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classes package
-
-
Submodules
-
-
-
classes.abstract_importer module
-
--
-class
classes.abstract_importer.AbstractImporter(file_path: str)
-Bases: abc.ABC
-Abstract class that exposes all the necessary methods to process the trajectories and the net structure.
-
-- Parameters
-file_path (str) – the file path
-- _concatenated_samples
-Dataframe containing the concatenation of all the processed trajectories
-- _df_structure
-Dataframe containing the structure of the network (edges)
-- _df_variables
-Dataframe containing the nodes cardinalities
-- _sorter
-A list containing the columns header (excluding the time column) of the _concatenated_samples
-
-
-
Warning
-
The class members _df_variables and _df_structure HAVE to be properly constructed
-as Pandas Dataframes with the following structure:
-Header of _df_structure = [From_Node | To_Node]
-Header of _df_variables = [Variable_Label | Variable_Cardinality]
-
-
Note
-
If you don’t have prior network structure just leave _df_structure set to None.
-
-
Note
-
See :class:JsonImporter for an example implementation
-
--
-
build_list_of_samples_array(data_frame: pandas.core.frame.DataFrame) → List
-Builds a List containing the columns of data_frame and converts them to a numpy array.
-
-- Parameters
-data_frame (pandas.Dataframe) – the dataframe from which the columns have to be extracted and converted
-- Returns
-the resulting list of numpy arrays
-- Return type
-List
-
-
-
-
--
-abstract
build_sorter(sample_frame: pandas.core.frame.DataFrame) → List
-Initializes the _sorter class member from a trajectory dataframe, exctracting the header of the frame
-and keeping ONLY the variables symbolic labels, cutting out the time label in the header.
-
-- Parameters
-sample_frame (pandas.DataFrame) – The dataframe from which extract the header
-- Returns
-A list containing the processed header.
-- Return type
-List
-
-
-
-
--
-
clear_concatenated_frame() → None
-Removes all values in the dataframe concatenated_samples.
-
-
-
--
-
compute_row_delta_in_all_samples_frames(df_samples_list: List) → None
-Calls the method compute_row_delta_sigle_samples_frame on every dataframe present in the list
-df_samples_list.
-Concatenates the result in the dataframe concatanated_samples
-
-- Parameters
-df_samples_list (List) – the datframe’s list to be processed and concatenated
-
-
-
Warning
-
The Dataframe sample_frame has to follow the column structure of this header:
-Header of sample_frame = [Time | Variable values]
-The class member self._sorter HAS to be properly INITIALIZED (See class members definition doc)
-
-
Note
-
After the call of this method the class member concatanated_samples will contain all processed
-and merged trajectories
-
-
-
--
-
compute_row_delta_sigle_samples_frame(sample_frame: pandas.core.frame.DataFrame, columns_header: List, shifted_cols_header: List) → pandas.core.frame.DataFrame
-Computes the difference between each value present in th time column.
-Copies and shift by one position up all the values present in the remaining columns.
-
-- Parameters
-
-sample_frame (pandas.Dataframe) – the traj to be processed
columns_header (List) – the original header of sample_frame
shifted_cols_header (List) – a copy of columns_header with changed names of the contents
-- Returns
-The processed dataframe
-- Return type
-pandas.Dataframe
-
-
-
Warning
-
the Dataframe sample_frame has to follow the column structure of this header:
-Header of sample_frame = [Time | Variable values]
-
-
-
--
-property
concatenated_samples
-
-
-
--
-abstract
dataset_id() → object
-If the original dataset contains multiple dataset, this method returns a unique id to identify the current
-dataset
-
-
-
--
-property
file_path
-
-
-
--
-property
sorter
-
-
-
--
-property
structure
-
-
-
--
-property
variables
-
-
-
-
-
-
-
classes.cache module
-
--
-class
classes.cache.Cache
-Bases: object
-This class acts as a cache of SetOfCims objects for a node.
-
-- _list_of_sets_of_parents
-a list of Sets objects of the parents to which the cim in cache at SAME
-index is related
-- _actual_cache
-a list of setOfCims objects
-
-
--
-
clear() → None
-Clear the contents both of _actual_cache and _list_of_sets_of_parents.
-
-
-
--
-
find(parents_comb: Set) → classes.set_of_cims.SetOfCims
-Tries to find in cache given the symbolic parents combination parents_comb the SetOfCims
-related to that parents_comb.
-
-- Parameters
-parents_comb (Set) – the parents related to that SetOfCims
-- Returns
-A SetOfCims object if the parents_comb index is found in _list_of_sets_of_parents.
-None otherwise.
-- Return type
-SetOfCims
-
-
-
-
--
-
put(parents_comb: Set, socim: classes.set_of_cims.SetOfCims) → None
-Place in cache the SetOfCims object, and the related symbolic index parents_comb in
-_list_of_sets_of_parents.
-
-- Parameters
--
-
-
-
-
-
-
-
-
-
classes.conditional_intensity_matrix module
-
--
-class
classes.conditional_intensity_matrix.ConditionalIntensityMatrix(state_residence_times: numpy.array, state_transition_matrix: numpy.array)
-Bases: object
-Abstracts the Conditional Intesity matrix of a node as aggregation of the state residence times vector
-and state transition matrix and the actual CIM matrix.
-
-- Parameters
--
-
-- _cim
-the actual cim of the node
-
-
--
-property
cim
-
-
-
--
-
compute_cim_coefficients() → None
-Compute the coefficients of the matrix _cim by using the following equality q_xx’ = M[x, x’] / T[x].
-The class member _cim will contain the computed cim
-
-
-
--
-property
state_residence_times
-
-
-
--
-property
state_transition_matrix
-
-
-
-
-
-
-
classes.json_importer module
-
--
-class
classes.json_importer.JsonImporter(file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str, variables_key: str)
-Bases: classes.abstract_importer.AbstractImporter
-Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare
-the data in json extension.
-
-- Parameters
-
-file_path (string) – the path of the file that contains tha data to be imported
samples_label (string) – the reference key for the samples in the trajectories
structure_label (string) – the reference key for the structure of the network data
variables_label (string) – the reference key for the cardinalites of the nodes data
time_key (string) – the key used to identify the timestamps in each trajectory
variables_key (string) – the key used to identify the names of the variables in the net
-- _array_indx
-the index of the outer JsonArray to extract the data from
-- _df_samples_list
-a Dataframe list in which every dataframe contains a trajectory
-- _raw_data
-The raw contents of the json file to import
-
-
--
-
build_sorter(sample_frame: pandas.core.frame.DataFrame) → List
-Implements the abstract method build_sorter of the AbstractImporter for this dataset.
-
-
-
--
-
clear_data_frame_list() → None
-Removes all values present in the dataframes in the list _df_samples_list.
-
-
-
--
-
dataset_id() → object
-If the original dataset contains multiple dataset, this method returns a unique id to identify the current
-dataset
-
-
-
--
-
import_data(indx: int) → None
-Implements the abstract method of AbstractImporter.
-
-- Parameters
-indx (int) – the index of the outer JsonArray to extract the data from
-
-
-
-
--
-
import_sampled_cims(raw_data: List, indx: int, cims_key: str) → Dict
-Imports the synthetic CIMS in the dataset in a dictionary, using variables labels
-as keys for the set of CIMS of a particular node.
-
-- Parameters
-
-raw_data (List) – List of Dicts
indx (int) – The index of the array from which the data have to be extracted
cims_key (string) – the key where the json object cims are placed
-- Returns
-a dictionary containing the sampled CIMS for all the variables in the net
-- Return type
-Dictionary
-
-
-
-
--
-
import_structure(raw_data: List) → pandas.core.frame.DataFrame
-Imports in a dataframe the data in the list raw_data at the key _structure_label
-
-- Parameters
-raw_data (List) – List of Dicts
-- Returns
-Dataframe containg the starting node a ending node of every arc of the network
-- Return type
-pandas.Dataframe
-
-
-
-
--
-
import_trajectories(raw_data: List) → List
-Imports the trajectories from the list of dicts raw_data.
-
-- Parameters
-raw_data (List) – List of Dicts
-- Returns
-List of dataframes containing all the trajectories
-- Return type
-List
-
-
-
-
--
-
import_variables(raw_data: List) → pandas.core.frame.DataFrame
-Imports the data in raw_data at the key _variables_label.
-
-- Parameters
-raw_data (List) – List of Dicts
-- Returns
-Datframe containg the variables simbolic labels and their cardinalities
-- Return type
-pandas.Dataframe
-
-
-
-
--
-
normalize_trajectories(raw_data: List, indx: int, trajectories_key: str) → List
-Extracts the trajectories in raw_data at the index index at the key trajectories key.
-
-- Parameters
-
-raw_data (List) – List of Dicts
indx (int) – The index of the array from which the data have to be extracted
trajectories_key (string) – the key of the trajectories objects
-- Returns
-A list of daframes containg the trajectories
-- Return type
-List
-
-
-
-
--
-
one_level_normalizing(raw_data: List, indx: int, key: str) → pandas.core.frame.DataFrame
-Extracts the one-level nested data in the list raw_data at the index indx at the key key.
-
-- Parameters
-
-raw_data (List) – List of Dicts
indx (int) – The index of the array from which the data have to be extracted
key (string) – the key for the Dicts from which exctract data
-- Returns
-A normalized dataframe
-- Return type
-pandas.Datframe
-
-
-
-
--
-
read_json_file() → List
-Reads the JSON file in the path self.filePath.
-
-- Returns
-The contents of the json file
-- Return type
-List
-
-
-
-
-
-
-
-
classes.network_graph module
-
--
-class
classes.network_graph.NetworkGraph(graph_struct: classes.structure.Structure)
-Bases: object
-Abstracts the infos contained in the Structure class in the form of a directed graph.
-Has the task of creating all the necessary filtering and indexing structures for parameters estimation
-
-- Parameters
-graph_struct (Structure) – the Structure object from which infos about the net will be extracted
-- _graph
-directed graph
-- _aggregated_info_about_nodes_parents
-a structure that contains all the necessary infos
-about every parents of the node of which all the indexing and filtering structures will be constructed.
-- _time_scalar_indexing_structure
-the indexing structure for state res time estimation
-- _transition_scalar_indexing_structure
-the indexing structure for transition computation
-- _time_filtering
-the columns filtering structure used in the computation of the state res times
-- _transition_filtering
-the columns filtering structure used in the computation of the transition
-from one state to another
-- _p_combs_structure
-all the possible parents states combination for the node of interest
-
-
--
-
add_edges(list_of_edges: List) → None
-Add the edges to the _graph contained in the list list_of_edges.
-
-- Parameters
-list_of_edges (List) – the list containing of tuples containing the edges
-
-
-
-
--
-
add_nodes(list_of_nodes: List) → None
-Adds the nodes to the _graph contained in the list of nodes list_of_nodes.
-Sets all the properties that identify a nodes (index, positional index, cardinality)
-
-- Parameters
-list_of_nodes (List) – the nodes to add to _graph
-
-
-
-
--
-static
build_p_comb_structure_for_a_node(parents_values: List) → numpy.ndarray
-Builds the combinatorial structure that contains the combinations of all the values contained in
-parents_values.
-
-- Parameters
-parents_values (List) – the cardinalities of the nodes
-- Returns
-A numpy matrix containing a grid of the combinations
-- Return type
-numpy.ndArray
-
-
-
-
--
-static
build_time_columns_filtering_for_a_node(node_indx: int, p_indxs: List) → numpy.ndarray
-Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs
-in the dataset.
-This structute will be used in the computation of the state res times.
-:param node_indx: the index of the node
-:type node_indx: int
-:param p_indxs: the indexes of the node’s parents
-:type p_indxs: List
-:return: The filtering structure for times estimation
-:rtype: numpy.ndArray
-
-
-
--
-static
build_time_scalar_indexing_structure_for_a_node(node_states: int, parents_vals: List) → numpy.ndarray
-Builds an indexing structure for the computation of state residence times values.
-
-- Parameters
--
-
-- Returns
-The time indexing structure
-- Return type
-numpy.ndArray
-
-
-
-
--
-static
build_transition_filtering_for_a_node(node_indx: int, p_indxs: List, nodes_number: int) → numpy.ndarray
-Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs
-in the dataset.
-This structure will be used in the computation of the state transitions values.
-:param node_indx: the index of the node
-:type node_indx: int
-:param p_indxs: the indexes of the node’s parents
-:type p_indxs: List
-:param nodes_number: the total number of nodes in the dataset
-:type nodes_number: int
-:return: The filtering structure for transitions estimation
-:rtype: numpy.ndArray
-
-
-
--
-static
build_transition_scalar_indexing_structure_for_a_node(node_states_number: int, parents_vals: List) → numpy.ndarray
-Builds an indexing structure for the computation of state transitions values.
-
-- Parameters
--
-
-- Returns
-The transition indexing structure
-- Return type
-numpy.ndArray
-
-
-
-
--
-
clear_indexing_filtering_structures() → None
-Initialize all the filtering/indexing structures.
-
-
-
--
-property
edges
-
-
-
--
-
fast_init(node_id: str) → None
-Initializes all the necessary structures for parameters estimation of the node identified by the label
-node_id
-
-- Parameters
-node_id (string) – the label of the node
-
-
-
-
--
-
get_node_indx(node_id) → int
-
-
-
--
-
get_ordered_by_indx_set_of_parents(node: str) → Tuple
-Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely
-(parents_labels, parents_indexes, parents_cardinalities).
-
-- Parameters
-node (string) – the label of the node
-- Returns
-a tuple containing all the parent set infos
-- Return type
-Tuple
-
-
-
-
--
-
get_parents_by_id(node_id) → List
-Returns a list of labels of the parents of the node node_id
-
-- Parameters
-node_id (string) – the node label
-- Returns
-a List of labels of the parents
-- Return type
-List
-
-
-
-
--
-
get_positional_node_indx(node_id) → int
-
-
-
--
-
get_states_number(node_id) → int
-
-
-
--
-property
nodes
-
-
-
--
-property
nodes_indexes
-
-
-
--
-property
nodes_values
-
-
-
--
-property
p_combs
-
-
-
--
-
remove_node(node_id: str) → None
-Remove the node node_id from all the class members.
-Initialize all the filtering/indexing structures.
-
-
-
--
-property
time_filtering
-
-
-
--
-property
time_scalar_indexing_strucure
-
-
-
--
-property
transition_filtering
-
-
-
--
-property
transition_scalar_indexing_structure
-
-
-
-
-
-
-
classes.parameters_estimator module
-
--
-class
classes.parameters_estimator.ParametersEstimator(trajectories: classes.trajectory.Trajectory, net_graph: classes.network_graph.NetworkGraph)
-Bases: object
-Has the task of computing the cims of particular node given the trajectories and the net structure
-in the graph _net_graph.
-
-- Parameters
--
-
-- _single_set_of_cims
-the set of cims object that will hold the cims of the node
-
-
--
-
compute_parameters_for_node(node_id: str) → classes.set_of_cims.SetOfCims
-Compute the CIMS of the node identified by the label node_id.
-
-- Parameters
-node_id (string) – the node label
-- Returns
-A SetOfCims object filled with the computed CIMS
-- Return type
-SetOfCims
-
-
-
-
--
-static
compute_state_res_time_for_node(times: numpy.ndarray, trajectory: numpy.ndarray, cols_filter: numpy.ndarray, scalar_indexes_struct: numpy.ndarray, T: numpy.ndarray) → None
-Compute the state residence times for a node and fill the matrix T with the results
-
-- Parameters
-
-node_indx (int) – the index of the node
times (numpy.array) – the times deltas vector
trajectory (numpy.ndArray) – the trajectory
cols_filter (numpy.array) – the columns filtering structure
scalar_indexes_struct (numpy.array) – the indexing structure
T (numpy.ndArray) – the state residence times vectors
-
-
-
-
--
-static
compute_state_transitions_for_a_node(node_indx: int, trajectory: numpy.ndarray, cols_filter: numpy.ndarray, scalar_indexing: numpy.ndarray, M: numpy.ndarray) → None
-Compute the state residence times for a node and fill the matrices M with the results.
-
-- Parameters
-
-node_indx (int) – the index of the node
trajectory (numpy.ndArray) – the trajectory
cols_filter (numpy.array) – the columns filtering structure
scalar_indexing (numpy.array) – the indexing structure
M (numpy.ndArray) – the state transitions matrices
-
-
-
-
--
-
fast_init(node_id: str) → None
-Initializes all the necessary structures for the parameters estimation for the node node_id.
-
-- Parameters
-node_id (string) – the node label
-
-
-
-
-
-
-
-
classes.sample_path module
-
--
-class
classes.sample_path.SamplePath(importer: classes.abstract_importer.AbstractImporter)
-Bases: object
-Aggregates all the informations about the trajectories, the real structure of the sampled net and variables
-cardinalites. Has the task of creating the objects Trajectory and Structure that will
-contain the mentioned data.
-
-- Parameters
-importer (AbstractImporter) – the Importer object which contains the imported and processed data
-- _trajectories
-the Trajectory object that will contain all the concatenated trajectories
-- _structure
-the Structure Object that will contain all the structural infos about the net
-- _total_variables_count
-the number of variables in the net
-
-
--
-
build_structure() → None
-Builds the Structure object that aggregates all the infos about the net.
-
-
-
--
-
build_trajectories() → None
-Builds the Trajectory object that will contain all the trajectories.
-Clears all the unused dataframes in _importer Object
-
-
-
--
-property
structure
-
-
-
--
-property
total_variables_count
-
-
-
--
-property
trajectories
-
-
-
-
-
-
-
classes.set_of_cims module
-
--
-class
classes.set_of_cims.SetOfCims(node_id: str, parents_states_number: List, node_states_number: int, p_combs: numpy.ndarray)
-Bases: object
-Aggregates all the CIMS of the node identified by the label _node_id.
-
-- Parameters
-
-node_id – the node label
parents_states_number (List) – the cardinalities of the parents
node_states_number (int) – the caridinality of the node
p_combs (numpy.ndArray) – the p_comb structure bound to this node
-- _state_residence_time
-matrix containing all the state residence time vectors for the node
-- _transition_matrices
-matrix containing all the transition matrices for the node
-- _actual_cims
-the cims of the node
-
-
--
-property
actual_cims
-
-
-
--
-
build_cims(state_res_times: numpy.ndarray, transition_matrices: numpy.ndarray) → None
-Build the ConditionalIntensityMatrix objects given the state residence times and transitions matrices.
-Compute the cim coefficients.The class member _actual_cims will contain the computed cims.
-
-- Parameters
--
-
-
-
-
-
--
-
build_times_and_transitions_structures() → None
-Initializes at the correct dimensions the state residence times matrix and the state transition matrices.
-
-
-
--
-
filter_cims_with_mask(mask_arr: numpy.ndarray, comb: List) → numpy.ndarray
-Filter the cims contained in the array _actual_cims given the boolean mask mask_arr and the index
-comb.
-
-- Parameters
--
-
-- Returns
-Array of ConditionalIntensityMatrix objects
-- Return type
-numpy.array
-
-
-
-
--
-
get_cims_number()
-
-
-
--
-property
p_combs
-
-
-
-
-
-
-
classes.structure module
-
--
-class
classes.structure.Structure(nodes_labels_list: List, nodes_indexes_arr: numpy.ndarray, nodes_vals_arr: numpy.ndarray, edges_list: List, total_variables_number: int)
-Bases: object
-Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)
-
-- Parameters
-
-nodes_labels_list (List) – the symbolic names of the variables
nodes_indexes_arr (numpy.ndArray) – the indexes of the nodes
nodes_vals_arr (numpy.ndArray) – the cardinalites of the nodes
edges_list (List) – the edges of the network
total_variables_number (int) – the total number of variables in the dataset
-
-
--
-property
edges
-
-
-
--
-
get_node_id(node_indx: int) → str
-Given the node_index returns the node label.
-
-- Parameters
-node_indx (int) – the node index
-- Returns
-the node label
-- Return type
-string
-
-
-
-
--
-
get_node_indx(node_id: str) → int
-Given the node_index returns the node label.
-
-- Parameters
-node_id (string) – the node label
-- Returns
-the node index
-- Return type
-int
-
-
-
-
--
-
get_positional_node_indx(node_id: str) → int
-
-
-
--
-
get_states_number(node: str) → int
-Given the node label node returns the cardinality of the node.
-
-- Parameters
-node (string) – the node label
-- Returns
-the node cardinality
-- Return type
-int
-
-
-
-
--
-property
nodes_indexes
-
-
-
--
-property
nodes_labels
-
-
-
--
-property
nodes_values
-
-
-
--
-
remove_node(node_id: str) → None
-Remove the node node_id from all the class members.
-The class member _total_variables_number since it refers to the total number of variables in the dataset.
-
-
-
--
-property
total_variables_number
-
-
-
-
-
-
-
classes.structure_estimator module
-
--
-class
classes.structure_estimator.StructureEstimator(sample_path: classes.sample_path.SamplePath, exp_test_alfa: float, chi_test_alfa: float)
-Bases: object
-Has the task of estimating the network structure given the trajectories in samplepath.
-
-- Parameters
-
-sample_path (SamplePath) – the _sample_path object containing the trajectories and the real structure
exp_test_alfa (float) – the significance level for the exponential Hp test
chi_test_alfa (float) – the significance level for the chi Hp test
-- _nodes
-the nodes labels
-- _nodes_vals
-the nodes cardinalities
-- _nodes_indxs
-the nodes indexes
-- _complete_graph
-the complete directed graph built using the nodes labels in _nodes
-- _cache
-the Cache object
-
-
--
-
adjacency_matrix() → numpy.ndarray
-Converts the estimated structrure _complete_graph to a boolean adjacency matrix representation.
-
-- Returns
-The adjacency matrix of the graph _complete_graph
-- Return type
-numpy.ndArray
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--
-static
build_complete_graph(node_ids: List) → networkx.classes.digraph.DiGraph
-Builds a complete directed graph (no self loops) given the nodes labels in the list node_ids:
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-- Parameters
-node_ids (List) – the list of nodes labels
-- Returns
-a complete Digraph Object
-- Return type
-networkx.DiGraph
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--
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complete_test(test_parent: str, test_child: str, parent_set: List, child_states_numb: int, tot_vars_count: int) → bool
-Performs a complete independence test on the directed graphs G1 = {test_child U parent_set}
-G2 = {G1 U test_parent} (added as an additional parent of the test_child).
-Generates all the necessary structures and datas to perform the tests.
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-- Parameters
-
-test_parent (string) – the node label of the test parent
test_child (string) – the node label of the child
parent_set (List) – the common parent set
child_states_numb (int) – the cardinality of the test_child
tot_vars_count (int) – the total number of variables in the net
-- Returns
-True iff test_child and test_parent are independent given the sep_set parent_set. False otherwise
-- Return type
-bool
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--
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ctpc_algorithm() → None
-Compute the CTPC algorithm over the entire net.
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--
-static
generate_possible_sub_sets_of_size(u: List, size: int, parent_label: str) → Iterator
-Creates a list containing all possible subsets of the list u of size size,
-that do not contains a the node identified by parent_label.
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-- Parameters
-
-u (List) – the list of nodes
size (int) – the size of the subsets
parent_label (string) – the node to exclude in the subsets generation
-- Returns
-an Iterator Object containing a list of lists
-- Return type
-Iterator
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--
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independence_test(child_states_numb: int, cim1: classes.conditional_intensity_matrix.ConditionalIntensityMatrix, cim2: classes.conditional_intensity_matrix.ConditionalIntensityMatrix) → bool
-Compute the actual independence test using two cims.
-It is performed first the exponential test and if the null hypothesis is not rejected,
-it is performed also the chi_test.
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-- Parameters
--
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-- Returns
-True iff both tests do NOT reject the null hypothesis of independence. False otherwise.
-- Return type
-bool
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--
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one_iteration_of_CTPC_algorithm(var_id: str, tot_vars_count: int) → None
-Performs an iteration of the CTPC algorithm using the node var_id as test_child.
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-- Parameters
--
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--
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save_results() → None
-Save the estimated Structure to a .json file in the path where the data are loaded from.
-The file is named as the input dataset but the results_ word is appended to the results file.
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classes.trajectory module
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--
-class
classes.trajectory.Trajectory(list_of_columns: List, original_cols_number: int)
-Bases: object
-Abstracts the infos about a complete set of trajectories, represented as a numpy array of doubles
-(the time deltas) and a numpy matrix of ints (the changes of states).
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-- Parameters
--
-
-- _actual_trajectory
-the trajectory containing also the duplicated/shifted values
-- _times
-the array containing the time deltas
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--
-property
complete_trajectory
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--
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size()
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--
-property
times
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--
-property
trajectory
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Module contents
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