Minor decoding fixes (#819)

* Fix name and filtering

* Fix linting

* New methods for mua

* Fix name of part table

* Add detection interval and update notebooks

* Add figurl for ripple for debugging

* Update CHANGELOG.md

* Add a way to filter the channels

* Add z-score threshold and raster

* Add demo and paper code references

* Fix ripple trace

* Fix ripple and mua z-score

* Remove raster from mua figurl, update notebooks

* Go back to red color

* Remove because doesn't belong here

* Apply suggestions from code review

Co-authored-by: Chris Brozdowski <[email protected]>

* Handle no zscore case

* Move ripple times inside function

* Simplify getting the time and speed

* Update notebooks

---------

Co-authored-by: Chris Brozdowski <[email protected]>

Author: edeno

CHANGELOG.md

@@ -28,7 +28,6 @@
 - Spike sorting:
     - Add SpikeSorting V1 pipeline. #651
     - Move modules into spikesorting.v0 #807
-    - Add MUA analysis to spike sorting pipeline
 - LFP:
     - Minor fixes to LFPBandV1 populator and `make`. #706, #795
     - LFPV1: Fix error for multiple lfp settings on same data #775
@@ -41,7 +40,7 @@
     - DLC path handling from config, and normalize naming convention. #722
     - Fix in place column bug #752
 - Decoding:
-    - Add `decoding` pipeline V1. #731, #769
+    - Add `decoding` pipeline V1. #731, #769, #819
     - Add a table to store the decoding results #731
     - Use the new `non_local_detector` package for decoding #731
     - Allow multiple spike waveform features for clusterless decoding #731
@@ -51,6 +50,10 @@
     - Rename SortedSpikesGroup.SortGroup to SortedSpikesGroup.Units #807
     - Change methods with load\_... to fetch\_... for consistency #807
     - Use merge table methods to access part methods #807
+- MUA
+    - Add MUA pipeline V1. #731, #819
+- Ripple
+    - Add figurl to Ripple pipeline #819
 
 ## [0.4.3] (November 7, 2023)
 

README.md

@@ -10,6 +10,8 @@ visualization, and sharing of neuroscience data to support reproducible
 research. It is designed to be interoperable with the NWB format and integrates
 open-source tools into a coherent framework.
 
+Try out a demo [here](https://spyglass.hhmi.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FLorenFrankLab%2Fspyglass-demo&urlpath=lab%2Ftree%2Fspyglass-demo%2Fnotebooks%2F01_Insert_Data.ipynb&branch=main)!
+
 Features of Spyglass include:
 
 - **Standardized data storage** - Spyglass uses the open-source
@@ -86,3 +88,5 @@ a data analysis framework for reproducible and shareable neuroscience research.
    [10.1101/2024.01.25.577295](https://doi.org/10.1101/2024.01.25.577295 ).
 
 *\* Equal contribution*
+
+See paper related code [here](https://github.com/LorenFrankLab/spyglass-paper).

notebooks/43_Decoding_SortedSpikes.ipynb

@@ -655,7 +655,7 @@
    ],
    "source": [
     "# look at the sorting within the group we just made\n",
-    "SortedSpikesGroup.SortGroup & {\n",
+    "SortedSpikesGroup.Units & {\n",
     "    \"nwb_file_name\": nwb_copy_file_name,\n",
     "    \"sorted_spikes_group_name\": \"test_group\",\n",
     "    \"unit_filter_params_name\": unit_filter_params_name,\n",

notebooks/51_MUA_Detection.ipynb

@@ -108,7 +108,7 @@
 # -
 
 # look at the sorting within the group we just made
-SortedSpikesGroup.SortGroup & {
+SortedSpikesGroup.Units & {
     "nwb_file_name": nwb_copy_file_name,
     "sorted_spikes_group_name": "test_group",
     "unit_filter_params_name": unit_filter_params_name,

notebooks/py_scripts/43_Decoding_SortedSpikes.py

@@ -13,229 +13,99 @@
 # ---
 
 # +
-from pathlib import Path
 import datajoint as dj
+from pathlib import Path
 
 dj.config.load(
     Path("../dj_local_conf.json").absolute()
 )  # load config for database connection info
+
+from spyglass.mua.v1.mua import MuaEventsV1, MuaEventsParameters
+
 # -
 
-# # MUA Analysis and Detection
-#
-# NOTE: This notebook is a work in progress. It is not yet complete and may contain errors.
+MuaEventsParameters()
+
+MuaEventsV1()
 
 # +
-from spyglass.spikesorting.spikesorting_merge import SpikeSortingOutput
-import spyglass.spikesorting.v1 as sgs
-
+from spyglass.position import PositionOutput
 
 nwb_copy_file_name = "mediumnwb20230802_.nwb"
 
-sorter_keys = {
+trodes_s_key = {
     "nwb_file_name": nwb_copy_file_name,
-    "sorter": "clusterless_thresholder",
-    "sorter_param_name": "default_clusterless",
+    "interval_list_name": "pos 0 valid times",
+    "trodes_pos_params_name": "single_led_upsampled",
 }
 
-(sgs.SpikeSortingSelection & sorter_keys) * SpikeSortingOutput.CurationV1
-
-# +
-spikesorting_merge_ids = (
-    (sgs.SpikeSortingSelection & sorter_keys) * SpikeSortingOutput.CurationV1
-).fetch("merge_id")
-
-spikesorting_merge_ids
-
-# +
-from spyglass.spikesorting.unit_inclusion_merge import (
-    ImportedUnitInclusionV1,
-    UnitInclusionOutput,
-)
-
-ImportedUnitInclusionV1().insert_all_units(spikesorting_merge_ids)
-
-UnitInclusionOutput.ImportedUnitInclusionV1() & [
-    {"spikesorting_merge_id": id} for id in spikesorting_merge_ids
-]
-
-# +
-from spyglass.spikesorting.unit_inclusion_merge import (
-    ImportedUnitInclusionV1,
-    UnitInclusionOutput,
-)
-
-ImportedUnitInclusionV1().insert_all_units(spikesorting_merge_ids)
-
-UnitInclusionOutput.ImportedUnitInclusionV1() & [
-    {"spikesorting_merge_id": id} for id in spikesorting_merge_ids
-]
+pos_merge_id = (PositionOutput.TrodesPosV1 & trodes_s_key).fetch1("merge_id")
+pos_merge_id
 
 # +
-from spyglass.spikesorting.unit_inclusion_merge import SortedSpikesGroup
-
-unit_inclusion_merge_ids = (
-    UnitInclusionOutput.ImportedUnitInclusionV1
-    & [{"spikesorting_merge_id": id} for id in spikesorting_merge_ids]
-).fetch("merge_id")
-
-SortedSpikesGroup().create_group(
-    group_name="test_group",
-    nwb_file_name=nwb_copy_file_name,
-    unit_inclusion_merge_ids=unit_inclusion_merge_ids,
+from spyglass.spikesorting.analysis.v1.group import (
+    SortedSpikesGroup,
 )
 
-group_key = {
+sorted_spikes_group_key = {
     "nwb_file_name": nwb_copy_file_name,
     "sorted_spikes_group_name": "test_group",
+    "unit_filter_params_name": "default_exclusion",
 }
 
-SortedSpikesGroup & group_key
-# -
-
-SortedSpikesGroup.Units() & group_key
-
-# An example of how to get spike times
-
-spike_times = SortedSpikesGroup.fetch_spike_data(group_key)
-spike_times[0]
+SortedSpikesGroup & sorted_spikes_group_key
 
 # +
-from spyglass.position import PositionOutput
-
-position_merge_id = (
-    PositionOutput.TrodesPosV1
-    & {
-        "nwb_file_name": nwb_copy_file_name,
-        "interval_list_name": "pos 0 valid times",
-        "trodes_pos_params_name": "default_decoding",
-    }
-).fetch1("merge_id")
-
-position_info = (
-    (PositionOutput & {"merge_id": position_merge_id})
-    .fetch1_dataframe()
-    .dropna()
-)
-position_info
-
-# +
-time_ind_slice = slice(63_000, 70_000)
-time = position_info.index[time_ind_slice]
-
-SortedSpikesGroup.get_spike_indicator(group_key, time)
-
-# +
-import matplotlib.pyplot as plt
-
-fig, axes = plt.subplots(2, 1, sharex=True, figsize=(15, 4))
-multiunit_firing_rate = SortedSpikesGroup.get_firing_rate(
-    group_key, time, multiunit=True
-)
-axes[0].plot(
-    time,
-    multiunit_firing_rate,
-)
-axes[0].set_ylabel("firing rate (Hz)")
-axes[0].set_title("multiunit")
-axes[1].fill_between(
-    time, position_info["speed"].iloc[time_ind_slice], color="lightgrey"
-)
-axes[1].set_ylabel("speed (cm/s)")
-axes[1].set_xlabel("time (s)")
-
-# +
-from spyglass.mua.v1.mua import MuaEventsParameters, MuaEventsV1
-
-MuaEventsParameters().insert_default()
-MuaEventsParameters()
-
-# +
-selection_key = {
+mua_key = {
     "mua_param_name": "default",
-    "nwb_file_name": nwb_copy_file_name,
-    "sorted_spikes_group_name": "test_group",
-    "pos_merge_id": position_merge_id,
-    "artifact_interval_list_name": "test_artifact_times",
+    **sorted_spikes_group_key,
+    "pos_merge_id": pos_merge_id,
+    "detection_interval": "pos 0 valid times",
 }
 
-MuaEventsV1.populate(selection_key)
+MuaEventsV1().populate(mua_key)
+MuaEventsV1 & mua_key
 # -
 
-MuaEventsV1 & selection_key
-
-mua_times = (MuaEventsV1 & selection_key).fetch1_dataframe()
+mua_times = (MuaEventsV1 & mua_key).fetch1_dataframe()
 mua_times
 
 # +
 import matplotlib.pyplot as plt
 import numpy as np
 
 fig, axes = plt.subplots(2, 1, sharex=True, figsize=(15, 4))
+speed = MuaEventsV1.get_speed(mua_key).to_numpy()
+time = speed.index.to_numpy()
+multiunit_firing_rate = MuaEventsV1.get_firing_rate(mua_key, time)
+
+time_slice = slice(
+    np.searchsorted(time, mua_times.loc[10].start_time) - 1_000,
+    np.searchsorted(time, mua_times.loc[10].start_time) + 5_000,
+)
+
 axes[0].plot(
-    time,
-    multiunit_firing_rate,
+    time[time_slice],
+    multiunit_firing_rate[time_slice],
+    color="black",
 )
 axes[0].set_ylabel("firing rate (Hz)")
 axes[0].set_title("multiunit")
-axes[1].fill_between(
-    time, position_info["speed"].iloc[time_ind_slice], color="lightgrey"
-)
+axes[1].fill_between(time[time_slice], speed[time_slice], color="lightgrey")
 axes[1].set_ylabel("speed (cm/s)")
 axes[1].set_xlabel("time (s)")
 
-in_bounds = np.logical_and(
-    mua_times.start_time >= time[0], mua_times.end_time <= time[-1]
-)
-
-for mua_time in mua_times.loc[in_bounds].itertuples():
-    axes[0].axvspan(
-        mua_time.start_time, mua_time.end_time, color="red", alpha=0.3
+for id, mua_time in mua_times.loc[
+    np.logical_and(
+        mua_times["start_time"] > time[time_slice].min(),
+        mua_times["end_time"] < time[time_slice].max(),
     )
-    axes[1].axvspan(
-        mua_time.start_time, mua_time.end_time, color="red", alpha=0.3
+].iterrows():
+    axes[0].axvspan(
+        mua_time["start_time"], mua_time["end_time"], color="red", alpha=0.5
     )
-axes[1].set_ylim((0, 80))
-axes[1].axhline(4, color="black", linestyle="--")
-axes[1].set_xlim((time[0], time[-1]))
-
-# +
-from spyglass.common import IntervalList
-
-IntervalList() & {
-    "nwb_file_name": nwb_copy_file_name,
-    "pipeline": "spikesorting_artifact_v1",
-}
 # -
 
-(
-    sgs.ArtifactDetectionParameters
-    * sgs.SpikeSortingRecording
-    * sgs.ArtifactDetectionSelection
-)
-
-SpikeSortingOutput.CurationV1() * (
-    sgs.ArtifactDetectionParameters
-    * sgs.SpikeSortingRecording
-    * sgs.ArtifactDetectionSelection
-)
-
-(
-    IntervalList()
-    & {
-        "nwb_file_name": nwb_copy_file_name,
-        "pipeline": "spikesorting_artifact_v1",
-    }
-).proj(artifact_id="interval_list_name")
-
-sgs.SpikeSortingRecording() * sgs.ArtifactDetectionSelection()
-
-SpikeSortingOutput.CurationV1() * sgs.SpikeSortingRecording()
-
-IntervalList.insert1(
-    {
-        "nwb_file_name": nwb_copy_file_name,
-        "interval_list_name": "test_artifact_times",
-        "valid_times": [],
-    }
+(MuaEventsV1 & mua_key).create_figurl(
+    zscore_mua=True,
 )

notebooks/py_scripts/51_MUA_Detection.py

@@ -0,0 +1 @@
+from spyglass.mua.v1.mua import MuaEventsParameters, MuaEventsV1  # noqa: F401

src/spyglass/mua/__init__.py

@@ -0,0 +1 @@
+from spyglass.mua.v1.mua import MuaEventsParameters, MuaEventsV1  # noqa: F401