import pandas as pd
import streamlit as st
from config.config import CRRA_COLORS, CRRA_FONT, PLOTLY_TEMPLATE


def render_resource_input(name_clean, r, tooltip=None):
    """Render a number_input widget for a single resource availability cap.

    Writes the entered value into st.session_state.resource_caps[r].

    Args:
        name_clean (str): display label (resource name without unit suffix).
        r (str): canonical resource name used as the session state key.
        tooltip (str | None): optional help text shown on hover.

    Returns:
        None
    """
    widget_key = f"cap_{r}"

    if widget_key not in st.session_state:
        existing = st.session_state.resource_caps.get(r, 0.0)
        st.session_state[widget_key] = existing if existing else None

    cap = st.number_input(
        label=name_clean,
        min_value=0.0,
        format="%.10g",
        value=None,
        placeholder="0.0",
        key=widget_key,
        help=tooltip,
    )

    st.session_state.resource_caps[r] = cap if cap is not None else 0.0


def apply_chart_style(fig, height=None):
    """Apply the standard CRRA theme (font, colours, margins) to a Plotly figure.

    Args:
        fig: Plotly figure object.
        height (int | None): optional fixed height in pixels.

    Returns:
        fig: the same figure with updated layout.
    """
    fig.update_layout(
        template=PLOTLY_TEMPLATE,
        font=dict(family=CRRA_FONT, size=12, color=CRRA_COLORS["black"]),
        margin=dict(t=40, b=40, l=40, r=20),
        legend=dict(
            orientation="v",
            yanchor="middle", y=0.5,
            xanchor="left", x=1.02,
        ),
    )
    if height:
        fig.update_layout(height=height)
    return fig


def register_chart_data(name: str, df: pd.DataFrame):
    """Store a chart's underlying DataFrame in the session-level registry for bulk export.

    Args:
        name (str): unique chart label used as the dict key and export sheet name.
        df (pd.DataFrame): data behind the chart.

    Returns:
        None
    """
    st.session_state.setdefault("chart_data_registry", {})
    st.session_state["chart_data_registry"][name] = df.copy()


def show_bar():
    """Render the CRRA four-colour decorative bar below page headings.

    Returns:
        None
    """
    st.markdown(
        """
        <div class="et_pb_module">
            <div style="display:flex;width:100%;height:12px;margin:0 auto 0 0;">
                <span style="flex:1;background:#FF5F4D;"></span>
                <span style="flex:1;background:#A0A0A0;"></span>
                <span style="flex:1;background:#D9E210;"></span>
                <span style="flex:1;background:#55C95B;"></span>
            </div>
        </div>
        """,
        unsafe_allow_html=True,
    )


def smart_display_format(val):
    """Format a numeric value showing all significant digits, no rounding.

    Uses repr() which gives the shortest decimal string that round-trips
    exactly to the same float — avoids floating-point noise like
    462961.099999999976717 when the value is actually 462961.1.
    Falls back to fixed-decimal notation when repr() produces scientific
    notation (very small numbers such as 1e-11).

    Args:
        val (float): numeric value to format.

    Returns:
        str: formatted string, or "" for near-zero values.
    """
    if abs(val) < 1e-14:
        return ""
    s = repr(val)
    if "e" not in s.lower():
        return s
    # Scientific notation → convert to fixed decimal (e.g. 1e-11 → 0.00000000001)
    return f"{val:.15f}".rstrip("0").rstrip(".")


def format_efficiency_summary(df: pd.DataFrame, df_default: pd.DataFrame):
    """Build a styled Pandas Styler for the resource-efficiency coefficient summary table.

    Cells whose value differs from the default median are highlighted in red.

    Args:
        df (pd.DataFrame): current coefficient values (methods × resources).
        df_default (pd.DataFrame): default median values for the same shape.

    Returns:
        pandas.io.formats.style.Styler: styled dataframe ready for st.dataframe().
    """
    df_numeric = df.copy()
    df_display = df_numeric.map(smart_display_format)

    def highlight_changes(data):
        styled = pd.DataFrame('', index=data.index, columns=data.columns)
        for m in data.index:
            for r in data.columns:
                try:
                    current = df_numeric.at[m, r]
                    default = df_default.at[m, r]
                    if abs(current - default) > 1e-6:
                        styled.at[m, r] = 'background-color: #ff5f4d'
                except:
                    continue
        return styled

    return (
        df_display
        .style
        .apply(highlight_changes, axis=None)
    )