palmari.data_structure package#

Submodules#

palmari.data_structure.acquisition module#

class palmari.data_structure.acquisition.Acquisition(image_file, experiment: Experiment, image_pipeline: ImagePipeline)[source]#

Bases: object

An acquisition corresponds to a PALM movie. It is part of an Experiment, and bound to a ImagePipeline with which it is processed.

property ID: str#
add_columns_to_loc(columns: pandas.core.frame.DataFrame, save: bool = False)[source]#
add_traj_cols_to_locs(traj_columns: pandas.core.frame.DataFrame)[source]#

traj_columns is a dataframe whose index corresponds to the ‘n’ column (traj ID) We merge it with the locs dataframe

basic_stats()[source]#
compute_intensity()[source]#
compute_tubeness()[source]#
correct_drift()[source]#
property drift_is_corrected: bool#
get_property(col: str) → Any[source]#

Access an acquisition’s property, read from its experiment’s index table.

Parameters

col (str) – name of the index table column to look into

Returns

value of the corresponding row x column in the experiment’s index table

Return type

Any

get_traj(n: int)[source]#
property image: dask.array.core.Array#

The actual movie, loaded with Dask.

Returns

the movie.

Return type

da.Array

property intensity: pandas.core.frame.DataFrame#
property intensity_is_computed: bool#
property intensity_path: str#
property is_localized: bool#
property is_processed: bool#
property is_tracked#
localize()[source]#
property locs: pandas.core.frame.DataFrame#
property locs_path: str#
property polygon_files: List#
property polygon_folder: str#
property raw_locs: pandas.core.frame.DataFrame#
property raw_locs_path: str#
track()[source]#
trajectories_list(min_length: int = 7, return_indices: bool = True, filter: Optional[Callable] = None)[source]#

Returns a list of trajectories whose length is above a given threshold, possibly filtered according to the locs they’re based on

Parameters

  • min_length (int, optional) – Defaults to 7.

  • return_indices (bool, optional) – Whether to return indices (‘n’ column of the locs DataFrame) along with coordinates. Defaults to True.

  • filter (Callable, optional) – Callable, which takes as input the locs DataFrame and returns a boolean Series with the same index. Defaults to None.

Returns

either a list of trajectories, or a tuple containing this same list and the list of indices

Return type

_type_

property tubeness: numpy.array#
property tubeness_is_computed: bool#
property tubeness_path: str#
view(min_traj_length=1, polygon_ID_col: Optional[str] = None, short_for_tests: bool = False, contrast_limits: tuple = (100, 500))[source]#
view_points(viewer=None, polygon_ID_col: Optional[str] = None, subsample: Optional[int] = None)[source]#
view_polygons(viewer=None)[source]#
view_tracks(viewer=None, min_length=1, polygon_ID_col: Optional[str] = None)[source]#

palmari.data_structure.experiment module#

class palmari.data_structure.experiment.Experiment(data_folder: str, export_folder: str, DT: Optional[float] = None, pixel_size: Optional[float] = None, file_pattern: Optional[str] = None)[source]#

Bases: object

add_new_roi_to_index(f: str)[source]#
property all_files: List[str]#

Return all files indexed in self.index_df

Returns

all files indexed in self.index_df

Return type

List[Acquisition]

check_export_folder_and_load_info()[source]#
property custom_fields: dict#

Override this in a subclass of Experience to meet your needs keys of the dict are column names values are used to fill the columns, using the TIF file name of each acquisition values can be :

  • string : True if the file name contains that string

  • int : the i-th part of the file name, when split using the filesystem separator

  • callable : callable(filename)

for instance

{"condition":get_condition_from_name}
classmethod from_single_tif(image_file: str, export_folder: str)[source]#
get_ID_of_acq(acquisition: palmari.data_structure.acquisition.Acquisition)[source]#
property index_df: pandas.core.frame.DataFrame#
look_for_new_columns(overwrite=False)[source]#

Computes custom columns

Parameters

overwrite (bool, optional) – Whether to overwrite pre-existing values. Defaults to False.

look_for_updates()[source]#

Look if the index dataframe matches the reality of present/absent files And computes custom columns if needed

parameter_influence_on_stats(param_name: str, stats: list = ['n_locs'], mode: str = 'hist')[source]#

Plots the influence of a processing parameter on some statistics.

Parameters

  • param_name (str) – The parameter whose influence is studied. it should be one returned by acquisition.basic_stats

  • stats (list, optional) – Statistics to compare. Defaults to [“n_locs”].

  • mode (str, optional) – Plotting mode. supported : “bars” and “hist”. Defaults to “hist”.

remove_old_roi_from_index(f: str)[source]#
property runs_stats: pandas.core.frame.DataFrame#
save_index()[source]#

Just saves index_df

scan_folder() → list[source]#

Module contents#