def plot_mean_sr(sr_list, time_sr, title, y_title, x_title, color=None):
'''Plot a list of series using its mean, with error bar using std'''
mean_sr, std_sr = util.calc_srs_mean_std(sr_list)
max_sr = mean_sr + std_sr
min_sr = mean_sr - std_sr
max_y = max_sr.tolist()
min_y = min_sr.tolist()
x = time_sr.tolist()
color = color or get_palette(1)[0]
main_trace = go.Scatter(
x=x,
y=mean_sr,
mode='lines',
showlegend=False,
line={
'color': color,
'width': 1
},
)
envelope_trace = go.Scatter(
x=x + x[::-1],
y=max_y + min_y[::-1],
showlegend=False,
line={'color': 'rgba(0, 0, 0, 0)'},
fill='tozerox',
fillcolor=lower_opacity(color, 0.2),
)
data = [main_trace, envelope_trace]
layout = create_layout(title=title, y_title=y_title, x_title=x_title)
fig = go.Figure(data, layout)
return fig
def calc_consistency(local_strs_list):
'''
Calculate consistency for metric
con &= 1 - \frac{\sum_{i=0}^N 2 stdev_j(str_{i,j})}{\sum_{i=0}^N avg_j(str_{i,j})}
@param Series:local_strs_list A list of multiple series of local strengths from different sessions
@returns float:con, Series:local_cons
'''
mean_local_strs, std_local_strs = util.calc_srs_mean_std(local_strs_list)
local_cons = 1 - 2 * std_local_strs / mean_local_strs
con = 1 - 2 * std_local_strs.sum() / mean_local_strs.sum()
return con, local_cons