import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cm
import matplotlib.gridspec as gridspec


def show_two_plots(gdf, name1, name2, vmin, vmax, colorscheme, title1, title2, label, split=False):
    #dynamically change marker size
    n_points = len(gdf)
    marker_size = 8500 / n_points  
    marker_size = max(2, min(marker_size, 200))
    filter_marker_size = marker_size*0.6
    print(marker_size)

    print('split', split)
    if gdf.empty:
        raise ValueError("GeoDataFrame is empty")

    required_cols = [name1, name2]
    for col in required_cols:
        if col not in gdf.columns:
            raise ValueError(f"Column '{col}' not found in gdf")

    #Create a shared colormap and normalization
    cmap = plt.colormaps[colorscheme]
    norm = colors.Normalize(vmin=vmin, vmax=vmax)
    
    # Create subplots
    fig = plt.figure(figsize=(12, 6))
    gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 0.05], wspace=0.3)

    axes = [fig.add_subplot(gs[0]), fig.add_subplot(gs[1]), fig.add_subplot(gs[2]) ]

    if split == False:
        # First plot 
        gdf.plot(column=name1, cmap=cmap, norm=norm, ax=axes[0], marker='s', markersize=marker_size)
    
    else:
        # Split data into three GeoDataFrames
        # below_100 = gdf[gdf[split] <= 100]
        above_capacity = gdf[gdf[split] > (100-gdf['Pct_Construccion'])]
        above_100 = gdf[gdf[split] > 100]
        above_colors = {"capacity":"chocolate", "100":"firebrick"}
        
        # Plot values ≤ 100 using colormap
        gdf.plot(column=name1, cmap=colorscheme, ax=axes[0], vmin=vmin, vmax=vmax, marker='s', markersize=marker_size)
        
        # Plot values > capacity in orange
        above_capacity.plot(color=above_colors['capacity'], ax=axes[0], label='> capacidad debido a la construcción', marker='x', markersize=filter_marker_size)
        
        # Plot values > 100 in red
        above_100.plot(color=above_colors['100'], ax=axes[0], label='> 100% cobertura arbórea', marker='x', markersize=filter_marker_size)
        
        # Add legend manually for orange points
        orange_patch = plt.Line2D([0], [0], marker='o', color='w', label='> capacidad debido a la construcción', 
                               markerfacecolor=above_colors['capacity'], markersize=8)
    
        # Add legend manually for red points
        red_patch = plt.Line2D([0], [0], marker='o', color='w', label='> 100% cobertura arbórea', 
                               markerfacecolor=above_colors['100'], markersize=8)
        
        axes[0].legend(handles=[orange_patch, red_patch])
    # axes[0].set_title(title1)
    axes[0].set_title(title1, pad=-30)
    axes[0].set_axis_off()
    


    # Second plot (original)
    gdf.plot(column=name2, cmap=cmap, norm=norm, ax=axes[1],marker='s', markersize=marker_size)
    axes[1].set_title(title2, pad=-30)
    axes[1].set_axis_off()
    

    # Shared colorbar
    sm = cm.ScalarMappable(cmap=cmap, norm=norm)
    sm._A = []  # Dummy array for the colormap
    cbar = fig.colorbar(sm, cax=axes[2], orientation='vertical', fraction=0.03, pad=0.02)
    cbar.set_label(label)
    
    return fig

def show_one_plot(gdf, name, vmin, vmax, colorscheme, title, label):
    #dynamically change marker size
    n_points = len(gdf)
    marker_size = 8500 / n_points  
    marker_size = max(2, min(marker_size, 200))

    # Second set of plots
    cmap = plt.colormaps[colorscheme]
    norm = colors.Normalize(vmin, vmax)

    # Create subplots
    fig = plt.figure(figsize=(12, 6))
    gs = gridspec.GridSpec(1, 2, width_ratios=[1, 0.025], wspace=0.3)

    axes = [fig.add_subplot(gs[0]), fig.add_subplot(gs[1])]
    

    gdf.plot(column=name, cmap=cmap, norm=norm, ax=axes[0], marker='s', markersize=marker_size)
    axes[0].set_title(title, pad=-30)
    axes[0].set_axis_off()

    # Add a colorbar
    sm = cm.ScalarMappable(cmap=cmap, norm=norm)
    sm._A = []  # Dummy data for the colorbar
    cbar = fig.colorbar(sm, cax=axes[1], orientation='vertical', fraction=0.03, pad=0.02)
    cbar.set_label(label)


    return fig