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Commit 14890f6c authored by Jan David Mol's avatar Jan David Mol
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L2SS-577: Various syntax fixes

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tangostationcontrol/tangostationcontrol/clients/statistics_client.py
+ 3
− 3
View file @ 14890f6c
...@@ -99,10 +99,10 @@ class StatisticsClient(AsyncCommClient): ...@@ -99,10 +99,10 @@ class StatisticsClient(AsyncCommClient):
def read_function(): def read_function():
if annotation.get("reshape", False): if annotation.get("reshape", False):
# force array into the shape of the attribute # force array into the shape of the attribute
if attribute.max_dim_y > 1: if attribute.dim_y > 1:
return self.collector.parameters[parameter].reshape(attribute.max_dim_y, attribute.max_dim_x) return self.collector.parameters[parameter].reshape(attribute.dim_y, attribute.dim_x)
else: else:
return self.collector.parameters[parameter].reshape(attribute.max_dim_x) return self.collector.parameters[parameter].reshape(attribute.dim_x)
else: else:
return self.collector.parameters[parameter] return self.collector.parameters[parameter]
elif annotation["type"] == "udp": elif annotation["type"] == "udp":
......
tangostationcontrol/tangostationcontrol/devices/sdp/statistics_collector.py
+ 2
− 2
View file @ 14890f6c
...@@ -151,7 +151,7 @@ class XSTCollector(StatisticsCollector): ...@@ -151,7 +151,7 @@ class XSTCollector(StatisticsCollector):
# When the youngest data for each subband was received # When the youngest data for each subband was received
"xst_timestamps": numpy.zeros((self.MAX_PARALLEL_SUBBANDS,), dtype=numpy.float64), "xst_timestamps": numpy.zeros((self.MAX_PARALLEL_SUBBANDS,), dtype=numpy.float64),
"xst_subbands": numpy.zeros((self.MAX_PARALLEL_SUBBANDS,), dtype=numpy.uint16), "xst_subbands": numpy.zeros((self.MAX_PARALLEL_SUBBANDS,), dtype=numpy.uint16),
"integration_intervals": numpy.zeros((self.MAX_PARALLEL_SUBBANDS,), dtype=numpy.float32), "xst_integration_intervals": numpy.zeros((self.MAX_PARALLEL_SUBBANDS,), dtype=numpy.float32),
}) })
return defaults return defaults
...@@ -234,7 +234,7 @@ class XSTCollector(StatisticsCollector): ...@@ -234,7 +234,7 @@ class XSTCollector(StatisticsCollector):
self.parameters["xst_timestamps"][subband_slot] = numpy.float64(fields.timestamp().timestamp()) self.parameters["xst_timestamps"][subband_slot] = numpy.float64(fields.timestamp().timestamp())
self.parameters["xst_conjugated"][subband_slot, block_index] = conjugated self.parameters["xst_conjugated"][subband_slot, block_index] = conjugated
self.parameters["xst_subbands"][subband_slot] = numpy.uint16(fields.subband_index) self.parameters["xst_subbands"][subband_slot] = numpy.uint16(fields.subband_index)
self.parameters["integration_intervals"][subband_slot] = fields.integration_interval() self.parameters["xst_integration_intervals"][subband_slot] = fields.integration_interval()
def xst_values(self, subband_indices=range(MAX_PARALLEL_SUBBANDS)): def xst_values(self, subband_indices=range(MAX_PARALLEL_SUBBANDS)):
""" xst_blocks, but as a matrix[len(subband_indices)][MAX_INPUTS][MAX_INPUTS] of complex values. """ xst_blocks, but as a matrix[len(subband_indices)][MAX_INPUTS][MAX_INPUTS] of complex values.
......
tangostationcontrol/tangostationcontrol/devices/sdp/xst.py
+ 46
− 46
View file @ 14890f6c
...@@ -116,15 +116,15 @@ class XST(Statistics): ...@@ -116,15 +116,15 @@ class XST(Statistics):
# number of packets with invalid payloads # 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=(XSTCollector.MAX_FPGAS,), datatype=numpy.uint64)
# latest XSTs # latest XSTs
xst_blocks_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_blocks", "reshape": True}, dims=(XSTCollector.BLOCK_LENGTH * XSTCollector.BLOCK_LENGTH * XSTCollector.VALUES_PER_COMPLEX, XSTCollector.MAX_BLOCKS), datatype=numpy.int64) xst_blocks_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_blocks", "reshape": True}, dims=(XSTCollector.MAX_BLOCKS * XSTCollector.BLOCK_LENGTH * XSTCollector.BLOCK_LENGTH * XSTCollector.VALUES_PER_COMPLEX, XSTCollector.MAX_PARALLEL_SUBBANDS), datatype=numpy.int64)
# whether the values in the block are conjugated and transposed # 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_BLOCKS,), datatype=numpy.bool_) xst_conjugated_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "xst_conjugated", "reshape": True}, dims=(XSTCollector.MAX_BLOCKS, XSTCollector.MAX_PARALLEL_SUBBANDS), datatype=numpy.bool_)
# reported timestamp for each subband in the latest XSTs # 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=(XSTCollector.MAX_PARALLEL_SUBBANDS,), datatype=numpy.uint64)
# which subband the XSTs describe # 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=(XSTCollector.MAX_PARALLEL_SUBBANDS,), datatype=numpy.uint16)
# integration interval for each subband in the latest XSTs # integration interval for each subband in the latest XSTs
integration_interval_R = attribute_wrapper(comms_id=StatisticsClient, comms_annotation={"type": "statistics", "parameter": "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=(XSTCollector.MAX_PARALLEL_SUBBANDS,), datatype=numpy.float32)
# xst_R, but as a matrix of subband x (input x input) # 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_real_R = attribute(max_dim_x=XSTCollector.MAX_INPUTS * XSTCollector.MAX_INPUTS, max_dim_y=XSTCollector.MAX_PARALLEL_SUBBANDS, dtype=((numpy.float32,),))
...@@ -145,57 +145,57 @@ class XST(Statistics): ...@@ -145,57 +145,57 @@ class XST(Statistics):
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(XSTCollector.MAX_PARALLEL_SUBBANDS, XSTCollector.MAX_INPUTS * XSTCollector.MAX_INPUTS)
# xst_R, but as a matrix of input x input, for each specific subband index # 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(0)) 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(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(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(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(1)) 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(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(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(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(2)) 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(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(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(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(3)) 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(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(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(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(4)) 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(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(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(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(5)) 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(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(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(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(6)) 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(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(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(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(7)) 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(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(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(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))
def read_xst_N_real_R(self, subband_idx): def read_xst_N_real_R(self, subband_idx):
return numpy.real(self.statistics_client.collector.xst_values(subband_idx)[0]) return numpy.real(self.statistics_client.collector.xst_values([subband_idx])[0])
def read_xst_N_imag_R(self, subband_idx): def read_xst_N_imag_R(self, subband_idx):
return numpy.imag(self.statistics_client.collector.xst_values(subband_idx)[0]) return numpy.imag(self.statistics_client.collector.xst_values([subband_idx])[0])
def read_xst_N_power_R(self, subband_idx): def read_xst_N_power_R(self, subband_idx):
return numpy.abs(self.statistics_client.collector.xst_values(subband_idx)[0]) return numpy.abs(self.statistics_client.collector.xst_values([subband_idx])[0])
def read_xst_N_phase_R(self, subband_idx): def read_xst_N_phase_R(self, subband_idx):
return numpy.angle(self.statistics_client.collector.xst_values(subband_idx)[0]) return numpy.angle(self.statistics_client.collector.xst_values([subband_idx])[0])
# ---------- # ----------
# Summarising Attributes # Summarising Attributes
......
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