points to csv

table_format.md

Points csv

coord_id	dim_0	dim_1	...	dim_n
0
1
...
n

Shapes csv

shape_id	shape_type	coord_id	dim_0	dim_1	dim_2	...	dim_n
0	Ellipse	0
0	Ellipse	1
0	Ellipse	2
0	Ellipse	3
1	Rectangle	0
1	Rectangle	1
...	...	...
n	Path	n-1
n	Path	n

Vectors csv

??? This can be left for another time

Surfaces csv

??? This can be left for another time

to_table.py

# Points class method
from napari.io import write_csv

...
    def to_table(self, path=None):
        """Constructs a table of point coordinate data.

        Parameters
        ----------
        path : str, optional
            If a filename is provided, the table will be saved as a csv file.

        Returns
        -------
        tuple
            (table, column_names) where table is a list of lists, and
            column_names is a list of strings.

        Notes
        -----
        You can construct a pandas dataframe from the results like this:
        ```
        table, column_names = my_points.to_table()
        points_dataframe = pandas.DataFrame(table, columns=column_names)
        ```
        """
        n_dimensions = self.data.shape[1]
        column_names = ['coord_id'] + [
            'dim_' + str(n) for n in range(n_dimensions)
        ]
        table = []
        for idx, row in enumerate(self.data):
            data = [idx] + list(row)
            table.append(data)
        if path is not None:
            write_csv(path, table, column_names)
        return table, column_names


# Shapes class method
from napari.io import write_csv

...
    def to_table(self, path=None):
        """Constructs a table of shape coordinate data.

        Parameters
        ----------
        path : str, optional
            If a filename is provided, the table will be saved as a csv file.

        Returns
        -------
        tuple
            (table, column_names) where table is a list of lists, and
            column_names is a list of strings.

        Notes
        -----
        You can construct a pandas dataframe from the results like this:
        ```
        table, column_names = my_shapes.to_table()
        points_dataframe = pandas.DataFrame(table, columns=column_names)
        ```
        """
        n_dimensions = max([i.shape[1] for i in self.data])
        column_names = ['shape_id', 'shape_type', 'coord_id'] + [
            'dim_' + str(n) for n in range(n_dimensions)
        ]
        table = []
        for idx, row in enumerate(self.data):
            data = [idx_shape, shape_type, idx] + list(row)
            table.append[data]
        if path is not None:
            write_csv(path, table, column_names)
        return table, column_names


# Vector class method
# ... this can be left for another time

# Surface class method
# ... this can be left for another time

write_csv.py

#napari.io.py
import csv


def write_csv(path, data, column_names=None):
    """Write a csv file.

    Parameters
    ----------
    path : str
        Filename for saving csv.
    data : list or ndarray
        Table values, contained in a list of lists or an ndarray.
    column_names : list, optional
        List of column names for table data.
    """
    with open(path, mode='w') as csvfile:
        writer = csv.writer(csvfile, delimiter=',', quotechar='"',
            quoting=csv.QUOTE_MINIMAL
        )
        if column_names is not None:
            writer.writerow(column_names)
        for row in data:
            writer.writerow(row)