def mass_excess(self, ions):
"""
TODO:
generally supplement by data from
http://www.nndc.bnl.gov/masses/mass.mas12
"""
if not is_iterable(ions):
shape = ()
ions = ions,
else:
shape = np.shape(ions)
if not hasattr(self, '_mass_excess_data'):
self._mass_excess_data = dict()
me = []
spec_ions_me = {
isotope.ion('nt1') : 8.07131714,
isotope.ion('h1' ) : 7.28897059,
isotope.ion('he4') : 2.42491561,
isotope.ion('be8') : 4.941671,
}
for i in ions:
x = self._mass_excess_data.get(i, None)
if x is None:
try:
x = self.__getitem__(i).mass_excess()
except KeyError:
x = spec_ions_me[isotope.ion(i)]
self._mass_excess_data[i] = x
me.append(x)
if shape == ():
return me[0]
return np.array(me)
def build_tree(self, data, tagname, attrs=None, depth=0):
r"""Build xml tree.
:param data: data for build xml.
:param tagname: element tag name.
:param attrs: element attributes. Default：``None``.
:type attrs: dict or None
:param depth: element depth of the hierarchy. Default：``0``.
:type depth: int
"""
if data is None:
data = ''
indent = ('\n%s' % (self.__options['indent'] * depth)) if self.__options['indent'] else ''
if isinstance(data, utils.DictTypes):
if self.__options['hasattr'] and self.check_structure(data.keys()):
attrs, values = self.pickdata(data)
self.build_tree(values, tagname, attrs, depth)
else:
self.__tree.append('%s%s' % (indent, self.tag_start(tagname, attrs)))
iter = data.iteritems()
if self.__options['ksort']:
iter = sorted(iter, key=lambda x:x[0], reverse=self.__options['reverse'])
for k, v in iter:
attrs = {}
if self.__options['hasattr'] and isinstance(v, utils.DictTypes) and self.check_structure(v.keys()):
attrs, v = self.pickdata(v)
self.build_tree(v, k, attrs, depth+1)
self.__tree.append('%s%s' % (indent, self.tag_end(tagname)))
elif utils.is_iterable(data):
for v in data:
self.build_tree(v, tagname, attrs, depth)
else:
self.__tree.append(indent)
data = self.safedata(data, self.__options['cdata'])
self.__tree.append(self.build_tag(tagname, data, attrs))
def font(self, value):
if is_iterable(value):
filename = value[0]
font_size = value[1]
else:
filename = value
font_size = DEFAULT_FONT_SIZE
font_path = os.path.abspath(filename)
self._image_font = ImageFont.truetype(font_path, font_size)
def __setitem__(self, name, value):
if is_iterable(name):
base_set = self._set.intersection(
self.transform_args_to_mishmash_set_values(*name))
else:
base_set = self._set.intersection(
self.transform_args_to_mishmash_set_values(name))
self._set = base_set
self.__mutate(base_set, value)
def __init__(self, enc_out_features):
super(MNISTDenseClassifier2, self).__init__()
if is_iterable(enc_out_features):
enc_out_features = product(enc_out_features)
self.classifier = nn.Sequential(
nn.Linear(enc_out_features, 1000),
nn.ReLU(),
nn.Linear(1000, 10),
nn.LogSoftmax(dim=1),
)
def font(self, value):
if is_iterable(value):
filename = value[0]
size = value[1]
else:
filename = value
size = None
if type(filename) is not str:
raise TypeError("Filename must be a string.")
if type(size) not in (int, NoneType):
raise TypeError("Size must be an integer.")
self._init_font(filename, size)
def build_post_body(self, args, kargs):
self.parameters = {}
if not self.post_data == {}:
return
post_body = []
for idx, arg in enumerate(args):
if arg is None:
continue
try:
if not is_iterable(arg):
post_body.append((self.allowed_param[idx], convert_to_utf8_str(arg)))
else:
self.parameters[self.allowed_param[idx]] = []
for value in arg:
post_body.append((self.allowed_param[idx], convert_to_utf8_str(value)))
self.parameters[self.allowed_param[idx]].append(convert_to_utf8_str(arg))
except:
raise MicardError('Too many parameters supplied!')
for k, arg in kargs.items():
if arg is None:
continue
if not is_iterable(arg):
post_body.append((k, convert_to_utf8_str(arg)))
self.parameters[k] = convert_to_utf8_str(arg)
else:
self.parameters[k]=[]
for value in arg:
post_body.append((k,convert_to_utf8_str(value)))
self.parameters[k].append(convert_to_utf8_str(value))
post_body.sort()
self.post_data = post_body
def test_is_iterable():
assert is_iterable([1, 2, 3])
assert is_iterable((1, 2, 3))
assert is_iterable('string')
assert is_iterable(u'unicode')
assert is_iterable({'key':'val'})
def f():
for i in xrange(3):
yield i
assert is_iterable(f())
assert not is_iterable(123)
def __init__(self, values):
"""
`values` must be an iterable, usually a list of lists.
"""
if not ut.is_iterable(values):
raise TypeError("`values` must be iterable for a pascal.Array!")
len_vals = len(values)
if len_vals == 0:
raise ValueError("`values` must not be empty for a pascal.Array!")
len_first = len(values[0])
if any(len(x) != len_first for x in values[1:]):
raise ValueError("All iterables in `values` must be of the same length.")
self._values = values
self.rank = len_vals
def build_tree(self, data, tagname, attrs=None, depth=0):
r"""Build xml tree.
:param data: data for build xml.
:param tagname: element tag name.
:param attrs: element attributes. Default：``None``.
:type attrs: dict or None
:param depth: element depth of the hierarchy. Default：``0``.
:type depth: int
"""
if data is None:
data = ''
indent = ('\n%s' % (self.__options['indent'] * depth)
) if self.__options['indent'] else ''
if isinstance(data, utils.DictTypes):
if self.__options['hasattr'] and self.check_structure(data.keys()):
attrs, values = self.pickdata(data)
self.build_tree(values, tagname, attrs, depth)
else:
self.__tree.append('%s%s' %
(indent, self.tag_start(tagname, attrs)))
iter = data.iteritems()
if self.__options['ksort']:
iter = sorted(iter,
key=lambda x: x[0],
reverse=self.__options['reverse'])
for k, v in iter:
attrs = {}
if self.__options['hasattr'] and isinstance(
v, utils.DictTypes) and self.check_structure(
v.keys()):
attrs, v = self.pickdata(v)
self.build_tree(v, k, attrs, depth + 1)
self.__tree.append('%s%s' % (indent, self.tag_end(tagname)))
elif utils.is_iterable(data):
for v in data:
self.build_tree(v, tagname, attrs, depth)
else:
self.__tree.append(indent)
data = self.safedata(data, self.__options['cdata'])
self.__tree.append(self.build_tag(tagname, data, attrs))
def mass_excess(self, ions):
"""
TODO:
generally supplement by data from
http://www.nndc.bnl.gov/masses/mass.mas12
"""
if not is_iterable(ions):
shape = ()
ions = ions,
else:
shape = np.shape(ions)
if not hasattr(self, '_mass_excess_data'):
self._mass_excess_data = dict()
me = []
spec_ions_me = {
isotope.ion('nt1') : 8.07131714,
isotope.ion('h1' ) : 7.28897059,
isotope.ion('he4') : 2.42491561,
isotope.ion('be8') : 4.941671,
}
for i in ions:
x = self._mass_excess_data.get(i, None)
if x is None:
try:
x = self.__getitem__(i).mass_excess()
except KeyError:
try:
x = spec_ions_me[isotope.ion(i)]
except KeyError:
x = 0
#print(f' [ERROR] NOT FOUND: {i} (returning {x})')
print(' [ERROR] NOT FOUND: {} (returning {})'.format(i,x))
self._mass_excess_data[i] = x
me.append(x)
if shape == ():
return me[0]
return np.array(me)
def actions(self, actions):
if not is_iterable(actions):
raise TypeError('actions must be list-like')
self._actions = list(actions)
def _construct_children_params(self,
children,
child_type,
child_params,
params,
num_children=None):
"""overridden from CompoundObject. this incorporates the specified
ShapeObject's shape into the child parameters.
Parameters
----------
children : list[string | list] | None
see CompoundObject. if unspecified, the number of children will be
determined from the specified shape.
child_type : string | list[string] | None
see CompoundObject
child_params : dict | list[dict] | None
see CompoundObject
params : dict
a dict of the keyword arguments passed to the ShapeObject
constructor. this might include a 'shape' element that overrides the
default shape.
num_children : Number, optional
the number of children that should be created. if unspecified,
determined from the specified shape.
Returns
-------
children : list[list[string, dict]]
the children specification list
"""
#process the shape
shape = params.pop('shape', None)
if shape is None:
shape = copy(self._default_shape)
if shape is not None:
shape = self._attributes['shape'].coerce_value(shape)
assert is_iterable(shape), 'shape must be iterable'
num_shape = len(shape)
if num_children is None:
num_children = num_shape
else:
assert num_children == num_shape, 'number of children (%d) and shape length (%d) do not match' % (
num_children, num_shape)
#pop the parent position out of params (so the super method doesn't give
#the children a position based on the parent)
if not 'position' in params:
position = self._attributes['position']
params['position'] = self._get_init_value(params,
position,
evaluate=False)
parent_pos = params.pop('position')
#parse callable positions
if callable(parent_pos):
if 'shape' in inspect.getargspec(parent_pos)[0]:
parent_pos = parent_pos(self, shape=shape)
else:
parent_pos = parent_pos(self)
#get the children without positions specified
children = super(ShapeObject, self)._construct_children_params(
children,
child_type,
child_params,
params,
num_children=num_children)
#put position back in params
params['position'] = parent_pos
#parse the absolute child positions
if shape is not None:
#absolute shape positions
children_pos = parent_pos + shape
#make sure int coordinates are respected
if isinstance(parent_pos, np.ndarray) and parent_pos.dtype == int:
children_pos = children_pos.astype(int)
for idx, child in enumerate(children):
if not 'position' in child[1]:
child[1]['position'] = children_pos[idx]
return children
