Merge pull request #697 from NeurodataWithoutBorders/alessio_recommen…

…dations

Recommendations from Alessio

pyflask/manageNeuroconv/manage_neuroconv.py

@@ -1058,6 +1058,7 @@ def generate_test_data(output_path: str):
     import spikeinterface
     from spikeinterface.extractors import NumpyRecording
     from spikeinterface.exporters import export_to_phy
+    from spikeinterface.preprocessing import scale, bandpass_filter, resample
 
     base_path = Path(output_path)
     spikeglx_output_folder = base_path / "spikeglx"
@@ -1067,33 +1068,29 @@ def generate_test_data(output_path: str):
     duration_in_s = 3.0
     number_of_units = 50
     number_of_channels = 385  # Have to include 'sync' channel to be proper SpikeGLX. TODO: artificiate sync pulses
-    ap_conversion_factor_to_uV = 2.34375
+    conversion_factor_to_uV = 2.34375
     ap_sampling_frequency = 30_000.0
     lf_sampling_frequency = 2_500.0
     downsample_factor = int(ap_sampling_frequency / lf_sampling_frequency)
 
     # Generate synthetic spiking and voltage traces with waveforms around them
-    artificial_ap_band, spiking = spikeinterface.generate_ground_truth_recording(
+    artificial_ap_band_in_uV, spiking = spikeinterface.generate_ground_truth_recording(
         durations=[duration_in_s],
         sampling_frequency=ap_sampling_frequency,
         num_channels=number_of_channels,
         dtype="float32",
         num_units=number_of_units,
         seed=0,  # Fixed seed for reproducibility
     )
-    artificial_ap_band.set_channel_gains(gains=ap_conversion_factor_to_uV)
-    waveform_extractor = spikeinterface.extract_waveforms(recording=artificial_ap_band, sorting=spiking, mode="memory")
-    int16_artificial_ap_band = artificial_ap_band.astype(dtype="int16")
-
-    # Approximate behavior of LF band with filter and downsampling
-    # TODO: currently looks a little out of scale?
-    artificial_lf_filter = spikeinterface.preprocessing.bandpass_filter(
-        recording=artificial_ap_band, freq_min=10, freq_max=300
-    )
-    int16_artificial_lf_band = NumpyRecording(
-        traces_list=artificial_lf_filter.get_traces()[::downsample_factor],
-        sampling_frequency=lf_sampling_frequency,
-    )
+
+    unscaled_artificial_ap_band = scale(recording=artificial_ap_band_in_uV, gain=1 / conversion_factor_to_uV)
+    int16_artificial_ap_band = unscaled_artificial_ap_band.astype(dtype="int16")
+    int16_artificial_ap_band.set_channel_gains(conversion_factor_to_uV)
+
+    unscaled_artificial_lf_filter = bandpass_filter(recording=unscaled_artificial_ap_band, freq_min=0.5, freq_max=1_000)
+    unscaled_artificial_lf_band = resample(recording=unscaled_artificial_lf_filter, resample_rate=2_500)
+    int16_artificial_lf_band = unscaled_artificial_lf_band.astype(dtype="int16")
+    int16_artificial_lf_band.set_channel_gains(conversion_factor_to_uV)
 
     ap_file_path = spikeglx_output_folder / "Session1_g0" / "Session1_g0_imec0" / "Session1_g0_t0.imec0.ap.bin"
     ap_meta_file_path = spikeglx_output_folder / "Session1_g0" / "Session1_g0_imec0" / "Session1_g0_t0.imec0.ap.meta"
@@ -1115,6 +1112,10 @@ def generate_test_data(output_path: str):
         io.write(lf_meta_content)
 
     # Make Phy folder
+    waveform_extractor = spikeinterface.extract_waveforms(
+        recording=artificial_ap_band_in_uV, sorting=spiking, mode="memory"
+    )
+
     export_to_phy(
         waveform_extractor=waveform_extractor, output_folder=phy_output_folder, remove_if_exists=True, copy_binary=False
     )