def make_mask_map_4d(data, infile, outfile):
""" Make mask map with 4d dimeions
data: values for levels in infile. Shape = [4th dimension, regions]
infile: input file to replace levels with values
outfile: output file name
"""
from neurosynth.base.mask import Masker
from neurosynth.base import imageutils
from nibabel import nifti1
data = np.array(data)
# Load image with masker
masker = Masker(infile)
img = imageutils.load_imgs(infile, masker)
header = masker.get_header()
shape = header.get_data_shape()[0:3] + (data.shape[0], )
header.set_data_shape(shape)
result = []
for t_dim, t_val in enumerate(data):
result.append(img.copy())
for num, value in enumerate(t_val):
np.place(result[t_dim], img == num + 1, [value])
result = np.hstack(result)
header.set_data_dtype(result.dtype) # Avoids loss of precision
img = nifti1.Nifti1Image(masker.unmask(result).squeeze(), None, header)
img.to_filename(outfile)
def make_mask_map(data, infile, outfile, index=None):
from neurosynth.base.mask import Masker
from neurosynth.base import imageutils
# Load image with masker
masker = Masker(infile)
img = imageutils.load_imgs(infile, masker)
data = list(data)
if index is None:
index = np.arange(0, len(data))
rev_index = None
else:
all_reg = np.arange(0, img.max())
rev_index = all_reg[np.invert(np.in1d(all_reg, index))]
min_val = img.min()
for num, value in enumerate(data):
n = index[num]
np.place(img, img == n + min_val, [value])
if rev_index is not None:
for value in rev_index:
np.place(img, img == value + min_val, 0)
img = img.astype('float32')
imageutils.save_img(img, outfile, masker)
def decode(self, filenames, method=None, save=None, fmt='%.3f'):
""" Decodes a set of images.
Args:
files: A list of filenames of images to decode.
method: Optional string indicating decoding method to use. If None, use
the method set when the Decoder instance was initialized.
save: Optional filename to save results to. If None (default), returns
all results as an array.
fmt: Optional format to pass to numpy.savetxt() if saving to file.
Returns:
An n_files x n_features numpy array, where each feature is a row and
each image is a column. The meaning of the values depends on the
decoding method used. """
if method is None: method = self.method
imgs_to_decode = imageutils.load_imgs(filenames, self.mask)
methods = {
'pearson': self._pearson_correlation(imgs_to_decode),
# 'nb': self._naive_bayes(imgs_to_decode),
'pattern': self._pattern_expression(imgs_to_decode)
}
result = methods[method]
if save is not None:
f = open(save, 'w')
f.write('\t'.join(self.feature_names) + '\n')
np.savetxt(f, result, fmt='%.3f', delimiter='\t')
else:
return methods[method]
def make_mask_map_4d(data, infile, outfile):
""" Make mask map with 4d dimeions
data: values for levels in infile. Shape = [4th dimension, regions]
infile: input file to replace levels with values
outfile: output file name
"""
from neurosynth.base.mask import Masker
from neurosynth.base import imageutils
from nibabel import nifti1
data = np.array(data)
# Load image with masker
masker = Masker(infile)
img = imageutils.load_imgs(infile, masker)
header = masker.get_header()
shape = header.get_data_shape()[0:3] + (data.shape[0],)
header.set_data_shape(shape)
result = []
for t_dim, t_val in enumerate(data):
result.append(img.copy())
for num, value in enumerate(t_val):
np.place(result[t_dim], img == num + 1, [value])
result = np.hstack(result)
header.set_data_dtype(result.dtype) # Avoids loss of precision
img = nifti1.Nifti1Image(masker.unmask(result).squeeze(), None, header)
img.to_filename(outfile)
def make_mask_map(data, infile, outfile, index=None):
from neurosynth.base.mask import Masker
from neurosynth.base import imageutils
# Load image with masker
masker = Masker(infile)
img = imageutils.load_imgs(infile, masker)
img = np.round(img)
data = list(data)
if index is None:
index = np.unique(img)
rev_index = None
else:
all_reg = np.arange(0, img.max())
rev_index = all_reg[np.invert(np.in1d(all_reg, index))]
min_val = img.min()
for num, value in enumerate(data):
ix = index[num]
np.place(img, img == ix, [value])
if rev_index is not None:
for value in rev_index:
np.place(img, img == value + min_val, 0)
img = img.astype('float32')
imageutils.save_img(img, outfile, masker)
def decode(self, images, method=None, save=None, round=4, names=None):
""" Decodes a set of images.
Args:
images: The images to decode. Can be:
- A single String specifying the filename of the image to decode
- A list of filenames
- A single NumPy array containing the image data
method: Optional string indicating decoding method to use. If None, use
the method set when the Decoder instance was initialized.
save: Optional filename to save results to. If None (default), returns
all results as an array.
round: Optional integer indicating number of decimals to round result
to. Defaults to 4.
names: Optional list of names corresponding to the images in filenames.
If passed, must be of same length and in same order as filenames.
By default, the columns in the output will be named using the image
filenames.
Returns:
An n_features x n_files numpy array, where each feature is a row and
each image is a column. The meaning of the values depends on the
decoding method used. """
if method is None:
method = self.method
if isinstance(images, basestring) or isinstance(images, list):
imgs_to_decode = imageutils.load_imgs(images, self.mask)
else:
imgs_to_decode = images
methods = {
'pearson': self._pearson_correlation(imgs_to_decode),
# 'nb': self._naive_bayes(imgs_to_decode),
'pattern': self._pattern_expression(imgs_to_decode)
}
result = np.around(methods[method], round)
if save is not None:
if names is None:
if type(images).__module__ == np.__name__:
names = ['image_%d' for i in range(images.shape[1])]
else:
names = images
rownames = np.array(self.feature_names, dtype='|S32')[:,
np.newaxis]
f = open(save, 'w')
f.write('\t'.join(['Feature'] + names) + '\n')
np.savetxt(f,
np.hstack((rownames, result)),
fmt='%s',
delimiter='\t')
else:
return methods[method]
def decode(self, images, method=None, save=None, round=4, names=None):
""" Decodes a set of images.
Args:
images: The images to decode. Can be:
- A single String specifying the filename of the image to decode
- A list of filenames
- A single NumPy array containing the image data
method: Optional string indicating decoding method to use. If None, use
the method set when the Decoder instance was initialized.
save: Optional filename to save results to. If None (default), returns
all results as an array.
round: Optional integer indicating number of decimals to round result
to. Defaults to 4.
names: Optional list of names corresponding to the images in filenames.
If passed, must be of same length and in same order as filenames.
By default, the columns in the output will be named using the image
filenames.
Returns:
An n_features x n_files numpy array, where each feature is a row and
each image is a column. The meaning of the values depends on the
decoding method used. """
if method is None:
method = self.method
if isinstance(images, basestring) or isinstance(images, list):
imgs_to_decode = imageutils.load_imgs(images, self.mask)
else:
imgs_to_decode = images
methods = {
'pearson': self._pearson_correlation(imgs_to_decode),
# 'nb': self._naive_bayes(imgs_to_decode),
'pattern': self._pattern_expression(imgs_to_decode)
}
result = np.around(methods[method], round)
if save is not None:
if names is None:
if type(images).__module__ == np.__name__:
names = ['image_%d' for i in range(images.shape[1])]
else:
names = filenames
rownames = np.array(
self.feature_names, dtype='|S32')[:, np.newaxis]
f = open(save, 'w')
f.write('\t'.join(['Feature'] + names) + '\n')
np.savetxt(f, np.hstack((
rownames, result)), fmt='%s', delimiter='\t')
else:
return methods[method]
def remove_value(infile, vals_rm, outfile):
masker = mask.Masker(infile)
img = imageutils.load_imgs(infile, masker)
# Remove value
for val in vals_rm:
np.place(img, img == val, [0])
# Save
imageutils.save_img(img, outfile, masker)
def decode(self, images, save=None, round=4, names=None, **kwargs):
""" Decodes a set of images.
Args:
images: The images to decode. Can be:
- A single String specifying the filename of the image to decode
- A list of filenames
- A single NumPy array containing the image data
save: Optional filename to save results to. If None (default), returns
all results as an array.
round: Optional integer indicating number of decimals to round result
to. Defaults to 4.
names: Optional list of names corresponding to the images in filenames.
If passed, must be of same length and in same order as filenames.
By default, the columns in the output will be named using the image
filenames.
Returns:
An n_features x n_files numpy array, where each feature is a row and
each image is a column. The meaning of the values depends on the
decoding method used. """
if isinstance(images, string_types):
images = [images]
if isinstance(images, list):
imgs_to_decode = imageutils.load_imgs(images, self.masker)
else:
imgs_to_decode = images
methods = {
'pearson': self._pearson_correlation,
'dot': self._dot_product,
'roi': self._roi_association
}
result = np.around(
methods[self.method](imgs_to_decode, **kwargs), round)
# if save is not None:
if names is None:
if type(images).__module__ == np.__name__:
names = ['image_%d' % i for i in range(images.shape[1])]
elif self.method == 'roi':
names = ['cluster_%d' % i for i in range(result.shape[1])]
else:
names = images
result = pd.DataFrame(result, columns=names, index=self.feature_names)
if save is not None:
result.to_csv(save, index_label='Feature')
return result
def decode(self, images, save=None, round=4, names=None, **kwargs):
""" Decodes a set of images.
Args:
images: The images to decode. Can be:
- A single String specifying the filename of the image to decode
- A list of filenames
- A single NumPy array containing the image data
save: Optional filename to save results to. If None (default), returns
all results as an array.
round: Optional integer indicating number of decimals to round result
to. Defaults to 4.
names: Optional list of names corresponding to the images in filenames.
If passed, must be of same length and in same order as filenames.
By default, the columns in the output will be named using the image
filenames.
Returns:
An n_features x n_files numpy array, where each feature is a row and
each image is a column. The meaning of the values depends on the
decoding method used. """
if isinstance(images, basestring):
images = [images]
if isinstance(images, list):
imgs_to_decode = imageutils.load_imgs(images, self.masker)
else:
imgs_to_decode = images
methods = {
'pearson': self._pearson_correlation,
'dot': self._dot_product,
'roi': self._roi_association
}
result = np.around(
methods[self.method](imgs_to_decode, **kwargs), round)
# if save is not None:
if names is None:
if type(images).__module__ == np.__name__:
names = ['image_%d' % i for i in range(images.shape[1])]
elif self.method == 'roi':
names = ['cluster_%d' % i for i in range(result.shape[1])]
else:
names = images
result = pd.DataFrame(result, columns=names, index=self.feature_names)
if save is not None:
result.to_csv(save, index_label='Feature')
return result
def remove_value(infile, vals_rm, outfile):
masker = mask.Masker(infile)
img = imageutils.load_imgs(infile, masker)
img = np.round(img)
# Remove value
for val in vals_rm:
np.place(img, img == val, [0])
# Save
imageutils.save_img(img, outfile, masker)
def extract_roi(mask_img, data_img, masker): """ Exctract values from an image for each region in an atlas Args: mask_img: Mask image or atlas image with multiple integer values denoting different regions data_img: Image to be extract_roi masker: A masker instance Returns: A list of tuples containing the region number mean value within each ROI, """ mask = imageutils.load_imgs(mask_img, masker) data = imageutils.load_imgs(data_img, masker) mask_values = np.unique(mask)[1:] mean_vals = [] for region in mask_values: mean_vals.append((region, data[np.where(mask == region)[0]].mean())) return pd.DataFrame(mean_vals, columns = ['roi', 'value'])
def _load_features_from_images(self, images, names=None):
""" Load feature image data from image files.
Args:
images: A list of image filenames.
names: An optional list of strings to use as the feature names. Must be
in the same order as the images.
"""
if names is not None and len(names) != len(images):
raise Exception( "Lists of feature names and image files must be of same length!")
self.feature_names = names if names is not None else images
self.feature_images = imageutils.load_imgs(images, self.masker)
def extract_roi(mask_img, data_img, masker):
""" Exctract values from an image for each region in an atlas
Args:
mask_img: Mask image or atlas image with multiple integer values denoting different regions
data_img: Image to be extract_roi
masker: A masker instance
Returns: A list of tuples containing the region number mean value within each ROI,
"""
mask = imageutils.load_imgs(mask_img, masker)
data = imageutils.load_imgs(data_img, masker)
mask_values = np.unique(mask)[1:]
mean_vals = []
for region in mask_values:
mean_vals.append((region, data[np.where(mask == region)[0]].mean()))
return pd.DataFrame(mean_vals, columns=['roi', 'value'])
def _load_features_from_images(self, images, names=None):
""" Load feature image data from image files.
Args:
images: A list of image filenames.
names: An optional list of strings to use as the feature names. Must
be in the same order as the images.
"""
if names is not None and len(names) != len(images):
raise Exception(
"Lists of feature names and images must be of same length!")
self.feature_names = names if names is not None else images
self.feature_images = imageutils.load_imgs(images, self.masker)
def decode(self,images,outfile,mrs=None,round=4):
if not self.decoder:
self.decoder = decode.Decoder(self.db)
# If mrs is not specified, do decoding against neurosynth database
if not mrs:
result = self.decoder.decode(images, save=outfile)
# If mrs is specified, do decoding against custom set of images
else:
# This is akin to traditional neurosynth method - pearson's r correlation
imgs_to_compare = imageutils.load_imgs(mrs,self.masker)
imgs_to_decode = imageutils.load_imgs(images,self.masker)
x, y = imgs_to_compare.astype(float),imgs_to_decode.astype(float)
x, y = x - x.mean(0), y - y.mean(0)
x, y = x / np.sqrt((x ** 2).sum(0)), y / np.sqrt((y ** 2).sum(0))
result = np.around(x.T.dot(y).T,round)
features = [os.path.basename(m) for m in mrs]
rownames = [os.path.basename(m) for m in images]
df = pd.DataFrame(result,columns=features)
df.index = rownames
df.to_csv(outfile,sep="\t")
return result
def decode(self, images, outfile, mrs=None, round=4):
if not self.decoder:
self.decoder = decode.Decoder(self.db)
# If mrs is not specified, do decoding against neurosynth database
if not mrs:
result = self.decoder.decode(images, save=outfile)
# If mrs is specified, do decoding against custom set of images
else:
# This is akin to traditional neurosynth method - pearson's r correlation
imgs_to_compare = imageutils.load_imgs(mrs, self.masker)
imgs_to_decode = imageutils.load_imgs(images, self.masker)
x, y = imgs_to_compare.astype(float), imgs_to_decode.astype(float)
x, y = x - x.mean(0), y - y.mean(0)
x, y = x / np.sqrt((x ** 2).sum(0)), y / np.sqrt((y ** 2).sum(0))
result = np.around(x.T.dot(y).T, round)
features = [os.path.basename(m) for m in mrs]
rownames = [os.path.basename(m) for m in images]
df = pd.DataFrame(result, columns=features)
df.index = rownames
df.to_csv(outfile, sep="\t")
return result
from neurosynth.base.dataset import Dataset
import neurosynth.base.imageutils as it
dataset = Dataset.load("../data/datasets/abs_topics_filt.pkl")
print "Filtering voxels..."
data = dataset.image_table.data.toarray()
voxel_mask = data.mean(axis=1) > 0.005
img = it.load_imgs('../masks/ward/30.nii.gz', dataset.masker)
good_voxels = img[voxel_mask]
it.save_img(good_voxels, "../masks/ward/30_masked.nii.gz", dataset.masker)
# removes parcels below some size and reorders in order
# while also outputting the region and community number
from neurosynth.base.mask import Masker
from neurosynth.base import imageutils
import numpy as np
import csv
min_vox = 300
file = '../masks/Andy/aal_MNI_V4.nii'
outfile = '../masks/Andy/aal_MNI_V4_' + str(min_vox) + '.nii'
# Load image with masker
masker = Masker(file)
img = imageutils.load_imgs(file, masker)
# How many levels in the original image
print "Original shape:"
print np.bincount([int(vox) for vox in img]).shape
# Get how many voxels per level and calc those that pass min_vox
count = np.bincount(img.astype('int').squeeze())
non_0_ix = np.where(count >= min_vox)[0]
zero_ix = np.where(count < min_vox)[0]
# Remove those not in a good community
bad = list(set(zero_ix))
# Remove
for value in bad:
dat.fullpath = '/Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/Friend.nii'; write(dat) # 2) Reorient using FSL - Unix fslreorient2std Friend Friend_Or # 3) Coregister to 2mm MNI space - Unix /usr/local/fsl/bin/flirt -in /Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/Friend_Or.nii.gz -ref /usr/local/fsl/data/standard/MNI152_T1_2mm_brain -out /Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/Friend_Or_Mni.nii.gz -omat /Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/Friend_Or_Mni.mat -bins 256 -cost corratio -searchrx -90 90 -searchry -90 90 -searchrz -90 90 -dof 12 -interp trilinear # 4) Decode - Python DATASET_FILE = '/Users/lukechang/Dropbox/Github/neurosynth/topics.pkl' PREFIX = '/Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/' INFILE = 'Friend_Or_Mni.nii.gz' dataset = Dataset.load(DATASET_FILE) decoder = decode.Decoder(dataset) #takes awhile to load, should only do this once. img = imageutils.load_imgs(PREFIX + INFILE, decoder.mask) result = decoder.decode(img) np.savetxt(PREFIX + 'Friend_Decoded.txt', result) # 5) Threshold at .001 - unix fslmaths Friend_Or_Mni -thr 3 Friend_Or_Mni_001 # 6) Decode thresholded map - python DATASET_FILE = '/Users/lukechang/Dropbox/Github/neurosynth/topics.pkl' PREFIX = '/Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/' INFILE = 'Friend_Or_Mni_001.nii.gz' dataset = Dataset.load(DATASET_FILE) decoder = decode.Decoder(dataset) #takes awhile to load, should only do this once. img = imageutils.load_imgs(PREFIX + INFILE, decoder.mask) result = decoder.decode(img) np.savetxt(PREFIX + 'Friend_001_Decoded.txt', result)
