"""This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.

This is the Bar class which lets you build your Bar charts just passing
the arguments to the Chart class and calling the proper functions.
It also add a new chained stacked method.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-----------------------------------------------------------------------------

#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
from __future__ import absolute_import, print_function, division

from ..builder import Builder, create_and_build
from ...models import FactorRange, Range1d
from ..glyphs import BarGlyph
from ...core.properties import Float, Enum, Bool, Override
from ..properties import Dimension
from ..attributes import ColorAttr, CatAttr
from ..operations import Stack, Dodge
from ...core.enums import Aggregation
from ..stats import stats
from ...models.sources import ColumnDataSource
from ..utils import help

#-----------------------------------------------------------------------------
# Classes and functions
#-----------------------------------------------------------------------------


class BarBuilder(Builder):
    """This is the Bar builder and it is in charge of plotting
    Bar chart (grouped and stacked) in an easy and intuitive way.

    Essentially, it utilizes a standardized way to ingest the data,
    make the proper calculations and generate renderers. The renderers
    reference the transformed data, which represent the groups of data
    that were derived from the inputs. We additionally make calculations
    for the ranges.

    The x_range is categorical, and is made either from the label argument
    or from the `pandas.DataFrame.index`. The y_range can be supplied as the
    parameter continuous_range, or will be calculated as a linear range
    (Range1d) based on the supplied values.

    The bar builder is and can be further used as a base class for other
    builders that might also be performing some aggregation across
    derived groups of data.

    """

    # ToDo: add label back as a discrete dimension
    values = Dimension('values')

    dimensions = ['values']
    #req_dimensions = [['values']]

    default_attributes = {'label': CatAttr(),
                          'color': ColorAttr(),
                          'line_color': ColorAttr(default='white'),
                          'stack': CatAttr(),
                          'group': CatAttr()}

    agg = Enum(Aggregation, default='sum')

    max_height = Float(1.0)
    min_height = Float(0.0)
    bar_width = Float(default=0.8)
    fill_alpha = Float(default=0.8)

    glyph = BarGlyph
    comp_glyph_types = Override(default=[BarGlyph])
    label_attributes = ['stack', 'group']

    label_only = Bool(False)
    values_only = Bool(False)

    def setup(self):

        if self.attributes['color'].columns is None:
            if self.attributes['stack'].columns is not None:
                self.attributes['color'].set_columns(self.attributes['stack'].columns)
            if self.attributes['group'].columns is not None:
                self.attributes['color'].set_columns(self.attributes['group'].columns)

        # ToDo: perform aggregation validation
        # Not given values kw, so using only categorical data
        if self.values.dtype.name == 'object' and len(self.attribute_columns) == 0:
            # agg must be count
            self.agg = 'count'
            self.attributes['label'].set_columns(self.values.selection)
        else:
            pass

        self._apply_inferred_index()

        if self.xlabel is None:
            if self.attributes['label'].columns is not None:
                self.xlabel = str(', '.join(self.attributes['label'].columns).title()).title()
            else:
                self.xlabel = self.values.selection

        if self.ylabel is None:
            if not self.label_only:
                self.ylabel = '%s( %s )' % (self.agg.title(), str(self.values.selection).title())
            else:
                self.ylabel = '%s( %s )' % (self.agg.title(), ', '.join(self.attributes['label'].columns).title())

    def _apply_inferred_index(self):
        """Configure chart when labels are provided as index instead of as kwarg."""

        # try to infer grouping vs stacking labels
        if (self.attributes['label'].columns is None and
                    self.values.selection is not None):

            if self.attributes['stack'].columns is not None:
                special_column = 'unity'
            else:
                special_column = 'index'

            self._data['label'] = special_column
            self.attributes['label'].setup(data=ColumnDataSource(self._data.df),
                                           columns=special_column)

            self.xlabel = ''

    def set_ranges(self):
        """Push the Bar data into the ColumnDataSource and calculate
        the proper ranges.
        """
        x_items = self.attributes['label'].items
        if x_items is None:
            x_items = ''
        x_labels = []

        # Items are identified by tuples. If the tuple has a single value,
        # we unpack it
        for item in x_items:
            item = self._get_label(item)

            x_labels.append(str(item))

        self.x_range = FactorRange(factors=x_labels)

        y_shift = abs(0.1 * ((self.min_height + self.max_height) / 2))

        if self.min_height < 0:
            start = self.min_height - y_shift
        else:
            start = 0.0

        if self.max_height > 0:
            end = self.max_height + y_shift
        else:
            end = 0.0

        self.y_range = Range1d(start=start, end=end)

    def get_extra_args(self):
        if self.__class__ is not BarBuilder:
            attrs = self.properties(with_bases=False)
            return {attr: getattr(self, attr) for attr in attrs}
        else:
            return {}

    def yield_renderers(self):
        """Use the rect glyphs to display the bars.

        Takes reference points from data loaded at the ColumnDataSource.
        """
        kwargs = self.get_extra_args()
        attrs = self.collect_attr_kwargs()

        for group in self._data.groupby(**self.attributes):
            glyph_kwargs = self.get_group_kwargs(group, attrs)
            group_kwargs = kwargs.copy()
            group_kwargs.update(glyph_kwargs)
            props = self.glyph.properties().difference(set(['label']))

            # make sure we always pass the color and line color
            for k in ['color', 'line_color']:
                group_kwargs[k] = group[k]

            # TODO(fpliger): we shouldn't need to do this to ensure we don't
            #               have extra kwargs... this is needed now because
            #               of label, group and stack being "special"
            for k in set(group_kwargs):
                if k not in props:
                    group_kwargs.pop(k)

            bg = self.glyph(label=group.label,
                            x_label=self._get_label(group['label']),
                            values=group.data[self.values.selection].values,
                            agg=stats[self.agg](),
                            width=self.bar_width,
                            fill_alpha=self.fill_alpha,
                            stack_label=self._get_label(group['stack']),
                            dodge_label=self._get_label(group['group']),
                            **group_kwargs)

            self.add_glyph(group, bg)

        Stack().apply(self.comp_glyphs)
        Dodge().apply(self.comp_glyphs)

        # a higher level function of bar chart is to keep track of max height of all bars
        self.max_height = max([renderer.y_max for renderer in self.comp_glyphs])
        self.min_height = min([renderer.y_min for renderer in self.comp_glyphs])

        for renderer in self.comp_glyphs:
            for sub_renderer in renderer.renderers:
                yield sub_renderer


@help(BarBuilder)
def Bar(data, label=None, values=None, color=None, stack=None, group=None, agg="sum",
        xscale="categorical", yscale="linear", xgrid=False, ygrid=True,
        continuous_range=None, **kw):
    """ Create a Bar chart using :class:`BarBuilder <bokeh.charts.builders.bar_builder.BarBuilder>`
    render the geometry from values, cat and stacked.

    Args:
        data (:ref:`userguide_charts_data_types`): the data
            source for the chart.
        label (list(str) or str, optional): list of string representing the categories.
            (Defaults to None)
        values (str, optional): iterable 2d representing the data series
            values matrix.
        color (str or list(str) or `~bokeh.charts._attributes.ColorAttr`): string color,
            string column name, list of string columns or a custom `ColorAttr`,
            which replaces the default `ColorAttr` for the builder.
        stack (list(str) or str, optional): columns to use for stacking.
            (Defaults to False, so grouping is assumed)
        group (list(str) or str, optional): columns to use for grouping.
        agg (str): how to aggregate the `values`. (Defaults to 'sum', or only label is
            provided, then performs a `count`)
        continuous_range(Range1d, optional): Custom continuous_range to be
            used. (Defaults to None)

    In addition to the parameters specific to this chart,
    :ref:`userguide_charts_defaults` are also accepted as keyword parameters.

    Returns:
        :class:`Chart`: includes glyph renderers that generate bars

    Examples:

        .. bokeh-plot::
            :source-position: above

            from bokeh.charts import Bar, output_file, show, hplot

            # best support is with data in a format that is table-like
            data = {
                'sample': ['1st', '2nd', '1st', '2nd', '1st', '2nd'],
                'interpreter': ['python', 'python', 'pypy', 'pypy', 'jython', 'jython'],
                'timing': [-2, 5, 12, 40, 22, 30]
            }

            # x-axis labels pulled from the interpreter column, stacking labels from sample column
            bar = Bar(data, values='timing', label='interpreter', stack='sample', agg='mean',
                      title="Python Interpreter Sampling", legend='top_right', width=400)

            # table-like data results in reconfiguration of the chart with no data manipulation
            bar2 = Bar(data, values='timing', label=['interpreter', 'sample'],
                       agg='mean', title="Python Interpreters", width=400)

            output_file("stacked_bar.html")
            show(hplot(bar, bar2))

    """
    if continuous_range and not isinstance(continuous_range, Range1d):
        raise ValueError(
            "continuous_range must be an instance of bokeh.models.ranges.Range1d"
        )

    if label is not None and values is None:
        kw['label_only'] = True
        if (agg == 'sum') or (agg == 'mean'):
            agg = 'count'
            values = label

    # The continuous_range is the y_range (until we implement HBar charts)
    y_range = continuous_range
    kw['label'] = label
    kw['values'] = values
    kw['color'] = color
    kw['stack'] = stack
    kw['group'] = group
    kw['agg'] = agg
    kw['xscale'] = xscale
    kw['yscale'] = yscale
    kw['xgrid'] = xgrid
    kw['ygrid'] = ygrid
    kw['y_range'] = y_range

    chart = create_and_build(BarBuilder, data, **kw)

    # hide x labels if there is a single value, implying stacking only
    if len(chart.x_range.factors) == 1:
        chart.below[0].visible = False

    return chart
