import seaborn as sns

from ..style.template import _header, _footer


def multicount(data,
               x,
               hue=None,
               row=None,
               col=None,
               col_wrap=4,
               palette='default',
               style='astetik',
               dpi=72,
               title='',
               sub_title='',
               x_label='',
               y_label='',
               x_scale='linear',
               y_scale='linear',
               x_limit='auto',
               y_limit='auto',
               legend=True,
               save=False):

    '''MULTICOUNT PLOT

    A bar plot for counting values in a single feature. Useful for
    categoricals and stepped data. Prints out multiple plots.

    Inputs: 3 to 4
    Features: 1 continuous and 2 to 3 categoricals

    1. USE
    ======
    ast.multicount(data=patients,
                   x='expired',
                   hue='gender',
                   col='insurance')

    2. PARAMETERS
    =============
    2.1 INPUT PARAMETERS
    --------------------
    data :: pandas dataframe

    x :: x-axis data (categorical)

    y :: y-axis data (continuous or categorical)

    hue :: color highlight (categorical)

    col :: the side-by-side plot comparison feature

    --------------------
    2.2. PLOT PARAMETERS
    --------------------
    col_wrap :: the number of plots to show per row

    ----------------------
    2.3. COMMON PARAMETERS
    ----------------------
    palette :: One of the hand-crafted palettes:
                'default'
                'colorblind'
                'blue_to_red'
                'blue_to_green'
                'red_to_green'
                'green_to_red'
                'violet_to_blue'
                'brown_to_green'
                'green_to_marine'

                Or use any cmap, seaborn or matplotlib
                color or palette code, or hex value.

    style :: Use one of the three core styles:
                'astetik'     # white
                '538'         # grey
                'solarized'   # sepia

              Or alternatively use any matplotlib or seaborn
              style definition.

    dpi :: the resolution of the plot (int value)

    title :: the title of the plot (string value)

    sub_title :: a secondary title to be shown below the title

    x_label :: string value for x-axis label

    y_label :: string value for y-axis label

    x_scale :: 'linear' or 'log' or 'symlog'

    y_scale :: 'linear' or 'log' or 'symlog'

    x_limit :: int or list with two ints

    y_limit :: int or list with two ints

    outliers :: Remove outliers using either 'zscore' or 'iqr'

    '''

    if row != None:
        col_wrap = None

    if hue != None:
        n_colors = len(data[hue].unique())
    else:
        n_colors = len(data[x].unique())

    # HEADER STARTS >>>
    palette = _header(palette,
                      style,
                      n_colors=n_colors,
                      dpi=dpi,
                      fig_height=None,
                      fig_width=None)
    # <<< HEADER ENDS

    p = sns.factorplot(data=data,
                       x=x,
                       y=None,
                       hue=hue,
                       row=row,
                       col=col,
                       col_wrap=col_wrap,
                       palette=palette,
                       size=4,
                       kind='count',
                       legend=legend,
                       legend_out=False)

    # FOOTER STARTS >>>
    _footer(p, x_label, y_label, save=save)
