Merge pull request #90 from yupidevs/develop

Fix trajectory save and load when dt is estimated

docs/source/conf.py

@@ -28,7 +28,7 @@
 author = 'Gustavo Viera-López, Alfredo Reyes, Jorge Morgado, Ernesto Altshuler'
 
 # The full version, including alpha/beta/rc tags
-release = '0.8.3'
+release = '0.8.4'
 
 
 # -- General configuration ---------------------------------------------------

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "yupi"
-version = "0.8.3"
+version = "0.8.4"
 description = "A package for tracking and analysing objects trajectories"
 authors = [
     "Gustavo Viera-López <[email protected]>",

tests/test_trajectory/test_io.py

@@ -5,17 +5,20 @@
 
 
 def test_save():
-    t1 = Trajectory(x=[1,2,3], y=[4,5,6])
+    x = [1,2,3]
+    y = [4,5,6]
+    t1 = Trajectory(x=x, y=y)
 
     # Wrong trajectory file extension at saving
     with pytest.raises(ValueError):
         t1.save('t1', file_type='abc')
 
-    # Saving json
     t1.save('t1', file_type='json')
-
-    # Saving csv
     t1.save('t1', file_type='csv')
+
+    t2 = Trajectory(x=x, y=y, t=[0.0, 0.5, 2.0])
+    t2.save('t2', file_type='json')
+    t2.save('t2', file_type='csv')
 
 
 def test_load():
@@ -27,11 +30,15 @@ def test_load():
     t1 = Trajectory.load('t1.json')
     for tp, point in zip(t1, [[1,4], [2,5], [3,6]]):
         assert (np.array(point) == tp.r).all()
+    t2 = Trajectory.load('t2.json')
 
     # Loading csv
     t1 = Trajectory.load('t1.csv')
     for tp, point in zip(t1, [[1,4], [2,5], [3,6]]):
         assert (np.array(point) == tp.r).all()
+    t2 = Trajectory.load('t2.csv')
 
     os.remove('t1.json')
     os.remove('t1.csv')
+    os.remove('t2.json')
+    os.remove('t2.csv')

yupi/__init__.py

@@ -21,4 +21,4 @@
     'Vector'
 ]
 
-__version__ = '0.8.3'
+__version__ = '0.8.4'

yupi/trajectory.py

@@ -158,23 +158,25 @@ def __init__(self, x: np.ndarray = None, y: np.ndarray = None,
         if lengths.count(lengths[0]) != len(lengths):
             raise ValueError('All input arrays must have the same shape.')
 
+        self.__dt = dt
+        self.dt_mean = dt
         self.__t0 = t0
         self.__t = data[0]
         self.ang = data[1]
         self.traj_id= traj_id
         self.lazy = lazy
 
         if self.__t is None:
-            self.dt = dt if dt is not None else 1.0
+            self.dt_mean = dt if dt is not None else 1.0
             self.dt_std = 0
             self.__v: Vector = self.r.delta / self.dt
         else:
-            self.dt = np.mean(np.array(self.__t.delta))
+            self.dt_mean = np.mean(np.array(self.__t.delta))
             self.dt_std = np.std(np.array(self.__t.delta))
             self.__v: Vector = (self.r.delta.T / self.__t.delta).T
 
         if t is not None and dt is not None:
-            if abs(self.dt - dt) > _threshold:
+            if abs(self.dt_mean - dt) > _threshold:
                 raise ValueError("You are giving 'dt' and 't' but 'dt' "
                                 "does not match with time values delta.")
             if abs(self.dt_std - 0) > _threshold:
@@ -186,6 +188,16 @@ def __init__(self, x: np.ndarray = None, y: np.ndarray = None,
 
         self.features = Features(self)
 
+    @property
+    def dt(self) -> float:
+        """
+        Returns the time between each position data value.
+
+        If the time data is not uniformly spaced it returns an
+        estimated value.
+        """
+        return self.dt_mean if self.__dt is None else self.__dt
+
     @property
     def uniformly_spaced(self) -> bool:
         """bool : True if the time data is uniformly spaced"""
@@ -530,7 +542,7 @@ def convert_to_list(vec: Vector):
         ang = None if self.ang is None else self.ang.T
         json_dict = {
             'id': self.traj_id,
-            'dt': self.dt,
+            'dt': self.__dt,
             'r': convert_to_list(self.r.T),
             'ang': convert_to_list(ang),
             't': convert_to_list(self.__t)
@@ -542,7 +554,7 @@ def _save_csv(self, path):
         with open(str(path), 'w', newline='') as traj_file:
             writer = csv.writer(traj_file, delimiter=',')
             ang_shape = 0 if self.ang is None else self.ang.shape[1]
-            writer.writerow([self.traj_id, self.dt, self.dim, ang_shape])
+            writer.writerow([self.traj_id, self.__dt, self.dim, ang_shape])
             for tp in self:
                 row = np.hstack(np.array([tp.r, tp.ang, tp.t], dtype=object))
                 writer.writerow(row)