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stemflow.utils.sphere_quadtree

A function module to get quadtree results for spherical indexing system. Twins to quadtree.py, Returns ensemble_df and plotting axes.

get_one_ensemble_sphere_quadtree(ensemble_count, data, Temporal1='DOY', grid_len_upper_threshold=8000, grid_len_lower_threshold=500, points_lower_threshold=50, temporal_start=1, temporal_end=366, temporal_step=20, temporal_bin_interval=50, temporal_bin_start_jitter='adaptive', spatio_bin_jitter_magnitude='adaptive', save_gridding_plotly=True, save_gridding_plot=False, ax=None, radius=6371.0, plot_empty=False)

Generate QuadTree gridding based on the input dataframe A function to get quadtree results for spherical indexing system. Twins to get_ensemble_quadtree in quadtree.py, Returns ensemble_df and plotting axes.

Parameters:

  • data (DataFrame) –

    Input pandas-like dataframe

  • Temporal1 (str, default: 'DOY' ) –

    Temporal column name 1 in data

  • size –

    How many ensemble to generate (how many round the data are gone through)

  • grid_len_upper_threshold (Union[float, int], default: 8000 ) –

    force divide if grid longitude larger than the threshold (in km)

  • grid_len_lower_threshold (Union[float, int], default: 500 ) –

    stop divide if grid longitude will be below than the threshold (in km)

  • points_lower_threshold (int, default: 50 ) –

    Do not train the model if the available data records for this stixel is less than this threshold, and directly set the value to np.nan.

  • temporal_start (Union[float, int], default: 1 ) –

    start of the temporal sequence

  • temporal_end (Union[float, int], default: 366 ) –

    end of the temporal sequence

  • temporal_step (Union[float, int], default: 20 ) –

    step of the sliding window

  • temporal_bin_interval (Union[float, int], default: 50 ) –

    size of the sliding window

  • temporal_bin_start_jitter (Union[float, int, str], default: 'adaptive' ) –

    jitter of the start of the sliding window. If 'adaptive', a adaptive jitter of range (-bin_interval, 0) will be generated for the start.

  • spatio_bin_jitter_magnitude (Union[float, int], default: 'adaptive' ) –

    jitter of the spatial gridding.

  • save_gridding_plotly (bool, default: True ) –

    Whether to save the plotly interactive gridding plot.

  • save_gridding_plot (bool, default: False ) –

    Whether ot save gridding plots

  • ax –

    Matplotlib Axes to add to.

  • radius (Union[int, float], default: 6371.0 ) –

    The radius of earth in km. Defaults to 6371.0.

Returns:

  • –

    A tuple of
    1. ensemble dataframe;
    2. grid plot. np.nan if save_gridding_plot=False

Source code in stemflow/utils/sphere_quadtree.py 
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def get_one_ensemble_sphere_quadtree(
    ensemble_count,
    data: pandas.core.frame.DataFrame,
    Temporal1: str = "DOY",
    grid_len_upper_threshold: Union[float, int] = 8000,
    grid_len_lower_threshold: Union[float, int] = 500,
    points_lower_threshold: int = 50,
    temporal_start: Union[float, int] = 1,
    temporal_end: Union[float, int] = 366,
    temporal_step: Union[float, int] = 20,
    temporal_bin_interval: Union[float, int] = 50,
    temporal_bin_start_jitter: Union[float, int, str] = "adaptive",
    spatio_bin_jitter_magnitude: Union[float, int] = "adaptive",
    save_gridding_plotly: bool = True,
    save_gridding_plot: bool = False,
    ax=None,
    radius: Union[int, float] = 6371.0,
    plot_empty: bool = False,
):
    """Generate QuadTree gridding based on the input dataframe
    A function to get quadtree results for spherical indexing system. Twins to `get_ensemble_quadtree` in `quadtree.py`, Returns ensemble_df and plotting axes.

    Args:
        data:
            Input pandas-like dataframe
        Temporal1:
            Temporal column name 1 in data
        size:
            How many ensemble to generate (how many round the data are gone through)
        grid_len_upper_threshold:
            force divide if grid longitude larger than the threshold (in km)
        grid_len_lower_threshold:
            stop divide if grid longitude **will** be below than the threshold (in km)
        points_lower_threshold:
            Do not train the model if the available data records for this stixel is less than this threshold,
            and directly set the value to np.nan.
        temporal_start:
            start of the temporal sequence
        temporal_end:
            end of the temporal sequence
        temporal_step:
            step of the sliding window
        temporal_bin_interval:
            size of the sliding window
        temporal_bin_start_jitter:
            jitter of the start of the sliding window.
            If 'adaptive', a adaptive jitter of range (-bin_interval, 0) will be generated
            for the start.
        spatio_bin_jitter_magnitude:
            jitter of the spatial gridding.
        save_gridding_plotly:
            Whether to save the plotly interactive gridding plot.
        save_gridding_plot:
            Whether ot save gridding plots
        ax:
            Matplotlib Axes to add to.
        radius (Union[int, float]):
            The radius of earth in km. Defaults to 6371.0.

    Returns:
        A tuple of <br>
            1. ensemble dataframe;<br>
            2. grid plot. np.nan if save_gridding_plot=False<br>

    """

    if spatio_bin_jitter_magnitude == "adaptive":
        rotation_angle = np.random.uniform(0, 90)
        rotation_axis = np.random.uniform(-1, 1, 3)

    temporal_bins = generate_temporal_bins(
        start=temporal_start,
        end=temporal_end,
        step=temporal_step,
        bin_interval=temporal_bin_interval,
        temporal_bin_start_jitter=temporal_bin_start_jitter,
    )

    ensemble_all_df_list = []

    for time_block_index, bin_ in enumerate(temporal_bins):
        time_start = bin_[0]
        time_end = bin_[1]
        sub_data = data[(data[Temporal1] >= time_start) & (data[Temporal1] < time_end)].copy()
        if len(sub_data) == 0:
            continue

        # Transform lon lat to 3D cartesian
        x, y, z = lonlat_cartesian_3D_transformer.transform(sub_data["longitude"], sub_data["latitude"], radius=radius)
        sub_data.loc[:, "x_3D"] = x
        sub_data.loc[:, "y_3D"] = y
        sub_data.loc[:, "z_3D"] = z

        QT_obj = Sphere_QTree(
            grid_len_upper_threshold=grid_len_upper_threshold,
            grid_len_lower_threshold=grid_len_lower_threshold,
            points_lower_threshold=points_lower_threshold,
            rotation_angle=rotation_angle,
            rotation_axis=rotation_axis,
            radius=radius,
            plot_empty=plot_empty,
        )

        # Give the data and indexes. The indexes should be used to assign points data so that base model can run on those points,
        # You need to generate the splitting parameters once giving the data. Like the calibration point and min,max.
        QT_obj.add_3D_data(sub_data.index, sub_data["x_3D"].values, sub_data["y_3D"].values, sub_data["z_3D"].values)
        QT_obj.generate_gridding_params()

        # Call subdivide to precess
        QT_obj.subdivide()
        this_slice = QT_obj.get_final_result()

        if save_gridding_plot:
            if time_block_index == int(len(temporal_bins) / 2):
                QT_obj.graph(scatter=False, ax=ax)

        if save_gridding_plotly:
            if time_block_index == int(len(temporal_bins) / 2):
                ax = QT_obj.plotly_graph(scatter=False, ax=ax)

        this_slice["ensemble_index"] = ensemble_count
        this_slice[f"{Temporal1}_start"] = time_start
        this_slice[f"{Temporal1}_end"] = time_end
        this_slice[f"{Temporal1}_start"] = round(this_slice[f"{Temporal1}_start"], 1)
        this_slice[f"{Temporal1}_end"] = round(this_slice[f"{Temporal1}_end"], 1)
        this_slice["unique_stixel_id"] = [
            str(time_block_index) + "_" + str(i) + "_" + str(k)
            for i, k in zip(this_slice["ensemble_index"].values, this_slice["stixel_indexes"].values)
        ]
        ensemble_all_df_list.append(this_slice)

    this_ensemble_df = pd.concat(ensemble_all_df_list).reset_index(drop=True)
    this_ensemble_df = this_ensemble_df.reset_index(drop=True)
    return this_ensemble_df