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🚀 Run your own SFPPy on Google Colab ⋆.˚☁️⋆ in Just 2 Minutes! 🎉

 

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📌 1 | Quick Setup Guide

Follow these simple steps to get SFPPy running in Google Colab:

Instructions	Demo
🔹Step 1: Open Google Colab. 🔹 Connect using your Google Account (free). ✅
🔹 Step 2: Open a Notebook from GitHub. 🔹 In Google Colab, select "Open Notebook" ➝ Click the GitHub tab. 🔹 Paste this repository URL: 👉 https://github.com/ovitrac/SFPPy 🔹 Select index.ipynb and open it. 📂
🔹 Step 3: Scroll to "Minimal Setup for SFPPy Core". 🔹 Run the cell by clicking ▶️ or pressing Ctrl+Enter. 🔹 The entire SFPPy framework will be cloned into your private workspace. 🔹 Once you see the SFPPy banner, everything is ready! 🎨✅
🔹 Step 4: Run the next cells one by one. 🔹 This will activate the graphical interface to explore examples and notebooks. 🖥️✨
🔹 Step 5: The interface provides interactive widgets to explore examples and notebooks. 📊🔧
🔹 Step 6: Run examples directly inside the notebook. 🏃💡
🔹 Step 7: Google Colab allows one notebook open per session. 🔹 Use the final widget to browse other notebooks and find relevant code snippets. 📚🔍
🔹 Step 8: The left panel gives access to all cloned SFPPy files. 🔹 You can open and edit Python scripts directly in Google Colab! 📝👨‍💻

🎯 Congratulations! SFPPy is running in Google Colab! 🎉🚀

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🤖 2 | AI-Powered Code Assistance!

Colab AI can generate and modify Python code based on SFPPy.

Example prompt:
"Generate a list of properties for BHT, Irganox 1076, Irganox 1010, Irgafos 168, and DEHP using migrant."

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## 🏗 3 | **Advanced Features & Installation**

from patankar.loadpubchem import migrant, migrantToxtree  # Chemical database tools
m1a = migrant("decabromodiphenyl ether")  # Without ToxTree outputs
m1b = migrantToxtree("decabromodiphenyl ether")  # With ToxTree outputs
m2 = migrant("BHT")  # Positively listed substance
m1b  # Display results

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📝 4 | Using Forms in Google Colab

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# Example Colab Form
%%form --col 2
# @title **Substance Contact Details** { display-mode: "form" }
# @markdown ### **Enter the details below:**
substance_name = "Irganox 1076" # @param {type:"string"}
contact_temp = 25.0 # @param {type:"number"}
contact_time = 10 # @param {type:"integer"}
form_date = "2025-03-18" # @param {type:"date"}
print(f"Substance: {substance_name}")
print(f"Contact Temperature: {contact_temp} °C")
print(f"Contact Time: {contact_time} days")
print(f"Date: {form_date}")

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🎯 5 | Tips & Best Practices with Cloud Resources

 

✅ 5.1 | Running All Cells

Execute all notebook cells via Runtime → Run All.

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⚡ 5.2 | Resource Optimization

Even without a GPU, SFPPy runs efficiently on Colab’s CPU-optimized environment, completing most calculations in typically under one or two seconds 🏎️.

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📂 5.3 | Managing Notebooks

Open additional SFPPy notebooks from index.ipynb.
Each notebook runs in an isolated session (Google policy).

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• 🏗️ 5.4 | Graphical User Interface

  SFPPy provides an intuitive, interactive interface that simplifies the design of complex migration scenarios with minimal coding effort. These fully functional widgets are available in Google Colab and can be accessed directly from the gui.ipynb notebook.

  While these tools streamline workflows and enhance usability, AI assistants may have difficulty interpreting user intent when working with graphical elements. Nevertheless, SFPPy’s visual approach significantly reduces the coding barrier and enables seamless model setup. 🚀

  📌 Example SFPPy GUI Screenshots:

  
  

   

   

  . .

  Positively listed substances within 🇪🇺 appear have blue information including details onon their applicable restrictions.
  Non-postively listed Substances are shown with toxilogical information in red, including relevant warnings and alerts.

   

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  📊 5.5 | Export Data in Multiple Formats

  No external plotting libraries are required—everything runs directly within the notebook. SFPPy provides built-in tools for exporting kinetic and concentration profile data in multiple formats.

  📌 Example Output:

  

  🔹 SFPPy exports both CF (kinetics) and Cx (concentration profiles) in four formats:
  ✔️ CSV – For easy data handling
  ✔️ XLS – Compatible with Excel
  ✔️ PDF – For reports and documentation
  ✔️ PNG – For visualization and presentations

   

  📌 Example Exported Data:

Since the notebook contains all assumptions used for the simulation, it is recommended to download it as a Python script (.py) or as a Jupyter Notebook (.ipynb) for reproducibility.

📌 Example Notebook Export Options:

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🔄 5.6 | Collaborative & Shareable

Google Colab enables real-time collaboration, making it easy to:

✅ Share SFPPy notebooks with colleagues
✅ Work together on chemical safety analyses
✅ Use GitHub for version control
✅ Enable one-click access to SFPPy workflows via shared links

 

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💾 6 | Persistent Storage via Google Drive

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🚀 6.1 - Step 1 | Mount Google Drive

Before saving any data, you need to connect Google Drive to Google Colab.

📌 Run the following code:

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from google.colab import drive
drive.mount('/content/drive')

🔹 This will prompt you to authorize access to your Google Drive.
🔹 Follow the on-screen instructions, copy the authentication code, and paste it into the prompt.
🔹 Once authenticated, Google Drive will be available under the /content/drive/My Drive/ path.

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📂 6.2 - Step 2 | Create a Folder in Google Drive

To keep your files organized, create a dedicated folder in your Google Drive. You can do this manually or using Python:

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import os
from google.colab import drive

# Mount Google Drive
drive.mount('/content/drive')

# Create a folder inside Google Drive
folder_path = "/content/drive/My Drive/Colab_Files"
os.makedirs(folder_path, exist_ok=True)

print(f"Folder created at: {folder_path}")

🔹 This code will create a folder named "Colab_Files" inside Google Drive.
🔹 If the folder already exists, the script will not overwrite it.

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📜 6.3 - Step 3 | Save a File to Google Drive

To save a simple text file or any generated data:

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file_path = "/content/drive/My Drive/Colab_Files/sample.txt"

# Write a sample file
with open(file_path, "w") as file:
    file.write("Hello, World from Google Colab!")

print(f"File saved at: {file_path}")

🔹 This saves "sample.txt" in Colab_Files inside Google Drive.
🔹 You can open Google Drive and check My Drive > Colab_Files > sample.txt.

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🗂 6.4 - Step 4 | Save an Entire Repository to Google Drive

To save a GitHub repository or any directory to Google Drive, follow these steps:

6.4.1 | Clone the Repository to Colab

First, clone your GitHub repo inside Colab:

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!git clone https://github.com/ovitrac/SFPPy.git /content/SFPPy

This will create a local copy of the repository in Colab under /content/SFPPy/.

6.4.2 | Copy the Repository to Google Drive

Now, move the entire repository to Google Drive:

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import shutil

repo_path = "/content/SFPPy"
drive_repo_path = "/content/drive/My Drive/SFPPy_Backup"

# Copy the repository to Google Drive
shutil.copytree(repo_path, drive_repo_path)

print(f"Repository saved at: {drive_repo_path}")

🔹 This copies the entire SFPPy repository to your Google Drive under My Drive > SFPPy_Backup.

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🔄 6.5 - Step 5 | Load Files Back from Google Drive

If you need to reload a saved file in a future Colab session:

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file_path = "/content/drive/My Drive/Colab_Files/sample.txt"

# Read the file
with open(file_path, "r") as file:
    content = file.read()

print("File Content:", content)

Similarly, to restore the full repository:

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import shutil

# Define paths
drive_repo_path = "/content/drive/My Drive/SFPPy_Backup"
local_repo_path = "/content/SFPPy"

# Copy back to Colab
shutil.copytree(drive_repo_path, local_repo_path)

print("Repository restored in:", local_repo_path)

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✅ 6.6 | Final Notes

🔹 Google Colab's storage is temporary – always save important work to Google Drive.
🔹 Use shutil.copytree to back up and restore large folders.
🔹 Google Drive syncs automatically, so you can access files from anywhere.

🔗 Now your files and repository are safe inside Google Drive! 🚀

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🎉 You're now ready to explore SFPPy, run examples, and customize scripts!

Do not forget to download your scripts on Google Drive on your local disk.💡🔬

 

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🎓 7 | Learning with External Resources

To deepen your understanding of Jupyter Notebooks and Google Colab, here are some highly recommended tutorials and learning resources from reputable sources.

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🐍 7.1 | Jupyter Notebook Tutorials

1️⃣ Dataquest's Beginner Tutorial

• Step-by-step guide to creating and managing Jupyter Notebooks

• Covers essential components and best practices

2️⃣ DataCamp's Ultimate Guide

• Comprehensive overview of Jupyter's structure and functionalities

• Explains markdown, cells, and advanced features

3️⃣ GeeksforGeeks: How to Use Jupyter Notebook

• Installation, basic operations, and key shortcuts

4️⃣ YouTube Video: Jupyter Notebook Tutorial

• Interactive walkthrough of Jupyter’s interface and features

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☁️ 7.2 | Google Colab Tutorials

1️⃣ Google's Official Colab Guide

• Introduction to Google Colab’s cloud-based Python environment

• How to write and execute Python code in a browser

2️⃣ GeeksforGeeks: How to Use Google Colab

• Covers interface navigation, running code, and collaboration

3️⃣ Marqo’s Beginner Guide to Google Colab

• Overview of Colab’s capabilities, including GPU usage and file management

4️⃣ YouTube Video: Google Colab for Beginners

• Step-by-step video tutorial on using Colab effectively

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🔹 7.3 | Key Takeaways

✅ Jupyter Notebooks are great for local Python development with high flexibility.
✅ Google Colab provides a cloud-based alternative with free resources and easy sharing.
✅ Both platforms support interactive computing, data visualization, and AI workflows.

With these resources, you can quickly master Jupyter and Colab to enhance your SFPPy experience! 🚀

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⋆.˚🦋༘⋆🍃°.☁️ • ๑ ˙☕

🔗 More details 👉 SFPPy GitHub Repository

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