def global_std(self):
'''Filters vectors by a multiple of the standard deviation.
See Also
--------
openpiv.validation.global_std()
'''
result_fnames = []
for i, f in enumerate(self.p['fnames']):
data = np.loadtxt(f)
u, v, mask = piv_vld.global_std(
data[:, 2],
data[:, 3],
std_threshold=self.p['global_std_threshold'])
save_fname = create_save_vec_fname(path=f, postfix='_std_thrhld')
save(data[:, 0],
data[:, 1],
u,
v,
data[:, 4] + mask,
data[:, 5],
save_fname,
delimiter=delimiter)
result_fnames.append(save_fname)
return (result_fnames)
def global_val(self):
'''Filter vectors based on a global min-max threshold.
See:
openpiv.validation.global_val()
'''
result_fnames = []
for i, f in enumerate(self.p['fnames']):
data = np.loadtxt(f)
u, v, mask = piv_vld.global_val(data[:, 2],
data[:, 3],
u_thresholds=(self.p['MinU'],
self.p['MaxU']),
v_thresholds=(self.p['MinV'],
self.p['MaxV']))
save_fname = create_save_vec_fname(path=f, postfix='_glob_thrhld')
save(data[:, 0],
data[:, 1],
u,
v,
data[:, 4] + mask,
data[:, 5],
save_fname,
delimiter=delimiter)
result_fnames.append(save_fname)
return (result_fnames)
def sig2noise(self):
'''Filter vectors based on the signal to noise threshold.
See:
openpiv.validation.sig2noise_val()
'''
result_fnames = []
for i, f in enumerate(self.p['fnames']):
data = np.loadtxt(f)
u, v, mask = piv_vld.sig2noise_val(
data[:, 2],
data[:, 3],
data[:, 5],
threshold=self.p['sig2noise_threshold'])
save_fname = create_save_vec_fname(path=f, postfix='_sig2noise')
save(data[:, 0],
data[:, 1],
u,
v,
data[:, 4] + mask,
sig2noise=data[:, 5],
filename=save_fname,
delimiter=delimiter)
result_fnames.append(save_fname)
return (result_fnames)
def local_median(self):
'''Filter vectors based on a local median threshold.
See Also
--------
openpiv.validation.local_median_val()
'''
result_fnames = []
for i, f in enumerate(self.p['fnames']):
data = np.loadtxt(f)
u, v, mask = piv_vld.local_median_val(
data[:, 2],
data[:, 3],
u_threshold=self.p['local_median_threshold'],
v_threshold=self.p['local_median_threshold'],
size=self.p['local_median_size'])
save_fname = create_save_vec_fname(path=f, postfix='_med_thrhld')
save(data[:, 0],
data[:, 1],
u,
v,
data[:, 4] + mask,
data[:, 5],
save_fname,
delimiter=delimiter)
result_fnames.append(save_fname)
return (result_fnames)
def local_median(self):
"""
Filter vectors based on a local median threshold.
See Also
--------
openpiv.validation.local_median_val()
"""
result_fnames = []
for i, f in enumerate(self.p['fnames']):
data = np.loadtxt(f)
shape = get_dim(data)
u, v, mask = piv_vld.local_median_val(
np.reshape(data[:, 2], shape),
np.reshape(data[:, 3], shape),
self.p['local_median_threshold'],
self.p['local_median_threshold'],
size=self.p['local_median_size'])
save_fname = create_save_vec_fname(
path=f,
postfix='_med_thrhld')
n = len(data)
save(data[:, 0],
data[:, 1],
np.reshape(u, (n,)),
np.reshape(v, (n,)),
data[:, 4] + np.reshape(mask, (n,)),
data[:, 5],
save_fname,
delimiter=self.delimiter)
result_fnames.append(save_fname)
return result_fnames
def repl_outliers(self):
'''Replace outliers.'''
result_fnames = []
for i, f in enumerate(self.p['fnames']):
data = np.loadtxt(f)
u, v = piv_flt.replace_outliers(np.array([data[:, 2]]),
np.array([data[:, 3]]),
method=self.p['repl_method'],
max_iter=self.p['repl_iter'],
kernel_size=self.p['repl_kernel'])
save_fname = create_save_vec_fname(path=f, postfix='_repl')
save(data[:, 0],
data[:, 1],
u,
v,
data[:, 4],
data[:, 5],
save_fname,
delimiter=delimiter)
result_fnames.append(save_fname)
return (result_fnames)
def smoothn_r(self):
"""Smoothn postprocessing results."""
result_fnames = []
for i, f in enumerate(self.p['fnames']):
data = np.loadtxt(f)
u, _, _, _ = piv_smt.smoothn(
data[:, 2], s=self.p['smoothn_val'], isrobust=self.p['robust'])
v, _, _, _ = piv_smt.smoothn(
data[:, 3], s=self.p['smoothn_val'], isrobust=self.p['robust'])
save_fname = create_save_vec_fname(
path=f,
postfix='_smthn')
save(data[:, 0],
data[:, 1],
u, v,
data[:, 4],
data[:, 5],
save_fname,
delimiter=self.delimiter)
result_fnames.append(save_fname)
return result_fnames