#!/usr/bin/env python
# coding: utf-8

# # Module 6
# 
# ## Video 27: Multiple Vessels in a Movement
# **Python for the Energy Industry**
# 
# Sometimes, a Cargo Movement will contain information on multiple vessels. There are two reasons this might happen:
# - The cargo is loaded to, or unloaded from, a floating storage vessel
# - A ship to ship transfer (STS) occurs
# 
# Let's find some example Cargo Movements where this is the case.
# 
# [Cargo Movements documentation](https://vortechsa.github.io/python-sdk/endpoints/cargo_movements/)

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from datetime import datetime
from dateutil.relativedelta import relativedelta
import pandas as pd
import vortexasdk as v
now = datetime.utcnow()


# We start as before with a simple query:

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cm_query = v.CargoMovements().search(
    filter_activity="any_activity",
    filter_time_min=now,
    filter_time_max=now)

print(len(cm_query))


# We can find Cargo Movements with STS events by getting those there multiple vessels are detailed:

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multi_vessel = [q for q in cm_query if len(q['vessels']) > 1]
print(len(multi_vessel))


# Let's take a look at the types of event happening in each of these:

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for cm in multi_vessel:
    event_types = [e['event_type'] for e in cm['events']]
    print(event_types)


# ## STS Events
# 
# We'll look in more detail at those Cargo Movements with STS events.

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sts_movements = [cm for cm in multi_vessel if 'cargo_sts_event' in [e['event_type'] for e in cm['events']]]
print(len(sts_movements))


# Let's take a look at the vessels in one of them:

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sts_movements[0]['vessels'][0]


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sts_movements[0]['vessels'][1]


#  And also the STS event:

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sts_movements[0]['events'][2]


# Let's view the data on movements with STS transfers in a DataFrame. The easiest way to do this is to convert our initial search into a DataFrame using the `to_df()` function, and then drop all movements with no STS event.

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required_columns = ["vessels.0.name","vessels.0.vessel_class","vessels.1.name","vessels.1.vessel_class","product.group.label","quantity",
                    "events.cargo_port_load_event.0.location.port.label","events.cargo_sts_event.0.location.sts_zone.label",
                    "events.cargo_port_unload_event.0.location.port.label"]

new_labels = ["vessel0","vessel0_class","vessel1","vessel1_class","product_group","quantity","loading_port","sts_location","unloading_port"]

relabel = dict(zip(required_columns,new_labels))


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cm_df = cm_query.to_df(columns=required_columns).rename(relabel,axis=1)


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cm_df_multi = cm_df.dropna(subset=['loading_port','sts_location','unloading_port'])


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cm_df_multi


# ### Exercise
# 
# Expand the range of the Cargo Movements - say to a ~2 week period. Can you find any movements involving 3 or more vessels? If so, isolate and have a look at these.

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