Update README.md

README.md

@@ -108,36 +108,24 @@ Beam_0_Data.h5
 You can load the dataset with the following lines of code.
 
 ```python
-from datasets import load_dataset
-
-dataset = load_dataset("PortPy-Project/PortPy_Datset", split="split_0")
-print(dataset)
-Dataset({
-    features: ['patient_id', 'ct_data', 'ct_metadata', 'structureset_data', 'structureset_metadata', 'beam_data_paths', 'beam_metadata_paths', 'optimization_voxels_data', 'optimization_voxels_metadata', 'planner_beams', 'rt_dose_echo_imrt', 'rt_plan_echo_imrt'],
-    num_rows: 50
-})
-
-# load ct metadata for 1st patient
-ct_metadata = load_dataset("json", data_files=dataset[0]['ct_metadata'])
-print(ct_metadata)
-
-DatasetDict({
-    train: Dataset({
-        features: ['origin_xyz_mm', 'resolution_xyz_mm', 'size_xyz_mm', 'direction', 'ct_hu_3d_File'],
-        num_rows: 1
-    })
-})
-
-# load structures metadata for 1st patient
-structureset_metadata = load_dataset("json", data_files=dataset[0]['structureset_metadata'])
-print(structureset_metadata)
-
-DatasetDict({
-    train: Dataset({
-        features: ['name', 'volume_cc', 'dicom_structure_name', 'fraction_of_vol_in_calc_box', 'structure_mask_3d_File'],
-        num_rows: 9
-    })
-})
+#!pip install portpy (# Install portpy )
+import portpy.photon as pp
+
+# Use PortPy DataExplorer class to explore and download PortPy data from HuggingFace. Please modify 'local_download_dir' in case
+# if you want to download data to custom local directory
+data = pp.DataExplorer(hf_repo_id="PortPy-Project/PortPy_Dataset", local_download_dir=r'../hugging_face_data')
+
+# display overview of the patients available in portpy
+data.display_list_of_patients()
+
+#pick up a patient to create a treament plan
+data.patient_id = 'Lung_Patient_16'
+
+# download data for the given patient
+data.filter_and_download_hf_dataset()
+
+# display patient metadata
+data.display_patient_metadata()
 
 ```
 
@@ -157,14 +145,3 @@ The original imaging data was processed by the authors of this repository by:
 - Adding benchmark plans using in-house automated treatment planning system **ECHO**.
 - Storing the data in the standardized [PortPy](https://github.com/PortPy/PortPy) data format for research and educational purposes.
  
-
-# Reference <a name="Reference"></a>
-If you find our work useful in your research or if you use parts of this code please cite our [AAPM'23 abstract](https://aapm.confex.com/aapm/2023am/meetingapp.cgi/Paper/4208) :
-```
-@article{jhanwar2023portpy,
-  title={Portpy: An Open-Source Python Package for Planning and Optimization in Radiation Therapy Including Benchmark Data and Algorithms},
-  author={Jhanwar, Gourav and Tefagh, Mojtaba and Taasti, Vicki T and Alam, Sadegh R and Tuomaala, Seppo and Nadeem, Saad and Zarepisheh, Masoud},
-  journal={AAPM 65th Annual Meeting & Exhibition},
-  year={2023}
-}
-```