Essex Senior Cup stats & predictions
Essex Senior Cup: Tomorrow's Football Fixtures and Expert Betting Predictions
The Essex Senior Cup is one of the most anticipated football tournaments in England, drawing teams from across the county to compete for glory. As we look forward to tomorrow's fixtures, fans and bettors alike are eager to see how the matches will unfold. This article provides a detailed overview of the upcoming games, along with expert betting predictions to help you make informed decisions.
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Overview of the Essex Senior Cup
The Essex Senior Cup has a rich history dating back over a century, making it one of the oldest football competitions in England. It serves as a prestigious platform for local clubs to showcase their talent and compete against some of the best teams in the region. The tournament is divided into various stages, culminating in an exciting final where the champion is crowned.
Tomorrow's Fixtures
Tomorrow's schedule is packed with thrilling encounters that promise to keep fans on the edge of their seats. Here are the key matches to look out for:
- Team A vs Team B: This match-up features two strong contenders who have been performing exceptionally well this season. Both teams are known for their aggressive playstyle and tactical prowess.
- Team C vs Team D: A classic derby that always draws large crowds. Team C has been in excellent form recently, while Team D relies on their experienced squad to turn things around.
- Team E vs Team F: An intriguing clash between two underdogs who have surprised many with their performances this year. This match could go either way, making it a must-watch for any football enthusiast.
Betting Predictions and Analysis
Betting on football can be both exciting and rewarding if done wisely. Below are expert predictions for tomorrow's matches, based on current form, head-to-head statistics, and other relevant factors.
Team A vs Team B
Prediction: Team A to win
Odds: 1.85
Rationale: Team A has been dominant at home this season, winning most of their fixtures. Their attacking lineup is in top form, making them favorites against Team B.
Team C vs Team D
Prediction: Draw
Odds: 3.10
Rationale: Both teams have a balanced record against each other historically. Given their current form, a draw seems likely as both sides will be wary of conceding goals.
Team E vs Team F
Prediction: Over 2.5 goals
Odds: 2.20
Rationale: Both teams have shown an inclination towards high-scoring games this season. With neither side having strong defensive records, expect plenty of goals in this encounter.
In-Depth Match Analysis
Team A vs Team B: Tactical Breakdown
This match promises to be a tactical battle between two well-drilled squads. Team A's manager is known for his strategic acumen, often setting up his team to exploit opponents' weaknesses effectively. On the other hand, Team B relies heavily on counter-attacks and quick transitions from defense to offense.
- Squad News:
- Team A:
- All key players are fit and available for selection.
- Team B:
- A key defender is doubtful due to injury concerns.
- Potential Lineups:
- Team A (4-3-3):
- GK: Player X
- D: Player Y - Player Z - Player W - Player V
- M: Player T - Player U - Player S
- F: Player R - Player Q - Player P
- Team B (4-2-3-1):
- GK: Player M
- D: Player N - Player O - Player L - Player K len(set(kmeans.labels_)):
silhouette_run.append(-1) # Silhouette score not defined when there's only one cluster or all points assigned same cluster label
else:
silhouette_run.append(silhouette_score(x,kmeans.labels_))
davies_bouldin_run.append(davies_bouldin_score(x,kmeans.labels_))
wcss_avg.append(wcss_run)
silhouette_avg.append(silhouette_run)
davies_bouldin_avg.append(davies_bouldin_run)
# Average results across runs
wcss_mean = np.mean(wcss_avg,axis=0)
silhouette_mean = np.mean([s for s in silhouette_avg if s != [-1]], axis=0) # Remove invalid entries (-1)
davies_bouldin_mean = np.mean(davies_bouldin_avg,axis=0)
# Plotting results
plt.figure(figsize=(18,6))
plt.subplot(131)
plt.plot(range(1,max_clusters+1), wcss_mean,'bo-', markersize=8)
plt.title('Elbow Method For Optimal Clusters')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.subplot(132)
plt.plot(range(2,max_clusters+1), silhouette_mean,'go-', markersize=8) # Start from cluster size >=2 since silhouettes doesn't apply otherwise
plt.title('Silhouette Score For Optimal Clusters')
plt.xlabel('Number of clusters')
plt.ylabel('Silhouette Score')
plt.subplot(133)
plt.plot(range(2,max_clusters+1), davies_bouldin_mean,'ro-', markersize=8) # Start from cluster size >=2 since DB doesn't apply otherwise
plt.title('Davies-Bouldin Index For Optimal Clusters')
plt.xlabel('Number of clusters')
plt.ylabel('Davies-Bouldin Index')
plt.tight_layout()
plt.show()
## Follow-up exercise
### Task Description:
Extend your previous solution by introducing dimensionality reduction before clustering using PCA (Principal Component Analysis). Additionally:
* Allow user input parameters via command-line arguments or configuration file specifying number of PCA components (`n_components`) before clustering.
* Compare performance metrics before and after applying PCA.
* Provide detailed analysis report summarizing findings including visualizations comparing original data versus reduced data clustering outcomes.
### Requirements:
* Implement PCA transformation before applying K-Means clustering.
* Accept user-defined parameters (`n_components`).
* Compare WCSS, Silhouette Score & Davies-Bouldin Index pre-and post PCA transformation visually & numerically.
* Generate summary report detailing findings.
## Solution
python
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score,davies_bouldin_score
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
def load_data(file_path):
return pd.read_csv(file_path)
def preprocess_data(df):
return df.iloc[:, [3 ,4]].values
def perform_clustering(x,n_runs,max_clusters):
wcss_avg,silhouette_avg,davies_boudlin_avg=[],[],[]
for run in range(n_runs):
wcss_run,silhouette_run,davies_boudlin_run=[],[],[]
for i in range(1,max_clusters+1):
kmeans=KMeans(n_clusters=i,
init='k-means++',
max_iter=300,
n_init=10,
random_state=np.random.randint(10000))
kmeans.fit(x)
wcss_run.append(kmeans.inertia_)
if i > len(set(kmeans.labels_)):
silhouette_run.append(-1)
else:
silhouette_run.append(silhouette_score(x,kmeans.labels_))
davies_boudlin_run.append(davies_bouldin_score(x,kmeans.labels_))
wcss_avg.append(wcss_run)
silhouette_avg.append(silhouette_run)
davies_boudlin_avg.append(davies_boudlin_run)
def calculate_metrics(wcss,silhouettes,dbscores,n_runs):
data_path="Mall_Customers.csv"
df_load_data(load_data(data_path))
x_preprocess_data(preprocess_data(df))
# Dynamic determination based on data size/sqrt(N)
max_clust=int(np.sqrt(len(x)))
runs_num=n_runs=max_cluster=max_clust
perform_clustering(x_preprocess_data(preprocess_data(df)),runs_num,max_cluster)
wccs_mean=np.mean(wccs,axis=0)
silhouet_means=np.mean([s[s!=-i]for s=silhouettes],axis=-i)#remove invalid entries (-i)
dbscores_means=np.mean(dbscores,axis=-i)
#Plotting Results
fig=plt.figure(figsize=(18,-6))
fig.add_subplot(131)
plot(range(max_clust),wccs_means,'bo-',markersize=-i)
title("Elbow Method For Optimal Clusters")
xlabel("Number Of Clusters")
ylabel("WCSS")
fig.add_subplot(132)
plot(range(max_clust),silhouet_means,'go-',markersize=-i)#start from >=cluster size since silouette doesn't apply otherwise
title("Silouette Score For Optimal Clusters")
xlabel("Number Of Clusters")
ylabel("Silouette Score")
fig.add_subplot(133)
plot(range(max_clust),dbscores_means,'ro-',markersize=-i)#start from >=cluster size since DB doesn't apply otherwise
title("Davie-Boulid Index For Optimal Clusters")
xlabel("Number Of Clusters")
ylabel("Daviess-Boulid Index")
tight_layout()
show()
[0]: #
[1]: # PySNMP MIB module CISCO-FLOW-MONITOR-MIB (http://snmplabs.com/pysmi)
[2]: # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CISCO-FLOW-MONITOR-MIB
[3]: # Produced by pysmi-0.3.4 at Wed May １８７５７７７７７７００６７３５ CST (http://pysmi.googlecode.com)
[4]: # On host DAVWANG4-M-1475 platform Darwin version 16.7.0 by user davwang4
[5]: # Using Python version 3.7.3 (default, Mar 26 2019, noon) '
"""
.. _cisco-flow-monitor-mib-textual-conventions:
CISCO-FLOW-MONITOR-MIB Textual Conventions
"""
class CiscoFlowMonitorIndex(TextualConvention, Unsigned32):
description = 'The value uniquely identifies an instance within Cisco Flow Monitor.'
status = 'current'
class CiscoFlowMonitorName(TextualConvention, OctetString):
description = 'The value uniquely identifies an instance within Cisco Flow Monitor.'
status = 'current'
displayHint = '255a'
class CiscoFlowMonitorIntervalType(TextualConvention,Integer32):
description ="An enumerated value indicating whether flow monitoring interval specifiedn is configured manually by user or automatically determinedn by device."
status ="current"
subtypeSpec += ConstraintsUnion(SingleValueConstraint(0,65535))
_namedValues = NamedValues(("manual",0),"automatic",65535)
subtypeSpec += NamedValues(("manual",0),"automatic",65535)
class CiscoFlowMonitorIntervalValue(TextualConvention,Integer32):
description ="Represents an interval value which specifies frequency at whichn flow statistics should be collected.nn When CiscoFlowMonitorIntervalType indicates manual intervaln configuration then this object represents manually configuredn interval value.nn When CiscoFlowMonitorIntervalType indicates automatic intervaln configuration then this object represents automatically determinedn interval value.nn Automatic interval determination logic takes into account devicen resources availability such CPU utilization level etc."
status ="current"
ciscoFlowMonitorMIBModuleIdentifier =",Cisco Flow Monitor MIB Module Identifier"
ciscoFlowMonitorMIBNotificationsPrefix =",Cisco Flow Monitor MIB Notifications Prefix"
ciscoFlowMonitorMIBObjectsPrefix =",Cisco Flow Monitor MIB Objects Prefix"
ciscoFlowMonitorMIBConformancePrefix =",Cisco Flow Monitor MIB Conformance Prefix"
ciscoFlowMonBaseGroup =",Cisco Flow Mon Base Group"
ciscoFlowMonNotificationGroup =",Cisco Flow Mon Notification Group"
ciscoFlowMonConformRev01Group =",Cisco Flow Mon Conform Rev01 Group"
ciscoflowmonitormibModulesSupported =[InterfaceIndex(), InetAddressIPv6(), InetAddressIPv6PrefixLength()]
class CfmComplianceV01(CFMComplianceV01):
cfmComplianceV01Groups =(CfMonBaseGroup,CfMonNotificationGroup,CfMonConformRev01Group,)
cfmComplianceV01Compliances =(CfMonCompliance,)
cfmonObjectsGroupName ="CFMON-OBJECTS-GROUP"
cfmonNotificationsGroupName ="CFMON-NOTIFICATIONS-GROUP"
cfmonConformanceRev01GroupName ="CFMON-CNFREV01-GROUP"
cfmonCompliancesGroupName ="CFMON-CNFGROUPS-GROUP"
cfmonGroupsGroupName ="CFMON-GROUPS-GROUP"
cfmonConformanceRev02GroupName ="CFMON-CNFREV02-GROUP"
class CfMonBaseGroup(ObjectGroup):
cfmObjectsGroup =[ObjectIdentity(cflowmonObjectsGroupName,"cfmCfgObjs")]
cfMonCfgObjsDescription ='A collection CFMON objects used for configuring CFMON.'
cfMonCfgObjsStatus ='current'
cfMonCfgObjsObjects =(CfmConfigMode,CfmGlobalEnable,CfmAdminIntvlType,CfmAdminIntvlValue,)
cfMonCfgObjsAgs =(CfmConfigMode.syntax,cfmGlobalEnable.syntax,cfmAdminIntvlType.syntax,cfmAdminIntvlValue.syntax,)
cfmObjectsGroup =[ObjectIdentity(cflowmonObjectsGroupName,"cfmStatsObjs")]
cfMonStatsObjsDescription ='A collection CFMON objects used collecting CFMON statistics.'
cfMonStatsObjsStatus ='current'
cfMonStatsObjsObjects =(CfmCurrentIntvlValue,CfmMaxFlows,CfmNumFlows,CfmNumFlowsWithNoErrors,CfmNumFlowsWithErrors,CfmNumInPktsByProtoUnmatchedByFiltCountOrNoMatchedFiltCountOrNoMatchedMaskCountOrMatchedMaskCountAndNoMatchedDstPortCountOrMatchedDstPortCountAndNoMatchedSrcPortCountOrMatchedSrcPortCountAndNoMatchedDstIpAddrCountOrMatchedDstIpAddrCountAndNoMatchedSrcIpAddrCountOrMatchedSrcIpAddrCountAndNoMatchedLlcSnapOuiAndNoMatchedLlcSnapOrgCodeAndNoMatchedLlcSnapPduTypeAndNoMatchedLlcSnapPduSubtypeAndNoMatchedEthertypeAndNoMatchedIcmpCodeAndIcmpTypeAndNoDirectionFilterFlagSetAndDirectionFilterFlagNotSet,CfmNumInPktsByProtoUnmatchedByFiltMaskOrUnmatchedByFiltMaskButFiltExists,CfmNumInPktsByProtoUnmatchedByFiltMaskButFiltExists,CfmonStatInPktsByDirFilterErrorCountsTableEntry,cfmonStatInPktsByDirFilterErrorCountsEntryActiveTime,cfmonStatInPktsByDirFilterErrorCountsEntryTotalTime,cfmonStatInPktsByDirFilterErrorCountsEntryTotalPkts,cfmonStatInPktsByDirFilterErrorCountsEntryTotalBytes,cfmonStatInPktsByDirFilterErrorCountsEntryTotalErrors,)
cfMonStatsObjsAgs =(CfmCurrentIntvlValue.syntax,cfmMaxFlows.syntax,cfmNumFlows.syntax,cfmNumFlowsWithNoErrors.syntax,cfmNumFlowsWithErrors.syntax,cfmNumInPktsByProtoUnmatchedByFiltCountOrNoMatchedFiltCountOrNoMatchedMaskCountOrMatchedMaskCountAndNoMatchedDstPortCountOrMatchedDstPortCountAndNoMatchedSrcPortCountOrMatchedSrcPortCountAndNoMatchedDstIpAddrCountOrMatchedDstIpAddrCountAndNoMatchedSrcIpAddrCountOrMatchedSrcIpAddrCountAndNoDirectionFilterFlagSet.cmfomonStatInPktsByDirFilterErrorCountsTableEntry.cfmondStatInPktsByDirFilterErrorCountsEntryActiveTime.cfmondStatInPktsByDirFilterErrorCountsEntryTotalTime.cfmondStatInPktsByDirFilterErrorCountsEntryTotalPkt.cfmondStatInPktsBypDiReFilErorrCntsEntyTtoalBtyes,)
cfgGroups =[ObjectIdentity(cfmonGroupsGroupName,"cfg")]
cfgDescr ='Configuration objects group.'
cfgStatus ='current'
cfgObjects =(CfnmCfgMode.CfnmCfgModeSyntax,),
cfgAgs =(CfnmCfgMode.CfnmCfgModeSyntax,)
statsGroups =[ObjectIdentity(cfmonGroupsGroupName,"stats")]
statsDescr ='Statistics objects group.'
statsStatus ='current'
statsObjects =(CfnmCurIntvlVal.CfnmCurIntvlValSyntax,),CfnmMaxFlows.CfnmMaxFlowsSyntax,),CfnmNumOfFlws.CfnmNumOfFlwsSyntax,),CfnmNoflwsWthnoErrrs.CfnmNoflwsWthnoErrrsSyntax,),CfnmNoflwsWthErros.CfnmAflwsWthErrosSyntax,),CfnmnIPkstsBypRtUnmtchdbyFLtcntornmtchdFLtcntornmtchdmaskcntormtchdmaskcntandnmthtdstprtcntormtchdstprtcntandnmthsrprtcntormtchsrtprtcntandnmthtdstipaddrcntormtchdstipaddrcntandnmthsrripaddrcntormtchsripaddrcntandnlcmnsnapouisndmlcmnsnaporgcdsndmlcmsnappdutypsndmlcmsnappsubtypsndmlcmsethtertypsndmlcmseticmpctpsndmlcmsetirctptdirfltflgnsdtmrstfltflgns,),CfmdIPkstsBypRtUmntchdbymskrbtmnthdmstrblksbtwnumtmnthdmstrblksbtrmnthdbtmnthdmstrblksbtrnumtmnthdbtmnthdmstrblksbtumtnumtmnthdbtmnthdmstrblksbtrnumtmnthdbymskrbtumtnumtmnthdbymskrbtumnmtmskrtbmnummskrtbmnum,.cfmpStatIPkstsBypDiRfiltErrocCntTblEnty,.cfmpStatIPkstsBypDiRfiltErrocCntTblEntyActvTme,.cfmpStatIPkstsBypDiRfiltErrocCntTblEntyTotlTme,.cfmpStatIPkstsBypDiRfiltErrocCntTblEntyTotlPkt,.cfmpStatIPkstsBypDiRfiltErrocCntTblEntyTotlBytes,)
statsAgs =(CfnmnCurIntvlVal.cfhmnCurIntvlValSytnax,) ,(cnmmxflws.cnmmxflwsSytnax,) ,(cnmnoflwswtnoerrrs.cnmnoflwswtnoerrrsSytnax,) ,(cnmnoflwswterros.cnmnoflwswterrosSytnax,) ,(cnmnipckstsbprtounmtchbdyltcntonmtchdyltcnonmtchdmaskcntormtchdmaskcntandnmthtdstprtcountormatchdstprtcountandnmthsrprrcountormatchsrpprrcountandnmthtdstripcountormatchdstripcountandnmthsrstripcountormatchsrstripcountandonlmclmsnapouisndlmclmsnaporgcdsndlmclmsnappdutypsndlmclmsnappsubtypsndlmclmssetethertypsndlmclmsseticmpctpsndlmclmssetircptdirfltflgnsmatchfrtlfilterflagnotsmatchfrtlfilterflagnots,) ,(cpmdipckstsbprtounmtchbdymasrkbrtmnthdamaskbrtmnthdamaskbrtnmaskbrtmastkrbrtamaskbrtamastkrbrtamastkrbrtamastkrbrtanalmclmasapousndlmcmasaporgcodsndlmcasappdutypsndlmcasapsuptypsndlmcmasaethertypesndlmcmasaicmpctypesndlmcmasaircptdirfltflgsmatchfrtlfilterflagnotsmatchfrtlfilterflagnots,) ,(cpfmpstatipckstsbprdirofilterrortableentry.cfmpstatipckstsbprdirofilterrortableentryactive.time.cfmpstatipckstsbprdirofilterrortableentrytotal.time.cfmpstatipckstsbprdirofilterrortableentrytotal.pkt.cfmpstatipckstsbprdirofilterrortableentrytotal.bytes,)
configGroups =[ObjectIdentity(cfmonConformanceRev02GroupName,"config")]
configDescr ='Configuration objects group.'
configStatus ='current'
configObjects =(CfmoConfVersionIdentifer.,),
configAgs =(ConfVersionIdentifer.ConfVersionIdentiferSyntax,)
statisticGroups =[ObjectIdentity(cfmonConformanceRev02GroupName,"statistics")]
statisticDescr ='Statistics objects group.'
statisticStatus ='current'
statisticObjects =(ConfVerErrThresHold.ConfVerErrThresHoldSyntax,),
statisticAgs =(ConfVerErrThresHold.ConfVerErrThresHoldSyntax,)
conformanceRevision02=[configGroups,[statsGroups]]
conformanceRevision02Description="This revision adds conformance groups related nto Configuration and Statistics."
conformanceRevision02Status="current"
def __init__(self):
self.name +"="
CFMComplianceV01
self.status +="current"
self.groups +=[cfgGroups,[statsGroups]]
class CfMoConfVersionIdentifier(TextualConvention,OCTET STRING):
description +'The object identifies version number associated nwith specific revision level nof CFMo compliance statement.'
status +="current"
pattern +'^(d+).(\d+).(\d+)$'
maxAccess +="readonly"
class ConfVerErrThresHold(TextualConvention,Gauge32):
description +'Represents threshold value associated nwith errors encountered during nversion negotiation process.'
status +="current"
maxAccess +="readwrite"
class CfMoNotificationGroup(NotificationGroup):
cmfmoNotificationDescr +'The notification group consists notifications generated nas result error conditions encountered during nversion negotiation process.'
cmfmoNotificationStatus +"='current'"
cmfmoNotificationNotifs +(confVerNegotiationFailed.,confVerNegotiationSuccessful., )
def __init__(self):
self.name +"="
CFMoNotification
self.status +"="
current
self.notifs +=[confVerNegotiationFailed.confVerNegotiationFailedNotice.confVerNegotiationFailedNoticeVar]
class CfMoCompliance(CFMoCompliance):
compliances +"="
CFMoComplianceV01
compliancesDescription +"="
CFMoCompV01Des
compliancesStatus +"="
current
mib +"="
CFMOMibModuleIden
mibModulesSupported +=[interfaceIndex.interfaceIndexModule.interfaceIndexModuleVar]
mibModulesSupported +=[inetAddressIPv6.inetAddressIPv6Module.inetAddressIPv6ModuleVar]
mibModulesSupported +=[inetAddressIPv6PrefixLength.inetAddressIPv6PrefixLengthModule.inetAddressIPv6PrefixLengthModuleVar]
def __init__(self):
self.name +"="
CFMoComp
self.status +"="
current
self.mibModIden +=CMFOModIden
self.mibsModSupp +=interfaceIndex.interfaceIndexModule.interfaceIndexModuleVar
self.mibsModSupp +=inetAddressIPv6.inetAddressIPv6Module.inetAddressIPv6ModuleVar
self.mibsModSupp +=inetAddressIPv6PrefixLength.inetAddressIPv6PrefixLengthModule.inetAddressIPv6PrefixLengthModuleVar
class CfMoConformRev02Grup(ObjectGrup):
conformanceRevision02GrupName +'CFMO-CNFREV02-GROUP'"
conformanceRevision02GrupDescri +'This revision adds conformance groups related nto Configuration and Statistics."'
conformanceRevision02GrupStatus +'=' "current"'
conformanceRevision02GrupObjcts +(confVersionIdentifier.confVersionIdentifierObj.confVersionIdentifierObjAg,),
confVersioNidentifierConfVersioNidentifierObjAg +'+' confVersionIdentifier.confVersionIdentifierObj.confVersionIdentifierObjAg
confVersioNidentifierConfVersioNidentifierObj +'+' confVersionIdentifier.confVersionIdentifierObj
statisticGruops +(confvererrorthreshold.confvererrorthresholdobj.confvererrorthresholdobjag,),
confvererrorthresholdconfvererrorthresholdobjag +'+' confvererrorthreshold.confvererrorthresholdobj.confvererrorthresholdobjag
confvererrorthresholdconfvererrorthresholdobj '+'+' confvererrorthreshold.confvererrorthresholdobj
def __init__(self):
self.name +
cfmoconformrevgrpname
self.status +
cfmoconformrevgrpstatus
self.objects +=[(confversionidentifier.conffersionidentifiereobject.conffersionidentifiereobjectag)]
self.aggregates +=[(confversionidentifier.conffersionidentifiereobject.conffersionidentifiereobjectag)]
self.objects +=
[(confversionidentifier.conffersionidentifiereobject)]
self.aggregates +=
[(confversionidentifier.conffersionidentifiereobject)]
self.statgroups +=
[(confvererrorethreshhold.confererrorethreshholdobject.confererrorethreshholdobjectag)]
self.aggregates +=
[(confvererrorethreshhold.confererrorethreshholdobject.confererrorethreshholdobjectag)]
self.objects +=
[(confvererrorethreshhold.confererrorethreshholdobject)]
self.aggregates +=
[(confvererrorethreshhold.confererrorethreshholdobject)]
<|file_sep|># Copyright (c) Twisted Matrix Laboratories.
# See LICENSE for details.
"""
Tests related specifically around parsing Windows registry files.
These tests rely upon having some Windows registry files available.
They live under test/winreg/data/.
"""
from twisted.trial.unittest import TestCase
from twisted.python.filepath import FilePath
from twisted.python.win32.registryparser import RegistryParser
class RegistryParserTestCase(TestCase):
def testParse(self):
pathToFileToParseFromDisk =
FilePath(__file__).parent().child(u"data").child(u"testreg.hiv")
parser =
RegistryParser(
pathToFileToParseFromDisk,
None)
result =
parser.parse()
expectedResult =
<|repo_name|>zhmu/analytics<|file_sep@REM Copyright (c) Twisted Matrix Laboratories.
@REM See LICENSE.txt for details.
@echo off
rem These tests require cygwin installed.
cygcheck.exe ntfs > ntfs.txt || goto :fail
cygcheck.exe fat >> ntfs.txt || goto :fail
findstr /i /c:"GNU libiconv" ntfs.txt > nul || goto :fail
findstr /i /c:"GNU libcrypt" ntfs.txt > nul || goto :fail
findstr /i /c:"GNU libintl" ntfs.txt > nul || goto :fail
findstr /i /c:"GNU gettext-runtime" ntfs.txt > nul || goto :fail
findstr /i /c:"GNU glibc" ntfs.txt > nul || goto :fail
findstr /i /c:"GNU libstdcxx" ntfs.txt > nul || goto :fail
del ntfs.txt > nul
goto pass
rem ----------------------------------------------------------------------
rem If we got here something failed.
rem Write some information about what happened.
echo Some tests failed:
echo .
type ntfs.txt | more
del ntfs.txt > nul
goto fail
rem ----------------------------------------------------------------------
rem We succeeded! Do some cleanup tasks first.
pass:
del ntfs.txt > nul
exit %ERRORLEVEL%
rem ----------------------------------------------------------------------
rem If we get here something went wrong.
fail:
exit %ERRORLEVEL%
<|file_septvremotecontrolagent.pypp.cpp
/* python wrapper */
#define PY_SSIZE_T_CLEAN
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"
#include "__main__.cpp"
#include "tvremotecontrolagent.h"
namespace py = pybind11;
PYBIND11_MODULE(tvremotecontrolagent,std::shared_ptr< ::boost::python::objects::proxy>) {
// Generated TVRemoteControlAgent python wrapper class.
// public types declared with PyBind11
// module constants
// classes
// functions
// templates tried:
// template arguments indices table:
// constructor
// function wrappers
// methods
// static methods
}
<|file_sepgetattr.pypp.cpp
/* python wrapper */
#define PY_SSIZE_T_CLEAN
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"
#include "__main__.cpp"
#include "getattribute.hpp"
namespace py = pybind11;
PYBIND11_DECLARE_HOLDER_TYPE(Tuple,&::std::tuple);
PYBIND11_DECLARE_HOLDER_TYPE(Tuple,&::std::pair
);
PYBIND11_MODULE(getattr,std::shared_ptr< ::boost::python::objects::proxy>) {
// Generated getattr python wrapper class.
using namespace std;
using namespace pystdf;
// module constants
// functions
namespace detail {namespace override {
void export_getattribute(){
{
py::scope g(m);
}
} // namespace detail::
} // namespace detail:: } // namespace pystdf <|repo_name|>zhmu/analytics<|file_sepaceserial.pypp.cpp /* python wrapper */ #define PY_SSIZE_T_CLEAN #include "pybind11/pybind11.h" #include "pybind11/stl.h" #include "__main__.cpp" #include "ace_serialization.hpp" namespace py ={}; PYBIND11_MODULE(a_c_e_s_e_r_i_a_l_i_z_a_t_i_o_n,std::shared_ptr< ::boost::python::objects::proxy>) { using namespace std; using namespace pystdf; namespace detail {namespace override { void export_ACE_Serialization(){ { py::scope g(m); py::enum_
