Merge pull request #94 from yupidevs/develop

Develop

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 = '0.8.6'
+release = '0.8.7'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/tutorials/analyzing.rst

@@ -11,7 +11,7 @@ detailed explanation of the parameters.)
 .. code-block:: python
 
     from yupi.generators import LangevinGenerator
-    lg = LangevinGenerator(500, 2, 500, 0.5, 2,  0.1)
+    lg = LangevinGenerator(500, 2, 500, 0.5, 2, 0.1, seed=0)
     trajs = lg.generate()
 
 
@@ -45,7 +45,7 @@ Plotting in three dimensions can be achieved in a similar way. Let us generate 3
 .. code-block:: python
 
     from yupi.generators import LangevinGenerator
-    lg = LangevinGenerator(500, 3, 5, 0.5, 2,  0.1)
+    lg = LangevinGenerator(500, 3, 5, 0.5, 2, 0.1, seed=0)
     trajs3D = lg.generate()
 
 
@@ -128,8 +128,8 @@ a linear function of time. To estimate the MSD of a list of
 Kurtosis
 ========
 
-Another useful quantity is the kurtosis, $\kappa$, a measure of the disparity of
-spatial scales of a dispersal process and also an intuitive means to understand
+Another useful quantity is the kurtosis, a measure of the disparity of spatial
+scales of a dispersal process and also an intuitive means to understand
 normality. It can be estimated using:
 
 .. code-block:: python
@@ -138,7 +138,7 @@ normality. It can be estimated using:
     from yupi.graphics import plot_kurtosis
 
     ref = yupi.stats.kurtosis_reference(trajs)
-    kurtosis = yupi.stats.kurtosis(trajs, time_avg=False, lag=30)
+    kurtosis, _ = yupi.stats.kurtosis(trajs, time_avg=False, lag=30)
     yupi.graphics.plot_kurtosis(kurtosis, kurtosis_ref=ref, dt=dt)
   
 
@@ -181,9 +181,9 @@ enables the characterization of the motion in terms of the frequency components.
     from yupi.stats import psd
     from yupi.graphics import plot_psd
 
-    psd_mean, psd_std, omega = psd(trajs, lag=150, omega=True)
-    plot_psd(psd_mean, omega, psd_std)
+    psd_mean, psd_std, frec = psd(trajs, lag=150)
+    plot_psd(psd_mean, frec, psd_std)
 
 .. figure:: /images/tutorial009.png
    :alt: PSD IMAGE
-   :align: center
+   :align: center

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "yupi"
-version = "0.8.6"
+version = "0.8.7"
 description = "A package for tracking and analysing objects trajectories"
 authors = [
     "Gustavo Viera-López <[email protected]>",

tests/test_stats/test_stats.py

@@ -1,6 +1,6 @@
 import numpy as np
 import pytest
-from yupi import Trajectory
+from yupi import Trajectory, VelMethod
 from yupi.stats import *
 
 APPROX_REL_TOLERANCE = 1e-10
@@ -9,19 +9,19 @@
 @pytest.fixture
 def traj():
     points = [[0, 0], [1, 0], [1, 1], [2, 1]]
-    return Trajectory(points=points)
+    return Trajectory(points=points, vel_est={"method": VelMethod.FORWARD})
 
 
 @pytest.fixture
 def traj1():
     x = [0, 8, 5, 11]
-    return Trajectory(x, dt=2)
+    return Trajectory(x, dt=2, vel_est={"method": VelMethod.FORWARD})
 
 
 @pytest.fixture
 def traj2():
     x = [0, 8.5, 4.9, 10.5]
-    return Trajectory(x, dt=2)
+    return Trajectory(x, dt=2, vel_est={"method": VelMethod.FORWARD})
 
 
 def test_turning_angles(traj):
@@ -33,8 +33,9 @@ def test_turning_angles(traj):
 
 
 def test_speed_ensemble(traj1):
+    print(traj1.v)
     se = speed_ensemble([traj1, traj1])
-    assert se == pytest.approx([4, 1.5, 3, 4, 1.5, 3], APPROX_REL_TOLERANCE)
+    assert se == pytest.approx([4, 1.5, 3, 3, 4, 1.5, 3, 3], APPROX_REL_TOLERANCE)
 
 
 def test_msd(traj1, traj2):
@@ -49,23 +50,17 @@ def test_msd(traj1, traj2):
     assert msd_t[1] == pytest.approx([1.26166667, 1.4975])
 
 
-def test_vacf():
-    traj1 = Trajectory([0, 8, 5, 11])
-    traj2 = Trajectory([0, 8.5, 4.9, 10.5])
-
+def test_vacf(traj1, traj2):
     vacf_e = vacf([traj1, traj2], time_avg=False)
-    assert vacf_e[0] == pytest.approx([68.125, -27.3, 47.8])
-    assert vacf_e[1] == pytest.approx([4.125, 3.3, 0.2])
+    assert vacf_e[0] == pytest.approx([17.03125, -6.825, 11.95, 11.95])
+    assert vacf_e[1] == pytest.approx([1.03125, 0.825, 0.05, 0.05])
 
     vacf_t = vacf([traj1, traj2], time_avg=True, lag=2)
-    assert vacf_t[0] == pytest.approx([-23.19, 47.8])
-    assert vacf_t[1] == pytest.approx([2.19, 0.2])
-
+    assert vacf_t[0] == pytest.approx([-1.05833333, 3.59])
+    assert vacf_t[1] == pytest.approx([0.55833333, 0.16])
 
-def test_kurtosis():
-    traj1 = Trajectory([0, 8, 5, 11])
-    traj2 = Trajectory([0, 8.5, 4.9, 10.5])
 
+def test_kurtosis(traj1, traj2):
     kurt_e = kurtosis([traj1, traj2], time_avg=False)
     assert kurt_e[0] == pytest.approx([0, 1, 1, 1])
 
@@ -75,13 +70,12 @@ def test_kurtosis():
     assert kurt_t[1] == pytest.approx([0, 0])
 
 
-def test_psd():
-    traj1 = Trajectory([0, 8, 5, 11])
+def test_psd(traj1):
     lag = 2
     psd_o = psd([traj1], lag=lag, omega=True)
-    assert psd_o[0] == pytest.approx([69, 27])
+    assert psd_o[0] == pytest.approx([8.5, 6.5])
     assert psd_o[1] == pytest.approx([0, 0])
-    assert psd_o[2] == pytest.approx([-3.14159265, 0])
+    assert psd_o[2] == pytest.approx([-9.8696044,  0.])
 
 
 def test_checkers():

tests/test_trajectory/test_creation.py

@@ -1,5 +1,7 @@
 import pytest
-from yupi import Trajectory
+from yupi import Trajectory, VelMethod, VelPadding
+
+APPROX_REL_TOLERANCE = 1e-10
 
 
 def test_creation_by_xyz():
@@ -68,6 +70,7 @@ def test_creation_with_time():
     with pytest.raises(ValueError):
         Trajectory(x=[1, 2], y=[2, 3], t=[0.4, 0.5], dt=0.1)
 
+
 def test_creation_with_ang():
     Trajectory(x=[1, 2], y=[2, 3], ang=[0, 0.1])
 
@@ -76,8 +79,54 @@ def test_creation_with_ang():
 
 
 def test_creation_general():
-    Trajectory(x=[1, 2], y=[2, 3], t=[0, 1], ang=[0, 0], traj_id='test')
-    Trajectory(x=[1, 2], y=[2, 3], dt=0.5, ang=[0, 1.2], traj_id='test')
-    Trajectory(points=[[1, 2], [2, 3]], dt=0.5, ang=[0, 1.2], traj_id='test')
-    Trajectory(dimensions=[[1, 2], [2, 3]], dt=0.5, t=[1, 1.5], t0=1,
-               traj_id='test')
+    Trajectory(x=[1, 2], y=[2, 3], t=[0, 1], ang=[0, 0], traj_id="test")
+    Trajectory(x=[1, 2], y=[2, 3], dt=0.5, ang=[0, 1.2], traj_id="test")
+    Trajectory(points=[[1, 2], [2, 3]], dt=0.5, ang=[0, 1.2], traj_id="test")
+    Trajectory(dimensions=[[1, 2], [2, 3]], dt=0.5, t=[1, 1.5], t0=1, traj_id="test")
+
+
+def test_velocity_estimation_methods():
+    x = [1, 2, 4, 8, 16]
+
+    Trajectory.global_vel_method(VelMethod.FORWARD)
+    traj = Trajectory(x=x)
+
+    assert traj.v == pytest.approx([1, 2, 4, 8, 8], rel=APPROX_REL_TOLERANCE)
+
+    Trajectory.global_vel_method(VelMethod.CENTERED)
+    traj.set_vel_method(VelMethod.BACKWARD)
+
+    assert traj.v == pytest.approx([1, 1, 2, 4, 8], rel=APPROX_REL_TOLERANCE)
+
+    traj = Trajectory(x=x)
+
+    assert traj.v == pytest.approx([3 / 2, 3 / 2, 3, 6, 6], rel=APPROX_REL_TOLERANCE)
+
+    traj = Trajectory(x=x, vel_est={"method": VelMethod.FORWARD, "h": 2})
+
+    assert traj.v == pytest.approx([3 / 2, 3, 6, 6, 6], rel=APPROX_REL_TOLERANCE)
+
+    traj = Trajectory(
+        x=x,
+        vel_est={
+            "method": VelMethod.BACKWARD,
+            "h": 2,
+            "padding": VelPadding.VALUE,
+            "padding_val": 0,
+        },
+    )
+
+    assert traj.v == pytest.approx([0, 0, 3 / 2, 3, 6], rel=APPROX_REL_TOLERANCE)
+
+    traj.set_vel_method(VelMethod.CENTERED, h=2)
+
+    assert traj.v == pytest.approx(
+        [15 / 4, 15 / 4, 15 / 4, 15 / 4, 15 / 4], rel=APPROX_REL_TOLERANCE
+    )
+
+    with pytest.raises(ValueError):
+        traj.set_vel_method(VelMethod.CENTERED, h=0)
+
+    with pytest.raises(ValueError):
+        traj.set_vel_method(VelMethod.CENTERED, h=8)
+

tests/test_trajectory/test_operations.py

@@ -1,7 +1,7 @@
 import numpy as np
 from pytest import approx, fixture
 import pytest
-from yupi import Trajectory
+from yupi import Trajectory, VelMethod
 
 APPROX_REL_TOLERANCE = 1e-12
 
@@ -47,6 +47,7 @@ def test_copy(traj):
     assert traj.t == approx(copy_traj.t, APPROX_REL_TOLERANCE)
     assert traj.v == approx(copy_traj.v, APPROX_REL_TOLERANCE)
     assert traj.ang == approx(copy_traj.ang, APPROX_REL_TOLERANCE)
+    assert traj.vel_est == copy_traj.vel_est
 
 
 def test_iteration(points, angles, traj):

yupi/__init__.py

@@ -7,6 +7,7 @@
 from yupi.features import Features
 from yupi.trajectory import Trajectory, TrajectoryPoint
 from yupi.vector import Vector
+from yupi._vel_estimators import VelMethod, VelPadding
 
 logging.basicConfig(
     level=logging.INFO,
@@ -18,7 +19,9 @@
     'Trajectory',
     'TrajectoryPoint',
     'Features',
-    'Vector'
+    'Vector',
+    'VelMethod',
+    'VelPadding'
 ]
 
-__version__ = '0.8.6'
+__version__ = '0.8.7'