Templates

Ready-made analysis figures: diurnal pattern, correlation matrices, the Koschmieder visibility fit and its PM₂.₅ × gRH extinction contour, ammonium-rich classification and metal heatmaps. Usage: Visualization.

AeroViz.plot.templates.diurnal_pattern

Attributes

__all__ module-attribute

__all__ = ['diurnal_pattern']

Classes

Color

Unit

Methods:

load_jsonfile classmethod

load_jsonfile()

讀取 JSON 檔中數據并將其變成屬性

update_jsonfile classmethod

update_jsonfile(key, value)

更新JSON檔

del_jsonfile classmethod

del_jsonfile(key)

更新JSON檔

Functions:

set_figure

set_figure(func=None, *, figsize: tuple | None = None, fs: int | None = None, fw: str = None, autolayout: bool = True)

combine_legends

combine_legends(axes_list: list[Axes]) -> tuple[list, list]

auto_label_pct

auto_label_pct(pct, symbol: bool = True, include_pct: bool = False, ignore: Literal['inner', 'outer'] = 'inner', value: float = 2)

linear_regression_base

linear_regression_base(x_array: ndarray, y_array: ndarray, columns: str | list[str] | None = None, positive: bool = True, fit_intercept: bool = True)

diurnal_pattern

diurnal_pattern(df: DataFrame, y: str | list[str], std_area: float = 0.5, ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

AeroViz.plot.templates.corr_matrix

Attributes

__all__ module-attribute

__all__ = ['corr_matrix', 'cross_corr_matrix']

Classes

Color

Unit

Methods:

load_jsonfile classmethod

load_jsonfile()

讀取 JSON 檔中數據并將其變成屬性

update_jsonfile classmethod

update_jsonfile(key, value)

更新JSON檔

del_jsonfile classmethod

del_jsonfile(key)

更新JSON檔

Functions:

set_figure

set_figure(func=None, *, figsize: tuple | None = None, fs: int | None = None, fw: str = None, autolayout: bool = True)

combine_legends

combine_legends(axes_list: list[Axes]) -> tuple[list, list]

auto_label_pct

auto_label_pct(pct, symbol: bool = True, include_pct: bool = False, ignore: Literal['inner', 'outer'] = 'inner', value: float = 2)

linear_regression_base

linear_regression_base(x_array: ndarray, y_array: ndarray, columns: str | list[str] | None = None, positive: bool = True, fit_intercept: bool = True)

corr_matrix

corr_matrix(data: DataFrame, cmap: str = 'RdBu', ax: Axes | None = None, items_order: list = None, **kwargs) -> tuple[Figure, Axes]

cross_corr_matrix

cross_corr_matrix(data1: DataFrame, data2: DataFrame, cmap: str = 'RdBu', ax: Axes | None = None, items_order: list = None, **kwargs) -> tuple[Figure, Axes]

Create a correlation matrix between two different DataFrames.

Parameters:

data1 : pd.DataFrame First DataFrame data2 : pd.DataFrame Second DataFrame cmap : str, optional Color map for the correlation matrix ax : Axes, optional Matplotlib axes to plot on items_order : list, optional List specifying the order of items to display **kwargs : dict Additional keyword arguments

AeroViz.plot.templates.cross_corr_matrix

cross_corr_matrix(data1: DataFrame, data2: DataFrame, cmap: str = 'RdBu', ax: Axes | None = None, items_order: list = None, **kwargs) -> tuple[Figure, Axes]

Create a correlation matrix between two different DataFrames.

Parameters:

data1 : pd.DataFrame First DataFrame data2 : pd.DataFrame Second DataFrame cmap : str, optional Color map for the correlation matrix ax : Axes, optional Matplotlib axes to plot on items_order : list, optional List specifying the order of items to display **kwargs : dict Additional keyword arguments

AeroViz.plot.templates.contour

Attributes

__all__ module-attribute

__all__ = ['contour']

Classes

Color

Unit

Methods:

load_jsonfile classmethod

load_jsonfile()

讀取 JSON 檔中數據并將其變成屬性

update_jsonfile classmethod

update_jsonfile(key, value)

更新JSON檔

del_jsonfile classmethod

del_jsonfile(key)

更新JSON檔

Functions:

set_figure

set_figure(func=None, *, figsize: tuple | None = None, fs: int | None = None, fw: str = None, autolayout: bool = True)

combine_legends

combine_legends(axes_list: list[Axes]) -> tuple[list, list]

auto_label_pct

auto_label_pct(pct, symbol: bool = True, include_pct: bool = False, ignore: Literal['inner', 'outer'] = 'inner', value: float = 2)

linear_regression_base

linear_regression_base(x_array: ndarray, y_array: ndarray, columns: str | list[str] | None = None, positive: bool = True, fit_intercept: bool = True)

contour

contour(df, ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

AeroViz.plot.templates.koschmieder

Attributes

__all__ module-attribute

__all__ = ['koschmieder']

Classes

Color

Unit

Methods:

load_jsonfile classmethod

load_jsonfile()

讀取 JSON 檔中數據并將其變成屬性

update_jsonfile classmethod

update_jsonfile(key, value)

更新JSON檔

del_jsonfile classmethod

del_jsonfile(key)

更新JSON檔

Functions:

set_figure

set_figure(func=None, *, figsize: tuple | None = None, fs: int | None = None, fw: str = None, autolayout: bool = True)

combine_legends

combine_legends(axes_list: list[Axes]) -> tuple[list, list]

auto_label_pct

auto_label_pct(pct, symbol: bool = True, include_pct: bool = False, ignore: Literal['inner', 'outer'] = 'inner', value: float = 2)

linear_regression_base

linear_regression_base(x_array: ndarray, y_array: ndarray, columns: str | list[str] | None = None, positive: bool = True, fit_intercept: bool = True)

koschmieder

koschmieder(df: DataFrame, vis: str, ext: list[str], ax: Axes | None = None, savefig: str | Path | None = None, **kwargs) -> tuple[Figure, Axes]

Plot Koschmieder relationship between Visibility and Extinction.

x = Visibility, y = Extinction, log-log fit!!

AeroViz.plot.templates.ammonium_rich

Classes

Unit

Methods:

load_jsonfile classmethod

load_jsonfile()

讀取 JSON 檔中數據并將其變成屬性

update_jsonfile classmethod

update_jsonfile(key, value)

更新JSON檔

del_jsonfile classmethod

del_jsonfile(key)

更新JSON檔

Functions:

set_figure

set_figure(func=None, *, figsize: tuple | None = None, fs: int | None = None, fw: str = None, autolayout: bool = True)

ammonium_rich

ammonium_rich(df: DataFrame, **kwargs) -> tuple[Figure, Axes]

AeroViz.plot.templates.metal_heatmaps

metal_heatmaps(df, process=True, major_freq='10d', minor_freq='1d', cmap='jet', ax: Axes | None = None, savefig: str | Path | None = None, **kwargs) -> tuple[Figure, Axes]