AddTimeSeriesLog dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| Workspace | InOut | MatrixWorkspace | Mandatory | In/out workspace that will store the new log information |
| Name | Input | string | Mandatory | A string name for either a new time series log to be created or an existing name to update |
| Time | Input | string | Mandatory | An ISO formatted date/time string specifying the timestamp for the given log value, e.g 2010-09-14T04:20:12 |
| Value | Input | number | Mandatory | The value for the log at the given time |
| Type | Input | string | double | An optional type for the given value. A double value with a Type=int will have the fractional part chopped off. Allowed values: [‘double’, ‘int’] |
| DeleteExisting | Input | boolean | False | If true and the named log exists then the whole log is removed first. |
Creates/updates a time-series log entry on a chosen workspace. The given timestamp & value are appended to the named log entry. If the named entry does not exist then a new log is created. A time stamp must be given in ISO8601 format, e.g. 2010-09-14T04:20:12.
By default, the given value is interpreted as a double and a double series is either created or expected. However, if the “Type” is set to “int” then the value is interpreted as an integer and an integer is either created or expected.
Example
import numpy as np
ws = CreateSampleWorkspace("Event",BankPixelWidth=1)
AddTimeSeriesLog(ws, Name="my_log", Time="2010-01-01T00:00:00", Value=100)
AddTimeSeriesLog(ws, Name="my_log", Time="2010-01-01T00:30:00", Value=15)
AddTimeSeriesLog(ws, Name="my_log", Time="2010-01-01T00:50:00", Value=100.2)
log = ws.getRun().getLogData("my_log")
print("my_log has {} entries".format(log.size()))
for time, value in zip(log.times, log.value):
ts = np.datetime_as_string(time.astype(np.dtype('M8[s]')), timezone='UTC')
print("\t{}\t{:.6f}".format(ts, value))
AddTimeSeriesLog(ws, Name="my_log", Time="2010-01-01T00:00:00", Value=12, Type="int", DeleteExisting=True)
AddTimeSeriesLog(ws, Name="my_log", Time="2010-01-01T00:50:00", Value=34, Type="int")
log = ws.getRun().getLogData("my_log")
print("my_log now has {} entries".format(log.size()))
for time, value in zip(log.times, log.value):
ts = np.datetime_as_string(time.astype(np.dtype('M8[s]')), timezone='UTC')
print("\t{}\t{:.6f}".format(ts, value))
Output:
my_log has 3 entries
2010-01-01T00:00:00Z 100.000000
2010-01-01T00:30:00Z 15.000000
2010-01-01T00:50:00Z 100.200000
my_log now has 2 entries
2010-01-01T00:00:00Z 12.000000
2010-01-01T00:50:00Z 34.000000
Categories: Algorithms | DataHandling\Logs
C++ source: AddTimeSeriesLog.cpp (last modified: 2016-02-22)
C++ header: AddTimeSeriesLog.h (last modified: 2016-02-15)