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Ernad Husremovic 2026-03-09 09:30:27 +01:00
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odoo-bringout-oca-ocb-survey/survey/models/survey_question.py
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@ -3,9 +3,10 @@
import collections
import contextlib
import json
import itertools
import json
import operator
from textwrap import shorten
from odoo import api, fields, models, tools, _
from odoo.exceptions import UserError, ValidationError
@ -30,7 +31,7 @@ class SurveyQuestion(models.Model):
items around.
It also removes on level of encoding by directly having 'Add a page' and 'Add a question'
links on the tree view of questions, enabling a faster encoding.
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
@ -46,6 +47,17 @@ class SurveyQuestion(models.Model):
_rec_name = 'title'
_order = 'sequence,id'
@api.model
def default_get(self, fields):
res = super().default_get(fields)
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 and 'is_time_limited' not in res:
res['is_time_limited'] = survey.session_speed_rating
if 'time_limit' in fields 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(
@ -54,9 +66,13 @@ class SurveyQuestion(models.Model):
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')
survey_id = fields.Many2one('survey.survey', string='Survey', ondelete='cascade', index='btree_not_null')
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)
survey_session_speed_rating = fields.Boolean(related="survey_id.session_speed_rating")
survey_session_speed_rating_time_limit = fields.Integer(related="survey_id.session_speed_rating_time_limit", string="General Time limit (seconds)")
# page specific
is_page = fields.Boolean('Is a page?')
question_ids = fields.One2many('survey.question', string='Questions', compute="_compute_question_ids")
@ -74,6 +90,7 @@ class SurveyQuestion(models.Model):
('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',
@ -82,6 +99,8 @@ class SurveyQuestion(models.Model):
'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.")
@ -105,9 +124,16 @@ class SurveyQuestion(models.Model):
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')
@ -124,7 +150,7 @@ class SurveyQuestion(models.Model):
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 message', translate=True)
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
@ -132,36 +158,73 @@ class SurveyQuestion(models.Model):
'survey.user_input.line', 'question_id', string='Answers',
domain=[('skipped', '=', False)], groups='survey.group_survey_user')
# Conditional display
is_conditional = fields.Boolean(
string='Conditional Display', copy=True, help="""If checked, this question will be displayed only
if the specified conditional answer have been selected in a previous question""")
triggering_question_id = fields.Many2one(
'survey.question', string="Triggering Question", copy=False, compute="_compute_triggering_question_id",
store=True, readonly=False, help="Question containing the triggering answer to display the current question.",
domain="[('survey_id', '=', survey_id), \
'&', ('question_type', 'in', ['simple_choice', 'multiple_choice']), \
'|', \
('sequence', '<', sequence), \
'&', ('sequence', '=', sequence), ('id', '<', id)]")
triggering_answer_id = fields.Many2one(
'survey.question.answer', string="Triggering Answer", copy=False, compute="_compute_triggering_answer_id",
store=True, readonly=False, help="Answer that will trigger the display of the current question.",
domain="[('question_id', '=', triggering_question_id)]")
# 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.")
_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')
]
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)
]"""
)
_positive_len_min = models.Constraint(
'CHECK (validation_length_min >= 0)',
'A length must be positive!',
)
_positive_len_max = models.Constraint(
'CHECK (validation_length_max >= 0)',
'A length must be positive!',
)
_validation_length = models.Constraint(
'CHECK (validation_length_min <= validation_length_max)',
'Max length cannot be smaller than min length!',
)
_validation_float = models.Constraint(
'CHECK (validation_min_float_value <= validation_max_float_value)',
'Max value cannot be smaller than min value!',
)
_validation_date = models.Constraint(
'CHECK (validation_min_date <= validation_max_date)',
'Max date cannot be smaller than min date!',
)
_validation_datetime = models.Constraint(
'CHECK (validation_min_datetime <= validation_max_datetime)',
'Max datetime cannot be smaller than min datetime!',
)
_positive_answer_score = models.Constraint(
'CHECK (answer_score >= 0)',
'An answer score for a non-multiple choice question cannot be negative!',
)
_scored_datetime_have_answers = models.Constraint(
"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 = models.Constraint(
"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 = models.Constraint(
"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 = models.Constraint(
'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
@ -177,6 +240,13 @@ class SurveyQuestion(models.Model):
# 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:
@ -220,19 +290,10 @@ class SurveyQuestion(models.Model):
@api.depends('survey_id.question_and_page_ids.is_page', 'survey_id.question_and_page_ids.sequence')
def _compute_question_ids(self):
"""Will take all questions of the survey for which the index is higher than the index of this page
and lower than the index of the next page."""
for question in self:
if question.is_page:
next_page_index = False
for page in question.survey_id.page_ids:
if page._index() > question._index():
next_page_index = page._index()
break
question.question_ids = question.survey_id.question_ids.filtered(
lambda q: q._index() > question._index() and (not next_page_index or q._index() < next_page_index)
)
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']
@ -269,33 +330,59 @@ class SurveyQuestion(models.Model):
if not question.validation_required or question.question_type not in ['char_box', 'numerical_box', 'date', 'datetime']:
question.validation_required = False
@api.depends('is_conditional')
def _compute_triggering_question_id(self):
""" Used as an 'onchange' : Reset the triggering question if user uncheck 'Conditional Display'
Avoid CacheMiss : set the value to False if the value is not set yet."""
for question in self:
if not question.is_conditional or question.triggering_question_id is None:
question.triggering_question_id = 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
@api.depends('triggering_question_id')
def _compute_triggering_answer_id(self):
""" Used as an 'onchange' : Reset the triggering answer if user unset or change the triggering question
or uncheck 'Conditional Display'.
Avoid CacheMiss : set the value to False if the value is not set yet."""
for question in self:
if not question.triggering_question_id \
or question.triggering_question_id != question.triggering_answer_id.question_id\
or question.triggering_answer_id is None:
question.triggering_answer_id = False
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
@api.depends('question_type', 'scoring_type', 'answer_date', 'answer_datetime', 'answer_numerical_box')
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 even if logic is a bit different (coming from answers)
- '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:
@ -308,14 +395,45 @@ class SurveyQuestion(models.Model):
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 = True
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
@api.model_create_multi
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')
@ -331,24 +449,26 @@ class SurveyQuestion(models.Model):
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, ...] }
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-):
return dict {question.id (int): error (str)} -> empty dict if no validation error.
"""
- 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, ...] }``
:returns: A dict ``{question.id: error}``, or an empty dict if no validation error.
:rtype: dict[int, str]
"""
self.ensure_one()
if isinstance(answer, str):
answer = answer.strip()
# Empty answer to mandatory question
if self.constr_mandatory and not answer and self.question_type not in ['simple_choice', 'multiple_choice']:
return {self.id: self.constr_error_msg or _('This question requires an answer.')}
# because in choices question types, comment can count as answer
if answer or self.question_type in ['simple_choice', 'multiple_choice']:
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':
@ -359,6 +479,8 @@ class SurveyQuestion(models.Model):
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):
@ -413,11 +535,22 @@ class SurveyQuestion(models.Model):
return {}
def _validate_choice(self, answer, comment):
# Empty comment
if self.constr_mandatory \
and not answer \
and not (self.comments_allowed and self.comment_count_as_answer and comment):
""" Validates choice-based questions.
- Checks that mandatory questions have at least one answer.
- For 'simple_choice', ensures that exactly one answer is provided.
"""
answers = answer if isinstance(answer, list) else ([answer] if answer else [])
valid_answers_count = len(answers)
if comment and self.comment_count_as_answer:
valid_answers_count += 1
if valid_answers_count == 0 and 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.')}
if valid_answers_count > 1 and self.question_type == 'simple_choice':
return {self.id: _('For this question, you can only select one answer.')}
return {}
def _validate_matrix(self, answers):
@ -426,6 +559,13 @@ class SurveyQuestion(models.Model):
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.
@ -434,6 +574,36 @@ class SurveyQuestion(models.Model):
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
# ------------------------------------------------------------
@ -466,7 +636,7 @@ class SurveyQuestion(models.Model):
answer_line_ids=answer_lines,
answer_line_done_ids=done_lines,
answer_input_done_ids=done_lines.mapped('user_input_id'),
answer_input_skipped_ids=skipped_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))
@ -474,7 +644,12 @@ class SurveyQuestion(models.Model):
table_data, graph_data = question._get_stats_data(answer_lines)
question_data['table_data'] = table_data
question_data['graph_data'] = json.dumps(graph_data)
if question.question_type in ["text_box", "char_box", "numerical_box", "date", "datetime"]:
answers_data = [
[input_line.id, input_line._get_answer_value(), input_line.user_input_id.get_print_url()]
for input_line in table_data if not input_line.skipped
]
question_data["answers_data"] = json.dumps(answers_data, default=str)
all_questions_data.append(question_data)
return all_questions_data
@ -486,6 +661,9 @@ class SurveyQuestion(models.Model):
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):
@ -504,17 +682,17 @@ class SurveyQuestion(models.Model):
count_data[line.suggested_answer_id] += 1
table_data = [{
'value': _('Other (see comments)') if not sug_answer else sug_answer.value,
'suggested_answer': sug_answer,
'count': count_data[sug_answer],
'count_text': _("%s Votes", count_data[sug_answer]),
'value': _('Other (see comments)') if not suggested_answer else suggested_answer.value_label,
'suggested_answer': suggested_answer,
'count': count_data[suggested_answer],
'count_text': self.env._("%s Votes", count_data[suggested_answer]),
}
for sug_answer in suggested_answers]
for suggested_answer in suggested_answers]
graph_data = [{
'text': _('Other (see comments)') if not sug_answer else sug_answer.value,
'count': count_data[sug_answer]
'text': self.env._('Other (see comments)') if not suggested_answer else suggested_answer.value_label,
'count': count_data[suggested_answer]
}
for sug_answer in suggested_answers]
for suggested_answer in suggested_answers]
return table_data, graph_data
@ -530,19 +708,42 @@ class SurveyQuestion(models.Model):
table_data = [{
'row': row,
'columns': [{
'suggested_answer': sug_answer,
'count': count_data[(row, sug_answer)]
} for sug_answer in suggested_answers],
'suggested_answer': suggested_answer,
'count': count_data[(row, suggested_answer)]
} for suggested_answer in suggested_answers],
} for row in matrix_rows]
graph_data = [{
'key': sug_answer.value,
'key': suggested_answer.value,
'values': [{
'text': row.value,
'count': count_data[(row, sug_answer)]
'count': count_data[(row, suggested_answer)]
}
for row in matrix_rows
]
} for sug_answer in suggested_answers]
} 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],
'count_text': _("%s Votes", count_data[sug_answer]),
})
graph_data.append({'text': str(sug_answer),
'count': count_data[sug_answer]
})
return table_data, graph_data
@ -552,8 +753,10 @@ class SurveyQuestion(models.Model):
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']:
if self.question_type in ['numerical_box', 'date', 'datetime', 'scale']:
stats.update(self._get_stats_summary_data_scored(user_input_lines))
return stats
@ -575,8 +778,8 @@ class SurveyQuestion(models.Model):
'partial_inputs_count': len(partial_inputs),
}
def _get_stats_summary_data_numerical(self, user_input_lines):
all_values = user_input_lines.filtered(lambda line: not line.skipped).mapped('value_numerical_box')
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),
@ -592,6 +795,42 @@ class SurveyQuestion(models.Model):
'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
@ -604,25 +843,79 @@ class SurveyQuestionAnswer(models.Model):
"""
_name = 'survey.question.answer'
_rec_name = 'value'
_order = 'sequence, id'
_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')
matrix_question_id = fields.Many2one('survey.question', string='Question (as matrix row)', ondelete='cascade')
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, required=True)
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")
_value_not_empty = models.Constraint(
'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 []