tomoscan.esrf.volume.tiffvolume.TIFFVolume

class tomoscan.esrf.volume.tiffvolume.TIFFVolume(folder: Optional[str] = None, volume_basename: Optional[str] = None, data: Optional[ndarray] = None, source_scan: Optional[TomoScanBase] = None, metadata: Optional[dict] = None, data_url: Optional[DataUrl] = None, metadata_url: Optional[DataUrl] = None, overwrite: bool = False, start_index=0, data_extension='tiff', metadata_extension='txt')

Bases: VolumeSingleFrameBase

Save volume data to single frame tiff and metadata to .txt files

Warning:

each file saved under {volume_basename}_{index_zfill6}.tiff is considered to be a slice of the volume.

__init__(folder: Optional[str] = None, volume_basename: Optional[str] = None, data: Optional[ndarray] = None, source_scan: Optional[TomoScanBase] = None, metadata: Optional[dict] = None, data_url: Optional[DataUrl] = None, metadata_url: Optional[DataUrl] = None, overwrite: bool = False, start_index=0, data_extension='tiff', metadata_extension='txt') → None

Methods

__init__([folder, volume_basename, data, ...])

browse_data_files([url])

param url:

data url. If not provided will take self.data_url

browse_data_urls([url])

generator on data urls used.

browse_metadata_files([url])

param url:

metadata url. If not provided will take self.metadata_url

browse_slices([url])

generator of 2D numpy array representing a slice

check_can_provide_identifier()

clear_cache()

remove object stored in data and medatada

data_file_name_generator(n_frames, data_url)

browse output files for n_frames

data_file_saver_generator(n_frames, ...)

Provide a helper class to dump data frame by frame.

deduce_data_and_metadata_urls(url)

compute data and metadata urls from 'parent url' :return: data_url: Optional[DataUrl], metadata_url: Optional[DataUrl]

example_defined_from_str_identifier()

example as string to explain how users can defined identifiers from a string

format_data_path_for_data(data_path, index, ...)

Return file path to save the frame at index of the current volume

from_identifier(identifier)

Return the Dataset from a identifier

get_bounding_box([axis])

Return the bounding box covered by the Tomo object axis is expected to be in (0, 1, 2) or (x==0, y==1, z==2)

get_data_path_pattern_for_data(data_path, ...)

Return file path pattern (and not full path) to load data.

get_identifier()

dataset unique identifier.

get_min_max()

compute min max of the volume.

get_min_max_values([url])

compute min max over 'data' if exists else browsing the volume slice by slice

get_slice([index, axis, xy, xz, yz, url])

get_volume_basename([url])

get_volume_shape([url])

load()

load_chunk(chunk[, url])

Load a sub-volume.

load_data([url, store])

load_frame(file_name, scheme)

Function dedicated for volune saving each frame on a single file

load_metadata([url, store])

save([url])

save_data([url])

save data to the provided url or existing one if none is provided

save_frame(frame, file_name, scheme)

Function dedicated for volune saving each frame on a single file

save_metadata([url])

save metadata to the provided url or existing one if none is provided

select(volume[, xy, xz, yz, axis, index])

Attributes

DEFAULT_DATA_EXTENSION

DEFAULT_DATA_PATH_PATTERN

DEFAULT_DATA_SCHEME

DEFAULT_METADATA_EXTENSION

DEFAULT_METADATA_PATH_PATTERN

DEFAULT_METADATA_SCHEME

EXTENSION

data

data_extension

data_url

extension

metadata

metadata_extension

metadata_url

overwrite

pixel_size

position

position are provided as a tuple using the same reference for axis as the volume data.

source_scan

start_index

url

voxel_size

voxel size as (axis 0 dim - aka z, axis 1 dim - aka y, axis 2 dim aka z)
browse_data_files(url=None)
Parameters:

url – data url. If not provided will take self.data_url

return a generator go through all the existings files associated to the data volume

browse_data_urls(url=None)

generator on data urls used.

Parameters:

url – data url to be used. If not provided will take self.data_url

browse_metadata_files(url=None)
Parameters:

url – metadata url. If not provided will take self.metadata_url

return a generator go through all the existings files associated to the data volume

browse_slices(url=None)

generator of 2D numpy array representing a slice

Parameters:

url – data url to be used. If not provided will browse self.data if exists else self.data_url

Warning:

this will get the slice from the data on disk and never use data property. so before browsing slices you might want to check if data is already loaded

clear_cache()

remove object stored in data and medatada

data_file_name_generator(n_frames, data_url)

browse output files for n_frames

data_file_saver_generator(n_frames, data_url: DataUrl, overwrite: bool)

Provide a helper class to dump data frame by frame. For know the only possible interaction is Helper[:] = frame

Parameters:

  • n_frames (int) – number of frame the final volume will contain

  • data_url (DataUrl) – url to dump data

  • overwrite (bool) – overwrite existing file ?

deduce_data_and_metadata_urls(url: Optional[DataUrl]) → tuple

compute data and metadata urls from ‘parent url’ :return: data_url: Optional[DataUrl], metadata_url: Optional[DataUrl]

static example_defined_from_str_identifier() → str

example as string to explain how users can defined identifiers from a string

format_data_path_for_data(data_path: str, index: int, volume_basename: str) → str

Return file path to save the frame at index of the current volume

static from_identifier(identifier)

Return the Dataset from a identifier

get_bounding_box(axis: Optional[Union[str, int]] = None)

Return the bounding box covered by the Tomo object axis is expected to be in (0, 1, 2) or (x==0, y==1, z==2)

get_data_path_pattern_for_data(data_path: str, volume_basename: str) → str

Return file path pattern (and not full path) to load data. For example in edf it can return ‘myacquisition_*.edf’ in order to be handled by

get_identifier() → TIFFVolumeIdentifier

dataset unique identifier. Can be for example a hdf5 and en entry from which the dataset can be rebuild

get_min_max() → tuple

compute min max of the volume. Can take some time but avoid to load the full volume in memory

get_min_max_values(url=None) → tuple

compute min max over ‘data’ if exists else browsing the volume slice by slice

Parameters:

url – data url to be used. If not provided will take self.data_url

load_chunk(chunk, url=None)

Load a sub-volume.

Parameters:

  • chunk – tuple of slice objects indicating which chunk of the volume has to be loaded.

  • url – data url to be used. If not provided will take self.data_url

load_frame(file_name, scheme) → ndarray

Function dedicated for volune saving each frame on a single file

Parameters:

  • file_name (str) – path to store the data

  • scheme (str) – scheme to save the data

property position: Optional[tuple]

position are provided as a tuple using the same reference for axis as the volume data. position is returned as (axis_0_pos, axis_1_pos, axis_2_pos). Can also be see as (z_position, y_position, x_position)

save_data(url: Optional[DataUrl] = None) → None

save data to the provided url or existing one if none is provided

save_frame(frame, file_name, scheme)

Function dedicated for volune saving each frame on a single file

Parameters:

  • frame (numpy.ndarray) – frame to be save

  • file_name (str) – path to store the data

  • scheme (str) – scheme to save the data

save_metadata(url: Optional[DataUrl] = None) → None

save metadata to the provided url or existing one if none is provided

property voxel_size: Optional[tuple]

voxel size as (axis 0 dim - aka z, axis 1 dim - aka y, axis 2 dim aka z)