Quickstart
==========

This guide will get you started with ``daxs`` in just a few minutes.

Create a Source of Scans
------------------------

.. code-block:: python

   from daxs.sources import Hdf5Source

   # Define the mapping of data fields in the HDF5 file.
   data_mappings = {
       "x": ".1/measurement/hdh_energy",
       "signal": ".1/measurement/det_dtc",
       "monitor": ".1/measurement/I02"
   }

   # Define which scans to load.
   scan_ids = "1-3" 

   # Create the source of scans.
   source = Hdf5Source("sample_0001.h5", scan_ids, data_mappings)

   # List available scans.
   print(f"Available scans: {source.scans}")


Create a Measurement
--------------------

.. code-block:: python

   from daxs import Xas

   # Create a measurement from scan source.
   measurement = Xas(source)

   # Access individual scans.
   for scan in measurement.scans:
       print(f"Scan index: {scan.index}, X-axis: {scan.x}")

Process the Data
----------------

.. code-block:: python

   # Remove signal outliers using the Hampel filter method.
   measurement.remove_outliers(method="hampel")

   # Aggregate scans using fraction of the sum of signals over the sum of monitors.
   # Normalize the aggregated signal by the area under the curve.
   measurement.process(aggregation="fraction of sums", normalization="area")

Visualise the Results
---------------------

.. code-block:: python

   import matplotlib.pyplot as plt

   fig, ax = plt.subplots()
   
   # Plot on the provided axes.
   measurement.plot(ax=ax)

   # Customize the plot as needed.
   ax.set_title("Sample")
   plt.show()