def select_coverage_data(
data: pd.DataFrame,
selected: dict,
- csv: bool=False
+ csv: bool=False,
+ show_latency: bool=True
) -> list:
"""Select coverage data for the tables and generate tables as pandas data
frames.
:param selected: Dictionary with user selection.
:param csv: If True, pandas data frame with selected coverage data is
returned for "Download Data" feature.
+ :param show_latency: If True, latency is displayed in the tables.
:type data: pandas.DataFrame
:type selected: dict
:type csv: bool
+ :type show_latency: bool
:returns: List of tuples with suite name (str) and data (pandas dataframe)
or pandas dataframe if csv is True.
:rtype: list[tuple[str, pandas.DataFrame], ] or pandas.DataFrame
inplace=True
)
+ ttype = df["test_type"].to_list()[0]
+
# Prepare the coverage data
def _latency(hdrh_string: str, percentile: float) -> int:
"""Get latency from HDRH string for given percentile.
return test_id.split(".")[-1].replace("-ndrpdr", "")
cov = pd.DataFrame()
- cov["suite"] = df.apply(lambda row: _get_suite(row["test_id"]), axis=1)
+ cov["Suite"] = df.apply(lambda row: _get_suite(row["test_id"]), axis=1)
cov["Test Name"] = df.apply(lambda row: _get_test(row["test_id"]), axis=1)
- cov["Throughput_Unit"] = df["result_pdr_lower_rate_unit"]
- cov["Throughput_NDR"] = df.apply(
- lambda row: row["result_ndr_lower_rate_value"] / 1e6, axis=1
- )
- cov["Throughput_NDR_Mbps"] = df.apply(
- lambda row: row["result_ndr_lower_bandwidth_value"] /1e9, axis=1
- )
- cov["Throughput_PDR"] = \
- df.apply(lambda row: row["result_pdr_lower_rate_value"] / 1e6, axis=1)
- cov["Throughput_PDR_Mbps"] = df.apply(
- lambda row: row["result_pdr_lower_bandwidth_value"] /1e9, axis=1
- )
- cov["Latency Forward [us]_10% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_10% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_10% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 99.0),
- axis=1
- )
+
+ if ttype == "device":
+ cov = cov.assign(Result="PASS")
+ else:
+ cov["Throughput_Unit"] = df["result_pdr_lower_rate_unit"]
+ cov["Throughput_NDR"] = df.apply(
+ lambda row: row["result_ndr_lower_rate_value"] / 1e6, axis=1
+ )
+ cov["Throughput_NDR_Gbps"] = df.apply(
+ lambda row: row["result_ndr_lower_bandwidth_value"] / 1e9, axis=1
+ )
+ cov["Throughput_PDR"] = df.apply(
+ lambda row: row["result_pdr_lower_rate_value"] / 1e6, axis=1
+ )
+ cov["Throughput_PDR_Gbps"] = df.apply(
+ lambda row: row["result_pdr_lower_bandwidth_value"] / 1e9, axis=1
+ )
+ if show_latency:
+ for way in ("Forward", "Reverse"):
+ for pdr in (10, 50, 90):
+ for perc in (50, 90, 99):
+ latency = f"result_latency_{way.lower()}_pdr_{pdr}_hdrh"
+ cov[f"Latency {way} [us]_{pdr}% PDR_P{perc}"] = \
+ df.apply(
+ lambda row: _latency(row[latency], perc),
+ axis=1
+ )
if csv:
return cov
- # Split data into tabels depending on the test suite.
- for suite in cov["suite"].unique().tolist():
- df_suite = pd.DataFrame(cov.loc[(cov["suite"] == suite)])
- unit = df_suite["Throughput_Unit"].tolist()[0]
- df_suite.rename(
- columns={
- "Throughput_NDR": f"Throughput_NDR_M{unit}",
- "Throughput_PDR": f"Throughput_PDR_M{unit}"
- },
- inplace=True
- )
- df_suite.drop(["suite", "Throughput_Unit"], axis=1, inplace=True)
+ # Split data into tables depending on the test suite.
+ for suite in cov["Suite"].unique().tolist():
+ df_suite = pd.DataFrame(cov.loc[(cov["Suite"] == suite)])
+
+ if ttype !="device":
+ unit = df_suite["Throughput_Unit"].tolist()[0]
+ df_suite.rename(
+ columns={
+ "Throughput_NDR": f"Throughput_NDR_M{unit}",
+ "Throughput_PDR": f"Throughput_PDR_M{unit}"
+ },
+ inplace=True
+ )
+ df_suite.drop(["Suite", "Throughput_Unit"], axis=1, inplace=True)
+
l_data.append((suite, df_suite, ))
+
return l_data
-def coverage_tables(data: pd.DataFrame, selected: dict) -> list:
+def coverage_tables(
+ data: pd.DataFrame,
+ selected: dict,
+ show_latency: bool=True
+ ) -> list:
"""Generate an accordion with coverage tables.
:param data: Coverage data.
:param selected: Dictionary with user selection.
+ :param show_latency: If True, latency is displayed in the tables.
:type data: pandas.DataFrame
:type selected: dict
+ :type show_latency: bool
:returns: Accordion with suite names (titles) and tables.
:rtype: dash_bootstrap_components.Accordion
"""
accordion_items = list()
- for suite, cov_data in select_coverage_data(data, selected):
- cols = list()
- for idx, col in enumerate(cov_data.columns):
- if idx == 0:
- cols.append({
- "name": ["", "", col],
+ sel_data = select_coverage_data(data, selected, show_latency=show_latency)
+ for suite, cov_data in sel_data:
+ if len(cov_data.columns) == 3: # VPP Device
+ cols = [
+ {
+ "name": col,
"id": col,
"deletable": False,
"selectable": False,
"type": "text"
- })
- elif idx < 5:
- cols.append({
- "name": col.split("_"),
- "id": col,
- "deletable": False,
- "selectable": False,
- "type": "numeric",
- "format": Format(precision=2, scheme=Scheme.fixed)
- })
- else:
- cols.append({
- "name": col.split("_"),
- "id": col,
- "deletable": False,
- "selectable": False,
- "type": "numeric",
- "format": Format(precision=0, scheme=Scheme.fixed)
- })
+ } for col in cov_data.columns
+ ]
+ style_cell={"textAlign": "left"}
+ style_cell_conditional=[
+ {
+ "if": {"column_id": "Result"},
+ "textAlign": "right"
+ }
+ ]
+ else: # Performance
+ cols = list()
+ for idx, col in enumerate(cov_data.columns):
+ if idx == 0:
+ cols.append({
+ "name": ["", "", col],
+ "id": col,
+ "deletable": False,
+ "selectable": False,
+ "type": "text"
+ })
+ elif idx < 5:
+ cols.append({
+ "name": col.split("_"),
+ "id": col,
+ "deletable": False,
+ "selectable": False,
+ "type": "numeric",
+ "format": Format(precision=2, scheme=Scheme.fixed)
+ })
+ else:
+ cols.append({
+ "name": col.split("_"),
+ "id": col,
+ "deletable": False,
+ "selectable": False,
+ "type": "numeric",
+ "format": Format(precision=0, scheme=Scheme.fixed)
+ })
+ style_cell={"textAlign": "right"}
+ style_cell_conditional=[
+ {
+ "if": {"column_id": "Test Name"},
+ "textAlign": "left"
+ }
+ ]
accordion_items.append(
dbc.AccordionItem(
columns=cols,
data=cov_data.to_dict("records"),
merge_duplicate_headers=True,
- editable=True,
+ editable=False,
filter_action="none",
sort_action="native",
sort_mode="multi",
selected_columns=[],
selected_rows=[],
page_action="none",
- style_cell={"textAlign": "right"},
- style_cell_conditional=[{
- "if": {"column_id": "Test Name"},
- "textAlign": "left"
- }]
+ style_cell=style_cell,
+ style_cell_conditional=style_cell_conditional
)
)
)
-
return dbc.Accordion(
- children=accordion_items,
- class_name="gy-2 p-0",
- start_collapsed=True,
- always_open=True
- )
+ children=accordion_items,
+ class_name="gy-1 p-0",
+ start_collapsed=True,
+ always_open=True
+ )
Code Review / csit.git / blobdiff