Overview / Introduction about the Team
Poruba is a prominent ice hockey team hailing from the Czech Republic, competing in the top-tier league. Founded in 1948, the team has established itself as a formidable contender under the guidance of their current coach. With a rich history and passionate fanbase, Poruba continues to be a key player in the league.
Team History and Achievements
Poruba boasts an illustrious history marked by numerous titles and awards. They have consistently secured top positions in league standings, with notable seasons that have cemented their reputation. The team has won several championships and cups, showcasing their dominance on the ice.
Current Squad and Key Players
The current squad features a blend of experienced veterans and promising young talent. Key players include:
- John Doe – Forward, known for his scoring prowess.
- Jane Smith – Defender, renowned for her defensive skills.
- Rick Johnson – Goalie, celebrated for his remarkable saves.
Team Playing Style and Tactics
Poruba employs a dynamic playing style characterized by aggressive offense and solid defense. Their typical formation allows for flexibility in adapting to opponents’ strategies. Strengths include quick transitions and strong puck control, while weaknesses may involve occasional lapses in defense.
Interesting Facts and Unique Traits
Poruba is affectionately nicknamed “The Ice Warriors” by their fans. The team has intense rivalries with neighboring clubs, adding excitement to their matches. Traditions such as pre-game rituals contribute to the unique atmosphere surrounding Poruba games.
Lists & Rankings of Players, Stats, or Performance Metrics
Poruba’s players are ranked based on performance metrics:
- ✅ John Doe – Top scorer with an impressive goal tally.
- ❌ Jane Smith – Struggling with recent injuries affecting performance.
- 🎰 Rick Johnson – Consistent performer with high save percentage.
- 💡 Rick Johnson – Potential breakout star this season.
Comparisons with Other Teams in the League or Division
Poruba is often compared to other top teams like XYZ and ABC. While XYZ excels in defensive play, Poruba’s offensive capabilities set them apart. In head-to-head matchups, Poruba has shown superior adaptability against various opponents.
Case Studies or Notable Matches
A breakthrough game for Poruba was their victory against ABC last season, where they overturned a significant deficit to win in overtime. This match highlighted their resilience and strategic depth.
Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds
| Statistic | Poruba | Average League Team |
|---|---|---|
| Total Goals Scored | 150 | 120 |
| Total Goals Conceded | 110 | 130 |
| Last Five Games Form (W/L) | W-W-L-W-W | L-W-L-L-W |
| Odds for Next Game Win</td[0]: #!/usr/bin/env python3
[1]: # -*- coding: utf-8 -*-
[2]: """
[3]: Created on Thu Apr 28 14:23:04 2020
[4]: @author: karl
[5]: This file contains all functions used by 'fitting.py'.
[6]: """
[7]: import numpy as np
[8]: import matplotlib.pyplot as plt
[9]: from scipy.optimize import curve_fit
[10]: from scipy.special import erf
[11]: from lmfit.models import GaussianModel
[12]: def calc_r_squared(y,y_pred):
[13]: '''
[14]: Calculates R-squared value given y data points
[15]: (i.e., experimental data) and predicted y data points.
[16]: Parameters:
[17]: y : array-like
[18]: Experimental data
[19]: y_pred : array-like
[20]: Predicted data
'''
ss_res = np.sum((y-y_pred)**2)
ss_tot = np.sum((y-np.mean(y))**2)
r_squared = (ss_tot-ss_res)/ss_tot
return r_squared
***** Tag Data *****
ID: N1
description: Function `calc_r_squared` calculates R-squared value which measures how
well observed outcomes are replicated by the model.
start line: 12
end line: 21
dependencies:
– type: Function
name: calc_r_squared
start line: 12
end line: 21
context description: This function is central to evaluating model performance within
'fitting.py'. It computes R-squared using residuals sum of squares (ss_res) and total
sum of squares (ss_tot).
algorithmic depth: 4
algorithmic depth external: N
obscurity: 1
advanced coding concepts: 3
interesting for students: 4
self contained: Y
*************
## Suggestions for complexity
1. **Weighted R-squared Calculation**: Modify the function to compute a weighted R-squared value where different observations can have different weights based on some criteria.
2. **Confidence Intervals**: Extend the function to also calculate confidence intervals for the R-squared value.
3. **Multivariate Support**: Adapt the function so it can handle multivariate regression models where `y` could be multi-dimensional.
4. **Handling Missing Values**: Enhance the function to handle datasets with missing values gracefully without throwing errors.
5. **Parallel Computation**: Modify the code so that it can perform parallel computation when dealing with very large datasets.
## Conversation
: I need help understanding how this code works [SNIPPET]. Can you explain?
: Sure! The function `calc_r_squared` calculates the R-squared value which measures how well your model predictions (`y_pred`) fit your actual data (`y`). Here’s what each part does:
– `ss_res = np.sum((y-y_pred)**2)`: This computes the residual sum of squares (RSS), which is essentially measuring how much your predictions deviate from actual values.
– `ss_tot = np.sum((y-np.mean(y))**2)`: This computes the total sum of squares (TSS), which measures how much your actual values deviate from their mean.
– `r_squared = (ss_tot-ss_res)/ss_tot`: Finally, this calculates R-squared using these sums.
The closer `r_squared` is to `1`, the better your model fits your data.
: How would I modify it if I wanted weighted R-squared calculation?
: To calculate weighted R-squared, you’ll need an additional parameter for weights (`weights`). Here’s how you can modify your function:
python
def calc_weighted_r_squared(y, y_pred, weights):
“””
Calculates Weighted R-squared value given y data points,
predicted y data points, and weights.
Parameters:
y : array-like : Experimental data.
y_pred : array-like : Predicted data.
weights : array-like : Weights for each observation.
Returns:
float : Weighted R-squared value.
“””
weights = np.array(weights)
ss_res = np.sum(weights * (y – y_pred)**2)
ss_tot = np.sum(weights * (y – np.average(y, weights=weights))**2)
r_squared = (ss_tot – ss_res) / ss_tot
return r_squared
This modification uses weighted sums instead of regular sums.
: What if I want confidence intervals for this? Possible?
: Yes! You can calculate confidence intervals using bootstrapping methods or analytical approaches depending on assumptions about your residuals distribution. Here’s an outline using bootstrapping:
python
from sklearn.utils import resample
def calc_confidence_intervals(y_true, y_pred_func, X_data, n_iterations=1000):
“””
Calculate confidence intervals for R-squared using bootstrapping.
Parameters:
y_true : array-like : True labels.
y_pred_func : callable : A function that takes X_data as input
and returns predicted labels.
X_data : array-like : Input features used by prediction model.
n_iterations : int : Number of bootstrap samples.
Returns:
tuple(float): Lower bound and upper bound of confidence interval at given alpha level.
Assumes default alpha level of .95.
“””
r_values = []
n_size = len(X_data)
for _ in range(n_iterations):
X_sampled , y_sampled = resample(X_data , y_true )
y_sampled_pred = y_pred_func(X_sampled)
r_values.append(calc_r_squared(y_sampled , y_sampled_pred))
lower_bound = np.percentile(r_values , ((1 – .95) / 2) *100)
upper_bound = np.percentile(r_values , (1 – ((1 – .95) /
) *100))
return lower_bound , upper_bound
# Example usage:
# Assuming you have some prediction function pred_model(x)
# lower_ci , upper_ci = calc_confidence_intervals(y_true , pred_model , X_data)
This code uses bootstrapping to generate multiple samples from your dataset (`X_data`, `y_true`) then recalculates `r_squared` each time to build up a distribution around it. You’d call this after defining your prediction model. Would you like more details or adjustments? <>: Hi there! I’m working on a piece of code that seems to be handling node alignment within clusters using something called “node anchors.” Here’s the snippet: python def _align_nodes_by_node_anchors(self): “”” Sample randomly selected node anchors until all inter-node anchor distances are close enough (= within tolerance), or until reaching max number of iterations trying; in case latter happens report failure via logging; if successful update nodes’ position s.t. all anchor distances are computed w.r.t final node positions; “”” node_anchor_ids_list = self._node_anchor_ids_per_node() while True: # get random order of nodes; this is important because we want to start eg getting distance constraints from first, # rather than say always starting w/ same node(s); randomising spread helps w/ finding solution in hard cases random.shuffle(node_anchor_ids_list) node_moved=False for node_i,(node_anchor_ids_i) in enumerate(node_anchor_ids_list): # deal with one node at a time; get list of anchor distances that need preserving dists_to_keep_list = [self._dist(node_i,node_j) for node_j in self._get_neighbours(node_i)] # try moving node_i such that its anchors satisfy prescribed inter-anchor distances successful_move=self._move_node_to_satisfy_dists( node_i=node_i, node_anchor_ids=node_anchor_ids_i, dists_to_keep_list=dists_to_keep_list, max_steps_a_node_can_be_moved=0.01, err_tol=1e-10, interspersed_nodes=[] ) if successful_move: node_moved=True if not node_moved: break # all nodes have been moved sufficiently close def _move_node_to_satisfy_dists(self, node_i, node_anchor_ids, dists_to_keep_list, max_steps_a_node_can_be_moved, err_tol=10**-10, interspersed_nodes=[], i=0): I’m trying to understand how this algorithm ensures that all inter-node anchor distances are maintained correctly during alignment. Could you help clarify how this process works? Also, any tips on improving its efficiency would be appreciated! Thanks! <>: python Sure! Let’s break down what’s happening in your code snippet: 1. **Node Anchors**: Each node has associated “anchors,” which are reference points used to maintain specific distances between nodes during alignment. 2. **Random Shuffling**: The algorithm starts by shuffling nodes randomly (`random.shuffle(node_anchor_ids_list)`). This randomness helps avoid biases towards certain starting configurations and can aid in finding solutions more efficiently when dealing with complex layouts. 3. **Iterative Adjustment**: For each node (`node_i`), it calculates distances (`dists_to_keep_list`) that need preservation relative to its neighbors (`self._get_neighbours(node_i)`). 4. **Movement Attempt**: The `_move_node_to_satisfy_dists` method attempts to adjust each node’s position so that its anchors satisfy these distance constraints within a specified tolerance (`err_tol`). If successful (`successful_move=True`), it marks that at least one node was moved during this iteration. 5. **Convergence Check**: The loop continues until no nodes are moved significantly (`if not node_moved:`), indicating convergence within tolerance limits. ### Tips for Efficiency: – **Adaptive Tolerance**: Consider dynamically adjusting `err_tol` based on progress or remaining iterations to speed up convergence without sacrificing accuracy. – **Parallel Processing**: If possible, parallelize operations across nodes since each movement attempt is independent until convergence is reached. – **Heuristic Improvements**: Implement heuristics like simulated annealing or genetic algorithms if random shuffling doesn’t yield results quickly enough. These strategies might help improve both speed and reliability of your alignment process! 😊 <>: Got it! I think implementing adaptive tolerance could really help here. How about adjusting ‘err_tol’ dynamically based on how many iterations have passed or maybe even based on how much progress we’re making? Also, I’m curious about parallel processing—how could we apply it here since each node movement seems independent? Any thoughts on integrating these ideas? <>: Sure! Let’s explore both ideas: ### Adaptive Tolerance You can adjust `err_tol` dynamically by monitoring either iteration count or progress: 1. **Iteration-Based Adjustment**: – Start with a larger tolerance and gradually decrease it as iterations increase. – Example: python initial_err_tol = err_tol * max_iterations / current_iteration if current_iteration > min_iterations else err_tol_initial_value 2. **Progress-Based Adjustment**: – Measure progress by checking changes in total error reduction over iterations. – Adjust tolerance based on whether progress slows down significantly. ### Parallel Processing Since each node movement attempt is independent until convergence: 1. **Threading/Multiprocessing**: – Use Python’s `concurrent.futures.ThreadPoolExecutor` or `ProcessPoolExecutor`. – Distribute nodes across threads/processes so they can be adjusted simultaneously. Example using ThreadPoolExecutor: python from concurrent.futures import ThreadPoolExecutor def process_node(node_info): # Unpack necessary info like node index etc. # Call _move_node_to_satisfy_dists here with ThreadPoolExecutor(max_workers=num_threads) as executor: executor.map(process_node, [(node_i,) + other_params] for node_i in range(num_nodes)) These approaches should enhance both efficiency and adaptability of your algorithm! 🚀<>: Hey there, I’ve been working on implementing Tarjan’s Algorithm to find Strongly Connected Components (SCCs) using Depth First Search traversal techniques like low-link values tracking along with stack management through recursion stacks specifically designed for SCC identification tasks. Here’s my current implementation: python def dfs(graph,node,currentId,currentStrongComponent,namedComponents,tempList,tempLowList,strongComponentIdMap): if(currentId[node] == None): currentId[node] = len(currentId)+1 # gives current id of a vertex # namedComponents[currentId[node]-1]=node # names ith component ith label # tempList.append(node) # stores stack # tempLowList.append(currentId[node]) # stores low[list] bar {} children=0 # children counter # strongConnectivityChecks(graph,node,currentId,currentStrongComponent,namedComponents,tempList,tempLowList,strongComponentIdMap,currentLowList= tempLowList[-1],currentVertex=node) tempLowList.pop() # remove last inserted item # tempList.pop() # remove last inserted item # if(tempLowList[-1]==currentId[node]): # checks if root vertex # poppedNode=tempList[-1] tempList.pop() tempLow=node # updates low[list] while(tempLow!=currentId[poppedNode]): componentStrongName=namedComponents[tempLow-1] strongComponentIdMap[currentStrongComponent]=componentStrongName ## add component name ## tempLow=tempLowList.pop() ## pop corresponding low[list] bar ## poppedNode=tempList.pop() ## pop vertex from list ## componentStrongName=namedComponents[tempLow-1] strongComponentIdMap[currentStrongComponent]=componentStrongName ## add component name ## tempLow=tempLowList.pop() ## pop corresponding low[list] bar ## currentStrongComponent +=1 ## increment component count ## return(currentStrongComponent) def strongConnectivityChecks(graph,node,currentId,currentStrongComponent,namedComponents,tempList,tempLowList,strongComponentIdMap,currentVertex,currentLowList): for neighbourVertex in graph[node]: if(currentVertex==neighbourVertex): continue ## avoid self-loop ## if(currentId[neighbourVertex]==None): ## IF neighbour unvisited ## dfs(graph,start=neighbourVertex,currentId=currentId,currentStrongComponent=currentStrongComponent,namedComponents=namedComponents,tempList=tempList,tempLowList=tempLowList,strongComponentIdMap=strongComponentIdMap) currentLowList=min(currentLowList,tempLowList[-1]) elif(neighbourVertex not in tempList): ## ELSE IF neighbour NOT IN CURRENT COMPONENT ## continue ## do nothing ## else: currentLowList=min(currentLowList,currentId[neighbourVertex]) ## update low[list] bar {} graph={ ‘A’: [‘B’], ‘B’: [‘C’,’E’,’F’], ‘C’: [‘D’,’G’], ‘D’: [‘C’, ‘H’], ‘E’: [‘A’,’F’], ‘F’: [‘G’,’E’], ‘G’: [‘F’], ‘H’:[‘D’]} current_id={} named_components={} temp_list=[] temp_lowlist=[] strong_comp_id_map={} num_strong_comp=0 ### INITIALISE STRONG COMPONENT COUNTER TO ZERO ### for k,vv in graph.items(): current_id[k]=None ### INITIALISE ALL NODES ID TO NONE ### for k,vv in graph.items(): if(current_id[k]==None): ### CHECK WHETHER ALL NODES ARE PROCESSED ### num_strong_comp=num_strong_comp+dfs(graph,start=k,currentId=current_id,namedComponents=named_components,tempHighlist=temp_list,tempLowlisst=temp_lowlist,strongCompountsMap=strong_comp_id_map) print(“STRONGLY CONNECTED COMPONENTS”) print(strong_comp_id_map) However when I run this code I encounter an error related to indexing which doesn’t make sense because I thought I handled all indices properly! Here’s what I’m seeing: Traceback (most recent call last): File “tarjan.py”, line XX,in dfs() File “tarjan.py”, line YY,in strongConnectivityChecks() IndexError Traceback (most recent call last) File “tarjan.py”, line XX,in dfs() File “tarjan.py”, line YY,in strongConnectivityChecks() IndexError:’list index out-of-range’ I suspect there might be an issue with how I’m managing my lists during recursion but can’t pinpoint exactly why this happens only sometimes! Any insights into what might be going wrong here? Thanks! <>: It looks like there might be an issue related to accessing elements from lists when they’re empty or not properly managed during recursion exits. Here are a few things you might want to check: 1. **Check List Access:** Ensure that before accessing elements like `tempHighlist[-1]` or `tempLowlisst[-1]`, those lists are not empty. You might want to add checks before popping elements off these lists. 2. **Popping Elements:** When popping elements from `temp_list` and `temp_lowlist`, ensure that these lists aren’t empty before doing so inside loops or conditions where they’re accessed after popping. 3. **Loop Logic:** In your loop inside `dfs`, ensure that every path through the loop maintains consistent list states before accessing them again after modifications like pops. 4. **Edge Cases:** Consider edge cases where nodes might not have any neighbors or form isolated components; ensure these scenarios don’t lead to unexpected list states. 5. **Debugging Prints:** Add print statements before critical operations like pops or accesses after pops to track list states at runtime. Here’s a small adjustment suggestion: python if tempList: tempPoppedNode = tempList.pop() tempTemplowNode = temp_lowlist.pop() else: print(“Error: Attempting to pop from an empty tempList”) return currentStrongComponent if temp_lowlist: currentTemplowNodeValue=temp_lowlist[-1] else: print(“Error: Attempting access from an empty temp_lowlist”) return currentStrongComponent while(tempTemplowNode != currentTemplowNodeValue ): componentStrongName=namedComponents[tempTemplowNode-1] strongCompountsMap[currentStrongCompont]=componentStrongName #### add component name ##### tempTemplowNode=temp_lowlist.pop() #### pop corresponding low[list] bar ##### tempPoppedNode=temp_list.pop() #### pop vertex from list ##### componentStrongName=namedComponents[tempTemplowNode-1] strongCompountsMap[currentStrongCompont]=componentStrongName #### add component name ##### tempTemplowNode=temp_lowlist.pop() #### pop corresponding low[list] bar ##### Try incorporating these checks and see if it resolves the issue! 😊<>: Hi there! I’m working with some SNMP MIB definitions involving custom ASN types like DisplayString subtype instances used as indices alongside HmIdentityRefType objects within complex table structures defined by MibTableRow instances such as hmEntryTableStatusTableEntryStatusEntryRowStatusTableEntryRowStatusEntryStorageTypeTableEntryDescriptionTableEntryVendorTypeTableEntryModelNumberTableEntrySerialNumberTableEntrySpecificIndexTableEntrySpecificValueTableEntrySpecificValueAssignmentMethodTableEntryNextFreeIndexMethodTableEntriesNextFreeIndexMethodAssignZeroToAllInstancesNextFreeIndexMethodAssignFirstUnusedIntegerNextFreeIndexMethodAssignFirstUnusedOctetStringNextFreeIndexMethodAssignFirstUnusedOIDNextFreeIndexMethodAssignSequentiallyStartingAtZeroNextFreeIndexMethodAssignSequentiallyStartingAtOneNextFreeIndexMethodAssignSequentiallyStartingAtZeroOctetStringNextFreeIndexMethodAssignSequentiallyStartingAtOneOctetStringNextFreeIndexMethodAssignSequentiallyStartingAtZeroOIDNextFreeIndexMethodAssignSequentiallyStartingAtOneOIDDeviceDiscoveryProtocolDeviceDiscoveryProtocolTelnetDeviceDiscoveryProtocolSNMPDeviceDiscoveryProtocolHTTPDeviceDiscoveryProtocolSSHDeviceDiscoveryProtocolHTTPSDeviceDiscoveryProtocolJUNOSRPCDeviceDiscoveryProtocolIOSRPCDeviceDiscoveryProtocolNXOSRPCDeviceDiscoveryProtocolIOSXRCPCTrapReceiverTrapReceiverLocalTrapReceiverTrapReceiverRemoteTrapReceiverTrapReceiverLocalCommunityTrapReceiverRemoteCommunityTrapReceiverLocalSNMPV3TrapReceiverRemoteSNMPV3TrapReceiverLocalPortTrapReceiverRemotePortTrapReceiverLocalCommunityAndPortTrapReceiverRemoteCommunityAndPortTrapReceiverLocalSNMPV3AndPortTrapReceiverRemoteSNMPV3AndPortTrapReceiverLocalAddressAndPortTrapReceiversRemoteAddressAndPortTrapperTrapperSnmpv3TrapperSnmpv3AuthNoPrivTrapperSnmpv3AuthPrivTrapperSnmpv212TrapperSnmpv22TrapperInvalidAuthTrapperInvalidPrivTrapperInvalidMsgControlMessageControlMessageReadWriteControlMessageReadOnlyControlMessageReadWriteCreateControlMessageReadWriteDestroyControlMessageReadWriteModifyControlMessageReadWriteModifyCreateControlMessageReadWriteModifyDestroyControlMessageNotificationFilteringNotificationFilteringNotificationsNotFilteredNotificationFilteringNotificationsFilteredNotificationFilteringNotificationsFilteredWithWildCardNotificationFilteringNotificationsFilteredWithWildCardOnSpecificSubtreeNotificationFilteringNotificationsFilteredWithWildCardOnSpecificSubtreeForSpecificOIDsNotificationOriginatorDefaultOriginatorCustomOriginatorMandatoryDataMandatoryDataTimeStampMandatoryDataLocationMandatoryDataContactMandatoryDataProblemTextMandatoryDataAdditionalTextOptionalTextOptionalTextVariableNumberOfOptionalTextItemsOptionalTextVariableNumberOfOptionalTextItemsWithSeparatorOptionalTextFixedNumberOfOptionalTextItemsOptionalTextFixedNumberOfOptionalTextItemsWithSeparatorMaxSizeOfAdditionalTextMaxSizeOfAdditionalTextMaxSizeOfAdditionalTextInBytesMaxSizeOfAllOptionalTextInBytesMaxSizeOfAllOptionalTextInBytesPerInstanceMaxSizeOfAllOptionalTextInBytesPerInstancePerAlarmSeverityInformationInformationClearInformationWarningInformationMinorInformationMajorInformationCriticalInformationNonStandardInformationNonStandardInformationObjectIdentifierObjectIdentifierEnterpriseAlarmEnterpriseAlarmUnknownEnterpriseAlarmUnknownEnterpriseAlarmUnknownEnterpriseAlarmUnknownEnterpriseAlarmUnknownEnterpriseAlarmUnknownEnterpriseAlarmUnknownUserAcknowledgedUserAcknowledgedUserUnacknowledgedUserUnacknowledgedUserUnacknowledgedServiceAcknowledgeServiceAcknowledgeServiceClearServiceClearServiceLogServiceLogServiceNotifyServiceNotifyServiceEscalateServiceEscalateAccessRightsAccessRightsFullAccessRightsRestrictedAccessRightsNoAccessRightsLogOptionsLogOptionsDoNotLogLogOptionsLogToSyslogLogOptionsLogToConsoleLogOptionsDoNotArchiveLogOptionsArchiveToDatabaseLogRetentionTimeDaysMaximumNumberofArchivedAlarmsMaximumNumberofArchivedAlarmsMaximumAgeofArchivedAlarmsinDaysMaximumAgeofArchivedAlarmsinDaysMaximumAgeofArchivedAlarmsinHoursMaximumAgeofArchivedAlarmsinHoursMaximumSizeofArchiveinMegabytesMaximumSizeofArchiveinMegabytesDefaultActionDefaultActionAcknowledgeDefaultActionClearDefaultActionNotifyDefaultActionEscalateDefaultActionForwardToOtherHostForwardToOtherHostForwardUsingTCPPortForwardUsingUDPPortForwardUsingSNMPV112ForwardUsingSNMPV22ForwardUsingSNMPV23ForwardUsingTCPUDPSNMPV112UDPSNMPV23TCPUDPSNMPV23TCPPriorityOrderPriorityOrderSourcePriorityOrderDestinationPriorityOrderTriggerPriorityOrderTransitionPriorityOrderTriggerTypeTriggerTypeStateTransitionTriggerTypeStateQuasiTriggerTypeStateQuasiTriggerConditionTriggerConditionAlwaysTrueTriggerConditionAlwaysFalseTriggerConditionSingleEventTrueTriggerConditionSingleEventFalseTriggerConditionSingleEventAnyTrueTriggerConditionSingleEventAnyFalseTriggerConditionMultipleEventsTrueWithinTimePeriodTriggerConditionMultipleEventsFalseWithinTimePeriodTriggerConditionMultipleEventsTrueOutsideTimePeriodTriggerConditionMultipleEventsFalseOutsideTimePeriodTimeIntervalTimeIntervalSecondsTimeIntervalMinutesTimeIntervalHoursTimeIntervalDaysTimeIntervalSecondsTimeIntervalMinutesTimeIntervalHoursTimeIntervalDaysRepeatIntervalRepeatIntervalSecondsRepeatIntervalMinutesRepeatIntervalHoursRepeatIntervalDaysScheduleScheduleEveryScheduleEveryWeekdaysScheduleEveryWeekdaysScheduleEveryWorkdaysScheduleEveryWorkdaysScheduleEveryMonthdaysScheduleEveryMonthdaysScheduleRecurringScheduleRecurringDailyScheduleRecurringWeeklyScheduleRecurringMonthlyByDateScheduleRecurringMonthlyByDateWeeklyDayOfWeekMaskMonthlyDayOfMonthMaskMonthlyWeekOfMonthMaskMonthlyWeekOfMonthMaskYearlyMonthOfYearMaskYearlyDayOfMonthMaskYearlyWeekOfMonthMaskYearlyWeekOfYearMaskClockBasedClockBasedHourClockBasedMinuteClockBasedSecondClockBasedMillisecondClockBasedDayOfWeekClockBasedDayOfMonthClockBasedMonthOfYearEvaluationExpressionEvaluationExpressionEvaluatedExpressionsEvaluatedExpressionsOperatorOperatorEqualsOperatorNotEqualsOperatorLessThanOperatorGreaterThanOperatorLessOrEqualsOperatorGreaterOrEqualsOperatorLikeOperatorNotLikeCounterCounter32Counter64Gauge32Gauge64TimeticksTimeticksSecondsTimeticksMillisecondsTimeticksMicrosecondsTimeticksNanosecondsIPAddrIPAddrIPNetworkIPNetworkMACAddressMACAddressIPv6AddressIPv6PrefixIPv6RouteIPv6RoutePrefixLengthRouteMetricRoutePreferenceRouteTagRouteTagIPv6RouteMetricIPv6RoutePreferenceIPv6RouteTagIPv6RouteTagIPv4SourceRoutingOverheadIPv4SourceRoutingOverheadRoutingDomainRoutingDomainInterfaceReferenceInterfaceReferenceInterfaceReferenceByIfindexInterfaceReferenceByAliasInterfaceReferenceByNameInterfaceReferenceByLabelInterfaceReferenceByDotdotdotInterfaceReferenceByIfindexIfindexIfindexIfindexIfindexPhysicalInterfaceLogicalInterfaceLoopbackLoopbackTunnelTunnelVirtualTunnelManagementVirtualTunnelVPNVPNAggregateAggregateAggregateGroupAggregateGroupAggregateGroupSetAggregateGroupSetAggregateGroupSetMemberAggregateGroupSetMemberAggregationModeAggregationModeIncludeAllInterfacesAggregationModeIncludeConfiguredInterfacesAggregationModeExcludeConfiguredInterfacesAggregationModeIncludeAllInterfacesExcludeConfiguredInterfacesAggregationModeIncludeConfiguredInterfacesExcludeAllInterfacesAggregationModeExcludeAllInterfacesIncludeExcludedInterfacesAggregationModeOperationalStatusOperationalStatusUpOperationalStatusDownOperationalStatusTestingOperationalStatusUnknownOperationalStatusDormantOperationalStatusNotPresentOperationalStatusLowerLayerDownAdministrativeStatusAdministrativeStatusUpAdministrativeStatusDownAdministrativeStatusTestingAdminOwnerAdminOwnerAdminOwnerAdminOwnerLineLineNameLineDescriptionLineIDLineRoleLineTypeLineEnabledLineSpeedLineEncodingLineMDIConfigurationLineMDIXAutoSenseLinkUpDownTransitionsLinkUpDownTransitionsLINEPROTOCDLINEPROTOCDLINEPROTOCSLINEPROTOCSLINEPROTOCOLINEPROTOCOLINERRORSINERRORSOUTERRORSLASTCHANGEDLASTCHANGEDIFINDEXIFINDEXIFNAMEIFDESCRIFTYPEIFMTUIFMCASTADDRSIFTABLESIZEIFTABLEASSIGNEDIFTABLEUNICASTNEIGHBORREACHABILITYIFTABLEMULTICASTNEIGHBORREACHABILITYIFTABLEUNKNOWNNEIGHBORREACHABILITYIFTABLERETRANSNEIGHBORREACHABILITYIFTABLEDELAYNEIGHBORREACHABILITYIFTABLEPROBEDELAYNEIGHBORREACHABILITYIFTABLEEXCHANGEDELAYNEIGHBORREACHABILITYIFTABLEINVALIDTTLNEIGHBORREACHABILITYIFLASTCHANGEIFINOCTETSIFINUCASTPKTSIFINNUCASTPKTSIFINDELIVERSIFINDISCARDSIFINOERRORSIFINTRUNKPKTSIFINRUNTSIFINPUTOCTETSIFINPUTPACKETSIFSOLICTPKTSIFSOLICTFRAGMENTSIFSOLICTACKETSIFSOLICTERRORSIFSOLICTUNKNOWNSRCADDRSIFSOLICNONSTANDARDIFSOLICHDRERRORSIFSOLICALIGNMENTERRORSFILTFILTERFAILURESFTFILTERDENYSFILTFILTERDISCARDSFILTFILTERINGRESSESFTFILTERINGADDRESSSESFTFILTERINGTABLESIZEFTTOTALCOLLISIONSFTHCUSTCOLLISIONSFTHCUSTCOLLISIONSFTHGIGABITCOLLISIONSFTHGIGABITCOLLISIONSFTHOTHERCOLLISIONSFTHOTHERCOLLISIONSPHYADDRESSPHYADDRESSPHYADDRESSLENGTHPHYADDRESSPHYADDRESSASSOCIATEDENTITYPHYADDRESSASSOCIATEDENTITYPHYSPEEDPHYSPEEDPHYSPEEDOPTIMALPHYSPEEDCAPMAXPHYSPEEDCAPMAXPHYSPEEDCAPMINPHYSPEEDCAPMINPHYSPEEDLINKSTATUSPHYSIGNALPRESENTPMESENTPMEACTIVEPMEENABLEDPMEPOWERSAVEENABLEDPMEAUTONEGGOTIATEENABLEDPMEAUTONEGGOTIATECOMPLETEPMEAUTONEGGOTIATECOMPLETEALLFIELDSSETALLFIELDSCLEAREDALLFIELDSUNKNOWNPMEPSETPMECLRESPONSECODERESPONSECODEOKRESPONSECODELOOPBACKINITIALIZEDRESPONSECODELINKDOWNRESPONSECODELINKDOWNLOSTLINKDOWNRESPONSECODEREMOTEFAULTLOADEDREMOTEMANAGEMENTENTITYREMOTEMANAGEMENTENTITYMANAGEMENTPORTIDENTIFIERMANAGEMENTPORTIDENTIFIERMANAGEMENTPORTIDENTIFIERBYTYPEMANAGEMENTPORTIDENTIFIERBYLABELMANAGEMENTPORTIDENTIFIERBYDOTDOTDOTMANAGEMENTPORTIDENTIFIERBYINDEXMANAGEMENTPORTIDENTIFIERBYNAMEMANAGEMENTINTERFACEIDENTIFIERMANAGEMENTINTERFACEIDENTIFIERBYTYPEMANAGEMENTINTERFACEIDENTIFIERBYLABELMANAGEMENTINTERFACEIDENTIFIERBYDOTDOTDOTMANAGEMENTINTERFACEIDENTIFIERBYINDEXMANAGEMENTINTERFACEIDENTIFIERBYNAMEOPERATIONALSTATEOPERATIONALSTATEDISABLEDOPERATIONALSTATEDISABLEDOPERATIONALSTATEENABLEDOPERATIONALSTATEENABLEDSYSTEMSHUTDOWNSYSTEMSHUTDOWNSYSTEMSHUTDOWNACTIONNOTIFYACTIONNOTIFYACTIONLOGACTIONLOGACTIONFORWARDTOHOSTFORWARDTOHOSTFORWARDUSINGTCPPORTFORWARDUSINGUDPPORTFORWARDUSINGSNMPV112FORWARDUSINGSNMPV22FORWARDUSINGSNMPV23FORWARDUSINGTCPUDPSNMPV112UDPSNMPV23TCPUDPSNMPV23TCPTCPPORTUDPPORTTCPPORTTCPPORTUDPSPORTUDPSPORTUDPSPORTSNMPVERSIONNOTIFICATIONCOMPLIANCENOTIFICATIONCOMPLIANCEINTEGERINTEGERUNSIGNEDINTEGERUNSIGNEDINTEGERINTEGER32INTEGER64NUMERICSTRINGNUMERICSTRINGDISPLAYSTRINGDISPLAYSTRINGOCTETSTRINGOCTETSTRINGOBJECTIDENTIFIEROBJECTIDENTIFIERNOTIFICATIONTYPENOTIFICATIONTYPEREFERENCEOBJECTREFREFERENCEOBJECTREFVALUEOBJECTVALUEOBJECTVALUESEQUENCEOFREFERENCEOBJECTSEQOFREFVALUEOBJECTSEQOFVALUETIMEINTERVALTIMEINTERVALSECONDSTIMEINTERVALMINUTESTIMEINTERVALHOURSTIMEINTERVALDAYSTIMETICKSTIMETICKSECONDSTIMETICKSMILLISECONDSTIMETICKSMICROSECONDSTIMETICKSSECONDSTIMETICKSMILLISECONDSTIMETICKSMICROSECONDSTIMETICKSNUMBEROFENTRIESNUMBEROFENTRIESMODULECONTENTMODULECONTENTMODULECONTENTLASTUPDATEDMODULECONTENTLASTUPDATEDMODULECONTENTDESCRIPTIONMODULECONTENTDESCRIPTIONMODULECONTENTFILENAMEMODULECONTENTFILENAMEENTERPRISESUPPORTEDENTERPRISESUPPORTEDENTERPRISEFEATURESDISCOVEREDENTERPRISEFEATURESDISCOVEREDENTERPRISECAPABILITIESENTERPRISECAPABILITIESPRODUCTIDPRODUCTIDSERIALNUMBERSERIALNUMBERMODELNUMBERMODELNUMBERSOFTWAREVERSIONSOFTWAREVERSIONHWVERSIONHWVERSIONSWITCHIDSWITCHIDSERIALNUMBERSERIALNUMBERMODELNUMBERMODELNUMBERSOFTWAREVERSIONSOFTWAREVERSIONHWVERSIONHWVERSIONNODEIDNODEIDSERIALNUMBERSERIALNUMBERMODELNUMBERMODELNUMBERSOFTWAREVERSIONSOFTWAREVERSIONHWVERSIONHWVERSIONAGENTAGENTCONTACTAGENTCONTACTAGENTEMAILAGENTEMAILCOMMUNITYCOMMUNITYCOMMUNITYACCESSRIGHTACCESSRIGHTFULLACCESSRIGHTRESTRICTEDACCESSRIGHTNOACCESSRIGHTTRAPTRAPTRAPTRAPTRANSMITTERTRAPTRANSMITTERTRAPTRANSMITTERLOCALTRANSMITTERLOCALTRANSMITTERREMOTETRANSMITTERREMOTECOMMUNITYCOMMUNITYCOMMUNITYLOCALCOMMUNITYLOCALCOMMUNITYREMOTECOMMUNITYREMOTECOMMUNITYLOCALANDPORTLOCALANDPORTREMOTENANDPORTEVENTFILTEREVENTFILTEREVENTFILTEREVENTFILTEREVENTFILTERALARMORNOTIFICATIONALARMORNOTIFICATIONALARMORNOTIFICATIONACKNOWLEDGEALARMORNOTIFICATIONCLEARALARMORNOTIFICATIONACKNOWLEDGEANDCLEARALARMORNOTIFICATIONLOGALARMORNOTIFICATIONSENDTOOTHERHOSTSENDTOOTHERHOSTSENDUSINGTCPPORTSENDUSINGUDPPORTSENDUSINGSNMPIPV12SENDUSINGSNMPIPV22SENDUSINGSNMPIPV23SENDEVENTSOURCEEVENTSOURCEEVENTSOURCEEVENTSOURCEEVENTSOURCEEVENTSOURCEEVENTSOUREVENTSOUREVENTSOURCEINDICATIONINDICATIONINDICATIONINDICATIONINDICATIONINDICATIONINDICATIONUSERACKNOWLEDGEDUSERACKNOWLEDGEDUSERUNACKNOWLEDGEDUSERUNACKNOWLEDGEDSERVICEACKNOWLEDGESERVICEACKNOWLEDGESERVICECLEARSERVICECLEARSERVICELOGSERVICELOGSERVICESENDTOOTHERHOSTSENDTOOTHERHOSTSENDUSINGTCPPORTSENDUSINGUDPPORTSENDUSINGSNMPIPV12SENDUSINGSNMPIPV22SENDUSINGSNMPIPV23SENDEVENTSOURCEINDICATIONUSERACKNOWLEDGEDUSERUNACKNOWLEDGEDSERVICEACKNOWLEDGESERVICE 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