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Updates for version v1.0.7 (Major)
- Add photon dose calculation module for custom patients outside PortPy dataset - Create example notebook for dose calculation of custom patient outside PortPy data and optimize the plan using PortPy Minor bug fixes - Add more colors to visualization.py for additional structures - Add create_ct_dose_voxel_map.py for photon dose calc - Expand functionality for convert_dose_rt_dicom_to_portpy.py if ct object is not available.
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‎README.md‎

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</h2>
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![Version](https://img.shields.io/static/v1?label=latest&message=v1.0.6&color=darkgreen)
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![Version](https://img.shields.io/static/v1?label=latest&message=v1.0.7&color=darkgreen)
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[![Total Downloads](https://static.pepy.tech/personalized-badge/portpy?period=total&units=international_system&left_color=grey&right_color=blue&left_text=total%20downloads)](https://pepy.tech/project/portpy?&left_text=totalusers)
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[![Monthly Downloads](https://static.pepy.tech/badge/portpy/month)](https://pepy.tech/project/portpy)
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# Quick start and examples <a name="QuickStart"></a>
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The easiest way to start is through the PorPy following Jupiter Notebook examples.
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| Example File | Description |
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|------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [1_basic_tutorial.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/1_basic_tutorial.ipynb) | Demonstrates the main functionalities of PortPy (e.g., Access data, create an IMRT plan, visualize) |
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| [vmat_scp_tutorial.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/vmat_scp_tutorial.ipynb) | Creates a VMAT plan using sequential convex programming |
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| [vmat_scp_dose_prediction.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/vmat_scp_dose_prediction.ipynb) | Predicts 3D dose distribution using deep learning and converts it into a deliverable VMAT plan |
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| [3d_slicer_integration.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/3d_slicer_integration.ipynb) | Creates an IMRT plan and visualizes it in 3D-Slicer |
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| [imrt_tps_import.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/imrt_tps_import.ipynb) | 1. Outputs IMRT plan in DICOM RT format and imports it into TPS. <br>2. Outputs IMRT plan optimal fluence in an Eclipse-compatable format and imports it into Eclipse |
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| [vmat_tps_import.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/vmat_tps_import.ipynb) | Outputs VMAT plan in DICOM RT format and imports it into TPS |
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| [imrt_dose_prediction.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/imrt_dose_prediction.ipynb) | Predicts 3D dose distribution using deep learning and converts it into a deliverable IMRT plan |
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| [vmat_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/vmat_global_optimal.ipynb) | Finds a globally optimal VMAT plan |
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| [beam_orientation_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/beam_orientation_global_optimal.ipynb) | Finds globally optimal beam angles for IMRT |
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| [dvh_constraint_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/dvh_constraint_global_optimal.ipynb) | Finds a globally optimal plan meeting Dose Volume Histogram (DVH) constraints |
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| Example File | Description |
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|------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [1_basic_tutorial.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/1_basic_tutorial.ipynb) | Demonstrates the main functionalities of PortPy (e.g., Access data, create an IMRT plan, visualize) |
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| [eclipse_photon_dose_calculation.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/eclipse_photon_dose_calculation.ipynb) | Demonstrates the capability of running dose calculation for patients outside PortPy dataset using Varian's photon dose calculation module and perform optimization in PortPy |
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| [vmat_scp_tutorial.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/vmat_scp_tutorial.ipynb) | Creates a VMAT plan using sequential convex programming |
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| [vmat_scp_dose_prediction.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/vmat_scp_dose_prediction.ipynb) | Predicts 3D dose distribution using deep learning and converts it into a deliverable VMAT plan |
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| [3d_slicer_integration.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/3d_slicer_integration.ipynb) | Creates an IMRT plan and visualizes it in 3D-Slicer |
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| [imrt_tps_import.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/imrt_tps_import.ipynb) | 1. Outputs IMRT plan in DICOM RT format and imports it into TPS. <br>2. Outputs IMRT plan optimal fluence in an Eclipse-compatable format and imports it into Eclipse |
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| [vmat_tps_import.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/vmat_tps_import.ipynb) | Outputs VMAT plan in DICOM RT format and imports it into TPS |
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| [imrt_dose_prediction.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/imrt_dose_prediction.ipynb) | Predicts 3D dose distribution using deep learning and converts it into a deliverable IMRT plan |
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| [vmat_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/vmat_global_optimal.ipynb) | Finds a globally optimal VMAT plan |
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| [beam_orientation_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/beam_orientation_global_optimal.ipynb) | Finds globally optimal beam angles for IMRT |
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| [dvh_constraint_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/dvh_constraint_global_optimal.ipynb) | Finds a globally optimal plan meeting Dose Volume Histogram (DVH) constraints |
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cannot use your own dataset for now. We will address this problem in the near future
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```python
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# Use PortPy DataExplorer class to explore PortPy data
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data = pp.DataExplorer(data_dir=''../data)
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data = pp.DataExplorer(data_dir='../data')
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# Load ct, structure set, beams for the above patient using CT, Structures, and Beams classes
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ct = pp.CT(data)
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structs = pp.Structures(data)
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```
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pip install portpy[mosek, pydicom]
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```
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* For installing all the additional packages
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```
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pip install portpy[full]
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```
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2. Install using conda:
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