Editorial changes to correct typos, rename parameters, and add comments

docs/_static/custom.css

@@ -107,7 +107,7 @@ blockquote p {
 }
 
 /* Mermaid
-to avoid the lable text being cut off
+to avoid the label text being cut off
 */
 .edgeTerminals {
     font-size: 9px !important;

docs/source/developing_with_sdk/packaging_app.md

@@ -13,7 +13,7 @@ It is required that the application configuration yaml file as well as the depen
 ### Basic Usage of MONAI Application Packager
 
 ```bash
-monai-deploy package <APP_PATH> --config <COMFIG> --tag <TAG> --platform <x64-workstation> [--models <MODEL_PATH>] [--log-level <LEVEL>] [-h]
+monai-deploy package <APP_PATH> --config <CONFIG> --tag <TAG> --platform <x64-workstation> [--models <MODEL_PATH>] [--log-level <LEVEL>] [-h]
 ```
 
 #### Required Arguments

docs/source/getting_started/tutorials/mednist_app.md

@@ -42,7 +42,7 @@ jupyter-lab
 <div style="text-align: center;">
     <iframe width="560" height="315" src="https://www.youtube.com/embed/WwjilJFHuU4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 
-    <p>Video may show the use of previous SDK verson.</p>
+    <p>Video may show the use of previous SDK version.</p>
 </div>
 ```
 

docs/source/getting_started/tutorials/monai_bundle_app.md

@@ -32,7 +32,7 @@ jupyter-lab
 <div style="text-align: center;">
     <iframe width="560" height="315" src="https://www.youtube.com/embed/nivgfD4pwWE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 
-    <p>Video may show the use of previous SDK verson.</p>
+    <p>Video may show the use of previous SDK version.</p>
 </div>
 ```
 

docs/source/release_notes/v0.3.0.md

@@ -16,7 +16,7 @@ This operator uses [Clara Viz](https://pypi.org/project/clara-viz/) to provide i
 
 ### STL Surface Mesh Conversion Operator
 
-This operator converts a volume image to surface mesh, in [STL file format](https://en.wikipedia.org/wiki/STL_(file_format)). Its API allows the client to control if smoothing needs to be applied, and if only keeping the largest connected component; the latter is useful when muliple disjoint segments are in a volume image, and the application needs to control if all or only the largest to be included in the output.
+This operator converts a volume image to surface mesh, in [STL file format](https://en.wikipedia.org/wiki/STL_(file_format)). Its API allows the client to control if smoothing needs to be applied, and if only keeping the largest connected component; the latter is useful when multiple disjoint segments are in a volume image, and the application needs to control if all or only the largest to be included in the output.
 
 ## What's fixed/updated
 

docs/source/release_notes/v0.4.0.md

@@ -9,7 +9,7 @@
 
 The new operator, `MONAI Bundle Inference Operator`, is intended to automate the inference with a MONAI Bundle in TorchScript with the following functionalities:
 - Parse the model metadata and extra configuration data in the TorchScript file
-- Instanciate MONAI transforms and inferer objects per bundle configuration data
+- Instantiate MONAI transforms and inferer objects per bundle configuration data
 - Convert input/output of the operator to and from model network input
 - Support named model and can be used in a multi-model application
 

docs/source/release_notes/v0.5.0.md

@@ -7,7 +7,7 @@
 - Generated DICOM instances as AI evidence now have the attribute <a href="https://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.12.html#table_C.12-1">(0008,0201) Timezone Offset From UTC</a>, in addition to the DICOM date and time which are set with values from the underlying operating system. The OS is expected to be synchronized with a known good timing source and have the correct timezone setting
 - Generated DICOM instance file names are now based on the SOP instance UID
 - Support DICOM instance level attribute matching in the DICOM Series Selection Operator
-- Operators and example applications are verified to be re-runable without needing to reinitialize the application object or re-load the AI model network. This will allow a main function or an external script to instantiate the application object once and use it to process multiple discreet inputs, either in a batch processing mode or in a long running service
+- Operators and example applications are verified to be re-runable without needing to reinitialize the application object or re-load the AI model network. This will allow a main function or an external script to instantiate the application object once and use it to process multiple discrete inputs, either in a batch processing mode or in a long running service
 - Tutorials, in Jupyter notebooks, are re-organized and updated
 - Reference added for <a href="https://github.com/Project-MONAI/monai-deploy/releases">MONAI Deploy Express</a> for hosting MAPs in development environments
 - Removed is the reference to the deprecated MONAI Inference Service

docs/srs.md

@@ -521,7 +521,7 @@ The SDK shall allow the packaging of the application in a standardized format so
 
 ### Background
 
-Please refer to [MONAI Application Packge Spec](https://github.com/Project-MONAI/monai-deploy-experimental/blob/main/guidelines/monai-application-package.md)for details.
+Please refer to [MONAI Application Package Spec](https://github.com/Project-MONAI/monai-deploy-experimental/blob/main/guidelines/monai-application-package.md)for details.
 
 ### Verification Strategy
 

examples/apps/ai_unetr_seg_app/app.py

@@ -80,7 +80,7 @@ def compose(self):
         _algorithm_family = codes.DCM.ArtificialIntelligence
         _algorithm_version = "0.1.0"
 
-        # List of (Segment name, [Code menaing str]), not including background which is value of 0.
+        # List of (Segment name, [Code meaning str]), not including background which is value of 0.
         # User must provide correct codes, which can be looked at, e.g.
         # https://bioportal.bioontology.org/ontologies/SNOMEDCT
         # Alternatively, consult the concept and code dictionaries in PyDicom

examples/apps/ai_unetr_seg_app/unetr_seg_operator.py

@@ -56,7 +56,7 @@ class UnetrSegOperator(Operator):
 
     def __init__(
         self,
-        frament: Fragment,
+        fragment: Fragment,
         *args,
         app_context: AppContext,
         model_path: Path,
@@ -71,13 +71,13 @@ def __init__(
         self.model_path = model_path
         self.output_folder = output_folder
         self.output_folder.mkdir(parents=True, exist_ok=True)
-        self.fragement = frament  # Cache and later pass the Fragment/Application to contained operator(s)
+        self.app_fragment = fragment  # Cache and later pass the Fragment/Application to contained operator(s)
         self.app_context = app_context
         self.input_name_image = "image"
         self.output_name_seg = "seg_image"
         self.output_name_saved_images_folder = "saved_images_folder"
 
-        super().__init__(frament, *args, **kwargs)
+        super().__init__(fragment, *args, **kwargs)
 
     def setup(self, spec: OperatorSpec):
         spec.input(self.input_name_image)
@@ -102,7 +102,7 @@ def compute(self, op_input, op_output, context):
 
         # Delegates inference and saving output to the built-in operator.
         infer_operator = MonaiSegInferenceOperator(
-            self.fragement,
+            self.app_fragment,
             roi_size=(
                 96,
                 96,

examples/apps/breast_density_classifier_app/README.md

@@ -9,7 +9,7 @@ Sample data and a torchscript model can be downloaded from https://drive.google.
 python app.py -i <input_dir> -o <out_dir> -m <breast_density_model>
 ```
 
-## Package the application as a MONAI Application Package (contianer image)
+## Package the application as a MONAI Application Package (container image)
 In order to build the MONAI App Package, go a level up and execute the following command.
 ```
 monai-deploy package breast_density_classification_app -m <breast_density_model> -c breast_density_classifer_app/app.yaml --tag breast_density:0.1.0 --platform x64-workstation -l DEBUG
@@ -20,4 +20,4 @@ monai-deploy package breast_density_classification_app -m <breast_density_model>
 monai-deploy run breast_density-x64-workstation-dgpu-linux-amd64:0.1.0 -i <input_dir> -o <output_dir>
 ```
 
-Once the container exits successfully, check the results in the output directory. There should be a newly creeated DICOM instance file and a `output.json` file containing the classification results.
+Once the container exits successfully, check the results in the output directory. There should be a newly created DICOM instance file and a `output.json` file containing the classification results.

examples/apps/breast_density_classifier_app/breast_density_classifier_operator.py

@@ -40,7 +40,7 @@ class ClassifierOperator(Operator):
 
     def __init__(
         self,
-        frament: Fragment,
+        fragment: Fragment,
         *args,
         model_name: Optional[str] = "",
         app_context: AppContext,
@@ -67,7 +67,7 @@ def __init__(
         # The name of the optional input port for passing data to override the output folder path.
         self.input_name_output_folder = "output_folder"
 
-        # The output folder set on the object can be overriden at each compute by data in the optional named input
+        # The output folder set on the object can be overridden at each compute by data in the optional named input
         self.output_folder = output_folder
 
         # Need the name when there are multiple models loaded
@@ -80,7 +80,7 @@ def __init__(
 
         self.model = self._get_model(self.app_context, self.model_path, self._model_name)
 
-        super().__init__(frament, *args, **kwargs)
+        super().__init__(fragment, *args, **kwargs)
 
     def _get_model(self, app_context: AppContext, model_path: Path, model_name: str):
         """Load the model with the given name from context or model path
@@ -116,7 +116,7 @@ def _convert_dicom_metadata_datatype(self, metadata: Dict):
         if not metadata:
             return metadata
 
-        # Try to convert data type for the well knowned attributes. Add more as needed.
+        # Try to convert data type for the well known attributes. Add more as needed.
         if metadata.get("SeriesInstanceUID", None):
             try:
                 metadata["SeriesInstanceUID"] = str(metadata["SeriesInstanceUID"])

examples/apps/hugging_face_integration_app/med_image_generation/app.py

@@ -1,39 +1,35 @@
-import logging 
-from pathlib import Path
-import torch 
-from diffusers import StableDiffusionPipeline
-from monai.deploy.core import AppContext, Application
-from PIL import Image
-import numpy as np
 import argparse
+import logging
+
+import torch
+from diffusers import StableDiffusionPipeline
 
+from monai.deploy.core import Application
 
 
 class App(Application):
-	name = "Diffusion Image App"
-	description = "Simple application showing diffusion to generate Images"
-	def compose(self):
-		model_id = "Nihirc/Prompt2MedImage"
-		device = "cuda"
-		parser = argparse.ArgumentParser()
-		parser.add_argument("--input_prompt", type=str, default="Generate a X-ray")
-		parser.add_argument("--output", type=str, default="./out.jpg")
-		args = parser.parse_args()
-
-		input_prompt = args.input_prompt
-		output_path = args.output
-		print("Input Prompt: ", input_prompt)
-		pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-		pipe = pipe.to(device)
-		prompt = "Show me an X ray pevic fracture"
-		image = pipe(prompt).images[0]
-		image.save(output_path)
+    name = "Diffusion Image App"
+    description = "Simple application showing diffusion to generate Images"
+
+    def compose(self):
+        model_id = "Nihirc/Prompt2MedImage"
+        device = "cuda"
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--input_prompt", type=str, default="Generate a X-ray")
+        parser.add_argument("--output", type=str, default="./out.jpg")
+        args = parser.parse_args()
+
+        input_prompt = args.input_prompt
+        output_path = args.output
+        print("Input Prompt: ", input_prompt)
+        pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+        pipe = pipe.to(device)
+        prompt = "Show me an X ray pevic fracture"
+        image = pipe(prompt).images[0]
+        image.save(output_path)
 
 
 if __name__ == "__main__":
-	logging.info(f"Begin {__name__}")
-	App().run()
-	logging.info(f"End {__name__}")
-
-
-
+    logging.info(f"Begin {__name__}")
+    App().run()
+    logging.info(f"End {__name__}")

examples/apps/mednist_classifier_monaideploy/mednist_classifier_monaideploy.py

@@ -100,7 +100,7 @@ class MedNISTClassifierOperator(Operator):
 
     def __init__(
         self,
-        frament: Fragment,
+        fragment: Fragment,
         *args,
         app_context: AppContext,
         model_name: Optional[str] = "",
@@ -127,7 +127,7 @@ def __init__(
         # The name of the optional input port for passing data to override the output folder path.
         self.input_name_output_folder = "output_folder"
 
-        # The output folder set on the object can be overriden at each compute by data in the optional named input
+        # The output folder set on the object can be overridden at each compute by data in the optional named input
         self.output_folder = output_folder
 
         # Need the name when there are multiple models loaded
@@ -138,7 +138,7 @@ def __init__(
         self.model = self._get_model(self.app_context, self.model_path, self._model_name)
 
         # This needs to be at the end of the constructor.
-        super().__init__(frament, *args, **kwargs)
+        super().__init__(fragment, *args, **kwargs)
 
     def _get_model(self, app_context: AppContext, model_path: Path, model_name: str):
         """Load the model with the given name from context or model path

examples/apps/simple_imaging_app/_version.py

@@ -307,7 +307,7 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
         # TAG-NUM-gHEX
         mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
         if not mo:
-            # unparseable. Maybe git-describe is misbehaving?
+            # unparsable. Maybe git-describe is misbehaving?
             pieces["error"] = ("unable to parse git-describe output: '%s'"
                                % describe_out)
             return pieces

examples/apps/simple_imaging_app/app.py

@@ -49,10 +49,10 @@ def compose(self):
         output_data_path = Path(app_context.output_path)
         logging.info(f"sample_data_path: {sample_data_path}")
 
-        # Please note that the Application object, self, is passed as the first positonal argument
+        # Please note that the Application object, self, is passed as the first positional argument
         # and the others as kwargs.
         # Also note the CountCondition of 1 on the first operator, indicating to the application executor
-        # to invoke this operator, hence the pipleline, only once.
+        # to invoke this operator, hence the pipeline, only once.
         sobel_op = SobelOperator(self, CountCondition(self, 1), input_path=sample_data_path, name="sobel_op")
         median_op = MedianOperator(self, name="median_op")
         gaussian_op = GaussianOperator(self, output_folder=output_data_path, name="gaussian_op")

examples/apps/simple_imaging_app/gaussian_operator.py

@@ -24,7 +24,7 @@ class GaussianOperator(Operator):
         single input:
           an image array object
         single output:
-          an image arrary object, without enforcing a downsteam receiver
+          an image array object, without enforcing a downstream receiver
 
     Besides, this operator also saves the image file in the given output folder.
     """

monai/deploy/core/domain/dicom_series.py

@@ -51,7 +51,7 @@ def get_sop_instances(self):
         return self._sop_instances
 
     # Properties named after DICOM Series module attribute keywords
-    # There are two required (Type 1) attrbutes for a series:
+    # There are two required (Type 1) attributes for a series:
     #     Keyword: SeriesInstanceUID, Tag: (0020,000E)
     #     Keyword: Modality, Tag: (0008,0060)
     #

monai/deploy/core/domain/dicom_sop_instance.py

@@ -27,7 +27,7 @@
 
 
 class DICOMSOPInstance(Domain):
-    """This class representes a SOP Instance.
+    """This class represents a SOP Instance.
 
     An attribute can be looked up with a slice ([group_number, element number]).
     """

monai/deploy/core/domain/dicom_study.py

@@ -35,7 +35,7 @@ def get_all_series(self):
         return list(self._series_dict.values())
 
     # Properties named after DICOM Study module attribute keywords
-    # There is only one required (Type 1) attrbute for a study:
+    # There is only one required (Type 1) attribute for a study:
     #     Keyword: StudyInstanceUID, Tag: (0020,000D)
     #
     @property

monai/deploy/operators/clara_viz_operator.py

@@ -33,7 +33,7 @@ class ClaraVizOperator(Operator):
         seg_image: Image object of the segmentation image derived from the input image.
     """
 
-    def __init__(self, fragement: Fragment, *args, **kwargs):
+    def __init__(self, fragment: Fragment, *args, **kwargs):
         """Constructor of the operator.
 
         Args:
@@ -43,7 +43,7 @@ def __init__(self, fragement: Fragment, *args, **kwargs):
         self.input_name_image = "image"
         self.input_name_seg_image = "seg_image"
 
-        super().__init__(fragement, *args, **kwargs)
+        super().__init__(fragment, *args, **kwargs)
 
     def setup(self, spec: OperatorSpec):
         spec.input(self.input_name_image)

monai/deploy/operators/dicom_encapsulated_pdf_writer_operator.py

@@ -45,12 +45,12 @@ class DICOMEncapsulatedPDFWriterOperator(Operator):
         None
 
     File output:
-        Generaed DICOM instance file in the provided output folder.
+        Generated DICOM instance file in the provided output folder.
     """
 
     # File extension for the generated DICOM Part 10 file.
     DCM_EXTENSION = ".dcm"
-    # The default output folder for saveing the generated DICOM instance file.
+    # The default output folder for saving the generated DICOM instance file.
     DEFAULT_OUTPUT_FOLDER = Path(os.getcwd()) / "output"
 
     def __init__(
@@ -249,7 +249,7 @@ def _is_pdf_bytes(self, content: bytes):
         return True
 
 
-# Commenting out the following as pttype complains about the contructor for no reason
+# Commenting out the following as pttype complains about the constructor for no reason
 # def test(test_copy_tags: bool = True):
 #     from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator
 #     from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator

monai/deploy/operators/dicom_seg_writer_operator.py

@@ -206,7 +206,7 @@ def __init__(
                 Object encapsulating the description of each segment present in the segmentation.
             output_folder: Folder for file output, overridden by named input on compute.
                            Defaults to current working dir's child folder, output.
-            custom_tags: Optonal[Dict[str, str]], optional
+            custom_tags: Optional[Dict[str, str]], optional
                 Dictionary for setting custom DICOM tags using Keywords and str values only
             omit_empty_frames: bool, optional
                 Whether to omit frames that contain no segmented pixels from the output segmentation.

monai/deploy/operators/dicom_text_sr_writer_operator.py

@@ -42,12 +42,12 @@ class DICOMTextSRWriterOperator(Operator):
         None
 
     File output:
-        Generaed DICOM instance file in the provided output folder.
+        Generated DICOM instance file in the provided output folder.
     """
 
     # File extension for the generated DICOM Part 10 file.
     DCM_EXTENSION = ".dcm"
-    # The default output folder for saveing the generated DICOM instance file.
+    # The default output folder for saving the generated DICOM instance file.
     # DEFAULT_OUTPUT_FOLDER = Path(os.path.join(os.path.dirname(__file__))) / "output"
     DEFAULT_OUTPUT_FOLDER = Path.cwd() / "output"
 
@@ -259,7 +259,7 @@ def write(self, content_text, dicom_series: Optional[DICOMSeries], output_dir: P
         self._logger.info(f"DICOM SOP instance saved in {file_path}")
 
 
-# Commenting out the following as pttype complains about the contructor for no reason
+# Commenting out the following as pttype complains about the constructor for no reason
 # def test(test_copy_tags: bool = True):
 #     from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator
 #     from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator