From f66ea6a567a36d82ba8b4dc0a27c6f6485a334e0 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Mon, 24 Nov 2025 18:20:26 +0100 Subject: [PATCH 01/19] auto-estimate margins based on freq_min/f0+Q for filters --- src/spikeinterface/preprocessing/filter.py | 58 +++++++++++++++++----- 1 file changed, 46 insertions(+), 12 deletions(-) diff --git a/src/spikeinterface/preprocessing/filter.py b/src/spikeinterface/preprocessing/filter.py index 78542e1f37..928d07d14b 100644 --- a/src/spikeinterface/preprocessing/filter.py +++ b/src/spikeinterface/preprocessing/filter.py @@ -43,7 +43,7 @@ class FilterRecording(BasePreprocessor): btype : "bandpass" | "highpass", default: "bandpass" Type of the filter margin_ms : float, default: 5.0 - Margin in ms on border to avoid border effect + Margin in ms on border to avoid border effect. coeff : array | None, default: None Filter coefficients in the filter_mode form. dtype : dtype or None, default: None @@ -78,7 +78,8 @@ def __init__( filter_order=5, ftype="butter", filter_mode="sos", - margin_ms=5.0, + margin_ms=5, + max_margin_s=5, add_reflect_padding=False, coeff=None, dtype=None, @@ -138,6 +139,22 @@ def __init__( direction=direction, ) + def adjust_margin_ms_for_highpass(self, freq_min, max_margin_s): + # compute margin as 3 times the period of the highpass cutoff + margin_ms = 3 * (1000.0 / freq_min) + # limit max margin + max_margin_ms = max_margin_s * 1000.0 + if margin_ms > max_margin_ms: + margin_ms = max_margin_ms + return margin_ms + + def adjust_margin_ms_for_notch(self, max_margin_s, q, f0): + margin_ms = (3 / np.pi) * (q / f0) * 1000.0 + max_margin_ms = max_margin_s * 1000.0 + if margin_ms < max_margin_ms: + margin_ms = max_margin_ms + return margin_ms + class FilterRecordingSegment(BasePreprocessorSegment): def __init__( @@ -217,8 +234,11 @@ class BandpassFilterRecording(FilterRecording): The highpass cutoff frequency in Hz freq_max : float The lowpass cutoff frequency in Hz - margin_ms : float - Margin in ms on border to avoid border effect + margin_ms : float | str, default: "auto" + Margin in ms on border to avoid border effect. + If "auto", margin is computed as 3 times the filter highpass cutoff period. + max_margin_s : float, default: 5 + Maximum margin in seconds when margin_ms is set to "auto". dtype : dtype or None The dtype of the returned traces. If None, the dtype of the parent recording is used {} @@ -229,7 +249,11 @@ class BandpassFilterRecording(FilterRecording): The bandpass-filtered recording extractor object """ - def __init__(self, recording, freq_min=300.0, freq_max=6000.0, margin_ms=5.0, dtype=None, **filter_kwargs): + def __init__( + self, recording, freq_min=300.0, freq_max=6000.0, margin_ms="auto", max_margin_s=5, dtype=None, **filter_kwargs + ): + if margin_ms == "auto": + margin_ms = self.adjust_margin_ms_for_highpass(freq_min, max_margin_s) FilterRecording.__init__( self, recording, band=[freq_min, freq_max], margin_ms=margin_ms, dtype=dtype, **filter_kwargs ) @@ -250,8 +274,11 @@ class HighpassFilterRecording(FilterRecording): The recording extractor to be re-referenced freq_min : float The highpass cutoff frequency in Hz - margin_ms : float - Margin in ms on border to avoid border effect + margin_ms : float | str, default: "auto" + Margin in ms on border to avoid border effect. + If "auto", margin is computed as 3 times the filter highpass cutoff period. + max_margin_s : float, default: 5 + Maximum margin in seconds when margin_ms is set to "auto". dtype : dtype or None The dtype of the returned traces. If None, the dtype of the parent recording is used {} @@ -262,7 +289,9 @@ class HighpassFilterRecording(FilterRecording): The highpass-filtered recording extractor object """ - def __init__(self, recording, freq_min=300.0, margin_ms=5.0, dtype=None, **filter_kwargs): + def __init__(self, recording, freq_min=300.0, margin_ms="auto", max_margin_s=5, dtype=None, **filter_kwargs): + if margin_ms == "auto": + margin_ms = self.adjust_margin_ms_for_highpass(freq_min, max_margin_s) FilterRecording.__init__( self, recording, band=freq_min, margin_ms=margin_ms, dtype=dtype, btype="highpass", **filter_kwargs ) @@ -283,8 +312,10 @@ class NotchFilterRecording(BasePreprocessor): The quality factor of the notch filter dtype : None | dtype, default: None dtype of recording. If None, will take from `recording` - margin_ms : float, default: 5.0 + margin_ms : float | str, default: "auto" Margin in ms on border to avoid border effect + max_margin_s : float, default: 5 + Maximum margin in seconds when margin_ms is set to "auto". Returns ------- @@ -292,11 +323,14 @@ class NotchFilterRecording(BasePreprocessor): The notch-filtered recording extractor object """ - def __init__(self, recording, freq=3000, q=30, margin_ms=5.0, dtype=None): - # coeef is 'ba' type - fn = 0.5 * float(recording.get_sampling_frequency()) + def __init__(self, recording, freq=3000, q=30, margin_ms="auto", max_margin_s=5, dtype=None): import scipy.signal + if margin_ms == "auto": + margin_ms = self.adjust_margin_ms_for_notch(max_margin_s, q, freq) + + fn = 0.5 * float(recording.get_sampling_frequency()) + coeff = scipy.signal.iirnotch(freq / fn, q) if dtype is None: From 7e7846421ee49061f65581e40a03e352ef38c579 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 25 Nov 2025 10:47:02 +0100 Subject: [PATCH 02/19] NotchFilter uses FilterRecording --- src/spikeinterface/preprocessing/filter.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/src/spikeinterface/preprocessing/filter.py b/src/spikeinterface/preprocessing/filter.py index 928d07d14b..89ed52d0fd 100644 --- a/src/spikeinterface/preprocessing/filter.py +++ b/src/spikeinterface/preprocessing/filter.py @@ -300,7 +300,7 @@ def __init__(self, recording, freq_min=300.0, margin_ms="auto", max_margin_s=5, self._kwargs.update(filter_kwargs) -class NotchFilterRecording(BasePreprocessor): +class NotchFilterRecording(FilterRecording): """ Parameters ---------- @@ -323,19 +323,16 @@ class NotchFilterRecording(BasePreprocessor): The notch-filtered recording extractor object """ - def __init__(self, recording, freq=3000, q=30, margin_ms="auto", max_margin_s=5, dtype=None): + def __init__(self, recording, freq=3000, q=30, margin_ms="auto", max_margin_s=5, dtype=None, **filter_kwargs): import scipy.signal if margin_ms == "auto": margin_ms = self.adjust_margin_ms_for_notch(max_margin_s, q, freq) fn = 0.5 * float(recording.get_sampling_frequency()) - coeff = scipy.signal.iirnotch(freq / fn, q) - if dtype is None: - dtype = recording.get_dtype() - dtype = np.dtype(dtype) + dtype = fix_dtype(recording, dtype) # if uint --> unsupported if dtype.kind == "u": @@ -344,15 +341,10 @@ def __init__(self, recording, freq=3000, q=30, margin_ms="auto", max_margin_s=5, "to specify a signed type (e.g. 'int16', 'float32')" ) - BasePreprocessor.__init__(self, recording, dtype=dtype) + FilterRecording.__init__(self, recording, coeff=coeff, margin_ms=margin_ms, dtype=dtype, **filter_kwargs) self.annotate(is_filtered=True) - - sf = recording.get_sampling_frequency() - margin = int(margin_ms * sf / 1000.0) - for parent_segment in recording._recording_segments: - self.add_recording_segment(FilterRecordingSegment(parent_segment, coeff, "ba", margin, dtype)) - self._kwargs = dict(recording=recording, freq=freq, q=q, margin_ms=margin_ms, dtype=dtype.str) + self._kwargs.update(filter_kwargs) # functions for API From b846118a1ff9bf2c80aeaaebc2ba62db9e270f3f Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 25 Nov 2025 11:40:58 +0100 Subject: [PATCH 03/19] Notch filter is 'ba' --- src/spikeinterface/preprocessing/filter.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/spikeinterface/preprocessing/filter.py b/src/spikeinterface/preprocessing/filter.py index 89ed52d0fd..38485aff4a 100644 --- a/src/spikeinterface/preprocessing/filter.py +++ b/src/spikeinterface/preprocessing/filter.py @@ -341,7 +341,9 @@ def __init__(self, recording, freq=3000, q=30, margin_ms="auto", max_margin_s=5, "to specify a signed type (e.g. 'int16', 'float32')" ) - FilterRecording.__init__(self, recording, coeff=coeff, margin_ms=margin_ms, dtype=dtype, **filter_kwargs) + FilterRecording.__init__( + self, recording, coeff=coeff, filter_mode="ba", margin_ms=margin_ms, dtype=dtype, **filter_kwargs + ) self.annotate(is_filtered=True) self._kwargs = dict(recording=recording, freq=freq, q=q, margin_ms=margin_ms, dtype=dtype.str) self._kwargs.update(filter_kwargs) From 4149305a366a97a17461a71e34f0a9ed2527fef3 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Fri, 5 Dec 2025 13:00:01 +0100 Subject: [PATCH 04/19] Better margin behavior and 'Extract LFPs' tutorial --- doc/how_to/extract_lfps.rst | 565 ++++++++++++++++++ .../extract_lfps_files/extract_lfps_17_2.png | Bin 0 -> 29067 bytes .../extract_lfps_files/extract_lfps_19_1.png | Bin 0 -> 20533 bytes .../extract_lfps_files/extract_lfps_28_2.png | Bin 0 -> 75077 bytes .../extract_lfps_files/extract_lfps_31_0.png | Bin 0 -> 26729 bytes .../extract_lfps_files/extract_lfps_8_1.png | Bin 0 -> 25985 bytes examples/how_to/extract_lfps.py | 310 ++++++++++ src/spikeinterface/core/recording_tools.py | 8 +- src/spikeinterface/preprocessing/filter.py | 86 ++- 9 files changed, 939 insertions(+), 30 deletions(-) create mode 100644 doc/how_to/extract_lfps.rst create mode 100644 doc/how_to/extract_lfps_files/extract_lfps_17_2.png create mode 100644 doc/how_to/extract_lfps_files/extract_lfps_19_1.png create mode 100644 doc/how_to/extract_lfps_files/extract_lfps_28_2.png create mode 100644 doc/how_to/extract_lfps_files/extract_lfps_31_0.png create mode 100644 doc/how_to/extract_lfps_files/extract_lfps_8_1.png create mode 100644 examples/how_to/extract_lfps.py diff --git a/doc/how_to/extract_lfps.rst b/doc/how_to/extract_lfps.rst new file mode 100644 index 0000000000..4a7689b65e --- /dev/null +++ b/doc/how_to/extract_lfps.rst @@ -0,0 +1,565 @@ +Extract LFPs +============ + +Understanding filtering artifacts and chunking when extracting LFPs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Local Field Potentials (LFPs) are low-frequency signals (<300 Hz) that +reflect the summed activity of many neurons. Extracting LFPs from +high-sampling-rate recordings requires bandpass filtering, but this can +introduce artifacts when not done carefully, especially when data is +processed in chunks (for memory efficiency). + +This tutorial demonstrates: 1. How to generate simulated LFP data 2. +Common pitfalls when filtering with low cutoff frequencies 3. How +chunking and margins affect filtering artifacts 4. Summary + +**Key takeaway**: For LFP extraction, use large chunks (30-60s) and +large margins (several seconds) to minimize edge artifacts, even though +this is less memory-efficient. + +.. code:: ipython3 + + import time + import numpy as np + import matplotlib.pyplot as plt + from pathlib import Path + import pandas as pd + import seaborn as sns + + import spikeinterface as si + import spikeinterface.extractors as se + import spikeinterface.preprocessing as spre + import spikeinterface.widgets as sw + from spikeinterface.core import generate_ground_truth_recording + +.. code:: ipython3 + + %matplotlib inline + +1. Generate simulated recording with low-frequency signals +---------------------------------------------------------- + +Let’s create a simulated recording and add some low-frequency sinusoids +that mimic LFP activity. + +.. code:: ipython3 + + # Generate a ground truth recording with spikes + # Use a higher sampling rate (30 kHz) to simulate raw neural data + recording, sorting = generate_ground_truth_recording( + durations=[300.0], # 300 s + sampling_frequency=30000.0, + num_channels=1, + num_units=4, + seed=2305, + ) + + print(f"Recording: {recording}") + print(f"Duration: {recording.get_total_duration():.1f} s") + print(f"Sampling frequency: {recording.sampling_frequency} Hz") + print(f"Number of channels: {recording.get_num_channels()}") + + +.. parsed-literal:: + + Recording: GroundTruthRecording (InjectTemplatesRecording): 1 channels - 30.0kHz - 1 segments + 9,000,000 samples - 300.00s (5.00 minutes) - float32 dtype - 34.33 MiB + Duration: 300.0 s + Sampling frequency: 30000.0 Hz + Number of channels: 1 + + +Now let’s add some low-frequency sinusoidal components to simulate LFP +signals + +.. code:: ipython3 + + # Add low-frequency sinusoids with different frequencies and phases per channel + np.random.seed(2305) + num_channels = recording.get_num_channels() + lfp_signals = np.zeros((recording.get_num_samples(), recording.get_num_channels())) + time_vector = recording.get_times() + + for ch in range(num_channels): + # Add multiple frequency components (theta, alpha, beta ranges) + # Theta-like: 4-8 Hz + freq_theta = 4 + np.random.rand() * 4 + phase_theta = np.random.rand() * 2 * np.pi + amp_theta = 50 + np.random.rand() * 50 + + # Alpha-like: 8-12 Hz + freq_alpha = 8 + np.random.rand() * 4 + phase_alpha = np.random.rand() * 2 * np.pi + amp_alpha = 30 + np.random.rand() * 30 + + # Beta-like: 12-30 Hz + freq_beta = 12 + np.random.rand() * 18 + phase_beta = np.random.rand() * 2 * np.pi + amp_beta = 20 + np.random.rand() * 20 + + lfp_signals[:, ch] = ( + amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta) + + amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha) + + amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta) + ) + + # Create a recording with the added LFP signals + recording_lfp = si.NumpyRecording(traces_list=[lfp_signals], sampling_frequency=recording.sampling_frequency, + channel_ids=recording.channel_ids) + recording_with_lfp = recording + recording_lfp + + print("Added low-frequency components to simulate LFP signals") + + +.. parsed-literal:: + + Added low-frequency components to simulate LFP signals + + +Let’s visualize a short segment of the signal + +.. code:: ipython3 + + sw.plot_traces(recording_with_lfp, time_range=[0, 3]) + + + + +.. parsed-literal:: + + + + + + +.. image:: extract_lfps_files/extract_lfps_8_1.png + + +2. Filtering with low cutoff frequencies: the problem +----------------------------------------------------- + +Now let’s try to extract LFPs using a bandpass filter with a low +highpass cutoff (1 Hz). This will demonstrate a common issue. + +.. code:: ipython3 + + # Try to filter with 1 Hz highpass + try: + recording_lfp_1hz = spre.bandpass_filter(recording_with_lfp, freq_min=1.0, freq_max=300.0) + print("Filtering succeeded!") + except Exception as e: + print(f"Error message:\n{str(e)}") + + +.. parsed-literal:: + + Error message: + The freq_min (1.0 Hz) is too low and may cause artifacts during chunk processing. You can set 'ignore_low_freq_error=True' to bypass this error, but make sure you understand the implications. It is recommended to use large chunks when processing/saving your filtered recording to minimize IO overhead.Refer to this documentation on LFP filtering and chunking artifacts for more details: https://spikeinterface.readthedocs.io/en/stable/how-to/extracting_lfps.html. + + +**Why does this fail?** + +The error occurs because by default in SpikeInterface when highpass +filtering below 100 Hz. Filters with very low cutoff frequencies have +long impulse responses, which require larger margins to avoid edge +artifacts between chunks. + +The filter length (and required margin) scales inversely with the +highpass frequency. A 1 Hz highpass filter requires a margin of several +seconds, while a 300 Hz highpass (for spike extraction) only needs a few +milliseconds. + +3. Understanding chunking and margins +------------------------------------- + +SpikeInterface processes recordings in chunks to handle large datasets +efficiently. Each chunk needs a “margin” (extra samples at the edges) to +avoid edge artifacts when filtering. Let’s demonstrate this by saving +the filtered data with different chunking strategies. + +**This error is to inform the user that extra care should be used when +dealing with LFP signals!** + +We can ignore this error, but let’s make sure we understand what it’s +happening. + +.. code:: ipython3 + + # We can ignore this error, but let's see what is happening + recording_filt = spre.bandpass_filter(recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True) + +When retrieving traces, extra samples will be retrieved at the left and +right edges. By default, the filter function will set a margin to 5x the +sampling period associated to ``freq_min``. So for a 1 Hz cutoff +frequency, the margin will be 5 seconds! + +.. code:: ipython3 + + margin_in_s = recording_filt.margin_samples / recording_lfp.sampling_frequency + print(f"Margin: {margin_in_s} s") + + +.. parsed-literal:: + + Margin: 5.0 s + + +This effectively means that if we plot 1-s snippet of traces, a total of +11 s will actually be read and filtered. Note that the margin can be +overridden with the ``margin_ms`` argument, but we do not recommend +changing it. + +.. code:: ipython3 + + sw.plot_traces(recording_filt, time_range=[20, 21]) + + +.. parsed-literal:: + + /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:169: UserWarning: The margin size (150000 samples) is more than 20% of the chunk size 30000 samples. This may lead to performance bottlenecks when chunking. Consider increasing the chunk size to minimize margin overhead. + warnings.warn( + + + + +.. parsed-literal:: + + + + + + +.. image:: extract_lfps_files/extract_lfps_17_2.png + + +A warning tells us that what we are doing is not optimized, since in +order to get the requested traces the marging “overhead” is very large. + +If we ask or plot longer snippets, the warning is not displayed. + +.. code:: ipython3 + + sw.plot_traces(recording_filt, time_range=[20, 80]) + + + + +.. parsed-literal:: + + + + + + +.. image:: extract_lfps_files/extract_lfps_19_1.png + + +4. Quantification and visualization the artifacts +------------------------------------------------- + +Let’s extract the traces and visualize the differences between chunking +strategies. We’ll focus on the chunk boundaries where artifacts appear. + +.. code:: ipython3 + + margins_ms = [100, 1000, 5000] + chunk_durations = ["1s", "10s", "30s"] + +The best we can do is to save the full recording in one chunk. This will +cause no artifacts and chunking effects, but in practice it’s not +possible due to the duration and number of channels of most setups. + +Since in this toy case we have a single channel 5-min recording, we can +use this as “optimal”. + +.. code:: ipython3 + + recording_optimal = recording_filt.save(format="memory", chunk_duration="1000s") + + + +.. parsed-literal:: + + write_memory_recording (no parallelization): 0%| | 0/1 [00:00SharedMemoryRecording: 1 channels - 30.0kHz - 1 segments - 9,000,000 samples - 300.00s (5.00 minutes) - float32 dtype - 34.33 MiB
Channel IDs
    ['0']
Annotations
  • is_filtered : True
Properties
    + + + +Now we can do the same with our various options: + +.. code:: ipython3 + + recordings_chunked = {} + + for margin_ms in margins_ms: + for chunk_duration in chunk_durations: + print(f"Margin ms: {margin_ms} - Chunk duration: {chunk_duration}") + t_start = time.perf_counter() + recording_chunk = spre.bandpass_filter( + recording_with_lfp, + freq_min=1.0, + freq_max=300.0, + margin_ms=margin_ms, + ignore_low_freq_error=True + ) + recording_chunk = recording_chunk.save( + format="memory", + chunk_duration=chunk_duration, + verbose=False, + ) + t_stop = time.perf_counter() + result_dict = { + "recording": recording_chunk, + "time": t_stop - t_start + } + recordings_chunked[(margin_ms, chunk_duration)] = result_dict + + +.. parsed-literal:: + + Margin ms: 100 - Chunk duration: 1s + + +.. parsed-literal:: + + /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:453: UserWarning: The provided margin_ms (100 ms) is smaller than the recommended margin for the given freq_min (1.0 Hz). This may lead to artifacts at the edges of chunks during processing. Consider increasing the margin_ms to at least 5000.0 ms. + warnings.warn( + + + +.. parsed-literal:: + + write_memory_recording (no parallelization): 0%| | 0/300 [00:00 + + + + +.. image:: extract_lfps_files/extract_lfps_28_2.png + + +For smaller chunk sizes, these artifact will happen more often. In +addition, the margin “overhead” will make processing slower. Let’s +quantify these concepts by computing the overall absolute error with +respect to the optimal case and processing time. + +.. code:: ipython3 + + trace_plotted = False + traces_optimal = recording_optimal.get_traces() + data = {"margin": [], "chunk": [], "error": [], "time": []} + for recording_key, recording_dict in recordings_chunked.items(): + recording_chunk = recording_dict["recording"] + time = recording_dict["time"] + margin, chunk = recording_key + traces_chunk = recording_chunk.get_traces() + error = np.sum(np.abs(traces_optimal - traces_chunk)) + data["margin"].append(margin) + data["chunk"].append(chunk) + data["error"].append(error) + data["time"].append(time) + + df = pd.DataFrame(data=data) + +.. code:: ipython3 + + fig, axs = plt.subplots(ncols=2, figsize=(10, 5)) + sns.barplot(data=data, x="margin", y="error", hue="chunk", ax=axs[0]) + axs[0].set_yscale("log") + sns.barplot(data=data, x="margin", y="time", hue="chunk", ax=axs[1]) + axs[0].set_title("Error VS margin x chunk size") + axs[1].set_title("Processing time VS margin x chunk size") + + + sns.despine(fig) + + + +.. image:: extract_lfps_files/extract_lfps_31_0.png + + +4. Summary +---------- + +1. **Low-frequency filters require special care**: Filters with low + cutoff frequencies (< 10 Hz) have long impulse responses that require + large margins to avoid edge artifacts. + +2. **Chunking artifacts are real**: When processing data in chunks, + insufficient margins lead to visible discontinuities and errors at + chunk boundaries. + +3. **The solution: large chunks and large margins**: For LFP extraction + (1-300 Hz), use: + + - Chunk size: 30-60 seconds + - Margin size: 5 seconds (for 1 Hz highpass) (**use defaults!**) + - This is less memory-efficient but more accurate + +4. **Downsample after filtering**: After bandpass filtering, downsample + to reduce data size (e.g., to 1-2.5 kHz for 300 Hz max frequency). + +5. **Trade-offs**: There’s always a trade-off between computational + efficiency (smaller chunks, less memory) and accuracy (larger chunks, + fewer artifacts). 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How to generate simulated LFP data +# 2. Common pitfalls when filtering with low cutoff frequencies +# 3. How chunking and margins affect filtering artifacts +# 4. Summary +# +# **Key takeaway**: For LFP extraction, use large chunks (30-60s) and large margins (several seconds) to minimize +# edge artifacts, even though this is less memory-efficient. + +# %% +import time +import numpy as np +import matplotlib.pyplot as plt +from pathlib import Path +import pandas as pd +import seaborn as sns + +import spikeinterface as si +import spikeinterface.extractors as se +import spikeinterface.preprocessing as spre +import spikeinterface.widgets as sw +from spikeinterface.core import generate_ground_truth_recording + +# %% +# %matplotlib inline + +# %% [markdown] +# ## 1. Generate simulated recording with low-frequency signals +# +# Let's create a simulated recording and add some low-frequency sinusoids that mimic LFP activity. + +# %% +# Generate a ground truth recording with spikes +# Use a higher sampling rate (30 kHz) to simulate raw neural data +recording, sorting = generate_ground_truth_recording( + durations=[300.0], # 300 s + sampling_frequency=30000.0, + num_channels=1, + num_units=4, + seed=2305, +) + +print(f"Recording: {recording}") +print(f"Duration: {recording.get_total_duration():.1f} s") +print(f"Sampling frequency: {recording.sampling_frequency} Hz") +print(f"Number of channels: {recording.get_num_channels()}") + +# %% [markdown] +# Now let's add some low-frequency sinusoidal components to simulate LFP signals + +# %% +# Add low-frequency sinusoids with different frequencies and phases per channel +np.random.seed(2305) +num_channels = recording.get_num_channels() +lfp_signals = np.zeros((recording.get_num_samples(), recording.get_num_channels())) +time_vector = recording.get_times() + +for ch in range(num_channels): + # Add multiple frequency components (theta, alpha, beta ranges) + # Theta-like: 4-8 Hz + freq_theta = 4 + np.random.rand() * 4 + phase_theta = np.random.rand() * 2 * np.pi + amp_theta = 50 + np.random.rand() * 50 + + # Alpha-like: 8-12 Hz + freq_alpha = 8 + np.random.rand() * 4 + phase_alpha = np.random.rand() * 2 * np.pi + amp_alpha = 30 + np.random.rand() * 30 + + # Beta-like: 12-30 Hz + freq_beta = 12 + np.random.rand() * 18 + phase_beta = np.random.rand() * 2 * np.pi + amp_beta = 20 + np.random.rand() * 20 + + lfp_signals[:, ch] = ( + amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta) + + amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha) + + amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta) + ) + +# Create a recording with the added LFP signals +recording_lfp = si.NumpyRecording(traces_list=[lfp_signals], sampling_frequency=recording.sampling_frequency, + channel_ids=recording.channel_ids) +recording_with_lfp = recording + recording_lfp + +print("Added low-frequency components to simulate LFP signals") + +# %% [markdown] +# Let's visualize a short segment of the signal + +# %% +sw.plot_traces(recording_with_lfp, time_range=[0, 3]) + +# %% [markdown] +# ## 2. Filtering with low cutoff frequencies: the problem +# +# Now let's try to extract LFPs using a bandpass filter with a low highpass cutoff (1 Hz). +# This will demonstrate a common issue. + +# %% +# Try to filter with 1 Hz highpass +try: + recording_lfp_1hz = spre.bandpass_filter(recording_with_lfp, freq_min=1.0, freq_max=300.0) + print("Filtering succeeded!") +except Exception as e: + print(f"Error message:\n{str(e)}") + +# %% [markdown] +# **Why does this fail?** +# +# The error occurs because by default in SpikeInterface when highpass filtering below 100 Hz. +# Filters with very low cutoff frequencies have long impulse responses, which require larger margins to avoid edge artifacts between chunks. +# +# The filter length (and required margin) scales inversely with the highpass frequency. A 1 Hz highpass +# filter requires a margin of several seconds, while a 300 Hz highpass (for spike extraction) only needs +# a few milliseconds. + +# %% [markdown] +# ## 3. Understanding chunking and margins +# +# SpikeInterface processes recordings in chunks to handle large datasets efficiently. Each chunk needs +# a "margin" (extra samples at the edges) to avoid edge artifacts when filtering. Let's demonstrate +# this by saving the filtered data with different chunking strategies. +# +# **This error is to inform the user that extra care should be used when dealing with LFP signals!** +# +# We can ignore this error, but let's make sure we understand what it's happening. + +# %% +# We can ignore this error, but let's see what is happening +recording_filt = spre.bandpass_filter(recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True) + +# %% [markdown] +# When retrieving traces, extra samples will be retrieved at the left and right edges. +# By default, the filter function will set a margin to 5x the sampling period associated to `freq_min`. +# So for a 1 Hz cutoff frequency, the margin will be 5 seconds! + +# %% +margin_in_s = recording_filt.margin_samples / recording_lfp.sampling_frequency +print(f"Margin: {margin_in_s} s") + +# %% [markdown] +# This effectively means that if we plot 1-s snippet of traces, a total of 11 s will actually be read and filtered. Note that the margin can be overridden with the `margin_ms` argument, but we do not recommend changing it. + +# %% +sw.plot_traces(recording_filt, time_range=[20, 21]) + +# %% [markdown] +# A warning tells us that what we are doing is not optimized, since in order to get the requested traces the marging "overhead" is very large. +# +# If we ask or plot longer snippets, the warning is not displayed. + +# %% +sw.plot_traces(recording_filt, time_range=[20, 80]) + +# %% [markdown] +# ## 4. Quantification and visualization the artifacts +# +# Let's extract the traces and visualize the differences between chunking strategies. +# We'll focus on the chunk boundaries where artifacts appear. + +# %% +margins_ms = [100, 1000, 5000] +chunk_durations = ["1s", "10s", "30s"] + +# %% [markdown] +# The best we can do is to save the full recording in one chunk. This will cause no artifacts and chunking effects, but in practice it's not possible due to the duration and number of channels of most setups. +# +# Since in this toy case we have a single channel 5-min recording, we can use this as "optimal". + +# %% +recording_optimal = recording_filt.save(format="memory", chunk_duration="1000s") + +# %% +recording_optimal + +# %% [markdown] +# Now we can do the same with our various options: + +# %% +recordings_chunked = {} + +for margin_ms in margins_ms: + for chunk_duration in chunk_durations: + print(f"Margin ms: {margin_ms} - Chunk duration: {chunk_duration}") + t_start = time.perf_counter() + recording_chunk = spre.bandpass_filter( + recording_with_lfp, + freq_min=1.0, + freq_max=300.0, + margin_ms=margin_ms, + ignore_low_freq_error=True + ) + recording_chunk = recording_chunk.save( + format="memory", + chunk_duration=chunk_duration, + verbose=False, + ) + t_stop = time.perf_counter() + result_dict = { + "recording": recording_chunk, + "time": t_stop - t_start + } + recordings_chunked[(margin_ms, chunk_duration)] = result_dict + +# %% [markdown] +# Let's visualize the error for the "10s" chunks and different margins, centered around 30s (which is a chunk edge): + +# %% +fig, ax = plt.subplots(figsize=(10, 5)) +trace_plotted = False +for recording_key, recording_dict in recordings_chunked.items(): + recording_chunk = recording_dict["recording"] + margin, chunk = recording_key + start_frame = int(25 * recording_optimal.sampling_frequency) + end_frame = int(35 * recording_optimal.sampling_frequency) + traces_opt = recording_optimal.get_traces(start_frame=start_frame, end_frame=end_frame) + if not trace_plotted: + ax.plot(traces_opt, color="grey", label="traces", alpha=0.5) + trace_plotted = True + if chunk != "10s": + continue + diff = recording_optimal - recording_chunk + traces_diff = diff.get_traces(start_frame=start_frame, end_frame=end_frame) + ax.plot(traces_diff, label=recording_key) + +ax.legend() + +# %% [markdown] +# For smaller chunk sizes, these artifact will happen more often. In addition, the margin "overhead" will make processing slower. +# Let's quantify these concepts by computing the overall absolute error with respect to the optimal case and processing time. + +# %% +trace_plotted = False +traces_optimal = recording_optimal.get_traces() +data = {"margin": [], "chunk": [], "error": [], "time": []} +for recording_key, recording_dict in recordings_chunked.items(): + recording_chunk = recording_dict["recording"] + time = recording_dict["time"] + margin, chunk = recording_key + traces_chunk = recording_chunk.get_traces() + error = np.sum(np.abs(traces_optimal - traces_chunk)) + data["margin"].append(margin) + data["chunk"].append(chunk) + data["error"].append(error) + data["time"].append(time) + +df = pd.DataFrame(data=data) + +# %% +fig, axs = plt.subplots(ncols=2, figsize=(10, 5)) +sns.barplot(data=data, x="margin", y="error", hue="chunk", ax=axs[0]) +axs[0].set_yscale("log") +sns.barplot(data=data, x="margin", y="time", hue="chunk", ax=axs[1]) +axs[0].set_title("Error VS margin x chunk size") +axs[1].set_title("Processing time VS margin x chunk size") + + +sns.despine(fig) + +# %% [markdown] +# ## 4. Summary +# +# 1. **Low-frequency filters require special care**: Filters with low cutoff frequencies (< 10 Hz) have long +# impulse responses that require large margins to avoid edge artifacts. +# +# 2. **Chunking artifacts are real**: When processing data in chunks, insufficient margins lead to visible +# discontinuities and errors at chunk boundaries. +# +# 3. **The solution: large chunks and large margins**: For LFP extraction (1-300 Hz), use: +# - Chunk size: 30-60 seconds +# - Margin size: 5 seconds (for 1 Hz highpass) (**use defaults!**) +# - This is less memory-efficient but more accurate +# +# 4. **Downsample after filtering**: After bandpass filtering, downsample to reduce data size (e.g., to 1-2.5 kHz +# for 300 Hz max frequency). +# +# 5. **Trade-offs**: There's always a trade-off between computational efficiency (smaller chunks, less memory) +# and accuracy (larger chunks, fewer artifacts). For LFP analysis, accuracy should take priority. +# +# **When processing your own data:** +# - If you have memory constraints, use the largest chunk size your system can handle +# - Always verify your filtering parameters on a small test segment first +# - Consider the lowest frequency component you want to preserve when setting margins +# - Save the processed LFP data to disk to avoid recomputing diff --git a/src/spikeinterface/core/recording_tools.py b/src/spikeinterface/core/recording_tools.py index fd95b11e6a..992a48c589 100644 --- a/src/spikeinterface/core/recording_tools.py +++ b/src/spikeinterface/core/recording_tools.py @@ -214,13 +214,7 @@ def write_binary_recording_file_handle( def _init_memory_worker(recording, arrays, shm_names, shapes, dtype): # create a local dict per worker worker_ctx = {} - if isinstance(recording, dict): - from spikeinterface.core import load - - worker_ctx["recording"] = load(recording) - else: - worker_ctx["recording"] = recording - + worker_ctx["recording"] = recording worker_ctx["dtype"] = np.dtype(dtype) if arrays is None: diff --git a/src/spikeinterface/preprocessing/filter.py b/src/spikeinterface/preprocessing/filter.py index 38485aff4a..710610f289 100644 --- a/src/spikeinterface/preprocessing/filter.py +++ b/src/spikeinterface/preprocessing/filter.py @@ -1,4 +1,5 @@ from __future__ import annotations +import warnings import numpy as np @@ -8,6 +9,10 @@ from spikeinterface.core import get_chunk_with_margin +HIGHPASS_ERROR_THRESHOLD_HZ = 100 +MARGIN_TO_CHUNK_PERCENT_WARNING = 0.2 # 20% + + _common_filter_docs = """**filter_kwargs : dict Certain keyword arguments for `scipy.signal` filters: filter_order : order @@ -79,7 +84,6 @@ def __init__( ftype="butter", filter_mode="sos", margin_ms=5, - max_margin_s=5, add_reflect_padding=False, coeff=None, dtype=None, @@ -112,6 +116,7 @@ def __init__( self.set_channel_offsets(0) margin = int(margin_ms * fs / 1000.0) + self.margin_samples = margin for parent_segment in recording._recording_segments: self.add_recording_segment( FilterRecordingSegment( @@ -139,22 +144,6 @@ def __init__( direction=direction, ) - def adjust_margin_ms_for_highpass(self, freq_min, max_margin_s): - # compute margin as 3 times the period of the highpass cutoff - margin_ms = 3 * (1000.0 / freq_min) - # limit max margin - max_margin_ms = max_margin_s * 1000.0 - if margin_ms > max_margin_ms: - margin_ms = max_margin_ms - return margin_ms - - def adjust_margin_ms_for_notch(self, max_margin_s, q, f0): - margin_ms = (3 / np.pi) * (q / f0) * 1000.0 - max_margin_ms = max_margin_s * 1000.0 - if margin_ms < max_margin_ms: - margin_ms = max_margin_ms - return margin_ms - class FilterRecordingSegment(BasePreprocessorSegment): def __init__( @@ -176,6 +165,12 @@ def __init__( self.dtype = dtype def get_traces(self, start_frame, end_frame, channel_indices): + if self.margin > MARGIN_TO_CHUNK_PERCENT_WARNING * (end_frame - start_frame): + warnings.warn( + f"The margin size ({self.margin} samples) is more than {int(MARGIN_TO_CHUNK_PERCENT_WARNING * 100)}% " + f"of the chunk size {(end_frame - start_frame)} samples. This may lead to performance bottlenecks when " + f"chunking. Consider increasing the chunk size to minimize margin overhead." + ) traces_chunk, left_margin, right_margin = get_chunk_with_margin( self.parent_recording_segment, start_frame, @@ -241,6 +236,8 @@ class BandpassFilterRecording(FilterRecording): Maximum margin in seconds when margin_ms is set to "auto". dtype : dtype or None The dtype of the returned traces. If None, the dtype of the parent recording is used + ignore_low_freq_error : bool, default: False + If True, does not raise an error if freq_min is too low for the sampling frequency. {} Returns @@ -250,10 +247,18 @@ class BandpassFilterRecording(FilterRecording): """ def __init__( - self, recording, freq_min=300.0, freq_max=6000.0, margin_ms="auto", max_margin_s=5, dtype=None, **filter_kwargs + self, + recording, + freq_min=300.0, + freq_max=6000.0, + margin_ms="auto", + dtype=None, + ignore_low_freq_error=False, + **filter_kwargs, ): if margin_ms == "auto": - margin_ms = self.adjust_margin_ms_for_highpass(freq_min, max_margin_s) + margin_ms = adjust_margin_ms_for_highpass(freq_min) + highpass_check(freq_min, margin_ms, ignore_low_freq_error=ignore_low_freq_error) FilterRecording.__init__( self, recording, band=[freq_min, freq_max], margin_ms=margin_ms, dtype=dtype, **filter_kwargs ) @@ -277,10 +282,10 @@ class HighpassFilterRecording(FilterRecording): margin_ms : float | str, default: "auto" Margin in ms on border to avoid border effect. If "auto", margin is computed as 3 times the filter highpass cutoff period. - max_margin_s : float, default: 5 - Maximum margin in seconds when margin_ms is set to "auto". dtype : dtype or None The dtype of the returned traces. If None, the dtype of the parent recording is used + ignore_low_freq_error : bool, default: False + If True, does not raise an error if freq_min is too low for the sampling frequency. {} Returns @@ -289,9 +294,12 @@ class HighpassFilterRecording(FilterRecording): The highpass-filtered recording extractor object """ - def __init__(self, recording, freq_min=300.0, margin_ms="auto", max_margin_s=5, dtype=None, **filter_kwargs): + def __init__( + self, recording, freq_min=300.0, margin_ms="auto", dtype=None, ignore_low_freq_error=False, **filter_kwargs + ): if margin_ms == "auto": - margin_ms = self.adjust_margin_ms_for_highpass(freq_min, max_margin_s) + margin_ms = adjust_margin_ms_for_highpass(freq_min) + highpass_check(freq_min, margin_ms, ignore_low_freq_error=ignore_low_freq_error) FilterRecording.__init__( self, recording, band=freq_min, margin_ms=margin_ms, dtype=dtype, btype="highpass", **filter_kwargs ) @@ -426,6 +434,38 @@ def causal_filter( highpass_filter.__doc__ = highpass_filter.__doc__.format(_common_filter_docs) +def adjust_margin_ms_for_highpass(freq_min, multiplier=5): + margin_ms = multiplier * (1000.0 / freq_min) + return margin_ms + + +def adjust_margin_ms_for_notch(q, f0, multiplier=5): + margin_ms = (multiplier / np.pi) * (q / f0) * 1000.0 + return margin_ms + + +def highpass_check(freq_min, margin_ms, ignore_low_freq_error=False): + if freq_min < HIGHPASS_ERROR_THRESHOLD_HZ: + if not ignore_low_freq_error: + raise ValueError( + f"The freq_min ({freq_min} Hz) is too low and may cause artifacts during chunk processing. " + f"You can set 'ignore_low_freq_error=True' to bypass this error, but make sure you understand the implications. " + f"It is recommended to use large chunks when processing/saving your filtered recording to minimize IO overhead." + f"Refer to this documentation on LFP filtering and chunking artifacts for more details: " + f"https://spikeinterface.readthedocs.io/en/latest/how-to/extract_lfps.html. " + ) + if margin_ms == "auto": + margin_ms = adjust_margin_ms_for_highpass(freq_min) + else: + auto_margin_ms = adjust_margin_ms_for_highpass(freq_min) + if margin_ms < auto_margin_ms: + warnings.warn( + f"The provided margin_ms ({margin_ms} ms) is smaller than the recommended margin for the given freq_min ({freq_min} Hz). " + f"This may lead to artifacts at the edges of chunks during processing. " + f"Consider increasing the margin_ms to at least {auto_margin_ms} ms." + ) + + def fix_dtype(recording, dtype): """ Fix recording dtype for preprocessing, by always returning a numpy.dtype. From d08c378dea7dd957db5bbfbdeb65afedea56965d Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Fri, 5 Dec 2025 13:06:51 +0100 Subject: [PATCH 05/19] Add new how-to to index --- doc/how_to/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/how_to/index.rst b/doc/how_to/index.rst index 2d490c207d..0f59ed5114 100644 --- a/doc/how_to/index.rst +++ b/doc/how_to/index.rst @@ -10,6 +10,7 @@ Guides on how to solve specific, short problems in SpikeInterface. Learn how to. customize_a_plot combine_recordings process_by_channel_group + extract_lfps build_pipeline_with_dicts physical_units unsigned_to_signed From 79e4469140068a755b6fdc2c9d1fa7d1895451f4 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Fri, 5 Dec 2025 14:07:12 +0100 Subject: [PATCH 06/19] Fix notch --- src/spikeinterface/preprocessing/filter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/spikeinterface/preprocessing/filter.py b/src/spikeinterface/preprocessing/filter.py index 710610f289..4ee7347962 100644 --- a/src/spikeinterface/preprocessing/filter.py +++ b/src/spikeinterface/preprocessing/filter.py @@ -335,7 +335,7 @@ def __init__(self, recording, freq=3000, q=30, margin_ms="auto", max_margin_s=5, import scipy.signal if margin_ms == "auto": - margin_ms = self.adjust_margin_ms_for_notch(max_margin_s, q, freq) + margin_ms = adjust_margin_ms_for_notch(q, freq) fn = 0.5 * float(recording.get_sampling_frequency()) coeff = scipy.signal.iirnotch(freq / fn, q) From c1e80f38126687e188a0c393d53c94664ea21ce4 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 9 Dec 2025 12:08:46 +0100 Subject: [PATCH 07/19] Move how-to in gallery and fox ibl export tests --- doc/conf.py | 6 +- doc/forhowto/forhowto_jupyter.zip | Bin 0 -> 24771 bytes doc/forhowto/forhowto_python.zip | Bin 0 -> 18570 bytes .../images/sphx_glr_plot_extract_lfps_001.png | Bin 0 -> 27316 bytes .../images/sphx_glr_plot_extract_lfps_002.png | Bin 0 -> 30481 bytes .../images/sphx_glr_plot_extract_lfps_003.png | Bin 0 -> 20875 bytes .../images/sphx_glr_plot_extract_lfps_004.png | Bin 0 -> 69160 bytes .../images/sphx_glr_plot_extract_lfps_005.png | Bin 0 -> 27413 bytes ...phx_glr_plot_working_with_tetrodes_001.png | Bin 0 -> 18554 bytes ...phx_glr_plot_working_with_tetrodes_002.png | Bin 0 -> 41202 bytes .../sphx_glr_plot_extract_lfps_thumb.png | Bin 0 -> 23684 bytes ...x_glr_plot_working_with_tetrodes_thumb.png | Bin 0 -> 15759 bytes doc/forhowto/index.rst | 81 + doc/forhowto/plot_extract_lfps.codeobj.json | 2637 +++++++++++++++++ doc/forhowto/plot_extract_lfps.ipynb | 316 ++ .../forhowto/plot_extract_lfps.py | 247 +- doc/forhowto/plot_extract_lfps.py.md5 | 1 + doc/forhowto/plot_extract_lfps.rst | 640 ++++ doc/forhowto/plot_extract_lfps.zip | Bin 0 -> 30149 bytes .../plot_working_with_tetrodes.codeobj.json | 743 +++++ doc/forhowto/plot_working_with_tetrodes.ipynb | 194 ++ .../forhowto/plot_working_with_tetrodes.py | 0 .../plot_working_with_tetrodes.py.md5 | 1 + doc/forhowto/plot_working_with_tetrodes.rst | 319 ++ doc/forhowto/plot_working_with_tetrodes.zip | Bin 0 -> 13192 bytes doc/how_to/index.rst | 4 +- examples/{tutorials => }/forhowto/README.rst | 0 examples/forhowto/plot_extract_lfps.py | 313 ++ .../forhowto/plot_working_with_tetrodes.py | 117 + .../exporters/tests/test_export_to_ibl.py | 2 +- src/spikeinterface/preprocessing/filter.py | 6 +- 31 files changed, 5494 insertions(+), 133 deletions(-) create mode 100644 doc/forhowto/forhowto_jupyter.zip create mode 100644 doc/forhowto/forhowto_python.zip create mode 100644 doc/forhowto/images/sphx_glr_plot_extract_lfps_001.png create mode 100644 doc/forhowto/images/sphx_glr_plot_extract_lfps_002.png create mode 100644 doc/forhowto/images/sphx_glr_plot_extract_lfps_003.png create mode 100644 doc/forhowto/images/sphx_glr_plot_extract_lfps_004.png create mode 100644 doc/forhowto/images/sphx_glr_plot_extract_lfps_005.png create mode 100644 doc/forhowto/images/sphx_glr_plot_working_with_tetrodes_001.png create mode 100644 doc/forhowto/images/sphx_glr_plot_working_with_tetrodes_002.png create mode 100644 doc/forhowto/images/thumb/sphx_glr_plot_extract_lfps_thumb.png create mode 100644 doc/forhowto/images/thumb/sphx_glr_plot_working_with_tetrodes_thumb.png create mode 100644 doc/forhowto/index.rst create mode 100644 doc/forhowto/plot_extract_lfps.codeobj.json create mode 100644 doc/forhowto/plot_extract_lfps.ipynb rename examples/how_to/extract_lfps.py => doc/forhowto/plot_extract_lfps.py (54%) create mode 100644 doc/forhowto/plot_extract_lfps.py.md5 create mode 100644 doc/forhowto/plot_extract_lfps.rst create mode 100644 doc/forhowto/plot_extract_lfps.zip create mode 100644 doc/forhowto/plot_working_with_tetrodes.codeobj.json create mode 100644 doc/forhowto/plot_working_with_tetrodes.ipynb rename examples/tutorials/forhowto/plot_1_working_with_tetrodes.py => doc/forhowto/plot_working_with_tetrodes.py (100%) create mode 100644 doc/forhowto/plot_working_with_tetrodes.py.md5 create mode 100644 doc/forhowto/plot_working_with_tetrodes.rst create mode 100644 doc/forhowto/plot_working_with_tetrodes.zip rename examples/{tutorials => }/forhowto/README.rst (100%) create mode 100644 examples/forhowto/plot_extract_lfps.py create mode 100644 examples/forhowto/plot_working_with_tetrodes.py diff --git a/doc/conf.py b/doc/conf.py index 4f9b60a926..6a50bf634d 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -123,8 +123,8 @@ # This is the default but including here explicitly. Should build all docs and fail on gallery failures only. # other option would be abort_on_example_error, but this fails on first failure. So we decided against this. 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    + +.. thumbnail-parent-div-open + +.. raw:: html + +
    + +.. only:: html + + .. image:: /forhowto/images/thumb/sphx_glr_plot_extract_lfps_thumb.png + :alt: + + :ref:`sphx_glr_forhowto_plot_extract_lfps.py` + +.. raw:: html + +
    Extract LFPs
    +
    + + +.. raw:: html + +
    + +.. only:: html + + .. image:: /forhowto/images/thumb/sphx_glr_plot_working_with_tetrodes_thumb.png + :alt: + + :ref:`sphx_glr_forhowto_plot_working_with_tetrodes.py` + +.. raw:: html + +
    Work with tetrodes
    +
    + + +.. thumbnail-parent-div-close + +.. raw:: html + +
    + + +.. toctree:: + :hidden: + + /forhowto/plot_extract_lfps + /forhowto/plot_working_with_tetrodes + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: forhowto_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: forhowto_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/forhowto/plot_extract_lfps.codeobj.json b/doc/forhowto/plot_extract_lfps.codeobj.json new file mode 100644 index 0000000000..ae763dc01b --- /dev/null +++ b/doc/forhowto/plot_extract_lfps.codeobj.json @@ -0,0 +1,2637 @@ +{ + "_": [ + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.legend", + "module_short": "matplotlib.legend", + "name": "Legend" + }, + { + "is_class": false, + 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false, + "module": "spikeinterface.core.operatorrecordings", + "module_short": "spikeinterface.core.operatorrecordings", + "name": "AddRecordings" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface.core", + "name": "AddRecordings" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "AddRecordings" + } + ], + "recordings_chunked": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "dict" + } + ], + "recordings_chunked.items": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ], + "result_dict": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "dict" + } + ], + "rng": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy.random._generator", + "module_short": "numpy.random", + "name": "Generator" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy.random", + "module_short": "numpy.random", + "name": "Generator" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "Generator" + } + ], + "rng.random": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy.random._generator", + "module_short": "numpy.random", + "name": "Generator.random" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy.random", + "module_short": "numpy.random", + "name": "Generator.random" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "Generator.random" + } + ], + "si.NumpyRecording": [ + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.core.numpyextractors", + "module_short": "spikeinterface", + "name": "NumpyRecording" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface", + "name": "NumpyRecording" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "NumpyRecording" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.core.baserecording", + "module_short": "spikeinterface", + "name": "BaseRecording" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface", + "name": "BaseRecording" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "BaseRecording" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.core.baserecordingsnippets", + "module_short": "spikeinterface", + "name": "BaseRecordingSnippets" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface", + "name": "BaseRecordingSnippets" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "BaseRecordingSnippets" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.core.base", + "module_short": "spikeinterface.core.base", + "name": "BaseExtractor" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface.core", + "name": "BaseExtractor" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "BaseExtractor" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "NumpyRecording" + } + ], + "sns.barplot": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "seaborn", + "module_short": "seaborn", + "name": "barplot" + } + ], + "sns.despine": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "seaborn", + "module_short": "seaborn", + "name": "despine" + } + ], + "sorting": [ + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core.numpyextractors", + "module_short": "spikeinterface", + "name": "NumpySorting" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface", + "name": "NumpySorting" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "NumpySorting" + } + ], + "spre.bandpass_filter": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.preprocessing", + "module_short": "spikeinterface.preprocessing", + "name": "bandpass_filter" + } + ], + "start_frame": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "int" + } + ], + "start_time": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "int" + } + ], + "sw.plot_traces": [ + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.widgets.traces", + "module_short": "spikeinterface.widgets", + "name": "TracesWidget" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.widgets", + "module_short": "spikeinterface.widgets", + "name": "TracesWidget" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "TracesWidget" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.widgets.base", + "module_short": "spikeinterface.widgets.base", + "name": "BaseWidget" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface.widgets", + "module_short": "spikeinterface.widgets", + "name": "BaseWidget" + }, + { + "is_class": true, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "BaseWidget" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.widgets", + "module_short": "spikeinterface.widgets", + "name": "plot_traces" + } + ], + "t_start": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "float" + } + ], + "t_stop": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "float" + } + ], + "time": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "float" + }, + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "time" + } + ], + "time.perf_counter": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "float" + }, + { + "is_class": false, + "is_explicit": false, + "module": "time", + "module_short": "time", + "name": "perf_counter" + } + ], + "time_vector": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "timestamps": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "trace_plotted": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "bool" + } + ], + "traces_chunk": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "traces_diff": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "traces_opt": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "traces_optimal": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ] +} diff --git a/doc/forhowto/plot_extract_lfps.ipynb b/doc/forhowto/plot_extract_lfps.ipynb new file mode 100644 index 0000000000..8f72547112 --- /dev/null +++ b/doc/forhowto/plot_extract_lfps.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Extract LFPs\n\nUnderstanding filtering artifacts and chunking when extracting LFPs\n\nLocal Field Potentials (LFPs) are low-frequency signals (<300 Hz) that reflect the summed activity of many neurons.\nExtracting LFPs from high-sampling-rate recordings requires bandpass filtering, but this can introduce artifacts\nwhen not done carefully, especially when data is processed in chunks (for memory efficiency).\n\nThis tutorial demonstrates:\n\n1. How to generate simulated LFP data\n2. Common pitfalls when filtering with low cutoff frequencies\n3. How chunking and margins affect filtering artifacts\n4. Summary\n\n**Key takeaway**: For LFP extraction, use large chunks (30-60s) and large margins (several seconds) to minimize\nedge artifacts, even though this is less memory-efficient.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import necessary modules\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import time\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\nimport pandas as pd\nimport seaborn as sns\n\nimport spikeinterface as si\nimport spikeinterface.extractors as se\nimport spikeinterface.preprocessing as spre\nimport spikeinterface.widgets as sw\nfrom spikeinterface.core import generate_ground_truth_recording" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Generate simulated recording with low-frequency signals\n\nLet's create a simulated recording and add some low-frequency sinusoids that mimic LFP activity.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Generate a ground truth recording with spikes\n# Use a higher sampling rate (30 kHz) to simulate raw neural data\nrecording, sorting = generate_ground_truth_recording(\n durations=[300.0], # 300 s\n sampling_frequency=30000.0,\n num_channels=1,\n num_units=4,\n seed=2305,\n)\n\nprint(recording)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's add some low-frequency sinusoidal components to simulate LFP signals\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Add low-frequency sinusoids with different frequencies and phases per channel\nrng = np.random.default_rng(42)\nnum_channels = recording.get_num_channels()\nlfp_signals = np.zeros(\n (recording.get_num_samples(), recording.get_num_channels())\n)\ntime_vector = recording.get_times()\n\nfor ch in range(num_channels):\n # Add multiple frequency components (theta, alpha, beta ranges)\n # Theta-like: 4-8 Hz\n freq_theta = 4 + rng.random() * 4\n phase_theta = rng.random() * 2 * np.pi\n amp_theta = 50 + rng.random() * 50\n\n # Alpha-like: 8-12 Hz\n freq_alpha = 8 + rng.random() * 4\n phase_alpha = rng.random() * 2 * np.pi\n amp_alpha = 30 + rng.random() * 30\n\n # Beta-like: 12-30 Hz\n freq_beta = 12 + rng.random() * 18\n phase_beta = rng.random() * 2 * np.pi\n amp_beta = 20 + rng.random() * 20\n\n lfp_signals[:, ch] = (\n amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta)\n + amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha)\n + amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta)\n )\n\n# Create a recording with the added LFP signals\nrecording_lfp = si.NumpyRecording(\n traces_list=[lfp_signals],\n sampling_frequency=recording.sampling_frequency,\n channel_ids=recording.channel_ids,\n)\nrecording_with_lfp = recording + recording_lfp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize a short segment of the signal\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = sw.plot_traces(recording_with_lfp, time_range=[0, 3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Filtering with low cutoff frequencies: the problem\n\nNow let's try to extract LFPs using a bandpass filter with a low highpass cutoff (1 Hz).\nThis will demonstrate a common issue.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Try to filter with 1 Hz highpass\ntry:\n recording_lfp_1hz = spre.bandpass_filter(\n recording_with_lfp, freq_min=1.0, freq_max=300.0\n )\nexcept Exception as e:\n print(f\"Error message:\\n{str(e)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Why does this fail?**\n\nThe error occurs because by default in SpikeInterface when highpass filtering below 100 Hz.\nFilters with very low cutoff frequencies have long impulse responses, which require larger margins to avoid edge artifacts between chunks.\n\nThe filter length (and required margin) scales inversely with the highpass frequency. A 1 Hz highpass\nfilter requires a margin of several seconds, while a 300 Hz highpass (for spike extraction) only needs\na few milliseconds.\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Understanding chunking and margins\n\nSpikeInterface processes recordings in chunks to handle large datasets efficiently. Each chunk needs\na \"margin\" (extra samples at the edges) to avoid edge artifacts when filtering. Let's demonstrate\nthis by saving the filtered data with different chunking strategies.\n\n**This error is to inform the user that extra care should be used when dealing with LFP signals!**\n\nWe can ignore this error, but let's make sure we understand what it's happening.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# We can ignore this error, but let's see what is happening\nrecording_filt = spre.bandpass_filter(\n recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When retrieving traces, extra samples will be retrieved at the left and right edges.\nBy default, the filter function will set a margin to 5x the sampling period associated to `freq_min`.\nSo for a 1 Hz cutoff frequency, the margin will be 5 seconds!\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "margin_in_s = recording_filt.margin_samples / recording_lfp.sampling_frequency\nprint(f\"Margin: {margin_in_s} s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This effectively means that if we plot 1-s snippet of traces, a total of 11 s will actually be read and filtered.\nNote that the margin can be overridden with the `margin_ms` argument, but we do not recommend changing it.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = sw.plot_traces(recording_filt, time_range=[20, 21])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A warning tells us that what we are doing is not optimized, since in order to get the requested traces\nthe margin \"overhead\" is very large.\n\nIf we ask or plot longer snippets, the warning is not displayed.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = sw.plot_traces(recording_filt, time_range=[20, 80])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Quantification and visualization of the artifacts\n\nLet's extract the traces and visualize the differences between chunking strategies.\nWe'll focus on the chunk boundaries where artifacts appear.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "margins_ms = [100, 1000, 5000]\nchunk_durations = [\"1s\", \"10s\", \"30s\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The best we can do is to save the full recording in one chunk. This will cause no artifacts and chunking effects,\nbut in practice it's not possible due to the duration and number of channels of most setups.\n\nSince in this toy case we have a single channel 5-min recording, we can use this as \"optimal\".\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "recording_optimal = recording_filt.save(\n format=\"memory\",\n chunk_duration=\"1000s\",\n progress_bar=False\n)\n\nprint(recording_optimal)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can do the same with our various options:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "recordings_chunked = {}\n\nfor margin_ms in margins_ms:\n for chunk_duration in chunk_durations:\n print(f\"Margin ms: {margin_ms} - Chunk duration: {chunk_duration}\")\n t_start = time.perf_counter()\n recording_chunk = spre.bandpass_filter(\n recording_with_lfp,\n freq_min=1.0,\n freq_max=300.0,\n margin_ms=margin_ms,\n ignore_low_freq_error=True,\n )\n recording_chunk = recording_chunk.save(\n format=\"memory\",\n chunk_duration=chunk_duration,\n verbose=False,\n progress_bar=False\n )\n t_stop = time.perf_counter()\n result_dict = {\"recording\": recording_chunk, \"time\": t_stop - t_start}\n recordings_chunked[(margin_ms, chunk_duration)] = result_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize the error for the \"10s\" chunks and different margins, centered around 30s (which is a chunk edge):\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 5))\ntrace_plotted = False\nstart_time = 15 # seconds\nend_time = 45 # seconds\nstart_frame = int(start_time * recording_optimal.sampling_frequency)\nend_frame = int(end_time * recording_optimal.sampling_frequency)\ntimestamps = recording_optimal.get_times()[start_frame:end_frame]\nfor recording_key, recording_dict in recordings_chunked.items():\n recording_chunk = recording_dict[\"recording\"]\n margin, chunk = recording_key\n # only plot \"10s\" chunks\n if chunk != \"10s\":\n continue\n traces_opt = recording_optimal.get_traces(\n start_frame=start_frame, end_frame=end_frame\n )\n if not trace_plotted:\n ax.plot(timestamps, traces_opt, color=\"grey\", label=\"traces\", alpha=0.5)\n trace_plotted = True\n diff = recording_optimal - recording_chunk\n traces_diff = diff.get_traces(start_frame=start_frame, end_frame=end_frame)\n ax.plot(timestamps, traces_diff, label=f\"Margin: {margin}\")\n for chunk in [20, 30, 40]: # chunk boundaries at 10s intervals\n ax.axvline(x=chunk, color=\"red\", linestyle=\"--\", alpha=0.5)\n\nax.set_xlabel(\"Time (s)\")\nax.set_ylabel(\"Voltage ($\\\\mu V$)\")\n_ = ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the plot, we can see that there is a very small error when the margin size is large (green),\na larger error when the margin is smaller (orange) and a large error when the margin is small (blue).\nSo we need large margins (compared to the chunk size) if we want accurate filtered.\n\nThe artifacts do not depend on chunk size, but for smaller chunk sizes, these artifacts will happen more often.\nIn addition, the margin \"overhead\" will make processing slower. Let's quantify these concepts by computing the\noverall absolute error with respect to the optimal case and processing time.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "trace_plotted = False\ntraces_optimal = recording_optimal.get_traces()\ndata = {\"margin\": [], \"chunk\": [], \"error\": [], \"time\": []}\nfor recording_key, recording_dict in recordings_chunked.items():\n recording_chunk = recording_dict[\"recording\"]\n time = recording_dict[\"time\"]\n margin, chunk = recording_key\n traces_chunk = recording_chunk.get_traces()\n error = np.sum(np.abs(traces_optimal - traces_chunk))\n data[\"margin\"].append(margin)\n data[\"chunk\"].append(chunk)\n data[\"error\"].append(error)\n data[\"time\"].append(time)\n\ndf = pd.DataFrame(data=data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's visualize the error and processing time for different margin and chunk size combinations\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(ncols=2, figsize=(10, 5))\nsns.barplot(data=data, x=\"margin\", y=\"error\", hue=\"chunk\", ax=axs[0])\naxs[0].set_yscale(\"log\")\nsns.barplot(data=data, x=\"margin\", y=\"time\", hue=\"chunk\", ax=axs[1])\naxs[0].set_title(\"Error VS margin x chunk size\")\naxs[1].set_title(\"Processing time VS margin x chunk size\")\n\nsns.despine(fig)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n\n1. **Low-frequency filters require special care**: Filters with low cutoff frequencies (< 10 Hz) have long\n impulse responses that require large margins to avoid edge artifacts.\n\n2. **Chunking artifacts are real**: When processing data in chunks, insufficient margins lead to visible\n discontinuities and errors at chunk boundaries.\n\n3. **The solution: large chunks and large margins**: For LFP extraction (1-300 Hz), use:\n - Chunk size: 30-60 seconds\n - Margin size: 5 seconds (for 1 Hz highpass) (**use defaults!**)\n - This is less memory-efficient but more accurate\n\n4. **Downsample after filtering**: After bandpass filtering, downsample to reduce data size (e.g., to 1-2.5 kHz\n for 300 Hz max frequency).\n\n5. **Trade-offs**: There's always a trade-off between computational efficiency (smaller chunks, less memory)\n and accuracy (larger chunks, fewer artifacts). For LFP analysis, accuracy should take priority.\n\n**When processing your own data:**\n\n- If you have memory constraints, use the largest chunk size your system can handle\n- Always verify your filtering parameters on a small test segment first\n- Consider the lowest frequency component you want to preserve when setting margins\n- Save the processed LFP data to disk to avoid recomputing\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/how_to/extract_lfps.py b/doc/forhowto/plot_extract_lfps.py similarity index 54% rename from examples/how_to/extract_lfps.py rename to doc/forhowto/plot_extract_lfps.py index a8a952d59a..4eb4393c22 100644 --- a/examples/how_to/extract_lfps.py +++ b/doc/forhowto/plot_extract_lfps.py @@ -1,38 +1,27 @@ -# --- -# jupyter: -# jupytext: -# cell_metadata_filter: -all -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.18.1 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# %% [markdown] -# # Extract LFPs -# -# ### Understanding filtering artifacts and chunking when extracting LFPs -# -# Local Field Potentials (LFPs) are low-frequency signals (<300 Hz) that reflect the summed activity of many neurons. -# Extracting LFPs from high-sampling-rate recordings requires bandpass filtering, but this can introduce artifacts -# when not done carefully, especially when data is processed in chunks (for memory efficiency). -# -# This tutorial demonstrates: -# 1. How to generate simulated LFP data -# 2. Common pitfalls when filtering with low cutoff frequencies -# 3. How chunking and margins affect filtering artifacts -# 4. Summary -# -# **Key takeaway**: For LFP extraction, use large chunks (30-60s) and large margins (several seconds) to minimize -# edge artifacts, even though this is less memory-efficient. +""" +Extract LFPs +============ + +Understanding filtering artifacts and chunking when extracting LFPs + +Local Field Potentials (LFPs) are low-frequency signals (<300 Hz) that reflect the summed activity of many neurons. +Extracting LFPs from high-sampling-rate recordings requires bandpass filtering, but this can introduce artifacts +when not done carefully, especially when data is processed in chunks (for memory efficiency). + +This tutorial demonstrates: + +1. How to generate simulated LFP data +2. Common pitfalls when filtering with low cutoff frequencies +3. How chunking and margins affect filtering artifacts +4. Summary + +**Key takeaway**: For LFP extraction, use large chunks (30-60s) and large margins (several seconds) to minimize +edge artifacts, even though this is less memory-efficient. +""" + +############################################################################## +# Import necessary modules -# %% import time import numpy as np import matplotlib.pyplot as plt @@ -46,15 +35,12 @@ import spikeinterface.widgets as sw from spikeinterface.core import generate_ground_truth_recording -# %% -# %matplotlib inline - -# %% [markdown] -# ## 1. Generate simulated recording with low-frequency signals +############################################################################## +# 1. Generate simulated recording with low-frequency signals +# ----------------------------------------------------------- # # Let's create a simulated recording and add some low-frequency sinusoids that mimic LFP activity. -# %% # Generate a ground truth recording with spikes # Use a higher sampling rate (30 kHz) to simulate raw neural data recording, sorting = generate_ground_truth_recording( @@ -65,72 +51,72 @@ seed=2305, ) -print(f"Recording: {recording}") -print(f"Duration: {recording.get_total_duration():.1f} s") -print(f"Sampling frequency: {recording.sampling_frequency} Hz") -print(f"Number of channels: {recording.get_num_channels()}") +print(recording) -# %% [markdown] +############################################################################## # Now let's add some low-frequency sinusoidal components to simulate LFP signals -# %% # Add low-frequency sinusoids with different frequencies and phases per channel -np.random.seed(2305) +rng = np.random.default_rng(42) num_channels = recording.get_num_channels() -lfp_signals = np.zeros((recording.get_num_samples(), recording.get_num_channels())) +lfp_signals = np.zeros( + (recording.get_num_samples(), recording.get_num_channels()) +) time_vector = recording.get_times() for ch in range(num_channels): # Add multiple frequency components (theta, alpha, beta ranges) # Theta-like: 4-8 Hz - freq_theta = 4 + np.random.rand() * 4 - phase_theta = np.random.rand() * 2 * np.pi - amp_theta = 50 + np.random.rand() * 50 + freq_theta = 4 + rng.random() * 4 + phase_theta = rng.random() * 2 * np.pi + amp_theta = 50 + rng.random() * 50 # Alpha-like: 8-12 Hz - freq_alpha = 8 + np.random.rand() * 4 - phase_alpha = np.random.rand() * 2 * np.pi - amp_alpha = 30 + np.random.rand() * 30 + freq_alpha = 8 + rng.random() * 4 + phase_alpha = rng.random() * 2 * np.pi + amp_alpha = 30 + rng.random() * 30 # Beta-like: 12-30 Hz - freq_beta = 12 + np.random.rand() * 18 - phase_beta = np.random.rand() * 2 * np.pi - amp_beta = 20 + np.random.rand() * 20 + freq_beta = 12 + rng.random() * 18 + phase_beta = rng.random() * 2 * np.pi + amp_beta = 20 + rng.random() * 20 lfp_signals[:, ch] = ( - amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta) + - amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha) + - amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta) + amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta) + + amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha) + + amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta) ) # Create a recording with the added LFP signals -recording_lfp = si.NumpyRecording(traces_list=[lfp_signals], sampling_frequency=recording.sampling_frequency, - channel_ids=recording.channel_ids) +recording_lfp = si.NumpyRecording( + traces_list=[lfp_signals], + sampling_frequency=recording.sampling_frequency, + channel_ids=recording.channel_ids, +) recording_with_lfp = recording + recording_lfp -print("Added low-frequency components to simulate LFP signals") -# %% [markdown] +############################################################################## # Let's visualize a short segment of the signal -# %% -sw.plot_traces(recording_with_lfp, time_range=[0, 3]) +_ = sw.plot_traces(recording_with_lfp, time_range=[0, 3]) -# %% [markdown] -# ## 2. Filtering with low cutoff frequencies: the problem +############################################################################## +# 2. Filtering with low cutoff frequencies: the problem +# ------------------------------------------------------ # # Now let's try to extract LFPs using a bandpass filter with a low highpass cutoff (1 Hz). # This will demonstrate a common issue. -# %% # Try to filter with 1 Hz highpass try: - recording_lfp_1hz = spre.bandpass_filter(recording_with_lfp, freq_min=1.0, freq_max=300.0) - print("Filtering succeeded!") + recording_lfp_1hz = spre.bandpass_filter( + recording_with_lfp, freq_min=1.0, freq_max=300.0 + ) except Exception as e: print(f"Error message:\n{str(e)}") -# %% [markdown] +############################################################################## # **Why does this fail?** # # The error occurs because by default in SpikeInterface when highpass filtering below 100 Hz. @@ -140,8 +126,9 @@ # filter requires a margin of several seconds, while a 300 Hz highpass (for spike extraction) only needs # a few milliseconds. -# %% [markdown] -# ## 3. Understanding chunking and margins +############################################################################## +# 3. Understanding chunking and margins +# -------------------------------------- # # SpikeInterface processes recordings in chunks to handle large datasets efficiently. Each chunk needs # a "margin" (extra samples at the edges) to avoid edge artifacts when filtering. Let's demonstrate @@ -151,58 +138,60 @@ # # We can ignore this error, but let's make sure we understand what it's happening. -# %% # We can ignore this error, but let's see what is happening -recording_filt = spre.bandpass_filter(recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True) +recording_filt = spre.bandpass_filter( + recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True +) -# %% [markdown] +############################################################################## # When retrieving traces, extra samples will be retrieved at the left and right edges. # By default, the filter function will set a margin to 5x the sampling period associated to `freq_min`. # So for a 1 Hz cutoff frequency, the margin will be 5 seconds! -# %% margin_in_s = recording_filt.margin_samples / recording_lfp.sampling_frequency print(f"Margin: {margin_in_s} s") -# %% [markdown] -# This effectively means that if we plot 1-s snippet of traces, a total of 11 s will actually be read and filtered. Note that the margin can be overridden with the `margin_ms` argument, but we do not recommend changing it. +############################################################################## +# This effectively means that if we plot 1-s snippet of traces, a total of 11 s will actually be read and filtered. +# Note that the margin can be overridden with the `margin_ms` argument, but we do not recommend changing it. -# %% -sw.plot_traces(recording_filt, time_range=[20, 21]) +_ = sw.plot_traces(recording_filt, time_range=[20, 21]) -# %% [markdown] -# A warning tells us that what we are doing is not optimized, since in order to get the requested traces the marging "overhead" is very large. +############################################################################## +# A warning tells us that what we are doing is not optimized, since in order to get the requested traces +# the margin "overhead" is very large. # # If we ask or plot longer snippets, the warning is not displayed. -# %% -sw.plot_traces(recording_filt, time_range=[20, 80]) +_ = sw.plot_traces(recording_filt, time_range=[20, 80]) -# %% [markdown] -# ## 4. Quantification and visualization the artifacts +############################################################################## +# 4. Quantification and visualization of the artifacts +# ----------------------------------------------------- # # Let's extract the traces and visualize the differences between chunking strategies. # We'll focus on the chunk boundaries where artifacts appear. -# %% margins_ms = [100, 1000, 5000] chunk_durations = ["1s", "10s", "30s"] -# %% [markdown] -# The best we can do is to save the full recording in one chunk. This will cause no artifacts and chunking effects, but in practice it's not possible due to the duration and number of channels of most setups. +############################################################################## +# The best we can do is to save the full recording in one chunk. This will cause no artifacts and chunking effects, +# but in practice it's not possible due to the duration and number of channels of most setups. # # Since in this toy case we have a single channel 5-min recording, we can use this as "optimal". -# %% -recording_optimal = recording_filt.save(format="memory", chunk_duration="1000s") +recording_optimal = recording_filt.save( + format="memory", + chunk_duration="1000s", + progress_bar=False +) -# %% -recording_optimal +print(recording_optimal) -# %% [markdown] +############################################################################## # Now we can do the same with our various options: -# %% recordings_chunked = {} for margin_ms in margins_ms: @@ -214,48 +203,59 @@ freq_min=1.0, freq_max=300.0, margin_ms=margin_ms, - ignore_low_freq_error=True + ignore_low_freq_error=True, ) recording_chunk = recording_chunk.save( format="memory", chunk_duration=chunk_duration, verbose=False, + progress_bar=False ) t_stop = time.perf_counter() - result_dict = { - "recording": recording_chunk, - "time": t_stop - t_start - } + result_dict = {"recording": recording_chunk, "time": t_stop - t_start} recordings_chunked[(margin_ms, chunk_duration)] = result_dict -# %% [markdown] +############################################################################## # Let's visualize the error for the "10s" chunks and different margins, centered around 30s (which is a chunk edge): -# %% fig, ax = plt.subplots(figsize=(10, 5)) trace_plotted = False +start_time = 15 # seconds +end_time = 45 # seconds +start_frame = int(start_time * recording_optimal.sampling_frequency) +end_frame = int(end_time * recording_optimal.sampling_frequency) +timestamps = recording_optimal.get_times()[start_frame:end_frame] for recording_key, recording_dict in recordings_chunked.items(): recording_chunk = recording_dict["recording"] margin, chunk = recording_key - start_frame = int(25 * recording_optimal.sampling_frequency) - end_frame = int(35 * recording_optimal.sampling_frequency) - traces_opt = recording_optimal.get_traces(start_frame=start_frame, end_frame=end_frame) - if not trace_plotted: - ax.plot(traces_opt, color="grey", label="traces", alpha=0.5) - trace_plotted = True + # only plot "10s" chunks if chunk != "10s": continue + traces_opt = recording_optimal.get_traces( + start_frame=start_frame, end_frame=end_frame + ) + if not trace_plotted: + ax.plot(timestamps, traces_opt, color="grey", label="traces", alpha=0.5) + trace_plotted = True diff = recording_optimal - recording_chunk traces_diff = diff.get_traces(start_frame=start_frame, end_frame=end_frame) - ax.plot(traces_diff, label=recording_key) - -ax.legend() - -# %% [markdown] -# For smaller chunk sizes, these artifact will happen more often. In addition, the margin "overhead" will make processing slower. -# Let's quantify these concepts by computing the overall absolute error with respect to the optimal case and processing time. + ax.plot(timestamps, traces_diff, label=f"Margin: {margin}") + for chunk in [20, 30, 40]: # chunk boundaries at 10s intervals + ax.axvline(x=chunk, color="red", linestyle="--", alpha=0.5) + +ax.set_xlabel("Time (s)") +ax.set_ylabel("Voltage ($\\mu V$)") +_ = ax.legend() + +############################################################################## +# From the plot, we can see that there is a very small error when the margin size is large (green), +# a larger error when the margin is smaller (orange) and a large error when the margin is small (blue). +# So we need large margins (compared to the chunk size) if we want accurate filtered. +# +# The artifacts do not depend on chunk size, but for smaller chunk sizes, these artifacts will happen more often. +# In addition, the margin "overhead" will make processing slower. Let's quantify these concepts by computing the +# overall absolute error with respect to the optimal case and processing time. -# %% trace_plotted = False traces_optimal = recording_optimal.get_traces() data = {"margin": [], "chunk": [], "error": [], "time": []} @@ -272,7 +272,9 @@ df = pd.DataFrame(data=data) -# %% +############################################################################## +# Now let's visualize the error and processing time for different margin and chunk size combinations + fig, axs = plt.subplots(ncols=2, figsize=(10, 5)) sns.barplot(data=data, x="margin", y="error", hue="chunk", ax=axs[0]) axs[0].set_yscale("log") @@ -280,11 +282,11 @@ axs[0].set_title("Error VS margin x chunk size") axs[1].set_title("Processing time VS margin x chunk size") - sns.despine(fig) -# %% [markdown] -# ## 4. Summary +############################################################################## +# Summary +# ------- # # 1. **Low-frequency filters require special care**: Filters with low cutoff frequencies (< 10 Hz) have long # impulse responses that require large margins to avoid edge artifacts. @@ -304,6 +306,7 @@ # and accuracy (larger chunks, fewer artifacts). For LFP analysis, accuracy should take priority. # # **When processing your own data:** +# # - If you have memory constraints, use the largest chunk size your system can handle # - Always verify your filtering parameters on a small test segment first # - Consider the lowest frequency component you want to preserve when setting margins diff --git a/doc/forhowto/plot_extract_lfps.py.md5 b/doc/forhowto/plot_extract_lfps.py.md5 new file mode 100644 index 0000000000..850f803ce2 --- /dev/null +++ b/doc/forhowto/plot_extract_lfps.py.md5 @@ -0,0 +1 @@ +fb59f45750a1df962d590b4f861b08eb diff --git a/doc/forhowto/plot_extract_lfps.rst b/doc/forhowto/plot_extract_lfps.rst new file mode 100644 index 0000000000..419714de2d --- /dev/null +++ b/doc/forhowto/plot_extract_lfps.rst @@ -0,0 +1,640 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "forhowto/plot_extract_lfps.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_forhowto_plot_extract_lfps.py: + + +Extract LFPs +============ + +Understanding filtering artifacts and chunking when extracting LFPs + +Local Field Potentials (LFPs) are low-frequency signals (<300 Hz) that reflect the summed activity of many neurons. +Extracting LFPs from high-sampling-rate recordings requires bandpass filtering, but this can introduce artifacts +when not done carefully, especially when data is processed in chunks (for memory efficiency). + +This tutorial demonstrates: + +1. How to generate simulated LFP data +2. Common pitfalls when filtering with low cutoff frequencies +3. How chunking and margins affect filtering artifacts +4. Summary + +**Key takeaway**: For LFP extraction, use large chunks (30-60s) and large margins (several seconds) to minimize +edge artifacts, even though this is less memory-efficient. + +.. GENERATED FROM PYTHON SOURCE LINES 23-24 + +Import necessary modules + +.. GENERATED FROM PYTHON SOURCE LINES 24-38 + +.. code-block:: Python + + + import time + import numpy as np + import matplotlib.pyplot as plt + from pathlib import Path + import pandas as pd + import seaborn as sns + + import spikeinterface as si + import spikeinterface.extractors as se + import spikeinterface.preprocessing as spre + import spikeinterface.widgets as sw + from spikeinterface.core import generate_ground_truth_recording + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 39-43 + +1. Generate simulated recording with low-frequency signals +----------------------------------------------------------- + +Let's create a simulated recording and add some low-frequency sinusoids that mimic LFP activity. + +.. GENERATED FROM PYTHON SOURCE LINES 43-56 + +.. code-block:: Python + + + # Generate a ground truth recording with spikes + # Use a higher sampling rate (30 kHz) to simulate raw neural data + recording, sorting = generate_ground_truth_recording( + durations=[300.0], # 300 s + sampling_frequency=30000.0, + num_channels=1, + num_units=4, + seed=2305, + ) + + print(recording) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + GroundTruthRecording (InjectTemplatesRecording): 1 channels - 30.0kHz - 1 segments + 9,000,000 samples - 300.00s (5.00 minutes) - float32 dtype - 34.33 MiB + + + + +.. GENERATED FROM PYTHON SOURCE LINES 57-58 + +Now let's add some low-frequency sinusoidal components to simulate LFP signals + +.. GENERATED FROM PYTHON SOURCE LINES 58-99 + +.. code-block:: Python + + + # Add low-frequency sinusoids with different frequencies and phases per channel + rng = np.random.default_rng(42) + num_channels = recording.get_num_channels() + lfp_signals = np.zeros( + (recording.get_num_samples(), recording.get_num_channels()) + ) + time_vector = recording.get_times() + + for ch in range(num_channels): + # Add multiple frequency components (theta, alpha, beta ranges) + # Theta-like: 4-8 Hz + freq_theta = 4 + rng.random() * 4 + phase_theta = rng.random() * 2 * np.pi + amp_theta = 50 + rng.random() * 50 + + # Alpha-like: 8-12 Hz + freq_alpha = 8 + rng.random() * 4 + phase_alpha = rng.random() * 2 * np.pi + amp_alpha = 30 + rng.random() * 30 + + # Beta-like: 12-30 Hz + freq_beta = 12 + rng.random() * 18 + phase_beta = rng.random() * 2 * np.pi + amp_beta = 20 + rng.random() * 20 + + lfp_signals[:, ch] = ( + amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta) + + amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha) + + amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta) + ) + + # Create a recording with the added LFP signals + recording_lfp = si.NumpyRecording( + traces_list=[lfp_signals], + sampling_frequency=recording.sampling_frequency, + channel_ids=recording.channel_ids, + ) + recording_with_lfp = recording + recording_lfp + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 100-101 + +Let's visualize a short segment of the signal + +.. GENERATED FROM PYTHON SOURCE LINES 101-104 + +.. code-block:: Python + + + _ = sw.plot_traces(recording_with_lfp, time_range=[0, 3]) + + + + +.. image-sg:: /forhowto/images/sphx_glr_plot_extract_lfps_001.png + :alt: plot extract lfps + :srcset: /forhowto/images/sphx_glr_plot_extract_lfps_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 105-110 + +2. Filtering with low cutoff frequencies: the problem +------------------------------------------------------ + +Now let's try to extract LFPs using a bandpass filter with a low highpass cutoff (1 Hz). +This will demonstrate a common issue. + +.. GENERATED FROM PYTHON SOURCE LINES 110-119 + +.. code-block:: Python + + + # Try to filter with 1 Hz highpass + try: + recording_lfp_1hz = spre.bandpass_filter( + recording_with_lfp, freq_min=1.0, freq_max=300.0 + ) + except Exception as e: + print(f"Error message:\n{str(e)}") + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Error message: + The freq_min (1.0 Hz) is too low and may cause artifacts during chunk processing. You can set 'ignore_low_freq_error=True' to bypass this error, but make sure you understand the implications. It is recommended to use large chunks when processing/saving your filtered recording to minimize IO overhead.Refer to this documentation on LFP filtering and chunking artifacts for more details: https://spikeinterface.readthedocs.io/en/latest/how-to/extract_lfps.html. + + + + +.. GENERATED FROM PYTHON SOURCE LINES 120-128 + +**Why does this fail?** + +The error occurs because by default in SpikeInterface when highpass filtering below 100 Hz. +Filters with very low cutoff frequencies have long impulse responses, which require larger margins to avoid edge artifacts between chunks. + +The filter length (and required margin) scales inversely with the highpass frequency. A 1 Hz highpass +filter requires a margin of several seconds, while a 300 Hz highpass (for spike extraction) only needs +a few milliseconds. + +.. GENERATED FROM PYTHON SOURCE LINES 130-140 + +3. Understanding chunking and margins +-------------------------------------- + +SpikeInterface processes recordings in chunks to handle large datasets efficiently. Each chunk needs +a "margin" (extra samples at the edges) to avoid edge artifacts when filtering. Let's demonstrate +this by saving the filtered data with different chunking strategies. + +**This error is to inform the user that extra care should be used when dealing with LFP signals!** + +We can ignore this error, but let's make sure we understand what it's happening. + +.. GENERATED FROM PYTHON SOURCE LINES 140-146 + +.. code-block:: Python + + + # We can ignore this error, but let's see what is happening + recording_filt = spre.bandpass_filter( + recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True + ) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 147-150 + +When retrieving traces, extra samples will be retrieved at the left and right edges. +By default, the filter function will set a margin to 5x the sampling period associated to `freq_min`. +So for a 1 Hz cutoff frequency, the margin will be 5 seconds! + +.. GENERATED FROM PYTHON SOURCE LINES 150-154 + +.. code-block:: Python + + + margin_in_s = recording_filt.margin_samples / recording_lfp.sampling_frequency + print(f"Margin: {margin_in_s} s") + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Margin: 5.0 s + + + + +.. GENERATED FROM PYTHON SOURCE LINES 155-157 + +This effectively means that if we plot 1-s snippet of traces, a total of 11 s will actually be read and filtered. +Note that the margin can be overridden with the `margin_ms` argument, but we do not recommend changing it. + +.. GENERATED FROM PYTHON SOURCE LINES 157-160 + +.. code-block:: Python + + + _ = sw.plot_traces(recording_filt, time_range=[20, 21]) + + + + +.. image-sg:: /forhowto/images/sphx_glr_plot_extract_lfps_002.png + :alt: plot extract lfps + :srcset: /forhowto/images/sphx_glr_plot_extract_lfps_002.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:169: UserWarning: The margin size (150000 samples) is more than 20% of the chunk size 30000 samples. This may lead to performance bottlenecks when chunking. Consider increasing the chunk size to minimize margin overhead. + warnings.warn( + + + + +.. GENERATED FROM PYTHON SOURCE LINES 161-165 + +A warning tells us that what we are doing is not optimized, since in order to get the requested traces +the margin "overhead" is very large. + +If we ask or plot longer snippets, the warning is not displayed. + +.. GENERATED FROM PYTHON SOURCE LINES 165-168 + +.. code-block:: Python + + + _ = sw.plot_traces(recording_filt, time_range=[20, 80]) + + + + +.. image-sg:: /forhowto/images/sphx_glr_plot_extract_lfps_003.png + :alt: plot extract lfps + :srcset: /forhowto/images/sphx_glr_plot_extract_lfps_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 169-174 + +4. Quantification and visualization of the artifacts +----------------------------------------------------- + +Let's extract the traces and visualize the differences between chunking strategies. +We'll focus on the chunk boundaries where artifacts appear. + +.. GENERATED FROM PYTHON SOURCE LINES 174-178 + +.. code-block:: Python + + + margins_ms = [100, 1000, 5000] + chunk_durations = ["1s", "10s", "30s"] + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 179-183 + +The best we can do is to save the full recording in one chunk. This will cause no artifacts and chunking effects, +but in practice it's not possible due to the duration and number of channels of most setups. + +Since in this toy case we have a single channel 5-min recording, we can use this as "optimal". + +.. GENERATED FROM PYTHON SOURCE LINES 183-192 + +.. code-block:: Python + + + recording_optimal = recording_filt.save( + format="memory", + chunk_duration="1000s", + progress_bar=False + ) + + print(recording_optimal) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + SharedMemoryRecording: 1 channels - 30.0kHz - 1 segments - 9,000,000 samples + 300.00s (5.00 minutes) - float32 dtype - 34.33 MiB + + + + +.. GENERATED FROM PYTHON SOURCE LINES 193-194 + +Now we can do the same with our various options: + +.. GENERATED FROM PYTHON SOURCE LINES 194-218 + +.. code-block:: Python + + + recordings_chunked = {} + + for margin_ms in margins_ms: + for chunk_duration in chunk_durations: + print(f"Margin ms: {margin_ms} - Chunk duration: {chunk_duration}") + t_start = time.perf_counter() + recording_chunk = spre.bandpass_filter( + recording_with_lfp, + freq_min=1.0, + freq_max=300.0, + margin_ms=margin_ms, + ignore_low_freq_error=True, + ) + recording_chunk = recording_chunk.save( + format="memory", + chunk_duration=chunk_duration, + verbose=False, + progress_bar=False + ) + t_stop = time.perf_counter() + result_dict = {"recording": recording_chunk, "time": t_stop - t_start} + recordings_chunked[(margin_ms, chunk_duration)] = result_dict + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Margin ms: 100 - Chunk duration: 1s + /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:458: UserWarning: The provided margin_ms (100 ms) is smaller than the recommended margin for the given freq_min (1.0 Hz). This may lead to artifacts at the edges of chunks during processing. Consider increasing the margin_ms to at least 5000.0 ms. + warnings.warn( + Margin ms: 100 - Chunk duration: 10s + Margin ms: 100 - Chunk duration: 30s + Margin ms: 1000 - Chunk duration: 1s + /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:458: UserWarning: The provided margin_ms (1000 ms) is smaller than the recommended margin for the given freq_min (1.0 Hz). This may lead to artifacts at the edges of chunks during processing. Consider increasing the margin_ms to at least 5000.0 ms. + warnings.warn( + /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:169: UserWarning: The margin size (30000 samples) is more than 20% of the chunk size 30000 samples. This may lead to performance bottlenecks when chunking. Consider increasing the chunk size to minimize margin overhead. + warnings.warn( + Margin ms: 1000 - Chunk duration: 10s + Margin ms: 1000 - Chunk duration: 30s + Margin ms: 5000 - Chunk duration: 1s + Margin ms: 5000 - Chunk duration: 10s + /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:169: UserWarning: The margin size (150000 samples) is more than 20% of the chunk size 300000 samples. This may lead to performance bottlenecks when chunking. Consider increasing the chunk size to minimize margin overhead. + warnings.warn( + Margin ms: 5000 - Chunk duration: 30s + + + + +.. GENERATED FROM PYTHON SOURCE LINES 219-220 + +Let's visualize the error for the "10s" chunks and different margins, centered around 30s (which is a chunk edge): + +.. GENERATED FROM PYTHON SOURCE LINES 220-250 + +.. code-block:: Python + + + fig, ax = plt.subplots(figsize=(10, 5)) + trace_plotted = False + start_time = 15 # seconds + end_time = 45 # seconds + start_frame = int(start_time * recording_optimal.sampling_frequency) + end_frame = int(end_time * recording_optimal.sampling_frequency) + timestamps = recording_optimal.get_times()[start_frame:end_frame] + for recording_key, recording_dict in recordings_chunked.items(): + recording_chunk = recording_dict["recording"] + margin, chunk = recording_key + # only plot "10s" chunks + if chunk != "10s": + continue + traces_opt = recording_optimal.get_traces( + start_frame=start_frame, end_frame=end_frame + ) + if not trace_plotted: + ax.plot(timestamps, traces_opt, color="grey", label="traces", alpha=0.5) + trace_plotted = True + diff = recording_optimal - recording_chunk + traces_diff = diff.get_traces(start_frame=start_frame, end_frame=end_frame) + ax.plot(timestamps, traces_diff, label=f"Margin: {margin}") + for chunk in [20, 30, 40]: # chunk boundaries at 10s intervals + ax.axvline(x=chunk, color="red", linestyle="--", alpha=0.5) + + ax.set_xlabel("Time (s)") + ax.set_ylabel("Voltage ($\\mu V$)") + _ = ax.legend() + + + + +.. image-sg:: /forhowto/images/sphx_glr_plot_extract_lfps_004.png + :alt: plot extract lfps + :srcset: /forhowto/images/sphx_glr_plot_extract_lfps_004.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 251-258 + +From the plot, we can see that there is a very small error when the margin size is large (green), +a larger error when the margin is smaller (orange) and a large error when the margin is small (blue). +So we need large margins (compared to the chunk size) if we want accurate filtered. + +The artifacts do not depend on chunk size, but for smaller chunk sizes, these artifacts will happen more often. +In addition, the margin "overhead" will make processing slower. Let's quantify these concepts by computing the +overall absolute error with respect to the optimal case and processing time. + +.. GENERATED FROM PYTHON SOURCE LINES 258-275 + +.. code-block:: Python + + + trace_plotted = False + traces_optimal = recording_optimal.get_traces() + data = {"margin": [], "chunk": [], "error": [], "time": []} + for recording_key, recording_dict in recordings_chunked.items(): + recording_chunk = recording_dict["recording"] + time = recording_dict["time"] + margin, chunk = recording_key + traces_chunk = recording_chunk.get_traces() + error = np.sum(np.abs(traces_optimal - traces_chunk)) + data["margin"].append(margin) + data["chunk"].append(chunk) + data["error"].append(error) + data["time"].append(time) + + df = pd.DataFrame(data=data) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 276-277 + +Now let's visualize the error and processing time for different margin and chunk size combinations + +.. GENERATED FROM PYTHON SOURCE LINES 277-287 + +.. code-block:: Python + + + fig, axs = plt.subplots(ncols=2, figsize=(10, 5)) + sns.barplot(data=data, x="margin", y="error", hue="chunk", ax=axs[0]) + axs[0].set_yscale("log") + sns.barplot(data=data, x="margin", y="time", hue="chunk", ax=axs[1]) + axs[0].set_title("Error VS margin x chunk size") + axs[1].set_title("Processing time VS margin x chunk size") + + sns.despine(fig) + + + + +.. image-sg:: /forhowto/images/sphx_glr_plot_extract_lfps_005.png + :alt: Error VS margin x chunk size, Processing time VS margin x chunk size + :srcset: /forhowto/images/sphx_glr_plot_extract_lfps_005.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 288-314 + +Summary +------- + +1. **Low-frequency filters require special care**: Filters with low cutoff frequencies (< 10 Hz) have long + impulse responses that require large margins to avoid edge artifacts. + +2. **Chunking artifacts are real**: When processing data in chunks, insufficient margins lead to visible + discontinuities and errors at chunk boundaries. + +3. **The solution: large chunks and large margins**: For LFP extraction (1-300 Hz), use: + - Chunk size: 30-60 seconds + - Margin size: 5 seconds (for 1 Hz highpass) (**use defaults!**) + - This is less memory-efficient but more accurate + +4. **Downsample after filtering**: After bandpass filtering, downsample to reduce data size (e.g., to 1-2.5 kHz + for 300 Hz max frequency). + +5. **Trade-offs**: There's always a trade-off between computational efficiency (smaller chunks, less memory) + and accuracy (larger chunks, fewer artifacts). For LFP analysis, accuracy should take priority. + +**When processing your own data:** + +- If you have memory constraints, use the largest chunk size your system can handle +- Always verify your filtering parameters on a small test segment first +- Consider the lowest frequency component you want to preserve when setting margins +- Save the processed LFP data to disk to avoid recomputing + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 10.342 seconds) + + +.. _sphx_glr_download_forhowto_plot_extract_lfps.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_extract_lfps.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_extract_lfps.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_extract_lfps.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/forhowto/plot_extract_lfps.zip b/doc/forhowto/plot_extract_lfps.zip new file mode 100644 index 0000000000000000000000000000000000000000..091d42cb5cd772b49a3a3fb9b31a617b46e30000 GIT binary patch literal 30149 zcmd^I&u=5ib>7%X4!i|0l53e`z@#kE(r6}F2quE@dMCTsADCpv-h(J`TVzpc4B1V0 zH%H@{0HXy-m9vvrbzu7QTD(xyd!p1SG{`g)%#WTQLn!E@!$UB zBmVsFcmHwxpa1pWfBeV8k3Qo6`E)+btD(KDieyv`r;~Zvn=jk#cI#7pWWId)s%-V6 zpH}O)+1M6km1N^IJ2R7XTG@iXB}J7^FidICWJc$U>||<3 z6+T!~E@m@3HlQGVn^sGcPs}XImL{`{BG1a6Uy~=oOp1JF&eOB=y)v22r+B_sB$dUW zqrBkaO8ol!MOxU>oMKJ$q%7<8?wZpDCr(Qop%8c{Oq8F%L zOsC6TW6QZ6VZqa-vS*xBiNQegA|KhZ#46HEIS7_a^1{sQEH9SEPA2InB}2NsR_itA zt`=2ZU??+2m#l;!D_b75S_eJzS$<)v+??6W%DT#QwwR)MOc@x?*?QVDpX4*VXy$1( z0YRk_R7>?DtVblKw4U|MH;`3Q zEL*LeoiFUtRLL8gTqMh#og?!S%O!nYW%6v-EK2AD1_l$UR$$8E{@y?DQ&TXdw)lxV zrG1O_PE83hW@GdL4`ykW&eHE~%Z@QfAO(_m3+=4V^TpY@$`UM{LIjTEd!FM}uf=WA zdh(z@ttaO5**q^QC@BRGmY5l|2o$thskT(<%=+K6#caMbNolgVe>6)f?%`>Aik-{f z*gT(BEz$0IQk|p8xUpX0qknM@)}bqVkNvaKCZ~Ck@nMu8ml$QL5vJC5QkN(~DbfG8*sX~eR$CZv zT8)?>>|66jtal!0MazX4X&Nyar9k>FXogdPhV+9K+aT9Y%iw3cz$_`uVE-6yqPKst zYs?cvk5NjyCuZoa*s_mC_S}_r?62YIJjpUUE&B)cvqhFxW&fEzD6Jj$pC0Z%-)(hU ztvP&O)d?iy$%9%5%J^$o->Jy?1~Gzxqa0QvgFA&tBNo%3Iu}ZbzJNNc`d6p0aaL_pcRS3uSBS#NA7$zoa!@x1fwX}1+~6_O0R>S0?C zz0<~qQyL;`hT}X;{bodrN3I1i32)1F6R*SxN zbWZP!wVc^bOkMX#bVRv_ZC0i8sSW&D#;ork9;uRDlT5)b1Pb_|1D0Jk#%p%io5HRh znP+<+BchQ8>g7;g293|mFAR9-*xKou9rH|ji3~!wl`c=w4SbuY(iMSC==gm9>VVJp zTaKa^q}5UM@!rAH21&9Iko57@5n_^jw;M_F9bpcxtno0A^y`{O2T%9VH|CNswQoS^ zl@Sg;j@ab--AIz__jC(avmS;LQzh@64gywpPJVqfilfHK^PE9B-dN1yVpJC!B8J$aPItNTEd!S14s*7~m$f+O>_*Q00X<06; z1V*oQX3cSQYz`u!4~K4nG<6yc*KCk5Tr8w0|YGY zQ*y}=rnfH^uiLVhBRj9mr}7UXAx6zsY0yYz(*Cq4B%?&Sab}MO*$+q>JGT3-y$#LS z+4=T-iToEKgQUunB%S`t&W?x>KG0YpCLfI!$QVxTC}FC1iZ?WdpeO!@8OG-!`;eqM z2(QjBPc5_51E~&BimH&vXPLkvBV2CCV$FH-mT@tH7-UI{DdtDQk01(R&Mvay6!Cy7 z18Am+Y~1Cz(7NO;!e`UWD(UYpEb>ZCRzZv?6=DV1BZ%d)GtkswVD3itd2`p4sEdGE zX$F!@%K`!E=f{9S$&%7{Ag?r-XaN{Cta@5j%tYpTrI)pHZM97ieYRy67dB3fo zN?ns@peIB4%P2oF6MKP_VG3KVnKt*tgZ^L^lo*=zrgg;z^t+uU5lO7*ldrk3*c0kL zb@>G{MnW(Ko3zZp45ef`8Y%-*$l=ojS|X%H{AsJ6v`t5BlcqwDEix_2lS-r2iM2{3 zdLGof=Q6#h%7azQmcni+lee^Y997?d^Oc&CC{G>C2j*#==nO?1)y|zAF?zZiSyjrR zfe40YGA8z9fnPwkE`*gVI;RDScc+%snX#*5S-36`BGX1W$4}jk|CUuSSkSYK8M{y~ zQ)(?ubDSA!L8#@S{Q^UWS|cXF{3)~D^JG4^8Riiqe48dBme<>`#`RLjl#Rm5b>8A z6fflPKShc#og7?C4SquyoLgO~hg>V#Fv7~&CYzPtg81<+$Lje@t z0S5%MiL9=xH5Z>;igJrgz}|?dZL;&+CgV2C{2Gn0o~_pRbCGXSzQM36W`-jyo~fh} zhj1)UHb#&`g|wVclcgQskGmi5Z-u)k?*H2&!Ep{wDx?-CTIaJGed02iP=*JuZs)&O zdWk#Nw8=;R>(BfU8==rv?n1CU zB*T80V^dTq_YOTc)pwqkWr{oyMO)0BtAJenR%D0T+bQ+{l*Og$kOYqOa?lTtv%pcM zhI4*PN;pyFD8wN^;}RvXXH7gi!!&LX^L!6JyIv!LU}Xjq4;ca(Q(LxtGHpYcwKs7O zuX-4=Pct`$Oi9&m>v>(<*B`*XAn!h;yiYl6jjG7ckZzR2)1>IX#0jT_2P;M8p__Rd zfdljCS{!;CtJ>XcLhiMy{eFU) zc8V@%$&fY4&5|p5y>fBN0JQAjafx84-#MT|>*8RA@y?KqjDGq? 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"BaseRecordingSnippets.set_probegroup" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core.base", + "module_short": "spikeinterface.core.base", + "name": "BaseExtractor.set_probegroup" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface.core", + "name": "BaseExtractor.set_probegroup" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "BaseExtractor.set_probegroup" + } + ], + "recording_good_channels": [ + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.preprocessing.detect_bad_channels", + "module_short": "spikeinterface.preprocessing.detect_bad_channels", + "name": "DetectAndRemoveBadChannelsRecording" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.preprocessing", + "module_short": "spikeinterface.preprocessing", + "name": "DetectAndRemoveBadChannelsRecording" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "DetectAndRemoveBadChannelsRecording" + } + ], + "recording_good_channels.get_property": [ + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.preprocessing.detect_bad_channels", + "module_short": "spikeinterface.preprocessing.detect_bad_channels", + "name": "DetectAndRemoveBadChannelsRecording.get_property" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.preprocessing", + "module_short": "spikeinterface.preprocessing", + "name": "DetectAndRemoveBadChannelsRecording.get_property" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "DetectAndRemoveBadChannelsRecording.get_property" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core.channelslice", + "module_short": "spikeinterface", + "name": "ChannelSliceRecording.get_property" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface", + "name": "ChannelSliceRecording.get_property" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "ChannelSliceRecording.get_property" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core.baserecording", + "module_short": "spikeinterface", + "name": "BaseRecording.get_property" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface", + "name": "BaseRecording.get_property" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "BaseRecording.get_property" + }, + { + "is_class": false, + "is_explicit": false, + "module": 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"recording_good_channels.split_by": [ + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.preprocessing.detect_bad_channels", + "module_short": "spikeinterface.preprocessing.detect_bad_channels", + "name": "DetectAndRemoveBadChannelsRecording.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.preprocessing", + "module_short": "spikeinterface.preprocessing", + "name": "DetectAndRemoveBadChannelsRecording.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "DetectAndRemoveBadChannelsRecording.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core.channelslice", + "module_short": "spikeinterface", + "name": "ChannelSliceRecording.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface", + "name": "ChannelSliceRecording.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "ChannelSliceRecording.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core.baserecording", + "module_short": "spikeinterface", + "name": "BaseRecording.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface", + "name": "BaseRecording.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "BaseRecording.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core.baserecordingsnippets", + "module_short": "spikeinterface", + "name": "BaseRecordingSnippets.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface", + "name": "BaseRecordingSnippets.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "BaseRecordingSnippets.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core.base", + "module_short": "spikeinterface.core.base", + "name": "BaseExtractor.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface.core", + "name": "BaseExtractor.split_by" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "BaseExtractor.split_by" + } + ], + "recording_with_probe": [ + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core.generate", + "module_short": "spikeinterface", + "name": "InjectTemplatesRecording" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.core", + "module_short": "spikeinterface", + "name": "InjectTemplatesRecording" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface", + "module_short": "spikeinterface", + "name": "InjectTemplatesRecording" + } + ], + "spre.bandpass_filter": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.preprocessing", + "module_short": "spikeinterface.preprocessing", + "name": "bandpass_filter" + } + ], + "spre.detect_and_remove_bad_channels": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "spikeinterface.preprocessing", + "module_short": "spikeinterface.preprocessing", + "name": "detect_and_remove_bad_channels" + } + ], + "tetrode_1": [ + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface.probe", + "module_short": "probeinterface", + "name": "Probe" + }, + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface", + "module_short": "probeinterface", + "name": "Probe" + } + ], + "tetrode_1.create_auto_shape": [ + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface.probe", + "module_short": "probeinterface", + "name": "Probe.create_auto_shape" + }, + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface", + "module_short": "probeinterface", + "name": "Probe.create_auto_shape" + } + ], + "tetrode_2": [ + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface.probe", + "module_short": "probeinterface", + "name": "Probe" + }, + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface", + "module_short": "probeinterface", + "name": "Probe" + } + ], + "tetrode_2.create_auto_shape": [ + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface.probe", + "module_short": "probeinterface", + "name": "Probe.create_auto_shape" + }, + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface", + "module_short": "probeinterface", + "name": "Probe.create_auto_shape" + } + ], + "tetrode_2.move": [ + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface.probe", + "module_short": "probeinterface", + "name": "Probe.move" + }, + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface", + "module_short": "probeinterface", + "name": "Probe.move" + } + ], + "tetrode_group": [ + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface.probegroup", + "module_short": "probeinterface", + "name": "ProbeGroup" + }, + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface", + "module_short": "probeinterface", + "name": "ProbeGroup" + } + ], + "tetrode_group.add_probe": [ + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface.probegroup", + "module_short": "probeinterface", + "name": "ProbeGroup.add_probe" + }, + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface", + "module_short": "probeinterface", + "name": "ProbeGroup.add_probe" + } + ], + "tetrode_group.set_global_device_channel_indices": [ + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface.probegroup", + "module_short": "probeinterface", + "name": "ProbeGroup.set_global_device_channel_indices" + }, + { + "is_class": false, + "is_explicit": false, + "module": "probeinterface", + "module_short": "probeinterface", + "name": "ProbeGroup.set_global_device_channel_indices" + } + ] +} diff --git a/doc/forhowto/plot_working_with_tetrodes.ipynb b/doc/forhowto/plot_working_with_tetrodes.ipynb new file mode 100644 index 0000000000..5e27d20069 --- /dev/null +++ b/doc/forhowto/plot_working_with_tetrodes.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Work with tetrodes\n\nTetrodes are a common recording method for electrophysiological data. It is also common\nto record from several tetrodes at the same time. In this 'how to' we'll see how to\nwork with data from two tetrodes, each with four channels.\n\nWe'll start by importing some functions we'll use in this How To guide\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import spikeinterface.preprocessing as spre\nfrom spikeinterface.widgets import plot_traces, plot_probe_map\nfrom spikeinterface import generate_ground_truth_recording\n\nfrom probeinterface import generate_tetrode, ProbeGroup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In practice, you would read in your raw data from a file. Instead, we will generate a\nrecording with eight channels. We can also set a duration, number of units and\nsampling frequency.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "recording, _ = generate_ground_truth_recording(\n durations = [60], # make the recording 60s long\n sampling_frequency=30_000,\n num_channels=8,\n num_units=10,\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to define the probe. This will tell the recording which channels came from\nwhich tetrode. To do this, we will use the :code:`generate_tetrode` function from :code:`ProbeInterface`\nto generate two 4-channel probes (representing one tetrode each). In our case, since we\ndon't know the relative distances between the tetrodes, we will move the second\ntetrode away from the first by 100 microns. This is just so we can visualize the\nresults more easily. Eventually, we will sort each tetrode separately, so their\nrelative distance won't affect the results.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Technically, we will add each tetrode to a :code:`ProbeGroup`. Read more in the ProbeInterface\n# docs.\n\n# Create each individual tetrode\ntetrode_1 = generate_tetrode()\ntetrode_1.create_auto_shape()\n\ntetrode_2 = generate_tetrode()\ntetrode_2.move([100, 0])\ntetrode_2.create_auto_shape()\n\n# Add the two tetrodes to a ProbeGroup\ntetrode_group = ProbeGroup()\ntetrode_group.add_probe(tetrode_1)\ntetrode_group.add_probe(tetrode_2)\n\n# Now we need to \"wire\" our tetrodes to ensure that each contact\n# can be associated with the correct channel when we attach it\n# to the recording. In this example we are just using `range`\n# but see ProbeInterface for more tutorials on wiring\ntetrode_group.set_global_device_channel_indices(range(8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now attach the :code:`tetrode_group` to our recording. To check if this worked, we'll\nplot the probe map\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "recording_with_probe = recording.set_probegroup(tetrode_group)\nplot_probe_map(recording_with_probe)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good! Now that the recording is aware of the probe geometry, we can\nbegin a standard spike sorting pipeline. First, we can apply preprocessing.\nNote that we apply this preprocessing on the entire bundle of tetrodes.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "preprocessed_recording = spre.bandpass_filter(recording_with_probe)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "WARNING: a very common preprocessing step is to apply a common median\nreference. This subtracts the median signal from all channels to help\nremove noise. However, for a tetrode, a spike is often seen on all\nchannels. So removing the median can remove the entire spike!\nThis is still a danger if you have two tetrodes in a bundle, which\nmight pick up the same spike, but becomes less dangerous\nas the number of tetrodes in your bundle increases.\n\nTetrodes often have dead channels, so it is advised to try and detect\nand remove these. For tetrodes, we should use a detection method which\ndoesn't depend on the channel locations such as std or mad:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "recording_good_channels = spre.detect_and_remove_bad_channels(\n preprocessed_recording,\n method = \"std\",\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It can be a good idea to sort your tetrode data separately for each tetrode.\nWhen we use :code:`set_probegroup`, the channels are automatically\nlabelled by which probe in the probe group they belong to. We can access\nthis labeling using the \"group\" property.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(recording_good_channels.get_property(\"group\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then use this information to split the recording by the group property:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "grouped_recordings = recording_good_channels.split_by('group')\nprint(grouped_recordings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've got preprocess, clean data. Let's take a look at a\nsnippet of data from the first group:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "plot_traces(grouped_recordings[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beautiful! We are now ready to sort. To read more about sorting by group, see\n`sorting-by-channel-group`. Note that many modern sorters are designed\nto sort data from high-density probes and will fail for tetrodes. Please read\neach spike sorter's documentation to find out if it is appropriate for tetrodes.\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/tutorials/forhowto/plot_1_working_with_tetrodes.py b/doc/forhowto/plot_working_with_tetrodes.py similarity index 100% rename from examples/tutorials/forhowto/plot_1_working_with_tetrodes.py rename to doc/forhowto/plot_working_with_tetrodes.py diff --git a/doc/forhowto/plot_working_with_tetrodes.py.md5 b/doc/forhowto/plot_working_with_tetrodes.py.md5 new file mode 100644 index 0000000000..b6de93f1f2 --- /dev/null +++ b/doc/forhowto/plot_working_with_tetrodes.py.md5 @@ -0,0 +1 @@ +f828444cacd54f1e64ce26991cd1f9d6 diff --git a/doc/forhowto/plot_working_with_tetrodes.rst b/doc/forhowto/plot_working_with_tetrodes.rst new file mode 100644 index 0000000000..57ad57b03f --- /dev/null +++ b/doc/forhowto/plot_working_with_tetrodes.rst @@ -0,0 +1,319 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "forhowto/plot_working_with_tetrodes.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_forhowto_plot_working_with_tetrodes.py: + + +Work with tetrodes +================== + +Tetrodes are a common recording method for electrophysiological data. It is also common +to record from several tetrodes at the same time. In this 'how to' we'll see how to +work with data from two tetrodes, each with four channels. + +We'll start by importing some functions we'll use in this How To guide + +.. GENERATED FROM PYTHON SOURCE LINES 11-18 + +.. code-block:: Python + + + import spikeinterface.preprocessing as spre + from spikeinterface.widgets import plot_traces, plot_probe_map + from spikeinterface import generate_ground_truth_recording + + from probeinterface import generate_tetrode, ProbeGroup + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 19-22 + +In practice, you would read in your raw data from a file. Instead, we will generate a +recording with eight channels. We can also set a duration, number of units and +sampling frequency. + +.. GENERATED FROM PYTHON SOURCE LINES 22-30 + +.. code-block:: Python + + + recording, _ = generate_ground_truth_recording( + durations = [60], # make the recording 60s long + sampling_frequency=30_000, + num_channels=8, + num_units=10, + ) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 31-38 + +We now need to define the probe. This will tell the recording which channels came from +which tetrode. To do this, we will use the :code:`generate_tetrode` function from :code:`ProbeInterface` +to generate two 4-channel probes (representing one tetrode each). In our case, since we +don't know the relative distances between the tetrodes, we will move the second +tetrode away from the first by 100 microns. This is just so we can visualize the +results more easily. Eventually, we will sort each tetrode separately, so their +relative distance won't affect the results. + +.. GENERATED FROM PYTHON SOURCE LINES 38-61 + +.. code-block:: Python + + + # Technically, we will add each tetrode to a :code:`ProbeGroup`. Read more in the ProbeInterface + # docs. + + # Create each individual tetrode + tetrode_1 = generate_tetrode() + tetrode_1.create_auto_shape() + + tetrode_2 = generate_tetrode() + tetrode_2.move([100, 0]) + tetrode_2.create_auto_shape() + + # Add the two tetrodes to a ProbeGroup + tetrode_group = ProbeGroup() + tetrode_group.add_probe(tetrode_1) + tetrode_group.add_probe(tetrode_2) + + # Now we need to "wire" our tetrodes to ensure that each contact + # can be associated with the correct channel when we attach it + # to the recording. In this example we are just using `range` + # but see ProbeInterface for more tutorials on wiring + tetrode_group.set_global_device_channel_indices(range(8)) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 62-64 + +We can now attach the :code:`tetrode_group` to our recording. To check if this worked, we'll +plot the probe map + +.. GENERATED FROM PYTHON SOURCE LINES 64-68 + +.. code-block:: Python + + + recording_with_probe = recording.set_probegroup(tetrode_group) + plot_probe_map(recording_with_probe) + + + + +.. image-sg:: /forhowto/images/sphx_glr_plot_working_with_tetrodes_001.png + :alt: plot working with tetrodes + :srcset: /forhowto/images/sphx_glr_plot_working_with_tetrodes_001.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 69-72 + +Looks good! Now that the recording is aware of the probe geometry, we can +begin a standard spike sorting pipeline. First, we can apply preprocessing. +Note that we apply this preprocessing on the entire bundle of tetrodes. + +.. GENERATED FROM PYTHON SOURCE LINES 72-75 + +.. code-block:: Python + + + preprocessed_recording = spre.bandpass_filter(recording_with_probe) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 76-87 + +WARNING: a very common preprocessing step is to apply a common median +reference. This subtracts the median signal from all channels to help +remove noise. However, for a tetrode, a spike is often seen on all +channels. So removing the median can remove the entire spike! +This is still a danger if you have two tetrodes in a bundle, which +might pick up the same spike, but becomes less dangerous +as the number of tetrodes in your bundle increases. + +Tetrodes often have dead channels, so it is advised to try and detect +and remove these. For tetrodes, we should use a detection method which +doesn't depend on the channel locations such as std or mad: + +.. GENERATED FROM PYTHON SOURCE LINES 87-93 + +.. code-block:: Python + + + recording_good_channels = spre.detect_and_remove_bad_channels( + preprocessed_recording, + method = "std", + ) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 94-98 + +It can be a good idea to sort your tetrode data separately for each tetrode. +When we use :code:`set_probegroup`, the channels are automatically +labelled by which probe in the probe group they belong to. We can access +this labeling using the "group" property. + +.. GENERATED FROM PYTHON SOURCE LINES 98-101 + +.. code-block:: Python + + + print(recording_good_channels.get_property("group")) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + [0 0 0 0 1 1 1 1] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 102-103 + +We can then use this information to split the recording by the group property: + +.. GENERATED FROM PYTHON SOURCE LINES 103-107 + +.. code-block:: Python + + + grouped_recordings = recording_good_channels.split_by('group') + print(grouped_recordings) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + {0: GroundTruthRecording (ChannelSliceRecording): 4 channels - 30.0kHz - 1 segments + 1,800,000 samples - 60.00s (1.00 minutes) - float32 dtype - 27.47 MiB, 1: GroundTruthRecording (ChannelSliceRecording): 4 channels - 30.0kHz - 1 segments + 1,800,000 samples - 60.00s (1.00 minutes) - float32 dtype - 27.47 MiB} + + + + +.. GENERATED FROM PYTHON SOURCE LINES 108-110 + +Now that we've got preprocess, clean data. Let's take a look at a +snippet of data from the first group: + +.. GENERATED FROM PYTHON SOURCE LINES 110-113 + +.. code-block:: Python + + + plot_traces(grouped_recordings[0]) + + + + +.. image-sg:: /forhowto/images/sphx_glr_plot_working_with_tetrodes_002.png + :alt: plot working with tetrodes + :srcset: /forhowto/images/sphx_glr_plot_working_with_tetrodes_002.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 114-118 + +Beautiful! We are now ready to sort. To read more about sorting by group, see +:ref:`sorting-by-channel-group`. Note that many modern sorters are designed +to sort data from high-density probes and will fail for tetrodes. Please read +each spike sorter's documentation to find out if it is appropriate for tetrodes. + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 0.644 seconds) + + +.. _sphx_glr_download_forhowto_plot_working_with_tetrodes.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_working_with_tetrodes.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_working_with_tetrodes.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: plot_working_with_tetrodes.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/forhowto/plot_working_with_tetrodes.zip b/doc/forhowto/plot_working_with_tetrodes.zip new file mode 100644 index 0000000000000000000000000000000000000000..12c4fb6c23042a68f0bf7972533f414208db03ec GIT binary patch literal 13192 zcmdU0Pj4H?6?c=iD0r<;1qw{4gCu}vWhn>(0Xn2blNt^TAr;_**H{gAhvY`fneEQ3 zXg0_p$9{yOhkl8CnfB5z(PIz&y*E3%E0MBAkrE;rhQOVfH#6_=@4dG?efjavpFW|_ zuj9YHIs5m2ev{%W{r@eV6}fTo+>{rYp2YLaO=G8AX;Ni}#iHNu_g-U6(0~X7y_2O+ zuXk3j60%f6ip0!jMvGD4cjqVdYE-m(3JtXgmRx-Km)iXLiM0?dCaFD&e2> z=(_$&w5uoV=B6P!5K1OfKVxjFQY2HUwaV?V*L&@WIaxX}T8L~`n9|V&t-%S#l}=n{ zw5>O-tP)v$o|i4VF3;PfAniGRO?T*GeQ(d$qL6LA}!o9D|$(LRYhqDuo$~N@tLT z)F}u;9gCdAH!js*Dy5SJRHxZxAYyUSoZtq&@AU*eVIzwLZ$3MCI}kf!CNH4tQ2K_T z`0T)n+~^6f37f~w<|ju7@xj5tfQND7I4JbV=j}5-|H*UA+P??UfrlCfpp{Btlpf0Jy)KnVGwUQ~Sus*>t`xbFhG^%8GlR)AWZ`gG z1H;C0E*JhFKmSL{W7za%(K5TWgHSWt8%FL z%)rGfX|sGW6o0(LZ80rhwEVVkiQa{VQ&?3<@-Z|E8{jBe$rP{M69!F;kmE5zM9m7` z6y~!d&Qvnh6fL@&$uzyX7o;SEs9SGzJ`~@Oo8ZHK*xJvO9(o#1?EkoAPinu ze;njlW`HRI7zbB_r*EX{`GPPJPjWMod7P?C#H-+PV^Sv=^d4`v_xb)kIa}f%xs#eH z#Ejan1zGYz&xunUHUm;)7n)2}av`!Ym`Gk3cu8^W1dsxI2xu^SSve4o5bGOGdl2bI zPC5c0j__07x6vs6w%=py!ovht;74oR*r9eGjQZM`3o9nZq@S?LvL;?p9LA$_0<{S7 zG~#bIQGk1{^iB`FCZ$mm_!TM0W2drAJ+6bh^Z2mH3I#|2Eb=8eVfL0JBv};s0#rMo zZwP9y4EzrthZL3Puq>`jB=w^+QNY0oM}Tk8Y&vMb@V#EUK&36JgW(gb9FB0-0!9`C zCINsx7~@}m@y)9juf944zX0eLAzx?@%aGcsg7{A^j5*Nc6*HA)%=A)?RSA3rwlaXQ z)rb%Q(n~a?@e z5}qtQ?z%E^48d^|0%w!)o5naR%!#xn9?5o=hwZH%ca6?t?K%@a5q<31zaO-XJeqWk z0w{Yoft(Y1Y}gz5JgMfY&y`wy<}-APqxA%GMz8A#OI)v`$W?cKK48TO;`T)V1j!lr z%y$tTmA{`=qrl7==_v+R68If^Fjq{Rchn;5{K=_h1^lrezb zP&NUt=wf`Y=33BEe$dq|QqMMOUcDs~jz^2VU0$|}aLZg-CUPG!mY`rA$adkuCcqA@ zc??97D`=`O|9q|7E?f}hw-WiRxuEh6CFoXXMS;v4p{-N00b_hZ!xsTpIu($;<;%Sx z5WW4P#P1d0eKxN0Pe@M*DG=_0drToaZ*h#N=!s81gn z*Q3uyi;!eK<5|dKT4*qndI6HDDiMl6PL(J?-~g!JA>=8lLM+hu6Ni_ooX*3zRJ^U%|h|pDg{esL4 zm>H<%3i;k?Q=WW^^5j4Md-SXSp)N`P#rw;XS+UTg?@-d{C-5t_e=Oc`eS;p+habmo zQ78=cXR^FV&0NFb*2D0_lDLA=@4j!IT7cn%R(N7eDAI6&P0cWbqew^kECfE{dV@|H zI}BloZoE#_Gtv;7r{Yn|Hp-rnzI)x1?z)}IXQcbJ0N(Q7n)9oLM)jIjVCtPpU};9j z2?~`CGSXF^H@qOS0nK*|rh=d|i2Ib*;zUlI+V?==-ZnbF8?JvUH@kT$RR2eYh0cDoT^hZ?z^(1aDoEe$>w3zN_hknScxJ zkht`rf_kGFH&oO+;_XZ%`Ky*sj^TJKMPo5Ffah;jP0%KEj@m8JC$ z&pF)p=b@AeWx?RHA}+5-x3900L_&SwE(La44}?mYX%ke|sIb%WJCxY3^Jk43`@_W& zVk^ayt=HWnyVG_t7Hy>`LI`FHEfFHKX-6c|p&opvh6t)FTqND3 z9}?+1w?iU*hi(X2%2}uhZPE^?D?-=;O zrjHX*BPZ(P@k8aT+idTku(xq{C(_$$?f~PF(XZ(3M7})(lp7j0!$+;3Yc!^stM5OQ zf}tovKz2**sz)s+&gQz%VXaOZVCqe+wg*BLlq+u3enTPjHr=;JE#wb34j)KbTWuyr zdOK}IIOY1yfvt2ABfae=;tJhEn}@qO{iU{0{{rn&yv$#ygj#=fKd6saZHrtkVvTo; z@w|V!zz-I*IBNS(IbO%9W77R_it>vds0F^%gWM}F?De} zL+D%ulIU~NNt(Sdgm;th(+Q3FM@#K2dfz=(@9=g~J*7`S#E$&(*{_$T&z4=HX+pT7LXFKNX``1g1G{nsIV`#(>7Y$^Z% literal 0 HcmV?d00001 diff --git a/doc/how_to/index.rst b/doc/how_to/index.rst index 0f59ed5114..e99706a9d4 100644 --- a/doc/how_to/index.rst +++ b/doc/how_to/index.rst @@ -10,11 +10,11 @@ Guides on how to solve specific, short problems in SpikeInterface. Learn how to. customize_a_plot combine_recordings process_by_channel_group - extract_lfps + ../forhowto/plot_extract_lfps build_pipeline_with_dicts physical_units unsigned_to_signed - ../tutorials/forhowto/plot_1_working_with_tetrodes + ../forhowto/plot_working_with_tetrodes analyze_neuropixels handle_drift drift_with_lfp diff --git a/examples/tutorials/forhowto/README.rst b/examples/forhowto/README.rst similarity index 100% rename from examples/tutorials/forhowto/README.rst rename to examples/forhowto/README.rst diff --git a/examples/forhowto/plot_extract_lfps.py b/examples/forhowto/plot_extract_lfps.py new file mode 100644 index 0000000000..4eb4393c22 --- /dev/null +++ b/examples/forhowto/plot_extract_lfps.py @@ -0,0 +1,313 @@ +""" +Extract LFPs +============ + +Understanding filtering artifacts and chunking when extracting LFPs + +Local Field Potentials (LFPs) are low-frequency signals (<300 Hz) that reflect the summed activity of many neurons. +Extracting LFPs from high-sampling-rate recordings requires bandpass filtering, but this can introduce artifacts +when not done carefully, especially when data is processed in chunks (for memory efficiency). + +This tutorial demonstrates: + +1. How to generate simulated LFP data +2. Common pitfalls when filtering with low cutoff frequencies +3. How chunking and margins affect filtering artifacts +4. Summary + +**Key takeaway**: For LFP extraction, use large chunks (30-60s) and large margins (several seconds) to minimize +edge artifacts, even though this is less memory-efficient. +""" + +############################################################################## +# Import necessary modules + +import time +import numpy as np +import matplotlib.pyplot as plt +from pathlib import Path +import pandas as pd +import seaborn as sns + +import spikeinterface as si +import spikeinterface.extractors as se +import spikeinterface.preprocessing as spre +import spikeinterface.widgets as sw +from spikeinterface.core import generate_ground_truth_recording + +############################################################################## +# 1. Generate simulated recording with low-frequency signals +# ----------------------------------------------------------- +# +# Let's create a simulated recording and add some low-frequency sinusoids that mimic LFP activity. + +# Generate a ground truth recording with spikes +# Use a higher sampling rate (30 kHz) to simulate raw neural data +recording, sorting = generate_ground_truth_recording( + durations=[300.0], # 300 s + sampling_frequency=30000.0, + num_channels=1, + num_units=4, + seed=2305, +) + +print(recording) + +############################################################################## +# Now let's add some low-frequency sinusoidal components to simulate LFP signals + +# Add low-frequency sinusoids with different frequencies and phases per channel +rng = np.random.default_rng(42) +num_channels = recording.get_num_channels() +lfp_signals = np.zeros( + (recording.get_num_samples(), recording.get_num_channels()) +) +time_vector = recording.get_times() + +for ch in range(num_channels): + # Add multiple frequency components (theta, alpha, beta ranges) + # Theta-like: 4-8 Hz + freq_theta = 4 + rng.random() * 4 + phase_theta = rng.random() * 2 * np.pi + amp_theta = 50 + rng.random() * 50 + + # Alpha-like: 8-12 Hz + freq_alpha = 8 + rng.random() * 4 + phase_alpha = rng.random() * 2 * np.pi + amp_alpha = 30 + rng.random() * 30 + + # Beta-like: 12-30 Hz + freq_beta = 12 + rng.random() * 18 + phase_beta = rng.random() * 2 * np.pi + amp_beta = 20 + rng.random() * 20 + + lfp_signals[:, ch] = ( + amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta) + + amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha) + + amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta) + ) + +# Create a recording with the added LFP signals +recording_lfp = si.NumpyRecording( + traces_list=[lfp_signals], + sampling_frequency=recording.sampling_frequency, + channel_ids=recording.channel_ids, +) +recording_with_lfp = recording + recording_lfp + + +############################################################################## +# Let's visualize a short segment of the signal + +_ = sw.plot_traces(recording_with_lfp, time_range=[0, 3]) + +############################################################################## +# 2. Filtering with low cutoff frequencies: the problem +# ------------------------------------------------------ +# +# Now let's try to extract LFPs using a bandpass filter with a low highpass cutoff (1 Hz). +# This will demonstrate a common issue. + +# Try to filter with 1 Hz highpass +try: + recording_lfp_1hz = spre.bandpass_filter( + recording_with_lfp, freq_min=1.0, freq_max=300.0 + ) +except Exception as e: + print(f"Error message:\n{str(e)}") + +############################################################################## +# **Why does this fail?** +# +# The error occurs because by default in SpikeInterface when highpass filtering below 100 Hz. +# Filters with very low cutoff frequencies have long impulse responses, which require larger margins to avoid edge artifacts between chunks. +# +# The filter length (and required margin) scales inversely with the highpass frequency. A 1 Hz highpass +# filter requires a margin of several seconds, while a 300 Hz highpass (for spike extraction) only needs +# a few milliseconds. + +############################################################################## +# 3. Understanding chunking and margins +# -------------------------------------- +# +# SpikeInterface processes recordings in chunks to handle large datasets efficiently. Each chunk needs +# a "margin" (extra samples at the edges) to avoid edge artifacts when filtering. Let's demonstrate +# this by saving the filtered data with different chunking strategies. +# +# **This error is to inform the user that extra care should be used when dealing with LFP signals!** +# +# We can ignore this error, but let's make sure we understand what it's happening. + +# We can ignore this error, but let's see what is happening +recording_filt = spre.bandpass_filter( + recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True +) + +############################################################################## +# When retrieving traces, extra samples will be retrieved at the left and right edges. +# By default, the filter function will set a margin to 5x the sampling period associated to `freq_min`. +# So for a 1 Hz cutoff frequency, the margin will be 5 seconds! + +margin_in_s = recording_filt.margin_samples / recording_lfp.sampling_frequency +print(f"Margin: {margin_in_s} s") + +############################################################################## +# This effectively means that if we plot 1-s snippet of traces, a total of 11 s will actually be read and filtered. +# Note that the margin can be overridden with the `margin_ms` argument, but we do not recommend changing it. + +_ = sw.plot_traces(recording_filt, time_range=[20, 21]) + +############################################################################## +# A warning tells us that what we are doing is not optimized, since in order to get the requested traces +# the margin "overhead" is very large. +# +# If we ask or plot longer snippets, the warning is not displayed. + +_ = sw.plot_traces(recording_filt, time_range=[20, 80]) + +############################################################################## +# 4. Quantification and visualization of the artifacts +# ----------------------------------------------------- +# +# Let's extract the traces and visualize the differences between chunking strategies. +# We'll focus on the chunk boundaries where artifacts appear. + +margins_ms = [100, 1000, 5000] +chunk_durations = ["1s", "10s", "30s"] + +############################################################################## +# The best we can do is to save the full recording in one chunk. This will cause no artifacts and chunking effects, +# but in practice it's not possible due to the duration and number of channels of most setups. +# +# Since in this toy case we have a single channel 5-min recording, we can use this as "optimal". + +recording_optimal = recording_filt.save( + format="memory", + chunk_duration="1000s", + progress_bar=False +) + +print(recording_optimal) + +############################################################################## +# Now we can do the same with our various options: + +recordings_chunked = {} + +for margin_ms in margins_ms: + for chunk_duration in chunk_durations: + print(f"Margin ms: {margin_ms} - Chunk duration: {chunk_duration}") + t_start = time.perf_counter() + recording_chunk = spre.bandpass_filter( + recording_with_lfp, + freq_min=1.0, + freq_max=300.0, + margin_ms=margin_ms, + ignore_low_freq_error=True, + ) + recording_chunk = recording_chunk.save( + format="memory", + chunk_duration=chunk_duration, + verbose=False, + progress_bar=False + ) + t_stop = time.perf_counter() + result_dict = {"recording": recording_chunk, "time": t_stop - t_start} + recordings_chunked[(margin_ms, chunk_duration)] = result_dict + +############################################################################## +# Let's visualize the error for the "10s" chunks and different margins, centered around 30s (which is a chunk edge): + +fig, ax = plt.subplots(figsize=(10, 5)) +trace_plotted = False +start_time = 15 # seconds +end_time = 45 # seconds +start_frame = int(start_time * recording_optimal.sampling_frequency) +end_frame = int(end_time * recording_optimal.sampling_frequency) +timestamps = recording_optimal.get_times()[start_frame:end_frame] +for recording_key, recording_dict in recordings_chunked.items(): + recording_chunk = recording_dict["recording"] + margin, chunk = recording_key + # only plot "10s" chunks + if chunk != "10s": + continue + traces_opt = recording_optimal.get_traces( + start_frame=start_frame, end_frame=end_frame + ) + if not trace_plotted: + ax.plot(timestamps, traces_opt, color="grey", label="traces", alpha=0.5) + trace_plotted = True + diff = recording_optimal - recording_chunk + traces_diff = diff.get_traces(start_frame=start_frame, end_frame=end_frame) + ax.plot(timestamps, traces_diff, label=f"Margin: {margin}") + for chunk in [20, 30, 40]: # chunk boundaries at 10s intervals + ax.axvline(x=chunk, color="red", linestyle="--", alpha=0.5) + +ax.set_xlabel("Time (s)") +ax.set_ylabel("Voltage ($\\mu V$)") +_ = ax.legend() + +############################################################################## +# From the plot, we can see that there is a very small error when the margin size is large (green), +# a larger error when the margin is smaller (orange) and a large error when the margin is small (blue). +# So we need large margins (compared to the chunk size) if we want accurate filtered. +# +# The artifacts do not depend on chunk size, but for smaller chunk sizes, these artifacts will happen more often. +# In addition, the margin "overhead" will make processing slower. Let's quantify these concepts by computing the +# overall absolute error with respect to the optimal case and processing time. + +trace_plotted = False +traces_optimal = recording_optimal.get_traces() +data = {"margin": [], "chunk": [], "error": [], "time": []} +for recording_key, recording_dict in recordings_chunked.items(): + recording_chunk = recording_dict["recording"] + time = recording_dict["time"] + margin, chunk = recording_key + traces_chunk = recording_chunk.get_traces() + error = np.sum(np.abs(traces_optimal - traces_chunk)) + data["margin"].append(margin) + data["chunk"].append(chunk) + data["error"].append(error) + data["time"].append(time) + +df = pd.DataFrame(data=data) + +############################################################################## +# Now let's visualize the error and processing time for different margin and chunk size combinations + +fig, axs = plt.subplots(ncols=2, figsize=(10, 5)) +sns.barplot(data=data, x="margin", y="error", hue="chunk", ax=axs[0]) +axs[0].set_yscale("log") +sns.barplot(data=data, x="margin", y="time", hue="chunk", ax=axs[1]) +axs[0].set_title("Error VS margin x chunk size") +axs[1].set_title("Processing time VS margin x chunk size") + +sns.despine(fig) + +############################################################################## +# Summary +# ------- +# +# 1. **Low-frequency filters require special care**: Filters with low cutoff frequencies (< 10 Hz) have long +# impulse responses that require large margins to avoid edge artifacts. +# +# 2. **Chunking artifacts are real**: When processing data in chunks, insufficient margins lead to visible +# discontinuities and errors at chunk boundaries. +# +# 3. **The solution: large chunks and large margins**: For LFP extraction (1-300 Hz), use: +# - Chunk size: 30-60 seconds +# - Margin size: 5 seconds (for 1 Hz highpass) (**use defaults!**) +# - This is less memory-efficient but more accurate +# +# 4. **Downsample after filtering**: After bandpass filtering, downsample to reduce data size (e.g., to 1-2.5 kHz +# for 300 Hz max frequency). +# +# 5. **Trade-offs**: There's always a trade-off between computational efficiency (smaller chunks, less memory) +# and accuracy (larger chunks, fewer artifacts). For LFP analysis, accuracy should take priority. +# +# **When processing your own data:** +# +# - If you have memory constraints, use the largest chunk size your system can handle +# - Always verify your filtering parameters on a small test segment first +# - Consider the lowest frequency component you want to preserve when setting margins +# - Save the processed LFP data to disk to avoid recomputing diff --git a/examples/forhowto/plot_working_with_tetrodes.py b/examples/forhowto/plot_working_with_tetrodes.py new file mode 100644 index 0000000000..0c652a5186 --- /dev/null +++ b/examples/forhowto/plot_working_with_tetrodes.py @@ -0,0 +1,117 @@ +""" +Work with tetrodes +================== + +Tetrodes are a common recording method for electrophysiological data. It is also common +to record from several tetrodes at the same time. In this 'how to' we'll see how to +work with data from two tetrodes, each with four channels. + +We'll start by importing some functions we'll use in this How To guide +""" + +import spikeinterface.preprocessing as spre +from spikeinterface.widgets import plot_traces, plot_probe_map +from spikeinterface import generate_ground_truth_recording + +from probeinterface import generate_tetrode, ProbeGroup + +############################################################################## +# In practice, you would read in your raw data from a file. Instead, we will generate a +# recording with eight channels. We can also set a duration, number of units and +# sampling frequency. + +recording, _ = generate_ground_truth_recording( + durations = [60], # make the recording 60s long + sampling_frequency=30_000, + num_channels=8, + num_units=10, +) + +############################################################################## +# We now need to define the probe. This will tell the recording which channels came from +# which tetrode. To do this, we will use the :code:`generate_tetrode` function from :code:`ProbeInterface` +# to generate two 4-channel probes (representing one tetrode each). In our case, since we +# don't know the relative distances between the tetrodes, we will move the second +# tetrode away from the first by 100 microns. This is just so we can visualize the +# results more easily. Eventually, we will sort each tetrode separately, so their +# relative distance won't affect the results. + +# Technically, we will add each tetrode to a :code:`ProbeGroup`. Read more in the ProbeInterface +# docs. + +# Create each individual tetrode +tetrode_1 = generate_tetrode() +tetrode_1.create_auto_shape() + +tetrode_2 = generate_tetrode() +tetrode_2.move([100, 0]) +tetrode_2.create_auto_shape() + +# Add the two tetrodes to a ProbeGroup +tetrode_group = ProbeGroup() +tetrode_group.add_probe(tetrode_1) +tetrode_group.add_probe(tetrode_2) + +# Now we need to "wire" our tetrodes to ensure that each contact +# can be associated with the correct channel when we attach it +# to the recording. In this example we are just using `range` +# but see ProbeInterface for more tutorials on wiring +tetrode_group.set_global_device_channel_indices(range(8)) + +############################################################################## +# We can now attach the :code:`tetrode_group` to our recording. To check if this worked, we'll +# plot the probe map + +recording_with_probe = recording.set_probegroup(tetrode_group) +plot_probe_map(recording_with_probe) + +############################################################################## +# Looks good! Now that the recording is aware of the probe geometry, we can +# begin a standard spike sorting pipeline. First, we can apply preprocessing. +# Note that we apply this preprocessing on the entire bundle of tetrodes. + +preprocessed_recording = spre.bandpass_filter(recording_with_probe) + +############################################################################## +# WARNING: a very common preprocessing step is to apply a common median +# reference. This subtracts the median signal from all channels to help +# remove noise. However, for a tetrode, a spike is often seen on all +# channels. So removing the median can remove the entire spike! +# This is still a danger if you have two tetrodes in a bundle, which +# might pick up the same spike, but becomes less dangerous +# as the number of tetrodes in your bundle increases. +# +# Tetrodes often have dead channels, so it is advised to try and detect +# and remove these. For tetrodes, we should use a detection method which +# doesn't depend on the channel locations such as std or mad: + +recording_good_channels = spre.detect_and_remove_bad_channels( + preprocessed_recording, + method = "std", +) + +############################################################################## +# It can be a good idea to sort your tetrode data separately for each tetrode. +# When we use :code:`set_probegroup`, the channels are automatically +# labelled by which probe in the probe group they belong to. We can access +# this labeling using the "group" property. + +print(recording_good_channels.get_property("group")) + +############################################################################## +# We can then use this information to split the recording by the group property: + +grouped_recordings = recording_good_channels.split_by('group') +print(grouped_recordings) + +############################################################################## +# Now that we've got preprocess, clean data. Let's take a look at a +# snippet of data from the first group: + +plot_traces(grouped_recordings[0]) + +############################################################################## +# Beautiful! We are now ready to sort. To read more about sorting by group, see +# :ref:`sorting-by-channel-group`. Note that many modern sorters are designed +# to sort data from high-density probes and will fail for tetrodes. Please read +# each spike sorter's documentation to find out if it is appropriate for tetrodes. diff --git a/src/spikeinterface/exporters/tests/test_export_to_ibl.py b/src/spikeinterface/exporters/tests/test_export_to_ibl.py index 3b859634df..96038ced59 100644 --- a/src/spikeinterface/exporters/tests/test_export_to_ibl.py +++ b/src/spikeinterface/exporters/tests/test_export_to_ibl.py @@ -78,7 +78,7 @@ def test_export_lfp_to_ibl(sorting_analyzer_sparse_for_export, create_cache_fold sorting_analyzer = sorting_analyzer_sparse_for_export recording = sorting_analyzer.recording - recording_lfp = bandpass_filter(recording, freq_min=0.5, freq_max=300) + recording_lfp = bandpass_filter(recording, freq_min=0.5, freq_max=300, ignore_low_freq_error=True) recording_lfp = decimate(recording_lfp, 10) # LFP, but no AP export_to_ibl_gui( diff --git a/src/spikeinterface/preprocessing/filter.py b/src/spikeinterface/preprocessing/filter.py index 4ee7347962..b57ebe5519 100644 --- a/src/spikeinterface/preprocessing/filter.py +++ b/src/spikeinterface/preprocessing/filter.py @@ -232,8 +232,6 @@ class BandpassFilterRecording(FilterRecording): margin_ms : float | str, default: "auto" Margin in ms on border to avoid border effect. If "auto", margin is computed as 3 times the filter highpass cutoff period. - max_margin_s : float, default: 5 - Maximum margin in seconds when margin_ms is set to "auto". dtype : dtype or None The dtype of the returned traces. If None, the dtype of the parent recording is used ignore_low_freq_error : bool, default: False @@ -322,8 +320,6 @@ class NotchFilterRecording(FilterRecording): dtype of recording. If None, will take from `recording` margin_ms : float | str, default: "auto" Margin in ms on border to avoid border effect - max_margin_s : float, default: 5 - Maximum margin in seconds when margin_ms is set to "auto". Returns ------- @@ -331,7 +327,7 @@ class NotchFilterRecording(FilterRecording): The notch-filtered recording extractor object """ - def __init__(self, recording, freq=3000, q=30, margin_ms="auto", max_margin_s=5, dtype=None, **filter_kwargs): + def __init__(self, recording, freq=3000, q=30, margin_ms="auto", dtype=None, **filter_kwargs): import scipy.signal if margin_ms == "auto": From a8df35779546ab0398ce2aa613033f8e8f9771c5 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 9 Dec 2025 12:10:01 +0100 Subject: [PATCH 08/19] Remove pre-generated how-to files for extract lfp --- doc/how_to/extract_lfps.rst | 565 ------------------ .../extract_lfps_files/extract_lfps_17_2.png | Bin 29067 -> 0 bytes .../extract_lfps_files/extract_lfps_19_1.png | Bin 20533 -> 0 bytes .../extract_lfps_files/extract_lfps_28_2.png | Bin 75077 -> 0 bytes .../extract_lfps_files/extract_lfps_31_0.png | Bin 26729 -> 0 bytes .../extract_lfps_files/extract_lfps_8_1.png | Bin 25985 -> 0 bytes 6 files changed, 565 deletions(-) delete mode 100644 doc/how_to/extract_lfps.rst delete mode 100644 doc/how_to/extract_lfps_files/extract_lfps_17_2.png delete mode 100644 doc/how_to/extract_lfps_files/extract_lfps_19_1.png delete mode 100644 doc/how_to/extract_lfps_files/extract_lfps_28_2.png delete mode 100644 doc/how_to/extract_lfps_files/extract_lfps_31_0.png delete mode 100644 doc/how_to/extract_lfps_files/extract_lfps_8_1.png diff --git a/doc/how_to/extract_lfps.rst b/doc/how_to/extract_lfps.rst deleted file mode 100644 index 4a7689b65e..0000000000 --- a/doc/how_to/extract_lfps.rst +++ /dev/null @@ -1,565 +0,0 @@ -Extract LFPs -============ - -Understanding filtering artifacts and chunking when extracting LFPs -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Local Field Potentials (LFPs) are low-frequency signals (<300 Hz) that -reflect the summed activity of many neurons. Extracting LFPs from -high-sampling-rate recordings requires bandpass filtering, but this can -introduce artifacts when not done carefully, especially when data is -processed in chunks (for memory efficiency). - -This tutorial demonstrates: 1. How to generate simulated LFP data 2. -Common pitfalls when filtering with low cutoff frequencies 3. How -chunking and margins affect filtering artifacts 4. Summary - -**Key takeaway**: For LFP extraction, use large chunks (30-60s) and -large margins (several seconds) to minimize edge artifacts, even though -this is less memory-efficient. - -.. code:: ipython3 - - import time - import numpy as np - import matplotlib.pyplot as plt - from pathlib import Path - import pandas as pd - import seaborn as sns - - import spikeinterface as si - import spikeinterface.extractors as se - import spikeinterface.preprocessing as spre - import spikeinterface.widgets as sw - from spikeinterface.core import generate_ground_truth_recording - -.. code:: ipython3 - - %matplotlib inline - -1. Generate simulated recording with low-frequency signals ----------------------------------------------------------- - -Let’s create a simulated recording and add some low-frequency sinusoids -that mimic LFP activity. - -.. code:: ipython3 - - # Generate a ground truth recording with spikes - # Use a higher sampling rate (30 kHz) to simulate raw neural data - recording, sorting = generate_ground_truth_recording( - durations=[300.0], # 300 s - sampling_frequency=30000.0, - num_channels=1, - num_units=4, - seed=2305, - ) - - print(f"Recording: {recording}") - print(f"Duration: {recording.get_total_duration():.1f} s") - print(f"Sampling frequency: {recording.sampling_frequency} Hz") - print(f"Number of channels: {recording.get_num_channels()}") - - -.. parsed-literal:: - - Recording: GroundTruthRecording (InjectTemplatesRecording): 1 channels - 30.0kHz - 1 segments - 9,000,000 samples - 300.00s (5.00 minutes) - float32 dtype - 34.33 MiB - Duration: 300.0 s - Sampling frequency: 30000.0 Hz - Number of channels: 1 - - -Now let’s add some low-frequency sinusoidal components to simulate LFP -signals - -.. code:: ipython3 - - # Add low-frequency sinusoids with different frequencies and phases per channel - np.random.seed(2305) - num_channels = recording.get_num_channels() - lfp_signals = np.zeros((recording.get_num_samples(), recording.get_num_channels())) - time_vector = recording.get_times() - - for ch in range(num_channels): - # Add multiple frequency components (theta, alpha, beta ranges) - # Theta-like: 4-8 Hz - freq_theta = 4 + np.random.rand() * 4 - phase_theta = np.random.rand() * 2 * np.pi - amp_theta = 50 + np.random.rand() * 50 - - # Alpha-like: 8-12 Hz - freq_alpha = 8 + np.random.rand() * 4 - phase_alpha = np.random.rand() * 2 * np.pi - amp_alpha = 30 + np.random.rand() * 30 - - # Beta-like: 12-30 Hz - freq_beta = 12 + np.random.rand() * 18 - phase_beta = np.random.rand() * 2 * np.pi - amp_beta = 20 + np.random.rand() * 20 - - lfp_signals[:, ch] = ( - amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta) + - amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha) + - amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta) - ) - - # Create a recording with the added LFP signals - recording_lfp = si.NumpyRecording(traces_list=[lfp_signals], sampling_frequency=recording.sampling_frequency, - channel_ids=recording.channel_ids) - recording_with_lfp = recording + recording_lfp - - print("Added low-frequency components to simulate LFP signals") - - -.. parsed-literal:: - - Added low-frequency components to simulate LFP signals - - -Let’s visualize a short segment of the signal - -.. code:: ipython3 - - sw.plot_traces(recording_with_lfp, time_range=[0, 3]) - - - - -.. parsed-literal:: - - - - - - -.. image:: extract_lfps_files/extract_lfps_8_1.png - - -2. Filtering with low cutoff frequencies: the problem ------------------------------------------------------ - -Now let’s try to extract LFPs using a bandpass filter with a low -highpass cutoff (1 Hz). This will demonstrate a common issue. - -.. code:: ipython3 - - # Try to filter with 1 Hz highpass - try: - recording_lfp_1hz = spre.bandpass_filter(recording_with_lfp, freq_min=1.0, freq_max=300.0) - print("Filtering succeeded!") - except Exception as e: - print(f"Error message:\n{str(e)}") - - -.. parsed-literal:: - - Error message: - The freq_min (1.0 Hz) is too low and may cause artifacts during chunk processing. You can set 'ignore_low_freq_error=True' to bypass this error, but make sure you understand the implications. It is recommended to use large chunks when processing/saving your filtered recording to minimize IO overhead.Refer to this documentation on LFP filtering and chunking artifacts for more details: https://spikeinterface.readthedocs.io/en/stable/how-to/extracting_lfps.html. - - -**Why does this fail?** - -The error occurs because by default in SpikeInterface when highpass -filtering below 100 Hz. Filters with very low cutoff frequencies have -long impulse responses, which require larger margins to avoid edge -artifacts between chunks. - -The filter length (and required margin) scales inversely with the -highpass frequency. A 1 Hz highpass filter requires a margin of several -seconds, while a 300 Hz highpass (for spike extraction) only needs a few -milliseconds. - -3. Understanding chunking and margins -------------------------------------- - -SpikeInterface processes recordings in chunks to handle large datasets -efficiently. Each chunk needs a “margin” (extra samples at the edges) to -avoid edge artifacts when filtering. Let’s demonstrate this by saving -the filtered data with different chunking strategies. - -**This error is to inform the user that extra care should be used when -dealing with LFP signals!** - -We can ignore this error, but let’s make sure we understand what it’s -happening. - -.. code:: ipython3 - - # We can ignore this error, but let's see what is happening - recording_filt = spre.bandpass_filter(recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True) - -When retrieving traces, extra samples will be retrieved at the left and -right edges. By default, the filter function will set a margin to 5x the -sampling period associated to ``freq_min``. So for a 1 Hz cutoff -frequency, the margin will be 5 seconds! - -.. code:: ipython3 - - margin_in_s = recording_filt.margin_samples / recording_lfp.sampling_frequency - print(f"Margin: {margin_in_s} s") - - -.. parsed-literal:: - - Margin: 5.0 s - - -This effectively means that if we plot 1-s snippet of traces, a total of -11 s will actually be read and filtered. Note that the margin can be -overridden with the ``margin_ms`` argument, but we do not recommend -changing it. - -.. code:: ipython3 - - sw.plot_traces(recording_filt, time_range=[20, 21]) - - -.. parsed-literal:: - - /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:169: UserWarning: The margin size (150000 samples) is more than 20% of the chunk size 30000 samples. This may lead to performance bottlenecks when chunking. Consider increasing the chunk size to minimize margin overhead. - warnings.warn( - - - - -.. parsed-literal:: - - - - - - -.. image:: extract_lfps_files/extract_lfps_17_2.png - - -A warning tells us that what we are doing is not optimized, since in -order to get the requested traces the marging “overhead” is very large. - -If we ask or plot longer snippets, the warning is not displayed. - -.. code:: ipython3 - - sw.plot_traces(recording_filt, time_range=[20, 80]) - - - - -.. parsed-literal:: - - - - - - -.. image:: extract_lfps_files/extract_lfps_19_1.png - - -4. Quantification and visualization the artifacts -------------------------------------------------- - -Let’s extract the traces and visualize the differences between chunking -strategies. We’ll focus on the chunk boundaries where artifacts appear. - -.. code:: ipython3 - - margins_ms = [100, 1000, 5000] - chunk_durations = ["1s", "10s", "30s"] - -The best we can do is to save the full recording in one chunk. This will -cause no artifacts and chunking effects, but in practice it’s not -possible due to the duration and number of channels of most setups. - -Since in this toy case we have a single channel 5-min recording, we can -use this as “optimal”. - -.. code:: ipython3 - - recording_optimal = recording_filt.save(format="memory", chunk_duration="1000s") - - - -.. parsed-literal:: - - write_memory_recording (no parallelization): 0%| | 0/1 [00:00SharedMemoryRecording: 1 channels - 30.0kHz - 1 segments - 9,000,000 samples - 300.00s (5.00 minutes) - float32 dtype - 34.33 MiB
    Channel IDs
      ['0']
    Annotations
    • is_filtered : True
    Properties
      - - - -Now we can do the same with our various options: - -.. code:: ipython3 - - recordings_chunked = {} - - for margin_ms in margins_ms: - for chunk_duration in chunk_durations: - print(f"Margin ms: {margin_ms} - Chunk duration: {chunk_duration}") - t_start = time.perf_counter() - recording_chunk = spre.bandpass_filter( - recording_with_lfp, - freq_min=1.0, - freq_max=300.0, - margin_ms=margin_ms, - ignore_low_freq_error=True - ) - recording_chunk = recording_chunk.save( - format="memory", - chunk_duration=chunk_duration, - verbose=False, - ) - t_stop = time.perf_counter() - result_dict = { - "recording": recording_chunk, - "time": t_stop - t_start - } - recordings_chunked[(margin_ms, chunk_duration)] = result_dict - - -.. parsed-literal:: - - Margin ms: 100 - Chunk duration: 1s - - -.. parsed-literal:: - - /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:453: UserWarning: The provided margin_ms (100 ms) is smaller than the recommended margin for the given freq_min (1.0 Hz). This may lead to artifacts at the edges of chunks during processing. Consider increasing the margin_ms to at least 5000.0 ms. - warnings.warn( - - - -.. parsed-literal:: - - write_memory_recording (no parallelization): 0%| | 0/300 [00:00 - - - - -.. image:: extract_lfps_files/extract_lfps_28_2.png - - -For smaller chunk sizes, these artifact will happen more often. In -addition, the margin “overhead” will make processing slower. Let’s -quantify these concepts by computing the overall absolute error with -respect to the optimal case and processing time. - -.. code:: ipython3 - - trace_plotted = False - traces_optimal = recording_optimal.get_traces() - data = {"margin": [], "chunk": [], "error": [], "time": []} - for recording_key, recording_dict in recordings_chunked.items(): - recording_chunk = recording_dict["recording"] - time = recording_dict["time"] - margin, chunk = recording_key - traces_chunk = recording_chunk.get_traces() - error = np.sum(np.abs(traces_optimal - traces_chunk)) - data["margin"].append(margin) - data["chunk"].append(chunk) - data["error"].append(error) - data["time"].append(time) - - df = pd.DataFrame(data=data) - -.. code:: ipython3 - - fig, axs = plt.subplots(ncols=2, figsize=(10, 5)) - sns.barplot(data=data, x="margin", y="error", hue="chunk", ax=axs[0]) - axs[0].set_yscale("log") - sns.barplot(data=data, x="margin", y="time", hue="chunk", ax=axs[1]) - axs[0].set_title("Error VS margin x chunk size") - axs[1].set_title("Processing time VS margin x chunk size") - - - sns.despine(fig) - - - -.. image:: extract_lfps_files/extract_lfps_31_0.png - - -4. Summary ----------- - -1. **Low-frequency filters require special care**: Filters with low - cutoff frequencies (< 10 Hz) have long impulse responses that require - large margins to avoid edge artifacts. - -2. **Chunking artifacts are real**: When processing data in chunks, - insufficient margins lead to visible discontinuities and errors at - chunk boundaries. - -3. **The solution: large chunks and large margins**: For LFP extraction - (1-300 Hz), use: - - - Chunk size: 30-60 seconds - - Margin size: 5 seconds (for 1 Hz highpass) (**use defaults!**) - - This is less memory-efficient but more accurate - -4. **Downsample after filtering**: After bandpass filtering, downsample - to reduce data size (e.g., to 1-2.5 kHz for 300 Hz max frequency). - -5. **Trade-offs**: There’s always a trade-off between computational - efficiency (smaller chunks, less memory) and accuracy (larger chunks, - fewer artifacts). 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      - -.. thumbnail-parent-div-open - -.. raw:: html - -
      - -.. only:: html - - .. image:: /forhowto/images/thumb/sphx_glr_plot_extract_lfps_thumb.png - :alt: - - :ref:`sphx_glr_forhowto_plot_extract_lfps.py` - -.. raw:: html - -
      Extract LFPs
      -
      - - -.. raw:: html - -
      - -.. only:: html - - .. image:: /forhowto/images/thumb/sphx_glr_plot_working_with_tetrodes_thumb.png - :alt: - - :ref:`sphx_glr_forhowto_plot_working_with_tetrodes.py` - -.. raw:: html - -
      Work with tetrodes
      -
      - - -.. thumbnail-parent-div-close - -.. raw:: html - -
      - - -.. toctree:: - :hidden: - - /forhowto/plot_extract_lfps - /forhowto/plot_working_with_tetrodes - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: forhowto_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: forhowto_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/forhowto/plot_extract_lfps.codeobj.json b/doc/forhowto/plot_extract_lfps.codeobj.json deleted file mode 100644 index ae763dc01b..0000000000 --- a/doc/forhowto/plot_extract_lfps.codeobj.json +++ /dev/null @@ -1,2637 +0,0 @@ -{ - "_": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.legend", - "module_short": "matplotlib.legend", - "name": "Legend" - }, - { - "is_class": false, - 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"module_short": "numpy.random", - "name": "Generator" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "Generator" - } - ], - "rng.random": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy.random._generator", - "module_short": "numpy.random", - "name": "Generator.random" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy.random", - "module_short": "numpy.random", - "name": "Generator.random" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "Generator.random" - } - ], - "si.NumpyRecording": [ - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.core.numpyextractors", - "module_short": "spikeinterface", - "name": "NumpyRecording" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "NumpyRecording" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "NumpyRecording" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.core.baserecording", - "module_short": "spikeinterface", - "name": "BaseRecording" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "BaseRecording" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseRecording" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.core.baserecordingsnippets", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.core.base", - "module_short": "spikeinterface.core.base", - "name": "BaseExtractor" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface.core", - "name": "BaseExtractor" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseExtractor" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "NumpyRecording" - } - ], - "sns.barplot": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "seaborn", - "module_short": "seaborn", - "name": "barplot" - } - ], - "sns.despine": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "seaborn", - "module_short": "seaborn", - "name": "despine" - } - ], - "sorting": [ - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.numpyextractors", - "module_short": "spikeinterface", - "name": "NumpySorting" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "NumpySorting" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "NumpySorting" - } - ], - "spre.bandpass_filter": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing", - "module_short": "spikeinterface.preprocessing", - "name": "bandpass_filter" - } - ], - "start_frame": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "start_time": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "sw.plot_traces": [ - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets.traces", - "module_short": "spikeinterface.widgets", - "name": "TracesWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets", - "module_short": "spikeinterface.widgets", - "name": "TracesWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "TracesWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets.base", - "module_short": "spikeinterface.widgets.base", - "name": "BaseWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets", - "module_short": "spikeinterface.widgets", - "name": "BaseWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseWidget" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.widgets", - "module_short": "spikeinterface.widgets", - "name": "plot_traces" - } - ], - "t_start": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "float" - } - ], - "t_stop": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "float" - } - ], - "time": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "float" - }, - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "time" - } - ], - "time.perf_counter": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "float" - }, - { - "is_class": false, - "is_explicit": false, - "module": "time", - "module_short": "time", - "name": "perf_counter" - } - ], - "time_vector": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "timestamps": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "trace_plotted": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "bool" - } - ], - "traces_chunk": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "traces_diff": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "traces_opt": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "traces_optimal": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ] -} diff --git a/doc/forhowto/plot_extract_lfps.ipynb b/doc/forhowto/plot_extract_lfps.ipynb deleted file mode 100644 index 8f72547112..0000000000 --- a/doc/forhowto/plot_extract_lfps.ipynb +++ /dev/null @@ -1,316 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Extract LFPs\n\nUnderstanding filtering artifacts and chunking when extracting LFPs\n\nLocal Field Potentials (LFPs) are low-frequency signals (<300 Hz) that reflect the summed activity of many neurons.\nExtracting LFPs from high-sampling-rate recordings requires bandpass filtering, but this can introduce artifacts\nwhen not done carefully, especially when data is processed in chunks (for memory efficiency).\n\nThis tutorial demonstrates:\n\n1. How to generate simulated LFP data\n2. Common pitfalls when filtering with low cutoff frequencies\n3. How chunking and margins affect filtering artifacts\n4. Summary\n\n**Key takeaway**: For LFP extraction, use large chunks (30-60s) and large margins (several seconds) to minimize\nedge artifacts, even though this is less memory-efficient.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import necessary modules\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import time\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\nimport pandas as pd\nimport seaborn as sns\n\nimport spikeinterface as si\nimport spikeinterface.extractors as se\nimport spikeinterface.preprocessing as spre\nimport spikeinterface.widgets as sw\nfrom spikeinterface.core import generate_ground_truth_recording" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Generate simulated recording with low-frequency signals\n\nLet's create a simulated recording and add some low-frequency sinusoids that mimic LFP activity.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Generate a ground truth recording with spikes\n# Use a higher sampling rate (30 kHz) to simulate raw neural data\nrecording, sorting = generate_ground_truth_recording(\n durations=[300.0], # 300 s\n sampling_frequency=30000.0,\n num_channels=1,\n num_units=4,\n seed=2305,\n)\n\nprint(recording)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's add some low-frequency sinusoidal components to simulate LFP signals\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Add low-frequency sinusoids with different frequencies and phases per channel\nrng = np.random.default_rng(42)\nnum_channels = recording.get_num_channels()\nlfp_signals = np.zeros(\n (recording.get_num_samples(), recording.get_num_channels())\n)\ntime_vector = recording.get_times()\n\nfor ch in range(num_channels):\n # Add multiple frequency components (theta, alpha, beta ranges)\n # Theta-like: 4-8 Hz\n freq_theta = 4 + rng.random() * 4\n phase_theta = rng.random() * 2 * np.pi\n amp_theta = 50 + rng.random() * 50\n\n # Alpha-like: 8-12 Hz\n freq_alpha = 8 + rng.random() * 4\n phase_alpha = rng.random() * 2 * np.pi\n amp_alpha = 30 + rng.random() * 30\n\n # Beta-like: 12-30 Hz\n freq_beta = 12 + rng.random() * 18\n phase_beta = rng.random() * 2 * np.pi\n amp_beta = 20 + rng.random() * 20\n\n lfp_signals[:, ch] = (\n amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta)\n + amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha)\n + amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta)\n )\n\n# Create a recording with the added LFP signals\nrecording_lfp = si.NumpyRecording(\n traces_list=[lfp_signals],\n sampling_frequency=recording.sampling_frequency,\n channel_ids=recording.channel_ids,\n)\nrecording_with_lfp = recording + recording_lfp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's visualize a short segment of the signal\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "_ = sw.plot_traces(recording_with_lfp, time_range=[0, 3])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Filtering with low cutoff frequencies: the problem\n\nNow let's try to extract LFPs using a bandpass filter with a low highpass cutoff (1 Hz).\nThis will demonstrate a common issue.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Try to filter with 1 Hz highpass\ntry:\n recording_lfp_1hz = spre.bandpass_filter(\n recording_with_lfp, freq_min=1.0, freq_max=300.0\n )\nexcept Exception as e:\n print(f\"Error message:\\n{str(e)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Why does this fail?**\n\nThe error occurs because by default in SpikeInterface when highpass filtering below 100 Hz.\nFilters with very low cutoff frequencies have long impulse responses, which require larger margins to avoid edge artifacts between chunks.\n\nThe filter length (and required margin) scales inversely with the highpass frequency. A 1 Hz highpass\nfilter requires a margin of several seconds, while a 300 Hz highpass (for spike extraction) only needs\na few milliseconds.\n\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Understanding chunking and margins\n\nSpikeInterface processes recordings in chunks to handle large datasets efficiently. Each chunk needs\na \"margin\" (extra samples at the edges) to avoid edge artifacts when filtering. Let's demonstrate\nthis by saving the filtered data with different chunking strategies.\n\n**This error is to inform the user that extra care should be used when dealing with LFP signals!**\n\nWe can ignore this error, but let's make sure we understand what it's happening.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# We can ignore this error, but let's see what is happening\nrecording_filt = spre.bandpass_filter(\n recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True\n)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When retrieving traces, extra samples will be retrieved at the left and right edges.\nBy default, the filter function will set a margin to 5x the sampling period associated to `freq_min`.\nSo for a 1 Hz cutoff frequency, the margin will be 5 seconds!\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "margin_in_s = recording_filt.margin_samples / recording_lfp.sampling_frequency\nprint(f\"Margin: {margin_in_s} s\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This effectively means that if we plot 1-s snippet of traces, a total of 11 s will actually be read and filtered.\nNote that the margin can be overridden with the `margin_ms` argument, but we do not recommend changing it.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "_ = sw.plot_traces(recording_filt, time_range=[20, 21])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A warning tells us that what we are doing is not optimized, since in order to get the requested traces\nthe margin \"overhead\" is very large.\n\nIf we ask or plot longer snippets, the warning is not displayed.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "_ = sw.plot_traces(recording_filt, time_range=[20, 80])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Quantification and visualization of the artifacts\n\nLet's extract the traces and visualize the differences between chunking strategies.\nWe'll focus on the chunk boundaries where artifacts appear.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "margins_ms = [100, 1000, 5000]\nchunk_durations = [\"1s\", \"10s\", \"30s\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The best we can do is to save the full recording in one chunk. This will cause no artifacts and chunking effects,\nbut in practice it's not possible due to the duration and number of channels of most setups.\n\nSince in this toy case we have a single channel 5-min recording, we can use this as \"optimal\".\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "recording_optimal = recording_filt.save(\n format=\"memory\",\n chunk_duration=\"1000s\",\n progress_bar=False\n)\n\nprint(recording_optimal)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can do the same with our various options:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "recordings_chunked = {}\n\nfor margin_ms in margins_ms:\n for chunk_duration in chunk_durations:\n print(f\"Margin ms: {margin_ms} - Chunk duration: {chunk_duration}\")\n t_start = time.perf_counter()\n recording_chunk = spre.bandpass_filter(\n recording_with_lfp,\n freq_min=1.0,\n freq_max=300.0,\n margin_ms=margin_ms,\n ignore_low_freq_error=True,\n )\n recording_chunk = recording_chunk.save(\n format=\"memory\",\n chunk_duration=chunk_duration,\n verbose=False,\n progress_bar=False\n )\n t_stop = time.perf_counter()\n result_dict = {\"recording\": recording_chunk, \"time\": t_stop - t_start}\n recordings_chunked[(margin_ms, chunk_duration)] = result_dict" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's visualize the error for the \"10s\" chunks and different margins, centered around 30s (which is a chunk edge):\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 5))\ntrace_plotted = False\nstart_time = 15 # seconds\nend_time = 45 # seconds\nstart_frame = int(start_time * recording_optimal.sampling_frequency)\nend_frame = int(end_time * recording_optimal.sampling_frequency)\ntimestamps = recording_optimal.get_times()[start_frame:end_frame]\nfor recording_key, recording_dict in recordings_chunked.items():\n recording_chunk = recording_dict[\"recording\"]\n margin, chunk = recording_key\n # only plot \"10s\" chunks\n if chunk != \"10s\":\n continue\n traces_opt = recording_optimal.get_traces(\n start_frame=start_frame, end_frame=end_frame\n )\n if not trace_plotted:\n ax.plot(timestamps, traces_opt, color=\"grey\", label=\"traces\", alpha=0.5)\n trace_plotted = True\n diff = recording_optimal - recording_chunk\n traces_diff = diff.get_traces(start_frame=start_frame, end_frame=end_frame)\n ax.plot(timestamps, traces_diff, label=f\"Margin: {margin}\")\n for chunk in [20, 30, 40]: # chunk boundaries at 10s intervals\n ax.axvline(x=chunk, color=\"red\", linestyle=\"--\", alpha=0.5)\n\nax.set_xlabel(\"Time (s)\")\nax.set_ylabel(\"Voltage ($\\\\mu V$)\")\n_ = ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the plot, we can see that there is a very small error when the margin size is large (green),\na larger error when the margin is smaller (orange) and a large error when the margin is small (blue).\nSo we need large margins (compared to the chunk size) if we want accurate filtered.\n\nThe artifacts do not depend on chunk size, but for smaller chunk sizes, these artifacts will happen more often.\nIn addition, the margin \"overhead\" will make processing slower. Let's quantify these concepts by computing the\noverall absolute error with respect to the optimal case and processing time.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "trace_plotted = False\ntraces_optimal = recording_optimal.get_traces()\ndata = {\"margin\": [], \"chunk\": [], \"error\": [], \"time\": []}\nfor recording_key, recording_dict in recordings_chunked.items():\n recording_chunk = recording_dict[\"recording\"]\n time = recording_dict[\"time\"]\n margin, chunk = recording_key\n traces_chunk = recording_chunk.get_traces()\n error = np.sum(np.abs(traces_optimal - traces_chunk))\n data[\"margin\"].append(margin)\n data[\"chunk\"].append(chunk)\n data[\"error\"].append(error)\n data[\"time\"].append(time)\n\ndf = pd.DataFrame(data=data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's visualize the error and processing time for different margin and chunk size combinations\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(ncols=2, figsize=(10, 5))\nsns.barplot(data=data, x=\"margin\", y=\"error\", hue=\"chunk\", ax=axs[0])\naxs[0].set_yscale(\"log\")\nsns.barplot(data=data, x=\"margin\", y=\"time\", hue=\"chunk\", ax=axs[1])\naxs[0].set_title(\"Error VS margin x chunk size\")\naxs[1].set_title(\"Processing time VS margin x chunk size\")\n\nsns.despine(fig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n\n1. **Low-frequency filters require special care**: Filters with low cutoff frequencies (< 10 Hz) have long\n impulse responses that require large margins to avoid edge artifacts.\n\n2. **Chunking artifacts are real**: When processing data in chunks, insufficient margins lead to visible\n discontinuities and errors at chunk boundaries.\n\n3. **The solution: large chunks and large margins**: For LFP extraction (1-300 Hz), use:\n - Chunk size: 30-60 seconds\n - Margin size: 5 seconds (for 1 Hz highpass) (**use defaults!**)\n - This is less memory-efficient but more accurate\n\n4. **Downsample after filtering**: After bandpass filtering, downsample to reduce data size (e.g., to 1-2.5 kHz\n for 300 Hz max frequency).\n\n5. **Trade-offs**: There's always a trade-off between computational efficiency (smaller chunks, less memory)\n and accuracy (larger chunks, fewer artifacts). For LFP analysis, accuracy should take priority.\n\n**When processing your own data:**\n\n- If you have memory constraints, use the largest chunk size your system can handle\n- Always verify your filtering parameters on a small test segment first\n- Consider the lowest frequency component you want to preserve when setting margins\n- Save the processed LFP data to disk to avoid recomputing\n\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.2" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/doc/forhowto/plot_extract_lfps.py b/doc/forhowto/plot_extract_lfps.py deleted file mode 100644 index 4eb4393c22..0000000000 --- a/doc/forhowto/plot_extract_lfps.py +++ /dev/null @@ -1,313 +0,0 @@ -""" -Extract LFPs -============ - -Understanding filtering artifacts and chunking when extracting LFPs - -Local Field Potentials (LFPs) are low-frequency signals (<300 Hz) that reflect the summed activity of many neurons. -Extracting LFPs from high-sampling-rate recordings requires bandpass filtering, but this can introduce artifacts -when not done carefully, especially when data is processed in chunks (for memory efficiency). - -This tutorial demonstrates: - -1. How to generate simulated LFP data -2. Common pitfalls when filtering with low cutoff frequencies -3. How chunking and margins affect filtering artifacts -4. Summary - -**Key takeaway**: For LFP extraction, use large chunks (30-60s) and large margins (several seconds) to minimize -edge artifacts, even though this is less memory-efficient. -""" - -############################################################################## -# Import necessary modules - -import time -import numpy as np -import matplotlib.pyplot as plt -from pathlib import Path -import pandas as pd -import seaborn as sns - -import spikeinterface as si -import spikeinterface.extractors as se -import spikeinterface.preprocessing as spre -import spikeinterface.widgets as sw -from spikeinterface.core import generate_ground_truth_recording - -############################################################################## -# 1. Generate simulated recording with low-frequency signals -# ----------------------------------------------------------- -# -# Let's create a simulated recording and add some low-frequency sinusoids that mimic LFP activity. - -# Generate a ground truth recording with spikes -# Use a higher sampling rate (30 kHz) to simulate raw neural data -recording, sorting = generate_ground_truth_recording( - durations=[300.0], # 300 s - sampling_frequency=30000.0, - num_channels=1, - num_units=4, - seed=2305, -) - -print(recording) - -############################################################################## -# Now let's add some low-frequency sinusoidal components to simulate LFP signals - -# Add low-frequency sinusoids with different frequencies and phases per channel -rng = np.random.default_rng(42) -num_channels = recording.get_num_channels() -lfp_signals = np.zeros( - (recording.get_num_samples(), recording.get_num_channels()) -) -time_vector = recording.get_times() - -for ch in range(num_channels): - # Add multiple frequency components (theta, alpha, beta ranges) - # Theta-like: 4-8 Hz - freq_theta = 4 + rng.random() * 4 - phase_theta = rng.random() * 2 * np.pi - amp_theta = 50 + rng.random() * 50 - - # Alpha-like: 8-12 Hz - freq_alpha = 8 + rng.random() * 4 - phase_alpha = rng.random() * 2 * np.pi - amp_alpha = 30 + rng.random() * 30 - - # Beta-like: 12-30 Hz - freq_beta = 12 + rng.random() * 18 - phase_beta = rng.random() * 2 * np.pi - amp_beta = 20 + rng.random() * 20 - - lfp_signals[:, ch] = ( - amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta) - + amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha) - + amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta) - ) - -# Create a recording with the added LFP signals -recording_lfp = si.NumpyRecording( - traces_list=[lfp_signals], - sampling_frequency=recording.sampling_frequency, - channel_ids=recording.channel_ids, -) -recording_with_lfp = recording + recording_lfp - - -############################################################################## -# Let's visualize a short segment of the signal - -_ = sw.plot_traces(recording_with_lfp, time_range=[0, 3]) - -############################################################################## -# 2. Filtering with low cutoff frequencies: the problem -# ------------------------------------------------------ -# -# Now let's try to extract LFPs using a bandpass filter with a low highpass cutoff (1 Hz). -# This will demonstrate a common issue. - -# Try to filter with 1 Hz highpass -try: - recording_lfp_1hz = spre.bandpass_filter( - recording_with_lfp, freq_min=1.0, freq_max=300.0 - ) -except Exception as e: - print(f"Error message:\n{str(e)}") - -############################################################################## -# **Why does this fail?** -# -# The error occurs because by default in SpikeInterface when highpass filtering below 100 Hz. -# Filters with very low cutoff frequencies have long impulse responses, which require larger margins to avoid edge artifacts between chunks. -# -# The filter length (and required margin) scales inversely with the highpass frequency. A 1 Hz highpass -# filter requires a margin of several seconds, while a 300 Hz highpass (for spike extraction) only needs -# a few milliseconds. - -############################################################################## -# 3. Understanding chunking and margins -# -------------------------------------- -# -# SpikeInterface processes recordings in chunks to handle large datasets efficiently. Each chunk needs -# a "margin" (extra samples at the edges) to avoid edge artifacts when filtering. Let's demonstrate -# this by saving the filtered data with different chunking strategies. -# -# **This error is to inform the user that extra care should be used when dealing with LFP signals!** -# -# We can ignore this error, but let's make sure we understand what it's happening. - -# We can ignore this error, but let's see what is happening -recording_filt = spre.bandpass_filter( - recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True -) - -############################################################################## -# When retrieving traces, extra samples will be retrieved at the left and right edges. -# By default, the filter function will set a margin to 5x the sampling period associated to `freq_min`. -# So for a 1 Hz cutoff frequency, the margin will be 5 seconds! - -margin_in_s = recording_filt.margin_samples / recording_lfp.sampling_frequency -print(f"Margin: {margin_in_s} s") - -############################################################################## -# This effectively means that if we plot 1-s snippet of traces, a total of 11 s will actually be read and filtered. -# Note that the margin can be overridden with the `margin_ms` argument, but we do not recommend changing it. - -_ = sw.plot_traces(recording_filt, time_range=[20, 21]) - -############################################################################## -# A warning tells us that what we are doing is not optimized, since in order to get the requested traces -# the margin "overhead" is very large. -# -# If we ask or plot longer snippets, the warning is not displayed. - -_ = sw.plot_traces(recording_filt, time_range=[20, 80]) - -############################################################################## -# 4. Quantification and visualization of the artifacts -# ----------------------------------------------------- -# -# Let's extract the traces and visualize the differences between chunking strategies. -# We'll focus on the chunk boundaries where artifacts appear. - -margins_ms = [100, 1000, 5000] -chunk_durations = ["1s", "10s", "30s"] - -############################################################################## -# The best we can do is to save the full recording in one chunk. This will cause no artifacts and chunking effects, -# but in practice it's not possible due to the duration and number of channels of most setups. -# -# Since in this toy case we have a single channel 5-min recording, we can use this as "optimal". - -recording_optimal = recording_filt.save( - format="memory", - chunk_duration="1000s", - progress_bar=False -) - -print(recording_optimal) - -############################################################################## -# Now we can do the same with our various options: - -recordings_chunked = {} - -for margin_ms in margins_ms: - for chunk_duration in chunk_durations: - print(f"Margin ms: {margin_ms} - Chunk duration: {chunk_duration}") - t_start = time.perf_counter() - recording_chunk = spre.bandpass_filter( - recording_with_lfp, - freq_min=1.0, - freq_max=300.0, - margin_ms=margin_ms, - ignore_low_freq_error=True, - ) - recording_chunk = recording_chunk.save( - format="memory", - chunk_duration=chunk_duration, - verbose=False, - progress_bar=False - ) - t_stop = time.perf_counter() - result_dict = {"recording": recording_chunk, "time": t_stop - t_start} - recordings_chunked[(margin_ms, chunk_duration)] = result_dict - -############################################################################## -# Let's visualize the error for the "10s" chunks and different margins, centered around 30s (which is a chunk edge): - -fig, ax = plt.subplots(figsize=(10, 5)) -trace_plotted = False -start_time = 15 # seconds -end_time = 45 # seconds -start_frame = int(start_time * recording_optimal.sampling_frequency) -end_frame = int(end_time * recording_optimal.sampling_frequency) -timestamps = recording_optimal.get_times()[start_frame:end_frame] -for recording_key, recording_dict in recordings_chunked.items(): - recording_chunk = recording_dict["recording"] - margin, chunk = recording_key - # only plot "10s" chunks - if chunk != "10s": - continue - traces_opt = recording_optimal.get_traces( - start_frame=start_frame, end_frame=end_frame - ) - if not trace_plotted: - ax.plot(timestamps, traces_opt, color="grey", label="traces", alpha=0.5) - trace_plotted = True - diff = recording_optimal - recording_chunk - traces_diff = diff.get_traces(start_frame=start_frame, end_frame=end_frame) - ax.plot(timestamps, traces_diff, label=f"Margin: {margin}") - for chunk in [20, 30, 40]: # chunk boundaries at 10s intervals - ax.axvline(x=chunk, color="red", linestyle="--", alpha=0.5) - -ax.set_xlabel("Time (s)") -ax.set_ylabel("Voltage ($\\mu V$)") -_ = ax.legend() - -############################################################################## -# From the plot, we can see that there is a very small error when the margin size is large (green), -# a larger error when the margin is smaller (orange) and a large error when the margin is small (blue). -# So we need large margins (compared to the chunk size) if we want accurate filtered. -# -# The artifacts do not depend on chunk size, but for smaller chunk sizes, these artifacts will happen more often. -# In addition, the margin "overhead" will make processing slower. Let's quantify these concepts by computing the -# overall absolute error with respect to the optimal case and processing time. - -trace_plotted = False -traces_optimal = recording_optimal.get_traces() -data = {"margin": [], "chunk": [], "error": [], "time": []} -for recording_key, recording_dict in recordings_chunked.items(): - recording_chunk = recording_dict["recording"] - time = recording_dict["time"] - margin, chunk = recording_key - traces_chunk = recording_chunk.get_traces() - error = np.sum(np.abs(traces_optimal - traces_chunk)) - data["margin"].append(margin) - data["chunk"].append(chunk) - data["error"].append(error) - data["time"].append(time) - -df = pd.DataFrame(data=data) - -############################################################################## -# Now let's visualize the error and processing time for different margin and chunk size combinations - -fig, axs = plt.subplots(ncols=2, figsize=(10, 5)) -sns.barplot(data=data, x="margin", y="error", hue="chunk", ax=axs[0]) -axs[0].set_yscale("log") -sns.barplot(data=data, x="margin", y="time", hue="chunk", ax=axs[1]) -axs[0].set_title("Error VS margin x chunk size") -axs[1].set_title("Processing time VS margin x chunk size") - -sns.despine(fig) - -############################################################################## -# Summary -# ------- -# -# 1. **Low-frequency filters require special care**: Filters with low cutoff frequencies (< 10 Hz) have long -# impulse responses that require large margins to avoid edge artifacts. -# -# 2. **Chunking artifacts are real**: When processing data in chunks, insufficient margins lead to visible -# discontinuities and errors at chunk boundaries. -# -# 3. **The solution: large chunks and large margins**: For LFP extraction (1-300 Hz), use: -# - Chunk size: 30-60 seconds -# - Margin size: 5 seconds (for 1 Hz highpass) (**use defaults!**) -# - This is less memory-efficient but more accurate -# -# 4. **Downsample after filtering**: After bandpass filtering, downsample to reduce data size (e.g., to 1-2.5 kHz -# for 300 Hz max frequency). -# -# 5. **Trade-offs**: There's always a trade-off between computational efficiency (smaller chunks, less memory) -# and accuracy (larger chunks, fewer artifacts). For LFP analysis, accuracy should take priority. -# -# **When processing your own data:** -# -# - If you have memory constraints, use the largest chunk size your system can handle -# - Always verify your filtering parameters on a small test segment first -# - Consider the lowest frequency component you want to preserve when setting margins -# - Save the processed LFP data to disk to avoid recomputing diff --git a/doc/forhowto/plot_extract_lfps.py.md5 b/doc/forhowto/plot_extract_lfps.py.md5 deleted file mode 100644 index 850f803ce2..0000000000 --- a/doc/forhowto/plot_extract_lfps.py.md5 +++ /dev/null @@ -1 +0,0 @@ -fb59f45750a1df962d590b4f861b08eb diff --git a/doc/forhowto/plot_extract_lfps.rst b/doc/forhowto/plot_extract_lfps.rst deleted file mode 100644 index 419714de2d..0000000000 --- a/doc/forhowto/plot_extract_lfps.rst +++ /dev/null @@ -1,640 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "forhowto/plot_extract_lfps.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_forhowto_plot_extract_lfps.py: - - -Extract LFPs -============ - -Understanding filtering artifacts and chunking when extracting LFPs - -Local Field Potentials (LFPs) are low-frequency signals (<300 Hz) that reflect the summed activity of many neurons. -Extracting LFPs from high-sampling-rate recordings requires bandpass filtering, but this can introduce artifacts -when not done carefully, especially when data is processed in chunks (for memory efficiency). - -This tutorial demonstrates: - -1. How to generate simulated LFP data -2. Common pitfalls when filtering with low cutoff frequencies -3. How chunking and margins affect filtering artifacts -4. Summary - -**Key takeaway**: For LFP extraction, use large chunks (30-60s) and large margins (several seconds) to minimize -edge artifacts, even though this is less memory-efficient. - -.. GENERATED FROM PYTHON SOURCE LINES 23-24 - -Import necessary modules - -.. GENERATED FROM PYTHON SOURCE LINES 24-38 - -.. code-block:: Python - - - import time - import numpy as np - import matplotlib.pyplot as plt - from pathlib import Path - import pandas as pd - import seaborn as sns - - import spikeinterface as si - import spikeinterface.extractors as se - import spikeinterface.preprocessing as spre - import spikeinterface.widgets as sw - from spikeinterface.core import generate_ground_truth_recording - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 39-43 - -1. Generate simulated recording with low-frequency signals ------------------------------------------------------------ - -Let's create a simulated recording and add some low-frequency sinusoids that mimic LFP activity. - -.. GENERATED FROM PYTHON SOURCE LINES 43-56 - -.. code-block:: Python - - - # Generate a ground truth recording with spikes - # Use a higher sampling rate (30 kHz) to simulate raw neural data - recording, sorting = generate_ground_truth_recording( - durations=[300.0], # 300 s - sampling_frequency=30000.0, - num_channels=1, - num_units=4, - seed=2305, - ) - - print(recording) - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - GroundTruthRecording (InjectTemplatesRecording): 1 channels - 30.0kHz - 1 segments - 9,000,000 samples - 300.00s (5.00 minutes) - float32 dtype - 34.33 MiB - - - - -.. GENERATED FROM PYTHON SOURCE LINES 57-58 - -Now let's add some low-frequency sinusoidal components to simulate LFP signals - -.. GENERATED FROM PYTHON SOURCE LINES 58-99 - -.. code-block:: Python - - - # Add low-frequency sinusoids with different frequencies and phases per channel - rng = np.random.default_rng(42) - num_channels = recording.get_num_channels() - lfp_signals = np.zeros( - (recording.get_num_samples(), recording.get_num_channels()) - ) - time_vector = recording.get_times() - - for ch in range(num_channels): - # Add multiple frequency components (theta, alpha, beta ranges) - # Theta-like: 4-8 Hz - freq_theta = 4 + rng.random() * 4 - phase_theta = rng.random() * 2 * np.pi - amp_theta = 50 + rng.random() * 50 - - # Alpha-like: 8-12 Hz - freq_alpha = 8 + rng.random() * 4 - phase_alpha = rng.random() * 2 * np.pi - amp_alpha = 30 + rng.random() * 30 - - # Beta-like: 12-30 Hz - freq_beta = 12 + rng.random() * 18 - phase_beta = rng.random() * 2 * np.pi - amp_beta = 20 + rng.random() * 20 - - lfp_signals[:, ch] = ( - amp_theta * np.sin(2 * np.pi * freq_theta * time_vector + phase_theta) - + amp_alpha * np.sin(2 * np.pi * freq_alpha * time_vector + phase_alpha) - + amp_beta * np.sin(2 * np.pi * freq_beta * time_vector + phase_beta) - ) - - # Create a recording with the added LFP signals - recording_lfp = si.NumpyRecording( - traces_list=[lfp_signals], - sampling_frequency=recording.sampling_frequency, - channel_ids=recording.channel_ids, - ) - recording_with_lfp = recording + recording_lfp - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 100-101 - -Let's visualize a short segment of the signal - -.. GENERATED FROM PYTHON SOURCE LINES 101-104 - -.. code-block:: Python - - - _ = sw.plot_traces(recording_with_lfp, time_range=[0, 3]) - - - - -.. image-sg:: /forhowto/images/sphx_glr_plot_extract_lfps_001.png - :alt: plot extract lfps - :srcset: /forhowto/images/sphx_glr_plot_extract_lfps_001.png - :class: sphx-glr-single-img - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 105-110 - -2. Filtering with low cutoff frequencies: the problem ------------------------------------------------------- - -Now let's try to extract LFPs using a bandpass filter with a low highpass cutoff (1 Hz). -This will demonstrate a common issue. - -.. GENERATED FROM PYTHON SOURCE LINES 110-119 - -.. code-block:: Python - - - # Try to filter with 1 Hz highpass - try: - recording_lfp_1hz = spre.bandpass_filter( - recording_with_lfp, freq_min=1.0, freq_max=300.0 - ) - except Exception as e: - print(f"Error message:\n{str(e)}") - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - Error message: - The freq_min (1.0 Hz) is too low and may cause artifacts during chunk processing. You can set 'ignore_low_freq_error=True' to bypass this error, but make sure you understand the implications. It is recommended to use large chunks when processing/saving your filtered recording to minimize IO overhead.Refer to this documentation on LFP filtering and chunking artifacts for more details: https://spikeinterface.readthedocs.io/en/latest/how-to/extract_lfps.html. - - - - -.. GENERATED FROM PYTHON SOURCE LINES 120-128 - -**Why does this fail?** - -The error occurs because by default in SpikeInterface when highpass filtering below 100 Hz. -Filters with very low cutoff frequencies have long impulse responses, which require larger margins to avoid edge artifacts between chunks. - -The filter length (and required margin) scales inversely with the highpass frequency. A 1 Hz highpass -filter requires a margin of several seconds, while a 300 Hz highpass (for spike extraction) only needs -a few milliseconds. - -.. GENERATED FROM PYTHON SOURCE LINES 130-140 - -3. Understanding chunking and margins --------------------------------------- - -SpikeInterface processes recordings in chunks to handle large datasets efficiently. Each chunk needs -a "margin" (extra samples at the edges) to avoid edge artifacts when filtering. Let's demonstrate -this by saving the filtered data with different chunking strategies. - -**This error is to inform the user that extra care should be used when dealing with LFP signals!** - -We can ignore this error, but let's make sure we understand what it's happening. - -.. GENERATED FROM PYTHON SOURCE LINES 140-146 - -.. code-block:: Python - - - # We can ignore this error, but let's see what is happening - recording_filt = spre.bandpass_filter( - recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True - ) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 147-150 - -When retrieving traces, extra samples will be retrieved at the left and right edges. -By default, the filter function will set a margin to 5x the sampling period associated to `freq_min`. -So for a 1 Hz cutoff frequency, the margin will be 5 seconds! - -.. GENERATED FROM PYTHON SOURCE LINES 150-154 - -.. code-block:: Python - - - margin_in_s = recording_filt.margin_samples / recording_lfp.sampling_frequency - print(f"Margin: {margin_in_s} s") - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - Margin: 5.0 s - - - - -.. GENERATED FROM PYTHON SOURCE LINES 155-157 - -This effectively means that if we plot 1-s snippet of traces, a total of 11 s will actually be read and filtered. -Note that the margin can be overridden with the `margin_ms` argument, but we do not recommend changing it. - -.. GENERATED FROM PYTHON SOURCE LINES 157-160 - -.. code-block:: Python - - - _ = sw.plot_traces(recording_filt, time_range=[20, 21]) - - - - -.. image-sg:: /forhowto/images/sphx_glr_plot_extract_lfps_002.png - :alt: plot extract lfps - :srcset: /forhowto/images/sphx_glr_plot_extract_lfps_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:169: UserWarning: The margin size (150000 samples) is more than 20% of the chunk size 30000 samples. This may lead to performance bottlenecks when chunking. Consider increasing the chunk size to minimize margin overhead. - warnings.warn( - - - - -.. GENERATED FROM PYTHON SOURCE LINES 161-165 - -A warning tells us that what we are doing is not optimized, since in order to get the requested traces -the margin "overhead" is very large. - -If we ask or plot longer snippets, the warning is not displayed. - -.. GENERATED FROM PYTHON SOURCE LINES 165-168 - -.. code-block:: Python - - - _ = sw.plot_traces(recording_filt, time_range=[20, 80]) - - - - -.. image-sg:: /forhowto/images/sphx_glr_plot_extract_lfps_003.png - :alt: plot extract lfps - :srcset: /forhowto/images/sphx_glr_plot_extract_lfps_003.png - :class: sphx-glr-single-img - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 169-174 - -4. Quantification and visualization of the artifacts ------------------------------------------------------ - -Let's extract the traces and visualize the differences between chunking strategies. -We'll focus on the chunk boundaries where artifacts appear. - -.. GENERATED FROM PYTHON SOURCE LINES 174-178 - -.. code-block:: Python - - - margins_ms = [100, 1000, 5000] - chunk_durations = ["1s", "10s", "30s"] - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 179-183 - -The best we can do is to save the full recording in one chunk. This will cause no artifacts and chunking effects, -but in practice it's not possible due to the duration and number of channels of most setups. - -Since in this toy case we have a single channel 5-min recording, we can use this as "optimal". - -.. GENERATED FROM PYTHON SOURCE LINES 183-192 - -.. code-block:: Python - - - recording_optimal = recording_filt.save( - format="memory", - chunk_duration="1000s", - progress_bar=False - ) - - print(recording_optimal) - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - SharedMemoryRecording: 1 channels - 30.0kHz - 1 segments - 9,000,000 samples - 300.00s (5.00 minutes) - float32 dtype - 34.33 MiB - - - - -.. GENERATED FROM PYTHON SOURCE LINES 193-194 - -Now we can do the same with our various options: - -.. GENERATED FROM PYTHON SOURCE LINES 194-218 - -.. code-block:: Python - - - recordings_chunked = {} - - for margin_ms in margins_ms: - for chunk_duration in chunk_durations: - print(f"Margin ms: {margin_ms} - Chunk duration: {chunk_duration}") - t_start = time.perf_counter() - recording_chunk = spre.bandpass_filter( - recording_with_lfp, - freq_min=1.0, - freq_max=300.0, - margin_ms=margin_ms, - ignore_low_freq_error=True, - ) - recording_chunk = recording_chunk.save( - format="memory", - chunk_duration=chunk_duration, - verbose=False, - progress_bar=False - ) - t_stop = time.perf_counter() - result_dict = {"recording": recording_chunk, "time": t_stop - t_start} - recordings_chunked[(margin_ms, chunk_duration)] = result_dict - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - Margin ms: 100 - Chunk duration: 1s - /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:458: UserWarning: The provided margin_ms (100 ms) is smaller than the recommended margin for the given freq_min (1.0 Hz). This may lead to artifacts at the edges of chunks during processing. Consider increasing the margin_ms to at least 5000.0 ms. - warnings.warn( - Margin ms: 100 - Chunk duration: 10s - Margin ms: 100 - Chunk duration: 30s - Margin ms: 1000 - Chunk duration: 1s - /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:458: UserWarning: The provided margin_ms (1000 ms) is smaller than the recommended margin for the given freq_min (1.0 Hz). This may lead to artifacts at the edges of chunks during processing. Consider increasing the margin_ms to at least 5000.0 ms. - warnings.warn( - /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:169: UserWarning: The margin size (30000 samples) is more than 20% of the chunk size 30000 samples. This may lead to performance bottlenecks when chunking. Consider increasing the chunk size to minimize margin overhead. - warnings.warn( - Margin ms: 1000 - Chunk duration: 10s - Margin ms: 1000 - Chunk duration: 30s - Margin ms: 5000 - Chunk duration: 1s - Margin ms: 5000 - Chunk duration: 10s - /home/alessio/Documents/Codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/preprocessing/filter.py:169: UserWarning: The margin size (150000 samples) is more than 20% of the chunk size 300000 samples. This may lead to performance bottlenecks when chunking. Consider increasing the chunk size to minimize margin overhead. - warnings.warn( - Margin ms: 5000 - Chunk duration: 30s - - - - -.. GENERATED FROM PYTHON SOURCE LINES 219-220 - -Let's visualize the error for the "10s" chunks and different margins, centered around 30s (which is a chunk edge): - -.. GENERATED FROM PYTHON SOURCE LINES 220-250 - -.. code-block:: Python - - - fig, ax = plt.subplots(figsize=(10, 5)) - trace_plotted = False - start_time = 15 # seconds - end_time = 45 # seconds - start_frame = int(start_time * recording_optimal.sampling_frequency) - end_frame = int(end_time * recording_optimal.sampling_frequency) - timestamps = recording_optimal.get_times()[start_frame:end_frame] - for recording_key, recording_dict in recordings_chunked.items(): - recording_chunk = recording_dict["recording"] - margin, chunk = recording_key - # only plot "10s" chunks - if chunk != "10s": - continue - traces_opt = recording_optimal.get_traces( - start_frame=start_frame, end_frame=end_frame - ) - if not trace_plotted: - ax.plot(timestamps, traces_opt, color="grey", label="traces", alpha=0.5) - trace_plotted = True - diff = recording_optimal - recording_chunk - traces_diff = diff.get_traces(start_frame=start_frame, end_frame=end_frame) - ax.plot(timestamps, traces_diff, label=f"Margin: {margin}") - for chunk in [20, 30, 40]: # chunk boundaries at 10s intervals - ax.axvline(x=chunk, color="red", linestyle="--", alpha=0.5) - - ax.set_xlabel("Time (s)") - ax.set_ylabel("Voltage ($\\mu V$)") - _ = ax.legend() - - - - -.. image-sg:: /forhowto/images/sphx_glr_plot_extract_lfps_004.png - :alt: plot extract lfps - :srcset: /forhowto/images/sphx_glr_plot_extract_lfps_004.png - :class: sphx-glr-single-img - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 251-258 - -From the plot, we can see that there is a very small error when the margin size is large (green), -a larger error when the margin is smaller (orange) and a large error when the margin is small (blue). -So we need large margins (compared to the chunk size) if we want accurate filtered. - -The artifacts do not depend on chunk size, but for smaller chunk sizes, these artifacts will happen more often. -In addition, the margin "overhead" will make processing slower. Let's quantify these concepts by computing the -overall absolute error with respect to the optimal case and processing time. - -.. GENERATED FROM PYTHON SOURCE LINES 258-275 - -.. code-block:: Python - - - trace_plotted = False - traces_optimal = recording_optimal.get_traces() - data = {"margin": [], "chunk": [], "error": [], "time": []} - for recording_key, recording_dict in recordings_chunked.items(): - recording_chunk = recording_dict["recording"] - time = recording_dict["time"] - margin, chunk = recording_key - traces_chunk = recording_chunk.get_traces() - error = np.sum(np.abs(traces_optimal - traces_chunk)) - data["margin"].append(margin) - data["chunk"].append(chunk) - data["error"].append(error) - data["time"].append(time) - - df = pd.DataFrame(data=data) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 276-277 - -Now let's visualize the error and processing time for different margin and chunk size combinations - -.. GENERATED FROM PYTHON SOURCE LINES 277-287 - -.. code-block:: Python - - - fig, axs = plt.subplots(ncols=2, figsize=(10, 5)) - sns.barplot(data=data, x="margin", y="error", hue="chunk", ax=axs[0]) - axs[0].set_yscale("log") - sns.barplot(data=data, x="margin", y="time", hue="chunk", ax=axs[1]) - axs[0].set_title("Error VS margin x chunk size") - axs[1].set_title("Processing time VS margin x chunk size") - - sns.despine(fig) - - - - -.. image-sg:: /forhowto/images/sphx_glr_plot_extract_lfps_005.png - :alt: Error VS margin x chunk size, Processing time VS margin x chunk size - :srcset: /forhowto/images/sphx_glr_plot_extract_lfps_005.png - :class: sphx-glr-single-img - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 288-314 - -Summary -------- - -1. **Low-frequency filters require special care**: Filters with low cutoff frequencies (< 10 Hz) have long - impulse responses that require large margins to avoid edge artifacts. - -2. **Chunking artifacts are real**: When processing data in chunks, insufficient margins lead to visible - discontinuities and errors at chunk boundaries. - -3. **The solution: large chunks and large margins**: For LFP extraction (1-300 Hz), use: - - Chunk size: 30-60 seconds - - Margin size: 5 seconds (for 1 Hz highpass) (**use defaults!**) - - This is less memory-efficient but more accurate - -4. **Downsample after filtering**: After bandpass filtering, downsample to reduce data size (e.g., to 1-2.5 kHz - for 300 Hz max frequency). - -5. **Trade-offs**: There's always a trade-off between computational efficiency (smaller chunks, less memory) - and accuracy (larger chunks, fewer artifacts). For LFP analysis, accuracy should take priority. - -**When processing your own data:** - -- If you have memory constraints, use the largest chunk size your system can handle -- Always verify your filtering parameters on a small test segment first -- Consider the lowest frequency component you want to preserve when setting margins -- Save the processed LFP data to disk to avoid recomputing - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** (0 minutes 10.342 seconds) - - -.. _sphx_glr_download_forhowto_plot_extract_lfps.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_extract_lfps.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_extract_lfps.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: plot_extract_lfps.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/forhowto/plot_extract_lfps.zip b/doc/forhowto/plot_extract_lfps.zip deleted file mode 100644 index 091d42cb5cd772b49a3a3fb9b31a617b46e30000..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 30149 zcmd^I&u=5ib>7%X4!i|0l53e`z@#kE(r6}F2quE@dMCTsADCpv-h(J`TVzpc4B1V0 zH%H@{0HXy-m9vvrbzu7QTD(xyd!p1SG{`g)%#WTQLn!E@!$UB zBmVsFcmHwxpa1pWfBeV8k3Qo6`E)+btD(KDieyv`r;~Zvn=jk#cI#7pWWId)s%-V6 zpH}O)+1M6km1N^IJ2R7XTG@iXB}J7^FidICWJc$U>||<3 z6+T!~E@m@3HlQGVn^sGcPs}XImL{`{BG1a6Uy~=oOp1JF&eOB=y)v22r+B_sB$dUW zqrBkaO8ol!MOxU>oMKJ$q%7<8?wZpDCr(Qop%8c{Oq8F%L zOsC6TW6QZ6VZqa-vS*xBiNQegA|KhZ#46HEIS7_a^1{sQEH9SEPA2InB}2NsR_itA zt`=2ZU??+2m#l;!D_b75S_eJzS$<)v+??6W%DT#QwwR)MOc@x?*?QVDpX4*VXy$1( z0YRk_R7>?DtVblKw4U|MH;`3Q zEL*LeoiFUtRLL8gTqMh#og?!S%O!nYW%6v-EK2AD1_l$UR$$8E{@y?DQ&TXdw)lxV zrG1O_PE83hW@GdL4`ykW&eHE~%Z@QfAO(_m3+=4V^TpY@$`UM{LIjTEd!FM}uf=WA zdh(z@ttaO5**q^QC@BRGmY5l|2o$thskT(<%=+K6#caMbNolgVe>6)f?%`>Aik-{f z*gT(BEz$0IQk|p8xUpX0qknM@)}bqVkNvaKCZ~Ck@nMu8ml$QL5vJC5QkN(~DbfG8*sX~eR$CZv zT8)?>>|66jtal!0MazX4X&Nyar9k>FXogdPhV+9K+aT9Y%iw3cz$_`uVE-6yqPKst zYs?cvk5NjyCuZoa*s_mC_S}_r?62YIJjpUUE&B)cvqhFxW&fEzD6Jj$pC0Z%-)(hU ztvP&O)d?iy$%9%5%J^$o->Jy?1~Gzxqa0QvgFA&tBNo%3Iu}ZbzJNNc`d6p0aaL_pcRS3uSBS#NA7$zoa!@x1fwX}1+~6_O0R>S0?C zz0<~qQyL;`hT}X;{bodrN3I1i32)1F6R*SxN zbWZP!wVc^bOkMX#bVRv_ZC0i8sSW&D#;ork9;uRDlT5)b1Pb_|1D0Jk#%p%io5HRh znP+<+BchQ8>g7;g293|mFAR9-*xKou9rH|ji3~!wl`c=w4SbuY(iMSC==gm9>VVJp zTaKa^q}5UM@!rAH21&9Iko57@5n_^jw;M_F9bpcxtno0A^y`{O2T%9VH|CNswQoS^ zl@Sg;j@ab--AIz__jC(avmS;LQzh@64gywpPJVqfilfHK^PE9B-dN1yVpJC!B8J$aPItNTEd!S14s*7~m$f+O>_*Q00X<06; z1V*oQX3cSQYz`u!4~K4nG<6yc*KCk5Tr8w0|YGY zQ*y}=rnfH^uiLVhBRj9mr}7UXAx6zsY0yYz(*Cq4B%?&Sab}MO*$+q>JGT3-y$#LS z+4=T-iToEKgQUunB%S`t&W?x>KG0YpCLfI!$QVxTC}FC1iZ?WdpeO!@8OG-!`;eqM z2(QjBPc5_51E~&BimH&vXPLkvBV2CCV$FH-mT@tH7-UI{DdtDQk01(R&Mvay6!Cy7 z18Am+Y~1Cz(7NO;!e`UWD(UYpEb>ZCRzZv?6=DV1BZ%d)GtkswVD3itd2`p4sEdGE zX$F!@%K`!E=f{9S$&%7{Ag?r-XaN{Cta@5j%tYpTrI)pHZM97ieYRy67dB3fo zN?ns@peIB4%P2oF6MKP_VG3KVnKt*tgZ^L^lo*=zrgg;z^t+uU5lO7*ldrk3*c0kL zb@>G{MnW(Ko3zZp45ef`8Y%-*$l=ojS|X%H{AsJ6v`t5BlcqwDEix_2lS-r2iM2{3 zdLGof=Q6#h%7azQmcni+lee^Y997?d^Oc&CC{G>C2j*#==nO?1)y|zAF?zZiSyjrR zfe40YGA8z9fnPwkE`*gVI;RDScc+%snX#*5S-36`BGX1W$4}jk|CUuSSkSYK8M{y~ zQ)(?ubDSA!L8#@S{Q^UWS|cXF{3)~D^JG4^8Riiqe48dBme<>`#`RLjl#Rm5b>8A z6fflPKShc#og7?C4SquyoLgO~hg>V#Fv7~&CYzPtg81<+$Lje@t z0S5%MiL9=xH5Z>;igJrgz}|?dZL;&+CgV2C{2Gn0o~_pRbCGXSzQM36W`-jyo~fh} zhj1)UHb#&`g|wVclcgQskGmi5Z-u)k?*H2&!Ep{wDx?-CTIaJGed02iP=*JuZs)&O zdWk#Nw8=;R>(BfU8==rv?n1CU zB*T80V^dTq_YOTc)pwqkWr{oyMO)0BtAJenR%D0T+bQ+{l*Og$kOYqOa?lTtv%pcM zhI4*PN;pyFD8wN^;}RvXXH7gi!!&LX^L!6JyIv!LU}Xjq4;ca(Q(LxtGHpYcwKs7O zuX-4=Pct`$Oi9&m>v>(<*B`*XAn!h;yiYl6jjG7ckZzR2)1>IX#0jT_2P;M8p__Rd zfdljCS{!;CtJ>XcLhiMy{eFU) zc8V@%$&fY4&5|p5y>fBN0JQAjafx84-#MT|>*8RA@y?KqjDGq? 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"module_short": "spikeinterface", - "name": "NumpySorting" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "NumpySorting" - } - ], - "generate_ground_truth_recording": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "generate_ground_truth_recording" - } - ], - "generate_tetrode": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface", - "module_short": "probeinterface", - "name": "generate_tetrode" - } - ], - "grouped_recordings": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "dict" - } - ], - "plot_probe_map": [ - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets.probe_map", - "module_short": "spikeinterface.widgets", - "name": "ProbeMapWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets", - "module_short": "spikeinterface.widgets", - "name": "ProbeMapWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "ProbeMapWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets.base", - "module_short": "spikeinterface.widgets.base", - "name": "BaseWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets", - "module_short": "spikeinterface.widgets", - "name": "BaseWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseWidget" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.widgets", - "module_short": "spikeinterface.widgets", - "name": "plot_probe_map" - } - ], - "plot_traces": [ - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets.traces", - "module_short": "spikeinterface.widgets", - "name": "TracesWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets", - "module_short": "spikeinterface.widgets", - "name": "TracesWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "TracesWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets.base", - "module_short": "spikeinterface.widgets.base", - "name": "BaseWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface.widgets", - "module_short": "spikeinterface.widgets", - "name": "BaseWidget" - }, - { - "is_class": true, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseWidget" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.widgets", - "module_short": "spikeinterface.widgets", - "name": "plot_traces" - } - ], - "preprocessed_recording": [ - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing.filter", - "module_short": "spikeinterface.preprocessing.filter", - "name": "BandpassFilterRecording" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing", - "module_short": "spikeinterface.preprocessing", - "name": "BandpassFilterRecording" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BandpassFilterRecording" - } - ], - "recording": [ - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.generate", - "module_short": "spikeinterface", - "name": "InjectTemplatesRecording" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "InjectTemplatesRecording" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "InjectTemplatesRecording" - } - ], - "recording.set_probegroup": [ - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.generate", - "module_short": "spikeinterface", - "name": "InjectTemplatesRecording.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "InjectTemplatesRecording.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "InjectTemplatesRecording.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.baserecording", - "module_short": "spikeinterface", - "name": "BaseRecording.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "BaseRecording.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseRecording.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.baserecordingsnippets", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.base", - "module_short": "spikeinterface.core.base", - "name": "BaseExtractor.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface.core", - "name": "BaseExtractor.set_probegroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseExtractor.set_probegroup" - } - ], - "recording_good_channels": [ - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing.detect_bad_channels", - "module_short": "spikeinterface.preprocessing.detect_bad_channels", - "name": "DetectAndRemoveBadChannelsRecording" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing", - "module_short": "spikeinterface.preprocessing", - "name": "DetectAndRemoveBadChannelsRecording" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "DetectAndRemoveBadChannelsRecording" - } - ], - "recording_good_channels.get_property": [ - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing.detect_bad_channels", - "module_short": "spikeinterface.preprocessing.detect_bad_channels", - "name": "DetectAndRemoveBadChannelsRecording.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing", - "module_short": "spikeinterface.preprocessing", - "name": "DetectAndRemoveBadChannelsRecording.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "DetectAndRemoveBadChannelsRecording.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.channelslice", - "module_short": "spikeinterface", - "name": "ChannelSliceRecording.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "ChannelSliceRecording.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "ChannelSliceRecording.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.baserecording", - "module_short": "spikeinterface", - "name": "BaseRecording.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "BaseRecording.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseRecording.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.baserecordingsnippets", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.base", - "module_short": "spikeinterface.core.base", - "name": "BaseExtractor.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface.core", - "name": "BaseExtractor.get_property" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseExtractor.get_property" - } - ], - "recording_good_channels.split_by": [ - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing.detect_bad_channels", - "module_short": "spikeinterface.preprocessing.detect_bad_channels", - "name": "DetectAndRemoveBadChannelsRecording.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing", - "module_short": "spikeinterface.preprocessing", - "name": "DetectAndRemoveBadChannelsRecording.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "DetectAndRemoveBadChannelsRecording.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.channelslice", - "module_short": "spikeinterface", - "name": "ChannelSliceRecording.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "ChannelSliceRecording.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "ChannelSliceRecording.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.baserecording", - "module_short": "spikeinterface", - "name": "BaseRecording.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "BaseRecording.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseRecording.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.baserecordingsnippets", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseRecordingSnippets.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.base", - "module_short": "spikeinterface.core.base", - "name": "BaseExtractor.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface.core", - "name": "BaseExtractor.split_by" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "BaseExtractor.split_by" - } - ], - "recording_with_probe": [ - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core.generate", - "module_short": "spikeinterface", - "name": "InjectTemplatesRecording" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.core", - "module_short": "spikeinterface", - "name": "InjectTemplatesRecording" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface", - "module_short": "spikeinterface", - "name": "InjectTemplatesRecording" - } - ], - "spre.bandpass_filter": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing", - "module_short": "spikeinterface.preprocessing", - "name": "bandpass_filter" - } - ], - "spre.detect_and_remove_bad_channels": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "spikeinterface.preprocessing", - "module_short": "spikeinterface.preprocessing", - "name": "detect_and_remove_bad_channels" - } - ], - "tetrode_1": [ - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface.probe", - "module_short": "probeinterface", - "name": "Probe" - }, - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface", - "module_short": "probeinterface", - "name": "Probe" - } - ], - "tetrode_1.create_auto_shape": [ - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface.probe", - "module_short": "probeinterface", - "name": "Probe.create_auto_shape" - }, - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface", - "module_short": "probeinterface", - "name": "Probe.create_auto_shape" - } - ], - "tetrode_2": [ - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface.probe", - "module_short": "probeinterface", - "name": "Probe" - }, - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface", - "module_short": "probeinterface", - "name": "Probe" - } - ], - "tetrode_2.create_auto_shape": [ - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface.probe", - "module_short": "probeinterface", - "name": "Probe.create_auto_shape" - }, - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface", - "module_short": "probeinterface", - "name": "Probe.create_auto_shape" - } - ], - "tetrode_2.move": [ - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface.probe", - "module_short": "probeinterface", - "name": "Probe.move" - }, - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface", - "module_short": "probeinterface", - "name": "Probe.move" - } - ], - "tetrode_group": [ - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface.probegroup", - "module_short": "probeinterface", - "name": "ProbeGroup" - }, - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface", - "module_short": "probeinterface", - "name": "ProbeGroup" - } - ], - "tetrode_group.add_probe": [ - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface.probegroup", - "module_short": "probeinterface", - "name": "ProbeGroup.add_probe" - }, - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface", - "module_short": "probeinterface", - "name": "ProbeGroup.add_probe" - } - ], - "tetrode_group.set_global_device_channel_indices": [ - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface.probegroup", - "module_short": "probeinterface", - "name": "ProbeGroup.set_global_device_channel_indices" - }, - { - "is_class": false, - "is_explicit": false, - "module": "probeinterface", - "module_short": "probeinterface", - "name": "ProbeGroup.set_global_device_channel_indices" - } - ] -} diff --git a/doc/forhowto/plot_working_with_tetrodes.ipynb b/doc/forhowto/plot_working_with_tetrodes.ipynb deleted file mode 100644 index 5e27d20069..0000000000 --- a/doc/forhowto/plot_working_with_tetrodes.ipynb +++ /dev/null @@ -1,194 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Work with tetrodes\n\nTetrodes are a common recording method for electrophysiological data. It is also common\nto record from several tetrodes at the same time. In this 'how to' we'll see how to\nwork with data from two tetrodes, each with four channels.\n\nWe'll start by importing some functions we'll use in this How To guide\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import spikeinterface.preprocessing as spre\nfrom spikeinterface.widgets import plot_traces, plot_probe_map\nfrom spikeinterface import generate_ground_truth_recording\n\nfrom probeinterface import generate_tetrode, ProbeGroup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In practice, you would read in your raw data from a file. Instead, we will generate a\nrecording with eight channels. We can also set a duration, number of units and\nsampling frequency.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "recording, _ = generate_ground_truth_recording(\n durations = [60], # make the recording 60s long\n sampling_frequency=30_000,\n num_channels=8,\n num_units=10,\n)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to define the probe. This will tell the recording which channels came from\nwhich tetrode. To do this, we will use the :code:`generate_tetrode` function from :code:`ProbeInterface`\nto generate two 4-channel probes (representing one tetrode each). In our case, since we\ndon't know the relative distances between the tetrodes, we will move the second\ntetrode away from the first by 100 microns. This is just so we can visualize the\nresults more easily. Eventually, we will sort each tetrode separately, so their\nrelative distance won't affect the results.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Technically, we will add each tetrode to a :code:`ProbeGroup`. Read more in the ProbeInterface\n# docs.\n\n# Create each individual tetrode\ntetrode_1 = generate_tetrode()\ntetrode_1.create_auto_shape()\n\ntetrode_2 = generate_tetrode()\ntetrode_2.move([100, 0])\ntetrode_2.create_auto_shape()\n\n# Add the two tetrodes to a ProbeGroup\ntetrode_group = ProbeGroup()\ntetrode_group.add_probe(tetrode_1)\ntetrode_group.add_probe(tetrode_2)\n\n# Now we need to \"wire\" our tetrodes to ensure that each contact\n# can be associated with the correct channel when we attach it\n# to the recording. In this example we are just using `range`\n# but see ProbeInterface for more tutorials on wiring\ntetrode_group.set_global_device_channel_indices(range(8))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now attach the :code:`tetrode_group` to our recording. To check if this worked, we'll\nplot the probe map\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "recording_with_probe = recording.set_probegroup(tetrode_group)\nplot_probe_map(recording_with_probe)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks good! Now that the recording is aware of the probe geometry, we can\nbegin a standard spike sorting pipeline. First, we can apply preprocessing.\nNote that we apply this preprocessing on the entire bundle of tetrodes.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "preprocessed_recording = spre.bandpass_filter(recording_with_probe)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "WARNING: a very common preprocessing step is to apply a common median\nreference. This subtracts the median signal from all channels to help\nremove noise. However, for a tetrode, a spike is often seen on all\nchannels. So removing the median can remove the entire spike!\nThis is still a danger if you have two tetrodes in a bundle, which\nmight pick up the same spike, but becomes less dangerous\nas the number of tetrodes in your bundle increases.\n\nTetrodes often have dead channels, so it is advised to try and detect\nand remove these. For tetrodes, we should use a detection method which\ndoesn't depend on the channel locations such as std or mad:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "recording_good_channels = spre.detect_and_remove_bad_channels(\n preprocessed_recording,\n method = \"std\",\n)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It can be a good idea to sort your tetrode data separately for each tetrode.\nWhen we use :code:`set_probegroup`, the channels are automatically\nlabelled by which probe in the probe group they belong to. We can access\nthis labeling using the \"group\" property.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(recording_good_channels.get_property(\"group\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then use this information to split the recording by the group property:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "grouped_recordings = recording_good_channels.split_by('group')\nprint(grouped_recordings)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we've got preprocess, clean data. Let's take a look at a\nsnippet of data from the first group:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plot_traces(grouped_recordings[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Beautiful! We are now ready to sort. To read more about sorting by group, see\n`sorting-by-channel-group`. Note that many modern sorters are designed\nto sort data from high-density probes and will fail for tetrodes. Please read\neach spike sorter's documentation to find out if it is appropriate for tetrodes.\n\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.2" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/doc/forhowto/plot_working_with_tetrodes.py b/doc/forhowto/plot_working_with_tetrodes.py deleted file mode 100644 index 0c652a5186..0000000000 --- a/doc/forhowto/plot_working_with_tetrodes.py +++ /dev/null @@ -1,117 +0,0 @@ -""" -Work with tetrodes -================== - -Tetrodes are a common recording method for electrophysiological data. It is also common -to record from several tetrodes at the same time. In this 'how to' we'll see how to -work with data from two tetrodes, each with four channels. - -We'll start by importing some functions we'll use in this How To guide -""" - -import spikeinterface.preprocessing as spre -from spikeinterface.widgets import plot_traces, plot_probe_map -from spikeinterface import generate_ground_truth_recording - -from probeinterface import generate_tetrode, ProbeGroup - -############################################################################## -# In practice, you would read in your raw data from a file. Instead, we will generate a -# recording with eight channels. We can also set a duration, number of units and -# sampling frequency. - -recording, _ = generate_ground_truth_recording( - durations = [60], # make the recording 60s long - sampling_frequency=30_000, - num_channels=8, - num_units=10, -) - -############################################################################## -# We now need to define the probe. This will tell the recording which channels came from -# which tetrode. To do this, we will use the :code:`generate_tetrode` function from :code:`ProbeInterface` -# to generate two 4-channel probes (representing one tetrode each). In our case, since we -# don't know the relative distances between the tetrodes, we will move the second -# tetrode away from the first by 100 microns. This is just so we can visualize the -# results more easily. Eventually, we will sort each tetrode separately, so their -# relative distance won't affect the results. - -# Technically, we will add each tetrode to a :code:`ProbeGroup`. Read more in the ProbeInterface -# docs. - -# Create each individual tetrode -tetrode_1 = generate_tetrode() -tetrode_1.create_auto_shape() - -tetrode_2 = generate_tetrode() -tetrode_2.move([100, 0]) -tetrode_2.create_auto_shape() - -# Add the two tetrodes to a ProbeGroup -tetrode_group = ProbeGroup() -tetrode_group.add_probe(tetrode_1) -tetrode_group.add_probe(tetrode_2) - -# Now we need to "wire" our tetrodes to ensure that each contact -# can be associated with the correct channel when we attach it -# to the recording. In this example we are just using `range` -# but see ProbeInterface for more tutorials on wiring -tetrode_group.set_global_device_channel_indices(range(8)) - -############################################################################## -# We can now attach the :code:`tetrode_group` to our recording. To check if this worked, we'll -# plot the probe map - -recording_with_probe = recording.set_probegroup(tetrode_group) -plot_probe_map(recording_with_probe) - -############################################################################## -# Looks good! Now that the recording is aware of the probe geometry, we can -# begin a standard spike sorting pipeline. First, we can apply preprocessing. -# Note that we apply this preprocessing on the entire bundle of tetrodes. - -preprocessed_recording = spre.bandpass_filter(recording_with_probe) - -############################################################################## -# WARNING: a very common preprocessing step is to apply a common median -# reference. This subtracts the median signal from all channels to help -# remove noise. However, for a tetrode, a spike is often seen on all -# channels. So removing the median can remove the entire spike! -# This is still a danger if you have two tetrodes in a bundle, which -# might pick up the same spike, but becomes less dangerous -# as the number of tetrodes in your bundle increases. -# -# Tetrodes often have dead channels, so it is advised to try and detect -# and remove these. For tetrodes, we should use a detection method which -# doesn't depend on the channel locations such as std or mad: - -recording_good_channels = spre.detect_and_remove_bad_channels( - preprocessed_recording, - method = "std", -) - -############################################################################## -# It can be a good idea to sort your tetrode data separately for each tetrode. -# When we use :code:`set_probegroup`, the channels are automatically -# labelled by which probe in the probe group they belong to. We can access -# this labeling using the "group" property. - -print(recording_good_channels.get_property("group")) - -############################################################################## -# We can then use this information to split the recording by the group property: - -grouped_recordings = recording_good_channels.split_by('group') -print(grouped_recordings) - -############################################################################## -# Now that we've got preprocess, clean data. Let's take a look at a -# snippet of data from the first group: - -plot_traces(grouped_recordings[0]) - -############################################################################## -# Beautiful! We are now ready to sort. To read more about sorting by group, see -# :ref:`sorting-by-channel-group`. Note that many modern sorters are designed -# to sort data from high-density probes and will fail for tetrodes. Please read -# each spike sorter's documentation to find out if it is appropriate for tetrodes. diff --git a/doc/forhowto/plot_working_with_tetrodes.py.md5 b/doc/forhowto/plot_working_with_tetrodes.py.md5 deleted file mode 100644 index b6de93f1f2..0000000000 --- a/doc/forhowto/plot_working_with_tetrodes.py.md5 +++ /dev/null @@ -1 +0,0 @@ -f828444cacd54f1e64ce26991cd1f9d6 diff --git a/doc/forhowto/plot_working_with_tetrodes.rst b/doc/forhowto/plot_working_with_tetrodes.rst deleted file mode 100644 index 57ad57b03f..0000000000 --- a/doc/forhowto/plot_working_with_tetrodes.rst +++ /dev/null @@ -1,319 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "forhowto/plot_working_with_tetrodes.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_forhowto_plot_working_with_tetrodes.py: - - -Work with tetrodes -================== - -Tetrodes are a common recording method for electrophysiological data. It is also common -to record from several tetrodes at the same time. In this 'how to' we'll see how to -work with data from two tetrodes, each with four channels. - -We'll start by importing some functions we'll use in this How To guide - -.. GENERATED FROM PYTHON SOURCE LINES 11-18 - -.. code-block:: Python - - - import spikeinterface.preprocessing as spre - from spikeinterface.widgets import plot_traces, plot_probe_map - from spikeinterface import generate_ground_truth_recording - - from probeinterface import generate_tetrode, ProbeGroup - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 19-22 - -In practice, you would read in your raw data from a file. Instead, we will generate a -recording with eight channels. We can also set a duration, number of units and -sampling frequency. - -.. GENERATED FROM PYTHON SOURCE LINES 22-30 - -.. code-block:: Python - - - recording, _ = generate_ground_truth_recording( - durations = [60], # make the recording 60s long - sampling_frequency=30_000, - num_channels=8, - num_units=10, - ) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 31-38 - -We now need to define the probe. This will tell the recording which channels came from -which tetrode. To do this, we will use the :code:`generate_tetrode` function from :code:`ProbeInterface` -to generate two 4-channel probes (representing one tetrode each). In our case, since we -don't know the relative distances between the tetrodes, we will move the second -tetrode away from the first by 100 microns. This is just so we can visualize the -results more easily. Eventually, we will sort each tetrode separately, so their -relative distance won't affect the results. - -.. GENERATED FROM PYTHON SOURCE LINES 38-61 - -.. code-block:: Python - - - # Technically, we will add each tetrode to a :code:`ProbeGroup`. Read more in the ProbeInterface - # docs. - - # Create each individual tetrode - tetrode_1 = generate_tetrode() - tetrode_1.create_auto_shape() - - tetrode_2 = generate_tetrode() - tetrode_2.move([100, 0]) - tetrode_2.create_auto_shape() - - # Add the two tetrodes to a ProbeGroup - tetrode_group = ProbeGroup() - tetrode_group.add_probe(tetrode_1) - tetrode_group.add_probe(tetrode_2) - - # Now we need to "wire" our tetrodes to ensure that each contact - # can be associated with the correct channel when we attach it - # to the recording. In this example we are just using `range` - # but see ProbeInterface for more tutorials on wiring - tetrode_group.set_global_device_channel_indices(range(8)) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 62-64 - -We can now attach the :code:`tetrode_group` to our recording. To check if this worked, we'll -plot the probe map - -.. GENERATED FROM PYTHON SOURCE LINES 64-68 - -.. code-block:: Python - - - recording_with_probe = recording.set_probegroup(tetrode_group) - plot_probe_map(recording_with_probe) - - - - -.. image-sg:: /forhowto/images/sphx_glr_plot_working_with_tetrodes_001.png - :alt: plot working with tetrodes - :srcset: /forhowto/images/sphx_glr_plot_working_with_tetrodes_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 69-72 - -Looks good! Now that the recording is aware of the probe geometry, we can -begin a standard spike sorting pipeline. First, we can apply preprocessing. -Note that we apply this preprocessing on the entire bundle of tetrodes. - -.. GENERATED FROM PYTHON SOURCE LINES 72-75 - -.. code-block:: Python - - - preprocessed_recording = spre.bandpass_filter(recording_with_probe) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 76-87 - -WARNING: a very common preprocessing step is to apply a common median -reference. This subtracts the median signal from all channels to help -remove noise. However, for a tetrode, a spike is often seen on all -channels. So removing the median can remove the entire spike! -This is still a danger if you have two tetrodes in a bundle, which -might pick up the same spike, but becomes less dangerous -as the number of tetrodes in your bundle increases. - -Tetrodes often have dead channels, so it is advised to try and detect -and remove these. For tetrodes, we should use a detection method which -doesn't depend on the channel locations such as std or mad: - -.. GENERATED FROM PYTHON SOURCE LINES 87-93 - -.. code-block:: Python - - - recording_good_channels = spre.detect_and_remove_bad_channels( - preprocessed_recording, - method = "std", - ) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 94-98 - -It can be a good idea to sort your tetrode data separately for each tetrode. -When we use :code:`set_probegroup`, the channels are automatically -labelled by which probe in the probe group they belong to. We can access -this labeling using the "group" property. - -.. GENERATED FROM PYTHON SOURCE LINES 98-101 - -.. code-block:: Python - - - print(recording_good_channels.get_property("group")) - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - [0 0 0 0 1 1 1 1] - - - - -.. GENERATED FROM PYTHON SOURCE LINES 102-103 - -We can then use this information to split the recording by the group property: - -.. GENERATED FROM PYTHON SOURCE LINES 103-107 - -.. code-block:: Python - - - grouped_recordings = recording_good_channels.split_by('group') - print(grouped_recordings) - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - {0: GroundTruthRecording (ChannelSliceRecording): 4 channels - 30.0kHz - 1 segments - 1,800,000 samples - 60.00s (1.00 minutes) - float32 dtype - 27.47 MiB, 1: GroundTruthRecording (ChannelSliceRecording): 4 channels - 30.0kHz - 1 segments - 1,800,000 samples - 60.00s (1.00 minutes) - float32 dtype - 27.47 MiB} - - - - -.. GENERATED FROM PYTHON SOURCE LINES 108-110 - -Now that we've got preprocess, clean data. Let's take a look at a -snippet of data from the first group: - -.. GENERATED FROM PYTHON SOURCE LINES 110-113 - -.. code-block:: Python - - - plot_traces(grouped_recordings[0]) - - - - -.. image-sg:: /forhowto/images/sphx_glr_plot_working_with_tetrodes_002.png - :alt: plot working with tetrodes - :srcset: /forhowto/images/sphx_glr_plot_working_with_tetrodes_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 114-118 - -Beautiful! We are now ready to sort. To read more about sorting by group, see -:ref:`sorting-by-channel-group`. Note that many modern sorters are designed -to sort data from high-density probes and will fail for tetrodes. Please read -each spike sorter's documentation to find out if it is appropriate for tetrodes. - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** (0 minutes 0.644 seconds) - - -.. _sphx_glr_download_forhowto_plot_working_with_tetrodes.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_working_with_tetrodes.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_working_with_tetrodes.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: plot_working_with_tetrodes.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/forhowto/plot_working_with_tetrodes.zip b/doc/forhowto/plot_working_with_tetrodes.zip deleted file mode 100644 index 12c4fb6c23042a68f0bf7972533f414208db03ec..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 13192 zcmdU0Pj4H?6?c=iD0r<;1qw{4gCu}vWhn>(0Xn2blNt^TAr;_**H{gAhvY`fneEQ3 zXg0_p$9{yOhkl8CnfB5z(PIz&y*E3%E0MBAkrE;rhQOVfH#6_=@4dG?efjavpFW|_ zuj9YHIs5m2ev{%W{r@eV6}fTo+>{rYp2YLaO=G8AX;Ni}#iHNu_g-U6(0~X7y_2O+ zuXk3j60%f6ip0!jMvGD4cjqVdYE-m(3JtXgmRx-Km)iXLiM0?dCaFD&e2> z=(_$&w5uoV=B6P!5K1OfKVxjFQY2HUwaV?V*L&@WIaxX}T8L~`n9|V&t-%S#l}=n{ zw5>O-tP)v$o|i4VF3;PfAniGRO?T*GeQ(d$qL6LA}!o9D|$(LRYhqDuo$~N@tLT z)F}u;9gCdAH!js*Dy5SJRHxZxAYyUSoZtq&@AU*eVIzwLZ$3MCI}kf!CNH4tQ2K_T z`0T)n+~^6f37f~w<|ju7@xj5tfQND7I4JbV=j}5-|H*UA+P??UfrlCfpp{Btlpf0Jy)KnVGwUQ~Sus*>t`xbFhG^%8GlR)AWZ`gG z1H;C0E*JhFKmSL{W7za%(K5TWgHSWt8%FL z%)rGfX|sGW6o0(LZ80rhwEVVkiQa{VQ&?3<@-Z|E8{jBe$rP{M69!F;kmE5zM9m7` z6y~!d&Qvnh6fL@&$uzyX7o;SEs9SGzJ`~@Oo8ZHK*xJvO9(o#1?EkoAPinu ze;njlW`HRI7zbB_r*EX{`GPPJPjWMod7P?C#H-+PV^Sv=^d4`v_xb)kIa}f%xs#eH z#Ejan1zGYz&xunUHUm;)7n)2}av`!Ym`Gk3cu8^W1dsxI2xu^SSve4o5bGOGdl2bI zPC5c0j__07x6vs6w%=py!ovht;74oR*r9eGjQZM`3o9nZq@S?LvL;?p9LA$_0<{S7 zG~#bIQGk1{^iB`FCZ$mm_!TM0W2drAJ+6bh^Z2mH3I#|2Eb=8eVfL0JBv};s0#rMo zZwP9y4EzrthZL3Puq>`jB=w^+QNY0oM}Tk8Y&vMb@V#EUK&36JgW(gb9FB0-0!9`C zCINsx7~@}m@y)9juf944zX0eLAzx?@%aGcsg7{A^j5*Nc6*HA)%=A)?RSA3rwlaXQ z)rb%Q(n~a?@e z5}qtQ?z%E^48d^|0%w!)o5naR%!#xn9?5o=hwZH%ca6?t?K%@a5q<31zaO-XJeqWk z0w{Yoft(Y1Y}gz5JgMfY&y`wy<}-APqxA%GMz8A#OI)v`$W?cKK48TO;`T)V1j!lr z%y$tTmA{`=qrl7==_v+R68If^Fjq{Rchn;5{K=_h1^lrezb zP&NUt=wf`Y=33BEe$dq|QqMMOUcDs~jz^2VU0$|}aLZg-CUPG!mY`rA$adkuCcqA@ zc??97D`=`O|9q|7E?f}hw-WiRxuEh6CFoXXMS;v4p{-N00b_hZ!xsTpIu($;<;%Sx z5WW4P#P1d0eKxN0Pe@M*DG=_0drToaZ*h#N=!s81gn z*Q3uyi;!eK<5|dKT4*qndI6HDDiMl6PL(J?-~g!JA>=8lLM+hu6Ni_ooX*3zRJ^U%|h|pDg{esL4 zm>H<%3i;k?Q=WW^^5j4Md-SXSp)N`P#rw;XS+UTg?@-d{C-5t_e=Oc`eS;p+habmo zQ78=cXR^FV&0NFb*2D0_lDLA=@4j!IT7cn%R(N7eDAI6&P0cWbqew^kECfE{dV@|H zI}BloZoE#_Gtv;7r{Yn|Hp-rnzI)x1?z)}IXQcbJ0N(Q7n)9oLM)jIjVCtPpU};9j z2?~`CGSXF^H@qOS0nK*|rh=d|i2Ib*;zUlI+V?==-ZnbF8?JvUH@kT$RR2eYh0cDoT^hZ?z^(1aDoEe$>w3zN_hknScxJ zkht`rf_kGFH&oO+;_XZ%`Ky*sj^TJKMPo5Ffah;jP0%KEj@m8JC$ z&pF)p=b@AeWx?RHA}+5-x3900L_&SwE(La44}?mYX%ke|sIb%WJCxY3^Jk43`@_W& zVk^ayt=HWnyVG_t7Hy>`LI`FHEfFHKX-6c|p&opvh6t)FTqND3 z9}?+1w?iU*hi(X2%2}uhZPE^?D?-=;O zrjHX*BPZ(P@k8aT+idTku(xq{C(_$$?f~PF(XZ(3M7})(lp7j0!$+;3Yc!^stM5OQ zf}tovKz2**sz)s+&gQz%VXaOZVCqe+wg*BLlq+u3enTPjHr=;JE#wb34j)KbTWuyr zdOK}IIOY1yfvt2ABfae=;tJhEn}@qO{iU{0{{rn&yv$#ygj#=fKd6saZHrtkVvTo; z@w|V!zz-I*IBNS(IbO%9W77R_it>vds0F^%gWM}F?De} zL+D%ulIU~NNt(Sdgm;th(+Q3FM@#K2dfz=(@9=g~J*7`S#E$&(*{_$T&z4=HX+pT7LXFKNX``1g1G{nsIV`#(>7Y$^Z% From 3de9a15cd312ca75beed9c77b2fe3425d5ca39e3 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 9 Dec 2025 12:14:17 +0100 Subject: [PATCH 10/19] More info on forhowto --- examples/forhowto/README.md | 8 ++++++++ examples/forhowto/README.rst | 4 ---- 2 files changed, 8 insertions(+), 4 deletions(-) create mode 100644 examples/forhowto/README.md delete mode 100644 examples/forhowto/README.rst diff --git a/examples/forhowto/README.md b/examples/forhowto/README.md new file mode 100644 index 0000000000..5a68e1a1bd --- /dev/null +++ b/examples/forhowto/README.md @@ -0,0 +1,8 @@ +# For "How to" + +These python scripts are files which we would like sphinx-gallery to run, +but are linked by the how to guide. + +All the python files in this folder are built by sphinx-gallery. The resulting +documentation page can be added to the "How to" section of the documentation by modifying +the file `doc/how_to/index.rst`. diff --git a/examples/forhowto/README.rst b/examples/forhowto/README.rst deleted file mode 100644 index 34805dba84..0000000000 --- a/examples/forhowto/README.rst +++ /dev/null @@ -1,4 +0,0 @@ -For how to ----------- - -These documents are files which we would like sphinx-gallery to run, but are linked by the how to guide. From 76cfcc4709975d0db93ee613266a29bb9437ba3c Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 9 Dec 2025 12:24:33 +0100 Subject: [PATCH 11/19] rst needed to be rst... --- examples/forhowto/README.md | 8 -------- 1 file changed, 8 deletions(-) delete mode 100644 examples/forhowto/README.md diff --git a/examples/forhowto/README.md b/examples/forhowto/README.md deleted file mode 100644 index 5a68e1a1bd..0000000000 --- a/examples/forhowto/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# For "How to" - -These python scripts are files which we would like sphinx-gallery to run, -but are linked by the how to guide. - -All the python files in this folder are built by sphinx-gallery. The resulting -documentation page can be added to the "How to" section of the documentation by modifying -the file `doc/how_to/index.rst`. From a0d243f5fda86fec67d00c5c814955a13fd99874 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 9 Dec 2025 12:24:54 +0100 Subject: [PATCH 12/19] rst needed to be rst... --- examples/forhowto/README.rst | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 examples/forhowto/README.rst diff --git a/examples/forhowto/README.rst b/examples/forhowto/README.rst new file mode 100644 index 0000000000..09ef2f2b24 --- /dev/null +++ b/examples/forhowto/README.rst @@ -0,0 +1,9 @@ +For "How to" +============ + +These python scripts are files which we would like sphinx-gallery to run, +but are linked by the how to guide. + +All the python files in this folder are built by sphinx-gallery. The resulting +documentation page can be added to the "How to" section of the documentation by modifying +the file ``doc/how_to/index.rst``. From 6c0dd0f9f782b9d29b81c761f75c0327e4467c28 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 9 Dec 2025 12:27:44 +0100 Subject: [PATCH 13/19] Add seaborn to docs requirements --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 91b3b5d45a..929b1e62f8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -208,6 +208,7 @@ docs = [ "hdbscan>=0.8.33", # For sorters spykingcircus2 + tridesclous "numba", # For many postprocessing functions "networkx", + "seaborn", "skops", # For automated curation "scikit-learn", # For automated curation "huggingface_hub", # For automated curation From f0fdd34853ca732fc8ffac91e07ae461b4628cfc Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 9 Dec 2025 12:37:20 +0100 Subject: [PATCH 14/19] Make recording smaller --- examples/forhowto/plot_extract_lfps.py | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/examples/forhowto/plot_extract_lfps.py b/examples/forhowto/plot_extract_lfps.py index 4eb4393c22..714c39af0b 100644 --- a/examples/forhowto/plot_extract_lfps.py +++ b/examples/forhowto/plot_extract_lfps.py @@ -3,10 +3,29 @@ ============ Understanding filtering artifacts and chunking when extracting LFPs +------------------------------------------------------------------- Local Field Potentials (LFPs) are low-frequency signals (<300 Hz) that reflect the summed activity of many neurons. Extracting LFPs from high-sampling-rate recordings requires bandpass filtering, but this can introduce artifacts -when not done carefully, especially when data is processed in chunks (for memory efficiency). +when not done carefully, especially when data is processed in chunks (which is usually the required because datasets +cannot be loaded entirely into memory). + +Before we get started, let's introduce some important concepts: + +Chunk +~~~~~ + +A "chunk" is a piece of recording that gets processed in parallel by SpikeInterface. +The default chunk duration for most operations is 1 second, but we'll see how this is not adequate for LFP +processing. + + +Margin +~~~~~~ + +When we apply a filter on chunked data, we extract additional "margins" of traces at the chunk borders. +This is done to reduce border artifacts. + This tutorial demonstrates: @@ -44,7 +63,7 @@ # Generate a ground truth recording with spikes # Use a higher sampling rate (30 kHz) to simulate raw neural data recording, sorting = generate_ground_truth_recording( - durations=[300.0], # 300 s + durations=[60.0], sampling_frequency=30000.0, num_channels=1, num_units=4, From f1f47bb15ef9cc914f49fd1eb0e092c4ff248a36 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 9 Dec 2025 12:41:10 +0100 Subject: [PATCH 15/19] cache to folder instead of memory --- doc/conf.py | 1 + examples/forhowto/plot_extract_lfps.py | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index 6a50bf634d..b272e290e0 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -38,6 +38,7 @@ '../examples/tutorials/qualitymetrics/curated_sorting', '../examples/tutorials/qualitymetrics/clean_analyzer.zarr', '../examples/tutorials/widgets/waveforms_mearec', + '../examples/forhowto/cached' ] diff --git a/examples/forhowto/plot_extract_lfps.py b/examples/forhowto/plot_extract_lfps.py index 714c39af0b..81a1c213d8 100644 --- a/examples/forhowto/plot_extract_lfps.py +++ b/examples/forhowto/plot_extract_lfps.py @@ -201,7 +201,7 @@ # Since in this toy case we have a single channel 5-min recording, we can use this as "optimal". recording_optimal = recording_filt.save( - format="memory", + folder="./cached/optimal", chunk_duration="1000s", progress_bar=False ) @@ -225,7 +225,7 @@ ignore_low_freq_error=True, ) recording_chunk = recording_chunk.save( - format="memory", + folder=f"./cached/{margin_ms}_{chunk_duration}", chunk_duration=chunk_duration, verbose=False, progress_bar=False From 22bc2d716b34defafbf1db57db8e29bea6df3700 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 9 Dec 2025 14:50:09 +0100 Subject: [PATCH 16/19] Add ignore_low_freq_error to kwargs --- src/spikeinterface/preprocessing/filter.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/spikeinterface/preprocessing/filter.py b/src/spikeinterface/preprocessing/filter.py index b57ebe5519..1ccab2b833 100644 --- a/src/spikeinterface/preprocessing/filter.py +++ b/src/spikeinterface/preprocessing/filter.py @@ -262,7 +262,12 @@ def __init__( ) dtype = fix_dtype(recording, dtype) self._kwargs = dict( - recording=recording, freq_min=freq_min, freq_max=freq_max, margin_ms=margin_ms, dtype=dtype.str + recording=recording, + freq_min=freq_min, + freq_max=freq_max, + margin_ms=margin_ms, + dtype=dtype.str, + ignore_low_freq_error=ignore_low_freq_error, ) self._kwargs.update(filter_kwargs) From d561025ed8394a36e8f4cbe498ca2bcee51e1501 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 9 Dec 2025 16:58:07 +0100 Subject: [PATCH 17/19] Chris' comments --- examples/forhowto/plot_extract_lfps.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/examples/forhowto/plot_extract_lfps.py b/examples/forhowto/plot_extract_lfps.py index 81a1c213d8..53c4c256c2 100644 --- a/examples/forhowto/plot_extract_lfps.py +++ b/examples/forhowto/plot_extract_lfps.py @@ -138,12 +138,15 @@ ############################################################################## # **Why does this fail?** # -# The error occurs because by default in SpikeInterface when highpass filtering below 100 Hz. +# The error always occurs in SpikeInterface when highpass filtering below 100 Hz, to remind the user that they need to be careful. # Filters with very low cutoff frequencies have long impulse responses, which require larger margins to avoid edge artifacts between chunks. # # The filter length (and required margin) scales inversely with the highpass frequency. A 1 Hz highpass # filter requires a margin of several seconds, while a 300 Hz highpass (for spike extraction) only needs # a few milliseconds. +# +# **This error is to inform the user that extra care should be used when dealing with LFP signals!** + ############################################################################## # 3. Understanding chunking and margins @@ -153,11 +156,8 @@ # a "margin" (extra samples at the edges) to avoid edge artifacts when filtering. Let's demonstrate # this by saving the filtered data with different chunking strategies. # -# **This error is to inform the user that extra care should be used when dealing with LFP signals!** -# -# We can ignore this error, but let's make sure we understand what it's happening. +# We can explicitly ignore the previous error, but let's make sure we understand what is happening. -# We can ignore this error, but let's see what is happening recording_filt = spre.bandpass_filter( recording_with_lfp, freq_min=1.0, freq_max=300.0, ignore_low_freq_error=True ) @@ -172,6 +172,7 @@ ############################################################################## # This effectively means that if we plot 1-s snippet of traces, a total of 11 s will actually be read and filtered. +# Hence the computational "overhead" is very large. # Note that the margin can be overridden with the `margin_ms` argument, but we do not recommend changing it. _ = sw.plot_traces(recording_filt, time_range=[20, 21]) From b70b2fa9658a3da8c974026b7a6b6b26444ecba8 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 17 Dec 2025 10:34:06 +0100 Subject: [PATCH 18/19] Make margin_ms None by default in BaseFilterRecording --- src/spikeinterface/preprocessing/filter.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/preprocessing/filter.py b/src/spikeinterface/preprocessing/filter.py index 1ccab2b833..732b310123 100644 --- a/src/spikeinterface/preprocessing/filter.py +++ b/src/spikeinterface/preprocessing/filter.py @@ -47,8 +47,9 @@ class FilterRecording(BasePreprocessor): If list. band (low, high) in Hz for "bandpass" filter type btype : "bandpass" | "highpass", default: "bandpass" Type of the filter - margin_ms : float, default: 5.0 + margin_ms : float, default: None Margin in ms on border to avoid border effect. + Must be provided by sub-class. coeff : array | None, default: None Filter coefficients in the filter_mode form. dtype : dtype or None, default: None @@ -83,7 +84,7 @@ def __init__( filter_order=5, ftype="butter", filter_mode="sos", - margin_ms=5, + margin_ms=None, add_reflect_padding=False, coeff=None, dtype=None, @@ -115,6 +116,7 @@ def __init__( if "offset_to_uV" in self.get_property_keys(): self.set_channel_offsets(0) + assert margin_ms is not None, "margin_ms must be provided!" margin = int(margin_ms * fs / 1000.0) self.margin_samples = margin for parent_segment in recording._recording_segments: From 9675c31b96a0c2e1ac7c26f1bb794a4696171ec1 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 17 Dec 2025 11:15:33 +0100 Subject: [PATCH 19/19] Fix tests --- src/spikeinterface/preprocessing/tests/test_filter.py | 4 ++-- src/spikeinterface/preprocessing/tests/test_pipeline.py | 3 +++ 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/preprocessing/tests/test_filter.py b/src/spikeinterface/preprocessing/tests/test_filter.py index 66889bf095..398b6cbc0e 100644 --- a/src/spikeinterface/preprocessing/tests/test_filter.py +++ b/src/spikeinterface/preprocessing/tests/test_filter.py @@ -159,7 +159,7 @@ def test_filter(): trace1 = rec2_cached1.get_traces(segment_index=0) # other filtering types - rec3 = filter(rec, band=500.0, btype="highpass", filter_mode="ba", filter_order=2) + rec3 = filter(rec, band=500.0, btype="highpass", filter_mode="ba", filter_order=2, margin_ms=5.0) rec4 = notch_filter(rec, freq=3000, q=30, margin_ms=5.0) rec5 = causal_filter(rec, direction="forward") rec6 = causal_filter(rec, direction="backward") @@ -168,7 +168,7 @@ def test_filter(): from scipy.signal import iirfilter coeff = iirfilter(8, [0.02, 0.4], rs=30, btype="band", analog=False, ftype="cheby2", output="sos") - rec5 = filter(rec, coeff=coeff, filter_mode="sos") + rec5 = filter(rec, coeff=coeff, filter_mode="sos", margin_ms=5.0) # compute by chunk rec5_cached0 = rec5.save(chunk_size=100000, verbose=False, progress_bar=True) diff --git a/src/spikeinterface/preprocessing/tests/test_pipeline.py b/src/spikeinterface/preprocessing/tests/test_pipeline.py index addcb5d172..27432bb156 100644 --- a/src/spikeinterface/preprocessing/tests/test_pipeline.py +++ b/src/spikeinterface/preprocessing/tests/test_pipeline.py @@ -80,6 +80,9 @@ def test_pipeline_equiv_to_step(): elif pp_name == "decimate": pp_dict[pp_name] = {"decimation_factor": 2} pp_rec_from_class = pp_class(rec, decimation_factor=2) + elif pp_name == "filter": + pp_dict[pp_name] = {"margin_ms": 5.0} + pp_rec_from_class = pp_class(rec, margin_ms=5.0) else: pp_rec_from_class = pp_class(rec)

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like sphinx-gallery to run, but are linked by the how to guide. - - - -.. raw:: html - -