spm1d.util¶
Utility module
This module contains a variety of convenience functions, including:
get_dataset
interp
p_corrected_bonf
p_critical_bonf
smooth
get_dataset¶
interp¶
- spm1d.util.interp(y, Q=101)[source]¶
Simple linear interpolation to n values.
- Parameters:
y — a 1D array or list of J separate 1D arrays
Q — number of nodes in the interpolated continuum
- Returns:
Q-component 1D array or a (J x Q) array
- Example:
>>> y0 = np.random.rand(51) >>> y1 = np.random.rand(87) >>> y2 = np.random.rand(68) >>> Y = [y0, y1, y2]
>>> Y = spm1d.util.interp(Y, Q=101)
p_corrected_bonf¶
- spm1d.util.p_corrected_bonf(p, n)[source]¶
Bonferroni-corrected p value.
Warning
This correction assumes independence amongst multiple tests.
- Parameters:
p — probability value computed from one of multiple tests
n — number of tests
- Returns:
Bonferroni-corrected p value.
- Example:
>>> p = spm1d.util.p_corrected_bonf(0.03, 8) # yields p = 0.216
p_critical_bonf¶
- spm1d.util.p_critical_bonf(alpha, n)[source]¶
Bonferroni-corrected critical Type I error rate.
Warning
This crticial threshold assumes independence amongst multiple tests.
- Parameters:
alpha — original Type I error rate (usually 0.05)
n — number of tests
- Returns:
Bonferroni-corrected critical p value; retains alpha across all tests.
- Example:
>>> p = spm1d.util.p_critical_bonf(0.05, 20) # yields p = 0.00256
smooth¶
- spm1d.util.smooth(Y, fwhm=5.0)[source]¶
Smooth a set of 1D continua. This method uses scipy.ndimage.filters.gaussian_filter1d but uses the fwhm instead of the standard deviation.
- Parameters:
Y — a (J x Q) numpy array
fwhm — Full-width at half-maximum of a Gaussian kernel used for smoothing.
- Returns:
(J x Q) numpy array
- Example:
>>> Y0 = np.random.rand(5, 101) >>> Y = spm1d.util.smooth(Y0, fwhm=10.0)
Note
A Gaussian kernel’s fwhm is related to its standard deviation (sd) as follows:
>>> fwhm = sd * sqrt(8*log(2))