def rawniml2string(p, form='text'):
if type(p) is list:
nb = '\n'.encode()
return nb.join(rawniml2string(v, form) for v in p)
if not form in ['text', 'binary', 'base64']:
raise ValueError("Illegal form %s" % form)
q = p.copy() # make a shallow copy
if 'nodes' in q:
s_body = rawniml2string(q.pop('nodes'), form) # recursion
else:
data = q.pop('data')
data = types.nimldataassupporteddtype(
data) # ensure the data format is supported by NIML
s_body = _data2string(data, form)
if form == 'text':
q.pop('ni_form', None) # defaults to text, remove if already there
else:
byteorder = types.data2ni_form(data, form)
if byteorder:
q['ni_form'] = byteorder
# remove some unncessary fields
for f in ['vec_typ', 'vec_len', 'vec_num']:
q.pop(f, None)
s_name = q.pop('name', None).encode()
s_header = _header2string(q)
d = map(lambda x: x.encode(), ['<', '\n', ' >', '</', '>'])
return b''.join(
(d[0], s_name, d[1], s_header, d[2], s_body, d[3], s_name, d[4]))
def rawniml2string(p, form='text'):
if type(p) is list:
nb = '\n'.encode()
return nb.join(rawniml2string(v, form) for v in p)
if not form in ['text', 'binary', 'base64']:
raise ValueError("Illegal form %s" % form)
q = p.copy() # make a shallow copy
if 'nodes' in q:
s_body = rawniml2string(q.pop('nodes'), form) # recursion
else:
data = q.pop('data')
data = types.nimldataassupporteddtype(data) # ensure the data format is supported by NIML
s_body = _data2string(data, form)
if form == 'text':
q.pop('ni_form', None) # defaults to text, remove if already there
else:
byteorder = types.data2ni_form(data, form)
if byteorder:
q['ni_form'] = byteorder
# remove some unncessary fields
for f in ['vec_typ', 'vec_len', 'vec_num']:
q.pop(f, None)
s_name = q.pop('name', None).encode()
s_header = _header2string(q)
d = map(lambda x:x.encode(), ['<', '\n', ' >', '</', '>'])
return b''.join((d[0], s_name, d[1], s_header, d[2], s_body, d[3], s_name, d[4]))
def _dset2rawniml_complete(r):
'''adds any missing information and ensures data is formatted properly'''
# if data is a list of strings, join it and store it as a string
# otherwise leave data untouched
if types.numpy_data_isstring(r['data']):
r['data'] = list(r['data'])
while True:
data = r['data']
tp = type(data)
if types.numpy_data_isstring(r['data']):
r['data'] = list(r['data'])
elif tp is list:
r['data'] = ";".join(data)
else:
break # we're done
if issubclass(tp, basestring):
r['ni_dimen'] = '1'
r['ni_type'] = 'String'
elif tp is np.ndarray:
data = types.nimldataassupporteddtype(data)
if len(data.shape) == 1:
data = np.reshape(data, (data.shape[0], 1))
r['data'] = data # ensure we store a supported type
nrows, ncols = data.shape
r['ni_dimen'] = str(nrows)
tpstr = types.numpy_type2name(data.dtype)
r['ni_type'] = '%d*%s' % (ncols, tpstr) if nrows > 1 else tpstr
else:
raise TypeError('Illegal type %r in %r' % (tp, data))
if not 'name' in r:
r['name'] = 'AFNI_atr'
return r
def _dset2rawniml_complete(r):
'''adds any missing information and ensures data is formatted properly'''
# if data is a list of strings, join it and store it as a string
# otherwise leave data untouched
if types.numpy_data_isstring(r['data']):
r['data'] = list(r['data'])
while True:
data = r['data']
tp = type(data)
if types.numpy_data_isstring(r['data']):
r['data'] = list(r['data'])
elif tp is list:
if all(isinstance(d, basestring) for d in data):
r['data'] = ";".join(data)
else:
tp = 'mixed'
break
else:
break # we're done
if tp == 'mixed':
#data = [types.nimldataassupporteddtype(d) for d in data]
#r['data'] = data
nrows, ncols = _dset_nrows_ncols(data)
r['ni_dimen'] = str(nrows)
tpstrs = []
for d in data:
if isinstance(d, basestring) or \
(type(d) is list and
all(isinstance(di, basestring) for di in d)):
tpstr = 'String'
elif isinstance(d, np.ndarray):
tpstr = types.numpy_type2name(d.dtype)
else:
raise ValueError('unrecognized type %s' % type(d))
tpstrs.append(tpstr)
r['ni_type'] = ','.join(tpstrs)
elif issubclass(tp, basestring):
r['ni_dimen'] = '1'
r['ni_type'] = 'String'
elif tp is np.ndarray:
data = types.nimldataassupporteddtype(data)
if len(data.shape) == 1:
data = np.reshape(data, (data.shape[0], 1))
r['data'] = data # ensure we store a supported type
nrows, ncols = data.shape
r['ni_dimen'] = str(nrows)
tpstr = types.numpy_type2name(data.dtype)
r['ni_type'] = '%d*%s' % (ncols, tpstr) if nrows > 1 else tpstr
elif not data is None:
raise TypeError('Illegal type %r in %r' % (tp, data))
if not 'name' in r:
r['name'] = 'AFNI_atr'
return r
def rawniml2string(p, form='text'):
'''Converts a raw NIML element to string representation
Parameters
----------
niml: dict
Raw NIML element
form: 'text', 'binary', 'base64'
Output form of niml
Returns
-------
s: bytearray
String representation of niml in output form 'form'.
'''
if type(p) is list:
nb = '\n'.encode()
return nb.join(rawniml2string(v, form) for v in p)
if not form in ['text', 'binary', 'base64']:
raise ValueError("Illegal form %s" % form)
q = p.copy() # make a shallow copy
has_body = True
if 'nodes' in q:
s_body = rawniml2string(q.pop('nodes'), form) # recursion
elif 'data' in q:
data = q.pop('data')
data = types.nimldataassupporteddtype(
data) # ensure the data format is supported by NIML
s_body = _data2string(data, form)
if form == 'text':
q.pop('ni_form', None) # defaults to text, remove if already there
else:
byteorder = types.data2ni_form(data, form)
if byteorder:
q['ni_form'] = byteorder
# remove some unncessary fields
for f in ['vec_typ', 'vec_len', 'vec_num']:
q.pop(f, None)
else:
has_body = False
s_name = q.pop('name', None).encode()
s_header = _header2string(q)
if has_body:
delim = ['<', '\n', ' >', '</', '>']
values = [s_name, s_header, s_body, s_name]
else:
delim = ['<', '\n', '/>']
values = [s_name, s_header]
delim_enc = map(lambda x: x.encode(), delim)
n_delim = len(delim_enc)
assert (n_delim == len(values) + 1)
# zip with unequal length
elems = []
for i in xrange(n_delim):
elems.append(delim_enc[i])
if i + 1 < n_delim:
# one element less than the number of delimeters
elems.append(values[i])
return b''.join(elems)
def rawniml2string(p, form='text'):
'''Converts a raw NIML element to string representation
Parameters
----------
niml: dict
Raw NIML element
form: 'text', 'binary', 'base64'
Output form of niml
Returns
-------
s: bytearray
String representation of niml in output form 'form'.
'''
if type(p) is list:
nb = '\n'.encode()
return nb.join(rawniml2string(v, form) for v in p)
if not form in ['text', 'binary', 'base64']:
raise ValueError("Illegal form %s" % form)
q = p.copy() # make a shallow copy
has_body = True
if 'nodes' in q:
s_body = rawniml2string(q.pop('nodes'), form) # recursion
elif 'data' in q:
data = q.pop('data')
data = types.nimldataassupporteddtype(data) # ensure the data format is supported by NIML
s_body = _data2string(data, form)
if form == 'text':
q.pop('ni_form', None) # defaults to text, remove if already there
else:
byteorder = types.data2ni_form(data, form)
if byteorder:
q['ni_form'] = byteorder
# remove some unncessary fields
for f in ['vec_typ', 'vec_len', 'vec_num']:
q.pop(f, None)
else:
has_body = False
s_name = q.pop('name', None).encode()
s_header = _header2string(q)
if has_body:
delim = ['<', '\n', ' >', '</', '>']
values = [s_name, s_header, s_body, s_name]
else:
delim = ['<', '\n', '/>']
values = [s_name, s_header]
delim_enc = map(lambda x: x.encode(), delim)
n_delim = len(delim_enc)
assert (n_delim == len(values) + 1)
# zip with unequal length
elems = []
for i in xrange(n_delim):
elems.append(delim_enc[i])
if i + 1 < n_delim:
# one element less than the number of delimeters
elems.append(values[i])
return b''.join(elems)
def _dset2rawniml_complete(r):
'''adds any missing information and ensures data is formatted properly'''
# if data is a list of strings, join it and store it as a string
# otherwise leave data untouched
if types.numpy_data_isstring(r['data']):
r['data'] = list(r['data'])
while True:
data = r['data']
tp = type(data)
if types.numpy_data_isstring(r['data']):
r['data'] = list(r['data'])
elif tp is list:
if all(isinstance(d, basestring) for d in data):
r['data'] = ";".join(data)
else:
tp = 'mixed'
break
else:
break # we're done
if tp == 'mixed':
#data = [types.nimldataassupporteddtype(d) for d in data]
#r['data'] = data
nrows, ncols = _dset_nrows_ncols(data)
r['ni_dimen'] = str(nrows)
tpstrs = []
for d in data:
if isinstance(d, basestring) or \
(type(d) is list and
all(isinstance(di, basestring) for di in d)):
tpstr = 'String'
elif isinstance(d, np.ndarray):
tpstr = types.numpy_type2name(d.dtype)
else:
raise ValueError('unrecognized type %s' % type(d))
tpstrs.append(tpstr)
r['ni_type'] = ','.join(tpstrs)
elif issubclass(tp, basestring):
r['ni_dimen'] = '1'
r['ni_type'] = 'String'
elif tp is np.ndarray:
data = types.nimldataassupporteddtype(data)
if len(data.shape) == 1:
data = np.reshape(data, (data.shape[0], 1))
r['data'] = data # ensure we store a supported type
nrows, ncols = data.shape
r['ni_dimen'] = str(nrows)
tpstr = types.numpy_type2name(data.dtype)
r['ni_type'] = '%d*%s' % (ncols, tpstr) if nrows > 1 else tpstr
elif not data is None:
raise TypeError('Illegal type %r in %r' % (tp, data))
if not 'name' in r:
r['name'] = 'AFNI_atr'
return r
def _dset2rawniml_complete(r):
"""adds any missing information and ensures data is formatted properly"""
# if data is a list of strings, join it and store it as a string
# otherwise leave data untouched
if types.numpy_data_isstring(r["data"]):
r["data"] = list(r["data"])
while True:
data = r["data"]
tp = type(data)
if types.numpy_data_isstring(r["data"]):
r["data"] = list(r["data"])
elif tp is list:
if all(isinstance(d, basestring) for d in data):
r["data"] = ";".join(data)
else:
tp = "mixed"
break
else:
break # we're done
if tp == "mixed":
# data = [types.nimldataassupporteddtype(d) for d in data]
# r['data'] = data
nrows, ncols = _dset_nrows_ncols(data)
r["ni_dimen"] = str(nrows)
tpstrs = []
for d in data:
if isinstance(d, basestring) or (type(d) is list and all(isinstance(di, basestring) for di in d)):
tpstr = "String"
elif isinstance(d, np.ndarray):
tpstr = types.numpy_type2name(d.dtype)
else:
raise ValueError("unrecognized type %s" % type(d))
tpstrs.append(tpstr)
r["ni_type"] = ",".join(tpstrs)
elif issubclass(tp, basestring):
r["ni_dimen"] = "1"
r["ni_type"] = "String"
elif tp is np.ndarray:
data = types.nimldataassupporteddtype(data)
if len(data.shape) == 1:
data = np.reshape(data, (data.shape[0], 1))
r["data"] = data # ensure we store a supported type
nrows, ncols = data.shape
r["ni_dimen"] = str(nrows)
tpstr = types.numpy_type2name(data.dtype)
r["ni_type"] = "%d*%s" % (ncols, tpstr) if nrows > 1 else tpstr
elif data is not None:
raise TypeError("Illegal type %r in %r" % (tp, data))
if not "name" in r:
r["name"] = "AFNI_atr"
return r
