Code Review
/
csit.git
/ blobdiff
commit
grep
author
committer
pickaxe
?
search:
re
summary
|
shortlog
|
log
|
commit
|
commitdiff
|
review
|
tree
raw
|
inline
| side by side
UTI: Add normalization to iterative data
[csit.git]
/
resources
/
tools
/
dash
/
app
/
pal
/
report
/
graphs.py
diff --git
a/resources/tools/dash/app/pal/report/graphs.py
b/resources/tools/dash/app/pal/report/graphs.py
index
d5dd0b8
..
92cf5ca
100644
(file)
--- 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
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",
_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
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
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":
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_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
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":
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)
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
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(
"""
"""
table = pd.DataFrame(