https://github.com/andrewzhoumq123/sci_graph_api

Generates different types of detailed plots for scientific data
https://github.com/andrewzhoumq123/sci_graph_api

fastapi h5py json matplotlib numpy pandas scipy

Last synced: about 1 month ago
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Generates different types of detailed plots for scientific data

• Host: GitHub
• URL: https://github.com/andrewzhoumq123/sci_graph_api
• Owner: AndrewZhouMQ123
• Created: 2025-02-11T01:55:39.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-03-04T07:46:11.000Z (2 months ago)
• Last Synced: 2025-04-05T19:35:59.759Z (about 1 month ago)
• Topics: fastapi, h5py, json, matplotlib, numpy, pandas, scipy
• Language: Python
• Homepage:
• Size: 108 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# SCIENTIFIC GRAPH API

- Generates data graphs and best fits plots by calling API endpoints with data files and parameters

- Plots and fits are available as downloadable pdfs (best format for latex)

- Hosted on Heroku, URL: https://sci-graph-api-5df4e4bebbdd.herokuapp.com/

- Web version hosted on Vercel, URL: https://web-goodies.vercel.app/

## General Guidelines

- Acceptable data formats: csv, npy, npz, HDF5, JSON

- First row of csv should be the headers (x labels, y labels etc)

- Size Option: Single Column Plot (3.375 inches * 3 inches), Double Column Plot (7 inches * 3 inches)

- Try to not have duplicate headers, Pandas automatically appends numbers to duplicate colummns, so if that is not what you want, try to have unique column headers

# Plot API

## Scatter Plot

- **Endpoint:** `/plot/scatter`

- **Format:** `x : y`

- **Example Data:**  

  ![data](/public/data.png)

## Errorbar Plot

- **Endpoints:**  

  - `/plot/errorbar1x` → `x : y : error x`

  - `/plot/errorbar1y` → `x : y : error y`

  - `/plot/errorbar2xy` → `x : y : error x : error y`

## Bar Graph

- **Endpoint:** `/plot/bar`

- **Format:**

  - Column 1: Data

  - Column 2: Labels

## Pie Chart

- **Endpoint:** `/plot/pie`

- **Format:**

  - Column 1: Category

  - Column 2: Percentage

## Boxplot

- **Endpoint:** `/plot/boxplot`

- **Format:**

  - Column 1: Category

  - Column 2: Value

## Single Histogram

- **Endpoints:**  

  - `/plot/eqhistogram`

  - `/plot/varyhistogram`

- **Formats:**  

  - Single file → Data → Counts/Frequencies

  - Two files → Data, Weights → Counts/Frequencies : Bins

- **Other Parameters:**  

  - `bins`:  

    - Integer (e.g., `bins=10`): Automatically bins data into evenly spaced intervals.

    - List (e.g., `bins=[1, 2, 5]`): Uses exact numbers for bin spacings.

## Heatmap and Contour Map (CSV Format)

- **Endpoints:**  

  - `/plot/imshowhmap`

  - `/plot/pmeshhmap`

  - `/plot/pmeshfunchmap`

  - `/plot/imshowhmap`

  - `/plot/contour`

- **Data Format:**  

  - Matrices must have a single header value `"m"` to distinguish them from ordinary data.

  - **Example Data:**  

    ![matrix](/public/matrix.png)

- **Normalization:**  

  - `"minmax"` or `"zscore"`

- **Missing Values Handling:**  

  - `"mean"`, `"median"`, or `0`

- **Colormap Selection:**  

  - Choose from: [Matplotlib Colormaps](https://matplotlib.org/stable/users/explain/colors/colormaps.html)

- **Contour Levels:**  

  - Integer (e.g., `levels=10`): Automatically generates evenly spaced contour levels.

  - List (e.g., `levels=[-1, 0, 1]`): Uses exact numbers as contour levels.

## Best Fit API

###