diff --git a/.gitignore b/.gitignore
index 779b76f9..d993763c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,14 @@
 !workspaces/CMS_project_v1/CMS_project_v1/config/CMS_project_v1_config.py
 !workspaces/CFD_workspace/CFD_project_animation/config/CFD_project_animation_config.py
 
+# This directory is used to store libraries that are built on the target machine and are not
+# intended to be shared across different machines (e.g. SZ3).
+/external/*
+
+# Exclude results tracking files
+green_code_tracking.txt
+compression_comparison_results.txt
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/baler/baler.py b/baler/baler.py
index f7bcfb2e..7294526a 100644
--- a/baler/baler.py
+++ b/baler/baler.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Baler Contributors
+# Copyright 2022-2025 Baler Contributors
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,13 +15,13 @@
 import os
 import time
 from math import ceil
-
 import numpy as np
 
 from .modules import helper
+from .modules import compare
 import gzip
-from .modules.profiling import pytorch_profile
-
+# from .modules.profiling import pytorch_profile
+import blosc2
 
 __all__ = (
     "perform_compression",
@@ -30,6 +30,7 @@
     "perform_plotting",
     "perform_training",
     "print_info",
+    "perform_comparison",
 )
 
 
@@ -38,10 +39,13 @@ def main():
 
         - if --mode=newProject: call `helper.create_new_project` and create a new project sub directory with config file
         - if --mode=train: call `perform_training` and train the network on given data and based on the config file and check if profilers are enabled
+        - if --mode=diagnose: call `perform_diagnostics` and diagnose the training process by plotting the activations of the layers.
         - if --mode=compress: call `perform_compression` and compress the given data using the model trained in `--mode=train`
         - if --mode=decompress: call `perform_decompression` and decompress the compressed file outputted from `--mode=compress`
         - if --mode=plot: call `perform_plotting` and plot the comparison between the original data and the decompressed data from `--mode=decompress`. Also plots the loss plot from the trained network.
+        - if --mode=info: call `print_info` and print information about the compression ratio and file sizes.
         - if --mode=convert_with_hls4ml: call `helper.perform_hls4ml_conversion` and create an hls4ml project containing the converted model.
+        - if --mode=compare: call `perform_comparison` and compare the compressed data with non-AE lossy compression algorithms.
 
 
     Raises:
@@ -60,7 +64,7 @@ def main():
     if mode == "newProject":
         helper.create_new_project(workspace_name, project_name, verbose)
     elif mode == "train":
-        perform_training(output_path=output_path, config=config, verbose=verbose)
+        perform_training(output_path, config, project_name, verbose)
     elif mode == "diagnose":
         perform_diagnostics(output_path, verbose)
     elif mode == "compress":
@@ -73,6 +77,8 @@ def main():
         print_info(output_path, config)
     elif mode == "convert_with_hls4ml":
         helper.perform_hls4ml_conversion(output_path, config)
+    elif mode == "compare":
+        perform_comparison(output_path, config, project_name, verbose)
     else:
         raise NameError(
             "Baler mode "
@@ -81,7 +87,7 @@ def main():
         )
 
 
-def perform_training(output_path, config, verbose: bool):
+def perform_training(output_path, config, project_name, verbose: bool):
     """Main function calling the training functions, ran when --mode=train is selected.
         The three functions called are: `helper.process`, `helper.mode_init` and `helper.training`.
 
@@ -95,6 +101,7 @@ def perform_training(output_path, config, verbose: bool):
     Raises:
         NameError: Baler currently only supports 1D (e.g. HEP) or 2D (e.g. CFD) data as inputs.
     """
+    green_code_timer_start = time.perf_counter()
     (
         train_set_norm,
         test_set_norm,
@@ -206,6 +213,13 @@ def perform_training(output_path, config, verbose: bool):
         print("\nThe model has the following structure:")
         print(model.type)
 
+    green_code_timer_end = time.perf_counter()
+    helper.green_code_tracking(
+        start=green_code_timer_start,
+        end=green_code_timer_end,
+        title=f"{project_name} - Model Training",
+    )
+
 
 def perform_diagnostics(project_path, verbose: bool):
     output_path = os.path.join(project_path, "plotting")
@@ -255,7 +269,7 @@ def perform_compression(output_path, config, verbose: bool):
         - Normalization features if `config.apply_normalization=True`
     """
     print("Compressing...")
-    start = time.time()
+    start = time.perf_counter()
     normalization_features = []
 
     if config.apply_normalization:
@@ -283,9 +297,9 @@ def perform_compression(output_path, config, verbose: bool):
             config=config,
         )
 
-    end = time.time()
+    end = time.perf_counter()
 
-    print("Compression took:", f"{(end - start) / 60:.3} minutes")
+    print("Compression took:", f"{(end - start):.4f} seconds")
 
     names = np.load(config.input_path)["names"]
 
@@ -351,7 +365,7 @@ def perform_decompression(output_path, config, verbose: bool):
     """
     print("Decompressing...")
 
-    start = time.time()
+    start = time.perf_counter()
     model_name = config.model_name
     data_before = np.load(config.input_path)["data"]
     if config.separate_model_saving:
@@ -434,8 +448,8 @@ def perform_decompression(output_path, config, verbose: bool):
     except AttributeError:
         pass
 
-    end = time.time()
-    print("Decompression took:", f"{(end - start) / 60:.3} minutes")
+    end = time.perf_counter()
+    print("Decompression took:", f"{(end - start):.4f} seconds")
 
     if config.extra_compression:
         if verbose:
@@ -478,10 +492,16 @@ def print_info(output_path, config):
 
     meta_data = [
         model,
-        os.path.join(training_path, "loss_data.npy"),
-        os.path.join(training_path, "normalization_features.npy"),
     ]
 
+    loss_path = os.path.join(training_path, "loss_data.npy")
+    if os.path.exists(loss_path):
+        meta_data.append(os.path.join(training_path, "loss_data.npy"))
+
+    norm_path = os.path.join(training_path, "normalization_features.npy")
+    if os.path.exists(norm_path):
+        meta_data.append(os.path.join(training_path, "normalization_features.npy"))
+
     meta_data_stats = [
         os.stat(meta_data[file]).st_size / (1024 * 1024)
         for file in range(len(meta_data))
@@ -508,3 +528,178 @@ def print_info(output_path, config):
     print("\n ==================================")
 
     ## TODO: Add way to print how much your data has been distorted
+
+
+def perform_comparison(output_path, config, project_name, verbose):
+    """
+    Runs a series of compression benchmarks and prints a summary table.
+    This function orchestrates a comparison between the project's Baler model
+    and other standard compression algorithms like Downcasting, ZFP, Blosc2,
+    and SZ3. It loads the original data, sets up each benchmark with specific
+    configurations, and then executes them sequentially.
+    After running all benchmarks, it collects performance metrics such as
+    compression ratio, reconstruction error (RMSE, Max Error), PSNR, and
+    compression/decompression times. Finally, it sorts the results by RMSE
+    and prints a formatted summary table to the console for easy comparison.
+    Args:
+        output_path (str): The base directory path where benchmark outputs,
+            including compressed files, will be stored. Each benchmark may
+            create its own subdirectory within this path.
+        config (object): A configuration object containing project settings,
+            such as the input data path (`config.input_path`) and the Baler
+            model name (`config.model_name`).
+        verbose (bool): A flag passed to the compression and decompression
+            functions to control the verbosity of their output.
+    """
+
+    green_code_timer_start = time.perf_counter()
+
+    original_path = config.input_path
+    original_npz = np.load(original_path)
+    data_original = original_npz["data"]
+    names_original = original_npz["names"]
+
+    # Calculate original file size for compression ratio calculation
+    try:
+        original_size_bytes = os.path.getsize(original_path)
+        original_size_mb = original_size_bytes / (1024 * 1024)
+        print(f"Original input file size: {original_size_mb:.3f} MB ({original_path})")
+    except FileNotFoundError:
+        print(
+            f"Warning: Could not find original file at {original_path} to calculate compression ratio."
+        )
+        original_size_mb = 0
+
+    # Define all the benchmarks we want to run
+    benchmarks_to_run = []
+
+    # 1. Baler
+    benchmarks_to_run.append(
+        compare.BalerBenchmark(
+            name=f"Baler ({config.model_name})",
+            output_path=output_path,  # Baler uses the main project output path
+            compress_func=lambda: perform_compression(output_path, config, verbose),
+            decompress_func=lambda: perform_decompression(output_path, config, verbose),
+            data_original=data_original,
+            names_original=names_original,
+        )
+    )
+
+
+    # 2. Downcast float32 - only valid when using float64 inputs
+    if config.float_dtype == "float32":
+        pass
+    else:
+        benchmarks_to_run.append(
+            compare.DowncastBenchmark(
+                output_dir=os.path.join(output_path, "downcast_float32"),
+                data_original=data_original,
+                names_original=names_original,
+                target_dtype=np.float32,
+            )
+        )
+
+    # 3a. ZFP using 'precision' mode (the original test)
+    # The 'precision' parameter specifies the number of uncompressed bits to keep.
+    # A higher precision means lower compression but better quality.
+    zfp_precision = 22
+    benchmarks_to_run.append(
+        compare.ZFPBenchmark(
+            output_dir=os.path.join(output_path, f"zfp_prec{zfp_precision}"),
+            data_original=data_original,
+            names_original=names_original,
+            zfp_params={"precision": zfp_precision},
+        )
+    )
+
+    # 3b. ZFP using 'rate' mode
+    # The 'rate' parameter specifies a fixed size budget in bits per value.
+    # A smaller rate means higher compression.
+    zfp_rate = 8.0
+    benchmarks_to_run.append(
+        compare.ZFPBenchmark(
+            output_dir=os.path.join(output_path, f"zfp_rate{zfp_rate}"),
+            data_original=data_original,
+            names_original=names_original,
+            zfp_params={"rate": zfp_rate},
+        )
+    )
+
+    # 3c. ZFP using 'tolerance' mode
+    # The 'tolerance' parameter specifies the maximum allowed error in the compressed data.
+    # A smaller tolerance means higher compression but potentially more error.
+    zfp_tolerance = 1e-3
+    benchmarks_to_run.append(
+        compare.ZFPBenchmark(
+            output_dir=os.path.join(output_path, f"zfp_tol{zfp_tolerance}"),
+            data_original=data_original,
+            names_original=names_original,
+            zfp_params={"tolerance": zfp_tolerance},
+        )
+    )
+
+    # 4a. Blosc2 with LZ4 (Lossy, Fast)
+    blosc_trunc_prec = 18
+    benchmarks_to_run.append(
+        compare.BloscBenchmark(
+            name=f"Blosc2-LZ4(prec={blosc_trunc_prec})",
+            output_dir=os.path.join(output_path, f"blosc2_lz4_prec{blosc_trunc_prec}"),
+            data_original=data_original,
+            names_original=names_original,
+            cparams={
+                "filters": [blosc2.Filter.TRUNC_PREC, blosc2.Filter.SHUFFLE],
+                "filters_meta": [blosc_trunc_prec, 0],
+                "codec": blosc2.Codec.LZ4,
+                "clevel": 5,
+            },
+        )
+    )
+
+    # 4b. Blosc2 with ZSTD (Lossy, Aggressive Compression)
+    blosc_zstd_clevel = 9
+    benchmarks_to_run.append(
+        compare.BloscBenchmark(
+            name=f"Blosc2-ZSTD(L{blosc_zstd_clevel})",
+            output_dir=os.path.join(
+                output_path, f"blosc2_zstd_lossy_l{blosc_zstd_clevel}"
+            ),
+            data_original=data_original,
+            names_original=names_original,
+            cparams={
+                "filters": [blosc2.Filter.TRUNC_PREC, blosc2.Filter.BITSHUFFLE],
+                "filters_meta": [blosc_trunc_prec, 0],
+                "codec": blosc2.Codec.ZSTD,
+                "clevel": blosc_zstd_clevel,
+            },
+        )
+    )
+
+    # TODO Implement SZ3 Benchmark
+
+    # --- Run all benchmarks and collect results ---
+    all_results = []
+    for benchmark in benchmarks_to_run:
+        result = benchmark.run()
+        all_results.append(result)
+
+    compare.output_benchmark_results(
+        original_size_mb, all_results, project_name, output_path, verbose=verbose
+    )
+
+    if all_results:
+        ## helper.plot_comparison_summary(all_results, output_path, original_size_mb)
+        helper.plot_all_results(output_path, config)
+
+    green_code_timer_end = time.perf_counter()
+
+    with open("compression_comparison_results.txt", "a") as f:
+        f.write(
+            f"Total time taken: {green_code_timer_end - green_code_timer_start:.3f} seconds\n"
+        )
+
+    helper.green_code_tracking(
+        start=green_code_timer_start,
+        end=green_code_timer_end,
+        title=f"{project_name} - Compression Comparison",
+        verbose=verbose,
+    )
diff --git a/baler/modules/compare.py b/baler/modules/compare.py
new file mode 100644
index 00000000..e3247833
--- /dev/null
+++ b/baler/modules/compare.py
@@ -0,0 +1,427 @@
+# Copyright 2022-2025 Baler Contributors
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import os
+import time
+import abc
+import csv
+from datetime import datetime
+from dataclasses import dataclass
+import numpy as np
+import zfpy
+import blosc2
+
+
+@dataclass
+class BenchmarkResult:
+    """A container for the results of a single compression benchmark."""
+
+    name: str
+    size_mb: float
+    compress_time_sec: float
+    decompress_time_sec: float
+    rmse: float
+    max_err: float
+    psnr: float
+
+
+class Benchmark(abc.ABC):
+    """
+    Abstract Base Class for a compression benchmark.
+    This class defines the template for running a benchmark.
+    """
+
+    def __init__(
+        self,
+        name: str,
+        output_dir: str,
+        data_original: np.ndarray,
+        names_original: np.ndarray,
+        verbose: bool = True,
+    ):
+        self.name = name
+        self.output_dir = output_dir
+        self.data_original = data_original
+        self.names_original = names_original
+        self.verbose = verbose
+        os.makedirs(self.output_dir, exist_ok=True)
+
+    def run(self) -> BenchmarkResult:
+        """
+        Executes the full benchmark process: compress, decompress, and analyze.
+        This is the public-facing method to run a benchmark.
+        """
+        # 1. Compression
+        start_compress = time.perf_counter()
+        compressed_data = self._compress()
+        end_compress = time.perf_counter()
+        compress_time = end_compress - start_compress
+        compressed_path = self._save_compressed(compressed_data)
+        compressed_file_size_bytes = os.path.getsize(compressed_path)
+
+        # 2. Decompression
+        start_decompress = time.perf_counter()
+        decompressed_data = self._decompress(compressed_data)
+        end_decompress = time.perf_counter()
+        decompress_time = end_decompress - start_decompress
+        self._save_decompressed(decompressed_data)
+
+        # 3. Error Analysis
+        metrics = self._analyze_errors(decompressed_data)
+
+        # 4. Print results if verbose
+        if self.verbose:
+            print(f"\nBenchmarking: {self.name}")
+            print(f"  Compression time: {compress_time:.3f} seconds")
+            print(
+                f"  Compressed size: {compressed_file_size_bytes / (1024 * 1024):.3f} MB"
+            )
+            print(f"  Decompression time: {decompress_time:.3f} seconds")
+            print(
+                f"  -> Done. RMSE: {metrics['rmse']:.2e}, Max Error: {metrics['max_err']:.2e}"
+            )
+
+        # 5. Create and return result object
+        return BenchmarkResult(
+            name=self.name,
+            size_mb=compressed_file_size_bytes / (1024 * 1024),
+            compress_time_sec=compress_time,
+            decompress_time_sec=decompress_time,
+            **metrics,
+        )
+
+    def _analyze_errors(self, decompressed_data: np.ndarray) -> dict:
+        """Performs error analysis and returns a dictionary of metrics."""
+        if decompressed_data.shape != self.data_original.shape:
+            raise ValueError(f"Shape mismatch after decompression for {self.name}!")
+
+        diff = self.data_original.astype(np.float64) - decompressed_data.astype(
+            np.float64
+        )
+        mse = np.mean(diff**2)
+        rmse = np.sqrt(mse)
+        max_abs_err = np.max(np.abs(diff))
+
+        data_range = np.max(self.data_original) - np.min(self.data_original)
+        psnr = (
+            20 * np.log10(data_range) - 10 * np.log10(mse) if mse > 0 else float("inf")
+        )
+
+        return {"rmse": rmse, "max_err": max_abs_err, "psnr": psnr}
+
+    def _save_decompressed(self, decompressed_data: np.ndarray):
+        """Saves the decompressed data and names to an NPZ file for inspection."""
+        path = os.path.join(self.output_dir, "decompressed.npz")
+        np.savez(path, data=decompressed_data, names=self.names_original)
+        if self.verbose:
+            print(f"  Decompressed file saved to: {path}")
+
+    @abc.abstractmethod
+    def _compress(self):
+        """Subclasses must implement this. Should return the compressed data."""
+        pass
+
+    @abc.abstractmethod
+    def _decompress(self, compressed_data) -> np.ndarray:
+        """Subclasses must implement this. Should return the decompressed numpy array."""
+        pass
+
+    def _save_compressed(self, compressed_data) -> str:
+        """Default method to save compressed data as a binary blob."""
+        path = os.path.join(self.output_dir, "compressed.bin")
+        with open(path, "wb") as f:
+            f.write(compressed_data)
+        return path
+
+
+# --- Specific Benchmark Implementations ---
+
+
+class BalerBenchmark(Benchmark):
+    """Benchmark for the main 'baler' autoencoder compression."""
+
+    def __init__(
+        self,
+        name: str,
+        output_path: str,
+        compress_func,
+        decompress_func,
+        data_original,
+        names_original,
+    ):
+        # Baler manages its own output directory, so we pass the project's output_path
+        super().__init__(name, output_path, data_original, names_original)
+        self.compress_func = compress_func
+        self.decompress_func = decompress_func
+
+    def _compress(self):
+        # Baler's compression function saves its own file and doesn't return data
+        self.compress_func()
+        return None  # No data to return
+
+    def _decompress(self, compressed_data):
+        # Baler's decompression also works on files, then we load the result
+        self.decompress_func()
+        decompressed_path = os.path.join(
+            self.output_dir, "decompressed_output", "decompressed.npz"
+        )
+        return np.load(decompressed_path)["data"]
+
+    def _save_compressed(self, compressed_data):
+        # This is a no-op because _compress() already saved the file.
+        # We just need to return the path for size calculation.
+        return os.path.join(self.output_dir, "compressed_output", "compressed.npz")
+
+    def _save_decompressed(self, decompressed_data):
+        # This is also a no-op because _decompress() already handled it.
+        path = os.path.join(self.output_dir, "decompressed_output", "decompressed.npz")
+        if self.verbose:
+            print(f"  Decompressed file already saved to: {path}")
+
+
+class DowncastBenchmark(Benchmark):
+    """
+    Benchmark for downcasting data to a specified NumPy data type (e.g., float32, float16).
+    """
+
+    def __init__(
+        self,
+        output_dir: str,
+        data_original: np.ndarray,
+        names_original: np.ndarray,
+        target_dtype: np.dtype,
+    ):
+        """
+        Initializes the benchmark for a specific target data type.
+
+        Args:
+            output_dir (str): Directory to save benchmark artifacts.
+            data_original (np.ndarray): The original, high-precision data.
+            names_original (np.ndarray): The names corresponding to the data features.
+            target_dtype (np.dtype): The NumPy data type to cast to (e.g., np.float32, np.float16).
+        """
+        self.target_dtype = target_dtype
+        # Automatically generate the name based on the target type
+        name = f"Downcast (to {np.dtype(self.target_dtype).name})"
+        super().__init__(name, output_dir, data_original, names_original)
+
+    def _compress(self):
+        """Performs the downcasting. This is our 'compression' step."""
+        return self.data_original.astype(self.target_dtype)
+
+    def _decompress(self, compressed_data):
+        """Decompression is a no-op; the compressed data is the final data."""
+        return compressed_data
+
+    def _save_compressed(self, compressed_data: np.ndarray) -> str:
+        """Override to save as .npz since the 'compressed' data is just a NumPy array."""
+        path = os.path.join(self.output_dir, "compressed.npz")
+        np.savez(path, data=compressed_data, names=self.names_original)
+        return path
+
+
+class ZFPBenchmark(Benchmark):
+    """
+    A benchmark class specifically for evaluating the ZFP compression algorithm.
+    This class extends the base `Benchmark` to implement compression and decompression
+    using the `zfpy` library. It is configured with a dictionary of ZFP-specific
+    parameters, such as precision, rate, or tolerance, which are directly passed
+    to the `zfpy.compress_numpy` function.
+    The name of the benchmark is automatically generated based on the provided
+    ZFP parameters to ensure clear identification of the results.
+        output_dir (str): The directory where benchmark artifacts (like plots and data) will be saved.
+        data_original (np.ndarray): The original, uncompressed data array to be used for the benchmark.
+        names_original (np.ndarray): An array of names corresponding to the features in `data_original`.
+        zfp_params (dict): A dictionary containing the parameters for ZFP compression. These are passed
+                           directly to `zfpy.compress_numpy`. Example: `{'precision': 22}` or `{'rate': 8.0}`.
+    Attributes:
+        zfp_params (dict): The dictionary of ZFP parameters used for compression.
+    """
+
+    def __init__(
+        self,
+        output_dir: str,
+        data_original: np.ndarray,
+        names_original: np.ndarray,
+        zfp_params: dict,
+    ):
+        """
+        Initializes the benchmark for a specific set of ZFP parameters.
+
+        Args:
+            output_dir (str): Directory to save benchmark artifacts.
+            data_original (np.ndarray): The original, high-precision data.
+            names_original (np.ndarray): The names corresponding to the data features.
+            zfp_params (dict): A dictionary of parameters to pass to zfpy.
+                               Example: {'precision': 22} or {'rate': 8.0}
+        """
+        if not zfp_params:
+            raise ValueError("zfp_params dictionary cannot be empty for ZFPBenchmark.")
+
+        self.zfp_params = zfp_params
+
+        # Automatically generate a descriptive name from the parameters
+        param_str = ", ".join([f"{k}={v}" for k, v in self.zfp_params.items()])
+        name = f"ZFP({param_str})"
+
+        super().__init__(name, output_dir, data_original, names_original)
+
+    def _compress(self):
+        """Compresses data using the stored ZFP parameters."""
+        # The ** operator unpacks the dictionary into keyword arguments
+        # e.g., {'rate': 8.0} becomes rate=8.0
+        return zfpy.compress_numpy(self.data_original, **self.zfp_params)
+
+    def _decompress(self, compressed_data):
+        """Decompression does not require the original parameters."""
+        return zfpy.decompress_numpy(compressed_data)
+
+
+class BloscBenchmark(Benchmark):
+    """Benchmark for Blosc2 compression."""
+
+    def __init__(
+        self, name: str, output_dir: str, data_original, names_original, cparams: dict
+    ):
+        super().__init__(name, output_dir, data_original, names_original)
+        self.cparams = cparams
+
+    def _compress(self):
+        return blosc2.pack_array2(self.data_original, cparams=self.cparams)
+
+    def _decompress(self, compressed_data):
+        return blosc2.unpack_array2(compressed_data)
+
+
+# TODO Implement a benchmark for SZ3 compression
+
+
+def output_benchmark_results(original_size_mb, all_results, title, output_path, verbose):
+    """
+    Formats and outputs the results of multiple compression benchmarks.
+
+    This function takes a list of BenchmarkResult objects, sorts them by RMSE,
+    and presents them in a formatted table. The summary is printed to the
+    console if `verbose` is True, and is always appended to a log file named
+    'compression_comparison_results.txt'.
+
+    Args:
+        original_size_mb (float): The size of the original, uncompressed data
+            in megabytes, used for calculating the compression ratio.
+        all_results (list[BenchmarkResult]): A list of result objects from all
+            the benchmarks that were run.
+        title (str): A title for the summary table, which will be included in
+            the header.
+        verbose (bool): If True, the summary table is printed to standard
+            output.
+    """
+    # --- Prepare the header for the summary table ---
+    header = f"{'Method':<30} | {'Size (MB)':>10} | {'Comp Ratio':>11} | {'RMSE':>10} | {'Max Error':>11} | {'PSNR (dB)':>10} | {'Comp Time(s)':>12} | {'Decomp Time(s)':>14}"
+
+    if verbose:
+        # --- Print Final Summary Table ---
+        print("\n" + "=" * 150)
+        print(
+            f"                          COMPRESSION SUMMARY - {title} - Original Size: {original_size_mb:.3f} MB                          "
+        )
+        print("-" * 150)
+        print(header)
+        print("-" * 150)
+
+    # --- CSV Logging Setup ---
+    timestamp = datetime.now().strftime("%Y-%m-%d %H.%M.%S")
+    csv_file_path = f"{output_path}/{timestamp}_compression_comparison_results.csv"
+    csv_header = [
+        "Original Size (MB)",
+        "Method",
+        "Size (MB)",
+        "Comp Ratio",
+        "RMSE",
+        "Max Error",
+        "PSNR (dB)",
+        "Comp Time(s)",
+        "Decomp Time(s)",
+    ]
+    # Check if file exists to decide whether to write the header
+    file_exists = os.path.isfile(csv_file_path)
+
+    # --- Write to CSV File ---
+
+    with open(csv_file_path, "a", newline="") as csvfile:
+        writer = csv.writer(csvfile)
+        if not file_exists:
+            writer.writerow(csv_header)
+
+        # Sort results by a desired metric, e.g., RMSE, before writing
+        sorted_results_for_csv = sorted(all_results, key=lambda r: r.rmse)
+        for r in sorted_results_for_csv:
+            if original_size_mb > 0 and r.size_mb > 0:
+                ratio = original_size_mb / r.size_mb
+                ratio_str = f"{ratio:.2f}:1"
+            else:
+                ratio_str = "N/A"
+
+            row = [
+                timestamp,
+                title,
+                f"{original_size_mb:.3f}",
+                r.name,
+                f"{r.size_mb:.3f}",
+                ratio_str,
+                f"{r.rmse:.2e}",
+                f"{r.max_err:.2e}",
+                f"{r.psnr:.1f}",
+                f"{r.compress_time_sec:.3f}",
+                f"{r.decompress_time_sec:.3f}",
+            ]
+            writer.writerow(row)
+
+    # --- Write to Formatted Text Log File ---
+    with open("compression_comparison_results.txt", "a") as f:
+        f.write("\n" + "=" * 150 + "\n")
+        f.write(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
+        f.write(
+            f"COMPRESSION SUMMARY - {title} - Original Size: {original_size_mb:.3f} MB\n"
+        )
+        f.write("-" * 150 + "\n")
+        f.write(f"{header}\n")
+        f.write("-" * 150 + "\n")
+
+    # Sort results for console/text output
+    sorted_results = sorted(all_results, key=lambda r: r.rmse)
+
+    for r in sorted_results:
+        # Calculate compression ratio
+        if original_size_mb > 0 and r.size_mb > 0:
+            ratio = original_size_mb / r.size_mb
+            ratio_str = f"{ratio:.2f}:1"
+        else:
+            ratio_str = "N/A"
+
+        result_string = (
+            f"{r.name:<30} | {r.size_mb:>10.3f} | {ratio_str:>11} | {r.rmse:>10.2e} | {r.max_err:>11.2e} | "
+            f"{r.psnr:>10.1f} | {r.compress_time_sec:>12.3f} | {r.decompress_time_sec:>14.3f}"
+        )
+
+        if verbose:
+            # Print each result in a formatted manner
+            print(result_string)
+
+        # Write each result to the results tracking file
+        with open("compression_comparison_results.txt", "a") as f:
+            f.write(result_string + "\n")
+
+    if verbose:
+        print("=" * 150 + "\n")
diff --git a/baler/modules/data_processing.py b/baler/modules/data_processing.py
index d14d9958..6d6a9ec0 100644
--- a/baler/modules/data_processing.py
+++ b/baler/modules/data_processing.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Baler Contributors
+# Copyright 2022-2025 Baler Contributors
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/baler/modules/diagnostics.py b/baler/modules/diagnostics.py
index a80f5221..28bc8661 100644
--- a/baler/modules/diagnostics.py
+++ b/baler/modules/diagnostics.py
@@ -71,8 +71,16 @@ def plot(data: np.array, output_path: str) -> None:
 
 
 def diagnose(input_path: str, output_path: str) -> None:
-    input = np.load(input_path)
-    plot(input, output_path)
-    print(
-        "Diagnostics saved as diagnostics.pdf in the diagnostics folder of your project."
-    )
+    """Function to create diagnostics for the neural activation pattern of a model.
+    Args:
+        input_path (str): Path to the input file containing the neural activation data.
+        output_path (str): Path to save the diagnostics plot.
+    """
+    if not os.path.exists(input_path):
+        raise FileNotFoundError(f"Input file not found: {input_path}")
+    else:
+        input = np.load(input_path)
+        plot(input, output_path)
+        print(
+            "Diagnostics saved as diagnostics.pdf in the diagnostics folder of your project."
+        )
diff --git a/baler/modules/helper.py b/baler/modules/helper.py
index dcacdc21..365a944f 100644
--- a/baler/modules/helper.py
+++ b/baler/modules/helper.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Baler Contributors
+# Copyright 2022-2025 Baler Contributors
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,6 +16,7 @@
 import importlib
 import os
 import sys
+from datetime import datetime
 from dataclasses import dataclass
 from math import ceil
 import gzip
@@ -35,20 +36,26 @@ def get_arguments():
     """Determines the arguments one is able to apply in the command line when running Baler. Use `--help` to see what
     options are available.
 
-    Returns: .py, string, folder: `.py` file containing the config options, string determining what mode to run,
-    projects directory where outputs go.
+    Returns:
+        tuple: (config (Config or None), mode (str), workspace_name (str), project_name (str), verbose (bool))
     """
     parser = argparse.ArgumentParser(
         prog="baler",
         description=(
             "Baler is a machine learning based compression tool for big data.\n\n"
-            "Baler has three running modes:\n\n"
-            '\t1. Derivation: Using a configuration file and a "small" input dataset, Baler derives a '
+            "Baler has nine running modes:\n\n"
+            "\t1. newProject: Creates a new project directory, with default configuration files.\n\n"
+            '\t2. train: Using a configuration file and a "small" input dataset, Baler derives a '
             "machine learning model optimized to compress and decompress your data.\n\n"
-            "\t2. Compression: Using a previously derived model and a large input dataset, Baler compresses "
+            "\t3. compress: Using a previously derived model and a large input dataset, Baler compresses "
             "your data and outputs a smaller compressed file.\n\n"
-            "\t3. Decompression: Using a previously compressed file as input and a model, Baler decompresses "
-            "your data into a larger file."
+            "\t4. decompress: Using a previously compressed file as input and a model, Baler decompresses "
+            "your data into a larger file.\n\n"
+            "\t5. plot: Creates plots from the training output data.\n\n"
+            "\t6. info: Displays information about the project and workspace.\n\n"
+            "\t7. hls4ml: Converts the trained model to an HLS4ML project for FPGA deployment.\n\n"
+            "\t8. diagnose: Runs diagnostics on the activations of the trained model.\n\n"
+            "\t9. compare: Compares the performance of the trained model with other models.\n\n"
         ),
         epilog="Enjoy!",
         formatter_class=argparse.RawTextHelpFormatter,
@@ -562,7 +569,10 @@ def compress(model_path, config):
                 data.shape[0], data.shape[1] * data.shape[2]
             )
     elif config.data_dimension == 1:
-        data_tensor = torch.tensor(data, dtype=torch.float64)
+        if hasattr(config, "float_dtype") and config.float_dtype == "float32":
+            data_tensor = torch.tensor(data, dtype=torch.float32)
+        else:
+            data_tensor = torch.tensor(data, dtype=torch.float64)
 
     # Batching data to avoid memory leaks
     data_dl = DataLoader(
@@ -740,7 +750,17 @@ def diagnose(input_path: str, output_path: str) -> None:
         input_path (str): path to the np.array contataining the activations values
         output_path (str): path to store the diagnostics pdf
     """
-    diagnostics.diagnose(input_path, output_path)
+    try:
+        diagnostics.diagnose(input_path, output_path)
+    except FileNotFoundError as e:
+        print(
+            "An error occurred while running diagnostics. "
+            "Please ensure that the activations.npy file exists in the training folder and try again."
+        )
+    except Exception as e:
+        print("An unexpected error occurred while running diagnostics. ")
+        print(f"Error details: {e}")
+        raise
 
 
 def perform_hls4ml_conversion(output_path, config):
@@ -849,3 +869,54 @@ def perform_hls4ml_conversion(output_path, config):
     hls_model.build(
         csim=config.csim, synth=config.synth, cosim=config.cosim, export=config.export
     )
+
+
+def green_code_tracking(start, end, title, verbose=False, testing=False):
+    if testing:
+        file_name = "green_code_tracking_test.txt"
+    else:
+        file_name = "green_code_tracking.txt"
+    if verbose:
+        print("\n" + "=" * 150)
+        print(
+            f"                    GREEN CODE INITIATIVE - {title}                          "
+        )
+        print("-" * 150)
+        print(f"Total time taken for {title}: {end - start:.3f} seconds")
+        print(f"{title} complete. All results saved in the output directory.")
+        print("\n" + "=" * 150)
+    with open(file_name, "a") as f:
+        f.write(
+            f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} - {title} - Total time taken: {end - start:.3f} seconds\n"
+        )
+
+
+def plot_comparison_summary(results, output_path, original_size_mb):
+    """
+    Calls `plotting.plot_comparison_summary()`
+    """
+    plotting.plot_comparison_summary(results, output_path, original_size_mb)
+    print("=== Done ===")
+    print("Summary Comparison plot is available in:", os.path.join(output_path, "plotting"))
+
+
+def find_decompressed_results(output_path):
+    results_dirs = []
+    for root, dirs, files in os.walk(output_path):
+        if "decompressed.npz" in files:
+            results_dirs.append(root)
+    return results_dirs
+
+
+def plot_all_results(output_path, config):
+    """
+
+    """
+    results_list = find_decompressed_results(output_path)
+
+    for path in results_list:
+        print(f"Plotting results from: {path}")
+        if config.data_dimension == 1:
+            plotting.plot_1D(output_path, config, path)
+        elif config.data_dimension == 2:
+            plotting.plot_2D(output_path, config, path)
\ No newline at end of file
diff --git a/baler/modules/models.py b/baler/modules/models.py
index 62075b92..26641267 100644
--- a/baler/modules/models.py
+++ b/baler/modules/models.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Baler Contributors
+# Copyright 2022-2025 Baler Contributors
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -183,6 +183,26 @@ def detach_hooks(self, hooks: list) -> None:
             hook.remove()
 
 
+class AE_float32(AE):
+    # This class defines an Autoencoder that inherits from the base `AE` class.
+    # All linear layers are explicitly defined with `dtype=torch.float32`.
+    # This model addresses an issue with the original AE inflating the compressed data size
+    #   caused by float64 precision layers operating on float32 data.
+    # To utilise this model, add [c.float_dtype = "float32"] to the project config file.
+
+    def __init__(self, n_features, z_dim, *args, **kwargs):
+        super(AE_float32, self).__init__(n_features, z_dim, *args, **kwargs)
+
+        self.en1 = nn.Linear(n_features, 200, dtype=torch.float32)
+        self.en2 = nn.Linear(200, 100, dtype=torch.float32)
+        self.en3 = nn.Linear(100, 50, dtype=torch.float32)
+        self.en4 = nn.Linear(50, z_dim, dtype=torch.float32)
+        self.de1 = nn.Linear(z_dim, 50, dtype=torch.float32)
+        self.de2 = nn.Linear(50, 100, dtype=torch.float32)
+        self.de3 = nn.Linear(100, 200, dtype=torch.float32)
+        self.de4 = nn.Linear(200, n_features, dtype=torch.float32)
+
+
 class CFD_dense_AE(nn.Module):
     # This class is a modified version of the original class by George Dialektakis found at
     # https://github.com/Autoencoders-compression-anomaly/Deep-Autoencoders-Data-Compression-GSoC-2021
diff --git a/baler/modules/plotting.py b/baler/modules/plotting.py
index aa59656f..a7a47119 100644
--- a/baler/modules/plotting.py
+++ b/baler/modules/plotting.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Baler Contributors
+# Copyright 2022-2025 Baler Contributors
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -98,7 +98,7 @@ def plot_box_and_whisker(names, residual, pdf):
     pdf.savefig()
 
 
-def plot_1D(output_path: str, config):
+def plot_1D(output_path: str, config, extra_path):
     """General plotting for 1D data, for example data from a '.csv' file. This function generates a pdf
         document where each page contains the before/after performance
         of each column of the 1D data
@@ -106,10 +106,16 @@ def plot_1D(output_path: str, config):
     Args:
         output_path (path): The path to the project directory
         config (dataclass): The config class containing attributes set in the config file
+        extra_path (path): The path to the directory where the decompressed data is stored. 
     """
 
     before_path = config.input_path
-    after_path = os.path.join(output_path, "decompressed_output", "decompressed.npz")
+    if extra_path == "decompressed_output":
+        after_path = os.path.join(output_path, extra_path, "decompressed.npz")
+        write_path = os.path.join(output_path, "plotting", "comparison.pdf")
+    else:
+        after_path = os.path.join(extra_path, "decompressed.npz")
+        write_path = os.path.join(extra_path, "comparison.pdf")
 
     before = np.transpose(np.load(before_path)["data"])
     after = np.transpose(np.load(after_path)["data"])
@@ -119,10 +125,16 @@ def plot_1D(output_path: str, config):
     before = np.delete(before, index_to_cut, axis=1)
     after = np.delete(after, index_to_cut, axis=1)
 
+    # with np.errstate(divide='raise'):
+    #     try:
+    #         response = np.divide(np.subtract(after, before), before) * 100
+    #     except FloatingPointError:
+    #         print("caught divide by zero")
+    #         response = 0    
     response = np.divide(np.subtract(after, before), before) * 100
     residual = np.subtract(after, before)
 
-    with PdfPages(os.path.join(output_path, "plotting", "comparison.pdf")) as pdf:
+    with PdfPages(write_path) as pdf:
         plot_box_and_whisker(names, residual, pdf)
         fig = plt.figure(constrained_layout=True, figsize=(10, 4))
         subfigs = fig.subfigures(1, 2, wspace=0.07, width_ratios=[1, 1])
@@ -172,7 +184,13 @@ def plot_1D(output_path: str, config):
             ax1.set_ylabel("Counts", ha="right", y=1.0)
             ax1.set_yscale("log")
             ax1.legend(loc="best")
-            ax1.set_xlim(x_min - 0.1 * x_diff, x_max + 0.1 * x_diff)
+            try:
+                ax1.set_xlim(x_min - 0.1 * x_diff, x_max + 0.1 * x_diff)
+            except ValueError:
+                print(
+                    f"Error setting xlim for {column_name}. Data may be too sparse or not well defined."
+                )
+                ax1.set_xlim(0, 10)
             ax1.set_ylim(ymin=1)
 
             data_bin_centers = bins_after[:-1] + (bins_after[1:] - bins_after[:-1]) / 2
@@ -367,7 +385,7 @@ def plot_2D_old(project_path, config):
     #         writer.append_data(image)
 
 
-def plot_2D(project_path, config):
+def plot_2D(project_path, config, extra_path):
     import sys
 
     """General plotting for 2D data, for example 2D arraysfrom computational fluid
@@ -437,14 +455,122 @@ def plot_2D(project_path, config):
         # sys.exit()
 
 
-def plot(output_path, config):
+def plot(project_path, config, extra_path="decompressed_output"):
     """Runs the appropriate plotting function based on the data dimension 1D or 2D
 
     Args:
-        output_path (path): The path to the project directory
+        extra_path (path): The path to the project directory
         config (dataclass): The config class containing attributes set in the config file
     """
     if config.data_dimension == 1:
-        plot_1D(output_path, config)
+        plot_1D(project_path, config, extra_path)
     elif config.data_dimension == 2:
-        plot_2D(output_path, config)
+        plot_2D(project_path, config, extra_path)
+
+
+def plot_comparison_summary(results, output_path, original_size_mb):
+    """
+    Generates a single PDF page with summary plots comparing all benchmark results.
+
+    Args:
+        results (list[BenchmarkResult]): A list of BenchmarkResult data objects.
+        output_path (str): The main output directory for the project.
+        original_size_mb (float): The size of the original uncompressed file in MB.
+    """
+    print("=== Plotting Comparison Summary ===")
+
+    # Sort results by RMSE for consistent plotting order
+    sorted_results = sorted(results, key=lambda r: r.rmse)
+
+    # Extract data into lists for easy plotting
+    names = [r.name for r in sorted_results]
+    rmse_values = [r.rmse for r in sorted_results]
+    compress_times = [r.compress_time_sec for r in sorted_results]
+    decompress_times = [r.decompress_time_sec for r in sorted_results]
+
+    # Calculate compression ratios
+    ratios = [
+        original_size_mb / r.size_mb if r.size_mb > 0 else 0 for r in sorted_results
+    ]
+
+    # --- Create the plots ---
+    fig, axs = plt.subplots(2, 2, figsize=(18, 24))
+    fig.suptitle("Compression Benchmark Summary", fontsize=20, y=1.02)
+
+    # 1. RMSE (Error) Plot
+    ax1 = axs[0, 0]
+    ax1.bar(names, rmse_values, color="skyblue")
+    ax1.set_title("Reconstruction Error (RMSE)")
+    ax1.set_ylabel("RMSE (lower is better)")
+    ax1.set_yscale("log")
+    ax1.grid(axis="y", linestyle="--", alpha=0.7)
+
+    # 2. Performance (Time) Plot - Grouped Bar Chart
+    ax2 = axs[0, 1]
+    x = np.arange(len(names))
+    width = 0.35
+    ax2.bar(
+        x - width / 2, compress_times, width, label="Compression Time", color="coral"
+    )
+    ax2.bar(
+        x + width / 2,
+        decompress_times,
+        width,
+        label="Decompression Time",
+        color="lightgreen",
+    )
+    ax2.set_title("Performance")
+    ax2.set_ylabel("Time (s, lower is better)")
+    ax2.set_xticks(x, names)
+    ax2.legend()
+    ax2.grid(axis="y", linestyle="--", alpha=0.7)
+
+    # 3. Compression Ratio Plot
+    ax3 = axs[1, 0]
+    ax3.bar(names, ratios, color="mediumpurple")
+    ax3.set_title("Compression Ratio")
+    ax3.set_ylabel("Ratio (Original / Compressed, higher is better)")
+    ax3.axhline(
+        y=1, color="gray", linestyle="--", linewidth=1
+    )  # Line for no compression
+    ax3.grid(axis="y", linestyle="--", alpha=0.7)
+
+    # 4. Trade-off Plot (Ratio vs. Error)
+    ax4 = axs[1, 1]
+    ax4.scatter(ratios, rmse_values, color="crimson", zorder=5)
+    for i, name in enumerate(names):
+        ax4.text(ratios[i] * 1.02, rmse_values[i], name, fontsize=9, rotation=45)
+    ax4.set_title("Trade-off: Compression Ratio vs. Error")
+    ax4.set_xlabel("Compression Ratio (higher is better)")
+    ax4.set_ylabel("RMSE (lower is better)")
+    ax4.set_yscale("log")
+    ax4.set_xscale("log")  # Log scale for ratio can also be useful
+    ax4.grid(True, which="both", ls="--", alpha=0.6)
+    # Highlight the "Pareto frontier" or ideal corner
+    ax4.annotate(
+        "Ideal Region",
+        xy=(0.95, 0.05),
+        xycoords="axes fraction",
+        xytext=(0.6, 0.3),
+        textcoords="axes fraction",
+        ha="center",
+        va="center",
+        arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=-0.2"),
+        bbox=dict(boxstyle="round,pad=0.3", fc="yellow", ec="k", lw=1, alpha=0.3),
+    )
+
+    # Improve layout for all subplots
+    for ax in axs.flat:
+        plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
+
+    fig.tight_layout(rect=[0, 0.03, 1, 0.97], h_pad=3)
+
+    # Save the figure
+    plot_dir = os.path.join(output_path, "plotting")
+    os.makedirs(plot_dir, exist_ok=True)
+    save_path = os.path.join(plot_dir, "comparison_summary.pdf")
+    fig.savefig(save_path, bbox_inches="tight")
+    plt.close(fig)
+    # print(f"  -> Comparison summary plot saved to: {save_path}")
+
+
diff --git a/baler/modules/training.py b/baler/modules/training.py
index ca189def..8945386d 100644
--- a/baler/modules/training.py
+++ b/baler/modules/training.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Baler Contributors
+# Copyright 2022-2025 Baler Contributors
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -227,8 +227,12 @@ def train(model, variables, train_data, test_data, project_path, config):
                 train_data.shape[0], 1, train_data.shape[1], train_data.shape[2]
             )
     elif config.data_dimension == 1:
-        train_ds = torch.tensor(train_data, dtype=torch.float64, device=device)
-        valid_ds = torch.tensor(test_data, dtype=torch.float64, device=device)
+        if hasattr(config, "float_dtype") and config.float_dtype == "float32":
+            train_ds = torch.tensor(train_data, dtype=torch.float32, device=device)
+            valid_ds = torch.tensor(test_data, dtype=torch.float32, device=device)
+        else:
+            train_ds = torch.tensor(train_data, dtype=torch.float64, device=device)
+            valid_ds = torch.tensor(test_data, dtype=torch.float64, device=device)
 
     # Pushing input data into the torch-DataLoader object and combines into one DataLoader object (a basic wrapper
     # around several DataLoader objects).
@@ -280,7 +284,7 @@ def train(model, variables, train_data, test_data, project_path, config):
     # Training and Validation of the model
     train_loss = []
     val_loss = []
-    start = time.time()
+    start = time.perf_counter()
 
     # Registering hooks for activation extraction
     if config.activation_extraction:
@@ -328,7 +332,7 @@ def train(model, variables, train_data, test_data, project_path, config):
                 path = os.path.join(project_path, f"model_{epoch}.pt")
                 helper.model_saver(model, path)
 
-    end = time.time()
+    end = time.perf_counter()
 
     # Saving activations values
     if config.activation_extraction:
@@ -336,7 +340,7 @@ def train(model, variables, train_data, test_data, project_path, config):
         model.detach_hooks(hooks)
         np.save(os.path.join(project_path, "activations.npy"), activations)
 
-    print(f"{(end - start) / 60:.3} minutes")
+    print(f"{(end - start):.4f} seconds")
     np.save(
         os.path.join(project_path, "loss_data.npy"), np.array([train_loss, val_loss])
     )
diff --git a/baler/modules/utils.py b/baler/modules/utils.py
index 23b63687..5b242af4 100644
--- a/baler/modules/utils.py
+++ b/baler/modules/utils.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Baler Contributors
+# Copyright 2022-2025 Baler Contributors
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -316,7 +316,7 @@ def __init__(self, optimizer, patience, min_lr=min_lr, factor=factor):
             patience=self.patience,
             factor=self.factor,
             min_lr=self.min_lr,
-            verbose=True,
+            # verbose=True, # commented out as now deprecated, use get_last_lr() instead
         )
 
     def __call__(self, train_loss):
diff --git a/poetry.lock b/poetry.lock
index 7b949e57..ba9a4626 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -6,6 +6,8 @@ version = "1.4.0"
 description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
 optional = true
 python-versions = ">=3.6"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "absl-py-1.4.0.tar.gz", hash = "sha256:d2c244d01048ba476e7c080bd2c6df5e141d211de80223460d5b3b8a2a58433d"},
     {file = "absl_py-1.4.0-py3-none-any.whl", hash = "sha256:0d3fe606adfa4f7db64792dd4c7aee4ee0c38ab75dfd353b7a83ed3e957fcb47"},
@@ -17,6 +19,8 @@ version = "1.6.3"
 description = "An AST unparser for Python"
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"},
     {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
@@ -32,6 +36,7 @@ version = "23.12.1"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "black-23.12.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e0aaf6041986767a5e0ce663c7a2f0e9eaf21e6ff87a5f95cbf3675bfd4c41d2"},
     {file = "black-23.12.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c88b3711d12905b74206227109272673edce0cb29f27e1385f33b0163c414bba"},
@@ -68,27 +73,63 @@ typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
+d = ["aiohttp (>=3.7.4) ; sys_platform != \"win32\" or implementation_name != \"pypy\"", "aiohttp (>=3.7.4,!=3.9.0) ; sys_platform == \"win32\" and implementation_name == \"pypy\""]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
-name = "cachetools"
-version = "5.3.3"
-description = "Extensible memoizing collections and decorators"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
-    {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
+name = "blosc2"
+version = "3.5.1"
+description = "A fast & compressed ndarray library with a flexible compute engine."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "blosc2-3.5.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ff031df2707cd7143b7c744bf10c55932151001cc5ff9d79a899714714732a2d"},
+    {file = "blosc2-3.5.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0fc7976024a89d490911ce9ef78968369acca908f0f771cf00e072a457ee342d"},
+    {file = "blosc2-3.5.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0fcc6c64315121f02555a398e6fbaf4b71edc39a4ca7567e089ce1af7288e9ed"},
+    {file = "blosc2-3.5.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d3174273decf69b9e61b2a57d4cad5bf651de04d22526568f5a5e3036631831f"},
+    {file = "blosc2-3.5.1-cp310-cp310-win_amd64.whl", hash = "sha256:a4dcaf581cd7ec91d91f379e7e3dc7d5dd6c300e88ae162b04b106d21f10f5c1"},
+    {file = "blosc2-3.5.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ebf860878dd7a4fb41dcb90b1c6b3c5db7e34dc64ab51bea3679534af4a54a4a"},
+    {file = "blosc2-3.5.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1a9fc80970d5bb135bd9579db26d96ec79739bd1f187bbaf52e04102e7c664de"},
+    {file = "blosc2-3.5.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fcf6658546da153fc8537dbf8edab1b22d6cedba32ec8c2a9a4165e450375b28"},
+    {file = "blosc2-3.5.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d77e0abe67675ec21ce812a4da3d01406dd4942c8929a5a78d91b2f558dd8c9e"},
+    {file = "blosc2-3.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:0e6f5ccfb817363191577dd0e40839788f664891908b82c2dcd969fb37e0e954"},
+    {file = "blosc2-3.5.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:780213c917d9ac28b52a09ac82baa761262e8421688937b67f6516987a96fc58"},
+    {file = "blosc2-3.5.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6e3d1d0885e955e184efae81168448106644d5a0a60c0911b98e94fe9d756fa9"},
+    {file = "blosc2-3.5.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d7c23e48414283dcfa5676abc6a4be9fb92a7b372a20f7acf90438abc69a746e"},
+    {file = "blosc2-3.5.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5df464652ddcae9ef8d8b3354ad224b1e147187dde5b6470e0e9f95bec328756"},
+    {file = "blosc2-3.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:530965c444186bfb92a4406ab4c2d801939df35a5c58658131000f68ce1a37ec"},
+    {file = "blosc2-3.5.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:44b4e51fd0b5a7e6ccae8053e133037f5984f6990a1e9b787376f4ec42aaf5bf"},
+    {file = "blosc2-3.5.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:b59805aa56ff1f9e6e4ba2f293e54cdf356b64db983bb019dbdbac72f88f2e38"},
+    {file = "blosc2-3.5.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:485ca015db7352fa0e3b78b912c66ec495d60b94cab36a82abfb9f87715e6abd"},
+    {file = "blosc2-3.5.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f88cfa1f1d64d77278c7fae0ab8c42291b7b2e8f36fcee154a0c55388e697fe9"},
+    {file = "blosc2-3.5.1-cp313-cp313-win_amd64.whl", hash = "sha256:9d36c4a8489c0f8719040a7543918e2701821340b8643d508c450d0d012259bf"},
+    {file = "blosc2-3.5.1.tar.gz", hash = "sha256:5d72f7a9a8b3b523c588be9d66e9e7f2463483716c4c01e5056c1f7e37167f85"},
 ]
 
+[package.dependencies]
+msgpack = "*"
+ndindex = "*"
+numexpr = {version = "*", markers = "platform_machine != \"wasm32\""}
+numpy = ">=1.26"
+platformdirs = "*"
+py-cpuinfo = {version = "*", markers = "platform_machine != \"wasm32\""}
+requests = "*"
+
+[package.extras]
+dev = ["dask", "h5py", "hdf5plugin", "jupyterlab", "matplotlib", "pandas", "plotly", "pre-commit", "pyarrow", "ruff", "s3fs", "xarray", "zarr"]
+doc = ["myst-parser", "nbsphinx", "numpydoc", "pydata-sphinx-theme", "sphinx (>=8)", "sphinx-panels", "sphinx-paramlinks"]
+test = ["psutil ; platform_machine != \"wasm32\"", "pytest", "torch ; platform_machine != \"wasm32\""]
+
 [[package]]
 name = "calmjs-parse"
 version = "1.3.1"
 description = "Various parsers for ECMA standards."
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "calmjs.parse-1.3.1-py2-none-any.whl", hash = "sha256:9d3cec13fcc914f635c4940d2bfe007800093435b4ad4ef7d4e91d81dda66c80"},
     {file = "calmjs.parse-1.3.1-py3-none-any.whl", hash = "sha256:34e087b2a7a3117264df416d7d6793000bc59773e182ffef7da2dd7820f88028"},
@@ -103,8 +144,9 @@ setuptools = "*"
 name = "certifi"
 version = "2024.2.2"
 description = "Python package for providing Mozilla's CA Bundle."
-optional = true
+optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
     {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
@@ -114,8 +156,9 @@ files = [
 name = "charset-normalizer"
 version = "3.3.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-optional = true
+optional = false
 python-versions = ">=3.7.0"
+groups = ["main"]
 files = [
     {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
     {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
@@ -215,6 +258,7 @@ version = "8.1.7"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
     {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
@@ -229,10 +273,12 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["main", "dev"]
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
+markers = {main = "platform_system == \"Windows\"", dev = "sys_platform == \"win32\" or platform_system == \"Windows\""}
 
 [[package]]
 name = "contourpy"
@@ -240,6 +286,7 @@ version = "1.1.1"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "contourpy-1.1.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:46e24f5412c948d81736509377e255f6040e94216bf1a9b5ea1eaa9d29f6ec1b"},
     {file = "contourpy-1.1.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0e48694d6a9c5a26ee85b10130c77a011a4fedf50a7279fa0bdaf44bafb4299d"},
@@ -311,6 +358,7 @@ version = "0.12.1"
 description = "Composable style cycles"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
     {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
@@ -326,6 +374,8 @@ version = "0.1.8"
 description = "Tree is a library for working with nested data structures."
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "dm-tree-0.1.8.tar.gz", hash = "sha256:0fcaabbb14e7980377439e7140bd05552739ca5e515ecb3119f234acee4b9430"},
     {file = "dm_tree-0.1.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:35cc164a79336bfcfafb47e5f297898359123bbd3330c1967f0c4994f9cf9f60"},
@@ -381,6 +431,8 @@ version = "1.2.0"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version == \"3.10\""
 files = [
     {file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
     {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
@@ -395,6 +447,7 @@ version = "3.13.1"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
     {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"},
@@ -403,17 +456,19 @@ files = [
 [package.extras]
 docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
 testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
-typing = ["typing-extensions (>=4.8)"]
+typing = ["typing-extensions (>=4.8) ; python_version < \"3.11\""]
 
 [[package]]
 name = "flatbuffers"
-version = "23.5.26"
+version = "25.2.10"
 description = "The FlatBuffers serialization format for Python"
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
-    {file = "flatbuffers-23.5.26-py2.py3-none-any.whl", hash = "sha256:c0ff356da363087b915fde4b8b45bdda73432fc17cddb3c8157472eab1422ad1"},
-    {file = "flatbuffers-23.5.26.tar.gz", hash = "sha256:9ea1144cac05ce5d86e2859f431c6cd5e66cd9c78c558317c7955fb8d4c78d89"},
+    {file = "flatbuffers-25.2.10-py2.py3-none-any.whl", hash = "sha256:ebba5f4d5ea615af3f7fd70fc310636fbb2bbd1f566ac0a23d98dd412de50051"},
+    {file = "flatbuffers-25.2.10.tar.gz", hash = "sha256:97e451377a41262f8d9bd4295cc836133415cc03d8cb966410a4af92eb00d26e"},
 ]
 
 [[package]]
@@ -422,6 +477,7 @@ version = "4.49.0"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "fonttools-4.49.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d970ecca0aac90d399e458f0b7a8a597e08f95de021f17785fb68e2dc0b99717"},
     {file = "fonttools-4.49.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ac9a745b7609f489faa65e1dc842168c18530874a5f5b742ac3dd79e26bca8bc"},
@@ -468,18 +524,18 @@ files = [
 ]
 
 [package.extras]
-all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"]
+all = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\"", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0) ; python_version <= \"3.12\"", "xattr ; sys_platform == \"darwin\"", "zopfli (>=0.1.4)"]
 graphite = ["lz4 (>=1.7.4.2)"]
-interpolatable = ["munkres", "pycairo", "scipy"]
+interpolatable = ["munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\""]
 lxml = ["lxml (>=4.0)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
 repacker = ["uharfbuzz (>=0.23.0)"]
 symfont = ["sympy"]
-type1 = ["xattr"]
+type1 = ["xattr ; sys_platform == \"darwin\""]
 ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=15.1.0)"]
-woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
+unicode = ["unicodedata2 (>=15.1.0) ; python_version <= \"3.12\""]
+woff = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "zopfli (>=0.1.4)"]
 
 [[package]]
 name = "fsspec"
@@ -487,6 +543,7 @@ version = "2024.2.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
     {file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
@@ -522,58 +579,21 @@ version = "0.4.0"
 description = "Python AST that abstracts the underlying Python version"
 optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "gast-0.4.0-py3-none-any.whl", hash = "sha256:b7adcdd5adbebf1adf17378da5ba3f543684dbec47b1cda1f3997e573cd542c4"},
     {file = "gast-0.4.0.tar.gz", hash = "sha256:40feb7b8b8434785585ab224d1568b857edb18297e5a3047f1ba012bc83b42c1"},
 ]
 
-[[package]]
-name = "google-auth"
-version = "2.28.1"
-description = "Google Authentication Library"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "google-auth-2.28.1.tar.gz", hash = "sha256:34fc3046c257cedcf1622fc4b31fc2be7923d9b4d44973d481125ecc50d83885"},
-    {file = "google_auth-2.28.1-py2.py3-none-any.whl", hash = "sha256:25141e2d7a14bfcba945f5e9827f98092716e99482562f15306e5b026e21aa72"},
-]
-
-[package.dependencies]
-cachetools = ">=2.0.0,<6.0"
-pyasn1-modules = ">=0.2.1"
-rsa = ">=3.1.4,<5"
-
-[package.extras]
-aiohttp = ["aiohttp (>=3.6.2,<4.0.0.dev0)", "requests (>=2.20.0,<3.0.0.dev0)"]
-enterprise-cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"]
-pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
-reauth = ["pyu2f (>=0.1.5)"]
-requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
-
-[[package]]
-name = "google-auth-oauthlib"
-version = "1.0.0"
-description = "Google Authentication Library"
-optional = true
-python-versions = ">=3.6"
-files = [
-    {file = "google-auth-oauthlib-1.0.0.tar.gz", hash = "sha256:e375064964820b47221a7e1b7ee1fd77051b6323c3f9e3e19785f78ab67ecfc5"},
-    {file = "google_auth_oauthlib-1.0.0-py2.py3-none-any.whl", hash = "sha256:95880ca704928c300f48194d1770cf5b1462835b6e49db61445a520f793fd5fb"},
-]
-
-[package.dependencies]
-google-auth = ">=2.15.0"
-requests-oauthlib = ">=0.7.0"
-
-[package.extras]
-tool = ["click (>=6.0.0)"]
-
 [[package]]
 name = "google-pasta"
 version = "0.2.0"
 description = "pasta is an AST-based Python refactoring library"
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "google-pasta-0.2.0.tar.gz", hash = "sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e"},
     {file = "google_pasta-0.2.0-py2-none-any.whl", hash = "sha256:4612951da876b1a10fe3960d7226f0c7682cf901e16ac06e473b267a5afa8954"},
@@ -589,6 +609,8 @@ version = "1.62.0"
 description = "HTTP/2-based RPC framework"
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "grpcio-1.62.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:136ffd79791b1eddda8d827b607a6285474ff8a1a5735c4947b58c481e5e4271"},
     {file = "grpcio-1.62.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:d6a56ba703be6b6267bf19423d888600c3f574ac7c2cc5e6220af90662a4d6b0"},
@@ -655,6 +677,8 @@ version = "3.10.0"
 description = "Read and write HDF5 files from Python"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "h5py-3.10.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b963fb772964fc1d1563c57e4e2e874022ce11f75ddc6df1a626f42bd49ab99f"},
     {file = "h5py-3.10.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:012ab448590e3c4f5a8dd0f3533255bc57f80629bf7c5054cf4c87b30085063c"},
@@ -692,6 +716,8 @@ version = "0.7.1"
 description = "Machine learning in FPGAs using HLS"
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "hls4ml-0.7.1-py3-none-any.whl", hash = "sha256:086c578c192908c12e1e579632faeb97635aeb0a31cd844ca8e7978cd0069df7"},
     {file = "hls4ml-0.7.1.tar.gz", hash = "sha256:a2ef3754243987f0295c3c123207405e96834541373aed4e4a4369f002199097"},
@@ -715,97 +741,33 @@ profiling = ["matplotlib", "pandas", "seaborn"]
 name = "idna"
 version = "3.6"
 description = "Internationalized Domain Names in Applications (IDNA)"
-optional = true
+optional = false
 python-versions = ">=3.5"
+groups = ["main"]
 files = [
     {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
     {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
 ]
 
-[[package]]
-name = "importlib-metadata"
-version = "7.0.1"
-description = "Read metadata from Python packages"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "importlib_metadata-7.0.1-py3-none-any.whl", hash = "sha256:4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e"},
-    {file = "importlib_metadata-7.0.1.tar.gz", hash = "sha256:f238736bb06590ae52ac1fab06a3a9ef1d8dce2b7a35b5ab329371d6c8f5d2cc"},
-]
-
-[package.dependencies]
-zipp = ">=0.5"
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
-
-[[package]]
-name = "importlib-resources"
-version = "6.1.2"
-description = "Read resources from Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "importlib_resources-6.1.2-py3-none-any.whl", hash = "sha256:9a0a862501dc38b68adebc82970140c9e4209fc99601782925178f8386339938"},
-    {file = "importlib_resources-6.1.2.tar.gz", hash = "sha256:308abf8474e2dba5f867d279237cd4076482c3de7104a40b41426370e891549b"},
-]
-
-[package.dependencies]
-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
-
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
-[[package]]
-name = "jax"
-version = "0.4.13"
-description = "Differentiate, compile, and transform Numpy code."
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "jax-0.4.13.tar.gz", hash = "sha256:03bfe6749dfe647f16f15f6616638adae6c4a7ca7167c75c21961ecfd3a3baaa"},
-]
-
-[package.dependencies]
-importlib_metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
-ml_dtypes = ">=0.1.0"
-numpy = ">=1.21"
-opt_einsum = "*"
-scipy = ">=1.7"
-
-[package.extras]
-australis = ["protobuf (>=3.13,<4)"]
-ci = ["jaxlib (==0.4.12)"]
-cpu = ["jaxlib (==0.4.13)"]
-cuda = ["jaxlib (==0.4.13+cuda11.cudnn86)"]
-cuda11-cudnn86 = ["jaxlib (==0.4.13+cuda11.cudnn86)"]
-cuda11-local = ["jaxlib (==0.4.13+cuda11.cudnn86)"]
-cuda11-pip = ["jaxlib (==0.4.13+cuda11.cudnn86)", "nvidia-cublas-cu11 (>=11.11)", "nvidia-cuda-cupti-cu11 (>=11.8)", "nvidia-cuda-nvcc-cu11 (>=11.8)", "nvidia-cuda-runtime-cu11 (>=11.8)", "nvidia-cudnn-cu11 (>=8.8)", "nvidia-cufft-cu11 (>=10.9)", "nvidia-cusolver-cu11 (>=11.4)", "nvidia-cusparse-cu11 (>=11.7)"]
-cuda12-local = ["jaxlib (==0.4.13+cuda12.cudnn89)"]
-cuda12-pip = ["jaxlib (==0.4.13+cuda12.cudnn89)", "nvidia-cublas-cu12", "nvidia-cuda-cupti-cu12", "nvidia-cuda-nvcc-cu12", "nvidia-cuda-runtime-cu12", "nvidia-cudnn-cu12 (>=8.9)", "nvidia-cufft-cu12", "nvidia-cusolver-cu12", "nvidia-cusparse-cu12"]
-minimum-jaxlib = ["jaxlib (==0.4.11)"]
-tpu = ["jaxlib (==0.4.13)", "libtpu-nightly (==0.1.dev20230622)"]
-
 [[package]]
 name = "jinja2"
 version = "3.1.3"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
     {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
@@ -823,6 +785,7 @@ version = "1.3.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
     {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
@@ -830,20 +793,35 @@ files = [
 
 [[package]]
 name = "keras"
-version = "2.12.0"
-description = "Deep learning for humans."
+version = "3.10.0"
+description = "Multi-backend Keras"
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
-    {file = "keras-2.12.0-py2.py3-none-any.whl", hash = "sha256:35c39534011e909645fb93515452e98e1a0ce23727b55d4918b9c58b2308c15e"},
+    {file = "keras-3.10.0-py3-none-any.whl", hash = "sha256:c095a6bf90cd50defadf73d4859ff794fad76b775357ef7bd1dbf96388dae7d3"},
+    {file = "keras-3.10.0.tar.gz", hash = "sha256:6e9100bf66eaf6de4b7f288d34ef9bb8b5dcdd62f42c64cfd910226bb34ad2d2"},
 ]
 
+[package.dependencies]
+absl-py = "*"
+h5py = "*"
+ml-dtypes = "*"
+namex = "*"
+numpy = "*"
+optree = "*"
+packaging = "*"
+rich = "*"
+
 [[package]]
 name = "keras-tuner"
 version = "1.4.7"
 description = "A Hyperparameter Tuning Library for Keras"
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "keras-tuner-1.4.7.tar.gz", hash = "sha256:6befd25ee81476e6207d8ca7ed7dc674b8194437cfa0b127294cd00da905ff22"},
     {file = "keras_tuner-1.4.7-py3-none-any.whl", hash = "sha256:0bcf0220eccc74e7a6a9bd7c8e58531a1af8515019e6bc2dc495833155c07fe2"},
@@ -868,6 +846,7 @@ version = "1.4.5"
 description = "A fast implementation of the Cassowary constraint solver"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af"},
     {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:146d14bebb7f1dc4d5fbf74f8a6cb15ac42baadee8912eb84ac0b3b2a3dc6ac3"},
@@ -981,6 +960,8 @@ version = "1.0.5"
 description = "Legacy import names for Keras Tuner"
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "kt-legacy-1.0.5.tar.gz", hash = "sha256:dbbade58f12c6a6da6062f4b045a6395a8d4195815e3e064bc3e609b69c8a26c"},
     {file = "kt_legacy-1.0.5-py3-none-any.whl", hash = "sha256:8d5c5b3dccf348367fe9ca5b006e7ad2d1babcce62976cfc199e831ea699dcd3"},
@@ -992,6 +973,8 @@ version = "16.0.6"
 description = "Clang Python Bindings, mirrored from the official LLVM repo: https://github.com/llvm/llvm-project/tree/main/clang/bindings/python, to make the installation process easier."
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "libclang-16.0.6-1-py2.py3-none-manylinux2014_aarch64.whl", hash = "sha256:88bc7e7b393c32e41e03ba77ef02fdd647da1f764c2cd028e69e0837080b79f6"},
     {file = "libclang-16.0.6-1-py2.py3-none-manylinux2014_armv7l.whl", hash = "sha256:d80ed5827736ed5ec2bcedf536720476fd9d4fa4c79ef0cb24aea4c59332f361"},
@@ -1012,24 +995,50 @@ version = "3.5.2"
 description = "Python implementation of John Gruber's Markdown."
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "Markdown-3.5.2-py3-none-any.whl", hash = "sha256:d43323865d89fc0cb9b20c75fc8ad313af307cc087e84b657d9eec768eddeadd"},
     {file = "Markdown-3.5.2.tar.gz", hash = "sha256:e1ac7b3dc550ee80e602e71c1d168002f062e49f1b11e26a36264dafd4df2ef8"},
 ]
 
-[package.dependencies]
-importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
-
 [package.extras]
 docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
 testing = ["coverage", "pyyaml"]
 
+[[package]]
+name = "markdown-it-py"
+version = "3.0.0"
+description = "Python port of markdown-it. Markdown parsing, done right!"
+optional = true
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
+files = [
+    {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
+    {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"},
+]
+
+[package.dependencies]
+mdurl = ">=0.1,<1.0"
+
+[package.extras]
+benchmarking = ["psutil", "pytest", "pytest-benchmark"]
+code-style = ["pre-commit (>=3.0,<4.0)"]
+compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
+linkify = ["linkify-it-py (>=1,<3)"]
+plugins = ["mdit-py-plugins"]
+profiling = ["gprof2dot"]
+rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"},
     {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72b6be590cc35924b02c78ef34b467da4ba07e4e0f0454a2c5907f473fc50ce5"},
@@ -1099,6 +1108,7 @@ version = "3.7.5"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "matplotlib-3.7.5-cp310-cp310-macosx_10_12_universal2.whl", hash = "sha256:4a87b69cb1cb20943010f63feb0b2901c17a3b435f75349fd9865713bfa63925"},
     {file = "matplotlib-3.7.5-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:d3ce45010fefb028359accebb852ca0c21bd77ec0f281952831d235228f15810"},
@@ -1153,7 +1163,6 @@ files = [
 contourpy = ">=1.0.1"
 cycler = ">=0.10"
 fonttools = ">=4.22.0"
-importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
 kiwisolver = ">=1.0.1"
 numpy = ">=1.20,<2"
 packaging = ">=20.0"
@@ -1162,36 +1171,50 @@ pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
 [[package]]
-name = "ml-dtypes"
-version = "0.2.0"
-description = ""
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
-    {file = "ml_dtypes-0.2.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:df6a76e1c8adf484feb138ed323f9f40a7b6c21788f120f7c78bec20ac37ee81"},
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+    {version = ">=1.21.2", markers = "python_version == \"3.10\""},
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+gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""]
 tests = ["pytest (>=4.6)"]
 
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@@ -1330,6 +1571,8 @@ version = "8.9.2.26"
 description = "cuDNN runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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 ]
@@ -1343,6 +1586,8 @@ version = "11.0.2.54"
 description = "CUFFT native runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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@@ -1354,6 +1599,8 @@ version = "10.3.2.106"
 description = "CURAND native runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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@@ -1365,6 +1612,8 @@ version = "11.4.5.107"
 description = "CUDA solver native runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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@@ -1381,6 +1630,8 @@ version = "12.1.0.106"
 description = "CUSPARSE native runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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@@ -1395,6 +1646,8 @@ version = "2.19.3"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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 ]
@@ -1405,7 +1658,10 @@ version = "12.4.99"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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 ]
@@ -1416,33 +1672,21 @@ version = "12.1.105"
 description = "NVIDIA Tools Extension"
 optional = false
 python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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 ]
 
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-description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
-optional = true
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-
-[package.extras]
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-signals = ["blinker (>=1.4.0)"]
-signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
-
 [[package]]
 name = "onnx"
 version = "1.15.0"
 description = "Open Neural Network Exchange"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
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@@ -1484,6 +1728,8 @@ version = "3.3.0"
 description = "Optimizing numpys einsum function"
 optional = true
 python-versions = ">=3.5"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
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@@ -1496,12 +1742,108 @@ numpy = ">=1.7"
 docs = ["numpydoc", "sphinx (==1.2.3)", "sphinx-rtd-theme", "sphinxcontrib-napoleon"]
 tests = ["pytest", "pytest-cov", "pytest-pep8"]
 
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+    {file = "optree-0.16.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4e1618d1fbe4ebc3f977aba80575c84c2ad1c54153b8a3d500fd3303bdb02be1"},
+    {file = "optree-0.16.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:69dea72a039f04549ca9d759e7037c9d3106ec6b7b7223bb66b6a9d07fba4b3b"},
+    {file = "optree-0.16.0.tar.gz", hash = "sha256:3b3432754b0753f5166a0899c693e99fe00e02c48f90b511c0604aa6e4b4a59e"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.6.0"
+
+[package.extras]
+docs = ["docutils", "jax[cpu]", "numpy", "sphinx", "sphinx-autoapi", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx-copybutton", "sphinx-rtd-theme", "sphinxcontrib-bibtex", "torch"]
+jax = ["jax"]
+lint = ["cpplint", "doc8", "mypy", "pre-commit", "pyenchant", "pylint[spelling]", "ruff", "xdoctest"]
+numpy = ["numpy"]
+test = ["covdefaults", "pytest", "pytest-cov", "rich"]
+torch = ["torch"]
+
 [[package]]
 name = "packaging"
 version = "23.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "packaging-23.2-py3-none-any.whl", hash = "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"},
     {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
@@ -1513,6 +1855,8 @@ version = "1.6.5"
 description = "parse() is the opposite of format()"
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "parse-1.6.5.tar.gz", hash = "sha256:c9545154ba6b0ede5b52c2877443a463dfc02f14fe43525d15255311939c3a72"},
 ]
@@ -1523,6 +1867,7 @@ version = "0.12.1"
 description = "Utility library for gitignore style pattern matching of file paths."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"},
     {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
@@ -1534,6 +1879,7 @@ version = "10.2.0"
 description = "Python Imaging Library (Fork)"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "pillow-10.2.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:7823bdd049099efa16e4246bdf15e5a13dbb18a51b68fa06d6c1d4d8b99a796e"},
     {file = "pillow-10.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:83b2021f2ade7d1ed556bc50a399127d7fb245e725aa0113ebd05cfe88aaf588"},
@@ -1610,7 +1956,7 @@ docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline
 fpx = ["olefile"]
 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
-typing = ["typing-extensions"]
+typing = ["typing-extensions ; python_version < \"3.10\""]
 xmp = ["defusedxml"]
 
 [[package]]
@@ -1619,6 +1965,7 @@ version = "4.2.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "platformdirs-4.2.0-py3-none-any.whl", hash = "sha256:0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068"},
     {file = "platformdirs-4.2.0.tar.gz", hash = "sha256:ef0cc731df711022c174543cb70a9b5bd22e5a9337c8624ef2c2ceb8ddad8768"},
@@ -1634,6 +1981,7 @@ version = "1.4.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pluggy-1.4.0-py3-none-any.whl", hash = "sha256:7db9f7b503d67d1c5b95f59773ebb58a8c1c288129a88665838012cfb07b8981"},
     {file = "pluggy-1.4.0.tar.gz", hash = "sha256:8c85c2876142a764e5b7548e7d9a0e0ddb46f5185161049a79b7e974454223be"},
@@ -1649,6 +1997,8 @@ version = "3.11"
 description = "Python Lex & Yacc"
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "ply-3.11-py2.py3-none-any.whl", hash = "sha256:096f9b8350b65ebd2fd1346b12452efe5b9607f7482813ffca50c22722a807ce"},
     {file = "ply-3.11.tar.gz", hash = "sha256:00c7c1aaa88358b9c765b6d3000c6eec0ba42abca5351b095321aef446081da3"},
@@ -1660,6 +2010,8 @@ version = "4.25.3"
 description = ""
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "protobuf-4.25.3-cp310-abi3-win32.whl", hash = "sha256:d4198877797a83cbfe9bffa3803602bbe1625dc30d8a097365dbc762e5790faa"},
     {file = "protobuf-4.25.3-cp310-abi3-win_amd64.whl", hash = "sha256:209ba4cc916bab46f64e56b85b090607a676f66b473e6b762e6f1d9d591eb2e8"},
@@ -1675,47 +2027,55 @@ files = [
 ]
 
 [[package]]
-name = "pyasn1"
-version = "0.5.1"
-description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
-optional = true
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
-files = [
-    {file = "pyasn1-0.5.1-py2.py3-none-any.whl", hash = "sha256:4439847c58d40b1d0a573d07e3856e95333f1976294494c325775aeca506eb58"},
-    {file = "pyasn1-0.5.1.tar.gz", hash = "sha256:6d391a96e59b23130a5cfa74d6fd7f388dbbe26cc8f1edf39fdddf08d9d6676c"},
-]
-
-[[package]]
-name = "pyasn1-modules"
-version = "0.3.0"
-description = "A collection of ASN.1-based protocols modules"
-optional = true
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+name = "py-cpuinfo"
+version = "9.0.0"
+description = "Get CPU info with pure Python"
+optional = false
+python-versions = "*"
+groups = ["main"]
+markers = "platform_machine != \"wasm32\""
 files = [
-    {file = "pyasn1_modules-0.3.0-py2.py3-none-any.whl", hash = "sha256:d3ccd6ed470d9ffbc716be08bd90efbd44d0734bc9303818f7336070984a162d"},
-    {file = "pyasn1_modules-0.3.0.tar.gz", hash = "sha256:5bd01446b736eb9d31512a30d46c1ac3395d676c6f3cafa4c03eb54b9925631c"},
+    {file = "py-cpuinfo-9.0.0.tar.gz", hash = "sha256:3cdbbf3fac90dc6f118bfd64384f309edeadd902d7c8fb17f02ffa1fc3f49690"},
+    {file = "py_cpuinfo-9.0.0-py3-none-any.whl", hash = "sha256:859625bc251f64e21f077d099d4162689c762b5d6a4c3c97553d56241c9674d5"},
 ]
 
-[package.dependencies]
-pyasn1 = ">=0.4.6,<0.6.0"
-
 [[package]]
 name = "pydigitalwavetools"
 version = "1.1"
 description = "Library for operations with VCD and other digital wave files"
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "pyDigitalWaveTools-1.1-py3-none-any.whl", hash = "sha256:aa1c3b2447786afab3412baa4d86bb4d1df9723a8df7076a1d9976ebdda1874c"},
     {file = "pyDigitalWaveTools-1.1.tar.gz", hash = "sha256:c7f0e06f7ed543a2f5f7110a4a8f2aae9e3ae328bb67ebd5b2a6db862efe7d33"},
 ]
 
+[[package]]
+name = "pygments"
+version = "2.19.2"
+description = "Pygments is a syntax highlighting package written in Python."
+optional = true
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
+files = [
+    {file = "pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b"},
+    {file = "pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887"},
+]
+
+[package.extras]
+windows-terminal = ["colorama (>=0.4.6)"]
+
 [[package]]
 name = "pyparser"
 version = "1.0"
 description = "pyparser is a collection of classes to make it easier to parse text data in a pythonic way."
 optional = true
 python-versions = "*"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "pyparser-1.0.tar.gz", hash = "sha256:d1b76e2dabdd2952cadfd545229cc144afee6130bf171a031d5bf53f11b912f5"},
 ]
@@ -1729,6 +2089,7 @@ version = "3.1.2"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
+groups = ["main"]
 files = [
     {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
     {file = "pyparsing-3.1.2.tar.gz", hash = "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad"},
@@ -1743,6 +2104,7 @@ version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
     {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
@@ -1765,6 +2127,7 @@ version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
 files = [
     {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
     {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
@@ -1779,6 +2142,8 @@ version = "6.0.1"
 description = "YAML parser and emitter for Python"
 optional = true
 python-versions = ">=3.6"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "PyYAML-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d858aa552c999bc8a8d57426ed01e40bef403cd8ccdd0fc5f6f04a00414cac2a"},
     {file = "PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f"},
@@ -1839,6 +2204,8 @@ version = "0.9.0"
 description = "Quantization package for Keras"
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "QKeras-0.9.0-py3-none-any.whl", hash = "sha256:087d9973725c285e4be69ba71b0aa92b5c5dab36c41ad8e8f1480b9a0d6397c8"},
     {file = "QKeras-0.9.0.tar.gz", hash = "sha256:b30f751420fa9172e53bab59ed65275ff9ec4085124491f3f5c89b7563d00b2a"},
@@ -1859,8 +2226,9 @@ tqdm = ">=4.48.0"
 name = "requests"
 version = "2.31.0"
 description = "Python HTTP for Humans."
-optional = true
+optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "requests-2.31.0-py3-none-any.whl", hash = "sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f"},
     {file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
@@ -1877,36 +2245,25 @@ socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
-name = "requests-oauthlib"
-version = "1.3.1"
-description = "OAuthlib authentication support for Requests."
+name = "rich"
+version = "14.0.0"
+description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 optional = true
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.8.0"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
-    {file = "requests-oauthlib-1.3.1.tar.gz", hash = "sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a"},
-    {file = "requests_oauthlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5"},
+    {file = "rich-14.0.0-py3-none-any.whl", hash = "sha256:1c9491e1951aac09caffd42f448ee3d04e58923ffe14993f6e83068dc395d7e0"},
+    {file = "rich-14.0.0.tar.gz", hash = "sha256:82f1bc23a6a21ebca4ae0c45af9bdbc492ed20231dcb63f297d6d1021a9d5725"},
 ]
 
 [package.dependencies]
-oauthlib = ">=3.0.0"
-requests = ">=2.0.0"
+markdown-it-py = ">=2.2.0"
+pygments = ">=2.13.0,<3.0.0"
+typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
-rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
-
-[[package]]
-name = "rsa"
-version = "4.9"
-description = "Pure-Python RSA implementation"
-optional = true
-python-versions = ">=3.6,<4"
-files = [
-    {file = "rsa-4.9-py3-none-any.whl", hash = "sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7"},
-    {file = "rsa-4.9.tar.gz", hash = "sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21"},
-]
-
-[package.dependencies]
-pyasn1 = ">=0.1.3"
+jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
 name = "scikit-learn"
@@ -1914,6 +2271,7 @@ version = "1.3.2"
 description = "A set of python modules for machine learning and data mining"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "scikit-learn-1.3.2.tar.gz", hash = "sha256:a2f54c76accc15a34bfb9066e6c7a56c1e7235dda5762b990792330b52ccfb05"},
     {file = "scikit_learn-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e326c0eb5cf4d6ba40f93776a20e9a7a69524c4db0757e7ce24ba222471ee8a1"},
@@ -1961,6 +2319,7 @@ version = "1.10.1"
 description = "Fundamental algorithms for scientific computing in Python"
 optional = false
 python-versions = "<3.12,>=3.8"
+groups = ["main"]
 files = [
     {file = "scipy-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e7354fd7527a4b0377ce55f286805b34e8c54b91be865bac273f527e1b839019"},
     {file = "scipy-1.10.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:4b3f429188c66603a1a5c549fb414e4d3bdc2a24792e061ffbd607d3d75fd84e"},
@@ -1999,6 +2358,8 @@ version = "69.1.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "setuptools-69.1.1-py3-none-any.whl", hash = "sha256:02fa291a0471b3a18b2b2481ed902af520c69e8ae0919c13da936542754b4c56"},
     {file = "setuptools-69.1.1.tar.gz", hash = "sha256:5c0806c7d9af348e6dd3777b4f4dbb42c7ad85b190104837488eab9a7c945cf8"},
@@ -2006,7 +2367,7 @@ files = [
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
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 [[package]]
@@ -2015,6 +2376,7 @@ version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+groups = ["main"]
 files = [
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@@ -2026,6 +2388,7 @@ version = "1.12"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
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@@ -2040,6 +2403,8 @@ version = "0.9.0"
 description = "Pretty-print tabular data"
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
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@@ -2050,27 +2415,27 @@ widechars = ["wcwidth"]
 
 [[package]]
 name = "tensorboard"
-version = "2.12.3"
+version = "2.17.1"
 description = "TensorBoard lets you watch Tensors Flow"
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
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 [package.dependencies]
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 markdown = ">=2.6.8"
 numpy = ">=1.12.0"
-protobuf = ">=3.19.6"
-requests = ">=2.21.0,<3"
+packaging = "*"
+protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
 setuptools = ">=41.0.0"
+six = ">1.9"
 tensorboard-data-server = ">=0.7.0,<0.8.0"
 werkzeug = ">=1.0.1"
-wheel = ">=0.26"
 
 [[package]]
 name = "tensorboard-data-server"
@@ -2078,6 +2443,8 @@ version = "0.7.2"
 description = "Fast data loading for TensorBoard"
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
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@@ -2086,62 +2453,57 @@ files = [
 
 [[package]]
 name = "tensorflow"
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+version = "2.17.1"
 description = "TensorFlow is an open source machine learning framework for everyone."
 optional = true
-python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 absl-py = ">=1.0.0"
 astunparse = ">=1.6.0"
-flatbuffers = ">=2.0"
-gast = ">=0.2.1,<=0.4.0"
+flatbuffers = ">=24.3.25"
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 google-pasta = ">=0.1.1"
 grpcio = ">=1.24.3,<2.0"
-h5py = ">=2.9.0"
-jax = ">=0.3.15"
-keras = ">=2.12.0,<2.13"
+h5py = ">=3.10.0"
+keras = ">=3.2.0"
 libclang = ">=13.0.0"
-numpy = ">=1.22,<1.24"
+ml-dtypes = ">=0.3.1,<0.5.0"
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 opt-einsum = ">=2.3.2"
 packaging = "*"
 protobuf = ">=3.20.3,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
+requests = ">=2.21.0,<3"
 setuptools = "*"
 six = ">=1.12.0"
-tensorboard = ">=2.12,<2.13"
-tensorflow-estimator = ">=2.12.0,<2.13"
-tensorflow-io-gcs-filesystem = {version = ">=0.23.1", markers = "platform_machine != \"arm64\" or platform_system != \"Darwin\""}
+tensorboard = ">=2.17,<2.18"
+tensorflow-io-gcs-filesystem = {version = ">=0.23.1", markers = "python_version < \"3.12\""}
 termcolor = ">=1.1.0"
 typing-extensions = ">=3.6.6"
-wrapt = ">=1.11.0,<1.15"
+wrapt = ">=1.11.0"
 
-[[package]]
-name = "tensorflow-estimator"
-version = "2.12.0"
-description = "TensorFlow Estimator."
-optional = true
-python-versions = ">=3.7"
-files = [
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-]
+[package.extras]
+and-cuda = ["nvidia-cublas-cu12 (==12.3.4.1)", "nvidia-cuda-cupti-cu12 (==12.3.101)", "nvidia-cuda-nvcc-cu12 (==12.3.107)", "nvidia-cuda-nvrtc-cu12 (==12.3.107)", "nvidia-cuda-runtime-cu12 (==12.3.101)", "nvidia-cudnn-cu12 (==8.9.7.29)", "nvidia-cufft-cu12 (==11.0.12.1)", "nvidia-curand-cu12 (==10.3.4.107)", "nvidia-cusolver-cu12 (==11.5.4.101)", "nvidia-cusparse-cu12 (==12.2.0.103)", "nvidia-nccl-cu12 (==2.19.3)", "nvidia-nvjitlink-cu12 (==12.3.101)"]
 
 [[package]]
 name = "tensorflow-io-gcs-filesystem"
@@ -2149,6 +2511,8 @@ version = "0.36.0"
 description = "TensorFlow IO"
 optional = true
 python-versions = ">=3.7, <3.12"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "tensorflow_io_gcs_filesystem-0.36.0-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:702c6df62b38095ff613c433546d9424d4f33902a5ab26b00fd26457e27a99fa"},
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@@ -2178,6 +2542,8 @@ version = "0.8.0"
 description = "A suite of tools that users, both novice and advanced can use to optimize machine learning models for deployment and execution."
 optional = true
 python-versions = ">=3"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "tensorflow_model_optimization-0.8.0-py2.py3-none-any.whl", hash = "sha256:50303e6ed6d07c1a7801215f5cc854dad388ea8de319e46746ca35789e4ee7b3"},
 ]
@@ -2194,6 +2560,8 @@ version = "2.4.0"
 description = "ANSI color formatting for output in terminal"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "termcolor-2.4.0-py3-none-any.whl", hash = "sha256:9297c0df9c99445c2412e832e882a7884038a25617c60cea2ad69488d4040d63"},
     {file = "termcolor-2.4.0.tar.gz", hash = "sha256:aab9e56047c8ac41ed798fa36d892a37aca6b3e9159f3e0c24bc64a9b3ac7b7a"},
@@ -2208,6 +2576,7 @@ version = "3.3.0"
 description = "threadpoolctl"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "threadpoolctl-3.3.0-py3-none-any.whl", hash = "sha256:6155be1f4a39f31a18ea70f94a77e0ccd57dced08122ea61109e7da89883781e"},
     {file = "threadpoolctl-3.3.0.tar.gz", hash = "sha256:5dac632b4fa2d43f42130267929af3ba01399ef4bd1882918e92dbc30365d30c"},
@@ -2219,6 +2588,8 @@ version = "2.0.1"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version == \"3.10\""
 files = [
     {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
@@ -2230,6 +2601,7 @@ version = "2.2.1"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
+groups = ["main"]
 files = [
     {file = "torch-2.2.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:8d3bad336dd2c93c6bcb3268e8e9876185bda50ebde325ef211fb565c7d15273"},
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@@ -2288,6 +2660,7 @@ version = "4.66.2"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
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@@ -2308,6 +2681,8 @@ version = "2.2.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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@@ -2331,24 +2706,27 @@ version = "4.10.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
     {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
 ]
+markers = {dev = "python_version == \"3.10\""}
 
 [[package]]
 name = "urllib3"
 version = "2.2.1"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
-optional = true
+optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
     {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
 ]
 
 [package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
 h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
@@ -2359,6 +2737,8 @@ version = "3.0.1"
 description = "The comprehensive WSGI web application library."
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "werkzeug-3.0.1-py3-none-any.whl", hash = "sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10"},
     {file = "werkzeug-3.0.1.tar.gz", hash = "sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc"},
@@ -2376,6 +2756,8 @@ version = "0.42.0"
 description = "A built-package format for Python"
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "wheel-0.42.0-py3-none-any.whl", hash = "sha256:177f9c9b0d45c47873b619f5b650346d632cdc35fb5e4d25058e09c9e581433d"},
     {file = "wheel-0.42.0.tar.gz", hash = "sha256:c45be39f7882c9d34243236f2d63cbd58039e360f85d0913425fbd7ceea617a8"},
@@ -2390,6 +2772,8 @@ version = "1.14.1"
 description = "Module for decorators, wrappers and monkey patching."
 optional = true
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+groups = ["main"]
+markers = "extra == \"hls4ml\""
 files = [
     {file = "wrapt-1.14.1-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:1b376b3f4896e7930f1f772ac4b064ac12598d1c38d04907e696cc4d794b43d3"},
     {file = "wrapt-1.14.1-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:903500616422a40a98a5a3c4ff4ed9d0066f3b4c951fa286018ecdf0750194ef"},
@@ -2468,24 +2852,44 @@ files = [
 ]
 
 [[package]]
-name = "zipp"
-version = "3.17.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
+name = "zfpy"
+version = "1.0.1"
+description = "zfp compression in Python"
 optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "zfpy-1.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1417ec8595c2ccb467d228410f7570b1e804bf042ffe84664d7851a267e03239"},
+    {file = "zfpy-1.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:893aa8239a9cbae34f8277c6633c145f2dc11230f0f51a39c010a594a4581976"},
+    {file = "zfpy-1.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:910cac6c191f7bf9d100bd348486d2e53a35a2ba4aa7091020f7154b9f26454a"},
+    {file = "zfpy-1.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b15012ed906b89b43474d5b1400a955348c6bc9cc4c216c2049c16c249a9b51f"},
+    {file = "zfpy-1.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:a71c0f8c4bfba2433ad11332d6b8493ae661a1c01048292f5e539103a605ec2d"},
+    {file = "zfpy-1.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:13146293a5132627feb2c9f7adc6d95c528dd19dc3260f000e4b6284b2696b1f"},
+    {file = "zfpy-1.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:06d6d6a46fbd198c6fec7f31b111e11f5be3a254859f5ac3ab9488d05b240337"},
+    {file = "zfpy-1.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d204b17ee8c1eef6a2e650c123d627c0f64b3d8957df4c788dd2aaf2a72eb4ae"},
+    {file = "zfpy-1.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a0ea9d0bcf191ec345145592d2e7586413bcfd385034f85d00238e1eaa857185"},
+    {file = "zfpy-1.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:78ff5cc656da9414e264bbdfb316a0c30bf930a426f9c2a1f1a5131620135439"},
+    {file = "zfpy-1.0.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:bb83fa31c943475c4ed8182c0bd1db518beca319809612c9851d78545e7d31c9"},
+    {file = "zfpy-1.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d401da303fe926b18df1e93483a2099762d61db87597ccdaf89e25fb71b107cc"},
+    {file = "zfpy-1.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b5e7c1f0ebdecc6850e65004e7fbba8d5e78bf4b626654e463c3e0aa5b3fe54"},
+    {file = "zfpy-1.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58180c0917656ab0eb2dadad7e033e10271a3c615dc8972657137dba16e2bd42"},
+    {file = "zfpy-1.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:f15c91425cebca0ab5c88b3b2ba0eb800ec5310e3ea2a6b5f3e4b198a97288d9"},
+    {file = "zfpy-1.0.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:791fa42b651a26be67e52043b619e3a039862900e7080711d07a5de956158b8c"},
+    {file = "zfpy-1.0.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f811480ebff2e028c50bf3491d1d2e3869eb4388883543d0698f5fdc917e4386"},
+    {file = "zfpy-1.0.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d4892bc441ce59de98c4b3b29f04e44657aa6ccfe3ae5ca23660efb010b829d6"},
+    {file = "zfpy-1.0.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c0a64590ef3c182988f4ac5745ec6e1623b06c348583cbdb96d296e4899f4469"},
+    {file = "zfpy-1.0.1-cp313-cp313-win_amd64.whl", hash = "sha256:f9a71cfce2fb7a82497e90e7152f847250664f30ca4dfedcca500c35d4c63e44"},
+    {file = "zfpy-1.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:46ec529b729b553604a3509cd0d6d0c28169f0be0cd7744f69f16324912d463b"},
+    {file = "zfpy-1.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9b717a14b3ae18fc0130d440706dc08cde9c8149423bb0df9f478f52a07a5ed6"},
+    {file = "zfpy-1.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cf82232d4f6e0092f54a7bf2ec52ae70024f0a2839ce54b77fb2a29fb417791e"},
+    {file = "zfpy-1.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9c86a576d65557f83e2c758664ee96710c06352f8b79dca5867199e94ccfb2ce"},
+    {file = "zfpy-1.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:2f0dea9281b6ce1623eb45316f000a0fdaad01459a01bc50dd866a5fff37ec7b"},
 ]
 
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
-
 [extras]
 hls4ml = ["hls4ml", "tensorflow"]
 
 [metadata]
-lock-version = "2.0"
-python-versions = ">=3.8, <3.11.10"
-content-hash = "0b0cf4871b8e171e15c6001e9f68266ef3585ecf750d1aee374b68f94ee3cf72"
+lock-version = "2.1"
+python-versions = ">=3.10, <3.11.10"
+content-hash = "7b49942de265e19e1910d4899e9a00214861b4eb050fc59016986068e393946c"
diff --git a/pyproject.toml b/pyproject.toml
index 193d2759..b9c1f0eb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,14 +9,16 @@ readme = "README.md"
 baler = "baler.baler:main"
 
 [tool.poetry.dependencies]
-python = ">=3.8, <3.11.10"
+python = ">=3.10, <3.11.10"
 torch = ">=2.0.0, !=2.0.1"
 tqdm = "^4.64.1"
 matplotlib = "^3.6.2"
 scikit-learn = "^1.2.0"
 hls4ml = { version = "^0.7.1", optional = true }
 tensorflow = { version = "^2.12.0", optional = true }
-numpy = "1.23.5"
+numpy = "^1.26"
+zfpy = "^1.0.1"
+blosc2 = "^3.5.1"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.2.1"
diff --git a/requirements.txt b/requirements.txt
index 86f2bb21..171a4222 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,436 +1,794 @@
-cmake==3.27.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:073e4f196d0888216e6794c08cd984ddabc108c0e4e66f48fbd7610d1e6d726d \
-    --hash=sha256:199bfaefb752e82d8067aeee5d6a6e0414fe0d60e9a3fd08e95d537a97e0db16 \
-    --hash=sha256:1b3189171665f5c8d748ae7fe10a29fff1ebeedeaef57b16f1ea54b1ec0fe514 \
-    --hash=sha256:1f38d87b2c65763a0113f4a6c652e6f4b5adf90b384c1e1d69e4f8a3104a57d6 \
-    --hash=sha256:35a8d397ce883e93b5e6561e2803ce9470df52283862264093c1078530f98189 \
-    --hash=sha256:48be3afe62c9513a49be007896a4058fafec512cb1f269a50126da30aacad97f \
-    --hash=sha256:5561aca62b65aac844f3931e74cfeb696e4534de145e3307bf942e735736541e \
-    --hash=sha256:58a3f39d3d1bc897f05e531bfa676246a2b25d424c6a47e4b6bbc193fb560db7 \
-    --hash=sha256:6f46a170b0c9c552d52da4346534570f818195dfc4f1d0c03264e24cc348fc60 \
-    --hash=sha256:8745eff805f36762d3e8e904698b853cb4a9da8b4b07d1c12bcd1e1a6c4a1709 \
-    --hash=sha256:9740ed9f61a3bd8708a41cadd5c057c04f38e5b89bd773e369df2e210a1c55a3 \
-    --hash=sha256:9ccab4cd93578d3c2df32e66b44b313b75a7484032645040431dc06a583ca4aa \
-    --hash=sha256:b470ccd3f86cf19a63f6b221c9cceebcc58e32d3787d0d5f9f43d1d91a095090 \
-    --hash=sha256:b9d5811954dcedcaa6c915c4a9bb6d64b55ac189e9cbc74be726307d9d084804 \
-    --hash=sha256:c4c968c188e7518deb463a14e64f3a19f242c9dcf7f24e1dbcc1419690cd54e0 \
-    --hash=sha256:d03f0a76a2b96805044ad1178b92aeeb5f695caa6776a32522bb5c430a55b4e8 \
-    --hash=sha256:e58e48643903e6fad76274337f9a4d3c575b8e21cd05c6214780b2c98bb0c706
-colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.11" and platform_system == "Windows" \
+blosc2==3.5.1 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:0e6f5ccfb817363191577dd0e40839788f664891908b82c2dcd969fb37e0e954 \
+    --hash=sha256:0fc7976024a89d490911ce9ef78968369acca908f0f771cf00e072a457ee342d \
+    --hash=sha256:0fcc6c64315121f02555a398e6fbaf4b71edc39a4ca7567e089ce1af7288e9ed \
+    --hash=sha256:1a9fc80970d5bb135bd9579db26d96ec79739bd1f187bbaf52e04102e7c664de \
+    --hash=sha256:44b4e51fd0b5a7e6ccae8053e133037f5984f6990a1e9b787376f4ec42aaf5bf \
+    --hash=sha256:485ca015db7352fa0e3b78b912c66ec495d60b94cab36a82abfb9f87715e6abd \
+    --hash=sha256:530965c444186bfb92a4406ab4c2d801939df35a5c58658131000f68ce1a37ec \
+    --hash=sha256:5d72f7a9a8b3b523c588be9d66e9e7f2463483716c4c01e5056c1f7e37167f85 \
+    --hash=sha256:5df464652ddcae9ef8d8b3354ad224b1e147187dde5b6470e0e9f95bec328756 \
+    --hash=sha256:6e3d1d0885e955e184efae81168448106644d5a0a60c0911b98e94fe9d756fa9 \
+    --hash=sha256:780213c917d9ac28b52a09ac82baa761262e8421688937b67f6516987a96fc58 \
+    --hash=sha256:9d36c4a8489c0f8719040a7543918e2701821340b8643d508c450d0d012259bf \
+    --hash=sha256:a4dcaf581cd7ec91d91f379e7e3dc7d5dd6c300e88ae162b04b106d21f10f5c1 \
+    --hash=sha256:b59805aa56ff1f9e6e4ba2f293e54cdf356b64db983bb019dbdbac72f88f2e38 \
+    --hash=sha256:d3174273decf69b9e61b2a57d4cad5bf651de04d22526568f5a5e3036631831f \
+    --hash=sha256:d77e0abe67675ec21ce812a4da3d01406dd4942c8929a5a78d91b2f558dd8c9e \
+    --hash=sha256:d7c23e48414283dcfa5676abc6a4be9fb92a7b372a20f7acf90438abc69a746e \
+    --hash=sha256:ebf860878dd7a4fb41dcb90b1c6b3c5db7e34dc64ab51bea3679534af4a54a4a \
+    --hash=sha256:f88cfa1f1d64d77278c7fae0ab8c42291b7b2e8f36fcee154a0c55388e697fe9 \
+    --hash=sha256:fcf6658546da153fc8537dbf8edab1b22d6cedba32ec8c2a9a4165e450375b28 \
+    --hash=sha256:ff031df2707cd7143b7c744bf10c55932151001cc5ff9d79a899714714732a2d
+certifi==2024.2.2 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f \
+    --hash=sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1
+charset-normalizer==3.3.2 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027 \
+    --hash=sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087 \
+    --hash=sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786 \
+    --hash=sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8 \
+    --hash=sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09 \
+    --hash=sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185 \
+    --hash=sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574 \
+    --hash=sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e \
+    --hash=sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519 \
+    --hash=sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898 \
+    --hash=sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269 \
+    --hash=sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3 \
+    --hash=sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f \
+    --hash=sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6 \
+    --hash=sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8 \
+    --hash=sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a \
+    --hash=sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73 \
+    --hash=sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc \
+    --hash=sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714 \
+    --hash=sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2 \
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+    --hash=sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d \
+    --hash=sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e \
+    --hash=sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6 \
+    --hash=sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269 \
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+    --hash=sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d \
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+markupsafe==2.1.5 ; python_version >= "3.10" and python_full_version < "3.11.10" \
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+    --hash=sha256:ffee1f21e5ef0d712f9033568f8344d5da8cc2869dbd08d87c84656e6a2d2f68
+matplotlib==3.7.5 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:039ad54683a814002ff37bf7981aa1faa40b91f4ff84149beb53d1eb64617980 \
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+    --hash=sha256:e530ab6a0afd082d2e9c17eb1eb064a63c5b09bb607b2b74fa41adbe3e162286 \
+    --hash=sha256:ec0e1adc0ad70ba8227e957551e25a9d2995e319c29f94a97575bb90fa1d4469 \
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+    --hash=sha256:f098ffbaab9df1e3ef04e5a5586a1e6b1791380698e84938d8640961c79b1fc0 \
+    --hash=sha256:f0ad550da9f160737d7890217c5eeed4337d07e83ca1b2ca6535078f354e7675 \
+    --hash=sha256:f0b60993ed3488b4532ec6b697059897891927cbfc2b8d458a891b60ec03d9d7 \
+    --hash=sha256:f65342c147572673f02a4abec2d5a23ad9c3898167df9b47c149f32ce61ca078 \
+    --hash=sha256:fa7ebc995a7d747dacf0a717d0eb3aa0f0c6a0e9ea88b0194d3a3cd241a1500f \
+    --hash=sha256:fbea1e762b28400393d71be1a02144aa16692a3c4c676ba0178ce83fc2928fdd \
+    --hash=sha256:fbf730fca3e1f23713bc1fae0a57db386e39dc81ea57dc305c67f628c1d7a342 \
+    --hash=sha256:fd4028d570fa4b31b7b165d4a685942ae9cdc669f33741e388c01857d9723eab \
+    --hash=sha256:fe184b4625b4052fa88ef350b815559dd90cc6cc8e97b62f966e1ca84074aafa
+mpmath==1.3.0 ; python_version >= "3.10" and python_full_version < "3.11.10" \
     --hash=sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f \
     --hash=sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c
-networkx==3.1 ; python_version >= "3.8" and python_version < "3.11" \
+msgpack==1.1.1 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:196a736f0526a03653d829d7d4c5500a97eea3648aebfd4b6743875f28aa2af8 \
+    --hash=sha256:1abfc6e949b352dadf4bce0eb78023212ec5ac42f6abfd469ce91d783c149c2a \
+    --hash=sha256:1b13fe0fb4aac1aa5320cd693b297fe6fdef0e7bea5518cbc2dd5299f873ae90 \
+    --hash=sha256:1d75f3807a9900a7d575d8d6674a3a47e9f227e8716256f35bc6f03fc597ffbf \
+    --hash=sha256:2fbbc0b906a24038c9958a1ba7ae0918ad35b06cb449d398b76a7d08470b0ed9 \
+    --hash=sha256:33be9ab121df9b6b461ff91baac6f2731f83d9b27ed948c5b9d1978ae28bf157 \
+    --hash=sha256:353b6fc0c36fde68b661a12949d7d49f8f51ff5fa019c1e47c87c4ff34b080ed \
+    --hash=sha256:36043272c6aede309d29d56851f8841ba907a1a3d04435e43e8a19928e243c1d \
+    --hash=sha256:3765afa6bd4832fc11c3749be4ba4b69a0e8d7b728f78e68120a157a4c5d41f0 \
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+networkx==3.1 ; python_version >= "3.10" and python_full_version < "3.11.10" \
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-    --hash=sha256:f9a909a8bae284d46bbfdefbdd4a262ba19d3bc9921b1e76126b1d21c3c34135
-nvidia-cublas-cu11==11.10.3.66 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:8ac17ba6ade3ed56ab898a036f9ae0756f1e81052a317bf98f8c6d18dc3ae49e \
-    --hash=sha256:d32e4d75f94ddfb93ea0a5dda08389bcc65d8916a25cb9f37ac89edaeed3bded
-nvidia-cuda-cupti-cu11==11.7.101 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:7cc5b8f91ae5e1389c3c0ad8866b3b016a175e827ea8f162a672990a402ab2b0 \
-    --hash=sha256:e0cfd9854e1f2edaa36ca20d21cd0bdd5dcfca4e3b9e130a082e05b33b6c5895
-nvidia-cuda-nvrtc-cu11==11.7.99 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:9f1562822ea264b7e34ed5930567e89242d266448e936b85bc97a3370feabb03 \
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-    --hash=sha256:f7d9610d9b7c331fa0da2d1b2858a4a8315e6d49765091d28711c8946e7425e7
-nvidia-cuda-runtime-cu11==11.7.99 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:bc77fa59a7679310df9d5c70ab13c4e34c64ae2124dd1efd7e5474b71be125c7 \
-    --hash=sha256:cc768314ae58d2641f07eac350f40f99dcb35719c4faff4bc458a7cd2b119e31
-nvidia-cudnn-cu11==8.5.0.96 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:402f40adfc6f418f9dae9ab402e773cfed9beae52333f6d86ae3107a1b9527e7 \
-    --hash=sha256:71f8111eb830879ff2836db3cccf03bbd735df9b0d17cd93761732ac50a8a108
-nvidia-cufft-cu11==10.9.0.58 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:222f9da70c80384632fd6035e4c3f16762d64ea7a843829cb278f98b3cb7dd81 \
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+    --hash=sha256:eae430ecf5794cb7ae7fa3808740b015aa80747e5266153128ef055975a72b99 \
+    --hash=sha256:ee84ca3c58fe48b8ddafdeb1db87388dce2c3c3f701bf447b05e4cfcc3679112 \
+    --hash=sha256:f042f66d0b4ae6d48e70e28d487376204d3cbf43b84c03bac57e28dac6151581 \
+    --hash=sha256:f8db2f125746e44dce707dd44d4f4efeea8d7e2b43aace3f8d1f235cfa2733dd \
+    --hash=sha256:f93fc78fe8bf15afe2b8d6b6499f1c73953169fad1e9a8dd086cdff3190e7fdf
+nvidia-cublas-cu12==12.1.3.1 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906 \
+    --hash=sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728
+nvidia-cuda-cupti-cu12==12.1.105 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4 \
+    --hash=sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e
+nvidia-cuda-nvrtc-cu12==12.1.105 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed \
+    --hash=sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2
+nvidia-cuda-runtime-cu12==12.1.105 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40 \
+    --hash=sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344
+nvidia-cudnn-cu12==8.9.2.26 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9
+nvidia-cufft-cu12==11.0.2.54 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56 \
+    --hash=sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253
+nvidia-curand-cu12==10.3.2.106 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a \
+    --hash=sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0
+nvidia-cusolver-cu12==11.4.5.107 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5 \
+    --hash=sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd
+nvidia-cusparse-cu12==12.1.0.106 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a \
+    --hash=sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c
+nvidia-nccl-cu12==2.19.3 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:a9734707a2c96443331c1e48c717024aa6678a0e2a4cb66b2c364d18cee6b48d
+nvidia-nvjitlink-cu12==12.4.99 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:75d6498c96d9adb9435f2bbdbddb479805ddfb97b5c1b32395c694185c20ca57 \
+    --hash=sha256:991905ffa2144cb603d8ca7962d75c35334ae82bf92820b6ba78157277da1ad2 \
+    --hash=sha256:c6428836d20fe7e327191c175791d38570e10762edc588fb46749217cd444c74
+nvidia-nvtx-cu12==12.1.105 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82 \
+    --hash=sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5
+packaging==23.2 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5 \
+    --hash=sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7
+pillow==10.2.0 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:0304004f8067386b477d20a518b50f3fa658a28d44e4116970abfcd94fac34a8 \
+    --hash=sha256:0689b5a8c5288bc0504d9fcee48f61a6a586b9b98514d7d29b840143d6734f39 \
+    --hash=sha256:0eae2073305f451d8ecacb5474997c08569fb4eb4ac231ffa4ad7d342fdc25ac \
+    --hash=sha256:0fb3e7fc88a14eacd303e90481ad983fd5b69c761e9e6ef94c983f91025da869 \
+    --hash=sha256:11fa2e5984b949b0dd6d7a94d967743d87c577ff0b83392f17cb3990d0d2fd6e \
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+    --hash=sha256:154e939c5f0053a383de4fd3d3da48d9427a7e985f58af8e94d0b3c9fcfcf4f9 \
+    --hash=sha256:15587643b9e5eb26c48e49a7b33659790d28f190fc514a322d55da2fb5c2950e \
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+    --hash=sha256:babf5acfede515f176833ed6028754cbcd0d206f7f614ea3447d67c33be12516 \
+    --hash=sha256:c365fd1703040de1ec284b176d6af5abe21b427cb3a5ff68e0759e1e313a5e7e \
+    --hash=sha256:c4225f5220f46b2fde568c74fca27ae9771536c2e29d7c04f4fb62c83275ac4e \
+    --hash=sha256:c570f24be1e468e3f0ce7ef56a89a60f0e05b30a3669a459e419c6eac2c35364 \
+    --hash=sha256:c6dafac9e0f2b3c78df97e79af707cdc5ef8e88208d686a4847bab8266870023 \
+    --hash=sha256:c8de2789052ed501dd829e9cae8d3dcce7acb4777ea4a479c14521c942d395b1 \
+    --hash=sha256:cb28c753fd5eb3dd859b4ee95de66cc62af91bcff5db5f2571d32a520baf1f04 \
+    --hash=sha256:cb4c38abeef13c61d6916f264d4845fab99d7b711be96c326b84df9e3e0ff62d \
+    --hash=sha256:d1b35bcd6c5543b9cb547dee3150c93008f8dd0f1fef78fc0cd2b141c5baf58a \
+    --hash=sha256:d8e6aeb9201e655354b3ad049cb77d19813ad4ece0df1249d3c793de3774f8c7 \
+    --hash=sha256:d8ecd059fdaf60c1963c58ceb8997b32e9dc1b911f5da5307aab614f1ce5c2fb \
+    --hash=sha256:da2b52b37dad6d9ec64e653637a096905b258d2fc2b984c41ae7d08b938a67e4 \
+    --hash=sha256:e87f0b2c78157e12d7686b27d63c070fd65d994e8ddae6f328e0dcf4a0cd007e \
+    --hash=sha256:edca80cbfb2b68d7b56930b84a0e45ae1694aeba0541f798e908a49d66b837f1 \
+    --hash=sha256:f379abd2f1e3dddb2b61bc67977a6b5a0a3f7485538bcc6f39ec76163891ee48 \
+    --hash=sha256:fe4c15f6c9285dc54ce6553a3ce908ed37c8f3825b5a51a15c91442bb955b868
+platformdirs==4.2.0 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068 \
+    --hash=sha256:ef0cc731df711022c174543cb70a9b5bd22e5a9337c8624ef2c2ceb8ddad8768
+py-cpuinfo==9.0.0 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_machine != "wasm32" \
+    --hash=sha256:3cdbbf3fac90dc6f118bfd64384f309edeadd902d7c8fb17f02ffa1fc3f49690 \
+    --hash=sha256:859625bc251f64e21f077d099d4162689c762b5d6a4c3c97553d56241c9674d5
+pyparsing==3.1.2 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad \
+    --hash=sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742
+python-dateutil==2.9.0.post0 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \
+    --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
+requests==2.31.0 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f \
+    --hash=sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1
+scikit-learn==1.3.2 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:0402638c9a7c219ee52c94cbebc8fcb5eb9fe9c773717965c1f4185588ad3107 \
+    --hash=sha256:0ee107923a623b9f517754ea2f69ea3b62fc898a3641766cb7deb2f2ce450161 \
+    --hash=sha256:1215e5e58e9880b554b01187b8c9390bf4dc4692eedeaf542d3273f4785e342c \
+    --hash=sha256:15e1e94cc23d04d39da797ee34236ce2375ddea158b10bee3c343647d615581d \
+    --hash=sha256:18424efee518a1cde7b0b53a422cde2f6625197de6af36da0b57ec502f126157 \
+    --hash=sha256:1d08ada33e955c54355d909b9c06a4789a729977f165b8bae6f225ff0a60ec4a \
+    --hash=sha256:3271552a5eb16f208a6f7f617b8cc6d1f137b52c8a1ef8edf547db0259b2c9fb \
+    --hash=sha256:35a22e8015048c628ad099da9df5ab3004cdbf81edc75b396fd0cff8699ac58c \
+    --hash=sha256:535805c2a01ccb40ca4ab7d081d771aea67e535153e35a1fd99418fcedd1648a \
+    --hash=sha256:5b2de18d86f630d68fe1f87af690d451388bb186480afc719e5f770590c2ef6c \
+    --hash=sha256:61a6efd384258789aa89415a410dcdb39a50e19d3d8410bd29be365bcdd512d5 \
+    --hash=sha256:64381066f8aa63c2710e6b56edc9f0894cc7bf59bd71b8ce5613a4559b6145e0 \
+    --hash=sha256:67f37d708f042a9b8d59551cf94d30431e01374e00dc2645fa186059c6c5d78b \
+    --hash=sha256:6c43290337f7a4b969d207e620658372ba3c1ffb611f8bc2b6f031dc5c6d1d03 \
+    --hash=sha256:6fb6bc98f234fda43163ddbe36df8bcde1d13ee176c6dc9b92bb7d3fc842eb66 \
+    --hash=sha256:763f0ae4b79b0ff9cca0bf3716bcc9915bdacff3cebea15ec79652d1cc4fa5c9 \
+    --hash=sha256:785a2213086b7b1abf037aeadbbd6d67159feb3e30263434139c98425e3dcfcf \
+    --hash=sha256:8db94cd8a2e038b37a80a04df8783e09caac77cbe052146432e67800e430c028 \
+    --hash=sha256:a19f90f95ba93c1a7f7924906d0576a84da7f3b2282ac3bfb7a08a32801add93 \
+    --hash=sha256:a2f54c76accc15a34bfb9066e6c7a56c1e7235dda5762b990792330b52ccfb05 \
+    --hash=sha256:b8692e395a03a60cd927125eef3a8e3424d86dde9b2370d544f0ea35f78a8073 \
+    --hash=sha256:cb06f8dce3f5ddc5dee1715a9b9f19f20d295bed8e3cd4fa51e1d050347de525 \
+    --hash=sha256:dc9002fc200bed597d5d34e90c752b74df516d592db162f756cc52836b38fe0e \
+    --hash=sha256:e326c0eb5cf4d6ba40f93776a20e9a7a69524c4db0757e7ce24ba222471ee8a1 \
+    --hash=sha256:ed932ea780517b00dae7431e031faae6b49b20eb6950918eb83bd043237950e0 \
+    --hash=sha256:fc4144a5004a676d5022b798d9e573b05139e77f271253a4703eed295bde0433
+scipy==1.10.1 ; python_version >= "3.10" and python_full_version < "3.11.10" \
     --hash=sha256:049a8bbf0ad95277ffba9b3b7d23e5369cc39e66406d60422c8cfef40ccc8415 \
     --hash=sha256:07c3457ce0b3ad5124f98a86533106b643dd811dd61b548e78cf4c8786652f6f \
     --hash=sha256:0f1564ea217e82c1bbe75ddf7285ba0709ecd503f048cb1236ae9995f64217bd \
@@ -452,71 +810,80 @@ scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.11" \
     --hash=sha256:d925fa1c81b772882aa55bcc10bf88324dadb66ff85d548c71515f6689c6dac5 \
     --hash=sha256:e7354fd7527a4b0377ce55f286805b34e8c54b91be865bac273f527e1b839019 \
     --hash=sha256:fae8a7b898c42dffe3f7361c40d5952b6bf32d10c4569098d276b4c547905ee1
-setuptools==68.0.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:11e52c67415a381d10d6b462ced9cfb97066179f0e871399e006c4ab101fc85f \
-    --hash=sha256:baf1fdb41c6da4cd2eae722e135500da913332ab3f2f5c7d33af9b492acb5235
-six==1.16.0 ; python_version >= "3.8" and python_version < "3.11" \
+six==1.16.0 ; python_version >= "3.10" and python_full_version < "3.11.10" \
     --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
     --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
-sympy==1.12 ; python_version >= "3.8" and python_version < "3.11" \
+sympy==1.12 ; python_version >= "3.10" and python_full_version < "3.11.10" \
     --hash=sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5 \
     --hash=sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8
-threadpoolctl==3.2.0 ; python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032 \
-    --hash=sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355
-torch==2.0.0 ; python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:01858620f25f25e7a9ec4b547ff38e5e27c92d38ec4ccba9cfbfb31d7071ed9c \
-    --hash=sha256:09651bff72e439d004c991f15add0c397c66f98ab36fe60d5514b44e4da722e8 \
-    --hash=sha256:11b0384fe3c18c01b8fc5992e70fc519cde65e44c51cc87be1838c1803daf42f \
-    --hash=sha256:2802f84f021907deee7e9470ed10c0e78af7457ac9a08a6cd7d55adef835fede \
-    --hash=sha256:297a4919aff1c0f98a58ebe969200f71350a1d4d4f986dbfd60c02ffce780e99 \
-    --hash=sha256:527f4ae68df7b8301ee6b1158ca56350282ea633686537b30dbb5d7b4a52622a \
-    --hash=sha256:53e1c33c6896583cdb9a583693e22e99266444c4a43392dddc562640d39e542b \
-    --hash=sha256:6befaad784004b7af357e3d87fa0863c1f642866291f12a4c2af2de435e8ac5c \
-    --hash=sha256:6e0b97beb037a165669c312591f242382e9109a240e20054d5a5782d9236cad0 \
-    --hash=sha256:7a9319a67294ef02459a19738bbfa8727bb5307b822dadd708bc2ccf6c901aca \
-    --hash=sha256:8969aa8375bcbc0c2993e7ede0a7f889df9515f18b9b548433f412affed478d9 \
-    --hash=sha256:9a2e53b5783ef5896a6af338b36d782f28e83c8ddfc2ac44b67b066d9d76f498 \
-    --hash=sha256:9f01fe1f6263f31bd04e1757946fd63ad531ae37f28bb2dbf66f5c826ee089f4 \
-    --hash=sha256:a83b26bd6ae36fbf5fee3d56973d9816e2002e8a3b7d9205531167c28aaa38a7 \
-    --hash=sha256:ab2da16567cb55b67ae39e32d520d68ec736191d88ac79526ca5874754c32203 \
-    --hash=sha256:bd42db2a48a20574d2c33489e120e9f32789c4dc13c514b0c44272972d14a2d7 \
-    --hash=sha256:c7e67195e1c3e33da53954b026e89a8e1ff3bc1aeb9eb32b677172d4a9b5dcbf \
-    --hash=sha256:c9090bda7d2eeeecd74f51b721420dbeb44f838d4536cc1b284e879417e3064a \
-    --hash=sha256:cc788cbbbbc6eb4c90e52c550efd067586c2693092cf367c135b34893a64ae78 \
-    --hash=sha256:ce9b5a49bd513dff7950a5a07d6e26594dd51989cee05ba388b03e8e366fd5d5 \
-    --hash=sha256:d292640f0fd72b7a31b2a6e3b635eb5065fcbedd4478f9cad1a1e7a9ec861d35 \
-    --hash=sha256:d439aec349c98f12819e8564b8c54008e4613dd4428582af0e6e14c24ca85870 \
-    --hash=sha256:e54846aa63855298cfb1195487f032e413e7ac9cbfa978fda32354cc39551475 \
-    --hash=sha256:ec5fff2447663e369682838ff0f82187b4d846057ef4d119a8dea7772a0b17dd
-tqdm==4.65.0 ; python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:1871fb68a86b8fb3b59ca4cdd3dcccbc7e6d613eeed31f4c332531977b89beb5 \
-    --hash=sha256:c4f53a17fe37e132815abceec022631be8ffe1b9381c2e6e30aa70edc99e9671
-triton==2.0.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:0117722f8c2b579cd429e0bee80f7731ae05f63fe8e9414acd9a679885fcbf42 \
-    --hash=sha256:1aca3303629cd3136375b82cb9921727f804e47ebee27b2677fef23005c3851a \
-    --hash=sha256:226941c7b8595219ddef59a1fdb821e8c744289a132415ddd584facedeb475b1 \
-    --hash=sha256:38806ee9663f4b0f7cd64790e96c579374089e58f49aac4a6608121aa55e2505 \
-    --hash=sha256:42a0d2c3fc2eab4ba71384f2e785fbfd47aa41ae05fa58bf12cb31dcbd0aeceb \
-    --hash=sha256:47b4d70dc92fb40af553b4460492c31dc7d3a114a979ffb7a5cdedb7eb546c08 \
-    --hash=sha256:4c9fc8c89874bc48eb7e7b2107a9b8d2c0bf139778637be5bfccb09191685cfd \
-    --hash=sha256:52c47b72c72693198163ece9d90a721299e4fb3b8e24fd13141e384ad952724f \
-    --hash=sha256:74f118c12b437fb2ca25e1a04759173b517582fcf4c7be11913316c764213656 \
-    --hash=sha256:75834f27926eab6c7f00ce73aaf1ab5bfb9bec6eb57ab7c0bfc0a23fac803b4c \
-    --hash=sha256:8f05a7e64e4ca0565535e3d5d3405d7e49f9d308505bb7773d21fb26a4c008c2 \
-    --hash=sha256:9618815a8da1d9157514f08f855d9e9ff92e329cd81c0305003eb9ec25cc5add \
-    --hash=sha256:9d4978298b74fcf59a75fe71e535c092b023088933b2f1df933ec32615e4beef \
-    --hash=sha256:bb4b99ca3c6844066e516658541d876c28a5f6e3a852286bbc97ad57134827fd \
-    --hash=sha256:bcd9be5d0c2e45d2b7e6ddc6da20112b6862d69741576f9c3dbaf941d745ecae \
-    --hash=sha256:d2684b6a60b9f174f447f36f933e9a45f31db96cb723723ecd2dcfd1c57b778b \
-    --hash=sha256:e3e13aa8b527c9b642e3a9defcc0fbd8ffbe1c80d8ac8c15a01692478dc64d8a \
-    --hash=sha256:fedce6a381901b1547e0e7e1f2546e4f65dca6d91e2d8a7305a2d1f5551895be
-typing-extensions==4.5.0 ; python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:5cb5f4a79139d699607b3ef622a1dedafa84e115ab0024e0d9c044a9479ca7cb \
-    --hash=sha256:fb33085c39dd998ac16d1431ebc293a8b3eedd00fd4a32de0ff79002c19511b4
-wheel==0.41.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.11" \
-    --hash=sha256:55a0f0a5a84869bce5ba775abfd9c462e3a6b1b7b7ec69d72c0b83d673a5114d \
-    --hash=sha256:7e9be3bbd0078f6147d82ed9ed957e323e7708f57e134743d2edef3a7b7972a9
-zipp==3.16.2 ; python_version >= "3.8" and python_version < "3.10" \
-    --hash=sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0 \
-    --hash=sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147
+threadpoolctl==3.3.0 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:5dac632b4fa2d43f42130267929af3ba01399ef4bd1882918e92dbc30365d30c \
+    --hash=sha256:6155be1f4a39f31a18ea70f94a77e0ccd57dced08122ea61109e7da89883781e
+torch==2.2.1 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:0952549bcb43448c8d860d5e3e947dd18cbab491b14638e21750cb3090d5ad3e \
+    --hash=sha256:0e8bdd4c77ac2584f33ee14c6cd3b12767b4da508ec4eed109520be7212d1069 \
+    --hash=sha256:26bd2272ec46fc62dcf7d24b2fb284d44fcb7be9d529ebf336b9860350d674ed \
+    --hash=sha256:2d9e7e5ecbb002257cf98fae13003abbd620196c35f85c9e34c2adfb961321ec \
+    --hash=sha256:46085e328d9b738c261f470231e987930f4cc9472d9ffb7087c7a1343826ac51 \
+    --hash=sha256:5297f13370fdaca05959134b26a06a7f232ae254bf2e11a50eddec62525c9006 \
+    --hash=sha256:5c0c83aa7d94569997f1f474595e808072d80b04d34912ce6f1a0e1c24b0c12a \
+    --hash=sha256:5f5dee8433798888ca1415055f5e3faf28a3bad660e4c29e1014acd3275ab11a \
+    --hash=sha256:6a21bcd7076677c97ca7db7506d683e4e9db137e8420eb4a68fb67c3668232a7 \
+    --hash=sha256:6ab3ea2e29d1aac962e905142bbe50943758f55292f1b4fdfb6f4792aae3323e \
+    --hash=sha256:77e990af75fb1675490deb374d36e726f84732cd5677d16f19124934b2409ce9 \
+    --hash=sha256:79848f46196750367dcdf1d2132b722180b9d889571e14d579ae82d2f50596c5 \
+    --hash=sha256:7ee804847be6be0032fbd2d1e6742fea2814c92bebccb177f0d3b8e92b2d2b18 \
+    --hash=sha256:84b2fb322ab091039fdfe74e17442ff046b258eb5e513a28093152c5b07325a7 \
+    --hash=sha256:8d3bad336dd2c93c6bcb3268e8e9876185bda50ebde325ef211fb565c7d15273 \
+    --hash=sha256:8f93ddf3001ecec16568390b507652644a3a103baa72de3ad3b9c530e3277098 \
+    --hash=sha256:91a1b598055ba06b2c386415d2e7f6ac818545e94c5def597a74754940188513 \
+    --hash=sha256:ada53aebede1c89570e56861b08d12ba4518a1f8b82d467c32665ec4d1f4b3c8 \
+    --hash=sha256:b6d78338acabf1fb2e88bf4559d837d30230cf9c3e4337261f4d83200df1fcbe \
+    --hash=sha256:be21d4c41ecebed9e99430dac87de1439a8c7882faf23bba7fea3fea7b906ac1 \
+    --hash=sha256:c47bc25744c743f3835831a20efdcfd60aeb7c3f9804a213f61e45803d16c2a5 \
+    --hash=sha256:d6227060f268894f92c61af0a44c0d8212e19cb98d05c20141c73312d923bc0a \
+    --hash=sha256:d86664ec85902967d902e78272e97d1aff1d331f7619d398d3ffab1c9b8e9157 \
+    --hash=sha256:ed9e29eb94cd493b36bca9cb0b1fd7f06a0688215ad1e4b3ab4931726e0ec092 \
+    --hash=sha256:f1b90ac61f862634039265cd0f746cc9879feee03ff962c803486301b778714b
+tqdm==4.66.2 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9 \
+    --hash=sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531
+triton==2.2.0 ; python_version >= "3.10" and python_full_version < "3.11.10" and platform_system == "Linux" and platform_machine == "x86_64" \
+    --hash=sha256:0af58716e721460a61886668b205963dc4d1e4ac20508cc3f623aef0d70283d5 \
+    --hash=sha256:227cc6f357c5efcb357f3867ac2a8e7ecea2298cd4606a8ba1e931d1d5a947df \
+    --hash=sha256:a2294514340cfe4e8f4f9e5c66c702744c4a117d25e618bd08469d0bfed1e2e5 \
+    --hash=sha256:b8ce26093e539d727e7cf6f6f0d932b1ab0574dc02567e684377630d86723ace \
+    --hash=sha256:da58a152bddb62cafa9a857dd2bc1f886dbf9f9c90a2b5da82157cd2b34392b0 \
+    --hash=sha256:e8fe46d3ab94a8103e291bd44c741cc294b91d1d81c1a2888254cbf7ff846dab
+typing-extensions==4.10.0 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475 \
+    --hash=sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb
+urllib3==2.2.1 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d \
+    --hash=sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19
+zfpy==1.0.1 ; python_version >= "3.10" and python_full_version < "3.11.10" \
+    --hash=sha256:06d6d6a46fbd198c6fec7f31b111e11f5be3a254859f5ac3ab9488d05b240337 \
+    --hash=sha256:13146293a5132627feb2c9f7adc6d95c528dd19dc3260f000e4b6284b2696b1f \
+    --hash=sha256:1417ec8595c2ccb467d228410f7570b1e804bf042ffe84664d7851a267e03239 \
+    --hash=sha256:2f0dea9281b6ce1623eb45316f000a0fdaad01459a01bc50dd866a5fff37ec7b \
+    --hash=sha256:3b5e7c1f0ebdecc6850e65004e7fbba8d5e78bf4b626654e463c3e0aa5b3fe54 \
+    --hash=sha256:46ec529b729b553604a3509cd0d6d0c28169f0be0cd7744f69f16324912d463b \
+    --hash=sha256:58180c0917656ab0eb2dadad7e033e10271a3c615dc8972657137dba16e2bd42 \
+    --hash=sha256:78ff5cc656da9414e264bbdfb316a0c30bf930a426f9c2a1f1a5131620135439 \
+    --hash=sha256:791fa42b651a26be67e52043b619e3a039862900e7080711d07a5de956158b8c \
+    --hash=sha256:893aa8239a9cbae34f8277c6633c145f2dc11230f0f51a39c010a594a4581976 \
+    --hash=sha256:910cac6c191f7bf9d100bd348486d2e53a35a2ba4aa7091020f7154b9f26454a \
+    --hash=sha256:9b717a14b3ae18fc0130d440706dc08cde9c8149423bb0df9f478f52a07a5ed6 \
+    --hash=sha256:9c86a576d65557f83e2c758664ee96710c06352f8b79dca5867199e94ccfb2ce \
+    --hash=sha256:a0ea9d0bcf191ec345145592d2e7586413bcfd385034f85d00238e1eaa857185 \
+    --hash=sha256:a71c0f8c4bfba2433ad11332d6b8493ae661a1c01048292f5e539103a605ec2d \
+    --hash=sha256:b15012ed906b89b43474d5b1400a955348c6bc9cc4c216c2049c16c249a9b51f \
+    --hash=sha256:bb83fa31c943475c4ed8182c0bd1db518beca319809612c9851d78545e7d31c9 \
+    --hash=sha256:c0a64590ef3c182988f4ac5745ec6e1623b06c348583cbdb96d296e4899f4469 \
+    --hash=sha256:cf82232d4f6e0092f54a7bf2ec52ae70024f0a2839ce54b77fb2a29fb417791e \
+    --hash=sha256:d204b17ee8c1eef6a2e650c123d627c0f64b3d8957df4c788dd2aaf2a72eb4ae \
+    --hash=sha256:d401da303fe926b18df1e93483a2099762d61db87597ccdaf89e25fb71b107cc \
+    --hash=sha256:d4892bc441ce59de98c4b3b29f04e44657aa6ccfe3ae5ca23660efb010b829d6 \
+    --hash=sha256:f15c91425cebca0ab5c88b3b2ba0eb800ec5310e3ea2a6b5f3e4b198a97288d9 \
+    --hash=sha256:f811480ebff2e028c50bf3491d1d2e3869eb4388883543d0698f5fdc917e4386 \
+    --hash=sha256:f9a71cfce2fb7a82497e90e7152f847250664f30ca4dfedcca500c35d4c63e44
diff --git a/tests/test_compare.py b/tests/test_compare.py
new file mode 100644
index 00000000..f4933ba5
--- /dev/null
+++ b/tests/test_compare.py
@@ -0,0 +1,142 @@
+import os
+import shutil
+import numpy as np
+import pytest
+from baler.modules.compare import Benchmark, BenchmarkResult
+
+
+# A concrete implementation of the abstract Benchmark class for testing purposes
+class MockBenchmark(Benchmark):
+    """A mock benchmark for testing the base Benchmark class functionality."""
+
+    def __init__(self, *args, **kwargs):
+        self.compress_called = False
+        self.decompress_called = False
+        self.noise_level = kwargs.pop("noise_level", 0.1)
+        super().__init__(*args, **kwargs)
+
+    def _compress(self):
+        """Mock compression: returns the data as a byte string."""
+        self.compress_called = True
+        return self.data_original.tobytes()
+
+    def _decompress(self, compressed_data) -> np.ndarray:
+        """Mock decompression: converts bytes back to array and adds some noise."""
+        self.decompress_called = True
+        decompressed = np.frombuffer(compressed_data, dtype=self.data_original.dtype)
+        decompressed = decompressed.reshape(self.data_original.shape)
+        # Add a small, predictable error for analysis
+        return decompressed + self.noise_level
+
+
+@pytest.fixture
+def benchmark_setup(tmp_path):
+    """Provides a setup for benchmark tests, including a temp directory and data."""
+    output_dir = tmp_path / "benchmark_output"
+    data = np.arange(100, dtype=np.float64).reshape(10, 10)
+    names = np.array([f"var_{i}" for i in range(10)])
+
+    # Use a specific noise level for predictable results
+    noise = 0.1
+    benchmark = MockBenchmark(
+        name="MockTest",
+        output_dir=str(output_dir),
+        data_original=data,
+        names_original=names,
+        verbose=False,
+        noise_level=noise,
+    )
+
+    yield benchmark, data, names, output_dir, noise
+
+    # Teardown is handled by tmp_path fixture
+
+
+def test_benchmark_initialization(benchmark_setup):
+    """Tests if the Benchmark class initializes correctly."""
+    benchmark, data, names, output_dir, _ = benchmark_setup
+    assert benchmark.name == "MockTest"
+    assert benchmark.output_dir == str(output_dir)
+    np.testing.assert_array_equal(benchmark.data_original, data)
+    np.testing.assert_array_equal(benchmark.names_original, names)
+    assert os.path.exists(output_dir)
+
+
+def test_analyze_errors(benchmark_setup):
+    """Tests the _analyze_errors method with various scenarios."""
+    benchmark, original_data, _, _, _ = benchmark_setup
+
+    # Scenario 1: No error
+    metrics_no_error = benchmark._analyze_errors(original_data)
+    assert metrics_no_error["rmse"] == 0.0
+    assert metrics_no_error["max_err"] == 0.0
+    assert metrics_no_error["psnr"] == float("inf")
+
+    # Scenario 2: With a known, constant error
+    decompressed_with_error = original_data + 0.5
+    metrics_with_error = benchmark._analyze_errors(decompressed_with_error)
+    assert np.isclose(metrics_with_error["rmse"], 0.5)
+    assert np.isclose(metrics_with_error["max_err"], 0.5)
+
+    # Scenario 3: Shape mismatch
+    with pytest.raises(ValueError, match="Shape mismatch after decompression"):
+        benchmark._analyze_errors(original_data.flatten())
+
+
+def test_save_decompressed(benchmark_setup):
+    """Tests if the decompressed file is saved correctly."""
+    benchmark, _, names, output_dir, _ = benchmark_setup
+    decompressed_data = np.random.rand(10, 10)
+
+    benchmark._save_decompressed(decompressed_data)
+
+    saved_path = output_dir / "decompressed.npz"
+    assert os.path.exists(saved_path)
+
+    loaded_file = np.load(saved_path)
+    np.testing.assert_array_equal(loaded_file["data"], decompressed_data)
+    np.testing.assert_array_equal(loaded_file["names"], names)
+
+
+def test_save_compressed(benchmark_setup):
+    """Tests the default binary blob saving for compressed data."""
+    benchmark, _, _, output_dir, _ = benchmark_setup
+    compressed_content = b"\x01\x02\x03\x04\x05"
+
+    saved_path_str = benchmark._save_compressed(compressed_content)
+    saved_path = output_dir / "compressed.bin"
+
+    assert saved_path_str == str(saved_path)
+    assert os.path.exists(saved_path)
+
+    with open(saved_path, "rb") as f:
+        read_content = f.read()
+
+    assert read_content == compressed_content
+
+
+def test_run_full_workflow(benchmark_setup):
+    """Tests the entire run() method from compression to result generation."""
+    benchmark, original_data, _, _, noise = benchmark_setup
+
+    result = benchmark.run()
+
+    # Check if mock methods were called
+    assert benchmark.compress_called
+    assert benchmark.decompress_called
+
+    # Check the result object
+    assert isinstance(result, BenchmarkResult)
+    assert result.name == "MockTest"
+    assert result.compress_time_sec > 0
+    assert result.decompress_time_sec > 0
+    assert result.size_mb > 0
+
+    # Check error metrics based on the known noise
+    assert np.isclose(result.rmse, noise)
+    assert np.isclose(result.max_err, noise)
+    assert result.psnr > 0
+
+    # Check if files were created
+    assert os.path.exists(os.path.join(benchmark.output_dir, "compressed.bin"))
+    assert os.path.exists(os.path.join(benchmark.output_dir, "decompressed.npz"))
diff --git a/tests/test_helper.py b/tests/test_helper.py
index 44023099..8cd94cf8 100644
--- a/tests/test_helper.py
+++ b/tests/test_helper.py
@@ -16,6 +16,8 @@
 import shutil
 
 from baler.modules import helper
+import time
+from datetime import datetime
 
 
 def test_create_new_project():
@@ -49,3 +51,108 @@ def test_create_new_project():
 
     # Clean up after the test
     shutil.rmtree(base_path)
+
+
+def test_green_code_tracking_no_verbose(capsys):
+    log_file = "green_code_tracking_test.txt"
+    if os.path.exists(log_file):
+        os.remove(log_file)
+
+    try:
+        start_time = time.time()
+        time.sleep(0.01)
+        end_time = time.time()
+        title = "Test Process No Verbose"
+
+        helper.green_code_tracking(
+            start_time, end_time, title, verbose=False, testing=True
+        )
+
+        # Check file content
+        assert os.path.exists(log_file)
+        with open(log_file, "r") as f:
+            content = f.read()
+            assert title in content
+            assert f"Total time taken: {end_time - start_time:.3f} seconds" in content
+
+        # Check that nothing was printed to stdout
+        captured = capsys.readouterr()
+        assert captured.out == ""
+
+    finally:
+        # Cleanup
+        if os.path.exists(log_file):
+            os.remove(log_file)
+
+
+def test_green_code_tracking_with_verbose(capsys):
+    log_file = "green_code_tracking_test.txt"
+    if os.path.exists(log_file):
+        os.remove(log_file)
+
+    try:
+        start_time = time.time()
+        time.sleep(0.01)
+        end_time = time.time()
+        title = "Test Process Verbose"
+
+        helper.green_code_tracking(
+            start_time, end_time, title, verbose=True, testing=True
+        )
+
+        # Check file content
+        assert os.path.exists(log_file)
+        with open(log_file, "r") as f:
+            content = f.read()
+            assert title in content
+            assert f"Total time taken: {end_time - start_time:.3f} seconds" in content
+
+        # Check stdout
+        captured = capsys.readouterr()
+        assert "GREEN CODE INITIATIVE" in captured.out
+        assert (
+            f"Total time taken for {title}: {end_time - start_time:.3f} seconds"
+            in captured.out
+        )
+        assert f"{title} complete." in captured.out
+
+    finally:
+        # Cleanup
+        if os.path.exists(log_file):
+            os.remove(log_file)
+
+
+def test_green_code_tracking_appends_to_file():
+    log_file = "green_code_tracking_test.txt"
+    if os.path.exists(log_file):
+        os.remove(log_file)
+
+    try:
+        # First call
+        start_time1 = time.time()
+        time.sleep(0.01)
+        end_time1 = time.time()
+        title1 = "PYTEST - First Process"
+        helper.green_code_tracking(start_time1, end_time1, title1, testing=True)
+
+        # Second call
+        start_time2 = time.time()
+        time.sleep(0.01)
+        end_time2 = time.time()
+        title2 = "PYTEST - Second Process"
+        helper.green_code_tracking(start_time2, end_time2, title2, testing=True)
+
+        # Check file content
+        assert os.path.exists(log_file)
+        with open(log_file, "r") as f:
+            lines = f.readlines()
+            assert len(lines) == 2
+            assert title1 in lines[0]
+            assert f"{end_time1 - start_time1:.3f}" in lines[0]
+            assert title2 in lines[1]
+            assert f"{end_time2 - start_time2:.3f}" in lines[1]
+
+    finally:
+        # Cleanup
+        if os.path.exists(log_file):
+            os.remove(log_file)