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C-Dash: Hover info and y-axis in trending and iterative graphs
[csit.git]
/
csit.infra.dash
/
app
/
cdash
/
report
/
graphs.py
diff --git
a/csit.infra.dash/app/cdash/report/graphs.py
b/csit.infra.dash/app/cdash/report/graphs.py
index
411a599
..
2d1f4b1
100644
(file)
--- a/
csit.infra.dash/app/cdash/report/graphs.py
+++ b/
csit.infra.dash/app/cdash/report/graphs.py
@@
-98,28
+98,40
@@
def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
lat_traces = list()
y_lat_max = 0
x_lat = list()
lat_traces = list()
y_lat_max = 0
x_lat = list()
+ y_units = set()
show_latency = False
show_tput = False
for idx, itm in enumerate(sel):
show_latency = False
show_tput = False
for idx, itm in enumerate(sel):
+
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 = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
if normalize else 1.0
phy = itm["phy"].split("-")
topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
if normalize else 1.0
+
+ if itm["area"] == "hoststack":
+ ttype = f"hoststack-{itm['testtype']}"
+ else:
+ ttype = itm["testtype"]
+
+ y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
+
if itm["testtype"] == "mrr":
if itm["testtype"] == "mrr":
- y_data_raw = itm_data[C.VALUE_ITER[
itm["testtype"]
]].to_list()[0]
+ y_data_raw = itm_data[C.VALUE_ITER[
ttype
]].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 = [(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_raw = itm_data[C.VALUE_ITER[
itm["testtype"]
]].to_list()
+ y_data_raw = itm_data[C.VALUE_ITER[
ttype
]].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
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
+
nr_of_samples = len(y_data)
tput_kwargs = dict(
y=y_data,
nr_of_samples = len(y_data)
tput_kwargs = dict(
y=y_data,
@@
-137,7
+149,7
@@
def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
tput_traces.append(go.Box(**tput_kwargs))
show_tput = True
tput_traces.append(go.Box(**tput_kwargs))
show_tput = True
- if
itm["testtype"]
== "pdr":
+ if
ttype
== "pdr":
y_lat_row = itm_data[C.VALUE_ITER["pdr-lat"]].to_list()
y_lat = [(y / norm_factor) for y in y_lat_row]
if y_lat:
y_lat_row = itm_data[C.VALUE_ITER["pdr-lat"]].to_list()
y_lat = [(y / norm_factor) for y in y_lat_row]
if y_lat:
@@
-166,8
+178,9
@@
def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
pl_tput = deepcopy(layout["plot-throughput"])
pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
pl_tput = deepcopy(layout["plot-throughput"])
pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
+ pl_tput["yaxis"]["title"] = f"Throughput [{'|'.join(sorted(y_units))}]"
if y_tput_max:
if y_tput_max:
- pl_tput["yaxis"]["range"] = [0, (int(y_tput_max / 1e6) +
1
) * 1e6]
+ pl_tput["yaxis"]["range"] = [0, (int(y_tput_max / 1e6) +
2
) * 1e6]
fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
if show_latency:
fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
if show_latency: