diff --git a/lattices2d.py b/lattices2d.py
deleted file mode 100644
index 5ea07cb..0000000
--- a/lattices2d.py
+++ /dev/null
@@ -1,247 +0,0 @@
-import numpy as np
-from enum import Enum
-
-nx = None
-
-class LatticeType(Enum):
-    """
-    All the five Bravais lattices in 2D
-    """
-    OBLIQUE=1
-    RECTANGULAR=2
-    SQUARE=4
-    RHOMBIC=5
-    EQUILATERAL_TRIANGULAR=3
-    RIGHT_ISOSCELES=SQUARE
-    PARALLELOGRAMMIC=OBLIQUE
-    CENTERED_RHOMBIC=RECTANGULAR
-    RIGHT_TRIANGULAR=RECTANGULAR
-    CENTERED_RECTANGULAR=RHOMBIC
-    ISOSCELE_TRIANGULAR=RHOMBIC
-    RIGHT_ISOSCELE_TRIANGULAR=SQUARE
-    HEXAGONAL=EQUILATERAL_TRIANGULAR
-
-def reduceBasisSingle(b1, b2):
-    """
-    Lagrange-Gauss reduction of a 2D basis.
-    cf. https://www.math.auckland.ac.nz/~sgal018/crypto-book/ch17.pdf
-    inputs and outputs are (2,)-shaped numpy arrays
-    The output shall satisfy |b1| <= |b2| <= |b2 - b1| 
-    TODO doc
-    
-    TODO perhaps have the (on-demand?) guarantee of obtuse angle between b1, b2?
-    TODO possibility of returning the (in-order, no-obtuse angles) b as well?
-    """
-    b1 = np.array(b1)
-    b2 = np.array(b2)
-    if b1.shape != (2,) or b2.shape != (2,):
-        raise ValueError('Shape of b1 and b2 must be (2,)')
-    B1 = np.sum(b1 * b1, axis=-1, keepdims=True)
-    mu = np.sum(b1 * b2, axis=-1, keepdims=True) / B1
-    b2 = b2 - np.rint(mu) * b1
-    B2 = np.sum(b2 * b2, axis=-1, keepdims=True)
-    while(np.any(B2 < B1)):
-        b2t = b1
-        b1 = b2
-        b2 = b2t
-        B1 = B2
-        mu = np.sum(b1 * b2, axis=-1, keepdims=True) / B1
-        b2 = b2 - np.rint(mu) * b1
-        B2 = np.sum(b2*b2, axis=-1, keepdims=True)
-    return(b1,b2)
-
-def shortestBase3(b1, b2):
-    ''' 
-    returns the "ordered shortest triple" of base vectors (each pair from 
-    the triple is a base) and there may not be obtuse angle between b1, b2
-    and between b2, b3
-    '''
-    b1, b2 = reduceBasisSingle(b1,b2)
-    if is_obtuse(b1, b2, tolerance=0):
-        b3 = b2
-        b2 = b2 + b1
-    else:
-        b3 = b2 - b1
-    return (b1, b2, b3)
-
-def shortestBase46(b1, b2, tolerance=1e-13):
-    b1, b2 = reduceBasisSingle(b1,b2)
-    b1s = np.sum(b1 ** 2)
-    b2s = np.sum(b2 ** 2)
-    b3 = b2 - b1
-    b3s = np.sum(b3 ** 2)
-    eps = tolerance * (b2s + b1s)
-    if abs(b3s - b2s - b1s) < eps:
-        return(b1, b2, -b1, -b2)
-    else:
-        if b3s - b2s - b1s > eps: #obtuse
-            b3 = b2
-            b2 = b2 + b1
-        return (b1, b2, b3, -b1, -b2, -b3)
-    
-
-def is_obtuse(b1, b2, tolerance=1e-13):
-    b1s = np.sum(b1 ** 2)
-    b2s = np.sum(b2 ** 2)
-    b3 = b2 - b1
-    b3s = np.sum(b3 ** 2)
-    eps = tolerance * (b2s + b1s)
-    return (b3s - b2s - b1s > eps)
-
-def classifyLatticeSingle(b1, b2, tolerance=1e-13):
-    """
-    Given two basis vectors, returns 2D Bravais lattice type.
-    Tolerance is relative.
-    TODO doc
-    """
-    b1, b2 = reduceBasisSingle(b1, b2)
-    b1s = np.sum(b1 ** 2)
-    b2s = np.sum(b2 ** 2)
-    b3 = b2 - b1
-    b3s = np.sum(b3 ** 2)
-    eps = tolerance * (b2s + b1s)
-    # Avoid obtuse angle between b1 and b2. TODO This should be yet thoroughly tested.
-    # TODO use is_obtuse here?
-    if b3s - b2s - b1s > eps:
-        b3 = b2
-        b2 = b2 + b1
-        # N. B. now the assumption |b3| >= |b2| is no longer valid
-        #b3 = b2 - b1
-        b2s = np.sum(b2 ** 2)
-        b3s = np.sum(b3 ** 2)
-    if abs(b2s - b1s) < eps or abs(b2s - b3s) < eps: # isoscele
-        if abs(b3s - b1s) < eps:
-            return LatticeType.EQUILATERAL_TRIANGULAR
-        elif abs(b3s - 2 * b1s) < eps:
-            return LatticeType.SQUARE
-        else:
-            return LatticeType.RHOMBIC
-    elif abs(b3s - b2s - b1s) < eps:
-        return LatticeType.RECTANGULAR
-    else:
-        return LatticeType.OBLIQUE
-
-def range2D(maxN, mini=1, minj=0, minN = 0):
-    """
-    "Triangle indices"
-    Generates pairs of non-negative integer indices (i, j) such that
-    minN ≤ i + j ≤ maxN, i ≥ mini, j ≥ minj.
-    TODO doc and possibly different orderings
-    """
-    for maxn in range(min(mini, minj, minN), floor(maxN+1)): # i + j == maxn
-        for i in range(mini, maxn + 1):
-            yield (i, maxn - i)
-
-            
-def generateLattice(b1, b2, maxlayer=5, include_origin=False, order='leaves'):
-    bvs = shortestBase46(b1, b2)
-    cc = len(bvs) # "corner count"
-    
-    if order == 'leaves':
-        indices = np.array(list(range2D(maxlayer)))
-        ia = indices[:,0]
-        ib = indices[:,1]
-        cc = len(bvs) # 4 for square/rec,
-        leaves = list()
-        if include_origin: leaves.append(np.array([[0,0]]))
-        for c in range(cc):
-            ba = bvs[c]
-            bb = bvs[(c+1)%cc]
-            leaves.append(ia[:,nx]*ba + ib[:,nx]*bb)
-        return np.concatenate(leaves)
-    else: 
-        raise ValueError('Lattice point order not implemented: ', order)
-        
-def generateLatticeDisk(b1, b2, r, include_origin=False, order='leaves'):
-    b1, b2 = reduceBasisSingle(b1,b2)
-    blen = np.linalg.norm(b1, ord=2)
-    maxlayer = 2*r/blen # FIXME kanon na vrabce? Nestačí odmocnina ze 2?
-    points = generateLattice(b1,b2, maxlayer=maxlayer, include_origin=include_origin, order=order)
-    mask = (np.linalg.norm(points, axis=-1, ord=2) <= r)
-    return points[mask]
-
-def cellCornersWS(b1, b2,):
-    """
-    Given basis vectors, returns the corners of the Wigner-Seitz unit cell
-    (w1, w2, -w1, w2) for rectangular and square lattice or
-    (w1, w2, w3, -w1, -w2, -w3) otherwise
-    """
-    def solveWS(v1, v2):
-        v1x = v1[0]
-        v1y = v1[1]
-        v2x = v2[0]
-        v2y = v2[1]
-        lsm = ((-v1y, v2y), (v1x, -v2x))
-        rs = ((v1x-v2x)/2, (v1y - v2y)/2)
-        t = np.linalg.solve(lsm, rs)
-        return np.array(v1)/2 + t[0]*np.array((v1y, -v1x))
-    b1, b2 = reduceBasisSingle(b1, b2)
-    latticeType = classifyLatticeSingle(b1, b2)
-    if latticeType is LatticeType.RECTANGULAR or latticeType is LatticeType.SQUARE:
-        return np.array( (
-            (+b1+b2),
-            (+b2-b1),
-            (-b1-b2),
-            (-b2+b1),
-        )) / 2
-    else:
-        bvs = shortestBase46(b1,b2,tolerance=0)
-        return np.array([solveWS(bvs[i], bvs[(i+1)%6]) for i in range(6)])
-
-def cutWS(points, b1, b2, scale=1., tolerance=1e-13):
-    """ 
-    From given points, return only those that are inside (or on the edge of)
-    the Wigner-Seitz cell of a (scale*b1, scale*b2)-based lattice.
-    """
-    # TODO check input dimensions?
-    bvs = shortestBase46(b1, b2)
-    points = np.array(points)
-    for b in bvs: 
-        mask = (np.tensordot(points, b, axes=(-1, 0)) <=  (scale * (1+tolerance) / 2) *np.linalg.norm(b, ord=2)**2 )
-        points = points[mask]
-    return points
-
-def filledWS(b1, b2, density=10, scale=1.):
-    """
-    TODO doc
-    TODO more intelligent generation, anisotropy balancing etc.
-    """
-    points = generateLattice(b1,b2,maxlayer=density*scale, include_origin=True)
-    points = cutWS(points/density, np.array(b1)*scale, np.array(b2)*scale)
-    return points
-    
-    
-rot90_ = np.array([[0,1],[-1,0]])
-def reciprocalBasis(a1, a2):
-    a1, a2 = reduceBasisSingle(a1,a2) # this can be replaced with the vector version of reduceBasis when it is made
-    prefac = 2*np.pi/np.sum(np.tensordot(a1, rot90_, axes=[-1,0]) * a2, axis=-1)
-    b1 = np.tensordot(rot90_, a2, axes=[-1,-1]) * prefac
-    b2 = np.tensordot(rot90_, a1, axes=[-1,-1]) * prefac
-    return (b1, b2)
-
-
-# TODO fill it with "points from reciprocal space" instead
-def filledWS2(b1,b2, density=10, scale=1.):
-    b1, b2 = reduceBasisSingle(b1,b2)
-    b1r, b2r = reciprocalBasis(b1,b2)
-    b1l = np.linalg.norm(b1, ord=2)
-    b2l = np.linalg.norm(b2, ord=2)
-    b1rl = np.linalg.norm(b1r, ord=2)
-    b2rl = np.linalg.norm(b2r, ord=2)
-    # Black magick. Think later.™ Really. FIXME
-    sicher_ratio = np.maximum(b1rl/b2rl, b2rl/b1rl) * np.maximum(b1l/b2l, b2l/b1l) # This really has to be adjusted
-    points = generateLattice(b1r,b2r,maxlayer=density*scale*sicher_ratio, include_origin=True)
-    points = cutWS(points*b1l/b1rl/density, b1*scale, b2*scale)
-    return points
-
-
-
-"""
-TODO
-====
-
-- DOC!!!!!
-- (nehoří) výhledově pořešit problém „hodně anisotropních“ mřížek (tj. kompensovat
-rozdílné délky základních vektorů).
-
-"""
diff --git a/qpms/lattices2d.py b/qpms/lattices2d.py
index 670b763..5ea07cb 100644
--- a/qpms/lattices2d.py
+++ b/qpms/lattices2d.py
@@ -151,6 +151,14 @@ def generateLattice(b1, b2, maxlayer=5, include_origin=False, order='leaves'):
         return np.concatenate(leaves)
     else: 
         raise ValueError('Lattice point order not implemented: ', order)
+        
+def generateLatticeDisk(b1, b2, r, include_origin=False, order='leaves'):
+    b1, b2 = reduceBasisSingle(b1,b2)
+    blen = np.linalg.norm(b1, ord=2)
+    maxlayer = 2*r/blen # FIXME kanon na vrabce? Nestačí odmocnina ze 2?
+    points = generateLattice(b1,b2, maxlayer=maxlayer, include_origin=include_origin, order=order)
+    mask = (np.linalg.norm(points, axis=-1, ord=2) <= r)
+    return points[mask]
 
 def cellCornersWS(b1, b2,):
     """