NNPE
Description
A code that automatically calculates missing data of any group of weather stations based on the station with the most consistent records of the group, called Neural Network Precipitation Estimator (NNPE) Initial preparation: *Install Python: If you don't have Python installed, download it from python.org and install it. *Install the necessary libraries: Open the terminal or command prompt (depending on your operating system) and run the following command to install the required libraries: pip install pandas numpy scikit-learn matplotlib openpyxl #You can run this code from the terminal or command prompt: Steps to run the code from the terminal or command prompt: *Prepare the files: Save the code in a text file with .py extension. Make sure you have an Excel file called Input.xlsx in the same directory as the .py file. This file should have a structure similar to what the code expects. *Execute the code: Open the terminal or command prompt. Navigate to the directory where your .py and Input.xlsx file are located. Type python filename.py (replace filename.py with the actual name of your Python file) and press Enter. *Interact with the program: The program will ask you to enter a number, either 1 or 2, to determine the number of base stations to use. Follow the instructions that appear in the terminal or command prompt depending on the questions the program asks. Or You can run this code from Jupyter Lab: Steps to run the code in Jupyter Lab: Initial preparation: *Install Jupyter Lab: If you don't already have Jupyter Lab installed, you can do so by using pip in your terminal or command prompt with the command: pip install jupyterlab *Open Jupyter Lab: Open the terminal or command prompt. Navigate to the directory where your .ipynb and Input.xlsx file is located. Run the jupyter lab command. This will open Jupyter Lab in your default web browser. Code execution: *Create a new notebook: In Jupyter Lab, click the "New" button and select "Notebook: Python" to create a new notebook. *Copy and paste the code: In a cell of the notebook, copy and paste the provided code. *Execute the code in sections: Divide the code into logical sections (cells). You can do this by separating each block of code with comments indicating sections #0, #1, #2, etc. Click on a cell and press Shift + Enter to run that section of code. *Interact with the program: When the program prompts for input (as in section #6), enter the answer in the appropriate cell and press Enter. *Observe the results: If there are parts of the code that generate graphs, these will be displayed directly below the corresponding code cells. *Save results (optional): You can save the generated results to files if the part of the code that saves the graphs or data to CSV files has been uncommented. *End execution: Continue executing the cells until you have finished using the code. Once finished, close Jupyter Lab or save and close the notebook if you want to resume work later.
Files
Steps to reproduce
Initial preparation: *Install Python: If you don't have Python installed, download it from python.org and install it. *Install the necessary libraries: Open the terminal or command prompt (depending on your operating system) and run the following command to install the required libraries: pip install pandas numpy scikit-learn matplotlib openpyxl #You can run this code from the terminal or command prompt: Steps to run the code from the terminal or command prompt: *Prepare the files: Save the code in a text file with .py extension. Make sure you have an Excel file called Input.xlsx in the same directory as the .py file. This file should have a structure similar to what the code expects. *Execute the code: Open the terminal or command prompt. Navigate to the directory where your .py and Input.xlsx file are located. Type python filename.py (replace filename.py with the actual name of your Python file) and press Enter. *Interact with the program: The program will ask you to enter a number, either 1 or 2, to determine the number of base stations to use. Follow the instructions that appear in the terminal or command prompt depending on the questions the program asks. Or You can run this code from Jupyter Lab: Steps to run the code in Jupyter Lab: Initial preparation: *Install Jupyter Lab: If you don't already have Jupyter Lab installed, you can do so by using pip in your terminal or command prompt with the command: pip install jupyterlab *Open Jupyter Lab: Open the terminal or command prompt. Navigate to the directory where your .ipynb and Input.xlsx file is located. Run the jupyter lab command. This will open Jupyter Lab in your default web browser. Code execution: *Create a new notebook: In Jupyter Lab, click the "New" button and select "Notebook: Python" to create a new notebook. *Copy and paste the code: In a cell of the notebook, copy and paste the provided code. *Execute the code in sections: Divide the code into logical sections (cells). You can do this by separating each block of code with comments indicating sections #0, #1, #2, etc. Click on a cell and press Shift + Enter to run that section of code. *Interact with the program: When the program prompts for input (as in section #6), enter the answer in the appropriate cell and press Enter. *Observe the results: If there are parts of the code that generate graphs, these will be displayed directly below the corresponding code cells. *Save results (optional): You can save the generated results to files if the part of the code that saves the graphs or data to CSV files has been uncommented. *End execution: Continue executing the cells until you have finished using the code. Once finished, close Jupyter Lab or save and close the notebook if you want to resume work later.