Update the exp8 to reduce the size

Experiments/exp8/SweepParameters.py

@@ -8,124 +8,14 @@
 """
 This script is used to sweep the parameters of the Tetris Ballistic simultaions.
 """
-
-import os
-import sys
-from multiprocessing import Pool
-from joblib import dump
-from tetris_ballistic.tetris_ballistic import Tetris_Ballistic, load_density_from_config
-
-
-class DualLogger:
-    def __init__(self, filepath, mode='a'):
-        self.terminal = sys.stdout
-        self.log = open(filepath, mode)
-
-    def write(self, message):
-        self.terminal.write(message)
-        self.log.write(message)
-
-    def flush(self):  # This flush method is needed for python 3 compatibility.
-        # This flushes the stream to the file, but not the terminal
-        self.terminal.flush()
-        self.log.flush()
-
-    def close(self):
-        self.log.close()
-
-
-def simulate(params, total_iterations):
-    # Extract parameters
-    w, seed, config_name, density, current_iteration = params
-
-    basename = os.path.basename(config_name).replace(".yaml", "")
-    joblib_filename = f'{basename}_w={w}_seed={seed}.joblib'
-    config_filename = f'{basename}_w={w}_seed={seed}.yaml'
-    fig_filename = f'{basename}_w={w}_seed={seed}.png'
-    log_file_path = f'{basename}_w={w}_seed={seed}.log'
-
-    sys.stdout = DualLogger(log_file_path, mode='a')
-
-    # Check if this simulation has already been completed
-    if os.path.exists(joblib_filename):
-        print(f"Skipping completed simulation: {joblib_filename}")
-        return
-
-    print(f"Running simulation: {joblib_filename}")
-
-    TB = Tetris_Ballistic(width=w,
-                          height=w * 10,
-                          steps=10 * w * w,
-                          seed=seed,
-                          density=density)
-
-    # Check if this config file has already been saved
-    if not os.path.exists(config_filename):
-        print(f"Save the config file: {config_filename}")
-        TB.save_config(config_filename)
-
-    TB.Simulate()
-    # TB.ComputeSlope()
-    title = basename.replace("_", " ")
-    title = title.replace("config", "Config: ")
-    list_images = TB.list_tetromino_images()
-    if len(list_images) > 10:
-        print("Too many images to display: ", len(list_images))
-        list_images = None
-    else:
-        print("List Images: ", list_images)
-
-    TB.ShowData(fig_filename=fig_filename,
-                custom_text=title,
-                images=list_images)
-
-    dump(TB, joblib_filename)
-
-    print(f"Finished simulation: {joblib_filename}")
-
-    # Log progress
-    progress = (current_iteration / total_iterations) * 100
-    progress_message = f"Progress: {progress:.2f}% Completed simulation: {joblib_filename}"
-    log_progress(progress_message)
-
-    sys.stdout.close()  # Assuming sys.stdout was set to an instance of DualLogger
-    sys.stdout = sys.__stdout__
-
-
-def log_progress(progress_message):
-    with open("simulation_progress.log", "a") as log_file:
-        log_file.write(progress_message + "\n")
-
-
-if __name__ == "__main__":
-    ListWidth = [50, 100, 200, 500, 1000]
-    ListRandomSeeds = [10 * i for i in range(800)]
-    configs = [
-        "../../tetris_ballistic/configs/config_piece_19_sticky.yaml",
-        "../../tetris_ballistic/configs/config_piece_19_nonsticky.yaml",
-        "../../tetris_ballistic/configs/config_piece_0_sticky.yaml",
-        "../../tetris_ballistic/configs/config_piece_0_nonsticky.yaml",
-        "../../tetris_ballistic/configs/config_piece_0_combined.yaml",
-    ]
-
-    # Generate all combinations of parameters
-    param_combinations = [
-        (w,
-         seed,
-         os.path.basename(config),
-         load_density_from_config(f"../../tetris_ballistic/configs/{config}"))
-        for w in ListWidth
-        for seed in ListRandomSeeds
-        for config in configs
-    ]
-
-    total_iterations = len(param_combinations)
-    param_combinations_with_progress = [
-        (w, seed, config_name, density, idx + 1)
-        for idx, (w, seed, config_name, density) in enumerate(param_combinations)
-    ]
-
-    # Use multiprocessing Pool to run simulations in parallel
-    with Pool() as pool:
-        # Note: Modify the simulate function to accept the total_iterations parameter if needed
-        pool.starmap(simulate, [(params, total_iterations) for params in param_combinations_with_progress])
+from tetris_ballistic.SweepParameters import SweepParameters as SP
+
+ListWidth = [50, 100, 200, 500, 1000, 1500]
+ListRandomSeeds = [10 * i for i in range(20)]
+config_patterns = ["*piece_19_sticky.yaml",
+                   "*piece_19_nonsticky.yaml",
+                   "*piece_0*.yaml"]
+SP(list_width=ListWidth,
+   list_random_seeds=ListRandomSeeds,
+   config_patterns=config_patterns,
+   ratio=10)