def _add_regression_line(self, ax: plt.subplot, x: np.array, y: np.array):
"""
Build linear regression of x y data and plot on included ax
Parameters
----------
ax
pyplot AxesSubplot instance
x
numpy array, 1d
y
numpy array, 1d
Returns
-------
str
regression line slope rounded to 6 places as string
"""
# nan not allowed for regression line
filter_nan = np.logical_or(np.isnan(x), np.isnan(y))
x = x[~filter_nan]
y = y[~filter_nan]
slopes, intercepts, stderrs, percent_deviation = linear_regression(x, y)
slope_label = np.round(slopes, 6)
ax.plot(x, slopes * x + intercepts,
label='y = {}x + {}'.format(slope_label, np.round(intercepts, 6)), color='red')
ax.legend()
return slope_label
def plot_allowable_percent_deviation(self, subplot: plt.subplot = None):
"""
Plot the correlation plot between ping time and percent deviation in the ping slope linear regression. Percent
deviation here is related to the standard error of the y in the regression. Include bounds for invalid data
in the plot as a filled in red area. According to source paper, greater than 5% should be rejected.
Need to include segment identification in final version for exluding greater than 5%
Parameters
----------
subplot
pyplot AxesSubplot instance to add to, if None will generate new instance
"""
if subplot is None:
subplot = plt.subplots(1)[1]
subplot.plot(self.times, self.slope_percent_deviation)
subplot.set_ylim(self.slope_percent_deviation.min(),
np.max([6,
self.slope_percent_deviation.max() * 1.5]))
subplot.axhline(5, c='red', linestyle='--')
subplot.fill_between(self.times,
5,
np.max(
[6,
self.slope_percent_deviation.max() * 1.5]),
color='red',
alpha=0.2)
subplot.set_title('Allowable Percent Deviation (red = data unusable)')
subplot.set_xlabel('Time (s)')
subplot.set_ylabel('% deviation')