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Merge pull request #10 from puls200/feature/hotfix_20231025_traffic_lights

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Feature/hotfix 20231025 traffic lights
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Daniel Schick 2023-10-25 20:43:04 +02:00 committed by GitHub
commit 4bb73fe80f
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1 changed files with 12 additions and 6 deletions
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18
src/server/BreCal/validators/validation_rule_functions.py
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@ -62,6 +62,8 @@ class ValidationRuleBaseFunctions():
Instead of comparing each individual result, this function counts the amount of unique instances. Instead of comparing each individual result, this function counts the amount of unique instances.
When there is not only one unique value, there are deviating time estimates, and a violation occurs When there is not only one unique value, there are deviating time estimates, and a violation occurs
To reduce the potential of false violations, the agreement is rounded (e.g., by minute).
returns: violation_state (bool) returns: violation_state (bool)
""" """
# shipcall type filter: consider only shipcalls, where the type matches # shipcall type filter: consider only shipcalls, where the type matches
@ -73,19 +75,23 @@ class ValidationRuleBaseFunctions():
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]
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 # exclude missing entries and consider only pd.Timestamp entries (which ignores pd.NaT/null entries)
df_times.loc[~df_times[query].isnull(),:] estimated_times = [type(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(df_times)==0: if len(estimated_times)==0:
violation_state = False violation_state = False
return violation_state return violation_state
# 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
unique_times = len(pd.unique(df_times.loc[:,query])) n_unique_times = len(np.unique(estimated_times))
violation_state = unique_times!=1 violation_state = n_unique_times!=1
return violation_state return violation_state
def check_unique_shipcall_counts(self, query:str, rounding="min", maximum_threshold=3)->bool: def check_unique_shipcall_counts(self, query:str, rounding="min", maximum_threshold=3)->bool:
@ -673,7 +679,7 @@ class ValidationRuleFunctions(ValidationRuleBaseFunctions):
times_agency = self.sql_handler.get_times_for_participant_type(df_times, participant_type=ParticipantType.AGENCY.value) times_agency = self.sql_handler.get_times_for_participant_type(df_times, participant_type=ParticipantType.AGENCY.value)
# requirements: tidal window (from & to) is filled in # requirements: tidal window (from & to) is filled in
if (shipcall.tidal_window_from is pd.NaT) or (shipcall.tidal_window_to is pd.NaT) or (df_times.eta_berth is pd.NaT): if (shipcall.tidal_window_from is pd.NaT) or (shipcall.tidal_window_to is pd.NaT) or (df_times.etd_berth is pd.NaT):
return (StatusFlags.GREEN, None) return (StatusFlags.GREEN, None)
# check, whether the query time is between start & end time # check, whether the query time is between start & end time