wally/report.py

import abc
import logging
from typing import Dict, Any, Iterator, Tuple, cast, List

import numpy
import scipy
import matplotlib
import matplotlib.pyplot as plt


matplotlib.use('svg')


from .utils import ssize2b
from .stage import Stage, StepOrder
from .test_run_class import TestRun
from .result_classes import NormStatProps


logger = logging.getLogger("wally")


class ConsoleReportStage(Stage):

    priority = StepOrder.REPORT

    def run(self, ctx: TestRun) -> None:
        # TODO(koder): load data from storage
        raise NotImplementedError("...")


class HtmlReportStage(Stage):

    priority = StepOrder.REPORT

    def run(self, ctx: TestRun) -> None:
        # TODO(koder): load data from storage
        raise NotImplementedError("...")


# TODO: need to be revised, have to user StatProps fields instead
class StoragePerfSummary:
    def __init__(self, name: str) -> None:
        self.direct_iops_r_max = 0  # type: int
        self.direct_iops_w_max = 0  # type: int

        # 64 used instead of 4k to faster feed caches
        self.direct_iops_w64_max = 0  # type: int

        self.rws4k_10ms = 0  # type: int
        self.rws4k_30ms = 0  # type: int
        self.rws4k_100ms = 0  # type: int
        self.bw_write_max = 0  # type: int
        self.bw_read_max = 0  # type: int

        self.bw = None  # type: float
        self.iops = None  # type: float
        self.lat = None  # type: float
        self.lat_50 = None  # type: float
        self.lat_95 = None  # type: float


class HTMLBlock:
    data = None  # type: str
    js_links = []  # type: List[str]
    css_links = []  # type: List[str]


class Reporter(metaclass=abc.ABCMeta):
    @abc.abstractmethod
    def get_divs(self, config, storage) -> Iterator[Tuple[str, str, HTMLBlock]]:
        pass


# Main performance report
class PerformanceSummary(Reporter):
    """Creates graph, which show how IOPS and Latency depend on QD"""


# Main performance report
class IOPS_QD(Reporter):
    """Creates graph, which show how IOPS and Latency depend on QD"""


# Linearization report
class IOPS_Bsize(Reporter):
    """Creates graphs, which show how IOPS and Latency depend on block size"""


# IOPS/latency distribution
class IOPSHist(Reporter):
    """IOPS.latency distribution histogram"""


# IOPS/latency over test time
class IOPSTime(Reporter):
    """IOPS/latency during test"""
    def get_divs(self, config, storage) -> Iterator[Tuple[str, str, HTMLBlock]]:
        pass


# Cluster load over test time
class ClusterLoad(Reporter):
    """IOPS/latency during test"""


# Node load over test time
class NodeLoad(Reporter):
    """IOPS/latency during test"""


# Ceph cluster summary
class CephClusterSummary(Reporter):
    """IOPS/latency during test"""


# TODO: Resource consumption report
# TODO: Ceph operation breakout report
# TODO: Resource consumption for different type of test


#
# # disk_info = None
# # base = None
# # linearity = None
#
#
# def group_by_name(test_data):
#     name_map = collections.defaultdict(lambda: [])
#
#     for data in test_data:
#         name_map[(data.name, data.summary())].append(data)
#
#     return name_map
#
#
# def report(name, required_fields):
#     def closure(func):
#         report_funcs.append((required_fields.split(","), name, func))
#         return func
#     return closure
#
#
# def get_test_lcheck_params(pinfo):
#     res = [{
#         's': 'sync',
#         'd': 'direct',
#         'a': 'async',
#         'x': 'sync direct'
#     }[pinfo.sync_mode]]
#
#     res.append(pinfo.p.rw)
#
#     return " ".join(res)
#
#
# def get_emb_data_svg(plt):
#     sio = StringIO()
#     plt.savefig(sio, format='svg')
#     img_start = "<!-- Created with matplotlib (http://matplotlib.org/) -->"
#     return sio.getvalue().split(img_start, 1)[1]
#
#
# def get_template(templ_name):
#     very_root_dir = os.path.dirname(os.path.dirname(wally.__file__))
#     templ_dir = os.path.join(very_root_dir, 'report_templates')
#     templ_file = os.path.join(templ_dir, templ_name)
#     return open(templ_file, 'r').read()
#
#
# def group_by(data, func):
#     if len(data) < 2:
#         yield data
#         return
#
#     ndata = [(func(dt), dt) for dt in data]
#     ndata.sort(key=func)
#     pkey, dt = ndata[0]
#     curr_list = [dt]
#
#     for key, val in ndata[1:]:
#         if pkey != key:
#             yield curr_list
#             curr_list = [val]
#         else:
#             curr_list.append(val)
#         pkey = key
#
#     yield curr_list
#
#
# @report('linearity', 'linearity_test')
# def linearity_report(processed_results, lab_info, comment):
#     labels_and_data_mp = collections.defaultdict(lambda: [])
#     vls = {}
#
#     # plot io_time = func(bsize)
#     for res in processed_results.values():
#         if res.name.startswith('linearity_test'):
#             iotimes = [1000. / val for val in res.iops.raw]
#
#             op_summ = get_test_summary(res.params)[:3]
#
#             labels_and_data_mp[op_summ].append(
#                 [res.p.blocksize, res.iops.raw, iotimes])
#
#             cvls = res.params.vals.copy()
#             del cvls['blocksize']
#             del cvls['rw']
#
#             cvls.pop('sync', None)
#             cvls.pop('direct', None)
#             cvls.pop('buffered', None)
#
#             if op_summ not in vls:
#                 vls[op_summ] = cvls
#             else:
#                 assert cvls == vls[op_summ]
#
#     all_labels = None
#     _, ax1 = plt.subplots()
#     for name, labels_and_data in labels_and_data_mp.items():
#         labels_and_data.sort(key=lambda x: ssize2b(x[0]))
#
#         labels, _, iotimes = zip(*labels_and_data)
#
#         if all_labels is None:
#             all_labels = labels
#         else:
#             assert all_labels == labels
#
#         plt.boxplot(iotimes)
#         if len(labels_and_data) > 2 and \
#            ssize2b(labels_and_data[-2][0]) >= 4096:
#
#             xt = range(1, len(labels) + 1)
#
#             def io_time(sz, bw, initial_lat):
#                 return sz / bw + initial_lat
#
#             x = numpy.array(map(ssize2b, labels))
#             y = numpy.array([sum(dt) / len(dt) for dt in iotimes])
#             popt, _ = scipy.optimize.curve_fit(io_time, x, y, p0=(100., 1.))
#
#             y1 = io_time(x, *popt)
#             plt.plot(xt, y1, linestyle='--',
#                      label=name + ' LS linear approx')
#
#             for idx, (sz, _, _) in enumerate(labels_and_data):
#                 if ssize2b(sz) >= 4096:
#                     break
#
#             bw = (x[-1] - x[idx]) / (y[-1] - y[idx])
#             lat = y[-1] - x[-1] / bw
#             y2 = io_time(x, bw, lat)
#             plt.plot(xt, y2, linestyle='--',
#                      label=abbv_name_to_full(name) +
#                      ' (4k & max) linear approx')
#
#     plt.setp(ax1, xticklabels=labels)
#
#     plt.xlabel("Block size")
#     plt.ylabel("IO time, ms")
#
#     plt.subplots_adjust(top=0.85)
#     plt.legend(bbox_to_anchor=(0.5, 1.15),
#                loc='upper center',
#                prop={'size': 10}, ncol=2)
#     plt.grid()
#     iotime_plot = get_emb_data_svg(plt)
#     plt.clf()
#
#     # plot IOPS = func(bsize)
#     _, ax1 = plt.subplots()
#
#     for name, labels_and_data in labels_and_data_mp.items():
#         labels_and_data.sort(key=lambda x: ssize2b(x[0]))
#         _, data, _ = zip(*labels_and_data)
#         plt.boxplot(data)
#         avg = [float(sum(arr)) / len(arr) for arr in data]
#         xt = range(1, len(data) + 1)
#         plt.plot(xt, avg, linestyle='--',
#                  label=abbv_name_to_full(name) + " avg")
#
#     plt.setp(ax1, xticklabels=labels)
#     plt.xlabel("Block size")
#     plt.ylabel("IOPS")
#     plt.legend(bbox_to_anchor=(0.5, 1.15),
#                loc='upper center',
#                prop={'size': 10}, ncol=2)
#     plt.grid()
#     plt.subplots_adjust(top=0.85)
#
#     iops_plot = get_emb_data_svg(plt)
#
#     res = set(get_test_lcheck_params(res) for res in processed_results.values())
#     ncount = list(set(res.testnodes_count for res in processed_results.values()))
#     conc = list(set(res.concurence for res in processed_results.values()))
#
#     assert len(conc) == 1
#     assert len(ncount) == 1
#
#     descr = {
#         'vm_count': ncount[0],
#         'concurence': conc[0],
#         'oper_descr': ", ".join(res).capitalize()
#     }
#
#     params_map = {'iotime_vs_size': iotime_plot,
#                   'iops_vs_size': iops_plot,
#                   'descr': descr}
#
#     return get_template('report_linearity.html').format(**params_map)
#
#
# @report('lat_vs_iops', 'lat_vs_iops')
# def lat_vs_iops(processed_results, lab_info, comment):
#     lat_iops = collections.defaultdict(lambda: [])
#     requsted_vs_real = collections.defaultdict(lambda: {})
#
#     for res in processed_results.values():
#         if res.name.startswith('lat_vs_iops'):
#             lat_iops[res.concurence].append((res.lat,
#                                              0,
#                                              res.iops.average,
#                                              res.iops.deviation))
#             # lat_iops[res.concurence].append((res.lat.average / 1000.0,
#             #                                  res.lat.deviation / 1000.0,
#             #                                  res.iops.average,
#             #                                  res.iops.deviation))
#             requested_iops = res.p.rate_iops * res.concurence
#             requsted_vs_real[res.concurence][requested_iops] = \
#                 (res.iops.average, res.iops.deviation)
#
#     colors = ['red', 'green', 'blue', 'orange', 'magenta', "teal"]
#     colors_it = iter(colors)
#     for conc, lat_iops in sorted(lat_iops.items()):
#         lat, dev, iops, iops_dev = zip(*lat_iops)
#         plt.errorbar(iops, lat, xerr=iops_dev, yerr=dev, fmt='ro',
#                      label=str(conc) + " threads",
#                      color=next(colors_it))
#
#     plt.xlabel("IOPS")
#     plt.ylabel("Latency, ms")
#     plt.grid()
#     plt.legend(loc=0)
#     plt_iops_vs_lat = get_emb_data_svg(plt)
#     plt.clf()
#
#     colors_it = iter(colors)
#     for conc, req_vs_real in sorted(requsted_vs_real.items()):
#         req, real = zip(*sorted(req_vs_real.items()))
#         iops, dev = zip(*real)
#         plt.errorbar(req, iops, yerr=dev, fmt='ro',
#                      label=str(conc) + " threads",
#                      color=next(colors_it))
#     plt.xlabel("Requested IOPS")
#     plt.ylabel("Get IOPS")
#     plt.grid()
#     plt.legend(loc=0)
#     plt_iops_vs_requested = get_emb_data_svg(plt)
#
#     res1 = processed_results.values()[0]
#     params_map = {'iops_vs_lat': plt_iops_vs_lat,
#                   'iops_vs_requested': plt_iops_vs_requested,
#                   'oper_descr': get_test_lcheck_params(res1).capitalize()}
#
#     return get_template('report_iops_vs_lat.html').format(**params_map)
#
#
# def render_all_html(comment, info, lab_description, images, templ_name):
#     data = info.__dict__.copy()
#     for name, val in data.items():
#         if not name.startswith('__'):
#             if val is None:
#                 if name in ('direct_iops_w64_max', 'direct_iops_w_max'):
#                     data[name] = ('-', '-', '-')
#                 else:
#                     data[name] = '-'
#             elif isinstance(val, (int, float, long)):
#                 data[name] = round_3_digit(val)
#
#     data['bw_read_max'] = (data['bw_read_max'][0] // 1024,
#                            data['bw_read_max'][1],
#                            data['bw_read_max'][2])
#
#     data['bw_write_max'] = (data['bw_write_max'][0] // 1024,
#                             data['bw_write_max'][1],
#                             data['bw_write_max'][2])
#
#     images.update(data)
#     templ = get_template(templ_name)
#     return templ.format(lab_info=lab_description,
#                         comment=comment,
#                         **images)
#
#
# def io_chart(title, concurence,
#              latv, latv_min, latv_max,
#              iops_or_bw, iops_or_bw_err,
#              legend,
#              log_iops=False,
#              log_lat=False,
#              boxplots=False,
#              latv_50=None,
#              latv_95=None,
#              error2=None):
#
#     matplotlib.rcParams.update({'font.size': 10})
#     points = " MiBps" if legend == 'BW' else ""
#     lc = len(concurence)
#     width = 0.35
#     xt = range(1, lc + 1)
#
#     op_per_vm = [v / (vm * th) for v, (vm, th) in zip(iops_or_bw, concurence)]
#     fig, p1 = plt.subplots()
#     xpos = [i - width / 2 for i in xt]
#
#     p1.bar(xpos, iops_or_bw,
#            width=width,
#            color='y',
#            label=legend)
#
#     err1_leg = None
#     for pos, y, err in zip(xpos, iops_or_bw, iops_or_bw_err):
#         err1_leg = p1.errorbar(pos + width / 2,
#                                y,
#                                err,
#                                color='magenta')
#
#     err2_leg = None
#     if error2 is not None:
#         for pos, y, err in zip(xpos, iops_or_bw, error2):
#             err2_leg = p1.errorbar(pos + width / 2 + 0.08,
#                                    y,
#                                    err,
#                                    lw=2,
#                                    alpha=0.5,
#                                    color='teal')
#
#     p1.grid(True)
#     p1.plot(xt, op_per_vm, '--', label=legend + "/thread", color='black')
#     handles1, labels1 = p1.get_legend_handles_labels()
#
#     handles1 += [err1_leg]
#     labels1 += ["95% conf"]
#
#     if err2_leg is not None:
#         handles1 += [err2_leg]
#         labels1 += ["95% dev"]
#
#     p2 = p1.twinx()
#
#     if latv_50 is None:
#         p2.plot(xt, latv_max, label="lat max")
#         p2.plot(xt, latv, label="lat avg")
#         p2.plot(xt, latv_min, label="lat min")
#     else:
#         p2.plot(xt, latv_50, label="lat med")
#         p2.plot(xt, latv_95, label="lat 95%")
#
#     plt.xlim(0.5, lc + 0.5)
#     plt.xticks(xt, ["{0} * {1}".format(vm, th) for (vm, th) in concurence])
#     p1.set_xlabel("VM Count * Thread per VM")
#     p1.set_ylabel(legend + points)
#     p2.set_ylabel("Latency ms")
#     plt.title(title)
#     handles2, labels2 = p2.get_legend_handles_labels()
#
#     plt.legend(handles1 + handles2, labels1 + labels2,
#                loc='center left', bbox_to_anchor=(1.1, 0.81))
#
#     if log_iops:
#         p1.set_yscale('log')
#
#     if log_lat:
#         p2.set_yscale('log')
#
#     plt.subplots_adjust(right=0.68)
#
#     return get_emb_data_svg(plt)
#
#
# def make_plots(processed_results, plots):
#     """
#     processed_results: [PerfInfo]
#     plots = [(test_name_prefix:str, fname:str, description:str)]
#     """
#     files = {}
#     for name_pref, fname, desc in plots:
#         chart_data = []
#
#         for res in processed_results:
#             summ = res.name + "_" + res.summary
#             if summ.startswith(name_pref):
#                 chart_data.append(res)
#
#         if len(chart_data) == 0:
#             raise ValueError("Can't found any date for " + name_pref)
#
#         use_bw = ssize2b(chart_data[0].p.blocksize) > 16 * 1024
#
#         chart_data.sort(key=lambda x: x.params['vals']['numjobs'])
#
#         lat = None
#         lat_min = None
#         lat_max = None
#
#         lat_50 = [x.lat_50 for x in chart_data]
#         lat_95 = [x.lat_95 for x in chart_data]
#
#         lat_diff_max = max(x.lat_95 / x.lat_50 for x in chart_data)
#         lat_log_scale = (lat_diff_max > 10)
#
#         testnodes_count = x.testnodes_count
#         concurence = [(testnodes_count, x.concurence)
#                       for x in chart_data]
#
#         if use_bw:
#             data = [x.bw.average / 1000 for x in chart_data]
#             data_conf = [x.bw.confidence / 1000 for x in chart_data]
#             data_dev = [x.bw.deviation * 2.5 / 1000 for x in chart_data]
#             name = "BW"
#         else:
#             data = [x.iops.average for x in chart_data]
#             data_conf = [x.iops.confidence for x in chart_data]
#             data_dev = [x.iops.deviation * 2 for x in chart_data]
#             name = "IOPS"
#
#         fc = io_chart(title=desc,
#                       concurence=concurence,
#
#                       latv=lat,
#                       latv_min=lat_min,
#                       latv_max=lat_max,
#
#                       iops_or_bw=data,
#                       iops_or_bw_err=data_conf,
#
#                       legend=name,
#                       log_lat=lat_log_scale,
#
#                       latv_50=lat_50,
#                       latv_95=lat_95,
#
#                       error2=data_dev)
#         files[fname] = fc
#
#     return files
#
#
# def find_max_where(processed_results, sync_mode, blocksize, rw, iops=True):
#     result = None
#     attr = 'iops' if iops else 'bw'
#     for measurement in processed_results:
#         ok = measurement.sync_mode == sync_mode
#         ok = ok and (measurement.p.blocksize == blocksize)
#         ok = ok and (measurement.p.rw == rw)
#
#         if ok:
#             field = getattr(measurement, attr)
#
#             if result is None:
#                 result = field
#             elif field.average > result.average:
#                 result = field
#
#     return result
#
#
# def get_disk_info(processed_results):
#     di = DiskInfo()
#     di.direct_iops_w_max = find_max_where(processed_results,
#                                           'd', '4k', 'randwrite')
#     di.direct_iops_r_max = find_max_where(processed_results,
#                                           'd', '4k', 'randread')
#
#     di.direct_iops_w64_max = find_max_where(processed_results,
#                                             'd', '64k', 'randwrite')
#
#     for sz in ('16m', '64m'):
#         di.bw_write_max = find_max_where(processed_results,
#                                          'd', sz, 'randwrite', False)
#         if di.bw_write_max is not None:
#             break
#
#     if di.bw_write_max is None:
#         for sz in ('1m', '2m', '4m', '8m'):
#             di.bw_write_max = find_max_where(processed_results,
#                                              'd', sz, 'write', False)
#             if di.bw_write_max is not None:
#                 break
#
#     for sz in ('16m', '64m'):
#         di.bw_read_max = find_max_where(processed_results,
#                                         'd', sz, 'randread', False)
#         if di.bw_read_max is not None:
#             break
#
#     if di.bw_read_max is None:
#         di.bw_read_max = find_max_where(processed_results,
#                                         'd', '1m', 'read', False)
#
#     rws4k_iops_lat_th = []
#     for res in processed_results:
#         if res.sync_mode in 'xs' and res.p.blocksize == '4k':
#             if res.p.rw != 'randwrite':
#                 continue
#             rws4k_iops_lat_th.append((res.iops.average,
#                                       res.lat,
#                                       # res.lat.average,
#                                       res.concurence))
#
#     rws4k_iops_lat_th.sort(key=lambda x: x[2])
#
#     latv = [lat for _, lat, _ in rws4k_iops_lat_th]
#
#     for tlat in [10, 30, 100]:
#         pos = bisect.bisect_left(latv, tlat)
#         if 0 == pos:
#             setattr(di, 'rws4k_{}ms'.format(tlat), 0)
#         elif pos == len(latv):
#             iops3, _, _ = rws4k_iops_lat_th[-1]
#             iops3 = int(round_3_digit(iops3))
#             setattr(di, 'rws4k_{}ms'.format(tlat), ">=" + str(iops3))
#         else:
#             lat1 = latv[pos - 1]
#             lat2 = latv[pos]
#
#             iops1, _, th1 = rws4k_iops_lat_th[pos - 1]
#             iops2, _, th2 = rws4k_iops_lat_th[pos]
#
#             th_lat_coef = (th2 - th1) / (lat2 - lat1)
#             th3 = th_lat_coef * (tlat - lat1) + th1
#
#             th_iops_coef = (iops2 - iops1) / (th2 - th1)
#             iops3 = th_iops_coef * (th3 - th1) + iops1
#             iops3 = int(round_3_digit(iops3))
#             setattr(di, 'rws4k_{}ms'.format(tlat), iops3)
#
#     hdi = DiskInfo()
#
#     def pp(x):
#         med, conf = x.rounded_average_conf()
#         conf_perc = int(float(conf) / med * 100)
#         dev_perc = int(float(x.deviation) / med * 100)
#         return (round_3_digit(med), conf_perc, dev_perc)
#
#     hdi.direct_iops_r_max = pp(di.direct_iops_r_max)
#
#     if di.direct_iops_w_max is not None:
#         hdi.direct_iops_w_max = pp(di.direct_iops_w_max)
#     else:
#         hdi.direct_iops_w_max = None
#
#     if di.direct_iops_w64_max is not None:
#         hdi.direct_iops_w64_max = pp(di.direct_iops_w64_max)
#     else:
#         hdi.direct_iops_w64_max = None
#
#     hdi.bw_write_max = pp(di.bw_write_max)
#     hdi.bw_read_max = pp(di.bw_read_max)
#
#     hdi.rws4k_10ms = di.rws4k_10ms if 0 != di.rws4k_10ms else None
#     hdi.rws4k_30ms = di.rws4k_30ms if 0 != di.rws4k_30ms else None
#     hdi.rws4k_100ms = di.rws4k_100ms if 0 != di.rws4k_100ms else None
#     return hdi
#
#
# @report('hdd', 'hdd')
# def make_hdd_report(processed_results, lab_info, comment):
#     plots = [
#         ('hdd_rrd4k', 'rand_read_4k', 'Random read 4k direct IOPS'),
#         ('hdd_rwx4k', 'rand_write_4k', 'Random write 4k sync IOPS')
#     ]
#     perf_infos = [res.disk_perf_info() for res in processed_results]
#     images = make_plots(perf_infos, plots)
#     di = get_disk_info(perf_infos)
#     return render_all_html(comment, di, lab_info, images, "report_hdd.html")
#
#
# @report('cinder_iscsi', 'cinder_iscsi')
# def make_cinder_iscsi_report(processed_results, lab_info, comment):
#     plots = [
#         ('cinder_iscsi_rrd4k', 'rand_read_4k', 'Random read 4k direct IOPS'),
#         ('cinder_iscsi_rwx4k', 'rand_write_4k', 'Random write 4k sync IOPS')
#     ]
#     perf_infos = [res.disk_perf_info() for res in processed_results]
#     try:
#         images = make_plots(perf_infos, plots)
#     except ValueError:
#         plots = [
#             ('cinder_iscsi_rrd4k', 'rand_read_4k', 'Random read 4k direct IOPS'),
#             ('cinder_iscsi_rws4k', 'rand_write_4k', 'Random write 4k sync IOPS')
#         ]
#         images = make_plots(perf_infos, plots)
#     di = get_disk_info(perf_infos)
#
#     return render_all_html(comment, di, lab_info, images, "report_cinder_iscsi.html")
#
#
# @report('ceph', 'ceph')
# def make_ceph_report(processed_results, lab_info, comment):
#     plots = [
#         ('ceph_rrd4k', 'rand_read_4k', 'Random read 4k direct IOPS'),
#         ('ceph_rws4k', 'rand_write_4k', 'Random write 4k sync IOPS'),
#         ('ceph_rrd16m', 'rand_read_16m', 'Random read 16m direct MiBps'),
#         ('ceph_rwd16m', 'rand_write_16m',
#          'Random write 16m direct MiBps'),
#     ]
#
#     perf_infos = [res.disk_perf_info() for res in processed_results]
#     images = make_plots(perf_infos, plots)
#     di = get_disk_info(perf_infos)
#     return render_all_html(comment, di, lab_info, images, "report_ceph.html")
#
#
# @report('mixed', 'mixed')
# def make_mixed_report(processed_results, lab_info, comment):
#     #
#     # IOPS(X% read) = 100 / ( X / IOPS_W + (100 - X) / IOPS_R )
#     #
#
#     perf_infos = [res.disk_perf_info() for res in processed_results]
#     mixed = collections.defaultdict(lambda: [])
#
#     is_ssd = False
#     for res in perf_infos:
#         if res.name.startswith('mixed'):
#             if res.name.startswith('mixed-ssd'):
#                 is_ssd = True
#             mixed[res.concurence].append((res.p.rwmixread,
#                                           res.lat,
#                                           0,
#                                           # res.lat.average / 1000.0,
#                                           # res.lat.deviation / 1000.0,
#                                           res.iops.average,
#                                           res.iops.deviation))
#
#     if len(mixed) == 0:
#         raise ValueError("No mixed load found")
#
#     fig, p1 = plt.subplots()
#     p2 = p1.twinx()
#
#     colors = ['red', 'green', 'blue', 'orange', 'magenta', "teal"]
#     colors_it = iter(colors)
#     for conc, mix_lat_iops in sorted(mixed.items()):
#         mix_lat_iops = sorted(mix_lat_iops)
#         read_perc, lat, dev, iops, iops_dev = zip(*mix_lat_iops)
#         p1.errorbar(read_perc, iops, color=next(colors_it),
#                     yerr=iops_dev, label=str(conc) + " th")
#
#         p2.errorbar(read_perc, lat, color=next(colors_it),
#                     ls='--', yerr=dev, label=str(conc) + " th lat")
#
#     if is_ssd:
#         p1.set_yscale('log')
#         p2.set_yscale('log')
#
#     p1.set_xlim(-5, 105)
#
#     read_perc = set(read_perc)
#     read_perc.add(0)
#     read_perc.add(100)
#     read_perc = sorted(read_perc)
#
#     plt.xticks(read_perc, map(str, read_perc))
#
#     p1.grid(True)
#     p1.set_xlabel("% of reads")
#     p1.set_ylabel("Mixed IOPS")
#     p2.set_ylabel("Latency, ms")
#
#     handles1, labels1 = p1.get_legend_handles_labels()
#     handles2, labels2 = p2.get_legend_handles_labels()
#     plt.subplots_adjust(top=0.85)
#     plt.legend(handles1 + handles2, labels1 + labels2,
#                bbox_to_anchor=(0.5, 1.15),
#                loc='upper center',
#                prop={'size': 12}, ncol=3)
#     plt.show()
#
#
# def make_load_report(idx, results_dir, fname):
#     dpath = os.path.join(results_dir, "io_" + str(idx))
#     files = sorted(os.listdir(dpath))
#     gf = lambda x: "_".join(x.rsplit(".", 1)[0].split('_')[:3])
#
#     for key, group in itertools.groupby(files, gf):
#         fname = os.path.join(dpath, key + ".fio")
#
#         cfgs = list(parse_all_in_1(open(fname).read(), fname))
#
#         fname = os.path.join(dpath, key + "_lat.log")
#
#         curr = []
#         arrays = []
#
#         with open(fname) as fd:
#             for offset, lat, _, _ in csv.reader(fd):
#                 offset = int(offset)
#                 lat = int(lat)
#                 if len(curr) > 0 and curr[-1][0] > offset:
#                     arrays.append(curr)
#                     curr = []
#                 curr.append((offset, lat))
#             arrays.append(curr)
#         conc = int(cfgs[0].vals.get('numjobs', 1))
#
#         if conc != 5:
#             continue
#
#         assert len(arrays) == len(cfgs) * conc
#
#         garrays = [[(0, 0)] for _ in range(conc)]
#
#         for offset in range(len(cfgs)):
#             for acc, new_arr in zip(garrays, arrays[offset * conc:(offset + 1) * conc]):
#                 last = acc[-1][0]
#                 for off, lat in new_arr:
#                     acc.append((off / 1000. + last, lat / 1000.))
#
#         for cfg, arr in zip(cfgs, garrays):
#             plt.plot(*zip(*arr[1:]))
#         plt.show()
#         exit(1)
#
#
# def make_io_report(dinfo, comment, path, lab_info=None):
#     lab_info = {
#         "total_disk": "None",
#         "total_memory": "None",
#         "nodes_count": "None",
#         "processor_count": "None"
#     }
#
#     try:
#         res_fields = sorted(v.name for v in dinfo)
#
#         found = False
#         for fields, name, func in report_funcs:
#             for field in fields:
#                 pos = bisect.bisect_left(res_fields, field)
#
#                 if pos == len(res_fields):
#                     break
#
#                 if not res_fields[pos].startswith(field):
#                     break
#             else:
#                 found = True
#                 hpath = path.format(name)
#
#                 try:
#                     report = func(dinfo, lab_info, comment)
#                 except:
#                     logger.exception("Diring {0} report generation".format(name))
#                     continue
#
#                 if report is not None:
#                     try:
#                         with open(hpath, "w") as fd:
#                             fd.write(report)
#                     except:
#                         logger.exception("Diring saving {0} report".format(name))
#                         continue
#                     logger.info("Report {0} saved into {1}".format(name, hpath))
#                 else:
#                     logger.warning("No report produced by {0!r}".format(name))
#
#         if not found:
#             logger.warning("No report generator found for this load")
#
#     except Exception as exc:
#         import traceback
#         traceback.print_exc()
#         logger.error("Failed to generate html report:" + str(exc))
#
#
#     # @classmethod
#     # def prepare_data(cls, results) -> List[Dict[str, Any]]:
#     #     """create a table with io performance report for console"""
#     #
#     #     def key_func(data: FioRunResult) -> Tuple[str, str, str, str, int]:
#     #         tpl = data.summary_tpl()
#     #         return (data.name,
#     #                 tpl.oper,
#     #                 tpl.mode,
#     #                 ssize2b(tpl.bsize),
#     #                 int(tpl.th_count) * int(tpl.vm_count))
#     #     res = []
#     #
#     #     for item in sorted(results, key=key_func):
#     #         test_dinfo = item.disk_perf_info()
#     #         testnodes_count = len(item.config.nodes)
#     #
#     #         iops, _ = test_dinfo.iops.rounded_average_conf()
#     #
#     #         if test_dinfo.iops_sys is not None:
#     #             iops_sys, iops_sys_conf = test_dinfo.iops_sys.rounded_average_conf()
#     #             _, iops_sys_dev = test_dinfo.iops_sys.rounded_average_dev()
#     #             iops_sys_per_vm = round_3_digit(iops_sys / testnodes_count)
#     #             iops_sys = round_3_digit(iops_sys)
#     #         else:
#     #             iops_sys = None
#     #             iops_sys_per_vm = None
#     #             iops_sys_dev = None
#     #             iops_sys_conf = None
#     #
#     #         bw, bw_conf = test_dinfo.bw.rounded_average_conf()
#     #         _, bw_dev = test_dinfo.bw.rounded_average_dev()
#     #         conf_perc = int(round(bw_conf * 100 / bw))
#     #         dev_perc = int(round(bw_dev * 100 / bw))
#     #
#     #         lat_50 = round_3_digit(int(test_dinfo.lat_50))
#     #         lat_95 = round_3_digit(int(test_dinfo.lat_95))
#     #         lat_avg = round_3_digit(int(test_dinfo.lat_avg))
#     #
#     #         iops_per_vm = round_3_digit(iops / testnodes_count)
#     #         bw_per_vm = round_3_digit(bw / testnodes_count)
#     #
#     #         iops = round_3_digit(iops)
#     #         bw = round_3_digit(bw)
#     #
#     #         summ = "{0.oper}{0.mode} {0.bsize:>4} {0.th_count:>3}th {0.vm_count:>2}vm".format(item.summary_tpl())
#     #
#     #         res.append({"name": key_func(item)[0],
#     #                     "key": key_func(item)[:4],
#     #                     "summ": summ,
#     #                     "iops": int(iops),
#     #                     "bw": int(bw),
#     #                     "conf": str(conf_perc),
#     #                     "dev": str(dev_perc),
#     #                     "iops_per_vm": int(iops_per_vm),
#     #                     "bw_per_vm": int(bw_per_vm),
#     #                     "lat_50": lat_50,
#     #                     "lat_95": lat_95,
#     #                     "lat_avg": lat_avg,
#     #
#     #                     "iops_sys": iops_sys,
#     #                     "iops_sys_per_vm": iops_sys_per_vm,
#     #                     "sys_conf": iops_sys_conf,
#     #                     "sys_dev": iops_sys_dev})
#     #
#     #     return res
#     #
#     # Field = collections.namedtuple("Field", ("header", "attr", "allign", "size"))
#     # fiels_and_header = [
#     #     Field("Name",           "name",        "l",  7),
#     #     Field("Description",    "summ",        "l", 19),
#     #     Field("IOPS\ncum",      "iops",        "r",  3),
#     #     # Field("IOPS_sys\ncum",  "iops_sys",    "r",  3),
#     #     Field("KiBps\ncum",     "bw",          "r",  6),
#     #     Field("Cnf %\n95%",     "conf",        "r",  3),
#     #     Field("Dev%",           "dev",         "r",  3),
#     #     Field("iops\n/vm",      "iops_per_vm", "r",  3),
#     #     Field("KiBps\n/vm",     "bw_per_vm",   "r",  6),
#     #     Field("lat ms\nmedian", "lat_50",      "r",  3),
#     #     Field("lat ms\n95%",    "lat_95",      "r",  3),
#     #     Field("lat\navg",       "lat_avg",     "r",  3),
#     # ]
#     #
#     # fiels_and_header_dct = dict((item.attr, item) for item in fiels_and_header)
#     #
#     # @classmethod
#     # def format_for_console(cls, results) -> str:
#     #     """create a table with io performance report for console"""
#     #
#     #     tab = texttable.Texttable(max_width=120)
#     #     tab.set_deco(tab.HEADER | tab.VLINES | tab.BORDER)
#     #     tab.set_cols_align([f.allign for f in cls.fiels_and_header])
#     #     sep = ["-" * f.size for f in cls.fiels_and_header]
#     #     tab.header([f.header for f in cls.fiels_and_header])
#     #     prev_k = None
#     #     for item in cls.prepare_data(results):
#     #         if prev_k is not None:
#     #             if prev_k != item["key"]:
#     #                 tab.add_row(sep)
#     #
#     #         prev_k = item["key"]
#     #         tab.add_row([item[f.attr] for f in cls.fiels_and_header])
#     #
#     #     return tab.draw()
#     #
#     # @classmethod
#     # def format_diff_for_console(cls, list_of_results: List[Any]) -> str:
#     #     """create a table with io performance report for console"""
#     #
#     #     tab = texttable.Texttable(max_width=200)
#     #     tab.set_deco(tab.HEADER | tab.VLINES | tab.BORDER)
#     #
#     #     header = [
#     #         cls.fiels_and_header_dct["name"].header,
#     #         cls.fiels_and_header_dct["summ"].header,
#     #     ]
#     #     allign = ["l", "l"]
#     #
#     #     header.append("IOPS ~ Cnf% ~ Dev%")
#     #     allign.extend(["r"] * len(list_of_results))
#     #     header.extend(
#     #         "IOPS_{0} %".format(i + 2) for i in range(len(list_of_results[1:]))
#     #     )
#     #
#     #     header.append("BW")
#     #     allign.extend(["r"] * len(list_of_results))
#     #     header.extend(
#     #         "BW_{0} %".format(i + 2) for i in range(len(list_of_results[1:]))
#     #     )
#     #
#     #     header.append("LAT")
#     #     allign.extend(["r"] * len(list_of_results))
#     #     header.extend(
#     #         "LAT_{0}".format(i + 2) for i in range(len(list_of_results[1:]))
#     #     )
#     #
#     #     tab.header(header)
#     #     sep = ["-" * 3] * len(header)
#     #     processed_results = map(cls.prepare_data, list_of_results)
#     #
#     #     key2results = []
#     #     for res in processed_results:
#     #         key2results.append(dict(
#     #             ((item["name"], item["summ"]), item) for item in res
#     #         ))
#     #
#     #     prev_k = None
#     #     iops_frmt = "{0[iops]} ~ {0[conf]:>2} ~ {0[dev]:>2}"
#     #     for item in processed_results[0]:
#     #         if prev_k is not None:
#     #             if prev_k != item["key"]:
#     #                 tab.add_row(sep)
#     #
#     #         prev_k = item["key"]
#     #
#     #         key = (item['name'], item['summ'])
#     #         line = list(key)
#     #         base = key2results[0][key]
#     #
#     #         line.append(iops_frmt.format(base))
#     #
#     #         for test_results in key2results[1:]:
#     #             val = test_results.get(key)
#     #             if val is None:
#     #                 line.append("-")
#     #             elif base['iops'] == 0:
#     #                 line.append("Nan")
#     #             else:
#     #                 prc_val = {'dev': val['dev'], 'conf': val['conf']}
#     #                 prc_val['iops'] = int(100 * val['iops'] / base['iops'])
#     #                 line.append(iops_frmt.format(prc_val))
#     #
#     #         line.append(base['bw'])
#     #
#     #         for test_results in key2results[1:]:
#     #             val = test_results.get(key)
#     #             if val is None:
#     #                 line.append("-")
#     #             elif base['bw'] == 0:
#     #                 line.append("Nan")
#     #             else:
#     #                 line.append(int(100 * val['bw'] / base['bw']))
#     #
#     #         for test_results in key2results:
#     #             val = test_results.get(key)
#     #             if val is None:
#     #                 line.append("-")
#     #             else:
#     #                 line.append("{0[lat_50]} - {0[lat_95]}".format(val))
#     #
#     #         tab.add_row(line)
#     #
#     #     tab.set_cols_align(allign)
#     #     return tab.draw()
#
#
# # READ_IOPS_DISCSTAT_POS = 3
# # WRITE_IOPS_DISCSTAT_POS = 7
# #
# #
# # def load_sys_log_file(ftype: str, fname: str) -> TimeSeriesValue:
# #     assert ftype == 'iops'
# #     pval = None
# #     with open(fname) as fd:
# #         iops = []
# #         for ln in fd:
# #             params = ln.split()
# #             cval = int(params[WRITE_IOPS_DISCSTAT_POS]) + \
# #                 int(params[READ_IOPS_DISCSTAT_POS])
# #             if pval is not None:
# #                 iops.append(cval - pval)
# #             pval = cval
# #
# #     vals = [(idx * 1000, val) for idx, val in enumerate(iops)]
# #     return TimeSeriesValue(vals)
# #
# #
# # def load_test_results(folder: str, run_num: int) -> 'FioRunResult':
# #     res = {}
# #     params = None
# #
# #     fn = os.path.join(folder, str(run_num) + '_params.yaml')
# #     params = yaml.load(open(fn).read())
# #
# #     conn_ids_set = set()
# #     rr = r"{}_(?P<conn_id>.*?)_(?P<type>[^_.]*)\.\d+\.log$".format(run_num)
# #     for fname in os.listdir(folder):
# #         rm = re.match(rr, fname)
# #         if rm is None:
# #             continue
# #
# #         conn_id_s = rm.group('conn_id')
# #         conn_id = conn_id_s.replace('_', ':')
# #         ftype = rm.group('type')
# #
# #         if ftype not in ('iops', 'bw', 'lat'):
# #             continue
# #
# #         ts = load_fio_log_file(os.path.join(folder, fname))
# #         res.setdefault(ftype, {}).setdefault(conn_id, []).append(ts)
# #
# #         conn_ids_set.add(conn_id)
# #
# #     rr = r"{}_(?P<conn_id>.*?)_(?P<type>[^_.]*)\.sys\.log$".format(run_num)
# #     for fname in os.listdir(folder):
# #         rm = re.match(rr, fname)
# #         if rm is None:
# #             continue
# #
# #         conn_id_s = rm.group('conn_id')
# #         conn_id = conn_id_s.replace('_', ':')
# #         ftype = rm.group('type')
# #
# #         if ftype not in ('iops', 'bw', 'lat'):
# #             continue
# #
# #         ts = load_sys_log_file(ftype, os.path.join(folder, fname))
# #         res.setdefault(ftype + ":sys", {}).setdefault(conn_id, []).append(ts)
# #
# #         conn_ids_set.add(conn_id)
# #
# #     mm_res = {}
# #
# #     if len(res) == 0:
# #         raise ValueError("No data was found")
# #
# #     for key, data in res.items():
# #         conn_ids = sorted(conn_ids_set)
# #         awail_ids = [conn_id for conn_id in conn_ids if conn_id in data]
# #         matr = [data[conn_id] for conn_id in awail_ids]
# #         mm_res[key] = MeasurementMatrix(matr, awail_ids)
# #
# #     raw_res = {}
# #     for conn_id in conn_ids:
# #         fn = os.path.join(folder, "{0}_{1}_rawres.json".format(run_num, conn_id_s))
# #
# #         # remove message hack
# #         fc = "{" + open(fn).read().split('{', 1)[1]
# #         raw_res[conn_id] = json.loads(fc)
# #
# #     fio_task = FioJobSection(params['name'])
# #     fio_task.vals.update(params['vals'])
# #
# #     config = TestConfig('io', params, None, params['nodes'], folder, None)
# #     return FioRunResult(config, fio_task, mm_res, raw_res, params['intervals'], run_num)
# #
#
# # class DiskPerfInfo:
# #     def __init__(self, name: str, summary: str, params: Dict[str, Any], testnodes_count: int) -> None:
# #         self.name = name
# #         self.bw = None
# #         self.iops = None
# #         self.lat = None
# #         self.lat_50 = None
# #         self.lat_95 = None
# #         self.lat_avg = None
# #
# #         self.raw_bw = []
# #         self.raw_iops = []
# #         self.raw_lat = []
# #
# #         self.params = params
# #         self.testnodes_count = testnodes_count
# #         self.summary = summary
# #
# #         self.sync_mode = get_test_sync_mode(self.params['vals'])
# #         self.concurence = self.params['vals'].get('numjobs', 1)
# #
# #
# # class IOTestResults:
# #     def __init__(self, suite_name: str, fio_results: 'FioRunResult', log_directory: str):
# #         self.suite_name = suite_name
# #         self.fio_results = fio_results
# #         self.log_directory = log_directory
# #
# #     def __iter__(self):
# #         return iter(self.fio_results)
# #
# #     def __len__(self):
# #         return len(self.fio_results)
# #
# #     def get_yamable(self) -> Dict[str, List[str]]:
# #         items = [(fio_res.summary(), fio_res.idx) for fio_res in self]
# #         return {self.suite_name: [self.log_directory] + items}
#
#
# # class FioRunResult(TestResults):
# #     """
# #     Fio run results
# #     config: TestConfig
# #     fio_task: FioJobSection
# #     ts_results: {str: MeasurementMatrix[TimeSeriesValue]}
# #     raw_result: ????
# #     run_interval:(float, float) - test tun time, used for sensors
# #     """
# #     def __init__(self, config, fio_task, ts_results, raw_result, run_interval, idx):
# #
# #         self.name = fio_task.name.rsplit("_", 1)[0]
# #         self.fio_task = fio_task
# #         self.idx = idx
# #
# #         self.bw = ts_results['bw']
# #         self.lat = ts_results['lat']
# #         self.iops = ts_results['iops']
# #
# #         if 'iops:sys' in ts_results:
# #             self.iops_sys = ts_results['iops:sys']
# #         else:
# #             self.iops_sys = None
# #
# #         res = {"bw": self.bw,
# #                "lat": self.lat,
# #                "iops": self.iops,
# #                "iops:sys": self.iops_sys}
# #
# #         self.sensors_data = None
# #         self._pinfo = None
# #         TestResults.__init__(self, config, res, raw_result, run_interval)
# #
# #     def get_params_from_fio_report(self):
# #         nodes = self.bw.connections_ids
# #
# #         iops = [self.raw_result[node]['jobs'][0]['mixed']['iops'] for node in nodes]
# #         total_ios = [self.raw_result[node]['jobs'][0]['mixed']['total_ios'] for node in nodes]
# #         runtime = [self.raw_result[node]['jobs'][0]['mixed']['runtime'] / 1000 for node in nodes]
# #         flt_iops = [float(ios) / rtime for ios, rtime in zip(total_ios, runtime)]
# #
# #         bw = [self.raw_result[node]['jobs'][0]['mixed']['bw'] for node in nodes]
# #         total_bytes = [self.raw_result[node]['jobs'][0]['mixed']['io_bytes'] for node in nodes]
# #         flt_bw = [float(tbytes) / rtime for tbytes, rtime in zip(total_bytes, runtime)]
# #
# #         return {'iops': iops,
# #                 'flt_iops': flt_iops,
# #                 'bw': bw,
# #                 'flt_bw': flt_bw}
# #
# #     def summary(self):
# #         return get_test_summary(self.fio_task, len(self.config.nodes))
# #
# #     def summary_tpl(self):
# #         return get_test_summary_tuple(self.fio_task, len(self.config.nodes))
# #
# #     def get_lat_perc_50_95_multy(self):
# #         lat_mks = collections.defaultdict(lambda: 0)
# #         num_res = 0
# #
# #         for result in self.raw_result.values():
# #             num_res += len(result['jobs'])
# #             for job_info in result['jobs']:
# #                 for k, v in job_info['latency_ms'].items():
# #                     if isinstance(k, basestring) and k.startswith('>='):
# #                         lat_mks[int(k[2:]) * 1000] += v
# #                     else:
# #                         lat_mks[int(k) * 1000] += v
# #
# #                 for k, v in job_info['latency_us'].items():
# #                     lat_mks[int(k)] += v
# #
# #         for k, v in lat_mks.items():
# #             lat_mks[k] = float(v) / num_res
# #         return get_lat_perc_50_95(lat_mks)
# #
# #     def disk_perf_info(self, avg_interval=2.0):
# #
# #         if self._pinfo is not None:
# #             return self._pinfo
# #
# #         testnodes_count = len(self.config.nodes)
# #
# #         pinfo = DiskPerfInfo(self.name,
# #                              self.summary(),
# #                              self.params,
# #                              testnodes_count)
# #
# #         def prepare(data, drop=1):
# #             if data is None:
# #                 return data
# #
# #             res = []
# #             for ts_data in data:
# #                 if ts_data.average_interval() < avg_interval:
# #                     ts_data = ts_data.derived(avg_interval)
# #
# #                 # drop last value on bounds
# #                 # as they may contains ranges without activities
# #                 assert len(ts_data.values) >= drop + 1, str(drop) + " " + str(ts_data.values)
# #
# #                 if drop > 0:
# #                     res.append(ts_data.values[:-drop])
# #                 else:
# #                     res.append(ts_data.values)
# #
# #             return res
# #
# #         def agg_data(matr):
# #             arr = sum(matr, [])
# #             min_len = min(map(len, arr))
# #             res = []
# #             for idx in range(min_len):
# #                 res.append(sum(dt[idx] for dt in arr))
# #             return res
# #
# #         pinfo.raw_lat = map(prepare, self.lat.per_vm())
# #         num_th = sum(map(len, pinfo.raw_lat))
# #         lat_avg = [val / num_th for val in agg_data(pinfo.raw_lat)]
# #         pinfo.lat_avg = data_property(lat_avg).average / 1000  # us to ms
# #
# #         pinfo.lat_50, pinfo.lat_95 = self.get_lat_perc_50_95_multy()
# #         pinfo.lat = pinfo.lat_50
# #
# #         pinfo.raw_bw = map(prepare, self.bw.per_vm())
# #         pinfo.raw_iops = map(prepare, self.iops.per_vm())
# #
# #         if self.iops_sys is not None:
# #             pinfo.raw_iops_sys = map(prepare, self.iops_sys.per_vm())
# #             pinfo.iops_sys = data_property(agg_data(pinfo.raw_iops_sys))
# #         else:
# #             pinfo.raw_iops_sys = None
# #             pinfo.iops_sys = None
# #
# #         fparams = self.get_params_from_fio_report()
# #         fio_report_bw = sum(fparams['flt_bw'])
# #         fio_report_iops = sum(fparams['flt_iops'])
# #
# #         agg_bw = agg_data(pinfo.raw_bw)
# #         agg_iops = agg_data(pinfo.raw_iops)
# #
# #         log_bw_avg = average(agg_bw)
# #         log_iops_avg = average(agg_iops)
# #
# #         # update values to match average from fio report
# #         coef_iops = fio_report_iops / float(log_iops_avg)
# #         coef_bw = fio_report_bw / float(log_bw_avg)
# #
# #         bw_log = data_property([val * coef_bw for val in agg_bw])
# #         iops_log = data_property([val * coef_iops for val in agg_iops])
# #
# #         bw_report = data_property([fio_report_bw])
# #         iops_report = data_property([fio_report_iops])
# #
# #         # When IOPS/BW per thread is too low
# #         # data from logs is rounded to match
# #         iops_per_th = sum(sum(pinfo.raw_iops, []), [])
# #         if average(iops_per_th) > 10:
# #             pinfo.iops = iops_log
# #             pinfo.iops2 = iops_report
# #         else:
# #             pinfo.iops = iops_report
# #             pinfo.iops2 = iops_log
# #
# #         bw_per_th = sum(sum(pinfo.raw_bw, []), [])
# #         if average(bw_per_th) > 10:
# #             pinfo.bw = bw_log
# #             pinfo.bw2 = bw_report
# #         else:
# #             pinfo.bw = bw_report
# #             pinfo.bw2 = bw_log
# #
# #         self._pinfo = pinfo
# #
# #         return pinfo
#
# # class TestResult:
# #     """Hold all information for a given test - test info,
# #     sensors data and performance results for test period from all nodes"""
# #     run_id = None  # type: int
# #     test_info = None  # type: Any
# #     begin_time = None  # type: int
# #     end_time = None  # type: int
# #     sensors = None  # Dict[Tuple[str, str, str], TimeSeries]
# #     performance = None  # Dict[Tuple[str, str], TimeSeries]
# #
# #     class TestResults:
# #         """
# #         this class describe test results
# #
# #         config:TestConfig - test config object
# #         params:dict - parameters from yaml file for this test
# #         results:{str:MeasurementMesh} - test results object
# #         raw_result:Any - opaque object to store raw results
# #         run_interval:(float, float) - test tun time, used for sensors
# #         """
# #
# #         def __init__(self,
# #                      config: TestConfig,
# #                      results: Dict[str, Any],
# #                      raw_result: Any,
# #                      run_interval: Tuple[float, float]) -> None:
# #             self.config = config
# #             self.params = config.params
# #             self.results = results
# #             self.raw_result = raw_result
# #             self.run_interval = run_interval
# #
# #         def __str__(self) -> str:
# #             res = "{0}({1}):\n    results:\n".format(
# #                 self.__class__.__name__,
# #                 self.summary())
# #
# #             for name, val in self.results.items():
# #                 res += "        {0}={1}\n".format(name, val)
# #
# #             res += "    params:\n"
# #
# #             for name, val in self.params.items():
# #                 res += "        {0}={1}\n".format(name, val)
# #
# #             return res
# #
# #         def summary(self) -> str:
# #             raise NotImplementedError()
# #             return ""
# #
# #         def get_yamable(self) -> Any:
# #             raise NotImplementedError()
# #             return None
#
#
#
#             # class MeasurementMatrix:
# #     """
# #     data:[[MeasurementResult]] - VM_COUNT x TH_COUNT matrix of MeasurementResult
# #     """
# #     def __init__(self, data, connections_ids):
# #         self.data = data
# #         self.connections_ids = connections_ids
# #
# #     def per_vm(self):
# #         return self.data
# #
# #     def per_th(self):
# #         return sum(self.data, [])
#
#
# # class MeasurementResults:
# #     data = None  # type: List[Any]
# #
# #     def stat(self) -> StatProps:
# #         return data_property(self.data)
# #
# #     def __str__(self) -> str:
# #         return 'TS([' + ", ".join(map(str, self.data)) + '])'
# #
# #
# # class SimpleVals(MeasurementResults):
# #     """
# #     data:[float] - list of values
# #     """
# #     def __init__(self, data: List[float]) -> None:
# #         self.data = data
# #
# #
# # class TimeSeriesValue(MeasurementResults):
# #     """
# #     data:[(float, float, float)] - list of (start_time, lenght, average_value_for_interval)
# #     odata: original values
# #     """
# #     def __init__(self, data: List[Tuple[float, float]]) -> None:
# #         assert len(data) > 0
# #         self.odata = data[:]
# #         self.data = []  # type: List[Tuple[float, float, float]]
# #
# #         cstart = 0.0
# #         for nstart, nval in data:
# #             self.data.append((cstart, nstart - cstart, nval))
# #             cstart = nstart
# #
# #     @property
# #     def values(self) -> List[float]:
# #         return [val[2] for val in self.data]
# #
# #     def average_interval(self) -> float:
# #         return float(sum([val[1] for val in self.data])) / len(self.data)
# #
# #     def skip(self, seconds) -> 'TimeSeriesValue':
# #         nres = []
# #         for start, ln, val in self.data:
# #             nstart = start + ln - seconds
# #             if nstart > 0:
# #                 nres.append([nstart, val])
# #         return self.__class__(nres)
# #
# #     def derived(self, tdelta) -> 'TimeSeriesValue':
# #         end = self.data[-1][0] + self.data[-1][1]
# #         tdelta = float(tdelta)
# #
# #         ln = end / tdelta
# #
# #         if ln - int(ln) > 0:
# #             ln += 1
# #
# #         res = [[tdelta * i, 0.0] for i in range(int(ln))]
# #
# #         for start, lenght, val in self.data:
# #             start_idx = int(start / tdelta)
# #             end_idx = int((start + lenght) / tdelta)
# #
# #             for idx in range(start_idx, end_idx + 1):
# #                 rstart = tdelta * idx
# #                 rend = tdelta * (idx + 1)
# #
# #                 intersection_ln = min(rend, start + lenght) - max(start, rstart)
# #                 if intersection_ln > 0:
# #                     try:
# #                         res[idx][1] += val * intersection_ln / tdelta
# #                     except IndexError:
# #                         raise
# #
# #         return self.__class__(res)
#
#
# def console_report_stage(ctx: TestRun) -> None:
#     # TODO(koder): load data from storage
#     raise NotImplementedError("...")
#     # first_report = True
#     # text_rep_fname = ctx.config.text_report_file
#     #
#     # with open(text_rep_fname, "w") as fd:
#     #     for tp, data in ctx.results.items():
#     #         if 'io' == tp and data is not None:
#     #             rep_lst = []
#     #             for result in data:
#     #                 rep_lst.append(
#     #                     IOPerfTest.format_for_console(list(result)))
#     #             rep = "\n\n".join(rep_lst)
#     #         elif tp in ['mysql', 'pgbench'] and data is not None:
#     #             rep = MysqlTest.format_for_console(data)
#     #         elif tp == 'omg':
#     #             rep = OmgTest.format_for_console(data)
#     #         else:
#     #             logger.warning("Can't generate text report for " + tp)
#     #             continue
#     #
#     #         fd.write(rep)
#     #         fd.write("\n")
#     #
#     #         if first_report:
#     #             logger.info("Text report were stored in " + text_rep_fname)
#     #             first_report = False
#     #
#     #         print("\n" + rep + "\n")
#
#
# # def test_load_report_stage(cfg: Config, ctx: TestRun) -> None:
# #     load_rep_fname = cfg.load_report_file
# #     found = False
# #     for idx, (tp, data) in enumerate(ctx.results.items()):
# #         if 'io' == tp and data is not None:
# #             if found:
# #                 logger.error("Making reports for more than one " +
# #                              "io block isn't supported! All " +
# #                              "report, except first are skipped")
# #                 continue
# #             found = True
# #             report.make_load_report(idx, cfg['results'], load_rep_fname)
# #
# #
#
# # def html_report_stage(ctx: TestRun) -> None:
#     # TODO(koder): load data from storage
#     # raise NotImplementedError("...")
#     # html_rep_fname = cfg.html_report_file
#     # found = False
#     # for tp, data in ctx.results.items():
#     #     if 'io' == tp and data is not None:
#     #         if found or len(data) > 1:
#     #             logger.error("Making reports for more than one " +
#     #                          "io block isn't supported! All " +
#     #                          "report, except first are skipped")
#     #             continue
#     #         found = True
#     #         report.make_io_report(list(data[0]),
#     #                               cfg.get('comment', ''),
#     #                               html_rep_fname,
#     #                               lab_info=ctx.nodes)
#
# #
# # def load_data_from_path(test_res_dir: str) -> Mapping[str, List[Any]]:
# #     files = get_test_files(test_res_dir)
# #     raw_res = yaml_load(open(files['raw_results']).read())
# #     res = collections.defaultdict(list)
# #
# #     for tp, test_lists in raw_res:
# #         for tests in test_lists:
# #             for suite_name, suite_data in tests.items():
# #                 result_folder = suite_data[0]
# #                 res[tp].append(TOOL_TYPE_MAPPER[tp].load(suite_name, result_folder))
# #
# #     return res
# #
# #
# # def load_data_from_path_stage(var_dir: str, _, ctx: TestRun) -> None:
# #     for tp, vals in load_data_from_path(var_dir).items():
# #         ctx.results.setdefault(tp, []).extend(vals)
# #
# #
# # def load_data_from(var_dir: str) -> Callable[[TestRun], None]:
# #     return functools.partial(load_data_from_path_stage, var_dir)