forked from ScoDoc/DocScoDoc
237 lines
9.0 KiB
Python
237 lines
9.0 KiB
Python
# -*- mode: python -*-
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# -*- coding: utf-8 -*-
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##############################################################################
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#
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# Gestion scolarite IUT
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#
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# Copyright (c) 1999 - 2022 Emmanuel Viennet. All rights reserved.
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#
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# This program is free software; you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation; either version 2 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program; if not, write to the Free Software
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# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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#
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# Emmanuel Viennet emmanuel.viennet@viennet.net
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#
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##############################################################################
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"""Fonctions de calcul des moyennes d'UE
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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 import db
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from app import models
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from app.models import UniteEns, Module, ModuleImpl, ModuleUECoef
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from app.comp import moy_mod
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from app.models.formsemestre import FormSemestre
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from app.scodoc import sco_codes_parcours
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def df_load_module_coefs(formation_id: int, semestre_idx: int = None) -> pd.DataFrame:
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"""Charge les coefs des modules de la formation pour le semestre indiqué.
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Ces coefs lient les modules à chaque UE.
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Résultat: (module_coefs_df, ues, modules)
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DataFrame rows = UEs, columns = modules, value = coef.
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Considère toutes les UE (sauf sport) et modules du semestre.
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Les coefs non définis (pas en base) sont mis à zéro.
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Si semestre_idx None, prend toutes les UE de la formation.
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"""
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ues = (
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UniteEns.query.filter_by(formation_id=formation_id)
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.filter(UniteEns.type != sco_codes_parcours.UE_SPORT)
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.order_by(UniteEns.semestre_idx, UniteEns.numero, UniteEns.acronyme)
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)
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modules = Module.query.filter_by(formation_id=formation_id).order_by(
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Module.semestre_id, Module.module_type.desc(), Module.numero, Module.code
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)
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if semestre_idx is not None:
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ues = ues.filter_by(semestre_idx=semestre_idx)
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modules = modules.filter_by(semestre_id=semestre_idx)
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ues = ues.all()
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modules = modules.all()
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ue_ids = [ue.id for ue in ues]
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module_ids = [module.id for module in modules]
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module_coefs_df = pd.DataFrame(columns=module_ids, index=ue_ids, dtype=float)
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query = (
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db.session.query(ModuleUECoef)
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.filter(UniteEns.formation_id == formation_id)
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.filter(ModuleUECoef.ue_id == UniteEns.id)
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)
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if semestre_idx is not None:
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query = query.filter(UniteEns.semestre_idx == semestre_idx)
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for mod_coef in query:
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module_coefs_df[mod_coef.module_id][mod_coef.ue_id] = mod_coef.coef
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module_coefs_df.fillna(value=0, inplace=True)
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return module_coefs_df, ues, modules
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def df_load_modimpl_coefs(
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formsemestre: models.FormSemestre, ues=None, modimpls=None
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) -> pd.DataFrame:
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"""Charge les coefs des modules du formsemestre indiqué.
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Comme df_load_module_coefs mais prend seulement les UE
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et modules du formsemestre.
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Si ues et modimpls sont None, prend tous ceux du formsemestre.
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Résultat: (module_coefs_df, ues, modules)
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DataFrame rows = UEs, columns = modimpl, value = coef.
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"""
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if ues is None:
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ues = formsemestre.query_ues().all()
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ue_ids = [x.id for x in ues]
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if modimpls is None:
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modimpls = formsemestre.modimpls.all()
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modimpl_ids = [x.id for x in modimpls]
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mod2impl = {m.module.id: m.id for m in modimpls}
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modimpl_coefs_df = pd.DataFrame(columns=modimpl_ids, index=ue_ids, dtype=float)
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mod_coefs = (
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db.session.query(ModuleUECoef)
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.filter(ModuleUECoef.module_id == ModuleImpl.module_id)
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.filter(ModuleImpl.formsemestre_id == formsemestre.id)
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)
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for mod_coef in mod_coefs:
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modimpl_coefs_df[mod2impl[mod_coef.module_id]][mod_coef.ue_id] = mod_coef.coef
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modimpl_coefs_df.fillna(value=0, inplace=True)
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return modimpl_coefs_df, ues, modimpls
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def notes_sem_assemble_cube(modimpls_notes: list[pd.DataFrame]) -> np.ndarray:
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"""Réuni les notes moyennes des modules du semestre en un "cube"
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modimpls_notes : liste des moyennes de module
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(DataFrames rendus par compute_module_moy, (etud x UE))
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Resultat: ndarray (etud x module x UE)
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"""
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assert len(modimpls_notes)
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modimpls_notes_arr = [df.values for df in modimpls_notes]
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modimpls_notes = np.stack(modimpls_notes_arr)
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# passe de (mod x etud x ue) à (etud x mod x UE)
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return modimpls_notes.swapaxes(0, 1)
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def notes_sem_load_cube(formsemestre):
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"""Calcule le cube des notes du semestre
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(charge toutes les notes, calcule les moyenne des modules
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et assemble le cube)
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Resultat:
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sem_cube : ndarray (etuds x modimpls x UEs)
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modimpls_evals_poids dict { modimpl.id : evals_poids }
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modimpls_evals_notes dict { modimpl.id : evals_notes }
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modimpls_evaluations dict { modimpl.id : liste des évaluations }
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modimpls_evaluations_complete: {modimpl_id : liste de booleens (complete/non)}
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"""
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modimpls_evals_poids = {}
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modimpls_evals_notes = {}
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modimpls_evaluations = {}
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modimpls_evaluations_complete = {}
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modimpls_notes = []
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for modimpl in formsemestre.modimpls:
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evals_notes, evaluations, evaluations_completes = moy_mod.df_load_modimpl_notes(
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modimpl.id
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)
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evals_poids, ues = moy_mod.df_load_evaluations_poids(modimpl.id)
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etuds_moy_module = moy_mod.compute_module_moy(
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evals_notes, evals_poids, evaluations, evaluations_completes
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)
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modimpls_evals_poids[modimpl.id] = evals_poids
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modimpls_evals_notes[modimpl.id] = evals_notes
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modimpls_evaluations[modimpl.id] = evaluations
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modimpls_evaluations_complete[modimpl.id] = evaluations_completes
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modimpls_notes.append(etuds_moy_module)
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if len(modimpls_notes):
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cube = notes_sem_assemble_cube(modimpls_notes)
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else:
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nb_etuds = formsemestre.etuds.count()
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cube = np.zeros((nb_etuds, 0, 0), dtype=float)
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return (
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cube,
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modimpls_evals_poids,
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modimpls_evals_notes,
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modimpls_evaluations,
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modimpls_evaluations_complete,
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)
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def compute_ue_moys(
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sem_cube: np.array,
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etuds: list,
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modimpls: list,
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ues: list,
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modimpl_inscr_df: pd.DataFrame,
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modimpl_coefs_df: pd.DataFrame,
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) -> pd.DataFrame:
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"""Calcul de la moyenne d'UE
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La moyenne d'UE est un nombre (note/20), ou NI ou NA ou ERR
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NI non inscrit à (au moins un) module de cette UE
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NA pas de notes disponibles
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ERR erreur dans une formule utilisateur. [XXX pas encore gérées ici]
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sem_cube: notes moyennes aux modules
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ndarray (etuds x modimpls x UEs)
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(floats avec des NaN)
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etuds : lites des étudiants (dim. 0 du cube)
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modimpls : liste des modules à considérer (dim. 1 du cube)
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ues : liste des UE (dim. 2 du cube)
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module_inscr_df: matrice d'inscription du semestre (etud x modimpl)
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module_coefs_df: matrice coefficients (UE x modimpl)
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Resultat: DataFrame columns UE, rows etudid
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"""
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nb_etuds, nb_modules, nb_ues = sem_cube.shape
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assert len(modimpls) == nb_modules
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if nb_modules == 0 or nb_etuds == 0:
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return pd.DataFrame(
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index=modimpl_inscr_df.index, columns=modimpl_coefs_df.index
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)
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assert len(etuds) == nb_etuds
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assert len(ues) == nb_ues
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assert modimpl_inscr_df.shape[0] == nb_etuds
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assert modimpl_inscr_df.shape[1] == nb_modules
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assert modimpl_coefs_df.shape[0] == nb_ues
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assert modimpl_coefs_df.shape[1] == nb_modules
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modimpl_inscr = modimpl_inscr_df.values
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modimpl_coefs = modimpl_coefs_df.values
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# Duplique les inscriptions sur les UEs:
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modimpl_inscr_stacked = np.stack([modimpl_inscr] * nb_ues, axis=2)
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# Enlève les NaN du numérateur:
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# si on veut prendre en compte les modules avec notes neutralisées ?
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sem_cube_no_nan = np.nan_to_num(sem_cube, nan=0.0)
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# Ne prend pas en compte les notes des étudiants non inscrits au module:
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# Annule les notes:
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sem_cube_inscrits = np.where(modimpl_inscr_stacked, sem_cube_no_nan, 0.0)
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# Annule les coefs des modules où l'étudiant n'est pas inscrit:
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modimpl_coefs_etuds = np.where(
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modimpl_inscr_stacked, np.stack([modimpl_coefs.T] * nb_etuds), 0.0
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)
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# Annule les coefs des modules NaN
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modimpl_coefs_etuds_no_nan = np.where(np.isnan(sem_cube), 0.0, modimpl_coefs_etuds)
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#
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# Version vectorisée
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#
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etud_moy_ue = np.sum(
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modimpl_coefs_etuds_no_nan * sem_cube_inscrits, axis=1
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) / np.sum(modimpl_coefs_etuds_no_nan, axis=1)
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return pd.DataFrame(
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etud_moy_ue, index=modimpl_inscr_df.index, columns=modimpl_coefs_df.index
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)
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