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author | 2022-06-29 14:27:19 +0200 | |
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committer | 2022-07-14 14:57:00 +0200 | |
commit | 419b091b7e79e8f35c99fd10d6ea34c6ff3d7723 (patch) | |
tree | f3a79f479d1f6f64673ff1b20ec3278a8b235dca /resources/tools/dash/app/pal/report/graphs.py | |
parent | 6513c1b1663ae60f6870a2989a2d8e794b53a67e (diff) |
UTI: Add normalization to iterative data
Change-Id: Iedfadd0a6a2b360e047b348bc21b35c2d2be1f24
Signed-off-by: Tibor Frank <tifrank@cisco.com>
Diffstat (limited to 'resources/tools/dash/app/pal/report/graphs.py')
-rw-r--r-- | resources/tools/dash/app/pal/report/graphs.py | 36 |
1 files changed, 30 insertions, 6 deletions
diff --git a/resources/tools/dash/app/pal/report/graphs.py b/resources/tools/dash/app/pal/report/graphs.py index d5dd0b8cce..92cf5ca989 100644 --- a/resources/tools/dash/app/pal/report/graphs.py +++ b/resources/tools/dash/app/pal/report/graphs.py @@ -24,6 +24,22 @@ import hdrh.histogram import hdrh.codec +_FREQURENCY = { # [GHz] + "2n-aws": 1.000, + "2n-dnv": 2.000, + "2n-clx": 2.300, + "2n-icx": 2.600, + "2n-skx": 2.500, + "2n-tx2": 2.500, + "2n-zn2": 2.900, + "3n-alt": 3.000, + "3n-aws": 1.000, + "3n-dnv": 2.000, + "3n-icx": 2.600, + "3n-skx": 2.500, + "3n-tsh": 2.200 +} + _VALUE = { "mrr": "result_receive_rate_rate_values", "ndr": "result_ndr_lower_rate_value", @@ -144,7 +160,8 @@ def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame: return df -def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict) -> tuple: +def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict, + normalize: bool) -> tuple: """ """ @@ -162,13 +179,18 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict) -> tuple: itm_data = select_iterative_data(data, itm) if itm_data.empty: continue + phy = itm["phy"].split("-") + topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str() + norm_factor = 2.0 / _FREQURENCY[topo_arch] if normalize else 1.0 if itm["testtype"] == "mrr": - y_data = itm_data[_VALUE[itm["testtype"]]].to_list()[0] - if y_data.size > 0: + y_data_raw = itm_data[_VALUE[itm["testtype"]]].to_list()[0] + y_data = [y * norm_factor for y in y_data_raw] + if len(y_data) > 0: y_tput_max = \ max(y_data) if max(y_data) > y_tput_max else y_tput_max else: - y_data = itm_data[_VALUE[itm["testtype"]]].to_list() + y_data_raw = itm_data[_VALUE[itm["testtype"]]].to_list() + y_data = [y * norm_factor for y in y_data_raw] if y_data: y_tput_max = \ max(y_data) if max(y_data) > y_tput_max else y_tput_max @@ -190,7 +212,8 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict) -> tuple: show_tput = True if itm["testtype"] == "pdr": - y_lat = itm_data[_VALUE["pdr-lat"]].to_list() + y_lat_row = itm_data[_VALUE["pdr-lat"]].to_list() + y_lat = [y * norm_factor for y in y_lat_row] if y_lat: y_lat_max = max(y_lat) if max(y_lat) > y_lat_max else y_lat_max nr_of_samples = len(y_lat) @@ -232,7 +255,8 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict) -> tuple: return fig_tput, fig_lat -def table_comparison(data: pd.DataFrame, sel:dict) -> pd.DataFrame: +def table_comparison(data: pd.DataFrame, sel:dict, + normalize: bool) -> pd.DataFrame: """ """ table = pd.DataFrame( |