diff options
Diffstat (limited to 'csit.infra.dash/app/cdash/coverage')
| -rw-r--r-- | csit.infra.dash/app/cdash/coverage/layout.py | 2 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/coverage/tables.py | 298 |
2 files changed, 166 insertions, 134 deletions
diff --git a/csit.infra.dash/app/cdash/coverage/layout.py b/csit.infra.dash/app/cdash/coverage/layout.py index 03d2da7fb7..f519f5a8ac 100644 --- a/csit.infra.dash/app/cdash/coverage/layout.py +++ b/csit.infra.dash/app/cdash/coverage/layout.py @@ -92,7 +92,7 @@ class Layout: if dut == "dpdk": area = "dpdk" else: - area = "-".join(lst_test_id[3:-2]) + area = ".".join(lst_test_id[3:-2]) suite = lst_test_id[-2].replace("2n1l-", "").replace("1n1l-", "").\ replace("2n-", "") test = lst_test_id[-1] diff --git a/csit.infra.dash/app/cdash/coverage/tables.py b/csit.infra.dash/app/cdash/coverage/tables.py index a773a2280c..31b227e9a8 100644 --- a/csit.infra.dash/app/cdash/coverage/tables.py +++ b/csit.infra.dash/app/cdash/coverage/tables.py @@ -75,8 +75,10 @@ def select_coverage_data( inplace=True ) + ttype = df["test_type"].to_list()[0] + # Prepare the coverage data - def _latency(hdrh_string: str, percentile: float) -> int: + def _laten(hdrh_string: str, percentile: float) -> int: """Get latency from HDRH string for given percentile. :param hdrh_string: Encoded HDRH string. @@ -105,109 +107,118 @@ def select_coverage_data( 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_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: _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 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 @@ -224,34 +235,59 @@ def coverage_tables(data: pd.DataFrame, selected: dict) -> list: 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], + 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( @@ -267,18 +303,14 @@ def coverage_tables(data: pd.DataFrame, selected: dict) -> list: 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 + ) |