Merge pull request #26 from BrainLesion/better-documentation

Better documentation

Author: neuronflow

README.md

@@ -12,29 +12,12 @@ The outcome of this processing sequence is a set of 4 NIFTI images, skull-stripp
 
 ## Atlas Reference
 
-The atlas employed in our workflow is based on the following publication:
-[Link to the Atlas Article](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2915788/)
+We use the SRI-24 atlas from this [publication](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2915788/)
 
 
 ## Installation
+With a Python 3.10+ environment you can install directly from  [pypi.org](https://pypi.org/project/brainles-preprocessing/):
 
-
-1. **Directly from the GitHub Repository**:
-   Using pip, you can directly install the preprocessing tool:
-   
-   ```bash
-   pip install git+https://github.com/BrainLesion/preprocessing.git
-   ```
-
-2. **Clone and Install Locally**:
-   For a local installation, you can clone the repository and then install it:
-   
-   ```bash
-   git clone https://github.com/BrainLesion/preprocessing.git
-   cd preprocessing
-   pip install .
-   ```
-
-## Directory Reference
-
-`/home/florian/flow/BrainLesion/BrainLes/preprocessing`
+```
+pip install brainles-preprocessing
+```

brainles_preprocessing/brain_extraction/brain_extractor.py

@@ -1,8 +1,8 @@
+# TODO add typing and docs
 from abc import abstractmethod
 
 import nibabel as nib
 import numpy as np
-
 from brainles_hd_bet import run_hd_bet
 
 

brainles_preprocessing/modality.py

@@ -1,10 +1,13 @@
+# todo add typing and docs
 import os
 import shutil
 
 from auxiliary.nifti.io import read_nifti, write_nifti
 from auxiliary.normalization.normalizer_base import Normalizer
 from auxiliary.turbopath import turbopath
 
+from brainles_preprocessing.registration.registrator import Registrator
+
 
 class Modality:
     """
@@ -82,9 +85,9 @@ def normalize(
     def register(
         self,
         registrator,
-        fixed_image_path,
-        registration_dir,
-        moving_image_name,
+        fixed_image_path: str,
+        registration_dir: str,
+        moving_image_name: str,
     ):
         registered = os.path.join(registration_dir, f"{moving_image_name}.nii.gz")
         registered_matrix = os.path.join(registration_dir, f"{moving_image_name}.txt")
@@ -120,7 +123,7 @@ def apply_mask(
 
     def transform(
         self,
-        registrator,
+        registrator: Registrator,
         fixed_image_path,
         registration_dir,
         moving_image_name,

brainles_preprocessing/preprocessor.py

@@ -1,11 +1,12 @@
+# TODO add typing and documentation
 import os
 import shutil
 import tempfile
 
 from auxiliary.turbopath import turbopath
 
-from .modality import Modality
 from .brain_extraction.brain_extractor import BrainExtractor
+from .modality import Modality
 from .registration.registrator import Registrator
 
 
@@ -14,9 +15,9 @@ def __init__(
         self,
         center_modality: Modality,
         moving_modalities: list[Modality],
-        registrator,
-        brain_extractor,
-        atlas_image_path=turbopath(__file__).parent
+        registrator: Registrator,
+        brain_extractor: BrainExtractor,
+        atlas_image_path: str = turbopath(__file__).parent
         + "/registration/atlas/t1_brats_space.nii",
         temp_folder=None,
     ):
@@ -170,9 +171,9 @@ def __init__(
         self,
         center_modality: Modality,
         moving_modalities: list[Modality],
-        registrator,
-        brain_extractor,
-        atlas_image_path=turbopath(__file__).parent
+        registrator: Registrator,
+        brain_extractor: BrainExtractor,
+        atlas_image_path: str = turbopath(__file__).parent
         + "/registration/atlas/t1_brats_space.nii",
         temp_folder=None,
         limit_cuda_visible_devices: str = None,

brainles_preprocessing/registration/niftyreg.py

@@ -1,8 +1,8 @@
+# TODO add typing and docs
 import os
 
 from auxiliary.runscript import ScriptRunner
 from auxiliary.turbopath import turbopath
-from ttictoc import Timer
 
 from brainles_preprocessing.registration.registrator import Registrator
 

brainles_preprocessing/registration/registrator.py

@@ -1,3 +1,4 @@
+# TODO add typing and docs
 from abc import ABC, abstractmethod
 
 

example_modality_centric_preprocessor.py

@@ -1,14 +1,15 @@
 # TODO move this to tutorial
+# TODO polish this
 import datetime
 
 from auxiliary.normalization.percentile_normalizer import PercentileNormalizer
 from auxiliary.turbopath import turbopath
 from tqdm import tqdm
 
+from brainles_preprocessing.brain_extraction import HDBetExtractor
 from brainles_preprocessing.modality import Modality
 from brainles_preprocessing.preprocessor import Preprocessor, PreprocessorGPU
 from brainles_preprocessing.registration import NiftyRegRegistrator
-from brainles_preprocessing.brain_extraction import HDBetExtractor
 
 
 def preprocess(inputDir):

example_preprocessing_brats.py

@@ -1,75 +1,76 @@
-# TODO move this to tutorial
-import datetime
-
-from auxiliary.turbopath import turbopath
-from tqdm import tqdm
-
-from brainles_preprocessing.brats import preprocess_brats_style_t1_centric
-
-
-def preprocess(inputDir):
-    inputDir = turbopath(inputDir)
-    try:
-        print("*** start ***")
-
-        # where are the raw mr files?
-        btk_raw_dir = turbopath(inputDir)
-
-        # is the exam already processed?
-        brainles_dir = turbopath(inputDir) + "/" + inputDir.name + "_brainles"
-        prep_dir = brainles_dir + "/preprocessed"
-
-        # if not os.path.exists(prep_dir):
-        # if os.path.exists(prep_dir):
-        t1_file = btk_raw_dir.files("*t1.nii.gz")
-        t1c_file = btk_raw_dir.files("*t1c.nii.gz")
-        t2_file = btk_raw_dir.files("*t2.nii.gz")
-        flair_file = btk_raw_dir.files("*fla.nii.gz")
-
-        if len(t1_file) == len(t1c_file) == len(t2_file) == len(flair_file) == 1:
-            print(t1_file)
-            print(t1c_file)
-            print(t2_file)
-            print(flair_file)
-
-            t1File = t1_file[0]
-            t1cFile = t1c_file[0]
-            t2File = t2_file[0]
-            flaFile = flair_file[0]
-
-            # execute it
-            preprocess_brats_style_t1_centric(
-                input_t1=t1File,
-                output_t1=prep_dir + "/" + inputDir.name + "_t1.nii.gz",
-                input_t1c=t1cFile,
-                output_t1c=prep_dir + "/" + inputDir.name + "_t1c.nii.gz",
-                input_t2=t2File,
-                output_t2=prep_dir + "/" + inputDir.name + "_t2.nii.gz",
-                input_flair=flaFile,
-                output_flair=prep_dir + "/" + inputDir.name + "_fla.nii.gz",
-                bet_mode="gpu",
-                limit_cuda_visible_devices="0",
-                temporary_directory="tempo/" + inputDir.name,
-                keep_coregistration=brainles_dir + "/co-registration",
-                keep_atlas_registration=brainles_dir + "/atlas-registration",
-                keep_brainextraction=brainles_dir + "/brain-extraction",
-            )
-
-    except Exception as e:
-        print("error: " + str(e))
-        print("conversion error for:", inputDir)
-
-        time = str(datetime.datetime.now().time())
-
-        print("** finished:", inputDir.name, "at:", time)
-
-
-### *** GOGOGO *** ###
-if __name__ == "__main__":
-    EXAMPLE_DATA_DIR = turbopath("example_data")
-
-    exams = EXAMPLE_DATA_DIR.dirs()
-
-    for exam in tqdm(exams):
-        print(exam)
-        preprocess(exam)
+# TODO non-functional atm
+# # TODO move this to tutorial
+# import datetime
+
+# from auxiliary.turbopath import turbopath
+# from tqdm import tqdm
+
+# from brainles_preprocessing.brats import preprocess_brats_style_t1_centric
+
+
+# def preprocess(inputDir):
+#     inputDir = turbopath(inputDir)
+#     try:
+#         print("*** start ***")
+
+#         # where are the raw mr files?
+#         btk_raw_dir = turbopath(inputDir)
+
+#         # is the exam already processed?
+#         brainles_dir = turbopath(inputDir) + "/" + inputDir.name + "_brainles"
+#         prep_dir = brainles_dir + "/preprocessed"
+
+#         # if not os.path.exists(prep_dir):
+#         # if os.path.exists(prep_dir):
+#         t1_file = btk_raw_dir.files("*t1.nii.gz")
+#         t1c_file = btk_raw_dir.files("*t1c.nii.gz")
+#         t2_file = btk_raw_dir.files("*t2.nii.gz")
+#         flair_file = btk_raw_dir.files("*fla.nii.gz")
+
+#         if len(t1_file) == len(t1c_file) == len(t2_file) == len(flair_file) == 1:
+#             print(t1_file)
+#             print(t1c_file)
+#             print(t2_file)
+#             print(flair_file)
+
+#             t1File = t1_file[0]
+#             t1cFile = t1c_file[0]
+#             t2File = t2_file[0]
+#             flaFile = flair_file[0]
+
+#             # execute it
+#             preprocess_brats_style_t1_centric(
+#                 input_t1=t1File,
+#                 output_t1=prep_dir + "/" + inputDir.name + "_t1.nii.gz",
+#                 input_t1c=t1cFile,
+#                 output_t1c=prep_dir + "/" + inputDir.name + "_t1c.nii.gz",
+#                 input_t2=t2File,
+#                 output_t2=prep_dir + "/" + inputDir.name + "_t2.nii.gz",
+#                 input_flair=flaFile,
+#                 output_flair=prep_dir + "/" + inputDir.name + "_fla.nii.gz",
+#                 bet_mode="gpu",
+#                 limit_cuda_visible_devices="0",
+#                 temporary_directory="tempo/" + inputDir.name,
+#                 keep_coregistration=brainles_dir + "/co-registration",
+#                 keep_atlas_registration=brainles_dir + "/atlas-registration",
+#                 keep_brainextraction=brainles_dir + "/brain-extraction",
+#             )
+
+#     except Exception as e:
+#         print("error: " + str(e))
+#         print("conversion error for:", inputDir)
+
+#         time = str(datetime.datetime.now().time())
+
+#         print("** finished:", inputDir.name, "at:", time)
+
+
+# ### *** GOGOGO *** ###
+# if __name__ == "__main__":
+#     EXAMPLE_DATA_DIR = turbopath("example_data")
+
+#     exams = EXAMPLE_DATA_DIR.dirs()
+
+#     for exam in tqdm(exams):
+#         print(exam)
+#         preprocess(exam)