feat: adds ability to parse pair measurements

kalpaa/read_bin_csv.py

@@ -334,6 +334,8 @@ def _parse_bin_header(field: str) -> typing.Optional[ParsedBinHeader]:
 class CSV_BinnedData:
 	measurement_type: MeasurementTypeEnum
 	single_dot_dict: typing.Dict[str, typing.Any]
+	pair_dot_dict: typing.Dict[typing.Tuple[str, str], typing.Any]
+	freqs: typing.Sequence[float]
 
 
 def read_bin_csv(
@@ -363,9 +365,11 @@ def read_bin_csv(
 			_logger.debug(f"Going to read frequencies from {freq_field=}")
 
 			parsed_headers = {}
+			freq_list = []
 			aggregated_dict: typing.Dict[str, typing.Any] = {
 				RETURNED_FREQUENCIES_KEY: []
 			}
+			pair_aggregated_dict: typing.Dict[typing.Tuple[str, str], typing.Any] = {}
 
 			for field in remaining_fields:
 				parsed_header = _parse_bin_header(field)
@@ -374,6 +378,23 @@ def read_bin_csv(
 					continue
 				parsed_headers[field] = parsed_header
 
+				# Get our dictionary structures set up by initialising empty dictionaries for each new field as we go
+				if parsed_header.pair:
+					if parsed_header.dot_name2 is None:
+						raise ValueError(
+							f"Pair measurement {field=} has no dot_name2, but it should"
+						)
+					dot_names = (parsed_header.dot_name, parsed_header.dot_name2)
+					if dot_names not in pair_aggregated_dict:
+						pair_aggregated_dict[dot_names] = {}
+
+					if (
+						parsed_header.summary_stat
+						not in pair_aggregated_dict[dot_names]
+					):
+						pair_aggregated_dict[dot_names][parsed_header.summary_stat] = []
+
+				else:
 					if parsed_header.dot_name not in aggregated_dict:
 						aggregated_dict[parsed_header.dot_name] = {}
 
@@ -385,6 +406,8 @@ def read_bin_csv(
 							parsed_header.summary_stat
 						] = []
 
+				# Realistically we'll always have the same measurement type, but this warning may help us catch out cases where this didn't happen correctly
+				# We should only need to set it once, so the fact we keep checking is more about catching errors than anything else
 				if measurement_type is not None:
 					if measurement_type != parsed_header.measurement_type:
 						_logger.warning(
@@ -397,10 +420,23 @@ def read_bin_csv(
 
 			for row in reader:
 				# _logger.debug(f"Got {row=}")
+				freq_list.append(float(row[freq_field].strip()))
+				# don't need to set, but keep for legacy
 				aggregated_dict[RETURNED_FREQUENCIES_KEY].append(
 					float(row[freq_field].strip())
 				)
 				for field, parsed_header in parsed_headers.items():
+					if parsed_header.pair:
+						if parsed_header.dot_name2 is None:
+							raise ValueError(
+								f"Pair measurement {field=} has no dot_name2, but it should"
+							)
+						value = float(row[field].strip())
+						dot_names = (parsed_header.dot_name, parsed_header.dot_name2)
+						pair_aggregated_dict[dot_names][
+							parsed_header.summary_stat
+						].append(value)
+					else:
 						value = float(row[field].strip())
 						aggregated_dict[parsed_header.dot_name][
 							parsed_header.summary_stat
@@ -412,7 +448,10 @@ def read_bin_csv(
 				)
 
 			return CSV_BinnedData(
-				measurement_type=measurement_type, single_dot_dict=aggregated_dict
+				measurement_type=measurement_type,
+				single_dot_dict=aggregated_dict,
+				freqs=freq_list,
+				pair_dot_dict=pair_aggregated_dict,
 			)
 	except Exception as e:
 		_logger.error(

tests/read_bin_csv/__snapshots__/test_read_bin_csv.ambr

@@ -341,3 +341,93 @@
     }),
   ])
 # ---
+# name: test_read_csv_with_pairs
+  dict({
+    'freqs': list([
+      0.0125,
+      0.024999999999999998,
+      0.045,
+      0.0775,
+      0.1375,
+      0.24749999999999997,
+      0.41,
+    ]),
+    'measurement_type': <MeasurementTypeEnum.POTENTIAL: 'electric-potential'>,
+    'pair_dot_dict': dict({
+      tuple(
+        'dot1',
+        'dot2',
+      ): dict({
+        'mean': list([
+          3.15,
+          3.13,
+          3.0,
+          2.7,
+          0.1,
+          0.25,
+          0.002,
+        ]),
+        'stdev': list([
+          0.02,
+          0.015,
+          0.8,
+          1.5,
+          0.3,
+          0.01,
+          0.1,
+        ]),
+      }),
+    }),
+    'single_dot_dict': dict({
+      'dot1': dict({
+        'mean': list([
+          10.638916947949246,
+          4.808960230987057,
+          1.8458074293863327,
+          1.0990901962765007,
+          0.6425140116757488,
+          0.4844873135633905,
+          0.448232552,
+        ]),
+        'stdev': list([
+          5.688165841523548,
+          1.5555855859097745,
+          0.5112103163244077,
+          0.37605535,
+          0.1411676088216461,
+          0.11795510686231957,
+          0.081977941,
+        ]),
+      }),
+      'dot2': dict({
+        'mean': list([
+          14.780311491085596,
+          7.413101036489984,
+          3.081527317039941,
+          1.198719434472466,
+          0.44608783800009594,
+          0.16750150967807267,
+          0.095604286,
+        ]),
+        'stdev': list([
+          5.085761250807487,
+          2.7753690312876014,
+          1.3009911753215875,
+          0.3361763625979774,
+          0.18042157503806078,
+          0.05820931,
+          0.022567042968929727,
+        ]),
+      }),
+      'frequencies': list([
+        0.0125,
+        0.024999999999999998,
+        0.045,
+        0.0775,
+        0.1375,
+        0.24749999999999997,
+        0.41,
+      ]),
+    }),
+  })
+# ---

tests/read_bin_csv/test_files/test_simple_pair_V.csv

@@ -0,0 +1,8 @@
+mean bin f (Hz), APSD_V_dot1_mean, APSD_V_dot1_stdev, APSD_V_dot2_mean, APSD_V_dot2_stdev,CPSD_phase_V_dot1_dot2_mean,CPSD_phase_V_dot1_dot2_stdev
+0.0125, 10.638916947949246, 5.688165841523548, 14.780311491085596, 5.085761250807487,3.15,0.02
+0.024999999999999998, 4.808960230987057, 1.5555855859097745, 7.413101036489984, 2.7753690312876014,3.13,0.015
+0.045, 1.8458074293863327, 0.5112103163244077, 3.081527317039941, 1.3009911753215875,3,0.8
+0.0775, 1.0990901962765007,0.37605535, 1.198719434472466, 0.3361763625979774,2.7,1.5
+0.1375, 0.6425140116757488, 0.1411676088216461, 0.44608783800009594, 0.18042157503806078,0.1,0.3
+0.24749999999999997, 0.4844873135633905, 0.11795510686231957, 0.16750150967807267,0.05820931,0.25,0.01
+0.41,0.448232552,0.081977941,0.095604286, 0.022567042968929727,0.002,0.1

tests/read_bin_csv/test_read_bin_csv.py

@@ -131,3 +131,15 @@ def test_binned_data_dot_measurement_costs(snapshot):
 	}
 
 	assert result_dict == snapshot
+
+
+def test_read_csv_with_pairs(snapshot):
+
+	# dots_json = TEST_DATA_DIR / "dots.json"
+	# v_csv_file = TEST_DATA_DIR / "test_binned_apsd_V.csv"
+	# ex_csv_file = TEST_DATA_DIR / "test_binned_apsd_Ex.csv"
+	pair_data_csv = TEST_DATA_DIR / "test_simple_pair_V.csv"
+
+	actual_read = kalpaa.read_bin_csv.read_bin_csv(pair_data_csv)
+
+	assert dataclasses.asdict(actual_read) == snapshot