import numpy as np
from abc import ABC, abstractmethod
import xarray as xr
# from enstools.feature.identification._proto_gen import identification_pb2
from enstools.feature.util.data_utils import get_subset_by_description, squeeze_nodes
from multiprocessing.pool import ThreadPool as Pool
from functools import partial
class TrackingTechnique(ABC):
"""
Base abstract class for feature tracking algorithms.
Implementations need to override the abstract track() method.
"""
def __init__(self):
self.pb_reference = None
self.graph = None
@abstractmethod
def track(self, track_set, subset: xr.Dataset): # TODO update docstrings
"""
Abstract tracking method. This gets called for each timesteps list of the feature descriptions.
This timeline can be for example a reforecast or a member of an ensemble forecast, in which detected objects should
be tracked over multiple timestamps. This method gets called in parallel for all timelines in the dataset.
This method should compute links of corresponding objects of two consecutive timestamps. Each object in a
timestep has its unique ID, and a computed tuple (id1, id2) remarks that object id1 from timestamp t is the
same object as id2 from timestamp t+1. One tuple is an edge in a tracking graph.
Parameters
----------
track_set : iterable of identification_pb2.TrackingSet
The data to be tracked
subset : xarray.Dataset
Subset to be tracked. Forwarded from identification
Returns
-------
connections : list of tuples of int
The connections forming the path of the objects in the timesteps.
"""
connections = []
return connections
@abstractmethod
def postprocess(self, object_desc): # TODO update docstrings
pass
def track_set(self, set_idx, obj_desc=None, dataset=None):
obj_set = obj_desc.sets[set_idx]
# get according subset
dataset_sel = get_subset_by_description(dataset, obj_set)
print("Track " + str(obj_set)[:40] + " (" + str(set_idx) + ")")
nodes = self.track(obj_set, dataset_sel) # TODO check subsets in here on more complex DS
nodes = squeeze_nodes(nodes)
obj_set.ref_graph.nodes.extend(nodes)
# create object connections from index connections for output graph
# not really efficient...
def execute(self, object_desc, dataset_ref: xr.Dataset):
"""
Execute the tracking procedure.
The description is split into the different timelines which can be executed in parallel.
Parameters
----------
object_desc : identification_pb2.DatasetDescription TODO?
The description of the detected features from the identificaiton technique.
dataset_ref : xarray.Dataset
Reference to the dataset used in the pipeline.
"""
# parallel for all tracking sets
pool = Pool()
pool.map(partial(self.track_set, obj_desc=object_desc, dataset=dataset_ref),
range(len(object_desc.sets))) # iterate over sets.
self.postprocess(object_desc) # only postprocess object desc, THEN to graph
# TODO what if tracks are the identification? e.g. AEW identification in Hovmoller
pass
def get_graph(self):
if self.graph is not None:
return self.graph # use cached
self.generate_graph()
return self.graph
# generate object graph from ref graph
def generate_graph(self, object_desc):
self.graph = self.pb_reference.DatasetDescription()
self.graph.CopyFrom(object_desc)
for set_idx, objdesc_set in enumerate(object_desc.sets):
graph_set = self.graph.sets[set_idx]
# empty the graph set
del graph_set.timesteps[:]
graph_set.ref_graph.Clear()
# for each object add (n1,[]) to graph. then add connections.
for idx_ts, ts in enumerate(objdesc_set.timesteps):
cur_time = ts.valid_time
for obj in ts.objects:
obj_connection = graph_set.object_graph.nodes.add()
obj_connection.this_node.time = cur_time
obj_connection.this_node.object.CopyFrom(obj)
# for each connection in ref graph: if n1 is current object: get n2 objects and add them
for ref_connection in objdesc_set.ref_graph.nodes:
if ref_connection.this_node.time_index == idx_ts and ref_connection.this_node.object_id == obj.id:
for ref_n2 in ref_connection.connected_nodes:
obj_n2 = obj_connection.connected_nodes.add()
obj_n2.time = objdesc_set.timesteps[ref_n2.time_index].valid_time
obj_index_of_n2_obj = [objindex_ for objindex_, obj_ in enumerate(objdesc_set.timesteps[ref_n2.time_index].objects) if obj_.id == ref_n2.object_id][0]
obj_n2.object.CopyFrom(objdesc_set.timesteps[ref_n2.time_index].objects[obj_index_of_n2_obj])
return self.graph
# filter the generated tracks: for each track call the keep_track() function.
# TODO assert sorted?
def filter_tracks(self):
for set_ in self.graph.sets:
for t_id in range(len(set_.tracks) - 1, -1, -1):
track = set_.tracks[t_id]
if not self.keep_track(track):
del set_.tracks[t_id]
def keep_track(self, track):
return True
# TODO overlap tracking def. input just field name
# after graph has been computed, compute "tracks", which is disjunct list of graphs of the total graph
# TODO mention heuristic in docstring.
def generate_tracks(self):
if self.graph is None:
print("Compute graph first.")
exit(1)
# for each set:
# order connections by time of first node
for graph_set in self.graph.sets:
# object_set = self.obj_ref.set_idx
# sort nodes by time of first node (is in key), as list here
# TODO assert comp by string yields time sorted list (yyyymmddd)
time_sorted_nodes = list(sorted(graph_set.object_graph.nodes, key=lambda item: item.this_node.time))
wave_id_per_node = [None] * len(time_sorted_nodes)
cur_id = 0
# iterate over all time sorted identified connections
# search temporal downstream tracked troughs and group them using a set id
for con_idx, oc in enumerate(time_sorted_nodes):
if wave_id_per_node[con_idx] is not None: # already part of a wave
continue
# not part of wave -> get (temporal) downstream connections = wave (return indices of them)
downstream_wave_node_indices = TrackingTechnique.get_downstream_node_indices(time_sorted_nodes, con_idx)
print(str(con_idx) + " -> " + str(downstream_wave_node_indices))
# any of downstream nodes already part of a wave?
connected_wave_id = None
for ds_node_idx in downstream_wave_node_indices:
if wave_id_per_node[ds_node_idx] is not None:
if connected_wave_id is not None:
print("Double ID, better resolve todo...")
connected_wave_id = wave_id_per_node[ds_node_idx]
# if so set all nodes to this found id
if connected_wave_id is not None:
for ds_node_idx in downstream_wave_node_indices:
wave_id_per_node[ds_node_idx] = connected_wave_id
continue
# else new path for all wave_nodes
cur_id_needs_update = False
for ds_node_idx in downstream_wave_node_indices:
wave_id_per_node[ds_node_idx] = cur_id
cur_id_needs_update = True
if cur_id_needs_update:
cur_id += 1
# done, now extract every wave by id and put them into subgraphs
waves = [] # generate list of waves according to above policy
for wave_id in range(cur_id):
track = self.pb_reference.ObjectGraph()
wave_idxs = [i for i in range(len(wave_id_per_node)) if wave_id_per_node[i] == wave_id]
cur_wave_nodes = [time_sorted_nodes[i] for i in wave_idxs] # troughs of this wave
# cur_troughs = cur_troughs.sortbytime # already sorted?
track.nodes.extend(cur_wave_nodes)
graph_set.tracks.append(track)
# print(wave)
# print(waves) # waves for this set only
return None
@staticmethod
def get_downstream_node_indices(graph_list, start_idx):
node_indices = [start_idx]
co = graph_list[start_idx]
node, connected_nodes = co.this_node, co.connected_nodes
for c_node in connected_nodes:
# get index of connected node in graph
obj_node_list = [con_.this_node for con_ in graph_list]
c_node_idx = obj_node_list.index(c_node)
# call recursively on this connected node
c_node_downstream_indices = TrackingTechnique.get_downstream_node_indices(graph_list, c_node_idx)
node_indices.extend(c_node_downstream_indices)
return list(set(node_indices))
"""
def get_items(self):
return self._graph.items()
# extract wave objects from graph: question here: what is a wave?
# e.g. before/after merging, what belongs to same wave
# wave object is list of wavetroughs, so wavetroughs for consecutive timesteps
def extract_waves(self):
"""