Updated file_io.rst to give a MVP for production

docs/fileio_usage.rst

@@ -3,60 +3,104 @@ Usage
 =====
 
 The easiest way to use :py:mod:`orsopy.fileio` (the module of :py:mod:`orsopy` that includes file reading and writing) to produce **metadata-rich .ort reduced reflectometry files** involves integrating this into your data reduction workflow.
-Early in the workflow, the :py:mod:`orsopy.fileio` should be imported and an empty :code:`orsopy.fileio.orso.Orso` header object (here we also import :py:mod:`numpy` which will be used later). 
+Early in the workflow, the :py:mod:`orsopy.fileio` should be imported and an empty :code:`orsopy.fileio.orso.Orso` header object (here we also import :py:mod:`numpy` which will be used later).
 
-.. code-block:: python 
+.. code-block:: python
 
-    import numpy as np
     from orsopy import fileio
+    from orsopy.fileio import (Reduction, Person, Orso, Experiment,
+                               Sample, DataSource, Measurement, InstrumentSettings)
+    import numpy as np
+    import datetime
 
     header = fileio.orso.Orso.empty()
 
-Having created the empty header object we can start to populate the appropriate components of it. 
-It is generally a good idea to populate the components as particular steps occur in the reduction process. 
-For example, if we want to identify the probing radiation as neutrons, we include this as follows. 
+Having created the empty header object we can start to populate the appropriate components of it.
+It is generally a good idea to populate the components as particular steps occur in the reduction process.
+In this example we fill out the experiment details and then wrap them in the appropriate class.
 
-.. code-block:: python 
+.. code-block:: python
 
-    header.data_source.experiment.probe = 'neutron'
+    title = 'my title'
+    instrument = 'PLATYPUS'
+    start_date = datetime.date.today()  # This needs to be an object of the type datetime
+    probe = 'neutron'
+    facility = 'ESS'
+    proposalID = 999999
+    doi = f'10.5286/ISIS.E.RB{99999}'
+
+    experiment = Experiment(title, instrument, start_date, probe, facility, proposalID, doi)
 
 Full details of the different components that can be populated can be found in the `documentation`_ here or in the `file format specification`_.
 Note that this specification includes information regarding the **required** and optional components to be included for a file to be considered a **valid** .ort file.
-It is not possible to write a .ort file without defining the columns present in the dataset, in this example we will have four columns of data, namely q, R, dR and dq (the final column is a description of the resolution function). 
-Columns are defined as follows, using the :code:`orsopy.fileio.base.Column` and :code:`orsopy.fileio.base.ErrorColumn` class objects (note that there are other `base classes`_ that can be used for a variety of objects).
+What we show here is not the minimum needed to write an `.ort` file, rather the minium to fill out all the major fields with information which is likely to be available at time of writing.
+
+We now write out the user and sample details, but note that the sample can be alot more descriptive than just a name.
+
+.. code-block:: python
+
+    user_name = 'Jos Cooper'
+    user_affil = 'ESS'
+
+    user = Person(user_name, user_affil)
+
+    # Now fill the sample details
+    sample_name = 'Silicon'
+
+    sample = Sample(sample_name)
+
+    # The measurement details needs some instrument settings assigned first
+    angle = 0.3  # This can be a float, or a range, though currently not a list
+    wavelength = (1.8, 8.8)  # Again a float or range
 
-.. code-block:: python 
-
+    instrument_settings = InstrumentSettings(angle, wavelength)
+
+    # Now add the instrument settings to the names of the files used
+    files = ['test_data.raw']  # A list of all raw files used reduced
+
+    measurment = Measurement(instrument_settings, files)
+
+We can now wrap together the user, experiment, sample and measurement information into a single object.
+
+.. code-block:: python
+    data_source_info = DataSource(user, experiment, sample, measurment)
+
+It is not possible to write an `.ort` file without defining the columns present in the dataset, in this example we will have four columns of data, namely q, R, dR and dq (the final column is a description of the resolution function).
+Columns are defined as follows, using the :code:`orsopy.fileio.base.Column` and :code:`orsopy.fileio.base.ErrorColumn` class objects.
+
+.. code-block:: python
+
+    # Interpreted units are ["1/angstrom", "1/nm", "1", "1/s", None]
     q_column = fileio.base.Column(name='Qz', unit='1/angstrom', physical_quantity='wavevector transfer')
     r_column = fileio.base.Column(name='R', unit=None, physical_quantity='reflectivity')
     dr_column = fileio.base.ErrorColumn(error_of='R', error_type='uncertainty', value_is='sigma')
     dq_column = fileio.base.ErrorColumn(error_of='Qz', error_type='resolution', value_is='sigma')
 
     header.columns = [q_column, r_column, dr_column, dq_column]
+    # We can also make some data so that this code example will write something out
+    q = np.array([0.01,0.02,0.03])
+    R = np.array([0.1,0.2,0.3])
+    dR = np.array([0.001,0.002,0.003])
+    dq = q * 0.02
 
-Any **required** metadata that is not included in the head will be written in the file as containing :code:`null`. 
-Having populated the metadata, we can now ensure that the metadata is correct with the following, 
+Any **required** metadata that is not included in the head will be written in the file as containing :code:`null`.
 
-.. code-block:: python 
-
-    correct_header = fileio.orso.Orso(**header.to_dict())
-
-This will produce a new object, if the metadata is correct. 
 
 Now, we then want to assign the data that we want to write (this will be after your data reduction has been performed).
-This is achieved by producing a :code:`fileio.orso.OrsoDataset` object, which takes the header and the relevant data columns (below these are :code:`q`, :code:`R`, :code:`dR`, and :code:`dq`) as inputs. 
+This is achieved by producing a :code:`fileio.orso.OrsoDataset` object, which takes the header and the relevant data columns (below these are :code:`q`, :code:`R`, :code:`dR`, and :code:`dq`) as inputs.
 
-.. code-block:: python 
+.. code-block:: python
 
-    dataset = fileio.orso.OrsoDataset(info=header, data=np.array([q, R, dR, dq]).T)
+    orso_class = Orso(data_source_info, reduction=Reduction('My own code'), columns=header.columns)  # reduction can also be assigned out of this funciton call
+    dataset = fileio.orso.OrsoDataset(info=orso_class, data=np.array([q, R, dR, dq]).T)
 
-The dataset can then be saved with the following function, where :code:`'my_file.ort'` is the name for the file to be saved under. 
+The dataset can then be saved with the following function, where :code:`'my_file.ort'` is the name for the file to be saved under.
 
 .. code-block:: python
 
-    fileio.orso.save_orso(datasets=[dataset], fname='my_file.ort') 
+    fileio.orso.save_orso(datasets=[dataset], fname='my_file.ort')  # note that the first input is a list of datasets
 
-Note that if you want to save more than one dataset in a single file, this can be achieved by including these in the list that is passed to this function. 
+Note that if you want to save more than one dataset in a single file, this can be achieved by including these in the list that is passed to this function.
 
 
 .. _`documentation`: ./modules.html#fileio