vania-janet/multiturn-rag-retrieval-data huggingface dataset:
(https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=messages-for-rabbitmq-monitoringibm_messages_for_rabbitmq_memory_used_bytes) 





 IO utilization in percent 5-minute average 

How much disk I/O has been used over 5 minutes as a percentage of total disk I/O available.



Table 2. IO utilization in percent 5 minute average metric metadata

 Metadata Description 

 Metric Name ibm_messages_for_rabbitmq_disk_io_utilization_percent_average_5m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 15-minute average 

How much disk I/O has been used over 15 minutes as a percentage of total disk I/O available.



Table 3. IO utilization in percent 15 minute average metric metadata

 Metadata Description 

 Metric Name ibm_messages_for_rabbitmq_disk_io_utilization_percent_average_15m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 30-minute average 

How much disk I/O has been used over 30 minutes as a percentage of total disk I/O available.



Table 4. IO utilization in percent 30 minute average metric metadata

 Metadata Description 

 Metric Name ibm_messages_for_rabbitmq_disk_io_utilization_percent_average_30m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 60-minute average 

How much disk I/O has been used over 60 minutes as a percentage of total disk I/O available.



Table 5. IO utilization in percent 60 minute average metric metadata

 Metadata Description 

 Metric Name ibm_messages_for_rabbitmq_disk_io_utilization_percent_average_60m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IOPS read and write total count for an instance 

How many input/output operations per second your deployment is performing.



Table 6. IOPS read and write total count for an instance metric metadata

 Metadata Description
[IO usage in percent 30-minute average](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_disk_io_utilization_percent_average_30m) 
 [IO usage in percent 60-minute average](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_disk_io_utilization_percent_average_60m) 
 [IOPS read and write total count for an instance.](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_disk_iops_read_write_total) 
 [Max allowed memory for an instance.](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_memory_limit_bytes) 
 [Read replica replication lag.](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_read_replica_replication_lag_bytes) 
 [Successful archive rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_successful_archive_rate) 
 [Temporary files size in bytes](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_temp_bytes_count)
[Transaction commit count](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_transaction_commit_count) 
 [Transaction commit rate](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_transaction_commit_rate) 
 [Transaction rollback count](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_transaction_rollback_count) 
 [Transaction rollback rate](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_transaction_rollback_rate) 
 [Tuples deleted count](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_deleted_count) 
 [Tuples deleted rate](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_deleted_rate) 
 [Tuples fetched count](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_fetched_count) 
 [Tuples fetched rate](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_fetched_rate)
1512989170
]
],
"target": "myclustername.myloadbalancername Connections SYN_SENT"
},
{
"datapoints":

28,
1512989170
]
],
"target": "myclustername.myloadbalancername Connections TIME_WAIT"
}
]
}
Show more

You must explicitly specify the load balancer in the request.





 disk_use 

See an example of a disk_use monitoring request:

curl "https://$ACCOUNT.cloudant.com/_api/v2/monitoring/disk_use?cluster=myclustername&format=json"

See example results (abbreviated) from a disk_use monitoring request:

{
"start": 1435935759,
"end": 1435936059,
"target_responses": [
{
"target": "myclustername Used disk space (bytes)",
"datapoints":

6855438336.0,
1435935780
],

null,
1435935795
],

null,
1435935810
],
...

null,
1435936065
]
]
},
{
"target": "myclustername Free disk space (bytes)",
"datapoints":

7141069422592.0,
1435935780
],

null,
1435935795
],

null,
1435935810
],
...

null,
1435936065
]
]
}
]
}
Show more





 kv_emits 

See an example of a kv_emits monitoring request:
[Tuples fetched count](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_fetched_count) 
 [Tuples fetched rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_fetched_rate) 
 [Tuples inserted count](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_inserted_count) 
 [Tuples inserted rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_inserted_rate) 
 [Tuples returned rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_returned_rate) 
 [Tuples updated count](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_updated_count) 
 [Tuples updated rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_updated_rate) 
 [Used CPU for an instance.]
[Interval total destructive get - byte count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_destructive_get_bytes_total) 
 [Interval total topic bytes put](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_topic_put_bytes_total) 
 [MQCB count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_mqcb_total) 
 [MQCLOSE count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_mqclose_total) 
 [MQCONN/MQCONNX count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_mqconn_mqconnx_total) 
 [MQCTL count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_mqctl_total) 
 [MQDISC count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_mqdisc_total) 
 [MQINQ count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_mqinq_total)
[Tuples fetched count](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_fetched_count) 
 [Tuples fetched rate](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_fetched_rate) 
 [Tuples inserted count](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_inserted_count) 
 [Tuples inserted rate](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_inserted_rate) 
 [Tuples returned rate](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_returned_rate) 
 [Tuples updated count](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_updated_count) 
 [Tuples updated rate](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_updated_rate) 
 [Used CPU for an instance.](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_cpu_used_percent)
(https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_cpu_used_percent) 
 [Used disk space for an instance.](https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_disk_used_bytes) 
 [Used memory for an instance.](https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_memory_used_bytes) 





 Disk read latency mean 

Disk read latency mean



Table 2: Disk read latency mean metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_etcd_disk_read_latency_mean 
 Metric Type gauge 
 Value Type count 
 Segment By Service instance 







 Disk write latency mean 

Disk write latency mean



Table 3: Disk write latency mean metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_etcd_disk_write_latency_mean 
 Metric Type gauge 
 Value Type count 
 Segment By Service instance 







 IO utilization in percent 5-minute average 

How much disk I/O has been used over 5 minutes as a percentage of total disk I/O available



Table 4. IO utilization in percent 5 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_etcd_disk_io_utilization_percent_average_5m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 15-minute average 

How much disk I/O has been used over 15 minutes as a percentage of total disk I/O available.



Table 5. IO utilization in percent 15 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_etcd_disk_io_utilization_percent_average_15m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 30-minute average
How much disk I/O has been used over 5 minutes as a percentage of total disk I/O available.



Table 7. IO utilization in percent 5 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mongodb_disk_io_utilization_percent_average_5m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 15 minute average 

How much disk I/O has been used over 15 minutes as a percentage of total disk I/O available.



Table 8. IO utilization in percent 15 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mongodb_disk_io_utilization_percent_average_15m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 30 minute average 

How much disk I/O has been used over 30 minutes as a percentage of total disk I/O available.



Table 9. IO utilization in percent 30 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mongodb_disk_io_utilization_percent_average_30m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 60 minute average 

How much disk I/O has been used over 60 minutes as a percentage of total disk I/O available.



Table 10. IO utilization in percent 60 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mongodb_disk_io_utilization_percent_average_60m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IOPS read & write total count for an instance 

How many input/output operations per second your deployment is performing.



Table 11. IOPS read & write total count for an instance metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mongodb_disk_iops_read_write_total 
 Metric Type gauge 
 Value Type count 
 Segment By Service instance 







 Max allowed memory for an instance 

The maximum amount of memory available to your deployment.



Table 12. Max allowed memory for an instance metric metadata

 Metadata Description
TimeStamp: 2019-01-01T00:01Z Value: 2.0
TimeStamp: 2019-01-01T00:02Z Value: 4.0

 numeric timestamps are now minutes elapsed since 1 Jan 2019, 12 midnight GMT
TimeStamp: 0 Value: 1.0
TimeStamp: 1 Value: 2.0
TimeStamp: 2 Value: 4.0

To better understand how it impacts post processing, let's examine the following. Note that get_values on numeric timestamps operates on the underlying numeric timestamps associated with the time series.

print(y.get_values(0,2))
print(y_minutely_1970.get_values(0,2))
print(y_minutely.get_values(0,2))

This returns:

 numeric timestamps in y are in the range 1546300800, 1546300920 and thus y.get_values(0,2) is empty
[]
 numeric timestamps in y_minutely_1970 are in the range 25771680, 25771682 and thus y_minutely_1970.get_values(0,2) is empty
[]
 numeric timestamps in y_minutely are in the range 0, 2
[(0,1.0),(1,2.0),(2,4.0)]

The method get_values can also be applied to datetime objects. This results in an exception if the underlying time series is not associated with a TRS (if TRS is None). Assuming the underlying time series has a TRS, the datetime objects are mapped to a numeric range using the TRS.

 Jan 1 2019, 12 midnight GMT
dt_beg = datetime.datetime(2019,1,1,0,0,0,0,tzinfo=datetime.timezone.utc)
 Jan 1 2019, 12:02 AM GMT
[(1, 1.2, 2.0]), (2, 1.2, 2.8]), (3, 2.2, 2.8]), (4, 2.2, 3.4]), (5, 3.6, 3.4]), (6, 3.6, NaN]), (7, 4.4, NaN]), (9, 5.4, 8.0])]

Where no matching timetick exists in one table, this statement uses an interpolator of type TS_INTERPOLATOR_NEAREST to generate the missing value.







 Temporal align 

A temporal align is similar to a temporal join, except that, instead of producing a single array time series based on the observations of the two input time series, it produces two output time series with identical timeticks:

SELECT TS_INNER_ALIGN(table1.col7, table2.col8)

Result:

[(1, 1.2), (9, 5.4)], [(1, 2.0), (9, 8.0)]

SELECT TS_LEFT_ALIGN(table1.col7, table2.col8, TS_INTERPOLATOR_PREV(-1.0))

Result:

[(1, 1.2), (3, 2.2), (5, 3.6), (7, 4.4), (9, 5.4)], [(1, 2.0), (3, 2.8), (5, 3.4), (7, NaN), (9, 8.0)]

SELECT TS_LEFT_OUTER_ALIGN(table1.col7, table2.col8, TS_INTERPOLATOR_PREV(-1.0))

Result:

[(3, 2.2), (5, 3.6), (7, 4.4)], [(3, 2.8), (5, 3.4), (7, NaN)]

SELECT TS_FULL_ALIGN(table1.col7, table2.col8, TS_INTERPOLATOR_NEAREST(-1.0))

Result:
IO utilization in percent 5-minute average 

How much disk I/O was used over 5 minutes as a percentage of total disk I/O available.



Table 4. IO utilization in percent 5 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases-for-cassandra_disk_io_utilization_percent_average_5m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 15-minute average 

How much disk I/O was used over 15 minutes as a percentage of total disk I/O available.



Table 5. IO utilization in percent 15 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases-for-cassandra_disk_io_utilization_percent_average_15m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 30-minute average 

How much disk I/O was used over 30 minutes as a percentage of total disk I/O available.



Table 6. IO utilization in percent 30 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases-for-cassandra_disk_io_utilization_percent_average_30m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 60-minute average 

How much disk I/O was used over 60 minutes as a percentage of total disk I/O available.



Table 7. IO utilization in percent 60 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases-for-cassandra_disk_io_utilization_percent_average_60m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IOPS read and write total count for an instance 

How many input/output operations per second your deployment is performing.



Table 8. IOPS read and write total count for an instance metric metadata

 Metadata Description 

 Metric Name ibm_databases-for-cassandra_disk_iops_read_write_total 
 Metric Type gauge 
 Value Type count 
 Segment By Service instance 







 Max allowed memory for an instance 

The maximum amount of memory available to your deployment.



Table 9. Max allowed memory for an instance metric metadata

 Metadata Description
[Transaction commit rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_transaction_commit_rate) 
 [Transaction rollback count](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_transaction_rollback_count) 
 [Transaction rollback rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_transaction_rollback_rate) 
 [Tuples deleted count](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_deleted_count) 
 [Tuples deleted rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_deleted_rate) 
 [Tuples fetched count](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_fetched_count) 
 [Tuples fetched rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_tuples_fetched_rate)
SELECT TS_LEFT_OUTER_ALIGN(table1.col7, table2.col8, TS_INTERPOLATOR_PREV(-1.0))

Result:

[(3, 2.2), (5, 3.6), (7, 4.4)], [(3, 2.8), (5, 3.4), (7, NaN)]

SELECT TS_FULL_ALIGN(table1.col7, table2.col8, TS_INTERPOLATOR_NEAREST(-1.0))

Result:

[(1, 1.2), (2, 1.2), (3, 2.2), (4, 2.2),(5, 3.6), (6, 3.6), (7, 4.4), (9, 5.4)], [(1, 2.0), (2, 2.8), (3, 2.8), (4, 3.4),(5, 3.4), (6, NaN), (7, NaN), (9, 8.0)]
[Disk write latency mean](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_disk_write_latency_mean) 
 [IO utilization in percent 5-minute average](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_disk_io_utilization_percent_average_5m) 
 [IO utilization in percent 15-minute average](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_disk_io_utilization_percent_average_15m) 
 [IO utilization in percent 30-minute average](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_disk_io_utilization_percent_average_30m) 
 [IO utilization in percent 60-minute average](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_disk_io_utilization_percent_average_60m) 
 [IOPS read and write total count for an instance.](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_disk_iops_read_write_total) 
 [Max allowed memory for an instance.](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_memory_limit_bytes) 
 [Read replica replication lag.]
![newvisualization.png](https://cloud.ibm.com/docs-content/v1/content/78bb71851d95d7580503eb9ba10cf3ae31490ade/apiconnect/tutorials/images/newvisualization.png)
2. Select Line chart as your visualization type. The initialized line chart has the Y-Axis setup with a count of API calls. This is appropriate for our chart.
3. Select the following:



* Buckets type: X-Axis
* Aggregation: Date Histogram
* Custom Label: Time



4. Click Run to see your chart. Tip: You might need to adjust your time frame to see data.

![apichart1.png](https://cloud.ibm.com/docs-content/v1/content/78bb71851d95d7580503eb9ba10cf3ae31490ade/apiconnect/tutorials/images/apichart1.png)



This chart (so far) shows a time series of API calls. We want to see API calls by app name.



5. Click the Add sub-buckets button.
6. Select the following:



* Buckets type: Split Lines
* Sub Aggregation: Terms
* Field: app_name
* Custom Label: App



![subbucket.png](https://cloud.ibm.com/docs-content/v1/content/78bb71851d95d7580503eb9ba10cf3ae31490ade/apiconnect/tutorials/images/subbucket.png)
7. Click Run to see your chart.
8. Click Save and give your chart a name API Calls by App.
9. To see your visualization in context, add it to the Subscriber dashboard.
TimeStamp: 1 Value: -3.0
TimeStamp: 2 Value: 2.0
TimeStamp: 3 Value: 2.0
TimeStamp: 4 Value: 0.0
TimeStamp: 5 Value: 1.0





 Using reducers 

Similar to the transformers package, you can reduce a time series by using methods provided by the reducers package. You can import the reducers package as follows:

from tspy.functions import reducers

After you have imported the package, use the reduce method to get the average over a time-series for example:

avg = ts.reduce(reducers.average())
avg

This outputs:

5.0

Reducers have a special property that enables them to be used alongside segmentation transformations (hourly sum, avg in the window prior to an error occurring, and others). Because the output of a segmentation + reducer is a time series, the transform method is used.

For example, to segment into windows of size 3 and get the average across each window, use:

avg_windows_ts = ts.segment(3).transform(reducers.average())

This results in:

imeStamp: 0 Value: 3.6666666666666665
TimeStamp: 1 Value: 4.0
TimeStamp: 2 Value: 5.333333333333333
TimeStamp: 3 Value: 6.333333333333333







 Graphing time series 

Lazy evaluation is used when graphing a time series. When you graph a time series, you can do one of the following:



* Collect the observations of the time series, which returns an ObservationCollection
* Reduce the time series to a value or collection of values
* Perform save or print operations



For example, to collect and return all of the values of a timeseries:

observations = ts.collect()
observations

This results in:

[(0,5.0),(1,2.0),(2,4.0),(3,6.0),(4,6.0),(5,7.0)]