args = parser.parse_args()
job_ids=args.jobids
data_file_name = args.file
con = mdb.connect(server, user, password, table);
with con:
cur = con.cursor()
plot_xlabel = "benchmarks solved"
plot_ylabel = "cummulative time"
gnuplot_command = cStringIO.StringIO()
data_file = open(data_file_name, 'w')
util.setupPlot(gnuplot_command, plot_xlabel, plot_ylabel, "")
util.setupCanvasPlot(gnuplot_command, "gnuplot_canvas")
util.setupCactusPlot(gnuplot_command)
util.startPlot(gnuplot_command)
for i in range(len(job_ids)) :
job_id = job_ids[i]
result_i = util.getSortedResults(cur, job_id)
util.dumpCactusToFile(data_file, util.getJobName(cur, job_id), result_i)
for i in range(len(job_ids)) :
util.plotOneCactus(gnuplot_command, data_file_name, i, 1, 2)
if ( i < len(job_ids) - 1) :
util.plotSeparator(gnuplot_command)
with con:
cur = con.cursor()
plot_xlabel = "benchmarks solved"
plot_ylabel = "cummulative time"
base_name = path + generateCactusBaseName(job_ids)
gnuplot_file_name = base_name + ".gnuplot"
gnuplot_file = open(gnuplot_file_name, 'w')
data_file_name = base_name + ".dat"
data_file = open(data_file_name, 'w')
pdf_file_name = base_name + ".pdf"
util.setupPlot(gnuplot_file, plot_xlabel, plot_ylabel, "")
util.setupPdfPlot(gnuplot_file, pdf_file_name)
util.setupCactusPlot(gnuplot_file)
util.startPlot(gnuplot_file)
for i in range(len(job_ids)) :
job_id = job_ids[i]
result_i = util.getSortedResults(cur, job_id)
util.dumpCactusToFile(data_file, util.getJobName(cur, job_id), result_i)
for i in range(len(job_ids)) :
util.plotOneCactus(gnuplot_file, data_file_name, i, 1, 2)
if ( i < len(job_ids) - 1) :
util.plotSeparator(gnuplot_file)
filtered_results.append((path, xvalue, yvalue))
return filtered_results
con = mdb.connect(server, user, password, database);
with con:
cur = con.cursor()
plot_xlabel = util.getJobName(cur, xjob) + " ("+ str(xjob) + ")"
plot_ylabel = util.getJobName(cur, yjob) + " ("+ str(yjob) + ")"
plot_title = plot_xlabel + " vs " + plot_ylabel
data_file = open(data_file_name, 'w')
javascript_file = open(javascript_file_name, 'w')
gnuplot_command = cStringIO.StringIO()
util.setupPlot(gnuplot_command, plot_xlabel, plot_ylabel, plot_title)
util.setupCanvasPlot(gnuplot_command, "gnuplot_canvas")
util.startPlot(gnuplot_command)
results_and_answers = util.getRunTimesAndAnswer(cur, xjob, yjob)
results = filterUnknowns(results_and_answers)
families = util.groupByFamilies(results)
if input_family == None or input_family == "":
run_over_families = families
else:
if input_family[0] == '-':
input_family = input_family[1:]
exclude_families = input_family.split(',')
run_over_families = [item for item in families if item not in exclude_families]
else:
plot_xlabel = util.getJobName(cur, xjob)
plot_ylabel = util.getJobName(cur, yjob)
plot_title = plot_xlabel + " vs " + plot_ylabel
base_name = path + generateScatterBaseName(xjob, yjob, xfield, yfield)
gnuplot_file_name = base_name + ".gnuplot"
gnuplot_file = open(gnuplot_file_name, 'w')
data_file_name = base_name + ".dat"
data_file = open(data_file_name, 'w')
pdf_file_name = base_name + ".pdf"
util.setupPlot(gnuplot_file, plot_xlabel, plot_ylabel, plot_title)
util.setupPdfPlot(gnuplot_file, pdf_file_name)
util.startPlot(gnuplot_file)
results = util.selectAllResult(cur, xjob, yjob, xfield, yfield)
families = util.groupByFamilies(results)
for family in families:
family_results = families[family]
util.dumpFamilyToFile(data_file, family, family_results)
for i in range(len(families)):
util.plotOneScatter(gnuplot_file, data_file_name, i, 2, 3)
util.plotSeparator(gnuplot_file)