aboutsummaryrefslogtreecommitdiffstats
path: root/resources/tools/presentation/generator_CPTA.py
blob: 72aef537cf8e09a92813fe4f8521c3b290c68053 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
# Copyright (c) 2018 Cisco and/or its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at:
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Generation of Continuous Performance Trending and Analysis.
"""

import datetime
import logging
import csv
import prettytable
import plotly.offline as ploff
import plotly.graph_objs as plgo
import plotly.exceptions as plerr
import numpy as np
import pandas as pd

from collections import OrderedDict
from utils import find_outliers, archive_input_data, execute_command


# Command to build the html format of the report
HTML_BUILDER = 'sphinx-build -v -c conf_cpta -a ' \
               '-b html -E ' \
               '-t html ' \
               '-D version="Generated on {date}" ' \
               '{working_dir} ' \
               '{build_dir}/'

# .css file for the html format of the report
THEME_OVERRIDES = """/* override table width restrictions */
.wy-nav-content {
    max-width: 1200px !important;
}
"""

COLORS = ["SkyBlue", "Olive", "Purple", "Coral", "Indigo", "Pink",
          "Chocolate", "Brown", "Magenta", "Cyan", "Orange", "Black",
          "Violet", "Blue", "Yellow"]


def generate_cpta(spec, data):
    """Generate all formats and versions of the Continuous Performance Trending
    and Analysis.

    :param spec: Specification read from the specification file.
    :param data: Full data set.
    :type spec: Specification
    :type data: InputData
    """

    logging.info("Generating the Continuous Performance Trending and Analysis "
                 "...")

    ret_code = _generate_all_charts(spec, data)

    cmd = HTML_BUILDER.format(
        date=datetime.date.today().strftime('%d-%b-%Y'),
        working_dir=spec.environment["paths"]["DIR[WORKING,SRC]"],
        build_dir=spec.environment["paths"]["DIR[BUILD,HTML]"])
    execute_command(cmd)

    with open(spec.environment["paths"]["DIR[CSS_PATCH_FILE]"], "w") as \
            css_file:
        css_file.write(THEME_OVERRIDES)

    with open(spec.environment["paths"]["DIR[CSS_PATCH_FILE2]"], "w") as \
            css_file:
        css_file.write(THEME_OVERRIDES)

    archive_input_data(spec)

    logging.info("Done.")

    return ret_code


def _select_data(in_data, period, fill_missing=False, use_first=False):
    """Select the data from the full data set. The selection is done by picking
    the samples depending on the period: period = 1: All, period = 2: every
    second sample, period = 3: every third sample ...

    :param in_data: Full set of data.
    :param period: Sampling period.
    :param fill_missing: If the chosen sample is missing in the full set, its
    nearest neighbour is used.
    :param use_first: Use the first sample even though it is not chosen.
    :type in_data: OrderedDict
    :type period: int
    :type fill_missing: bool
    :type use_first: bool
    :returns: Reduced data.
    :rtype: OrderedDict
    """

    first_idx = min(in_data.keys())
    last_idx = max(in_data.keys())

    idx = last_idx
    data_dict = dict()
    if use_first:
        data_dict[first_idx] = in_data[first_idx]
    while idx >= first_idx:
        data = in_data.get(idx, None)
        if data is None:
            if fill_missing:
                threshold = int(round(idx - period / 2)) + 1 - period % 2
                idx_low = first_idx if threshold < first_idx else threshold
                threshold = int(round(idx + period / 2))
                idx_high = last_idx if threshold > last_idx else threshold

                flag_l = True
                flag_h = True
                idx_lst = list()
                inc = 1
                while flag_l or flag_h:
                    if idx + inc > idx_high:
                        flag_h = False
                    else:
                        idx_lst.append(idx + inc)
                    if idx - inc < idx_low:
                        flag_l = False
                    else:
                        idx_lst.append(idx - inc)
                    inc += 1

                for i in idx_lst:
                    if i in in_data.keys():
                        data_dict[i] = in_data[i]
                        break
        else:
            data_dict[idx] = data
        idx -= period

    return OrderedDict(sorted(data_dict.items(), key=lambda t: t[0]))


def _evaluate_results(in_data, trimmed_data, window=10):
    """Evaluates if the sample value is regress, normal or progress compared to
    previous data within the window.
    We use the intervals defined as:
    - regress: less than median - 3 * stdev
    - normal: between median - 3 * stdev and median + 3 * stdev
    - progress: more than median + 3 * stdev

    :param in_data: Full data set.
    :param trimmed_data: Full data set without the outliers.
    :param window: Window size used to calculate moving median and moving stdev.
    :type in_data: pandas.Series
    :type trimmed_data: pandas.Series
    :type window: int
    :returns: Evaluated results.
    :rtype: list
    """

    if len(in_data) > 2:
        win_size = in_data.size if in_data.size < window else window
        results = [0.66, ]
        median = in_data.rolling(window=win_size, min_periods=2).median()
        stdev_t = trimmed_data.rolling(window=win_size, min_periods=2).std()

        first = True
        for build_nr, value in in_data.iteritems():
            if first:
                first = False
                continue
            if np.isnan(trimmed_data[build_nr]) \
                    or np.isnan(median[build_nr]) \
                    or np.isnan(stdev_t[build_nr]) \
                    or np.isnan(value):
                results.append(0.0)
            elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
                results.append(0.33)
            elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
                results.append(1.0)
            else:
                results.append(0.66)
    else:
        results = [0.0, ]
        try:
            median = np.median(in_data)
            stdev = np.std(in_data)
            if in_data.values[-1] < (median - 3 * stdev):
                results.append(0.33)
            elif (median - 3 * stdev) <= in_data.values[-1] <= (
                    median + 3 * stdev):
                results.append(0.66)
            else:
                results.append(1.0)
        except TypeError:
            results.append(None)
    return results


def _generate_trending_traces(in_data, build_info, period, moving_win_size=10,
                              fill_missing=True, use_first=False,
                              show_moving_median=True, name="", color=""):
    """Generate the trending traces:
     - samples,
     - moving median (trending plot)
     - outliers, regress, progress

    :param in_data: Full data set.
    :param build_info: Information about the builds.
    :param period: Sampling period.
    :param moving_win_size: Window size.
    :param fill_missing: If the chosen sample is missing in the full set, its
    nearest neighbour is used.
    :param use_first: Use the first sample even though it is not chosen.
    :param show_moving_median: Show moving median (trending plot).
    :param name: Name of the plot
    :param color: Name of the color for the plot.
    :type in_data: OrderedDict
    :type build_info: dict
    :type period: int
    :type moving_win_size: int
    :type fill_missing: bool
    :type use_first: bool
    :type show_moving_median: bool
    :type name: str
    :type color: str
    :returns: Generated traces (list) and the evaluated result (float).
    :rtype: tuple(traces, result)
    """

    if period > 1:
        in_data = _select_data(in_data, period,
                               fill_missing=fill_missing,
                               use_first=use_first)

    data_x = [key for key in in_data.keys()]
    data_y = [val for val in in_data.values()]

    hover_text = list()
    for idx in data_x:
        hover_text.append("vpp-build: {0}".
                          format(build_info[str(idx)][1].split("~")[-1]))

    data_pd = pd.Series(data_y, index=data_x)

    t_data, outliers = find_outliers(data_pd, outlier_const=1.5)
    results = _evaluate_results(data_pd, t_data, window=moving_win_size)

    anomalies = pd.Series()
    anomalies_res = list()
    for idx, item in enumerate(in_data.items()):
        item_pd = pd.Series([item[1], ], index=[item[0], ])
        if item[0] in outliers.keys():
            anomalies = anomalies.append(item_pd)
            anomalies_res.append(0.0)
        elif results[idx] in (0.33, 1.0):
            anomalies = anomalies.append(item_pd)
            anomalies_res.append(results[idx])
    anomalies_res.extend([0.0, 0.33, 0.66, 1.0])

    # Create traces
    color_scale = [[0.00, "grey"],
                   [0.25, "grey"],
                   [0.25, "red"],
                   [0.50, "red"],
                   [0.50, "white"],
                   [0.75, "white"],
                   [0.75, "green"],
                   [1.00, "green"]]

    trace_samples = plgo.Scatter(
        x=data_x,
        y=data_y,
        mode='markers',
        line={
            "width": 1
        },
        name="{name}-thput".format(name=name),
        marker={
            "size": 5,
            "color": color,
            "symbol": "circle",
        },
        text=hover_text,
        hoverinfo="x+y+text+name"
    )
    traces = [trace_samples, ]

    trace_anomalies = plgo.Scatter(
        x=anomalies.keys(),
        y=anomalies.values,
        mode='markers',
        hoverinfo="none",
        showlegend=False,
        legendgroup=name,
        name="{name}: outliers".format(name=name),
        marker={
            "size": 15,
            "symbol": "circle-open",
            "color": anomalies_res,
            "colorscale": color_scale,
            "showscale": True,
            "line": {
                "width": 2
            },
            "colorbar": {
                "y": 0.5,
                "len": 0.8,
                "title": "Circles Marking Data Classification",
                "titleside": 'right',
                "titlefont": {
                    "size": 14
                },
                "tickmode": 'array',
                "tickvals": [0.125, 0.375, 0.625, 0.875],
                "ticktext": ["Outlier", "Regression", "Normal", "Progression"],
                "ticks": "",
                "ticklen": 0,
                "tickangle": -90,
                "thickness": 10
            }
        }
    )
    traces.append(trace_anomalies)

    if show_moving_median:
        data_mean_y = pd.Series(data_y).rolling(
            window=moving_win_size, min_periods=2).median()
        trace_median = plgo.Scatter(
            x=data_x,
            y=data_mean_y,
            mode='lines',
            line={
                "shape": "spline",
                "width": 1,
                "color": color,
            },
            name='{name}-trend'.format(name=name)
        )
        traces.append(trace_median)

    return traces, results[-1]


def _generate_chart(traces, layout, file_name):
    """Generates the whole chart using pre-generated traces.

    :param traces: Traces for the chart.
    :param layout: Layout of the chart.
    :param file_name: File name for the generated chart.
    :type traces: list
    :type layout: dict
    :type file_name: str
    """

    # Create plot
    logging.info("    Writing the file '{0}' ...".format(file_name))
    plpl = plgo.Figure(data=traces, layout=layout)
    try:
        ploff.plot(plpl, show_link=False, auto_open=False, filename=file_name)
    except plerr.PlotlyEmptyDataError:
        logging.warning(" No data for the plot. Skipped.")


def _generate_all_charts(spec, input_data):
    """Generate all charts specified in the specification file.

    :param spec: Specification.
    :param input_data: Full data set.
    :type spec: Specification
    :type input_data: InputData
    """

    job_name = spec.cpta["data"].keys()[0]

    builds_lst = list()
    for build in spec.input["builds"][job_name]:
        status = build["status"]
        if status != "failed" and status != "not found":
            builds_lst.append(str(build["build"]))

    # Get "build ID": "date" dict:
    build_info = OrderedDict()
    for build in builds_lst:
        try:
            build_info[build] = (
                input_data.metadata(job_name, build)["generated"][:14],
                input_data.metadata(job_name, build)["version"]
            )
        except KeyError:
            build_info[build] = ("", "")
        logging.info("{}: {}, {}".format(build,
                                         build_info[build][0],
                                         build_info[build][1]))

    # Create the header:
    csv_table = list()
    header = "Build Number:," + ",".join(builds_lst) + '\n'
    csv_table.append(header)
    build_dates = [x[0] for x in build_info.values()]
    header = "Build Date:," + ",".join(build_dates) + '\n'
    csv_table.append(header)
    vpp_versions = [x[1] for x in build_info.values()]
    header = "VPP Version:," + ",".join(vpp_versions) + '\n'
    csv_table.append(header)

    results = list()
    for chart in spec.cpta["plots"]:
        logging.info("  Generating the chart '{0}' ...".
                     format(chart.get("title", "")))

        # Transform the data
        data = input_data.filter_data(chart, continue_on_error=True)
        if data is None:
            logging.error("No data.")
            return

        chart_data = dict()
        for job in data:
            for idx, build in job.items():
                for test_name, test in build.items():
                    if chart_data.get(test_name, None) is None:
                        chart_data[test_name] = OrderedDict()
                    try:
                        chart_data[test_name][int(idx)] = \
                            test["result"]["throughput"]
                    except (KeyError, TypeError):
                        pass

        # Add items to the csv table:
        for tst_name, tst_data in chart_data.items():
            tst_lst = list()
            for build in builds_lst:
                item = tst_data.get(int(build), '')
                tst_lst.append(str(item) if item else '')
            csv_table.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')

        for period in chart["periods"]:
            # Generate traces:
            traces = list()
            win_size = 14 if period == 1 else 5 if period < 20 else 3
            idx = 0
            for test_name, test_data in chart_data.items():
                if not test_data:
                    logging.warning("No data for the test '{0}'".
                                    format(test_name))
                    continue
                test_name = test_name.split('.')[-1]
                trace, result = _generate_trending_traces(
                    test_data,
                    build_info=build_info,
                    period=period,
                    moving_win_size=win_size,
                    fill_missing=True,
                    use_first=False,
                    name='-'.join(test_name.split('-')[3:-1]),
                    color=COLORS[idx])
                traces.extend(trace)
                results.append(result)
                idx += 1

            # Generate the chart:
            chart["layout"]["xaxis"]["title"] = \
                chart["layout"]["xaxis"]["title"].format(job=job_name)
            _generate_chart(traces,
                            chart["layout"],
                            file_name="{0}-{1}-{2}{3}".format(
                                spec.cpta["output-file"],
                                chart["output-file-name"],
                                period,
                                spec.cpta["output-file-type"]))

        logging.info("  Done.")

    # Write the tables:
    file_name = spec.cpta["output-file"] + "-trending"
    with open("{0}.csv".format(file_name), 'w') as file_handler:
        file_handler.writelines(csv_table)

    txt_table = None
    with open("{0}.csv".format(file_name), 'rb') as csv_file:
        csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
        line_nr = 0
        for row in csv_content:
            if txt_table is None:
                txt_table = prettytable.PrettyTable(row)
            else:
                if line_nr > 1:
                    for idx, item in enumerate(row):
                        try:
                            row[idx] = str(round(float(item) / 1000000, 2))
                        except ValueError:
                            pass
                try:
                    txt_table.add_row(row)
                except Exception as err:
                    logging.warning("Error occurred while generating TXT table:"
                                    "\n{0}".format(err))
            line_nr += 1
        txt_table.align["Build Number:"] = "l"
    with open("{0}.txt".format(file_name), "w") as txt_file:
        txt_file.write(str(txt_table))

    # Evaluate result:
    result = "PASS"
    for item in results:
        if item is None:
            result = "FAIL"
            break
        if item == 0.66 and result == "PASS":
            result = "PASS"
        elif item == 0.33 or item == 0.0:
            result = "FAIL"

    logging.info("Partial results: {0}".format(results))
    logging.info("Result: {0}".format(result))

    return result