line lengths adjusted
@@ -184,8 +184,9 @@ def plot_model_comparison(result, sort=False, colors=None,
experimental conditions.
'dots':
- Draws dots for each data-point, i.e. first dimension of the evaluation tensor.
- This is primarily sensible for fixed evaluation where this dimension
+ Draws dots for each data-point, i.e. first dimension of
+ the evaluation tensor. This is primarily sensible for
+ fixed evaluation where this dimension
corresponds to the subjects in the experiment.
test_type (string):
@@ -203,9 +204,10 @@ def plot_model_comparison(result, sort=False, colors=None,
performs wilcoxon signed rank sum tests
Returns:
- (matplotlib.pyplot.Figure, matplotlib.pyplot.Axis, matplotlib.pyplot.Axis):
- the figure and axes the plots were made into. This allows further modification
- saving and printing of the figure.
+ (matplotlib.pyplot.Figure, matplotlib.pyplot.Axis,
+ matplotlib.pyplot.Axis):
+ the figure and axes the plots were made into.
+ This allows further modification, saving and printing.
"""
@@ -341,7 +343,8 @@ def plot_model_comparison(result, sort=False, colors=None,
errorbar_low = np.sqrt(np.maximum(model_var, 0))
errorbar_high = np.sqrt(np.maximum(model_var, 0))
ax.errorbar(np.arange(evaluations.shape[1]), perf,
- yerr=[errorbar_low, errorbar_high], fmt='none', ecolor='k',
+ yerr=[errorbar_low, errorbar_high],
+ fmt='none', ecolor='k',
capsize=0, linewidth=3)
elif error_bars[0:2].lower() == 'ci':
if len(error_bars) == 2:
@@ -379,7 +382,8 @@ def plot_model_comparison(result, sort=False, colors=None,
for i in range(evaluations.shape[1]):
ax.plot(i - 0.2 + 0.4 * np.random.rand(evaluations.shape[2]),
np.mean(evaluations[:, i], 0), '.', markersize=10,
- markerfacecolor=(0, 0, 0, 0.4), markeredgecolor=(0, 0, 0, 0))
+ markerfacecolor=(0, 0, 0, 0.4),
+ markeredgecolor=(0, 0, 0, 0))
else:
raise Exception('plot_model_comparison: Argument ' +
'error_bars is incorrectly defined as '
@@ -977,7 +981,8 @@ def _get_model_comp_descr(test_type, n_models, multiple_pair_testing, alpha,
model_comp_descr = (model_comp_descr + ' ' +
str(CI_percent) + '% confidence interval.')
elif error_bars.lower() == 'sem':
- model_comp_descr = (model_comp_descr +
+ model_comp_descr = (
+ model_comp_descr +
'Error bars indicate the standard error of the mean.')
elif error_bars.lower() == 'sem':
model_comp_descr = (model_comp_descr +
