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# Grid

Sampling grids.

## `Grid` `dataclass`

Bases: `BaseMixin`

A single survey grid.

Attributes:

Name | Type | Description
--- | --- | ---
`ident` | `str` | unique identifier
`size` | `int` | grid size in cells
`spacing` | `float` | size of individual cell (m)
`lat0` | `float` | reference latitude of cell (0, 0)
`lon0` | `float` | reference longitude of cell (0, 0)
`cells` | `list[float]` | pollution measurements for cells

Source code in `src/snailz/grid.py`

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    @dataclass
    class Grid(BaseMixin):
        """
        A single survey grid.

        Attributes:
            ident: unique identifier
            size: grid size in cells
            spacing: size of individual cell (m)
            lat0: reference latitude of cell (0, 0)
            lon0: reference longitude of cell (0, 0)
            cells: pollution measurements for cells
        """

        primary_key: ClassVar[str] = "ident"
        pivot_keys: ClassVar[set[str]] = {"cells"}
        table_name: ClassVar[str] = "grid"
        _next_id: IdGeneratorType = id_generator("G", 4)

        ident: str = ""
        size: int = 0
        spacing: float = 0.0
        lat0: float = 0.0
        lon0: float = 0.0
        cells: list[float] = field(default_factory=list)
        params: InitVar[Parameters] = None

        def __post_init__(self, params: Parameters):
            """
            Validate fields, generate unique identifier, and fill in cells.

            Args:
                params: Parameters object.

            Raises:
                ValueError: If validation fails.
            """

            validate(self.ident == "", "grid ID cannot be set externally")
            validate(self.size > 0, f"grid size must be positive not {self.size}")
            validate(
                self.spacing > 0.0, f"grid spacing must be positive not {self.spacing}"
            )
            validate_lat_lon("grid", self.lat0, self.lon0)
            validate(params is not None, "params required for initializing grid")

            self.ident = next(self._next_id)
            self.cells = [0.0 for _ in range(self.size * self.size)]
            self._fill()
            self._randomize(params)

        def __str__(self) -> str:
            """
            Convert grid values to headerless CSV text.

            Returns:
                Printable CSV string representation of grid values.
            """

            return "\n".join(
                ",".join(str(self[x, y]) for x in range(self.size))
                for y in range(self.size - 1, -1, -1)
            )

        def __getitem__(self, key: tuple[int, int]) -> Any:
            """
            Get grid element.

            Args:
                key: (x, y) coordinates.

            Returns:
                Value at that location.

            Raises:
                ValueError: If either coordinate out of range.
            """

            self._validate_coords(key)
            x, y = key
            return self.cells[x * self.size + y]

        def __setitem__(self, key: tuple[int, int], value: float):
            """
            Set grid element.

            Args:
                key: (x, y) coordinates.
                value: new value.

            Raises:
                ValueError: If either coordinate out of range.
            """

            x, y = key
            self.cells[x * self.size + y] = value

        @classmethod
        def make(cls, params: Parameters) -> list[Self]:
            """
            Construct multiple grids.

            Args:
                params: Parameters object.

            Returns:
                List of grids.
            """

            origins = cls._make_origins(params)
            return [
                Grid(
                    size=params.grid_size,
                    spacing=params.grid_spacing,
                    lat0=origin[0],
                    lon0=origin[1],
                    params=params,
                )
                for origin in origins
            ]

        @classmethod
        def save_csv(cls, outdir: Path | str, grids: list[Self]):
            """
            Save grids as CSV. Scalar properties of all grids are saved in
            one file; grid cell values are pivoted to long form and saved
            in a separate file.

            Args:
                outdir: Output directory.
                grids: `Grid` objects to save.
            """

            super().save_csv(outdir, grids)

            with open(Path(outdir, "grid_cells.csv"), "w", newline="") as stream:
                objects = cls._grid_cells(grids)
                writer = cls._csv_dict_writer(stream, list(objects[0].keys()))
                for obj in objects:
                    writer.writerow(obj)

        @classmethod
        def save_db(cls, db: Database, grids: list[Self]):
            """
            Save grids to database. Scalar properties of all grids are
            saved in one table; grid cell values are pivoted to long form
            and saved in a separate table.

            Args:
                db: Database connector.
                grids: `Grid` objects to save.
            """

            super().save_db(db, grids)

            table = db["grid_cells"]
            table.insert_all(
                cls._grid_cells(grids),
                pk=("grid_id", "lat", "lon"),
                foreign_keys=[("grid_id", "grid", "ident")],
            )

        @classmethod
        def _grid_cells(cls, grids):
            """
            Pivot grid cell values to long format for persistence.

            Args:
                grids: `Grid` objects to pivot.
            """

            return [
                {"grid_id": g.ident, **g.lat_lon(x, y, True), "value": g[x, y]}
                for g in grids
                for x in range(g.size)
                for y in range(g.size)
            ]

        @classmethod
        def _make_origins(cls, params):
            """
            Construct grid origins.

            Args:
                params: Parameters object.

            Returns:
                List of `params.num_grids` (lat, lon) origins.
            """

            possible = list(
                itertools.product(range(params.num_grids), range(params.num_grids))
            )
            actual = random.sample(possible, k=params.num_grids)
            dim = params.grid_size * params.grid_spacing * params.grid_separation
            return [lat_lon(params.lat0, params.lon0, x * dim, y * dim) for x, y in actual]

        def as_image(self, scale: float) -> Image.Image:
            """
            Convert grid to image.

            Args:
                scale: Scaling factor for grid values to ensure largest is black.

            Returns:
                `Image` object.
            """

            if scale == 0.0:
                scale = self.min_max()[1]
            img_size = (self.size * CELL_SIZE) + ((self.size + 1) * BORDER_WIDTH)
            array = np.full((img_size, img_size), WHITE, dtype=np.uint8)
            spacing = CELL_SIZE + BORDER_WIDTH
            for ix, x in enumerate(range(BORDER_WIDTH, img_size, spacing)):
                for iy, y in enumerate(range(BORDER_WIDTH, img_size, spacing)):
                    color = WHITE - math.floor(WHITE * self[ix, iy] / scale)
                    array[y : y + CELL_SIZE + 1, x : x + CELL_SIZE + 1] = color

            return Image.fromarray(array)

        def lat_lon(
            self, x: int, y: int, as_dict: bool = False
        ) -> tuple[float, float] | dict[str, float]:
            """
            Calculate latitude and longitude of grid cell.

            Args:
                x: Grid X coordinate.
                y: Grid Y coordinate.
                as_dict: Return result as dict instead of pair.

            Returns:
                `(lat, lon)` pair or `{"lat": lat, "lon": lon}` dictionary.

            Raises:
                ValueError: if either coordinate out of range.
            """

            self._validate_coords((x, y))
            lat, lon = lat_lon(self.lat0, self.lon0, x * self.spacing, y * self.spacing)
            return {"lat": lat, "lon": lon} if as_dict else (lat, lon)

        def min_max(self) -> tuple[float, float]:
            """
            Find smallest and largest values in grid.

            Returns:
                `(min, max)` pair.
            """

            return min(self.cells), max(self.cells)

        def _fill(self):
            """Fill in grid values using random walk."""

            center = self.size // 2
            size_1 = self.size - 1
            x, y = center, center

            while (x != 0) and (y != 0) and (x != size_1) and (y != size_1):
                self[x, y] += 1
                m = random.choice(MOVES)
                x += m[0]
                y += m[1]

        def _randomize(self, params: Parameters):
            """
            Randomize values in grid after filling.

            Args:
                params: Parameters object.
            """

            for i, val in enumerate(self.cells):
                if val > 0.0:
                    self.cells[i] = round(
                        abs(random.normalvariate(self.cells[i], params.grid_std_dev)),
                        GRID_PRECISION,
                    )
                else:
                    self.cells[i] = 0.0

        def _validate_coords(self, key: tuple[int, int]):
            """
            Validate (x, y) coordinate pair.

            Raises:
                ValueError: If either coordinate is out of range.
            """
            validate(0 <= key[0] < self.size, "invalid X coordinate {key[0]}")
            validate(0 <= key[1] < self.size, "invalid Y coordinate {key[1]}")

### `__post_init__(params)`

Validate fields, generate unique identifier, and fill in cells.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`params` | `Parameters` | Parameters object. | required

Raises:

Type | Description
--- | ---
`ValueError` | If validation fails.

Source code in `src/snailz/grid.py`

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    def __post_init__(self, params: Parameters):
        """
        Validate fields, generate unique identifier, and fill in cells.

        Args:
            params: Parameters object.

        Raises:
            ValueError: If validation fails.
        """

        validate(self.ident == "", "grid ID cannot be set externally")
        validate(self.size > 0, f"grid size must be positive not {self.size}")
        validate(
            self.spacing > 0.0, f"grid spacing must be positive not {self.spacing}"
        )
        validate_lat_lon("grid", self.lat0, self.lon0)
        validate(params is not None, "params required for initializing grid")

        self.ident = next(self._next_id)
        self.cells = [0.0 for _ in range(self.size * self.size)]
        self._fill()
        self._randomize(params)

### `__str__()`

Convert grid values to headerless CSV text.

Returns:

Type | Description
--- | ---
`str` | Printable CSV string representation of grid values.

Source code in `src/snailz/grid.py`

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    def __str__(self) -> str:
        """
        Convert grid values to headerless CSV text.

        Returns:
            Printable CSV string representation of grid values.
        """

        return "\n".join(
            ",".join(str(self[x, y]) for x in range(self.size))
            for y in range(self.size - 1, -1, -1)
        )

### `__getitem__(key)`

Get grid element.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`key` | `tuple[int, int]` | (x, y) coordinates. | required

Returns:

Type | Description
--- | ---
`Any` | Value at that location.

Raises:

Type | Description
--- | ---
`ValueError` | If either coordinate out of range.

Source code in `src/snailz/grid.py`

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    def __getitem__(self, key: tuple[int, int]) -> Any:
        """
        Get grid element.

        Args:
            key: (x, y) coordinates.

        Returns:
            Value at that location.

        Raises:
            ValueError: If either coordinate out of range.
        """

        self._validate_coords(key)
        x, y = key
        return self.cells[x * self.size + y]

### `__setitem__(key, value)`

Set grid element.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`key` | `tuple[int, int]` | (x, y) coordinates. | required
`value` | `float` | new value. | required

Raises:

Type | Description
--- | ---
`ValueError` | If either coordinate out of range.

Source code in `src/snailz/grid.py`

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    def __setitem__(self, key: tuple[int, int], value: float):
        """
        Set grid element.

        Args:
            key: (x, y) coordinates.
            value: new value.

        Raises:
            ValueError: If either coordinate out of range.
        """

        x, y = key
        self.cells[x * self.size + y] = value

### `make(params)` `classmethod`

Construct multiple grids.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`params` | `Parameters` | Parameters object. | required

Returns:

Type | Description
--- | ---
`list[Self]` | List of grids.

Source code in `src/snailz/grid.py`

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    @classmethod
    def make(cls, params: Parameters) -> list[Self]:
        """
        Construct multiple grids.

        Args:
            params: Parameters object.

        Returns:
            List of grids.
        """

        origins = cls._make_origins(params)
        return [
            Grid(
                size=params.grid_size,
                spacing=params.grid_spacing,
                lat0=origin[0],
                lon0=origin[1],
                params=params,
            )
            for origin in origins
        ]

### `save_csv(outdir, grids)` `classmethod`

Save grids as CSV. Scalar properties of all grids are saved in one file; grid cell values are pivoted to long form and saved in a separate file.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`outdir` | `Path | str` | Output directory. | required
`grids` | `list[Self]` | `Grid` objects to save. | required

Source code in `src/snailz/grid.py`

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    @classmethod
    def save_csv(cls, outdir: Path | str, grids: list[Self]):
        """
        Save grids as CSV. Scalar properties of all grids are saved in
        one file; grid cell values are pivoted to long form and saved
        in a separate file.

        Args:
            outdir: Output directory.
            grids: `Grid` objects to save.
        """

        super().save_csv(outdir, grids)

        with open(Path(outdir, "grid_cells.csv"), "w", newline="") as stream:
            objects = cls._grid_cells(grids)
            writer = cls._csv_dict_writer(stream, list(objects[0].keys()))
            for obj in objects:
                writer.writerow(obj)

### `save_db(db, grids)` `classmethod`

Save grids to database. Scalar properties of all grids are saved in one table; grid cell values are pivoted to long form and saved in a separate table.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`db` | `Database` | Database connector. | required
`grids` | `list[Self]` | `Grid` objects to save. | required

Source code in `src/snailz/grid.py`

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    @classmethod
    def save_db(cls, db: Database, grids: list[Self]):
        """
        Save grids to database. Scalar properties of all grids are
        saved in one table; grid cell values are pivoted to long form
        and saved in a separate table.

        Args:
            db: Database connector.
            grids: `Grid` objects to save.
        """

        super().save_db(db, grids)

        table = db["grid_cells"]
        table.insert_all(
            cls._grid_cells(grids),
            pk=("grid_id", "lat", "lon"),
            foreign_keys=[("grid_id", "grid", "ident")],
        )

### `as_image(scale)`

Convert grid to image.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`scale` | `float` | Scaling factor for grid values to ensure largest is black. | required

Returns:

Type | Description
--- | ---
`Image` | `Image` object.

Source code in `src/snailz/grid.py`

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    def as_image(self, scale: float) -> Image.Image:
        """
        Convert grid to image.

        Args:
            scale: Scaling factor for grid values to ensure largest is black.

        Returns:
            `Image` object.
        """

        if scale == 0.0:
            scale = self.min_max()[1]
        img_size = (self.size * CELL_SIZE) + ((self.size + 1) * BORDER_WIDTH)
        array = np.full((img_size, img_size), WHITE, dtype=np.uint8)
        spacing = CELL_SIZE + BORDER_WIDTH
        for ix, x in enumerate(range(BORDER_WIDTH, img_size, spacing)):
            for iy, y in enumerate(range(BORDER_WIDTH, img_size, spacing)):
                color = WHITE - math.floor(WHITE * self[ix, iy] / scale)
                array[y : y + CELL_SIZE + 1, x : x + CELL_SIZE + 1] = color

        return Image.fromarray(array)

### `lat_lon(x, y, as_dict=False)`

Calculate latitude and longitude of grid cell.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`x` | `int` | Grid X coordinate. | required
`y` | `int` | Grid Y coordinate. | required
`as_dict` | `bool` | Return result as dict instead of pair. | `False`

Returns:

Type | Description
--- | ---
`tuple[float, float] | dict[str, float]` | `(lat, lon)` pair or `{"lat": lat, "lon": lon}` dictionary.

Raises:

Type | Description
--- | ---
`ValueError` | if either coordinate out of range.

Source code in `src/snailz/grid.py`

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    def lat_lon(
        self, x: int, y: int, as_dict: bool = False
    ) -> tuple[float, float] | dict[str, float]:
        """
        Calculate latitude and longitude of grid cell.

        Args:
            x: Grid X coordinate.
            y: Grid Y coordinate.
            as_dict: Return result as dict instead of pair.

        Returns:
            `(lat, lon)` pair or `{"lat": lat, "lon": lon}` dictionary.

        Raises:
            ValueError: if either coordinate out of range.
        """

        self._validate_coords((x, y))
        lat, lon = lat_lon(self.lat0, self.lon0, x * self.spacing, y * self.spacing)
        return {"lat": lat, "lon": lon} if as_dict else (lat, lon)

### `min_max()`

Find smallest and largest values in grid.

Returns:

Type | Description
--- | ---
`tuple[float, float]` | `(min, max)` pair.

Source code in `src/snailz/grid.py`

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    def min_max(self) -> tuple[float, float]:
        """
        Find smallest and largest values in grid.

        Returns:
            `(min, max)` pair.
        """

        return min(self.cells), max(self.cells)