bqplot/bqplot/pyplot.py at master · bqplot/bqplot · GitHub

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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import sys

from collections import OrderedDict

from IPython.display import display

from ipywidgets import Image as ipyImage

from numpy import arange, issubdtype, array, column_stack, shape

from .figure import Figure

from bqscales import Scale, LinearScale, Mercator

from .axes import Axis

from .marks import (Lines, Scatter, Hist, Bars, OHLC, Pie, Map, Image,

Label, HeatMap, GridHeatMap, topo_load, Boxplot, Bins)

from .interacts import (BrushIntervalSelector, FastIntervalSelector,

BrushSelector, IndexSelector, MultiSelector,

LassoSelector)

from traitlets.utils.sentinel import Sentinel

import functools

Keep = Sentinel('Keep', 'bqplot.pyplot', '''

Used in bqplot.pyplot to specify that the same scale should be used for

a certain dimension.

''')

# `_context` object contains the global information for pyplot.

# `figure`: refers to the current figure to which marks will be added.

# `scales`: The current set of scales which will be used for drawing a mark. if

# the scale for an attribute is not present, it is created based on the range

# type.

# `scale_registry`: This is a dictionary where the keys are the context

# names and the values are the set of scales which were used on the last plot

# in that context. This is useful when switching context.

# `last_mark`: refers to the last mark that has been plotted.

# `current_key`: The key for the current context figure. If there is no key,

# then the value is `None`.

_context = {

'figure': None,

'figure_registry': {},

'scales': {},

'scale_registry': {},

'last_mark': None,

'current_key': None

}

LINE_STYLE_CODES = OrderedDict([(':', 'dotted'), ('-.', 'dash_dotted'),

('--', 'dashed'), ('-', 'solid')])

COLOR_CODES = {'b': 'blue', 'g': 'green', 'r': 'red', 'c': 'cyan',

'm': 'magenta', 'y': 'yellow', 'k': 'black'}

MARKER_CODES = {'o': 'circle', 'v': 'triangle-down', '^': 'triangle-up',

's': 'square', 'd': 'diamond', '+': 'cross', 'p': 'plus',

'x': 'crosshair', '.': 'point'}

PY2 = sys.version_info[0] == 2

if PY2:

string_types = basestring, # noqa

else:

string_types = str,

# Determine whether `v` can be hashed.

def hashable(data, v):

try:

data[v]

except (TypeError, KeyError, IndexError):

return False

return True

def show(key=None, display_toolbar=True):

"""Shows the current context figure in the output area.

Parameters

----------

key : hashable, optional

Any variable that can be used as a key for a dictionary.

display_toolbar: bool (default: True)

If True, a toolbar for different mouse interaction is displayed with

the figure.

Raises

------

KeyError

When no context figure is associated with the provided key.

Examples

--------

>>> import numpy as np

>>> import pyplot as plt

>>> n = 100

>>> x = np.arange(n)

>>> y = np.cumsum(np.random.randn(n))

>>> plt.plot(x,y)

>>> plt.show()

"""

if key is None:

figure = current_figure()

else:

figure = _context['figure_registry'][key]

figure.display_toolbar = display_toolbar

display(figure)

def figure(key=None, fig=None, **kwargs):

"""Creates figures and switches between figures.

If a ``bqplot.Figure`` object is provided via the fig optional argument,

this figure becomes the current context figure.

Otherwise:

- If no key is provided, a new empty context figure is created.

- If a key is provided for which a context already exists, the

corresponding context becomes current.

- If a key is provided and no corresponding context exists, a new context

is reated for that key and becomes current.

Besides, optional arguments allow to set or modify Attributes

of the selected context figure.

Parameters

----------

key: hashable, optional

Any variable that can be used as a key for a dictionary

fig: Figure, optional

A bqplot Figure

"""

scales_arg = kwargs.pop('scales', {})

_context['current_key'] = key

if fig is not None: # fig provided

_context['figure'] = fig

if key is not None:

_context['figure_registry'][key] = fig

for arg in kwargs:

setattr(_context['figure'], arg, kwargs[arg])

else: # no fig provided

if key is None: # no key provided

_context['figure'] = Figure(**kwargs)

else: # a key is provided

if key not in _context['figure_registry']:

if 'title' not in kwargs:

kwargs['title'] = 'Figure' + ' ' + str(key)

_context['figure_registry'][key] = Figure(**kwargs)

_context['figure'] = _context['figure_registry'][key]

for arg in kwargs:

setattr(_context['figure'], arg, kwargs[arg])

scales(key, scales=scales_arg)

# Set the axis reference dictionary. This dictionary contains the mapping

# from the possible dimensions in the figure to the list of scales with

# respect to which axes have been drawn for this figure.

# Used to automatically generate axis.

if getattr(_context['figure'], 'axis_registry', None) is None:

setattr(_context['figure'], 'axis_registry', {})

return _context['figure']

def close(key):

"""Closes and unregister the context figure corresponding to the key.

Parameters

----------

key: hashable

Any variable that can be used as a key for a dictionary

"""

figure_registry = _context['figure_registry']

if key not in figure_registry:

return

if _context['figure'] == figure_registry[key]:

figure()

fig = figure_registry[key]

if hasattr(fig, 'pyplot'):

fig.pyplot.close()

fig.pyplot_vbox.close()

fig.close()

del figure_registry[key]

del _context['scale_registry'][key]

def _process_data(*kwarg_names):

"""Helper function to handle data keyword argument

"""

def _data_decorator(func):

@functools.wraps(func)

def _mark_with_data(*args, **kwargs):

data = kwargs.pop('data', None)

if data is None:

return func(*args, **kwargs)

else:

data_args = [data[i] if hashable(data, i) else i for i in args]

data_kwargs = {

kw: data[kwargs[kw]] if hashable(data, kwargs[kw]) else kwargs[kw] for kw in set(kwarg_names).intersection(list(kwargs.keys()))

}

try:

# if any of the plots want to use the index_data, they can

# use it by referring to this attribute.

data_kwargs['index_data'] = data.index

except AttributeError:

pass

kwargs_update = kwargs.copy()

kwargs_update.update(data_kwargs)

return func(*data_args, **kwargs_update)

return _mark_with_data

return _data_decorator

def scales(key=None, scales={}):

"""Creates and switches between context scales.

If no key is provided, a new blank context is created.

If a key is provided for which a context already exists, the existing

context is set as the current context.

If a key is provided and no corresponding context exists, a new context is

created for that key and set as the current context.

Parameters

----------

key: hashable, optional

Any variable that can be used as a key for a dictionary

scales: dictionary

Dictionary of scales to be used in the new context

Example

-------

>>> scales(scales={

>>> 'x': Keep,

>>> 'color': ColorScale(min=0, max=1)

>>> })

This creates a new scales context, where the 'x' scale is kept from the

previous context, the 'color' scale is an instance of ColorScale

provided by the user. Other scales, potentially needed such as the 'y'

scale in the case of a line chart will be created on the fly when

needed.

Notes

-----

Every call to the function figure triggers a call to scales.

The `scales` parameter is ignored if the `key` argument is not Keep and

context scales already exist for that key.

"""

old_ctxt = _context['scales']

if key is None: # No key provided

_context['scales'] = {_get_attribute_dimension(k): scales[k] if scales[k] is not Keep

else old_ctxt[_get_attribute_dimension(k)] for k in scales}

else: # A key is provided

if key not in _context['scale_registry']:

_context['scale_registry'][key] = {

_get_attribute_dimension(k): scales[k]

if scales[k] is not Keep

else old_ctxt[_get_attribute_dimension(k)]

for k in scales

}

_context['scales'] = _context['scale_registry'][key]

def xlim(min, max):

"""Set the domain bounds of the current 'x' scale.

"""

return set_lim(min, max, 'x')

def ylim(min, max):

"""Set the domain bounds of the current 'y' scale.

"""

return set_lim(min, max, 'y')

def set_lim(min, max, name):

"""Set the domain bounds of the scale associated with the provided key.

Parameters

----------

name: hashable

Any variable that can be used as a key for a dictionary

Raises

------

KeyError

When no context figure is associated with the provided key.

"""

scale = _context['scales'][_get_attribute_dimension(name)]

scale.min = min

scale.max = max

return scale

def axes(mark=None, options={}, **kwargs):

"""Draws axes corresponding to the scales of a given mark.

It also returns a dictionary of drawn axes. If the mark is not provided,

the last drawn mark is used.

Parameters

----------

mark: Mark or None (default: None)

The mark to inspect to create axes. If None, the last mark drawn is

used instead.

options: dict (default: {})

Options for the axes to be created. If a scale labeled 'x' is required

for that mark, options['x'] contains optional keyword arguments for the

constructor of the corresponding axis type.

"""

if mark is None:

mark = _context['last_mark']

if mark is None:

return {}

fig = kwargs.get('figure', current_figure())

scales = mark.scales

fig_axes = [axis for axis in fig.axes]

axes = {}

for name in scales:

if name not in mark.class_trait_names(scaled=True):

# The scale is not needed.

continue

scale_metadata = mark.scales_metadata.get(name, {})

dimension = scale_metadata.get('dimension', scales[name])

axis_args = dict(scale_metadata,

**(options.get(name, {})))

axis = _fetch_axis(fig, dimension, scales[name])

if axis is not None:

# For this figure, an axis exists for the scale in the given

# dimension. Apply the properties and return back the object.

_apply_properties(axis, options.get(name, {}))

axes[name] = axis

continue

# An axis must be created. We fetch the type from the registry

# the key being provided in the scaled attribute decoration

key = mark.class_traits()[name].get_metadata('atype')

if key is not None:

axis_type = Axis.axis_types[key]

axis = axis_type(scale=scales[name], **axis_args)

axes[name] = axis

fig_axes.append(axis)

# Update the axis registry of the figure once the axis is added

_update_fig_axis_registry(fig, dimension, scales[name], axis)

fig.axes = fig_axes

return axes

def _set_label(label, mark, dim, **kwargs):

"""Helper function to set labels for an axis

"""

if mark is None:

mark = _context['last_mark']

if mark is None:

return {}

fig = kwargs.get('figure', current_figure())

scales = mark.scales

scale_metadata = mark.scales_metadata.get(dim, {})

scale = scales.get(dim, None)

if scale is None:

return

dimension = scale_metadata.get('dimension', scales[dim])

axis = _fetch_axis(fig, dimension, scales[dim])

if axis is not None:

_apply_properties(axis, {'label': label})

def xlabel(label=None, mark=None, **kwargs):

"""Sets the value of label for an axis whose associated scale has the

dimension `x`.

Parameters

----------

label: Unicode or None (default: None)

The label for x axis

"""

_set_label(label, mark, 'x', **kwargs)

def ylabel(label=None, mark=None, **kwargs):

"""Sets the value of label for an axis whose associated scale has the

dimension `y`.

Parameters

----------

label: Unicode or None (default: None)

The label for y axis

"""

_set_label(label, mark, 'y', **kwargs)

def grids(fig=None, value='solid'):

"""Sets the value of the grid_lines for the axis to the passed value.

The default value is `solid`.

Parameters

----------

fig: Figure or None(default: None)

The figure for which the axes should be edited. If the value is None,

the current figure is used.

value: {'none', 'solid', 'dashed'}

The display of the grid_lines

"""

if fig is None:

fig = current_figure()

for a in fig.axes:

a.grid_lines = value

def title(label, style=None):

"""Sets the title for the current figure.

Parameters

----------

label : str

The new title for the current figure.

style: dict

The CSS style to be applied to the figure title

"""

fig = current_figure()

fig.title = label

if style is not None:

fig.title_style = style

def legend():

"""Places legend in the current figure."""

for m in current_figure().marks:

m.display_legend = True

def hline(level, **kwargs):

"""Draws a horizontal line at the given level.

Parameters

----------

level: float

The level at which to draw the horizontal line.

preserve_domain: boolean (default: False)

If true, the line does not affect the domain of the 'y' scale.

"""

kwargs.setdefault('colors', ['dodgerblue'])

kwargs.setdefault('stroke_width', 1)

scales = kwargs.pop('scales', {})

fig = kwargs.get('figure', current_figure())

scales['x'] = fig.scale_x

level = array(level)

if len(level.shape) == 0:

x = [0, 1]

y = [level, level]

else:

x = [0, 1]

y = column_stack([level, level])

return plot(x, y, scales=scales, preserve_domain={

'x': True,

'y': kwargs.get('preserve_domain', False)

}, axes=False, update_context=False, **kwargs)

def vline(level, **kwargs):

"""Draws a vertical line at the given level.

Parameters

----------

level: float

The level at which to draw the vertical line.

preserve_domain: boolean (default: False)

If true, the line does not affect the domain of the 'x' scale.

"""

kwargs.setdefault('colors', ['dodgerblue'])

kwargs.setdefault('stroke_width', 1)

scales = kwargs.pop('scales', {})

fig = kwargs.get('figure', current_figure())

scales['y'] = fig.scale_y

level = array(level)

if len(level.shape) == 0:

x = [level, level]

y = [0, 1]

else:

x = column_stack([level, level])

# TODO: repeating [0, 1] should not be required once we allow for

# 2-D x and 1-D y

y = [[0, 1]] * len(level)

return plot(x, y, scales=scales, preserve_domain={

'x': kwargs.get('preserve_domain', False),

'y': True

}, axes=False, update_context=False, **kwargs)

def _process_cmap(cmap):

'''

Returns a kwarg dict suitable for a ColorScale

'''

option = {}

if isinstance(cmap, str):

option['scheme'] = cmap

elif isinstance(cmap, list):

option['colors'] = cmap

else:

raise ValueError('''`cmap` must be a string (name of a color scheme)

or a list of colors, but a value of {} was given

'''.format(cmap))

return option

def set_cmap(cmap):

'''

Set the color map of the current 'color' scale.

'''

scale = _context['scales']['color']

for k, v in _process_cmap(cmap).items():

setattr(scale, k, v)

return scale

def _draw_mark(mark_type, options={}, axes_options={}, **kwargs):

"""Draw the mark of specified mark type.

Parameters

----------

mark_type: type

The type of mark to be drawn

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

figure: Figure or None

The figure to which the mark is to be added.

If the value is None, the current figure is used.

cmap: list or string

List of css colors, or name of bqplot color scheme

"""

fig = kwargs.pop('figure', current_figure())

scales = kwargs.pop('scales', {})

update_context = kwargs.pop('update_context', True)

# Set the color map of the color scale

cmap = kwargs.pop('cmap', None)

if cmap is not None:

# Add the colors or scheme to the color scale options

options['color'] = dict(options.get('color', {}),

**_process_cmap(cmap))

# Going through the list of data attributes

for name in mark_type.class_trait_names(scaled=True):

dimension = _get_attribute_dimension(name, mark_type)

# TODO: the following should also happen if name in kwargs and

# scales[name] is incompatible.

if name not in kwargs:

# The scaled attribute is not being passed to the mark. So no need

# create a scale for this.

continue

elif name in scales:

if update_context:

_context['scales'][dimension] = scales[name]

# Scale has to be fetched from the context or created as it has not

# been passed.

elif dimension not in _context['scales']:

# Creating a scale for the dimension if a matching scale is not

# present in _context['scales']

traitlet = mark_type.class_traits()[name]

rtype = traitlet.get_metadata('rtype')

dtype = traitlet.validate(None, kwargs[name]).dtype

# Fetching the first matching scale for the rtype and dtype of the

# scaled attributes of the mark.

compat_scale_types = [

Scale.scale_types[key]

for key in Scale.scale_types

if Scale.scale_types[key].rtype == rtype and issubdtype(dtype, Scale.scale_types[key].dtype)

]

sorted_scales = sorted(compat_scale_types,

key=lambda x: x.precedence)

scales[name] = sorted_scales[-1](**options.get(name, {}))

# Adding the scale to the context scales

if update_context:

_context['scales'][dimension] = scales[name]

else:

scales[name] = _context['scales'][dimension]

mark = mark_type(scales=scales, **kwargs)

_context['last_mark'] = mark

fig.marks = [m for m in fig.marks] + [mark]

if kwargs.get('axes', True):

axes(mark, options=axes_options)

return mark

def _infer_x_for_line(y):

"""

Infers the x for a line if no x is provided.

"""

array_shape = shape(y)

if len(array_shape) == 0:

return []

if len(array_shape) == 1:

return arange(array_shape[0])

if len(array_shape) > 1:

return arange(array_shape[1])

@_process_data('color')

def plot(*args, **kwargs):

"""Draw lines in the current context figure.

Signature: `plot(x, y, **kwargs)` or `plot(y, **kwargs)`, depending of the

length of the list of positional arguments. In the case where the `x` array

is not provided.

Parameters

----------

x: numpy.ndarray or list, 1d or 2d (optional)

The x-coordinates of the plotted line. When not provided, the function

defaults to `numpy.arange(len(y))`

x can be 1-dimensional or 2-dimensional.

y: numpy.ndarray or list, 1d or 2d

The y-coordinates of the plotted line. If argument `x` is 2-dimensional

it must also be 2-dimensional.

marker_str: string

string representing line_style, marker and color.

For e.g. 'g--o', 'sr' etc

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

figure: Figure or None

The figure to which the line is to be added.

If the value is None, the current figure is used.

"""

marker_str = None

if len(args) == 1:

kwargs['y'] = args[0]

if kwargs.get('index_data', None) is not None:

kwargs['x'] = kwargs['index_data']

else:

kwargs['x'] = _infer_x_for_line(args[0])

elif len(args) == 2:

if isinstance(args[1], str):

kwargs['y'] = args[0]

kwargs['x'] = _infer_x_for_line(args[0])

marker_str = args[1].strip()

else:

kwargs['x'] = args[0]

kwargs['y'] = args[1]

elif len(args) == 3:

kwargs['x'] = args[0]

kwargs['y'] = args[1]

if isinstance(args[2], str):

marker_str = args[2].strip()

if marker_str:

line_style, color, marker = _get_line_styles(marker_str)

# only marker specified => draw scatter

if marker and not line_style:

kwargs['marker'] = marker

if color:

kwargs['colors'] = [color]

return _draw_mark(Scatter, **kwargs)

else: # draw lines in all other cases

kwargs['line_style'] = line_style or 'solid'

if marker:

kwargs['marker'] = marker

if color:

kwargs['colors'] = [color]

return _draw_mark(Lines, **kwargs)

else:

return _draw_mark(Lines, **kwargs)

def imshow(image, format, **kwargs):

"""Draw an image in the current context figure.

Parameters

----------

image: image data

Image data, depending on the passed format, can be one of:

- an instance of an ipywidgets Image

- a file name

- a raw byte string

format: {'widget', 'filename', ...}

Type of the input argument.

If not 'widget' or 'filename', must be a format supported by

the ipywidgets Image.

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

"""

if format == 'widget':

ipyimage = image

elif format == 'filename':

with open(image, 'rb') as f:

data = f.read()

ipyimage = ipyImage(value=data)

else:

ipyimage = ipyImage(value=image, format=format)

kwargs['image'] = ipyimage

kwargs.setdefault('x', [0., 1.])

kwargs.setdefault('y', [0., 1.])

return _draw_mark(Image, **kwargs)

def ohlc(*args, **kwargs):

"""Draw OHLC bars or candle bars in the current context figure.

Signature: `ohlc(x, y, **kwargs)` or `ohlc(y, **kwargs)`, depending of the

length of the list of positional arguments. In the case where the `x` array

is not provided

Parameters

----------

x: numpy.ndarray or list, 1d (optional)

The x-coordinates of the plotted line. When not provided, the function

defaults to `numpy.arange(len(y))`.

y: numpy.ndarray or list, 2d

The ohlc (open/high/low/close) information. A two dimensional array. y

must have the shape (n, 4).

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

"""

if len(args) == 2:

kwargs['x'] = args[0]

kwargs['y'] = args[1]

elif len(args) == 1:

kwargs['y'] = args[0]

length = len(args[0])

kwargs['x'] = arange(length)

return _draw_mark(OHLC, **kwargs)

@_process_data('color', 'opacity', 'size', 'skew', 'rotation')

def scatter(x, y, **kwargs):

"""Draw a scatter in the current context figure.

Parameters

----------

x: numpy.ndarray, 1d

The x-coordinates of the data points.

y: numpy.ndarray, 1d

The y-coordinates of the data points.

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

"""

kwargs['x'] = x

kwargs['y'] = y

return _draw_mark(Scatter, **kwargs)

@_process_data()

def hist(sample, options={}, **kwargs):

"""Draw a histogram in the current context figure.

Parameters

----------

sample: numpy.ndarray, 1d

The sample for which the histogram must be generated.

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'counts'

is required for that mark, options['counts'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'counts' is

required for that mark, axes_options['counts'] contains optional

keyword arguments for the constructor of the corresponding axis type.

"""

kwargs['sample'] = sample

scales = kwargs.pop('scales', {})

if 'count' not in scales:

dimension = _get_attribute_dimension('count', Hist)

if dimension in _context['scales']:

scales['count'] = _context['scales'][dimension]

else:

scales['count'] = LinearScale(**options.get('count', {}))

_context['scales'][dimension] = scales['count']

kwargs['scales'] = scales

return _draw_mark(Hist, options=options, **kwargs)

@_process_data()

def bin(sample, options={}, **kwargs):

"""Draw a histogram in the current context figure.

Parameters

----------

sample: numpy.ndarray, 1d

The sample for which the histogram must be generated.

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x'

is required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is

required for that mark, axes_options['x'] contains optional

keyword arguments for the constructor of the corresponding axis type.

"""

kwargs['sample'] = sample

scales = kwargs.pop('scales', {})

for xy in ['x', 'y']:

if xy not in scales:

dimension = _get_attribute_dimension(xy, Bars)

if dimension in _context['scales']:

scales[xy] = _context['scales'][dimension]

else:

scales[xy] = LinearScale(**options.get(xy, {}))

_context['scales'][dimension] = scales[xy]

kwargs['scales'] = scales

return _draw_mark(Bins, options=options, **kwargs)

@_process_data('color')

def bar(x, y, **kwargs):

"""Draws a bar chart in the current context figure.

Parameters

----------

x: numpy.ndarray, 1d

The x-coordinates of the data points.

y: numpy.ndarray, 1d

The y-coordinates of the data pints.

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

"""

kwargs['x'] = x

kwargs['y'] = y

return _draw_mark(Bars, **kwargs)

@_process_data()

def boxplot(x, y, **kwargs):

"""Draws a boxplot in the current context figure.

Parameters

----------

x: numpy.ndarray, 1d

The x-coordinates of the data points.

y: numpy.ndarray, 2d

The data from which the boxes are to be created. Each row of the data

corresponds to one box drawn in the plot.

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

"""

kwargs['x'] = x

kwargs['y'] = y

return _draw_mark(Boxplot, **kwargs)

@_process_data('color')

def barh(*args, **kwargs):

"""Draws a horizontal bar chart in the current context figure.

Parameters

----------

x: numpy.ndarray, 1d

The domain of the data points.

y: numpy.ndarray, 1d

The range of the data pints.

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

"""

kwargs['orientation'] = "horizontal"

return bar(*args, **kwargs)

@_process_data('color')

def pie(sizes, **kwargs):

"""Draws a Pie in the current context figure.

Parameters

----------

sizes: numpy.ndarray, 1d

The proportions to be represented by the Pie.

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

"""

kwargs['sizes'] = sizes

return _draw_mark(Pie, **kwargs)

def label(text, **kwargs):

"""Draws a Label in the current context figure.

Parameters

----------

text: string

The label to be displayed.

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

"""

kwargs['text'] = text

return _draw_mark(Label, **kwargs)

def geo(map_data, **kwargs):

"""Draw a map in the current context figure.

Parameters

----------

map_data: string or bqplot.map (default: WorldMap)

Name of the map or json file required for the map data.

options: dict (default: {})

Options for the scales to be created. If a scale labeled 'x' is

required for that mark, options['x'] contains optional keyword

arguments for the constructor of the corresponding scale type.

axes_options: dict (default: {})

Options for the axes to be created. If an axis labeled 'x' is required

for that mark, axes_options['x'] contains optional keyword arguments

for the constructor of the corresponding axis type.

"""

scales = kwargs.pop('scales', _context['scales'])

options = kwargs.get('options', {})

if 'projection' not in scales:

scales['projection'] = Mercator(**options.get('projection', {}))

kwargs['scales'] = scales

if isinstance(map_data, string_types):

kwargs['map_data'] = topo_load('map_data/' + map_data + '.json')

else:

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