def savanalysis(self, adataname, adatarray):
'''
prerequisite: accesstructure, mkanalysis
'''
m, n = adatarray.shape[0], adatarray.shape[1]
with open_file(self.analysispath / (self.analysisfolder + ".h5"),
'w') as f:
filters = Filters(complevel=5, complib='blosc')
acontainer = f.create_carray(f.root,
adataname,
Float64Atom(),
shape=(m, n),
filters=filters)
acontainer[:, :] = adatarray
# Create a table in the root directory and append data...
class About(IsDescription):
task = StringCol(len(self.task), pos=1) # N-character String
comment = StringCol(len(self.comment),
pos=2) # N-character String
tableroot = f.create_table(f.root, 'info', About,
"A table at root", Filters(1))
tableroot.append(
[(self.task, self.comment)]
) # , ("Mediterranean", 11, -1, 11*11, 11**2), ("Adriatic", 12, -2, 12*12, 12**2)])
return
def save(self, db):
"""Save the input data to disk.
Notes
-----
Saves predictions, measurements, observables, and prior_pops to the
HDF5 PyMC database.
"""
if db != "hdf5":
return
from tables import Float64Atom, Filters
compression = Filters(complevel=9, complib='blosc', shuffle=True)
F = self.mcmc.db._h5file
F.createCArray("/",
"predictions",
Float64Atom(),
self.predictions.shape,
filters=compression)
F.root.predictions[:] = self.predictions
F.createCArray("/",
"measurements",
Float64Atom(),
self.measurements.shape,
filters=compression)
F.root.measurements[:] = self.measurements
F.createCArray("/",
"uncertainties",
Float64Atom(),
self.uncertainties.shape,
filters=compression)
F.root.uncertainties[:] = self.uncertainties
F.createCArray("/",
"prior_pops",
Float64Atom(),
self.prior_pops.shape,
filters=compression)
F.root.prior_pops[:] = self.prior_pops
def create_correlation_matrix(infiles, roi, out_type, package):
import os
import numpy as np
import scipy.io as sio
import nibabel as nb
from nipype.utils.filemanip import split_filename, filename_to_list
for idx, fname in enumerate(filename_to_list(infiles)):
data = np.squeeze(nb.load(fname).get_data())
if idx == 0:
timeseries = data
else:
timeseries = np.vstack((timeseries, data))
roi_data = np.genfromtxt(roi)
if not len(roi_data.shape) == 2:
roi_data = roi_data[:, None]
corrmat = np.zeros((roi_data.shape[1], timeseries.shape[0]))
print timeseries.shape
for i in xrange(roi_data.shape[1]):
for j in xrange(timeseries.shape[0]):
r = np.corrcoef(timeseries[j, :], roi_data[:, i])[0][1]
corrmat[i, j] = np.sqrt(timeseries.shape[1] - 3) * 0.5 * np.log(
(1 + r) / (1 - r))
#corrmat = np.corrcoef(timeseries,roi_data.T)
print corrmat.shape
_, name, _ = split_filename(filename_to_list(infiles)[0])
if len(filename_to_list(infiles)) > 1:
name = 'combined_' + name
if 'mat' in out_type:
matfile = os.path.abspath(name + '.mat')
sio.savemat(matfile, {'corrmat': corrmat})
output = matfile
elif 'hdf5' in out_type:
hdf5file = os.path.abspath(name + '.hf5')
if package == 'h5py':
import h5py
f = h5py.File(hdf5file, 'w')
f.create_dataset('corrmat', data=corrmat, compression=5)
f.close()
else:
from tables import openFile, Float64Atom, Filters
h5file = openFile(hdf5file, 'w')
arr = h5file.createCArray(h5file.root,
'corrmat',
Float64Atom(),
corrmat.shape,
filters=Filters(complevel=5))
arr[:] = corrmat
h5file.close()
output = hdf5file
else:
raise Exception('Unknown output type')
return output
def create_correlation_matrix(infiles, out_type, package):
import os
import numpy as np
import scipy.io as sio
import nibabel as nb
from nipype.utils.filemanip import split_filename, filename_to_list
for idx, fname in enumerate(filename_to_list(infiles)):
data = np.squeeze(nb.load(fname).get_data())
if idx == 0:
timeseries = data
else:
timeseries = np.vstack((timeseries, data))
corrmat = np.corrcoef(timeseries)
_, name, _ = split_filename(filename_to_list(infiles)[0])
if len(filename_to_list(infiles)) > 1:
name = 'combined_' + name
if 'mat' in out_type:
matfile = os.path.abspath(name + '.mat')
sio.savemat(matfile, {'corrmat': corrmat})
output = matfile
elif 'hdf5' in out_type:
hdf5file = os.path.abspath(name + '.hf5')
if package == 'h5py':
import h5py
f = h5py.File(hdf5file, 'w')
f.create_dataset('corrmat', data=corrmat, compression=5)
f.close()
else:
from tables import openFile, Float64Atom, Filters
h5file = openFile(hdf5file, 'w')
arr = h5file.createCArray(h5file.root,
'corrmat',
Float64Atom(),
corrmat.shape,
filters=Filters(complevel=5))
arr[:] = corrmat
h5file.close()
output = hdf5file
else:
raise Exception('Unknown output type')
return output
def test_from_dtype_04(self):
atom1 = Atom.from_dtype(numpy.dtype('Float64'))
atom2 = Float64Atom(shape=(), dflt=0.0)
self.assertEqual(atom1, atom2)
self.assertEqual(str(atom1), str(atom2))