def __init__(self, left, right, op):
from .data import ComponentID
self._left = left
self._right = right
self._op = op
from_ = []
if isinstance(left, ComponentID):
from_.append(left)
elif isinstance(left, ComponentLink):
from_.extend(left.get_from_ids())
elif not operator.isNumberType(left):
raise TypeError("Cannot create BinaryComponentLink using %s" %
left)
if isinstance(right, ComponentID):
from_.append(right)
elif isinstance(right, ComponentLink):
from_.extend(right.get_from_ids())
elif not operator.isNumberType(right):
raise TypeError("Cannot create BinaryComponentLink using %s" %
right)
to = ComponentID("")
null = lambda *args: None
super(BinaryComponentLink, self).__init__(from_, to, null)
def _writeRecord(self, record):
if not self.isInitialized(): return
vals = []
for colname in self.labels:
val = record.data.get(colname)
if (not val is None) and (operator.isNumberType(val) and (type(val) in [int, float, long])):
vals.append(val)
elif (not val is None) and (operator.isNumberType(val)):
valsmca = []
for i in range(0, len(val)):
valsmca.append(val[i])
sufix = "1D"
if self.array.endswith(sufix):
valsmca = numpy.array(valsmca)
self.sps.putdatarow(self.program, self.array, record.recordno, valsmca)
sufix = "0D"
if self.array.endswith(sufix):
vals = numpy.array(vals)
self.sps.putdatarow(self.program, self.array, record.recordno, vals)
self.nopts += 1
env = { 'nopts' : self.nopts,
'peak' : 111,
'peakpos' : 34,
'fwhm' : 12.3,
'fwhmpos' : 45,
'com' : 23 }
self.putAllEnv(env)
def do_gui(disagreeable_restraints_list, interesting_list):
#print "BL DEBUG:: DR: ", disagreeable_restraints_list
dis_res_button_list = []
for dr_list in disagreeable_restraints_list:
restraint_type = dr_list[4]
drl_dict = {"BUMP": 0, "DANG": 0, "FLAT": 0, "SIMU": 1, "ISOR": 1}
if (drl_dict.has_key(restraint_type)):
drl_index = drl_dict[restraint_type]
else:
drl_index = 0
atom_parts = make_atom_parts(dr_list[5][drl_index])
stats_string = "not sure what?"
n2 = dr_list[2]
n3 = dr_list[3]
if (operator.isNumberType(n2) and operator.isNumberType(n3)
and (n2 <> 0)):
z = n3 / abs(n2)
stats_string = " " + str(n2) + " [Z=" + str(z) + "]"
else:
stats_string = ""
if (drl_index == 0):
rt = restraint_type + " "
else:
rt = restraint_type + " " + dr_list[5][0]
button_label = "Disagreeable Restraint " + rt + " " + atom_parts[0] + " " + \
str(atom_parts[1]) + " " + atom_parts[3] + stats_string
interesting_thing = [button_label, imol] + atom_parts
dis_res_button_list.append(interesting_thing)
gui_interesting_list(interesting_list + dis_res_button_list)
def get_cuts(cuts):
""" Convert `cuts` argument to vector_double type.
Argument can be None, a number or a sequence of numbers. If None, there is
only one cut at 0.5. If `cuts` is a number, function returns a vector_double
with that number as a single element. If its a sequence, that sequence will
be converted to vector_double type.
Parameters
----------
cuts : None, number or sequence of numbers
Cut values
Returns
-------
cuts : vector_double
Cut values.
Raises
------
TypeError
If `cuts` is not None, a number or a sequence of numbers.
"""
if cuts is None:
return sfr.vector_double([0.5])
elif op.isSequenceType(cuts) and np.array([op.isNumberType(a) for a in cuts]).all():
return sfr.vector_double(cuts)
elif op.isNumberType(cuts):
return sfr.vector_double([cuts])
else:
raise TypeError("'cuts' must be either None, a number or a " + "sequence of numbers.")
def test_isNumberType(self):
self.failUnlessRaises(TypeError, operator.isNumberType)
self.failUnless(operator.isNumberType(8))
self.failUnless(operator.isNumberType(8j))
self.failUnless(operator.isNumberType(8L))
self.failUnless(operator.isNumberType(8.3))
self.failIf(operator.isNumberType(dir()))
def __init__(self, left, right, op):
from .data import ComponentID
self._left = left
self._right = right
self._op = op
from_ = []
if isinstance(left, ComponentID):
from_.append(left)
elif isinstance(left, ComponentLink):
from_.extend(left.get_from_ids())
elif not operator.isNumberType(left):
raise TypeError("Cannot create BinaryComponentLink using %s" %
left)
if isinstance(right, ComponentID):
from_.append(right)
elif isinstance(right, ComponentLink):
from_.extend(right.get_from_ids())
elif not operator.isNumberType(right):
raise TypeError("Cannot create BinaryComponentLink using %s" %
right)
to = ComponentID("")
null = lambda *args: None
super(BinaryComponentLink, self).__init__(from_, to, null)
def do_gui(disagreeable_restraints_list, interesting_list):
#print "BL DEBUG:: DR: ", disagreeable_restraints_list
dis_res_button_list = []
for dr_list in disagreeable_restraints_list:
restraint_type = dr_list[4]
drl_dict = {"BUMP": 0, "DANG": 0, "FLAT": 0,
"SIMU": 1, "ISOR": 1}
if (drl_dict.has_key(restraint_type)):
drl_index = drl_dict[restraint_type]
else:
drl_index = 0
atom_parts = make_atom_parts(dr_list[5][drl_index])
stats_string = "not sure what?"
n2 = dr_list[2]
n3 = dr_list[3]
if (operator.isNumberType(n2) and operator.isNumberType(n3) and (n2 <> 0)):
z = n3 / abs(n2)
stats_string = " " + str(n2) + " [Z=" + str(z) +"]"
else:
stats_string = ""
if (drl_index == 0):
rt = restraint_type + " "
else:
rt = restraint_type + " " + dr_list[5][0]
button_label = "Disagreeable Restraint " + rt + " " + atom_parts[0] + " " + \
str(atom_parts[1]) + " " + atom_parts[3] + stats_string
interesting_thing = [button_label, imol] + atom_parts
dis_res_button_list.append(interesting_thing)
gui_interesting_list(interesting_list + dis_res_button_list)
def test_isNumberType(self):
self.failUnlessRaises(TypeError, operator.isNumberType)
self.failUnless(operator.isNumberType(8))
self.failUnless(operator.isNumberType(8j))
self.failUnless(operator.isNumberType(8))
self.failUnless(operator.isNumberType(8.3))
self.failIf(operator.isNumberType(dir()))
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 compute(self, data, view=None):
l = self._left
r = self._right
if not operator.isNumberType(self._left):
l = data[self._left, view]
if not operator.isNumberType(self._right):
r = data[self._right, view]
return self._op(l, r)
def compute(self, data, view=None):
l = self._left
r = self._right
if not operator.isNumberType(self._left):
l = data[self._left, view]
if not operator.isNumberType(self._right):
r = data[self._right, view]
return self._op(l, r)
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 to_mask(self, data, view=None):
from .data import ComponentID
left = self._left
if not operator.isNumberType(self._left):
left = data[self._left, view]
right = self._right
if not operator.isNumberType(self._right):
right = data[self._right, view]
return self._operator(left, right)
def update_progress_bar(progress_bar):
if file_name_for_progress_bar:
curl_info = curl_progress_info(file_name_for_progress_bar)
if curl_info:
v1 = curl_info['content-length-download']
v2 = curl_info['size-download']
if operator.isNumberType(v1):
if operator.isNumberType(v2):
f = v2 / v1
#print "count %s, active_count %s, f: %s" %(count, active_count, f)
progress_bar.set_fraction(f)
def to_mask(self, view=None):
from .data import ComponentID
left = self._left
if not operator.isNumberType(self._left):
left = self.parent.data[self._left, view]
right = self._right
if not operator.isNumberType(self._right):
right = self.parent.data[self._right, view]
return self._operator(left, right)
def _load_anat(anat_img=MNI152TEMPLATE, dim=False, black_bg='auto'):
""" Internal function used to load anatomy, for optional diming
"""
vmin = None
vmax = None
if anat_img is not False and anat_img is not None:
if anat_img is MNI152TEMPLATE:
anat_img.load()
# We special-case the 'canonical anat', as we don't need
# to do a few transforms to it.
vmin = 0
vmax = anat_img.vmax
if black_bg == 'auto':
black_bg = False
else:
anat_img = _utils.check_niimg(anat_img, ensure_3d=True)
if dim or black_bg == 'auto':
# We need to inspect the values of the image
data = anat_img.get_data()
vmin = data.min()
vmax = data.max()
if black_bg == 'auto':
# Guess if the background is rather black or light based on
# the values of voxels near the border
border_size = 2
border_data = np.concatenate([
data[:border_size, :, :].ravel(),
data[-border_size:, :, :].ravel(),
data[:, :border_size, :].ravel(),
data[:, -border_size:, :].ravel(),
data[:, :, :border_size].ravel(),
data[:, :, -border_size:].ravel(),
])
background = np.median(border_data)
if background > .5 * (vmin + vmax):
black_bg = False
else:
black_bg = True
if dim:
vmean = .5 * (vmin + vmax)
ptp = .5 * (vmax - vmin)
if black_bg:
if not operator.isNumberType(dim):
dim = .8
vmax = vmean + (1 + dim) * ptp
else:
if not operator.isNumberType(dim):
dim = .6
vmin = vmean - (1 + dim) * ptp
if black_bg == 'auto':
# No anatomy given: no need to turn black_bg on
black_bg = False
return anat_img, black_bg, vmin, vmax
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 _load_anat(anat_img=MNI152TEMPLATE, dim=False, black_bg='auto'):
""" Internal function used to load anatomy, for optional diming
"""
vmin = None
vmax = None
if anat_img is not False and anat_img is not None:
if anat_img is MNI152TEMPLATE:
anat_img.load()
# We special-case the 'canonical anat', as we don't need
# to do a few transforms to it.
vmin = 0
vmax = anat_img.vmax
if black_bg == 'auto':
black_bg = False
else:
anat_img = _utils.check_niimg(anat_img, ensure_3d=True)
if dim or black_bg == 'auto':
# We need to inspect the values of the image
data = anat_img.get_data()
vmin = data.min()
vmax = data.max()
if black_bg == 'auto':
# Guess if the background is rather black or light based on
# the values of voxels near the border
border_size = 2
border_data = np.concatenate([
data[:border_size, :, :].ravel(),
data[-border_size:, :, :].ravel(),
data[:, :border_size, :].ravel(),
data[:, -border_size:, :].ravel(),
data[:, :, :border_size].ravel(),
data[:, :, -border_size:].ravel(),
])
background = np.median(border_data)
if background > .5 * (vmin + vmax):
black_bg = False
else:
black_bg = True
if dim:
vmean = .5 * (vmin + vmax)
ptp = .5 * (vmax - vmin)
if black_bg:
if not operator.isNumberType(dim):
dim = .8
vmax = vmean + (1 + dim) * ptp
else:
if not operator.isNumberType(dim):
dim = .6
vmin = vmean - (1 + dim) * ptp
if black_bg == 'auto':
# No anatomy given: no need to turn black_bg on
black_bg = False
return anat_img, black_bg, vmin, vmax
def getDataMembers(self, obj, objName, tab = " "):
import operator
if operator.isNumberType(obj):
#print "Attribute name = ", objName, ", value = ", obj
print(tab, objName, " = ", obj)
else:
objType = type(obj)
# If Object is a method, replace obj with the result of the method
if objType.__name__ == 'MethodProxy':
try:
obj = obj()
objType = type(obj)
# Exception is thrown for functions that take arguments. We
# ignore them for the moment. It may be that there are two
# methods, one providing access to individual values, and
# another giving a whole vector of values.
except:
pass
if operator.isNumberType(obj):
print("obj type = ",objType.__name__)
#print "Attribute name = ", objName, ", value = ", obj
print(tab, objName, " = ", obj)
return
print("obj type =",objType.__name__)
# Is this a vector?
isVector = False
if objType.__name__.find('vector') == 0:
isVector = True
elif hasattr(obj,'begin') and hasattr(obj,'end') and hasattr(obj,'size'):
isVector = True
# If a vector, loop over elements and print each one out
if isVector:
i = 0
newTab = tab + " "
for o in obj:
self.getDataMembers(o, objName + '_' + str(i), newTab)
i += 1
# Assume a complex type, loop over attributes and print them out
else:
attrNames = dir(obj)
#print ("found attributes:",attrNames)
newTab = tab + " "
for attrName in attrNames:
# Select attributes that begin with m_ or get
if attrName.find("m_") == 0 or attrName.find("get") == 0:
try:
attr = getattr(obj, attrName)
except:
print("Could not get attr", attrName)
self.getDataMembers(attr, attrName, newTab)
def __rpow__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
ox = oy = other
else:
ox, oy = getattr(other, "__vec2__", other)
return Vec2(None, (ox**vx, oy**vy))
def __mul__(self, val):
if not operator.isNumberType(val):
raise TypeError
return AffineTransform( self.obj,
self.a * val,
self.b * val )
def check_sampling(sampling, n):
if sampling is None:
sampling = 1.0
if operator.isNumberType(sampling):
sampling = SplitSampling(n, evaluation_fraction=sampling)
return sampling
def _readline(self, line):
"""
Convert the pseudocode into a list of commands.
The block associated with the command is stored as the second element
in a tuple, e.g., (#forward, 16)
"""
# debug_output(line, self.tw.running_sugar)
res = []
while line:
token = line.pop(0)
bindex = None
if isinstance(token, tuple):
(token, bindex) = token
if isNumberType(token):
res.append(token)
elif token.isdigit():
res.append(float(token))
elif token[0] == '-' and token[1:].isdigit():
res.append(-float(token[1:]))
elif token[0] == '"':
res.append(token[1:])
elif token[0:2] == "#s":
res.append(token[2:])
elif token == '[':
res.append(self._readline(line))
elif token == ']':
return res
elif bindex is None or not isinstance(bindex, int):
res.append(self._intern(token))
else:
res.append((self._intern(token), bindex))
return res
def __rpow__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
ox = oy = other
else:
ox, oy = getattr(other, "__vec2__", other)
return Vec2(None, (ox ** vx, oy ** vy))
def __rmod__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
ox = oy = other
else:
ox, oy = getattr(other, "__vec2__", other)
return ox * vy - oy * vx
def __imul__(self, val):
if not operator.isNumberType(val):
raise TypeError
self.a *= val
self.b *= val
return self
def __rpow__(self, other):
vx, vy, vz = self.__vec3__
if isNumberType(other):
ox = oy = oz = other
else:
ox, oy, oz = getattr(other, "__vec3__", other)
return Vec3((ox**vx, oy**vy, oz**vz), _InternalCall)
def isfloat(f):
if not operator.isNumberType(f):
return 0
if f % 1:
return 1
else:
return 0
def check_sampling(sampling, n):
if sampling is None:
sampling = 1.0
if operator.isNumberType(sampling):
sampling = SplitSampling(n, evaluation_fraction=sampling)
return sampling
def __div__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
ox = oy = other
else:
ox, oy = getattr(other, "__vec2__", other)
return Vec2(None, (vx / ox, vy / oy))
def __imul__(self, val):
if not operator.isNumberType(val):
raise TypeError
self.a *= val
self.b *= val
return self
def isNumeric(obj):
"""
Returns True if an object is a number type, otherwise returns False.
:rtype: bool
"""
return operator.isNumberType(obj)
def __pow__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
return Vec2(None, (vx * other, vy * other))
else:
ox, oy = getattr(other, "__vec2__", other)
return Vec2(None, (vx**ox, vy**oy))
def __pow__(self, other):
vx, vy, vz = self.__vec3__
if isNumberType(other):
return Vec3((vx * other, vy * other), _InternalCall)
else:
ox, oy = getattr(other, "__vec2__", other)
return Vec3((vx**ox, vy**oy, vz**oz), _InternalCall)
def __rmod__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
ox = oy = other
else:
ox, oy = getattr(other, "__vec2__", other)
return ox * vy - oy * vx
def getFlatHeader(self):
r = {}
for k, v in sorted(self.header.iteritems(), key=itemgetter(0)):
if isinstance(v, dict):
for k1, v1 in sorted(v.iteritems(), key=itemgetter(0)):
if isinstance(v1, dict):
for k2, v2 in sorted(v1.iteritems(),
key=itemgetter(0)):
r[k2.upper()] = v2 if isNumberType(v2) else str(
v2).upper().strip()
else:
r[k1.upper()] = v1 if isNumberType(v1) else str(
v1).upper().strip()
else:
r[k.upper()] = v if isNumberType(v) else str(v).upper().strip()
return r
def check_cv(cv, X=None, y=None, classifier=False):
"""Creates a valid and usable cv generator
Parameters
===========
cv: an integer, a cv generator instance, or None
The input specifying which cv generator to use. It can be an
integer, in which case it is the number of folds in a KFold,
None, in which case 3 fold is used, or another object, that
will then be used as a cv generator.
X: 2D ndarray
the data the cross-val object will be applied on
y: 1D ndarray
the target variable for a supervised learning problem
classifier: boolean optional
whether the task is a classification task, in which case
stratified KFold will be used.
"""
if cv is None:
cv = 3
if operator.isNumberType(cv):
is_sparse = hasattr(X, 'tocsr')
if classifier:
cv = StratifiedKFold(y, cv, indices=is_sparse)
else:
if not is_sparse:
n_samples = len(X)
else:
n_samples = X.shape[0]
cv = KFold(n_samples, cv, indices=is_sparse)
return cv
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 __mul__(self, other):
vx, vy, vz = self.__vec3__
if isNumberType(other):
ox = oy = oz = other
else:
ox, oy, oz = getattr(other, "__vec3__", other)
return Vec3((vx * ox, vy * oy, vz * oz), _InternalCall)
def _readline(self, line):
"""
Convert the pseudocode into a list of commands.
The block associated with the command is stored as the second element
in a tuple, e.g., (#forward, 16)
"""
# debug_output(line, self.tw.running_sugar)
res = []
while line:
token = line.pop(0)
bindex = None
if isinstance(token, tuple):
(token, bindex) = token
if isNumberType(token):
res.append(token)
elif token.isdigit():
res.append(float(token))
elif token[0] == '-' and token[1:].isdigit():
res.append(-float(token[1:]))
elif token[0] == '"':
res.append(token[1:])
elif token[0:2] == "#s":
res.append(token[2:])
elif token == '[':
res.append(self._readline(line))
elif token == ']':
return res
elif bindex is None or not isinstance(bindex, int):
res.append(self._intern(token))
else:
res.append((self._intern(token), bindex))
return res
def __add__(self, other):
# Order of testing is important!
if isinstance(other, numpy.ndarray):
# If adding sparse and dense, result is dense
# rv = SparseVector( max( self.n, other.shape[0] ), {} )
rv = numpy.zeros(max(self.n, other.shape[0]), "d")
for k in range(other.shape[0]):
rv[k] = other[k]
for k in self.values.keys():
rv[k] += self[k]
return rv
elif isSparseVector(other):
rv = SparseVector(max(self.n, other.n), {})
for k in self.values.keys():
rv[k] += self[k]
for k in other.values.keys():
rv[k] += other[k]
return rv
elif operator.isNumberType(other):
rv = SparseVector(self.n, {})
for k in self.values.keys():
rv[k] = self[k] + other
return rv
else:
raise TypeError, "Cannot add with SparseVector"
def __add__(self, val):
if not operator.isNumberType(val):
raise TypeError, type(val)
return AffineTransform( self.obj,
self.a,
self.b + val )
def __floordiv__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
ox = oy = oz = other
else:
ox, oy, oz = getattr(other, "__vec3__", other)
return Vec3((vx // ox, vy // oy, vz // oz), _InternalCall)
def __rmod__(self, other):
vx, vy, vz = self.__vec3__
if isNumberType(other):
ox = oy = oz = other
else:
ox, oy, oz = getattr(other, "__vec3__", other)
return Vec3(vy * oz - vz * oy, vz * ox - vx * oz, vx * oy - vy * ox)
def __rsub__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
ox = oy = oz = other
else:
ox, oy, oz = getattr(other, "__vec3__", other)
return Vec2((ox - vx, oy - vy, oz - vz), _InternalCall)
def check_cv(cv, n):
if cv is None:
cv = 3
if operator.isNumberType(cv):
cv = KFold(n, cv, indices=True)
return cv
def isfloat(f):
if not operator.isNumberType(f):
return 0
if f % 1:
return 1
else:
return 0
def __rfloordiv__(self, other):
vx, vy, vz = self.__vec3__
if isNumberType(other):
ox = oy = oz = other
else:
ox, oy, oz = getattr(other, "__vec3__", other)
return Vec3((ox // vx, oy // vy, oz // vz), _InternalCall)
def ok_button_event(*args):
global pending_install_in_place
import operator
# only start when ok button is pressed
gobject.timeout_add(500, idle_func) # update every 500ms is god enough?!
if not operator.isNumberType(revision):
info_dialog("Failed to communicate with server")
else:
# BL says:: check if we have a download available already?! (from before)
if (version_string in get_latest_pending_version()):
# installer already here (win only?!)
pending_install_in_place = True
else:
# download
ok_button.set_sensitive(False)
#align.show()
progress_bar.show()
cancel_button.show()
def threaded_func():
global pending_install_in_place
ret = run_download_binary_curl(revision, version_string,
pending_install_in_place_func,
set_file_name_func,
progress_bar,
use_curl)
if ((not ret) and
(not pending_install_in_place == "cancelled")):
print "run_download_binary_curl failed"
pending_install_in_place = "fail"
run_python_thread(threaded_func, [])
def check_cv(cv, X=None, y=None, classifier=False):
"""Input checker utility for building a CV in a user friendly way.
Parameters
===========
cv: an integer, a cv generator instance, or None
The input specifying which cv generator to use. It can be an
integer, in which case it is the number of folds in a KFold,
None, in which case 3 fold is used, or another object, that
will then be used as a cv generator.
X: 2D ndarray
the data the cross-val object will be applied on
y: 1D ndarray
the target variable for a supervised learning problem
classifier: boolean optional
whether the task is a classification task, in which case
stratified KFold will be used.
"""
if cv is None:
cv = 3
if operator.isNumberType(cv):
is_sparse = hasattr(X, 'tocsr')
if classifier:
cv = StratifiedKFold(y, cv, indices=is_sparse)
else:
if not is_sparse:
n_samples = len(X)
else:
n_samples = X.shape[0]
cv = KFold(n_samples, cv, indices=is_sparse)
return cv
def __add__(self, other):
# Order of testing is important!
if isinstance(other, numpy.ndarray):
# If adding sparse and dense, result is dense
#rv = SparseVector( max( self.n, other.shape[0] ), {} )
rv = numpy.zeros(max(self.n, other.shape[0]), 'd')
for k in range(other.shape[0]):
rv[k] = other[k]
for k in self.values.keys():
rv[k] += self[k]
return rv
elif isSparseVector(other):
rv = SparseVector(max(self.n, other.n), {})
for k in self.values.keys():
rv[k] += self[k]
for k in other.values.keys():
rv[k] += other[k]
return rv
elif operator.isNumberType(other):
rv = SparseVector(self.n, {})
for k in self.values.keys():
rv[k] = self[k] + other
return rv
else:
raise TypeError, "Cannot add with SparseVector"
def __div__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
ox = oy = other
else:
ox, oy = getattr(other, "__vec2__", other)
return Vec2(None, (vx / ox, vy / oy))
def isNumeric(obj):
"""
Returns True if an object is a number type, otherwise returns False.
:rtype: bool
"""
return operator.isNumberType(obj)
def __rfloordiv__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
ox = oy = other
else:
ox, oy = getattr(other, "__vec2__", other)
return Vec2(None, (ox // vx, oy // vy))
def __pow__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
return Vec2(None, (vx * other, vy * other))
else:
ox, oy = getattr(other, "__vec2__", other)
return Vec2(None, (vx ** ox, vy ** oy))
def __rfloordiv__(self, other):
vx, vy = self.__vec2__
if isNumberType(other):
ox = oy = other
else:
ox, oy = getattr(other, "__vec2__", other)
return Vec2(None, (ox // vx, oy // vy))
def validate(self, object, name, value):
if value is None:
return value
elif operator.isNumberType(value):
# Local import to avoid explicit dependency.
import numpy
value = numpy.atleast_1d(value)
return super(ArrayNumberOrNone, self).validate(object, name, value)
def _getscaleoffset(expr):
stub = ["stub"]
data = expr(_E(stub)).data
try:
(a, b, c) = data # simplified syntax
if (a is stub and b == "__mul__" and isNumberType(c)):
return c, 0.0
if (a is stub and b == "__add__" and isNumberType(c)):
return 1.0, c
except TypeError: pass
try:
((a, b, c), d, e) = data # full syntax
if (a is stub and b == "__mul__" and isNumberType(c) and
d == "__add__" and isNumberType(e)):
return c, e
except TypeError: pass
raise ValueError("illegal expression")
def chpri(search="", sortby=[], limit=None, priority=None):
"""Change priority of matching jobs. priority= specifies the new priority."""
if priority is None:
raise TractorQueryError("chpri(): priority must be specified")
if not operator.isNumberType(priority):
raise TractorQueryError("chpri(): priority is not numeric")
jids = _jidsForArgs(search, sortby, limit)
ModuleEngineClient.setJobPriority(jids, priority)