X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_CPTA.py;h=f96fab0f8863df09eed349fb5ca753ebffcf5e7e;hp=d4ac06d09fb858600a63ef5fa106f4823d4a6819;hb=4b0df8e7baea755e2e1a1c27a7707fb0a3f28b6e;hpb=b8bf181cafb0f4e8a317c308cfe83a3e022ce7c5 diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py index d4ac06d09f..f96fab0f88 100644 --- a/resources/tools/presentation/generator_CPTA.py +++ b/resources/tools/presentation/generator_CPTA.py @@ -22,7 +22,6 @@ import prettytable import plotly.offline as ploff import plotly.graph_objs as plgo import plotly.exceptions as plerr -import pandas as pd from collections import OrderedDict from datetime import datetime @@ -116,23 +115,40 @@ def _generate_trending_traces(in_data, job_name, build_info, hover_text = list() xaxis = list() for idx in data_x: + date = build_info[job_name][str(idx)][0] + hover_str = ("date: {0}
" + "value: {1:,}
" + "{2}-ref: {3}
" + "csit-ref: mrr-{4}-build-{5}") if "dpdk" in job_name: - hover_text.append("dpdk-ref: {0}
csit-ref: mrr-weekly-build-{1}". - format(build_info[job_name][str(idx)][1]. - rsplit('~', 1)[0], idx)) + hover_text.append(hover_str.format( + date, + int(in_data[idx].avg), + "dpdk", + build_info[job_name][str(idx)][1]. + rsplit('~', 1)[0], + "weekly", + idx)) elif "vpp" in job_name: - hover_text.append("vpp-ref: {0}
csit-ref: mrr-daily-build-{1}". - format(build_info[job_name][str(idx)][1]. - rsplit('~', 1)[0], idx)) - date = build_info[job_name][str(idx)][0] + hover_text.append(hover_str.format( + date, + int(in_data[idx].avg), + "vpp", + build_info[job_name][str(idx)][1]. + rsplit('~', 1)[0], + "daily", + idx)) + xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]), int(date[9:11]), int(date[12:]))) - data_pd = pd.Series(data_y, index=xaxis) + data_pd = OrderedDict() + for key, value in zip(xaxis, data_y): + data_pd[key] = value anomaly_classification, avgs = classify_anomalies(data_pd) - anomalies = pd.Series() + anomalies = OrderedDict() anomalies_colors = list() anomalies_avgs = list() anomaly_color = { @@ -141,11 +157,10 @@ def _generate_trending_traces(in_data, job_name, build_info, "progression": 1.0 } if anomaly_classification: - for idx, item in enumerate(data_pd.items()): + for idx, (key, value) in enumerate(data_pd.iteritems()): if anomaly_classification[idx] in \ ("outlier", "regression", "progression"): - anomalies = anomalies.append(pd.Series([item[1], ], - index=[item[0], ])) + anomalies[key] = value anomalies_colors.append( anomaly_color[anomaly_classification[idx]]) anomalies_avgs.append(avgs[idx]) @@ -155,7 +170,7 @@ def _generate_trending_traces(in_data, job_name, build_info, trace_samples = plgo.Scatter( x=xaxis, - y=data_y, + y=[y.avg for y in data_y], mode='markers', line={ "width": 1 @@ -169,7 +184,7 @@ def _generate_trending_traces(in_data, job_name, build_info, "symbol": "circle", }, text=hover_text, - hoverinfo="x+y+text+name" + hoverinfo="text" ) traces = [trace_samples, ] @@ -185,7 +200,9 @@ def _generate_trending_traces(in_data, job_name, build_info, }, showlegend=False, legendgroup=name, - name='{name}-trend'.format(name=name) + name='{name}'.format(name=name), + text=["trend: {0:,}".format(int(avg)) for avg in avgs], + hoverinfo="text+name" ) traces.append(trace_trend) @@ -280,7 +297,7 @@ def _generate_all_charts(spec, input_data): chart_data[test_name] = OrderedDict() try: chart_data[test_name][int(index)] = \ - test["result"]["throughput"] + test["result"]["receive-rate"] except (KeyError, TypeError): pass @@ -289,6 +306,8 @@ def _generate_all_charts(spec, input_data): tst_lst = list() for bld in builds_dict[job_name]: itm = tst_data.get(int(bld), '') + if not isinstance(itm, str): + itm = itm.avg tst_lst.append(str(itm)) csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n') # Generate traces: