evaluation.py 11.7 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
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
LUSTIG Peter's avatar
LUSTIG Peter committed
25
26
27
import dill as pickle
from matplotlib.ticker import FormatStrFormatter
from collections import OrderedDict
LUSTIG Peter's avatar
LUSTIG Peter committed
28
from utils import completness_purity_wcs, completness_worker, purity_worker
LUSTIG Peter's avatar
LUSTIG Peter committed
29
from utils import find_nearest
LUSTIG Peter's avatar
LUSTIG Peter committed
30
31


LUSTIG Peter's avatar
LUSTIG Peter committed
32
33
34
35
36
37
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]
LUSTIG Peter's avatar
LUSTIG Peter committed
38
39
        self.sources = [sources[i] for i in idxsort]
        self.fake_sources = [fake_sources[i] for i in idxsort]
LUSTIG Peter's avatar
LUSTIG Peter committed
40
41
42
43
44
45
46
47
48
49
50

        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

51
52
        self.thresholds = np.linspace(threshold_range[0], threshold_range[1],
                                      threshold_bins)
LUSTIG Peter's avatar
LUSTIG Peter committed
53
54
55
56
57
58
59
60
61
62
63
64
65
66
        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)
LUSTIG Peter's avatar
LUSTIG Peter committed
67
        self.completness, self.purity, self.hitmap = self.GetCP()
LUSTIG Peter's avatar
LUSTIG Peter committed
68

69
70
71
72
73
74
75
76
77
    def GetCompletnessBin(self, xbin, ybin):
        return self.completness[:, ybin, xbin, :]

    def GetPurityBin(self, xbin, ybin):
        return self.purity[:, ybin, xbin, :]

    def GetHitsBin(self, xbin, ybin):
        return self.purity[:, ybin, xbin]

LUSTIG Peter's avatar
LUSTIG Peter committed
78
    def GetCP(self, sources=None, fake_sources=None, wcs=None, shape=None,
79
              pool=None):
LUSTIG Peter's avatar
LUSTIG Peter committed
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
        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)):
LUSTIG Peter's avatar
LUSTIG Peter committed
97
                tmpres = self.completness_purity(sources[i], fake_sources[i],
LUSTIG Peter's avatar
LUSTIG Peter committed
98
99
100
101
                                                 wcs, shape)
                comp.append(tmpres[0])
                pur.append(tmpres[1])
                hitm.append(tmpres[2])
LUSTIG Peter's avatar
LUSTIG Peter committed
102
            return np.array(comp), np.array(pur), np.array(hitm)
LUSTIG Peter's avatar
LUSTIG Peter committed
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133

    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
LUSTIG Peter's avatar
LUSTIG Peter committed
134

135
136
    def PlotBin(self, data, title='', flux=None, thresh=None,
                nfluxlabels=None, nthreshlabels=None, **kwargs):
LUSTIG Peter's avatar
LUSTIG Peter committed
137
138
139
        tickfs = 20
        labelfs = 25

140
141
142
        if flux is None:
            flux = self.flux.to_value(u.mJy)
        if thresh is None:
143
            thresh = self.thresholds
144

LUSTIG Peter's avatar
LUSTIG Peter committed
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
        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)
174
        plt.imshow(data, origin='lower', aspect='auto', **kwargs)
LUSTIG Peter's avatar
LUSTIG Peter committed
175
176
177
        cbar = plt.colorbar()
        cbar.ax.tick_params(labelsize=tickfs)

178
    def PlotFixedThreshold(self, data, thresholds, nfluxlabels=None,
179
                           hlines=None, ylabel=''):
180
181
182
183

        linestyles = ['-', '--', '-.', ':']
        real_thresholds = find_nearest(self.thresholds, thresholds)
        _x = self.flux.to_value(u.mJy)
184
185

        plt.figure()
186
187
188
189
190
191
192
193
194
195
        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)
196
        plt.ylabel(ylabel, fontsize=25)
197
198
199
200
201
202
203
204
205
206
        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)

207
208
    def PlotOverview(self, flux=None, completness=None, purity=None,
                     threshold=None):
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
        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):
                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")

240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
    def PlotHitmap(self, flux=None, **kwargs):
        fluxidx = find_nearest(self.flux.to_value(u.mJy), flux.to_value(u.mJy))
        for iidx in fluxidx:
            plt.figure()
            plt.imshow(self.hitmap[iidx], origin='lower', **kwargs)
            plt.title('Hitmap {:.1f}'.format(flux), fontsize=30, y=1.02)


def UglyLoader(filename):
    hdul = fits.HDUList(fits.open(filename))
    nfluxes = hdul[0].header['NFLUXES']
    print('{} different fluxes found'.format(nfluxes))

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

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

        SOURCE.append(Table.read(hdul['DETECTED_SOURCES{}'
262
                                      .format(isimu)]))
263
        FSOURCE.append(Table.read(hdul['FAKE_SOURCES{}'
264
                                       .format(isimu)]))
265
266
267
268
269
270
271
272
273
274
275
    return u.Quantity(FLUX), SOURCE, FSOURCE


if __name__ == '__main__':

    DATA_DIR = "/home/peter/Dokumente/Uni/Paris/Stage/data/v_1"
    data = NikaMap.read(Path(DATA_DIR) / '..' / 'map.fits')
    sh = data.data.shape
    wcs = data.wcs

    fname = ('/home/peter/Dokumente/Uni/Paris/Stage/'
276
             'FirstSteps/Completness/NEWcombined_tables_long.fits')
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297

    FLUX, SOURCE, FSOURCE = UglyLoader(fname)

    xx = PCEvaluation(SOURCE, FSOURCE, sh, wcs, FLUX, mapbins=19,
                      threshold_bins=6, threshold_range=(2.5, 5))
    xx.PlotBin(xx.GetCompletnessBin(9, 9), nfluxlabels=10, title='Completness')
    xx.PlotBin(xx.GetPurityBin(9, 9), nfluxlabels=10, title='Purity')
    xx.PlotFixedThreshold(xx.GetCompletnessBin(9, 9), np.array([3, 5]),
                          ylabel='Completness')
    xx.PlotFixedThreshold(xx.GetPurityBin(9, 9), np.array([3, 5]),
                          ylabel='Purity')
    xx.PlotOverview(flux=5*u.mJy)
    xx.PlotHitmap(flux=5*u.mJy)
    yy = PCEvaluation(SOURCE, FSOURCE, sh, wcs, FLUX, mapbins=9,
                      threshold_bins=6, threshold_range=(2.5, 5))
    yy.PlotFixedThreshold(yy.GetCompletnessBin(4, 4), np.array([3, 5]),
                          ylabel='Completness')
    yy.PlotFixedThreshold(yy.GetPurityBin(4, 4), np.array([3, 5]),
                          ylabel='Purity')

    plt.show(block=True)