Flatten plot API: replace Plotter classes with module-level functions

autofit/non_linear/plot/__init__.py

@@ -1,5 +1,4 @@
-from autofit.non_linear.plot.samples_plotters import SamplesPlotter
-from autofit.non_linear.plot.mcmc_plotters import MCMCPlotter
-from autofit.non_linear.plot.mle_plotters import MLEPlotter
-from autofit.non_linear.plot.nest_plotters import NestPlotter
-from autofit.non_linear.plot.output import Output
+from autofit.non_linear.plot.samples_plotters import corner_cornerpy
+from autofit.non_linear.plot.nest_plotters import corner_anesthetic
+from autofit.non_linear.plot.mle_plotters import subplot_parameters, log_likelihood_vs_iteration
+from autofit.non_linear.plot.plot_util import output_figure

autofit/non_linear/plot/mle_plotters.py

@@ -1,136 +1,94 @@
-import logging
-
-from autofit.non_linear.plot.samples_plotters import SamplesPlotter
-
-from autofit.non_linear.plot.samples_plotters import skip_plot_in_test_mode
-
-logger = logging.getLogger(__name__)
-
-class MLEPlotter(SamplesPlotter):
-
-    def subplot_parameters(self, use_log_y : bool = False, use_last_50_percent : bool = False, **kwargs):
-        """
-        Plots a subplot of every parameter against iteration number.
-
-        The subplot extends over all free parameters in the model-fit, with the number of parameters per subplot
-        given by the total number of free parameters in the model-fit.
-
-        This often produces a large dynamic range in the y-axis. Plotting the y-axis on a log-scale or only
-        plotting the last 50% of samples can make the plot easier to inspect.
-
-        Parameters
-        ----------
-        use_log_y
-            If True, the y-axis is plotted on a log-scale.
-        use_last_50_percent
-            If True, only the last 50% of samples are plotted.
-        kwargs
-            Additional key word arguments can be passed to the `plt.subplots` method.
-
-        Returns
-        -------
-
-        """
-
-        import matplotlib.pyplot as plt
-
-        parameter_lists = self.samples.parameters_extract
-
-        plt.subplots(self.model.total_free_parameters, 1, figsize=(12, 3 * len(parameter_lists)))
-
-        for i, parameters in enumerate(parameter_lists):
-
-            iteration_list = range(len(parameter_lists[0]))
-
-            plt.subplot(self.model.total_free_parameters, 1, i + 1)
-
-            if use_last_50_percent:
-
-                iteration_list = iteration_list[int(len(iteration_list) / 2) :]
-                parameters = parameters[int(len(parameters) / 2) :]
-
-            if use_log_y:
-                plt.semilogy(iteration_list, parameters, c="k")
-            else:
-                plt.plot(iteration_list, parameters, c="k")
-
-            plt.xlabel("Iteration", fontsize=16)
-            plt.ylabel(self.model.parameter_labels_with_superscripts_latex[i], fontsize=16)
-            plt.xticks(fontsize=16)
-            plt.yticks(fontsize=16)
-
-        filename = "subplot_parameters"
-
-        if use_log_y:
-            filename += "_log_y"
-
-        if use_last_50_percent:
-            filename += "_last_50_percent"
-
-        self.output.subplot_to_figure(
-            auto_filename=filename
-        )
-        plt.close()
-
-    @skip_plot_in_test_mode
-    def log_likelihood_vs_iteration(self, use_log_y : bool = False, use_last_50_percent : bool = False, **kwargs):
-        """
-        Plot the log likelihood of a model fit as a function of iteration number.
-
-        For a maximum likelihood estimate, the log likelihood should increase with iteration number.
-
-        This often produces a large dynamic range in the y-axis. Plotting the y-axis on a log-scale or only
-        plotting the last 50% of samples can make the plot easier to inspect.
-
-        Parameters
-        ----------
-        use_log_y
-            If True, the y-axis is plotted on a log-scale.
-        """
-
-        import matplotlib.pyplot as plt
-
-        log_likelihood_list = self.samples.log_likelihood_list
-        iteration_list = range(len(log_likelihood_list))
-
-        if use_last_50_percent:
-
-            iteration_list = iteration_list[int(len(iteration_list) / 2) :]
-            log_likelihood_list = log_likelihood_list[int(len(log_likelihood_list) / 2) :]
-
-        plt.figure(figsize=(12, 12))
-
-        if use_log_y:
-            plt.semilogy(iteration_list, log_likelihood_list, c="k")
-        else:
-            plt.plot(iteration_list, log_likelihood_list, c="k")
-
-        plt.xlabel("Iteration", fontsize=16)
-        plt.ylabel("Log Likelihood", fontsize=16)
-        plt.xticks(fontsize=16)
-        plt.yticks(fontsize=16)
-
-        title = "Log Likelihood vs Iteration"
-
-        if use_log_y:
-
-            title += " (Log Scale)"
-
-        if use_last_50_percent:
-
-            title += " (Last 50 Percent)"
-
-        plt.title("Log Likelihood vs Iteration", fontsize=24)
-
-        filename = "log_likelihood_vs_iteration"
-
-        if use_log_y:
-            filename += "_log_y"
-
-        if use_last_50_percent:
-            filename += "_last_50_percent"
-
-        self.output.to_figure(
-            auto_filename=filename,
-        )
-        plt.close()
+from autofit.non_linear.plot.plot_util import skip_in_test_mode, output_figure
+
+
+@skip_in_test_mode
+def subplot_parameters(
+    samples,
+    use_log_y=False,
+    use_last_50_percent=False,
+    path=None,
+    filename="subplot_parameters",
+    format="show",
+    **kwargs,
+):
+    import matplotlib.pyplot as plt
+
+    model = samples.model
+    parameter_lists = samples.parameters_extract
+
+    plt.subplots(model.total_free_parameters, 1, figsize=(12, 3 * len(parameter_lists)))
+
+    for i, parameters in enumerate(parameter_lists):
+        iteration_list = range(len(parameter_lists[0]))
+
+        plt.subplot(model.total_free_parameters, 1, i + 1)
+
+        if use_last_50_percent:
+            iteration_list = iteration_list[int(len(iteration_list) / 2) :]
+            parameters = parameters[int(len(parameters) / 2) :]
+
+        if use_log_y:
+            plt.semilogy(iteration_list, parameters, c="k")
+        else:
+            plt.plot(iteration_list, parameters, c="k")
+
+        plt.xlabel("Iteration", fontsize=16)
+        plt.ylabel(model.parameter_labels_with_superscripts_latex[i], fontsize=16)
+        plt.xticks(fontsize=16)
+        plt.yticks(fontsize=16)
+
+    actual_filename = filename
+    if use_log_y:
+        actual_filename += "_log_y"
+    if use_last_50_percent:
+        actual_filename += "_last_50_percent"
+
+    output_figure(path=path, filename=actual_filename, format=format)
+
+
+@skip_in_test_mode
+def log_likelihood_vs_iteration(
+    samples,
+    use_log_y=False,
+    use_last_50_percent=False,
+    path=None,
+    filename="log_likelihood_vs_iteration",
+    format="show",
+    **kwargs,
+):
+    import matplotlib.pyplot as plt
+
+    log_likelihood_list = samples.log_likelihood_list
+    iteration_list = range(len(log_likelihood_list))
+
+    if use_last_50_percent:
+        iteration_list = iteration_list[int(len(iteration_list) / 2) :]
+        log_likelihood_list = log_likelihood_list[int(len(log_likelihood_list) / 2) :]
+
+    plt.figure(figsize=(12, 12))
+
+    if use_log_y:
+        plt.semilogy(iteration_list, log_likelihood_list, c="k")
+    else:
+        plt.plot(iteration_list, log_likelihood_list, c="k")
+
+    plt.xlabel("Iteration", fontsize=16)
+    plt.ylabel("Log Likelihood", fontsize=16)
+    plt.xticks(fontsize=16)
+    plt.yticks(fontsize=16)
+
+    title = "Log Likelihood vs Iteration"
+    if use_log_y:
+        title += " (Log Scale)"
+    if use_last_50_percent:
+        title += " (Last 50 Percent)"
+
+    plt.title(title, fontsize=24)
+
+    actual_filename = filename
+    if use_log_y:
+        actual_filename += "_log_y"
+    if use_last_50_percent:
+        actual_filename += "_last_50_percent"
+
+    output_figure(path=path, filename=actual_filename, format=format)