def generate_umami(dataframe, plot_metrics, highlight_index=-1, show_empty=False, show_invariant=False):
"""
Generate a full UMAMI diagram based on an input dataframe and a list of
column names. Relies on abcutil.CONFIG to create labels and determine if
big_is_good
"""
umami = tokio.analysis.umami.Umami()
for metric in plot_metrics:
try:
num_nans = sum(numpy.isnan(dataframe[metric]))
except TypeError:
num_nans = 0
if (len(dataframe[metric].unique()) == 1) and (not show_invariant):
warnings.warn("Skipping %s (no change in value)" % metric)
continue
if (len(dataframe[metric]) == num_nans) and (not show_empty):
warnings.warn("Skipping %s (all values are NaN)" % metric)
continue
label = abcutils.CONFIG['metric_labels'].get(metric, metric)
big_is_good = abcutils.CONFIG['metric_big_is_good'].get(metric, True)
umami[metric] = tokio.analysis.umami.UmamiMetric(
timestamps=dataframe['_datetime_start'],
values=dataframe[metric],
label=label,
big_is_good=big_is_good)
return umami.plot(highlight_index=highlight_index)
def test_umami_plot_to_file():
"""
Ensure that basic UMAMI plot can be generated
"""
umami = build_umami_from_sample()
fig = umami.plot(output_file=tokiotest.TEMP_FILE.name)
print "Wrote output to %s" % tokiotest.TEMP_FILE.name
verify_umami_fig(fig)
def test_umami_plot_uneven_data():
"""
Ensure that basic UMAMI plot can be generated with uneven data
"""
umami = build_umami_from_sample(datasets=SAMPLE_DATA_UNEVEN)
fig = umami.plot(output_file=tokiotest.TEMP_FILE.name)
print("Wrote output to %s" % tokiotest.TEMP_FILE.name)
verify_umami_fig(fig, datasets=SAMPLE_DATA_UNEVEN)
def test_umamimetric_append():
"""
UmamiMetric append functionality
"""
umami_metrics = []
for index, sample_data in enumerate(SAMPLE_DATA):
umami_metric = tokio.analysis.umami.UmamiMetric(
timestamps=[],
values=[],
label="Test Metric %d" % index,
big_is_good=True)
print "%d: sample_data is %d units long (%s)" % (
index, len(sample_data), json.dumps(sample_data))
for jndex, sample_datum in enumerate(sample_data):
umami_metric.append(SAMPLE_TIMES[jndex], sample_datum)
umami_metrics.append(umami_metric)
umami = tokio.analysis.umami.Umami()
for index, umami_metric in enumerate(umami_metrics):
umami["test_metric_%d" % index] = umami_metric
fig = umami.plot()
verify_umami_fig(fig)