def test_matplotlib_plotly_with_multiple_axes():
"""Ensures that wandb.Plotly constructor can accept a plotly figure
reference in which the figure has multiple axes. Importantly, there is
no requirement that any of the axes have plotted data.
"""
for fig in utils.matplotlib_multiple_axes_figures():
wandb.Plotly(fig) # this should not error.
for fig in utils.matplotlib_multiple_axes_figures():
wandb.Plotly(plt) # this should not error.
def test_plotly_from_matplotlib_with_image():
"""Ensures that wandb.Plotly constructor properly errors when
a pyplot with image is passed
"""
# try the figure version
fig = utils.matplotlib_with_image()
with pytest.raises(ValueError):
wandb.Plotly(fig)
plt.close()
# try the plt version
fig = utils.matplotlib_with_image()
with pytest.raises(ValueError):
wandb.Plotly(plt)
plt.close()
def log_figure(summary_name: str, figure: plotly.graph_objects.Figure,
epoch: int) -> None:
"""
Logs a figure to wandb.
:param summary_name: The name of the summary to use on wandb.
:param figure: The figure to be logged.
:param epoch: The current epoch to log with.
"""
wandb.log({summary_name: wandb.Plotly(figure)}, step=epoch)
def plot_all_output_sensitivities(jacobians: Mapping[str, OutputSensitivity]):
"""
Create a plotly heatmap for each input sensitivity matrix for each model
output.
jacobians: mapping of each out variable to a sensitivity for each input
e.g.,
air_temperature_after_precpd:
air_temperature_input: sensitivity matrix (nlev x nlev)
specific_humidity_input: sensitivity matrix
specific_humidity_after_precpd:
...
"""
all_plots = {
out_name: _plot_single_output_sensitivities(out_name,
out_sensitivities)
for out_name, out_sensitivities in jacobians.items()
}
for out_name, fig in all_plots.items():
wandb.log({f"jacobian/{out_name}": wandb.Plotly(fig)})
def func(ds):
return {
f"lat_p_skill/{field}":
wandb.Plotly(plot_cloud_skill_zonal(ds, field, "mean"))
}
