some new features

.venv/lib/python3.12/site-packages/mpl_toolkits/mplot3d/proj3d.py

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+"""
+Various transforms used for by the 3D code
+"""
+
+import numpy as np
+
+from matplotlib import _api
+
+
+def world_transformation(xmin, xmax,
+                         ymin, ymax,
+                         zmin, zmax, pb_aspect=None):
+    """
+    Produce a matrix that scales homogeneous coords in the specified ranges
+    to [0, 1], or [0, pb_aspect[i]] if the plotbox aspect ratio is specified.
+    """
+    dx = xmax - xmin
+    dy = ymax - ymin
+    dz = zmax - zmin
+    if pb_aspect is not None:
+        ax, ay, az = pb_aspect
+        dx /= ax
+        dy /= ay
+        dz /= az
+
+    return np.array([[1/dx,    0,    0, -xmin/dx],
+                     [   0, 1/dy,    0, -ymin/dy],
+                     [   0,    0, 1/dz, -zmin/dz],
+                     [   0,    0,    0,        1]])
+
+
+def _rotation_about_vector(v, angle):
+    """
+    Produce a rotation matrix for an angle in radians about a vector.
+    """
+    vx, vy, vz = v / np.linalg.norm(v)
+    s = np.sin(angle)
+    c = np.cos(angle)
+    t = 2*np.sin(angle/2)**2  # more numerically stable than t = 1-c
+
+    R = np.array([
+        [t*vx*vx + c,    t*vx*vy - vz*s, t*vx*vz + vy*s],
+        [t*vy*vx + vz*s, t*vy*vy + c,    t*vy*vz - vx*s],
+        [t*vz*vx - vy*s, t*vz*vy + vx*s, t*vz*vz + c]])
+
+    return R
+
+
+def _view_axes(E, R, V, roll):
+    """
+    Get the unit viewing axes in data coordinates.
+
+    Parameters
+    ----------
+    E : 3-element numpy array
+        The coordinates of the eye/camera.
+    R : 3-element numpy array
+        The coordinates of the center of the view box.
+    V : 3-element numpy array
+        Unit vector in the direction of the vertical axis.
+    roll : float
+        The roll angle in radians.
+
+    Returns
+    -------
+    u : 3-element numpy array
+        Unit vector pointing towards the right of the screen.
+    v : 3-element numpy array
+        Unit vector pointing towards the top of the screen.
+    w : 3-element numpy array
+        Unit vector pointing out of the screen.
+    """
+    w = (E - R)
+    w = w/np.linalg.norm(w)
+    u = np.cross(V, w)
+    u = u/np.linalg.norm(u)
+    v = np.cross(w, u)  # Will be a unit vector
+
+    # Save some computation for the default roll=0
+    if roll != 0:
+        # A positive rotation of the camera is a negative rotation of the world
+        Rroll = _rotation_about_vector(w, -roll)
+        u = np.dot(Rroll, u)
+        v = np.dot(Rroll, v)
+    return u, v, w
+
+
+def _view_transformation_uvw(u, v, w, E):
+    """
+    Return the view transformation matrix.
+
+    Parameters
+    ----------
+    u : 3-element numpy array
+        Unit vector pointing towards the right of the screen.
+    v : 3-element numpy array
+        Unit vector pointing towards the top of the screen.
+    w : 3-element numpy array
+        Unit vector pointing out of the screen.
+    E : 3-element numpy array
+        The coordinates of the eye/camera.
+    """
+    Mr = np.eye(4)
+    Mt = np.eye(4)
+    Mr[:3, :3] = [u, v, w]
+    Mt[:3, -1] = -E
+    M = np.dot(Mr, Mt)
+    return M
+
+
+def _persp_transformation(zfront, zback, focal_length):
+    e = focal_length
+    a = 1  # aspect ratio
+    b = (zfront+zback)/(zfront-zback)
+    c = -2*(zfront*zback)/(zfront-zback)
+    proj_matrix = np.array([[e,   0,  0, 0],
+                            [0, e/a,  0, 0],
+                            [0,   0,  b, c],
+                            [0,   0, -1, 0]])
+    return proj_matrix
+
+
+def _ortho_transformation(zfront, zback):
+    # note: w component in the resulting vector will be (zback-zfront), not 1
+    a = -(zfront + zback)
+    b = -(zfront - zback)
+    proj_matrix = np.array([[2, 0,  0, 0],
+                            [0, 2,  0, 0],
+                            [0, 0, -2, 0],
+                            [0, 0,  a, b]])
+    return proj_matrix
+
+
+def _proj_transform_vec(vec, M):
+    vecw = np.dot(M, vec.data)
+    w = vecw[3]
+    txs, tys, tzs = vecw[0]/w, vecw[1]/w, vecw[2]/w
+    if np.ma.isMA(vec[0]):  # we check each to protect for scalars
+        txs = np.ma.array(txs, mask=vec[0].mask)
+    if np.ma.isMA(vec[1]):
+        tys = np.ma.array(tys, mask=vec[1].mask)
+    if np.ma.isMA(vec[2]):
+        tzs = np.ma.array(tzs, mask=vec[2].mask)
+    return txs, tys, tzs
+
+
+def _proj_transform_vec_clip(vec, M, focal_length):
+    vecw = np.dot(M, vec.data)
+    w = vecw[3]
+    txs, tys, tzs = vecw[0] / w, vecw[1] / w, vecw[2] / w
+    if np.isinf(focal_length):  # don't clip orthographic projection
+        tis = np.ones(txs.shape, dtype=bool)
+    else:
+        tis = (-1 <= txs) & (txs <= 1) & (-1 <= tys) & (tys <= 1) & (tzs <= 0)
+    if np.ma.isMA(vec[0]):
+        tis = tis & ~vec[0].mask
+    if np.ma.isMA(vec[1]):
+        tis = tis & ~vec[1].mask
+    if np.ma.isMA(vec[2]):
+        tis = tis & ~vec[2].mask
+
+    txs = np.ma.masked_array(txs, ~tis)
+    tys = np.ma.masked_array(tys, ~tis)
+    tzs = np.ma.masked_array(tzs, ~tis)
+    return txs, tys, tzs, tis
+
+
+def inv_transform(xs, ys, zs, invM):
+    """
+    Transform the points by the inverse of the projection matrix, *invM*.
+    """
+    vec = _vec_pad_ones(xs, ys, zs)
+    vecr = np.dot(invM, vec)
+    if vecr.shape == (4,):
+        vecr = vecr.reshape((4, 1))
+    for i in range(vecr.shape[1]):
+        if vecr[3][i] != 0:
+            vecr[:, i] = vecr[:, i] / vecr[3][i]
+    return vecr[0], vecr[1], vecr[2]
+
+
+def _vec_pad_ones(xs, ys, zs):
+    if np.ma.isMA(xs) or np.ma.isMA(ys) or np.ma.isMA(zs):
+        return np.ma.array([xs, ys, zs, np.ones_like(xs)])
+    else:
+        return np.array([xs, ys, zs, np.ones_like(xs)])
+
+
+def proj_transform(xs, ys, zs, M):
+    """
+    Transform the points by the projection matrix *M*.
+    """
+    vec = _vec_pad_ones(xs, ys, zs)
+    return _proj_transform_vec(vec, M)
+
+
+@_api.deprecated("3.10")
+def proj_transform_clip(xs, ys, zs, M):
+    return _proj_transform_clip(xs, ys, zs, M, focal_length=np.inf)
+
+
+def _proj_transform_clip(xs, ys, zs, M, focal_length):
+    """
+    Transform the points by the projection matrix
+    and return the clipping result
+    returns txs, tys, tzs, tis
+    """
+    vec = _vec_pad_ones(xs, ys, zs)
+    return _proj_transform_vec_clip(vec, M, focal_length)
+
+
+def _proj_points(points, M):
+    return np.column_stack(_proj_trans_points(points, M))
+
+
+def _proj_trans_points(points, M):
+    points = np.asanyarray(points)
+    xs, ys, zs = points[:, 0], points[:, 1], points[:, 2]
+    return proj_transform(xs, ys, zs, M)