chainconsumer.PlotConfig

Bases: BetterBase

Source code in src/chainconsumer/plotting/config.py

class PlotConfig(BetterBase):
    labels: dict[ColumnName, str] = Field(default={}, description="Labels for parameters")
    max_ticks: int = Field(default=5, ge=0, description="Maximum number of ticks to use on axes")
    plot_hists: bool = Field(default=True, description="Whether to plot the 1D histograms")
    flip: bool = Field(default=False, description="Whether to flip the 1D histograms")
    serif: bool | None = Field(
        default=False, description="Whether to use a serif font. None to not override the rc param."
    )
    usetex: bool | None = Field(
        default=False, description="Whether to use LaTeX for text rendering. None to not override the rc param."
    )
    diagonal_tick_labels: bool = Field(default=True, description="Whether to show tick labels on the diagonal")
    label_font_size: int = Field(default=12, ge=0, description="Font size for axis labels")
    tick_font_size: int = Field(default=10, ge=0, description="Font size for axis ticks")
    spacing: float | None = Field(default=None, ge=0, description="Spacing between subplots")
    contour_label_font_size: int = Field(default=10, ge=0, description="Font size for contour labels")
    show_legend: bool | None = Field(
        default=None,
        description="Whether to show the legend. None means determine automatically",
    )
    legend_kwargs: dict[str, Any] = Field(default={}, description="Kwargs to pass to the legend")
    legend_location: tuple[int, int] | None = Field(default=None, description="Which subplot to put the legend in")
    legend_artists: bool | None = Field(default=None, description="Whether to show artists in the legend")
    legend_color_text: bool = Field(default=True, description="Whether to color the legend text")
    watermark: str | None = Field(default=None, description="Watermark text to add to the plot")
    watermark_text_kwargs: dict[str, Any] = Field(default={}, description="Kwargs to pass to the watermark text")
    summarise: bool = Field(default=True, description="Whether to annotate the plot with summary statistics")
    summary_font_size: int = Field(default=12, ge=0, description="Font size for parameter summaries")
    sigma2d: bool = Field(
        default=False,
        description=(
            "Whether to use 2D sigmas for summary statistics. Ie in 2D a 1sigma contour"
            r" does *not* encapsulate 68% of the volume, it covers 39.3% of the volume."
        ),
    )
    blind: bool | list[str] = Field(default=False, description="Whether to blind some parameters")
    log_scales: list[ColumnName] = Field(default=[], description="Whether to use log scales for some parameters")
    extents: dict[ColumnName, tuple[float, float]] = Field(
        default={}, description="Extents for parameters. Any you don't specify are determined automatically"
    )
    dpi: int = Field(default=300, ge=0, description="DPI for the figure")

    @property
    def legend_kwargs_final(self) -> dict[str, Any]:
        default = {
            "labelspacing": 0.3,
            "loc": "upper right",
            "frameon": False,
            "fontsize": self.label_font_size,
            "handlelength": 1,
            "handletextpad": 0.2,
            "borderaxespad": 0.0,
        }
        return default | self.legend_kwargs

    @property
    def watermark_text_kwargs_final(self) -> dict[str, Any]:
        default = {
            "color": "#333333",
            "alpha": 0.7,
            "verticalalignment": "center",
            "horizontalalignment": "center",
            "weight": "bold",
        }
        return default | self.watermark_text_kwargs

    def get_label(self, column: ColumnName) -> str:
        return self.labels.get(column, column)

labels class-attribute instance-attribute

labels: dict[ColumnName, str] = Field(
    default={}, description="Labels for parameters"
)

max_ticks class-attribute instance-attribute

max_ticks: int = Field(
    default=5,
    ge=0,
    description="Maximum number of ticks to use on axes",
)

plot_hists class-attribute instance-attribute

plot_hists: bool = Field(
    default=True,
    description="Whether to plot the 1D histograms",
)

flip class-attribute instance-attribute

flip: bool = Field(
    default=False,
    description="Whether to flip the 1D histograms",
)

serif class-attribute instance-attribute

serif: bool | None = Field(
    default=False,
    description="Whether to use a serif font. None to not override the rc param.",
)

usetex class-attribute instance-attribute

usetex: bool | None = Field(
    default=False,
    description="Whether to use LaTeX for text rendering. None to not override the rc param.",
)

diagonal_tick_labels class-attribute instance-attribute

diagonal_tick_labels: bool = Field(
    default=True,
    description="Whether to show tick labels on the diagonal",
)

label_font_size class-attribute instance-attribute

label_font_size: int = Field(
    default=12,
    ge=0,
    description="Font size for axis labels",
)

tick_font_size class-attribute instance-attribute

tick_font_size: int = Field(
    default=10, ge=0, description="Font size for axis ticks"
)

spacing class-attribute instance-attribute

spacing: float | None = Field(
    default=None,
    ge=0,
    description="Spacing between subplots",
)

contour_label_font_size class-attribute instance-attribute

contour_label_font_size: int = Field(
    default=10,
    ge=0,
    description="Font size for contour labels",
)

show_legend class-attribute instance-attribute

show_legend: bool | None = Field(
    default=None,
    description="Whether to show the legend. None means determine automatically",
)

legend_kwargs class-attribute instance-attribute

legend_kwargs: dict[str, Any] = Field(
    default={}, description="Kwargs to pass to the legend"
)

legend_location class-attribute instance-attribute

legend_location: tuple[int, int] | None = Field(
    default=None,
    description="Which subplot to put the legend in",
)

legend_artists class-attribute instance-attribute

legend_artists: bool | None = Field(
    default=None,
    description="Whether to show artists in the legend",
)

legend_color_text class-attribute instance-attribute

legend_color_text: bool = Field(
    default=True,
    description="Whether to color the legend text",
)

watermark class-attribute instance-attribute

watermark: str | None = Field(
    default=None,
    description="Watermark text to add to the plot",
)

watermark_text_kwargs class-attribute instance-attribute

watermark_text_kwargs: dict[str, Any] = Field(
    default={},
    description="Kwargs to pass to the watermark text",
)

summarise class-attribute instance-attribute

summarise: bool = Field(
    default=True,
    description="Whether to annotate the plot with summary statistics",
)

summary_font_size class-attribute instance-attribute

summary_font_size: int = Field(
    default=12,
    ge=0,
    description="Font size for parameter summaries",
)

sigma2d class-attribute instance-attribute

sigma2d: bool = Field(
    default=False,
    description="Whether to use 2D sigmas for summary statistics. Ie in 2D a 1sigma contour does *not* encapsulate 68% of the volume, it covers 39.3% of the volume.",
)

blind class-attribute instance-attribute

blind: bool | list[str] = Field(
    default=False,
    description="Whether to blind some parameters",
)

log_scales class-attribute instance-attribute

log_scales: list[ColumnName] = Field(
    default=[],
    description="Whether to use log scales for some parameters",
)

extents class-attribute instance-attribute

extents: dict[ColumnName, tuple[float, float]] = Field(
    default={},
    description="Extents for parameters. Any you don't specify are determined automatically",
)

dpi class-attribute instance-attribute

dpi: int = Field(
    default=300, ge=0, description="DPI for the figure"
)

legend_kwargs_final property

legend_kwargs_final: dict[str, Any]

watermark_text_kwargs_final property

watermark_text_kwargs_final: dict[str, Any]

get_label

get_label(column: ColumnName) -> str

Source code in src/chainconsumer/plotting/config.py

def get_label(self, column: ColumnName) -> str:
    return self.labels.get(column, column)