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Graph Plotting Utility

Author: Muhammed Emre Bayraktaroglu

This documentation provides an overview of the Graph Plotting Utility, which is designed to generate and encode food consumption-related graphs for different countries. The utility processes country-level predictions and outputs visualizations as base64-encoded images.

Overview​

The Graph Plotting Utility:

  • Generates Food Consumption Score (FCS) and Reduced Coping Strategy Index (rCSI) graphs.
  • Applies confidence intervals for visual clarity.
  • Encodes graphs as base64 strings for seamless integration.
  • Cleans up temporary image files after encoding.

Requirements and Setup​

Before using this utility, ensure you have the following:

  • Python 3.8+ installed.
  • matplotlib installed for graph generation.

Directory Structure​

The script assumes the following folder structure:

project_root/
├── plots/  # Stores temporary image files before encoding
│   ├── country_FCS.png
│   ├── country_RCSI.png

After execution, the plots folder will be removed, leaving only base64-encoded images.

Key Features​

Graph Generation​

The script generates FCS and rCSI visualizations based on country-specific predictions.

Base64 Encoding​

The generated graphs are converted into base64 strings for easy embedding.

Temporary File Cleanup​

Ensures that all intermediate image files are deleted after encoding.

Function(s)​

Function: plot_fcs(country_data)​

Generates and encodes an FCS graph for a given country.

Process:​

  1. Extracts FCS data (dates, values, confidence intervals) from country_data.
  2. Plots a time-series graph with confidence shading.
  3. Saves the graph as a temporary PNG file.
  4. Encodes the image in base64 format.
  5. Deletes the temporary image file.

Exceptions Handled:​

  • KeyError: If necessary data is missing.
  • FileNotFoundError: If the image file cannot be located post-generation.
  • PermissionError: If the script lacks the necessary permissions to delete files.

Function: plot_rcsi(country_data)​

Generates and encodes an rCSI graph for a given country.

Process:​

  1. Extracts rCSI data (dates, values, confidence intervals) from country_data.
  2. Plots a time-series graph with confidence shading.
  3. Saves the graph as a temporary PNG file.
  4. Encodes the image in base64 format.
  5. Deletes the temporary image file.

Exceptions Handled:​

  • KeyError: If necessary data is missing.
  • FileNotFoundError: If the image file cannot be located post-generation.
  • PermissionError: If the script lacks the necessary permissions to delete files.

Function: generate_graphs(data)​

Processes a dataset to generate and encode FCS and rCSI graphs for multiple countries.

Process:​

  1. Iterates through a list of country dictionaries.
  2. Calls plot_fcs() and plot_rcsi() for each country.
  3. Stores the resulting base64-encoded images in the dataset.
  4. Cleans up the plots directory after processing.

Exceptions Handled:​

  • OSError: If the script encounters issues removing the plots directory.

Function: base64_encode_image(image_path)​

Encodes an image file as a base64 string.

Process:​

  1. Reads the image file in binary mode.
  2. Encodes the file using base64.
  3. Returns the encoded string.

Example Usage​

To execute the script, call generate_graphs() with a dataset:

country_data_list = [
    {"country_name": "Country A", "fcs_prediction": [...], "rcsi_prediction": [...]},
    {"country_name": "Country B", "fcs_prediction": [...], "rcsi_prediction": [...]},
]
generate_graphs(country_data_list)

If no data is available for a country, the script will print:

No FCS data available for Country A
No RCSI data available for Country A

Upon successful encoding, it prints:

FCS Graph saved: plots/Country_A_FCS.png
RCSI Graph saved: plots/Country_A_RCSI.png

What Happens Under the Hood?​

  1. Data Extraction: The script retrieves FCS and rCSI predictions from country data.
  2. Graph Generation: Uses Matplotlib to generate visual representations.
  3. Base64 Encoding: Converts the images into a string format for easy transfer.

Conclusion​

The Graph Plotting Utility is an efficient tool for visualizing food consumption trends across different countries.