Transfert ready to ECG

Author: Nicolas Marc Simon Legrand

.coverage

@@ -1 +1 @@
-!coverage.py: This is a private format, don't read it directly!{"lines":{"C:\\Users\\au646069\\github\\systole\\systole\\__init__.py":[1,2,3,4,5,6,8],"C:\\Users\\au646069\\github\\systole\\systole\\detection.py":[3,4,5,6,7,8,12,105,168,215,282,322,55,59,60,61,62,63,66,69,70,306,309,312,313,314,317,319,72,74,75,76,77,78,80,83,84,85,86,87,90,93,96,97,99,102,130,131,133,135,136,143,144,150,151,152,154,155,156,158,159,162,163,165,245,247,248,249,250,251,252,254,253,255,258,259,260,261,262,263,264,265,266,269,270,271,272,273,274,276,277,275,279,186,187,190,191,193,195,197,200,201,203,206,207,208,209,210,212,56],"C:\\Users\\au646069\\github\\systole\\systole\\utils.py":[3,4,5,8,50,79,137,180,108,112,115,117,120,121,126,128,130,131,132,134,123,30,33,34,41,42,43,46,47,35,36,152,154,158,162,163,165,168,171,174,176,208,213,215,216,218,219,220,221,224,225,229,230,233,234,235,239,242,245],"C:\\Users\\au646069\\github\\systole\\systole\\plotting.py":[3,4,5,6,7,8,9,10,13,85,116,157,298,420,357,360,361,363,364,367,370,372,374,376,377,389,390,393,394,395,398,406,409,411,412,413,414,415,417,378,381,382,384,385,379,380,400,401,402,403,451,452,461,463,467,469,471,473,475,477,101,102,103,104,105,106,109,110,111,113,40,42,43,46,53,56,59,60,61,63,64,67,70,71,74,78,79,80,82,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,153,192,193,200,201,202,203,204,205,207,208,214,215,216,219,220,221,223,224,227,228,229,230,233,234,235,236,237,238,241,242,243,244,245,246,249,250,251,253,254,255,256,257,262,263,264,265,268,269,270,271,272,275,276,277,278,279,282,283,284,286,287,288,289,290,291,292,294],"C:\\Users\\au646069\\github\\systole\\systole\\hrv.py":[3,4,5,6,7,10,35,64,98,177,250,339,274,275,276,277,279,282,283,284,287,289,291,292,293,294,298,299,300,301,302,303,306,307,308,311,312,313,315,316,317,320,321,322,325,326,328,329,330,331,333,334,336,206,207,208,209,211,214,215,216,219,221,223,224,225,226,228,230,231,232,233,234,235,236,237,238,239,240,241,242,243,245,247,25,27,31,32,353,354,355,356,357,358,360,362,50,52,56,59,61,88,90,93,95,124,126,130,133,136,139,142,145,148,151,154,157,160,163,166,167,168,169,171,173],"C:\\Users\\au646069\\github\\systole\\systole\\datasets\\__init__.py":[1,2,3,4,6,8,13,16,18,23,32,47,51,69,82,84,64,66,19,20,21,48,34,38,39,42,43,45,24,25,26,30,28],"C:\\Users\\au646069\\github\\systole\\systole\\reports.py":[3,4,5,6,7,10,23,26,29,30,31,33,35,36,37,39,40,41,42,43,44,46,47,48,49,50,51,52,55,57,58,59,60,61,62,64,65,67,68,69,70,71,72,73,75],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\__init__.py":[1],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_detection.py":[3,4,5,6,8,11,13,20,28,37,45,22,23,24,25,26,38,39,40,41,42,30,31,32,33,34,35,15,16,17,18],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_hrv.py":[3,4,5,6,7,9,11,14,16,21,26,31,37,44,50,57,46,47,48,39,40,41,42,18,19,52,53,54,23,24,28,29,33,34,35],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_plotting.py":[3,4,5,6,7,9,10,12,13,14,18,19,20,21,22,23,24,25,26,27,28,31,32,33,34,36,38,42,46,50,57,68,74,82,59,60,61,62,63,64,65,66,70,71,72,43,44,39,40,47,48,75,76,77,78,79],"C:\\Users\\au646069\\github\\systole\\systole\\recording.py":[3,4,5,6,9,98,99,121,187,206,230,242,254,266,285,310,332,101,102,103,104,107,108,109,110,111,112,113,114,115,116,117,143,144,147,148,149,152,153,158,159,160,163,164,179,183,185,155,166,169,172,175,176,180,181,323,324,325,326,327,195,196,197,198,199,200,201,202,204,328,329,274,275,276,278,279,280,283,330,219,220,223,226,228,238,240,250,252,262,264,294,296,297,298,335,336,338,339,340,341,342,345],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_recording.py":[3,4,5,6,7,8,9,12,13,16,18,39,19,20,21,24,25,26,27,29,30,32,33,35,36],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_reports.py":[3,4,5,6,9,11,16,12,13],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_utils.py":[3,4,5,6,7,8,11,13,24,34,43,50,26,27,28,29,30,31,32,14,15,16,17,18,19,20,21,22,36,38,39,40,41,45,46,47,48]}}
+!coverage.py: This is a private format, don't read it directly!{"lines":{"C:\\Users\\au646069\\github\\systole\\systole\\__init__.py":[1,2,3,4,5,6,8],"C:\\Users\\au646069\\github\\systole\\systole\\detection.py":[3,4,5,6,7,8,12,105,168,215,282,322,55,59,60,61,62,63,66,69,70,306,309,312,313,314,317,319,72,74,75,76,77,78,80,83,84,85,86,87,90,93,96,97,99,102,130,131,133,135,136,143,144,150,151,152,154,155,156,158,159,162,163,165,245,247,248,249,250,251,252,254,253,255,258,259,260,261,262,263,264,265,266,269,270,271,272,273,274,276,277,275,279,186,187,190,191,193,195,197,200,201,203,206,207,208,209,210,212,56],"C:\\Users\\au646069\\github\\systole\\systole\\utils.py":[3,4,5,8,50,79,139,182,108,110,114,117,119,122,123,128,130,132,133,134,136,125,30,33,34,41,42,43,46,47,35,36,154,156,160,164,165,167,170,173,176,178,210,215,217,218,220,221,222,223,226,227,231,232,235,236,237,241,244,247],"C:\\Users\\au646069\\github\\systole\\systole\\plotting.py":[3,4,5,6,7,8,9,10,11,14,87,121,179,320,394,516,453,456,457,459,460,463,466,468,470,472,473,485,486,489,490,491,494,502,505,507,508,509,510,511,513,474,477,478,480,481,475,476,496,497,498,499,547,548,557,559,563,565,567,569,571,573,103,104,105,106,107,108,109,110,113,114,115,116,118,41,43,54,57,60,61,62,63,65,66,69,72,73,76,80,81,82,84,42,45,52,138,145,146,147,148,149,151,152,153,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,175,349,350,351,352,354,357,358,359,362,364,366,367,368,369,371,373,374,375,376,377,378,379,380,381,382,383,384,385,386,388,390,214,215,222,223,224,225,226,227,229,230,236,237,238,241,242,243,245,246,249,250,251,252,255,256,257,258,259,260,263,264,265,266,267,268,271,272,273,275,276,277,278,279,284,285,286,287,290,291,292,293,294,297,298,299,300,301,304,305,306,308,309,310,311,312,313,314,316,139,140,141,142,143],"C:\\Users\\au646069\\github\\systole\\systole\\hrv.py":[3,4,5,6,9,34,63,97,175,264,199,200,201,202,204,207,208,209,212,214,216,217,218,219,223,224,225,226,227,228,231,232,233,236,237,238,240,241,242,245,246,247,250,251,253,254,255,256,258,259,261,24,26,30,31,278,279,280,281,282,283,285,287,49,51,55,58,60,87,89,92,94,123,125,129,132,135,138,141,144,147,150,153,156,159,162,165,166,167,168,170,172],"C:\\Users\\au646069\\github\\systole\\systole\\datasets\\__init__.py":[1,2,3,4,6,8,13,16,18,23,32,47,51,69,82,84,64,66,19,20,21,48,34,38,39,42,43,45,24,25,26,30,28],"C:\\Users\\au646069\\github\\systole\\systole\\reports.py":[3,4,5,6,7,10,22,23,24,27,30,33,34,35,37,39,40,41,43,44,45,46,47,48,50,51,52,53,54,55,56,59,61,62,63,64,65,66,68,69,71,72,73,74,75,76,77,79],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\__init__.py":[1],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_detection.py":[3,4,5,6,8,11,13,20,28,37,45,22,23,24,25,26,38,39,40,41,42,30,31,32,33,34,35,15,16,17,18],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_hrv.py":[3,4,5,6,7,9,11,14,16,21,26,31,37,43,50,39,40,41,18,19,45,46,47,23,24,28,29,33,34,35],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_plotting.py":[3,4,5,6,7,9,11,12,13,17,18,19,20,21,22,23,24,25,26,27,30,31,32,33,35,37,45,49,53,60,68,79,85,93,70,71,72,73,74,75,76,77,81,82,83,46,47,38,39,40,41,42,43,50,51,62,63,64,65,66,86,87,88,89,90],"C:\\Users\\au646069\\github\\systole\\systole\\recording.py":[3,4,5,6,9,98,99,121,187,206,230,242,254,266,285,310,341,101,102,103,104,107,108,109,110,111,112,113,114,115,116,117,143,144,147,148,149,152,153,158,159,160,163,164,179,183,185,155,166,169,172,175,176,180,181,327,328,329,330,331,195,196,197,198,199,200,201,202,204,332,333,274,275,276,278,279,280,283,336,337,339,219,220,223,226,228,238,240,250,252,262,264,294,296,297,298,344,345,347,348,349,350,351,354],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_recording.py":[3,4,5,6,7,8,11,12,15,17,38,18,19,20,23,24,25,26,28,29,31,32,34,35],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_reports.py":[3,4,5,6,9,11,16,12,13],"C:\\Users\\au646069\\github\\systole\\systole\\tests\\test_utils.py":[3,4,5,6,7,8,11,13,24,34,43,50,26,27,28,29,30,31,32,14,15,16,17,18,19,20,21,22,36,38,39,40,41,45,46,47,48]}}

build/lib/systole/hrv.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
 from scipy import interpolate
 from scipy.signal import welch
 
@@ -173,80 +172,6 @@ def time_domain(x):
     return stats
 
 
-def hrv_psd(x, sfreq=5, method='welch', fbands=None, low=0.003,
-            high=0.4, show=True, ax=None):
-    """Plot PSD of heart rate variability.
-
-    Parameters
-    ----------
-    x : 1d array-like
-        Length of R-R intervals (default is in miliseconds).
-    sfreq : int
-        The sampling frequency.
-    method : str
-        The method used to extract freauency power. Default set to `'welch'`.
-    fbands : None or dict, optional
-        Dictionary containing the names of the frequency bands of interest
-        (str), their range (tuples) and their color in the PSD plot. Default is
-        {'vlf': ['Very low frequency', (0.003, 0.04), 'b'],
-        'lf': ['Low frequency', (0.04, 0.15), 'g'],
-        'hf': ['High frequency', (0.15, 0.4), 'r']}
-    show : boolean
-        Plot the power spectrum density. Default is `True`.
-    ax : Matplotlib.Axes instance | None
-        Where to draw the plot. Default is ´None´ (create a new figure).
-
-    Returns
-    -------
-    ax | freq, psd : Matplotlib instance | numpy array
-        If `show=True`, return the PSD plot. If `show=False`, will return the
-        frequencies and PSD level as arrays.
-    """
-    # Interpolate R-R interval
-    time = np.cumsum(x)
-    f = interpolate.interp1d(time, x, kind='cubic')
-    new_time = np.arange(time[0], time[-1], 1000/sfreq)  # Sampling rate = 5 Hz
-    x = f(new_time)
-
-    if method == 'welch':
-
-        # Define window length
-        nperseg = 256 * sfreq
-        if nperseg > len(x):
-            nperseg = len(x)
-
-        # Compute Power Spectral Density
-        freq, psd = welch(x=x, fs=sfreq, nperseg=nperseg, nfft=nperseg)
-
-        psd = psd/1000000
-
-    if fbands is None:
-        fbands = {'vlf': ['Very low frequency', (0.003, 0.04), 'b'],
-                  'lf':	['Low frequency', (0.04, 0.15), 'g'],
-                  'hf':	['High frequency', (0.15, 0.4), 'r']}
-
-    if show is True:
-        # Plot the PSD
-        if ax is None:
-            fig, ax = plt.subplots(figsize=(8, 4))
-        ax.plot(freq, psd, 'k')
-        for f in ['vlf', 'lf', 'hf']:
-            mask = (freq >= fbands[f][1][0]) & (freq <= fbands[f][1][1])
-            ax.fill_between(freq, psd, where=mask, alpha=0.5,
-                            color=fbands[f][2])
-            ax.axvline(x=fbands[f][1][0],
-                       linestyle='--',
-                       color='gray')
-        ax.set_xlim(0.003, 0.4)
-        ax.set_xlabel('Frequency [Hz]')
-        ax.set_ylabel('PSD [$s^2$/Hz]')
-        ax.set_title('Power Spectral Density', fontweight='bold')
-
-        return ax
-    else:
-        return freq, psd
-
-
 def frequency_domain(x, sfreq=5, method='welch', fbands=None):
     """Extract the frequency domain features of heart rate variability.
 

build/lib/systole/plotting.py

@@ -5,9 +5,10 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-from systole.detection import hrv_subspaces
+from systole.detection import hrv_subspaces, oxi_peaks
 from systole.utils import heart_rate
 from scipy.interpolate import interp1d
+from scipy.signal import welch
 
 
 def plot_hr(x, sfreq=75, outliers=None, unit='rr', kind='cubic', ax=None):
@@ -39,25 +40,26 @@ def plot_hr(x, sfreq=75, outliers=None, unit='rr', kind='cubic', ax=None):
     """
     if isinstance(x, list):
         x = np.asarray(x)
-    if not isinstance(x, np.ndarray):
-        x = np.asarray(x.peaks)
-
-    # If a RR time serie is provided, transform to peaks vector
-    if not ((x == 0) | (x == 1)).all():
-        x = np.round(x).astype(int)
-        peaks = np.zeros(np.cumsum(x)[-1])
-        peaks = np.insert(peaks, 0, 1)
-        peaks[np.cumsum(x)] = 1
-        sfreq = 1000
-    else:
-        peaks = x
+    if isinstance(x, np.ndarray):
+        # If a RR time serie is provided, transform to peaks vector
+        if not ((x == 0) | (x == 1)).all():
+            x = np.round(x).astype(int)
+            peaks = np.zeros(np.cumsum(x)[-1])
+            peaks = np.insert(peaks, 0, 1)
+            peaks[np.cumsum(x)] = 1
+            sfreq = 1000
+        else:
+            peaks = x
+    else:  # Oximeter instance
+        peaks = np.asarray(x.peaks)
 
     # Compute the interpolated instantaneous heart rate
     hr, times = heart_rate(peaks, sfreq=sfreq, unit=unit, kind=kind)
 
     # New peaks vector
     f = interp1d(np.arange(0, len(peaks)/sfreq, 1/sfreq), peaks,
-                 kind='linear', bounds_error=False, fill_value=(0, 0))
+                 kind='linear', bounds_error=False,
+                 fill_value=(np.nan, np.nan))
     new_peaks = f(times)
 
     if ax is None:
@@ -83,7 +85,7 @@ def plot_hr(x, sfreq=75, outliers=None, unit='rr', kind='cubic', ax=None):
 
 
 def plot_events(oximeter, ax=None):
-    """Plot events distribution.
+    """Plot events occurence across recording.
 
     Parameters
     ----------
@@ -116,13 +118,15 @@ def plot_events(oximeter, ax=None):
     return ax
 
 
-def plot_oximeter(oximeter, ax=None):
-    """Plot recorded PPG signal.
+def plot_oximeter(x, sfreq=75, ax=None):
+    """Plot PPG signal.
 
     Parameters
     ----------
-    oximeter : `systole.recording.Oximeter`
-        The Oximeter instance used to record the signal.
+    x : 1d array-like or `systole.recording.Oximeter`
+        The ppg signal, or the Oximeter instance used to record the signal.
+    sfreq : int
+        Signal sampling frequency. Default is 75 Hz.
     ax : `Matplotlib.Axes` or None
         Where to draw the plot. Default is *None* (create a new figure).
 
@@ -131,25 +135,39 @@ def plot_oximeter(oximeter, ax=None):
     ax : `Matplotlib.Axes`
         The figure.
     """
+    if isinstance(x, (list, np.ndarray)):
+        times = np.arange(0, len(x)/sfreq, 1/sfreq)
+        recording = np.asarray(x)
+        signal, peaks = oxi_peaks(x, new_sfreq=sfreq)
+        threshold = None
+        label = 'Offline estimation'
+    else:
+        times = np.asarray(x.times)
+        recording = np.asarray(x.recording)
+        peaks = np.asarray(x.recording)
+        threshold = np.asarray(x.threshold)
+        label = 'Online estimation'
+
     if ax is None:
         fig, ax = plt.subplots(figsize=(13, 5))
     ax.set_title('Oximeter recording', fontweight='bold')
-    ax.plot(oximeter.times, oximeter.threshold, linestyle='--', color='gray',
-            label='Threshold')
-    ax.fill_between(x=oximeter.times,
-                    y1=oximeter.threshold,
-                    y2=np.asarray(oximeter.recording).min(),
-                    alpha=0.2,
-                    color='gray')
-    ax.plot(oximeter.times, oximeter.recording, label='Recording',
+
+    if threshold is not None:
+        ax.plot(times, threshold, linestyle='--', color='gray',
+                label='Threshold')
+        ax.fill_between(x=times,
+                        y1=threshold,
+                        y2=recording.min(),
+                        alpha=0.2,
+                        color='gray')
+    ax.plot(times, recording, label='Recording', linewidth=.2,
             color='#4c72b0')
-    ax.fill_between(x=oximeter.times,
-                    y1=oximeter.recording,
-                    y2=np.asarray(oximeter.recording).min(),
+    ax.fill_between(x=times,
+                    y1=recording,
+                    y2=recording.min(),
                     color='w')
-    ax.plot(np.asarray(oximeter.times)[np.where(oximeter.peaks)[0]],
-            np.asarray(oximeter.recording)[np.where(oximeter.peaks)[0]],
-            color='#c44e52', linestyle=None, label='Online estimation')
+    ax.plot(times[np.where(peaks)[0]], recording[np.where(peaks)[0]], 'o',
+            color='#c44e52', markersize=0.8, label=label)
     ax.set_ylabel('PPG level')
     ax.set_xlabel('Time (s)')
     ax.legend()
@@ -298,6 +316,80 @@ def f2(x): return -c1*x - c2
     return ax
 
 
+def plot_psd(x, sfreq=5, method='welch', fbands=None, low=0.003,
+             high=0.4, show=True, ax=None):
+    """Plot PSD of heart rate variability.
+
+    Parameters
+    ----------
+    x : 1d array-like
+        Length of R-R intervals (default is in miliseconds).
+    sfreq : int
+        The sampling frequency.
+    method : str
+        The method used to extract freauency power. Default set to `'welch'`.
+    fbands : None or dict, optional
+        Dictionary containing the names of the frequency bands of interest
+        (str), their range (tuples) and their color in the PSD plot. Default is
+        {'vlf': ['Very low frequency', (0.003, 0.04), 'b'],
+        'lf': ['Low frequency', (0.04, 0.15), 'g'],
+        'hf': ['High frequency', (0.15, 0.4), 'r']}
+    show : boolean
+        Plot the power spectrum density. Default is `True`.
+    ax : Matplotlib.Axes instance | None
+        Where to draw the plot. Default is ´None´ (create a new figure).
+
+    Returns
+    -------
+    ax | freq, psd : Matplotlib instance | numpy array
+        If `show=True`, return the PSD plot. If `show=False`, will return the
+        frequencies and PSD level as arrays.
+    """
+    # Interpolate R-R interval
+    time = np.cumsum(x)
+    f = interp1d(time, x, kind='cubic')
+    new_time = np.arange(time[0], time[-1], 1000/sfreq)  # Sampling rate = 5 Hz
+    x = f(new_time)
+
+    if method == 'welch':
+
+        # Define window length
+        nperseg = 256 * sfreq
+        if nperseg > len(x):
+            nperseg = len(x)
+
+        # Compute Power Spectral Density
+        freq, psd = welch(x=x, fs=sfreq, nperseg=nperseg, nfft=nperseg)
+
+        psd = psd/1000000
+
+    if fbands is None:
+        fbands = {'vlf': ['Very low frequency', (0.003, 0.04), 'b'],
+                  'lf':	['Low frequency', (0.04, 0.15), 'g'],
+                  'hf':	['High frequency', (0.15, 0.4), 'r']}
+
+    if show is True:
+        # Plot the PSD
+        if ax is None:
+            fig, ax = plt.subplots(figsize=(8, 4))
+        ax.plot(freq, psd, 'k')
+        for f in ['vlf', 'lf', 'hf']:
+            mask = (freq >= fbands[f][1][0]) & (freq <= fbands[f][1][1])
+            ax.fill_between(freq, psd, where=mask, alpha=0.5,
+                            color=fbands[f][2])
+            ax.axvline(x=fbands[f][1][0],
+                       linestyle='--',
+                       color='gray')
+        ax.set_xlim(0.003, 0.4)
+        ax.set_xlabel('Frequency [Hz]')
+        ax.set_ylabel('PSD [$s^2$/Hz]')
+        ax.set_title('Power Spectral Density', fontweight='bold')
+
+        return ax
+    else:
+        return freq, psd
+
+
 def circular(data, bins=32, density='area', offset=0, mean=False, norm=True,
              units='radians', color=None, ax=None):
     """Plot polar histogram.
@@ -445,7 +537,7 @@ def plot_circular(data, y=None, hue=None, **kwargs):
 
        import numpy as np
        import pandas as pd
-       from systole.circular import plot_circular
+       from systole.plotting import plot_circular
        x = np.random.normal(np.pi, 0.5, 100)
        y = np.random.uniform(0, np.pi*2, 100)
        data = pd.DataFrame(data={'x': x, 'y': y}).melt()