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-rw-r--r--csit.infra.dash/app/cdash/coverage/tables.py100
1 files changed, 24 insertions, 76 deletions
diff --git a/csit.infra.dash/app/cdash/coverage/tables.py b/csit.infra.dash/app/cdash/coverage/tables.py
index a34b80f024..6cc2956637 100644
--- a/csit.infra.dash/app/cdash/coverage/tables.py
+++ b/csit.infra.dash/app/cdash/coverage/tables.py
@@ -28,7 +28,8 @@ from ..utils.constants import Constants as C
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.
@@ -37,9 +38,11 @@ def select_coverage_data(
: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
@@ -78,7 +81,7 @@ def select_coverage_data(
ttype = df["test_type"].to_list()[0]
# Prepare the coverage data
- def _laten(hdrh_string: str, percentile: float) -> int:
+ def _latency(hdrh_string: str, percentile: float) -> int:
"""Get latency from HDRH string for given percentile.
:param hdrh_string: Encoded HDRH string.
@@ -126,78 +129,16 @@ def select_coverage_data(
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: _laten(row["result_latency_forward_pdr_10_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_10% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_10_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_10% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_10_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P50"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_50_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_50_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_50_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P50"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_90_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_90_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_90_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P50"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_10_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_10_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_10_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P50"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_50_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_50_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_50_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P50"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_90_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_90_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_90_hdrh"], 99.0),
- 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
@@ -222,19 +163,26 @@ def select_coverage_data(
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):
+ 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 = [
{