cellpack.autopack.writers package

Submodules

cellpack.autopack.writers.ImageWriter module

class cellpack.autopack.writers.ImageWriter.ImageWriter(env, name=None, output_path=None, voxel_size=None, hollow=False, convolution_options=None, projection_axis='z')

Bases: object

concatenate_image_data()

Creates a voxelized representation of the current scene

static convolve_channel(channel, psf)

Convolves a channel with a psf

Parameters:

• channel (numpy.ndarray) – channel to be convolved

• psf (numpy.ndarray) – psf

Returns:

convolved channel

Return type:

numpy.ndarray

convolve_image(image, psf='gaussian', psf_parameters=None)

Convolves the image with a psf

Parameters:

• image (numpy.ndarray) – image to be convolved

• psf (str or numpy.ndarray) – psf type

• psf_parameters (dict) – psf parameters

Returns:

convolved image

Return type:

numpy.ndarray

static create_box_psf(size=None)

Creates a box psf

Parameters:

size (np.ndarray) – size of the psf

Returns:

psf

Return type:

numpy.ndarray

static create_gaussian_psf(sigma=1.5, size=None)

Creates a gaussian psf

Parameters:

• sigma (float) – sigma of the gaussian

• size (np.ndarray) – size of the psf

Returns:

psf

Return type:

numpy.ndarray

export_image()

Saves the results as a tiff file

static transpose_image_for_projection(image, projection_axis)

cellpack.autopack.writers.MarkdownWriter module

class cellpack.autopack.writers.MarkdownWriter.MarkdownWriter(title: str, output_path: Path, output_image_location: Path, report_name: str)

Bases: object

add_header(header, level: int = 2)

add_images(header, image_text, filepaths)

add_inline_image(text, filepath)

add_line(line)

add_list(list_items)

add_table(header, table, text_align='center')

add_table_from_csv(header, filepath, text_align='center')

write_file()

Module contents

Write out data for cellpack.

class cellpack.autopack.writers.IOingredientTool(env=None)

Bases: object

ingrJsonNode(ingr, result=False, kwds=None, transpose=False)

write(ingr, filename, ingr_format='xml', kwds=None, result=False)

class cellpack.autopack.writers.NumpyArrayEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)

Bases: JSONEncoder

Constructor for JSONEncoder, with sensible defaults.

If skipkeys is false, then it is a TypeError to attempt encoding of keys that are not str, int, float or None. If skipkeys is True, such items are simply skipped.

If ensure_ascii is true, the output is guaranteed to be str objects with all incoming non-ASCII characters escaped. If ensure_ascii is false, the output can contain non-ASCII characters.

If check_circular is true, then lists, dicts, and custom encoded objects will be checked for circular references during encoding to prevent an infinite recursion (which would cause an RecursionError). Otherwise, no such check takes place.

If allow_nan is true, then NaN, Infinity, and -Infinity will be encoded as such. This behavior is not JSON specification compliant, but is consistent with most JavaScript based encoders and decoders. Otherwise, it will be a ValueError to encode such floats.

If sort_keys is true, then the output of dictionaries will be sorted by key; this is useful for regression tests to ensure that JSON serializations can be compared on a day-to-day basis.

If indent is a non-negative integer, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0 will only insert newlines. None is the most compact representation.

If specified, separators should be an (item_separator, key_separator) tuple. The default is (’, ‘, ‘: ‘) if indent is None and (‘,’, ‘: ‘) otherwise. To get the most compact JSON representation, you should specify (‘,’, ‘:’) to eliminate whitespace.

If specified, default is a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a TypeError.

default(obj)

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return JSONEncoder.default(self, o)

class cellpack.autopack.writers.Writer(format='simularium')

Bases: object

static return_object_value(data)

save(env, kwds=None, result=False, grid=False, packing_options=False, indent=False, quaternion=False, transpose=False, seed_to_results_map=None)

save_Mixed_asJson(env, useXref=True, kwds=None, result=False, grid=False, packing_options=False, indent=True, quaternion=False, transpose=False)

Save the current environment setup as an json file. env is the environment / recipe to be exported.

save_as_simularium(env, seed_to_results_map)

static setValueToJsonNode(value, attrname)

cellpack.autopack.writers.updatePositionsRadii(ingr)