Merge pull request #27 from yupidevs/develop

Minor changes

.github/workflows/tests.yml

@@ -20,10 +20,5 @@ jobs:
       run: echo "${HOME}/.poetry/bin" >> $GITHUB_PATH
     - name: Install yupi on venv
       run: poetry install
-    - name: Install Pytest
-      run: |
-        python -m pip install --upgrade pip
-        pip install pytest
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
     - name: Run tests
       run: poetry run pytest ./tests/

CHANGELOG.md

@@ -1,5 +1,55 @@
 # CHANGELOG
 
+## [0.4.2] 2021-06-12
+
+### Added
+
+- Add documentation for example002
+- Implement offset add and sub on trajectoies
+- Implement add and sub between trajectories
+- Implement polar offset
+
+### Fixed
+
+- Fix examples links on documentation
+
+## [0.4.1] 2021-06-09
+
+### Added
+
+- Add yupi exceptions
+- Add recursively parameter in load_folder method
+- Add framedifferencing tracking algorithm
+- Add vector docstrings
+- Add a background estimator for videos
+- Add lazy paremeter on trajectory
+- Add background subtraction as tracking algoritm
+- Add `plot_trajectory` function on `__init__.py`
+- Add template matching algorithm
+- Add optical flow algorithm
+
+### Fixed
+
+- Fix exception handling on trajectory loading
+- Change velocity calculation on trajectory
+- Fix docstring on load_folder
+- Change scipy dependency version
+- Fix start_in_frame param in track trackingscenario
+- Fix trackers for algorithms requiring 2 frames
+- Fix drifting in trackers failing detecting a frame
+- Fix docstring in frame differencing
+- Fix docstring of some algorithms
+- Fix wrong references in the docs of algorithms
+- Fix a typo in readme
+
+### Others
+
+- Update the api of tracking algorithms
+- Update the readme
+- Update docs trajectory creation methods
+- All tracking algorithms to return an empty mask
+- Set version 0.4.1
+
 ## [0.4.0] 2021-05-7
 
 ### Added

docs/source/conf.py

@@ -26,7 +26,7 @@
 author = 'Gustavo Viera-López, Alfredo Reyes, Jorge Morgado, Ernesto Altshuler'
 
 # The full version, including alpha/beta/rc tags
-release = '0.4.1'
+release = '0.4.2'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/examples/examples.rst

@@ -28,7 +28,7 @@ In this table you can easily find the examples that better suits you.
        | of a lysozyme molecule in water. Several molecule 
        | trajectories are generated and later analyzed.
      - * Generation:
-          * :py:class:`~generators.LangevinGenerator`
+          * :py:class:`~generating.LangevinGenerator`
        * Statistics:
           * :py:func:`~statistics.estimate_velocity_samples`
           * :py:func:`~statistics.estimate_turning_angles`
@@ -50,15 +50,15 @@ In this table you can easily find the examples that better suits you.
      - * Visualization:
           * :py:func:`~visualization.plot_trajectories`
        * Tracking:
-          * :py:func:`~tracking.ROI`
-          * :py:func:`~tracking.ObjectTracker`
-          * :py:func:`~tracking.TrackingScenario`
-          * :py:func:`~tracking.ColorMatching`
-          * :py:func:`~tracking.FrameDifferencing`
-          * :py:func:`~tracking.BackgroundEstimator`
-          * :py:func:`~tracking.BackgroundSubtraction`
-          * :py:func:`~tracking.TemplateMatching`
-          * :py:func:`~tracking.OpticalFlow`
+          * :py:func:`~tracking.trackers.ROI`
+          * :py:func:`~tracking.trackers.ObjectTracker`
+          * :py:func:`~tracking.trackers.TrackingScenario`
+          * :py:func:`~tracking.algorithms.ColorMatching`
+          * :py:func:`~tracking.algorithms.FrameDifferencing`
+          * :py:func:`~tracking.algorithms.BackgroundEstimator`
+          * :py:func:`~tracking.algorithms.BackgroundSubtraction`
+          * :py:func:`~tracking.algorithms.TemplateMatching`
+          * :py:func:`~tracking.algorithms.OpticalFlow`
 
 
 .. toctree::

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "yupi"
-version = "0.4.1"
+version = "0.4.2"
 description = "A package for tracking and analysing objects trajectories"
 authors = [
     "Gustavo Viera-López <gvieralopez@gmail.com>",

yupi/__init__.py

@@ -27,4 +27,4 @@
     'Vector'
 ]
 
-__version__ = '0.4.1'
+__version__ = '0.4.2'

yupi/analyzing/processing.py

@@ -1,5 +1,5 @@
-from yupi.trajectory import Trajectory
 import numpy as np
+from yupi.trajectory import Trajectory
 from yupi.analyzing.transformations import wrap_theta
 
 

yupi/analyzing/statistics.py

@@ -1,7 +1,7 @@
 from typing import List
-from yupi.trajectory import Trajectory
-import numpy as np
 import scipy.stats
+import numpy as np
+from yupi.trajectory import Trajectory
 from yupi.analyzing import turning_angles, subsample_trajectory
 
 
@@ -148,8 +148,8 @@ def estimate_msd_time(trajs: List[Trajectory], lag: int):
 def estimate_msd(trajs: List[Trajectory], time_avg=True, lag=None):
     """
     Estimate the mean square displacement of the list of Trajectory
-    objects, ``trajs``, providing the options of averaging over the ensemble
-    of realizations or over time.
+    objects, ``trajs``, providing the options of averaging over the
+    ensemble of realizations or over time.
 
     Parameters
     ----------
@@ -384,5 +384,4 @@ def estimate_kurtosis(trajs: List[Trajectory], time_avg=True, lag=None):
 
     if not time_avg:
         return estimate_kurtosis_ensemble(trajs)
-    else:
-        return estimate_kurtosis_time(trajs, lag)
+    return estimate_kurtosis_time(trajs, lag)

yupi/analyzing/transformations.py

@@ -130,6 +130,6 @@ def wrap_theta(ang: np.ndarray, degrees=False, centered=False):
     discont = 360 if degrees else 2 * np.pi
     if not centered:
         return ang % discont
-    else:
-        discont_half = discont / 2
-        return -((discont_half - ang) % discont - discont_half)
+
+    discont_half = discont / 2
+    return -((discont_half - ang) % discont - discont_half)

yupi/generating/generators.py

@@ -1,5 +1,5 @@
-import numpy as np
 import abc
+import numpy as np
 from yupi import Trajectory
 
 
@@ -89,7 +89,7 @@ def __init__(self, T: float, dim: int = 1, N: int = 1, dt: float = 1,
         # Only right/left jumps, uniform probabilities for it
         # And equal length for all jumps are set as default
         # TODO: Check that the model parameters received have the
-        #Eexpected shape
+        # expected shape
         if actions is None:
             actions = np.array([1, -1])
         if actions_prob is None:
@@ -103,7 +103,7 @@ def __init__(self, T: float, dim: int = 1, N: int = 1, dt: float = 1,
 
     # Compute vector position as a function of time for
     # All the walkers of the ensemble
-    def get_r(self):
+    def _get_r(self):
         # Get movements for every space coordinates according
         # To the sample space of probabilities in self.actions_prob
         dr = [np.random.choice(self.actions, p=p, size=(self.n - 1, self.N))
@@ -122,14 +122,14 @@ def get_r(self):
     # Get position vectors and generate RandomWalk object
     def generate(self):
         # Get position vectors
-        r = self.get_r()
+        r = self._get_r()
 
         # Generate RandomWalk object
         trajs = []
         for i in range(self.N):
             points = r[:, :, i]
             trajs.append(Trajectory(points=points, dt=self.dt, t=self.t,
-                                    traj_id="Random Walker {}".format(i+1)))
+                                    traj_id=f"Random Walker {i + 1}"))
         return trajs
 
 
@@ -211,12 +211,12 @@ def set_scale(self, v_scale=None, r_scale=None, t_scale=None):
             self.t_scale = t_scale
 
     # Fill noise array with custom noise properties
-    def get_noise(self):
+    def _get_noise(self):
         dist = getattr(np.random, self.noise_pdf)
         self.noise = dist(scale=self.noise_scale, size=self.shape)
 
     # Solve Langevin Equation using the numerical method of Euler-Maruyama
-    def solve_rv(self):
+    def _solve_rv(self):
         for i in range(self.n - 1):
             # Solving for position
             self.r[i + 1] = self.r[i] + \
@@ -228,13 +228,13 @@ def solve_rv(self):
                             self.noise[i] * np.sqrt(self.dt)
 
     # Simulate the process
-    def simulate(self):
-        self.get_noise()  # Set the attribute self.noise
-        self.solve_rv()   # Solve the Langevin equation
+    def _simulate(self):
+        self._get_noise()  # Set the attribute self.noise
+        self._solve_rv()   # Solve the Langevin equation
 
     # Generate yupi Trajectory objects
     def generate(self):
-        self.simulate()
+        self._simulate()
 
         self.r *= self.r_scale
         self.v *= self.v_scale
@@ -245,28 +245,5 @@ def generate(self):
         for i in range(self.N):
             points = self.r[:, :, i]
             trajs.append(Trajectory(points=points, dt=self.dt, t=self.t,
-                                    traj_id="LangevinSolution {}".format(i+1)))
+                                    traj_id=f"LangevinSolution {i + 1}"))
         return trajs
-
-
-# Testing
-if __name__ == '__main__':
-    from yupi.analyzing.visualization import plot_trajectories
-
-    np.random.seed(0)
-
-    print("Testing RandomWalker")
-
-    # Set parameter values
-    T = 500
-    dim = 2
-    N = 5
-    dt = 1
-    actions = [1, 0, -1]
-    prob = [[.5, .1, .4],
-            [.5, 0, .5]]
-
-    # Get RandomWalk object and get position vectors
-    rw = LatticeRandomWalkGenerator(T, dim, N, dt, actions, prob)
-    tr = rw.generate()
-    plot_trajectories(tr, max_trajectories=3, legend=False)