forked from ScoDoc/ScoDoc
71 lines
2.8 KiB
Python
71 lines
2.8 KiB
Python
# -*- mode: python -*-
|
|
# -*- coding: utf-8 -*-
|
|
|
|
"""Matrices d'inscription aux modules d'un semestre
|
|
"""
|
|
import pandas as pd
|
|
|
|
from app import db
|
|
|
|
#
|
|
# Le chargement des inscriptions est long: matrice nb_module x nb_etuds
|
|
# sur test debug 116 etuds, 18 modules, on est autour de 250ms.
|
|
# On a testé trois approches, ci-dessous (et retenu la 1ere)
|
|
#
|
|
def df_load_modimpl_inscr(formsemestre) -> pd.DataFrame:
|
|
"""Charge la matrice des inscriptions aux modules du semestre
|
|
rows: etudid (inscrits au semestre, avec DEM et DEF)
|
|
columns: moduleimpl_id
|
|
value: bool (0/1 inscrit ou pas)
|
|
"""
|
|
# méthode la moins lente: une requete par module, merge les dataframes
|
|
moduleimpl_ids = [m.id for m in formsemestre.modimpls_sorted]
|
|
etudids = [inscr.etudid for inscr in formsemestre.inscriptions]
|
|
df = pd.DataFrame(index=etudids, dtype=int)
|
|
for moduleimpl_id in moduleimpl_ids:
|
|
ins_df = pd.read_sql_query(
|
|
"""SELECT etudid, 1 AS "%(moduleimpl_id)s"
|
|
FROM notes_moduleimpl_inscription
|
|
WHERE moduleimpl_id=%(moduleimpl_id)s""",
|
|
db.engine,
|
|
params={"moduleimpl_id": moduleimpl_id},
|
|
index_col="etudid",
|
|
dtype=int,
|
|
)
|
|
df = df.merge(ins_df, how="left", left_index=True, right_index=True)
|
|
# Force columns names to integers (moduleimpl ids)
|
|
df.columns = pd.Int64Index([int(x) for x in df.columns], dtype="int")
|
|
# les colonnes de df sont en float (Nan) quand il n'y a
|
|
# aucun inscrit au module.
|
|
df.fillna(0, inplace=True) # les non-inscrits
|
|
return df.astype(bool) # x100 25.5s 15s 17s
|
|
|
|
|
|
# chrono avec timeit:
|
|
# timeit.timeit('x = df_load_module_inscr_v0(696)', number=100, globals=globals())
|
|
|
|
|
|
def df_load_modimpl_inscr_v0(formsemestre):
|
|
# methode 0, pur SQL Alchemy, 1.5 à 2 fois plus lente
|
|
moduleimpl_ids = [m.id for m in formsemestre.modimpls_sorted]
|
|
etudids = [i.etudid for i in formsemestre.inscriptions]
|
|
df = pd.DataFrame(False, columns=moduleimpl_ids, index=etudids, dtype=bool)
|
|
for modimpl in formsemestre.modimpls_sorted:
|
|
ins_mod = df[modimpl.id]
|
|
for inscr in modimpl.inscriptions:
|
|
ins_mod[inscr.etudid] = True
|
|
return df # x100 30.7s 46s 32s
|
|
|
|
|
|
def df_load_modimpl_inscr_v2(formsemestre):
|
|
moduleimpl_ids = [m.id for m in formsemestre.modimpls_sorted]
|
|
etudids = [i.etudid for i in formsemestre.inscriptions]
|
|
df = pd.DataFrame(False, columns=moduleimpl_ids, index=etudids, dtype=bool)
|
|
cursor = db.engine.execute(
|
|
"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",
|
|
{"formsemestre_id": formsemestre.id},
|
|
)
|
|
for moduleimpl_id, etudid in cursor:
|
|
df[moduleimpl_id][etudid] = True
|
|
return df # x100 44s, 31s, 29s, 28s
|