#hard_agent.py import matplotlib.pyplot as plt import cartopy.crs as ccrs import streamlit as st import cartopy.feature as cfeature import pandas as pd import numpy as np import os import cartopy.io.shapereader as shpreader import xarray as xr import time import logging from matplotlib.colors import ListedColormap, LinearSegmentedColormap from mpl_toolkits.axes_grid1 import make_axes_locatable from adjustText import adjust_text import uuid # Add this function to generate unique image paths def generate_unique_image_path(): figs_dir = os.path.join('tmp', 'figs') os.makedirs(figs_dir, exist_ok=True) unique_path = os.path.join(figs_dir, f'fig_{uuid.uuid4()}.png') logging.debug(f"Generated unique image path: {unique_path}") return unique_path # Path to local shapefiles and bathymetry file base_dir = os.path.join('data', 'plotting_data', 'shape_files') bathymetry_file = os.path.join('data', 'plotting_data', 'bathymetry', 'etopo', 'ETOPO2v2c_f4.nc') #output_file = os.path.join('plotting_tools', 'temp_files', 'plot.png') # Define the base color palette and levels for master track map color_dict_master_track = { '0-50': '#E0F7FF', '50-100': '#D4F1FF', '100-250': '#C6EBFF', '250-500': '#B9E5FF', '500-750': '#ACE0FF', '750-1000': '#9FD8FF', '1000-1250': '#93D2FF', '1250-1500': '#86CCFF', '1500-2000': '#79C6FF', '2000-2500': '#6DBFFF', '2500-3000': '#60B9FF', '3000-3500': '#53B2FF', '3500-4000': '#47ABFF', '4000-4500': '#3AA5FF', '4500-5000': '#2D9EFF', '5000-5500': '#2098FF', '5500-6000': '#1491FF', '6000-6500': '#078BFF', '6500-7000': '#007FFF' } def create_colormap(min_depth, max_depth, color_dict): start_time = time.time() colors = [] levels = [] for key, color in reversed(color_dict.items()): # Reverse the order of colors depth_range = key.split('-') start_depth = 0 end_depth = -int(depth_range[0]) if start_depth >= min_depth and end_depth <= max_depth: levels.extend([start_depth, end_depth]) colors.append(color) if min_depth > 0: # Ensure colormap starts from 0 min_depth = 0 levels = np.linspace(min_depth, max_depth, len(colors) + 1) cmap = LinearSegmentedColormap.from_list('custom_cmap', colors, N=len(levels) - 1) end_time = time.time() print(f"Colormap creation took {end_time - start_time:.2f} seconds") return levels, cmap #Plot the master track map def plot_master_track_map(main_title, lat_col, lon_col, date_col, dataset_df): total_start_time = time.time() step_start_time = time.time() #dataset_path = os.path.join('data', 'current_data', 'dataset.csv') dataset = dataset_df step_end_time = time.time() print(f"Loading dataset took {step_end_time - step_start_time:.2f} seconds") step_start_time = time.time() # Ensure the longitude, latitude, and date columns are numeric or datetime dataset[lon_col] = pd.to_numeric(dataset[lon_col], errors='coerce') dataset[lat_col] = pd.to_numeric(dataset[lat_col], errors='coerce') dataset[date_col] = pd.to_datetime(dataset[date_col], errors='coerce') # Drop rows with invalid longitude, latitude, or date dataset = dataset.dropna(subset=[lon_col, lat_col, date_col]) dataset = dataset.sort_values(by=date_col) step_end_time = time.time() print(f"Data cleaning took {step_end_time - step_start_time:.2f} seconds") step_start_time = time.time() # Calculate the extent with padding min_lon = dataset[lon_col].min() - 5 max_lon = dataset[lon_col].max() + 5 min_lat = dataset[lat_col].min() - 5 max_lat = dataset[lat_col].max() + 5 # Print debug information print(f"Min Lon: {min_lon}, Max Lon: {max_lon}, Min Lat: {min_lat}, Max Lat: {max_lat}") # Ensure the extent is within valid bounds min_lon = max(min_lon, -180) max_lon = min(max_lon, 180) min_lat = max(min_lat, -90) max_lat = min(max_lat, 90) # Print debug information after bounds check print(f"Adjusted Min Lon: {min_lon}, Adjusted Max Lon: {max_lon}, Adjusted Min Lat: {min_lat}, Adjusted Max Lat: {max_lat}") # Calculate aspect ratio lon_range = max_lon - min_lon lat_range = max_lat - min_lat aspect_ratio = lon_range / lat_range # Dynamically set figure size based on aspect ratio width = 10 height = width / aspect_ratio step_end_time = time.time() print(f"Extent calculation took {step_end_time - step_start_time:.2f} seconds") step_start_time = time.time() # Load bathymetry data within the extent ds = xr.open_dataset(bathymetry_file) bathymetry = ds['z'].sel(x=slice(min_lon, max_lon), y=slice(min_lat, max_lat)) # Filter to include only depths (negative values) bathymetry = bathymetry.where(bathymetry < 0, drop=True) # Get the min and max elevation values in the bathymetry data min_depth = bathymetry.min().item() max_depth = bathymetry.max().item() # Ensure colormap includes 0 if min_depth > 0: min_depth = 0 # Create the colormap and levels levels, custom_cmap = create_colormap(min_depth, max_depth, color_dict_master_track) step_end_time = time.time() print(f"Bathymetry data loading and colormap creation took {step_end_time - step_start_time:.2f} seconds") step_start_time = time.time() fig, ax = plt.subplots(figsize=(width, height), subplot_kw={'projection': ccrs.PlateCarree()}) ax.set_extent([min_lon, max_lon, min_lat, max_lat], crs=ccrs.PlateCarree()) # Plot bathymetry data with the custom gradient bathy_plot = ax.contourf(bathymetry.x, bathymetry.y, bathymetry, levels=levels, cmap=custom_cmap, transform=ccrs.PlateCarree()) # Adding features from local shapefiles ocean_shp = shpreader.Reader(os.path.join(base_dir, 'ne_10m_ocean', 'ne_10m_ocean.shp')) land_shp = shpreader.Reader(os.path.join(base_dir, 'ne_10m_land', 'ne_10m_land.shp')) coastline_shp = shpreader.Reader(os.path.join(base_dir, 'ne_10m_coastline', 'ne_10m_coastline.shp')) ax.add_geometries(ocean_shp.geometries(), ccrs.PlateCarree(), facecolor='none', edgecolor='black', zorder=0) ax.add_geometries(land_shp.geometries(), ccrs.PlateCarree(), facecolor='lightgray', edgecolor='black', zorder=1) ax.add_geometries(coastline_shp.geometries(), ccrs.PlateCarree(), facecolor='none', edgecolor='black', zorder=2) ax.gridlines(draw_labels=True) # Plotting the master track map master_track_data = dataset[[lon_col, lat_col, date_col]] ax.plot(master_track_data[lon_col], master_track_data[lat_col], color='red', linestyle='-', linewidth=1, transform=ccrs.PlateCarree()) # Plot dates including Start and End dates start_date = dataset[date_col].iloc[0] end_date = dataset[date_col].iloc[-1] # Randomly select 4 dates between start and end dates, excluding the first and last points middle_dates = dataset[date_col].iloc[1:-1].sample(n=4, random_state=1).sort_values() dates_to_plot = pd.concat([pd.Series(start_date), middle_dates, pd.Series(end_date)]) lon_offset = (max_lon - min_lon) * 0.015 # 1.5% of the longitude range lat_offset = (max_lat - min_lat) * 0.015 # 1.5% of the latitude range texts = [] for date in dates_to_plot: point = dataset.loc[dataset[date_col] == date].iloc[0] if date == start_date: label = f"Start: {date.strftime('%Y-%m-%d')}" elif date == end_date: label = f"End: {date.strftime('%Y-%m-%d')}" else: label = date.strftime('%Y-%m-%d') texts.append(ax.text(point[lon_col], point[lat_col], label, transform=ccrs.PlateCarree(), fontsize=12, ha='left', color='black', weight='bold', bbox=dict(facecolor='white', alpha=0.7, boxstyle='round,pad=0.3'))) adjust_text(texts, arrowprops=dict(arrowstyle='->', color='red')) plt.title(f'{main_title}', y=1.05, fontsize=25, weight='bold') # Create an axis on the right side of ax. The width of cax will be 5% # of ax and the padding between cax and ax will be fixed at 0.05 inch. divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="4%", pad=0.75, axes_class=plt.Axes) # Create the colorbar cbar = plt.colorbar(bathy_plot, cax=cax, orientation='vertical', label='Depth (m)') # Ensure temp_files directory exists plot_dir = os.path.join('src', 'plotting_tools', 'temp_files') if not os.path.exists(plot_dir): os.makedirs(plot_dir) # Save the plot as a PNG file output_file = generate_unique_image_path() plt.savefig(output_file, format='png') step_end_time = time.time() print(f"Plotting and saving the figure took {step_end_time - step_start_time:.2f} seconds") total_end_time = time.time() print(f"Total time for plot_master_track_map: {total_end_time - total_start_time:.2f} seconds") if os.path.exists(output_file): st.session_state.new_plot_path = output_file print(f"Plot saved to {output_file}")