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mirror of https://gitlab.crans.org/bde/nk20 synced 2025-06-21 01:48:21 +02:00

more tests

This commit is contained in:
bleizi
2023-08-27 23:11:40 +02:00
parent 13b9b6edea
commit 1760196578
3 changed files with 61 additions and 44 deletions

View File

@ -1,14 +1,11 @@
# Copyright (C) 2018-2023 by BDE ENS Paris-Saclay
# SPDX-License-Identifier: GPL-3.0-or-later
# import time
from functools import lru_cache
# from random import Random
from django import forms
from django.db import transaction
from django.db.models import Q
# from django.utils.translation import gettext_lazy as _
from .base import WEISurvey, WEISurveyInformation, WEISurveyAlgorithm, WEIBusInformation
from ...models import WEIMembership
@ -48,12 +45,6 @@ class WEISurveyForm2023(forms.Form):
"""
information = WEISurveyInformation2023(registration)
# if self.data:
# for question in WORDS:
# self.fields[question].choices = [answer for answer in WORDS[question][1]]
# if self.is_valid():
# return
question = information.questions[information.step]
self.fields[question] = forms.ChoiceField(
label=WORDS[question][0] + question,
@ -86,14 +77,6 @@ class WEISurveyInformation2023(WEISurveyInformation):
questions = list(WORDS.keys())
def __init__(self, registration):
# print(hasattr(self,"questions"))
# if not hasattr(self, "questions"):
# rng = Random(int(1000 * time.time()))
# questions = list(WORDS.keys())
# rng.shuffle(questions)
# setattr(self, "questions", questions)
# print(questions)
for question in WORDS:
setattr(self, str(question), None)
super().__init__(registration)
@ -143,15 +126,6 @@ class WEISurvey2023(WEISurvey):
return False
return True
# @classmethod
# @lru_cache()
# def word_mean(cls, word):
# """
# Calculate the mid-score given by all buses.
# """
# buses = cls.get_algorithm_class().get_buses()
# return sum([cls.get_algorithm_class().get_bus_information(bus).scores[word] for bus in buses]) / buses.count()
@lru_cache()
def score(self, bus):
if not self.is_complete():
@ -176,7 +150,6 @@ class WEISurvey2023(WEISurvey):
@classmethod
def clear_cache(cls):
# cls.word_mean.cache_clear()
return super().clear_cache()