Utility functions

Check input quality

bigfish.stack.check_array(array, ndim=None, dtype=None, allow_nan=True)

Full safety check of an array.

Parameters
arraynp.ndarray

Array to check.

ndimint or List[int]

Number of dimensions expected.

dtypetype or List[type]

Types expected.

allow_nanbool

Allow NaN values or not.

Returns
_bool

Assert if the array is well formatted.

bigfish.stack.check_df(df, features=None, features_without_nan=None)

Full safety check of a dataframe.

Parameters
dfpd.DataFrame or pd.Series

Dataframe or Series to check.

featuresList[str]

Names of the expected features.

features_without_nanList[str]

Names of the features to check for the missing values

Returns
_bool

Assert if the dataframe is well formatted.

bigfish.stack.check_parameter(**kwargs)

Check dtype of the function’s parameters.

Parameters
kwargsType or Tuple[Type]

Map of each parameter with its expected dtype.

Returns
_bool

Assert if the array is well formatted.

bigfish.stack.check_range_value(array, min_=None, max_=None)

Check the support of the array.

Parameters
arraynp.ndarray

Array to check.

min_int

Minimum value allowed.

max_int

Maximum value allowed.

Returns
_bool

Assert if the array has the right range of values.

Get constant values

bigfish.stack.get_margin_value()

Return the margin pixel around a cell coordinate used to define its bounding box.

Returns
_int

Margin value (in pixels).

bigfish.stack.get_eps_float32()

Return the epsilon value for a 32 bit float.

Returns
_np.float32

Epsilon value.

Load and check stored data

bigfish.stack.load_and_save_url(remote_url, directory, filename=None)

Download remote data and save them

Parameters
remote_urlstr

Remote url of the data to download.

directorystr

Directory to save the download content.

filenamestr

Filename of the object to save.

Returns
pathstr

Path of the downloaded file.

bigfish.stack.check_hash(path, expected_hash)

Check hash value of a file.

Parameters
pathstr

Path of the file to check.

expected_hashstr

Expected hash value.

Returns
_bool

True if hash values match.

bigfish.stack.compute_hash(path)

Compute sha256 hash of a file.

Parameters
pathstr

Path to read the file.

Returns
sha256str

Hash value of the file.

bigfish.stack.check_input_data(input_directory, input_segmentation=False)

Check input images exists and download them if necessary.

Parameters
input_directorystr

Path of the image directory.

input_segmentationbool

Check 2-d example images for segmentation.

Compute moving average

bigfish.stack.moving_average(array, n)

Compute a trailing average.

Parameters
arraynp.ndarray

Array used to compute moving average.

nint

Window width of the moving average.

Returns
resultsnp.ndarray

Moving average values.

bigfish.stack.centered_moving_average(array, n)

Compute a centered moving average.

Parameters
arraynp.ndarray

Array used to compute moving average.

nint

Window width of the moving average.

Returns
resultsnp.ndarray

Centered moving average values.

Convert pixels and nanometers

bigfish.detection.convert_spot_coordinates(spots, voxel_size)

Convert spots coordinates from pixel to nanometer.

Parameters
spotsnp.ndarray

Coordinates of the detected spots with shape (nb_spots, 3) or (nb_spots, 2).

voxel_sizeint, float, Tuple(int, float) or List(int, float)

Size of a voxel, in nanometer. One value per spatial dimension (zyx or yx dimensions). If it’s a scalar, the same value is applied to every dimensions.

Returns
spots_nanometernp.ndarray

Coordinates of the detected spots with shape (nb_spots, 3) or (nb_spots, 3), in nanometer.

bigfish.detection.get_object_radius_pixel(voxel_size_nm, object_radius_nm, ndim)

Convert the object radius in pixel.

When the object considered is a spot this value can be interpreted as the standard deviation of the spot PSF, in pixel. For any object modelled with a gaussian signal, this value can be interpreted as the standard deviation of the gaussian.

Parameters
voxel_size_nmint, float, Tuple(int, float) or List(int, float)

Size of a voxel, in nanometer. One value per spatial dimension (zyx or yx dimensions). If it’s a scalar, the same value is applied to every dimensions.

object_radius_nmint, float, Tuple(int, float) or List(int, float)

Radius of the object, in nanometer. One value per spatial dimension (zyx or yx dimensions). If it’s a scalar, the same radius is applied to every dimensions.

ndimint

Number of spatial dimension to consider.

Returns
object_radius_pxTuple[float]

Radius of the object in pixel, one element per dimension (zyx or yx dimensions).

bigfish.detection.get_object_radius_nm(voxel_size_nm, object_radius_px, ndim)

Convert the object radius in nanometer.

When the object considered is a spot this value can be interpreted as the standard deviation of the spot PSF, in nanometer. For any object modelled with a gaussian signal, this value can be interpreted as the standard deviation of the gaussian.

Parameters
voxel_size_nmint, float, Tuple(int, float) or List(int, float)

Size of a voxel, in nanometer. One value per spatial dimension (zyx or yx dimensions). If it’s a scalar, the same value is applied to every dimensions.

object_radius_pxint, float, Tuple(int, float) or List(int, float)

Radius of the object, in pixel. One value per spatial dimension (zyx or yx dimensions). If it’s a scalar, the same radius is applied to every dimensions.

ndimint

Number of spatial dimension to consider.

Returns
object_radius_nmTuple[float]

Radius of the object in nanometer, one element per dimension (zyx or yx dimensions).

Format and save plots

bigfish.plot.save_plot(path_output, ext)

Save the plot.

Parameters
path_outputstr

Path to save the image (without extension).

extstr or List[str]

Extension used to save the plot. If it is a list of strings, the plot will be saved several times.

bigfish.plot.get_minmax_values(tensor)

Get the minimum and maximum value of the image according to its dtype.

Parameters
tensornp.ndarray

A 2-d, 3-d or 5-d tensor with shape (y, x), (z, y, x) or (r, c, z, y, x) respectively.

Returns
vminint

Minimum value display in the plot.

vmaxint

Maximum value display in the plot.

bigfish.plot.create_colormap()

Create a shuffled colormap to display segmentation masks.

Returns
colormapListedColormap object

Colormap for matplotlib.