kwix

Developer Guide

Setup

# create conda environment
$ mamba env create -f env.yml

# update conda environment
$ mamba env update -n kwix --file env.yml

Install

pip install -e .

# install from pypi
pip install kwix

nbdev

# activate conda environment
$ conda activate kwix

# make sure the kwix package is installed in development mode
$ pip install -e .

# make changes under nbs/ directory
# ...

# compile to have changes apply to the kwix package
$ nbdev_prepare

Publishing

# publish to pypi
$ nbdev_pypi

# publish to conda
$ nbdev_conda --build_args '-c conda-forge'
$ nbdev_conda --mambabuild --build_args '-c conda-forge -c dsm-72'

Usage

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/dsm-72/kwix.git

or from conda

$ conda install -c dsm-72 kwix

or from pypi

$ pip install kwix

Documentation

Documentation can be found hosted on GitHub repository pages. Additionally you can find package manager specific guidelines on conda and pypi respectively.

Toy Inheritance

@dataclass
class Foo(KWix):
    _: KW_ONLY
    a: int = 1
    b: int = 2
    c: int = 3

@dataclass
class Qux(KWix):
    _: KW_ONLY
    q: str = -2
    u: str = -1
    x: int = 0

@dataclass
class Bar(Foo):
    _: KW_ONLY
    x: int = 7
    y: int = 8
    z: int = 9

@dataclass
class Baz(Bar, Qux):
    _: KW_ONLY
    a: int = 6
    b: int = 7
    q: int = 0

@dataclass
class Auz(Bar):
    _: KW_ONLY

Seaborn

Since Seaborn Clustermap passes all extra keyword-arguments to Heatmap using KWix can make it easy to know what these parameters are and easy to pass them accordingly

@dataclass
class SeabornHeatmap(KWix):
    _: KW_ONLY    
   = None
    cmap: Optional[str] = 'inferno'
    center: Optional[float] = None
    robust: bool = True
    annot: Optional[bool] = None
    fmt: Optional[str] = '.2g'
    annot_kws: Optional[dict] = None
    linewidths: Optional[float] = 0
    linecolor: Optional[str] = 'white'
    cbar: bool = True
    cbar_kws: Optional[dict] = None
    cbar_ax: Optional['Axes'] = None
    square: bool = False
    xticklabels: Union[bool, list, str, bool] = 'auto'
    yticklabels: Union[bool, list, str, bool] = 'auto'
    mask: Optional[Union['NPArray', 'DataFrame']] = None
    ax: Optional['Axes'] = None

@dataclass
class SeabornClustermap(SeabornHeatmap, KWix):
    _: KW_ONLY    
    # clustermap parameters
    pivot_kws: Optional[dict] = None
    method: 'LinkageMethod' = 'SINGLE'
    metric: 'PDistMetric' = 'CORRELATION'
    z_score: Optional[int] = 0
    standard_scale: Optional[int] = None
    figsize: Tuple[int, int] = (10, 10)
    cbar_kws: Optional[dict] = None
    row_cluster: bool = True
    col_cluster: bool = True
    row_linkage: Optional[dict] = None
    col_linkage: Optional[dict] = None
    row_colors: Optional[dict] = None
    col_colors: Optional[dict] = None
    mask: Optional[dict] = None
    dendrogram_ratio: float = 0.2
    colors_ratio: float = 0.03
    cbar_pos: 'CBarPos' = (0.02, 0.8, 0.05, 0.18)
    tree_kws: Optional[dict] = None

clstmap = SeabornClustermap()

clstmap.curvals()

{'cbar_kws': None,
 'cbar_pos': (0.02, 0.8, 0.05, 0.18),
 'col_cluster': True,
 'col_colors': None,
 'col_linkage': None,
 'colors_ratio': 0.03,
 'dendrogram_ratio': 0.2,
 'figsize': (10, 10),
 'mask': None,
 'method': 'SINGLE',
 'metric': 'CORRELATION',
 'pivot_kws': None,
 'row_cluster': True,
 'row_colors': None,
 'row_linkage': None,
 'standard_scale': None,
 'tree_kws': None,
 'z_score': 0}

clstmap.inhvals()

{'annot': None,
 'annot_kws': None,
 'ax': None,
 'cbar': True,
 'cbar_ax': None,
 'center': None,
 'cmap': 'inferno',
 'fmt': '.2g',
 'linecolor': 'white',
 'linewidths': 0,
 'robust': True,
 'square': False,
 'vmax': None,
 'vmin': None,
 'xticklabels': 'auto',
 'yticklabels': 'auto'}

clstmap.allvals()

{'annot': None,
 'annot_kws': None,
 'ax': None,
 'cbar': True,
 'cbar_ax': None,
 'cbar_kws': None,
 'cbar_pos': (0.02, 0.8, 0.05, 0.18),
 'center': None,
 'cmap': 'inferno',
 'col_cluster': True,
 'col_colors': None,
 'col_linkage': None,
 'colors_ratio': 0.03,
 'dendrogram_ratio': 0.2,
 'figsize': (10, 10),
 'fmt': '.2g',
 'linecolor': 'white',
 'linewidths': 0,
 'mask': None,
 'method': 'SINGLE',
 'metric': 'CORRELATION',
 'pivot_kws': None,
 'robust': True,
 'row_cluster': True,
 'row_colors': None,
 'row_linkage': None,
 'square': False,
 'standard_scale': None,
 'tree_kws': None,
 'vmax': None,
 'vmin': None,
 'xticklabels': 'auto',
 'yticklabels': 'auto',
 'z_score': 0}

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