Fix: bonus St Quentin / Ville d'Avray
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@ -338,9 +338,12 @@ class BonusAisneStQuentin(BonusSportAdditif):
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# pas d'étudiants ou pas d'UE ou pas de module...
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return
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# Calcule moyenne pondérée des notes de sport:
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bonus_moy_arr = np.sum(
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sem_modimpl_moys_inscrits * modimpl_coefs_etuds_no_nan, axis=1
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) / np.sum(modimpl_coefs_etuds_no_nan, axis=1)
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with np.errstate(invalid="ignore"): # ignore les 0/0 (-> NaN)
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bonus_moy_arr = np.sum(
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sem_modimpl_moys_inscrits * modimpl_coefs_etuds_no_nan, axis=1
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) / np.sum(modimpl_coefs_etuds_no_nan, axis=1)
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np.nan_to_num(bonus_moy_arr, nan=0.0, copy=False)
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bonus_moy_arr[bonus_moy_arr < 10.0] = 0.0
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bonus_moy_arr[bonus_moy_arr >= 18.1] = 0.5
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bonus_moy_arr[bonus_moy_arr >= 16.1] = 0.4
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@ -823,9 +826,11 @@ class BonusVilleAvray(BonusSport):
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# pas d'étudiants ou pas d'UE ou pas de module...
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return
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# Calcule moyenne pondérée des notes de sport:
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bonus_moy_arr = np.sum(
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sem_modimpl_moys_inscrits * modimpl_coefs_etuds_no_nan, axis=1
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) / np.sum(modimpl_coefs_etuds_no_nan, axis=1)
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with np.errstate(invalid="ignore"): # ignore les 0/0 (-> NaN)
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bonus_moy_arr = np.sum(
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sem_modimpl_moys_inscrits * modimpl_coefs_etuds_no_nan, axis=1
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) / np.sum(modimpl_coefs_etuds_no_nan, axis=1)
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np.nan_to_num(bonus_moy_arr, nan=0.0, copy=False)
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bonus_moy_arr[bonus_moy_arr < 10.0] = 0.0
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bonus_moy_arr[bonus_moy_arr >= 16.0] = 0.3
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bonus_moy_arr[bonus_moy_arr >= 12.0] = 0.2
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@ -1,7 +1,7 @@
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# -*- mode: python -*-
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# -*- coding: utf-8 -*-
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SCOVERSION = "9.1.66"
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SCOVERSION = "9.1.67"
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SCONAME = "ScoDoc"
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