Oil Flows¶

Run this example in Colab.¶

Setup¶

Install the Signal Ocean SDK:

pip install signal-ocean

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

In [ ]:

!pip install signal-ocean

!pip install signal-ocean

In [ ]:

signal_ocean_api_key = '' #replace with your subscription key

signal_ocean_api_key = '' #replace with your subscription key

Call the Voyages API¶

The Voyages API retrieves information about vessel voyages.

In [2]:

from signal_ocean import Connection
from signal_ocean.voyages import VoyagesAPI
import pandas as pd
from datetime import date, datetime
import plotly.express as px

from signal_ocean import Connection from signal_ocean.voyages import VoyagesAPI import pandas as pd from datetime import date, datetime import plotly.express as px

In [3]:

connection = Connection(signal_ocean_api_key)
api = VoyagesAPI(connection)

connection = Connection(signal_ocean_api_key) api = VoyagesAPI(connection)

Get vessel class id for VLCCsTankers

In [4]:

vessel_classes = api.get_vessel_classes()
vessel_classes_df = pd.DataFrame(v.__dict__ for v in vessel_classes)
vessel_classes_df[vessel_classes_df['vessel_type']=='Tanker'].head(7)

vessel_classes = api.get_vessel_classes() vessel_classes_df = pd.DataFrame(v.__dict__ for v in vessel_classes) vessel_classes_df[vessel_classes_df['vessel_type']=='Tanker'].head(7)

Out[4]:

	vessel_class_id	vessel_class_name	vessel_type_id	vessel_type
11	84	VLCC	1	Tanker
12	85	Suezmax	1	Tanker
13	86	Aframax	1	Tanker
14	87	Panamax	1	Tanker
15	88	MR2	1	Tanker
16	89	MR1	1	Tanker
17	90	Small	1	Tanker

Get voyages for VLCCs¶

For VLCCs we will use (vessel_class_id = 84) when calling the API

In [5]:

voyages = api.get_voyages_condensed(vessel_class_id=84, date_from=datetime.strptime("2021-09-01", "%Y-%m-%d"))
voyages = pd.DataFrame([v.__dict__ for v in voyages])
voyages.tail()

voyages = api.get_voyages_condensed(vessel_class_id=84, date_from=datetime.strptime("2021-09-01", "%Y-%m-%d")) voyages = pd.DataFrame([v.__dict__ for v in voyages]) voyages.tail()

Out[5]:

	imo	voyage_number	vessel_type_id	vessel_class_id	vessel_status_id	commercial_operator_id	deleted	events	id	horizon_id	...	last_discharge_sailing_date	last_discharge_country_id	last_discharge_country_name	last_discharge_area_id_level0	last_discharge_area_name_level0	repairs_ind	storage_ind	sts_load_ind	sts_discharge_ind	local_trade_ind
10341	9943748	1	1	84	1	1645.0	False	None	I97BAC454VEDBD3C900	1	...	2023-08-19 19:57:33+00:00	173.0	Netherlands	None	Continent	True	False	True	False	False
10342	9943748	2	1	84	1	1645.0	False	None	I97BAC454VEDC71FD00	1	...	2023-11-12 03:58:16+00:00	112.0	India	None	Pakistan / West Coast India	False	False	True	False	False
10343	9943748	3	1	84	1	1645.0	False	None	I97BAC454VEDCC11700	1	...	2023-12-20 23:54:35+00:00	55.0	China	None	Central China	False	False	False	False	False
10344	9943748	4	1	84	1	558.0	False	None	I97BAC454VEDD103100	2	...	2024-02-17 15:42:08.398000+00:00	55.0	China	None	North China	False	False	False	False	False
10345	9946673	1	1	84	1	1441.0	False	None	I97C63154VEDB84AF00	2	...	NaT	NaN	None	None	None	False	False	False	False	True

5 rows × 97 columns

Convert Timestamp to datetime, drop voyages with first_load_sailing_date in the future (ongoing or future)

In [6]:

voyages['first_load_sailing_date'] = voyages['first_load_sailing_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None))
voyages=voyages[voyages['first_load_sailing_date']<datetime.now()]

voyages['first_load_sailing_date'] = voyages['first_load_sailing_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None)) voyages=voyages[voyages['first_load_sailing_date']

Examine US VLCC Dirty Exports¶

Group by origin and destination (first load, last discharge), and use first_load_sailing_date as date of export

In [7]:

dirty_exports_country_level = voyages[(voyages['first_load_sailing_date']>"2022-01-01")&(voyages['cargo_group']=='Dirty')] \
    .set_index('first_load_sailing_date') \
    .groupby(['first_load_country_name', 'last_discharge_country_name'])['quantity'] \
    .resample('MS') \
    .sum() \
    .reset_index() \
    .rename(columns={'first_load_sailing_date': 'Date'})

dirty_exports_country_level = voyages[(voyages['first_load_sailing_date']>"2022-01-01")&(voyages['cargo_group']=='Dirty')] \ .set_index('first_load_sailing_date') \ .groupby(['first_load_country_name', 'last_discharge_country_name'])['quantity'] \ .resample('MS') \ .sum() \ .reset_index() \ .rename(columns={'first_load_sailing_date': 'Date'})

In [8]:

dirty_exports_country_level.head()

dirty_exports_country_level.head()

Out[8]:

	first_load_country_name	last_discharge_country_name	Date	quantity
0	Angola	Benin	2023-04-01	192000.0
1	Angola	Brazil	2022-11-01	260000.0
2	Angola	Caribs	2023-12-01	246000.0
3	Angola	China	2022-01-01	2601000.0
4	Angola	China	2022-02-01	3171000.0

Drop intra country flows

In [9]:

dirty_exports_country_level = dirty_exports_country_level[dirty_exports_country_level['first_load_country_name']!=dirty_exports_country_level['last_discharge_country_name']]

dirty_exports_country_level = dirty_exports_country_level[dirty_exports_country_level['first_load_country_name']!=dirty_exports_country_level['last_discharge_country_name']]

• Keep only US exports
• Aggregate all exports monthly
• Drop unnecessary columns

In [10]:

us_dirty_exports = dirty_exports_country_level[(dirty_exports_country_level['first_load_country_name']=='United States')].copy()

us_dirty_exports['TotalExports'] = us_dirty_exports['quantity'].groupby(us_dirty_exports['Date']).transform('sum')
us_dirty_exports.drop_duplicates(subset=['Date'], keep='last', inplace = True)
us_dirty_exports.drop(['last_discharge_country_name', 'quantity'], axis=1, inplace = True)
us_dirty_exports.head()

us_dirty_exports = dirty_exports_country_level[(dirty_exports_country_level['first_load_country_name']=='United States')].copy() us_dirty_exports['TotalExports'] = us_dirty_exports['quantity'].groupby(us_dirty_exports['Date']).transform('sum') us_dirty_exports.drop_duplicates(subset=['Date'], keep='last', inplace = True) us_dirty_exports.drop(['last_discharge_country_name', 'quantity'], axis=1, inplace = True) us_dirty_exports.head()

Out[10]:

	first_load_country_name	Date	TotalExports
4276	United States	2022-01-01	6661000.0
4277	United States	2022-02-01	4330000.0
4301	United States	2024-02-01	1956000.0
4302	United States	2022-03-01	5702000.0
4303	United States	2022-04-01	5561000.0

Plot US VLCC Dirty exports¶

In [11]:

us_dirty_exports['Year'] = us_dirty_exports['Date'].apply(lambda date : date.year)
us_dirty_exports['Month'] = us_dirty_exports['Date'].apply(lambda date : date.month)
us_dirty_exports.sort_values(['Month', 'Year'], inplace = True)
us_dirty_exports['Month'] = us_dirty_exports['Date'].apply(lambda date : date.strftime("%b"))

fig = px.histogram(us_dirty_exports, x="Month", y="TotalExports", color='Year',
             barmode='group', height=500, width=1000,
             title='US VLCC Dirty Exports (MT)',
             color_discrete_sequence=["lightgray", "gray", "lightblue"])
fig.show()

us_dirty_exports['Year'] = us_dirty_exports['Date'].apply(lambda date : date.year) us_dirty_exports['Month'] = us_dirty_exports['Date'].apply(lambda date : date.month) us_dirty_exports.sort_values(['Month', 'Year'], inplace = True) us_dirty_exports['Month'] = us_dirty_exports['Date'].apply(lambda date : date.strftime("%b")) fig = px.histogram(us_dirty_exports, x="Month", y="TotalExports", color='Year', barmode='group', height=500, width=1000, title='US VLCC Dirty Exports (MT)', color_discrete_sequence=["lightgray", "gray", "lightblue"]) fig.show()

Examine China VLCC Dirty Imports

Convert Timestamp to datetime, drop uncompleted voyages (with last_discharge_arrival_date in the future)

In [12]:

voyages['last_discharge_arrival_date'] = voyages['last_discharge_arrival_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None))
voyages=voyages[voyages['last_discharge_arrival_date']<datetime.now()]

voyages['last_discharge_arrival_date'] = voyages['last_discharge_arrival_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None)) voyages=voyages[voyages['last_discharge_arrival_date']

Group by origin and destination (first load, last discharge), and use last_discharge_arrival_date as date of import

In [13]:

dirty_imports_country_level = voyages[(voyages['last_discharge_arrival_date']>"2022-01-01")&(voyages['cargo_group']=='Dirty')] \
    .set_index('last_discharge_arrival_date') \
    .groupby(['first_load_country_name', 'last_discharge_country_name'])['quantity'] \
    .resample('MS') \
    .sum() \
    .reset_index() \
    .rename(columns={'last_discharge_arrival_date': 'Date'})

dirty_imports_country_level = voyages[(voyages['last_discharge_arrival_date']>"2022-01-01")&(voyages['cargo_group']=='Dirty')] \ .set_index('last_discharge_arrival_date') \ .groupby(['first_load_country_name', 'last_discharge_country_name'])['quantity'] \ .resample('MS') \ .sum() \ .reset_index() \ .rename(columns={'last_discharge_arrival_date': 'Date'})

Drop intra country flows

In [14]:

dirty_imports_country_level = dirty_imports_country_level[dirty_imports_country_level['first_load_country_name']!=dirty_imports_country_level['last_discharge_country_name']]
dirty_imports_country_level.head()

dirty_imports_country_level = dirty_imports_country_level[dirty_imports_country_level['first_load_country_name']!=dirty_imports_country_level['last_discharge_country_name']] dirty_imports_country_level.head()

Out[14]:

	first_load_country_name	last_discharge_country_name	Date	quantity
0	Angola	Benin	2023-05-01	192000.0
1	Angola	Brazil	2022-12-01	260000.0
2	Angola	Caribs	2024-01-01	246000.0
3	Angola	China	2022-01-01	2346000.0
4	Angola	China	2022-02-01	2333000.0

• Keep only China imports
• Aggregate all exports monthly
• Drop unnecessary columns

In [15]:

china_dirty_imports = dirty_imports_country_level[(dirty_imports_country_level['last_discharge_country_name']=='China')].copy()

china_dirty_imports['TotalImports'] = china_dirty_imports['quantity'].groupby(china_dirty_imports['Date']).transform('sum')
china_dirty_imports.drop_duplicates(subset=['Date'], keep='last', inplace = True)
china_dirty_imports.drop(['first_load_country_name', 'quantity'], axis=1, inplace = True)
china_dirty_imports.head()

china_dirty_imports = dirty_imports_country_level[(dirty_imports_country_level['last_discharge_country_name']=='China')].copy() china_dirty_imports['TotalImports'] = china_dirty_imports['quantity'].groupby(china_dirty_imports['Date']).transform('sum') china_dirty_imports.drop_duplicates(subset=['Date'], keep='last', inplace = True) china_dirty_imports.drop(['first_load_country_name', 'quantity'], axis=1, inplace = True) china_dirty_imports.head()

Out[15]:

	last_discharge_country_name	Date	TotalImports
3825	China	2022-01-01	29399000.0
3826	China	2022-02-01	26518000.0
3850	China	2024-02-01	9693000.0
4249	China	2022-12-01	33871000.0
4250	China	2023-01-01	29848000.0

Plot China VLCC Dirty Imports¶

In [16]:

china_dirty_imports['Year'] = china_dirty_imports['Date'].apply(lambda date : date.year)
china_dirty_imports['Month'] = china_dirty_imports['Date'].apply(lambda date : date.month)
china_dirty_imports.sort_values(['Month', 'Year'], inplace = True)
china_dirty_imports['Month'] = china_dirty_imports['Date'].apply(lambda date : date.strftime("%b"))

fig = px.histogram(china_dirty_imports, x="Month", y="TotalImports", color='Year',
             barmode='group', height=500, width=1000,
             title='China VLCC dirty Imports',
             color_discrete_sequence=["lightgray", "gray", "lightblue"])
fig.show()

china_dirty_imports['Year'] = china_dirty_imports['Date'].apply(lambda date : date.year) china_dirty_imports['Month'] = china_dirty_imports['Date'].apply(lambda date : date.month) china_dirty_imports.sort_values(['Month', 'Year'], inplace = True) china_dirty_imports['Month'] = china_dirty_imports['Date'].apply(lambda date : date.strftime("%b")) fig = px.histogram(china_dirty_imports, x="Month", y="TotalImports", color='Year', barmode='group', height=500, width=1000, title='China VLCC dirty Imports', color_discrete_sequence=["lightgray", "gray", "lightblue"]) fig.show()

Build your custom Flows - US Clean Exports to specific countries by Panamax and MR2¶

Get vessel class ids for Tanker vessel classes

In [17]:

vessel_classes = api.get_vessel_classes()
vessel_classes_df = pd.DataFrame(v.__dict__ for v in vessel_classes)
vessel_classes_df[vessel_classes_df['vessel_type']=='Tanker'].head(10)

vessel_classes = api.get_vessel_classes() vessel_classes_df = pd.DataFrame(v.__dict__ for v in vessel_classes) vessel_classes_df[vessel_classes_df['vessel_type']=='Tanker'].head(10)

Out[17]:

	vessel_class_id	vessel_class_name	vessel_type_id	vessel_type
11	84	VLCC	1	Tanker
12	85	Suezmax	1	Tanker
13	86	Aframax	1	Tanker
14	87	Panamax	1	Tanker
15	88	MR2	1	Tanker
16	89	MR1	1	Tanker
17	90	Small	1	Tanker

Select the vessel classes of interest¶

Get voyages for selected vessel classes (Panamax and MR2) after a given date

In [18]:

vessel_class_ids = (87, 88) # Get only voyages for Aframax, Panamax and MR2
date_from = datetime.strptime("2022-09-01", "%Y-%m-%d")

voyages = []
for vessel_class_id in vessel_class_ids:    
    voyages.append(api.get_voyages_condensed(vessel_class_id=vessel_class_id, date_from=date_from))
    
voyages = pd.DataFrame([v.__dict__ for vc in voyages for v in vc])
voyages.tail(5)

vessel_class_ids = (87, 88) # Get only voyages for Aframax, Panamax and MR2 date_from = datetime.strptime("2022-09-01", "%Y-%m-%d") voyages = [] for vessel_class_id in vessel_class_ids: voyages.append(api.get_voyages_condensed(vessel_class_id=vessel_class_id, date_from=date_from)) voyages = pd.DataFrame([v.__dict__ for vc in voyages for v in vc]) voyages.tail(5)

Out[18]:

	imo	voyage_number	vessel_type_id	vessel_class_id	vessel_status_id	commercial_operator_id	deleted	events	id	horizon_id	...	last_discharge_sailing_date	last_discharge_country_id	last_discharge_country_name	last_discharge_area_id_level0	last_discharge_area_name_level0	repairs_ind	storage_ind	sts_load_ind	sts_discharge_ind	local_trade_ind
36728	9970363	2	1	88	1	NaN	False	None	I9822BB58VEDCE8A400	2	...	2024-02-17 04:22:33.820000+00:00	240.0	United States	None	US Atlantic Coast	False	False	False	False	False
36729	9970375	1	1	88	1	NaN	False	None	I9822C758VEDC998A00	2	...	2024-02-28 21:48:29.342000+00:00	38.0	Brazil	None	Brazil	True	False	False	False	False
36730	9973913	1	1	88	1	NaN	False	None	I98309958VEDD37BE00	2	...	NaT	NaN	None	None	None	True	False	False	False	True
36731	9974981	1	1	88	1	NaN	False	None	I9834C558VEDD103100	2	...	NaT	NaN	None	None	None	True	False	False	False	True
36732	9983889	1	1	88	1	1216.0	False	None	I98579158VEDD37BE00	2	...	NaT	NaN	None	None	None	True	False	False	False	True

5 rows × 97 columns

Date of flows will be based on the export date

• Convert Timestamp to datetime, drop voyages with first_load_sailing_date in the future (ongoing or future)

In [19]:

voyages['first_load_sailing_date'] = voyages['first_load_sailing_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None))
voyages=voyages[voyages['first_load_sailing_date']<datetime.now()]
voyages.head()

voyages['first_load_sailing_date'] = voyages['first_load_sailing_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None)) voyages=voyages[voyages['first_load_sailing_date']

Out[19]:

	imo	voyage_number	vessel_type_id	vessel_class_id	vessel_status_id	commercial_operator_id	deleted	events	id	horizon_id	...	last_discharge_sailing_date	last_discharge_country_id	last_discharge_country_name	last_discharge_area_id_level0	last_discharge_area_name_level0	repairs_ind	storage_ind	sts_load_ind	sts_discharge_ind	local_trade_ind
0	9063108	207	1	87	1	356.0	False	None	I8A4AC4VEDA976100	1	...	2022-09-16 04:50:52+00:00	55.0	China	None	South China	False	False	False	False	True
1	9063108	208	1	87	1	356.0	False	None	I8A4AC4VEDAB1BF00	1	...	2022-10-06 23:01:32.800000+00:00	55.0	China	None	South China	False	False	False	False	True
2	9063108	209	1	87	1	356.0	False	None	I8A4AC4VEDABEEE00	1	...	2022-10-24 03:57:34+00:00	55.0	China	None	South China	False	False	False	False	True
3	9063108	210	1	87	1	356.0	False	None	I8A4AC4VEDAE67B00	1	...	2022-11-09 03:33:00+00:00	55.0	China	None	South China	False	False	False	False	True
4	9063108	211	1	87	1	356.0	False	None	I8A4AC4VEDAF3AA00	1	...	2022-12-05 07:47:48+00:00	55.0	China	None	South China	False	False	False	False	True

5 rows × 97 columns

Select the cargo types of interest¶

Examine different cargo types under the "Clean" cargo group

In [20]:

voyages[voyages['cargo_group']=='Clean'].drop_duplicates(subset=['cargo_group', 'cargo_type'], keep='last')[['cargo_group', 'cargo_type']].head()

voyages[voyages['cargo_group']=='Clean'].drop_duplicates(subset=['cargo_group', 'cargo_type'], keep='last')[['cargo_group', 'cargo_type']].head()

Out[20]:

	cargo_group	cargo_type
36029	Clean	Mixed Aromatics
36410	Clean	Clean Condensate
36614	Clean	Intermediate
36647	Clean	Gasoline Blending Component
36697	Clean	Jet

In [21]:

voyages[(voyages['first_load_country_name']=='United States')&(voyages['cargo_group']=='Clean')]['cargo_type'].value_counts().head(10)

voyages[(voyages['first_load_country_name']=='United States')&(voyages['cargo_group']=='Clean')]['cargo_type'].value_counts().head(10)

Out[21]:

Gasoline                       2319
Diesel                         1932
Gasoil                          354
Jet                             334
Naphtha                         270
Gasoline Blending Component     120
Intermediate                     15
Mixed Aromatics                   4
Clean Condensate                  2
Name: cargo_type, dtype: int64

• Keep cargo types of interest
• Group by origin and destination (first load, last discharge) and cargo type
• Use last_discharge_arrival_date as date of flow

In [22]:

cargo_types_of_interest = ('Gasoline', 'Diesel', 'Ultra Low Sulphur Diesel', 'Gasoil', 'Naphtha', 'Unleaded Motor Spirit', 'Jet')

custom_flows_country_level = voyages[(voyages['first_load_sailing_date']>"2023-01-01")&(voyages['cargo_type'].isin(cargo_types_of_interest))] \
    .set_index('first_load_sailing_date') \
    .groupby(['first_load_country_name', 'last_discharge_country_name', 'cargo_type'])['quantity'] \
    .resample('MS') \
    .sum() \
    .reset_index() \
    .rename(columns={'first_load_sailing_date': 'Date'})
custom_flows_country_level.head()

cargo_types_of_interest = ('Gasoline', 'Diesel', 'Ultra Low Sulphur Diesel', 'Gasoil', 'Naphtha', 'Unleaded Motor Spirit', 'Jet') custom_flows_country_level = voyages[(voyages['first_load_sailing_date']>"2023-01-01")&(voyages['cargo_type'].isin(cargo_types_of_interest))] \ .set_index('first_load_sailing_date') \ .groupby(['first_load_country_name', 'last_discharge_country_name', 'cargo_type'])['quantity'] \ .resample('MS') \ .sum() \ .reset_index() \ .rename(columns={'first_load_sailing_date': 'Date'}) custom_flows_country_level.head()

Out[22]:

	first_load_country_name	last_discharge_country_name	cargo_type	Date	quantity
0	Albania	Slovenia	Diesel	2023-08-01	30000.0
1	Algeria	Belgium	Gasoline	2023-04-01	37000.0
2	Algeria	Belgium	Jet	2023-02-01	37000.0
3	Algeria	Belgium	Naphtha	2023-02-01	146000.0
4	Algeria	Belgium	Naphtha	2023-03-01	37000.0

Select flows of interest¶

Keep only flows between US and (Mexico, Brazil, Argentina)

In [23]:

import_countries = ('Mexico', 'Brazil', 'Argentina')
custom_flows = custom_flows_country_level[(custom_flows_country_level['first_load_country_name']=='United States')&(custom_flows_country_level['last_discharge_country_name'].isin(import_countries))].copy()
custom_flows.head()

import_countries = ('Mexico', 'Brazil', 'Argentina') custom_flows = custom_flows_country_level[(custom_flows_country_level['first_load_country_name']=='United States')&(custom_flows_country_level['last_discharge_country_name'].isin(import_countries))].copy() custom_flows.head()

Out[23]:

	first_load_country_name	last_discharge_country_name	cargo_type	Date	quantity
12798	United States	Argentina	Diesel	2023-01-01	76000.0
12799	United States	Argentina	Diesel	2023-02-01	97000.0
12800	United States	Argentina	Diesel	2023-03-01	60000.0
12801	United States	Argentina	Diesel	2023-04-01	98000.0
12802	United States	Argentina	Diesel	2023-05-01	108000.0

Plot custom flows - US Clean Exports to specific countries¶

In [24]:

custom_flows['Year'] = custom_flows['Date'].apply(lambda date : date.year)
custom_flows['Month'] = custom_flows['Date'].apply(lambda date : date.month)
custom_flows.sort_values(['Year', 'Month'], inplace = True)

custom_flows['Month'] = custom_flows['Date'].apply(lambda date : date.strftime("%b"))
custom_flows['Year'] = custom_flows['Date'].apply(lambda date : date.strftime("%Y"))
custom_flows['MonthYear'] = custom_flows['Month'].astype(str) + ' ' + custom_flows['Year'].astype(str)

fig = px.histogram(custom_flows, x="MonthYear", y="quantity", color='cargo_type',
            height=500, width=1000, title='custom Flows - US Clean Exports to (Mexico, Brazil and Argentina)',
             color_discrete_sequence=px.colors.qualitative.Pastel)
fig.update_layout(bargap=0.2)
fig.show()

custom_flows['Year'] = custom_flows['Date'].apply(lambda date : date.year) custom_flows['Month'] = custom_flows['Date'].apply(lambda date : date.month) custom_flows.sort_values(['Year', 'Month'], inplace = True) custom_flows['Month'] = custom_flows['Date'].apply(lambda date : date.strftime("%b")) custom_flows['Year'] = custom_flows['Date'].apply(lambda date : date.strftime("%Y")) custom_flows['MonthYear'] = custom_flows['Month'].astype(str) + ' ' + custom_flows['Year'].astype(str) fig = px.histogram(custom_flows, x="MonthYear", y="quantity", color='cargo_type', height=500, width=1000, title='custom Flows - US Clean Exports to (Mexico, Brazil and Argentina)', color_discrete_sequence=px.colors.qualitative.Pastel) fig.update_layout(bargap=0.2) fig.show()