### Install and Run Documentation Tools

Source: https://github.com/nixtla/datasetsforecast/blob/main/CONTRIBUTING.md

Steps to install 'mintlify' and 'lazydocs', generate documentation files, and serve the documentation locally.

```bash
npm i -g mint

uv pip install -e '.[dev]' lazydocs
lazydocs .datasetsforecast --no-watermark
python docs/to_mdx.py

cd docs/mintlify
mintlify dev
```

--------------------------------

### Install Pre-commit Hooks

Source: https://github.com/nixtla/datasetsforecast/blob/main/CONTRIBUTING.md

Commands to install and run pre-commit hooks for maintaining code quality.

```bash
pre-commit install
pre-commit run --all-files
```

--------------------------------

### Install datasetsforecast

Source: https://github.com/nixtla/datasetsforecast/blob/main/README.md

Installs the datasetsforecast library using pip. This is the primary method for adding the library to your Python environment.

```sh
pip install datasetsforecast
```

--------------------------------

### Create Development Environment with uv

Source: https://github.com/nixtla/datasetsforecast/blob/main/CONTRIBUTING.md

Steps to create a Python virtual environment using 'uv' and install the project in editable mode with development dependencies.

```bash
pip install uv
uv venv --python 3.10
source .venv/bin/activate

# Install the library in editable mode for development
uv pip install -e ".[dev]" -U
```

--------------------------------

### Create Development Environment with conda

Source: https://github.com/nixtla/datasetsforecast/blob/main/CONTRIBUTING.md

Instructions to set up the development environment using 'conda' and the provided 'environment.yml' file.

```bash
conda env create -f environment.yml
```

--------------------------------

### Clone Repository

Source: https://github.com/nixtla/datasetsforecast/blob/main/CONTRIBUTING.md

Methods for cloning the datasetsforecast repository using HTTPS, SSH, or GitHub CLI.

```bash
git clone https://github.com/Nixtla/datasetsforecast.git
```

```bash
git clone git@github.com:Nixtla/datasetsforecast.git
```

```bash
gh repo clone Nixtla/datasetsforecast
```

--------------------------------

### Running Tests

Source: https://github.com/nixtla/datasetsforecast/blob/main/CONTRIBUTING.md

Command to execute tests using pytest within the development environment.

```bash
uv run pytest
```

--------------------------------

### Load PHM2008 Dataset

Source: https://github.com/nixtla/datasetsforecast/blob/main/README.md

Demonstrates how to load the PHM2008 dataset using the datasetsforecast library. It shows importing the dataset class and using the `load` method to fetch training and testing data, specifying a directory for storage and a group for the data subset. The output displays the shapes of the resulting training and testing dataframes.

```python
from datasetsforecast.phm2008 import PHM2008

train_df, test_df = PHM2008.load(directory='data', group='FD001')
train_df.shape, test_df.shape
```

--------------------------------

### Processing Notebook with Initialized Processor

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Executes the processing logic defined in the initialized NBProcessor instance.

```python
processor.process()
```

--------------------------------

### Applying Specific Directives with NBProcessor

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Demonstrates using NBProcessor to apply a specific function (`extract_dir`) with custom directives ('hide', 'polars') to a notebook.

```python
NBProcessor(nb_path, procs=partial(extract_dir, dirs='hide,polars')).process()
```

--------------------------------

### Mapping Processor Functions

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

A dictionary mapping string names to their corresponding notebook processing functions. This allows for dynamic selection of processors.

```python
#|export
mapper = {
    'print_execs': print_execs,
    'print_hide': print_hide,
    'other_tests': other_tests,
    'get_markdown': get_markdown,
    'extract_dir': extract_dir,
    'no_dir_and_dir': no_dir_and_dir,
    'get_all_tests':get_all_tests
}
```

--------------------------------

### Initializing NBProcessor with Specific Directives

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Initializes an NBProcessor instance configured to extract cells containing the 'distributed' directive.

```python
processor = NBProcessor(nb_path, partial(extract_dir, dirs='distributed'))
```

--------------------------------

### Notebook Reading

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Reads a specified notebook file into a notebook object. This is a foundational step for processing notebook content using nbdev.

```python
from execnb.nbio import read_nb

nb_path = "../nbs/favorita.ipynb"
nb = read_nb(nb_path)
```

--------------------------------

### Applying All Test Cells with NBProcessor

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Applies the `get_all_tests` function to a notebook using NBProcessor, filtering for cells relevant to testing.

```python
NBProcessor(nb_path, procs=get_all_tests).process()
```

--------------------------------

### CLI Function for Notebook Processing

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

A command-line function to process notebooks based on specified directives and processor functions. It utilizes nbdev's NBProcessor for applying custom logic to notebook cells.

```python
#|export
from functools import partial
from fastcore.script import call_parse
from nbdev.config import get_config
from nbdev.processors import NBProcessor

@call_parse
def print_dir_in_nb(nb_path:str,
                    dir:str=None,
                    dir_name:str=None,
                    ):
    if dir_name not in mapper.keys():
        raise ValueError(f'Choose processor from the the following: {mapper.keys()}')

    if dir_name == 'extract_dir':
        processor = NBProcessor(nb_path, partial(extract_dir, dir=dir))
        processor.process()
        return
    elif dir_name == 'no_dir_and_dir':
        processor = NBProcessor(nb_path, partial(no_dir_and_dir, dir=dir))
        processor.process()
        return

    processor = NBProcessor(nb_path, mapper[dir_name])
    processor.process()
```

--------------------------------

### Extracting Cells by Specific Directives

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Prints the source code of cells that contain any of the specified directives. Allows for targeted extraction based on notebook metadata.

```python
#|export
def extract_dir(cell, dirs):
    for directive in dirs.split(','):
        if directive in cell.directives_:
            print(cell.source)
```

--------------------------------

### Notebook Export

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Exports a notebook to a Python module. This function is part of the nbdev library, used for managing and exporting notebook content.

```python
#|export
nb_export('cli.ipynb', lib_path='.', name='cli')
```

--------------------------------

### Extracting Markdown Cells

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Prints the source code of cells that are of the 'markdown' type. Used for isolating and displaying markdown content.

```python
#|export
def get_markdown(cell):
    if cell.cell_type == "markdown":
        print(cell.source)
```

--------------------------------

### Extracting All Test-Related Cells

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Prints code cells that are either untagged or tagged with test-related directives (e.g., 'hide', 'datasets', 'distributed'). Skips cells containing specific skip keywords.

```python
#|export
tst_flags = 'datasets distributed matplotlib polars pyarrow scipy'.split()
to_skip = [
    'showdoc',
    'load_ext',
    'from nbdev'
]

def get_all_tests(cell):
    if cell.cell_type == "code":

        if len(cell.directives_) == 0:
            print(cell.source)


        elif any(x in tst_flags + ['hide'] for x in cell.directives_):
            if not (x in cell.source for x in to_skip):
                print(cell.source)
```

--------------------------------

### Notebook Cell Access

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Accesses the cells within a loaded notebook object. This allows for iterating through and processing individual cells.

```python
nb.cells
```

--------------------------------

### Extracting Code Cells

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Prints the source code of cells that are of the 'code' type. Used for isolating and displaying executable code.

```python
#|export
def get_code(cell):
    if cell.cell_type == "code":
        print(cell.source)
```

--------------------------------

### Conditional Cell Printing (Exec)

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Prints the source code of a notebook cell if it contains the 'exec' directive. Useful for identifying executable code blocks.

```python
#|export
def print_execs(cell):
    if 'exec' in cell.source: print(cell.source)
```

--------------------------------

### Printing Cells Without Directives

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Prints the source code of a notebook cell if it has no directives. Useful for identifying standard code cells.

```python
#|export
def other_tests(cell):
    if len(cell.directives_) == 0:
        print(cell.source)
```

--------------------------------

### Conditional Cell Printing (Hide Directive)

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Prints the source code of a notebook cell if it has the 'hide' directive. Helps in selectively displaying or hiding code.

```python
#|export
def print_hide(cell):
    if 'hide' in cell.directives_: print(cell.source)
```

--------------------------------

### Printing Cells with or without a Specific Directive

Source: https://github.com/nixtla/datasetsforecast/blob/main/scripts/cli.ipynb

Prints cells that either have no directives or contain a specific directive. Useful for conditional processing based on directive presence.

```python
#|export
def no_dir_and_dir(cell, dir):
    if len(cell.directives_) == 0:
        print(cell.source)

    if dir in cell.directives_:
        print(cell.source)
```

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