forked from ScoDoc/DocScoDoc
80 lines
2.8 KiB
Python
80 lines
2.8 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 - 2021 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 de semestre (indicatives dans le BUT)
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"""
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import numpy as np
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import pandas as pd
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def compute_sem_moys(etud_moy_ue_df, modimpl_coefs_df):
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"""Calcule la moyenne générale indicative
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= moyenne des moyennes d'UE, pondérée par la somme de leurs coefs
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etud_moy_ue_df: DataFrame, colonnes ue_id, lignes etudid
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modimpl_coefs_df: DataFrame, colonnes moduleimpl_id, lignes UE
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Result: panda Series, index etudid, valeur float (moyenne générale)
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"""
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moy_gen = (etud_moy_ue_df * modimpl_coefs_df.values.sum(axis=1)).sum(
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axis=1
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) / modimpl_coefs_df.values.sum()
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return moy_gen
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def comp_ranks_series(notes: pd.Series):
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"""Calcul rangs à partir d'une séries ("vecteur") de notes (index etudid, valeur numérique)
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en tenant compte des ex-aequos
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Le resultat est: { etudid : rang } où rang est une chaine decrivant le rang
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"""
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notes = notes.sort_values(ascending=False) # Serie, tri par ordre décroissant
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rangs = pd.Series(index=notes.index, dtype=str) # le rang est une chaîne
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N = len(notes)
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nb_ex = 0 # nb d'ex-aequo consécutifs en cours
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notes_i = notes.iat
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for i, etudid in enumerate(notes.index):
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# test ex-aequo
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if i < (N - 1):
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next = notes_i[i + 1]
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else:
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next = None
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val = notes_i[i]
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if nb_ex:
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srang = "%d ex" % (i + 1 - nb_ex)
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if val == next:
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nb_ex += 1
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else:
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nb_ex = 0
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else:
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if val == next:
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srang = "%d ex" % (i + 1 - nb_ex)
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nb_ex = 1
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else:
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srang = "%d" % (i + 1)
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rangs[etudid] = srang
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return rangs
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