preprocessing¶
preprocessing pipeline¶

ardent.preprocessing.__init__.
preprocess
(data: (<class 'numpy.ndarray'>, <class 'list'>), processes: list)[source]¶ Perform each preprocessing function in processes, in the order listed, on data if it is an array, or on each element in data if it is a list of arrays.
Parameters: Raises: TypeError
 Raised if data is a list whose elements are not all of type np.ndarray.TypeError
 Raised if data is neither a np.ndarray or a list of np.ndarrays.ValueError
 Raised if processes cannot be cast to a np.ndarray with dtype str.ValueError
 Raised if any element of processes is not a recognized preprocessing function.
Returns: A copy of data after having each function in processes applied.
Return type: np.ndarray, list
resampling¶

ardent.preprocessing.resampling.
downsample_image
(image, scale_factors, truncate=False)[source]¶ Downsample an image by averaging.
Parameters: Raises: ValueError
 Raised if any of scale_factors is less than 1.Returns: A downsampled copy of image.
Return type: np.ndarray

ardent.preprocessing.resampling.
change_resolution_to
(image, xyz_resolution, desired_xyz_resolution, pad_to_match_res=True, err_to_higher_res=True, average_on_downsample=True, truncate=False, return_true_resolution=False, **resample_kwargs)[source]¶ Resamples <image> to get its resolution as close as possible to <desired_xyz_resolution>.
Parameters:  image (np.ndarray)  The image to be resampled, allowing arbitrary dimensions.
 xyz_resolution (float, sequence)  The peraxis resolution of <image>.
 desired_xyz_resolution (float, sequence)  The desired peraxis resolution of <image> after resampling.
 pad_to_match_res (bool, optional)  If True, pads a copy of <image> to guarantee that <desired_xyz_resolution> is achieved. Defaults to True.
 err_to_higher_res (bool, optional)  If True and <pad_to_match_res> is False, rounds the shape of the new image up rather than down. Defaults to True.
 average_on_downsample (bool, optional)  If True, performs downsample_image on a copy of <image> before resampling to prevent aliasing. It scales the image by the largest integer possible along each axis without reducing the resolution past the final resolution. Defaults to True.
 truncate (bool, optional)  A kwarg passed to downsample_image. If true, evenly truncates the image down to the nearest multiple of the scale_factor for each axis. Defaults to False.
 return_true_resolution (bool, optional)  If True, rather than just returning the resampled image, returns a tuple containing the resampled image and its actual resolution. Defaults to False.
Returns:  A resampled copy of <image>.
If <return_true_resolution> was provided as True, then the return value is a tuple containing the resampled copy of <image> and its actual resolution.
Return type: np.ndarray, tuple

ardent.preprocessing.resampling.
change_resolution_by
(image, xyz_scales, xyz_resolution=1, pad_to_match_res=True, err_to_higher_res=True, average_on_downsample=True, truncate=False, return_true_resolution=False, **resample_kwargs)[source]¶ Resample image such that its resolution is scaled by 1 / <xyz_scales>[dim] or abs(xyz_scales[dim]) if xyz_scales[dim] is negative, in each dimension dim.
Parameters:  image (np.ndarray)  The image to be resampled, allowing arbitrary dimensions.
 xyz_scales (float, sequence) 
The peraxis factors by which to adjust the resolution of <image>. Negative values are treated as the reciprocal of their positive counterparts.
xyz_scales[dim] > 1 implies upsampling  increasing resolution and image size. xyz_scales[dim] = 1 implies unity  no change in resolution for this dimension. xyz_scales[dim] < 1 implies downsampling  decreasing resolution and image size. xyz_scales[dim] < 0 implies downsampling by this factor  cast to 1 / xyz_scales[dim].
Examples: xyz_scales[dim] = 2 > upsample by 2 xyz_scales[dim] = 1 > do nothing xyz_scales[dim] = 1/2 > downsample by 2 xyz_scales[dim] = 3 > downsample by 3 xyz_scales[dim] = 1/5 > upsample by 5
 xyz_resolution (float, sequence)  The peraxis resolution of <image>. Defaults to 1.
 pad_to_match_res (bool)  If True, pads a copy of <image> to guarantee that <desired_xyz_resolution> is achieved. Defaults to True.
 err_to_higher_res (bool)  If True and <pad_to_match_res> is False, rounds the shape of the new image up rather than down. Defaults to True.
 average_on_downsample (bool)  If True, performs downsample_image on a copy of <image> before resampling to prevent aliasing. It scales the image by the largest integer possible along each axis without reducing the resolution past the final resolution. Defaults to True.
 truncate (bool)  A kwarg passed to downsample_image. If true, evenly truncates the image down to the nearest multiple of the scale_factor for each axis. Defaults to False.
 return_true_resolution (bool)  If True, rather than just returning the resampled image, returns a tuple containing the resampled image and its actual resolution. Defaults to False.
Returns:  A resampled copy of <image>.
If <return_true_resolution> was provided as True, then the return value is a tuple containing the resampled copy of <image> and its actual resolution.
Return type: np.ndarray, tuple
normalization¶

ardent.preprocessing.normalization.
cast_to_typed_array
(data, dtype=<class 'float'>)[source]¶ Returns a copy of data cast as a np.ndarray of type dtype.
Parameters:  data (np.ndarray)  The array to be cast.
 dtype (type, optional)  The dtype to cast data to. Defaults to float. Defaults to float.
Returns: A copy of data cast to type dtype.
Return type: np.ndarray

ardent.preprocessing.normalization.
normalize_by_MAD
(data)[source]¶ Returns a copy of data divided by its mean absolute deviation.
Parameters: data (np.ndarray)  The array to be normalized. Returns: A copy of data divided by its mean absolute deviation. Return type: np.ndarray

ardent.preprocessing.normalization.
center_to_mean
(data)[source]¶ Returns a copy of data subtracted by its mean.
Parameters: data (np.ndarray)  The array to be subtracted from. Returns: A copy of data subtracted by its mean. Return type: np.ndarray