1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870
|
# -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
import collections
import contextlib
import itertools
import json
import operator
from textwrap import shorten
from odoo import api, fields, models, tools, _
from odoo.exceptions import UserError, ValidationError
class SurveyQuestion(models.Model):
""" Questions that will be asked in a survey.
Each question can have one of more suggested answers (eg. in case of
multi-answer checkboxes, radio buttons...).
Technical note:
survey.question is also the model used for the survey's pages (with the "is_page" field set to True).
A page corresponds to a "section" in the interface, and the fact that it separates the survey in
actual pages in the interface depends on the "questions_layout" parameter on the survey.survey model.
Pages are also used when randomizing questions. The randomization can happen within a "page".
Using the same model for questions and pages allows to put all the pages and questions together in a o2m field
(see survey.survey.question_and_page_ids) on the view side and easily reorganize your survey by dragging the
items around.
It also removes on level of encoding by directly having 'Add a page' and 'Add a question'
links on the list view of questions, enabling a faster encoding.
However, this has the downside of making the code reading a little bit more complicated.
Efforts were made at the model level to create computed fields so that the use of these models
still seems somewhat logical. That means:
- A survey still has "page_ids" (question_and_page_ids filtered on is_page = True)
- These "page_ids" still have question_ids (questions located between this page and the next)
- These "question_ids" still have a "page_id"
That makes the use and display of these information at view and controller levels easier to understand.
"""
_name = 'survey.question'
_description = 'Survey Question'
_rec_name = 'title'
_order = 'sequence,id'
@api.model
def default_get(self, fields_list):
res = super().default_get(fields_list)
if default_survey_id := self.env.context.get('default_survey_id'):
survey = self.env['survey.survey'].browse(default_survey_id)
if 'is_time_limited' in fields_list and 'is_time_limited' not in res:
res['is_time_limited'] = survey.session_speed_rating
if 'time_limit' in fields_list and 'time_limit' not in res:
res['time_limit'] = survey.session_speed_rating_time_limit
return res
# question generic data
title = fields.Char('Title', required=True, translate=True)
description = fields.Html(
'Description', translate=True, sanitize=True, sanitize_overridable=True,
help="Use this field to add additional explanations about your question or to illustrate it with pictures or a video")
question_placeholder = fields.Char("Placeholder", translate=True, compute="_compute_question_placeholder", store=True, readonly=False)
background_image = fields.Image("Background Image", compute="_compute_background_image", store=True, readonly=False)
background_image_url = fields.Char("Background Url", compute="_compute_background_image_url")
survey_id = fields.Many2one('survey.survey', string='Survey', ondelete='cascade')
scoring_type = fields.Selection(related='survey_id.scoring_type', string='Scoring Type', readonly=True)
sequence = fields.Integer('Sequence', default=10)
session_available = fields.Boolean(related='survey_id.session_available', string='Live Session available', readonly=True)
# page specific
is_page = fields.Boolean('Is a page?')
question_ids = fields.One2many('survey.question', string='Questions', compute="_compute_question_ids")
questions_selection = fields.Selection(
related='survey_id.questions_selection', readonly=True,
help="If randomized is selected, add the number of random questions next to the section.")
random_questions_count = fields.Integer(
'# Questions Randomly Picked', default=1,
help="Used on randomized sections to take X random questions from all the questions of that section.")
# question specific
page_id = fields.Many2one('survey.question', string='Page', compute="_compute_page_id", store=True)
question_type = fields.Selection([
('simple_choice', 'Multiple choice: only one answer'),
('multiple_choice', 'Multiple choice: multiple answers allowed'),
('text_box', 'Multiple Lines Text Box'),
('char_box', 'Single Line Text Box'),
('numerical_box', 'Numerical Value'),
('scale', 'Scale'),
('date', 'Date'),
('datetime', 'Datetime'),
('matrix', 'Matrix')], string='Question Type',
compute='_compute_question_type', readonly=False, store=True)
is_scored_question = fields.Boolean(
'Scored', compute='_compute_is_scored_question',
readonly=False, store=True, copy=True,
help="Include this question as part of quiz scoring. Requires an answer and answer score to be taken into account.")
has_image_only_suggested_answer = fields.Boolean(
"Has image only suggested answer", compute='_compute_has_image_only_suggested_answer')
# -- scoreable/answerable simple answer_types: numerical_box / date / datetime
answer_numerical_box = fields.Float('Correct numerical answer', help="Correct number answer for this question.")
answer_date = fields.Date('Correct date answer', help="Correct date answer for this question.")
answer_datetime = fields.Datetime('Correct datetime answer', help="Correct date and time answer for this question.")
answer_score = fields.Float('Score', help="Score value for a correct answer to this question.")
# -- char_box
save_as_email = fields.Boolean(
"Save as user email", compute='_compute_save_as_email', readonly=False, store=True, copy=True,
help="If checked, this option will save the user's answer as its email address.")
save_as_nickname = fields.Boolean(
"Save as user nickname", compute='_compute_save_as_nickname', readonly=False, store=True, copy=True,
help="If checked, this option will save the user's answer as its nickname.")
# -- simple choice / multiple choice / matrix
suggested_answer_ids = fields.One2many(
'survey.question.answer', 'question_id', string='Types of answers', copy=True,
help='Labels used for proposed choices: simple choice, multiple choice and columns of matrix')
# -- matrix
matrix_subtype = fields.Selection([
('simple', 'One choice per row'),
('multiple', 'Multiple choices per row')], string='Matrix Type', default='simple')
matrix_row_ids = fields.One2many(
'survey.question.answer', 'matrix_question_id', string='Matrix Rows', copy=True,
help='Labels used for proposed choices: rows of matrix')
# -- scale
scale_min = fields.Integer("Scale Minimum Value", default=0)
scale_max = fields.Integer("Scale Maximum Value", default=10)
scale_min_label = fields.Char("Scale Minimum Label", translate=True)
scale_mid_label = fields.Char("Scale Middle Label", translate=True)
scale_max_label = fields.Char("Scale Maximum Label", translate=True)
# -- display & timing options
is_time_limited = fields.Boolean("The question is limited in time",
help="Currently only supported for live sessions.")
is_time_customized = fields.Boolean("Customized speed rewards")
time_limit = fields.Integer("Time limit (seconds)")
# -- comments (simple choice, multiple choice, matrix (without count as an answer))
comments_allowed = fields.Boolean('Show Comments Field')
comments_message = fields.Char('Comment Message', translate=True)
comment_count_as_answer = fields.Boolean('Comment is an answer')
# question validation
validation_required = fields.Boolean('Validate entry', compute='_compute_validation_required', readonly=False, store=True)
validation_email = fields.Boolean('Input must be an email')
validation_length_min = fields.Integer('Minimum Text Length', default=0)
validation_length_max = fields.Integer('Maximum Text Length', default=0)
validation_min_float_value = fields.Float('Minimum value', default=0.0)
validation_max_float_value = fields.Float('Maximum value', default=0.0)
validation_min_date = fields.Date('Minimum Date')
validation_max_date = fields.Date('Maximum Date')
validation_min_datetime = fields.Datetime('Minimum Datetime')
validation_max_datetime = fields.Datetime('Maximum Datetime')
validation_error_msg = fields.Char('Validation Error', translate=True)
constr_mandatory = fields.Boolean('Mandatory Answer')
constr_error_msg = fields.Char('Error message', translate=True)
# answers
user_input_line_ids = fields.One2many(
'survey.user_input.line', 'question_id', string='Answers',
domain=[('skipped', '=', False)], groups='survey.group_survey_user')
# Not stored, convenient for trigger display computation.
triggering_question_ids = fields.Many2many(
'survey.question', string="Triggering Questions", compute="_compute_triggering_question_ids",
store=False, help="Questions containing the triggering answer(s) to display the current question.")
allowed_triggering_question_ids = fields.Many2many(
'survey.question', string="Allowed Triggering Questions", copy=False, compute="_compute_allowed_triggering_question_ids")
is_placed_before_trigger = fields.Boolean(
string='Is misplaced?', help="Is this question placed before any of its trigger questions?",
compute="_compute_allowed_triggering_question_ids")
triggering_answer_ids = fields.Many2many(
'survey.question.answer', string="Triggering Answers", copy=False, store=True,
readonly=False, help="Picking any of these answers will trigger this question.\n"
"Leave the field empty if the question should always be displayed.",
domain="""[
('question_id.survey_id', '=', survey_id),
'&', ('question_id.question_type', 'in', ['simple_choice', 'multiple_choice']),
'|',
('question_id.sequence', '<', sequence),
'&', ('question_id.sequence', '=', sequence), ('question_id.id', '<', id)
]"""
)
_sql_constraints = [
('positive_len_min', 'CHECK (validation_length_min >= 0)', 'A length must be positive!'),
('positive_len_max', 'CHECK (validation_length_max >= 0)', 'A length must be positive!'),
('validation_length', 'CHECK (validation_length_min <= validation_length_max)', 'Max length cannot be smaller than min length!'),
('validation_float', 'CHECK (validation_min_float_value <= validation_max_float_value)', 'Max value cannot be smaller than min value!'),
('validation_date', 'CHECK (validation_min_date <= validation_max_date)', 'Max date cannot be smaller than min date!'),
('validation_datetime', 'CHECK (validation_min_datetime <= validation_max_datetime)', 'Max datetime cannot be smaller than min datetime!'),
('positive_answer_score', 'CHECK (answer_score >= 0)', 'An answer score for a non-multiple choice question cannot be negative!'),
('scored_datetime_have_answers', "CHECK (is_scored_question != True OR question_type != 'datetime' OR answer_datetime is not null)",
'All "Is a scored question = True" and "Question Type: Datetime" questions need an answer'),
('scored_date_have_answers', "CHECK (is_scored_question != True OR question_type != 'date' OR answer_date is not null)",
'All "Is a scored question = True" and "Question Type: Date" questions need an answer'),
('scale', "CHECK (question_type != 'scale' OR (scale_min >= 0 AND scale_max <= 10 AND scale_min < scale_max))",
'The scale must be a growing non-empty range between 0 and 10 (inclusive)'),
('is_time_limited_have_time_limit', "CHECK (is_time_limited != TRUE OR time_limit IS NOT NULL AND time_limit > 0)",
'All time-limited questions need a positive time limit'),
]
# -------------------------------------------------------------------------
# CONSTRAINT METHODS
# -------------------------------------------------------------------------
@api.constrains("is_page")
def _check_question_type_for_pages(self):
invalid_pages = self.filtered(lambda question: question.is_page and question.question_type)
if invalid_pages:
raise ValidationError(_("Question type should be empty for these pages: %s", ', '.join(invalid_pages.mapped('title'))))
# -------------------------------------------------------------------------
# COMPUTE METHODS
# -------------------------------------------------------------------------
@api.depends('suggested_answer_ids', 'suggested_answer_ids.value')
def _compute_has_image_only_suggested_answer(self):
questions_with_image_only_answer = self.env['survey.question'].search(
[('id', 'in', self.ids), ('suggested_answer_ids.value', 'in', [False, ''])])
questions_with_image_only_answer.has_image_only_suggested_answer = True
(self - questions_with_image_only_answer).has_image_only_suggested_answer = False
@api.depends('question_type')
def _compute_question_placeholder(self):
for question in self:
if question.question_type in ('simple_choice', 'multiple_choice', 'matrix') \
or not question.question_placeholder: # avoid CacheMiss errors
question.question_placeholder = False
@api.depends('is_page')
def _compute_background_image(self):
""" Background image is only available on sections. """
for question in self.filtered(lambda q: not q.is_page):
question.background_image = False
@api.depends('survey_id.access_token', 'background_image', 'page_id', 'survey_id.background_image_url')
def _compute_background_image_url(self):
""" How the background url is computed:
- For a question: it depends on the related section (see below)
- For a section:
- if a section has a background, then we create the background URL using this section's ID
- if not, then we fallback on the survey background url """
base_bg_url = "/survey/%s/%s/get_background_image"
for question in self:
if question.is_page:
background_section_id = question.id if question.background_image else False
else:
background_section_id = question.page_id.id if question.page_id.background_image else False
if background_section_id:
question.background_image_url = base_bg_url % (
question.survey_id.access_token,
background_section_id
)
else:
question.background_image_url = question.survey_id.background_image_url
@api.depends('is_page')
def _compute_question_type(self):
pages = self.filtered(lambda question: question.is_page)
pages.question_type = False
(self - pages).filtered(lambda question: not question.question_type).question_type = 'simple_choice'
@api.depends('survey_id.question_and_page_ids.is_page', 'survey_id.question_and_page_ids.sequence')
def _compute_question_ids(self):
for question in self:
if question.is_page:
question.question_ids = question.survey_id.question_ids\
.filtered(lambda q: q.page_id == question).sorted(lambda q: q._index())
else:
question.question_ids = self.env['survey.question']
@api.depends('survey_id.question_and_page_ids.is_page', 'survey_id.question_and_page_ids.sequence')
def _compute_page_id(self):
"""Will find the page to which this question belongs to by looking inside the corresponding survey"""
for question in self:
if question.is_page:
question.page_id = None
else:
page = None
for q in question.survey_id.question_and_page_ids.sorted():
if q == question:
break
if q.is_page:
page = q
question.page_id = page
@api.depends('question_type', 'validation_email')
def _compute_save_as_email(self):
for question in self:
if question.question_type != 'char_box' or not question.validation_email:
question.save_as_email = False
@api.depends('question_type')
def _compute_save_as_nickname(self):
for question in self:
if question.question_type != 'char_box':
question.save_as_nickname = False
@api.depends('question_type')
def _compute_validation_required(self):
for question in self:
if not question.validation_required or question.question_type not in ['char_box', 'numerical_box', 'date', 'datetime']:
question.validation_required = False
@api.depends('survey_id', 'survey_id.question_ids', 'triggering_answer_ids')
def _compute_allowed_triggering_question_ids(self):
"""Although the question (and possible trigger questions) sequence
is used here, we do not add these fields to the dependency list to
avoid cascading rpc calls when reordering questions via the webclient.
"""
possible_trigger_questions = self.search([
('is_page', '=', False),
('question_type', 'in', ['simple_choice', 'multiple_choice']),
('suggested_answer_ids', '!=', False),
('survey_id', 'in', self.survey_id.ids)
])
# Using the sequence stored in db is necessary for existing questions that are passed as
# NewIds because the sequence provided by the JS client can be incorrect.
(self | possible_trigger_questions).flush_recordset()
self.env.cr.execute(
"SELECT id, sequence FROM survey_question WHERE id =ANY(%s)",
[self.ids]
)
conditional_questions_sequences = dict(self.env.cr.fetchall()) # id: sequence mapping
for question in self:
question_id = question._origin.id
if not question_id: # New question
question.allowed_triggering_question_ids = possible_trigger_questions.filtered(
lambda q: q.survey_id.id == question.survey_id._origin.id)
question.is_placed_before_trigger = False
continue
question_sequence = conditional_questions_sequences[question_id]
question.allowed_triggering_question_ids = possible_trigger_questions.filtered(
lambda q: q.survey_id.id == question.survey_id._origin.id
and (q.sequence < question_sequence or q.sequence == question_sequence and q.id < question_id)
)
question.is_placed_before_trigger = bool(
set(question.triggering_answer_ids.question_id.ids)
- set(question.allowed_triggering_question_ids.ids) # .ids necessary to match ids with newIds
)
@api.depends('triggering_answer_ids')
def _compute_triggering_question_ids(self):
for question in self:
question.triggering_question_ids = question.triggering_answer_ids.question_id
@api.depends('question_type', 'scoring_type', 'answer_date', 'answer_datetime', 'answer_numerical_box', 'suggested_answer_ids.is_correct')
def _compute_is_scored_question(self):
""" Computes whether a question "is scored" or not. Handles following cases:
- inconsistent Boolean=None edge case that breaks tests => False
- survey is not scored => False
- 'date'/'datetime'/'numerical_box' question types w/correct answer => True
(implied without user having to activate, except for numerical whose correct value is 0.0)
- 'simple_choice / multiple_choice': set to True if any of suggested answers are marked as correct
- question_type isn't scoreable (note: choice questions scoring logic handled separately) => False
"""
for question in self:
if question.is_scored_question is None or question.scoring_type == 'no_scoring':
question.is_scored_question = False
elif question.question_type == 'date':
question.is_scored_question = bool(question.answer_date)
elif question.question_type == 'datetime':
question.is_scored_question = bool(question.answer_datetime)
elif question.question_type == 'numerical_box' and question.answer_numerical_box:
question.is_scored_question = True
elif question.question_type in ['simple_choice', 'multiple_choice']:
question.is_scored_question = any(question.suggested_answer_ids.mapped('is_correct'))
else:
question.is_scored_question = False
@api.onchange('question_type', 'validation_required')
def _onchange_validation_parameters(self):
"""Ensure no value stays set but not visible on form,
preventing saving (+consistency with question type)."""
self.validation_email = False
self.validation_length_min = 0
self.validation_length_max = 0
self.validation_min_date = False
self.validation_max_date = False
self.validation_min_datetime = False
self.validation_max_datetime = False
self.validation_min_float_value = 0
self.validation_max_float_value = 0
# ------------------------------------------------------------
# CRUD
# ------------------------------------------------------------
def copy(self, default=None):
new_questions = super().copy(default)
for old_question, new_question in zip(self, new_questions):
if old_question.triggering_answer_ids:
new_question.triggering_answer_ids = old_question.triggering_answer_ids
return new_questions
def create(self, vals_list):
questions = super().create(vals_list)
questions.filtered(
lambda q: q.survey_id
and (q.survey_id.session_speed_rating != q.is_time_limited
or q.is_time_limited and q.survey_id.session_speed_rating_time_limit != q.time_limit)
).is_time_customized = True
return questions
@api.ondelete(at_uninstall=False)
def _unlink_except_live_sessions_in_progress(self):
running_surveys = self.survey_id.filtered(lambda survey: survey.session_state == 'in_progress')
if running_surveys:
raise UserError(_(
'You cannot delete questions from surveys "%(survey_names)s" while live sessions are in progress.',
survey_names=', '.join(running_surveys.mapped('title')),
))
# ------------------------------------------------------------
# VALIDATION
# ------------------------------------------------------------
def validate_question(self, answer, comment=None):
""" Validate question, depending on question type and parameters
for simple choice, text, date and number, answer is simply the answer of the question.
For other multiple choices questions, answer is a list of answers (the selected choices
or a list of selected answers per question -for matrix type-):
- Simple answer : answer = 'example' or 2 or question_answer_id or 2019/10/10
- Multiple choice : answer = [question_answer_id1, question_answer_id2, question_answer_id3]
- Matrix: answer = { 'rowId1' : [colId1, colId2,...], 'rowId2' : [colId1, colId3, ...] }
return dict {question.id (int): error (str)} -> empty dict if no validation error.
"""
self.ensure_one()
if isinstance(answer, str):
answer = answer.strip()
# Empty answer to mandatory question
# because in choices question types, comment can count as answer
if not answer and self.question_type not in ['simple_choice', 'multiple_choice']:
if self.constr_mandatory and not self.survey_id.users_can_go_back:
return {self.id: self.constr_error_msg or _('This question requires an answer.')}
else:
if self.question_type == 'char_box':
return self._validate_char_box(answer)
elif self.question_type == 'numerical_box':
return self._validate_numerical_box(answer)
elif self.question_type in ['date', 'datetime']:
return self._validate_date(answer)
elif self.question_type in ['simple_choice', 'multiple_choice']:
return self._validate_choice(answer, comment)
elif self.question_type == 'matrix':
return self._validate_matrix(answer)
elif self.question_type == 'scale':
return self._validate_scale(answer)
return {}
def _validate_char_box(self, answer):
# Email format validation
# all the strings of the form "<something>@<anything>.<extension>" will be accepted
if self.validation_email:
if not tools.email_normalize(answer):
return {self.id: _('This answer must be an email address')}
# Answer validation (if properly defined)
# Length of the answer must be in a range
if self.validation_required:
if not (self.validation_length_min <= len(answer) <= self.validation_length_max):
return {self.id: self.validation_error_msg or _('The answer you entered is not valid.')}
return {}
def _validate_numerical_box(self, answer):
try:
floatanswer = float(answer)
except ValueError:
return {self.id: _('This is not a number')}
if self.validation_required:
# Answer is not in the right range
with contextlib.suppress(Exception):
if not (self.validation_min_float_value <= floatanswer <= self.validation_max_float_value):
return {self.id: self.validation_error_msg or _('The answer you entered is not valid.')}
return {}
def _validate_date(self, answer):
isDatetime = self.question_type == 'datetime'
# Checks if user input is a date
try:
dateanswer = fields.Datetime.from_string(answer) if isDatetime else fields.Date.from_string(answer)
except ValueError:
return {self.id: _('This is not a date')}
if self.validation_required:
# Check if answer is in the right range
if isDatetime:
min_date = fields.Datetime.from_string(self.validation_min_datetime)
max_date = fields.Datetime.from_string(self.validation_max_datetime)
dateanswer = fields.Datetime.from_string(answer)
else:
min_date = fields.Date.from_string(self.validation_min_date)
max_date = fields.Date.from_string(self.validation_max_date)
dateanswer = fields.Date.from_string(answer)
if (min_date and max_date and not (min_date <= dateanswer <= max_date))\
or (min_date and not min_date <= dateanswer)\
or (max_date and not dateanswer <= max_date):
return {self.id: self.validation_error_msg or _('The answer you entered is not valid.')}
return {}
def _validate_choice(self, answer, comment):
# Empty comment
if not self.survey_id.users_can_go_back \
and self.constr_mandatory \
and not answer \
and not (self.comments_allowed and self.comment_count_as_answer and comment):
return {self.id: self.constr_error_msg or _('This question requires an answer.')}
return {}
def _validate_matrix(self, answers):
# Validate that each line has been answered
if self.constr_mandatory and len(self.matrix_row_ids) != len(answers):
return {self.id: self.constr_error_msg or _('This question requires an answer.')}
return {}
def _validate_scale(self, answer):
if not self.survey_id.users_can_go_back \
and self.constr_mandatory \
and not answer:
return {self.id: self.constr_error_msg or _('This question requires an answer.')}
return {}
def _index(self):
"""We would normally just use the 'sequence' field of questions BUT, if the pages and questions are
created without ever moving records around, the sequence field can be set to 0 for all the questions.
However, the order of the recordset is always correct so we can rely on the index method."""
self.ensure_one()
return list(self.survey_id.question_and_page_ids).index(self)
# ------------------------------------------------------------
# SPEED RATING
# ------------------------------------------------------------
def _update_time_limit_from_survey(self, is_time_limited=None, time_limit=None):
"""Update the speed rating values after a change in survey's speed rating configuration.
* Questions that were not customized will take the new default values from the survey
* Questions that were customized will not change their values, but this method will check
and update the `is_time_customized` flag if necessary (to `False`) such that the user
won't need to "actively" do it to make the question sensitive to change in survey values.
This is not done with `_compute`s because `is_time_limited` (and `time_limit`) would depend
on `is_time_customized` and vice versa.
"""
write_vals = {}
if is_time_limited is not None:
write_vals['is_time_limited'] = is_time_limited
if time_limit is not None:
write_vals['time_limit'] = time_limit
non_time_customized_questions = self.filtered(lambda s: not s.is_time_customized)
non_time_customized_questions.write(write_vals)
# Reset `is_time_customized` as necessary
customized_questions = self - non_time_customized_questions
back_to_default_questions = customized_questions.filtered(
lambda q: q.is_time_limited == q.survey_id.session_speed_rating
and (q.is_time_limited is False or q.time_limit == q.survey_id.session_speed_rating_time_limit))
back_to_default_questions.is_time_customized = False
# ------------------------------------------------------------
# STATISTICS / REPORTING
# ------------------------------------------------------------
def _prepare_statistics(self, user_input_lines):
""" Compute statistical data for questions by counting number of vote per choice on basis of filter """
all_questions_data = []
for question in self:
question_data = {'question': question, 'is_page': question.is_page}
if question.is_page:
all_questions_data.append(question_data)
continue
# fetch answer lines, separate comments from real answers
all_lines = user_input_lines.filtered(lambda line: line.question_id == question)
if question.question_type in ['simple_choice', 'multiple_choice', 'matrix']:
answer_lines = all_lines.filtered(
lambda line: line.answer_type == 'suggestion' or (
line.skipped and not line.answer_type) or (
line.answer_type == 'char_box' and question.comment_count_as_answer)
)
comment_line_ids = all_lines.filtered(lambda line: line.answer_type == 'char_box')
else:
answer_lines = all_lines
comment_line_ids = self.env['survey.user_input.line']
skipped_lines = answer_lines.filtered(lambda line: line.skipped)
done_lines = answer_lines - skipped_lines
question_data.update(
answer_line_ids=answer_lines,
answer_line_done_ids=done_lines,
answer_input_done_ids=done_lines.mapped('user_input_id'),
answer_input_ids=answer_lines.mapped('user_input_id'),
comment_line_ids=comment_line_ids)
question_data.update(question._get_stats_summary_data(answer_lines))
# prepare table and graph data
table_data, graph_data = question._get_stats_data(answer_lines)
question_data['table_data'] = table_data
question_data['graph_data'] = json.dumps(graph_data)
all_questions_data.append(question_data)
return all_questions_data
def _get_stats_data(self, user_input_lines):
if self.question_type == 'simple_choice':
return self._get_stats_data_answers(user_input_lines)
elif self.question_type == 'multiple_choice':
table_data, graph_data = self._get_stats_data_answers(user_input_lines)
return table_data, [{'key': self.title, 'values': graph_data}]
elif self.question_type == 'matrix':
return self._get_stats_graph_data_matrix(user_input_lines)
elif self.question_type == 'scale':
table_data, graph_data = self._get_stats_data_scale(user_input_lines)
return table_data, [{'key': self.title, 'values': graph_data}]
return [line for line in user_input_lines], []
def _get_stats_data_answers(self, user_input_lines):
""" Statistics for question.answer based questions (simple choice, multiple
choice.). A corner case with a void record survey.question.answer is added
to count comments that should be considered as valid answers. This small hack
allow to have everything available in the same standard structure. """
suggested_answers = [answer for answer in self.mapped('suggested_answer_ids')]
if self.comment_count_as_answer:
suggested_answers += [self.env['survey.question.answer']]
count_data = dict.fromkeys(suggested_answers, 0)
for line in user_input_lines:
if line.suggested_answer_id in count_data\
or (line.value_char_box and self.comment_count_as_answer):
count_data[line.suggested_answer_id] += 1
table_data = [{
'value': _('Other (see comments)') if not suggested_answer else suggested_answer.value_label,
'suggested_answer': suggested_answer,
'count': count_data[suggested_answer]
}
for suggested_answer in suggested_answers]
graph_data = [{
'text': _('Other (see comments)') if not suggested_answer else suggested_answer.value_label,
'count': count_data[suggested_answer]
}
for suggested_answer in suggested_answers]
return table_data, graph_data
def _get_stats_graph_data_matrix(self, user_input_lines):
suggested_answers = self.mapped('suggested_answer_ids')
matrix_rows = self.mapped('matrix_row_ids')
count_data = dict.fromkeys(itertools.product(matrix_rows, suggested_answers), 0)
for line in user_input_lines:
if line.matrix_row_id and line.suggested_answer_id:
count_data[(line.matrix_row_id, line.suggested_answer_id)] += 1
table_data = [{
'row': row,
'columns': [{
'suggested_answer': suggested_answer,
'count': count_data[(row, suggested_answer)]
} for suggested_answer in suggested_answers],
} for row in matrix_rows]
graph_data = [{
'key': suggested_answer.value,
'values': [{
'text': row.value,
'count': count_data[(row, suggested_answer)]
}
for row in matrix_rows
]
} for suggested_answer in suggested_answers]
return table_data, graph_data
def _get_stats_data_scale(self, user_input_lines):
suggested_answers = range(self.scale_min, self.scale_max + 1)
# Scale doesn't support comment as answer, so no extra value added
count_data = dict.fromkeys(suggested_answers, 0)
for line in user_input_lines:
if not line.skipped and line.value_scale in count_data:
count_data[line.value_scale] += 1
table_data = []
graph_data = []
for sug_answer in suggested_answers:
table_data.append({'value': str(sug_answer),
'suggested_answer': self.env['survey.question.answer'],
'count': count_data[sug_answer],
})
graph_data.append({'text': str(sug_answer),
'count': count_data[sug_answer]
})
return table_data, graph_data
def _get_stats_summary_data(self, user_input_lines):
stats = {}
if self.question_type in ['simple_choice', 'multiple_choice']:
stats.update(self._get_stats_summary_data_choice(user_input_lines))
elif self.question_type == 'numerical_box':
stats.update(self._get_stats_summary_data_numerical(user_input_lines))
elif self.question_type == 'scale':
stats.update(self._get_stats_summary_data_numerical(user_input_lines, 'value_scale'))
if self.question_type in ['numerical_box', 'date', 'datetime', 'scale']:
stats.update(self._get_stats_summary_data_scored(user_input_lines))
return stats
def _get_stats_summary_data_choice(self, user_input_lines):
right_inputs, partial_inputs = self.env['survey.user_input'], self.env['survey.user_input']
right_answers = self.suggested_answer_ids.filtered(lambda label: label.is_correct)
if self.question_type == 'multiple_choice':
for user_input, lines in tools.groupby(user_input_lines, operator.itemgetter('user_input_id')):
user_input_answers = self.env['survey.user_input.line'].concat(*lines).filtered(lambda l: l.answer_is_correct).mapped('suggested_answer_id')
if user_input_answers and user_input_answers < right_answers:
partial_inputs += user_input
elif user_input_answers:
right_inputs += user_input
else:
right_inputs = user_input_lines.filtered(lambda line: line.answer_is_correct).mapped('user_input_id')
return {
'right_answers': right_answers,
'right_inputs_count': len(right_inputs),
'partial_inputs_count': len(partial_inputs),
}
def _get_stats_summary_data_numerical(self, user_input_lines, fname='value_numerical_box'):
all_values = user_input_lines.filtered(lambda line: not line.skipped).mapped(fname)
lines_sum = sum(all_values)
return {
'numerical_max': max(all_values, default=0),
'numerical_min': min(all_values, default=0),
'numerical_average': round(lines_sum / (len(all_values) or 1), 2),
}
def _get_stats_summary_data_scored(self, user_input_lines):
return {
'common_lines': collections.Counter(
user_input_lines.filtered(lambda line: not line.skipped).mapped('value_%s' % self.question_type)
).most_common(5),
'right_inputs_count': len(user_input_lines.filtered(lambda line: line.answer_is_correct).mapped('user_input_id'))
}
# ------------------------------------------------------------
# OTHERS
# ------------------------------------------------------------
def _get_correct_answers(self):
""" Return a dictionary linking the scorable question ids to their correct answers.
The questions without correct answers are not considered.
"""
correct_answers = {}
# Simple and multiple choice
choices_questions = self.filtered(lambda q: q.question_type in ['simple_choice', 'multiple_choice'])
if choices_questions:
suggested_answers_data = self.env['survey.question.answer'].search_read(
[('question_id', 'in', choices_questions.ids), ('is_correct', '=', True)],
['question_id', 'id'],
load='', # prevent computing display_names
)
for data in suggested_answers_data:
if not data.get('id'):
continue
correct_answers.setdefault(data['question_id'], []).append(data['id'])
# Numerical box, date, datetime
for question in self - choices_questions:
if question.question_type not in ['numerical_box', 'date', 'datetime']:
continue
answer = question[f'answer_{question.question_type}']
if question.question_type == 'date':
answer = tools.format_date(self.env, answer)
elif question.question_type == 'datetime':
answer = tools.format_datetime(self.env, answer, tz='UTC', dt_format=False)
correct_answers[question.id] = answer
return correct_answers
class SurveyQuestionAnswer(models.Model):
""" A preconfigured answer for a question. This model stores values used
for
* simple choice, multiple choice: proposed values for the selection /
radio;
* matrix: row and column values;
"""
_name = 'survey.question.answer'
_rec_name = 'value'
_rec_names_search = ['question_id.title', 'value']
_order = 'question_id, sequence, id'
_description = 'Survey Label'
MAX_ANSWER_NAME_LENGTH = 90 # empirically tested in client dropdown
# question and question related fields
question_id = fields.Many2one('survey.question', string='Question', ondelete='cascade', index='btree_not_null')
matrix_question_id = fields.Many2one('survey.question', string='Question (as matrix row)', ondelete='cascade', index='btree_not_null')
question_type = fields.Selection(related='question_id.question_type')
sequence = fields.Integer('Label Sequence order', default=10)
scoring_type = fields.Selection(related='question_id.scoring_type')
# answer related fields
value = fields.Char('Suggested value', translate=True)
value_image = fields.Image('Image', max_width=1024, max_height=1024)
value_image_filename = fields.Char('Image Filename')
value_label = fields.Char('Value Label', compute='_compute_value_label',
help="Answer label as either the value itself if not empty "
"or a letter representing the index of the answer otherwise.")
is_correct = fields.Boolean('Correct')
answer_score = fields.Float('Score', help="A positive score indicates a correct choice; a negative or null score indicates a wrong answer")
_sql_constraints = [
('value_not_empty', "CHECK (value IS NOT NULL OR value_image_filename IS NOT NULL)",
'Suggested answer value must not be empty (a text and/or an image must be provided).'),
]
@api.depends('value_label', 'question_id.question_type', 'question_id.title', 'matrix_question_id')
def _compute_display_name(self):
"""Render an answer name as "Question title : Answer label value", making sure it is not too long.
Unless the answer is part of a matrix-type question, this implementation makes sure we have
at least 30 characters for the question title, then we elide it, leaving the rest of the
space for the answer.
"""
for answer in self:
answer_label = answer.value_label
if not answer.question_id or answer.question_id.question_type == 'matrix':
answer.display_name = answer_label
continue
title = answer.question_id.title or _("[Question Title]")
n_extra_characters = len(title) + len(answer_label) + 3 - self.MAX_ANSWER_NAME_LENGTH # 3 for `" : "`
if n_extra_characters <= 0:
answer.display_name = f'{title} : {answer_label}'
else:
answer.display_name = shorten(
f'{shorten(title, max(30, len(title) - n_extra_characters), placeholder="...")} : {answer_label}',
self.MAX_ANSWER_NAME_LENGTH,
placeholder="..."
)
@api.depends('question_id.suggested_answer_ids', 'sequence', 'value')
def _compute_value_label(self):
""" Compute the label as the value if not empty or a letter representing the index of the answer otherwise. """
for answer in self:
# using image -> use a letter to represent the value
if not answer.value and answer.question_id and answer.id:
answer_idx = answer.question_id.suggested_answer_ids.ids.index(answer.id)
answer.value_label = chr(65 + answer_idx) if answer_idx < 27 else ''
else:
answer.value_label = answer.value or ''
@api.constrains('question_id', 'matrix_question_id')
def _check_question_not_empty(self):
"""Ensure that field question_id XOR field matrix_question_id is not null"""
for label in self:
if not bool(label.question_id) != bool(label.matrix_question_id):
raise ValidationError(_("A label must be attached to only one question."))
def _get_answer_matching_domain(self, row_id=False):
self.ensure_one()
if self.question_type == "matrix":
return ['&', '&', ('question_id', '=', self.question_id.id), ('matrix_row_id', '=', row_id), ('suggested_answer_id', '=', self.id)]
elif self.question_type in ('multiple_choice', 'simple_choice'):
return ['&', ('question_id', '=', self.question_id.id), ('suggested_answer_id', '=', self.id)]
return []
|