napari_ndev.widgets._measure_container

MeasureContainer

Bases: Container

Widget to measure labels from folders.

This class provides functionality to measure labels and compare them against intensity images, which can be microscopic images or other labels. It initializes various widgets and containers for user input and interaction, and connects events to handle user actions.

Parameters:

• viewer

  (Viewer, default: None ) –

  The napari viewer instance. Optional.

Attributes:

• viewer (Viewer) –

  The napari viewer instance.

• _label_choices (list) –

  List of label choices.

• _intensity_choices (list) –

  List of intensity image choices.

• _p_sizes (None) –

  Placeholder for pixel sizes.

• _squeezed_dims (None) –

  Placeholder for squeezed dimensions.

• _prop (object) –

  Dynamic object to hold region properties checkboxes.

• _label_directory (FileEdit) –

  Widget for selecting label directory.

• _image_directory (FileEdit) –

  Widget for selecting image directory.

• _region_directory (FileEdit) –

  Widget for selecting region directory.

• _output_directory (FileEdit) –

  Widget for selecting output directory.

• _label_image (ComboBox) –

  Widget for selecting label image.

• _intensity_images (Select) –

  Widget for selecting intensity images.

• _scale_tuple (TupleEdit) –

  Widget for setting physical pixel sizes.

• _measure_button (PushButton) –

  Button to start measurement.

• _progress_bar (ProgressBar) –

  Progress bar to show measurement progress.

• _props_container (Container) –

  Container for region properties checkboxes.

• _sk_props (list) –

  List of region properties.

• _id_regex_container (Container) –

  Container for ID regex settings.

• _example_id_string (LineEdit) –

  Widget for example ID string.

• _id_regex_dict (TextEdit) –

  Widget for ID regex dictionary.

• _tx_map_container (Container) –

  Container for treatment map settings.

• _tx_id (LineEdit) –

  Widget for treatment ID.

• _tx_n_well (ComboBox) –

  Widget for number of wells.

• _tx_dict (TextEdit) –

  Widget for treatment dictionary.

• _grouping_container (Container) –

  Container for grouping settings.

• _create_grouped (CheckBox) –

  Checkbox to create grouped data.

• _group_by_sample_id (CheckBox) –

  Checkbox to group by sample ID.

Methods:

• _init_widgets –

  Initializes the widgets for user input.

• _init_regionprops_container –

  Initializes the container for region properties checkboxes.

• _init_id_regex_container –

  Initializes the container for ID regex settings.

• _init_tx_map_container –

  Initializes the container for treatment map settings.

• _init_grouping_container –

  Initializes the container for grouping settings.

• _init_layout –

  Initializes the layout of the container.

• _connect_events –

  Connects events to handle user actions.

• _get_0th_img_from_dir –

  Gets the first image from a directory.

• _update_dim_and_scales –

  Updates the dimensions and scales based on the image.

• _update_choices –

  Updates the choices for labels and intensity images.

• _update_image_choices –

  Updates the choices for intensity images.

• _update_label_choices –

  Updates the choices for label images.

• _update_region_choices –

  Updates the choices for region images.

• _safe_dict_eval –

  Safely evaluates a dictionary string.

• batch_measure –

  Performs batch measurement of labels and intensity images, and returns the measurement results as a DataFrame.

Source code in src/napari_ndev/widgets/_measure_container.py

class MeasureContainer(Container):
    """
    Widget to measure labels from folders.

    This class provides functionality to measure labels and compare them against intensity images, which can be microscopic images or other labels. It initializes various widgets and containers for user input and interaction, and connects events to handle user actions.

    Parameters
    ----------
    viewer : napari.viewer.Viewer
        The napari viewer instance. Optional.

    Attributes
    ----------
    viewer : napari.viewer.Viewer
        The napari viewer instance.

    _label_choices : list
        List of label choices.
    _intensity_choices : list
        List of intensity image choices.
    _p_sizes : None
        Placeholder for pixel sizes.
    _squeezed_dims : None
        Placeholder for squeezed dimensions.
    _prop : object
        Dynamic object to hold region properties checkboxes.
    _label_directory : FileEdit
        Widget for selecting label directory.
    _image_directory : FileEdit
        Widget for selecting image directory.
    _region_directory : FileEdit
        Widget for selecting region directory.
    _output_directory : FileEdit
        Widget for selecting output directory.
    _label_image : ComboBox
        Widget for selecting label image.
    _intensity_images : Select
        Widget for selecting intensity images.
    _scale_tuple : TupleEdit
        Widget for setting physical pixel sizes.
    _measure_button : PushButton
        Button to start measurement.
    _progress_bar : ProgressBar
        Progress bar to show measurement progress.
    _props_container : Container
        Container for region properties checkboxes.
    _sk_props : list
        List of region properties.
    _id_regex_container : Container
        Container for ID regex settings.
    _example_id_string : LineEdit
        Widget for example ID string.
    _id_regex_dict : TextEdit
        Widget for ID regex dictionary.
    _tx_map_container : Container
        Container for treatment map settings.
    _tx_id : LineEdit
        Widget for treatment ID.
    _tx_n_well : ComboBox
        Widget for number of wells.
    _tx_dict : TextEdit
        Widget for treatment dictionary.
    _grouping_container : Container
        Container for grouping settings.
    _create_grouped : CheckBox
        Checkbox to create grouped data.
    _group_by_sample_id : CheckBox
        Checkbox to group by sample ID.

    Methods
    -------
    _init_widgets()
        Initializes the widgets for user input.
    _init_regionprops_container()
        Initializes the container for region properties checkboxes.
    _init_id_regex_container()
        Initializes the container for ID regex settings.
    _init_tx_map_container()
        Initializes the container for treatment map settings.
    _init_grouping_container()
        Initializes the container for grouping settings.
    _init_layout()
        Initializes the layout of the container.
    _connect_events()
        Connects events to handle user actions.
    _get_0th_img_from_dir(directory)
        Gets the first image from a directory.
    _update_dim_and_scales(img)
        Updates the dimensions and scales based on the image.
    _update_choices(directory, prefix, update_label=False)
        Updates the choices for labels and intensity images.
    _update_image_choices()
        Updates the choices for intensity images.
    _update_label_choices()
        Updates the choices for label images.
    _update_region_choices()
        Updates the choices for region images.
    _safe_dict_eval(dict_string, dict_name=None)
        Safely evaluates a dictionary string.
    batch_measure()
        Performs batch measurement of labels and intensity images, and returns the measurement results as a DataFrame.

    """

    def __init__(
        self,
        viewer: napari.viewer.Viewer = None,
    ):
        """
        Initialize the MeasureContainer.

        Parameters
        ----------
        viewer : napari.viewer.Viewer
            The napari viewer instance. Optional.

        """
        super().__init__()

        self.viewer = viewer if viewer is not None else None
        self._label_choices = []
        self._intensity_choices = []
        self._p_sizes = None
        self._squeezed_dims = None
        self._prop = type('', (), {})()

        self._init_widgets()
        self._init_regionprops_container()
        self._init_id_regex_container()
        self._init_tx_map_container()
        self._init_grouping_container()
        self._init_layout()
        self._connect_events()

    def _init_widgets(self):
        """Initialize non-container widgets."""
        self._label_directory = FileEdit(label='Label directory', mode='d')
        self._image_directory = FileEdit(
            label='Image directory', mode='d', nullable=True
        )
        self._region_directory = FileEdit(
            label='Region directory', mode='d', nullable=True
        )
        self._output_directory = FileEdit(label='Output directory', mode='d')

        self._label_images = Select(
            label='Label image',
            choices=self._label_choices,
            allow_multiple=True,
            nullable=False,
            tooltip='Select label images to measure',
        )
        self._intensity_images = Select(
            label='Intensity images',
            choices=self._intensity_choices,
            allow_multiple=True,
            nullable=True,
            tooltip='Select intensity images to compare against labels',
        )
        self._scale_tuple = TupleEdit(
            value=(0.0000, 1.0000, 1.0000),
            label='Physical Pixel Sizes, ZYX',
            tooltip='Pixel size, usually in μm/px',
            options={'step': 0.0001},
        )
        self._measure_button = PushButton(label='Measure')

        self._progress_bar = ProgressBar(label='Progress:')

    def _init_regionprops_container(self):
        """Initialize the container for region properties checkboxes."""
        self._props_container = Container(layout='vertical')

        self._sk_props = [
            'label',
            'area',
            'area_convex',
            'bbox',
            'centroid',
            'eccentricity',
            'extent',
            'feret_diameter_max',
            'intensity_max',
            'intensity_mean',
            'intensity_min',
            'intensity_std',
            'num_pixels',
            'orientation',
            'perimeter',
            'solidity',
        ]

        for feature in self._sk_props:
            setattr(self._prop, feature, CheckBox(label=feature))
            self._props_container.extend([getattr(self._prop, feature)])

        self._prop.label.value = True
        self._prop.area.value = True

    def _init_id_regex_container(self):
        """Initialize the container for ID regex settings."""
        self._id_regex_container = Container(layout='vertical')
        self._example_id_string = LineEdit(
            label='Example ID String',
            value=None,
            nullable=True,
        )
        self._id_regex_dict = TextEdit(
            label='ID Regex Dict',
            value='{\n\n}',
        )
        self._id_regex_container.extend(
            [self._example_id_string, self._id_regex_dict]
        )

    def _init_tx_map_container(self):
        """Initialize the container for treatment map settings."""
        self._tx_map_container = Container(layout='vertical')
        self._update_tx_id_choices_button = PushButton(label='Update Treatment ID Choices')
        self._tx_id = ComboBox(
            label='Treatment ID',
            choices=['id'],
            value=None,
            nullable=True,
            tooltip='Usually, the treatment ID is the well ID or a unique identifier for each sample'
            "The treatment dict will be looked up against whatever this value is. If it is 'file', then will match against the filename",
        )
        self._tx_n_well = ComboBox(
            label='Number of Wells',
            value=None,
            choices=[6, 12, 24, 48, 96, 384],
            nullable=True,
            tooltip='By default, treatments must be verbosely defined for each condition and sample id '
            'If you have a known plate map, then selecting wells will allow a sparse treatment map to be passed to PlateMapper',
        )
        self._tx_dict = TextEdit(label='Treatment Dict', value='{\n\n}')
        # TODO: Add example treatment regex result widget when example id string or id regex dict is changed

        self._tx_map_container.extend(
            [
                self._update_tx_id_choices_button,
                self._tx_id,
                self._tx_n_well,
                self._tx_dict,
            ]
        )

    def _init_grouping_container(self):
        """Initialize the container for grouping settings."""
        self._grouping_container = Container(layout='vertical')

        self._measured_data_path = FileEdit(
            label='Measured Data Path',
            tooltip='Path to the measured data',
        )
        self._grouping_cols = Select(
            label='Grouping Columns',
            choices=[],
            allow_multiple=True,
            tooltip='Select columns to group the data by',
        )
        self._count_col = ComboBox(
            label='Count Column',
            choices=[],
            tooltip='Select column that will be counted',
        )
        self._agg_cols = Select(
            label='Aggregation Columns',
            choices=[],
            allow_multiple=True,
            nullable=True,
            value=None,
            tooltip='Select columns to aggregate with functions',
        )
        self._agg_funcs = Select(
            label='Aggregation Functions',
            choices=[
                'mean', 'median',
                'std', 'sem',
                'min', 'max',
                'sum', 'nunique'
            ],
            value=['mean'],
            allow_multiple=True,
            tooltip='Select functions performed on aggregation columns',
        )
        self._pivot_wider = CheckBox(label='Pivot Wider', value=True)
        self._group_measurements_button = PushButton(label='Group Measurements')


        self._grouping_container.extend([
            self._measured_data_path,
            self._grouping_cols,
            self._count_col,
            self._agg_cols,
            self._agg_funcs,
            self._pivot_wider,
            self._group_measurements_button,
        ])

    def _init_layout(self):
        """Initialize the layout of the container."""
        self.extend(
            [
                self._label_directory,
                self._image_directory,
                self._region_directory,
                self._output_directory,
                self._label_images,
                self._intensity_images,
                self._scale_tuple,
                self._measure_button,
                self._progress_bar,
            ]
        )

        tabs = QTabWidget()
        tabs.addTab(self._props_container.native, 'Region Props')
        tabs.addTab(self._id_regex_container.native, 'ID Regex')
        tabs.addTab(self._tx_map_container.native, 'Tx Map')
        tabs.addTab(self._grouping_container.native, 'Grouping')
        self.native.layout().addWidget(tabs)

    def _connect_events(self):
        """Connect events to handle user actions."""
        self._image_directory.changed.connect(self._update_image_choices)
        self._label_directory.changed.connect(self._update_label_choices)
        self._region_directory.changed.connect(self._update_region_choices)
        self._update_tx_id_choices_button.clicked.connect(self._update_tx_id_choices)
        self._measure_button.clicked.connect(self.batch_measure)
        self._measured_data_path.changed.connect(self._update_grouping_cols)
        self._group_measurements_button.clicked.connect(self.group_measurements)

    def _update_tx_id_choices(self):
        """Update the choices for treatment ID."""
        id_regex_dict = self._safe_dict_eval(self._id_regex_dict.value)
        if id_regex_dict is None:
            return
        # add the keys to a list which already contains 'id'
        regex_choices = list(id_regex_dict.keys())
        self._tx_id.choices = ['id'] + regex_choices

    def _update_grouping_cols(self):
        """Update the columns for grouping."""
        if self._measured_data_path.value is None:
            return

        df = pd.read_csv(self._measured_data_path.value)
        self._grouping_cols.choices = df.columns
        self._count_col.choices = df.columns
        self._agg_cols.choices = df.columns

        # set default value to label_name and id if exists
        grouping_cols = []
        if 'label_name' in df.columns:
            grouping_cols.append('label_name')
        if 'id' in df.columns:
            grouping_cols.append('id')
        self._grouping_cols.value = grouping_cols

        if 'label' in df.columns:
            self._count_col.value = 'label'

        return

    def _get_0th_img_from_dir(
        self, directory: str | None = None
    ) -> tuple[BioImage, pathlib.Path]:
        """Get the first image from a directory."""
        from bioio import BioImage

        _, files = helpers.get_directory_and_files(directory)
        return BioImage(files[0]), files[0]

    def _update_dim_and_scales(self, img):
        """Update the dimensions and scales based on the image."""
        self._squeezed_dims = helpers.get_squeezed_dim_order(img)
        self._scale_tuple.value = (
            img.physical_pixel_sizes.Z or 1,
            img.physical_pixel_sizes.Y or 1,
            img.physical_pixel_sizes.X or 1,
        )

    def _update_choices(self, directory, prefix, update_label=False):
        """Update the choices for labels and intensity images."""
        img, _ = self._get_0th_img_from_dir(directory)
        img_channels = helpers.get_channel_names(img)
        img_channels = [f'{prefix}: {channel}' for channel in img_channels]

        if update_label:
            self._update_dim_and_scales(img)
            self._label_choices.extend(img_channels)
            self._label_images.choices = self._label_choices

        self._intensity_choices.extend(img_channels)
        self._intensity_images.choices = self._intensity_choices

    def _update_image_choices(self):
        """Update the choices for intensity images."""
        self._update_choices(self._image_directory.value, 'Intensity')

    def _update_label_choices(self):
        """Update the choices for label images."""
        self._update_choices(
            self._label_directory.value, 'Labels', update_label=True
        )
        img, file_id = self._get_0th_img_from_dir(self._label_directory.value)
        id_string = helpers.create_id_string(img, file_id.stem)
        self._example_id_string.value = id_string

    def _update_region_choices(self):
        """Update the choices for region images."""
        self._update_choices(self._region_directory.value, 'Region')

    def _safe_dict_eval(self, dict_string, dict_name=None):
        """Safely evaluate a string as a dictionary."""
        if dict_string is None:
            return None

        stripped_string = dict_string.strip()
        if stripped_string == '{}' or not stripped_string:
            return None
        try:
            return ast.literal_eval(stripped_string)
        except (ValueError, SyntaxError):
            return None

    def batch_measure(self) -> pd.DataFrame:
        """
        Perform batch measurement of labels and intensity images.

        Use scikit-image's regionprops to measure properties of labels and
        intensity images. The measurements are saved to a CSV file in the
        output directory.

        Returns
        -------
        pd.DataFrame
            The measurement results as a DataFrame.

        """
        from bioio import BioImage

        # from skimage import measure
        from napari_ndev import measure as ndev_measure

        # get all the files in the label directory
        label_dir, label_files = helpers.get_directory_and_files(
            self._label_directory.value
        )
        image_dir, image_files = helpers.get_directory_and_files(
            self._image_directory.value
        )
        region_dir, region_files = helpers.get_directory_and_files(
            self._region_directory.value
        )

        log_loc = self._output_directory.value / 'measure.log.txt'
        logger, handler = helpers.setup_logger(log_loc)

        logger.info(
            """
            Label Images: %s
            Intensity Channels: %s
            Num. Files: %d
            Label Directory: %s
            Image Directory: %s
            Region Directory: %s
            Output Directory: %s
            ID Example: %s
            ID Regex Dict: %s
            Tx ID: %s
            Tx N Well: %s
            Tx Dict: %s
            """,
            self._label_images.value,
            self._intensity_images.value,
            len(label_files),
            label_dir,
            image_dir,
            region_dir,
            self._output_directory.value,
            self._example_id_string.value,
            self._id_regex_dict.value,
            self._tx_id.value,
            self._tx_n_well.value,
            self._tx_dict.value,
        )

        # check if the label files are the same as the image files
        if self._image_directory.value is not None and len(label_files) != len(image_files):
            logger.error(
                'Number of label files (%s) and image files (%s) do not match',
                len(label_files), len(image_files),
            )
        if self._region_directory.value is not None and len(label_files) != len(region_files):
            logger.error(
                'Number of label files (%s) and region files (%s) do not match',
                len(label_files), len(region_files),
            )

        self._progress_bar.label = f'Measuring {len(label_files)} Images'
        self._progress_bar.value = 0
        self._progress_bar.max = len(label_files)
        # get the relevant spacing for regionprops, depending on length
        props_scale = self._scale_tuple.value
        props_scale = props_scale[-len(self._squeezed_dims) :]
        # get the properties list
        properties = [
            prop.label for prop in self._props_container if prop.value
        ]

        id_regex_dict = self._safe_dict_eval(
            self._id_regex_dict.value, 'ID Regex Dict'
        )
        tx_dict = self._safe_dict_eval(self._tx_dict.value, 'Tx Dict')
        measure_props_concat = []

        for idx, file in enumerate(label_files):
            # TODO: Add scene processing
            logger.info('Processing file %s', file.name)
            lbl = BioImage(label_dir / file.name)
            id_string = helpers.create_id_string(lbl, file.stem)

            # get the itnensity image only if the image directory is not empty
            if self._image_directory.value:
                image_path = image_dir / file.name
                if not image_path.exists():
                    logger.error(
                        'Image file %s not found in intensity directory',
                        file.name,
                    )
                    self._progress_bar.value = idx + 1
                    continue
                img = BioImage(image_path)
            if self._region_directory.value:
                region_path = region_dir / file.name
                if not region_path.exists():
                    logger.error(
                        'Region file %s not found in region directory',
                        file.name,
                    )
                    self._progress_bar.value = idx + 1
                    continue
                reg = BioImage(region_path)

            for scene_idx, scene in enumerate(lbl.scenes):
                logger.info('Processing scene: %s :: %s', scene_idx, scene)
                lbl.set_scene(scene_idx)

                label_images = []
                label_names = []

                # iterate through each channel in the label image
                for label_chan in self._label_images.value:
                    label_chan = label_chan[8:]
                    label_names.append(label_chan)

                    lbl_C = lbl.channel_names.index(label_chan)
                    label = lbl.get_image_data(self._squeezed_dims, C=lbl_C)
                    label_images.append(label)

                intensity_images = []
                intensity_names = []

                # id_string = helpers.create_id_string(lbl, file.stem)

                # Get stack of intensity images if there are any selected
                if self._intensity_images.value and not None:
                    for channel in self._intensity_images.value:
                        if channel.startswith('Labels: '):
                            chan = channel[8:]
                            lbl_C = lbl.channel_names.index(chan)
                            lbl.set_scene(scene_idx)
                            inten_img = lbl.get_image_data(
                                self._squeezed_dims, C=lbl_C
                            )
                        elif channel.startswith('Intensity: '):
                            chan = channel[11:]
                            img_C = img.channel_names.index(chan)
                            img.set_scene(scene_idx)
                            inten_img = img.get_image_data(
                                self._squeezed_dims, C=img_C
                            )
                        elif channel.startswith('Region: '):
                            chan = channel[8:]
                            reg_C = reg.channel_names.index(chan)
                            reg.set_scene(scene_idx)
                            inten_img = reg.get_image_data(
                                self._squeezed_dims, C=reg_C
                            )
                        intensity_names.append(chan)
                        intensity_images.append(inten_img)

                    # the last dim is the multi-channel dim for regionprops
                    intensity_stack = np.stack(intensity_images, axis=-1)

                else:
                    intensity_stack = None
                    intensity_names = None

                # start the measuring here
                # TODO: Add optional scaling, in case images have different scales?
                measure_props_df = ndev_measure.measure_regionprops(
                    label_images=label_images,
                    label_names=label_names,
                    intensity_images=intensity_stack,
                    intensity_names=intensity_names,
                    properties=properties,
                    scale=props_scale,
                    id_string=id_string,
                    id_regex_dict=id_regex_dict,
                    tx_id=self._tx_id.value,
                    tx_dict=tx_dict,
                    tx_n_well=self._tx_n_well.value,
                    save_data_path=None,
                )

                measure_props_concat.append(measure_props_df)
                self._progress_bar.value = idx + 1

        measure_props_df = pd.concat(measure_props_concat)
        labels_string = '_'.join(label_names)
        save_loc = self._output_directory.value / f'measure_props_{labels_string}.csv'
        measure_props_df.to_csv(save_loc, index=False)

        logger.removeHandler(handler)

        return measure_props_df

    def group_measurements(self):
        """
        Group measurements based on user input.

        Uses the values in the Grouping Container of the Widget and passes them
        to the group_and_agg_measurements function in the measure module. The
        grouped measurements are saved to a CSV file in the same directory as
        the measured data with '_grouped' appended.

        Returns
        -------
        pd.DataFrame
            The grouped measurements as a DataFrame.

        """
        from napari_ndev import measure as ndev_measure

        self._progress_bar.label = 'Grouping Measurements'
        self._progress_bar.value = 0
        self._progress_bar.max = 1

        df = pd.read_csv(self._measured_data_path.value)

        # Filter out None values from agg_cols
        agg_cols = [col for col in self._agg_cols.value if col is not None]

        grouped_df = ndev_measure.group_and_agg_measurements(
            df=df,
            grouping_cols=self._grouping_cols.value,
            count_col=self._count_col.value,
            agg_cols=agg_cols,
            agg_funcs=self._agg_funcs.value,
        )
        # use the label_name column to make the dataframe wider
        if self._pivot_wider.value:
            # get grouping calls without label name
            index_cols = [col for col in self._grouping_cols.value if col != 'label_name']

            # alternatively, pivot every values column that is not present in index or columns
            value_cols = [col for col in grouped_df.columns if col not in self._grouping_cols.value]

            pivot_df = grouped_df.pivot(
                index=index_cols,
                columns='label_name',
                values=value_cols,
            )
            # # flatten the multiindex columns
            # pivot_df.columns = [f'{col[1]}_{col[0]}' for col in pivot_df.columns]

            # reset index so that it is saved in the csv
            pivot_df.reset_index(inplace=True)

            grouped_df = pivot_df

        save_loc = (
            self._measured_data_path.value.parent /
            f'{self._measured_data_path.value.stem}_grouped.csv'
        )
        grouped_df.to_csv(save_loc, index=False)

        self._progress_bar.value = 1
        return grouped_df

__init__

__init__(viewer=None)

Initialize the MeasureContainer.

Parameters:

• viewer

  (Viewer, default: None ) –

  The napari viewer instance. Optional.

Source code in src/napari_ndev/widgets/_measure_container.py

def __init__(
    self,
    viewer: napari.viewer.Viewer = None,
):
    """
    Initialize the MeasureContainer.

    Parameters
    ----------
    viewer : napari.viewer.Viewer
        The napari viewer instance. Optional.

    """
    super().__init__()

    self.viewer = viewer if viewer is not None else None
    self._label_choices = []
    self._intensity_choices = []
    self._p_sizes = None
    self._squeezed_dims = None
    self._prop = type('', (), {})()

    self._init_widgets()
    self._init_regionprops_container()
    self._init_id_regex_container()
    self._init_tx_map_container()
    self._init_grouping_container()
    self._init_layout()
    self._connect_events()

batch_measure

batch_measure()

Perform batch measurement of labels and intensity images.

Use scikit-image's regionprops to measure properties of labels and intensity images. The measurements are saved to a CSV file in the output directory.

Returns:

• DataFrame –

  The measurement results as a DataFrame.

Source code in src/napari_ndev/widgets/_measure_container.py

def batch_measure(self) -> pd.DataFrame:
    """
    Perform batch measurement of labels and intensity images.

    Use scikit-image's regionprops to measure properties of labels and
    intensity images. The measurements are saved to a CSV file in the
    output directory.

    Returns
    -------
    pd.DataFrame
        The measurement results as a DataFrame.

    """
    from bioio import BioImage

    # from skimage import measure
    from napari_ndev import measure as ndev_measure

    # get all the files in the label directory
    label_dir, label_files = helpers.get_directory_and_files(
        self._label_directory.value
    )
    image_dir, image_files = helpers.get_directory_and_files(
        self._image_directory.value
    )
    region_dir, region_files = helpers.get_directory_and_files(
        self._region_directory.value
    )

    log_loc = self._output_directory.value / 'measure.log.txt'
    logger, handler = helpers.setup_logger(log_loc)

    logger.info(
        """
        Label Images: %s
        Intensity Channels: %s
        Num. Files: %d
        Label Directory: %s
        Image Directory: %s
        Region Directory: %s
        Output Directory: %s
        ID Example: %s
        ID Regex Dict: %s
        Tx ID: %s
        Tx N Well: %s
        Tx Dict: %s
        """,
        self._label_images.value,
        self._intensity_images.value,
        len(label_files),
        label_dir,
        image_dir,
        region_dir,
        self._output_directory.value,
        self._example_id_string.value,
        self._id_regex_dict.value,
        self._tx_id.value,
        self._tx_n_well.value,
        self._tx_dict.value,
    )

    # check if the label files are the same as the image files
    if self._image_directory.value is not None and len(label_files) != len(image_files):
        logger.error(
            'Number of label files (%s) and image files (%s) do not match',
            len(label_files), len(image_files),
        )
    if self._region_directory.value is not None and len(label_files) != len(region_files):
        logger.error(
            'Number of label files (%s) and region files (%s) do not match',
            len(label_files), len(region_files),
        )

    self._progress_bar.label = f'Measuring {len(label_files)} Images'
    self._progress_bar.value = 0
    self._progress_bar.max = len(label_files)
    # get the relevant spacing for regionprops, depending on length
    props_scale = self._scale_tuple.value
    props_scale = props_scale[-len(self._squeezed_dims) :]
    # get the properties list
    properties = [
        prop.label for prop in self._props_container if prop.value
    ]

    id_regex_dict = self._safe_dict_eval(
        self._id_regex_dict.value, 'ID Regex Dict'
    )
    tx_dict = self._safe_dict_eval(self._tx_dict.value, 'Tx Dict')
    measure_props_concat = []

    for idx, file in enumerate(label_files):
        # TODO: Add scene processing
        logger.info('Processing file %s', file.name)
        lbl = BioImage(label_dir / file.name)
        id_string = helpers.create_id_string(lbl, file.stem)

        # get the itnensity image only if the image directory is not empty
        if self._image_directory.value:
            image_path = image_dir / file.name
            if not image_path.exists():
                logger.error(
                    'Image file %s not found in intensity directory',
                    file.name,
                )
                self._progress_bar.value = idx + 1
                continue
            img = BioImage(image_path)
        if self._region_directory.value:
            region_path = region_dir / file.name
            if not region_path.exists():
                logger.error(
                    'Region file %s not found in region directory',
                    file.name,
                )
                self._progress_bar.value = idx + 1
                continue
            reg = BioImage(region_path)

        for scene_idx, scene in enumerate(lbl.scenes):
            logger.info('Processing scene: %s :: %s', scene_idx, scene)
            lbl.set_scene(scene_idx)

            label_images = []
            label_names = []

            # iterate through each channel in the label image
            for label_chan in self._label_images.value:
                label_chan = label_chan[8:]
                label_names.append(label_chan)

                lbl_C = lbl.channel_names.index(label_chan)
                label = lbl.get_image_data(self._squeezed_dims, C=lbl_C)
                label_images.append(label)

            intensity_images = []
            intensity_names = []

            # id_string = helpers.create_id_string(lbl, file.stem)

            # Get stack of intensity images if there are any selected
            if self._intensity_images.value and not None:
                for channel in self._intensity_images.value:
                    if channel.startswith('Labels: '):
                        chan = channel[8:]
                        lbl_C = lbl.channel_names.index(chan)
                        lbl.set_scene(scene_idx)
                        inten_img = lbl.get_image_data(
                            self._squeezed_dims, C=lbl_C
                        )
                    elif channel.startswith('Intensity: '):
                        chan = channel[11:]
                        img_C = img.channel_names.index(chan)
                        img.set_scene(scene_idx)
                        inten_img = img.get_image_data(
                            self._squeezed_dims, C=img_C
                        )
                    elif channel.startswith('Region: '):
                        chan = channel[8:]
                        reg_C = reg.channel_names.index(chan)
                        reg.set_scene(scene_idx)
                        inten_img = reg.get_image_data(
                            self._squeezed_dims, C=reg_C
                        )
                    intensity_names.append(chan)
                    intensity_images.append(inten_img)

                # the last dim is the multi-channel dim for regionprops
                intensity_stack = np.stack(intensity_images, axis=-1)

            else:
                intensity_stack = None
                intensity_names = None

            # start the measuring here
            # TODO: Add optional scaling, in case images have different scales?
            measure_props_df = ndev_measure.measure_regionprops(
                label_images=label_images,
                label_names=label_names,
                intensity_images=intensity_stack,
                intensity_names=intensity_names,
                properties=properties,
                scale=props_scale,
                id_string=id_string,
                id_regex_dict=id_regex_dict,
                tx_id=self._tx_id.value,
                tx_dict=tx_dict,
                tx_n_well=self._tx_n_well.value,
                save_data_path=None,
            )

            measure_props_concat.append(measure_props_df)
            self._progress_bar.value = idx + 1

    measure_props_df = pd.concat(measure_props_concat)
    labels_string = '_'.join(label_names)
    save_loc = self._output_directory.value / f'measure_props_{labels_string}.csv'
    measure_props_df.to_csv(save_loc, index=False)

    logger.removeHandler(handler)

    return measure_props_df

group_measurements

group_measurements()

Group measurements based on user input.

Uses the values in the Grouping Container of the Widget and passes them to the group_and_agg_measurements function in the measure module. The grouped measurements are saved to a CSV file in the same directory as the measured data with '_grouped' appended.

Returns:

• DataFrame –

  The grouped measurements as a DataFrame.

Source code in src/napari_ndev/widgets/_measure_container.py

def group_measurements(self):
    """
    Group measurements based on user input.

    Uses the values in the Grouping Container of the Widget and passes them
    to the group_and_agg_measurements function in the measure module. The
    grouped measurements are saved to a CSV file in the same directory as
    the measured data with '_grouped' appended.

    Returns
    -------
    pd.DataFrame
        The grouped measurements as a DataFrame.

    """
    from napari_ndev import measure as ndev_measure

    self._progress_bar.label = 'Grouping Measurements'
    self._progress_bar.value = 0
    self._progress_bar.max = 1

    df = pd.read_csv(self._measured_data_path.value)

    # Filter out None values from agg_cols
    agg_cols = [col for col in self._agg_cols.value if col is not None]

    grouped_df = ndev_measure.group_and_agg_measurements(
        df=df,
        grouping_cols=self._grouping_cols.value,
        count_col=self._count_col.value,
        agg_cols=agg_cols,
        agg_funcs=self._agg_funcs.value,
    )
    # use the label_name column to make the dataframe wider
    if self._pivot_wider.value:
        # get grouping calls without label name
        index_cols = [col for col in self._grouping_cols.value if col != 'label_name']

        # alternatively, pivot every values column that is not present in index or columns
        value_cols = [col for col in grouped_df.columns if col not in self._grouping_cols.value]

        pivot_df = grouped_df.pivot(
            index=index_cols,
            columns='label_name',
            values=value_cols,
        )
        # # flatten the multiindex columns
        # pivot_df.columns = [f'{col[1]}_{col[0]}' for col in pivot_df.columns]

        # reset index so that it is saved in the csv
        pivot_df.reset_index(inplace=True)

        grouped_df = pivot_df

    save_loc = (
        self._measured_data_path.value.parent /
        f'{self._measured_data_path.value.stem}_grouped.csv'
    )
    grouped_df.to_csv(save_loc, index=False)

    self._progress_bar.value = 1
    return grouped_df