How to use extract_file_paths method in gabbi

Best Python code snippet using gabbi_python

csv_pipeline2.py

Source:csv_pipeline2.py

...25        source_paths_t2 = list()26        source_paths_t1 = list()27        target_paths = list()28        for subject in sorted(os.listdir(os.path.join(self._source_dir))):29            source_paths_t1.append(extract_file_paths(os.path.join(self._source_dir, subject, "T1")))30            source_paths_t2.append(extract_file_paths(os.path.join(self._source_dir, subject, "T2")))31            target_paths.append(extract_file_paths(os.path.join(self._source_dir, subject, "Labels")))32        subjects = np.arange(1, 11)33        source_paths_t1 = natural_sort([item for sublist in source_paths_t1 for item in sublist])34        source_paths_t2 = natural_sort([item for sublist in source_paths_t2 for item in sublist])35        target_paths = natural_sort([item for sublist in target_paths for item in sublist])36        with open(os.path.join(self._output_dir, output_filename), mode='a+') as output_file:37            writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)38            writer.writerow(39                ["T1", "T2", "labels", "subject", "T1_min", "T1_max", "T1_mean", "T1_std", "T2_min", "T2_max",40                 "T2_mean", "T2_std"])41            for source_path, source_path_t2, target_path, subject in zip(source_paths_t1, source_paths_t2,42                                                                         target_paths, subjects):43                self.LOGGER.info("Processing file {}".format(source_path))44                t1 = ToNumpyArray()(source_path)45                t2 = ToNumpyArray()(source_path_t2)46                csv_data = np.vstack((source_path, source_path_t2, target_path, subject, str(t1.min()), str(t1.max()),47                                      str(t1.mean()), str(t1.std()), str(t2.min()), str(t2.max()), str(t2.mean()),48                                      str(t2.std())))49                for item in range(csv_data.shape[1]):50                    writer.writerow(51                        [csv_data[0][item], csv_data[1][item], csv_data[2][item], csv_data[3][item], csv_data[4][item],52                         csv_data[5][item], csv_data[6][item], csv_data[7][item], csv_data[8][item], csv_data[9][item],53                         csv_data[10][item], csv_data[11][item]])54            output_file.close()55class ToCSVMRBrainSPipeline(object):56    LOGGER = logging.getLogger("MRBrainSPipeline")57    def __init__(self, root_dir: str, output_dir: str):58        self._source_dir = root_dir59        self._output_dir = output_dir60        self._transforms = Compose([ToNumpyArray()])61    def run(self, output_filename: str):62        source_paths_t1_1mm = list()63        source_paths_t2 = list()64        source_paths_t1 = list()65        source_paths_t1_ir = list()66        target_paths = list()67        target_paths_training = list()68        for subject in sorted(os.listdir(os.path.join(self._source_dir))):69            source_paths_t2.append(extract_file_paths(os.path.join(self._source_dir, subject, "T2_FLAIR")))70            source_paths_t1_ir.append(extract_file_paths(os.path.join(self._source_dir, subject, "T1_IR")))71            source_paths_t1_1mm.append(extract_file_paths(os.path.join(self._source_dir, subject, "T1_1mm")))72            source_paths_t1.append(extract_file_paths(os.path.join(self._source_dir, subject, "T1")))73            target_paths.append(extract_file_paths(os.path.join(self._source_dir, subject, "LabelsForTesting")))74            target_paths_training.append(75                extract_file_paths(os.path.join(self._source_dir, subject, "LabelsForTraining")))76        subjects = np.arange(1, 6)77        with open(os.path.join(self._output_dir, output_filename), mode='a+') as output_file:78            writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)79            writer.writerow(80                ["T1_1mm", "T1", "T1_IR", "T2_FLAIR", "LabelsForTesting", "LabelsForTraining", "subject", "T1_min",81                 "T1_max", "T1_mean", "T1_std", "T2_min", "T2_max", "T2_mean", "T2_std"])82            for source_path_t2, source_path_t1_ir, source_path_t1_1mm, source_path_t1, target_path, target_path_training, subject in zip(83                    source_paths_t2, source_paths_t1_ir, source_paths_t1_1mm, source_paths_t1, target_paths,84                    target_paths_training, subjects):85                self.LOGGER.info("Processing file {}".format(source_path_t1))86                t1 = ToNumpyArray()(source_path_t1[0])87                t2 = ToNumpyArray()(source_path_t2[0])88                csv_data = np.vstack((89                    source_path_t1_1mm, source_path_t1, source_path_t1_ir, source_path_t2, target_path,90                    target_path_training, subject, str(t1.min()), str(t1.max()), str(t1.mean()), str(t1.std()),91                    str(t2.min()), str(t2.max()), str(t2.mean()), str(t2.std())))92                for item in range(csv_data.shape[1]):93                    writer.writerow(94                        [csv_data[0][item], csv_data[1][item], csv_data[2][item], csv_data[3][item], csv_data[4][item],95                         csv_data[5][item], csv_data[6][item], csv_data[7][item], csv_data[8][item], csv_data[9][item],96                         csv_data[10][item], csv_data[11][item], csv_data[12][item], csv_data[13][item],97                         csv_data[14][item]])98            output_file.close()99class ToCSVABIDEPipeline(object):100    LOGGER = logging.getLogger("ABIDEPipeline")101    def __init__(self, root_dir: str, output_dir: str):102        self._source_dir = root_dir103        self._output_dir = output_dir104        self._transforms = Compose([ToNumpyArray()])105    def run(self, output_filename: str):106        source_paths = list()107        target_paths = list()108        subjects = list()109        sites = list()110        for dir in sorted(os.listdir(self._source_dir)):111            source_paths_ = extract_file_paths(os.path.join(self._source_dir, dir, "mri", "T1"), "T1.nii.gz")112            target_paths_ = extract_file_paths(os.path.join(self._source_dir, dir, "mri", "Labels"), "Labels.nii.gz")113            subject_ = dir114            source_paths.append(source_paths_)115            target_paths.append(target_paths_)116            if len(source_paths_) is not 0:117                match = re.search('(?P<site>.*)_(?P<patient_id>[0-9]*)', str(dir))118                site_ = match.group("site")119                sites.append(site_)120                subjects.append(subject_)121        source_paths = list(filter(None, source_paths))122        target_paths = list(filter(None, target_paths))123        with open(os.path.join(self._output_dir, output_filename), mode='a+') as output_file:124            writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)125            writer.writerow(["T1", "labels", "subject", "site", "min", "max", "mean", "std"])126            for source_path, target_path, subject, site in zip(source_paths, target_paths, subjects, sites):...

image_slicer_test.py

Source:image_slicer_test.py

...13    PATH = "/mnt/md0/Data/Preprocessed/iSEG/Training/Patches/Aligned/Full/T1/10"14    # TARGET_PATH = "/mnt/md0/Data/Preprocessed/iSEG/Patches/Aligned/label/6"15    FULL_IMAGE_PATH = "/mnt/md0/Data/Preprocessed/iSEG/Training/Aligned/T1/subject-10-T1.nii"16    def setUp(self) -> None:17        paths = extract_file_paths(self.PATH)18        self._dataset = iSEGSegmentationFactory.create(natural_sort(paths), None, modalities=Modality.T1,19                                                       dataset_id=0)20        self._reconstructor = ImageReconstructor([128, 160, 128], [32, 32, 32], [8, 8, 8], )21        transforms = Compose([ToNumpyArray(), PadToPatchShape([1, 32, 32, 32], [1, 8, 8, 8])])22        self._full_image = transforms(self.FULL_IMAGE_PATH)23    def test_should_output_reconstructed_image(self):24        all_patches = []25        all_labels = []26        for current_batch, input in enumerate(self._dataset):27            all_patches.append(input.x)28            all_labels.append(input.y)29        img = self._reconstructor.reconstruct_from_patches_3d(all_patches)30        plt.imshow(img[64, :, :], cmap="gray")31        plt.show()32        np.testing.assert_array_almost_equal(img, self._full_image.squeeze(0), 6)33class ImageReconstructorMRBrainSTest(unittest.TestCase):34    PATH = "/mnt/md0/Data/Preprocessed_4/MRBrainS/DataNii/TrainingData/1/T1"35    # TARGET_PATH = "/mnt/md0/Data/Preprocessed/iSEG/Patches/Aligned/label/6"36    FULL_IMAGE_PATH = "/mnt/md0/Data/Preprocessed/MRBrainS/DataNii/TrainingData/1/T1/T1.nii.gz"37    def setUp(self) -> None:38        paths = extract_file_paths(self.PATH)39        self._dataset = MRBrainSSegmentationFactory.create(natural_sort(paths), None, modalities=Modality.T1,40                                                           dataset_id=0)41        self._reconstructor = ImageReconstructor([256, 256, 192], [1, 32, 32, 32], [1, 8, 8, 8], )42        transforms = Compose([ToNumpyArray(), PadToPatchShape([1, 32, 32, 32], [1, 8, 8, 8])])43        self._full_image = transforms(self.FULL_IMAGE_PATH)44    def test_should_output_reconstructed_image(self):45        all_patches = []46        all_labels = []47        for current_batch, input in enumerate(self._dataset):48            all_patches.append(input.x)49            all_labels.append(input.y)50        img = self._reconstructor.reconstruct_from_patches_3d(all_patches)51        plt.imshow(img[64, :, :], cmap="gray")52        plt.show()53        np.testing.assert_array_almost_equal(img, self._full_image.squeeze(0), 6)54class ImageReconstructorABIDETest(unittest.TestCase):55    PATH = "/home/pierre-luc-delisle/ABIDE/5.1/Stanford_0051160/mri/patches/image"56    # TARGET_PATH = "/home/pierre-luc-delisle/ABIDE/5.1/Stanford_0051160/mri/patches/labels"57    FULL_IMAGE_PATH = "/home/pierre-luc-delisle/ABIDE/5.1/Stanford_0051160/mri/real_brainmask.nii.gz"58    def setUp(self) -> None:59        paths = extract_file_paths(self.PATH)60        self._dataset = ABIDESegmentationFactory.create(natural_sort(paths), None, modalities=Modality.T1,61                                                        dataset_id=0)62        self._reconstructor = ImageReconstructor([224, 224, 192], [1, 32, 32, 32], [1, 8, 8, 8], )63        transforms = Compose([ToNumpyArray(), PadToPatchShape([1, 32, 32, 32], [1, 8, 8, 8])])64        self._full_image = transforms(self.FULL_IMAGE_PATH)65    def test_should_output_reconstructed_image(self):66        all_patches = []67        all_labels = []68        for current_batch, input in enumerate(self._dataset):69            all_patches.append(input.x)70            all_labels.append(input.y)71        img = self._reconstructor.reconstruct_from_patches_3d(all_patches)72        plt.imshow(img[112, :, :], cmap="gray")73        plt.show()...

json_to_csv.py

Source:json_to_csv.py

...5class processing:6    def __init__(self):7        pass89    def extract_file_paths(self):10        file_path = fp11        self.snomed_json_loc = fp.run("snomed_json")12        self.data_loc = fp.run("initial_data")1314    def create_dataframe(self):15        dataframe = pd.read_json(self.snomed_json_loc)16        self.df = dataframe1718    def cloumn_to_delete(self):19        self.to_remove = "system"2021    def delete_idx(self):22        self.df = self.df.drop(self.to_remove, axis=1)2324    def converter(self):25        self.df.to_csv(self.data_loc, index = None)2627class preprocess:28    def extract_file_paths(self):29        file_path = fp30        self.file_name = file_path.run("data")3132    def filechecker(self):       33        check = path.exists(self.file_name)34        return check3536class postprocess:37    def __init__(self):38        pass3940    def runall_pre(self):41        self.pre_process = preprocess()42        self.pre_process.extract_file_paths()43        self.file_checker = self.pre_process.filechecker()44        return self.file_checker4546    def runall_processing(self):47        process = processing()48        process.extract_file_paths()49        process.create_dataframe()50        process.cloumn_to_delete()51        process.delete_idx()52        process.converter()53        54def runall():55    post_process = postprocess()
...

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