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Divisions par zéro non controlées si les arrays vides passent en object

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This commit is contained in:
Emmanuel Viennet 2022-02-15 22:20:17 +01:00
parent 41b41c6c55
commit 9a57f362dc
1 changed files with 6 additions and 1 deletions
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7
app/comp/moy_ue.py
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@ -266,6 +266,8 @@ def compute_ue_moys_apc(
) )
# Annule les coefs des modules NaN # Annule les coefs des modules NaN
modimpl_coefs_etuds_no_nan = np.where(np.isnan(sem_cube), 0.0, modimpl_coefs_etuds) modimpl_coefs_etuds_no_nan = np.where(np.isnan(sem_cube), 0.0, modimpl_coefs_etuds)
if modimpl_coefs_etuds_no_nan.dtype == np.object: # arrive sur des tableaux vides
modimpl_coefs_etuds_no_nan = modimpl_coefs_etuds_no_nan.astype(np.float)
# #
# Version vectorisée # Version vectorisée
# #
@ -348,7 +350,8 @@ def compute_ue_moys_classic(
modimpl_coefs_etuds_no_nan = np.where( modimpl_coefs_etuds_no_nan = np.where(
np.isnan(sem_matrix), 0.0, modimpl_coefs_etuds np.isnan(sem_matrix), 0.0, modimpl_coefs_etuds
) )
if modimpl_coefs_etuds_no_nan.dtype == np.object: # arrive sur des tableaux vides
modimpl_coefs_etuds_no_nan = modimpl_coefs_etuds_no_nan.astype(np.float)
# --------------------- Calcul des moyennes d'UE # --------------------- Calcul des moyennes d'UE
ue_modules = np.array( ue_modules = np.array(
[[m.module.ue == ue for m in formsemestre.modimpls_sorted] for ue in ues] [[m.module.ue == ue for m in formsemestre.modimpls_sorted] for ue in ues]
@ -358,6 +361,8 @@ def compute_ue_moys_classic(
) )
# nb_ue x nb_etuds x nb_mods : coefs prenant en compte NaN et inscriptions # nb_ue x nb_etuds x nb_mods : coefs prenant en compte NaN et inscriptions
coefs = (modimpl_coefs_etuds_no_nan_stacked * ue_modules).swapaxes(1, 2) coefs = (modimpl_coefs_etuds_no_nan_stacked * ue_modules).swapaxes(1, 2)
if coefs.dtype == np.object: # arrive sur des tableaux vides
coefs = coefs.astype(np.float)
with np.errstate(invalid="ignore"): # ignore les 0/0 (-> NaN) with np.errstate(invalid="ignore"): # ignore les 0/0 (-> NaN)
etud_moy_ue = ( etud_moy_ue = (
np.sum(coefs * sem_matrix_inscrits, axis=2) / np.sum(coefs, axis=2) np.sum(coefs * sem_matrix_inscrits, axis=2) / np.sum(coefs, axis=2)