"""Classes for easy interpolation of trajectories and curves."""
import numpy as np
[docs]
class Interpolator:
"""Poorman's linear interpolator.
Parameters
----------
tt : list, optional
List of time frames for the interpolator.
ss : list, optional
List of values for the interpolator.
ttss : list, optional
Lists of time frames and their correspondients values for the
interpolator. This argument can be used instead of ``tt`` and ``ss``
to instantiate the interpolator using an unique argument.
left : float, optional
Value to return when ``t < tt[0]``.
right : float, optional
Value to return when ``t > tt[-1]``.
Examples
--------
.. code:: python
# instantiate using `tt` and `ss`
interpolator = Interpolator(tt=[0, 1, 2], ss=[3, 4, 5])
# instantiate using `ttss`
interpolator = Interpolator(ttss=[[0, 3], [1, 4], [2, 5]]) # [t, value]
"""
def __init__(self, tt=None, ss=None, ttss=None, left=None, right=None):
if ttss is not None:
tt, ss = zip(*ttss)
self.tt = 1.0 * np.array(tt)
self.ss = 1.0 * np.array(ss)
self.left = left
self.right = right
self.tmin, self.tmax = min(tt), max(tt)
def __call__(self, t):
"""Interpolates ``t``.
Parameters
----------
t : float
Time frame for which the correspondent value will be returned.
"""
return np.interp(t, self.tt, self.ss, self.left, self.right)
[docs]
class Trajectory:
"""Trajectory compound by time frames and (x, y) pixels.
It's designed as an interpolator, so you can get the position at a given
time ``t``. You can instantiate it from a file using the methods
``from_file`` and ``load_list``.
Parameters
----------
tt : list or numpy.ndarray
Time frames.
xx : list or numpy.ndarray
X positions in the trajectory.
yy : list or numpy.ndarray
Y positions in the trajectory.
Examples
--------
>>> trajectory = Trajectory([0, .166, .333], [554, 474, 384], [100, 90, 91])
"""
def __init__(self, tt, xx, yy):
self.tt = 1.0 * np.array(tt)
self.xx = np.array(xx)
self.yy = np.array(yy)
self.update_interpolators()
def __call__(self, t):
"""Interpolates the trajectory at the given time ``t``.
Parameters
----------
t : float
Time for which to the corresponding position will be returned.
"""
return np.array([self.xi(t), self.yi(t)])
[docs]
def addx(self, x):
"""Adds a value to the ``xx`` position of the trajectory.
Parameters
----------
x : int
Value added to ``xx`` in the trajectory.
Returns
-------
Trajectory : new instance with the new X position included.
"""
return Trajectory(self.tt, self.xx + x, self.yy)
[docs]
def addy(self, y):
"""Adds a value to the ``yy`` position of the trajectory.
Parameters
----------
y : int
Value added to ``yy`` in the trajectory.
Returns
-------
Trajectory : new instance with the new Y position included.
"""
return Trajectory(self.tt, self.xx, self.yy + y)
[docs]
def update_interpolators(self):
"""Updates the internal X and Y position interpolators for the instance."""
self.xi = Interpolator(self.tt, self.xx)
self.yi = Interpolator(self.tt, self.yy)
[docs]
def txy(self, tms=False):
"""Returns all times with the X and Y values of each position.
Parameters
----------
tms : bool, optional
If is ``True``, the time will be returned in milliseconds.
"""
return zip((1000 if tms else 1) * self.tt, self.xx, self.yy)
[docs]
def to_file(self, filename):
"""Saves the trajectory data in a text file.
Parameters
----------
filename : str
Path to the location of the new trajectory text file.
"""
np.savetxt(
filename,
np.array(list(self.txy(tms=True))),
fmt="%d",
delimiter="\t",
)
[docs]
@staticmethod
def from_file(filename):
"""Instantiates an object of Trajectory using a data text file.
Parameters
----------
filename : str
Path to the location of trajectory text file to load.
Returns
-------
Trajectory : new instance loaded from text file.
"""
arr = np.loadtxt(filename, delimiter="\t")
tt, xx, yy = arr.T
return Trajectory(1.0 * tt / 1000, xx, yy)
[docs]
@staticmethod
def save_list(trajs, filename):
"""Saves a set of trajectories into a text file.
Parameters
----------
trajs : list
List of trajectories to be saved.
filename : str
Path of the text file that will store the trajectories data.
"""
N = len(trajs)
arr = np.hstack([np.array(list(t.txy(tms=True))) for t in trajs])
np.savetxt(
filename,
arr,
fmt="%d",
delimiter="\t",
header="\t".join(N * ["t(ms)", "x", "y"]),
)
[docs]
@staticmethod
def load_list(filename):
"""Loads a list of trajectories from a data text file.
Parameters
----------
filename : str
Path of the text file that stores the data of a set of trajectories.
Returns
-------
list : List of trajectories loaded from the file.
"""
arr = np.loadtxt(filename, delimiter="\t").T
Nlines = arr.shape[0]
return [
Trajectory(tt=1.0 * a[0] / 1000, xx=a[1], yy=a[2])
for a in np.split(arr, Nlines / 3)
]
