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Merge pull request #10 from catalystneuro/docker-proxy
change worker to docker proxy
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@@ -159,4 +159,5 @@ yarn-error.log* | |
| # Miscellaneous | ||
| *.fuse_hidden* | ||
| results/ | ||
| logs/ | ||
| *.nwb | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,154 @@ | ||
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| source_data = { | ||
| "source": "dandi", # or "s3" | ||
| "source_data_type": "nwb", # or "spikeglx" | ||
| "source_data_paths": { | ||
| "file": "https://dandiarchive.org/dandiset/000003/0.210813.1807" | ||
| }, | ||
| "recording_kwargs": { | ||
| "electrical_series_name": "ElectricalSeries", | ||
| } | ||
| } | ||
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| source_data_2 = { | ||
| "source": "s3", | ||
| "source_data_type": "spikeglx", | ||
| "source_data_paths": { | ||
| "file_bin": "s3://bucket/path/to/file.ap.bin", | ||
| "file_meta": "s3://bucket/path/to/file2.ap.meta", | ||
| }, | ||
| "recording_kwargs": {} | ||
| } | ||
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| preprocessing_params = dict( | ||
| preprocessing_strategy="cmr", # 'destripe' or 'cmr' | ||
| highpass_filter=dict(freq_min=300.0, margin_ms=5.0), | ||
| phase_shift=dict(margin_ms=100.0), | ||
| detect_bad_channels=dict( | ||
| method="coherence+psd", | ||
| dead_channel_threshold=-0.5, | ||
| noisy_channel_threshold=1.0, | ||
| outside_channel_threshold=-0.3, | ||
| n_neighbors=11, | ||
| seed=0, | ||
| ), | ||
| remove_out_channels=False, | ||
| remove_bad_channels=False, | ||
| max_bad_channel_fraction_to_remove=1.1, | ||
| common_reference=dict(reference="global", operator="median"), | ||
| highpass_spatial_filter=dict( | ||
| n_channel_pad=60, | ||
| n_channel_taper=None, | ||
| direction="y", | ||
| apply_agc=True, | ||
| agc_window_length_s=0.01, | ||
| highpass_butter_order=3, | ||
| highpass_butter_wn=0.01, | ||
| ), | ||
| ) | ||
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| qm_params = { | ||
| "presence_ratio": {"bin_duration_s": 60}, | ||
| "snr": {"peak_sign": "neg", "peak_mode": "extremum", "random_chunk_kwargs_dict": None}, | ||
| "isi_violation": {"isi_threshold_ms": 1.5, "min_isi_ms": 0}, | ||
| "rp_violation": {"refractory_period_ms": 1, "censored_period_ms": 0.0}, | ||
| "sliding_rp_violation": { | ||
| "bin_size_ms": 0.25, | ||
| "window_size_s": 1, | ||
| "exclude_ref_period_below_ms": 0.5, | ||
| "max_ref_period_ms": 10, | ||
| "contamination_values": None, | ||
| }, | ||
| "amplitude_cutoff": { | ||
| "peak_sign": "neg", | ||
| "num_histogram_bins": 100, | ||
| "histogram_smoothing_value": 3, | ||
| "amplitudes_bins_min_ratio": 5, | ||
| }, | ||
| "amplitude_median": {"peak_sign": "neg"}, | ||
| "nearest_neighbor": {"max_spikes": 10000, "min_spikes": 10, "n_neighbors": 4}, | ||
| "nn_isolation": {"max_spikes": 10000, "min_spikes": 10, "n_neighbors": 4, "n_components": 10, "radius_um": 100}, | ||
| "nn_noise_overlap": {"max_spikes": 10000, "min_spikes": 10, "n_neighbors": 4, "n_components": 10, "radius_um": 100}, | ||
| } | ||
| qm_metric_names = [ | ||
| "num_spikes", | ||
| "firing_rate", | ||
| "presence_ratio", | ||
| "snr", | ||
| "isi_violation", | ||
| "rp_violation", | ||
| "sliding_rp_violation", | ||
| "amplitude_cutoff", | ||
| "drift", | ||
| "isolation_distance", | ||
| "l_ratio", | ||
| "d_prime", | ||
| ] | ||
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| postprocessing_params = dict( | ||
| sparsity=dict(method="radius", radius_um=100), | ||
| waveforms_deduplicate=dict( | ||
| ms_before=0.5, | ||
| ms_after=1.5, | ||
| max_spikes_per_unit=100, | ||
| return_scaled=False, | ||
| dtype=None, | ||
| precompute_template=("average",), | ||
| use_relative_path=True, | ||
| ), | ||
| waveforms=dict( | ||
| ms_before=3.0, | ||
| ms_after=4.0, | ||
| max_spikes_per_unit=500, | ||
| return_scaled=True, | ||
| dtype=None, | ||
| precompute_template=("average", "std"), | ||
| use_relative_path=True, | ||
| ), | ||
| spike_amplitudes=dict( | ||
| peak_sign="neg", | ||
| return_scaled=True, | ||
| outputs="concatenated", | ||
| ), | ||
| similarity=dict(method="cosine_similarity"), | ||
| correlograms=dict( | ||
| window_ms=100.0, | ||
| bin_ms=2.0, | ||
| ), | ||
| isis=dict( | ||
| window_ms=100.0, | ||
| bin_ms=5.0, | ||
| ), | ||
| locations=dict(method="monopolar_triangulation"), | ||
| template_metrics=dict(upsampling_factor=10, sparsity=None), | ||
| principal_components=dict(n_components=5, mode="by_channel_local", whiten=True), | ||
| quality_metrics=dict( | ||
| qm_params=qm_params, | ||
| metric_names=qm_metric_names, | ||
| n_jobs=1 | ||
| ), | ||
| ) | ||
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| curation_params = dict( | ||
| duplicate_threshold=0.9, | ||
| isi_violations_ratio_threshold=0.5, | ||
| presence_ratio_threshold=0.8, | ||
| amplitude_cutoff_threshold=0.1, | ||
| ) | ||
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| visualization_params = dict( | ||
| timeseries=dict(n_snippets_per_segment=2, snippet_duration_s=0.5, skip=False), | ||
| drift=dict( | ||
| detection=dict(method="locally_exclusive", peak_sign="neg", detect_threshold=5, exclude_sweep_ms=0.1), | ||
| localization=dict(ms_before=0.1, ms_after=0.3, local_radius_um=100.0), | ||
| n_skip=30, | ||
| alpha=0.15, | ||
| vmin=-200, | ||
| vmax=0, | ||
| cmap="Greys_r", | ||
| figsize=(10, 10), | ||
| ), | ||
| ) |
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