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Remove intervals dimension from decoding results #1476

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Remove intervals dimension from decoding results #1476

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dd14b94
Initial plan
Copilot Nov 20, 2025
3aaebec
Remove intervals dimension and add interval_labels coordinate
Copilot Nov 20, 2025
5dd72b7
Update docstrings and format code with black
Copilot Nov 20, 2025
281d949
Apply suggestions from code review
edeno Nov 21, 2025
5bbb3fa
Add interval_labels coordinate to decoding results
edeno Nov 21, 2025
e54d4dd
Add tests for interval_labels with estimate_decoding_params
edeno Nov 21, 2025
b8b9ce0
Fix test fixture to create time points outside decoding interval
edeno Nov 22, 2025
e5612ba
Remove redundant coordinates causing xarray broadcasting error
edeno Nov 22, 2025
0b61c6f
Fix mock results to match non_local_detector output format
edeno Nov 22, 2025
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52 changes: 38 additions & 14 deletions src/spyglass/decoding/v1/clusterless.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
import pandas as pd
import xarray as xr
from non_local_detector.models.base import ClusterlessDetector
from scipy.ndimage import label
from track_linearization import get_linearized_position

from spyglass.common.common_interval import IntervalList # noqa: F401
Expand Down Expand Up @@ -237,7 +238,9 @@ def _run_decoder(
classifier : ClusterlessDetector
Fitted classifier instance
results : xr.Dataset
Decoding results with posteriors
Decoding results with posteriors. Results from multiple intervals
are concatenated along the time dimension with an interval_labels
coordinate to track which interval each time point belongs to.
"""
classifier = ClusterlessDetector(**decoding_params)

Expand Down Expand Up @@ -266,6 +269,14 @@ def _run_decoder(
time=position_info.index.to_numpy(),
**decoding_kwargs,
)
# Add interval_labels coordinate for consistency with predict branch
# label() returns 1-indexed labels; subtract 1 for 0-indexed intervals
# Result: -1 = outside intervals, 0, 1, 2... = interval index
labels, _ = label(~is_missing)
interval_labels = labels - 1
results = results.assign_coords(
interval_labels=("time", interval_labels)
)
else:
VALID_FIT_KWARGS = [
"is_training",
Expand Down Expand Up @@ -300,7 +311,10 @@ def _run_decoder(

# We treat each decoding interval as a separate sequence
results = []
for interval_start, interval_end in decoding_interval:
interval_labels = []
for interval_idx, (interval_start, interval_end) in enumerate(
decoding_interval
):
interval_time = position_info.loc[
interval_start:interval_end
].index.to_numpy()
Expand All @@ -310,19 +324,29 @@ def _run_decoder(
f"Interval {interval_start}:{interval_end} is empty"
)
continue
results.append(
classifier.predict(
position_time=interval_time,
position=position_info.loc[interval_start:interval_end][
position_variable_names
].to_numpy(),
spike_times=spike_times,
spike_waveform_features=spike_waveform_features,
time=interval_time,
**predict_kwargs,
)
interval_result = classifier.predict(
position_time=interval_time,
position=position_info.loc[interval_start:interval_end][
position_variable_names
].to_numpy(),
spike_times=spike_times,
spike_waveform_features=spike_waveform_features,
time=interval_time,
**predict_kwargs,
)
results = xr.concat(results, dim="intervals")
results.append(interval_result)
# Track which interval each time point belongs to
interval_labels.extend(
[interval_idx] * len(interval_result.time)
)
# Concatenate along time dimension instead of intervals dimension
if not results:
raise ValueError("All decoding intervals are empty")
results = xr.concat(results, dim="time")
# Add interval_labels as a coordinate for groupby/selection operations
results = results.assign_coords(
interval_labels=("time", interval_labels)
)

# Save discrete transition and initial conditions
results["initial_conditions"] = xr.DataArray(
Expand Down
50 changes: 37 additions & 13 deletions src/spyglass/decoding/v1/sorted_spikes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
import numpy as np
import pandas as pd
import xarray as xr
from scipy.ndimage import label
from non_local_detector.models.base import SortedSpikesDetector
from track_linearization import get_linearized_position

Expand Down Expand Up @@ -195,7 +196,9 @@ def _run_decoder(
classifier : SortedSpikesDetector
Fitted classifier instance
results : xr.Dataset
Decoding results with posteriors
Decoding results with posteriors. Results from multiple intervals
are concatenated along the time dimension with an interval_labels
coordinate to track which interval each time point belongs to.
"""
classifier = SortedSpikesDetector(**decoding_params)

Expand Down Expand Up @@ -223,6 +226,14 @@ def _run_decoder(
time=position_info.index.to_numpy(),
**decoding_kwargs,
)
# Add interval_labels coordinate for consistency with predict branch
# label() returns 1-indexed labels; subtract 1 for 0-indexed intervals
# Result: -1 = outside intervals, 0, 1, 2... = interval index
labels, _ = label(~is_missing)
interval_labels = labels - 1
results = results.assign_coords(
interval_labels=("time", interval_labels)
)
else:
VALID_FIT_KWARGS = [
"is_training",
Expand Down Expand Up @@ -255,7 +266,10 @@ def _run_decoder(

# We treat each decoding interval as a separate sequence
results = []
for interval_start, interval_end in decoding_interval:
interval_labels = []
for interval_idx, (interval_start, interval_end) in enumerate(
decoding_interval
):
interval_time = position_info.loc[
interval_start:interval_end
].index.to_numpy()
Expand All @@ -265,18 +279,28 @@ def _run_decoder(
f"Interval {interval_start}:{interval_end} is empty"
)
continue
results.append(
classifier.predict(
position_time=interval_time,
position=position_info.loc[interval_start:interval_end][
position_variable_names
].to_numpy(),
spike_times=spike_times,
time=interval_time,
**predict_kwargs,
)
interval_result = classifier.predict(
position_time=interval_time,
position=position_info.loc[interval_start:interval_end][
position_variable_names
].to_numpy(),
spike_times=spike_times,
time=interval_time,
**predict_kwargs,
)
results = xr.concat(results, dim="intervals")
results.append(interval_result)
# Track which interval each time point belongs to
interval_labels.extend(
[interval_idx] * len(interval_result.time)
)
# Concatenate along time dimension instead of intervals dimension
if not results:
raise ValueError("All decoding intervals are empty")
results = xr.concat(results, dim="time")
# Add interval_labels as a coordinate for groupby/selection operations
results = results.assign_coords(
interval_labels=("time", interval_labels)
)

# Save discrete transition and initial conditions
results["initial_conditions"] = xr.DataArray(
Expand Down
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