pandora.check_configuration

This module contains functions allowing to check the configuration given to Pandora pipeline.

Module Contents

Functions

rasterio_can_open_mandatory(→ bool)

Test if file can be open by rasterio

rasterio_can_open(→ bool)

Test if file can be open by rasterio

check_shape(→ None)

Check if two data_vars are the same dimensions

check_attributes(→ None)

Check if attributes are in the dataset

check_dataset(→ None)

Check if input dataset is correct

check_datasets(→ None)

Check that left and right datasets are correct

check_image_dimension(→ None)

Check width and height are the same between two images

check_images(→ None)

Check the images

check_band_names(→ None)

Check that band names have the correct format : band names must be strings.

check_disparities_from_input(→ None)

Check disparities from user configuration

check_disparities_from_dataset(→ None)

Check disparities with this format

get_config_input(→ Dict[str, dict])

Get the input configuration

get_config_pipeline(→ Dict[str, dict])

Get the pipeline configuration

memory_consumption_estimation(→ Union[Tuple[float, ...)

Return the approximate memory consumption for a given pipeline in GiB.

check_pipeline_section(→ Dict[str, dict])

Check if the pipeline is correct by

check_input_section(→ Dict[str, dict])

Complete and check if the dictionary is correct

check_conf(→ dict)

Complete and check if the dictionary is correct

concat_conf(→ Dict[str, dict])

Concatenate dictionaries

read_multiscale_params(→ Tuple[int, int])

Returns the multiscale parameters

read_config_file(→ Dict[str, dict])

Read a json configuration file

update_conf(→ Dict[str, dict])

Update the default configuration with the user configuration,

Attributes

input_configuration_schema

input_configuration_schema_integer_disparity

input_configuration_schema_left_disparity_grids_right_none

input_configuration_schema_left_disparity_grids_right_grids

default_short_configuration_input

MEMORY_CONSUMPTION_LIST

default_short_configuration_pipeline

default_short_configuration

pandora.check_configuration.rasterio_can_open_mandatory(file_: str) → bool[source]

Test if file can be open by rasterio

Parameters:

file (string) – File to test

Returns:

True if rasterio can open file and False otherwise

Return type:

bool

pandora.check_configuration.rasterio_can_open(file_: str) → bool[source]

Test if file can be open by rasterio

Parameters:

file (string) – File to test

Returns:

True if rasterio can open file and False otherwise

Return type:

bool

pandora.check_configuration.check_shape(dataset: xarray.Dataset, ref: str, test: str) → None[source]

Check if two data_vars are the same dimensions

Parameters:

  • dataset (xr.Dataset) – dataset

  • ref (str) – name of the reference image

  • test (str) – the tested image

Returns:

None

pandora.check_configuration.check_attributes(dataset: xarray.Dataset, attribute_list: set) → None[source]

Check if attributes are in the dataset

Parameters:

  • dataset (xr.Dataset) – dataset

  • attribute_list (list) – the attribute to test

Returns:

None

pandora.check_configuration.check_dataset(dataset: xarray.Dataset) → None[source]

Check if input dataset is correct

Parameters:

dataset (xr.Dataset) – dataset

Returns:

None

pandora.check_configuration.check_datasets(left: xarray.Dataset, right: xarray.Dataset) → None[source]

Check that left and right datasets are correct

Parameters:

  • left (xr.Dataset) – left dataset

  • right (xr.Dataset) – right dataset

Returns:

None

pandora.check_configuration.check_image_dimension(img1: rasterio.io.DatasetReader, img2: rasterio.io.DatasetReader) → None[source]

Check width and height are the same between two images

Parameters:

  • img1 (rasterio.io.DatasetReader) – image DatasetReader with width and height

  • img2 (rasterio.io.DatasetReader) – image DatasetReader with width and height

Returns:

None

pandora.check_configuration.check_images(user_cfg: Dict[str, dict]) → None[source]

Check the images

Parameters:

user_cfg (dict) – user configuration

Returns:

None

pandora.check_configuration.check_band_names(dataset: xarray.Dataset) → None[source]

Check that band names have the correct format : band names must be strings.

Parameters:

dataset (xr.Dataset) – dataset

Returns:

None

pandora.check_configuration.check_disparities_from_input(disparity: list[int] | str | None, img_left: str) → None[source]

Check disparities from user configuration

Parameters:

  • disparity (list[int] | str | None) – disparity to check if disparity is a list of two values: min and max.

  • img_left (str) – path to the left image

Returns:

None

pandora.check_configuration.check_disparities_from_dataset(disparity: xarray.DataArray) → None[source]

Check disparities with this format

disparity: 3D (band_disp, row, col) xarray.DataArray float32 and band_disp = (min, max)

Parameters:

disparity (xr.DataArray) – disparity to check

Returns:

None

pandora.check_configuration.get_config_input(user_cfg: Dict[str, dict]) → Dict[str, dict][source]

Get the input configuration

Parameters:

user_cfg (dict) – user configuration

Return cfg:

partial configuration

Rtype cfg:

dict

pandora.check_configuration.get_config_pipeline(user_cfg: Dict[str, dict]) → Dict[str, dict][source]

Get the pipeline configuration

Parameters:

user_cfg (dict) – user configuration

Return cfg:

partial configuration

Rtype cfg:

dict

pandora.check_configuration.memory_consumption_estimation(user_pipeline_cfg: Dict[str, dict], user_input: Dict[str, dict] | Tuple[str, int, int] | Tuple[str, str], pandora_machine: pandora.state_machine.PandoraMachine, checked_cfg_flag: bool = False) → Tuple[float, float] | None[source]

Return the approximate memory consumption for a given pipeline in GiB.

Parameters:

  • user_pipeline_cfg (dict) – user pipeline configuration

  • user_input (dict or Tuple[str, int, int] or Tuple[str, str]) – user input configuration, may be given as a dict or directly as (img_path, disp_min, disp_max) where [disp_min, disp_max] is the disparity interval used, or as (img_path, disparity_path) where disparity_path leads to a disparity grid containing two bands: min and max.

  • pandora_machine (PandoraMachine object) – instance of PandoraMachine

  • checked_cfg_flag (bool) – Flag for checking pipeline

Returns:

minimum and maximum memory consumption

Return type:

Tuple[float, float]

pandora.check_configuration.check_pipeline_section(user_cfg: Dict[str, dict], img_left: xarray.Dataset, img_right: xarray.Dataset, pandora_machine: pandora.state_machine.PandoraMachine) → Dict[str, dict][source]

Check if the pipeline is correct by - Checking the sequence of steps according to the machine transitions - Checking parameters, define in dictionary, of each Pandora step

Parameters:

  • user_cfg (dict) – pipeline user configuration

  • img_left (xarray.Dataset) – image left with metadata

  • img_right (xarray.Dataset) – image right with metadata

  • pandora_machine (PandoraMachine object) – instance of PandoraMachine

Returns:

cfg: pipeline configuration

Return type:

cfg: dict

pandora.check_configuration.check_input_section(user_cfg: Dict[str, dict]) → Dict[str, dict][source]

Complete and check if the dictionary is correct

Parameters:

user_cfg (dict) – user configuration

Returns:

cfg: global configuration

Return type:

cfg: dict

pandora.check_configuration.check_conf(user_cfg: Dict[str, dict], pandora_machine: pandora.state_machine.PandoraMachine) → dict[source]

Complete and check if the dictionary is correct

Parameters:

  • user_cfg (dict) – user configuration

  • pandora_machine (PandoraMachine) – instance of PandoraMachine

Returns:

cfg: global configuration

Return type:

cfg: dict

pandora.check_configuration.concat_conf(cfg_list: List[Dict[str, dict]]) → Dict[str, dict][source]

Concatenate dictionaries

Parameters:

cfg_list (List of dict) – list of configurations

Returns:

cfg: global configuration

Return type:

cfg: dict

pandora.check_configuration.read_multiscale_params(cfg: Dict[str, dict]) → Tuple[int, int][source]

Returns the multiscale parameters

Parameters:

cfg (dict) – configuration

Returns:

  • num_scales: number of scales

  • scale_factor: factor by which each coarser layer is downsampled

Return type:

tuple(int, int )

pandora.check_configuration.input_configuration_schema[source]
pandora.check_configuration.input_configuration_schema_integer_disparity: collections.abc.Mapping[source]
pandora.check_configuration.input_configuration_schema_left_disparity_grids_right_none: collections.abc.Mapping[source]
pandora.check_configuration.input_configuration_schema_left_disparity_grids_right_grids: collections.abc.Mapping[source]
pandora.check_configuration.default_short_configuration_input[source]
pandora.check_configuration.MEMORY_CONSUMPTION_LIST = [['matching_cost', 'matching_cost_method', 'mc_cnn', 1.57e-05, 265], ['optimization',...[source]
pandora.check_configuration.default_short_configuration_pipeline: dict[source]
pandora.check_configuration.default_short_configuration[source]
pandora.check_configuration.read_config_file(config_file: os.PathLike | str) → Dict[str, dict][source]

Read a json configuration file

Parameters:

config_file (PathLike | string) – path to a json file containing the algorithm parameters

Return user_cfg:

configuration dictionary

Return type:

dict

pandora.check_configuration.update_conf(def_cfg: Dict[str, dict], user_cfg: Dict[str, dict]) → Dict[str, dict][source]

Update the default configuration with the user configuration,

Parameters:

  • def_cfg (dict) – default configuration

  • user_cfg (dict) – user configuration

Returns:

the user and default configuration

Return type:

dict