def test_is_image():
# tests for tests for image
img = load_image(anatfile)
yield assert_true(is_image(img))
class C(object): pass
yield assert_false(is_image(C()))
class C(object):
def __array__(self): pass
yield assert_false(is_image(C()))
class C(object):
coordmap = None
def __array__(self): pass
yield assert_true(is_image(img))
def as_image(image_input):
''' Load image from filename or pass through image instance
Parameters
----------
image_input : str or Image instance
image or string filename of image. If a string, load image and
return. If an image, pass through without modification
Returns
-------
img : Image or Image-like instance
Input object if `image_input` seemed to be an image, loaded Image
object if `image_input` was a string.
Raises
------
TypeError : if neither string nor image-like passed
Examples
--------
>>> from nipy.testing import anatfile
>>> from nipy.io.api import load_image
>>> img = as_image(anatfile)
>>> img2 = as_image(img)
>>> img2 is img
True
'''
if is_image(image_input):
return image_input
if isinstance(image_input, basestring):
return load(image_input)
raise TypeError('Expecting an image-like object or filename string')
def group_analysis_signs(design, contrast, mask, signs=None):
""" Refit the EM model with a vector of signs.
Used in the permutation tests.
Returns the maximum of the T-statistic within mask
Parameters
----------
design: one of 'block', 'event'
contrast: str
name of contrast to estimate
mask : ``Image`` instance or array-like
image containing mask, or array-like
signs: ndarray, optional
Defaults to np.ones. Should have shape (*,nsubj)
where nsubj is the number of effects combined in the group analysis.
Returns
-------
minT: np.ndarray, minima of T statistic within mask, one for each
vector of signs
maxT: np.ndarray, maxima of T statistic within mask, one for each
vector of signs
"""
if api.is_image(mask):
maska = mask.get_data()
else:
maska = np.asarray(mask)
maska = maska.astype(np.bool)
# Which subjects have this (contrast, design) pair?
subj_con_dirs = futil.subj_des_con_dirs(design, contrast)
# Assemble effects and sds into 4D arrays
sds = []
Ys = []
for s in subj_con_dirs:
sd_img = load_image(pjoin(s, "sd.nii"))
effect_img = load_image(pjoin(s, "effect.nii"))
sds.append(sd_img.get_data()[maska])
Ys.append(effect_img.get_data()[maska])
sd = np.array(sds)
Y = np.array(Ys)
if signs is None:
signs = np.ones((1, Y.shape[0]))
maxT = np.empty(signs.shape[0])
minT = np.empty(signs.shape[0])
for i, sign in enumerate(signs):
signY = sign[:,np.newaxis] * Y
varest = onesample.estimate_varatio(signY, sd)
random_var = varest['random']
adjusted_var = sd**2 + random_var
adjusted_sd = np.sqrt(adjusted_var)
results = onesample.estimate_mean(Y, adjusted_sd)
T = results['t']
minT[i], maxT[i] = np.nanmin(T), np.nanmax(T)
return minT, maxT
def group_analysis_signs(design, contrast, mask, signs=None):
""" Refit the EM model with a vector of signs.
Used in the permutation tests.
Returns the maximum of the T-statistic within mask
Parameters
----------
design: one of 'block', 'event'
contrast: str
name of contrast to estimate
mask : ``Image`` instance or array-like
image containing mask, or array-like
signs: ndarray, optional
Defaults to np.ones. Should have shape (*,nsubj)
where nsubj is the number of effects combined in the group analysis.
Returns
-------
minT: np.ndarray, minima of T statistic within mask, one for each
vector of signs
maxT: np.ndarray, maxima of T statistic within mask, one for each
vector of signs
"""
if api.is_image(mask):
maska = mask.get_data()
else:
maska = np.asarray(mask)
maska = maska.astype(np.bool)
# Which subjects have this (contrast, design) pair?
subj_con_dirs = futil.subj_des_con_dirs(design, contrast)
# Assemble effects and sds into 4D arrays
sds = []
Ys = []
for s in subj_con_dirs:
sd_img = load_image(pjoin(s, "sd.nii"))
effect_img = load_image(pjoin(s, "effect.nii"))
sds.append(sd_img.get_data()[maska])
Ys.append(effect_img.get_data()[maska])
sd = np.array(sds)
Y = np.array(Ys)
if signs is None:
signs = np.ones((1, Y.shape[0]))
maxT = np.empty(signs.shape[0])
minT = np.empty(signs.shape[0])
for i, sign in enumerate(signs):
signY = sign[:, np.newaxis] * Y
varest = onesample.estimate_varatio(signY, sd)
random_var = varest['random']
adjusted_var = sd**2 + random_var
adjusted_sd = np.sqrt(adjusted_var)
results = onesample.estimate_mean(Y, adjusted_sd)
T = results['t']
minT[i], maxT[i] = np.nanmin(T), np.nanmax(T)
return minT, maxT
