forked from ScoDoc/ScoDoc
71 lines
2.8 KiB
Python
71 lines
2.8 KiB
Python
# -*- mode: python -*-
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# -*- coding: utf-8 -*-
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"""Matrices d'inscription aux modules d'un semestre
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"""
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import pandas as pd
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from app import db
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#
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# Le chargement des inscriptions est long: matrice nb_module x nb_etuds
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# sur test debug 116 etuds, 18 modules, on est autour de 250ms.
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# On a testé trois approches, ci-dessous (et retenu la 1ere)
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#
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def df_load_modimpl_inscr(formsemestre) -> pd.DataFrame:
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"""Charge la matrice des inscriptions aux modules du semestre
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rows: etudid (inscrits au semestre, avec DEM et DEF)
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columns: moduleimpl_id
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value: bool (0/1 inscrit ou pas)
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"""
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# méthode la moins lente: une requete par module, merge les dataframes
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moduleimpl_ids = [m.id for m in formsemestre.modimpls_sorted]
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etudids = [inscr.etudid for inscr in formsemestre.inscriptions]
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df = pd.DataFrame(index=etudids, dtype=int)
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for moduleimpl_id in moduleimpl_ids:
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ins_df = pd.read_sql_query(
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"""SELECT etudid, 1 AS "%(moduleimpl_id)s"
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FROM notes_moduleimpl_inscription
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WHERE moduleimpl_id=%(moduleimpl_id)s""",
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db.engine,
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params={"moduleimpl_id": moduleimpl_id},
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index_col="etudid",
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dtype=int,
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)
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df = df.merge(ins_df, how="left", left_index=True, right_index=True)
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# Force columns names to integers (moduleimpl ids)
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df.columns = pd.Index([int(x) for x in df.columns], dtype=int)
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# les colonnes de df sont en float (Nan) quand il n'y a
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# aucun inscrit au module.
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df.fillna(0, inplace=True) # les non-inscrits
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return df.astype(bool) # x100 25.5s 15s 17s
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# chrono avec timeit:
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# timeit.timeit('x = df_load_module_inscr_v0(696)', number=100, globals=globals())
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def df_load_modimpl_inscr_v0(formsemestre):
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# methode 0, pur SQL Alchemy, 1.5 à 2 fois plus lente
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moduleimpl_ids = [m.id for m in formsemestre.modimpls_sorted]
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etudids = [i.etudid for i in formsemestre.inscriptions]
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df = pd.DataFrame(False, columns=moduleimpl_ids, index=etudids, dtype=bool)
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for modimpl in formsemestre.modimpls_sorted:
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ins_mod = df[modimpl.id]
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for inscr in modimpl.inscriptions:
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ins_mod[inscr.etudid] = True
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return df # x100 30.7s 46s 32s
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def df_load_modimpl_inscr_v2(formsemestre):
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moduleimpl_ids = [m.id for m in formsemestre.modimpls_sorted]
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etudids = [i.etudid for i in formsemestre.inscriptions]
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df = pd.DataFrame(False, columns=moduleimpl_ids, index=etudids, dtype=bool)
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cursor = db.engine.execute(
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"select moduleimpl_id, etudid from notes_moduleimpl_inscription i, notes_moduleimpl m where i.moduleimpl_id = m.id and m.formsemestre_id = %(formsemestre_id)s",
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{"formsemestre_id": formsemestre.id},
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)
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for moduleimpl_id, etudid in cursor:
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df[moduleimpl_id][etudid] = True
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return df # x100 44s, 31s, 29s, 28s
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