Merge pull request #152 from neurodsp-tools/plts

Add Plotting Funcs

doc/Makefile

@@ -29,6 +29,7 @@ clean:
 ## make install:
 # Build the html site, and push it to gh-pages branch of repo to deploy
 install:
+	make clean
 	rm -rf _build/doctrees _build/tmp_html
 	# Clone, specifically, the gh-pages branch:
 	#   --no-checkout just fetches the root folder without content

doc/api.rst

@@ -247,3 +247,57 @@ Combined Signals
   :toctree: generated/
 
   sim_combined
+
+Plots
+-----
+
+Functions for plotting time series and analysis outputs.
+
+Time Series
+~~~~~~~~~~~
+
+.. currentmodule:: neurodsp.plts.time_series
+
+.. autosummary::
+  :toctree: generated/
+
+  plot_time_series
+  plot_instantaneous_measure
+  plot_bursts
+
+Spectral
+~~~~~~~~
+
+.. currentmodule:: neurodsp.plts.spectral
+
+.. autosummary::
+  :toctree: generated/
+
+  plot_power_spectra
+  plot_scv
+  plot_scv_rs_lines
+  plot_scv_rs_matrix
+  plot_spectral_hist
+
+Filter
+~~~~~~
+
+.. currentmodule:: neurodsp.plts.filt
+
+.. autosummary::
+  :toctree: generated/
+
+  plot_filter_properties
+  plot_frequency_response
+  plot_impulse_response
+
+Rhythm
+~~~~~~
+
+.. currentmodule:: neurodsp.plts.rhythm
+
+.. autosummary::
+  :toctree: generated/
+
+  plot_swm_pattern
+  plot_lagged_coherence

examples/plot_mne_example.py

@@ -38,6 +38,9 @@
 from neurodsp.burst import detect_bursts_dual_threshold
 from neurodsp.rhythm import lagged_coherence
 
+# Import NeuroDSP plotting functions
+from neurodsp.plts import plot_time_series, plot_power_spectra, plot_bursts, plot_lagged_coherence
+
 ###################################################################################################
 # Load & Check MNE Data
 # ---------------------
@@ -87,8 +90,7 @@
 ###################################################################################################
 
 # Plot a segment of the extracted time series data
-plt.figure(figsize=(16, 3))
-plt.plot(times, sig, 'k')
+plot_time_series(times, sig)
 
 ###################################################################################################
 # Calculate Power Spectra
@@ -112,9 +114,8 @@
 
 ###################################################################################################
 
-# Plot the power spectra
-plt.figure(figsize=(8, 8))
-plt.semilogy(freqs, powers)
+# Plot the power spectra, and note the peak power
+plot_power_spectra(freqs, powers)
 plt.plot(freqs[np.argmax(powers)], np.max(powers), '.r', ms=12)
 
 ###################################################################################################
@@ -139,10 +140,7 @@
 ###################################################################################################
 
 # Plot original signal and burst activity
-plt.figure(figsize=(16, 3))
-plt.plot(times, sig, 'k', label='Raw Data')
-plt.plot(times[bursting], sig[bursting], 'r', label='Detected Bursts')
-plt.legend(loc='best')
+plot_bursts(times, sig, bursting, labels=['Raw Data', 'Detected Bursts'])
 
 ###################################################################################################
 # Measure Rhythmicity with Lagged Coherence
@@ -174,11 +172,7 @@
 ###################################################################################################
 
 # Visualize lagged coherence across all frequencies
-plt.figure(figsize=(6, 3))
-plt.plot(freqs, lcs, 'k.-')
-plt.xlabel('Frequency (Hz)')
-plt.ylabel('Lagged coherence')
-plt.tight_layout()
+plot_lagged_coherence(freqs, lcs)
 
 ###################################################################################################
 

neurodsp/plts/__init__.py

@@ -1 +1,6 @@
 """Plotting functions."""
+
+from .time_series import plot_time_series, plot_bursts
+from .filt import plot_filter_properties, plot_frequency_response, plot_impulse_response
+from .rhythm import plot_swm_pattern, plot_lagged_coherence
+from .spectral import plot_power_spectra, plot_scv, plot_scv_rs_lines, plot_scv_rs_matrix, plot_spectral_hist

neurodsp/plts/filt.py

@@ -3,9 +3,13 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+from neurodsp.plts.style import style_plot
+from neurodsp.plts.utils import check_ax, savefig
+
 ###################################################################################################
 ###################################################################################################
 
+@savefig
 def plot_filter_properties(f_db, db, fs, impulse_response):
     """Plot filter properties, including frequency response and filter kernel.
 
@@ -25,6 +29,8 @@ def plot_filter_properties(f_db, db, fs, impulse_response):
     plot_impulse_response(fs, impulse_response, ax=ax[1])
 
 
+@savefig
+@style_plot
 def plot_frequency_response(f_db, db, ax=None):
     """Plot the frequency response of a filter.
 
@@ -38,8 +44,7 @@ def plot_frequency_response(f_db, db, ax=None):
         Figure axes upon which to plot.
     """
 
-    if not ax:
-        _, ax = plt.subplots(figsize=(5, 5))
+    ax = check_ax(ax, (5, 5))
 
     ax.plot(f_db, db, 'k')
 
@@ -48,6 +53,8 @@ def plot_frequency_response(f_db, db, ax=None):
     ax.set_ylabel('Attenuation (dB)')
 
 
+@savefig
+@style_plot
 def plot_impulse_response(fs, impulse_response, ax=None):
     """Plot the impulse response of a filter.
 
@@ -59,8 +66,7 @@ def plot_impulse_response(fs, impulse_response, ax=None):
         Figure axes upon which to plot.
     """
 
-    if not ax:
-        _, ax = plt.subplots(figsize=(5, 5))
+    ax = check_ax(ax, (5, 5))
 
     # Create a samples vector, center to zero, and convert to time
     samples = np.arange(len(impulse_response))

neurodsp/plts/rhythm.py

@@ -0,0 +1,53 @@
+"""Plotting functions for neurodsp.rhythm."""
+
+import matplotlib.pyplot as plt
+
+from neurodsp.plts.style import style_plot
+from neurodsp.plts.utils import check_ax, savefig
+
+###################################################################################################
+###################################################################################################
+
+@savefig
+@style_plot
+def plot_swm_pattern(pattern, ax=None):
+    """Plot the resulting pattern from a sliding window matching analysis.
+
+    Parameters
+    ----------
+    pattern : 1d array
+        The resulting average pattern from applying sliding window matching.
+    ax : matplotlib.Axes, optional
+        Figure axes upon which to plot.
+    """
+
+    ax = check_ax(ax, (4, 4))
+
+    plt.plot(pattern, 'k')
+
+    plt.title('Average Pattern')
+    plt.xlabel('Time (samples)')
+    plt.ylabel('Voltage (a.u.)')
+
+
+@savefig
+@style_plot
+def plot_lagged_coherence(freqs, lcs, ax=None):
+    """Plot lagged coherence values across frequencies.
+
+    Parameters
+    ----------
+    freqs : 1d array
+        Vector of frequencies at which lagged coherence was computed.
+    lcs : 1d array
+        Lagged coherence values across the computed frequencies.
+    ax : matplotlib.Axes, optional
+        Figure axes upon which to plot.
+    """
+
+    ax = check_ax(ax, (6, 3))
+
+    plt.plot(freqs, lcs, 'k.-')
+
+    plt.xlabel('Frequency (Hz)')
+    plt.ylabel('Lagged Coherence')

neurodsp/plts/spectral.py

@@ -1,11 +1,130 @@
 """Plotting functions for neurodsp.spectral."""
 
+from itertools import repeat
+
 import numpy as np
 import matplotlib.pyplot as plt
 
+from neurodsp.plts.style import style_plot
+from neurodsp.plts.utils import check_ax, savefig
+
 ###################################################################################################
 ###################################################################################################
 
+@savefig
+@style_plot
+def plot_power_spectra(freqs, powers, labels=None, colors=None, ax=None):
+    """Plot power spectra.
+
+    Parameters
+    ----------
+    freqs : 1d array or list of 1d array
+        Frequency vector.
+    powers : 1d array or list of 1d array
+        Power values.
+    labels : str or list of str, optional
+        Labels for each time series.
+    colors : str or list of str
+        Colors to use to plot lines.
+    ax : matplotlib.Axes, optional
+        Figure axes upon which to plot.
+    """
+
+    ax = check_ax(ax, (6, 6))
+
+    freqs = repeat(freqs) if isinstance(freqs, np.ndarray) else freqs
+    powers = [powers] if isinstance(powers, np.ndarray) else powers
+
+    if labels is not None:
+        labels = [labels] if not isinstance(labels, list) else labels
+    else:
+        labels = repeat(labels)
+
+    if colors is not None:
+        colors = repeat(colors) if not isinstance(colors, list) else cycle(colors)
+
+    for freq, power, label in zip(freqs, powers, labels):
+        ax.loglog(freq, power, label=label)
+
+    ax.set_xlabel('Frequency (Hz)')
+    ax.set_ylabel('Power (V^2/Hz)')
+
+
+@savefig
+@style_plot
+def plot_scv(freqs, scv, ax=None):
+    """Plot the SCV.
+
+    Parameters
+    ----------
+    freqs : 1d array
+        Frequency vector.
+    scv : 1d array
+        Spectral coefficient of variation.
+    ax : matplotlib.Axes, optional
+        Figure axes upon which to plot.
+    """
+
+    ax = check_ax(ax, (5, 5))
+
+    ax.loglog(freqs, scv)
+
+    ax.set_xlabel('Frequency (Hz)')
+    ax.set_ylabel('SCV')
+
+
+@savefig
+@style_plot
+def plot_scv_rs_lines(freqs, scv_rs, ax=None):
+    """Plot the SCV, from the resampling method.
+
+    Parameters
+    ----------
+    freqs : 1d array
+        Frequency vector.
+    scv_rs :
+        Spectral coefficient of variation, from resampling procedure.
+    ax : matplotlib.Axes, optional
+        Figure axes upon which to plot.
+    """
+
+    ax = check_ax(ax, (8, 8))
+
+    ax.loglog(freqs, scv_rs, 'k', alpha=0.1)
+    ax.loglog(freqs, np.mean(scv_rs, axis=1), lw=2)
+    ax.loglog(freqs, len(freqs)*[1.])
+
+    ax.set_xlabel('Frequency (Hz)')
+    ax.set_ylabel('SCV')
+
+
+@savefig
+@style_plot
+def plot_scv_rs_matrix(freqs, t_inds, scv_rs):
+    """Plot the SCV, from the resampling method.
+
+    Parameters
+    ----------
+    freqs : 1d array
+        Frequency vector.
+    t_inds : 1d array
+        Time indices
+    scv_rs : 1d array
+        Spectral coefficient of variation, from resampling procedure.
+    """
+
+    fig, ax = plt.subplots(figsize=(10, 5))
+
+    plt.imshow(np.log10(scv_rs), aspect='auto',
+               extent=(t_inds[0], t_inds[-1], freqs[-1], freqs[0]))
+    plt.colorbar(label='SCV')
+
+    plt.xlabel('Time (s)')
+    plt.ylabel('Frequency (Hz)')
+
+
+@savefig
+@style_plot
 def plot_spectral_hist(freqs, power_bins, spectral_hist, spectrum_freqs=None, spectrum=None):
     """Plot the spectral histogram.
 
@@ -27,12 +146,14 @@ def plot_spectral_hist(freqs, power_bins, spectral_hist, spectrum_freqs=None, sp
     plt.figure(figsize=(8, 12 * len(power_bins) / len(freqs)))
 
     # Plot histogram intensity as image and automatically adjust aspect ratio
-    plt.imshow(spectral_hist, extent=[freqs[0], freqs[-1],
-               power_bins[0], power_bins[-1]], aspect='auto')
+    plt.imshow(spectral_hist, extent=[freqs[0], freqs[-1], power_bins[0], power_bins[-1]], aspect='auto')
     plt.xlabel('Frequency (Hz)', fontsize=15)
     plt.ylabel('Log10 Power', fontsize=15)
     plt.colorbar(label='Probability')
 
+    plt.xlabel('Frequency (Hz)')
+    plt.ylabel('Log10 Power')
+
     # If a PSD is provided, plot over the histogram data
     if spectrum is not None:
         plt_inds = np.logical_and(spectrum_freqs >= freqs[0], spectrum_freqs <= freqs[-1])