def read_hdf5_arc(file_name):
"""reads a .hdf file and returns data contents mapped in a dict
:type file_name: str
:param file_name: path to the file to read
:returns: ndarray: raw data [f32], float: sampling rate in Hz,
or None, None on read error
"""
data, srate = None, None
with openFile(file_name, 'r') as arc:
for node in arc:
# XXX: any more identifiers we used or the data in an archive?!
if node._v_name.lower() in ['data', 'x']:
data = sp.asanyarray(node.read())
try:
data = data.astype(sp.float32)
if data.shape[0] <= data.shape[1]:
data = data.T.copy()
except:
data = None
if node._v_name.lower() == 'srate':
try:
srate = float(node.read())
except:
srate = None
return data, srate
def create_hdf5_arc(file_name, rdata, srate=1000.0, **kwargs):
"""creates a valid hdf5 archive for :rdata:
:type file_name: str
:param file_name: path to the file to write
:type rdata: ndarray
:param rdata: raw data array [samples, channels] castable to f32
:type srate: float
:param srate: sampling rate of rdata in Hz
Default=1000.0
:returns: True on success, False else
"""
with openFile(file_name, 'w') as arc:
# XXX: we go with 'data' here
try:
data = sp.asanyarray(rdata, dtype=sp.float32)
if data.shape[0] <= rdata.shape[1]:
data = data.T
arc.createArray(arc.root, 'data', data)
arc.createArray(arc.root, 'srate', srate)
for k, v in kwargs.iteritems():
arc.createArray(arc.root, str(k), v)
return True
except:
return False
def read_hdf5_arc(file_name):
"""reads a .hdf file and returns data contents mapped in a dict
:type file_name: str
:param file_name: path to the file to read
:returns: ndarray: raw data [f32], float: sampling rate in Hz,
or None, None on read error
"""
data, srate = None, None
with openFile(file_name, 'r') as arc:
for node in arc:
# XXX: any more identifiers we used or the data in an archive?!
if node._v_name.lower() in ['data', 'x']:
data = sp.asanyarray(node.read())
try:
data = data.astype(sp.float32)
if data.shape[0] <= data.shape[1]:
data = data.T.copy()
except:
data = None
if node._v_name.lower() == 'srate':
try:
srate = float(node.read())
except:
srate = None
return data, srate
def create_hdf5_arc(file_name, rdata, srate=1000.0, **kwargs):
"""creates a valid hdf5 archive for :rdata:
:type file_name: str
:param file_name: path to the file to write
:type rdata: ndarray
:param rdata: raw data array [samples, channels] castable to f32
:type srate: float
:param srate: sampling rate of rdata in Hz
Default=1000.0
:returns: True on success, False else
"""
with openFile(file_name, 'w') as arc:
# XXX: we go with 'data' here
try:
data = sp.asanyarray(rdata, dtype=sp.float32)
if data.shape[0] <= rdata.shape[1]:
data = data.T
arc.createArray(arc.root, 'data', data)
arc.createArray(arc.root, 'srate', srate)
for k, v in kwargs.iteritems():
arc.createArray(arc.root, str(k), v)
return True
except:
return False
def __coerce__(self, other):
try:
other = sp.asanyarray(other)
if other.shape == self.shape:
return (self.toarray(), other)
else:
return None
except:
return None
def __coerce__(self, other):
try:
other = sp.asanyarray(other)
if other.shape == self.shape or sp.isscalar(other):
return (self.toarray(), other)
else:
return NotImplemented
except:
return NotImplemented
def __coerce__(self, other):
try:
other = sp.asanyarray(other)
if other.shape == self.shape:
return (self.toarray(), other)
else:
return None
except:
return None
def __coerce__(self, other):
try:
other = sp.asanyarray(other)
if other.shape == self.shape or sp.isscalar(other):
return (self.toarray(), other)
else:
return NotImplemented
except:
return NotImplemented
def __mul__(self, other):
if sp.isscalar(other):
return simple_diag_matrix(self.diag * other)
try:
other = sp.asanyarray(other)
if other.shape == self.shape:
return simple_diag_matrix(self.diag * other.diagonal())
return self.toarray() * other
except:
return NotImplemented
def __mul__(self, other):
if sp.isscalar(other):
return simple_diag_matrix(self.diag * other)
try:
other = sp.asanyarray(other)
if other.shape == self.shape:
return simple_diag_matrix(self.diag * other.diagonal())
return self.toarray() * other
except:
return NotImplemented
def __init__(self, value):
"""
:type value: scalar dtype
:param value: single digit value
"""
super(MRScalar, self).__init__()
value_ = value
if sp.isscalar(value_):
value_ = sp.asanyarray(value_)
try:
assert value_.ndim == 0
except:
raise ValueError('%s is not a scalar!' % value)
self._value = value_
def __init__(self, value):
"""
:type value: scalar dtype
:param value: single digit value
"""
super(MRScalar, self).__init__()
value_ = value
if sp.isscalar(value_):
value_ = sp.asanyarray(value_)
try:
assert value_.ndim == 0
except:
raise ValueError('%s is not a scalar!' % value)
self._value = value_
def append_data_peaks(self, data, force=False):
"""append bin(s) calculated from a strip of data
with this method the data is first queried for peaks. this should
reduce the noise/smoothness of the histogram as observed from the
amplitude distribution of the pure signal.
:type data: ndarray
:param data: the data to generate the bin(s) to append from
:type force: bool
:param force: if True, immediately start a new bin before calculation
"""
# check data
data_ = sp.asanyarray(data)
if data.ndim < 2:
data_ = sp.atleast_2d(data_)
if data_.shape[0] < data_.shape[1]:
data_ = data_.T
nsmpl, nchan = data_.shape
if nchan != self._nchan:
raise ValueError('data has channel count %s, expected %s' %
(nchan, self._nchan))
# generate bin set
bin_set = [0]
if self._cur_bin_smpl != 0:
bin_set.append(self._bin_size - self._cur_bin_smpl)
while bin_set[-1] < nsmpl:
bin_set.append(bin_set[-1] + self._bin_size)
if bin_set[-1] > nsmpl:
bin_set[-1] = nsmpl
# process bins
idx = 1
while idx < len(bin_set):
data_bin = data_[bin_set[idx - 1]:bin_set[idx], :]
for c in xrange(self._nchan):
self._cur_bin[c] += sp.histogram(data_bin[:, c],
bins=self._ampl_range)[0]
self._cur_bin_smpl += data_bin.shape[0]
if self._cur_bin_smpl == self._bin_size:
self.append_bin(self._cur_bin)
self._cur_bin[:] = 0
self._cur_bin_smpl = 0
idx += 1
def append_bin(self, bin):
"""append an AmplHistBin instance
:type bin: ndarray like
:param bin: the amplHistBin to append
"""
# checks
bin_ = sp.asanyarray(bin)
if bin_.shape != self._cur_bin.shape:
raise ValueError('shape does not match! expected %s, got %s' %
(self._cur_bin.shape, bin_.shape))
if bin_.sum() == 0:
print '!!appending zero bin!!'
# append bin
self._hist_data.append(bin_)
def append_data_peaks(self, data, force=False):
"""append bin(s) calculated from a strip of data
with this method the data is first queried for peaks. this should
reduce the noise/smoothness of the histogram as observed from the
amplitude distribution of the pure signal.
:type data: ndarray
:param data: the data to generate the bin(s) to append from
:type force: bool
:param force: if True, immediately start a new bin before calculation
"""
# check data
data_ = sp.asanyarray(data)
if data.ndim < 2:
data_ = sp.atleast_2d(data_)
if data_.shape[0] < data_.shape[1]:
data_ = data_.T
nsmpl, nchan = data_.shape
if nchan != self._nchan:
raise ValueError('data has channel count %s, expected %s' %
(nchan, self._nchan))
# generate bin set
bin_set = [0]
if self._cur_bin_smpl != 0:
bin_set.append(self._bin_size - self._cur_bin_smpl)
while bin_set[-1] < nsmpl:
bin_set.append(bin_set[-1] + self._bin_size)
if bin_set[-1] > nsmpl:
bin_set[-1] = nsmpl
# process bins
idx = 1
while idx < len(bin_set):
data_bin = data_[bin_set[idx - 1]:bin_set[idx], :]
for c in xrange(self._nchan):
self._cur_bin[c] += sp.histogram(data_bin[:, c],
bins=self._ampl_range)[0]
self._cur_bin_smpl += data_bin.shape[0]
if self._cur_bin_smpl == self._bin_size:
self.append_bin(self._cur_bin)
self._cur_bin[:] = 0
self._cur_bin_smpl = 0
idx += 1
def append_bin(self, bin):
"""append an AmplHistBin instance
:type bin: ndarray like
:param bin: the amplHistBin to append
"""
# checks
bin_ = sp.asanyarray(bin)
if bin_.shape != self._cur_bin.shape:
raise ValueError('shape does not match! expected %s, got %s' %
(self._cur_bin.shape, bin_.shape))
if bin_.sum() == 0:
print '!!appending zero bin!!'
# append bin
self._hist_data.append(bin_)
def dict_list2arr(in_dict):
"""converts all lists in a dictionary to `ndarray`. [in place!]
If there are instances of dict found as values, this function will be
applied recursively.
:Parameters:
in_dict : dict
"""
try:
for k in in_dict:
if isinstance(in_dict[k], list):
in_dict[k] = sp.asanyarray(in_dict[k])
elif isinstance(in_dict[k], dict):
dict_list2arr(in_dict[k])
else:
pass
finally:
return in_dict
def dict_list2arr(in_dict):
"""converts all lists in a dictionary to `ndarray`. [in place!]
If there are instances of dict found as values, this function will be
applied recursively.
:Parameters:
in_dict : dict
"""
try:
for k in in_dict:
if isinstance(in_dict[k], list):
in_dict[k] = sp.asanyarray(in_dict[k])
elif isinstance(in_dict[k], dict):
dict_list2arr(in_dict[k])
else:
pass
finally:
return in_dict
def __init__(self, value, header=None):
"""
:type value: ndarray
:param value: single digit _value
:type header: list
:param header: list of str with as many entries as columns in _value
"""
super(MRTable, self).__init__()
val = sp.asanyarray(value)
if val.dtype == object:
raise ValueError('%s is not a compatible type: %s' %
(value, value.__class__.__name__))
if val.ndim != 2:
raise ValueError('%s is not ndim==2: _value.ndim==%s' % val.ndim)
self._value = val
self.header = None
if header is not None:
if len(header) == self._value.shape[1]:
self.header = map(str, header)
def __init__(self, value, header=None):
"""
:type value: ndarray
:param value: single digit _value
:type header: list
:param header: list of str with as many entries as columns in _value
"""
super(MRTable, self).__init__()
val = sp.asanyarray(value)
if val.dtype == object:
raise ValueError('%s is not a compatible type: %s' %
(value, value.__class__.__name__))
if val.ndim != 2:
raise ValueError('%s is not ndim==2: _value.ndim==%s' % val.ndim)
self._value = val
self.header = None
if header is not None:
if len(header) == self._value.shape[1]:
self.header = map(str, header)
def __init__(self, diag, dtype=None):
diag = sp.asanyarray(diag, dtype=dtype)
self.dtype = diag.dtype
assert diag.ndim == 1
self.diag = diag
self.shape = (diag.shape[0], diag.shape[0])
def __init__(self, diag, dtype=None):
diag = sp.asanyarray(diag, dtype=dtype)
self.dtype = diag.dtype
assert diag.ndim == 1
self.diag = diag
self.shape = (diag.shape[0], diag.shape[0])