forked from ScoDoc/ScoDoc
120 lines
4.4 KiB
Python
120 lines
4.4 KiB
Python
##############################################################################
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# ScoDoc
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# Copyright (c) 1999 - 2022 Emmanuel Viennet. All rights reserved.
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# See LICENSE
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##############################################################################
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"""Résultats semestres classiques (non APC)
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"""
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import numpy as np
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import pandas as pd
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from app.comp import moy_mod, moy_ue, moy_sem, inscr_mod
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from app.comp.res_common import NotesTableCompat
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from app.models.formsemestre import FormSemestre
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class ResultatsSemestreClassic(NotesTableCompat):
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"""Résultats du semestre (formation classique): organisation des calculs."""
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_cached_attrs = NotesTableCompat._cached_attrs + (
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"modimpl_coefs",
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"modimpl_idx",
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"sem_matrix",
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)
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def __init__(self, formsemestre):
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super().__init__(formsemestre)
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if not self.load_cached():
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self.compute()
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self.store()
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# recalculé (aussi rapide que de les cacher)
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self.moy_min = self.etud_moy_gen.min()
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self.moy_max = self.etud_moy_gen.max()
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self.moy_moy = self.etud_moy_gen.mean()
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def compute(self):
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"Charge les notes et inscriptions et calcule les moyennes d'UE et gen."
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self.sem_matrix, self.modimpls_results = notes_sem_load_matrix(
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self.formsemestre
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)
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self.modimpl_inscr_df = inscr_mod.df_load_modimpl_inscr(self.formsemestre)
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self.modimpl_coefs = np.array(
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[m.module.coefficient for m in self.formsemestre.modimpls]
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)
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self.modimpl_idx = {m.id: i for i, m in enumerate(self.formsemestre.modimpls)}
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"l'idx de la colonne du mod modimpl.id est modimpl_idx[modimpl.id]"
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(
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self.etud_moy_gen,
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self.etud_moy_ue,
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self.etud_coef_ue_df,
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) = moy_ue.compute_ue_moys_classic(
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self.formsemestre,
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self.sem_matrix,
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self.ues,
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self.modimpl_inscr_df,
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self.modimpl_coefs,
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)
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self.etud_moy_gen_ranks = moy_sem.comp_ranks_series(self.etud_moy_gen)
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def get_etud_mod_moy(self, moduleimpl_id: int, etudid: int) -> float:
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"""La moyenne de l'étudiant dans le moduleimpl
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Result: valeur float (peut être NaN) ou chaîne "NI" (non inscrit ou DEM)
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"""
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return self.modimpls_results[moduleimpl_id].etuds_moy_module.get(etudid, "NI")
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def get_mod_stats(self, moduleimpl_id: int) -> dict:
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"""Stats sur les notes obtenues dans un modimpl"""
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notes_series: pd.Series = self.modimpls_results[moduleimpl_id].etuds_moy_module
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nb_notes = len(notes_series)
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if not nb_notes:
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super().get_mod_stats(moduleimpl_id)
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return {
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# Series: Statistical methods from ndarray have been overridden to automatically
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# exclude missing data (currently represented as NaN)
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"moy": notes_series.mean(), # donc sans prendre en compte les NaN
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"max": notes_series.max(),
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"min": notes_series.min(),
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"nb_notes": nb_notes,
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"nb_missing": sum(notes_series.isna()),
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"nb_valid_evals": sum(
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self.modimpls_results[moduleimpl_id].evaluations_completes
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),
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}
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def notes_sem_load_matrix(formsemestre: FormSemestre) -> tuple:
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"""Calcule la matrice des notes du semestre
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(charge toutes les notes, calcule les moyenne des modules
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et assemble la matrice)
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Resultat:
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sem_matrix : 2d-array (etuds x modimpls)
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modimpls_results dict { modimpl.id : ModuleImplResultsClassic }
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"""
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modimpls_results = {}
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modimpls_notes = []
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for modimpl in formsemestre.modimpls:
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mod_results = moy_mod.ModuleImplResultsClassic(modimpl)
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etuds_moy_module = mod_results.compute_module_moy()
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modimpls_results[modimpl.id] = mod_results
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modimpls_notes.append(etuds_moy_module)
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return (
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notes_sem_assemble_matrix(modimpls_notes),
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modimpls_results,
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)
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def notes_sem_assemble_matrix(modimpls_notes: list[pd.Series]) -> np.ndarray:
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"""Réuni les notes moyennes des modules du semestre en une matrice
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modimpls_notes : liste des moyennes de module
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(Series rendus par compute_module_moy, index: etud)
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Resultat: ndarray (etud x module)
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"""
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modimpls_notes_arr = [s.values for s in modimpls_notes]
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modimpls_notes = np.stack(modimpls_notes_arr)
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# passe de (mod x etud) à (etud x mod)
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return modimpls_notes.T
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