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Inspect

Inspecting Experimental Data

Class for inspecting HDF5 cluster data, checking for NaNs, adding track counts, and visualizing clusters.

Parameters

number_to_viz: (int) Number of events to visualize. num_tracks: (int) Number of tracks to filter for visualization.

Return

None

Source code in scripts/data_inspect.py 
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class Inspect:
    """
    Class for inspecting HDF5 cluster data, checking for NaNs, adding track counts,
    and visualizing clusters.

    Parameters
    ----------
    number_to_viz: (int)
        Number of events to visualize.
    num_tracks: (int)
        Number of tracks to filter for visualization.

    Return
    ----------
    None
    """

    def __init__(self, number_to_viz: int | None, num_tracks: int | None):
        """
        Initialize the Inspect object.

        Args:
            number_to_viz (int): Number of events to visualize.
            num_tracks (int): Number of tracks to filter for visualization.
        """
        self.number_to_viz = number_to_viz
        self.num_tracks = num_tracks

    def h5_keys_extract(self, file_h5):
        """
        Extract the first group from an HDF5 file.

        Args:
            file_h5 (h5py.File): HDF5 file object.

        Returns:
            h5py.Group: The first HDF5 group in the file.
        """
        group_ls = list(file_h5.keys())[0]  # Getting the first group
        group = file_h5[group_ls]  # Accessing the group
        return group

    def check_nans(self, group) -> bool:
        """
        Check for NaN values in each dataset of the HDF5 group.

        Args:
            group (h5py.Group): HDF5 group object.

        Returns:
            bool: True if any NaNs are found, False otherwise.
        """
        for key in tqdm(group, desc="Checking for NaNs"):
            data_array = group[key][:]
            if np.isnan(data_array).any():
                return True  # NaNs found
        return False  # No NaNs found

    def add_attr_tracks(self, group, file_est: str):
        """
        Add a new attribute 'tracks' to each dataset in the HDF5 group from parquet files.

        Args:
            group (h5py.Group): HDF5 group object.
            est_path (str): Path to the parquet file with estimate classification.

        Returns:
            h5py.Group: Modified HDF5 group with new 'tracks' attribute.
        """
        grouped = file_est.groupby("event")
        group_sizes = grouped.size()

        for event, size in tqdm(group_sizes.items(), total=len(group_sizes), desc="Adding class attribute"):
            key = "cloud_" + str(event)
            if key in group:
                group[key].attrs["tracks"] = size
        return group

    def viz_cluster(self, group) -> None:
        """
        Visualize clusters in the HDF5 group and save to a PDF.

        Args:
            group (h5py.Group): HDF5 group object.

        Returns:
            None
        """
        attribute = "tracks"  # Attribute corresponding to the number of tracks
        counter = 0  # Counter to limit the number of visualizations

        pdf_path = f"/Users/pranjalsingh/Desktop/research_space_spyral/AT-TPC_ML_workflow/plots/visualising_{self.num_tracks}_tracks.pdf"
        with PdfPages(pdf_path) as pdf:
            for key in enumerate(group, desc="Visualizing clusters"):
                if attribute in group[key].attrs:
                    num_tracks = group[key].attrs[attribute]
                    if num_tracks == self.num_tracks and counter < self.number_to_viz:
                        fig = plt.figure()
                        ax = fig.add_subplot(111, projection='3d')

                        x = group[key][:, 0]
                        y = group[key][:, 1]
                        z = group[key][:, 2]
                        charge = group[key][:, 3]

                        ax.scatter(x / 250, y / 250, (z - 500) / 500, marker='o', s=10)
                        ax.set_xlabel('X')
                        ax.set_ylabel('Y')
                        ax.set_zlabel('Z')
                        ax.set_xlim(-1, 1)
                        ax.set_ylim(-1, 1)
                        ax.set_zlim(-1, 1)
                        ax.set_title(f'Event {key}')

                        ax.text2D(0.05, 0.95, f'Estimator Data: {num_tracks}', transform=ax.transAxes, color='red')

                        pdf.savefig(fig)
                        plt.close(fig)

                        counter += 1  # Increment counter

__init__(number_to_viz, num_tracks)

Initialize the Inspect object.

Parameters:

Name Type Description Default
number_to_viz int

Number of events to visualize.

 required
num_tracks int

Number of tracks to filter for visualization.

 required
Source code in scripts/data_inspect.py 
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def __init__(self, number_to_viz: int | None, num_tracks: int | None):
    """
    Initialize the Inspect object.

    Args:
        number_to_viz (int): Number of events to visualize.
        num_tracks (int): Number of tracks to filter for visualization.
    """
    self.number_to_viz = number_to_viz
    self.num_tracks = num_tracks

add_attr_tracks(group, file_est)

Add a new attribute 'tracks' to each dataset in the HDF5 group from parquet files.

Parameters:

Name Type Description Default
group Group

HDF5 group object.

 required
est_path str

Path to the parquet file with estimate classification.

 required

Returns:

Type Description

h5py.Group: Modified HDF5 group with new 'tracks' attribute.
Source code in scripts/data_inspect.py 
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def add_attr_tracks(self, group, file_est: str):
    """
    Add a new attribute 'tracks' to each dataset in the HDF5 group from parquet files.

    Args:
        group (h5py.Group): HDF5 group object.
        est_path (str): Path to the parquet file with estimate classification.

    Returns:
        h5py.Group: Modified HDF5 group with new 'tracks' attribute.
    """
    grouped = file_est.groupby("event")
    group_sizes = grouped.size()

    for event, size in tqdm(group_sizes.items(), total=len(group_sizes), desc="Adding class attribute"):
        key = "cloud_" + str(event)
        if key in group:
            group[key].attrs["tracks"] = size
    return group

check_nans(group)

Check for NaN values in each dataset of the HDF5 group.

Parameters:

Name Type Description Default
group Group

HDF5 group object.

 required

Returns:

Name Type Description
bool bool

True if any NaNs are found, False otherwise.
Source code in scripts/data_inspect.py 
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def check_nans(self, group) -> bool:
    """
    Check for NaN values in each dataset of the HDF5 group.

    Args:
        group (h5py.Group): HDF5 group object.

    Returns:
        bool: True if any NaNs are found, False otherwise.
    """
    for key in tqdm(group, desc="Checking for NaNs"):
        data_array = group[key][:]
        if np.isnan(data_array).any():
            return True  # NaNs found
    return False  # No NaNs found

h5_keys_extract(file_h5)

Extract the first group from an HDF5 file.

Parameters:

Name Type Description Default
file_h5 File

HDF5 file object.

 required

Returns:

Type Description

h5py.Group: The first HDF5 group in the file.
Source code in scripts/data_inspect.py 
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def h5_keys_extract(self, file_h5):
    """
    Extract the first group from an HDF5 file.

    Args:
        file_h5 (h5py.File): HDF5 file object.

    Returns:
        h5py.Group: The first HDF5 group in the file.
    """
    group_ls = list(file_h5.keys())[0]  # Getting the first group
    group = file_h5[group_ls]  # Accessing the group
    return group

viz_cluster(group)

Visualize clusters in the HDF5 group and save to a PDF.

Parameters:

Name Type Description Default
group Group

HDF5 group object.

 required

Returns:

Type Description
None

None
Source code in scripts/data_inspect.py 
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def viz_cluster(self, group) -> None:
    """
    Visualize clusters in the HDF5 group and save to a PDF.

    Args:
        group (h5py.Group): HDF5 group object.

    Returns:
        None
    """
    attribute = "tracks"  # Attribute corresponding to the number of tracks
    counter = 0  # Counter to limit the number of visualizations

    pdf_path = f"/Users/pranjalsingh/Desktop/research_space_spyral/AT-TPC_ML_workflow/plots/visualising_{self.num_tracks}_tracks.pdf"
    with PdfPages(pdf_path) as pdf:
        for key in enumerate(group, desc="Visualizing clusters"):
            if attribute in group[key].attrs:
                num_tracks = group[key].attrs[attribute]
                if num_tracks == self.num_tracks and counter < self.number_to_viz:
                    fig = plt.figure()
                    ax = fig.add_subplot(111, projection='3d')

                    x = group[key][:, 0]
                    y = group[key][:, 1]
                    z = group[key][:, 2]
                    charge = group[key][:, 3]

                    ax.scatter(x / 250, y / 250, (z - 500) / 500, marker='o', s=10)
                    ax.set_xlabel('X')
                    ax.set_ylabel('Y')
                    ax.set_zlabel('Z')
                    ax.set_xlim(-1, 1)
                    ax.set_ylim(-1, 1)
                    ax.set_zlim(-1, 1)
                    ax.set_title(f'Event {key}')

                    ax.text2D(0.05, 0.95, f'Estimator Data: {num_tracks}', transform=ax.transAxes, color='red')

                    pdf.savefig(fig)
                    plt.close(fig)

                    counter += 1  # Increment counter