diff --git a/CAL/CalibrationCommon/lib/datacontainers/holography_dataset.py b/CAL/CalibrationCommon/lib/datacontainers/holography_dataset.py
index 76d76b368ecbbf3a3fd10b4691e4d3d9952cc8e3..7baf7477c187c5d30d068823edee58af31c44338 100644
--- a/CAL/CalibrationCommon/lib/datacontainers/holography_dataset.py
+++ b/CAL/CalibrationCommon/lib/datacontainers/holography_dataset.py
@@ -173,7 +173,8 @@ class HolographyDataset():
return False
return equality
- def find_central_beamlets(_, source, ra_dec, frequencies, beamlets):
+
+ def find_central_beamlets(self, source, ra_dec, frequencies, beamlets):
'''
This function finds the central beamlet of a target station for every
frequency.
@@ -187,68 +188,34 @@ class HolographyDataset():
@param beamlets: A list of beamlet numbers.
'''
logger.debug("Finding the central beamlets...")
- source_ra = source["RA"]
- source_dec = source["DEC"]
- # Store each pseudo distance in a dict of dicts: pd[frequency][beamlet]
- pseudo_distance = dict()
- # Iterate over all _frequencies...
- for frequency in frequencies:
- frequency_string = str(frequency)
- pseudo_distance[frequency_string] = dict()
-
- # and iterate over the beamlets.
- for beamlet in beamlets:
- beamlet_string = str(beamlet)
- if beamlet_string not in ra_dec[frequency_string]:
- continue
- ra = ra_dec[frequency_string][beamlet_string]['RA']
- dec = ra_dec[frequency_string][beamlet_string]['DEC']
-
- # calculate a pseudo distance that is an indicator if this
- # beamlet is closer to the source than the ones before.
- # Note that I am too lazy to calculate the spherical distance.
- # But keep in mind that this pseudo distance like its cousin
- # the geometrical distance are not really cutting it since RA
- # and DEC are coordinates of a spherical coordinate system.
- # But here the pseudo distance is good enough.
- pseudo_distance[frequency_string][beamlet_string] = abs(ra - source_ra) + abs(
- dec - source_dec)
-
+ # calculate a pseudo distance that is an indicator if this
+ # beamlet is closer to the source than the ones before.
+ # Note that I am too lazy to calculate the spherical distance.
+ # But keep in mind that this pseudo distance like its cousin
+ # the geometrical distance are not really cutting it since RA
+ # and DEC are coordinates of a spherical coordinate system.
+ # But here the pseudo distance is good enough.
+ pseudo_distance = self._compute_pseudo_distance_per_frequency_beam(ra_dec, source["RA"], source["DEC"])
# OK. Done with all the iteration business. Now check if across all
# _frequencies the same beamlet is the central one. It is allowed that
# the central beamlets are not always the same but it would be better
# and let the astronomers sleep better if it would always be the same.
# And also check if there is actually only one central beamlet per
# frequency.
- central_beamlet = dict()
- for (frequency_string, beamlets) in pseudo_distance.items():
- values = list(beamlets.values())
- keys = list(beamlets.keys())
- minimal_distance = min(values)
- # If I find a second beamlet that is 'closest' to the source for the
- # same frequency then that is a problem. The outset was that there
- # is only a single central beamlet and not a bunch of 'em.
- if values.count(minimal_distance) == 1:
- # All is good. Only one central beamlet.
- central_beamlet[frequency_string] = keys[values.index(minimal_distance)]
- else:
- text = "Found %d beamlets that have the same distance from the source position. Therefore a unique central beamlet does not exist." % (
- values.count(minimal_distance))
- logger.error(text)
- raise ValueError(text)
+ central_beamlets = self._find_minimal_distance_per_frequency(pseudo_distance)
# Now check if the central beamlet is the same across all _frequencies.
# I do that by creating a set from the central beamlets of all stations.
# If there is only one central beamlet for all _frequencies, the size of
# the set will be 1.
- central_beamlet_set = set(central_beamlet.values())
+ central_beamlet_set = set(central_beamlets.values())
if len(central_beamlet_set) == 1:
logger.debug(
"All is good, unicorns everywhere, there is only one central beamlet \"%s\" for all _frequencies.",
central_beamlet_set)
else:
- logger.warning("Multiple central beamlets have been identified: ", central_beamlet)
- return central_beamlet
+ logger.warning("Multiple central beamlets have been identified: ", central_beamlets)
+ return central_beamlets
def load_from_beam_specification_and_ms(self, station_name, list_of_hbs_ms_tuples):
"""
@@ -273,6 +240,50 @@ class HolographyDataset():
logger.exception("Error creating dataset for station \"%s\": %s", station_name, e)
raise e
+ def _compute_pseudo_distance_per_frequency_beam(self, ra_dec, source_ra, source_dec):
+ """
+ Compute the pseudo distance defined as abs(ra-source_ra) + abs(dec-source_dec) for all the ra_dec in
+ ra_dec a function of the frequencies and beamlet
+ :param ra_dec: ra and dec
+ :type ra_dec: Dict[str, Dict[str, numpy.ndarray]]
+ :param source_ra:
+ :param source_dec:
+ :return:
+ """
+ # Store each pseudo distance in a dict of dicts: pd[frequency][beamlet]
+ pseudo_distance = dict()
+ # Iterate over all _frequencies...
+ for frequency_string in ra_dec.keys():
+ pseudo_distance[frequency_string] = dict()
+ # and iterate over the beamlets.
+ for beamlet_string in ra_dec[frequency_string].keys():
+ ra = ra_dec[frequency_string][beamlet_string]['RA']
+ dec = ra_dec[frequency_string][beamlet_string]['DEC']
+
+ pseudo_distance[frequency_string][beamlet_string] = abs(ra - source_ra) + abs(
+ dec - source_dec)
+ return pseudo_distance
+
+ def _find_minimal_distance_per_frequency(_, distance_per_frequency_beam):
+ minimal_distance_per_frequency = dict()
+ for (frequency_string, beamlets) in distance_per_frequency_beam.items():
+ values = list(beamlets.values())
+ keys = list(beamlets.keys())
+ minimal_distance = min(values)
+ # If I find a second beamlet that is 'closest' to the source for the
+ # same frequency then that is a problem. The outset was that there
+ # is only a single central beamlet and not a bunch of 'em.
+ if values.count(minimal_distance) == 1:
+ # All is good. Only one central beamlet.
+ minimal_distance_per_frequency[frequency_string] = keys[values.index(minimal_distance)]
+ else:
+ text = "Found %d beamlets that have the same distance from the source position." \
+ " Therefore a unique central beamlet does not exist." % (
+ values.count(minimal_distance))
+ logger.error(text)
+ raise ValueError(text)
+ return minimal_distance_per_frequency
+
def __read_data(self, station_name, list_of_hbs_ms_tuples):
"""