#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2019, Anaconda, Inc., and Bokeh Contributors. # All rights reserved. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- ''' Provide the DataSpec properties and helpers. ''' #----------------------------------------------------------------------------- # Boilerplate #----------------------------------------------------------------------------- from __future__ import absolute_import, division, print_function, unicode_literals import logging log = logging.getLogger(__name__) #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Standard library imports # External imports from six import string_types # Bokeh imports from ... import colors from ...util.serialization import convert_datetime_type, convert_timedelta_type from .. import enums from .color import Color from .container import Dict, List from .datetime import Datetime, TimeDelta from .descriptors import DataSpecPropertyDescriptor, UnitsSpecPropertyDescriptor from .either import Either from .enum import Enum from .instance import Instance from .primitive import Float, String from .visual import FontSize, HatchPatternType, MarkerType #----------------------------------------------------------------------------- # Globals and constants #----------------------------------------------------------------------------- __all__ = ( 'AngleSpec', 'ColorSpec', 'DataSpec', 'DataDistanceSpec', 'DistanceSpec', 'expr', 'field', 'FontSizeSpec', 'HatchPatternSpec', 'MarkerSpec', 'NumberSpec', 'ScreenDistanceSpec', 'StringSpec', 'UnitsSpec', 'value', ) #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- _ExprFieldValueTransform = Enum("expr", "field", "value", "transform") _ExprFieldValueTransformUnits = Enum("expr", "field", "value", "transform", "units") #----------------------------------------------------------------------------- # General API #----------------------------------------------------------------------------- [docs]class DataSpec(Either): ''' Base class for properties that accept either a fixed value, or a string name that references a column in a :class:`~bokeh.models.sources.ColumnDataSource`. Many Bokeh models have properties that a user might want to set either to a single fixed value, or to have the property take values from some column in a data source. As a concrete example consider a glyph with an ``x`` property for location. We might want to set all the glyphs that get drawn to have the same location, say ``x=10``. It would be convenient to just be able to write: .. code-block:: python glyph.x = 10 Alternatively, maybe the each glyph that gets drawn should have a different location, according to the "pressure" column of a data source. In this case we would like to be able to write: .. code-block:: python glyph.x = "pressure" Bokeh ``DataSpec`` properties (and subclasses) afford this ease of and consistency of expression. Ultimately, all ``DataSpec`` properties resolve to dictionary values, with either a ``"value"`` key, or a ``"field"`` key, depending on how it is set. For instance: .. code-block:: python glyph.x = 10 # => { 'value': 10 } glyph.x = "pressure" # => { 'field': 'pressure' } When these underlying dictionary dictionary values are received in the browser, BokehJS knows how to interpret them and take the correct, expected action (i.e., draw the glyph at ``x=10``, or draw the glyph with ``x`` coordinates from the "pressure" column). In this way, both use-cases may be expressed easily in python, without having to handle anything differently, from the user perspective. It is worth noting that ``DataSpec`` properties can also be set directly with properly formed dictionary values: .. code-block:: python glyph.x = { 'value': 10 } # same as glyph.x = 10 glyph.x = { 'field': 'pressure' } # same as glyph.x = "pressure" Setting the property directly as a dict can be useful in certain situations. For instance some ``DataSpec`` subclasses also add a ``"units"`` key to the dictionary. This key is often set automatically, but the dictionary format provides a direct mechanism to override as necessary. Additionally, ``DataSpec`` can have a ``"transform"`` key, that specifies a client-side transform that should be applied to any fixed or field values before they are uses. As an example, you might want to apply a ``Jitter`` transform to the ``x`` values: .. code-block:: python glyph.x = { 'value': 10, 'transform': Jitter(width=0.4) } Note that ``DataSpec`` is not normally useful on its own. Typically, a model will define properties using one of the subclasses such as :class:`~bokeh.core.properties.NumberSpec` or :class:`~bokeh.core.properties.ColorSpec`. For example, a Bokeh model with ``x``, ``y`` and ``color`` properties that can handle fixed values or columns automatically might look like: .. code-block:: python class SomeModel(Model): x = NumberSpec(default=0, help="docs for x") y = NumberSpec(default=0, help="docs for y") color = ColorSpec(help="docs for color") # defaults to None ''' def __init__(self, key_type, value_type, default, help=None): super(DataSpec, self).__init__( String, Dict( key_type, Either( String, Instance('bokeh.models.transforms.Transform'), Instance('bokeh.models.expressions.Expression'), value_type)), value_type, default=default, help=help ) self._type = self._validate_type_param(value_type) # TODO (bev) add stricter validation on keys def make_descriptors(self, base_name): ''' Return a list of ``DataSpecPropertyDescriptor`` instances to install on a class, in order to delegate attribute access to this property. Args: base_name (str) : the name of the property these descriptors are for Returns: list[DataSpecPropertyDescriptor] The descriptors returned are collected by the ``MetaHasProps`` metaclass and added to ``HasProps`` subclasses during class creation. ''' return [ DataSpecPropertyDescriptor(base_name, self) ] def to_serializable(self, obj, name, val): # Check for None value; this means "the whole thing is # unset," not "the value is None." if val is None: return None # Check for spec type value try: self._type.validate(val, False) return dict(value=val) except ValueError: pass # Check for data source field name if isinstance(val, string_types): return dict(field=val) # Must be dict, return a new dict return dict(val) def _sphinx_type(self): return self._sphinx_prop_link() [docs]class NumberSpec(DataSpec): ''' A |DataSpec| property that accepts numeric and datetime fixed values. By default, date and datetime values are immediately converted to milliseconds since epoch. It is possible to disable processing of datetime values by passing ``accept_datetime=False``. By default, timedelta values are immediately converted to absolute milliseconds. It is possible to disable processing of timedelta values by passing ``accept_timedelta=False`` Timedelta values are interpreted as absolute milliseconds. .. code-block:: python m.location = 10.3 # value m.location = "foo" # field ''' def __init__(self, default=None, help=None, key_type=_ExprFieldValueTransform, accept_datetime=True, accept_timedelta=True): super(NumberSpec, self).__init__(key_type, Float, default=default, help=help) if accept_timedelta: self.accepts(TimeDelta, convert_timedelta_type) if accept_datetime: self.accepts(Datetime, convert_datetime_type) [docs]class StringSpec(DataSpec): ''' A |DataSpec| property that accepts string fixed values. Because acceptable fixed values and field names are both strings, it can be necessary explicitly to disambiguate these possibilities. By default, string values are interpreted as fields, but the |value| function can be used to specify that a string should interpreted as a value: .. code-block:: python m.title = value("foo") # value m.title = "foo" # field ''' def __init__(self, default, help=None, key_type=_ExprFieldValueTransform): super(StringSpec, self).__init__(key_type, List(String), default=default, help=help) def prepare_value(self, cls, name, value): if isinstance(value, list): if len(value) != 1: raise TypeError("StringSpec convenience list values must have length 1") value = dict(value=value[0]) return super(StringSpec, self).prepare_value(cls, name, value) [docs]class FontSizeSpec(DataSpec): ''' A |DataSpec| property that accepts font-size fixed values. The ``FontSizeSpec`` property attempts to first interpret string values as font sizes (i.e. valid CSS length values). Otherwise string values are interpreted as field names. For example: .. code-block:: python m.font_size = "10pt" # value m.font_size = "1.5em" # value m.font_size = "foo" # field A full list of all valid CSS length units can be found here: https://drafts.csswg.org/css-values/#lengths ''' def __init__(self, default, help=None, key_type=_ExprFieldValueTransform): super(FontSizeSpec, self).__init__(key_type, FontSize, default=default, help=help) def validate(self, value, detail=True): # We want to preserve existing semantics and be a little more restrictive. This # validations makes m.font_size = "" or m.font_size = "6" an error super(FontSizeSpec, self).validate(value, detail) if isinstance(value, string_types): if len(value) == 0 or value[0].isdigit() and FontSize._font_size_re.match(value) is None: msg = "" if not detail else "%r is not a valid font size value" % value raise ValueError(msg) class HatchPatternSpec(DataSpec): ''' A |DataSpec| property that accepts hatch pattern types as fixed values. The ``HatchPatternSpec`` property attempts to first interpret string values as hatch pattern types. Otherwise string values are interpreted as field names. For example: .. code-block:: python m.font_size = "." # value m.font_size = "ring" # value m.font_size = "foo" # field ''' def __init__(self, default, help=None, key_type=_ExprFieldValueTransform): super(HatchPatternSpec, self).__init__(key_type, HatchPatternType, default=default, help=help) [docs]class MarkerSpec(DataSpec): ''' A |DataSpec| property that accepts marker types as fixed values. The ``MarkerSpec`` property attempts to first interpret string values as marker types. Otherwise string values are interpreted as field names. For example: .. code-block:: python m.font_size = "circle" # value m.font_size = "square" # value m.font_size = "foo" # field ''' def __init__(self, default, help=None, key_type=_ExprFieldValueTransform): super(MarkerSpec, self).__init__(key_type, MarkerType, default=default, help=help) [docs]class UnitsSpec(NumberSpec): ''' A |DataSpec| property that accepts numeric fixed values, and also provides an associated units property to store units information. ''' def __init__(self, default, units_type, units_default, help=None): super(UnitsSpec, self).__init__(default=default, help=help, key_type=_ExprFieldValueTransformUnits) self._units_type = self._validate_type_param(units_type) # this is a hack because we already constructed units_type self._units_type.validate(units_default) self._units_type._default = units_default # this is sort of a hack because we don't have a # serialized= kwarg on every Property subtype self._units_type._serialized = False def __str__(self): return "%s(units_default=%r)" % (self.__class__.__name__, self._units_type._default) def make_descriptors(self, base_name): ''' Return a list of ``PropertyDescriptor`` instances to install on a class, in order to delegate attribute access to this property. Unlike simpler property types, ``UnitsSpec`` returns multiple descriptors to install. In particular, descriptors for the base property as well as the associated units property are returned. Args: name (str) : the name of the property these descriptors are for Returns: list[PropertyDescriptor] The descriptors returned are collected by the ``MetaHasProps`` metaclass and added to ``HasProps`` subclasses during class creation. ''' units_name = base_name + "_units" units_props = self._units_type.make_descriptors(units_name) return units_props + [ UnitsSpecPropertyDescriptor(base_name, self, units_props[0]) ] def to_serializable(self, obj, name, val): d = super(UnitsSpec, self).to_serializable(obj, name, val) if d is not None and 'units' not in d: # d is a PropertyValueDict at this point, we need to convert it to # a plain dict if we are going to modify its value, otherwise a # notify_change that should not happen will be triggered d = dict(d) d["units"] = getattr(obj, name+"_units") return d [docs]class AngleSpec(UnitsSpec): ''' A |DataSpec| property that accepts numeric fixed values, and also provides an associated units property to store angle units. Acceptable values for units are ``"rad"`` and ``"deg"``. ''' def __init__(self, default=None, units_default="rad", help=None): super(AngleSpec, self).__init__(default=default, units_type=Enum(enums.AngleUnits), units_default=units_default, help=help) [docs]class DistanceSpec(UnitsSpec): ''' A |DataSpec| property that accepts numeric fixed values or strings that refer to columns in a :class:`~bokeh.models.sources.ColumnDataSource`, and also provides an associated units property to store units information. Acceptable values for units are ``"screen"`` and ``"data"``. ''' def __init__(self, default=None, units_default="data", help=None): super(DistanceSpec, self).__init__(default=default, units_type=Enum(enums.SpatialUnits), units_default=units_default, help=help) def prepare_value(self, cls, name, value): try: if value is not None and value < 0: raise ValueError("Distances must be positive or None!") except TypeError: pass return super(DistanceSpec, self).prepare_value(cls, name, value) [docs]class ScreenDistanceSpec(UnitsSpec): ''' A |DataSpec| property that accepts numeric fixed values for screen distances, and also provides an associated units property that reports ``"screen"`` as the units. .. note:: Units are always ``"screen"``. ''' def __init__(self, default=None, help=None): super(ScreenDistanceSpec, self).__init__(default=default, units_type=Enum(enums.enumeration("screen")), units_default="screen", help=help) def prepare_value(self, cls, name, value): try: if value is not None and value < 0: raise ValueError("Distances must be positive or None!") except TypeError: pass return super(ScreenDistanceSpec, self).prepare_value(cls, name, value) def make_descriptors(self, base_name): ''' Return a list of ``PropertyDescriptor`` instances to install on a class, in order to delegate attribute access to this property. Unlike simpler property types, ``UnitsSpec`` returns multiple descriptors to install. In particular, descriptors for the base property as well as the associated units property are returned. Args: name (str) : the name of the property these descriptors are for Returns: list[PropertyDescriptor] The descriptors returned are collected by the ``MetaHasProps`` metaclass and added to ``HasProps`` subclasses during class creation. ''' units_props = self._units_type.make_descriptors("unused") return [ UnitsSpecPropertyDescriptor(base_name, self, units_props[0]) ] def to_serializable(self, obj, name, val): d = super(UnitsSpec, self).to_serializable(obj, name, val) if d is not None and 'units' not in d: # d is a PropertyValueDict at this point, we need to convert it to # a plain dict if we are going to modify its value, otherwise a # notify_change that should not happen will be triggered d = dict(d) d["units"] = "screen" return d [docs]class DataDistanceSpec(UnitsSpec): ''' A |DataSpec| property that accepts numeric fixed values for data-space distances, and also provides an associated units property that reports ``"data"`` as the units. .. note:: Units are always ``"data"``. ''' def __init__(self, default=None, help=None): super(DataDistanceSpec, self).__init__(default=default, units_type=Enum(enums.enumeration("data")), units_default="data", help=help) def prepare_value(self, cls, name, value): try: if value is not None and value < 0: raise ValueError("Distances must be positive or None!") except TypeError: pass return super(DataDistanceSpec, self).prepare_value(cls, name, value) def make_descriptors(self, base_name): ''' Return a list of ``PropertyDescriptor`` instances to install on a class, in order to delegate attribute access to this property. Unlike simpler property types, ``UnitsSpec`` returns multiple descriptors to install. In particular, descriptors for the base property as well as the associated units property are returned. Args: name (str) : the name of the property these descriptors are for Returns: list[PropertyDescriptor] The descriptors returned are collected by the ``MetaHasProps`` metaclass and added to ``HasProps`` subclasses during class creation. ''' units_props = self._units_type.make_descriptors("unused") return [ UnitsSpecPropertyDescriptor(base_name, self, units_props[0]) ] def to_serializable(self, obj, name, val): d = super(UnitsSpec, self).to_serializable(obj, name, val) if d is not None and 'units' not in d: # d is a PropertyValueDict at this point, we need to convert it to # a plain dict if we are going to modify its value, otherwise a # notify_change that should not happen will be triggered d = dict(d) d["units"] = "data" return d [docs]class ColorSpec(DataSpec): ''' A |DataSpec| property that accepts |Color| fixed values. The ``ColorSpec`` property attempts to first interpret string values as colors. Otherwise, string values are interpreted as field names. For example: .. code-block:: python m.color = "#a4225f" # value (hex color string) m.color = "firebrick" # value (named CSS color string) m.color = "foo" # field (named "foo") This automatic interpretation can be override using the dict format directly, or by using the |field| function: .. code-block:: python m.color = { "field": "firebrick" } # field (named "firebrick") m.color = field("firebrick") # field (named "firebrick") ''' def __init__(self, default, help=None, key_type=_ExprFieldValueTransform): super(ColorSpec, self).__init__(key_type, Color, default=default, help=help) @classmethod def isconst(cls, val): ''' Whether the value is a string color literal. Checks for a well-formed hexadecimal color value or a named color. Args: val (str) : the value to check Returns: True, if the value is a string color literal ''' return isinstance(val, string_types) and \ ((len(val) == 7 and val[0] == "#") or val in enums.NamedColor) def to_serializable(self, obj, name, val): if val is None: return dict(value=None) # Check for hexadecimal or named color if self.isconst(val): return dict(value=val) # Check for RGB or RGBa tuple if isinstance(val, tuple): return dict(value=colors.RGB(*val).to_css()) # Check for data source field name if isinstance(val, colors.RGB): return val.to_css() # Check for data source field name or rgb(a) string if isinstance(val, string_types): if val.startswith(("rgb(", "rgba(")): return val return dict(field=val) # Must be dict, return new dict return dict(val) def prepare_value(self, cls, name, value): # Some explanation is in order. We want to accept tuples like # (12.0, 100.0, 52.0) i.e. that have "float" byte values. The # ColorSpec has a transform to adapt values like this to tuples # of integers, but Property validation happens before the # transform step, so values like that will fail Color validation # at this point, since Color is very strict about only accepting # tuples of (integer) bytes. This conditions tuple values to only # have integer RGB components if isinstance(value, tuple): # TODO (bev) verify that all original floats are integer values? value = tuple(int(v) if i < 3 else v for i, v in enumerate(value)) return super(ColorSpec, self).prepare_value(cls, name, value) # DataSpec helpers ------------------------------------------------------------ [docs]def expr(expression, transform=None): ''' Convenience function to explicitly return an "expr" specification for a Bokeh :class:`~bokeh.core.properties.DataSpec` property. Args: expression (Expression) : a computed expression for a ``DataSpec`` property. transform (Transform, optional) : a transform to apply (default: None) Returns: dict : ``{ "expr": expression }`` .. note:: This function is included for completeness. String values for property specifications are by default interpreted as field names. ''' if transform: return dict(expr=expression, transform=transform) return dict(expr=expression) [docs]def field(name, transform=None): ''' Convenience function to explicitly return a "field" specification for a Bokeh :class:`~bokeh.core.properties.DataSpec` property. Args: name (str) : name of a data source field to reference for a ``DataSpec`` property. transform (Transform, optional) : a transform to apply (default: None) Returns: dict : ``{ "field": name }`` .. note:: This function is included for completeness. String values for property specifications are by default interpreted as field names. ''' if transform: return dict(field=name, transform=transform) return dict(field=name) [docs]def value(val, transform=None): ''' Convenience function to explicitly return a "value" specification for a Bokeh :class:`~bokeh.core.properties.DataSpec` property. Args: val (any) : a fixed value to specify for a ``DataSpec`` property. transform (Transform, optional) : a transform to apply (default: None) Returns: dict : ``{ "value": name }`` .. note:: String values for property specifications are by default interpreted as field names. This function is especially useful when you want to specify a fixed value with text properties. Example: .. code-block:: python # The following will take text values to render from a data source # column "text_column", but use a fixed value "12pt" for font size p.text("x", "y", text="text_column", text_font_size=value("12pt"), source=source) ''' if transform: return dict(value=val, transform=transform) return dict(value=val) #----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------