Time series

timeseries (matplotlib, one or two axes, line / scatter / bar styles), timeseries_stacked, timeseries_template, and the Plotly timeseries_interactive. Usage: Visualization.

AeroViz.plot.timeseries.timeseries

Attributes

all `module-attribute`

__all__ = ['timeseries', 'timeseries_stacked']

default_bar_kws `module-attribute`

default_bar_kws = dict(width=0.0417, edgecolor=None, linewidth=0, cmap='jet')

default_scatter_kws `module-attribute`

default_scatter_kws = dict(marker='o', s=5, edgecolor=None, linewidths=0.3, alpha=0.9, cmap='jet')

default_insert_kws `module-attribute`

default_insert_kws = dict(width='1.5%', height='100%', loc='lower left', bbox_to_anchor=(1.01, 0, 1.2, 1), borderpad=0)

default_plot_kws `module-attribute`

default_plot_kws = dict()

default_cbar_kws `module-attribute`

default_cbar_kws = dict()

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)

_scatter

_scatter(ax, df, _y, _c, scatter_kws, cbar_kws, inset_kws)

_bar

_bar(ax, df, _y, _c, bar_kws, cbar_kws, inset_kws)

_plot

_plot(ax, df, _y, _color, plot_kws)

_wind_arrow

_wind_arrow(ax, df, y, c, scatter_kws, cbar_kws, inset_kws)

Plot wind arrows on a scatter plot.

:param ax: matplotlib axes :param df: pandas DataFrame :param y: column name for wind speed :param c: column name for wind direction :param scatter_kws: keyword arguments for scatter plot :param cbar_kws: keyword arguments for colorbar :param inset_kws: keyword arguments for inset axes

process_timeseries_data

process_timeseries_data(df, rolling=None, interpolate_limit=None, full_time_index=None)

timeseries

timeseries(df: DataFrame, y: list[str] | str, y2: list[str] | str = None, yi: list[str] | str = None, color: list[str] | str | None = None, label: list[str] | str | None = None, rolling: int | str | None = 3, interpolate_limit: int | None = 6, major_freq: str = '1MS', minor_freq: str = '10d', style: list[Literal['scatter', 'bar', 'line', 'arrow']] | str | None = None, ax: Axes | None = None, set_xaxis_visible: bool | None = None, legend_loc: Literal['best', 'upper right', 'upper left', 'lower left', 'lower right'] = 'best', legend_ncol: int = 1, **kwargs) -> tuple[Figure, Axes]

Plot the timeseries data with the option of scatterplot, barplot, and lineplot.

Parameters:

Name	Type	Description	Default
`df`	`DataFrame`	The data to plot.	required
`y`	`list[str] \| str`	The primary y-axis data columns.	required
`y2`	`list[str] \| str`	The secondary y-axis data columns. Defaults to None.	`None`
`yi`	`list[str] \| str`	The components for percentage calculation. Defaults to None.	`None`
`color`	`str`	The column for color mapping or the color. Defaults to None.	`None`
`label`	`str`	The label for the legend. Defaults to None.	`None`
`rolling`	`str \| int \| None`	Rolling window size for smoothing. Defaults to None.	`3`
`interpolate_limit`	`int`	Interpolation limit for missing values. Defaults to None.	`6`
`major_freq`	`str`	Frequency for x-axis ticks. Defaults to '1MS'.	`'1MS'`
`minor_freq`	`str`	Frequency for x-axis minor ticks. Defaults to '10d'.	`'10d'`
`style`	`Literal['scatter', 'bar', 'line'] \| None`	Style of the plot. Defaults to 'scatter'.	`None`
`ax`	`Axes \| None`	Matplotlib Axes object to plot on. Defaults to None.	`None`
`set_xaxis_visible`	`bool \| None`	Whether to set x-axis visibility. Defaults to None.	`None`
`legend_loc`	`Literal['best', 'upper right', 'upper left', 'lower left', 'lower right']`	Location of the legend. Defaults to 'best'.	`'best'`
`legend_ncol`	`int`	Number of columns in the legend. Defaults to 1.	`1`
`**kwargs`	`Additional keyword arguments for customization.`	fig_kws : dict, optional Additional keyword arguments for the figure. Defaults to {}. scatter_kws : dict, optional Additional keyword arguments for the scatter plot. Defaults to {}. bar_kws : dict, optional Additional keyword arguments for the bar plot. Defaults to {}. ax_plot_kws : dict, optional Additional keyword arguments for the primary y-axis plot. Defaults to {}. ax2_plot_kws : dict, optional Additional keyword arguments for the secondary y-axis plot. Defaults to {}. cbar_kws : dict, optional Additional keyword arguments for the colorbar. Defaults to {}. inset_kws : dict, optional Additional keyword arguments for the inset axes. Defaults to {}.	`{}`

Returns:

Name	Type	Description
`ax`	`AxesSubplot`	Matplotlib AxesSubplot.

Example

timeseries(df, y='WS', color='WD', scatter_kws=dict(cmap='hsv'), cbar_kws=dict(ticks=[0, 90, 180, 270, 360]), ylim=[0, None])

timeseries_stacked

timeseries_stacked(df, y: list[str] | str, yi: list[str] | str, label: list[str] | str, plot_type: Literal['absolute', 'percentage', 'both'] | str = 'both', rolling: int | str | None = 4, interpolate_limit: int | None = 4, major_freq: str = '10d', minor_freq: str = '1d', support_df: DataFrame | None = None, ax: Axes | None = None, savefig: str | Path | None = None, **kwargs) -> tuple[Figure, Axes]

AeroViz.plot.timeseries.timeseries_stacked

timeseries_stacked(df, y: list[str] | str, yi: list[str] | str, label: list[str] | str, plot_type: Literal['absolute', 'percentage', 'both'] | str = 'both', rolling: int | str | None = 4, interpolate_limit: int | None = 4, major_freq: str = '10d', minor_freq: str = '1d', support_df: DataFrame | None = None, ax: Axes | None = None, savefig: str | Path | None = None, **kwargs) -> tuple[Figure, Axes]

AeroViz.plot.timeseries.timeseries_template

timeseries_template(df: DataFrame) -> tuple[Figure, Axes]

Interactive viewer

timeseries_interactive renders an interactive Plotly chart for a RawDataReader result — one trace per column, with the legend acting as the column selector (click an entry to show/hide it). Pan, zoom, hover and a time range-slider are built in, and the figure can be saved as a standalone HTML file. It returns the Plotly figure, not (fig, ax).

from AeroViz import RawDataReader
from AeroViz.plot import timeseries_interactive

df = RawDataReader('AE33', '/data/AE33')          # native resolution, full coverage
timeseries_interactive(df, columns=['eBC', 'BC1', 'BC6', 'AAE'])  # click legend to toggle
timeseries_interactive(df, save='ae33.html', show=False)          # export to HTML

AeroViz.plot.timeseries_interactive

timeseries_interactive(df: DataFrame, columns: list | None = None, *, save: str | None = None, show: bool = True, title: str | None = None)

Interactive timeseries plot (Plotly); the legend toggles columns.