diff --git a/__pycache__/__init__.cpython-311.pyc b/__pycache__/__init__.cpython-311.pyc
index a50469ed17864f696db773fddcadf9eca9a3dc9d..a56ee1f6b4dae7b5e88e838093159afdbc581359 100644
Binary files a/__pycache__/__init__.cpython-311.pyc and b/__pycache__/__init__.cpython-311.pyc differ
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index a9aaefd34811381879ceac66adbbea5d1a9703c4..3776743e48c3bafc152410a79f45c93f582243eb 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -36,7 +36,16 @@ class PostProcessing:
Name of the PostProcessing object to be used for saving the generated files.
The default is 'calib'.
out_dir : string
- Output directory in which the images are placed. The default is ''.
+ Output directory in which the PostProcessing results are placed.
+ The results are contained in a subfolder '/Outputs_PostProcessing_name'
+ The default is ''.
+ out_format : string
+ Format of the saved plots. Supports 'png' and 'pdf'. The default is 'pdf'.
+
+ Raises
+ ------
+ AttributeError
+ `engine` must be trained.
"""
@@ -71,17 +80,21 @@ class PostProcessing:
self.stds = None
# -------------------------------------------------------------------------
- def plot_moments(self, plot_type: str = None):
+ def plot_moments(self, plot_type: str = 'line'):
"""
Plots the moments in a user defined output format (standard is pdf) in the directory
`Outputs_PostProcessing`.
Parameters
----------
- xlabel : str, optional
- String to be displayed as x-label. The default is `'Time [s]'`.
plot_type : str, optional
- Options: bar or line. The default is `None`.
+ Supports 'bar' for barplots and 'line'
+ for lineplots The default is `line`.
+
+ Raises
+ ------
+ AttributeError
+ Plot type must be 'bar' or 'line'.
Returns
-------
@@ -91,6 +104,8 @@ class PostProcessing:
Standard deviation of the model outputs.
"""
+ if plot_type not in ['bar', 'line']:
+ raise AttributeError('The wanted plot-type is not supported.')
bar_plot = bool(plot_type == "bar")
meta_model_type = self.engine.MetaModel.meta_model_type
@@ -205,12 +220,10 @@ class PostProcessing:
Samples to be evaluated. The default is None.
model_out_dict: dict
The model runs using the samples provided.
- x_axis : str, optional
- Label of x axis. The default is `'Time [s]'`.
Returns
-------
- None.
+ None
"""
if samples is None:
@@ -263,7 +276,7 @@ class PostProcessing:
Returns
-------
- None.
+ None
"""
# Set the number of samples
@@ -324,19 +337,19 @@ class PostProcessing:
Parameters
----------
- ref_BME_KLD : array, optional
+ ref_bme_kld : array, optional
Reference BME and KLD . The default is `None`.
Returns
-------
- None.
+ None
"""
engine = self.engine
n_init_samples = engine.ExpDesign.n_init_samples
n_total_samples = engine.ExpDesign.X.shape[0]
- newpath = f"Outputs_PostProcessing_{self.name}/seq_design_diagnostics/"
+ newpath = f"{self.out_dir}/seq_design_diagnostics/"
if not os.path.exists(newpath):
os.makedirs(newpath)
@@ -614,26 +627,27 @@ class PostProcessing:
np.save(f"./{newpath}/seq_{plot_name}.npy", seq_values)
# -------------------------------------------------------------------------
- def sobol_indices(self, plot_type: str = None, save: bool = True):
+ def sobol_indices(self, plot_type: str = 'line', save: bool = True):
"""
Visualizes and writes out Sobol' and Total Sobol' indices of the trained metamodel.
One file is created for each index and output key.
Parameters
----------
- xlabel : str, optional
- Label of the x-axis. The default is `'Time [s]'`.
plot_type : str, optional
- Plot type. The default is `None`. This corresponds to line plot.
+ Plot type, supports 'line' for lineplots and 'bar' for barplots.
+ The default is `line`.
Bar chart can be selected by `bar`.
save : bool, optional
Write out the inidces as csv files if set to True. The default
- is True
+ is True.
Raises
------
AttributeError
MetaModel in given Engine needs to be of type 'pce' or 'apce'.
+ AttributeError
+ Plot-type must be 'line' or 'bar'.
Returns
-------
@@ -649,6 +663,9 @@ class PostProcessing:
raise AttributeError("Sobol indices only support PCE-type models!")
if metamod.meta_model_type.lower() not in ["pce", "apce"]:
raise AttributeError("Sobol indices only support PCE-type models!")
+
+ if plot_type not in ['line', 'bar']:
+ raise AttributeError('The wanted plot type is not supported.')
# Extract the necessary variables
max_order = np.max(metamod._pce_deg)
@@ -722,7 +739,7 @@ class PostProcessing:
Returns
-------
- None.
+ None
"""
sobol = None
@@ -820,7 +837,7 @@ class PostProcessing:
Returns
-------
- None.
+ None
"""
if samples is None:
@@ -830,11 +847,6 @@ class PostProcessing:
else:
n_samples = samples.shape[0]
- # if samples is not isinstance(samples, np.ndarray):
- # raise TypeError
-
- if outputs is None or outputs == {}:
- raise AttributeError("Please provide the outputs of the model!")
# Evaluate the original and the surrogate model
y_val = outputs
if y_val is None:
@@ -946,7 +958,7 @@ class PostProcessing:
Returns
-------
- None.
+ None
"""
if self.engine.ExpDesign.ndim != 2:
@@ -1000,7 +1012,7 @@ class PostProcessing:
Returns
-------
- None.
+ None
"""
# List of markers and colors
diff --git a/tests/test_PostProcessing.py b/tests/test_PostProcessing.py
index 5ba76449b9f90f0ea0e093bd9d1f341a5ceb93cd..6ec891854fcfa50e9c4b7c932029b64e9e16d0e7 100644
--- a/tests/test_PostProcessing.py
+++ b/tests/test_PostProcessing.py
@@ -255,22 +255,6 @@ def test_plot_moments_gpebar(gpe_engine) -> None:
assert stdev['Z'][0] == pytest.approx(0.1, abs=0.01)
#%% valid_metamodel
-def test_plot_validation_multi_pce(pce_engine):
- engine = pce_engine
- post = PostProcessing(engine)
- out_mean = {'Z': np.array([[0.4], [0.5], [0.45], [0.4]])}
- out_std = {'Z': np.array([[0.1], [0.1], [0.1], [0.1]])}
- post.model_out_dict = {'Z': np.array([[0.4], [0.5],[0.3],[0.4]])}
- post._plot_validation_multi(out_mean, out_std)
-
-def test_plot_validation_multi_gpe(gpe_engine):
- engine = gpe_engine
- post = PostProcessing(engine)
- out_mean = {'Z': np.array([[0.4], [0.5], [0.45]])}
- out_std = {'Z': np.array([[0.1], [0.1], [0.1]])}
- post.model_out_dict = {'Z': np.array([[0.4], [0.5], [0.45]])}
- post._plot_validation_multi(out_mean, out_std)
-
def test_valid_metamodel_pce(pce_engine):
engine = pce_engine
post = PostProcessing(engine)
@@ -311,7 +295,7 @@ def test_plot_seq_design_diagnostics(basic_engine_trained):
post = PostProcessing(engine)
post.plot_seq_design_diagnostics()
# Check if the plot was created and saved
- assert os.path.exists(f"./{engine.out_dir}/Seq_Design_Diagnostics.{engine.out_format}")
+ assert os.path.exists(f"./{post.out_dir}/Seq_Design_Diagnostics.{post.out_format}")
def test_plot_seq_design_diagnostics_with_custom_values(basic_engine_trained):
"""
@@ -323,7 +307,7 @@ def test_plot_seq_design_diagnostics_with_custom_values(basic_engine_trained):
post = PostProcessing(engine)
post.plot_seq_design_diagnostics()
# Check if the plot was created and saved
- assert os.path.exists(f"./{engine.out_dir}/Seq_Design_Diagnostics.{engine.out_format}")
+ assert os.path.exists(f"./{post.out_dir}/Seq_Design_Diagnostics.{post.out_format}")
def test_plot_seq_design_diagnostics_with_empty_values(basic_engine_trained):
"""
@@ -428,13 +412,18 @@ def test_plot_validation_multi(pce_engine_3d_plot):
assert os.path.exists(f"./{post.out_dir}/Model_vs_pceModel_Y.{post.out_format}")
assert os.path.exists(f"./{post.out_dir}/Model_vs_pceModel_Z.{post.out_format}")
-def test_plot_validation_multi_with_empty_values(pce_engine_3d_plot) -> None:
- """
- Test the _plot_validation_multi method with empty values
- """
- engine = pce_engine_3d_plot
+def test_plot_validation_multi_pce(pce_engine):
+ engine = pce_engine
post = PostProcessing(engine)
- with pytest.raises(ValueError) as excinfo:
- post._plot_validation_multi({},{},{})
- assert "y_val and y_val_std cannot be empty" in str(excinfo.value)
-
\ No newline at end of file
+ out_mean = {'Z': np.array([[0.4], [0.5], [0.45], [0.4]])}
+ out_std = {'Z': np.array([[0.1], [0.1], [0.1], [0.1]])}
+ model_out_dict = {'Z': np.array([[0.4], [0.5],[0.3],[0.4]])}
+ post._plot_validation_multi(out_mean, out_std, model_out_dict)
+
+def test_plot_validation_multi_gpe(gpe_engine):
+ engine = gpe_engine
+ post = PostProcessing(engine)
+ out_mean = {'Z': np.array([[0.4], [0.5], [0.45]])}
+ out_std = {'Z': np.array([[0.1], [0.1], [0.1]])}
+ model_out_dict = {'Z': np.array([[0.4], [0.5], [0.45]])}
+ post._plot_validation_multi(out_mean, out_std, model_out_dict)