From ec95f2abfec7bd181f96260d5f35cbb761bf04b9 Mon Sep 17 00:00:00 2001
From: Mattia Mancini <mancini@astron.nl>
Date: Wed, 19 Jun 2019 14:21:57 +0000
Subject: [PATCH] SSB-47: fixing space domain for antenna positions
---
.../lib/processing/inspect.py | 38 ++++++++++++++++---
.../lib/processing/interpolate.py | 14 +------
2 files changed, 34 insertions(+), 18 deletions(-)
diff --git a/CAL/CalibrationProcessing/lib/processing/inspect.py b/CAL/CalibrationProcessing/lib/processing/inspect.py
index dfff4e8db52..4c93361369a 100644
--- a/CAL/CalibrationProcessing/lib/processing/inspect.py
+++ b/CAL/CalibrationProcessing/lib/processing/inspect.py
@@ -13,6 +13,13 @@ import numba
from numba import jit
import numpy
+from matplotlib.ticker import AutoMinorLocator
+
+
+def __set_minor_ticks_for_canvas(canvas):
+ canvas.xaxis.set_minor_locator(AutoMinorLocator())
+ canvas.yaxis.set_minor_locator(AutoMinorLocator())
+
__LIST_OR_NPARRAY = typing.Union[typing.List[float], numpy.ndarray]
__DATATABLE_TYPE = typing.Dict[
@@ -212,7 +219,7 @@ def _plot_gains_as_frequency(data_table: __DATATABLE_TYPE, target_station):
for polarization_index, polarization in enumerate(['XX', 'XY', 'YX', 'YY']):
extracted_data = [
- (float(frequency_str), data_per_frequency[polarization]['gains'][:, 0])
+ (float(frequency_str), data_per_frequency[polarization]['gains'])
for frequency_str, data_per_frequency in data_per_station.items()]
frequencies, gains = list(zip(*extracted_data))
@@ -261,8 +268,9 @@ def plot_gains_fit_per_antenna(dset: HolographyDataset, interpolation_parameters
for station, data_per_station in gains.items():
for polarization_index, polarization in enumerate(['XX', 'YY']):
+
extracted_data = [
- (float(frequency_str), data_per_frequency[polarization]['gains'][:, 0])
+ (float(frequency_str), data_per_frequency[polarization]['gains'])
for frequency_str, data_per_frequency in data_per_station.items()]
frequencies, gains = list(zip(*extracted_data))
@@ -270,10 +278,30 @@ def plot_gains_fit_per_antenna(dset: HolographyDataset, interpolation_parameters
gains = numpy.stack(gains, axis=1)
for antenna, antenna_data in enumerate(gains):
+ amplitude_par = interpolation_parameters[station][polarization][str(antenna)]['amplitude']['parameters']
+ phase_par = \
+ interpolation_parameters[station][polarization][str(antenna)]['phase'][
+ 'parameters']
- plt.figure('station %s antenna %s polarization %s' %
- (station, antenna, polarization))
+ figure = plt.figure('station %s antenna %s' %
+ (station, antenna))
- plt.plot(frequencies, antenna_data)
+ canvas = figure.add_subplot(2, 2, polarization_index + 1)
+ __set_minor_ticks_for_canvas(canvas)
+ canvas.set_title(polarization)
+ canvas.set_xlabel('frequency (MHz)')
+ canvas.set_ylabel('Amplitude')
+ canvas.plot(frequencies/__MHZ_IN_HZ, numpy.abs(antenna_data), '+')
+ canvas.plot(frequencies/__MHZ_IN_HZ, amplitude_par['m'] * frequencies + amplitude_par['q'])
+
+ canvas = figure.add_subplot(2, 2, polarization_index + 3)
+ __set_minor_ticks_for_canvas(canvas)
+
+ canvas.set_title(polarization)
+ canvas.set_xlabel('frequency (MHz)')
+ canvas.set_ylabel('Phase')
+ canvas.plot(frequencies/__MHZ_IN_HZ, numpy.angle(antenna_data), '+')
+ canvas.plot(frequencies/__MHZ_IN_HZ, phase_par['m'] * frequencies + phase_par['q'])
+ plt.tight_layout()
plt.show()
diff --git a/CAL/CalibrationProcessing/lib/processing/interpolate.py b/CAL/CalibrationProcessing/lib/processing/interpolate.py
index 61ae39584cc..ffcd7fccb14 100644
--- a/CAL/CalibrationProcessing/lib/processing/interpolate.py
+++ b/CAL/CalibrationProcessing/lib/processing/interpolate.py
@@ -83,18 +83,6 @@ def _first_guess(x, y, function):
return numpy.array((m, q, sigma))
-def _ln_likelihood_phase(parameters, x, y):
- m, q, sigma = parameters
- y_model = m * x + q
-
- y_model_wrapped = wrap_linear(y_model)
- inv_variance = sigma ** -2.
- residual = numpy.square(y - y_model_wrapped) * inv_variance
-
- ln_likelihood = -.5 * numpy.sum(residual)
- return ln_likelihood
-
-
def __ln_prior(parameters):
m, b, sigma = parameters
if sigma >= 0:
@@ -150,7 +138,7 @@ def _interpolate_gains(x, y):
phase = numpy.angle(y).flatten()
amplitude_parameters, amp_mc_samples = _interpolate_mc_mc(x, amplitude, linear, _ln_likelihood_abs)
- phase_parameters, phase_mc_samples = _interpolate_mc_mc(x, phase, linear, _ln_likelihood_phase)
+ phase_parameters, phase_mc_samples = _interpolate_mc_mc(x, phase, linear, _ln_likelihood_abs)
result = dict(amplitude=dict(parameters=amplitude_parameters, mc_samples=amp_mc_samples),
phase=dict(parameters=phase_parameters, mc_samples=phase_mc_samples))
--
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