def construct_data_model(parameterized, name=None, ignore=[], types={}):
properties = {}
for pname in parameterized.param:
if pname in ignore:
continue
p = parameterized.param[pname]
if p.precedence and p.precedence < 0:
continue
ptype = types.get(pname, type(p))
prop = PARAM_MAPPING.get(ptype)
pname = parameterized._rename.get(pname, pname)
if pname == 'name' or pname is None:
continue
nullable = getattr(p, 'allow_None', False)
kwargs = {'default': p.default, 'help': p.doc}
if prop is None:
bk_prop, accepts = bp.Any(**kwargs), []
else:
bkp = prop(p, {} if nullable else kwargs)
bk_prop, accepts = bkp if isinstance(bkp, tuple) else (bkp, [])
if nullable:
bk_prop = bp.Nullable(bk_prop, **kwargs)
for bkp, convert in accepts:
bk_prop = bk_prop.accepts(bkp, convert)
properties[pname] = bk_prop
name = name or parameterized.name
return type(name, (DataModel, ), properties)
def construct_data_model(parameterized, name=None, ignore=[], types={}):
"""
Dynamically creates a Bokeh DataModel class from a Parameterized
object.
Arguments
---------
parameterized: param.Parameterized
The Parameterized class or instance from which to create the
DataModel
name: str or None
Name of the dynamically created DataModel class
ignore: list(str)
List of parameters to ignore.
types: dict
A dictionary mapping from parameter name to a Parameter type,
making it possible to override the default parameter types.
Returns
-------
DataModel
"""
properties = {}
for pname in parameterized.param:
if pname in ignore:
continue
p = parameterized.param[pname]
if p.precedence and p.precedence < 0:
continue
ptype = types.get(pname, type(p))
prop = PARAM_MAPPING.get(ptype)
if isinstance(parameterized, Syncable):
pname = parameterized._rename.get(pname, pname)
if pname == 'name' or pname is None:
continue
nullable = getattr(p, 'allow_None', False)
kwargs = {'default': p.default, 'help': p.doc}
if prop is None:
bk_prop, accepts = bp.Any(**kwargs), []
else:
bkp = prop(p, {} if nullable else kwargs)
bk_prop, accepts = bkp if isinstance(bkp, tuple) else (bkp, [])
if nullable:
bk_prop = bp.Nullable(bk_prop, **kwargs)
for bkp, convert in accepts:
bk_prop = bk_prop.accepts(bkp, convert)
properties[pname] = bk_prop
name = name or parameterized.name
return type(name, (DataModel, ), properties)
def construct_data_model(parameterized, name=None, ignore=['name']):
properties = {}
for pname in parameterized.param:
if pname in ignore:
continue
p = parameterized.param[pname]
prop = PARAM_MAPPING.get(type(p))
kwargs = {'default': p.default, 'help': p.doc}
if prop is None:
properties[pname] = bp.Any(**kwargs)
else:
properties[pname] = prop(p, kwargs)
name = name or parameterized.name
return type(name, (DataModel, ), properties)
_DATA_MODELS = weakref.WeakKeyDictionary()
PARAM_MAPPING = {
pm.Array:
lambda p, kwargs: bp.Array(bp.Any, **kwargs),
pm.Boolean:
lambda p, kwargs: bp.Bool(**kwargs),
pm.CalendarDate:
lambda p, kwargs: bp.Date(**kwargs),
pm.CalendarDateRange:
lambda p, kwargs: bp.Tuple(bp.Date, bp.Date, **kwargs),
pm.ClassSelector:
lambda p, kwargs: ((bp.Instance(DataModel, **kwargs), [
(Parameterized, create_linked_datamodel)
]) if isinstance(p.class_, type) and issubclass(
p.class_, param.Parameterized) else bp.Any(**kwargs)),
pm.Color:
lambda p, kwargs: bp.Color(**kwargs),
pm.DataFrame:
lambda p, kwargs: (bp.ColumnData(bp.Any, bp.Seq(bp.Any), **kwargs), [(
bp.PandasDataFrame, lambda x: ColumnDataSource._data_from_df(x))]),
pm.DateRange:
lambda p, kwargs: bp.Tuple(bp.Datetime, bp.Datetime, **kwargs),
pm.Date:
lambda p, kwargs: bp.Datetime(**kwargs),
pm.Dict:
lambda p, kwargs: bp.Dict(bp.String, bp.Any, **kwargs),
pm.Event:
lambda p, kwargs: bp.Bool(**kwargs),
pm.Integer:
lambda p, kwargs: bp.Int(**kwargs),
