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

ddb5bc9e87cfc65e9510b627a37f2f35ff9512ab

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/pysnmp-with-texts/WWP-LEOS-TABLE-CHG-NOTIF-MIB.py

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permissive

agustinhenze/mibs.snmplabs.com

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refs/heads/master

2020-12-26T12:41:41.132395

2019-08-16T15:51:41

2019-08-16T15:53:57

237,512,469

0

Apache-2.0

2020-01-31T20:41:36

2020-01-31T20:41:35

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# # PySNMP MIB module WWP-LEOS-TABLE-CHG-NOTIF-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/WWP-LEOS-TABLE-CHG-NOTIF-MIB # Produced by pysmi-0.3.4 at Wed May 1 15:38:29 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # ObjectIdentifier, Integer, OctetString = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "Integer", "OctetString") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ConstraintsIntersection, ValueRangeConstraint, ConstraintsUnion, ValueSizeConstraint, SingleValueConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "ValueRangeConstraint", "ConstraintsUnion", "ValueSizeConstraint", "SingleValueConstraint") AddressFamilyNumbers, = mibBuilder.importSymbols("IANA-ADDRESS-FAMILY-NUMBERS-MIB", "AddressFamilyNumbers") ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup") Counter64, ObjectIdentity, MibScalar, MibTable, MibTableRow, MibTableColumn, Unsigned32, MibIdentifier, Integer32, ModuleIdentity, NotificationType, iso, Bits, Gauge32, Counter32, TimeTicks, IpAddress = mibBuilder.importSymbols("SNMPv2-SMI", "Counter64", "ObjectIdentity", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Unsigned32", "MibIdentifier", "Integer32", "ModuleIdentity", "NotificationType", "iso", "Bits", "Gauge32", "Counter32", "TimeTicks", "IpAddress") RowStatus, TruthValue, MacAddress, DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "RowStatus", "TruthValue", "MacAddress", "DisplayString", "TextualConvention") wwpModulesLeos, wwpModules = mibBuilder.importSymbols("WWP-SMI", "wwpModulesLeos", "wwpModules") wwpLeosTableChgNotifMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9)) wwpLeosTableChgNotifMIB.setRevisions(('2002-03-12 00:00',)) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): if mibBuilder.loadTexts: wwpLeosTableChgNotifMIB.setRevisionsDescriptions(('Initial creation.',)) if mibBuilder.loadTexts: wwpLeosTableChgNotifMIB.setLastUpdated('200203120000Z') if mibBuilder.loadTexts: wwpLeosTableChgNotifMIB.setOrganization('World Wide Packets, Inc') if mibBuilder.loadTexts: wwpLeosTableChgNotifMIB.setContactInfo(' Mib Meister Postal: World Wide Packets P.O. Box 950 Veradale, WA 99037 USA Phone: +1 509 242 9000 Email: [email protected]') if mibBuilder.loadTexts: wwpLeosTableChgNotifMIB.setDescription('This MIB module is used to maintain the table of (Table, Ems IP)') wwpLeosTableChgNotifMIBObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1)) wwpLeosTableChgNotif = MibIdentifier((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 7)) wwpLeosTableChgNotifMIBNotificationPrefix = MibIdentifier((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 2)) wwpLeosTableChgNotifMIBNotifications = MibIdentifier((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 2, 0)) wwpLeosTableChgNotifMIBConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 3)) wwpLeosTableChgNotifMIBCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 3, 1)) wwpLeosTableChgNotifMIBGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 3, 2)) wwpLeosTableTrapNotifTimer = MibScalar((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(30, 3600))).setMaxAccess("readwrite") if mibBuilder.loadTexts: wwpLeosTableTrapNotifTimer.setStatus('current') if mibBuilder.loadTexts: wwpLeosTableTrapNotifTimer.setDescription('Setting this value will set the trap timer. Device will send the trap notification after every wwpLeosTableTrapNotifTimer seconds if any of the table changes.') wwpLeosListenerRefreshTimer = MibScalar((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(30, 1000))).setMaxAccess("readwrite") if mibBuilder.loadTexts: wwpLeosListenerRefreshTimer.setStatus('current') if mibBuilder.loadTexts: wwpLeosListenerRefreshTimer.setDescription("Setting this value will set the listener refresh timer. Device will send the trap notification 'wwpLeosTableRefreshTrap' after every 'wwpLeosListenerRefreshTimer' seconds if count value associated with 'wwpLeosListenerEntry' becomes 20 or 10 or 5. Device will delete entry from wwpLeosListenerEntry once count = 0. Minimum value is 30 sec and maximum = 1000 seconds.") wwpLeosTableChgNotifTable = MibTable((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 4), ) if mibBuilder.loadTexts: wwpLeosTableChgNotifTable.setStatus('current') if mibBuilder.loadTexts: wwpLeosTableChgNotifTable.setDescription('Table of Ems.') wwpLeosTableChgNotifEntry = MibTableRow((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 4, 1), ).setIndexNames((0, "WWP-LEOS-TABLE-CHG-NOTIF-MIB", "wwpLeosTableChgNotifIndex")) if mibBuilder.loadTexts: wwpLeosTableChgNotifEntry.setStatus('current') if mibBuilder.loadTexts: wwpLeosTableChgNotifEntry.setDescription('Ems Entry in the ems Port Table.') wwpLeosTableChgNotifIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 4, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 100))).setMaxAccess("readonly") if mibBuilder.loadTexts: wwpLeosTableChgNotifIndex.setStatus('current') if mibBuilder.loadTexts: wwpLeosTableChgNotifIndex.setDescription('This is the index used for this table.') wwpLeosTableChgNotifOid = MibTableColumn((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 4, 1, 2), ObjectIdentifier()).setMaxAccess("readonly") if mibBuilder.loadTexts: wwpLeosTableChgNotifOid.setStatus('current') if mibBuilder.loadTexts: wwpLeosTableChgNotifOid.setDescription('This is the OID of the table for which device will send trap if anything changed in this table.') wwpLeosTableChgNotifNumChanges = MibTableColumn((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 4, 1, 3), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: wwpLeosTableChgNotifNumChanges.setStatus('current') if mibBuilder.loadTexts: wwpLeosTableChgNotifNumChanges.setDescription('This variables returns the counter value which keeps track of how many times wwpLeosTableChgNotifOid has changed. This counter is 32 bit counter and will never reset, except if device is rebooted.') wwpLeosListenerTable = MibTable((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 5), ) if mibBuilder.loadTexts: wwpLeosListenerTable.setStatus('current') if mibBuilder.loadTexts: wwpLeosListenerTable.setDescription("Table of EMS/NMS Ip's. This table uses Multiple Set operation to create entry in the table.") wwpLeosListenerEntry = MibTableRow((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 5, 1), ).setIndexNames((0, "WWP-LEOS-TABLE-CHG-NOTIF-MIB", "wwpLeosListenerIndex")) if mibBuilder.loadTexts: wwpLeosListenerEntry.setStatus('current') if mibBuilder.loadTexts: wwpLeosListenerEntry.setDescription('Entry of each EMS/NMS who is interested to receive table change notification.') wwpLeosListenerIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 5, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 10))).setMaxAccess("readonly") if mibBuilder.loadTexts: wwpLeosListenerIndex.setStatus('current') if mibBuilder.loadTexts: wwpLeosListenerIndex.setDescription('Specifies the unique index in the wwpLeosListenerTable.') wwpLeosListenerAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 5, 1, 2), DisplayString()).setMaxAccess("readcreate") if mibBuilder.loadTexts: wwpLeosListenerAddr.setStatus('current') if mibBuilder.loadTexts: wwpLeosListenerAddr.setDescription('This is the host name or ip address of the EMS/NMS.') wwpLeosListenerResolvedIp = MibTableColumn((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 5, 1, 3), IpAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: wwpLeosListenerResolvedIp.setStatus('current') if mibBuilder.loadTexts: wwpLeosListenerResolvedIp.setDescription('This shows the resolved IP address of the host name specified in wwpLeosListenerAddr. If wwpLeosListenerAddr is set to IpV4 type then wwpLeosListenerResolvedIp is equal to wwpLeosListenerAddr. If wwpLeosListenerAddr is set to dns type then wwpLeosListenerResolvedIp is equal to 0 if it is unresolved or is equal to resolved IP address.') wwpLeosListenerStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 5, 1, 4), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: wwpLeosListenerStatus.setStatus('current') if mibBuilder.loadTexts: wwpLeosListenerStatus.setDescription("Used to manage the creation and deletion of the conceptual rows in this table. To create a row in this table, a manager must set this object to 'createAndGo'. Setting this object to 'active' if it is already active will result in refreshing this entry. Setting this object to 'create and go' if this entry is already active will result in refreshing this entry. This table uses Multiple Set operation to create entry in the table. wwpLeosListenerAddr and wwpLeosListenerStatus and index are mandatory objects that need to be set when creating entry. While deleting entry ony index needs to be specified. ") wwpLeosTableChgNotifIndexStr = MibScalar((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 7, 1), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess("readonly") if mibBuilder.loadTexts: wwpLeosTableChgNotifIndexStr.setStatus('current') if mibBuilder.loadTexts: wwpLeosTableChgNotifIndexStr.setDescription("This variable contains the string of indexes that changed corresponding to table wwpLeosTableChgNotifOid. This variable is sent in the trap 'wwpLeosTableChgTrap'.") wwpLeosListenerRefreshCount = MibScalar((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 1, 7, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(30, 1000))).setMaxAccess("readonly") if mibBuilder.loadTexts: wwpLeosListenerRefreshCount.setStatus('current') if mibBuilder.loadTexts: wwpLeosListenerRefreshCount.setDescription('This is the count value for each listener entry. when this count value becomes zero, the listener entry corresponding to this value will be deleted. This count value is sent in the trap.') wwpLeosTableChgTrap = NotificationType((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 2, 0, 1)).setObjects(("WWP-LEOS-TABLE-CHG-NOTIF-MIB", "wwpLeosTableChgNotifOid"), ("WWP-LEOS-TABLE-CHG-NOTIF-MIB", "wwpLeosTableChgNotifNumChanges"), ("WWP-LEOS-TABLE-CHG-NOTIF-MIB", "wwpLeosTableChgNotifIndexStr")) if mibBuilder.loadTexts: wwpLeosTableChgTrap.setStatus('current') if mibBuilder.loadTexts: wwpLeosTableChgTrap.setDescription('A notification is sent whenever the table has changed. OID of the table and the counter associated with this table is sent in the trap.') wwpLeosTableRefreshTrap = NotificationType((1, 3, 6, 1, 4, 1, 6141, 2, 60, 9, 2, 0, 2)).setObjects(("WWP-LEOS-TABLE-CHG-NOTIF-MIB", "wwpLeosListenerRefreshCount")) if mibBuilder.loadTexts: wwpLeosTableRefreshTrap.setStatus('current') if mibBuilder.loadTexts: wwpLeosTableRefreshTrap.setDescription('A notification is sent whenever the listener table entry needs to be refreshed.') mibBuilder.exportSymbols("WWP-LEOS-TABLE-CHG-NOTIF-MIB", wwpLeosTableChgNotifTable=wwpLeosTableChgNotifTable, wwpLeosTableChgNotifMIBObjects=wwpLeosTableChgNotifMIBObjects, wwpLeosTableChgNotifEntry=wwpLeosTableChgNotifEntry, wwpLeosTableRefreshTrap=wwpLeosTableRefreshTrap, wwpLeosTableChgNotif=wwpLeosTableChgNotif, wwpLeosTableChgNotifMIBNotificationPrefix=wwpLeosTableChgNotifMIBNotificationPrefix, wwpLeosTableChgNotifMIB=wwpLeosTableChgNotifMIB, wwpLeosListenerResolvedIp=wwpLeosListenerResolvedIp, wwpLeosTableChgNotifMIBGroups=wwpLeosTableChgNotifMIBGroups, wwpLeosTableChgNotifMIBConformance=wwpLeosTableChgNotifMIBConformance, wwpLeosTableChgNotifOid=wwpLeosTableChgNotifOid, wwpLeosTableChgNotifMIBCompliances=wwpLeosTableChgNotifMIBCompliances, wwpLeosTableTrapNotifTimer=wwpLeosTableTrapNotifTimer, wwpLeosListenerAddr=wwpLeosListenerAddr, wwpLeosTableChgNotifIndexStr=wwpLeosTableChgNotifIndexStr, wwpLeosListenerRefreshTimer=wwpLeosListenerRefreshTimer, wwpLeosTableChgNotifNumChanges=wwpLeosTableChgNotifNumChanges, wwpLeosListenerRefreshCount=wwpLeosListenerRefreshCount, wwpLeosTableChgTrap=wwpLeosTableChgTrap, wwpLeosListenerTable=wwpLeosListenerTable, wwpLeosListenerIndex=wwpLeosListenerIndex, wwpLeosTableChgNotifIndex=wwpLeosTableChgNotifIndex, wwpLeosListenerEntry=wwpLeosListenerEntry, wwpLeosListenerStatus=wwpLeosListenerStatus, PYSNMP_MODULE_ID=wwpLeosTableChgNotifMIB, wwpLeosTableChgNotifMIBNotifications=wwpLeosTableChgNotifMIBNotifications)

[ "[email protected]" ]

[email protected]

feff5ab73695206b96ceecd566fb5f3a48277960

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/EL266.py

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Pumafied/Project-Euler

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# The divisors of 12 are: 1,2,3,4,6 and 12. # The largest divisor of 12 that does not exceed the square root of 12 is 3. # We shall call the largest divisor of an integer n that does not exceed the square root of n the pseudo square root (PSR) of n. # It can be seen that PSR(3102)=47. # Let p be the product of the primes below 190. # Find PSR(p) mod 1016.

[ "[email protected]" ]

[email protected]

a8c147db047fa4bcc792392e8c7a98ea3ea03be4

18576820ca4cfbecbfc8e8c05e9aaba0809e6138

/fizeau_control_loop.py

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[]

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mwanakijiji/lbti_fizeau_control

d12e3fcfa12725ab24c42acaaea6e83bd97e2f02

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refs/heads/master

2021-07-09T09:13:06.175214

2018-12-10T16:24:44

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#!/usr/bin/python from lmircam_tools import * from lmircam_tools.overlap_psfs import overlap_airy_psfs, overlap_grism_psfs from lmircam_tools.dial_opd import optimize_opd_fizeau_grism, optimize_opd_fizeau_airy from lmircam_tools.change_tt import optimize_tt_fizeau_airy ############## BEGIN GROSS OVERLAP OF NON-FIZEAU AIRY PSFS psf_loc_setpoint = [1220,800] # pixel location for PSFs to be at overlap_airy_psfs(psf_loc_setpoint) # filter-agnostic ############## END GROSS OVERLAP OF AIRY PSFS ############## BEGIN PUT IN GRISM AND REFINE GRISM-PSF OVERLAP put_in_grism() overlap_grism_psfs(psf_loc_setpoint) ############## END PUT IN GRISM AND REFINE GRISM-PSF OVERLAP ############## BEGIN DIAL OPD WITH HPC AND FIND CENTER OF COHERENCE ENVELOPE, THEN REMOVE GRISM optimize_opd_fizeau_grism(psf_loc_setpoint) # might also use argument of the re-established Fizeau/grism PSF instead of the coordinate where it's supposed to be remove_grism() ############## END DIAL OPD WITH HPC AND FIND CENTER OF COHERENCE ENVELOPE, THEN REMOVE GRISM ############## BEGIN HOLD CENTER OF SCIENCE COHERENCE ENVELOPE WITH HIGH-CONTRAST FRINGES # maybe allow HPC to correct pathlength using science readouts, even though Phasecam not closed yet? or does this not have to be automatic, and we can just correct the Fizeau/Airy PSFs once Phasecam is closed? ############## END HOLD CENTER OF SCIENCE COHERENCE ENVELOPE WITH HIGH-CONTRAST FRINGES ############## TRANSLATE NIL + CLOSE PHASECAM LOOP HERE? # might be manual step ############## BEGIN OPTIMIZE SCIENCE PSF BY FINDING OPD AND TT SETPOINTS ITERATIVELY optimize_opd_fizeau_airy(psf_location) optimize_tt_fizeau_airy(psf_location) ## adjust TT to optimize PSF; maybe iterate with OPD? ## note OPD movements cannot be more than 5 um with Phasecam closed ############## END OPTIMIZE SCIENCE PSF BY FINDING OPD AND TT SETPOINTS ITERATIVELY ############## ANY RUNNING STATS I WANT TO KEEP OF THE SCIENCE PSFS? ############## REDO EVERYTHING ONCE NOD HAPPENS

[ "[email protected]" ]

[email protected]

ed86d450a200b1a3716031f000c74d3c8bc7d0b5

449f410b621049c4049a4f7d4b0858f53d56a7d7

/tests/test_text.py

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permissive

mvwicky/holdmypics

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refs/heads/main

2023-05-10T19:36:20.978697

2023-05-06T21:27:29

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from __future__ import annotations import imghdr import io import os import time from collections.abc import Callable from typing import TYPE_CHECKING from urllib.parse import urlencode import pytest from flask.testing import FlaskClient from hypothesis import example, given, strategies as st from loguru import logger from PIL import Image from tests.strategies import ( color_strategy, dpi_strategy, fmt_strategy, opt_color_strategy, size_strategy, ) from tests.utils import compact_dict, make_route, size_id if TYPE_CHECKING: from holdmypics import Holdmypics char_stragegy = st.characters(blacklist_categories=("Cc", "Cf", "Cs", "Co", "Cn")) text_strategy = st.text(min_size=1, max_size=255, alphabet=char_stragegy) long_text_strategy = st.text(min_size=16, max_size=255, alphabet=char_stragegy) opt_text_strategt = st.one_of(st.none(), text_strategy) args_strategy = st.fixed_dictionaries({"text": opt_text_strategt, "dpi": dpi_strategy}) def make_args(**kwargs: str | int | None): from holdmypics.api.args import TextImageArgs return TextImageArgs(**compact_dict(kwargs)) @given( size=size_strategy, img_fmt=fmt_strategy, fg=color_strategy, bg=color_strategy, args=args_strategy, ) @example( size=(1920, 1080), img_fmt="png", fg="fff", bg="000", args={"text": "Some Text", "dpi": 300}, ) def test_create_images_using_function( app_factory: Callable[[], Holdmypics], size: tuple[int, int], img_fmt: str, fg: str, bg: str, args: dict[str, str | int | None], ): from holdmypics.api.text import GeneratedTextImage start = time.perf_counter() with app_factory().test_request_context(): img_args = make_args(**args) img = GeneratedTextImage(size, img_fmt, bg, fg, img_args) assert img.get_save_kw() p = img.get_path() assert os.path.isfile(p) assert os.path.getsize(p) im = Image.open(p) assert im.size == size logger.debug("Elapsed: {0:.4f}", time.perf_counter() - start) @given( size=size_strategy, img_fmt=fmt_strategy, fg=opt_color_strategy, bg=opt_color_strategy, args=args_strategy, ) def test_create_images_using_client( app_factory: Callable[[], Holdmypics], size: tuple[int, int], img_fmt: str, fg: str | None, bg: str | None, args: dict[str, str | int | None], ): if bg is None and fg: bg, fg = fg, None start = time.perf_counter() app = app_factory() with app.test_client() as client: url = make_route( app, "api.image_route", size=size, bg_color=bg, fg_color=fg, fmt=img_fmt, **compact_dict(args), ) # if args: # url = "?".join((url, urlencode(compact_dict(args)))) res = client.get(url, follow_redirects=False) assert res.status_code == 200 img_type = imghdr.what("filename", h=res.data) assert img_type == img_fmt im = Image.open(io.BytesIO(res.data)) assert im.size == size logger.debug("Elapsed: {0:.4f}", time.perf_counter() - start) def test_random_text_header(client: FlaskClient): path = make_route( client, "api.image_route", size=(638, 328), bg_color="cef", fg_color="555", fmt="png", random_text=True, ) res = client.get(path, follow_redirects=False) assert res.status_code == 200 assert "X-Random-Text" in res.headers def test_random_text_ocr(client: FlaskClient): pytesseract = pytest.importorskip("pytesseract", reason="pytesseract not installed") path = make_route( client, "api.image_route", size=(638, 328), bg_color="cef", fg_color="555", fmt="png", ) args = {"text": "Some Random Text", "dpi": None, "random_text": True} query = urlencode({k: v for (k, v) in args.items() if v}) url = "?".join((path, query)) res = client.get(url, follow_redirects=False) assert res.status_code == 200 img_type = imghdr.what("filename", h=res.data) assert img_type == "png" im = Image.open(io.BytesIO(res.data)) from_header = res.headers.get("X-Random-Text") assert from_header is not None from_ocr = pytesseract.image_to_string(im).strip() logger.info("Got text from OCR: {0}", from_ocr) assert from_ocr.casefold() == from_header.casefold() @pytest.mark.parametrize( "font_name", ["overpass", "fira-mono", "fira-sans", "roboto", "spectral"] ) @pytest.mark.parametrize("size", [(3840, 2160), (960, 540)], ids=size_id) def test_text_with_fonts( app: Holdmypics, image_format: str, font_name: str, size: tuple[int, int] ): from holdmypics.api.text import GeneratedTextImage with app.test_request_context(): img_args = make_args(text=f"Text with font: {font_name}", font_name=font_name) img = GeneratedTextImage(size, image_format, "cef", "555", img_args) assert img.get_save_kw() p = img.get_path() assert os.path.isfile(p) assert os.path.getsize(p)

[ "[email protected]" ]

[email protected]

738b8cf3f59c9df7da1c0c7c55adfefafc2b9a16

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/mnist_data/mnist_app.py

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csliuchang/tensorflow_project

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# coding utf-8 import tensorflow as tf import numpy as np from PIL import Image import mnist_backward import mnist_forward # from .mnist_backward import MODEL_SAVE_PATH def restore_model(testPicArr): """ " 创建一个默认图，在改图中执行以下操作" args: MOVING_AVERAGE_DECAY: 用于控制模型更新的速度，训练过程中会对每一个变量维护一个影子变量，这个影子变量的初始值就是相应变量的初始值，每次变量更新时，影子变量就是随之更新。 preValue： axis返回每一行最大值的位置索引,得到概率最大的预测值 variables_to_restore：通过使用variables_to_restore函数，可以使在加载模型的时候将影子变量直接映射到变量的本身，所以我们在获取变量的滑动平均值的时候只需要获取到变量的本身值而不需要去获取影子变量。 """ with tf.Graph().as_default() as tg: x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE]) y = mnist_forward.forward(x, None) preValue = tf.argmax(y, 1) variable_averages = tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY) variables_to_restore = variable_averages.variables_to_restore() saver = tf.train.Saver(variables_to_restore) MODEL_SAVE_PATH = "./model/" with tf.Session() as sess: ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) preValue = sess.run(preValue, feed_dict={x: testPicArr}) return preValue else: print("No checkpoint file found") return -1 def pre_pic(picName): """ ANTIALIAS: 抗锯齿 convert('L')：变为灰度图 threshold ：阈值 """ img = Image.open(picName) reIm = img.resize((28, 28), Image.ANTIALIAS) im_arr = np.array(reIm.convert('L')) threshold = 50 for i in range(28): for j in range(28): im_arr[i][j] = 255 - im_arr[i][j] if (im_arr[i][j] < threshold): im_arr[i][j] = 0 # 纯黑色0 else: im_arr[i][j] = 255 nm_arr = im_arr.reshape([1, 784]) nm_arr = nm_arr.astype(np.float32) img_ready = np.multiply(nm_arr, 1.0/255.0) return img_ready def application(): # testNum = input("input the number of test pictures:") # for i in range(testNum): # testPic = raw_input("the path of test picture:") # testPicArr = pre_pic('./1.png') # preValue = restore_model(testPicArr) # print("The prediction number is", preValue) # testPicArr = pre_pic('./2.png') preValue = restore_model(pre_pic(raw_input("the path of test picture :"))) print("The prediction number is ", preValue) def main(): application() if __name__ == "__main__": try: raw_input # Python 2 except NameError: raw_input = input # Python 3 main()

[ "[email protected]" ]

[email protected]

0e529e884f05b337cd282d3977342a4aee9e70b4

5f73a8162b8c22f8f65c2c1a98b5a61502fb0ed3

/viskit/frontend.py

4a3a355fec35cebee5d079cd865cd968af6996c8

[]

no_license

brandontrabucco/viskit

b6d18ba2d8f32779b0bb98fa550a80c6d6463ec2

a546e51e13bd1cf0eb9fa5284f1c596b88eded50

refs/heads/master

2020-04-29T21:40:45.270104

2019-03-19T04:03:44

176,420,386

0

null

2019-03-19T04:01:00

null

UTF-8

Python

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py

import sys from viskit.core import AttrDict sys.path.append('.') import matplotlib import os matplotlib.use('Agg') import flask # import Flask, render_template, send_from_directory from viskit import core import sys import argparse import json import numpy as np from plotly import tools import plotly.offline as po import plotly.graph_objs as go def flatten(xs): return [x for y in xs for x in y] def sliding_mean(data_array, window=5): data_array = np.array(data_array) new_list = [] for i in range(len(data_array)): indices = list(range(max(i - window + 1, 0), min(i + window + 1, len(data_array)))) avg = 0 for j in indices: avg += data_array[j] avg /= float(len(indices)) new_list.append(avg) return np.array(new_list) import itertools app = flask.Flask(__name__, static_url_path='/static') exps_data = None plottable_keys = None distinct_params = None @app.route('/js/<path:path>') def send_js(path): return flask.send_from_directory('js', path) @app.route('/css/<path:path>') def send_css(path): return flask.send_from_directory('css', path) def make_plot( plot_lists, use_median=False, plot_width=None, plot_height=None, title=None, ): """ plot_lists is a list of lists. Each outer list represents different y-axis attributes. Each inner list represents different experiments to run, within that y-axis attribute. Each plot is an AttrDict which should have the elements used below. """ p25, p50, p75 = [], [], [] num_y_axes = len(plot_lists) fig = tools.make_subplots(rows=num_y_axes, cols=1, print_grid=False) fig['layout'].update( width=plot_width, height=plot_height, title=title, ) for y_idx, plot_list in enumerate(plot_lists): for idx, plt in enumerate(plot_list): color = core.color_defaults[idx % len(core.color_defaults)] if use_median: p25.append(np.mean(plt.percentile25)) p50.append(np.mean(plt.percentile50)) p75.append(np.mean(plt.percentile75)) x = list(range(len(plt.percentile50))) y = list(plt.percentile50) y_upper = list(plt.percentile75) y_lower = list(plt.percentile25) else: x = list(range(len(plt.means))) y = list(plt.means) y_upper = list(plt.means + plt.stds) y_lower = list(plt.means - plt.stds) errors = go.Scatter( x=x + x[::-1], y=y_upper + y_lower[::-1], fill='tozerox', fillcolor=core.hex_to_rgb(color, 0.2), line=go.scatter.Line(color=core.hex_to_rgb(color, 0)), showlegend=False, legendgroup=plt.legend, hoverinfo='none' ) values = go.Scatter( x=x, y=y, name=plt.legend, legendgroup=plt.legend, line=dict(color=core.hex_to_rgb(color)), ) # plotly is 1-indexed like matplotlib for subplots y_idx_plotly = y_idx + 1 fig.append_trace(values, y_idx_plotly, 1) fig.append_trace(errors, y_idx_plotly, 1) fig['layout']['yaxis{}'.format(y_idx_plotly)].update( title=plt.plot_key, ) fig_div = po.plot(fig, output_type='div', include_plotlyjs=False) if "footnote" in plot_list[0]: footnote = " ".join([ r"%s: %s" % ( plt.legend, plt.footnote) for plt in plot_list ]) return r"%s<div>%s</div>" % (fig_div, footnote) else: return fig_div def make_plot_eps(plot_list, use_median=False, counter=0): import matplotlib.pyplot as _plt f, ax = _plt.subplots(figsize=(8, 5)) for idx, plt in enumerate(plot_list): color = core.color_defaults[idx % len(core.color_defaults)] if use_median: x = list(range(len(plt.percentile50))) y = list(plt.percentile50) y_upper = list(plt.percentile75) y_lower = list(plt.percentile25) else: x = list(range(len(plt.means))) y = list(plt.means) y_upper = list(plt.means + plt.stds) y_lower = list(plt.means - plt.stds) plt.legend = plt.legend.replace('rllab.algos.trpo.TRPO', 'TRPO') plt.legend = plt.legend.replace('rllab.algos.vpg.VPG', 'REINFORCE') plt.legend = plt.legend.replace('rllab.algos.erwr.ERWR', 'ERWR') plt.legend = plt.legend.replace('sandbox.rein.algos.trpo_vime.TRPO', 'TRPO+VIME') plt.legend = plt.legend.replace('sandbox.rein.algos.vpg_vime.VPG', 'REINFORCE+VIME') plt.legend = plt.legend.replace('sandbox.rein.algos.erwr_vime.ERWR', 'ERWR+VIME') plt.legend = plt.legend.replace('0.0001', '1e-4') # plt.legend = plt.legend.replace('0.001', 'TRPO+VIME') # plt.legend = plt.legend.replace('0', 'TRPO') # plt.legend = plt.legend.replace('0.005', 'TRPO+L2') if idx == 0: plt.legend = 'TRPO (0.0)' if idx == 1: plt.legend = 'TRPO+VIME (103.7)' if idx == 2: plt.legend = 'TRPO+L2 (0.0)' ax.fill_between( x, y_lower, y_upper, interpolate=True, facecolor=color, linewidth=0.0, alpha=0.3) if idx == 2: ax.plot(x, y, color=color, label=plt.legend, linewidth=2.0, linestyle="--") else: ax.plot(x, y, color=color, label=plt.legend, linewidth=2.0) ax.grid(True) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) if counter == 1: # ax.set_xlim([0, 120]) ax.set_ylim([-3, 60]) # ax.set_xlim([0, 80]) loc = 'upper left' elif counter == 2: ax.set_ylim([-0.04, 0.4]) # ax.set_ylim([-0.1, 0.4]) ax.set_xlim([0, 2000]) loc = 'upper left' elif counter == 3: # ax.set_xlim([0, 1000]) loc = 'lower right' elif counter == 4: # ax.set_xlim([0, 800]) # ax.set_ylim([0, 2]) loc = 'lower right' leg = ax.legend(loc=loc, prop={'size': 12}, ncol=1) for legobj in leg.legendHandles: legobj.set_linewidth(5.0) def y_fmt(x, y): return str(int(np.round(x / 1000.0))) + 'K' import matplotlib.ticker as tick # ax.xaxis.set_major_formatter(tick.FuncFormatter(y_fmt)) _plt.savefig('tmp' + str(counter) + '.pdf', bbox_inches='tight') def summary_name(exp, selector=None): # if selector is not None: # exclude_params = set([x[0] for x in selector._filters]) # else: # exclude_params = set() # rest_params = set([x[0] for x in distinct_params]).difference(exclude_params) # if len(rest_params) > 0: # name = "" # for k in rest_params: # name += "%s=%s;" % (k.split(".")[-1], str(exp.flat_params.get(k, "")).split(".")[-1]) # return name return exp.params["exp_name"] def check_nan(exp): return all( not np.any(np.isnan(vals)) for vals in list(exp.progress.values())) def get_plot_instruction( plot_keys, split_keys=None, group_keys=None, best_filter_key=None, filters=None, exclusions=None, use_median=False, only_show_best=False, best_based_on_final=False, gen_eps=False, only_show_best_sofar=False, best_is_lowest=False, clip_plot_value=None, plot_width=None, plot_height=None, filter_nan=False, smooth_curve=False, custom_filter=None, legend_post_processor=None, normalize_error=False, custom_series_splitter=None, ): """ A custom filter might look like "lambda exp: exp.flat_params['algo_params_base_kwargs.batch_size'] == 64" """ if filter_nan: nonnan_exps_data = list(filter(check_nan, exps_data)) selector = core.Selector(nonnan_exps_data) else: selector = core.Selector(exps_data) if legend_post_processor is None: legend_post_processor = lambda x: x if filters is None: filters = dict() if exclusions is None: exclusions = [] if split_keys is None: split_keys = [] if group_keys is None: group_keys = [] if plot_height is None: plot_height = 300 * len(plot_keys) for k, v in filters.items(): selector = selector.where(k, str(v)) for k, v in exclusions: selector = selector.where_not(k, str(v)) if custom_filter is not None: selector = selector.custom_filter(custom_filter) if len(split_keys) > 0: split_selectors, split_titles = split_by_keys( selector, split_keys, distinct_params ) else: split_selectors = [selector] split_titles = ["Plot"] plots = [] counter = 1 print("Plot_keys:", plot_keys) print("split_keys:", split_keys) print("group_keys:", group_keys) print("filters:", filters) print("exclusions:", exclusions) for split_selector, split_title in zip(split_selectors, split_titles): if custom_series_splitter is not None: exps = split_selector.extract() splitted_dict = dict() for exp in exps: key = custom_series_splitter(exp) if key not in splitted_dict: splitted_dict[key] = list() splitted_dict[key].append(exp) splitted = list(splitted_dict.items()) group_selectors = [core.Selector(list(x[1])) for x in splitted] group_legends = [x[0] for x in splitted] else: if len(group_keys) > 0: group_selectors, group_legends = split_by_keys( split_selector, group_keys, distinct_params ) else: group_selectors = [split_selector] group_legends = [split_title] list_of_list_of_plot_dicts = [] for plot_key in plot_keys: to_plot = [] for group_selector, group_legend in zip(group_selectors, group_legends): filtered_data = group_selector.extract() if len(filtered_data) == 0: continue if (best_filter_key and best_filter_key not in group_keys and best_filter_key not in split_keys): selectors = split_by_key( group_selector, best_filter_key, distinct_params ) scores = [ get_selector_score(plot_key, selector, use_median, best_based_on_final) for selector in selectors ] if np.isfinite(scores).any(): if best_is_lowest: best_idx = np.nanargmin(scores) else: best_idx = np.nanargmax(scores) best_selector = selectors[best_idx] filtered_data = best_selector.extract() print("For split '{0}', group '{1}':".format( split_title, group_legend, )) print(" best '{0}': {1}".format( best_filter_key, dict(best_selector._filters)[best_filter_key] )) if only_show_best or only_show_best_sofar: # Group by seed and sort. # ----------------------- filtered_params = core.extract_distinct_params( filtered_data, l=0) filtered_params2 = [p[1] for p in filtered_params] filtered_params_k = [p[0] for p in filtered_params] product_space = list(itertools.product( *filtered_params2 )) data_best_regret = None best_regret = np.inf if best_is_lowest else -np.inf kv_string_best_regret = None for idx, params in enumerate(product_space): selector = core.Selector(exps_data) for k, v in zip(filtered_params_k, params): selector = selector.where(k, str(v)) data = selector.extract() if len(data) > 0: progresses = [ exp.progress.get(plot_key, np.array([np.nan])) for exp in data ] sizes = list(map(len, progresses)) max_size = max(sizes) progresses = [ np.concatenate( [ps, np.ones(max_size - len(ps)) * np.nan]) for ps in progresses] if best_based_on_final: progresses = np.asarray(progresses)[:, -1] if only_show_best_sofar: if best_is_lowest: progresses = np.min(np.asarray(progresses), axis=1) else: progresses = np.max(np.asarray(progresses), axis=1) if use_median: medians = np.nanmedian(progresses, axis=0) regret = np.mean(medians) else: means = np.nanmean(progresses, axis=0) regret = np.mean(means) distinct_params_k = [p[0] for p in distinct_params] distinct_params_v = [ v for k, v in zip(filtered_params_k, params) if k in distinct_params_k] distinct_params_kv = [ (k, v) for k, v in zip(distinct_params_k, distinct_params_v)] distinct_params_kv_string = str( distinct_params_kv).replace('), ', ')\t') print( '{}\t{}\t{}'.format(regret, len(progresses), distinct_params_kv_string)) if best_is_lowest: change_regret = regret < best_regret else: change_regret = regret > best_regret if change_regret: best_regret = regret best_progress = progresses data_best_regret = data kv_string_best_regret = distinct_params_kv_string print(group_selector._filters) print('best regret: {}'.format(best_regret)) # ----------------------- if np.isfinite(best_regret): progresses = [ exp.progress.get(plot_key, np.array([np.nan])) for exp in data_best_regret] # progresses = [progress[:500] for progress in progresses ] sizes = list(map(len, progresses)) # more intelligent: max_size = max(sizes) progresses = [ np.concatenate( [ps, np.ones(max_size - len(ps)) * np.nan]) for ps in progresses] legend = '{} (mu: {:.3f}, std: {:.5f})'.format( group_legend, best_regret, np.std(best_progress)) window_size = np.maximum( int(np.round(max_size / float(1000))), 1) statistics = get_statistics( progresses, use_median, normalize_error, ) statistics = process_statistics( statistics, smooth_curve, clip_plot_value, window_size, ) to_plot.append( AttrDict( legend=legend_post_processor(legend), plot_key=plot_key, **statistics ) ) if len(to_plot) > 0 and len(data) > 0: to_plot[-1]["footnote"] = "%s; e.g. %s" % ( kv_string_best_regret, data[0].params.get("exp_name", "NA")) else: to_plot[-1]["footnote"] = "" else: progresses = [ exp.progress.get(plot_key, np.array([np.nan])) for exp in filtered_data ] sizes = list(map(len, progresses)) # more intelligent: max_size = max(sizes) progresses = [ np.concatenate( [ps, np.ones(max_size - len(ps)) * np.nan]) for ps in progresses] window_size = np.maximum( int(np.round(max_size / float(100))), 1, ) statistics = get_statistics( progresses, use_median, normalize_error, ) statistics = process_statistics( statistics, smooth_curve, clip_plot_value, window_size, ) to_plot.append( AttrDict( legend=legend_post_processor(group_legend), plot_key=plot_key, **statistics ) ) if len(to_plot) > 0: list_of_list_of_plot_dicts.append(to_plot) if len(list_of_list_of_plot_dicts) > 0 and not gen_eps: fig_title = split_title plots.append(make_plot( list_of_list_of_plot_dicts, use_median=use_median, title=fig_title, plot_width=plot_width, plot_height=plot_height )) if gen_eps: make_plot_eps(to_plot, use_median=use_median, counter=counter) counter += 1 return "\n".join(plots) def shorten_key(key): """ Convert a dot-map string like "foo.bar.baz" into "f.b.baz" """ *heads, tail = key.split(".") new_key_builder = [] for subkey in heads: if len(subkey) > 0: new_key_builder.append(subkey[0]) new_key_builder.append(tail) return ".".join(new_key_builder) def get_selector_score(key, selector, use_median, best_based_on_final): """ :param key: Thing to measure (e.g. Average Returns, Loss, etc.) :param selector: Selector instance :param use_median: Use the median? Else use the mean :param best_based_on_final: Only look at the final value? Else use all values. :return: A single number that gives the score of `key` inside `selector` """ data = selector.extract() if best_based_on_final: values = [ exp.progress.get(key, np.array([np.nan]))[-1] for exp in data ] else: values = np.concatenate([ exp.progress.get(key, np.array([np.nan])) for exp in data ] or [[np.nan]]) if len(values) == 0 or not np.isfinite(values).all(): return np.nan if use_median: return np.nanpercentile(values, q=50, axis=0) else: return np.nanmean(values) def get_statistics(progresses, use_median, normalize_errors): """ Get some dictionary of statistics (e.g. the median, mean). :param progresses: :param use_median: :param normalize_errors: :return: """ if use_median: return dict( percentile25=np.nanpercentile(progresses, q=25, axis=0), percentile50=np.nanpercentile(progresses, q=50, axis=0), percentile75=np.nanpercentile(progresses, q=75, axis=0), ) else: stds = np.nanstd(progresses, axis=0) if normalize_errors: stds /= np.sqrt(np.sum((1. - np.isnan(progresses)), axis=0)) return dict( means=np.nanmean(progresses, axis=0), stds=stds, ) def process_statistics( statistics, smooth_curve, clip_plot_value, window_size ): """ Smoothen and clip time-series data. """ clean_statistics = {} for k, v in statistics.items(): clean_statistics[k] = v if smooth_curve: clean_statistics[k] = sliding_mean(v, window=window_size) if clip_plot_value is not None: clean_statistics[k] = np.clip( clean_statistics[k], -clip_plot_value, clip_plot_value, ) return clean_statistics def get_possible_values(distinct_params, key): return [vs for k, vs in distinct_params if k == key][0] def split_by_key(selector, key, distinct_params): """ Return a list of selectors based on this selector. Each selector represents one distinct value of `key`. """ values = get_possible_values(distinct_params, key) return [selector.where(key, v) for v in values] def split_by_keys(base_selector, keys, distinct_params): """ Return a list of selectors based on the base_selector. Each selector represents one distinct set of values for each key in `keys`. :param base_selector: :param keys: :param distinct_params: :return: """ list_of_key_and_unique_value = [ [ (key, v) for v in get_possible_values(distinct_params, key) ] for key in keys ] """ elements of list_of_key_and_unique_value should look like: - [(color, red), (color, blue), (color, green), ...] - [(season, spring), (season, summer), (season, fall), ...] We now take the cartesian product so that we get all the combinations, like: - [(color, red), (season, spring)] - [(color, blue), (season, spring)] - ... """ selectors = [] descriptions = [] for key_and_value_list in itertools.product( *list_of_key_and_unique_value ): selector = None keys = [] for key, value in key_and_value_list: keys.append(key) if selector is None: selector = base_selector.where(key, value) else: selector = selector.where(key, value) selectors.append(selector) descriptions.append(", ".join([ "{0}={1}".format( shorten_key(key), value, ) for key, value in key_and_value_list ])) return selectors, descriptions def parse_float_arg(args, key): x = args.get(key, "") try: return float(x) except Exception: return None @app.route("/plot_div") def plot_div(): args = flask.request.args plot_keys_json = args.get("plot_keys") plot_keys = json.loads(plot_keys_json) split_keys_json = args.get("split_keys", "[]") split_keys = json.loads(split_keys_json) group_keys_json = args.get("group_keys", "[]") group_keys = json.loads(group_keys_json) best_filter_key = args.get("best_filter_key", "") filters_json = args.get("filters", "{}") filters = json.loads(filters_json) exclusions_json = args.get("exclusions", "{}") exclusions = json.loads(exclusions_json) if len(best_filter_key) == 0: best_filter_key = None use_median = args.get("use_median", "") == 'True' gen_eps = args.get("eps", "") == 'True' only_show_best = args.get("only_show_best", "") == 'True' best_based_on_final = args.get("best_based_on_final", "") == 'True' only_show_best_sofar = args.get("only_show_best_sofar", "") == 'True' best_is_lowest = args.get("best_is_lowest", "") == 'True' normalize_error = args.get("normalize_error", "") == 'True' filter_nan = args.get("filter_nan", "") == 'True' smooth_curve = args.get("smooth_curve", "") == 'True' clip_plot_value = parse_float_arg(args, "clip_plot_value") plot_width = parse_float_arg(args, "plot_width") plot_height = parse_float_arg(args, "plot_height") custom_filter = args.get("custom_filter", None) custom_series_splitter = args.get("custom_series_splitter", None) if custom_filter is not None and len(custom_filter.strip()) > 0: custom_filter = safer_eval(custom_filter) else: custom_filter = None legend_post_processor = args.get("legend_post_processor", None) if legend_post_processor is not None and len( legend_post_processor.strip()) > 0: legend_post_processor = safer_eval(legend_post_processor) else: legend_post_processor = None if custom_series_splitter is not None and len( custom_series_splitter.strip()) > 0: custom_series_splitter = safer_eval(custom_series_splitter) else: custom_series_splitter = None plot_div = get_plot_instruction( plot_keys=plot_keys, split_keys=split_keys, filter_nan=filter_nan, group_keys=group_keys, best_filter_key=best_filter_key, filters=filters, exclusions=exclusions, use_median=use_median, gen_eps=gen_eps, only_show_best=only_show_best, best_based_on_final=best_based_on_final, only_show_best_sofar=only_show_best_sofar, best_is_lowest=best_is_lowest, clip_plot_value=clip_plot_value, plot_width=plot_width, plot_height=plot_height, smooth_curve=smooth_curve, custom_filter=custom_filter, legend_post_processor=legend_post_processor, normalize_error=normalize_error, custom_series_splitter=custom_series_splitter, ) return plot_div def safer_eval(some_string): """ Not full-proof, but taking advice from: https://nedbatchelder.com/blog/201206/eval_really_is_dangerous.html """ if "__" in some_string or "import" in some_string: raise Exception("string to eval looks suspicious") return eval(some_string, {'__builtins__': {}}) @app.route("/") def index(): if "AverageReturn" in plottable_keys: plot_keys = ["AverageReturn"] elif len(plottable_keys) > 0: plot_keys = plottable_keys[0:1] else: plot_keys = None plot_div = get_plot_instruction(plot_keys=plot_keys) return flask.render_template( "main.html", plot_div=plot_div, plot_keys=plot_keys, group_keys=[], plottable_keys=plottable_keys, distinct_param_keys=[str(k) for k, v in distinct_params], distinct_params=dict([(str(k), list(map(str, v))) for k, v in distinct_params]), ) def reload_data(data_filename): global exps_data global plottable_keys global distinct_params exps_data = core.load_exps_data( args.data_paths, data_filename, args.disable_variant, ) plottable_keys = list( set(flatten(list(exp.progress.keys()) for exp in exps_data))) plottable_keys = sorted([k for k in plottable_keys if k is not None]) distinct_params = sorted(core.extract_distinct_params(exps_data)) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("data_paths", type=str, nargs='*') parser.add_argument("--prefix", type=str, nargs='?', default="???") parser.add_argument("--debug", action="store_true", default=False) parser.add_argument("--port", type=int, default=5000) parser.add_argument("--disable-variant", default=False, action='store_true') parser.add_argument("--dname", default='progress.csv', help='name of data file') args = parser.parse_args(sys.argv[1:]) # load all folders following a prefix if args.prefix != "???": args.data_paths = [] dirname = os.path.dirname(args.prefix) subdirprefix = os.path.basename(args.prefix) for subdirname in os.listdir(dirname): path = os.path.join(dirname, subdirname) if os.path.isdir(path) and (subdirprefix in subdirname): args.data_paths.append(path) print("Importing data from {path}...".format(path=args.data_paths)) reload_data(args.dname) port = args.port try: print("View http://localhost:%d in your browser" % port) app.run(host='0.0.0.0', port=port, debug=args.debug) except OSError as e: if e.strerror == 'Address already in use': print("Port {} is busy. Try specifying a different port with (" "e.g.) --port=5001".format(port))

[ "[email protected]" ]

[email protected]

e87fd0af770bdebc927b353eaa39dd78d958400f

d75f2550f0493a153e144759c8c6c378a4c436b2

/base_kivy_app/graphics.py

e766af9a31226890faa523de76926c8702df8b2d

[ "MIT" ]

permissive

healthonrails/base_kivy_app

c946fab1e130cd4b40eadc45116a2c4eabf460cf

47ade9665c79850909d4f2b6bae3acebaa1a0a3c

refs/heads/master

2020-12-10T18:55:36.819462

2020-01-03T23:36:46

null

0

null

UTF-8

Python

false

35,652

py

'''Graphics ============ ''' from os.path import join, dirname import math from time import perf_counter from functools import partial from inspect import isclass from math import pow, fabs from kivy.lang import Builder from kivy.clock import Clock from kivy.properties import ( NumericProperty, ReferenceListProperty, ObjectProperty, ListProperty, StringProperty, BooleanProperty, DictProperty, AliasProperty, OptionProperty) from kivy.uix.boxlayout import BoxLayout from kivy.uix.scatter import Scatter from kivy.uix.spinner import Spinner, SpinnerOption from kivy.graphics.texture import Texture from kivy.graphics import Rectangle, BindTexture from kivy.graphics.transformation import Matrix from kivy.graphics.fbo import Fbo from kivy.uix.widget import Widget from kivy.uix.label import Label from kivy.core.window import Window from kivy.metrics import dp from kivy.uix.behaviors.button import ButtonBehavior from kivy.uix.behaviors.focus import FocusBehavior from kivy.animation import Sequence, Animation from kivy.factory import Factory from kivy.compat import string_types from kivy.uix.dropdown import DropDown from kivy.uix.textinput import TextInput from kivy.uix.slider import Slider from base_kivy_app.utils import pretty_time __all__ = ( 'EventFocusBehavior', 'BufferImage', 'ErrorIndicatorBase', 'TimeLineSlice', 'TimeLine', 'AutoSizedSpinner', 'EmptyDropDown', 'HighightButtonBehavior') Builder.load_file(join(dirname(__file__), 'graphics.kv')) class AutoSizedSpinnerBehavior(object): '''Spinner that exposes :attr:`minimum_size`, which is the size required to display the texture of the largest item in the spinner. ''' minimum_size = ObjectProperty((0, 0)) '''A 2-tuple containing the texture width and height of the spinner item with the largest texture. Can be used to set the spinner size to ensure it will be big enough to display nicely the largest item. ''' def __init__(self, **kwargs): cls = kwargs.pop('option_cls', self.option_cls) if isinstance(cls, string_types): cls = Factory.get(cls) self.option_cls = partial(self._decorate_class, cls) def decorate_cls(*largs): cls = self.option_cls if isinstance(cls, string_types): cls = Factory.get(cls) if not isclass(cls) or not issubclass(cls, Widget): return self.option_cls = partial(self._decorate_class, cls) self.fbind('option_cls', decorate_cls) self.fbind('texture_size', self._update_min_size) self.fbind('padding', self._update_min_size) super(AutoSizedSpinnerBehavior, self).__init__(**kwargs) self._update_min_size() def _decorate_class(self, cls, *l, **kw): wid = cls(*l, **kw) wid.fbind('texture_size', self._update_min_size) self._update_min_size() return wid def _update_min_size(self, *largs): if not self._dropdown or not self._dropdown.container: widgets = [self] else: widgets = self._dropdown.container.children + [self] w = max((c.texture_size[0] for c in widgets)) h = max((c.texture_size[1] for c in widgets)) self.minimum_size = w + 2 * self.padding_x, h + 2 * self.padding_y class EmptyDropDown(DropDown): def __init__(self, **kwargs): super(EmptyDropDown, self).__init__(container=None, **kwargs) class FollowingLabel(Label): attached_widget = None def show_label(self, widget): self.attached_widget = widget Window.add_widget(self) widget.fbind('center', self._reposition) self.fbind('size', self._reposition) def hide_label(self): Window.remove_widget(self) self.attached_widget.funbind('center', self._reposition) self.funbind('size', self._reposition) def _reposition(self, *largs): # calculate the coordinate of the attached widget in the window # coordinate system win = Window widget = self.attached_widget wx, wy = widget.to_window(*widget.pos) _, wtop = widget.to_window(widget.right, widget.top) # ensure the dropdown list doesn't get out on the X axis, with a # preference to 0 in case the list is too wide. x = wx if x + self.width > win.width: x = win.width - self.width if x < 0: x = 0 self.x = x # determine if we display the dropdown upper or lower to the widget height = self.height h_bottom = wy - height h_top = win.height - (wtop + height) if h_bottom > 0: self.top = wy elif h_top > 0: self.y = wtop else: if h_top < h_bottom: self.top = self.height = wy else: self.y = wtop Builder.load_string(''' <FollowingLabel>: size_hint: None, None size: self.texture_size padding: '6dp', '6dp' color: 0, 0, 0, 1 canvas.before: Color: rgba: 1, 1, 1, 1 Rectangle: size: self.size pos: self.pos <HighightButtonBehavior>: canvas.after: Color: a: .3 if self.hovering else 0 Rectangle: pos: self.pos size: self.size ''') class HighightButtonBehavior(object): show_hover = BooleanProperty(True) hover_text = StringProperty('') hovering = BooleanProperty(False) attached_widget = None tracked_widgets = [] label = None def __init__(self, **kwargs): super(HighightButtonBehavior, self).__init__(**kwargs) if self.show_hover: self.tracked_widgets.append(self.proxy_ref) def on_show_hover(self, *largs): if self.show_hover: self.tracked_widgets.append(self.proxy_ref) else: if self.hovering: self.detach_widget() self.tracked_widgets.remove(self.proxy_ref) def on_hover_text(self, *largs): if self.hovering and self.label: self.label.text = self.hover_text @staticmethod def init_class(): Window.fbind('mouse_pos', HighightButtonBehavior.track_mouse) HighightButtonBehavior.label = FollowingLabel(markup=True) @staticmethod def uninit_class(): Window.funbind('mouse_pos', HighightButtonBehavior.track_mouse) HighightButtonBehavior.label = None HighightButtonBehavior.attached_widget = None del HighightButtonBehavior.tracked_widgets[:] def attach_widget(self): self.hovering = True if self.hover_text and self.label is not None: self.label.show_label(self) self.label.text = self.hover_text HighightButtonBehavior.attached_widget = self def detach_widget(self): self.hovering = False HighightButtonBehavior.attached_widget = None if self.hover_text and self.label is not None: self.label.hide_label() @staticmethod def track_mouse(instance, pos): widget = HighightButtonBehavior.attached_widget if widget: if widget.collide_point(*widget.to_widget(*pos)): return else: widget.detach_widget() for widget in HighightButtonBehavior.tracked_widgets: try: if widget.collide_point(*widget.to_widget(*pos)): widget.attach_widget() break except ReferenceError: pass class SpinnerBehavior(AutoSizedSpinnerBehavior): values = ListProperty() text_autoupdate = BooleanProperty(False) option_cls = ObjectProperty(SpinnerOption) dropdown_cls = ObjectProperty(DropDown) is_open = BooleanProperty(False) sync_height = BooleanProperty(False) def __init__(self, **kwargs): self._dropdown = None super(SpinnerBehavior, self).__init__(**kwargs) fbind = self.fbind build_dropdown = self._build_dropdown fbind('on_release', self._toggle_dropdown) fbind('dropdown_cls', build_dropdown) fbind('option_cls', build_dropdown) fbind('values', self._update_dropdown) fbind('size', self._update_dropdown_size) fbind('text_autoupdate', self._update_dropdown) build_dropdown() def _build_dropdown(self, *largs): if self._dropdown: self._dropdown.unbind(on_select=self._on_dropdown_select) self._dropdown.unbind(on_dismiss=self._close_dropdown) self._dropdown.dismiss() self._dropdown = None cls = self.dropdown_cls if isinstance(cls, string_types): cls = Factory.get(cls) self._dropdown = cls() self._dropdown.bind(on_select=self._on_dropdown_select) self._dropdown.bind(on_dismiss=self._close_dropdown) self._update_dropdown() def _update_dropdown_size(self, *largs): if not self.sync_height: return dp = self._dropdown if not dp: return container = dp.container if not container: return h = self.height for item in container.children[:]: item.height = h def _update_dropdown(self, *largs): dp = self._dropdown cls = self.option_cls values = self.values text_autoupdate = self.text_autoupdate if isinstance(cls, string_types): cls = Factory.get(cls) dp.clear_widgets() for value in values: item = cls(text=value) item.height = self.height if self.sync_height else item.height item.bind(on_release=lambda option: dp.select(option.text)) dp.add_widget(item) if text_autoupdate: if values: if not self.text or self.text not in values: self.text = values[0] else: self.text = '' def _toggle_dropdown(self, *largs): if self.values: self.is_open = not self.is_open def _close_dropdown(self, *largs): self.is_open = False def _on_dropdown_select(self, instance, data, *largs): self.text = data self.is_open = False def on_is_open(self, instance, value): if value: self._dropdown.open(self) else: if self._dropdown.attach_to: self._dropdown.dismiss() class AutoSizedSpinner(AutoSizedSpinnerBehavior, Spinner): pass class EventFocusBehavior(FocusBehavior): ''':class:`~kivy.uix.behaviors.focus.FocusBehavior` based class which converts keyboard events listed in :attr:`keys` into a ``on_key_press`` or ``on_key_release`` event. :Events: `on_key_press`: Triggered when a key that is in :attr:`keys` is pressed. `on_key_release`: Triggered when a key that is in :attr:`keys` is released. ''' __events__ = ('on_key_press', 'on_key_release') keys = ListProperty(['spacebar', 'escape', 'enter']) '''A list of strings that are potential keyboard keys, which trigger key press or key release events. Defaults to `['spacebar', 'escape', 'enter']`. ''' def keyboard_on_key_down(self, window, keycode, text, modifiers): if super(EventFocusBehavior, self).keyboard_on_key_down( window, keycode, text, modifiers): return True if keycode[1] in self.keys: return self.dispatch('on_key_press', keycode[1]) def keyboard_on_key_up(self, window, keycode): if super(EventFocusBehavior, self).keyboard_on_key_up(window, keycode): return True if keycode[1] in self.keys: return self.dispatch('on_key_release', keycode[1]) def on_key_press(self, key): pass def on_key_release(self, key): pass class BufferImage(Scatter): '''Class that displays an image and allows its manipulation using touch. It receives an ffpyplayer :py:class:`~ffpyplayer.pic.Image` object. ''' scale_to_image = BooleanProperty(True) flip = BooleanProperty(False) _iw = NumericProperty(0.) '''The width of the input image. ''' _ih = NumericProperty(0.) '''The height of the input image. ''' available_size = ObjectProperty(None, allownone=True) '''The size that the widget has available for drawing. ''' _last_w = 0 '''The width of the screen region available to display the image. Can be used to determine if the screen size changed and we need to output a different sized image. ''' _last_h = 0 '''The width of the screen region available to display the image. ''' _last_rotation = 0 image_size = ObjectProperty((0, 0)) '''The size of the last image. ''' _fmt = '' '''The input format of the last image passed in, if the format is supported. E.g. rgb24, yuv420p, etc. Otherwise, it's the forma into which the unsupported image is converted into. ''' _sw_src_fmt = '' '''The input format of the last image passed in. ''' _swscale = None '''The SWScale object that converts the image into a supported format. ''' img = None '''Holds the last :class:`~ffpyplayer.pic.Image` passed in. ''' texture_size = ObjectProperty((0, 0)) '''A tuple with the size of the last :class:`~ffpyplayer.pic.Image` that was passed in. ''' img_texture = ObjectProperty(None) '''The texture into which the images are blitted. Defaults to None. ''' color = ListProperty([1, 1, 1, 1]) '''The color in which to display the image. ''' _kivy_ofmt = '' '''Kivy's color format of the image passed in. ''' _tex_y = None ''' The y texture into which the y plane of the images are blitted when yuv420p. ''' _tex_u = None ''' The u texture into which the u plane of the images are blitted when yuv420p. ''' _tex_v = None ''' The v texture into which the v plane of the images are blitted when yuv420p. ''' _fbo = None ''' The Fbo used when blitting yuv420p images. ''' _YUV_RGB_FS = ''' $HEADER$ uniform sampler2D tex_y; uniform sampler2D tex_u; uniform sampler2D tex_v; void main(void) { float y = texture2D(tex_y, tex_coord0).r; float u = texture2D(tex_u, tex_coord0).r - 0.5; float v = texture2D(tex_v, tex_coord0).r - 0.5; float r = y + 1.402 * v; float g = y - 0.344 * u - 0.714 * v; float b = y + 1.772 * u; gl_FragColor = vec4(r, g, b, 1.0); } ''' def on_flip(self, *largs): self.update_img(self.img, True) def update_img(self, img, force=False): ''' Updates the screen with a new image. :Parameters: `img`: :class:`~ffpyplayer.pic.Image` instance The image to be displayed. ''' from ffpyplayer.tools import get_best_pix_fmt from ffpyplayer.pic import SWScale if img is None: return img_fmt = img.get_pixel_format() self.image_size = img_w, img_h = img.get_size() update = force if self._iw != img_w or self._ih != img_h: update = True if img_fmt not in ('yuv420p', 'rgba', 'rgb24', 'gray', 'bgr24', 'bgra'): swscale = self._swscale if img_fmt != self._sw_src_fmt or swscale is None or update: ofmt = get_best_pix_fmt( img_fmt, ( 'yuv420p', 'rgba', 'rgb24', 'gray', 'bgr24', 'bgra')) self._swscale = swscale = SWScale( iw=img_w, ih=img_h, ifmt=img_fmt, ow=0, oh=0, ofmt=ofmt) self._sw_src_fmt = img_fmt img = swscale.scale(img) img_fmt = img.get_pixel_format() w, h = self.available_size or self.size if (not w) or not h: self.img = img return if self._fmt != img_fmt: self._fmt = img_fmt self._kivy_ofmt = { 'yuv420p': 'yuv420p', 'rgba': 'rgba', 'rgb24': 'rgb', 'gray': 'luminance', 'bgr24': 'bgr', 'bgra': 'bgra'}[img_fmt] update = True if update or w != self._last_w or h != self._last_h or \ self.rotation != self._last_rotation: if self.scale_to_image: rotation = self.rotation rot = self.rotation * math.pi / 180 rot_w = abs(img_w * math.cos(rot)) + abs(img_h * math.sin(rot)) rot_h = abs(img_h * math.cos(rot)) + abs(img_w * math.sin(rot)) scalew, scaleh = w / rot_w, h / rot_h scale = min(min(scalew, scaleh), 1) self.transform = Matrix() self.rotation = rotation self.apply_transform(Matrix().scale(scale, scale, 1), post_multiply=True) self.pos = 0, 0 self._iw, self._ih = img_w, img_h self._last_h = h self._last_w = w self._last_rotation = self.rotation self.img = img kivy_ofmt = self._kivy_ofmt if update: self.canvas.remove_group(str(self) + 'image_display') if kivy_ofmt == 'yuv420p': w2 = int(img_w / 2) h2 = int(img_h / 2) self._tex_y = Texture.create(size=(img_w, img_h), colorfmt='luminance') self._tex_u = Texture.create(size=(w2, h2), colorfmt='luminance') self._tex_v = Texture.create(size=(w2, h2), colorfmt='luminance') with self.canvas: self._fbo = fbo = Fbo(size=(img_w, img_h), group=str(self) + 'image_display') with fbo: BindTexture(texture=self._tex_u, index=1) BindTexture(texture=self._tex_v, index=2) Rectangle(size=fbo.size, texture=self._tex_y) fbo.shader.fs = BufferImage._YUV_RGB_FS fbo['tex_y'] = 0 fbo['tex_u'] = 1 fbo['tex_v'] = 2 tex = self.img_texture = fbo.texture fbo.add_reload_observer(self.reload_buffer) else: tex = self.img_texture = Texture.create( size=(img_w, img_h), colorfmt=kivy_ofmt) tex.add_reload_observer(self.reload_buffer) tex.flip_vertical() if self.flip: tex.flip_horizontal() self.texture_size = img_w, img_h if kivy_ofmt == 'yuv420p': dy, du, dv, _ = img.to_memoryview() self._tex_y.blit_buffer(dy, colorfmt='luminance') self._tex_u.blit_buffer(du, colorfmt='luminance') self._tex_v.blit_buffer(dv, colorfmt='luminance') self._fbo.ask_update() self._fbo.draw() else: self.img_texture.blit_buffer(img.to_memoryview()[0], colorfmt=kivy_ofmt) self.canvas.ask_update() def reload_buffer(self, *args): ''' Reloads the last displayed image. It is and should be called whenever the screen size changes or the last image need to be recalculated. ''' if self.img is not None: self.update_img(self.img) def rotate_right_reposition(self): rotation = self.rotation - 90 factor = abs(int(round(rotation / 90))) % 4 self.rotation = math.copysign(factor * 90, rotation) self.reload_buffer() class ErrorIndicatorBehavior(ButtonBehavior): '''A Button based class that visualizes and notifies on the current error status. When pressed, it stops the notification and displays in a popup the list of errors/warnings/infos. Errors are added to the log with :meth:`add_item.` ''' _container = None _level = StringProperty('ok') _alpha = NumericProperty(1.) _anim = None levels = {'error': 0, 'warning': 1, 'info': 2} icon_names = {} count = NumericProperty(0) __events__ = ('on_log_event', ) def __init__(self, **kw): super(ErrorIndicatorBehavior, self).__init__(**kw) a = self._anim = Sequence( Animation(t='in_bounce', _alpha=1.), Animation(t='out_bounce', _alpha=0)) a.repeat = True def add_item(self, text, level='error'): '''Adds a log item to the log. Upon addition, the button will notify with an animation of the item. :Parameters: `text`: str The text of the item. `level`: str Can be one of `error`, `warning`, or `info` indicating the importance of the item. Defaults to `error`. ''' levels = self.levels if level not in levels: raise ValueError('"{}" is not a valid level within "{}"'. format(level, levels.keys())) self.count += 1 if self._level == 'ok': if levels[level] < levels['info']: self._level = level self._anim.start(self) elif levels[level] < levels[self._level]: self._level = level self._container.data.append( {'text': text, 'icon_name': self.icon_names.get(level, level)}) self.dispatch('on_log_event', self, text, level) def on_log_event(self, *largs): pass class ErrorIndicatorBase(ErrorIndicatorBehavior, Widget): pass class TimeLineSlice(Widget): '''A representation of a time slice of :class:`TimeLine`. ''' duration = NumericProperty(0) '''The duration of the slice. ''' elapsed_t = NumericProperty(0) '''The amount of time that has elapsed since the start of this slice. Can be larger than :attr:`duration`, but visually it gets clipped to :attr:`duration`. ''' _scale = NumericProperty(0) color = ObjectProperty(None, allownone=True) '''If not None, it's a list of size 2 indicating the color to use for when the slice is not yet done and when it's done, respectively. When not None, it overwrites the values provided with :attr:`TimeLine.color_odd` and ::`attr.color_even`. ''' _color = ListProperty([(1, 1, 1, 1), (1, 1, 1, 1)]) name = StringProperty('') '''The name of the slice. ''' text = StringProperty('') '''If not empty, rather than displaying :attr:`name` when this slice is active, it'll display this :attr:`text`. ''' class TimeLine(BoxLayout): '''A widget that displays an elapsing time line. It has named time slices indicating e.g. timed stages and the time line progresses through them. Slices are added/removed with :meth:`add_slice`, :meth:`remove_slice`, and :meth:`clear_slices`. :meth:`smear_slices` is used to smear the width of the slices so that they are non-linearly proportional to the provided duration of each slice. To move from one slice to another, :meth:`set_active_slice` must be called. It sets all the previous slices preceding this slice as done. Slices do not automatically finish, without this method being called. Properties of ''' slices = ListProperty([]) '''The list of :class:`TimeLineSlice` visualizing all the slices. ''' slice_names = ListProperty([]) '''The given name corresponding to the slices in :attr:`slices`. They should be unique. ''' current_slice = NumericProperty(None, allownone=True) '''The index in :attr:`slices` that is the current slice. ''' timer = StringProperty('') '''A string version of the amount of time elapsed within the current slice. It gets reset when :meth:`set_active_slice` is called. ''' text = StringProperty('') '''The name of the current slice displayed in the status field. ''' color_odd = ListProperty([(0, .7, .2, 1), (.5, .5, 0, 1)]) '''A list of size 2 indicating the color to use when the slice is not yet done and when it's done for odd slices, respectively. Each item is a 4 tuple indicating the rgba value (0-1) to use. ''' color_even = ListProperty( [(0, .2, .7, 1), (135 / 255., 206 / 255., 250 / 255., 1)]) '''A list of size 2 indicating the color to use when the slice is not yet done and when it's done for even slices, respectively. Each item is a 4 tuple indicating the rgba value (0-1) to use. ''' _start_t = perf_counter() def __init__(self, **kwargs): super(TimeLine, self).__init__(**kwargs) Clock.schedule_interval(self._update_clock, .15) def _update_clock(self, dt): elapsed = perf_counter() - self._start_t self.timer = pretty_time(elapsed) if self.slices and self.current_slice is not None: self.slices[self.current_slice].elapsed_t = elapsed def set_active_slice(self, name, after=None): '''Sets the slice that is the active slice. All the slices preceding this slice will be marked as done and the timer will restart. :Parameters: `name`: str The name of the slice to set as the current slice. It can be the name of a non-existing slice. `after`: str If ``name`` is a non-existing slice, if ``after`` is None, then all the slices preceding, and including the current slice will be marked as done. Otherwise, all the slices preceding and including the named slice will be marked as done. ''' try: idx = self.slice_names.index(name) for s in self.slices[:idx]: s.elapsed_t = max(s.duration, 10000) for s in self.slices[idx:]: s.elapsed_t = 0. self.current_slice = idx except ValueError: if after is not None: idx = self.slice_names.index(after) for s in self.slices[:idx + 1]: s.elapsed_t = max(s.duration, 10000) for s in self.slices[idx + 1:]: s.elapsed_t = 0. elif self.current_slice is not None: for s in self.slices[:self.current_slice + 1]: s.elapsed_t = max(s.duration, 10000) self.current_slice = None self.text = name self._start_t = perf_counter() def clear_slices(self): '''Removes all the slices and clears the time line. ''' for ch in self.box.children[:]: self.box.remove_widget(ch) self.current_slice = None self.slice_names = [] self.slices = [] self._start_t = perf_counter() def update_slice_attrs(self, current_name, **kwargs): '''Called to update the attributes of the :class:`TimeLineSlice` instance associated with the name such as :attr:`TimeLineSlice.duration` etc. Can be used to even rename the slice. :Parameters: `name`: str The name of the slice to update. `**kwargs`: keyword args The names and values of the slice to change. ''' s = self.slices[self.slice_names.index(current_name)] for key, val in kwargs.items(): setattr(s, key, val) self._update_attrs() def _update_attrs(self): widgets = list(reversed(self.box.children)) self.slice_names = [widget.name for widget in widgets] for i, wid in enumerate(widgets): wid._color = self.color_odd if i % 2 else self.color_even def add_slice( self, name, before=None, duration=0, size_hint_x=None, **kwargs): '''Adds a new slice to the timeline. :Parameters: `name`: str The unique name of the new slice to create. `before`: str If not None, the name of the slice before which to create the new slice. Otherwise, the default, it's added at the end. `duration`: float, int The estimated duration of the slice. Defaults to 0. A slice of duration 0 is allowed. `size_hint_x`: float The width size_hint of the slice display. If None, the default, the duration is used as the size hint, otherwise the provided value is used. Since Kivy normalizes the size hints to 1.0, by default the duration is used to scale the displayed width of the slices to their durations. ''' if 'text' not in kwargs: kwargs['text'] = name s = TimeLineSlice( duration=duration, name=name, size_hint_x=size_hint_x if size_hint_x is not None else duration, **kwargs) if before is not None: i = self.slice_names.index(before) old_len = len(self.slices) self.slices.insert(s, i) i = old_len - i else: self.slices.append(s) i = 0 self.box.add_widget(s, index=i) self._update_attrs() def remove_slice(self, name): '''Removes the named slice. :Parameters: `name`: str The name of the slice to remove. ''' s = self.slices.pop(self.slice_names.index(name)) self.box.remove_widget(s) self._update_attrs() def smear_slices(self, exponent=3): '''Smears the width of the slices in a non-linear manner so that the width of each slice become less exactly related to the duration of the slice. It is useful to prevent some slices being huge and other tiny. Overall, the algorithm normalizes exponentiated durations to their mean exponentiated value. :Parameters: `exponent`: float, int The exponent to use when smearing the slices. Defaults to 3. ''' widgets = self.box.children vals = [w.duration for w in widgets if w.duration] mn, mx = min(vals), max(vals) center = (mn + mx) / 2. a = pow(mx - center, exponent) offset = abs(pow(mn - center, exponent) / a) def f(x): return max((2 * pow(x - center, exponent) / a) + offset, offset) for w in widgets: w.size_hint_x = f(w.duration) class FlatTextInput(TextInput): pass class TimeSliceSelection(Widget): low_handle = NumericProperty(0) high_handle = NumericProperty(1) min = NumericProperty(0) max = NumericProperty(1) low_val = NumericProperty(0) high_val = NumericProperty(1) _working = False def __init__(self, **kwargs): super(TimeSliceSelection, self).__init__(**kwargs) self.fbind('min', self._update_handles) self.fbind('max', self._update_handles) self.fbind('width', self._update_handles) self.fbind('x', self._update_handles) self.fbind('low_val', self._update_handles) self.fbind('high_val', self._update_handles) def _update_handles(self, *largs): if self._working: return lowval = self.low_val highval = self.high_val mn = self.min mx = self.max if lowval < mn: self.low_val = mn return if highval > mx: self.high_val = mx return if lowval > highval: self._working = True self.low_val = highval self._working = False self.high_val = lowval return self.low_handle = self.to_size(lowval - mn) + self.x self.high_handle = self.to_size(highval - mn) + self.x def to_size(self, value): '''value is the state value. returns in size. ''' diff = float(self.max - self.min) w = self.width if not diff or not w: return 0 return value / diff * w def to_state(self, value): '''value is the size value. returns in state. ''' diff = float(self.max - self.min) w = float(self.width) if not diff or not w: return 0 return value / w * diff def on_touch_down(self, touch): if super(TimeSliceSelection, self).on_touch_down(touch): return True if not self.collide_point(*touch.pos): return False tol = dp(2) if self.low_handle - tol <= touch.x <= self.high_handle + tol: if self.low_handle + tol <= touch.x <= self.high_handle - tol: touch.ud['{0}.{1}'.format('timeslice', self.uid)] = 'center' else: touch.ud['{0}.{1}'.format('timeslice', self.uid)] = 'side' return True return False def on_touch_move(self, touch): if super(TimeSliceSelection, self).on_touch_move(touch): return True drag_type = touch.ud.get('{0}.{1}'.format('timeslice', self.uid)) if drag_type not in ('center', 'side'): return False dx = touch.dx start = touch.x - dx positive = dx > 0 tol = dp(2) diff = self.to_state(dx) if drag_type == 'center': if self.low_handle <= start <= self.high_handle: if positive: diff = min(diff, self.max - self.high_val) self.high_val += diff # right side should move first self.low_val += diff else: diff = max(diff, self.min - self.low_val) self.low_val += diff # left side should move first self.high_val += diff return True is_low = self.low_handle - tol <= start <= self.low_handle + tol is_high = self.high_handle - tol <= start <= self.high_handle + tol if is_low and is_high: if self.low_handle == self.high_handle: if positive: is_low = False else: is_high = False else: if fabs(self.low_handle - start) <= \ fabs(self.high_handle - start): is_high = False else: is_low = False if is_low: self.low_val = min( max(self.min, self.low_val + diff), self.high_val) else: self.high_val = min( max(self.low_val, self.high_val + diff), self.max) class FlatSlider(Slider): __events__ = ('on_release', ) def on_release(self, *largs): pass def on_touch_up(self, touch): if super(FlatSlider, self).on_touch_up(touch): if touch.grab_current == self: self.dispatch('on_release', self) return True Factory.register('AutoSizedSpinnerBehavior', cls=AutoSizedSpinnerBehavior) Factory.register('SpinnerBehavior', cls=SpinnerBehavior) Factory.register('EventFocusBehavior', cls=EventFocusBehavior) Factory.register('ErrorIndicatorBehavior', cls=ErrorIndicatorBehavior) Factory.register('HighightButtonBehavior', cls=HighightButtonBehavior)

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[email protected]

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""" https://en.wikipedia.org/wiki/Bubble_sort Worst-case performance: O(N^2) If you call bubble_sort(arr,True), you can see the process of the sort Default is simulation = False """ def bubble_sort(arr, simulation=False): def swap(i, j): arr[i], arr[j] = arr[j], arr[i] n = len(arr) swapped = True iteration = 0 if simulation: print("iteration", iteration, ":", *arr) x = -1 while swapped: swapped = False x = x + 1 for i in range(1, n - x): if arr[i - 1] > arr[i]: swap(i - 1, i) swapped = True if simulation: iteration = iteration + 1 print("iteration", iteration, ":", *arr) return arr

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[email protected]

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gswyhq/hello-world

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#!/usr/bin/env python # coding: utf-8 # # 3.10 多层感知机的简洁实现 # In[ ]: import torch from torch import nn from torch.nn import init import numpy as np import sys import torchvision print(torch.__version__) # ## 3.10.1 定义模型 # In[11]: num_inputs, num_outputs, num_hiddens = 784, 10, 256 class FlattenLayer(torch.nn.Module): def __init__(self): super(FlattenLayer, self).__init__() def forward(self, x): # x shape: (batch, *, *, ...) return x.view(x.shape[0], -1) net = nn.Sequential( FlattenLayer(), nn.Linear(num_inputs, num_hiddens), nn.ReLU(), nn.Linear(num_hiddens, num_outputs), ) for params in net.parameters(): init.normal_(params, mean=0, std=0.01) # ## 3.10.2 读取数据并训练模型 # In[12]: batch_size = 256 def load_data_fashion_mnist(batch_size, resize=None, root='~/Datasets/FashionMNIST'): """Download the fashion mnist dataset and then load into memory.""" trans = [] if resize: trans.append(torchvision.transforms.Resize(size=resize)) trans.append(torchvision.transforms.ToTensor()) transform = torchvision.transforms.Compose(trans) mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=transform) mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, download=True, transform=transform) if sys.platform.startswith('win'): num_workers = 0 # 0表示不用额外的进程来加速读取数据 else: num_workers = 4 train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers) test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers) return train_iter, test_iter def evaluate_accuracy(data_iter, net, device=None): if device is None and isinstance(net, torch.nn.Module): # 如果没指定device就使用net的device device = list(net.parameters())[0].device acc_sum, n = 0.0, 0 with torch.no_grad(): for X, y in data_iter: if isinstance(net, torch.nn.Module): net.eval() # 评估模式, 这会关闭dropout acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item() net.train() # 改回训练模式 else: # 自定义的模型, 3.13节之后不会用到, 不考虑GPU if('is_training' in net.__code__.co_varnames): # 如果有is_training这个参数 # 将is_training设置成False acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item() else: acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() n += y.shape[0] return acc_sum / n def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params=None, lr=None, optimizer=None): for epoch in range(num_epochs): train_l_sum, train_acc_sum, n = 0.0, 0.0, 0 for X, y in train_iter: y_hat = net(X) l = loss(y_hat, y).sum() # 梯度清零 if optimizer is not None: optimizer.zero_grad() elif params is not None and params[0].grad is not None: for param in params: param.grad.data.zero_() l.backward() if optimizer is None: sgd(params, lr, batch_size) else: optimizer.step() # “softmax回归的简洁实现”一节将用到 train_l_sum += l.item() train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item() n += y.shape[0] test_acc = evaluate_accuracy(test_iter, net) print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f' % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc)) train_iter, test_iter = load_data_fashion_mnist(batch_size) loss = torch.nn.CrossEntropyLoss() optimizer = torch.optim.SGD(net.parameters(), lr=0.5) num_epochs = 5 train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer) # 来源： https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/code/chapter03_DL-basics/3.10_mlp-pytorch.ipynb

[ "[email protected]" ]

[email protected]

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/homepage/core/admin.py

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# -*- Python -*- # -*- coding: utf-8 -*- # # alec aivazis # # this file describes the base administration for homepage # homepage imports from .models import Project, ProjectScreenshot # import the django admin from django.contrib import admin # register the base models admin.site.register(Project) admin.site.register(ProjectScreenshot) # end of file

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# 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 azure.identity import DefaultAzureCredential from azure.mgmt.networkcloud import NetworkCloudMgmtClient """ # PREREQUISITES pip install azure-identity pip install azure-mgmt-networkcloud # USAGE python agent_pools_create.py Before run the sample, please set the values of the client ID, tenant ID and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET. For more info about how to get the value, please see: https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal """ def main(): client = NetworkCloudMgmtClient( credential=DefaultAzureCredential(), subscription_id="123e4567-e89b-12d3-a456-426655440000", ) response = client.agent_pools.begin_create_or_update( resource_group_name="resourceGroupName", kubernetes_cluster_name="kubernetesClusterName", agent_pool_name="agentPoolName", agent_pool_parameters={ "extendedLocation": { "name": "/subscriptions/123e4567-e89b-12d3-a456-426655440000/resourceGroups/resourceGroupName/providers/Microsoft.ExtendedLocation/customLocations/clusterExtendedLocationName", "type": "CustomLocation", }, "location": "location", "properties": { "administratorConfiguration": { "adminUsername": "azure", "sshPublicKeys": [ { "keyData": "ssh-rsa AAtsE3njSONzDYRIZv/WLjVuMfrUSByHp+jfaaOLHTIIB4fJvo6dQUZxE20w2iDHV3tEkmnTo84eba97VMueQD6OzJPEyWZMRpz8UYWOd0IXeRqiFu1lawNblZhwNT/ojNZfpB3af/YDzwQCZgTcTRyNNhL4o/blKUmug0daSsSXISTRnIDpcf5qytjs1Xo+yYyJMvzLL59mhAyb3p/cD+Y3/s3WhAx+l0XOKpzXnblrv9d3q4c2tWmm/SyFqthaqd0= admin@vm" } ], }, "agentOptions": {"hugepagesCount": 96, "hugepagesSize": "1G"}, "attachedNetworkConfiguration": { "l2Networks": [ { "networkId": "/subscriptions/123e4567-e89b-12d3-a456-426655440000/resourceGroups/resourceGroupName/providers/Microsoft.NetworkCloud/l2Networks/l2NetworkName", "pluginType": "DPDK", } ], "l3Networks": [ { "ipamEnabled": "False", "networkId": "/subscriptions/123e4567-e89b-12d3-a456-426655440000/resourceGroups/resourceGroupName/providers/Microsoft.NetworkCloud/l3Networks/l3NetworkName", "pluginType": "SRIOV", } ], "trunkedNetworks": [ { "networkId": "/subscriptions/123e4567-e89b-12d3-a456-426655440000/resourceGroups/resourceGroupName/providers/Microsoft.NetworkCloud/trunkedNetworks/trunkedNetworkName", "pluginType": "MACVLAN", } ], }, "availabilityZones": ["1", "2", "3"], "count": 3, "labels": [{"key": "kubernetes.label", "value": "true"}], "mode": "System", "taints": [{"key": "kubernetes.taint", "value": "true"}], "upgradeSettings": {"maxSurge": "1"}, "vmSkuName": "NC_M16_v1", }, "tags": {"key1": "myvalue1", "key2": "myvalue2"}, }, ).result() print(response) # x-ms-original-file: specification/networkcloud/resource-manager/Microsoft.NetworkCloud/stable/2023-07-01/examples/AgentPools_Create.json if __name__ == "__main__": main()

[ "[email protected]" ]

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import logging import ibmsecurity.utilities.tools logger = logging.getLogger(__name__) def get_all(isdsAppliance, check_mode=False, force=False): """ Retrieving all interfaces :rtype: (str, dict) """ return isdsAppliance.invoke_get("Retrieving all interfaces", "/widgets/mgmtinterface") def get_all_app(isdsAppliance, check_mode=False, force=False): """ Retrieving all application interfaces :rtype: (str, dict) """ return isdsAppliance.invoke_get("Retrieving all application interfaces", "/application_interfaces") def get(isdsAppliance, uuid, check_mode=False, force=False): """ Retrieving a single interface """ return isdsAppliance.invoke_get("Retrieving a single interface", "/application_interfaces/" + uuid + "/addresses/1") def compare(isdsAppliance1, isdsAppliance2): """ Compare interfaces between 2 appliances """ ret_obj1 = get_all(isdsAppliance1) ret_obj2 = get_all(isdsAppliance2) return ibmsecurity.utilities.tools.json_compare(ret_obj1, ret_obj2)

[ "[email protected]" ]

[email protected]

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Apache-2.0

2021-12-30T13:43:08

2020-06-22T08:09:05

Python

UTF-8

Python

false

3,805

py

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """External function interface to MIOpen library.""" # pylint: disable-msg=C0103 import ctypes import numpy as np from .. import api as _api from .. import intrin as _intrin from .. import get_global_func as _get_global_func def _get_np_int32_array_handle(arr): """Return a void_p handle for a numpy array Parameters ---------- arr: numpy.NDArray source numpy array Returns ------- ptr: ctypes.c_void_p pointer to the data """ assert arr.dtype == np.int32 ptr = arr.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)) return ctypes.cast(ptr, ctypes.c_void_p) def conv2d_forward(x, w, stride_h=1, stride_w=1, pad_h=0, pad_w=0, dilation_h=1, dilation_w=1, conv_mode=0, data_type=1, group_count=1): """Create an extern op that compute 2D convolution with MIOpen Parameters ---------- x: Tensor input feature map w: Tensor convolution weight stride_h: int height stride stride_w: int width stride pad_h: int height pad pad_w: int weight pad dilation_h: int height dilation dilation_w: int width dilation conv_mode: int 0: miopenConvolution 1: miopenTranspose data_type: int 0: miopenHalf (fp16) 1: miopenFloat (fp32) group_count: int number of groups Returns ------- y: Tensor The result tensor """ assert (0 <= conv_mode <= 2), "0: miopenConvolution / 1: miopenTranspose / 2: miopenGroupConv" if group_count > 1: conv_mode = 2 oshape = np.zeros((len(x.shape)), dtype=np.int32) xshape = x.shape wshape = w.shape setup_func = _get_global_func("tvm.contrib.miopen.conv2d.setup") algo = setup_func(conv_mode, data_type, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, xshape[0].value, xshape[1].value, xshape[2].value, xshape[3].value, wshape[0].value, wshape[1].value, wshape[2].value, wshape[3].value, group_count, _get_np_int32_array_handle(oshape)) return _api.extern( list(oshape), [x, w], lambda ins, outs: _intrin.call_packed( "tvm.contrib.miopen.conv2d.forward", conv_mode, data_type, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, algo, ins[0], ins[1], outs[0]), name="y")

[ "[email protected]" ]

[email protected]

ef9da3bac625f676f56fdc1d3a6ff80c1630d9da

62e58c051128baef9452e7e0eb0b5a83367add26

/x12/4060/224004060.py

0cfd110dffd480626cfac7c195c4d02697106c2d

[]

no_license

dougvanhorn/bots-grammars

2eb6c0a6b5231c14a6faf194b932aa614809076c

09db18d9d9bd9d92cefbf00f1c0de1c590fe3d0d

refs/heads/master

2021-05-16T12:55:58.022904

2019-05-17T15:22:23

105,274,633

0

null

2017-09-29T13:21:21

null

UTF-8

Python

false

416

py

from bots.botsconfig import * from records004060 import recorddefs syntax = { 'version' : '00403', #version of ISA to send 'functionalgroup' : 'MA', } structure = [ {ID: 'ST', MIN: 1, MAX: 1, LEVEL: [ {ID: 'CF1', MIN: 1, MAX: 1}, {ID: 'CF2', MIN: 1, MAX: 9999, LEVEL: [ {ID: 'L11', MIN: 0, MAX: 99}, ]}, {ID: 'SE', MIN: 1, MAX: 1}, ]} ]

[ "[email protected]" ]

[email protected]

cfd2f57ade3b18556861fc73ac6feeca514adc38

e4d39c514c8f555a706d4d293b91e253a23614aa

/manage.py

572ea681f23fba520e85784690e8183a78c12971

[]

no_license

shubhamjain31/AskForum

ddda79528026ed027e2268b99cfc5cb9ea412c54

f234f9ad1fb8d55520ced6987b1aec5d74fc7c08

refs/heads/main

2023-03-10T21:41:25.559793

2021-02-26T16:52:58

309,034,117

0

null

UTF-8

Python

false

664

py

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): """Run administrative tasks.""" os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'AskForum.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "[email protected]" ]

[email protected]

50ef32c94d5e5aee734232dd3e61fc70bf587629

8773e8c9b9a0a6e407f91b6f7c6321141d7e8356

/P0113.py

0b8fdc5ce3434443cc3611041a335ba30d4bcad7

[]

no_license

westgate458/LeetCode

1836bb21e8dd95386ccab390f5fd04567a429a02

36d7f9e967a62db77622e0888f61999d7f37579a

refs/heads/master

2021-12-28T04:16:36.875737

2021-12-17T05:48:09

152,928,584

0

null

UTF-8

Python

false

1,231

py

# -*- coding: utf-8 -*- """ Created on Wed Apr 3 13:23:08 2019 @author: Tianqi Guo """ class Solution(object): def pathSum(self, root, sum): """ :type root: TreeNode :type sum: int :rtype: List[List[int]] """ # deal with trivial case if not root: return [] # if current node is a leaf elif not root.left and not root.right: # if remaining sum is equal to current value if sum == root.val: # current leaf is part of the desired path return [[root.val]] # if current leaf doesn't give the desired sum else: # return empty list return [] # if current node is not a leaf else: # 1) recursively check the child branches, which return lists of values that add up to the desired sum # 2) for the combined lists, add the value current node to each entry # 3) return the updated list of path values to previous level return [[root.val] + combo for combo in self.pathSum(root.left, sum - root.val) + self.pathSum(root.right, sum - root.val)]

[ "[email protected]" ]

[email protected]

f4440770a165003e26a1fe82ab270ff926180faa

b822fd48d109c59a07cfef5196888c3f22c792b3

/aae/train.py

0c434501c97952da300c1a76736c15a79a31d1cb

[ "MIT" ]

permissive

cupOJoseph/drawlikebobross

2e179f24bc59303be2782d95880235c57995a460

e4c33745c605d17ea6b9e5bea3cf339eb875a58a

refs/heads/master

2022-05-05T12:18:45.504161

2018-07-05T02:21:40

null

0

null

UTF-8

Python

false

1,237

py

import os import sys import argparse import torch import torchvision.transforms as transforms sys.path.append(os.path.dirname(__file__)) from trainer import gan_trainer from loader import BobRossDataset # Params parser = argparse.ArgumentParser(description='GAN trainer') parser.add_argument('--epoch', default=500, type=int) parser.add_argument('--lr', default=0.001, type=float) parser.add_argument('--cuda', default='true', type=str) parser.add_argument('--resume', default='', type=str) args, unknown = parser.parse_known_args() cuda = True if 'true' in args.cuda.lower() else False # cuda = True transformers = transforms.Compose([ transforms.ToTensor(), ]) # Gan trainer trainer = gan_trainer(z_dim=8, h_dim=128, filter_num=64, channel_num=3, lr=args.lr, cuda=cuda) if __name__ == '__main__': if args.resume: trainer.load_(args.resume) # dataset train_dataset = BobRossDataset('../dataset/bobross.h5py', transform=transformers) train_loader = torch.utils.data.DataLoader( dataset=train_dataset, batch_size=8, shuffle=True, pin_memory=cuda, num_workers=4 ) for e in range(trainer.start_epoch, args.epoch): trainer.train(train_loader, e) trainer.save_(e)

[ "[email protected]" ]

[email protected]

75302cb9e4d1e3d5f8bf29b9814776112b3b0823

ab0f2794733a129d4073542a1c9315f95f1b7ca8

/hello.py

ddddd1c76ee9aebeeffa87a41cc017b685828c26

[]

no_license

krishna-prasath/guvi

d0fbe631e932888ba426f77ba4feaa32d4e66781

98fd72ffa03e5d8e7a9fe68989924b690cd92c1b

refs/heads/master

2020-04-15T04:59:38.243149

2019-06-12T19:32:59

164,404,997

0

null

UTF-8

Python

false

62

py

a=int(input()) if a==0: print(" ") else: print("Hello"*a)

[ "[email protected]" ]

[email protected]

faa0232a40c211a3852add071f93ba865508361c

f445450ac693b466ca20b42f1ac82071d32dd991

/generated_tempdir_2019_09_15_163300/generated_part002186.py

828488582f050dee16fc5a1431eafefa81c4dca2

[]

no_license

Upabjojr/rubi_generated

76e43cbafe70b4e1516fb761cabd9e5257691374

cd35e9e51722b04fb159ada3d5811d62a423e429

refs/heads/master

2020-07-25T17:26:19.227918

2019-09-15T15:41:48

208,357,412

4

1

null

UTF-8

Python

false

1,304

py

from sympy.abc import * from matchpy.matching.many_to_one import CommutativeMatcher from matchpy import * from matchpy.utils import VariableWithCount from collections import deque from multiset import Multiset from sympy.integrals.rubi.constraints import * from sympy.integrals.rubi.utility_function import * from sympy.integrals.rubi.rules.miscellaneous_integration import * from sympy import * class CommutativeMatcher14833(CommutativeMatcher): _instance = None patterns = { 0: (0, Multiset({}), [ (VariableWithCount('i2.2.1.4.1.1.0', 1, 1, None), Mul), (VariableWithCount('i2.2.1.4.1.1.0_1', 1, 1, S(1)), Mul) ]) } subjects = {} subjects_by_id = {} bipartite = BipartiteGraph() associative = Mul max_optional_count = 1 anonymous_patterns = set() def __init__(self): self.add_subject(None) @staticmethod def get(): if CommutativeMatcher14833._instance is None: CommutativeMatcher14833._instance = CommutativeMatcher14833() return CommutativeMatcher14833._instance @staticmethod def get_match_iter(subject): subjects = deque([subject]) if subject is not None else deque() subst0 = Substitution() # State 14832 return yield from collections import deque

[ "[email protected]" ]

[email protected]

67c3f9ec9c939f9d356a5fd38f10e6df68ba4e5e

1d928c3f90d4a0a9a3919a804597aa0a4aab19a3

/python/celery/2016/4/test_filesystem.py

e96039d394100b3efa36fe17417d2f22b8d73151

[]

no_license

rosoareslv/SED99

d8b2ff5811e7f0ffc59be066a5a0349a92cbb845

a062c118f12b93172e31e8ca115ce3f871b64461

refs/heads/main

2023-02-22T21:59:02.703005

2021-01-28T19:40:51

306,497,459

1

null

2020-11-24T20:56:18

2020-10-23T01:18:07

null

UTF-8

Python

false

2,471

py

# -*- coding: utf-8 -*- from __future__ import absolute_import, unicode_literals import os import shutil import tempfile from celery import states from celery.backends.filesystem import FilesystemBackend from celery.exceptions import ImproperlyConfigured from celery.utils import uuid from celery.tests.case import AppCase, skip @skip.if_win32() class test_FilesystemBackend(AppCase): def setup(self): self.directory = tempfile.mkdtemp() self.url = 'file://' + self.directory self.path = self.directory.encode('ascii') def teardown(self): shutil.rmtree(self.directory) def test_a_path_is_required(self): with self.assertRaises(ImproperlyConfigured): FilesystemBackend(app=self.app) def test_a_path_in_url(self): tb = FilesystemBackend(app=self.app, url=self.url) self.assertEqual(tb.path, self.path) def test_path_is_incorrect(self): with self.assertRaises(ImproperlyConfigured): FilesystemBackend(app=self.app, url=self.url + '-incorrect') def test_missing_task_is_PENDING(self): tb = FilesystemBackend(app=self.app, url=self.url) self.assertEqual(tb.get_state('xxx-does-not-exist'), states.PENDING) def test_mark_as_done_writes_file(self): tb = FilesystemBackend(app=self.app, url=self.url) tb.mark_as_done(uuid(), 42) self.assertEqual(len(os.listdir(self.directory)), 1) def test_done_task_is_SUCCESS(self): tb = FilesystemBackend(app=self.app, url=self.url) tid = uuid() tb.mark_as_done(tid, 42) self.assertEqual(tb.get_state(tid), states.SUCCESS) def test_correct_result(self): data = {'foo': 'bar'} tb = FilesystemBackend(app=self.app, url=self.url) tid = uuid() tb.mark_as_done(tid, data) self.assertEqual(tb.get_result(tid), data) def test_get_many(self): data = {uuid(): 'foo', uuid(): 'bar', uuid(): 'baz'} tb = FilesystemBackend(app=self.app, url=self.url) for key, value in data.items(): tb.mark_as_done(key, value) for key, result in tb.get_many(data.keys()): self.assertEqual(result['result'], data[key]) def test_forget_deletes_file(self): tb = FilesystemBackend(app=self.app, url=self.url) tid = uuid() tb.mark_as_done(tid, 42) tb.forget(tid) self.assertEqual(len(os.listdir(self.directory)), 0)

[ "[email protected]" ]

[email protected]

37ec8231587eda9a2df9ede1cf5ec2282956d8c1

d4d3b859f136154427c36924f87525590853873a

/Tools.py

b104a92fdf8abdde26ffeaa6ddae63c1d381c369

[]

no_license

lijiunderstand/Semantic_Segmentation_RefineNet

46b002f53254d5cc0bb4b9565382d2386a1d01c9

ec7ea477096dafc2052fa74fdb3277199251a35f

refs/heads/master

2020-04-18T10:16:16.303654

2018-07-11T16:41:18

null

0

null

UTF-8

Python

false

2,346

py

import time import os from tensorflow.python import pywrap_tensorflow import numpy as np from matplotlib import pyplot as plt import cv2 class Tools: def __init__(self): pass @staticmethod def print_info(info): print(time.strftime("%H:%M:%S", time.localtime()), info) pass # 新建目录 @staticmethod def new_dir(path): if not os.path.exists(path): os.makedirs(path) return path @staticmethod def print_ckpt(ckpt_path): reader = pywrap_tensorflow.NewCheckpointReader(ckpt_path) var_to_shape_map = reader.get_variable_to_shape_map() for key in var_to_shape_map: print("tensor_name: ", key) print(reader.get_tensor(key)) pass pass pass class Visualize: @staticmethod def _discrete_matshow_adaptive(data, labels_names=[], title=""): fig_size = [7, 6] plt.rcParams["figure.figsize"] = fig_size cmap = plt.get_cmap('Paired', np.max(data) - np.min(data) + 1) mat = plt.matshow(data, cmap=cmap, vmin=np.min(data) - .5, vmax=np.max(data) + .5) cax = plt.colorbar(mat, ticks=np.arange(np.min(data), np.max(data) + 1)) if labels_names: cax.ax.set_yticklabels(labels_names) if title: plt.suptitle(title, fontsize=15, fontweight='bold') fig = plt.gcf() fig.savefig('data/tmp.jpg', dpi=300) img = cv2.imread('data/tmp.jpg') return img @staticmethod def visualize_segmentation_adaptive(predictions, segmentation_class_lut, title="Segmentation"): # TODO: add non-adaptive visualization function, where the colorbar # will be constant with names unique_classes, relabeled_image = np.unique(predictions, return_inverse=True) relabeled_image = relabeled_image.reshape(predictions.shape) labels_names = [] for index, current_class_number in enumerate(unique_classes): labels_names.append(str(index) + ' ' + segmentation_class_lut[current_class_number]) im = Visualize._discrete_matshow_adaptive(data=relabeled_image, labels_names=labels_names, title=title) return im pass

[ "[email protected]" ]

[email protected]

428f590f1df538a492bd7a601fcb55bf5ce4ee3b

81fff1188c6918fbe7ccbcd9e61b62456f0abef5

/backend/settings.py

4531b84a2b3ef8fe367cd5479e52c1a85a274c8d

[ "MIT" ]

permissive

mugash/cookbook-graphql-backend

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refs/heads/master

2020-12-02T16:13:25.043539

2017-07-07T09:02:42

96,519,903

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2

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""" Django settings for backend project. Generated by 'django-admin startproject' using Django 1.11.3. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'ng5ptohji7_9@u(e&az$ljy4(#ai+tj#dcj-hg92wdrjxdpcx6' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'graphene_django', 'ingredients' ] 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 = 'backend.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 = 'backend.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.11/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/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Africa/Nairobi' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' GRAPHENE = { 'SCHEMA': 'backend.schema.schema' }

[ "[email protected]" ]

[email protected]

9d12a804190337836e997d0e7f88d8a22da76e8b

84147502b81451a2f9bcaabc00a35789afe132f0

/fastapistudy/test_chapter08.py

abf3208c284c9c77487e9a7b074f1a6fe1c9b59a

[]

no_license

teng-tt/Fastapi_Study

bfdb3ca9f97cf8e2a928f56a77d0fc17c5bb9692

946decd07b0de98ce353d4008c7920c778a94a6f

refs/heads/master

2023-06-01T14:57:43.811721

2021-06-13T03:50:55

360,474,922

1

0

null

UTF-8

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py

# !/usr/bin/python3 # -*- coding:utf-8 -*- # __author__ = "Teng" from fastapi.testclient import TestClient from run import app """ 测试用例 """ client = TestClient(app) # 先安装pytest def test_run_bg_task(): # 主义不是async def h函数test开头是一种pytest规范 response = client.post(url="/chapter08/background_tasks?framework=FastApi") assert response.status_code == 200 assert response.json() == {"message": "任务已在后台运行"} def test_dependency_run_bg_task(): response = client.post(url="/chapter08/dependency/background_tasks") assert response.status_code == 200 assert response.json() is None def test_dependency_run_bg_task_q(): response = client.post(url="/chapter08/dependency/background_tasks?q=1") assert response.status_code == 200 assert response.json() == {"message": "README.md更新成功"}

[ "[email protected]" ]

[email protected]

6127053660627a2dde6c74165c90c823c64c299b

564d6a4d305a8ac6a7e01c761831fb2081c02d0f

/sdk/network/azure-mgmt-network/azure/mgmt/network/v2019_04_01/aio/operations/_ddos_custom_policies_operations.py

eac7c4ae02f97170fa3e3be18ebca8027cc97483

[ "LicenseRef-scancode-generic-cla", "LGPL-2.1-or-later", "MIT" ]

permissive

paultaiton/azure-sdk-for-python

69af4d889bac8012b38f5b7e8108707be679b472

d435a1a25fd6097454b7fdfbbdefd53e05029160

refs/heads/master

2023-01-30T16:15:10.647335

2020-11-14T01:09:50

283,343,691

0

MIT

2020-07-28T22:43:43

null

UTF-8

Python

false

22,058

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, Callable, Dict, Generic, Optional, TypeVar, Union import warnings 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 T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class DdosCustomPoliciesOperations: """DdosCustomPoliciesOperations 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.v2019_04_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, ddos_custom_policy_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 = "2019-04-01" accept = "application/json" # Construct URL url = self._delete_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'ddosCustomPolicyName': self._serialize.url("ddos_custom_policy_name", ddos_custom_policy_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.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/ddosCustomPolicies/{ddosCustomPolicyName}'} # type: ignore async def begin_delete( self, resource_group_name: str, ddos_custom_policy_name: str, **kwargs ) -> AsyncLROPoller[None]: """Deletes the specified DDoS custom policy. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param ddos_custom_policy_name: The name of the DDoS custom policy. :type ddos_custom_policy_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: True for ARMPolling, False for no polling, or a polling object for 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, ddos_custom_policy_name=ddos_custom_policy_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, {}) if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **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/ddosCustomPolicies/{ddosCustomPolicyName}'} # type: ignore async def get( self, resource_group_name: str, ddos_custom_policy_name: str, **kwargs ) -> "models.DdosCustomPolicy": """Gets information about the specified DDoS custom policy. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param ddos_custom_policy_name: The name of the DDoS custom policy. :type ddos_custom_policy_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: DdosCustomPolicy, or the result of cls(response) :rtype: ~azure.mgmt.network.v2019_04_01.models.DdosCustomPolicy :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.DdosCustomPolicy"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" # Construct URL url = self.get.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'ddosCustomPolicyName': self._serialize.url("ddos_custom_policy_name", ddos_custom_policy_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('DdosCustomPolicy', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosCustomPolicies/{ddosCustomPolicyName}'} # type: ignore async def _create_or_update_initial( self, resource_group_name: str, ddos_custom_policy_name: str, parameters: "models.DdosCustomPolicy", **kwargs ) -> "models.DdosCustomPolicy": cls = kwargs.pop('cls', None) # type: ClsType["models.DdosCustomPolicy"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/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'), 'ddosCustomPolicyName': self._serialize.url("ddos_custom_policy_name", ddos_custom_policy_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(parameters, 'DdosCustomPolicy') 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('DdosCustomPolicy', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('DdosCustomPolicy', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosCustomPolicies/{ddosCustomPolicyName}'} # type: ignore async def begin_create_or_update( self, resource_group_name: str, ddos_custom_policy_name: str, parameters: "models.DdosCustomPolicy", **kwargs ) -> AsyncLROPoller["models.DdosCustomPolicy"]: """Creates or updates a DDoS custom policy. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param ddos_custom_policy_name: The name of the DDoS custom policy. :type ddos_custom_policy_name: str :param parameters: Parameters supplied to the create or update operation. :type parameters: ~azure.mgmt.network.v2019_04_01.models.DdosCustomPolicy :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: True for ARMPolling, False for no polling, or a polling object for 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 DdosCustomPolicy or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.DdosCustomPolicy] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["models.DdosCustomPolicy"] 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, ddos_custom_policy_name=ddos_custom_policy_name, parameters=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('DdosCustomPolicy', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **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/ddosCustomPolicies/{ddosCustomPolicyName}'} # type: ignore async def _update_tags_initial( self, resource_group_name: str, ddos_custom_policy_name: str, parameters: "models.TagsObject", **kwargs ) -> "models.DdosCustomPolicy": cls = kwargs.pop('cls', None) # type: ClsType["models.DdosCustomPolicy"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._update_tags_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'ddosCustomPolicyName': self._serialize.url("ddos_custom_policy_name", ddos_custom_policy_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(parameters, 'TagsObject') body_content_kwargs['content'] = body_content request = self._client.patch(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]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('DdosCustomPolicy', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _update_tags_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosCustomPolicies/{ddosCustomPolicyName}'} # type: ignore async def begin_update_tags( self, resource_group_name: str, ddos_custom_policy_name: str, parameters: "models.TagsObject", **kwargs ) -> AsyncLROPoller["models.DdosCustomPolicy"]: """Update a DDoS custom policy tags. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param ddos_custom_policy_name: The name of the DDoS custom policy. :type ddos_custom_policy_name: str :param parameters: Parameters supplied to the update DDoS custom policy resource tags. :type parameters: ~azure.mgmt.network.v2019_04_01.models.TagsObject :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: True for ARMPolling, False for no polling, or a polling object for 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 DdosCustomPolicy or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.DdosCustomPolicy] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["models.DdosCustomPolicy"] 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._update_tags_initial( resource_group_name=resource_group_name, ddos_custom_policy_name=ddos_custom_policy_name, parameters=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('DdosCustomPolicy', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, **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_update_tags.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosCustomPolicies/{ddosCustomPolicyName}'} # type: ignore

[ "[email protected]" ]

[email protected]

a6e2b69a7dd2c15bf8f960ee53cf86c2fca9e9cd

9eb35d6df7b0490d556623f84dba12bb05f30ee2

/models_and_validation/cross_validation.py

6b2524e4812c1b21e05c66400b87e28df0741375

[ "MIT" ]

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FelSiq/statistics-related

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refs/heads/master

2021-11-24T12:31:08.660652

2021-11-03T23:42:39

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"""Tests with cross-validation. General and simple method used for estimating unknown parameters from data. General algorithm: 1. Randomly partition the data X of size n into X_{train} and X_{test} Let m = X_{test}.size Therefore, X_{train}.size = n - m 2. Fit the model using X_{train} 3. Test the fitted model using X_{test} 4. Repeat t times and average the results Some of the most known Cross-validation procedures: k-fold CV: partition the data X into k (approximately) equal-sized subsets. t = k and m = n/k (tests of every subset once.) Leave-one-out (LOO) CV: m = 1, t = n, testing on every sample once. (The same as K-fold CV with k = n). Monte Carlo CV: randomly sample subsets of suitable size for the desired number of times. """ import typing as t import numpy as np def kfold_cv( X: np.ndarray, k: int = 10, shuffle: bool = True, return_inds: bool = False, random_state: t.Optional[int] = None, ) -> t.Iterator[t.Tuple[np.ndarray, np.ndarray]]: """K-fold Cross Validation.""" if not isinstance(k, (int, np.int, np.int32, np.int64)): raise TypeError("'k' must be an integer (got {}.)".format(type(k))) if k <= 1: raise ValueError("'k' must be a greater than 1 (got {}.)".format(k)) n_samples = X.size if X.ndim == 1 else X.shape[0] if n_samples < max(2, k): raise ValueError("Insufficient number of instances ({}). " "Required num_inst >= max(2, k)".format(n_samples)) test_size = int(n_samples / k) uneven_extra_inds = n_samples - k * test_size indices = np.arange(n_samples) if shuffle: if random_state is not None: np.random.seed(random_state) np.random.shuffle(indices) for _ in np.arange(k): split_index = test_size + int(uneven_extra_inds > 0) uneven_extra_inds -= 1 if return_inds: yield indices[:split_index], indices[split_index:] else: yield X[indices[:split_index]], X[indices[split_index:]] indices = np.roll(indices, -split_index) def loo_cv( X: np.ndarray, shuffle: bool = True, return_inds: bool = False, random_state: t.Optional[int] = None, ) -> t.Iterator[t.Tuple[np.ndarray, np.ndarray]]: """LOOCV (Leave-one-out Cross Validation). This is the same as n-fold Cross Validation (k = n). """ n_samples = X.size if X.ndim == 1 else X.shape[0] for fold in kfold_cv( X=X, k=n_samples, shuffle=shuffle, return_inds=return_inds, random_state=random_state): yield fold def jackknife( X: np.ndarray, k: int = 0, shuffle: bool = True, return_inds: bool = False, random_state: t.Optional[int] = None, ) -> t.Iterator[np.ndarray]: """Jackknife iterator. The jackknife procedure partitions the ``X`` data into k folds, and, unlike the Cross Validation procedure, returns just the `kept/train` examples. If k <= 0, then k = `number of instances` is used. """ n_samples = X.size if X.ndim == 1 else X.shape[0] k = n_samples if k <= 0 else k for _, train_vals in kfold_cv( X=X, k=k, shuffle=shuffle, return_inds=return_inds, random_state=random_state): yield train_vals def monte_carlo_cv(X: np.ndarray, test_frac: float = 0.2, n: int = 10, return_inds: bool = False, random_state: t.Optional[int] = None ) -> t.Iterator[t.Tuple[np.ndarray, np.ndarray]]: """Monte Carlo Cross Validation.""" if not isinstance(test_frac, float): raise ValueError("'test_frac' must be float type (got {}.)".format( type(test_frac))) if not isinstance(n, int): raise TypeError("'n' must be an integer (got {}.)".format(type(n))) if n <= 0: raise ValueError("'n' must be a positive value (got {}.)".format(n)) if not 0 < test_frac < 1: raise ValueError( "'test_frac' must be in (0.0, 1.0) interval (got {}.)".format( test_frac)) n_samples = X.size if X.ndim == 1 else X.shape[0] if n_samples < 2: raise ValueError("Number of samples must be greater than 1 " "(got {}.)".format(n_samples)) test_size = int(test_frac * n_samples) if test_size == 0: raise ValueError( "Test subset with 0 instances. Please choose a higher 'test_frac' (got {}.)" .format(test_frac)) if random_state is not None: np.random.seed(random_state) indices = np.arange(n_samples) for _ in np.arange(n): np.random.shuffle(indices) inds_test, inds_train = np.split(indices, [test_size]) if return_inds: yield inds_test, inds_train else: yield X[inds_test], X[inds_train] def _test(): for fold in monte_carlo_cv(np.arange(2), test_frac=0.99, random_state=1): print(fold) if __name__ == "__main__": _test()

[ "[email protected]" ]

[email protected]

abc24fb1a2d224e5ce351a3c7c1e216546bed8fa

e6208febf7e34d4108422c6da54453373733a421

/sdks/python/client/argo_workflows/model/io_argoproj_workflow_v1alpha1_gcs_artifact_repository.py

721344cf443c2cbdacfa8ae5df74a17003b32c37

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permissive

wreed4/argo

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41f94310b0f7fee1ccd533849bb3af7f1ad4f672

refs/heads/master

2023-01-22T05:32:12.254485

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""" Argo Server API You can get examples of requests and responses by using the CLI with `--gloglevel=9`, e.g. `argo list --gloglevel=9` # noqa: E501 The version of the OpenAPI document: VERSION Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from argo_workflows.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) from ..model_utils import OpenApiModel from argo_workflows.exceptions import ApiAttributeError def lazy_import(): from argo_workflows.model.secret_key_selector import SecretKeySelector globals()['SecretKeySelector'] = SecretKeySelector class IoArgoprojWorkflowV1alpha1GCSArtifactRepository(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ lazy_import() return { 'bucket': (str,), # noqa: E501 'key_format': (str,), # noqa: E501 'service_account_key_secret': (SecretKeySelector,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'bucket': 'bucket', # noqa: E501 'key_format': 'keyFormat', # noqa: E501 'service_account_key_secret': 'serviceAccountKeySecret', # noqa: E501 } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 """IoArgoprojWorkflowV1alpha1GCSArtifactRepository - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) bucket (str): Bucket is the name of the bucket. [optional] # noqa: E501 key_format (str): KeyFormat is defines the format of how to store keys. Can reference workflow variables. [optional] # noqa: E501 service_account_key_secret (SecretKeySelector): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """IoArgoprojWorkflowV1alpha1GCSArtifactRepository - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) bucket (str): Bucket is the name of the bucket. [optional] # noqa: E501 key_format (str): KeyFormat is defines the format of how to store keys. Can reference workflow variables. [optional] # noqa: E501 service_account_key_secret (SecretKeySelector): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

[ "[email protected]" ]

[email protected]

feb5e81dff37b3e59bb6b25fe4a2ad1dd53ee5f0

4591684136ac81244d5337197e97f58864d7fff3

/keras/keras18_ensemble1.py

afb0ad3b17ce43849797d7ecfda41ba1b2a9b692

[]

no_license

marattang/AI_training

4b15e9d9734d77ae04beaae078749c85d832c9c5

f7f1a2b762dcf770335b62ee668ad1c54ccf1ceb

refs/heads/main

2023-06-20T19:05:10.385238

2021-07-26T00:29:10

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from tensorflow.keras.models import Model from tensorflow.keras.layers import Dense, Input import numpy as np from tensorflow.keras.layers import concatenate, Concatenate from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score x1 = np.array([range(100), range(301, 401), range(1, 101)]) x2 = np.array([range(101, 201), range(411, 511), range(100, 200)]) x1 = np.transpose(x1) x2 = np.transpose(x2) y1 = np.array([range(1001, 1101)]) # y1 = np.array(range(1001, 1101)) [] 빼면 (100,) y1 = np.transpose(y1) print(x1.shape, x2.shape, y1.shape) # x1_train, x1_test, x2_train, x2_test, y_train, y_test = train_test_split(x1, x2, y1, test_size=0.2, random_state=8, shuffle=True) x1_train, x1_test, x2_train, x2_test, y_train, y_test = train_test_split(x1, x2, y1, random_state=8, shuffle=True) print(x1_train.shape, x2_train.shape, y_train.shape) print(x1_test.shape, x2_test.shape, y_test.shape) # 모델 구성 # 실습 # #2-1 모델1 input1 = Input(shape=(3,)) dense1 = Dense(55, activation='relu', name='dense1')(input1) dense2 = Dense(32, activation='relu', name='dense2')(dense1) dense3 = Dense(26, activation='relu', name='dense3')(dense2) output1 = Dense(18)(dense3) # #2-2 모델2 input2 = Input(shape=(3,)) dense11 = Dense(45, activation='relu', name='dense11')(input2) dense12 = Dense(28, activation='relu', name='dense12')(dense11) dense13 = Dense(20, activation='relu', name='dense13')(dense12) dense14 = Dense(10, activation='relu', name='dense14')(dense13) output2 = Dense(7)(dense14) merge1 = concatenate([output1, output2]) # 첫번째 모델의 가장 마지막 부분, 두번째 모델의 가장 마지막 부분 병합. # 과제 4. Concentenate로 코딩 merge1 = Concatenate(axis=1)([output1, output2]) merge2 = Dense(24)(merge1) merge3 = Dense(15, activation='relu')(merge2) last_output = Dense(1)(merge3) # last_output = Dense(1)(merge1) model = Model(inputs=[input1, input2], outputs=last_output) # model.summary() # 3. 컴파일, 훈련 model.compile(loss = 'mse', optimizer='adam', metrics=['mae']) # metrics=['mae','mse] # 매트릭스를 보면 list로 받아들이고 있기 때문에 2개 이상을 쓰는 것도 가능하다. model.fit([x1_train, x2_train], y_train, epochs=400, batch_size=25, verbose=1, validation_split=0.1) # # 4. 평가, 예측 result = model.evaluate([x1_test, x2_test], y_test) # evaluate는 loss와 metrics를 출력한다. print('result : ', result) y_predict = model.predict([x1_test, x2_test]) r2 = r2_score(y_test, y_predict) print('r2 스코어 : ', r2) print('loss : ', result[0]) print('metrics["mae"] : ', result[1]) #r2 스코어 : 0.9914715240776343 -> 0.9997684219501827 # loss 소수점단위까지 낮추기 -> 0.20147289335727692

[ "[email protected]" ]

[email protected]

e23cff71c1b73f9b5b94aefde10c99cbf6be3d6d

66fb1005aaeb25735a1ae9197ab7dd371862bbf2

/sysadmin_scripts/mongodb_data_model_3/updateDB.py

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[ "MIT" ]

permissive

jfnavarro/st_misc

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bb8c1f2c4f05343f6dd5cc8b8cd8f405d825bd31

refs/heads/master

2021-01-01T17:13:58.540991

2017-08-22T13:04:26

98,029,163

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#! /usr/bin/env python """ Script to convert ST API database model 2 to model 3 @author: Jose Fernandez """ import argparse import os import sys try: from pymongo import MongoClient from pymongo import errors from bson.objectid import ObjectId except ImportError, e: sys.stderr.write("Pymongo was not found, aborting...\n") sys.exit(1) def usage(): print "Usage:" print " python updateDB.py [options]" print "Options:" print " [-a, --user] => username for the MongoDB admin" print " [-d, --password] => password for the MongoDB admin" print " [-c, --host] => (default localhost)" print " [-p, --port] => (default 27017)" print "Description:" print " Updates the ST database from data model 2 to data model 3." print " NOTE: It is a wise idea to manually run mongodump to create a backup of the data state prior to the update!" def main(user, password, host, port): print "Connecting to database..." mongoConnection = 0 try: mongoConnection = MongoClient(host, port) except errors.AutoReconnect: print 'Cannot connect to database. \nPlease manually start up MongoDB.' sys.exit(1) print "mongoConnection" , mongoConnection print "Authorizing..." try: db_admin = mongoConnection["admin"] db_admin.authenticate(user, password) print "Authorization Ok!" except TypeError,e: sys.stderr.write("There was an error in the authentication: " + str(e) + "\n") sys.exit(1) ############################################################################################################### db_analysis = mongoConnection["analysis"] datasets = db_analysis["dataset"] datasetinfos = db_analysis["datasetinfo"] imagealignments = db_analysis["imagealignment"] chips = db_analysis["chip"] # Remove the experiment database mongoConnection.drop_database("experiment") # Remove some fields in analysis.dataset datasets.update_many({}, {'$unset' : { 'overall_feature_count' : 1}}) datasets.update_many({}, {'$unset' : { 'overall_hit_count' : 1}}) datasets.update_many({}, {'$unset' : { 'unique_barcode_count' : 1}}) datasets.update_many({}, {'$unset' : { 'overall_hit_quartiles' : 1}}) datasets.update_many({}, {'$unset' : { 'gene_pooled_hit_quartiles' : 1}}) datasets.update_many({}, {'$unset' : { 'obo_foundry_terms' : 1}}) # Remove one field in analaysis.dataset datasetinfos.update_many({}, {'$unset' : { 'comment' : 1}}) # Update the analysis.dataset collection to add the fields from analysis.imagealignment for ele in datasets.find(): try: dataset_id = ele["_id"] al_id = ele["image_alignment_id"] valid = True if al_id is None or al_id == "": valid = False else: al = imagealignments.find_one({"_id": ObjectId(al_id)}) if al is None or al == "": valid = False if valid: datasets.update_one({"_id": dataset_id}, {"$set": {"figureHE": al["figure_blue"]}}) datasets.update_one({"_id": dataset_id}, {"$set": {"figureCy3": al["figure_red"]}}) datasets.update_one({"_id": dataset_id}, {"$set": {"alignmentMatrix": al["alignment_matrix"]}}) datasets.update_one({"_id": dataset_id}, {"$set": {"dataFile": str(dataset_id) + "_stdata.tsv.gz"}}) datasets.update_one({"_id": dataset_id}, {"$set": {"files": []}}) else: datasets.delete_one({"_id": dataset_id}) except KeyError: continue datasets.delete_one({"_id": dataset_id}) # Remove image_alignment_id field from analysis.dataset datasets.update_many({}, {'$unset' : { 'image_alignment_id' : 1}}) # Remove analysis.imagealignment and analysis.chip imagealignments.drop() chips.drop() if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-c', '--host', type=str, default="localhost", help='Address of the host to connect to') parser.add_argument('-p', '--port', type=int, default=27017, help='Port of the host to connect to') parser.add_argument('-a', '--user', required=True, type=str, help='the user name for the admin of the database') parser.add_argument('-d', '--password', required=True, type=str, help='the password for the admin of the database') args = parser.parse_args() main(args.user, args.password, args.host, args.port)

[ "[email protected]" ]

[email protected]

b0a4eaabb5ac8a0fbcc6c8266ba143827102a7db

6b98eeaf6eb485e1cc4d56c3eda15b6482f21296

/app/grandchallenge/evaluation/migrations/0003_config_new_results_are_public.py

d09b3fcc4484ec754dc2e22c6730305c3811c093

[ "Apache-2.0" ]

permissive

cnbillow/grand-challenge.org

ef2db96c7bc6919aa7ee993d43978f8c3185a71f

de90bd01ca6aa883dcb47c4d005bd15f38549752

refs/heads/master

2020-03-28T03:07:41.905924

2018-09-06T04:45:57

null

0

null

UTF-8

Python

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# -*- coding: utf-8 -*- # Generated by Django 1.11.12 on 2018-04-03 10:27 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [("evaluation", "0002_config_submission_page_html")] operations = [ migrations.AddField( model_name="config", name="new_results_are_public", field=models.BooleanField( default=True, help_text="If true, new results are automatically made public. If false, the challenge administrator must manually publish each new result.", ), ) ]

[ "[email protected]" ]

[email protected]

5ba4cad4c0f578ee5023846bff798403f454451d

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p03548/s413867262.py

f0f0fea98a3638b5746c91a4b886907789bfdba3

[]

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

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false

66

py

x,y,z = map(int,input().split()) x -= z r = x // (y + z) print(r)

[ "[email protected]" ]

[email protected]

25901eb4746bde899cc2b41588f21da0d665c13b

3d19e1a316de4d6d96471c64332fff7acfaf1308

/Users/E/elleryq/haodooscraper.py

7afbaca75ca4d510bc65104eb77ff5d74af43eac

[]

no_license

BerilBBJ/scraperwiki-scraper-vault

4e98837ac3b1cc3a3edb01b8954ed00f341c8fcc

65ea6a943cc348a9caf3782b900b36446f7e137d

refs/heads/master

2021-12-02T23:55:58.481210

2013-09-30T17:02:59

null

0

null

UTF-8

Python

false

13,488

py

# For scrape Haodoo (http://www.haodoo.net) import scraperwiki import simplejson import lxml.html import sys from urlparse import parse_qs from urllib import urlencode import traceback base_url = 'http://www.haodoo.net' def parse_books_from_html( html ): """ Parse the url of each book from the book list page. The book's title and url will be stored in sqlite database provided by scraperwiki. """ root = lxml.html.fromstring(html) for a in root.cssselect("a"): href = a.attrib['href'] if href.startswith( '?' ): s = href[1:] else: s = href book_title = a.text_content() d = parse_qs( s ) if d.has_key('M') and d['M'][0] in ('book', 'Share'): if d.has_key('P'): book_id = d['P'][0] book = { 'id': book_id, 'url': base_url + href, 'title': book_title } scraperwiki.sqlite.save(unique_keys=["id"], data=book, table_name="bookpages" ) def find_volume_id( onclick ): """ Find book id from the given string. The string actually is javascript function. """ # find which kind of quote, ' or " quote = "'" start = onclick.find( quote ) if start==-1: quote = '"' id = '' start = onclick.find( quote ) end = onclick.rfind( quote ) if start!=-1 and end!=-1: id = onclick[ start+1:end ] return id def convert_to_dl_url( id, ext ): """ According book_id and book type to generate download url. """ result = base_url + "/?" + urlencode( { "M": "d", "P": id + "." + ext } ) #print( "__convert_to_dl_url()=%s" % result ) return result def extract_set_title( html ): start_pos = html.find( 'SetTitle("' ) if start_pos == -1: return ("", "") start_quote = html.find( '"', start_pos ) if start_quote == -1: return ("", "") end_quote = html.find( '"', start_quote+1 ) if end_quote == -1: return ("", "") set_title = html[ start_quote+1: end_quote-1 ] set_title = set_title.replace( '《', ',' ) r = set_title.split(',') if len(r)!=2: return ("", "" ) return r def analysis_book_html_and_save( book, html ): doc = lxml.html.fromstring( html ) volume_author, volume_name = extract_set_title( html ) pdb_download_elements = doc.xpath( '//a[contains(@href, "pdb")]' ) if len(pdb_download_elements): # old style page, only readonline and download link. save_item = pdb_download_elements[0] title = save_item.getprevious().text author = None if save_item is not None and save_item.getprevious() and save_item.getprevious().getprevious(): author = save_item.getprevious().getprevious().text volume = { 'id': book['id'], 'bookid': book['id'], } if title: volume['title'] = title else: volume['title'] = volume_name if author: volume['author'] = author else: volume['author'] = volume_author scraperwiki.sqlite.save( unique_keys=["volumeid", "type"], data={ "volumeid": book['id'], "type": "pdb", "link": base_url + "/" + save_item.attrib['href'] }, table_name="volumeexts" ) scraperwiki.sqlite.save(unique_keys=["id"], data=volume, table_name="bookvolumes" ) else: volume = None exts = [] for save_item in doc.xpath('//input[contains(@type, "button")]'): onclick = save_item.get('onclick') id = find_volume_id( onclick ) skip = False if "ReadOnline" in onclick or "ReadPdbOnline" in onclick: if volume is not None: for ext in exts: scraperwiki.sqlite.save(unique_keys=["volumeid", "type"], data=ext, table_name="volumeexts" ) scraperwiki.sqlite.save(unique_keys=["id"], data=volume, table_name="bookvolumes" ) volume = { 'id': id, 'author': save_item.getprevious().text, 'title': save_item.getprevious().tail, 'bookid': book['id'], } exts = [] elif "DownloadEpub" in onclick: dl_link = convert_to_dl_url( id, "epub" ) exts.append( { "volumeid": id, "type": "epub", "link": dl_link } ) elif "DownloadUpdb" in onclick: dl_link = convert_to_dl_url( id, "updb" ) exts.append( { "volumeid": id, "type": "updb", "link": dl_link } ) elif "DownloadPdb" in onclick: dl_link = convert_to_dl_url( id, "pdb" ) exts.append( { "volumeid": id, "type": "pdb", "link": dl_link } ) if volume: for ext in exts: scraperwiki.sqlite.save(unique_keys=["volumeid", "type"], data=ext, table_name="volumeexts" ) scraperwiki.sqlite.save(unique_keys=["id"], data=volume, table_name="bookvolumes" ) # # Main # def main(): urls = [ 'http://www.haodoo.net/?M=hd&P=wisdom', 'http://www.haodoo.net/?M=hd&P=history', 'http://www.haodoo.net/?M=hd&P=martial', 'http://www.haodoo.net/?M=hd&P=mystery', 'http://www.haodoo.net/?M=hd&P=romance', 'http://www.haodoo.net/?M=hd&P=scifi', 'http://www.haodoo.net/?M=hd&P=fiction', ] skip_stage1 = False try: print( ">>> Stage 1 - Collecting all book urls <<<" ) if not skip_stage1: for url in urls: html = scraperwiki.scrape(url) page = 1 while True: suburl = "{0}-{1}".format( url, page ) if html.find( suburl[suburl.find('?'):] ): html = scraperwiki.scrape( suburl ) if html.find("404")!=-1: break parse_books_from_html( html ) page = page + 1 else: break print( ">>> Stage 2 - Analysising all book urls <<<" ) for book in scraperwiki.sqlite.select("* from bookpages"): # grab html html = scraperwiki.scrape( book['url'] ) # analysis and store information into book analysis_book_html_and_save( book, html ) print( ">>> State 3 - done <<<" ) except Exception, e: print( "Got exception:" ) print( e ) print( traceback.format_exc() ) main() # For scrape Haodoo (http://www.haodoo.net) import scraperwiki import simplejson import lxml.html import sys from urlparse import parse_qs from urllib import urlencode import traceback base_url = 'http://www.haodoo.net' def parse_books_from_html( html ): """ Parse the url of each book from the book list page. The book's title and url will be stored in sqlite database provided by scraperwiki. """ root = lxml.html.fromstring(html) for a in root.cssselect("a"): href = a.attrib['href'] if href.startswith( '?' ): s = href[1:] else: s = href book_title = a.text_content() d = parse_qs( s ) if d.has_key('M') and d['M'][0] in ('book', 'Share'): if d.has_key('P'): book_id = d['P'][0] book = { 'id': book_id, 'url': base_url + href, 'title': book_title } scraperwiki.sqlite.save(unique_keys=["id"], data=book, table_name="bookpages" ) def find_volume_id( onclick ): """ Find book id from the given string. The string actually is javascript function. """ # find which kind of quote, ' or " quote = "'" start = onclick.find( quote ) if start==-1: quote = '"' id = '' start = onclick.find( quote ) end = onclick.rfind( quote ) if start!=-1 and end!=-1: id = onclick[ start+1:end ] return id def convert_to_dl_url( id, ext ): """ According book_id and book type to generate download url. """ result = base_url + "/?" + urlencode( { "M": "d", "P": id + "." + ext } ) #print( "__convert_to_dl_url()=%s" % result ) return result def extract_set_title( html ): start_pos = html.find( 'SetTitle("' ) if start_pos == -1: return ("", "") start_quote = html.find( '"', start_pos ) if start_quote == -1: return ("", "") end_quote = html.find( '"', start_quote+1 ) if end_quote == -1: return ("", "") set_title = html[ start_quote+1: end_quote-1 ] set_title = set_title.replace( '《', ',' ) r = set_title.split(',') if len(r)!=2: return ("", "" ) return r def analysis_book_html_and_save( book, html ): doc = lxml.html.fromstring( html ) volume_author, volume_name = extract_set_title( html ) pdb_download_elements = doc.xpath( '//a[contains(@href, "pdb")]' ) if len(pdb_download_elements): # old style page, only readonline and download link. save_item = pdb_download_elements[0] title = save_item.getprevious().text author = None if save_item is not None and save_item.getprevious() and save_item.getprevious().getprevious(): author = save_item.getprevious().getprevious().text volume = { 'id': book['id'], 'bookid': book['id'], } if title: volume['title'] = title else: volume['title'] = volume_name if author: volume['author'] = author else: volume['author'] = volume_author scraperwiki.sqlite.save( unique_keys=["volumeid", "type"], data={ "volumeid": book['id'], "type": "pdb", "link": base_url + "/" + save_item.attrib['href'] }, table_name="volumeexts" ) scraperwiki.sqlite.save(unique_keys=["id"], data=volume, table_name="bookvolumes" ) else: volume = None exts = [] for save_item in doc.xpath('//input[contains(@type, "button")]'): onclick = save_item.get('onclick') id = find_volume_id( onclick ) skip = False if "ReadOnline" in onclick or "ReadPdbOnline" in onclick: if volume is not None: for ext in exts: scraperwiki.sqlite.save(unique_keys=["volumeid", "type"], data=ext, table_name="volumeexts" ) scraperwiki.sqlite.save(unique_keys=["id"], data=volume, table_name="bookvolumes" ) volume = { 'id': id, 'author': save_item.getprevious().text, 'title': save_item.getprevious().tail, 'bookid': book['id'], } exts = [] elif "DownloadEpub" in onclick: dl_link = convert_to_dl_url( id, "epub" ) exts.append( { "volumeid": id, "type": "epub", "link": dl_link } ) elif "DownloadUpdb" in onclick: dl_link = convert_to_dl_url( id, "updb" ) exts.append( { "volumeid": id, "type": "updb", "link": dl_link } ) elif "DownloadPdb" in onclick: dl_link = convert_to_dl_url( id, "pdb" ) exts.append( { "volumeid": id, "type": "pdb", "link": dl_link } ) if volume: for ext in exts: scraperwiki.sqlite.save(unique_keys=["volumeid", "type"], data=ext, table_name="volumeexts" ) scraperwiki.sqlite.save(unique_keys=["id"], data=volume, table_name="bookvolumes" ) # # Main # def main(): urls = [ 'http://www.haodoo.net/?M=hd&P=wisdom', 'http://www.haodoo.net/?M=hd&P=history', 'http://www.haodoo.net/?M=hd&P=martial', 'http://www.haodoo.net/?M=hd&P=mystery', 'http://www.haodoo.net/?M=hd&P=romance', 'http://www.haodoo.net/?M=hd&P=scifi', 'http://www.haodoo.net/?M=hd&P=fiction', ] skip_stage1 = False try: print( ">>> Stage 1 - Collecting all book urls <<<" ) if not skip_stage1: for url in urls: html = scraperwiki.scrape(url) page = 1 while True: suburl = "{0}-{1}".format( url, page ) if html.find( suburl[suburl.find('?'):] ): html = scraperwiki.scrape( suburl ) if html.find("404")!=-1: break parse_books_from_html( html ) page = page + 1 else: break print( ">>> Stage 2 - Analysising all book urls <<<" ) for book in scraperwiki.sqlite.select("* from bookpages"): # grab html html = scraperwiki.scrape( book['url'] ) # analysis and store information into book analysis_book_html_and_save( book, html ) print( ">>> State 3 - done <<<" ) except Exception, e: print( "Got exception:" ) print( e ) print( traceback.format_exc() ) main()

[ "[email protected]" ]

[email protected]

7291d8ba50828814176f7abb193c7cde1e7ba1c6

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/tests/test_decompressor_decompressobj.py

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glandium/python-zstandard

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import unittest import zstandard as zstd class TestDecompressor_decompressobj(unittest.TestCase): def test_simple(self): data = zstd.ZstdCompressor(level=1).compress(b"foobar") dctx = zstd.ZstdDecompressor() dobj = dctx.decompressobj() self.assertEqual(dobj.unused_data, b"") self.assertEqual(dobj.unconsumed_tail, b"") self.assertFalse(dobj.eof) self.assertEqual(dobj.decompress(data), b"foobar") self.assertEqual(dobj.unused_data, b"") self.assertEqual(dobj.unconsumed_tail, b"") self.assertTrue(dobj.eof) self.assertEqual(dobj.flush(), b"") self.assertEqual(dobj.flush(10), b"") self.assertEqual(dobj.flush(length=100), b"") self.assertEqual(dobj.unused_data, b"") self.assertEqual(dobj.unconsumed_tail, b"") def test_input_types(self): compressed = zstd.ZstdCompressor(level=1).compress(b"foo") dctx = zstd.ZstdDecompressor() mutable_array = bytearray(len(compressed)) mutable_array[:] = compressed sources = [ memoryview(compressed), bytearray(compressed), mutable_array, ] for source in sources: dobj = dctx.decompressobj() self.assertEqual(dobj.unused_data, b"") self.assertEqual(dobj.unconsumed_tail, b"") self.assertFalse(dobj.eof) self.assertEqual(dobj.flush(), b"") self.assertEqual(dobj.flush(10), b"") self.assertEqual(dobj.flush(length=100), b"") self.assertEqual(dobj.decompress(source), b"foo") self.assertEqual(dobj.unused_data, b"") self.assertEqual(dobj.unconsumed_tail, b"") self.assertTrue(dobj.eof) self.assertEqual(dobj.flush(), b"") def test_unused_data(self): data = zstd.ZstdCompressor(level=1).compress(b"foobar") dctx = zstd.ZstdDecompressor() dobj = dctx.decompressobj() self.assertEqual(dobj.unused_data, b"") self.assertEqual(dobj.decompress(data + b"extra"), b"foobar") self.assertTrue(dobj.eof) self.assertEqual(dobj.unused_data, b"extra") def test_reuse(self): data = zstd.ZstdCompressor(level=1).compress(b"foobar") dctx = zstd.ZstdDecompressor() dobj = dctx.decompressobj() dobj.decompress(data) with self.assertRaisesRegex( zstd.ZstdError, "cannot use a decompressobj" ): dobj.decompress(data) self.assertEqual(dobj.flush(), b"") def test_multiple_decompress_calls(self): expected = b"foobar" * 10 data = zstd.ZstdCompressor(level=1).compress(expected) N = 3 partitioned_data = [ data[len(data) * i // N : len(data) * (i + 1) // N] for i in range(N) ] dctx = zstd.ZstdDecompressor() dobj = dctx.decompressobj() for partition in partitioned_data[:-1]: decompressed = dobj.decompress(partition) self.assertEqual(decompressed, b"") self.assertEqual(dobj.unused_data, b"") decompressed = dobj.decompress(partitioned_data[-1]) self.assertEqual(decompressed, expected) def test_bad_write_size(self): dctx = zstd.ZstdDecompressor() with self.assertRaisesRegex(ValueError, "write_size must be positive"): dctx.decompressobj(write_size=0) def test_write_size(self): source = b"foo" * 64 + b"bar" * 128 data = zstd.ZstdCompressor(level=1).compress(source) dctx = zstd.ZstdDecompressor() for i in range(128): dobj = dctx.decompressobj(write_size=i + 1) self.assertEqual(dobj.decompress(data), source)

[ "[email protected]" ]

[email protected]

57848684f29088f1594e93d18a9cca0f11cda17c

c8781d3dc17202fcc1b5358475071c0a834c7f82

/ShowAndSearch/utils/parser.py

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''' lanhuage: python Descripttion: version: beta Author: xiaoshuyui Date: 2020-09-15 15:59:10 LastEditors: xiaoshuyui LastEditTime: 2020-09-22 11:19:20 ''' import argparse from ShowAndSearch.utils.logger import logger class BaseParser(object): def __init__(self, args: list, method: str): """ args type:list arg type:tuple arg example : ('-f','--force','force to show message even do not contain the module') """ self.args = args self.method = method self.parser = argparse.ArgumentParser( description='{} method or module information'.format(self.method)) def get_parser(self): self.parser.add_argument( 'question', metavar='QUESTION', type=str, nargs='*', help='the question to answer') self.parser.add_argument( '-v', '--version', help='show current version', action='store_true') if len(self.args) > 0: # self.parser.add_argument('-f','--force',help='force to show message even do not contain the module') # self.parser.add_argument('-s','--simple',help='show simple message') for i in self.args: self.parser.add_argument( i[0], i[1], help=i[2], action='store_true') else: logger.warning('args list is null') return self.parser def add_parser(self, arg): if type(arg) is tuple and len(arg) == 3: self.parser.add_argument( arg[0], arg[1], help=arg[2], action='store_true') else: logger.error('input error') return self.parser

[ "[email protected]" ]

[email protected]

b4922344953251b60d5c915c16fcf704c828a4f4

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/hvad/tests/contrib/restframework.py

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# -*- coding: utf-8 -*- from rest_framework.serializers import ModelSerializer, CharField from hvad.test_utils.context_managers import LanguageOverride from hvad.test_utils.testcase import HvadTestCase from hvad.test_utils.project.app.models import Normal from hvad.test_utils.data import NORMAL from hvad.test_utils.fixtures import NormalFixture from hvad.contrib.restframework import (TranslationsMixin, TranslatableModelSerializer) from hvad.contrib.restframework.serializers import TranslationListSerializer #============================================================================= class AutoSerializer(TranslatableModelSerializer): class Meta: model = Normal class ManualSerializer(TranslatableModelSerializer): class Meta: model = Normal fields = ['shared_field', 'translated_field'] class ExcludeSerializer(TranslatableModelSerializer): class Meta: model = Normal exclude = ['translated_field'] class TranslationsSerializer(TranslationsMixin, ModelSerializer): class Meta: model = Normal class CombinedSerializer(TranslationsMixin, TranslatableModelSerializer): class Meta: model = Normal class CustomTranslationSerializer(ModelSerializer): # 'cheat' tests that shared fields are accessible to the translation serializer # It is relevant, it ensures custom serializers see the full object, along with # any @property. Default serializer will just get to translated fields through # their accessors on the shared object and work transparently. cheat = CharField(max_length=250, source='shared_field') custom = CharField(max_length=250, source='translated_field') class Meta: exclude = ('translated_field',) class CustomSerializer(TranslationsMixin, ModelSerializer): class Meta: model = Normal translations_serializer = CustomTranslationSerializer #============================================================================= class TranslatableModelSerializerTests(HvadTestCase, NormalFixture): 'Checking the serializer representation of objects' normal_count = 1 #--------------------------------------------------------------------- def test_modelserializer_fields(self): 'Check serializers fields are properly set' serializer = AutoSerializer() self.assertCountEqual(serializer.fields, ['id', 'shared_field', 'translated_field', 'language_code']) serializer = ManualSerializer() self.assertCountEqual(serializer.fields, ['shared_field', 'translated_field']) serializer = ExcludeSerializer() self.assertCountEqual(serializer.fields, ['id', 'shared_field', 'language_code']) #--------------------------------------------------------------------- def test_serialize_normal(self): 'Serialize translated fields using instance language' obj = Normal.objects.language('ja').get(pk=self.normal_id[1]) serializer = AutoSerializer(instance=obj) data = serializer.data self.assertCountEqual(data, ['id', 'shared_field', 'translated_field', 'language_code']) self.assertEqual(data['id'], self.normal_id[1]) self.assertEqual(data['shared_field'], NORMAL[1].shared_field) self.assertEqual(data['translated_field'], NORMAL[1].translated_field['ja']) self.assertEqual(data['language_code'], 'ja') def test_serialize_enforce_wrong(self): 'Serialize translated fields while enforcing a language - wrong translation' obj = Normal.objects.language('ja').get(pk=self.normal_id[1]) serializer = AutoSerializer(instance=obj, language='en') data = serializer.data self.assertCountEqual(data, ['id', 'shared_field', 'translated_field', 'language_code']) self.assertEqual(data['id'], self.normal_id[1]) self.assertEqual(data['shared_field'], NORMAL[1].shared_field) self.assertEqual(data['translated_field'], NORMAL[1].translated_field['en']) self.assertEqual(data['language_code'], 'en') def test_serialize_enforce_nonexistent(self): 'Serialize translated fields while enforcing a language - nonexistent translation' obj = Normal.objects.language('ja').get(pk=self.normal_id[1]) serializer = AutoSerializer(instance=obj, language='xx') data = serializer.data self.assertCountEqual(data, ['id', 'shared_field', 'translated_field', 'language_code']) self.assertEqual(data['id'], self.normal_id[1]) self.assertEqual(data['shared_field'], NORMAL[1].shared_field) self.assertEqual(data['translated_field'], '') self.assertEqual(data['language_code'], 'xx') #--------------------------------------------------------------------- def test_create_normal(self): 'Deserialize a new instance' data = { 'shared_field': 'shared', 'translated_field': 'translated', 'language_code': 'en' } serializer = AutoSerializer(data=data) self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertIsNotNone(obj.pk) self.assertSavedObject(obj, 'en', **data) def test_create_enforce(self): 'Deserialize a new instance, with language-enforcing mode' data = { 'shared_field': 'shared', 'translated_field': 'translated', } serializer = AutoSerializer(data=data, language='sr') self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertIsNotNone(obj.pk) self.assertSavedObject(obj, 'sr', **data) def test_create_enforce_violation(self): 'Deserialize a new instance, with language-enforcing mode and language_code' data = { 'shared_field': 'shared', 'translated_field': 'translated', 'language_code': 'en', } serializer = AutoSerializer(data=data, language='en') self.assertFalse(serializer.is_valid()) serializer = AutoSerializer(data=data, language='xx') self.assertFalse(serializer.is_valid()) def test_update_normal_default(self): 'Deserialize an existing instance using instance-loaded language' obj = Normal.objects.language('ja').get(pk=self.normal_id[1]) data = { 'shared_field': 'shared', 'translated_field': 'translated', } serializer = AutoSerializer(instance=obj, data=data) self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) self.assertSavedObject(obj, 'ja', **data) obj = Normal.objects.untranslated().get(pk=self.normal_id[1]) serializer = AutoSerializer(instance=obj, data=data) self.assertTrue(serializer.is_valid()) with LanguageOverride('en'): obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) self.assertSavedObject(obj, 'en', **data) def test_update_normal_language_code(self): 'Deserialize an existing instance using submitted language' obj = Normal.objects.language('ja').get(pk=self.normal_id[1]) data = { 'shared_field': 'shared', 'translated_field': 'translated', 'language_code': 'sr' } serializer = AutoSerializer(instance=obj, data=data) self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) self.assertSavedObject(obj, 'sr', **data) obj = Normal.objects.untranslated().get(pk=self.normal_id[1]) data['translated_field'] = 'translated_bis' serializer = AutoSerializer(instance=obj, data=data) self.assertTrue(serializer.is_valid()) with LanguageOverride('en'): obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) self.assertSavedObject(obj, 'sr', **data) def test_update_enforce(self): 'Deserialize an existing intance in language-enforcing mode' data = { 'shared_field': 'shared', 'translated_field': 'translated', } # Correct translation obj = Normal.objects.language('ja').get(pk=self.normal_id[1]) serializer = AutoSerializer(instance=obj, data=data, language='ja') self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) self.assertSavedObject(obj, 'ja', **data) # Wrong translation obj = Normal.objects.language('en').get(pk=self.normal_id[1]) serializer = AutoSerializer(instance=obj, data=data, language='ja') self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) self.assertSavedObject(obj, 'ja', **data) # Nonexistent translation obj = Normal.objects.language('en').get(pk=self.normal_id[1]) serializer = AutoSerializer(instance=obj, data=data, language='sr') self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) self.assertSavedObject(obj, 'sr', **data) #============================================================================= class TranslationsMixinTests(HvadTestCase, NormalFixture): normal_count = 1 def test_translations_mixin_fields(self): 'Check serializers fields are properly set' serializer = TranslationsSerializer() self.assertCountEqual(serializer.fields, ['id', 'shared_field', 'translations']) self.assertIsInstance(serializer.fields['translations'], TranslationListSerializer) self.assertCountEqual(serializer.fields['translations'].child.fields, ['translated_field']) serializer = CustomSerializer() self.assertCountEqual(serializer.fields, ['id', 'shared_field', 'translations']) self.assertIsInstance(serializer.fields['translations'], TranslationListSerializer) self.assertIsInstance(serializer.fields['translations'].child, CustomTranslationSerializer) self.assertCountEqual(serializer.fields['translations'].child.fields, ['cheat', 'custom']) #--------------------------------------------------------------------- def test_serialize(self): 'Serialize nested translations as a language => fields dict' obj = Normal.objects.prefetch_related('translations').get(pk=self.normal_id[1]) serializer = TranslationsSerializer(instance=obj) data = serializer.data self.assertCountEqual(data, ['id', 'shared_field', 'translations']) self.assertEqual(data['id'], self.normal_id[1]) self.assertEqual(data['shared_field'], NORMAL[1].shared_field) self.assertIsInstance(data['translations'], dict) self.assertCountEqual(data['translations'], self.translations) for language in self.translations: translation = data['translations'][language] self.assertCountEqual(translation, ['translated_field']) self.assertEqual(translation['translated_field'], NORMAL[1].translated_field[language]) def test_serialize_custom(self): 'Serialize nested translations as a language => fields dict' obj = Normal.objects.prefetch_related('translations').get(pk=self.normal_id[1]) serializer = CustomSerializer(instance=obj) data = serializer.data self.assertCountEqual(data, ['id', 'shared_field', 'translations']) self.assertEqual(data['id'], self.normal_id[1]) self.assertEqual(data['shared_field'], NORMAL[1].shared_field) self.assertIsInstance(data['translations'], dict) self.assertCountEqual(data['translations'], self.translations) for language in self.translations: translation = data['translations'][language] self.assertCountEqual(translation, ['cheat', 'custom']) self.assertEqual(translation['cheat'], NORMAL[1].shared_field) self.assertEqual(translation['custom'], NORMAL[1].translated_field[language]) #--------------------------------------------------------------------- def test_invalid(self): 'Submit invalid data' # No translations data = { 'shared_field': 'shared', 'translations': {}, } serializer = TranslationsSerializer(data=data) self.assertFalse(serializer.is_valid()) self.assertTrue(serializer.errors['translations']) # Invalid translations type data = { 'shared_field': 'shared', 'translations': [ { 'translated_field': 'English', }, ], } serializer = TranslationsSerializer(data=data) self.assertFalse(serializer.is_valid()) self.assertTrue(serializer.errors['translations']) # Cascade invalid child data = { 'shared_field': 'shared', 'translations': { 'en': { 'translated_field': 'x'*999 }, }, } serializer = TranslationsSerializer(data=data) self.assertFalse(serializer.is_valid()) self.assertTrue(serializer.errors['translations']) self.assertTrue(serializer.errors['translations']['en']) self.assertTrue(serializer.errors['translations']['en']['translated_field']) #--------------------------------------------------------------------- def test_create(self): 'Create a new Normal instance, with two translations' data = { 'shared_field': 'shared', 'translations': { 'en': { 'translated_field': 'English', }, 'sr': { 'translated_field': u'српски', }, }, } serializer = TranslationsSerializer(data=data) self.assertTrue(serializer.is_valid()) with self.assertNumQueries(3): # insert shared, insert "en", insert "sr" obj = serializer.save() self.assertIsNot(obj.pk, None) qs = Normal.objects.language('all').filter(pk=obj.pk) self.assertCountEqual([(obj.language_code, obj.translated_field) for obj in qs], [('en', 'English'), ('sr', u'српски')]) def test_update(self): 'Update an existing normal instance: 1 new, 1 updated, 1 deleted translations' obj = Normal.objects.untranslated().prefetch_related('translations').get(pk=self.normal_id[1]) data = { 'shared_field': 'shared', 'translations': { 'en': { 'translated_field': 'English', }, # should updated 'sr': { 'translated_field': u'српски', }, # should create }, # Japanese should be deleted } serializer = TranslationsSerializer(instance=obj, data=data) self.assertTrue(serializer.is_valid()) with self.assertNumQueries(4): # update shared, update "en", insert "sr", delete others obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) qs = Normal.objects.language('all').filter(pk=self.normal_id[1]) self.assertCountEqual([(obj.language_code, obj.translated_field) for obj in qs], [('en', 'English'), ('sr', u'српски')]) def test_update_partial(self): 'Update an existing instance, but just some fields' obj = Normal.objects.untranslated().get(pk=self.normal_id[1]) data = { 'shared_field': 'shared' } serializer = TranslationsSerializer(instance=obj, data=data, partial=True) self.assertTrue(serializer.is_valid()) with self.assertNumQueries(1): # update shared obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) qs = Normal.objects.language('all').filter(pk=self.normal_id[1], shared_field='shared') self.assertCountEqual([obj.language_code for obj in qs], self.translations) #============================================================================= class CombinedTests(HvadTestCase, NormalFixture): normal_count = 1 def test_combined_fields(self): 'Check serializers fields are properly set' serializer = CombinedSerializer() self.assertCountEqual(serializer.fields, ['id', 'shared_field', 'translated_field', 'language_code', 'translations']) self.assertIsInstance(serializer.fields['translations'], TranslationListSerializer) self.assertCountEqual(serializer.fields['translations'].child.fields, ['translated_field']) #--------------------------------------------------------------------- def test_serialize(self): 'Serialize translations as a language => fields dict + naive fields' obj = Normal.objects.language('ja').prefetch_related('translations').get(pk=self.normal_id[1]) serializer = CombinedSerializer(instance=obj) data = serializer.data self.assertCountEqual(data, ['id', 'shared_field', 'translated_field', 'language_code', 'translations']) self.assertEqual(data['id'], self.normal_id[1]) self.assertEqual(data['shared_field'], NORMAL[1].shared_field) self.assertEqual(data['translated_field'], NORMAL[1].translated_field['ja']) self.assertEqual(data['language_code'], 'ja') self.assertIsInstance(data['translations'], dict) self.assertCountEqual(data['translations'], self.translations) for language in self.translations: translation = data['translations'][language] self.assertCountEqual(translation, ['translated_field']) self.assertEqual(translation['translated_field'], NORMAL[1].translated_field[language]) #--------------------------------------------------------------------- def test_create_translations(self): 'Create a new Normal instance, with two translations' data = { 'shared_field': 'shared', 'translated_field': 'should be ignored', 'language_code': 'sr', 'translations': { 'en': { 'translated_field': 'English', }, 'sr': { 'translated_field': u'српски', }, }, } serializer = CombinedSerializer(data=data) self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertIsNot(obj.pk, None) qs = Normal.objects.language('all').filter(pk=obj.pk) self.assertCountEqual([(obj.language_code, obj.translated_field) for obj in qs], [('en', 'English'), ('sr', u'српски')]) def test_create_translatable(self): 'Create a new Normal instance, in translatablemodelserializer style' data = { 'shared_field': 'shared', 'translated_field': u'српски', 'language_code': 'sr' } serializer = CombinedSerializer(data=data) self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertIsNot(obj.pk, None) qs = Normal.objects.language('all').filter(pk=obj.pk) self.assertCountEqual([(obj.language_code, obj.translated_field) for obj in qs], [('sr', u'српски')]) def test_update_translations(self): 'Update an existing normal instance: 1 new, 1 updated, 1 deleted translations' obj = Normal.objects.untranslated().get(pk=self.normal_id[1]) data = { 'shared_field': 'shared', 'language_code': 'ignored', 'translations': { 'en': { 'translated_field': 'English', }, # should updated 'sr': { 'translated_field': u'српски', }, # should create }, # Japanese should be deleted } serializer = CombinedSerializer(instance=obj, data=data) self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) qs = Normal.objects.language('all').filter(pk=self.normal_id[1]) self.assertCountEqual([(obj.language_code, obj.translated_field) for obj in qs], [('en', 'English'), ('sr', u'српски')]) def test_update_translatable(self): 'Update an existing normal instance translation in translatablemodel mode' obj = Normal.objects.untranslated().get(pk=self.normal_id[1]) data = { 'shared_field': 'shared', 'translated_field': u'српски', 'language_code': 'sr' } serializer = CombinedSerializer(instance=obj, data=data) self.assertTrue(serializer.is_valid()) obj = serializer.save() self.assertEqual(obj.pk, self.normal_id[1]) qs = Normal.objects.language('all').filter(pk=self.normal_id[1]) self.assertCountEqual([(obj.language_code, obj.translated_field) for obj in qs], [('en', NORMAL[1].translated_field['en']), ('ja', NORMAL[1].translated_field['ja']), ('sr', u'српски')])

[ "[email protected]" ]

[email protected]

6a0aec763b5e253145873cd3bed3a39e26344b7f

016b7b0cdd60900ca9b2e26f959142c30313e00d

/report/views.py

1c7bc22d70a1ad296f8eed19d0e4747783cedc6b

[]

no_license

groob/imagr_server

1e2abdab290b020225359103e72f56ecec7d52b5

81dfa968ed48ec719803dd0d53f17b92130e76da

refs/heads/master

2020-04-05T23:06:40.972867

2015-06-11T14:46:15

51,090,790

0

null

2016-02-04T16:58:34

null

UTF-8

Python

false

773

py

from django.shortcuts import render from django.http import HttpResponse from django.views.decorators.csrf import csrf_exempt from models import * # Create your views here. @csrf_exempt def index(request): data = request.POST serial = data['serial'] message = data['message'] status = data['status'] # see if the computer exists if serial: try: computer = Computer.objects.get(serial_number=serial) except Computer.DoesNotExist: computer = Computer(serial_number=serial) computer.current_status = status computer.save() # create a new report object report = Report(computer=computer, message=message, status=status) report.save() return HttpResponse(data)

[ "[email protected]" ]

[email protected]

beefc26ee5cc6b2af147350338002391621f0297

80e6e31054fe9105d2c26be7aac53c4cd6a4a33f

/scripts/kettle/oracle2hive.py

532ed84e5b965fec9f9c21de191d5f8bc008386d

[]

no_license

alionishere/learn_python

8a7f6dc7d754a357d4cb720f4bc0d5c3e6e5e895

832b8e0579da0b7ab37e815be10204f8de1ad22d

refs/heads/master

2021-06-24T11:02:05.111027

2021-06-23T08:47:06

223,834,194

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null

UTF-8

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5,666

py

# -*- coding: utf-8 -*- import json import dbutils get_ora_meta_sql = ''' SELECT t1.OWNER ,t1.TABLE_NAME ,t1.COLUMN_NAME ,t1.DATA_TYPE ,t1.DATA_LENGTH ,t1.DATA_PRECISION ,t1.DATA_SCALE ,t2.COMMENTS FROM DBA_TAB_COLUMNS t1 LEFT JOIN DBA_COL_COMMENTS t2 ON t1.OWNER = t2.OWNER AND t1.TABLE_NAME = t2.TABLE_NAME AND t1.COLUMN_NAME = t2.COLUMN_NAME WHERE t1.OWNER = '%s' AND t1.TABLE_NAME = '%s' ORDER BY COLUMN_ID ''' get_mysql_meta_sql = ''' SELECT TABLE_SCHEMA ,TABLE_NAME ,COLUMN_NAME ,ORDINAL_POSITION ,DATA_TYPE ,CHARACTER_MAXIMUM_LENGTH ,CHARACTER_OCTET_LENGTH ,NUMERIC_PRECISION ,NUMERIC_SCALE ,COLUMN_TYPE FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = '%s' AND TABLE_NAME = '%s' ; ''' def get_ora_meta(conn, sql, src_schema, src_tb, hive_schema='', hive_tb=''): fields = [] field_attrs = [] cur = conn.cursor() sql = sql % (src_schema.upper(), src_tb.upper()) print(sql) print('--' * 30) cur.execute(sql) res = cur.fetchall() for field in res: if field[3] == 'CLOB' or field == 'DATE': field_attr = field[2] + ' STRING ' + 'COMMENT \'' + str(field[7]) + '\'' field_attrs.append(field_attr) elif field[3] == 'VARCHAR2' or field[3] == 'VARCHAR' or field[3] == 'CHAR': field_attr = field[2] + ' VARCHAR(' + str(field[4]) + ') COMMENT \'' + str(field[7]) + '\'' field_attrs.append(field_attr) elif field[3] == 'NUMBER': field_attr = '' if field[6] == 0: field_attr = field[2] + ' BIGINT ' + 'COMMENT \'' + str(field[7]) + '\'' elif field[5] is not None and field[6] is not None: field_attr = field[2] + ' DECIMAL(' + str(field[5]) + ',' + str(field[6]) + ') COMMENT \'' + str(field[7]) + '\'' else: field_attr = field[2] + ' DECIMAL(23,4)' + ' COMMENT \'' + str(field[7]) + '\'' field_attrs.append(field_attr) else: field_attr = field[2] + ' STRING ' + ' COMMENT \'' + str(field[7]) + '\'' field_attrs.append(field_attr) # print(field) fields.append(field[2]) # break cur.close() fields = ','.join(fields) field_attrs = ',\n'.join(field_attrs) # print(field_attrs) create_str = ''' CREATE TABLE %s.%s (\n%s\n) PARTITIONED BY (TX_DATE STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\u0001' STORED AS PARQUE LOCATION '/DWZQ/%s/%s'; ''' hive_tb = '%s_%s' % (src_schema, src_tb) hive_tb_temp = '%s_TEMP' % hive_tb create_stmt = create_str % (hive_schema.upper(), hive_tb.upper(), field_attrs, hive_schema.upper(), hive_tb.upper()) create_stmt_temp = create_str % (hive_schema.upper(), hive_tb_temp.upper(), field_attrs, hive_schema.upper(), hive_tb_temp.upper()) print(create_stmt) print(create_stmt_temp) return create_stmt def get_mysql_meta(conn, sql, src_schema, src_tb, hive_schema='', hive_tb=''): fields = [] field_attrs = [] cur = conn.cursor() sql = sql % (src_schema.upper(), src_tb.upper()) print(sql) print('--' * 30) cur.execute(sql) res = cur.fetchall() for field in res: if field[3] == 'CLOB' or field == 'DATE': field_attr = field[2] + ' STRING ' + 'COMMENT \'' + str(field[7]) + '\'' field_attrs.append(field_attr) elif field[3] == 'VARCHAR2' or field[3] == 'VARCHAR' or field[3] == 'CHAR': field_attr = field[2] + ' VARCHAR(' + str(field[4]) + ') COMMENT \'' + str(field[7]) + '\'' field_attrs.append(field_attr) elif field[3] == 'NUMBER': field_attr = '' if field[6] == 0: field_attr = field[2] + ' BIGINT ' + 'COMMENT \'' + str(field[7]) + '\'' elif field[5] is not None and field[6] is not None: field_attr = field[2] + ' DECIMAL(' + str(field[5]) + ',' + str(field[6]) + ') COMMENT \'' + str(field[7]) + '\'' else: field_attr = field[2] + ' DECIMAL(23,4)' + ' COMMENT \'' + str(field[7]) + '\'' field_attrs.append(field_attr) else: field_attr = field[2] + ' STRING ' + ' COMMENT \'' + str(field[7]) + '\'' field_attrs.append(field_attr) # print(field) fields.append(field[2]) # break cur.close() fields = ','.join(fields) field_attrs = ',\n'.join(field_attrs) # print(field_attrs) create_str = ''' CREATE TABLE %s.%s (\n%s\n) PARTITIONED BY (TX_DATE STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\u0001' STORED AS PARQUE LOCATION '/DWZQ/%s/%s'; ''' hive_tb = '%s_%s' % (src_schema, src_tb) hive_tb_temp = '%s_TEMP' % hive_tb create_stmt = create_str % (hive_schema.upper(), hive_tb.upper(), field_attrs, hive_schema.upper(), hive_tb.upper()) create_stmt_temp = create_str % (hive_schema.upper(), hive_tb_temp.upper(), field_attrs, hive_schema.upper(), hive_tb_temp.upper()) print(create_stmt) print(create_stmt_temp) return create_stmt def run(tb_info_details): for tb_info in tb_info_details: conn = dbutils.get_conn(tb_info['data_src'].lower()) src_owner = tb_info['src_tb'].split('.')[0] src_tb = tb_info['src_tb'].split('.')[1] hive_schema = tb_info['data_src'] # hive_tb = get_ora_meta(conn, get_ora_meta_sql, src_owner, src_tb, hive_schema) if __name__ == '__main__': with open('cfg.json', 'r') as f: tb_info_details = json.load(f) run(tb_info_details)

[ "[email protected]" ]

[email protected]

ed264a15f7a93c1ffc3c24393851337420b1c8c5

5f67c696967456c063e5f8a0d14cf18cf845ad38

/archiv/_python/py4inf/gmane/gyear.py

30e892a7e7d666c4991703bf713d123ac276373c

[]

no_license

wuxi20/Pythonista

3f2abf8c40fd6554a4d7596982c510e6ba3d6d38

acf12d264615749f605a0a6b6ea7ab72442e049c

refs/heads/master

2020-04-02T01:17:39.264328

2019-04-16T18:26:59

153,848,116

1

0

null

UTF-8

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false

1,705

py

import sqlite3 import time import urllib.request, urllib.parse, urllib.error import zlib conn = sqlite3.connect('index.sqlite') conn.text_factory = str cur = conn.cursor() # Determine the top ten organizations cur.execute('''SELECT Messages.id, sender FROM Messages JOIN Senders ON Messages.sender_id = Senders.id''') sendorgs = dict() for message_row in cur : sender = message_row[1] pieces = sender.split("@") if len(pieces) != 2 : continue dns = pieces[1] sendorgs[dns] = sendorgs.get(dns,0) + 1 # pick the top schools orgs = sorted(sendorgs, key=sendorgs.get, reverse=True) orgs = orgs[:10] print("Top 10 Organizations") print(orgs) # orgs = ['total'] + orgs # Read through the messages counts = dict() years = list() cur.execute('''SELECT Messages.id, sender, sent_at FROM Messages JOIN Senders ON Messages.sender_id = Senders.id''') for message_row in cur : sender = message_row[1] pieces = sender.split("@") if len(pieces) != 2 : continue dns = pieces[1] if dns not in orgs : continue year = message_row[2][:4] if year not in years : years.append(year) key = (year, dns) counts[key] = counts.get(key,0) + 1 tkey = (year, 'total') counts[tkey] = counts.get(tkey,0) + 1 years.sort() print(counts) print(years) fhand = open('gline.js','w') fhand.write("gline = [ ['Year'") for org in orgs: fhand.write(",'"+org+"'") fhand.write("]") # for year in years[1:-1]: for year in years: fhand.write(",\n['"+year+"'") for org in orgs: key = (year, org) val = counts.get(key,0) fhand.write(","+str(val)) fhand.write("]"); fhand.write("\n];\n") print("Output written to gline.js")

[ "[email protected]" ]

[email protected]

0e688ee0061c5df180a71f4e16541de90c10d0b4

39bc099123097e1a183b44437954a3f037125891

/tests/ext/modeling.py

b4844cc12cbbd9efb8749f282a606990ae3eb1a8

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permissive

pranaya-mathur/bert-for-tf2

81c5f1db765ca6e06651e284f0911ba9099c6f99

cad915ee9d20802a05181373fe30b716a70bc870

refs/heads/master

2020-09-05T18:00:24.987757

2019-11-05T16:19:44

220,175,286

1

0

MIT

2019-11-07T07:19:26

2019-11-07T07:19:25

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Python

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38,936

py

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """The main BERT model and related functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import copy import json import math import re import numpy as np import six import tensorflow as tf class BertConfig(object): """Configuration for `BertModel`.""" def __init__(self, vocab_size, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, initializer_range=0.02): """Constructs BertConfig. Args: vocab_size: Vocabulary size of `inputs_ids` in `BertModel`. hidden_size: Size of the encoder layers and the pooler layer. num_hidden_layers: Number of hidden layers in the Transformer encoder. num_attention_heads: Number of attention heads for each attention layer in the Transformer encoder. intermediate_size: The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act: The non-linear activation function (function or string) in the encoder and pooler. hidden_dropout_prob: The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob: The dropout ratio for the attention probabilities. max_position_embeddings: The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size: The vocabulary size of the `token_type_ids` passed into `BertModel`. initializer_range: The stdev of the truncated_normal_initializer for initializing all weight matrices. """ self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range @classmethod def from_dict(cls, json_object): """Constructs a `BertConfig` from a Python dictionary of parameters.""" config = BertConfig(vocab_size=None) for (key, value) in six.iteritems(json_object): config.__dict__[key] = value return config @classmethod def from_json_file(cls, json_file): """Constructs a `BertConfig` from a json file of parameters.""" with tf.io.gfile.GFile(json_file, "r") as reader: text = reader.read() return cls.from_dict(json.loads(text)) def to_dict(self): """Serializes this instance to a Python dictionary.""" output = copy.deepcopy(self.__dict__) return output def to_json_string(self): """Serializes this instance to a JSON string.""" return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" class BertModel(object): """BERT model ("Bidirectional Encoder Representations from Transformers"). Example usage: ```python # Already been converted into WordPiece token ids input_ids = tf.constant([[31, 51, 99], [15, 5, 0]]) input_mask = tf.constant([[1, 1, 1], [1, 1, 0]]) token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]]) config = modeling.BertConfig(vocab_size=32000, hidden_size=512, num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) model = modeling.BertModel(config=config, is_training=True, input_ids=input_ids, input_mask=input_mask, token_type_ids=token_type_ids) label_embeddings = tf.get_variable(...) pooled_output = model.get_pooled_output() logits = tf.matmul(pooled_output, label_embeddings) ... ``` """ def __init__(self, config, is_training, input_ids, input_mask=None, token_type_ids=None, use_one_hot_embeddings=False, scope=None): """Constructor for BertModel. Args: config: `BertConfig` instance. is_training: bool. true for training model, false for eval model. Controls whether dropout will be applied. input_ids: int32 Tensor of shape [batch_size, seq_length]. input_mask: (optional) int32 Tensor of shape [batch_size, seq_length]. token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length]. use_one_hot_embeddings: (optional) bool. Whether to use one-hot word embeddings or tf.embedding_lookup() for the word embeddings. scope: (optional) variable scope. Defaults to "bert". Raises: ValueError: The config is invalid or one of the input tensor shapes is invalid. """ config = copy.deepcopy(config) if not is_training: config.hidden_dropout_prob = 0.0 config.attention_probs_dropout_prob = 0.0 input_shape = get_shape_list(input_ids, expected_rank=2) batch_size = input_shape[0] seq_length = input_shape[1] if input_mask is None: input_mask = tf.ones(shape=[batch_size, seq_length], dtype=tf.int32) if token_type_ids is None: token_type_ids = tf.zeros(shape=[batch_size, seq_length], dtype=tf.int32) with tf.compat.v1.variable_scope(scope, default_name="bert"): with tf.compat.v1.variable_scope("embeddings"): # Perform embedding lookup on the word ids. (self.embedding_output, self.embedding_table) = embedding_lookup( input_ids=input_ids, vocab_size=config.vocab_size, embedding_size=config.hidden_size, initializer_range=config.initializer_range, word_embedding_name="word_embeddings", use_one_hot_embeddings=use_one_hot_embeddings) # Add positional embeddings and token type embeddings, then layer # normalize and perform dropout. self.embedding_output = embedding_postprocessor( input_tensor=self.embedding_output, use_token_type=True, token_type_ids=token_type_ids, token_type_vocab_size=config.type_vocab_size, token_type_embedding_name="token_type_embeddings", use_position_embeddings=True, position_embedding_name="position_embeddings", initializer_range=config.initializer_range, max_position_embeddings=config.max_position_embeddings, dropout_prob=config.hidden_dropout_prob) with tf.compat.v1.variable_scope("encoder"): # This converts a 2D mask of shape [batch_size, seq_length] to a 3D # mask of shape [batch_size, seq_length, seq_length] which is used # for the attention scores. attention_mask = create_attention_mask_from_input_mask( input_ids, input_mask) # Run the stacked transformer. # `sequence_output` shape = [batch_size, seq_length, hidden_size]. self.all_encoder_layers = transformer_model( input_tensor=self.embedding_output, attention_mask=attention_mask, hidden_size=config.hidden_size, num_hidden_layers=config.num_hidden_layers, num_attention_heads=config.num_attention_heads, intermediate_size=config.intermediate_size, intermediate_act_fn=get_activation(config.hidden_act), hidden_dropout_prob=config.hidden_dropout_prob, attention_probs_dropout_prob=config.attention_probs_dropout_prob, initializer_range=config.initializer_range, do_return_all_layers=True) self.sequence_output = self.all_encoder_layers[-1] # The "pooler" converts the encoded sequence tensor of shape # [batch_size, seq_length, hidden_size] to a tensor of shape # [batch_size, hidden_size]. This is necessary for segment-level # (or segment-pair-level) classification tasks where we need a fixed # dimensional representation of the segment. with tf.compat.v1.variable_scope("pooler"): # We "pool" the model by simply taking the hidden state corresponding # to the first token. We assume that this has been pre-trained first_token_tensor = tf.squeeze(self.sequence_output[:, 0:1, :], axis=1) self.pooled_output = tf.compat.v1.layers.dense( first_token_tensor, config.hidden_size, activation=tf.tanh, kernel_initializer=create_initializer(config.initializer_range)) def get_pooled_output(self): return self.pooled_output def get_sequence_output(self): """Gets final hidden layer of encoder. Returns: float Tensor of shape [batch_size, seq_length, hidden_size] corresponding to the final hidden of the transformer encoder. """ return self.sequence_output def get_all_encoder_layers(self): return self.all_encoder_layers def get_embedding_output(self): """Gets output of the embedding lookup (i.e., input to the transformer). Returns: float Tensor of shape [batch_size, seq_length, hidden_size] corresponding to the output of the embedding layer, after summing the word embeddings with the positional embeddings and the token type embeddings, then performing layer normalization. This is the input to the transformer. """ return self.embedding_output def get_embedding_table(self): return self.embedding_table def gelu(x): """Gaussian Error Linear Unit. This is a smoother version of the RELU. Original paper: https://arxiv.org/abs/1606.08415 Args: x: float Tensor to perform activation. Returns: `x` with the GELU activation applied. """ cdf = 0.5 * (1.0 + tf.tanh( (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))) return x * cdf def get_activation(activation_string): """Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`. Args: activation_string: String name of the activation function. Returns: A Python function corresponding to the activation function. If `activation_string` is None, empty, or "linear", this will return None. If `activation_string` is not a string, it will return `activation_string`. Raises: ValueError: The `activation_string` does not correspond to a known activation. """ # We assume that anything that"s not a string is already an activation # function, so we just return it. if not isinstance(activation_string, six.string_types): return activation_string if not activation_string: return None act = activation_string.lower() if act == "linear": return None elif act == "relu": return tf.nn.relu elif act == "gelu": return gelu elif act == "tanh": return tf.tanh else: raise ValueError("Unsupported activation: %s" % act) def get_assignment_map_from_checkpoint(tvars, init_checkpoint): """Compute the union of the current variables and checkpoint variables.""" assignment_map = {} initialized_variable_names = {} name_to_variable = collections.OrderedDict() for var in tvars: name = var.name m = re.match("^(.*):\\d+$", name) if m is not None: name = m.group(1) name_to_variable[name] = var init_vars = tf.train.list_variables(init_checkpoint) assignment_map = collections.OrderedDict() for x in init_vars: (name, var) = (x[0], x[1]) if name not in name_to_variable: continue assignment_map[name] = name initialized_variable_names[name] = 1 initialized_variable_names[name + ":0"] = 1 return (assignment_map, initialized_variable_names) def dropout(input_tensor, dropout_prob): """Perform dropout. Args: input_tensor: float Tensor. dropout_prob: Python float. The probability of dropping out a value (NOT of *keeping* a dimension as in `tf.nn.dropout`). Returns: A version of `input_tensor` with dropout applied. """ if dropout_prob is None or dropout_prob == 0.0: return input_tensor output = tf.nn.dropout(input_tensor, rate=dropout_prob) return output def layer_norm(input_tensor, name=None): """Run layer normalization on the last dimension of the tensor.""" # return tf.contrib.layers.layer_norm( # inputs=input_tensor, begin_norm_axis=-1, begin_params_axis=-1, scope=name) epsilon = 1e-12 input_shape = input_tensor.shape with tf.compat.v1.variable_scope("LayerNorm"): gamma = tf.compat.v1.get_variable(name="gamma", shape=input_shape[-1:], initializer=tf.compat.v1.initializers.ones(), trainable=True) beta = tf.compat.v1.get_variable(name="beta", shape=input_shape[-1:], initializer=tf.compat.v1.initializers.zeros(), trainable=True) x = input_tensor if tf.__version__.startswith("2."): mean, var = tf.nn.moments(x=x, axes=-1, keepdims=True) else: mean, var = tf.nn.moments(x, axes=-1, keep_dims=True) inv = gamma * tf.math.rsqrt(var + epsilon) res = x * tf.cast(inv, x.dtype) + tf.cast(beta - mean * inv, x.dtype) return res def layer_norm_and_dropout(input_tensor, dropout_prob, name=None): """Runs layer normalization followed by dropout.""" output_tensor = layer_norm(input_tensor, name) output_tensor = dropout(output_tensor, dropout_prob) return output_tensor def create_initializer(initializer_range=0.02): """Creates a `truncated_normal_initializer` with the given range.""" return tf.compat.v1.initializers.truncated_normal(stddev=initializer_range) def embedding_lookup(input_ids, vocab_size, embedding_size=128, initializer_range=0.02, word_embedding_name="word_embeddings", use_one_hot_embeddings=False): """Looks up words embeddings for id tensor. Args: input_ids: int32 Tensor of shape [batch_size, seq_length] containing word ids. vocab_size: int. Size of the embedding vocabulary. embedding_size: int. Width of the word embeddings. initializer_range: float. Embedding initialization range. word_embedding_name: string. Name of the embedding table. use_one_hot_embeddings: bool. If True, use one-hot method for word embeddings. If False, use `tf.gather()`. Returns: float Tensor of shape [batch_size, seq_length, embedding_size]. """ # This function assumes that the input is of shape [batch_size, seq_length, # num_inputs]. # # If the input is a 2D tensor of shape [batch_size, seq_length], we # reshape to [batch_size, seq_length, 1]. if input_ids.shape.ndims == 2: input_ids = tf.expand_dims(input_ids, axis=[-1]) embedding_table = tf.compat.v1.get_variable( name=word_embedding_name, shape=[vocab_size, embedding_size], initializer=create_initializer(initializer_range)) flat_input_ids = tf.reshape(input_ids, [-1]) if use_one_hot_embeddings: one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size) output = tf.matmul(one_hot_input_ids, embedding_table) else: output = tf.gather(embedding_table, flat_input_ids) input_shape = get_shape_list(input_ids) output = tf.reshape(output, input_shape[0:-1] + [input_shape[-1] * embedding_size]) return (output, embedding_table) def embedding_postprocessor(input_tensor, use_token_type=False, token_type_ids=None, token_type_vocab_size=16, token_type_embedding_name="token_type_embeddings", use_position_embeddings=True, position_embedding_name="position_embeddings", initializer_range=0.02, max_position_embeddings=512, dropout_prob=0.1): """Performs various post-processing on a word embedding tensor. Args: input_tensor: float Tensor of shape [batch_size, seq_length, embedding_size]. use_token_type: bool. Whether to add embeddings for `token_type_ids`. token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length]. Must be specified if `use_token_type` is True. token_type_vocab_size: int. The vocabulary size of `token_type_ids`. token_type_embedding_name: string. The name of the embedding table variable for token type ids. use_position_embeddings: bool. Whether to add position embeddings for the position of each token in the sequence. position_embedding_name: string. The name of the embedding table variable for positional embeddings. initializer_range: float. Range of the weight initialization. max_position_embeddings: int. Maximum sequence length that might ever be used with this model. This can be longer than the sequence length of input_tensor, but cannot be shorter. dropout_prob: float. Dropout probability applied to the final output tensor. Returns: float tensor with same shape as `input_tensor`. Raises: ValueError: One of the tensor shapes or input values is invalid. """ input_shape = get_shape_list(input_tensor, expected_rank=3) batch_size = input_shape[0] seq_length = input_shape[1] width = input_shape[2] output = input_tensor if use_token_type: if token_type_ids is None: raise ValueError("`token_type_ids` must be specified if" "`use_token_type` is True.") token_type_table = tf.compat.v1.get_variable( name=token_type_embedding_name, shape=[token_type_vocab_size, width], initializer=create_initializer(initializer_range), use_resource=False) # This vocab will be small so we always do one-hot here, since it is always # faster for a small vocabulary. flat_token_type_ids = tf.reshape(token_type_ids, [-1]) one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size) token_type_embeddings = tf.matmul(one_hot_ids, token_type_table) token_type_embeddings = tf.reshape(token_type_embeddings, [batch_size, seq_length, width]) output += token_type_embeddings if use_position_embeddings: assert_op = tf.compat.v1.assert_less_equal(seq_length, max_position_embeddings) with tf.control_dependencies([assert_op]): full_position_embeddings = tf.compat.v1.get_variable( name=position_embedding_name, shape=[max_position_embeddings, width], initializer=create_initializer(initializer_range), use_resource=False) # Since the position embedding table is a learned variable, we create it # using a (long) sequence length `max_position_embeddings`. The actual # sequence length might be shorter than this, for faster training of # tasks that do not have long sequences. # # So `full_position_embeddings` is effectively an embedding table # for position [0, 1, 2, ..., max_position_embeddings-1], and the current # sequence has positions [0, 1, 2, ... seq_length-1], so we can just # perform a slice. position_embeddings = tf.slice(full_position_embeddings, [0, 0], [seq_length, -1]) num_dims = len(output.shape.as_list()) # Only the last two dimensions are relevant (`seq_length` and `width`), so # we broadcast among the first dimensions, which is typically just # the batch size. position_broadcast_shape = [] for _ in range(num_dims - 2): position_broadcast_shape.append(1) position_broadcast_shape.extend([seq_length, width]) position_embeddings = tf.reshape(position_embeddings, position_broadcast_shape) output += position_embeddings output = layer_norm_and_dropout(output, dropout_prob) return output def create_attention_mask_from_input_mask(from_tensor, to_mask): """Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: int32 Tensor of shape [batch_size, to_seq_length]. Returns: float Tensor of shape [batch_size, from_seq_length, to_seq_length]. """ from_shape = get_shape_list(from_tensor, expected_rank=[2, 3]) batch_size = from_shape[0] from_seq_length = from_shape[1] to_shape = get_shape_list(to_mask, expected_rank=2) to_seq_length = to_shape[1] to_mask = tf.cast( tf.reshape(to_mask, [batch_size, 1, to_seq_length]), tf.float32) # We don't assume that `from_tensor` is a mask (although it could be). We # don't actually care if we attend *from* padding tokens (only *to* padding) # tokens so we create a tensor of all ones. # # `broadcast_ones` = [batch_size, from_seq_length, 1] broadcast_ones = tf.ones( shape=[batch_size, from_seq_length, 1], dtype=tf.float32) # Here we broadcast along two dimensions to create the mask. mask = broadcast_ones * to_mask return mask def attention_layer(from_tensor, to_tensor, attention_mask=None, num_attention_heads=1, size_per_head=512, query_act=None, key_act=None, value_act=None, attention_probs_dropout_prob=0.0, initializer_range=0.02, do_return_2d_tensor=False, batch_size=None, from_seq_length=None, to_seq_length=None): """Performs multi-headed attention from `from_tensor` to `to_tensor`. This is an implementation of multi-headed attention based on "Attention is all you Need". If `from_tensor` and `to_tensor` are the same, then this is self-attention. Each timestep in `from_tensor` attends to the corresponding sequence in `to_tensor`, and returns a fixed-with vector. This function first projects `from_tensor` into a "query" tensor and `to_tensor` into "key" and "value" tensors. These are (effectively) a list of tensors of length `num_attention_heads`, where each tensor is of shape [batch_size, seq_length, size_per_head]. Then, the query and key tensors are dot-producted and scaled. These are softmaxed to obtain attention probabilities. The value tensors are then interpolated by these probabilities, then concatenated back to a single tensor and returned. In practice, the multi-headed attention are done with transposes and reshapes rather than actual separate tensors. Args: from_tensor: float Tensor of shape [batch_size, from_seq_length, from_width]. to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width]. attention_mask: (optional) int32 Tensor of shape [batch_size, from_seq_length, to_seq_length]. The values should be 1 or 0. The attention scores will effectively be set to -infinity for any positions in the mask that are 0, and will be unchanged for positions that are 1. num_attention_heads: int. Number of attention heads. size_per_head: int. Size of each attention head. query_act: (optional) Activation function for the query transform. key_act: (optional) Activation function for the key transform. value_act: (optional) Activation function for the value transform. attention_probs_dropout_prob: (optional) float. Dropout probability of the attention probabilities. initializer_range: float. Range of the weight initializer. do_return_2d_tensor: bool. If True, the output will be of shape [batch_size * from_seq_length, num_attention_heads * size_per_head]. If False, the output will be of shape [batch_size, from_seq_length, num_attention_heads * size_per_head]. batch_size: (Optional) int. If the input is 2D, this might be the batch size of the 3D version of the `from_tensor` and `to_tensor`. from_seq_length: (Optional) If the input is 2D, this might be the seq length of the 3D version of the `from_tensor`. to_seq_length: (Optional) If the input is 2D, this might be the seq length of the 3D version of the `to_tensor`. Returns: float Tensor of shape [batch_size, from_seq_length, num_attention_heads * size_per_head]. (If `do_return_2d_tensor` is true, this will be of shape [batch_size * from_seq_length, num_attention_heads * size_per_head]). Raises: ValueError: Any of the arguments or tensor shapes are invalid. """ def transpose_for_scores(input_tensor, batch_size, num_attention_heads, seq_length, width): output_tensor = tf.reshape( input_tensor, [batch_size, seq_length, num_attention_heads, width]) output_tensor = tf.transpose(a=output_tensor, perm=[0, 2, 1, 3]) return output_tensor from_shape = get_shape_list(from_tensor, expected_rank=[2, 3]) to_shape = get_shape_list(to_tensor, expected_rank=[2, 3]) if len(from_shape) != len(to_shape): raise ValueError( "The rank of `from_tensor` must match the rank of `to_tensor`.") if len(from_shape) == 3: batch_size = from_shape[0] from_seq_length = from_shape[1] to_seq_length = to_shape[1] elif len(from_shape) == 2: if (batch_size is None or from_seq_length is None or to_seq_length is None): raise ValueError( "When passing in rank 2 tensors to attention_layer, the values " "for `batch_size`, `from_seq_length`, and `to_seq_length` " "must all be specified.") # Scalar dimensions referenced here: # B = batch size (number of sequences) # F = `from_tensor` sequence length # T = `to_tensor` sequence length # N = `num_attention_heads` # H = `size_per_head` from_tensor_2d = reshape_to_matrix(from_tensor) to_tensor_2d = reshape_to_matrix(to_tensor) # `query_layer` = [B*F, N*H] query_layer = tf.compat.v1.layers.dense( from_tensor_2d, num_attention_heads * size_per_head, activation=query_act, name="query", kernel_initializer=create_initializer(initializer_range)) # `key_layer` = [B*T, N*H] key_layer = tf.compat.v1.layers.dense( to_tensor_2d, num_attention_heads * size_per_head, activation=key_act, name="key", kernel_initializer=create_initializer(initializer_range)) # `value_layer` = [B*T, N*H] value_layer = tf.compat.v1.layers.dense( to_tensor_2d, num_attention_heads * size_per_head, activation=value_act, name="value", kernel_initializer=create_initializer(initializer_range)) # `query_layer` = [B, N, F, H] query_layer = transpose_for_scores(query_layer, batch_size, num_attention_heads, from_seq_length, size_per_head) # `key_layer` = [B, N, T, H] key_layer = transpose_for_scores(key_layer, batch_size, num_attention_heads, to_seq_length, size_per_head) # Take the dot product between "query" and "key" to get the raw # attention scores. # `attention_scores` = [B, N, F, T] attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) attention_scores = tf.multiply(attention_scores, 1.0 / math.sqrt(float(size_per_head))) if attention_mask is not None: # `attention_mask` = [B, 1, F, T] attention_mask = tf.expand_dims(attention_mask, axis=[1]) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. adder = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0 # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. attention_scores += adder # Normalize the attention scores to probabilities. # `attention_probs` = [B, N, F, T] attention_probs = tf.nn.softmax(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = dropout(attention_probs, attention_probs_dropout_prob) # `value_layer` = [B, T, N, H] value_layer = tf.reshape( value_layer, [batch_size, to_seq_length, num_attention_heads, size_per_head]) # `value_layer` = [B, N, T, H] value_layer = tf.transpose(a=value_layer, perm=[0, 2, 1, 3]) # `context_layer` = [B, N, F, H] context_layer = tf.matmul(attention_probs, value_layer) # `context_layer` = [B, F, N, H] context_layer = tf.transpose(a=context_layer, perm=[0, 2, 1, 3]) if do_return_2d_tensor: # `context_layer` = [B*F, N*H] context_layer = tf.reshape( context_layer, [batch_size * from_seq_length, num_attention_heads * size_per_head]) else: # `context_layer` = [B, F, N*H] context_layer = tf.reshape( context_layer, [batch_size, from_seq_length, num_attention_heads * size_per_head]) return context_layer def transformer_model(input_tensor, attention_mask=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, intermediate_act_fn=gelu, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, do_return_all_layers=False): """Multi-headed, multi-layer Transformer from "Attention is All You Need". This is almost an exact implementation of the original Transformer encoder. See the original paper: https://arxiv.org/abs/1706.03762 Also see: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py Args: input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size]. attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length, seq_length], with 1 for positions that can be attended to and 0 in positions that should not be. hidden_size: int. Hidden size of the Transformer. num_hidden_layers: int. Number of layers (blocks) in the Transformer. num_attention_heads: int. Number of attention heads in the Transformer. intermediate_size: int. The size of the "intermediate" (a.k.a., feed forward) layer. intermediate_act_fn: function. The non-linear activation function to apply to the output of the intermediate/feed-forward layer. hidden_dropout_prob: float. Dropout probability for the hidden layers. attention_probs_dropout_prob: float. Dropout probability of the attention probabilities. initializer_range: float. Range of the initializer (stddev of truncated normal). do_return_all_layers: Whether to also return all layers or just the final layer. Returns: float Tensor of shape [batch_size, seq_length, hidden_size], the final hidden layer of the Transformer. Raises: ValueError: A Tensor shape or parameter is invalid. """ if hidden_size % num_attention_heads != 0: raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (hidden_size, num_attention_heads)) attention_head_size = int(hidden_size / num_attention_heads) input_shape = get_shape_list(input_tensor, expected_rank=3) batch_size = input_shape[0] seq_length = input_shape[1] input_width = input_shape[2] # The Transformer performs sum residuals on all layers so the input needs # to be the same as the hidden size. if input_width != hidden_size: raise ValueError("The width of the input tensor (%d) != hidden size (%d)" % (input_width, hidden_size)) # We keep the representation as a 2D tensor to avoid re-shaping it back and # forth from a 3D tensor to a 2D tensor. Re-shapes are normally free on # the GPU/CPU but may not be free on the TPU, so we want to minimize them to # help the optimizer. prev_output = reshape_to_matrix(input_tensor) all_layer_outputs = [] for layer_idx in range(num_hidden_layers): with tf.compat.v1.variable_scope("layer_%d" % layer_idx): layer_input = prev_output with tf.compat.v1.variable_scope("attention"): attention_heads = [] with tf.compat.v1.variable_scope("self"): attention_head = attention_layer( from_tensor=layer_input, to_tensor=layer_input, attention_mask=attention_mask, num_attention_heads=num_attention_heads, size_per_head=attention_head_size, attention_probs_dropout_prob=attention_probs_dropout_prob, initializer_range=initializer_range, do_return_2d_tensor=True, batch_size=batch_size, from_seq_length=seq_length, to_seq_length=seq_length) attention_heads.append(attention_head) attention_output = None if len(attention_heads) == 1: attention_output = attention_heads[0] else: # In the case where we have other sequences, we just concatenate # them to the self-attention head before the projection. attention_output = tf.concat(attention_heads, axis=-1) # Run a linear projection of `hidden_size` then add a residual # with `layer_input`. with tf.compat.v1.variable_scope("output"): attention_output = tf.compat.v1.layers.dense( attention_output, hidden_size, kernel_initializer=create_initializer(initializer_range)) attention_output = dropout(attention_output, hidden_dropout_prob) attention_output = layer_norm(attention_output + layer_input) # The activation is only applied to the "intermediate" hidden layer. with tf.compat.v1.variable_scope("intermediate"): intermediate_output = tf.compat.v1.layers.dense( attention_output, intermediate_size, activation=intermediate_act_fn, kernel_initializer=create_initializer(initializer_range)) # Down-project back to `hidden_size` then add the residual. with tf.compat.v1.variable_scope("output"): layer_output = tf.compat.v1.layers.dense( intermediate_output, hidden_size, kernel_initializer=create_initializer(initializer_range)) layer_output = dropout(layer_output, hidden_dropout_prob) layer_output = layer_norm(layer_output + attention_output) prev_output = layer_output all_layer_outputs.append(layer_output) if do_return_all_layers: final_outputs = [] for layer_output in all_layer_outputs: final_output = reshape_from_matrix(layer_output, input_shape) final_outputs.append(final_output) return final_outputs else: final_output = reshape_from_matrix(prev_output, input_shape) return final_output def get_shape_list(tensor, expected_rank=None, name=None): """Returns a list of the shape of tensor, preferring static dimensions. Args: tensor: A tf.Tensor object to find the shape of. expected_rank: (optional) int. The expected rank of `tensor`. If this is specified and the `tensor` has a different rank, and exception will be thrown. name: Optional name of the tensor for the error message. Returns: A list of dimensions of the shape of tensor. All static dimensions will be returned as python integers, and dynamic dimensions will be returned as tf.Tensor scalars. """ if name is None: name = tensor.name if expected_rank is not None: assert_rank(tensor, expected_rank, name) shape = tensor.shape.as_list() non_static_indexes = [] for (index, dim) in enumerate(shape): if dim is None: non_static_indexes.append(index) if not non_static_indexes: return shape dyn_shape = tf.shape(input=tensor) for index in non_static_indexes: shape[index] = dyn_shape[index] return shape def reshape_to_matrix(input_tensor): """Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix).""" ndims = input_tensor.shape.ndims if ndims < 2: raise ValueError("Input tensor must have at least rank 2. Shape = %s" % (input_tensor.shape)) if ndims == 2: return input_tensor width = input_tensor.shape[-1] output_tensor = tf.reshape(input_tensor, [-1, width]) return output_tensor def reshape_from_matrix(output_tensor, orig_shape_list): """Reshapes a rank 2 tensor back to its original rank >= 2 tensor.""" if len(orig_shape_list) == 2: return output_tensor output_shape = get_shape_list(output_tensor) orig_dims = orig_shape_list[0:-1] width = output_shape[-1] return tf.reshape(output_tensor, orig_dims + [width]) def assert_rank(tensor, expected_rank, name=None): """Raises an exception if the tensor rank is not of the expected rank. Args: tensor: A tf.Tensor to check the rank of. expected_rank: Python integer or list of integers, expected rank. name: Optional name of the tensor for the error message. Raises: ValueError: If the expected shape doesn't match the actual shape. """ if name is None: name = tensor.name expected_rank_dict = {} if isinstance(expected_rank, six.integer_types): expected_rank_dict[expected_rank] = True else: for x in expected_rank: expected_rank_dict[x] = True actual_rank = tensor.shape.ndims if actual_rank not in expected_rank_dict: scope_name = tf.compat.v1.get_variable_scope().name raise ValueError( "For the tensor `%s` in scope `%s`, the actual rank " "`%d` (shape = %s) is not equal to the expected rank `%s`" % (name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))

[ "[email protected]" ]

[email protected]

e8001a656cae6b21c00f398deca4b950fda490ed

ab98c033b4c2e80b304e9f77b740b6d545870b66

/data_aggregation/CreationBDD2_3_aliases+GoTerms/researchDG.py

86a42e0a2c3605a4904beaa95bb1e64303e6338a

[]

no_license

yannistannier/textmining-light

503384c28f5fb4763293ced15337295685d84ba3

864210d127684d5af55336ceb8c0718d0f2c3e3c

refs/heads/master

2020-04-14T23:37:38.751779

2019-01-07T09:10:50

164,209,545

0

1

null

UTF-8

Python

false

4,808

py

from Bio import Entrez, SeqIO, Medline import scipy.sparse as sp import numpy as np import pandas as pd import sklearn import sys Entrez.email = "[email protected]" def recupDictionnaires(): ens = [] file = open("dict.txt", "r") doc = file.read() dim = len(doc.split("##")) i = 0 print ("STARTING RECUP") for line in doc.split("##"): if (i < dim-1): symbol, aliases, name, diseases, goTerms, pubmedIds = line.split("|") dico = {} dico['symbol'] = symbol dico['aliases'] = [] for alias in aliases.split(","): if (not alias == ""): dico['aliases'].append(alias) dico['name'] = name dico['diseases'] = [] for disease in diseases.split(","): if (not disease == ""): dico['diseases'].append(disease) dico['goTerms'] = [] for goTerm in goTerms.split(","): if (not goTerm == ""): dico['goTerms'].append(goTerm) dico['pubmedIds'] = [] for pubmedId in pubmedIds.split(","): if (not pubmedId == ""): dico['pubmedIds'].append(pubmedId) ens.append(dico) #print (dico) i += 1 print ("END RECUP") return ens def pbmd_search(maladie,gene): handle = Entrez.esearch(db = 'pubmed', term = maladie + " AND " + gene, retmax = '1000000000') print (maladie + " AND " + gene) result = Entrez.read(handle) handle.close() return(result) def ecriture_file(maladie, gene, value): output = open('output.txt', 'a') output.write(maladie+"|" +gene+ "|" + str(value) + "##") output.close() def ecriture_end_dg(): output = open('output.txt', 'a') output.write("@@") output.close() if __name__ == '__main__': print("################### START SEARCHING ###################") ###### RECUPERATION DE LA LISTE DES DICTIONNAIRES DE GENES ens = recupDictionnaires() dim = len(ens) print (dim) ###### OUVERTURE DU FIHCIER D'OUTPUT ET VERIFICATION DE SON CONTENU POUR REPRISE DE LA RECHERCHE try : file = open("output.txt", "r") except IOError: file = open("output.txt", "x") file = open("output.txt", "r") doc = file.read() file.close() lim = 0 already = doc.split("@@") for line in already: lim += 1 finalPart = already[-1] print (finalPart) print (lim) delim = 0 for part in finalPart.split("##"): delim += 1 i = 0 #while (i < dim): ###### POUR CHAQUE DICTIONNAIRE : for dico in ens: print ("Loading...") print(str(i+1) + "/" + str(dim)) # S'IL EST DEJA ECRIT DANS LE FICHIER, PASSER AU SUIVANT if (i < lim -1): print ("... GENE : Already done") # SINON : # VERIFIER QUELLES MALADIES ONT DEJA ETE ECRITES else : print ("Preparing GENE...") # PREPARATION DE LA REQUETE genes = set([]) if (not len(dico['aliases']) == 0): genes.update(dico['aliases']) if (not len(dico['symbol']) == 0): genes.add(dico['symbol']) if (not len(dico['name']) == 0): genes.add(dico['name']) genes_string = " OR ".join(genes) genes_string = "(" + genes_string + ")" print ("GENE : ", genes_string) nbD = len(dico['diseases']) cptD = 0 #while (cptD < nbD): ##### POUR CHAQUE MALADIE for disease in dico['diseases']: # SI DEJA FAIT if (cptD < delim -1): print(str(cptD+1) + "/" + str(nbD)) print ("... DISEASE Already done") # SI PAS ENCORE FAIT else: # REQUETE PUBMED print ("SEARCHING DISEASE (" + str(cptD+1) + "/" + str(nbD) + ")" ) result = [] idList = set([]) result = pbmd_search(disease,genes_string) idList.update(result['IdList']) key = len(idList) key = key + 1 print("PRINTING IN FILE ...") ecriture_file(disease, dico['symbol'], key) print ("***** OK !!") cptD += 1 ecriture_end_dg() delim = 0 i+=1 print("################### END SEARCHING ###################")

[ "[email protected]" ]

[email protected]

049c12ca9c2ec403bf4f152a25d45aee9f1d0c8c

6cb1bd6816af5964c82e127e9e28cd6d0fd5fd7d

/05-05finally.py

e54a7e88b941cc9bae40f8afbc18c38fba553199

[]

no_license

jinju-lee/Python-study

660f952b3c16d675147f870e1cab473177106636

c226bcb2c501c49ac157b6d3a3d18e515f3011f8

refs/heads/master

2021-05-08T21:15:32.782635

2018-02-11T14:32:26

119,610,114

0

null

UTF-8

Python

false

200

py

try: num =int(input('숫자를 입력하세요:')) except ValueError: print('숫자가 아닙니다.') else: print(num) finally: print('finally는 무조건 실행됩니다.')

[ "[email protected]" ]

[email protected]

158affbd0c69bdb708234a227040c705d8a0b2f4

88853b9b6c3ae0c12cbd5bf83be3d48f2fe0159e

/document/eggs_package/gflux_egg/gflux/gflux/apps/station/management/commands/obselete/deal_with_shihua_none_fuel_data.py

e12cd0c2aed3e9ab1d6084886feb5e89e19b5a76

[]

no_license

laoyin/nyf

9151da3368c2e636501bcf1ad52f895fe446c04b

a2b12f54827b6088548f060881b6dafa2d791a3a

refs/heads/master

2021-01-17T17:11:14.174968

2015-09-28T01:12:28

null

0

null

UTF-8

Python

false

2,588

py

# coding=utf-8 import sys reload(sys) sys.setdefaultencoding('utf-8') from django.core.management.base import BaseCommand from dash.core.backends.sql.models import get_dash_session_maker from gflux.apps.common.models import SiteDayBatch from datetime import datetime from optparse import make_option import sys,pdb,re from dash.core.utils import getPaymentTypeByCard import hashlib import xlrd import random from xlrd import xldate_as_tuple class Command(BaseCommand): help = 'Deal with None Fuel Data' option_list = BaseCommand.option_list + ( make_option('--file',help="set file path",type="string"), make_option('--save_path',help="save file path",type="string"), ) def handle(self, *args, **options): print 'start...' save_path=options['save_path'] try: trans_count=30150001 book=xlrd.open_workbook(options['file'],encoding_override='gb2312') sheets=book.sheets() for sheet in sheets: nrows=sheet.nrows for row_idx in xrange(nrows): #忽略表头 if row_idx == 0: continue #最后一行的总计忽略掉 if row_idx==nrows-1: return row=sheet.row_values(row_idx) with open(save_path+'/changjiangdao.txt','a') as lf: site=eval(repr(row[1])[1:]).decode('gbk','ignore') #处理unicode编码，并去掉文字后面到空格 site = site.decode('unicode-escape').rstrip()+',' trans_type='1,' cardnum='0,' payment_type='1000,' timestamp = row[3]+',' barcode=str(row[11][0:11])+',' pay=str(row[38])+',' quantity=str(row[31])+',' desc=eval(repr(row[10])[1:]).decode('gbk','ignore') desc=desc.decode('unicode-escape')+',' price=str(row[35])+',' unitname=row[12]+',' pump_id='0,' trans_id=str(trans_count)+'\n' trans_count+=1 lf.write(site+trans_type+cardnum+payment_type+timestamp+barcode+pay+quantity+desc+price+unitname+pump_id+trans_id) print 'ok' except Exception,e: print e print 'end...'

[ "[email protected]" ]

[email protected]

38769bed99e3caf79b45b1c948a5142c38462485

dee1aa5ce988f59165a8a651b28f471c468fff99

/tributary/lazy/output/__init__.py

3d73f9fd9636c043435f23c15dff8fc754280c14

[ "Apache-2.0" ]

permissive

ceball/tributary

04f22e57048a3cb0375b57cdb30e62f69cf4a380

5e30f90d1a5cc176c0f231f525d9dc5a81353925

refs/heads/master

2022-12-05T20:35:33.631468

2020-08-28T13:14:24

291,319,040

0

Apache-2.0

2020-08-29T17:39:39

2020-08-29T17:39:38

null

UTF-8

Python

false

3,819

py

from ..node import Node def _print(node, cache=None): if cache is None: cache = {} cache[id(node)] = node ret = {node: []} if node._dependencies: for call, deps in node._dependencies.items(): # callable node if hasattr(call, '_node_wrapper') and \ call._node_wrapper is not None: val = call._node_wrapper._print(cache) ret[node].append(val) # args for arg in deps[0]: val = arg._print(cache) ret[node].append(val) # kwargs for kwarg in deps[1].values(): val = kwarg._print(cache) ret[node].append(val) return ret def Print(node): return node._print({}) def Graph(node): return node.print() def GraphViz(node): d = node.graph() from graphviz import Digraph dot = Digraph(node._name, strict=True) dot.format = 'png' def rec(nodes, parent): for d in nodes: if not isinstance(d, dict): if d.isDirty(): dot.node(d._name, color='red', shape=d._graphvizshape) dot.edge(d._name, parent._name, color='red') else: dot.node(d._name, shape=d._graphvizshape) dot.edge(d._name, parent._name) else: for k in d: if k.isDirty(): dot.node(k._name, color='red', shape=k._graphvizshape) rec(d[k], k) dot.edge(k._name, parent._name, color='red') else: dot.node(k._name, shape=k._graphvizshape) rec(d[k], k) dot.edge(k._name, parent._name) for k in d: if k.isDirty(): dot.node(k._name, color='red', shape=k._graphvizshape) else: dot.node(k._name, shape=k._graphvizshape) rec(d[k], k) return dot def Dagre(node): import ipydagred3 as dd3 G = dd3.Graph() d = Graph(node) def rec(nodes, parent): for d in nodes: if not isinstance(d, dict): d._dd3g = G if d.isDirty(): G.setNode(d._name, style='fill: #f00', shape="rect" if d._graphvizshape == "box" else d._graphvizshape) # G.setEdge(d._name, parent._name, style='stroke: #f00') else: G.setNode(d._name, style='fill: #fff', shape="rect" if d._graphvizshape == "box" else d._graphvizshape) G.setEdge(d._name, parent._name, style='stroke: #000') else: for k in d: k._dd3g = G if k.isDirty(): G.setNode(k._name, style='fill: #f00', shape="rect" if k._graphvizshape == "box" else k._graphvizshape) rec(d[k], k) # G.setEdge(k._name, parent._name, style='stroke: #f00') else: G.setNode(k._name, style='fill: #fff', shape="rect" if k._graphvizshape == "box" else k._graphvizshape) rec(d[k], k) G.setEdge(k._name, parent._name, style='stroke: #000') for k in d: k._dd3g = G if k.isDirty(): G.setNode(k._name, style='fill: #f00', shape="rect" if k._graphvizshape == "box" else k._graphvizshape) else: G.setNode(k._name, style='fill: #fff', shape="rect" if k._graphvizshape == "box" else k._graphvizshape) rec(d[k], k) graph = dd3.DagreD3Widget(graph=G) return graph Node._print = _print Node.print = Print Node.graph = Graph Node.graphviz = GraphViz Node.dagre = Dagre

[ "[email protected]" ]

[email protected]

7e5da0fbf908161bc4084fef3c8bf28c92b54ad9

c868d681415d152ba331bd80e0ed542832f20f0e

/week 3/todo_project/todo_project/main/migrations/0005_auto_20200205_2301.py

819409651f247514434443d5e5acf7e6a3948904

[]

no_license

Yeldarmt/BFDjango

a297a6b0c00ffb1a269f05c7e6665c5d34a51097

b8256ff1d5f2125495df66eabf267fc17e667aeb

refs/heads/master

2022-11-30T12:45:17.356453

2020-04-19T16:50:26

233,515,749

0

null

UTF-8

Python

false

906

py

# Generated by Django 2.0 on 2020-02-05 17:01 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('main', '0004_auto_20200203_1126'), ] operations = [ migrations.CreateModel( name='Todo', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=200)), ('description', models.TextField()), ('completed', models.BooleanField(default=False)), ], ), migrations.DeleteModel( name='Employee', ), migrations.RemoveField( model_name='myuser', name='name', ), migrations.RemoveField( model_name='myuser', name='sur_name', ), ]

[ "[email protected]" ]

[email protected]

3467e9fbec6ceb28a2b2a98d25b2a0dbb03e4122

78e60a7d8a67ed76244004e8a3ed573fbf396e41

/samples/sq__unbind_skill.py

5e488f7e1a0d6a6147070822fc075df323fa31d5

[ "MIT" ]

permissive

Crivez/apiclient-python

837a9f7cc0453ccd3121311adc7920b5fe6b3e33

860fc054f546152a101e29b1af388c381075ac47

refs/heads/master

2023-06-08T13:24:09.249704

2021-06-17T12:16:35

null

0

null

UTF-8

Python

false

490

py

from voximplant.apiclient import VoximplantAPI, VoximplantException if __name__ == "__main__": voxapi = VoximplantAPI("credentials.json") # Unbind the skill with id = 1 from the user with id = 1. APPLICATION_ID = 1 USER_ID = 1 SQ_SKILL_ID = 1 try: res = voxapi.sq__unbind_skill(APPLICATION_ID, USER_ID, SQ_SKILL_ID) print(res) except VoximplantException as e: print("Error: {}".format(e.message))

[ "[email protected]" ]

[email protected]

21282b4075722ef249ada742b5404b049ef993c0

9d278285f2bc899ac93ec887b1c31880ed39bf56

/ondoc/diagnostic/migrations/0014_auto_20180427_1159.py

32ba48033c717f5b1bc268bb1082d320a0021623

[]

no_license

ronit29/docprime

945c21f8787387b99e4916cb3ba1618bc2a85034

60d4caf6c52a8b70174a1f654bc792d825ba1054

refs/heads/master

2023-04-01T14:54:10.811765

2020-04-07T18:57:34

353,953,576

0

null

UTF-8

Python

false

993

py

# Generated by Django 2.0.2 on 2018-04-27 06:29 import django.core.validators from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('diagnostic', '0013_auto_20180426_1939'), ] operations = [ migrations.AddField( model_name='lab', name='onboarding_status', field=models.PositiveSmallIntegerField(choices=[(1, 'Not Onboarded'), (2, 'Onboarding Request Sent'), (3, 'Onboarded')], default=1), ), migrations.AddField( model_name='labonboardingtoken', name='email', field=models.EmailField(blank=True, max_length=100), ), migrations.AddField( model_name='labonboardingtoken', name='mobile', field=models.BigIntegerField(blank=True, null=True, validators=[django.core.validators.MaxValueValidator(9999999999), django.core.validators.MinValueValidator(1000000000)]), ), ]

[ "[email protected]" ]

[email protected]

91a320683b315b2ec3f1fff36159fbc626d0fef6

2ccb6448e4783275350a388f2b71ace5c68a8024

/mars/dataframe/reduction/custom_reduction.py

c08a1e6da4ecdc13fc077ff6f87e6c0a2c1dcdf8

[ "BSD-3-Clause", "OFL-1.1", "LicenseRef-scancode-unknown-license-reference", "CC0-1.0", "Apache-2.0", "BSD-2-Clause", "MIT" ]

permissive

songroger/mars

3fb286513d039944134c84c3e56f5d23cbe4562a

ae79f70599b5253418a05daed88ae835b8303649

refs/heads/master

2021-06-20T17:13:58.186858

2021-01-25T02:53:44

166,158,684

2

0

Apache-2.0

2019-01-17T04:12:10

2019-01-17T04:12:09

null

UTF-8

Python

false

1,730

py

# Copyright 1999-2020 Alibaba Group Holding Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ... import opcodes as OperandDef from ...config import options from ...core import OutputType from ...serialize import AnyField from .core import DataFrameReductionOperand, DataFrameReductionMixin class DataFrameCustomReduction(DataFrameReductionOperand, DataFrameReductionMixin): _op_type_ = OperandDef.CUSTOM_REDUCTION _func_name = 'custom_reduction' _custom_reduction = AnyField('custom_reduction') def __init__(self, custom_reduction=None, **kw): super().__init__(_custom_reduction=custom_reduction, **kw) @property def custom_reduction(self): return self._custom_reduction @property def is_atomic(self): return True def get_reduction_args(self, axis=None): return dict() def build_custom_reduction_result(df, custom_reduction_obj): use_inf_as_na = options.dataframe.mode.use_inf_as_na output_type = OutputType.series if df.ndim == 2 else OutputType.scalar op = DataFrameCustomReduction(custom_reduction=custom_reduction_obj, output_types=[output_type], use_inf_as_na=use_inf_as_na) return op(df)

[ "[email protected]" ]

[email protected]

455de03e8f49274c758ae023bb63a631d4a1a7be

0b1e404a165c960677d07015bc26aac0569cf84a

/src/combustion/models/efficientdet.py

9ad75fdd19fa31300cf789c8bae8a09322c9b3c9

[ "Apache-2.0" ]

permissive

johndpope/combustion

d3ec349cd7be086f55b4e3deebd571c97842e1ed

c3f91e62a10a873cfeeae8c675b0683bc5158818

refs/heads/master

2023-03-01T14:34:42.149415

2021-02-07T17:55:58

2021-02-13T17:17:23

null

0

null

UTF-8

Python

false

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#!/usr/bin/env python # -*- coding: utf-8 -*- from copy import deepcopy from typing import Any, Dict, List, Optional import torch import torch.nn as nn from torch import Tensor from combustion.nn import BiFPN1d, BiFPN2d, BiFPN3d, MatchShapes, MobileNetBlockConfig from .efficientnet import _EfficientNet class _EfficientDetMeta(type): def __new__(cls, name, bases, dct): x = super().__new__(cls, name, bases, dct) if "3d" in name: x.Conv = nn.Conv3d x.BatchNorm = nn.BatchNorm3d x.BiFPN = BiFPN3d x._get_blocks = MobileNetBlockConfig.get_3d_blocks elif "2d" in name: x.Conv = nn.Conv2d x.BatchNorm = nn.BatchNorm2d x.BiFPN = BiFPN2d x._get_blocks = MobileNetBlockConfig.get_2d_blocks elif "1d" in name: x.Conv = nn.Conv1d x.BatchNorm = nn.BatchNorm1d x.BiFPN = BiFPN1d x._get_blocks = MobileNetBlockConfig.get_1d_blocks else: raise RuntimeError(f"Metaclass: error processing name {cls.__name__}") return x class _EfficientDet(_EfficientNet): __constants__ = ["fpn_levels"] def __init__( self, block_configs: List[MobileNetBlockConfig], fpn_levels: List[int] = [3, 5, 7, 8, 9], fpn_filters: int = 64, fpn_repeats: int = 3, width_coeff: float = 1.0, depth_coeff: float = 1.0, width_divisor: float = 8.0, min_width: Optional[int] = None, stem: Optional[nn.Module] = None, head: Optional[nn.Module] = None, fpn_kwargs: dict = {}, ): super(_EfficientDet, self).__init__( block_configs, width_coeff, depth_coeff, width_divisor, min_width, stem, head ) self.fpn_levels = fpn_levels # convolutions mapping backbone feature maps to constant number of channels fpn_convs = [] output_filters = self.round_filters(fpn_filters, 1.0, width_divisor, min_width) self.__fpn_filters = output_filters for i, config in enumerate(self.block_configs): if i + 1 in fpn_levels: input_filters = config.output_filters conv = self.Conv(input_filters, output_filters, kernel_size=1) fpn_convs.append(conv) for i in fpn_levels: if i == len(self.block_configs) + 1: input_filters = self.block_configs[-1].output_filters conv = self.Conv(input_filters, output_filters, kernel_size=3, stride=2, padding=1) fpn_convs.append(conv) elif i > len(self.block_configs) + 1: input_filters = output_filters conv = self.Conv(input_filters, output_filters, kernel_size=3, stride=2, padding=1) fpn_convs.append(nn.Sequential(nn.ReLU(), conv)) self.fpn_convs = nn.ModuleList(fpn_convs) self.match = MatchShapes() # defaults for batch norm params _ = {"bn_momentum": 0.01, "bn_epsilon": 1e-3} _.update(fpn_kwargs) fpn_kwargs = _ # build bifpn bifpn_layers = [] for i in range(fpn_repeats): bifpn = self.BiFPN(output_filters, levels=len(fpn_levels), **fpn_kwargs) bifpn_layers.append(bifpn) self.bifpn_layers = nn.ModuleList(bifpn_layers) @torch.jit.unused @property def fpn_filters(self) -> int: r"""Number of filters in each level of the BiFPN. When using a custom head, use this property to determine the number of filters in the head's input. """ return self.__fpn_filters def extract_features(self, inputs: Tensor) -> List[Tensor]: r"""Runs the EfficientDet stem and body to extract features, returning a list of tensors representing features extracted from each block. Args: inputs (:class:`torch.Tensor`): Model inputs """ # efficientnet feature extractor backbone_features: List[Tensor] = [] x = self.stem(inputs) prev_x = x for block in self.blocks: x = block(prev_x) backbone_features.append(x) prev_x = x # pull out feature maps to be used in BiFPN captured_features: List[Tensor] = [] for i in self.fpn_levels: if i - 1 < len(backbone_features): captured_features.append(backbone_features[i - 1]) # map to constant channel number using trivial convs for i, conv in enumerate(self.fpn_convs): if i < len(captured_features): captured_features[i] = conv(captured_features[i]) else: prev_x = conv(prev_x) captured_features.append(prev_x) for bifpn in self.bifpn_layers: captured_features = bifpn(captured_features) return captured_features def forward(self, inputs: Tensor) -> List[Tensor]: r"""Runs the entire EfficientDet model, including stem, body, and head. If no head was supplied, the output of :func:`extract_features` will be returned. Otherwise, the output of the given head will be returned. .. note:: The returned output will always be a list of tensors. If a custom head is given and it returns a single tensor, that tensor will be wrapped in a list before being returned. Args: inputs (:class:`torch.Tensor`): Model inputs """ output = self.extract_features(inputs) if self.head is not None: output = self.head(output) if not isinstance(output, list): output = [ output, ] return output @classmethod def from_predefined(cls, compound_coeff: int, block_overrides: Dict[str, Any] = {}, **kwargs) -> "_EfficientDet": r"""Creates an EfficientDet model using one of the parameterizations defined in the `EfficientDet paper`_. Args: compound_coeff (int): Compound scaling parameter :math:`\phi`. For example, to construct EfficientDet-D0, set ``compound_coeff=0``. block_overrides (dict): Overrides to be applied to each :class:`combustion.nn.MobileNetBlockConfig`. **kwargs: Additional parameters/overrides for model constructor. .. _EfficientNet paper: https://arxiv.org/abs/1905.11946 """ # from paper alpha = 1.2 beta = 1.1 width_divisor = 8.0 depth_coeff = alpha ** compound_coeff width_coeff = beta ** compound_coeff fpn_filters = int(64 * 1.35 ** compound_coeff) fpn_repeats = 3 + compound_coeff fpn_levels = [3, 5, 7, 8, 9] # apply config overrides at each block block_configs = deepcopy(cls.DEFAULT_BLOCKS) for k, v in block_overrides.items(): for config in block_configs: setattr(config, str(k), v) final_kwargs = { "block_configs": block_configs, "width_coeff": width_coeff, "depth_coeff": depth_coeff, "width_divisor": width_divisor, "fpn_filters": fpn_filters, "fpn_repeats": fpn_repeats, "fpn_levels": fpn_levels, } final_kwargs.update(kwargs) result = cls(**final_kwargs) result.compound_coeff = compound_coeff return result class EfficientDet1d(_EfficientDet, metaclass=_EfficientDetMeta): pass class EfficientDet2d(_EfficientDet, metaclass=_EfficientDetMeta): r"""Implementation of EfficientDet as described in the `EfficientDet paper`_. EfficientDet is built on an EfficientNet backbone (see :class:`combustion.models.EfficientNet2d` for details). EfficientDet adds a bidirectional feature pyramid network (see :class:`combustion.nn.BiFPN2d`), which mixes information across the various feature maps produced by the EfficientNet backbone. .. image:: ./efficientdet.png :width: 800px :align: center :height: 300px :alt: Diagram of EfficientDet The authors of EfficientDet used the default EfficientNet scaling parameters for the backbone: .. math:: \alpha = 1.2 \\ \beta = 1.1 \\ \gamma = 1.15 The BiFPN was scaled as follows: .. math:: W_\text{bifpn} = 64 \cdot \big(1.35^\phi\big) \\ D_\text{bifpn} = 3 + \phi In the original EfficientDet implementation, the authors extract feature maps from levels 3, 5, and 7 of the backbone. Two additional coarse levels are created by performing additional strided convolutions to the final level in the backbone, for a total of 5 levels in the BiFPN. .. note:: Currently, DropConnect ratios are not scaled based on depth of the given block. This is a deviation from the true EfficientNet implementation. Args: block_configs (list of :class:`combustion.nn.MobileNetBlockConfig`) Configs for each of the :class:`combustion.nn.MobileNetConvBlock2d` blocks used in the model. fpn_levels (list of ints): Indicies of EfficientNet feature levels to include in the BiFPN, starting at index 1. Values in ``fpn_levels`` greater than the total number of blocks in the backbone denote levels that should be created by applying additional strided convolutions to the final level in the backbone. fpn_filters (int): Number of filters to use for the BiFPN. The filter count given here should be the desired number of filters after width scaling. fpn_repeats (int): Number of repeats to use for the BiFPN. The repeat count given here should be the desired number of repeats after depth scaling. width_coeff (float): The width scaling coefficient. Increasing this increases the width of the model. depth_coeff (float): The depth scaling coefficient. Increasing this increases the depth of the model. width_divisor (float): Used in calculating number of filters under width scaling. Filters at each block will be a multiple of ``width_divisor``. min_width (int): The minimum width of the model at any block stem (:class:`torch.nn.Module`): An optional stem to use for the model. The default stem is a single 3x3/2 conolution that expects 3 input channels. head (:class:`torch.nn.Module`): An optional head to use for the model. By default, no head will be used and ``forward`` will return a list of tensors containing extracted features. fpn_kwargs (dict): Keyword args to be passed to all :class:`combustion.nn.BiFPN2d` layers. Shapes * Input: :math:`(N, C, H, W)` * Output: List of tensors of shape :math:`(N, C, H', W')`, where height and width vary depending on the amount of downsampling for that feature map. .. _EfficientDet paper: https://arxiv.org/abs/1911.09070 """ class EfficientDet3d(_EfficientDet, metaclass=_EfficientDetMeta): pass

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#!/usr/bin/env python3 from .common import get_parser, IS_SYSTEMD from .ntfy_common import run_ntfy BACKEND = 'linux' if IS_SYSTEMD else 'darwin' def main() -> None: p = get_parser() args = p.parse_args() run_ntfy(job=args.job, backend=BACKEND) if __name__ == '__main__': main()

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import json from channels.generic.websocket import AsyncWebsocketConsumer class ChatConsumer(AsyncWebsocketConsumer): async def connect(self): self.room_name = self.scope['url_route']['kwargs']['room_name'] self.room_group_name = 'chat_%s' % self.room_name # Join room group await self.channel_layer.group_add( self.room_group_name, self.channel_name ) await self.accept() async def disconnect(self, close_code): # Leave room group await self.channel_layer.group_discard( self.room_group_name, self.channel_name ) # Receive message from WebSocket async def receive(self, text_data): text_data_json = json.loads(text_data) message = text_data_json['message'] # Send message to room group await self.channel_layer.group_send( self.room_group_name, { 'type': 'chat_message', 'message': message } ) # Receive message from room group async def chat_message(self, event): message = event['message'] # Send message to WebSocket await self.send(text_data=json.dumps({ 'message': message }))

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# -*- coding: utf-8 -*- # 使用两个算法模型并行识别 import os, sys import base64 #from datetime import datetime from models.predict_plus import predict_parallel, predict_thread_db if __name__ == "__main__": if len(sys.argv)<4: print("usage: python3 %s <knn|keras> <group_id> <test dir or file>" % sys.argv[0]) sys.exit(2) #from facelib import api_func classifier = sys.argv[1] group_id = sys.argv[2] test_thing = sys.argv[3] if classifier not in ['knn', 'keras']: print('invalid classifier!') sys.exit(3) if os.path.isdir(test_thing): images = os.listdir(test_thing) images = [os.path.join(test_thing, i) for i in images] else: images = [ test_thing ] # Using the trained classifier, make predictions for unknown images for image_file in images: print("Looking for faces in {}".format(image_file)) with open(image_file, 'rb') as f: image_data = f.read() image_b64 = base64.b64encode(image_data) # Find all people in the image using a trained classifier model # Note: You can pass in either a classifier file name or a classifier model instance #predictions = api_func.face_search('', image_b64, group_id) #start_time = datetime.now() predictions = predict_parallel(predict_thread_db, image_b64, group_id, classifier=classifier) #print('[Time taken: {!s}]'.format(datetime.now() - start_time)) # Print results on the console for name, (top, right, bottom, left), distance, count in predictions: print("- Found {} at ({}, {}), distance={}, count={}".format(name, left, top, distance, count)) #for i in predictions: # print("- Found {} at {}, distance={}".format(i['user_id'], i['location'], i['score'])) if len(predictions)==0: print('Face not found!') #print(predictions) # Display results overlaid on an image #knn.show_prediction_labels_on_image(image_file, predictions)

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from . import logging, renderer import io import itertools import os import requests import stat import tarfile __all__ = ['Builder'] LOG = logging.getLogger(__name__) class Builder: def __init__(self, config, *, validators=False): self.config = config self.validators = validators self._file_cache = None @property def file_cache(self): if not self._file_cache: self._build_file_cache() return self._file_cache def _build_file_cache(self): self._file_cache = {} for file_spec in self._file_specs: path = file_spec['path'] if 'content' in file_spec: data = file_spec['content'] elif 'tar_url' in file_spec: data = _fetch_tar_content( url=file_spec['tar_url'], path=file_spec['tar_path']) self._file_cache[path] = { 'path': path, 'data': data, 'mode': file_spec['mode'], } @property def _file_specs(self): return itertools.chain( self.config.get_path('HostSystem:files', []), self.config.get_path('Genesis:files', [])) def build_all(self, *, output_dir): self.build_genesis(output_dir=output_dir) for node_document in self.config.iterate( schema='promenade/KubernetesNode/v1'): self.build_node(node_document, output_dir=output_dir) if self.validators: validate_script = renderer.render_template( self.config, template='scripts/validate-cluster.sh') _write_script(output_dir, 'validate-cluster.sh', validate_script) def build_genesis(self, *, output_dir): LOG.info('Building genesis script') sub_config = self.config.extract_genesis_config() tarball = renderer.build_tarball_from_roles( config=sub_config, roles=['common', 'genesis'], file_specs=self.file_cache.values()) script = renderer.render_template( sub_config, template='scripts/genesis.sh', context={ 'tarball': tarball }) _write_script(output_dir, 'genesis.sh', script) if self.validators: validate_script = renderer.render_template( sub_config, template='scripts/validate-genesis.sh') _write_script(output_dir, 'validate-genesis.sh', validate_script) def build_node(self, node_document, *, output_dir): node_name = node_document['metadata']['name'] LOG.info('Building script for node %s', node_name) script = self.build_node_script(node_name) _write_script(output_dir, _join_name(node_name), script) if self.validators: validate_script = self._build_node_validate_script(node_name) _write_script(output_dir, 'validate-%s.sh' % node_name, validate_script) def build_node_script(self, node_name): sub_config = self.config.extract_node_config(node_name) file_spec_paths = [ f['path'] for f in self.config.get_path('HostSystem:files', []) ] file_specs = [self.file_cache[p] for p in file_spec_paths] tarball = renderer.build_tarball_from_roles( config=sub_config, roles=['common', 'join'], file_specs=file_specs) return renderer.render_template( sub_config, template='scripts/join.sh', context={ 'tarball': tarball }) def _build_node_validate_script(self, node_name): sub_config = self.config.extract_node_config(node_name) return renderer.render_template( sub_config, template='scripts/validate-join.sh') def _fetch_tar_content(*, url, path): LOG.debug('Fetching url=%s (tar path=%s)', url, path) response = requests.get(url) response.raise_for_status() LOG.debug('Finished downloading url=%s (tar path=%s)', url, path) f = io.BytesIO(response.content) tf = tarfile.open(fileobj=f, mode='r') buf_reader = tf.extractfile(path) return buf_reader.read() def _join_name(node_name): return 'join-%s.sh' % node_name def _write_script(output_dir, name, script): path = os.path.join(output_dir, name) with open(path, 'w') as f: f.write(script) os.chmod( path, os.stat(path).st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)

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[email protected]

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#!/usr/bin/env python # -*- coding:utf-8 -*- # 什么是进程：是计算机资源分配的最小单位 # 什么是线程 # 线程和进程的关系： # 每一个进程中都至少有一个线程 # python中线程的特点 # 其他语言中线程的特点 # import os # import time # from threading import Thread # n = 100 # def func(i): # global n # time.sleep(1) # n -= 1 # print(os.getpid(),'thread%s'%i) # t_l = [] # for i in range(100): # t = Thread(target=func,args=(i,)) # t.start() # t_l.append(t) # for t in t_l:t.join() # print('main : ',n) # 每个进程里至少有一个主线程负责执行代码 # 在主线程中可以再开启一个新的线程 # 在同一个进程中就有两个线程同时在工作了 # 线程才是CPU调度的最小单位 # 多个线程之间的数据时共享的 # GIL锁全局解释器锁 # 解释器的锅 Cpython解释器的问题 # 在同一个进程中同一个时刻只能有一个线程被CPU执行 # 导致高计算型代码不适合用python的多线程来解决 # 用多进程或者分布式来解决高计算型代码

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#encoding=utf8 """ werkzeug/routing/ Rule __repr__方法直接print某个对象时打印出的信息 """ class Test(object): def __repr__(self): return "<%s, called>" % (self.__class__.__name__) t = Test() print t

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# -*- coding: utf-8 -*- # Generated by Django 1.9.7 on 2016-07-14 14:19 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('customer', '0010_auto_20160714_1930'), ] operations = [ migrations.RemoveField( model_name='customer', name='services_required', ), ]

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/home/action/.parts/packages/googleappengine/1.9.4/lib/django-1.2/tests/regressiontests/forms/localflavor/__init__.py

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[email protected]

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from django.shortcuts import render from django.http import HttpResponse from django.core.mail import send_mail import math, random def generateOTP() : digits = "0123456789" OTP = "" for i in range(4) : OTP += digits[math.floor(random.random() * 10)] return OTP def send_email_otp(request): email=request.GET.get ("email") print(email) o=generateOTP() htmlgen = 'Your OTP is o' send_mail('OTP request',o,'<your gmail id>',[email], fail_silently=False, html_message=htmlgen) return HttpResponse(o)

[ "[email protected]" ]

[email protected]

f4acc1b6983de406da0a4d2d27544abda966e6da

e82b761f53d6a3ae023ee65a219eea38e66946a0

/All_In_One/addons/learnbgame_hops/operators/misc/triangulate_ngons.py

392d2147834108f25c1e15670053e2c33eba33e5

[]

no_license

2434325680/Learnbgame

f3a050c28df588cbb3b14e1067a58221252e2e40

7b796d30dfd22b7706a93e4419ed913d18d29a44

refs/heads/master

2023-08-22T23:59:55.711050

2021-10-17T07:26:07

null

0

null

UTF-8

Python

false

1,310

py

import bpy class HOPS_OT_TriangulateNgons(bpy.types.Operator): bl_idname = "hops.triangulate_ngons" bl_label = "triangulate ngons" bl_description = "triangulate ngons" bl_options = {"REGISTER"} @classmethod def poll(cls, context): return True def execute(self, context): bpy.ops.object.convert(target='MESH') for obj in bpy.context.selected_objects: bpy.context.view_layer.objects.active = obj bpy.ops.object.editmode_toggle() bpy.ops.mesh.select_all(action='DESELECT') bpy.ops.mesh.select_face_by_sides(number=4, type='GREATER') bpy.ops.mesh.quads_convert_to_tris(quad_method='BEAUTY', ngon_method='BEAUTY') bpy.ops.object.editmode_toggle() return {"FINISHED"} class HOPS_OT_TriangulateModifier(bpy.types.Operator): bl_idname = "hops.triangulate_mod" bl_label = "triangulate mod" bl_description = "triangulate mod" bl_options = {"REGISTER"} @classmethod def poll(cls, context): return True def execute(self, context): selected = context.selected_objects for obj in selected: obj.modifiers.new(name="Triangulate", type="TRIANGULATE") return {"FINISHED"}

[ "[email protected]" ]

[email protected]

681d91cef8af005ef2529196c8d13b4eddf0314d

dc39ccc50b7d34e5de84f3cc132c5cc096a32656

/Sanic/4-NamedURLRedirect/main.py

035aee847b4d83b28db14dd020c1e8de99eb6971

[]

no_license

Shukladas1115/Python

0947aefd62a9ce4c3140360cb7259b031368709c

feb32bc2e2e7df377fc2d92330bfdacb83f31a55

refs/heads/master

2022-02-20T04:15:56.036495

2019-08-26T16:36:52

null

0

null

UTF-8

Python

false

506

py

from sanic import Sanic from sanic import response app = Sanic(__name__) @app.route('/') async def index(request): # generate a URL for the endpoint `post_handler` url = app.url_for('post_handler', post_id=5) # the URL is `/posts/5`, redirect to it return response.redirect(url) @app.route('/posts/<post_id>') async def post_handler(request, post_id): return response.text('Post - {}'.format(post_id)) if __name__ == '__main__': app.run(host="0.0.0.0", port=8000, debug=True)

[ "[email protected]" ]

[email protected]

892a164d926d0ef0796ff7c322ef22178f33cb1e

3f16e66b33b39df8866947fcf1d8249476725c03

/mymodule/test1/file1.py

051436656cfe2394e1be22e4365ad29c246bff90

[]

no_license

VadimVolynkin/learning_python3

ea3559e0f01b4c9e09ae82b76ca315de8e41ecc4

872f0a2ac296ec3242ac9b81f63a29f09bc614fa

refs/heads/main

2023-08-10T05:39:04.376172

2021-09-07T14:44:20

339,133,214

0

null

UTF-8

Python

false

113

py

print('hello from file1.py') a1 = 'its a1 from file1' def __a2(): a2 = 'its a2 from file1' return a2

[ "[email protected]" ]

[email protected]

5a7c1fe7ce5663916fe48e08b1c9a759329dca0c

9bdd421f0bd5cb30a0429e11b23bd85ed34b006a

/account/views.py

019f9c68589e98f18cf1df20bd508d7642623be2

[]

no_license

MrAch26/yugioh_django_proj

8f0f0fbf0cb6e4ec4fac8757a7236fbb08099689

9cd363a3ab9019c92973454dab5eb812894c4c37

refs/heads/main

2023-03-27T21:53:45.240113

2020-10-25T07:39:22

305,049,382

0

null

UTF-8

Python

false

2,289

py

from django.contrib.auth import authenticate, login from django.contrib.auth.models import User from django.shortcuts import render, redirect from django.urls import reverse, reverse_lazy from django.views.generic import CreateView, UpdateView, DetailView from account.forms import UserSignupForm, ProfileViewForm from account.models import Profile from trading_cards.models import Card, Trade class UserSignUp(CreateView): template_name = "registration/signup.html" model = User form_class = UserSignupForm success_url = 'home' failed_message = "The User couldn't be added" def form_valid(self, form): user_to_add = form.cleaned_data # check the data we get when the form is valid print("user_to_add", user_to_add) super(UserSignUp, self).form_valid(form) # inherit from ModelFormMixin : form_valid(form) # Saves the form instance, sets the current object for the view, # and redirects to get_success_url(). print("---------form valid") # The form is valid, automatically sign-in the user user = authenticate(self.request, username=form.cleaned_data['username'], password=form.cleaned_data['password1']) if user is None: print("---------user none") # User not validated for some reason, return standard form_valid() response # Inherit from TemplateResponseMixin : # render_to_response(context, **response_kwargs)¶ return self.render_to_response( self.get_context_data(form=form, failed_message=self.failed_message)) else: print("-----------user good") # Log the user in login(self.request, user) # Redirect to success url return redirect(reverse(self.get_success_url())) class ProfileView(UpdateView): model = Profile template_name = 'profile.html' form_class = ProfileViewForm success_url = reverse_lazy('home') def my_deck(request): trade = Trade.objects.all() return render(request, 'my_deck.html', {'trade': trade}) class MyCard(DetailView): model = Card # todo: add details view for deck if relevant Maybe if time

[ "[email protected]" ]

[email protected]

68f3fb9a96aa5c00e2fb8dedab67d2f23725c127

edb88981aa1420af7e074068ed7818b9d904a3dd

/trunk/minds/test/test_app_httpserver.py

91a8c2880f3ee0c0508f61d743a9000402944147

[]

no_license

BackupTheBerlios/mindretrieve-svn

101c0f1dfc25d20d5f828b6fd0d43301b773af4e

463745fcf1c1d5b1f6c201c30bcc339c99b437ed

refs/heads/master

2021-01-22T13:57:31.225772

2006-04-28T04:24:43

40,801,743

0

null

UTF-8

Python

false

3,765

py

""" """ import StringIO import unittest from minds.safe_config import cfg as testcfg from minds import app_httpserver class AppHTTPRequestHandlerFixture(app_httpserver.AppHTTPRequestHandler): def __init__(self): pass class TestAppHTTPRequestHandler(unittest.TestCase): def _test_lookup(self, url, expected): handler = AppHTTPRequestHandlerFixture() self.assertEqual(handler._lookup_cgi(url), expected) def test_lookup_cgi(self): from minds.cgibin import history from minds.cgibin import weblib from minds.cgibin import weblibMultiForm self._test_lookup('', (weblib, '/', '', '')) self._test_lookup('/', (weblib, '/', '', '')) self._test_lookup('/history/item?1', (history, '/history', '/item', '1')) self._test_lookup('/weblib/multiform/100', (weblibMultiForm, '/weblib/multiform', '/100', '')) class TestMisc(unittest.TestCase): def test_convertPath2Module1(self): self.assertEqual( app_httpserver._convertPath2Module(r'./minds\admin\tmpl/home.html'), ('minds.admin.tmpl.home','home'), ) def test_convertPath2Module2(self): self.assertEqual( app_httpserver._convertPath2Module(r'./minds\admin\snoop'), ('minds.admin.snoop','snoop'), ) def test_convertPath2Module3(self): self.assertEqual( app_httpserver._convertPath2Module(r'/minds/admin/snoop.py'), ('minds.admin.snoop','snoop'), ) class TestCGIFileFilter(unittest.TestCase): DATA1 = """date:04/19/05\r \r line1 line2 """ DATA2 = """line3 line4""" def setUp(self): self.buf = StringIO.StringIO() self.fp = app_httpserver.CGIFileFilter(self.buf) def test1(self): self.fp.write('\r\n\r\n') self.fp.flush() self.assertEqual(self.buf.getvalue(), 'HTTP/1.0 200 OK\r\n\r\n\r\n') def test_nodirective(self): self.fp.write(self.DATA1) self.fp.write(self.DATA2) self.fp.flush() self.assertEqual(self.buf.getvalue(), 'HTTP/1.0 200 OK\r\n' + self.DATA1 + self.DATA2) def test_status(self): self.fp.write('404 not found\r\n') self.fp.write(self.DATA1) self.fp.write(self.DATA2) self.fp.flush() self.assertEqual(self.buf.getvalue(), 'HTTP/1.0 404 not found\r\n' + self.DATA1 + self.DATA2) def test_location(self): self.fp.write('loCATion : http://abc.com/index.html\r\n') self.fp.write(self.DATA1) self.fp.write(self.DATA2) self.fp.flush() self.assertEqual(self.buf.getvalue(), """HTTP/1.0 302 Found\r loCATion : http://abc.com/index.html\r """ + \ self.DATA1 + self.DATA2) def test_states(self): # verify CGIFileFilter has gone through each state self.assertEqual(self.fp.state, self.fp.INIT) self.fp.write('200 ok\r\n\r\n') self.assertEqual(self.fp.state, self.fp.BUFFER) self.fp.write('.'*(self.fp.MAX_BUFFER+1)) self.assertEqual(self.fp.state, self.fp.SENT) buf_size = len(self.buf.getvalue()) self.assert_(buf_size > self.fp.MAX_BUFFER+1) # some HTTP info + content # still accepting output at SENT state self.fp.write('.') self.assertEqual(len(self.buf.getvalue()), buf_size+1) def test_buffer(self): # verify data is buffered until flush self.fp.write('200 ok\r\n\r\n') self.fp.write('.') self.assertEqual(len(self.buf.getvalue()), 0) self.fp.flush() self.assert_(len(self.buf.getvalue()) > 0) if __name__ == '__main__': unittest.main()

[ "tungwaiyip@785ff9d5-dded-0310-b5f2-a5aff206d990" ]

tungwaiyip@785ff9d5-dded-0310-b5f2-a5aff206d990

3668b4615b62655571841c3fe2962d8a50e0b33f

1b5ab3f252069181b5e07d4d6d177ab82e942e51

/Homework3/Part 1/tt1.py

e219c5bb06a667aa68f443395c6215ac7c9e253b

[]

no_license

phamhailongg/C4T9

59214081224f37b356e209d57f0865632dccc8f6

c400005012fb349c1388dd92c8e590322bb203e4

refs/heads/master

2021-07-06T11:10:05.283974

2019-05-05T21:46:04

152,599,080

0

null

UTF-8

Python

false

185

py

from turtle import * speed(10) color("red") for i in range(4): lt(120) fd(100) lt(-60) fd(100) lt(-120) fd(100) lt(-60) fd(100) lt(30) mainloop()

[ "[email protected]" ]

[email protected]

f7a0a2fa0e865a49765a53208422402c335ba849

9743d5fd24822f79c156ad112229e25adb9ed6f6

/xai/brain/wordbase/otherforms/_discomposing.py

bafc1a64c9a8785f9dbb06454a7311b879d585bc

[ "MIT" ]

permissive

cash2one/xai

de7adad1758f50dd6786bf0111e71a903f039b64

e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6

refs/heads/master

2021-01-19T12:33:54.964379

2017-01-28T02:00:50

null

0

null

UTF-8

Python

false

240

py

#calss header class _DISCOMPOSING(): def __init__(self,): self.name = "DISCOMPOSING" self.definitions = discompose self.parents = [] self.childen = [] self.properties = [] self.jsondata = {} self.basic = ['discompose']

[ "[email protected]" ]

[email protected]

dd9ea44609ed4c96315ef9e7285fbe5f871730ce

30bd7e8abe0a15fbb8f1b1e4a3a9a15a3ad124a9

/romans/src/utils/roman.py

c2a15aca81d4265a81dc04ee59ee231d8b7b4fca

[]

no_license

TiagoArrazi/Romans

c96cac19a36e5e89ea719b084693b2af0f6e1cf2

f2841931fb9b7428acdc4604dae0535508002781

refs/heads/master

2020-07-12T01:53:44.220206

2019-08-27T12:31:34

204,688,868

0

null

UTF-8

Python

false

3,057

py

from resources.symbols import symbols class Roman: @classmethod def make_it_roman(cls, number): if 900 <= int(number) <= 3000: mult = divmod(int(number), 1000) if mult[0] > 0 and mult[1] == 0: return symbols["1000"] * mult[0] c_amount = (1000 - int(number)) // 100 if c_amount > 0: return f"{symbols['100']}{symbols['1000']}" if c_amount < 0: return f"{symbols['1000']}{abs(c_amount) * symbols['100']}" elif 400 <= int(number) <= 800: if number == "500": return symbols["500"] c_amount = (500 - int(number)) // 100 if c_amount > 0: return f"{symbols['100']}{symbols['500']}" if c_amount < 0: return f"{symbols['500']}{abs(c_amount) * symbols['100']}" elif 90 <= int(number) <= 300: mult = divmod(int(number), 100) if mult[0] > 0 and mult[1] == 0: return symbols["100"] * mult[0] c_amount = (100 - int(number)) // 10 if c_amount > 0: return f"{symbols['10']}{symbols['100']}" if c_amount < 0: return f"{symbols['100']}{abs(c_amount) * symbols['10']}" elif 40 <= int(number) <= 80: if number == "50": return symbols["50"] c_amount = (50 - int(number)) // 10 if c_amount > 0: return f"{symbols['10']}{symbols['50']}" if c_amount < 0: return f"{symbols['50']}{abs(c_amount) * symbols['10']}" elif 9 <= int(number) <= 30: mult = divmod(int(number), 10) if mult[0] > 0 and mult[1] == 0: return symbols["10"] * mult[0] c_amount = (10 - int(number)) if c_amount > 0: return f"{symbols['1']}{symbols['10']}" if c_amount < 0: return f"{symbols['10']}{abs(c_amount) * symbols['1']}" elif 4 <= int(number) <= 8: if number == "5": return symbols["5"] c_amount = (5 - int(number)) if c_amount > 0: return f"{symbols['1']}{symbols['5']}" if c_amount < 0: return f"{symbols['5']}{abs(c_amount) * symbols['1']}" else: return int(number) * symbols["1"] @classmethod def convert_digits(cls, number): try: if 1 <= int(number) <= 3000: strip_number_list = [(10 ** index) // 10 * int(n) for index, n in zip(range(len(number), 0, -1), number)] converted_number_list = list() for item in strip_number_list: converted_number_list.append(cls.make_it_roman(str(item))) return ''.join(converted_number_list) except ValueError: return False return False

[ "[email protected]" ]

[email protected]

f14c49b90c661b6ac6e514e6ecfda425e0621418

17f2ea360d2cc77ff45ab7b61f0e03d3c9d96247

/Stock/Trade/AccountManager/StopMode/DyStockStopLossMaMode.py

e53c9179190315d960e3820c1281b459be2d9596

[ "MIT" ]

permissive

yutiansut/DevilYuan

89aba2728d42a686cf989b74283a5197edfe6b49

6467f8c33c4692d3616f0eb0b0bf974d16e95836

refs/heads/master

2020-03-19T18:43:11.674992

2018-06-11T03:17:32

136,821,539

2

1

MIT

2018-06-11T03:17:33

2018-06-10T15:37:24

Python

UTF-8

Python

false

2,409

py

from .DyStockStopMode import * from ...DyStockTradeCommon import * class DyStockStopLossMaMode(DyStockStopMode): stopLossPnlRatio = -5 def __init__(self, accountManager, dataEngine, ma): super().__init__(accountManager) self._dataEngine = dataEngine self._daysEngine = self._dataEngine.daysEngine self._ma = ma self._tradeStartTime = '14:55:00' self._curInit() def _curInit(self): self._preparedData = {} def onOpen(self, date): self._curInit() preDate = self._daysEngine.tDaysOffsetInDb(date, -1) for code in self._accountManager.curPos: if not self._daysEngine.loadCode(code, [preDate, -self._ma+2], latestAdjFactorInDb=False): return False df = self._daysEngine.getDataFrame(code) if df.shape[0] != (self._ma - 1): return False self._preparedData[code] = df['close'].values.tolist() return True def _processAdj(self, code, tick): """ 处理除复权 """ if tick.preClose is None: return if code not in self._preparedData: return False if code not in self._accountManager.curPos: return False closes = self._preparedData[code] if tick.preClose == closes[-1]: return True # 复权 adjFactor = tick.preClose/closes[-1] # 价格 closes = list(map(lambda x,y:x*y, closes, [adjFactor]*len(closes))) closes[-1] = tick.preClose # 浮点数的精度问题 self._preparedData[code] = closes return True def _stopLoss(self, code, tick): ma = (sum(self._preparedData[code]) + tick.price)/self._ma pos = self._accountManager.curPos[code] if tick.price < ma and pos.pnlRatio < self.stopLossPnlRatio: self._accountManager.closePos(tick.datetime, code, getattr(tick, DyStockTradeCommon.sellPrice), DyStockSellReason.stopLoss) def onTicks(self, ticks): for code, pos in self._accountManager.curPos.items(): tick = ticks.get(code) if tick is None: continue if tick.time < self._tradeStartTime: return if not self._processAdj(code, tick): continue self._stopLoss(code, tick) def onBars(self, bars): self.onTicks(bars)

[ "[email protected]" ]

[email protected]

05ff5f5a599c92b2f689b4a53313597783b6caef

727cdc7c9af6fdf6b4eb8444197718e5c6760019

/asin_keyword/cookie_sele_local.py

f538225e776bc68fc0a60a43531ef76c1f359afa

[]

no_license

newer027/amazon_crawler

0cc6feb30f9180ae48ac936eeb6af41ec06eadfd

39d6867a8dd56b90dae5e98aa44e6df274439f8e

refs/heads/master

2022-11-23T17:04:33.995126

2020-04-03T15:42:42

252,774,253

1

0

null

2022-11-22T01:44:53

2020-04-03T15:42:31

CSS

UTF-8

Python

false

4,831

py

import time, pickle from selenium import webdriver from .validation import validation, validation_jp from PIL import Image from pytesseract import image_to_string from random import * from urllib.request import urlretrieve from bs4 import BeautifulSoup #from .emails import send_email from pyvirtualdisplay import Display import requests, shutil def get_captcha(driver, element, path): # now that we have the preliminary stuff out of the way time to get that image :D location = element.location size = element.size # saves screenshot of entire page driver.save_screenshot(path) # uses PIL library to open image in memory image = Image.open(path) #image.show() left = location['x'] top = location['y'] right = location['x'] + size['width'] bottom = location['y'] + size['height'] print(left, top, right, bottom) # image = image.crop((left, top, right, bottom)) # defines crop points image = image.crop((left*2, top*2, right*2, bottom*2)) # defines crop points image.save(path, 'jpeg') # saves new cropped image def validate(driver,country): im = driver.find_element_by_id('auth-captcha-image') # im = im.get_attribute('src') # urlretrieve(im, "captcha.jpeg") get_captcha(driver,im,"captcha.jpeg") """ agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11; rv:52.0) Gecko/20100101 Firefox/52.0' headers = { 'User-Agent': agent, 'Host': "opfcaptcha-prod.s3.amazonaws.com", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8", "Accept-Encoding": "gzip, deflate, sdch, br", "Accept-Language": "zh-CN,zh;q=0.8", "Connection": "keep-alive" } print(im) response = requests.get(im, stream=True, headers=headers) with open('captcha.jpeg', 'wb') as out_file: shutil.copyfileobj(response.raw, out_file) del response """ im = 'captcha.jpeg' im = Image.open(im) im = im.convert('L') def initTable(threshold=140): table = [] for i in range(256): if i < threshold: table.append(0) else: table.append(1) return table binaryImage = im.point(initTable(), '1') binaryImage.show() characters = image_to_string(binaryImage,config='-psm 7') char_list = characters.split(' ') characters = ''.join(char_list) print(characters) search_box = driver.find_element_by_id('ap_password') if country=='jp': for i in validation_jp['password']: time.sleep(0.8-random()*0.5) search_box.send_keys(i) else: for i in validation['password']: time.sleep(0.8-random()*0.5) search_box.send_keys(i) time.sleep(4) search_box = driver.find_element_by_id('auth-captcha-guess') for i in characters: time.sleep(0.5-random()*0.2) search_box.send_keys(i) time.sleep(3) driver.find_element_by_id('signInSubmit').click() # amazon = AmazonAPI('AKIAJ2TPWCFJMKXPSJVQ','ixmfea5B2xKFukyuR/aiBzkI6f+umvISvYlzzBBy','newer027-20') # asin="B01LCDJ7LG" # ean='0848061039726' # product = amazon.lookup(ItemId=asin) # ean = product.ean # print(ean) # driver = webdriver.Chrome("/Users/Jacob/Desktop/amazon_keyword/chromedriver") def cookie_sele(country): # display = Display(visible=0, size=(1920, 1080)).start() # driver = webdriver.Firefox() product_url_am = "https://vendorexpress.amazon.com/home?ref=VE_LANDING" product_url_eu = "https://vendorexpress.amazon.eu/home?ref=VE_LANDING" product_url_jp = "https://vendorexpress.amazon.co.jp/home?ref=VE_LANDING" driver = webdriver.Chrome("/Users/Jacob/Desktop/amazon_keyword/chromedriver") try: if country=='am': driver.get(product_url_am) start_url="https://vendorexpress.amazon.com/ap/signin" cookies="cookies_am.pkl" elif country=='eu': driver.get(product_url_eu) start_url="https://vendorexpress.amazon.eu/ap/signin" cookies="cookies_eu.pkl" else: driver.get(product_url_jp) start_url="https://vendorexpress.amazon.co.jp/ap/signin" cookies="cookies_jp.pkl" search_box = driver.find_element_by_id('ap_email') if country=='jp': search_box.send_keys(validation_jp['id']) else: search_box.send_keys(validation['id']) time.sleep(5) while driver.current_url.startswith(start_url): validate(driver,country) time.sleep(15) html = driver.page_source soup = BeautifulSoup(html, "lxml") print(soup.title.string) pickle.dump( driver.get_cookies() , open(cookies,"wb")) finally: driver.quit() #display.stop()

[ "[email protected]" ]

[email protected]

3d7e4e20678a99f2171c8af491263ebaaf9b1f39

34a5921552537d96d9680f88b94be1706e5c8f1a

/facets/common/consts.py

a69b16a8e80bc022fd6cd90a8ebb69d306714fae

[ "Apache-2.0" ]

permissive

hunterhector/DDSemantics

11f1a85486349627036626d3b638db39f70030fe

65235d8897bce403e5d628ed912e516b28254c74

refs/heads/master

2023-07-13T05:20:13.211363

2023-06-21T21:44:37

123,484,643

0

2

null

UTF-8

Python

false

150

py

"""Define constants""" MENTION_START_TOKEN = "[unused1]" MENTION_END_TOKEN = "[unused2]" HEADER_END_TOKEN = "[unused3]" CLS = "[CLS]" SEP = "[SEP]"

[ "[email protected]" ]

[email protected]

2897a346fb526a6e0e57f8e45f21e07c4f5a4bb0

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p02267/s584392095.py

6a5daa87a557535ac58fe4e73ddfee745deeec4f

[]

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

166

py

n = int(input()) s = [int(x) for x in input().split()] q = int(input()) t = [int(x) for x in input().split()] cnt = 0 for x in t: if x in s: cnt += 1 print(cnt)

[ "[email protected]" ]

[email protected]

57ca134de961ddf6a494d2abcf622a29832b057d

f07a42f652f46106dee4749277d41c302e2b7406

/Data Set/bug-fixing-2/07acc579db839170122fc66505a886ef023d5f4f-<execute_install>-bug.py

46e7feb105b1c13945aa837f5c23514abfcee9e9

[]

no_license

wsgan001/PyFPattern

e0fe06341cc5d51b3ad0fe29b84098d140ed54d1

cc347e32745f99c0cd95e79a18ddacc4574d7faa

refs/heads/main

2023-08-25T23:48:26.112133

2021-10-23T14:11:22

null

0

null

UTF-8

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py

def execute_install(self): '\n uses the args list of roles to be installed, unless -f was specified. The list of roles\n can be a name (which will be downloaded via the galaxy API and github), or it can be a local .tar.gz file.\n ' role_file = self.options.role_file if ((len(self.args) == 0) and (role_file is None)): raise AnsibleOptionsError('- you must specify a user/role name or a roles file') no_deps = self.options.no_deps force = self.options.force roles_left = [] if role_file: try: f = open(role_file, 'r') if (role_file.endswith('.yaml') or role_file.endswith('.yml')): try: required_roles = yaml.safe_load(f.read()) except Exception as e: raise AnsibleError(('Unable to load data from the requirements file: %s' % role_file)) if (required_roles is None): raise AnsibleError(('No roles found in file: %s' % role_file)) for role in required_roles: if ('include' not in role): role = RoleRequirement.role_yaml_parse(role) display.vvv(('found role %s in yaml file' % str(role))) if (('name' not in role) and ('scm' not in role)): raise AnsibleError('Must specify name or src for role') roles_left.append(GalaxyRole(self.galaxy, **role)) else: with open(role['include']) as f_include: try: roles_left += [GalaxyRole(self.galaxy, **r) for r in (RoleRequirement.role_yaml_parse(i) for i in yaml.safe_load(f_include))] except Exception as e: msg = 'Unable to load data from the include requirements file: %s %s' raise AnsibleError((msg % (role_file, e))) else: display.deprecated('going forward only the yaml format will be supported', version='2.6') for rline in f.readlines(): if (rline.startswith('#') or (rline.strip() == '')): continue display.debug(('found role %s in text file' % str(rline))) role = RoleRequirement.role_yaml_parse(rline.strip()) roles_left.append(GalaxyRole(self.galaxy, **role)) f.close() except (IOError, OSError) as e: raise AnsibleError(('Unable to open %s: %s' % (role_file, str(e)))) else: for rname in self.args: role = RoleRequirement.role_yaml_parse(rname.strip()) roles_left.append(GalaxyRole(self.galaxy, **role)) for role in roles_left: if (role_file and self.args and (role.name not in self.args)): display.vvv(('Skipping role %s' % role.name)) continue display.vvv(('Processing role %s ' % role.name)) if (role.install_info is not None): if ((role.install_info['version'] != role.version) or force): if force: display.display(('- changing role %s from %s to %s' % (role.name, role.install_info['version'], (role.version or 'unspecified')))) role.remove() else: display.warning(('- %s (%s) is already installed - use --force to change version to %s' % (role.name, role.install_info['version'], (role.version or 'unspecified')))) continue elif (not force): display.display(('- %s is already installed, skipping.' % str(role))) continue try: installed = role.install() except AnsibleError as e: display.warning(('- %s was NOT installed successfully: %s ' % (role.name, str(e)))) self.exit_without_ignore() continue if ((not no_deps) and installed): role_dependencies = (role.metadata.get('dependencies') or []) for dep in role_dependencies: display.debug(('Installing dep %s' % dep)) dep_req = RoleRequirement() dep_info = dep_req.role_yaml_parse(dep) dep_role = GalaxyRole(self.galaxy, **dep_info) if (('.' not in dep_role.name) and ('.' not in dep_role.src) and (dep_role.scm is None)): continue if (dep_role.install_info is None): if (dep_role not in roles_left): display.display(('- adding dependency: %s' % str(dep_role))) roles_left.append(dep_role) else: display.display(('- dependency %s already pending installation.' % dep_role.name)) elif (dep_role.install_info['version'] != dep_role.version): display.warning(('- dependency %s from role %s differs from already installed version (%s), skipping' % (str(dep_role), role.name, dep_role.install_info['version']))) else: display.display(('- dependency %s is already installed, skipping.' % dep_role.name)) if (not installed): display.warning(('- %s was NOT installed successfully.' % role.name)) self.exit_without_ignore() return 0

[ "[email protected]" ]

[email protected]

e3f8022857e30db6341da4c8f88e97f4e7063f57

6d8faae66dd6332836bb11d7f02d6867c95d2a65

/glast/pointlike/python/uw/like/likelihood_fit.py

7dec093d277b975937d18b773ffedc0d0b11c596

[]

no_license

Areustle/fermi-glast

9085f32f732bec6bf33079ce8e2ea2a0374d0228

c51b821522a5521af253973fdd080e304fae88cc

refs/heads/master

2021-01-01T16:04:44.289772

2017-09-12T16:35:52

97,769,090

0

1

null

UTF-8

Python

false

6,382

py

"""A module providing functionality for parametrizing a likelihood curve by a simple function. Classes: LogLikelihood: a representation of a likelihood curve Authors: Eric Wallace, Matthew Kerr """ __version__ = "$Revision: 1.1.2.1 $" #$Header: /glast/ScienceTools/glast/pointlike/python/uw/like/Attic/likelihood_fit.py,v 1.1.2.1 2015/08/13 18:03:08 jasercio Exp $ import numpy as np import scipy.optimize as opt class LogLikelihood(object): """A representation of a log likelihood curve by a Poisson-like function The representation used here follows the approach of Nolan, et al. The likelihood is represented by a three-parameter function of a form similar to the Poisson distribution PMF. The peak is found by maximizing the provided log likelihood function. The parameters are then found by a least squares fit using the peak, two points equispaced around it, and zero. The parametrizing function is f(s) = logL - logL_max = n*np.log(e*(s+b)) - e*(s+b) - n*np.log(n) + n with n = e*(s_peak+b) """ def __init__(self,loglike,initial_value=1e-10,fit_max = True,pars=None): """Create a LogLikelihood instance. loglike: The log likelihood function to be parametrized. Should be a callable with one argument. initial_value: An initial guess at the maximum of the provided function. The default of 1e-10 should be a reasonable guess for the normalization of a PowerLaw model. fit_max: Whether to use fmin to maximize the log likelihood. If False initial_value will be taken as the position of the maximum of the log likelihood, so this should only be set to False if the value passed as initial_value is the result of a previous maximization of the provided function. pars: A length three sequence providing the values for the parameters of the fit function: s_peak,e, and b. If provided, these values will be used and the loglike argument will be ignored. """ self.function = self._setup_function(loglike) self.saved_points = np.array([]) self.saved_values = np.array([]) if pars is not None: try: assert(hasattr(pars,'__iter__') and len(pars)==3) self.pars = pars except AssertionError: print('Keyword argument pars must be a sequence of length 3.') print('Will attempt to derive parameters from provided function') self.pars = self._find_pars(initial_value,fit_max = fit_max) else: self.pars = self._find_pars(initial_value,fit_max = fit_max) self._check_agreement() def _setup_function(self,function): """Setup caching of values passed to the log likelihood function.""" def _function(x): if x in self.saved_points: ll = self.saved_values[self.saved_points==x][0] else: ll = function(x) self.saved_points = np.append(self.saved_points,x) self.saved_values = np.append(self.saved_values,ll) return ll return _function def _find_pars(self,initial_value,fit_max = False): """Find the best fit parameters for the fit function""" if fit_max: self.mode = opt.fmin(lambda x: -self.function(x),initial_value)[0] else: self.mode = initial_value self.max = self.function(self.mode) #xs = np.array([0,max/2,max,max*2]) #ys = np.array([self.function(x) for x in xs]) xs = self.saved_points.copy() ys = self.saved_values.copy() ys = ys - ys.max() return opt.leastsq(lambda x:self._poisson(x,xs)-ys,np.array([self.mode,10/self.mode,xs[-1]]),maxfev=5000)[0] def _poisson(self,pars,s): """Calculate the value of the parametrizing function for some parameters. pars: A sequence of length 3 providing the parameters s_peak, e, and b. s: The point at which to evaluate the function. Can be a numpy array. """ if pars[0]<0: return -1e10 s_peak,e,b = pars[0],pars[1],pars[2];n = e*(s_peak+b) #logL - logL_max = n*np.log(e*(s+b))-e*(s+b) - n*np.log(e*(s_peak+b))+e*(s_peak+b) #simplified: return n*np.log((s+b)/(s_peak+b)) + e*(s_peak-s) def __call__(self,x): """Return the value of the parametrizing function at point x.""" return self._poisson(self.pars,x) + self.max def find_logl_change(self,initial_value,delta_logl): """Find the points where the likelihood has decreased by delta_logl. Returns a tuple of the (low, high) values. If the likelihood at zero differs from the max by less than the specified change, return zero for the lower value. """ #First, find lower value lo = 1e-20 #basically zero hi = initial_value ll_0 = self.function(hi) if ll_0-self.function(lo)>delta_logl: for i in xrange(20): avg = .5*(hi+lo) ll = self.function(avg) if ll_0-ll<delta_logl: hi = avg else: lo = avg if abs(ll_0-ll-delta_logl)<.01: break lo_val = avg else: lo_val = lo #Now the upper value lo = initial_value hi = initial_value*10 while ll_0-self.function(hi)<delta_logl: hi+=1 for i in xrange(20): avg = .5*(lo+hi) ll = self.function(avg) if ll_0-ll<delta_logl: lo = avg else: hi = avg if abs(ll_0-ll-delta_logl)<.01: break hi_val = avg return (lo_val,hi_val) def _check_agreement(self): lo,hi = self.find_logl_change(self.mode,10) lo_ll,hi_ll = self.function(lo),self.function(hi) lo_val,hi_val = self(lo),self(hi) if abs(1-lo_ll/lo_val) > .05: print("Warning: fit function differs from log likelihood by {0:.02}\% in the low tail".format((1-lo_ll/lo_val)*100)) if abs(1-hi_ll/hi_val) > .05: print("Warning: fit function differs from log likelihood by {0:.02}\% in the high tail".format((1-lo_ll/lo_val)*100)) def ts(self): return self(self.mode)-self(0)

[ "[email protected]" ]

[email protected]

a95c88307396ee0164e6f263644fc07b185a3d85

1089bc72856fe3ef0edd4b17b2f07b8ec5de8e14

/firecares/settings/base.py

1090ad22ec43635bb3cf9e8ae536318c7280b299

[ "MIT" ]

permissive

JWileczek/firecares

e521c9d9f829fc60f13c2d051be89b5feadb5fc0

dd82e6e720cdaaf0bacd7a2cc51669341a29ffae

refs/heads/master

2020-12-25T12:41:04.124970

2015-08-30T15:38:56

41,690,086

0

null

2015-08-31T17:29:12

null

UTF-8

Python

false

7,905

py

import os from kombu import Queue DEBUG = True TEMPLATE_DEBUG = DEBUG PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)) ADMINS = ( # ('Your Name', '[email protected]'), ) MANAGERS = ADMINS DATABASES = { 'default': { 'ENGINE': 'django.contrib.gis.db.backends.postgis', 'NAME': os.getenv('DATABASE_NAME', 'firecares'), 'USER': os.getenv('DATABASE_USER', 'firecares'), 'PASSWORD': os.getenv('DATABASE_PASSWORD', 'password'), 'HOST': os.getenv('DATABASE_HOST', 'localhost'), 'PORT': os.getenv('DATABASE_PORT', '5432'), }, 'nfirs': { 'ENGINE': 'django.contrib.gis.db.backends.postgis', 'NAME': os.getenv('NFIRS_DATABASE_NAME', 'nfirs'), 'USER': os.getenv('NFIRS_DATABASE_USER', 'firecares'), 'PASSWORD': os.getenv('NFIRS_DATABASE_PASSWORD', 'password'), 'PORT': os.getenv('NFIRS_DATABASE_PORT', '5432'), 'HOST': os.getenv('NFIRS_DATABASE_HOST', 'localhost'), } } # Hosts/domain names that are valid for this site; required if DEBUG is False # See https://docs.djangoproject.com/en/1.5/ref/settings/#allowed-hosts ALLOWED_HOSTS = [] # Local time zone for this installation. Choices can be found here: # http://en.wikipedia.org/wiki/List_of_tz_zones_by_name # although not all choices may be available on all operating systems. # In a Windows environment this must be set to your system time zone. TIME_ZONE = 'America/Chicago' # Language code for this installation. All choices can be found here: # http://www.i18nguy.com/unicode/language-identifiers.html LANGUAGE_CODE = 'en-us' SITE_ID = 1 # If you set this to False, Django will make some optimizations so as not # to load the internationalization machinery. USE_I18N = True # If you set this to False, Django will not format dates, numbers and # calendars according to the current locale. USE_L10N = True # If you set this to False, Django will not use timezone-aware datetimes. USE_TZ = True # Absolute filesystem path to the directory that will hold user-uploaded files. # Example: "/var/www/example.com/media/" MEDIA_ROOT = '/var/www/firecares/media/' # URL that handles the media served from MEDIA_ROOT. Make sure to use a # trailing slash. # Examples: "http://example.com/media/", "http://media.example.com/" MEDIA_URL = '/media/' # Absolute path to the directory static files should be collected to. # Don't put anything in this directory yourself; store your static files # in apps' "static/" subdirectories and in STATICFILES_DIRS. # Example: "/var/www/example.com/static/" STATIC_ROOT = '/var/www/firecares/static/' # URL prefix for static files. # Example: "http://example.com/static/", "http://static.example.com/" STATIC_URL = '/static/' # Additional locations of static files STATICFILES_DIRS = ( # Put strings here, like "/home/html/static" or "C:/www/django/static". # Always use forward slashes, even on Windows. # Don't forget to use absolute paths, not relative paths. os.path.join(PROJECT_ROOT, 'static'), ) # List of finder classes that know how to find static files in # various locations. STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', 'compressor.finders.CompressorFinder', ) # Make this unique, and don't share it with anybody. SECRET_KEY = '$keb7sv^%c+_7+94u6_!lc3%a-3ima9eh!xyj%$xa8yibv&ogr' # List of callables that know how to import templates from various sources. TEMPLATE_LOADERS = ( 'django.template.loaders.filesystem.Loader', 'django.template.loaders.app_directories.Loader', # 'django.template.loaders.eggs.Loader', ) TEMPLATE_CONTEXT_PROCESSORS = ( "django.core.context_processors.request", "django.contrib.auth.context_processors.auth", "django.template.context_processors.debug", "django.template.context_processors.i18n", "django.template.context_processors.media", "django.template.context_processors.static", "django.template.context_processors.tz", "django.contrib.messages.context_processors.messages", "firecares.firecares_core.context_processors.third_party_tracking_ids", ) MIDDLEWARE_CLASSES = ( 'django.middleware.common.CommonMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', # Uncomment the next line for simple clickjacking protection: # 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) ROOT_URLCONF = 'firecares.urls' # Python dotted path to the WSGI application used by Django's runserver. WSGI_APPLICATION = 'firecares.wsgi.application' TEMPLATE_DIRS = ( # Put strings here, like "/home/html/django_templates" or "C:/www/django/templates". # Always use forward slashes, even on Windows. # Don't forget to use absolute paths, not relative paths. os.path.join(PROJECT_ROOT, 'templates'), ) INSTALLED_APPS = ( 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.messages', 'django.contrib.staticfiles', 'autocomplete_light', 'django.contrib.admin', 'django.contrib.gis', 'django.contrib.humanize', 'firecares.firecares_core', 'firecares.firestation', 'firecares.usgs', 'jsonfield', 'compressor', 'storages', 'widget_tweaks', 'firecares.tasks', 'registration' ) SESSION_SERIALIZER = 'django.contrib.sessions.serializers.JSONSerializer' # A sample logging configuration. The only tangible logging # performed by this configuration is to send an email to # the site admins on every HTTP 500 error when DEBUG=False. # See http://docs.djangoproject.com/en/dev/topics/logging for # more details on how to customize your logging configuration. LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'filters': { 'require_debug_false': { '()': 'django.utils.log.RequireDebugFalse' } }, 'handlers': { 'mail_admins': { 'level': 'ERROR', 'filters': ['require_debug_false'], 'class': 'django.utils.log.AdminEmailHandler' } }, 'loggers': { 'django.request': { 'handlers': ['mail_admins'], 'level': 'ERROR', 'propagate': True, }, } } # Celery settings. BROKER_URL = os.getenv('BROKER_URL', 'amqp://guest:[email protected]//') CELERY_RESULT_BACKEND = os.getenv('CELERY_RESULT_BACKEND', 'amqp') AWS_STORAGE_BUCKET_NAME = os.getenv('AWS_STORAGE_BUCKET_NAME', None) AWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID', None) AWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY', None) MAPBOX_ACCESS_TOKEN = os.getenv('MAPBOX_ACCESS_TOKEN', None) GOOGLE_ANALYTICS_TRACKING_ID = os.getenv('GOOGLE_ANALYTICS_TRACKING_ID', None) LOGIN_REDIRECT_URL = '/' LOGIN_URL = '/login' CELERY_DEFAULT_QUEUE = "default" CELERY_DEFAULT_EXCHANGE = "default" CELERY_DEFAULT_EXCHANGE_TYPE = "direct" CELERY_DEFAULT_ROUTING_KEY = "default" CELERY_CREATE_MISSING_QUEUES = True CELERY_IMPORTS = ( 'firecares.tasks.cache', 'firecares.tasks.update', ) CELERY_QUEUES = [ Queue('default', routing_key='default'), Queue('cache', routing_key='cache'), Queue('update', routing_key='update'), Queue('email', routing_key='email'), ] ACCOUNT_ACTIVATION_DAYS = 7 REGISTRATION_OPEN = False EMAIL_HOST = os.getenv('EMAIL_HOST', 'localhost') EMAIL_HOST_PASSWORD = os.getenv('EMAIL_HOST_PASSWORD', '') EMAIL_HOST_USER = os.getenv('EMAIL_HOST_USER', '') EMAIL_PORT = os.getenv('EMAIL_PORT', 25) EMAIL_SUBJECT_PREFIX = '[FireCARES] ' SERVER_EMAIL = os.getenv('SERVER_EMAIL', '') DEFAULT_FROM_EMAIL = os.getenv('DEFAULT_FROM_EMAIL', '')

[ "[email protected]" ]

[email protected]

4ceda0b049891a9c2963a7c0c48c3f511140ac69

9e988c0dfbea15cd23a3de860cb0c88c3dcdbd97

/sdBs/HundredRun/PG_0132+151/sdB_PG_0132+151_lc.py

8918441d2824176148a8e7a956f7ebd08c4bc153

[]

no_license

tboudreaux/SummerSTScICode

73b2e5839b10c0bf733808f4316d34be91c5a3bd

4dd1ffbb09e0a599257d21872f9d62b5420028b0

refs/heads/master

2021-01-20T18:07:44.723496

2016-08-08T16:49:53

65,221,159

0

null

UTF-8

Python

false

346

py

from gPhoton.gAperture import gAperture def main(): gAperture(band="NUV", skypos=[356.757495,15.400942], stepsz=30., csvfile="/data2/fleming/GPHOTON_OUTPU/LIGHTCURVES/sdBs/sdB_PG_0132+151 /sdB_PG_0132+151_lc.csv", maxgap=1000., overwrite=True, radius=0.00555556, annulus=[0.005972227,0.0103888972], verbose=3) if __name__ == "__main__": main()

[ "[email protected]" ]

[email protected]

a3b152abeff9b59f25a919958f7e36901eaaa4ad

385a63d3c9e6f5815979165001f78ec3d7b90cd2

/DrivingTDM_SetupMatlabOOP/headerAndFunctionsMotor/ximc/python-profiles/STANDA/10MCWA168-20.py

f6af4a4b73c9f33f4b74a776ed1ee7067c55ade2

[ "BSD-2-Clause" ]

permissive

Rasedujjaman/matlabOOP

5abb6ec94998fda5e9214ed94cf67a42bf243d4f

e1f025ab9b00a3646719df23852079736d2b5701

refs/heads/main

2023-07-23T21:40:53.905045

2021-08-31T16:12:39

378,249,559

1

null

UTF-8

Python

false

22,523

py

def set_profile_10MCWA168_20(lib, id): worst_result = Result.Ok result = Result.Ok feedback_settings = feedback_settings_t() feedback_settings.IPS = 4000 class FeedbackType_: FEEDBACK_ENCODER_MEDIATED = 6 FEEDBACK_NONE = 5 FEEDBACK_EMF = 4 FEEDBACK_ENCODER = 1 feedback_settings.FeedbackType = FeedbackType_.FEEDBACK_NONE class FeedbackFlags_: FEEDBACK_ENC_TYPE_BITS = 192 FEEDBACK_ENC_TYPE_DIFFERENTIAL = 128 FEEDBACK_ENC_TYPE_SINGLE_ENDED = 64 FEEDBACK_ENC_REVERSE = 1 FEEDBACK_ENC_TYPE_AUTO = 0 feedback_settings.FeedbackFlags = FeedbackFlags_.FEEDBACK_ENC_TYPE_SINGLE_ENDED | FeedbackFlags_.FEEDBACK_ENC_TYPE_AUTO feedback_settings.CountsPerTurn = 4000 result = lib.set_feedback_settings(id, byref(feedback_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result home_settings = home_settings_t() home_settings.FastHome = 55 home_settings.uFastHome = 0 home_settings.SlowHome = 55 home_settings.uSlowHome = 0 home_settings.HomeDelta = 0 home_settings.uHomeDelta = 0 class HomeFlags_: HOME_USE_FAST = 256 HOME_STOP_SECOND_BITS = 192 HOME_STOP_SECOND_LIM = 192 HOME_STOP_SECOND_SYN = 128 HOME_STOP_SECOND_REV = 64 HOME_STOP_FIRST_BITS = 48 HOME_STOP_FIRST_LIM = 48 HOME_STOP_FIRST_SYN = 32 HOME_STOP_FIRST_REV = 16 HOME_HALF_MV = 8 HOME_MV_SEC_EN = 4 HOME_DIR_SECOND = 2 HOME_DIR_FIRST = 1 home_settings.HomeFlags = HomeFlags_.HOME_USE_FAST | HomeFlags_.HOME_STOP_SECOND_BITS | HomeFlags_.HOME_STOP_FIRST_BITS | HomeFlags_.HOME_HALF_MV | HomeFlags_.HOME_DIR_FIRST result = lib.set_home_settings(id, byref(home_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result move_settings = move_settings_t() move_settings.Speed = 27 move_settings.uSpeed = 0 move_settings.Accel = 200 move_settings.Decel = 200 move_settings.AntiplaySpeed = 27 move_settings.uAntiplaySpeed = 0 class MoveFlags_: RPM_DIV_1000 = 1 result = lib.set_move_settings(id, byref(move_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result engine_settings = engine_settings_t() engine_settings.NomVoltage = 1 engine_settings.NomCurrent = 1200 engine_settings.NomSpeed = 55 engine_settings.uNomSpeed = 0 class EngineFlags_: ENGINE_LIMIT_RPM = 128 ENGINE_LIMIT_CURR = 64 ENGINE_LIMIT_VOLT = 32 ENGINE_ACCEL_ON = 16 ENGINE_ANTIPLAY = 8 ENGINE_MAX_SPEED = 4 ENGINE_CURRENT_AS_RMS = 2 ENGINE_REVERSE = 1 engine_settings.EngineFlags = EngineFlags_.ENGINE_LIMIT_RPM | EngineFlags_.ENGINE_ACCEL_ON engine_settings.Antiplay = 9 class MicrostepMode_: MICROSTEP_MODE_FRAC_256 = 9 MICROSTEP_MODE_FRAC_128 = 8 MICROSTEP_MODE_FRAC_64 = 7 MICROSTEP_MODE_FRAC_32 = 6 MICROSTEP_MODE_FRAC_16 = 5 MICROSTEP_MODE_FRAC_8 = 4 MICROSTEP_MODE_FRAC_4 = 3 MICROSTEP_MODE_FRAC_2 = 2 MICROSTEP_MODE_FULL = 1 engine_settings.MicrostepMode = MicrostepMode_.MICROSTEP_MODE_FRAC_256 engine_settings.StepsPerRev = 200 result = lib.set_engine_settings(id, byref(engine_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result entype_settings = entype_settings_t() class EngineType_: ENGINE_TYPE_BRUSHLESS = 5 ENGINE_TYPE_TEST = 4 ENGINE_TYPE_STEP = 3 ENGINE_TYPE_2DC = 2 ENGINE_TYPE_DC = 1 ENGINE_TYPE_NONE = 0 entype_settings.EngineType = EngineType_.ENGINE_TYPE_STEP | EngineType_.ENGINE_TYPE_NONE class DriverType_: DRIVER_TYPE_EXTERNAL = 3 DRIVER_TYPE_INTEGRATE = 2 DRIVER_TYPE_DISCRETE_FET = 1 entype_settings.DriverType = DriverType_.DRIVER_TYPE_INTEGRATE result = lib.set_entype_settings(id, byref(entype_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result power_settings = power_settings_t() power_settings.HoldCurrent = 50 power_settings.CurrReductDelay = 1000 power_settings.PowerOffDelay = 60 power_settings.CurrentSetTime = 300 class PowerFlags_: POWER_SMOOTH_CURRENT = 4 POWER_OFF_ENABLED = 2 POWER_REDUCT_ENABLED = 1 power_settings.PowerFlags = PowerFlags_.POWER_SMOOTH_CURRENT | PowerFlags_.POWER_OFF_ENABLED | PowerFlags_.POWER_REDUCT_ENABLED result = lib.set_power_settings(id, byref(power_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result secure_settings = secure_settings_t() secure_settings.LowUpwrOff = 800 secure_settings.CriticalIpwr = 4000 secure_settings.CriticalUpwr = 5500 secure_settings.CriticalT = 800 secure_settings.CriticalIusb = 450 secure_settings.CriticalUusb = 520 secure_settings.MinimumUusb = 420 class Flags_: ALARM_ENGINE_RESPONSE = 128 ALARM_WINDING_MISMATCH = 64 USB_BREAK_RECONNECT = 32 ALARM_FLAGS_STICKING = 16 ALARM_ON_BORDERS_SWAP_MISSET = 8 H_BRIDGE_ALERT = 4 LOW_UPWR_PROTECTION = 2 ALARM_ON_DRIVER_OVERHEATING = 1 secure_settings.Flags = Flags_.ALARM_ENGINE_RESPONSE | Flags_.ALARM_FLAGS_STICKING | Flags_.ALARM_ON_BORDERS_SWAP_MISSET | Flags_.H_BRIDGE_ALERT | Flags_.ALARM_ON_DRIVER_OVERHEATING result = lib.set_secure_settings(id, byref(secure_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result edges_settings = edges_settings_t() class BorderFlags_: BORDERS_SWAP_MISSET_DETECTION = 8 BORDER_STOP_RIGHT = 4 BORDER_STOP_LEFT = 2 BORDER_IS_ENCODER = 1 class EnderFlags_: ENDER_SW2_ACTIVE_LOW = 4 ENDER_SW1_ACTIVE_LOW = 2 ENDER_SWAP = 1 edges_settings.EnderFlags = EnderFlags_.ENDER_SW2_ACTIVE_LOW edges_settings.LeftBorder = 5 edges_settings.uLeftBorder = 0 edges_settings.RightBorder = 195 edges_settings.uRightBorder = 0 result = lib.set_edges_settings(id, byref(edges_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result pid_settings = pid_settings_t() pid_settings.KpU = 0 pid_settings.KiU = 0 pid_settings.KdU = 0 pid_settings.Kpf = 0.003599999938160181 pid_settings.Kif = 0.03799999877810478 pid_settings.Kdf = 2.8000000384054147e-05 result = lib.set_pid_settings(id, byref(pid_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result sync_in_settings = sync_in_settings_t() class SyncInFlags_: SYNCIN_GOTOPOSITION = 4 SYNCIN_INVERT = 2 SYNCIN_ENABLED = 1 sync_in_settings.ClutterTime = 4 sync_in_settings.Position = 0 sync_in_settings.uPosition = 0 sync_in_settings.Speed = 0 sync_in_settings.uSpeed = 0 result = lib.set_sync_in_settings(id, byref(sync_in_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result sync_out_settings = sync_out_settings_t() class SyncOutFlags_: SYNCOUT_ONPERIOD = 64 SYNCOUT_ONSTOP = 32 SYNCOUT_ONSTART = 16 SYNCOUT_IN_STEPS = 8 SYNCOUT_INVERT = 4 SYNCOUT_STATE = 2 SYNCOUT_ENABLED = 1 sync_out_settings.SyncOutFlags = SyncOutFlags_.SYNCOUT_ONSTOP | SyncOutFlags_.SYNCOUT_ONSTART sync_out_settings.SyncOutPulseSteps = 100 sync_out_settings.SyncOutPeriod = 2000 sync_out_settings.Accuracy = 0 sync_out_settings.uAccuracy = 0 result = lib.set_sync_out_settings(id, byref(sync_out_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result extio_settings = extio_settings_t() class EXTIOSetupFlags_: EXTIO_SETUP_INVERT = 2 EXTIO_SETUP_OUTPUT = 1 extio_settings.EXTIOSetupFlags = EXTIOSetupFlags_.EXTIO_SETUP_OUTPUT class EXTIOModeFlags_: EXTIO_SETUP_MODE_OUT_BITS = 240 EXTIO_SETUP_MODE_OUT_MOTOR_ON = 64 EXTIO_SETUP_MODE_OUT_ALARM = 48 EXTIO_SETUP_MODE_OUT_MOVING = 32 EXTIO_SETUP_MODE_OUT_ON = 16 EXTIO_SETUP_MODE_IN_BITS = 15 EXTIO_SETUP_MODE_IN_ALARM = 5 EXTIO_SETUP_MODE_IN_HOME = 4 EXTIO_SETUP_MODE_IN_MOVR = 3 EXTIO_SETUP_MODE_IN_PWOF = 2 EXTIO_SETUP_MODE_IN_STOP = 1 EXTIO_SETUP_MODE_IN_NOP = 0 EXTIO_SETUP_MODE_OUT_OFF = 0 extio_settings.EXTIOModeFlags = EXTIOModeFlags_.EXTIO_SETUP_MODE_IN_STOP | EXTIOModeFlags_.EXTIO_SETUP_MODE_IN_NOP | EXTIOModeFlags_.EXTIO_SETUP_MODE_OUT_OFF result = lib.set_extio_settings(id, byref(extio_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result brake_settings = brake_settings_t() brake_settings.t1 = 300 brake_settings.t2 = 500 brake_settings.t3 = 300 brake_settings.t4 = 400 class BrakeFlags_: BRAKE_ENG_PWROFF = 2 BRAKE_ENABLED = 1 brake_settings.BrakeFlags = BrakeFlags_.BRAKE_ENG_PWROFF result = lib.set_brake_settings(id, byref(brake_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result control_settings = control_settings_t() control_settings.MaxSpeed[0] = 27 control_settings.MaxSpeed[1] = 0 control_settings.MaxSpeed[2] = 0 control_settings.MaxSpeed[3] = 0 control_settings.MaxSpeed[4] = 0 control_settings.MaxSpeed[5] = 0 control_settings.MaxSpeed[6] = 0 control_settings.MaxSpeed[7] = 0 control_settings.MaxSpeed[8] = 0 control_settings.MaxSpeed[9] = 0 control_settings.uMaxSpeed[0] = 0 control_settings.uMaxSpeed[1] = 0 control_settings.uMaxSpeed[2] = 0 control_settings.uMaxSpeed[3] = 0 control_settings.uMaxSpeed[4] = 0 control_settings.uMaxSpeed[5] = 0 control_settings.uMaxSpeed[6] = 0 control_settings.uMaxSpeed[7] = 0 control_settings.uMaxSpeed[8] = 0 control_settings.uMaxSpeed[9] = 0 control_settings.Timeout[0] = 1000 control_settings.Timeout[1] = 1000 control_settings.Timeout[2] = 1000 control_settings.Timeout[3] = 1000 control_settings.Timeout[4] = 1000 control_settings.Timeout[5] = 1000 control_settings.Timeout[6] = 1000 control_settings.Timeout[7] = 1000 control_settings.Timeout[8] = 1000 control_settings.MaxClickTime = 300 class Flags_: CONTROL_BTN_RIGHT_PUSHED_OPEN = 8 CONTROL_BTN_LEFT_PUSHED_OPEN = 4 CONTROL_MODE_BITS = 3 CONTROL_MODE_LR = 2 CONTROL_MODE_JOY = 1 CONTROL_MODE_OFF = 0 control_settings.Flags = Flags_.CONTROL_MODE_LR | Flags_.CONTROL_MODE_OFF control_settings.DeltaPosition = 1 control_settings.uDeltaPosition = 0 result = lib.set_control_settings(id, byref(control_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result joystick_settings = joystick_settings_t() joystick_settings.JoyLowEnd = 0 joystick_settings.JoyCenter = 5000 joystick_settings.JoyHighEnd = 10000 joystick_settings.ExpFactor = 100 joystick_settings.DeadZone = 50 class JoyFlags_: JOY_REVERSE = 1 result = lib.set_joystick_settings(id, byref(joystick_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result ctp_settings = ctp_settings_t() ctp_settings.CTPMinError = 3 class CTPFlags_: CTP_ERROR_CORRECTION = 16 REV_SENS_INV = 8 CTP_ALARM_ON_ERROR = 4 CTP_BASE = 2 CTP_ENABLED = 1 ctp_settings.CTPFlags = CTPFlags_.CTP_ERROR_CORRECTION result = lib.set_ctp_settings(id, byref(ctp_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result uart_settings = uart_settings_t() uart_settings.Speed = 115200 class UARTSetupFlags_: UART_STOP_BIT = 8 UART_PARITY_BIT_USE = 4 UART_PARITY_BITS = 3 UART_PARITY_BIT_MARK = 3 UART_PARITY_BIT_SPACE = 2 UART_PARITY_BIT_ODD = 1 UART_PARITY_BIT_EVEN = 0 uart_settings.UARTSetupFlags = UARTSetupFlags_.UART_PARITY_BIT_EVEN result = lib.set_uart_settings(id, byref(uart_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result controller_name = controller_name_t() controller_name.ControllerName = bytes([0, 113, 252, 118, 36, 0, 72, 0, 3, 0, 0, 0, 104, 101, 103, 0]) class CtrlFlags_: EEPROM_PRECEDENCE = 1 result = lib.set_controller_name(id, byref(controller_name)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result emf_settings = emf_settings_t() emf_settings.L = 0 emf_settings.R = 0 emf_settings.Km = 0 class BackEMFFlags_: BACK_EMF_KM_AUTO = 4 BACK_EMF_RESISTANCE_AUTO = 2 BACK_EMF_INDUCTANCE_AUTO = 1 result = lib.set_emf_settings(id, byref(emf_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result engine_advansed_setup = engine_advansed_setup_t() engine_advansed_setup.stepcloseloop_Kw = 50 engine_advansed_setup.stepcloseloop_Kp_low = 1000 engine_advansed_setup.stepcloseloop_Kp_high = 33 result = lib.set_engine_advansed_setup(id, byref(engine_advansed_setup)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result extended_settings = extended_settings_t() extended_settings.Param1 = 0 result = lib.set_extended_settings(id, byref(extended_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_name = stage_name_t() stage_name.PositionerName = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_stage_name(id, byref(stage_name)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_information = stage_information_t() stage_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) stage_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_stage_information(id, byref(stage_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_settings = stage_settings_t() stage_settings.LeadScrewPitch = 0 stage_settings.Units = bytes([0, 0, 0, 0, 0, 0, 0, 0]) stage_settings.MaxSpeed = 0 stage_settings.TravelRange = 0 stage_settings.SupplyVoltageMin = 0 stage_settings.SupplyVoltageMax = 0 stage_settings.MaxCurrentConsumption = 0 stage_settings.HorizontalLoadCapacity = 0 stage_settings.VerticalLoadCapacity = 0 result = lib.set_stage_settings(id, byref(stage_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result motor_information = motor_information_t() motor_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) motor_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_motor_information(id, byref(motor_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result motor_settings = motor_settings_t() class MotorType_: MOTOR_TYPE_BLDC = 3 MOTOR_TYPE_DC = 2 MOTOR_TYPE_STEP = 1 MOTOR_TYPE_UNKNOWN = 0 motor_settings.MotorType = MotorType_.MOTOR_TYPE_UNKNOWN motor_settings.ReservedField = 0 motor_settings.Poles = 0 motor_settings.Phases = 0 motor_settings.NominalVoltage = 0 motor_settings.NominalCurrent = 0 motor_settings.NominalSpeed = 0 motor_settings.NominalTorque = 0 motor_settings.NominalPower = 0 motor_settings.WindingResistance = 0 motor_settings.WindingInductance = 0 motor_settings.RotorInertia = 0 motor_settings.StallTorque = 0 motor_settings.DetentTorque = 0 motor_settings.TorqueConstant = 0 motor_settings.SpeedConstant = 0 motor_settings.SpeedTorqueGradient = 0 motor_settings.MechanicalTimeConstant = 0 motor_settings.MaxSpeed = 0 motor_settings.MaxCurrent = 0 motor_settings.MaxCurrentTime = 0 motor_settings.NoLoadCurrent = 0 motor_settings.NoLoadSpeed = 0 result = lib.set_motor_settings(id, byref(motor_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result encoder_information = encoder_information_t() encoder_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) encoder_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_encoder_information(id, byref(encoder_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result encoder_settings = encoder_settings_t() encoder_settings.MaxOperatingFrequency = 0 encoder_settings.SupplyVoltageMin = 0 encoder_settings.SupplyVoltageMax = 0 encoder_settings.MaxCurrentConsumption = 0 encoder_settings.PPR = 0 class EncoderSettings_: ENCSET_REVOLUTIONSENSOR_ACTIVE_HIGH = 256 ENCSET_REVOLUTIONSENSOR_PRESENT = 64 ENCSET_INDEXCHANNEL_PRESENT = 16 ENCSET_PUSHPULL_OUTPUT = 4 ENCSET_DIFFERENTIAL_OUTPUT = 1 result = lib.set_encoder_settings(id, byref(encoder_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result hallsensor_information = hallsensor_information_t() hallsensor_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) hallsensor_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_hallsensor_information(id, byref(hallsensor_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result hallsensor_settings = hallsensor_settings_t() hallsensor_settings.MaxOperatingFrequency = 0 hallsensor_settings.SupplyVoltageMin = 0 hallsensor_settings.SupplyVoltageMax = 0 hallsensor_settings.MaxCurrentConsumption = 0 hallsensor_settings.PPR = 0 result = lib.set_hallsensor_settings(id, byref(hallsensor_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result gear_information = gear_information_t() gear_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) gear_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_gear_information(id, byref(gear_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result gear_settings = gear_settings_t() gear_settings.ReductionIn = 1 gear_settings.ReductionOut = 1 gear_settings.RatedInputTorque = 0 gear_settings.RatedInputSpeed = 0 gear_settings.MaxOutputBacklash = 0 gear_settings.InputInertia = 0 gear_settings.Efficiency = 0 result = lib.set_gear_settings(id, byref(gear_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result accessories_settings = accessories_settings_t() accessories_settings.MagneticBrakeInfo = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) accessories_settings.MBRatedVoltage = 0 accessories_settings.MBRatedCurrent = 0 accessories_settings.MBTorque = 0 class MBSettings_: MB_POWERED_HOLD = 2 MB_AVAILABLE = 1 accessories_settings.TemperatureSensorInfo = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) accessories_settings.TSMin = 0 accessories_settings.TSMax = 0 accessories_settings.TSGrad = 0 class TSSettings_: TS_AVAILABLE = 8 TS_TYPE_BITS = 7 TS_TYPE_SEMICONDUCTOR = 2 TS_TYPE_THERMOCOUPLE = 1 TS_TYPE_UNKNOWN = 0 accessories_settings.TSSettings = TSSettings_.TS_TYPE_THERMOCOUPLE | TSSettings_.TS_TYPE_UNKNOWN class LimitSwitchesSettings_: LS_SHORTED = 16 LS_SW2_ACTIVE_LOW = 8 LS_SW1_ACTIVE_LOW = 4 LS_ON_SW2_AVAILABLE = 2 LS_ON_SW1_AVAILABLE = 1 result = lib.set_accessories_settings(id, byref(accessories_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result return worst_result

[ "[email protected]" ]

[email protected]

a8824158345cddc59be0477b3353fbdb3dbef6da

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/tests/benchmarks/micro/NestedFunctionClosure.py

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# Copyright 2017, Kay Hayen, mailto:[email protected] # # Python test originally created or extracted from other peoples work. The # parts from me are licensed as below. It is at least Free Software where # it's copied from other people. In these cases, that will normally be # indicated. # # 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. # x = 1 def f(): c = x+1 def g(): return c return g() def caller(): for i in range(10000): f() if __name__ == "__main__": caller()

[ "[email protected]" ]

[email protected]

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/opsgenie_sdk/api/alert/add_details_to_alert_payload.py

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# coding: utf-8 """ Python SDK for Opsgenie REST API Python SDK for Opsgenie REST API # noqa: E501 The version of the OpenAPI document: 2.0.0 Contact: [email protected] Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six class AddDetailsToAlertPayload(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'user': 'str', 'note': 'str', 'source': 'str', 'details': 'dict(str, str)' } attribute_map = { 'user': 'user', 'note': 'note', 'source': 'source', 'details': 'details' } def __init__(self, user=None, note=None, source=None, details=None): # noqa: E501 """AddDetailsToAlertPayload - a model defined in OpenAPI""" # noqa: E501 self._user = None self._note = None self._source = None self._details = None self.discriminator = None if user is not None: self.user = user if note is not None: self.note = note if source is not None: self.source = source self.details = details @property def user(self): """Gets the user of this AddDetailsToAlertPayload. # noqa: E501 Display name of the request owner # noqa: E501 :return: The user of this AddDetailsToAlertPayload. # noqa: E501 :rtype: str """ return self._user @user.setter def user(self, user): """Sets the user of this AddDetailsToAlertPayload. Display name of the request owner # noqa: E501 :param user: The user of this AddDetailsToAlertPayload. # noqa: E501 :type: str """ self._user = user @property def note(self): """Gets the note of this AddDetailsToAlertPayload. # noqa: E501 Additional note that will be added while creating the alert # noqa: E501 :return: The note of this AddDetailsToAlertPayload. # noqa: E501 :rtype: str """ return self._note @note.setter def note(self, note): """Sets the note of this AddDetailsToAlertPayload. Additional note that will be added while creating the alert # noqa: E501 :param note: The note of this AddDetailsToAlertPayload. # noqa: E501 :type: str """ self._note = note @property def source(self): """Gets the source of this AddDetailsToAlertPayload. # noqa: E501 Source field of the alert. Default value is IP address of the incoming request # noqa: E501 :return: The source of this AddDetailsToAlertPayload. # noqa: E501 :rtype: str """ return self._source @source.setter def source(self, source): """Sets the source of this AddDetailsToAlertPayload. Source field of the alert. Default value is IP address of the incoming request # noqa: E501 :param source: The source of this AddDetailsToAlertPayload. # noqa: E501 :type: str """ self._source = source @property def details(self): """Gets the details of this AddDetailsToAlertPayload. # noqa: E501 Key-value pairs to add as custom property into alert. # noqa: E501 :return: The details of this AddDetailsToAlertPayload. # noqa: E501 :rtype: dict(str, str) """ return self._details @details.setter def details(self, details): """Sets the details of this AddDetailsToAlertPayload. Key-value pairs to add as custom property into alert. # noqa: E501 :param details: The details of this AddDetailsToAlertPayload. # noqa: E501 :type: dict(str, str) """ if details is None: raise ValueError("Invalid value for `details`, must not be `None`") # noqa: E501 self._details = details def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_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 pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, AddDetailsToAlertPayload): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

[ "[email protected]" ]

[email protected]

44d6d277ce2adec9b2f94b011620101c7ccec4f4

1f31dc44aca58992aa478635cfe036566a7eebe2

/py/kubeflow/kfctl/testing/ci/update_jupyter_web_app.py

ff987dbaf3747e37071a3b1067faf73ea5262fbd

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permissive

adrian555/kfctl

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refs/heads/master

2020-12-15T18:38:30.190800

2020-10-26T20:50:24

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0

5

Apache-2.0

2020-06-03T22:38:51

2020-06-03T22:38:50

null

UTF-8

Python

false

10,444

py

"""Script to build and update the Jupyter WebApp image. Requires python3 hub CLI depends on an OAuth token with repo permissions: https://hub.github.com/hub.1.html * It will look for environment variable GITHUB_TOKEN """ import logging import os import tempfile import yaml import fire import git import httplib2 from kubeflow.kfctl.testing.util import application_util from kubeflow.testing import util # pylint: disable=no-name-in-module from containerregistry.client import docker_creds from containerregistry.client import docker_name from containerregistry.client.v2_2 import docker_http from containerregistry.client.v2_2 import docker_image as v2_2_image from containerregistry.transport import transport_pool # The image name as defined in the kustomization file JUPYTER_WEB_APP_IMAGE_NAME = "gcr.io/kubeflow-images-public/jupyter-web-app" class WebAppUpdater(object): # pylint: disable=useless-object-inheritance def __init__(self): self._last_commit = None self.manifests_repo_dir = None def build_image(self, build_project, registry_project): """Build the image. Args: build_project: GCP project used to build the image. registry_project: GCP project used to host the image. """ env = dict() env.update(os.environ) env["PROJECT"] = build_project env["REGISTRY_PROJECT"] = registry_project env["GIT_TAG"] = self._last_commit with tempfile.NamedTemporaryFile() as hf: name = hf.name env["OUTPUT"] = name web_dir = self._component_dir() util.run(["make", "build-gcb"], env=env, cwd=web_dir) # TODO(jlewi): We want to get the actual image produced by GCB. Right # now this is a bit brittle because we have multiple layers of substitution # e.g. in the Makefile and then the GCB YAML. # It might be better to parse the stdout of make-build-gcb to get the # GCB job name and then fetch the GCB info specifying the images. with open(name) as hf: data = yaml.load(hf) return data["image"] @property def last_commit(self): """Get the last commit of a change to the source for the jupyter-web-app.""" if not self._last_commit: # Get the hash of the last commit to modify the source for the Jupyter web # app image self._last_commit = util.run(["git", "log", "-n", "1", "--pretty=format:\"%h\"", "components/jupyter-web-app"], cwd=self._root_dir()).strip("\"") return self._last_commit def _find_remote_repo(self, repo, remote_url): # pylint: disable=no-self-use """Find the remote repo if it has already been added. Args: repo: The git python repo object. remote_url: The URL of the remote repo e.g. [email protected]:jlewi/kubeflow.git Returns: remote: git-python object representing the remote repo or none if it isn't present. """ for r in repo.remotes: for u in r.urls: if remote_url == u: return r return None def all(self, build_project, registry_project, remote_fork, # pylint: disable=too-many-statements,too-many-branches kustomize_file, add_github_host=False): """Build the latest image and update the prototype. Args: build_project: GCP project used to build the image. registry_project: GCP project used to host the image. remote_fork: Url of the remote fork. The remote fork used to create the PR; e.g. [email protected]:jlewi/kubeflow.git. currently only ssh is supported. kustomize_file: Path to the kustomize file add_github_host: If true will add the github ssh host to known ssh hosts. """ # TODO(jlewi): How can we automatically determine the root of the git # repo containing the kustomize_file? self.manifests_repo_dir = util.run(["git", "rev-parse", "--show-toplevel"], cwd=os.path.dirname(kustomize_file)) repo = git.Repo(self.manifests_repo_dir) util.maybe_activate_service_account() last_commit = self.last_commit # Ensure github.com is in the known hosts if add_github_host: output = util.run(["ssh-keyscan", "github.com"]) with open(os.path.join(os.getenv("HOME"), ".ssh", "known_hosts"), mode='a') as hf: hf.write(output) if not remote_fork.startswith("[email protected]"): raise ValueError("Remote fork currently only supports ssh") remote_repo = self._find_remote_repo(repo, remote_fork) if not remote_repo: fork_name = remote_fork.split(":", 1)[-1].split("/", 1)[0] logging.info("Adding remote %s=%s", fork_name, remote_fork) remote_repo = repo.create_remote(fork_name, remote_fork) logging.info("Last change to components-jupyter-web-app was %s", last_commit) base = "gcr.io/{0}/jupyter-web-app".format(registry_project) # Check if there is already an image tagged with this commit. image = base + ":" + self.last_commit transport = transport_pool.Http(httplib2.Http) src = docker_name.from_string(image) creds = docker_creds.DefaultKeychain.Resolve(src) image_exists = False try: with v2_2_image.FromRegistry(src, creds, transport) as src_image: logging.info("Image %s exists; digest: %s", image, src_image.digest()) image_exists = True except docker_http.V2DiagnosticException as e: if e.status == 404: logging.info("%s doesn't exist", image) else: raise if not image_exists: logging.info("Building the image") image = self.build_image(build_project, registry_project) logging.info("Created image: %s", image) else: logging.info("Image %s already exists", image) # TODO(jlewi): What if the file was already modified so we didn't # modify it in this run but we still need to commit it? image_updated = application_util.set_kustomize_image( kustomize_file, JUPYTER_WEB_APP_IMAGE_NAME, image) if not image_updated: logging.info("kustomization not updated so not creating a PR.") return application_util.regenerate_manifest_tests(self.manifests_repo_dir) branch_name = "update_jupyter_{0}".format(last_commit) if repo.active_branch.name != branch_name: logging.info("Creating branch %s", branch_name) branch_names = [b.name for b in repo.branches] if branch_name in branch_names: logging.info("Branch %s exists", branch_name) util.run(["git", "checkout", branch_name], cwd=self.manifests_repo_dir) else: util.run(["git", "checkout", "-b", branch_name], cwd=self.manifests_repo_dir) if self._check_if_pr_exists(commit=last_commit): # Since a PR already exists updating to the specified commit # don't create a new one. # We don't want to just push -f because if the PR already exists # git push -f will retrigger the tests. # To force a recreate of the PR someone could close the existing # PR and a new PR will be created on the next cron run. return logging.info("Add file %s to repo", kustomize_file) repo.index.add([kustomize_file]) repo.index.add([os.path.join(self.manifests_repo_dir, "tests/*")]) repo.index.commit("Update the jupyter web app image to {0}".format(image)) util.run(["git", "push", "-f", remote_repo.name, "{0}:{0}".format(branch_name)], cwd=self.manifests_repo_dir) self.create_pull_request(commit=last_commit) def _pr_title(self, commit): pr_title = "[auto PR] Update the jupyter-web-app image to {0}".format( commit) return pr_title def _check_if_pr_exists(self, commit=None): """Check if a PR is already open. Returns: exists: True if a PR updating the image to the specified commit already exists and false otherwise. """ # TODO(jlewi): Modeled on # https://github.com/kubeflow/examples/blob/master/code_search/docker/ks/update_index.sh # TODO(jlewi): We should use the GitHub API and check if there is an # existing open pull request. Or potentially just use the hub CLI. if not commit: commit = self.last_commit logging.info("No commit specified defaulting to %s", commit) pr_title = self._pr_title(commit) # See hub conventions: # https://hub.github.com/hub.1.html # The GitHub repository is determined automatically based on the name # of remote repositories output = util.run(["hub", "pr", "list", "--format=%U;%t\n"], cwd=self.manifests_repo_dir) lines = output.splitlines() prs = {} for l in lines: n, t = l.split(";", 1) prs[t] = n if pr_title in prs: logging.info("PR %s already exists to update the Jupyter web app image " "to %s", prs[pr_title], commit) return True return False def create_pull_request(self, base="kubeflow:master", commit=None): """Create a pull request. Args: base: The base to use. Defaults to "kubeflow:master". This should be in the form <GitHub OWNER>:<branch> """ pr_title = self._pr_title(commit) with tempfile.NamedTemporaryFile(delete=False, mode="w") as hf: hf.write(pr_title) hf.write("\n") hf.write("\n") hf.write( "Image built from commit https://github.com/kubeflow/kubeflow/" "commit/{0}".format(self._last_commit)) message_file = hf.name # TODO(jlewi): -f creates the pull requests even if there are local changes # this was useful during development but we may want to drop it. util.run(["hub", "pull-request", "-f", "--base=" + base, "-F", message_file], cwd=self.manifests_repo_dir) def _root_dir(self): this_dir = os.path.dirname(__file__) return os.path.abspath(os.path.join(this_dir, "..", "..", "..", "..")) def _component_dir(self): return os.path.join(self._root_dir(), "components", "jupyter-web-app") if __name__ == '__main__': logging.basicConfig(level=logging.INFO, format=('%(levelname)s|%(asctime)s' '|%(pathname)s|%(lineno)d| %(message)s'), datefmt='%Y-%m-%dT%H:%M:%S', ) logging.getLogger().setLevel(logging.INFO) fire.Fire(WebAppUpdater)

[ "[email protected]" ]

[email protected]

50a0e0a631826408e3f3cd6fd38ce599131e4588

163bbb4e0920dedd5941e3edfb2d8706ba75627d

/Code/CodeRecords/2506/60832/280621.py

f1113858470ab2074adaab3a1b0c3b72695ba34b

[]

no_license

AdamZhouSE/pythonHomework

a25c120b03a158d60aaa9fdc5fb203b1bb377a19

ffc5606817a666aa6241cfab27364326f5c066ff

refs/heads/master

2022-11-24T08:05:22.122011

2020-07-28T16:21:24

259,576,640

2

1

null

UTF-8

Python

false

666

py

import numpy as np ar = list(map(int, input().split(','))) length = len(ar) if length == 0: print(0) exit() Max = np.zeros(length) opt = np.zeros(length) opt[0] = 1 Max[0] = ar[0] for i in range(1, length): a = opt[i - 1] temp = ar[i] j = i - 1 has = False for j in range(i - 1, -1, -1): if Max[j] < temp: has = True break if has: b = opt[j] + 1 else: b = 1 if b > a: Max[i] = ar[i] opt[i] = b elif b == a: Max[i] = min(Max[i - 1], ar[i]) opt[i] = b else: Max[i] = Max[i - 1] opt[i] = a print(int(opt[length - 1]))

[ "[email protected]" ]

[email protected]

d5edadbe66cf157c9e28ddd27acc016d3102d6ac

abef98cfa3fb2c4626eb8c0a77c1080992d9b11b

/python/bindiff/bindiff.py

00885d7c9351425c339702dff4eee4738485d185

[]

no_license

mikebentley15/sandbox

ff88ed9dc4b9ac37668142a319d0a8162e88e9e3

4f5869544de18be21f415a9d6f9b71c362307f27

refs/heads/main

2023-04-14T00:22:34.623441

2023-03-24T21:43:56

116,987,549

6

3

null

2022-10-26T03:02:06

2018-01-10T17:14:54

C++

UTF-8

Python

false

1,521

py

#!/usr/bin/env python3 ''' Diff two binary files byte by byte. Do not try to do insertions or deletions, just a straight side-by-side comparison. ''' import sys import argparse def parse_args(arguments): 'Parse and return parsed arguments' parser = argparse.ArgumentParser( description=''' Diff two binary files byte-by-byte. This is a simple comparison operation, so no attempts to align based on insertions or deletions, just a straight side-by-side comparison. ''') parser.add_argument('file1') parser.add_argument('file2') args = parser.parse_args(arguments) return args def count_byte_diffs(file1, file2): 'Return # bytes different between file1 and file2 side-by-side' diff_bytes = 0 with open(file1, 'rb') as fin1: with open(file2, 'rb') as fin2: while True: c1 = fin1.read(1) c2 = fin2.read(1) # Handle end of file if c1 == bytes(): return diff_bytes + len(c2) + len(fin2.read()) if c2 == bytes(): return diff_bytes + len(c1) + len(fin1.read()) # Diff if c1 != c2: diff_bytes += 1 def main(arguments): 'Main logic here' args = parse_args(arguments) diff_bytes = count_byte_diffs(args.file1, args.file2) print(diff_bytes, 'bytes are different') if __name__ == '__main__': sys.exit(main(sys.argv[1:]))

[ "[email protected]" ]

[email protected]

bbf76565b6f64ab27199de1efc446badfd0ba38a

1bad7d2b7fc920ecf2789755ed7f44b039d4134d

/ABC/173/A.py

b409f92bf2697a9ef34d7cb880b50eb8c4da2cfe

[]

no_license

kanekyo1234/AtCoder_solve

ce95caafd31f7c953c0fc699f0f4897dddd7a159

e5ea7b080b72a2a2fd3fcb826cd10c4ab2e2720e

refs/heads/master

2023-04-01T04:01:15.885945

2021-04-06T04:03:31

266,151,065

0

null

UTF-8

Python

false

79

py

n = int(input()) if n % 1000 == 0: print(0) else: print(1000-n % 1000)

[ "[email protected]" ]

[email protected]

a0acdd42e55260598a360131a282d5f7852e0d57

ef187d259d33e97c7b9ed07dfbf065cec3e41f59

/work/atcoder/abc/abc054/D/answers/105471_s484.py

e511180c2fae7d4bf9960eae07c6d72b6540daf4

[]

no_license

kjnh10/pcw

847f7295ea3174490485ffe14ce4cdea0931c032

8f677701bce15517fb9362cc5b596644da62dca8

refs/heads/master

2020-03-18T09:54:23.442772

2018-07-19T00:26:09

134,586,379

0

null

UTF-8

Python

false

567

py

N, Ma, Mb = map(int, input().split()) G = [] for i in range(N): a, b, c = map(int, input().split()) G.append( (a,b,c) ) M = 1 + min(sum([ a for a, b, c in G ]), sum([ b for a, b, c in G ]) ) INF = 1000000000 dp = [INF] * (M * M) dp[0] = 0 for a, b, c in G: i = M - a - 1 while i >= 0: j = M - b - 1 while j >= 0: dp[(i+a)*M+(j+b)] = min(dp[(i+a)*M+(j+b)], dp[i*M+j] + c) j -= 1 i -= 1 ans = INF x = 1 while Ma * x < M and Mb * x < M: ans = min(ans, dp[Ma * x * M + Mb * x]) x += 1 if ans >= INF: ans = -1 print(ans)

[ "[email protected]" ]

[email protected]

1b2863c931ac97cc512170299b6e7d5844ead205

add72f4d6f9f7af1f437d19213c14efb218b2194

/icekit/page_types/author/tests.py

afcd6534c4231a59e310a4f281e7a4b7faa61d11

[ "MIT" ]

permissive

ic-labs/django-icekit

6abe859f97c709fcf51207b54778501b50436ff7

c507ea5b1864303732c53ad7c5800571fca5fa94

refs/heads/develop

2022-08-08T21:26:04.144852

2018-01-08T02:55:17

65,470,395

53

12

MIT

2022-07-06T19:59:39

2016-08-11T13:11:02

Python

UTF-8

Python

false

2,128

py

from django.contrib.auth import get_user_model from django.core.urlresolvers import reverse from django_dynamic_fixture import G from django_webtest import WebTest from fluent_contents.models import Placeholder from . import models User = get_user_model() class AuthorTests(WebTest): def setUp(self): self.staff_1 = User.objects.create( email='[email protected]', is_staff=True, is_active=True, is_superuser=True, ) # used to make the author's URL self.author_listing = models.AuthorListing.objects.create( author=self.staff_1, title="Authors", slug="authors", ) self.author_1 = G(models.Author) self.author_2 = G(models.Author) def test_get_absolute_url(self): self.assertEqual( self.author_1.get_absolute_url(), '/authors/%s/' % ( self.author_1.slug ) ) def test_admin(self): admin_app_list = ( ('icekit_authors_author', self.author_1), ) for admin_app, obj in admin_app_list: response = self.app.get( reverse('admin:%s_changelist' % admin_app), user=self.staff_1 ) self.assertEqual(response.status_code, 200) response = self.app.get(reverse('admin:%s_add' % admin_app), user=self.staff_1) self.assertEqual(response.status_code, 200) response = self.app.get( reverse('admin:%s_history' % admin_app, args=(obj.id,)), user=self.staff_1 ) self.assertEqual(response.status_code, 200) response = self.app.get( reverse('admin:%s_delete' % admin_app, args=(obj.id,)), user=self.staff_1 ) self.assertEqual(response.status_code, 200) response = self.app.get( reverse('admin:%s_change' % admin_app, args=(obj.id,)), user=self.staff_1 ) self.assertEqual(response.status_code, 200)

[ "[email protected]" ]

[email protected]

f0de2600bd1e07a39f42bbe91fae645d210e66f7

e489172f6e49e1239db56c047a78a29a6ffc0b36

/via_account_taxform/account_tax.py

b1245ef236db95e377f0531f854b5c0588e892e0

[]

no_license

eksotama/prln-via-custom-addons

f05d0059353ae1de89ccc8d1625a896c0215cfc7

f2b44a8af0e7bee87d52d258fca012bf44ca876f

refs/heads/master

2020-03-25T19:49:08.117628

2015-12-01T07:29:43

null

0

null

UTF-8

Python

false

1,478

py

# -*- encoding: utf-8 -*- ############################################################################## # # Vikasa Infinity Anugrah, PT # Copyright (c) 2011 - 2013 Vikasa Infinity Anugrah <http://www.infi-nity.com> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see http://www.gnu.org/licenses/. # ############################################################################## from osv import fields, osv class account_tax(osv.osv): _inherit = "account.tax" def _get_tax_category(self, cr, uid, context=None): res = self.pool.get('code.decode').get_company_selection_for_category(cr, uid, 'via_account_taxform', 'tax_category', context=context) return res _columns = { 'tax_category': fields.selection(_get_tax_category, 'Tax Category'), } account_tax() # vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:

[ "aero@aero.(none)" ]

aero@aero.(none)

a2514f32e71a028a6e1421e5456b756a92898f22

ccfc0566cd646cbe1837affef08baec8cd245d3b

/src/robot/model/control.py

bafd41a03b8be08ca4906403ab2cae40e68f9a71

[ "Apache-2.0", "CC-BY-3.0" ]

permissive

bmalhi/robotframework

9f395d3197cdd7925b8def3aeb50b14fc31e83e2

eaadffabc98b587c108cc904e0e54ce368020dd7

refs/heads/master

2023-03-01T14:30:23.110935

2021-02-05T16:01:30

2021-02-05T16:02:18

null

0

null

UTF-8

Python

false

3,750

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# Copyright 2008-2015 Nokia Networks # Copyright 2016- Robot Framework Foundation # # 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 robot.utils import setter, py3to2 from .body import Body, BodyItem from .keyword import Keywords @py3to2 @Body.register class For(BodyItem): type = BodyItem.FOR_TYPE body_class = Body repr_args = ('variables', 'flavor', 'values') __slots__ = ['variables', 'flavor', 'values'] deprecate_keyword_attributes = True def __init__(self, variables=(), flavor='IN', values=(), parent=None): self.variables = variables self.flavor = flavor self.values = values self.parent = parent self.body = None @setter def body(self, body): return self.body_class(self, body) @property def keywords(self): """Deprecated since Robot Framework 4.0. Use :attr:`body` instead.""" return Keywords(self, self.body) @keywords.setter def keywords(self, keywords): Keywords.raise_deprecation_error() @property def source(self): return self.parent.source if self.parent is not None else None def visit(self, visitor): visitor.visit_for(self) def __str__(self): variables = ' '.join(self.variables) values = ' '.join(self.values) return u'FOR %s %s %s' % (variables, self.flavor, values) @py3to2 @Body.register class If(BodyItem): body_class = Body repr_args = ('condition', 'type') __slots__ = ['condition', 'type', '_orelse'] deprecate_keyword_attributes = True def __init__(self, condition=None, type=BodyItem.IF_TYPE, parent=None): self.condition = condition self.type = type self.parent = parent self.body = None self._orelse = None @setter def body(self, body): return self.body_class(self, body) @property # Cannot use @setter because it would create orelses recursively. def orelse(self): if self._orelse is None and self: self._orelse = type(self)(type=None, parent=self) return self._orelse @orelse.setter def orelse(self, orelse): if orelse is None: self._orelse = None elif not isinstance(orelse, type(self)): raise TypeError("Only %s objects accepted, got %s." % (type(self).__name__, type(orelse).__name__)) else: orelse.parent = self self._orelse = orelse @property def source(self): return self.parent.source if self.parent is not None else None def config(self, **attributes): BodyItem.config(self, **attributes) if self.type is None: self.type = self.ELSE_IF_TYPE if self.condition else self.ELSE_TYPE return self def visit(self, visitor): if self: visitor.visit_if(self) def __str__(self): if not self: return u'None' if not isinstance(self.parent, If): return u'IF %s' % self.condition if self.condition: return u'ELSE IF %s' % self.condition return u'ELSE' def __bool__(self): return self.type is not None

[ "[email protected]" ]

[email protected]

c253c273fb8c240f6eda595e492a460e88fd798c

ebdb33a86794a779714318f8a0b8397c3d6002b5

/processing/state_processing_dataframe.py

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ShabbirHasan1/interactive-corporate-report

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refs/heads/master

2023-05-16T17:15:52.651033

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import plotly.plotly as py import pandas as pd import numpy as np import os import _pickle as pickle # rd = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv') my_path = os.path.abspath(os.path.dirname('__file__')) path_in_file = os.path.join(my_path, "../data/google/addresses_google.csv") df_own = pd.read_csv(path_in_file) path_in_ngrams = os.path.join(my_path, "../data/cpickle/") df_own = df_own[df_own["country"] == "United States"].reset_index(drop=True) df_own = df_own.fillna(df_own.mean()) df_own = df_own.rename(columns={"city_long": "state", "city_short": "code", "Female": "Female Rating" , "Male": "Male Rating", "Patrons": "Patrons Rating", 'Average Customer Network': 'Connectedness', "Male": "Male Rating", "Patrons": "Patrons Rating", 'Food Aestheticist': 'Food Aestheticist Rating', 'High Network': 'High Network Rating', 'Low Network': 'Low Network Rating', 'Connoisseur': 'Connoisseur Rating'}) df_own.loc[:, ["Total Network", "Number of Reviewers"]] = df_own.loc[:, ["Total Network", "Number of Reviewers"]].applymap(np.int32) df_own.loc[:, ['Male to Female', 'Foreign to Local', 'Male Rating', 'Female Rating', 'Local', 'Foreign', 'High Network Rating', 'Low Network Rating', 'Connoisseur Rating', 'Food Aestheticist Rating', 'Patrons Rating', 'First Visit', 'Visual Importance', 'Female Importance', 'Foreign Importance', 'Connectedness', 'Average Rating']] = df_own.loc[:, ['Male to Female', 'Foreign to Local', 'Male Rating', 'Female Rating', 'Local', 'Foreign', 'High Network Rating', 'Low Network Rating', 'Connoisseur Rating', 'Food Aestheticist Rating', 'Patrons Rating', 'First Visit', 'Visual Importance', 'Female Importance', 'Foreign Importance', 'Connectedness', 'Average Rating']].applymap( np.float32).round(3) df_own.replace({'county': {'Anchorage': 'Anchorage Borough', 'Fairbanks North Star': 'Fairbanks North Star Borough', 'Matanuska-Susitna': 'Matanuska-Susitna Borough'}}) df_own["county_state"] = df_own["county"] + ", " + df_own["code"] us = df_own[df_own["country"] == "United States"].reset_index(drop=True) sep = us[["Total Network", "Number of Reviewers", "code"]] us = us.drop(["Total Network", "Number of Reviewers"], axis=1) all_firms_mean = us.groupby("code").mean().reset_index() all_firms_sum = sep.groupby("code").sum().reset_index() all_firms = pd.concat((all_firms_mean, all_firms_sum), axis=1) all_firms.drop(["Unnamed: 0"], axis=1, inplace=True) all_firms = all_firms.iloc[:, 1:] all_dicts = {} for i in df_own["target_small_name"].unique(): firm_lvl = df_own[df_own["target_small_name"] == i].reset_index() sep_fir = firm_lvl[["Total Network", "Number of Reviewers", "code"]] firm_lvl = firm_lvl.drop(["Total Network", "Number of Reviewers"], axis=1) firm_lvl = firm_lvl.groupby("code").mean().reset_index() sep_fir = sep_fir.groupby("code").sum().reset_index() firms = pd.concat((firm_lvl, sep_fir), axis=1) firms.drop(["index", "Unnamed: 0"], axis=1, inplace=True) firms = firms.iloc[:, 1:] all_dicts[i] = firms all_dicts["All"] = all_firms pickle.dump(all_dicts, open(path_in_ngrams + "all_dicts_state.p", "wb")) # go = input_fields["short_name"].tolist() # go.append("All") # [dict(args=['z', value["Female"] ], label=key, method='restyle') for key, value in all_dicts.items()] # updatemenus=list([dict(buttons = [[dict(args=['z', value["Female"] ], label=key, method='update') for key, value in all_dicts.items()]])])

[ "[email protected]" ]

[email protected]

062ad9dc571345a7f470daa1d624c95fab71381b

8f21513b8ba9e583246908006cac98e5e473e245

/2_date_time.py

4e845b6d46313ee7c1aa2f575d6f4ed98c8d58b0

[]

no_license

MatsakB/Lesson3

6335bdeb86e3216e4576c170096d7bcf57ec4b2d

69493f147720ac7d975421d7400e6964ecfac3a2

refs/heads/master

2020-04-10T06:35:22.069664

2018-12-08T08:45:20

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from datetime import datetime, timedelta date_today = datetime.now() delta_days = timedelta(days=1) delta_months = timedelta(days=30) date_yesterday = date_today-delta_days date_month_before = date_today-delta_months print(date_today.strftime('%d.%m.%y')) print(date_yesterday.strftime('%d.%m.%y')) print(date_month_before.strftime('%d.%m.%y')) #Превратите строку "01/01/17 12:10:03.234567" в объект datetime d = "01/01/17 12:10:03.234567" d_datetime_object = datetime.strptime(d,'%d/%m/%y %H:%M:%S.%f') print(d_datetime_object)

[ "[email protected]" ]

[email protected]

dc768c5a2982a554ecbde52148df68d05e357efd

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/tools/project-creator/Python2.6.6/Lib/test/test_transformer.py

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xcode1986/nineck.ca

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refs/heads/master

2020-04-15T14:48:08.551821

2019-01-15T07:36:06

164,768,581

1

MIT

2019-01-15T08:30:27

2019-01-09T02:09:21

C++

UTF-8

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1,146

py

import unittest from test import test_support # Silence Py3k warning test_support.import_module('compiler', deprecated=True) from compiler import transformer, ast from compiler import compile class Tests(unittest.TestCase): def testMultipleLHS(self): """ Test multiple targets on the left hand side. """ snippets = ['a, b = 1, 2', '(a, b) = 1, 2', '((a, b), c) = (1, 2), 3'] for s in snippets: a = transformer.parse(s) assert isinstance(a, ast.Module) child1 = a.getChildNodes()[0] assert isinstance(child1, ast.Stmt) child2 = child1.getChildNodes()[0] assert isinstance(child2, ast.Assign) # This actually tests the compiler, but it's a way to assure the ast # is correct c = compile(s, '<string>', 'single') vals = {} exec c in vals assert vals['a'] == 1 assert vals['b'] == 2 def test_main(): test_support.run_unittest(Tests) if __name__ == "__main__": test_main()

[ "[email protected]" ]

[email protected]

f144fec17ff955f0806f4a5f976eb5a2072ff5dc

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/newsletter/settings.py

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[ "MIT" ]

permissive

Williano/Final-Senior-Year-Project-

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4bd988575537b37b5cf852b616d3db5666c95e7f

refs/heads/master

2023-08-07T16:11:42.778492

2023-06-05T04:59:06

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2023-06-05T04:59:07

2018-02-13T06:17:16

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from importlib import import_module from django.conf import settings as django_settings from django.core.exceptions import ImproperlyConfigured from .utils import Singleton class Settings(object): """ A settings object that proxies settings and handles defaults, inspired by `django-appconf` and the way it works in `django-rest-framework`. By default, a single instance of this class is created as `<app>_settings`, from which `<APP>_SETTING_NAME` can be accessed as `SETTING_NAME`, i.e.:: from myapp.settings import myapp_settings if myapp_settings.SETTING_NAME: # DO FUNKY DANCE If a setting has not been explicitly defined in Django's settings, defaults can be specified as `DEFAULT_SETTING_NAME` class variable or property. """ __metaclass__ = Singleton def __init__(self): """ Assert app-specific prefix. """ assert hasattr(self, 'settings_prefix'), 'No prefix specified.' def __getattr__(self, attr): """ Return Django setting `PREFIX_SETTING` if explicitly specified, otherwise return `PREFIX_SETTING_DEFAULT` if specified. """ if attr.isupper(): # Require settings to have uppercase characters try: setting = getattr( django_settings, '%s_%s' % (self.settings_prefix, attr), ) except AttributeError: if not attr.startswith('DEFAULT_'): setting = getattr(self, 'DEFAULT_%s' % attr) else: raise return setting else: # Default behaviour raise AttributeError( 'No setting or default available for \'%s\'' % attr ) class NewsletterSettings(Settings): """ Django-newsletter specific settings. """ settings_prefix = 'NEWSLETTER' DEFAULT_CONFIRM_EMAIL = True @property def DEFAULT_CONFIRM_EMAIL_SUBSCRIBE(self): return self.CONFIRM_EMAIL @property def DEFAULT_CONFIRM_EMAIL_UNSUBSCRIBE(self): return self.CONFIRM_EMAIL @property def DEFAULT_CONFIRM_EMAIL_UPDATE(self): return self.CONFIRM_EMAIL @property def RICHTEXT_WIDGET(self): # Import and set the richtext field NEWSLETTER_RICHTEXT_WIDGET = getattr( django_settings, "NEWSLETTER_RICHTEXT_WIDGET", "" ) if NEWSLETTER_RICHTEXT_WIDGET: try: module, attr = NEWSLETTER_RICHTEXT_WIDGET.rsplit(".", 1) mod = import_module(module) return getattr(mod, attr) except Exception as e: # Catch ImportError and other exceptions too # (e.g. user sets setting to an integer) raise ImproperlyConfigured( "Error while importing setting " "NEWSLETTER_RICHTEXT_WIDGET %r: %s" % ( NEWSLETTER_RICHTEXT_WIDGET, e ) ) return None newsletter_settings = NewsletterSettings()

[ "[email protected]" ]

[email protected]

ff3771b5c0f1674a5c668a6a8e2b5f78017cab70

d83fde3c891f44014f5339572dc72ebf62c38663

/_bin/google-cloud-sdk/.install/.backup/lib/googlecloudsdk/api_lib/auth/exceptions.py

b07bd1f9d71b922ba849cb4dc2a0eac9562fdd33

[ "LicenseRef-scancode-unknown-license-reference", "Apache-2.0" ]

permissive

gyaresu/dotfiles

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e5e533b3a081b42e9492b228f308f6833b670cfe

refs/heads/master

2022-11-24T01:12:49.435037

2022-11-01T16:58:13

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1

null

2020-07-25T14:11:43

2014-02-24T14:59:59

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UTF-8

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1,206

py

# Copyright 2017 Google Inc. 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. """User errors raised by auth commands.""" from __future__ import absolute_import from __future__ import unicode_literals from googlecloudsdk.core import exceptions class AuthenticationError(exceptions.Error): """Raised for errors reported by Oauth2client library.""" class InvalidCredentialsError(exceptions.Error): """Raised if credentials are not usable.""" class WrongAccountError(exceptions.Error): """Raised when credential account does not match expected account.""" class GitCredentialHelperError(exceptions.Error): """Raised for issues related to passing auth credentials to Git."""

[ "[email protected]" ]

[email protected]

a2859f4ac719600fa16c18391c0265afda7857f5

238e46a903cf7fac4f83fa8681094bf3c417d22d

/VTK/vtk_7.1.1_x64_Release/lib/python2.7/site-packages/twisted/scripts/test/test_scripts.py

8705a90f7287a1f2a71871bea4ec36b22d67d56a

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permissive

baojunli/FastCAE

da1277f90e584084d461590a3699b941d8c4030b

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refs/heads/master

2023-02-25T20:25:31.815729

2021-02-01T03:17:33

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BSD-3-Clause

2020-06-01T00:39:31

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# Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ Tests for the command-line scripts in the top-level I{bin/} directory. Tests for actual functionality belong elsewhere, written in a way that doesn't involve launching child processes. """ from os import devnull, getcwd, chdir from sys import executable from subprocess import PIPE, Popen from twisted.trial.unittest import SkipTest, TestCase from twisted.python.modules import getModule from twisted.python.filepath import FilePath from twisted.python.test.test_shellcomp import ZshScriptTestMixin def outputFromPythonScript(script, *args): """ Synchronously run a Python script, with the same Python interpreter that ran the process calling this function, using L{Popen}, using the given command-line arguments, with standard input and standard error both redirected to L{os.devnull}, and return its output as a string. @param script: The path to the script. @type script: L{FilePath} @param args: The command-line arguments to follow the script in its invocation (the desired C{sys.argv[1:]}). @type args: L{tuple} of L{str} @return: the output passed to the proces's C{stdout}, without any messages from C{stderr}. @rtype: L{bytes} """ nullInput = file(devnull, "rb") nullError = file(devnull, "wb") stdout = Popen([executable, script.path] + list(args), stdout=PIPE, stderr=nullError, stdin=nullInput).stdout.read() nullInput.close() nullError.close() return stdout class ScriptTestsMixin: """ Mixin for L{TestCase} subclasses which defines a helper function for testing a Twisted-using script. """ bin = getModule("twisted").pathEntry.filePath.child("bin") def scriptTest(self, name): """ Verify that the given script runs and uses the version of Twisted currently being tested. This only works when running tests against a vcs checkout of Twisted, since it relies on the scripts being in the place they are kept in version control, and exercises their logic for finding the right version of Twisted to use in that situation. @param name: A path fragment, relative to the I{bin} directory of a Twisted source checkout, identifying a script to test. @type name: C{str} @raise SkipTest: if the script is not where it is expected to be. """ script = self.bin.preauthChild(name) if not script.exists(): raise SkipTest( "Script tests do not apply to installed configuration.") from twisted.copyright import version scriptVersion = outputFromPythonScript(script, '--version') self.assertIn(str(version), scriptVersion) class ScriptTests(TestCase, ScriptTestsMixin): """ Tests for the core scripts. """ def test_twistd(self): self.scriptTest("twistd") def test_twistdPathInsert(self): """ The twistd script adds the current working directory to sys.path so that it's able to import modules from it. """ script = self.bin.child("twistd") if not script.exists(): raise SkipTest( "Script tests do not apply to installed configuration.") cwd = getcwd() self.addCleanup(chdir, cwd) testDir = FilePath(self.mktemp()) testDir.makedirs() chdir(testDir.path) testDir.child("bar.tac").setContent( "import sys\n" "print sys.path\n") output = outputFromPythonScript(script, '-ny', 'bar.tac') self.assertIn(repr(testDir.path), output) def test_manhole(self): self.scriptTest("manhole") def test_trial(self): self.scriptTest("trial") def test_trialPathInsert(self): """ The trial script adds the current working directory to sys.path so that it's able to import modules from it. """ script = self.bin.child("trial") if not script.exists(): raise SkipTest( "Script tests do not apply to installed configuration.") cwd = getcwd() self.addCleanup(chdir, cwd) testDir = FilePath(self.mktemp()) testDir.makedirs() chdir(testDir.path) testDir.child("foo.py").setContent("") output = outputFromPythonScript(script, 'foo') self.assertIn("PASSED", output) def test_pyhtmlizer(self): self.scriptTest("pyhtmlizer") def test_tap2rpm(self): self.scriptTest("tap2rpm") def test_tap2deb(self): self.scriptTest("tap2deb") def test_tapconvert(self): self.scriptTest("tapconvert") def test_deprecatedTkunzip(self): """ The entire L{twisted.scripts.tkunzip} module, part of the old Windows installer tool chain, is deprecated. """ from twisted.scripts import tkunzip warnings = self.flushWarnings( offendingFunctions=[self.test_deprecatedTkunzip]) self.assertEqual(DeprecationWarning, warnings[0]['category']) self.assertEqual( "twisted.scripts.tkunzip was deprecated in Twisted 11.1.0: " "Seek unzipping software outside of Twisted.", warnings[0]['message']) self.assertEqual(1, len(warnings)) def test_deprecatedTapconvert(self): """ The entire L{twisted.scripts.tapconvert} module is deprecated. """ from twisted.scripts import tapconvert warnings = self.flushWarnings( offendingFunctions=[self.test_deprecatedTapconvert]) self.assertEqual(DeprecationWarning, warnings[0]['category']) self.assertEqual( "twisted.scripts.tapconvert was deprecated in Twisted 12.1.0: " "tapconvert has been deprecated.", warnings[0]['message']) self.assertEqual(1, len(warnings)) class ZshIntegrationTestCase(TestCase, ZshScriptTestMixin): """ Test that zsh completion functions are generated without error """ generateFor = [('twistd', 'twisted.scripts.twistd.ServerOptions'), ('trial', 'twisted.scripts.trial.Options'), ('pyhtmlizer', 'twisted.scripts.htmlizer.Options'), ('tap2rpm', 'twisted.scripts.tap2rpm.MyOptions'), ('tap2deb', 'twisted.scripts.tap2deb.MyOptions'), ('tapconvert', 'twisted.scripts.tapconvert.ConvertOptions'), ('manhole', 'twisted.scripts.manhole.MyOptions') ]

[ "l”[email protected]“" ]

l”[email protected]“

70bc331f3ab7dcdf0904d00a928becf959b12a5e

f3bd271bf00325881fb5b2533b9ef7f7448a75ec

/xcp2k/classes/_wf_correlation1.py

946851bae269b8bc3790a4acf9f9905235827943

[]

no_license

obaica/xcp2k

7f99fc9d494859e16b9b0ea8e217b0493f4b2f59

6e15c2c95658f545102595dc1783f5e03a9e6916

refs/heads/master

2020-07-15T17:27:43.378835

2019-02-11T16:32:24

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py

from xcp2k.inputsection import InputSection from _mp2_info1 import _mp2_info1 from _direct_canonical1 import _direct_canonical1 from _wfc_gpw1 import _wfc_gpw1 from _ri_mp21 import _ri_mp21 from _opt_ri_basis1 import _opt_ri_basis1 from _ri_rpa1 import _ri_rpa1 from _ri_laplace1 import _ri_laplace1 from _cphf1 import _cphf1 from _interaction_potential3 import _interaction_potential3 from _eri_mme2 import _eri_mme2 class _wf_correlation1(InputSection): def __init__(self): InputSection.__init__(self) self.Method = None self.Memory = None self.Scale_s = None self.Scale_t = None self.Group_size = None self.Row_block = None self.Col_block = None self.Calc_cond_num = None self.Ri_metric = None self.Eri_method = None self.Eri_blksize = None self.Minimal_gap = None self.MP2_INFO = _mp2_info1() self.DIRECT_CANONICAL = _direct_canonical1() self.WFC_GPW = _wfc_gpw1() self.RI_MP2 = _ri_mp21() self.OPT_RI_BASIS = _opt_ri_basis1() self.RI_RPA = _ri_rpa1() self.RI_LAPLACE = _ri_laplace1() self.CPHF = _cphf1() self.INTERACTION_POTENTIAL = _interaction_potential3() self.ERI_MME = _eri_mme2() self._name = "WF_CORRELATION" self._keywords = {'Minimal_gap': 'MINIMAL_GAP', 'Group_size': 'GROUP_SIZE', 'Row_block': 'ROW_BLOCK', 'Calc_cond_num': 'CALC_COND_NUM', 'Scale_s': 'SCALE_S', 'Scale_t': 'SCALE_T', 'Memory': 'MEMORY', 'Eri_method': 'ERI_METHOD', 'Col_block': 'COL_BLOCK', 'Method': 'METHOD', 'Eri_blksize': 'ERI_BLKSIZE', 'Ri_metric': 'RI_METRIC'} self._subsections = {'MP2_INFO': 'MP2_INFO', 'RI_RPA': 'RI_RPA', 'WFC_GPW': 'WFC_GPW', 'RI_LAPLACE': 'RI_LAPLACE', 'RI_MP2': 'RI_MP2', 'CPHF': 'CPHF', 'INTERACTION_POTENTIAL': 'INTERACTION_POTENTIAL', 'OPT_RI_BASIS': 'OPT_RI_BASIS', 'ERI_MME': 'ERI_MME', 'DIRECT_CANONICAL': 'DIRECT_CANONICAL'} self._aliases = {'Row_block_size': 'Row_block', 'Number_proc': 'Group_size', 'Col_block_size': 'Col_block', 'Calc_condition_number': 'Calc_cond_num', 'Ri': 'Ri_metric'} @property def Number_proc(self): """ See documentation for Group_size """ return self.Group_size @property def Row_block_size(self): """ See documentation for Row_block """ return self.Row_block @property def Col_block_size(self): """ See documentation for Col_block """ return self.Col_block @property def Calc_condition_number(self): """ See documentation for Calc_cond_num """ return self.Calc_cond_num @property def Ri(self): """ See documentation for Ri_metric """ return self.Ri_metric @Number_proc.setter def Number_proc(self, value): self.Group_size = value @Row_block_size.setter def Row_block_size(self, value): self.Row_block = value @Col_block_size.setter def Col_block_size(self, value): self.Col_block = value @Calc_condition_number.setter def Calc_condition_number(self, value): self.Calc_cond_num = value @Ri.setter def Ri(self, value): self.Ri_metric = value

[ "[email protected]" ]

[email protected]

7588e0b50d82ff81490c180db39e55febd0d85ab

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start-jsk/jsk_apc

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refs/heads/master

2023-09-05T09:06:24.855510

2023-09-01T17:10:12

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2023-09-01T17:10:14

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Common Lisp

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py

#!/usr/bin/env python import chainer_mask_rcnn import instance_occlsegm_lib import contrib if __name__ == '__main__': dataset = contrib.datasets.ARC2017OcclusionDataset( split='train', do_aug=True) def visualize_func(dataset, index): img, bboxes, labels, lbls = dataset[index] class_names = dataset.class_names captions = [class_names[l] for l in labels] vizs = [] for bbox, label, lbl, caption in \ zip(bboxes, labels, lbls, captions): mask_bg = lbl == 0 mask_visible = lbl == 1 mask_invisible = lbl == 2 viz = chainer_mask_rcnn.utils.draw_instance_bboxes( img, [bbox], [label], n_class=len(class_names), masks=[mask_bg], captions=[caption]) vizs.append(viz) viz = chainer_mask_rcnn.utils.draw_instance_bboxes( img, [bbox], [label], n_class=len(class_names), masks=[mask_visible], captions=[caption]) vizs.append(viz) viz = chainer_mask_rcnn.utils.draw_instance_bboxes( img, [bbox], [label], n_class=len(class_names), masks=[mask_invisible], captions=[caption]) vizs.append(viz) viz = instance_occlsegm_lib.image.tile( vizs, (max(1, len(vizs) // 3), 3)) return viz instance_occlsegm_lib.datasets.view_dataset(dataset, visualize_func) # viz = instance_occlsegm_lib.image.resize(viz, size=1000 * 1000) # instance_occlsegm_lib.io.imshow(viz) # instance_occlsegm_lib.io.waitkey()

[ "[email protected]" ]

[email protected]

3e7af994a6235be22aa1a34320c806ffcc69e7cd

0ca1d8363439e0e34d7eaa54f021ff0b2940cac7

/facturacion/migrations/0016_auto_20181130_1741.py

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[]

no_license

geovanniberdugo/medhis

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b8f8df111432bfab537853ed8e8dbd4603e9707d

refs/heads/main

2023-02-13T19:44:33.699689

2021-01-15T22:08:35

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# -*- coding: utf-8 -*- # Generated by Django 1.11.15 on 2018-11-30 22:41 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('facturacion', '0015_auto_20181120_1824'), ] operations = [ migrations.AlterField( model_name='caja', name='verificacion_correcta', field=models.BooleanField(default=True, help_text='Indica si los valores son correctos'), ), ]

[ "[email protected]" ]

[email protected]

00f502880899ebc4ff3c56f1b131f1ba2ae7846c

0e25538b2f24f1bc002b19a61391017c17667d3d

/xsharepoint/win_xspmanagedpath.py

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trondhindenes/Ansible-Auto-Generated-Modules

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efa6ac8cd2b545116f24c1929936eb8cc5c8d337

refs/heads/master

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#!/usr/bin/python # -*- coding: utf-8 -*- # <COPYRIGHT> # <CODEGENMETA> # # This file is part of Ansible # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. # this is a windows documentation stub. actual code lives in the .ps1 # file of the same name DOCUMENTATION = ''' --- module: win_xspmanagedpath version_added: short_description: Generated from DSC module xsharepoint version 0.12.0.0 at 07.10.2016 02.56.36 description: - This DSC module is used to deploy and configure SharePoint Server 2013, and convers a wide range of areas including web apps, service apps and farm configuration. options: Explicit: description: - required: True default: aliases: [] HostHeader: description: - required: True default: aliases: [] RelativeUrl: description: - required: True default: aliases: [] WebAppUrl: description: - required: True default: aliases: [] InstallAccount_username: description: - required: False default: aliases: [] InstallAccount_password: description: - required: False default: aliases: [] PsDscRunAsCredential_username: description: - required: False default: aliases: [] PsDscRunAsCredential_password: description: - required: False default: aliases: [] AutoInstallModule: description: - If true, the required dsc resource/module will be auto-installed using the Powershell package manager required: False default: false aliases: [] choices: - true - false AutoConfigureLcm: description: - If true, LCM will be auto-configured for directly invoking DSC resources (which is a one-time requirement for Ansible DSC modules) required: False default: false aliases: [] choices: - true - false

[ "[email protected]" ]

[email protected]

d454ee79742bbf7b555240e05ae0700d83559c75

29fad6273eb43fcbaff7460b2b68fea66d9ebc8c

/custom-vpc/custom_vpc/custom_vpc_stack.py

99ef9dd2bd53bf2c57edf14bd1ebc1b98829cff7

[]

no_license

satishbr/cdk-demos

a3b2c7ca32551eb1c0264f8125f2ffc9413d00ff

14568c885322f561d548de0d1175f3b60ee87df0

refs/heads/master

2022-11-10T16:58:38.105403

2020-04-15T10:20:19

275,331,921

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from aws_cdk import ( aws_ec2 as ec2, core ) class CustomVpcStack(core.Stack): def __init__(self, scope: core.Construct, id: str, **kwargs) -> None: super().__init__(scope, id, **kwargs) # The code that defines your stack goes here # https://docs.aws.amazon.com/cdk/api/latest/python/aws_cdk.aws_ec2/Vpc.html vpc = ec2.Vpc( self, "MyVpc", cidr="10.13.0.0/21", max_azs=2, nat_gateways=0, subnet_configuration=[ ec2.SubnetConfiguration(name="public", cidr_mask=24, subnet_type=ec2.SubnetType.PUBLIC), # ec2.SubnetConfiguration(name="private", cidr_mask=24, subnet_type=ec2.SubnetType.PRIVATE) ec2.SubnetConfiguration(name="private", cidr_mask=24, subnet_type=ec2.SubnetType.ISOLATED) ] ) # Tag all VPC Resources core.Tag.add(vpc,key="Owner",value="KonStone",include_resource_types=[])

[ "[email protected]" ]

[email protected]

96d3ef7124f1d20922a37c482305578a536be494

2e682fd72e3feaa70e3f7bf2a3b83c50d783ec02

/PyTorch/contrib/cv/video/QVI_ID2930_for_PyTorch/datas/__init__.py

3f2183896479f5bf7d870a252d3f4a6d07fccc07

[ "GPL-1.0-or-later", "Apache-2.0", "MIT", "BSD-2-Clause", "BSD-3-Clause", "LicenseRef-scancode-generic-cla", "LicenseRef-scancode-unknown-license-reference" ]

permissive

Ascend/ModelZoo-PyTorch

4c89414b9e2582cef9926d4670108a090c839d2d

92acc188d3a0f634de58463b6676e70df83ef808

refs/heads/master

2023-07-19T12:40:00.512853

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# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ from .Adobe240all import Adobe240all from .QVI960 import QVI960 from .AIMSequence import AIMSequence __all__ = ['Adobe240all', 'QVI960', 'AIMSequence',]

[ "[email protected]" ]

[email protected]

84187813982783ed1d30663de5146be4acf7ac64

98e4686742146ec557a6c61a83540ca69f27f077

/classification/Net/ResNetHeatMap/generate_heatmap.py

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[]

no_license

UpCoder/Secret

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15a9b7c50eaf4a5f4fd4856446d51fa4d7917c88

refs/heads/master

2021-07-09T19:13:41.816810

2017-10-10T04:16:30

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# # -*- coding=utf-8 -*- # # 根据我们训练好的模型来生成概率图模型 # from tools.image_operations import extract_patchs_return # import tensorflow as tf # from net_config import Net_Config as net_config # from resnet import inference # import numpy as np # import sys # import math # from PIL import Image # import os # MOMENTUM = 0.9 # # FLAGS = tf.app.flags.FLAGS # tf.app.flags.DEFINE_string('train_dir', '/tmp/resnet_train', # """Directory where to write event logs """ # """and checkpoint.""") # tf.app.flags.DEFINE_string('save_model_dir', './models', 'the path using to save model') # tf.app.flags.DEFINE_float('learning_rate', 0.01, "learning rate.") # tf.app.flags.DEFINE_integer('batch_size', net_config.BATCH_SIZE, "batch size") # tf.app.flags.DEFINE_integer('max_steps', 500000, "max steps") # tf.app.flags.DEFINE_boolean('resume', True, # 'resume from latest saved state') # # def train(logits, label_value, image_pathes): # # from image_processing import image_preprocessing # # filenames = image_pathes # # labels = label_value # # filename, label = tf.train.slice_input_producer([filenames, labels], shuffle=True) # # num_process_threads = 4 # # images_and_labels = [] # # for thread_id in range(num_process_threads): # # image_buffer = tf.read_file(filename) # # bbox = [] # # image = image_preprocessing( # # image_buffer, # # bbox=bbox, # # train=False, # # thread_id=thread_id # # ) # # # image = tf.image.rgb_to_hsv(image) # # images_and_labels.append([image, label]) # # batch_image, batch_label = tf.train.batch_join( # # images_and_labels, # # batch_size=FLAGS.batch_size, # # capacity=2 * num_process_threads * FLAGS.batch_size # # ) # # height = net_config.IMAGE_W # # width = net_config.IMAGE_H # # depth = 3 # # # # images = tf.cast(batch_image, tf.float32) # # images = tf.reshape(images, shape=[FLAGS.batch_size, height, width, depth]) # # # # # # print 'image shape is ', np.shape(images) # # logits = inference(images, # # num_classes=2, # # is_training=False, # # bottleneck=False) # # global_step = tf.get_variable('global_step', [], # # initializer=tf.constant_initializer(0), # # trainable=False) # # val_step = tf.get_variable('val_step', [], # # initializer=tf.constant_initializer(0), # # trainable=False) # # predictions = tf.nn.softmax(logits) # # # # saver = tf.train.Saver(tf.all_variables()) # # # # init = tf.global_variables_initializer() # # # # sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)) # # sess.run(init) # # sess.run(tf.initialize_local_variables()) # # tf.train.start_queue_runners(sess=sess) # # print images.eval(session=sess) # # if FLAGS.resume: # # latest = tf.train.latest_checkpoint('/home/give/PycharmProjects/StomachCanner/classification/Net/ResNet/models/instance/5500.0/') # # if not latest: # # print "No checkpoint to continue from in", FLAGS.train_dir # # sys.exit(1) # # print "resume", latest # # saver.restore(sess, latest) # # # # is_training = tf.placeholder('bool', [], name='is_training') # # predictions_values = sess.run( # # [predictions], # # { # # is_training: False # # }) # # print predictions_values # # predictions_values = np.argmax(predictions_values, axis=1) # # print predictions_values # ''' # 根据测试集的heating map得到分类结果 # ''' # def get_classification_result(image_dir): # names = os.listdir(image_dir) # image_pathes = [os.path.join(image_dir, name) for name in names] # filenames = image_pathes # print image_pathes # [filename] = tf.train.slice_input_producer([filenames], shuffle=False, num_epochs=1) # num_process_threads = 4 # images_and_labels = [] # from image_processing import image_preprocessing # for thread_id in range(num_process_threads): # image_buffer = tf.read_file(filename) # bbox = [] # image = image_preprocessing( # image_buffer, # bbox=bbox, # train=False, # thread_id=thread_id # ) # # image = tf.image.rgb_to_hsv(image) # images_and_labels.append([image]) # batch_image = tf.train.batch_join( # images_and_labels, # batch_size=1, # capacity=2 * num_process_threads * FLAGS.batch_size # ) # height = net_config.IMAGE_W # width = net_config.IMAGE_H # depth = 3 # # images = tf.cast(batch_image, tf.float32) # images = tf.reshape(images, shape=[1, height, width, depth]) # print images # # logits = inference(images, # num_classes=2, # is_training=True, # bottleneck=False, ) # # saver = tf.train.Saver(tf.all_variables()) # # init = tf.global_variables_initializer() # # sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)) # sess.run(init) # sess.run(tf.initialize_local_variables()) # tf.train.start_queue_runners(sess=sess) # # latest = tf.train.latest_checkpoint( # '/home/give/PycharmProjects/StomachCanner/classification/Net/ResNetHeatMap/models/method5-512') # if not latest: # print "No checkpoint to continue from in", FLAGS.train_dir # sys.exit(1) # print "resume", latest # saver.restore(sess, latest) # predictions = tf.nn.softmax(logits) # predictions_label = tf.argmax(predictions, axis=1) # print predictions_label # while True: # prediction_value = sess.run(predictions_label) # print prediction_value # return images # # ''' # 针对heating map分类，返回每个heating map的分类结果 # ''' # def generate_prediction(patches): # # probability = tf.nn.softmax(logits) # if FLAGS.resume: # latest = tf.train.latest_checkpoint( # '/home/give/PycharmProjects/StomachCanner/classification/Net/ResNetHeatMap/models/method5-512') # if not latest: # print "No checkpoint to continue from in", FLAGS.train_dir # sys.exit(1) # print "resume", latest # saver.restore(sess, latest) # probability_values = [] # start = 0 # batch_size = 512 # while start < len(patches): # end = start + batch_size # if end >= len(patches): # end = len(patches) # cur_patches = patches[start:end] # probability_value = sess.run( # probability, # { # img_tensor: cur_patches # } # ) # # print probability_value # probability_values.extend(probability_value) # # print 'logits value shape is ', np.shape(probability_value) # start = end # probability_values = np.asarray(probability_values, np.float32) # return np.argmax(probability_values, axis=1) # # ''' # 加载已知模型，计算一个tiff文件的heat map # :param tiff_path 一个tiff文件的path # :param save_path 保存heat map 的路径如果是None的话，则show # ''' # def generate_heatmap(tiff_path, save_path): # if os.path.exists(save_path): # print 'Exists' # return # patches = extract_patchs_return( # tiff_path=tiff_path, # mask_dir=None, # occupy_rate=None, # stride=16, # patch_size=256 # ) # patches = np.asarray(patches, np.float32) # for index, patch in enumerate(patches): # patch = np.asarray(patch, np.float32) # patch = patch * (1.0 / np.max(patch)) # patches[index] = patch # probability_value = generate_prediction(patches) # print np.max(probability_value), np.min(probability_value) # print probability_value # w = int(math.sqrt(len(probability_value))) # probability_img = Image.fromarray(np.asarray(np.reshape(probability_value, [w, w]) * 255, np.uint8)) # if save_path is not None: # probability_img.save(save_path) # else: # probability_img.show() # # # ''' # 加载已知模型，计算一个文件夹下面所有tiff文件的heat mapping # :param tiff_path 一个tiff文件的path # :param save_path 保存heat map 的路径如果是None的话，则show # ''' # def generate_heatmap_one_floder(tiff_dir, save_dir): # names = os.listdir(tiff_dir) # tiff_paths = [os.path.join(tiff_dir, name) for name in names] # for index, tiff_path in enumerate(tiff_paths): # name = names[index].split('.tiff')[0] # generate_heatmap(tiff_path, os.path.join(save_dir, name+'.png')) # # # ''' # 加载已知模型，计算多个文件夹下面所有tiff文件的heat mapping # :param tiff_dirs 多个文件夹的路径 # :param save_dirs 对上面参数对应的保存的路径 # # ''' # def generate_heatmap_multi_floder(tiff_dirs, save_dirs): # for tiff_dir_index, tiff_dir in enumerate(tiff_dirs): # save_dir = save_dirs[tiff_dir_index] # names = os.listdir(tiff_dir) # tiff_paths = [os.path.join(tiff_dir, name) for name in names] # for index, tiff_path in enumerate(tiff_paths): # name = names[index].split('.tiff')[0] # generate_heatmap(tiff_path, os.path.join(save_dir, name+'.png')) # # if __name__ == '__main__': # # generate_heatmap_multi_floder( # # tiff_dirs=[ # # '/home/give/Documents/dataset/BOT_Game/val/positive', # # '/home/give/Documents/dataset/BOT_Game/val/negative' # # # '/home/give/Documents/dataset/BOT_Game/0-testdataset' # # ], # # save_dirs=[ # # # '/home/give/Documents/dataset/BOT_Game/0-testdataset-hm' # # '/home/give/Documents/dataset/BOT_Game/val/positive-hm', # # '/home/give/Documents/dataset/BOT_Game/val/negative-hm' # # ] # # ) # # from tools.image_operations import read_images # # image_dir = '/home/give/Documents/dataset/BOT_Game/train/positive-test' # # names = os.listdir(image_dir) # # pathes = [os.path.join(image_dir, name) for name in names] # # patches = read_images('/home/give/Documents/dataset/BOT_Game/train/positive-test') # # for index, patch in enumerate(patches): # # patch = np.asarray(patch, np.float32) # # patch = patch * (1.0 / np.max(patch)) # # patches[index] = patch # # print 'patch shape is ', np.shape(patches) # # predicted = generate_prediction(patches) # # print np.max(predicted), np.min(predicted) # # print predicted # # train(None, [0]*len(pathes), pathes) # # get_classification_result('/home/give/Documents/dataset/BOT_Game/0-testdataset-hm/method5') # get_classification_result('/home/give/Documents/dataset/BOT_Game/train/positive-hm/method5') import tensorflow as tf import os from net_config import Net_Config as net_config from resnet import inference from DataSetBase import DataSetBase as DataSet from image_processing import image_preprocessing from resnet_val import val import numpy as np from PIL import Image def file_list(data_dir): dir_txt = data_dir + ".txt" filenames = [] with open(dir_txt, 'r') as f: for line in f: if line[0] == '.': continue line = line.rstrip() fn = os.path.join(data_dir, line) filenames.append(fn) return filenames def distorted_inputs_unit(dataset, trainable, shuffle=True): filenames = dataset.images_names labels = dataset.labels filename, label = tf.train.slice_input_producer([filenames, labels], shuffle=shuffle) num_process_threads = 4 images_and_labels = [] for thread_id in range(num_process_threads): image_buffer = tf.read_file(filename) bbox = [] image = image_preprocessing( image_buffer, bbox=bbox, train=trainable, thread_id=thread_id ) # image = tf.image.rgb_to_hsv(image) images_and_labels.append([image, label]) batch_image, batch_label = tf.train.batch_join( images_and_labels, batch_size=net_config.BATCH_SIZE, capacity=2*num_process_threads*net_config.BATCH_SIZE ) height = net_config.IMAGE_W width = net_config.IMAGE_H depth = 3 images = tf.cast(batch_image, tf.float32) images = tf.reshape(images, shape=[net_config.BATCH_SIZE, height, width, depth]) return images, tf.reshape(batch_label, [net_config.BATCH_SIZE]) def distorted_inputs(): # data = load_data(FLAGS.data_dir) # filenames = [ d['filename'] for d in data ] # label_indexes = [ d['label_index'] for d in data ] # train_positive_path = '/home/give/Documents/dataset/BOT_Game/train/positive-png' # train_negative_path = '/home/give/Documents/dataset/BOT_Game/train/negative-copy' # val_positive_path = '/home/give/Documents/dataset/BOT_Game/val/positive-png' # val_negative_path = '/home/give/Documents/dataset/BOT_Game/val/negative-png' train_positive_path = '/home/give/Documents/dataset/BOT_Game/train/negative-hm/method6' train_negative_path = '/home/give/Documents/dataset/BOT_Game/train/negative-hm/method6' val_positive_path = '/home/give/Documents/dataset/BOT_Game/val/positive-hm/method5' val_negative_path = '/home/give/Documents/dataset/BOT_Game/val/negative-hm/method5' val_dataset = DataSet( positive_path=val_positive_path, negative_path=val_negative_path ) train_dataset = DataSet( positive_path=train_positive_path, negative_path=train_negative_path ) return distorted_inputs_unit(train_dataset, False), distorted_inputs_unit(val_dataset, False) def main(_): predict_dir = '/home/give/Documents/dataset/BOT_Game/train/positive-hm/method5' file_names = os.listdir(predict_dir) file_pathes = [os.path.join(predict_dir, file_name) for file_name in file_names] image_values = [np.array(Image.open(file_path).convert('RGB')) for file_path in file_pathes] image_values = np.asarray(image_values, np.float32) image_values = image_values[:net_config.BATCH_SIZE] new_image_values = [] for index, image_value in enumerate(image_values): image_value = np.asarray(image_value, np.float32) image_value = image_value * (1.0 / np.max(image_value)) image_value = np.asarray(image_value, np.float32) img = np.zeros([net_config.IMAGE_W, net_config.IMAGE_H, net_config.IMAGE_CHANNEL]) for j in range(net_config.IMAGE_CHANNEL): img[:, :, j] = np.array( Image.fromarray(image_value[:, :, j]).resize([net_config.IMAGE_W, net_config.IMAGE_H]) ) new_image_values.append(np.array(img)) image_values = np.array(new_image_values) image_tensor = tf.placeholder( tf.float32, [net_config.BATCH_SIZE, net_config.IMAGE_W, net_config.IMAGE_H, net_config.IMAGE_CHANNEL] ) label_tensor = tf.placeholder( tf.int32, [net_config.BATCH_SIZE] ) logits = inference(image_tensor, num_classes=2, is_training=True, bottleneck=False,) save_model_path = '/home/give/PycharmProjects/StomachCanner/classification/Net/ResNetHeatMap/models/method5-512' print 'image_tensor is ', image_tensor print np.shape(image_values) val(image_tensor, logits, image_values, label_tensor, [0]*len(image_values), save_model_path=save_model_path) if __name__ == '__main__': tf.app.run()

[ "[email protected]" ]

[email protected]

258c7e7730f7dcfc58404705362466c414aa2af4

8b865eca2facf190369df4303fd6550c31614f72

/project04/bagInterface.py

49ca757766fdb7a64aa4d786bbe30b5e0f3d4fe9

[]

no_license

Yamase31/cs112

16ba1732441e70065f2aded7542907ccb35e048e

199c5731b0bcbd475d8a8d2c9429eaebfbc1d180

refs/heads/main

2023-06-30T13:34:43.086674

2021-08-10T03:14:45

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""" Author: James Lawson, Harry Pinkerton, Laurie Jones File: baginterface.py Speficitactions of the methods for all bag classes. Running this code will not produce any results, but it shows the headers and docstrings of the methods that MUST be included or supported in any bag class. """ class BagInterface(object): """Interface for all bag types.""" # Constructor def __init__(self, sourceCollection = None): """Sets the initial state of self, which includes the contents of sourceCollection, if it's present.""" self._size = 0 self._modCount = 0 if sourceCollection: for item in sourceCollection: self.add(item) #pass # Accessor methods def isEmpty(self): """Returns True if len(self) == 0, or False otherwise.""" if len(self) == 0: return True else: return False def count(self, target): """Returns the number of a specific items in self.""" """ Returns the number of instances of item in self""" itemCount = 0 for nextItem in self: if nextItem == target: itemCount += 1 return itemCount def __len__(self): """-Returns the number of items in self.""" return self._size def __str__(self): """Returns the string representation of self.""" return "{" + ",".join(map(str, self)) + "}" def __iter__(self): """Supports iteration over a view of self.""" return None def __add__(self, other): """Returns a new bag containing the contents of self and other.""" result = ArrayBag(self) for item in other: result.add(item) return result def __eq__(self, other): """Returns True if self equals other, or False otherwise.""" return False # Mutator methods def clear(self): """Makes self become empty.""" pass def add(self, item): """Adds item to self.""" self._items = Node(item, self._items) self._size += 1 pass def remove(self, item): """Precondition: item is in self. Raises: KeyError if item in not in self. Postcondition: item is removed from self.""" pass

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0681e4919822450a149df6a9ebf09f9bd101b37f

2d837bca6989f61996e4e8e96635d722c97241c3

/core/gtk_table.py

eaae443c2d7d162408c56f74d7190bb125d20de0

[]

no_license

gsy/gmusic

1485e11f4d63241f012b9e2ee27bbdb1ef563ce5

277e70c83a0ffcc00f2fc93933668dc16add11a8

refs/heads/master

2020-03-26T20:48:54.145376

2013-05-03T07:28:37

9,724,424

1

0

null

UTF-8

Python

false

1,189

py

#!/usr/bin/env python # -*- coding: utf-8 import pygtk pygtk.require('2.0') import gtk class albumViewer: def __init__(self): self.window = gtk.Window(gtk.WINDOW_TOPLEVEL) self.window.connect("destroy", lambda w:gtk.main_quit()) self.table = gtk.Table(1, 2, False) self.table.set_row_spacings(10) self.table.set_col_spacings(10) image1 = gtk.Image() image1.set_from_file("1.jpg") self.table.attach(image1, 0, 1, 0, 1, gtk.FILL|gtk.EXPAND, gtk.FILL|gtk.EXPAND, 10, 10) self.scaleImage(image1, "1.jpg") image2 = gtk.Image() image2.set_from_file("2.jpg") self.table.attach(image2, 0, 1, 1, 2, gtk.FILL|gtk.EXPAND, gtk.FILL|gtk.EXPAND, 10, 10) self.scaleImage(image2, "2.jpg") self.window.add(self.table) self.window.show_all() def scaleImage(self, image, file): pixbuf = gtk.gdk.pixbuf_new_from_file(file) scaled_buf = pixbuf.scale_simple(200, 200, gtk.gdk.INTERP_BILINEAR) image.set_from_pixbuf(scaled_buf) def main(self): gtk.main() if __name__ == '__main__': albumViewer().main()

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