def xyzthick2thetazthick(path,
alphadeg_measured,
H_measured,
R_expected=10.,
use_best_fit=True,
sample_size=None,
stretch_H=False,
z_offset_bot=None,
r_TOL=1.,
save=True,
fmt='%1.6f',
rotatedeg=None):
r"""Transforms an imperfection file from the format: "`x` `y` `z` `thick`"
to the format "`\theta` `z` `thick`".
The input file::
x1 y1 z1 thick1
x2 y2 z2 thick2
...
xn yn zn thickn
Is transformed in a file::
theta1 z1 thick1
theta2 z2 thick2
...
thetan zn thickn
Parameters
----------
path : str
The path to the imperfection file.
alphadeg_measured : float
The semi-vertex angle of the measured sample (it is ``0.`` for a
cylinder).
H_measured : float
The total height of the measured test specimen, including eventual
resin rings at the edges.
R_expected : float, optional
If ``use_best_fit is True`` this will be used as a first estimative that
can reduce the number of iterations up to convergence of the best fit
algorithms. If ``use_best_fit is False`` this will be considered the
``R_best_fit``.
use_best_fit : bool, optional
If ``True`` it overwrites the values for: ``R_expected`` (for
cylinders and cones) and ``z_offset_bot`` (for cones), which are
automatically determined with functions :func:`.best_fit_cylinder` and
:func:`.best_fit_cone`.
sample_size : int, optional
If the input file containing the measured data is too large it may
become convenient to use only a sample of it in order to calculate
the best fit.
z_offset_bot : float, optional
The offset that should be used from the bottom of the measured points
to the bottom of the test specimen.
r_TOL : float, optional
The tolerance used to ignore points farer than ``r_TOL*R_best_fit``,
given in percent.
save : bool, optional
If the returned array ``mps`` should also be saved to a ``.txt`` file.
fmt : str or sequence of strs, optional
See ``np.savetxt()`` documentation for more details.
rotatedeg : float or None, optional
Rotation angle in degrees telling how much the imperfection pattern
should be rotated about the `X_3` (or `Z`) axis.
Returns
-------
mps : np.ndarray
A 2-D array with `\theta`, `z`, `imp` in the first, second
and third columns, respectively.
"""
inputa = np.loadtxt(path)
if inputa.shape[1] != 4:
raise ValueError('Input file does not have the format: "x y z thick"')
xyz = inputa[:, :3]
thick = inputa[:, 3]
if use_best_fit:
log('Finding the best-fit ...')
if alphadeg_measured == 0.:
out = best_fit_cylinder(xyz, R_expected=R_expected, H=H_measured,
save=False, sample_size=sample_size)
R_best_fit = out['R_best_fit']
input_pts = np.loadtxt(path, unpack=True)[:, :3]
pts = np.vstack((input_pts, np.ones_like(input_pts[0, :])))
x, y, z = out['T'].dot(pts)
z -= z.min()
H_points = z.max() - z.min()
if z_offset_bot:
z += z_offset_bot
else:
z += (H_measured - H_points)/2. # centralizes the points
else:
R_best_fit = R_expected
log('Reading the data ...')
d, data, d = read_xyz(xyz, alphadeg_measured, R_best_fit,
H_measured, False, z_offset_bot, r_TOL)
x, y, z = data.T
theta = np.arctan2(y, x)
log('Minimum thickness: {0}'.format(thick.min()))
log('Maximum thickness: {0}'.format(thick.max()))
if rotatedeg is not None:
theta += np.deg2rad(rotatedeg)
mps = np.vstack((theta, z, thick)).T
if save:
outpath = ('.'.join(os.path.basename(path).split('.')[:-1]) +
'_theta_z_thick.txt')
np.savetxt(outpath, mps, fmt=fmt)
return mps
def xyz2thetazimp(path,
alphadeg_measured,
H_measured,
R_expected=10.,
use_best_fit=True,
sample_size=None,
best_fit_output=False,
errorRtol=1.e-9,
stretch_H=False,
z_offset_bot=None,
r_TOL=1.,
clip_bottom=None,
clip_top=None,
save=True,
fmt='%1.6f',
rotatedeg=None):
r"""Transforms an imperfection file from the format "`x` `y` `z`"
to the format "`\theta` `z` `imp`".
The input file::
x1 y1 z1
x2 y2 z2
...
xn yn zn
Is transformed to a file::
theta1 z1 imp1
theta2 z2 imp2
...
thetan zn impn
Parameters
----------
path : str
The path to the imperfection file.
alphadeg_measured : float
The semi-vertex angle of the measured sample (it is ``0.`` for a
cylinder).
H_measured : float
The total height of the measured test specimen, including eventual
resin rings at the edges.
R_expected : float, optional
If ``use_best_fit==True`` this will be used as a first estimative that
can reduce the number of iterations up to convergence of the best fit
algorithms. If ``use_best_fit==False`` this will be considered the
``R_best_fit``.
use_best_fit : bool, optional
If ``True`` it overwrites the values for: ``R_expected`` (for
cylinders and cones) and ``z_offset_bot`` (for cones), which are
automatically determined with functions :func:`.best_fit_cylinder` and
:func:`.best_fit_cone`.
best_fit_output : bool, optional
If the output from the best fit routines should be also returned. In
case ``True`` the output of this function will be a tuple with
``(mps, out)``. For a description of ``out`` see
:func:`.best_fit_cylinder`.
errorRtol : float, optional
The error tolerance for the best-fit radius to stop the iterations.
sample_size : int, optional
If the input file containing the measured data is too large it may
become convenient to use only a sample of it in order to calculate
the best fit.
z_offset_bot : float, optional
The offset that should be used from the bottom of the measured points
to the bottom of the test specimen.
r_TOL : float, optional
The tolerance used to ignore points farer than ``r_TOL*R_best_fit``,
given in percent.
clip_bottom : float, optional
How much of the measured points close to the bottom edge should be
cut off, convenient to remove spurious measured data. Example: if
the minimum ``z`` coordinate of the measured points is ``25.4``
and ``clip_bottom=10.``, all points with ``z<=35.4`` will be
ignored.
clip_top : float, optional
Same as ``clip_bottom``, but applicable to the points close to the
top edge.
save : bool, optional
If the returned array ``mps`` should also be saved to a ``.txt`` file.
fmt : str or sequence of strs, optional
See ``np.savetxt()`` documentation for more details.
rotatedeg : float or None, optional
Rotation angle in degrees telling how much the imperfection pattern
should be rotated about the `X_3` (or `Z`) axis.
Returns
-------
mps : np.ndarray
A 2-D array with `\theta`, `z`, `imp` in the first, second
and third columns, respectively.
mps, out : np.ndarray, dict
If ``best_fit_output==True`` it returns ``(mps, out)`` as described
above.
"""
if use_best_fit:
log('Finding the best-fit ...')
if alphadeg_measured==0.:
out = best_fit_cylinder(path, R_expected=R_expected, H=H_measured,
save=False, sample_size=sample_size,
errorRtol=errorRtol)
R_best_fit = out['R_best_fit']
input_pts = np.loadtxt(path, unpack=True)
pts = np.vstack((input_pts, np.ones_like(input_pts[0, :])))
x, y, z = out['T'].dot(pts)
zmin = z.min()
zmax = z.max()
z -= zmin
H_points = zmax - zmin
if z_offset_bot:
z += z_offset_bot
else:
z += (H_measured - H_points)/2. # centralizes the points
else:
R_best_fit = R_expected
log('Reading the data ...')
d, data, d = read_xyz(path, alphadeg_measured, R_best_fit,
H_measured, None, z_offset_bot, r_TOL)
x, y, z = data.T
if clip_bottom or clip_top:
mask = np.ones(z.shape[0], dtype=bool)
if clip_bottom:
zmin = z.min()
log('Removing points with z <= {0:1.6f}'.format(zmin+clip_bottom))
mask &= (z > (zmin + clip_bottom))
if clip_top:
zmax = z.max()
log('Removing points with z >= {0:1.6f}'.format(zmax-clip_top))
mask &= (z < (zmax - clip_top))
shape1 = x.shape[0]
x = x[mask]
y = y[mask]
z = z[mask]
shape2 = x.shape[0]
log('Total of {0} points excluded.'.format(shape1 - shape2),
level=1)
r = np.sqrt(x**2 + y**2)
theta = np.arctan2(y, x)
imp = r - R_best_fit
log('Minimum imperfection: {0}'.format(imp.min()))
log('Maximum imperfection: {0}'.format(imp.max()))
if rotatedeg is not None:
theta += np.deg2rad(rotatedeg)
mps = np.vstack((theta, z, imp)).T
if save:
outpath = ('.'.join(os.path.basename(path).split('.')[:-1]) +
'_theta_z_imp.txt')
np.savetxt(outpath, mps, fmt=fmt)
if best_fit_output:
return mps, out
else:
return mps