ErrorBar Update Two

Created the kwargs pipeline, initialized errorbars in summaryplot as 0. Now the user will define errorbars as the level of confidence if they wish to use them and then they will play a role in conducting the standard error.

neurokit2/hrv/hrv.py

@@ -10,7 +10,7 @@
 from .hrv_utils import _hrv_get_rri, _hrv_sanitize_input
 
 
-def hrv(peaks, sampling_rate=1000, show=False, errorbar=False):
+def hrv(peaks, sampling_rate=1000, show=False, **kwargs):
     """Computes indices of Heart Rate Variability (HRV).
 
     Computes HRV indices in the time-, frequency-, and nonlinear domain. Note that a minimum duration
@@ -29,9 +29,6 @@ def hrv(peaks, sampling_rate=1000, show=False, errorbar=False):
     show : bool, optional
         If True, returns the plots that are generates for each of the domains.
 
-    errorbar : bool, optional
-        If true, returns on the histogram plot an error bar for each bin.
-
     Returns
     -------
     DataFrame
@@ -84,12 +81,12 @@ def hrv(peaks, sampling_rate=1000, show=False, errorbar=False):
         # Indices for plotting
         out_plot = out.copy(deep=False)
 
-        _hrv_plot(peaks, out_plot, sampling_rate, errorbar)
+        _hrv_plot(peaks, out_plot, sampling_rate, **kwargs)
 
     return out
 
 
-def _hrv_plot(peaks, out, sampling_rate=1000, err=False):
+def _hrv_plot(peaks, out, sampling_rate=1000, **kwargs):
 
     fig = plt.figure(constrained_layout=False)
     spec = gs.GridSpec(ncols=2, nrows=2, height_ratios=[1, 1], width_ratios=[1, 1])
@@ -110,7 +107,7 @@ def _hrv_plot(peaks, out, sampling_rate=1000, err=False):
     # Distribution of RR intervals
     peaks = _hrv_sanitize_input(peaks)
     rri = _hrv_get_rri(peaks, sampling_rate=sampling_rate, interpolate=False)
-    ax_distrib = summary_plot(rri, errorbar=err, ax=ax_distrib)
+    ax_distrib = summary_plot(rri, ax=ax_distrib, **kwargs)
 
     # Poincare plot
     out.columns = [col.replace("HRV_", "") for col in out.columns]

neurokit2/hrv/hrv_time.py

@@ -8,7 +8,7 @@
 from .hrv_utils import _hrv_get_rri, _hrv_sanitize_input
 
 
-def hrv_time(peaks, sampling_rate=1000, show=False, errorbar=False):
+def hrv_time(peaks, sampling_rate=1000, show=False, **kwargs):
     """Computes time-domain indices of Heart Rate Variability (HRV).
 
      See references for details.
@@ -23,8 +23,6 @@ def hrv_time(peaks, sampling_rate=1000, show=False, errorbar=False):
         least twice as high as the highest frequency in vhf. By default 1000.
     show : bool
         If True, will plot the distribution of R-R intervals.
-    errorbar : bool, optional
-        If true, returns on the histogram plot an error bar for each bin.
 
     Returns
     -------
@@ -121,15 +119,15 @@ def hrv_time(peaks, sampling_rate=1000, show=False, errorbar=False):
     out["HTI"] = len(rri) / np.max(bar_y)  # HRV Triangular Index
 
     if show:
-        _hrv_time_show(rri, errorbar)
+        _hrv_time_show(rri, **kwargs)
 
     out = pd.DataFrame.from_dict(out, orient="index").T.add_prefix("HRV_")
     return out
 
 
-def _hrv_time_show(rri, errorbar, **kwargs):
+def _hrv_time_show(rri, **kwargs):
 
-    fig = summary_plot(rri, errorbar, **kwargs)
+    fig = summary_plot(rri, **kwargs)
     plt.xlabel("R-R intervals (ms)")
     fig.suptitle("Distribution of R-R intervals")
 

neurokit2/stats/summary.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 import matplotlib.pyplot as plt
 import numpy as np
+import scipy.stats as st
 
 from .density import density
 from .rescale import rescale
 
 
-def summary_plot(x, errorbar=False, **kwargs):
+def summary_plot(x, errorbars=0, **kwargs):
     """Descriptive plot.
 
     Visualize a distribution with density, histogram, boxplot and rugs plots all at once.
@@ -32,9 +33,9 @@ def summary_plot(x, errorbar=False, **kwargs):
     # Histogram
     counts, bins = np.histogram(x, **kwargs)
     bin_centers = 0.5*(bins[1:] + bins[:-1])
-    menStd = np.sqrt(counts)
-    if errorbar:
-        ax.errorbar(bin_centers, counts, yerr=menStd, ecolor="#228B22", fmt='.', capsize=5, capthick=2)
+    if errorbars > 0:
+        samperr = np.std(counts) / np.sqrt(counts) * (st.norm.ppf(1-(1-errorbars)/2))
+        ax.errorbar(bin_centers, counts, yerr=samperr, ecolor="#FF8C00", fmt='.', capsize=5, capthick=2)
     ax.hist(bins[:-1], bins, weights=counts, color="#2196F3", edgecolor="white", zorder=1, **kwargs)
 
     # Density