]
ext = make_extension('matplotlib.backends._tkagg', sources)
self.add_flags(ext)
Numpy().add_flags(ext)
LibAgg().add_flags(ext, add_sources=False)
return ext
def add_flags(self, ext):
ext.include_dirs.insert(0, 'src')
if sys.platform == 'win32':
# PSAPI library needed for finding Tcl / Tk at run time
ext.libraries.extend(['psapi'])
class BackendGtk(OptionalBackendPackage):
name = "gtk"
def check_requirements(self):
try:
import gtk
except ImportError:
raise CheckFailed("Requires pygtk")
except RuntimeError:
raise CheckFailed('pygtk present, but import failed.')
else:
version = (2, 2, 0)
if gtk.pygtk_version < version:
raise CheckFailed(
"Requires pygtk %d.%d.%d or later. "
"Found %d.%d.%d" % (version + gtk.pygtk_version))
ext = self.get_extension()
self.add_flags(ext)
check_include_file(ext.include_dirs,
os.path.join("gtk", "gtk.h"),
'gtk')
check_include_file(ext.include_dirs,
os.path.join("pygtk", "pygtk.h"),
'pygtk')
return 'Gtk: %s pygtk: %s' % (
".".join(str(x) for x in gtk.gtk_version),
".".join(str(x) for x in gtk.pygtk_version))
def get_package_data(self):
return {'matplotlib': ['mpl-data/*.glade']}
def get_extension(self):
sources = [
'src/_backend_gdk.c'
]
ext = make_extension('matplotlib.backends._backend_gdk', sources)
self.add_flags(ext)
Numpy().add_flags(ext)
return ext
def add_flags(self, ext):
if sys.platform == 'win32':
def getoutput(s):
ret = os.popen(s).read().strip()
return ret
if 'PKG_CONFIG_PATH' not in os.environ:
# If Gtk+ is installed, pkg-config is required to be installed
os.environ['PKG_CONFIG_PATH'] = 'C:\\GTK\\lib\\pkgconfig'
# popen broken on my win32 plaform so I can't use pkgconfig
ext.library_dirs.extend(
['C:/GTK/bin', 'C:/GTK/lib'])
ext.include_dirs.extend(
['win32_static/include/pygtk-2.0',
'C:/GTK/include',
'C:/GTK/include/gobject',
'C:/GTK/include/gext',
'C:/GTK/include/glib',
'C:/GTK/include/pango',
'C:/GTK/include/atk',
'C:/GTK/include/X11',
'C:/GTK/include/cairo',
'C:/GTK/include/gdk',
'C:/GTK/include/gdk-pixbuf',
'C:/GTK/include/gtk',
])
pygtkIncludes = getoutput(
'pkg-config --cflags-only-I pygtk-2.0').split()
gtkIncludes = getoutput(
'pkg-config --cflags-only-I gtk+-2.0').split()
includes = pygtkIncludes + gtkIncludes
ext.include_dirs.extend([include[2:] for include in includes])
pygtkLinker = getoutput('pkg-config --libs pygtk-2.0').split()
gtkLinker = getoutput('pkg-config --libs gtk+-2.0').split()
linkerFlags = pygtkLinker + gtkLinker
ext.libraries.extend(
[flag[2:] for flag in linkerFlags if flag.startswith('-l')])
ext.library_dirs.extend(
[flag[2:] for flag in linkerFlags if flag.startswith('-L')])
ext.extra_link_args.extend(
[flag for flag in linkerFlags if not
(flag.startswith('-l') or flag.startswith('-L'))])
# visual studio doesn't need the math library
if (sys.platform == 'win32' and
win32_compiler == 'msvc' and
'm' in ext.libraries):
ext.libraries.remove('m')
elif sys.platform != 'win32':
pkg_config.setup_extension(ext, 'pygtk-2.0')
pkg_config.setup_extension(ext, 'gtk+-2.0')
class BackendGtkAgg(BackendGtk):
name = "gtkagg"
def get_package_data(self):
return {'matplotlib': ['mpl-data/*.glade']}
def get_extension(self):
sources = [
'src/py_converters.cpp',
'src/_gtkagg.cpp',
'src/mplutils.cpp'
]
ext = make_extension('matplotlib.backends._gtkagg', sources)
self.add_flags(ext)
LibAgg().add_flags(ext)
Numpy().add_flags(ext)
return ext
def backend_gtk3agg_internal_check(x):
try:
import gi
except ImportError:
return (False, "Requires pygobject to be installed.")
try:
gi.require_version("Gtk", "3.0")
except ValueError:
return (False, "Requires gtk3 development files to be installed.")
except AttributeError:
return (False, "pygobject version too old.")
try:
from gi.repository import Gtk, Gdk, GObject
except (ImportError, RuntimeError):
return (False, "Requires pygobject to be installed.")
return (True, "version %s.%s.%s" % (
Gtk.get_major_version(),
Gtk.get_micro_version(),
Gtk.get_minor_version()))
class BackendGtk3Agg(OptionalBackendPackage):
name = "gtk3agg"
def check_requirements(self):
if 'TRAVIS' in os.environ:
raise CheckFailed("Can't build with Travis")
# This check needs to be performed out-of-process, because
# importing gi and then importing regular old pygtk afterward
# segfaults the interpreter.
try:
p = multiprocessing.Pool()
except:
return "unknown (can not use multiprocessing to determine)"
try:
res = p.map_async(backend_gtk3agg_internal_check, [0])
success, msg = res.get(timeout=10)[0]
except multiprocessing.TimeoutError:
p.terminate()
# No result returned. Probaly hanging, terminate the process.
success = False
raise CheckFailed("Check timed out")
except:
p.close()
# Some other error.
success = False
msg = "Could not determine"
raise
else:
p.close()
finally:
p.join()
if success:
return msg
else:
raise CheckFailed(msg)
def get_package_data(self):
return {'matplotlib': ['mpl-data/*.glade']}
def backend_gtk3cairo_internal_check(x):
try:
import cairocffi
except ImportError:
try:
import cairo
except ImportError:
return (False, "Requires cairocffi or pycairo to be installed.")
try:
import gi
except ImportError:
return (False, "Requires pygobject to be installed.")
try:
gi.require_version("Gtk", "3.0")
except ValueError:
return (False, "Requires gtk3 development files to be installed.")
except AttributeError:
return (False, "pygobject version too old.")
try:
from gi.repository import Gtk, Gdk, GObject
except (RuntimeError, ImportError):
return (False, "Requires pygobject to be installed.")
return (True, "version %s.%s.%s" % (
Gtk.get_major_version(),
Gtk.get_micro_version(),
Gtk.get_minor_version()))
class BackendGtk3Cairo(OptionalBackendPackage):
name = "gtk3cairo"
def check_requirements(self):
if 'TRAVIS' in os.environ:
raise CheckFailed("Can't build with Travis")
# This check needs to be performed out-of-process, because
# importing gi and then importing regular old pygtk afterward
# segfaults the interpreter.
try:
p = multiprocessing.Pool()
except:
return "unknown (can not use multiprocessing to determine)"
try:
res = p.map_async(backend_gtk3cairo_internal_check, [0])
success, msg = res.get(timeout=10)[0]
except multiprocessing.TimeoutError:
p.terminate()
# No result returned. Probaly hanging, terminate the process.
success = False
raise CheckFailed("Check timed out")
except:
p.close()
success = False
raise
else:
p.close()
finally:
p.join()
if success:
return msg
else:
raise CheckFailed(msg)
def get_package_data(self):
return {'matplotlib': ['mpl-data/*.glade']}
class BackendWxAgg(OptionalBackendPackage):
name = "wxagg"
def check_requirements(self):
wxversioninstalled = True
try:
import wxversion
except ImportError:
wxversioninstalled = False
if wxversioninstalled:
try:
_wx_ensure_failed = wxversion.AlreadyImportedError
except AttributeError:
_wx_ensure_failed = wxversion.VersionError
try:
wxversion.ensureMinimal('2.9')
except _wx_ensure_failed:
pass
try:
import wx
backend_version = wx.VERSION_STRING
except ImportError:
raise CheckFailed("requires wxPython")
if not is_min_version(backend_version, "2.9"):
raise CheckFailed(
"Requires wxPython 2.9, found %s" % backend_version)
return "version %s" % backend_version
class BackendMacOSX(OptionalBackendPackage):
name = 'macosx'
def check_requirements(self):
if sys.platform != 'darwin':
raise CheckFailed("Mac OS-X only")
return 'darwin'
def get_extension(self):
sources = [
'src/_macosx.m'
]
ext = make_extension('matplotlib.backends._macosx', sources)
ext.extra_link_args.extend(['-framework', 'Cocoa'])
return ext
class Windowing(OptionalBackendPackage):
"""
Builds the windowing extension.
"""
name = "windowing"
def check_requirements(self):
if sys.platform != 'win32':
raise CheckFailed("Microsoft Windows only")
config = self.get_config()
if config is False:
raise CheckFailed("skipping due to configuration")
return ""
def get_extension(self):
sources = [
"src/_windowing.cpp"
]
ext = make_extension('matplotlib._windowing', sources)
ext.include_dirs.extend(['C:/include'])
ext.libraries.extend(['user32'])
ext.library_dirs.extend(['C:/lib'])
ext.extra_link_args.append("-mwindows")
return ext
class BackendQtBase(OptionalBackendPackage):
def convert_qt_version(self, version):
version = '%x' % version
temp = []
while len(version) > 0:
version, chunk = version[:-2], version[-2:]
temp.insert(0, str(int(chunk, 16)))
return '.'.join(temp)
def check_requirements(self):
'''
If PyQt4/PyQt5 is already imported, importing PyQt5/PyQt4 will fail
so we need to test in a subprocess (as for Gtk3).
'''
try:
p = multiprocessing.Pool()
except:
# Can't do multiprocessing, fall back to normal approach
# (this will fail if importing both PyQt4 and PyQt5).
try:
# Try in-process
msg = self.callback(self)
except RuntimeError:
raise CheckFailed(
"Could not import: are PyQt4 & PyQt5 both installed?")
else:
# Multiprocessing OK
try:
res = p.map_async(self.callback, [self])
msg = res.get(timeout=10)[0]
except multiprocessing.TimeoutError:
p.terminate()
# No result returned. Probaly hanging, terminate the process.
raise CheckFailed("Check timed out")
except:
# Some other error.
p.close()
raise
else:
# Clean exit
p.close()
finally:
# Tidy up multiprocessing
p.join()
return msg
def backend_pyside_internal_check(self):
try:
from PySide import __version__
from PySide import QtCore
except ImportError:
raise CheckFailed("PySide not found")
else:
return ("Qt: %s, PySide: %s" %
(QtCore.__version__, __version__))
def backend_pyqt4_internal_check(self):
try:
from PyQt4 import QtCore
except ImportError:
raise CheckFailed("PyQt4 not found")
try:
qt_version = QtCore.QT_VERSION
pyqt_version_str = QtCore.PYQT_VERSION_STR
except AttributeError:
raise CheckFailed('PyQt4 not correctly imported')
else:
return ("Qt: %s, PyQt: %s" % (self.convert_qt_version(qt_version), pyqt_version_str))
def backend_qt4_internal_check(self):
successes = []
failures = []
try:
successes.append(backend_pyside_internal_check(self))
except CheckFailed as e:
failures.append(str(e))
try:
successes.append(backend_pyqt4_internal_check(self))
except CheckFailed as e:
failures.append(str(e))
if len(successes) == 0:
raise CheckFailed('; '.join(failures))
return '; '.join(successes + failures)
class BackendQt4(BackendQtBase):
name = "qt4agg"
def __init__(self, *args, **kwargs):
BackendQtBase.__init__(self, *args, **kwargs)
self.callback = backend_qt4_internal_check
def backend_pyside2_internal_check(self):
try:
from PySide2 import __version__
from PySide2 import QtCore
except ImportError:
raise CheckFailed("PySide2 not found")
else:
return ("Qt: %s, PySide2: %s" %
(QtCore.__version__, __version__))
def backend_pyqt5_internal_check(self):
try:
from PyQt5 import QtCore
except ImportError:
raise CheckFailed("PyQt5 not found")
try:
qt_version = QtCore.QT_VERSION
pyqt_version_str = QtCore.PYQT_VERSION_STR
except AttributeError:
raise CheckFailed('PyQt5 not correctly imported')
else:
return ("Qt: %s, PyQt: %s" % (self.convert_qt_version(qt_version), pyqt_version_str))
def backend_qt5_internal_check(self):
successes = []
failures = []
try:
successes.append(backend_pyside2_internal_check(self))
except CheckFailed as e:
failures.append(str(e))
try:
successes.append(backend_pyqt5_internal_check(self))
except CheckFailed as e:
failures.append(str(e))
if len(successes) == 0:
raise CheckFailed('; '.join(failures))
return '; '.join(successes + failures)
class BackendQt5(BackendQtBase):
name = "qt5agg"
def __init__(self, *args, **kwargs):
BackendQtBase.__init__(self, *args, **kwargs)
self.callback = backend_qt5_internal_check
class BackendCairo(OptionalBackendPackage):
name = "cairo"
def check_requirements(self):
try:
import cairocffi
except ImportError:
try:
import cairo
except ImportError:
raise CheckFailed("cairocffi or pycairo not found")
else:
return "pycairo version %s" % cairo.version
else:
return "cairocffi version %s" % cairocffi.version
class DviPng(SetupPackage):
name = "dvipng"
optional = True
def check(self):
try:
output = check_output('dvipng -version', shell=True,
stderr=subprocess.STDOUT)
return "version %s" % output.splitlines()[1].decode().split()[-1]
except (IndexError, ValueError, subprocess.CalledProcessError):
raise CheckFailed()
class Ghostscript(SetupPackage):
name = "ghostscript"
optional = True
def check(self):
if sys.platform == 'win32':
# mgs is the name in miktex
gs_execs = ['gswin32c', 'gswin64c', 'mgs', 'gs']
else:
gs_execs = ['gs']
for gs_exec in gs_execs:
try:
command = gs_exec + ' --version'
output = check_output(command, shell=True,
stderr=subprocess.STDOUT)
return "version %s" % output.decode()[:-1]
except (IndexError, ValueError, subprocess.CalledProcessError):
pass
raise CheckFailed()
class LaTeX(SetupPackage):
name = "latex"
optional = True
def check(self):
try:
output = check_output('latex -version', shell=True,
stderr=subprocess.STDOUT)
line = output.splitlines()[0].decode()
pattern = '(3\.1\d+)|(MiKTeX \d+.\d+)'
match = re.search(pattern, line)
return "version %s" % match.group(0)
except (IndexError, ValueError, AttributeError, subprocess.CalledProcessError):
raise CheckFailed()
class PdfToPs(SetupPackage):
name = "pdftops"
optional = True
def check(self):
try:
output = check_output('pdftops -v', shell=True,
stderr=subprocess.STDOUT)
for line in output.splitlines():
line = line.decode()
if 'version' in line:
return "version %s" % line.split()[2]
except (IndexError, ValueError, subprocess.CalledProcessError):
pass
raise CheckFailed()
class OptionalPackageData(OptionalPackage):
config_category = "package_data"
class Dlls(OptionalPackageData):
"""
On Windows, this packages any DLL files that can be found in the
lib/matplotlib/* directories.
"""
name = "dlls"
def check_requirements(self):
if sys.platform != 'win32':
raise CheckFailed("Microsoft Windows only")
def get_package_data(self):
return {'': ['*.dll']}
@classmethod
def get_config(cls):
"""
Look at `setup.cfg` and return one of ["auto", True, False] indicating
if the package is at default state ("auto"), forced by the user (True)
or opted-out (False).
"""
try:
return config.getboolean(cls.config_category, cls.name)
except:
return False # <-- default
| [
"[email protected]"
] | |
7d31fe18877c6078bd75cf9d7badeddd503d0e55 | 0466559817d3a1be9409da2c83db99c4db3bacfe | /hubcheck/shell/container_manager.py | 0d0d575c450a0cd9d608a7c9e7729ac697b061c0 | [
"MIT"
] | permissive | ken2190/hubcheck | 955cf9b75a1ee77e28256dfd3a780cfbc17de961 | 2ff506eb56ba00f035300862f8848e4168452a17 | refs/heads/master | 2023-03-20T15:17:12.949715 | 2015-09-29T16:11:18 | 2015-09-29T16:11:18 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,451 | py | from .toolsession import ToolSession
from hubcheck.exceptions import ConnectionClosedError
from hubcheck.exceptions import SessionCreateError
import logging
import pprint
import re
import hubcheck.conf
class Singleton(type):
_instances = {}
def __call__(cls, *args, **kwargs):
if cls not in cls._instances:
cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
class ContainerManager(object):
__metaclass__ = Singleton
def __init__(self):
self.logger = logging.getLogger(__name__)
self._lookup = {
# Example:
# host : {
# username : {
# 'sessionobj' : sessionObj,
# 'sessions' : [ {'number' : sessionNum, 'toolname' : toolname},
# {'number' : sessionNum, 'toolname' : toolname},
# ... ],
# }
# }
}
def __repr__(self):
return "ContainerManager(%s)" % (pprint.pformat(self._lookup))
# def __del__(self):
#
# self.stop_all()
def _find_session_number_for(self,host,username,toolname=None):
self.logger.debug(
'cm looking for session number for %s on %s with toolname %s' \
% (username,host,toolname))
self.logger.debug(
'session dictionary:\n%s' \
% (pprint.pformat(self._lookup)))
session_obj = None
session_number = None
session = None
# check if the host,user combination exists
try:
sessions = self._lookup[host][username]['sessions']
session_obj = self._lookup[host][username]['sessionobj']
except KeyError:
return session_obj,session_number
if len(sessions) == 0:
session_number = None
return session_obj,session_number
if toolname is None:
# return the first available session
session_number = sessions[0]['number']
return session_obj,session_number
# find a session that matches the toolname
for session in sessions:
if session['toolname'] == toolname:
session_number = session['number']
break
return session_obj,session_number
def _create_session_number_record(self,host,username,session_number,
session_obj,toolname):
if host not in self._lookup:
self._lookup[host] = {}
session_number = int(session_number)
if username not in self._lookup[host]:
# add a new record
self.logger.debug(
'adding cm record for %s:%s -> %s,%s' \
% (host,username,session_number,toolname))
self._lookup[host][username] = {
'sessionobj' : session_obj,
'sessions' : [{'number':session_number,'toolname':toolname}],
}
else:
# update an existing record
self.logger.debug(
'updating cm record for %s:%s -> %s,%s' \
% (host,username,session_number,toolname))
self._lookup[host][username]['sessions'].append(
{'number':session_number,'toolname':toolname})
self.logger.info(
"cm user sessions: host='%s' username='%s' sessions='%s'" \
% (host,username,self._lookup[host][username]['sessions']))
def _delete_session_number_record(self,host,username,session_number):
session_number = int(session_number)
self.logger.debug(
"removing cm session record for %s:%s -> %s" \
% (host,username,session_number))
# update an existing record
for i in xrange(0,len(self._lookup[host][username]['sessions'])):
session = self._lookup[host][username]['sessions'][i]
if session['number'] == int(session_number):
del self._lookup[host][username]['sessions'][i]
break
self.logger.info(
"cm user sessions: host='%s' username='%s' sessions='%s'" \
% (host,username,self._lookup[host][username]['sessions']))
def create(self,host,username,password,session=None,title=None,toolname=None):
self.logger.info("cm creating new session")
if session is None:
session = ToolSession(host=host,
username=username,
password=password)
# read the configuration to find the name of the default workspace
if toolname is None:
toolname = hubcheck.conf.settings.default_workspace_toolname
# create the session
i,o,e = session.create(title,toolname)
output = o.read(1024)
try:
session_number = int(re.search('(\d+)',output).group(0))
except:
msg = "Failed to locate session number: %s" % (output)
raise SessionCreateError(msg)
# enter the session
ws = session.access(session_number=session_number)
# store the session number
self._create_session_number_record(host,username,session_number,session,toolname)
return ws
def access(self,host,username,password,toolname=None):
ws = None
# FIXME:
# we should probably grab all of the open sessions
# and loop through them, trying to connect. if we
# get to the end, then we open a new session.
session,session_number = self._find_session_number_for(host,username,toolname=toolname)
if session_number is not None:
# an open session was returned
# open a shell in that session
self.logger.info("cm accessing session %s" % (session_number))
try:
ws = session.access(session_number=session_number)
except ConnectionClosedError as e:
self.logger.exception(e)
self.logger.debug("session access failed, trying to recover...")
self.logger.debug("checking if closed")
# accessing the session failed
# check if the session is closed
d = session.get_open_session_detail()
for k,v in d.items():
if int(v['session_number']) == session_number:
# session is still listed in table
# probably something wrong trying to connect to it.
self.logger.debug("session %d appears open"
% (session_number))
raise
# session was not in the table, it is probably closed
# force a fall through to the next if clause
self.logger.debug("session appears closed, open a new one")
self._delete_session_number_record(host,username,session_number)
session_number = None
if session_number is None:
# no stored open sessions for the user on this host
# create a new session and store it
ws = self.create(host,username,password,session,toolname=toolname)
return ws
def sync_open_sessions(self,host=None,username=None):
self.logger.info("sync'ing open sessions: host = %s, username = %s"
% (host,username))
for key_host in self._lookup.keys():
if (host is not None) and (key_host != host):
continue
for key_user in self._lookup[key_host].keys():
if (username is not None) and (key_user != username):
continue
# get the list of open session from the "session list" command
session = self._lookup[key_host][key_user]['sessionobj']
open_sessions_dict = session.get_open_session_detail()
open_sessions = []
open_session_data = {}
for k,v in open_sessions_dict.items():
open_sessions.append(int(v['session_number']))
toolname = re.sub('_r\d+$','',v['name'])
open_session_data[int(v['session_number'])] = toolname
# figure out which sessions cm has listed as open,
# verses the sessions listed as open by "session list"
# closed_sessions = set(userd['sessions']) - set(open_sessions)
stored_session_data = self._lookup[key_host][key_user]['sessions']
stored_sessions = []
for session in stored_session_data:
stored_sessions.append(session['number'])
self.logger.debug("stored open sessions: %s" % (stored_sessions))
self.logger.debug("session list results: %s" % (open_sessions))
new_open_sessions = set(stored_sessions) & set(open_sessions)
# rebuild the container manager's open session data
self._lookup[key_host][key_user]['sessions'] = []
for session_number in new_open_sessions:
self._lookup[key_host][key_user]['sessions'].append(
{'number':session_number,
'toolname':open_session_data[session_number]}
)
self.logger.debug("new open sessions: %s"
% (self._lookup[key_host][key_user]['sessions']))
def stop(self,host,username,session_number):
"""
stop a session container
"""
self.logger.info("cm stopping session %s" % (session_number))
session = self._lookup[host][username]['sessionobj']
# check if the session is open
is_session_open = False
open_sessions_dict = session.get_open_session_detail()
for k,v in open_sessions_dict.items():
if int(v['session_number']) == int(session_number):
is_session_open = True
break
if is_session_open is False:
self.logger.info("session %s is not listed as open" % (session_number))
try:
self._delete_session_number_record(host,username,session_number)
except:
pass
return
i,o,e = session.stop(session_number=session_number)
output = o.read(1024)
self.logger.debug("session stop output: %s" % (output))
#FIXME:
# should probably read the output to make sure
# there were no errors
self._delete_session_number_record(host,username,session_number)
def stop_all(self):
for host in self._lookup.keys():
for user in self._lookup[host].keys():
sessions = list(self._lookup[host][user]['sessions'])
self.logger.debug('closing %s:%s\'s open sessions: %s'
% (host,user,sessions))
# stop each session
for s in sessions:
self.stop(host,user,s['number'])
# kill the session object
del self._lookup[host][user]['sessionobj']
self._lookup[host][user]['sessionobj'] = None
# delete the user record
del self._lookup[host][user]
self.clear()
def clear(self):
self._lookup = {}
| [
"[email protected]"
] | |
694553df0c0aa0de72c6cd3372d907b36a37b9fa | 487ce91881032c1de16e35ed8bc187d6034205f7 | /codes/CodeJamCrawler/16_0_3_neat/16_0_3_RTN8_solve.py | 7578551770778fbca70157c20919e407da47b880 | [] | no_license | DaHuO/Supergraph | 9cd26d8c5a081803015d93cf5f2674009e92ef7e | c88059dc66297af577ad2b8afa4e0ac0ad622915 | refs/heads/master | 2021-06-14T16:07:52.405091 | 2016-08-21T13:39:13 | 2016-08-21T13:39:13 | 49,829,508 | 2 | 0 | null | 2021-03-19T21:55:46 | 2016-01-17T18:23:00 | Python | UTF-8 | Python | false | false | 2,357 | py | #!/usr/bin/python3
# -*- coding: utf-8 -*-
import math
def optimal(from_, to_):
if from_ % 2 == 0:
yield from_
from_ += 1
for divider_candidate in range(from_, to_, 2):
yield divider_candidate
def get_divider(x, from_, to_):
for divider_candidate in optimal(from_, min(to_, int(math.sqrt(x)) + 1)):
if x % divider_candidate == 0:
return divider_candidate
def solve(n_and_j):
n, j = n_and_j.split(' ')
n, j = int(n), int(j)
results_candidates = []
results = []
def generate_jamcoin_candidate():
for bin_number in range(0, 2 ** (n - 1)):
yield ('1{:0%sb}1' % (n - 2)).format(bin_number)
jamcoin_candidate_generator = generate_jamcoin_candidate()
def get_jamcoin_candidate(i):
if i >= len(results_candidates):
jamcoin_candidate = next(jamcoin_candidate_generator)
results_candidates.append((
jamcoin_candidate,
{'nums': [int(jamcoin_candidate, b) for b in range(2, 11)],
'step': 2,
'results': [None] * 9}))
return results_candidates[i]
jamcoin_candidate_i = 0
max_divider = 4
max_jamcoin_i = 2
max_bin_number = 2 ** (n - 1)
while True:
jamcoin_candidate, stats = get_jamcoin_candidate(jamcoin_candidate_i)
all_done = True
for i, num in enumerate(stats['nums']):
if stats['results'][i]:
continue
divider = get_divider(num, stats['step'], max_divider)
if divider:
stats['results'][i] = divider
else:
all_done = False
if all_done:
results.append(jamcoin_candidate + ' ' + ' '.join(map(str, stats['results'])))
results_candidates.pop(jamcoin_candidate_i)
if len(results) == j:
return '\n'.join(results)
else:
jamcoin_candidate_i += 1
if jamcoin_candidate_i >= max_jamcoin_i:
max_divider += 2
jamcoin_candidate_i = 0
max_jamcoin_i = min(max_bin_number, max_jamcoin_i * 2)
if __name__ == '__main__':
cases_number = int(input())
for case_number in range(1, cases_number + 1):
input_args = input()
print('Case #%s:\n%s' % (case_number, solve(input_args)))
| [
"[[email protected]]"
] | |
3c851c00f3168cf06f90684e89022ab2bc3965e0 | c9697437c292df7fefd68559fdd9636066bdb2f1 | /dev/animations/quick_sph_harm_anim.py | 70d6bba7b23d2c08505d1efe4f8e75ea2ef961bf | [] | no_license | JoshKarpel/ionization | ebdb387483a9bc3fdb52818ab8e897e562ffcc67 | 3056df523ee90147d262b0e8bfaaef6f2678ea11 | refs/heads/master | 2021-03-24T13:03:57.469388 | 2020-04-06T03:37:04 | 2020-04-06T03:37:04 | 62,348,115 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,491 | py | import logging
import os
from copy import deepcopy
import simulacra as si
from simulacra.units import *
import ionization as ion
import matplotlib.pyplot as plt
FILE_NAME = os.path.splitext(os.path.basename(__file__))[0]
OUT_DIR = os.path.join(os.getcwd(), "out", FILE_NAME)
if __name__ == "__main__":
with si.utils.LogManager(
"simulacra", "ionization", stdout_logs=True, stdout_level=logging.DEBUG
) as logger:
anim_kwargs = dict(length=10, target_dir=OUT_DIR)
epot_axman = animation.animators.ElectricPotentialPlotAxis(
show_electric_field=True,
show_vector_potential=False,
show_y_label=False,
show_ticks_right=True,
)
test_state_axman = animation.animators.TestStateStackplotAxis(
states=tuple(
ion.HydrogenBoundState(n, l) for n in range(5) for l in range(n)
)[:8]
)
wavefunction_axman = animation.animators.WavefunctionStackplotAxis(
states=(
ion.HydrogenBoundState(1, 0),
ion.HydrogenBoundState(2, 0),
ion.HydrogenBoundState(3, 1),
)
)
animators = [
animation.animators.PolarAnimator(
postfix="g2",
axman_wavefunction=animation.animators.SphericalHarmonicPhiSliceMeshAxis(
shading="flat"
),
axman_lower_right=deepcopy(epot_axman),
axman_upper_right=deepcopy(test_state_axman),
axman_colorbar=animation.animators.ColorBarAxis(),
**anim_kwargs,
),
animation.animators.PolarAnimator(
postfix="g",
axman_wavefunction=animation.animators.SphericalHarmonicPhiSliceMeshAxis(
which="g",
colormap=plt.get_cmap("richardson"),
norm=si.vis.RichardsonNormalization(),
shading="flat",
),
axman_lower_right=deepcopy(epot_axman),
axman_upper_right=deepcopy(test_state_axman),
axman_colorbar=None,
**anim_kwargs,
),
animation.animators.PolarAnimator(
postfix="g_angmom",
axman_wavefunction=animation.animators.SphericalHarmonicPhiSliceMeshAxis(
which="g",
colormap=plt.get_cmap("richardson"),
norm=si.vis.RichardsonNormalization(),
shading="flat",
),
axman_lower_right=deepcopy(epot_axman),
axman_upper_right=animation.animators.AngularMomentumDecompositionAxis(
maximum_l=10
),
axman_colorbar=None,
**anim_kwargs,
),
animation.animators.PolarAnimator(
postfix="g_wavefunction",
axman_wavefunction=animation.animators.SphericalHarmonicPhiSliceMeshAxis(
which="g",
colormap=plt.get_cmap("richardson"),
norm=si.vis.RichardsonNormalization(),
shading="flat",
),
axman_lower_right=deepcopy(epot_axman),
axman_upper_right=deepcopy(wavefunction_axman),
axman_colorbar=None,
**anim_kwargs,
),
animation.animators.PolarAnimator(
postfix="g_wavefunction_again",
axman_wavefunction=animation.animators.SphericalHarmonicPhiSliceMeshAxis(
which="g",
colormap=plt.get_cmap("richardson"),
norm=si.vis.RichardsonNormalization(),
shading="flat",
),
axman_lower_right=deepcopy(epot_axman),
axman_upper_right=deepcopy(wavefunction_axman),
axman_colorbar=None,
**anim_kwargs,
),
animation.animators.PolarAnimator(
postfix="g_wavefunction_again_hires",
axman_wavefunction=animation.animators.SphericalHarmonicPhiSliceMeshAxis(
which="g",
colormap=plt.get_cmap("richardson"),
norm=si.vis.RichardsonNormalization(),
shading="flat",
),
axman_lower_right=deepcopy(epot_axman),
axman_upper_right=deepcopy(wavefunction_axman),
axman_colorbar=None,
fig_dpi_scale=2,
**anim_kwargs,
),
]
sim = ion.SphericalHarmonicSpecification(
"sph_harm",
time_initial=0 * asec,
time_final=100 * asec,
r_bound=50 * bohr_radius,
l_bound=20,
r_points=200,
electric_potential=ion.potentials.Rectangle(
start_time=25 * asec,
end_time=75 * asec,
amplitude=1 * atomic_electric_field,
),
# test_states = (ion.HydrogenBoundState(n, l) for n in range(5) for l in range(n)),
use_numeric_eigenstates=True,
numeric_eigenstate_max_energy=10 * eV,
numeric_eigenstate_max_angular_momentum=5,
animators=animators,
).to_sim()
sim.info().log()
sim.run()
sim.info().log()
| [
"[email protected]"
] | |
1c68371a7e2d8eaddb197d4d63eff1c8935ef143 | 5c8346597e3690eec3939f56f233eb5fafd336bc | /varsom_regobs_client/models/snow_temp_view_model.py | 761a19ec81381882d6deee0093d85ef0c634d216 | [] | no_license | NVE/python-varsom-regobs-client | be44befd04ca07058f8b46ec69bf1659d3ee422b | 8bb7fc06d2f6da36a5fa4a475d4f036ebe3cfd72 | refs/heads/master | 2022-12-27T19:09:54.761318 | 2020-06-24T08:56:15 | 2020-06-24T08:56:15 | 274,619,205 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,254 | py | # coding: utf-8
"""
RegObs API
## Introduction RegObs is a tool for collecting observations and events related to natural hazards. It is currently used by the Norwegian flood, landslide and avalanche warning service in Norway, but the data is openly available for anyone through this API. Regobs has been developed by the Norwegian Water resources and Energy Directorate (NVE), in collaboration with the Norwegian Meteorological Institute (MET) and the Norwegian Public Roads Administration (NPRA). You can check out our representation of the data at [regobs.no](http://regobs.no). ## Authentication Some endpoints require an api key. You can get an API key by sending an email to [[email protected]](mailto:[email protected]?subject=RegObs%20API%20Key). To use the api key with the swagger ui, fill in the api\\_key input above. It should then be included with every request in the `regObs_apptoken` header. ## Getting started Get the last 10 observations using python: ```python import requests r = requests.post('https://api.regobs.no/v4/Search', data={'NumberOfRecords': 10}, headers={'Content-Type': 'application/json'} ) data = r.json() print(len(data)) # 10 ``` # noqa: E501
OpenAPI spec version: v4
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class SnowTempViewModel(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'layers': 'list[SnowTempObsViewModel]'
}
attribute_map = {
'layers': 'Layers'
}
def __init__(self, layers=None): # noqa: E501
"""SnowTempViewModel - a model defined in Swagger""" # noqa: E501
self._layers = None
self.discriminator = None
if layers is not None:
self.layers = layers
@property
def layers(self):
"""Gets the layers of this SnowTempViewModel. # noqa: E501
:return: The layers of this SnowTempViewModel. # noqa: E501
:rtype: list[SnowTempObsViewModel]
"""
return self._layers
@layers.setter
def layers(self, layers):
"""Sets the layers of this SnowTempViewModel.
:param layers: The layers of this SnowTempViewModel. # noqa: E501
:type: list[SnowTempObsViewModel]
"""
self._layers = layers
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(SnowTempViewModel, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, SnowTempViewModel):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| [
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] | |
72dff18867a5ecc45e8a6feb50567cf3be592ed6 | 6c951ca04d6c0db92b05972d651d370302d98a2c | /tests/test_sensitivity_analyzer.py | 35a1db44b33b09a91687ae8644cb8603a1c9727c | [
"MIT"
] | permissive | nickderobertis/sensitivity | 9309bba0aadbac6e8dba09e7c7b1477d063a6d6d | 8f0d0e676213772bdb8cbc8c6fc08fdba6dc6b53 | refs/heads/master | 2023-02-23T20:33:45.118907 | 2022-10-09T01:17:01 | 2022-10-09T01:17:01 | 239,607,375 | 12 | 0 | MIT | 2023-02-11T02:07:41 | 2020-02-10T20:33:30 | Jupyter Notebook | UTF-8 | Python | false | false | 2,995 | py | import uuid
from pandas.testing import assert_frame_equal
from sensitivity import SensitivityAnalyzer
from tests.base import EXPECT_DF_TWO_VALUE, SENSITIVITY_VALUES_TWO_VALUE, add_5_to_values, RESULT_NAME, \
SENSITIVITY_VALUES_THREE_VALUE, add_10_to_values, EXPECT_DF_THREE_VALUE, assert_styled_matches, \
DF_STYLED_NUM_FMT_PATH, assert_graph_matches, PLOT_THREE_PATH, PLOT_OPTIONS_PATH, TWO_VALUE_LABELS, DF_LABELED_PATH
class TestSensitivityAnalyzer:
def create_sa(self, **kwargs) -> SensitivityAnalyzer:
sa_config = dict(
sensitivity_values=SENSITIVITY_VALUES_TWO_VALUE,
func=add_5_to_values,
result_name=RESULT_NAME
)
sa_config.update(**kwargs)
sa = SensitivityAnalyzer(**sa_config)
return sa
def test_create(self):
sa = self.create_sa()
def test_create_df(self):
sa = self.create_sa()
assert_frame_equal(sa.df, EXPECT_DF_TWO_VALUE, check_dtype=False)
def test_create_df_three_values(self):
sa = self.create_sa(
sensitivity_values=SENSITIVITY_VALUES_THREE_VALUE,
func=add_10_to_values,
)
assert_frame_equal(sa.df, EXPECT_DF_THREE_VALUE, check_dtype=False)
def test_create_styled_dfs(self):
sa = self.create_sa()
result = sa.styled_dfs()
assert_styled_matches(result)
def test_create_styled_dfs_with_num_fmt(self):
sa = self.create_sa(num_fmt='${:,.0f}')
result = sa.styled_dfs()
sa2 = self.create_sa()
result2 = sa2.styled_dfs(num_fmt='${:,.0f}')
assert_styled_matches(result, DF_STYLED_NUM_FMT_PATH)
assert_styled_matches(result2, DF_STYLED_NUM_FMT_PATH)
def test_create_styled_dfs_with_labels(self):
sa = self.create_sa(labels=TWO_VALUE_LABELS)
result = sa.styled_dfs()
assert_styled_matches(result, DF_LABELED_PATH)
def test_create_styled_dfs_three_values(self):
sa = self.create_sa(
sensitivity_values=SENSITIVITY_VALUES_THREE_VALUE,
func=add_10_to_values,
)
result = sa.styled_dfs()
def test_create_plot(self):
sa = self.create_sa()
result = sa.plot()
assert_graph_matches(result)
def test_create_plot_three_values(self):
sa = self.create_sa(
sensitivity_values=SENSITIVITY_VALUES_THREE_VALUE,
func=add_10_to_values,
)
result = sa.plot()
assert_graph_matches(result, file_path=PLOT_THREE_PATH)
def test_create_plot_with_options(self):
options = dict(
grid_size=2, color_map='viridis', reverse_colors=True
)
sa = self.create_sa(labels=TWO_VALUE_LABELS, **options)
result = sa.plot()
assert_graph_matches(result, file_path=PLOT_OPTIONS_PATH)
sa = self.create_sa(labels=TWO_VALUE_LABELS)
result = sa.plot(**options)
assert_graph_matches(result, file_path=PLOT_OPTIONS_PATH)
| [
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] | |
0497e0262a8ee739513125f73d20dec716f79060 | 52b5773617a1b972a905de4d692540d26ff74926 | /.history/cylicRot_20200714234806.py | 755b17fab1acf221b7f045ba530fc306bc41432f | [] | no_license | MaryanneNjeri/pythonModules | 56f54bf098ae58ea069bf33f11ae94fa8eedcabc | f4e56b1e4dda2349267af634a46f6b9df6686020 | refs/heads/master | 2022-12-16T02:59:19.896129 | 2020-09-11T12:05:22 | 2020-09-11T12:05:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 536 | py | # given an array rotate it k times to the right
def rotate(arr,k):
# first I'd rotate the array once
# so how do we rotate the array
# we move the last element to the firs place and
# the rest follow suit
# [1,2,3,4]
# [4,2,3,1]
# [4,1,3,2]
# [4,1,2,3]
# [4,1,2,3]
# all we are doing is swapping the elements
newArr = []
for i in range(len(arr)):
k = len(arr) - 1
print('k',k,'i',i)
arr[i],arr[k] = arr[k],arr[i]
print(arr)
rotate([1,2,3,4],4)
| [
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] | |
e615006a23c81dc60b0a5cdc99d864b0a4c4a7d4 | c724fad90be2e221cb0f5c0005ebcfbdfdb35d27 | /backend/fitsii_19945/wsgi.py | cfa9f31b691c6399a7797d950bc243dc2bb70ac9 | [] | no_license | crowdbotics-apps/fitsii-19945 | d461349a510febd39f4edcaeb2b8b722664e3bf0 | 040621b4053e58b9c323ef7222a6a36465c4806e | refs/heads/master | 2022-12-07T18:18:50.580128 | 2020-09-02T16:56:11 | 2020-09-02T16:56:11 | 292,342,025 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 401 | py | """
WSGI config for fitsii_19945 project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'fitsii_19945.settings')
application = get_wsgi_application()
| [
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] | |
c5968b91f1e8556b70007f764784c56df35cfef6 | 2c89037666a3c3c9be55b53055c73aa9fcbde2b7 | /webrobot/app/main/service/user_service.py | 1aa181ef641092046126c96166d66c61d9b54523 | [
"MIT"
] | permissive | kakawaa/Auto-Test-System | 844284de1eb5fac8fa8c5318371c99991caff62d | 76b0690e4e49769ec5d6e65ab6c499396880c0bd | refs/heads/master | 2020-06-17T11:42:38.121124 | 2019-07-05T03:32:39 | 2019-07-05T03:32:39 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,451 | py | # import uuid
import datetime
import os
from pathlib import Path
from app.main import db
from app.main.model.database import User, Organization
from task_runner.runner import start_threads
from ..config import get_config
from ..util.errors import *
from ..util.identicon import *
USERS_ROOT = Path(get_config().USERS_ROOT)
def save_new_user(data, admin=None):
user = User.objects(email=data['email']).first()
if not user:
new_user = User(
# public_id=str(uuid.uuid4()),
email=data['email'],
name=data.get('username', ''),
registered_on=datetime.datetime.utcnow(),
roles=data.get('roles', ['admin']),
avatar=data.get('avatar', ''),
introduction=data.get('introduction', '')
)
new_user.password = data['password']
try:
new_user.save()
except Exception as e:
print(e)
return error_message(EINVAL, 'Field validating for User failed'), 401
user_root = USERS_ROOT / data['email']
try:
os.mkdir(user_root)
except FileExistsError as e:
return error_message(EEXIST), 401
try:
os.mkdir(user_root / 'test_results')
except FileExistsError as e:
return error_message(EEXIST), 401
if new_user.avatar == '':
img = render_identicon(hash(data['email']), 27)
img.save(user_root / ('%s.png' % new_user.id))
new_user.avatar = '%s.png' % new_user.id
if new_user.name == '':
new_user.name = new_user.email.split('@')[0]
if not admin:
organization = Organization(name='Personal')
organization.owner = new_user
organization.path = new_user.email
organization.save()
new_user.organizations = [organization]
new_user.save()
start_threads(new_user)
return generate_token(new_user)
else:
return error_message(USER_ALREADY_EXIST), 409
def get_all_users():
return User.objects()
def get_a_user(user_id):
return User.objects(pk=user_id).first()
def generate_token(user):
try:
# generate the auth token
auth_token = User.encode_auth_token(str(user.id))
return error_message(SUCCESS, token=auth_token.decode()), 201
except Exception as e:
print(e)
return error_message(UNKNOWN_ERROR), 401
| [
"[email protected]"
] | |
56a9016f9048bf93ced9d3230e3e07125c5674b2 | 01bd00e6498190aac53210689c111d72018956fa | /companies/migrations/0047_auto_20190917_1011.py | a0c9fdef406a96c4ea5f7cbf5a40000ea2755162 | [] | no_license | dchaplinsky/edrdr | 0494b31fe3a0ce54d0cf087fb11ef709cb002810 | e9fd5295f8c7ca7db81fce2427456e779ff6637e | refs/heads/master | 2022-06-01T07:01:59.049162 | 2020-10-12T08:04:42 | 2020-10-12T08:04:42 | 122,268,695 | 0 | 1 | null | 2022-04-22T20:52:45 | 2018-02-20T23:14:48 | CSS | UTF-8 | Python | false | false | 571 | py | # Generated by Django 2.2.3 on 2019-09-17 10:11
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('companies', '0046_pepowner_person_type'),
]
operations = [
migrations.AddField(
model_name='companyrecord',
name='charter_capital',
field=models.FloatField(default=None, null=True),
),
migrations.AddField(
model_name='companyrecord',
name='reg_date',
field=models.DateField(null=True),
),
]
| [
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] | |
62468571196349acaac805658ec61d5532fcb955 | dc4a42ad81013a1fdaa0c6be0559504e17bacb7e | /products/admin.py | a845d9021b184ff03ccdeed387467a77c73d2d28 | [] | no_license | deone/eqsupply | 15afbda692779431357d2c69475da8503c4728b1 | 3af726b65c1658d364c6485ad36ef98d5c6e7fc3 | refs/heads/master | 2020-04-20T05:29:53.020966 | 2010-05-13T09:16:18 | 2010-05-13T09:16:18 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 229 | py | from django.contrib import admin
from eqsupply.products.models import *
admin.site.register(Division)
admin.site.register(Category)
admin.site.register(Product)
admin.site.register(Accessory)
admin.site.register(ProductVariant)
| [
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] | |
07fed4cb0ac0a9c9fe7cf77a4577b118c598fd1f | 6147d3da9c7f31a658f13892de457ed5a9314b22 | /multithreading/without_threading.py | 4f637839a61975629dea515f930117251368c52c | [] | no_license | ashish-bisht/must_do_geeks_for_geeks | 17ba77608eb2d24cf4adb217c8e5a65980e85609 | 7ee5711c4438660db78916cf876c831259109ecc | refs/heads/master | 2023-02-11T22:37:03.302401 | 2021-01-03T05:53:03 | 2021-01-03T05:53:03 | 320,353,079 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 285 | py | import threading
import time
start = time.perf_counter()
def working_on_something():
print("Sleeping for a sec")
time.sleep(1)
print("Woke up")
working_on_something()
working_on_something()
finish = time.perf_counter()
print("total time taken is ", finish - start)
| [
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] | |
9ab7745e8b4d48edd0fe67af3de20eca60454dcc | f59a3641f488dd40b0af4c0024a252170ab59998 | /chap4/p35.py | d89dca31848be92a9ad88a15209c75b1fe2ad076 | [] | no_license | ujiuji1259/NLP100 | 478a5276514d2f21ac5ee5ec9b50f00dcba67d1a | c19f9ba00eec108dbc93d4cb7d33e86f539d3397 | refs/heads/master | 2023-04-01T23:05:14.376652 | 2021-04-13T05:21:37 | 2021-04-13T05:21:37 | 255,311,319 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 297 | py | # mecab neko.txt > neko.txt.mecab
from p30 import load_mecab_output
import collections
if __name__ == '__main__':
lines = load_mecab_output('neko.txt.mecab')
lines = [l['surface'] for line in lines for l in line]
counter = collections.Counter(lines)
print(counter.most_common())
| [
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] | |
f0ec9069cd636274166bcd07ca0cebc104ee447b | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03598/s680963277.py | c8861d19ff2e2ce27d5b6a660a4fb273c93d87c7 | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 173 | py | N = int(input())
K = int(input())
x = list(map(int, input().split()))
A=[]
B=[]
for i in range(len(x)):
a = min(2*(x[i]), 2*abs(K-x[i]))
A.append(a)
print(sum(A)) | [
"[email protected]"
] | |
0d843d4556bf97c40beacc40c239357fa08e4b8a | 05263538c3ad0f577cdbbdb9bac87dcf450230ce | /alexa/ask-sdk/ask_sdk_dynamodb/__version__.py | 5cfdf120d47b16330d48f329ae8c0e26ce048100 | [] | no_license | blairharper/ISS-GoogleMap-project | cea027324fc675a9a309b5277de99fc0265dcb80 | 3df119036b454a0bb219af2d703195f4154a2471 | refs/heads/master | 2020-03-21T16:47:21.046174 | 2018-10-24T08:05:57 | 2018-10-24T08:05:57 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,140 | py | #
# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights
# Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file 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.
#
__pip_package_name__ = 'ask-sdk-dynamodb-persistence-adapter'
__description__ = (
'The ASK SDK DynamoDB Persistence Adapter package provides DynamoDB '
'Adapter, that can be used with ASK SDK Core, for persistence management')
__url__ = 'http://developer.amazon.com/ask'
__version__ = '0.1'
__author__ = 'Alexa Skills Kit'
__author_email__ = '[email protected]'
__license__ = 'Apache 2.0'
__keywords__ = ['ASK SDK', 'Alexa Skills Kit', 'Alexa', 'ASK SDK Core',
'Persistence', 'DynamoDB']
__install_requires__ = ["boto3", "ask-sdk-core"]
| [
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] | |
d07d964851d7ea84722cc1c566fdb976f5049c0a | 10d98fecb882d4c84595364f715f4e8b8309a66f | /non_semantic_speech_benchmark/distillation/train_keras_test.py | 58293b999787e89c984afb7ffed56dbb033ecc48 | [
"CC-BY-4.0",
"Apache-2.0"
] | permissive | afcarl/google-research | 51c7b70d176c0d70a5ee31ea1d87590f3d6c6f42 | 320a49f768cea27200044c0d12f394aa6c795feb | refs/heads/master | 2021-12-02T18:36:03.760434 | 2021-09-30T20:59:01 | 2021-09-30T21:07:02 | 156,725,548 | 1 | 0 | Apache-2.0 | 2018-11-08T15:13:53 | 2018-11-08T15:13:52 | null | UTF-8 | Python | false | false | 3,089 | py | # coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Tests for non_semantic_speech_benchmark.eval_embedding.keras.train_keras."""
from absl import flags
from absl.testing import absltest
from absl.testing import flagsaver
from absl.testing import parameterized
import mock
import tensorflow as tf
from non_semantic_speech_benchmark.distillation import train_keras
def _get_data(*args, **kwargs):
del args
assert 'samples_key' in kwargs
assert 'min_length' in kwargs
assert 'batch_size' in kwargs
bs = kwargs['batch_size']
samples = tf.zeros((bs, 16000), tf.float32)
targets = tf.ones([bs, 10], tf.float32)
return tf.data.Dataset.from_tensors((samples, targets)).repeat()
class TrainKerasTest(parameterized.TestCase):
@parameterized.parameters(
{'bottleneck_dimension': 3, 'alpha': 1.0},
{'bottleneck_dimension': 5, 'alpha': 0.5},
)
def test_get_model(self, bottleneck_dimension, alpha):
batched_samples = tf.zeros([3, 16000])
output_dimension = 10
targets = tf.ones([3, output_dimension])
model = train_keras.models.get_keras_model(
f'mobilenet_debug_{alpha}_False',
bottleneck_dimension=bottleneck_dimension,
output_dimension=output_dimension)
loss_obj = tf.keras.losses.MeanSquaredError()
opt = tf.keras.optimizers.Adam()
train_loss = tf.keras.metrics.MeanSquaredError()
train_mae = tf.keras.metrics.MeanAbsoluteError()
summary_writer = tf.summary.create_file_writer(
absltest.get_default_test_tmpdir())
train_step = train_keras.get_train_step(
model, loss_obj, opt, train_loss, train_mae, summary_writer)
gstep = opt.iterations
train_step(batched_samples, targets, gstep)
self.assertEqual(1, gstep)
train_step(batched_samples, targets, gstep)
self.assertEqual(2, gstep)
@mock.patch.object(train_keras.get_data, 'get_data', new=_get_data)
@mock.patch.object(train_keras.hub, 'load')
@flagsaver.flagsaver
def test_full_flow(self, mock_load):
del mock_load
flags.FLAGS.file_pattern = 'dummy'
flags.FLAGS.teacher_model_hub = 'dummy'
flags.FLAGS.output_key = 'dummmy'
flags.FLAGS.bottleneck_dimension = 2
flags.FLAGS.output_dimension = 10
flags.FLAGS.shuffle_buffer_size = 4
flags.FLAGS.samples_key = 'audio'
flags.FLAGS.logdir = absltest.get_default_test_tmpdir()
train_keras.train_and_report(debug=True)
if __name__ == '__main__':
tf.compat.v2.enable_v2_behavior()
assert tf.executing_eagerly()
absltest.main()
| [
"[email protected]"
] | |
ba106a98267a6ec0d424113b2870654dbf4698b9 | 3154e6d1a9e9e9919cae75570969da36c45429d7 | /codigo/tutorial/tut0C_camara.py | 9e54589237d6c51292d941cdce95c822a95243c0 | [] | no_license | javacasm/TutorialPyGame | 0d458c7155794668fc1464c466e4d740b3ac77ee | baeb7ce5dda151f8093e39f8b14182a8ee5de926 | refs/heads/master | 2021-07-25T20:01:04.504958 | 2021-05-10T12:33:26 | 2021-05-10T12:33:26 | 250,080,620 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,568 | py | https://www.pygame.org/docs/tut/CameraIntro.html
```python
class Capture(object):
def __init__(self):
self.size = (640,480)
# create a display surface. standard pygame stuff
self.display = pygame.display.set_mode(self.size, 0)
# this is the same as what we saw before
self.clist = pygame.camera.list_cameras()
if not self.clist:
raise ValueError("Sorry, no cameras detected.")
self.cam = pygame.camera.Camera(self.clist[0], self.size)
self.cam.start()
# create a surface to capture to. for performance purposes
# bit depth is the same as that of the display surface.
self.snapshot = pygame.surface.Surface(self.size, 0, self.display)
def get_and_flip(self):
# if you don't want to tie the framerate to the camera, you can check
# if the camera has an image ready. note that while this works
# on most cameras, some will never return true.
if self.cam.query_image():
self.snapshot = self.cam.get_image(self.snapshot)
# blit it to the display surface. simple!
self.display.blit(self.snapshot, (0,0))
pygame.display.flip()
def main(self):
going = True
while going:
events = pygame.event.get()
for e in events:
if e.type == QUIT or (e.type == KEYDOWN and e.key == K_ESCAPE):
# close the camera safely
self.cam.stop()
going = False
self.get_and_flip()
``` | [
"[email protected]"
] | |
f62111deb74e279775448c7d5a97f5ea7f6a8255 | 9f835d53232e954805b7ed1d93889e409209b36b | /1541_복습.py | 134932438e9def1182112113c24eb401c83df29d | [] | no_license | dmswl0311/Baekjoon | 7c8a862fceff086b3d7740eef23b80164e1d5aeb | 22040aff6b64d5081e86d91b0d118d1a718a4316 | refs/heads/master | 2023-04-29T13:48:51.448245 | 2021-05-26T14:35:32 | 2021-05-26T14:35:32 | 323,482,711 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 319 | py | s = input().split('-')
sum = 0
result = []
for i in s:
if '+' in i:
a = i.split('+')
for j in a:
sum += int(j)
result.append(sum)
else:
result.append(int(i))
minus = result[0]
for i in range(1, len(result)):
minus -= result[i]
print(minus)
| [
"[email protected]"
] | |
bcdcdba6ff316a16065b95a2bba284abc290a417 | 9d25d1205da84db33bc425266bc3021cd7529cb1 | /digitalearthau/testing/plugin.py | b73fdee1aba0e11cd5d8c9a183a595c1b7c6e754 | [] | no_license | GeoscienceAustralia/digitalearthau | 9068970b2794a4ac55a34f910caa5877b548bb37 | 4cf486eb2a93d7de23f86ce6de0c3af549fe42a9 | refs/heads/develop | 2023-06-22T14:31:41.516829 | 2022-11-14T05:22:05 | 2022-11-14T05:22:05 | 51,411,119 | 31 | 21 | null | 2023-06-14T06:36:31 | 2016-02-10T00:16:36 | Python | UTF-8 | Python | false | false | 2,081 | py | import itertools
import os
import pytest
from pathlib import Path
from typing import Iterable
import datacube
import digitalearthau
import digitalearthau.system
from datacube.config import LocalConfig
from . import factories
# These are unavoidable in pytests due to fixtures
# pylint: disable=redefined-outer-name,protected-access,invalid-name
try:
from yaml import CSafeLoader as SafeLoader
except ImportError:
from yaml import SafeLoader
# The default test config options.
# The user overrides these by creating their own file in ~/.datacube_integration.conf
INTEGRATION_DEFAULT_CONFIG_PATH = Path(__file__).parent.joinpath('testing-default.conf')
def pytest_report_header(config):
if config.getoption('verbose') > 0:
return (
f"digitaleathau {digitalearthau.__version__}, "
f"opendatacube {datacube.__version__}"
)
return None
@pytest.fixture(scope='session')
def integration_config_paths():
if not INTEGRATION_DEFAULT_CONFIG_PATH.exists():
# Safety check. We never want it falling back to the default config,
# as it will alter/wipe the user's own datacube to run tests
raise RuntimeError(
'Integration default file not found. This should be built-in?')
return (
str(INTEGRATION_DEFAULT_CONFIG_PATH),
os.path.expanduser('~/.datacube_integration.conf')
)
@pytest.fixture(scope='session')
def global_integration_cli_args(integration_config_paths: Iterable[str]):
"""
The first arguments to pass to a cli command for integration test configuration.
"""
# List of a config files in order.
return list(
itertools.chain(*(('--config_file', f) for f in integration_config_paths)))
@pytest.fixture(scope='session')
def local_config(integration_config_paths):
return LocalConfig.find(integration_config_paths)
# Default fixtures which will drop/create on every individual test function.
db = factories.db_fixture('local_config')
index = factories.index_fixture('db')
dea_index = factories.dea_index_fixture('index')
| [
"[email protected]"
] | |
76f794ba7b0ecbb4b8044008f296f605ccca2439 | 94838674ffd175df6194437c1ccc3f90ab409d6c | /pillowV3/log/2018-12-30 14:25:26.954969 | 574f467c8fb2f82034a73060e36f0973007e6bd0 | [] | no_license | WojciechKoz/MyFirstNeuralNetwork | 4fdb3140d8f02257599d005638598f78055c1ac8 | 3cd032aba80ecd71edb0286724ae9ba565b75a81 | refs/heads/master | 2020-04-02T03:02:48.680433 | 2020-02-29T17:57:43 | 2020-02-29T17:57:43 | 153,943,121 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 498,667 | 954969 | #!/usr/bin/env python3
# -*- coding: utf8 -*-
from __future__ import print_function # new print() on python2
from datetime import datetime
import sys
import numpy as np
from mnist import MNIST
# Display full arrays
np.set_printoptions(threshold=np.inf)
mndata = MNIST('./data')
images_full, labels_full = mndata.load_training()
images = []
labels = []
# dynamic arguments
batch_size = int(sys.argv[1])
size_1 = int(sys.argv[2])
size_2 = int(sys.argv[3])
batch_training_size = int(sys.argv[4])
data_part = 5 # only one fifth of the whole dataset to speed up training
for i in range(len(labels_full) // batch_size // data_part):
images.append(images_full[i*batch_size : (i+1)*batch_size])
labels.append(labels_full[i*batch_size : (i+1)*batch_size])
def sigmoid_prime(x):
return np.exp(-x) / ((np.exp(-x) + 1) ** 2)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# nowe, przyda się?
def relu(x):
return np.maximum(x, x * 0.01)
def relu_prime(x):
if x >= 0:
return 1
# ej nie jest tak xd
# a jak xd?
type(x) == no.ndarray
# no x to macierz xd
# np.exp jest przeładowane ale jakakoleiwk funkcja to chyba nie
# to co foreach ? :(
# właśnie nie wiem, a co z gpu?
# to miało być szybsze a nie xd
# mamy duzo mozliwosci zmian ale nie na raz trzeba ustalic jakos
# hm TODO gpu TODO wincyj procent TODO gui gotowe
# xd
# tamto myliło hah
# to co najpierw? :p
# ssh daje wglad do basha tylko tak ?
# nie, to jest taki fajny programik, byobu
# i ten pasek na dole też jest z byobu
# on udostepnia tylko basha ?
# tak, ale basha multiplayer xd
# szkoda że 2 kursorow nie ma
# hm
return 0.01 # chyba tak xd nikt nie widzial xd
# ale x to macierz :p
# ale to jest przeciazone i jak jest funkcja od macierzy to bierze po kolei kazdy element
# w sumie
# zobacze na drugiej karcie xd
#X = np.array([[0, 0],
# [0, 1],
# [1, 0],
# [1, 1]])
#X = np.array(images)
y = []
for batch in labels:
y.append([])
for label in batch:
y[-1].append([1.0 if i == label else 0.0 for i in range(10)])
y = np.array(y)
#y = np.array([[0],
# [1],
# [1],
# [0]])
np.random.seed(1)
LEN = len(labels)
SIZES = [ 784, size_1, size_2, 10 ]
syn0 = 2 * np.random.random((SIZES[0], SIZES[1])) - 1
syn1 = 2 * np.random.random((SIZES[1], SIZES[2])) - 1
syn2 = 2 * np.random.random((SIZES[2], SIZES[3])) - 1
# biases for respective layers
b0 = 2 * np.random.random((1, SIZES[1])) - 1
b1 = 2 * np.random.random((1, SIZES[2])) - 1
b2 = 2 * np.random.random((1, SIZES[3])) - 1
for i, batch in list(enumerate(images)):
X = np.array(batch)
print("x:")
print(np.shape(X))
print("======================= BATCH {} =======================".format(i))
error = 1
j = 0
while j < batch_training_size:
l0 = X
l1 = sigmoid(np.dot(l0, syn0) + b0)
l2 = sigmoid(np.dot(l1, syn1) + b1)
l3 = sigmoid(np.dot(l2, syn2) + b2)
l3_error = (y[i] - l3)#** 2
error = np.mean(np.abs(l3_error))
j += 1
if j % 20 == 0:
print(("[%d] error: " % j) + str(error))
l3_delta = l3_error * sigmoid_prime(l3)
l2_error = l3_delta.dot(syn2.T)
l2_delta = l2_error * sigmoid_prime(l2)
l1_error = l2_delta.dot(syn1.T)
l1_delta = l1_error * sigmoid_prime(l1)
syn2 += l2.T.dot(l3_delta)
syn1 += l1.T.dot(l2_delta)
syn0 += l0.T.dot(l1_delta)
b0 += l1_delta.mean(axis=0)
b1 += l2_delta.mean(axis=0)
b2 += l3_delta.mean(axis=0)
def predict(data):
l0 = [data]
l1 = sigmoid(np.dot(l0, syn0) + b0)
l2 = sigmoid(np.dot(l1, syn1) + b1)
l3 = sigmoid(np.dot(l2, syn2) + b2)
return np.argmax(l3)
print("Output after training: ")
print(l3)
for i, el in enumerate(l3):
print(labels[0][i], "=", np.argmax(el), " predictions: ", el)
testing_images, testing_labels = mndata.load_testing()
correct = 0.0
for i, (image, label) in enumerate(zip(testing_images, testing_labels)):
prediction = predict(image)
if label == prediction:
correct += 1.0
correct_rate = correct / (i + 1.0)
print("{} = {} (correct {}%)".format(label, prediction, 100 * correct_rate))
with open('log/' + str(datetime.now()), 'a') as f:
with open(__file__, 'r') as myself:
print(myself.read(), file=f)
print("", file=f)
print("#### answers:", file=f)
print("argv =", sys.argv, file=f)
print("correct_rate =", correct_rate, file=f)
print("SIZES =", SIZES, file=f)
print("syn0 =", syn0, file=f)
print("syn1 =", syn1, file=f)
print("syn2 =", syn2, file=f)
print("b0 =", b0, file=f)
print("b1 =", b1, file=f)
print("b2 =", b2, file=f)
#### answers:
argv = ['./main.py', '59', '36', '34', '25']
correct_rate = 0.594
SIZES = [784, 36, 34, 10]
syn0 = [[-1.65955991e-01 4.40648987e-01 -9.99771250e-01 -3.95334855e-01
-7.06488218e-01 -8.15322810e-01 -6.27479577e-01 -3.08878546e-01
-2.06465052e-01 7.76334680e-02 -1.61610971e-01 3.70439001e-01
-5.91095501e-01 7.56234873e-01 -9.45224814e-01 3.40935020e-01
-1.65390395e-01 1.17379657e-01 -7.19226123e-01 -6.03797022e-01
6.01489137e-01 9.36523151e-01 -3.73151644e-01 3.84645231e-01
7.52778305e-01 7.89213327e-01 -8.29911577e-01 -9.21890434e-01
-6.60339161e-01 7.56285007e-01 -8.03306332e-01 -1.57784750e-01
9.15779060e-01 6.63305699e-02 3.83754228e-01 -3.68968738e-01]
[ 3.73001855e-01 6.69251344e-01 -9.63423445e-01 5.00288630e-01
9.77722178e-01 4.96331309e-01 -4.39112016e-01 5.78558657e-01
-7.93547987e-01 -1.04212948e-01 8.17191006e-01 -4.12771703e-01
-4.24449323e-01 -7.39942856e-01 -9.61266084e-01 3.57671066e-01
-5.76743768e-01 -4.68906681e-01 -1.68536814e-02 -8.93274910e-01
1.48235211e-01 -7.06542850e-01 1.78611074e-01 3.99516720e-01
-7.95331142e-01 -1.71888024e-01 3.88800315e-01 -1.71641461e-01
-9.00093082e-01 7.17928118e-02 3.27589290e-01 2.97782241e-02
8.89189512e-01 1.73110081e-01 8.06803831e-01 -7.25050592e-01]
[-7.21447305e-01 6.14782577e-01 -2.04646326e-01 -6.69291606e-01
8.55017161e-01 -3.04468281e-01 5.01624206e-01 4.51995971e-01
7.66612182e-01 2.47344414e-01 5.01884868e-01 -3.02203316e-01
-4.60144216e-01 7.91772436e-01 -1.43817620e-01 9.29680094e-01
3.26882996e-01 2.43391440e-01 -7.70508054e-01 8.98978517e-01
-1.00175733e-01 1.56779229e-01 -1.83726394e-01 -5.25946040e-01
8.06759041e-01 1.47358973e-01 -9.94259346e-01 2.34289827e-01
-3.46710196e-01 5.41162045e-02 7.71884199e-01 -2.85460480e-01
8.17070302e-01 2.46720232e-01 -9.68357514e-01 8.58874467e-01]
[ 3.81793835e-01 9.94645701e-01 -6.55318983e-01 -7.25728501e-01
8.65190926e-01 3.93636323e-01 -8.67999655e-01 5.10926105e-01
5.07752377e-01 8.46049071e-01 4.23049517e-01 -7.51458076e-01
-9.60239732e-01 -9.47578026e-01 -9.43387024e-01 -5.07577865e-01
7.20055897e-01 7.76621287e-02 1.05643957e-01 6.84061785e-01
-7.51653370e-01 -4.41632642e-01 1.71518543e-01 9.39191497e-01
1.22060439e-01 -9.62705421e-01 6.01265345e-01 -5.34051452e-01
6.14210391e-01 -2.24278712e-01 7.27083709e-01 4.94243285e-01
1.12480468e-01 -7.27089549e-01 -8.80164621e-01 -7.57313089e-01]
[-9.10896243e-01 -7.85011742e-01 -5.48581323e-01 4.25977961e-01
1.19433964e-01 -9.74888040e-01 -8.56051441e-01 9.34552660e-01
1.36200924e-01 -5.93413531e-01 -4.95348511e-01 4.87651708e-01
-6.09141038e-01 1.62717855e-01 9.40039978e-01 6.93657603e-01
-5.20304482e-01 -1.24605715e-02 2.39911437e-01 6.57961799e-01
-6.86417211e-01 -9.62847596e-01 -8.59955713e-01 -2.73097781e-02
2.12658923e-01 1.37702874e-01 -3.65275181e-01 9.77232309e-01
1.59490438e-01 -2.39717655e-01 1.01896438e-01 4.90668862e-01
3.38465787e-01 -4.70160885e-01 -8.67330331e-01 -2.59831604e-01]
[ 2.59435014e-01 -5.79651980e-01 5.05511107e-01 -8.66927037e-01
-4.79369803e-01 6.09509127e-01 -6.13131435e-01 2.78921762e-01
4.93406182e-02 8.49615941e-01 -4.73406459e-01 -8.68077819e-01
4.70131927e-01 5.44356059e-01 8.15631705e-01 8.63944138e-01
-9.72096854e-01 -5.31275828e-01 2.33556714e-01 8.98032641e-01
9.00352238e-01 1.13306376e-01 8.31212700e-01 2.83132418e-01
-2.19984572e-01 -2.80186658e-02 2.08620966e-01 9.90958430e-02
8.52362853e-01 8.37466871e-01 -2.10248774e-01 9.26525057e-01
-6.52088667e-01 -7.47340961e-01 -7.29841684e-01 1.13243314e-02]
[-9.56950389e-01 8.95940422e-01 6.54230942e-01 -9.69962039e-01
-6.47607489e-01 -3.35872851e-01 -7.38006310e-01 6.18981384e-01
-3.10526695e-01 8.80214965e-01 1.64028360e-01 7.57663969e-01
6.89468891e-01 8.10784637e-01 -8.02394684e-02 9.26936320e-02
5.97207182e-01 -4.28562297e-01 -1.94929548e-02 1.98220615e-01
-9.68933449e-01 1.86962816e-01 -1.32647302e-01 6.14721058e-01
-3.69510394e-01 7.85777417e-01 1.55714431e-01 -6.31979597e-01
5.75858468e-01 2.24062354e-01 -8.92181456e-01 -1.59612640e-01
3.58137673e-01 8.37203556e-01 -9.99195950e-01 9.53518298e-01]
[-2.46839371e-01 9.47567077e-01 2.09432202e-01 6.57691616e-01
1.49423009e-01 2.56152397e-01 -4.28847437e-01 1.73666681e-01
5.00043527e-01 7.16627673e-01 5.10164377e-01 3.96114497e-01
7.28958860e-01 -3.54638006e-01 3.41577582e-01 -9.82521272e-02
-2.35794496e-01 -1.78377300e-01 -1.97040833e-01 -3.65232108e-01
2.43838736e-01 -1.39505458e-01 9.47604156e-01 3.55601783e-01
-6.02860223e-01 -1.46597981e-01 -3.13307520e-01 5.95277608e-01
7.59996577e-01 8.07683912e-01 3.25439625e-01 -4.59583476e-01
-4.95266597e-01 7.09795885e-01 5.54292926e-02 6.04322168e-01]
[ 1.44977034e-01 4.66285051e-01 3.80232549e-02 5.41767821e-01
1.37715981e-01 -6.85802428e-02 -3.14622184e-01 -8.63581303e-01
-2.44151641e-01 -8.40747845e-01 9.65634227e-01 -6.36774297e-01
6.23717395e-01 7.49923290e-01 3.76826505e-01 1.38988825e-01
-6.78057126e-01 -6.62399545e-02 -3.09655898e-01 -5.49920084e-01
1.85023738e-01 -3.75460325e-01 8.32611107e-01 8.19271050e-01
-4.85763412e-01 -7.78217399e-01 -6.14074536e-01 -8.31658642e-04
4.57171336e-01 -5.83611123e-01 -5.03932883e-01 7.03343750e-01
-1.68302563e-01 2.33370134e-01 -5.32667722e-01 -7.96065481e-01]
[ 3.17140339e-02 -4.57180259e-02 -6.94656712e-01 2.43612463e-01
8.80202376e-02 3.08274694e-01 -7.10908920e-01 5.03055634e-01
-5.55901720e-01 3.87036487e-02 5.70592056e-01 -9.55339144e-01
-3.51275081e-01 7.45844753e-01 6.89419215e-01 7.68811852e-02
7.33216548e-01 8.99611983e-01 6.52813995e-01 7.08230888e-01
-8.02513196e-01 3.02608665e-01 4.07033976e-01 2.20481625e-01
5.99230523e-01 -9.30857560e-01 5.40477469e-01 4.63457201e-01
-4.80603213e-01 -4.85861402e-01 2.64606635e-01 -3.09405077e-01
5.93177356e-01 -1.07707536e-01 5.65498830e-01 9.80943567e-01]
[-3.99503321e-01 -7.13988343e-01 8.02616873e-01 8.31187578e-02
9.49480742e-01 2.73208800e-01 9.87826049e-01 9.21416083e-02
5.28518678e-02 -7.29144194e-01 -2.88589658e-01 -9.47562865e-01
-6.79209641e-01 4.91274385e-01 -9.39200620e-01 -2.66913806e-01
7.24692506e-01 3.85355435e-01 3.81884284e-01 -6.22726398e-01
-1.16191439e-01 1.63154815e-01 9.79503415e-01 -5.92187550e-01
-5.04534196e-01 -4.75653832e-01 5.00344827e-01 -8.60493451e-02
-8.86141123e-01 1.70324812e-02 -5.76079671e-01 5.97208490e-01
-4.05337237e-01 -9.44787976e-01 1.86864899e-01 6.87680858e-01]
[-2.37967752e-01 4.99716621e-01 2.22829566e-02 8.19036099e-02
9.18868642e-01 6.07921783e-01 -9.35353867e-01 4.18774502e-01
-6.99970369e-02 8.95097883e-01 -5.57134531e-01 -4.65855961e-01
-8.37052070e-01 -1.42762343e-01 -7.81962472e-01 2.67573521e-01
6.05926475e-01 3.93600992e-01 5.32422762e-01 -3.15091760e-01
6.91702966e-01 -1.42462450e-01 6.48019741e-01 2.52992317e-01
-7.13153903e-01 -8.43226200e-01 -9.63334714e-01 -8.66550005e-01
-8.28323726e-02 -7.73316154e-01 -9.44433302e-01 5.09722963e-01
-2.10299039e-01 4.93876991e-01 -9.51903465e-02 -9.98265060e-02]
[-4.38549866e-02 -5.19921469e-02 6.06326684e-01 -1.95214960e-01
8.09372321e-01 -9.25877904e-01 5.47748685e-01 -7.48717238e-01
2.37027134e-01 -9.79271477e-01 7.72545652e-02 -9.93964087e-01
9.02387571e-01 8.10804067e-01 5.91933884e-01 8.30548640e-01
-7.08883538e-01 -6.84539860e-01 -6.24736654e-01 2.44991805e-01
8.11618992e-01 9.79910357e-01 4.22244918e-01 4.63600818e-01
8.18586409e-01 -1.98252535e-01 -5.00298640e-01 -6.53139658e-01
-7.61085899e-01 6.25221176e-01 -7.06415253e-01 -4.71405035e-01
6.38178357e-01 -3.78825496e-01 9.64834899e-01 -4.66722596e-01]
[ 6.73066899e-02 -3.71065978e-01 8.21545662e-01 -2.66886712e-01
-1.32815345e-01 2.45853846e-02 8.77772955e-01 -9.38101987e-01
4.33757327e-01 7.82037909e-01 -9.45425553e-01 4.41024945e-02
-3.48020376e-01 7.18978642e-01 1.17033102e-01 3.80455736e-01
-9.42930001e-02 2.56618075e-01 -4.19806297e-01 -9.81302844e-01
1.53511870e-01 -3.77111572e-01 3.45351970e-02 8.32811706e-01
-1.47050423e-01 -5.05207927e-01 -2.57412477e-01 8.63722233e-01
8.73736763e-01 6.88659897e-01 8.40413029e-01 -5.44199420e-01
-8.25035581e-01 -5.45380527e-01 -3.71246768e-01 -6.50468247e-01]
[ 2.14188324e-01 -1.72827170e-01 6.32703024e-01 -6.29739203e-01
4.03753060e-01 -5.19288750e-01 1.48438178e-01 -3.02024806e-01
-8.86071201e-01 -5.42372658e-01 3.28205111e-01 -5.49981328e-03
3.80319681e-02 -6.50559700e-01 1.41431703e-01 9.93506850e-01
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-1.06773596e+02 -1.31499536e+02 -1.26318851e+01 -1.78433136e+02
1.38802254e+01 -5.37448572e+00 6.14873455e+01 -1.31403249e+02
-3.37067474e+01 -8.63057270e+01]
[ 1.15965734e+01 -1.57326728e-01 -1.43718273e+01 -2.79314847e+01
-2.15681621e+01 -5.00244874e+01 -1.49641018e+01 -6.48231096e+00
6.40123776e-01 1.54126631e+01 -3.84286559e+01 -2.24599987e+01
-2.02736760e+01 -4.13527926e+01 -9.76593096e-01 -4.41248900e+01
-4.54843424e+01 -3.60197805e+01 5.79545448e+00 1.81979552e+01
3.00901702e+00 -1.11604102e+01 8.77769475e+00 2.51582929e+01
-2.54449404e+01 1.17606812e+01 4.01481502e+00 -2.87987100e+01
5.61387258e+00 -6.72899703e+01 1.71698964e+01 -1.86237351e+01
1.10420766e+00 1.53391160e+01]
[-2.78675569e+01 3.08737667e+01 -2.11716488e+01 -2.40875721e+01
-1.71479262e+01 -6.96996139e+01 -4.26116774e+01 -1.41359160e+01
-6.14868751e+00 3.57383664e+01 -4.50651109e+01 -5.76869011e+01
-4.64614018e+01 -7.64263059e+01 -9.84521326e+00 -6.42376332e+01
-3.13494960e+01 -6.60791372e+01 -1.48416931e+01 3.28828789e+01
-5.46226953e+01 -3.16833171e+01 -4.96763399e+01 -1.16002208e+01
-6.32383579e+01 -1.52073335e+01 -7.98653458e+00 -3.30740943e+01
-4.24968665e+01 -1.08343715e+02 -1.16697688e+00 -2.72836390e+01
-2.64135944e+00 -1.60111912e+01]
[-1.59081376e+01 1.44263594e+01 -1.19427379e+01 -3.56822523e+01
-9.95374222e+00 -6.63055550e-01 6.43725837e+01 -4.44912970e+01
1.02717943e+01 -5.20030329e+00 -2.29532837e+01 4.50124082e+01
1.33136638e+01 4.19750937e+01 -1.44659033e+01 1.27175210e+02
3.06025751e+01 -2.18393501e+01 9.06530293e+00 2.40656548e+01
-3.39519436e+00 -1.31706866e+01 -3.98734200e+01 -2.43087457e+01
2.24954027e+01 -3.00978424e+01 -3.93640439e+01 -2.66671352e+01
-7.79393522e+01 -1.03650324e+02 2.40266307e+01 5.79173693e-01
3.50804371e+01 -3.38669138e+01]
[-2.04533072e+01 1.26044606e+01 -3.17354768e+01 -4.15872116e+01
-1.72731920e+00 3.18797440e+01 6.61529152e+01 -3.11735189e+01
9.16725646e+00 -9.96178562e+00 -2.22334459e+01 4.51382155e+01
1.32813340e+01 2.39853166e+01 -1.90368460e+00 9.64402410e+01
1.18143830e+01 -2.20427060e+01 -8.96384252e-01 2.87679861e+01
-5.88828214e+00 -2.20656112e+01 -3.63774424e+01 -2.02683918e+01
3.55033060e+01 -3.32454619e+01 -5.15658581e+01 -3.50872574e+01
-1.18010751e+02 -5.35763251e+01 2.04927639e+01 -1.45961362e+01
-6.78791843e+00 -5.25786326e+01]]
syn2 = [[ 6.53255608e-01 -7.81726238e-01 1.50873204e+00 -3.94529830e+00
-2.05232343e+00 3.92407385e+00 -1.14457075e-02 -3.27455221e+00
1.84395833e+00 -3.35020040e+00]
[-2.74766419e+00 -3.24420947e-01 3.55588049e-01 1.21950411e-03
-5.57903366e+00 -3.36522443e+00 6.12355719e+00 -3.19343506e+00
-1.75433576e+00 -5.34801533e+00]
[ 4.02695165e-01 -1.07518024e+00 -1.70123094e+00 3.83551550e+00
1.14277753e-01 6.05846222e-01 -9.76958056e-01 -7.03324281e-01
-2.32236188e-01 6.69535983e-01]
[-4.82281910e-01 -2.05046345e+00 -1.43298198e+00 2.13073857e-01
4.87762371e-01 -1.72161041e+00 -5.23464525e+00 -2.99146671e+00
3.03699344e-01 -3.15034181e+00]
[ 4.06290082e+00 -7.79086267e+00 4.25344763e+00 -6.83517779e-01
2.58091338e+00 -6.66348001e+00 -1.18876372e+00 4.99575897e-01
-3.89736754e+00 -1.74076411e+00]
[-5.00540950e+00 -3.49876586e+00 -3.11779151e+00 -2.14650813e-02
-5.66139992e-01 -2.81711436e+00 -5.97783978e+00 -1.13178080e+00
-2.77213977e+00 -1.11986234e+00]
[-3.47233185e+00 1.29780938e+00 -2.90674393e+00 3.28820844e-02
-4.82698459e+00 -2.57233305e+00 -5.41093051e+00 5.69418769e+00
1.65149651e+00 -3.55879729e+00]
[ 4.58523219e-01 -6.72622424e+00 -7.96778439e-01 2.99154804e+00
1.51677234e+00 -5.23850856e-01 -2.10215802e+00 -2.98234369e+00
-2.42397901e+00 -2.75781051e+00]
[ 1.11061493e-01 -7.22705687e-01 -3.24230904e+00 -3.37240545e-01
6.95915214e-01 -5.34915124e+00 -3.30293405e+00 2.78062533e+00
-3.69412028e+00 -1.23650432e+00]
[-3.40218255e+00 6.48048851e+00 -1.19797547e+00 2.35100135e-01
-8.19271819e-01 -5.33014661e+00 -7.01056864e+00 -8.35403624e-01
-4.73585684e+00 2.38736310e+00]
[-1.94355025e+00 -7.47636253e+00 -2.74446150e+00 8.02378211e-01
-1.37208079e+00 -3.15010520e+00 -3.84930544e+00 2.64969553e+00
2.41581683e+00 1.55866600e-01]
[-2.25130566e+00 -5.43921205e+00 -2.08139357e+00 5.65200882e-01
-1.13702658e+00 -1.31291179e+00 -5.47260219e+00 3.51871746e+00
-3.06519286e+00 1.16760709e+00]
[-2.65811802e-01 -4.04890070e+00 2.04261188e+00 -2.35510310e+00
7.99617337e-01 -1.96396383e+00 -2.82885688e+00 7.16753195e-02
1.24591074e+00 -5.43892736e-01]
[-1.97046977e+00 -1.62590201e+00 1.06351058e+00 3.91604558e-02
-3.94948616e-01 -2.15979345e-01 -5.15585517e+00 3.23560995e+00
2.49619224e+00 2.95721909e+00]
[-2.73366636e+00 -2.16611740e+00 -5.42693297e+00 -3.12563919e+00
4.65572352e+00 -1.54487468e+00 2.49614023e-01 -3.18167945e+00
7.70637610e-01 -2.08279123e+00]
[-2.81299675e+00 -6.33568421e+00 -3.08082598e+00 -1.95871434e+00
-3.03971293e-01 -2.22888205e+00 -4.16561437e+00 -3.33057712e+00
-2.43312593e-01 1.68456585e+00]
[ 2.67729150e+00 -1.18095459e+00 -4.14665587e+00 -1.22593296e+00
-1.92761190e+00 -2.76775554e-02 -3.82779114e-01 -2.62412572e+00
-4.77439733e+00 3.22377066e+00]
[ 6.44110414e+00 -3.96538273e+00 -5.00794565e+00 -1.01063410e+00
-1.67352125e+00 -8.18392785e-01 -6.18773236e+00 3.35161386e+00
-3.88472254e+00 -3.49437111e-01]
[-7.46861777e+00 -9.75667482e-01 -2.07156291e+00 -1.15526082e+00
-1.25729298e+00 1.33198751e-02 -2.61970421e+00 -6.63320411e+00
4.56103327e-01 -1.12475657e-01]
[-5.24298738e+00 1.48085404e+00 6.78771162e-01 -2.85955274e+00
3.09910462e+00 -1.39610107e+00 -3.21238512e+00 4.77340343e-01
1.38217897e+00 -6.52941379e+00]
[-2.12551954e+00 -2.46819933e+00 -7.70764569e-01 -1.41946590e+00
-3.82581800e+00 4.80083639e+00 -2.12782064e+00 4.32236620e+00
-5.03320869e+00 -8.95322677e-01]
[-2.33533871e-01 -5.68410969e+00 3.68648523e+00 -1.01615772e+00
-7.00216437e-01 -4.89914433e-02 -5.22572635e-01 -2.47941779e-01
-2.36659849e+00 3.39124112e+00]
[-8.60503345e-01 -2.59453438e+00 6.33893657e-01 -1.26455802e+00
-2.79479067e+00 4.71301419e+00 -5.23377831e+00 2.12566261e+00
-6.85342766e-01 -1.97481775e+00]
[-3.01019172e+00 3.03084461e+00 -6.36654147e-01 3.10246625e-01
7.25535301e-01 1.42265009e+00 -3.90313693e+00 2.35706273e+00
-1.55371903e+00 -1.94184803e-01]
[-2.51686524e+00 -8.27406926e-01 -3.93807549e+00 7.17862265e-01
-5.52424423e-01 1.78066048e+00 -4.17260083e+00 4.63216439e+00
-3.43925439e+00 6.77243178e-01]
[-1.65359357e-01 -2.18030892e+00 -1.76545898e+00 -3.96749333e-01
2.22075536e+00 2.59673099e+00 1.48400204e-01 -3.91178321e-01
-6.71836591e-01 1.02435614e+00]
[-2.96126101e+00 -5.66150537e-01 -3.03042301e+00 3.60454753e+00
-3.74489853e+00 8.86472593e-01 -2.68186065e+00 -3.90179658e+00
1.97754003e+00 -2.11475778e+00]
[-4.19420977e-01 -6.64976468e+00 -2.80863799e+00 4.17531061e+00
-4.52969619e-01 -2.53312500e+00 -2.04243429e+00 -1.93778209e+00
-1.59148232e-02 5.02316842e+00]
[ 7.77756034e-01 -2.87151110e+00 2.43779910e+00 1.08941564e+00
-2.65841603e+00 1.89653658e+00 -7.38960685e-01 -7.36362648e+00
1.12415557e-01 -1.41625224e+00]
[ 1.33079760e+00 -6.60172572e+00 -2.26338025e+00 -5.07170852e+00
2.19034334e+00 1.40784744e-01 -4.04383085e-01 -6.57130084e-01
8.20751426e-01 1.98176397e-01]
[-4.72883332e-01 3.57740762e-01 -4.65194027e+00 -4.01463192e+00
-3.47407602e+00 -1.41359965e+00 2.89756373e+00 2.80812333e-01
-2.63416377e+00 1.82721019e+00]
[ 5.64979039e-01 1.57499196e+00 -2.02698719e+00 2.53095086e+00
1.40221816e+00 -8.30099075e-01 -2.02834490e+00 -3.27276885e+00
-3.24942111e+00 1.33428065e+00]
[-2.93535955e+00 2.13567448e+00 -7.52365913e-01 2.31130072e+00
-1.56005211e+00 -5.26342415e+00 -3.08157013e+00 2.72945538e-01
-2.84029347e+00 1.78330794e+00]
[-2.56831877e+00 -1.68549930e+00 -2.93974885e+00 -2.87900416e+00
-1.73070613e+00 3.59006710e+00 -4.01741500e+00 -3.75313207e+00
-2.20974683e+00 4.10056117e+00]]
b0 = [[ -875.73663618 -813.85866971 -840.27174486 -1066.0344334
-826.36406162 -877.04031066 -916.60081508 -515.31463157
-856.81938453 -934.05288301 -882.58779013 -884.11254226
-344.67593241 -503.62217279 -882.00032229 -899.51823195
-921.02182561 -1127.18384338 -628.77626535 -831.6744478
-846.43811066 -941.3049701 -301.58188636 -890.1993107
-862.5599114 -661.3040105 -254.89316881 -871.50264689
-869.4125715 -820.86846255 -840.98990615 -850.6850811
-861.05567862 -914.68797341 -250.68963748 -257.9943676 ]]
b1 = [[-1.66278210e+00 -3.65324540e+00 -4.51958146e+00 -2.57870597e+00
-4.87897223e+00 5.29131753e-01 -6.82521641e-01 -3.82414882e+00
-3.59395758e+00 -4.49837233e+00 -2.93700807e+00 -2.09688231e+00
-2.71914216e+00 -7.11979726e-01 -4.03584819e+00 3.47651147e-03
1.68643496e-01 -2.81502351e+00 -4.15120256e+00 -5.23985551e+00
-4.01459467e+00 -4.10819489e+00 -3.10466809e+00 -4.72698550e+00
-5.82501507e-01 -3.52325194e+00 -2.77943189e+00 -4.12160271e+00
-3.03927975e+00 -2.62742330e+00 -3.49018083e+00 -2.38325761e+00
-5.95594166e+00 -1.73942359e+00]]
b2 = [[-1.29065252 -1.07410532 -1.12565259 -1.91341438 -0.90875645 -1.56695869
-1.52767328 -0.36236739 -1.04558348 -0.45456078]]
| [
"[email protected]"
] | |
d1ddaf333839d2b4c77c8c4265b2240ac9836035 | 8d6fa96da4220ba886ef8e858f1925b6dca34e58 | /examples/wtf/wtf/config.py | 7cf539ff078f59cb14f772090950734c0d091acb | [] | no_license | FZambia/cyclone-wtforms | 6ee26c920171685e027529e8f1fbb99c765edc30 | c266b5f3bfff77e3a721b3335b74a294966f7daf | refs/heads/master | 2016-09-05T15:23:08.336180 | 2012-10-05T18:55:00 | 2012-10-05T18:55:00 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,041 | py | # coding: utf-8
#
# Copyright 2010 Alexandre Fiori
# based on the original Tornado by Facebook
#
# 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 ConfigParser
from cyclone.util import ObjectDict
def xget(func, section, option, default=None):
try:
return func(section, option)
except:
return default
def parse_config(filename):
cfg = ConfigParser.RawConfigParser()
with open(filename) as fp:
cfg.readfp(fp)
fp.close()
settings = {'raw': cfg}
# web server settings
settings["debug"] = xget(cfg.getboolean, "server", "debug", False)
settings["xheaders"] = xget(cfg.getboolean, "server", "xheaders", False)
settings["cookie_secret"] = cfg.get("server", "cookie_secret")
settings["xsrf_cookies"] = xget(cfg.getboolean, "server", "xsrf_cookies",
False)
# get project's absolute path
root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
getpath = lambda k, v: os.path.join(root, xget(cfg.get, k, v))
# locale, template and static directories' path
settings["locale_path"] = getpath("frontend", "locale_path")
settings["static_path"] = getpath("frontend", "static_path")
settings["template_path"] = getpath("frontend", "template_path")
# sqlite support
if xget(cfg.getboolean, "sqlite", "enabled", False):
settings["sqlite_settings"] = ObjectDict(database=cfg.get("sqlite",
"database"))
else:
settings["sqlite_settings"] = None
# redis support
if xget(cfg.getboolean, "redis", "enabled", False):
settings["redis_settings"] = ObjectDict(
host=cfg.get("redis", "host"),
port=cfg.getint("redis", "port"),
dbid=cfg.getint("redis", "dbid"),
poolsize=cfg.getint("redis", "poolsize"))
else:
settings["redis_settings"] = None
# mysql support
if xget(cfg.getboolean, "mysql", "enabled", False):
settings["mysql_settings"] = ObjectDict(
host=cfg.get("mysql", "host"),
port=cfg.getint("mysql", "port"),
username=xget(cfg.get, "mysql", "username"),
password=xget(cfg.get, "mysql", "password"),
database=xget(cfg.get, "mysql", "database"),
poolsize=xget(cfg.getint, "mysql", "poolsize", 10),
debug=xget(cfg.getboolean, "mysql", "debug", False))
else:
settings["mysql_settings"] = None
return settings
| [
"[email protected]"
] | |
643a7e8fab27c002a3adec8754905d174c27db19 | ab4f74d127bfc89813ee359bb9c779eca5426ddc | /script/label_image.runfiles/org_tensorflow/tensorflow/contrib/resampler/python/ops/resampler_ops.py | 77aba28802d7d37842990c9062030322f5f2eb39 | [
"MIT"
] | permissive | harshit-jain-git/ImageNET | cdfd5a340b62862ad8d1cc3b9a0f30cccc481744 | 1cd4c2b70917e4709ce75422c0205fe3735a1b01 | refs/heads/master | 2022-12-11T12:47:46.795376 | 2017-12-19T05:47:26 | 2017-12-19T05:47:26 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 102 | py | /home/co/Documents/ImageClassifier/tensorflow/tensorflow/contrib/resampler/python/ops/resampler_ops.py | [
"[email protected]"
] | |
128efb9b492a29c2e87a97b932e626a724b6af9f | 52b9016932aa426eeaaade5d856af6a1a771683f | /tests/testapp/serializers.py | 3c4be81a47c21da377120bda5b7ee7eb6deb647d | [
"MIT"
] | permissive | marlncpe/django-rest-pandas | 33033627d88c6467a9677133402fb519d5ea5a75 | 89a93c3ce8d30688f9137f5a9beacc7d63f621e0 | refs/heads/master | 2021-01-23T11:55:02.722962 | 2017-09-01T20:47:46 | 2017-09-01T20:47:46 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,844 | py | from rest_framework.serializers import ModelSerializer
from rest_framework import serializers
from rest_pandas import PandasUnstackedSerializer
from .models import TimeSeries, MultiTimeSeries, ComplexTimeSeries
class TimeSeriesSerializer(ModelSerializer):
date = serializers.DateField(format=None)
class Meta:
model = TimeSeries
fields = '__all__'
class TimeSeriesNoIdSerializer(TimeSeriesSerializer):
class Meta:
model = TimeSeries
exclude = ['id']
class MultiTimeSeriesSerializer(ModelSerializer):
class Meta:
model = MultiTimeSeries
exclude = ['id']
pandas_index = ['date']
pandas_unstacked_header = ['series']
pandas_scatter_coord = ['series']
pandas_boxplot_group = 'series'
pandas_boxplot_date = 'date'
class ComplexTimeSeriesSerializer(ModelSerializer):
class Meta:
model = ComplexTimeSeries
exclude = ['id']
pandas_index = ['date', 'type']
pandas_unstacked_header = ['site', 'parameter', 'units']
class ComplexScatterSerializer(ComplexTimeSeriesSerializer):
class Meta(ComplexTimeSeriesSerializer.Meta):
exclude = ['id', 'flag']
pandas_scatter_coord = ['units', 'parameter']
pandas_scatter_header = ['site']
class ComplexBoxplotSerializer(ComplexTimeSeriesSerializer):
class Meta(ComplexTimeSeriesSerializer.Meta):
exclude = ['id', 'flag', 'type']
pandas_boxplot_group = 'site'
pandas_boxplot_date = 'date'
pandas_boxplot_header = ['units', 'parameter']
class NotUnstackableSerializer(ModelSerializer):
class Meta:
model = MultiTimeSeries
fields = '__all__'
list_serializer_class = PandasUnstackedSerializer
# pandas_unstacked_header = Missing
pandas_index = ['series']
| [
"[email protected]"
] | |
626e284b40ec0447bfcba31a165d86827eb7df2a | 6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4 | /gHrMmA7emP6CFAMnb_6.py | 35eeb43f5be552b55e650249bf1ff464b8e37754 | [] | no_license | daniel-reich/ubiquitous-fiesta | 26e80f0082f8589e51d359ce7953117a3da7d38c | 9af2700dbe59284f5697e612491499841a6c126f | refs/heads/master | 2023-04-05T06:40:37.328213 | 2021-04-06T20:17:44 | 2021-04-06T20:17:44 | 355,318,759 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 200 | py |
def is_apocalyptic(n):
L=str(2**n).split('666')
if len(L)==1:
return "Safe"
elif len(L)==2:
return "Single"
elif len(L)==3:
return "Double"
elif len(L)==4:
return "Triple"
| [
"[email protected]"
] | |
85041057b18077c038426fd96461f5dbd0ed30a7 | 747febe786dd6b7fd6c63cfe73dbe3023354daa8 | /src/the_tale/the_tale/game/companions/tests/test_abilities_effects.py | 433f74ad27be13a54043c1f878032a3d17dfde97 | [
"BSD-3-Clause"
] | permissive | the-tale/the-tale | 4e4b8d91dc873a5fb935fe58e9721a877baa6d3f | e8450bd2332344da805b1851e728da5a3e5bf0ef | refs/heads/develop | 2023-08-01T13:53:46.835667 | 2022-12-25T18:04:56 | 2022-12-25T18:04:56 | 1,949,167 | 98 | 52 | BSD-3-Clause | 2023-02-15T18:57:33 | 2011-06-24T18:49:48 | Python | UTF-8 | Python | false | false | 35,535 | py |
import smart_imports
smart_imports.all()
effects = companions_abilities_effects
MODIFIERS = heroes_relations.MODIFIERS
class BaseEffectsTests(utils_testcase.TestCase):
def setUp(self):
super(BaseEffectsTests, self).setUp()
game_logic.create_test_map()
self.account = self.accounts_factory.create_account()
self.storage = game_logic_storage.LogicStorage()
self.storage.load_account_data(self.account.id)
self.hero = self.storage.accounts_to_heroes[self.account.id]
self.companion_record = logic.create_companion_record(utg_name=game_names.generator().get_test_name(),
description='description',
type=tt_beings_relations.TYPE.random(),
max_health=10,
dedication=relations.DEDICATION.random(),
archetype=game_relations.ARCHETYPE.random(),
mode=relations.MODE.random(),
abilities=companions_abilities_container.Container(),
communication_verbal=tt_beings_relations.COMMUNICATION_VERBAL.random(),
communication_gestures=tt_beings_relations.COMMUNICATION_GESTURES.random(),
communication_telepathic=tt_beings_relations.COMMUNICATION_TELEPATHIC.random(),
intellect_level=tt_beings_relations.INTELLECT_LEVEL.random(),
structure=tt_beings_relations.STRUCTURE.random(),
features=frozenset((tt_beings_relations.FEATURE.random(), tt_beings_relations.FEATURE.random())),
movement=tt_beings_relations.MOVEMENT.random(),
body=tt_beings_relations.BODY.random(),
size=tt_beings_relations.SIZE.random(),
orientation=tt_beings_relations.ORIENTATION.random(),
weapons=[artifacts_objects.Weapon(weapon=artifacts_relations.STANDARD_WEAPON.random(),
material=tt_artifacts_relations.MATERIAL.random(),
power_type=artifacts_relations.ARTIFACT_POWER_TYPE.random())],
state=relations.STATE.ENABLED)
self.hero.set_companion(logic.create_companion(self.companion_record))
def apply_ability(self, ability):
container = companions_abilities_container.Container(common=(),
start=frozenset((ability,)),
coherence=None,
honor=None,
peacefulness=None)
self.companion_record.abilities = container
self.hero.reset_accessors_cache()
def get_ability(self, *argv):
return random.choice([ability
for ability in effects.ABILITIES.records
if any(isinstance(ability.effect, effect) for effect in argv)])
class CommonTests(BaseEffectsTests):
def test_aprox(self):
self.assertEqual(effects.aprox(1, 2, 1), 1.2)
self.assertEqual(effects.aprox(1, 2, 2), 1.4)
self.assertEqual(effects.aprox(1, 2, 3), 1.6)
self.assertEqual(effects.aprox(1, 2, 4), 1.8)
self.assertEqual(effects.aprox(1, 2, 5), 2)
class CoherenceSpeedTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CoherenceSpeed(0.8)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COHERENCE_EXPERIENCE, 10), 8)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COHERENCE_EXPERIENCE, 11), 8.8)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COHERENCE_EXPERIENCE,)), 11), 11)
effect = effects.CoherenceSpeed(1.2)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COHERENCE_EXPERIENCE, 10), 12)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COHERENCE_EXPERIENCE, 11), 13.2)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COHERENCE_EXPERIENCE,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CoherenceSpeed)
self.hero.companion.coherence = c.COMPANIONS_MAX_COHERENCE - 1
self.hero.companion.experience = 0
self.hero.companion.add_experience(10)
old_delta = self.hero.companion.experience
self.hero.companion.experience = 0
self.apply_ability(ability)
self.hero.companion.add_experience(10)
new_delta = self.hero.companion.experience
self.assertEqual(int(round(old_delta * ability.effect.multiplier_left)), new_delta)
class ChangeHabitsTests(BaseEffectsTests):
def test_effect(self):
effect = effects.ChangeHabits(habit_type=game_relations.HABIT_TYPE.HONOR,
habit_sources=(heroes_relations.HABIT_CHANGE_SOURCE.COMPANION_HONOR_NEUTRAL_1,
heroes_relations.HABIT_CHANGE_SOURCE.COMPANION_HONOR_NEUTRAL_2))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.HABITS_SOURCES, set()), set((heroes_relations.HABIT_CHANGE_SOURCE.COMPANION_HONOR_NEUTRAL_1,
heroes_relations.HABIT_CHANGE_SOURCE.COMPANION_HONOR_NEUTRAL_2)))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.HABITS_SOURCES,)), set()), set())
def check_habits_changed(self, honor, peacefulness, honor_check, peacefulness_check):
self.hero.habit_honor.set_habit(honor)
self.hero.habit_peacefulness.set_habit(peacefulness)
for habit_source in self.hero.companion.modify_attribute(heroes_relations.MODIFIERS.HABITS_SOURCES, set()):
self.hero.update_habits(habit_source)
self.assertTrue(honor_check(self.hero.habit_honor.raw_value))
self.assertTrue(peacefulness_check(self.hero.habit_peacefulness.raw_value))
def test_in_game__aggressive(self):
self.apply_ability(effects.ABILITIES.AGGRESSIVE)
self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0,
honor_check=lambda v: v == -c.HABITS_BORDER,
peacefulness_check=lambda v: v < 0)
self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER,
honor_check=lambda v: v == 0,
peacefulness_check=lambda v: v < c.HABITS_BORDER)
self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER,
honor_check=lambda v: v == c.HABITS_BORDER,
peacefulness_check=lambda v: v == -c.HABITS_BORDER)
def test_in_game__peaceful(self):
self.apply_ability(effects.ABILITIES.PEACEFUL)
self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0,
honor_check=lambda v: v == -c.HABITS_BORDER,
peacefulness_check=lambda v: v > 0)
self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER,
honor_check=lambda v: v == 0,
peacefulness_check=lambda v: v == c.HABITS_BORDER)
self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER,
honor_check=lambda v: v == c.HABITS_BORDER,
peacefulness_check=lambda v: v > -c.HABITS_BORDER)
def test_in_game__reserved(self):
self.apply_ability(effects.ABILITIES.RESERVED)
self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0,
honor_check=lambda v: v == -c.HABITS_BORDER,
peacefulness_check=lambda v: v == 0)
self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER,
honor_check=lambda v: v == 0,
peacefulness_check=lambda v: v < c.HABITS_BORDER)
self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER,
honor_check=lambda v: v == c.HABITS_BORDER,
peacefulness_check=lambda v: v > -c.HABITS_BORDER)
def test_in_game__canny(self):
self.apply_ability(effects.ABILITIES.CANNY)
self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0,
honor_check=lambda v: v > -c.HABITS_BORDER,
peacefulness_check=lambda v: v == 0)
self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER,
honor_check=lambda v: v == 0,
peacefulness_check=lambda v: v == c.HABITS_BORDER)
self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER,
honor_check=lambda v: v < c.HABITS_BORDER,
peacefulness_check=lambda v: v == -c.HABITS_BORDER)
def test_in_game__honest(self):
self.apply_ability(effects.ABILITIES.HONEST)
self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0,
honor_check=lambda v: v > -c.HABITS_BORDER,
peacefulness_check=lambda v: v == 0)
self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER,
honor_check=lambda v: v > 0,
peacefulness_check=lambda v: v == c.HABITS_BORDER)
self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER,
honor_check=lambda v: v == c.HABITS_BORDER,
peacefulness_check=lambda v: v == -c.HABITS_BORDER)
def test_in_game__sneaky(self):
self.apply_ability(effects.ABILITIES.SNEAKY)
self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0,
honor_check=lambda v: v == -c.HABITS_BORDER,
peacefulness_check=lambda v: v == 0)
self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER,
honor_check=lambda v: v < 0,
peacefulness_check=lambda v: v == c.HABITS_BORDER)
self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER,
honor_check=lambda v: v < c.HABITS_BORDER,
peacefulness_check=lambda v: v == -c.HABITS_BORDER)
class QuestMoneyRewardTests(BaseEffectsTests):
def test_effect(self):
effect = effects.QuestMoneyReward(0.5)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.QUEST_MONEY_REWARD, 10), 10.5)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.QUEST_MONEY_REWARD, 11), 11.5)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.QUEST_MONEY_REWARD,)), 11), 11)
effect = effects.QuestMoneyReward(2.0)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.QUEST_MONEY_REWARD, 10), 12)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.QUEST_MONEY_REWARD, 11), 13)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.QUEST_MONEY_REWARD,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.QuestMoneyReward)
with self.check_changed(lambda: self.hero.quest_money_reward_multiplier()):
self.apply_ability(ability)
class MaxBagSizeTests(BaseEffectsTests):
def test_effect(self):
effect = effects.MaxBagSize(666)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.MAX_BAG_SIZE, 10), 676)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.MAX_BAG_SIZE, 11), 677)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.MAX_BAG_SIZE,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.MaxBagSize)
with self.check_changed(lambda: self.hero.max_bag_size):
self.apply_ability(ability)
class PoliticsPowerTests(BaseEffectsTests):
def test_effect(self):
effect = effects.PoliticsPower(3)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.POWER, 11), 14.0)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.POWER, )), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.PoliticsPower)
with self.check_changed(lambda: self.hero.politics_power_modifier):
self.apply_ability(ability)
class MagicDamageBonusTests(BaseEffectsTests):
def test_effect(self):
effect = effects.MagicDamageBonus(2)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.MAGIC_DAMAGE, 10), 20)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.PHYSIC_DAMAGE, 10), 10)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.MAGIC_DAMAGE,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.MagicDamageBonus)
with self.check_changed(lambda: self.hero.magic_damage_modifier):
self.apply_ability(ability)
class PhysicDamageBonusTests(BaseEffectsTests):
def test_effect(self):
effect = effects.PhysicDamageBonus(2)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.MAGIC_DAMAGE, 10), 10)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.PHYSIC_DAMAGE, 10), 20)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.PHYSIC_DAMAGE,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.PhysicDamageBonus)
with self.check_changed(lambda: self.hero.physic_damage_modifier):
self.apply_ability(ability)
class SpeedTests(BaseEffectsTests):
def test_effect(self):
effect = effects.Speed(2)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.SPEED, 10), 20)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.SPEED,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.Speed)
with self.check_changed(lambda: self.hero.move_speed):
self.apply_ability(ability)
class BattleAbilityTests(BaseEffectsTests):
def test_effect(self):
effect = effects.BattleAbilityFireball()
self.assertEqual(effect._modify_attribute({}, MODIFIERS.INITIATIVE, 10), 10.25)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.ADDITIONAL_ABILITIES, []), [effect.ABILITY])
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.INITIATIVE, MODIFIERS.ADDITIONAL_ABILITIES)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.BattleAbilityHit,
effects.BattleAbilityStrongHit,
effects.BattleAbilityRunUpPush,
effects.BattleAbilityFireball,
effects.BattleAbilityPoisonCloud,
effects.BattleAbilityFreezing)
with self.check_changed(lambda: self.hero.initiative):
with self.check_changed(lambda: len(self.hero.companion.modify_attribute(heroes_relations.MODIFIERS.ADDITIONAL_ABILITIES,
heroes_relations.MODIFIERS.ADDITIONAL_ABILITIES.default()))):
self.apply_ability(ability)
class InitiativeTests(BaseEffectsTests):
def test_effect(self):
effect = effects.Initiative(2)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.INITIATIVE, 10), 20)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.INITIATIVE,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.Initiative)
with self.check_changed(lambda: self.hero.initiative):
self.apply_ability(ability)
class BattleProbabilityTests(BaseEffectsTests):
def test_effect(self):
effect = effects.BattleProbability(1.5)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.BATTLES_PER_TURN, 10), 11.5)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.BATTLES_PER_TURN,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.BattleProbability)
with self.check_changed(lambda: self.hero.battles_per_turn_summand):
self.apply_ability(ability)
class LootProbabilityTests(BaseEffectsTests):
def test_effect(self):
effect = effects.LootProbability(2)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.LOOT_PROBABILITY, 10), 20)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.LOOT_PROBABILITY,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.LootProbability)
with self.check_changed(lambda: self.hero.loot_probability(mobs_storage.mobs.all()[0])):
self.apply_ability(ability)
class CompanionDamageTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionDamage(3)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_DAMAGE, 10), 13)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_DAMAGE,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionDamage)
with mock.patch('the_tale.game.balance.constants.COMPANIONS_BONUS_DAMAGE_PROBABILITY', 6666666666):
with self.check_changed(lambda: self.hero.companion_damage):
self.apply_ability(ability)
class CompanionDamageProbabilityTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionDamageProbability(3)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_DAMAGE_PROBABILITY, 10), 30)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_DAMAGE_PROBABILITY,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionDamageProbability)
with self.check_changed(lambda: self.hero.companion_damage_probability):
self.apply_ability(ability)
class CompanionStealMoneyTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionStealMoney(3)
self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_STEAL_MONEY))
self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_STEAL_MONEY_MULTIPLIER))
self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_STEAL_MONEY, MODIFIERS.COMPANION_STEAL_MONEY_MULTIPLIER))))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_STEAL_MONEY, 10), 10)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_STEAL_MONEY_MULTIPLIER, 10), 30)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_STEAL_MONEY, MODIFIERS.COMPANION_STEAL_MONEY_MULTIPLIER)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionStealMoney)
with self.check_changed(lambda: self.hero.can_companion_steal_money()):
with self.check_changed(lambda: self.hero.companion_steal_money_modifier):
self.apply_ability(ability)
class CompanionStealItemTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionStealItem(3)
self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_STEAL_ITEM))
self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_STEAL_ITEM_MULTIPLIER))
self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_STEAL_ITEM, MODIFIERS.COMPANION_STEAL_ITEM))))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_STEAL_ITEM, 10), 10)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_STEAL_ITEM_MULTIPLIER, 10), 30)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_STEAL_ITEM, MODIFIERS.COMPANION_STEAL_ITEM_MULTIPLIER)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionStealItem)
with self.check_changed(lambda: self.hero.can_companion_steal_item()):
with self.check_changed(lambda: self.hero.companion_steal_artifact_probability_multiplier):
self.apply_ability(ability)
class CompanionSparePartsTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionSpareParts()
self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_SPARE_PARTS))
self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_SPARE_PARTS,))))
def test_in_game(self):
ability = self.get_ability(effects.CompanionSpareParts)
with self.check_changed(lambda: self.hero.can_companion_broke_to_spare_parts()):
self.apply_ability(ability)
class CompanionSayWisdomTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionSayWisdom(3)
self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_SAY_WISDOM))
self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_SAY_WISDOM_PROBABILITY))
self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_SAY_WISDOM, MODIFIERS.COMPANION_SAY_WISDOM_PROBABILITY))))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_SAY_WISDOM, 10), 10)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_SAY_WISDOM_PROBABILITY, 10), 30)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_SAY_WISDOM, MODIFIERS.COMPANION_SAY_WISDOM_PROBABILITY)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionSayWisdom)
with self.check_changed(lambda: self.hero.can_companion_say_wisdom()):
with self.check_changed(lambda: self.hero.companion_say_wisdom_probability):
self.apply_ability(ability)
class CompanionExpPerHealTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionExpPerHeal(2)
self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_EXP_PER_HEAL))
self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_EXP_PER_HEAL_PROBABILITY))
self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EXP_PER_HEAL, MODIFIERS.COMPANION_EXP_PER_HEAL_PROBABILITY))))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EXP_PER_HEAL, 10), 10)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EXP_PER_HEAL_PROBABILITY, 10), 20)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EXP_PER_HEAL, MODIFIERS.COMPANION_EXP_PER_HEAL_PROBABILITY)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionExpPerHeal)
with self.check_changed(lambda: self.hero.can_companion_exp_per_heal()):
with self.check_changed(lambda: self.hero.companion_exp_per_heal_probability):
self.apply_ability(ability)
class DoubleReligionProfitTests(BaseEffectsTests):
def test_effect(self):
effect = effects.DoubleReligionProfit(0.1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.DOUBLE_RELIGION_PROFIT, 0), 0.1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.DOUBLE_RELIGION_PROFIT,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.DoubleReligionProfit)
with self.check_changed(lambda: self.hero.double_religion_profit_probability):
self.apply_ability(ability)
class CompanionEatCorpsesTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionEatCorpses(3)
self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_EAT_CORPSES))
self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_EAT_CORPSES_PROBABILITY))
self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EAT_CORPSES, MODIFIERS.COMPANION_EAT_CORPSES_PROBABILITY))))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EAT_CORPSES, 1), 1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EAT_CORPSES_PROBABILITY, 1), 3)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EAT_CORPSES, MODIFIERS.COMPANION_EAT_CORPSES_PROBABILITY)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionEatCorpses)
with self.check_changed(lambda: self.hero.can_companion_eat_corpses()):
with self.check_changed(lambda: self.hero.companion_eat_corpses_probability):
self.apply_ability(ability)
class CompanionRegenerateTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionRegenerate(2)
self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_REGENERATE))
self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_REGENERATE_PROBABILITY))
self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_REGENERATE, MODIFIERS.COMPANION_REGENERATE_PROBABILITY))))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_REGENERATE, 10), 10)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_REGENERATE_PROBABILITY, 10), 20)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_REGENERATE, MODIFIERS.COMPANION_REGENERATE_PROBABILITY)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionRegenerate)
with self.check_changed(lambda: self.hero.can_companion_regenerate()):
with self.check_changed(lambda: self.hero.companion_regenerate_probability):
self.apply_ability(ability)
class CompanionEatAndDiscountTest(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionEat(0.5)
self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_MONEY_FOR_FOOD))
self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_MONEY_FOR_FOOD,))))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_MONEY_FOR_FOOD, 2), 1.0)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_MONEY_FOR_FOOD,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionEat)
with self.check_changed(lambda: self.hero.can_companion_eat()):
with self.check_changed(lambda: self.hero.companion_money_for_food_multiplier):
self.apply_ability(ability)
class CompanionDrinkArtifactTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionDrinkArtifact(0.5)
self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_DRINK_ARTIFACT))
self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_DRINK_ARTIFACT_PROBABILITY))
self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_DRINK_ARTIFACT, MODIFIERS.COMPANION_DRINK_ARTIFACT_PROBABILITY))))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_DRINK_ARTIFACT, 2), 2)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_DRINK_ARTIFACT_PROBABILITY, 2), 1.0)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_DRINK_ARTIFACT, MODIFIERS.COMPANION_DRINK_ARTIFACT_PROBABILITY,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionDrinkArtifact)
with self.check_changed(lambda: self.hero.can_companion_drink_artifact()):
with self.check_changed(lambda: self.hero.companion_drink_artifact_probability):
self.apply_ability(ability)
class CompanionExorcistTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionExorcist(0.5)
self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_EXORCIST))
self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_EXORCIST_PROBABILITY))
self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EXORCIST, MODIFIERS.COMPANION_EXORCIST_PROBABILITY))))
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EXORCIST, 2), 2)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EXORCIST_PROBABILITY, 2), 1.0)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EXORCIST, MODIFIERS.COMPANION_EXORCIST_PROBABILITY,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionExorcist)
with self.check_changed(lambda: self.hero.can_companion_do_exorcism()):
with self.check_changed(lambda: self.hero.companion_do_exorcism_probability):
self.apply_ability(ability)
class RestLenghtTests(BaseEffectsTests):
def test_effect(self):
effect = effects.RestLenght(3)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.REST_LENGTH, 12), 36)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.REST_LENGTH,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.RestLenght)
with self.check_changed(lambda: self.hero.rest_length):
self.apply_ability(ability)
class IDLELenghtTests(BaseEffectsTests):
def test_effect(self):
effect = effects.IDLELenght(3)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.IDLE_LENGTH, 12), 36)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.IDLE_LENGTH,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.IDLELenght)
with self.check_changed(lambda: self.hero.idle_length):
self.apply_ability(ability)
class CompanionBlockProbabilityTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionBlockProbability(3)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_BLOCK_PROBABILITY, 12), 36)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_BLOCK_PROBABILITY, )), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionBlockProbability)
with self.check_changed(lambda: self.hero.companion_block_probability_multiplier):
self.apply_ability(ability)
class HucksterTests(BaseEffectsTests):
def test_effect(self):
effect = effects.Huckster(buy_bonus_left=3, buy_bonus_right=3,
sell_bonus_left=2, sell_bonus_right=2)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.BUY_PRICE, 12), 15)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.BUY_PRICE, MODIFIERS.SELL_PRICE)), 11), 11)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.SELL_PRICE, 130), 132)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.BUY_PRICE, MODIFIERS.SELL_PRICE)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.Huckster)
with self.check_changed(self.hero.buy_price):
with self.check_changed(self.hero.sell_price):
self.apply_ability(ability)
class EtherealMagnetTests(BaseEffectsTests):
def test_effect(self):
effect = effects.EtherealMagnet(0.1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.CHARACTER_QUEST_PRIORITY, 0), 0.1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.CHARACTER_QUEST_PRIORITY,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.EtherealMagnet)
with self.check_changed(lambda: self.hero.attribute_modifier(heroes_relations.MODIFIERS.CHARACTER_QUEST_PRIORITY)):
self.apply_ability(ability)
class CompanionTeleportTests(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionTeleport(0.1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_TELEPORTATOR, 0), 0.1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_TELEPORTATOR,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionTeleport)
with self.check_changed(lambda: self.hero.companion_teleport_probability):
self.apply_ability(ability)
class CompanionFly(BaseEffectsTests):
def test_effect(self):
effect = effects.CompanionFly(0.1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_FLYER, 0), 0.1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_FLYER,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.CompanionFly)
with self.check_changed(lambda: self.hero.companion_fly_probability):
self.apply_ability(ability)
class UnsociableTests(BaseEffectsTests):
def test_effect(self):
effect = effects.Unsociable(0.1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_LEAVE_IN_PLACE, 0), 0.1)
self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_LEAVE_IN_PLACE,)), 11), 11)
def test_in_game(self):
ability = self.get_ability(effects.Unsociable)
with self.check_changed(lambda: self.hero.companion_leave_in_place_probability):
self.apply_ability(ability)
| [
"[email protected]"
] | |
db266f900c6525725d8b23ccc316b88a594ad197 | e10a6d844a286db26ef56469e31dc8488a8c6f0e | /dql_grasping/q_graph.py | c0b5360b942f4f2acb0762e32bcf24e28f12f9a4 | [
"Apache-2.0",
"CC-BY-4.0"
] | permissive | Jimmy-INL/google-research | 54ad5551f97977f01297abddbfc8a99a7900b791 | 5573d9c5822f4e866b6692769963ae819cb3f10d | refs/heads/master | 2023-04-07T19:43:54.483068 | 2023-03-24T16:27:28 | 2023-03-24T16:32:17 | 282,682,170 | 1 | 0 | Apache-2.0 | 2020-07-26T15:50:32 | 2020-07-26T15:50:31 | null | UTF-8 | Python | false | false | 22,583 | py | # coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Continuous Q-Learning via random sampling."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
import enum
import gin
import tensorflow.compat.v1 as tf
class DQNTarget(enum.Enum):
"""Enum constants for DQN target network variants.
Attributes:
notarget: No target network used. Next-step action-value computed using
using online Q network.
normal: Target network used to select action and evaluate next-step
action-value.
doubleq: Double-Q Learning as proposed by https://arxiv.org/abs/1509.06461.
Action is selected by online Q network but evaluated using target network.
"""
notarget = 'notarget'
normal = 'normal'
doubleq = 'doubleq'
gin.constant('DQNTarget.notarget', DQNTarget.notarget)
gin.constant('DQNTarget.normal', DQNTarget.normal)
gin.constant('DQNTarget.doubleq', DQNTarget.doubleq)
@gin.configurable
def discrete_q_graph(q_func,
transition,
target_network_type=DQNTarget.normal,
gamma=1.0,
loss_fn=tf.losses.huber_loss,
extra_callback=None):
"""Construct loss/summary graph for discrete Q-Learning (DQN).
This Q-function loss implementation is derived from OpenAI baselines.
This function supports dynamic batch sizes.
Args:
q_func: Python function that takes in state, scope as input
and returns a tensor Q(a_0...a_N) for each action a_0...a_N, and
intermediate endpoints dictionary.
transition: SARSTransition namedtuple.
target_network_type: Option to use Q Learning without target network, Q
Learning with a target network (default), or Double-Q Learning with a
target network.
gamma: Discount factor.
loss_fn: Function that computes the td_loss tensor. Takes as arguments
(target value tensor, predicted value tensor).
extra_callback: Optional function that takes in (transition, end_points_t,
end_points_tp1) and adds additional TF graph elements.
Returns:
A tuple (loss, summaries) where loss is a scalar loss tensor to minimize,
summaries are TensorFlow summaries.
"""
state = transition.state
action = transition.action
state_p1 = transition.state_p1
reward = transition.reward
done = transition.done
q_t, end_points_t = q_func(state, scope='q_func')
num_actions = q_t.get_shape().as_list()[1]
q_t_selected = tf.reduce_sum(q_t * tf.one_hot(action, num_actions), 1)
if gamma != 0:
if target_network_type == DQNTarget.notarget:
# Evaluate target values using the current net only.
q_tp1_using_online_net, end_points_tp1 = q_func(
state_p1, scope='q_func', reuse=True)
q_tp1_best = tf.reduce_max(q_tp1_using_online_net, 1)
elif target_network_type == DQNTarget.normal:
# Target network Q values at t+1.
q_tp1_target, end_points_tp1 = q_func(state_p1, scope='target_q_func')
q_tp1_best = tf.reduce_max(q_tp1_target, 1)
elif target_network_type == DQNTarget.doubleq:
q_tp1_target, end_points_tp1 = q_func(state_p1, scope='target_q_func')
# Q values at t+1.
q_tp1_using_online_net, _ = q_func(state_p1, scope='q_func', reuse=True)
# Q values for action we select at t+1.
q_tp1_best_using_online_net = tf.one_hot(
tf.argmax(q_tp1_using_online_net, 1), num_actions)
# Q value of target network at t+1, but for action selected by online net.
q_tp1_best = tf.reduce_sum(q_tp1_target * q_tp1_best_using_online_net, 1)
else:
logging.error('Invalid target_network_mode %s', target_network_type)
q_tp1_best_masked = (1.0 - done) * q_tp1_best
q_t_selected_target = tf.stop_gradient(reward + gamma * q_tp1_best_masked)
else:
q_t_selected_target = tf.stop_gradient(reward)
td_error = q_t_selected - q_t_selected_target
if extra_callback is not None:
extra_callback(transition, end_points_t, end_points_tp1)
tf.summary.histogram('td_error', td_error)
tf.summary.histogram('q_t_selected', q_t_selected)
tf.summary.histogram('q_t_selected_target', q_t_selected_target)
tf.summary.scalar('mean_q_t_selected', tf.reduce_mean(q_t_selected))
td_loss = loss_fn(q_t_selected_target, q_t_selected)
tf.summary.scalar('td_loss', td_loss)
reg_loss = tf.losses.get_regularization_loss()
tf.summary.scalar('reg_loss', reg_loss)
loss = tf.losses.get_total_loss()
tf.summary.scalar('total_loss', loss)
summaries = tf.summary.merge_all()
return loss, summaries
@gin.configurable
def random_sample_box(batch_size,
action_size,
num_samples,
minval=-1.,
maxval=1.):
"""Samples actions for each batch element uniformly from a hyperrectangle.
Args:
batch_size: tf.Tensor (dtype=tf.int32) or int representing the minibatch
size of the state tensors.
action_size: (int) Size of continuous actio space.
num_samples: (int) Number of action samples for each minibatch element.
minval: (float) Minimum value for each action dimension.
maxval: (float) Maximum value for each action dimension.
Returns:
Tensor (dtype=tf.float32) of shape (batch_size * num_samples, action_size).
"""
return tf.random_uniform(
(batch_size * num_samples, action_size), minval=minval, maxval=maxval)
def _q_tp1_notarget(q_func, state_p1, batch_size, num_samples, random_actions):
"""Evaluate target values at t+1 using online Q function (no target network).
Args:
q_func: Python function that takes in state, action, scope as input
and returns Q(state, action) and intermediate endpoints dictionary.
state_p1: Tensor (potentially any dtype) representing next .
batch_size: tf.Tensor (dtype=tf.int32) or int representing the minibatch
size of the state tensors.
num_samples: (int) Number of action samples for each minibatch element.
random_actions: tf.Tensor (dtype=tf.float32) of candidate actions.
Returns:
Tuple (q_tp1_best, end_points_tp1). See _get_q_tp1 docs for description.
"""
# Evaluate target values using the current net only.
q_tp1_using_online_net, end_points_tp1 = q_func(
state_p1, random_actions, scope='q_func', reuse=True)
q_tp1_using_online_net_2d = tf.reshape(
q_tp1_using_online_net, (batch_size, num_samples))
q_tp1_best = tf.reduce_max(q_tp1_using_online_net_2d, 1)
return q_tp1_best, end_points_tp1
def _q_tp1_normal(q_func, state_p1, batch_size, num_samples, random_actions):
"""Evaluate target values at t+1 using separate target network network.
Args:
q_func: Python function that takes in state, action, scope as input
and returns Q(state, action) and intermediate endpoints dictionary.
state_p1: Tensor (potentially any dtype) representing next .
batch_size: tf.Tensor (dtype=tf.int32) or int representing the minibatch
size of the state tensors.
num_samples: (int) Number of action samples for each minibatch element.
random_actions: tf.Tensor (dtype=tf.float32) of candidate actions.
Returns:
Tuple (q_tp1_best, end_points_tp1). See _get_q_tp1 docs for description.
"""
q_tp1_target, end_points_tp1 = q_func(
state_p1, random_actions, scope='target_q_func')
q_tp1_target_2d = tf.reshape(q_tp1_target, (batch_size, num_samples))
q_tp1_best = tf.reduce_max(q_tp1_target_2d, 1)
return q_tp1_best, end_points_tp1
def _q_tp1_doubleq(q_func,
state_p1,
batch_size,
action_size,
num_samples,
random_actions):
"""Q(s_p1, a_p1) via Double Q Learning with stochastic sampling.
Args:
q_func: Python function that takes in state, action, scope as input
and returns Q(state, action) and intermediate endpoints dictionary.
state_p1: Tensor (potentially any dtype) representing next .
batch_size: tf.Tensor (dtype=tf.int32) or int representing the minibatch
size of the state tensors.
action_size: (int) Size of continuous actio space.
num_samples: (int) Number of action samples for each minibatch element.
random_actions: tf.Tensor (dtype=tf.float32) of candidate actions.
Returns:
Tuple (q_tp1_best, end_points_tp1). See _get_q_tp1 docs for description.
"""
# Target Q values at t+1, for action selected by online net.
q_tp1_using_online_net, end_points_tp1 = q_func(
state_p1, random_actions, scope='q_func', reuse=True)
q_tp1_using_online_net_2d = tf.reshape(
q_tp1_using_online_net, (batch_size, num_samples))
q_tp1_indices_using_online_net = tf.argmax(q_tp1_using_online_net_2d, 1)
random_actions = tf.reshape(
random_actions, (batch_size, num_samples, action_size))
batch_indices = tf.cast(tf.range(batch_size), tf.int64)
indices = tf.stack([batch_indices, q_tp1_indices_using_online_net], axis=1)
# For each batch item, slice into the num_samples,
# action subarray using the corresponding to yield the chosen action.
q_tp1_best_action = tf.gather_nd(random_actions, indices)
q_tp1_best, end_points_tp1 = q_func(
state_p1, q_tp1_best_action, scope='target_q_func')
return q_tp1_best, end_points_tp1
def _get_q_tp1(q_func,
state_p1,
batch_size,
action_size,
num_samples,
random_sample_fn,
target_network_type):
"""Computes non-discounted Bellman target Q(s_p1, a_p1).
Args:
q_func: Python function that takes in state, action, scope as input
and returns Q(state, action) and intermediate endpoints dictionary.
state_p1: Tensor (potentially any dtype) representing next .
batch_size: tf.Tensor (dtype=tf.int32) or int representing the minibatch
size of the state tensors.
action_size: (int) Size of continuous action space.
num_samples: (int) Number of action samples for each minibatch element.
random_sample_fn: See random_continuous_q_graph.
target_network_type: See random_continuous_q_graph.
Returns:
Tuple (q_tp1_best, end_points_tp1) where q_tp1_best is a tensor of best
next-actions as computed by a greedy stochastic policy for each minibatch
element in state_p1. end_points_tp1 is any auxiliary ouputs computed via
q_func.
"""
random_actions = random_sample_fn(batch_size, action_size, num_samples)
if target_network_type == DQNTarget.notarget:
q_tp1_best, end_points_tp1 = _q_tp1_notarget(
q_func, state_p1, batch_size, num_samples, random_actions)
elif target_network_type == DQNTarget.normal:
q_tp1_best, end_points_tp1 = _q_tp1_normal(
q_func, state_p1, batch_size, num_samples, random_actions)
elif target_network_type == DQNTarget.doubleq:
q_tp1_best, end_points_tp1 = _q_tp1_doubleq(
q_func, state_p1, batch_size, action_size, num_samples, random_actions)
else:
logging.error('Invalid target_network_mode %s', target_network_type)
return q_tp1_best, end_points_tp1
@gin.configurable
def random_continuous_q_graph(q_func,
transition,
random_sample_fn=random_sample_box,
num_samples=10,
target_network_type=DQNTarget.normal,
gamma=1.0,
loss_fn=tf.losses.huber_loss,
extra_callback=None,
log_input_image=True):
"""Construct loss/summary graph for continuous Q-Learning via sampling.
This Q-function loss implementation is derived from OpenAI baselines, extended
to work in the continuous case. This function supports batch sizes whose value
is only known at runtime.
Args:
q_func: Python function that takes in state, action, scope as input
and returns Q(state, action) and intermediate endpoints dictionary.
transition: SARSTransition namedtuple.
random_sample_fn: Function that samples actions for Bellman Target
maximization.
num_samples: For each state, how many actions to randomly sample in order
to compute approximate max over Q values.
target_network_type: Option to use Q Learning without target network, Q
Learning with a target network (default), or Double-Q Learning with a
target network.
gamma: Discount factor.
loss_fn: Function that computes the td_loss tensor. Takes as arguments
(target value tensor, predicted value tensor).
extra_callback: Optional function that takes in (transition, end_points_t,
end_points_tp1) and adds additional TF graph elements.
log_input_image: If True, creates an image summary of the first element of
the state tuple (assumed to be an image tensor).
Returns:
A tuple (loss, summaries) where loss is a scalar loss tensor to minimize,
summaries are TensorFlow summaries.
"""
state = transition.state
action = transition.action
state_p1 = transition.state_p1
reward = transition.reward
done = transition.done
q_t_selected, end_points_t = q_func(state, action, scope='q_func')
if log_input_image:
tf.summary.image('input_image', state[0])
if gamma != 0:
action_size = action.get_shape().as_list()[1]
batch_size = tf.shape(done)[0]
q_tp1_best, end_points_tp1 = _get_q_tp1(
q_func, state_p1, batch_size, action_size, num_samples,
random_sample_fn, target_network_type)
# Bellman eq is Q(s,a) = r + max_{a_p1} Q(s_p1, a_p1)
# Q(s_T, a_T) is regressed to r, and the max_{a_p1} Q(s_p1, a_p1)
# term is masked to zero.
q_tp1_best_masked = (1.0 - done) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = tf.stop_gradient(reward + gamma * q_tp1_best_masked)
else:
# Supervised Target.
end_points_tp1 = None
q_t_selected_target = reward
td_error = q_t_selected - q_t_selected_target
if extra_callback is not None:
extra_callback(transition, end_points_t, end_points_tp1)
tf.summary.histogram('td_error', td_error)
tf.summary.histogram('q_t_selected', q_t_selected)
tf.summary.histogram('q_t_selected_target', q_t_selected_target)
tf.summary.scalar('mean_q_t_selected', tf.reduce_mean(q_t_selected))
td_loss = loss_fn(q_t_selected_target, q_t_selected)
tf.summary.scalar('td_loss', td_loss)
reg_loss = tf.losses.get_regularization_loss()
tf.summary.scalar('reg_loss', reg_loss)
loss = tf.losses.get_total_loss()
tf.summary.scalar('total_loss', loss)
summaries = tf.summary.merge_all()
return loss, summaries
def _get_tau_var(tau, tau_curriculum_steps):
"""Variable which increases linearly from 0 to tau over so many steps."""
if tau_curriculum_steps > 0:
tau_var = tf.get_variable('tau', [],
initializer=tf.constant_initializer(0.0),
trainable=False)
tau_var = tau_var.assign(
tf.minimum(float(tau), tau_var + float(tau) / tau_curriculum_steps))
else:
tau_var = tf.get_variable('tau', [],
initializer=tf.constant_initializer(float(tau)),
trainable=False)
return tau_var
def _get_pcl_values(q_func, not_pad, state, tstep, action,
random_sample_fn, num_samples, target_network_type):
"""Computes Q- and V-values for batch of episodes."""
# get dimensions of input
batch_size = tf.shape(state)[0]
episode_length = tf.shape(state)[1]
img_height = state.get_shape().as_list()[2]
img_width = state.get_shape().as_list()[3]
img_channels = state.get_shape().as_list()[4]
action_size = action.get_shape().as_list()[2]
# flatten input so each row corresponds to single transition
flattened_state = tf.reshape(state, [batch_size * episode_length,
img_height, img_width, img_channels])
flattened_tstep = tf.reshape(tstep, [batch_size * episode_length])
flattened_action = tf.reshape(action,
[batch_size * episode_length, action_size])
flattened_q_t, end_points_q_t = q_func(
(flattened_state, flattened_tstep), flattened_action, scope='q_func')
flattened_v_t, end_points_v_t = _get_q_tp1(
q_func, (flattened_state, flattened_tstep),
batch_size * episode_length, action_size, num_samples,
random_sample_fn, target_network_type)
# reshape to correspond to original input
q_t = not_pad * tf.reshape(flattened_q_t, [batch_size, episode_length])
v_t = not_pad * tf.reshape(flattened_v_t, [batch_size, episode_length])
v_t = tf.stop_gradient(v_t)
return q_t, v_t, end_points_q_t, end_points_v_t
@gin.configurable
def random_continuous_pcl_graph(q_func,
transition,
random_sample_fn=random_sample_box,
num_samples=10,
target_network_type=None,
gamma=1.0,
rollout=20,
loss_fn=tf.losses.huber_loss,
tau=1.0,
tau_curriculum_steps=0,
stop_gradient_on_adv=False,
extra_callback=None):
"""Construct loss/summary graph for continuous PCL via sampling.
This is an implementation of "Corrected MC", a specific variant of PCL.
See https://arxiv.org/abs/1802.10264
Args:
q_func: Python function that takes in state, action, scope as input
and returns Q(state, action) and intermediate endpoints dictionary.
transition: SARSTransition namedtuple containing a batch of episodes.
random_sample_fn: Function that samples actions for Bellman Target
maximization.
num_samples: For each state, how many actions to randomly sample in order
to compute approximate max over Q values.
target_network_type: Option to use Q Learning without target network, Q
Learning with a target network (default), or Double-Q Learning with a
target network.
gamma: Float discount factor.
rollout: Integer rollout parameter. When rollout = 1 we recover Q-learning.
loss_fn: Function that computes the td_loss tensor. Takes as arguments
(target value tensor, predicted value tensor).
tau: Coefficient on correction terms (i.e. on advantages).
tau_curriculum_steps: Increase tau linearly from 0 over this many steps.
stop_gradient_on_adv: Whether to allow training of q-values to targets in
the past.
extra_callback: Optional function that takes in (transition, end_points_t,
end_points_tp1) and adds additional TF graph elements.
Returns:
A tuple (loss, summaries) where loss is a scalar loss tensor to minimize,
summaries are TensorFlow summaries.
"""
if target_network_type is None:
target_network_type = DQNTarget.normal
tau_var = _get_tau_var(tau, tau_curriculum_steps)
state, tstep = transition.state
action = transition.action
reward = transition.reward
done = transition.done
not_pad = tf.to_float(tf.equal(tf.cumsum(done, axis=1, exclusive=True), 0.0))
reward *= not_pad
q_t, v_t, end_points_q_t, end_points_v_t = _get_pcl_values(
q_func, not_pad, state, tstep, action,
random_sample_fn, num_samples, target_network_type)
discounted_sum_rewards = discounted_future_sum(reward, gamma, rollout)
advantage = q_t - v_t # equivalent to tau * log_pi in PCL
if stop_gradient_on_adv:
advantage = tf.stop_gradient(advantage)
discounted_sum_adv = discounted_future_sum(
shift_values(advantage, gamma, 1), gamma, rollout - 1)
last_v = shift_values(v_t, gamma, rollout)
# values we regress on
pcl_values = q_t
# targets we regress to
pcl_targets = -tau_var * discounted_sum_adv + discounted_sum_rewards + last_v
# error is discrepancy between values and targets
pcl_error = pcl_values - pcl_targets
if extra_callback:
extra_callback(transition, end_points_q_t, end_points_v_t)
tf.summary.histogram('pcl_error', pcl_error)
tf.summary.histogram('q_t', q_t)
tf.summary.histogram('v_t', v_t)
tf.summary.scalar('mean_q_t', tf.reduce_mean(q_t))
pcl_loss = loss_fn(pcl_values, pcl_targets, weights=not_pad)
tf.summary.scalar('pcl_loss', pcl_loss)
reg_loss = tf.losses.get_regularization_loss()
tf.summary.scalar('reg_loss', reg_loss)
loss = tf.losses.get_total_loss()
tf.summary.scalar('total_loss', loss)
summaries = tf.summary.merge_all()
return loss, summaries
def shift_values(values, discount, rollout):
"""Shift values up by some amount of time.
Args:
values: Tensor of shape [batch_size, time].
discount: Scalar (float) representing discount factor.
rollout: Amount (int) to shift values in time by.
Returns:
Tensor of shape [batch_size, time] with values shifted.
"""
final_values = tf.zeros_like(values[:, 0])
roll_range = tf.cumsum(tf.ones_like(values[:, :rollout]), 0,
exclusive=True, reverse=True)
final_pad = tf.expand_dims(final_values, 1) * discount ** roll_range
return tf.concat([discount ** rollout * values[:, rollout:],
final_pad], 1)
def discounted_future_sum(values, discount, rollout):
"""Discounted future sum of values.
Args:
values: A tensor of shape [batch_size, episode_length].
discount: Scalar discount factor.
rollout: Number of steps to compute sum.
Returns:
Tensor of same shape as values.
"""
if not rollout:
return tf.zeros_like(values)
discount_filter = tf.reshape(
discount ** tf.range(float(rollout)), [-1, 1, 1])
expanded_values = tf.concat(
[values, tf.zeros([tf.shape(values)[0], rollout - 1])], 1)
conv_values = tf.squeeze(tf.nn.conv1d(
tf.expand_dims(expanded_values, -1), discount_filter,
stride=1, padding='VALID'), -1)
return conv_values
| [
"[email protected]"
] | |
4e8d14003c2e112ef076b89c4c8a3ad6613f9a2c | 8da91c26d423bacbeee1163ac7e969904c7e4338 | /pyvisdk/do/customization_failed.py | b63b14e03d5fddb6d06ae4f32d77239d433f8930 | [] | no_license | pexip/os-python-infi-pyvisdk | 5d8f3a3858cdd61fb76485574e74ae525cdc7e25 | 1aadea0afbc306d09f6ecb9af0e683dbbf961d20 | refs/heads/master | 2023-08-28T02:40:28.789786 | 2020-07-16T04:00:53 | 2020-07-16T04:00:53 | 10,032,240 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,169 | py |
import logging
from pyvisdk.exceptions import InvalidArgumentError
########################################
# Automatically generated, do not edit.
########################################
log = logging.getLogger(__name__)
def CustomizationFailed(vim, *args, **kwargs):
'''The customization sequence in the guest failed.'''
obj = vim.client.factory.create('{urn:vim25}CustomizationFailed')
# do some validation checking...
if (len(args) + len(kwargs)) < 5:
raise IndexError('Expected at least 6 arguments got: %d' % len(args))
required = [ 'template', 'chainId', 'createdTime', 'key', 'userName' ]
optional = [ 'logLocation', 'changeTag', 'computeResource', 'datacenter', 'ds', 'dvs',
'fullFormattedMessage', 'host', 'net', 'vm', 'dynamicProperty', 'dynamicType' ]
for name, arg in zip(required+optional, args):
setattr(obj, name, arg)
for name, value in kwargs.items():
if name in required + optional:
setattr(obj, name, value)
else:
raise InvalidArgumentError("Invalid argument: %s. Expected one of %s" % (name, ", ".join(required + optional)))
return obj
| [
"[email protected]"
] | |
23cb6c73db0e3711ff0ecbd0b6aa7165e94b3584 | a01fb7bb8e8738a3170083d84bc3fcfd40e7e44f | /python3/module/pandas/df/sql/join.py | 540fb2077f46a30f47e810c2b98ebc2c0a79da73 | [] | no_license | jk983294/CommonScript | f07acf603611b4691b176aa4a02791ef7d4d9370 | 774bcbbae9c146f37312c771c9e867fb93a0c452 | refs/heads/master | 2023-08-21T17:50:19.036159 | 2023-08-16T00:22:03 | 2023-08-16T00:22:03 | 42,732,160 | 5 | 0 | null | null | null | null | UTF-8 | Python | false | false | 739 | py | import pandas as pd
import numpy as np
df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': np.random.randn(4)})
df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'], 'value': np.random.randn(4)})
print(df1)
print(df2)
# SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key;
print(pd.merge(df1, df2, on='key'))
# in case join key is different
print(pd.merge(df1, df2, left_on='key', right_on='key'))
# SELECT * FROM df1 LEFT OUTER JOIN df2 ON df1.key = df2.key;
print(pd.merge(df1, df2, on='key', how='left'))
# SELECT * FROM df1 RIGHT OUTER JOIN df2 ON df1.key = df2.key;
print(pd.merge(df1, df2, on='key', how='right'))
# SELECT * FROM df1 FULL OUTER JOIN df2 ON df1.key = df2.key;
print(pd.merge(df1, df2, on='key', how='outer'))
| [
"[email protected]"
] | |
478b126ab280b9343347c1ee8bc9238dd9f45703 | 86da8c4d616a78afc7cd09711b0151e5f852a8b8 | /pythonprograms/LanguageFundamentals/Logicaloperator.py | 98dae1b5e2d38ecb15661dfb77541e77356b7768 | [] | no_license | sharijamusthafa/luminarpython | d1d3274d23d93af2c5e4db7d2652e8cb46b133aa | 8ebd75ea5f734e5061a7138153a2c6b1cd43a738 | refs/heads/master | 2022-12-23T22:45:40.194242 | 2020-10-07T16:40:09 | 2020-10-07T16:40:09 | 290,109,565 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 32 | py | num1=2
num2=4
print(num1&num2)
| [
"[email protected]"
] | |
f46f3f29cb80c2826087623308da18f78f72a5fc | 91f948b849a03f27c96aa6b76980a5fa68970b70 | /experiments/__init__.py | de913a706b51dac74f50aafe9917d627f649419c | [
"MIT"
] | permissive | satyam-cyc/MASS-Learning | 3d987af7622f604db02b64313179590651285170 | 0d40de5227c94d1a5e4b18e44d16374e12821ad2 | refs/heads/master | 2022-01-10T02:23:06.670225 | 2019-06-11T19:41:35 | 2019-06-11T19:41:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 304 | py | from .LogModelParameters import LogModelParameters
from .MASSLossTerms import MASSLossTerms
from .ModelLossAndAccuracy import ModelLossAndAccuracy
from .OODDetection import OODDetection
from .SaveModelParameters import SaveModelParameters
from .UncertaintyQuantification import UncertaintyQuantification
| [
"[email protected]"
] | |
cfe436e359c52cb80c53b6b3d45d67431576f12c | 16f173135e81215d05ee8f475c13a16e3796e1fa | /Deep_Learning_with_Keras_in_Python/3.Improving_Your_Model_Performance/Learning the digits.py | 4219e773851dd4e8ea25cc68e96088e4bed25bb3 | [] | no_license | jerry-mkpong/DataCamp | 1b53821f1a32b48efdc8465251401721ba75bb56 | 10445bad35ef11567910ffab6ac70a980555a1b7 | refs/heads/master | 2022-11-11T03:57:21.923366 | 2020-06-28T17:36:10 | 2020-06-28T17:36:10 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,060 | py | '''
You're going to build a model on the digits dataset, a sample dataset that comes pre-loaded with scikit learn. The digits dataset consist of 8x8 pixel handwritten digits from 0 to 9:
You want to distinguish between each of the 10 possible digits given an image, so we are dealing with multi-class classification.
The dataset has already been partitioned into X_train, y_train, X_test, and y_test using 30% of the data as testing data. The labels are one-hot encoded vectors, so you don't need to use Keras to_categorical() function.
Let's build this new model!
'''
# Instantiate a Sequential model
model = Sequential()
# Input and hidden layer with input_shape, 16 neurons, and relu
model.add(Dense(16, input_shape = (64,), activation = 'relu'))
# Output layer with 10 neurons (one per digit) and softmax
model.add(Dense(10, activation='softmax'))
# Compile your model
model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
# Test if your model works and can process input data
print(model.predict(X_train)) | [
"[email protected]"
] | |
cefea000be2b8713b9d4ea548c735c4984caf7de | 3904a5773c5aa047692895dce1225be7d84f5cc7 | /ML_AI_TechWithTim/K-Means/K_Means.py | f33bc323b87c4aba7ff873f2b6d3cbe38641d449 | [] | no_license | snehilk1312/ML_1 | 063038586296c4f6f0ab92422a6c60dd007c4068 | 8e3b081b1037ab999ca78fa282ce7041730d082a | refs/heads/master | 2020-09-07T20:01:45.509060 | 2020-03-15T15:44:54 | 2020-03-15T15:44:54 | 220,898,676 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,057 | py | # Importing modules
import numpy as np
import sklearn
from sklearn.preprocessing import scale
from sklearn.datasets import load_digits
from sklearn.cluster import KMeans
from sklearn import metrics
# Loading Data sets
digits = load_digits()
data = scale(digits.data)
y = digits.target
k = len(np.unique(y)) # or here k=10
samples, features = data.shape
def bench_k_means(estimator, name, data):
estimator.fit(data)
print('%-9s\t%i\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f'
% (name, estimator.inertia_,
metrics.homogeneity_score(y, estimator.labels_),
metrics.completeness_score(y, estimator.labels_),
metrics.v_measure_score(y, estimator.labels_),
metrics.adjusted_rand_score(y, estimator.labels_),
metrics.adjusted_mutual_info_score(y, estimator.labels_),
metrics.silhouette_score(data, estimator.labels_,
metric='euclidean')))
clf = KMeans(n_clusters=k, init="random", n_init=10)
bench_k_means(clf, "1", data)
| [
"[email protected]"
] | |
00bb139bc7606403b576ce7cbadcf0745f8fc7fb | cc1eeb43eb9e4e83078f4c87e40a5c7fe56b109f | /Day05/shuixianhua.py | 8cb8f1e3429d4bb2394b367a322d9a2886c2fb28 | [] | no_license | test-wsl/learn_100 | d57ac4e8e7c062472273622351374decbae6d213 | 9fbb83455c15115b3cdec80d17c542e0aba2a6df | refs/heads/master | 2020-08-29T22:43:10.800177 | 2019-11-04T08:17:38 | 2019-11-04T08:17:38 | 218,192,964 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 326 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
寻找水仙花数
水仙花数为一个三位数，每位上的立方之和正好等于本身
version: 0.1
"""
for num in range(100, 1000):
low = num %10
mid = num // 10 %10
high = num // 100
if num == low ** 3 + mid ** 3 + high **3:
print (num)
| [
"weishl"
] | weishl |
e953daf74af26ba80d58f622e7985c62eaf4cadd | 76de53bd3923a57a36d0ed4b4a900b56050ebb31 | /SW Expert Academy/190926/1263_사람 네트워크2.py | 61dbab0dcf1c40b17376a408ca7e36d21934b1bb | [] | no_license | Seungjin22/Algorithm | 5b4fd53ae5742d830594d116e536531959b3454d | 753dda47334e445f7a9e1e41df5e44564d99e79e | refs/heads/master | 2020-09-04T08:54:01.359518 | 2020-02-03T10:41:05 | 2020-02-03T10:41:05 | 219,697,780 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 821 | py | import sys
sys.stdin = open('1263_input.txt')
def AllPairsShortest(D):
for k in range(N):
for i in range(N):
if i != k:
for j in range(N):
if j != k and j != i:
D[i][j] = min(D[i][k] + D[k][j], D[i][j])
T = int(input())
for tc in range(1, T + 1):
data = list(map(int, input().split()))
N = data.pop(0)
dist = [[987654321] * N for _ in range(N)]
idx = 0
for i in range(N):
for j in range(N):
if i == j:
dist[i][j] = 0
if data[idx]:
dist[i][j] = data[idx]
idx += 1
AllPairsShortest(dist)
mini = 987654321
for i in range(N):
if sum(dist[i]) < mini:
mini = sum(dist[i])
print('#{} {}'.format(tc, mini))
| [
"[email protected]"
] | |
b4577f6dc2ca7a3c75449f92e21cad3aa1b6b5fe | 163bbb4e0920dedd5941e3edfb2d8706ba75627d | /Code/CodeRecords/2814/60652/240209.py | 19b8d713af73e09dfece90f18c9ba12646de0b4a | [] | no_license | AdamZhouSE/pythonHomework | a25c120b03a158d60aaa9fdc5fb203b1bb377a19 | ffc5606817a666aa6241cfab27364326f5c066ff | refs/heads/master | 2022-11-24T08:05:22.122011 | 2020-07-28T16:21:24 | 2020-07-28T16:21:24 | 259,576,640 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 162 | py | n = int(input())
l=list(map(int,input().split()))
l.sort()
num_s=0
wait_time=0
for i in l:
if i>=wait_time:
num_s+=1
wait_time+=i
print(num_s) | [
"[email protected]"
] |
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