summaryrefslogtreecommitdiffstats
path: root/src/vppinfra/macros.c
blob: 6649277d897659812d61fe5ee1638c4f447cddd4 (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
/*
  macros.c - a simple macro expander

  Copyright (c) 2010, 2014 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 <vppinfra/macros.h>

static inline int
macro_isalnum (i8 c)
{
  if ((c >= 'A' && c <= 'Z')
      || (c >= 'a' && c <= 'z') || (c >= '0' && c <= '9') || (c == '_'))
    return 1;
  return 0;
}

static i8 *
builtin_eval (macro_main_t * mm, i8 * varname, i32 complain)
{
  uword *p;
  i8 *(*fp) (macro_main_t *, i32);

  p = hash_get_mem (mm->the_builtin_eval_hash, varname);
  if (p == 0)
    return 0;
  fp = (void *) (p[0]);
  return (*fp) (mm, complain);
}

int
clib_macro_unset (macro_main_t * mm, char *name)
{
  hash_pair_t *p;
  u8 *key, *value;

  p = hash_get_pair (mm->the_value_table_hash, name);

  if (p == 0)
    return 1;

  key = (u8 *) (p->key);
  value = (u8 *) (p->value[0]);
  hash_unset_mem (mm->the_value_table_hash, name);

  vec_free (value);
  vec_free (key);
  return 0;
}

int
clib_macro_set_value (macro_main_t * mm, char *name, char *value)
{
  u8 *key_copy, *value_copy;
  int rv;

  rv = clib_macro_unset (mm, name);

  key_copy = format (0, "%s%c", name, 0);
  value_copy = format (0, "%s%c", value, 0);

  hash_set_mem (mm->the_value_table_hash, key_copy, value_copy);
  return rv;
}

i8 *
clib_macro_get_value (macro_main_t * mm, char *name)
{
  uword *p;

  p = hash_get_mem (mm->the_value_table_hash, name);
  if (p)
    return (i8 *) (p[0]);
  else
    return 0;
}

/*
 * eval: takes a string, returns a vector.
 * looks up $foobar in the variable table.
 */
i8 *
clib_macro_eval (macro_main_t * mm, i8 * s, i32 complain)
{
  i8 *rv = 0;
  i8 *varname, *varvalue;
  i8 *ts;

  while (*s)
    {
      switch (*s)
	{
	case '\\':
	  s++;
	  /* fallthrough */

	default:
	  vec_add1 (rv, *s);
	  s++;
	  break;

	case '$':
	  s++;
	  varname = 0;
	  /*
	   * Make vector with variable name in it.
	   */
	  while (*s && (macro_isalnum (*s) || (*s == '_') || (*s == '(')))
	    {

	      /* handle $(foo) */
	      if (*s == '(')
		{
		  s++;		/* skip '(' */
		  while (*s && *s != ')')
		    {
		      vec_add1 (varname, *s);
		      s++;
		    }
		  if (*s)
		    s++;	/* skip ')' */
		  break;
		}
	      vec_add1 (varname, *s);
	      s++;
	    }
	  /* null terminate */
	  vec_add1 (varname, 0);
	  /* Look for a builtin, e.g. $my_hostname */
	  if (!(varvalue = builtin_eval (mm, varname, complain)))
	    {
	      /* Look in value table */
	      if (!varvalue)
		{
		  i8 *tmp = clib_macro_get_value (mm, (char *) varname);
		  if (tmp)
		    varvalue = (i8 *) format (0, "%s%c", tmp, 0);
		}
#ifdef CLIB_UNIX
	      /* Look in environment. */
	      if (!varvalue)
		{
		  char *tmp = getenv ((char *) varname);
		  if (tmp)
		    varvalue = (i8 *) format (0, "%s%c", tmp, 0);
		}
#endif /* CLIB_UNIX */
	    }
	  if (varvalue)
	    {
	      /* recursively evaluate */
	      ts = clib_macro_eval (mm, varvalue, complain);
	      vec_free (varvalue);
	      /* add results to answer */
	      vec_append (rv, ts);
	      /* Remove NULL termination or the results are sad */
	      _vec_len (rv) = vec_len (rv) - 1;
	      vec_free (ts);
	    }
	  else
	    {
	      if (complain)
		clib_warning ("Undefined Variable Reference: %s\n", varname);
	      vec_append (rv, format (0, "UNSET "));
	      _vec_len (rv) = vec_len (rv) - 1;

	    }
	  vec_free (varname);
	}
    }
  vec_add1 (rv, 0);
  return (rv);
}

/*
 * eval: takes a string, returns a vector.
 * looks up $foobar in the variable table.
 */
i8 *
clib_macro_eval_dollar (macro_main_t * mm, i8 * s, i32 complain)
{
  i8 *s2;
  i8 *rv;

  s2 = (i8 *) format (0, "$(%s)%c", s, 0);
  rv = clib_macro_eval (mm, s2, complain);
  vec_free (s2);
  return (rv);
}

void
clib_macro_add_builtin (macro_main_t * mm, char *name, void *eval_fn)
{
  hash_set_mem (mm->the_builtin_eval_hash, name, (uword) eval_fn);
}

#ifdef CLIB_UNIX
static i8 *
eval_hostname (macro_main_t * mm, i32 complain)
{
  char tmp[128];
  if (gethostname (tmp, sizeof (tmp)))
    return ((i8 *) format (0, "gethostname-error%c", 0));
  return ((i8 *) format (0, "%s%c", tmp, 0));
}
#endif

void
clib_macro_init (macro_main_t * mm)
{
  if (mm->the_builtin_eval_hash != 0)
    {
      clib_warning ("mm %p already initialized", mm);
      return;
    }

  mm->the_builtin_eval_hash = hash_create_string (0, sizeof (uword));
  mm->the_value_table_hash = hash_create_string (0, sizeof (uword));

#ifdef CLIB_UNIX
  hash_set_mem (mm->the_builtin_eval_hash, "hostname", (uword) eval_hostname);
#endif
}

void
clib_macro_free (macro_main_t * mm)
{
  hash_pair_t *p;
  u8 **strings_to_free = 0;
  int i;

  hash_free (mm->the_builtin_eval_hash);

  /* *INDENT-OFF* */
  hash_foreach_pair (p, mm->the_value_table_hash,
  ({
    vec_add1 (strings_to_free, (u8 *) (p->key));
    vec_add1 (strings_to_free, (u8 *) (p->value[0]));
  }));
  /* *INDENT-ON* */

  for (i = 0; i < vec_len (strings_to_free); i++)
    vec_free (strings_to_free[i]);
  vec_free (strings_to_free);
  hash_free (mm->the_value_table_hash);
}

/*
 * fd.io coding-style-patch-verification: ON
 *
 * Local Variables:
 * eval: (c-set-style "gnu")
 * End:
 */
pan class="p">("Done.") def plot_performance_box(plot, input_data): """Generate the plot(s) with algorithm: plot_performance_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 plot_title = plot.get("title", "") logging.info(" Creating the data set for the {0} '{1}'.". format(plot.get("type", ""), plot_title)) data = input_data.filter_data(plot) if data is None: logging.error("No data.") return # Prepare the data for the plot y_vals = dict() y_tags = dict() for job in data: for build in job: for test in build: if y_vals.get(test["parent"], None) is None: y_vals[test["parent"]] = list() y_tags[test["parent"]] = test.get("tags", None) try: if test["type"] in ("NDRPDR", ): if "-pdr" in plot_title.lower(): y_vals[test["parent"]].\ append(test["throughput"]["PDR"]["LOWER"]) elif "-ndr" in plot_title.lower(): y_vals[test["parent"]]. \ append(test["throughput"]["NDR"]["LOWER"]) else: continue else: continue except (KeyError, TypeError): y_vals[test["parent"]].append(None) # Sort the tests order = plot.get("sort", None) if order and y_tags: y_sorted = OrderedDict() y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()} for tag in order: logging.info(tag) for suite, tags in y_tags_l.items(): if "not " in tag: tag = tag.split(" ")[-1] if tag.lower() in tags: continue else: if tag.lower() not in tags: continue try: y_sorted[suite] = y_vals.pop(suite) y_tags_l.pop(suite) logging.info(suite) except KeyError as err: logging.error("Not found: {0}".format(err)) finally: break else: y_sorted = y_vals # Add None to the lists with missing data max_len = 0 for val in y_sorted.values(): if len(val) > max_len: max_len = len(val) for key, val in y_sorted.items(): if len(val) < max_len: val.extend([None for _ in range(max_len - len(val))]) # Add plot traces traces = list() df = pd.DataFrame(y_sorted) df.head() y_max = list() for i, col in enumerate(df.columns): name = "{0}. {1}".format(i + 1, col.lower().replace('-ndrpdrdisc', ''). replace('-ndrpdr', '')) logging.info(name) traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]), y=[y / 1000000 if y else None for y in df[col]], name=name, **plot["traces"])) try: val_max = max(df[col]) except ValueError as err: logging.error(err) continue if val_max: y_max.append(int(val_max / 1000000) + 1) try: # Create plot layout = deepcopy(plot["layout"]) if layout.get("title", None): layout["title"] = "<b>Packet Throughput:</b> {0}". \ format(layout["title"]) if y_max: layout["yaxis"]["range"] = [0, max(y_max)] plpl = plgo.Figure(data=traces, layout=layout) # Export Plot logging.info(" Writing file '{0}{1}'.". format(plot["output-file"], plot["output-file-type"])) ploff.plot(plpl, show_link=False, auto_open=False, filename='{0}{1}'.format(plot["output-file"], plot["output-file-type"])) except PlotlyError as err: logging.error(" Finished with error: {}". format(str(err).replace("\n", " "))) return def plot_latency_error_bars(plot, input_data): """Generate the plot(s) with algorithm: plot_latency_error_bars 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("title", "") logging.info(" Creating the data set for the {0} '{1}'.". format(plot.get("type", ""), plot_title)) data = input_data.filter_data(plot) if data is None: logging.error("No data.") return # Prepare the data for the plot y_tmp_vals = dict() y_tags = dict() for job in data: for build in job: for test in build: if y_tmp_vals.get(test["parent"], None) is None: y_tmp_vals[test["parent"]] = [ list(), # direction1, min list(), # direction1, avg list(), # direction1, max list(), # direction2, min list(), # direction2, avg list() # direction2, max ] y_tags[test["parent"]] = test.get("tags", None) try: if test["type"] in ("NDRPDR", ): if "-pdr" in plot_title.lower(): ttype = "PDR" elif "-ndr" in plot_title.lower(): ttype = "NDR" else: continue y_tmp_vals[test["parent"]][0].append( test["latency"][ttype]["direction1"]["min"]) y_tmp_vals[test["parent"]][1].append( test["latency"][ttype]["direction1"]["avg"]) y_tmp_vals[test["parent"]][2].append( test["latency"][ttype]["direction1"]["max"]) y_tmp_vals[test["parent"]][3].append( test["latency"][ttype]["direction2"]["min"]) y_tmp_vals[test["parent"]][4].append( test["latency"][ttype]["direction2"]["avg"]) y_tmp_vals[test["parent"]][5].append( test["latency"][ttype]["direction2"]["max"]) else: continue except (KeyError, TypeError): pass # Sort the tests order = plot.get("sort", None) if order and y_tags: y_sorted = OrderedDict() y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()} for tag in order: for suite, tags in y_tags_l.items(): if tag.lower() in tags: try: y_sorted[suite] = y_tmp_vals.pop(suite) y_tags_l.pop(suite) except KeyError as err: logging.error("Not found: {0}".format(err)) finally: break else: y_sorted = y_tmp_vals x_vals = list() y_vals = list() y_mins = list() y_maxs = list() for key, val in y_sorted.items(): key = "-".join(key.split("-")[1:-1]) x_vals.append(key) # dir 1 y_vals.append(mean(val[1]) if val[1] else None) y_mins.append(mean(val[0]) if val[0] else None) y_maxs.append(mean(val[2]) if val[2] else None) x_vals.append(key) # dir 2 y_vals.append(mean(val[4]) if val[4] else None) y_mins.append(mean(val[3]) if val[3] else None) y_maxs.append(mean(val[5]) if val[5] else None) traces = list() annotations = list() for idx in range(len(x_vals)): if not bool(int(idx % 2)): direction = "West - East" else: direction = "East - West" hovertext = ("Test: {test}<br>" "Direction: {dir}<br>".format(test=x_vals[idx], dir=direction)) if isinstance(y_maxs[idx], float): hovertext += "Max: {max:.2f}uSec<br>".format(max=y_maxs[idx]) if isinstance(y_vals[idx], float): hovertext += "Avg: {avg:.2f}uSec<br>".format(avg=y_vals[idx]) if isinstance(y_mins[idx], float): hovertext += "Min: {min:.2f}uSec".format(min=y_mins[idx]) if isinstance(y_maxs[idx], float) and isinstance(y_vals[idx], float): array = [y_maxs[idx] - y_vals[idx], ] else: array = [None, ] if isinstance(y_mins[idx], float) and isinstance(y_vals[idx], float): arrayminus = [y_vals[idx] - y_mins[idx], ] else: arrayminus = [None, ] traces.append(plgo.Scatter( x=[idx, ], y=[y_vals[idx], ], name=x_vals[idx], legendgroup=x_vals[idx], showlegend=bool(int(idx % 2)), mode="markers", error_y=dict( type='data', symmetric=False, array=array, arrayminus=arrayminus, color=COLORS[int(idx / 2)] ), marker=dict( size=10, color=COLORS[int(idx / 2)], ), text=hovertext, hoverinfo="text", )) annotations.append(dict( x=idx, y=0, xref="x", yref="y", xanchor="center", yanchor="top", text="E-W" if bool(int(idx % 2)) else "W-E", font=dict( size=16, ), align="center", showarrow=False )) try: # Create plot logging.info(" Writing file '{0}{1}'.". format(plot["output-file"], plot["output-file-type"])) layout = deepcopy(plot["layout"]) if layout.get("title", None): layout["title"] = "<b>Packet Latency:</b> {0}".\ format(layout["title"]) layout["annotations"] = annotations plpl = plgo.Figure(data=traces, layout=layout) # Export Plot ploff.plot(plpl, show_link=False, auto_open=False, filename='{0}{1}'.format(plot["output-file"], plot["output-file-type"])) except PlotlyError as err: logging.error(" Finished with error: {}". format(str(err).replace("\n", " "))) return def plot_throughput_speedup_analysis(plot, input_data): """Generate the plot(s) with algorithm: plot_throughput_speedup_analysis 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("title", "") logging.info(" Creating the data set for the {0} '{1}'.". format(plot.get("type", ""), plot_title)) data = input_data.filter_data(plot) if data is None: logging.error("No data.") return y_vals = dict() y_tags = dict() for job in data: for build in job: for test in build: if y_vals.get(test["parent"], None) is None: y_vals[test["parent"]] = {"1": list(), "2": list(), "4": list()} y_tags[test["parent"]] = test.get("tags", None) try: if test["type"] in ("NDRPDR",): if "-pdr" in plot_title.lower(): ttype = "PDR" elif "-ndr" in plot_title.lower(): ttype = "NDR" else: continue if "1C" in test["tags"]: y_vals[test["parent"]]["1"]. \ append(test["throughput"][ttype]["LOWER"]) elif "2C" in test["tags"]: y_vals[test["parent"]]["2"]. \ append(test["throughput"][ttype]["LOWER"]) elif "4C" in test["tags"]: y_vals[test["parent"]]["4"]. \ append(test["throughput"][ttype]["LOWER"]) except (KeyError, TypeError): pass if not y_vals: logging.warning("No data for the plot '{}'". format(plot.get("title", ""))) 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: y_vals[test_name][key] = sum(test_val) / len(test_val) if key == "1": y_1c_max[test_name] = max(test_val) / 1000000.0 vals = dict() y_max = list() nic_limit = 0 lnk_limit = 0 pci_limit = plot["limits"]["pci"]["pci-g3-x8"] for test_name, test_vals in y_vals.items(): if test_vals["1"]: name = "-".join(test_name.split('-')[1:-1]) vals[name] = dict() y_val_1 = test_vals["1"] / 1000000.0 y_val_2 = test_vals["2"] / 1000000.0 if test_vals["2"] else None y_val_4 = test_vals["4"] / 1000000.0 if test_vals["4"] else None vals[name]["val"] = [y_val_1, y_val_2, y_val_4] vals[name]["rel"] = [1.0, None, None] vals[name]["ideal"] = [y_1c_max[test_name], y_1c_max[test_name] * 2, y_1c_max[test_name] * 4] vals[name]["diff"] = \ [(y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None] try: val_max = max(max(vals[name]["val"], vals[name]["ideal"])) except ValueError as err: logging.error(err) continue if val_max: y_max.append(int((val_max / 10) + 1) * 10) if y_val_2: vals[name]["rel"][1] = round(y_val_2 / y_val_1, 2) vals[name]["diff"][1] = \ (y_val_2 - vals[name]["ideal"][1]) * 100 / y_val_2 if y_val_4: vals[name]["rel"][2] = round(y_val_4 / y_val_1, 2) vals[name]["diff"][2] = \ (y_val_4 - vals[name]["ideal"][2]) * 100 / y_val_4 # Limits: if "x520" in test_name: limit = plot["limits"]["nic"]["x520"] elif "x710" in test_name: limit = plot["limits"]["nic"]["x710"] elif "xxv710" in test_name: limit = plot["limits"]["nic"]["xxv710"] elif "xl710" in test_name: limit = plot["limits"]["nic"]["xl710"] else: limit = 0 if limit > nic_limit: nic_limit = limit mul = 2 if "ge2p" in test_name else 1 if "10ge" in test_name: limit = plot["limits"]["link"]["10ge"] * mul elif "25ge" in test_name: limit = plot["limits"]["link"]["25ge"] * mul elif "40ge" in test_name: limit = plot["limits"]["link"]["40ge"] * mul elif "100ge" in test_name: limit = plot["limits"]["link"]["100ge"] * mul else: limit = 0 if limit > lnk_limit: lnk_limit = limit # Sort the tests order = plot.get("sort", None) if order and y_tags: y_sorted = OrderedDict() y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()} for tag in order: for test, tags in y_tags_l.items(): if tag.lower() in tags: name = "-".join(test.split('-')[1:-1]) try: y_sorted[name] = vals.pop(name) y_tags_l.pop(test) except KeyError as err: logging.error("Not found: {0}".format(err)) finally: break else: y_sorted = vals traces = list() annotations = list() x_vals = [1, 2, 4] # Limits: try: threshold = 1.1 * max(y_max) # 10% except ValueError as err: logging.error(err) return nic_limit /= 1000000.0 if nic_limit < threshold: traces.append(plgo.Scatter( x=x_vals, y=[nic_limit, ] * len(x_vals), name="NIC: {0:.2f}Mpps".format(nic_limit), showlegend=False, mode="lines", line=dict( dash="dot", color=COLORS[-1], width=1), hoverinfo="none" )) annotations.append(dict( x=1, y=nic_limit, xref="x", yref="y", xanchor="left", yanchor="bottom", text="NIC: {0:.2f}Mpps".format(nic_limit), font=dict( size=14, color=COLORS[-1], ), align="left", showarrow=False )) y_max.append(int((nic_limit / 10) + 1) * 10) lnk_limit /= 1000000.0 if lnk_limit < threshold: traces.append(plgo.Scatter( x=x_vals, y=[lnk_limit, ] * len(x_vals), name="Link: {0:.2f}Mpps".format(lnk_limit), showlegend=False, mode="lines", line=dict( dash="dot", color=COLORS[-2], width=1), hoverinfo="none" )) annotations.append(dict( x=1, y=lnk_limit, xref="x", yref="y", xanchor="left", yanchor="bottom", text="Link: {0:.2f}Mpps".format(lnk_limit), font=dict( size=14, color=COLORS[-2], ), align="left", showarrow=False )) y_max.append(int((lnk_limit / 10) + 1) * 10) pci_limit /= 1000000.0 if pci_limit < threshold: traces.append(plgo.Scatter( x=x_vals, y=[pci_limit, ] * len(x_vals), name="PCIe: {0:.2f}Mpps".format(pci_limit), showlegend=False, mode="lines", line=dict( dash="dot", color=COLORS[-3], width=1), hoverinfo="none" )) annotations.append(dict( x=1, y=pci_limit, xref="x", yref="y", xanchor="left", yanchor="bottom", text="PCIe: {0:.2f}Mpps".format(pci_limit), font=dict( size=14, color=COLORS[-3], ), align="left", showarrow=False )) y_max.append(int((pci_limit / 10) + 1) * 10) # Perfect and measured: cidx = 0 for name, val in y_sorted.iteritems(): hovertext = list() for idx in range(len(val["val"])): htext = "" if isinstance(val["val"][idx], float): htext += "value: {0:.2f}Mpps<br>".format(val["val"][idx]) if isinstance(val["diff"][idx], float): htext += "diff: {0:.0f}%<br>".format(round(val["diff"][idx])) if isinstance(val["rel"][idx], float): htext += "speedup: {0:.2f}".format(val["rel"][idx]) hovertext.append(htext) traces.append(plgo.Scatter(x=x_vals, y=val["val"], name=name, legendgroup=name, mode="lines+markers", line=dict( color=COLORS[cidx], width=2), marker=dict( symbol="circle", size=10 ), text=hovertext, hoverinfo="text+name" )) traces.append(plgo.Scatter(x=x_vals, y=val["ideal"], name="{0} perfect".format(name), legendgroup=name, showlegend=False, mode="lines", line=dict( color=COLORS[cidx], width=2, dash="dash"), text=["perfect: {0:.2f}Mpps".format(y) for y in val["ideal"]], hoverinfo="text" )) cidx += 1 try: # Create plot logging.info(" Writing file '{0}{1}'.". format(plot["output-file"], plot["output-file-type"])) layout = deepcopy(plot["layout"]) if layout.get("title", None): layout["title"] = "<b>Speedup Multi-core:</b> {0}". \ format(layout["title"]) layout["annotations"].extend(annotations) plpl = plgo.Figure(data=traces, layout=layout) # Export Plot ploff.plot(plpl, show_link=False, auto_open=False, filename='{0}{1}'.format(plot["output-file"], plot["output-file-type"])) except PlotlyError as err: logging.error(" Finished with error: {}". format(str(err).replace("\n", " "))) return def plot_http_server_performance_box(plot, input_data): """Generate the plot(s) with algorithm: plot_http_server_performance_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(" Creating the data set for the {0} '{1}'.". format(plot.get("type", ""), plot.get("title", ""))) data = input_data.filter_data(plot) if data is None: logging.error("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["name"], None) is None: y_vals[test["name"]] = list() try: y_vals[test["name"]].append(test["result"]) except (KeyError, TypeError): y_vals[test["name"]].append(None) # Add None to the lists with missing data max_len = 0 for val in y_vals.values(): if len(val) > max_len: max_len = len(val) for key, val in y_vals.items(): if len(val) < max_len: val.extend([None for _ in range(max_len - len(val))]) # Add plot traces traces = list() df = pd.DataFrame(y_vals) df.head() for i, col in enumerate(df.columns): name = "{0}. {1}".format(i + 1, col.lower().replace('-cps', ''). replace('-rps', '')) traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]), y=df[col], name=name, **plot["traces"])) try: # Create plot plpl = plgo.Figure(data=traces, layout=plot["layout"]) # Export Plot logging.info(" Writing file '{0}{1}'.". format(plot["output-file"], plot["output-file-type"])) ploff.plot(plpl, show_link=False, auto_open=False, filename='{0}{1}'.format(plot["output-file"], plot["output-file-type"])) except PlotlyError as err: logging.error(" Finished with error: {}". format(str(err).replace("\n", " "))) return