diff --git a/mlair/plotting/data_insight_plotting.py b/mlair/plotting/data_insight_plotting.py
index f159e6fca1390b12891a511f30eeb1fbbb0672e9..fec4ef6ec5e5fb89c3fedd46fc1fd6fd845ff1ea 100644
--- a/mlair/plotting/data_insight_plotting.py
+++ b/mlair/plotting/data_insight_plotting.py
@@ -446,51 +446,90 @@ class PlotAvailabilityHistogram(AbstractPlotClass): # pragma: no cover
class PlotDataHistogram(AbstractPlotClass): # pragma: no cover
- def __init__(self, generator: Dict[str, DataCollection], plot_folder: str = ".", plot_name="histogram",
+ def __init__(self, generators: Dict[str, DataCollection], plot_folder: str = ".", plot_name="histogram",
variables_dim="variables", time_dim="datetime", window_dim="window"):
super().__init__(plot_folder, plot_name)
self.variables_dim = variables_dim
self.time_dim = time_dim
self.window_dim = window_dim
- self.inputs = to_list(generator[0].get_X(as_numpy=False)[0].coords[self.variables_dim].values.tolist())
- self.targets = to_list(generator[0].get_Y(as_numpy=False).coords[self.variables_dim].values.tolist())
-
- # normalized versions
- self._calculate_hist(generator, self.inputs, input_data=True)
- self._plot(add_name="input")
- self._calculate_hist(generator, self.targets, input_data=False)
- self._plot(add_name="target")
-
- def _calculate_hist(self, generator, variables, input_data=True):
- bins = {}
- n_bins = 100
- interval_width = None
- bin_edges = None
- f = lambda x: x.get_X(as_numpy=False)[0] if input_data is True else x.get_Y(as_numpy=False)
- for gen in generator:
- w = min(abs(f(gen).coords[self.window_dim].values))
- data = f(gen).sel({self.window_dim: w})
- res, interval_width, bin_edges = f_proc_hist(data, variables, n_bins, self.variables_dim)
- for var in variables:
- n_var = bins.get(var, np.zeros(n_bins))
- n_var += res[var]
- bins[var] = n_var
- self.bins = bins
- self.interval_width = interval_width
- self.bin_edges = bin_edges
-
- def _plot(self, add_name):
- plot_path = os.path.join(os.path.abspath(self.plot_folder), f"{self.plot_name}_{add_name}.pdf")
+ self.inputs, self.targets = self._get_inputs_targets(generators, self.variables_dim)
+ self.bins = {}
+
+ # input plots
+ self._calculate_hist(generators, self.inputs, input_data=True)
+ for subset in generators.keys():
+ self._plot(add_name="input", subset=subset)
+ self._plot_combined(add_name="input")
+
+ # target plots
+ self._calculate_hist(generators, self.targets, input_data=False)
+ for subset in generators.keys():
+ self._plot(add_name="target", subset=subset)
+ self._plot_combined(add_name="target")
+
+ @staticmethod
+ def _get_inputs_targets(gens, dim):
+ k = list(gens.keys())[0]
+ gen = gens[k][0]
+ inputs = to_list(gen.get_X(as_numpy=False)[0].coords[dim].values.tolist())
+ targets = to_list(gen.get_Y(as_numpy=False).coords[dim].values.tolist())
+ return inputs, targets
+
+ def _calculate_hist(self, generators, variables, input_data=True):
+ for set_type, generator in generators.items():
+ bins = {}
+ n_bins = 100
+ interval_width = None
+ bin_edges = None
+ f = lambda x: x.get_X(as_numpy=False)[0] if input_data is True else x.get_Y(as_numpy=False)
+ for gen in generator:
+ w = min(abs(f(gen).coords[self.window_dim].values))
+ data = f(gen).sel({self.window_dim: w})
+ res, interval_width, bin_edges = f_proc_hist(data, variables, n_bins, self.variables_dim)
+ for var in variables:
+ n_var = bins.get(var, np.zeros(n_bins))
+ n_var += res[var]
+ bins[var] = n_var
+ self.bins[set_type] = bins
+ self.interval_width = interval_width
+ self.bin_edges = bin_edges
+
+ def _plot(self, add_name, subset):
+ plot_path = os.path.join(os.path.abspath(self.plot_folder), f"{self.plot_name}_{subset}_{add_name}.pdf")
pdf_pages = matplotlib.backends.backend_pdf.PdfPages(plot_path)
- for var in self.bins.keys():
+ bins = self.bins[subset]
+ colors = self.get_dataset_colors()
+ for var in bins.keys():
fig, ax = plt.subplots()
- hist_var = self.bins[var]
+ hist_var = bins[var]
n_var = sum(hist_var)
weights = hist_var / (self.interval_width * n_var)
- ax.hist(self.bin_edges[:-1], self.bin_edges, weights=weights)
+ ax.hist(self.bin_edges[:-1], self.bin_edges, weights=weights, color=colors[subset])
+ ax.set_ylabel("probability density")
+ ax.set_xlabel(f"values ({subset})")
+ ax.set_title(f"Histogram ({var}, n={int(n_var)})")
+ pdf_pages.savefig()
+ # close all open figures / plots
+ pdf_pages.close()
+ plt.close('all')
+
+ def _plot_combined(self, add_name):
+ plot_path = os.path.join(os.path.abspath(self.plot_folder), f"{self.plot_name}_{add_name}.pdf")
+ pdf_pages = matplotlib.backends.backend_pdf.PdfPages(plot_path)
+ variables = self.bins[list(self.bins.keys())[0]].keys()
+ colors = self.get_dataset_colors()
+ for var in variables:
+ fig, ax = plt.subplots()
+ for subset in self.bins.keys():
+ hist_var = self.bins[subset][var]
+ n_var = sum(hist_var)
+ weights = hist_var / (self.interval_width * n_var)
+ ax.plot(self.bin_edges[:-1] + 0.5 * self.interval_width, weights, label=f"{subset}",
+ c=colors[subset])
ax.set_ylabel("probability density")
ax.set_xlabel(f"{var}")
- ax.set_title(f"Histogram (n={int(n_var)})")
+ ax.legend(loc="upper right")
+ ax.set_title(f"Histogram")
pdf_pages.savefig()
# close all open figures / plots
pdf_pages.close()
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index fafcff5e13930930f298c99750990642c22cded8..89a6f205d03892c57c55a66399a43c9ba2987b42 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -400,8 +400,8 @@ class PostProcessing(RunEnvironment):
try:
if "PlotDataHistogram" in plot_list:
- PlotDataHistogram(self.train_data, plot_folder=self.plot_path, time_dim=time_dim,
- variables_dim=target_dim)
+ gens = {"train": self.train_data, "val": self.val_data, "test": self.test_data}
+ PlotDataHistogram(gens, plot_folder=self.plot_path, time_dim=time_dim, variables_dim=target_dim)
except Exception as e:
logging.error(f"Could not create plot PlotDataHistogram due to the following error: {e}")