diff --git a/tangostationcontrol/tangostationcontrol/clients/attribute_wrapper.py b/tangostationcontrol/tangostationcontrol/clients/attribute_wrapper.py
index ee898ff561ea916431a7100f098069c68b4e5a13..46589552326436c926c98750b91b9b42b8b52d4f 100644
--- a/tangostationcontrol/tangostationcontrol/clients/attribute_wrapper.py
+++ b/tangostationcontrol/tangostationcontrol/clients/attribute_wrapper.py
@@ -89,22 +89,26 @@ class attribute_wrapper(attribute):
self.datatype = datatype
if dims == (1,):
+ # scalar
# Tango defines a scalar as having dimensions (1,0), see https://pytango.readthedocs.io/en/stable/server_api/attribute.html
max_dim_x = 1
max_dim_y = 0
dtype = datatype
shape = ()
elif len(dims) == 1:
+ # spectrum
max_dim_x = dims[0]
max_dim_y = 0
dtype = (datatype,)
shape = (max_dim_x,)
elif len(dims) == 2:
+ # image
max_dim_x = dims[1]
max_dim_y = dims[0]
dtype = ((datatype,),)
shape = (max_dim_y, max_dim_x)
else:
+ # higher dimensional
# >2D arrays collapse into the X and Y dimensions. The Y (major) dimension mirrors the first dimension given, the
# rest collapses into the X (minor) dimension.
max_dim_x = reduce(mul, dims[1:])
diff --git a/tangostationcontrol/tangostationcontrol/clients/statistics/client.py b/tangostationcontrol/tangostationcontrol/clients/statistics/client.py
index 8cbcffc445fc2d3124e11f1f1f42ced225860c23..9ea336ad9e81df319f8596ab76d3b6080db7f567 100644
--- a/tangostationcontrol/tangostationcontrol/clients/statistics/client.py
+++ b/tangostationcontrol/tangostationcontrol/clients/statistics/client.py
@@ -15,6 +15,7 @@ from tangostationcontrol.clients.comms_client import AsyncCommClient
from tangostationcontrol.clients.tcp_replicator import TCPReplicator
from tangostationcontrol.clients.udp_receiver import UDPReceiver
from tangostationcontrol.clients.statistics.consumer import StatisticsConsumer
+from tangostationcontrol.common.constants import constants
logger = logging.getLogger()
@@ -25,7 +26,7 @@ class StatisticsClient(AsyncCommClient):
and provides a CommClient interface to expose points to a Device Server.
"""
- def __init__(self, collector, udp_options, tcp_options, fault_func, event_loop=None, queuesize=1024):
+ def __init__(self, collector, udp_options, tcp_options, fault_func, event_loop=None, queuesize=constants.STATISTICS_CLIENT_QUEUE_SIZE):
"""
Create the statistics client and connect() to it and get the object node.
diff --git a/tangostationcontrol/tangostationcontrol/clients/udp_receiver.py b/tangostationcontrol/tangostationcontrol/clients/udp_receiver.py
index fbe17bc22ffde29bc90adfb036193590617c0990..585655172530fa7efbacc32c576f76362cb4785f 100644
--- a/tangostationcontrol/tangostationcontrol/clients/udp_receiver.py
+++ b/tangostationcontrol/tangostationcontrol/clients/udp_receiver.py
@@ -90,7 +90,7 @@ class UDPReceiver(Thread, StatisticsClientThread):
while self.stream_on:
try:
- packet, _, _, _ = self.sock.recvmsg(9000)
+ packet, _, _, _ = self.sock.recvmsg(constants.MAX_PACKET_SIZE)
self.parameters["nof_packets_received"] += numpy.uint64(1)
self.parameters["nof_bytes_received"] += numpy.uint64(len(packet))
diff --git a/tangostationcontrol/tangostationcontrol/common/constants.py b/tangostationcontrol/tangostationcontrol/common/constants.py
index cc0a3a0f14595bb0dc946fb531ab347de6bd04c6..69dc7c2f435838fc620b90d7491ca36732ed3397 100644
--- a/tangostationcontrol/tangostationcontrol/common/constants.py
+++ b/tangostationcontrol/tangostationcontrol/common/constants.py
@@ -48,7 +48,8 @@ class constants:
N_beamsets_sdp = 2
N_beamsets_ctrl = 1
- N_beamlets_sdp = 976
+ # The maximum amount of beamlets the SDP (and we) support
+ N_beamlets_max = 976
# number of actively controlled beamlets
N_beamlets_ctrl = 488
N_beamlets_out = 488
@@ -61,11 +62,23 @@ class constants:
# Maximum array size to allocate for beam_device pointings,
MAX_POINTINGS = 1024
+ # Maximum number of antenna inputs we support (used to determine array sizes)
+ MAX_INPUTS = 192
+ # Maximum number of subbands for which we collect XSTs simultaneously
+ MAX_PARALLEL_SUBBANDS = 8
+ # Expected block for XST's
+ BLOCK_LENGTH = 12
+ # Complex values are (real, imag). A bit silly, but we don't want magical
+ VALUES_PER_COMPLEX = 2
+ # Max blocks for the BST statistics
+ MAX_BLOCKS = 2
+
# number of sockets the psoc has
PSOC_SOCKETS = 8
# main clock frequency of the subrack is 200MHz NOTE: type is a float here
- F_CLOCK = 200e6
+ CLK_200 = 200_000_000
+ CLK_160 = 160_000_000
# default subband we use because of its low RFI
DEFAULT_SUBBAND = 102
@@ -86,12 +99,21 @@ class constants:
# Maximum number of supported simultaneous replicator clients
MAX_STATISTICS_CLIENTS = 128
+ # the maximum amount of packets unhandled packets the statistics client thread will queue
+ STATISTICS_CLIENT_QUEUE_SIZE = 1024
+
# maximum number of devices boot.py supports
MAX_BOOT_DEVICES = 128
# The default polling period for polled attributes
DEFAULT_POLLING_PERIOD = 1000
+ # the three spatial dimensions XYZ used a lot for pointing directions and ITRF coordinates.
+ N_xyz = 3
+ # number of values for latitude/longitude coordinates
+ N_coord = 2
+
+
"""
These classes for now exist for future use
diff --git a/tangostationcontrol/tangostationcontrol/devices/antennafield.py b/tangostationcontrol/tangostationcontrol/devices/antennafield.py
index 59b0d076c09d764da7b52176d908cafbd3322749..fbd4506ebec411d37ccda3ac855f2756239fc784 100644
--- a/tangostationcontrol/tangostationcontrol/devices/antennafield.py
+++ b/tangostationcontrol/tangostationcontrol/devices/antennafield.py
@@ -235,41 +235,41 @@ class AntennaField(lofar_device):
ANT_mask_RW = mapped_attribute("ANT_mask_RW", dtype=(bool,), max_dim_x=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
RCU_PWR_ANT_on_R = mapped_attribute("RCU_PWR_ANT_on_R", dtype=(bool,), max_dim_x=MAX_NUMBER_OF_HBAT)
RCU_PWR_ANT_on_RW = mapped_attribute("RCU_PWR_ANT_on_RW", dtype=(bool,), max_dim_x=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
- HBAT_BF_delay_steps_R = mapped_attribute("HBAT_BF_delay_steps_R", dtype=((numpy.int64,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * 2, max_dim_y=MAX_NUMBER_OF_HBAT)
- HBAT_BF_delay_steps_RW = mapped_attribute("HBAT_BF_delay_steps_RW", dtype=((numpy.int64,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * 2, max_dim_y=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
- HBAT_LED_on_R = mapped_attribute("HBAT_LED_on_R", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * 2, max_dim_y=MAX_NUMBER_OF_HBAT)
- HBAT_LED_on_RW = mapped_attribute("HBAT_LED_on_RW", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * 2, max_dim_y=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
- HBAT_PWR_LNA_on_R = mapped_attribute("HBAT_PWR_LNA_on_R", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * 2, max_dim_y=MAX_NUMBER_OF_HBAT)
- HBAT_PWR_LNA_on_RW = mapped_attribute("HBAT_PWR_LNA_on_RW", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * 2, max_dim_y=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
- HBAT_PWR_on_R = mapped_attribute("HBAT_PWR_on_R", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * 2, max_dim_y=MAX_NUMBER_OF_HBAT)
- HBAT_PWR_on_RW = mapped_attribute("HBAT_PWR_on_RW", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * 2, max_dim_y=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
+ HBAT_BF_delay_steps_R = mapped_attribute("HBAT_BF_delay_steps_R", dtype=((numpy.int64,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * constants.N_pol, max_dim_y=MAX_NUMBER_OF_HBAT)
+ HBAT_BF_delay_steps_RW = mapped_attribute("HBAT_BF_delay_steps_RW", dtype=((numpy.int64,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * constants.N_pol, max_dim_y=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
+ HBAT_LED_on_R = mapped_attribute("HBAT_LED_on_R", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * constants.N_pol, max_dim_y=MAX_NUMBER_OF_HBAT)
+ HBAT_LED_on_RW = mapped_attribute("HBAT_LED_on_RW", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * constants.N_pol, max_dim_y=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
+ HBAT_PWR_LNA_on_R = mapped_attribute("HBAT_PWR_LNA_on_R", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * constants.N_pol, max_dim_y=MAX_NUMBER_OF_HBAT)
+ HBAT_PWR_LNA_on_RW = mapped_attribute("HBAT_PWR_LNA_on_RW", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * constants.N_pol, max_dim_y=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
+ HBAT_PWR_on_R = mapped_attribute("HBAT_PWR_on_R", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * constants.N_pol, max_dim_y=MAX_NUMBER_OF_HBAT)
+ HBAT_PWR_on_RW = mapped_attribute("HBAT_PWR_on_RW", dtype=((bool,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * constants.N_pol, max_dim_y=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
RCU_band_select_RW = mapped_attribute("RCU_band_select_RW", dtype=(numpy.int64,), max_dim_x=MAX_NUMBER_OF_HBAT, access=AttrWriteType.READ_WRITE)
# ----- Position information
Antenna_Field_Reference_ITRF_R = attribute(access=AttrWriteType.READ,
- doc='Absolute reference position of antenna field, in ITRF (XYZ)',
- dtype=(numpy.float64,), max_dim_x=3)
+ doc='Absolute reference position of antenna field, in ITRF (XYZ)',
+ dtype=(numpy.float64,), max_dim_x=constants.N_xyz)
Antenna_Field_Reference_GEO_R = attribute(access=AttrWriteType.READ,
doc='Absolute reference position of antenna field, in latitude/longitude (degrees)',
- dtype=(numpy.float64,), max_dim_x=2)
+ dtype=(numpy.float64,), max_dim_x=constants.N_coord)
Antenna_Field_Reference_GEOHASH_R = attribute(access=AttrWriteType.READ,
doc='Absolute reference position of antenna field, as a geohash string',
dtype=str)
HBAT_antenna_ITRF_offsets_R = attribute(access=AttrWriteType.READ,
- doc='For each tile, the offsets of the antennas within that, in ITRF ("iHBADeltas"). True shape: nrtiles x 16 x 3.',
- dtype=((numpy.float64,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * 3, max_dim_y=96)
+ doc='For each tile, the offsets of the antennas within that, in ITRF ("iHBADeltas"). True shape: nrtiles x 16 x 3.',
+ dtype=((numpy.float64,),), max_dim_x=NUMBER_OF_ELEMENTS_PER_TILE * constants.N_xyz, max_dim_y=MAX_NUMBER_OF_HBAT)
Antenna_Reference_ITRF_R = attribute(access=AttrWriteType.READ,
- doc='Absolute reference position of each tile, in ITRF (XYZ)',
- dtype=((numpy.float64,),), max_dim_x=3, max_dim_y=MAX_NUMBER_OF_HBAT)
+ doc='Absolute reference position of each tile, in ITRF (XYZ)',
+ dtype=((numpy.float64,),), max_dim_x=constants.N_xyz, max_dim_y=MAX_NUMBER_OF_HBAT)
Antenna_Reference_GEO_R = attribute(access=AttrWriteType.READ,
doc='Absolute reference position of each tile, in latitude/longitude (degrees)',
- dtype=((numpy.float64,),), max_dim_x=2, max_dim_y=MAX_NUMBER_OF_HBAT)
+ dtype=((numpy.float64,),), max_dim_x=constants.N_coord, max_dim_y=MAX_NUMBER_OF_HBAT)
Antenna_Reference_GEOHASH_R = attribute(access=AttrWriteType.READ,
doc='Absolute reference position of each tile, as geohash strings',
@@ -317,10 +317,10 @@ class AntennaField(lofar_device):
def read_Antenna_Field_Reference_ITRF_R(self):
# provide ITRF field coordinates if they were configured
if self.Antenna_Field_Reference_ITRF:
- return numpy.array(self.Antenna_Field_Reference_ITRF).reshape(3)
+ return numpy.array(self.Antenna_Field_Reference_ITRF).reshape(constants.N_xyz)
# calculate them from ETRS coordinates if not, using the configured ITRF reference
- ETRS_coordinates = numpy.array(self.Antenna_Field_Reference_ETRS).reshape(3)
+ ETRS_coordinates = numpy.array(self.Antenna_Field_Reference_ETRS).reshape(constants.N_xyz)
return ETRS_to_ITRF(ETRS_coordinates, self.ITRF_Reference_Frame, self.ITRF_Reference_Epoch)
def read_Antenna_Field_Reference_GEO_R(self):
@@ -345,9 +345,9 @@ class AntennaField(lofar_device):
tiles lie on the same plane in ITRF. """
# the relative offsets between the elements is fixed in HBAT_base_antenna_offsets
- base_antenna_offsets = numpy.array(self.HBAT_base_antenna_offsets).reshape(constants.N_te, 3)
+ base_antenna_offsets = numpy.array(self.HBAT_base_antenna_offsets).reshape(constants.N_te, constants.N_xyz)
- PQR_to_ETRS_rotation_matrix = numpy.array(self.PQR_to_ETRS_rotation_matrix).reshape(3,3)
+ PQR_to_ETRS_rotation_matrix = numpy.array(self.PQR_to_ETRS_rotation_matrix).reshape(constants.N_xyz, constants.N_xyz)
# each tile has its own rotation angle, resulting in different offsets per tile
all_offsets = numpy.array(
@@ -357,15 +357,15 @@ class AntennaField(lofar_device):
PQR_to_ETRS_rotation_matrix)
for angle_deg in self.HBAT_PQR_rotation_angles_deg])
- return all_offsets.reshape(-1, constants.N_te * 3)
+ return all_offsets.reshape(-1, constants.N_te * constants.N_xyz)
def read_Antenna_Reference_ITRF_R(self):
# provide ITRF coordinates if they were configured
if self.Antenna_Reference_ITRF:
- return numpy.array(self.Antenna_Reference_ITRF).reshape(-1,3)
+ return numpy.array(self.Antenna_Reference_ITRF).reshape(-1, constants.N_xyz)
# calculate them from ETRS coordinates if not, using the configured ITRF reference
- ETRS_coordinates = numpy.array(self.Antenna_Reference_ETRS).reshape(-1,3)
+ ETRS_coordinates = numpy.array(self.Antenna_Reference_ETRS).reshape(-1, constants.N_xyz)
return ETRS_to_ITRF(ETRS_coordinates, self.ITRF_Reference_Frame, self.ITRF_Reference_Epoch)
def read_Antenna_Reference_GEO_R(self):
@@ -450,8 +450,8 @@ class AntennaField(lofar_device):
delays = delays.reshape(num_tiles, constants.N_te)
- result_values = numpy.zeros((num_tiles, constants.N_te * 2), dtype=numpy.int64)
- control_mapping = numpy.reshape(self.Control_to_RECV_mapping, (-1, 2))
+ result_values = numpy.zeros((num_tiles, constants.N_te * constants.N_pol_bf), dtype=numpy.int64)
+ control_mapping = numpy.reshape(self.Control_to_RECV_mapping, (-1, constants.N_pol_bf))
for recv_idx, recv_proxy in enumerate(self.recv_proxies):
# collect all delays for this recv_proxy
@@ -464,7 +464,7 @@ class AntennaField(lofar_device):
# convert them into delay steps
flatten_delay_steps = numpy.array(recv_proxy.calculate_HBAT_bf_delay_steps(recv_delays.flatten()), dtype=numpy.int64)
- delay_steps = numpy.reshape(flatten_delay_steps, (-1, constants.N_te * 2))
+ delay_steps = numpy.reshape(flatten_delay_steps, (-1, constants.N_te * constants.N_pol))
# write back into same positions we collected them from
result_values[recv_result_indices] = delay_steps
diff --git a/tangostationcontrol/tangostationcontrol/devices/beam_device.py b/tangostationcontrol/tangostationcontrol/devices/beam_device.py
index 832df357230ce562e3b6e87cbf077b680b4e2978..4f64b44044ba2724aac3667c47eee33c10a5b13b 100644
--- a/tangostationcontrol/tangostationcontrol/devices/beam_device.py
+++ b/tangostationcontrol/tangostationcontrol/devices/beam_device.py
@@ -74,11 +74,11 @@ class beam_device(lofar_device):
# will be stored as self._num_pointings.
Pointing_direction_R = attribute(access=AttrWriteType.READ,
- dtype=((str,),), max_dim_x=3, max_dim_y=constants.MAX_POINTINGS,
+ dtype=((str,),), max_dim_x=constants.N_xyz, max_dim_y=constants.MAX_POINTINGS,
fget=lambda self: self._pointing_direction_r)
Pointing_direction_RW = attribute(access=AttrWriteType.READ_WRITE,
- dtype=((str,),), max_dim_x=3, max_dim_y=constants.MAX_POINTINGS,
+ dtype=((str,),), max_dim_x=constants.N_xyz, max_dim_y=constants.MAX_POINTINGS,
fget=lambda self: self._pointing_direction_rw)
Pointing_direction_str_R = attribute(access=AttrWriteType.READ,
@@ -230,7 +230,7 @@ class beam_device(lofar_device):
# Initialise tracking control
self._num_pointings = num_pointings
self._pointing_timestamp_r = numpy.zeros(num_pointings, dtype=numpy.double)
- self._pointing_direction_r = numpy.zeros((num_pointings, 3), dtype="<U32")
+ self._pointing_direction_r = numpy.zeros((num_pointings, constants.N_xyz), dtype="<U32")
self._pointing_direction_rw = numpy.array([["AZELGEO","0deg","90deg"]] * num_pointings, dtype="<U32")
self._tracking_enabled_rw = self.Tracking_enabled_RW_default
diff --git a/tangostationcontrol/tangostationcontrol/devices/sdp/beamlet.py b/tangostationcontrol/tangostationcontrol/devices/sdp/beamlet.py
index 4c42faea1da8b590a2be00fec75fb48e9d98995e..e86bd456220b85c2d2b3e3124e873e6a729929f4 100644
--- a/tangostationcontrol/tangostationcontrol/devices/sdp/beamlet.py
+++ b/tangostationcontrol/tangostationcontrol/devices/sdp/beamlet.py
@@ -293,7 +293,7 @@ class Beamlet(opcua_device):
def _subband_frequencies(subbands: numpy.ndarray, clock: int, nyquist_zone: int) -> numpy.ndarray:
""" Obtain the frequencies of each subband, given a clock and an antenna type. """
- subband_width = clock / 1024
+ subband_width = clock / constants.N_fft
base_subband = nyquist_zone * constants.N_sub
# broadcast clock across frequencies
diff --git a/tangostationcontrol/tangostationcontrol/devices/sdp/bst.py b/tangostationcontrol/tangostationcontrol/devices/sdp/bst.py
index e7f1cf4e27f30fa7d957312aa35fcecbaaf53737..016fa9c16adc5cba577f654fca11cc11916b7263 100644
--- a/tangostationcontrol/tangostationcontrol/devices/sdp/bst.py
+++ b/tangostationcontrol/tangostationcontrol/devices/sdp/bst.py
@@ -84,14 +84,14 @@ class BST(Statistics):
FPGA_bst_offload_nof_valid_R = attribute_wrapper(comms_annotation=["FPGA_bst_offload_nof_valid_R"], datatype=numpy.int32, dims=(constants.N_pn,))
# number of packets with valid payloads
- nof_valid_payloads_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "nof_valid_payloads"}, dims=(BSTCollector.MAX_FPGAS,), datatype=numpy.uint64)
+ nof_valid_payloads_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "nof_valid_payloads"}, dims=(constants.N_pn,), datatype=numpy.uint64)
# number of packets with invalid payloads
- nof_payload_errors_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "nof_payload_errors"}, dims=(BSTCollector.MAX_FPGAS,), datatype=numpy.uint64)
+ nof_payload_errors_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "nof_payload_errors"}, dims=(constants.N_pn,), datatype=numpy.uint64)
# latest BSTs
- bst_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "bst_values"}, dims=(BSTCollector.MAX_BLOCKS, BSTCollector.MAX_BEAMLETS), datatype=numpy.uint64)
+ bst_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "bst_values"}, dims=(constants.MAX_BLOCKS, constants.N_beamlets_max), datatype=numpy.uint64)
# reported timestamp
# for each row in the latest BSTs
- bst_timestamp_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "bst_timestamps"}, dims=(BSTCollector.MAX_BLOCKS,), datatype=numpy.uint64)
+ bst_timestamp_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "bst_timestamps"}, dims=(constants.MAX_BLOCKS,), datatype=numpy.uint64)
# ----------
# Summarising Attributes
diff --git a/tangostationcontrol/tangostationcontrol/devices/sdp/digitalbeam.py b/tangostationcontrol/tangostationcontrol/devices/sdp/digitalbeam.py
index a5a5c9d96e04b02c90a2c7e940f15e1a8f8101b4..633a8d56988d254674d5f46ea540e1935773c52b 100644
--- a/tangostationcontrol/tangostationcontrol/devices/sdp/digitalbeam.py
+++ b/tangostationcontrol/tangostationcontrol/devices/sdp/digitalbeam.py
@@ -144,7 +144,7 @@ class DigitalBeam(beam_device):
# Retrieve positions from RECV device
reference_itrf = self.antennafield_proxy.Antenna_Field_Reference_ITRF_R
- antenna_itrf = self.antennafield_proxy.Antenna_Reference_ITRF_R.reshape(-1, 3)
+ antenna_itrf = self.antennafield_proxy.Antenna_Reference_ITRF_R.reshape(-1, constants.N_xyz)
# Generate positions for all FPGA inputs.
# Use reference position for any missing antennas so they always get a delay of 0
diff --git a/tangostationcontrol/tangostationcontrol/devices/sdp/sdp.py b/tangostationcontrol/tangostationcontrol/devices/sdp/sdp.py
index def422eb51f67c9ce052356afc48ed4c1fd9644d..4ede62a8c8e7042d3916abdd70b9f0b182d16baa 100644
--- a/tangostationcontrol/tangostationcontrol/devices/sdp/sdp.py
+++ b/tangostationcontrol/tangostationcontrol/devices/sdp/sdp.py
@@ -76,7 +76,7 @@ class SDP(opcua_device):
dtype='DevVarDoubleArray',
mandatory=False,
# Emit a signal on subband 102
- default_value=[[constants.DEFAULT_SUBBAND * constants.F_CLOCK / 1024] * S_pn] * N_pn
+ default_value=[[constants.DEFAULT_SUBBAND * float(constants.CLK_200) / constants.N_fft] * S_pn] * N_pn
)
FPGA_wg_phase_RW_default = device_property(
@@ -106,7 +106,7 @@ class SDP(opcua_device):
clock_RW_default = device_property(
dtype='DevULong',
mandatory=False,
- default_value = int(constants.F_CLOCK)
+ default_value = constants.CLK_200
)
TRANSLATOR_DEFAULT_SETTINGS = [
@@ -236,7 +236,7 @@ class SDP(opcua_device):
return numpy.uint32(clocks_in_mask[0]) if clocks_in_mask else self.clock_RW_default
def write_clock_RW(self, clock):
- if clock not in (160*1000000, 200*1000000):
+ if clock not in (constants.CLK_160, constants.CLK_200):
raise ValueError(f"Unsupported clock frequency: {clock}")
# Tell all FPGAs to use this clock
diff --git a/tangostationcontrol/tangostationcontrol/devices/sdp/sst.py b/tangostationcontrol/tangostationcontrol/devices/sdp/sst.py
index cc0bc9b0687166edcc5f028346b3592b67d12576..0a7d7c8186d8d69d53011f2bd86b8a45ee5981bd 100644
--- a/tangostationcontrol/tangostationcontrol/devices/sdp/sst.py
+++ b/tangostationcontrol/tangostationcontrol/devices/sdp/sst.py
@@ -100,14 +100,14 @@ class SST(Statistics):
# number of packets with invalid payloads
nof_payload_errors_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "nof_payload_errors"}, dims=(constants.N_pn,), datatype=numpy.uint64)
# latest SSTs
- sst_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "sst_values"}, dims=(StationSSTCollector.MAX_INPUTS, constants.N_sub), datatype=numpy.uint64)
+ sst_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "sst_values"}, dims=(constants.MAX_INPUTS, constants.N_sub), datatype=numpy.uint64)
# reported timestamp
# for each row in the latest SSTs
- sst_timestamp_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "sst_timestamps"}, dims=(StationSSTCollector.MAX_INPUTS,), datatype=numpy.uint64)
+ sst_timestamp_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "sst_timestamps"}, dims=(constants.MAX_INPUTS,), datatype=numpy.uint64)
# integration interval for each row in the latest SSTs
- integration_interval_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "integration_intervals"}, dims=(StationSSTCollector.MAX_INPUTS,), datatype=numpy.float32)
+ integration_interval_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "integration_intervals"}, dims=(constants.MAX_INPUTS,), datatype=numpy.float32)
# whether the subband data was calibrated by the SDP (that is, were subband weights applied)
- subbands_calibrated_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "subbands_calibrated"}, dims=(StationSSTCollector.MAX_INPUTS,), datatype=bool)
+ subbands_calibrated_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "subbands_calibrated"}, dims=(constants.MAX_INPUTS,), datatype=bool)
# ----------
# Summarising Attributes
diff --git a/tangostationcontrol/tangostationcontrol/devices/sdp/xst.py b/tangostationcontrol/tangostationcontrol/devices/sdp/xst.py
index b044bc84c7fd2e312fb9dbf9078029b832011ff7..42ead3ecf56a7965256c70eeeed92660b4c3f606 100644
--- a/tangostationcontrol/tangostationcontrol/devices/sdp/xst.py
+++ b/tangostationcontrol/tangostationcontrol/devices/sdp/xst.py
@@ -111,8 +111,8 @@ class XST(Statistics):
FPGA_xst_offload_bsn_R = attribute_wrapper(comms_id=OPCUAConnection, comms_annotation=["FPGA_xst_offload_bsn_R"], datatype=numpy.int64, dims=(N_pn,))
FPGA_xst_processing_enable_RW = attribute_wrapper(comms_id=OPCUAConnection, comms_annotation=["FPGA_xst_processing_enable_RW"], datatype=bool, dims=(N_pn,), access=AttrWriteType.READ_WRITE)
FPGA_xst_processing_enable_R = attribute_wrapper(comms_id=OPCUAConnection, comms_annotation=["FPGA_xst_processing_enable_R"], datatype=bool, dims=(N_pn,))
- FPGA_xst_subband_select_RW = attribute_wrapper(comms_id=OPCUAConnection, comms_annotation=["FPGA_xst_subband_select_RW"], datatype=numpy.uint32, dims=(XSTCollector.MAX_PARALLEL_SUBBANDS,N_pn), access=AttrWriteType.READ_WRITE)
- FPGA_xst_subband_select_R = attribute_wrapper(comms_id=OPCUAConnection, comms_annotation=["FPGA_xst_subband_select_R"], datatype=numpy.uint32, dims=(XSTCollector.MAX_PARALLEL_SUBBANDS,N_pn))
+ FPGA_xst_subband_select_RW = attribute_wrapper(comms_id=OPCUAConnection, comms_annotation=["FPGA_xst_subband_select_RW"], datatype=numpy.uint32, dims=(constants.MAX_PARALLEL_SUBBANDS,N_pn), access=AttrWriteType.READ_WRITE)
+ FPGA_xst_subband_select_R = attribute_wrapper(comms_id=OPCUAConnection, comms_annotation=["FPGA_xst_subband_select_R"], datatype=numpy.uint32, dims=(constants.MAX_PARALLEL_SUBBANDS,N_pn))
FPGA_xst_offload_nof_crosslets_RW = attribute_wrapper(comms_id=OPCUAConnection, comms_annotation=["FPGA_xst_offload_nof_crosslets_RW"], datatype=numpy.uint32, dims=(N_pn,), access=AttrWriteType.READ_WRITE)
FPGA_xst_offload_nof_crosslets_R = attribute_wrapper(comms_id=OPCUAConnection, comms_annotation=["FPGA_xst_offload_nof_crosslets_R"], datatype=numpy.uint32, dims=(N_pn,))
@@ -149,78 +149,78 @@ class XST(Statistics):
FPGA_xst_ring_tx_latency_R = attribute_wrapper(comms_id=OPCUAConnection, comms_annotation=["FPGA_xst_ring_tx_latency_R"], datatype=numpy.int32, dims=(N_pn,N_pn))
# number of packets with valid payloads
- nof_valid_payloads_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "nof_valid_payloads"}, dims=(XSTCollector.MAX_FPGAS,), datatype=numpy.uint64)
+ nof_valid_payloads_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "nof_valid_payloads"}, dims=(constants.N_pn,), datatype=numpy.uint64)
# number of packets with invalid payloads
- nof_payload_errors_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "nof_payload_errors"}, dims=(XSTCollector.MAX_FPGAS,), datatype=numpy.uint64)
+ nof_payload_errors_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "nof_payload_errors"}, dims=(constants.N_pn,), datatype=numpy.uint64)
# latest XSTs
- xst_blocks_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_blocks", "reshape": True}, dims=(XSTCollector.MAX_PARALLEL_SUBBANDS, XSTCollector.MAX_BLOCKS, XSTCollector.BLOCK_LENGTH, XSTCollector.BLOCK_LENGTH, XSTCollector.VALUES_PER_COMPLEX), datatype=numpy.int64)
+ xst_blocks_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_blocks", "reshape": True}, dims=(constants.MAX_PARALLEL_SUBBANDS, XSTCollector.MAX_BLOCKS, constants.BLOCK_LENGTH, constants.BLOCK_LENGTH, constants.VALUES_PER_COMPLEX), datatype=numpy.int64)
# whether the values in the block are conjugated and transposed
- xst_conjugated_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_conjugated", "reshape": True}, dims=(XSTCollector.MAX_PARALLEL_SUBBANDS, XSTCollector.MAX_BLOCKS), datatype=bool)
+ xst_conjugated_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_conjugated", "reshape": True}, dims=(constants.MAX_PARALLEL_SUBBANDS, XSTCollector.MAX_BLOCKS), datatype=bool)
# reported timestamp for each subband in the latest XSTs
- xst_timestamp_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_timestamps"}, dims=(XSTCollector.MAX_PARALLEL_SUBBANDS,), datatype=numpy.uint64)
+ xst_timestamp_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_timestamps"}, dims=(constants.MAX_PARALLEL_SUBBANDS,), datatype=numpy.uint64)
# which subband the XSTs describe
- xst_subbands_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_subbands"}, dims=(XSTCollector.MAX_PARALLEL_SUBBANDS,), datatype=numpy.uint16)
+ xst_subbands_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_subbands"}, dims=(constants.MAX_PARALLEL_SUBBANDS,), datatype=numpy.uint16)
# integration interval for each subband in the latest XSTs
- xst_integration_interval_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_integration_intervals"}, dims=(XSTCollector.MAX_PARALLEL_SUBBANDS,), datatype=numpy.float32)
+ xst_integration_interval_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_integration_intervals"}, dims=(constants.MAX_PARALLEL_SUBBANDS,), datatype=numpy.float32)
# xst_R, but as a matrix of subband x (input x input)
- xst_real_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS * XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_PARALLEL_SUBBANDS, dtype=((numpy.float32,),))
- xst_imag_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS * XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_PARALLEL_SUBBANDS, dtype=((numpy.float32,),))
- xst_power_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS * XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_PARALLEL_SUBBANDS, dtype=((numpy.float32,),))
- xst_phase_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS * XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_PARALLEL_SUBBANDS, dtype=((numpy.float32,),))
+ xst_real_R = attribute(max_dim_x=constants.MAX_INPUTS * constants.MAX_INPUTS, max_dim_y=constants.MAX_PARALLEL_SUBBANDS, dtype=((numpy.float32,),))
+ xst_imag_R = attribute(max_dim_x=constants.MAX_INPUTS * constants.MAX_INPUTS, max_dim_y=constants.MAX_PARALLEL_SUBBANDS, dtype=((numpy.float32,),))
+ xst_power_R = attribute(max_dim_x=constants.MAX_INPUTS * constants.MAX_INPUTS, max_dim_y=constants.MAX_PARALLEL_SUBBANDS, dtype=((numpy.float32,),))
+ xst_phase_R = attribute(max_dim_x=constants.MAX_INPUTS * constants.MAX_INPUTS, max_dim_y=constants.MAX_PARALLEL_SUBBANDS, dtype=((numpy.float32,),))
def read_xst_real_R(self):
- return numpy.real(self.statistics_client.collector.xst_values()).reshape(XSTCollector.MAX_PARALLEL_SUBBANDS, XSTCollector.MAX_INPUTS * XSTCollector.MAX_INPUTS)
+ return numpy.real(self.statistics_client.collector.xst_values()).reshape(constants.MAX_PARALLEL_SUBBANDS, constants.MAX_INPUTS * constants.MAX_INPUTS)
def read_xst_imag_R(self):
- return numpy.imag(self.statistics_client.collector.xst_values()).reshape(XSTCollector.MAX_PARALLEL_SUBBANDS, XSTCollector.MAX_INPUTS * XSTCollector.MAX_INPUTS)
+ return numpy.imag(self.statistics_client.collector.xst_values()).reshape(constants.MAX_PARALLEL_SUBBANDS, constants.MAX_INPUTS * constants.MAX_INPUTS)
def read_xst_power_R(self):
- return numpy.abs(self.statistics_client.collector.xst_values()).reshape(XSTCollector.MAX_PARALLEL_SUBBANDS, XSTCollector.MAX_INPUTS * XSTCollector.MAX_INPUTS)
+ return numpy.abs(self.statistics_client.collector.xst_values()).reshape(constants.MAX_PARALLEL_SUBBANDS, constants.MAX_INPUTS * constants.MAX_INPUTS)
def read_xst_phase_R(self):
- return numpy.angle(self.statistics_client.collector.xst_values()).reshape(XSTCollector.MAX_PARALLEL_SUBBANDS, XSTCollector.MAX_INPUTS * XSTCollector.MAX_INPUTS)
+ return numpy.angle(self.statistics_client.collector.xst_values()).reshape(constants.MAX_PARALLEL_SUBBANDS, constants.MAX_INPUTS * constants.MAX_INPUTS)
# xst_R, but as a matrix of input x input, for each specific subband index
- xst_0_real_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(0))
- xst_0_imag_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(0))
- xst_0_power_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(0))
- xst_0_phase_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(0))
-
- xst_1_real_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(1))
- xst_1_imag_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(1))
- xst_1_power_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(1))
- xst_1_phase_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(1))
-
- xst_2_real_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(2))
- xst_2_imag_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(2))
- xst_2_power_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(2))
- xst_2_phase_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(2))
-
- xst_3_real_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(3))
- xst_3_imag_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(3))
- xst_3_power_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(3))
- xst_3_phase_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(3))
-
- xst_4_real_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(4))
- xst_4_imag_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(4))
- xst_4_power_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(4))
- xst_4_phase_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(4))
-
- xst_5_real_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(5))
- xst_5_imag_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(5))
- xst_5_power_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(5))
- xst_5_phase_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(5))
-
- xst_6_real_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(6))
- xst_6_imag_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(6))
- xst_6_power_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(6))
- xst_6_phase_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(6))
-
- xst_7_real_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(7))
- xst_7_imag_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(7))
- xst_7_power_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(7))
- xst_7_phase_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(7))
+ xst_0_real_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(0))
+ xst_0_imag_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(0))
+ xst_0_power_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(0))
+ xst_0_phase_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(0))
+
+ xst_1_real_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(1))
+ xst_1_imag_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(1))
+ xst_1_power_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(1))
+ xst_1_phase_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(1))
+
+ xst_2_real_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(2))
+ xst_2_imag_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(2))
+ xst_2_power_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(2))
+ xst_2_phase_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(2))
+
+ xst_3_real_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(3))
+ xst_3_imag_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(3))
+ xst_3_power_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(3))
+ xst_3_phase_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(3))
+
+ xst_4_real_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(4))
+ xst_4_imag_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(4))
+ xst_4_power_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(4))
+ xst_4_phase_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(4))
+
+ xst_5_real_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(5))
+ xst_5_imag_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(5))
+ xst_5_power_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(5))
+ xst_5_phase_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(5))
+
+ xst_6_real_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(6))
+ xst_6_imag_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(6))
+ xst_6_power_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(6))
+ xst_6_phase_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(6))
+
+ xst_7_real_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_real_R(7))
+ xst_7_imag_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_imag_R(7))
+ xst_7_power_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_power_R(7))
+ xst_7_phase_R = attribute(max_dim_x=constants.MAX_INPUTS, max_dim_y=constants.MAX_INPUTS, dtype=((numpy.float32,),), fget = lambda self: self.read_xst_N_phase_R(7))
def read_xst_N_real_R(self, subband_idx):
return numpy.real(self.statistics_client.collector.xst_values([subband_idx])[0])
diff --git a/tangostationcontrol/tangostationcontrol/devices/tilebeam.py b/tangostationcontrol/tangostationcontrol/devices/tilebeam.py
index 0e8a7fd947b60b76448d7a14e53365c30123a48f..c175963a3533163e888119d8d7ac811b410dab37 100644
--- a/tangostationcontrol/tangostationcontrol/devices/tilebeam.py
+++ b/tangostationcontrol/tangostationcontrol/devices/tilebeam.py
@@ -58,7 +58,7 @@ class TileBeam(beam_device):
# Retrieve positions from AntennaField device
Antenna_Reference_itrf = self.antennafield_proxy.Antenna_Reference_itrf_R
- HBAT_antenna_itrf_offsets = self.antennafield_proxy.HBAT_antenna_itrf_offsets_R.reshape(self._nr_tiles, constants.N_te, 3)
+ HBAT_antenna_itrf_offsets = self.antennafield_proxy.HBAT_antenna_itrf_offsets_R.reshape(self._nr_tiles, constants.N_te, constants.N_xyz)
# a delay calculator for each tile
self.HBAT_delay_calculators = [Delays(reference_itrf) for reference_itrf in Antenna_Reference_itrf]