SSB-47: redefinition of l, m and the scale of immage in the station plane, minor look and feel

CAL/CalibrationProcessing/lib/processing/inspect.py

@@ -7,9 +7,10 @@ from matplotlib import cm
 from matplotlib.figure import Figure
 from numpy import mgrid
 from scipy.constants import c as light_speed
-from scipy.fftpack import fft2, fftshift
+from numpy.fft import fft2, fftshift
 from scipy.interpolate import griddata
 
+
 __LIST_OR_NPARRAY = typing.Union[typing.List[float], numpy.ndarray]
 __DATATABLE_TYPE = typing.Dict[
     str, typing.Dict[str, typing.Dict[str, typing.Dict[str, numpy.ndarray]]]]
@@ -36,7 +37,7 @@ def complex_value_to_color(complex_array: numpy.ndarray, abs_max=None, abs_min=N
     norm_abs_array = (abs_array - abs_min) / normalization
     phase_array = (numpy.angle(linearized_complex_array) + numpy.pi) / 2 / numpy.pi
 
-    color_map = cm.get_cmap('autumn')
+    color_map = cm.get_cmap('hsv')
 
     colors = color_map(phase_array)
     colors[:, 0] *= norm_abs_array
@@ -66,11 +67,12 @@ def _grid_visibilities_lm_plane(l, m, v, sampling):
     points = numpy.stack((l, m), axis=1)
     grid_x, grid_y = mgrid[-1: 1: sampling * 1.j, -1: 1: sampling * 1.j]
 
-    from scipy.ndimage.filters import gaussian_filter
-    vis = griddata(points, v, (grid_x, grid_y), fill_value=0., method='cubic')
-    sigma = [2, 2]
-    vis.real = gaussian_filter(vis.real, sigma)
-    vis.imag = gaussian_filter(vis.imag, sigma)
+    #from scipy.ndimage.filters import gaussian_filter
+    vis = griddata(points, v, (grid_x, grid_y), fill_value=0., method='linear')
+    #sigma = [2, 2]
+    #vis.real = gaussian_filter(vis.real, sigma)
+    #vis.imag = gaussian_filter(vis.imag, sigma)
+
     return vis
 
 
@@ -82,7 +84,7 @@ def _fft_visibilities_lm_plane(l, m, v, sampling):
 
 
 def _plot_station_averaged_visibilities_lm_plane_single_frequency(figure: Figure, l_m_v,
-                                                                  sampling=250):
+                                                                  sampling=512):
     l, m, v, flagged = list(zip(*l_m_v))
 
     for index, polarization in enumerate(('XX', 'XY', 'YX', 'YY')):
@@ -95,7 +97,7 @@ def _plot_station_averaged_visibilities_lm_plane_single_frequency(figure: Figure
         canvas.set_ylim(-1, 1)
 
         vis = _grid_visibilities_lm_plane(l, m, v_pol, sampling)
-        color_mapped_vis = complex_value_to_color(vis, log=False)
+        color_mapped_vis = complex_value_to_color(vis, log=True)
         canvas.imshow(color_mapped_vis, extent=[-1, 1, -1, 1], origin='lower', resample=True)
 
 
@@ -110,13 +112,13 @@ def _plot_station_averaged_visibilities_station_plane_single_frequency(figure: F
         fft_vis = _fft_visibilities_lm_plane(l, m, v_pol, sampling)
 
         canvas = figure.add_subplot(2, 2, index + 1)
-        canvas.set_xlim(-60, 60)
-        canvas.set_ylim(-60, 60)
+        canvas.set_xlim(-30, 30)
+        canvas.set_ylim(-30, 30)
 
         canvas.set_xlabel('x [m]')
         canvas.set_ylabel('y [m]')
 
-        antenna_array_length = light_speed / frequency * sampling
+        antenna_array_length = light_speed / frequency * sampling / 2. / 2.
 
         color_mapped_fft = complex_value_to_color(fft_vis, log=False)
         canvas.imshow(color_mapped_fft[:, ::-1], origin='lower', extent=[-1 * antenna_array_length,
@@ -127,11 +129,15 @@ def _plot_station_averaged_visibilities_station_plane_single_frequency(figure: F
         canvas.set_title(polarization)
 
 
-def _plot_station_averaged_visibilities_lm_plane_datatable(data_table: __DATATABLE_TYPE):
+def _plot_station_averaged_visibilities_lm_plane_datatable(data_table: __DATATABLE_TYPE,
+                                                           central_beamlets):
     for station, data_per_station in data_table.items():
         for frequency_str, data_per_frequency in data_per_station.items():
-            l_m_v = [(data_per_beam['mean']['l'][0],
-                      data_per_beam['mean']['m'][0],
+
+            central_beam = data_per_frequency[central_beamlets[frequency_str]]['mean']
+
+            l_m_v = [(data_per_beam['mean']['l'][0] - central_beam['l'][0],
+                      data_per_beam['mean']['m'][0] - central_beam['m'][0],
                       dict(XX=data_per_beam['mean']['XX'][0], XY=data_per_beam['mean']['XY'][0],
                            YX=data_per_beam['mean']['YX'][0], YY=data_per_beam['mean']['YY'][0]),
                       data_per_beam['mean']['flag'][0])
@@ -171,6 +177,7 @@ def _plot_gains_as_frequency(data_table: __DATATABLE_TYPE, target_station):
 
                 canvas = figure.add_subplot(2, 2, polarization_index + 1)
                 canvas.set_title(polarization)
+                canvas.set_ylim(-numpy.pi, numpy.pi)
 
                 canvas.plot(frequencies / __MHZ_IN_HZ, numpy.angle(gains_per_antenna), '+-')
     plt.tight_layout()
@@ -187,7 +194,8 @@ def plot_station_averaged_visibilities_lm_plane(dset: HolographyDataset,
                                                derived_datatable_name='STATION_AVERAGED'):
     __apply_to_datatable(dset,
                          derived_datatable_name,
-                         _plot_station_averaged_visibilities_lm_plane_datatable)
+                         _plot_station_averaged_visibilities_lm_plane_datatable,
+                         dset.central_beamlets)
 
 
 def plot_gains_per_antenna(dset: HolographyDataset, derived_datatable_name='GAINS'):