1 # Copyright (c) 2017 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
6 # http://www.apache.org/licenses/LICENSE-2.0
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
14 """Algorithms to generate tables.
23 from string import replace
24 from math import isnan
25 from xml.etree import ElementTree as ET
27 from errors import PresentationError
28 from utils import mean, stdev, relative_change, remove_outliers, find_outliers
31 def generate_tables(spec, data):
32 """Generate all tables specified in the specification file.
34 :param spec: Specification read from the specification file.
35 :param data: Data to process.
36 :type spec: Specification
40 logging.info("Generating the tables ...")
41 for table in spec.tables:
43 eval(table["algorithm"])(table, data)
45 logging.error("The algorithm '{0}' is not defined.".
46 format(table["algorithm"]))
50 def table_details(table, input_data):
51 """Generate the table(s) with algorithm: table_detailed_test_results
52 specified in the specification file.
54 :param table: Table to generate.
55 :param input_data: Data to process.
56 :type table: pandas.Series
57 :type input_data: InputData
60 logging.info(" Generating the table {0} ...".
61 format(table.get("title", "")))
64 data = input_data.filter_data(table)
66 # Prepare the header of the tables
68 for column in table["columns"]:
69 header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
71 # Generate the data for the table according to the model in the table
73 job = table["data"].keys()[0]
74 build = str(table["data"][job][0])
76 suites = input_data.suites(job, build)
78 logging.error(" No data available. The table will not be generated.")
81 for suite_longname, suite in suites.iteritems():
83 suite_name = suite["name"]
85 for test in data[job][build].keys():
86 if data[job][build][test]["parent"] in suite_name:
88 for column in table["columns"]:
90 col_data = str(data[job][build][test][column["data"].
91 split(" ")[1]]).replace('"', '""')
92 if column["data"].split(" ")[1] in ("vat-history",
94 col_data = replace(col_data, " |br| ", "",
96 col_data = " |prein| {0} |preout| ".\
98 row_lst.append('"{0}"'.format(col_data))
100 row_lst.append("No data")
101 table_lst.append(row_lst)
103 # Write the data to file
105 file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
106 table["output-file-ext"])
107 logging.info(" Writing file: '{}'".format(file_name))
108 with open(file_name, "w") as file_handler:
109 file_handler.write(",".join(header) + "\n")
110 for item in table_lst:
111 file_handler.write(",".join(item) + "\n")
113 logging.info(" Done.")
116 def table_merged_details(table, input_data):
117 """Generate the table(s) with algorithm: table_merged_details
118 specified in the specification file.
120 :param table: Table to generate.
121 :param input_data: Data to process.
122 :type table: pandas.Series
123 :type input_data: InputData
126 logging.info(" Generating the table {0} ...".
127 format(table.get("title", "")))
130 data = input_data.filter_data(table)
131 data = input_data.merge_data(data)
132 data.sort_index(inplace=True)
134 suites = input_data.filter_data(table, data_set="suites")
135 suites = input_data.merge_data(suites)
137 # Prepare the header of the tables
139 for column in table["columns"]:
140 header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
142 for _, suite in suites.iteritems():
144 suite_name = suite["name"]
146 for test in data.keys():
147 if data[test]["parent"] in suite_name:
149 for column in table["columns"]:
151 col_data = str(data[test][column["data"].
152 split(" ")[1]]).replace('"', '""')
153 if column["data"].split(" ")[1] in ("vat-history",
155 col_data = replace(col_data, " |br| ", "",
157 col_data = " |prein| {0} |preout| ".\
158 format(col_data[:-5])
159 row_lst.append('"{0}"'.format(col_data))
161 row_lst.append("No data")
162 table_lst.append(row_lst)
164 # Write the data to file
166 file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
167 table["output-file-ext"])
168 logging.info(" Writing file: '{}'".format(file_name))
169 with open(file_name, "w") as file_handler:
170 file_handler.write(",".join(header) + "\n")
171 for item in table_lst:
172 file_handler.write(",".join(item) + "\n")
174 logging.info(" Done.")
177 def table_performance_improvements(table, input_data):
178 """Generate the table(s) with algorithm: table_performance_improvements
179 specified in the specification file.
181 :param table: Table to generate.
182 :param input_data: Data to process.
183 :type table: pandas.Series
184 :type input_data: InputData
187 def _write_line_to_file(file_handler, data):
188 """Write a line to the .csv file.
190 :param file_handler: File handler for the csv file. It must be open for
192 :param data: Item to be written to the file.
193 :type file_handler: BinaryIO
199 if isinstance(item["data"], str):
200 # Remove -?drdisc from the end
201 if item["data"].endswith("drdisc"):
202 item["data"] = item["data"][:-8]
203 line_lst.append(item["data"])
204 elif isinstance(item["data"], float):
205 line_lst.append("{:.1f}".format(item["data"]))
206 elif item["data"] is None:
208 file_handler.write(",".join(line_lst) + "\n")
210 logging.info(" Generating the table {0} ...".
211 format(table.get("title", "")))
214 file_name = table.get("template", None)
217 tmpl = _read_csv_template(file_name)
218 except PresentationError:
219 logging.error(" The template '{0}' does not exist. Skipping the "
220 "table.".format(file_name))
223 logging.error("The template is not defined. Skipping the table.")
227 data = input_data.filter_data(table)
229 # Prepare the header of the tables
231 for column in table["columns"]:
232 header.append(column["title"])
234 # Generate the data for the table according to the model in the table
237 for tmpl_item in tmpl:
239 for column in table["columns"]:
240 cmd = column["data"].split(" ")[0]
241 args = column["data"].split(" ")[1:]
242 if cmd == "template":
244 val = float(tmpl_item[int(args[0])])
246 val = tmpl_item[int(args[0])]
247 tbl_item.append({"data": val})
253 for build in data[job]:
255 data_lst.append(float(build[tmpl_item[0]]
256 ["throughput"]["value"]))
257 except (KeyError, TypeError):
261 tbl_item.append({"data": (eval(operation)(data_lst)) /
264 tbl_item.append({"data": None})
265 elif cmd == "operation":
268 nr1 = float(tbl_item[int(args[1])]["data"])
269 nr2 = float(tbl_item[int(args[2])]["data"])
271 tbl_item.append({"data": eval(operation)(nr1, nr2)})
273 tbl_item.append({"data": None})
274 except (IndexError, ValueError, TypeError):
275 logging.error("No data for {0}".format(tbl_item[0]["data"]))
276 tbl_item.append({"data": None})
279 logging.error("Not supported command {0}. Skipping the table.".
282 tbl_lst.append(tbl_item)
284 # Sort the table according to the relative change
285 tbl_lst.sort(key=lambda rel: rel[-1]["data"], reverse=True)
287 # Create the tables and write them to the files
289 "{0}_ndr_top{1}".format(table["output-file"], table["output-file-ext"]),
290 "{0}_pdr_top{1}".format(table["output-file"], table["output-file-ext"]),
291 "{0}_ndr_low{1}".format(table["output-file"], table["output-file-ext"]),
292 "{0}_pdr_low{1}".format(table["output-file"], table["output-file-ext"])
295 for file_name in file_names:
296 logging.info(" Writing the file '{0}'".format(file_name))
297 with open(file_name, "w") as file_handler:
298 file_handler.write(",".join(header) + "\n")
300 if isinstance(item[-1]["data"], float):
301 rel_change = round(item[-1]["data"], 1)
303 rel_change = item[-1]["data"]
304 if "ndr_top" in file_name \
305 and "ndr" in item[0]["data"] \
306 and rel_change >= 10.0:
307 _write_line_to_file(file_handler, item)
308 elif "pdr_top" in file_name \
309 and "pdr" in item[0]["data"] \
310 and rel_change >= 10.0:
311 _write_line_to_file(file_handler, item)
312 elif "ndr_low" in file_name \
313 and "ndr" in item[0]["data"] \
314 and rel_change < 10.0:
315 _write_line_to_file(file_handler, item)
316 elif "pdr_low" in file_name \
317 and "pdr" in item[0]["data"] \
318 and rel_change < 10.0:
319 _write_line_to_file(file_handler, item)
321 logging.info(" Done.")
324 def _read_csv_template(file_name):
325 """Read the template from a .csv file.
327 :param file_name: Name / full path / relative path of the file to read.
329 :returns: Data from the template as list (lines) of lists (items on line).
331 :raises: PresentationError if it is not possible to read the file.
335 with open(file_name, 'r') as csv_file:
337 for line in csv_file:
338 tmpl_data.append(line[:-1].split(","))
340 except IOError as err:
341 raise PresentationError(str(err), level="ERROR")
344 def table_performance_comparison(table, input_data):
345 """Generate the table(s) with algorithm: table_performance_comparison
346 specified in the specification file.
348 :param table: Table to generate.
349 :param input_data: Data to process.
350 :type table: pandas.Series
351 :type input_data: InputData
354 logging.info(" Generating the table {0} ...".
355 format(table.get("title", "")))
358 data = input_data.filter_data(table)
360 # Prepare the header of the tables
362 header = ["Test case",
363 "{0} Throughput [Mpps]".format(table["reference"]["title"]),
364 "{0} stdev [Mpps]".format(table["reference"]["title"]),
365 "{0} Throughput [Mpps]".format(table["compare"]["title"]),
366 "{0} stdev [Mpps]".format(table["compare"]["title"]),
368 header_str = ",".join(header) + "\n"
369 except (AttributeError, KeyError) as err:
370 logging.error("The model is invalid, missing parameter: {0}".
374 # Prepare data to the table:
376 for job, builds in table["reference"]["data"].items():
378 for tst_name, tst_data in data[job][str(build)].iteritems():
379 if tbl_dict.get(tst_name, None) is None:
380 name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
381 "-".join(tst_data["name"].
383 tbl_dict[tst_name] = {"name": name,
387 tbl_dict[tst_name]["ref-data"].\
388 append(tst_data["throughput"]["value"])
390 pass # No data in output.xml for this test
392 for job, builds in table["compare"]["data"].items():
394 for tst_name, tst_data in data[job][str(build)].iteritems():
396 tbl_dict[tst_name]["cmp-data"].\
397 append(tst_data["throughput"]["value"])
401 tbl_dict.pop(tst_name, None)
404 for tst_name in tbl_dict.keys():
405 item = [tbl_dict[tst_name]["name"], ]
406 if tbl_dict[tst_name]["ref-data"]:
407 data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
408 table["outlier-const"])
409 item.append(round(mean(data_t) / 1000000, 2))
410 item.append(round(stdev(data_t) / 1000000, 2))
412 item.extend([None, None])
413 if tbl_dict[tst_name]["cmp-data"]:
414 data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
415 table["outlier-const"])
416 item.append(round(mean(data_t) / 1000000, 2))
417 item.append(round(stdev(data_t) / 1000000, 2))
419 item.extend([None, None])
420 if item[1] is not None and item[3] is not None:
421 item.append(int(relative_change(float(item[1]), float(item[3]))))
425 # Sort the table according to the relative change
426 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
430 tbl_names = ["{0}-ndr-1t1c-full{1}".format(table["output-file"],
431 table["output-file-ext"]),
432 "{0}-ndr-2t2c-full{1}".format(table["output-file"],
433 table["output-file-ext"]),
434 "{0}-ndr-4t4c-full{1}".format(table["output-file"],
435 table["output-file-ext"]),
436 "{0}-pdr-1t1c-full{1}".format(table["output-file"],
437 table["output-file-ext"]),
438 "{0}-pdr-2t2c-full{1}".format(table["output-file"],
439 table["output-file-ext"]),
440 "{0}-pdr-4t4c-full{1}".format(table["output-file"],
441 table["output-file-ext"])
443 for file_name in tbl_names:
444 logging.info(" Writing file: '{0}'".format(file_name))
445 with open(file_name, "w") as file_handler:
446 file_handler.write(header_str)
448 if (file_name.split("-")[-3] in test[0] and # NDR vs PDR
449 file_name.split("-")[-2] in test[0]): # cores
450 test[0] = "-".join(test[0].split("-")[:-1])
451 file_handler.write(",".join([str(item) for item in test]) +
455 tbl_names_txt = ["{0}-ndr-1t1c-full.txt".format(table["output-file"]),
456 "{0}-ndr-2t2c-full.txt".format(table["output-file"]),
457 "{0}-ndr-4t4c-full.txt".format(table["output-file"]),
458 "{0}-pdr-1t1c-full.txt".format(table["output-file"]),
459 "{0}-pdr-2t2c-full.txt".format(table["output-file"]),
460 "{0}-pdr-4t4c-full.txt".format(table["output-file"])
463 for i, txt_name in enumerate(tbl_names_txt):
465 logging.info(" Writing file: '{0}'".format(txt_name))
466 with open(tbl_names[i], 'rb') as csv_file:
467 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
468 for row in csv_content:
469 if txt_table is None:
470 txt_table = prettytable.PrettyTable(row)
472 txt_table.add_row(row)
473 txt_table.align["Test case"] = "l"
474 with open(txt_name, "w") as txt_file:
475 txt_file.write(str(txt_table))
477 # Selected tests in csv:
478 input_file = "{0}-ndr-1t1c-full{1}".format(table["output-file"],
479 table["output-file-ext"])
480 with open(input_file, "r") as in_file:
485 output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"],
486 table["output-file-ext"])
487 logging.info(" Writing file: '{0}'".format(output_file))
488 with open(output_file, "w") as out_file:
489 out_file.write(header_str)
490 for i, line in enumerate(lines[1:]):
491 if i == table["nr-of-tests-shown"]:
495 output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"],
496 table["output-file-ext"])
497 logging.info(" Writing file: '{0}'".format(output_file))
498 with open(output_file, "w") as out_file:
499 out_file.write(header_str)
500 for i, line in enumerate(lines[-1:0:-1]):
501 if i == table["nr-of-tests-shown"]:
505 input_file = "{0}-pdr-1t1c-full{1}".format(table["output-file"],
506 table["output-file-ext"])
507 with open(input_file, "r") as in_file:
512 output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"],
513 table["output-file-ext"])
514 logging.info(" Writing file: '{0}'".format(output_file))
515 with open(output_file, "w") as out_file:
516 out_file.write(header_str)
517 for i, line in enumerate(lines[1:]):
518 if i == table["nr-of-tests-shown"]:
522 output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"],
523 table["output-file-ext"])
524 logging.info(" Writing file: '{0}'".format(output_file))
525 with open(output_file, "w") as out_file:
526 out_file.write(header_str)
527 for i, line in enumerate(lines[-1:0:-1]):
528 if i == table["nr-of-tests-shown"]:
533 def table_performance_comparison_mrr(table, input_data):
534 """Generate the table(s) with algorithm: table_performance_comparison_mrr
535 specified in the specification file.
537 :param table: Table to generate.
538 :param input_data: Data to process.
539 :type table: pandas.Series
540 :type input_data: InputData
543 logging.info(" Generating the table {0} ...".
544 format(table.get("title", "")))
547 data = input_data.filter_data(table)
549 # Prepare the header of the tables
551 header = ["Test case",
552 "{0} Throughput [Mpps]".format(table["reference"]["title"]),
553 "{0} stdev [Mpps]".format(table["reference"]["title"]),
554 "{0} Throughput [Mpps]".format(table["compare"]["title"]),
555 "{0} stdev [Mpps]".format(table["compare"]["title"]),
557 header_str = ",".join(header) + "\n"
558 except (AttributeError, KeyError) as err:
559 logging.error("The model is invalid, missing parameter: {0}".
563 # Prepare data to the table:
565 for job, builds in table["reference"]["data"].items():
567 for tst_name, tst_data in data[job][str(build)].iteritems():
568 if tbl_dict.get(tst_name, None) is None:
569 name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
570 "-".join(tst_data["name"].
572 tbl_dict[tst_name] = {"name": name,
576 tbl_dict[tst_name]["ref-data"].\
577 append(tst_data["result"]["throughput"])
579 pass # No data in output.xml for this test
581 for job, builds in table["compare"]["data"].items():
583 for tst_name, tst_data in data[job][str(build)].iteritems():
585 tbl_dict[tst_name]["cmp-data"].\
586 append(tst_data["result"]["throughput"])
590 tbl_dict.pop(tst_name, None)
593 for tst_name in tbl_dict.keys():
594 item = [tbl_dict[tst_name]["name"], ]
595 if tbl_dict[tst_name]["ref-data"]:
596 data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
597 table["outlier-const"])
598 item.append(round(mean(data_t) / 1000000, 2))
599 item.append(round(stdev(data_t) / 1000000, 2))
601 item.extend([None, None])
602 if tbl_dict[tst_name]["cmp-data"]:
603 data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
604 table["outlier-const"])
605 item.append(round(mean(data_t) / 1000000, 2))
606 item.append(round(stdev(data_t) / 1000000, 2))
608 item.extend([None, None])
609 if item[1] is not None and item[3] is not None:
610 item.append(int(relative_change(float(item[1]), float(item[3]))))
614 # Sort the table according to the relative change
615 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
619 tbl_names = ["{0}-1t1c-full{1}".format(table["output-file"],
620 table["output-file-ext"]),
621 "{0}-2t2c-full{1}".format(table["output-file"],
622 table["output-file-ext"]),
623 "{0}-4t4c-full{1}".format(table["output-file"],
624 table["output-file-ext"])
626 for file_name in tbl_names:
627 logging.info(" Writing file: '{0}'".format(file_name))
628 with open(file_name, "w") as file_handler:
629 file_handler.write(header_str)
631 if file_name.split("-")[-2] in test[0]: # cores
632 test[0] = "-".join(test[0].split("-")[:-1])
633 file_handler.write(",".join([str(item) for item in test]) +
637 tbl_names_txt = ["{0}-1t1c-full.txt".format(table["output-file"]),
638 "{0}-2t2c-full.txt".format(table["output-file"]),
639 "{0}-4t4c-full.txt".format(table["output-file"])
642 for i, txt_name in enumerate(tbl_names_txt):
644 logging.info(" Writing file: '{0}'".format(txt_name))
645 with open(tbl_names[i], 'rb') as csv_file:
646 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
647 for row in csv_content:
648 if txt_table is None:
649 txt_table = prettytable.PrettyTable(row)
651 txt_table.add_row(row)
652 txt_table.align["Test case"] = "l"
653 with open(txt_name, "w") as txt_file:
654 txt_file.write(str(txt_table))
657 def table_performance_trending_dashboard(table, input_data):
658 """Generate the table(s) with algorithm: table_performance_comparison
659 specified in the specification file.
661 :param table: Table to generate.
662 :param input_data: Data to process.
663 :type table: pandas.Series
664 :type input_data: InputData
667 logging.info(" Generating the table {0} ...".
668 format(table.get("title", "")))
671 data = input_data.filter_data(table)
673 # Prepare the header of the tables
674 header = ["Test case",
675 "Thput trend [Mpps]",
679 header_str = ",".join(header) + "\n"
681 # Prepare data to the table:
683 for job, builds in table["data"].items():
685 for tst_name, tst_data in data[job][str(build)].iteritems():
686 if tbl_dict.get(tst_name, None) is None:
687 name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
688 "-".join(tst_data["name"].
690 tbl_dict[tst_name] = {"name": name,
693 tbl_dict[tst_name]["data"]. \
694 append(tst_data["result"]["throughput"])
695 except (TypeError, KeyError):
696 pass # No data in output.xml for this test
699 for tst_name in tbl_dict.keys():
700 if len(tbl_dict[tst_name]["data"]) > 2:
701 sample_lst = tbl_dict[tst_name]["data"]
702 pd_data = pd.Series(sample_lst)
703 win_size = pd_data.size \
704 if pd_data.size < table["window"] else table["window"]
706 name = tbl_dict[tst_name]["name"]
709 trend_lst = list(pd_data.rolling(window=win_size, min_periods=2).
712 t_data, _ = find_outliers(pd_data)
713 t_data_lst = list(t_data)
714 stdev_lst = list(t_data.rolling(window=win_size, min_periods=2).
717 rel_change_lst = [None, ]
718 classification_lst = [None, ]
719 for idx in range(1, len(trend_lst)):
720 # Relative changes list:
721 if not isnan(sample_lst[idx]) \
722 and not isnan(trend_lst[idx])\
723 and trend_lst[idx] != 0:
724 rel_change_lst.append(
725 int(relative_change(float(trend_lst[idx]),
726 float(sample_lst[idx]))))
728 rel_change_lst.append(None)
729 # Classification list:
730 if isnan(t_data_lst[idx]) or isnan(stdev_lst[idx]):
731 classification_lst.append("outlier")
732 elif sample_lst[idx] < (trend_lst[idx] - 3*stdev_lst[idx]):
733 classification_lst.append("regression")
734 elif sample_lst[idx] > (trend_lst[idx] + 3*stdev_lst[idx]):
735 classification_lst.append("progression")
737 classification_lst.append("normal")
739 last_idx = len(sample_lst) - 1
740 first_idx = last_idx - int(table["evaluated-window"])
744 if "regression" in classification_lst[first_idx:]:
745 classification = "regression"
746 elif "outlier" in classification_lst[first_idx:]:
747 classification = "outlier"
748 elif "progression" in classification_lst[first_idx:]:
749 classification = "progression"
751 classification = "normal"
753 idx = len(classification_lst) - 1
755 if classification_lst[idx] == classification:
759 trend = round(float(trend_lst[-2]) / 1000000, 2) \
760 if not isnan(trend_lst[-2]) else ''
761 sample = round(float(sample_lst[idx]) / 1000000, 2) \
762 if not isnan(sample_lst[idx]) else ''
763 rel_change = rel_change_lst[idx] \
764 if rel_change_lst[idx] is not None else ''
765 tbl_lst.append([name,
771 # Sort the table according to the classification
773 for classification in ("regression", "outlier", "progression", "normal"):
774 tbl_tmp = [item for item in tbl_lst if item[4] == classification]
775 tbl_tmp.sort(key=lambda rel: rel[0])
776 tbl_sorted.extend(tbl_tmp)
778 file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
780 logging.info(" Writing file: '{0}'".format(file_name))
781 with open(file_name, "w") as file_handler:
782 file_handler.write(header_str)
783 for test in tbl_sorted:
784 file_handler.write(",".join([str(item) for item in test]) + '\n')
786 txt_file_name = "{0}.txt".format(table["output-file"])
788 logging.info(" Writing file: '{0}'".format(txt_file_name))
789 with open(file_name, 'rb') as csv_file:
790 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
791 for row in csv_content:
792 if txt_table is None:
793 txt_table = prettytable.PrettyTable(row)
795 txt_table.add_row(row)
796 txt_table.align["Test case"] = "l"
797 with open(txt_file_name, "w") as txt_file:
798 txt_file.write(str(txt_table))
801 def table_performance_trending_dashboard_html(table, input_data):
802 """Generate the table(s) with algorithm:
803 table_performance_trending_dashboard_html specified in the specification
806 :param table: Table to generate.
807 :param input_data: Data to process.
808 :type table: pandas.Series
809 :type input_data: InputData
812 logging.info(" Generating the table {0} ...".
813 format(table.get("title", "")))
816 with open(table["input-file"], 'rb') as csv_file:
817 csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
818 csv_lst = [item for item in csv_content]
820 logging.warning("The input file is not defined.")
822 except csv.Error as err:
823 logging.warning("Not possible to process the file '{0}'.\n{1}".
824 format(table["input-file"], err))
828 dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
831 tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#6699ff"))
832 for idx, item in enumerate(csv_lst[0]):
833 alignment = "left" if idx == 0 else "right"
834 th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
838 for r_idx, row in enumerate(csv_lst[1:]):
839 background = "#D4E4F7" if r_idx % 2 else "white"
840 tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
843 for c_idx, item in enumerate(row):
844 alignment = "left" if c_idx == 0 else "center"
845 td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
847 if item == "regression":
848 td.set("bgcolor", "#eca1a6")
849 elif item == "outlier":
850 td.set("bgcolor", "#d6cbd3")
851 elif item == "progression":
852 td.set("bgcolor", "#bdcebe")
856 with open(table["output-file"], 'w') as html_file:
857 logging.info(" Writing file: '{0}'".
858 format(table["output-file"]))
859 html_file.write(".. raw:: html\n\n\t")
860 html_file.write(ET.tostring(dashboard))
861 html_file.write("\n\t<p><br><br></p>\n")
863 logging.warning("The output file is not defined.")