Minor changes

- in prep for doi relase,
- updated branches of mth5,mth_metadata to mains
- pretty up some logging messages

aurora/pipelines/transfer_function_helpers.py

@@ -96,6 +96,7 @@ def process_transfer_functions(
     transfer_function_obj,
     segment_weights=None,
     channel_weights=None,
+    #    use_multiple_coherence_weights=False,
 ):
     """
     This method based on TTFestBand.m
@@ -127,10 +128,12 @@ def process_transfer_functions(
     -------
 
     """
-    # PUT COHERENCE SORTING HERE IF WIDE BAND?
     estimator_class = get_estimator_class(dec_level_config.estimator.engine)
+    iter_control = set_up_iter_control(dec_level_config)
     for band in transfer_function_obj.frequency_bands.bands():
-        iter_control = set_up_iter_control(dec_level_config)
+        # if use_multiple_coherence_weights:
+        #     from aurora.transfer_function.weights.coherence_weights import compute_multiple_coherence_weights
+        #     Wmc = compute_multiple_coherence_weights(band, local_stft_obj, remote_stft_obj)
         X, Y, RR = get_band_for_tf_estimate(
             band, dec_level_config, local_stft_obj, remote_stft_obj
         )
@@ -143,6 +146,8 @@ def process_transfer_functions(
             RR = RR.stack(observation=("frequency", "time"))
 
         W = effective_degrees_of_freedom_weights(X, RR, edf_obj=None)
+        # if use_multiple_coherence_weights:
+        #     W *= Wmc
         W[W == 0] = np.nan  # use this to drop values in the handle_nan
         # apply weights
         X *= W

aurora/pipelines/transfer_function_kernel.py

@@ -316,13 +316,11 @@ def make_processing_summary(self):
         for group, df in grouper:
             try:
                 try:
-                    assert (
-                        df.dec_level.diff()[1:] == 1
-                    ).all()  # dec levels increment by 1
+                    cond = (df.dec_level.diff()[1:] == 1).all()
+                    assert cond  # dec levels increment by 1
                 except AssertionError:
-                    logger.info(
-                        f"Skipping {group} because decimation levels are messy."
-                    )
+                    msg = f"Skipping {group} because decimation levels are messy."
+                    logger.info(msg)
                     continue
                 assert df.dec_factor.iloc[0] == 1
                 assert df.dec_level.iloc[0] == 0

aurora/time_series/frequency_band_helpers.py

@@ -28,7 +28,9 @@ def get_band_for_tf_estimate(band, dec_level_config, local_stft_obj, remote_stft
         reference_channels and also the frequency axes are restricted to
         being within the frequency band given as an input argument.
     """
-    logger.info(f"Processing band {band.center_period:.6f}s")
+    logger.info(
+        f"Processing band {band.center_period:.6f}s  ({1./band.center_period:.6f}Hz)"
+    )
     band_dataset = extract_band(band, local_stft_obj)
     X = band_dataset[dec_level_config.input_channels]
     Y = band_dataset[dec_level_config.output_channels]

aurora/time_series/window_helpers.py

@@ -5,13 +5,14 @@
 
 """
 import numpy as np
-from numpy.lib.stride_tricks import as_strided
-from numba import jit
 import time
+
 from loguru import logger
+from numba import jit
+from numpy.lib.stride_tricks import as_strided
 
 
-# Window-to-timeseries relationshp
+# Window-to-timeseries relationship
 def available_number_of_windows_in_array(n_samples_array, n_samples_window, n_advance):
     """
 

tests/synthetic/test_compare_aurora_vs_archived_emtf.py

@@ -211,6 +211,16 @@ def test_pipeline(merged=True):
 
     tfk_dataset = KernelDataset()
     tfk_dataset.from_run_summary(run_summary, "test2", "test1")
+    # Uncomment to sanity check the problem is linear
+    # scale_factors = {
+    #     "ex": 20.0,
+    #     "ey": 20.0,
+    #     "hx": 20.0,
+    #     "hy": 20.0,
+    #     "hz": 20.0,
+    # }
+    # tfk_dataset.df["channel_scale_factors"].at[0] = scale_factors
+    # tfk_dataset.df["channel_scale_factors"].at[1] = scale_factors
     run_test2r1(tfk_dataset)