pipeline.src.flows.vessels

Functions

`extract_french_vessels`(→ pandas.DataFrame)	Extracts french vessels from Navpro.
`extract_eu_vessels`(→ pandas.DataFrame)	Extracts EU vessels from Navpro.
`extract_non_eu_vessels`(→ pandas.DataFrame)	Extracts non-EU vessels from Navpro.
`extract_vessels_operators`(→ pandas.DataFrame)	Extracts vessel operators (in the sense of "the people or organisation that operate
`extract_vessels_logbook_equipement`(→ pandas.DataFrame)	Extracts vessels logbook equipement data
`extract_french_vessels_navigation_licences`(...)	Extracts the navigation licence sailing category and expiration date of french
`extract_control_charters`(→ pandas.DataFrame)	Extracts vessels with the information of whether they are under control charter or
`concat_merge_vessels`(→ pandas.DataFrame)	Concatenates french_vessels, eu_vessels and non_eu_vessels, then performs a
`clean_vessels`(→ pandas.DataFrame)	Combines and concatenates data of some columns as coalesced values or lists (phone
`add_unknown_vessel`(→ pandas.DataFrame)	Adds an "UNKNOWN" vessel to the list, to be used when reporting an action on a
`load_vessels`(all_vessels)	Replaces the content of the vessels table with the content of the all_vessels
`vessels_flow`()

Module Contents

pipeline.src.flows.vessels.extract_french_vessels() → pandas.DataFrame[source]

Extracts french vessels from Navpro.

Returns:: french vessels
Return type:: pd.DataFrame

pipeline.src.flows.vessels.extract_eu_vessels() → pandas.DataFrame[source]

Extracts EU vessels from Navpro.

Returns:: EU vessels
Return type:: pd.DataFrame

pipeline.src.flows.vessels.extract_non_eu_vessels() → pandas.DataFrame[source]

Extracts non-EU vessels from Navpro.

Returns:: non-EU vessels
Return type:: pd.DataFrame

pipeline.src.flows.vessels.extract_vessels_operators() → pandas.DataFrame[source]

Extracts vessel operators (in the sense of “the people or organisation that operate or manage the operations of the vessel”) data from Poséidon (name, contact info).

Returns:: Vessels operators
Return type:: pd.DataFrame

pipeline.src.flows.vessels.extract_vessels_logbook_equipement() → pandas.DataFrame[source]

Extracts vessels logbook equipement data

Returns:: Vessels logbook equipment data
Return type:: pd.DataFrame

pipeline.src.flows.vessels.extract_french_vessels_navigation_licences() → pandas.DataFrame[source]

Extracts the navigation licence sailing category and expiration date of french vessels from Gina.

Returns:: French vessels navigation licence information
Return type:: pd.DataFrame

pipeline.src.flows.vessels.extract_control_charters() → pandas.DataFrame[source]

Extracts vessels with the information of whether they are under control charter or not from Monitorfish, based on historical control data.

Returns:: Vessels with a boolean column under_charter
Return type:: pd.DataFrame

pipeline.src.flows.vessels.concat_merge_vessels(french_vessels: pandas.DataFrame, eu_vessels: pandas.DataFrame, non_eu_vessels: pandas.DataFrame, vessels_logbook_equipement: pandas.DataFrame, vessels_operators: pandas.DataFrame, licences: pandas.DataFrame, control_charters: pandas.DataFrame) → pandas.DataFrame[source]

Concatenates french_vessels, eu_vessels and non_eu_vessels, then performs a left join of the resulting DataFrame with vessels_operators, licences and control_charters successively.

Vessels, identified by their id should be unique :

accross french_vessels, eu_vessels and non_eu_vessels : a given id cannot be in more than one of the three DataFrames, and it must be present just once (a single row)

in vessels_operators, licences and control_charters: a given id can be in 1, 2 or all 3 DataFrames, but it cannot have more than one row in each DataFrame.

Parameters:

french_vessels (pd.DataFrame) – French vessels
eu_vessels (pd.DataFrame) – EU vessels
non_eu_vessels (pd.DataFrame) – non-EU vessels
vessels_logbook_equipement (pd.DataFrame) – vessels logbook equipment data
vessels_operators (pd.DataFrame) – vessels’ operators data
licences (pd.DataFrame) – french vessels navigation licences data
control_charters (pd.DataFrame) – vessels under_charter status

Raises:

ValueError – if a vessel id is duplicated

Returns:

merged vessels data

Return type:

pd.DataFrame

pipeline.src.flows.vessels.clean_vessels(all_vessels: pandas.DataFrame) → pandas.DataFrame[source]

Combines and concatenates data of some columns as coalesced values or lists (phone numbers, emails…)

Parameters:: all_vessels (pd.DataFrame) – Output of concat_merge_vessels
Returns:: vessels data ready to be loaded.
Return type:: pd.DataFrame

pipeline.src.flows.vessels.add_unknown_vessel(all_vessels: pandas.DataFrame) → pandas.DataFrame[source]

Adds an “UNKNOWN” vessel to the list, to be used when reporting an action on a vessel that is not part of the officiel vessels list.

Parameters:

all_vessels (pd.DataFrame) – List of vessels

Returns:

Same as input with one added “Unknown vessel”

Return type:

pd.DataFrame

Raises:

AssertionError – if one of the input vessels has the id reserved for the UNKNOWN
vessel –

pipeline.src.flows.vessels.load_vessels(all_vessels: pandas.DataFrame)[source]

Replaces the content of the vessels table with the content of the all_vessels DataFrame.

Parameters:: all_vessels (pd.DataFrame) – vessels data to load

pipeline.src.flows.vessels.vessels_flow()[source]