def do_analysis():
riffdata = Riffdata()
meetings = riffdata.get_meetings_with_participant_utterances()
print(f'Found utterances from {len(meetings)} meetings')
meeting_ids = list(meetings)
# Just to get an idea of what is in a meeting w/ participant utterances print 1
_print_participant_uts_info(meeting_ids[0], meetings[meeting_ids[0]])
durations = get_utterance_durations(meetings)
print(f'Found {len(durations)} utterances')
durations.sort()
print(
f'shortest utterance was {durations[0]}ms and longest was {durations[-1]}ms'
)
buckets, bucket_cnt, graph_ranges = _distribute_durations(durations)
_print_bucket_data(buckets, bucket_cnt)
x, y = _make_xy_sets_to_plot(x_src=buckets,
y_src=bucket_cnt,
ranges=graph_ranges)
# pylint: disable=consider-using-enumerate
fig, ax = plt.subplots(len(x), 1)
for plot in range(0, len(x)):
my_plotter(ax[plot], x[plot], y[plot], {'marker': 'x'})
fig.savefig('plot.png')
def do_analysis():
riffdata = Riffdata()
meetings = riffdata.get_meetings_with_participant_utterances()
print(f'Found utterances from {len(meetings)} meetings')
gaps = get_utterance_gaps(meetings)
x = np.array(gaps)
fig, ax = plt.subplots()
# the histogram of the data (see example: https://matplotlib.org/gallery/statistics/histogram_features.html)
num_bins = 50
ax.hist(x, num_bins, range=(0, 4000))
fig.savefig('plot_gap.png')
def do_analysis():
riffdata = Riffdata()
meetings = riffdata.get_meetings_with_participant_utterances()
print(f'Found utterances from {len(meetings)} meetings')
zerolen_ut_distribution = get_zerolen_ut_distribution(meetings)
percentile_size = 100 // len(zerolen_ut_distribution)
x = np.array(range(percentile_size, 101, percentile_size))
y = np.array(zerolen_ut_distribution)
# pylint: disable=consider-using-enumerate
fig, ax = plt.subplots()
my_plotter(ax, x, y, {'marker': 'x'})
fig.savefig('plot_0_distrib.png')