class Transform(schema.Transform):
filter = T.Union([
T.Unicode(allow_none=True, default_value=None),
T.Instance(expr.Expression),
T.Instance(schema.EqualFilter),
T.Instance(schema.RangeFilter),
T.Instance(schema.OneOfFilter),
T.List(
T.Union([
T.Unicode(),
T.Instance(expr.Expression),
T.Instance(schema.EqualFilter),
T.Instance(schema.RangeFilter),
T.Instance(schema.OneOfFilter)
]))
],
allow_none=True,
default_value=None,
help=schema.Transform.filter.help)
def _finalize(self, **kwargs):
"""Finalize object: convert filter expressions to string"""
convert = lambda f: repr(f) if isinstance(f, expr.Expression) else f
self.filter = convert(self.filter)
if isinstance(self.filter, list):
self.filter = [convert(f) for f in self.filter]
super(Transform, self)._finalize(**kwargs)
class FieldDef(BaseObject):
"""Wrapper for Vega-Lite FieldDef definition.
Attributes
----------
aggregate: AggregateOp
Aggregation function for the field .
bin: Union(Bool, Bin)
Flag for binning a `quantitative` field, or a bin property object for binning parameters.
field: Unicode
Name of the field from which to pull a data value.
timeUnit: TimeUnit
Time unit for a `temporal` field .
title: Unicode
Title for axis or legend.
type: Type
The encoded field's type of measurement.
value: Union(CFloat, Unicode, Bool)
A constant value in visual domain.
"""
aggregate = AggregateOp(allow_none=True, default_value=None, help="""Aggregation function for the field .""")
bin = T.Union([T.Bool(allow_none=True, default_value=None), T.Instance(Bin, allow_none=True, default_value=None)])
field = T.Unicode(allow_none=True, default_value=None, help="""Name of the field from which to pull a data value.""")
timeUnit = TimeUnit(allow_none=True, default_value=None, help="""Time unit for a `temporal` field .""")
title = T.Unicode(allow_none=True, default_value=None, help="""Title for axis or legend.""")
type = Type(allow_none=True, default_value=None, help="""The encoded field's type of measurement.""")
value = T.Union([T.CFloat(allow_none=True, default_value=None), T.Unicode(allow_none=True, default_value=None), T.Bool(allow_none=True, default_value=None)])
def __init__(self, aggregate=None, bin=None, field=None, timeUnit=None, title=None, type=None, value=None, **kwargs):
kwds = dict(aggregate=aggregate, bin=bin, field=field, timeUnit=timeUnit, title=title, type=type, value=value)
kwargs.update({k:v for k, v in kwds.items() if v is not None})
super(FieldDef, self).__init__(**kwargs)
class Scatter(widgets.DOMWidget):
_view_name = Unicode('ScatterView').tag(sync=True)
_view_module = Unicode('ipyvolume').tag(sync=True)
_model_name = Unicode('ScatterModel').tag(sync=True)
_model_module = Unicode('ipyvolume').tag(sync=True)
x = Array(default_value=None).tag(sync=True, **create_array_binary_serialization('x'))
y = Array(default_value=None).tag(sync=True, **create_array_binary_serialization('y'))
z = Array(default_value=None).tag(sync=True, **create_array_binary_serialization('z'))
vx = Array(default_value=None,allow_none=True).tag(sync=True, **create_array_binary_serialization('vx'))
vy = Array(default_value=None,allow_none=True).tag(sync=True, **create_array_binary_serialization('vy'))
vz = Array(default_value=None,allow_none=True).tag(sync=True, **create_array_binary_serialization('vz'))
selected = Array(default_value=None,allow_none=True).tag(sync=True, **create_array_binary_serialization('selection', update_from_js=True))
sequence_index = Integer(default_value=0).tag(sync=True)
size = traitlets.Union([traitlets.Float().tag(sync=True),
Array(default_value=None,allow_none=True).tag(sync=True, **create_array_binary_serialization('size'))],
default_value=5).tag(sync=True)
size_selected = traitlets.Union([traitlets.Float().tag(sync=True),
Array(default_value=None,allow_none=True).tag(sync=True, **create_array_binary_serialization('size_selected'))],
default_value=7).tag(sync=True)
color = traitlets.Union([Unicode().tag(sync=True),
Array(default_value=None,allow_none=True).tag(sync=True, **create_array_binary_serialization('color'))],
default_value="red").tag(sync=True)
color_selected = traitlets.Union([Unicode().tag(sync=True),
Array(default_value=None,allow_none=True).tag(sync=True, **create_array_binary_serialization('color_selected'))],
default_value="green").tag(sync=True)
geo = traitlets.Unicode('diamond').tag(sync=True)
class Scatter(widgets.Widget):
_view_name = Unicode('ScatterView').tag(sync=True)
_view_module = Unicode('ipyvolume').tag(sync=True)
_model_name = Unicode('ScatterModel').tag(sync=True)
_model_module = Unicode('ipyvolume').tag(sync=True)
_view_module_version = Unicode(semver_range_frontend).tag(sync=True)
_model_module_version = Unicode(semver_range_frontend).tag(sync=True)
x = Array(default_value=None).tag(sync=True, **array_sequence_serialization)
y = Array(default_value=None).tag(sync=True, **array_sequence_serialization)
z = Array(default_value=None).tag(sync=True, **array_sequence_serialization)
vx = Array(default_value=None, allow_none=True).tag(sync=True, **array_sequence_serialization)
vy = Array(default_value=None, allow_none=True).tag(sync=True, **array_sequence_serialization)
vz = Array(default_value=None, allow_none=True).tag(sync=True, **array_sequence_serialization)
selected = Array(default_value=None, allow_none=True).tag(sync=True, **array_sequence_serialization)
sequence_index = Integer(default_value=0).tag(sync=True)
size = traitlets.Union([Array(default_value=None, allow_none=True).tag(sync=True, **array_sequence_serialization),
traitlets.Float().tag(sync=True)],
default_value=5).tag(sync=True)
size_selected = traitlets.Union([Array(default_value=None, allow_none=True).tag(sync=True, **array_sequence_serialization),
traitlets.Float().tag(sync=True)],
default_value=7).tag(sync=True)
color = Array(default_value="red", allow_none=True).tag(sync=True, **color_serialization)
color_selected = traitlets.Union([Array(default_value=None, allow_none=True).tag(sync=True, **color_serialization),
Unicode().tag(sync=True)],
default_value="green").tag(sync=True)
geo = traitlets.Unicode('diamond').tag(sync=True)
connected = traitlets.CBool(default_value=False).tag(sync=True)
visible = traitlets.CBool(default_value=True).tag(sync=True)
texture = traitlets.Union([
traitlets.Instance(ipywebrtc.MediaStream),
Unicode(),
traitlets.List(Unicode, [], allow_none=True),
Image(default_value=None, allow_none=True),
traitlets.List(Image(default_value=None, allow_none=True))
]).tag(sync=True, **texture_serialization)
material = traitlets.Instance(pythreejs.ShaderMaterial, help='A :any:`pythreejs.ShaderMaterial` that is used for the mesh')\
.tag(sync=True, **ipywidgets.widget_serialization)
@traitlets.default('material')
def _default_material(self):
return pythreejs.ShaderMaterial()
line_material = traitlets.Instance(pythreejs.ShaderMaterial, help='A :any:`pythreejs.ShaderMaterial` that is used for the lines/wireframe')\
.tag(sync=True, **ipywidgets.widget_serialization)
@traitlets.default('line_material')
def _default_line_material(self):
return pythreejs.ShaderMaterial()
class Scatter(widgets.DOMWidget):
_view_name = Unicode('ScatterView').tag(sync=True)
_view_module = Unicode('ipyvolume').tag(sync=True)
_model_name = Unicode('ScatterModel').tag(sync=True)
_model_module = Unicode('ipyvolume').tag(sync=True)
_view_module_version = Unicode(semver_range_frontend).tag(sync=True)
_model_module_version = Unicode(semver_range_frontend).tag(sync=True)
x = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
y = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
z = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
vx = Array(default_value=None,
allow_none=True).tag(sync=True, **array_sequence_serialization)
vy = Array(default_value=None,
allow_none=True).tag(sync=True, **array_sequence_serialization)
vz = Array(default_value=None,
allow_none=True).tag(sync=True, **array_sequence_serialization)
selected = Array(default_value=None,
allow_none=True).tag(sync=True,
**array_sequence_serialization)
sequence_index = Integer(default_value=0).tag(sync=True)
size = traitlets.Union([
Array(default_value=None, allow_none=True).tag(
sync=True, **array_sequence_serialization),
traitlets.Float().tag(sync=True)
],
default_value=5).tag(sync=True)
size_selected = traitlets.Union([
Array(default_value=None, allow_none=True).tag(
sync=True, **array_sequence_serialization),
traitlets.Float().tag(sync=True)
],
default_value=7).tag(sync=True)
color = Array(default_value="red",
allow_none=True).tag(sync=True, **color_serialization)
color_selected = traitlets.Union([
Array(default_value=None, allow_none=True).tag(sync=True,
**color_serialization),
Unicode().tag(sync=True)
],
default_value="green").tag(sync=True)
geo = traitlets.Unicode('diamond').tag(sync=True)
connected = traitlets.CBool(default_value=False).tag(sync=True)
visible = traitlets.CBool(default_value=True).tag(sync=True)
visible_lines = traitlets.CBool(default_value=False).tag(sync=True)
visible_markers = traitlets.CBool(default_value=True).tag(sync=True)
class FacetSpec(BaseObject):
"""Wrapper for Vega-Lite FacetSpec definition.
Attributes
----------
config: Config
Configuration object.
data: Data
An object describing the data source.
description: Unicode
An optional description of this mark for commenting purpose.
facet: Facet
name: Unicode
Name of the visualization for later reference.
spec: Union(LayerSpec, UnitSpec)
transform: Transform
An object describing filter and new field calculation.
"""
config = T.Instance(Config, allow_none=True, default_value=None, help="""Configuration object.""")
data = T.Instance(Data, allow_none=True, default_value=None, help="""An object describing the data source.""")
description = T.Unicode(allow_none=True, default_value=None, help="""An optional description of this mark for commenting purpose.""")
facet = T.Instance(Facet, allow_none=True, default_value=None)
name = T.Unicode(allow_none=True, default_value=None, help="""Name of the visualization for later reference.""")
spec = T.Union([T.Instance(LayerSpec, allow_none=True, default_value=None), T.Instance(UnitSpec, allow_none=True, default_value=None)])
transform = T.Instance(Transform, allow_none=True, default_value=None, help="""An object describing filter and new field calculation.""")
def __init__(self, config=None, data=None, description=None, facet=None, name=None, spec=None, transform=None, **kwargs):
kwds = dict(config=config, data=data, description=description, facet=facet, name=name, spec=spec, transform=transform)
kwargs.update({k:v for k, v in kwds.items() if v is not None})
super(FacetSpec, self).__init__(**kwargs)
class Shelf(BaseObject):
skip = ['shorthand', 'config']
def __init__(self, shorthand, **kwargs):
kwargs['shorthand'] = shorthand
super(Shelf, self).__init__(**kwargs)
def _infer_type(self, data):
if self.type is None and self.name in data:
self.type = infer_vegalite_type(data[self.name])
def _shorthand_changed(self, name, old, new):
D = parse_shorthand(self.shorthand)
for key, val in D.items():
setattr(self, key, val)
shorthand = T.Unicode('')
name = T.Unicode('', config=True)
type = T.Enum(['N', 'O', 'Q', 'T'],
default_value=None,
allow_none=True,
config=True)
timeUnit = T.Enum(
['year', 'month', 'day', 'date', 'hours', 'minutes', 'seconds'],
default_value=None,
allow_none=True)
bin = T.Union([T.Bool(), T.Instance(Bin)], default_value=False)
sort = T.List(T.Instance(SortItems), default_value=None, allow_none=True)
aggregate = T.Enum(['avg', 'sum', 'median', 'min', 'max', 'count'],
default_value=None,
allow_none=True,
config=True)
class VisualizerBase(W.VBox):
"""
The basic Visualization class that takes the shape of an ipywidgets.VBox
:param graph: an rdflib.graph.Graph object or a networkx.classes.graph.Graph object to visualize.
:param edge_color: a string, the desired color of edges.
:param node_color: a string, the desired color of nodes.
:param selected_nodes: a tuple of URIRefs of nodes currently selected either via tap or box select.
:param selected_edges: a list of edges currently selected, currently only working with ipycytoscape.
"""
graph = T.Union((T.Instance(Graph), T.Instance(nx.classes.graph.Graph)),
allow_none=True)
_vis = T.Instance(W.Box, allow_none=True)
edge_color = T.Unicode(default_value="pink")
node_color = T.Unicode(default_value="grey")
selected_nodes = W.trait_types.TypedTuple(trait=T.Instance(URIRef))
selected_edges = T.List()
hovered_nodes = W.trait_types.TypedTuple(trait=T.Instance(URIRef))
hovered_edges = T.List()
graph_layout = T.Unicode()
graph_layout_options = W.trait_types.TypedTuple(trait=T.Unicode())
graph_layout_params = T.Dict()
@T.default("graph_layout_params")
def make_params(self):
return {}
class RangeFilter(BaseObject):
"""Wrapper for Vega-Lite RangeFilter definition.
Attributes
----------
field: Unicode
Field to be filtered.
range: List(Union(CFloat, DateTime))
Array of inclusive minimum and maximum values for a field value of a data item to be included in the filtered data.
timeUnit: TimeUnit
time unit for the field to be filtered.
"""
field = T.Unicode(allow_none=True,
default_value=None,
help="""Field to be filtered.""")
range = T.List(
T.Union([
T.CFloat(allow_none=True, default_value=None),
T.Instance(DateTime, allow_none=True, default_value=None)
]),
allow_none=True,
default_value=None,
maxlen=2,
minlen=2,
help=
"""Array of inclusive minimum and maximum values for a field value of a data item to be included in the filtered data."""
)
timeUnit = TimeUnit(allow_none=True,
default_value=None,
help="""time unit for the field to be filtered.""")
def __init__(self, field=None, range=None, timeUnit=None, **kwargs):
kwds = dict(field=field, range=range, timeUnit=timeUnit)
kwargs.update({k: v for k, v in kwds.items() if v is not None})
super(RangeFilter, self).__init__(**kwargs)
class TimeScale(QuantitativeScale):
type = T.Unicode('time')
nice = T.Union([
T.Unicode, T.CFloat,
T.Enum(['second', 'minute', 'hour', 'day', 'week', 'month', 'year'])
])
class QuantitativeScale(Scale):
type = T.Unicode('quantitative')
clamp = T.Bool()
interploate = T.Unicode()
nice = T.Union([T.Bool(), T.CFloat])
zero = T.Bool()
class OneOfFilter(BaseObject):
"""Wrapper for Vega-Lite OneOfFilter definition.
Attributes
----------
field: Unicode
Field to be filtered.
oneOf: List(Union(Unicode, CFloat, Bool, DateTime))
A set of values that the `field`'s value should be a member of, for a data item included in the filtered data.
timeUnit: TimeUnit
time unit for the field to be filtered.
"""
field = T.Unicode(allow_none=True,
default_value=None,
help="""Field to be filtered.""")
oneOf = T.List(
T.Union([
T.Unicode(allow_none=True, default_value=None),
T.CFloat(allow_none=True, default_value=None),
T.Bool(allow_none=True, default_value=None),
T.Instance(DateTime, allow_none=True, default_value=None)
]),
allow_none=True,
default_value=None,
help=
"""A set of values that the `field`'s value should be a member of, for a data item included in the filtered data."""
)
timeUnit = TimeUnit(allow_none=True,
default_value=None,
help="""time unit for the field to be filtered.""")
def __init__(self, field=None, oneOf=None, timeUnit=None, **kwargs):
kwds = dict(field=field, oneOf=oneOf, timeUnit=timeUnit)
kwargs.update({k: v for k, v in kwds.items() if v is not None})
super(OneOfFilter, self).__init__(**kwargs)
class Reproject(Interpolate):
"""
Create a Algorithm that evalutes a Node with one set of coordinates, and then interpolates it.
This can be used to bilinearly interpolate an averaged dataset, for example.
Attributes
----------
source : Node
The source node. This node will use it's own, specified interpolation scheme
interpolation : str
Type of interpolation method to use for the interpolation
coordinates: Coordinates, Node, str, dict
Coordinates used to evaluate the source. These can be specified as a dictionary, json-formatted string,
PODPAC Coordinates, or a PODPAC Node, where the node MUST implement the 'coordinates' attribute.
"""
coordinates = tl.Union(
[NodeTrait(),
tl.Dict(),
tl.Unicode(),
tl.Instance(Coordinates)],
help=
"""Coordinates used to evaluate the source. These can be specified as a dictionary,
json-formatted string, PODPAC Coordinates, or a PODPAC Node, where the node MUST implement
the 'coordinates' attribute""",
).tag(attr=True)
@tl.validate("coordinates")
def _validate_coordinates(self, d):
if isinstance(d["value"],
Node) and not hasattr(d["value"], "coordinates"):
raise ValueError(
"When specifying the coordinates as a PODPAC Node, this Node must have a 'coordinates' attribute"
)
return d["value"]
@property
def _coordinates(self):
if isinstance(self.coordinates, Coordinates):
return self.coordinates
elif isinstance(self.coordinates, Node):
return self.coordinates.coordinates
elif isinstance(self.coordinates, dict):
return Coordinates.from_definition(self.coordinates)
elif isinstance(self.coordinates, string_types):
return Coordinates.from_json(self.coordinates)
else:
raise TypeError("The coordinates attribute is of the wrong type.")
def _source_eval(self, coordinates, selector, output=None):
return self.source.eval(self._coordinates,
output=output,
_selector=selector)
@property
def base_ref(self):
return "{}_reprojected".format(self.source.base_ref)
class PathLoader(W.HBox, BaseLoader):
"""Loader that selects from a list of files in a path"""
label = T.Instance(W.Label)
path = T.Union([T.Unicode(), T.Instance(Path)], allow_none=True)
file_picker = T.Instance(W.Dropdown)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.children = tuple([self.label, self.file_picker])
T.link((self, "description"), (self.label, "value"))
self.file_picker.observe(self._file_picked, "value")
self._path_changed()
self._file_picked
@T.default("description")
def make_default_description(self):
return "Select file"
@T.default("label")
def make_default_label(self):
return W.Label()
@T.default("file_picker")
def make_default_file_picker(self):
"""TODO: revisit for multiple files, e.g. checkboxes"""
return W.Dropdown()
def _file_picked(self, change=None):
value = self.file_picker.value
if not value:
self.file_upload_value = {}
return
text = value.read_text(encoding="utf-8")
self.file_upload_value = {
value.name: dict(metadata=dict(size=len(text)), content=text)
}
@T.observe("path")
def _path_changed(self, change=None):
options = {"Select a file...": ""}
if not self.path:
self.file_picker.options = options
return
path = Path(self.path)
if path.is_dir():
globs = [
sorted(path.glob(f"*.{ext}")) for ext in SUFFIX_FORMAT_MAP
]
options.update(**{p.name: p for p in sorted(sum([*globs], []))})
self.file_picker.options = options
class FacetedChart(schema.FacetSpec, TopLevelMixin):
_data = None
# Use specialized version of Facet, spec, and Transform
facet = T.Instance(Facet, allow_none=True, default_value=None,
help=schema.FacetSpec.facet.help)
spec = T.Union([T.Instance(LayeredChart), T.Instance(Chart)],
allow_none=True, default_value=None,
help=schema.FacetSpec.spec.help)
transform = T.Instance(Transform, allow_none=True, default_value=None,
help=schema.FacetSpec.transform.help)
@property
def data(self):
return self._data
@data.setter
def data(self, new):
if isinstance(new, string_types):
self._data = Data(url=new)
elif (new is None or isinstance(new, pd.DataFrame)
or isinstance(new, expr.DataFrame) or isinstance(new, Data)):
self._data = new
else:
raise TypeError('Expected DataFrame or altair.Data, got: {0}'.format(new))
skip = ['data', '_data']
def __init__(self, data=None, **kwargs):
super(FacetedChart, self).__init__(**kwargs)
self.data = data
def __dir__(self):
base = super(Chart, self).__dir__()
methods = [
'to_dict', 'from_dict', 'to_altair', 'display',
'configure', 'configure_axis', 'configure_cell',
'configure_legend', 'configure_mark', 'configure_scale',
'configure_facet_axis', 'configure_facet_cell',
'configure_facet_grid', 'configure_facet_scale',
'transform_data',
'set_facet',
]
return base + methods
@use_signature(Facet)
def set_facet(self, *args, **kwargs):
"""Define the facet encoding for the Chart."""
return self._update_subtraits('facet', *args, **kwargs)
def _finalize(self, **kwargs):
self._finalize_data()
# data comes from wrappers, but self.data overrides this if defined
if self.data is not None:
kwargs['data'] = self.data
super(FacetedChart, self)._finalize(**kwargs)
class FacetedChart(schema.FacetSpec, TopLevelMixin):
_data = None
# Use specialized version of Facet, spec, and Transform
facet = T.Instance(Facet,
allow_none=True,
default_value=None,
help=schema.FacetSpec.facet.help)
spec = T.Union(
[T.Instance(LayeredChart), T.Instance(Chart)],
allow_none=True,
default_value=None,
help=schema.FacetSpec.spec.help)
transform = T.Instance(Transform,
allow_none=True,
default_value=None,
help=schema.FacetSpec.transform.help)
max_rows = T.Int(default_value=DEFAULT_MAX_ROWS,
help="Maximum number of rows in the dataset to accept.")
@property
def data(self):
return self._data
@data.setter
def data(self, new):
if isinstance(new, string_types):
self._data = Data(url=new)
elif (new is None or isinstance(new, pd.DataFrame)
or isinstance(new, expr.DataFrame) or isinstance(new, Data)):
self._data = new
else:
raise TypeError(
'Expected DataFrame or altair.Data, got: {0}'.format(new))
skip = ['data', '_data', 'max_rows']
def __init__(self, data=None, **kwargs):
super(FacetedChart, self).__init__(**kwargs)
self.data = data
def __dir__(self):
return [m for m in dir(self.__class__) if m not in dir(T.HasTraits)]
@use_signature(Facet)
def set_facet(self, *args, **kwargs):
"""Define the facet encoding for the Chart."""
return self._update_subtraits('facet', *args, **kwargs)
def _finalize(self, **kwargs):
self._finalize_data()
# data comes from wrappers, but self.data overrides this if defined
if self.data is not None:
kwargs['data'] = self.data
super(FacetedChart, self)._finalize(**kwargs)
class MultiToggleButtons(Box):
description = traitlets.Unicode()
value = traitlets.Tuple()
options = traitlets.Union([traitlets.List(), traitlets.Dict()])
style = traitlets.Dict()
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._selection_obj = widget_selection._MultipleSelection()
traitlets.link((self, 'options'), (self._selection_obj, 'options'))
traitlets.link((self, 'value'), (self._selection_obj, 'value'))
@observer(self, 'options')
def _(*_):
self.buttons = [
ToggleButton(description=label,
layout=Layout(margin='1', width='auto'))
for label in self._selection_obj._options_labels
]
if self.description:
self.label = Label(
self.description,
layout=Layout(
width=self.style.get('description_width', '100px')))
else:
self.label = Label(
self.description,
layout=Layout(
width=self.style.get('description_width', '0px')))
self.children = [self.label] + self.buttons
@observer(self.buttons, 'value')
def _(*_):
self.value = tuple(value for btn, value in zip(
self.buttons, self._selection_obj._options_values)
if btn.value)
self.add_class('btn-group')
def reset(self):
opts = self.options
self.options = []
self.options = opts
def set_value(self, x):
for b, opt in zip(self.buttons, self.options):
b.value = (opt in x)
def set_all_on(self):
for b, opt in zip(self.buttons, self.options):
b.value = True
def set_all_off(self):
for b, opt in zip(self.buttons, self.options):
b.value = False
class Transform(schema.Transform):
filter = T.Union([T.Unicode(), T.Instance(expr.Expression)],
allow_none=True,
default_value=None,
help=schema.Transform.filter.help)
def _finalize(self, **kwargs):
"""Finalize object: convert filter expression to string"""
if isinstance(self.filter, expr.Expression):
self.filter = repr(self.filter)
super(Transform, self)._finalize(**kwargs)
class Mesh(widgets.Widget):
_view_name = Unicode('MeshView').tag(sync=True)
_view_module = Unicode('ipyvolume').tag(sync=True)
_model_name = Unicode('MeshModel').tag(sync=True)
_model_module = Unicode('ipyvolume').tag(sync=True)
_view_module_version = Unicode(semver_range_frontend).tag(sync=True)
_model_module_version = Unicode(semver_range_frontend).tag(sync=True)
x = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
y = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
z = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
u = Array(default_value=None,
allow_none=True).tag(sync=True, **array_sequence_serialization)
v = Array(default_value=None,
allow_none=True).tag(sync=True, **array_sequence_serialization)
triangles = Array(default_value=None,
allow_none=True).tag(sync=True, **array_serialization)
lines = Array(default_value=None,
allow_none=True).tag(sync=True, **array_serialization)
texture = traitlets.Union([
traitlets.Instance(ipywebrtc.MediaStream),
Unicode(),
traitlets.List(Unicode, [], allow_none=True),
Image(default_value=None, allow_none=True),
traitlets.List(Image(default_value=None, allow_none=True)),
]).tag(sync=True, **texture_serialization)
sequence_index = Integer(default_value=0).tag(sync=True)
color = Array(default_value="red",
allow_none=True).tag(sync=True, **color_serialization)
visible = traitlets.CBool(default_value=True).tag(sync=True)
material = traitlets.Instance(
pythreejs.ShaderMaterial,
help='A :any:`pythreejs.ShaderMaterial` that is used for the mesh'
).tag(sync=True, **widgets.widget_serialization)
@traitlets.default('material')
def _default_material(self):
return pythreejs.ShaderMaterial(side=pythreejs.enums.Side.DoubleSide)
line_material = traitlets.Instance(
pythreejs.ShaderMaterial,
help=
'A :any:`pythreejs.ShaderMaterial` that is used for the lines/wireframe'
).tag(sync=True, **widgets.widget_serialization)
@traitlets.default('line_material')
def _default_line_material(self):
return pythreejs.ShaderMaterial()
class Mesh(widgets.DOMWidget):
_view_name = Unicode('MeshView').tag(sync=True)
_view_module = Unicode('ipyvolume').tag(sync=True)
_model_name = Unicode('MeshModel').tag(sync=True)
_model_module = Unicode('ipyvolume').tag(sync=True)
_view_module_version = Unicode(semver_range_frontend).tag(sync=True)
_model_module_version = Unicode(semver_range_frontend).tag(sync=True)
x = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
y = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
z = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
u = Array(default_value=None,
allow_none=True).tag(sync=True, **array_sequence_serialization)
v = Array(default_value=None,
allow_none=True).tag(sync=True, **array_sequence_serialization)
triangles = Array(default_value=None,
allow_none=True).tag(sync=True, **array_serialization)
lines = Array(default_value=None,
allow_none=True).tag(sync=True, **array_serialization)
texture = traitlets.Union([
traitlets.Instance(ipywebrtc.MediaStream),
Unicode(),
traitlets.List(Unicode, [], allow_none=True),
Image(default_value=None, allow_none=True),
traitlets.List(Image(default_value=None, allow_none=True))
]).tag(sync=True, **texture_serialization)
# selected = Array(default_value=None, allow_none=True).tag(sync=True, **array_sequence_serialization)
sequence_index = Integer(default_value=0).tag(sync=True)
color = Array(default_value="red",
allow_none=True).tag(sync=True, **color_serialization)
# color_selected = traitlets.Union([Array(default_value=None, allow_none=True).tag(sync=True, **color_serialization),
# Unicode().tag(sync=True)],
# default_value="green").tag(sync=True)
# geo = traitlets.Unicode('diamond').tag(sync=True)
visible = traitlets.CBool(default_value=True).tag(sync=True)
material = traitlets.Instance(pythreejs.ShaderMaterial).tag(
sync=True, **ipywidgets.widget_serialization)
@traitlets.default('material')
def _default_material(self):
return pythreejs.ShaderMaterial(side=pythreejs.Side.DoubleSide)
line_material = traitlets.Instance(pythreejs.ShaderMaterial).tag(
sync=True, **ipywidgets.widget_serialization)
@traitlets.default('line_material')
def _default_line_material(self):
return pythreejs.ShaderMaterial()
class Formula(schema.Formula):
expr = T.Union([T.Unicode(), T.Instance(expr.Expression)],
allow_none=True, default_value=None,
help=schema.Formula.expr.help)
def __init__(self, field, expr=None, **kwargs):
super(Formula, self).__init__(field=field, expr=expr, **kwargs)
def _finalize(self, **kwargs):
"""Finalize object: convert expr expression to string if necessary"""
if isinstance(self.expr, expr.Expression):
self.expr = repr(self.expr)
super(Formula, self)._finalize(**kwargs)
class Scale(T.HasTraits):
name = T.Unicode('')
type = T.Unicode('')
domain = T.Union([T.List, T.Tuple])
domainMax = T.CFloat()
domainMin = T.CFloat()
domainMid = T.CFloat()
domainRaw = T.Union([T.List, T.Tuple])
range = T.Union([
T.List, T.Tuple,
T.Enum([
'width', 'height', 'symbol', 'category', 'diverging', 'ordinal',
'ramp', 'heatmap'
])
])
reverse = T.Bool()
round = T.Bool()
def __call__(self, *args, **kwargs):
raise NotImplementedError(
'Scale operation not implemented, use a subclass.')
def interpolation_trait(default_value=INTERPOLATION_DEFAULT):
"""Create a new interpolation trait
Returns
-------
tl.Union
Union trait for an interpolation definition
"""
return tl.Union([
tl.Dict(),
tl.Enum(INTERPOLATION_SHORTCUTS),
tl.Instance(Interpolation)
],
allow_none=True,
default_value=default_value)
class Shelf(BaseObject):
# TODO: Supported enums & supported types for aggregate
# TODO: Supported types for timeunit
# TODO: Supported types for bin
# TODO: supported role?
# TODO: supported mark types?
# TODO: assert name and type are required
skip = ['shorthand', 'config']
def __init__(self, shorthand, **kwargs):
kwargs['shorthand'] = shorthand
super(Shelf, self).__init__(**kwargs)
def _infer_type(self, data):
if self.type is None and self.name in data:
self.type = infer_vegalite_type(data[self.name])
def _shorthand_changed(self, name, old, new):
# TODO: if name of shorthand changed, should it reset all properties of obj?
D = parse_shorthand(self.shorthand)
for key, val in D.items():
setattr(self, key, val)
def to_dict(self):
if not self.name:
return None
return super(Shelf, self).to_dict()
shorthand = T.Unicode('')
name = T.Unicode('', config=True)
type = T.Enum(['N', 'O', 'Q', 'T'],
default_value=None,
allow_none=True,
config=True)
timeUnit = T.Enum(
['year', 'month', 'day', 'date', 'hours', 'minutes', 'seconds'],
default_value=None,
allow_none=True)
bin = T.Union([T.Bool(), T.Instance(Bin)], default_value=False)
sort = T.List(T.Instance(SortItems), default_value=None, allow_none=True)
aggregate = T.Enum(['avg', 'sum', 'median', 'min', 'max', 'count'],
default_value=None,
allow_none=True,
config=True)
class Mesh(widgets.DOMWidget):
_view_name = Unicode('MeshView').tag(sync=True)
_view_module = Unicode('ipyvolume').tag(sync=True)
_model_name = Unicode('MeshModel').tag(sync=True)
_model_module = Unicode('ipyvolume').tag(sync=True)
_view_module_version = Unicode(semver_range_frontend).tag(sync=True)
_model_module_version = Unicode(semver_range_frontend).tag(sync=True)
x = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
y = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
z = Array(default_value=None).tag(sync=True,
**array_sequence_serialization)
u = Array(default_value=None,
allow_none=True).tag(sync=True, **array_sequence_serialization)
v = Array(default_value=None,
allow_none=True).tag(sync=True, **array_sequence_serialization)
triangles = Array(default_value=None,
allow_none=True).tag(sync=True, **array_serialization)
lines = Array(default_value=None,
allow_none=True).tag(sync=True, **array_serialization)
texture = traitlets.Union([
traitlets.Instance(ipywebrtc.MediaStream),
Unicode(),
traitlets.List(Unicode, [], allow_none=True),
Image(default_value=None, allow_none=True),
traitlets.List(Image(default_value=None, allow_none=True))
]).tag(sync=True, **texture_serialization)
# selected = Array(default_value=None, allow_none=True).tag(sync=True, **array_sequence_serialization)
sequence_index = Integer(default_value=0).tag(sync=True)
color = Array(default_value="red",
allow_none=True).tag(sync=True, **color_serialization)
# color_selected = traitlets.Union([Array(default_value=None, allow_none=True).tag(sync=True, **color_serialization),
# Unicode().tag(sync=True)],
# default_value="green").tag(sync=True)
# geo = traitlets.Unicode('diamond').tag(sync=True)
visible = traitlets.CBool(default_value=True).tag(sync=True)
visible_lines = traitlets.CBool(default_value=True).tag(sync=True)
visible_faces = traitlets.CBool(default_value=True).tag(sync=True)
side = traitlets.CaselessStrEnum(['front', 'back', 'both'],
'both').tag(sync=True)
class Transform(BaseObject):
"""Wrapper for Vega-Lite Transform definition.
Attributes
----------
calculate: List(Formula)
Calculate new field(s) using the provided expresssion(s).
filter: Union(Unicode, EqualFilter, RangeFilter, OneOfFilter, List(Union(Unicode, EqualFilter, RangeFilter, OneOfFilter)))
A string containing the filter Vega expression.
filterInvalid: Bool
Whether to filter invalid values (`null` and `NaN`) from the data.
"""
calculate = T.List(T.Instance(Formula), allow_none=True, default_value=None, help="""Calculate new field(s) using the provided expresssion(s).""")
filter = T.Union([T.Unicode(allow_none=True, default_value=None), T.Instance(EqualFilter, allow_none=True, default_value=None), T.Instance(RangeFilter, allow_none=True, default_value=None), T.Instance(OneOfFilter, allow_none=True, default_value=None), T.List(T.Union([T.Unicode(allow_none=True, default_value=None), T.Instance(EqualFilter, allow_none=True, default_value=None), T.Instance(RangeFilter, allow_none=True, default_value=None), T.Instance(OneOfFilter, allow_none=True, default_value=None)]), allow_none=True, default_value=None)])
filterInvalid = T.Bool(allow_none=True, default_value=None, help="""Whether to filter invalid values (`null` and `NaN`) from the data.""")
def __init__(self, calculate=None, filter=None, filterInvalid=None, **kwargs):
kwds = dict(calculate=calculate, filter=filter, filterInvalid=filterInvalid)
kwargs.update({k:v for k, v in kwds.items() if v is not None})
super(Transform, self).__init__(**kwargs)
class EqualFilter(BaseObject):
"""Wrapper for Vega-Lite EqualFilter definition.
Attributes
----------
equal: Union(Unicode, CFloat, Bool, DateTime)
Value that the field should be equal to.
field: Unicode
Field to be filtered.
timeUnit: TimeUnit
Time unit for the field to be filtered.
"""
equal = T.Union([T.Unicode(allow_none=True, default_value=None), T.CFloat(allow_none=True, default_value=None), T.Bool(allow_none=True, default_value=None), T.Instance(DateTime, allow_none=True, default_value=None)])
field = T.Unicode(allow_none=True, default_value=None, help="""Field to be filtered.""")
timeUnit = TimeUnit(allow_none=True, default_value=None, help="""Time unit for the field to be filtered.""")
def __init__(self, equal=None, field=None, timeUnit=None, **kwargs):
kwds = dict(equal=equal, field=field, timeUnit=timeUnit)
kwargs.update({k:v for k, v in kwds.items() if v is not None})
super(EqualFilter, self).__init__(**kwargs)
class ColorScale(Scale):
"""Scale object that adds additional properties to the Scale property for Color"""
range = T.Union([T.Unicode(), T.List(T.Unicode)],
default_value=None,
allow_none=True)
c10palette = T.Enum([
'category10', 'category10k', 'Pastel1', 'Pastel2', 'Set1', 'Set2',
'Set3'
],
default_value='category10')
c20palette = T.Enum(['category20', 'category20b', 'category20c'],
default_value='category20')
ordinalPalette = T.Enum([
'YlGn', 'YlGnBu', 'GnBu', 'BuGn', 'PuBuGn', 'PuBu', 'BuPu', 'RdPu',
'PuRd', 'OrRd', 'YlOrRd', 'YlOrBr', 'Purples', 'Blues', 'Greens',
'Oranges', 'Reds', 'Greys', 'PuOr', 'BrBG', 'PRGn', 'PiYG', 'RdBu',
'RdGy', 'RdYlBu', 'Spectral', 'RdYlGn', 'Accent', 'Dark2', 'Paired',
'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3'
],
default_value='BuGn')
class FractionalSelectionModel(traitlets.HasTraits):
response_fn = SubclassName(SelectionResponseFn)
@property
def response_impl(self):
return resolve_subclass(SelectionResponseFn, self.response_fn)()
#sel_k = traitlets.Dict(
# traits = dict(
# __class__ = SubclassName(Continuous)
# ),
# default_value = dict(
# __class__="FlatNormal",
# mu=1.63, #4.0, #1.63,
# sd=0.00002, #0.0001, #0.00002,
# w=1.02 #2.5 #1.02,
# )
#)
#
#@property
#def sel_k_class(self):
# return resolve_subclass(Continuous, self.sel_k["__class__"])
#
#@property
#def sel_k_kwargs(self):
# kwargs = dict(self.sel_k)
# kwargs.pop("__class__")
# return kwargs
sel_k = traitlets.Float()#min_selection_rate
min_selection_rate = traitlets.Union([
traitlets.Bool(),
traitlets.Float()
])
min_selection_mass = traitlets.Union([
traitlets.Enum(["global", "per_selection"]),
traitlets.Float()
])
homogenous_k = traitlets.Bool(default_value=True)
outlier_detection_opt_cycles = traitlets.Integer(default_value=1)
def __init__(self, **kwargs):
# Override 'super' error-handling logic in HasTraits base __init__
# __init__ swallows errors from unused kwargs until v4.3
for key in kwargs:
if not self.has_trait(key):
raise TypeError("__init__() got an unexpected keyword argument '%s'" % key )
traitlets.HasTraits.__init__(self, **kwargs)
@staticmethod
def lognorm_params(sd, mode):
return dict(
tau = sd**-2.,
mu = numpy.log(mode) + sd ** 2,
)
@staticmethod
def parent_depth(start_key, pop_specs):
seen_keys = set()
cur_depth = 0
key = start_key
while pop_specs[key]["parent"] is not None:
seen_keys.add(key)
parent = pop_specs[key]["parent"]
if parent in seen_keys:
raise ValueError(
"Cycle in population parents. start_key: %s cycle_key: %s" %
(start_key, pop_specs[key]["parent"]))
if parent not in pop_specs:
raise ValueError(
"Invalid parent specified: %s pop_specs: %s" %
(parent, list(pop_specs.keys())))
cur_depth += 1
key = parent
return cur_depth
def generate_data(self, pop_specs, sel_k, sel_ec50, init_pop):
populations = {}
for pkey in sorted(list(pop_specs.keys()), key=lambda pkey: self.parent_depth(pkey, pop_specs)):
p = pop_specs[pkey]
if p["parent"] is None:
start_pop = init_pop
else:
start_pop = populations[p["parent"]]["P_sel"]
start_dist = unorm(start_pop)
if p["selection_level"] is not None:
src_dist = start_dist * self.response_impl.selection_mass(
sel_level = p["selection_level"], sel_k = sel_k, sel_ec50 = sel_ec50,
**{param : p[param] for param in self.response_impl.population_params}
)
fraction_selected = src_dist.sum() / start_dist.sum()
else:
src_dist = start_dist
fraction_selected = 1
selected = numpy.random.multinomial(p["P_sel"], unorm(src_dist)).astype(float)
populations[pkey] = {}
populations[pkey].update(p)
populations[pkey].update({
"P_sel" : selected,
"Frac_sel_pop" : fraction_selected
})
return populations
def add_fit_param(self, name, dist):
var = self.model.Var(name, dist, data=None)
if dist.transform:
forward_trans = dist.transform.forward(var)
self.to_trans[name] = (
"%s_%s_" % (name, dist.transform.name),
lambda v: forward_trans.eval({var : v})
)
self.fit_params[name] = var
return var
def build_model(self, population_data):
for k, p in list(population_data.items()):
unused_keys = set(p.keys()).difference(
["P_sel", "Frac_sel_pop", "selection_level", "parent"] +
list(self.response_impl.population_params)
)
if unused_keys:
logger.warning("Unused keys in population_data[%r] : %s", k, unused_keys)
num_members = set( len(p["P_sel"]) for p in list(population_data.values()) )
assert len(num_members) == 1, "Different observed population memberships: %s" % num_members
self.num_members = num_members.pop()
selected_observations = {
v["selection_level"] : v["P_sel"]
for v in list(population_data.values()) if v["selection_level"] is not None
}
start_ec50 = numpy.full_like(list(selected_observations.values())[0], min(selected_observations) - 1)
for sl in selected_observations:
start_ec50[ ((sl - 1) > start_ec50) & (selected_observations[sl] > 0) ] = sl - 1
self.model = pymc3.Model()
self.to_trans = {}
self.fit_params = {}
self.model_populations = {}
self.population_data = population_data
pops_by_depth = sorted(
list(population_data.keys()),
key=lambda pkey: self.parent_depth(pkey, population_data))
self.modeled_populations = [ p for p in pops_by_depth if population_data[p]["parent"] is not None ]
with self.model:
#sel_k = self.add_fit_param(
# "sel_k",
# self.sel_k_class.dist(**self.sel_k_kwargs))
#sel_k = self.add_fit_param(
# "sel_k",
# FlatNormal.dist(**default_selk_dict[self.sel_k_dict]))
sel_k=self.sel_k
sel_values = set(
float(p["selection_level"])
for p in list(self.population_data.values())
if p["selection_level"] is not None
)
sel_mag = max(sel_values) - min(sel_values)
self.sel_range = dict(lower=min(sel_values) - sel_mag * .5, upper=max(sel_values)+sel_mag*.5)
logger.info("Inferred sel_ec50 range: %s", self.sel_range)
sel_ec50 = self.add_fit_param(
"sel_ec50",
pymc3.Uniform.dist(
shape=self.num_members,
testval=start_ec50,
**self.sel_range)
)
if self.min_selection_rate:
if isinstance(self.min_selection_rate, bool):
logger.info("Adding adaptive min_selection_rate.")
min_selection_rate = self.add_fit_param(
"min_selection_rate",
pymc3.HalfNormal.dist(sd=.0002, testval=.0001))
else:
logger.info("Adding const min_selection_rate: %.03f" % self.min_selection_rate)
min_selection_rate = float(self.min_selection_rate)
else:
min_selection_rate = 0.0
if self.min_selection_mass:
if self.min_selection_mass == "global":
logger.info("Adding global adaptive min_selection_mass.")
min_selection_mass = self.add_fit_param(
"min_selection_mass",
pymc3.HalfNormal.dist(sd=1e-3, testval=1e-12))
elif self.min_selection_mass == "per_selection":
logger.info("Adding per-selection adaptive min_selection_mass.")
min_selection_mass = self.add_fit_param(
"min_selection_mass",
pymc3.HalfNormal.dist(shape=len(self.modeled_populations), sd=1e-3, testval=1e-12))
else:
logger.info("Adding const min_selection_mass: %.03f" % self.min_selection_mass)
min_selection_mass = float(self.min_selection_mass)
else:
min_selection_mass = 0.0
for pidx, pkey in enumerate(self.modeled_populations): # Sum over all rounds as in Eq. 15
pdat = population_data[pkey]
p_min_selection_mass = (
min_selection_mass[pidx]
if self.min_selection_mass == "per_selection" else
min_selection_mass
)
start_pop = population_data[pdat["parent"]]["P_sel"].astype(float)
P_in = unorm(start_pop)
if pdat["selection_level"] is not None:
Frac_sel = self.response_impl.selection_mass( # Eq. 10, where "sel_k" is K_sel and
sel_level = pdat["selection_level"], sel_k = sel_k, sel_ec50 = sel_ec50, # sel_ec50 is the vector of EC_50s, and
**{param : pdat[param] for param in self.response_impl.population_params} # selection_level is the enzyme "concentration" on a
) # log scale, with base specified in the input.
Frac_sel_star = min_selection_rate + (Frac_sel * (1 - min_selection_rate)) # Eq. 11, with min_selection_rate as "a"
P_cleave_prenormalized = P_in * Frac_sel_star # The numerator of Eq. 12, normalized shortly
Frac_sel_pop = P_cleave_prenormalized.sum() / P_in.sum() # Precalculate this for Eq. 14
else:
P_cleave = P_in
Frac_sel_pop = 1.0
#multinomial formula returns nan if any p == 0, file bug?
# Add epsilon to selection prob to avoid nan-results when p==0
P_cleave = unorm( # Finish calculating P_cleave per Eq. 12 by normalizingNormalize P_cleave and ensure all probabilities
T.clip(P_cleave_prenormalized, (p_min_selection_mass + 1e-9) * Frac_sel_pop, 1)) # it to sum to 1. A lower threshold is added to all probabilities to
pop_mask = numpy.flatnonzero(start_pop > 0) # prevent crashes. This low threshold is rarely relevant because
n_sel = pdat["P_sel"][pop_mask].sum()
selected = pymc3.distributions.Multinomial( # Eq. 13
name = "selected_%s" % pkey, #
n=n_sel , #
p=P_cleave[pop_mask],
observed=pdat["P_sel"][pop_mask]
)
if pdat.get("Frac_sel_pop", None) is not None:
n_sel = pdat["P_sel"].sum()
n_assay = numpy.floor(float(n_sel) / pdat["Frac_sel_pop"])
total_selected = pymc3.distributions.Binomial( #
name = "total_selected_%s" % pkey, # Eq. 14
n = n_assay, #
p = Frac_sel_pop, #
observed = n_sel) #
else:
total_selected = pdat["P_sel"].sum()
self.model_populations[pkey] = {
"selection_mass" : self._function(P_cleave * Frac_sel_pop),
"P_cleave" : self._function(P_cleave),
"Frac_sel_pop" : self._function(Frac_sel_pop),
"P_sel" : self._function(selected),
"n_sel" : self._function(total_selected),
}
self.fit_params = { k : self._function(v) for k, v in list(self.fit_params.items()) }
self.logp = self._function(self.model.logpt)
return self
def optimize_params(self, start = None):
logger.info("optimize_params: %i members", self.num_members)
if start is not None:
start = self.to_transformed(start)
for k in self.model.test_point:
if k not in start:
start[k] = self.model.test_point[k]
MAP = pymc3.find_MAP(start=start, model=self.model, fmin=scipy.optimize.fmin_l_bfgs_b)
return { k : v(MAP) for k, v in list(self.fit_params.items()) }
def opt_ec50_cred_outliers(self, src_params):
logger.info("scan_ec50_outliers: %i members", self.num_members)
params = copy.deepcopy(src_params)
num_outlier = 0
for i in range(self.num_members):
if i % 1000 == 0:
logger.info("scan_ec50_outliers: %i / %i outlier count: %s", i, self.num_members, num_outlier)
cred_summary = self.estimate_ec50_cred(params, i)
current = numpy.searchsorted(cred_summary["xs"], cred_summary["sel_ec50"], "left")
#rb = numpy.searchsorted(cred_summary["xs"], cred_summary["sel_ec50"], "right")
m_pmf = cred_summary["pmf"].argmax()
if m_pmf < current - 1 or m_pmf > current:
num_outlier += 1
params["sel_ec50"][i] = cred_summary["xs"][m_pmf]
logger.info(
"Modified %.3f outliers. (%i/%i)",
num_outlier / self.num_members, num_outlier, self.num_members)
return params
def find_MAP(self, start = None):
params = self.optimize_params(start)
for _ in range(self.outlier_detection_opt_cycles):
resampled = self.opt_ec50_cred_outliers(params)
params = self.optimize_params(resampled)
return params
def ec50_logp_trace(self, base_params, sample_i, ec50_range, include_global_terms=True):
llh_by_ec50_gen = numpy.zeros((len(ec50_range), len(self.model_populations)))
if self.min_selection_rate:
if self.min_selection_rate == True:
min_selection_rate = base_params["min_selection_rate"]
else:
min_selection_rate = self.min_selection_rate
else:
min_selection_rate = 0
if self.min_selection_mass:
if isinstance(self.min_selection_mass, str):
min_selection_mass = base_params["min_selection_mass"]
else:
min_selection_mass = self.min_selection_mass
else:
min_selection_mass = 0
for pidx, pkey in enumerate(self.modeled_populations):
pdat = self.population_data[pkey]
p_min_selection_mass = (
min_selection_mass[pidx]
if self.min_selection_mass == "per_selection" else
min_selection_mass
)
parent_pop_fraction = unorm(self.population_data[pdat['parent']]["P_sel"])[sample_i]
if parent_pop_fraction == 0:
continue
# calculate selection results for full ec50 range
# base_params['sel_k'] * (pdat['conc_factor'] ** (pdat['selection_level'] - ec50_range) - 1.0 ))
if "sel_k" in base_params:
sel_k = base_params["sel_k"]
else:
sel_k = self.sel_k
selected_fraction = self.response_impl.selection_mass(
sel_level = pdat["selection_level"], sel_k = sel_k, sel_ec50 = ec50_range,
**{param : pdat[param] for param in self.response_impl.population_params}
)
selected_fraction = min_selection_rate + (selected_fraction * (1 - min_selection_rate))
sel_pop_fraction = parent_pop_fraction * selected_fraction / self.model_populations[pkey]["Frac_sel_pop"](base_params)
sample_llhs = scipy.stats.binom.logpmf(
pdat["P_sel"][sample_i],
n=pdat["P_sel"].sum(),
p=numpy.clip(sel_pop_fraction, p_min_selection_mass + 1e-9, 1.0)
)
if include_global_terms and pdat.get("Frac_sel_pop") is not None:
prev_selected_fraction = self.response_impl.selection_mass(
sel_level = pdat["selection_level"], sel_k = sel_k, sel_ec50 = base_params['sel_ec50'][sample_i],
**{param : pdat[param] for param in self.response_impl.population_params}
)
prev_selected_mass = parent_pop_fraction * prev_selected_fraction
selected_mass = parent_pop_fraction * selected_fraction
selected_count = pdat["P_sel"].sum()
source_count = numpy.floor(float(selected_count) / pdat["Frac_sel_pop"])
modified_global_selection_fractions = (
self.model_populations[pkey]["Frac_sel_pop"](base_params)
+ selected_mass - prev_selected_mass
)
sample_llhs += scipy.stats.binom.logpmf(
selected_count,
n=source_count,
p=modified_global_selection_fractions
)
llh_by_ec50_gen[:,pidx] = sample_llhs
llh_by_ec50 = llh_by_ec50_gen.sum(axis=1)
return llh_by_ec50 - numpy.nanmax(llh_by_ec50)
def estimate_ec50_cred(self, base_params, ec50_i, cred_spans = [.68, .95]):
"""Estimate EC50 credible interval for a single ec50 parameter via model probability."""
#xs = numpy.arange(self.sel_range["lower"]+0.1, self.sel_range["upper"]-0.1, .1)
xs=numpy.linspace(self.sel_range['lower']+1,self.sel_range['upper']-1, (self.sel_range['upper'] - self.sel_range['lower'] - 2)*10 + 1)
logp = numpy.nan_to_num(self.ec50_logp_trace(base_params, ec50_i, xs))
pmf = numpy.exp(logp) / numpy.sum(numpy.exp(logp))
cdf = numpy.cumsum(pmf)
cred_intervals = {}
for cred_i in cred_spans:
cdf_b = (1 - cred_i) / 2
l_b = xs[numpy.searchsorted(cdf, cdf_b, side="left")]
u_b = xs[numpy.searchsorted(cdf, 1 - cdf_b, side="right")]
cred_intervals[cred_i] = (l_b, u_b)
return dict(
xs = xs,
pmf = pmf,
cdf = cdf,
logp = logp,
sel_ec50 = base_params["sel_ec50"][ec50_i],
cred_intervals = cred_intervals
)
@staticmethod
def plot_cred_summary(ec50_cred, ax=None):
if ax is None:
from matplotlib import pylab
ax = pylab.gca()
ax.plot( ec50_cred["xs"], ec50_cred["pmf"], label="pmf" )
ax.plot( ec50_cred["xs"], ec50_cred["cdf"], label="cdf" )
ax.axvline(ec50_cred["sel_ec50"], alpha=.5, label="sel_e50: %.2f" % ec50_cred["sel_ec50"])
for ci, (cl, cu) in list(ec50_cred["cred_intervals"].items()):
ax.axvspan(cl, cu, color="red", alpha=.2, label="%.2f cred" % ci)
def model_selection_summary(self, params):
def normed_pop(v):
return v / v.sum()
return {
pkey : {
"P_sel" : self.population_data[pkey]["P_sel"],
"selected_fraction" : normed_pop(self.population_data[pkey]["P_sel"].astype(float)),
"P_cleave" : normed_pop(self.model_populations[pkey]["P_cleave"](params))
}
for pkey in self.model_populations
}
def plot_fit_summary(model, i, fit):
import scipy.stats
from matplotlib import pylab
sel_sum = model.model_selection_summary(fit)
sel_levels = {
k : p["selection_level"] if p["selection_level"] else 0
for k, p in list(model.population_data.items())}
sel_fracs = {
k : p["P_sel"][i] / p["P_sel"].sum()
for k, p in list(model.population_data.items())}
pylab.xticks(
list(sel_levels.values()), list(sel_levels.keys()))
pylab.xlim((-1, 7))
# porder = [
# k for k, p in
# sorted(model.population_data.items(), key=lambda (k, p): p["selection_level"])]
pylab.plot(
[sel_levels[k] for k in porder],
[sel_fracs[k] for k in porder],
"-o",
color="black", label="observed")
lbl = False
for k in sel_sum:
n = sel_sum[k]["P_sel"].sum()
p = sel_sum[k]["pop_fraction"][i]
sel_level = model.population_data[k]["selection_level"]
counts=sel_sum[k]["P_sel"][i]
plt.text(sel_levels[k] + 0.2, sel_fracs[k], '%.0f' % counts)
if p<=0:
continue
bn = scipy.stats.binom(n=n, p=p)
parkey = model.population_data[k]["parent"]
pylab.plot(
[sel_levels[parkey], sel_levels[k]],
[sel_fracs[parkey], float(bn.ppf(.5)) / n],
"--", color="red", alpha=.25
)
for ci in (.68, .95, .99):
pylab.plot(
[sel_level] * 2, bn.ppf([ci, 1-ci]) / n,
linewidth=10, color="red", alpha=.25,
label="predicted" if not lbl else None
)
lbl=True
pylab.legend(fontsize="large", loc="best")
pylab.twinx()
xs = numpy.linspace(-2, 8)
sel_ec50 = fit["sel_ec50"][i]
sel_k = fit["sel_k"][i] if len(fit["sel_k"]) > 1 else fit["sel_k"]
pylab.plot(xs, scipy.special.expit(-sel_k * (xs - sel_ec50)), alpha=.75)
pylab.yticks([], [])
pylab.title("%s - ec50: %.2f - k: %.2f" % (i, sel_ec50, sel_k))
def model_outlier_summary(self, params):
selection_summary = self.model_selection_summary(params)
for v in list(selection_summary.values()):
logpmf = scipy.stats.binom.logpmf(
v["P_sel"],
n=v["P_sel"].sum(),
p=v["P_cleave"])
max_logpmf = scipy.stats.binom.logpmf(
numpy.round(v["P_sel"].sum() * v["P_cleave"]),
n=v["P_sel"].sum(),
p=v["P_cleave"])
sel_llh = logpmf - max_logpmf
v["sel_log_likelihood"] = numpy.where(sel_llh != -numpy.inf, sel_llh, numpy.nan)
sel_error = (v["P_sel"] / v["P_sel"].sum()) - v["P_cleave"]
v["sel_log_likelihood_signed"] = -v["sel_log_likelihood"] * numpy.sign(sel_error)
return selection_summary
def to_transformed(self, val_dict):
r = {}
for n, val in list(val_dict.items()):
if n in self.to_trans:
k, f = self.to_trans[n]
r[k] = f(val)
else:
r[n] = val
return r
def _function(self, f):
if isinstance(f, theano.tensor.TensorVariable):
fn = theano.function(self.model.free_RVs, f, on_unused_input="ignore")
def call_fn(val_dict):
val_dict = self.to_transformed(val_dict)
return fn(*[val_dict[str(n)] for n in self.model.free_RVs])
return call_fn
else:
return lambda _: f
class XarrayInterpolator(Interpolator):
"""Xarray interpolation Interpolation
Attributes
----------
{interpolator_attributes}
fill_nan: bool
Default is False. If True, nan values will be filled before interpolation.
fill_value: float,str
Default is None. The value that will be used to fill nan values. This can be a number, or "extrapolate", see `scipy.interpn`/`scipy/interp1d`
kwargs: dict
Default is {{"bounds_error": False}}. Additional values to pass to xarray's `interp` method.
"""
dims_supported = ["lat", "lon", "alt", "time"]
methods_supported = [
"nearest",
"linear",
"bilinear",
"quadratic",
"cubic",
"zero",
"slinear",
"next",
"previous",
"splinef2d",
]
# defined at instantiation
method = tl.Unicode(default_value="nearest")
fill_value = tl.Union([tl.Unicode(), tl.Float()],
default_value=None,
allow_none=True)
fill_nan = tl.Bool(False)
kwargs = tl.Dict({"bounds_error": False})
def __repr__(self):
rep = super(XarrayInterpolator, self).__repr__()
# rep += '\n\tspatial_tolerance: {}\n\ttime_tolerance: {}'.format(self.spatial_tolerance, self.time_tolerance)
return rep
@common_doc(COMMON_INTERPOLATOR_DOCS)
def can_interpolate(self, udims, source_coordinates, eval_coordinates):
"""
{interpolator_interpolate}
"""
udims_subset = self._filter_udims_supported(udims)
# confirm that udims are in both source and eval coordinates
if self._dim_in(udims_subset, source_coordinates, unstacked=True):
for d in source_coordinates.udims: # Cannot handle stacked dimensions
if source_coordinates.is_stacked(d):
return tuple()
return udims_subset
else:
return tuple()
@common_doc(COMMON_INTERPOLATOR_DOCS)
def interpolate(self, udims, source_coordinates, source_data,
eval_coordinates, output_data):
"""
{interpolator_interpolate}
"""
coords = {}
nn_coords = {}
for d in udims:
# Note: This interpolator cannot handle stacked source -- and this is handled in the can_interpolate function
if source_coordinates[d].size == 1:
# If the source only has a single coordinate, xarray will automatically throw an error asking for at least 2 coordinates
# So, we prevent this. Main problem is that this won't respect any tolerances.
new_dim = [dd for dd in eval_coordinates.dims if d in dd][0]
nn_coords[d] = xr.DataArray(
eval_coordinates[d].coordinates,
dims=[new_dim],
coords=[eval_coordinates.xcoords[new_dim]],
)
continue
if not source_coordinates.is_stacked(
d) and eval_coordinates.is_stacked(d):
new_dim = [dd for dd in eval_coordinates.dims if d in dd][0]
coords[d] = xr.DataArray(
eval_coordinates[d].coordinates,
dims=[new_dim],
coords=[eval_coordinates.xcoords[new_dim]])
else:
# TODO: Check dependent coordinates
coords[d] = eval_coordinates[d].coordinates
kwargs = self.kwargs.copy()
kwargs.update({"fill_value": self.fill_value})
coords["kwargs"] = kwargs
if self.method == "bilinear":
self.method = "linear"
if self.fill_nan:
for d in source_coordinates.dims:
if not np.any(np.isnan(source_data)):
break
# use_coordinate=False allows for interpolation when dimension is not monotonically increasing
source_data = source_data.interpolate_na(method=self.method,
dim=d,
use_coordinate=False)
if nn_coords:
source_data = source_data.sel(method="nearest", **nn_coords)
output_data = source_data.interp(method=self.method, **coords)
return output_data.transpose(*eval_coordinates.dims)