Fix model iv py halo import

.gitignore

@@ -0,0 +1 @@
+venv/

DeepLense_Physics_Informed_Super_Resolution_Anirudh_Shankar/simulations.txt

@@ -1,7 +1,7 @@
 apturl==0.5.2
 bcrypt==3.2.0
 blinker==1.4
-Brlapi==0.8.3
+Brlapi==0.8.3; platform_system == "Linux"
 certifi==2020.6.20
 chardet==4.0.0
 click==8.0.3
@@ -62,7 +62,7 @@ scipy==1.14.1
 screen-resolution-extra==0.0.0
 SecretStorage==3.3.1
 six==1.16.0
-systemd-python==234
+systemd-python==234; platform_system == "Linux"
 ubuntu-advantage-tools==8001
 ubuntu-drivers-common==0.0.0
 ufw==0.36.1

Grid_based_strong_lensing_for_unsupervised_super_resolution_Anirudh_Shankar/requirements.txt

@@ -3,7 +3,7 @@ apturl==0.5.2
 asttokens==3.0.0
 bcrypt==3.2.0
 blinker==1.4
-Brlapi==0.8.3
+Brlapi==0.8.3; platform_system == "Linux"
 CacheControl==0.12.10
 cachy==0.3.0
 certifi==2020.6.20
@@ -109,7 +109,7 @@ pymacaroons==0.13.0
 PyNaCl==1.5.0
 pyparsing==2.4.7
 pyRFC3339==1.1
-python-apt==2.4.0+ubuntu4
+python-apt==2.4.0+ubuntu4; platform_system == "Linux"
 python-dateutil==2.9.0.post0
 python-debian==0.1.43+ubuntu1.1
 pytz==2022.1
@@ -127,7 +127,7 @@ six==1.16.0
 ssh-import-id==5.11
 stack-data==0.6.3
 sympy==1.13.1
-systemd-python==234
+systemd-python==234; platform_system == "Linux"
 tensorboard==2.19.0
 tensorboard-data-server==0.7.2
 tomlkit==0.9.2

README.md

@@ -115,4 +115,60 @@ Finally, DeepLense help combat the problem of noisy and low-resolution of real l
 **Pranath Reddy** performs a comparative study of the super-resolution of strong lensing images in their [GSoC 2023 project](https://summerofcode.withgoogle.com/archive/2023/projects/Rh8kJLr4), using Residual Models with Content Loss and Conditional Diffusion Models, on the Model 1 dataset.
 
 #### 3.3.3 Physics-Informed Unsupervised Super-Resolution of Strong Lensing Images
-**Anirudh Shankar** explores the unsupervised super-resolution of strong lensing images through a Physics-Informed approach in his [GSoC 2024 project](https://summerofcode.withgoogle.com/programs/2024/projects/AvlaMMJJ), built to handle sparse datasets. They use custom datasets using different lens models and light profiles. 
+**Anirudh Shankar** explores the unsupervised super-resolution of strong lensing images through a Physics-Informed approach in his [GSoC 2024 project](https://summerofcode.withgoogle.com/programs/2024/projects/AvlaMMJJ), built to handle sparse datasets. They use custom datasets using different lens models and light profiles. 
+
+> ⚠️ **Note**
+>
+> This repository is a script-based research monorepo and is **not**
+> distributed as a Python package.  
+> It does **not** support `pip install .` or `pip install -e .`.
+> Dependencies must be installed using `requirements.txt`.
+
+
+
+## 3. Installation (Windows)
+
+> ⚠️ **Installation Note**
+>
+> This repository does **not** support:
+> - `pip install .`
+> - `pip install -e .`
+>
+> Dependencies must be installed using `requirements.txt`.
+
+### 3.1 Create and activate a virtual environment
+
+```bat
+python -m venv venv
+venv\Scripts\activate
+
+3.2 Install common dependencies
+python -m pip install --upgrade pip
+pip install -r requirements.txt
+
+
+This installs common dependencies used across multiple DeepLense projects, such as:
+
+numpy
+scip
+matplotlib
+lenstronomy
+pyHalo
+
+Some projects may require additional dependencies (e.g. torch).
+Please refer to the README inside the relevant project directory if available.
+
+
+After installing dependencies, you can verify that the environment is correctly set up.
+
+python -c "import numpy, scipy, matplotlib, lenstronomy, pyHalo"
+
+If no error is raised, the core dependencies are installed successfully.
+
+cd <project_directory>
+
+python <script_name>.py
+python <script_name>.py
+
+Jupyter issue if any 
+pip install jupyter

requirements.txt

@@ -0,0 +1,6 @@
+numpy>=1.23
+scipy>=1.9
+matplotlib>=3.7
+mcfit>=0.0.21
+pyHalo>=1.4.3
+lenstronomy>=1.11,<1.13