ScoDoc-Lille/app/comp/res_classic.py
2022-01-26 22:59:53 +01:00

158 lines
6.0 KiB
Python

##############################################################################
# ScoDoc
# Copyright (c) 1999 - 2022 Emmanuel Viennet. All rights reserved.
# See LICENSE
##############################################################################
"""Résultats semestres classiques (non APC)
"""
import numpy as np
import pandas as pd
from app.comp import moy_mod, moy_ue, moy_sem, inscr_mod
from app.comp.res_common import NotesTableCompat
from app.comp.bonus_spo import BonusSport
from app.models import ScoDocSiteConfig
from app.models.formsemestre import FormSemestre
from app.scodoc.sco_codes_parcours import UE_SPORT
from app.scodoc.sco_utils import ModuleType
class ResultatsSemestreClassic(NotesTableCompat):
"""Résultats du semestre (formation classique): organisation des calculs."""
_cached_attrs = NotesTableCompat._cached_attrs + (
"modimpl_coefs",
"modimpl_idx",
"sem_matrix",
)
def __init__(self, formsemestre):
super().__init__(formsemestre)
if not self.load_cached():
self.compute()
self.store()
# recalculé (aussi rapide que de les cacher)
self.moy_min = self.etud_moy_gen.min()
self.moy_max = self.etud_moy_gen.max()
self.moy_moy = self.etud_moy_gen.mean()
def compute(self):
"Charge les notes et inscriptions et calcule les moyennes d'UE et gen."
self.sem_matrix, self.modimpls_results = notes_sem_load_matrix(
self.formsemestre
)
self.modimpl_inscr_df = inscr_mod.df_load_modimpl_inscr(self.formsemestre)
self.modimpl_coefs = np.array(
[m.module.coefficient for m in self.formsemestre.modimpls_sorted]
)
self.modimpl_idx = {
m.id: i for i, m in enumerate(self.formsemestre.modimpls_sorted)
}
"l'idx de la colonne du mod modimpl.id est modimpl_idx[modimpl.id]"
modimpl_standards_mask = np.array(
[
(m.module.module_type == ModuleType.STANDARD)
and (m.module.ue.type != UE_SPORT)
for m in self.formsemestre.modimpls_sorted
]
)
(
self.etud_moy_gen,
self.etud_moy_ue,
self.etud_coef_ue_df,
) = moy_ue.compute_ue_moys_classic(
self.formsemestre,
self.sem_matrix,
self.ues,
self.modimpl_inscr_df,
self.modimpl_coefs,
modimpl_standards_mask,
)
# --- Bonus Sport & Culture
bonus_class = ScoDocSiteConfig.get_bonus_sport_class()
if bonus_class is not None:
bonus: BonusSport = bonus_class(
self.formsemestre,
self.sem_matrix,
self.ues,
self.modimpl_inscr_df,
self.modimpl_coefs,
self.etud_moy_gen,
self.etud_moy_ue,
)
self.bonus_ues = bonus.get_bonus_ues()
if self.bonus_ues is not None:
self.etud_moy_ue += self.bonus_ues # somme les dataframes
self.etud_moy_ue.clip(lower=0.0, upper=20.0, inplace=True)
bonus_mg = bonus.get_bonus_moy_gen()
if bonus_mg is not None:
self.etud_moy_gen += bonus_mg
self.etud_moy_gen.clip(lower=0.0, upper=20.0, inplace=True)
self.bonus = (
bonus_mg # compat nt, utilisé pour l'afficher sur les bulletins
)
# --- Classements:
self.etud_moy_gen_ranks = moy_sem.comp_ranks_series(self.etud_moy_gen)
def get_etud_mod_moy(self, moduleimpl_id: int, etudid: int) -> float:
"""La moyenne de l'étudiant dans le moduleimpl
Result: valeur float (peut être NaN) ou chaîne "NI" (non inscrit ou DEM)
"""
return self.modimpls_results[moduleimpl_id].etuds_moy_module.get(etudid, "NI")
def get_mod_stats(self, moduleimpl_id: int) -> dict:
"""Stats sur les notes obtenues dans un modimpl"""
notes_series: pd.Series = self.modimpls_results[moduleimpl_id].etuds_moy_module
nb_notes = len(notes_series)
if not nb_notes:
super().get_mod_stats(moduleimpl_id)
return {
# Series: Statistical methods from ndarray have been overridden to automatically
# exclude missing data (currently represented as NaN)
"moy": notes_series.mean(), # donc sans prendre en compte les NaN
"max": notes_series.max(),
"min": notes_series.min(),
"nb_notes": nb_notes,
"nb_missing": sum(notes_series.isna()),
"nb_valid_evals": sum(
self.modimpls_results[moduleimpl_id].evaluations_completes
),
}
def notes_sem_load_matrix(formsemestre: FormSemestre) -> tuple[np.ndarray, dict]:
"""Calcule la matrice des notes du semestre
(charge toutes les notes, calcule les moyennes des modules
et assemble la matrice)
Resultat:
sem_matrix : 2d-array (etuds x modimpls)
modimpls_results dict { modimpl.id : ModuleImplResultsClassic }
"""
modimpls_results = {}
modimpls_notes = []
for modimpl in formsemestre.modimpls_sorted:
mod_results = moy_mod.ModuleImplResultsClassic(modimpl)
etuds_moy_module = mod_results.compute_module_moy()
modimpls_results[modimpl.id] = mod_results
modimpls_notes.append(etuds_moy_module)
return (
notes_sem_assemble_matrix(modimpls_notes),
modimpls_results,
)
def notes_sem_assemble_matrix(modimpls_notes: list[pd.Series]) -> np.ndarray:
"""Réuni les notes moyennes des modules du semestre en une matrice
modimpls_notes : liste des moyennes de module
(Series rendus par compute_module_moy, index: etud)
Resultat: ndarray (etud x module)
"""
modimpls_notes_arr = [s.values for s in modimpls_notes]
modimpls_notes = np.stack(modimpls_notes_arr)
# passe de (mod x etud) à (etud x mod)
return modimpls_notes.T