<s> current_lib = [ None ] <EOL> supported_libraries = [ '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT:none>' ] <EOL> error_suggestion = "<STR_LIT>" . format ( "<STR_LIT:U+002CU+0020>" . join ( supported_libraries ) ) <EOL> def change_plotting_library ( lib ) : <EOL> try : <EOL> if lib not in supported_libraries : <EOL> raise ValueError ( "<STR_LIT>" . format ( lib , "<STR_LIT:U+002CU+0020>" . join ( supported_libraries ) ) ) <EOL> if lib == '<STR_LIT>' : <EOL> import matplotlib <EOL> from . matplot_dep . plot_definitions import MatplotlibPlots <EOL> from . matplot_dep import visualize , mapping_plots , priors_plots , ssgplvm , svig_plots , variational_plots , img_plots <EOL> current_lib [ <NUM_LIT:0> ] = MatplotlibPlots ( ) <EOL> if lib == '<STR_LIT>' : <EOL> import plotly <EOL> from . plotly_dep . plot_definitions import PlotlyPlots <EOL> current_lib [ <NUM_LIT:0> ] = PlotlyPlots ( ) <EOL> if lib == '<STR_LIT:none>' : <EOL> current_lib [ <NUM_LIT:0> ] = None <EOL> inject_plotting ( ) <EOL> except ( ImportError , NameError ) : <EOL> config . set ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT:none>' ) <EOL> raise <EOL> import warnings <EOL> warnings . warn ( ImportWarning ( "<STR_LIT>" . format ( lib , lib ) ) ) <EOL> def inject_plotting ( ) : <EOL> if current_lib [ <NUM_LIT:0> ] is not None : <EOL> from . import gpy_plot <EOL> from . . core import GP <EOL> GP . plot_data = gpy_plot . data_plots . plot_data <EOL> GP . plot_data_error = gpy_plot . data_plots . plot_data_error <EOL> GP . plot_errorbars_trainset = gpy_plot . data_plots . plot_errorbars_trainset <EOL> GP . plot_mean = gpy_plot . gp_plots . plot_mean <EOL> GP . plot_confidence = gpy_plot . gp_plots . plot_confidence <EOL> GP . plot_density = gpy_plot . gp_plots . plot_density <EOL> GP . plot_samples = gpy_plot . gp_plots . plot_samples <EOL> GP . plot = gpy_plot . gp_plots . plot <EOL> GP . plot_f = gpy_plot . gp_plots . plot_f <EOL> GP . plot_magnification = gpy_plot . latent_plots . plot_magnification <EOL> from . . models import StateSpace <EOL> StateSpace . plot_data = gpy_plot . data_plots . plot_data <EOL> StateSpace . plot_data_error = gpy_plot . data_plots . plot_data_error <EOL> StateSpace . plot_errorbars_trainset = gpy_plot . data_plots . plot_errorbars_trainset <EOL> StateSpace . plot_mean = gpy_plot . gp_plots . plot_mean <EOL> StateSpace . plot_confidence = gpy_plot . gp_plots . plot_confidence <EOL> StateSpace . plot_density = gpy_plot . gp_plots . plot_density <EOL> StateSpace . plot_samples = gpy_plot . gp_plots . plot_samples <EOL> StateSpace . plot = gpy_plot . gp_plots . plot <EOL> StateSpace . plot_f = gpy_plot . gp_plots . plot_f <EOL> from . . core import SparseGP <EOL> SparseGP . plot_inducing = gpy_plot . data_plots . plot_inducing <EOL> from . . models import GPLVM , BayesianGPLVM , bayesian_gplvm_minibatch , SSGPLVM , SSMRD <EOL> GPLVM . plot_latent = gpy_plot . latent_plots . plot_latent <EOL> GPLVM . plot_scatter = gpy_plot . latent_plots . plot_latent_scatter <EOL> GPLVM . plot_inducing = gpy_plot . latent_plots . plot_latent_inducing <EOL> GPLVM . plot_steepest_gradient_map = gpy_plot . latent_plots . plot_steepest_gradient_map <EOL> BayesianGPLVM . plot_latent = gpy_plot . latent_plots . plot_latent <EOL> BayesianGPLVM . plot_scatter = gpy_plot . latent_plots . plot_latent_scatter <EOL> BayesianGPLVM . plot_inducing = gpy_plot . latent_plots . plot_latent_inducing <EOL> BayesianGPLVM . plot_steepest_gradient_map = gpy_plot . latent_plots . plot_steepest_gradient_map <EOL> bayesian_gplvm_minibatch . BayesianGPLVMMiniBatch . plot_latent = gpy_plot . latent_plots . plot_latent <EOL> bayesian_gplvm_minibatch . BayesianGPLVMMiniBatch . plot_scatter = gpy_plot . latent_plots . plot_latent_scatter <EOL> bayesian_gplvm_minibatch . BayesianGPLVMMiniBatch . plot_inducing = gpy_plot . latent_plots . plot_latent_inducing <EOL> bayesian_gplvm_minibatch . BayesianGPLVMMiniBatch . plot_steepest_gradient_map = gpy_plot . latent_plots . plot_steepest_gradient_map <EOL> SSGPLVM . plot_latent = gpy_plot . latent_plots . plot_latent <EOL> SSGPLVM . plot_scatter = gpy_plot . latent_plots . plot_latent_scatter <EOL> SSGPLVM . plot_inducing = gpy_plot . latent_plots . plot_latent_inducing <EOL> SSGPLVM . plot_steepest_gradient_map = gpy_plot . latent_plots . plot_steepest_gradient_map <EOL> from . . kern import Kern <EOL> Kern . plot_covariance = gpy_plot . kernel_plots . plot_covariance <EOL> def deprecate_plot ( self , * args , ** kwargs ) : <EOL> import warnings <EOL> warnings . warn ( DeprecationWarning ( '<STR_LIT>' ) ) <EOL> return self . plot_covariance ( * args , ** kwargs ) <EOL> Kern . plot = deprecate_plot <EOL> Kern . plot_ARD = gpy_plot . kernel_plots . plot_ARD <EOL> from . . inference . optimization import Optimizer <EOL> Optimizer . plot = gpy_plot . inference_plots . plot_optimizer <EOL> def plotting_library ( ) : <EOL> if current_lib [ <NUM_LIT:0> ] is None : <EOL> raise RuntimeError ( "<STR_LIT>" . format ( error_suggestion ) ) <EOL> return current_lib [ <NUM_LIT:0> ] <EOL> def show ( figure , ** kwargs ) : <EOL> """<STR_LIT>""" <EOL> return plotting_library ( ) . show_canvas ( figure , ** kwargs ) <EOL> from . . util . config import config , NoOptionError <EOL> try : <EOL> lib = config . get ( '<STR_LIT>' , '<STR_LIT>' ) <EOL> change_plotting_library ( lib ) <EOL> except NoOptionError : <EOL> print ( "<STR_LIT>" . format ( error_suggestion ) ) </s>
