forked from ScoDoc/ScoDoc
recapcomplet sans NotesTable
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@ -69,6 +69,8 @@ class ModuleImplResults:
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"nombre d'inscrits (non DEM) au module"
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self.evaluations_completes = []
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"séquence de booléens, indiquant les évals à prendre en compte."
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self.evaluations_completes_dict = {}
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"{ evaluation.id : bool } indique si à prendre en compte ou non."
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self.evaluations_etat = {}
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"{ evaluation_id: EvaluationEtat }"
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#
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@ -122,6 +124,7 @@ class ModuleImplResults:
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# dataFrame vide, index = tous les inscrits au SEMESTRE
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evals_notes = pd.DataFrame(index=self.etudids, dtype=float)
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self.evaluations_completes = []
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self.evaluations_completes_dict = {}
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for evaluation in moduleimpl.evaluations:
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eval_df = self._load_evaluation_notes(evaluation)
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# is_complete ssi tous les inscrits (non dem) au semestre ont une note
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@ -131,6 +134,7 @@ class ModuleImplResults:
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== self.nb_inscrits_module
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) or evaluation.publish_incomplete # immédiate
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self.evaluations_completes.append(is_complete)
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self.evaluations_completes_dict[evaluation.id] = is_complete
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# NULL en base => ABS (= -999)
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eval_df.fillna(scu.NOTES_ABSENCE, inplace=True)
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@ -60,6 +60,25 @@ class ResultatsSemestreClassic(NotesTableCompat):
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"""
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return self.modimpls_results[moduleimpl_id].etuds_moy_module.get(etudid, "NI")
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def get_mod_stats(self, moduleimpl_id: int) -> dict:
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"""Stats sur les notes obtenues dans un modimpl"""
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notes_series: pd.Series = self.modimpls_results[moduleimpl_id].etuds_moy_module
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nb_notes = len(notes_series)
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if not nb_notes:
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super().get_mod_stats(moduleimpl_id)
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return {
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# Series: Statistical methods from ndarray have been overridden to automatically
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# exclude missing data (currently represented as NaN)
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"moy": notes_series.mean(), # donc sans prendre en compte les NaN
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"max": notes_series.max(),
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"min": notes_series.min(),
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"nb_notes": nb_notes,
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"nb_missing": sum(notes_series.isna()),
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"nb_valid_evals": sum(
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self.modimpls_results[moduleimpl_id].evaluations_completes
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),
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}
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def notes_sem_load_matrix(formsemestre: FormSemestre) -> tuple:
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"""Calcule la matrice des notes du semestre
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@ -9,6 +9,7 @@ from functools import cached_property
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import numpy as np
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import pandas as pd
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from app.comp.aux import StatsMoyenne
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from app.comp.moy_mod import ModuleImplResults
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from app.models import FormSemestre, ModuleImpl
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from app.scodoc import sco_utils as scu
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from app.scodoc.sco_cache import ResultatsSemestreCache
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@ -36,11 +37,12 @@ class ResultatsSemestre:
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self.formsemestre = formsemestre
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# BUT ou standard ? (apc == "approche par compétences")
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self.is_apc = formsemestre.formation.is_apc()
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# Attributs "virtuels", définis pas les sous-classes
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# Attributs "virtuels", définis dans les sous-classes
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# ResultatsSemestreBUT ou ResultatsSemestreStd
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self.etud_moy_ue = {}
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self.etud_moy_gen = {}
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self.etud_moy_gen_ranks = {}
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self.modimpls_results: ModuleImplResults = None
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# TODO
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def load_cached(self) -> bool:
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@ -215,7 +217,7 @@ class NotesTableCompat(ResultatsSemestre):
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def get_etud_moy_gen(self, etudid): # -> float | str
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"""Moyenne générale de cet etudiant dans ce semestre.
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Prend en compte les UE capitalisées. (TODO)
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Prend(ra) en compte les UE capitalisées. (TODO) XXX
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Si apc, moyenne indicative.
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Si pas de notes: 'NA'
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"""
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@ -241,30 +243,37 @@ class NotesTableCompat(ResultatsSemestre):
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return (None, 0) # XXX unimplemented TODO
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def get_evals_in_mod(self, moduleimpl_id: int) -> list[dict]:
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"liste des évaluations valides dans un module"
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"""Liste d'informations (compat NotesTable) sur évaluations completes
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de ce module.
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Évaluation "complete" ssi toutes notes saisies ou en attente.
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"""
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modimpl = ModuleImpl.query.get(moduleimpl_id)
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evals_results = []
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for e in modimpl.evaluations:
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d = e.to_dict()
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d["heure_debut"] = e.heure_debut # datetime.time
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d["heure_fin"] = e.heure_fin
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d["jour"] = e.jour # datetime
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d["notes"] = {
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etud.id: {
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"etudid": etud.id,
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"value": self.results.modimpls_evals_notes[e.moduleimpl_id][e.id][
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etud.id
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],
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if self.modimpls_results[moduleimpl_id].evaluations_completes_dict[e.id]:
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d = e.to_dict()
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d["heure_debut"] = e.heure_debut # datetime.time
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d["heure_fin"] = e.heure_fin
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d["jour"] = e.jour # datetime
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d["notes"] = {
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etud.id: {
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"etudid": etud.id,
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"value": self.modimpls_evals_notes[e.moduleimpl_id][e.id][
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etud.id
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],
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}
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for etud in self.etuds
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}
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for etud in self.results.etuds
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}
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evals_results.append(d)
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evals_results.append(d)
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return evals_results
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def get_moduleimpls_attente(self):
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return [] # XXX TODO
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def get_mod_stats(self, moduleimpl_id):
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def get_mod_stats(self, moduleimpl_id: int) -> dict:
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"""Stats sur les notes obtenues dans un modimpl
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Vide en APC
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"""
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return {
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"moy": "-",
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"max": "-",
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@ -170,6 +170,7 @@ class NotesTable:
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"""
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def __init__(self, formsemestre_id):
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# XXX breakpoint()
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log(f"NotesTable( formsemestre_id={formsemestre_id} )")
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if not formsemestre_id:
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raise ValueError("invalid formsemestre_id (%s)" % formsemestre_id)
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@ -303,9 +303,11 @@ def make_formsemestre_recapcomplet(
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sem = sco_formsemestre.do_formsemestre_list(
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args={"formsemestre_id": formsemestre_id}
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)[0]
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nt = sco_cache.NotesTableCache.get(formsemestre_id)
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parcours = formsemestre.formation.get_parcours()
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# nt = sco_cache.NotesTableCache.get(formsemestre_id)
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# XXX EXPERIMENTAL
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# nt = ResultatsSemestreClassic(formsemestre)
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nt = ResultatsSemestreClassic(formsemestre)
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modimpls = nt.get_modimpls_dict()
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ues = nt.get_ues_stat_dict() # incluant le(s) UE de sport
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#
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@ -701,9 +703,7 @@ def make_formsemestre_recapcomplet(
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idx_col_moy = idx_col_gr + 1
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cssclass = "recap_col_moy"
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try:
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if float(nsn[idx_col_moy]) < (
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nt.parcours.BARRE_MOY - scu.NOTES_TOLERANCE
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):
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if float(nsn[idx_col_moy]) < (parcours.BARRE_MOY - scu.NOTES_TOLERANCE):
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cssclass = "recap_col_moy_inf"
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except:
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pass
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@ -718,11 +718,11 @@ def make_formsemestre_recapcomplet(
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if (ir < (nblines - 4)) or (ir == nblines - 3):
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try:
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if float(nsn[i]) < nt.parcours.get_barre_ue(
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if float(nsn[i]) < parcours.get_barre_ue(
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ue["type"]
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): # NOTES_BARRE_UE
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cssclass = "recap_col_ue_inf"
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elif float(nsn[i]) >= nt.parcours.NOTES_BARRE_VALID_UE:
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elif float(nsn[i]) >= parcours.NOTES_BARRE_VALID_UE:
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cssclass = "recap_col_ue_val"
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except:
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pass
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@ -732,7 +732,7 @@ def make_formsemestre_recapcomplet(
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ir == nblines - 3
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): # si moyenne generale module < barre ue, surligne:
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try:
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if float(nsn[i]) < nt.parcours.get_barre_ue(ue["type"]):
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if float(nsn[i]) < parcours.get_barre_ue(ue["type"]):
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cssclass = "recap_col_moy_inf"
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except:
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pass
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