class _BigDumbParams(param.Parameterized):
action = param.Action(default_action, allow_None=True)
array = param.Array(np.array([1.0, 2.0]))
boolean = param.Boolean(True, allow_None=True)
callable = param.Callable(default_action, allow_None=True)
class_selector = param.ClassSelector(int, is_instance=False, allow_None=True)
color = param.Color("#FFFFFF", allow_None=True)
composite = param.Composite(["action", "array"], allow_None=True)
try:
data_frame = param.DataFrame(
pd.DataFrame({"A": 1.0, "B": np.arange(5)}), allow_None=True
)
except TypeError:
data_frame = param.DataFrame(pd.DataFrame({"A": 1.0, "B": np.arange(5)}))
date = param.Date(datetime.now(), allow_None=True)
date_range = param.DateRange((datetime.min, datetime.max), allow_None=True)
dict_ = param.Dict({"foo": "bar"}, allow_None=True, doc="dict means dictionary")
dynamic = param.Dynamic(default=default_action, allow_None=True)
file_selector = param.FileSelector(
os.path.join(FILE_DIR_DIR, "LICENSE"),
path=os.path.join(FILE_DIR_DIR, "*"),
allow_None=True,
)
filename = param.Filename(
os.path.join(FILE_DIR_DIR, "LICENSE"), allow_None=True
)
foldername = param.Foldername(os.path.join(FILE_DIR_DIR), allow_None=True)
hook_list = param.HookList(
[CallableObject(), CallableObject()], class_=CallableObject, allow_None=True
)
integer = param.Integer(10, allow_None=True)
list_ = param.List([1, 2, 3], allow_None=True, class_=int)
list_selector = param.ListSelector([2, 2], objects=[1, 2, 3], allow_None=True)
magnitude = param.Magnitude(0.5, allow_None=True)
multi_file_selector = param.MultiFileSelector(
[],
path=os.path.join(FILE_DIR_DIR, "*"),
allow_None=True,
check_on_set=True,
)
number = param.Number(-10.0, allow_None=True, doc="here is a number")
numeric_tuple = param.NumericTuple((5.0, 10.0), allow_None=True)
object_selector = param.ObjectSelector(
False, objects={"False": False, "True": 1}, allow_None=True
)
path = param.Path(os.path.join(FILE_DIR_DIR, "LICENSE"), allow_None=True)
range_ = param.Range((-1.0, 2.0), allow_None=True)
series = param.Series(pd.Series(range(5)), allow_None=True)
string = param.String("foo", allow_None=True, doc="this is a string")
tuple_ = param.Tuple((3, 4, "fi"), allow_None=True)
x_y_coordinates = param.XYCoordinates((1.0, 2.0), allow_None=True)
class Test(param.Parameterized):
series = param.Series(allow_None=True, rows=3)
class Test(param.Parameterized):
series = param.Series(default=None, rows=3)
class Test(param.Parameterized):
series = param.Series(default=invalid_series, rows=(5, 7))
class Test(param.Parameterized):
series = param.Series(default=valid_series, rows=(None, 3))
class Test(param.Parameterized):
series = param.Series(valid_series, rows=2)
class ScatterDashBoard(MultiCollectionHandler, param.Parameterized):
'''Dashboard to view 3 dimensions (x,y,color) of a multidimensional dataset
as a scatter plot.'''
x_key = param.Selector()
y_key = param.Selector()
color_key = param.Selector()
hover_keys = param.List()
filter_columns = param.List()
selected_row = param.Series(pd.Series([], dtype=int))
selection_ids = param.List([])
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
available_keys = self.df.columns.tolist()
self.param.x_key.objects = available_keys
self.param.y_key.objects = available_keys
self.param.color_key.objects = available_keys
if not self.x_key:
self.x_key = available_keys[0]
if not self.y_key:
self.y_key = available_keys[1]
if not self.color_key:
self.color_key = available_keys[2]
if isinstance(self.df.index, pd.MultiIndex):
self.multi_select_levels = list(self.df.index.names)
else:
self.multi_select_levels = [self.df.index.name]
self.file_widgets = []
self._build_widgets()
def _selected_hook(self, plot, element):
'''Directly access bokeh figure and set selection'''
# NOTE only works when called during plot creation
# --> redraw entire figure to update selection
# In principle it's possible, from a handle, to update only
# the selection but update is not triggered
plot.handles['selected'].indices = self.selection_ids
@param.depends('x_key', 'y_key', 'color_key', 'selection_ids', 'subdf')
def plot_scatter(self):
points = hv.Points(self.subdf,
kdims=[self.x_key, self.y_key],
vdims=self.hover_keys + [self.color_key],
group='props_scatter')
# change colormap for categorical values
if self.subdf[self.color_key].dtype == 'O':
if len(self.subdf[self.color_key].unique()) <= 10:
cmap = 'Category10'
else:
cmap = 'Category20'
colorbar = False
color_levels = None
else:
cmap = 'viridis'
colorbar = True
color_levels = len(self.subdf[self.color_key].unique())
points.opts(
color=self.color_key,
color_levels=color_levels,
cmap=cmap,
colorbar=colorbar,
tools=['hover', 'box_select', 'lasso_select', 'tap'],
)
# add selection stream and attach callback to update sample/image selection
self._selection = Selection1D(source=points)
@param.depends(self._selection.param.index, watch=True)
def update_image_selectors(index):
if len(index) > 0:
self.selected_row = self.subdf.iloc[index[0]]
points.opts(hooks=[self._selected_hook])
# TODO fix broken adjoint histograms, broken in latest holviews when points color is set
return points #.hist(dimension=[self.x_key, self.y_key], num_bins=100).opts(opts.Histogram(color='grey', yaxis='bare', xaxis='bare'))
def widget(self):
scatter_wg = pn.WidgetBox(
'Scatter',
self.param.x_key,
self.param.y_key,
self.param.color_key,
)
return pn.Row(scatter_wg, self.io_widgets)
def panel(self):
return pn.Column(self.plot_scatter, self.widget())
