Finding vessels opening east and west of Suez¶

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 [12]:

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 [13]:

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()

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

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

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

In [14]:

from signal_ocean.tonnage_list import IndexLevel

data_frame = historical_tonnage_list.to_data_frame()
open_wide_area_west_of_suez = [
"Caribs",
"UK Continent",
"Mediterranean",
"West Africa",
"Black Sea / Sea Of Marmara",
"North Sea",
"Baltic",
"East Coast Canada",
"US Atlantic Coast",
"West Coast Central America",
"US Gulf & Mainland",
"East Coast Mexico",
"East Coast Central America"
]

open_wide_area_east_of_suez = [
"South East Asia",
"Arabian Gulf",
"Red Sea",
"Korea / Japan",
"India / Pakistan",
"South East Africa",
"China / Taiwan",
"Australia / New Zealand",
"Russian Pacific",
"West Coast South America",
"West Coast North America",
"Pacific Islands"
]
west_areas_filter = data_frame.open_wide_area_point_in_time.isin(
    open_wide_area_west_of_suez
)


east_areas_filter = data_frame.open_wide_area_point_in_time.isin(
    open_wide_area_east_of_suez
)

west_vessels = (
    data_frame[west_areas_filter]
    .groupby(IndexLevel.DATE, sort=True)
    .size()
)

east_vessels = (
    data_frame[east_areas_filter]
    .groupby(IndexLevel.DATE, sort=True)
    .size()
)

ax = west_vessels.plot(
    markerfacecolor="blue",
    color="skyblue",
    linewidth=4,
    label="West",
    figsize=(12, 9),
)
ax2 = east_vessels.plot(
    ax=ax,
    markerfacecolor="darkgreen",
    color="lime",
    linewidth=4,
    label="East",
    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_wide_area_west_of_suez = [ "Caribs", "UK Continent", "Mediterranean", "West Africa", "Black Sea / Sea Of Marmara", "North Sea", "Baltic", "East Coast Canada", "US Atlantic Coast", "West Coast Central America", "US Gulf & Mainland", "East Coast Mexico", "East Coast Central America" ] open_wide_area_east_of_suez = [ "South East Asia", "Arabian Gulf", "Red Sea", "Korea / Japan", "India / Pakistan", "South East Africa", "China / Taiwan", "Australia / New Zealand", "Russian Pacific", "West Coast South America", "West Coast North America", "Pacific Islands" ] west_areas_filter = data_frame.open_wide_area_point_in_time.isin( open_wide_area_west_of_suez ) east_areas_filter = data_frame.open_wide_area_point_in_time.isin( open_wide_area_east_of_suez ) west_vessels = ( data_frame[west_areas_filter] .groupby(IndexLevel.DATE, sort=True) .size() ) east_vessels = ( data_frame[east_areas_filter] .groupby(IndexLevel.DATE, sort=True) .size() ) ax = west_vessels.plot( markerfacecolor="blue", color="skyblue", linewidth=4, label="West", figsize=(12, 9), ) ax2 = east_vessels.plot( ax=ax, markerfacecolor="darkgreen", color="lime", linewidth=4, label="East", figsize=(12, 9), ) ax2.set_ylabel("Vessel count") ax2.legend()

Out[14]:

<matplotlib.legend.Legend at 0x21c5b2aea60>