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@ -7,6 +7,7 @@ import pandas as pd |
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import re |
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import json |
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from numpy import random |
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from multiprocessing import Process, Manager |
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class TrajectoryGenerator(object): |
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"""Provides the methods to generate a trajectory basing on the network defined |
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@ -132,6 +133,48 @@ class TrajectoryGenerator(object): |
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if self._vnames[next] in self._parents[v]: |
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time[i] = np.NaN |
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def worker(self, t_end, max_tr, trajectories): |
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"""Single process that will be executed in parallel in order to generate one trajectory. |
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:param t_end: If defined, the sampling ends when end time is reached |
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:type t_end: float |
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:param max_tr: Parameter taken in consideration in case that t_end isn't defined. It specifies the number of transitions to execute |
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:type max_tr: int |
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:param trajectories: Shared list that contains to which the generated trajectory is added |
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:type trajectories: list |
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""" |
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trajectory = self.CTBN_Sample(t_end = t_end, max_tr = max_tr) |
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trajectories.append(trajectory) |
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def multi_trajectory(self, t_ends: list = None, max_trs: list = None): |
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"""Generate n trajectories in parallel, where n is the number of items in |
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t_ends, if defined, or the number of items in max_trs otherwise |
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:param t_ends: List of t_end values for the trajectories that will be generated |
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:type t_ends: list |
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:param max_trs: List of max_tr values for the trajectories that will be generated |
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:type max_trs: list |
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""" |
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if t_ends is None and max_trs is None: |
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return |
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trajectories = Manager().list() |
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if t_ends is not None: |
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processes = [Process(target = self.worker, args = (t, -1, trajectories)) for t in t_ends] |
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else: |
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processes = [Process(target = self.worker, args = (-1, m, trajectories)) for m in max_trs] |
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for p in processes: |
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p.start() |
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for p in processes: |
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p.join() |
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return trajectories |
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def to_json(self): |
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"""Convert the last generated trajectory from pandas.DataFrame object type to JSON format |
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(suitable to do input/output of the trajectory with file) |
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