Merge pull request #889 from neuropsychology/ppg_fixpeaks

[Breaking] for *_plot() and ecg_fixpeaks(method="neurokit")
neuropsychology · Aug 23, 2023 · 4bf8071 · 4bf8071
2 parents 6493c6e + b47335f
commit 4bf8071
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diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml
@@ -12,7 +12,7 @@ jobs:
             - name: Set up Python ${{ matrix.python-version }}
               uses: actions/setup-python@v4
               with:
-                  python-version: "3.10"
+                  python-version: "3.11"
 
             - name: Install dependencies
               run: |

diff --git a/.github/workflows/releasePR.yml b/.github/workflows/releasePR.yml
@@ -15,7 +15,7 @@ jobs:
             - name: Set up Python ${{ matrix.python-version }}
               uses: actions/setup-python@v4
               with:
-                  python-version: "3.7"
+                  python-version: "3.11"
 
             - name: Install dependencies
               run: |
@@ -50,7 +50,7 @@ jobs:
             - name: Set up Python
               uses: actions/setup-python@v4
               with:
-                  python-version: "3.x"
+                  python-version: "3.11"
 
             - name: Install pep517
               run: >-

diff --git a/README.rst b/README.rst
@@ -248,7 +248,7 @@ Electrodermal Activity (EDA/GSR)
     signals, info = nk.eda_process(eda, sampling_rate=250)
 
     # Visualise the processing
-    nk.eda_plot(signals, sampling_rate=250)
+    nk.eda_plot(signals, info)
 
 .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_eda.png
         :target: https://neuropsychology.github.io/NeuroKit/examples/eda_peaks/eda_peaks.html
@@ -266,7 +266,7 @@ Cardiac activity (ECG)
     signals, info = nk.ecg_process(ecg, sampling_rate=250)
 
     # Visualise the processing
-    nk.ecg_plot(signals, sampling_rate=250)
+    nk.ecg_plot(signals, info)
 
 
 .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_ecg.png
@@ -285,47 +285,48 @@ Respiration (RSP)
     signals, info = nk.rsp_process(rsp, sampling_rate=250)
 
     # Visualise the processing
-    nk.rsp_plot(signals, sampling_rate=250)
+    nk.rsp_plot(signals, info)
 
 
 .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_rsp.png
         :target: https://neuropsychology.github.io/NeuroKit/examples/rsp_rrv/rsp_rrv.html
 
 
-Electromyography (EMG)
-^^^^^^^^^^^^^^^^^^^^^^^
+Photoplethysmography (PPG/BVP)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. code-block:: python
 
-    # Generate 10 seconds of EMG signal (recorded at 250 samples/second)
-    emg = nk.emg_simulate(duration=10, sampling_rate=250, burst_number=3)
+    # Generate 15 seconds of PPG signal (recorded at 250 samples/second)
+    ppg = nk.ppg_simulate(duration=15, sampling_rate=250, heart_rate=70)
 
     # Process it
-    signals, info = nk.emg_process(emg, sampling_rate=250)
+    signals, info = nk.ppg_process(ppg, sampling_rate=250)
 
-    # Visualise the processing
-    nk.emg_plot(signals, sampling_rate=250)
+    # Visualize the processing
+    nk.ppg_plot(signals, info)
 
 
-.. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_emg.png
+.. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_ppg.png
 
 
-Photoplethysmography (PPG/BVP)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Electromyography (EMG)
+^^^^^^^^^^^^^^^^^^^^^^^
 
 .. code-block:: python
 
-    # Generate 15 seconds of PPG signal (recorded at 250 samples/second)
-    ppg = nk.ppg_simulate(duration=15, sampling_rate=250, heart_rate=70)
+    # Generate 10 seconds of EMG signal (recorded at 250 samples/second)
+    emg = nk.emg_simulate(duration=10, sampling_rate=250, burst_number=3)
 
     # Process it
-    signals, info = nk.ppg_process(ppg, sampling_rate=250)
+    signals, info = nk.emg_process(emg, sampling_rate=250)
 
-    # Visualize the processing
-    nk.ppg_plot(signals, sampling_rate=250)
+    # Visualise the processing
+    nk.emg_plot(signals, info)
 
 
-.. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_ppg.png
+.. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_emg.png
+
 
 
 Electrooculography (EOG)
@@ -340,7 +341,7 @@ Electrooculography (EOG)
     signals, info = nk.eog_process(eog_signal, sampling_rate=100)
 
     # Plot
-    nk.eog_plot(signals, info, sampling_rate=100)
+    nk.eog_plot(signals, info)
 
 
 .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_eog.png

diff --git a/docs/readme/README_ecg.png b/docs/readme/README_ecg.png
diff --git a/docs/readme/README_eda.png b/docs/readme/README_eda.png
diff --git a/docs/readme/README_emg.png b/docs/readme/README_emg.png
diff --git a/docs/readme/README_eog.png b/docs/readme/README_eog.png
diff --git a/docs/readme/README_examples.py b/docs/readme/README_examples.py
@@ -9,7 +9,7 @@
 
 # Setup matplotlib with Agg to run on server
 matplotlib.use("Agg")
-plt.rcParams["figure.figsize"] = (10, 6.5)
+plt.rcParams["figure.figsize"] = (2**0.5 * 10, 10)
 plt.rcParams["savefig.facecolor"] = "white"
 
 # =============================================================================
@@ -55,13 +55,15 @@
     }
 )
 plot = data.plot(
-    subplots=True, layout=(5, 1), color=["#f44336", "#E91E63", "#2196F3", "#9C27B0", "#FF9800"]
+    subplots=True,
+    layout=(5, 1),
+    color=["#f44336", "#E91E63", "#2196F3", "#9C27B0", "#FF9800"],
 )
 fig = plt.gcf()
 fig.set_size_inches(10, 6, forward=True)
 [ax.legend(loc=1) for ax in plt.gcf().axes]
 plt.tight_layout()
-fig.savefig("README_simulation.png", dpi=300)
+fig.savefig("README_simulation.png", dpi=150)
 
 # =============================================================================
 # Electrodermal Activity (EDA) processing
@@ -73,13 +75,10 @@
 # Process it
 signals, info = nk.eda_process(eda, sampling_rate=250)
 
-# Visualise the processing
-nk.eda_plot(signals, sampling_rate=None)
-
 # Save it
-nk.eda_plot(signals, sampling_rate=None)
+nk.eda_plot(signals, info)
 plt.tight_layout()
-plt.savefig("README_eda.png", dpi=300)
+plt.savefig("README_eda.png", dpi=150)
 
 # =============================================================================
 # Cardiac activity (ECG) processing
@@ -91,13 +90,10 @@
 # Process it
 signals, info = nk.ecg_process(ecg, sampling_rate=250)
 
-# Visualise the processing
-nk.ecg_plot(signals, sampling_rate=250)
-
 # Save it
-nk.ecg_plot(signals, sampling_rate=250)
+nk.ecg_plot(signals, info)
 plt.tight_layout()
-plt.savefig("README_ecg.png", dpi=300)
+plt.savefig("README_ecg.png", dpi=150)
 
 # =============================================================================
 # Respiration (RSP) processing
@@ -109,51 +105,43 @@
 # Process it
 signals, info = nk.rsp_process(rsp, sampling_rate=250)
 
-# Visualise the processing
-nk.rsp_plot(signals, sampling_rate=250)
-
 # Save it
-nk.rsp_plot(signals, sampling_rate=250)
+nk.rsp_plot(signals, info)
 fig = plt.gcf()
 fig.set_size_inches(10, 12, forward=True)
 plt.tight_layout()
-plt.savefig("README_rsp.png", dpi=300)
+plt.savefig("README_rsp.png", dpi=150)
 
 # =============================================================================
-# Electromyography (EMG) processing
+# Photoplethysmography (PPG/BVP)
 # =============================================================================
 
-# Generate 10 seconds of EMG signal (recorded at 250 samples / second)
-emg = nk.emg_simulate(duration=10, sampling_rate=250, burst_number=3)
+# Generate 15 seconds of PPG signal (recorded at 250 samples / second)
+ppg = nk.ppg_simulate(duration=15, sampling_rate=250, heart_rate=70, random_state=333)
 
 # Process it
-signals, info = nk.emg_process(emg, sampling_rate=250)
-
-# Visualise the processing
-nk.emg_plot(signals, sampling_rate=250)
+signals, info = nk.ppg_process(ppg, sampling_rate=250)
 
 # Save it
-nk.emg_plot(signals, sampling_rate=250)
+nk.ppg_plot(signals, info)
 plt.tight_layout()
-plt.savefig("README_emg.png", dpi=300)
+plt.savefig("README_ppg.png", dpi=150)
 
 # =============================================================================
-# Photoplethysmography (PPG/BVP)
+# Electromyography (EMG) processing
 # =============================================================================
 
-# Generate 15 seconds of PPG signal (recorded at 250 samples / second)
-ppg = nk.ppg_simulate(duration=15, sampling_rate=250, heart_rate=70, random_state=333)
+# Generate 10 seconds of EMG signal (recorded at 250 samples / second)
+emg = nk.emg_simulate(duration=10, sampling_rate=250, burst_number=3)
 
 # Process it
-signals, info = nk.ppg_process(ppg, sampling_rate=250)
-
-# Visualize the processing
-nk.ppg_plot(signals, sampling_rate=250)
+signals, info = nk.emg_process(emg, sampling_rate=250)
 
 # Save it
-nk.ppg_plot(signals, sampling_rate=250)
+nk.emg_plot(signals, info)
 plt.tight_layout()
-plt.savefig("README_ppg.png", dpi=300)
+plt.savefig("README_emg.png", dpi=150)
+
 
 # =============================================================================
 # Electrooculography (EOG)
@@ -166,9 +154,9 @@
 signals, info = nk.eog_process(eog_signal, sampling_rate=100)
 
 # Plot
-nk.eog_plot(signals, peaks=info, sampling_rate=100)
+nk.eog_plot(signals, info)
 plt.tight_layout()
-plt.savefig("README_eog.png", dpi=300)
+plt.savefig("README_eog.png", dpi=150)
 
 # =============================================================================
 # Signal Processing
@@ -280,7 +268,9 @@
 signal = nk.signal_simulate(duration=10, frequency=1)  # High freq
 signal += 3 * nk.signal_simulate(duration=10, frequency=3)  # Higher freq
 signal += 3 * np.linspace(0, 2, len(signal))  # Add baseline and linear trend
-signal += 2 * nk.signal_simulate(duration=10, frequency=0.1, noise=0)  # Non-linear trend
+signal += 2 * nk.signal_simulate(
+    duration=10, frequency=0.1, noise=0
+)  # Non-linear trend
 signal += np.random.normal(0, 0.02, len(signal))  # Add noise
 
 # Decompose signal using Empirical Mode Decomposition (EMD)
@@ -323,10 +313,16 @@
 )
 
 # Get the PSD using different methods
-welch = nk.signal_psd(signal, method="welch", min_frequency=1, max_frequency=20, show=True)
+welch = nk.signal_psd(
+    signal, method="welch", min_frequency=1, max_frequency=20, show=True
+)
 multitaper = nk.signal_psd(signal, method="multitapers", max_frequency=20, show=True)
-lomb = nk.signal_psd(signal, method="lomb", min_frequency=1, max_frequency=20, show=True)
-burg = nk.signal_psd(signal, method="burg", min_frequency=1, max_frequency=20, order=10, show=True)
+lomb = nk.signal_psd(
+    signal, method="lomb", min_frequency=1, max_frequency=20, show=True
+)
+burg = nk.signal_psd(
+    signal, method="burg", min_frequency=1, max_frequency=20, order=10, show=True
+)
 
 
 # Visualize the different methods together
@@ -337,7 +333,12 @@
 axes[0].set_xlabel("Time (s)")
 
 axes[1].plot(
-    welch["Frequency"], welch["Power"], label="Welch", color="#E91E63", linewidth=2, zorder=1
+    welch["Frequency"],
+    welch["Power"],
+    label="Welch",
+    color="#E91E63",
+    linewidth=2,
+    zorder=1,
 )
 axes[1].plot(
     multitaper["Frequency"],
@@ -347,9 +348,21 @@
     linewidth=2,
     zorder=2,
 )
-axes[1].plot(burg["Frequency"], burg["Power"], label="Burg", color="#4CAF50", linewidth=2, zorder=3)
 axes[1].plot(
-    lomb["Frequency"], lomb["Power"], label="Lomb", color="#FFC107", linewidth=0.5, zorder=4
+    burg["Frequency"],
+    burg["Power"],
+    label="Burg",
+    color="#4CAF50",
+    linewidth=2,
+    zorder=3,
+)
+axes[1].plot(
+    lomb["Frequency"],
+    lomb["Power"],
+    label="Lomb",
+    color="#FFC107",
+    linewidth=0.5,
+    zorder=4,
 )
 
 axes[1].set_title("Power Spectrum Density (PSD)")

diff --git a/docs/readme/README_ppg.png b/docs/readme/README_ppg.png
diff --git a/docs/readme/README_rsp.png b/docs/readme/README_rsp.png
diff --git a/docs/readme/README_simulation.png b/docs/readme/README_simulation.png
diff --git a/neurokit2/ecg/ecg_clean.py b/neurokit2/ecg/ecg_clean.py
@@ -6,7 +6,7 @@
 import scipy.signal
 
 from ..misc import NeuroKitWarning, as_vector
-from ..signal import signal_filter, signal_resample
+from ..signal import signal_filter
 
 
 def ecg_clean(ecg_signal, sampling_rate=1000, method="neurokit", **kwargs):
@@ -335,7 +335,9 @@ def _ecg_clean_templateconvolution(ecg_signal, sampling_rate=1000):
     """Filter and Convolve ECG signal with QRS complex template.
     Totally exploratory method by Dominique Makowski, use at your own risks.
 
-    The idea is to use a QRS template to convolve the signal with, in order to magnify the QRS features. However, it doens't work well and creates a lot of artifacts. If you have ideas for improvement please let me know!
+    The idea is to use a QRS template to convolve the signal with, in order to magnify the QRS
+    features. However, it doens't work well and creates a lot of artifacts. If you have ideas for
+    improvement please let me know!
     """
 
     window_size = int(np.round(sampling_rate / 4))