# ribs.visualize.grid_archive_heatmap¶

- ribs.visualize.grid_archive_heatmap(
*archive*,*ax=None*,***,*df=None*,*transpose_measures=False*,*cmap='magma'*,*aspect=None*,*vmin=None*,*vmax=None*,*cbar='auto'*,*cbar_kwargs=None*,*rasterized=False*,*pcm_kwargs=None*)[source]¶ Plots heatmap of a

`GridArchive`

with 1D or 2D measure space.This function creates a grid of cells and shades each cell with a color corresponding to the objective value of that cell’s elite. This function uses

`pcolormesh()`

to generate the grid. For further customization, pass extra kwargs to`pcolormesh()`

through the`pcm_kwargs`

parameter. For instance, to create black boundaries of width 0.1, pass in`pcm_kwargs={"edgecolor": "black", "linewidth": 0.1}`

.Examples

Heatmap of a 2D GridArchive

>>> import numpy as np >>> import matplotlib.pyplot as plt >>> from ribs.archives import GridArchive >>> from ribs.visualize import grid_archive_heatmap >>> # Populate the archive with the negative sphere function. >>> archive = GridArchive(solution_dim=2, ... dims=[20, 20], ... ranges=[(-1, 1), (-1, 1)]) >>> x = np.random.uniform(-1, 1, 10000) >>> y = np.random.uniform(-1, 1, 10000) >>> archive.add(solution_batch=np.stack((x, y), axis=1), ... objective_batch=-(x**2 + y**2), ... measures_batch=np.stack((x, y), axis=1)) >>> # Plot a heatmap of the archive. >>> plt.figure(figsize=(8, 6)) >>> grid_archive_heatmap(archive) >>> plt.title("Negative sphere function") >>> plt.xlabel("x coords") >>> plt.ylabel("y coords") >>> plt.show()

Heatmap of a 1D GridArchive

>>> import numpy as np >>> import matplotlib.pyplot as plt >>> from ribs.archives import GridArchive >>> from ribs.visualize import grid_archive_heatmap >>> # Populate the archive with the negative sphere function. >>> archive = GridArchive(solution_dim=2, ... dims=[20], ranges=[(-1, 1)]) >>> x = np.random.uniform(-1, 1, 1000) >>> archive.add(solution_batch=np.stack((x, x), axis=1), ... objective_batch=-x**2, ... measures_batch=x[:, None]) >>> # Plot a heatmap of the archive. >>> plt.figure(figsize=(8, 6)) >>> grid_archive_heatmap(archive) >>> plt.title("Negative sphere function with 1D measures") >>> plt.xlabel("x coords") >>> plt.show()

- Parameters
**archive**(*GridArchive*) – A 1D or 2D`GridArchive`

.**ax**(*matplotlib.axes.Axes*) – Axes on which to plot the heatmap. If`None`

, the current axis will be used.**df**(*ribs.archives.ArchiveDataFrame*) – If provided, we will plot data from this argument instead of the data currently in the archive. This data can be obtained by, for instance, calling`ribs.archives.ArchiveBase.as_pandas()`

and modifying the resulting`ArchiveDataFrame`

. Note that, at a minimum, the data must contain columns for index, objective, and measures. To display a custom metric, replace the “objective” column.**transpose_measures**(*bool*) – By default, the first measure in the archive will appear along the x-axis, and the second will be along the y-axis. To switch this behavior (i.e. to transpose the axes), set this to`True`

. Does not apply for 1D archives.**cmap**(*str**,**list**,**matplotlib.colors.Colormap*) – The colormap to use when plotting intensity. Either the name of a`Colormap`

, a list of RGB or RGBA colors (i.e. an \(N \times 3\) or \(N \times 4\) array), or a`Colormap`

object.**aspect**(*'auto'**,**'equal'**,**float*) – The aspect ratio of the heatmap (i.e. height/width). Defaults to`'auto'`

for 2D and`0.5`

for 1D.`'equal'`

is the same as`aspect=1`

. See`matplotlib.axes.Axes.set_aspect()`

for more info.**vmin**(*float*) – Minimum objective value to use in the plot. If`None`

, the minimum objective value in the archive is used.**vmax**(*float*) – Maximum objective value to use in the plot. If`None`

, the maximum objective value in the archive is used.**cbar**(*'auto'**,**None**,**matplotlib.axes.Axes*) – By default, this is set to`'auto'`

which displays the colorbar on the archive’s current`Axes`

. If`None`

, then colorbar is not displayed. If this is an`Axes`

, displays the colorbar on the specified Axes.**cbar_kwargs**(*dict*) – Additional kwargs to pass to`colorbar()`

.**rasterized**(*bool*) – Whether to rasterize the heatmap. This can be useful for saving to a vector format like PDF. Essentially, only the heatmap will be converted to a raster graphic so that the archive cells will not have to be individually rendered. Meanwhile, the surrounding axes, particularly text labels, will remain in vector format. This is implemented by passing`rasterized`

to`pcolormesh()`

, so passing`"rasterized"`

in the`pcm_kwargs`

below will raise an error.**pcm_kwargs**(*dict*) – Additional kwargs to pass to`pcolormesh()`

.

- Raises
**ValueError**– The archive’s measure dimension must be 1D or 2D.