from pandas import DataFrame
from plotnine import (
    ggplot, 
    aes, 
    geom_col, 
    geom_line, 
    annotate, 
    theme, 
    element_blank, 
    labs, 
    coord_flip, 
    scale_x_discrete, 
    scale_x_datetime, 
    scale_y_continuous, 
    theme_light, 
    )


class DataAvailabilityError(Exception):
    """Raised when not enough data available to vizualize."""
    pass


def plot_NA(placeholder_text: str = "Not enough data available to visualize.", placeholder_size: int = 14):
    """Placeholder plot for when there is not enough data available to visualize.
    """
    return (
        ggplot() 
        + annotate("text", x=0, y=0, label=placeholder_text, size=placeholder_size)
        + theme(axis_ticks=element_blank(), axis_text=element_blank(), panel_grid=element_blank())
        + labs(x="", y="", title="")
        )


def generate_rule_axis_label(rule_types: list | None = None):
    """Generate axis label for rules, accounting for rule type ("all", "3f1-significant", or "other-significant").
    """
    categories = ""
    if (rule_types is None) or ("all" in rule_types):
        pass
    elif all(True if cat in rule_types else False for cat in ("3f1-significant", "other-significant")):
        categories = "significant"
    elif ("3f1-significant" in rule_types) and ("other-significant" not in rule_types):
        categories = "Section 3(f)(1) Significant"
    elif ("3f1-significant" not in rule_types) and ("other-significant" in rule_types):
        categories = "Other Significant"
    return f"Number of {categories} rules".replace("  ", " ")


def plot_agency(df, group_col = "acronym", value_col = "rules", color="#033C5A", rule_types: list | None = None):
    """Plot rules by agency.

    Args:
        df (DataFrame): Input data.
        group_col (str, optional): Column on which the data are grouped. Defaults to "acronym".
        value_col (str, optional): Column of values to be plotted. Defaults to "rules".
        color (str, optional): Color of values in plot. Defaults to "#033C5A" ([GW Blue](https://communications.gwu.edu/visual-identity/color-palette)).
        rule_types (list | None, optional): One or more rule types to include in plot. Accepts "all", "3f1-significant", or "other-significant". Defaults to None.
    
    Returns:
        ggplot: Plotted data.
    """
    order_list = df.loc[:, group_col].to_list()[::-1]

    y_lab = generate_rule_axis_label(rule_types)
    
    plot = (
        ggplot(
            df, 
            aes(x=group_col, y=value_col), 
            )
        + geom_col(color="#FFFFFF", fill=color)
        + coord_flip()
        + scale_x_discrete(limits=order_list)
        + labs(y=y_lab, x="", title="Rules Published by Agency")
        + theme_light()
        )
    return plot


def plot_month(
        df: DataFrame, 
        group_cols: tuple = ("publication_year", "publication_month"), 
        value_col: str = "rules", 
        color: str = "#033C5A", 
        title: str | None = None, 
        y_lab: str = "",
        ):
    """Plot rules by month.

    Args:
        df (DataFrame): Input data.
        group_cols (tuple, optional): Columns on which the data are grouped. Defaults to ("publication_year", "publication_month").
        value_col (str, optional): Column of values to be plotted. Defaults to "rules".
        color (str, optional): Color of values in plot. Defaults to "#033C5A" ([GW Blue](https://communications.gwu.edu/visual-identity/color-palette)).
        title (str | None, optional): Plot title. Defaults to None.
        y_lab (str, optional): Plot y label. Defaults to "" (empty string).
    
    Returns:
        ggplot: Plotted data.
    """
    df.loc[:, "ym"] = df[group_cols[0]].astype(str) + "-" + df[group_cols[1]].astype(str).str.pad(2, fillchar="0")
    order_list = df.loc[:, "ym"].to_list()
    if title is None:
        title = "Rules Published by Month"

    plot = (
        ggplot(
            df, 
            aes(x="ym", y=value_col), 
            )
        + geom_col(color="#FFFFFF", fill=color)
        + scale_x_discrete(limits=order_list)
        + labs(y=y_lab, x="", title=title)
        + theme_light()
        )
    return plot


def plot_day(
        df: DataFrame, 
        group_col: str = "publication_date", 
        value_col: str = "rules", 
        color: str = "#033C5A", 
        title: str | None = None, 
        y_lab: str = "",
    ):
    """Plot rules by day.

    Args:
        df (DataFrame): Input data.
        group_col (str, optional): Column on which the data are grouped. Defaults to "publication_date".
        value_col (str, optional): Column of values to be plotted. Defaults to "rules".
        color (str, optional): Color of values in plot. Defaults to "#033C5A" ([GW Blue](https://communications.gwu.edu/visual-identity/color-palette)).
        title (str | None, optional): Plot title. Defaults to None.
        y_lab (str, optional): Plot y label. Defaults to "" (empty string).

    Returns:
        ggplot: Plotted data.
    """ 
    min_date = df.loc[:, group_col].min()
    max_date = df.loc[:, group_col].max()
    diff = (max_date - min_date).days
    if diff in range(0, 61):
        freq = "1 week"
    elif diff in range(61, 91):
        freq = "2 weeks"
    else:
        freq = "1 month"
    
    max_value = df.loc[:, value_col].max()

    if title is None:
        title = "Rules Published by Date"
    
    plot = (
        ggplot(
            df, 
            aes(x=group_col, y=value_col), 
            )
        + geom_line(group=1, color=color)
        + scale_x_datetime(date_breaks=freq, date_labels="%m-%d")
        + scale_y_continuous(limits=(0, max_value), expand=(0, 0, 0.1, 0))
        + labs(y=y_lab, x="", title=title)
        + theme_light()
        )
    return plot


def plot_week(
        df: DataFrame, 
        group_col: str = "week_of", 
        value_col: str = "rules", 
        color: str = "#033C5A", 
        title: str | None = None, 
        y_lab: str = "",
        show_significant: bool = False, 
    ):
    """Plot rules by week.

    Args:
        df (DataFrame): Input data.
        group_col (str, optional): Column on which the data are grouped. Defaults to "week_of".
        value_col (str, optional): Column of values to be plotted. Defaults to "rules".
        color (str, optional): Color of values in plot. Defaults to "#033C5A" ([GW Blue](https://communications.gwu.edu/visual-identity/color-palette)).
        title (str | None, optional): Plot title. Defaults to None.
        y_lab (str, optional): Plot y label. Defaults to "" (empty string).
    
    Returns:
        ggplot: Plotted data.
    """
    max_value = df.loc[:, value_col].max()
    
    date_values = df[group_col].to_list()
    num_weeks = len(date_values)

    if num_weeks in range(8, 16):
        reduce_by = 2
    elif num_weeks in range(16, 24):
        reduce_by = 3
    elif num_weeks in range(24, 32):
        reduce_by = 4
    elif num_weeks >= 32:
        reduce_by = 5
    else:
        reduce_by = 1
    
    breaks = [val for idx, val in enumerate(date_values) if idx % reduce_by == 0]

    if title is None:
        title = "Rules Published by Week"

    plot = (
        ggplot(
            df, 
            aes(x=group_col, y=value_col), 
            )
        + geom_line(group=1, color=color)
        + scale_x_datetime(breaks=breaks, labels=[f"{w.strftime('%m-%d')}" for w in breaks])
        + scale_y_continuous(limits=(0, max_value), expand=(0, 0, 0.1, 0))
        + labs(y=y_lab, x="", title=title)
        + theme_light()
        )
    return plot


def plot_tf(df: DataFrame, frequency: str, rule_types: list | None = None, **kwargs) -> ggplot:
    """Plot rules over time by given frequency.

    Args:
        df (DataFrame): Input data.
        frequency (str): Frequency of time for aggregating rules. Accepts "monthly" or "daily".
        rule_types (list | None, optional): One or more rule types to include in plot. Accepts "all", "3f1-significant", or "other-significant". Defaults to None.

    Raises:
        ValueError: Frequency parameter received invalid value.

    Returns:
        ggplot: Plotted data.
    """    
    freq_options = {
        "monthly": plot_month, 
        "daily": plot_day, 
        "weekly": plot_week, 
        }
    plot_freq = freq_options.get(frequency, None)
    if plot_freq is None:
        raise ValueError(f"Frequency must be one of: {', '.join(freq_options.keys())}")
    
    y_lab = generate_rule_axis_label(rule_types)

    return plot_freq(df, y_lab=y_lab, **kwargs)