palmari.processing package#

Subpackages#

Submodules#

palmari.processing.edit_pipeline_window module#

palmari.processing.image_pipeline module#

class palmari.processing.image_pipeline.ImagePipeline(name: str, movie_preprocessors: List[palmari.processing.steps.base.MoviePreProcessor], detector: palmari.processing.steps.base.Detector, localizer: palmari.processing.steps.base.SubpixelLocalizer, loc_processors: List[palmari.processing.steps.base.LocProcessor], tracker: palmari.processing.steps.base.Tracker)[source]#

Bases: object

property available_steps#
can_be_removed(step)[source]#
contains_class(step)[source]#
classmethod default_with_name(name: str)[source]#
exp_params_path(acq: palmari.data_structure.acquisition.Acquisition) str[source]#
exp_run_df_path(acq: palmari.data_structure.acquisition.Acquisition) str[source]#
classmethod from_dict(p: Dict)[source]#

Instantiate from a dictionnary. Here’s an example Dictionnary which could be passed as an argument :

{
    "name":"my_pipeline",
    "movie_preprocessors":[
        {
            "MyPreProcessingClass":{"param1":value,"param2":other_value}
        },
        {
            "WindowPercentileFilter":{}
            # If the parameter's dict is empty, default parameters will be used
        }
    }],
    "localizer":{
        "DefaultLocalizer":{"threshold_factor":1.5}
        },
    "tracker":{
        "UnknownClass":{"bla":bla}
        # If the class is not found, this will raise an exception
        # Similarly, if the class provided does not inherit Tracker, an exception will be raised
    }
    # If some step is not mentioned (e.g. here, there's nothing about localization processing), then
    # if it's movie_preprocessors, then no movie_preprocessors will be used (same for loc_processors)
    # it it's localizer or tracker, then the default classes will be used.
}
classmethod from_yaml(file)[source]#
has_alternatives_to(step)[source]#
index_of(step)[source]#
is_already_localized(acq: palmari.data_structure.acquisition.Acquisition)[source]#
is_already_tracked(acq: palmari.data_structure.acquisition.Acquisition)[source]#
is_mandatory(step)[source]#
loc_processing(mov: dask.array.core.Array, locs: pandas.core.frame.DataFrame, pixel_size: float = 1.0) pandas.core.frame.DataFrame[source]#
mark_as_localized(acq: palmari.data_structure.acquisition.Acquisition)[source]#
mark_as_tracked(acq: palmari.data_structure.acquisition.Acquisition)[source]#
movie_localization(mov: dask.array.core.Array, DT: float, pixel_size: float) pandas.core.frame.DataFrame[source]#
movie_preprocessing(mov: dask.array.core.Array) dask.array.core.Array[source]#
process(to_process: Union[palmari.data_structure.acquisition.Acquisition, palmari.data_structure.experiment.Experiment], force_reprocess: bool = False)[source]#
step_class_of(step)[source]#
step_type_of(step)[source]#
to_dict() dict[source]#
to_yaml(fileName)[source]#
tracking(locs: pandas.core.frame.DataFrame) pandas.core.frame.DataFrame[source]#

palmari.processing.image_pipeline_widget module#

palmari.processing.utils module#

palmari.processing.utils.get_values_as_in_dict(dict_to_copy: dict, summary_string: str) dict[source]#

Module contents#