# Installation


# Prerequisites

To install the latest version of `imfp`, you will need to have [Python 3.10 or later](https://www.python.org/downloads/) installed on your system.

If you don't already have Python, we recommend installing [the `uv` package manager](https://astral.sh/setup-uv/) and installing Python with `uv python install`.


# Installation

To install the latest stable `imfp` wheel from PyPi using pip:

``` bash
pip install --upgrade imfp
```

Alternatively, to install from the source code on Github, you can use the following command:

``` bash
pip install --upgrade git+https://github.com/Promptly-Technologies-LLC/imfp.git
```

You can then import the package in your Python script:

``` python
import imfp
```


# Suggested Dependencies for Data Analysis

`imfp` outputs data in a `pandas` data frame, so you will want to use the `pandas` package (which is installed with `imfp`) for its functions for viewing and manipulating this object type. For data visualization, we recommend installing these additional packages:

``` bash
pip install -q matplotlib seaborn
```

You can then import these packages in your Python script:

``` python
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
```


# Development Installation

To get started with development of `imfp`,

1.  Fork and clone the repository

2.  Install [uv](https://astral.sh/setup-uv/) with `curl -LsSf https://astral.sh/uv/install.sh | sh`

3.  Install the dependencies with `uv sync`

4.  Install a git pre-commit hook to enforce conventional commits:

    ``` bash
    curl -o- https://raw.githubusercontent.com/tapsellorg/conventional-commits-git-hook/master/scripts/install.sh | sh
    ```

To edit and preview the documentation, install the [Quarto CLI tool](https://quarto.org/docs/download/) and run `uv run great-docs build` (or `uv run great-docs preview`).