Source code for pelagos_py.steps.quality_control.impossible_location_qc

# This file is part of pelagos_py.
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# Copyright 2025-2026 National Oceanography Centre and The Contributors
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#    http://www.apache.org/licenses/LICENSE-2.0
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"""QC test to identify impossible locations in LATITUDE and LONGITUDE variables."""

#### Mandatory imports ####
from pelagos_py.steps.base_qc import BaseQC, register_qc, flag_cols

#### Custom imports ####
import polars as pl
import xarray as xr
import matplotlib
import matplotlib.pyplot as plt


@register_qc


class impossible_location_qc(BaseQC):
    """
    Target Variable: LATITUDE, LONGITUDE
    Flag Number: 4 (bad data)
    Variables Flagged: LATITUDE, LONGITUDE
    Checks that the latitude and longitude are valid.
    """

    qc_name = "impossible location qc"
    parameter_schema = {}
    required_variables = ["LATITUDE", "LONGITUDE"]
    qc_outputs = ["LATITUDE_QC", "LONGITUDE_QC"]



    def return_qc(self):
        # Convert to polars
        self.df = pl.from_pandas(
            self.data[self.required_variables].to_dataframe(), nan_to_null=False
        )

        # Check LAT/LONG exist within expected bounds
        # TODO: Add optional bounds via parameters (such as Southern Hemisphere, for example)
        for label, bounds in zip(["LATITUDE", "LONGITUDE"], [(-90, 90), (-180, 180)]):
            self.df = self.df.with_columns(
                pl.when(pl.col(label).is_nan())
                .then(9)
                .when((pl.col(label) > bounds[0]) & (pl.col(label) < bounds[1]))
                .then(1)
                .otherwise(4)
                .alias(f"{label}_QC")
            )

        # Convert back to xarray
        flags = self.df.select(pl.col("^.*_QC$"))
        self.flags = xr.Dataset(
            data_vars={
                col: ("N_MEASUREMENTS", flags[col].to_numpy()) for col in flags.columns
            },
            coords={"N_MEASUREMENTS": self.data["N_MEASUREMENTS"]},
        )

        return self.flags




    def plot_diagnostics(self):
        matplotlib.use("tkagg")
        fig, axs = plt.subplots(nrows=2, figsize=(8, 6), sharex=True, dpi=200)

        for ax, var, bounds in zip(
            axs, ["LATITUDE", "LONGITUDE"], [(-90, 90), (-180, 180)]
        ):
            for i in range(10):
                # Plot by flag number
                plot_data = self.df.with_row_index().filter(pl.col(f"{var}_QC") == i)
                if len(plot_data) == 0:
                    continue

                # Plot the data
                ax.plot(
                    plot_data["index"],
                    plot_data[var],
                    c=flag_cols[i],
                    ls="",
                    marker="o",
                    label=f"{i}",
                )
            ax.set(
                xlabel="Index",
                ylabel=var,
            )
            ax.legend(title="Flags", loc="upper right")
            for bound in bounds:
                ax.axhline(bound, ls="--", c="k")

        fig.suptitle("Impossible Location Test")
        fig.tight_layout()
        plt.show(block=True)