Guide to analyse a ``Grid``
===========================

- A grid of secondaries is analysed for a given primary
- Recovered parameters of secondaries are compared with input parameters

.. code:: ipython3

    from astropy import units as u
    import numpy as np
    import matplotlib.pyplot as plt
    import sed_analysis_tools as st
    import warnings
    warnings.filterwarnings("ignore")
    
    # Assumed filter system
    filter_set = st.FilterSet(list_pivot_wavelengths=np.logspace(3.1, 4.7, 20) * u.Angstrom)
    print(filter_set.list_pivot_wavelengths)


.. parsed-literal::

    [ 1258.92541179  1528.30673266  1855.32951135  2252.32770499
      2734.27445617  3319.34681844  4029.6113202   4891.85622354
      5938.60186759  7209.32720222  8751.95877204 10624.67830894
     12898.11710826 15658.01995138 19008.47904698 23075.85996195
     28013.56761199 34007.83206543 41284.73237715 50118.72336273] Angstrom


Creating a grid of secondaries
------------------------------

.. code:: ipython3

    logT_A, logL_A = 3.7, 0
    frac_err = 0.01
    logT_B_list = np.linspace(3.4,5.2,10)
    logL_B_list = np.linspace(-4.5,0,10)
    logT_B_list, logL_B = np.meshgrid(logT_B_list, logL_B_list)
    logT_B_list, logL_B_list = logT_B_list.flatten(), logL_B.flatten()
    
    print("Total secondaries:", len(logT_B_list) * len(logL_B_list))
    fig, ax = plt.subplots()
    st.Plotter.plot_isochrone_and_wd(ax)
    plt.scatter(logT_A, logL_A, marker="s", edgecolors="k", facecolor="none")
    plt.scatter(logT_B_list, logL_B_list, marker=".", color="0.5")


.. parsed-literal::

    Total secondaries: 10000




.. parsed-literal::

    <matplotlib.collections.PathCollection at 0x15f44eea0>




.. image:: guide_Grid/output_3_2.png


.. code:: ipython3

    grid = st.Grid(
        T_A=10**logT_A * u.K,
        L_A=10**logL_A * u.solLum,
        logT_B_list=logT_B_list,
        logL_B_list=logL_B_list,
        niter=100,
        frac_err=frac_err,
        name="test_grid",
        filter_set=filter_set
    )

Calculating fitting parameter across the grid and plotting
----------------------------------------------------------

.. code:: ipython3

    grid.calculate_params(refit=True)

.. code:: ipython3

    grid.plot()



.. image:: guide_Grid/output_7_0.png


.. code:: ipython3

    # Input and recovered positions connected by lines
    # Average of all random realisation
    grid.plot_Double_fitting_lines()



.. image:: guide_Grid/output_8_0.png


.. code:: ipython3

    # Input and recovered positions connected by lines
    # Only 1st random realisation
    grid.plot_Double_fitting_lines(niter=0)



.. image:: guide_Grid/output_9_0.png


.. code:: ipython3

    # Input and recovered positions connected by lines
    # Diamond markers show Monte-Carlo errors
    # Average of all random realisation
    fig, ax = plt.subplots(figsize=(6,6))
    grid.plot_Double_fitting_points(ax=ax)
    grid.plot_skeleton(ax=ax)
    st.Plotter.plot_isochrone_and_wd(ax=ax)
    ax.set_ylim(-5.5,1.9)
    ax.set_xlim(5.55,3.15)
    ax.legend()




.. parsed-literal::

    <matplotlib.legend.Legend at 0x16b6e5700>




.. image:: guide_Grid/output_10_1.png


.. code:: ipython3

    # Recovered % errors in T_2
    grid.plot_Double_fitting_T2err()



.. image:: guide_Grid/output_11_0.png


.. code:: ipython3

    # SEDs of secondaries coloured with EWR
    # Only 1st random realisation
    grid.plot_sed_patches()



.. image:: guide_Grid/output_12_0.png