def nest(self, inner_axes=None, outer_axes=None):
"""Return a new xpSpace with axes `outer_axes`,
obtained by projecting along the `inner_axes`.
The entries of this `xpSpace` are themselves `xpSpace`s,
with axes `inner_axes`,
each one regrouping the entries with the same (projected) coordinate.
Note: is also called by `__getitem__(key)` if `key` is dict.
"""
# Default: a singleton outer space,
# with everything contained in the inner (projection) space.
if inner_axes is None and outer_axes is None:
outer_axes = ()
# Validate axes
if inner_axes is None:
assert outer_axes is not None
inner_axes = struct_tools.complement(self.axes, outer_axes)
else:
assert outer_axes is None
outer_axes = struct_tools.complement(self.axes, inner_axes)
# Fill spaces
outer_space = self.__class__(outer_axes)
for coord, entry in self.items():
outer_coord = outer_space.__getkey__(coord)
try:
inner_space = outer_space[outer_coord]
except KeyError:
inner_space = self.__class__(inner_axes)
outer_space[outer_coord] = inner_space
inner_space[inner_space.__getkey__(coord)] = entry
return outer_space
def intersect_axes(self, attrs):
"""Rm those a in attrs that are not in self.axes.
This allows errors in the axes allotment, for ease-of-use.
"""
absent = struct_tools.complement(attrs, self.axes)
if absent:
print(color_text("Warning:",
colorama.Fore.RED), "The requested attributes",
color_text(str(absent), colorama.Fore.RED),
("were not found among the"
" xpSpace axes (attrs. used as coordinates"
" for the set of experiments)."
" This may be no problem if the attr. is redundant"
" for the coord-sys."
" However, if it is caused by confusion or mis-spelling,"
" then it is likely to cause mis-interpretation"
" of the shown results."))
attrs = struct_tools.complement(attrs, absent)
return attrs
def for_params(method, **fixed_params):
dc_fields = [f.name for f in dcs.fields(method)]
params = struct_tools.intersect(param_dict, dc_fields)
params = struct_tools.complement(params, fixed_params)
params = {**glob_dict, **params} # glob_dict 1st
def xp1(dct):
xp = method(**struct_tools.intersect(dct, dc_fields), **fixed_params)
for key, v in struct_tools.intersect(dct, glob_dict).items():
setattr(xp, key, v)
return xp
return [xp1(dct) for dct in struct_tools.prodct(params)]
def unpack1(arr, i, uq):
if uq is None:
return
# Columns: val/prec
if decimals is None:
# v, c = uq.round()
v, c = str(uq).split("±")
else:
v, c = np.round([uq.val, uq.prec], decimals)
v = pad0(str(v))
c = pad0(str(c))
arr["val"][i], arr["prec"][i] = v.replace(" ", ""), c.replace(" ", "")
# Columns: others
for col in struct_tools.complement(cols, ["val", "prec"]):
try:
arr[col][i] = getattr(uq, col)
except AttributeError:
pass
def _aggregate_keys():
"""Aggregate keys from all `xp`"""
if len(self) == 0:
return []
# Start with da_method
aggregate = ['da_method']
# Aggregate all other keys
for xp in self:
# Get dataclass fields
try:
dc_fields = dcs.fields(xp.__class__)
dc_names = [F.name for F in dc_fields]
keys = xp.__dict__.keys()
except TypeError:
# Assume namedtuple
dc_names = []
keys = xp._fields
# For all potential keys:
for k in keys:
# If not already present:
if k not in aggregate:
# If dataclass, check repr:
if k in dc_names:
if dc_fields[dc_names.index(k)].repr:
aggregate.append(k)
# Else, just append
else:
aggregate.append(k)
# Remove unwanted
excluded = [re.compile('^_'), 'avrgs', 'stats', 'HMM', 'duration']
aggregate = struct_tools.complement(aggregate, excluded)
return aggregate