Analyzing historical data for all vessels in a specific vessel class¶

Run this example in Colab.¶

Setup¶

Install the Signal Ocean SDK:

In [ ]:

!pip install signal-ocean

!pip install signal-ocean

Set your subscription key, acquired here: https://apis.signalocean.com/profile

In [2]:

signal_ocean_api_key = '' #replace with your subscription key

signal_ocean_api_key = '' #replace with your subscription key

Example 1: Number of spot and relet vessels over time¶

In this example, a supply trend is generated in order to calculate the number of spot and relet vessels which open in specific areas. A time period of 5 days is explored here but this number can go up until the date you subscribed to The Signal Ocean Platform.

First, get a historical tonnage list containing the relevant data:

In [3]:

from datetime import date, timedelta
from signal_ocean import Connection
from signal_ocean.tonnage_list import (
    TonnageListAPI,
    VesselClassFilter,
    PortFilter,
    VesselFilter,
    MarketDeployment,
    VesselSubclass,
    DateRange
)

connection = Connection(signal_ocean_api_key)
api = TonnageListAPI(connection)

vessel_class_filter = VesselClassFilter(name_like="aframax")
vessel_class = api.get_vessel_classes(vessel_class_filter)[0]

port_filter = PortFilter(name_like="ceyhan")
port = api.get_ports(port_filter)[0]

laycan_end_in_days = 512
start_date = date.today() - timedelta(days=5)
end_date = date.today()

vessel_filter = VesselFilter(
    market_deployments=[MarketDeployment.RELET, MarketDeployment.SPOT],
    vessel_subclass=VesselSubclass.DIRTY,
    latest_ais_since=5,
)

historical_tonnage_list = api.get_historical_tonnage_list(
    port,
    vessel_class,
    laycan_end_in_days,
    DateRange(start_date, end_date),
    vessel_filter=vessel_filter,
)

from datetime import date, timedelta from signal_ocean import Connection from signal_ocean.tonnage_list import ( TonnageListAPI, VesselClassFilter, PortFilter, VesselFilter, MarketDeployment, VesselSubclass, DateRange ) connection = Connection(signal_ocean_api_key) api = TonnageListAPI(connection) vessel_class_filter = VesselClassFilter(name_like="aframax") vessel_class = api.get_vessel_classes(vessel_class_filter)[0] port_filter = PortFilter(name_like="ceyhan") port = api.get_ports(port_filter)[0] laycan_end_in_days = 512 start_date = date.today() - timedelta(days=5) end_date = date.today() vessel_filter = VesselFilter( market_deployments=[MarketDeployment.RELET, MarketDeployment.SPOT], vessel_subclass=VesselSubclass.DIRTY, latest_ais_since=5, ) historical_tonnage_list = api.get_historical_tonnage_list( port, vessel_class, laycan_end_in_days, DateRange(start_date, end_date), vessel_filter=vessel_filter, )

Then, convert the data to a data frame, filter, and plot:

In [4]:

from signal_ocean.tonnage_list import IndexLevel

data_frame = historical_tonnage_list.to_data_frame()
open_areas_of_interest = [
    "Iberian Atlantic Coast",
    "British Isles",
    "North Sea",
    "Baltic Sea Low",
    "Baltic Sea Upper",
    "Continent",
]
areas_filter = data_frame.open_narrow_area_point_in_time.isin(
    open_areas_of_interest
)

spot_filter = data_frame.market_deployment_point_in_time == "Spot"
spot_vessels = (
    data_frame[spot_filter & areas_filter]
    .groupby(IndexLevel.DATE, sort=True)
    .size()
)

relet_filter = data_frame.market_deployment_point_in_time == "Relet"
relet_vessels = (
    data_frame[relet_filter & areas_filter]
    .groupby(IndexLevel.DATE, sort=True)
    .size()
)

ax = spot_vessels.plot(
    markerfacecolor="blue",
    color="skyblue",
    linewidth=4,
    label="Spot",
    figsize=(12, 9),
)
ax2 = relet_vessels.plot(
    ax=ax,
    markerfacecolor="darkgreen",
    color="lime",
    linewidth=4,
    label="Relet",
    figsize=(12, 9),
)

ax2.set_ylabel("Vessel count")
ax2.legend()

from signal_ocean.tonnage_list import IndexLevel data_frame = historical_tonnage_list.to_data_frame() open_areas_of_interest = [ "Iberian Atlantic Coast", "British Isles", "North Sea", "Baltic Sea Low", "Baltic Sea Upper", "Continent", ] areas_filter = data_frame.open_narrow_area_point_in_time.isin( open_areas_of_interest ) spot_filter = data_frame.market_deployment_point_in_time == "Spot" spot_vessels = ( data_frame[spot_filter & areas_filter] .groupby(IndexLevel.DATE, sort=True) .size() ) relet_filter = data_frame.market_deployment_point_in_time == "Relet" relet_vessels = ( data_frame[relet_filter & areas_filter] .groupby(IndexLevel.DATE, sort=True) .size() ) ax = spot_vessels.plot( markerfacecolor="blue", color="skyblue", linewidth=4, label="Spot", figsize=(12, 9), ) ax2 = relet_vessels.plot( ax=ax, markerfacecolor="darkgreen", color="lime", linewidth=4, label="Relet", figsize=(12, 9), ) ax2.set_ylabel("Vessel count") ax2.legend()

Out[4]:

<matplotlib.legend.Legend at 0x7f4a14c3b130>

Example 2: Number of ballast unfixed vessels in the Mediterranean and Black Sea¶

In this example, the same data collected from the first call will be used to detect ballast unfixed vessels in the Mediterranean and Black Sea areas.

In [5]:

from signal_ocean.tonnage_list import OperationalStatus

data_frame = historical_tonnage_list.to_data_frame()
open_areas_of_interest = [
    "East Mediterranean",
    "Central Mediterranean",
    "West Mediterranean",
    "Black Sea",
]

operationalStatuses = [OperationalStatus.BALLAST_UNFIXED]
areas_filter = data_frame.open_narrow_area_point_in_time.isin(
    open_areas_of_interest
)
operational_status_filter = data_frame.operational_status_point_in_time.isin(
    operationalStatuses
)

aggregatedData = (
    data_frame[operational_status_filter & areas_filter]
    .groupby(IndexLevel.DATE, sort=True)
    .size()
)


ax = aggregatedData.plot(
    markerfacecolor="blue",
    color="skyblue",
    linewidth=4,
    label="Spot",
    figsize=(12, 9),
)

ax2.set_ylabel("Vessel count")
ax2.legend()

from signal_ocean.tonnage_list import OperationalStatus data_frame = historical_tonnage_list.to_data_frame() open_areas_of_interest = [ "East Mediterranean", "Central Mediterranean", "West Mediterranean", "Black Sea", ] operationalStatuses = [OperationalStatus.BALLAST_UNFIXED] areas_filter = data_frame.open_narrow_area_point_in_time.isin( open_areas_of_interest ) operational_status_filter = data_frame.operational_status_point_in_time.isin( operationalStatuses ) aggregatedData = ( data_frame[operational_status_filter & areas_filter] .groupby(IndexLevel.DATE, sort=True) .size() ) ax = aggregatedData.plot( markerfacecolor="blue", color="skyblue", linewidth=4, label="Spot", figsize=(12, 9), ) ax2.set_ylabel("Vessel count") ax2.legend()

Out[5]:

<matplotlib.legend.Legend at 0x7f4a161b7280>