0.2.9

.github/workflows/releasePR.yml

@@ -68,13 +68,13 @@ jobs:
               run: python setup.py sdist bdist_wheel
 
             - name: Publish distribution 📦 to Test PyPI
-              uses: pypa/gh-action-pypi-publish@master
+              uses: pypa/gh-action-pypi-publish@release/v1
               with:
                   password: ${{ secrets.TEST_PYPI_PASSWORD }}
                   repository_url: https://test.pypi.org/legacy/
 
             - name: Publish distribution 📦 to PyPI
               #if: startsWith(github.event.ref, 'refs/tags')
-              uses: pypa/gh-action-pypi-publish@master
+              uses: pypa/gh-action-pypi-publish@release/v1
               with:
                   password: ${{ secrets.PYPI_PASSWORD }}

README.rst

@@ -16,9 +16,7 @@
 .. image:: https://codecov.io/gh/neuropsychology/NeuroKit/branch/master/graph/badge.svg
         :target: https://codecov.io/gh/neuropsychology/NeuroKit
 
-.. image:: https://api.codeclimate.com/v1/badges/517cb22bd60238174acf/maintainability
-        :target: https://codeclimate.com/github/neuropsychology/NeuroKit/maintainability
-        :alt: Maintainability
+
 
 
 **The Python Toolbox for Neurophysiological Signal Processing**
@@ -44,6 +42,7 @@ Quick Example
     # Compute relevant features
     results = nk.bio_analyze(processed_data, sampling_rate=100)
 
+
 And **boom** 💥 your analysis is done 😎
 
 Download

docs/conf.py

@@ -133,6 +133,7 @@ def find_version():
     "use_edit_page_button": True,
     "logo_only": True,
     "show_toc_level": 1,
+    "navigation_with_keys": False,
 }
 
 

neurokit2/__init__.py

@@ -33,7 +33,7 @@
 from .video import *
 
 # Info
-__version__ = "0.2.8"
+__version__ = "0.2.9"
 
 
 # Maintainer info

neurokit2/complexity/entropy_phase.py

@@ -123,7 +123,8 @@ def entropy_phase(signal, delay=1, k=4, show=False, **kwargs):
         Tx = Tx.astype(bool)
         Ys = np.sin(angles) * limx * np.sqrt(2)
         Xs = np.cos(angles) * limx * np.sqrt(2)
-        colors = plt.get_cmap("jet")(np.linspace(0, 1, k))
+        resampled_cmap = plt.get_cmap("jet").resampled(k)
+        colors = resampled_cmap(np.linspace(0, 1, k))
 
         plt.figure()
         for i in range(k):

neurokit2/events/events_plot.py

@@ -1,5 +1,4 @@
 # -*- coding: utf-8 -*-
-import matplotlib.cm
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -127,7 +126,7 @@ def events_plot(events, signal=None, color="red", linestyle="--"):
     else:
         # Convert color and style to list
         if isinstance(color, str):
-            color_map = matplotlib.cm.get_cmap("rainbow")
+            color_map = plt.get_cmap("rainbow")
             color = color_map(np.linspace(0, 1, num=len(events)))
         if isinstance(linestyle, str):
             linestyle = np.full(len(events), linestyle)

neurokit2/hrv/hrv_nonlinear.py

@@ -542,7 +542,7 @@ def _hrv_nonlinear_show(rri, rri_time=None, rri_missing=False, out={}, ax=None,
     kernel = scipy.stats.gaussian_kde(values)
     f = np.reshape(kernel(positions).T, xx.shape)
 
-    cmap = matplotlib.cm.get_cmap("Blues", 10)
+    cmap = plt.get_cmap("Blues").resampled(10)
     ax.contourf(xx, yy, f, cmap=cmap)
     ax.imshow(np.rot90(f), extent=[ax1_min, ax1_max, ax2_min, ax2_max], aspect="auto")
 

neurokit2/microstates/microstates_plot.py

@@ -99,7 +99,7 @@ def microstates_plot(microstates, segmentation=None, gfp=None, info=None, epoch=
     if epoch is None:
         epoch = (0, len(gfp))
 
-    cmap = plt.cm.get_cmap("plasma", n)
+    cmap = plt.get_cmap("plasma").resampled(n)
     # Plot the GFP line above the area
     ax["GFP"].plot(
         times[epoch[0] : epoch[1]], gfp[epoch[0] : epoch[1]], color="black", linewidth=0.5

neurokit2/misc/report.py

@@ -100,7 +100,7 @@ def summarize_table(signals):
 def text_combine(info):
     """Reformat dictionary describing processing methods as strings to be inserted into HTML file."""
     preprocessing = '<h2 style="background-color: #FB1CF0">Preprocessing</h1>'
-    for key in ["text_cleaning", "text_peaks"]:
+    for key in ["text_cleaning", "text_peaks", "text_quality"]:
         if key in info.keys():
             preprocessing += info[key] + "<br>"
     ref = '<h2 style="background-color: #FBB41C">References</h1>'

neurokit2/ppg/__init__.py

@@ -11,6 +11,7 @@
 from .ppg_peaks import ppg_peaks
 from .ppg_plot import ppg_plot
 from .ppg_process import ppg_process
+from .ppg_quality import ppg_quality
 from .ppg_segment import ppg_segment
 from .ppg_simulate import ppg_simulate
 
@@ -23,6 +24,7 @@
     "ppg_rate",
     "ppg_process",
     "ppg_plot",
+    "ppg_quality",
     "ppg_methods",
     "ppg_intervalrelated",
     "ppg_eventrelated",

neurokit2/ppg/ppg_methods.py

@@ -4,13 +4,15 @@
 from ..misc.report import get_kwargs
 from .ppg_clean import ppg_clean
 from .ppg_findpeaks import ppg_findpeaks
+from .ppg_quality import ppg_quality
 
 
 def ppg_methods(
     sampling_rate=1000,
     method="elgendi",
     method_cleaning="default",
     method_peaks="default",
+    method_quality="default",
     **kwargs,
 ):
     """**PPG Preprocessing Methods**
@@ -38,6 +40,11 @@ def ppg_methods(
         will be set to the value of ``"method"``. Defaults to ``"default"``.
         For more information, see the ``"method"`` argument
         of :func:`.ppg_findpeaks`.
+    method_quality: str
+        The method used to assess PPG signal quality. If ``"default"``,
+        will be set to the value of ``"templatematch"``. Defaults to ``"templatematch"``.
+        For more information, see the ``"method"`` argument
+        of :func:`.ppg_quality`.
     **kwargs
         Other arguments to be passed to :func:`.ppg_clean` and
         :func:`.ppg_findpeaks`.
@@ -51,15 +58,17 @@ def ppg_methods(
 
     See Also
     --------
-    ppg_process, ppg_clean, ppg_findpeaks
+    ppg_process, ppg_clean, ppg_findpeaks, ppg_quality
 
     Examples
     --------
     .. ipython:: python
 
       import neurokit2 as nk
 
-      methods = nk.ppg_methods(sampling_rate=100, method="elgendi", method_cleaning="nabian2018")
+      methods = nk.ppg_methods(
+          sampling_rate=100, method="elgendi",
+          method_cleaning="nabian2018", method_quality="templatematch")
       print(methods["text_cleaning"])
       print(methods["references"][0])
 
@@ -71,7 +80,12 @@ def ppg_methods(
         else str(method_cleaning).lower()
     )
     method_peaks = (
-        str(method).lower() if method_peaks == "default" else str(method_peaks).lower()
+        str(method).lower()
+        if method_peaks == "default"
+        else str(method_peaks).lower()
+    )
+    method_quality = (
+        str(method_quality).lower()
     )
 
     # Create dictionary with all inputs
@@ -80,16 +94,19 @@ def ppg_methods(
         "method": method,
         "method_cleaning": method_cleaning,
         "method_peaks": method_peaks,
+        "method_quality": method_quality,
         **kwargs,
     }
 
-    # Get arguments to be passed to cleaning and peak finding functions
+    # Get arguments to be passed to cleaning, peak finding, and quality assessment functions
     kwargs_cleaning, report_info = get_kwargs(report_info, ppg_clean)
     kwargs_peaks, report_info = get_kwargs(report_info, ppg_findpeaks)
+    kwargs_quality, report_info = get_kwargs(report_info, ppg_quality)
 
     # Save keyword arguments in dictionary
     report_info["kwargs_cleaning"] = kwargs_cleaning
     report_info["kwargs_peaks"] = kwargs_peaks
+    report_info["kwargs_quality"] = kwargs_quality
 
     # Initialize refs list with NeuroKit2 reference
     refs = ["""Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H.,
@@ -158,5 +175,40 @@ def ppg_methods(
             "text_peaks"
         ] = f"The peak detection was carried out using the method {method_peaks}."
 
+    # 2. Quality
+    # ----------
+    if method_quality in ["templatematch"]:
+        report_info[
+            "text_quality"
+        ] = (
+            "The quality assessment was carried out using template-matching, approximately as described "
+            + "in Orphanidou et al. (2015)."
+        )
+        refs.append(
+            """Orphanidou C, Bonnici T, Charlton P, Clifton D, Vallance D, Tarassenko L (2015)
+            Signal-quality indices for the electrocardiogram and photoplethysmogram: Derivation
+            and applications to wireless monitoring
+            IEEE Journal of Biomedical and Health Informatics 19(3): 832–838. doi:10.1109/JBHI.2014.2338351."""
+        )
+    elif method_quality in ["disimilarity"]:
+        report_info[
+            "text_quality"
+        ] = (
+            "The quality assessment was carried out using a disimilarity measure of positive-peaked beats, "
+            + "approximately as described in Sabeti et al. (2019)."
+        )
+        refs.append(
+            """Sabeti E, Reamaroon N, Mathis M, Gryak J, Sjoding M, Najarian K (2019)
+            Signal quality measure for pulsatile physiological signals using
+            morphological features: Applications in reliability measure for pulse oximetry
+            Informatics in Medicine Unlocked 16: 100222. doi:10.1016/j.imu.2019.100222."""
+        )
+    elif method_quality in ["none"]:
+        report_info["text_quality"] = "There was no quality assessment carried out."
+    else:
+        report_info[
+            "text_quality"
+        ] = f"The quality assessment was carried out using the method {method_quality}."
+
     report_info["references"] = list(np.unique(refs))
     return report_info

neurokit2/ppg/ppg_peaks.py

@@ -3,6 +3,7 @@
 
 from ..ecg.ecg_peaks import _ecg_peaks_plot_artefacts
 from ..signal import signal_fixpeaks, signal_formatpeaks
+from ..stats import rescale
 from .ppg_findpeaks import ppg_findpeaks
 
 
@@ -143,6 +144,7 @@ def _ppg_peaks_plot(
     info=None,
     sampling_rate=1000,
     raw=None,
+    quality=None,
     ax=None,
 ):
     x_axis = np.linspace(0, len(ppg_cleaned) / sampling_rate, len(ppg_cleaned))
@@ -154,6 +156,29 @@ def _ppg_peaks_plot(
     ax.set_xlabel("Time (seconds)")
     ax.set_title("PPG signal and peaks")
 
+    # Quality Area -------------------------------------------------------------
+    if quality is not None:
+        quality = rescale(
+            quality,
+            to=[
+                np.min([np.min(raw), np.min(ppg_cleaned)]),
+                np.max([np.max(raw), np.max(ppg_cleaned)]),
+            ],
+        )
+        minimum_line = np.full(len(x_axis), quality.min())
+
+        # Plot quality area first
+        ax.fill_between(
+            x_axis,
+            minimum_line,
+            quality,
+            alpha=0.12,
+            zorder=0,
+            interpolate=True,
+            facecolor="#4CAF50",
+            label="Signal quality",
+        )
+
     # Raw Signal ---------------------------------------------------------------
     if raw is not None:
         ax.plot(x_axis, raw, color="#B0BEC5", label="Raw signal", zorder=1)

neurokit2/ppg/ppg_plot.py

@@ -92,6 +92,7 @@ def ppg_plot(ppg_signals, info=None, static=True):
             info=info,
             sampling_rate=info["sampling_rate"],
             raw=ppg_signals["PPG_Raw"].values,
+            quality=ppg_signals["PPG_Quality"].values,
             ax=ax0,
         )
 

neurokit2/ppg/ppg_process.py

@@ -8,10 +8,11 @@
 from .ppg_methods import ppg_methods
 from .ppg_peaks import ppg_peaks
 from .ppg_plot import ppg_plot
+from .ppg_quality import ppg_quality
 
 
 def ppg_process(
-    ppg_signal, sampling_rate=1000, method="elgendi", report=None, **kwargs
+    ppg_signal, sampling_rate=1000, method="elgendi", method_quality="templatematch", report=None, **kwargs
 ):
     """**Process a photoplethysmogram (PPG)  signal**
 
@@ -26,6 +27,9 @@ def ppg_process(
     method : str
         The processing pipeline to apply. Can be one of ``"elgendi"``.
         Defaults to ``"elgendi"``.
+    method_quality : str
+        The quality assessment approach to use. Can be one of ``"templatematch"``, ``"disimilarity"``.
+        Defaults to ``"templatematch"``.
     report : str
         The filename of a report containing description and figures of processing
         (e.g. ``"myreport.html"``). Needs to be supplied if a report file
@@ -69,7 +73,7 @@ def ppg_process(
     """
     # Sanitize input
     ppg_signal = as_vector(ppg_signal)
-    methods = ppg_methods(sampling_rate=sampling_rate, method=method, **kwargs)
+    methods = ppg_methods(sampling_rate=sampling_rate, method=method, method_quality=method_quality, **kwargs)
 
     # Clean signal
     ppg_cleaned = ppg_clean(
@@ -94,12 +98,22 @@ def ppg_process(
         info["PPG_Peaks"], sampling_rate=sampling_rate, desired_length=len(ppg_cleaned)
     )
 
+    # Assess signal quality
+    quality = ppg_quality(
+        ppg_cleaned,
+        ppg_pw_peaks=info["PPG_Peaks"],
+        sampling_rate=sampling_rate,
+        method=methods["method_quality"],
+        **methods["kwargs_quality"]
+    )
+
     # Prepare output
     signals = pd.DataFrame(
         {
             "PPG_Raw": ppg_signal,
             "PPG_Clean": ppg_cleaned,
             "PPG_Rate": rate,
+            "PPG_Quality": quality,
             "PPG_Peaks": peaks_signal["PPG_Peaks"].values,
         }
     )