def imshow(self, *, data=None, x=None, y=None, widget='bqplot', show=True):
"""
Open an interactive image viewer.
Parameters
----------
data : str or `~glue.core.data.Data`, optional
The initial dataset to show in the viewer. Additional
datasets can be added later using the ``add_data`` method on
the viewer object.
x : str or `~glue.core.component_id.ComponentID`, optional
The attribute to show on the x axis. This should be one of the
pixel axis attributes.
y : str or `~glue.core.component_id.ComponentID`, optional
The attribute to show on the y axis. This should be one of the
pixel axis attributes.
widget : {'bqplot', 'matplotlib'}
Whether to use bqplot or Matplotlib as the front-end.
show : bool, optional
Whether to show the view immediately (`True`) or whether to only
show it later if the ``show()`` method is called explicitly
(`False`).
"""
if widget == 'bqplot':
from .bqplot.image import BqplotImageView
viewer_cls = BqplotImageView
elif widget == 'matplotlib':
from .matplotlib.image import ImageJupyterViewer
viewer_cls = ImageJupyterViewer
else:
raise ValueError("Widget type should be 'bqplot' or 'matplotlib'")
data = validate_data_argument(self.data_collection, data)
if len(data.pixel_component_ids) < 2:
raise ValueError(
'Only data with two or more dimensions can be used '
'as the initial dataset in the image viewer')
view = self.new_data_viewer(viewer_cls, data=data, show=show)
if x is not None:
x = data.id[x]
view.state.x_att = x
if y is not None:
y = data.id[y]
view.state.y_att = y
return view
def vue_create_viewer(self, name):
viewer_cls = viewers.members[name]['cls']
selected = self.components.get('g-data-tree').selected
for idx in selected:
data = validate_data_argument(self.data_collection,
self.data_collection[idx])
new_viewer_message = NewViewerMessage(viewer_cls,
data=data,
sender=self)
self.hub.broadcast(new_viewer_message)
def add_data(self, data, color=None, alpha=None, **layer_state):
data = validate_data_argument(self._data, data)
result = super().add_data(data)
if not result:
return
layer = list(self._layer_artist_container[data])[-1]
layer_state = dict(layer_state)
_update_not_none(layer_state, color=color, alpha=alpha)
layer.state.update_from_dict(layer_state)
return True
def table(self,
*,
data=None,
x=None,
widget='ipyvuetify',
viewer_state=None,
layer_state=None,
show=True):
"""
Open an interactive table viewer.
Parameters
----------
data : str or `~glue.core.data.Data`, optional
The dataset to show in the viewer.
widget : {'ipyvuetify', 'matplotlib'}
Whether to use ipyvuetify or ... as table viewer
viewer_state : `~glue.viewers.common.state.ViewerState`
The initial state for the viewer (advanced).
layer_state : `~glue.viewers.common.state.LayerState`
The initial state for the data layer (advanced).
show : bool, optional
Whether to show the view immediately (`True`) or whether to only
show it later if the ``show()`` method is called explicitly
(`False`).
"""
if widget == 'ipyvuetify':
from .table import TableViewer
viewer_cls = TableViewer
else:
raise ValueError("Widget type should be 'ipyvuetify'")
data = validate_data_argument(self.data_collection, data)
viewer_state_obj = viewer_cls._state_cls()
viewer_state = viewer_state or {}
viewer_state_obj.update_from_dict(viewer_state)
view = self.new_data_viewer(viewer_cls,
data=data,
state=viewer_state_obj,
show=show)
layer_state = layer_state or {}
view.layers[0].state.update_from_dict(layer_state)
return view
def volshow(self, *, data=None, x=None, y=None, z=None, show=True):
"""
Open an interactive volume viewer.
Parameters
----------
data : str or `~glue.core.data.Data`, optional
The initial dataset to show in the viewer. Additional
datasets can be added later using the ``add_data`` method on
the viewer object.
x : str or `~glue.core.component_id.ComponentID`, optional
The attribute to show on the x axis. This should be one of the
pixel axis attributes.
y : str or `~glue.core.component_id.ComponentID`, optional
The attribute to show on the y axis. This should be one of the
pixel axis attributes.
z : str or `~glue.core.component_id.ComponentID`, optional
The attribute to show on the z axis. This should be one of the
pixel axis attributes.
show : bool, optional
Whether to show the view immediately (`True`) or whether to only
show it later if the ``show()`` method is called explicitly
(`False`).
"""
from .ipyvolume import IpyvolumeVolumeView
data = validate_data_argument(self.data_collection, data)
view = self.new_data_viewer(IpyvolumeVolumeView, data=data, show=show)
if x is not None:
x = data.id[x]
view.state.x_att = x
if y is not None:
y = data.id[y]
view.state.y_att = y
if z is not None:
z = data.id[z]
view.state.z_att = z
return view
def profile1d(self, *, data=None, x=None, widget='bqplot', show=True):
"""
Open an interactive 1d profile viewer.
Parameters
----------
data : str or `~glue.core.data.Data`, optional
The initial dataset to show in the viewer. Additional
datasets can be added later using the ``add_data`` method on
the viewer object.
x : str or `~glue.core.component_id.ComponentID`, optional
The attribute to show on the x axis. This should be a pixel or
world coordinate `~glue.core.component_id.ComponentID`.
widget : {'bqplot', 'matplotlib'}
Whether to use bqplot or Matplotlib as the front-end.
show : bool, optional
Whether to show the view immediately (`True`) or whether to only
show it later if the ``show()`` method is called explicitly
(`False`).
"""
if widget == 'bqplot':
from .bqplot.profile import BqplotProfileView
viewer_cls = BqplotProfileView
elif widget == 'matplotlib':
from .matplotlib.profile import ProfileJupyterViewer
viewer_cls = ProfileJupyterViewer
else:
raise ValueError("Widget type should be 'matplotlib'")
data = validate_data_argument(self.data_collection, data)
view = self.new_data_viewer(viewer_cls, data=data, show=show)
if x is not None:
x = data.id[x]
view.state.x_att = x
return view
def scatter2d(self,
*,
data=None,
x=None,
y=None,
widget='bqplot',
color=None,
size=None,
viewer_state=None,
layer_state=None,
show=True):
"""
Open an interactive 2d scatter plot viewer.
Parameters
----------
data : str or `~glue.core.data.Data`, optional
The initial dataset to show in the viewer. Additional
datasets can be added later using the ``add_data`` method on
the viewer object.
x : str or `~glue.core.component_id.ComponentID`, optional
The attribute to show on the x axis.
y : str or `~glue.core.component_id.ComponentID`, optional
The attribute to show on the y axis.
widget : {'bqplot', 'matplotlib'}
Whether to use bqplot or Matplotlib as the front-end.
color : str or tuple, optional
The color to use for the markers. Note that this will have the
effect of setting the data color for all viewers.
size : int or float
The size to use for the markers. Note that this will have the
effect of setting the marker size for all viewers.
viewer_state : `~glue.viewers.common.state.ViewerState`
The initial state for the viewer (advanced).
layer_state : `~glue.viewers.common.state.LayerState`
The initial state for the data layer (advanced).
show : bool, optional
Whether to show the view immediately (`True`) or whether to only
show it later if the ``show()`` method is called explicitly
(`False`).
"""
if widget == 'bqplot':
from .bqplot.scatter import BqplotScatterView
viewer_cls = BqplotScatterView
elif widget == 'matplotlib':
from .matplotlib.scatter import ScatterJupyterViewer
viewer_cls = ScatterJupyterViewer
else:
raise ValueError("Widget type should be 'bqplot' or 'matplotlib'")
data = validate_data_argument(self.data_collection, data)
viewer_state_obj = viewer_cls._state_cls()
viewer_state_obj.x_att_helper.append_data(data)
viewer_state_obj.y_att_helper.append_data(data)
viewer_state = viewer_state or {}
if x is not None:
viewer_state['x_att'] = data.id[x]
if y is not None:
viewer_state['y_att'] = data.id[y]
viewer_state_obj.update_from_dict(viewer_state)
view = self.new_data_viewer(viewer_cls,
data=data,
state=viewer_state_obj,
show=show)
layer_state = layer_state or {}
_update_not_none(layer_state, color=color, size=size)
view.layers[0].state.update_from_dict(layer_state)
return view
def histogram1d(self,
*,
data=None,
x=None,
widget='bqplot',
color=None,
x_min=None,
x_max=None,
n_bin=None,
normalize=False,
cumulative=False,
viewer_state=None,
layer_state=None,
show=True):
"""
Open an interactive histogram viewer.
Parameters
----------
data : str or `~glue.core.data.Data`, optional
The initial dataset to show in the viewer. Additional
datasets can be added later using the ``add_data`` method on
the viewer object.
x : str or `~glue.core.component_id.ComponentID`, optional
The attribute to show on the x axis.
widget : {'bqplot', 'matplotlib'}
Whether to use bqplot or Matplotlib as the front-end.
color : str or tuple, optional
The color to use for the data. Note that this will have the
effect of setting the data color for all viewers.
x_min : float, optional
The lower value of the range to compute the histogram in.
x_max : float, optional
The upper value of the range to compute the histogram in.
n_bin : int, optional
The number of bins in the histogram.
normalize : bool, optional
Whether to normalize the histogram.
cumulative : bool, optional
Whether to show a cumulative histogram.
viewer_state : `~glue.viewers.common.state.ViewerState`
The initial state for the viewer (advanced).
layer_state : `~glue.viewers.common.state.LayerState`
The initial state for the data layer (advanced).
show : bool, optional
Whether to show the view immediately (`True`) or whether to only
show it later if the ``show()`` method is called explicitly
(`False`).
"""
if widget == 'bqplot':
from .bqplot.histogram import BqplotHistogramView
viewer_cls = BqplotHistogramView
elif widget == 'matplotlib':
from .matplotlib.histogram import HistogramJupyterViewer
viewer_cls = HistogramJupyterViewer
else:
raise ValueError("Widget type should be 'bqplot' or 'matplotlib'")
data = validate_data_argument(self.data_collection, data)
viewer_state_obj = viewer_cls._state_cls()
viewer_state_obj.x_att_helper.append_data(data)
viewer_state = viewer_state or {}
if x is not None:
viewer_state['x_att'] = data.id[x]
# x_min and x_max get set to the hist_x_min/max in
# glue.viewers.histogram.state for this API it make more sense to call
# it x_min and x_max, and for consistency with the rest
_update_not_none(viewer_state,
hist_x_min=x_min,
hist_x_max=x_max,
hist_n_bin=n_bin,
normalize=normalize,
cumulative=cumulative)
viewer_state_obj.update_from_dict(viewer_state)
view = self.new_data_viewer(viewer_cls,
data=data,
state=viewer_state_obj,
show=show)
layer_state = layer_state or {}
_update_not_none(layer_state, color=color)
view.layers[0].state.update_from_dict(layer_state)
return view
