Removed code duplication

eyepy/preprocess/loggabor.py

@@ -1,103 +1,12 @@
-# -*- coding: utf-8 -*-
 from functools import lru_cache
 from typing import Tuple, Union
 
 import numpy as np
 
-Shape = Union[int, Tuple[int, int]]
-
-
-
-# -*- coding: utf-8 -*-
-from functools import lru_cache
-from typing import Tuple, Union
-
-import numpy as np
+from eyepy.utils.filters import theta_filtergrid, radius_filtergrid
 
 Shape = Union[int, Tuple[int, int]]
 
-
-@lru_cache(maxsize=8)
-def filtergrid(size: Shape, quadrant_shift: bool =True, normalize:bool =True) -> Tuple[np.ndarray, np.ndarray]:
-    """Generates grid for constructing frequency domain filters.
-    Parameters
-    ----------
-    size : Size of the filter
-    quadrant_shift : Quadrant shift such that 0 values / frequencies are at the corners
-    normalize: Normalize the range to [-0.5,0.5]
-    Returns
-    -------
-    Coordinate matrices for x and y value for a 2D array. The out can be quadrant shifted 
-    and / or normalized. This is basically a wrapper around np.meshgrid.
-    
-    Inspired by filtergrid.m found at https://www.peterkovesi.com/matlabfns/
-    """
-    if type(size) is int:
-        rows = cols = size
-    else:
-        rows = size[0]
-        cols = size[1]
-
-    range_1 = np.linspace(-(cols // 2), np.floor((cols - 1) / 2), cols)    
-    range_2 = np.linspace(-(rows // 2), np.floor((rows - 1) / 2), rows)
-
-    if normalize: 
-        range_1 = range_1 / cols
-        range_2 = range_2 / rows
-
-    x, y = np.meshgrid(range_1, range_2)
-
-    # Quadrant shift so that filters are constructed with 0 frequency at the corners
-    if quadrant_shift:
-        x = np.fft.ifftshift(x)
-        y = np.fft.ifftshift(y)
-
-    return x.T, y.T
-
-
-@lru_cache(maxsize=8)
-def radius_filtergrid(size: Shape, quadrant_shift: bool =True, normalize:bool =True) -> np.ndarray:
-    """
-    Parameters
-    ----------
-    size : Size of the filter
-    quadrant_shift : Quadrant shift such that 0 values / frequencies are at the corners
-    normalize: Normalize radius to [0 ,0.5]
-    Returns
-    -------
-    A matrix containing the radius from the center. This radius is in range [0, 0.5] if normalized.
-    The result can be quadrant shifted such that the 0 values are in the corners. 
-    """
-    x, y = filtergrid(size, quadrant_shift, normalize)
-    radius = np.sqrt(x ** 2 + y ** 2)
-    return radius
-
-
-@lru_cache(maxsize=8)
-def theta_filtergrid(size: Shape, quadrant_shift: bool =True) -> np.ndarray:
-    """
-    Parameters
-    ----------
-    size : Size of the filter
-    quadrant_shift : Quadrant shift such that 0 values / frequencies are at the corners
-    Returns
-    -------
-    A matrix containing the polar angle in radian at the respective position for a circle centered in the matrix.
-    The result can be returned quadrant shifted. The angle is 0 for all points on the positive x-axis. 
-    The angles are pi/2 (90°) and -pi/2 (-90°) on the positive and negative y-axis respectively. On the negative
-    x-axis the angle is pi (180°). If you need the angle to be in range [0, 2pi] instead of [-pi, pi], you can simply 
-    add 2pi whenever the angle is negative.
-    """
-
-    y, x = filtergrid(size, quadrant_shift)
-
-    # Matrix values contain polar angle.
-    # 0 angle starts on the horizontal line and runs counter clock-wise
-    theta = np.arctan2(-y, x)
-
-    return theta
-
-
 @lru_cache(maxsize=8)
 def lowpassfilter(size: Shape, cutoff: float, order: int) -> np.ndarray:
     """Constructs a low-pass butterworth filter.