# See the License for the specific language governing permissions and
# limitations under the License.
-"""Function used by Dash applications.
+"""Functions used by Dash applications.
"""
import pandas as pd
+import plotly.graph_objects as go
import dash_bootstrap_components as dbc
-from numpy import isnan
+import hdrh.histogram
+import hdrh.codec
+
+from math import sqrt
from dash import dcc
from datetime import datetime
-from ..jumpavg import classify
from ..utils.constants import Constants as C
from ..utils.url_processing import url_encode
-def classify_anomalies(data):
- """Process the data and return anomalies and trending values.
-
- Gather data into groups with average as trend value.
- Decorate values within groups to be normal,
- the first value of changed average as a regression, or a progression.
-
- :param data: Full data set with unavailable samples replaced by nan.
- :type data: OrderedDict
- :returns: Classification and trend values
- :rtype: 3-tuple, list of strings, list of floats and list of floats
- """
- # NaN means something went wrong.
- # Use 0.0 to cause that being reported as a severe regression.
- bare_data = [0.0 if isnan(sample) else sample for sample in data.values()]
- # TODO: Make BitCountingGroupList a subclass of list again?
- group_list = classify(bare_data).group_list
- group_list.reverse() # Just to use .pop() for FIFO.
- classification = list()
- avgs = list()
- stdevs = list()
- active_group = None
- values_left = 0
- avg = 0.0
- stdv = 0.0
- for sample in data.values():
- if isnan(sample):
- classification.append("outlier")
- avgs.append(sample)
- stdevs.append(sample)
- continue
- if values_left < 1 or active_group is None:
- values_left = 0
- while values_left < 1: # Ignore empty groups (should not happen).
- active_group = group_list.pop()
- values_left = len(active_group.run_list)
- avg = active_group.stats.avg
- stdv = active_group.stats.stdev
- classification.append(active_group.comment)
- avgs.append(avg)
- stdevs.append(stdv)
- values_left -= 1
- continue
- classification.append("normal")
- avgs.append(avg)
- stdevs.append(stdv)
- values_left -= 1
- return classification, avgs, stdevs
-
-
def get_color(idx: int) -> str:
"""Returns a color from the list defined in Constants.PLOT_COLORS defined by
its index.
}
-def get_list_group_items(tests: list) -> list:
- """Generate list of ListGroupItems with checkboxes with selected tests.
-
- :param tests: List of tests to be displayed in the ListGroup.
- :type tests: list
- :returns: List of ListGroupItems with checkboxes with selected tests.
+def get_list_group_items(
+ items: list,
+ type: str,
+ colorize: bool=True,
+ add_index: bool=False
+ ) -> list:
+ """Generate list of ListGroupItems with checkboxes with selected items.
+
+ :param items: List of items to be displayed in the ListGroup.
+ :param type: The type part of an element ID.
+ :param colorize: If True, the color of labels is set, otherwise the default
+ color is used.
+ :param add_index: Add index to the list items.
+ :type items: list
+ :type type: str
+ :type colorize: bool
+ :type add_index: bool
+ :returns: List of ListGroupItems with checkboxes with selected items.
:rtype: list
"""
- return [
- dbc.ListGroupItem(
- children=[
- dbc.Checkbox(
- id={"type": "sel-cl", "index": i},
- label=l["id"],
- value=False,
- label_class_name="m-0 p-0",
- label_style={
- "font-size": ".875em",
- "color": get_color(i)
- },
- class_name="info"
- )
- ],
- class_name="p-0"
- ) for i, l in enumerate(tests)
- ]
+
+ children = list()
+ for i, l in enumerate(items):
+ idx = f"{i + 1}. " if add_index else str()
+ label = f"{idx}{l['id']}" if isinstance(l, dict) else f"{idx}{l}"
+ children.append(
+ dbc.ListGroupItem(
+ children=[
+ dbc.Checkbox(
+ id={"type": type, "index": i},
+ label=label,
+ value=False,
+ label_class_name="m-0 p-0",
+ label_style={
+ "font-size": ".875em",
+ "color": get_color(i) if colorize else "#55595c"
+ },
+ class_name="info"
+ )
+ ],
+ class_name="p-0"
+ )
+ )
+
+ return children
+
+
+def relative_change_stdev(mean1, mean2, std1, std2):
+ """Compute relative standard deviation of change of two values.
+
+ The "1" values are the base for comparison.
+ Results are returned as percentage (and percentual points for stdev).
+ Linearized theory is used, so results are wrong for relatively large stdev.
+
+ :param mean1: Mean of the first number.
+ :param mean2: Mean of the second number.
+ :param std1: Standard deviation estimate of the first number.
+ :param std2: Standard deviation estimate of the second number.
+ :type mean1: float
+ :type mean2: float
+ :type std1: float
+ :type std2: float
+ :returns: Relative change and its stdev.
+ :rtype: float
+ """
+ mean1, mean2 = float(mean1), float(mean2)
+ quotient = mean2 / mean1
+ first = std1 / mean1
+ second = std2 / mean2
+ std = quotient * sqrt(first * first + second * second)
+ return (quotient - 1) * 100, std * 100
+
+
+def get_hdrh_latencies(row: pd.Series, name: str) -> dict:
+ """Get the HDRH latencies from the test data.
+
+ :param row: A row fron the data frame with test data.
+ :param name: The test name to be displayed as the graph title.
+ :type row: pandas.Series
+ :type name: str
+ :returns: Dictionary with HDRH latencies.
+ :rtype: dict
+ """
+
+ latencies = {"name": name}
+ for key in C.LAT_HDRH:
+ try:
+ latencies[key] = row[key]
+ except KeyError:
+ return None
+
+ return latencies
+
+
+def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
+ """Generate HDR Latency histogram graphs.
+
+ :param data: HDRH data.
+ :param layout: Layout of plot.ly graph.
+ :type data: dict
+ :type layout: dict
+ :returns: HDR latency Histogram.
+ :rtype: plotly.graph_objects.Figure
+ """
+
+ fig = None
+
+ traces = list()
+ for idx, (lat_name, lat_hdrh) in enumerate(data.items()):
+ try:
+ decoded = hdrh.histogram.HdrHistogram.decode(lat_hdrh)
+ except (hdrh.codec.HdrLengthException, TypeError):
+ continue
+ previous_x = 0.0
+ prev_perc = 0.0
+ xaxis = list()
+ yaxis = list()
+ hovertext = list()
+ for item in decoded.get_recorded_iterator():
+ # The real value is "percentile".
+ # For 100%, we cut that down to "x_perc" to avoid
+ # infinity.
+ percentile = item.percentile_level_iterated_to
+ x_perc = min(percentile, C.PERCENTILE_MAX)
+ xaxis.append(previous_x)
+ yaxis.append(item.value_iterated_to)
+ hovertext.append(
+ f"<b>{C.GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
+ f"Direction: {('W-E', 'E-W')[idx % 2]}<br>"
+ f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
+ f"Latency: {item.value_iterated_to}uSec"
+ )
+ next_x = 100.0 / (100.0 - x_perc)
+ xaxis.append(next_x)
+ yaxis.append(item.value_iterated_to)
+ hovertext.append(
+ f"<b>{C.GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
+ f"Direction: {('W-E', 'E-W')[idx % 2]}<br>"
+ f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
+ f"Latency: {item.value_iterated_to}uSec"
+ )
+ previous_x = next_x
+ prev_perc = percentile
+
+ traces.append(
+ go.Scatter(
+ x=xaxis,
+ y=yaxis,
+ name=C.GRAPH_LAT_HDRH_DESC[lat_name],
+ mode="lines",
+ legendgroup=C.GRAPH_LAT_HDRH_DESC[lat_name],
+ showlegend=bool(idx % 2),
+ line=dict(
+ color=get_color(int(idx/2)),
+ dash="solid",
+ width=1 if idx % 2 else 2
+ ),
+ hovertext=hovertext,
+ hoverinfo="text"
+ )
+ )
+ if traces:
+ fig = go.Figure()
+ fig.add_traces(traces)
+ layout_hdrh = layout.get("plot-hdrh-latency", None)
+ if lat_hdrh:
+ fig.update_layout(layout_hdrh)
+
+ return fig