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Fixed time comparison validation func. Now compares min/max value of array.

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This commit is contained in:
Daniel Schick 2024-01-05 10:51:13 +01:00
parent c24bc981b0
commit d8fabe0f97
1 changed files with 118 additions and 115 deletions
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17
src/server/BreCal/validators/validation_rule_functions.py
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@ -133,26 +133,29 @@ class ValidationRuleBaseFunctions():
if not self.ignore_port_administration_flag: if not self.ignore_port_administration_flag:
participant_types = [ParticipantType.AGENCY.value, ParticipantType.MOORING.value, ParticipantType.PORT_ADMINISTRATION.value, ParticipantType.PILOT.value, ParticipantType.TUG.value] participant_types = [ParticipantType.AGENCY.value, ParticipantType.MOORING.value, ParticipantType.PORT_ADMINISTRATION.value, ParticipantType.PILOT.value, ParticipantType.TUG.value]
else: else:
participant_types = [ParticipantType.AGENCY.value, ParticipantType.MOORING.value, ParticipantType.PORT_ADMINISTRATION.value, ParticipantType.PILOT.value, ParticipantType.TUG.value] participant_types = [ParticipantType.AGENCY.value, ParticipantType.MOORING.value, ParticipantType.PILOT.value, ParticipantType.TUG.value]
df_times = df_times.loc[df_times["participant_type"].isin(participant_types),:] df_times = df_times.loc[df_times["participant_type"].isin(participant_types),:]
# exclude missing entries and consider only pd.Timestamp entries (which ignores pd.NaT/null entries) # exclude missing entries and consider only pd.Timestamp entries (which ignores pd.NaT/null entries)
estimated_times = [time_ for time_ in df_times.loc[:,query].tolist() if isinstance(time_, pd.Timestamp)] # df_times = df_times.loc[~df_times[query].isnull(),:] estimated_times = [time_ for time_ in df_times.loc[:,query].tolist() if isinstance(time_, pd.Timestamp)] # df_times = df_times.loc[~df_times[query].isnull(),:]
# apply rounding. For example, the agreement of different participants may be required to match minute-wise
# '15min' rounds to 'every 15 minutes'. E.g., '2023-09-22 08:18:49' becomes '2023-09-22 08:15:00'
estimated_times = [time_.round("15min") for time_ in estimated_times]
# when there are no entries left (no entries are provided), skip # when there are no entries left (no entries are provided), skip
if len(estimated_times)==0: if len(estimated_times)==0:
violation_state = False violation_state = False
return violation_state return violation_state
difference = np.max(estimated_times) - np.min(estimated_times)
violation_state = difference > pd.Timedelta("15min")
# apply rounding. For example, the agreement of different participants may be required to match minute-wise
# '15min' rounds to 'every 15 minutes'. E.g., '2023-09-22 08:18:49' becomes '2023-09-22 08:15:00'
# estimated_times = [time_.round("15min") for time_ in estimated_times]
# there should only be one eta_berth, when all participants have provided the same time # there should only be one eta_berth, when all participants have provided the same time
# this equates to the same criteria as checking, whether # this equates to the same criteria as checking, whether
# times_agency.eta_berth==times_mooring.eta_berth==times_portadministration.eta_berth==times_pilot.eta_berth==times_tug.eta_berth # times_agency.eta_berth==times_mooring.eta_berth==times_portadministration.eta_berth==times_pilot.eta_berth==times_tug.eta_berth
n_unique_times = len(np.unique(estimated_times)) # n_unique_times = len(np.unique(estimated_times))
violation_state = n_unique_times!=1 # violation_state = n_unique_times!=1
return violation_state return violation_state
def check_unique_shipcall_counts(self, query:str, times_agency:pd.DataFrame, rounding="min", maximum_threshold=3, all_times_agency=None)->bool: def check_unique_shipcall_counts(self, query:str, times_agency:pd.DataFrame, rounding="min", maximum_threshold=3, all_times_agency=None)->bool: