from __future__ import annotations
import typing
import matplotlib.axis
import numpy as np
import pandas as pd
from conflowgen.domain_models.data_types.mode_of_transport import ModeOfTransport
from conflowgen.analyses.inbound_and_outbound_vehicle_capacity_analysis import \
InboundAndOutboundVehicleCapacityAnalysis
from conflowgen.reporting import AbstractReportWithMatplotlib
[docs]
class InboundAndOutboundVehicleCapacityAnalysisReport(AbstractReportWithMatplotlib):
"""
This analysis report takes the data structure as generated by :class:`.InboundAndOutboundVehicleCapacityAnalysis`
and creates a comprehensible representation for the user, either as text or as a graph.
The visual and table are expected to approximately look like in the
`example InboundAndOutboundVehicleCapacityAnalysisReport \
<notebooks/analyses.ipynb#Inbound-And-Outbound-Vehicle-Capacity-Analysis-Report>`_.
"""
report_description = """
Analyze the container volumes transported by vehicle type for the inbound and outbound journeys and check for the
maximum capacity of each vehicle type on its outbound journey.
If, e.g., for the vehicle type 'feeder' the maximum capacity is reached, most likely there are more vehicles that
deliver containers destined for feeders but could not get a free spot.
You might consider adding more vehicles of that type or adjusting the modal split.
"""
def __init__(self):
super().__init__()
self._df = None
self.analysis = InboundAndOutboundVehicleCapacityAnalysis(
transportation_buffer=self.transportation_buffer
)
[docs]
def get_report_as_text(self, **kwargs) -> str:
"""
Keyword Args:
start_date (datetime.datetime):
Only include containers that arrive after the given start time.
end_date (datetime.datetime):
Only include containers that depart before the given end time.
Returns:
The report in text format spanning over several lines.
"""
inbound_capacities, outbound_actual_capacities, outbound_maximum_capacities = \
self._get_container_volumes_in_teu(kwargs)
assert len(kwargs) == 0, f"Keyword(s) {list(kwargs.keys())} have not been processed."
# create string representation
report = "\n(all numbers are reported in TEU)\n"
report += "vehicle type "
report += "inbound volume "
report += "outbound volume "
report += "outbound max capacity"
report += "\n"
for vehicle_type in self.order_of_vehicle_types_in_report:
vehicle_type_as_text = str(vehicle_type).replace("_", " ")
max_capacities_repr = -1 if np.isnan(outbound_maximum_capacities[vehicle_type]) \
else outbound_maximum_capacities[vehicle_type]
report += f"{vehicle_type_as_text:<15} "
report += f"{inbound_capacities[vehicle_type]:>16.1f} "
report += f"{outbound_actual_capacities[vehicle_type]:>20.1f} "
report += f"{max_capacities_repr:>25.1f}"
report += "\n"
report += "(rounding errors might exist)\n"
return report
[docs]
def get_report_as_graph(self, **kwargs) -> matplotlib.axis.Axis:
"""
The report as a graph is represented as a bar chart using pandas.
Keyword Args:
start_date (datetime.datetime):
Only include containers that arrive after the given start time.
end_date (datetime.datetime):
Only include containers that depart before the given end time.
Returns:
The matplotlib axis of the bar chart.
"""
inbound_capacities, outbound_actual_capacities, outbound_maximum_capacities = \
self._get_container_volumes_in_teu(kwargs)
assert len(kwargs) == 0, f"Keyword(s) {list(kwargs.keys())} have not been processed."
self._df = pd.DataFrame({
"inbound volume (in TEU)": inbound_capacities,
"outbound volume (in TEU)": outbound_actual_capacities,
"outbound maximum capacity": outbound_maximum_capacities
})
self._df.index = [str(i).replace("_", " ") for i in self._df.index]
ax = self._df.plot.barh()
ax.set_xlabel("Capacity (in TEU)")
ax.set_title("Inbound and outbound vehicle capacity analysis")
return ax
def _get_container_volumes_in_teu(
self,
kwargs
) -> typing.Tuple[typing.Dict[ModeOfTransport, float], typing.Dict[
ModeOfTransport, float], typing.Dict[ModeOfTransport, float]]:
assert self.transportation_buffer is not None
self.analysis.update(
transportation_buffer=self.transportation_buffer
)
start_date = kwargs.pop("start_date", None)
end_date = kwargs.pop("end_date", None)
# gather data
inbound_container_volume = self.analysis.get_inbound_container_volumes_by_vehicle_type(
start_date=start_date,
end_date=end_date
)
outbound_container_volume, outbound_maximum_container_volume = \
self.analysis.get_outbound_container_volume_by_vehicle_type(
start_date=start_date,
end_date=end_date
)
return inbound_container_volume.teu, outbound_container_volume.teu, outbound_maximum_container_volume.teu