Add wrapper to determine whether to return a metric or a physio object

phys2denoise/metrics/cardiac.py

@@ -7,11 +7,17 @@
 from ..due import due
 from .responses import crf
 from .utils import apply_function_in_sliding_window as afsw
-from .utils import convolve_and_rescale
+from .utils import convolve_and_rescale, return_physio_or_metric
 
 
 def _cardiac_metrics(
-    data, metric, fs=None, peaks=None, window=6, central_measure="mean"
+    data,
+    metric,
+    fs=None,
+    peaks=None,
+    window=6,
+    central_measure="mean",
+    return_physio=False,
 ):
     """
     Compute cardiac metrics.
@@ -146,6 +152,7 @@ def _cardiac_metrics(
 
 
 @due.dcite(references.CHANG_CUNNINGHAM_GLOVER_2009)
+@return_physio_or_metric()
 @physio.make_operation()
 def heart_rate(data, fs=None, peaks=None, window=6, central_measure="mean"):
     """
@@ -200,14 +207,19 @@ def heart_rate(data, fs=None, peaks=None, window=6, central_measure="mean"):
     Biology Society (EMBC), doi: 10.1109/EMBC.2016.7591347.
     """
     data, hr = _cardiac_metrics(
-        data, metric="hr", fs=fs, peaks=peaks, window=6, central_measure="mean"
+        data,
+        metric="hr",
+        fs=fs,
+        peaks=peaks,
+        window=window,
+        central_measure=central_measure,
     )
-    data._computed_metrics["heart_rate"] = dict(metric=hr, has_lags=False)
 
     return data, hr
 
 
 @due.dcite(references.PINHERO_ET_AL_2016)
+@return_physio_or_metric()
 @physio.make_operation()
 def heart_rate_variability(data, fs=None, peaks=None, window=6, central_measure="mean"):
     """
@@ -260,14 +272,19 @@ def heart_rate_variability(data, fs=None, peaks=None, window=6, central_measure=
     Biology Society (EMBC), doi: 10.1109/EMBC.2016.7591347.
     """
     data, hrv = _cardiac_metrics(
-        data, metric="hrv", fs=None, peaks=None, window=6, central_measure="std"
+        data,
+        metric="hrv",
+        fs=fs,
+        peaks=peaks,
+        window=window,
+        central_measure=central_measure,
     )
-    data._computed_metrics["heart_rate_variability"] = dict(metric=hrv)
 
     return data, hrv
 
 
 @due.dcite(references.CHEN_2020)
+@return_physio_or_metric()
 @physio.make_operation()
 def heart_beat_interval(data, fs=None, peaks=None, window=6, central_measure="mean"):
     """
@@ -313,13 +330,18 @@ def heart_beat_interval(data, fs=None, peaks=None, window=6, central_measure="me
         vol. 213, pp. 116707, 2020.
     """
     data, hbi = _cardiac_metrics(
-        data, metric="hbi", fs=None, peaks=None, window=6, central_measure="mean"
+        data,
+        metric="hbi",
+        fs=fs,
+        peaks=peaks,
+        window=window,
+        central_measure=central_measure,
     )
-    data._computed_metrics["heart_beat_interval"] = dict(metric=hbi)
 
     return data, hbi
 
 
+@return_physio_or_metric()
 @physio.make_operation()
 def cardiac_phase(data, slice_timings, n_scans, t_r, fs=None, peaks=None):
     """Calculate cardiac phase from cardiac peaks.
@@ -402,6 +424,4 @@ def cardiac_phase(data, slice_timings, n_scans, t_r, fs=None, peaks=None):
             ) / (t2 - t1)
         phase_card[:, i_slice] = phase_card_crSlice
 
-    data._computed_metrics["cardiac_phase"] = dict(metric=phase_card)
-
     return data, phase_card

phys2denoise/metrics/chest_belt.py

@@ -9,10 +9,11 @@
 from ..due import due
 from .responses import rrf
 from .utils import apply_function_in_sliding_window as afsw
-from .utils import convolve_and_rescale, rms_envelope_1d
+from .utils import convolve_and_rescale, return_physio_or_metric, rms_envelope_1d
 
 
 @due.dcite(references.BIRN_2006)
+@return_physio_or_metric()
 @physio.make_operation()
 def respiratory_variance_time(
     data, fs=None, peaks=None, troughs=None, lags=(0, 4, 8, 12)
@@ -106,11 +107,11 @@ def respiratory_variance_time(
         )
         rvt_lags[:, ind] = temp_rvt
 
-    data._computed_metrics["rvt"] = dict(metric=rvt_lags, has_lags=True)
     return data, rvt_lags
 
 
 @due.dcite(references.POWER_2018)
+@return_physio_or_metric()
 @physio.make_operation()
 def respiratory_pattern_variability(data, window):
     """Calculate respiratory pattern variability.
@@ -154,11 +155,11 @@ def respiratory_pattern_variability(data, window):
     # Calculate standard deviation
     rpv_val = np.std(rpv_upper_env)
 
-    data._computed_metrics["rpv"] = dict(metric=rpv_val)
     return data, rpv_val
 
 
 @due.dcite(references.POWER_2020)
+@return_physio_or_metric()
 @physio.make_operation()
 def env(data, fs=None, window=10):
     """Calculate respiratory pattern variability across a sliding window.
@@ -238,11 +239,11 @@ def _respiratory_pattern_variability(data, window):
     )
     env_arr[np.isnan(env_arr)] = 0.0
 
-    data._computed_metrics["env"] = dict(metric=env_arr)
     return data, env_arr
 
 
 @due.dcite(references.CHANG_GLOVER_2009)
+@return_physio_or_metric()
 @physio.make_operation()
 def respiratory_variance(data, fs=None, window=6):
     """Calculate respiratory variance.
@@ -304,11 +305,10 @@ def respiratory_variance(data, fs=None, window=6):
     # Convolve with rrf
     rv_out = convolve_and_rescale(rv_arr, rrf(data.fs), rescale="zscore")
 
-    data._computed_metrics["respiratory_variance"] = dict(metric=rv_out)
-
     return data, rv_out
 
 
+@return_physio_or_metric()
 @physio.make_operation()
 def respiratory_phase(data, n_scans, slice_timings, t_r, fs=None):
     """Calculate respiratory phase from respiratory signal.
@@ -374,6 +374,4 @@ def respiratory_phase(data, n_scans, slice_timings, t_r, fs=None):
 
         phase_resp[:, i_slice] = phase_resp_crSlice
 
-    data._computed_metrics["respiratory_phase"] = dict(metric=phase_resp)
-
     return data, phase_resp

phys2denoise/metrics/utils.py

@@ -1,8 +1,11 @@
 """Miscellaneous utility functions for metric calculation."""
+import functools
 import logging
 
 import numpy as np
+from loguru import logger
 from numpy.lib.stride_tricks import sliding_window_view as swv
+from physutils.physio import Physio
 from scipy.interpolate import interp1d
 from scipy.stats import zscore
 
@@ -332,3 +335,38 @@ def export_metric(
                 )
 
     return fileprefix
+
+
+def return_physio_or_metric(*, return_physio=True):
+    """
+    Decorator to check if the input is a Physio object.
+
+    Parameters
+    ----------
+    func : function
+        Function to be decorated
+
+    Returns
+    -------
+    function
+        Decorated function
+    """
+
+    def determine_return_type(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            physio, metric = func(*args, **kwargs)
+            if isinstance(args[0], Physio):
+                physio._computed_metrics[func.__name__] = dict(
+                    metric=metric, args=kwargs
+                )
+                if return_physio:
+                    return physio, metric
+                else:
+                    return metric
+            else:
+                return metric
+
+        return wrapper
+
+    return determine_return_type

phys2denoise/tests/test_metrics_cardiac.py

@@ -28,7 +28,7 @@ def test_cardiac_phase_smoke():
     slice_timings = np.linspace(0, t_r, 22)[1:-1]
     peaks = np.array([0.534, 0.577, 10.45, 20.66, 50.55, 90.22])
     data = np.zeros(peaks.shape)
-    _, card_phase = cardiac.cardiac_phase(
+    card_phase = cardiac.cardiac_phase(
         data,
         peaks=peaks,
         fs=sample_rate,

phys2denoise/tests/test_metrics_chest_belt.py

@@ -30,7 +30,7 @@ def test_respiratory_phase_smoke():
     slice_timings = np.linspace(0, t_r, 22)[1:-1]
     n_samples = int(np.rint((n_scans * t_r) * sample_rate))
     resp = np.random.normal(size=n_samples)
-    _, resp_phase = chest_belt.respiratory_phase(
+    resp_phase = chest_belt.respiratory_phase(
         resp,
         fs=sample_rate,
         slice_timings=slice_timings,
@@ -46,7 +46,7 @@ def test_respiratory_pattern_variability_smoke():
     n_samples = 2000
     resp = np.random.normal(size=n_samples)
     window = 50
-    _, rpv_val = chest_belt.respiratory_pattern_variability(resp, window)
+    rpv_val = chest_belt.respiratory_pattern_variability(resp, window)
     assert isinstance(rpv_val, float)
 
 
@@ -56,7 +56,7 @@ def test_env_smoke():
     resp = np.random.normal(size=n_samples)
     samplerate = 1 / 0.01
     window = 6
-    _, env_arr = chest_belt.env(resp, fs=samplerate, window=window)
+    env_arr = chest_belt.env(resp, fs=samplerate, window=window)
     assert env_arr.ndim == 1
     assert env_arr.shape == (n_samples,)
 
@@ -67,6 +67,6 @@ def test_respiratory_variance_smoke():
     resp = np.random.normal(size=n_samples)
     samplerate = 1 / 0.01
     window = 6
-    _, rv_arr = chest_belt.respiratory_variance(resp, fs=samplerate, window=window)
+    rv_arr = chest_belt.respiratory_variance(resp, fs=samplerate, window=window)
     assert rv_arr.ndim == 2
     assert rv_arr.shape == (n_samples, 2)

phys2denoise/tests/test_rvt.py

@@ -17,8 +17,8 @@ def test_respiratory_variance_time(fake_phys):
     phys = peakdet.operations.peakfind_physio(phys)
 
     # TODO: Change to a simpler call once physutils are
-    # integrated to peakdet
-    phys, r = respiratory_variance_time(
+    # integrated to peakdet/prep4phys
+    r = respiratory_variance_time(
         phys.data, fs=phys.fs, peaks=phys.peaks, troughs=phys.troughs
     )
     assert r is not None