tslumen.plot.static package

Default static Matplotlib plots.

tslumen.plot.static.comparison module

Comparison plots.

class tslumen.plot.static.comparison.Heatmap(df: pandas.core.frame.DataFrame, figsize: Tuple[float, float] = (4, 0.45), min_figsize: Tuple[float, float] = (4, 4), cmap: str = 'PuBu', valfmt: str = '{x:.3f}', textcolors: Tuple[str, …] = ('black', 'white'))

Bases: tslumen.plot.static.base.Figure

Heatmap plot.

cmap: str = 'PuBu'

df: pandas.core.frame.DataFrame

figsize: Tuple[float, float] = (4, 0.45)

min_figsize: Tuple[float, float] = (4, 4)

textcolors: Tuple[str, …] = ('black', 'white')

valfmt: str = '{x:.3f}'

class tslumen.plot.static.comparison.Radar(df: pandas.core.frame.DataFrame, nticks: int = 5, figsize: Tuple[float, float] = (2.1, 2.1), linewidth: float = 0.75, alpha: float = 0.45, legend: bool = False)

Bases: tslumen.plot.static.base.Figure

Radar (aka spider) plot for comparing data bearing multiple dimensions.

alpha: float = 0.45

df: pandas.core.frame.DataFrame

figsize: Tuple[float, float] = (2.1, 2.1)

legend: bool = False

linewidth: float = 0.75

nticks: int = 5

tslumen.plot.static.correlation module

Correlation plots.

class tslumen.plot.static.correlation.LagCorrelation(df: pandas.core.frame.DataFrame, title: Optional[str] = None, col_lag: str = 'lag', col_correlation: str = 'correlation', col_up: str = 'up', col_low: str = 'low', figsize: Tuple[float, float] = (3.3, 2))

Bases: tslumen.plot.static.base.Figure

Lag correlation (ACF/PACF) plots, commonly known as lollipop, for analysing correlation on a given lag. Useful for auto-, partial-, cross- and partial-cross-correlation.

col_correlation: str = 'correlation'

col_lag: str = 'lag'

col_low: str = 'low'

col_up: str = 'up'

df: pandas.core.frame.DataFrame

figsize: Tuple[float, float] = (3.3, 2)

title: Optional[str] = None

class tslumen.plot.static.correlation.LagMatrix(original: pandas.core.series.Series, lags: pandas.core.frame.DataFrame, corr: pandas.core.series.Series, ncols: int = 4, cellsize: Tuple[float, …] = (1.7, 1.7))

Bases: tslumen.plot.static.base.Figure

Lag Matrix plot.

cellsize: Tuple[float, …] = (1.7, 1.7)

corr: pandas.core.series.Series

lags: pandas.core.frame.DataFrame

ncols: int = 4

original: pandas.core.series.Series

class tslumen.plot.static.correlation.ScatterMatrix(df: pandas.core.frame.DataFrame, df_corr: pandas.core.frame.DataFrame, figsize: Tuple[float, float] = (0.8, 0.8), min_figsize: Tuple[float, float] = (8, 8))

Bases: tslumen.plot.static.base.Figure

Scatter matrix (aka pair plot) with scatters, KDE and correlation Heatmap.

df: pandas.core.frame.DataFrame

df_corr: pandas.core.frame.DataFrame

figsize: Tuple[float, float] = (0.8, 0.8)

min_figsize: Tuple[float, float] = (8, 8)

tslumen.plot.static.distribution module

Distribution plots.

class tslumen.plot.static.distribution.BoxPlot(df: pandas.core.frame.DataFrame, figsize: Tuple[float, float] = (8, 3))

Bases: tslumen.plot.static.base.Figure

BoxPlot w/ whiskers.

df: pandas.core.frame.DataFrame

figsize: Tuple[float, float] = (8, 3)

class tslumen.plot.static.distribution.Distribution(series: pandas.core.series.Series, df_quantiles: pandas.core.frame.DataFrame, df_percentiles: pandas.core.frame.DataFrame, col_theoretical_quantiles: str = 'theoretical_quantiles', col_sample_quantiles: str = 'sample_quantiles', col_theoretical_percentiles: str = 'theoretical_percentiles', col_sample_percentiles: str = 'sample_percentiles', figsize: Tuple[float, float] = (8, 6))

Bases: tslumen.plot.static.base.Figure

Combined Histogram, P-P and Q-Q plots.

col_pp_ref = 'reference'

col_qq_ref = 'reference'

col_sample_percentiles: str = 'sample_percentiles'

col_sample_quantiles: str = 'sample_quantiles'

col_theoretical_percentiles: str = 'theoretical_percentiles'

col_theoretical_quantiles: str = 'theoretical_quantiles'

df_percentiles: pandas.core.frame.DataFrame

df_quantiles: pandas.core.frame.DataFrame

figsize: Tuple[float, float] = (8, 6)

series: pandas.core.series.Series

tslumen.plot.static.line module

Line plots.

class tslumen.plot.static.line.TS(df: pandas.core.frame.DataFrame, figsize: Tuple[float, float] = (8, 3), xaxis: bool = True, yaxis: bool = True, legend: bool = True, line_width: Optional[List[float]] = None, colors: Optional[Union[str, List[str]]] = None)

Bases: tslumen.plot.static.base.Figure

Time series line plot.

colors: Optional[Union[str, List[str]]] = None

df: pandas.core.frame.DataFrame

figsize: Tuple[float, float] = (8, 3)

legend: bool = True

line_width: Optional[List[float]] = None

xaxis: bool = True

yaxis: bool = True

class tslumen.plot.static.line.TSStack(df: pandas.core.frame.DataFrame, figsize: Tuple[float, float] = (10, 3))

Bases: tslumen.plot.static.base.Figure

Stacked time series line plot.

df: pandas.core.frame.DataFrame

figsize: Tuple[float, float] = (10, 3)

tslumen.plot.static.misc module

Miscellaneous plots.

class tslumen.plot.static.misc.GrangerGraph(dfp: pandas.core.frame.DataFrame, figsize: Tuple[float, float] = (8, 6), critical: float = 0.05, cmap: str = 'PuBu_r', node_color: str = 'white', edgecolors: str = '#555555', edge_hi: str = '#b60982')

Bases: tslumen.plot.static.base.Figure

Granger causality graph.

cmap: str = 'PuBu_r'

critical: float = 0.05

dfp: pandas.core.frame.DataFrame

edge_hi: str = '#b60982'

edgecolors: str = '#555555'

figsize: Tuple[float, float] = (8, 6)

node_color: str = 'white'

class tslumen.plot.static.misc.GrangerMatrix(dfl: pandas.core.frame.DataFrame, dfp: pandas.core.frame.DataFrame, figsize: Tuple[float, float] = (0.8, 0.4), min_figsize: Tuple[float, float] = (8, 4), critical: float = 0.05)

Bases: tslumen.plot.static.base.Figure

Granger causality matrix.

critical: float = 0.05

dfl: pandas.core.frame.DataFrame

dfp: pandas.core.frame.DataFrame

figsize: Tuple[float, float] = (0.8, 0.4)

min_figsize: Tuple[float, float] = (8, 4)