def test_histograms_read_yaml(self):
values = [
V1Event(
timestamp=dt_parser.parse("2018-12-11 10:24:57"),
histogram=V1EventHistogram(values=[10], counts=[1]),
step=12,
),
V1Event(
timestamp=dt_parser.parse("2018-12-11 10:25:57"),
histogram=V1EventHistogram(values=[10, 1, 1], counts=[1, 1,
1]),
step=13,
),
V1Event(
timestamp=dt_parser.parse("2018-12-11 10:26:57"),
histogram=V1EventHistogram(values=[10, 112, 12, 1],
counts=[12, 1, 1, 1]),
step=14,
),
]
events = V1Events.read(
name="foo",
kind="histogram",
data=os.path.abspath(
"tests/fixtures/polyboard/histogram/histogram_events.plx"),
)
assert events.name == "foo"
assert len(events.df.values) == 3
for i in range(3):
assert events.get_event_at(i).to_dict() == values[i].to_dict()
def histogram(values, bins, max_bins=None):
if not np:
logger.warning(NUMPY_ERROR_MESSAGE)
return UNKNOWN
values = to_np(values).astype(float)
if values.size == 0:
raise ValueError("The input has no element.")
values = values.reshape(-1)
counts, limits = np.histogram(values, bins=bins)
num_bins = len(counts)
if max_bins is not None and num_bins > max_bins:
subsampling = num_bins // max_bins
subsampling_remainder = num_bins % subsampling
if subsampling_remainder != 0:
counts = np.pad(
counts,
pad_width=[[0, subsampling - subsampling_remainder]],
mode="constant",
constant_values=0,
)
counts = counts.reshape(-1, subsampling).sum(axis=-1)
if counts.size == 0:
logger.warning("Tracking an empty histogram")
return UNKNOWN
return V1EventHistogram(values=values, counts=counts)
def test_histogram(self):
events = LoggedEventListSpec(
name="foo",
kind="histogram",
events=[
V1Event(
timestamp=dt_parser.parse("2018-12-11 10:24:57"),
histogram=V1EventHistogram(values=[10], counts=[1]),
step=12,
),
V1Event(
timestamp=dt_parser.parse("2018-12-11 11:24:57"),
histogram=V1EventHistogram(values=[10], counts=[1]),
step=13,
),
V1Event(
timestamp=dt_parser.parse("2018-12-11 12:24:57"),
histogram=V1EventHistogram(values=[10], counts=[1]),
step=14,
),
],
)
events_dict = events.to_dict()
assert events_dict == events.from_dict(events_dict).to_dict()
def np_histogram(values, counts):
return V1EventHistogram(values=values, counts=counts)