#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2018, Anaconda, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with this software.
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''' Provide proprties for various visual attrributes.
'''
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# Boilerplate
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from __future__ import absolute_import, division, print_function, unicode_literals
import logging
log = logging.getLogger(__name__)
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# Imports
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# Standard library imports
import base64
from io import BytesIO
import re
# External imports
import PIL.Image
from six import string_types
# Bokeh imports
from .. import enums
from .auto import Auto
from .bases import Property
from .container import Seq, Tuple
from .datetime import Datetime, TimeDelta
from .either import Either
from .enum import Enum
from .numeric import Int, Float
from .regex import Regex
from .primitive import String
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# Globals and constants
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__all__ = (
'DashPattern',
'FontSize',
'Image',
'MinMaxBounds',
'MarkerType',
)
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# General API
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[docs]class DashPattern(Either):
''' Accept line dash specifications.
Express patterns that describe line dashes. ``DashPattern`` values
can be specified in a variety of ways:
* An enum: "solid", "dashed", "dotted", "dotdash", "dashdot"
* a tuple or list of integers in the `HTML5 Canvas dash specification style`_.
Note that if the list of integers has an odd number of elements, then
it is duplicated, and that duplicated list becomes the new dash list.
To indicate that dashing is turned off (solid lines), specify the empty
list [].
.. _HTML5 Canvas dash specification style: http://www.w3.org/html/wg/drafts/2dcontext/html5_canvas/#dash-list
'''
_dash_patterns = {
"solid": [],
"dashed": [6],
"dotted": [2,4],
"dotdash": [2,4,6,4],
"dashdot": [6,4,2,4],
}
def __init__(self, default=[], help=None):
types = Enum(enums.DashPattern), Regex(r"^(\d+(\s+\d+)*)?$"), Seq(Int)
super(DashPattern, self).__init__(*types, default=default, help=help)
def __str__(self):
return self.__class__.__name__
def transform(self, value):
value = super(DashPattern, self).transform(value)
if isinstance(value, string_types):
try:
return self._dash_patterns[value]
except KeyError:
return [int(x) for x in value.split()]
else:
return value
def _sphinx_type(self):
return self._sphinx_prop_link()
[docs]class FontSize(String):
_font_size_re = re.compile(r"^[0-9]+(.[0-9]+)?(%|em|ex|ch|ic|rem|vw|vh|vi|vb|vmin|vmax|cm|mm|q|in|pc|pt|px)$", re.I)
def validate(self, value, detail=True):
super(FontSize, self).validate(value, detail)
if isinstance(value, string_types):
if len(value) == 0:
msg = "" if not detail else "empty string is not a valid font size value"
raise ValueError(msg)
elif self._font_size_re.match(value) is None:
msg = "" if not detail else "%r is not a valid font size value" % value
raise ValueError(msg)
[docs]class Image(Property):
''' Accept image file types, e.g PNG, JPEG, TIFF, etc.
This property can be configured with:
* A string filename to be loaded with ``PIL.Image.open``
* An RGB(A) NumPy array, will be converted to PNG
* A ``PIL.Image.Image`` object
In all cases, the image data is serialized as a Base64 encoded string.
'''
def validate(self, value, detail=True):
import numpy as np
valid = False
if value is None or isinstance(value, (string_types, PIL.Image.Image)):
valid = True
if isinstance(value, np.ndarray):
valid = value.dtype == "uint8" and len(value.shape) == 3 and value.shape[2] in (3, 4)
if not valid:
msg = "" if not detail else "invalid value: %r; allowed values are string filenames, PIL.Image.Image instances, or RGB(A) NumPy arrays" % value
raise ValueError(msg)
def transform(self, value):
if value is None:
return None
import numpy as np
if isinstance(value, np.ndarray):
value = PIL.Image.fromarray(value)
if isinstance(value, string_types):
value = PIL.Image.open(value)
if isinstance(value, PIL.Image.Image):
out = BytesIO()
fmt = value.format or "PNG"
value.save(out, fmt)
return "data:image/%s;base64," % fmt.lower() + base64.b64encode(out.getvalue()).decode('ascii')
raise ValueError("Could not transform %r" % value)
[docs]class MinMaxBounds(Either):
''' Accept (min, max) bounds tuples for use with Ranges.
Bounds are provided as a tuple of ``(min, max)`` so regardless of whether your range is
increasing or decreasing, the first item should be the minimum value of the range and the
second item should be the maximum. Setting min > max will result in a ``ValueError``.
Setting bounds to None will allow your plot to pan/zoom as far as you want. If you only
want to constrain one end of the plot, you can set min or max to
``None`` e.g. ``DataRange1d(bounds=(None, 12))`` '''
def __init__(self, accept_datetime=False, default='auto', help=None):
if accept_datetime:
types = (
Auto,
Tuple(Float, Float),
Tuple(TimeDelta, TimeDelta),
Tuple(Datetime, Datetime),
)
else:
types = (
Auto,
Tuple(Float, Float),
Tuple(TimeDelta, TimeDelta),
)
super(MinMaxBounds, self).__init__(*types, default=default, help=help)
def validate(self, value, detail=True):
super(MinMaxBounds, self).validate(value, detail)
if value is None:
pass
elif value[0] is None or value[1] is None:
pass
elif value[0] >= value[1]:
msg = "" if not detail else "Invalid bounds: maximum smaller than minimum. Correct usage: bounds=(min, max)"
raise ValueError(msg)
return True
def _sphinx_type(self):
return self._sphinx_prop_link()
[docs]class MarkerType(Enum):
'''
'''
def __init__(self, **kw):
super(MarkerType, self).__init__(enums.MarkerType, **kw)
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# Dev API
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#-----------------------------------------------------------------------------
# Private API
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#-----------------------------------------------------------------------------
# Code
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