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validation.py
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validation.py
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"""A module for spurious vector detection."""
__licence__ = """
Copyright (C) 2011 www.openpiv.net
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import numpy as np
from scipy.ndimage import median_filter
def global_val(u, v, u_thresholds, v_thresholds):
"""Eliminate spurious vectors with a global threshold.
This validation method tests for the spatial consistency of the data
and outliers vector are replaced with Nan (Not a Number) if at
least one of the two velocity components is out of a specified global range.
Parameters
----------
u : 2d np.ndarray
a two dimensional array containing the u velocity component.
v : 2d np.ndarray
a two dimensional array containing the v velocity component.
u_thresholds: two elements tuple
u_thresholds = (u_min, u_max). If ``u<u_min`` or ``u>u_max``
the vector is treated as an outlier.
v_thresholds: two elements tuple
``v_thresholds = (v_min, v_max)``. If ``v<v_min`` or ``v>v_max``
the vector is treated as an outlier.
Returns
-------
u : 2d np.ndarray
a two dimensional array containing the u velocity component,
where spurious vectors have been replaced by NaN.
v : 2d np.ndarray
a two dimensional array containing the v velocity component,
where spurious vectors have been replaced by NaN.
mask : boolean 2d np.ndarray
a boolean array. True elements corresponds to outliers.
"""
np.warnings.filterwarnings("ignore")
ind = np.logical_or(
np.logical_or(u < u_thresholds[0], u > u_thresholds[1]),
np.logical_or(v < v_thresholds[0], v > v_thresholds[1]),
)
u[ind] = np.nan
v[ind] = np.nan
mask = np.zeros(u.shape, dtype=bool)
mask[ind] = True
return u, v, mask
def global_std(u, v, std_threshold=3):
"""Eliminate spurious vectors with a global threshold defined by the standard deviation
This validation method tests for the spatial consistency of the data
and outliers vector are replaced with NaN (Not a Number) if at least
one of the two velocity components is out of a specified global range.
Parameters
----------
u : 2d np.ndarray
a two dimensional array containing the u velocity component.
v : 2d np.ndarray
a two dimensional array containing the v velocity component.
std_threshold: float
If the length of the vector (actually the sum of squared components) is
larger than std_threshold times standard deviation of the flow field,
then the vector is treated as an outlier. [default = 3]
Returns
-------
u : 2d np.ndarray
a two dimensional array containing the u velocity component,
where spurious vectors have been replaced by NaN.
v : 2d np.ndarray
a two dimensional array containing the v velocity component,
where spurious vectors have been replaced by NaN.
mask : boolean 2d np.ndarray
a boolean array. True elements corresponds to outliers.
"""
vel_magnitude = u ** 2 + v ** 2
ind = vel_magnitude > std_threshold * np.std(vel_magnitude)
u[ind] = np.nan
v[ind] = np.nan
mask = np.zeros(u.shape, dtype=bool)
mask[ind] = True
return u, v, mask
def sig2noise_val(u, v, sig2noise, w=None, threshold=1.3):
"""Eliminate spurious vectors from cross-correlation signal to noise ratio.
Replace spurious vectors with zero if signal to noise ratio
is below a specified threshold.
Parameters
----------
u : 2d or 3d np.ndarray
a two or three dimensional array containing the u velocity component.
v : 2d or 3d np.ndarray
a two or three dimensional array containing the v velocity component.
sig2noise : 2d np.ndarray
a two or three dimensional array containing the value of the signal to
noise ratio from cross-correlation function.
w : 2d or 3d np.ndarray
a two or three dimensional array containing the w (in z-direction) velocity component.
threshold: float
the signal to noise ratio threshold value.
Returns
-------
u : 2d or 3d np.ndarray
a two or three dimensional array containing the u velocity component,
where spurious vectors have been replaced by NaN.
v : 2d or 3d np.ndarray
a two or three dimensional array containing the v velocity component,
where spurious vectors have been replaced by NaN.
w : 2d or 3d np.ndarray
optional, a two or three dimensional array containing the w (in z-direction) velocity component.
where spurious vectors have been replaced by NaN.
mask : boolean 2d np.ndarray
a boolean array. True elements corresponds to outliers.
References
----------
R. D. Keane and R. J. Adrian, Measurement Science & Technology,1990, 1, 1202-1215.
"""
ind = sig2noise < threshold
u[ind] = np.nan
v[ind] = np.nan
if isinstance(w, np.ndarray):
w[ind] = np.nan
return u, v, w, ind
return u, v, ind
def local_median_val(u, v, u_threshold, v_threshold, size=1):
"""Eliminate spurious vectors with a local median threshold.
This validation method tests for the spatial consistency of the data.
Vectors are classified as outliers and replaced with Nan (Not a Number) if
the absolute difference with the local median is greater than a user
specified threshold. The median is computed for both velocity components.
Parameters
----------
u : 2d np.ndarray
a two dimensional array containing the u velocity component.
v : 2d np.ndarray
a two dimensional array containing the v velocity component.
u_threshold : float
the threshold value for component u
v_threshold : float
the threshold value for component v
Returns
-------
u : 2d np.ndarray
a two dimensional array containing the u velocity component,
where spurious vectors have been replaced by NaN.
v : 2d np.ndarray
a two dimensional array containing the v velocity component,
where spurious vectors have been replaced by NaN.
mask : boolean 2d np.ndarray
a boolean array. True elements corresponds to outliers.
"""
um = median_filter(u, size=2 * size + 1)
vm = median_filter(v, size=2 * size + 1)
ind = (np.abs((u - um)) > u_threshold) | (np.abs((v - vm)) > v_threshold)
u[ind] = np.nan
v[ind] = np.nan
mask = np.zeros(u.shape, dtype=bool)
mask[ind] = True
return u, v, mask