fix conflicts

Former-commit-id: caad95a

imgtools/autopipeline.py

@@ -1,4 +1,4 @@
-import os
+import os, pathlib
 import shutil
 import glob
 import pickle
@@ -173,20 +173,20 @@ def process_one_subject(self, subject_id):
                 metadata[f"metadata_{colname}"] = [read_results[i].get_metadata()]
                 print(subject_id, " SAVED PET")
         #Saving all the metadata in multiple text files
-        with open(os.path.join(self.output_directory,".temp",f'temp_{subject_id}.pkl'),'wb') as f:
+        with open(os.path.join(self.output_directory,".temp",f'{subject_id}.pkl'),'wb') as f:
             pickle.dump(metadata,f)
         return 
 
     def save_data(self):
         files = glob.glob(os.path.join(self.output_directory,".temp","*.pkl"))
         for file in files:
-            subject_id = ("_").join(file.replace("/","_").replace(".","_").split("_")[-3:-1])
-            subject_id = file.replace(".","/").split("/")[-2][5:] #From temp_ considers
+            filename = pathlib.Path(file).name
+            subject_id = os.path.splitext(filename)[0]
             with open(file,"rb") as f:
                 metadata = pickle.load(f)
             self.output_df.loc[subject_id, list(metadata.keys())] = list(metadata.values())
         self.output_df.to_csv(self.output_df_path)
-        shutil.rmtree(os.path.join(self.output_directory,".temp"))
+        shutil.rmtree(os.path.join(self.output_directory, ".temp"))
 
     def run(self):
         """Execute the pipeline, possibly in parallel.

imgtools/io/dataset.py

@@ -1,21 +1,16 @@
 from genericpath import exists
 import os
-import numpy as np
 from typing import List, Sequence, Optional, Callable, Iterable, Dict,Tuple
+from tqdm import tqdm
+
+import SimpleITK as sitk
 import torchio as tio
 import pandas as pd
-<<<<<<< HEAD
-=======
-# from . import file_name_convention
-# from ..ops import StructureSetToSegmentation, ImageAutoInput, Resample, BaseOp
->>>>>>> 700fbe6ff316bf36536be5bf6cca67df144096fa
+
 from imgtools.io import file_name_convention
 from imgtools.ops import StructureSetToSegmentation, ImageAutoInput, Resample, BaseOp
-from tqdm import tqdm
-from joblib import Parallel, delayed
-import SimpleITK as sitk
-import warnings
 from imgtools.pipeline import Pipeline
+from joblib import Parallel, delayed
 
 class Dataset(tio.SubjectsDataset):
     """
@@ -49,7 +44,7 @@ def load_from_nrrd(
         if not os.path.exists(path_metadata):
             raise ValueError("The specified path has no file name {}".format(path_metadata))
         df_metadata = pd.read_csv(path_metadata,index_col=0)
-        output_streams = [("_").join(cols.split("_")[1:]) for cols in df_metadata.columns if cols.split("_")[0]=="folder"]
+        output_streams = [("_").join(cols.split("_")[1:]) for cols in df_metadata.columns if cols.split("_")[0] == "folder"]
         imp_metadata = [cols for cols in df_metadata.columns if cols.split("_")[0] in ("metadata")]
         #Ignores multiple connection to single modality
         if ignore_multi:

imgtools/io/loaders.py

@@ -1,4 +1,4 @@
-import os
+import os, pathlib
 import glob
 import re
 from typing import Optional, List
@@ -272,8 +272,7 @@ def crawl(self, top, n_jobs=1):
                 database_dict[key] = db[key]
 
         # save one level above imaging folders
-        parent  = os.path.dirname(top)
-        dataset = top.split("/")[-1]
+        parent, dataset  = os.path.split(top)
 
         # save as json
         with open(os.path.join(parent, f'imgtools_{dataset}.json'), 'w') as f:

imgtools/modules/datagraph.py

@@ -4,7 +4,6 @@
 import numpy as np
 import pandas as pd
 from tqdm import tqdm
-from pyvis.network import Network
 
 
 class DataGraph:
@@ -90,6 +89,7 @@ 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
         print("Generating visualizations...")
         data_net = Network(height='100%', width='100%', bgcolor='#222222', font_color='white')
 
@@ -117,7 +117,8 @@ def visualize_graph(self):
             node["title"] += "<br>Number of connections: {}".format(len(neigbour_map[node['id']])) 
             node["value"] = len(neigbour_map[node['id']])
 
-        vis_path = os.path.join(("/").join(self.edge_path.split("/")[:-1]),"datanet.html")
+
+        vis_path = os.path.join(os.path.basename(self.edge_path),"datanet.html")
         data_net.show(vis_path)
 
     def _form_edge_study(self, df, all_study, study_id):

imgtools/ops/ops.py

@@ -79,8 +79,7 @@ def __init__(self,
                  visualize: bool = False):
         self.dir_path = dir_path
         self.modalities = modalities
-        self.dataset_name = self.dir_path.split("/")[-1]
-        self.parent  = os.path.dirname(self.dir_path)
+        self.parent, self.dataset_name = os.path.split(self.dir_path)
 
         ####### CRAWLER ############
         # Checks if dataset has already been indexed
@@ -96,12 +95,12 @@ def __init__(self,
         ####### GRAPH ##########
         # Form the graph
         edge_path = os.path.join(self.parent,f"imgtools_{self.dataset_name}_edges.csv")
-        graph = DataGraph(path_crawl=path_crawl,edge_path=edge_path,visualize=visualize)
+        graph = DataGraph(path_crawl=path_crawl, edge_path=edge_path, visualize=visualize)
         print(f"Forming the graph based on the given modalities: {self.modalities}")
         self.df_combined = graph.parser(self.modalities)
-        self.output_streams = [("_").join(cols.split("_")[1:]) for cols in self.df_combined.columns if cols.split("_")[0]=="folder"]
-        self.column_names = [cols for cols in self.df_combined.columns if cols.split("_")[0]=="folder"]
-        self.series_names = [cols for cols in self.df_combined.columns if cols.split("_")[0]=="series"]
+        self.output_streams = [("_").join(cols.split("_")[1:]) for cols in self.df_combined.columns if cols.split("_")[0] == "folder"]
+        self.column_names = [cols for cols in self.df_combined.columns if cols.split("_")[0] == "folder"]
+        self.series_names = [cols for cols in self.df_combined.columns if cols.split("_")[0] == "series"]
 
         print(f"There are {len(self.df_combined)} cases containing all {modalities} modalities.")
 

setup.py

@@ -1,4 +1,4 @@
-from setuptools import setup
+from setuptools import setup, find_packages
 
 with open("README.md", "r") as fh:
     long_description = fh.read()
@@ -16,7 +16,10 @@
     long_description_content_type="text/markdown",
     url="https://github.com/bhklab/med-imagetools",
     install_requires=reqs,
-    packages=setuptools.find_packages(),
+    packages=find_packages(),
+    extras_require={
+        'debug': ['pyvis'],
+    },
     # classifiers=[
     #     "Programming Language :: Python :: 3",
     #     "License :: OSI Approved :: MIT License",

tests/test_autopipe.py

@@ -53,6 +53,8 @@ def test_pipeline(dataset_path, modalities):
 
     #Check if the dataset.csv is having the correct number of components and has all the fields
     comp_table = pd.read_csv(comp_path)
+    print(len(comp_table))
+    print(comp_table)
     assert len(comp_table) == 2, "There was some error in making components, check datagraph.parser"
 
     #Check the nrrd files
@@ -73,7 +75,6 @@ def test_pipeline(dataset_path, modalities):
         dicom_ct, _     = nrrd.read(path_ct)
         dicom_dose, _   = nrrd.read(path_dose)
         dicom_pet, _    = nrrd.read(path_pet)
-        print(dicom_ct.shape, dicom_dose.shape, dicom_pet.shape)
         assert dicom_ct.shape == dicom_dose.shape == dicom_pet.shape
     elif modalities == "CT,RTSTRUCT,RTDOSE":
         path_ct         = os.path.join(output_path_mod, "image", os.listdir(os.path.join(output_path_mod,"image"))[0])

tests/test_modalities.py

@@ -2,16 +2,12 @@
 This code is for testing functioning of different modalities 
 '''
 
-<<<<<<< HEAD
 
 import os
-=======
-import os
-import pathlib
->>>>>>> 700fbe6ff316bf36536be5bf6cca67df144096fa
 from posixpath import dirname
 import shutil
 import warnings
+import pathlib
 from multiprocessing import cpu_count
 
 import numpy as np
@@ -66,5 +62,5 @@ def test_modalities(modalities, modalities_path):
         make_binary_mask = StructureSetToSegmentation(roi_names=['GTV.?', 'LARYNX'], continuous=False)
         mask = make_binary_mask(struc, img)
         A = sitk.GetArrayFromImage(mask)
-        assert len(A.shape)==4
+        assert len(A.shape) == 4
         assert A.shape[0:3] == (img.GetDepth(),img.GetHeight(),img.GetWidth())