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path: root/src/tools/g2/cpel.c
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/* 
 *------------------------------------------------------------------
 * Copyright (c) 2005-2016 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.
 */

#include <stdio.h>
#include <stdlib.h>
#include <netinet/in.h>
#include <string.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <sys/fcntl.h>
#include <sys/mman.h>
#include <unistd.h>
#include <ctype.h>
#include <vppinfra/clib.h>
#include <vppinfra/vec.h>
#include <vppinfra/hash.h>
#include <pwd.h>
#include <stdarg.h>
#include <time.h>
#include "cpel.h"
#include "g2.h"

typedef struct bound_event_ {
    u32 event_code;
    u8  *event_str;
    u8  *datum_str;
} bound_event_t;

bound_event_t *bound_events;

int widest_track_format=8;

typedef struct bound_track_ {
    u32 track;
    u8  *track_str;
} bound_track_t;

bound_track_t *bound_tracks;

uword *the_strtab_hash; /* (name, base-VA) hash of all string tables */
uword *the_evtdef_hash; /* (event-id, event-definition) hash */
uword *the_trackdef_hash; /* (track-id, track-definition) hash */
u8 *event_strtab;         /* event string-table */

void fatal(char *s)
{
    fprintf(stderr, "%s", s);
    exit(1);
}

typedef enum {
    PASS1=1,
    PASS2=2,
} pass_t;

typedef struct {
    int (*pass1)(cpel_section_header_t *, int, FILE *);
    int (*pass2)(cpel_section_header_t *, int, FILE *);
} section_processor_t;

int bad_section(cpel_section_header_t *sh, int verbose, FILE *ofp)
{
    fprintf(ofp, "Bad (type 0) section, skipped...\n");
    return(0);
}

int noop_pass(cpel_section_header_t *sh, int verbose, FILE *ofp)
{
    return(0);
}

int strtab_pass1(cpel_section_header_t *sh, int verbose, FILE *ofp)
{
    uword *p;
    u8 *strtab_data_area = (u8 *)(sh+1);
    
    /* Multiple string tables with the same name are Bad... */
    p = hash_get_mem(the_strtab_hash, strtab_data_area);
    if (p) {
        fprintf(ofp, "Duplicate string table name %s", strtab_data_area);
    }
    /*
     * Looks funny, but we really do want key = first string in the
     * table, value = address(first string in the table) 
     */
    hash_set_mem(the_strtab_hash, strtab_data_area, strtab_data_area);
    if (verbose) {
        fprintf(ofp, "String Table %s\n", strtab_data_area);
    }
    return(0);
}

int evtdef_pass1(cpel_section_header_t *sh, int verbose, FILE *ofp)
{
    int i, nevents;
    event_definition_section_header_t *edh;
    event_definition_t *ep;
    u8 *this_strtab;
    u32 event_code;
    uword *p;
    bound_event_t *bp;

    edh = (event_definition_section_header_t *)(sh+1);
    nevents = ntohl(edh->number_of_event_definitions);
    
    if (verbose) {
        fprintf(ofp, "Event Definition Section: %d definitions\n",
                nevents);
    }

    p = hash_get_mem(the_strtab_hash, edh->string_table_name);
    if (!p) {
        fprintf(ofp, "Fatal: couldn't find string table\n");
        return(1);
    }
    this_strtab = (u8 *)p[0];

    initialize_events();

    ep = (event_definition_t *)(edh+1);
    
    for (i = 0; i < nevents; i++) {
        event_code = ntohl(ep->event);
        p = hash_get(the_evtdef_hash, event_code);
        if (p) {
            fprintf(ofp, "Event %d redefined, retain first definition\n",
                    event_code);
            continue;
        }
        vec_add2(bound_events, bp, 1);
        bp->event_code = event_code;
        bp->event_str = this_strtab + ntohl(ep->event_format);
        bp->datum_str = this_strtab + ntohl(ep->datum_format);
        hash_set(the_evtdef_hash, event_code, bp - bound_events);

        add_event_from_cpel_file(event_code, (char *) bp->event_str, 
                                 (char *)bp->datum_str);

        ep++;
    }

    finalize_events();
    return (0);
}

int trackdef_pass1(cpel_section_header_t *sh, int verbose, FILE *ofp)
{
    int i, nevents;
    track_definition_section_header_t *tdh;
    track_definition_t *tp;
    u8 *this_strtab;
    u32 track_code;
    uword *p;
    bound_track_t *btp;
    int track_strlen;

    tdh = (track_definition_section_header_t *)(sh+1);
    nevents = ntohl(tdh->number_of_track_definitions);
    
    if (verbose) {
        fprintf(ofp, "Track Definition Section: %d definitions\n",
                nevents);
    }

    p = hash_get_mem(the_strtab_hash, tdh->string_table_name);
    if (!p) {
        fprintf(ofp, "Fatal: couldn't find string table\n");
        return(1);
    }
    this_strtab = (u8 *)p[0];

    tp = (track_definition_t *)(tdh+1);
    
    for (i = 0; i < nevents; i++) {
        track_code = ntohl(tp->track);
        p = hash_get(the_trackdef_hash, track_code);
        if (p) {
            fprintf(ofp, "track %d redefined, retain first definition\n",
                    track_code);
            continue;
        }
        vec_add2(bound_tracks, btp, 1);
        btp->track = track_code;
        btp->track_str = this_strtab + ntohl(tp->track_format);
        hash_set(the_trackdef_hash, track_code, btp - bound_tracks);

        track_strlen = strlen((char *)btp->track_str);
        if (track_strlen > widest_track_format)
            widest_track_format = track_strlen;
        tp++;
    }
    return (0);
}

int unsupported_pass (cpel_section_header_t *sh, int verbose, FILE *ofp)
{
    if (verbose) {
        fprintf(ofp, "Unsupported type %d section\n",
                ntohl(sh->section_type));
    }
    return(0);
}

int event_pass2(cpel_section_header_t *sh, int verbose, FILE *ofp)
{
    event_section_header_t *eh;
    u32 event_code, track_code, datum;
    u64 starttime = ~0ULL;
    int nevents;
    int i;
    event_entry_t *ep;
    u64 now;
    u64 delta;
    u32 time0, time1;
    double d;
    uword *p;

    eh = (event_section_header_t *)(sh+1);
    nevents = ntohl(eh->number_of_events);
    ticks_per_ns = ntohl(eh->clock_ticks_per_second)/1e9;
    ep = (event_entry_t *)(eh+1);

    p = hash_get_mem(the_strtab_hash, eh->string_table_name);
    if (!p) {
        fprintf(ofp, "Fatal: couldn't find string table\n");
        return(1);
    }
    event_strtab = (u8 *)p[0];

    cpel_event_init(nevents);

    for (i = 0; i < nevents; i++) {
        time0 = ntohl (ep->time[0]);
        time1 = ntohl (ep->time[1]);

        now = (((u64) time0)<<32) | time1;
        
        /* Convert from bus ticks to usec */
        d = now;
        d /= ticks_per_ns;

        now = d;

        if (starttime == ~0ULL)
            starttime = now;
        
        delta = now - starttime;

        /* Delta = time since first event, in usec */
        event_code = ntohl(ep->event_code);
        track_code = ntohl(ep->track);
        datum = ntohl(ep->event_datum);

        add_cpel_event(delta, track_code, event_code, datum);

        ep++;
    }
    cpel_event_finalize();
    return(0);
}

char *strtab_ref(unsigned long datum)
{
    return ((char *)(event_strtab + datum));
}

/* 
 * Note: If necessary, add passes / columns to this table to 
 * handle section order dependencies.
 */

section_processor_t processors[CPEL_NUM_SECTION_TYPES+1] =
{
    {bad_section,	noop_pass}, 		/* type 0 -- f**ked */
    {strtab_pass1, 	noop_pass}, 		/* type 1 -- STRTAB */
    {unsupported_pass,  noop_pass}, 		/* type 2 -- SYMTAB */
    {evtdef_pass1,      noop_pass},             /* type 3 -- EVTDEF */
    {trackdef_pass1,    noop_pass},		/* type 4 -- TRACKDEF */
    {noop_pass,         event_pass2},           /* type 5 -- EVENTS */
};


int process_section(cpel_section_header_t *sh, int verbose, FILE *ofp,
                    pass_t pass)
{
    u32 type;
    type = ntohl(sh->section_type);
    int rv;
    int (*fp)(cpel_section_header_t *, int, FILE *);

    if (type > CPEL_NUM_SECTION_TYPES) {
        fprintf(stderr, "Unknown section type %d\n", type);
        return(1);
    }
    switch(pass) {
    case PASS1:
        fp = processors[type].pass1;
        break;

    case PASS2:
        fp = processors[type].pass2;
        break;
        
    default:
        fprintf(stderr, "Unknown pass %d\n", pass);
        return(1);
    }

    rv = (*fp)(sh, verbose, ofp);

    return(rv);
}

int cpel_dump_file_header(cpel_file_header_t *fh, int verbose, FILE *ofp)
{
    time_t file_time;

    if (verbose) {
        fprintf(ofp, "CPEL file: %s-endian, version %d\n",
                ((fh->endian_version & CPEL_FILE_LITTLE_ENDIAN) ? 
                 "little" : "big"), 
                fh->endian_version & CPEL_FILE_VERSION_MASK);

        file_time = ntohl(fh->file_date);

        fprintf(ofp, "File created %s", ctime(&file_time));
    }

    return(0);
}


int cpel_process(u8 *cpel, int verbose, FILE *ofp)
{
    cpel_file_header_t *fh;
    cpel_section_header_t *sh;
    u16 nsections;
    u32 section_size;
    int i;

    /* First, the file header */
    fh = (cpel_file_header_t *)cpel;
    if (fh->endian_version != CPEL_FILE_VERSION) {
        if (fh->endian_version & CPEL_FILE_LITTLE_ENDIAN) {
            fprintf(stderr, "Little endian data format not supported\n");
            return(1);
        }
        fprintf(stderr, "Unsupported file version 0x%x\n", 
                fh->endian_version);
        return(1);
    }
    cpel_dump_file_header(fh, verbose, ofp);
    nsections = ntohs(fh->nsections);

    /*
     * Take two passes through the file. PASS1 builds
     * data structures, PASS2 actually dumps the file.
     * Just in case the sections are in an unobvious order.
     */
    sh = (cpel_section_header_t *)(fh+1);
    for (i = 0; i < nsections; i++) {
        section_size = ntohl(sh->data_length);

        if(verbose) {
            fprintf(ofp, "Section type %d, size %d\n", ntohl(sh->section_type),
                    section_size);
        }

        if(process_section(sh, verbose, ofp, PASS1))
            return(1);

        sh++;
        sh = (cpel_section_header_t *)(((u8 *)sh)+section_size);
    }

    sh = (cpel_section_header_t *)(fh+1);
    for (i = 0; i < nsections; i++) {
        if(process_section(sh, verbose, ofp, PASS2))
            return(1);
        section_size = ntohl(sh->data_length);
        sh++;
        sh = (cpel_section_header_t *)(((u8 *)sh)+section_size);
    }


    return(0);
}

/*
 * read_cpel_file
 */
int read_cpel_file(char *cpel_file)
{
    int verbose = 0;
    int rv;
    static u8 *cpel;
    static unsigned long size;
    static FILE *ofp;

    if (cpel) {
        unmapfile((char *)cpel, size);
        hash_free(the_strtab_hash);
        the_strtab_hash = 0;
        hash_free(the_evtdef_hash);
        the_evtdef_hash = 0;
        hash_free(the_trackdef_hash);
        the_trackdef_hash = 0;
    }

    cpel = (u8 *)mapfile((char *)cpel_file, &size);
    if (cpel == 0) {
        fprintf(stderr, "Couldn't map %s...\n", cpel_file);
        exit(1);
    }

    if (ofp == NULL) {
        ofp = fdopen(2, "w");
        if (ofp == NULL) {
            fprintf(stderr, "Couldn't fdopen(2)?\n");
            exit(1);
        }
    }

    the_strtab_hash = hash_create_string (0, sizeof (uword));
    the_evtdef_hash = hash_create (0, sizeof (uword));
    the_trackdef_hash = hash_create (0, sizeof (uword));

    rv = cpel_process(cpel, verbose, ofp);

    set_pid_ax_width(8*widest_track_format);

    return(rv);
}

static bound_track_t generic_hex_track = {0, (u8 *) "0x%08x"};
static bound_track_t generic_decimal_track = {0, (u8 *) "%8ld"};

/*
 * get_track_label
 */
char *get_track_label(unsigned long track)
{
    uword *p;
    bound_track_t *tp;

    p = hash_get(the_trackdef_hash, track);
    if (p) {
        tp = &bound_tracks[p[0]];
    } else {
        if (track > 65535) 
            tp = &generic_hex_track;
        else
            tp = &generic_decimal_track;
    }
    return((char *)tp->track_str);
}
[plot[u"algorithm"]](plot, data) logging.info(u" Done.") except NameError as err: logging.error( f"Probably algorithm {plot[u'algorithm']} is not defined: " f"{repr(err)}" ) logging.info(u"Done.") def plot_hdrh_lat_by_percentile(plot, input_data): """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile specified in the specification file. :param plot: Plot to generate. :param input_data: Data to process. :type plot: pandas.Series :type input_data: InputData """ # Transform the data logging.info( f" Creating the data set for the {plot.get(u'type', u'')} " f"{plot.get(u'title', u'')}." ) if plot.get(u"include", None): data = input_data.filter_tests_by_name( plot, params=[u"name", u"latency", u"parent", u"tags", u"type"] )[0][0] elif plot.get(u"filter", None): data = input_data.filter_data( plot, params=[u"name", u"latency", u"parent", u"tags", u"type"], continue_on_error=True )[0][0] else: job = list(plot[u"data"].keys())[0] build = str(plot[u"data"][job][0]) data = input_data.tests(job, build) if data is None or len(data) == 0: logging.error(u"No data.") return desc = { u"LAT0": u"No-load.", u"PDR10": u"Low-load, 10% PDR.", u"PDR50": u"Mid-load, 50% PDR.", u"PDR90": u"High-load, 90% PDR.", u"PDR": u"Full-load, 100% PDR.", u"NDR10": u"Low-load, 10% NDR.", u"NDR50": u"Mid-load, 50% NDR.", u"NDR90": u"High-load, 90% NDR.", u"NDR": u"Full-load, 100% NDR." } graphs = [ u"LAT0", u"PDR10", u"PDR50", u"PDR90" ] file_links = plot.get(u"output-file-links", None) target_links = plot.get(u"target-links", None) for test in data: try: if test[u"type"] not in (u"NDRPDR",): logging.warning(f"Invalid test type: {test[u'type']}") continue name = re.sub(REGEX_NIC, u"", test[u"parent"]. replace(u'-ndrpdr', u'').replace(u'2n1l-', u'')) try: nic = re.search(REGEX_NIC, test[u"parent"]).group(1) except (IndexError, AttributeError, KeyError, ValueError): nic = u"" name_link = f"{nic}-{test[u'name']}".replace(u'-ndrpdr', u'') logging.info(f" Generating the graph: {name_link}") fig = plgo.Figure() layout = deepcopy(plot[u"layout"]) for color, graph in enumerate(graphs): for idx, direction in enumerate((u"direction1", u"direction2")): previous_x = 0.0 xaxis = list() yaxis = list() hovertext = list() try: decoded = hdrh.histogram.HdrHistogram.decode( test[u"latency"][graph][direction][u"hdrh"] ) except hdrh.codec.HdrLengthException: logging.warning( f"No data for direction {(u'W-E', u'E-W')[idx % 2]}" ) continue for item in decoded.get_recorded_iterator(): percentile = item.percentile_level_iterated_to xaxis.append(previous_x) yaxis.append(item.value_iterated_to) hovertext.append( f"<b>{desc[graph]}</b><br>" f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>" f"Percentile: {previous_x:.5f}-{percentile:.5f}%<br>" f"Latency: {item.value_iterated_to}uSec" ) xaxis.append(percentile) yaxis.append(item.value_iterated_to) hovertext.append( f"<b>{desc[graph]}</b><br>" f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>" f"Percentile: {previous_x:.5f}-{percentile:.5f}%<br>" f"Latency: {item.value_iterated_to}uSec" ) previous_x = percentile fig.add_trace( plgo.Scatter( x=xaxis, y=yaxis, name=desc[graph], mode=u"lines", legendgroup=desc[graph], showlegend=bool(idx), line=dict( color=COLORS[color], dash=u"solid", width=1 if idx % 2 else 2 ), hovertext=hovertext, hoverinfo=u"text" ) ) layout[u"title"][u"text"] = f"<b>Latency:</b> {name}" fig.update_layout(layout) # Create plot file_name = f"{plot[u'output-file']}-{name_link}.html" logging.info(f" Writing file {file_name}") try: # Export Plot ploff.plot(fig, show_link=False, auto_open=False, filename=file_name) # Add link to the file: if file_links and target_links: with open(file_links, u"a") as file_handler: file_handler.write( f"- `{name_link} " f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n" ) except FileNotFoundError as err: logging.error( f"Not possible to write the link to the file " f"{file_links}\n{err}" ) except PlotlyError as err: logging.error(f" Finished with error: {repr(err)}") except hdrh.codec.HdrLengthException as err: logging.warning(repr(err)) continue except (ValueError, KeyError) as err: logging.warning(repr(err)) continue def plot_hdrh_lat_by_percentile_x_log(plot, input_data): """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile_x_log specified in the specification file. :param plot: Plot to generate. :param input_data: Data to process. :type plot: pandas.Series :type input_data: InputData """ # Transform the data logging.info( f" Creating the data set for the {plot.get(u'type', u'')} " f"{plot.get(u'title', u'')}." ) if plot.get(u"include", None): data = input_data.filter_tests_by_name( plot, params=[u"name", u"latency", u"parent", u"tags", u"type"] )[0][0] elif plot.get(u"filter", None): data = input_data.filter_data( plot, params=[u"name", u"latency", u"parent", u"tags", u"type"], continue_on_error=True )[0][0] else: job = list(plot[u"data"].keys())[0] build = str(plot[u"data"][job][0]) data = input_data.tests(job, build) if data is None or len(data) == 0: logging.error(u"No data.") return desc = { u"LAT0": u"No-load.", u"PDR10": u"Low-load, 10% PDR.", u"PDR50": u"Mid-load, 50% PDR.", u"PDR90": u"High-load, 90% PDR.", u"PDR": u"Full-load, 100% PDR.", u"NDR10": u"Low-load, 10% NDR.", u"NDR50": u"Mid-load, 50% NDR.", u"NDR90": u"High-load, 90% NDR.", u"NDR": u"Full-load, 100% NDR." } graphs = [ u"LAT0", u"PDR10", u"PDR50", u"PDR90" ] file_links = plot.get(u"output-file-links", None) target_links = plot.get(u"target-links", None) for test in data: try: if test[u"type"] not in (u"NDRPDR",): logging.warning(f"Invalid test type: {test[u'type']}") continue name = re.sub(REGEX_NIC, u"", test[u"parent"]. replace(u'-ndrpdr', u'').replace(u'2n1l-', u'')) try: nic = re.search(REGEX_NIC, test[u"parent"]).group(1) except (IndexError, AttributeError, KeyError, ValueError): nic = u"" name_link = f"{nic}-{test[u'name']}".replace(u'-ndrpdr', u'') logging.info(f" Generating the graph: {name_link}") fig = plgo.Figure() layout = deepcopy(plot[u"layout"]) for color, graph in enumerate(graphs): for idx, direction in enumerate((u"direction1", u"direction2")): previous_x = 0.0 prev_perc = 0.0 xaxis = list() yaxis = list() hovertext = list() try: decoded = hdrh.histogram.HdrHistogram.decode( test[u"latency"][graph][direction][u"hdrh"] ) except hdrh.codec.HdrLengthException: logging.warning( f"No data for direction {(u'W-E', u'E-W')[idx % 2]}" ) continue for item in decoded.get_recorded_iterator(): # The real value is "percentile". # For 100%, we cut that down to "x_perc" to avoid # infinity. percentile = item.percentile_level_iterated_to x_perc = min(percentile, PERCENTILE_MAX) xaxis.append(previous_x) yaxis.append(item.value_iterated_to) hovertext.append( f"<b>{desc[graph]}</b><br>" f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>" f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>" f"Latency: {item.value_iterated_to}uSec" ) next_x = 100.0 / (100.0 - x_perc) xaxis.append(next_x) yaxis.append(item.value_iterated_to) hovertext.append( f"<b>{desc[graph]}</b><br>" f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>" f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>" f"Latency: {item.value_iterated_to}uSec" ) previous_x = next_x prev_perc = percentile fig.add_trace( plgo.Scatter( x=xaxis, y=yaxis, name=desc[graph], mode=u"lines", legendgroup=desc[graph], showlegend=not(bool(idx)), line=dict( color=COLORS[color], dash=u"solid", width=1 if idx % 2 else 2 ), hovertext=hovertext, hoverinfo=u"text" ) ) layout[u"title"][u"text"] = f"<b>Latency:</b> {name}" x_max = log(100.0 / (100.0 - PERCENTILE_MAX), 10) layout[u"xaxis"][u"range"] = [0, x_max] fig.update_layout(layout) # Create plot file_name = f"{plot[u'output-file']}-{name_link}.html" logging.info(f" Writing file {file_name}") try: # Export Plot ploff.plot(fig, show_link=False, auto_open=False, filename=file_name) # Add link to the file: if file_links and target_links: with open(file_links, u"a") as file_handler: file_handler.write( f"- `{name_link} " f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n" ) except FileNotFoundError as err: logging.error( f"Not possible to write the link to the file " f"{file_links}\n{err}" ) except PlotlyError as err: logging.error(f" Finished with error: {repr(err)}") except hdrh.codec.HdrLengthException as err: logging.warning(repr(err)) continue except (ValueError, KeyError) as err: logging.warning(repr(err)) continue def plot_nf_reconf_box_name(plot, input_data): """Generate the plot(s) with algorithm: plot_nf_reconf_box_name specified in the specification file. :param plot: Plot to generate. :param input_data: Data to process. :type plot: pandas.Series :type input_data: InputData """ # Transform the data logging.info( f" Creating the data set for the {plot.get(u'type', u'')} " f"{plot.get(u'title', u'')}." ) data = input_data.filter_tests_by_name( plot, params=[u"result", u"parent", u"tags", u"type"] ) if data is None: logging.error(u"No data.") return for core in plot.get(u"core", tuple()): # Prepare the data for the plot y_vals = OrderedDict() loss = dict() for item in plot.get(u"include", tuple()): reg_ex = re.compile(str(item.format(core=core)).lower()) for job in data: for build in job: for test_id, test in build.iteritems(): if not re.match(reg_ex, str(test_id).lower()): continue if y_vals.get(test[u"parent"], None) is None: y_vals[test[u"parent"]] = list() loss[test[u"parent"]] = list() try: y_vals[test[u"parent"]].append( test[u"result"][u"time"] ) loss[test[u"parent"]].append( test[u"result"][u"loss"] ) except (KeyError, TypeError): y_vals[test[u"parent"]].append(None) # Add None to the lists with missing data max_len = 0 nr_of_samples = list() for val in y_vals.values(): if len(val) > max_len: max_len = len(val) nr_of_samples.append(len(val)) for val in y_vals.values(): if len(val) < max_len: val.extend([None for _ in range(max_len - len(val))]) # Add plot traces traces = list() df_y = pd.DataFrame(y_vals) df_y.head() for i, col in enumerate(df_y.columns): tst_name = re.sub( REGEX_NIC, u"", col.lower().replace(u'-reconf', u'').replace(u'2n1l-', u''). replace(u'2n-', u'').replace(u'-testpmd', u'') ) traces.append(plgo.Box( x=[str(i + 1) + u'.'] * len(df_y[col]), y=df_y[col], name=( f"{i + 1}. " f"({nr_of_samples[i]:02d} " f"run{u's' if nr_of_samples[i] > 1 else u''}, " f"packets lost average: {mean(loss[col]):.1f}) " f"{u'-'.join(tst_name.split(u'-')[2:])}" ), hoverinfo=u"y+name" )) try: # Create plot layout = deepcopy(plot[u"layout"]) layout[u"title"] = f"<b>Time Lost:</b> {layout[u'title']}" layout[u"yaxis"][u"title"] = u"<b>Effective Blocked Time [s]</b>" layout[u"legend"][u"font"][u"size"] = 14 layout[u"yaxis"].pop(u"range") plpl = plgo.Figure(data=traces, layout=layout) # Export Plot file_name = f"{plot[u'output-file'].format(core=core)}.html" logging.info(f" Writing file {file_name}") ploff.plot( plpl, show_link=False, auto_open=False, filename=file_name ) except PlotlyError as err: logging.error( f" Finished with error: {repr(err)}".replace(u"\n", u" ") ) def plot_perf_box_name(plot, input_data): """Generate the plot(s) with algorithm: plot_perf_box_name specified in the specification file. Use only for soak and hoststack tests. :param plot: Plot to generate. :param input_data: Data to process. :type plot: pandas.Series :type input_data: InputData """ # Transform the data logging.info( f" Creating data set for the {plot.get(u'type', u'')} " f"{plot.get(u'title', u'')}." ) data = input_data.filter_tests_by_name( plot, params=[u"throughput", u"gbps", u"result", u"parent", u"tags", u"type"]) if data is None: logging.error(u"No data.") return # Prepare the data for the plot y_vals = OrderedDict() test_type = u"" for item in plot.get(u"include", tuple()): reg_ex = re.compile(str(item).lower()) for job in data: for build in job: for test_id, test in build.iteritems(): if not re.match(reg_ex, str(test_id).lower()): continue if y_vals.get(test[u"parent"], None) is None: y_vals[test[u"parent"]] = list() try: if test[u"type"] in (u"SOAK",): y_vals[test[u"parent"]]. \ append(test[u"throughput"][u"LOWER"]) test_type = u"SOAK" elif test[u"type"] in (u"HOSTSTACK",): if u"LDPRELOAD" in test[u"tags"]: y_vals[test[u"parent"]].append( float( test[u"result"][u"bits_per_second"] ) / 1e3 ) elif u"VPPECHO" in test[u"tags"]: y_vals[test[u"parent"]].append( (float( test[u"result"][u"client"][u"tx_data"] ) * 8 / 1e3) / ((float( test[u"result"][u"client"][u"time"] ) + float( test[u"result"][u"server"][u"time"]) ) / 2) ) test_type = u"HOSTSTACK" else: continue except (KeyError, TypeError): y_vals[test[u"parent"]].append(None) # Add None to the lists with missing data max_len = 0 nr_of_samples = list() for val in y_vals.values(): if len(val) > max_len: max_len = len(val) nr_of_samples.append(len(val)) for val in y_vals.values(): if len(val) < max_len: val.extend([None for _ in range(max_len - len(val))]) # Add plot traces traces = list() df_y = pd.DataFrame(y_vals) df_y.head() y_max = list() for i, col in enumerate(df_y.columns): tst_name = re.sub(REGEX_NIC, u"", col.lower().replace(u'-ndrpdr', u''). replace(u'2n1l-', u'')) kwargs = dict( x=[str(i + 1) + u'.'] * len(df_y[col]), y=[y / 1e6 if y else None for y in df_y[col]], name=( f"{i + 1}. " f"({nr_of_samples[i]:02d} " f"run{u's' if nr_of_samples[i] > 1 else u''}) " f"{tst_name}" ), hoverinfo=u"y+name" ) if test_type in (u"SOAK", ): kwargs[u"boxpoints"] = u"all" traces.append(plgo.Box(**kwargs)) try: val_max = max(df_y[col]) if val_max: y_max.append(int(val_max / 1e6) + 2) except (ValueError, TypeError) as err: logging.error(repr(err)) continue try: # Create plot layout = deepcopy(plot[u"layout"]) if layout.get(u"title", None): if test_type in (u"HOSTSTACK", ): layout[u"title"] = f"<b>Bandwidth:</b> {layout[u'title']}" else: layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}" if y_max: layout[u"yaxis"][u"range"] = [0, max(y_max)] plpl = plgo.Figure(data=traces, layout=layout) # Export Plot logging.info(f" Writing file {plot[u'output-file']}.html.") ploff.plot( plpl, show_link=False, auto_open=False, filename=f"{plot[u'output-file']}.html" ) except PlotlyError as err: logging.error( f" Finished with error: {repr(err)}".replace(u"\n", u" ") ) return def plot_ndrpdr_box_name(plot, input_data): """Generate the plot(s) with algorithm: plot_ndrpdr_box_name specified in the specification file. :param plot: Plot to generate. :param input_data: Data to process. :type plot: pandas.Series :type input_data: InputData """ # Transform the data logging.info( f" Creating data set for the {plot.get(u'type', u'')} " f"{plot.get(u'title', u'')}." ) data = input_data.filter_tests_by_name( plot, params=[u"throughput", u"gbps", u"parent", u"tags", u"type"] ) if data is None: logging.error(u"No data.") return if u"-gbps" in plot.get(u"title", u"").lower(): value = u"gbps" multiplier = 1e6 else: value = u"throughput" multiplier = 1.0 test_type = u"" for ttype in plot.get(u"test-type", (u"ndr", u"pdr")): for core in plot.get(u"core", tuple()): # Prepare the data for the plot data_x = list() data_y = OrderedDict() data_y_max = list() idx = 1 for item in plot.get(u"include", tuple()): reg_ex = re.compile(str(item.format(core=core)).lower()) for job in data: for build in job: for test_id, test in build.iteritems(): if not re.match(reg_ex, str(test_id).lower()): continue if data_y.get(test[u"parent"], None) is None: data_y[test[u"parent"]] = list() test_type = test[u"type"] data_x.append(idx) idx += 1 try: data_y[test[u"parent"]].append( test[value][ttype.upper()][u"LOWER"] * multiplier ) except (KeyError, TypeError): pass # Add plot traces traces = list() for idx, (key, vals) in enumerate(data_y.items()): name = re.sub( REGEX_NIC, u'', key.lower().replace(u'-ndrpdr', u''). replace(u'2n1l-', u'') ) traces.append( plgo.Box( x=[data_x[idx], ] * len(data_x), y=[y / 1e6 if y else None for y in vals], name=( f"{idx+1}." f"({len(vals):02d} " f"run" f"{u's' if len(vals) > 1 else u''}) " f"{name}" ), hoverinfo=u"y+name" ) ) data_y_max.append(max(vals)) try: # Create plot layout = deepcopy(plot[u"layout"]) if layout.get(u"title", None): layout[u"title"] = \ layout[u'title'].format(core=core, test_type=ttype) if test_type in (u"CPS", ): layout[u"title"] = f"<b>CPS:</b> {layout[u'title']}" else: layout[u"title"] = \ f"<b>Throughput:</b> {layout[u'title']}" if data_y_max: layout[u"yaxis"][u"range"] = [0, max(data_y_max) / 1e6 + 1] plpl = plgo.Figure(data=traces, layout=layout) # Export Plot file_name = ( f"{plot[u'output-file'].format(core=core, test_type=ttype)}" f".html" ) logging.info(f" Writing file {file_name}") ploff.plot( plpl, show_link=False, auto_open=False, filename=file_name ) except PlotlyError as err: logging.error( f" Finished with error: {repr(err)}".replace(u"\n", u" ") ) def plot_mrr_box_name(plot, input_data): """Generate the plot(s) with algorithm: plot_mrr_box_name specified in the specification file. :param plot: Plot to generate. :param input_data: Data to process. :type plot: pandas.Series :type input_data: InputData """ # Transform the data logging.info( f" Creating data set for the {plot.get(u'type', u'')} " f"{plot.get(u'title', u'')}." ) data = input_data.filter_tests_by_name( plot, params=[u"result", u"parent", u"tags", u"type"] ) if data is None: logging.error(u"No data.") return for core in plot.get(u"core", tuple()): # Prepare the data for the plot data_x = list() data_names = list() data_y = list() data_y_max = list() idx = 1 for item in plot.get(u"include", tuple()): reg_ex = re.compile(str(item.format(core=core)).lower()) for job in data: for build in job: for test_id, test in build.iteritems(): if not re.match(reg_ex, str(test_id).lower()): continue try: data_x.append(idx) name = re.sub( REGEX_NIC, u'', test[u'parent'].lower(). replace(u'-mrr', u'').replace(u'2n1l-', u'') ) data_y.append(test[u"result"][u"samples"]) data_names.append( f"{idx}." f"({len(data_y[-1]):02d} " f"run{u's' if len(data_y[-1]) > 1 else u''}) " f"{name}" ) data_y_max.append(max(data_y[-1])) idx += 1 except (KeyError, TypeError): pass # Add plot traces traces = list() for idx in range(len(data_x)): traces.append( plgo.Box( x=[data_x[idx], ] * len(data_y[idx]), y=data_y[idx], name=data_names[idx], hoverinfo=u"y+name" ) ) try: # Create plot layout = deepcopy(plot[u"layout"]) if layout.get(u"title", None): layout[u"title"] = ( f"<b>Throughput:</b> {layout[u'title'].format(core=core)}" ) if data_y_max: layout[u"yaxis"][u"range"] = [0, max(data_y_max) + 1] plpl = plgo.Figure(data=traces, layout=layout) # Export Plot file_name = f"{plot[u'output-file'].format(core=core)}.html" logging.info(f" Writing file {file_name}") ploff.plot( plpl, show_link=False, auto_open=False, filename=file_name ) except PlotlyError as err: logging.error( f" Finished with error: {repr(err)}".replace(u"\n", u" ") ) def plot_tsa_name(plot, input_data): """Generate the plot(s) with algorithm: plot_tsa_name specified in the specification file. :param plot: Plot to generate. :param input_data: Data to process. :type plot: pandas.Series :type input_data: InputData """ # Transform the data plot_title = plot.get(u"title", u"") logging.info( f" Creating data set for the {plot.get(u'type', u'')} {plot_title}." ) data = input_data.filter_tests_by_name( plot, params=[u"throughput", u"gbps", u"parent", u"tags", u"type"] ) if data is None: logging.error(u"No data.") return plot_title = plot_title.lower() if u"-gbps" in plot_title: value = u"gbps" h_unit = u"Gbps" multiplier = 1e6 else: value = u"throughput" h_unit = u"Mpps" multiplier = 1.0 for ttype in plot.get(u"test-type", (u"ndr", u"pdr")): y_vals = OrderedDict() for item in plot.get(u"include", tuple()): reg_ex = re.compile(str(item).lower()) for job in data: for build in job: for test_id, test in build.iteritems(): if re.match(reg_ex, str(test_id).lower()): if y_vals.get(test[u"parent"], None) is None: y_vals[test[u"parent"]] = { u"1": list(), u"2": list(), u"4": list() } try: if test[u"type"] not in (u"NDRPDR", u"CPS"): continue if u"1C" in test[u"tags"]: y_vals[test[u"parent"]][u"1"].append( test[value][ttype.upper()][u"LOWER"] * multiplier ) elif u"2C" in test[u"tags"]: y_vals[test[u"parent"]][u"2"].append( test[value][ttype.upper()][u"LOWER"] * multiplier ) elif u"4C" in test[u"tags"]: y_vals[test[u"parent"]][u"4"].append( test[value][ttype.upper()][u"LOWER"] * multiplier ) except (KeyError, TypeError): pass if not y_vals: logging.warning(f"No data for the plot {plot.get(u'title', u'')}") return y_1c_max = dict() for test_name, test_vals in y_vals.items(): for key, test_val in test_vals.items(): if test_val: avg_val = sum(test_val) / len(test_val) y_vals[test_name][key] = [avg_val, len(test_val)] ideal = avg_val / (int(key) * 1e6) if test_name not in y_1c_max or ideal > y_1c_max[test_name]: y_1c_max[test_name] = ideal vals = OrderedDict() y_max = list() nic_limit = 0 lnk_limit = 0 pci_limit = 0 for test_name, test_vals in y_vals.items(): try: if test_vals[u"1"][1]: name = re.sub( REGEX_NIC, u"", test_name.replace(u'-ndrpdr', u''). replace(u'2n1l-', u'') ) vals[name] = OrderedDict() y_val_1 = test_vals[u"1"][0] / 1e6 y_val_2 = test_vals[u"2"][0] / 1e6 if test_vals[u"2"][0] \ else None y_val_4 = test_vals[u"4"][0] / 1e6 if test_vals[u"4"][0] \ else None vals[name][u"val"] = [y_val_1, y_val_2, y_val_4] vals[name][u"rel"] = [1.0, None, None] vals[name][u"ideal"] = [ y_1c_max[test_name], y_1c_max[test_name] * 2, y_1c_max[test_name] * 4 ] vals[name][u"diff"] = [ (y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None ] vals[name][u"count"] = [ test_vals[u"1"][1], test_vals[u"2"][1], test_vals[u"4"][1] ] try: val_max = max(vals[name][u"val"]) except ValueError as err: logging.error(repr(err)) continue if val_max: y_max.append(val_max) if y_val_2: vals[name][u"rel"][1] = round(y_val_2 / y_val_1, 2) vals[name][u"diff"][1] = \ (y_val_2 - vals[name][u"ideal"][1]) * 100 / y_val_2 if y_val_4: vals[name][u"rel"][2] = round(y_val_4 / y_val_1, 2) vals[name][u"diff"][2] = \ (y_val_4 - vals[name][u"ideal"][2]) * 100 / y_val_4 except IndexError as err: logging.warning(f"No data for {test_name}") logging.warning(repr(err)) # Limits: if u"x520" in test_name: limit = plot[u"limits"][u"nic"][u"x520"] elif u"x710" in test_name: limit = plot[u"limits"][u"nic"][u"x710"] elif u"xxv710" in test_name: limit = plot[u"limits"][u"nic"][u"xxv710"] elif u"xl710" in test_name: limit = plot[u"limits"][u"nic"][u"xl710"] elif u"x553" in test_name: limit = plot[u"limits"][u"nic"][u"x553"] elif u"cx556a" in test_name: limit = plot[u"limits"][u"nic"][u"cx556a"] elif u"e810cq" in test_name: limit = plot[u"limits"][u"nic"][u"e810cq"] else: limit = 0 if limit > nic_limit: nic_limit = limit mul = 2 if u"ge2p" in test_name else 1 if u"10ge" in test_name: limit = plot[u"limits"][u"link"][u"10ge"] * mul elif u"25ge" in test_name: limit = plot[u"limits"][u"link"][u"25ge"] * mul elif u"40ge" in test_name: limit = plot[u"limits"][u"link"][u"40ge"] * mul elif u"100ge" in test_name: limit = plot[u"limits"][u"link"][u"100ge"] * mul else: limit = 0 if limit > lnk_limit: lnk_limit = limit if u"cx556a" in test_name: limit = plot[u"limits"][u"pci"][u"pci-g3-x8"] else: limit = plot[u"limits"][u"pci"][u"pci-g3-x16"] if limit > pci_limit: pci_limit = limit traces = list() annotations = list() x_vals = [1, 2, 4] # Limits: if u"-gbps" not in plot_title and u"-cps-" not in plot_title: nic_limit /= 1e6 lnk_limit /= 1e6 pci_limit /= 1e6 min_limit = min((nic_limit, lnk_limit, pci_limit)) if nic_limit == min_limit: traces.append(plgo.Scatter( x=x_vals, y=[nic_limit, ] * len(x_vals), name=f"NIC: {nic_limit:.2f}Mpps", showlegend=False, mode=u"lines", line=dict( dash=u"dot", color=COLORS[-1], width=1), hoverinfo=u"none" )) annotations.append(dict( x=1, y=nic_limit, xref=u"x", yref=u"y", xanchor=u"left", yanchor=u"bottom", text=f"NIC: {nic_limit:.2f}Mpps", font=dict( size=14, color=COLORS[-1], ), align=u"left", showarrow=False )) y_max.append(nic_limit) elif lnk_limit == min_limit: traces.append(plgo.Scatter( x=x_vals, y=[lnk_limit, ] * len(x_vals), name=f"Link: {lnk_limit:.2f}Mpps", showlegend=False, mode=u"lines", line=dict( dash=u"dot", color=COLORS[-1], width=1), hoverinfo=u"none" )) annotations.append(dict( x=1, y=lnk_limit, xref=u"x", yref=u"y", xanchor=u"left", yanchor=u"bottom", text=f"Link: {lnk_limit:.2f}Mpps", font=dict( size=14, color=COLORS[-1], ), align=u"left", showarrow=False )) y_max.append(lnk_limit) elif pci_limit == min_limit: traces.append(plgo.Scatter( x=x_vals, y=[pci_limit, ] * len(x_vals), name=f"PCIe: {pci_limit:.2f}Mpps", showlegend=False, mode=u"lines", line=dict( dash=u"dot", color=COLORS[-1], width=1), hoverinfo=u"none" )) annotations.append(dict( x=1, y=pci_limit, xref=u"x", yref=u"y", xanchor=u"left", yanchor=u"bottom", text=f"PCIe: {pci_limit:.2f}Mpps", font=dict( size=14, color=COLORS[-1], ), align=u"left", showarrow=False )) y_max.append(pci_limit) # Perfect and measured: cidx = 0 for name, val in vals.items(): hovertext = list() try: for idx in range(len(val[u"val"])): htext = "" if isinstance(val[u"val"][idx], float): htext += ( f"No. of Runs: {val[u'count'][idx]}<br>" f"Mean: {val[u'val'][idx]:.2f}{h_unit}<br>" ) if isinstance(val[u"diff"][idx], float): htext += f"Diff: {round(val[u'diff'][idx]):.0f}%<br>" if isinstance(val[u"rel"][idx], float): htext += f"Speedup: {val[u'rel'][idx]:.2f}" hovertext.append(htext) traces.append( plgo.Scatter( x=x_vals, y=val[u"val"], name=name, legendgroup=name, mode=u"lines+markers", line=dict( color=COLORS[cidx], width=2), marker=dict( symbol=u"circle", size=10 ), text=hovertext, hoverinfo=u"text+name" ) ) traces.append( plgo.Scatter( x=x_vals, y=val[u"ideal"], name=f"{name} perfect", legendgroup=name, showlegend=False, mode=u"lines", line=dict( color=COLORS[cidx], width=2, dash=u"dash"), text=[f"Perfect: {y:.2f}Mpps" for y in val[u"ideal"]], hoverinfo=u"text" ) ) cidx += 1 except (IndexError, ValueError, KeyError) as err: logging.warning(f"No data for {name}\n{repr(err)}") try: # Create plot file_name = f"{plot[u'output-file'].format(test_type=ttype)}.html" logging.info(f" Writing file {file_name}") layout = deepcopy(plot[u"layout"]) if layout.get(u"title", None): layout[u"title"] = ( f"<b>Speedup Multi-core:</b> " f"{layout[u'title'].format(test_type=ttype)}" ) layout[u"yaxis"][u"range"] = [0, int(max(y_max) * 1.1)] layout[u"annotations"].extend(annotations) plpl = plgo.Figure(data=traces, layout=layout) # Export Plot ploff.plot( plpl, show_link=False, auto_open=False, filename=file_name ) except PlotlyError as err: logging.error( f" Finished with error: {repr(err)}".replace(u"\n", u" ") ) def plot_http_server_perf_box(plot, input_data): """Generate the plot(s) with algorithm: plot_http_server_perf_box specified in the specification file. :param plot: Plot to generate. :param input_data: Data to process. :type plot: pandas.Series :type input_data: InputData """ # Transform the data logging.info( f" Creating the data set for the {plot.get(u'type', u'')} " f"{plot.get(u'title', u'')}." ) data = input_data.filter_data(plot) if data is None: logging.error(u"No data.") return # Prepare the data for the plot y_vals = dict() for job in data: for build in job: for test in build: if y_vals.get(test[u"name"], None) is None: y_vals[test[u"name"]] = list() try: y_vals[test[u"name"]].append(test[u"result"]) except (KeyError, TypeError): y_vals[test[u"name"]].append(None) # Add None to the lists with missing data max_len = 0 nr_of_samples = list() for val in y_vals.values(): if len(val) > max_len: max_len = len(val) nr_of_samples.append(len(val)) for val in y_vals.values(): if len(val) < max_len: val.extend([None for _ in range(max_len - len(val))]) # Add plot traces traces = list() df_y = pd.DataFrame(y_vals) df_y.head() for i, col in enumerate(df_y.columns): name = \ f"{i + 1}. " \ f"({nr_of_samples[i]:02d} " \ f"run{u's' if nr_of_samples[i] > 1 else u''}) " \ f"{col.lower().replace(u'-ndrpdr', u'')}" if len(name) > 50: name_lst = name.split(u'-') name = u"" split_name = True for segment in name_lst: if (len(name) + len(segment) + 1) > 50 and split_name: name += u"<br> " split_name = False name += segment + u'-' name = name[:-1] traces.append(plgo.Box(x=[str(i + 1) + u'.'] * len(df_y[col]), y=df_y[col], name=name, **plot[u"traces"])) try: # Create plot plpl = plgo.Figure(data=traces, layout=plot[u"layout"]) # Export Plot logging.info( f" Writing file {plot[u'output-file']}" f"{plot[u'output-file-type']}." ) ploff.plot( plpl, show_link=False, auto_open=False, filename=f"{plot[u'output-file']}{plot[u'output-file-type']}" ) except PlotlyError as err: logging.error( f" Finished with error: {repr(err)}".replace(u"\n", u" ") ) return def plot_nf_heatmap(plot, input_data): """Generate the plot(s) with algorithm: plot_nf_heatmap specified in the specification file. :param plot: Plot to generate. :param input_data: Data to process. :type plot: pandas.Series :type input_data: InputData """ def sort_by_int(value): """Makes possible to sort a list of strings which represent integers. :param value: Integer as a string. :type value: str :returns: Integer representation of input parameter 'value'. :rtype: int """ return int(value) regex_cn = re.compile(r'^(\d*)R(\d*)C$') regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-' r'(\d+mif|\d+vh)-' r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$') vals = dict() # Transform the data logging.info( f" Creating the data set for the {plot.get(u'type', u'')} " f"{plot.get(u'title', u'')}." ) in_data = input_data.filter_tests_by_name( plot, continue_on_error=True, params=[u"throughput", u"result", u"name", u"tags", u"type"] ) if in_data is None or in_data.empty: logging.error(u"No data.") return for ttype in plot.get(u"test-type", (u"ndr", u"pdr")): for core in plot.get(u"core", tuple()): for item in plot.get(u"include", tuple()): reg_ex = re.compile(str(item.format(core=core)).lower()) for job in in_data: for build in job: for test_id, test in build.iteritems(): if not re.match(reg_ex, str(test_id).lower()): continue for tag in test[u"tags"]: groups = re.search(regex_cn, tag) if groups: chain = str(groups.group(1)) node = str(groups.group(2)) break else: continue groups = re.search(regex_test_name, test[u"name"]) if groups and len(groups.groups()) == 3: hover_name = ( f"{str(groups.group(1))}-" f"{str(groups.group(2))}-" f"{str(groups.group(3))}" ) else: hover_name = u"" if vals.get(chain, None) is None: vals[chain] = dict() if vals[chain].get(node, None) is None: vals[chain][node] = dict( name=hover_name, vals=list(), nr=None, mean=None, stdev=None ) try: if ttype == u"mrr": result = test[u"result"][u"receive-rate"] elif ttype == u"pdr": result = \ test[u"throughput"][u"PDR"][u"LOWER"] elif ttype == u"ndr": result = \ test[u"throughput"][u"NDR"][u"LOWER"] else: result = None except TypeError: result = None if result: vals[chain][node][u"vals"].append(result) if not vals: logging.error(u"No data.") return txt_chains = list() txt_nodes = list() for key_c in vals: txt_chains.append(key_c) for key_n in vals[key_c].keys(): txt_nodes.append(key_n) if vals[key_c][key_n][u"vals"]: vals[key_c][key_n][u"nr"] = \ len(vals[key_c][key_n][u"vals"]) vals[key_c][key_n][u"mean"] = \ round(mean(vals[key_c][key_n][u"vals"]) / 1e6, 1) vals[key_c][key_n][u"stdev"] = \ round(stdev(vals[key_c][key_n][u"vals"]) / 1e6, 1) txt_nodes = list(set(txt_nodes)) txt_chains = sorted(txt_chains, key=sort_by_int) txt_nodes = sorted(txt_nodes, key=sort_by_int) chains = [i + 1 for i in range(len(txt_chains))] nodes = [i + 1 for i in range(len(txt_nodes))] data = [list() for _ in range(len(chains))] for chain in chains: for node in nodes: try: val = vals[txt_chains[chain - 1]] \ [txt_nodes[node - 1]][u"mean"] except (KeyError, IndexError): val = None data[chain - 1].append(val) # Color scales: my_green = [[0.0, u"rgb(235, 249, 242)"], [1.0, u"rgb(45, 134, 89)"]] my_blue = [[0.0, u"rgb(236, 242, 248)"], [1.0, u"rgb(57, 115, 172)"]] my_grey = [[0.0, u"rgb(230, 230, 230)"], [1.0, u"rgb(102, 102, 102)"]] hovertext = list() annotations = list() text = (u"Test: {name}<br>" u"Runs: {nr}<br>" u"Thput: {val}<br>" u"StDev: {stdev}") for chain, _ in enumerate(txt_chains): hover_line = list() for node, _ in enumerate(txt_nodes): if data[chain][node] is not None: annotations.append( dict( x=node+1, y=chain+1, xref=u"x", yref=u"y", xanchor=u"center", yanchor=u"middle", text=str(data[chain][node]), font=dict( size=14, ), align=u"center", showarrow=False ) ) hover_line.append(text.format( name=vals[txt_chains[chain]][txt_nodes[node]] [u"name"], nr=vals[txt_chains[chain]][txt_nodes[node]][u"nr"], val=data[chain][node], stdev=vals[txt_chains[chain]][txt_nodes[node]] [u"stdev"] )) hovertext.append(hover_line) traces = [ plgo.Heatmap( x=nodes, y=chains, z=data, colorbar=dict( title=plot.get(u"z-axis", u"{test_type}"). format(test_type=ttype.upper()), titleside=u"right", titlefont=dict( size=16 ), tickfont=dict( size=16, ), tickformat=u".1f", yanchor=u"bottom", y=-0.02, len=0.925, ), showscale=True, colorscale=my_green, text=hovertext, hoverinfo=u"text" ) ] for idx, item in enumerate(txt_nodes): # X-axis, numbers: annotations.append( dict( x=idx+1, y=0.05, xref=u"x", yref=u"y", xanchor=u"center", yanchor=u"top", text=item, font=dict( size=16, ), align=u"center", showarrow=False ) ) for idx, item in enumerate(txt_chains): # Y-axis, numbers: annotations.append( dict( x=0.35, y=idx+1, xref=u"x", yref=u"y", xanchor=u"right", yanchor=u"middle", text=item, font=dict( size=16, ), align=u"center", showarrow=False ) ) # X-axis, title: annotations.append( dict( x=0.55, y=-0.15, xref=u"paper", yref=u"y", xanchor=u"center", yanchor=u"bottom", text=plot.get(u"x-axis", u""), font=dict( size=16, ), align=u"center", showarrow=False ) ) # Y-axis, title: annotations.append( dict( x=-0.1, y=0.5, xref=u"x", yref=u"paper", xanchor=u"center", yanchor=u"middle", text=plot.get(u"y-axis", u""), font=dict( size=16, ), align=u"center", textangle=270, showarrow=False ) ) updatemenus = list([ dict( x=1.0, y=0.0, xanchor=u"right", yanchor=u"bottom", direction=u"up", buttons=list([ dict( args=[ { u"colorscale": [my_green, ], u"reversescale": False } ], label=u"Green", method=u"update" ), dict( args=[ { u"colorscale": [my_blue, ], u"reversescale": False } ], label=u"Blue", method=u"update" ), dict( args=[ { u"colorscale": [my_grey, ], u"reversescale": False } ], label=u"Grey", method=u"update" ) ]) ) ]) try: layout = deepcopy(plot[u"layout"]) except KeyError as err: logging.error( f"Finished with error: No layout defined\n{repr(err)}" ) return layout[u"annotations"] = annotations layout[u'updatemenus'] = updatemenus if layout.get(u"title", None): layout[u"title"] = layout[u'title'].replace(u"test_type", ttype) try: # Create plot plpl = plgo.Figure(data=traces, layout=layout) # Export Plot file_name = ( f"{plot[u'output-file'].format(core=core, test_type=ttype)}" f".html" ) logging.info(f" Writing file {file_name}") ploff.plot( plpl, show_link=False, auto_open=False, filename=file_name ) except PlotlyError as err: logging.error( f" Finished with error: {repr(err)}".replace(u"\n", u" ") )