Plotting

Visualization utilities for calibration and validation data.

Plotting utilities for EyeLink calibration and validation data.

class syelink.plotting.CalibrationPlotStyle(color_left='cyan', color_right='magenta', marker='+', marker_size=300, marker_linewidth=2, show_grid=True, grid_alpha=0.3, figsize=(16, 7), dpi=150, title_fontsize=14, label_fontsize=12)

Bases: object

Style configuration for calibration plots.

Example

>>> style = CalibrationPlotStyle(
...     color_left="green",
...     color_right="purple",
... )
>>> plot_calibration_raw(session, style=style)

Parameters:

• color_left (str)

• color_right (str)

• marker (str)

• marker_size (float)

• marker_linewidth (float)

• show_grid (bool)

• grid_alpha (float)

• figsize (tuple[float, float])

• dpi (int)

• title_fontsize (float)

• label_fontsize (float)

color_left: str = 'cyan'

color_right: str = 'magenta'

marker: str = '+'

marker_size: float = 300

marker_linewidth: float = 2

show_grid: bool = True

grid_alpha: float = 0.3

figsize: tuple[float, float] = (16, 7)

dpi: int = 150

title_fontsize: float = 14

label_fontsize: float = 12

class syelink.plotting.ValidationPlotStyle(color_left='#00FFFF', color_right='#4169E1', color_target='#000000', color_screen='#888888', marker='+', marker_size=200, marker_linewidth=2, line_style='--', line_width=1.5, line_alpha=0.8, label_fontsize=11, label_fontweight='bold', show_labels=True, figsize=(12, 10), dpi=150, title_fontsize=14, show_legend=True, legend_loc='upper right', legend_fontsize=10)

Bases: object

Style configuration for validation plots.

All colors can be any matplotlib-compatible color string (hex, named colors, RGB tuples, etc.)

Example

>>> style = ValidationPlotStyle(
...     color_left="red",
...     color_right="blue",
...     marker_size=300,
... )
>>> plot_validation(session, style=style)

Parameters:

• color_left (str)

• color_right (str)

• color_target (str)

• color_screen (str)

• marker (str)

• marker_size (float)

• marker_linewidth (float)

• line_style (str)

• line_width (float)

• line_alpha (float)

• label_fontsize (float)

• label_fontweight (str)

• show_labels (bool)

• figsize (tuple[float, float])

• dpi (int)

• title_fontsize (float)

• show_legend (bool)

• legend_loc (str)

• legend_fontsize (float)

color_left: str = '#00FFFF'

color_right: str = '#4169E1'

color_target: str = '#000000'

color_screen: str = '#888888'

marker: str = '+'

marker_size: float = 200

marker_linewidth: float = 2

line_style: str = '--'

line_width: float = 1.5

line_alpha: float = 0.8

label_fontsize: float = 11

label_fontweight: str = 'bold'

show_labels: bool = True

figsize: tuple[float, float] = (12, 10)

dpi: int = 150

title_fontsize: float = 14

show_legend: bool = True

legend_loc: str = 'upper right'

legend_fontsize: float = 10

syelink.plotting.plot_calibration_raw(session, cal_index=0, save_path=None, style=None)

Plot RAW calibration points for both eyes on single plot.

Parameters:

• session (SessionData) – SessionData object containing calibration data

• cal_index (int) – Which calibration to plot (0-based index)

• save_path (str | Path | None) – Optional path to save the figure

• style (CalibrationPlotStyle | None) – Optional CalibrationPlotStyle for customizing colors, markers, etc.

Return type:

Figure

Returns:

matplotlib Figure object

Example

>>> from syelink.plotting import plot_calibration_raw, CalibrationPlotStyle
>>> style = CalibrationPlotStyle(color_left="green", color_right="orange")
>>> fig = plot_calibration_raw(session, style=style)

syelink.plotting.plot_validation(session, validation_index=0, save_path=None, target_scale=0.015, target_image_path=None, style=None, show_connectors=False)

Plot a single validation showing left and right eye data.

Parameters:

• session (SessionData) – SessionData object containing validation data

• validation_index (int) – Index of validation to plot (0-based)

• save_path (str | Path | None) – Optional path to save the plot

• target_scale (float) – Scaling factor for target image size (default: 0.015)

• target_image_path (str | Path | None) – Optional path to custom target image

• style (ValidationPlotStyle | None) – Optional ValidationPlotStyle for customizing colors, markers, etc.

• show_connectors (bool) – Whether to draw connector lines from original label positions to adjusted label positions (default: False)

Return type:

Figure

Returns:

matplotlib Figure object

Example

>>> from syelink.plotting import plot_validation, ValidationPlotStyle
>>> style = ValidationPlotStyle(color_left="red", color_right="green")
>>> fig = plot_validation(session, style=style, show_connectors=True)

calibration

Plot calibration points (P-CR in HREF angular space) for both eyes.

Shows the polynomial input — the P-CR feature in HREF coordinates — at each cal target.

syelink.plotting.calibration.plot_calibration_raw(session, cal_index=0, save_path=None, style=None)

Plot RAW calibration points for both eyes on single plot.

Parameters:

• session (SessionData) – SessionData object containing calibration data

• cal_index (int) – Which calibration to plot (0-based index)

• save_path (str | Path | None) – Optional path to save the figure

• style (CalibrationPlotStyle | None) – Optional CalibrationPlotStyle for customizing colors, markers, etc.

Return type:

Figure

Returns:

matplotlib Figure object

Example

>>> from syelink.plotting import plot_calibration_raw, CalibrationPlotStyle
>>> style = CalibrationPlotStyle(color_left="green", color_right="orange")
>>> fig = plot_calibration_raw(session, style=style)

validation

Create validation plots showing target positions and gaze offsets.

Shows left and right eye data with error vectors and offset labels.

syelink.plotting.validation.plot_validation(session, validation_index=0, save_path=None, target_scale=0.015, target_image_path=None, style=None, show_connectors=False)

Plot a single validation showing left and right eye data.

Parameters:

• session (SessionData) – SessionData object containing validation data

• validation_index (int) – Index of validation to plot (0-based)

• save_path (str | Path | None) – Optional path to save the plot

• target_scale (float) – Scaling factor for target image size (default: 0.015)

• target_image_path (str | Path | None) – Optional path to custom target image

• style (ValidationPlotStyle | None) – Optional ValidationPlotStyle for customizing colors, markers, etc.

• show_connectors (bool) – Whether to draw connector lines from original label positions to adjusted label positions (default: False)

Return type:

Figure

Returns:

matplotlib Figure object

Example

>>> from syelink.plotting import plot_validation, ValidationPlotStyle
>>> style = ValidationPlotStyle(color_left="red", color_right="green")
>>> fig = plot_validation(session, style=style, show_connectors=True)

style

Plot styling configuration for syelink visualizations.

class syelink.plotting.style.ValidationPlotStyle(color_left='#00FFFF', color_right='#4169E1', color_target='#000000', color_screen='#888888', marker='+', marker_size=200, marker_linewidth=2, line_style='--', line_width=1.5, line_alpha=0.8, label_fontsize=11, label_fontweight='bold', show_labels=True, figsize=(12, 10), dpi=150, title_fontsize=14, show_legend=True, legend_loc='upper right', legend_fontsize=10)

Bases: object

Style configuration for validation plots.

All colors can be any matplotlib-compatible color string (hex, named colors, RGB tuples, etc.)

Example

>>> style = ValidationPlotStyle(
...     color_left="red",
...     color_right="blue",
...     marker_size=300,
... )
>>> plot_validation(session, style=style)

Parameters:

• color_left (str)

• color_right (str)

• color_target (str)

• color_screen (str)

• marker (str)

• marker_size (float)

• marker_linewidth (float)

• line_style (str)

• line_width (float)

• line_alpha (float)

• label_fontsize (float)

• label_fontweight (str)

• show_labels (bool)

• figsize (tuple[float, float])

• dpi (int)

• title_fontsize (float)

• show_legend (bool)

• legend_loc (str)

• legend_fontsize (float)

color_left: str = '#00FFFF'

color_right: str = '#4169E1'

color_target: str = '#000000'

color_screen: str = '#888888'

marker: str = '+'

marker_size: float = 200

marker_linewidth: float = 2

line_style: str = '--'

line_width: float = 1.5

line_alpha: float = 0.8

label_fontsize: float = 11

label_fontweight: str = 'bold'

show_labels: bool = True

figsize: tuple[float, float] = (12, 10)

dpi: int = 150

title_fontsize: float = 14

show_legend: bool = True

legend_loc: str = 'upper right'

legend_fontsize: float = 10

class syelink.plotting.style.CalibrationPlotStyle(color_left='cyan', color_right='magenta', marker='+', marker_size=300, marker_linewidth=2, show_grid=True, grid_alpha=0.3, figsize=(16, 7), dpi=150, title_fontsize=14, label_fontsize=12)

Bases: object

Style configuration for calibration plots.

Example

>>> style = CalibrationPlotStyle(
...     color_left="green",
...     color_right="purple",
... )
>>> plot_calibration_raw(session, style=style)

Parameters:

• color_left (str)

• color_right (str)

• marker (str)

• marker_size (float)

• marker_linewidth (float)

• show_grid (bool)

• grid_alpha (float)

• figsize (tuple[float, float])

• dpi (int)

• title_fontsize (float)

• label_fontsize (float)

color_left: str = 'cyan'

color_right: str = 'magenta'

marker: str = '+'

marker_size: float = 300

marker_linewidth: float = 2

show_grid: bool = True

grid_alpha: float = 0.3

figsize: tuple[float, float] = (16, 7)

dpi: int = 150

title_fontsize: float = 14

label_fontsize: float = 12