Beispiel #1
0
def print_summary(mode, array, array_name, unit, df, item_value=None):
    if (utils.print_level & utils.Levels.SUMMARY) and (len(array) > 0):
        result = []
        before = ""
        after = ""
        RED = "\033[1;31m"
        ORANGE = "\033[1;33m"
        WHITE = "\033[1;m"
        GREEN = "\033[1;32m"

        for host in array:
            result.append(df[host].sum())
        if "unstable" in array_name:
            before = RED
            after = WHITE
        if "curious" in array_name:
            before = ORANGE
            after = WHITE

        mean = numpy.mean(result)
        perf_status = ""
        if array_name == "consistent":
            if item_value is not None:
                if mode == "loops_per_sec" or mode == "bogomips":
                    min_cpu_perf = perf_cpu_tables.get_cpu_min_perf(mode, item_value)
                    if min_cpu_perf == 0:
                        perf_status = ": " + ORANGE + "NO PERF ENTRY IN DB" + WHITE + " for " + item_value
                    elif (mean >= min_cpu_perf):
                        perf_status = ": " + GREEN + "PERF OK" + WHITE
                    else:
                        perf_status = ": " + RED + "PERF FAIL" + WHITE + " as min perf should have been : " + str(min_cpu_perf)
        utils.do_print(mode, utils.Levels.SUMMARY, "%3d %s%-10s%s hosts with %8.2f %-4s as average value and %8.2f standard deviation %s", len(array), before, array_name, after, mean, unit, numpy.std(result), perf_status)
Beispiel #2
0
def print_summary(mode, array, array_name, unit, df, item_value=None):
    if (utils.print_level & utils.Levels.SUMMARY) and (len(array) > 0):
        result = []
        before = ""
        after = ""
        RED = "\033[1;31m"
        ORANGE = "\033[1;33m"
        WHITE = "\033[1;m"
        GREEN = "\033[1;32m"

        for host in array:
            result.append(df[host].sum())
        if "unstable" in array_name:
            before = RED
            after = WHITE
        if "curious" in array_name:
            before = ORANGE
            after = WHITE

        mean = numpy.mean(result)
        perf_status = ""
        if array_name == "consistent":
            if item_value is not None:
                if mode == "loops_per_sec" or mode == "bogomips":
                    min_cpu_perf = perf_cpu_tables.get_cpu_min_perf(mode, item_value)
                    if min_cpu_perf == 0:
                        perf_status = ": " + ORANGE + "NO PERF ENTRY IN DB" + WHITE + " for " + item_value
                    elif (mean >= min_cpu_perf):
                        perf_status = ": " + GREEN + "PERF OK" + WHITE
                    else:
                        perf_status = ": " + RED + "PERF FAIL" + WHITE + " as min perf should have been : " + str(min_cpu_perf)
        utils.do_print(mode, utils.Levels.SUMMARY, "%3d %s%-10s%s hosts with %8.2f %-4s as average value and %8.2f standard deviation %s", len(array), before, array_name, after, mean, unit, numpy.std(result), perf_status)
Beispiel #3
0
 def test_cpu4(self):
     cpu_type = "Intel(R) Leon(R) CPU E7-2750 0 @ 2.20GHz"
     cpu_perf = perf_cpu_tables.get_cpu_min_perf("loops_per_sec", cpu_type)
     self.assertEqual(cpu_perf, 0)
Beispiel #4
0
 def test_cpu1(self):
     cpu_type = "Intel(R) Xeon(R) CPU E5-2650 0 @ 2.20GHz"
     cpu_perf = perf_cpu_tables.get_cpu_min_perf("loops_per_sec", cpu_type)
     self.assertEqual(cpu_perf, 420)