evaluation.py 10.4 KB
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from __future__ import absolute_import, division, print_function

from pathlib import Path
import os
import numpy as np
import matplotlib.pyplot as plt

from multiprocessing import Pool, cpu_count
from functools import partial

from astropy import units as u
from astropy.io import ascii
from astropy.wcs import WCS
from astropy.utils.console import ProgressBar
from astropy.table import vstack

from scipy.optimize import curve_fit

from nikamap import NikaMap, Jackknife
from nikamap.utils import pos_uniform
from astropy.io import fits
from astropy.table import Table, MaskedColumn
import sys
from mpl_toolkits.axes_grid1 import make_axes_locatable
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import dill as pickle
from matplotlib.ticker import FormatStrFormatter
from collections import OrderedDict
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from utils import completness_purity_wcs, completness_worker, purity_worker
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from utils import find_nearest
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import os
os.getcwd()
'''
%load_ext autoreload
%autoreload 2
%matplotlib tk
'''

plt.ion()


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class PCEvaluation:
    def __init__(self, sources, fake_sources, shape, wcs, flux=None,
                 mapbins=19, threshold_bins=5, threshold_range=(3, 5)):

        idxsort = np.argsort(flux.to_value(u.mJy))
        self.flux = flux[idxsort]
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        self.sources = [sources[i] for i in idxsort]
        self.fake_sources = [fake_sources[i] for i in idxsort]
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        self.completness = None
        self.purity = None
        assert len(sources) == len(fake_sources), ("Number of results for "
                                                   "sources and fake "
                                                   "sources is not the same.")
        assert len(sources) == len(flux), ("Number of provided fluxes differs "
                                           "from number of simulation results")
        assert type(mapbins) is int, "number of bins must be an integer"
        self.bins = mapbins

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        self.thresholds = np.linspace(threshold_range[0], threshold_range[1],
                                      threshold_bins)
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        threshold_edges = np.linspace(threshold_range[0], threshold_range[1],
                                      threshold_bins+1)

        self.shape_3D, self.wcs_3D = completness_purity_wcs(
                                        shape, wcs,
                                        bins=self.bins,
                                        threshold_range=threshold_range,
                                        threshold_bins=threshold_bins)

        # Testing the lower edges
        wcs_threshold = self.wcs_3D.sub([3])
        assert np.all(np.abs(wcs_threshold.all_pix2world(
                                np.arange(threshold_bins+1)-0.5, 0)
                             - threshold_edges) < 1e-15)
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        self.completness, self.purity, self.hitmap = self.GetCP()
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    def GetCP(self, sources=None, fake_sources=None, wcs=None, shape=None,
                pool=None):
        if sources is None:
            sources = self.sources
        if fake_sources is None:
            fake_sources = self.fake_sources
        if wcs is None:
            wcs = self.wcs_3D
        if shape is None:
            shape = self.shape_3D

        if pool is not None:
            f = partial(self.completness_purity, wcs=wcs, shape=shape)
            res = pool.starmap(f, (sources, fake_sources))
            res = list(zip(*res))
            return res[0], res[1], res[2]
        else:
            comp, pur, hitm = [], [], []
            for i in range(len(sources)):
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                tmpres = self.completness_purity(sources[i], fake_sources[i],
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                                                 wcs, shape)
                comp.append(tmpres[0])
                pur.append(tmpres[1])
                hitm.append(tmpres[2])
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            return np.array(comp), np.array(pur), np.array(hitm)
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    def completness_purity(self, sources, fake_sources, wcs=None,
                           shape=None):
        """Compute completness map for a given flux"""

        min_threshold, max_threshold = wcs.sub([3]).all_pix2world(
                                                        [-0.5, shape[2]-1],
                                                        0)[0]

        # %load_ext snakeviz
        # %snakeviz the following line.... all is spend in the find_peaks /
        # fit_2d_gaussian
        # TODO: Change the find_peaks routine, or maybe just the
        # fit_2d_gaussian to be FAST ! (Maybe look into gcntrd.pro routine
        # or photutils.centroid.centroid_1dg maybe ?)

        completness, norm_comp = completness_worker(shape, wcs, sources,
                                                    fake_sources,
                                                    min_threshold,
                                                    max_threshold)

        purity, norm_pur = purity_worker(shape, wcs, sources, max_threshold)

        # norm can be 0, so to avoid warning on invalid values...
        with np.errstate(divide='ignore', invalid='ignore'):
            completness /= norm_comp[..., np.newaxis]
            purity /= norm_pur

        # TODO: One should probably return completness AND norm if one want to
        # combine several fluxes
        return completness, purity, norm_comp
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    def PlotBin(self, data, title='', flux=None, thresh=None,
                nfluxlabels=None, nthreshlabels=None, **kwargs):
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        tickfs = 20
        labelfs = 25

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        if flux is None:
            flux = self.flux.to_value(u.mJy)
        if thresh is None:
            thresh = self.threshold

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        if nfluxlabels is not None:
            _label_flux = np.geomspace(flux[0], flux[-1], nfluxlabels)
            _f_idx = find_nearest(flux, _label_flux)
            flblpos, _flbl = _f_idx, flux[_f_idx]
        else:
            flblpos, _flbl = np.arange(len(flux)), flux

        if nthreshlabels is not None:
            _label_thresh = np.linspace(thresh[0], thresh[-1], nthreshlabels)
            _t_idx = find_nearest(thresh, _label_thresh)
            print(_t_idx)
            tlblpos, _tlbl = _t_idx, thresh[_t_idx]
        else:
            tlblpos, _tlbl = np.arange(len(thresh)), thresh

        flbl = []
        for i in range(len(_flbl)):
            flbl.append('{:.1f}'.format(_flbl[i]))

        tlbl = []
        for i in range(len(_tlbl)):
            tlbl.append('{:.1f}'.format(_tlbl[i]))

        plt.figure()
        plt.title(title, fontsize=30)
        plt.xlabel('Detection Threshold [SNR]', fontsize=labelfs)
        plt.ylabel('Flux [mJy]', fontsize=labelfs)
        plt.xticks(tlblpos, tlbl, fontsize=tickfs)
        plt.yticks(flblpos, flbl, fontsize=tickfs)
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        plt.imshow(data, origin='lower', aspect='auto', **kwargs)
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        cbar = plt.colorbar()
        cbar.ax.tick_params(labelsize=tickfs)

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    def PlotFixedThreshold(self, data, thresholds, nfluxlabels=None,
                           hlines=None):

        linestyles = ['-', '--', '-.', ':']
        real_thresholds = find_nearest(self.thresholds, thresholds)
        _x = self.flux.to_value(u.mJy)
        for i in range(len(real_thresholds)):
            _y = data[:, real_thresholds[i]]
            plt.plot(_x, _y, linestyle=linestyles[i],
                     label='{:.1f}'.format(
                                self.thresholds[real_thresholds[i]]))
        if hlines is not None:
            for i, val in enumerate(hlines):
                plt.axhline(val, color='r')
        plt.title('Fixed Threshold', fontsize=30, y=1.02)
        plt.xlabel('Source Flux [mJy]', fontsize=25)
        plt.ylabel('Completness', fontsize=25)
        plt.yticks(fontsize=20)
        plt.xticks(fontsize=20)
        plt.subplots_adjust(left=0.12)
        ax = plt.gca()
        ax.set_xscale("log", nonposx='clip')
        # legend = plt.legend(fontsize=25, title='SNR', loc='lower right')
        legend = plt.legend(fontsize=25, title='SNR', loc='upper left',
                            framealpha=1)
        plt.setp(legend.get_title(), fontsize=25)
        plt.show(block=True)

    def PlotOverview(self, completness=None, purity=None, threshold=None,
                     flux=None):
        if completness is None:
            completness = self.completness
        if purity is None:
            purity = self.purity
        if threshold is None:
            threshold = self.thresholds
        threshold_bins = completness.shape[-1]
        fluxidx = find_nearest(self.flux.to_value(u.mJy), flux.to_value(u.mJy))
        realflux = self.flux[fluxidx]
        for iimg in range(len(fluxidx)):
            _completness = completness[fluxidx[iimg]]
            _purity = purity[fluxidx[iimg]]
            fig, axes = plt.subplots(nrows=2, ncols=threshold_bins,
                                     sharex=True, sharey=True)

            for i in range(threshold_bins):
                print(i)
                axes[0, i].imshow(_completness[:, :, i], vmin=0, vmax=1)
                im = axes[1, i].imshow(_purity[:, :, i], vmin=0, vmax=1)
                axes[1, i].set_xlabel("thresh={:.2f}".format(threshold[i]))
                if i == (threshold_bins-1):
                    # print('-----------')
                    divider = make_axes_locatable(axes[1, i])
                    cax = divider.append_axes('right', size='5%', pad=0.0)
                    fig = plt.gcf()
                    fig.colorbar(im, cax=cax, orientation='vertical')

            if flux is not None:
                axes[0, 1].set_title("{:.1f}".format(realflux[iimg]))
            axes[0, 0].set_ylabel("completness")
            axes[1, 0].set_ylabel("purity")

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DATA_DIR = "/home/peter/Dokumente/Uni/Paris/Stage/data/v_1"
data = NikaMap.read(Path(DATA_DIR) / '..' / 'map.fits')
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sh = data.data.shape
wcs = data.wcs
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hdul = fits.HDUList(fits.open('/home/peter/Dokumente/Uni/Paris/Stage/'
                              'FirstSteps/Completness/'
                              'combined_tables_long.fits'))
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nfluxes = hdul[0].header['NFLUXES']
print('{} different fluxes found'.format(nfluxes))

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FLUX = []
SOURCE = []
FSOURCE = []

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# for isimu in range(nfluxes):
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for isimu in range(nfluxes):
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    FLUX.append(u.Quantity(hdul[0].header['flux{}'.format(isimu)]))

    SOURCE.append(Table.read(hdul['DETECTED_SOURCES{}'
                                  .format(FLUX[isimu])]))
    FSOURCE.append(Table.read(hdul['FAKE_SOURCES{}'
                                   .format(FLUX[isimu])]))

xx = PCEvaluation(SOURCE, FSOURCE, sh, wcs, u.Quantity(FLUX))
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# %% testfunctions
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dd = xx.completness
print('comp shape', dd.shape)
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# xx.PlotBin(xx.completness[:, 9, 9, :])
# xx.PlotFixedThreshold(xx.completness[:, 9, 9, :], np.array([3, 5]))
xx.PlotOverview(flux=5*u.mJy)
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plt.show(block=True)
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sys.exit('Done')