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a16f583 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 | import pandas as pd
import numpy as np
import xgboost as xgb
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
import geopandas as gpd
from config import (base_path_data , file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC , MAX_OC ,
TIME_BEGINNING , TIME_END , INFERENCE_TIME)
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataloader.dataloaderMapping import MultiRasterDatasetMapping
from sklearn.utils import shuffle
import copy
from torch.utils.data import DataLoader, Subset
import torch
import tqdm
##################################################################
# Loading the Data
##################################################################
def create_prediction_visualizations(year,coordinates, predictions, save_path):
"""
Create and save three separate map visualizations of predictions in Bavaria plus a triptych,
with timestamps in filenames.
Parameters:
coordinates (numpy.array): Array of coordinates (longitude, latitude)
predictions (numpy.array): Array of prediction values
save_path (str): Directory path where the images should be saved
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
import geopandas as gpd
from datetime import datetime
# Get current timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
# Create directories if they don't exist
os.makedirs(save_path, exist_ok=True)
individual_path = os.path.join(save_path, 'individual')
os.makedirs(individual_path, exist_ok=True)
# Load Bavaria boundaries
bavaria = gpd.read_file('https://raw.githubusercontent.com/isellsoap/deutschlandGeoJSON/main/2_bundeslaender/4_niedrig.geo.json')
bavaria = bavaria[bavaria['name'] == 'Bayern']
# Create interpolation grid with higher resolution
grid_x = np.linspace(coordinates[:, 0].min(), coordinates[:, 0].max(), 300)
grid_y = np.linspace(coordinates[:, 1].min(), coordinates[:, 1].max(), 300)
grid_x, grid_y = np.meshgrid(grid_x, grid_y)
# Interpolate values with cubic interpolation
grid_z = griddata(coordinates, predictions, (grid_x, grid_y), method='linear')
# Common plotting parameters
plot_params = {
'figsize': (12, 10),
'dpi': 300
}
# Function to set common elements for all plots
def set_common_elements(ax, title):
bavaria.boundary.plot(ax=ax, color='black', linewidth=1)
ax.set_title(title, fontsize=12, pad=20)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
ax.grid(True)
# Function to generate filename with timestamp
def get_filename(base_name):
return f"{base_name}_MAX_OC_{str(MAX_OC)}_Beginning_{TIME_BEGINNING}_End_{TIME_END}__InferenceTime{INFERENCE_TIME}_{timestamp}.png"
# 1. Interpolated surface
fig_interp, ax_interp = plt.subplots(**plot_params)
contour = ax_interp.contourf(grid_x, grid_y, grid_z,
levels=50,
cmap='viridis',
alpha=0.8)
set_common_elements(ax_interp, 'Interpolated Predicted Values')
plt.colorbar(contour, ax=ax_interp, label='Predicted Values')
plt.savefig(os.path.join(individual_path, get_filename(f'{year}_bavaria_interpolated')),
bbox_inches='tight')
plt.close()
# 2. Scatter plot
fig_scatter, ax_scatter = plt.subplots(**plot_params)
scatter = ax_scatter.scatter(coordinates[:, 0], coordinates[:, 1],
c=predictions,
cmap='viridis',
alpha=0.6,
s=50)
set_common_elements(ax_scatter, 'Scatter Plot of Predicted Values')
plt.colorbar(scatter, ax=ax_scatter, label='Predicted Values')
plt.savefig(os.path.join(individual_path, get_filename(f'{year}_bavaria_scatter')),
bbox_inches='tight')
plt.close()
# 3. Discrete points
fig_discrete, ax_discrete = plt.subplots(**plot_params)
discrete = ax_discrete.scatter(coordinates[:, 0], coordinates[:, 1],
c=predictions,
cmap='viridis',
alpha=1.0,
s=20)
set_common_elements(ax_discrete, 'Discrete Points of Predicted Values')
plt.colorbar(discrete, ax=ax_discrete, label='Predicted Values')
plt.savefig(os.path.join(individual_path, get_filename(f'{year}_bavaria_discrete')),
bbox_inches='tight')
plt.close()
# Create triptych
fig_triptych = plt.figure(figsize=(30, 10))
# Interpolated plot
ax1 = plt.subplot(131)
contour = ax1.contourf(grid_x, grid_y, grid_z,
levels=50,
cmap='viridis',
alpha=0.8)
set_common_elements(ax1, 'Interpolated Predicted Values')
plt.colorbar(contour, ax=ax1, label='Predicted Values')
# Scatter plot
ax2 = plt.subplot(132)
scatter = ax2.scatter(coordinates[:, 0], coordinates[:, 1],
c=predictions,
cmap='viridis',
alpha=0.6,
s=50)
set_common_elements(ax2, 'Scatter Plot of Predicted Values')
plt.colorbar(scatter, ax=ax2, label='Predicted Values')
# Discrete points
ax3 = plt.subplot(133)
discrete = ax3.scatter(coordinates[:, 0], coordinates[:, 1],
c=predictions,
cmap='viridis',
alpha=1.0,
s=20)
set_common_elements(ax3, 'Discrete Points of Predicted Values')
plt.colorbar(discrete, ax=ax3, label='Predicted Values')
plt.tight_layout()
plt.savefig(os.path.join(save_path, get_filename(f'{year}_bavaria_triptych')),
dpi=300,
bbox_inches='tight')
plt.close()
# Example usage:
# create_prediction_map(bavaria, coordinates, predictions, save_path='output/maps')
# Define the worker function
def process_batch(df_chunk, model_copy, bands_yearly, batch_size):
# Create dataset and dataloader for this chunk
chunk_dataset = MultiRasterDatasetMapping(bands_yearly, df_chunk)
chunk_dataloader = DataLoader(chunk_dataset, batch_size=batch_size, shuffle=True)
chunk_coordinates = []
chunk_features = []
for longitudes, latitudes, batch_features in chunk_dataloader:
# Store coordinates for plotting
chunk_coordinates.append(np.column_stack((longitudes.numpy(), latitudes.numpy())))
# Concatenate all values in the batch_features dictionary
concatenated_features = np.concatenate([value.numpy() for value in batch_features.values()], axis=1)
# Flatten the features
flattened_features = concatenated_features.reshape(concatenated_features.shape[0], -1)
chunk_features.extend(flattened_features)
# Convert to arrays
chunk_features = np.array(chunk_features)
chunk_coordinates = np.vstack(chunk_coordinates)
# Make predictions using the model copy
chunk_predictions = model_copy.predict(chunk_features)
return chunk_coordinates, chunk_predictions
def parallel_predict(df_full, model, bands_yearly, batch_size=4, num_threads=4):
# Shuffle the DataFrame
df_shuffled = df_full.sample(frac=1, random_state=42).reset_index(drop=True)
# Split DataFrame into chunks for each thread
chunk_size = len(df_shuffled) // num_threads
df_chunks = [df_shuffled[i:i + chunk_size] for i in range(0, len(df_shuffled), chunk_size)]
# Ensure that predictions and coordinates match
all_coordinates = []
all_predictions = []
# Use ThreadPoolExecutor for multithreading
print(num_threads)
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
futures = [
executor.submit(
process_batch,
chunk,
copy.deepcopy(model),
bands_yearly,
batch_size
) for chunk in df_chunks
]
for future in futures:
coordinates, predictions = future.result()
all_coordinates.append(coordinates)
all_predictions.append(predictions)
# Combine results from all threads
all_coordinates = np.vstack(all_coordinates)
all_predictions = np.concatenate(all_predictions)
return all_coordinates, all_predictions |