How to use the vortexasdk.VesselMovements function in vortexasdk

def test_search_to_dataframe(self):
        rotterdam = [
            g.id
            for g in Geographies().search("rotterdam").to_list()
            if "port" in g.layer
        ]
        df = (
            VesselMovements()
            .search(
                filter_time_min=datetime(2017, 10, 1, 0, 0),
                filter_time_max=datetime(2017, 10, 1, 0, 10),
                filter_origins=rotterdam,
            )
            .to_df()
            .head(2)
        )

        assert list(df.columns) == vessel_movements_result.DEFAULT_COLUMNS
        assert len(df) == 2

cols = [
            "vessel_movement_id",
            "vessel.name",
            "start_timestamp",
            "end_timestamp",
            "origin.location.country.id",
            "origin.location.country.label",
            "destination.location.country.id",
            "destination.location.country.label",
            "cargoes.0.product.group.label",
            "vessel.corporate_entities.charterer.id",
            "vessel.corporate_entities.charterer.label",
        ]

        df = (
            VesselMovements()
            .search(
                filter_origins=meg,
                exclude_origins=iraq,
                exclude_charterers=bahri[0],
                filter_time_min=datetime(2019, 10, 15),
                filter_time_max=datetime(2019, 11, 1),
            )
            .to_df(columns=cols)
        )

        mask = (df["origin.location.country.id"] == iraq[0]) | (
            df["vessel.corporate_entities.charterer.id"] == bahri[0]
        )
        df_excl = df.loc[mask]

        assert df_excl.empty

def test_search_vessel_status(self):
        v = VesselMovements().search(
            filter_time_min=datetime(2019, 10, 1),
            filter_time_max=datetime(2019, 10, 15),
            filter_vessel_classes=["vlcc"],
            filter_vessel_status="vessel_status_ballast",
        )

        assert len(v) > 50

def test_filter_activity(self):
        df = (
            VesselMovements()
            .search(
                filter_activity="storing_state",
                filter_time_min=datetime(2017, 10, 1),
                filter_time_max=datetime(2017, 10, 1),
            )
            .to_df()
            .head(2)
        )

        print(df.head())
        assert len(df) == 2

def test_search_to_dataframe_subset_of_columns(self):
        cols = ["vessel.imo", "vessel.name"]

        rotterdam = [
            g.id
            for g in Geographies().search("rotterdam").to_list()
            if "port" in g.layer
        ]
        df = (
            VesselMovements()
            .search(
                filter_time_min=datetime(2017, 10, 1, 0, 0),
                filter_time_max=datetime(2017, 10, 1, 0, 10),
                filter_origins=rotterdam,
            )
            .to_df(columns=cols)
            .head(2)
        )

        assert list(df.columns) == cols
        assert len(df) == 2

def test_age_flag_scrubbers_filters(self):
        panama = [
            g.id
            for g in Geographies().search("panama").to_list()
            if "country" in g.layer
        ]

        df = (
            VesselMovements()
            .search(
                filter_vessel_scrubbers="inc",
                filter_vessel_age_min=2,
                filter_vessel_age_max=15,
                filter_vessel_flags=panama,
                filter_time_min=datetime(2017, 10, 1),
                filter_time_max=datetime(2017, 10, 1),
            )
            .to_df()
            .head(2)
        )

        print(df.head())
        assert len(df) == 2

def test_to_df_all_columns(self):
        rotterdam = [
            g.id
            for g in Geographies().search("rotterdam").to_list()
            if "port" in g.layer
        ]
        df = (
            VesselMovements()
            .search(
                filter_time_min=datetime(2017, 10, 1, 0, 0),
                filter_time_max=datetime(2017, 10, 1, 0, 10),
                filter_origins=rotterdam,
            )
            .to_df(columns="all")
            .head(2)
        )

        assert len(df) == 2

| 10 | ABDIAS NASCIMENTO |  9.4539e+06  |     710032990 |                  171000 |       157055 | suezmax               | Marlim Sul Field [BR]                  |                              nan |                        nan |                      nan | Sao Francisco Do Sul, SC [BR]     |                                   nan |                             nan |                           nan | 2017-09-28T18:29:45+0000 | 2017-10-04T23:05:32+0000    | Crude                           | nan                             |                            nan        | nan                                         |                                              nan | PETROBRAS                                          |
| 11 | ABIOLA            |  8.61943e+06 |     657995000 |                   47261 |        35644 | handysize             | Port Harcourt [NG]                     |                              nan |                        nan |                      nan | nan                               |                                   nan |                             nan |                           nan | 2014-12-02T10:20:03+0000 | nan                         | Clean products                  | Full Range                      |                              0.490481 | nan                                         |                                              nan | nan                                                |
| 13 | ABLIANI           |  9.69307e+06 |     256903000 |                  124518 |       109999 | aframax               | Ceyhan [TR]                            |                              nan |                        nan |                      nan | Sarroch (Porto Foxi) [IT]         |                                   nan |                             nan |                           nan | 2017-09-26T21:33:43+0000 | 2017-10-03T15:45:15+0000    | Crude                           | Azeri Light                     |                              1        | nan                                         |                                              nan | Eastern Mediterranean Maritime Ltd                 |
| 19 | AC-D              |  9.42844e+06 |     256934000 |                    8628 |         7842 | tiny_tanker           | Varna [BG]                             |                              nan |                        nan |                      nan | Valencia [ES]                     |                                   nan |                             nan |                           nan | 2017-09-20T08:00:58+0000 | 2017-10-06T15:49:00+0000    | Clean products                  | Finished Biodiesel              |                              0.868073 | nan                                         |                                              nan | nan                                                |
| 20 | ACACIA            |  9.4766e+06  |     371044000 |                   14570 |        13566 | general_purpose       | Bontang, KL [ID]                       |                              nan |                        nan |                      nan | Lianyungang [CN]                  |                                   nan |                             nan |                           nan | 2017-09-28T13:13:57+0000 | 2017-10-07T00:30:46+0000    | Clean products                  | Chemicals                       |                              0.999186 | nan                                         |                                              nan | KOKUKA SANGYO                                      |
| 22 | ACACIA RUBRA      |  9.46853e+06 |     249374000 |                    6000 |         6065 | tiny_tanker           | Mosjoen [NO]                           |                              nan |                        nan |                      nan | Sigerfjord [NO]                   |                                   nan |                             nan |                           nan | 2017-10-01T00:01:53+0000 | 2017-10-06T10:53:20+0000    | Dirty products                  | nan                             |                            nan        | nan                                         |                                              nan | nan                                                |


"""
from datetime import datetime

from vortexasdk import VesselMovements

if __name__ == "__main__":
    # Query the API
    search_result = VesselMovements().search(
        filter_time_min=datetime(2017, 10, 1, 0),
        filter_time_max=datetime(2017, 10, 1, 1),
    )

    # A complete list of available columns can be found at https://vortechsa.github.io/python-sdk/endpoints/vessel_movements/#notes
    # We only require a subset of available columns here
    required_columns = [
        # Show metadata about the vessel
        "vessel.name",
        "vessel.imo",
        "vessel.mmsi",
        "vessel.cubic_capacity",
        "vessel.dwt",
        "vessel.vessel_class",
        # Show any corporate information associated with the vessel
        "vessel.corporate_entities.charterer.label",

"""
Let's find some ballast movements
"""
from datetime import datetime

from vortexasdk import VesselMovements

if __name__ == "__main__":
    # Query the API
    search_result = VesselMovements().search(
        filter_time_min=datetime(2017, 10, 1, 0),
        filter_time_max=datetime(2017, 10, 1, 1),
        filter_vessel_status="vessel_status_ballast",
    )

    # Convert the search result to a dataframe
    ballast_movements = search_result.to_df()

return [c.id for c in corporations_with_encapsulating_names if c.name.upper() == corporation_name.upper()]

    # We could have chosen to keep the other corporations (with similar, but not exact names) like so:
    # return [c.id for c in corporations_with_encapsulating_names]


if __name__ == "__main__":
    # Read our excel sheet of charterers into a dataframe
    charterers_df = pd.read_excel("./resources/my_charterers.xlsx")

    # Convert the charterer names into ids
    charterers_list_of_lists = charterers_df['charterers'].apply(convert_to_corporation_ids).to_list()
    charterers = [item for sublist in charterers_list_of_lists for item in sublist]

    # Query API
    df = VesselMovements().search(
        filter_charterers=charterers,
        filter_time_min=datetime.now() - timedelta(weeks=1),
        filter_time_max=datetime.now(),
    ).to_df()

    print(df)