.. _02_pim_sol:

============================
Python in Mantid: Solution 2
============================

All the data for these solutions can be found in the TrainingCourseData on the Downloads page.

A - Processing ISIS Data
========================

.. code-block:: python

    # import mantid algorithms, numpy and matplotlib
    from mantid.simpleapi import *
    import matplotlib.pyplot as plt
    import numpy as np

    # Load the main data file and its monitor
    Load(Filename='LOQ48097.raw', OutputWorkspace='Small_Angle', LoadMonitors='Separate')

    # Convert monitor to wavelength
    ConvertUnits(InputWorkspace='Small_Angle_monitors', OutputWorkspace='Small_Angle_monitors', Target='Wavelength')

    # Convert data to wavelength
    ConvertUnits(InputWorkspace='Small_Angle', OutputWorkspace='Small_Angle', Target='Wavelength')

    # Rebin the monitors with a suggested set of parameters
    rebin_var = Rebin(InputWorkspace='Small_Angle_monitors', OutputWorkspace='Small_Angle_monitors', Params='2.2,-0.035,10')

    # Extract binning params from the first Rebin-algm
    rebin_alg = rebin_var.getHistory().lastAlgorithm()
    params = rebin_alg.getPropertyValue('Params')

    # Log the Rebin params at the level information
    logger.information("Rebin parameters: {}".format(params))

    # Rebin the data with the params extracted from the earlier Rebin
    Rebin(InputWorkspace='Small_Angle', OutputWorkspace='Small_Angle', Params= params)

    # Extract the Spectrum for correcting the data
    ExtractSingleSpectrum(InputWorkspace='Small_Angle_monitors', OutputWorkspace='Small_Angle_monitors', WorkspaceIndex=1)

    # Correct the data by dividing by the monitor spectrum
    Divide(LHSWorkspace='Small_Angle', RHSWorkspace='Small_Angle_monitors', OutputWorkspace='Corrected_data')


B - Plotting ILL Data
=====================

.. plot::
    :include-source:

    # import mantid algorithms, numpy and matplotlib
    from mantid.simpleapi import *
    import matplotlib.pyplot as plt
    import numpy as np

    _164198 = Load('164198.nxs')

    fig, axes = plt.subplots(edgecolor='#ffffff', num='164198-1', subplot_kw={'projection': 'mantid'})
    axes.plot(_164198, color='#2ca02c', label='164198: spec 100', linewidth=1.0, specNum=100, zorder=2.1)
    axes.plot(_164198, color='#1f77b4', label='164198: spec 200', linewidth=1.0, specNum=200, zorder=2.1)
    axes.errorbar(_164198, capsize=1.0, color='#ff7f0e', label='A funky label', linewidth=1.0, specNum=50)
    axes.plot(_164198, color='#000000', label='164198: spec 300', linewidth=1.0, markeredgecolor='#d62728', markerfacecolor='#d62728', specNum=300, zorder=2.1)
    axes.set_title('My Beautiful Plot')
    axes.set_xlabel(r'Time-of-flight ($\mu s$)')
    axes.set_ylabel('Counts')
    axes.set_xlim([460.0, 600.0])
    axes.set_ylim([1.0, 2000.0])
    axes.set_yscale('log')
    axes.legend() #.draggable()

    #fig.show()


C - Processing and Plotting SNS Data
====================================

.. plot::
   :include-source:

    # import mantid algorithms, numpy and matplotlib
    from mantid.simpleapi import *
    import matplotlib.pyplot as plt
    import numpy as np

    Load(Filename=r'EQSANS_6071_event.nxs',OutputWorkspace='run',LoadMonitors='1')
    ConvertUnits(InputWorkspace='run_monitors',OutputWorkspace='run_monitors_lambda',Target='Wavelength')
    Rebin(InputWorkspace='run_monitors_lambda',OutputWorkspace='run_monitors_lambda_rebinned',Params='2.5,0.1,5.5')
    ConvertUnits(InputWorkspace='run',OutputWorkspace='run_lambda',Target='Wavelength')
    Rebin(InputWorkspace='run_lambda',OutputWorkspace='run_lambda_rebinned',Params='2.5,0.1,5.5')
    SumSpectra(InputWorkspace='run_lambda_rebinned', OutputWorkspace='run_lambda_summed')
    Divide(LHSWorkspace='run_lambda_summed', RHSWorkspace='run_monitors_lambda_rebinned', OutputWorkspace='normalized')

    from mantid.api import AnalysisDataService as ADS

    run_lambda_summed = ADS.retrieve('run_lambda_summed')
    run_monitors_lambda_rebinned = ADS.retrieve('run_monitors_lambda_rebinned')
    normalized = ADS.retrieve('normalized')

    fig, axes = plt.subplots(edgecolor='#ffffff', num='run_lambda_summed-1', subplot_kw={'projection': 'mantid'})
    axes.plot(run_lambda_summed, color='#1f77b4', label='run_lambda_summed: spec 1', linewidth=1.0, specNum=1)
    axes.plot(run_monitors_lambda_rebinned, color='#ff7f0e', label='run_monitors_lambda_rebinned: spec 1', linewidth=1.0, specNum=1)
    axes.plot(normalized, color='#2ca02c', distribution=False, label='normalized: spec 1', linewidth=1.0, specNum=1)
    axes.set_title('run_lambda_summed')
    axes.set_xlabel('Wavelength ($\AA$)')
    axes.set_ylabel('($\AA$)$^{-1}$')
    axes.set_xlim([2.405, 4.5])
    axes.set_yscale('log')
    axes.legend() #.draggable()

    #fig.show()

    # NOTE: This script could be improved further with adding comments,
    # and extracting and logging values for filename and binning params,
    # as in Exercise 2A above.