def to_dtype_dformat(data):
"""Transforms the given data parameter (string/ or sequence of string or
sequence of sequence of string/:obj:`DataType`) into a tuple of two
elements (:obj:`DataType`, :obj:`DataFormat`).
:param data: the data information to be transformed
:type data: str or seq<str> or seq<seq<str>>
:return: a tuple <:obj:`DataType`, :obj:`DataFormat`> for the given data
:rtype: tuple<:obj:`DataType`, :obj:`DataFormat`>
"""
import operator
dtype, dformat = data, DataFormat.Scalar
if isinstance(data, (str, unicode)):
dtype, dformat = from_dtype_str(data)
elif operator.isSequenceType(data):
dformat = DataFormat.OneD
dtype = data[0]
if type(dtype) == str:
dtype, dformat2 = from_dtype_str(dtype)
if dformat2 == DataFormat.OneD:
dformat = DataFormat.TwoD
elif operator.isSequenceType(dtype):
dformat = DataFormat.TwoD
dtype = dtype[0]
if type(dtype) == str:
dtype, _ = from_dtype_str(dtype)
dtype = DTYPE_MAP.get(dtype, DataType.Invalid)
return dtype, dformat
def _fitdata_fired(self,new):
from operator import isSequenceType,isMappingType
if self.model is not None:
if isSequenceType(new) and len(new) == 2:
kw={'x':new[0],'y':new[1]}
elif isSequenceType(new) and len(new) == 3:
kw={'x':new[0],'y':new[1],'weights':new[2]}
elif isMappingType(new):
kw = dict(new)
#add any missing pieces
for i,k in enumerate(('x','y','weights')):
if k not in new:
if self.model.fiteddata:
new[k] = self.model.fiteddata[i]
else:
raise ValueError('no pre-fitted data available')
elif new is True:
if self.model.fiteddata:
fd = self.model.fiteddata
kw= {'x':fd[0],'y':fd[1],'weights':fd[2]}
else:
raise ValueError('No data to fit')
else:
raise ValueError('unusable fitdata event input')
if 'fixedpars' not in kw:
kw['fixedpars'] = [tn.replace('fixfit_','') for tn in self.traits() if tn.startswith('fixfit_') if getattr(self,tn)]
try:
self.model.fitData(**kw)
self.updatetraitparams = True
self.lastfitfailure = None
except Exception,e:
self.lastfitfailure = e
def __init__(self, call, attribs=None, argfilter=None, expand_args=True,
copy_attribs=True):
"""Initialize
:Parameters:
expand_args : bool
Either to expand the output of looper into a list of arguments for
call
attribs : list of basestr
What attributes of call to store and return later on?
copy_attribs : bool
Force copying values of attributes
"""
self.call = call
"""Call which gets called in the harvester."""
if attribs is None:
attribs = []
if not isSequenceType(attribs):
raise ValueError, "'attribs' have to specified as a sequence."
if not (argfilter is None or isSequenceType(argfilter)):
raise ValueError, "'argfilter' have to be a sequence or None."
# now give it to me...
self.argfilter = argfilter
self.expand_args = expand_args
self.copy_attribs = copy_attribs
self.attribs = attribs
def test_isSequenceType(self):
self.failUnlessRaises(TypeError, operator.isSequenceType)
self.failUnless(operator.isSequenceType(dir()))
self.failUnless(operator.isSequenceType(()))
self.failUnless(operator.isSequenceType(xrange(10)))
self.failUnless(operator.isSequenceType('yeahbuddy'))
self.failIf(operator.isSequenceType(3))
def test_isSequenceType(self):
self.failUnlessRaises(TypeError, operator.isSequenceType)
self.failUnless(operator.isSequenceType(dir()))
self.failUnless(operator.isSequenceType(()))
self.failUnless(operator.isSequenceType(xrange(10)))
self.failUnless(operator.isSequenceType('yeahbuddy'))
self.failIf(operator.isSequenceType(3))
def to_dtype_dformat(data):
"""Transforms the given data parameter (string/ or sequence of string or
sequence of sequence of string/:obj:`DataType`) into a tuple of two
elements (:obj:`DataType`, :obj:`DataFormat`).
:param data: the data information to be transformed
:type data: :obj:`str` or seq<str> or seq<seq<str>>
:return: a tuple <:obj:`DataType`, :obj:`DataFormat`> for the given data
:rtype: tuple<:obj:`DataType`, :obj:`DataFormat`>
"""
import operator
dtype, dformat = data, DataFormat.Scalar
if isinstance(data, (str, unicode)):
dtype, dformat = from_dtype_str(data)
elif operator.isSequenceType(data):
dformat = DataFormat.OneD
dtype = data[0]
if isinstance(dtype, str):
dtype, dformat2 = from_dtype_str(dtype)
if dformat2 == DataFormat.OneD:
dformat = DataFormat.TwoD
elif operator.isSequenceType(dtype):
dformat = DataFormat.TwoD
dtype = dtype[0]
if isinstance(dtype, str):
dtype, _ = from_dtype_str(dtype)
dtype = DTYPE_MAP.get(dtype, DataType.Invalid)
return dtype, dformat
def __setitem__(self, index, W):
"""
Speed-up if x is a sparse matrix.
TODO: checks (first remove the data).
TODO: once we've got this working in all cases, should we submit to scipy?
"""
try:
i, j = index
except (ValueError, TypeError):
raise IndexError, "invalid index"
if isinstance(i, slice) and isinstance(j, slice) and\
(i.step is None) and (j.step is None) and\
(isinstance(W, sparse.lil_matrix) or isinstance(W, numpy.ndarray)):
rows = self.rows[i]
datas = self.data[i]
j0 = j.start
if isinstance(W, sparse.lil_matrix):
for row, data, rowW, dataW in izip(rows, datas, W.rows,
W.data):
jj = bisect.bisect(row, j0) # Find the insertion point
row[jj:jj] = [j0 + k for k in rowW]
data[jj:jj] = dataW
elif isinstance(W, ndarray):
nq = W.shape[1]
for row, data, rowW in izip(rows, datas, W):
jj = bisect.bisect(row, j0) # Find the insertion point
row[jj:jj] = range(j0, j0 + nq)
data[jj:jj] = rowW
elif isinstance(i, int) and isinstance(j,
(list, tuple, numpy.ndarray)):
if len(j) == 0:
return
row = dict(izip(self.rows[i], self.data[i]))
try:
row.update(dict(izip(j, W)))
except TypeError:
row.update(dict(izip(j, itertools.repeat(W))))
items = row.items()
items.sort()
row, data = izip(*items)
self.rows[i] = list(row)
self.data[i] = list(data)
elif isinstance(i, slice) and isinstance(j, int) and isSequenceType(W):
# This corrects a bug in scipy sparse matrix as of version 0.7.0, but
# it is not efficient!
for w, k in izip(W, xrange(*i.indices(self.shape[0]))):
sparse.lil_matrix.__setitem__(self, (k, j), w)
elif isinstance(i, int) and isinstance(
j, slice) and (isNumberType(W) and not isSequenceType(W)):
# this fixes a bug in scipy 0.7.1
sparse.lil_matrix.__setitem__(
self, index, [W] * len(xrange(*j.indices(self.shape[1]))))
elif isinstance(i, slice) and isinstance(j, slice) and isNumberType(W):
n = len(xrange(*i.indices(self.shape[0])))
m = len(xrange(*j.indices(self.shape[1])))
sparse.lil_matrix.__setitem__(self, index, W * numpy.ones((n, m)))
else:
sparse.lil_matrix.__setitem__(self, index, W)
def setup_analog_scan(self,
channels=(0, 1, 2, 3),
sweeps=-1,
rate=100,
gains=GAIN2_DIFF,
exttrig=0):
if self.scanning:
raise MeasurementComputingError, "cannot start new scan without stopping old one"
if not isSequenceType(channels):
channels = (channels, )
if not isSequenceType(gains):
gains = (gains, ) * len(channels)
if len(gains) != len(channels):
raise MeasurementComputingError, \
"gain list not compatible with channel list" + str(channels) + ":" + str(gains)
self.setup_gain_list(channels, gains)
timerPre, timerVal, setupTime, actRate = self.compute_timer_vals(
rate * len(channels))
self.actRate = actRate
if sweeps <= 0: # continuous scan !
blocking = self.DATA_SAMPLE_SIZE // len(channels)
if len(channels) * blocking != self.DATA_SAMPLE_SIZE:
raise MeasurementComputingError, \
"continuous scan channel count must be submultiple of %d" % self.DATA_SAMPLE_SIZE
tCount = self.DATA_SAMPLE_SIZE # each block is filled in continuous mode
scanmode = self.AD_CONT_MODE
elif sweeps == 1: # use AD_SINGLEEXEC mode for single sweep
tCount = len(channels)
scanmode = self.AD_SINGLE_MODE
else:
# round block count up to next block above requested samples
blocks = (len(channels) * sweeps + self.DATA_SAMPLE_SIZE -
1) // self.DATA_SAMPLE_SIZE
tCount = blocks * self.DATA_SAMPLE_SIZE
if tCount > self.MAX_STORED_SAMPLES:
raise MeasurementComputingError, \
"burst scan sample count must be < %d" % self.MAX_STORED_SAMPLES
scanmode = self.AD_BURST_MODE
self.burst_scan_count = tCount
self.burst_scan_blocks = blocks
tChigh = tCount >> 8
tClow = tCount & 255
self.write((self.CBAINSCAN, tClow, tChigh, timerVal + setupTime,
timerPre, scanmode
| exttrig))
self.continuous_scan_packet_index = 1
self.got_last_packet = 1
self.last_packet_time = 0.0 # long ago!
self.packet_dt = self.DATA_SAMPLE_SIZE / actRate
def merge_undo ( self, undo_item ):
""" Merges two undo items if possible.
"""
# Undo items are potentially mergeable only if they are of the same
# class and refer to the same object trait, so check that first:
if (isinstance( undo_item, self.__class__ ) and
(self.object is undo_item.object) and
(self.name == undo_item.name)):
v1 = self.new_value
v2 = undo_item.new_value
t1 = type( v1 )
if t1 is type( v2 ):
if t1 is str:
# Merge two undo items if they have new values which are
# strings which only differ by one character (corresponding
# to a single character insertion, deletion or replacement
# operation in a text editor):
n1 = len( v1 )
n2 = len( v2 )
n = min( n1, n2 )
i = 0
while (i < n) and (v1[i] == v2[i]):
i += 1
if v1[i + (n2 <= n1):] == v2[i + (n2 >= n1):]:
self.new_value = v2
return True
elif isSequenceType( v1 ):
# Merge sequence types only if a single element has changed
# from the 'original' value, and the element type is a
# simple Python type:
v1 = self.old_value
if isSequenceType( v1 ):
# Note: wxColour says it's a sequence type, but it
# doesn't support 'len', so we handle the exception
# just in case other classes have similar behavior:
try:
if len( v1 ) == len( v2 ):
diffs = 0
for i, item in enumerate( v1 ):
titem = type( item )
item2 = v2[i]
if ((titem not in SimpleTypes) or
(titem is not type( item2 )) or
(item != item2)):
diffs += 1
if diffs >= 2:
return False
self.new_value = v2
return True
except:
pass
elif t1 in NumericTypes:
# Always merge simple numeric trait changes:
self.new_value = v2
return True
return False
def merge_undo(self, undo_item):
""" Merges two undo items if possible.
"""
# Undo items are potentially mergeable only if they are of the same
# class and refer to the same object trait, so check that first:
if (
isinstance(undo_item, self.__class__)
and (self.object is undo_item.object)
and (self.name == undo_item.name)
):
v1 = self.new_value
v2 = undo_item.new_value
t1 = type(v1)
if t1 is type(v2):
if t1 is str:
# Merge two undo items if they have new values which are
# strings which only differ by one character (corresponding
# to a single character insertion, deletion or replacement
# operation in a text editor):
n1 = len(v1)
n2 = len(v2)
n = min(n1, n2)
i = 0
while (i < n) and (v1[i] == v2[i]):
i += 1
if v1[i + (n2 <= n1) :] == v2[i + (n2 >= n1) :]:
self.new_value = v2
return True
elif isSequenceType(v1):
# Merge sequence types only if a single element has changed
# from the 'original' value, and the element type is a
# simple Python type:
v1 = self.old_value
if isSequenceType(v1):
# Note: wxColour says it's a sequence type, but it
# doesn't support 'len', so we handle the exception
# just in case other classes have similar behavior:
try:
if len(v1) == len(v2):
diffs = 0
for i, item in enumerate(v1):
titem = type(item)
item2 = v2[i]
if (titem not in SimpleTypes) or (titem is not type(item2)) or (item != item2):
diffs += 1
if diffs >= 2:
return False
self.new_value = v2
return True
except:
pass
elif t1 in NumericTypes:
# Always merge simple numeric trait changes:
self.new_value = v2
return True
return False
def _flatten(seq):
from operator import isSequenceType
if not isSequenceType(seq): return seq
while isSequenceType(seq):
if type(item) in (TupleType, ListType):
res.extend(flatten(item))
else:
res.append(item)
return res
def _flatten(seq):
from operator import isSequenceType
if not isSequenceType(seq): return seq
while isSequenceType(seq):
if type(item) in (TupleType, ListType):
res.extend(flatten(item))
else:
res.append(item)
return res
def test_01_RequestCACerts(self):
cacert = self.proxy.RetrieveCACertificates()
self.failUnless(operator.isSequenceType(cacert),
"RetrieveCACertificate returns a non-sequence")
self.failUnless(
len(cacert) == 2,
"RetrieveCACertificate returns a tuple of len != 2")
self.failUnless(
type(cacert[1]) != str and operator.isSequenceType(cacert[1]),
"cacert[1] is not a sequence")
self.assert_(
cacert[0],
"RetrieveCACertificate returned failure: " + str(cacert[1]))
certList = cacert[1]
#
# It should be a list containing a single pair (cert, policy)
#
self.assert_(
len(certList) == 1, "Retrieved cert list is not of length 1")
self.assert_(
len(certList[0]) == 2,
"Retrieved cert list does not contain a single pair")
cert, policy = certList[0]
self.assert_(re.search("access_id_CA", policy),
"Policy string does not appear to be valid")
self.assert_(re.search("BEGIN CERTIFICATE", cert),
"Certificate does not appear to be valid")
tmp = tempfile.mktemp()
fh = open(tmp, "w")
fh.write(cert)
fh.close()
fh = os.popen("openssl x509 -noout -subject -in %s" % (tmp), "r")
subj = fh.read()
fh.close()
m = re.search(r"^subject=\s+(.*)", subj)
self.assert_(m is not None, "Did not match subject")
global issuer
issuer = m.group(1)
os.unlink(tmp)
def setup_analog_scan(
self, channels=(0, 1, 2, 3),
sweeps=-1, rate=100, gains=GAIN2_DIFF, exttrig=0):
if self.scanning:
raise MeasurementComputingError, "cannot start new scan without stopping old one"
if not isSequenceType(channels):
channels = (channels,)
if not isSequenceType(gains):
gains = (gains,) * len(channels)
if len(gains) != len(channels):
raise MeasurementComputingError, \
"gain list not compatible with channel list" + str(channels) + ":" + str(gains)
self.setup_gain_list(channels, gains)
timerPre, timerVal, setupTime, actRate = self.compute_timer_vals(
rate * len(channels))
self.actRate = actRate
if sweeps <= 0: # continuous scan !
blocking = self.DATA_SAMPLE_SIZE // len(channels)
if len(channels) * blocking != self.DATA_SAMPLE_SIZE:
raise MeasurementComputingError, \
"continuous scan channel count must be submultiple of %d" % self.DATA_SAMPLE_SIZE
tCount = self.DATA_SAMPLE_SIZE # each block is filled in continuous mode
scanmode = self.AD_CONT_MODE
elif sweeps == 1: # use AD_SINGLEEXEC mode for single sweep
tCount = len(channels)
scanmode = self.AD_SINGLE_MODE
else:
# round block count up to next block above requested samples
blocks = (
len(channels) * sweeps + self.DATA_SAMPLE_SIZE - 1) // self.DATA_SAMPLE_SIZE
tCount = blocks * self.DATA_SAMPLE_SIZE
if tCount > self.MAX_STORED_SAMPLES:
raise MeasurementComputingError, \
"burst scan sample count must be < %d" % self.MAX_STORED_SAMPLES
scanmode = self.AD_BURST_MODE
self.burst_scan_count = tCount
self.burst_scan_blocks = blocks
tChigh = tCount >> 8
tClow = tCount & 255
self.write(
(self.CBAINSCAN, tClow, tChigh, timerVal + setupTime, timerPre, scanmode
| exttrig))
self.continuous_scan_packet_index = 1
self.got_last_packet = 1
self.last_packet_time = 0.0 # long ago!
self.packet_dt = self.DATA_SAMPLE_SIZE / actRate
def __init__(self, values, shadow_values):
if not isSequenceType(values) or not isSequenceType(shadow_values):
raise TypeError("values and shadow_values must be sequences.")
if len(values) != len(shadow_values):
raise ValueError(
"values and shadow_values must have the same length.")
self.values = values
# Store always a copy: values and shadow_values may be identical,
# and if this is the case, the two reverse() calls in the method
# reverse() below will cancel each other.
self.shadow_values = shadow_values[:]
def __init__(self, values, shadow_values):
if not isSequenceType(values) or not isSequenceType(shadow_values):
raise TypeError("values and shadow_values must be sequences.")
if len(values) != len(shadow_values):
raise ValueError(
"values and shadow_values must have the same length.")
self.values = values
# Store always a copy: values and shadow_values may be identical,
# and if this is the case, the two reverse() calls in the method
# reverse() below will cancel each other.
self.shadow_values = shadow_values[:]
def test_isXxxType_more(self):
import operator
assert not operator.isSequenceType(list)
assert not operator.isSequenceType(dict)
assert not operator.isSequenceType({})
assert not operator.isMappingType(list)
assert not operator.isMappingType(dict)
assert not operator.isMappingType([])
assert not operator.isMappingType(())
assert not operator.isNumberType(int)
assert not operator.isNumberType(float)
def test_isXxxType_more(self):
import operator
assert not operator.isSequenceType(list)
assert not operator.isSequenceType(dict)
assert not operator.isSequenceType({})
assert not operator.isMappingType(list)
assert not operator.isMappingType(dict)
assert not operator.isMappingType([])
assert not operator.isMappingType(())
assert not operator.isNumberType(int)
assert not operator.isNumberType(float)
def server_init(args=None, groups=None):
"""
Initialize embedded server. If this client is not linked against
the embedded server library, this function does nothing.
args -- sequence of command-line arguments
groups -- sequence of groups to use in defaults files
"""
global server_init_done
if server_init_done:
raise ProgrammingError('already initialized')
args_count = 0
if args is not None:
if not operator.isSequenceType(args):
raise TypeError('args must be a sequence')
try:
args_count = len(args)
except Exception:
raise TypeError('args could not be sized')
for arg in args:
if not isinstance(arg, basestring):
raise TypeError('args must contain strings')
args_array = (ctypes.c_char_p * args_count)(*args)
if groups is not None:
if not operator.isSequenceType(groups):
raise TypeError('groups must be a sequence')
try:
len(groups)
except Exception:
raise TypeError('groups could not be sized')
for group in groups:
if not isinstance(group, basestring):
raise TypeError('groups must contain strings')
# create a null-terminated list (one longer than we need)
groups_array = (ctypes.c_char_p * (len(groups)+1))(*groups)
res = _mysql_api.mysql_server_init(args_count, args_array, groups)
if res:
return do_exception(None)
server_init_done = True
def test_01_RequestCACerts(self):
cacert = self.proxy.RetrieveCACertificates()
self.failUnless(operator.isSequenceType(cacert),
"RetrieveCACertificate returns a non-sequence")
self.failUnless(len(cacert) == 2,
"RetrieveCACertificate returns a tuple of len != 2")
self.failUnless(type(cacert[1]) != str and operator.isSequenceType(cacert[1]),
"cacert[1] is not a sequence")
self.assert_(cacert[0],
"RetrieveCACertificate returned failure: " + str(cacert[1]))
certList = cacert[1]
#
# It should be a list containing a single pair (cert, policy)
#
self.assert_(len(certList) == 1, "Retrieved cert list is not of length 1")
self.assert_(len(certList[0]) == 2, "Retrieved cert list does not contain a single pair")
cert, policy = certList[0]
self.assert_(re.search("access_id_CA", policy),
"Policy string does not appear to be valid")
self.assert_(re.search("BEGIN CERTIFICATE", cert),
"Certificate does not appear to be valid")
tmp = tempfile.mktemp()
fh = open(tmp, "w")
fh.write(cert)
fh.close()
fh = os.popen("openssl x509 -noout -subject -in %s" % (tmp), "r")
subj = fh.read()
fh.close()
m = re.search(r"^subject=\s+(.*)", subj)
self.assert_(m is not None, "Did not match subject")
global issuer
issuer = m.group(1)
os.unlink(tmp)
def constructFileName(fname, shaderName, shaderParams, shaderArgs): assert(kgUtils.isAString(shaderName) and kgUtils.isAString(fname)) assert(operator.isSequenceType(shaderParams) and operator.isSequenceType(shaderArgs)) fname = os.path.abspath(fname) fname1, ext = os.path.splitext(fname) resFileName=fname1 if(shaderParams != None or shaderArgs != None): print shaderParams print shaderArgs resFileName += '_' + '_'.join([ x[:2]\ + '_' + re.sub(r'[.]', '_', y) \ for x, y in map(None, shaderParams, shaderArgs)]) resFileName += ext print resFileName return resFileName
def _ExpandValue(var, specials, params, name, default):
"""Expand one value.
This expands the <field>.<field>...<field> part of the variable
expansion. A field may be of the form *<param> to use the value
of a parameter as the field name.
"""
if var == '_key':
return name
elif var == '_this':
return params
if var.startswith('_'):
value = specials
else:
value = params
for v in var.split('.'):
if v == '*_this':
v = params
if v.startswith('*'):
v = _GetValue(specials['_params'], v[1:])
if operator.isSequenceType(v):
v = v[0] # reduce repeated url param to single value
value = _GetValue(value, str(v), default)
return value
def add(self, *objs):
"""
Add an object or container of objects to the network
"""
self.unprepare()
for obj in objs:
if isinstance(obj, (NeuronGroup, Connection, NetworkOperation)):
self._added_objects.append(obj)
if isinstance(obj, NeuronGroup):
if obj not in self.groups:
self.groups.append(obj)
elif isinstance(obj, Connection):
if obj not in self.connections:
self.connections.append(obj)
elif isinstance(obj, NetworkOperation):
if obj not in self._all_operations:
self._operations_dict[obj.when].append(obj)
self._all_operations.append(obj)
elif isSequenceType(obj):
for o in obj:
self.add(o)
else:
raise TypeError('Only the following types of objects can be added to a network: NeuronGroup, Connection or NetworkOperation')
try:
gco = obj.contained_objects
if gco is not None:
self.add(gco)
except AttributeError:
pass
def init_with_figure(cls, data=None, affine=None, threshold=None,
cut_coords=None, figure=None, axes=None,
black_bg=False, leave_space=False):
cut_coords = cls.find_cut_coords(data, affine, threshold,
cut_coords)
if not isinstance(figure, pl.Figure):
# Make sure that we have a figure
figsize = cls._default_figsize[:]
# Adjust for the number of axes
figsize[0] *= len(cut_coords)
facecolor = 'k' if black_bg else 'w'
if leave_space:
figsize[0] += 3.4
figure = pl.figure(figure, figsize=figsize,
facecolor=facecolor)
else:
if isinstance(axes, pl.Axes):
assert axes.figure is figure, ("The axes passed are not "
"in the figure")
if axes is None:
axes = [0., 0., 1., 1.]
if leave_space:
axes = [0.3, 0, .7, 1.]
if operator.isSequenceType(axes):
axes = figure.add_axes(axes)
axes.axis('off')
return cls(cut_coords, axes, black_bg)
def size(X):
from operator import isSequenceType
out = marray()
while isSequenceType(X):
out.append(len(X))
X = X[0]
return out
def merge(*mappings, **opts):
'''
Merges all mappings given as arguments.
opts can have {'keyeq': predicate}
'''
mapping = {}
keytransform = opts.get('keytransform', lambda k: k)
use_list = opts.get('use_list', lambda k: k)
for elem in mappings:
if isMappingType(elem):
# {key: value, key: value, ...}
items = elem.iteritems()
elif isSequenceType(elem):
# [(key, value), (key, value), ...]
if not all(len(s) == 2 for s in elem):
raise TypeError(
'mapping sequences must be sequences of (key,'
'value) pairs: %s %s', type(elem), repr(elem)
)
items = elem
else:
raise TypeError('all arguments to merge must be mappings: %r',
elem)
for key, value in items:
merge_values(
mapping, keytransform(key), value, keytransform, use_list
)
return mapping
def fireEvent(self, event_type, event_value, listeners=None):
"""sends an event to all listeners or a specific one"""
if listeners is None:
listeners = self._listeners
if listeners is None:
return
if not operator.isSequenceType(listeners):
listeners = listeners,
for listener in listeners:
if isinstance(listener, weakref.ref) or isinstance(
listener, BoundMethodWeakref):
l = listener()
else:
l = listener
if l is None:
continue
meth = getattr(l, 'eventReceived', None)
if meth is not None and operator.isCallable(meth):
l.eventReceived(self, event_type, event_value)
elif operator.isCallable(l):
l(self, event_type, event_value)
def electrode(Re, Ce, v_el='v_el', vm='vm', i_inj='i_inj', i_cmd='i_cmd'):
'''
An intracellular electrode modeled as an RC circuit,
or multiple RC circuits in series (if Re, Ce are lists).
v_el = electrode (=recording) potential
vm = membrane potential
i_inj = current entering the membrane
i_cmd = electrode command current (None = no injection)
Returns an Equations() object.
'''
if isSequenceType(Re):
if len(Re) != len(Ce) or len(Re) < 2:
raise TypeError, "Re and Ce must have the same length"
v_mid, i_mid = [], []
for i in range(len(Re) - 1):
v_mid.append('v_mid_' + str(i) + unique_id())
i_mid.append('i_mid_' + str(i) + unique_id())
eqs = electrode(Re[0], Ce[0], v_mid[0], vm, i_inj, i_mid[0])
for i in range(1, len(Re) - 1):
eqs + electrode(Re[i], Ce[i], v_mid[i], v_mid[i - 1], i_mid[i - 1], i_mid[i])
eqs += electrode(Re[-1], Ce[-1], v_el, v_mid[-1], i_mid[-1], i_cmd)
return eqs
else:
if Ce > 0 * farad:
return Equations('''
dvr/dt = ((vm-vr)/Re+ic)/Ce : mV
ie = (vr-vm)/Re : nA''', vr=v_el, vm=vm, ic=i_cmd, ie=i_inj, \
Re=Re, Ce=Ce)
else: # ideal electrode - pb here
return Equations('''
vr = vm+Re*ic : volt
ie = ic : amp''', vr=v_el, vm=vm, ic=i_cmd, ie=i_inj)
def init_with_figure(cls, data=None, affine=None, threshold=None,
cut_coords=None, figure=None, axes=None,
black_bg=False, leave_space=False):
cut_coords = cls.find_cut_coords(data, affine, threshold,
cut_coords)
if isinstance(axes, pl.Axes) and figure is None:
figure = axes.figure
if not isinstance(figure, pl.Figure):
# Make sure that we have a figure
figsize = cls._default_figsize[:]
# Adjust for the number of axes
figsize[0] *= len(cut_coords)
facecolor = 'k' if black_bg else 'w'
if leave_space:
figsize[0] += 3.4
figure = pl.figure(figure, figsize=figsize,
facecolor=facecolor)
else:
if isinstance(axes, pl.Axes):
assert axes.figure is figure, ("The axes passed are not "
"in the figure")
if axes is None:
axes = [0., 0., 1., 1.]
if leave_space:
axes = [0.3, 0, .7, 1.]
if operator.isSequenceType(axes):
axes = figure.add_axes(axes)
# People forget to turn their axis off, or to set the zorder, and
# then they cannot see their slicer
axes.axis('off')
return cls(cut_coords, axes, black_bg)
def _uniquemerge2literal(attrs):
"""Compress a sequence into its unique elements (with string merge).
Whenever there is more then one unique element in `attrs`, these
are converted to a string and join with a '+' character inbetween.
Parameters
----------
attrs : sequence, arbitrary
Returns
-------
Non-sequence arguments are passed as is. Sequences are converted into
a single item representation (see above) and returned. None is returned
in case of an empty sequence.
"""
# only do something if multiple items are given
if not operator.isSequenceType(attrs):
return attrs
unq = np.unique(attrs)
lunq = len(unq)
if lunq > 1:
return '+'.join([str(l) for l in unq])
elif lunq: # first entry (non
return unq[0]
else:
return None
def _convert_to_svm_node_array(x): """ convert a sequence or mapping to an svm_node array """ import operator # Find non zero elements iter_range = [] if type(x) == dict: for k, v in x.iteritems(): # all zeros kept due to the precomputed kernel; no good solution yet # if v != 0: iter_range.append( k ) elif operator.isSequenceType(x): for j in range(len(x)): # if x[j] != 0: iter_range.append( j ) else: raise TypeError,"data must be a mapping or a sequence" iter_range.sort() data = svmc.svm_node_array(len(iter_range)+1) svmc.svm_node_array_set(data,len(iter_range),-1,0) j = 0 for k in iter_range: svmc.svm_node_array_set(data,j,k,x[k]) j = j + 1 return data
def init_with_figure(cls, img, threshold=None,
cut_coords=None, figure=None, axes=None,
black_bg=False, leave_space=False):
cut_coords = cls.find_cut_coords(img, threshold,
cut_coords)
if isinstance(axes, pl.Axes) and figure is None:
figure = axes.figure
if not isinstance(figure, pl.Figure):
# Make sure that we have a figure
figsize = cls._default_figsize[:]
# Adjust for the number of axes
figsize[0] *= len(cut_coords)
facecolor = 'k' if black_bg else 'w'
if leave_space:
figsize[0] += 3.4
figure = pl.figure(figure, figsize=figsize,
facecolor=facecolor)
if isinstance(axes, pl.Axes):
assert axes.figure is figure, ("The axes passed are not "
"in the figure")
if axes is None:
axes = [0., 0., 1., 1.]
if leave_space:
axes = [0.3, 0, .7, 1.]
if operator.isSequenceType(axes):
axes = figure.add_axes(axes)
# People forget to turn their axis off, or to set the zorder, and
# then they cannot see their slicer
axes.axis('off')
return cls(cut_coords, axes, black_bg)
def find_capacitance(model):
'''
Tries to find the membrane capacitance from
a set of differential equations or a model given as
a dictionnary (with typical keys 'model', 'threshold' and 'reset').
'''
if type(model) == types.DictType:
if 'Cm' in model:
return model['Cm']
if 'C' in model:
return model['C']
# Failed: look the equations
if 'model' in model:
model = model['model']
else: # no clue!
raise TypeError, 'Strange model!'
if isinstance(model, StateUpdater):
if hasattr(model, 'Cm'):
return model.Cm
if hasattr(model, 'C'):
return model.C
if isSequenceType(model):
model = model[0] # The first equation is the membrane equation
if type(model) == types.FunctionType:
if 'Cm' in model.func_globals:
return model.func_globals['Cm']
if 'C' in model.func_globals:
return model.func_globals['C']
# Nothing was found!
raise TypeError, 'No capacitance found!'
def _action(self, key, func, missingok=False, **kwargs):
"""Run specific func either on a single item or on all of them
Parameters
----------
key : str
Name of the conditional attribute
func
Function (not bound) to call given an item, and **kwargs
missingok : bool
If True - do not complain about wrong key
"""
if isinstance(key, basestring):
if key.upper() == 'ALL':
for key_ in self:
self._action(key_, func, missingok=missingok, **kwargs)
else:
try:
func(self[key], **kwargs)
except:
if missingok:
return
raise
elif operator.isSequenceType(key):
for item in key:
self._action(item, func, missingok=missingok, **kwargs)
else:
raise ValueError, \
"Don't know how to handle variable given by %s" % key
def merge(*mappings, **opts):
'''
Merges all mappings given as arguments.
opts can have {'keyeq': predicate}
'''
mapping = {}
keytransform = opts.get('keytransform', lambda k: k)
use_list = opts.get('use_list', lambda k: k)
for elem in mappings:
if isMappingType(elem):
# {key: value, key: value, ...}
items = elem.iteritems()
elif isSequenceType(elem):
# [(key, value), (key, value), ...]
if not all(len(s) == 2 for s in elem):
raise TypeError(
'mapping sequences must be sequences of (key,'
'value) pairs: %s %s', type(elem), repr(elem))
items = elem
else:
raise TypeError('all arguments to merge must be mappings: %r',
elem)
for key, value in items:
merge_values(mapping, keytransform(key), value, keytransform,
use_list)
return mapping
def _check_input_times(times):
issequence = False
if isSequenceType(times):
## Here assume sequence contains
## elements of same type.
test_tm = times[0]
issequence = True
else:
test_tm = times
if not issequence:
times = [times]
if isinstance(test_tm, datetime.datetime):
tticks = map(ticks, times)
tticks = array(tticks, float)
elif isNumberType(test_tm):
tticks = array(times, float)
else:
raise TypeError("Second input for interpolation functions" \
" must be list/array of number or datatime, or single" \
" number or datetime")
return tticks
def upgraderChangeUserAgentTuple(self, clients=None):
"Change the user agent to the newer tuple format for detection"
if not clients:
clients = self.getClients()
lookup = {
"Konqueror/2.2": ("Mozilla/5", "Konqueror", "2", "2"),
"Konqueror": ("Mozilla/5", "Konqueror", "", ""),
"MSIE": ("Mozilla/4", "MSIE", "", ""),
"MSIE 6": ("Mozilla/4", "MSIE", "6", ""),
"MSIE 6.0": ("Mozilla/4", "MSIE", "6", "0"),
"MSIE 5": ("Mozilla/4", "MSIE", "5", ""),
"MSIE 4": ("Mozilla/4", "MSIE", "4", ""),
"MSIE 3": ("Mozilla/3", "MSIE", "3", ""),
"MSIE 2": ("Mozilla/2", "MSIE", "2", ""),
"MSIE 5.5": ("Mozilla/4", "MSIE", "5", "5"),
"MSIE 5.0": ("Mozilla/4", "MSIE", "5", "0"),
"Opera": ("", "Opera", "", ""),
"Opera/6": ("", "Opera", "6", ""),
"Opera/6.0": ("", "Opera", "6", "0"),
"Opera/5": ("", "Opera", "5", ""),
"Opera/5.0": ("", "Opera", "5", "0"),
}
if operator.isSequenceType(clients):
list = [lookup[i] for i in clients if i in lookup]
if len(list):
self.setClients(tuple(list))
def __init__(self, intf, dict=None, inst=None) :
actualIntf = None
if isinstance(intf, str) or isinstance(intf, unicode) :
actualIntf = [ _jclass.JClass(intf) ]
elif isinstance(intf, _jclass._JavaClass) :
actualIntf = [ intf ]
elif operator.isSequenceType(intf) :
actualIntf = []
for i in intf :
if isinstance(i, str) or isinstance(i, unicode) :
actualIntf.append(_jclass.JClass(i))
elif isinstance(i, _jclass._JavaClass) :
actualIntf.append(i)
else:
raise TypeError, "JProxy requires java interface classes or the names of java interfaces classes"
else:
raise TypeError, "JProxy requires java interface classes or the names of java interfaces classes"
for i in actualIntf :
if not JClassUtil.isInterface(i) :
raise TypeError, "JProxy requires java interface classes or the names of java interfaces classes : "+i.__name__
if dict is not None and inst is not None :
raise RuntimeError, "Specify only one of dict and inst"
self._dict = dict
self._inst = inst
self._proxy = _jpype.createProxy(self, actualIntf)
def _build_parameter(self, param_def):
'''Builds a list of parameters, each of them represented by a dictionary
containing information: name, type, default_value, description, min and
max values. In case of simple parameters, type is the parameter type.
In case of ParamRepeat, type is a list containing definition of the
param repeat.
'''
ret = []
param_def = param_def or ()
for p in param_def:
t = p[1]
ret_p = {'min': 1, 'max': None}
# take care of old ParamRepeat
if isinstance(t, ParamRepeat):
t = t.obj()
if operator.isSequenceType(t) and not isinstance(t, (str, unicode)):
if operator.isMappingType(t[-1]):
ret_p.update(t[-1])
t = self._build_parameter(t[:-1])
else:
t = self._build_parameter(t)
ret_p['name'] = p[0]
ret_p['type'] = t
ret_p['default_value'] = p[2]
ret_p['description'] = p[3]
ret.append(ret_p)
return ret
def geocentric_to_geographic_latitude(geoclat):
"""
Converts a geocentric latitude to a geographic/geodetic latitude.
:param geoclat:
An :class:`astropysics.coords.AngularCoordinate` object (or arguments to
create one) or an angle in degrees for the geocentric latitude.
:returns:
An :class:`astropysics.coords.AngularCoordinate` object with the
geographic latitude.
"""
from astropysics.constants import Rea, Reb
from astropysics.coords import AngularCoordinate
from operator import isSequenceType
if not isinstance(geoclat, AngularCoordinate):
if isSequenceType(geoclat):
rads = AngularCoordinate(*geoclat).radians
else:
rads = AngularCoordinate(geoclat).radians
else:
rads = geoclat.radians
boasq = (Reb / Rea)**2
return AngularCoordinate(np.arctan((1 / boasq) * np.tan(rads)),
radians=True)
def __setitem__(self, key, value):
"""Add a new IndexedCollectable to the collection
Parameters
----------
item : IndexedCollectable
or of derived class. Must have 'name' assigned.
"""
# local binding
ulength = self._uniform_length
# XXX should we check whether it is some other Collectable?
if not isinstance(value, ArrayCollectable):
# if it is only a single element iterable, attempt broadcasting
if isSequenceType(value) and len(value) == 1 and not ulength is None:
if ulength > 1:
# cannot use np.repeat, because it destroys dimensionality
value = [value[0]] * ulength
value = ArrayCollectable(value)
if ulength is None:
ulength = len(value)
elif not len(value.value) == ulength:
raise ValueError(
"Collectable '%s' with length [%i] does not match "
"the required length [%i] of collection '%s'." % (key, len(value.value), ulength, str(self))
)
# tell the attribute to maintain the desired length
value.set_length_check(ulength)
Collection.__setitem__(self, key, value)
def isSequenceType(obj):
if six.PY2:
import operator
return operator.isSequenceType(obj)
else:
import collections.abc
return isinstance(obj, collections.abc.Sequence)
def _setValidjdrange(self, val):
"""
Sets the jd range over which this method is valid. Trying to get
something outside will result in an `exc`:EphemerisAccuracyWarning:
Intended for use in __init__.
:param val:
The range as (minjd,maxjd), can be None to indicate no bound. If set
to None, the result will be (None,None).
"""
if val is None:
self._validrange = (None, None)
else:
v1, v2 = val
if v1 is None and v2 is None:
self._validrange = (None, None)
else:
from operator import isSequenceType
from ..obstools import calendar_to_jd
from datetime import datetime
vs = []
for v in (v1, v2):
if v is None:
vs.append(None)
elif v == 'now':
vs.append(calendar_to_jd(datetime.utcnow(), tz=None))
elif hasattr(v, 'year') or isSequenceType(v):
vs.append(calendar_to_jd(v))
else:
vs.append(v)
self._validrange = tuple(vs)
def seq_to_svm_node(x):
"""convert a sequence or mapping to an SVMNode array"""
import operator
length = len(x)
# make two lists, one of indices, one of values
# YYY Use isinstance instead of type...is so we could
# easily use derived subclasses
if isinstance(x, np.ndarray):
iter_range = range(length)
iter_values = x
elif isinstance(x, dict):
iter_range = list(x).sort()
iter_values = np.ndarray(x.values())
elif operator.isSequenceType(x):
iter_range = range(length)
iter_values = np.asarray(x)
else:
raise TypeError, "data must be a mapping or an ndarray or a sequence"
# allocate c struct
data = svmc.svm_node_array(length + 1)
# insert markers into the c struct
svmc.svm_node_array_set(data, length, -1, 0.0)
# pass the list and the ndarray to the c struct
svmc.svm_node_array_set(data, iter_range, iter_values)
return data
def _convert_to_svm_node_array(x, keep_zeros = True):
"""
convert a sequence or mapping to an svm_node array.
3/12/09 -- originally, zeros were included (the in-line comment suggests this had something
to do with precomputed kernels?) test ?
"""
import operator
# Find non zero elements
iter_range = []
if type(x) == dict:
for k, v in x.iteritems():
# all zeros kept due to the precomputed kernel; no good solution yet
if v != 0 or keep_zeros:
iter_range.append( k )
elif operator.isSequenceType(x):
for j in range(len(x)):
if x[j] != 0 or keep_zeros:
iter_range.append( j )
else:
raise TypeError,"data must be a mapping or a sequence"
iter_range.sort()
data = svmc.svm_node_array(len(iter_range)+1)
svmc.svm_node_array_set(data,len(iter_range),-1,0)
j = 0
for k in iter_range:
svmc.svm_node_array_set(data,j,k,x[k])
j = j + 1
return data
def Vectorize(v):
"Converts v from a sequence into a Vector3."
if operator.isSequenceType(v):
v = DirectX.Vector3(System.Single(v[0]), System.Single(v[1]),
System.Single(v[2]))
return v
def __idiv__(self,arg):
if operator.isSequenceType(arg) :
assert len(arg) == len(self.__GFlist) , "list of incorrect length"
for l,g in izip(arg,self.__GFlist) : g /=l
else :
for i,g in self : g /= arg
return self
def _action(self, key, func, missingok=False, **kwargs):
"""Run specific func either on a single item or on all of them
Parameters
----------
key : str
Name of the conditional attribute
func
Function (not bound) to call given an item, and **kwargs
missingok : bool
If True - do not complain about wrong key
"""
if isinstance(key, basestring):
if key.upper() == 'ALL':
for key_ in self:
self._action(key_, func, missingok=missingok, **kwargs)
else:
try:
func(self[key], **kwargs)
except:
if missingok:
return
raise
elif operator.isSequenceType(key):
for item in key:
self._action(item, func, missingok=missingok, **kwargs)
else:
raise ValueError, \
"Don't know how to handle variable given by %s" % key
def _uniquemerge2literal(attrs):
"""Compress a sequence into its unique elements (with string merge).
Whenever there is more then one unique element in `attrs`, these
are converted to a string and join with a '+' character inbetween.
Parameters
----------
attrs : sequence, arbitrary
Returns
-------
Non-sequence arguments are passed as is. Sequences are converted into
a single item representation (see above) and returned. None is returned
in case of an empty sequence.
"""
# only do something if multiple items are given
if not operator.isSequenceType(attrs):
return attrs
unq = np.unique(attrs)
lunq = len(unq)
if lunq > 1:
return "+".join([str(l) for l in unq])
elif lunq: # first entry (non
return unq[0]
else:
return None
def connect(self, attrs):
if not operator.isSequenceType(attrs):
attrs = (attrs,)
self.disconnect()
self._attrs = attrs
for attr in self._attrs:
attr.addListener(self)
def remove(self, *objs, **kwds):
'''
Remove an object or sequence of objects from a Network (note that this
will force the Network to be prepared from scratch).
'''
unprepare = kwds.pop('unprepare', True)
for obj in objs:
if isinstance(obj, (NeuronGroup, Connection, NetworkOperation)):
self._added_objects = [o for o in self._added_objects if o is not obj]
if isinstance(obj, NeuronGroup):
self.groups = [o for o in self.groups if o is not obj]
if isinstance(obj, Connection):
self.connections = [o for o in self.connections if o is not obj]
if isinstance(obj, NetworkOperation):
self._all_operations = [o for o in self._all_operations if o is not obj]
self._operations_dict[obj.when] = [o for o in self._operations_dict[obj.when]
if o is not obj]
elif isSequenceType(obj):
for o in obj:
self.remove(o, unprepare=False)
else:
raise TypeError('Only the following types of objects can be removed from a network: NeuronGroup, Connection or NetworkOperation')
try:
gco = obj.contained_objects
if gco is not None:
self.remove(gco, unprepare=False)
except AttributeError:
pass
if unprepare:
self.unprepare()
def _convert_to_svm_node_array(x):
""" convert a sequence or mapping to an svm_node array """
import operator
# Find non zero elements
iter_range = []
if type(x) == dict:
for k, v in x.iteritems():
# all zeros kept due to the precomputed kernel; no good solution yet
# if v != 0:
iter_range.append(k)
elif operator.isSequenceType(x):
for j in range(len(x)):
# if x[j] != 0:
iter_range.append(j)
else:
raise TypeError, "data must be a mapping or a sequence"
iter_range.sort()
data = svmc.svm_node_array(len(iter_range) + 1)
svmc.svm_node_array_set(data, len(iter_range), -1, 0)
j = 0
for k in iter_range:
svmc.svm_node_array_set(data, j, k, x[k])
j = j + 1
return data
def __idiv__(self,arg):
if operator.isSequenceType(arg):
assert len(arg) == len(self.__GFlist) , "list of incorrect length"
for l,g in izip(arg,self.__GFlist): g /=l
else:
for i,g in self: self[i] /= arg
return self
def __calKeyframes(start, end, func, *additionalArgs, **keyArgs):
"""
helping to calculate and pack key values
"""
perc = start
values = func(perc, *additionalArgs)
samplingRate = 60.0
if keyArgs.has_key('samplingRate'):
samplingRate = keyArgs['samplingRate']
keyArrays = []
if operator.isSequenceType(values):
valueCount = len(values)
isSequence = True
for value in values:
keyArrays.append([value])
else:
isSequence = False
keyArrays.append(values)
offset = 1.0 / samplingRate
epsilon = offset * 0.0001
while perc != end:
perc += offset
if (end - start > 0) ^ ((perc + epsilon) < end):
perc = end
values = func(perc, *additionalArgs)
if isSequence:
for i in range(valueCount):
keyArrays[i].append(values[i])
else:
keyArrays.append(values)
return keyArrays
def size(X):
from operator import isSequenceType
out = marray()
while isSequenceType(X):
out.append(len(X))
X = X[0]
return out
def validate(self, data):
"""Checks that the data is a valid sequence."""
from operator import isSequenceType
if not isSequenceType(data):
return "List data has to be a sequence."
return True
