import plotly.offline as ploff
import plotly.graph_objs as plgo
-from plotly.subplots import make_subplots
from plotly.exceptions import PlotlyError
from pal_utils import mean, stdev
-COLORS = [u"SkyBlue", u"Olive", u"Purple", u"Coral", u"Indigo", u"Pink",
- u"Chocolate", u"Brown", u"Magenta", u"Cyan", u"Orange", u"Black",
- u"Violet", u"Blue", u"Yellow", u"BurlyWood", u"CadetBlue", u"Crimson",
- u"DarkBlue", u"DarkCyan", u"DarkGreen", u"Green", u"GoldenRod",
- u"LightGreen", u"LightSeaGreen", u"LightSkyBlue", u"Maroon",
- u"MediumSeaGreen", u"SeaGreen", u"LightSlateGrey"]
-
-REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*)-')
+COLORS = (
+ u"#1A1110",
+ u"#DA2647",
+ u"#214FC6",
+ u"#01786F",
+ u"#BD8260",
+ u"#FFD12A",
+ u"#A6E7FF",
+ u"#738276",
+ u"#C95A49",
+ u"#FC5A8D",
+ u"#CEC8EF",
+ u"#391285",
+ u"#6F2DA8",
+ u"#FF878D",
+ u"#45A27D",
+ u"#FFD0B9",
+ u"#FD5240",
+ u"#DB91EF",
+ u"#44D7A8",
+ u"#4F86F7",
+ u"#84DE02",
+ u"#FFCFF1",
+ u"#614051"
+)
+
+REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)-')
def generate_plots(spec, data):
generator = {
u"plot_nf_reconf_box_name": plot_nf_reconf_box_name,
u"plot_perf_box_name": plot_perf_box_name,
- u"plot_lat_err_bars_name": plot_lat_err_bars_name,
u"plot_tsa_name": plot_tsa_name,
u"plot_http_server_perf_box": plot_http_server_perf_box,
u"plot_nf_heatmap": plot_nf_heatmap,
- u"plot_lat_hdrh_bar_name": plot_lat_hdrh_bar_name,
- u"plot_lat_hdrh_percentile": plot_lat_hdrh_percentile,
u"plot_hdrh_lat_by_percentile": plot_hdrh_lat_by_percentile
}
logging.info(u"Done.")
-def plot_lat_hdrh_percentile(plot, input_data):
- """Generate the plot(s) with algorithm: plot_lat_hdrh_percentile
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- # Transform the data
- plot_title = plot.get(u"title", u"")
- logging.info(
- f" Creating the data set for the {plot.get(u'type', u'')} "
- f"{plot_title}."
- )
- data = input_data.filter_tests_by_name(
- plot, params=[u"latency", u"parent", u"tags", u"type"])
- if data is None or len(data[0][0]) == 0:
- logging.error(u"No data.")
- return
-
- fig = plgo.Figure()
-
- # Prepare the data for the plot
- directions = [u"W-E", u"E-W"]
- for color, test in enumerate(data[0][0]):
- try:
- if test[u"type"] in (u"NDRPDR",):
- if u"-pdr" in plot_title.lower():
- ttype = u"PDR"
- elif u"-ndr" in plot_title.lower():
- ttype = u"NDR"
- else:
- logging.warning(f"Invalid test type: {test[u'type']}")
- continue
- name = re.sub(REGEX_NIC, u"", test[u"parent"].
- replace(u'-ndrpdr', u'').
- replace(u'2n1l-', u''))
- for idx, direction in enumerate(
- (u"direction1", u"direction2", )):
- try:
- hdr_lat = test[u"latency"][ttype][direction][u"hdrh"]
- # TODO: Workaround, HDRH data must be aligned to 4
- # bytes, remove when not needed.
- hdr_lat += u"=" * (len(hdr_lat) % 4)
- xaxis = list()
- yaxis = list()
- hovertext = list()
- decoded = hdrh.histogram.HdrHistogram.decode(hdr_lat)
- for item in decoded.get_recorded_iterator():
- percentile = item.percentile_level_iterated_to
- if percentile != 100.0:
- xaxis.append(100.0 / (100.0 - percentile))
- yaxis.append(item.value_iterated_to)
- hovertext.append(
- f"Test: {name}<br>"
- f"Direction: {directions[idx]}<br>"
- f"Percentile: {percentile:.5f}%<br>"
- f"Latency: {item.value_iterated_to}uSec"
- )
- fig.add_trace(
- plgo.Scatter(
- x=xaxis,
- y=yaxis,
- name=name,
- mode=u"lines",
- legendgroup=name,
- showlegend=bool(idx),
- line=dict(
- color=COLORS[color]
- ),
- hovertext=hovertext,
- hoverinfo=u"text"
- )
- )
- except hdrh.codec.HdrLengthException as err:
- logging.warning(
- f"No or invalid data for HDRHistogram for the test "
- f"{name}\n{err}"
- )
- continue
- else:
- logging.warning(f"Invalid test type: {test[u'type']}")
- continue
- except (ValueError, KeyError) as err:
- logging.warning(repr(err))
-
- layout = deepcopy(plot[u"layout"])
-
- layout[u"title"][u"text"] = \
- f"<b>Latency:</b> {plot.get(u'graph-title', u'')}"
- fig[u"layout"].update(layout)
-
- # Create plot
- file_type = plot.get(u"output-file-type", u".html")
- logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
- try:
- # Export Plot
- ploff.plot(fig, show_link=False, auto_open=False,
- filename=f"{plot[u'output-file']}{file_type}")
- except PlotlyError as err:
- logging.error(f" Finished with error: {repr(err)}")
-
-
def plot_hdrh_lat_by_percentile(plot, input_data):
"""Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile
specified in the specification file.
if plot.get(u"include", None):
data = input_data.filter_tests_by_name(
plot,
- params=[u"latency", u"throughput", u"parent", u"tags", u"type"]
+ params=[u"name", u"latency", u"parent", u"tags", u"type"]
+ )[0][0]
+ elif plot.get(u"filter", None):
+ data = input_data.filter_data(
+ plot,
+ params=[u"name", u"latency", u"parent", u"tags", u"type"],
+ continue_on_error=True
)[0][0]
else:
job = list(plot[u"data"].keys())[0]
logging.error(u"No data.")
return
+ desc = {
+ u"LAT0": u"No-load.",
+ u"PDR10": u"Low-load, 10% PDR.",
+ u"PDR50": u"Mid-load, 50% PDR.",
+ u"PDR90": u"High-load, 90% PDR.",
+ u"PDR": u"Full-load, 100% PDR.",
+ u"NDR10": u"Low-load, 10% NDR.",
+ u"NDR50": u"Mid-load, 50% NDR.",
+ u"NDR90": u"High-load, 90% NDR.",
+ u"NDR": u"Full-load, 100% NDR."
+ }
+
graphs = [
u"LAT0",
u"PDR10",
logging.info(f" Generating the graph: {name_link}")
- desc = {
- u"LAT0": u"No-load.",
- u"PDR10": u"Low-load, 10% PDR.",
- u"PDR50": u"Mid-load, 50% PDR.",
- u"PDR90": u"High-load, 90% PDR.",
- u"PDR": u"Full-load, 100% PDR.",
- u"NDR10": u"Low-load, 10% NDR.",
- u"NDR50": u"Mid-load, 50% NDR.",
- u"NDR90": u"High-load, 90% NDR.",
- u"NDR": u"Full-load, 100% NDR."
- }
-
fig = plgo.Figure()
layout = deepcopy(plot[u"layout"])
filename=file_name)
# Add link to the file:
if file_links and target_links:
- with open(file_links, u"a") as fw:
- fw.write(
+ with open(file_links, u"a") as file_handler:
+ file_handler.write(
f"- `{name_link} "
f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n"
)
continue
-def plot_lat_hdrh_bar_name(plot, input_data):
- """Generate the plot(s) with algorithm: plot_lat_hdrh_bar_name
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- # Transform the data
- plot_title = plot.get(u"title", u"")
- logging.info(
- f" Creating the data set for the {plot.get(u'type', u'')} "
- f"{plot_title}."
- )
- data = input_data.filter_tests_by_name(
- plot, params=[u"latency", u"parent", u"tags", u"type"])
- if data is None or len(data[0][0]) == 0:
- logging.error(u"No data.")
- return
-
- # Prepare the data for the plot
- directions = [u"W-E", u"E-W"]
- tests = list()
- traces = list()
- for idx_row, test in enumerate(data[0][0]):
- try:
- if test[u"type"] in (u"NDRPDR",):
- if u"-pdr" in plot_title.lower():
- ttype = u"PDR"
- elif u"-ndr" in plot_title.lower():
- ttype = u"NDR"
- else:
- logging.warning(f"Invalid test type: {test[u'type']}")
- continue
- name = re.sub(REGEX_NIC, u"", test[u"parent"].
- replace(u'-ndrpdr', u'').
- replace(u'2n1l-', u''))
- histograms = list()
- for idx_col, direction in enumerate(
- (u"direction1", u"direction2", )):
- try:
- hdr_lat = test[u"latency"][ttype][direction][u"hdrh"]
- # TODO: Workaround, HDRH data must be aligned to 4
- # bytes, remove when not needed.
- hdr_lat += u"=" * (len(hdr_lat) % 4)
- xaxis = list()
- yaxis = list()
- hovertext = list()
- decoded = hdrh.histogram.HdrHistogram.decode(hdr_lat)
- total_count = decoded.get_total_count()
- for item in decoded.get_recorded_iterator():
- xaxis.append(item.value_iterated_to)
- prob = float(item.count_added_in_this_iter_step) / \
- total_count * 100
- yaxis.append(prob)
- hovertext.append(
- f"Test: {name}<br>"
- f"Direction: {directions[idx_col]}<br>"
- f"Latency: {item.value_iterated_to}uSec<br>"
- f"Probability: {prob:.2f}%<br>"
- f"Percentile: "
- f"{item.percentile_level_iterated_to:.2f}"
- )
- marker_color = [COLORS[idx_row], ] * len(yaxis)
- marker_color[xaxis.index(
- decoded.get_value_at_percentile(50.0))] = u"red"
- marker_color[xaxis.index(
- decoded.get_value_at_percentile(90.0))] = u"red"
- marker_color[xaxis.index(
- decoded.get_value_at_percentile(95.0))] = u"red"
- histograms.append(
- plgo.Bar(
- x=xaxis,
- y=yaxis,
- showlegend=False,
- name=name,
- marker={u"color": marker_color},
- hovertext=hovertext,
- hoverinfo=u"text"
- )
- )
- except hdrh.codec.HdrLengthException as err:
- logging.warning(
- f"No or invalid data for HDRHistogram for the test "
- f"{name}\n{err}"
- )
- continue
- if len(histograms) == 2:
- traces.append(histograms)
- tests.append(name)
- else:
- logging.warning(f"Invalid test type: {test[u'type']}")
- continue
- except (ValueError, KeyError) as err:
- logging.warning(repr(err))
-
- if not tests:
- logging.warning(f"No data for {plot_title}.")
- return
-
- fig = make_subplots(
- rows=len(tests),
- cols=2,
- specs=[
- [{u"type": u"bar"}, {u"type": u"bar"}] for _ in range(len(tests))
- ]
- )
-
- layout_axes = dict(
- gridcolor=u"rgb(220, 220, 220)",
- linecolor=u"rgb(220, 220, 220)",
- linewidth=1,
- showgrid=True,
- showline=True,
- showticklabels=True,
- tickcolor=u"rgb(220, 220, 220)",
- )
-
- for idx_row, test in enumerate(tests):
- for idx_col in range(2):
- fig.add_trace(
- traces[idx_row][idx_col],
- row=idx_row + 1,
- col=idx_col + 1
- )
- fig.update_xaxes(
- row=idx_row + 1,
- col=idx_col + 1,
- **layout_axes
- )
- fig.update_yaxes(
- row=idx_row + 1,
- col=idx_col + 1,
- **layout_axes
- )
-
- layout = deepcopy(plot[u"layout"])
-
- layout[u"title"][u"text"] = \
- f"<b>Latency:</b> {plot.get(u'graph-title', u'')}"
- layout[u"height"] = 250 * len(tests) + 130
-
- layout[u"annotations"][2][u"y"] = 1.06 - 0.008 * len(tests)
- layout[u"annotations"][3][u"y"] = 1.06 - 0.008 * len(tests)
-
- for idx, test in enumerate(tests):
- layout[u"annotations"].append({
- u"font": {
- u"size": 14
- },
- u"showarrow": False,
- u"text": f"<b>{test}</b>",
- u"textangle": 0,
- u"x": 0.5,
- u"xanchor": u"center",
- u"xref": u"paper",
- u"y": 1.0 - float(idx) * 1.06 / len(tests),
- u"yanchor": u"bottom",
- u"yref": u"paper"
- })
-
- fig[u"layout"].update(layout)
-
- # Create plot
- file_type = plot.get(u"output-file-type", u".html")
- logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
- try:
- # Export Plot
- ploff.plot(fig, show_link=False, auto_open=False,
- filename=f"{plot[u'output-file']}{file_type}")
- except PlotlyError as err:
- logging.error(f" Finished with error: {repr(err)}")
-
-
def plot_nf_reconf_box_name(plot, input_data):
"""Generate the plot(s) with algorithm: plot_nf_reconf_box_name
specified in the specification file.
f"{plot.get(u'title', u'')}."
)
data = input_data.filter_tests_by_name(
- plot, params=[u"throughput", u"parent", u"tags", u"type"])
+ plot, params=[u"throughput", u"result", u"parent", u"tags", u"type"])
if data is None:
logging.error(u"No data.")
return
# Prepare the data for the plot
y_vals = OrderedDict()
+ test_type = u""
for job in data:
for build in job:
for test in build:
u"-pdr" in plot.get(u"title", u"").lower()):
y_vals[test[u"parent"]].\
append(test[u"throughput"][u"PDR"][u"LOWER"])
+ test_type = u"NDRPDR"
elif (test[u"type"] in (u"NDRPDR", ) and
u"-ndr" in plot.get(u"title", u"").lower()):
y_vals[test[u"parent"]]. \
append(test[u"throughput"][u"NDR"][u"LOWER"])
+ test_type = u"NDRPDR"
elif test[u"type"] in (u"SOAK", ):
y_vals[test[u"parent"]].\
append(test[u"throughput"][u"LOWER"])
+ test_type = u"SOAK"
+ elif test[u"type"] in (u"HOSTSTACK", ):
+ if u"LDPRELOAD" in test[u"tags"]:
+ y_vals[test[u"parent"]].append(
+ float(test[u"result"][u"bits_per_second"]) / 1e3
+ )
+ elif u"VPPECHO" in test[u"tags"]:
+ y_vals[test[u"parent"]].append(
+ (float(test[u"result"][u"client"][u"tx_data"])
+ * 8 / 1e3) /
+ ((float(test[u"result"][u"client"][u"time"]) +
+ float(test[u"result"][u"server"][u"time"])) /
+ 2)
+ )
+ test_type = u"HOSTSTACK"
else:
continue
except (KeyError, TypeError):
tst_name = re.sub(REGEX_NIC, u"",
col.lower().replace(u'-ndrpdr', u'').
replace(u'2n1l-', u''))
- traces.append(
- plgo.Box(
- x=[str(i + 1) + u'.'] * len(df_y[col]),
- y=[y / 1000000 if y else None for y in df_y[col]],
- name=(
- f"{i + 1}. "
- f"({nr_of_samples[i]:02d} "
- f"run{u's' if nr_of_samples[i] > 1 else u''}) "
- f"{tst_name}"
- ),
- hoverinfo=u"y+name"
- )
+ kwargs = dict(
+ x=[str(i + 1) + u'.'] * len(df_y[col]),
+ y=[y / 1e6 if y else None for y in df_y[col]],
+ name=(
+ f"{i + 1}. "
+ f"({nr_of_samples[i]:02d} "
+ f"run{u's' if nr_of_samples[i] > 1 else u''}) "
+ f"{tst_name}"
+ ),
+ hoverinfo=u"y+name"
)
+ if test_type in (u"SOAK", ):
+ kwargs[u"boxpoints"] = u"all"
+
+ traces.append(plgo.Box(**kwargs))
+
try:
val_max = max(df_y[col])
if val_max:
- y_max.append(int(val_max / 1000000) + 2)
+ y_max.append(int(val_max / 1e6) + 2)
except (ValueError, TypeError) as err:
logging.error(repr(err))
continue
# Create plot
layout = deepcopy(plot[u"layout"])
if layout.get(u"title", None):
- layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
+ if test_type in (u"HOSTSTACK", ):
+ layout[u"title"] = f"<b>Bandwidth:</b> {layout[u'title']}"
+ else:
+ layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
if y_max:
layout[u"yaxis"][u"range"] = [0, max(y_max)]
plpl = plgo.Figure(data=traces, layout=layout)
return
-def plot_lat_err_bars_name(plot, input_data):
- """Generate the plot(s) with algorithm: plot_lat_err_bars_name
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- # Transform the data
- plot_title = plot.get(u"title", u"")
- logging.info(
- f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
- )
- data = input_data.filter_tests_by_name(
- plot, params=[u"latency", u"parent", u"tags", u"type"])
- if data is None:
- logging.error(u"No data.")
- return
-
- # Prepare the data for the plot
- y_tmp_vals = OrderedDict()
- for job in data:
- for build in job:
- for test in build:
- try:
- logging.debug(f"test[u'latency']: {test[u'latency']}\n")
- except ValueError as err:
- logging.warning(repr(err))
- if y_tmp_vals.get(test[u"parent"], None) is None:
- y_tmp_vals[test[u"parent"]] = [
- list(), # direction1, min
- list(), # direction1, avg
- list(), # direction1, max
- list(), # direction2, min
- list(), # direction2, avg
- list() # direction2, max
- ]
- try:
- if test[u"type"] not in (u"NDRPDR", ):
- logging.warning(f"Invalid test type: {test[u'type']}")
- continue
- if u"-pdr" in plot_title.lower():
- ttype = u"PDR"
- elif u"-ndr" in plot_title.lower():
- ttype = u"NDR"
- else:
- logging.warning(
- f"Invalid test type: {test[u'type']}"
- )
- continue
- y_tmp_vals[test[u"parent"]][0].append(
- test[u"latency"][ttype][u"direction1"][u"min"])
- y_tmp_vals[test[u"parent"]][1].append(
- test[u"latency"][ttype][u"direction1"][u"avg"])
- y_tmp_vals[test[u"parent"]][2].append(
- test[u"latency"][ttype][u"direction1"][u"max"])
- y_tmp_vals[test[u"parent"]][3].append(
- test[u"latency"][ttype][u"direction2"][u"min"])
- y_tmp_vals[test[u"parent"]][4].append(
- test[u"latency"][ttype][u"direction2"][u"avg"])
- y_tmp_vals[test[u"parent"]][5].append(
- test[u"latency"][ttype][u"direction2"][u"max"])
- except (KeyError, TypeError) as err:
- logging.warning(repr(err))
-
- x_vals = list()
- y_vals = list()
- y_mins = list()
- y_maxs = list()
- nr_of_samples = list()
- for key, val in y_tmp_vals.items():
- name = re.sub(REGEX_NIC, u"", key.replace(u'-ndrpdr', u'').
- replace(u'2n1l-', u''))
- x_vals.append(name) # dir 1
- y_vals.append(mean(val[1]) if val[1] else None)
- y_mins.append(mean(val[0]) if val[0] else None)
- y_maxs.append(mean(val[2]) if val[2] else None)
- nr_of_samples.append(len(val[1]) if val[1] else 0)
- x_vals.append(name) # dir 2
- y_vals.append(mean(val[4]) if val[4] else None)
- y_mins.append(mean(val[3]) if val[3] else None)
- y_maxs.append(mean(val[5]) if val[5] else None)
- nr_of_samples.append(len(val[3]) if val[3] else 0)
-
- traces = list()
- annotations = list()
-
- for idx, _ in enumerate(x_vals):
- if not bool(int(idx % 2)):
- direction = u"West-East"
- else:
- direction = u"East-West"
- hovertext = (
- f"No. of Runs: {nr_of_samples[idx]}<br>"
- f"Test: {x_vals[idx]}<br>"
- f"Direction: {direction}<br>"
- )
- if isinstance(y_maxs[idx], float):
- hovertext += f"Max: {y_maxs[idx]:.2f}uSec<br>"
- if isinstance(y_vals[idx], float):
- hovertext += f"Mean: {y_vals[idx]:.2f}uSec<br>"
- if isinstance(y_mins[idx], float):
- hovertext += f"Min: {y_mins[idx]:.2f}uSec"
-
- if isinstance(y_maxs[idx], float) and isinstance(y_vals[idx], float):
- array = [y_maxs[idx] - y_vals[idx], ]
- else:
- array = [None, ]
- if isinstance(y_mins[idx], float) and isinstance(y_vals[idx], float):
- arrayminus = [y_vals[idx] - y_mins[idx], ]
- else:
- arrayminus = [None, ]
- traces.append(plgo.Scatter(
- x=[idx, ],
- y=[y_vals[idx], ],
- name=x_vals[idx],
- legendgroup=x_vals[idx],
- showlegend=bool(int(idx % 2)),
- mode=u"markers",
- error_y=dict(
- type=u"data",
- symmetric=False,
- array=array,
- arrayminus=arrayminus,
- color=COLORS[int(idx / 2)]
- ),
- marker=dict(
- size=10,
- color=COLORS[int(idx / 2)],
- ),
- text=hovertext,
- hoverinfo=u"text",
- ))
- annotations.append(dict(
- x=idx,
- y=0,
- xref=u"x",
- yref=u"y",
- xanchor=u"center",
- yanchor=u"top",
- text=u"E-W" if bool(int(idx % 2)) else u"W-E",
- font=dict(
- size=16,
- ),
- align=u"center",
- showarrow=False
- ))
-
- try:
- # Create plot
- file_type = plot.get(u"output-file-type", u".html")
- logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
- layout = deepcopy(plot[u"layout"])
- if layout.get(u"title", None):
- layout[u"title"] = f"<b>Latency:</b> {layout[u'title']}"
- layout[u"annotations"] = annotations
- plpl = plgo.Figure(data=traces, layout=layout)
-
- # Export Plot
- ploff.plot(
- plpl,
- show_link=False, auto_open=False,
- filename=f"{plot[u'output-file']}{file_type}"
- )
- except PlotlyError as err:
- logging.error(
- f" Finished with error: {repr(err)}".replace(u"\n", u" ")
- )
- return
-
-
def plot_tsa_name(plot, input_data):
"""Generate the plot(s) with algorithm:
plot_tsa_name
if test_val:
avg_val = sum(test_val) / len(test_val)
y_vals[test_name][key] = [avg_val, len(test_val)]
- ideal = avg_val / (int(key) * 1000000.0)
+ ideal = avg_val / (int(key) * 1e6)
if test_name not in y_1c_max or ideal > y_1c_max[test_name]:
y_1c_max[test_name] = ideal
test_name.replace(u'-ndrpdr', u'').replace(u'2n1l-', u'')
)
vals[name] = OrderedDict()
- y_val_1 = test_vals[u"1"][0] / 1000000.0
- y_val_2 = test_vals[u"2"][0] / 1000000.0 if test_vals[u"2"][0] \
+ y_val_1 = test_vals[u"1"][0] / 1e6
+ y_val_2 = test_vals[u"2"][0] / 1e6 if test_vals[u"2"][0] \
else None
- y_val_4 = test_vals[u"4"][0] / 1000000.0 if test_vals[u"4"][0] \
+ y_val_4 = test_vals[u"4"][0] / 1e6 if test_vals[u"4"][0] \
else None
vals[name][u"val"] = [y_val_1, y_val_2, y_val_4]
limit = plot[u"limits"][u"nic"][u"xl710"]
elif u"x553" in test_name:
limit = plot[u"limits"][u"nic"][u"x553"]
+ elif u"cx556a" in test_name:
+ limit = plot[u"limits"][u"nic"][u"cx556a"]
else:
limit = 0
if limit > nic_limit:
except ValueError as err:
logging.error(err)
return
- nic_limit /= 1000000.0
+ nic_limit /= 1e6
traces.append(plgo.Scatter(
x=x_vals,
y=[nic_limit, ] * len(x_vals),
))
y_max.append(nic_limit)
- lnk_limit /= 1000000.0
+ lnk_limit /= 1e6
if lnk_limit < threshold:
traces.append(plgo.Scatter(
x=x_vals,
))
y_max.append(lnk_limit)
- pci_limit /= 1000000.0
+ pci_limit /= 1e6
if (pci_limit < threshold and
(pci_limit < lnk_limit * 0.95 or lnk_limit > lnk_limit * 1.05)):
traces.append(plgo.Scatter(
regex_cn = re.compile(r'^(\d*)R(\d*)C$')
regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
r'(\d+mif|\d+vh)-'
- r'(\d+vm\d+t|\d+dcr\d+t).*$')
+ r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$')
vals = dict()
# Transform the data
Code Review / csit.git / blobdiff