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zstd >=1.5.6,<1.5.7.0a0 - zstd >=1.5.6,<1.6.0a0 + arch: x86_64 + platform: linux license: BSD-3-Clause license_family: BSD purls: @@ -8801,6 +9017,8 @@ packages: - python_abi 3.12.* *_cp312 - zstd >=1.5.6,<1.5.7.0a0 - zstd >=1.5.6,<1.6.0a0 + arch: arm64 + platform: osx license: BSD-3-Clause license_family: BSD purls: @@ -8814,6 +9032,8 @@ packages: - libgcc-ng >=12 - libstdcxx-ng >=12 - libzlib >=1.2.13,<2.0.0a0 + arch: x86_64 + platform: linux license: BSD-3-Clause license_family: BSD purls: [] @@ -8825,6 +9045,8 @@ packages: depends: - __osx >=11.0 - libzlib >=1.2.13,<2.0.0a0 + arch: arm64 + platform: osx license: BSD-3-Clause license_family: BSD purls: [] diff --git a/src/imgtools/modules/datagraph.py b/src/imgtools/modules/datagraph.py index d03a7b5..b1a5b11 100644 --- a/src/imgtools/modules/datagraph.py +++ b/src/imgtools/modules/datagraph.py @@ -102,7 +102,11 @@ def visualize_graph(self): """ Generates visualization using Pyviz, a wrapper around visJS. The visualization can be found at datanet.html """ - from pyvis.network import Network # type: ignore (PyLance) + try: + from pyvis.network import Network + except ImportError: + logger.error("Please install `pyvis` to visualize the graph") + return logger.info("Generating visualizations...") data_net = Network(height='100%', width='100%', bgcolor='#222222', font_color='white') diff --git a/src/imgtools/ops/ops.py b/src/imgtools/ops/ops.py index 4a27087..74edf21 100644 --- a/src/imgtools/ops/ops.py +++ b/src/imgtools/ops/ops.py @@ -1,5 +1,4 @@ import json -import os import pathlib import re import time @@ -117,6 +116,9 @@ class ImageAutoInput(BaseInput): update: bool Whether to update crawled index + + n_jobs: int + Number of jobs to run in parallel when crawling. If -1, then the number of jobs is set to the number of CPU cores """ imgtools_dir : str = ".imgtools" @@ -133,13 +135,13 @@ def __init__(self, self.parent = self.dir_path.parent self.dataset_name = self.dir_path.name - # self.csv_path = pathlib.Path(self.parent, ".imgtools", f"imgtools_{self.dataset_name}.csv") - # self.json_path = pathlib.Path(self.parent, ".imgtools", f'imgtools_{self.dataset}.json') - # self.edge_path = pathlib.Path(self.parent,".imgtools",f"imgtools_{self.dataset_name}_edges.csv") self.csv_path = self.parent / self.imgtools_dir / f"imgtools_{self.dataset_name}.csv" self.json_path = self.parent / self.imgtools_dir / f'imgtools_{self.dataset_name}.json' self.edge_path = self.parent / self.imgtools_dir / f"imgtools_{self.dataset_name}_edges.csv" + # The result of the crawl + self.db: Dict[str, Any] + start = time.time() # CRAWLER # ------- @@ -147,16 +149,29 @@ def __init__(self, if not self.csv_path.exists() or update: logger.debug("Output exists, force updating.", path=self.csv_path) logger.info("Indexing the dataset...") - db = crawl(self.dir_path, n_jobs=n_jobs) - logger.info(f"Number of patients in the dataset: {len(db)}") + self.db = crawl(self.dir_path, n_jobs=n_jobs) + logger.info(f"Number of patients in the dataset: {len(self.db)}") else: + logger.info("Output exists, loading existing index.") + if not self.json_path.exists(): + raise FileNotFoundError(f"Index file {self.json_path} not found, please re-index the dataset.") + + with json_path.open("r") as f: + self.db = json.load(f) logger.warning("The dataset has already been indexed. Use --update to force update.") # GRAPH # ----- # Form the graph logger.debug("Creating edge path", edge_path=self.edge_path) - self.graph = DataGraph(path_crawl= self.csv_path.resolve().as_posix(), edge_path=self.edge_path.as_posix(), visualize=visualize, update=update) + self.graph = DataGraph( + path_crawl = self.csv_path.resolve().as_posix(), + edge_path=self.edge_path.as_posix(), + visualize=visualize, + update=update + ) + self.df_edges = self.graph.df_edges + logger.info(f"Forming the graph based on the given modalities: {self.modalities}") self.df_combined = self.graph.parser(self.modalities) @@ -366,7 +381,7 @@ def __init__(self, root_directory: str, output_streams: List[str], nnunet_info: Dict = None, - inference: bool = False,): + inference: bool = False): self.output = {} for colname in output_streams: