def bss_write_pvalue_as_log_color(nimg_datain, nimg_dataout):
nimg_data_type = NimgDataio.validatetype(nimg_datain)
if nimg_data_type == 'surface':
s1 = dfsio.readdfs(nimg_datain)
if not hasattr(s1, 'attributes'):
sys.stdout.write('Error: The surface ' + nimg_datain +
' is missing p-value attributes.\n')
return
if s1.attributes is None:
sys.stdout.write('Error: The surface ' + nimg_datain +
' is missing p-value attributes.\n')
return
log_pvalues = s1.attributes
s1.vColor, pex, cmap = cm.Colormap.log_pvalues_to_rgb(log_pvalues)
dfsio.writedfs(nimg_dataout, s1)
elif nimg_data_type == 'nifti_image':
sys.stdout.write('Not implemented.\n')
log_pvalues = NimgDataio.read(nimg_datain, attributes_only=True)
cdict_pvalues, pex, cmap = cm.Colormap.log_pvalues_to_rgb(log_pvalues)
LUT = cmap._lut[0:256, 0:3]
fid = open(nimg_dataout, 'wt')
for i in range(0, len(LUT)):
fid.write("{0:f} {1:f} {2:f}\n".format(float(LUT[i, 0]),
float(LUT[i, 1]),
float(LUT[i, 2])))
fid.close()
else:
raise TypeError(
'Error: Unsupported data type. Supported data types are: ' +
', '.join(NimgDataio.datatype.keys()) + '.\n')
sys.stdout.write('Done.\n')
def bss_write_corr_as_color(surfin, surfout):
s1 = dfsio.readdfs(surfin)
if not hasattr(s1, 'attributes'):
sys.stdout.write('Error: The surface ' + surfin + ' is missing p-value attributes.\n')
sys.exit(0)
s1.vColor, cex, cmap = cm.Colormap.correlation_to_rgb(s1.attributes)
# cm.Colormap.exportParaviewCmap(cdict_orig, surfout + '.xml')
dfsio.writedfs(surfout, s1)
return
def bss_mean_surface(args):
try:
# Parse the arguments
if args.txt: # If txt file present
fid = open(args.txt, 'rt')
filelist = [filename.rstrip('\n') for filename in fid.readlines()]
elif args.csv: # If csv file present
demographic_data = pandas.read_csv(args.csv[0])
# Iterate over all columns, and check if any of the values
# are NaN for missing data
# TODO: This code is repeated.
for columns in demographic_data.columns.values:
if len(np.where(demographic_data[columns].isnull())[0]) > 0:
sys.stdout.write(
'Error: Some data may be missing from the demographics file: '
+ args.csv[0] + '. ' +
'\nIf nothing seems wrong at the first glance and if its a csv file, please open it in a '
'\nplain text editor and check if there are missing values, rows, or columns.'
+ '. Will quit now.\n')
return
filelist = demographic_data[args.csv[1]]
# Read the file list and compute the average of the coordinates and attributes
surf_array = []
for fname in filelist:
surf_array.append(dfsio.readdfs(fname))
# Check if all surfaces have same number of vertices
nVertices = len(surf_array[0].vertices)
s1_average = surf_array[0]
for i in range(1, len(surf_array)):
if len(surf_array[i].vertices) == nVertices:
s1_average.vertices += surf_array[i].vertices
if surf_array[i].attributes.any():
s1_average.attributes += surf_array[i].attributes
else:
sys.stdout.write(
'Not all surfaces have the same number of vertices. Exiting...\n'
)
return
s1_average.vertices /= len(surf_array)
s1_average.attributes /= len(surf_array)
dfsio.writedfs(args.dfsfilename, s1_average)
except:
print "Something went wrong. Please send this error message to the developers." \
"\nUnexpected error:", sys.exc_info()[0]
print traceback.print_exc(file=sys.stdout)
def bss_resample_surface_to_target(srcfile, src_regfile, resampfile):
surf_src = dfsio.readdfs(srcfile)
surf_src_reg = dfsio.readdfs(src_regfile)
surf_src.uv = np.c_[surf_src.u, surf_src.v]
surf_src_reg.uv = np.c_[surf_src_reg.u, surf_src_reg.v]
sys.stdout.write("Resampling source to target...")
sys.stdout.write("x ")
res_x = griddata(surf_src.uv, surf_src.vertices[:, 0], surf_src_reg.uv, method='nearest')
sys.stdout.write("y ")
res_y = griddata(surf_src.uv, surf_src.vertices[:, 1], surf_src_reg.uv, method='nearest')
sys.stdout.write("z ")
res_z = griddata(surf_src.uv, surf_src.vertices[:, 2], surf_src_reg.uv, method='nearest')
sys.stdout.write("Done.\n")
res_vertices = np.zeros((surf_src_reg.vertices.shape[0], 3))
res_vertices[:, 0] = res_x
res_vertices[:, 1] = res_y
res_vertices[:, 2] = res_z
if len(surf_src.attributes) > 0:
sys.stdout.write("Resampling attributes to target ...")
res_attributes = griddata(surf_src.vertices, surf_src.attributes, res_vertices, method='nearest')
sys.stdout.write("Done.\n")
else:
res_attributes = []
if len(surf_src.labels) > 0:
sys.stdout.write("Resampling labels to target ...")
res_labels = griddata(surf_src.vertices, surf_src.labels, res_vertices, method='nearest')
sys.stdout.write("Done.\n")
else:
res_labels = []
class surf_resamp:
pass
surf_resamp.vertices = res_vertices
surf_resamp.faces = surf_src_reg.faces
surf_resamp.attributes = res_attributes
surf_resamp.labels = res_labels
dfsio.writedfs(resampfile, surf_resamp)
return None
def shape_fdr(shapein, shapeout, method):
rootnamein, extin = os.path.splitext(shapein)
rootnameout, extout = os.path.splitext(shapein)
if extin != extout:
sys.stdout.write('Error: Input and output extensions should be same.\n')
return
if extin == '.txt':
pvalues = np.loadtxt(shapein)
fdr_adjusted_pvalues = Stats_Multi_Comparisons.adjust(pvalues, method=method)
np.savetxt(shapeout, fdr_adjusted_pvalues)
elif extin == '.dfs':
s1 = dfsio.readdfs(shapein)
s1.attributes = Stats_Multi_Comparisons.adjust(s1.attributes, method=method)
dfsio.writedfs(shapeout, s1)
def bss_write_pvalue_as_log_color(surfin, surfout):
s1 = dfsio.readdfs(surfin)
if not hasattr(s1, 'attributes'):
sys.stdout.write('Error: The surface ' + surfin + ' is missing p-value attributes.\n')
sys.exit(0)
cmap = colormaps.Colormap('pvalue', s1.attributes)
rgb_list = cmap.get_rgb_list_from_attribute_list(s1.attributes)
s1.vColor = np.empty((3, len(s1.attributes)))
for idx, val in enumerate(rgb_list):
s1.vColor[:, idx] = [val[0], val[1], val[2]]
# s1.vColor[:, idx] = [abs(np.random.random()), abs(np.random.random()), abs(np.random.random())]
s1.vColor = np.ndarray.transpose(s1.vColor)
exportParaviewCmap(cmap.color_dict, surfout + '.xml')
dfsio.writedfs(surfout, s1)
def bss_write_pvalue_as_log_color(surfin, surfout):
s1 = dfsio.readdfs(surfin)
if not hasattr(s1, 'attributes'):
sys.stdout.write('Error: The surface ' + surfin +
' is missing p-value attributes.\n')
sys.exit(0)
cmap = colormaps.Colormap('pvalue', s1.attributes)
rgb_list = cmap.get_rgb_list_from_attribute_list(s1.attributes)
s1.vColor = np.empty((3, len(s1.attributes)))
for idx, val in enumerate(rgb_list):
s1.vColor[:, idx] = [val[0], val[1], val[2]]
# s1.vColor[:, idx] = [abs(np.random.random()), abs(np.random.random()), abs(np.random.random())]
s1.vColor = np.ndarray.transpose(s1.vColor)
exportParaviewCmap(cmap.color_dict, surfout + '.xml')
dfsio.writedfs(surfout, s1)
def shape_fdr(shapein, shapeout, method):
rootnamein, extin = os.path.splitext(shapein)
rootnameout, extout = os.path.splitext(shapein)
if extin != extout:
sys.stdout.write(
'Error: Input and output extensions should be same.\n')
return
if extin == '.txt':
pvalues = np.loadtxt(shapein)
fdr_adjusted_pvalues = Stats_Multi_Comparisons.adjust(pvalues,
method=method)
np.savetxt(shapeout, fdr_adjusted_pvalues)
elif extin == '.dfs':
s1 = dfsio.readdfs(shapein)
s1.attributes = Stats_Multi_Comparisons.adjust(s1.attributes,
method=method)
dfsio.writedfs(shapeout, s1)