ScoDoc-PE/app/comp/moy_sem.py

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# -*- mode: python -*-
# -*- coding: utf-8 -*-
##############################################################################
#
# Gestion scolarite IUT
#
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# Copyright (c) 1999 - 2022 Emmanuel Viennet. All rights reserved.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
# Emmanuel Viennet emmanuel.viennet@viennet.net
#
##############################################################################
"""Fonctions de calcul des moyennes de semestre (indicatives dans le BUT)
"""
import numpy as np
import pandas as pd
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def compute_sem_moys_apc(
etud_moy_ue_df: pd.DataFrame, modimpl_coefs_df: pd.DataFrame
) -> pd.Series:
"""Calcule les moyennes générales indicatives de tous les étudiants
= moyenne des moyennes d'UE, pondérée par la somme de leurs coefs
etud_moy_ue_df: DataFrame, colonnes ue_id, lignes etudid
modimpl_coefs_df: DataFrame, colonnes moduleimpl_id, lignes UE
Result: panda Series, index etudid, valeur float (moyenne générale)
"""
moy_gen = (etud_moy_ue_df * modimpl_coefs_df.values.sum(axis=1)).sum(
axis=1
) / modimpl_coefs_df.values.sum()
return moy_gen
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def comp_ranks_series(notes: pd.Series) -> dict[int, str]:
"""Calcul rangs à partir d'une séries ("vecteur") de notes (index etudid, valeur
numérique) en tenant compte des ex-aequos.
Result: { etudid : rang:str } rang est une chaine decrivant le rang.
"""
notes = notes.sort_values(ascending=False) # Serie, tri par ordre décroissant
rangs = pd.Series(index=notes.index, dtype=str) # le rang est une chaîne
N = len(notes)
nb_ex = 0 # nb d'ex-aequo consécutifs en cours
notes_i = notes.iat
for i, etudid in enumerate(notes.index):
# test ex-aequo
if i < (N - 1):
next = notes_i[i + 1]
else:
next = None
val = notes_i[i]
if nb_ex:
srang = "%d ex" % (i + 1 - nb_ex)
if val == next:
nb_ex += 1
else:
nb_ex = 0
else:
if val == next:
srang = "%d ex" % (i + 1 - nb_ex)
nb_ex = 1
else:
srang = "%d" % (i + 1)
rangs[etudid] = srang
return rangs