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How to use invocation_result method in localstack

Best Python code snippet using localstack_python

version_manager.py

Source:version_manager.py

...309        executor.invocation_done()310        self.available_environments.put(executor)311        self.store_logs(invocation_result=invocation_result, executor=executor)312    # Service Endpoint implementation313    def invocation_result(self, invoke_id: str, invocation_result: InvocationResult) -> None:314        LOG.debug("Got invocation result for invocation '%s'", invoke_id)315        self.invocation_response(invoke_id=invoke_id, invocation_result=invocation_result)316    def invocation_error(self, invoke_id: str, invocation_error: InvocationError) -> None:317        LOG.debug("Got invocation error for invocation '%s'", invoke_id)318        self.invocation_response(invoke_id=invoke_id, invocation_result=invocation_error)319    def invocation_logs(self, invoke_id: str, invocation_logs: InvocationLogs) -> None:320        LOG.debug("Got logs for invocation '%s'", invoke_id)321        for log_line in invocation_logs.logs.splitlines():322            LOG.debug("> %s", log_line)323        running_invocation = self.running_invocations.get(invoke_id, None)324        if running_invocation is None:325            raise Exception(f"Cannot map invocation result {invoke_id} to invocation")326        running_invocation.logs = invocation_logs.logs327    def status_ready(self, executor_id: str) -> None:...

full_preprocessing_utils.py

Source:full_preprocessing_utils.py

1import numpy as np # linear algebra2import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)3import dicom4import os5import matplotlib.pyplot as plt6from mpl_toolkits.mplot3d.art3d import Poly3DCollection7import scipy8import pandas9from skimage import measure, morphology1011import shutil12import zipfile1314# Load the scans in given folder path15def load_scan(path):16    slices = [dicom.read_file(path + '/' + s) for s in get_dcom_files(path)]17    slices.sort(key=lambda x: float(x.ImagePositionPatient[2]))18    try:19        slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2])20    except:21        slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation)2223    for s in slices:24        s.SliceThickness = slice_thickness2526    return slices272829def get_pixels_hu(slices):30    image = np.stack([s.pixel_array for s in slices])31    # Convert to int16 (from sometimes int16),32    # should be possible as values should always be low enough (<32k)33    image = image.astype(np.int16)3435    # Set outside-of-scan pixels to 036    # The intercept is usually -1024, so air is approximately 037    image[image == -2000] = 03839    # Convert to Hounsfield units (HU)40    for slice_number in range(len(slices)):4142        intercept = slices[slice_number].RescaleIntercept43        slope = slices[slice_number].RescaleSlope4445        if slope != 1:46            image[slice_number] = slope * image[slice_number].astype(np.float64)47            image[slice_number] = image[slice_number].astype(np.int16)4849        image[slice_number] += np.int16(intercept)5051    return np.array(image, dtype=np.int16)525354def resample(image, scan, new_spacing=[1, 1, 1]):55    print('In resample, original image dimensions ', image.shape)56    # Determine current pixel spacing57    spacing = np.array([scan[0].SliceThickness] + scan[0].PixelSpacing, dtype=np.float32)5859    resize_factor = spacing / new_spacing60    new_real_shape = image.shape * resize_factor61    new_shape = np.round(new_real_shape)62    real_resize_factor = new_shape / image.shape63    new_spacing = spacing / real_resize_factor6465    image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest').astype(np.uint8)66    #print(image[50:55,90:92,105:110])67    print('In resample, final image dimensions ', image.shape)686970    return image7172def get_average_pixel_value(patient_dir):73    slices = load_scan(patient_dir)74    image_3d = get_pixels_hu(slices)75    return np.mean(slices)7677def write_3d_image(patient, root_dir):78    slices = load_scan(root_dir + patient)79    image_3d = get_pixels_hu(slices)80    print('Resampling image ' + patient)81    #resampled_image = resample(image_3d, slices)82    print('Done. Writing ' + patient)83    # for i in range(resampled_image.shape[0]):84    #      np.savetxt('output/' + patient + '_' + str(i) + '.csv',85    #                 resampled_image[i,:,:],  '%5.0f', delimiter=',')86         #create_zip_file('c:/data/cosmin/kaggle/scan/output/', patient + "_" + str(i))87         #os.remove('c:/data/cosmin/kaggle/scan/output/' + patient + '_' + str(i) + '.csv')88    np.save('output/' + patient , image_3d)8990def get_3d_image(patient, root_dir):91    slices = load_scan(root_dir + patient)92    image_3d = get_pixels_hu(slices)93    return image_3d9495def create_zip_file(directory, file):96    zf = zipfile.ZipFile(file + ".zip", "w",compression=zipfile.ZIP_DEFLATED)97    path = os.path.normpath(os.path.join(directory, file + '.csv'))98    zf.write(path, path)99100101def write_resampled_images(root_dir):102    patients = os.listdir(root_dir)103    index = 0104    for patient in patients:105        write_resampled_image(patient, root_dir)106        index += 1107        print(index)108109110# apply method_to_invoke to all image3d of the patients in the directory111# return mapping patient-id -> invocation result112def apply_to_all_patients(root_dir, method_to_invoke):113    df = load_train_set_outcomes()114    res = pandas.DataFrame(columns=('patient','outcome','invocation_result'))115    patients = os.listdir(root_dir)116    index = 0117    for patient in patients:118        if(df.loc[df['id'] == patient].size == 0):119            continue120        slices = load_scan(root_dir + patient)121        image_3d = get_pixels_hu(slices)122        invocation_result = method_to_invoke(image_3d)123        print(patient)124        patient_outcome = (df.loc[df['id'] == patient]['cancer'] == 1).values[0]125        print(patient_outcome,invocation_result)126        res[index] = [patient, patient_outcome, invocation_result]127        index += 1128    res.to_csv('averages.csv')129    return res130131def get_all_patients(root_dir):132    return os.listdir(root_dir)133134#load patientId->1 or 0 from the input/stage1_labels.csv file135def load_train_set_outcomes():136    df = pandas.read_csv('input/stage1_labels.csv')137    return df138139140def plot_3d(image, threshold=-300):141    # Position the scan upright,142    # so the head of the patient would be at the top facing the camera143    p = image.transpose(2, 1, 0)144145    verts, faces, normals, values = measure.marching_cubes(p, threshold)146147    fig = plt.figure(figsize=(10, 10))148    ax = fig.add_subplot(111, projection='3d')149150    # Fancy indexing: `verts[faces]` to generate a collection of triangles151    mesh = Poly3DCollection(verts[faces], alpha=0.70)152    face_color = [0.45, 0.45, 0.75]153    mesh.set_facecolor(face_color)154    ax.add_collection3d(mesh)155156    ax.set_xlim(0, p.shape[0])157    ax.set_ylim(0, p.shape[1])158    ax.set_zlim(0, p.shape[2])159160    plt.show()161162163def get_dcom_files(path):164    res = []165    files = os.listdir(path)166    for file in files:167        if file.endswith(".dcm"):168            res.append(file)
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