Source code for inspec.sim140425

# -*- coding: utf-8 -*-
# Time-stamp: <2020-12-24 18:21 ycopin@lyonovae03>

"""
.. _sim140425:

Simulation 140425
=================

Interface elements to `Simulation 140425
<http://euclid.roe.ac.uk/projects/ou-spe/wiki/SimulationData>`_.
"""



__author__ = "Yannick Copin <y.copin@ipnl.in2p3.fr>"

import numpy as N
from . import statistics as S


def read_sim_input(filename,
                   colnames=['idx', 'type', 'z', 'fHa', 'ewHa', 'mag']):

    data = N.genfromtxt(filename, dtype=None, names=colnames)
    data.sort(order='idx')

    print("%s: %d spectra (columns: %s)" % (filename, len(data), colnames))

    return data


def fig_hist_survey(data, title=''):
    """
    Plot various histograms (Z, Ha, etc.) vs. morphological types
    """

    import matplotlib.pyplot as P

    # Morphological type
    morphtypes = {
        (1, 2, 3): 'High activity 1',
        (4, 5, 6): 'High activity 2',
        (7, 8, 9): 'Intermediate activity 1',
        (10, 11, 12): 'Intermediate activity 2',
        (13, 14, 15): 'Quiescent 1',
        (16, 17, 18): 'Quiescent 2'
    }

    # Simplified morphological type classification
    types = (
        ("High 1", N.in1d(data['type'], list(range(1, 4)))),
        ("High 2", N.in1d(data['type'], list(range(4, 7)))),
        ("Intermediate", N.in1d(data['type'], list(range(7, 13)))),
        ("Quiescent", N.in1d(data['type'], list(range(13, 19)))),
    )

    # Histogram range
    bkw = dict(range=(3., 97.), percentiles=True)

    fig = P.figure()
    axz = fig.add_subplot(2, 2, 1,
                          xlabel="Redshift")
    axz.hist([ data['z'][idxs] for lbl, idxs in types],
             bins=S.hist_bins(data['z'], **bkw),
             histtype='stepfilled', stacked=True)

    axf = fig.add_subplot(2, 2, 2,
                          xlabel="Hα flux")
    axf.hist([ data['fHa'][idxs] for lbl, idxs in types],
             bins=S.hist_bins(data['fHa'], **bkw),
             histtype='stepfilled', stacked=True,
             label=[ "%s (%d)" % (lbl, len(idxs[idxs]))
                     for lbl, idxs in types ])
    axf.legend(loc='upper right', fontsize='x-small')

    axe = fig.add_subplot(2, 2, 3,
                          xlabel="Hα EW")
    axe.hist([ data['ewHa'][idxs] for lbl, idxs in types],
             bins=S.hist_bins(data['ewHa'], **bkw),
             histtype='stepfilled', stacked=True)

    axm = fig.add_subplot(2, 2, 4,
                          xlabel="Mag")
    axm.hist([ data['mag'][idxs] for lbl, idxs in types],
             bins=S.hist_bins(data['mag'], **bkw),
             histtype='stepfilled', stacked=True)

    if title:
        fig.suptitle(title)

    return fig