Reset23/the-stack-v2-processed · Datasets at Hugging Face

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from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import Column, Integer, String Base = declarative_base() class FlashCard(Base): __tablename__ = "flashcard" id = Column(Integer, primary_key=True) question = Column(String) answer = Column(String) box_number = Column(Integer)

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# Generated by Django 3.1.3 on 2020-12-13 15:42 import datetime from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('manager', '0003_auto_20201213_2240'), ] operations = [ migrations.AlterField( model_name='purchaseproduct', name='time_create', field=models.DateTimeField(default=datetime.datetime(2020, 12, 13, 22, 42, 23, 127128)), ), ]

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""" Idempotent API for managing python packages """ from fabtools.python import * def package(pkg_name, virtualenv=None, use_sudo=False): """ I can haz python package """ if not is_installed(pkg_name): install(pkg_name, virtualenv, use_sudo) def packages(pkg_list, virtualenv=None, use_sudo=False): """ I can haz python packages """ pkg_list = [pkg for pkg in pkg_list if not is_installed(pkg)] if pkg_list: install(pkg_list, virtualenv, use_sudo)

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# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the License); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' model = dict( pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, groups=64, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), style='pytorch'))

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import scrapy from scrapy.loader import ItemLoader from itemloaders.processors import TakeFirst from datetime import datetime from umweltbank.items import Article class UmweltbankSpider(scrapy.Spider): name = 'umweltbank' start_urls = ['https://bankundumwelt.de/'] def parse(self, response): links = response.xpath('//a[@class="post-link link"]/@href').getall() yield from response.follow_all(links, self.parse_article) def parse_article(self, response): if 'pdf' in response.url: return item = ItemLoader(Article()) item.default_output_processor = TakeFirst() title = response.xpath('//h1/text()').get() if title: title = title.strip() date = response.xpath('//div[@class="post-meta-date meta-info"]/text()').get() if date: date = date.strip() content = response.xpath('//div[@class="post-content"]//text()').getall() content = [text for text in content if text.strip()] content = "\n".join(content).strip() item.add_value('title', title) item.add_value('date', date) item.add_value('link', response.url) item.add_value('content', content) return item.load_item()

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basket = [21, 12,33, 35, 99] print(basket) print(len(basket)) #33 gets pops because it is 2nd number in the array print(basket.pop(2)) print(basket) #extend basket1 = [1000, 2000, 3000] print(basket.extend(basket1)) print(basket) #append - last to the list print(basket.append(700)) print(basket) #index print(basket.index(21)) print(basket) print(basket.sort()) #insert print(basket.insert(5, 1020))

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""" WSGI config for ayush_crowdbotics_378 project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.11/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "ayush_crowdbotics_378.settings") application = get_wsgi_application()

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# ---------------------------------------------------------------------------- # Copyright (c) 2013--, BP development team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file LICENSE, distributed with this software. # ---------------------------------------------------------------------------- from ._bp import BP from ._io import parse_newick from ._conv import to_skbio_treenode, from_skbio_treenode, to_skbio_treearray __all__ = ['BP', 'parse_newick', 'to_skbio_treenode', 'from_skbio_treenode', 'to_skbio_treearray']

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#!/home/jinesh/Documents/djangoproj/bin/python # -*- coding: utf-8 -*- import re import sys from libpasteurize.main import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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#!/usr/bin/env python2.7 import argparse import os import qrcode import qrcode.image.pil import sqlite3 import sys import urllib class AuthenticatorAccount(object): def __init__(self, account_name, account_desc, secret): self.account_name = account_name self.account_desc = account_desc self.secret = secret def __repr__(self): return "AuthenticatorAccount@%s%s" % (hex(id(self))[2:], self.__dict__) def __main__(): parser = argparse.ArgumentParser() parser.add_argument("database", help="The SQLite database file.") args = parser.parse_args() if not os.path.isfile(args.database): sys.stderr.write("Unable to open %s.\n" % (args.database,)) sys.stderr.flush() sys.exit(1) conn = sqlite3.connect(args.database) conn.row_factory = sqlite3.Row cursor = conn.cursor() cursor.execute("SELECT * FROM accounts ORDER BY _id;") row = None while True: row = cursor.fetchone() if row is None: break account = AuthenticatorAccount(row['issuer'] or row['original_name'], row['email'], row['secret']) print """Saving "%s" to "qrcode-account-%02d.svg" """[:-1] % (account.account_desc, row['_id']) qr = qrcode.make("otpauth://totp/%s?secret=%s&issuer=%s" % (account.account_desc, account.secret, account.account_name), image_factory=qrcode.image.pil.PilImage) with open("qrcode-account-%02d.png" % (row['_id'],), "wb") as f: qr.save(f) if __name__ == "__main__": __main__()

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#!/usr/bin/env python # Copyright (c) 2014 Wladimir J. van der Laan # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. ''' Run this script from the root of the repository to update all translations from transifex. It will do the following automatically: - fetch all translations using the tx tool - post-process them into valid and committable format - remove invalid control characters - remove location tags (makes diffs less noisy) TODO: - auto-add new translations to the build system according to the translation process ''' from __future__ import division, print_function import subprocess import re import sys import os import io import xml.etree.ElementTree as ET # Name of transifex tool TX = 'tx' # Name of source language file SOURCE_LANG = 'deimos_en.ts' # Directory with locale files LOCALE_DIR = 'src/qt/locale' # Minimum number of messages for translation to be considered at all MIN_NUM_MESSAGES = 10 def check_at_repository_root(): if not os.path.exists('.git'): print('No .git directory found') print('Execute this script at the root of the repository', file=sys.stderr) exit(1) def fetch_all_translations(): if subprocess.call([TX, 'pull', '-f', '-a']): print('Error while fetching translations', file=sys.stderr) exit(1) def find_format_specifiers(s): '''Find all format specifiers in a string.''' pos = 0 specifiers = [] while True: percent = s.find('%', pos) if percent < 0: break specifiers.append(s[percent+1]) pos = percent+2 return specifiers def split_format_specifiers(specifiers): '''Split format specifiers between numeric (Qt) and others (strprintf)''' numeric = [] other = [] for s in specifiers: if s in {'1','2','3','4','5','6','7','8','9'}: numeric.append(s) else: other.append(s) # If both numeric format specifiers and "others" are used, assume we're dealing # with a Qt-formatted message. In the case of Qt formatting (see https://doc.qt.io/qt-5/qstring.html#arg) # only numeric formats are replaced at all. This means "(percentage: %1%)" is valid, without needing # any kind of escaping that would be necessary for strprintf. Without this, this function # would wrongly detect '%)' as a printf format specifier. if numeric: other = [] # numeric (Qt) can be present in any order, others (strprintf) must be in specified order return set(numeric),other def sanitize_string(s): '''Sanitize string for printing''' return s.replace('\n',' ') def check_format_specifiers(source, translation, errors, numerus): source_f = split_format_specifiers(find_format_specifiers(source)) # assert that no source messages contain both Qt and strprintf format specifiers # if this fails, go change the source as this is hacky and confusing! assert(not(source_f[0] and source_f[1])) try: translation_f = split_format_specifiers(find_format_specifiers(translation)) except IndexError: errors.append("Parse error in translation for '%s': '%s'" % (sanitize_string(source), sanitize_string(translation))) return False else: if source_f != translation_f: if numerus and source_f == (set(), ['n']) and translation_f == (set(), []) and translation.find('%') == -1: # Allow numerus translations to omit %n specifier (usually when it only has one possible value) return True errors.append("Mismatch between '%s' and '%s'" % (sanitize_string(source), sanitize_string(translation))) return False return True def all_ts_files(suffix=''): for filename in os.listdir(LOCALE_DIR): # process only language files, and do not process source language if not filename.endswith('.ts'+suffix) or filename == SOURCE_LANG+suffix: continue if suffix: # remove provided suffix filename = filename[0:-len(suffix)] filepath = os.path.join(LOCALE_DIR, filename) yield(filename, filepath) FIX_RE = re.compile(b'[\x00-\x09\x0b\x0c\x0e-\x1f]') def remove_invalid_characters(s): '''Remove invalid characters from translation string''' return FIX_RE.sub(b'', s) # Override cdata escape function to make our output match Qt's (optional, just for cleaner diffs for # comparison, disable by default) _orig_escape_cdata = None def escape_cdata(text): text = _orig_escape_cdata(text) text = text.replace("'", ''') text = text.replace('"', '"') return text def postprocess_translations(reduce_diff_hacks=False): print('Checking and postprocessing...') if reduce_diff_hacks: global _orig_escape_cdata _orig_escape_cdata = ET._escape_cdata ET._escape_cdata = escape_cdata for (filename,filepath) in all_ts_files(): os.rename(filepath, filepath+'.orig') have_errors = False for (filename,filepath) in all_ts_files('.orig'): # pre-fixups to cope with transifex output parser = ET.XMLParser(encoding='utf-8') # need to override encoding because 'utf8' is not understood only 'utf-8' with open(filepath + '.orig', 'rb') as f: data = f.read() # remove control characters; this must be done over the entire file otherwise the XML parser will fail data = remove_invalid_characters(data) tree = ET.parse(io.BytesIO(data), parser=parser) # iterate over all messages in file root = tree.getroot() for context in root.findall('context'): for message in context.findall('message'): numerus = message.get('numerus') == 'yes' source = message.find('source').text translation_node = message.find('translation') # pick all numerusforms if numerus: translations = [i.text for i in translation_node.findall('numerusform')] else: translations = [translation_node.text] for translation in translations: if translation is None: continue errors = [] valid = check_format_specifiers(source, translation, errors, numerus) for error in errors: print('%s: %s' % (filename, error)) if not valid: # set type to unfinished and clear string if invalid translation_node.clear() translation_node.set('type', 'unfinished') have_errors = True # Remove location tags for location in message.findall('location'): message.remove(location) # Remove entire message if it is an unfinished translation if translation_node.get('type') == 'unfinished': context.remove(message) # check if document is (virtually) empty, and remove it if so num_messages = 0 for context in root.findall('context'): for message in context.findall('message'): num_messages += 1 if num_messages < MIN_NUM_MESSAGES: print('Removing %s, as it contains only %i messages' % (filepath, num_messages)) continue # write fixed-up tree # if diff reduction requested, replace some XML to 'sanitize' to qt formatting if reduce_diff_hacks: out = io.BytesIO() tree.write(out, encoding='utf-8') out = out.getvalue() out = out.replace(b' />', b'/>') with open(filepath, 'wb') as f: f.write(out) else: tree.write(filepath, encoding='utf-8') return have_errors if __name__ == '__main__': check_at_repository_root() fetch_all_translations() postprocess_translations()

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#!/usr/bin/env python import subprocess import sys try: from setuptools import setup from setuptools.command.test import test as TestCommand class PyTest(TestCommand): extra_kwargs = {'tests_require': ['pytest', 'mock']} def finalize_options(self): TestCommand.finalize_options(self) self.test_args = [] self.test_suite = True def run_tests(self): import pytest sys.exit(pytest.main(self.test_args)) except ImportError: from distutils.core import setup, Command class PyTest(Command): extra_kwargs = {} user_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): raise SystemExit(subprocess.call([sys.executable, 'runtests.py'])) try: import pypandoc readme = pypandoc.convert('README.md', 'rst') except (IOError, ImportError, OSError, RuntimeError): readme = '' setup(name='hug', version='1.4.0', description='A Python framework that makes developing APIs as simple as possible, but no simpler.', long_description=readme, author='Timothy Crosley', author_email='timothy.crosley@gmail.com', url='https://github.com/timothycrosley/hug', license="MIT", entry_points={ 'console_scripts': [ 'hug = hug:run.terminal', ] }, packages=['hug'], requires=['falcon'], install_requires=['falcon'], cmdclass={'test': PyTest}, keywords='Web, Python, Python3, Refactoring, REST, Framework, RPC', classifiers=['Development Status :: 6 - Mature', 'Intended Audience :: Developers', 'Natural Language :: English', 'Environment :: Console', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Topic :: Software Development :: Libraries', 'Topic :: Utilities'], **PyTest.extra_kwargs)

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# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- # pylint: disable=unused-argument import logging from typing import Any from marshmallow import post_load from azure.ai.ml._schema.core.fields import NestedField from azure.ai.ml._schema.core.schema import PatchedSchemaMeta from .resource_settings_schema import ResourceSettingsSchema module_logger = logging.getLogger(__name__) class ResourceRequirementsSchema(metaclass=PatchedSchemaMeta): requests = NestedField(ResourceSettingsSchema) limits = NestedField(ResourceSettingsSchema) @post_load def make(self, data: Any, **kwargs: Any) -> "ResourceRequirementsSettings": from azure.ai.ml.entities import ResourceRequirementsSettings return ResourceRequirementsSettings(**data)

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import FWCore.ParameterSet.Config as cms from CMGTools.Common.factories.cmgDiObject_cfi import diObjectFactory cmgDiPFCandidateFactory = diObjectFactory.clone( leg1Collection = cms.InputTag("particleFlow"), leg2Collection = cms.InputTag("particleFlow"), ) cmgDiPFCandidate = cms.EDFilter( "DiPFCandidatePOProducer", cfg = cmgDiPFCandidateFactory.clone(), cuts = cms.PSet( ), )

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############################################################################### # # Tests for XlsxWriter. # # SPDX-License-Identifier: BSD-2-Clause # Copyright (c), 2013-2022, John McNamara, jmcnamara@cpan.org # from ..excel_comparison_test import ExcelComparisonTest from ...workbook import Workbook class TestCompareXLSXFiles(ExcelComparisonTest): """ Test file created by XlsxWriter against a file created by Excel. """ def setUp(self): self.set_filename('chart_layout03.xlsx') def test_create_file(self): """Test the creation of an XlsxWriter file with user defined layout.""" workbook = Workbook(self.got_filename) worksheet = workbook.add_worksheet() chart = workbook.add_chart({'type': 'column'}) chart.axis_ids = [68312064, 69198592] data = [ [1, 2, 3, 4, 5], [2, 4, 6, 8, 10], [3, 6, 9, 12, 15], ] worksheet.write_column('A1', data[0]) worksheet.write_column('B1', data[1]) worksheet.write_column('C1', data[2]) chart.add_series({'values': '=Sheet1!$A$1:$A$5'}) chart.add_series({'values': '=Sheet1!$B$1:$B$5'}) chart.add_series({'values': '=Sheet1!$C$1:$C$5'}) chart.set_legend({ 'position': 'overlay_right', 'layout': { 'x': 0.80197353455818043, 'y': 0.3744240303295423, 'width': 0.12858202099737534, 'height': 0.25115157480314959, } }) worksheet.insert_chart('E9', chart) workbook.close() self.assertExcelEqual()

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""" Given a string S and a string T, find the minimum window in S which will contain all the characters in T in complexity O(n). Example: Input: S = "ADOBECODEBANC", T = "ABC" Output: "BANC" Note: If there is no such window in S that covers all characters in T, return the empty string "". If there is such window, you are guaranteed that there will always be only one unique minimum window in S. """ import collections # 2018-6-26 # Minimum Window Substring # https://leetcode.com/problems/minimum-window-substring/discuss/26804/12-lines-Python class Solution: def minWindow(self, s, t): """ :type s: str :type t: str :rtype: str """ need, missing = collections.Counter(t), len(t) print(need,missing) i = I = J = 0 for j, c in enumerate(s, 1): missing -= need[c] > 0 # missing = missing -1 if need[c] > 0 else missing print(j,c,missing,need,i,j,I,J) need[c] -= 1 # 添加c并使得 c : -1 if not missing: # missing <= 0 while i < j and need[s[i]] < 0: need[s[i]] += 1 i += 1 if not J or j - i <= J - I: I, J = i, j #print(need) print(j,c,missing,need,i,j,I,J) return s[I:J] # test S = "ADOBECODEBANC" T = "ABC" test = Solution() res = test.minWindow(S,T) print(res)

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# -*- coding: utf-8 -*- # Copyright 2020 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 # # 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 collections import OrderedDict import functools import re from typing import Dict, Sequence, Tuple, Type, Union import pkg_resources import google.api_core.client_options as ClientOptions # type: ignore from google.api_core import exceptions as core_exceptions # type: ignore from google.api_core import gapic_v1 # type: ignore from google.api_core import retry as retries # type: ignore from google.auth import credentials as ga_credentials # type: ignore from google.oauth2 import service_account # type: ignore from google.cloud.dialogflowcx_v3beta1.services.webhooks import pagers from google.cloud.dialogflowcx_v3beta1.types import webhook from google.cloud.dialogflowcx_v3beta1.types import webhook as gcdc_webhook from google.protobuf import duration_pb2 # type: ignore from google.protobuf import field_mask_pb2 # type: ignore from .transports.base import WebhooksTransport, DEFAULT_CLIENT_INFO from .transports.grpc_asyncio import WebhooksGrpcAsyncIOTransport from .client import WebhooksClient class WebhooksAsyncClient: """Service for managing [Webhooks][google.cloud.dialogflow.cx.v3beta1.Webhook]. """ _client: WebhooksClient DEFAULT_ENDPOINT = WebhooksClient.DEFAULT_ENDPOINT DEFAULT_MTLS_ENDPOINT = WebhooksClient.DEFAULT_MTLS_ENDPOINT service_path = staticmethod(WebhooksClient.service_path) parse_service_path = staticmethod(WebhooksClient.parse_service_path) webhook_path = staticmethod(WebhooksClient.webhook_path) parse_webhook_path = staticmethod(WebhooksClient.parse_webhook_path) common_billing_account_path = staticmethod(WebhooksClient.common_billing_account_path) parse_common_billing_account_path = staticmethod(WebhooksClient.parse_common_billing_account_path) common_folder_path = staticmethod(WebhooksClient.common_folder_path) parse_common_folder_path = staticmethod(WebhooksClient.parse_common_folder_path) common_organization_path = staticmethod(WebhooksClient.common_organization_path) parse_common_organization_path = staticmethod(WebhooksClient.parse_common_organization_path) common_project_path = staticmethod(WebhooksClient.common_project_path) parse_common_project_path = staticmethod(WebhooksClient.parse_common_project_path) common_location_path = staticmethod(WebhooksClient.common_location_path) parse_common_location_path = staticmethod(WebhooksClient.parse_common_location_path) @classmethod def from_service_account_info(cls, info: dict, *args, **kwargs): """Creates an instance of this client using the provided credentials info. Args: info (dict): The service account private key info. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: WebhooksAsyncClient: The constructed client. """ return WebhooksClient.from_service_account_info.__func__(WebhooksAsyncClient, info, *args, **kwargs) # type: ignore @classmethod def from_service_account_file(cls, filename: str, *args, **kwargs): """Creates an instance of this client using the provided credentials file. Args: filename (str): The path to the service account private key json file. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: WebhooksAsyncClient: The constructed client. """ return WebhooksClient.from_service_account_file.__func__(WebhooksAsyncClient, filename, *args, **kwargs) # type: ignore from_service_account_json = from_service_account_file @property def transport(self) -> WebhooksTransport: """Returns the transport used by the client instance. Returns: WebhooksTransport: The transport used by the client instance. """ return self._client.transport get_transport_class = functools.partial(type(WebhooksClient).get_transport_class, type(WebhooksClient)) def __init__(self, *, credentials: ga_credentials.Credentials = None, transport: Union[str, WebhooksTransport] = "grpc_asyncio", client_options: ClientOptions = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, ) -> None: """Instantiates the webhooks client. Args: credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. transport (Union[str, ~.WebhooksTransport]): The transport to use. If set to None, a transport is chosen automatically. client_options (ClientOptions): Custom options for the client. It won't take effect if a ``transport`` instance is provided. (1) The ``api_endpoint`` property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the ``api_endpoint`` property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the ``client_cert_source`` property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used. Raises: google.auth.exceptions.MutualTlsChannelError: If mutual TLS transport creation failed for any reason. """ self._client = WebhooksClient( credentials=credentials, transport=transport, client_options=client_options, client_info=client_info, ) async def list_webhooks(self, request: webhook.ListWebhooksRequest = None, *, parent: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListWebhooksAsyncPager: r"""Returns the list of all webhooks in the specified agent. Args: request (:class:`google.cloud.dialogflowcx_v3beta1.types.ListWebhooksRequest`): The request object. The request message for [Webhooks.ListWebhooks][google.cloud.dialogflow.cx.v3beta1.Webhooks.ListWebhooks]. parent (:class:`str`): Required. The agent to list all webhooks for. Format: ``projects/<Project ID>/locations/<Location ID>/agents/<Agent ID>``. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.dialogflowcx_v3beta1.services.webhooks.pagers.ListWebhooksAsyncPager: The response message for [Webhooks.ListWebhooks][google.cloud.dialogflow.cx.v3beta1.Webhooks.ListWebhooks]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError("If the `request` argument is set, then none of " "the individual field arguments should be set.") request = webhook.ListWebhooksRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.list_webhooks, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata(( ("parent", request.parent), )), ) # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # This method is paged; wrap the response in a pager, which provides # an `__aiter__` convenience method. response = pagers.ListWebhooksAsyncPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response async def get_webhook(self, request: webhook.GetWebhookRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> webhook.Webhook: r"""Retrieves the specified webhook. Args: request (:class:`google.cloud.dialogflowcx_v3beta1.types.GetWebhookRequest`): The request object. The request message for [Webhooks.GetWebhook][google.cloud.dialogflow.cx.v3beta1.Webhooks.GetWebhook]. name (:class:`str`): Required. The name of the webhook. Format: ``projects/<Project ID>/locations/<Location ID>/agents/<Agent ID>/webhooks/<Webhook ID>``. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.dialogflowcx_v3beta1.types.Webhook: Webhooks host the developer's business logic. During a session, webhooks allow the developer to use the data extracted by Dialogflow's natural language processing to generate dynamic responses, validate collected data, or trigger actions on the backend. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError("If the `request` argument is set, then none of " "the individual field arguments should be set.") request = webhook.GetWebhookRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_webhook, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata(( ("name", request.name), )), ) # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response async def create_webhook(self, request: gcdc_webhook.CreateWebhookRequest = None, *, parent: str = None, webhook: gcdc_webhook.Webhook = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> gcdc_webhook.Webhook: r"""Creates a webhook in the specified agent. Args: request (:class:`google.cloud.dialogflowcx_v3beta1.types.CreateWebhookRequest`): The request object. The request message for [Webhooks.CreateWebhook][google.cloud.dialogflow.cx.v3beta1.Webhooks.CreateWebhook]. parent (:class:`str`): Required. The agent to create a webhook for. Format: ``projects/<Project ID>/locations/<Location ID>/agents/<Agent ID>``. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. webhook (:class:`google.cloud.dialogflowcx_v3beta1.types.Webhook`): Required. The webhook to create. This corresponds to the ``webhook`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.dialogflowcx_v3beta1.types.Webhook: Webhooks host the developer's business logic. During a session, webhooks allow the developer to use the data extracted by Dialogflow's natural language processing to generate dynamic responses, validate collected data, or trigger actions on the backend. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, webhook]) if request is not None and has_flattened_params: raise ValueError("If the `request` argument is set, then none of " "the individual field arguments should be set.") request = gcdc_webhook.CreateWebhookRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if webhook is not None: request.webhook = webhook # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.create_webhook, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata(( ("parent", request.parent), )), ) # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response async def update_webhook(self, request: gcdc_webhook.UpdateWebhookRequest = None, *, webhook: gcdc_webhook.Webhook = None, update_mask: field_mask_pb2.FieldMask = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> gcdc_webhook.Webhook: r"""Updates the specified webhook. Args: request (:class:`google.cloud.dialogflowcx_v3beta1.types.UpdateWebhookRequest`): The request object. The request message for [Webhooks.UpdateWebhook][google.cloud.dialogflow.cx.v3beta1.Webhooks.UpdateWebhook]. webhook (:class:`google.cloud.dialogflowcx_v3beta1.types.Webhook`): Required. The webhook to update. This corresponds to the ``webhook`` field on the ``request`` instance; if ``request`` is provided, this should not be set. update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`): The mask to control which fields get updated. If the mask is not present, all fields will be updated. This corresponds to the ``update_mask`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.dialogflowcx_v3beta1.types.Webhook: Webhooks host the developer's business logic. During a session, webhooks allow the developer to use the data extracted by Dialogflow's natural language processing to generate dynamic responses, validate collected data, or trigger actions on the backend. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([webhook, update_mask]) if request is not None and has_flattened_params: raise ValueError("If the `request` argument is set, then none of " "the individual field arguments should be set.") request = gcdc_webhook.UpdateWebhookRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if webhook is not None: request.webhook = webhook if update_mask is not None: request.update_mask = update_mask # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.update_webhook, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata(( ("webhook.name", request.webhook.name), )), ) # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response async def delete_webhook(self, request: webhook.DeleteWebhookRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> None: r"""Deletes the specified webhook. Args: request (:class:`google.cloud.dialogflowcx_v3beta1.types.DeleteWebhookRequest`): The request object. The request message for [Webhooks.DeleteWebhook][google.cloud.dialogflow.cx.v3beta1.Webhooks.DeleteWebhook]. name (:class:`str`): Required. The name of the webhook to delete. Format: ``projects/<Project ID>/locations/<Location ID>/agents/<Agent ID>/webhooks/<Webhook ID>``. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError("If the `request` argument is set, then none of " "the individual field arguments should be set.") request = webhook.DeleteWebhookRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.delete_webhook, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata(( ("name", request.name), )), ) # Send the request. await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) try: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=pkg_resources.get_distribution( "google-cloud-dialogflowcx", ).version, ) except pkg_resources.DistributionNotFound: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo() __all__ = ( "WebhooksAsyncClient", )

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from math import sqrt, floor class Solution: # 递归不可行, 因为包含一个 1, 在事先不知道四平方数定理的情况下, 最常的路径会导致栈被撑爆. # def numSquares(self, n: int) -> int: # if n in self.memo: # return self.memo[n] # result = min([self.numSquares(n-i**2) for i in range(1, floor(sqrt(n))+1)])+1 # self.memo[n] = result # return result # 将 memo 从 `def __init__(self)` 中取出来是关键, 从实例变量变成了类变量, 作弊啊 # 176 ms memo = {0: 0, 1: 1} def numSquares(self, n: int) -> int: if n in self.memo: return self.memo[n] i = 1 while n not in self.memo: i += 1 if i in self.memo: continue self.memo[i] = min((self.memo[i-j*j] for j in range(1, floor(sqrt(i))+1))) + 1 return self.memo[n] if __name__ == "__main__": sol = Solution() print(sol.memo) for i in [12,2,3,4,5,6,7,8,9,10,5373,5374]: print(sol.numSquares(i)) print('='*80) print(sol.numSquares(2)) print(sol.numSquares(8)) print(sol.numSquares(10)) print(sol.numSquares(11)) print(sol.numSquares(12)) print(sol.numSquares(13)) print(sol.numSquares(5673)) print(sol.numSquares(5674))

[ "enginechen07@gmail.com" ]

enginechen07@gmail.com

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/tensorflow/contrib/py2tf/utils/multiple_dispatch_test.py

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2021-04-09T11:51:32.393739

2019-10-16T16:44:23

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# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for multiple_dispatch.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.contrib.py2tf.utils import multiple_dispatch from tensorflow.python.client.session import Session from tensorflow.python.framework.constant_op import constant from tensorflow.python.platform import test class MultipleDispatchTest(test.TestCase): def test_dynamic_is_python(self): a = np.eye(3) also_a = a not_actually_a = np.eye(3) should_be_true1 = multiple_dispatch.dynamic_is(a, also_a) should_be_false1 = multiple_dispatch.dynamic_is_not(a, also_a) should_be_true2 = multiple_dispatch.dynamic_is_not(a, not_actually_a) should_be_false2 = multiple_dispatch.dynamic_is(a, not_actually_a) self.assertTrue(should_be_true1) self.assertTrue(should_be_true2) self.assertFalse(should_be_false1) self.assertFalse(should_be_false2) def test_dynamic_is_tf(self): with Session().as_default(): a = constant([2.0]) also_a = a not_actually_a = constant([2.0]) should_be_true1 = multiple_dispatch.dynamic_is(a, also_a) should_be_false1 = multiple_dispatch.dynamic_is_not(a, also_a) should_be_true2 = multiple_dispatch.dynamic_is_not(a, not_actually_a) should_be_false2 = multiple_dispatch.dynamic_is(a, not_actually_a) self.assertTrue(should_be_true1.eval()) self.assertTrue(should_be_true2.eval()) self.assertFalse(should_be_false1.eval()) self.assertFalse(should_be_false2.eval()) def test_run_cond_python(self): true_fn = lambda: 2.0 false_fn = lambda: 3.0 self.assertEqual(multiple_dispatch.run_cond(True, true_fn, false_fn), 2.0) self.assertEqual(multiple_dispatch.run_cond(False, true_fn, false_fn), 3.0) def test_run_cond_tf(self): true_fn = lambda: constant([2.0]) false_fn = lambda: constant([3.0]) with Session() as sess: out = multiple_dispatch.run_cond(constant(True), true_fn, false_fn) self.assertEqual(sess.run(out), 2.0) out = multiple_dispatch.run_cond(constant(False), true_fn, false_fn) self.assertEqual(sess.run(out), 3.0) def test_run_while_python(self): cond_fn = lambda x, t, s: x > t body_fn = lambda x, t, s: (x * s, t, s) x, _, _ = multiple_dispatch.run_while(cond_fn, body_fn, [3.0, 1.0, 0.5]) self.assertEqual(x, 0.75) x, _, _ = multiple_dispatch.run_while(cond_fn, body_fn, [3.0, 4.0, 0.5]) self.assertEqual(x, 3.0) def test_run_while_tf(self): cond_fn = lambda x, t, s: x > t body_fn = lambda x, t, s: (x * s, t, s) with Session() as sess: x, _, _ = multiple_dispatch.run_while(cond_fn, body_fn, [constant(3.0), 1.0, 0.5]) self.assertEqual(sess.run(x), 0.75) x, _, _ = multiple_dispatch.run_while(cond_fn, body_fn, [constant(3.0), 4.0, 0.5]) self.assertEqual(sess.run(x), 3.0) if __name__ == '__main__': test.main()

[ "gardener@tensorflow.org" ]

gardener@tensorflow.org

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conradylx/Python_Course

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import unittest from exc0 import triangle, trapezoid class FieldsTestCase(unittest.TestCase): def setUp(self): self.a = 2 self.b = 3 self.h = 5 def test_triangle_with_correct_result(self): result = triangle(self.a, self.h) self.assertEqual(result, 5) def test_triangle_with_incorrect_values(self): with self.assertRaises(TypeError): triangle("*", self.h) def test_trapezoid_with_correct_result(self): result = trapezoid(self.a, self.b, self.h) self.assertEqual(result, 12.5) def test_trapezoid_with_incorrect_value(self): with self.assertRaises(TypeError): trapezoid('**', self.b, self.h) def tearDown(self): del self.a, self.h if __name__ == '__main__': unittest.main()

[ "50596942+conradylx@users.noreply.github.com" ]

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/2021/Qualification/E. Cheating Detection/2021-q-e.py

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andy1li/codejam

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2022-06-28T02:42:53.980149

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# 2021 Qualification Round - E. Cheating Detection # https://codingcompetitions.withgoogle.com/codejam/round/000000000043580a/00000000006d12d7 import numpy as np #------------------------------------------------------------------------------# def solve(data): difficulty = np.mean(data, axis=0) correct_spreads = [difficulty[row].std() for row in data] return np.argmax(correct_spreads) + 1 #------------------------------------------------------------------------------# T, _ = int(input()), input() for i in range(T): data = [ [bool(int(x)) for x in input()] for _ in range(100) ] result = solve(data) print('Case #{}:'.format(i+1), result)

[ "li.chenxing@gmail.com" ]

li.chenxing@gmail.com

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/scripts/sources/S_EvaluationCornishFisherLogN.py

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permissive

s0ap/arpmRes

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2022-02-16T05:01:22.118959

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- # --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.4' # jupytext_version: 1.1.4 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # S_EvaluationCornishFisherLogN [<img src="https://www.arpm.co/lab/icons/icon_permalink.png" width=30 height=30 style="display: inline;">](https://www.arpm.co/lab/redirect.php?code=S_EvaluationCornishFisherLogN&codeLang=Python) # For details, see [here](https://www.arpm.co/lab/redirect.php?permalink=EBCornishFisherEvaluation). # ## Prepare the environment # + import os.path as path import sys sys.path.append(path.abspath('../../functions-legacy')) from numpy import array import matplotlib.pyplot as plt plt.style.use('seaborn') from PortfolioMomentsLogN import PortfolioMomentsLogN from CornishFisher import CornishFisher # parameters v_tnow = array([[2], [1.5]]) mu = array([[0.5], [-0.3]]) sigma2 = array([[0.55, 0.82], [0.82, 1.05]]) h = array([[2], [1]]) c = 0.95 # - # ## Computation of the expectation, the standard deviation and the skewness # ## of the portfolio's P&L using function PortfolioMomentsLogN muPL_h, sdPL_h, skPL_h = PortfolioMomentsLogN(v_tnow, h, mu, sigma2) # ## Using the skewness computed at the previous step, compute the third central # ## moment of the portfolio's P&L third_central = skPL_h@(sdPL_h) ** 3 # ## Computation of the Cornish-Fisher expansion of the quantile based-index # ## with confidence c=0.95 using function CornishFisher q = CornishFisher(muPL_h, sdPL_h, skPL_h, 1 - c)

[ "dario.popadic@yahoo.com" ]

dario.popadic@yahoo.com

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/python_processThreading/asynico_oo/coroutines_asyncio.py

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[]

no_license

xiaotiankeyi/PythonBase

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2023-03-16T19:43:45.589440

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# 概念:asyncio是3.4以后的协程模块,是python实现并发重要的包,使用事件循环驱动实现并发,实现异步io import asyncio async def aunt(): for i in range(10): print(f'{i}婶婶说,python是世界上最好的语言') await asyncio.sleep(1) # 释放cpu避免阻塞 async def uncle(): for i in range(10): print(f'{i}叔叔说,python是世界上最好的语言') await asyncio.sleep(0.5) if __name__ == "__main__": a1 = aunt() a2 = uncle() # 创建循环事件 loop = asyncio.get_event_loop() # 创建监听事件,会阻塞直到事件完成, loop.run_until_complete(asyncio.gather(a1, a2)) # 关闭事件循环 loop.close()

[ "laizhitian163@163.com" ]

laizhitian163@163.com

29858121c108ccddea63d18579cac7770cfd723a

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/find the duplicate number.py

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[]

no_license

shreyansh-tyagi/leetcode-problem

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f8679a7b639f874a52cf9081b84e7c7abff1d100

refs/heads/master

2023-08-26T13:50:27.769753

2021-10-29T17:39:41

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4

1

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''' Given an array of integers nums containing n + 1 integers where each integer is in the range [1, n] inclusive. There is only one repeated number in nums, return this repeated number. You must solve the problem without modifying the array nums and uses only constant extra space. Example 1: Input: nums = [1,3,4,2,2] Output: 2 Example 2: Input: nums = [3,1,3,4,2] Output: 3 Example 3: Input: nums = [1,1] Output: 1 Example 4: Input: nums = [1,1,2] Output: 1 Constraints: 1 <= n <= 105 nums.length == n + 1 1 <= nums[i] <= n All the integers in nums appear only once except for precisely one integer which appears two or more times. Follow up: How can we prove that at least one duplicate number must exist in nums? Can you solve the problem in linear runtime complexity? ''' class Solution: def findDuplicate(self, nums: List[int]) -> int: for i in range(len(nums)-1): if nums[i]==nums[i+1]: return nums[i]

[ "sunnytyagi886@gmail.com" ]

sunnytyagi886@gmail.com

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2021-06-01T09:51:56.452050

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from __future__ import absolute_import, division, print_function # flake8:noqa # Wrapper around PyQt4/5 from PyQt4.QtCore import *

[ "crallj@rpi.edu" ]

crallj@rpi.edu

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/k8sclient/models/v1alpha1_certificate_signing_request_spec.py

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Arvinhub/client-python

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refs/heads/master

2023-08-31T03:25:57.823810

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# coding: utf-8 """ Kubernetes No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: unversioned 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 pprint import pformat from six import iteritems import re class V1alpha1CertificateSigningRequestSpec(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ def __init__(self, groups=None, request=None, uid=None, username=None): """ V1alpha1CertificateSigningRequestSpec - a model defined in Swagger :param dict swaggerTypes: The key is attribute name and the value is attribute type. :param dict attributeMap: The key is attribute name and the value is json key in definition. """ self.swagger_types = { 'groups': 'list[str]', 'request': 'str', 'uid': 'str', 'username': 'str' } self.attribute_map = { 'groups': 'groups', 'request': 'request', 'uid': 'uid', 'username': 'username' } self._groups = groups self._request = request self._uid = uid self._username = username @property def groups(self): """ Gets the groups of this V1alpha1CertificateSigningRequestSpec. :return: The groups of this V1alpha1CertificateSigningRequestSpec. :rtype: list[str] """ return self._groups @groups.setter def groups(self, groups): """ Sets the groups of this V1alpha1CertificateSigningRequestSpec. :param groups: The groups of this V1alpha1CertificateSigningRequestSpec. :type: list[str] """ self._groups = groups @property def request(self): """ Gets the request of this V1alpha1CertificateSigningRequestSpec. Base64-encoded PKCS#10 CSR data :return: The request of this V1alpha1CertificateSigningRequestSpec. :rtype: str """ return self._request @request.setter def request(self, request): """ Sets the request of this V1alpha1CertificateSigningRequestSpec. Base64-encoded PKCS#10 CSR data :param request: The request of this V1alpha1CertificateSigningRequestSpec. :type: str """ if request is None: raise ValueError("Invalid value for `request`, must not be `None`") self._request = request @property def uid(self): """ Gets the uid of this V1alpha1CertificateSigningRequestSpec. :return: The uid of this V1alpha1CertificateSigningRequestSpec. :rtype: str """ return self._uid @uid.setter def uid(self, uid): """ Sets the uid of this V1alpha1CertificateSigningRequestSpec. :param uid: The uid of this V1alpha1CertificateSigningRequestSpec. :type: str """ self._uid = uid @property def username(self): """ Gets the username of this V1alpha1CertificateSigningRequestSpec. Information about the requesting user (if relevant) See user.Info interface for details :return: The username of this V1alpha1CertificateSigningRequestSpec. :rtype: str """ return self._username @username.setter def username(self, username): """ Sets the username of this V1alpha1CertificateSigningRequestSpec. Information about the requesting user (if relevant) See user.Info interface for details :param username: The username of this V1alpha1CertificateSigningRequestSpec. :type: str """ self._username = username def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

[ "mehdy@google.com" ]

mehdy@google.com

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avpakh/recon

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('prognoz', '0025_auto_20151110_0727'), ] operations = [ migrations.AddField( model_name='settlements', name='description', field=models.CharField(default=1, max_length=60, verbose_name=b'\xd0\x9e\xd0\xbf\xd0\xb8\xd1\x81\xd0\xb0\xd0\xbd\xd0\xb8\xd0\xb5 \xd1\x80\xd0\xb8\xd1\x81\xd0\xba\xd0\xb0'), preserve_default=False, ), ]

[ "aliaksandr.pakhomau@gmail.com" ]

aliaksandr.pakhomau@gmail.com

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# -*- coding: utf-8 -*- #!/usr/bin/env python # # Copyright 2015-2017 BigML # # 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. """Common auxiliary constants for all resources """ import re # Basic resources SOURCE_PATH = 'source' DATASET_PATH = 'dataset' MODEL_PATH = 'model' PREDICTION_PATH = 'prediction' EVALUATION_PATH = 'evaluation' ENSEMBLE_PATH = 'ensemble' BATCH_PREDICTION_PATH = 'batchprediction' CLUSTER_PATH = 'cluster' CENTROID_PATH = 'centroid' BATCH_CENTROID_PATH = 'batchcentroid' ANOMALY_PATH = 'anomaly' ANOMALY_SCORE_PATH = 'anomalyscore' BATCH_ANOMALY_SCORE_PATH = 'batchanomalyscore' PROJECT_PATH = 'project' SAMPLE_PATH = 'sample' CORRELATION_PATH = 'correlation' STATISTICAL_TEST_PATH = 'statisticaltest' LOGISTIC_REGRESSION_PATH = 'logisticregression' ASSOCIATION_PATH = 'association' ASSOCIATION_SET_PATH = 'associationset' TOPIC_MODEL_PATH = 'topicmodel' TOPIC_DISTRIBUTION_PATH = 'topicdistribution' BATCH_TOPIC_DISTRIBUTION_PATH = 'batchtopicdistribution' TIME_SERIES_PATH = 'timeseries' FORECAST_PATH = 'forecast' SCRIPT_PATH = 'script' EXECUTION_PATH = 'execution' LIBRARY_PATH = 'library' # Resource Ids patterns ID_PATTERN = '[a-f0-9]{24}' SHARED_PATTERN = '[a-zA-Z0-9]{24,30}' SOURCE_RE = re.compile(r'^%s/%s$' % (SOURCE_PATH, ID_PATTERN)) DATASET_RE = re.compile(r'^(public/)?%s/%s$|^shared/%s/%s$' % ( DATASET_PATH, ID_PATTERN, DATASET_PATH, SHARED_PATTERN)) MODEL_RE = re.compile(r'^(public/)?%s/%s$|^shared/%s/%s$' % ( MODEL_PATH, ID_PATTERN, MODEL_PATH, SHARED_PATTERN)) PREDICTION_RE = re.compile(r'^%s/%s$' % (PREDICTION_PATH, ID_PATTERN)) EVALUATION_RE = re.compile(r'^%s/%s$' % (EVALUATION_PATH, ID_PATTERN)) ENSEMBLE_RE = re.compile(r'^%s/%s$' % (ENSEMBLE_PATH, ID_PATTERN)) BATCH_PREDICTION_RE = re.compile(r'^%s/%s$' % (BATCH_PREDICTION_PATH, ID_PATTERN)) CLUSTER_RE = re.compile(r'^(public/)?%s/%s$|^shared/%s/%s$' % ( CLUSTER_PATH, ID_PATTERN, CLUSTER_PATH, SHARED_PATTERN)) CENTROID_RE = re.compile(r'^%s/%s$' % (CENTROID_PATH, ID_PATTERN)) BATCH_CENTROID_RE = re.compile(r'^%s/%s$' % (BATCH_CENTROID_PATH, ID_PATTERN)) ANOMALY_RE = re.compile(r'^(public/)?%s/%s$|^shared/%s/%s$' % ( ANOMALY_PATH, ID_PATTERN, ANOMALY_PATH, SHARED_PATTERN)) ANOMALY_SCORE_RE = re.compile(r'^%s/%s$' % (ANOMALY_SCORE_PATH, ID_PATTERN)) BATCH_ANOMALY_SCORE_RE = re.compile(r'^%s/%s$' % (BATCH_ANOMALY_SCORE_PATH, ID_PATTERN)) PROJECT_RE = re.compile(r'^%s/%s$' % (PROJECT_PATH, ID_PATTERN)) SAMPLE_RE = re.compile(r'^%s/%s|^shared/%s/%s$' % ( SAMPLE_PATH, ID_PATTERN, SAMPLE_PATH, SHARED_PATTERN)) CORRELATION_RE = re.compile(r'^%s/%s|^shared/%s/%s$' % ( CORRELATION_PATH, ID_PATTERN, CORRELATION_PATH, SHARED_PATTERN)) STATISTICAL_TEST_RE = re.compile(r'^%s/%s|^shared/%s/%s$' % \ (STATISTICAL_TEST_PATH, ID_PATTERN, STATISTICAL_TEST_PATH, SHARED_PATTERN)) LOGISTIC_REGRESSION_RE = re.compile(r'^%s/%s|^shared/%s/%s$' % \ (LOGISTIC_REGRESSION_PATH, ID_PATTERN, LOGISTIC_REGRESSION_PATH, SHARED_PATTERN)) ASSOCIATION_RE = re.compile(r'^%s/%s|^shared/%s/%s$' % \ (ASSOCIATION_PATH, ID_PATTERN, ASSOCIATION_PATH, SHARED_PATTERN)) ASSOCIATION_SET_RE = re.compile(r'^%s/%s$' % \ (ASSOCIATION_SET_PATH, ID_PATTERN)) TOPIC_MODEL_RE = re.compile(r'^(public/)?%s/%s$|^shared/%s/%s$' % ( TOPIC_MODEL_PATH, ID_PATTERN, TOPIC_MODEL_PATH, SHARED_PATTERN)) TOPIC_DISTRIBUTION_RE = re.compile(r'^(public/)?%s/%s$|^shared/%s/%s$' % ( TOPIC_DISTRIBUTION_PATH, ID_PATTERN, TOPIC_DISTRIBUTION_PATH, SHARED_PATTERN)) BATCH_TOPIC_DISTRIBUTION_RE = re.compile(r'^(public/)?%s/%s$|^shared/%s/%s$' % ( BATCH_TOPIC_DISTRIBUTION_PATH, ID_PATTERN, BATCH_TOPIC_DISTRIBUTION_PATH, SHARED_PATTERN)) TIME_SERIES_RE = re.compile(r'^%s/%s|^shared/%s/%s$' % \ (TIME_SERIES_PATH, ID_PATTERN, TIME_SERIES_PATH, SHARED_PATTERN)) FORECAST_RE = re.compile(r'^%s/%s$' % \ (FORECAST_PATH, ID_PATTERN)) SCRIPT_RE = re.compile(r'^%s/%s|^shared/%s/%s$' % \ (SCRIPT_PATH, ID_PATTERN, SCRIPT_PATH, SHARED_PATTERN)) EXECUTION_RE = re.compile(r'^%s/%s|^shared/%s/%s$' % \ (EXECUTION_PATH, ID_PATTERN, EXECUTION_PATH, SHARED_PATTERN)) LIBRARY_RE = re.compile(r'^%s/%s|^shared/%s/%s$' % \ (LIBRARY_PATH, ID_PATTERN, LIBRARY_PATH, SHARED_PATTERN)) RESOURCE_RE = { SOURCE_PATH: SOURCE_RE, DATASET_PATH: DATASET_RE, MODEL_PATH: MODEL_RE, PREDICTION_PATH: PREDICTION_RE, EVALUATION_PATH: EVALUATION_RE, ENSEMBLE_PATH: ENSEMBLE_RE, BATCH_PREDICTION_PATH: BATCH_PREDICTION_RE, CLUSTER_PATH: CLUSTER_RE, CENTROID_PATH: CENTROID_RE, BATCH_CENTROID_PATH: BATCH_CENTROID_RE, ANOMALY_PATH: ANOMALY_RE, ANOMALY_SCORE_PATH: ANOMALY_SCORE_RE, BATCH_ANOMALY_SCORE_PATH: BATCH_ANOMALY_SCORE_RE, PROJECT_PATH: PROJECT_RE, SAMPLE_PATH: SAMPLE_RE, CORRELATION_PATH: CORRELATION_RE, STATISTICAL_TEST_PATH: STATISTICAL_TEST_RE, LOGISTIC_REGRESSION_PATH: LOGISTIC_REGRESSION_RE, ASSOCIATION_PATH: ASSOCIATION_RE, ASSOCIATION_SET_PATH: ASSOCIATION_SET_RE, TOPIC_MODEL_PATH: TOPIC_MODEL_RE, TOPIC_DISTRIBUTION_PATH: TOPIC_DISTRIBUTION_RE, BATCH_TOPIC_DISTRIBUTION_PATH: BATCH_TOPIC_DISTRIBUTION_RE, TIME_SERIES_PATH: TIME_SERIES_RE, FORECAST_PATH: FORECAST_RE, SCRIPT_PATH: SCRIPT_RE, EXECUTION_PATH: EXECUTION_RE, LIBRARY_PATH: LIBRARY_RE} RENAMED_RESOURCES = { BATCH_PREDICTION_PATH: 'batch_prediction', BATCH_CENTROID_PATH: 'batch_centroid', ANOMALY_SCORE_PATH: 'anomaly_score', BATCH_ANOMALY_SCORE_PATH: 'batch_anomaly_score', STATISTICAL_TEST_PATH: 'statistical_test', LOGISTIC_REGRESSION_PATH: 'logistic_regression', ASSOCIATION_SET_PATH: 'association_set', TOPIC_MODEL_PATH: 'topic_model', TOPIC_DISTRIBUTION_PATH: 'topic_distribution', BATCH_TOPIC_DISTRIBUTION_PATH: 'batch_topic_distribution', TIME_SERIES_PATH: 'time_series' } # Resource status codes WAITING = 0 QUEUED = 1 STARTED = 2 IN_PROGRESS = 3 SUMMARIZED = 4 FINISHED = 5 UPLOADING = 6 FAULTY = -1 UNKNOWN = -2 RUNNABLE = -3 # Minimum query string to get model fields TINY_RESOURCE = "full=false"

[ "merce@bigml.com" ]

merce@bigml.com

1b037fe0c41ff59b93d4a24f7f450057b1cf6e7a

1ef164682fee7298732bce1037d91d895c108a02

/testapp/urls.py

9aede7bd9c0113635a94aeabd1f944180ca31580

[]

no_license

qwertypool/Django-location-locator

3ecf45a9bcc42fcbf587ee3ba9ff8637492c3460

e162fe61715b4d954a25a8af9592b3d64a357818

refs/heads/master

2022-09-29T17:44:03.260904

2020-06-10T08:34:13

271,224,122

3

0

null

UTF-8

Python

false

121

py

from django.urls import path from . import views urlpatterns = [ path('',views.location_generator,name="ipstack"), ]

[ "deepapandey364@gmail.com" ]

deepapandey364@gmail.com

d9fc547ef77070538f454815fa5e74d3c62c1312

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p03611/s329630070.py

2da7de28a0f7899a21bdeddfa3f4520533bf8ad0

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

462

py

import sys from collections import Counter read = sys.stdin.read readline = sys.stdin.readline readlines = sys.stdin.readlines sys.setrecursionlimit(10 ** 9) INF = 1 << 60 MOD = 1000000007 def main(): N, *A = map(int, read().split()) counter = Counter() for a in A: counter[a - 1] += 1 counter[a] += 1 counter[a + 1] += 1 ans = max(counter.values()) print(ans) return if __name__ == '__main__': main()

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

e9165487a666303d57c21ab719a94938efc5b12d

63d37b990c194a68fbb8d2d288de5faae374ed26

/main/migrations/0010_photo_isavatar.py

c396985b39a95cdfa776c6e3981996e77b117cce

[]

no_license

Dostoyewski/MH_Back

bf16cd83ff5103e65a5f3fe7d866a2b6dbe66624

6de68e1a14643a23e268f1c313224cf7bea89c75

refs/heads/master

2022-03-30T18:04:51.340938

2020-04-05T08:06:04

251,396,226

0

null

UTF-8

Python

false

377

py

# Generated by Django 2.2.1 on 2020-04-04 19:37 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('main', '0009_hero_stage'), ] operations = [ migrations.AddField( model_name='photo', name='isAvatar', field=models.BooleanField(default=False), ), ]

[ "dostoyewski@yandex.ru" ]

dostoyewski@yandex.ru

04cc8dff2a874e4be609a6ed7784fee98d47ec7d

ed850eb63494b61e84ff81e35eaf3ed6b96d1208

/autobahn/autobahn/wamp/tests/test_uri_pattern.py

1d1098b4949ace709dea7e65a6d283aa0b65a84c

[ "Apache-2.0" ]

permissive

Shareed2k/AutobahnPython

4e501fce496b7cd54529d1c181ee9d6b7d6be438

530bab04ae420db566a636b4f73e6cd2210b0c90

refs/heads/master

2021-01-20T16:35:02.773818

2014-04-01T10:40:35

null

0

null

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Python

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############################################################################### ## ## Copyright (C) 2014 Tavendo GmbH ## ## 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 from twisted.trial import unittest #import unittest #from autobahn import wamp2 as wamp from autobahn import wamp from autobahn.wamp.uri import Pattern class TestUris(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_invalid_uris(self): for u in ["", "123", "com.myapp.<product:foo>.update", "com.myapp.<123:int>.update", "com.myapp.<:product>.update", "com.myapp.<product:>.update", "com.myapp.<int:>.update", ]: self.assertRaises(Exception, Pattern, u, Pattern.URI_TARGET_ENDPOINT) def test_valid_uris(self): for u in ["com.myapp.proc1", "com.myapp.<product:int>.update", ]: p = Pattern(u, Pattern.URI_TARGET_ENDPOINT) self.assertIsInstance(p, Pattern) def test_parse_uris(self): tests = [ ("com.myapp.<product:int>.update", [ ("com.myapp.0.update", {'product': 0}), ("com.myapp.123456.update", {'product': 123456}), ("com.myapp.aaa.update", None), ("com.myapp..update", None), ("com.myapp.0.delete", None), ] ), ("com.myapp.<product:string>.update", [ ("com.myapp.box.update", {'product': 'box'}), ("com.myapp.123456.update", {'product': '123456'}), ("com.myapp..update", None), ] ) ] for test in tests: pat = Pattern(test[0], Pattern.URI_TARGET_ENDPOINT) for ptest in test[1]: uri = ptest[0] kwargs_should = ptest[1] if kwargs_should is not None: args_is, kwargs_is = pat.match(uri) self.assertEqual(kwargs_is, kwargs_should) else: self.assertRaises(Exception, pat.match, uri) class TestDecorators(unittest.TestCase): def test_decorate_endpoint(self): @wamp.procedure("com.calculator.square") def square(x): pass self.assertTrue(hasattr(square, '_wampuris')) self.assertTrue(type(square._wampuris) == list) self.assertEqual(len(square._wampuris), 1) self.assertIsInstance(square._wampuris[0], Pattern) self.assertTrue(square._wampuris[0].is_endpoint()) self.assertFalse(square._wampuris[0].is_handler()) self.assertFalse(square._wampuris[0].is_exception()) self.assertEqual(square._wampuris[0].uri(), "com.calculator.square") self.assertEqual(square._wampuris[0]._type, Pattern.URI_TYPE_EXACT) @wamp.procedure("com.myapp.product.<product:int>.update") def update_product(product = None, label = None): pass self.assertTrue(hasattr(update_product, '_wampuris')) self.assertTrue(type(update_product._wampuris) == list) self.assertEqual(len(update_product._wampuris), 1) self.assertIsInstance(update_product._wampuris[0], Pattern) self.assertTrue(update_product._wampuris[0].is_endpoint()) self.assertFalse(update_product._wampuris[0].is_handler()) self.assertFalse(update_product._wampuris[0].is_exception()) self.assertEqual(update_product._wampuris[0].uri(), "com.myapp.product.<product:int>.update") self.assertEqual(update_product._wampuris[0]._type, Pattern.URI_TYPE_WILDCARD) @wamp.procedure("com.myapp.<category:string>.<id:int>.update") def update(category = None, id = None): pass self.assertTrue(hasattr(update, '_wampuris')) self.assertTrue(type(update._wampuris) == list) self.assertEqual(len(update._wampuris), 1) self.assertIsInstance(update._wampuris[0], Pattern) self.assertTrue(update._wampuris[0].is_endpoint()) self.assertFalse(update._wampuris[0].is_handler()) self.assertFalse(update._wampuris[0].is_exception()) self.assertEqual(update._wampuris[0].uri(), "com.myapp.<category:string>.<id:int>.update") self.assertEqual(update._wampuris[0]._type, Pattern.URI_TYPE_WILDCARD) def test_decorate_handler(self): @wamp.topic("com.myapp.on_shutdown") def on_shutdown(): pass self.assertTrue(hasattr(on_shutdown, '_wampuris')) self.assertTrue(type(on_shutdown._wampuris) == list) self.assertEqual(len(on_shutdown._wampuris), 1) self.assertIsInstance(on_shutdown._wampuris[0], Pattern) self.assertFalse(on_shutdown._wampuris[0].is_endpoint()) self.assertTrue(on_shutdown._wampuris[0].is_handler()) self.assertFalse(on_shutdown._wampuris[0].is_exception()) self.assertEqual(on_shutdown._wampuris[0].uri(), "com.myapp.on_shutdown") self.assertEqual(on_shutdown._wampuris[0]._type, Pattern.URI_TYPE_EXACT) @wamp.topic("com.myapp.product.<product:int>.on_update") def on_product_update(product = None, label = None): pass self.assertTrue(hasattr(on_product_update, '_wampuris')) self.assertTrue(type(on_product_update._wampuris) == list) self.assertEqual(len(on_product_update._wampuris), 1) self.assertIsInstance(on_product_update._wampuris[0], Pattern) self.assertFalse(on_product_update._wampuris[0].is_endpoint()) self.assertTrue(on_product_update._wampuris[0].is_handler()) self.assertFalse(on_product_update._wampuris[0].is_exception()) self.assertEqual(on_product_update._wampuris[0].uri(), "com.myapp.product.<product:int>.on_update") self.assertEqual(on_product_update._wampuris[0]._type, Pattern.URI_TYPE_WILDCARD) @wamp.topic("com.myapp.<category:string>.<id:int>.on_update") def on_update(category = None, id = None, label = None): pass self.assertTrue(hasattr(on_update, '_wampuris')) self.assertTrue(type(on_update._wampuris) == list) self.assertEqual(len(on_update._wampuris), 1) self.assertIsInstance(on_update._wampuris[0], Pattern) self.assertFalse(on_update._wampuris[0].is_endpoint()) self.assertTrue(on_update._wampuris[0].is_handler()) self.assertFalse(on_update._wampuris[0].is_exception()) self.assertEqual(on_update._wampuris[0].uri(), "com.myapp.<category:string>.<id:int>.on_update") self.assertEqual(on_update._wampuris[0]._type, Pattern.URI_TYPE_WILDCARD) def test_decorate_exception(self): @wamp.error("com.myapp.error") class AppError(Exception): pass self.assertTrue(hasattr(AppError, '_wampuris')) self.assertTrue(type(AppError._wampuris) == list) self.assertEqual(len(AppError._wampuris), 1) self.assertIsInstance(AppError._wampuris[0], Pattern) self.assertFalse(AppError._wampuris[0].is_endpoint()) self.assertFalse(AppError._wampuris[0].is_handler()) self.assertTrue(AppError._wampuris[0].is_exception()) self.assertEqual(AppError._wampuris[0].uri(), "com.myapp.error") self.assertEqual(AppError._wampuris[0]._type, Pattern.URI_TYPE_EXACT) @wamp.error("com.myapp.product.<product:int>.product_inactive") class ProductInactiveError(Exception): pass self.assertTrue(hasattr(ProductInactiveError, '_wampuris')) self.assertTrue(type(ProductInactiveError._wampuris) == list) self.assertEqual(len(ProductInactiveError._wampuris), 1) self.assertIsInstance(ProductInactiveError._wampuris[0], Pattern) self.assertFalse(ProductInactiveError._wampuris[0].is_endpoint()) self.assertFalse(ProductInactiveError._wampuris[0].is_handler()) self.assertTrue(ProductInactiveError._wampuris[0].is_exception()) self.assertEqual(ProductInactiveError._wampuris[0].uri(), "com.myapp.product.<product:int>.product_inactive") self.assertEqual(ProductInactiveError._wampuris[0]._type, Pattern.URI_TYPE_WILDCARD) @wamp.error("com.myapp.<category:string>.<product:int>.inactive") class ObjectInactiveError(Exception): pass self.assertTrue(hasattr(ObjectInactiveError, '_wampuris')) self.assertTrue(type(ObjectInactiveError._wampuris) == list) self.assertEqual(len(ObjectInactiveError._wampuris), 1) self.assertIsInstance(ObjectInactiveError._wampuris[0], Pattern) self.assertFalse(ObjectInactiveError._wampuris[0].is_endpoint()) self.assertFalse(ObjectInactiveError._wampuris[0].is_handler()) self.assertTrue(ObjectInactiveError._wampuris[0].is_exception()) self.assertEqual(ObjectInactiveError._wampuris[0].uri(), "com.myapp.<category:string>.<product:int>.inactive") self.assertEqual(ObjectInactiveError._wampuris[0]._type, Pattern.URI_TYPE_WILDCARD) def test_match_decorated_endpoint(self): @wamp.procedure("com.calculator.square") def square(x): return x args, kwargs = square._wampuris[0].match("com.calculator.square") self.assertEqual(square(666, **kwargs), 666) @wamp.procedure("com.myapp.product.<product:int>.update") def update_product(product = None, label = None): return product, label args, kwargs = update_product._wampuris[0].match("com.myapp.product.123456.update") kwargs['label'] = "foobar" self.assertEqual(update_product(**kwargs), (123456, "foobar")) @wamp.procedure("com.myapp.<category:string>.<id:int>.update") def update(category = None, id = None, label = None): return category, id, label args, kwargs = update._wampuris[0].match("com.myapp.product.123456.update") kwargs['label'] = "foobar" self.assertEqual(update(**kwargs), ("product", 123456, "foobar")) def test_match_decorated_handler(self): @wamp.topic("com.myapp.on_shutdown") def on_shutdown(): pass args, kwargs = on_shutdown._wampuris[0].match("com.myapp.on_shutdown") self.assertEqual(on_shutdown(**kwargs), None) @wamp.topic("com.myapp.product.<product:int>.on_update") def on_product_update(product = None, label = None): return product, label args, kwargs = on_product_update._wampuris[0].match("com.myapp.product.123456.on_update") kwargs['label'] = "foobar" self.assertEqual(on_product_update(**kwargs), (123456, "foobar")) @wamp.topic("com.myapp.<category:string>.<id:int>.on_update") def on_update(category = None, id = None, label = None): return category, id, label args, kwargs = on_update._wampuris[0].match("com.myapp.product.123456.on_update") kwargs['label'] = "foobar" self.assertEqual(on_update(**kwargs), ("product", 123456, "foobar")) def test_match_decorated_exception(self): @wamp.error("com.myapp.error") class AppError(Exception): def __init__(self, msg): Exception.__init__(self, msg) def __eq__(self, other): return self.__class__ == other.__class__ and \ self.args == other.args args, kwargs = AppError._wampuris[0].match("com.myapp.error") self.assertEqual(AppError("fuck", **kwargs), AppError("fuck")) @wamp.error("com.myapp.product.<product:int>.product_inactive") class ProductInactiveError(Exception): def __init__(self, msg, product = None): Exception.__init__(self, msg) self.product = product def __eq__(self, other): return self.__class__ == other.__class__ and \ self.args == other.args and \ self.product == other.product args, kwargs = ProductInactiveError._wampuris[0].match("com.myapp.product.123456.product_inactive") self.assertEqual(ProductInactiveError("fuck", **kwargs), ProductInactiveError("fuck", 123456)) @wamp.error("com.myapp.<category:string>.<product:int>.inactive") class ObjectInactiveError(Exception): def __init__(self, msg, category = None, product = None): Exception.__init__(self, msg) self.category = category self.product = product def __eq__(self, other): return self.__class__ == other.__class__ and \ self.args == other.args and \ self.category == other.category and \ self.product == other.product args, kwargs = ObjectInactiveError._wampuris[0].match("com.myapp.product.123456.inactive") self.assertEqual(ObjectInactiveError("fuck", **kwargs), ObjectInactiveError("fuck", "product", 123456)) class KwException(Exception): def __init__(self, *args, **kwargs): Exception.__init__(self, *args) self.kwargs = kwargs # what if the WAMP error message received # contains args/kwargs that cannot be # consumed by the constructor of the exception # class defined for the WAMP error URI? # 1. we can bail out (but we are already signaling an error) # 2. we can require a generic constructor # 3. we can map only unconsumed args/kwargs to generic attributes # 4. we can silently drop unconsumed args/kwargs def getargs(fun): try: argspec = inspect.getargspec(fun) except: if fun == Exception.__init__: # `inspect.getargspec(Exception.__init__)` does work on PyPy, but not # on CPython, since `Exception.__init__` is C code in CPython that # cannot be reflected upon. argspec = inspect.ArgSpec(args = ['self'], varargs = 'args', keywords = None, defaults = None) else: raise Exception("could not inspect function {}".format(fun)) args = argspec.args[:-len(argspec.defaults)] kwargs = argspec.args[-len(argspec.defaults):] return args, kwargs, argspec.varargs, argspec.keywords class MockSession: def __init__(self): self._ecls_to_uri_pat = {} self._uri_to_ecls = {} def define(self, exception, error = None): if error is None: assert(hasattr(exception, '_wampuris')) self._ecls_to_uri_pat[exception] = exception._wampuris self._uri_to_ecls[exception._wampuris[0].uri()] = exception else: assert(not hasattr(exception, '_wampuris')) self._ecls_to_uri_pat[exception] = [Pattern(error, Pattern.URI_TARGET_HANDLER)] self._uri_to_ecls[error] = exception def map_error(self, error, args = [], kwargs = {}): # FIXME: # 1. map to ecls based on error URI wildcard/prefix # 2. extract additional args/kwargs from error URI if self._uri_to_ecls.has_key(error): ecls = self._uri_to_ecls[error] try: ## the following might fail, eg. TypeError when ## signature of exception constructor is incompatible ## with args/kwargs or when the exception constructor raises if kwargs: if args: exc = ecls(*args, **kwargs) else: exc = ecls(**kwargs) else: if args: exc = ecls(*args) else: exc = ecls() except Exception as e: ## FIXME: log e exc = KwException(error, *args, **kwargs) else: ## this never fails exc = KwException(error, *args, **kwargs) return exc class TestDecoratorsAdvanced(unittest.TestCase): def test_decorate_exception_non_exception(self): def test(): @wamp.error("com.test.error") class Foo: pass self.assertRaises(Exception, test) def test_decorate_endpoint_multiple(self): @wamp.procedure("com.oldapp.oldproc") @wamp.procedure("com.calculator.square") def square(x): pass self.assertTrue(hasattr(square, '_wampuris')) self.assertTrue(type(square._wampuris) == list) self.assertEqual(len(square._wampuris), 2) for i in range(2): self.assertIsInstance(square._wampuris[i], Pattern) self.assertTrue(square._wampuris[i].is_endpoint()) self.assertFalse(square._wampuris[i].is_handler()) self.assertFalse(square._wampuris[i].is_exception()) self.assertEqual(square._wampuris[i]._type, Pattern.URI_TYPE_EXACT) self.assertEqual(square._wampuris[0].uri(), "com.calculator.square") self.assertEqual(square._wampuris[1].uri(), "com.oldapp.oldproc") def test_marshal_decorated_exception(self): @wamp.error("com.myapp.error") class AppError(Exception): pass try: raise AppError("fuck") except Exception as e: self.assertEqual(e._wampuris[0].uri(), "com.myapp.error") @wamp.error("com.myapp.product.<product:int>.product_inactive") class ProductInactiveError(Exception): def __init__(self, msg, product = None): Exception.__init__(self, msg) self.product = product try: raise ProductInactiveError("fuck", 123456) except Exception as e: self.assertEqual(e._wampuris[0].uri(), "com.myapp.product.<product:int>.product_inactive") class AppErrorUndecorated(Exception): pass session = MockSession() session.define(AppError) def test_define_exception_undecorated(self): session = MockSession() class AppError(Exception): pass ## defining an undecorated exception requires ## an URI to be provided self.assertRaises(Exception, session.define, AppError) session.define(AppError, "com.myapp.error") exc = session.map_error("com.myapp.error") self.assertIsInstance(exc, AppError) def test_define_exception_decorated(self): session = MockSession() @wamp.error("com.myapp.error") class AppError(Exception): pass ## when defining a decorated exception ## an URI must not be provided self.assertRaises(Exception, session.define, AppError, "com.myapp.error") session.define(AppError) exc = session.map_error("com.myapp.error") self.assertIsInstance(exc, AppError) def test_map_exception_undefined(self): session = MockSession() exc = session.map_error("com.myapp.error") self.assertIsInstance(exc, Exception) def test_map_exception_args(self): session = MockSession() @wamp.error("com.myapp.error") class AppError(Exception): pass @wamp.error("com.myapp.error.product_inactive") class ProductInactiveError(Exception): def __init__(self, product = None): self.product = product ## define exceptions in mock session session.define(AppError) session.define(ProductInactiveError) for test in [ #("com.myapp.foo.error", [], {}, KwException), ("com.myapp.error", [], {}, AppError), ("com.myapp.error", ["you are doing it wrong"], {}, AppError), ("com.myapp.error", ["you are doing it wrong", 1, 2, 3], {}, AppError), ("com.myapp.error.product_inactive", [], {}, ProductInactiveError), ("com.myapp.error.product_inactive", [], {"product": 123456}, ProductInactiveError), ]: error, args, kwargs, ecls = test exc = session.map_error(error, args, kwargs) self.assertIsInstance(exc, ecls) self.assertEqual(list(exc.args), args) if __name__ == '__main__': unittest.main()

[ "tobias.oberstein@tavendo.de" ]

tobias.oberstein@tavendo.de

350249cd29157e132cd1e6549a7530a9fc74ae0f

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/examples/large_deformation/active_fibres.py

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# -*- coding: utf-8 -*- r""" Nearly incompressible hyperelastic material model with active fibres. Large deformation is described using the total Lagrangian formulation. Models of this kind can be used in biomechanics to model biological tissues, e.g. muscles. Find :math:`\ul{u}` such that: .. math:: \intl{\Omega\suz}{} \left( \ull{S}\eff(\ul{u}) + K(J-1)\; J \ull{C}^{-1} \right) : \delta \ull{E}(\ul{v}) \difd{V} = 0 \;, \quad \forall \ul{v} \;, where .. list-table:: :widths: 20 80 * - :math:`\ull{F}` - deformation gradient :math:`F_{ij} = \pdiff{x_i}{X_j}` * - :math:`J` - :math:`\det(F)` * - :math:`\ull{C}` - right Cauchy-Green deformation tensor :math:`C = F^T F` * - :math:`\ull{E}(\ul{u})` - Green strain tensor :math:`E_{ij} = \frac{1}{2}(\pdiff{u_i}{X_j} + \pdiff{u_j}{X_i} + \pdiff{u_m}{X_i}\pdiff{u_m}{X_j})` * - :math:`\ull{S}\eff(\ul{u})` - effective second Piola-Kirchhoff stress tensor The effective stress :math:`\ull{S}\eff(\ul{u})` incorporates also the effects of the active fibres in two preferential directions: .. math:: \ull{S}\eff(\ul{u}) = \mu J^{-\frac{2}{3}}(\ull{I} - \frac{1}{3}\tr(\ull{C}) \ull{C}^{-1}) + \sum_{k=1}^2 \tau^k \ull{\omega}^k \;. The first term is the neo-Hookean term and the sum add contributions of the two fibre systems. The tensors :math:`\ull{\omega}^k = \ul{d}^k\ul{d}^k` are defined by the fibre system direction vectors :math:`\ul{d}^k` (unit). For the one-dimensional tensions :math:`\tau^k` holds simply (:math:`^k` omitted): .. math:: \tau = A f_{\rm max} \exp{\left\{-(\frac{\epsilon - \varepsilon_{\rm opt}}{s})^2\right\}} \mbox{ , } \epsilon = \ull{E} : \ull{\omega} \;. """ from __future__ import print_function from __future__ import absolute_import import numpy as nm from sfepy import data_dir filename_mesh = data_dir + '/meshes/3d/cylinder.mesh' vf_matrix = 0.5 vf_fibres1 = 0.2 vf_fibres2 = 0.3 options = { 'nls' : 'newton', 'ls' : 'ls', 'ts' : 'ts', 'save_steps' : -1, 'post_process_hook' : 'stress_strain', } fields = { 'displacement': (nm.float64, 3, 'Omega', 1), } materials = { 'solid' : ({ 'K' : vf_matrix * 1e3, # bulk modulus 'mu' : vf_matrix * 20e0, # shear modulus of neoHookean term },), 'f1' : 'get_pars_fibres1', 'f2' : 'get_pars_fibres2', } def get_pars_fibres(ts, coors, mode=None, which=0, vf=1.0, **kwargs): """ Parameters ---------- ts : TimeStepper Time stepping info. coors : array_like The physical domain coordinates where the parameters shound be defined. mode : 'qp' or 'special' Call mode. which : int Fibre system id. vf : float Fibre system volume fraction. """ if mode != 'qp': return fmax = 10.0 eps_opt = 0.01 s = 1.0 tt = ts.nt * 2.0 * nm.pi if which == 0: # system 1 fdir = nm.array([1.0, 0.0, 0.0], dtype=nm.float64) act = 0.5 * (1.0 + nm.sin(tt - (0.5 * nm.pi))) elif which == 1: # system 2 fdir = nm.array([0.0, 1.0, 0.0], dtype=nm.float64) act = 0.5 * (1.0 + nm.sin(tt + (0.5 * nm.pi))) else: raise ValueError('unknown fibre system! (%d)' % which) fdir.shape = (3, 1) fdir /= nm.linalg.norm(fdir) print(act) shape = (coors.shape[0], 1, 1) out = { 'fmax' : vf * nm.tile(fmax, shape), 'eps_opt' : nm.tile(eps_opt, shape), 's' : nm.tile(s, shape), 'fdir' : nm.tile(fdir, shape), 'act' : nm.tile(act, shape), } return out functions = { 'get_pars_fibres1' : (lambda ts, coors, mode=None, **kwargs: get_pars_fibres(ts, coors, mode=mode, which=0, vf=vf_fibres1, **kwargs),), 'get_pars_fibres2' : (lambda ts, coors, mode=None, **kwargs: get_pars_fibres(ts, coors, mode=mode, which=1, vf=vf_fibres2, **kwargs),), } variables = { 'u' : ('unknown field', 'displacement', 0), 'v' : ('test field', 'displacement', 'u'), } regions = { 'Omega' : 'all', 'Left' : ('vertices in (x < 0.001)', 'facet'), 'Right' : ('vertices in (x > 0.099)', 'facet'), } ## # Dirichlet BC. ebcs = { 'l' : ('Left', {'u.all' : 0.0}), } ## # Balance of forces. integral_1 = { 'name' : 'i', 'order' : 1, } equations = { 'balance' : """dw_tl_he_neohook.i.Omega( solid.mu, v, u ) + dw_tl_bulk_penalty.i.Omega( solid.K, v, u ) + dw_tl_fib_a.i.Omega( f1.fmax, f1.eps_opt, f1.s, f1.fdir, f1.act, v, u ) + dw_tl_fib_a.i.Omega( f2.fmax, f2.eps_opt, f2.s, f2.fdir, f2.act, v, u ) = 0""", } def stress_strain(out, problem, state, extend=False): from sfepy.base.base import Struct, debug ev = problem.evaluate strain = ev('dw_tl_he_neohook.i.Omega( solid.mu, v, u )', mode='el_avg', term_mode='strain') out['green_strain'] = Struct(name='output_data', mode='cell', data=strain, dofs=None) stress = ev('dw_tl_he_neohook.i.Omega( solid.mu, v, u )', mode='el_avg', term_mode='stress') out['neohook_stress'] = Struct(name='output_data', mode='cell', data=stress, dofs=None ) stress = ev('dw_tl_bulk_penalty.i.Omega( solid.K, v, u )', mode='el_avg', term_mode= 'stress') out['bulk_stress'] = Struct(name='output_data', mode='cell', data=stress, dofs=None) return out ## # Solvers etc. solver_0 = { 'name' : 'ls', 'kind' : 'ls.scipy_direct', } solver_1 = { 'name' : 'newton', 'kind' : 'nls.newton', 'i_max' : 7, 'eps_a' : 1e-10, 'eps_r' : 1.0, 'macheps' : 1e-16, 'lin_red' : 1e-2, # Linear system error < (eps_a * lin_red). 'ls_red' : 0.1, 'ls_red_warp': 0.001, 'ls_on' : 1.1, 'ls_min' : 1e-5, 'check' : 0, 'delta' : 1e-6, } solver_2 = { 'name' : 'ts', 'kind' : 'ts.simple', 't0' : 0, 't1' : 1, 'dt' : None, 'n_step' : 21, # has precedence over dt! }

[ "cimrman3@ntc.zcu.cz" ]

cimrman3@ntc.zcu.cz

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/snippets/jinjaglobals.py

94483a9b22739b8556c77c61ada4f9ce77f30ce1

[]

no_license

Emilnurg/anas.ru

19000063c7db98c15261056bb9590382db362d42

20cee1aee02da192c9c79a51bd0898c1dba0c98f

refs/heads/master

2021-05-20T12:09:08.155749

2017-12-26T13:49:12

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# -*- coding: utf-8 -*- import os import random import re import time from django.conf import settings from django.contrib.humanize.templatetags.humanize import intcomma as int_comma from django.template.defaultfilters import floatformat as float_format from django.utils import formats from django.utils.dateformat import format as date_format from django.utils.timezone import template_localtime from django.utils.translation import ugettext_lazy as _ from easy_thumbnails.conf import settings as thumbnailer_settings from easy_thumbnails.exceptions import InvalidImageFormatError from easy_thumbnails.files import get_thumbnailer from easy_thumbnails.templatetags.thumbnail import RE_SIZE, VALID_OPTIONS from jinja2 import nodes from jinja2.exceptions import TemplateSyntaxError from jinja2.ext import Extension from markupsafe import escape from snippets.db_config import db_vars from snippets.template_backends.jinja2 import jinjaglobal, jinjafilter class SpacelessExtension(Extension): """ Removes whitespace between HTML tags at compile time, including tab and newline characters. It does not remove whitespace between jinja2 tags or variables. Neither does it remove whitespace between tags and their text content. Adapted from coffin: https://github.com/coffin/coffin/blob/master/coffin/template/defaulttags.py """ tags = {'spaceless'} def parse(self, parser): lineno = parser.stream.next().lineno body = parser.parse_statements(['name:endspaceless'], drop_needle=True) return nodes.CallBlock( self.call_method('_strip_spaces', [], [], None, None), [], [], body, ).set_lineno(lineno) def _strip_spaces(self, caller=None): return re.sub(r'>\s+<', '><', caller().strip()) class CacheExtension(Extension): """Exactly like Django's own tag, but supports full Jinja2 expressiveness for all arguments. {% cache gettimeout()*2 "foo"+options.cachename %} ... {% endcache %} This actually means that there is a considerable incompatibility to Django: In Django, the second argument is simply a name, but interpreted as a literal string. This tag, with Jinja2 stronger emphasis on consistent syntax, requires you to actually specify the quotes around the name to make it a string. Otherwise, allowing Jinja2 expressions would be very hard to impossible (one could use a lookahead to see if the name is followed by an operator, and evaluate it as an expression if so, or read it as a string if not. TODO: This may not be the right choice. Supporting expressions here is probably not very important, so compatibility should maybe prevail. Unfortunately, it is actually pretty hard to be compatibly in all cases, simply because Django's per-character parser will just eat everything until the next whitespace and consider it part of the fragment name, while we have to work token-based: ``x*2`` would actually be considered ``"x*2"`` in Django, while Jinja2 would give us three tokens: ``x``, ``*``, ``2``. General Syntax: {% cache [expire_time] [fragment_name] [var1] [var2] .. %} .. some expensive processing .. {% endcache %} Available by default (does not need to be loaded). Partly based on the ``FragmentCacheExtension`` from the Jinja2 docs. TODO: Should there be scoping issues with the internal dummy macro limited access to certain outer variables in some cases, there is a different way to write this. Generated code would look like this: internal_name = environment.extensions['..']._get_cache_value(): if internal_name is not None: yield internal_name else: internal_name = "" # or maybe use [] and append() for performance internalname += "..." internalname += "..." internalname += "..." environment.extensions['..']._set_cache_value(internalname): yield internalname In other words, instead of using a CallBlock which uses a local function and calls into python, we have to separate calls into python, but put the if-else logic itself into the compiled template. """ tags = {'cache'} def parse(self, parser): lineno = parser.stream.next().lineno expire_time = parser.parse_expression() fragment_name = parser.parse_expression() vary_on = [] while not parser.stream.current.test('block_end'): vary_on.append(parser.parse_expression()) body = parser.parse_statements(['name:endcache'], drop_needle=True) return nodes.CallBlock( self.call_method('_cache_support', [expire_time, fragment_name, nodes.List(vary_on), nodes.Const(lineno)]), [], [], body).set_lineno(lineno) def _cache_support(self, expire_time, fragm_name, vary_on, lineno, caller): from hashlib import md5 from django.core.cache import cache # delay depending in settings from django.utils.http import urlquote try: expire_time = int(expire_time) except (ValueError, TypeError): raise TemplateSyntaxError( '"%s" tag got a non-integer timeout value: %r' % (list(self.tags)[0], expire_time), lineno ) args_string = ':'.join([urlquote(v) for v in vary_on]) args_md5 = md5(args_string) cache_key = 'template.cache.%s.%s' % (fragm_name, args_md5.hexdigest()) value = cache.get(cache_key) if value is None: value = caller() cache.set(cache_key, value, expire_time) return value @jinjafilter def cropped_thumbnail(instance, field_name, width=None, height=None, scale=None, **opts): """Cropper""" ratiofield = instance._meta.get_field(field_name) image = getattr(instance, ratiofield.image_field) if ratiofield.image_fk_field: image = getattr(image, ratiofield.image_fk_field) if not image: return '' size = (int(ratiofield.width), int(ratiofield.height)) box = getattr(instance, field_name) if scale: scale = float(scale) width = size[0] * scale height = size[1] * scale elif width and height: width = float(width) h = size[1] * width / size[0] if h > height: width = height * size[0] / size[1] else: height = h elif width: width = float(width) height = size[1] * width / size[0] elif height: height = float(height) width = height * size[0] / size[1] if width and height: size = (int(width), int(height)) if ratiofield.adapt_rotation: if (image.height > image.width) != (size[1] > size[0]): size = (size[1], size[0]) thumbnailer = get_thumbnailer(image) thumbnail_options = { 'size': size, 'box': box, **opts } try: return thumbnailer.get_thumbnail(thumbnail_options).url except InvalidImageFormatError: return '' @jinjafilter def date(value, arg, use_l10n=True): value = template_localtime(value) if value in (None, ''): return '' if arg is None: arg = settings.DATE_FORMAT if arg == 'timestamp': return str(int(time.mktime(value.timetuple()))) try: return formats.date_format(value, arg, use_l10n=use_l10n) except AttributeError: try: return date_format(value, arg) except AttributeError: return '' @jinjaglobal def get_language_href(request, lang): url = request.get_full_path() parts = url.split('/') parts[1] = lang url = '/'.join(parts) return url if url.endswith('/') else url + '/' @jinjaglobal def get_languages(): return [x for x in settings.LANGUAGES if x[0] in settings.LANGUAGE_CODES_PUBLIC] @jinjafilter def floatformat(value, digits): """Порт floatformat""" return float_format(value, digits) @jinjafilter def intcomma(value, use_l10n=True): return int_comma(value, use_l10n=use_l10n) phone_re = re.compile(r'(\.|\s|-|\)|$)+') @jinjafilter def phone_url(val): val = strip_whitescapes(val, phone_re) # если не 8 800 if not val.startswith('8'): if not val.startswith('+'): val = '+7' + val return val @jinjaglobal def random_int(): return random.randint(1, 9999999) @jinjafilter def rjust(value, width, fillchar): return str(value).rjust(width, fillchar) @jinjaglobal def site_name(): return settings.SITE_NAME @jinjaglobal def site_url(): return settings.SITE_URL @jinjaglobal def static_with_mtime(file_path): filemtime = int( os.path.getmtime(os.path.join(settings.STATIC_ROOT, file_path)) ) return '%s%s?v=%s' % (settings.STATIC_URL, file_path, filemtime) whitespace_re = re.compile(r'(\s|-|$|\()+', re.MULTILINE) @jinjafilter def strip_whitescapes(val, re_obj=whitespace_re): return re_obj.sub('', val) @jinjafilter def thumbnail_obj(source, size, **opts): """Make thumbnail from source image""" if not source: return None raise_errors = thumbnailer_settings.THUMBNAIL_DEBUG accepted_opts = {} for key, value in opts.items(): if key in VALID_OPTIONS: accepted_opts[key] = value opts = accepted_opts m = RE_SIZE.match(size) if m: opts['size'] = (int(m.group(1)), int(m.group(2))) else: if raise_errors: raise TemplateSyntaxError('%r is not a valid size.' % size, 1) if 'quality' in opts: try: opts['quality'] = int(opts['quality']) except (TypeError, ValueError): if raise_errors: raise TemplateSyntaxError('%r is an invalid quality.' % opts['quality'], 1) try: curr_thumbnail = get_thumbnailer(source).get_thumbnail(opts) except Exception as e: if raise_errors: raise TemplateSyntaxError('Couldn\'t get the thumbnail %s: %s' % (source, e), 1) else: return None return curr_thumbnail @jinjafilter def thumbnail(source, size, **opts): thumb = thumbnail_obj(source, size, **opts) return escape(thumb.url) if thumb else '' @jinjaglobal def ugettext(value): return _(value) @jinjaglobal def var(key, request, **kwargs): result = db_vars.get(key, request.LANGUAGE_CODE, '') if kwargs: result = result.format(**kwargs) return result

[ "wizzzet@gmail.com" ]

wizzzet@gmail.com

fba58c1d1d2a511970d286307437a86f18d54932

8a3f10cd8f178e8452b0f1ecf747bec40ee87b31

/orders.py

2d627de6e0a1a078793a69db21cc83a1d14f48eb

[]

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Panda0229/flasky

70887be89b182f458ed6f1434e2a93afac048899

d6adb2dfe998a6836bc24443d23d3cb4ed8b8e58

refs/heads/master

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2019-12-31T13:04:24

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from flask import Blueprint # 创建一个蓝图的对象,蓝图就是一个小模块的抽象的概念,等号左边是蓝图对，等号右边是为这个对象所起的名字，可以不同 app_orders = Blueprint("app_orders", __name__) @app_orders.route("/get_orders") def get_orders(): return "get orders page" @app_orders.route("/post_orders") def post_orders(): return "post orders page"

[ "zhanghaining0229@163.com" ]

zhanghaining0229@163.com

b73253fd7fdc82aacabc674fd9ab679a7a0f6a51

1daf07aa6e1a602d69ab2a786dca43d093803a04

/module/module_using_sys.py

ecbb9b3638943e95bf22a6a2a0bf07396123cacf

[]

no_license

luteresa/python

89491c90788ccfcd49f554cd8e8db8f9d0ab715f

652dc34990f179094df64ef760fc03cc980556cd

refs/heads/master

2020-12-02T16:15:20.813439

2017-07-26T08:52:07

96,525,838

0

null

UTF-8

Python

false

253

py

import sys import os print('the command line arguments are:') for i in sys.argv: print(i) print('\n\nThe PYTHONPATH is', sys.path,'\n') print(os.getcwd()) for item in sys.path: print(item) from math import sqrt print('Squre root of 16 is ',sqrt(16))

[ "luteresa@163.com" ]

luteresa@163.com

868cbe6be728a3cb624a86979fd0c364286f5f63

5a52ccea88f90dd4f1acc2819997fce0dd5ffb7d

/alipay/aop/api/domain/AlipayMerchantComplainReconciliationCloseModel.py

0ef574bea5b34d603c969410707030ff70deb5cb

[ "Apache-2.0" ]

permissive

alipay/alipay-sdk-python-all

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1fad300587c9e7e099747305ba9077d4cd7afde9

refs/heads/master

2023-08-27T21:35:01.778771

2023-08-23T07:12:26

133,338,689

247

70

Apache-2.0

2023-04-25T04:54:02

2018-05-14T09:40:54

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Python

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1,508

py

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * class AlipayMerchantComplainReconciliationCloseModel(object): def __init__(self): self._merchant_id = None self._merchant_type = None @property def merchant_id(self): return self._merchant_id @merchant_id.setter def merchant_id(self, value): self._merchant_id = value @property def merchant_type(self): return self._merchant_type @merchant_type.setter def merchant_type(self, value): self._merchant_type = value def to_alipay_dict(self): params = dict() if self.merchant_id: if hasattr(self.merchant_id, 'to_alipay_dict'): params['merchant_id'] = self.merchant_id.to_alipay_dict() else: params['merchant_id'] = self.merchant_id if self.merchant_type: if hasattr(self.merchant_type, 'to_alipay_dict'): params['merchant_type'] = self.merchant_type.to_alipay_dict() else: params['merchant_type'] = self.merchant_type return params @staticmethod def from_alipay_dict(d): if not d: return None o = AlipayMerchantComplainReconciliationCloseModel() if 'merchant_id' in d: o.merchant_id = d['merchant_id'] if 'merchant_type' in d: o.merchant_type = d['merchant_type'] return o

[ "jishupei.jsp@alibaba-inc.com" ]

jishupei.jsp@alibaba-inc.com

168476b32a6333cbb5bb67e465d815cd3a211e1e

76c8a2593316a74078e5ebe3c280d393b058ff67

/vai/commands/BreakLineCommand.py

f5ae1c5829502cfe19b2fd6aa2d35ee9be32a0ea

[]

no_license

gavd89/vai

b7f746c3ba31397e8d85f477af9b9b71d01795fb

afa3a31b74ee81f9be8ab2c06cd8bdaebae1baad

refs/heads/master

2021-01-16T22:04:05.131998

2014-10-31T22:35:37

26,130,434

1

0

null

UTF-8

Python

false

1,830

py

from .BufferCommand import BufferCommand from .CommandResult import CommandResult from .NewLineCommand import NewLineCommand from .NewLineAfterCommand import NewLineAfterCommand from ..models.TextDocument import LineMeta class BreakLineCommand(BufferCommand): def execute(self): cursor = self._cursor document = self._document pos = cursor.pos self.saveCursorPos() if pos[1] == document.lineLength(pos[0]): command = NewLineAfterCommand(self._buffer) result = command.execute() if result.success: self._sub_command = command return result if pos[1] == 1: command = NewLineCommand(self._buffer) result = command.execute() if result.success: self._sub_command = command cursor.toPos((pos[0]+1, 1)) return result self.saveLineMemento(pos[0], BufferCommand.MEMENTO_REPLACE) current_text = document.lineText(pos[0]) current_indent = len(current_text) - len(current_text.lstrip(' ')) document.breakLine(pos) document.insertChars( (pos[0]+1, 1), ' '*current_indent ) cursor.toPos((pos[0]+1, current_indent+1)) line_meta = document.lineMeta(pos[0]) if line_meta.get(LineMeta.Change) == None: document.updateLineMeta(pos[0], {LineMeta.Change: "modified"}) document.updateLineMeta(pos[0]+1, {LineMeta.Change: "added"}) return CommandResult(success=True, info=None) def undo(self): self.restoreCursorPos() if self._sub_command is not None: self._sub_command.undo() self._sub_command = None return self.restoreLineMemento() self._document.deleteLine(self._cursor.pos[0]+1)

[ "stefano.borini@gmail.com" ]

stefano.borini@gmail.com

d88e83ce61f5c1cfefddc7edc3506b16a9221549

d8d0de926ac1a1de1abfca06aaed843d59f3674a

/python/easy/Solution_53.py

3823a1f61d12b196772daf89164680284c13e3b6

[]

no_license

nickest14/Leetcode-python

75b4919d1ac45c6a7b008e6336db38a06e337bc7

435deadfe2b3936dd7848a384d2d9a364352268c

refs/heads/master

2023-08-31T07:23:14.595745

2023-08-30T13:52:10

188,695,066

0

null

2019-07-20T12:29:09

2019-05-26T14:43:12

Python

UTF-8

Python

false

643

py

# 53. Maximum Subarray from typing import List class Solution: def maxSubArray(self, nums: List[int]) -> int: ans = nums[0] total = 0 for n in nums: total += n ans = max(ans, total) if total < 0: total = 0 return ans # def maxSubArray(self, nums: List[int]) -> int: # length = len(nums) # f = [None for _ in range(length+1)] # f[0] = 0 # for i in range(length): # f[i+1] = max(f[i]+nums[i], nums[i]) # return max(f[1:]) ans = Solution().maxSubArray([-2, 1, -3, 4, -1, 2, 1, -5, 4]) print(ans)

[ "nickest14@gmail.com" ]

nickest14@gmail.com

d8f4e1dab4f396180784cd27edd3f97818f00ff4

275a96a33ae1f89e7b2ee0ecdbac7d78abe6d6cc

/test/test_agreement.py

a23d4faa65adb95e44786545cc1594e00cee38cd

[]

no_license

cascadiarc/cyclos-python-client

8029ce07174f2fe92350a92dda9a60976b2bb6c2

a2e22a30e22944587293d51be2b8268bce808d70

refs/heads/main

2023-04-03T16:52:01.618444

2021-04-04T00:00:52

354,419,532

0

null

UTF-8

Python

false

827

py

# coding: utf-8 """ Cyclos 4.11.5 API The REST API for Cyclos 4.11.5 # noqa: E501 OpenAPI spec version: 4.11.5 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.models.agreement import Agreement # noqa: E501 from swagger_client.rest import ApiException class TestAgreement(unittest.TestCase): """Agreement unit test stubs""" def setUp(self): pass def tearDown(self): pass def testAgreement(self): """Test Agreement""" # FIXME: construct object with mandatory attributes with example values # model = swagger_client.models.agreement.Agreement() # noqa: E501 pass if __name__ == '__main__': unittest.main()

[ "dan@leftcoastfs.com" ]

dan@leftcoastfs.com

33daa2f6e6384682405e79bf9780044ac51c8720

d914604923dffab7a612b95f7e1b7ebcab0e9bb3

/insta/migrations/0001_initial.py

86409c50e4e9b1efd73aabc3b1f744ec7c895782

[]

no_license

MaryMbugua/Insta

8ca4adaa356d249a0d945cec653cf70fd2fe1589

a238cda86616cbfc26e95611ad63b7a42011ffc0

refs/heads/master

2020-03-18T03:27:33.141311

2018-05-23T16:05:06

134,241,994

0

1

null

UTF-8

Python

false

2,618

py

# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2018-05-23 06:31 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion import tinymce.models class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Comment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('comment', models.CharField(max_length=50)), ], ), migrations.CreateModel( name='Image', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('pic', models.ImageField(blank=True, upload_to='images/')), ('caption', models.CharField(max_length=60, null=True)), ('likes', models.ManyToManyField(blank=True, related_name='likes', to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Profile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('profile_pic', models.ImageField(blank=True, upload_to='avatar/')), ('bio', tinymce.models.HTMLField()), ('first_name', models.CharField(max_length=30, null=True)), ('last_name', models.CharField(max_length=30, null=True)), ('following', models.ManyToManyField(blank=True, related_name='followed_by', to=settings.AUTH_USER_MODEL)), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='profile', to=settings.AUTH_USER_MODEL)), ], ), migrations.AddField( model_name='image', name='profile', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='insta.Profile'), ), migrations.AddField( model_name='comment', name='image', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='insta.Image'), ), migrations.AddField( model_name='comment', name='user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), ]

[ "marymbugua.nm@gmail.com" ]

marymbugua.nm@gmail.com

d593edb29f5801f87b35e1ea86e83429d51735a0

215fd5c4f9893d9f38e4e48199ea16d7d6ef9430

/9.Dynmaic_Programming/9.6_L91_Decode_Ways.py

8f21ead3257f41860d9cc46e3d11e2e5d4e81dbb

[]

no_license

fztest/Classified

fd01622c097ca21b2e20285b06997ff0e9792dd1

b046d94657c0d04f3803ca15437dfe9a6f6f3252

refs/heads/master

2020-03-25T06:34:07.885108

2017-05-04T17:22:36

null

0

null

UTF-8

Python

false

924

py

""" Description ______________ A message containing letters from A-Z is being encoded to numbers using the following mapping: 'A' -> 1 'B' -> 2 ... 'Z' -> 26 Given an encoded message containing digits, determine the total number of ways to decode it. For example, Given encoded message "12", it could be decoded as "AB" (1 2) or "L" (12). The number of ways decoding "12" is 2. """ class Solution: # @param s, a string # @return an integer def numDecodings(self, s): if s is None or len(s) == 0: return 0 DP = [0 for _ in xrange(len(s))] DP[0] = 0 if s[0] == '0' else 1 for i in xrange(1, len(s)): first = s[i:i + 1] second = s[i - 1:i + 1] if int(first) != 0: DP[i] += DP[i - 1] if int(second) >= 10 and int(second) <= 26: DP[i] += DP[i - 2] if i >= 2 else 1 return DP[-1]

[ "cdzengpeiyun@gmail.com" ]

cdzengpeiyun@gmail.com

c9ee749e1278aca262b9a42c087dbe576822bd2e

bfaf89bdb222b5b1f31aa4ef2a6466ca0125e225

/students/view/logs.py

1ebabdab75d4327d83f3c4782c224b271a81e1d8

[]

no_license

smolynets/studentsdb13

ab70506571150745753f04923c1a62457368ee03

a76f9afa62ac6176a2f4dcea0098b6dd3986c3fc

refs/heads/master

2021-01-17T22:03:14.145146

2017-03-07T08:33:38

84,186,580

0

null

UTF-8

Python

false

962

py

from django.shortcuts import render from django.http import HttpResponse, HttpResponseRedirect from django.core.urlresolvers import reverse from ..models.monthjournal import logentry from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger from datetime import datetime from django.contrib import messages from datetime import datetime from django.contrib.auth.decorators import login_required @login_required def logs(request): logs = logentry.objects.order_by('asctime').reverse() # paginate logs paginator = Paginator(logs, 5) page = request.GET.get('page') try: logs = paginator.page(page) except PageNotAnInteger: # If page is not an integer, deliver first page. logs = paginator.page(1) except EmptyPage: # If page is out of range (e.g. 9999), deliver # last page of results. logs = paginator.page(paginator.num_pages) return render(request, 'students/logs.html', {'logs': logs})

[ "smolynets@gmail.com" ]

smolynets@gmail.com

b363b9ba1433e55ffe49fdb19adb2c5bade1ef05

6b2a8dd202fdce77c971c412717e305e1caaac51

/solutions_5738606668808192_0/Python/macieck9/c.py

272d253ddc792afadcf3d720f6bab5d5693a2d4b

[]

no_license

alexandraback/datacollection

0bc67a9ace00abbc843f4912562f3a064992e0e9

076a7bc7693f3abf07bfdbdac838cb4ef65ccfcf

refs/heads/master

2021-01-24T18:27:24.417992

2017-05-23T09:23:38

84,313,442

2

4

null

UTF-8

Python

false

869

py

def to_bin(n): l = [] while n != 0: l.append(n % 2) n /= 2 l.reverse() res = 0 for d in l: res = 10 * res + d return res def to_dec(n, b): l = str(n) res = 0 for d in l: res = b * res + int(d) return res def divisor(n): for p in primes: if n % p == 0: return p if p * p > n: return -1 return -1 N = 1<<17 T = [0] * (N + 5) primes = [] i = 2 while i*i <= N: if T[i] == 1: i += 1 continue for j in range(i * i, N + 1, i): T[j] = 1 i += 1 for i in range(2, N + 1): if T[i] == 0: primes.append(i) ans = [] for i in range((1<<15) + 1, 1<<16, 2): n = to_bin(i) div = [] for j in range(2, 11): x = to_dec(n, j) d = divisor(x) if d == -1: break div.append(d) if len(div) == 9: ans.append((n, div)) if len(ans) == 50: break print "Case #1:" for (n, l) in ans: print n, for x in l: print x, print

[ "alexandra1.back@gmail.com" ]

alexandra1.back@gmail.com

0901a8decebfd2e16adbeb8a5654d44c7e9cc093

a140fe192fd643ce556fa34bf2f84ddbdb97f091

/.history/예외처리_20200709152901.py

0f0b02c3f92435483b13b818496c0572d693295e

[]

no_license

sangha0719/py-practice

826f13cb422ef43992a69f822b9f04c2cb6d4815

6d71ce64bf91cc3bccee81378577d84ba9d9c121

refs/heads/master

2023-03-13T04:40:55.883279

2021-02-25T12:02:04

342,230,484

0

null

UTF-8

Python

false

443

py

try: print("나누기 전용 계산기입니다.") nums = [] nums.append(int(input("첫 번째 숫자를 입력하세요 : "))) nums.append(int(input("두 번째 숫자를 입력하세요 : "))) nums.append(int(nums[0] / nums[1])) print("{0} / {1} = {2}".format(num1, num2, int(num1/num2))) except ValueError: print("에러! 잘못된 값을 입력하였습니다.") except ZeroDivisionError as err: print(err)

[ "sangha0719@gmail.com" ]

sangha0719@gmail.com

91cb47500489f1a197928970caee8353d4676186

347299b495e1417dd4748c86f7696fb849c79c4f

/analisis_proyectos/servicios/configurador.py

5fd1e564e54d15c1808c25abe4961a5136ee9eb8

[]

no_license

vvalotto/python_uner

6a379cc7ab4cb6380c193863403409e5559e1cd6

8fbeb9fde0883427695b5065232ace452160034f

refs/heads/master

2020-07-25T23:32:50.829101

2019-10-18T18:02:16

208,457,042

0

null

UTF-8

Python

false

2,099

py

""" Clase que inicializa y arma las instancias necesarias iniciales de la aplicaccion: Contextos de datos Respositorios Gestores """ from analisis_proyectos.infraestructura.persistencia.contexto.contexto_database_sqlite import * from analisis_proyectos.infraestructura.persistencia.repositorios.DB_repositorio_proyecto import * from analisis_proyectos.infraestructura.persistencia.mapeador.proyecto import * from analisis_proyectos.aplicacion.gestores.gestor_proyecto import * from analisis_proyectos.aplicacion.gestores.gestor_componente import * from analisis_proyectos.aplicacion.gestores.gestor_elemento import * from analisis_proyectos.dominio.analitico.muestra import * from analisis_proyectos.dominio.analitico.analizador import * import os directorio_base = os.path.abspath(os.path.dirname(__file__)) URI_DATABASE = 'sqlite:///' + os.path.join(directorio_base, 'proyectos.sqlite') class Configurador: contexto = ContextoDBSQLite(URI_DATABASE) repositorio_proyecto = DBRepositorioProyecto(contexto, MapeadorDatosProyecto(contexto)) repositorio_componente = DBRepositorioComponente(contexto, MapeadorDatosComponente(contexto)) repositorio_elemento = DBRepositorioElemento(contexto, MapeadorDatosElemento(contexto)) gestor_proyecto = GestorProyecto() gestor_proyecto.asignar_repositorio(repositorio_proyecto) gestor_componente = GestorComponente() gestor_componente.asignar_repositorio(repositorio_componente) gestor_elemento = GestorElemento() gestor_elemento.asignar_repositorio(repositorio_elemento) muestra_proyectos=Muestra() analizador_proyecto = Analizador(muestra_proyectos) repositorio = "proyectos.sqlite" datos_origen = "SELECT * FROM mediciones_proyecto;" muestra_proyectos.cargar_valores_de_muestra(repositorio, datos_origen) if __name__ == '__main__': print(Configurador.contexto.recurso) if Configurador.gestor_proyecto.existe_proyecto("Sistema de Gestión de Flota"): proyecto = Configurador.gestor_proyecto.recuperar_proyecto_por_nombre("Sistema de Gestión de Flota") print(proyecto)

[ "vvalotto@gmail.com" ]

vvalotto@gmail.com

9a4060829fa3fbbf0940f5bfb96aa01245a3d0c5

1bccf7d57c7aa8d48b84fff187de4b6ff2599cb6

/pandora_rqt_gui/scripts/pandora_rqt_gui.py

c324b72f85afebafedfbb68e996684964493b158

[]

no_license

skohlbr/pandora_ros_pkgs

733ed34edb5b6d46e59df4acb01288f28ef3b50f

eecaf082b47e52582c5f009eefbf46dd692aba4f

refs/heads/indigo-devel

2021-01-21T18:06:14.967943

2015-11-04T15:08:03

53,413,573

0

1

null

2016-03-08T13:19:40

null

UTF-8

Python

false

140

py

#!/usr/bin/env python import sys from rqt_gui.main import Main main = Main() sys.exit(main.main(sys.argv, standalone='pandora_rqt_gui'))

[ "pandora@ee.auth.gr" ]

pandora@ee.auth.gr

4fff3230f52f4f0a934e4f4430ae04de4a2e3c0a

43b6bffc820d26dfd223728bed71241fb3d54983

/abc/223/c.py

b26920383e241d8bef73911853f1633fb651f49a

[]

no_license

kiccho1101/atcoder

3a163b6a38a62c578dad6d15ccb586d0fcd1e004

c86cb8e08b881a0a01dc2ef538f0699f3951e897

refs/heads/master

2023-03-02T13:27:17.747402

2022-05-30T13:51:00

223,152,693

1

0

null

2023-02-11T01:29:47

2019-11-21T10:52:49

Python

UTF-8

Python

false

345

py

N = int(input()) ab = [list(map(int, input().split())) for _ in range(N)] secs = [a / b for a, b in ab] middle = sum(secs) / 2 ans = 0 curr = 0 for i, (a, b) in enumerate(ab): diff = middle - (curr + secs[i]) if diff > 0: curr += secs[i] ans += a else: ans += (middle - curr) * b break print(ans)

[ "yodai.a.kishimoto@rakuten.com" ]

yodai.a.kishimoto@rakuten.com

c125b0cb7fc1c7088739d00ba172ced46b39efe7

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p02909/s936658151.py

b112400ac8521188a69b312edffaf79281fe8a75

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

157

py

S = input() weather = ['Sunny', 'Cloudy','Rainy'] if S == weather[0]: print(weather[1]) elif S == weather[1]: print(weather[2]) else: print(weather[0])

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

274bceda1d0097d2ed46e0f61982776854ed733c

de5eafa17b8c1e3a8aedb2848e03282eae775334

/augment/augment_intersect_Brent9060_v10.py

6ade04d52ef7f70c94a83767fdf168eedfbac5a4

[]

no_license

ekourkchi/HI

cba6601fbbb96231d457cc6a0a379c2fdb7a3e4d

796ff5b9826d9d64e2a761e3ef587411df8edd80

refs/heads/master

2020-05-25T19:21:05.663026

2020-03-14T04:08:47

187,949,260

1

0

null

UTF-8

Python

false

52,866

py

#!/usr/bin/python # encoding=utf8 import sys reload(sys) sys.setdefaultencoding('utf8') import os import subprocess import math import matplotlib.pyplot as plt import numpy as np import pylab as py from astropy.table import Table, Column import random from scipy.optimize import curve_fit from astropy.stats import sigma_clip import sqlcl import urllib2 inFile = 'wise_all.csv' table = np.genfromtxt( inFile , delimiter=',', filling_values=None, names=True, dtype=None) wise_name = table['ID'] wise_pgc = table['PGC'] ###################################### def xcmd(cmd,verbose): if verbose: print '\n'+cmd tmp=os.popen(cmd) output='' for x in tmp: output+=x if 'abort' in output: failure=True else: failure=tmp.close() if False: print 'execution of %s failed' % cmd print 'error is as follows',output sys.exit() else: return output ###################################### ################# class cd: """Context manager for changing the current working directory""" def __init__(self, newPath): self.newPath = os.path.expanduser(newPath) def __enter__(self): self.savedPath = os.getcwd() os.chdir(self.newPath) def __exit__(self, etype, value, traceback): os.chdir(self.savedPath) ############################### def get_scales(photometry, nskip=29): table = np.genfromtxt(photometry , delimiter=',', filling_values=None, names=True, dtype=None, skip_header=nskip) try: ebv = float(table['ebv']) except: ebv=-9.99 try: a_gal = float(table['a_gal']) except: a_gal=-9.99 try: central_mu = float(table['central_mu']) except: central_mu=-9.99 try: mu_90 = float(table['mu_90']) except: mu_90=-9.99 try: mu_50 = float(table['mu_50']) except: mu_50=-9.99 try: concentration = float(table['concentration']) except: concentration=-9.99 try: m_255 = float(table['m_255']) except: m_255=-9.99 try: disc_mu0 = float(table['disc_mu0']) except: disc_mu0=-9.99 try: scale_length_h = float(table['scale_length_h']) except: scale_length_h=-9.99 try: R_asy = float(table['R_asy']) except: R_asy=-9.99 try: R_90 = float(table['R_90']) except: R_90=-9.99 try: R_80 = float(table['R_80']) except: R_80=-9.99 try: R_50 = float(table['R_50']) except: R_50=-9.99 try: R_20 = float(table['R_20']) except: R_20=-9.99 try: R_255 = float(table['R_255']) except: R_255=-9.99 try: d_m_ext = float(table['d_m_ext']) except: d_m_ext=-9.99 return ebv, a_gal, central_mu, mu_50, mu_90, m_255, disc_mu0, scale_length_h, R_50, R_90, R_255, concentration, d_m_ext ############################### ### For a given photometry file, this returns the magnitude value def get_mag(photometry, index=2, header=None): if header is not None: if os.path.exists(photometry): with open(photometry) as f: for line in f: foundit = False once = True if line.split(" ")[0]== '#': line_split = line.split(" ") not_void = 0 key = None for thing in line_split: if thing != '': not_void+=1 if not_void==2 and once: key=thing once = False if not_void==3 and key==header: foundit = True break if foundit: return np.float(thing) if header is not None: return 0 if os.path.exists(photometry): with open(photometry) as f: counter = 1 for line in f: if counter == 14: line_split = line.split(" ") not_void = 0 for thing in line_split: if thing != '': not_void+=1 if not_void==index: break return np.float(thing) counter+=1 ############################### ############################### ### For a given photometry file, this returns the magnitude value def get_mag_f(photometry, index=2, header=None): if header is not None: if os.path.exists(photometry): with open(photometry) as f: for line in f: foundit = False once = True if line.split(" ")[0]== '#': line_split = line.split(" ") not_void = 0 key = None for thing in line_split: if thing != '': not_void+=1 if not_void==2 and once: key=thing once = False if not_void==3 and key==header: foundit = True break if foundit: return np.float(thing) if header is not None: return 0 if os.path.exists(photometry): with open(photometry) as f: counter = 1 for line in f: if line.split(" ")[0]!= '#' and line.split(" ")[0]!='#\n': line_split = line.split(" ") not_void = 0 for thing in line_split: if thing != '': not_void+=1 set_param = True if not_void==index: break return np.float(thing) counter+=1 def get_mag_wise(photometry, index=2): mag = get_mag_f(photometry, index=index) if mag!=None: return mag else: return -1000 ############################### def get_semimajor(filename): with open(filename) as f: counter = 1 for line in f: if counter == 14: line_split = line.split(" ") not_void = 0 for thing in line_split: if thing != '': not_void+=1 if not_void==1: break return np.float(thing) counter+=1 def get_ellipse(filename): ra_cen = -1 dec_cen = -1 semimajor = -1 semiminor = -1 PA = -1 with open(filename) as f: counter = 1 for line in f: if counter == 14: line_split = line.split(" ") not_void = 0 set_param = False for thing in line_split: if thing != '': not_void+=1 set_param = True if not_void==1 and set_param: set_param = False ra_cen=np.float(thing) if not_void==2 and set_param: dec_cen=np.float(thing) set_param = False if not_void==3 and set_param: semimajor=np.float(thing) set_param = False if not_void==4 and set_param: semiminor=np.float(thing) set_param = False if not_void==5 and set_param: PA=np.float(thing) break return ra_cen, dec_cen, semimajor, semiminor, PA counter+=1 ################################# ############################### def isInSDSS_DR12(ra, dec): querry = "select dbo.fInFootprintEq("+str(ra)+","+str(dec)+", 1)" lines = sqlcl.query(querry).readlines() if lines[2] == "True\n": return 1 else: return 0 ############################### ############################### def get_ellipse_wise(filename): ra_cen = -1 dec_cen = -1 semimajor = -1 semiminor = -1 PA = -1 with open(filename) as f: counter = 1 for line in f: if line.split(" ")[0]!= '#' and line.split(" ")[0]!='#\n': # counter == 17: line_split = line.split(" ") not_void = 0 set_param = False for thing in line_split: if thing != '': not_void+=1 set_param = True if not_void==1 and set_param: set_param = False ra_cen=np.float(thing) if not_void==2 and set_param: dec_cen=np.float(thing) set_param = False if not_void==3 and set_param: semimajor=np.float(thing) set_param = False if not_void==4 and set_param: semiminor=np.float(thing) set_param = False if not_void==5 and set_param: PA=np.float(thing) break return ra_cen, dec_cen, semimajor, semiminor, PA counter+=1 ################################# ################################# def get_quality(filename, nline=40): line_no = 0 seprator = ' ' for line in open(filename, 'r'): columns = line.split(seprator) line_no+=1 if len(columns) >= 2 and line_no==nline: key = columns[0] j = 1 while columns[j] == '' or columns[j] == '=': j+=1 return int(columns[j]) return -1 ################################# def read_note(filename): qa_note = filename note = ' ' if os.path.exists(qa_note): with open(qa_note) as f: counter = 1 for line in f: if counter == 11: line_split = line.split("=") note = line_split[1] note = note[0:len(note)-1] counter+=1 return note ################################# ################################# def ra_db(ra): # returns a string ra_id = str(int(np.floor(ra))) if ra < 10: ra_id = '00'+ra_id+'D' elif ra < 100: ra_id = '0'+ra_id+'D' else: ra_id = ra_id+'D' return ra_id ################################# ################# def QA_SDSS_DONE(pgc, ra): databse = '/home/ehsan/db_esn/'+'/cf4_sdss/data/' name = 'pgc'+str(pgc) if os.path.exists(databse+ra_db(ra)+'/sdss/fits/'+name+'_qa.txt'): return True return False ################# def QA_WISE_DONE(pgc, ra): global wise_name, wise_pgc databse = '/home/ehsan/db_esn/'+'/cf4_wise/data/' if pgc in wise_pgc: i_lst = np.where(pgc == wise_pgc) name = wise_name[i_lst][0] if os.path.exists(databse+ra_db(ra)+'/wise/fits/'+name+'_qa.txt'): return True name = 'pgc'+str(pgc) if os.path.exists(databse+ra_db(ra)+'/wise/fits/'+name+'_qa.txt'): return True return False ####################################### ###################################### def rnd_inc(inc): if inc==None: return None if inc>90: return 90. if inc<45: return 0. d_inc = inc - int(inc) if d_inc>0.5: inc=int(inc)+1 elif d_inc<0.5: inc=int(inc) else: rand = random.randint(0,1) if rand==0: inc=int(inc) else: inc=int(inc)+1 return inc ###################################### def inc_append(incs, email, inc): n = 1 if email=='rbtully1@gmail.com': n = 4 elif email=='ekourkchi@gmail.com': n = 4 elif email=='s.eftekharzadeh@gmail.com': n = 2 elif email=='mokelkea@hawaii.edu': n = 3 elif email=='chasemu@hawaii.edu': n = 2 elif email=='jrl2014@hawaii.edu': n = 3 elif email=='dschoen@hawaii.edu': n = 4 elif email=='adholtha@hawaii.edu': n = 4 elif email=='chuangj@hawaii.edu': n = 2 elif email=='mi24@hawaii.edu': n = 2 elif email=='mka7@hawaii.edu': n = 1 elif email=='a.danesh61@gmail.com': n = 1 elif email=='cgrubner0@gmail.com': n = 1 elif email=='pascal.jouve@free.fr': n = 2 elif email=='dlsaintsorny@gmail.com': n = 2 elif email=='arnaud.ohet@gmail.com': n = 1 elif email=='hawaii@udrea.fr': n = 1 elif email=='helenecourtois33@gmail.com': n = 4 elif email=='claude.rene21@gmail.com': n = 1 elif email=='fredwallet@gmail.com': n = 2 elif email=='henri140860@wanadoo.fr': n = 2 elif email=='joannin.lycee@free.fr': n = 2 elif email=='bevig434@gmail.com': n = 2 elif email=='echarraix69@gmail.com': n = 2 for i in range(n): incs.append(inc) return incs ###################################### def correction(i, email): a=1 b=0 if email=='chuangj@hawaii.edu': a = 0.9698391552105461 b = 3.582543838111245 if email=='mi24@hawaii.edu': a = 0.9819724300214063 b = 2.485648837307963 if email=='arnaud.ohet@gmail.com': a = 0.8925968302721691 b = 8.021973390519326 if email=='cgrubner0@gmail.com': a = 0.8957026107782403 b = 9.076420810780814 if email=='jrl2014@hawaii.edu': a = 0.9350710901954157 b = 5.178922022569104 a = 1./a b = -1.*b*a # -b/a return a*i+b ###################################### def fitFunc(x, a, b): return a*x+b ###################################### def addNote(note, text): if text=='': return note if note=='': note = '['+text+']' else: note = note+' '+'['+text+']' return note def addConcern(note, cncrn): if cncrn[0]>0: note = addNote(note, 'not_sure') if cncrn[1]>0: note = addNote(note, 'better_image') if cncrn[2]>0: note = addNote(note, 'bad_TF') if cncrn[3]>0: note = addNote(note, 'ambiguous') if cncrn[4]>0: note = addNote(note, 'disturbed') if cncrn[5]>0: note = addNote(note, 'HI') if cncrn[6]>0: note = addNote(note, 'face_on') if cncrn[7]>0: note = addNote(note, 'not_spiral') if cncrn[8]>0: note = addNote(note, 'multiple') return note ###################################### ####################################### def getINC(include_Email=None, exclude_Email=[]): if include_Email==None: emails = ['rbtully1@gmail.com','ekourkchi@gmail.com','mokelkea@hawaii.edu', 'jrl2014@hawaii.edu', 'dschoen@hawaii.edu', 'adholtha@hawaii.edu'] else: emails = include_Email #### Manoa inFile = 'EDD.inclination.All.Manoa.11Sep2018135319.txt' table = np.genfromtxt(inFile , delimiter='|', filling_values=None, names=True, dtype=None) pgc_incout = table['pgcID'] inc_incout = table['inc'] flag_incout = table['flag'] note = [' '.join(dummy.split()) for dummy in table['note']] email = [' '.join(dummy.split()) for dummy in table['email']] NS = table['not_sure'] BI = table['better_image'] TF = table['bad_TF'] AM = table['ambiguous'] DI = table['disturbed'] HI = table['HI'] FO = table['face_on'] NP = table['not_spiral'] MU = table['multiple'] #### Guest inFile = 'EDD.inclination.All.Guest.11Sep2018135307.txt' table = np.genfromtxt(inFile , delimiter='|', filling_values=None, names=True, dtype=None) pgc_incout_ = table['pgcID'] inc_incout_ = table['inc'] flag_incout_ = table['flag'] note_ = [' '.join(dummy.split()) for dummy in table['note']] email_ = [' '.join(dummy.split()) for dummy in table['email']] NS_ = table['not_sure'] BI_ = table['better_image'] TF_ = table['bad_TF'] AM_ = table['ambiguous'] DI_ = table['disturbed'] HI_ = table['HI'] FO_ = table['face_on'] NP_ = table['not_spiral'] MU_ = table['multiple'] PGC = [] for i in range(len(pgc_incout)): if not pgc_incout[i] in PGC: PGC.append(pgc_incout[i]) for i in range(len(pgc_incout_)): if not pgc_incout_[i] in PGC: PGC.append(pgc_incout_[i]) incDict = {} for i in range(len(PGC)): data = [] indx = np.where(PGC[i] == pgc_incout) for j in indx[0]: if email[j] in emails and not email[j] in exclude_Email: inc_incout[j] = correction(inc_incout[j], email[j]) data.append([email[j], inc_incout[j],flag_incout[j],note[j], [NS[j], BI[j], TF[j], AM[j], DI[j], HI[j], FO[j], NP[j], MU[j]]]) indx = np.where(PGC[i] == pgc_incout_) for j in indx[0]: if email_[j] in emails and not email_[j] in exclude_Email: inc_incout_[j] = correction(inc_incout_[j], email[j]) data.append([email[j], inc_incout_[j],flag_incout_[j],note_[j], [NS_[j], BI_[j], TF_[j], AM_[j], DI_[j], HI_[j], FO_[j], NP_[j], MU_[j]]]) incDict[PGC[i]] = data return incDict ########################################################### ###################################### def incMedian(incDic): boss = 'ekourkchi@gmail.com' Keypeople = [] for item in incDic: Keypeople.append(item[0]) if item[0] == 'rbtully1@gmail.com': boss = 'rbtully1@gmail.com' flag = 0 inc = 0 note = '' stdev = 0 n = 0 # number of good measurments concerns = np.zeros(9) if boss in Keypeople: poss_i = 0 for ppl in Keypeople: if ppl==boss: break poss_i+=1 if incDic[poss_i][2] != 0: # boss has flagged it flag = 1 for item in incDic: if item[2]==1: note = addNote(note, item[3]) concerns+=np.asarray(item[4]) n+=1 else: # boss has NOT flagged it flag = 0 incs = [] incs2 = [] for item in incDic: if item[2]==0: incs.append(item[1]) incs2 = inc_append(incs2, item[0], item[1]) note = addNote(note, item[3]) n+=1 incs = np.asarray(incs) filtered_data = sigma_clip(incs, sigma=2, iters=5, copy=False) incs = filtered_data.data[np.logical_not(filtered_data.mask)] stdev = np.std(incs) incs2 = np.asarray(incs2) filtered_data = sigma_clip(incs2, sigma=2, iters=5, copy=False) incs2 = filtered_data.data[np.logical_not(filtered_data.mask)] inc = np.median(incs2) else: flag = [] for item in incDic: flag.append(item[2]) flag = np.median(flag) if flag > 0: flag =1 incs = [] incs2 = [] for item in incDic: if item[2]==flag: incs.append(item[1]) incs2 = inc_append(incs2, item[0], item[1]) note = addNote(note, item[3]) concerns+=np.asarray(item[4]) n+=1 incs = np.asarray(incs) filtered_data = sigma_clip(incs, sigma=2, iters=5, copy=False) incs = filtered_data.data[np.logical_not(filtered_data.mask)] stdev = np.std(incs) incs2 = np.asarray(incs2) filtered_data = sigma_clip(incs2, sigma=2, iters=5, copy=False) incs2 = filtered_data.data[np.logical_not(filtered_data.mask)] inc = np.median(incs2) note = addConcern(note, concerns) inc = rnd_inc(inc) if inc>=89: err = 1. elif inc>=85: err = 2. elif inc>=69: err = 3. elif inc>=50: err = 4. elif inc>=45: err = 6. else: err = 0 flag = 1 inc = 0 stdev = 0 stdev = np.max([stdev, err]) stdev = np.round(stdev) return inc, stdev, flag, note, n ####################################### ################# def query_leda_lyon(pgc): leda = [] query="" if True: query=query+"%20or%20pgc%3D"+str(pgc) if True: query=query[5:] url='http://leda.univ-lyon1.fr/leda/fullsqlmean.cgi?Query=select%20*%20where'+query result=urllib2.urlopen(url) for myline in result: if "<" in myline: continue if myline=="": continue elements=myline.replace(" ","").split("|") elements=[x if x!="-" else None for x in elements] if ("pgc" in elements[0]): continue if (len(elements)<2): continue elements.pop() if (elements): #print elements[:3] leda.append((elements)) query="" pgc_leda = None ra_leda = None dec_leda = None l_leda = None b_leda = None sgl_leda = None sgb_leda = None logd25_leda = None logr25_leda = None pa_leda = None ty_leda = None type_leda = None if (leda): leda = leda[0] pgc_leda = int(leda[0]) ra_leda = float(leda[5])*15. dec_leda = float(leda[6]) l_leda = float(leda[7]) b_leda = float(leda[8]) sgl_leda = float(leda[9]) sgb_leda = float(leda[10]) logd25_leda = float(leda[20]) logr25_leda = float(leda[22]) pa_leda = float(leda[24]) ty_leda = float(leda[17]) type_leda = (leda[12]) return([pgc_leda, ra_leda, dec_leda, l_leda, b_leda, sgl_leda, sgb_leda, logd25_leda, logr25_leda, pa_leda, ty_leda, type_leda]) #return leda ############################### ########################################################### TEST #print get_mag_wise('/run/media/ehsan/6ccd3c78-12e8-4f00-815d-faf200b314cf/ehsan/db_esn/cf4_wise/data/001D/photometry/NGC7821_w1_asymptotic.dat', index=1) #print get_mag_f('/run/media/ehsan/6ccd3c78-12e8-4f00-815d-faf200b314cf/ehsan/db_esn/cf4_wise/data/001D/photometry/NGC7821_w1_asymptotic.dat', header='A_Gal:') #print get_mag('/run/media/ehsan/6ccd3c78-12e8-4f00-815d-faf200b314cf/ehsan/db_esn/cf4_sdss/data/018D/photometry/pgc1264576_g_asymptotic.dat') #print get_mag('/run/media/ehsan/6ccd3c78-12e8-4f00-815d-faf200b314cf/ehsan/db_esn/cf4_sdss/data/018D/photometry/pgc1264576_g_asymptotic.dat', index=1) #print get_mag('/run/media/ehsan/6ccd3c78-12e8-4f00-815d-faf200b314cf/ehsan/db_esn/cf4_sdss/data/018D/photometry/pgc1264576_g_asymptotic.dat', header='A_Gal:') #sys.exit() ########################################################### Begin inFile = 'EDD_distance_cf4_v21.csv' table = np.genfromtxt(inFile , delimiter='|', filling_values=None, names=True, dtype=None) pgc = table['pgc'] ra = table['ra'] dec = table['dec'] gl = table['gl'] gb = table['gb'] sgl = table['sgl'] sgb = table['sgb'] d25 = table['d25'] b_a = table['b_a'] pa = table['pa'] ty = table['ty'] type = table['type'] sdss = table['sdss'] alfa100_ = table['alfa100'] QA_sdss = table['QA_sdss'] QA_wise = table['QA_wise'] ############################################################ inFile = 'All_LEDA_EDD.csv' table = np.genfromtxt( inFile , delimiter=',', filling_values=None, names=True, dtype=None) pgc_leda = table['pgc'] ra_leda = table['al2000'] ra_leda *= 15. dec_leda = table['de2000'] l_leda = table['l2'] b_leda = table['b2'] sgl_leda = table['sgl'] sgb_leda = table['sgb'] logd25_leda = table['logd25'] d25_leda = 0.1*(10**logd25_leda) logr25_leda = table['logr25'] b_a_leda = 1./(10**logr25_leda) pa_leda = table['pa'] ty_leda = table['t'] type_leda = table['type'] ############################################################ pgc_ = [] ra_ = [] dec_ = [] l_ = [] b_ = [] sgl_ = [] sgb_ = [] sdss_ = [] d25_ = [] alfa100 = [] QA = [] QA_wise = [] pa_ = [] b_a_ = [] ty_ = [] type_ = [] for i in range(len(pgc)): # range(100): if not pgc[i] in pgc_: pgc_.append(pgc[i]) ra_.append(ra[i]) dec_.append(dec[i]) l_.append(gl[i]) b_.append(gb[i]) sgl_.append(sgl[i]) sgb_.append(sgb[i]) d25_.append(d25[i]) sdss_.append(sdss[i]) pa_.append(pa[i]) b_a_.append(b_a[i]) ty_.append(ty[i]) alfa100.append(alfa100_[i]) if QA_SDSS_DONE(pgc[i], ra[i]): QA.append(1) else: QA.append(0) if QA_WISE_DONE(pgc[i], ra[i]): QA_wise.append(1) else: QA_wise.append(0) type_.append(type[i]) ##################################################### inFile = 'Alfa100_EDD.csv' table = np.genfromtxt( inFile , delimiter='|', filling_values=None, names=True, dtype=None) pgc_edd_alfa100 = table['PGC'] inFile = 'tmp1' table = np.genfromtxt( inFile , delimiter='|', filling_values=None, names=True, dtype=None) pgc_sdss = table['pgc'] ############################################################ inFile = 'output_1_Results_alexandra.csv' table = np.genfromtxt(inFile , delimiter='|', filling_values=None, names=True, dtype=None) id_lexi = table['ID'] pgc_add = [] q = 0 for i in range(len(id_lexi)): new_pgc = int(id_lexi[i][3:10]) if not new_pgc in pgc_: pgc_add.append(new_pgc) q+=1 print "New TF gals: #", q for i in range(len(pgc_add)): if not pgc_add[i] in pgc_leda: leda_lexi = query_leda_lyon(pgc_add[i]) pgc_.append(pgc_add[i]) ra_.append(leda_lexi[1]) dec_.append(leda_lexi[2]) ra000 = leda_lexi[1] l_.append(leda_lexi[3]) b_.append(leda_lexi[4]) sgl_.append(leda_lexi[5]) sgb_.append(leda_lexi[6]) d25_.append(0.1*(10**leda_lexi[7])) b_a_.append(1./(10**leda_lexi[8])) pa_.append(leda_lexi[9]) ty_.append(leda_lexi[10]) type_.append(leda_lexi[11]) if pgc_add[i] in pgc_sdss: sdss_.append(1) else: sdss_.append(0) if pgc_add[i] in pgc_edd_alfa100: alfa100.append(1) else: alfa100.append(0) if QA_SDSS_DONE(pgc_add[i], ra000): QA.append(1) else: QA.append(0) if QA_WISE_DONE(pgc_add[i], ra000): QA_wise.append(1) else: QA_wise.append(0) else: for j in range(len(pgc_leda)): if pgc_leda[j] == pgc_add[i]: if not pgc_add[i] in pgc_: pgc_.append(pgc_leda[j]) if True: ra000 = ra_leda[j] ra_.append(ra_leda[j]) dec_.append(dec_leda[j]) b_a_.append(1./(10**logr25_leda[j])) ty_.append(ty_leda[j]) l_.append(l_leda[j]) b_.append(b_leda[j]) sgl_.append(sgl_leda[j]) sgb_.append(sgb_leda[j]) d25_.append(0.1*(10**logd25_leda[j])) if pgc_add[i] in pgc_sdss: sdss_.append(1) else: sdss_.append(0) pa_.append(pa_leda[j]) type_.append(type_leda[j]) if pgc_leda[j] in pgc_edd_alfa100: alfa100.append(1) else: alfa100.append(0) if QA_SDSS_DONE(pgc_leda[j], ra000): QA.append(1) else: QA.append(0) if QA_WISE_DONE(pgc_leda[j], ra000): QA_wise.append(1) else: QA_wise.append(0) break ##################################################### print "Adding Types from the LEDA catalog" pgc_ = np.asarray(pgc_) ra_ = np.asarray(ra_) dec_ = np.asarray(dec_) l_ = np.asarray(l_) b_ = np.asarray(b_) sgl_ = np.asarray(sgl_) sgb_ = np.asarray(sgb_) d25_ = np.asarray(d25_) b_a_ = np.asarray(b_a_) pa_ = np.asarray(pa_) ty_ = np.asarray(ty_) type_ = np.asarray(type_) sdss_ = np.asarray(sdss_) alfa100 = np.asarray(alfa100) QA = np.asarray(QA) QA_wise = np.asarray(QA_wise) index = np.argsort(pgc_) pgc_ = pgc_[index] ra_ = ra_[index] dec_ = dec_[index] l_ = l_[index] b_ = b_[index] sgl_ = sgl_[index] sgb_ = sgb_[index] d25_ = d25_[index] b_a_ = b_a_[index] pa_ = pa_[index] ty_ = ty_[index] type_ = type_[index] sdss_ = sdss_[index] alfa100 = alfa100[index] QA = QA[index] QA_wise = QA_wise[index] for i in range(len(pgc_)): gal = pgc_[i] if gal in [58411,58239,17170,1977897,9476]: sdss_[i] = 0 ##################################################################### print "Taking Care of inclinations ..." A_emails = ['rbtully1@gmail.com', 'mokelkea@hawaii.edu', 'jrl2014@hawaii.edu', 'dschoen@hawaii.edu', 'mi24@hawaii.edu', 'chuangj@hawaii.edu'] B_emails = ['ekourkchi@gmail.com', 's.eftekharzadeh@gmail.com', 'chasemu@hawaii.edu', 'adholtha@hawaii.edu', 'mka7@hawaii.edu', 'a.danesh61@gmail.com', 'helenecourtois33@gmail.com'] C_emails = ['cgrubner0@gmail.com', 'pascal.jouve@free.fr', 'dlsaintsorny@gmail.com', 'arnaud.ohet@gmail.com', 'hawaii@udrea.fr', 'henri140860@wanadoo.fr'] D_emails = ['henri140860@wanadoo.fr', 'claude.rene21@gmail.com', 'fredwallet@gmail.com', 'joannin.lycee@free.fr', 'bevig434@gmail.com', 'echarraix69@gmail.com'] incDic = getINC(include_Email=A_emails+B_emails+C_emails+D_emails) print "Taking Care of flags ..." location_sdss = '/home/ehsan/db_esn/cf4_sdss/data/' location_wise = '/home/ehsan/db_esn/cf4_wise/data/' N = len(pgc_) Squality = np.zeros(N) Wquality = np.zeros(N) disturbed = np.zeros((N,), dtype='a1') trail = np.zeros((N,), dtype='a1') not_spiral = np.zeros((N,), dtype='a1') face_on = np.zeros((N,), dtype='a1') faint = np.zeros((N,), dtype='a1') crowded = np.zeros((N,), dtype='a1') over_masked = np.zeros((N,), dtype='a1') fov = np.zeros((N,), dtype='a1') multiple = np.zeros((N,), dtype='a1') bright_star = np.zeros((N,), dtype='a1') uncertain = np.zeros((N,), dtype='a1') note = np.zeros((N,), dtype='a100') source = np.zeros((N,), dtype='a4') uu_mag = np.zeros((N,)) gg_mag = np.zeros((N,)) rr_mag = np.zeros((N,)) ii_mag = np.zeros((N,)) zz_mag = np.zeros((N,)) Sba = np.zeros((N,)) Spa = np.zeros((N,)) u_Rasy = np.zeros((N,)) g_Rasy = np.zeros((N,)) r_Rasy = np.zeros((N,)) i_Rasy = np.zeros((N,)) z_Rasy = np.zeros((N,)) A_u = np.zeros((N,)) A_g = np.zeros((N,)) A_r = np.zeros((N,)) A_i = np.zeros((N,)) A_z = np.zeros((N,)) ebv = np.zeros((N,)) mu0_u = np.zeros((N,)) mu0_g = np.zeros((N,)) mu0_r = np.zeros((N,)) mu0_i = np.zeros((N,)) mu0_z = np.zeros((N,)) mu50_u = np.zeros((N,)) mu50_g = np.zeros((N,)) mu50_r = np.zeros((N,)) mu50_i = np.zeros((N,)) mu50_z = np.zeros((N,)) mu90_u = np.zeros((N,)) mu90_g = np.zeros((N,)) mu90_r = np.zeros((N,)) mu90_i = np.zeros((N,)) mu90_z = np.zeros((N,)) m255_u = np.zeros((N,)) m255_g = np.zeros((N,)) m255_r = np.zeros((N,)) m255_i = np.zeros((N,)) m255_z = np.zeros((N,)) disc_mu0_u = np.zeros((N,)) disc_mu0_g = np.zeros((N,)) disc_mu0_r = np.zeros((N,)) disc_mu0_i = np.zeros((N,)) disc_mu0_z = np.zeros((N,)) SLh_u = np.zeros((N,)) SLh_g = np.zeros((N,)) SLh_r = np.zeros((N,)) SLh_i = np.zeros((N,)) SLh_z = np.zeros((N,)) R50_u = np.zeros((N,)) R50_g = np.zeros((N,)) R50_r = np.zeros((N,)) R50_i = np.zeros((N,)) R50_z = np.zeros((N,)) R90_u = np.zeros((N,)) R90_g = np.zeros((N,)) R90_r = np.zeros((N,)) R90_i = np.zeros((N,)) R90_z = np.zeros((N,)) R255_u = np.zeros((N,)) R255_g = np.zeros((N,)) R255_r = np.zeros((N,)) R255_i = np.zeros((N,)) R255_z = np.zeros((N,)) Cntion_u = np.zeros((N,)) Cntion_g = np.zeros((N,)) Cntion_r = np.zeros((N,)) Cntion_i = np.zeros((N,)) Cntion_z = np.zeros((N,)) d_m_ext_u = np.zeros((N,)) d_m_ext_g = np.zeros((N,)) d_m_ext_r = np.zeros((N,)) d_m_ext_i = np.zeros((N,)) d_m_ext_z = np.zeros((N,)) w1_mag = np.zeros((N,)) w2_mag = np.zeros((N,)) Wba = np.zeros((N,)) Wpa = np.zeros((N,)) w1_Rasy = np.zeros((N,)) w2_Rasy = np.zeros((N,)) A_w1 = np.zeros((N,)) A_w2 = np.zeros((N,)) mu0_w1 = np.zeros((N,)) mu50_w1 = np.zeros((N,)) mu90_w1 = np.zeros((N,)) m255_w1 = np.zeros((N,)) disc_mu0_w1 = np.zeros((N,)) SLh_w1 = np.zeros((N,)) R50_w1 = np.zeros((N,)) R90_w1 = np.zeros((N,)) R255_w1 = np.zeros((N,)) Cntion_w1 = np.zeros((N,)) mu0_w2 = np.zeros((N,)) mu50_w2 = np.zeros((N,)) mu90_w2 = np.zeros((N,)) m255_w2 = np.zeros((N,)) disc_mu0_w2 = np.zeros((N,)) SLh_w2 = np.zeros((N,)) R50_w2 = np.zeros((N,)) R90_w2 = np.zeros((N,)) R255_w2 = np.zeros((N,)) Cntion_w2 = np.zeros((N,)) d_m_ext_w1 = np.zeros((N,)) d_m_ext_w2 = np.zeros((N,)) inc = np.zeros((N,)) inc_e = np.zeros((N,)) inc_flg = np.zeros((N,)) inc_note = np.zeros((N,), dtype='a100') inc_n = np.zeros((N,)) for i in range(len(pgc_)): ## inclination if pgc_[i] in incDic: inc[i], inc_e[i], inc_flg[i], inc_note[i], inc_n[i]= incMedian(incDic[pgc_[i]]) radb = ra_db(ra_[i]) pgcname = 'pgc'+str(pgc_[i]) qa_txt_sdss = location_sdss + radb + '/sdss/fits/' + pgcname+'_qa.txt' photometry_sdss = location_sdss + radb +'/photometry/'+pgcname ##################################################################### Taking care of photometry results if os.path.exists(qa_txt_sdss): ebv[i] = -9.99 Squality[i] = get_quality(qa_txt_sdss) if os.path.exists(photometry_sdss+'_u_asymptotic.dat'): uu_mag[i] = get_mag(photometry_sdss+'_u_asymptotic.dat') u_Rasy[i] = get_mag(photometry_sdss+'_u_asymptotic.dat', index=1)/60. A_u[i] = get_mag(photometry_sdss+'_u_asymptotic.dat', header='A_Gal:') if os.path.exists(photometry_sdss+'_u.scales.dat'): ebv[i], A_u[i], mu0_u[i], mu50_u[i], mu90_u[i], m255_u[i], disc_mu0_u[i], SLh_u[i], R50_u[i], R90_u[i], R255_u[i], Cntion_u[i], d_m_ext_u[i] = get_scales(photometry_sdss+'_u.scales.dat') if os.path.exists(photometry_sdss+'_g_asymptotic.dat'): gg_mag[i] = get_mag(photometry_sdss+'_g_asymptotic.dat') g_Rasy[i] = get_mag(photometry_sdss+'_g_asymptotic.dat', index=1)/60. A_g[i] = get_mag(photometry_sdss+'_g_asymptotic.dat', header='A_Gal:') if os.path.exists(photometry_sdss+'_g.scales.dat'): ebv[i], A_g[i], mu0_g[i], mu50_g[i], mu90_g[i], m255_g[i], disc_mu0_g[i], SLh_g[i], R50_g[i], R90_g[i], R255_g[i], Cntion_g[i], d_m_ext_g[i] = get_scales(photometry_sdss+'_g.scales.dat') if os.path.exists(photometry_sdss+'_r_asymptotic.dat'): rr_mag[i] = get_mag(photometry_sdss+'_r_asymptotic.dat') r_Rasy[i] = get_mag(photometry_sdss+'_r_asymptotic.dat', index=1)/60. A_r[i] = get_mag(photometry_sdss+'_r_asymptotic.dat', header='A_Gal:') if os.path.exists(photometry_sdss+'_r.scales.dat'): ebv[i], A_r[i], mu0_r[i], mu50_r[i], mu90_r[i], m255_r[i], disc_mu0_r[i], SLh_r[i], R50_r[i], R90_r[i], R255_r[i], Cntion_r[i], d_m_ext_r[i] = get_scales(photometry_sdss+'_r.scales.dat') if os.path.exists(photometry_sdss+'_i_asymptotic.dat'): ii_mag[i] = get_mag(photometry_sdss+'_i_asymptotic.dat') i_Rasy[i] = get_mag(photometry_sdss+'_i_asymptotic.dat', index=1)/60. A_i[i] = get_mag(photometry_sdss+'_i_asymptotic.dat', header='A_Gal:') if os.path.exists(photometry_sdss+'_i.scales.dat'): ebv[i], A_i[i], mu0_i[i], mu50_i[i], mu90_i[i], m255_i[i], disc_mu0_i[i], SLh_i[i], R50_i[i], R90_i[i], R255_i[i], Cntion_i[i], d_m_ext_i[i] = get_scales(photometry_sdss+'_i.scales.dat') if os.path.exists(photometry_sdss+'_z_asymptotic.dat'): zz_mag[i] = get_mag(photometry_sdss+'_z_asymptotic.dat') z_Rasy[i] = get_mag(photometry_sdss+'_z_asymptotic.dat', index=1)/60. A_z[i] = get_mag(photometry_sdss+'_z_asymptotic.dat', header='A_Gal:') if os.path.exists(photometry_sdss+'_z.scales.dat'): ebv[i], A_z[i], mu0_z[i], mu50_z[i], mu90_z[i], m255_z[i], disc_mu0_z[i], SLh_z[i], R50_z[i], R90_z[i], R255_z[i], Cntion_z[i], d_m_ext_z[i] = get_scales(photometry_sdss+'_z.scales.dat') ellipsefile = location_sdss + radb +'/photometry/'+pgcname+'_g_ellipsepar.dat' if os.path.exists(ellipsefile): ra_cen, dec_cen, semimajor, semiminor, PA = get_ellipse(ellipsefile) Sba[i] = min([semimajor,semiminor])/max([semiminor,semimajor]) Spa[i] = PA ellipsefile = location_sdss + radb +'/photometry/'+pgcname+'_r_ellipsepar.dat' if os.path.exists(ellipsefile): ra_cen, dec_cen, semimajor, semiminor, PA = get_ellipse(ellipsefile) Sba[i] = min([semimajor,semiminor])/max([semiminor,semimajor]) Spa[i] = PA ellipsefile = location_sdss + radb +'/photometry/'+pgcname+'_i_ellipsepar.dat' if os.path.exists(ellipsefile): ra_cen, dec_cen, semimajor, semiminor, PA = get_ellipse(ellipsefile) Sba[i] = min([semimajor,semiminor])/max([semiminor,semimajor]) Spa[i] = PA if pgc_[i] in wise_pgc: i_lst = np.where(wise_pgc == pgc_[i]) galname = wise_name[i_lst][0] qa_txt_wise = location_wise + radb + '/wise/fits/' + galname+'_qa.txt' if not os.path.exists(qa_txt_wise): galname = 'pgc'+str(pgc_[i]) else: galname = 'pgc'+str(pgc_[i]) qa_txt_wise = location_wise + radb + '/wise/fits/' + galname+'_qa.txt' photometry_wise = location_wise + radb +'/photometry/'+galname if os.path.exists(qa_txt_wise): tmp = -9.99 Wquality[i] = get_quality(qa_txt_wise) if os.path.exists(photometry_wise+'_w1_asymptotic.dat'): w1_mag[i] = get_mag_wise(photometry_wise+'_w1_asymptotic.dat') w1_Rasy[i] = get_mag_wise(photometry_wise+'_w1_asymptotic.dat', index=1)/60. A_w1[i] = get_mag_f(photometry_wise+'_w1_asymptotic.dat', header='A_Gal:') if os.path.exists(photometry_wise+'_w1.scales.dat'): tmp, A_w1[i], mu0_w1[i], mu50_w1[i], mu90_w1[i], m255_w1[i], disc_mu0_w1[i], SLh_w1[i], R50_w1[i], R90_w1[i], R255_w1[i], Cntion_w1[i], d_m_ext_w1[i] = get_scales(photometry_wise+'_w1.scales.dat') if os.path.exists(photometry_wise+'_w2_asymptotic.dat'): w2_mag[i] = get_mag_wise(photometry_wise+'_w2_asymptotic.dat') w2_Rasy[i] = get_mag_wise(photometry_wise+'_w2_asymptotic.dat', index=1)/60. A_w2[i] = get_mag_f(photometry_wise+'_w2_asymptotic.dat', header='A_Gal:') if os.path.exists(photometry_wise+'_w2.scales.dat'): tmp, A_w2[i], mu0_w2[i], mu50_w2[i], mu90_w2[i], m255_w2[i], disc_mu0_w2[i], SLh_w2[i], R50_w2[i], R90_w2[i], R255_w2[i], Cntion_w2[i], d_m_ext_w2[i] = get_scales(photometry_wise+'_w2.scales.dat') if ebv[i]<0 and tmp>0: ebv[i] = tmp ellipsefile = location_wise + radb +'/photometry/'+galname+'_w2_ellipsepar.dat' if os.path.exists(ellipsefile): ra_cen, dec_cen, semimajor, semiminor, PA = get_ellipse_wise(ellipsefile) Wba[i] = min([semimajor,semiminor])/max([semiminor,semimajor]) Wpa[i] = PA ellipsefile = location_wise + radb +'/photometry/'+galname+'_w1_ellipsepar.dat' if os.path.exists(ellipsefile): ra_cen, dec_cen, semimajor, semiminor, PA = get_ellipse_wise(ellipsefile) Wba[i] = min([semimajor,semiminor])/max([semiminor,semimajor]) Wpa[i] = PA ##################################################################### Taking care of flags found = False if os.path.exists(qa_txt_sdss): qa_txt = qa_txt_sdss found = True source[i] = 'SDSS' else: if QA_wise[i]==1: if pgc_[i] in wise_pgc: i_lst = np.where(wise_pgc == pgc_[i]) galname = wise_name[i_lst][0] qa_txt_wise = location_wise + radb + '/wise/fits/' + galname+'_qa.txt' if not os.path.exists(qa_txt_wise): galname = 'pgc'+str(pgc_[i]) else: galname = 'pgc'+str(pgc_[i]) qa_txt_wise = location_wise + radb + '/wise/fits/' + galname+'_qa.txt' if os.path.exists(qa_txt_wise): qa_txt = qa_txt_wise found = True source[i] = 'WISE' else: #print galname #print galname, ra[i], dec[i], d25[i], d25[i]*b_a[i], PA[i], Ty[i] source[i] = 'NONE' if found: if get_quality(qa_txt, nline=41)==1: disturbed[i]='D' if get_quality(qa_txt, nline=42)==1: trail[i]='L' if get_quality(qa_txt, nline=43)==1: not_spiral[i]='P' if get_quality(qa_txt, nline=44)==1: face_on[i]='F' if get_quality(qa_txt, nline=45)==1: faint[i]='N' if get_quality(qa_txt, nline=46)==1: crowded[i]='C' if get_quality(qa_txt, nline=47)==1: over_masked[i]='O' if get_quality(qa_txt, nline=20)==1: fov[i]='V' if get_quality(qa_txt, nline=19)==1: multiple[i]='M' if get_quality(qa_txt, nline=18)==1: bright_star[i]='B' if get_quality(qa_txt, nline=17)==1: uncertain[i]='U' note[i]= read_note(qa_txt) ##################################################################### myTable = Table() myTable.add_column(Column(data=pgc_, name='pgc')) myTable.add_column(Column(data=ra_, name='ra', format='%0.4f')) myTable.add_column(Column(data=dec_, name='dec', format='%0.4f')) myTable.add_column(Column(data=l_, name='gl', format='%0.4f')) myTable.add_column(Column(data=b_, name='gb', format='%0.4f')) myTable.add_column(Column(data=sgl_, name='sgl', format='%0.4f')) myTable.add_column(Column(data=sgb_, name='sgb', format='%0.4f')) myTable.add_column(Column(data=d25_, name='d25', format='%0.2f')) myTable.add_column(Column(data=b_a_, name='b_a', format='%0.2f')) myTable.add_column(Column(data=pa_, name='pa', format='%0.1f')) myTable.add_column(Column(data=ty_, name='ty', format='%0.1f')) myTable.add_column(Column(data=type_, name='type')) myTable.add_column(Column(data=sdss_, name='sdss')) myTable.add_column(Column(data=alfa100, name='alfa100')) myTable.add_column(Column(data=QA, name='QA_sdss')) myTable.add_column(Column(data=QA_wise, name='QA_wise')) myTable.add_column(Column(data=ebv, name='ebv', format='%0.3f')) myTable.add_column(Column(data=uu_mag, name='u_mag', format='%0.2f')) myTable.add_column(Column(data=gg_mag, name='g_mag', format='%0.2f')) myTable.add_column(Column(data=rr_mag, name='r_mag', format='%0.2f')) myTable.add_column(Column(data=ii_mag, name='i_mag', format='%0.2f')) myTable.add_column(Column(data=zz_mag, name='z_mag', format='%0.2f')) myTable.add_column(Column(data=u_Rasy, name='u_Rasy', format='%0.2f')) myTable.add_column(Column(data=g_Rasy, name='g_Rasy', format='%0.2f')) myTable.add_column(Column(data=r_Rasy, name='r_Rasy', format='%0.2f')) myTable.add_column(Column(data=i_Rasy, name='i_Rasy', format='%0.2f')) myTable.add_column(Column(data=z_Rasy, name='z_Rasy', format='%0.2f')) myTable.add_column(Column(data=A_u, name='A_u', format='%0.3f')) myTable.add_column(Column(data=A_g, name='A_g', format='%0.3f')) myTable.add_column(Column(data=A_r, name='A_r', format='%0.3f')) myTable.add_column(Column(data=A_i, name='A_i', format='%0.3f')) myTable.add_column(Column(data=A_z, name='A_z', format='%0.3f')) myTable.add_column(Column(data=Sba, name='Sba', format='%0.2f')) myTable.add_column(Column(data=Spa, name='Spa', format='%0.2f')) myTable.add_column(Column(data=mu0_u, name='mu0_u', format='%0.2f')) myTable.add_column(Column(data=mu0_g, name='mu0_g', format='%0.2f')) myTable.add_column(Column(data=mu0_r, name='mu0_r', format='%0.2f')) myTable.add_column(Column(data=mu0_i, name='mu0_i', format='%0.2f')) myTable.add_column(Column(data=mu0_z, name='mu0_z', format='%0.2f')) myTable.add_column(Column(data=mu50_u, name='mu50_u', format='%0.2f')) myTable.add_column(Column(data=mu50_g, name='mu50_g', format='%0.2f')) myTable.add_column(Column(data=mu50_r, name='mu50_r', format='%0.2f')) myTable.add_column(Column(data=mu50_i, name='mu50_i', format='%0.2f')) myTable.add_column(Column(data=mu50_z, name='mu50_z', format='%0.2f')) myTable.add_column(Column(data=mu90_u, name='mu90_u', format='%0.2f')) myTable.add_column(Column(data=mu90_g, name='mu90_g', format='%0.2f')) myTable.add_column(Column(data=mu90_r, name='mu90_r', format='%0.2f')) myTable.add_column(Column(data=mu90_i, name='mu90_i', format='%0.2f')) myTable.add_column(Column(data=mu90_z, name='mu90_z', format='%0.2f')) myTable.add_column(Column(data=m255_u, name='m255_u', format='%0.2f')) myTable.add_column(Column(data=m255_g, name='m255_g', format='%0.2f')) myTable.add_column(Column(data=m255_r, name='m255_r', format='%0.2f')) myTable.add_column(Column(data=m255_i, name='m255_i', format='%0.2f')) myTable.add_column(Column(data=m255_z, name='m255_z', format='%0.2f')) myTable.add_column(Column(data=disc_mu0_u, name='disc_mu0_u', format='%0.2f')) myTable.add_column(Column(data=disc_mu0_g, name='disc_mu0_g', format='%0.2f')) myTable.add_column(Column(data=disc_mu0_r, name='disc_mu0_r', format='%0.2f')) myTable.add_column(Column(data=disc_mu0_i, name='disc_mu0_i', format='%0.2f')) myTable.add_column(Column(data=disc_mu0_z, name='disc_mu0_z', format='%0.2f')) myTable.add_column(Column(data=SLh_u, name='h_u', format='%0.2f')) myTable.add_column(Column(data=SLh_g, name='h_g', format='%0.2f')) myTable.add_column(Column(data=SLh_r, name='h_r', format='%0.2f')) myTable.add_column(Column(data=SLh_i, name='h_i', format='%0.2f')) myTable.add_column(Column(data=SLh_z, name='h_z', format='%0.2f')) myTable.add_column(Column(data=R50_u, name='R50_u', format='%0.2f')) myTable.add_column(Column(data=R50_g, name='R50_g', format='%0.2f')) myTable.add_column(Column(data=R50_r, name='R50_r', format='%0.2f')) myTable.add_column(Column(data=R50_i, name='R50_i', format='%0.2f')) myTable.add_column(Column(data=R50_z, name='R50_z', format='%0.2f')) myTable.add_column(Column(data=R90_u, name='R90_u', format='%0.2f')) myTable.add_column(Column(data=R90_g, name='R90_g', format='%0.2f')) myTable.add_column(Column(data=R90_r, name='R90_r', format='%0.2f')) myTable.add_column(Column(data=R90_i, name='R90_i', format='%0.2f')) myTable.add_column(Column(data=R90_z, name='R90_z', format='%0.2f')) myTable.add_column(Column(data=R255_u, name='R255_u', format='%0.2f')) myTable.add_column(Column(data=R255_g, name='R255_g', format='%0.2f')) myTable.add_column(Column(data=R255_r, name='R255_r', format='%0.2f')) myTable.add_column(Column(data=R255_i, name='R255_i', format='%0.2f')) myTable.add_column(Column(data=R255_z, name='R255_z', format='%0.2f')) myTable.add_column(Column(data=Cntion_u, name='C82_u', format='%0.2f')) myTable.add_column(Column(data=Cntion_g, name='C82_g', format='%0.2f')) myTable.add_column(Column(data=Cntion_r, name='C82_r', format='%0.2f')) myTable.add_column(Column(data=Cntion_i, name='C82_i', format='%0.2f')) myTable.add_column(Column(data=Cntion_z, name='C82_z', format='%0.2f')) myTable.add_column(Column(data=d_m_ext_u, name='d_m_ext_u', format='%0.4f')) myTable.add_column(Column(data=d_m_ext_g, name='d_m_ext_g', format='%0.4f')) myTable.add_column(Column(data=d_m_ext_r, name='d_m_ext_r', format='%0.4f')) myTable.add_column(Column(data=d_m_ext_i, name='d_m_ext_i', format='%0.4f')) myTable.add_column(Column(data=d_m_ext_z, name='d_m_ext_z', format='%0.4f')) myTable.add_column(Column(data=w1_mag, name='w1_mag', format='%0.2f')) myTable.add_column(Column(data=w2_mag, name='w2_mag', format='%0.2f')) myTable.add_column(Column(data=w1_Rasy, name='w1_Rasy', format='%0.2f')) myTable.add_column(Column(data=w2_Rasy, name='w2_Rasy', format='%0.2f')) myTable.add_column(Column(data=A_w1, name='A_w1', format='%0.3f')) myTable.add_column(Column(data=A_w2, name='A_w2', format='%0.3f')) myTable.add_column(Column(data=Wba, name='Wba', format='%0.2f')) myTable.add_column(Column(data=Wpa, name='Wpa', format='%0.2f')) myTable.add_column(Column(data=mu0_w1, name='mu0_w1', format='%0.2f')) myTable.add_column(Column(data=mu0_w2, name='mu0_w2', format='%0.2f')) myTable.add_column(Column(data=mu50_w1, name='mu50_w1', format='%0.2f')) myTable.add_column(Column(data=mu50_w2, name='mu50_w2', format='%0.2f')) myTable.add_column(Column(data=mu90_w1, name='mu90_w1', format='%0.2f')) myTable.add_column(Column(data=mu90_w2, name='mu90_w2', format='%0.2f')) myTable.add_column(Column(data=m255_w1, name='m255_w1', format='%0.2f')) myTable.add_column(Column(data=m255_w2, name='m255_w2', format='%0.2f')) myTable.add_column(Column(data=disc_mu0_w1, name='disc_mu0_w1', format='%0.2f')) myTable.add_column(Column(data=disc_mu0_w2, name='disc_mu0_w2', format='%0.2f')) myTable.add_column(Column(data=SLh_w1, name='h_w1', format='%0.2f')) myTable.add_column(Column(data=SLh_w2, name='h_w2', format='%0.2f')) myTable.add_column(Column(data=R50_w1, name='R50_w1', format='%0.2f')) myTable.add_column(Column(data=R50_w2, name='R50_w2', format='%0.2f')) myTable.add_column(Column(data=R90_w1, name='R90_w1', format='%0.2f')) myTable.add_column(Column(data=R90_w2, name='R90_w2', format='%0.2f')) myTable.add_column(Column(data=R255_w1, name='R255_w1', format='%0.2f')) myTable.add_column(Column(data=R255_w2, name='R255_w2', format='%0.2f')) myTable.add_column(Column(data=Cntion_w1, name='C82_w1', format='%0.2f')) myTable.add_column(Column(data=Cntion_w2, name='C82_w2', format='%0.2f')) myTable.add_column(Column(data=d_m_ext_w1, name='d_m_ext_w1', format='%0.4f')) myTable.add_column(Column(data=d_m_ext_w2, name='d_m_ext_w2', format='%0.4f')) myTable.add_column(Column(data=Squality, name='Sqlt', dtype=np.dtype(int))) myTable.add_column(Column(data=Wquality, name='Wqlt', dtype=np.dtype(int))) #myTable.add_column(Column(data=source, name='source', dtype='S4')) myTable.add_column(Column(data=disturbed, name='dst', dtype='S1')) myTable.add_column(Column(data=trail, name='trl', dtype='S1')) myTable.add_column(Column(data=not_spiral, name='nsp', dtype='S1')) myTable.add_column(Column(data=face_on, name='fon', dtype='S1')) myTable.add_column(Column(data=faint, name='fnt', dtype='S1')) myTable.add_column(Column(data=crowded, name='cwd', dtype='S1')) myTable.add_column(Column(data=over_masked, name='ovm', dtype='S1')) myTable.add_column(Column(data=fov, name='fov', dtype='S1')) myTable.add_column(Column(data=multiple, name='mlp', dtype='S1')) myTable.add_column(Column(data=bright_star, name='bts', dtype='S1')) myTable.add_column(Column(data=uncertain, name='unc', dtype='S1')) myTable.add_column(Column(data=note, name='note', dtype='S100')) myTable.add_column(Column(data=inc, name='inc', dtype=np.dtype(int))) myTable.add_column(Column(data=inc_e, name='inc_e', dtype=np.dtype(int))) myTable.add_column(Column(data=inc_flg, name='inc_flg', format='%1d')) myTable.add_column(Column(data=inc_n, name='inc_n', format='%2d')) myTable.add_column(Column(data=inc_note, name='inc_note', dtype='S100')) myTable.write('EDD_distance_cf4_v22.csv', format='ascii.fixed_width',delimiter='|', bookend=False, overwrite=True)

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ekourkchi@gmail.com

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# -*- coding: utf-8 -*- # Generated by Django 1.10 on 2018-12-12 20:01 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Shop', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('item', models.CharField(max_length=255)), ('price', models.FloatField()), ], ), ]

[ "noreply@github.com" ]

LeoKnox.noreply@github.com

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/product/migrations/0228_auto_20180502_0957.py

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rickyakilimali/approeco

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# -*- coding: utf-8 -*- # Generated by Django 1.11.8 on 2018-05-02 09:57 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('product', '0227_auto_20180502_0954'), ] operations = [ migrations.AlterField( model_name='productiontournage', name='nombre_minute', field=models.CharField(choices=[('0-10', '0-10'), ('10-20', '10-20'), ('20-40', '20-40'), ('40-80', '40-80')], max_length=20, verbose_name='DUREE DU TOURNAGE(MINUTE)'), ), ]

[ "ricky.akilimali@approeco.net" ]

ricky.akilimali@approeco.net

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/genfragments/SevenTeV/RSGravitonToWW_kMpl01_M_2000_TuneZ2_7TeV_pythia6_cff.py

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[]

no_license

cms-sw/genproductions

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refs/heads/master

2023-08-30T17:26:02.581596

2023-08-29T14:53:43

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import FWCore.ParameterSet.Config as cms from Configuration.Generator.PythiaUEZ2Settings_cfi import * generator = cms.EDFilter("Pythia6GeneratorFilter", pythiaHepMCVerbosity = cms.untracked.bool(False), maxEventsToPrint = cms.untracked.int32(0), pythiaPylistVerbosity = cms.untracked.int32(0), filterEfficiency = cms.untracked.double(1), comEnergy = cms.double(7000.0), crossSection = cms.untracked.double(1.83e-3), PythiaParameters = cms.PSet( pythiaUESettingsBlock, processParameters = cms.vstring( 'MSEL = 0', 'MSUB(391) = 1', 'MSUB(392) = 1', 'PMAS(347,1) = 2000', 'PARP(50) = 0.54', #0.54 '5000039:ALLOFF', '5000039:ONIFANY 24', ), parameterSets = cms.vstring( 'pythiaUESettings', 'processParameters') ) ) configurationMetadata = cms.untracked.PSet( version = cms.untracked.string('\$Revision: 1.1 $'), name = cms.untracked.string('\$Source: /cvs/CMSSW/UserCode/hinzmann/production/RSGravitonToWW_kMpl01_M_2000_TuneZ2_7TeV_pythia6_cff.py,v $'), annotation = cms.untracked.string('Fall2011 sample with PYTHIA6: RSG -> WW, TuneZ2') ) ProductionFilterSequence = cms.Sequence(generator)

[ "sha1-45889d0b422ced4b08fb223eae59c585c331ccec@cern.ch" ]

sha1-45889d0b422ced4b08fb223eae59c585c331ccec@cern.ch

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/mycalendar/serializers.py

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[]

no_license

rdahal35/django_fullcalendar

576023fa348391082ee82b50e27772b9c11c7b47

35623f562642816b9f501ea1390a03d96c0d188a

refs/heads/master

2022-12-13T12:32:33.098739

2018-08-13T10:28:35

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from rest_framework import serializers from .models import Event class eventSerializer(serializers.ModelSerializer): class Meta: model = Event fields = '__all__'

[ "rdahal35@gmail.com" ]

rdahal35@gmail.com

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/1501-2000/1678.Goal Parser Interpretation.py

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kaiwensun/leetcode

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class Solution(object): def interpret(self, command): """ :type command: str :rtype: str """ def tokenize(command): i = 0 while i < len(command): if command[i] == "G": yield "G" i += 1 elif command[i + 1] == ")": yield "o" i += 2 else: yield "al" i += 4 return "".join(token for token in tokenize(command))

[ "skw_kevin@126.com" ]

skw_kevin@126.com

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Adi19471/Djnago_Code-Daily

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"""ADI31 URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path,include urlpatterns = [ path('admin/', admin.site.urls), path('',include('enroll.urls')), ]

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akumatha@gmail.com

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# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Benchmarks for Cudnn RNN models.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import time from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops from tensorflow.contrib.rnn.python.ops import core_rnn from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl from tensorflow.contrib.rnn.python.ops import lstm_ops from tensorflow.python.client import session from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import init_ops from tensorflow.python.ops import variables from tensorflow.python.platform import flags from tensorflow.python.platform import test flags.DEFINE_integer("batch_size", 64, "batch size.") FLAGS = flags.FLAGS class CudnnRNNBenchmark(test.Benchmark): """Benchmarks Cudnn LSTM and other related models. """ def _GetTestConfig(self): return { "large": { "num_layers": 4, "num_units": 1024, "seq_length": 40, "batch_size": 64, }, "medium": { "num_layers": 4, "num_units": 512, "seq_length": 30, "batch_size": 64, }, "small": { "num_layers": 4, "num_units": 128, "seq_length": 20, "batch_size": 64, }, } def _GetConfigDesc(self, config): num_layers = config["num_layers"] num_units = config["num_units"] batch_size = config["batch_size"] seq_length = config["seq_length"] return "y%d_u%d_b%d_q%d" % (num_layers, num_units, batch_size, seq_length) def _BenchmarkOp(self, op, desc): burn_in_steps = 10 benchmark_steps = 40 with session.Session() as sess: sess.run(variables.global_variables_initializer()) for i in xrange(burn_in_steps + benchmark_steps): if i == burn_in_steps: start_time = time.time() sess.run(op) total_time = time.time() - start_time step_time = total_time / benchmark_steps print("%s takes %.4f sec/step" % (desc, step_time)) self.report_benchmark( name=desc, iters=benchmark_steps, wall_time=total_time) def benchmarkCudnnLSTMTraining(self): test_configs = self._GetTestConfig() for config_name, config in test_configs.items(): config = test_configs[config_name] num_layers = config["num_layers"] num_units = config["num_units"] batch_size = config["batch_size"] seq_length = config["seq_length"] with ops.Graph().as_default(), ops.device("/gpu:0"): model = cudnn_rnn_ops.CudnnLSTM(num_layers, num_units, num_units) params_size_t = model.params_size() input_data = variables.Variable( array_ops.ones([seq_length, batch_size, num_units])) input_h = variables.Variable( array_ops.ones([num_layers, batch_size, num_units])) input_c = variables.Variable( array_ops.ones([num_layers, batch_size, num_units])) params = variables.Variable( array_ops.ones([params_size_t]), validate_shape=False) output, output_h, output_c = model( is_training=True, input_data=input_data, input_h=input_h, input_c=input_c, params=params) all_grads = gradients_impl.gradients( [output, output_h, output_c], [params, input_data, input_h, input_c]) training_op = control_flow_ops.group(*all_grads) self._BenchmarkOp(training_op, "cudnn_lstm %s %s" % (config_name, self._GetConfigDesc(config))) def benchmarkTfRNNLSTMTraining(self): test_configs = self._GetTestConfig() for config_name, config in test_configs.items(): num_layers = config["num_layers"] num_units = config["num_units"] batch_size = config["batch_size"] seq_length = config["seq_length"] with ops.Graph().as_default(), ops.device("/gpu:0"): inputs = seq_length * [ array_ops.zeros([batch_size, num_units], dtypes.float32) ] initializer = init_ops.random_uniform_initializer(-0.01, 0.01, seed=127) cell = core_rnn_cell_impl.LSTMCell( num_units=num_units, initializer=initializer, state_is_tuple=True) multi_cell = core_rnn_cell_impl.MultiRNNCell([cell] * num_layers) outputs, final_state = core_rnn.static_rnn( multi_cell, inputs, dtype=dtypes.float32) trainable_variables = ops.get_collection( ops.GraphKeys.TRAINABLE_VARIABLES) gradients = gradients_impl.gradients([outputs, final_state], trainable_variables) training_op = control_flow_ops.group(*gradients) self._BenchmarkOp(training_op, "tf_rnn_lstm %s %s" % (config_name, self._GetConfigDesc(config))) def benchmarkTfRNNLSTMBlockCellTraining(self): test_configs = self._GetTestConfig() for config_name, config in test_configs.items(): num_layers = config["num_layers"] num_units = config["num_units"] batch_size = config["batch_size"] seq_length = config["seq_length"] with ops.Graph().as_default(), ops.device("/gpu:0"): inputs = seq_length * [ array_ops.zeros([batch_size, num_units], dtypes.float32) ] cell = lstm_ops.LSTMBlockCell(num_units=num_units) multi_cell = core_rnn_cell_impl.MultiRNNCell([cell] * num_layers) outputs, final_state = core_rnn.static_rnn( multi_cell, inputs, dtype=dtypes.float32) trainable_variables = ops.get_collection( ops.GraphKeys.TRAINABLE_VARIABLES) gradients = gradients_impl.gradients([outputs, final_state], trainable_variables) training_op = control_flow_ops.group(*gradients) self._BenchmarkOp(training_op, "tf_rnn_lstm_block_cell %s %s" % (config_name, self._GetConfigDesc(config))) if __name__ == "__main__": test.main()

[ "gardener@tensorflow.org" ]

gardener@tensorflow.org

f1e42f777f4166260daa99290c2416e05ac6c882

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/demos/great-expectations/venv/lib/python3.8/site-packages/notebook/services/kernels/tests/test_kernels_api.py

38a9dfb00c7393a15d2daeec1fa2ce9f94e46e1d

[]

no_license

franciscojavierarceo/Python

29aaea28642dde151255c5b4a813158e975a073d

02715ca6f19fd3c76cefa12de92deeae4ddf9684

refs/heads/main

2023-08-27T14:23:04.376095

2023-08-27T10:30:37

33,146,755

7

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null

2023-02-16T06:40:35

2015-03-30T20:38:00

Jupyter Notebook

UTF-8

Python

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9,675

py

"""Test the kernels service API.""" import json import time from requests import HTTPError from traitlets.config import Config from tornado.httpclient import HTTPRequest from tornado.ioloop import IOLoop from tornado.websocket import websocket_connect from unittest import SkipTest from jupyter_client.kernelspec import NATIVE_KERNEL_NAME from notebook.utils import url_path_join from notebook.tests.launchnotebook import NotebookTestBase, assert_http_error try: from jupyter_client import AsyncMultiKernelManager async_testing_enabled = True except ImportError: async_testing_enabled = False class KernelAPI: """Wrapper for kernel REST API requests""" def __init__(self, request, base_url, headers): self.request = request self.base_url = base_url self.headers = headers def _req(self, verb, path, body=None): response = self.request(verb, url_path_join('api/kernels', path), data=body) if 400 <= response.status_code < 600: try: response.reason = response.json()['message'] except: pass response.raise_for_status() return response def list(self): return self._req('GET', '') def get(self, id): return self._req('GET', id) def start(self, name=NATIVE_KERNEL_NAME): body = json.dumps({'name': name}) return self._req('POST', '', body) def shutdown(self, id): return self._req('DELETE', id) def interrupt(self, id): return self._req('POST', url_path_join(id, 'interrupt')) def restart(self, id): return self._req('POST', url_path_join(id, 'restart')) def websocket(self, id): loop = IOLoop() loop.make_current() req = HTTPRequest( url_path_join(self.base_url.replace('http', 'ws', 1), 'api/kernels', id, 'channels'), headers=self.headers, ) f = websocket_connect(req) return loop.run_sync(lambda : f) class KernelAPITest(NotebookTestBase): """Test the kernels web service API""" def setUp(self): self.kern_api = KernelAPI(self.request, base_url=self.base_url(), headers=self.auth_headers(), ) def tearDown(self): for k in self.kern_api.list().json(): self.kern_api.shutdown(k['id']) def test_no_kernels(self): """Make sure there are no kernels running at the start""" kernels = self.kern_api.list().json() self.assertEqual(kernels, []) def test_default_kernel(self): # POST request r = self.kern_api._req('POST', '') kern1 = r.json() self.assertEqual(r.headers['location'], url_path_join(self.url_prefix, 'api/kernels', kern1['id'])) self.assertEqual(r.status_code, 201) self.assertIsInstance(kern1, dict) report_uri = url_path_join(self.url_prefix, 'api/security/csp-report') expected_csp = '; '.join([ "frame-ancestors 'self'", 'report-uri ' + report_uri, "default-src 'none'" ]) self.assertEqual(r.headers['Content-Security-Policy'], expected_csp) def test_main_kernel_handler(self): # POST request r = self.kern_api.start() kern1 = r.json() self.assertEqual(r.headers['location'], url_path_join(self.url_prefix, 'api/kernels', kern1['id'])) self.assertEqual(r.status_code, 201) self.assertIsInstance(kern1, dict) report_uri = url_path_join(self.url_prefix, 'api/security/csp-report') expected_csp = '; '.join([ "frame-ancestors 'self'", 'report-uri ' + report_uri, "default-src 'none'" ]) self.assertEqual(r.headers['Content-Security-Policy'], expected_csp) # GET request r = self.kern_api.list() self.assertEqual(r.status_code, 200) assert isinstance(r.json(), list) self.assertEqual(r.json()[0]['id'], kern1['id']) self.assertEqual(r.json()[0]['name'], kern1['name']) # create another kernel and check that they both are added to the # list of kernels from a GET request kern2 = self.kern_api.start().json() assert isinstance(kern2, dict) r = self.kern_api.list() kernels = r.json() self.assertEqual(r.status_code, 200) assert isinstance(kernels, list) self.assertEqual(len(kernels), 2) # Interrupt a kernel r = self.kern_api.interrupt(kern2['id']) self.assertEqual(r.status_code, 204) # Restart a kernel r = self.kern_api.restart(kern2['id']) rekern = r.json() self.assertEqual(rekern['id'], kern2['id']) self.assertEqual(rekern['name'], kern2['name']) def test_kernel_handler(self): # GET kernel with given id kid = self.kern_api.start().json()['id'] r = self.kern_api.get(kid) kern1 = r.json() self.assertEqual(r.status_code, 200) assert isinstance(kern1, dict) self.assertIn('id', kern1) self.assertEqual(kern1['id'], kid) # Request a bad kernel id and check that a JSON # message is returned! bad_id = '111-111-111-111-111' with assert_http_error(404, 'Kernel does not exist: ' + bad_id): self.kern_api.get(bad_id) # DELETE kernel with id r = self.kern_api.shutdown(kid) self.assertEqual(r.status_code, 204) kernels = self.kern_api.list().json() self.assertEqual(kernels, []) # Request to delete a non-existent kernel id bad_id = '111-111-111-111-111' with assert_http_error(404, 'Kernel does not exist: ' + bad_id): self.kern_api.shutdown(bad_id) def test_connections(self): kid = self.kern_api.start().json()['id'] model = self.kern_api.get(kid).json() self.assertEqual(model['connections'], 0) ws = self.kern_api.websocket(kid) model = self.kern_api.get(kid).json() self.assertEqual(model['connections'], 1) ws.close() # give it some time to close on the other side: for i in range(10): model = self.kern_api.get(kid).json() if model['connections'] > 0: time.sleep(0.1) else: break model = self.kern_api.get(kid).json() self.assertEqual(model['connections'], 0) class AsyncKernelAPITest(KernelAPITest): """Test the kernels web service API using the AsyncMappingKernelManager""" @classmethod def setup_class(cls): if not async_testing_enabled: # Can be removed once jupyter_client >= 6.1 is required. raise SkipTest("AsyncKernelAPITest tests skipped due to down-level jupyter_client!") super().setup_class() @classmethod def get_argv(cls): argv = super().get_argv() # before we extend the argv with the class, ensure that appropriate jupyter_client is available. # if not available, don't set kernel_manager_class, resulting in the repeat of sync-based tests. if async_testing_enabled: argv.extend(['--NotebookApp.kernel_manager_class=' 'notebook.services.kernels.kernelmanager.AsyncMappingKernelManager']) return argv class KernelFilterTest(NotebookTestBase): # A special install of NotebookTestBase where only `kernel_info_request` # messages are allowed. config = Config({ 'NotebookApp': { 'MappingKernelManager': { 'allowed_message_types': ['kernel_info_request'] } } }) # Sanity check verifying that the configurable was properly set. def test_config(self): self.assertEqual(self.notebook.kernel_manager.allowed_message_types, ['kernel_info_request']) CULL_TIMEOUT = 5 CULL_INTERVAL = 1 class KernelCullingTest(NotebookTestBase): """Test kernel culling """ @classmethod def get_argv(cls): argv = super().get_argv() # Enable culling with 5s timeout and 1s intervals argv.extend([f'--MappingKernelManager.cull_idle_timeout={CULL_TIMEOUT}', f'--MappingKernelManager.cull_interval={CULL_INTERVAL}', '--MappingKernelManager.cull_connected=False']) return argv def setUp(self): self.kern_api = KernelAPI(self.request, base_url=self.base_url(), headers=self.auth_headers(), ) def tearDown(self): for k in self.kern_api.list().json(): self.kern_api.shutdown(k['id']) def test_culling(self): kid = self.kern_api.start().json()['id'] ws = self.kern_api.websocket(kid) model = self.kern_api.get(kid).json() self.assertEqual(model['connections'], 1) assert not self.get_cull_status(kid) # connected, should not be culled ws.close() assert self.get_cull_status(kid) # not connected, should be culled def get_cull_status(self, kid): frequency = 0.5 culled = False for _ in range(int((CULL_TIMEOUT + CULL_INTERVAL)/frequency)): # Timeout + Interval will ensure cull try: self.kern_api.get(kid) except HTTPError as e: assert e.response.status_code == 404 culled = True break else: time.sleep(frequency) return culled

[ "arceofrancisco@gmail.com" ]

arceofrancisco@gmail.com

d8d3b761b789cce584a071b57e2e9664d41b9c7b

7911da973079f325a515cd2ee66f7590a9f32e48

/asci_char.py

440a8d3234ddf3ac903b9fb83e313e80717b0991

[]

no_license

Ponkiruthika112/Guvi

5d2ff3dcf55d6c52c0f09a1e577d8b11632c7a92

319e5b4dab5654fabc25ef15c1d528f76d833c15

refs/heads/master

2020-04-21T06:05:03.581658

2018-08-02T05:53:48

null

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UTF-8

Python

false

48

py

ip=input("Enter any character:") print(ord(ip))

[ "noreply@github.com" ]

Ponkiruthika112.noreply@github.com

102be23096fba5b7196ac3e0c929f4ac9631cd95

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/dev_bc/models/product_uom.py

77461ba74a7add5a24d3a1415a7831e4131454f2

[]

no_license

dadysuarsa/Odoo

8d026a066c390cc8f72805d2672212e61260c1cb

c9becd0c192fa239520ad3e1a11d81f70832eddf

refs/heads/master

2023-03-11T06:02:06.011575

2021-02-26T02:17:37

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from odoo import models, fields, api, _ import openerp.addons.decimal_precision as dp from odoo.exceptions import UserError class productuom(models.Model): _inherit = 'product.uom' name_bc = fields.Char('Unit of bc')

[ "dads02_zetti@yahoo.com" ]

dads02_zetti@yahoo.com

294939bed03799b74ad9da32b6d03e81286ce9ed

8a83bb7acb9b62183fca817e1f196dd8075630a4

/24_fourthFolder/18_lazy_propagation.py

4435a51def47f2397589dc377acdb639e7fffb40

[]

no_license

sandeepkumar8713/pythonapps

ff5ad3da854aa58e60f2c14d27359f8b838cac57

5dcb5ad4873124fed2ec3a717bfa379a4bbd197d

refs/heads/main

2023-09-01T04:12:03.865755

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1

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Python

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6,404

py

# https://www.geeksforgeeks.org/lazy-propagation-in-segment-tree/ # Question : Write a function which takes start and end index and return their range sum. We # have used the same "Sum of given Range" problem to explain Lazy propagation. He then asked # range update in best optimised way. I told him Lazy propagation. I had to write code for # segment tree with lazy propagation. # # Question Type : Generic # Used : updateRange(us, ue) # 1) If current segment tree node has any pending update, then first add that # pending update to current node. # 2) If current node's range lies completely in update query range. # a) Update current node # b) Postpone updates to children by setting lazy value for children nodes. # 3) If current node's range overlaps with update range, follow the same approach as above simple update. # a) Recur for left and right children. # b) Update current node using results of left and right calls. # Logic : # def getSumUtil(self, segStart, segEnd, queryStart, queryEnd, index): # if self.lazy[index] != 0: # self.tree[index] += (segEnd - segStart + 1) * self.lazy[index] # if segStart != segEnd: # self.lazy[index * 2 + 1] += self.lazy[index] # self.lazy[index * 2 + 2] += self.lazy[index] # self.lazy[index] = 0 # if segStart > segEnd or segStart > queryEnd or segEnd < queryStart: return 0 # if segStart >= queryStart and segEnd <= queryEnd: return self.tree[index] # mid = (segStart + segEnd) / 2 # return self.getSumUtil(segStart, mid, queryStart, queryEnd, index * 2 + 1) + \ # self.getSumUtil(mid + 1, segEnd, queryStart, queryEnd, index * 2 + 2) # # def updateRangeUtil(self, segStart, segEnd, queryStart, queryEnd, index, diff): # if self.lazy[index] != 0: # self.tree[index] += (segEnd - segStart + 1) * self.lazy[index] # if segStart != segEnd: # self.lazy[index * 2 + 1] += self.lazy[index] # self.lazy[index * 2 + 2] += self.lazy[index] # self.lazy[index] = 0 # No overlap b/w segment and query range # if segStart > segEnd or segStart > queryEnd or segEnd < queryStart: # return 0 # Query start/end is more than segment range # if segStart >= queryStart and segEnd <= queryEnd: # self.tree[index] += (segEnd - segStart + 1) * diff # if segStart != segEnd: # self.lazy[index * 2 + 1] += diff # self.lazy[index * 2 + 2] += diff # return # mid = (segStart + segEnd) // 2 # self.updateRangeUtil(segStart, mid, queryStart, queryEnd, index * 2 + 1, diff) # self.updateRangeUtil(mid + 1, segEnd, queryStart, queryEnd, index * 2 + 2, diff) # self.tree[index] = self.tree[index * 2 + 1] + self.tree[index * 2 + 2] # Complexity : Tree construction O(n) Update O(log n) Sum O(log n) MAX = 1000 class LazySegmentTree: def __init__(self): self.tree = [0] * MAX self.lazy = [0] * MAX self.size = -1 def constructUtil(self, inpArr, segStart, segEnd, index): if segStart > segEnd: return if segStart == segEnd: self.tree[index] = inpArr[segStart] return mid = (segStart + segEnd) // 2 self.constructUtil(inpArr, segStart, mid, index * 2 + 1) self.constructUtil(inpArr, mid + 1, segEnd, index * 2 + 2) self.tree[segStart] = self.tree[segStart * 2 + 1] + self.tree[segStart * 2 + 2] def construct(self, inpArr): self.size = len(inpArr) self.constructUtil(arr, 0, self.size - 1, 0) def getSumUtil(self, segStart, segEnd, queryStart, queryEnd, index): if self.lazy[index] != 0: self.tree[index] += (segEnd - segStart + 1) * self.lazy[index] if segStart != segEnd: self.lazy[index * 2 + 1] += self.lazy[index] self.lazy[index * 2 + 2] += self.lazy[index] self.lazy[index] = 0 if segStart > segEnd or segStart > queryEnd or segEnd < queryStart: return 0 # If this segment lies in range if segStart >= queryStart and segEnd <= queryEnd: return self.tree[index] mid = (segStart + segEnd) // 2 return self.getSumUtil(segStart, mid, queryStart, queryEnd, index * 2 + 1) + \ self.getSumUtil(mid + 1, segEnd, queryStart, queryEnd, index * 2 + 2) def getSum(self, queryStart, queryEnd): if queryStart < 0 or queryEnd > self.size - 1 or queryStart > queryEnd: print("Invalid Input") return -1 return self.getSumUtil(0, self.size - 1, queryStart, queryEnd, 0) def updateRangeUtil(self, segStart, segEnd, queryStart, queryEnd, index, diff): if self.lazy[index] != 0: self.tree[index] += (segEnd - segStart + 1) * self.lazy[index] if segStart != segEnd: self.lazy[index * 2 + 1] += self.lazy[index] self.lazy[index * 2 + 2] += self.lazy[index] self.lazy[index] = 0 if segStart > segEnd or segStart > queryEnd or segEnd < queryStart: return 0 # If this segment lies in range if segStart >= queryStart and segEnd <= queryEnd: self.tree[index] += (segEnd - segStart + 1) * diff if segStart != segEnd: self.lazy[index * 2 + 1] += diff self.lazy[index * 2 + 2] += diff return mid = (segStart + segEnd) // 2 self.updateRangeUtil(segStart, mid, queryStart, queryEnd, index * 2 + 1, diff) self.updateRangeUtil(mid + 1, segEnd, queryStart, queryEnd, index * 2 + 2, diff) self.tree[index] = self.tree[index * 2 + 1] + self.tree[index * 2 + 2] def updateRange(self, queryStart, queryEnd, diff): self.updateRangeUtil(0, self.size - 1, queryStart, queryEnd, 0, diff) if __name__ == "__main__": arr = [1, 3, 5, 7, 9, 11] lazySegmentTree = LazySegmentTree() lazySegmentTree.construct(arr) print(lazySegmentTree.getSum(1, 3)) lazySegmentTree.updateRange(1, 5, 10) print(lazySegmentTree.getSum(1, 3))

[ "sandeepkumar8713@gmail.com" ]

sandeepkumar8713@gmail.com

570f37233f49a95dccabcfab99c73b34aed7b8a1

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/ghidra/scripts/find_duplicate_functions.py

eefaf1e3448e6d38d520887ec12f49284de1acca

[ "MIT" ]

permissive

qeedquan/debug

4ad1fd9c2f484190a0a64725653e47172e7595c6

aadeb3351f832bbd7210f0512037c93e48153062

refs/heads/master

2023-05-10T17:06:50.463693

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#@author #@category _NEW_ #@keybinding #@menupath #@toolbar class Func: def __init__(self): self.func = None self.body = [] self.hash = "" def getfuncs(): listing = currentProgram.getListing() result = [] func = getFirstFunction() while func is not None: f = Func() f.func = func f.body = list(listing.getCodeUnits(func.getBody(), True)) for op in f.body: f.hash += op.toString().split(" ")[0] result.append(f) func = getFunctionAfter(func) return result def getdiffs(funcs): dups = {} for f in funcs: key = f.hash if key not in dups: dups[key] = [] dups[key].append(f.func) for key in dups: if len(dups[key]) > 1: print(dups[key]) getdiffs(getfuncs())

[ "qeed.quan@gmail.com" ]

qeed.quan@gmail.com

2c8eb5561dfa8bcd89c70ae82192ec5011775a7f

1f190e0290513ede543c370b0428dff8079e32ed

/clusters/haswell/submit_dalton.py

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Computational-Chemistry-Research/personal_scripts

535062bc402088d1fd0ccbabae906eb6e7a65e84

0b51032582a2ee55b06a150009bb898e2b976606

refs/heads/master

2023-02-03T10:02:18.612244

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#!/usr/bin/env python """submit_dalton.py: A standalone script for submitting DALTON jobs to Haswell's SLURM scheduler. """ def template_slurmfile_dalton(inpfile, ppn, time, extrafiles): """The template for a SLURM jobfile that calls DALTON.""" copy_string_template = 'cp "$SLURM_SUBMIT_DIR"/{} "$LOCAL"\n' if extrafiles is None: joined_extrafiles = "" elif isinstance(extrafiles, list): copy_strings = [] for extrafile in extrafiles: copy_string = copy_string_template.format(extrafile) copy_strings.append(copy_string) joined_extrafiles = "".join(copy_strings) else: joined_extrafiles = copy_string_template.format(extrafiles) module = 'dalton/2016.2-i2017.1-mkl_parallel-omp' return '''#!/bin/bash #SBATCH --job-name={inpfile} #SBATCH --output={inpfile}.slurmout #SBATCH --nodes=1 #SBATCH --ntasks-per-node={ppn} #SBATCH --time=0-{time}:00:00 module purge module load intel/2017.1.132 module load mkl/2017.1.132 module load {module} mkdir -p "$LOCAL" cp "$SLURM_SUBMIT_DIR"/{inpfile}.dal "$LOCAL" {extrafiles}cd "$LOCAL" run_on_exit() {{ set -v find "$LOCAL" -type f -exec chmod 644 '{{}}' \; cp -v -R "$LOCAL"/DALTON_scratch_{username}/* "$SLURM_SUBMIT_DIR" }} trap run_on_exit EXIT $(which dalton) -omp {ppn} -noarch -nobackup -d -ow -w "$SLURM_SUBMIT_DIR" {inpfile}.dal chmod 644 "$SLURM_SUBMIT_DIR"/{inpfile}.out '''.format(inpfile=inpfile, ppn=ppn, time=time, module=module, username=os.environ['USER'], extrafiles=joined_extrafiles) if __name__ == "__main__": import argparse import os.path parser = argparse.ArgumentParser() parser.add_argument('inpfilename', help='the DALTON input file to submit', nargs='*') parser.add_argument('--ppn', type=int, default=12, help='number of cores to run on (max 12)') parser.add_argument('--time', type=int, default=24, help='walltime to reserve (max 144 hours)') parser.add_argument('--extrafiles', help='An arbitrary number of files to copy to $LOCAL.', nargs='*') args = parser.parse_args() for inpfilename in args.inpfilename: inpfilename = os.path.splitext(inpfilename)[0] slurmfilename = inpfilename + '.slurm' with open(slurmfilename, 'w') as slurmfile: slurmfile.write(template_slurmfile_dalton(inpfilename, args.ppn, args.time, args.extrafiles)) print(slurmfilename)

[ "eric.berquist@gmail.com" ]

eric.berquist@gmail.com

a963122d803d9c95d2f4da26529d3c3263e17c97

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/tests/problem_difference_operators/test_Dcd.py

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[]

no_license

ASU-CompMethodsPhysics-PHY494/Activity_11_differential_operators

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refs/heads/main

2023-03-12T15:45:59.381399

2021-03-04T10:09:50

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from ..base import _TestDxx class TestDcd(_TestDxx): name = "D_cd" @staticmethod def op(f, x, dx): dx2 = dx/2 return (f(x + dx2) - f(x - dx2))/dx

[ "orbeckst@gmail.com" ]

orbeckst@gmail.com

ca62d5843d222ded6598b0f8b2af7ca737d437d6

12579725d1c1e51a436136f465a8a7e60a76248b

/apps/almacen/views.py

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[]

no_license

kiritodeveloper/optica

f4f43b9ead720e0708974736ba6a798e7980ccb8

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refs/heads/master

2023-01-06T14:00:20.337601

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# -*- encoding: utf-8 -*- from django.shortcuts import render,redirect, HttpResponse from django.views.generic import View, ListView, DeleteView from apps.cliente.models import Cliente from apps.facturacion.models import Venta from apps.receta.models import Receta from .models import Producto,Proveedor from .forms import ProductoForm,IngresoProductosForm from django.contrib import messages from apps.usuarios.views import LoginRequiredMixin from decimal import Decimal import sys reload(sys) sys.setdefaultencoding("utf-8") class Index(LoginRequiredMixin,View): template_name = 'index.html' def get(self,request): # Calculando 6 meses anterior y sus totales import datetime # nombre_meses = { 1:"Enero",2:"Febrero",3:"Marzo",4:"Abril",5:"Mayo",6:"Junio",7:"Julio",8:"Agosto",9:"Setiembre",10:"Octubre", 11:"Noviembre",12:"Diciembre" } # contador = [0,0,0,0,0,0] # totales = [Decimal(0),Decimal(0),Decimal(0),Decimal(0),Decimal(0),Decimal(0)] # receta = Receta.objects.all().order_by('-fecha') # lista = [] # for item in receta: # diff = (datetime.date.today() - item.fecha).days # if(int(diff/30) >= 8): # lista.append(item) # receta = lista # ventas = Venta.objects.all() # suma_mes = Decimal(0) # suma_dia = Decimal(0) # flag = False # meses = [[datetime.date.today().month,datetime.date.today().year],] # #Obtener los ultimos 6 meses # if (datetime.date.today().month - 1 >0 and flag == False): # meses.append([datetime.date.today().month-1,datetime.date.today().year]) # else: # meses.append([12,datetime.date.today().year-1]) # flag = True # if (datetime.date.today().month - 2 >0 and flag == False): # meses.append([datetime.date.today().month-2,datetime.date.today().year]) # else: # meses.append([11,datetime.date.today().year-1]) # flag = True # if (datetime.date.today().month - 3 >0 and flag == False): # meses.append([datetime.date.today().month-3,datetime.date.today().year]) # else: # meses.append([10,datetime.date.today().year-1]) # flag = True # if (datetime.date.today().month - 4 >0 and flag == False): # meses.append([datetime.date.today().month-4,datetime.date.today().year]) # else: # meses.append([9,datetime.date.today().year-1]) # flag = True # if (datetime.date.today().month - 5 >0 and flag == False): # meses.append([datetime.date.today().month-5,datetime.date.today().year]) # else: # meses.append([8,datetime.date.today().year-1]) # flag = True # # for item in ventas:#Calcular totales del dia y del mes # if item.fecha == datetime.date.today(): # suma_dia += Decimal(item.total or 0) # if (item.fecha.month == datetime.date.today().month) and (item.fecha.year == datetime.date.today().year): # suma_mes += Decimal(item.total or 0) # #Cacular totales para los 6 meses # if (item.fecha.month == meses[0][0]) and (item.fecha.year == meses[0][1]): # totales[0] += Decimal(item.total or 0) # contador[0] += 1 # if (item.fecha.month == meses[1][0]) and (item.fecha.year == meses[1][1]): # totales[1] += Decimal(item.total or 0) # contador[1] += 1 # if (item.fecha.month == meses[2][0]) and (item.fecha.year == meses[2][1]): # totales[2] += Decimal(item.total or 0) # contador[2] += 1 # if (item.fecha.month == meses[3][0]) and (item.fecha.year == meses[3][1]): # totales[3] += Decimal(item.total or 0) # contador[3] += 1 # if (item.fecha.month == meses[4][0]) and (item.fecha.year == meses[4][1]): # totales[4] += Decimal(item.total or 0) # contador[4] += 1 # if (item.fecha.month == meses[5][0]) and (item.fecha.year == meses[5][1]): # totales[5] += Decimal(item.total or 0) # contador[5] += 1 # # #Renderizando datos a json # import json # index = 0 # for item in meses: # meses[index] = "%s - %s" %(str(nombre_meses[item[0]]),str(item[1])) # index +=1 # meses = json.dumps(meses) # contador = json.dumps(contador) # index = 0 # for item in totales: # totales[index] = float(totales[index]) # index+=1 # totales = json.dumps(totales) # # Clientes de cumpleaños clientes = Cliente.objects.filter(fecha_nacimiento__month=datetime.date.today().month, fecha_nacimiento__day=datetime.date.today().day) return render(request,self.template_name,locals()) class Productos(LoginRequiredMixin,View): template_name = 'productos/index.html' def get(self,request): productos = Producto.objects.all() producto_form = ProductoForm() ingreso_form = IngresoProductosForm() return render(request,self.template_name,locals()) def post(self,request): productos = Producto.objects.all() producto_form = ProductoForm(request.POST) ingreso_form = IngresoProductosForm(request.POST) if producto_form.is_valid(): producto = producto_form.save() messages.success(request, unicode('El producto '+unicode(producto.descripcion)+' de código '+unicode(producto.codigo)+' fue registrado con exito')) productos = Producto.objects.all() producto_form = ProductoForm() ingreso_form = IngresoProductosForm() return render(request,self.template_name,locals()) elif(ingreso_form.is_valid()): historial = ingreso_form.save() producto = Producto.objects.get(pk=historial.producto.id) producto.stock_actual += int(request.POST['cantidad']) producto.save() messages.success(request, 'Se ingresaron '+request.POST['cantidad']+' unidades de '+producto.descripcion) productos = Producto.objects.all() producto_form = ProductoForm() ingreso_form = IngresoProductosForm() return render(request,self.template_name,locals()) else: messages.error(request, 'No se pudo registrar la operación, porfavor intente denuevo.') return render(request,self.template_name,locals()) import json from django.views.decorators.csrf import csrf_exempt @csrf_exempt def ObtenerProducto(request,nro): item = Producto.objects.get(pk=nro) return HttpResponse(json.dumps({"precio":float(item.precio_sugerido),"max_value":item.stock_actual}),content_type='application/json')

[ "admin@example.com" ]

admin@example.com

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87d0de6a06451d7aa561b72d908d06e68074f650

/core/arxiv/submission/auth.py

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[ "MIT" ]

permissive

arXiv/arxiv-submission-core

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6077ce4e0685d67ce7010800083a898857158112

refs/heads/develop

2022-01-21T02:11:56.384920

2020-07-31T18:16:17

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MIT

2022-01-06T22:29:31

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py

from typing import List import uuid from datetime import datetime, timedelta from pytz import UTC from arxiv.users import auth, domain from arxiv.base.globals import get_application_config from .domain.agent import User, Agent, Client def get_system_token(name: str, agent: Agent, scopes: List[str]) -> str: start = datetime.now(tz=UTC) end = start + timedelta(seconds=36000) if isinstance(agent, User): user = domain.User( username=agent.username, email=agent.email, user_id=agent.identifier, name=agent.name, verified=True ) else: user = None session = domain.Session( session_id=str(uuid.uuid4()), start_time=datetime.now(), end_time=end, user=user, client=domain.Client( owner_id='system', client_id=name, name=name ), authorizations=domain.Authorizations(scopes=scopes) ) secret = get_application_config()['JWT_SECRET'] return str(auth.tokens.encode(session, secret)) def get_compiler_scopes(resource: str) -> List[str]: """Get minimal auth scopes necessary for compilation integration.""" return [auth.scopes.READ_COMPILE.for_resource(resource), auth.scopes.CREATE_COMPILE.for_resource(resource)]

[ "brp53@cornell.edu" ]

brp53@cornell.edu

f9f5f1c810bd6768490f56eed7d994bb84fc244d

d5682d2ef13ad63c68d59d3d0706853a88035ff1

/week4/netmiko_test.py

7d33f05d13548fa2bd2280fdb6c9095940601267

[ "Apache-2.0" ]

permissive

mikealford/ktbyers_automation

66467f5352a3fbb111fc18f9c90b83cf97a75e79

d8b30e7ddbe27b4bc62b74bfc051b6d1c099f7f9

refs/heads/master

2020-04-17T19:37:42.365653

2019-02-19T01:16:41

166,872,630

0

null

UTF-8

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779

py

from netmiko import ConnectHandler from getpass import getpass password = getpass() switch1 = { 'device_type': 'cisco_ios', 'ip': '192.168.122.172', 'username': 'malford', 'password': password, } switch2 = { 'device_type': 'cisco_ios', 'ip': '192.168.122.173', 'username': 'malford', 'password': password, } switch3 = { 'device_type': 'cisco_ios', 'ip': '192.168.122.174', 'username': 'malford', 'password': password, } ssh_switch1 = ConnectHandler(**switch1) ssh_switch2 = ConnectHandler(**switch2) ssh_switch3 = ConnectHandler(**switch3) #output = ssh_switch1.send_command("show ip int brief") config_commands = ['logging buffered 20000'] output = ssh_switch1.send_config_set(config_commands) print(output)

[ "mike.alford13@gmail.com" ]

mike.alford13@gmail.com

debf5d538dd470447e69bf1ceafc4368d95d2702

a276d03f34457c174d2e79fc4fdb17c90299e843

/projects/buttons/lib/markdown/__version__.py

65edeeddbe2024b9e9a9513b5ec0deaec794cfdb

[ "MIT" ]

permissive

lucidworks/streams

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89aaf02382494cf09041ca5dadb41dddb86cf9d8

refs/heads/master

2021-06-02T04:09:27.626504

2020-01-07T01:28:09

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2020-03-17T21:17:48

2017-10-12T20:34:28

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UTF-8

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907

py

# # markdown/__version__.py # # version_info should conform to PEP 386 # (major, minor, micro, alpha/beta/rc/final, #) # (1, 1, 2, 'alpha', 0) => "1.1.2.dev" # (1, 2, 0, 'beta', 2) => "1.2b2" version_info = (2, 4, 0, 'final', 0) def _get_version(): " Returns a PEP 386-compliant version number from version_info. " assert len(version_info) == 5 assert version_info[3] in ('alpha', 'beta', 'rc', 'final') parts = 2 if version_info[2] == 0 else 3 main = '.'.join(map(str, version_info[:parts])) sub = '' if version_info[3] == 'alpha' and version_info[4] == 0: # TODO: maybe append some sort of git info here?? sub = '.dev' elif version_info[3] != 'final': mapping = {'alpha': 'a', 'beta': 'b', 'rc': 'c'} sub = mapping[version_info[3]] + str(version_info[4]) return str(main + sub) version = _get_version()

[ "kordless@gmail.com" ]

kordless@gmail.com

a68ab708a3dd64fa6df53028e3ae4f92b71cde57

5d0edf31b17c5375faf6126c1a7be8e79bfe2ab8

/buildout-cache/eggs/collective.siterss-0.4-py2.7.egg/collective/siterss/tests.py

504d02d5c4f70d7c823545bd4735b78c9bb7ae7c

[]

no_license

renansfs/Plone_SP

27cba32ebd9fc03dae3941ec23cf1bf0a7b6667a

8a7bdbdb98c3f9fc1073c6061cd2d3a0ec80caf5

refs/heads/master

2021-01-15T15:32:43.138965

2016-08-24T15:30:19

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import unittest #from zope.testing import doctestunit #from zope.component import testing #from Testing import ZopeTestCase as ztc from Products.Five import zcml from Products.Five import fiveconfigure from Products.PloneTestCase import PloneTestCase as ptc from Products.PloneTestCase.layer import PloneSite ptc.setupPloneSite() import collective.siterss class TestCase(ptc.PloneTestCase): class layer(PloneSite): @classmethod def setUp(cls): fiveconfigure.debug_mode = True zcml.load_config('configure.zcml', collective.siterss) fiveconfigure.debug_mode = False @classmethod def tearDown(cls): pass def test_suite(): return unittest.TestSuite([ # Unit tests #doctestunit.DocFileSuite( # 'README.txt', package='collective.siterss', # setUp=testing.setUp, tearDown=testing.tearDown), #doctestunit.DocTestSuite( # module='collective.siterss.mymodule', # setUp=testing.setUp, tearDown=testing.tearDown), # Integration tests that use PloneTestCase #ztc.ZopeDocFileSuite( # 'README.txt', package='collective.siterss', # test_class=TestCase), #ztc.FunctionalDocFileSuite( # 'browser.txt', package='collective.siterss', # test_class=TestCase), ]) if __name__ == '__main__': unittest.main(defaultTest='test_suite')

[ "renansfs@gmail.com" ]

renansfs@gmail.com

9da24d7be31634385f5297b6853fdebc8cd2a1a6

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/restapi/restapi/urls.py

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[]

no_license

kagxin/recipes

3358a79267a18ba499145e2de64f44c6e14ddaaf

17a106478859cd8e82513456ecd03873e1fe9391

refs/heads/master

2021-05-06T08:13:14.851114

2018-07-21T11:50:48

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"""restapi URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include urlpatterns = [ path('api/', include('app.urls')), path('admin/', admin.site.urls), ]

[ "k506855660@163.com" ]

k506855660@163.com

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/jd_home1.py

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[]

no_license

luobodage/-

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refs/heads/master

2022-12-30T23:49:31.205151

2020-10-25T09:02:01

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import requests import UserAgent import lxml.etree as le import re import os def spider_home(): """ 获取物品的url以及标题价格 :return: 返回物品编码 """ shop = input("请输入你要搜索的商品：") global headers headers = UserAgent.get_headers() # 随机获取一个headers url = 'https://search.jd.com/Search?keyword={shop}&enc=utf-8&wq=%E5%B0%8F&pvid=469d5d51a3184cc9a053124dc020b31f'.format( shop=shop ) try: r = requests.get( url=url, headers=headers ).content content = le.HTML(r) href = content.xpath('//*[@id="J_goodsList"]/ul/li[1]/div/div[1]/a/@href') price = content.xpath('//*[@id="J_goodsList"]/ul/li[1]/div/div/strong/i/text()') title_01 = content.xpath('//*[@id="J_goodsList"]/ul/li[1]/div/div/a/em/text()') title = [x.strip() for x in title_01 if x.strip() != ''] # 提取标题将多余空格和\n去除 re_01 = re.compile(r'\d+') number = re_01.findall(str(href)) shop_price_01 = "".join(price) print("商品价格：" + shop_price_01) # for shop_price in price: # # print("商品价格：" + shop_price) global shop_title # 全局定义商品题目进行文件改标题 shop_title_01 = "".join(title) print("商品标题：" + shop_title_01) # for shop_title in title: # print("商品标题：" + shop_title) for index in href: global href_shop href_shop = 'http:' + index print(href_shop) for num in number: # print(num) return num # file_rename() except: print('爬取失败') def file_rename(): file_srcFile = 'id.txt' file_dstFile = shop_title + '.txt' os.rename(file_srcFile, file_dstFile) # 改标题 img_srcFile = 'ciyun.png' img_dstFile = shop_title + '.png' os.rename(img_srcFile, img_dstFile) if __name__ == '__main__': spider_home()

[ "fuyu16032001@gmail.com" ]

fuyu16032001@gmail.com

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/ixl/management/commands/createchallenges.py

dacb82b399b6bd7265dc6ed66a89f577c65727cc

[]

no_license

waffle-iron/Newton

ce60a8ccc66bbc23aa764742b197add4cfb4d2d3

f8f3df4127e88428db0cc73207ac51582db7cd42

refs/heads/master

2021-01-02T08:59:39.948797

2017-08-02T12:52:52

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2017-08-02T12:52:52

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# commands/createchallenges.py # Full path to your django project directory your_djangoproject_home="/home/alex/newton/" import django import datetime import sys,os import requests from variables import second_teachers as assigned_teachers from variables import mastery_skills, cbaExercises sys.path.append(your_djangoproject_home) os.environ.setdefault("DJANGO_SETTINGS_MODULE", "newton.settings") django.setup() from django.core.management.base import BaseCommand, CommandError from brain.models import StudentRoster, CurrentClass, Teacher from ixl.models import ChallengeAssignment, Challenge, ChallengeExercise, IXLSkillScores from libs.functions import nwea_recommended_skills_list as nwea_skills date = datetime.date.today() date = date.strftime('%-m/%-d') todays_date = datetime.date.today() class Command(BaseCommand): help = 'Assigns a custom challenge to all students' def add_arguments(self, parser): pass def make_mastery_challenge(self, student, current_challenge, exercise_count): for addition in mastery_skills: try: skill_score = IXLSkillScores.objects.get(student_id=student, ixl_skill_id__skill_id=addition) if skill_score.score < 96: exercise_count += 1 challenge_exercise = ChallengeExercise.objects.create(challenge=current_challenge, exercise_id=addition, required_score=100, ) except: exercise_count += 1 challenge_exercise = ChallengeExercise.objects.create(challenge=current_challenge, exercise_id=addition, required_score=100) if exercise_count == 1: return exercise_count return exercise_count def make_cba_challenge(self, student, current_challenge, exercise_count): for addition in cbaExercises: try: skill_score = IXLSkillScores.objects.get(student_id=student, ixl_skill_id__skill_id=addition) if skill_score.score < 78: exercise_count += 1 challenge_exercise = ChallengeExercise.objects.create(challenge=current_challenge, exercise_id=addition, required_score=80, ) else: print("Could not add {}".format(addition)) except: try: challenge_exercise = ChallengeExercise.objects.create(challenge=current_challenge, exercise_id=addition, required_score=80) exercise_count += 1 except: pass if exercise_count == 3: return exercise_count print("Ran out of cba exercises for {}!".format(student)) return exercise_count def make_nwea_challenge(self, student, current_challenge, exercise_count): skill_list = nwea_skills(student, "recommended_skill_list") domain_list = [] waiting_list = [] for skill in skill_list: previously_assigned = ChallengeExercise.objects.filter(challenge__challengeassignment__student_id=student, exercise_id=skill[0] ) pal = len(previously_assigned) print("{} Previously Assigned {} times".format(skill[0],pal)) if pal>3: continue if skill[3] in domain_list: waiting_list.append(skill[0]) elif exercise_count >=5: waiting_list.append(skill[0]) else: domain_list.append(skill[3]) # Add this domain to the list # Create a Challenge Exercise object with the challenge and skill try: challenge_exercise = ChallengeExercise.objects.create(challenge=current_challenge, exercise_id=skill[0]) exercise_count += 1 except: continue if exercise_count<5: for skill in waiting_list: try: challenge_exercise = ChallengeExercise.objects.create(challenge=current_challenge, exercise_id=skill[0]) exercise_count += 1 except: continue if exercise_count ==5: return exercise_count return exercise_count def handle(self, *args, **options): for teacher in assigned_teachers: try: #Get the class current_class = CurrentClass.objects.get(teacher__last_name=teacher) except: print('Teacher {} could not be found.'.format(teacher)) break student_list = StudentRoster.objects.filter(current_class=current_class) print("Got student list. Creating Challenges.") for student in student_list: #Go through one student at a time title = "{} {}'s {} Challenge".format(student.first_name, student.last_name[0],date) current_challenge = Challenge.objects.create(title=title, date=todays_date) exercise_count = 0 exercise_count = self.make_mastery_challenge(student, current_challenge, exercise_count) exercise_count = self.make_cba_challenge(student, current_challenge, exercise_count) exercise_count = self.make_nwea_challenge(student, current_challenge, exercise_count) print("Assigning {} to {}, length: {}".format(title,student, exercise_count)) obj, created = ChallengeAssignment.objects.get_or_create( student_id=student, challenge=current_challenge, ) #TODO: Email teachers previous week's scores # TODO: Add Bonus Exercises # IXL Challenge Creation # Create 5 main challenges # 2 for CBA # Map the CBAs to IXL Exercises for each of the three. # Make it change depending on the date # 2 for NWEA # 1 for Mastery - based on the current or passed curriculum - 100 Smart Score # Create 5 Bonus Challenges from django.core.mail import EmailMessage def send_an_email(): email = EmailMessage( subject='Hello', body='''Body goes here. How are you? I hope this email works!''', from_email='newton@newtonthinks.com', to=['ins-dauaprqb@isnotspam.com'], reply_to=['alextrostbtwa@gmail.com.com'], headers={'Content-Type': 'text/plain'}, ) email.send() def send_simple_message(): return requests.post( "https://api.mailgun.net/v3/sandbox791822b6aeca4aee8007134fecd331ec.mailgun.org/messages", auth=("api", "key-cedb9e331a1be78e57582e4e13cac442"), data={"from": "Mailgun Sandbox <postmaster@sandbox791822b6aeca4aee8007134fecd331ec.mailgun.org>", "to": "Alex <alextrostbtwa@gmail.com>", "subject": "Hello Alex", "text": "Congratulations Alex, you just sent an email with Mailgun! You are truly awesome!"}) send_simple_message()

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#!/usr/bin/python3 # -*- coding: utf-8 -*- import os import re import json import Titles import Print from bs4 import BeautifulSoup import requests import time import datetime import calendar import threading import Nsps import urllib.request import Config try: from PIL import Image except ImportError: import Image global grabUrlInit global urlCache global urlLock grabUrlInit = False urlCache = {} urlLock = threading.Lock() if os.path.isfile('titledb/redirectCache.json'): with open('titledb/redirectCache.json', encoding="utf-8-sig") as f: urlCache = json.loads(f.read()) if os.path.isfile('titledb/titleRedirects.json'): with open('titledb/titleRedirects.json', encoding="utf-8-sig") as f: titleRedirects = json.loads(f.read()) def grabCachedRedirectUrl(url, cookies = None): global grabUrlInit global urlCache global urlLock try: if url in urlCache: if not urlCache[url]: return None result = requests.get(urlCache[url], cookies=cookies) #result.url = urlCache[url] return result urlLock.acquire() # we need to slow this down so we dont get banned #Print.info('hitting ' + url) #time.sleep(0.1) result = requests.get(url, cookies=cookies) if result.status_code == 404: urlCache[url] = None elif result.status_code == 200: urlCache[url] = result.url else: #not sure but dont cache it return result with open('titledb/redirectCache.json', 'w') as outfile: json.dump(urlCache, outfile) urlLock.release() return result except: urlLock.release() raise def getBaseId(id): if not id: return None titleIdNum = int(id, 16) return '{:02X}'.format(titleIdNum & 0xFFFFFFFFFFFFE000).zfill(16) class Title: def __init__(self): self.id = None self.rightsId = None self.name = None self.isDLC = False self.isUpdate = False self.idExt = None self.updateId = None self.version = None self.key = None self.isDemo = None self.region = None self.isModified = False self.retailOnly = None self.releaseDate = None self.nsuId = None self.category = None self.ratingContent = None self.numberOfPlayers = None self.rating = None self.developer = None self.publisher = None self.frontBoxArt = None self.iconUrl = None self.screenshots = None self.bannerUrl = None self.intro = None self.description = None self.languages = None self.size = 0 def __lt__(self, other): if not self.name: return True if not other.name: return False return str(self.name) < str(other.name) def loadCsv(self, line, map = ['id', 'key', 'name']): split = line.split('|') for i, value in enumerate(split): if i >= len(map): Print.info('invalid map index: ' + str(i) + ', ' + str(len(map))) continue i = str(map[i]) methodName = 'set' + i[0].capitalize() + i[1:] method = getattr(self, methodName, lambda x: None) method(value.strip()) #self.setId(split[0].strip()) #self.setName(split[2].strip()) #self.setKey(split[1].strip()) def dict(self, map = ['id', 'rightsId', 'key', 'isUpdate', 'isDLC', 'isDemo', 'name', 'version', 'region', 'retailOnly']): r = {} for i in map: methodName = 'get' + i[0].capitalize() + i[1:] method = getattr(self, methodName, lambda: methodName) r[i] = method() return r def serialize(self, map = ['id', 'rightsId', 'key', 'isUpdate', 'isDLC', 'isDemo', 'name', 'version', 'region', 'retailOnly']): r = [] for i in map: methodName = 'get' + i[0].capitalize() + i[1:] method = getattr(self, methodName, lambda: methodName) r.append(str(method())) return '|'.join(r) def getFiles(self): files = [] for path, f in Nsps.files.items(): if(f.titleId == self.id): files.append(f) return files def getLatestFile(self): highest = None for nsp in self.getFiles(): if not highest or nsp.version > highest.version: highest = nsp return highest def isUpdateAvailable(self): nsp = self.getLatestFile() if not nsp: return True try: if int(nsp.version) < int(self.lastestVersion()): return True except: pass return False def getIsDLC(self): return self.isDLC*1 def setIsDLC(self, v): try: v = int(v, 10) if v == 1: self.isDLC = True elif v == 0: self.isDLC = False except: pass def getIsUpdate(self): return self.isUpdate*1 def setIsUpdate(self, v): try: v = int(v, 10) if v == 1: self.isUpdate = True elif v == 0: self.isUpdate = False except: pass def getRetailOnly(self): try: return self.retailOnly*1 except: return 0 def setRetailOnly(self, v): try: self.retailOnly = bool(int(v, 10)) except: pass def getIsDemo(self): try: return self.isDemo*1 except: return 0 def setIsDemo(self, v): try: v = int(v, 10) if v == 1: self.isDemo = True elif v == 0: self.isDemo = False except: pass def setRightsId(self, rightsId): if not id: self.setId(rightsId) if rightsId and len(rightsId) == 32 and rightsId != '00000000000000000000000000000000': self.rightsId = rightsId.upper() def getRightsId(self): return self.rightsId or '00000000000000000000000000000000' def setId(self, id): if not id or self.id: return id = id.upper(); try: i = int(id, 16) except: return if len(id) == 32: self.id = id[:16] self.setRightsId(id) elif len(id) == 16: self.id = id[:16] else: return titleIdNum = int(self.id, 16) if self.id: self.baseId = '{:02X}'.format(titleIdNum & 0xFFFFFFFFFFFFE000).zfill(16) else: self.baseId = None self.isDLC = (titleIdNum & 0xFFFFFFFFFFFFE000) != (titleIdNum & 0xFFFFFFFFFFFFF000) #self.isBase = self.id == titleIdNum & 0xFFFFFFFFFFFFE000 self.idExt = titleIdNum & 0x0000000000000FFF if self.isDLC: # dlc pass elif self.idExt == 0: # base self.updateId = '%s800' % self.id[:-3] else: # update self.isUpdate = True pass @staticmethod def baseDlcId(id): titleIdNum = int(id, 16) return (titleIdNum & 0xFFFFFFFFFFFFE000) + 0x1000 #return hex(dlcId) def getId(self): return self.id or '0000000000000000' def getBaseId(self): return self.baseId or '0000000000000000' def setRegion(self, region): if re.match('[A-Z]{2}', region): self.region = region def getRegion(self): return self.region or '' def setName(self, name): if not name: return self.name = name if self.isDemo == None: if re.match('.*\s[$\[]?Demo[$\]]?\s*$', self.name, re.I) or re.match('.*\s[$\[]?Demo[$\]]?\s+.*$', self.name, re.I): self.isDemo = True else: self.isDemo = False def getName(self): baseId = getBaseId(self.id) if self.isUpdate and Titles.contains(baseId): return Titles.get(baseId).name return self.name or '' def setKey(self, key): if not key: return key = key.upper() if len(key) != 32: return try: i = int(key, 16) if i <= 0: return except: return self.key = key def getKey(self): return self.key or '00000000000000000000000000000000' def setVersion(self, version, force = False): if version != None: try: n = int(str(version), 10) except: return try: o = int(str(self.version), 10) except: o = None if not o or n > o or force: self.version = version def getVersion(self): return self.version or '' def lastestVersion(self, force = False): #if self.isDLC: # return '0' if not self.id: return None if self.version and self.version.lower() == 'none': self.version = None if not self.version or force: self.version = Title.getCdnVersion(self.id) Print.info('Grabbed %s [%s] version, %s' % (str(self.name), str(self.id), str(self.version))) #Print.info('version: ' + str(self.version)) return self.version def isValid(self): if self.id: return True else: return False @staticmethod def getCdnVersion(id): r = CDNSP.get_version(id) #if len(r) == 0 or r[0] == 'none': # return ['0'] return r def download(self, base, fileName, url): path = os.path.join(base, fileName) if os.path.isfile(path): return path os.makedirs(base, exist_ok=True) urllib.request.urlretrieve(url, path) return path def getResizedImage(self, filePath, width = None, height = None): if not width and not height: return filePath base, name = os.path.split(filePath) path = os.path.join(base, '.' + str(width) + 'x' + str(height) + '_' + name) if not os.path.isfile(path): os.makedirs(base, exist_ok=True) im = Image.open(filePath) ar = im.size[0] / im.size[1] if height == None: height = int(width / ar) elif width == None: width = int(height * ar) out = im.resize((width, height), Image.ANTIALIAS) out.save(path, quality=100) return path def bannerFile(self, width = None, height = None): if not self.bannerUrl or self.bannerUrl.startswith('cocoon:/'): return None baseName, ext = os.path.splitext(self.bannerUrl) return self.getResizedImage(self.download(Config.paths.titleImages + self.id, 'banner' + ext, self.bannerUrl), width, height) def frontBoxArtFile(self, width = None, height = None): if not self.frontBoxArt or self.frontBoxArt.startswith('cocoon:/'): return None baseName, ext = os.path.splitext(self.frontBoxArt) return self.getResizedImage(self.download(Config.paths.titleImages + self.id, 'frontBoxArt' + ext, self.frontBoxArt), width, height) def iconFile(self, width = None, height = None): if not 'iconUrl' in self.__dict__: self.iconUrl = None if not self.iconUrl or self.iconUrl.startswith('cocoon:/'): return None baseName, ext = os.path.splitext(self.iconUrl) return self.getResizedImage(self.download(Config.paths.titleImages + self.id, 'icon' + ext, self.iconUrl), width, height) def screenshotFile(self, i, width = None, height = None): if not self.screenshots[i] or self.screenshots[i].startswith('cocoon:/'): return None baseName, ext = os.path.splitext(self.screenshots[i]) return self.getResizedImage(self.download(Config.paths.titleImages + self.id, 'screenshot' + str(i) + ext, self.screenshots[i]), width, height) def screenshotFiles(self): if not self.screenshots: return [] r = [] for i,u in enumerate(self.screenshots): r.append(self.screenshotFile(i)) return r def scrape(self, delta = True): if self.isUpdate or self.isDLC: return try: if (not delta or not self.bannerUrl): id = self.id if id in titleRedirects: id = titleRedirects[id] cookies = {'esrb.verified': 'true'} for region in ['JP', 'AU']: result = grabCachedRedirectUrl("https://ec.nintendo.com/apps/%s/%s" % (id, region), cookies=cookies) _json = '' if not result or result.status_code != 200: continue _json = json.loads(result.text.split('NXSTORE.titleDetail.jsonData = ')[1].split('NXSTORE.titleDetail')[0].replace(';','')) if _json == '' or _json == None: Print.error('Failed to parse json for ' + "https://ec.nintendo.com/apps/%s/%s" % (id, region)) continue if 'hero_banner_url' in _json: self.bannerUrl = _json['hero_banner_url'] if "release_date_on_eshop" in _json: self.releaseDate = int(_json["release_date_on_eshop"].replace('-','')) if "id" in _json: self.nsuId = int("%s" % _json["id"]) if "formal_name" in _json: self.name = _json["formal_name"].strip() if 'screenshots' in _json: self.screenshots = [] for i, k in enumerate(_json["screenshots"]): self.screenshots.append(k["images"][0]["url"]) if "demos" in _json: for demo in _json["demos"]: if "id" in demo: if id[0:12] != _json['applications'][0]['id'][0:12]: self.nsuId = int(demo["id"]) if "name" in demo: self.name = demo["name"].strip() if "languages" in _json: self.languages = [] for language in _json["languages"]: self.languages.append(language['iso_code']) if "genre" in _json: self.category = _json["genre"].split(' / ') if "total_rom_size" in _json: self.size = _json["total_rom_size"] if "rating_info" in _json: if "rating" in _json["rating_info"]: if "age" in _json["rating_info"]['rating']: self.rating = _json["rating_info"]['rating']['age'] if "content_descriptors" in _json["rating_info"]: content = [] for descriptor in _json["rating_info"]["content_descriptors"]: content.append(descriptor['name']) self.ratingContent = content if "player_number" in _json: if 'local_max' in _json["player_number"]: self.numberOfPlayers = _json["player_number"]["local_max"] if 'offline_max' in _json["player_number"]: self.numberOfPlayers = _json["player_number"]["offline_max"] if "publisher" in _json: if 'name' in _json["publisher"]: self.publisher = _json["publisher"]["name"] if 'title' in _json["publisher"]: self.publisher = _json["publisher"]["title"] if "applications" in _json: if "image_url" in _json["applications"][0]: self.iconUrl = _json["applications"][0]['image_url'] if "catch_copy" in _json: intro = re.sub('(?<!\n)\n(?!\n)', ' ',_json["catch_copy"]) intro = re.sub(' ', ' ', intro) self.intro = intro if "description" in _json: desc = re.sub('(?<!\n)\n(?!\n)', ' ',_json["description"]) desc = re.sub(' ', ' ', desc) self.description = desc #<img aria-hidden="true" data-src="https://media.nintendo.com/nintendo/bin/ZppwWK6BnjH5twBNvE5wEEI9aeMGR0XX/hQGr97SGMnlXBWoqOBtgtGX5noK3tNtD.jpg"/> result = grabCachedRedirectUrl("https://ec.nintendo.com/apps/%s/US" % id, cookies=cookies) if result and result.status_code == 200: if result.url != 'https://www.nintendo.com/games/': soup = BeautifulSoup(result.text, "html.parser") if not self.bannerUrl: m = re.search(r"#hero\s*{\s*background(-image)?:\s*url$'([^)]+)'$", result.text, re.DOTALL | re.UNICODE | re.MULTILINE | re.IGNORECASE) if m: banner = m.group(2) if banner[0] == '/': banner = 'https://www.nintendo.com' + banner self.bannerUrl = banner rem = re.finditer('<img aria-hidden="true" data-src="([^"]+)"', result.text) if rem: ss = [] for m in rem: ss.append(m.group(1)) if len(ss) > 0: self.screenshots = ss if soup.find("meta", {"property": "og:url"}) != None: slug = soup.find("meta", {"property": "og:url"})["content"].split('/')[-1] infoJson = json.loads(requests.get("https://www.nintendo.com/json/content/get/game/%s" % slug, cookies=cookies).text)["game"] if "release_date" in infoJson: self.releaseDate = int(datetime.datetime.strftime(datetime.datetime.strptime(infoJson["release_date"], "%b %d, %Y"),'%Y%m%d')) if "name" in infoJson: self.name = infoJson["name"].strip() if "nsuid" in infoJson: self.nsuId = int(infoJson["nsuid"]) catagories = [] if "game_category_ref" in infoJson: catindex = 0 if "name" in infoJson["game_category_ref"]: catagories.append(infoJson["game_category_ref"]["name"]) elif "title" in infoJson["game_category_ref"]: catagories.append(infoJson["game_category_ref"]["title"]) else: try: for game_category in infoJson["game_category_ref"]: catagories.append(infoJson["game_category_ref"][catindex]["name"]) catindex += 1 except: pass self.category = catagories esrbcontent = [] if "esrb_content_descriptor_ref" in infoJson: esrbindex = 0 if "name" in infoJson["esrb_content_descriptor_ref"]: esrbcontent.append(infoJson["esrb_content_descriptor_ref"]["name"]) elif "title" in infoJson["esrb_content_descriptor_ref"]: esrbcontent.append(infoJson["esrb_content_descriptor_ref"]["title"]) else: try: for descriptor in infoJson["esrb_content_descriptor_ref"]: if 'name' in descriptor: esrbcontent.append(descriptor["name"]) if 'title' in descriptor: esrbcontent.append(descriptor["title"]) except: pass self.ratingContent = esrbcontent if "number_of_players" in infoJson: self.numberOfPlayers = re.sub('[^0-9]', '', infoJson["number_of_players"]) if "esrb_rating_ref" in infoJson: if "esrb_rating" in infoJson["esrb_rating_ref"]: if "short_description" in infoJson["esrb_rating_ref"]["esrb_rating"]: self.rating = infoJson["esrb_rating_ref"]["esrb_rating"]["short_description"] ''' if not self.screenshots: try: ss = [] for s in infoJson["screenshot_gallery_ref"]["screenshot_gallery"]["screenshots"]: ss.append(s['image']['large_image']['include']['src'].replace('cocoon:/', '')) self.screenshots = ss except: pass ''' if "developer_ref" in infoJson: if "name" in infoJson["developer_ref"]: self.developer = infoJson["developer_ref"]["name"] if "publisher_ref" in infoJson: if "name" in infoJson["publisher_ref"]: self.publisher = infoJson["publisher_ref"]["name"] if 'title' in infoJson["publisher_ref"]: self.publisher = infoJson["publisher_ref"]["title"] if "front_box_art" in infoJson: if "image" in infoJson["front_box_art"]: if "image" in infoJson["front_box_art"]["image"]: if "url" in infoJson["front_box_art"]["image"]["image"]: self.frontBoxArt = infoJson["front_box_art"]["image"]["image"]["url"] if "intro" in infoJson: try: details = BeautifulSoup(infoJson["intro"][0],"html.parser") try: details = details.decode(formatter=None) except: details = details.decode() details = re.sub('<[^<]+?>', '', details).strip() details = re.sub(' +', ' ', details) details = re.sub('\n ', '\n', details) details = re.sub('\n\n+', '\n\n', details) details = re.sub('(?<!\n)\n(?!\n)', ' ',details) details = re.sub(' ', ' ', details) self.intro = details except Exception as e: pass if "game_overview_description" in infoJson: details = BeautifulSoup(infoJson["game_overview_description"][0],"html.parser") try: details = details.decode(formatter=None) except: details = details.decode() details = re.sub('<[^<]+?>', '', details).strip() details = re.sub(' +', ' ', details) details = re.sub('\n ', '\n', details) details = re.sub('\n\n+', '\n\n', details) details = re.sub('(?<!\n)\n(?!\n)', ' ',details) details = re.sub(' ', ' ', details) self.description = details #else: #f = open("missing.txt", 'a', encoding="utf8") #f.write(rid+"|title doesn't exist at ec.nintendo.com"+'\n') #f.close() except BaseException as e: pass print(repr(e) + ' ' + self.id) self.bannerFile() self.frontBoxArtFile() self.iconFile() self.screenshotFiles()

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# -*- coding: utf-8 -*- # # {project.authors} # {project.affiliations} # (c) {project.span} all rights reserved # # externals import {project.name} # declaration class About({project.name}.command, family='{project.name}.actions.about'): """ Display information about this application """ # user configurable state root = {project.name}.properties.str(default='/') root.tip = "specify the portion of the namespace to display" # commands @{project.name}.export(tip="the name of the app for configuration purposes") def name(self, plexus, **kwds): """ Print the name of the app for configuration purposes """ # show me plexus.info.log("{{!r}}".format(plexus.pyre_name) or "unknown") # all done return @{project.name}.export(tip="the application home directory") def home(self, plexus, **kwds): """ Print the application home directory """ # show me plexus.info.log("{{}}".format(plexus.home)) # all done return @{project.name}.export(tip="the application installation directory") def prefix(self, plexus, **kwds): """ Print the application installation directory """ # show me plexus.info.log("{{}}".format(plexus.prefix)) # all done return @{project.name}.export(tip="the application configuration directory") def defaults(self, plexus, **kwds): """ Print the application configuration directory """ # show me plexus.info.log("{{}}".format(plexus.defaults)) # all done return @{project.name}.export(tip="print the version number") def version(self, plexus, **kwds): """ Print the version of the {project.name} package """ # make some space plexus.info.log({project.name}.meta.header) # all done return @{project.name}.export(tip="print the copyright note") def copyright(self, plexus, **kwds): """ Print the copyright note of the {project.name} package """ # show the copyright note plexus.info.log({project.name}.meta.copyright) # all done return @{project.name}.export(tip="print out the acknowledgments") def credits(self, plexus, **kwds): """ Print out the license and terms of use of the {project.name} package """ # make some space plexus.info.log({project.name}.meta.header) # all done return @{project.name}.export(tip="print out the license and terms of use") def license(self, plexus, **kwds): """ Print out the license and terms of use of the {project.name} package """ # make some space plexus.info.log({project.name}.meta.license) # all done return @{project.name}.export(tip='dump the application configuration namespace') def nfs(self, plexus, **kwds): """ Dump the application configuration namespace """ # get the prefix prefix = self.root or '{project.name}' # show me plexus.pyre_nameserver.dump(prefix) # all done return @{project.name}.export(tip='dump the application private filesystem') def pfs(self, plexus, **kwds): """ Dump the application private filesystem """ # build the report report = '\n'.join(plexus.pfs.dump()) # sign in plexus.info.line('pfs:') # dump plexus.info.log(report) # all done return @{project.name}.export(tip='dump the application virtual filesystem') def vfs(self, plexus, **kwds): """ Dump the application virtual filesystem """ # get the prefix prefix = self.root or '/{project.name}' # build the report report = '\n'.join(plexus.vfs[prefix].dump()) # sign in plexus.info.line('vfs: root={{!r}}'.format(prefix)) # dump plexus.info.log(report) # all done return # end of file

[ "michael.aivazis@orthologue.com" ]

michael.aivazis@orthologue.com

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refs/heads/main

2023-08-25T23:48:26.112133

2021-10-23T14:11:22

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UTF-8

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389

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def test_qz_single(self): n = 5 A = random([n, n]).astype(float32) B = random([n, n]).astype(float32) (AA, BB, Q, Z) = qz(A, B) assert_array_almost_equal(dot(dot(Q, AA), Z.T), A) assert_array_almost_equal(dot(dot(Q, BB), Z.T), B) assert_array_almost_equal(dot(Q, Q.T), eye(n)) assert_array_almost_equal(dot(Z, Z.T), eye(n)) assert_(all((diag(BB) >= 0)))

[ "dg1732004@smail.nju.edu.cn" ]

dg1732004@smail.nju.edu.cn

923ba6a2cc6cf3ac0399432f8258fbbb7ef66708

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/sdk/network/azure-mgmt-network/azure/mgmt/network/v2018_01_01/aio/operations/_express_route_circuit_peerings_operations.py

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permissive

scbedd/azure-sdk-for-python

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2023-09-01T08:38:56.188954

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MIT

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2018-11-28T21:34:49

Python

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21,703

py

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union import warnings from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling from ... import models as _models T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class ExpressRouteCircuitPeeringsOperations: """ExpressRouteCircuitPeeringsOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.network.v2018_01_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config async def _delete_initial( self, resource_group_name: str, circuit_name: str, peering_name: str, **kwargs ) -> None: cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2018-01-01" # Construct URL url = self._delete_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'circuitName': self._serialize.url("circuit_name", circuit_name, 'str'), 'peeringName': self._serialize.url("peering_name", peering_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCircuits/{circuitName}/peerings/{peeringName}'} # type: ignore async def begin_delete( self, resource_group_name: str, circuit_name: str, peering_name: str, **kwargs ) -> AsyncLROPoller[None]: """Deletes the specified peering from the specified express route circuit. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param circuit_name: The name of the express route circuit. :type circuit_name: str :param peering_name: The name of the peering. :type peering_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: Pass in True if you'd like the AsyncARMPolling polling method, False for no polling, or your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either None or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[None] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType[None] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._delete_initial( resource_group_name=resource_group_name, circuit_name=circuit_name, peering_name=peering_name, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'circuitName': self._serialize.url("circuit_name", circuit_name, 'str'), 'peeringName': self._serialize.url("peering_name", peering_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCircuits/{circuitName}/peerings/{peeringName}'} # type: ignore async def get( self, resource_group_name: str, circuit_name: str, peering_name: str, **kwargs ) -> "_models.ExpressRouteCircuitPeering": """Gets the specified authorization from the specified express route circuit. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param circuit_name: The name of the express route circuit. :type circuit_name: str :param peering_name: The name of the peering. :type peering_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: ExpressRouteCircuitPeering, or the result of cls(response) :rtype: ~azure.mgmt.network.v2018_01_01.models.ExpressRouteCircuitPeering :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.ExpressRouteCircuitPeering"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2018-01-01" accept = "application/json, text/json" # Construct URL url = self.get.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'circuitName': self._serialize.url("circuit_name", circuit_name, 'str'), 'peeringName': self._serialize.url("peering_name", peering_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('ExpressRouteCircuitPeering', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCircuits/{circuitName}/peerings/{peeringName}'} # type: ignore async def _create_or_update_initial( self, resource_group_name: str, circuit_name: str, peering_name: str, peering_parameters: "_models.ExpressRouteCircuitPeering", **kwargs ) -> "_models.ExpressRouteCircuitPeering": cls = kwargs.pop('cls', None) # type: ClsType["_models.ExpressRouteCircuitPeering"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2018-01-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json, text/json" # Construct URL url = self._create_or_update_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'circuitName': self._serialize.url("circuit_name", circuit_name, 'str'), 'peeringName': self._serialize.url("peering_name", peering_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(peering_parameters, 'ExpressRouteCircuitPeering') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('ExpressRouteCircuitPeering', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('ExpressRouteCircuitPeering', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCircuits/{circuitName}/peerings/{peeringName}'} # type: ignore async def begin_create_or_update( self, resource_group_name: str, circuit_name: str, peering_name: str, peering_parameters: "_models.ExpressRouteCircuitPeering", **kwargs ) -> AsyncLROPoller["_models.ExpressRouteCircuitPeering"]: """Creates or updates a peering in the specified express route circuits. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param circuit_name: The name of the express route circuit. :type circuit_name: str :param peering_name: The name of the peering. :type peering_name: str :param peering_parameters: Parameters supplied to the create or update express route circuit peering operation. :type peering_parameters: ~azure.mgmt.network.v2018_01_01.models.ExpressRouteCircuitPeering :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: Pass in True if you'd like the AsyncARMPolling polling method, False for no polling, or your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either ExpressRouteCircuitPeering or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2018_01_01.models.ExpressRouteCircuitPeering] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.ExpressRouteCircuitPeering"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._create_or_update_initial( resource_group_name=resource_group_name, circuit_name=circuit_name, peering_name=peering_name, peering_parameters=peering_parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('ExpressRouteCircuitPeering', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'circuitName': self._serialize.url("circuit_name", circuit_name, 'str'), 'peeringName': self._serialize.url("peering_name", peering_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCircuits/{circuitName}/peerings/{peeringName}'} # type: ignore def list( self, resource_group_name: str, circuit_name: str, **kwargs ) -> AsyncIterable["_models.ExpressRouteCircuitPeeringListResult"]: """Gets all peerings in a specified express route circuit. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param circuit_name: The name of the express route circuit. :type circuit_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either ExpressRouteCircuitPeeringListResult or the result of cls(response) :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.network.v2018_01_01.models.ExpressRouteCircuitPeeringListResult] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.ExpressRouteCircuitPeeringListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2018-01-01" accept = "application/json, text/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'circuitName': self._serialize.url("circuit_name", circuit_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request async def extract_data(pipeline_response): deserialized = self._deserialize('ExpressRouteCircuitPeeringListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCircuits/{circuitName}/peerings'} # type: ignore

[ "noreply@github.com" ]

scbedd.noreply@github.com

32d24dea52ce64fed1b3599ceacbc71988bc0232

1310ca784c1b0b9238f2407eb59d0704b8ae5a08

/NextGen/circuitpython/adafruit-circuitpython-bundle-6.x-mpy-20201114/examples/requests_simpletest_cellular.py

6727815ba8df84aa15bd0a164f457469bad79927

[]

no_license

RyannDaGreat/LightWave

6b89838bfd48dba010eb5229b84b206be4e8ccbb

d055b0c01b01b3795d9e6c28b6b70f969893ed97

refs/heads/master

2023-07-20T08:23:47.526629

2023-07-18T00:25:02

123,113,725

1

0

null

UTF-8

Python

false

2,411

py

# pylint: disable=unused-import import time import board import busio import digitalio from adafruit_fona.adafruit_fona import FONA from adafruit_fona.fona_3g import FONA3G import adafruit_fona.adafruit_fona_network as network import adafruit_fona.adafruit_fona_socket as cellular_socket import adafruit_requests as requests # Get GPRS details and more from a secrets.py file try: from secrets import secrets except ImportError: print("GPRS secrets are kept in secrets.py, please add them there!") raise # Create a serial connection for the FONA connection uart = busio.UART(board.TX, board.RX) rst = digitalio.DigitalInOut(board.D4) # Use this for FONA800 and FONA808 fona = FONA(uart, rst) # Use this for FONA3G # fona = FONA3G(uart, rst) # Initialize cellular data network network = network.CELLULAR( fona, (secrets["apn"], secrets["apn_username"], secrets["apn_password"]) ) while not network.is_attached: print("Attaching to network...") time.sleep(0.5) print("Attached!") while not network.is_connected: print("Connecting to network...") network.connect() time.sleep(0.5) print("Network Connected!") # Initialize a requests object with a socket and cellular interface requests.set_socket(cellular_socket, fona) TEXT_URL = "http://wifitest.adafruit.com/testwifi/index.html" JSON_GET_URL = "http://httpbin.org/get" JSON_POST_URL = "http://httpbin.org/post" print("Fetching text from %s" % TEXT_URL) response = requests.get(TEXT_URL) print("-" * 40) print("Text Response: ", response.text) print("-" * 40) response.close() print("Fetching JSON data from %s" % JSON_GET_URL) response = requests.get(JSON_GET_URL) print("-" * 40) print("JSON Response: ", response.json()) print("-" * 40) response.close() data = "31F" print("POSTing data to {0}: {1}".format(JSON_POST_URL, data)) response = requests.post(JSON_POST_URL, data=data) print("-" * 40) json_resp = response.json() # Parse out the 'data' key from json_resp dict. print("Data received from server:", json_resp["data"]) print("-" * 40) response.close() json_data = {"Date": "July 25, 2019"} print("POSTing data to {0}: {1}".format(JSON_POST_URL, json_data)) response = requests.post(JSON_POST_URL, json=json_data) print("-" * 40) json_resp = response.json() # Parse out the 'json' key from json_resp dict. print("JSON Data received from server:", json_resp["json"]) print("-" * 40) response.close()

[ "sqrtryan@gmail.com" ]

sqrtryan@gmail.com

31b20c33f85cf51e5f5a85fc2e154cd5e696c05c

622a4baffb2c1e47aa9f1ac10eedeaf97e16c2a4

/DataFreaksSchool/apps/school/admin.py

0cb291f1c034da2523b31ef46444010cdfe6b23a

[]

no_license

Noeuclides/DataFreaks

81bff087ee813bff4529245a27c09ea5ff6086d8

4739316223e31feffe5a020505727be983001be0

refs/heads/master

2023-04-27T18:06:49.788374

2020-02-05T00:50:36

237,767,894

0

1

null

2023-04-21T20:47:07

2020-02-02T12:28:54

Python

UTF-8

Python

false

269

py

from django.contrib import admin from .models import CustomUser, Student, Teacher, Course, Note # Register your models here. admin.site.register(CustomUser) admin.site.register(Student) admin.site.register(Teacher) admin.site.register(Course) admin.site.register(Note)

[ "euclidesnoeuclides@gmail.com" ]

euclidesnoeuclides@gmail.com

8c5a37c5f4bd04e4bdee20dfee9587c03cbae32c

3e05276c6562bbca2c46daec0bf30d765bb6c8d5

/jobseeker/forms.py

7ce3e3e0bb7a3ade0d8400cacafb8924bcf73ddf

[]

no_license

mrpal39/portflioWebapp

762571a74979ddcd4abf90c8ab8684dcd2afa6fa

898023c0b528557d4ab5ece6c48707f5e61ea296

refs/heads/master

2023-08-07T06:17:28.081727

2021-10-05T16:30:27

414,057,726

0

null

UTF-8

Python

false

2,814

py

from crispy_forms.helper import FormHelper from crispy_forms.layout import Submit from django.db import models from django.db.models import fields from password_reset.forms import PasswordRecoveryForm, PasswordResetForm from django import forms from django.contrib.auth.forms import AuthenticationForm, UserCreationForm from django.utils.translation import ugettext_lazy as _ from .models import Profile from django.db import transaction from django.contrib.auth.forms import UserCreationForm class UserForm(forms.ModelForm): def __init__(self, *args, **kwargs): super(UserForm, self).__init__(*args, **kwargs) self.helper = FormHelper() self.fields['bio'].widget.attrs.update({"class":"form-control"}) self.fields['image'].widget.attrs.update({"class":"image"}) self.fields['email'].widget.attrs.update({"class":"form-control"}) self.fields['phone'].widget.attrs.update({"class":"form-control"}) self.fields['occupation'].widget.attrs.update({"class":"form-control"}) self.fields['social_account'].widget.attrs.update({"class":"form-control"}) self.fields['experence'].widget.attrs.update({"class":"form-control"}) self.fields['skills'].widget.attrs.update({"class":"form-control"}) self.fields['age'].widget.attrs.update({"class":"form-control"}) self.fields['status'].widget.attrs.update({"class":"form-control"}) self.fields['mobile'].widget.attrs.update({"class":"form-control"}) self.fields['gender'].widget.attrs.update({"class":"form-control"}) class ProfilUpdateForm(UserForm): class Meta: model = Profile fields = ( 'bio', 'image', 'email', 'phone', 'social_account', 'experence', 'skills', 'occupation', 'age', 'status', 'mobile', 'gender', ) def __init__(self, *args, **kwargs): super(ProfilUpdateForm, self).__init__(*args, **kwargs) self.helper = FormHelper() self.helper.add_input(Submit("submit", _("Save Changes"))) def save(self): user = super().save(commit=False) user.save() return user class ProfileForm(UserForm): class Meta: model = Profile fields = ( 'bio', 'experence', 'skills', 'age', 'status', 'mobile', 'gender', ) def __init__(self, *args, **kwargs): super(ProfileForm, self).__init__(*args, **kwargs) self.helper = FormHelper() self.helper.add_input(Submit("submit", _("Save Changes"))) def save(self): user = super().save(commit=False) user.save() return user

[ "rp9545416@gmail.com" ]

rp9545416@gmail.com

b321ddea753491e4d4c5b8d334c61a4c303b5cd0

96148bf17555c028f5650d51f496f349c89e8c79

/build/cob_driver/cob_utilities/catkin_generated/pkg.develspace.context.pc.py

a5e228897d169a8b0fdaa67c1507b846b574a730

[]

no_license

kerekare/ros_hydra_libphidgetsupdated

239daed94a95f60743c5659f1102183641761240

e05e58417fb03a14d627bc80d09af3b2a0fcceab

refs/heads/master

2016-09-05T23:35:43.792883

2014-03-25T16:32:01

null

0

null

UTF-8

Python

false

582

py

# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "/home/kerekare/workspace/care-o-bot/src/cob_driver/cob_utilities/common/include".split(';') if "/home/kerekare/workspace/care-o-bot/src/cob_driver/cob_utilities/common/include" != "" else [] PROJECT_CATKIN_DEPENDS = "roscpp".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "-lcob_utilities".split(';') if "-lcob_utilities" != "" else [] PROJECT_NAME = "cob_utilities" PROJECT_SPACE_DIR = "/home/kerekare/workspace/care-o-bot/devel" PROJECT_VERSION = "0.5.0"

[ "kerekare@i60sr2.(none)" ]

kerekare@i60sr2.(none)

986b1f3251a5308712b090a8167d90ed2cdb9b59

1c6283303ceb883add8de4ee07c5ffcfc2e93fab

/Jinja2/lib/python3.7/site-packages/uhd_restpy/testplatform/sessions/ixnetwork/statistics/view/layer23trafficflowdetectivefilter/layer23trafficflowdetectivefilter.py

55ef9c2492cdff0c2a063ba59e7c480e514620c1

[]

no_license

pdobrinskiy/devcore

0f5b3dfc2f3bf1e44abd716f008a01c443e14f18

580c7df6f5db8c118990cf01bc2b986285b9718b

refs/heads/main

2023-07-29T20:28:49.035475

2021-09-14T10:02:16

405,919,390

0

null

UTF-8

Python

false

16,939

py

# MIT LICENSE # # Copyright 1997 - 2020 by IXIA Keysight # # 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 uhd_restpy.base import Base from uhd_restpy.files import Files from typing import List, Any, Union class Layer23TrafficFlowDetectiveFilter(Base): """Filters associated with layer23TrafficFlowDetective view. The Layer23TrafficFlowDetectiveFilter class encapsulates a list of layer23TrafficFlowDetectiveFilter resources that are managed by the user. A list of resources can be retrieved from the server using the Layer23TrafficFlowDetectiveFilter.find() method. The list can be managed by using the Layer23TrafficFlowDetectiveFilter.add() and Layer23TrafficFlowDetectiveFilter.remove() methods. """ __slots__ = () _SDM_NAME = 'layer23TrafficFlowDetectiveFilter' _SDM_ATT_MAP = { 'DeadFlowsCount': 'deadFlowsCount', 'DeadFlowsThreshold': 'deadFlowsThreshold', 'FlowFilterType': 'flowFilterType', 'PortFilterIds': 'portFilterIds', 'ShowEgressFlows': 'showEgressFlows', 'TrafficItemFilterId': 'trafficItemFilterId', 'TrafficItemFilterIds': 'trafficItemFilterIds', } _SDM_ENUM_MAP = { 'flowFilterType': ['allFlows', 'deadFlows', 'liveFlows'], } def __init__(self, parent, list_op=False): super(Layer23TrafficFlowDetectiveFilter, self).__init__(parent, list_op) @property def AllFlowsFilter(self): """ Returns ------- - obj(uhd_restpy.testplatform.sessions.ixnetwork.statistics.view.layer23trafficflowdetectivefilter.allflowsfilter.allflowsfilter.AllFlowsFilter): An instance of the AllFlowsFilter class Raises ------ - ServerError: The server has encountered an uncategorized error condition """ from uhd_restpy.testplatform.sessions.ixnetwork.statistics.view.layer23trafficflowdetectivefilter.allflowsfilter.allflowsfilter import AllFlowsFilter if self._properties.get('AllFlowsFilter', None) is not None: return self._properties.get('AllFlowsFilter') else: return AllFlowsFilter(self) @property def DeadFlowsFilter(self): """ Returns ------- - obj(uhd_restpy.testplatform.sessions.ixnetwork.statistics.view.layer23trafficflowdetectivefilter.deadflowsfilter.deadflowsfilter.DeadFlowsFilter): An instance of the DeadFlowsFilter class Raises ------ - ServerError: The server has encountered an uncategorized error condition """ from uhd_restpy.testplatform.sessions.ixnetwork.statistics.view.layer23trafficflowdetectivefilter.deadflowsfilter.deadflowsfilter import DeadFlowsFilter if self._properties.get('DeadFlowsFilter', None) is not None: return self._properties.get('DeadFlowsFilter') else: return DeadFlowsFilter(self) @property def LiveFlowsFilter(self): """ Returns ------- - obj(uhd_restpy.testplatform.sessions.ixnetwork.statistics.view.layer23trafficflowdetectivefilter.liveflowsfilter.liveflowsfilter.LiveFlowsFilter): An instance of the LiveFlowsFilter class Raises ------ - ServerError: The server has encountered an uncategorized error condition """ from uhd_restpy.testplatform.sessions.ixnetwork.statistics.view.layer23trafficflowdetectivefilter.liveflowsfilter.liveflowsfilter import LiveFlowsFilter if self._properties.get('LiveFlowsFilter', None) is not None: return self._properties.get('LiveFlowsFilter') else: return LiveFlowsFilter(self) @property def StatisticFilter(self): """ Returns ------- - obj(uhd_restpy.testplatform.sessions.ixnetwork.statistics.view.layer23trafficflowdetectivefilter.statisticfilter.statisticfilter.StatisticFilter): An instance of the StatisticFilter class Raises ------ - ServerError: The server has encountered an uncategorized error condition """ from uhd_restpy.testplatform.sessions.ixnetwork.statistics.view.layer23trafficflowdetectivefilter.statisticfilter.statisticfilter import StatisticFilter if self._properties.get('StatisticFilter', None) is not None: return self._properties.get('StatisticFilter') else: return StatisticFilter(self) @property def TrackingFilter(self): """ Returns ------- - obj(uhd_restpy.testplatform.sessions.ixnetwork.statistics.view.layer23trafficflowdetectivefilter.trackingfilter.trackingfilter.TrackingFilter): An instance of the TrackingFilter class Raises ------ - ServerError: The server has encountered an uncategorized error condition """ from uhd_restpy.testplatform.sessions.ixnetwork.statistics.view.layer23trafficflowdetectivefilter.trackingfilter.trackingfilter import TrackingFilter if self._properties.get('TrackingFilter', None) is not None: return self._properties.get('TrackingFilter') else: return TrackingFilter(self) @property def DeadFlowsCount(self): # type: () -> int """ Returns ------- - number: The number of flows declared dead. A flow is declared dead if no traffic is received for a specified number of seconds. To change this threshold use the deadFlowsThreshold attribute. """ return self._get_attribute(self._SDM_ATT_MAP['DeadFlowsCount']) @property def DeadFlowsThreshold(self): # type: () -> int """ Returns ------- - number: Threshold in seconds after which the flows are declared dead if there are no packets received for a specified number of seconds. This is a global attibute and hence the latest value entered takes precedence over previous values in all the custom views. """ return self._get_attribute(self._SDM_ATT_MAP['DeadFlowsThreshold']) @DeadFlowsThreshold.setter def DeadFlowsThreshold(self, value): # type: (int) -> None self._set_attribute(self._SDM_ATT_MAP['DeadFlowsThreshold'], value) @property def FlowFilterType(self): # type: () -> str """ Returns ------- - str(allFlows | deadFlows | liveFlows): Indicates the flow detective filter settings. """ return self._get_attribute(self._SDM_ATT_MAP['FlowFilterType']) @FlowFilterType.setter def FlowFilterType(self, value): # type: (str) -> None self._set_attribute(self._SDM_ATT_MAP['FlowFilterType'], value) @property def PortFilterIds(self): # type: () -> List[str] """ Returns ------- - list(str[None | /api/v1/sessions/1/ixnetwork/statistics/.../availablePortFilter]): Selected port filters from the availablePortFilter list. """ return self._get_attribute(self._SDM_ATT_MAP['PortFilterIds']) @PortFilterIds.setter def PortFilterIds(self, value): # type: (List[str]) -> None self._set_attribute(self._SDM_ATT_MAP['PortFilterIds'], value) @property def ShowEgressFlows(self): # type: () -> bool """ Returns ------- - bool: NOT DEFINED """ return self._get_attribute(self._SDM_ATT_MAP['ShowEgressFlows']) @ShowEgressFlows.setter def ShowEgressFlows(self, value): # type: (bool) -> None self._set_attribute(self._SDM_ATT_MAP['ShowEgressFlows'], value) @property def TrafficItemFilterId(self): # type: () -> str """DEPRECATED Returns ------- - str(None | /api/v1/sessions/1/ixnetwork/statistics/.../availableTrafficItemFilter): Selected traffic flow detective filter from the availableTrafficItemFilter list. """ return self._get_attribute(self._SDM_ATT_MAP['TrafficItemFilterId']) @TrafficItemFilterId.setter def TrafficItemFilterId(self, value): # type: (str) -> None self._set_attribute(self._SDM_ATT_MAP['TrafficItemFilterId'], value) @property def TrafficItemFilterIds(self): # type: () -> List[str] """ Returns ------- - list(str[None | /api/v1/sessions/1/ixnetwork/statistics/.../availableTrafficItemFilter]): Selected traffic item filters from the availableTrafficItemFilter list. """ return self._get_attribute(self._SDM_ATT_MAP['TrafficItemFilterIds']) @TrafficItemFilterIds.setter def TrafficItemFilterIds(self, value): # type: (List[str]) -> None self._set_attribute(self._SDM_ATT_MAP['TrafficItemFilterIds'], value) def update(self, DeadFlowsThreshold=None, FlowFilterType=None, PortFilterIds=None, ShowEgressFlows=None, TrafficItemFilterId=None, TrafficItemFilterIds=None): # type: (int, str, List[str], bool, str, List[str]) -> Layer23TrafficFlowDetectiveFilter """Updates layer23TrafficFlowDetectiveFilter resource on the server. Args ---- - DeadFlowsThreshold (number): Threshold in seconds after which the flows are declared dead if there are no packets received for a specified number of seconds. This is a global attibute and hence the latest value entered takes precedence over previous values in all the custom views. - FlowFilterType (str(allFlows | deadFlows | liveFlows)): Indicates the flow detective filter settings. - PortFilterIds (list(str[None | /api/v1/sessions/1/ixnetwork/statistics/.../availablePortFilter])): Selected port filters from the availablePortFilter list. - ShowEgressFlows (bool): NOT DEFINED - TrafficItemFilterId (str(None | /api/v1/sessions/1/ixnetwork/statistics/.../availableTrafficItemFilter)): Selected traffic flow detective filter from the availableTrafficItemFilter list. - TrafficItemFilterIds (list(str[None | /api/v1/sessions/1/ixnetwork/statistics/.../availableTrafficItemFilter])): Selected traffic item filters from the availableTrafficItemFilter list. Raises ------ - ServerError: The server has encountered an uncategorized error condition """ return self._update(self._map_locals(self._SDM_ATT_MAP, locals())) def add(self, DeadFlowsThreshold=None, FlowFilterType=None, PortFilterIds=None, ShowEgressFlows=None, TrafficItemFilterId=None, TrafficItemFilterIds=None): # type: (int, str, List[str], bool, str, List[str]) -> Layer23TrafficFlowDetectiveFilter """Adds a new layer23TrafficFlowDetectiveFilter resource on the server and adds it to the container. Args ---- - DeadFlowsThreshold (number): Threshold in seconds after which the flows are declared dead if there are no packets received for a specified number of seconds. This is a global attibute and hence the latest value entered takes precedence over previous values in all the custom views. - FlowFilterType (str(allFlows | deadFlows | liveFlows)): Indicates the flow detective filter settings. - PortFilterIds (list(str[None | /api/v1/sessions/1/ixnetwork/statistics/.../availablePortFilter])): Selected port filters from the availablePortFilter list. - ShowEgressFlows (bool): NOT DEFINED - TrafficItemFilterId (str(None | /api/v1/sessions/1/ixnetwork/statistics/.../availableTrafficItemFilter)): Selected traffic flow detective filter from the availableTrafficItemFilter list. - TrafficItemFilterIds (list(str[None | /api/v1/sessions/1/ixnetwork/statistics/.../availableTrafficItemFilter])): Selected traffic item filters from the availableTrafficItemFilter list. Returns ------- - self: This instance with all currently retrieved layer23TrafficFlowDetectiveFilter resources using find and the newly added layer23TrafficFlowDetectiveFilter resources available through an iterator or index Raises ------ - ServerError: The server has encountered an uncategorized error condition """ return self._create(self._map_locals(self._SDM_ATT_MAP, locals())) def remove(self): """Deletes all the contained layer23TrafficFlowDetectiveFilter resources in this instance from the server. Raises ------ - NotFoundError: The requested resource does not exist on the server - ServerError: The server has encountered an uncategorized error condition """ self._delete() def find(self, DeadFlowsCount=None, DeadFlowsThreshold=None, FlowFilterType=None, PortFilterIds=None, ShowEgressFlows=None, TrafficItemFilterId=None, TrafficItemFilterIds=None): # type: (int, int, str, List[str], bool, str, List[str]) -> Layer23TrafficFlowDetectiveFilter """Finds and retrieves layer23TrafficFlowDetectiveFilter resources from the server. All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve layer23TrafficFlowDetectiveFilter resources from the server. To retrieve an exact match ensure the parameter value starts with ^ and ends with $ By default the find method takes no parameters and will retrieve all layer23TrafficFlowDetectiveFilter resources from the server. Args ---- - DeadFlowsCount (number): The number of flows declared dead. A flow is declared dead if no traffic is received for a specified number of seconds. To change this threshold use the deadFlowsThreshold attribute. - DeadFlowsThreshold (number): Threshold in seconds after which the flows are declared dead if there are no packets received for a specified number of seconds. This is a global attibute and hence the latest value entered takes precedence over previous values in all the custom views. - FlowFilterType (str(allFlows | deadFlows | liveFlows)): Indicates the flow detective filter settings. - PortFilterIds (list(str[None | /api/v1/sessions/1/ixnetwork/statistics/.../availablePortFilter])): Selected port filters from the availablePortFilter list. - ShowEgressFlows (bool): NOT DEFINED - TrafficItemFilterId (str(None | /api/v1/sessions/1/ixnetwork/statistics/.../availableTrafficItemFilter)): Selected traffic flow detective filter from the availableTrafficItemFilter list. - TrafficItemFilterIds (list(str[None | /api/v1/sessions/1/ixnetwork/statistics/.../availableTrafficItemFilter])): Selected traffic item filters from the availableTrafficItemFilter list. Returns ------- - self: This instance with matching layer23TrafficFlowDetectiveFilter resources retrieved from the server available through an iterator or index Raises ------ - ServerError: The server has encountered an uncategorized error condition """ return self._select(self._map_locals(self._SDM_ATT_MAP, locals())) def read(self, href): """Retrieves a single instance of layer23TrafficFlowDetectiveFilter data from the server. Args ---- - href (str): An href to the instance to be retrieved Returns ------- - self: This instance with the layer23TrafficFlowDetectiveFilter resources from the server available through an iterator or index Raises ------ - NotFoundError: The requested resource does not exist on the server - ServerError: The server has encountered an uncategorized error condition """ return self._read(href)

[ "pdobrinskiy@yahoo.com" ]

pdobrinskiy@yahoo.com

1519cd3690074f07ddfb744acb91bcd6f0e5a6a8

26e4bea46942b9afa5a00b9cde9a84f2cc58e3c9

/pygame/Astar/pathfinding/Graph_old.py

bb30a06775a80a8af4ce9dfcaed88b9af6e2cc5d

[]

no_license

MeetLuck/works

46da692138cb9741a913d84eff6822f107510dc7

ab61175bb7e2ed5c5113bf150e0541ae18eb04c4

refs/heads/master

2020-04-12T05:40:25.143075

2017-08-21T17:01:06

62,373,576

2

1

null

UTF-8

Python

false

4,690

py

from random import random from colors import * #bgcolor = lightgray grid = [" * ", " *** ", " ", "* ** ", "* "] def findNode(node,lst): return node in lst class Node: def __init__(self): self.adjacent = [] # UP,DOWN,LEFT,RIGHT(NWSE) self.previous = None self.label = "" def clear(self): self.previous = None self.cost = 'Infinity' def __str__(self): return self.label class Graph: def __init__(self,grid): self.rows = len(grid) self.cols = len(grid[0]) self.createLabels() self.createNodes() def createLabels(self): # create some labels self.labels = list() for i in range(65,91): self.labels.append( chr(i) ) # first = ["",'A','B','C'] # for i,val in enumerate(first): # prefix = first[i] # for j in range(65,91): # self.labels.append(prefix + chr(j)) def createNodes(self): # create one node per square in the grid self.nodes = list() for i in range(self.rows*self.cols): node = Node() node.label = self.labels[i] self.nodes.append(node) # add edges to adjacent nodes for r in range(self.rows): for c in range(self.cols): node = self.nodes[self.cols * r + c] # ignore blocked squares if grid[r][c] == '*': continue # figure out the adjacent nodes if r > 0 and grid[r-1][c] == ' ': # UP node.adjacent.append(self.nodes[self.cols*(r-1) + c]) if r < self.rows-1 and grid[r+1][c] == ' ': # DOWN node.adjacent.append(self.nodes[self.cols*(r+1) + c]) if c > 0 and grid[r][c-1] == ' ': # LEFT node.adjacent.append(self.nodes[self.cols*r + c-1]) if c < self.cols-1 and grid[r][c+1] == ' ': # RIGHT node.adjacent.append(self.nodes[self.cols*r + c+1]) def findNodeByLabel(self,label): for i,val in enumerate(self.nodes): if self.nodes[i].label == label: return val # Search class Search: def __init__(self, graph,start,goal): self.graph = graph self.reachable = list() self.explored = list() self.path = list() self.start_label = start self.goal_label = goal def reset(self): self.reachable = [ self.graph.findNodeByLabel(self.start_label) ] self.goal = self.graph.findNodeByLabel(self.goal_label) self.explored = list() self.path = list() self.iteration = 0 for i,node in enumerate(self.graph.nodes): self.graph.nodes[i].clear() #self.reachable[0].cost = 0 #self.render() def step(self): if len(self.path) > 0: # is the search already done ? return # if there are no more nodes to consider, we're done if len(self.reachable) == 0: self.finished = True return self.iteration += 1 # choose a node to examine next node = self.chooseNode() # are we done yet? if node== self.goal: while node: self.path.append(node) node = node.previous print '------------- find path ----------------' self.render() return # do not repeat self.reachable.remove(node) self.explored.append(node) # where can we get from here? # self.render() # if node is None: return for adjnode in node.adjacent: self.addAdjacent(node,adjnode) self.render() def chooseNode(self): return self.reachable[ int(random()* len(self.reachable)) ] def addAdjacent(self,node,adjacent): if findNode(adjacent,self.explored) or findNode(adjacent,self.reachable): return adjacent.previous = node self.reachable.append(adjacent) def render(self): print '================== render ==============' print 'reachable ==>' for rnode in self.reachable: print rnode.label, print print 'explored ==>' for enode in self.explored: print enode.label, print print 'path ==>' print self.path if __name__ == '__main__': g = Graph(grid) search = Search(g,'A','T') search.reset() search.render() for i in range(40): search.step() # search.step() # print g.labels # for node in g.nodes: # print node

[ "withpig1994@hanmail.net" ]

withpig1994@hanmail.net

67473f9f435beec220fa067cf392d561ef7b110b

d8a766184f7d2e4379a9578b6bd01451f4434fd8

/waynes_world/server.py

a8b013a456acdc8013c43b1f65b05507c9a6c9c7

[]

no_license

YaoQ/zmq_examples

1e9c386f3d8b51e04208bcededb8f64938a5200e

631867073b79087c4bf94dff7ff3c57c113fc9a1

refs/heads/master

2020-11-30T13:03:08.111975

2011-06-03T21:16:59

null

0

null

UTF-8

Python

false

339

py

#!/usr/bin/env python import zmq import time ctx = zmq.Context() # one socket s1 = ctx.socket(zmq.PUB) s1.bind("tcp://127.0.0.1:5566") # another s2 = ctx.socket(zmq.PUB) s2.bind("ipc://*:5567") while True: print 'Camera 1?' s1.send("Camera 1") time.sleep(1) print 'Camera 2?' s2.send("Camera 2") time.sleep(1)

[ "jd@j2labs.net" ]

jd@j2labs.net

dce067bc85e5dd7a18bcdd69eac1544a142aeea7

bc441bb06b8948288f110af63feda4e798f30225

/micro_app_sdk/model/cmdb/import_result_pb2.py

8d037a5e63e6e6059b267b9e37c5d661f6e72b1b

[ "Apache-2.0" ]

permissive

easyopsapis/easyops-api-python

23204f8846a332c30f5f3ff627bf220940137b6b

adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0

refs/heads/master

2020-06-26T23:38:27.308803

2020-06-16T07:25:41

199,773,131

5

0

null

UTF-8

Python

false

true

11,538

py

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: import_result.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from micro_app_sdk.model.cmdb import import_status_pb2 as micro__app__sdk_dot_model_dot_cmdb_dot_import__status__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='import_result.proto', package='cmdb', syntax='proto3', serialized_options=_b('Z>go.easyops.local/contracts/protorepo-models/easyops/model/cmdb'), serialized_pb=_b('\n\x13import_result.proto\x12\x04\x63mdb\x1a,micro_app_sdk/model/cmdb/import_status.proto\"\x83\x04\n\x0cImportResult\x12\x10\n\x08objectId\x18\x01 \x01(\t\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x10\n\x08\x63\x61tegory\x18\x03 \x01(\t\x12\x0c\n\x04memo\x18\x04 \x01(\t\x12\x11\n\tprotected\x18\x05 \x01(\x08\x12\x0e\n\x06system\x18\x06 \x01(\t\x12\x0c\n\x04\x63ode\x18\x07 \x01(\x05\x12\x0f\n\x07message\x18\x08 \x01(\t\x12\'\n\x0binfo_result\x18\t \x03(\x0b\x32\x12.cmdb.ImportStatus\x12\x31\n\x15relation_group_result\x18\n \x03(\x0b\x32\x12.cmdb.ImportStatus\x12,\n\x10\x61ttr_list_result\x18\x0b \x03(\x0b\x32\x12.cmdb.ImportStatus\x12\x30\n\x14relation_list_result\x18\x0c \x03(\x0b\x32\x12.cmdb.ImportStatus\x12=\n\x11index_list_result\x18\r \x03(\x0b\x32\".cmdb.ImportResult.IndexListResult\x12\x11\n\tis_create\x18\x0e \x01(\x08\x1a\x63\n\x0fIndexListResult\x12\x0c\n\x04\x63ode\x18\x01 \x01(\x05\x12\x0f\n\x07message\x18\x02 \x01(\t\x12\x0c\n\x04name\x18\x03 \x01(\t\x12\x13\n\x0bpropertyIds\x18\x04 \x03(\t\x12\x0e\n\x06unique\x18\x05 \x01(\x08\x42@Z>go.easyops.local/contracts/protorepo-models/easyops/model/cmdbb\x06proto3') , dependencies=[micro__app__sdk_dot_model_dot_cmdb_dot_import__status__pb2.DESCRIPTOR,]) _IMPORTRESULT_INDEXLISTRESULT = _descriptor.Descriptor( name='IndexListResult', full_name='cmdb.ImportResult.IndexListResult', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='code', full_name='cmdb.ImportResult.IndexListResult.code', index=0, number=1, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='message', full_name='cmdb.ImportResult.IndexListResult.message', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='name', full_name='cmdb.ImportResult.IndexListResult.name', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='propertyIds', full_name='cmdb.ImportResult.IndexListResult.propertyIds', index=3, number=4, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='unique', full_name='cmdb.ImportResult.IndexListResult.unique', index=4, number=5, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=492, serialized_end=591, ) _IMPORTRESULT = _descriptor.Descriptor( name='ImportResult', full_name='cmdb.ImportResult', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='objectId', full_name='cmdb.ImportResult.objectId', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='name', full_name='cmdb.ImportResult.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='category', full_name='cmdb.ImportResult.category', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='memo', full_name='cmdb.ImportResult.memo', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='protected', full_name='cmdb.ImportResult.protected', index=4, number=5, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='system', full_name='cmdb.ImportResult.system', index=5, number=6, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='code', full_name='cmdb.ImportResult.code', index=6, number=7, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='message', full_name='cmdb.ImportResult.message', index=7, number=8, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='info_result', full_name='cmdb.ImportResult.info_result', index=8, number=9, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='relation_group_result', full_name='cmdb.ImportResult.relation_group_result', index=9, number=10, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='attr_list_result', full_name='cmdb.ImportResult.attr_list_result', index=10, number=11, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='relation_list_result', full_name='cmdb.ImportResult.relation_list_result', index=11, number=12, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='index_list_result', full_name='cmdb.ImportResult.index_list_result', index=12, number=13, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='is_create', full_name='cmdb.ImportResult.is_create', index=13, number=14, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_IMPORTRESULT_INDEXLISTRESULT, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=76, serialized_end=591, ) _IMPORTRESULT_INDEXLISTRESULT.containing_type = _IMPORTRESULT _IMPORTRESULT.fields_by_name['info_result'].message_type = micro__app__sdk_dot_model_dot_cmdb_dot_import__status__pb2._IMPORTSTATUS _IMPORTRESULT.fields_by_name['relation_group_result'].message_type = micro__app__sdk_dot_model_dot_cmdb_dot_import__status__pb2._IMPORTSTATUS _IMPORTRESULT.fields_by_name['attr_list_result'].message_type = micro__app__sdk_dot_model_dot_cmdb_dot_import__status__pb2._IMPORTSTATUS _IMPORTRESULT.fields_by_name['relation_list_result'].message_type = micro__app__sdk_dot_model_dot_cmdb_dot_import__status__pb2._IMPORTSTATUS _IMPORTRESULT.fields_by_name['index_list_result'].message_type = _IMPORTRESULT_INDEXLISTRESULT DESCRIPTOR.message_types_by_name['ImportResult'] = _IMPORTRESULT _sym_db.RegisterFileDescriptor(DESCRIPTOR) ImportResult = _reflection.GeneratedProtocolMessageType('ImportResult', (_message.Message,), { 'IndexListResult' : _reflection.GeneratedProtocolMessageType('IndexListResult', (_message.Message,), { 'DESCRIPTOR' : _IMPORTRESULT_INDEXLISTRESULT, '__module__' : 'import_result_pb2' # @@protoc_insertion_point(class_scope:cmdb.ImportResult.IndexListResult) }) , 'DESCRIPTOR' : _IMPORTRESULT, '__module__' : 'import_result_pb2' # @@protoc_insertion_point(class_scope:cmdb.ImportResult) }) _sym_db.RegisterMessage(ImportResult) _sym_db.RegisterMessage(ImportResult.IndexListResult) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)

[ "service@easyops.cn" ]

service@easyops.cn

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/aliyun-python-sdk-vs/aliyunsdkvs/request/v20181212/DescribeAccountStatRequest.py

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aliyun/aliyun-openapi-python-sdk

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2023-08-04T12:32:57.028821

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2023-09-14T08:51:06

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# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # # http://www.apache.org/licenses/LICENSE-2.0 # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest from aliyunsdkvs.endpoint import endpoint_data class DescribeAccountStatRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'vs', '2018-12-12', 'DescribeAccountStat') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_Id(self): return self.get_query_params().get('Id') def set_Id(self,Id): self.add_query_param('Id',Id) def get_OwnerId(self): return self.get_query_params().get('OwnerId') def set_OwnerId(self,OwnerId): self.add_query_param('OwnerId',OwnerId)

[ "sdk-team@alibabacloud.com" ]

sdk-team@alibabacloud.com

1dc9d34098975e3ff9b1f7711301a6527338634f

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/backend/mcgill_cc_22904/settings.py

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[]

no_license

crowdbotics-apps/mcgill-cc-22904

ce47ef45ccc84bedb11e869d6119c77dcc98eda7

d24767a048d901d534f9006e2bcc8b3a0bde1495

refs/heads/master

2023-01-15T22:20:41.446718

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""" Django settings for mcgill_cc_22904 project. Generated by 'django-admin startproject' using Django 2.2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os import environ import logging env = environ.Env() # SECURITY WARNING: don't run with debug turned on in production! DEBUG = env.bool("DEBUG", default=False) # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = env.str("SECRET_KEY") ALLOWED_HOSTS = env.list("HOST", default=["*"]) SITE_ID = 1 SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https") SECURE_SSL_REDIRECT = env.bool("SECURE_REDIRECT", default=False) # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.sites' ] LOCAL_APPS = [ 'home', 'users.apps.UsersConfig', ] THIRD_PARTY_APPS = [ 'rest_framework', 'rest_framework.authtoken', 'rest_auth', 'rest_auth.registration', 'bootstrap4', 'allauth', 'allauth.account', 'allauth.socialaccount', 'allauth.socialaccount.providers.google', 'django_extensions', 'drf_yasg', 'storages', # start fcm_django push notifications 'fcm_django', # end fcm_django push notifications ] INSTALLED_APPS += LOCAL_APPS + THIRD_PARTY_APPS MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mcgill_cc_22904.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mcgill_cc_22904.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } if env.str("DATABASE_URL", default=None): DATABASES = { 'default': env.db() } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' MIDDLEWARE += ['whitenoise.middleware.WhiteNoiseMiddleware'] AUTHENTICATION_BACKENDS = ( 'django.contrib.auth.backends.ModelBackend', 'allauth.account.auth_backends.AuthenticationBackend' ) STATIC_ROOT = os.path.join(BASE_DIR, "staticfiles") STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static')] STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage' # allauth / users ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_AUTHENTICATION_METHOD = 'email' ACCOUNT_USERNAME_REQUIRED = False ACCOUNT_EMAIL_VERIFICATION = "optional" ACCOUNT_CONFIRM_EMAIL_ON_GET = True ACCOUNT_LOGIN_ON_EMAIL_CONFIRMATION = True ACCOUNT_UNIQUE_EMAIL = True LOGIN_REDIRECT_URL = "users:redirect" ACCOUNT_ADAPTER = "users.adapters.AccountAdapter" SOCIALACCOUNT_ADAPTER = "users.adapters.SocialAccountAdapter" ACCOUNT_ALLOW_REGISTRATION = env.bool("ACCOUNT_ALLOW_REGISTRATION", True) SOCIALACCOUNT_ALLOW_REGISTRATION = env.bool("SOCIALACCOUNT_ALLOW_REGISTRATION", True) REST_AUTH_SERIALIZERS = { # Replace password reset serializer to fix 500 error "PASSWORD_RESET_SERIALIZER": "home.api.v1.serializers.PasswordSerializer", } REST_AUTH_REGISTER_SERIALIZERS = { # Use custom serializer that has no username and matches web signup "REGISTER_SERIALIZER": "home.api.v1.serializers.SignupSerializer", } # Custom user model AUTH_USER_MODEL = "users.User" EMAIL_HOST = env.str("EMAIL_HOST", "smtp.sendgrid.net") EMAIL_HOST_USER = env.str("SENDGRID_USERNAME", "") EMAIL_HOST_PASSWORD = env.str("SENDGRID_PASSWORD", "") EMAIL_PORT = 587 EMAIL_USE_TLS = True # AWS S3 config AWS_ACCESS_KEY_ID = env.str("AWS_ACCESS_KEY_ID", "") AWS_SECRET_ACCESS_KEY = env.str("AWS_SECRET_ACCESS_KEY", "") AWS_STORAGE_BUCKET_NAME = env.str("AWS_STORAGE_BUCKET_NAME", "") AWS_STORAGE_REGION = env.str("AWS_STORAGE_REGION", "") USE_S3 = ( AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY and AWS_STORAGE_BUCKET_NAME and AWS_STORAGE_REGION ) if USE_S3: AWS_S3_CUSTOM_DOMAIN = env.str("AWS_S3_CUSTOM_DOMAIN", "") AWS_S3_OBJECT_PARAMETERS = {"CacheControl": "max-age=86400"} AWS_DEFAULT_ACL = env.str("AWS_DEFAULT_ACL", "public-read") AWS_MEDIA_LOCATION = env.str("AWS_MEDIA_LOCATION", "media") AWS_AUTO_CREATE_BUCKET = env.bool("AWS_AUTO_CREATE_BUCKET", True) DEFAULT_FILE_STORAGE = env.str( "DEFAULT_FILE_STORAGE", "home.storage_backends.MediaStorage" ) MEDIA_URL = '/mediafiles/' MEDIA_ROOT = os.path.join(BASE_DIR, 'mediafiles') # start fcm_django push notifications FCM_DJANGO_SETTINGS = { "FCM_SERVER_KEY": env.str("FCM_SERVER_KEY", "") } # end fcm_django push notifications # Swagger settings for api docs SWAGGER_SETTINGS = { "DEFAULT_INFO": f"{ROOT_URLCONF}.api_info", } if DEBUG or not (EMAIL_HOST_USER and EMAIL_HOST_PASSWORD): # output email to console instead of sending if not DEBUG: logging.warning("You should setup `SENDGRID_USERNAME` and `SENDGRID_PASSWORD` env vars to send emails.") EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend"

[ "team@crowdbotics.com" ]

team@crowdbotics.com

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/MIDI Remote Scripts/ableton/v2/control_surface/components/clip_slot.py

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[ "MIT" ]

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aarkwright/ableton_devices

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refs/heads/master

2020-07-02T08:11:21.137438

2019-08-09T13:48:06

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# uncompyle6 version 3.3.5 # Python bytecode 2.7 (62211) # Decompiled from: Python 3.7.3 (default, Apr 24 2019, 15:29:51) [MSC v.1915 64 bit (AMD64)] # Embedded file name: c:\Jenkins\live\output\win_64_static\Release\python-bundle\MIDI Remote Scripts\ableton\v2\control_surface\components\clip_slot.py # Compiled at: 2019-02-18 13:43:58 from __future__ import absolute_import, print_function, unicode_literals import Live from ...base import listens, liveobj_valid from ..component import Component from ..control import ButtonControl def find_nearest_color(rgb_table, src_hex_color): def hex_to_channels(color_in_hex): return ( (color_in_hex & 16711680) >> 16, (color_in_hex & 65280) >> 8, color_in_hex & 255) def squared_distance(color): return sum([ (a - b) ** 2 for a, b in zip(hex_to_channels(src_hex_color), hex_to_channels(color[1])) ]) return min(rgb_table, key=squared_distance)[0] def is_button_pressed(button): if button: return button.is_pressed() return False class ClipSlotComponent(Component): """ Component representing a ClipSlot within Live. """ launch_button = ButtonControl() def __init__(self, *a, **k): super(ClipSlotComponent, self).__init__(*a, **k) self._clip_slot = None self._triggered_to_play_color = b'Session.ClipTriggeredPlay' self._triggered_to_record_color = b'Session.ClipTriggeredRecord' self._started_value = b'Session.ClipStarted' self._recording_color = b'Session.ClipRecording' self._stopped_value = b'Session.ClipStopped' self._clip_palette = [] self._clip_rgb_table = None self._record_button_color = b'Session.RecordButton' self._empty_slot_color = b'Session.ClipEmpty' self._delete_button = None self._select_button = None self._duplicate_button = None return def set_clip_slot(self, clip_slot): self._clip_slot = clip_slot self._update_clip_property_slots() self.__on_slot_triggered_changed.subject = clip_slot self.__on_slot_playing_state_changed.subject = clip_slot self.__on_clip_state_changed.subject = clip_slot self.__on_controls_other_clips_changed.subject = clip_slot self.__on_has_stop_button_changed.subject = clip_slot self.__on_clip_slot_color_changed.subject = clip_slot track = clip_slot.canonical_parent if clip_slot else None if track and track.can_be_armed: self.__on_arm_value_changed.subject = track self.__on_implicit_arm_value_changed.subject = track self.update() return def set_launch_button(self, button): self.launch_button.set_control_element(button) self.update() def set_delete_button(self, button): self._delete_button = button def set_select_button(self, button): self._select_button = button def set_duplicate_button(self, button): self._duplicate_button = button def set_clip_palette(self, palette): assert palette != None self._clip_palette = palette return def set_clip_rgb_table(self, rgb_table): """ A list of velocity, hex-rgb color pairs that is used, if the color could not be matched to the clip palette """ self._clip_rgb_table = rgb_table def has_clip(self): assert liveobj_valid(self._clip_slot) return self._clip_slot.has_clip def update(self): super(ClipSlotComponent, self).update() self._update_launch_button_color() def _update_launch_button_color(self): if self.is_enabled(): value_to_send = self._empty_slot_color if liveobj_valid(self._clip_slot): track = self._clip_slot.canonical_parent slot_or_clip = self._clip_slot.clip if self.has_clip() else self._clip_slot value_to_send = self._feedback_value(track, slot_or_clip) self.launch_button.color = value_to_send def _color_value(self, slot_or_clip): color = slot_or_clip.color try: return self._clip_palette[color] except (KeyError, IndexError): if self._clip_rgb_table != None: return find_nearest_color(self._clip_rgb_table, color) else: return self._stopped_value return def _track_is_armed(self, track): return liveobj_valid(track) and track.can_be_armed and any([track.arm, track.implicit_arm]) def _feedback_value(self, track, slot_or_clip): if slot_or_clip.is_triggered: if slot_or_clip.will_record_on_start: return self._triggered_to_record_color return self._triggered_to_play_color else: if slot_or_clip.is_playing: if slot_or_clip.is_recording: return self._recording_color return self._started_value if slot_or_clip.color != None: return self._color_value(slot_or_clip) if getattr(slot_or_clip, b'controls_other_clips', True): return self._stopped_value if self._track_is_armed(track) and self._clip_slot.has_stop_button and self._record_button_color != None: return self._record_button_color return self._empty_slot_color def _update_clip_property_slots(self): clip = self._clip_slot.clip if self._clip_slot else None self.__on_clip_playing_state_changed.subject = clip self.__on_recording_state_changed.subject = clip self.__on_clip_color_changed.subject = clip return @listens(b'has_clip') def __on_clip_state_changed(self): self._update_clip_property_slots() self._update_launch_button_color() @listens(b'controls_other_clips') def __on_controls_other_clips_changed(self): self._update_clip_property_slots() self._update_launch_button_color() @listens(b'color') def __on_clip_color_changed(self): self._update_launch_button_color() @listens(b'color') def __on_clip_slot_color_changed(self): self._update_launch_button_color() @listens(b'playing_status') def __on_slot_playing_state_changed(self): self._update_launch_button_color() @listens(b'playing_status') def __on_clip_playing_state_changed(self): self._update_launch_button_color() @listens(b'is_recording') def __on_recording_state_changed(self): self._update_launch_button_color() @listens(b'arm') def __on_arm_value_changed(self): self._update_launch_button_color() @listens(b'implicit_arm') def __on_implicit_arm_value_changed(self): self._update_launch_button_color() @listens(b'has_stop_button') def __on_has_stop_button_changed(self): self._update_launch_button_color() @listens(b'is_triggered') def __on_slot_triggered_changed(self): if not self.has_clip(): self._update_launch_button_color() @launch_button.pressed def launch_button(self, button): self._on_launch_button_pressed() def _on_launch_button_pressed(self): if is_button_pressed(self._select_button): self._do_select_clip(self._clip_slot) elif liveobj_valid(self._clip_slot): if is_button_pressed(self._duplicate_button): self._do_duplicate_clip() elif is_button_pressed(self._delete_button): self._do_delete_clip() else: self._do_launch_clip(True) self._show_launched_clip_as_highlighted_clip() @launch_button.released def launch_button(self, button): self._on_launch_button_released() def _on_launch_button_released(self): if self.launch_button.is_momentary and not is_button_pressed(self._select_button) and liveobj_valid(self._clip_slot) and not is_button_pressed(self._duplicate_button) and not is_button_pressed(self._delete_button): self._do_launch_clip(False) def _do_delete_clip(self): if self._clip_slot and self._clip_slot.has_clip: self._clip_slot.delete_clip() def _do_select_clip(self, clip_slot): if liveobj_valid(self._clip_slot): if self.song.view.highlighted_clip_slot != self._clip_slot: self.song.view.highlighted_clip_slot = self._clip_slot def _do_duplicate_clip(self): if self._clip_slot and self._clip_slot.has_clip: try: track = self._clip_slot.canonical_parent track.duplicate_clip_slot(list(track.clip_slots).index(self._clip_slot)) except Live.Base.LimitationError: pass except RuntimeError: pass def _do_launch_clip(self, fire_state): object_to_launch = self._clip_slot if self.has_clip(): object_to_launch = self._clip_slot.clip object_to_launch.set_fire_button_state(fire_state) def _show_launched_clip_as_highlighted_clip(self): song = self.song if song.select_on_launch and self._clip_slot != song.view.highlighted_clip_slot: self.song.view.highlighted_clip_slot = self._clip_slot

[ "apollo.arkwright@gmail.com" ]

apollo.arkwright@gmail.com

dcf370a1bd6932ff3c4ff9bb217104dc2ff6961a

2fc849ee16732463779d4445954941538828879a

/source/webapp/migrations/0001_initial.py

04cf199a27d95189a00f4255ac850a68e5143e58

[]

no_license

Aisuluu1405/python_group_3_homework_48_Aisulu_Dzhusupova

83da037fc9200a1a213fdf0eb5b09ed2febae79a

d4272516816fb83ff4d8a1c64645a508aecf37ee

refs/heads/master

2023-05-02T16:13:24.948710

2019-09-23T12:30:14

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2023-04-21T20:38:01

2019-09-23T10:19:38

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# Generated by Django 2.2 on 2019-09-20 05:09 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Product', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100, verbose_name='Product name')), ('description', models.TextField(blank=True, max_length=2000, null=True, verbose_name='Description')), ('category', models.CharField(choices=[('other', 'Other'), ('clothes', 'Clothes'), ('shoes', 'Shoes'), ('accessories', 'Accessories'), ('beauty', 'Beauty')], default='other', max_length=30, verbose_name='Category')), ('count', models.FloatField(verbose_name='Count')), ('price', models.DecimalField(decimal_places=2, max_digits=7, verbose_name='Price')), ], ), ]

[ "aisuluueco2009@yandex.ru" ]

aisuluueco2009@yandex.ru

e432d89a1b17d0c1572e3c78718079a42a30ce0d

bc233c24523f05708dd1e091dca817f9095e6bb5

/bitmovin_api_sdk/encoding/encodings/muxings/packed_audio/customdata/customdata_api.py

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[ "MIT" ]

permissive

bitmovin/bitmovin-api-sdk-python

e3d6cf8eb8bdad62cb83ec77c0fc4950b06b9cdd

b0860c0b1be7747cf22ad060985504da625255eb

refs/heads/main

2023-09-01T15:41:03.628720

2023-08-30T10:52:13

175,209,828

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14

MIT

2021-04-29T12:30:31

2019-03-12T12:47:18

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# coding: utf-8 from __future__ import absolute_import from bitmovin_api_sdk.common import BaseApi, BitmovinApiLoggerBase from bitmovin_api_sdk.common.poscheck import poscheck_except from bitmovin_api_sdk.models.custom_data import CustomData from bitmovin_api_sdk.models.response_envelope import ResponseEnvelope from bitmovin_api_sdk.models.response_error import ResponseError class CustomdataApi(BaseApi): @poscheck_except(2) def __init__(self, api_key, tenant_org_id=None, base_url=None, logger=None): # type: (str, str, str, BitmovinApiLoggerBase) -> None super(CustomdataApi, self).__init__( api_key=api_key, tenant_org_id=tenant_org_id, base_url=base_url, logger=logger ) def get(self, encoding_id, muxing_id, **kwargs): # type: (string_types, string_types, dict) -> CustomData """Packed Audio muxing Custom Data :param encoding_id: Id of the encoding. :type encoding_id: string_types, required :param muxing_id: Id of the Packed Audio muxing :type muxing_id: string_types, required :return: Packed Audio muxing custom data :rtype: CustomData """ return self.api_client.get( '/encoding/encodings/{encoding_id}/muxings/packed-audio/{muxing_id}/customData', path_params={'encoding_id': encoding_id, 'muxing_id': muxing_id}, type=CustomData, **kwargs )

[ "openapi@bitmovin.com" ]

openapi@bitmovin.com

f9552e5fb9ea367cd1fb32326b63cd871d695afb

2b4668ba8ff74aa03d031786956c4d4802bfe02b

/util/samm_resolver.py

5b5a982d47eede73c9a4c109120b5b22b97f4f1d

[ "BSD-3-Clause" ]

permissive

ioggstream/dsomm-orm

a8397ab6f73d46a0acfb8928ad7e835bef9b759a

52c2040b1cb7263d568af548ab18acdcc3700292

refs/heads/main

2023-07-12T21:52:52.988267

2021-08-13T22:56:30

360,102,120

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# # Parses yaml files contained in https://github.com/OWASP/samm/tree/master/Supporting%20Resources/v2.0/Datamodel/Datafiles # from pathlib import Path import yaml def read_yaml(f): return yaml.safe_load(Path(f).read_text()) class SammResolver: def __init__(self, basepath="."): self.basepath = Path(basepath) self.functions = [ f.name[:-4].split(" ")[1] for f in self.basepath.glob("Function *yml") ] self.functions_map = {x[0]: x for x in self.functions} self.practices_map = self._parse_practices() self.streams = self._parse_streams() self.activities = list( set([f.name[:-4].split(" ")[1] for f in self.basepath.glob("Activit*.yml")]) ) def _parse_practices(self): practices = {} for f in self.basepath.glob("Practice *yml"): p = read_yaml(f) if "shortDescription" not in p: continue practices[p["shortName"]] = p["name"] return practices def _parse_streams(self): streams = {} for f in self.basepath.glob("Stream *yml"): s = read_yaml(f) s_id = f.name[7:-4] s_name = s["name"] streams[s_id] = s_name return streams def parse_activity(self, a): function, practice, maturity, stream = a.split("-") stream_id = f"{function}-{practice}-{stream}" return { "id": a, "function": self.functions_map[function], "practice": self.practices_map[practice], "maturity": maturity, "stream": self.streams[stream_id], } def test_parse_activities(): fpath = "downloads/Datafiles" samm = SammResolver(fpath) for a in samm.activities: print(samm.parse_activity(a)) def test_samm_to_csv(): fpath = "downloads/Datafiles" samm = SammResolver(fpath) import pandas as pd df = pd.DataFrame([samm.parse_activity(a) for a in samm.activities]) df.to_csv("samm_activities.csv") from sqlalchemy import create_engine engine = create_engine("mysql+mysqlconnector://root:root@127.0.0.1/dsomm") df.to_sql("samm", con=engine)

[ "robipolli@gmail.com" ]

robipolli@gmail.com

dca2aacac1572c1ca6111998fa21040bca5af015

bfc25f1ad7bfe061b57cfab82aba9d0af1453491

/data/external/repositories_2to3/208513/kaggle-liberty-hazard-prediction-master/models/neighbors.py

72c4ced2d31ec580b44e987575d068641d85f2de

[ "MIT" ]

permissive

Keesiu/meta-kaggle

77d134620ebce530d183467202cf45639d9c6ff2

87de739aba2399fd31072ee81b391f9b7a63f540

refs/heads/master

2020-03-28T00:23:10.584151

2018-12-20T19:09:50

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# import pandas as pd # import numpy as np # import pickle # from sklearn import preprocessing # from sklearn.cross_validation import train_test_split # from sklearn.grid_search import RandomizedSearchCV # from sklearn.neighbors import KNeighborsRegressor # from sklearn.neighbors import KNeighborsClassifier # ################################################################################## # # cal metric # def gini(solution, submission): # df = zip(solution, submission, range(len(solution))) # df = sorted(df, key=lambda x: (x[1],-x[2]), reverse=True) # rand = [float(i+1)/float(len(df)) for i in range(len(df))] # totalPos = float(sum([x[0] for x in df])) # cumPosFound = [df[0][0]] # for i in range(1,len(df)): # cumPosFound.append(cumPosFound[len(cumPosFound)-1] + df[i][0]) # Lorentz = [float(x)/totalPos for x in cumPosFound] # Gini = [Lorentz[i]-rand[i] for i in range(len(df))] # return sum(Gini) # def normalized_gini(solution, submission): # normalized_gini = gini(solution, submission)/gini(solution, solution) # return normalized_gini # ################################################################################## # #load train and test # train = pd.read_csv('./data/train.csv', index_col=0) # test = pd.read_csv('./data/test.csv', index_col=0) # train_y = train.Hazard # # drop train_y -> train_y # train.drop('Hazard', axis=1, inplace=True) # # drop noisy features # train.drop('T2_V10', axis=1, inplace=True) # train.drop('T2_V7', axis=1, inplace=True) # train.drop('T1_V13', axis=1, inplace=True) # train.drop('T1_V10', axis=1, inplace=True) # test.drop('T2_V10', axis=1, inplace=True) # test.drop('T2_V7', axis=1, inplace=True) # test.drop('T1_V13', axis=1, inplace=True) # test.drop('T1_V10', axis=1, inplace=True) # # columns and index for later use # columns = train.columns # test_ind = test.index # train = np.array(train) # test = np.array(test) # # label encode the categorical variables # for i in range(train.shape[1]): # lbl = preprocessing.LabelEncoder() # lbl.fit(list(train[:,i]) + list(test[:,i])) # train[:,i] = lbl.transform(train[:,i]) # test[:,i] = lbl.transform(test[:,i]) # train = train.astype(np.float32) # test = test.astype(np.float32) # ################################################################################## # with open('./data/train_denoise.vec', 'rb') as f: # train = pickle.load(f) # with open('./data/test_denoise.vec', 'rb') as f: # test = pickle.load(f) # with open('./data/train_y.vec', 'rb') as f: # train_y = pickle.load(f) # train_x_sp, test_x_sp, train_y_sp, test_y_sp = train_test_split(train, train_y, train_size=0.8, random_state=50) # rgrs = KNeighborsRegressor(n_neighbors =100) # rgrs.fit(train_x_sp, train_y_sp) # pred = rgrs.predict(test_x_sp) # score = normalized_gini(test_y_sp, pred) # print '{:.6f}'.format(score)

[ "keesiu.wong@gmail.com" ]

keesiu.wong@gmail.com

a37cdc5d8eead8699cb0d2c774558b30f8df8ebc

a1a43879a2da109d9fe8d9a75f4fda73f0d7166b

/api/json/select_configs.py

6bc4e323b127bc99231a8b7ccf6570f228d5d613

[]

no_license

PaddlePaddle/benchmark

a3ed62841598d079529c7440367385fc883835aa

f0e0a303e9af29abb2e86e8918c102b152a37883

refs/heads/master

2023-09-01T13:11:09.892877

2023-08-21T09:32:49

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null

2023-09-14T05:13:08

2019-02-28T03:14:16

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import warnings from operator import mul from functools import reduce import random import copy import os, sys package_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(package_path) from common.api_param import parse_list def select_configs_by_json(args, origin_configs, configs_without_input, all_shapes, input_type): """ Select configs according to json config and save the selected configs to the sepcified json file. """ ignored_params = [] if args.ignored_params: ignored_params += args.ignored_params all_config_info = [] for config in configs_without_input: config_info = "" for param in config["param_info"]: if param not in ignored_params: value = config["param_info"][param]["value"] config_info += param + "=" + value + " " all_config_info.append(config_info) config_groups = grouping_configs(all_config_info, all_shapes, input_type) all_selected_ids = select_and_print_configs(config_groups, all_shapes) out_dir = os.path.dirname(args.output_json_file) if not os.path.exists(out_dir): os.makedirs(out_dir) configs = [] for index in all_selected_ids: if args.similar_api: filename = os.path.basename(args.output_json_file) origin_configs[index]["op"] = os.path.splitext(filename)[0] configs.append(origin_configs[index]) with open(args.output_json_file, 'w') as f: json.dump(configs, f, indent=4, sort_keys=True) def select_configs_by_log(args, forward_logs, backward_logs): """ Select configs according to forward logs and backward logs and save the selected configs to the sepcified json file. Args: args(object): An object to take the attributes. forward_logs(list): A list of forward logs. backward_logs(list): A list of backward logs. """ ignored_params = ["op"] input_shape = ["input_shape"] if args.input_shape: input_shape = args.input_shape if args.ignored_params: ignored_params += args.ignored_params shapes_list = get_input_shapes(forward_logs, input_shape) removed_params = ignored_params + input_shape combined_logs = combine_logs_with_key_params(removed_params, forward_logs, backward_logs) config_groups = grouping_configs(combined_logs, shapes_list) all_selected_ids = select_and_print_configs(config_groups, shapes_list) with open(args.input_json_file, 'r') as f: all_configs = json.load(f) out_dir = os.path.dirname(args.output_json_file) if not os.path.exists(out_dir): os.makedirs(out_dir) configs = [] for index in all_selected_ids: configs.append(all_configs[index]) with open(args.output_json_file, 'w') as f: json.dump(configs, f, indent=4, sort_keys=True) def select_and_print_configs(config_groups, shapes_list): print("=" * 30 + "config_groups" + "=" * 30) all_selected_ids = [] i = 0 for key in config_groups: print("config {0}: {1}, total: {2}.".format( i, key, len(config_groups[key]['ids']))) shape_groups = config_groups[key]['shape_groups'] if not shape_groups: selected_ids = random.sample(config_groups[key]['ids'], 1) all_selected_ids.extend(selected_ids) select_ids_info = " Select {0} config_ids: {1}.".format( len(selected_ids), selected_ids) print(select_ids_info) else: all_selected_ids.extend( select_from_shape_groups(shape_groups, shapes_list)) i += 1 return all_selected_ids def select_from_shape_groups(shape_groups, shapes): """ Select configs from shape groups. The small, middle and large input will be selected. args: shape_groups(dict): A dict that groups input shapes. shapes(list): A list of input shapes. Returns: A list of selected config ids. """ all_selected_ids = [] j = 0 for label in shape_groups: candidate_ids = [] selected_ids = [] ids = shape_groups[label]['ids'] ids = rearrange_ids(shape_groups[label]['sizes'], ids) if len(ids) <= 3: candidate_ids = ids else: candidate_ids = [ids[0], ids[int(len(ids) / 2)], ids[-1]] selected_shapes = [] for idx in candidate_ids: if shapes[idx] not in selected_shapes: selected_shapes.append(shapes[idx]) selected_ids.append(idx) all_selected_ids += selected_ids selected_shapes_info = " The shapes are: " for shape in selected_shapes: selected_shapes_info += "{} ".format(shape) shape_groups_info = " " * 2 + "shape {0}: {1}, total: {2}.".format( j, label, len(ids)) select_ids_info = " Select {0} config_ids: {1}.".format( len(selected_ids), selected_ids) print(shape_groups_info + select_ids_info + selected_shapes_info) j += 1 return all_selected_ids def combine_logs_with_key_params(removed_params, forward_logs, backward_logs): """ Combine each forward log with the corresponding backward log. First, some params in forward and backward logs are removed. Second, the union of each forward and corresponding backward log is computed. Args: removed_params(list): A list of removed params. It usually contains "op" and "input_shape". forward_logs(list): A list of forward logs. backward_logs(list): A list og backward logs. Returns: A list of combined logs. """ for logs in [forward_logs, backward_logs]: for i in range(len(logs)): logs[i] = remove_params(logs[i], removed_params) combined_logs = forward_logs for i in range(len(forward_logs)): if forward_logs[i] != backward_logs[i]: intersection = list( set(forward_logs[i]).intersection(set(backward_logs[i]))) difference = list( set(forward_logs[i]).symmetric_difference( set(backward_logs[i]))) combined_logs[i] = intersection + difference combined_logs[i] = ' '.join(combined_logs[i]) return combined_logs def grouping_configs(logs, shapes, input_type="Variable"): """ Groups all configs according to the logs. First, all configs are grouped by key params without input shape. Second, the results of first step are grouped by input shape. Args: logs(list): A list of logs in which each item combines forward and backward log. shapes(list): A list of input shapes. Returns: A 2-D dict of config groups. """ config_groups = dict() # group all configs by key params without input shape. for i in range(len(logs)): if logs[i] not in config_groups.keys(): config_groups[logs[i]] = {'ids': [i]} else: config_groups[logs[i]]['ids'] += [i] # group config_groups by input shape. for key in config_groups: config_ids = config_groups[key]['ids'] shape_groups = group_input_shapes(shapes, config_ids, input_type) config_groups[key]['shape_groups'] = shape_groups return config_groups def remove_params(log, removed_params): """ Remove params from logs according to the names of removed params. Args: log(list): A list of logs in which each item is a string. removed_params(list): The names of removed params. Example: los=['op=conv data_format=NCHW filter_size=[1, 1]'] removed_params=['op'] result=['data_format=NCHW filter_size=[1, 1]'] Returns: A list of logs. """ result = list(log) if removed_params: for item in log: param_val = item.split("=") if param_val[0] in removed_params: result.remove(item) return result def get_input_shapes(logs, input_shape): """ Get input shapes from logs and parse them into lists. Args: logs(list): A list of forward logs or backward logs. It is used to extract input shapes. input_shape(list): The name of input shape in logs. For example, if input_shape=[10, 10] or x_shape=[10, 10] in logs. Then the name of input shape is "input_shape" or "x_shape". Returns: A list of input shapes and each input shape is also a list. """ all_shapes = [] for log in logs: shapes = [] for item in log: param_val = item.split("=") if param_val[0] in input_shape: shape = parse_list(param_val[1]) shapes.append(list(shape)) all_shapes.append(shapes) return all_shapes def group_input_shapes(shapes, config_ids, input_type): """ Group the input shapes according to the shape's label. Args: shapes(list): A list of input shapes. config_ids(list): A list of config ids in one group. Returns: A 2-D dict of shape groups. """ shape_groups = dict() if len(shapes) == 0: warnings.warn("Group configs regardless of input shape.") return shape_groups for index in config_ids: shape = shapes[index] label, size = label_shape(shape, input_type) if label not in shape_groups.keys(): shape_groups[label] = {'ids': [index], 'sizes': [size]} else: shape_groups[label]['ids'] += [index] shape_groups[label]['sizes'] += [size] return shape_groups def get_input_shapes_from_json(args, origin_configs): configs_without_input = [] all_shapes = [] input_type = "Variable" input_name = ["input", "x", "y"] for config in origin_configs: config_res = copy.deepcopy(config) input_shapes = [] var_shapes = [] for name, value in config["param_info"].items(): if args.ignored_params and name in args.ignored_params: continue if value["type"] in ["Variable", "numpy.ndarray"]: if value["type"] == "Variable": shape = list(parse_list(value["shape"])) else: shape = list(parse_list(value["value"])) for i in range(len(shape)): if shape[i] == -1: shape[i] = 16 if name in input_name: input_shapes.append(shape) else: var_shapes.append(shape) del config_res["param_info"][name] elif value["type"] == "list<Variable>": input_type = "list<Variable>" for key, var in value.items(): if key != "type": shape = list(parse_list(var["shape"])) for i in range(len(shape)): if shape[i] == -1: shape[i] = 16 input_shapes.append(shape) del config_res["param_info"][name] configs_without_input.append(config_res) if len(input_shapes) != 0: all_shapes.append(input_shapes) elif len(var_shapes) != 0 and len(var_shapes) <= 2: all_shapes.append(var_shapes) return configs_without_input, all_shapes, input_type def label_shape(shape, input_type): """ Label shape with the features. When only one shape is found, label the shape according to the number of dimensions and whether the size is a power of 2. When two shape is found, if the shapes are the same, label the shapes according to the rule of one input, if the shapes are not the same, label the shapes according to the number of dimensions. Args: shape: A list of input shapes. Each item is also a list containing 1 or 2 items. Returns: A label of shapes that is a string and the size of the input with the most dimensions. """ if input_type == "list<Variable>": is_same_shape = 'T' for s in shape: if s != shape[0]: is_same_shape = 'F' break label = 'is_same_shape=' + is_same_shape all_sizes = [reduce(mul, shape[i]) for i in range(len(shape))] size = max(all_sizes) elif len(shape) == 1: size = reduce(mul, shape[0]) is_power_of_2 = 'T' if size & (size - 1) == 0 else 'F' label = str(len(shape[0])) + '-D' + ' is_power_of_2=' + is_power_of_2 elif len(shape) == 2: if (shape[0] == shape[1]): is_same_shape = 'T' size = reduce(mul, shape[0]) is_power_of_2 = 'T' if size & (size - 1) == 0 else 'F' label = 'is_same_shape=' + is_same_shape + ' ' + str( len(shape[0])) + '-D-' + str(len(shape[ 1])) + '-D' + ' is_power_of_2=' + is_power_of_2 else: is_same_shape = 'F' size1 = reduce(mul, shape[0]) size2 = reduce(mul, shape[0]) label = 'is_same_shape=' + is_same_shape + ' ' + str( len(shape[0])) + '-D-' + str(len(shape[1])) + '-D' size = size1 if (len(shape[0]) > len(shape[1])) else size2 return label, size def rearrange_ids(sizes, ids): """ This function will sort sizes by ascending order and use the index of sorted sizes to rearrange ids. Example: sizes = [400, 256, 1000, 512] ids = [0, 3, 16, 10] sorted_sizes = [256, 400, 512, 1000] sorted_ids = [1, 0, 3, 2] return: [3, 0, 10, 16] Args: sizes(list): A list of sizes to be sorted. ids(list): A list of config ids to be rearrange by index of sorted sizes. Returns: A list of rearranged ids. """ sorted_nums = sorted(enumerate(sizes), key=lambda x: x[1]) sorted_ids = [i[0] for i in sorted_nums] return [ids[idx] for idx in sorted_ids] def get_logs(op_name, log_file): """ Given the name of the OP and the path of the log, split forward and backward logs. Args: op_name(str): The OP's name that is used to extract key logs. log_file(str): The path of Op's log. Returns: Two lists of forward and backward logs. """ forward_logs = [] backward_logs = [] forward_name = "op=" + op_name backward_name = forward_name + '_grad' with open(log_file, 'r') as f: for line in f.readlines(): line = line.strip() if backward_name in line: line = line.replace(', ', ',').split(' ') backward_logs.append(line) elif forward_name in line: line = line.replace(', ', ',').split(' ') forward_logs.append(line) if not forward_logs: raise ValueError("Could not find {0} in {1}.".format(forward_name, log_file)) if not backward_logs: warnings.warn("Only forward logs are used to select configs.") else: if len(forward_logs) != len(backward_logs): raise ValueError( "There are {0} logs containing {1}, but {2} logs containing {3}. They should be equal". format( len(forward_logs), forward_name, len(backward_logs), backward_name)) return forward_logs, backward_logs def parse_json_config(args): if not os.path.exists(args.input_json_file): raise ValueError( "The input_json_file {} is not found. Please check the path of json file.". format(args.input_json_file)) origin_configs = [] if os.path.isdir(args.input_json_file): if args.similar_api: for api in args.similar_api: json_path = os.path.join(args.input_json_file, api + '.json') if os.path.exists(json_path): with open(json_path, 'r') as f: api_configs = json.load(f) origin_configs.extend(api_configs) else: raise ValueError( "When input_json_file is a directory, the args similar_api should be set." ) elif os.path.isfile(args.input_json_file): with open(args.input_json_file, 'r') as f: origin_configs = json.load(f) configs_without_input, all_shapes, input_type = get_input_shapes_from_json( args, origin_configs) print("Total config of input json: {}".format(len(origin_configs))) return origin_configs, configs_without_input, all_shapes, input_type def parse_args(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( '--op_name', type=str, default=None, help='Specify the operator name.') parser.add_argument( '--log_file', type=str, default=None, help='Specify the path of log file.') parser.add_argument( '--input_json_file', type=str, default=None, required=True, help='Specify the path of input json file.') parser.add_argument( '--output_json_file', type=str, default=None, required=True, help='Specify the path of output json file.') parser.add_argument( '--input_shape', nargs='*', help='Specify the name of input shape. If None, ["input_shape"] will be used.' ) parser.add_argument( '--ignored_params', nargs='*', help='Specify the ignored param list, the configs will be filtered according to the other params.' ) parser.add_argument( '--parse_runtime_log', action='store_true', help='Whether selecting configs by parsing runtime log. ' 'Default: False, selecting configs by parsing json config.') parser.add_argument( '--similar_api', nargs='*', help='Specify the similar APIs, the output config will be ' 'selected from the json file of these APIs.') args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() print("ignored_params: {0}.".format(args.ignored_params)) if args.parse_runtime_log: forward_logs, backward_logs = get_logs(args.op_name, args.log_file) select_configs_by_log(args, forward_logs, backward_logs) else: origin_configs, configs_without_input, all_shapes, input_type = parse_json_config( args) select_configs_by_json(args, origin_configs, configs_without_input, all_shapes, input_type)

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CARLOSC10/T07_LIZA.DAMIAN_ROJAS.CUBAS

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#REPETITIVAS "PARA" QUE MUESTRA LOS N PRIMEROS NUMEROS, N SE ESCRIBE POR TECLADO import os n=0 #ARGUMENTOS n=int(os.sys.argv[1]) #INPUT VALIDA LOS DATOS datos_incorectos=(n<0) #WHILE #MIESTRAS LOS DATOS SEAN INCORECTOS A LA CONDICION ENTRA EN WHILE while(datos_incorectos==True): n=int(input("DATOS INGRESADOS INVALIDOS:Ingrese nuevamente los datos:")) datos_incorectos=(n<0) #fin_while print("FIN DEL BUCLE") #PROCESSING DE LA ESTRUCTURA "PARA" i=0 while(i<=n): print(i) i+=1 #fin_while

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permissive

alexjc/vispy

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# -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # Copyright (c) 2014, Vispy Development Team. All Rights Reserved. # Distributed under the (new) BSD License. See LICENSE.txt for more info. # ----------------------------------------------------------------------------- from nose.tools import assert_raises from vispy.testing import (assert_in, assert_not_in, assert_is, run_tests_if_main) def test_testing(): """Test testing ports""" assert_raises(AssertionError, assert_in, 'foo', 'bar') assert_in('foo', 'foobar') assert_raises(AssertionError, assert_not_in, 'foo', 'foobar') assert_not_in('foo', 'bar') assert_raises(AssertionError, assert_is, None, 0) assert_is(None, None) run_tests_if_main()

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larson.eric.d@gmail.com

6b73a7ebd7255118b70d146fd4edfd385a9c5be8

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[]

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KongBOy/kong_model2

33a94a9d2be5b0f28f9d479b3744e1d0e0ebd307

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refs/heads/master

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############################################################################################################################################################################################################# ############################################################################################################################################################################################################# ### 把 kong_model2 加入 sys.path import os code_exe_path = os.path.realpath(__file__) ### 目前執行 step10_b.py 的 path code_exe_path_element = code_exe_path.split("\\") ### 把 path 切分等等要找出 kong_model 在第幾層 code_dir = "\\".join(code_exe_path_element[:-1]) kong_layer = code_exe_path_element.index("kong_model2") ### 找出 kong_model2 在第幾層 kong_model2_dir = "\\".join(code_exe_path_element[:kong_layer + 1]) ### 定位出 kong_model2 的 dir import sys ### 把 kong_model2 加入 sys.path sys.path.append(kong_model2_dir) sys.path.append(code_dir) # print(__file__.split("\\")[-1]) # print(" code_exe_path:", code_exe_path) # print(" code_exe_path_element:", code_exe_path_element) # print(" code_dir:", code_dir) # print(" kong_layer:", kong_layer) # print(" kong_model2_dir:", kong_model2_dir) ############################################################################################################################################################################################################# kong_to_py_layer = len(code_exe_path_element) - 1 - kong_layer ### 中間 -1 是為了長度轉index # print(" kong_to_py_layer:", kong_to_py_layer) if (kong_to_py_layer == 0): template_dir = "" elif(kong_to_py_layer == 2): template_dir = code_exe_path_element[kong_layer + 1][0:] ### [7:] 是為了去掉 step1x_，後來覺得好像改有意義的名字不去掉也行所以改 0 elif(kong_to_py_layer == 3): template_dir = code_exe_path_element[kong_layer + 1][0:] + "/" + code_exe_path_element[kong_layer + 2][0:] ### [5:] 是為了去掉 mask_ ，前面的 mask_ 是為了python 的 module 不能數字開頭，隨便加的這樣子，後來覺得自動排的順序也可以接受，所以改0 elif(kong_to_py_layer > 3): template_dir = code_exe_path_element[kong_layer + 1][0:] + "/" + code_exe_path_element[kong_layer + 2][0:] + "/" + "/".join(code_exe_path_element[kong_layer + 3: -1]) # print(" template_dir:", template_dir) ### 舉例： template_dir: 7_mask_unet/5_os_book_and_paper_have_dtd_hdr_mix_bg_tv_s04_mae ############################################################################################################################################################################################################# exp_dir = template_dir ############################################################################################################################################################################################################# from step06_a_datas_obj import * from step09_5side_L5 import * from step10_a2_loss_info_obj import * from step10_b2_exp_builder import Exp_builder rm_paths = [path for path in sys.path if code_dir in path] for rm_path in rm_paths: sys.path.remove(rm_path) rm_moduless = [module for module in sys.modules if "step09" in module] for rm_module in rm_moduless: del sys.modules[rm_module] ############################################################################################################################################################################################################# ''' exp_dir 是決定 result_dir 的 "上一層"資料夾名字喔！ exp_dir要巢狀也沒問題～比如：exp_dir = "6_mask_unet/自己命的名字"，那 result_dir 就都在： 6_mask_unet/自己命的名字/result_a 6_mask_unet/自己命的名字/result_b 6_mask_unet/自己命的名字/... ''' use_db_obj = type9_mask_flow_have_bg_dtd_hdr_mix_and_paper use_loss_obj = [G_bce_s001_loss_info_builder.set_loss_target("UNet_Mask").copy()] ### z, y, x 順序是看 step07_b_0b_Multi_UNet 來對應的喔 ############################################################# ### 為了resul_analyze畫空白的圖，建一個empty的 Exp_builder empty = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="為了resul_analyze畫空白的圖，建一個empty的 Exp_builder") ################################## ### 1side1 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_1__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr__1s1__2s1__3s1__4s1__5s1-20220401_144959") ################################## ### 1side2 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_2__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr__1s2__2s1__3s1__4s1__5s1-20220401_162033") # 1 "3" 6 10 15 21 28 36 45 55 # 2side2 OK 4 ch032_1side_2__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr__1s2__2s2__3s1__4s1__5s1-20220401_175427") ch032_1side_2__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr__1s2__2s2__3s2__4s1__5s1-20220401_193152") ch032_1side_2__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr__1s2__2s2__3s2__4s2__5s1-20220401_211317") ch032_1side_2__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr__1s2__2s2__3s2__4s2__5s2-20220401_225740") ################################## ### 1side3 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_3__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 "3" 6 10 15 21 28 36 45 55 # 2side2 OK 4 ch032_1side_3__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s2_3s1_4s1_5s1-20220316_205936") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s2_3s2_4s1_5s1-20220316_221259") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s2_3s2_4s2_5s1-20220316_232850") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s2_3s2_4s2_5s2-20220317_004708") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) # 1 3 "6" 10 15 21 28 36 45 55 # 2side3 OK 10 ch032_1side_3__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s3_3s1_4s1_5s1-20220317_020758") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s3_3s2_4s1_5s1-20220317_032229") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s3_3s2_4s2_5s1-20220317_043933") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s3_3s2_4s2_5s2-20220317_055929") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s3_3s3_4s1_5s1-20220317_072133") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s3_3s3_4s2_5s1-20220317_084039") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s3_3s3_4s2_5s2-20220317_100201") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s3_3s3_4s3_5s1-20220317_112544") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s3_3s3_4s3_5s2-20220317_124820") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_3__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s3_2s3_3s3_4s3_5s3-20220317_141326") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ################################## ### 1side4 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_4__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 "3" 6 10 15 21 28 36 45 55 # 2side2 OK 4 ch032_1side_4__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s2_3s1_4s1_5s1-20220317_072910") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s2_3s2_4s1_5s1-20220317_084410") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s2_3s2_4s2_5s1-20220317_100139") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s2_3s2_4s2_5s2-20220317_112155") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) # 1 3 "6" 10 15 21 28 36 45 55 # 2side3 OK 10 ch032_1side_4__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s3_3s1_4s1_5s1-20220317_124420") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s3_3s2_4s1_5s1-20220317_140023") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s3_3s2_4s2_5s1-20220317_151925") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s3_3s2_4s2_5s2-20220317_164045") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s3_3s3_4s1_5s1-20220317_180440") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s3_3s3_4s2_5s1-20220317_192450") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s3_3s3_4s2_5s2-20220317_152510") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s3_3s3_4s3_5s1-20220317_165545") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s3_3s3_4s3_5s2-20220317_182514") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s3_3s3_4s3_5s3-20220317_195710") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) # 1 3 6 "10" 15 21 28 36 45 55 # 2side4 OK 20 ch032_1side_4__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s1_4s1_5s1-20220316_210316") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s2_4s1_5s1-20220316_222451") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s2_4s2_5s1-20220316_234909") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s2_4s2_5s2-20220317_011612") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s3_4s1_5s1-20220317_024555") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s3_4s2_5s1-20220317_041133") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s3_4s2_5s2-20220317_054003") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s3_4s3_5s1-20220317_071106") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s3_4s3_5s2-20220317_084107") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s3_4s3_5s3-20220317_101422") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s4_4s1_5s1-20220316_212016") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s4_4s2_5s1-20220316_224118") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s4_4s2_5s2-20220317_073559") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s4_4s3_5s1-20220317_090140") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s4_4s3_5s2-20220317_102548") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s4_4s3_5s3-20220317_115214") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s4_4s4_5s1-20220317_132021") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s4_4s4_5s2-20220317_144528") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s4_4s4_5s3-20220317_161321") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_4__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s4_2s4_3s4_4s4_5s4-20220317_174224") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ################################## ### 1side5 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_5__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s1_3s1_4s1_5s1-20220311_113503") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) # 1 "3" 6 10 15 21 28 36 45 55 # 2side2 OK 4 ch032_1side_5__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s2_3s1_4s1_5s1-20220311_131440") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s2_3s2_4s1_5s1-20220311_145742") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s2_3s2_4s2_5s1-20220311_164409") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s2_3s2_4s2_5s2-20220311_183355") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) # 1 3 "6" 10 15 21 28 36 45 55 # 2side3 OK 10 ch032_1side_5__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s3_3s1_4s1_5s1-20220311_202700") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s3_3s2_4s1_5s1-20220311_221325") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s3_3s2_4s2_5s1-20220312_000137") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s3_3s2_4s2_5s2-20220312_101242") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s3_3s3_4s1_5s1-20220312_120714") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s3_3s3_4s2_5s1-20220312_135625") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s3_3s3_4s2_5s2-20220312_154925") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s3_3s3_4s3_5s1-20220312_174618") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s3_3s3_4s3_5s2-20220312_194134") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s3_3s3_4s3_5s3-20220312_214024") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) # 1 3 6 "10" 15 21 28 36 45 55 # 2side4 OK 20 ch032_1side_5__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s4_3s1_4s1_5s1-20220316_210417") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s4_3s2_4s1_5s1-20220316_224954") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s4_3s2_4s2_5s1-20220317_003909") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s4_3s2_4s2_5s2-20220317_023150") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s4_3s3_4s1_5s1-20220317_042739") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s4_3s3_4s2_5s1-20220317_061855") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s4_3s3_4s2_5s2-20220317_081340") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s4_3s3_4s3_5s1-20220317_101153") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s4_3s3_4s3_5s2-20220317_120829") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s5_2s4_3s3_4s3_5s3-20220317_140844") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_5__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 6 10 "15" 21 28 36 45 55 # 2side5 OK 35 ch032_1side_5__2side_5__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_5_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_5_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_5_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_5_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_5_5s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ################################## ### 5side6 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_6__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 "3" 6 10 15 21 28 36 45 55 # 2side2 OK 4 ch032_1side_6__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s2_3s2_4s1_5s1-20220317_191218") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s2_3s2_4s2_5s1-20220317_203019") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s2_3s2_4s2_5s2-20220317_215051") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) # 1 3 "6" 10 15 21 28 36 45 55 # 2side3 OK 10 ch032_1side_6__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s3_3s1_4s1_5s1-20220317_161536") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s3_3s2_4s1_5s1-20220317_180235") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s3_3s2_4s2_5s1-20220317_195259") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s3_3s2_4s2_5s2-20220317_214651") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s3_3s3_4s1_5s1-20220317_154014") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s3_3s3_4s2_5s1-20220317_170201") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s3_3s3_4s2_5s2-20220317_182620") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s3_3s3_4s3_5s1-20220317_195311") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s3_3s3_4s3_5s2-20220317_211848") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6_2s3_3s3_4s3_5s3-20220317_224646") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) # 1 3 6 "10" 15 21 28 36 45 55 # 2side4 OK 20 ch032_1side_6__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6__2s4__3s1__4s1__5s1_-20220317_234833") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6__2s4__3s2__4s1__5s1_-20220318_013601") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6__2s4__3s2__4s2__5s1_-20220318_032721") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6__2s4__3s2__4s2__5s2_-20220318_052252") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="L5_ch032_bl_pyr_1s6__2s4__3s3__4s1__5s1_-20220318_072114") #.change_result_name_v4_Remove_sig_out(run_change=True, print_msg=True) #.change_result_name_v3_to_v4_Remove_db_name(run_change=True, print_msg=True) ch032_1side_6__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 6 10 "15" 21 28 36 45 55 # 2side5 OK 35 ch032_1side_6__2side_5__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_5_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_5_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_5_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_5_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_5_5s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 6 10 15 "21" 28 36 45 55 # 2side6 OK 56 ch032_1side_6__2side_6__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_5_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_5_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_5_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_5_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_5_5s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_5_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_5_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_5_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_5_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_5_5s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s1.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6.kong_model.model_describe) .set_train_args(epochs= 60).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ############################################################# if(__name__ == "__main__"): print("build exps cost time:", time.time() - start_time) if len(sys.argv) < 2: ############################################################################################################ ### 直接按 F5 或打 python step10_b1_exp_obj_load_and_train_and_test.py，後面沒有接東西喔！才不會跑到下面給 step10_b_subprocss.py 用的程式碼~~~ ch032_1side_1__2side_1__3side_1_4side_1_5s1.build().run() # print('no argument') sys.exit() ### 以下是給 step10_b_subprocess.py 用的，相當於cmd打 python step10_b1_exp_obj_load_and_train_and_test.py 某個exp.build().run() eval(sys.argv[1])

[ "s89334roy@yahoo.com.tw" ]

s89334roy@yahoo.com.tw

760cb2fd39f86a9b39f87005d16dbdd0b0dc1846

2bdedcda705f6dcf45a1e9a090377f892bcb58bb

/src/main/output/water/java_level_reason/lot/president.py

0dc9e9aba884377bf07220292becfd6d869e08ff

[]

no_license

matkosoric/GenericNameTesting

860a22af1098dda9ea9e24a1fc681bb728aa2d69

03f4a38229c28bc6d83258e5a84fce4b189d5f00

refs/heads/master

2021-01-08T22:35:20.022350

2020-02-21T11:28:21

242,123,053

1

0

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UTF-8

Python

false

1,144

py

# -*- coding: utf-8 -*- import http.client, urllib.parse # ********************************************** # *** Update or verify the following values. *** # ********************************************** # Replace the subscriptionKey string value with your valid subscription key. host = 'api.microsofttranslator.com' path = '/V2/Http.svc/TranslateArray' params = '' ns = "http://schemas.microsoft.com/2003/10/Serialization/Arrays"; # NOTE: AppId is required, but it can be empty because we are sending the Ocp-Apim-Subscription-Key header. body = """ <TranslateArrayRequest> <AppId /> <Texts> <string xmlns=\"%s\">Hello</string> <string xmlns=\"%s\">Goodbye</string> </Texts> <To>fr-fr</To> </TranslateArrayRequest> """ % (ns, ns) def TranslateArray (): subscriptionKey = '95de3c2322800cad9803e2d338616d8b' headers = { 'd3320239539e8ef417a1646394d0703b': subscriptionKey, 'Content-type': 'text/xml' } conn = http.client.HTTPSConnection(host) conn.request ("POST", path + params, body, headers) response = conn.getresponse () return response.read () result = TranslateArray () print (result.decode("utf-8"))

[ "soric.matko@gmail.com" ]

soric.matko@gmail.com

9b1be2255716dd81859aaf4f32c15e6443397f0a

a33a5d7d2a9b0b6030f39803553f2689f3d2c743

/ml/vcwiz/training/trainer.py

4024824cb15983e56849955a17f6b33eeebf0686

[]

no_license

yasyf/vc

6c1a4224d56049aff44f2ffa1c57922ccb7907ab

bd12d8b1248b008516b1547a693008428085de78

refs/heads/master

2023-01-10T08:39:34.285545

2019-07-29T10:20:31

2019-07-29T14:58:33

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2023-01-09T20:04:34

2016-05-25T21:52:18

Ruby

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Python

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py

from abc import ABC, abstractmethod import os, sys, tempfile from google.cloud import storage class Trainer(ABC): def __init__(self): self.model = None self.output_path = None self.upload_path = None self.client = storage.Client(os.environ['GC_PROJECT_ID']) self.bucket = self.client.bucket(os.environ['GOOGLE_MODEL_BUCKET']) @abstractmethod def _train(self, filename): raise NotImplementedError def train(self, filename): self.model = self._train(filename) def remote_train(self, path): fd, filename = tempfile.mkstemp(suffix='.csv') blob = self.bucket.blob(path) blob.download_to_filename(filename) self.train(filename) os.close(fd) @abstractmethod def _save(self, model, path): raise NotImplementedError def save(self, path): assert self.model self._save(self.model, path) self.output_path = path def upload(self, path): assert self.output_path blob = self.bucket.blob(path) blob.upload_from_filename(self.output_path) self.upload_path = path return blob.generation def remote_save(self, path): fd, filename = tempfile.mkstemp(suffix='.model') self.save(filename) os.close(fd) return self.upload(path) @abstractmethod def _test(self, model, *args): raise NotImplementedError def test(self, *args): assert self.model self._test(self.model, *args) @abstractmethod def _metrics(self, model, *args): raise NotImplementedError def metrics(self, *args): return self._metrics(self.model, *args) @classmethod def _train_and_test(cls, filename, args): instance = cls() instance.train(filename) instance.test(*args) @classmethod def train_and_test(cls): filename = sys.argv[1] args = sys.argv[2:] cls._train_and_test(filename, args)

[ "yasyfm@gmail.com" ]

yasyfm@gmail.com

98f34f3c1c4d17b6fb254028c9ec9441791c06b0

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/Algorithms/tf2algos/ppo.py

260f8d8b3e33d4bb3ac8b37fe11f677e6019b279

[ "Apache-2.0" ]

permissive

hititan/RLs

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refs/heads/master

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import numpy as np import tensorflow as tf import tensorflow_probability as tfp import Nn from utils.sth import sth from utils.tf2_utils import show_graph, get_TensorSpecs, gaussian_clip_rsample, gaussian_likelihood_sum, gaussian_entropy from Algorithms.tf2algos.base.on_policy import On_Policy class PPO(On_Policy): def __init__(self, s_dim, visual_sources, visual_resolution, a_dim_or_list, is_continuous, epoch=5, beta=1.0e-3, lr=5.0e-4, lambda_=0.95, epsilon=0.2, share_net=True, actor_lr=3e-4, critic_lr=1e-3, hidden_units={ 'share': { 'continuous': { 'share': [32, 32], 'mu': [32, 32], 'v': [32, 32] }, 'discrete': { 'share': [32, 32], 'logits': [32, 32], 'v': [32, 32] } }, 'actor_continuous': [32, 32], 'actor_discrete': [32, 32], 'critic': [32, 32] }, **kwargs): super().__init__( s_dim=s_dim, visual_sources=visual_sources, visual_resolution=visual_resolution, a_dim_or_list=a_dim_or_list, is_continuous=is_continuous, **kwargs) self.beta = beta self.epoch = epoch self.lambda_ = lambda_ self.epsilon = epsilon self.share_net = share_net if self.share_net: self.TensorSpecs = get_TensorSpecs([self.s_dim], self.visual_dim, [self.a_counts], [1], [1], [1]) if self.is_continuous: self.net = Nn.a_c_v_continuous(self.s_dim, self.visual_dim, self.a_counts, 'ppo_net', hidden_units['share']['continuous']) else: self.net = Nn.a_c_v_discrete(self.s_dim, self.visual_dim, self.a_counts, 'ppo_net', hidden_units['share']['discrete']) self.lr = tf.keras.optimizers.schedules.PolynomialDecay(lr, self.max_episode, 1e-10, power=1.0) self.optimizer = tf.keras.optimizers.Adam(learning_rate=self.lr(self.episode)) else: self.actor_TensorSpecs = get_TensorSpecs([self.s_dim], self.visual_dim, [self.a_counts], [1], [1]) self.critic_TensorSpecs = get_TensorSpecs([self.s_dim], self.visual_dim, [1]) if self.is_continuous: self.actor_net = Nn.actor_mu(self.s_dim, self.visual_dim, self.a_counts, 'actor_net', hidden_units['actor_continuous']) else: self.actor_net = Nn.actor_discrete(self.s_dim, self.visual_dim, self.a_counts, 'actor_net', hidden_units['actor_discrete']) self.critic_net = Nn.critic_v(self.s_dim, self.visual_dim, 'critic_net', hidden_units['critic']) self.actor_lr = tf.keras.optimizers.schedules.PolynomialDecay(actor_lr, self.max_episode, 1e-10, power=1.0) self.critic_lr = tf.keras.optimizers.schedules.PolynomialDecay(critic_lr, self.max_episode, 1e-10, power=1.0) self.optimizer_actor = tf.keras.optimizers.Adam(learning_rate=self.actor_lr(self.episode)) self.optimizer_critic = tf.keras.optimizers.Adam(learning_rate=self.critic_lr(self.episode)) if self.is_continuous: self.log_std = tf.Variable(initial_value=-0.5 * np.ones(self.a_counts, dtype=np.float32), trainable=True) self.recorder.logger.info(''' 　　　ｘｘｘｘｘｘｘｘ　　　　　　　ｘｘｘｘｘｘｘｘ　　　　　　　　　ｘｘｘｘｘ　　　　　　　　　　ｘｘ　　ｘｘ　　　　　　　　　ｘｘ　　ｘｘ　　　　　　　　ｘｘｘ　ｘｘｘ　　　　　　　　　ｘ　　　ｘｘｘ　　　　　　　　ｘ　　　ｘｘｘ　　　　　　　ｘｘ　　　ｘｘ　　　　　　　　　ｘ　　　ｘｘｘ　　　　　　　　ｘ　　　ｘｘｘ　　　　　　　ｘｘ　　　ｘｘｘ　　　　　　　　ｘｘｘｘｘｘ　　　　　　　　　ｘｘｘｘｘｘ　　　　　　　ｘｘｘ　　　ｘｘｘ　　　　　　　　ｘ　　　　　　　　　　　　　　ｘ　　　　　　　　　　　　　ｘｘ　　　ｘｘｘ　　　　　　　　ｘ　　　　　　　　　　　　　　ｘ　　　　　　　　　　　　　ｘｘ　　　ｘｘ　　　　　　　　　ｘ　　　　　　　　　　　　　　ｘ　　　　　　　　　　　　　ｘｘ　　ｘｘｘ　　　　　　　ｘｘｘｘｘ　　　　　　　　　　ｘｘｘｘｘ　　　　　　　　　　　　ｘｘｘｘｘ　　 ''') def choose_action(self, s, visual_s, evaluation=False): a = self._get_action(s, visual_s, evaluation).numpy() return a if self.is_continuous else sth.int2action_index(a, self.a_dim_or_list) @tf.function def _get_action(self, s, visual_s, evaluation): s, visual_s = self.cast(s, visual_s) with tf.device(self.device): if self.is_continuous: if self.share_net: mu, _ = self.net(s, visual_s) else: mu = self.actor_net(s, visual_s) sample_op, _ = gaussian_clip_rsample(mu, self.log_std) else: if self.share_net: logits, _ = self.net(s, visual_s) else: logits = self.actor_net(s, visual_s) norm_dist = tfp.distributions.Categorical(logits) sample_op = norm_dist.sample() return sample_op def store_data(self, s, visual_s, a, r, s_, visual_s_, done): assert isinstance(a, np.ndarray), "store_data need action type is np.ndarray" assert isinstance(r, np.ndarray), "store_data need reward type is np.ndarray" assert isinstance(done, np.ndarray), "store_data need done type is np.ndarray" if not self.is_continuous: a = sth.action_index2one_hot(a, self.a_dim_or_list) self.data = self.data.append({ 's': s, 'visual_s': visual_s, 'a': a, 'r': r, 'done': done, 'value': np.squeeze(self._get_value(s, visual_s).numpy()), 'log_prob': self._get_log_prob(s, visual_s, a).numpy() + 1e-10 }, ignore_index=True) self.s_ = s_ self.visual_s_ = visual_s_ @tf.function def _get_value(self, s, visual_s): s, visual_s = self.cast(s, visual_s) with tf.device(self.device): if self.share_net: _, value = self.net(s, visual_s) else: value = self.critic_net(s, visual_s) return value @tf.function def _get_log_prob(self, s, visual_s, a): s, visual_s, a = self.cast(s, visual_s, a) with tf.device(self.device): if self.is_continuous: if self.share_net: mu, _ = self.net(s, visual_s) else: mu = self.actor_net(s, visual_s) new_log_prob = gaussian_likelihood_sum(mu, a, self.log_std) else: if self.share_net: logits, _ = self.net(s, visual_s) else: logits = self.actor_net(s, visual_s) logp_all = tf.nn.log_softmax(logits) new_log_prob = tf.reduce_sum(a * logp_all, axis=1, keepdims=True) return new_log_prob def calculate_statistics(self): init_value = np.squeeze(self._get_value(self.s_, self.visual_s_).numpy()) self.data['total_reward'] = sth.discounted_sum(self.data.r.values, 1, init_value, self.data.done.values) self.data['discounted_reward'] = sth.discounted_sum(self.data.r.values, self.gamma, init_value, self.data.done.values) self.data['td_error'] = sth.discounted_sum_minus( self.data.r.values, self.gamma, init_value, self.data.done.values, self.data.value.values ) # GAE adv = np.asarray(sth.discounted_sum( self.data.td_error.values, self.lambda_ * self.gamma, 0, self.data.done.values )) self.data['advantage'] = [i for i in (adv - adv.mean()) / adv.std()] # self.data.to_excel(self.recorder.excel_writer, sheet_name=f'test{self.episode}', index=True) # self.recorder.excel_writer.save() def get_sample_data(self, index): i_data = self.data.iloc[index:index + self.batch_size] s = np.vstack(i_data.s.values).astype(np.float32) visual_s = np.vstack(i_data.visual_s.values).astype(np.float32) a = np.vstack(i_data.a.values).astype(np.float32) dc_r = np.vstack(i_data.discounted_reward.values).reshape(-1, 1).astype(np.float32) old_log_prob = np.vstack(i_data.log_prob.values).astype(np.float32) advantage = np.vstack(i_data.advantage.values).reshape(-1, 1).astype(np.float32) return s, visual_s, a, dc_r, old_log_prob, advantage # @show_graph(name='ppo_net') def learn(self, **kwargs): assert self.batch_size <= self.data.shape[0], "batch_size must less than the length of an episode" self.episode = kwargs['episode'] self.calculate_statistics() for _ in range(self.epoch): for index in range(0, self.data.shape[0], self.batch_size): s, visual_s, a, dc_r, old_log_prob, advantage = [tf.convert_to_tensor(i) for i in self.get_sample_data(index)] if self.share_net: actor_loss, critic_loss, entropy, kl = self.train_share.get_concrete_function( *self.TensorSpecs)(s, visual_s, a, dc_r, old_log_prob, advantage) else: actor_loss, entropy, kl = self.train_actor.get_concrete_function( *self.actor_TensorSpecs)(s, visual_s, a, old_log_prob, advantage) # if kl > 1.5 * 0.01: # break critic_loss = self.train_critic.get_concrete_function( *self.critic_TensorSpecs)(s, visual_s, dc_r) self.global_step.assign_add(1) summaries = dict([ ['LOSS/actor_loss', actor_loss], ['LOSS/critic_loss', critic_loss], ['Statistics/entropy', entropy] ]) if self.share_net: summaries.update(dict([['LEARNING_RATE/lr', self.lr(self.episode)]])) else: summaries.update(dict([ ['LEARNING_RATE/actor_lr', self.actor_lr(self.episode)], ['LEARNING_RATE/critic_lr', self.critic_lr(self.episode)] ])) self.write_training_summaries(self.episode, summaries) self.clear() @tf.function(experimental_relax_shapes=True) def train_share(self, s, visual_s, a, dc_r, old_log_prob, advantage): s, visual_s, a, dc_r, old_log_prob, advantage = self.cast(s, visual_s, a, dc_r, old_log_prob, advantage) with tf.device(self.device): with tf.GradientTape() as tape: if self.is_continuous: mu, value = self.net(s, visual_s) new_log_prob = gaussian_likelihood_sum(mu, a, self.log_std) entropy = gaussian_entropy(self.log_std) else: logits, value = self.net(s, visual_s) logp_all = tf.nn.log_softmax(logits) new_log_prob = tf.reduce_sum(a * logp_all, axis=1, keepdims=True) entropy = -tf.reduce_mean(tf.reduce_sum(tf.exp(logp_all) * logp_all, axis=1, keepdims=True)) ratio = tf.exp(new_log_prob - old_log_prob) kl = tf.reduce_mean(old_log_prob - new_log_prob) surrogate = ratio * advantage td_error = dc_r - value actor_loss = tf.reduce_mean( tf.minimum( surrogate, tf.clip_by_value(ratio, 1.0 - self.epsilon, 1.0 + self.epsilon) * advantage )) value_loss = tf.reduce_mean(tf.square(td_error)) loss = -(actor_loss - 1.0 * value_loss + self.beta * entropy) if self.is_continuous: loss_grads = tape.gradient(loss, self.net.trainable_variables + [self.log_std]) self.optimizer.apply_gradients( zip(loss_grads, self.net.trainable_variables + [self.log_std]) ) else: loss_grads = tape.gradient(loss, self.net.trainable_variables) self.optimizer.apply_gradients( zip(loss_grads, self.net.trainable_variables) ) return actor_loss, value_loss, entropy, kl @tf.function(experimental_relax_shapes=True) def train_actor(self, s, visual_s, a, old_log_prob, advantage): s, visual_s, a, old_log_prob, advantage = self.cast(s, visual_s, a, old_log_prob, advantage) with tf.device(self.device): with tf.GradientTape() as tape: if self.is_continuous: mu = self.actor_net(s, visual_s) new_log_prob = gaussian_likelihood_sum(mu, a, self.log_std) entropy = gaussian_entropy(self.log_std) else: logits = self.actor_net(s, visual_s) logp_all = tf.nn.log_softmax(logits) new_log_prob = tf.reduce_sum(a * logp_all, axis=1, keepdims=True) entropy = -tf.reduce_mean(tf.reduce_sum(tf.exp(logp_all) * logp_all, axis=1, keepdims=True)) ratio = tf.exp(new_log_prob - old_log_prob) kl = tf.reduce_mean(old_log_prob - new_log_prob) surrogate = ratio * advantage min_adv = tf.where(advantage > 0, (1 + self.epsilon) * advantage, (1 - self.epsilon) * advantage) actor_loss = -(tf.reduce_mean(tf.minimum(surrogate, min_adv)) + self.beta * entropy) if self.is_continuous: actor_grads = tape.gradient(actor_loss, self.actor_net.trainable_variables + [self.log_std]) self.optimizer_actor.apply_gradients( zip(actor_grads, self.actor_net.trainable_variables + [self.log_std]) ) else: actor_grads = tape.gradient(actor_loss, self.actor_net.trainable_variables) self.optimizer_actor.apply_gradients( zip(actor_grads, self.actor_net.trainable_variables) ) return actor_loss, entropy, kl @tf.function(experimental_relax_shapes=True) def train_critic(self, s, visual_s, dc_r): s, visual_s, dc_r = self.cast(s, visual_s, dc_r) with tf.device(self.device): with tf.GradientTape() as tape: value = self.critic_net(s, visual_s) td_error = dc_r - value value_loss = tf.reduce_mean(tf.square(td_error)) critic_grads = tape.gradient(value_loss, self.critic_net.trainable_variables) self.optimizer_critic.apply_gradients( zip(critic_grads, self.critic_net.trainable_variables) ) return value_loss

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/AlgoExpert_algorithms/Easy/FindThreeLargestNumbers/test_FindThreeLargestNumbersd.py

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2023-06-01T01:16:22.897097

2023-05-15T01:05:22

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''' Unittests for FindThreeLargestNumbers.py January 2021 Jakub Kazimierski ''' import unittest import FindThreeLargestNumbers class test_FindThreeLargestNumbers(unittest.TestCase): ''' Class with unittests for FindThreeLargestNumbers.py ''' # region Unittests def test_ExpectedOutput(self): ''' Checks if returned output is as expected. ''' input_arr = [10, 5, 9, 10, 12] output = FindThreeLargestNumbers.findThreeLargestNumbers(input_arr) self.assertEqual(output, [10, 10, 12]) # endregion if __name__ == "__main__": ''' Main method for test cases. ''' unittest.main()

[ "j.m.kazimierski@gmail.com" ]

j.m.kazimierski@gmail.com