diff --git a/wally/report.py b/wally/report.py
index f8d8c5a..0b0540e 100644
--- a/wally/report.py
+++ b/wally/report.py
@@ -1,36 +1,32 @@
 import os
-import re
 import abc
-import bisect
 import logging
 from io import BytesIO
 from functools import wraps
-from typing import Dict, Any, Iterator, Tuple, cast, List, Callable
+from typing import Dict, Any, Iterator, Tuple, cast, List, Callable, Set, Optional
 from collections import defaultdict
 
 import numpy
-import matplotlib
-# have to be before pyplot import to avoid tkinter(default graph frontend) import error
-matplotlib.use('svg')
-import matplotlib.pyplot as plt
 import scipy.stats
+import matplotlib.pyplot as plt
 
 import wally
 
 from . import html
-from .utils import b2ssize
 from .stage import Stage, StepOrder
 from .test_run_class import TestRun
 from .hlstorage import ResultStorage
 from .node_interfaces import NodeInfo
-from .storage import Storage
-from .statistic import calc_norm_stat_props, calc_histo_stat_props
-from .result_classes import (StatProps, DataSource, TimeSeries, TestSuiteConfig,
-                             NormStatProps, HistoStatProps, TestJobConfig)
+from .utils import b2ssize, b2ssize_10, STORAGE_ROLES
+from .statistic import (calc_norm_stat_props, calc_histo_stat_props, moving_average, moving_dev,
+                        hist_outliers_perc, ts_hist_outliers_perc, find_ouliers_ts, approximate_curve,
+                        rebin_histogram)
+from .result_classes import (StatProps, DataSource, TimeSeries, NormStatProps, HistoStatProps, SuiteConfig,
+                             IResultStorage)
 from .suits.io.fio_hist import get_lat_vals, expected_lat_bins
 from .suits.io.fio import FioTest, FioJobConfig
-from .suits.io.fio_task_parser import FioTestSumm
-from .statistic import approximate_curve, average, dev
+from .suits.io.fio_job import FioJobParams
+from .suits.job import JobConfig
 
 
 logger = logging.getLogger("wally")
@@ -48,34 +44,61 @@
 # ----------------  PROFILES  ------------------------------------------------------------------------------------------
 
 
+# this is default values, real values is loaded from config
+
 class ColorProfile:
     primary_color = 'b'
     suppl_color1 = 'teal'
     suppl_color2 = 'magenta'
+    suppl_color3 = 'orange'
     box_color = 'y'
+    err_color = 'red'
 
     noise_alpha = 0.3
     subinfo_alpha = 0.7
 
+    imshow_colormap = None  # type: str
+
 
 class StyleProfile:
     grid = True
     tide_layout = True
     hist_boxes = 10
+    hist_lat_boxes = 25
+    hm_hist_bins_count = 25
     min_points_for_dev = 5
 
     dev_range_x = 2.0
     dev_perc = 95
 
-    avg_range = 20
+    point_shape = 'o'
+    err_point_shape = '*'
 
-    curve_approx_level = 5
+    avg_range = 20
+    approx_average = True
+
+    curve_approx_level = 6
     curve_approx_points = 100
     assert avg_range >= min_points_for_dev
 
+    # figure size in inches
+    figsize = (10, 6)
+
     extra_io_spine = True
 
     legend_for_eng = True
+    heatmap_interpolation = '1d'
+    heatmap_interpolation_points = 300
+    outliers_q_nd = 3.0
+    outliers_hide_q_nd = 4.0
+    outliers_lat = (0.01, 0.995)
+
+    violin_instead_of_box = True
+    violin_point_count = 30000
+
+    heatmap_colorbar = False
+
+    min_iops_vs_qd_jobs = 3
 
     units = {
         'bw': ("MiBps", MiB2KiB, "bandwith"),
@@ -126,36 +149,10 @@
 
 
 # --------------  AGGREGATION AND STAT FUNCTIONS  ----------------------------------------------------------------------
-rexpr = {
-    'sensor': r'(?P<sensor>[-a-z]+)',
-    'dev': r'(?P<dev>[^.]+)',
-    'metric': r'(?P<metric>[a-z_]+)',
-    'node': r'(?P<node>\d+\.\d+\.\d+\.\d+:\d+)',
-}
 
-def iter_sensors(storage: Storage, node: str = None, sensor: str = None, dev: str = None, metric: str = None):
-    if node is None:
-        node = rexpr['node']
-    if sensor is None:
-        sensor = rexpr['sensor']
-    if dev is None:
-        dev = rexpr['dev']
-    if metric is None:
-        metric = rexpr['metric']
-
-    rr = r"{}_{}\.{}\.{}$".format(node, sensor, dev, metric)
-    sensor_name_re = re.compile(rr)
-
-    for is_file, sensor_data_name in storage.list("sensors"):
-        if is_file:
-            rr = sensor_name_re.match(sensor_data_name)
-            if rr:
-                yield 'sensors/' + sensor_data_name, rr.groupdict()
-
-
-def make_iosum(rstorage: ResultStorage, suite: TestSuiteConfig, job: FioJobConfig) -> IOSummary:
+def make_iosum(rstorage: ResultStorage, suite: SuiteConfig, job: FioJobConfig) -> IOSummary:
     lat = get_aggregated(rstorage, suite, job, "lat")
-    bins_edges = numpy.array(get_lat_vals(lat.second_axis_size), dtype='float32') / 1000
+    bins_edges = numpy.array(get_lat_vals(lat.data.shape[1]), dtype='float32') / 1000
     io = get_aggregated(rstorage, suite, job, "bw")
 
     return IOSummary(job.qd,
@@ -191,34 +188,38 @@
 #             yield suite, fjob
 
 
-def get_aggregated(rstorage: ResultStorage, suite: TestSuiteConfig, job: FioJobConfig, sensor: str) -> TimeSeries:
-    tss = list(rstorage.iter_ts(suite, job, sensor=sensor))
+AGG_TAG = 'ALL'
+
+
+def get_aggregated(rstorage: ResultStorage, suite: SuiteConfig, job: FioJobConfig, metric: str) -> TimeSeries:
+    tss = list(rstorage.iter_ts(suite, job, sensor=metric))
     ds = DataSource(suite_id=suite.storage_id,
                     job_id=job.storage_id,
-                    node_id="__all__",
-                    dev='fio',
-                    sensor=sensor,
-                    tag=None)
+                    node_id=AGG_TAG,
+                    sensor='fio',
+                    dev=AGG_TAG,
+                    metric=metric,
+                    tag='csv')
 
-    agg_ts = TimeSeries(sensor,
+    agg_ts = TimeSeries(metric,
                         raw=None,
                         source=ds,
                         data=numpy.zeros(tss[0].data.shape, dtype=tss[0].data.dtype),
                         times=tss[0].times.copy(),
-                        second_axis_size=tss[0].second_axis_size)
+                        units=tss[0].units)
 
     for ts in tss:
-        if sensor == 'lat' and ts.second_axis_size != expected_lat_bins:
+        if metric == 'lat' and (len(ts.data.shape) != 2 or ts.data.shape[1] != expected_lat_bins):
             logger.error("Sensor %s.%s on node %s has" +
-                         "second_axis_size=%s. Can only process sensors with second_axis_size=%s.",
+                         "shape=%s. Can only process sensors with shape=[X, %s].",
                          ts.source.dev, ts.source.sensor, ts.source.node_id,
-                         ts.second_axis_size, expected_lat_bins)
+                         ts.data.shape, expected_lat_bins)
             continue
 
-        if sensor != 'lat' and ts.second_axis_size != 1:
+        if metric != 'lat' and len(ts.data.shape) != 1:
             logger.error("Sensor %s.%s on node %s has" +
-                         "second_axis_size=%s. Can only process sensors with second_axis_size=1.",
-                         ts.source.dev, ts.source.sensor, ts.source.node_id, ts.second_axis_size)
+                         "shape=%s. Can only process 1D sensors.",
+                         ts.source.dev, ts.source.sensor, ts.source.node_id, ts.data.shape)
             continue
 
         # TODO: match times on different ts
@@ -227,24 +228,135 @@
     return agg_ts
 
 
+def is_sensor_numarray(sensor: str, metric: str) -> bool:
+    """Returns True if sensor provides one-dimension array of numeric values. One number per one measurement."""
+    return True
+
+
+LEVEL_SENSORS = {("block-io", "io_queue"),
+                 ("system-cpu", "procs_blocked"),
+                 ("system-cpu", "procs_queue")}
+
+
+def is_level_sensor(sensor: str, metric: str) -> bool:
+    """Returns True if sensor measure level of any kind, E.g. queue depth."""
+    return (sensor, metric) in LEVEL_SENSORS
+
+
+def is_delta_sensor(sensor: str, metric: str) -> bool:
+    """Returns True if sensor provides deltas for cumulative value. E.g. io completed in given period"""
+    return not is_level_sensor(sensor, metric)
+
+
+def get_sensor_for_time_range(storage: IResultStorage,
+                              node_id: str,
+                              sensor: str,
+                              dev: str,
+                              metric: str,
+                              time_range: Tuple[int, int]) -> numpy.array:
+    """Return sensor values for given node for given period. Return per second estimated values array
+
+    Raise an error if required range is not full covered by data in storage.
+    First it finds range of results from sensor, which fully covers requested range.
+    ...."""
+
+    ds = DataSource(node_id=node_id, sensor=sensor, dev=dev, metric=metric)
+    sensor_data = storage.load_sensor(ds)
+    assert sensor_data.time_units == 'us'
+
+    # collected_at is array of pairs (collection_started_at, collection_finished_at)
+    # extract start time from each pair
+    collection_start_at = sensor_data.times[::2]  # type: numpy.array
+
+    MICRO = 1000000
+
+    # convert seconds to us
+    begin = time_range[0] * MICRO
+    end = time_range[1] * MICRO
+
+    if begin < collection_start_at[0] or end > collection_start_at[-1] or end <= begin:
+        raise AssertionError(("Incorrect data for get_sensor - time_range={!r}, collected_at=[{}, ..., {}]," +
+                              "sensor = {}_{}.{}.{}").format(time_range,
+                                                             sensor_data.times[0] // MICRO,
+                                                             sensor_data.times[-1] // MICRO,
+                                                             node_id, sensor, dev, metric))
+
+    pos1, pos2 = numpy.searchsorted(collection_start_at, (begin, end))
+
+    # current real data time chunk begin time
+    edge_it = iter(collection_start_at[pos1 - 1: pos2 + 1])
+
+    # current real data value
+    val_it = iter(sensor_data.data[pos1 - 1: pos2 + 1])
+
+    # result array, cumulative value per second
+    result = numpy.zeros((end - begin) // MICRO)
+    idx = 0
+    curr_summ = 0
+
+    # end of current time slot
+    results_cell_ends = begin + MICRO
+
+    # hack to unify looping
+    real_data_end = next(edge_it)
+    while results_cell_ends <= end:
+        real_data_start = real_data_end
+        real_data_end = next(edge_it)
+        real_val_left = next(val_it)
+
+        # real data "speed" for interval [real_data_start, real_data_end]
+        real_val_ps = float(real_val_left) / (real_data_end - real_data_start)
+
+        while real_data_end >= results_cell_ends and results_cell_ends <= end:
+            # part of current real value, which is fit into current result cell
+            curr_real_chunk = int((results_cell_ends - real_data_start) * real_val_ps)
+
+            # calculate rest of real data for next result cell
+            real_val_left -= curr_real_chunk
+            result[idx] = curr_summ + curr_real_chunk
+            idx += 1
+            curr_summ = 0
+
+            # adjust real data start time
+            real_data_start = results_cell_ends
+            results_cell_ends += MICRO
+
+        # don't lost any real data
+        curr_summ += real_val_left
+
+    return result
+
+
 # --------------  PLOT HELPERS FUNCTIONS  ------------------------------------------------------------------------------
 
-def get_emb_data_svg(plt: Any) -> bytes:
+def get_emb_data_svg(plt: Any, format: str = 'svg') -> bytes:
     bio = BytesIO()
-    plt.savefig(bio, format='svg')
-    img_start = "<!-- Created with matplotlib (http://matplotlib.org/) -->"
-    return bio.getvalue().decode("utf8").split(img_start, 1)[1].encode("utf8")
+    if format in ('png', 'jpg'):
+        plt.savefig(bio, format=format)
+        return bio.getvalue()
+    elif format == 'svg':
+        plt.savefig(bio, format='svg')
+        img_start = "<!-- Created with matplotlib (http://matplotlib.org/) -->"
+        return bio.getvalue().decode("utf8").split(img_start, 1)[1].encode("utf8")
 
 
 def provide_plot(func: Callable[..., None]) -> Callable[..., str]:
     @wraps(func)
-    def closure1(storage: ResultStorage, path: DataSource, *args, **kwargs) -> str:
+    def closure1(storage: ResultStorage,
+                 path: DataSource,
+                 *args, **kwargs) -> str:
         fpath = storage.check_plot_file(path)
         if not fpath:
+            format = path.tag.split(".")[-1]
+
+            plt.figure(figsize=StyleProfile.figsize)
+            plt.subplots_adjust(right=0.66)
+
             func(*args, **kwargs)
-            fpath = storage.put_plot_file(get_emb_data_svg(plt), path)
+            fpath = storage.put_plot_file(get_emb_data_svg(plt, format=format), path)
+            logger.debug("Plot %s saved to %r", path, fpath)
             plt.clf()
-            logger.debug("Save plot for %s to %r", path, fpath)
+            plt.close('all')
         return fpath
     return closure1
 
@@ -269,11 +381,9 @@
               style: Any = StyleProfile) -> None:
 
     # TODO: unit should came from ts
-    total = sum(prop.bins_populations)
-    mids = prop.bins_mids
-    normed_bins = [population / total for population in prop.bins_populations]
-    bar_width = mids[1] - mids[0]
-    plt.bar(mids - bar_width / 2, normed_bins, color=colors.box_color, width=bar_width, label="Real data")
+    normed_bins = prop.bins_populations / prop.bins_populations.sum()
+    bar_width = prop.bins_edges[1] - prop.bins_edges[0]
+    plt.bar(prop.bins_edges, normed_bins, color=colors.box_color, width=bar_width, label="Real data")
 
     plt.xlabel(units)
     plt.ylabel("Value probability")
@@ -284,18 +394,20 @@
         nprop = cast(NormStatProps, prop)
         stats = scipy.stats.norm(nprop.average, nprop.deviation)
 
-        # xpoints = numpy.linspace(mids[0], mids[-1], style.curve_approx_points)
-        # ypoints = stats.pdf(xpoints) / style.curve_approx_points
+        new_edges, step = numpy.linspace(prop.bins_edges[0], prop.bins_edges[-1],
+                                         len(prop.bins_edges) * 10, retstep=True)
 
-        edges, step = numpy.linspace(mids[0], mids[-1], len(mids) * 10, retstep=True)
-
-        ypoints = stats.cdf(edges) * 11
+        ypoints = stats.cdf(new_edges) * 11
         ypoints = [next - prev for (next, prev) in zip(ypoints[1:], ypoints[:-1])]
-        xpoints = (edges[1:] + edges[:-1]) / 2
+        xpoints = (new_edges[1:] + new_edges[:-1]) / 2
 
-        plt.plot(xpoints, ypoints, color=colors.primary_color, label="Expected from\nnormal distribution")
+        plt.plot(xpoints, ypoints, color=colors.primary_color, label="Expected from\nnormal\ndistribution")
         dist_plotted = True
 
+    plt.gca().set_xlim(left=prop.bins_edges[0])
+    if prop.log_bins:
+        plt.xscale('log')
+
     apply_style(style, eng=True, no_legend=not dist_plotted)
 
 
@@ -308,58 +420,67 @@
     min_time = min(ts.times)
 
     # /1000 is us to ms conversion
-    time_points = [(val_time - min_time) / 1000 for val_time in ts.times]
+    time_points = numpy.array([(val_time - min_time) / 1000 for val_time in ts.times])
+
+    outliers_idxs = find_ouliers_ts(ts.data, cut_range=style.outliers_q_nd)
+    outliers_4q_idxs = find_ouliers_ts(ts.data, cut_range=style.outliers_hide_q_nd)
+    normal_idxs = numpy.logical_not(outliers_idxs)
+    outliers_idxs = outliers_idxs & numpy.logical_not(outliers_4q_idxs)
+    hidden_outliers_count = numpy.count_nonzero(outliers_4q_idxs)
+
+    data = ts.data[normal_idxs]
+    data_times = time_points[normal_idxs]
+    outliers = ts.data[outliers_idxs]
+    outliers_times = time_points[outliers_idxs]
 
     alpha = colors.noise_alpha if plot_avg_dev else 1.0
-    plt.plot(time_points, ts.data, "o", color=colors.primary_color, alpha=alpha, label="Data")
+    plt.plot(data_times, data, style.point_shape,
+             color=colors.primary_color, alpha=alpha, label="Data")
+    plt.plot(outliers_times, outliers, style.err_point_shape,
+             color=colors.err_color, label="Outliers")
 
-    if plot_avg_dev:
-        avg_vals = []
-        low_vals_dev = []
-        hight_vals_dev = []
-        avg_times = []
-        dev_times = []
+    has_negative_dev = False
+    plus_minus = "\xb1"
 
-        start = (len(ts.data) % style.avg_range) // 2
-        points = list(range(start, len(ts.data) + 1, style.avg_range))
+    if plot_avg_dev and len(data) < style.avg_range * 2:
+            logger.warning("Array %r to small to plot average over %s points", title, style.avg_range)
+    elif plot_avg_dev:
+        avg_vals = moving_average(data, style.avg_range)
+        dev_vals = moving_dev(data, style.avg_range)
+        avg_times = moving_average(data_times, style.avg_range)
 
-        for begin, end in zip(points[:-1], points[1:]):
-            vals = ts.data[begin: end]
+        if style.approx_average:
+            avg_vals = approximate_curve(avg_times, avg_vals, avg_times, style.curve_approx_level)
+            dev_vals = approximate_curve(avg_times, dev_vals, avg_times, style.curve_approx_level)
 
-            cavg = average(vals)
-            cdev = dev(vals)
-            tavg = average(time_points[begin: end])
+        plt.plot(avg_times, avg_vals, c=colors.suppl_color1, label="Average")
 
-            avg_vals.append(cavg)
-            avg_times.append(tavg)
-
-            low_vals_dev.append(cavg - style.dev_range_x * cdev)
-            hight_vals_dev.append(cavg + style.dev_range_x * cdev)
-            dev_times.append(tavg)
-
-        avg_timepoints = cast(List[float], numpy.linspace(avg_times[0], avg_times[-1], style.curve_approx_points))
-
-        low_vals_dev = approximate_curve(dev_times, low_vals_dev, avg_timepoints, style.curve_approx_level)
-        hight_vals_dev = approximate_curve(dev_times, hight_vals_dev, avg_timepoints, style.curve_approx_level)
-        new_vals_avg = approximate_curve(avg_times, avg_vals, avg_timepoints, style.curve_approx_level)
-
-        plt.plot(avg_timepoints, new_vals_avg, c=colors.suppl_color1,
-                 label="Average\nover {}s".format(style.avg_range))
-        plt.plot(avg_timepoints, low_vals_dev, c=colors.suppl_color2,
-                 label="Avg \xB1 {} * stdev\nover {}s".format(style.dev_range_x, style.avg_range))
-        plt.plot(avg_timepoints, hight_vals_dev, c=colors.suppl_color2)
+        low_vals_dev = avg_vals - dev_vals * style.dev_range_x
+        hight_vals_dev = avg_vals + dev_vals * style.dev_range_x
+        if style.dev_range_x - int(style.dev_range_x) < 0.01:
+            plt.plot(avg_times, low_vals_dev, c=colors.suppl_color2,
+                     label="{}{}*stdev".format(plus_minus, int(style.dev_range_x)))
+        else:
+            plt.plot(avg_times, low_vals_dev, c=colors.suppl_color2,
+                     label="{}{}*stdev".format(plus_minus, style.dev_range_x))
+        plt.plot(avg_times, hight_vals_dev, c=colors.suppl_color2)
+        has_negative_dev = low_vals_dev.min() < 0
 
     plt.xlim(-5, max(time_points) + 5)
-
     plt.xlabel("Time, seconds from test begin")
-    plt.ylabel("{}. Average and \xB1stddev over {} points".format(units, style.avg_range))
+    plt.ylabel("{}. Average and {}stddev over {} points".format(units, plus_minus, style.avg_range))
     plt.title(title)
+
+    if has_negative_dev:
+        plt.gca().set_ylim(bottom=0)
+
     apply_style(style, eng=True)
 
 
 @provide_plot
 def plot_lat_over_time(title: str, ts: TimeSeries, bins_vals: List[int], samples: int = 5,
-                       colors: Any = ColorProfile, style: Any = StyleProfile) -> None:
+                       colors: Any = ColorProfile,
+                       style: Any = StyleProfile) -> None:
 
     min_time = min(ts.times)
     times = [int(tm - min_time + 500) // 1000 for tm in ts.times]
@@ -368,42 +489,59 @@
     points = [times[int(i * step + 0.5)] for i in range(samples)]
     points.append(times[-1])
     bounds = list(zip(points[:-1], points[1:]))
-    data = numpy.array(ts.data, dtype='int32')
-    data.shape = [len(ts.data) // ts.second_axis_size, ts.second_axis_size]  # type: ignore
     agg_data = []
     positions = []
     labels = []
 
-    min_idxs = []
-    max_idxs = []
-
     for begin, end in bounds:
-        agg_hist = numpy.sum(data[begin:end], axis=0)
+        agg_hist = ts.data[begin:end].sum(axis=0)
+
+        if style.violin_instead_of_box:
+            # cut outliers
+            idx1, idx2 = hist_outliers_perc(agg_hist, style.outliers_lat)
+            agg_hist = agg_hist[idx1:idx2]
+            curr_bins_vals = bins_vals[idx1:idx2]
+
+            correct_coef = style.violin_point_count / sum(agg_hist)
+            if correct_coef > 1:
+                correct_coef = 1
+        else:
+            curr_bins_vals = bins_vals
+            correct_coef = 1
 
         vals = numpy.empty(shape=(numpy.sum(agg_hist),), dtype='float32')
         cidx = 0
-        non_zero = agg_hist.nonzero()[0]
-        min_idxs.append(non_zero[0])
-        max_idxs.append(non_zero[-1])
 
+        non_zero, = agg_hist.nonzero()
         for pos in non_zero:
-            vals[cidx:cidx + agg_hist[pos]] = bins_vals[pos]
-            cidx += agg_hist[pos]
+            count = int(agg_hist[pos] * correct_coef + 0.5)
 
-        agg_data.append(vals)
+            if count != 0:
+                vals[cidx: cidx + count] = curr_bins_vals[pos]
+                cidx += count
+
+        agg_data.append(vals[:cidx])
         positions.append((end + begin) / 2)
         labels.append(str((end + begin) // 2))
 
-    min_y = bins_vals[min(min_idxs)]
-    max_y = bins_vals[max(max_idxs)]
+    if style.violin_instead_of_box:
+        patches = plt.violinplot(agg_data,
+                                 positions=positions,
+                                 showmeans=True,
+                                 showmedians=True,
+                                 widths=step / 2)
 
-    min_y -= (max_y - min_y) * 0.05
-    max_y += (max_y - min_y) * 0.05
+        patches['cmeans'].set_color("blue")
+        patches['cmedians'].set_color("green")
+        if style.legend_for_eng:
+            legend_location = "center left"
+            legend_bbox_to_anchor = (1.03, 0.81)
+            plt.legend([patches['cmeans'], patches['cmedians']], ["mean", "median"],
+                       loc=legend_location, bbox_to_anchor=legend_bbox_to_anchor)
+    else:
+        plt.boxplot(agg_data, 0, '', positions=positions, labels=labels, widths=step / 4)
 
-    # plot box size adjust (only plot, not spines and legend)
-    plt.boxplot(agg_data, 0, '', positions=positions, labels=labels, widths=step / 4)
     plt.xlim(min(times), max(times))
-    plt.ylim(min_y, max_y)
     plt.xlabel("Time, seconds from test begin, sampled for ~{} seconds".format(int(step)))
     plt.ylabel("Latency, ms")
     plt.title(title)
@@ -411,19 +549,74 @@
 
 
 @provide_plot
-def plot_heatmap(title: str, ts: TimeSeries, bins_vals: List[int], samples: int = 5,
-                 colors: Any = ColorProfile, style: Any = StyleProfile) -> None:
-    hist_bins_count = 20
-    bin_top = [100 * 2 ** i for i in range(20)]
-    bin_ranges = [[0, 0]]
-    cborder_it = iter(bin_top)
-    cborder = next(cborder_it)
-    for bin_val in bins_vals:
-        if bin_val < cborder:
-            bin_ranges
+def plot_heatmap(title: str,
+                 ts: TimeSeries,
+                 bins_vals: List[int],
+                 colors: Any = ColorProfile,
+                 style: Any = StyleProfile) -> None:
 
-    # bins: [100us, 200us, ...., 104s]
-    # msp origin bins ranges to heatmap bins
+    assert len(ts.data.shape) == 2
+    assert ts.data.shape[1] == len(bins_vals)
+
+    total_hist = ts.data.sum(axis=0)
+
+    # idx1, idx2 = hist_outliers_perc(total_hist, style.outliers_lat)
+    idx1, idx2 = ts_hist_outliers_perc(ts.data, bounds_perc=style.outliers_lat)
+
+    # don't cut too many bins
+    min_bins_left = style.hm_hist_bins_count
+    if idx2 - idx1 < min_bins_left:
+        missed = min_bins_left - (idx2 - idx1) // 2
+        idx2 = min(len(total_hist), idx2 + missed)
+        idx1 = max(0, idx1 - missed)
+
+    data = ts.data[:, idx1:idx2]
+    bins_vals = bins_vals[idx1:idx2]
+
+    # don't using rebin_histogram here, as we need apply same bins for many arrays
+    step = (bins_vals[-1] - bins_vals[0]) / style.hm_hist_bins_count
+    new_bins_edges = numpy.arange(style.hm_hist_bins_count) * step + bins_vals[0]
+    bin_mapping = numpy.clip(numpy.searchsorted(new_bins_edges, bins_vals) - 1, 0, len(new_bins_edges) - 1)
+
+    # map origin bins ranges to heatmap bins, iterate over rows
+    cmap = []
+    for line in data:
+        curr_bins = [0] * style.hm_hist_bins_count
+        for idx, count in zip(bin_mapping, line):
+            curr_bins[idx] += count
+        cmap.append(curr_bins)
+    ncmap = numpy.array(cmap)
+
+    xmin = 0
+    xmax = (ts.times[-1] - ts.times[0]) / 1000 + 1
+    ymin = new_bins_edges[0]
+    ymax = new_bins_edges[-1]
+
+    fig, ax = plt.subplots(figsize=style.figsize)
+
+    if style.heatmap_interpolation == '1d':
+        interpolation = 'none'
+        res = []
+        for column in ncmap:
+            new_x = numpy.linspace(0, len(column), style.heatmap_interpolation_points)
+            old_x = numpy.arange(len(column)) + 0.5
+            new_vals = numpy.interp(new_x, old_x, column)
+            res.append(new_vals)
+        ncmap = numpy.array(res)
+    else:
+        interpolation = style.heatmap_interpolation
+
+    ax.imshow(ncmap[:,::-1].T,
+              interpolation=interpolation,
+              extent=(xmin, xmax, ymin, ymax),
+              cmap=colors.imshow_colormap)
+
+    ax.set_aspect((xmax - xmin) / (ymax - ymin) * (6 / 9))
+    ax.set_ylabel("Latency, ms")
+    ax.set_xlabel("Test time, s")
+
+    plt.title(title)
+
 
 @provide_plot
 def io_chart(title: str,
@@ -441,9 +634,6 @@
     # offset from center of bar to deviation/confidence range indicator
     err_x_offset = 0.05
 
-    # figure size in inches
-    figsize = (12, 6)
-
     # extra space on top and bottom, comparing to maximal tight layout
     extra_y_space = 0.05
 
@@ -472,7 +662,7 @@
     # gs = gridspec.GridSpec(1, 3, width_ratios=[1, 4, 1])
     # p1 = plt.subplot(gs[1])
 
-    fig, p1 = plt.subplots(figsize=figsize)
+    fig, p1 = plt.subplots(figsize=StyleProfile.figsize)
 
     # plot IOPS/BW bars
     if block_size >= LARGE_BLOCKS:
@@ -560,7 +750,9 @@
 
     # legend box
     handles2, labels2 = p2.get_legend_handles_labels()
-    plt.legend(handles1 + handles2, labels1 + labels2, loc=legend_location, bbox_to_anchor=legend_bbox_to_anchor)
+    plt.legend(handles1 + handles2, labels1 + labels2,
+               loc=legend_location,
+               bbox_to_anchor=legend_bbox_to_anchor)
 
     # adjust central box size to fit legend
     plt.subplots_adjust(**plot_box_adjust)
@@ -574,11 +766,35 @@
     data = None  # type: str
     js_links = []  # type: List[str]
     css_links = []  # type: List[str]
+    order_attr = None  # type: Any
+
+    def __init__(self, data: str, order_attr: Any = None) -> None:
+        self.data = data
+        self.order_attr = order_attr
+
+    def __eq__(self, o: object) -> bool:
+        return o.order_attr == self.order_attr  # type: ignore
+
+    def __lt__(self, o: object) -> bool:
+        return o.order_attr > self.order_attr  # type: ignore
+
+
+class Table:
+    def __init__(self, header: List[str]) -> None:
+        self.header = header
+        self.data = []
+
+    def add_line(self, values: List[str]) -> None:
+        self.data.append(values)
+
+    def html(self):
+        return html.table("", self.header, self.data)
 
 
 class Menu1st:
     engineering = "Engineering"
     summary = "Summary"
+    per_job = "Per Job"
 
 
 class Menu2ndEng:
@@ -591,56 +807,66 @@
     io_lat_qd = "IO & Lat vs QD"
 
 
-menu_1st_order = [Menu1st.summary, Menu1st.engineering]
+menu_1st_order = [Menu1st.summary, Menu1st.engineering, Menu1st.per_job]
 
 
 #  --------------------  REPORTS  --------------------------------------------------------------------------------------
 
 
 class Reporter(metaclass=abc.ABCMeta):
+    suite_types = set() # type: Set[str]
+
     @abc.abstractmethod
-    def get_divs(self, suite: TestSuiteConfig, storage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
+    def get_divs(self, suite: SuiteConfig, storage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
+        pass
+
+
+class JobReporter(metaclass=abc.ABCMeta):
+    suite_type = set()  # type: Set[str]
+
+    @abc.abstractmethod
+    def get_divs(self,
+                 suite: SuiteConfig,
+                 job: JobConfig,
+                 storage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
         pass
 
 
 # Main performance report
 class PerformanceSummary(Reporter):
-    """Creates graph, which show how IOPS and Latency depend on QD"""
+    """Aggregated summary fro storage"""
 
 
 # Main performance report
 class IO_QD(Reporter):
     """Creates graph, which show how IOPS and Latency depend on QD"""
-    def get_divs(self, suite: TestSuiteConfig, rstorage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
-        ts_map = {}  # type: Dict[FioTestSumm, List[IOSummary]]
-        str_summary = {}  # type: Dict[FioTestSumm, List[IOSummary]]
+    suite_types = {'fio'}
+
+    def get_divs(self, suite: SuiteConfig, rstorage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
+        ts_map = defaultdict(list)  # type: Dict[FioJobParams, List[Tuple[SuiteConfig, FioJobConfig]]]
+        str_summary = {}  # type: Dict[FioJobParams, List[IOSummary]]
         for job in rstorage.iter_job(suite):
             fjob = cast(FioJobConfig, job)
-            tpl_no_qd = fjob.characterized_tuple_no_qd()
-            io_summ = make_iosum(rstorage, suite, job)
+            fjob_no_qd = cast(FioJobParams, fjob.params.copy(qd=None))
+            str_summary[fjob_no_qd] = (fjob_no_qd.summary, fjob_no_qd.long_summary)
+            ts_map[fjob_no_qd].append((suite, fjob))
 
-            if tpl_no_qd not in ts_map:
-                ts_map[tpl_no_qd] = [io_summ]
-                str_summary[tpl_no_qd] = (fjob.summary_no_qd(), fjob.long_summary_no_qd())
-            else:
-                ts_map[tpl_no_qd].append(io_summ)
+        for tpl, suites_jobs in ts_map.items():
+            if len(suites_jobs) > StyleProfile.min_iops_vs_qd_jobs:
+                iosums = [make_iosum(rstorage, suite, job) for suite, job in suites_jobs]
+                iosums.sort(key=lambda x: x.qd)
+                summary, summary_long = str_summary[tpl]
+                ds = DataSource(suite_id=suite.storage_id,
+                                job_id=summary,
+                                node_id=AGG_TAG,
+                                sensor="fio",
+                                dev=AGG_TAG,
+                                metric="io_over_qd",
+                                tag="svg")
 
-        for tpl, iosums in ts_map.items():
-            iosums.sort(key=lambda x: x.qd)
-            summary, summary_long = str_summary[tlp]
-
-            ds = DataSource(suite_id=suite.storage_id,
-                            job_id="io_over_qd_".format(summary),
-                            node_id="__all__",
-                            dev='fio',
-                            sensor="io_over_qd",
-                            tag="svg")
-
-            title = "IOPS, BW, Lat vs. QD.\n" + summary_long
-            fpath = io_chart(rstorage, ds, title=title, legend="IOPS/BW", iosums=iosums)
-            yield Menu1st.summary, Menu2ndSumm.io_lat_qd, html.img(fpath)
-            if DEBUG:
-                return
+                title = "IOPS, BW, Lat vs. QD.\n" + summary_long
+                fpath = io_chart(rstorage, ds, title=title, legend="IOPS/BW", iosums=iosums)  # type: str
+                yield Menu1st.summary, Menu2ndSumm.io_lat_qd, HTMLBlock(html.img(fpath))
 
 
 # Linearization report
@@ -648,162 +874,234 @@
     """Creates graphs, which show how IOPS and Latency depend on block size"""
 
 
+def summ_sensors(rstorage: ResultStorage,
+                 nodes: List[str],
+                 sensor: str,
+                 metric: str,
+                 time_range: Tuple[int, int]) -> Optional[numpy.array]:
+
+    res = None  # type: Optional[numpy.array]
+    for node_id in nodes:
+        for _, groups in rstorage.iter_sensors(node_id=node_id, sensor=sensor, metric=metric):
+            data = get_sensor_for_time_range(rstorage,
+                                             node_id=node_id,
+                                             sensor=sensor,
+                                             dev=groups['dev'],
+                                             metric=metric,
+                                             time_range=time_range)
+            if res is None:
+                res = data
+            else:
+                res += data
+    return res
+
+
 # IOPS/latency distribution
-class IOHist(Reporter):
+class StatInfo(JobReporter):
+    """Statistic info for job results"""
+    suite_types = {'fio'}
+
+    def get_divs(self, suite: SuiteConfig, job: JobConfig,
+                 rstorage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
+
+        fjob = cast(FioJobConfig, job)
+        io_sum = make_iosum(rstorage, suite, fjob)
+
+        summary_data = [
+            ["Summary", job.params.long_summary],
+        ]
+
+        res = html.H2(html.center("Test summary"))
+        res += html.table("Test info", None, summary_data)
+        stat_data_headers = ["Name", "Average ~ Dev", "Conf interval", "Mediana", "Mode", "Kurt / Skew", "95%", "99%"]
+
+        KB = 1024
+        bw_data = ["Bandwidth",
+                   "{}Bps ~ {}Bps".format(b2ssize(io_sum.bw.average * KB), b2ssize(io_sum.bw.deviation * KB)),
+                   b2ssize(io_sum.bw.confidence * KB) + "Bps",
+                   b2ssize(io_sum.bw.perc_50 * KB) + "Bps",
+                   "-",
+                   "{:.2f} / {:.2f}".format(io_sum.bw.kurt, io_sum.bw.skew),
+                   b2ssize(io_sum.bw.perc_5 * KB) + "Bps",
+                   b2ssize(io_sum.bw.perc_1 * KB) + "Bps"]
+
+        iops_data = ["IOPS",
+                     "{}IOPS ~ {}IOPS".format(b2ssize_10(io_sum.bw.average / fjob.bsize),
+                                              b2ssize_10(io_sum.bw.deviation / fjob.bsize)),
+                     b2ssize_10(io_sum.bw.confidence / fjob.bsize) + "IOPS",
+                     b2ssize_10(io_sum.bw.perc_50 / fjob.bsize) + "IOPS",
+                     "-",
+                     "{:.2f} / {:.2f}".format(io_sum.bw.kurt, io_sum.bw.skew),
+                     b2ssize_10(io_sum.bw.perc_5 / fjob.bsize) + "IOPS",
+                     b2ssize_10(io_sum.bw.perc_1 / fjob.bsize) + "IOPS"]
+
+        MICRO = 1000000
+        # latency
+        lat_data = ["Latency",
+                    "-",
+                    "-",
+                    b2ssize_10(io_sum.bw.perc_50 / MICRO) + "s",
+                    "-",
+                    "-",
+                    b2ssize_10(io_sum.bw.perc_95 / MICRO) + "s",
+                    b2ssize_10(io_sum.bw.perc_99 / MICRO) + "s"]
+
+        # sensor usage
+        stat_data = [iops_data, bw_data, lat_data]
+        res += html.table("Load stats info", stat_data_headers, stat_data)
+
+        resource_headers = ["Resource", "Usage count", "Proportional to work done"]
+
+        io_transfered = io_sum.bw.data.sum() * KB
+        resource_data = [
+            ["IO made", b2ssize_10(io_transfered / KB / fjob.bsize) + "OP", "-"],
+            ["Data transfered", b2ssize(io_transfered) + "B", "-"]
+        ]
+
+
+        storage = rstorage.storage
+        nodes = storage.load_list(NodeInfo, 'all_nodes')  # type: List[NodeInfo]
+
+        storage_nodes = [node.node_id for node in nodes if node.roles.intersection(STORAGE_ROLES)]
+        test_nodes = [node.node_id for node in nodes if "testnode" in node.roles]
+
+        trange = [job.reliable_info_range[0] / 1000, job.reliable_info_range[1] / 1000]
+        ops_done = io_transfered / fjob.bsize / KB
+
+        all_metrics = [
+            ("Test nodes net send", 'net-io', 'send_bytes', b2ssize, test_nodes, "B", io_transfered),
+            ("Test nodes net recv", 'net-io', 'recv_bytes', b2ssize, test_nodes, "B", io_transfered),
+
+            ("Test nodes disk write", 'block-io', 'sectors_written', b2ssize, test_nodes, "B", io_transfered),
+            ("Test nodes disk read", 'block-io', 'sectors_read', b2ssize, test_nodes, "B", io_transfered),
+            ("Test nodes writes", 'block-io', 'writes_completed', b2ssize_10, test_nodes, "OP", ops_done),
+            ("Test nodes reads", 'block-io', 'reads_completed', b2ssize_10, test_nodes, "OP", ops_done),
+
+            ("Storage nodes net send", 'net-io', 'send_bytes', b2ssize, storage_nodes, "B", io_transfered),
+            ("Storage nodes net recv", 'net-io', 'recv_bytes', b2ssize, storage_nodes, "B", io_transfered),
+
+            ("Storage nodes disk write", 'block-io', 'sectors_written', b2ssize, storage_nodes, "B", io_transfered),
+            ("Storage nodes disk read", 'block-io', 'sectors_read', b2ssize, storage_nodes, "B", io_transfered),
+            ("Storage nodes writes", 'block-io', 'writes_completed', b2ssize_10, storage_nodes, "OP", ops_done),
+            ("Storage nodes reads", 'block-io', 'reads_completed', b2ssize_10, storage_nodes, "OP", ops_done),
+        ]
+
+        all_agg = {}
+
+        for descr, sensor, metric, ffunc, nodes, units, denom in all_metrics:
+            if not nodes:
+                continue
+
+            res_arr = summ_sensors(rstorage, nodes=nodes, sensor=sensor, metric=metric, time_range=trange)
+            if res_arr is None:
+                continue
+
+            agg = res_arr.sum()
+            resource_data.append([descr, ffunc(agg) + units, "{:.1f}".format(agg / denom)])
+            all_agg[descr] = agg
+
+
+        cums = [
+            ("Test nodes writes", "Test nodes reads", "Total test ops", b2ssize_10, "OP", ops_done),
+            ("Storage nodes writes", "Storage nodes reads", "Total storage ops", b2ssize_10, "OP", ops_done),
+            ("Storage nodes disk write", "Storage nodes disk read", "Total storage IO size", b2ssize,
+             "B", io_transfered),
+            ("Test nodes disk write", "Test nodes disk read", "Total test nodes IO size", b2ssize, "B", io_transfered),
+        ]
+
+        for name1, name2, descr, ffunc, units, denom in cums:
+            if name1 in all_agg and name2 in all_agg:
+                agg = all_agg[name1] + all_agg[name2]
+                resource_data.append([descr, ffunc(agg) + units, "{:.1f}".format(agg / denom)])
+
+        res += html.table("Resources usage", resource_headers, resource_data)
+
+        yield Menu1st.per_job, job.summary, HTMLBlock(res)
+
+
+# IOPS/latency distribution
+class IOHist(JobReporter):
     """IOPS.latency distribution histogram"""
-    def get_divs(self, suite: TestSuiteConfig, rstorage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
-        for job in rstorage.iter_job(suite):
-            fjob = cast(FioJobConfig, job)
-            agg_lat = get_aggregated(rstorage, suite, fjob, "lat")
-            bins_edges = numpy.array(get_lat_vals(agg_lat.second_axis_size), dtype='float32') / 1000  # convert us to ms
-            lat_stat_prop = calc_histo_stat_props(agg_lat, bins_edges, bins_count=StyleProfile.hist_boxes)
+    suite_types = {'fio'}
 
-            title = "Latency distribution. " + fjob.long_summary
-            units = "ms"
+    def get_divs(self,
+                 suite: SuiteConfig,
+                 job: JobConfig,
+                 rstorage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
 
-            fpath = plot_hist(rstorage, agg_lat.source(tag='hist.svg'), title, units, lat_stat_prop)
-            if DEBUG:
-                yield Menu1st.summary, Menu2ndSumm.io_lat_qd, html.img(fpath)
-            else:
-                yield Menu1st.engineering, Menu2ndEng.hist, html.img(fpath)
+        fjob = cast(FioJobConfig, job)
 
-            agg_io = get_aggregated(rstorage, suite, fjob, "bw")
+        yield Menu1st.per_job, fjob.summary, HTMLBlock(html.H2(html.center("Load histograms")))
 
-            if fjob.bsize >= LARGE_BLOCKS:
-                title = "BW distribution. " + fjob.long_summary
-                units = "MiBps"
-                agg_io.data /= MiB2KiB
-            else:
-                title = "IOPS distribution. " + fjob.long_summary
-                agg_io.data /= fjob.bsize
-                units = "IOPS"
+        agg_lat = get_aggregated(rstorage, suite, fjob, "lat")
+        bins_edges = numpy.array(get_lat_vals(agg_lat.data.shape[1]), dtype='float32') / 1000  # convert us to ms
+        lat_stat_prop = calc_histo_stat_props(agg_lat, bins_edges, bins_count=StyleProfile.hist_lat_boxes)
 
-            io_stat_prop = calc_norm_stat_props(agg_io, bins_count=StyleProfile.hist_boxes)
-            fpath = plot_hist(rstorage, agg_io.source(tag='hist.svg'), title, units, io_stat_prop)
-            if DEBUG:
-                yield Menu1st.summary, Menu2ndSumm.io_lat_qd, html.img(fpath)
-                return
-            else:
-                yield Menu1st.engineering, Menu2ndEng.hist, html.img(fpath)
+        # import IPython
+        # IPython.embed()
+
+        long_summary = cast(FioJobParams, fjob.params).long_summary
+
+        title = "Latency distribution"
+        units = "ms"
+
+        fpath = plot_hist(rstorage, agg_lat.source(tag='hist.svg'), title, units, lat_stat_prop)  # type: str
+        yield Menu1st.per_job, fjob.summary, HTMLBlock(html.img(fpath))
+
+        agg_io = get_aggregated(rstorage, suite, fjob, "bw")
+
+        if fjob.bsize >= LARGE_BLOCKS:
+            title = "BW distribution"
+            units = "MiBps"
+            agg_io.data //= MiB2KiB
+        else:
+            title = "IOPS distribution"
+            agg_io.data //= fjob.bsize
+            units = "IOPS"
+
+        io_stat_prop = calc_norm_stat_props(agg_io, bins_count=StyleProfile.hist_boxes)
+        fpath = plot_hist(rstorage, agg_io.source(tag='hist.svg'), title, units, io_stat_prop)  # type: str
+        yield Menu1st.per_job, fjob.summary, HTMLBlock(html.img(fpath))
 
 
 # IOPS/latency over test time for each job
-class IOTime(Reporter):
+class IOTime(JobReporter):
     """IOPS/latency during test"""
-    def get_divs(self, suite: TestSuiteConfig, rstorage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
-        for job in rstorage.iter_job(suite):
-            fjob = cast(FioJobConfig, job)
-            agg_lat = get_aggregated(rstorage, suite, fjob, "lat")
-            bins_edges = numpy.array(get_lat_vals(agg_lat.second_axis_size), dtype='float32') / 1000
-            title = "Latency during test. " + fjob.long_summary
+    suite_types = {'fio'}
 
-            fpath = plot_lat_over_time(rstorage, agg_lat.source(tag='ts.svg'), title, agg_lat, bins_edges)
-            if DEBUG:
-                yield Menu1st.summary, Menu2ndSumm.io_lat_qd, html.img(fpath)
-            else:
-                yield Menu1st.engineering, Menu2ndEng.lat_time, html.img(fpath)
+    def get_divs(self,
+                 suite: SuiteConfig,
+                 job: JobConfig,
+                 rstorage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
 
-            fpath = plot_heatmap(rstorage, agg_lat.source(tag='hmap.svg'), title, agg_lat, bins_edges)
-            if DEBUG:
-                yield Menu1st.summary, Menu2ndSumm.io_lat_qd, html.img(fpath)
-            else:
-                yield Menu1st.engineering, Menu2ndEng.lat_time, html.img(fpath)
+        fjob = cast(FioJobConfig, job)
 
-            agg_io = get_aggregated(rstorage, suite, fjob, "bw")
-            if fjob.bsize >= LARGE_BLOCKS:
-                title = "BW during test. " + fjob.long_summary
-                units = "MiBps"
-                agg_io.data /= MiB2KiB
-            else:
-                title = "IOPS during test. " + fjob.long_summary
-                agg_io.data /= fjob.bsize
-                units = "IOPS"
+        yield Menu1st.per_job, fjob.summary, HTMLBlock(html.H2(html.center("Load over time")))
 
-            fpath = plot_v_over_time(rstorage, agg_io.source(tag='ts.svg'), title, units, agg_io)
+        agg_io = get_aggregated(rstorage, suite, fjob, "bw")
+        if fjob.bsize >= LARGE_BLOCKS:
+            title = "Bandwidth"
+            units = "MiBps"
+            agg_io.data //= MiB2KiB
+        else:
+            title = "IOPS"
+            agg_io.data //= fjob.bsize
+            units = "IOPS"
 
-            if DEBUG:
-                yield Menu1st.summary, Menu2ndSumm.io_lat_qd, html.img(fpath)
-                return
-            else:
-                yield Menu1st.engineering, Menu2ndEng.iops_time, html.img(fpath)
+        fpath = plot_v_over_time(rstorage, agg_io.source(tag='ts.svg'), title, units, agg_io)  # type: str
+        yield Menu1st.per_job, fjob.summary, HTMLBlock(html.img(fpath))
 
+        agg_lat = get_aggregated(rstorage, suite, fjob, "lat")
+        bins_edges = numpy.array(get_lat_vals(agg_lat.data.shape[1]), dtype='float32') / 1000
+        title = "Latency"
 
-def is_sensor_numarray(sensor: str, metric: str) -> bool:
-    """Returns True if sensor provides one-dimension array of numeric values. One number per one measurement."""
-    return True
+        fpath = plot_lat_over_time(rstorage, agg_lat.source(tag='ts.svg'), title, agg_lat, bins_edges)  # type: str
+        yield Menu1st.per_job, fjob.summary, HTMLBlock(html.img(fpath))
 
+        title = "Latency heatmap"
+        fpath = plot_heatmap(rstorage, agg_lat.source(tag='hmap.png'), title, agg_lat, bins_edges)  # type: str
 
-LEVEL_SENSORS = {("block-io", "io_queue"),
-                 ("system-cpu", "procs_blocked"),
-                 ("system-cpu", "procs_queue")}
-
-
-def is_level_sensor(sensor: str, metric: str) -> bool:
-    """Returns True if sensor measure level of any kind, E.g. queue depth."""
-    return (sensor, metric) in LEVEL_SENSORS
-
-
-def is_delta_sensor(sensor: str, metric: str) -> bool:
-    """Returns True if sensor provides deltas for cumulative value. E.g. io completed in given period"""
-    return not is_level_sensor(sensor, metric)
-
-
-
-def get_sensor(storage: Storage, node: str, sensor: str, dev: str, metric: str,
-               time_range: Tuple[int, int]) -> numpy.array:
-    """Return sensor values for given node for given period. Return per second estimated values array
-
-    Raise an error if required range is not full covered by data in storage.
-    First it finds range of results from sensor, which fully covers requested range.
-    ...."""
-
-    collected_at = numpy.array(storage.get_array("sensors/{}_collected_at".format(node)), dtype="int")
-    data = numpy.array(storage.get_array("sensors/{}_{}.{}.{}".format(node, sensor, dev, metric)))
-
-    # collected_at is array of pairs (collection_started_at, collection_finished_at)
-    collection_start_at = collected_at[::2]
-
-    MICRO = 1000000
-
-    # convert secods to us
-    begin = time_range[0] * MICRO
-    end = time_range[1] * MICRO
-
-    if begin < collection_start_at[0] or end > collection_start_at[-1] or end <= begin:
-        raise AssertionError(("Incorrect data for get_sensor - time_range={!r}, collected_at=[{}, ..., {}]," +
-                              "sensor = {}_{}.{}.{}").format(time_range,
-                                                             collected_at[0] // MICRO,
-                                                             collected_at[-1] // MICRO,
-                                                             node, sensor, dev, metric))
-
-    pos1, pos2 = numpy.searchsorted(collection_start_at, (begin, end))
-    assert pos1 >= 1
-
-    time_bounds = collection_start_at[pos1 - 1: pos2]
-    edge_it = iter(time_bounds)
-    val_it = iter(data[pos1 - 1: pos2])
-
-    result = []
-    curr_summ = 0
-
-    results_cell_ends = begin + MICRO
-    curr_end = next(edge_it)
-
-    while results_cell_ends <= end:
-        curr_start = curr_end
-        curr_end = next(edge_it)
-        curr_val = next(val_it)
-        while curr_end >= results_cell_ends and results_cell_ends <= end:
-            current_part = (results_cell_ends - curr_start) / (curr_end - curr_start) * curr_val
-            result.append(curr_summ + current_part)
-            curr_summ = 0
-            curr_val -= current_part
-            curr_start = results_cell_ends
-            results_cell_ends += MICRO
-        curr_summ += curr_val
-
-    assert len(result) == (end - begin) // MICRO
-    return result
+        yield Menu1st.per_job, fjob.summary, HTMLBlock(html.img(fpath))
 
 
 class ResourceUsage:
@@ -824,50 +1122,65 @@
 
 
 # Cluster load over test time
-class ClusterLoad(Reporter):
+class ClusterLoad(JobReporter):
     """IOPS/latency during test"""
 
+    # TODO: units should came from sensor
     storage_sensors = [
-        ('block-io', 'reads_completed', "Read ops"),
-        ('block-io', 'writes_completed', "Write ops"),
-        ('block-io', 'sectors_read', "Read kb"),
-        ('block-io', 'sectors_written', "Write kb"),
+        ('block-io', 'reads_completed', "Read ops", 'iops'),
+        ('block-io', 'writes_completed', "Write ops", 'iops'),
+        ('block-io', 'sectors_read', "Read kb", 'kb'),
+        ('block-io', 'sectors_written', "Write kb", 'kb'),
     ]
 
-    def get_divs(self, suite: TestSuiteConfig, rstorage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
+    def get_divs(self,
+                 suite: SuiteConfig,
+                 job: JobConfig,
+                 rstorage: ResultStorage) -> Iterator[Tuple[str, str, HTMLBlock]]:
         # split nodes on test and other
         storage = rstorage.storage
         nodes = storage.load_list(NodeInfo, "all_nodes")  # type: List[NodeInfo]
 
+        yield Menu1st.per_job, job.summary, HTMLBlock(html.H2(html.center("Cluster load")))
         test_nodes = {node.node_id for node in nodes if 'testnode' in node.roles}
         cluster_nodes = {node.node_id for node in nodes if 'testnode' not in node.roles}
 
-        for job in rstorage.iter_job(suite):
-            # convert ms to s
-            time_range = (job.reliable_info_starts_at // MS2S, job.reliable_info_stops_at // MS2S)
-            len = time_range[1] - time_range[0]
+        # convert ms to s
+        time_range = (job.reliable_info_range[0] // MS2S, job.reliable_info_range[1] // MS2S)
+        len = time_range[1] - time_range[0]
+        for sensor, metric, sensor_title, units in self.storage_sensors:
+            sum_testnode = numpy.zeros((len,))
+            sum_other = numpy.zeros((len,))
+            for path, groups in rstorage.iter_sensors(sensor=sensor, metric=metric):
+                # todo: should return sensor units
+                data = get_sensor_for_time_range(rstorage,
+                                                 groups['node_id'],
+                                                 sensor,
+                                                 groups['dev'],
+                                                 metric, time_range)
+                if groups['node_id'] in test_nodes:
+                    sum_testnode += data
+                else:
+                    sum_other += data
 
-            for sensor, metric, sensor_title in self.storage_sensors:
-                sum_testnode = numpy.zeros((len,))
-                sum_other = numpy.zeros((len,))
+            ds = DataSource(suite_id=suite.storage_id,
+                            job_id=job.storage_id,
+                            node_id="test_nodes",
+                            sensor=sensor,
+                            dev=AGG_TAG,
+                            metric=metric,
+                            tag="ts.svg")
 
-                for path, groups in iter_sensors(rstorage.storage, sensor=sensor, metric=metric):
-                    data = get_sensor(rstorage.storage, groups['node'], sensor, groups['dev'], metric, time_range)
-                    if groups['node'] in test_nodes:
-                        sum_testnode += data
-                    else:
-                        sum_other += data
-
-                ds = DataSource(suite_id=suite.storage_id, job_id=job.summary, node_id="cluster",
-                                dev=sensor, sensor=metric, tag="ts.svg")
-
-                # s to ms
-                ts = TimeSeries(name="", times=numpy.arange(*time_range) * MS2S, data=sum_testnode, raw=None)
-                fpath = plot_v_over_time(rstorage, ds, "{}.{}".format(sensor, metric), sensor_title, ts=ts)
-                yield Menu1st.engineering, Menu2ndEng.iops_time, html.img(fpath)
-
-            if DEBUG:
-                return
+            # s to ms
+            ts = TimeSeries(name="",
+                            times=numpy.arange(*time_range) * MS2S,
+                            data=sum_testnode,
+                            raw=None,
+                            units=units,
+                            time_units="us",
+                            source=ds)
+            fpath = plot_v_over_time(rstorage, ds, sensor_title, sensor_title, ts=ts)  # type: str
+            yield Menu1st.per_job, job.summary, HTMLBlock(html.img(fpath))
 
 
 # Ceph cluster summary
@@ -897,7 +1210,12 @@
 
     def run(self, ctx: TestRun) -> None:
         rstorage = ResultStorage(ctx.storage)
-        reporters = [ClusterLoad()] # IO_QD(), IOTime(), IOHist()] # type: List[Reporter]
+
+        job_reporters = [StatInfo(), IOTime(), IOHist(), ClusterLoad()] # type: List[JobReporter]
+        reporters = [IO_QD()]  # type: List[Reporter]
+
+        # job_reporters = [ClusterLoad()]
+        # reporters = []
 
         root_dir = os.path.dirname(os.path.dirname(wally.__file__))
         doc_templ_path = os.path.join(root_dir, "report_templates/index.html")
@@ -909,14 +1227,30 @@
         content_block = []
         link_idx = 0
 
-        matplotlib.rcParams.update({'font.size': 10})
+        # matplotlib.rcParams.update(ctx.config.reporting.matplotlib_params.raw())
+        # ColorProfile.__dict__.update(ctx.config.reporting.colors.raw())
+        # StyleProfile.__dict__.update(ctx.config.reporting.style.raw())
 
-        items = defaultdict(lambda: defaultdict(list))  # type: Dict[str, Dict[str, list]]
+        items = defaultdict(lambda: defaultdict(list))  # type: Dict[str, Dict[str, List[HTMLBlock]]]
+
+        # TODO: filter reporters
         for suite in rstorage.iter_suite(FioTest.name):
+            all_jobs = list(rstorage.iter_job(suite))
+            all_jobs.sort(key=lambda job: job.params)
+            for job in all_jobs:
+                for reporter in job_reporters:
+                    for block, item, html in reporter.get_divs(suite, job, rstorage):
+                        items[block][item].append(html)
+                if DEBUG:
+                    break
+
             for reporter in reporters:
                 for block, item, html in reporter.get_divs(suite, rstorage):
                     items[block][item].append(html)
 
+            if DEBUG:
+                break
+
         for idx_1st, menu_1st in enumerate(sorted(items, key=lambda x: menu_1st_order.index(x))):
             menu_block.append(
                 '<a href="#item{}" class="nav-group" data-toggle="collapse" data-parent="#MainMenu">{}</a>'
@@ -927,7 +1261,7 @@
                 menu_block.append('    <a href="#content{}" class="nav-group-item">{}</a>'
                                   .format(link_idx, menu_2nd))
                 content_block.append('<div id="content{}">'.format(link_idx))
-                content_block.extend("    " + x for x in items[menu_1st][menu_2nd])
+                content_block.extend("    " + x.data for x in items[menu_1st][menu_2nd])
                 content_block.append('</div>')
                 link_idx += 1
             menu_block.append('</div>')
@@ -946,1504 +1280,3 @@
     def run(self, ctx: TestRun) -> None:
         # TODO(koder): load data from storage
         raise NotImplementedError("...")
-
-
-#  ---------------------------   LEGASY --------------------------------------------------------------------------------
-
-
-# # 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)