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 _data2string(data, form):
'''Converts a data element to binary, text or base64 representation'''
if isinstance(data, basestring):
return ('"%s"' % encode_escape(data)).encode()
elif type(data) is np.ndarray:
if form == 'text' or types.numpy_data_isstring(data):
f = types.numpy_data2printer(data)
nrows, ncols = data.shape
return _TEXT_ROWSEP.join([
_TEXT_COLSEP.join([f(data[row, col]) for col in xrange(ncols)])
for row in xrange(nrows)
]).encode()
elif form == 'binary':
data_reshaped = data.reshape((data.shape[1], data.shape[0]))
r = data_reshaped.tostring()
debug(
'NIML', 'Binary encoding (len %d -> %d): [%s]' %
(data_reshaped.size, len(r), _partial_string(r, 0)))
return r
elif form == 'base64':
data_reshaped = data.reshape((data.shape[1], data.shape[0]))
r = base64.b64encode(data_reshaped.tostring())
debug('NIML', 'Encoding ok: [%s]', _partial_string(r, 0))
return r
else:
raise ValueError("illegal format %s" % format)
elif type(data) is list:
# mixed types, each column in its own container
# always use text output format, even if requested form is binary of base64
ncols = len(data)
if ncols == 0:
return "".encode()
else:
nrows = len(data[0])
# separate formatter functions for each column
# if list of strings then take first element of the list to get a string formattr
# else use the entire np array to get a numeric formatter
fs = [
types.numpy_data2printer(d[0] if type(d) is list else d)
for d in data
]
return _TEXT_ROWSEP.join([
_TEXT_COLSEP.join(
[fs[col](data[col][row]) for col in xrange(ncols)])
for row in xrange(nrows)
]).encode()
else:
raise TypeError("Unknown type %r" % type(data))
def _data2string(data, form):
'''Converts a data element to binary, text or base64 representation'''
if isinstance(data, basestring):
return ('"%s"' % encode_escape(data)).encode()
elif type(data) is np.ndarray:
if form == 'text' or types.numpy_data_isstring(data):
f = types.numpy_data2printer(data)
nrows, ncols = data.shape
return _TEXT_ROWSEP.join([_TEXT_COLSEP.join([f(data[row, col])
for col in xrange(ncols)])
for row in xrange(nrows)]).encode()
elif form == 'binary':
data_reshaped = data.reshape((data.shape[1], data.shape[0]))
r = data_reshaped.tostring()
debug('NIML', 'Binary encoding (len %d -> %d): [%s]' %
(data_reshaped.size, len(r), _partial_string(r, 0)))
return r
elif form == 'base64':
data_reshaped = data.reshape((data.shape[1], data.shape[0]))
r = base64.b64encode(data_reshaped.tostring())
debug('NIML', 'Encoding ok: [%s]', _partial_string(r, 0))
return r
else:
raise ValueError("illegal format %s" % format)
elif type(data) is list:
# mixed types, each column in its own container
# always use text output format, even if requested form is binary of base64
ncols = len(data)
if ncols == 0:
return "".encode()
else:
nrows = len(data[0])
# separate formatter functions for each column
# if list of strings then take first element of the list to get a string formattr
# else use the entire np array to get a numeric formatter
fs = [types.numpy_data2printer(d[0] if type(d) is list else d) for d in data]
return _TEXT_ROWSEP.join([_TEXT_COLSEP.join([fs[col](data[col][row])
for col in xrange(ncols)])
for row in xrange(nrows)]).encode()
else:
raise TypeError("Unknown type %r" % type(data))
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 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
