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.
    selection = "1-3"

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