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.. algorithm::
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Description
-----------
Workspaces contain information in logs. Often these detail what happened
to the sample during the experiment. This algorithm allows one named log
to be entered.
The log can be either a String, a Number, or a Number Series. If you
select Number Series, the workspace start time will be used as the time
of the log entry, and the number in the text used as the (only) value.
If the LogText contains a numeric value, the created log will be of
integer type if an integer is passed and floating point (double)
otherwise. This applies to both the Number & Number Series options.
The algorithm can be forced to create a log of integer or floating point
type by using the optional NumberType property as 'Int' or 'Double'. For
example, with NumberType='Double' the log for LogText='12' would be
created as a floating point (double) rather than an integer. NumberType defaults
to 'AutoDetect' which decides whether to use integer or floating point based
on the format of the string, e.g. LogText='12' would create an integer type
log and LogText='12.0' would create a floating point type log.
To add logs that vary over time (Time Series Logs) use :ref:`algm-AddTimeSeriesLog`.
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Usage
-----
**Example - Add Sample Logs in different ways**
.. testcode:: AddSampleLogExample
# Create a host workspace
demo_ws = CreateWorkspace(DataX=range(0,3), DataY=(0,2))
# Add sample logs
AddSampleLog(Workspace=demo_ws, LogName='x', LogText='hello world', LogType='String')
AddSampleLog(Workspace=demo_ws, LogName='y', LogText='1', LogType='Number')
AddSampleLog(Workspace=demo_ws, LogName='z', LogText='2', LogType='Number Series')
# Fetch the generated logs
run = demo_ws.getRun()
log_x = run.getLogData('x')
log_y = run.getLogData('y')
log_z = run.getLogData('z')
# Print the log values
print log_x.value
print log_y.value
print log_z.value
Output:
.. testoutput:: AddSampleLogExample
hello world
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[2]
.. categories::