diff --git a/.coveragerc b/.coveragerc
index bad43d6e..19cab4b3 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -15,4 +15,5 @@ exclude_lines =
if __name__ == '__main__':
raise ImportError
except Exception as error:
- except ImportError
\ No newline at end of file
+ except ImportError
+ plt.show()
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 4bb8c33c..ca80b181 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,7 +5,8 @@
*.bak
.project
.pydevproject
-aixcalibuha/examples/testzone
+examples/testzone
+examples/results
# pip install #
#####################
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index fb02bb78..dfb58347 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -9,9 +9,10 @@ stages:
variables:
COVERAGE_TYPE: "FMU"
PAGES_BRANCH: master
- EXLUDE_PYTHON: 37
+ EXCLUDE_PYTHON: 37
GIT_REPO: RWTH-EBC/AixCaliBuHA
PYTHON_PACKAGE_NAME: "aixcalibuha"
+ PYLINT_INPUT: "aixcalibuha"
PYTHON_VERSION: "registry.git.rwth-aachen.de/ebc/ebc_all/gitlab_ci/templates:python_3.9"
TEST_ENGINE: "unittest"
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 701f077b..83414bde 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -51,3 +51,12 @@
- **v0.3.1**
- Issue 41: Fix logging and add kwarg
+
+- **v1.0.0**
+ - Issue 43: Improvement of sensitivity analysis
+ - Enables verbose sensitivity analysis and the reuse of simulations
+ - It is now possible to use verbose sensitivity analysis for an automatic selection of tuner parameters
+ - Enables multiprocessing for the entire sensitivity process
+ - Sensitivity analysis is now usable for large models and data
+ - Add time dependent sensitivity analysis
+ - Ends support for python 3.7
diff --git a/README.md b/README.md
index d3f3aa44..452a9627 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,11 @@
[](https://doi.org/10.21105/joss.03861)
-[](https://ebc.pages.rwth-aachen.de/EBC_all/github_ci/AixCaliBuHA/master/pylint/pylint.html)
-[](https://ebc.pages.rwth-aachen.de/EBC_all/github_ci/AixCaliBuHA/master/docs/index.html)
-[](https://ebc.pages.rwth-aachen.de/EBC_all/github_ci/AixCaliBuHA/master/coverage)
+[](https://rwth-ebc.github.io/AixCaliBuHA/master/pylint/pylint.html)
+[](https://rwth-ebc.github.io/AixCaliBuHA/master/docs/index.html)
+[](https://rwth-ebc.github.io/AixCaliBuHA/master/coverage)
[](https://opensource.org/licenses/MIT)
-[](https://ebc.pages.rwth-aachen.de/EBC_all/github_ci/AixCaliBuHA/master/build/build.svg)
+[](https://rwth-ebc.github.io/AixCaliBuHA/master/build/build.svg)
# AixCaliBuHA
@@ -16,7 +16,7 @@ This framework automates the process of calibrating models used in Building
and HVAC Simulations.
# Key features
-- Performing a **Sensitivity Analysis** to discover tuner parameters for the calibration
+- Performing a **Sensitivity Analysis** to analyze your model and discover tuner parameters for the calibration
- **Calibration** of a given model based on the tuner parameters, the calibration classes and specified goals to evaluate the objective function of the underlying optimization
# Installation
@@ -143,7 +143,7 @@ Please use the following metadata to cite `AixCaliBuHA` in your research:
```
# Documentation
-Visit our official [Documentation](https://ebc.pages.rwth-aachen.de/EBC_all/github_ci/AixCaliBuHA/master/docs).
+Visit our official [Documentation](https://rwth-ebc.github.io/AixCaliBuHA/master/docs).
# Problems?
Please [raise an issue here](https://github.com/RWTH-EBC/AixCaliBuHA/issues/new).
diff --git a/aixcalibuha/__init__.py b/aixcalibuha/__init__.py
index 7c51b46e..db00633f 100644
--- a/aixcalibuha/__init__.py
+++ b/aixcalibuha/__init__.py
@@ -4,5 +4,7 @@
"""
from .data_types import CalibrationClass, TunerParas, Goals
from .calibration import Calibrator, MultipleClassCalibrator
-from .sensitivity_analysis import SobolAnalyzer, MorrisAnalyzer
-__version__ = "0.3.1"
+from .sensitivity_analysis import SobolAnalyzer, MorrisAnalyzer, FASTAnalyzer, PAWNAnalyzer, \
+ plotting
+
+__version__ = "1.0.0"
diff --git a/aixcalibuha/calibration/calibrator.py b/aixcalibuha/calibration/calibrator.py
index 8b6c0dbb..69bad510 100644
--- a/aixcalibuha/calibration/calibrator.py
+++ b/aixcalibuha/calibration/calibrator.py
@@ -10,6 +10,7 @@
from typing import Dict
from copy import copy
import numpy as np
+import pandas as pd
from ebcpy import data_types, Optimizer
from ebcpy.simulationapi import SimulationAPI
from aixcalibuha.utils import visualizer, MaxIterationsReached
@@ -95,7 +96,7 @@ def __init__(self,
calibration_class: CalibrationClass,
**kwargs):
"""Instantiate instance attributes"""
- #%% Kwargs
+ # %% Kwargs
# Initialize supported keywords with default value
# Pop the items so they wont be added when calling the
# __init__ of the parent class. Always pop with a default value in case
@@ -131,7 +132,7 @@ def __init__(self,
raise TypeError(f"Given {bool_keyword} is of type "
f"{type(keyword_value).__name__} but should be type bool")
- #%% Initialize all public parameters
+ # %% Initialize all public parameters
super().__init__(cd, **kwargs)
# Set sim_api
self.sim_api = sim_api
@@ -153,7 +154,7 @@ def __init__(self,
"stop_time": self.calibration_class.stop_time}
)
- #%% Setup the logger
+ # %% Setup the logger
# De-register the logger setup in the optimization class:
if self.verbose_logging:
self.logger = visualizer.CalibrationVisualizer(
@@ -243,13 +244,13 @@ def obj(self, xk, *args):
)
return self.ret_val_on_error
- total_res = self._kpi_and_logging_calculation(
+ total_res, unweighted_objective = self._kpi_and_logging_calculation(
xk_descaled=xk_descaled,
counter=self._counter,
results=sim_target_data
)
- return total_res
+ return total_res, unweighted_objective
def mp_obj(self, x, *args):
# Initialize list for results
@@ -268,12 +269,14 @@ def mp_obj(self, x, *args):
xk_descaled_list.append(xk_descaled)
# Update Parameters
parameter_copy = parameters.copy()
- parameter_copy.update({name: value for name, value in zip(initial_names, xk_descaled.values)})
+ parameter_copy.update(
+ {name: value for name, value in zip(initial_names, xk_descaled.values)})
parameter_list.append(parameter_copy)
# Simulate
if self.save_files:
- result_file_names = [f"simulation_{self._counter + idx}" for idx in range(len(parameter_list))]
+ result_file_names = [f"simulation_{self._counter + idx}" for idx in
+ range(len(parameter_list))]
_filepaths = self.sim_api.simulate(
parameters=parameter_list,
return_option="savepath",
@@ -304,7 +307,7 @@ def mp_obj(self, x, *args):
if result is None:
total_res_list[idx] = self.ret_val_on_error
continue
- total_res = self._kpi_and_logging_calculation(
+ total_res, unweighted_objective = self._kpi_and_logging_calculation(
xk_descaled=xk_descaled_list[idx],
counter=self._counter,
results=result
@@ -370,7 +373,7 @@ def _kpi_and_logging_calculation(self, *, xk_descaled, counter, results):
f"of iterations {self.max_itercount} has been reached."
)
- return total_res
+ return total_res, unweighted_objective
def calibrate(self, framework, method=None, **kwargs) -> dict:
"""
@@ -380,7 +383,7 @@ def calibrate(self, framework, method=None, **kwargs) -> dict:
arguments in Optimizer.optimize(). Look at the docstring
in ebcpy to know which options are available.
"""
- #%% Start Calibration:
+ # %% Start Calibration:
self.at_calibration = True
self.logger.log(f"Start calibration of model: {self.sim_api.model_name}"
f" with framework-class {self.__class__.__name__}")
@@ -417,7 +420,7 @@ def calibrate(self, framework, method=None, **kwargs) -> dict:
"Can't save or return any results."
)
- #%% Save the relevant results.
+ # %% Save the relevant results.
self.logger.save_calibration_result(self._current_best_iterate,
self.sim_api.model_name,
duration=t_cal,
@@ -486,7 +489,7 @@ def save_results(self, parameter_values: dict, filename: str):
with open(s_path, 'w') as json_file:
json.dump(parameter_values, json_file, indent=4)
- def validate(self, validation_class: CalibrationClass, calibration_result: Dict):
+ def validate(self, validation_class: CalibrationClass, calibration_result: Dict, verbose=False):
"""
Validate the given calibration class based on the given
values for tuner_parameters.
@@ -501,10 +504,10 @@ def validate(self, validation_class: CalibrationClass, calibration_result: Dict)
self.logger.log(f"Start validation of model: {self.sim_api.model_name} with "
f"framework-class {self.__class__.__name__}")
# Use start-time of calibration class
+ self.calibration_class = validation_class
start_time = self._apply_start_time_method(
start_time=self.calibration_class.start_time
)
- self.calibration_class = validation_class
old_tuner_paras = copy(self.calibration_class.tuner_paras)
tuner_values = list(calibration_result.values())
self.calibration_class.tuner_paras = TunerParas(
@@ -526,12 +529,29 @@ def validate(self, validation_class: CalibrationClass, calibration_result: Dict)
# Scale the tuner parameters
xk = self.tuner_paras.scale(tuner_values)
# Evaluate objective
- obj = self.obj(xk=xk)
+ obj, unweighted_objective = self.obj(xk=xk)
self.logger.validation_callback_func(
obj=obj
)
# Reset tuner_parameters to avoid unwanted behaviour
self.calibration_class.tuner_paras = old_tuner_paras
+ if verbose:
+ weights = [1]
+ objectives = [obj]
+ goals = ['all']
+ for goal, val in unweighted_objective.items():
+ weights.append(val[0])
+ objectives.append(val[1])
+ goals.append(goal)
+ index = pd.MultiIndex.from_product(
+ [[validation_class.name], goals],
+ names=['Class', 'Goal']
+ )
+ obj_verbos = pd.DataFrame(
+ {'weight': weights, validation_class.goals.statistical_measure: objectives},
+ index=index
+ )
+ return obj_verbos
return obj
def _handle_error(self, error):
diff --git a/aixcalibuha/calibration/multi_class_calibrator.py b/aixcalibuha/calibration/multi_class_calibrator.py
index f7cb5af1..8ff888ab 100644
--- a/aixcalibuha/calibration/multi_class_calibrator.py
+++ b/aixcalibuha/calibration/multi_class_calibrator.py
@@ -176,6 +176,8 @@ def calibrate(self, framework, method=None, **kwargs) -> dict:
for cal_run in self._cal_history:
for p_name in cal_run['res']['Parameters'].index:
parameter_values[p_name] = cal_run['res']['Parameters'][p_name]
+ for p_name, res_intersection in res_tuner.items():
+ parameter_values[p_name] = res_intersection
self.save_results(parameter_values=parameter_values,
filename='MultiClassCalibrationResult')
diff --git a/aixcalibuha/data_types.py b/aixcalibuha/data_types.py
index a522a83b..7e96a66e 100644
--- a/aixcalibuha/data_types.py
+++ b/aixcalibuha/data_types.py
@@ -11,6 +11,7 @@
from ebcpy import TimeSeriesData
from ebcpy.utils.statistics_analyzer import StatisticsAnalyzer
from ebcpy.preprocessing import convert_datetime_index_to_float_index
+
# pylint: disable=I1101
logger = logging.getLogger(__name__)
@@ -146,8 +147,8 @@ def __init__(self,
# Set the weightings, if not specified.
self._num_goals = len(_columns)
if weightings is None:
- self._weightings = np.array([1/self._num_goals
- for i in range(self._num_goals)])
+ self.weightings = np.array([1 / self._num_goals
+ for i in range(self._num_goals)])
else:
if not isinstance(weightings, (list, np.ndarray)):
raise TypeError(f"weightings is of type {type(weightings).__name__} "
@@ -156,7 +157,7 @@ def __init__(self,
raise IndexError(f"The given number of weightings ({len(weightings)}) "
f"does not match the number of "
f"goals ({self._num_goals})")
- self._weightings = np.array(weightings) / sum(weightings)
+ self.weightings = np.array(weightings) / sum(weightings)
def __str__(self):
"""Overwrite string method to present the Goals-Object more
@@ -213,10 +214,8 @@ def eval_difference(self, verbose=False, penaltyfactor=1):
)
# Apply penalty function
_diff = _diff * penaltyfactor
-
- _verbose_calculation[self._weightings[i]] = _diff
- total_difference += self._weightings[i] * _diff
-
+ _verbose_calculation[goal_name] = (self.weightings[i], _diff)
+ total_difference += self.weightings[i] * _diff
if verbose:
return total_difference, _verbose_calculation
return total_difference
@@ -368,6 +367,7 @@ class TunerParas:
m_flow_2 0.02 0.01 0.1 0.09
heatConv_a 200.00 10.00 300.0 290.00
"""
+
def __init__(self, names, initial_values, bounds=None):
"""Initialize class-objects and check correct input."""
# Check if the given input-parameters are of correct format. If not, raise an error.
@@ -426,7 +426,7 @@ def scale(self, descaled):
# If no bounds are given, scaling is not possible--> descaled = scaled
if self._bounds is None:
return descaled
- _scaled = (descaled - self._df["min"])/self._df["scale"]
+ _scaled = (descaled - self._df["min"]) / self._df["scale"]
if not all((_scaled >= 0) & (_scaled <= 1)):
warnings.warn("Given descaled values are outside "
"of bounds. Automatically limiting "
@@ -446,12 +446,12 @@ def descale(self, scaled):
if not self._bounds:
return scaled
_scaled = np.array(scaled)
- if not all((_scaled >= 0-1e4) & (_scaled <= 1+1e4)):
+ if not all((_scaled >= 0 - 1e4) & (_scaled <= 1 + 1e4)):
warnings.warn("Given scaled values are outside of bounds. "
"Automatically limiting the values with "
"respect to the bounds.")
_scaled = np.clip(_scaled, a_min=0, a_max=1)
- return _scaled*self._df["scale"] + self._df["min"]
+ return _scaled * self._df["scale"] + self._df["min"]
@property
def bounds(self):
@@ -706,7 +706,7 @@ def merge_calibration_classes(calibration_classes):
"inputs": deepcopy(cal_class.inputs),
"input_kwargs": deepcopy(cal_class.input_kwargs)
}
-
+
# Convert dict to actual calibration-classes
cal_classes_merged = []
for _name, values in temp_merged.items():
diff --git a/aixcalibuha/sensitivity_analysis/__init__.py b/aixcalibuha/sensitivity_analysis/__init__.py
index 2b881b47..64eabaf2 100644
--- a/aixcalibuha/sensitivity_analysis/__init__.py
+++ b/aixcalibuha/sensitivity_analysis/__init__.py
@@ -2,6 +2,10 @@
This package contains classes to perform
sensitivity analysis with.
"""
+
+from aixcalibuha.sensitivity_analysis import plotting
from .sensitivity_analyzer import SenAnalyzer
from .sobol import SobolAnalyzer
from .morris import MorrisAnalyzer
+from .fast import FASTAnalyzer
+from .pawn import PAWNAnalyzer
diff --git a/aixcalibuha/sensitivity_analysis/fast.py b/aixcalibuha/sensitivity_analysis/fast.py
new file mode 100644
index 00000000..ffc3462a
--- /dev/null
+++ b/aixcalibuha/sensitivity_analysis/fast.py
@@ -0,0 +1,89 @@
+"""
+Adds the FASTAnalyzer to the available
+classes of sensitivity analysis.
+"""
+import numpy as np
+from SALib.sample import fast_sampler as fast
+from SALib.analyze import fast as analyze_fast
+from aixcalibuha.sensitivity_analysis import SenAnalyzer
+from aixcalibuha import CalibrationClass
+
+
+class FASTAnalyzer(SenAnalyzer):
+ """
+ FAST method from SALib
+ https://salib.readthedocs.io/en/latest/api.html#fast-fourier-amplitude-sensitivity-test
+ A variance-based method which can compute the sensitivity measures
+ 'S1' and 'ST'.
+
+ Additional arguments:
+
+ :keyword int M:
+ Default 4, used for the fast-method
+ :keyword seed:
+ Used for the fast-method
+ """
+
+ def __init__(self, sim_api, **kwargs):
+ super().__init__(
+ sim_api=sim_api,
+ **kwargs)
+ # Set additional kwargs
+ self.M = kwargs.pop("M", 4)
+ self.seed = kwargs.pop("seed", None)
+
+ @property
+ def analysis_variables(self):
+ """The analysis variables of the FAST method"""
+ return ['S1', 'ST']
+
+ def analysis_function(self, x, y):
+ """
+ Use the SALib.analyze.fast method to analyze the simulation results.
+
+ :param np.array x:
+ placeholder for the `X` parameter of the morris method not used for sobol
+ :param np.array y:
+ The NumPy array containing the model outputs
+ :return:
+ returns the result of the SALib.analyze.fast method (from the documentation:
+ Returns a dictionary with keys 'S1' and 'ST', where each entry is a list of
+ size D (the number of parameters) containing the indices in the same order
+ as the parameter file.)
+ """
+ return analyze_fast.analyze(self.problem, y,
+ M=self.M)
+
+ def create_sampler_demand(self):
+ """
+ Function to create the sampler parameters for the fast method
+ """
+ return {'M': self.M}
+
+ def generate_samples(self):
+ """
+ Run the sampler for fast and return the results.
+
+ :return:
+ The list of samples generated as a NumPy array with one row per sample
+ and each row containing one value for each variable name in `problem['names']`.
+ :rtype: np.ndarray
+ """
+ return fast.sample(self.problem,
+ N=self.num_samples,
+ **self.create_sampler_demand())
+
+ def _get_res_dict(self, result: dict, cal_class: CalibrationClass, analysis_variable: str):
+ """
+ Convert the result object to a dict with the key
+ being the variable name and the value being the result
+ associated to self.analysis_variable.
+ """
+ names = self.create_problem(cal_class.tuner_paras)['names']
+ if result is None:
+ return {var_name: np.abs(res_val)
+ for var_name, res_val in zip(names,
+ np.zeros(len(names)))}
+ return {var_name: np.abs(res_val)
+ for var_name, res_val in zip(names,
+ result[analysis_variable])}
diff --git a/aixcalibuha/sensitivity_analysis/morris.py b/aixcalibuha/sensitivity_analysis/morris.py
index 95ecec7f..a6067026 100644
--- a/aixcalibuha/sensitivity_analysis/morris.py
+++ b/aixcalibuha/sensitivity_analysis/morris.py
@@ -2,6 +2,7 @@
Adds the MorrisAnalyzer to the available
classes of sensitivity analysis.
"""
+import numpy as np
from SALib.sample import morris
from SALib.analyze import morris as analyze_morris
from aixcalibuha.sensitivity_analysis import SenAnalyzer
@@ -10,10 +11,14 @@
class MorrisAnalyzer(SenAnalyzer):
"""
+ Moris method from SALib https://salib.readthedocs.io/en/latest/api.html#method-of-morris
+ An elementary effects (One-At-A-Time) method which computes the sensitivity
+ measures 'mu', 'mu_star' and 'sigma' with a confidence interval for mu_star.
+
Additional arguments:
:keyword int num_levels:
- Default 4, used for the morris-method
+ Default num_samples, used for the morris-method
:keyword optimal_trajectories:
Used for the morris-method
:keyword bool local_optimization:
@@ -24,14 +29,14 @@ def __init__(self, sim_api, **kwargs):
sim_api=sim_api,
**kwargs)
# Set additional kwargs
- self.num_levels = kwargs.pop("num_levels", 4)
+ self.num_levels = kwargs.pop("num_levels", self.num_samples)
self.optimal_trajectories = kwargs.pop("optimal_trajectories", None)
self.local_optimization = kwargs.pop("local_optimization", True)
@property
def analysis_variables(self):
"""The analysis variables of the sobol method"""
- return ['mu_star', 'sigma', 'mu_star_conf']
+ return ['mu_star', 'mu', 'sigma', 'mu_star_conf']
def analysis_function(self, x, y):
"""
@@ -71,10 +76,13 @@ def generate_samples(self):
N=self.num_samples,
**self.create_sampler_demand())
- def _get_res_dict(self, result: dict, cal_class: CalibrationClass):
+ def _get_res_dict(self, result: dict, cal_class: CalibrationClass, analysis_variable: str):
"""
Convert the result object to a dict with the key
being the variable name and the value being the result
associated to self.analysis_variable.
"""
- return dict(zip(result['names'], result[self.analysis_variable]))
+ if result is None:
+ names = cal_class.tuner_paras.get_names()
+ return dict(zip(names, np.zeros(len(names))))
+ return dict(zip(result['names'], result[analysis_variable]))
diff --git a/aixcalibuha/sensitivity_analysis/pawn.py b/aixcalibuha/sensitivity_analysis/pawn.py
new file mode 100644
index 00000000..0c7e441f
--- /dev/null
+++ b/aixcalibuha/sensitivity_analysis/pawn.py
@@ -0,0 +1,124 @@
+"""
+Adds the PAWNAnalyzer to the available
+classes of sensitivity analysis.
+"""
+
+from SALib.sample import sobol
+from SALib.sample import morris
+from SALib.sample import fast_sampler as fast
+from SALib.analyze import pawn as analyze_pawn
+import numpy as np
+from aixcalibuha.sensitivity_analysis import SenAnalyzer
+from aixcalibuha import CalibrationClass
+
+
+class PAWNAnalyzer(SenAnalyzer):
+ """
+ PAWN method from SALib https://salib.readthedocs.io/en/latest/api.html#pawn-sensitivity-analysis
+ Density-based method which computes the PAWN index at 'min', 'max', 'mean',
+ 'median' and coefficient of variation 'cv'.
+
+ Additional arguments:
+
+ :keyword bool calc_second_order:
+ Default True, used for the sampler of the sobol-method
+ :keyword int s:
+ Default 10, used for the pawn-method.
+ :keyword str sampler:
+ Which sampler should be used. Default sobol.
+ Choose between 'sobol', 'morris' and 'fast'.
+ :keyword int num_levels:
+ Default num_samples, used for the sampler of the morris-method.
+ :keyword optimal_trajectories:
+ Used for the sampler of the morris-method.
+ :keyword bool local_optimization:
+ Default True, used for the sampler of the morris-method.
+ :keyword int M:
+ Default 4, used for the sampler of the fast-method.
+ """
+
+ def __init__(self, sim_api, **kwargs):
+ super().__init__(
+ sim_api=sim_api,
+ **kwargs)
+ # Set additional kwargs
+ self.calc_second_order = kwargs.pop("calc_second_order", True)
+ self.s = kwargs.pop("s", 10)
+ self.sampler = kwargs.pop("sampler", 'sobol')
+ self.num_levels = kwargs.pop("num_levels", self.num_samples)
+ self.optimal_trajectories = kwargs.pop("optimal_trajectories", None)
+ self.local_optimization = kwargs.pop("local_optimization", True)
+ self.M = kwargs.pop("M", 4)
+
+ @property
+ def analysis_variables(self):
+ """The analysis variables of the PAWN method"""
+ return ['minimum', 'mean', 'median', 'maximum', 'CV']
+
+ def analysis_function(self, x, y):
+ """
+ Use the SALib.analyze.pawn method to analyze the simulation results.
+
+ :param np.array x:
+ placeholder for the `X` parameter of the morris method not used for sobol
+ :param np.array y:
+ The NumPy array containing the model outputs
+ :return:
+ returns the result of the SALib.analyze.pawn method (from the documentation:
+ This implementation reports the PAWN index at the min, mean, median, and
+ max across the slides/conditioning intervals as well as the coefficient of
+ variation (``CV``). The median value is the typically reported value. As
+ the ``CV`` is (standard deviation / mean), it indicates the level of
+ variability across the slides, with values closer to zero indicating lower
+ variation.)
+ """
+ return analyze_pawn.analyze(self.problem, x, y,
+ S=self.s)
+
+ def create_sampler_demand(self):
+ """
+ Function to create the sampler parameters for the sampler method
+ """
+ if self.sampler == 'sobol':
+ return {'calc_second_order': self.calc_second_order}
+ if self.sampler == 'morris':
+ return {'num_levels': self.num_levels,
+ 'optimal_trajectories': self.optimal_trajectories,
+ 'local_optimization': self.local_optimization}
+ if self.sampler == 'fast':
+ return {'M': self.M}
+ raise NotImplementedError(f'{self.sampler} is not implemented yet')
+
+ def generate_samples(self):
+ """
+ Run the sampler for the selected sampler and return the results.
+
+ :return:
+ The list of samples generated as a NumPy array with one row per sample
+ and each row containing one value for each variable name in `problem['names']`.
+ :rtype: np.ndarray
+ """
+ if self.sampler == 'sobol':
+ return sobol.sample(self.problem,
+ N=self.num_samples,
+ **self.create_sampler_demand())
+ if self.sampler == 'morris':
+ return morris.sample(self.problem,
+ N=self.num_samples,
+ **self.create_sampler_demand())
+ if self.sampler == 'fast':
+ return fast.sample(self.problem,
+ N=self.num_samples,
+ **self.create_sampler_demand())
+ raise NotImplementedError(f'{self.sampler} is not implemented yet')
+
+ def _get_res_dict(self, result: dict, cal_class: CalibrationClass, analysis_variable: str):
+ """
+ Convert the result object to a dict with the key
+ being the variable name and the value being the result
+ associated to self.analysis_variable.
+ """
+ if result is None:
+ names = cal_class.tuner_paras.get_names()
+ return dict(zip(names, np.zeros(len(names))))
+ return dict(zip(result['names'], result[analysis_variable]))
diff --git a/aixcalibuha/sensitivity_analysis/plotting.py b/aixcalibuha/sensitivity_analysis/plotting.py
new file mode 100644
index 00000000..cb0cf91c
--- /dev/null
+++ b/aixcalibuha/sensitivity_analysis/plotting.py
@@ -0,0 +1,552 @@
+"""
+Module containing functions for plotting sensitivity results
+"""
+import sys
+import pandas as pd
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from SALib.plotting.bar import plot as barplot
+from aixcalibuha.utils.visualizer import short_name
+
+
+def plot_single(result: pd.DataFrame,
+ cal_classes: [str] = None,
+ goals: [str] = None,
+ max_name_len: int = 15,
+ **kwargs):
+ """
+ Plot sensitivity results of first and total order analysis variables.
+ For each calibration class one figure is created, which shows for each goal an axis
+ with a barplot of the values of the analysis variables.
+
+ :param pd.DataFrame result:
+ A result from run
+ :param int max_name_len:
+ Default is 15. Shortens the parameter names to max_name_len characters.
+ :param [str] cal_classes:
+ Default are all possible calibration classes. If a list of
+ names of calibration classes is given only plots for these
+ classes are created.
+ :param [str] goals:
+ Default are all possible goal names. If a list of specific
+ goal names is given only these will be plotted.
+ :keyword bool show_plot:
+ Default is True. If False, all created plots are not shown.
+ :keyword ([fig], [ax]) figs_axes:
+ Default None. Set own figures of subfigures with corresponding axes for customization.
+ :keyword bool use_suffix:
+ Default is True: If True, the last part after the last point
+ of Modelica variables is used for the x ticks.
+ :return:
+ Returns all created figures and axes in lists like [fig], [ax]
+ with shapes (len(cal_classes)), (len(cal_classes), len(goals))
+ """
+ use_suffix = kwargs.pop('use_suffix', False)
+ show_plot = kwargs.pop('show_plot', True)
+ figs_axes = kwargs.pop('figs_axes', None)
+ _func_name = kwargs.pop('_func_name', 'plot_single')
+
+ # get lists of the calibration classes and their goals in the result dataframe
+ if cal_classes is None:
+ cal_classes = _del_duplicates(list(result.index.get_level_values(0)))
+ if goals is None:
+ goals = _del_duplicates(list(result.index.get_level_values(1)))
+
+ result = _rename_tuner_names(result, use_suffix, max_name_len, _func_name)
+
+ # plotting with simple plot function of the SALib
+ figs = []
+ axes = []
+ for col, cal_class in enumerate(cal_classes):
+ fig, ax = _create_figs_axes(figs_axes, col, goals, f"Class: {cal_class}")
+ figs.append(fig)
+ axes.append(ax)
+ for row, goal in enumerate(goals):
+ result_df = result.loc[cal_class, goal]
+ axes[col][row].grid(True, which='both', axis='y')
+ barplot(result_df.T, ax=axes[col][row])
+ axes[col][row].set_title(f"Goal: {goal}")
+ axes[col][row].legend()
+
+ if show_plot:
+ plt.show()
+
+ return figs, axes
+
+
+def plot_second_order(result: pd.DataFrame,
+ cal_classes: [str] = None,
+ goals: [str] = None,
+ max_name_len: int = 15,
+ **kwargs):
+ """
+ Plot sensitivity results of second order analysis variables.
+ For each calibration class and goal one figure of a 3d plot is created
+ with the barplots of the interactions for each parameter.
+ Only working for more than 2 parameter.
+
+ :param pd.DataFrame result:
+ A result from run
+ :param int max_name_len:
+ Default is 15. Shortens the parameter names to max_name_len characters.
+ :param [str] cal_classes:
+ Default are all possible calibration classes. If a list of
+ names of calibration classes is given only plots for these
+ classes are created.
+ :param [str] goals:
+ Default are all possible goal names. If a list of specific
+ goal names is given only these will be plotted.
+ :keyword bool show_plot:
+ Default is True. If False, all created plots are not shown.
+ :keyword bool use_suffix:
+ Default is True: If True, the last part after the last point
+ of Modelica variables is used for the x ticks.
+ :keyword [[fig]] figs:
+ Default None. Set own figures of subfigures for customization.
+ Shape (len(cal_classes), len(goals))
+ :return:
+ Returns all created figures and axes in lists like [fig], [ax]
+ """
+ use_suffix = kwargs.pop('use_suffix', False)
+ show_plot = kwargs.pop('show_plot', True)
+ figs = kwargs.pop('figs', None)
+ result = result.fillna(0)
+ if cal_classes is None:
+ cal_classes = _del_duplicates(list(result.index.get_level_values(0)))
+ if goals is None:
+ goals = _del_duplicates(list(result.index.get_level_values(1)))
+
+ result = _rename_tuner_names(result, use_suffix, max_name_len, "plot_second_order")
+
+ tuner_names = result.columns
+ if len(tuner_names) < 2:
+ return None
+ xticks = np.arange(len(tuner_names))
+
+ # when the index is not sorted pandas throws a performance warning
+ result = result.sort_index()
+
+ # plot of S2 without S2_conf
+ all_figs = []
+ all_axes = []
+ for class_idx, cal_class in enumerate(cal_classes):
+ class_figs = []
+ class_axes = []
+ for goal_idx, goal in enumerate(goals):
+ if figs is None:
+ fig = plt.figure()
+ else:
+ fig = figs[class_idx][goal_idx]
+ ax = fig.add_subplot(projection='3d')
+ for idx, name in enumerate(tuner_names):
+ ax.bar(tuner_names,
+ result.loc[cal_class, goal, 'S2', name].to_numpy(),
+ zs=idx, zdir='y', alpha=0.8)
+ ax.set_title(f"Class: {cal_class} Goal: {goal}")
+ ax.set_zlabel('S2 [-]')
+ ax.set_yticks(xticks)
+ ax.set_yticklabels(tuner_names)
+ # rotate tick labels for better readability
+ plt.setp(ax.get_xticklabels(), rotation=90, ha="right", rotation_mode="anchor")
+ plt.setp(ax.get_yticklabels(), rotation=90, ha="right", rotation_mode="anchor")
+ class_figs.append(fig)
+ class_axes.append(ax)
+ all_figs.append(class_figs)
+ all_axes.append(class_axes)
+ if show_plot:
+ plt.show()
+ return all_figs, all_axes
+
+
+def plot_single_second_order(result: pd.DataFrame,
+ para_name: str,
+ cal_classes: [str] = None,
+ goals: [str] = None,
+ max_name_len: int = 15,
+ **kwargs):
+ """
+ Plot the value of S2 from one parameter with all other parameters.
+
+ :param pd.DataFrame result:
+ Second order result from run.
+ :param str para_name:
+ Name of the parameter of which the results should be plotted.
+ :param [str] cal_classes:
+ Default are all possible calibration classes. If a list of
+ names of calibration classes is given only plots for these
+ classes are created.
+ :param [str] goals:
+ Default are all possible goal names. If a list of specific
+ goal names is given only these will be plotted.
+ :param int max_name_len:
+ Default is 15. Shortens the parameter names to max_name_len characters.
+ :keyword bool show_plot:
+ Default is True. If False, all created plots are not shown.
+ :keyword bool use_suffix:
+ Default is True: If True, the last part after the last point
+ of Modelica variables is used for the x ticks.
+ :keyword ([fig], [ax]) figs_axes:
+ Default None. Set own figures of subfigures with corresponding axes for customization.
+ :return:
+ Returns all created figures and axes in lists like [fig], [ax]
+ with shapes (len(cal_classes)), (len(cal_classes), len(goals))
+ """
+ use_suffix = kwargs.pop('use_suffix', False)
+ show_plot = kwargs.pop('show_plot', True)
+ figs_axes = kwargs.pop('figs_axes', None)
+
+ result = result.loc[:, :, :, para_name][:].fillna(0)
+ figs, axes = plot_single(
+ result=result,
+ show_plot=False,
+ cal_classes=cal_classes,
+ goals=goals,
+ figs_axes=figs_axes,
+ use_suffix=use_suffix,
+ max_name_len=max_name_len,
+ _func_name="plot_single_second_order"
+ )
+ # set new title for the figures of each calibration class
+ for fig in figs:
+ fig.suptitle(f"Interaction of {para_name} in class {fig._suptitle.get_text()}")
+ if show_plot:
+ plt.show()
+ return figs, axes
+
+
+def heatmap(result: pd.DataFrame,
+ cal_class: str,
+ goal: str,
+ ax: matplotlib.axes.Axes = None,
+ max_name_len: int = 15,
+ **kwargs):
+ """
+ Plot S2 sensitivity results from one calibration class and goal as a heatmap.
+
+ :param pd.DataFrame result:
+ A second order result from run
+ :param str cal_class:
+ Name of the class to plot S2 from.
+ :param str goal:
+ Name of the goal to plot S2 from.
+ :param matplotlib.axes ax:
+ Default is None. If an axes is given the heatmap will be plotted on it, else
+ a new figure and axes is created.
+ :param int max_name_len:
+ Default is 15. Shortens the parameter names to max_name_len characters.
+ :keyword bool show_plot:
+ Default is True. If False, all created plots are not shown.
+ :keyword bool use_suffix:
+ Default is False. If True, only the last suffix of a Modelica variable is displayed.
+ :return:
+ Returns axes
+ """
+ use_suffix = kwargs.pop('use_suffix', False)
+ show_plot = kwargs.pop('show_plot', True)
+ _func_name = kwargs.pop('_func_name', "heatmap")
+
+ result = _rename_tuner_names(result, use_suffix, max_name_len, _func_name)
+ if ax is None:
+ _, ax = plt.subplots(layout="constrained")
+ data = result.sort_index().loc[cal_class, goal, 'S2'].fillna(0).reindex(
+ index=result.columns)
+ image = ax.imshow(data, cmap='Reds')
+ ax.set_title(f'Class: {cal_class} Goal: {goal}')
+ ax.set_xticks(np.arange(len(data.columns)))
+ ax.set_yticks(np.arange(len(data.index)))
+ ax.set_xticklabels(data.columns)
+ ax.set_yticklabels(data.index)
+ ax.spines[:].set_color('black')
+ plt.setp(ax.get_xticklabels(), rotation=90, ha="right", rotation_mode="anchor")
+ cbar = ax.figure.colorbar(image, ax=ax)
+ cbar.ax.set_ylabel("S2", rotation=90)
+ if show_plot:
+ plt.show()
+ return ax
+
+
+def heatmaps(result: pd.DataFrame,
+ cal_classes: [str] = None,
+ goals: [str] = None,
+ max_name_len: int = 15,
+ **kwargs):
+ """
+ Plot S2 sensitivity results as a heatmap for multiple
+ calibration classes and goals in one figure.
+
+ :param pd.DataFrame result:
+ A second order result from run
+ :param [str] cal_classes:
+ Default is a list of all calibration classes in the result.
+ If a list of classes is given only these classes are plotted.
+ :param [str] goals:
+ Default is a list of all goals in the result.
+ If a list of goals is given only these goals are plotted.
+ :param int max_name_len:
+ Default is 15. Shortens the parameter names to max_name_len characters.
+ :keyword bool show_plot:
+ Default is True. If False, all created plots are not shown.
+ :keyword bool use_suffix:
+ Default is False. If True, only the last suffix of a Modelica variable is displayed.
+ """
+ use_suffix = kwargs.pop('use_suffix', False)
+ show_plot = kwargs.pop('show_plot', True)
+
+ if cal_classes is None:
+ cal_classes = result.index.get_level_values("Class").unique()
+ if goals is None:
+ goals = result.index.get_level_values("Goal").unique()
+
+ _, axes = plt.subplots(ncols=len(cal_classes), nrows=len(goals), sharex='all', sharey='all',
+ layout="constrained")
+ if len(goals) == 1:
+ axes = [axes]
+ if len(cal_classes) == 1:
+ for idx, ax in enumerate(axes):
+ axes[idx] = [ax]
+ _func_name = "heatmaps"
+ for col, class_name in enumerate(cal_classes):
+ for row, goal_name in enumerate(goals):
+ heatmap(result,
+ class_name,
+ goal_name,
+ ax=axes[row][col],
+ show_plot=False,
+ use_suffix=use_suffix,
+ max_name_len=max_name_len,
+ _func_name=_func_name)
+ _func_name = None
+ if show_plot:
+ plt.show()
+
+
+def plot_time_dependent(result: pd.DataFrame,
+ parameters: [str] = None,
+ goals: [str] = None,
+ analysis_variables: [str] = None,
+ plot_conf: bool = True,
+ **kwargs):
+ """
+ Plot time dependent sensitivity results without interactions from run_time_dependent().
+
+ For each goal one figure is created with one axes for each analysis variable.
+ In these plots the time dependent sensitivity of the parameters is plotted.
+ The confidence interval can also be plotted.
+
+ :param pd.DataFrame result:
+ A result from run_time_dependent without second order results.
+ :param [str] parameters:
+ Default all parameters. List of parameters to plot the sensitivity.
+ :param [str] analysis_variables:
+ Default all analysis_variables. List of analysis variables to plot.
+ :param bool plot_conf:
+ Default True. If true, the confidence intervals for each parameter are plotted.
+ :param [str] goals:
+ Default are all possible goal names. If a list of specific
+ goal names is given only these will be plotted.
+ :keyword ([fig], [ax]) figs_axes:
+ Default None. Optional custom figures and axes
+ (see example for verbose sensitivity analysis).
+ :return:
+ Returns all created figures and axes in lists like [fig], [ax]
+ with shapes (len(goals)), (len(goals), len(analysis_variables))
+ :keyword bool show_plot:
+ Default is True. If False, all created plots are not shown.
+ :keyword bool use_suffix:
+ Default is True: If True, the last part after the last point
+ of Modelica variables is used for the x ticks.
+ :keyword int max_name_len:
+ Default is 50. Shortens the parameter names to max_name_len characters.
+ """
+ use_suffix = kwargs.pop('use_suffix', False)
+ max_name_len = kwargs.pop('max_name_len', 50)
+ show_plot = kwargs.pop('show_plot', True)
+ figs_axes = kwargs.pop('figs_axes', None)
+
+ if goals is None:
+ goals = _del_duplicates(list(result.index.get_level_values(0)))
+ all_analysis_variables = _del_duplicates(list(result.index.get_level_values(1)))
+ if analysis_variables is None:
+ analysis_variables = [av for av in all_analysis_variables if '_conf' not in av]
+ if parameters is None:
+ parameters = result.columns.values
+
+ result = _rename_tuner_names(result, use_suffix, max_name_len, "plot_time_dependent")
+
+ renamed_parameters = [_format_name(para, use_suffix, max_name_len) for para in parameters]
+
+ figs = []
+ axes = []
+ for idx_goal, goal in enumerate(goals):
+ fig, ax = _create_figs_axes(figs_axes, idx_goal, analysis_variables, f"Goal: {goal}")
+ figs.append(fig)
+ axes.append(ax)
+ for idx_av, analysis_var in enumerate(analysis_variables):
+ axes[idx_goal][idx_av].plot(result.loc[goal, analysis_var][renamed_parameters])
+ axes[idx_goal][idx_av].set_ylabel(analysis_var)
+ axes[idx_goal][idx_av].legend(renamed_parameters)
+ if plot_conf and analysis_var + '_conf' in all_analysis_variables:
+ for para in renamed_parameters:
+ y = result.loc[goal, analysis_var][para]
+ x = y.index.to_numpy()
+ conv_int = result.loc[goal, analysis_var + '_conf'][para]
+ large_values_indices = conv_int[conv_int > 1].index
+ if list(large_values_indices):
+ sys.stderr.write(
+ f"plot_time_dependent INFO:"
+ f"Confidence interval for {goal}, {analysis_var}, {para} was at the "
+ f"following times {list(large_values_indices)} lager than 1 "
+ f"and is smoothed out in the plot.\n")
+ for idx in large_values_indices:
+ prev_idx = conv_int.index.get_loc(idx) - 1
+ if prev_idx >= 0:
+ conv_int.iloc[conv_int.index.get_loc(idx)] = conv_int.iloc[prev_idx]
+ else:
+ conv_int.iloc[conv_int.index.get_loc(idx)] = 1
+ axes[idx_goal][idx_av].fill_between(x, (y - conv_int), (y + conv_int), alpha=.1)
+ axes[idx_goal][-1].set_xlabel('time')
+ if show_plot:
+ plt.show()
+ return figs, axes
+
+
+def plot_parameter_verbose(parameter: str,
+ single_result: pd.DataFrame,
+ second_order_result: pd.DataFrame = None,
+ goals: [str] = None,
+ **kwargs):
+ """
+ Plot all time dependent sensitivity measure for one parameter.
+ For each goal an axes is created within one figure.
+
+ If second_order_results form SobolAnalyzer.run_time_dependent are given
+ the S2 results of the interaction with each other parameter are added on top
+ of each other and the first order result.
+
+ :param str parameter:
+ Parameter to plot all sensitivity results for. If use_suffix=True, then
+ the name must also be only the suffix.
+ :param pd.DataFrame single_result:
+ First and total order result form run_time_dependent.
+ :param pd.DataFrame second_order_result:
+ Default None. Second order result of SobolAnalyzer.run_time_dependent.
+ :param [str] goals:
+ Default are all possible goal names. If a list of specific
+ goal names is given only these will be plotted.
+ :keyword (fig, [ax]) fig_axes:
+ Default None. Optional custom figures and axes
+ (see example for verbose sensitivity analysis).
+ :return:
+ Returns all created figures and axes in lists like fig, [ax]
+ with shape (len(goals)) for the axes list
+ :keyword bool show_plot:
+ Default is True. If False, all created plots are not shown.
+ :keyword bool use_suffix:
+ Default is True: If True, the last part after the last point
+ of Modelica variables is used for the x ticks.
+ :keyword int max_name_len:
+ Default is 15. Shortens the parameter names to max_name_len characters.
+ """
+ use_suffix = kwargs.pop('use_suffix', False)
+ max_name_len = kwargs.pop('max_name_len', 50)
+ show_plot = kwargs.pop('show_plot', True)
+ fig_axes = kwargs.pop('fig_axes', None)
+
+ if goals is None:
+ goals = _del_duplicates(list(single_result.index.get_level_values(0)))
+ all_analysis_variables = _del_duplicates(list(single_result.index.get_level_values(1)))
+ analysis_variables = [av for av in all_analysis_variables if '_conf' not in av]
+
+ renamed_parameter = _format_name(parameter, use_suffix, max_name_len)
+
+ single_result = _rename_tuner_names(single_result, use_suffix, max_name_len,
+ "plot_parameter_verbose")
+ if second_order_result is not None:
+ second_order_result = _rename_tuner_names(second_order_result, use_suffix, max_name_len)
+
+ if fig_axes is None:
+ fig, ax = plt.subplots(len(goals), sharex='all', layout="constrained")
+ else:
+ fig = fig_axes[0]
+ ax = fig_axes[1]
+ fig.suptitle(f"Parameter: {parameter}")
+ if not isinstance(ax, np.ndarray):
+ ax = [ax]
+ for g_i, goal in enumerate(goals):
+ if second_order_result is not None:
+ result_2_goal = second_order_result.loc[goal, 'S2', renamed_parameter]
+ mean = result_2_goal.mean().drop([renamed_parameter])
+ mean.sort_values(ascending=False, inplace=True)
+ sorted_interactions = list(mean.index)
+ time_ar = _del_duplicates(list(result_2_goal.index.get_level_values(0)))
+ value = single_result.loc[goal, 'S1'][renamed_parameter].to_numpy()
+ ax[g_i].plot(single_result.loc[goal, 'S1'][renamed_parameter], label='S1')
+ ax[g_i].fill_between(time_ar, np.zeros_like(value), value, alpha=0.1)
+ for para in sorted_interactions:
+ value_2 = value + result_2_goal[para].to_numpy()
+ ax[g_i].plot(time_ar, value_2, label='S2 ' + para)
+ ax[g_i].fill_between(time_ar, value, value_2, alpha=0.1)
+ value = value_2
+ ax[g_i].plot(single_result.loc[goal, 'ST'][renamed_parameter], label='ST')
+ legend = ['S1']
+ legend.extend(analysis_variables)
+ legend.append('ST')
+ ax[g_i].set_title(f"Goal: {goal}")
+ ax[g_i].legend()
+ else:
+ for analysis_var in analysis_variables:
+ ax[g_i].plot(single_result.loc[goal, analysis_var][renamed_parameter])
+ ax[g_i].legend(analysis_variables)
+ if show_plot:
+ plt.show()
+ return fig, ax
+
+
+def _del_duplicates(x):
+ """Helper function"""
+ return list(dict.fromkeys(x))
+
+
+def _rename_tuner_names(result, use_suffix, max_len, func_name=None):
+ """Helper function"""
+ tuner_names = list(result.columns)
+ renamed_names = {name: _format_name(name, use_suffix, max_len) for name in tuner_names}
+ result = result.rename(columns=renamed_names, index=renamed_names)
+ result = result.sort_index()
+ for old, new in renamed_names.items():
+ if old != new and func_name is not None:
+ sys.stderr.write(f"{func_name} INFO: parameter name {old} changed to {new}\n")
+ return result
+
+
+def _format_name(name, use_suffix, max_len):
+ """
+ Format tuner names.
+ """
+ if use_suffix:
+ name = _get_suffix(name)
+ name = short_name(name, max_len)
+ return name
+
+
+def _get_suffix(modelica_var_name):
+ """Helper function"""
+ index_last_dot = modelica_var_name.rfind('.')
+ suffix = modelica_var_name[index_last_dot + 1:]
+ return suffix
+
+
+def _create_figs_axes(figs_axes, fig_index, ax_len_list, fig_title):
+ """
+ Check if figs and axes are already given, if not create them.
+ """
+ if figs_axes is None:
+ fig, ax = plt.subplots(len(ax_len_list), sharex='all', layout="constrained")
+ else:
+ fig = figs_axes[0][fig_index]
+ ax = figs_axes[1][fig_index]
+ fig.suptitle(fig_title)
+ if not isinstance(ax, np.ndarray):
+ ax = [ax]
+ return fig, ax
diff --git a/aixcalibuha/sensitivity_analysis/sensitivity_analyzer.py b/aixcalibuha/sensitivity_analysis/sensitivity_analyzer.py
index afeec06b..6b4d288b 100644
--- a/aixcalibuha/sensitivity_analysis/sensitivity_analyzer.py
+++ b/aixcalibuha/sensitivity_analysis/sensitivity_analyzer.py
@@ -3,15 +3,84 @@
import abc
import copy
import os
-import time
+import pathlib
+import multiprocessing as mp
from typing import List
+from collections import Counter
import numpy as np
import pandas as pd
from ebcpy.utils import setup_logger
-from ebcpy import data_types
+from ebcpy.utils.reproduction import CopyFile
from ebcpy.simulationapi import SimulationAPI
from aixcalibuha import CalibrationClass, data_types
from aixcalibuha import utils
+from aixcalibuha.sensitivity_analysis.plotting import plot_single, plot_time_dependent
+
+
+def _load_single_file(_filepath, parquet_engine='pyarrow'):
+ """Helper function"""
+ if _filepath is None:
+ return None
+ return data_types.TimeSeriesData(_filepath, default_tag='sim', key='simulation',
+ engine=parquet_engine)
+
+
+def _load_files(_filepaths, parquet_engine='pyarrow'):
+ """Helper function"""
+ results = []
+ for _filepath in _filepaths:
+ results.append(_load_single_file(_filepath, parquet_engine=parquet_engine))
+ return results
+
+
+def _restruct_verbose(list_output_verbose):
+ """Helper function"""
+ output_verbose = {}
+ for key, val in list_output_verbose[0].items():
+ output_verbose[key] = np.array([])
+ for i in list_output_verbose:
+ for key, val in i.items():
+ output_verbose[key] = np.append(output_verbose[key], np.array([val[1]]))
+ return output_verbose
+
+
+def _concat_all_sims(sim_results_list):
+ """Helper function that concat all results in a list to one DataFrame."""
+ sim_results_list = [r.to_df() for r in sim_results_list]
+ sim_results_list = pd.concat(sim_results_list, keys=range(len(sim_results_list)),
+ axis='columns')
+ sim_results_list = sim_results_list.swaplevel(axis=1).sort_index(axis=1)
+ return sim_results_list
+
+
+def _restruct_time_dependent(sen_time_dependent_list, time_index):
+ """Helper function that restructures the time dependent sensitivity results."""
+
+ def _restruct_single(sen_time_dependent_list_s, second_order=False):
+ sen_time_dependent_df = pd.concat(sen_time_dependent_list_s, keys=time_index, axis=0)
+ sen_time_dependent_df = sen_time_dependent_df.droplevel('Class', axis='index')
+ sen_time_dependent_df = sen_time_dependent_df.swaplevel(0, 1)
+ sen_time_dependent_df = sen_time_dependent_df.swaplevel(1, 2).sort_index(axis=0)
+ if second_order:
+ sen_time_dependent_df = sen_time_dependent_df.swaplevel(2, 3).sort_index(axis=0)
+ sen_time_dependent_df.index.set_names(
+ ['Goal', 'Analysis variable', 'Interaction', 'time'], inplace=True)
+ else:
+ sen_time_dependent_df.index.set_names(['Goal', 'Analysis variable', 'time'],
+ inplace=True)
+ return sen_time_dependent_df
+
+ if isinstance(sen_time_dependent_list[0], tuple):
+ sen_time_dependent_list1, sen_time_dependent_list2 = zip(*sen_time_dependent_list)
+ return _restruct_single(sen_time_dependent_list1), _restruct_single(
+ sen_time_dependent_list2, True)
+ return _restruct_single(sen_time_dependent_list)
+
+
+def _divide_chunks(long_list, chunk_length):
+ """Helper function that divides all list into multiple list with a specific chunk length."""
+ for i in range(0, len(long_list), chunk_length):
+ yield long_list[i:i + chunk_length]
class SenAnalyzer(abc.ABC):
@@ -22,13 +91,12 @@ class SenAnalyzer(abc.ABC):
Simulation-API used to simulate the samples
:param int num_samples:
The parameter `N` to the sampler methods of sobol and morris. NOTE: This is not the
- the number of samples produced, but relates to the total number of samples produced in
- a manner dependent on the sampler method used. See the documentation of sobol and
- morris in the SALib for more information.
- :keyword str analysis_function:
- Used to automatically select result values.
+ number of samples produced, but relates to the total number of samples produced in
+ a manner dependent on the sampler method used. See the documentation of the specific
+ method in the SALib for more information.
:keyword str,os.path.normpath cd:
The path for the current working directory.
+ Logger and results will be stored here.
:keyword boolean fail_on_error:
Default is False. If True, the calibration will stop with an error if
the simulation fails. See also: ``ret_val_on_error``
@@ -38,7 +106,19 @@ class SenAnalyzer(abc.ABC):
options are np.NaN, np.inf or some other high numbers. be aware that this
max influence the solver.
:keyword boolean save_files:
- If true, all simulation files for each iteration will be saved!
+ Default False. If true, all simulation files for each iteration will be saved!
+ :keyword str suffix_files:
+ Default 'csv'. Specifies the data format to store the simulation files in.
+ Options are 'csv', 'hdf', 'parquet'.
+ :keyword str parquet_engine:
+ The engine to use for the data format parquet.
+ Supported options can be extracted
+ from the ebcpy.TimeSeriesData.save() function.
+ Default is 'pyarrow'.
+ :keyword str,os.path.normpath savepath_sim:
+ Default is cd. Own directory for the time series data sets of all simulations
+ during the sensitivity analysis. The own dir can be necessary for large data sets,
+ because they can crash IDE during indexing when they are in the project folder.
"""
@@ -54,20 +134,23 @@ def __init__(self,
# Update kwargs
self.fail_on_error = kwargs.pop("fail_on_error", True)
self.save_files = kwargs.pop("save_files", False)
+ self.suffix_files = kwargs.pop('suffix_files', 'csv')
+ self.parquet_engine = kwargs.pop('parquet_engine', 'pyarrow')
self.ret_val_on_error = kwargs.pop("ret_val_on_error", np.NAN)
self.cd = kwargs.pop("cd", os.getcwd())
- self.analysis_variable = kwargs.pop('analysis_variable',
- self.analysis_variables[0])
- if self.analysis_variable not in self.analysis_variables:
- raise TypeError(f'Given analysis_variable "{self.analysis_variable}" not '
- f'supported for class {self.__class__.__name__}. '
- f'Supported options are: {", ".join(self.analysis_variables)}.')
+ self.savepath_sim = kwargs.pop('savepath_sim', self.cd)
+
+ if isinstance(self.cd, str):
+ self.cd = pathlib.Path(self.cd)
+ if isinstance(self.savepath_sim, str):
+ self.savepath_sim = pathlib.Path(self.savepath_sim)
# Setup the logger
self.logger = setup_logger(cd=self.cd, name=self.__class__.__name__)
# Setup default values
self.problem: dict = None
+ self.reproduction_files = []
@property
@abc.abstractmethod
@@ -120,19 +203,23 @@ def generate_samples(self):
raise NotImplementedError(f'{self.__class__.__name__}.generate_samples '
f'function is not defined yet')
- def simulate_samples(self, samples, cal_class):
+ def simulate_samples(self, cal_class, **kwargs):
"""
- Put the parameters in the model and simulate it.
+ Creates the samples for the calibration class and simulates them.
- :param Union[list, np.ndarray] samples:
- Output variables in dymola
:param cal_class:
One class for calibration. Goals and tuner_paras have to be set
+ :keyword scale:
+ Default is False. If True the bounds of the tuner-parameters
+ will be scaled between 0 and 1.
- :returns: np.array
- An array containing the evaluated differences for each sample
+ :return:
+ Returns two lists. First a list with the simulation results for each sample.
+ If save_files the list contains the filepaths to the results
+ Second a list of the samples.
+ :rtype: list
"""
- output = []
+ scale = kwargs.pop('scale', False)
# Set the output interval according the given Goals
mean_freq = cal_class.goals.get_meas_frequency()
self.logger.info("Setting output_interval of simulation according "
@@ -142,31 +229,42 @@ def simulate_samples(self, samples, cal_class):
self.sim_api.set_sim_setup({"start_time": cal_class.start_time,
"stop_time": cal_class.stop_time})
self.sim_api.result_names = cal_class.goals.get_sim_var_names()
- self.logger.info('Starting %s parameter variations on %s cores',
- len(samples), self.sim_api.n_cpu)
+
+ self.problem = self.create_problem(cal_class.tuner_paras, scale=scale)
+ samples = self.generate_samples()
+
+ # creat df of samples with the result_file_names as the index
+ result_file_names = [f"simulation_{idx}" for idx in range(len(samples))]
+ samples_df = pd.DataFrame(samples, columns=initial_names, index=result_file_names)
+ samples_df.to_csv(self.cd.joinpath(f'samples_{cal_class.name}.csv'))
+
# Simulate the current values
parameters = []
- for i, initial_values in enumerate(samples):
- parameters.append({name: value for name, value in zip(initial_names, initial_values)})
+ for initial_values in samples:
+ if scale:
+ initial_values = cal_class.tuner_paras.descale(initial_values)
+ parameters.append(dict(zip(initial_names, initial_values)))
+ self.logger.info('Starting %s parameter variations on %s cores',
+ len(samples), self.sim_api.n_cpu)
if self.save_files:
- result_file_names = [f"simulation_{idx + 1}" for idx in range(len(parameters))]
+ sim_dir = self.savepath_sim.joinpath(f'simulations_{cal_class.name}')
+ os.makedirs(sim_dir, exist_ok=True)
+ samples_df.to_csv(self.savepath_sim.joinpath(f'samples_{cal_class.name}.csv'))
+ self.logger.info(f'Saving simulation files in: {sim_dir}')
_filepaths = self.sim_api.simulate(
parameters=parameters,
return_option="savepath",
- savepath=self.sim_api.cd,
+ savepath=sim_dir,
result_file_name=result_file_names,
+ result_file_suffix=self.suffix_files,
+ parquet_engine=self.parquet_engine,
fail_on_error=self.fail_on_error,
inputs=cal_class.inputs,
**cal_class.input_kwargs
)
- # Load results
- results = []
- for _filepath in _filepaths:
- if _filepath is None:
- results.append(None)
- else:
- results.append(data_types.TimeSeriesData(_filepath))
+ self.reproduction_files.extend(_filepaths)
+ results = _filepaths
else:
results = self.sim_api.simulate(
parameters=parameters,
@@ -175,95 +273,324 @@ def simulate_samples(self, samples, cal_class):
**cal_class.input_kwargs
)
self.logger.info('Finished %s simulations', len(samples))
- for i, result in enumerate(results):
- if result is None:
- output.append(self.ret_val_on_error)
- else:
- cal_class.goals.set_sim_target_data(result)
- cal_class.goals.set_relevant_time_intervals(cal_class.relevant_intervals)
- # Evaluate the current objective
- total_res = cal_class.goals.eval_difference()
- output.append(total_res)
+ return results, samples
+
+ def _check_index(self, tsd: data_types.TimeSeriesData, sim_num=None):
+ freq = tsd.frequency
+ if sim_num is None:
+ sim_num = tsd.filepath.name
+ if freq[0] != self.sim_api.sim_setup.output_interval:
+ self.logger.info(
+ f'The mean value of the frequency from {sim_num} does not match output '
+ 'interval index will be cleaned and spaced equally')
+ tsd.to_datetime_index()
+ tsd.clean_and_space_equally(f'{str(self.sim_api.sim_setup.output_interval * 1000)}ms')
+ tsd.to_float_index()
+ freq = tsd.frequency
+ if freq[1] > 0.0:
+ self.logger.info(f'The standard deviation of the frequency from {sim_num} is to high '
+ f'and will be rounded to the accuracy of the output interval')
+ tsd.index = np.round(tsd.index.astype("float64"),
+ str(self.sim_api.sim_setup.output_interval)[::-1].find('.'))
+ return tsd
+ def _single_eval_statistical_measure(self, kwargs_eval):
+ """Evaluates statistical measure of one result"""
+ cal_class = kwargs_eval.pop('cal_class')
+ result = kwargs_eval.pop('result')
+ num_sim = kwargs_eval.pop('sim_num', None)
+ if result is None:
+ verbose_error = {}
+ for goal, weight in zip(cal_class.goals.get_goals_list(), cal_class.goals.weightings):
+ verbose_error[goal] = (weight, self.ret_val_on_error)
+ return self.ret_val_on_error, verbose_error
+ result = self._check_index(result, num_sim)
+ cal_class.goals.set_sim_target_data(result)
+ cal_class.goals.set_relevant_time_intervals(cal_class.relevant_intervals)
+ # Evaluate the current objective
+ total_res, verbose_calculation = cal_class.goals.eval_difference(verbose=True)
+ return total_res, verbose_calculation
+
+ def eval_statistical_measure(self, cal_class, results, verbose=True):
+ """Evaluates statistical measures of results on single core"""
+ self.logger.info('Starting evaluation of statistical measure')
+ output = []
+ list_output_verbose = []
+ for i, result in enumerate(results):
+ total_res, verbose_calculation = self._single_eval_statistical_measure(
+ {'cal_class': cal_class, 'result': result, 'sim_num': f'simulation_{i}'}
+ )
+ output.append(total_res)
+ list_output_verbose.append(verbose_calculation)
+ if verbose:
+ # restructure output_verbose
+ output_verbose = _restruct_verbose(list_output_verbose)
+ return np.asarray(output), output_verbose
return np.asarray(output)
- def run(self, calibration_classes, merge_multiple_classes=True):
+ def _single_load_eval_file(self, kwargs_load_eval):
+ """For multiprocessing"""
+ filepath = kwargs_load_eval.pop('filepath')
+ _result = _load_single_file(filepath, self.parquet_engine)
+ kwargs_load_eval.update({'result': _result})
+ total_res, verbose_calculation = self._single_eval_statistical_measure(kwargs_load_eval)
+ return total_res, verbose_calculation
+
+ def _mp_load_eval(self, _filepaths, cal_class, n_cpu):
+ """
+ Loading and evaluating the statistical measure of saved simulation files on multiple cores
+ """
+ self.logger.info(f'Load files and evaluate statistical measure on {n_cpu} processes.')
+ kwargs_load_eval = []
+ for filepath in _filepaths:
+ kwargs_load_eval.append({'filepath': filepath, 'cal_class': cal_class})
+ output_array = []
+ list_output_verbose = []
+ with mp.Pool(processes=n_cpu) as pool:
+ for total, verbose in pool.imap(self._single_load_eval_file, kwargs_load_eval):
+ output_array.append(total)
+ list_output_verbose.append(verbose)
+ output_array = np.asarray(output_array)
+ output_verbose = _restruct_verbose(list_output_verbose)
+ return output_array, output_verbose
+
+ def _load_eval(self, _filepaths, cal_class, n_cpu):
+ """
+ Loading and evaluating the statistical measure of saved simulation files.
+ Single- or multiprocessing possible with definition of n_cpu.
+ """
+ if n_cpu == 1:
+ results = _load_files(_filepaths, self.parquet_engine)
+ output_array, output_verbose = self.eval_statistical_measure(
+ cal_class=cal_class,
+ results=results
+ )
+ return output_array, output_verbose
+ output_array, output_verbose = self._mp_load_eval(_filepaths, cal_class, n_cpu)
+ return output_array, output_verbose
+
+ def run(self, calibration_classes, merge_multiple_classes=True, **kwargs):
"""
Execute the sensitivity analysis for each class and
return the result.
:param CalibrationClass,list calibration_classes:
- Either one or multiple classes for calibration
+ Either one or multiple classes for calibration with same tuner-parameters.
:param bool merge_multiple_classes:
Default True. If False, the given list of calibration-classes
- is handeled as-is. This means if you pass two CalibrationClass objects
+ is handled as-is. This means if you pass two CalibrationClass objects
with the same name (e.g. "device on"), the calibration process will run
for both these classes stand-alone.
This will automatically yield an intersection of tuner-parameters, however may
have advantages in some cases.
+ :keyword bool verbose:
+ Default False. If True, in addition to the combined Goals of the Classes
+ (saved under index Goal: all), the sensitivity measures of the individual
+ Goals will also be calculated and returned.
+ :keyword scale:
+ Default is False. If True the bounds of the tuner-parameters
+ will be scaled between 0 and 1.
+ :keyword bool use_fist_sim:
+ Default False. If True, the simulations of the first calibration class will be used for
+ all other calibration classes with their relevant time intervals.
+ The simulations must be stored on a hard-drive, so it must be used with
+ either save_files or load_files.
+ :keyword int n_cpu:
+ Default is 1. The number of processes to use for the evaluation of the statistical
+ measure. For n_cpu > 1 only one simulation file is loaded at once in a process and
+ dumped directly after the evaluation of the statistical measure,
+ so that only minimal memory is used.
+ Use this option for large analyses.
+ Only implemented for save_files=True or load_sim_files=True.
+ :keyword bool load_sim_files:
+ Default False. If True, no new simulations are done and old simulations are loaded.
+ The simulations and corresponding samples will be loaded from self.savepath_sim like
+ they were saved from self.save_files. Currently, the name of the sim folder must be
+ "simulations_CAL_CLASS_NAME" and for the samples "samples_CAL_CLASS_NAME".
+ The usage of the same simulations for different
+ calibration classes is not supported yet.
+ :keyword bool save_results:
+ Default True. If True, all results are saved as a csv in cd.
+ (samples, statistical measures and analysis variables).
+ :keyword bool plot_result:
+ Default True. If True, the results will be plotted.
:return:
- Returns a list of dictionaries. One dict is the SALib-result for
- one calibration-class. The order is based on the order of the
- calibration-class list.
- :rtype: list
+ Returns a pandas.DataFrame. The DataFrame has a Multiindex with the
+ levels Class, Goal and Analysis variable. The Goal name of combined goals is 'all'.
+ The variables are the tuner-parameters.
+ For the Sobol Method and calc_second_order returns a tuple of DataFrames (df_1, df_2)
+ where df_2 contains the second oder analysis variables and has an extra index level
+ Interaction, which also contains the variables.
+ :rtype: pandas.DataFrame
"""
+ verbose = kwargs.pop('verbose', False)
+ scale = kwargs.pop('scale', False)
+ use_first_sim = kwargs.pop('use_first_sim', False)
+ n_cpu = kwargs.pop('n_cpu', 1)
+ save_results = kwargs.pop('save_results', True)
+ plot_result = kwargs.pop('plot_result', True)
+ load_sim_files = kwargs.pop('load_sim_files', False)
# Check correct input
calibration_classes = utils.validate_cal_class_input(calibration_classes)
# Merge the classes for avoiding possible intersection of tuner-parameters
if merge_multiple_classes:
calibration_classes = data_types.merge_calibration_classes(calibration_classes)
+ # Check n_cpu
+ if n_cpu > mp.cpu_count():
+ raise ValueError(f"Given n_cpu '{n_cpu}' is greater "
+ "than the available number of "
+ f"cpus on your machine '{mp.cpu_count()}'")
+
+ # Check if the usage of the simulations from the first calibration class for all is possible
+ if use_first_sim:
+ if not self.save_files and not load_sim_files:
+ raise AttributeError('To use the simulations of the first calibration class '
+ 'for all classes the simulation files must be saved. '
+ 'Either set save_files=True or load already exiting files '
+ 'with load_sim_files=True.')
+ start_time = 0
+ stop_time = 0
+ for idx, cal_class in enumerate(calibration_classes):
+ if idx == 0:
+ start_time = cal_class.start_time
+ stop_time = cal_class.stop_time
+ continue
+ if start_time > cal_class.start_time or stop_time < cal_class.stop_time:
+ raise ValueError(f'To use the simulations of the first calibration class '
+ f'for all classes the start and stop times of the other '
+ f'classes must be in the interval [{start_time}, {stop_time}] '
+ f'of the first calibration class.')
+
all_results = []
- for cal_class in calibration_classes:
- t_sen_start = time.time()
+ for idx, cal_class in enumerate(calibration_classes):
+
self.logger.info('Start sensitivity analysis of class: %s, '
'Time-Interval: %s-%s s', cal_class.name,
cal_class.start_time, cal_class.stop_time)
- self.problem = self.create_problem(cal_class.tuner_paras)
- samples = self.generate_samples()
# Generate list with metrics of every parameter variation
- output_array = self.simulate_samples(
- samples=samples,
- cal_class=cal_class)
- result = self.analysis_function(
- x=samples,
- y=output_array
- )
- t_sen_stop = time.time()
- result['duration[s]'] = t_sen_stop - t_sen_start
- all_results.append(result)
+ results_goals = {}
+ if load_sim_files:
+ self.problem = self.create_problem(cal_class.tuner_paras, scale=scale)
+ if use_first_sim:
+ class_name = calibration_classes[0].name
+ else:
+ class_name = cal_class.name
+ sim_dir = self.savepath_sim.joinpath(f'simulations_{class_name}')
+ samples_path = self.savepath_sim.joinpath(f'samples_{class_name}.csv')
+ self.logger.info(f'Loading samples from {samples_path}')
+ samples = pd.read_csv(samples_path,
+ header=0,
+ index_col=0)
+ samples = samples.to_numpy()
+ result_file_names = [f"simulation_{idx}.{self.suffix_files}" for idx in
+ range(len(samples))]
+ _filepaths = [sim_dir.joinpath(result_file_name) for result_file_name in
+ result_file_names]
+ self.logger.info(f'Loading simulation files from {sim_dir}')
+ output_array, output_verbose = self._load_eval(_filepaths, cal_class, n_cpu)
+ else:
+ results, samples = self.simulate_samples(
+ cal_class=cal_class,
+ scale=scale
+ )
+ if self.save_files:
+ output_array, output_verbose = self._load_eval(results, cal_class, n_cpu)
+ else:
+ output_array, output_verbose = self.eval_statistical_measure(
+ cal_class=cal_class,
+ results=results
+ )
+ if use_first_sim:
+ load_sim_files = True
+
+ # combine output_array and output_verbose
+ # set key for output_array depending on one or multiple goals
+ stat_mea = {'all': output_array}
+ if len(output_verbose) == 1:
+ stat_mea = output_verbose
+ if len(output_verbose) > 1 and verbose:
+ stat_mea.update(output_verbose)
+
+ # save statistical measure and corresponding samples for each cal_class in cd
+ if save_results:
+ result_file_names = [f"simulation_{idx}" for idx in range(len(output_array))]
+ stat_mea_df = pd.DataFrame(stat_mea, index=result_file_names)
+ savepath_stat_mea = self.cd.joinpath(
+ f'{cal_class.goals.statistical_measure}_{cal_class.name}.csv')
+ stat_mea_df.to_csv(savepath_stat_mea)
+ self.reproduction_files.append(savepath_stat_mea)
+ samples_df = pd.DataFrame(samples, columns=cal_class.tuner_paras.get_names(),
+ index=result_file_names)
+ savepath_samples = self.cd.joinpath(f'samples_{cal_class.name}.csv')
+ samples_df.to_csv(savepath_samples)
+ self.reproduction_files.append(savepath_samples)
+
+ self.logger.info('Starting calculation of analysis variables')
+ for key, val in stat_mea.items():
+ result_goal = self.analysis_function(
+ x=samples,
+ y=val
+ )
+ results_goals[key] = result_goal
+ all_results.append(results_goals)
+ self.logger.info('Finished sensitivity analysis of class: %s, '
+ 'Time-Interval: %s-%s s', cal_class.name,
+ cal_class.start_time, cal_class.stop_time)
result = self._conv_local_results(results=all_results,
local_classes=calibration_classes)
+ if save_results:
+ self._save(result)
+ if plot_result:
+ self.plot(result)
return result, calibration_classes
+ def _save(self, result: pd.DataFrame, time_dependent: bool = False):
+ """
+ Saves the result DataFrame of run and run_time_dependent.
+ Needs to be overwritten for Sobol results.
+ """
+ if time_dependent:
+ savepath_result = self.cd.joinpath(f'{self.__class__.__name__}_results_time.csv')
+ else:
+ savepath_result = self.cd.joinpath(f'{self.__class__.__name__}_results.csv')
+ result.to_csv(savepath_result)
+ self.reproduction_files.append(savepath_result)
+
@staticmethod
- def create_problem(tuner_paras) -> dict:
+ def create_problem(tuner_paras, scale=False) -> dict:
"""Create function for later access if multiple calibration-classes are used."""
num_vars = len(tuner_paras.get_names())
bounds = np.array(tuner_paras.get_bounds())
+ if scale:
+ bounds = [np.zeros_like(bounds[0]), np.ones_like(bounds[1])]
problem = {'num_vars': num_vars,
'names': tuner_paras.get_names(),
'bounds': np.transpose(bounds)}
return problem
@staticmethod
- def select_by_threshold(calibration_classes, result, threshold):
+ def select_by_threshold(calibration_classes, result, analysis_variable, threshold):
"""
Automatically select sensitive tuner parameters based on a given threshold
- and a key-word of the result.
+ of a given analysis variable from a sensitivity result.
+ Uses only the combined goals.
:param list calibration_classes:
List of aixcalibuha.data_types.CalibrationClass objects that you want to
automatically select sensitive tuner-parameters.
:param pd.DataFrame result:
Result object of sensitivity analysis run
+ :param str analysis_variable:
+ Analysis variable to use for the selection
:param float threshold:
Minimal required value of given key
:return: list calibration_classes
"""
- for num_class, cal_class in enumerate(calibration_classes):
- class_result = result.loc[cal_class.name]
+ for cal_class in calibration_classes:
+ first_goal = result.index.get_level_values(1)[0]
+ class_result = result.loc[cal_class.name, first_goal, analysis_variable]
tuner_paras = copy.deepcopy(cal_class.tuner_paras)
select_names = class_result[class_result < threshold].index.values
tuner_paras.remove_names(select_names)
@@ -278,22 +605,312 @@ def select_by_threshold(calibration_classes, result, threshold):
cal_class.tuner_paras = tuner_paras
return calibration_classes
- def _conv_global_result(self, result: dict, cal_class: CalibrationClass):
- glo_res_dict = self._get_res_dict(result=result, cal_class=cal_class)
+ @staticmethod
+ def select_by_threshold_verbose(calibration_class: CalibrationClass,
+ result: pd.DataFrame,
+ analysis_variable: str,
+ threshold: float,
+ calc_names_for_selection: List[str] = None):
+ """
+ Select tuner-parameters of single calibration class with verbose sensitivity results.
+ This function selects tuner-parameters if their sensitivity is equal or greater
+ than the threshold in just one target value of one calibration class in the
+ sensitivity result. This can be more robust because a small sensitivity in one target
+ value and state of the system can mean that the parameter can also be calibrated in
+ a global calibration class which calibrates multiple states and target values at
+ the same time and has there not directly the same sensitivity as in the isolated
+ view of a calibration class for only one state.
+
+ :param CalibrationClass calibration_class:
+ The calibration class from which the tuner parameters will be selected.
+ :param pd.DataFrame result:
+ Sensitivity results to use for the selection. Can include multiple classes.
+ :param str analysis_variable:
+ The analysis variable to use for the selection.
+ :param float threshold:
+ Minimal required value of given analysis variable.
+ :param List[str] calc_names_for_selection:
+ Specifies which calibration classes in the sensitivity results will be used for
+ the selection. Default are all classes.
+ """
+ if Counter(calibration_class.tuner_paras.get_names()) != Counter(list(result.columns)):
+ raise NameError("The tuner-parameter of the calibration class do not "
+ "match the tuner-parameters in the sensitivity result."
+ "They have to match.")
+
+ result = result.loc[:, :, analysis_variable]
+ calc_names_results = result.index.get_level_values("Class").unique()
+ if calc_names_for_selection:
+ for cal_class in calc_names_for_selection:
+ if cal_class not in calc_names_results:
+ raise NameError(f"The calibration class name {cal_class} "
+ f"does not match any class name "
+ f"in the given sensitivity result.")
+ result = result.loc[calc_names_for_selection, :, :]
+
+ selected_tuners = (result >= threshold).any()
+
+ remove_tuners = []
+ for tuner, selected in selected_tuners.items():
+ if not selected:
+ remove_tuners.append(tuner)
+ tuner_paras = copy.deepcopy(calibration_class.tuner_paras)
+ tuner_paras.remove_names(remove_tuners)
+ if not tuner_paras.get_names():
+ raise ValueError("Threshold to small. All tuner-parameters would be removed.")
+ calibration_class.tuner_paras = tuner_paras
+ return calibration_class
+
+ def run_time_dependent(self, cal_class: CalibrationClass, **kwargs):
+ """
+ Calculate the time dependent sensitivity for all the single goals in the calibration class.
+
+ :param CalibrationClass cal_class:
+ Calibration class with tuner-parameters to calculate sensitivity for.
+ Can include dummy target date.
+ :keyword scale:
+ Default is False. If True the bounds of the tuner-parameters
+ will be scaled between 0 and 1.
+ :keyword bool load_sim_files:
+ Default False. If True, no new simulations are done and old simulations are loaded.
+ The simulations and corresponding samples will be loaded from self.savepath_sim like
+ they were saved from self.save_files. Currently, the name of the sim folder must be
+ "simulations_CAL_CLASS_NAME" and for the samples "samples_CAL_CLASS_NAME".
+ :keyword bool save_results:
+ Default True. If True, all results are saved as a csv in cd.
+ (samples and analysis variables).
+ :keyword int n_steps:
+ Default is all time steps. If the problem is large, the evaluation of all time steps
+ at once can cause a memory error. Then n_steps defines how many time_steps
+ are evaluated at once in chunks. This increases the needed time exponentially and
+ the simulation files must be saved.
+ :keyword bool plot_result:
+ Default True. If True, the results will be plotted.
+ :return:
+ Returns a pandas.DataFrame.
+ :rtype: pandas.DataFrame
+ """
+ scale = kwargs.pop('scale', False)
+ save_results = kwargs.pop('save_results', True)
+ plot_result = kwargs.pop('plot_result', True)
+ load_sim_files = kwargs.pop('load_sim_files', False)
+ n_steps = kwargs.pop('n_steps', 'all')
+
+ self.logger.info("Start time dependent sensitivity analysis.")
+ if load_sim_files:
+ self.problem = self.create_problem(cal_class.tuner_paras, scale=scale)
+ sim_dir = self.savepath_sim.joinpath(f'simulations_{cal_class.name}')
+ samples_path = self.savepath_sim.joinpath(f'samples_{cal_class.name}.csv')
+ samples = pd.read_csv(samples_path,
+ header=0,
+ index_col=0)
+ samples = samples.to_numpy()
+ result_file_names = [f"simulation_{idx}.{self.suffix_files}" for idx in
+ range(len(samples))]
+ _filepaths = [sim_dir.joinpath(result_file_name) for result_file_name in
+ result_file_names]
+
+ sen_time_dependent_list, time_index = self._load_analyze_tsteps(_filepaths=_filepaths,
+ samples=samples,
+ n_steps=n_steps,
+ cal_class=cal_class)
+ sen_time_dependent_df = _restruct_time_dependent(sen_time_dependent_list, time_index)
+ else:
+ results, samples = self.simulate_samples(
+ cal_class=cal_class,
+ scale=scale
+ )
+ if self.save_files:
+ sen_time_dependent_list, time_index = self._load_analyze_tsteps(_filepaths=results,
+ samples=samples,
+ n_steps=n_steps,
+ cal_class=cal_class)
+ sen_time_dependent_df = _restruct_time_dependent(sen_time_dependent_list,
+ time_index)
+ else:
+ variables = results[0].get_variable_names()
+ time_index = results[0].index.to_numpy()
+ total_result = _concat_all_sims(results)
+ sen_time_dependent_list = []
+ for time_step in time_index:
+ result_df_tstep = self._analyze_tstep_df(time_step=time_step,
+ tsteps_sim_results=total_result,
+ variables=variables,
+ samples=samples,
+ cal_class=cal_class)
+ sen_time_dependent_list.append(result_df_tstep)
+ sen_time_dependent_df = _restruct_time_dependent(sen_time_dependent_list,
+ time_index)
+ self.logger.info("Finished time dependent sensitivity analysys.")
+ if save_results:
+ self._save(sen_time_dependent_df, time_dependent=True)
+ if plot_result:
+ if isinstance(sen_time_dependent_df, pd.DataFrame):
+ plot_time_dependent(sen_time_dependent_df)
+ else:
+ plot_time_dependent(sen_time_dependent_df[0])
+ return sen_time_dependent_df
+
+ def _analyze_tstep_df(self, time_step, tsteps_sim_results, variables, samples, cal_class):
+ """Analyze the sensitivity at a single time step."""
+ result_dict_tstep = {}
+ for var in variables:
+ result_tstep_var = tsteps_sim_results[var].loc[time_step].to_numpy()
+ if np.all(result_tstep_var == result_tstep_var[0]):
+ sen_tstep_var = None
+ else:
+ sen_tstep_var = self.analysis_function(
+ x=samples,
+ y=result_tstep_var
+ )
+ result_dict_tstep[var] = sen_tstep_var
+ result_df_tstep = self._conv_local_results(results=[result_dict_tstep],
+ local_classes=[cal_class])
+ return result_df_tstep
+
+ def _load_tsteps_df(self, tsteps, _filepaths):
+ """
+ Load all simulations and extract and concat the sim results of the time steps in tsteps.
+ """
+ self.logger.info(
+ f"Loading time steps from {tsteps[0]} to {tsteps[-1]} of the simulation files.")
+ tsteps_sim_results = []
+ for _filepath in _filepaths:
+ sim = _load_single_file(_filepath)
+ tsteps_sim_results.append(sim.loc[tsteps[0]:tsteps[-1]])
+ tsteps_sim_results = _concat_all_sims(tsteps_sim_results)
+ return tsteps_sim_results
+
+ def _load_analyze_tsteps(self, _filepaths, samples, n_steps, cal_class):
+ """
+ Load and analyze all time steps in chunks with n_steps time steps.
+ """
+ sim1 = _load_single_file(_filepaths[0])
+ time_index = sim1.index.to_numpy()
+ variables = sim1.get_variable_names()
+ sen_time_dependent_list = []
+ if n_steps == 'all':
+ list_tsteps = [time_index]
+ elif isinstance(n_steps, int) and not (n_steps <= 0 or n_steps > len(time_index)):
+ list_tsteps = _divide_chunks(time_index, n_steps)
+ else:
+ raise ValueError(
+ f"n_steps can only be between 1 and {len(time_index)} or the string all.")
+
+ for tsteps in list_tsteps:
+ tsteps_sim_results = self._load_tsteps_df(tsteps=tsteps, _filepaths=_filepaths)
+ self.logger.info("Analyzing these time steps.")
+ for tstep in tsteps:
+ result_df_tstep = self._analyze_tstep_df(time_step=tstep,
+ tsteps_sim_results=tsteps_sim_results,
+ variables=variables,
+ samples=samples,
+ cal_class=cal_class)
+ sen_time_dependent_list.append(result_df_tstep)
+ return sen_time_dependent_list, time_index
+
+ def _conv_global_result(self, result: dict, cal_class: CalibrationClass,
+ analysis_variable: str):
+ glo_res_dict = self._get_res_dict(result=result, cal_class=cal_class,
+ analysis_variable=analysis_variable)
return pd.DataFrame(glo_res_dict, index=['global'])
def _conv_local_results(self, results: list, local_classes: list):
- _conv_results = [self._get_res_dict(result=result, cal_class=local_class)
- for result, local_class in zip(results, local_classes)]
- df = pd.DataFrame(_conv_results)
- df.index = [c.name for c in local_classes]
+ """
+ Convert the result dictionaries form SALib of each class and goal into one DataFrame.
+ Overwritten for Sobol.
+ """
+ _conv_results = []
+ tuples = []
+ for class_results, local_class in zip(results, local_classes):
+ for goal, goal_results in class_results.items():
+ for analysis_var in self.analysis_variables:
+ _conv_results.append(self._get_res_dict(result=goal_results,
+ cal_class=local_class,
+ analysis_variable=analysis_var))
+ tuples.append((local_class.name, goal, analysis_var))
+ index = pd.MultiIndex.from_tuples(tuples=tuples,
+ names=['Class', 'Goal', 'Analysis variable'])
+ df = pd.DataFrame(_conv_results, index=index)
return df
@abc.abstractmethod
- def _get_res_dict(self, result: dict, cal_class: CalibrationClass):
+ def _get_res_dict(self, result: dict, cal_class: CalibrationClass, analysis_variable: str):
"""
Convert the result object to a dict with the key
being the variable name and the value being the result
- associated to self.analysis_variable.
+ associated to analysis_variable.
"""
raise NotImplementedError
+
+ def plot(self, result):
+ """
+ Plot the results of the sensitivity analysis method from run().
+
+ :param pd.DataFrame result:
+ Dataframe of the results like from the run() function.
+ :return tuple of matplotlib objects (fig, ax):
+ """
+ plot_single(result=result)
+
+ @staticmethod
+ def load_from_csv(path):
+ """
+ Load sensitivity results which were saved with the run() or run_time_dependent() function.
+
+ For second order results use the load_second_order_from_csv() function of the SobolAnalyzer.
+ """
+ result = pd.read_csv(path, index_col=[0, 1, 2])
+ return result
+
+ def save_for_reproduction(self,
+ title: str,
+ path: pathlib.Path = None,
+ files: list = None,
+ exclude_sim_files: bool = False,
+ remove_saved_files: bool = False,
+ **kwargs):
+ """
+ Save the settings of the SenAnalyzer and SimApi in order to
+ reproduce the simulations and sensitivity analysis method.
+ All saved results will be also saved in the reproduction
+ archive. The simulations can be excluded from saving.
+
+ :param str title:
+ Title of the study
+ :param pathlib.Path path:
+ Where to store the .zip file. If not given, self.cd is used.
+ :param list files:
+ List of files to save along the standard ones.
+ Examples would be plots, tables etc.
+ :param bool exclude_sim_files:
+ Default False. If True, the simulation files will not be saved in
+ the reproduction archive.
+ :param bool remove_saved_files:
+ Default False. If True, the result and simulation files will be moved
+ instead of just copied.
+ :param dict kwargs:
+ All keyword arguments except title, files, and path of the function
+ `save_reproduction_archive`. Most importantly, `log_message` may be
+ specified to avoid input during execution.
+ """
+ if files is None:
+ files = []
+
+ for file_path in self.reproduction_files:
+ if exclude_sim_files:
+ if 'simulation' in str(file_path):
+ continue
+ filename = "SenAnalyzer" + str(file_path).rsplit(self.cd.name, maxsplit=1)[-1]
+ files.append(CopyFile(
+ sourcepath=file_path,
+ filename=filename,
+ remove=remove_saved_files
+ ))
+
+ return self.sim_api.save_for_reproduction(
+ title=title,
+ path=path,
+ files=files,
+ **kwargs
+ )
diff --git a/aixcalibuha/sensitivity_analysis/sobol.py b/aixcalibuha/sensitivity_analysis/sobol.py
index fb3524f3..beb2cc2b 100644
--- a/aixcalibuha/sensitivity_analysis/sobol.py
+++ b/aixcalibuha/sensitivity_analysis/sobol.py
@@ -2,40 +2,58 @@
Adds the SobolAnalyzer to the available
classes of sensitivity analysis.
"""
-from SALib.sample import saltelli as sobol
+import pandas as pd
+from SALib.sample import sobol
from SALib.analyze import sobol as analyze_sobol
import numpy as np
from aixcalibuha.sensitivity_analysis import SenAnalyzer
from aixcalibuha import CalibrationClass
+from aixcalibuha.sensitivity_analysis.plotting import plot_single, heatmaps
class SobolAnalyzer(SenAnalyzer):
"""
+ Sobol method from SALib
+ https://salib.readthedocs.io/en/latest/api.html#sobol-sensitivity-analysis
+ A variance-based method which can compute the sensitivity measures
+ 'S1', 'ST' and 'S2' with their confidence intervals.
+
Additional arguments:
:keyword bool calc_second_order:
Default True, used for the sobol-method
- :keyword seed:
+ :keyword int seed:
Used for the sobol-method
"""
+ __analysis_variables = ['S1', 'ST', 'S1_conf', 'ST_conf']
+ __analysis_variables_1 = ['S1', 'ST', 'S1_conf', 'ST_conf']
+ __analysis_variables_2 = ['S2', 'S2_conf']
+
def __init__(self, sim_api, **kwargs):
+ # Additional kwarg which changes the possible analysis_variables
+ self.calc_second_order = kwargs.get("calc_second_order", True)
+ if self.calc_second_order:
+ self.__analysis_variables = ['S1', 'ST', 'S1_conf', 'ST_conf', 'S2', 'S2_conf']
+ # separately store first and total order (1) and second order (2) analysis variables
+ self.av_1_selected = []
+ self.av_2_selected = []
+ # Set additional kwargs
+ self.seed = kwargs.pop("seed", None)
+
super().__init__(
sim_api=sim_api,
**kwargs)
- # Set additional kwargs
- self.calc_second_order = kwargs.pop("calc_second_order", True)
- self.seed = kwargs.pop("seed", None)
@property
def analysis_variables(self):
"""The analysis variables of the sobol method"""
- return ['S1', 'ST', 'S1_conf', 'ST_conf']
+ return self.__analysis_variables
def analysis_function(self, x, y):
"""
Use the SALib.analyze.sobol method to analyze the simulation results.
- :param np.array: x
+ :param np.array x:
placeholder for the `X` parameter of the morris method not used for sobol
:param np.array y:
The NumPy array containing the model outputs
@@ -68,13 +86,108 @@ def generate_samples(self):
N=self.num_samples,
**self.create_sampler_demand())
- def _get_res_dict(self, result: dict, cal_class: CalibrationClass):
+ def _save(self, result: tuple, time_dependent: bool = False):
+ """
+ Save the results of the run and run_time_dependent function of the SobolAnalyzer.
+ """
+ if not result[0].empty:
+ super()._save(result=result[0], time_dependent=time_dependent)
+ if time_dependent:
+ savepath_result_2 = self.cd.joinpath(
+ f'{self.__class__.__name__}_results_second_order_time.csv')
+ else:
+ savepath_result_2 = self.cd.joinpath(
+ f'{self.__class__.__name__}_results_second_order.csv')
+ if not result[1].empty:
+ result[1].to_csv(savepath_result_2)
+ self.reproduction_files.append(savepath_result_2)
+
+ def _conv_local_results(self, results: list, local_classes: list):
+ """
+ Convert the result dictionaries form SALib
+ of each class and goal into a tuple of two DataFrames.
+ First is the single order and second is the second order result.
+ If one of the results is not computed an empty list is returned.
+ """
+ _conv_results = []
+ _conv_results_2 = []
+ tuples = []
+ tuples_2 = []
+ for class_results, local_class in zip(results, local_classes):
+ for goal, goal_results in class_results.items():
+ for analysis_var in self.analysis_variables:
+ res_dict = self._get_res_dict(result=goal_results,
+ cal_class=local_class,
+ analysis_variable=analysis_var)
+ if analysis_var in self.__analysis_variables_1:
+ _conv_results.append(res_dict)
+ tuples.append((local_class.name, goal, analysis_var))
+ elif analysis_var in self.__analysis_variables_2:
+ for tuner_para, res_dict in res_dict.items():
+ _conv_results_2.append(res_dict)
+ tuples_2.append((local_class.name, goal, analysis_var, tuner_para))
+ index = pd.MultiIndex.from_tuples(tuples=tuples,
+ names=['Class', 'Goal', 'Analysis variable'])
+ index_2 = pd.MultiIndex.from_tuples(tuples=tuples_2,
+ names=['Class', 'Goal', 'Analysis variable',
+ 'Interaction'])
+ df = pd.DataFrame(_conv_results, index=index)
+ df_2 = pd.DataFrame(_conv_results_2, index=index_2)
+ return df, df_2
+
+ def _get_res_dict(self, result: dict, cal_class: CalibrationClass, analysis_variable: str):
"""
Convert the result object to a dict with the key
being the variable name and the value being the result
- associated to self.analysis_variable.
+ associated to analysis_variable.
+ For second oder analysis variables the result is converted to a
+ dict with the key being the variable name and the value being another dict
+ with the variable names as the keys and the result associated to analysis_variable
+ from the interaction between the two variables.
"""
names = self.create_problem(cal_class.tuner_paras)['names']
- return {var_name: np.abs(res_val)
- for var_name, res_val in zip(names,
- result[self.analysis_variable])}
+ if analysis_variable in self.__analysis_variables_1:
+ if result is None:
+ res_dict_1 = {var_name: np.abs(res_val)
+ for var_name, res_val in zip(names,
+ np.zeros(len(names)))}
+ else:
+ res_dict_1 = {var_name: np.abs(res_val)
+ for var_name, res_val in zip(names,
+ result[analysis_variable])}
+ return res_dict_1
+ if analysis_variable in self.__analysis_variables_2:
+ if result is None:
+ res_dict_2 = {var_name: dict(zip(names, np.abs(res_val)))
+ for var_name, res_val in zip(names,
+ np.zeros((len(names), len(names))))}
+ else:
+ result_av = result[analysis_variable]
+ for i, _ in enumerate(result_av):
+ for j, _ in enumerate(result_av):
+ if i > j:
+ result_av[i][j] = result_av[j][i]
+ res_dict_2 = {var_name: dict(zip(names, np.abs(res_val)))
+ for var_name, res_val in zip(names,
+ result_av)}
+ return res_dict_2
+
+ def plot(self, result):
+ """
+ Plot the results of the sensitivity analysis method from run().
+
+ :param pd.DataFrame result:
+ Dataframe of the results like from the run() function.
+ :return tuple of matplotlib objects (fig, ax):
+ """
+ plot_single(result=result[0])
+ heatmaps(result=result[1])
+
+ @staticmethod
+ def load_second_order_from_csv(path):
+ """
+ Load second order sensitivity results which were saved with the run() or
+ run_time_dependent() function.
+ """
+ result = pd.read_csv(path, index_col=[0, 1, 2, 3])
+ return result
diff --git a/aixcalibuha/utils/configuration.py b/aixcalibuha/utils/configuration.py
index 84e1bb2c..8cc76e43 100644
--- a/aixcalibuha/utils/configuration.py
+++ b/aixcalibuha/utils/configuration.py
@@ -8,7 +8,6 @@
from ebcpy import data_types
from aixcalibuha import Goals, CalibrationClass, TunerParas
-
tsd_config = {"data": "TODO: Specify the path to the target values measured",
"key": None,
"sheet_name": None,
@@ -49,13 +48,13 @@
"TODO: Specify the boundaries of the tuner parameters as a list of tuples"}}
default_calibration_config = {
- "statistical_measure": "TODO: Specify the statistical "
- "measure for calibration (RMSE, MAE, etc.)",
- "calibration_classes": [default_cal_class_config],
- "start_time_method": 'fixstart',
- "settings": kwargs_calibrator,
- "settings multiple classes": kwargs_multiple_classes
- }
+ "statistical_measure": "TODO: Specify the statistical "
+ "measure for calibration (RMSE, MAE, etc.)",
+ "calibration_classes": [default_cal_class_config],
+ "start_time_method": 'fixstart',
+ "settings": kwargs_calibrator,
+ "settings multiple classes": kwargs_multiple_classes
+}
kwargs_scipy_dif_evo = {"maxiter": 30,
"popsize": 5,
@@ -100,7 +99,7 @@
"Optimization": default_optimization_config,
"Input Data": default_input_config,
"Calibration": default_calibration_config
- }
+}
def get_goals_from_config(config):
diff --git a/aixcalibuha/utils/visualizer.py b/aixcalibuha/utils/visualizer.py
index 53f0ee88..552f9bcb 100644
--- a/aixcalibuha/utils/visualizer.py
+++ b/aixcalibuha/utils/visualizer.py
@@ -15,6 +15,28 @@
import aixcalibuha
+def short_name(ini_name: str, max_len: int):
+ """
+ Shortens long strings to a max length from the front.
+ long_string_name => ...ing_name with len(new_name) = max_len
+
+ :param str ini_name:
+ Long string to shorten.
+ :param int max_len:
+ Max len of the new string.
+ :return: str
+ The shorten string.
+ """
+ if len(ini_name) > max_len:
+ num_dots = len(ini_name) - max_len
+ if num_dots > 3:
+ num_dots = 3
+ formatted_name = "." * num_dots + ini_name[-(max_len - num_dots):]
+ else:
+ formatted_name = ini_name
+ return formatted_name
+
+
class CalibrationLogger:
"""Base class for showing the process of functions in
this Framework with print-statements and saving everything
@@ -259,16 +281,11 @@ def _get_tuner_para_names_as_string(self):
for ini_name in initial_names:
# Limit string length to a certain amount.
# The full name has to be displayed somewhere else
- if len(ini_name) > self._width:
- num_dots = len(ini_name) - self._width
- if num_dots > 3:
- num_dots = 3
- formatted_name = "."*num_dots + ini_name[-(self._width-num_dots):]
- else:
- formatted_name = ini_name
+ formatted_name = short_name(ini_name=ini_name, max_len=self._width)
info_string += " {0:{width}s}".format(formatted_name, width=self._width)
# Add string for qualitative measurement used (e.g. NRMSE, MEA etc.)
- info_string += " {0:{width}s}".format(self.goals.statistical_measure, width=self._width)
+ info_string += " {0:{width}s}".format(self.goals.statistical_measure,
+ width=self._width)
info_string += "penaltyfactor"
info_string += f" Unweighted {self.goals.statistical_measure}"
return info_string
@@ -306,11 +323,12 @@ def _get_tuner_para_values_as_string(self,
prec=self._prec)
if penalty:
info_string += " {0:{width}.{prec}f}".format(penalty, width=self._width,
- prec=self._prec-3)
+ prec=self._prec - 3)
else:
info_string += " {}".format("-")
- _verbose_info = "= " + " + ".join(["{0:.{prec}}*{1:.{prec}}".format(weight, val, prec=4)
- for weight, val in unweighted_objective.items()])
+ _verbose_info = "= " + " + ".join(["{0:.{prec}}*{1:.{prec}}".format(val[0],
+ val[1], prec=4)
+ for goal, val in unweighted_objective.items()])
info_string += f" {_verbose_info}"
return info_string
@@ -462,18 +480,21 @@ def validation_callback_func(self, obj):
if self.goals is not None and self.create_tsd_plot:
self._plot_goals(at_validation=True)
- self._show_plot()
+ self._show_plot(for_validation=True)
- def _show_plot(self):
+ def _show_plot(self, for_validation=False):
"""Show plot if activated"""
if not self.show_plot:
return
plt.draw()
- self.fig_obj.canvas.draw_idle()
- self.fig_tuner.canvas.draw_idle()
if self.create_tsd_plot:
self.fig_goal.canvas.draw_idle()
- plt.pause(self.show_plot_pause_time)
+ if not for_validation:
+ self.fig_obj.canvas.draw_idle()
+ self.fig_tuner.canvas.draw_idle()
+ plt.pause(self.show_plot_pause_time)
+ else:
+ plt.show()
def save_calibration_result(self, best_iterate, model_name, **kwargs):
"""
@@ -568,8 +589,9 @@ def log_intersection_of_tuners(self, intersected_tuner_parameters, **kwargs):
os.makedirs(path_intersections)
if "itercount" in kwargs:
fig_intersection.savefig(
- os.path.join(path_intersections,
- f'tuner_parameter_intersection_plot_it{kwargs["itercount"]}.{self.file_type}')
+ os.path.join(
+ path_intersections,
+ f'tuner_parameter_intersection_plot_it{kwargs["itercount"]}.{self.file_type}')
)
else:
fig_intersection.savefig(
diff --git a/docs/source/conf.py b/docs/source/conf.py
index a717c97c..b8b9229b 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -65,7 +65,7 @@
# built documents.
#
# The full version, including alpha/beta/rc tags.
-release = '0.3.1'
+release = '1.0.0'
# The short X.Y version.
version = '.'.join(release.split('.')[:-1])
diff --git a/examples/README.md b/examples/README.md
index a8268bc6..5d82c759 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -12,7 +12,7 @@ You have three ways of accessing the examples:
For the latter two, be sure to:
-1. Create a clean environment of python 3.7 or 3.8. In Anaconda run: `conda create -n py38_ebcpy python=3.8`
+1. Create a clean environment of python 3.8 or 3.9. In Anaconda run: `conda create -n py38_ebcpy python=3.8`
2. Activate the environment in your terminal. In Anaconda run: `activate py38_ebcpy`
3. Clone the library using `git clone --recurse-submodules https://github.com/RWTH-EBC/AixCaliBuHA`
5. Install the library using `pip install -e AixCaliBuHA`
@@ -50,9 +50,21 @@ The examples 3-5 are written for both, so just alter the parameter in the `if __
## `e3_sensitivity_analysis_example.py`
-1. Learn how to execute a sensitivity analysis
+1. Learn how to execute a simple sensitivity analysis
2. Learn how to automatically select sensitive tuner parameters
+## `e3_1_verbose_sensitivity_analysis_example.py`
+
+1. Learn how to execute a verbose sensitivity analysis
+2. Learn the meaning of the results the analysis of your model
+3. Learn other sensitivity methods
+4. Learn time dependent sensitivity analysis
+5. Learn how to save the results for reproduction
+
+## `e3_1_verbose_dymola_sensitivity_analysis_example.py`
+
+1. Learn how to execute a sensitivity analysis with the dymola api
+
## `e4_calibration_example.py`
1. Learn the settings for a calibration
diff --git a/examples/__init__.py b/examples/__init__.py
index 7b0c821e..8a921fa4 100644
--- a/examples/__init__.py
+++ b/examples/__init__.py
@@ -3,7 +3,7 @@
"""
import pathlib
import sys
-from ebcpy import FMU_API
+from ebcpy import FMU_API, DymolaAPI
from examples import e2_A_optimization_problem_definition, e2_B_optimization_problem_definition
@@ -11,7 +11,7 @@ def setup_fmu(examples_dir, example="B", n_cpu=1):
"""
Setup the FMU used in all examples and tests.
- :param str examples_dir:
+ :param [pathlib.Path, str] examples_dir:
Path to the examples folder of AixCaliBuHA
:param str example:
Which example to run, "A" or "B"
@@ -38,6 +38,27 @@ def setup_fmu(examples_dir, example="B", n_cpu=1):
n_cpu=n_cpu)
+def setup_dym_api(examples_dir, aixlib_mo, example="A", n_cpu=1, dymola_version='Dymola 2023'):
+ examples_dir = pathlib.Path(examples_dir)
+ packages = []
+ if example == "A":
+ packages.append(examples_dir.joinpath('model', 'AixCaliBuHAExamples.mo'))
+ packages.append(aixlib_mo)
+ model_name = "AixCaliBuHAExamples.HeatPumpSystemWithTimTab"
+ elif example == "B":
+ model_name = "Modelica.Thermal.FluidHeatFlow.Examples.PumpAndValve"
+ else:
+ raise ValueError("Only example 'A' and 'B' are available")
+
+ return DymolaAPI(cd=examples_dir.joinpath("testzone"),
+ model_name=model_name,
+ packages=packages,
+ n_cpu=n_cpu,
+ dymola_version=dymola_version,
+ show_window=True,
+ equidistant_output=True, )
+
+
def setup_calibration_classes(examples_dir, example="B", multiple_classes=True):
"""Setup the CalibrationClasses used in all examples and tests."""
if example == "A":
diff --git a/examples/converter.toml b/examples/converter.toml
index e24f85e8..b1f176f2 100644
--- a/examples/converter.toml
+++ b/examples/converter.toml
@@ -3,39 +3,60 @@ filename = "examples/e1_A_energy_system_analysis.py"
func_name = "main"
md_path = "examples/docs"
ipynb_path = "examples/jupyter_notebooks"
+docstrings = true
[[ExampleFile]]
filename = "examples/e1_B_energy_system_analysis.py"
func_name = "main"
md_path = "examples/docs"
ipynb_path = "examples/jupyter_notebooks"
+docstrings = true
[[ExampleFile]]
filename = "examples/e2_A_optimization_problem_definition.py"
func_name = "main"
md_path = "examples/docs"
ipynb_path = "examples/jupyter_notebooks"
+docstrings = true
[[ExampleFile]]
filename = "examples/e2_B_optimization_problem_definition.py"
func_name = "main"
md_path = "examples/docs"
ipynb_path = "examples/jupyter_notebooks"
+docstrings = true
[[ExampleFile]]
filename = "examples/e3_sensitivity_analysis_example.py"
func_name = "run_sensitivity_analysis"
md_path = "examples/docs"
ipynb_path = "examples/jupyter_notebooks"
+docstrings = true
+
+[[ExampleFile]]
+filename = "examples/e3_1_verbose_sensitivity_analysis_example.py"
+func_name = "run_sensitivity_analysis"
+md_path = "examples/docs"
+ipynb_path = "examples/jupyter_notebooks"
+docstrings = true
+
+[[ExampleFile]]
+filename = "examples/e3_2_verbose_dymola_sensitivity_analysis_example.py"
+func_name = "run_sensitivity_analysis"
+md_path = "examples/docs"
+ipynb_path = "examples/jupyter_notebooks"
+docstrings = true
[[ExampleFile]]
filename = "examples/e4_calibration_example.py"
func_name = "run_calibration"
md_path = "examples/docs"
ipynb_path = "examples/jupyter_notebooks"
+docstrings = true
[[ExampleFile]]
filename = "examples/e5_automated_process.py"
func_name = "main"
md_path = "examples/docs"
ipynb_path = "examples/jupyter_notebooks"
+docstrings = true
diff --git a/examples/data/FASTAnalyzer_results_A.csv b/examples/data/FASTAnalyzer_results_A.csv
new file mode 100644
index 00000000..e1801c50
--- /dev/null
+++ b/examples/data/FASTAnalyzer_results_A.csv
@@ -0,0 +1,19 @@
+Class,Goal,Analysis variable,heatPumpSystem.declination,vol.V,heaCap.C,rad.n,theCon.G
+global,all,S1,3.699703925116529e-05,0.001281425172057828,0.0015227349583394145,0.0008281593221427361,0.8366451874300566
+global,all,ST,0.10796269929100588,0.12727189909324055,0.1155495847504745,0.14553950605124466,0.9611281699385661
+global,Electricity,S1,0.00031747319966370904,0.00024353085199873387,0.0003764898090155623,0.000135902101349077,0.2273758074248678
+global,Electricity,ST,0.7453376933545235,0.743962586600946,0.7341629863644938,0.6621874987030962,0.9102144331281674
+global,Room temperature,S1,1.9287204118830097e-06,0.0014180823531301531,0.0017859984591948764,0.0010890901444372842,0.8978566425268381
+global,Room temperature,ST,0.05035547650977379,0.06154859380863109,0.055193397879125805,0.09792075841836423,0.9851648659973355
+On,all,S1,0.00020018029301728152,0.0003732800506908317,0.00033910160370510047,0.0017703482120637846,0.6715942611517385
+On,all,ST,0.3058265124146551,0.31933112616005177,0.3106200608685906,0.2786203656702405,0.9541458753137816
+On,Electricity,S1,0.00031228249245786544,0.00022249786696166632,0.00018164902302868642,0.005685227589962284,0.499599259861104
+On,Electricity,ST,0.49059981121348806,0.49021126574835416,0.4861302770522937,0.43359040188437836,0.9465746302805614
+On,Room temperature,S1,2.31860405807331e-06,0.001757478870513649,0.0021660414281231893,0.002831257060395158,0.8827135350836856
+On,Room temperature,ST,0.06344275024747548,0.07565150101135554,0.06893193702463862,0.10856419232862935,0.9821831915877569
+Off,all,S1,2.1272005929025807e-05,0.0014832996244445206,0.0017744830119772596,0.0024849373841675845,0.8229577833899749
+Off,all,ST,0.10593723631230523,0.12327425458825259,0.11281478076102636,0.1668689639884684,0.9530787110466263
+Off,Electricity,S1,5.73192959411043e-05,6.155556543634787e-05,0.0001669727284376773,0.0023844957443155753,0.7454993026971005
+Off,Electricity,ST,0.24742569417945115,0.24087517643428302,0.24085902743964094,0.23074864973329057,0.9621483328487416
+Off,Room temperature,S1,1.6988774649118675e-06,0.001263602865036885,0.0016135715721661615,0.0005017013173918048,0.9040316061407335
+Off,Room temperature,ST,0.045317364760374956,0.055889602048548204,0.05000328217772776,0.09356248938590017,0.9860860387514716
diff --git a/examples/data/FASTAnalyzer_results_B.csv b/examples/data/FASTAnalyzer_results_B.csv
new file mode 100644
index 00000000..270dc33a
--- /dev/null
+++ b/examples/data/FASTAnalyzer_results_B.csv
@@ -0,0 +1,25 @@
+Class,Goal,Analysis variable,speedRamp.duration,valveRamp.duration
+global,all,S1,0.5917308997409292,0.379117599282294
+global,all,ST,0.6205950853256805,0.40804812070929186
+global,TCap,S1,0.5917215351510067,0.3793252681120051
+global,TCap,ST,0.6203912510129758,0.4080628127755115
+global,TPipe,S1,0.5917450423435522,0.3787723747099413
+global,TPipe,ST,0.6209337961580995,0.4080251130684973
+heat up,all,S1,0.8922962178451639,0.0005185141196262783
+heat up,all,ST,0.9901108280563709,0.001378956953640209
+heat up,TCap,S1,0.8872776153193306,0.00016329648721438874
+heat up,TCap,ST,0.9898164616675547,0.0008451859400043782
+heat up,TPipe,S1,0.8967070115849604,0.0011643877567905018
+heat up,TPipe,ST,0.9899167804946783,0.0024034782867773385
+cool down,all,S1,0.6550501792736069,0.2730958055928426
+cool down,all,ST,0.7209711406680488,0.3389372220286204
+cool down,TCap,S1,0.661603687917945,0.2663793850421064
+cool down,TCap,ST,0.7273424095448515,0.3317375000130648
+cool down,TPipe,S1,0.6384030411799604,0.2898932999369031
+cool down,TPipe,ST,0.7049802640048506,0.35701722533532554
+stationary,all,S1,0.5714442279815414,0.4072996331003543
+stationary,all,ST,0.592531413520754,0.4281910213812993
+stationary,TCap,S1,0.5715306908484292,0.4067983964399629
+stationary,TCap,ST,0.5930495257247834,0.42811243479953087
+stationary,TPipe,S1,0.5712363117614699,0.4083265219873574
+stationary,TPipe,ST,0.591467296301498,0.42838135939038535
diff --git a/examples/data/MorrisAnalyzer_results_A.csv b/examples/data/MorrisAnalyzer_results_A.csv
new file mode 100644
index 00000000..d94e2f19
--- /dev/null
+++ b/examples/data/MorrisAnalyzer_results_A.csv
@@ -0,0 +1,37 @@
+Class,Goal,Analysis variable,heatPumpSystem.declination,vol.V,heaCap.C,rad.n,theCon.G
+global,all,mu_star,0.0,0.022525642343656747,0.028602597553477715,0.14797276618789093,0.46153810785076105
+global,all,mu,0.0,-0.018767913806143843,-0.0265561023622424,-0.021522471938822743,0.10530785767624773
+global,all,sigma,0.0,0.02747390836331699,0.030861152605208737,0.1856747763706546,0.5436860141700762
+global,all,mu_star_conf,0.0,0.0053077431211565615,0.005813271114528823,0.02268129045479092,0.059987777922132175
+global,Electricity,mu_star,0.0,0.004375412875317221,0.007244229672193131,0.11913237492338556,0.19059766744876355
+global,Electricity,mu,0.0,0.0030978947991899325,-0.0023988833684108835,0.004296410304411573,0.08481088640629703
+global,Electricity,sigma,0.0,0.028820151031349515,0.024208637087167875,0.2015809201314141,0.22626477191618138
+global,Electricity,mu_star_conf,0.0,0.004997242122161083,0.004649098213004041,0.0310395647671873,0.027494134289429952
+global,Room temperature,mu_star,0.0,0.07060447967104885,0.08357909899488365,0.3516388158802559,1.576005722379027
+global,Room temperature,mu,0.0,-0.06978813388525604,-0.08292294668118264,-0.0817665305063695,0.15313412397279944
+global,Room temperature,sigma,0.0,0.0631217564459834,0.08638324458251034,0.39137828345965936,1.8633676485570183
+global,Room temperature,mu_star_conf,0.0,0.010910184108350806,0.016658611127499428,0.035492118493850505,0.20151871586023257
+On,all,mu_star,0.0,0.03810946025881845,0.05665082149799998,0.4983808352618271,1.069436078575448
+On,all,mu,0.0,-0.01706637960587919,-0.041120877949852704,0.06753187405105186,-0.7261402976998097
+On,all,sigma,0.0,0.09207566161918955,0.09148966222614398,0.7694871553013972,1.0353681427798644
+On,all,mu_star_conf,0.0,0.019080700726853472,0.016780190638428095,0.11281095477835927,0.12811225803842147
+On,Electricity,mu_star,0.0,0.018655084455900778,0.03717573523299986,0.6322843221693322,1.3607609499625914
+On,Electricity,mu,0.0,0.011949183332543602,-0.014637921364388464,0.1590817691241839,-1.0417512457540468
+On,Electricity,sigma,0.0,0.1274761096237838,0.11873817109360583,1.05979884216595,1.0807233115456716
+On,Electricity,mu_star_conf,0.0,0.02538892669508074,0.021533945528040527,0.15835984016059587,0.13117732568184381
+On,Room temperature,mu_star,0.0,0.08752172428254282,0.10517435690966856,0.3801194164995445,1.7131349750390694
+On,Room temperature,mu,0.0,-0.08476935979553231,-0.10291444331593595,-0.1460845477862561,0.010285247760075746
+On,Room temperature,sigma,0.0,0.07604203307091216,0.1090346974575414,0.4198778835240512,2.0759273321329728
+On,Room temperature,mu_star_conf,0.0,0.013428948135488143,0.019109780553662557,0.0475729436301634,0.21139410154937083
+Off,all,mu_star,0.0,0.023503707672766935,0.02814322845691414,0.1461657436620339,0.5151848617304223
+Off,all,mu,0.0,-0.02044360383531918,-0.026081382429958517,-0.03426091539569144,0.3373063792166661
+Off,all,sigma,0.0,0.027020849033224104,0.03207572974892224,0.1733537350075874,0.5013320858103784
+Off,all,mu_star_conf,0.0,0.0050215162410203445,0.006224976034865717,0.021348087651422383,0.06700337391447003
+Off,Electricity,mu_star,0.0,0.00421369243421552,0.005604729890351776,0.0936143724548465,0.39314130461527663
+Off,Electricity,mu,0.0,0.002669850989235836,-0.0004452263573276853,-0.02264257032988952,0.3746856329362358
+Off,Electricity,sigma,0.0,0.023090685412870716,0.02378181240720325,0.15804626816122963,0.3014983931663316
+Off,Electricity,mu_star_conf,0.0,0.004592739953397077,0.004340639591224935,0.02880801840348102,0.05540749191558049
+Off,Room temperature,mu_star,0.0,0.0750857103157915,0.08654912868039583,0.4047689181177338,1.7678251313905187
+Off,Room temperature,mu,0.0,-0.07437499842594748,-0.08589907993276377,-0.061370387215895966,0.2500881205376701
+Off,Room temperature,sigma,0.0,0.0730877866849492,0.09521806608394966,0.4461956890842288,2.065670584641397
+Off,Room temperature,mu_star_conf,0.0,0.015485790479409454,0.019117626755726543,0.0346645749678262,0.21251085487006927
diff --git a/examples/data/MorrisAnalyzer_results_B.csv b/examples/data/MorrisAnalyzer_results_B.csv
new file mode 100644
index 00000000..b2832f14
--- /dev/null
+++ b/examples/data/MorrisAnalyzer_results_B.csv
@@ -0,0 +1,49 @@
+Class,Goal,Analysis variable,speedRamp.duration,valveRamp.duration
+global,all,mu_star,0.31305743447674617,0.2438577687647123
+global,all,mu,0.3127102405492654,0.24073512993911111
+global,all,sigma,0.08508118771216477,0.08475373056594036
+global,all,mu_star_conf,0.019161094181496682,0.014063723818849624
+global,TCap,mu_star,0.2790018486237064,0.21737023880705805
+global,TCap,mu,0.2787084275028493,0.2146430064387138
+global,TCap,sigma,0.07562509647679039,0.07532508949412352
+global,TCap,mu_star_conf,0.015269278328582221,0.014320753329334024
+global,TPipe,mu_star,0.39252046813383934,0.3056620053325721
+global,TPipe,mu,0.39204780432423647,0.30161675144003813
+global,TPipe,sigma,0.10714679650045936,0.10675564044121401
+global,TPipe,mu_star_conf,0.019794076252614232,0.015933238371723796
+heat up,all,mu_star,0.04047180930247298,0.0012453633711093609
+heat up,all,mu,0.04047180930247298,0.000604602795518513
+heat up,all,sigma,0.04008432008503694,0.002869838119027461
+heat up,all,mu_star_conf,0.006926096333791133,0.0005187039854856136
+heat up,TCap,mu_star,0.03071051240422334,0.0005170131629745076
+heat up,TCap,mu,0.03071051240422334,0.0002864534864145227
+heat up,TCap,sigma,0.03056340733679365,0.0011782843686675107
+heat up,TCap,mu_star_conf,0.006147746994490932,0.00020888382654708014
+heat up,TPipe,mu_star,0.06324816873172216,0.003034946096783261
+heat up,TPipe,mu,0.06324816873172216,0.0013469511834278307
+heat up,TPipe,sigma,0.06239214093815815,0.006936561762070073
+heat up,TPipe,mu_star_conf,0.011976140392551259,0.0011254107425354642
+cool down,all,mu_star,1.769730414899365,1.1969401930252628
+cool down,all,mu,1.7364806700062225,1.048494694797056
+cool down,all,sigma,1.1806097556092914,1.009771894653784
+cool down,all,mu_star_conf,0.22030423949275754,0.14835748078362918
+cool down,TCap,mu_star,1.8048395624463764,1.2046696479939747
+cool down,TCap,mu,1.7706637908809941,1.0492729896624347
+cool down,TCap,sigma,1.2200744465504736,1.032538120458078
+cool down,TCap,mu_star_conf,0.2227941462869606,0.17493124500507318
+cool down,TPipe,mu_star,1.6878090706230073,1.1789047980982692
+cool down,TPipe,mu,1.6567200546317584,1.046678673444506
+cool down,TPipe,sigma,1.0893102467350555,0.957220794495985
+cool down,TPipe,mu_star_conf,0.20156688714792548,0.15011913343216818
+stationary,all,mu_star,1.3888562118897565,1.1341892352947918
+stationary,all,mu,1.3888562118897565,1.1341892352947918
+stationary,all,sigma,0.2881177509667847,0.2968232362236692
+stationary,all,mu_star_conf,0.058825105552902796,0.05599901566585096
+stationary,TCap,mu_star,1.3398622145793047,1.0931692061970422
+stationary,TCap,mu,1.3398622145793047,1.0931692061970422
+stationary,TCap,sigma,0.28112172414276876,0.28938860523544085
+stationary,TCap,mu_star_conf,0.056354906247524365,0.05037412914531874
+stationary,TPipe,mu_star,1.5031755389474784,1.2299026365228756
+stationary,TPipe,mu,1.5031755389474784,1.2299026365228756
+stationary,TPipe,sigma,0.30513199502486715,0.31475374712568294
+stationary,TPipe,mu_star_conf,0.061795456321479335,0.06497250778947744
diff --git a/examples/data/SobolAnalyzer_results_A.csv b/examples/data/SobolAnalyzer_results_A.csv
new file mode 100644
index 00000000..0cccbe73
--- /dev/null
+++ b/examples/data/SobolAnalyzer_results_A.csv
@@ -0,0 +1,37 @@
+Class,Goal,Analysis variable,heatPumpSystem.declination,vol.V,heaCap.C,rad.n,theCon.G
+global,all,S1,0.0,9.981171761535852e-05,0.0006857904459304658,0.002475490822438631,0.9034639249125812
+global,all,ST,0.0,0.003924512984803624,0.005754568135873106,0.08557418979309585,0.996242292472142
+global,all,S1_conf,0.0,0.006127994199815119,0.007079330293270881,0.02399533909683912,0.09344179756910223
+global,all,ST_conf,0.0,0.0009589708186608186,0.0012692922801728398,0.013898486050221329,0.0657804146571587
+global,Electricity,S1,0.0,0.004477503576317218,0.006310771628947152,0.00204836976045287,0.5678532847040125
+global,Electricity,ST,0.0,0.013763720073739955,0.022396392507374758,0.39660081655872514,0.9672826287670265
+global,Electricity,S1_conf,0.0,0.015470866341642447,0.017417005814734383,0.0649339382173066,0.11559057970804365
+global,Electricity,ST_conf,0.0,0.0069449048405892215,0.009900323949783203,0.07791229303480955,0.10082753378837742
+global,Room temperature,S1,0.0,0.0033096370749875143,0.001146806433852794,0.002413851502786947,0.9419961540472024
+global,Room temperature,ST,0.0,0.0026379181713934896,0.003581832030264988,0.044513984703652455,0.9964165506793828
+global,Room temperature,S1_conf,0.0,0.004423940070216972,0.004839345174559873,0.018500634013359336,0.10666025751334397
+global,Room temperature,ST_conf,0.0,0.0005138510038039782,0.0008041833135976853,0.00539837928247146,0.06899766363253093
+On,all,S1,0.0,0.0026068368373645782,0.0028751878924056485,0.009521743542029079,0.7920006147757741
+On,all,ST,0.0,0.006147796126319411,0.010659358187485516,0.19553595331722928,0.987302118023262
+On,all,S1_conf,0.0,0.0075902160199334415,0.008452324281255594,0.035255014530575154,0.08576669816482141
+On,all,ST_conf,0.0,0.0030602558680729404,0.005255851453639148,0.039812909298835006,0.07097571619906619
+On,Electricity,S1,0.0,0.0031019198825259484,0.005124928494282338,0.009701390838402867,0.6980120801756866
+On,Electricity,ST,0.0,0.008683574599556498,0.015489359257563002,0.2807328712345282,0.9735591382638042
+On,Electricity,S1_conf,0.0,0.010227763415624855,0.011370525734316801,0.04982606736346287,0.09324022268550188
+On,Electricity,ST_conf,0.0,0.004566892958008373,0.006201867415676126,0.050993107858154786,0.07750162414998682
+On,Room temperature,S1,0.0,0.0029933039637624194,0.0009301875580098956,0.00016093981027493417,0.9428611143479794
+On,Room temperature,ST,0.0,0.0034231262143536614,0.004484530618641006,0.04381099350863442,0.994051176596271
+On,Room temperature,S1_conf,0.0,0.004616920614669334,0.004999791841751198,0.01731718539750302,0.11471695294044447
+On,Room temperature,ST_conf,0.0,0.0006231299214510954,0.0009647445473571392,0.006128433646316758,0.06012647776512428
+Off,all,S1,0.0,0.000844647455915583,0.0013464195899526988,0.00354275763037027,0.9048517811798845
+Off,all,ST,0.0,0.0042322310847638656,0.005923099495160968,0.0840804056726998,0.9986180079159158
+Off,all,S1_conf,0.0,0.00728926308747947,0.007435125529932373,0.027468292118276526,0.08724140072263453
+Off,all,ST_conf,0.0,0.0009646044811803216,0.0011668302153965255,0.013502941144022932,0.06668288285873901
+Off,Electricity,S1,0.0,0.00278675622554076,0.0006291696318957217,0.004539947869769703,0.8773304445310295
+Off,Electricity,ST,0.0,0.004925693822333759,0.007104860219666679,0.1103424586810084,0.9966021882294881
+Off,Electricity,S1_conf,0.0,0.007578755136460543,0.00875440239308875,0.028765438194848458,0.06871983628415697
+Off,Electricity,ST_conf,0.0,0.002477096234671017,0.0029887350984999397,0.01890838214596232,0.056443330805657846
+Off,Room temperature,S1,0.0,0.0034799055229904645,0.0014853595907252887,0.003098722176793324,0.9407765733177197
+Off,Room temperature,ST,0.0,0.0024704165374669885,0.0033841994131140907,0.04520553459523207,0.9968220514703601
+Off,Room temperature,S1_conf,0.0,0.004208882503404611,0.004651472681642032,0.016913167020701277,0.09678062439511863
+Off,Room temperature,ST_conf,0.0,0.0005014151372478863,0.0007290413056634394,0.005422544915661947,0.06869125727764101
diff --git a/examples/data/SobolAnalyzer_results_B.csv b/examples/data/SobolAnalyzer_results_B.csv
new file mode 100644
index 00000000..e4e79344
--- /dev/null
+++ b/examples/data/SobolAnalyzer_results_B.csv
@@ -0,0 +1,49 @@
+Class,Goal,Analysis variable,speedRamp.duration,valveRamp.duration
+global,all,S1,0.5937802470561883,0.38208572826006765
+global,all,ST,0.6198934667423768,0.40813867769100104
+global,all,S1_conf,0.05927934379285615,0.04537800415317561
+global,all,ST_conf,0.05423029420652675,0.03276833043835816
+global,TCap,S1,0.593767135664744,0.3823027635340512
+global,TCap,ST,0.619670744722624,0.40815965357470885
+global,TCap,S1_conf,0.06220399791518888,0.051117416448008275
+global,TCap,ST_conf,0.04603250581658939,0.03505929347005915
+global,TPipe,S1,0.5938004842864535,0.38172498110708525
+global,TPipe,ST,0.6202636490007541,0.4081054179900795
+global,TPipe,S1_conf,0.0578191245014379,0.05051546945490835
+global,TPipe,ST_conf,0.04932727723753033,0.035402590550360304
+heat up,all,S1,0.9960017847412489,0.0001309489323739352
+heat up,all,ST,0.9967831515665558,0.0009675760476598921
+heat up,all,S1_conf,0.11281061857093035,0.002282031030027039
+heat up,all,ST_conf,0.06226298918861027,0.00025592314930269696
+heat up,TCap,S1,0.9968046304874463,4.456294614174331e-05
+heat up,TCap,ST,0.9972859865346204,0.0003236409507216123
+heat up,TCap,S1_conf,0.13253601701746343,0.001487202193566651
+heat up,TCap,ST_conf,0.061894634020611794,9.28916273166505e-05
+heat up,TPipe,S1,0.9946187206807962,0.0006987366637176041
+heat up,TPipe,ST,0.9958352973366467,0.002130999211514989
+heat up,TPipe,S1_conf,0.1098836191287225,0.00419208055324135
+heat up,TPipe,ST_conf,0.06076365072560032,0.0006294215141259365
+cool down,all,S1,0.662473147347816,0.28757989636005366
+cool down,all,ST,0.7184360410573779,0.3434917826335655
+cool down,all,S1_conf,0.08447646411516352,0.05434718716326143
+cool down,all,ST_conf,0.061194854983838476,0.03815207964976311
+cool down,TCap,S1,0.669363719279727,0.2810389346908704
+cool down,TCap,ST,0.7248603988562174,0.33660171853533416
+cool down,TCap,S1_conf,0.08059602453881608,0.055728065307068315
+cool down,TCap,ST_conf,0.05718725691482579,0.037545628893716
+cool down,TPipe,S1,0.645152844253433,0.3039484862000267
+cool down,TPipe,ST,0.7023380837797042,0.36082353581622273
+cool down,TPipe,S1_conf,0.06583242177574586,0.06356755996519355
+cool down,TPipe,ST_conf,0.05228182308752929,0.04102149803875612
+stationary,all,S1,0.5727872461146412,0.4076905846627818
+stationary,all,ST,0.5925119530775118,0.4276212828508054
+stationary,all,S1_conf,0.06249847211050626,0.04934835959472443
+stationary,all,ST_conf,0.047527054476383376,0.03730630620565084
+stationary,TCap,S1,0.5729247402261338,0.40726658290953377
+stationary,TCap,ST,0.5929959757665975,0.42754083415762084
+stationary,TCap,S1_conf,0.05648188575352477,0.052616156960121084
+stationary,TCap,ST_conf,0.04705211301080932,0.04036493638135444
+stationary,TPipe,S1,0.5724766349459368,0.4085618413210011
+stationary,TPipe,ST,0.5915183511101494,0.4278161826946474
+stationary,TPipe,S1_conf,0.06374470621654553,0.05020223720222382
+stationary,TPipe,ST_conf,0.04799686763454703,0.04016096218158261
diff --git a/examples/data/SobolAnalyzer_results_second_order_A.csv b/examples/data/SobolAnalyzer_results_second_order_A.csv
new file mode 100644
index 00000000..34fb7f14
--- /dev/null
+++ b/examples/data/SobolAnalyzer_results_second_order_A.csv
@@ -0,0 +1,91 @@
+Class,Goal,Analysis variable,Interaction,heatPumpSystem.declination,vol.V,heaCap.C,rad.n,theCon.G
+global,all,S2,heatPumpSystem.declination,,2.2768245622195593e-18,1.951563910473908e-18,4.336808689942018e-18,0.0
+global,all,S2,vol.V,2.2768245622195593e-18,,0.0009434796751958398,0.001349665834839888,0.0008712101367860381
+global,all,S2,heaCap.C,1.951563910473908e-18,0.0009434796751958398,,0.0031919273508358424,0.003742997239774337
+global,all,S2,rad.n,4.336808689942018e-18,0.001349665834839888,0.0031919273508358424,,0.07391700230996812
+global,all,S2,theCon.G,0.0,0.0008712101367860381,0.003742997239774337,0.07391700230996812,
+global,all,S2_conf,heatPumpSystem.declination,,6.3844938717002024e-18,7.383295339417844e-18,1.6012755729664826e-17,1.9328317118102763e-16
+global,all,S2_conf,vol.V,6.3844938717002024e-18,,0.008445416150214794,0.007923556873010072,0.012077636741657615
+global,all,S2_conf,heaCap.C,7.383295339417844e-18,0.008445416150214794,,0.010807518665064122,0.014585675197420675
+global,all,S2_conf,rad.n,1.6012755729664826e-17,0.007923556873010072,0.010807518665064122,,0.03747907348954445
+global,all,S2_conf,theCon.G,1.9328317118102763e-16,0.012077636741657615,0.014585675197420675,0.03747907348954445,
+global,Electricity,S2,heatPumpSystem.declination,,0.0,8.673617379884035e-19,6.5052130349130266e-18,1.1102230246251565e-16
+global,Electricity,S2,vol.V,0.0,,0.012286868908583073,0.0067684949821407246,0.004648745804725407
+global,Electricity,S2,heaCap.C,8.673617379884035e-19,0.012286868908583073,,0.016800006045762463,0.00935891471147643
+global,Electricity,S2,rad.n,6.5052130349130266e-18,0.0067684949821407246,0.016800006045762463,,0.2908826230658256
+global,Electricity,S2,theCon.G,1.1102230246251565e-16,0.004648745804725407,0.00935891471147643,0.2908826230658256,
+global,Electricity,S2_conf,heatPumpSystem.declination,,9.41661885783599e-18,1.0245364444250908e-17,2.3970692473369066e-17,1.6689810540597914e-16
+global,Electricity,S2_conf,vol.V,9.41661885783599e-18,,0.017403827364213455,0.016637258784640365,0.020034747218354042
+global,Electricity,S2_conf,heaCap.C,1.0245364444250908e-17,0.017403827364213455,,0.029927395207655312,0.020538494345680558
+global,Electricity,S2_conf,rad.n,2.3970692473369066e-17,0.016637258784640365,0.029927395207655312,,0.11817831076235108
+global,Electricity,S2_conf,theCon.G,1.6689810540597914e-16,0.020034747218354042,0.020538494345680558,0.11817831076235108,
+global,Room temperature,S2,heatPumpSystem.declination,,4.336808689942018e-18,2.3852447794681098e-18,1.734723475976807e-18,0.0
+global,Room temperature,S2,vol.V,4.336808689942018e-18,,0.003325000694632053,0.002953302661897171,0.0005579528276739021
+global,Room temperature,S2,heaCap.C,2.3852447794681098e-18,0.003325000694632053,,0.0011122629069048534,0.0021325274085535195
+global,Room temperature,S2,rad.n,1.734723475976807e-18,0.002953302661897171,0.0011122629069048534,,0.0488280700128535
+global,Room temperature,S2,theCon.G,0.0,0.0005579528276739021,0.0021325274085535195,0.0488280700128535,
+global,Room temperature,S2_conf,heatPumpSystem.declination,,7.393662097468368e-18,6.262983987110103e-18,1.3728037634602546e-17,1.7462840114333936e-16
+global,Room temperature,S2_conf,vol.V,7.393662097468368e-18,,0.006683452235837769,0.006698233743717954,0.012121967609502154
+global,Room temperature,S2_conf,heaCap.C,6.262983987110103e-18,0.006683452235837769,,0.007332941382947123,0.015047392539198966
+global,Room temperature,S2_conf,rad.n,1.3728037634602546e-17,0.006698233743717954,0.007332941382947123,,0.03017035381753847
+global,Room temperature,S2_conf,theCon.G,1.7462840114333936e-16,0.012121967609502154,0.015047392539198966,0.03017035381753847,
+On,all,S2,heatPumpSystem.declination,,0.0,1.3010426069826053e-18,5.204170427930421e-18,1.1102230246251565e-16
+On,all,S2,vol.V,0.0,,0.0010143816466899936,0.0017959596623205306,0.0011416642959209966
+On,all,S2,heaCap.C,1.3010426069826053e-18,0.0010143816466899936,,0.007260482280736972,0.00018933793403996813
+On,all,S2,rad.n,5.204170427930421e-18,0.0017959596623205306,0.007260482280736972,,0.1391820233739386
+On,all,S2,theCon.G,1.1102230246251565e-16,0.0011416642959209966,0.00018933793403996813,0.1391820233739386,
+On,all,S2_conf,heatPumpSystem.declination,,7.592851808382974e-18,7.887921955624244e-18,1.883943426136829e-17,2.4253567249097665e-16
+On,all,S2_conf,vol.V,7.592851808382974e-18,,0.010351198334042603,0.009628069064404552,0.010174590252513703
+On,all,S2_conf,heaCap.C,7.887921955624244e-18,0.010351198334042603,,0.012081812359311923,0.011557058577621087
+On,all,S2_conf,rad.n,1.883943426136829e-17,0.009628069064404552,0.012081812359311923,,0.0650775784571036
+On,all,S2_conf,theCon.G,2.4253567249097665e-16,0.010174590252513703,0.011557058577621087,0.0650775784571036,
+On,Electricity,S2,heatPumpSystem.declination,,8.673617379884035e-19,1.734723475976807e-18,8.673617379884035e-18,0.0
+On,Electricity,S2,vol.V,8.673617379884035e-19,,0.005271268351831465,0.0012667573081751142,0.0018192954547141538
+On,Electricity,S2,heaCap.C,1.734723475976807e-18,0.005271268351831465,,0.012755937805991735,0.00043805112272488067
+On,Electricity,S2,rad.n,8.673617379884035e-18,0.0012667573081751142,0.012755937805991735,,0.1985308689654931
+On,Electricity,S2,theCon.G,0.0,0.0018192954547141538,0.00043805112272488067,0.1985308689654931,
+On,Electricity,S2_conf,heatPumpSystem.declination,,1.1116176166041256e-17,1.3526852744647269e-17,2.7718905218416113e-17,1.2026288471501823e-16
+On,Electricity,S2_conf,vol.V,1.1116176166041256e-17,,0.014073753894595122,0.012666168993853914,0.014445659324622266
+On,Electricity,S2_conf,heaCap.C,1.3526852744647269e-17,0.014073753894595122,,0.018626896976920534,0.012691184530164962
+On,Electricity,S2_conf,rad.n,2.7718905218416113e-17,0.012666168993853914,0.018626896976920534,,0.08232452192112935
+On,Electricity,S2_conf,theCon.G,1.2026288471501823e-16,0.014445659324622266,0.012691184530164962,0.08232452192112935,
+On,Room temperature,S2,heatPumpSystem.declination,,1.734723475976807e-18,1.5178830414797062e-18,7.914675859144182e-18,0.0
+On,Room temperature,S2,vol.V,1.734723475976807e-18,,0.0031022408775590116,0.002836810687308447,7.499030206692403e-05
+On,Room temperature,S2,heaCap.C,1.5178830414797062e-18,0.0031022408775590116,,0.001046359044839581,0.0017718025338738785
+On,Room temperature,S2,rad.n,7.914675859144182e-18,0.002836810687308447,0.001046359044839581,,0.04410303112518099
+On,Room temperature,S2,theCon.G,0.0,7.499030206692403e-05,0.0017718025338738785,0.04410303112518099,
+On,Room temperature,S2_conf,heatPumpSystem.declination,,7.799090673111322e-18,6.444142203779651e-18,1.1450305268565058e-17,2.7727851172547907e-16
+On,Room temperature,S2_conf,vol.V,7.799090673111322e-18,,0.007176033519265127,0.007054759433084827,0.013229751043951616
+On,Room temperature,S2_conf,heaCap.C,6.444142203779651e-18,0.007176033519265127,,0.008032085960969075,0.014097813204162314
+On,Room temperature,S2_conf,rad.n,1.1450305268565058e-17,0.007054759433084827,0.008032085960969075,,0.03131143115746934
+On,Room temperature,S2_conf,theCon.G,2.7727851172547907e-16,0.013229751043951616,0.014097813204162314,0.03131143115746934,
+Off,all,S2,heatPumpSystem.declination,,3.0357660829594124e-18,3.903127820947816e-18,0.0,0.0
+Off,all,S2,vol.V,3.0357660829594124e-18,,0.000809740719460693,0.0005804211966973986,0.0003454899729710137
+Off,all,S2,heaCap.C,3.903127820947816e-18,0.000809740719460693,,0.004131025379532775,0.005793908759675337
+Off,all,S2,rad.n,0.0,0.0005804211966973986,0.004131025379532775,,0.07948685257854182
+Off,all,S2,theCon.G,0.0,0.0003454899729710137,0.005793908759675337,0.07948685257854182,
+Off,all,S2_conf,heatPumpSystem.declination,,7.57142463817432e-18,7.056857516105084e-18,1.6481328827545575e-17,2.1315843655366831e-16
+Off,all,S2_conf,vol.V,7.57142463817432e-18,,0.00982567554015715,0.009175343548334124,0.012914751862315837
+Off,all,S2_conf,heaCap.C,7.056857516105084e-18,0.00982567554015715,,0.009749632187608425,0.014820366210237851
+Off,all,S2_conf,rad.n,1.6481328827545575e-17,0.009175343548334124,0.009749632187608425,,0.038417691808124266
+Off,all,S2_conf,theCon.G,2.1315843655366831e-16,0.012914751862315837,0.014820366210237851,0.038417691808124266,
+Off,Electricity,S2,heatPumpSystem.declination,,3.469446951953614e-18,6.505213034913027e-19,2.6020852139652106e-18,1.1102230246251565e-16
+Off,Electricity,S2,vol.V,3.469446951953614e-18,,6.996823615311107e-05,0.00138519427742099,0.000162397792841662
+Off,Electricity,S2,heaCap.C,6.505213034913027e-19,6.996823615311107e-05,,0.0035855327083588896,0.006286369666079872
+Off,Electricity,S2,rad.n,2.6020852139652106e-18,0.00138519427742099,0.0035855327083588896,,0.08485607591232924
+Off,Electricity,S2,theCon.G,1.1102230246251565e-16,0.000162397792841662,0.006286369666079872,0.08485607591232924,
+Off,Electricity,S2_conf,heatPumpSystem.declination,,9.207264608941349e-18,1.0876240330647538e-17,1.421062444277043e-17,2.1511277875398801e-16
+Off,Electricity,S2_conf,vol.V,9.207264608941349e-18,,0.009805926794353478,0.00988239968681761,0.00875351289838158
+Off,Electricity,S2_conf,heaCap.C,1.0876240330647538e-17,0.009805926794353478,,0.011071340322830381,0.009862303983600234
+Off,Electricity,S2_conf,rad.n,1.421062444277043e-17,0.00988239968681761,0.011071340322830381,,0.042363775402363954
+Off,Electricity,S2_conf,theCon.G,2.1511277875398801e-16,0.00875351289838158,0.009862303983600234,0.042363775402363954,
+Off,Room temperature,S2,heatPumpSystem.declination,,3.903127820947816e-18,2.168404344971009e-18,3.0357660829594124e-18,0.0
+Off,Room temperature,S2,vol.V,3.903127820947816e-18,,0.003288220586336853,0.0030091126397154997,0.0011188479106749583
+Off,Room temperature,S2,heaCap.C,2.168404344971009e-18,0.003288220586336853,,0.0007218902651460837,0.002107649671139855
+Off,Room temperature,S2,rad.n,3.0357660829594124e-18,0.0030091126397154997,0.0007218902651460837,,0.05001533759434096
+Off,Room temperature,S2,theCon.G,0.0,0.0011188479106749583,0.002107649671139855,0.05001533759434096,
+Off,Room temperature,S2_conf,heatPumpSystem.declination,,7.280244257669331e-18,6.930284777592844e-18,1.1408219380144552e-17,1.7867605642290127e-16
+Off,Room temperature,S2_conf,vol.V,7.280244257669331e-18,,0.006545268949917151,0.006331104808848709,0.012134941223556442
+Off,Room temperature,S2_conf,heaCap.C,6.930284777592844e-18,0.006545268949917151,,0.0073908208461457795,0.0139985170430143
+Off,Room temperature,S2_conf,rad.n,1.1408219380144552e-17,0.006331104808848709,0.0073908208461457795,,0.029454309402864493
+Off,Room temperature,S2_conf,theCon.G,1.7867605642290127e-16,0.012134941223556442,0.0139985170430143,0.029454309402864493,
diff --git a/examples/data/SobolAnalyzer_results_second_order_B.csv b/examples/data/SobolAnalyzer_results_second_order_B.csv
new file mode 100644
index 00000000..90f891f8
--- /dev/null
+++ b/examples/data/SobolAnalyzer_results_second_order_B.csv
@@ -0,0 +1,49 @@
+Class,Goal,Analysis variable,Interaction,speedRamp.duration,valveRamp.duration
+global,all,S2,speedRamp.duration,,0.025310201538231436
+global,all,S2,valveRamp.duration,0.025310201538231436,
+global,all,S2_conf,speedRamp.duration,,0.09263496301447202
+global,all,S2_conf,valveRamp.duration,0.09263496301447202,
+global,TCap,S2,speedRamp.duration,,0.02510815334489852
+global,TCap,S2,valveRamp.duration,0.02510815334489852,
+global,TCap,S2_conf,speedRamp.duration,,0.08403574199838072
+global,TCap,S2_conf,valveRamp.duration,0.08403574199838072,
+global,TPipe,S2,speedRamp.duration,,0.02564762833065176
+global,TPipe,S2,valveRamp.duration,0.02564762833065176,
+global,TPipe,S2_conf,speedRamp.duration,,0.08707239835207532
+global,TPipe,S2_conf,valveRamp.duration,0.08707239835207532,
+heat up,all,S2,speedRamp.duration,,0.0021955576128004765
+heat up,all,S2,valveRamp.duration,0.0021955576128004765,
+heat up,all,S2_conf,speedRamp.duration,,0.20154302316420983
+heat up,all,S2_conf,valveRamp.duration,0.20154302316420983,
+heat up,TCap,S2,speedRamp.duration,,0.0012995270454584343
+heat up,TCap,S2,valveRamp.duration,0.0012995270454584343,
+heat up,TCap,S2_conf,speedRamp.duration,,0.2420898458105829
+heat up,TCap,S2_conf,valveRamp.duration,0.2420898458105829,
+heat up,TPipe,S2,speedRamp.duration,,0.0031993339164700474
+heat up,TPipe,S2,valveRamp.duration,0.0031993339164700474,
+heat up,TPipe,S2_conf,speedRamp.duration,,0.18805732378187293
+heat up,TPipe,S2_conf,valveRamp.duration,0.18805732378187293,
+cool down,all,S2,speedRamp.duration,,0.053354300386931786
+cool down,all,S2,valveRamp.duration,0.053354300386931786,
+cool down,all,S2_conf,speedRamp.duration,,0.11057871422127731
+cool down,all,S2_conf,valveRamp.duration,0.11057871422127731,
+cool down,TCap,S2,speedRamp.duration,,0.052955009211473
+cool down,TCap,S2,valveRamp.duration,0.052955009211473,
+cool down,TCap,S2_conf,speedRamp.duration,,0.0941273641207029
+cool down,TCap,S2_conf,valveRamp.duration,0.0941273641207029,
+cool down,TPipe,S2,speedRamp.duration,,0.054434830401888235
+cool down,TPipe,S2,valveRamp.duration,0.054434830401888235,
+cool down,TPipe,S2_conf,speedRamp.duration,,0.09416195219251533
+cool down,TPipe,S2_conf,valveRamp.duration,0.09416195219251533,
+stationary,all,S2,speedRamp.duration,,0.019799380701387148
+stationary,all,S2,valveRamp.duration,0.019799380701387148,
+stationary,all,S2_conf,speedRamp.duration,,0.09697025188939104
+stationary,all,S2_conf,valveRamp.duration,0.09697025188939104,
+stationary,TCap,S2,speedRamp.duration,,0.02009112317326811
+stationary,TCap,S2,valveRamp.duration,0.02009112317326811,
+stationary,TCap,S2_conf,speedRamp.duration,,0.09902866632577796
+stationary,TCap,S2_conf,valveRamp.duration,0.09902866632577796,
+stationary,TPipe,S2,speedRamp.duration,,0.019230472503300844
+stationary,TPipe,S2,valveRamp.duration,0.019230472503300844,
+stationary,TPipe,S2_conf,speedRamp.duration,,0.09120933886110148
+stationary,TPipe,S2_conf,valveRamp.duration,0.09120933886110148,
diff --git a/examples/data/SobolAnalyzer_results_second_order_time_A.csv b/examples/data/SobolAnalyzer_results_second_order_time_A.csv
new file mode 100644
index 00000000..95118630
--- /dev/null
+++ b/examples/data/SobolAnalyzer_results_second_order_time_A.csv
@@ -0,0 +1,4741 @@
+Goal,Analysis variable,Interaction,time,heatPumpSystem.declination,vol.V,heaCap.C,rad.n,theCon.G
+Pel,S2,heaCap.C,0.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,10.0,1.0842021724855044e-18,0.005739039639365278,,0.0062085305518572165,0.016306116701229542
+Pel,S2,heaCap.C,20.0,1.734723475976807e-18,0.013325480338809934,,0.010918385715956158,0.03334865325207567
+Pel,S2,heaCap.C,30.0,1.0408340855860843e-17,0.019190788860211208,,0.009995707643608687,0.03949849355456836
+Pel,S2,heaCap.C,40.0,0.0,0.022965826541887557,,0.00808267507714111,0.03907543319754342
+Pel,S2,heaCap.C,50.0,0.0,0.023128608142052788,,0.007010921418852123,0.037837186188788285
+Pel,S2,heaCap.C,60.0,0.0,0.020194018168738004,,0.006646145907492074,0.037962300440912033
+Pel,S2,heaCap.C,70.0,6.938893903907228e-18,0.01587054706851239,,0.00648339799093961,0.039593710650097536
+Pel,S2,heaCap.C,80.0,0.0,0.01191336782980356,,0.006041253445408928,0.04174147336609568
+Pel,S2,heaCap.C,90.0,0.0,0.009157187126794503,,0.005134517160991503,0.0430613385015699
+Pel,S2,heaCap.C,100.0,0.0,0.007464653825635787,,0.003921552445523696,0.04272903505895165
+Pel,S2,heaCap.C,110.0,0.0,0.0064086286568239455,,0.0026757575254916094,0.04077932023975461
+Pel,S2,heaCap.C,120.0,0.0,0.005729211348071608,,0.0015932582951217328,0.037768320746718875
+Pel,S2,heaCap.C,130.0,1.734723475976807e-18,0.005311451112668309,,0.0007638169622811025,0.03434203094140853
+Pel,S2,heaCap.C,140.0,1.734723475976807e-18,0.005044268084689871,,0.00021213914908115028,0.031016003659361968
+Pel,S2,heaCap.C,150.0,0.0,0.004788021875110791,,7.68465496471804e-05,0.028135052692368046
+Pel,S2,heaCap.C,160.0,1.734723475976807e-18,0.00443693236434704,,0.0001557201011860715,0.02589149926519352
+Pel,S2,heaCap.C,170.0,1.734723475976807e-18,0.003934996715239973,,0.00013347797383939097,0.024338864500652924
+Pel,S2,heaCap.C,180.0,0.0,0.003177969746063049,,5.622764582335904e-05,0.023149181319813583
+Pel,S2,heaCap.C,190.0,3.469446951953614e-18,0.002030894062030192,,1.0699522429186148e-05,0.022218372526017882
+Pel,S2,heaCap.C,200.0,0.0,0.001301591619668507,,0.00013902479475946805,0.020949768661085666
+Pel,S2,heaCap.C,210.0,3.469446951953614e-18,0.0012702193553189144,,0.00039610824216913115,0.01889400851772316
+Pel,S2,heaCap.C,220.0,1.734723475976807e-18,0.0016552008705105186,,0.0010234486424510192,0.016652042763941277
+Pel,S2,heaCap.C,230.0,0.0,0.0014419472579042065,,0.0008859713425806923,0.014118513707058522
+Pel,S2,heaCap.C,240.0,8.673617379884035e-19,0.0011503815640532032,,0.0007992435779199669,0.0117922880976844
+Pel,S2,heaCap.C,250.0,1.734723475976807e-18,0.000667133171845598,,0.000693330218510993,0.009896178840830605
+Pel,S2,heaCap.C,260.0,8.673617379884035e-19,0.0005457775521056328,,0.0006452331054068033,0.00911085978100279
+Pel,S2,heaCap.C,270.0,8.673617379884035e-19,0.0008270872686903501,,0.0006325102780531389,0.008714031557284119
+Pel,S2,heaCap.C,280.0,8.673617379884035e-19,0.0009513511034605054,,0.0006925392253877491,0.008279589741321258
+Pel,S2,heaCap.C,290.0,2.6020852139652106e-18,0.0007680348211216914,,0.0003680971656584396,0.00774161518183325
+Pel,S2,heaCap.C,300.0,8.673617379884035e-19,0.0006325192793562246,,0.00032612594273986537,0.007332451713746302
+Pel,S2,heaCap.C,310.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,320.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,330.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,340.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,350.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,360.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,370.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,380.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,390.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,400.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,410.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,420.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,430.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,440.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,450.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,460.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,470.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,480.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,490.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,500.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,510.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,520.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,530.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,540.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,550.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,560.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,570.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,580.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,590.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,600.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,610.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,620.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,630.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,640.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,650.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,660.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,670.0,0.0,0.0,0.0,0.0,0.0
+Pel,S2,heaCap.C,680.0,0.0,0.0003256003256003255,,0.0,0.0
+Pel,S2,heaCap.C,690.0,1.0842021724855044e-19,0.0009449149576538118,,1.0842021724855044e-19,0.14334709876111157
+Pel,S2,heaCap.C,700.0,1.0842021724855044e-19,0.0009449149576538118,,1.0842021724855044e-19,0.14334709876111157
+Pel,S2,heaCap.C,710.0,1.0842021724855044e-19,0.0009449149576538118,,1.0842021724855044e-19,0.14334709876111157
+Pel,S2,heaCap.C,720.0,1.0842021724855044e-19,0.0009449149576538118,,1.0842021724855044e-19,0.14334709876111157
+Pel,S2,heaCap.C,730.0,1.0842021724855044e-19,0.0009457973614548969,,2.0326476828169787e-08,0.14330158560270487
+Pel,S2,heaCap.C,740.0,5.421010862427522e-20,0.0009452312202029845,,8.673891518438994e-07,0.14319903957446198
+Pel,S2,heaCap.C,750.0,5.421010862427522e-20,0.0009445262154380587,,6.787163805188985e-07,0.143118963461662
+Pel,S2,heaCap.C,760.0,1.0842021724855044e-19,0.0009446642477714212,,3.4257192605334325e-07,0.14312108419113323
+Pel,S2,heaCap.C,770.0,3.2526065174565133e-19,0.0009450597740647002,,1.4598949274160432e-08,0.1431738827892073
+Pel,S2,heaCap.C,780.0,0.0,0.01494545844576434,,0.0039288628209447965,0.003925831505588784
+Pel,S2,heaCap.C,790.0,4.336808689942018e-19,0.005439015210829967,,0.0006241479785023991,0.0006235751390084587
+Pel,S2,heaCap.C,800.0,4.336808689942018e-19,0.005437497301188757,,0.0006227668013946142,0.0006251461698311145
+Pel,S2,heaCap.C,810.0,0.0,0.005402581162786026,,0.0006251174084317586,0.0006140298641946762
+Pel,S2,heaCap.C,820.0,3.903127820947816e-18,0.005348530678514499,,0.0006422916546892157,0.0005620600250630936
+Pel,S2,heaCap.C,830.0,8.673617379884035e-19,0.00545996628186224,,0.0006064108484960286,0.0005355436668912361
+Pel,S2,heaCap.C,840.0,3.0357660829594124e-18,0.005492505538183278,,0.0005798721573445909,0.0005258153142021493
+Pel,S2,heaCap.C,850.0,4.336808689942018e-19,0.0054820866920347235,,0.0005769473201195571,0.000520930636829342
+Pel,S2,heaCap.C,860.0,8.673617379884035e-19,0.005459777371735945,,0.0005867250337762732,0.000503014692541548
+Pel,S2,heaCap.C,870.0,3.0357660829594124e-18,0.005445340095555781,,0.0005900306816474452,0.0004967270045598271
+Pel,S2,heaCap.C,880.0,1.3010426069826053e-18,0.005798994403047504,,0.0002759657747656713,0.00023955710357492777
+Pel,S2,heaCap.C,890.0,1.734723475976807e-18,0.0003680270900598918,,0.006023989782662657,0.0022774387139186114
+Pel,S2,heaCap.C,900.0,0.0,0.00038957955345613,,0.006001611340015242,0.0022908604786721076
+Pel,S2,heaCap.C,910.0,1.734723475976807e-18,0.00041905662778073203,,0.006009454296564168,0.002301798110403541
+Pel,S2,heaCap.C,920.0,8.673617379884035e-19,0.00041733050798669746,,0.0060354802510110064,0.0022890377135416085
+Pel,S2,heaCap.C,930.0,8.673617379884035e-19,0.00038822642936782534,,0.006144987344578884,0.00222854436915354
+Pel,S2,heaCap.C,940.0,8.673617379884035e-19,0.00036748853205955095,,0.006192048888476092,0.002150651377094359
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diff --git a/examples/data/SobolAnalyzer_results_time_A.csv b/examples/data/SobolAnalyzer_results_time_A.csv
new file mode 100644
index 00000000..da6c7c93
--- /dev/null
+++ b/examples/data/SobolAnalyzer_results_time_A.csv
@@ -0,0 +1,1897 @@
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+vol.T,ST_conf,1980.0,0.0,0.0005313347460859442,0.0005930956310903346,0.002125345166776397,0.04967279623952688
+vol.T,ST_conf,1990.0,0.0,0.0005752082416003027,0.000741311540832395,0.002108158506068901,0.04143803247840873
+vol.T,ST_conf,2000.0,0.0,0.00029601574430107266,0.00032195913571094695,0.0021869568670739655,0.05050573321648083
+vol.T,ST_conf,2010.0,0.0,0.00011022176401993112,0.0001458913621900413,0.0021448454988290512,0.04851692273843478
+vol.T,ST_conf,2020.0,0.0,0.00021327915921223517,0.00022126695189315639,0.0021746786892176575,0.040939879711621635
+vol.T,ST_conf,2030.0,0.0,0.0004894896565287624,0.0005699599925069592,0.0023163164491032794,0.03856727581279801
+vol.T,ST_conf,2040.0,0.0,0.00047924609061606973,0.0005362331502096927,0.002757733840559556,0.045986579107159656
+vol.T,ST_conf,2050.0,0.0,0.0002746975716893851,0.0002777614533614591,0.00247479620846998,0.040651356693536755
+vol.T,ST_conf,2060.0,0.0,0.0001121235302922117,0.00011252997291704672,0.0023806948467244952,0.04112806735664506
+vol.T,ST_conf,2070.0,0.0,0.00029823113517385755,0.0002962348722720363,0.0023605380751676925,0.04475958569678079
+vol.T,ST_conf,2080.0,0.0,0.0003787933935988723,0.0005467108688990586,0.0020780094555253943,0.04288277702908661
+vol.T,ST_conf,2090.0,0.0,0.00041295403925375643,0.0005258475128741695,0.0018335788565457234,0.048542450088416504
+vol.T,ST_conf,2100.0,0.0,0.00021901105474159013,0.0002918250218855248,0.0018553890141205405,0.04547562885610112
+vol.T,ST_conf,2110.0,0.0,0.00010090395041540487,0.00010445538578681013,0.001902742275282812,0.047394932112720646
+vol.T,ST_conf,2120.0,0.0,0.000242068669324111,0.0002460904221501841,0.0023308720413417134,0.047035160788589804
+vol.T,ST_conf,2130.0,0.0,0.0006222812274753348,0.0006505891263297172,0.0023163124441579717,0.04160255910541565
+vol.T,ST_conf,2140.0,0.0,0.0006321548221687135,0.0007118088914381335,0.002360793218847632,0.049466622144500164
+vol.T,ST_conf,2150.0,0.0,0.0003389314913440829,0.0004419897299660621,0.0026142317237520508,0.04562589922024017
+vol.T,ST_conf,2160.0,0.0,0.00013484037568081003,0.00013900408451077257,0.002470552706588518,0.04401277293991247
+vol.T,ST_conf,2170.0,0.0,0.00019605250732648567,0.00025617733337502725,0.002254930428695135,0.0420830453928134
+vol.T,ST_conf,2180.0,0.0,0.00037323778550334467,0.0005027696809527052,0.0017985098700018052,0.04419740174080838
+vol.T,ST_conf,2190.0,0.0,0.00042377418932376497,0.00043986013586727043,0.0022815325868258424,0.0500997561294418
+vol.T,ST_conf,2200.0,0.0,0.00019743031327499583,0.00027130405322886916,0.002064496634351267,0.047937403227030655
+vol.T,ST_conf,2210.0,0.0,0.00010261195686674256,0.00012178381039451809,0.002275908180427143,0.04676705204672779
+vol.T,ST_conf,2220.0,0.0,0.0003424618531541925,0.00030511200426957635,0.0024644765881569543,0.03708720071509088
+vol.T,ST_conf,2230.0,0.0,0.0007356165860652211,0.0006909539597312581,0.0026224498469369995,0.04341207137903328
+vol.T,ST_conf,2240.0,0.0,0.0007443672666252617,0.0007979845116700096,0.00280132538445707,0.047518344819381865
+vol.T,ST_conf,2250.0,0.0,0.00044181989888338654,0.00048182110169518277,0.0030823895850611376,0.04459248684310764
+vol.T,ST_conf,2260.0,0.0,0.00014453005225536727,0.00017024403680616327,0.003015621753833127,0.044807737319057815
+vol.T,ST_conf,2270.0,0.0,0.00024305252127228524,0.0002313235863712953,0.0022611671599553065,0.04512050387857471
+vol.T,ST_conf,2280.0,0.0,0.0003843681726835901,0.00043547541076303757,0.0023080229106516425,0.04137833800307317
+vol.T,ST_conf,2290.0,0.0,0.0004037257412241127,0.0004435900995318039,0.002229500039241148,0.04383436596242808
+vol.T,ST_conf,2300.0,0.0,0.00021035560782775952,0.00024539837453645027,0.002036492020690815,0.042367859116262105
+vol.T,ST_conf,2310.0,0.0,9.261561614134398e-05,9.148506928303829e-05,0.0021665672800046504,0.04760431317262684
+vol.T,ST_conf,2320.0,0.0,0.00035918840239763884,0.0003775524308523397,0.0027729379263080754,0.04196652352430324
+vol.T,ST_conf,2330.0,0.0,0.0006678347929108024,0.0008152204496432671,0.0029295115236431784,0.04733423965091339
+vol.T,ST_conf,2340.0,0.0,0.0007197726959090483,0.0009426030264578798,0.0033556585586544327,0.045494697502212626
+vol.T,ST_conf,2350.0,0.0,0.0003873360412764021,0.0005171511252355608,0.0029828219564716116,0.04527319646531724
+vol.T,ST_conf,2360.0,0.0,0.00016750115601553723,0.00020620572646689662,0.00308383977390075,0.046428226428870414
diff --git a/examples/docs/e1_A_energy_system_analysis.md b/examples/docs/e1_A_energy_system_analysis.md
index f06de4ea..6669913d 100644
--- a/examples/docs/e1_A_energy_system_analysis.md
+++ b/examples/docs/e1_A_energy_system_analysis.md
@@ -19,6 +19,29 @@ Imports from ebcpy
from ebcpy import DymolaAPI, TimeSeriesData
```
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Arguments of this example:
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+:param str aixlib_mo:
+ Path to the package.mo of the AixLib.
+ This example was tested for AixLib version 1.0.0.
+:param str cd:
+ Path in which to store the output.
+ Default is the examples\results folder
+:param bool with_plot:
+ Show the plot at the end of the script. Default is True.
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+aixlib_mo = "TODO: Add a valid input according to the docstring above"
+cd = None
+with_plot = True
+```
+
General settings
```python
diff --git a/examples/docs/e1_B_energy_system_analysis.md b/examples/docs/e1_B_energy_system_analysis.md
index 7223dfc0..a65eeb7d 100644
--- a/examples/docs/e1_B_energy_system_analysis.md
+++ b/examples/docs/e1_B_energy_system_analysis.md
@@ -13,6 +13,21 @@ import pathlib
import matplotlib.pyplot as plt
```
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Arguments of this example:
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+:param bool with_plot:
+ Show the plot at the end of the script. Default is True.
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+with_plot = True
+```
+
## System analysis
The best way to analyze the model which we later want to calibrate
is to open the models in a GUI (OpenModelica, fmpy, Dymola, o.s.).
diff --git a/examples/docs/e2_A_optimization_problem_definition.md b/examples/docs/e2_A_optimization_problem_definition.md
index a1f41f03..a069194f 100644
--- a/examples/docs/e2_A_optimization_problem_definition.md
+++ b/examples/docs/e2_A_optimization_problem_definition.md
@@ -28,6 +28,28 @@ from aixcalibuha import TunerParas, Goals, \
from aixcalibuha.data_types import merge_calibration_classes
```
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Arguments of this example:
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+:param str statistical_measure:
+ Measure to calculate the scalar of the objective,
+ One of the supported methods in
+ ebcpy.utils.statistics_analyzer.StatisticsAnalyzer
+ e.g. RMSE, MAE, NRMSE
+:param bool multiple_classes:
+ If False, all CalibrationClasses will have the
+ same name
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+statistical_measure = "NRMSE"
+multiple_classes = True
+```
+
## Tuner Parameters
Tuner parameters are the optimization variables we will be
changing to match the simulated onto the measured output.
diff --git a/examples/docs/e2_B_optimization_problem_definition.md b/examples/docs/e2_B_optimization_problem_definition.md
index 6bb4d193..e8512779 100644
--- a/examples/docs/e2_B_optimization_problem_definition.md
+++ b/examples/docs/e2_B_optimization_problem_definition.md
@@ -28,6 +28,28 @@ from aixcalibuha import TunerParas, Goals, \
from aixcalibuha.data_types import merge_calibration_classes
```
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Arguments of this example:
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+:param str statistical_measure:
+ Measure to calculate the scalar of the objective,
+ One of the supported methods in
+ ebcpy.utils.statistics_analyzer.StatisticsAnalyzer
+ e.g. RMSE, MAE, NRMSE
+:param bool multiple_classes:
+ If False, all CalibrationClasses will have the
+ same name
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+statistical_measure = "NRMSE"
+multiple_classes = True
+```
+
## Tuner Parameters
Tuner parameters are the optimization variables we will be
changing to match the simulated onto the measured output.
diff --git a/examples/docs/e3_1_verbose_sensitivity_analysis_example.md b/examples/docs/e3_1_verbose_sensitivity_analysis_example.md
new file mode 100644
index 00000000..97f623fd
--- /dev/null
+++ b/examples/docs/e3_1_verbose_sensitivity_analysis_example.md
@@ -0,0 +1,519 @@
+
+## Example 3 verbose sensitivity analysis
+for the analysis of your model and the calibration process
+
+Goals of this part of the examples:
+1. Learn how to execute a verbose sensitivity analysis
+2. Learn the meaning of the results and the analysis of your model
+3 Learn how to use the results to select tuner-parameters for a calibration
+4. Learn other sensitivity methods and compare them
+5. Learn how to execute a time dependent sensitivity analysis
+6. Learn how to save the results for reproduction
+
+Start by importing all relevant packages
+
+```python
+import warnings
+import pathlib
+import matplotlib.pyplot as plt
+from aixcalibuha import SobolAnalyzer, FASTAnalyzer, MorrisAnalyzer
+from aixcalibuha.data_types import merge_calibration_classes
+from examples import setup_fmu, setup_calibration_classes
+from aixcalibuha import plotting
+```
+
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Example process of a verbose sensitivity analysis for calibration and analysis porpoises.
+First, the sensitivity problem is constructed, in this example
+the `sobol` method is chosen.
+Afterward, the SenAnalyzer class is instantiated to run the
+sensitivity analysis in the next step.
+The result of this analysis is then printed to the user.
+and tuner-parameters are selected with these results.
+A comparison between different methods is shown.
+At the end the option to save a reproduction archive is shown.
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+ e.g. r"LOCAL_PATH_TO\AixCaliBuHA\examples"
+:param str example:
+ Which example to run, "A" or "B"
+:param int n_cpu:
+ Number of cores to use
+
+:return: A list of calibration classes
+:rtype: list
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+example: str = "B"
+n_cpu: int = 1
+```
+
+## Setup
+Set up the class according to the documentation.
+You just have to pass a valid simulation api and
+some further settings for the analysis.
+Let's first load the necessary simulation api:
+
+```python
+examples_dir = pathlib.Path(examples_dir)
+sim_api = setup_fmu(examples_dir=examples_dir, example=example, n_cpu=n_cpu)
+```
+
+To conduct a sensitivity analysis, we need to define calibration classes that
+encompass the objectives (goals) for which sensitivity is to be assessed. In this
+context, we'll explore distinct calibration classes corresponding to various
+states of the models. Initially, we establish a comprehensive calibration class
+that spans the entire duration, denoted as `global`. Subsequently, we can
+leverage simulations from this global class for other classes targeting
+specific time intervals within the overall global range.
+
+For the specific states of the models with distinct time intervals, we adopt the
+calibration classes from the second example and tailor them for the verbose
+sensitivity analysis. Alternatively, custom classes could be created directly
+function (`validation_class`), we opt to ignore it by using the variable
+assignment `_` and omit any associated text output of the second example.
+
+```python
+calibration_classes, _ = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=False
+)
+merged_calibration_classes = merge_calibration_classes(calibration_classes)
+merged_calibration_classes[0].name = 'global'
+```
+
+Now, we add the calibration classes for the different states of the system
+
+```python
+calibration_classes, _ = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=True
+)
+merged_calibration_classes.extend(merge_calibration_classes(calibration_classes))
+```
+
+This results in the following calibration classes where we merge the time intervals directly.
+We could have also merged them with an option in the `run()` function of the
+sensitivity analyzer classes.
+
+```python
+print("Calibration classes for sensitivity analysis:",
+ [c.name for c in merged_calibration_classes])
+```
+
+In our detailed sensitivity analysis, it is essential for all classes to share
+identical tuner parameters. This ensures that we can employ the same set of
+simulations for calculating sensitivity across various calibration classes.
+However, the final class in example B deviates in tuner parameters; hence, we
+reset them to align with the tuner parameters of the other classes.
+
+```python
+if example == 'B':
+ merged_calibration_classes[-1].tuner_paras = merged_calibration_classes[0].tuner_paras
+```
+
+With the definition of the calibration classes and the loaded `sim_api`,
+we now take a look at the different options for sensitivity analysis.
+First, we perform the `Sobol` method, which is the most powerful currently
+supported method, but is also the most computational demanding one.
+Afterward, we will compare the results of the different methods.
+
+## Example of Sobol method
+First, we instantiate the Analyzer with the `sim_api` and the number of samples.
+For this example we will use a small sample size to reduce the time needed,
+which will lead to inaccurate results.
+In the comparison of the different methods, we will discuss the required sample size.
+We will also define a working directory were all results of the analysis will be stored.
+Additionally, we can choose if the samples and corresponding simulation files will be saved.
+These files can later be loaded and used for analysis of different calibration classes
+without performing new simulations. The simulations during the sensitivity analysis are the
+main computational time factor.
+
+```python
+sen_analyzer = SobolAnalyzer(
+ sim_api=sim_api,
+ num_samples=8,
+ calc_second_order=True,
+ cd=examples_dir.joinpath('testzone', f'verbose_sen_{example}'),
+ save_files=True,
+ savepath_sim=examples_dir.joinpath('testzone', f'verbose_sen_{example}', 'files'),
+ suffix_files='csv'
+)
+```
+
+Now, we run the sensitivity analysis with the verbose option.
+With that, we not only get the results for combined target values,
+but we also get the results for every target value alone.
+Because we defined the first calibration class global
+and every calibration class has the same
+tuner-parameters, we can use the option `use_first_sim`,
+where only the first class will be simulated and the simulation files saved.
+These simulations will be used for all other classes.
+
+For that the simulations are loaded for each class
+and the statistical measure is then evaluated for the
+relevant time intervals of each class.
+
+When we load simulation files we can use multiprocessing for loading the simulation
+and evaluating the statistical measure for each class in their time intervals.
+This multiprocessing option is especially
+useful for large models and large simulation data,
+because only one simulation at a time is stored
+in memory for each process.
+This can prevent possible memory errors.
+
+We disable the automatic plot option here,
+but we save all results. Later we can use the plot function
+of the plotting module to plot the results.
+
+```python
+result, classes = sen_analyzer.run(calibration_classes=merged_calibration_classes,
+ verbose=True,
+ use_first_sim=True,
+ plot_result=False,
+ save_results=True,
+ n_cpu=n_cpu,
+ scale=False)
+```
+
+After running the sensitivity analysis you can see
+that the working directory was created and the result
+files were saved here. First, the folder "files" was
+created as the result of `save_files=True`. In there, the
+simulation files are stored in an own folder which
+name contains the name of the used calibration class.
+Additionally, the corresponding samples are stored.
+The simulations are coupled with their samples.
+As one results of the analysis, the statistical
+measure and corresponding sample are saved for each class.
+This information could be used for surrogate-based
+calibration, which is currently not implemented in `AixCaliBuHA`.
+The main results of the sensitivity analysis are the sensitivity
+measures stored in "SobolAnalyzer_results.csv"
+and "SobolAnalyzer_results_second_order.csv".
+dataframes. This is specific to the sobol method, all other
+of the sobol method with possibly other analysis variables.
+Let´s take a look at these results.
+
+The first result has as columns the tuner-parameters
+and a multi level index with three levels.
+The first level defines the calibration class.
+The second level defines the Goals (target values). The index `all`
+is for the result of the combined target values in the goals.
+The last level defines the result of the
+sensitivity measure for each class and goal.
+These analysis variables are specific for each method.
+For their exact meaning I refer to the documentation of the SALib or the literature.
+In this example you get a short overview in the comparison later.
+
+```python
+print("Result of the sensitivity analysis")
+print('First and total order results of sobol method')
+print(result[0].to_string())
+```
+
+The second result of the sobol method is for second order sensitive measures.
+These describe the interaction between two parameters,
+so this dataframe has a fourth index level "Interaction".
+In this level, the tuner-parameters are listed again.
+
+```python
+print('Second order results of sobol method')
+print(result[1].to_string())
+```
+
+For a better understanding of the results we will now plot them.
+
+## Plotting Sensitivity results
+We start with the result which were calculated with the small sample size.
+Let's plot the first and total order results. These results
+are specific for each single parameter
+For each calibration class, a figure is created
+which shows for each goal the first order sensitivity `S1`
+and the total order sensitivity `ST` combined. For the small
+sample size the results have large confidence
+intervals, which show that these results are inaccurate as we
+noted earlier due to the small sample size.
+
+```python
+plotting.plot_single(result[0])
+```
+
+The plotting of second order results is only useful
+and working for more than 2 parameters.
+So we only can take a look at them in example A.
+If you run example B we will skip the plot of the second order results
+and load some sensitivity results of example A.
+
+Let's take a look at the second order results `S2` of example A we just created.
+This analysis variable shows the interaction of two
+parameters, so we can plot them as a heatmap.
+We can see that the parameters have no interaction with
+themselves what is obvious. Also, we see that the
+values for p1,p2 and p2,p1 are the same.
+In the heatmap we can't visualize the confidence intervals,
+so we will also take a look at the interaction of
+one specific parameter.
+For that the `plotting` module has also a function
+which looks simular to the `S1` and `ST` plots.
+Here we see again large confidence intervals,
+so now we will load results which were calculated with
+a much higher sample number.
+
+```python
+if example == 'A':
+ plotting.heatmaps(result[1])
+ plotting.plot_single_second_order(result[1], 'rad.n')
+```
+
+## Loading results
+We will now load sensitivity results of example A .
+These results were produced with this example and
+a samples number N=1024 and calc_second_order=True
+
+```python
+result_sobol = SobolAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_A.csv')
+)
+result_sobol_2 = SobolAnalyzer.load_second_order_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_second_order_A.csv')
+)
+```
+
+For a better understanding we will only take a
+look at the global class and Electricity goal
+and plot `S1`, `ST` and `S2` in the same window.
+For that we can use the plot function
+with some optional options. This shows how you can easily customize
+these plots, and you can also chang everything
+on the axes of the plots.
+
+```python
+fig = plt.figure(figsize=plt.figaspect(1. / 4.), layout="constrained") # creating one figure
+subfigs = fig.subfigures(1, 3, wspace=0) # creating subfigures for each type of plot
+ax0 = subfigs[0].subplots() # plotting `S1` and `ST`
+plotting.plot_single(
+ result=result_sobol,
+ cal_classes=['global'],
+ goals=['Electricity'],
+ show_plot=False,
+ figs_axes=([subfigs[0]], [ax0]),
+ max_name_len=14
+)
+ax1 = subfigs[1].subplots() # plotting heatmap
+plotting.heatmap(
+ result_sobol_2,
+ cal_class='global',
+ goal='Electricity',
+ ax=ax1,
+ show_plot=False,
+ max_name_len=14
+)
+ax2 = subfigs[2].subplots() # plotting the interactions of one single parameter
+plotting.plot_single_second_order(
+ result=result_sobol_2,
+ para_name='rad.n',
+ show_plot=False,
+ cal_classes=['global'],
+ goals=['Electricity'],
+ figs_axes=([subfigs[2]], [ax2]),
+ max_name_len=14
+)
+plt.show()
+```
+
+Now, what can we see in these results? First, the
+confidence intervals are now much smaller, so that we
+can interpret and connect the different analysis variables.
+`S1` stands for the variance in the objective,
+which is caused by the variation of one parameter
+while all other parameters are constant. The sobol analysis
+variables are normalized with the total variance caused
+by all parameter variations together within
+their bounds. This means that when the parameters had
+no interactions the sum of all `S1` values would
+be 1. `ST` shows the resulting variance of a parameter
+with all his interactions. Let's take a look at the
+interaction between `n` and `G`, which are the highest.
+Here, the `S2` value of the interaction
+between `n` and `G` has a similar value to each difference
+of `S1` and `ST` from these parameters. This show how this
+difference corresponds to the interactions of a parameter.
+All other parameters have only a very small sensitivity.
+These are just some basics, to understand what option you
+have in `AixCaliBuAH`. For more information look up relevant literature.
+
+## Comparison of different Methods
+We will now take a short look at a comparison of the
+`Sobol`, `Fast` and `Morris` method in `AixCaliBuAH`.
+The `SALib` provides more methods, which can
+be implemented here in the future or on your own.
+We already took a look at the `Sobol` method,
+which can compute `S1`, `ST`, and `S2` with their confidence intervals.
+The sobol method needs for that `(2+2k)N` simulations
+where `k` is the number of parameters and `N` is the sample
+number. For variance-based methods `N` should be greater
+than 1000. The sobol method can also compute only `S1` and
+`ST` with calc_second_order=False and (1+k)N simulations.
+The FAST method is another variance-based
+method and only computes `S1` and `ST` with k*N simulations.
+`Sobol` and FAST should show simular results which is the
+case for example B but in example A the FAST method overestimates `ST` in some cases.
+In the right plots, the results for the `Morris` method are shown.
+These are based on the mean of derivatives which
+represents the analysis variables mu. In the estimation
+of the derivatives, only one parameter is changed at a time.
+mu_star is the mean of the absolut values of the derivatives
+and is an approximation of `ST` but
+needs only an `N` of over 100 and `(1+k)N` simulations.
+In this comparison, mu_star shows simular results to `ST`
+of the `Sobol` method, but it is not normalized.
+Last, sigma is computed which is the standard deviation and
+is a sign for a non-linear model or for interactions in the model.
+
+```python
+result_sobol = SobolAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', f'SobolAnalyzer_results_{example}.csv')
+)
+result_fast = FASTAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', f'FASTAnalyzer_results_{example}.csv')
+)
+result_morris = MorrisAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', f'MorrisAnalyzer_results_{example}.csv')
+)
+
+global_class = classes[0]
+fig_comp = plt.figure(figsize=plt.figaspect(1. / 4.), layout="constrained")
+subfigs_comp = fig_comp.subfigures(1, 3, wspace=0)
+ax0_comp = subfigs_comp[0].subplots(3, 1, sharex=True)
+plotting.plot_single(
+ result=result_sobol,
+ cal_classes=[global_class.name],
+ show_plot=False,
+ figs_axes=([subfigs_comp[0]], [ax0_comp])
+)
+subfigs_comp[0].suptitle("Sobol")
+ax1_comp = subfigs_comp[1].subplots(3, 1, sharex=True)
+plotting.plot_single(
+ result=result_fast,
+ cal_classes=[global_class.name],
+ show_plot=False,
+ figs_axes=([subfigs_comp[1]], [ax1_comp])
+)
+subfigs_comp[1].suptitle("FAST")
+ax2_comp = subfigs_comp[2].subplots(3, 1, sharex=True)
+plotting.plot_single(
+ result=result_morris,
+ show_plot=False,
+ cal_classes=[global_class.name],
+ figs_axes=([subfigs_comp[2]], [ax2_comp])
+)
+subfigs_comp[2].suptitle("Morris")
+plt.show()
+```
+
+## Selection of tuner-parameters based on verbose sensitivity results
+We can now also use these verbose sensitivity
+results for a selection of relevant tuner parameters.
+We already saw that our models have interactions,
+so it will be necessary to calibrate them together
+in one calibration class. The calibration class
+global can be used for that because it includes all
+the other specific classes. But in the sensitivity
+results of this class it could be that parameters,
+which are only in one state sensitive can be missed.
+We can use the verbose sensitivity results so
+that a parameter will be selected when it has a
+sensitivity at least in one class and target value
+of the sensitivity results. This is enough that
+the parameter can be calibrated.
+Here, we will use `S1` because it is normalized instead of mu_star,
+and we can set on single threshold for all classes and goals.
+Also, if a parameter is only through interaction sensitive (only in `ST`)
+A real value can't be found and the parameters which interact will
+compensate each others deviation to a real world value.
+This still can happen when we choose with `S1` but it is less likely.
+
+```python
+calibration_class = SobolAnalyzer.select_by_threshold_verbose(classes[0],
+ result=result_sobol,
+ analysis_variable='S1',
+ threshold=0.001,
+ )
+
+print(calibration_class.tuner_paras)
+```
+
+## Time dependent sensitivity
+For analysis porpoises we can also evaluate the time dependent sensitivity
+instead of the sensitivity of larger time intervals. But these results can not yet
+be used to automatically selected tuner parameters for a calibration.
+The function for the time dependent sensitivity is similar to the other one.
+So we can also use the simulations of booth run functions for each other.
+The main difference is that we only need one calibration class from which
+the measured target data is not used and the sensitivity is directly
+calculated for the change of the separate target values and no combined goals.
+In the results, we then get just the additional index time.
+
+```python
+with warnings.catch_warnings():
+ warnings.simplefilter("ignore") # ignoring warnings that are caused by the low sample size
+ result = sen_analyzer.run_time_dependent(
+ cal_class=merged_calibration_classes[0],
+ load_sim_files=True,
+ plot_result=True
+ )
+print(result)
+```
+
+When we use the plot function we can see the sensitivity of each parameter
+changing over time. We can also see that the confidence intervals
+are again large for such a small sample size. When the confidence interval is larger
+than one an info is shown and the confidence interval of the previous
+time step is used to smooth it out for the visualisation.
+Let's load again results which were created with a larger sample size.
+
+## Loading time dependent results
+These results were produced with a samples number `N=1024` and `calc_second_order=True`
+
+```python
+result_sobol_time = SobolAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_time_A.csv')
+)
+result_sobol_2_time = SobolAnalyzer.load_second_order_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_second_order_time_A.csv')
+)
+plotting.plot_time_dependent(result=result_sobol_time, plot_conf=True, show_plot=False)
+```
+
+Now the confidence intervals are smaller and
+only at one time step they are still lager than 1.
+We can also see that the parameter `theCon.G` has the biggest influence.
+So we can take a closer look at this parameter with another plot
+function where all available sensitivity measures are plotted together.
+Second order results are plotted cumulative on top of `S1`.
+This resembles the definition of `ST = S1 + sum(S2_i) + sum(S3_i) + ...`
+
+```python
+plotting.plot_parameter_verbose(parameter='theCon.G',
+ single_result=result_sobol_time,
+ second_order_result=result_sobol_2_time)
+```
+
+At the end we also can create a reproduction
+archive which saves all settings and all created files
+automatically with the reproduction function of ebcpy.
+Not running in jupyter notebook
+
+```python
+file = sen_analyzer.save_for_reproduction(
+ title="SenAnalyzerTest",
+ path=examples_dir.joinpath('testzone'),
+ log_message="This is just an example",
+ remove_saved_files=False,
+ exclude_sim_files=True
+)
+print("ZIP-File to reproduce all this:", file)
+```
diff --git a/examples/docs/e3_2_verbose_dymola_sensitivity_analysis_example.md b/examples/docs/e3_2_verbose_dymola_sensitivity_analysis_example.md
new file mode 100644
index 00000000..f909b0fe
--- /dev/null
+++ b/examples/docs/e3_2_verbose_dymola_sensitivity_analysis_example.md
@@ -0,0 +1,128 @@
+
+# Example 3 sensitivity analysis with dymola api
+
+Goals of this part of the examples:
+1. Learn how to execute a sensitivity analysis with the dymola api
+
+
+```python
+import os
+import pathlib
+from examples import setup_dym_api, setup_calibration_classes
+```
+
+Import a valid analyzer, e.g. `SobolAnalyzer`
+
+```python
+from aixcalibuha import SobolAnalyzer
+from aixcalibuha.data_types import merge_calibration_classes
+from ebcpy.utils.conversion import convert_tsd_to_modelica_txt
+```
+
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Example process of a verbose sensitivity analysis with the dymola_api.
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+:param [pathlib.Path, str] aixlib_mo:
+ Path to the AixLib package.mo file.
+:param str example:
+ Which example to run, "A" or "B"
+:param int n_cpu:
+ Number of cores to use
+
+:return: A list of calibration classes
+:rtype: list
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+aixlib_mo = "TODO: Add a valid input according to the docstring above"
+example: str = "B"
+n_cpu: int = 1
+```
+
+## Setup
+Using a dymola api instead of the fmu api
+
+```python
+examples_dir = pathlib.Path(examples_dir)
+sim_api = setup_dym_api(examples_dir=examples_dir,
+ aixlib_mo=aixlib_mo,
+ example=example,
+ n_cpu=n_cpu)
+calibration_classes = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=False
+)[0]
+merged_calibration_classes = merge_calibration_classes(calibration_classes)
+merged_calibration_classes[0].name = 'global'
+calibration_classes = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=True
+)[0]
+merged_calibration_classes.extend(merge_calibration_classes(calibration_classes))
+
+if example == 'B':
+ merged_calibration_classes[-1].tuner_paras = merged_calibration_classes[0].tuner_paras
+```
+
+## Example of Sobol method
+Set up Sobol analyzer
+
+```python
+sen_analyzer = SobolAnalyzer(
+ sim_api=sim_api,
+ num_samples=2,
+ calc_second_order=True,
+ cd=examples_dir.joinpath('testzone', f'verbose_sen_dymola_{example}'),
+ save_files=True,
+ load_files=False,
+ savepath_sim=examples_dir.joinpath('testzone', f'verbose_sen_dymola_{example}', 'files'),
+ suffix_files='mat'
+)
+```
+
+The only difference to the fmu example is the handling of inputs.
+There we have in the model now a table for the inputs and generate
+an input file here. This is only necessary for example A because
+example B has no inputs. To generate the input in the correct format,
+use the convert_tsd_to_modelica_txt function:
+
+```python
+if example == "A":
+ table_name = "InputTDryBul"
+ file_name = r"D:\dymola_inputs_A.txt"
+ print(file_name)
+ filepath = convert_tsd_to_modelica_txt(
+ tsd=merged_calibration_classes[0].inputs,
+ table_name=table_name,
+ save_path_file=file_name
+ )
+ # Now we can remove the input from the old fmu calibration classes
+ for cal_class in merged_calibration_classes:
+ cal_class._inputs = None
+ print("Successfully created Dymola input file at", filepath)
+```
+
+run sensitivity analysis
+
+```python
+result, classes = sen_analyzer.run(calibration_classes=merged_calibration_classes,
+ verbose=True,
+ use_first_sim=True,
+ plot_result=True,
+ save_results=True,
+ n_cpu=2)
+print("Result of the sensitivity analysis")
+print('First and total order results of sobol method')
+print(result[0].to_string())
+print('Second order results of sobol method')
+print(result[1].to_string())
+```
+
+remove input file
+
+```python
+if example == "A":
+ os.remove(file_name)
+```
diff --git a/examples/docs/e3_sensitivity_analysis_example.md b/examples/docs/e3_sensitivity_analysis_example.md
index 1e0e99a0..6278a1ab 100644
--- a/examples/docs/e3_sensitivity_analysis_example.md
+++ b/examples/docs/e3_sensitivity_analysis_example.md
@@ -11,6 +11,34 @@ Import a valid analyzer, e.g. `SobolAnalyzer`
from aixcalibuha import SobolAnalyzer
```
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Example process of a sensitivity analysis.
+First, the sensitivity problem is constructed, in this example
+the `morris` method is chosen.
+Afterward, the sen_analyzer class is instantiated to run the
+sensitivity analysis in the next step.
+The result of this analysis is then printed to the user.
+The automatic_select function is presented as-well, using a threshold of 1
+and the default `mu_star` criterion.
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+:param str example:
+ Which example to run, "A" or "B"
+:param int n_cpu:
+ Number of cores to use
+
+:return: A list of calibration classes
+:rtype: list
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+example: str = "B"
+n_cpu: int = 1
+```
+
## Setup
Setup the class according to the documentation.
You just have to pass a valid simulation api and
@@ -24,8 +52,7 @@ sim_api = setup_fmu(examples_dir=examples_dir, example=example, n_cpu=n_cpu)
sen_analyzer = SobolAnalyzer(
sim_api=sim_api,
num_samples=10,
- cd=sim_api.cd,
- analysis_variable='S1'
+ cd=sim_api.cd
)
```
@@ -36,7 +63,9 @@ calibration_classes = setup_calibration_classes(
examples_dir=examples_dir, example=example
)[0]
-result, classes = sen_analyzer.run(calibration_classes=calibration_classes)
+result, classes = sen_analyzer.run(calibration_classes=calibration_classes,
+ plot_result=True,
+ save_results=False)
print("Result of the sensitivity analysis")
print(result)
```
@@ -49,8 +78,9 @@ to remove complexity from our calibration problem:
```python
print("Selecting relevant tuner-parameters using a fixed threshold:")
sen_analyzer.select_by_threshold(calibration_classes=classes,
- result=result,
- threshold=0.01)
+ result=result[0],
+ threshold=0.01,
+ analysis_variable='S1')
for cal_class in classes:
print(f"Class '{cal_class.name}' with parameters:\n{cal_class.tuner_paras}")
```
diff --git a/examples/docs/e3_verbose_dymola_sensitivity_analysis_example.md b/examples/docs/e3_verbose_dymola_sensitivity_analysis_example.md
new file mode 100644
index 00000000..f909b0fe
--- /dev/null
+++ b/examples/docs/e3_verbose_dymola_sensitivity_analysis_example.md
@@ -0,0 +1,128 @@
+
+# Example 3 sensitivity analysis with dymola api
+
+Goals of this part of the examples:
+1. Learn how to execute a sensitivity analysis with the dymola api
+
+
+```python
+import os
+import pathlib
+from examples import setup_dym_api, setup_calibration_classes
+```
+
+Import a valid analyzer, e.g. `SobolAnalyzer`
+
+```python
+from aixcalibuha import SobolAnalyzer
+from aixcalibuha.data_types import merge_calibration_classes
+from ebcpy.utils.conversion import convert_tsd_to_modelica_txt
+```
+
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Example process of a verbose sensitivity analysis with the dymola_api.
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+:param [pathlib.Path, str] aixlib_mo:
+ Path to the AixLib package.mo file.
+:param str example:
+ Which example to run, "A" or "B"
+:param int n_cpu:
+ Number of cores to use
+
+:return: A list of calibration classes
+:rtype: list
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+aixlib_mo = "TODO: Add a valid input according to the docstring above"
+example: str = "B"
+n_cpu: int = 1
+```
+
+## Setup
+Using a dymola api instead of the fmu api
+
+```python
+examples_dir = pathlib.Path(examples_dir)
+sim_api = setup_dym_api(examples_dir=examples_dir,
+ aixlib_mo=aixlib_mo,
+ example=example,
+ n_cpu=n_cpu)
+calibration_classes = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=False
+)[0]
+merged_calibration_classes = merge_calibration_classes(calibration_classes)
+merged_calibration_classes[0].name = 'global'
+calibration_classes = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=True
+)[0]
+merged_calibration_classes.extend(merge_calibration_classes(calibration_classes))
+
+if example == 'B':
+ merged_calibration_classes[-1].tuner_paras = merged_calibration_classes[0].tuner_paras
+```
+
+## Example of Sobol method
+Set up Sobol analyzer
+
+```python
+sen_analyzer = SobolAnalyzer(
+ sim_api=sim_api,
+ num_samples=2,
+ calc_second_order=True,
+ cd=examples_dir.joinpath('testzone', f'verbose_sen_dymola_{example}'),
+ save_files=True,
+ load_files=False,
+ savepath_sim=examples_dir.joinpath('testzone', f'verbose_sen_dymola_{example}', 'files'),
+ suffix_files='mat'
+)
+```
+
+The only difference to the fmu example is the handling of inputs.
+There we have in the model now a table for the inputs and generate
+an input file here. This is only necessary for example A because
+example B has no inputs. To generate the input in the correct format,
+use the convert_tsd_to_modelica_txt function:
+
+```python
+if example == "A":
+ table_name = "InputTDryBul"
+ file_name = r"D:\dymola_inputs_A.txt"
+ print(file_name)
+ filepath = convert_tsd_to_modelica_txt(
+ tsd=merged_calibration_classes[0].inputs,
+ table_name=table_name,
+ save_path_file=file_name
+ )
+ # Now we can remove the input from the old fmu calibration classes
+ for cal_class in merged_calibration_classes:
+ cal_class._inputs = None
+ print("Successfully created Dymola input file at", filepath)
+```
+
+run sensitivity analysis
+
+```python
+result, classes = sen_analyzer.run(calibration_classes=merged_calibration_classes,
+ verbose=True,
+ use_first_sim=True,
+ plot_result=True,
+ save_results=True,
+ n_cpu=2)
+print("Result of the sensitivity analysis")
+print('First and total order results of sobol method')
+print(result[0].to_string())
+print('Second order results of sobol method')
+print(result[1].to_string())
+```
+
+remove input file
+
+```python
+if example == "A":
+ os.remove(file_name)
+```
diff --git a/examples/docs/e3_verbose_sensitivity_analysis_example.md b/examples/docs/e3_verbose_sensitivity_analysis_example.md
new file mode 100644
index 00000000..97f623fd
--- /dev/null
+++ b/examples/docs/e3_verbose_sensitivity_analysis_example.md
@@ -0,0 +1,519 @@
+
+## Example 3 verbose sensitivity analysis
+for the analysis of your model and the calibration process
+
+Goals of this part of the examples:
+1. Learn how to execute a verbose sensitivity analysis
+2. Learn the meaning of the results and the analysis of your model
+3 Learn how to use the results to select tuner-parameters for a calibration
+4. Learn other sensitivity methods and compare them
+5. Learn how to execute a time dependent sensitivity analysis
+6. Learn how to save the results for reproduction
+
+Start by importing all relevant packages
+
+```python
+import warnings
+import pathlib
+import matplotlib.pyplot as plt
+from aixcalibuha import SobolAnalyzer, FASTAnalyzer, MorrisAnalyzer
+from aixcalibuha.data_types import merge_calibration_classes
+from examples import setup_fmu, setup_calibration_classes
+from aixcalibuha import plotting
+```
+
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Example process of a verbose sensitivity analysis for calibration and analysis porpoises.
+First, the sensitivity problem is constructed, in this example
+the `sobol` method is chosen.
+Afterward, the SenAnalyzer class is instantiated to run the
+sensitivity analysis in the next step.
+The result of this analysis is then printed to the user.
+and tuner-parameters are selected with these results.
+A comparison between different methods is shown.
+At the end the option to save a reproduction archive is shown.
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+ e.g. r"LOCAL_PATH_TO\AixCaliBuHA\examples"
+:param str example:
+ Which example to run, "A" or "B"
+:param int n_cpu:
+ Number of cores to use
+
+:return: A list of calibration classes
+:rtype: list
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+example: str = "B"
+n_cpu: int = 1
+```
+
+## Setup
+Set up the class according to the documentation.
+You just have to pass a valid simulation api and
+some further settings for the analysis.
+Let's first load the necessary simulation api:
+
+```python
+examples_dir = pathlib.Path(examples_dir)
+sim_api = setup_fmu(examples_dir=examples_dir, example=example, n_cpu=n_cpu)
+```
+
+To conduct a sensitivity analysis, we need to define calibration classes that
+encompass the objectives (goals) for which sensitivity is to be assessed. In this
+context, we'll explore distinct calibration classes corresponding to various
+states of the models. Initially, we establish a comprehensive calibration class
+that spans the entire duration, denoted as `global`. Subsequently, we can
+leverage simulations from this global class for other classes targeting
+specific time intervals within the overall global range.
+
+For the specific states of the models with distinct time intervals, we adopt the
+calibration classes from the second example and tailor them for the verbose
+sensitivity analysis. Alternatively, custom classes could be created directly
+function (`validation_class`), we opt to ignore it by using the variable
+assignment `_` and omit any associated text output of the second example.
+
+```python
+calibration_classes, _ = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=False
+)
+merged_calibration_classes = merge_calibration_classes(calibration_classes)
+merged_calibration_classes[0].name = 'global'
+```
+
+Now, we add the calibration classes for the different states of the system
+
+```python
+calibration_classes, _ = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=True
+)
+merged_calibration_classes.extend(merge_calibration_classes(calibration_classes))
+```
+
+This results in the following calibration classes where we merge the time intervals directly.
+We could have also merged them with an option in the `run()` function of the
+sensitivity analyzer classes.
+
+```python
+print("Calibration classes for sensitivity analysis:",
+ [c.name for c in merged_calibration_classes])
+```
+
+In our detailed sensitivity analysis, it is essential for all classes to share
+identical tuner parameters. This ensures that we can employ the same set of
+simulations for calculating sensitivity across various calibration classes.
+However, the final class in example B deviates in tuner parameters; hence, we
+reset them to align with the tuner parameters of the other classes.
+
+```python
+if example == 'B':
+ merged_calibration_classes[-1].tuner_paras = merged_calibration_classes[0].tuner_paras
+```
+
+With the definition of the calibration classes and the loaded `sim_api`,
+we now take a look at the different options for sensitivity analysis.
+First, we perform the `Sobol` method, which is the most powerful currently
+supported method, but is also the most computational demanding one.
+Afterward, we will compare the results of the different methods.
+
+## Example of Sobol method
+First, we instantiate the Analyzer with the `sim_api` and the number of samples.
+For this example we will use a small sample size to reduce the time needed,
+which will lead to inaccurate results.
+In the comparison of the different methods, we will discuss the required sample size.
+We will also define a working directory were all results of the analysis will be stored.
+Additionally, we can choose if the samples and corresponding simulation files will be saved.
+These files can later be loaded and used for analysis of different calibration classes
+without performing new simulations. The simulations during the sensitivity analysis are the
+main computational time factor.
+
+```python
+sen_analyzer = SobolAnalyzer(
+ sim_api=sim_api,
+ num_samples=8,
+ calc_second_order=True,
+ cd=examples_dir.joinpath('testzone', f'verbose_sen_{example}'),
+ save_files=True,
+ savepath_sim=examples_dir.joinpath('testzone', f'verbose_sen_{example}', 'files'),
+ suffix_files='csv'
+)
+```
+
+Now, we run the sensitivity analysis with the verbose option.
+With that, we not only get the results for combined target values,
+but we also get the results for every target value alone.
+Because we defined the first calibration class global
+and every calibration class has the same
+tuner-parameters, we can use the option `use_first_sim`,
+where only the first class will be simulated and the simulation files saved.
+These simulations will be used for all other classes.
+
+For that the simulations are loaded for each class
+and the statistical measure is then evaluated for the
+relevant time intervals of each class.
+
+When we load simulation files we can use multiprocessing for loading the simulation
+and evaluating the statistical measure for each class in their time intervals.
+This multiprocessing option is especially
+useful for large models and large simulation data,
+because only one simulation at a time is stored
+in memory for each process.
+This can prevent possible memory errors.
+
+We disable the automatic plot option here,
+but we save all results. Later we can use the plot function
+of the plotting module to plot the results.
+
+```python
+result, classes = sen_analyzer.run(calibration_classes=merged_calibration_classes,
+ verbose=True,
+ use_first_sim=True,
+ plot_result=False,
+ save_results=True,
+ n_cpu=n_cpu,
+ scale=False)
+```
+
+After running the sensitivity analysis you can see
+that the working directory was created and the result
+files were saved here. First, the folder "files" was
+created as the result of `save_files=True`. In there, the
+simulation files are stored in an own folder which
+name contains the name of the used calibration class.
+Additionally, the corresponding samples are stored.
+The simulations are coupled with their samples.
+As one results of the analysis, the statistical
+measure and corresponding sample are saved for each class.
+This information could be used for surrogate-based
+calibration, which is currently not implemented in `AixCaliBuHA`.
+The main results of the sensitivity analysis are the sensitivity
+measures stored in "SobolAnalyzer_results.csv"
+and "SobolAnalyzer_results_second_order.csv".
+dataframes. This is specific to the sobol method, all other
+of the sobol method with possibly other analysis variables.
+Let´s take a look at these results.
+
+The first result has as columns the tuner-parameters
+and a multi level index with three levels.
+The first level defines the calibration class.
+The second level defines the Goals (target values). The index `all`
+is for the result of the combined target values in the goals.
+The last level defines the result of the
+sensitivity measure for each class and goal.
+These analysis variables are specific for each method.
+For their exact meaning I refer to the documentation of the SALib or the literature.
+In this example you get a short overview in the comparison later.
+
+```python
+print("Result of the sensitivity analysis")
+print('First and total order results of sobol method')
+print(result[0].to_string())
+```
+
+The second result of the sobol method is for second order sensitive measures.
+These describe the interaction between two parameters,
+so this dataframe has a fourth index level "Interaction".
+In this level, the tuner-parameters are listed again.
+
+```python
+print('Second order results of sobol method')
+print(result[1].to_string())
+```
+
+For a better understanding of the results we will now plot them.
+
+## Plotting Sensitivity results
+We start with the result which were calculated with the small sample size.
+Let's plot the first and total order results. These results
+are specific for each single parameter
+For each calibration class, a figure is created
+which shows for each goal the first order sensitivity `S1`
+and the total order sensitivity `ST` combined. For the small
+sample size the results have large confidence
+intervals, which show that these results are inaccurate as we
+noted earlier due to the small sample size.
+
+```python
+plotting.plot_single(result[0])
+```
+
+The plotting of second order results is only useful
+and working for more than 2 parameters.
+So we only can take a look at them in example A.
+If you run example B we will skip the plot of the second order results
+and load some sensitivity results of example A.
+
+Let's take a look at the second order results `S2` of example A we just created.
+This analysis variable shows the interaction of two
+parameters, so we can plot them as a heatmap.
+We can see that the parameters have no interaction with
+themselves what is obvious. Also, we see that the
+values for p1,p2 and p2,p1 are the same.
+In the heatmap we can't visualize the confidence intervals,
+so we will also take a look at the interaction of
+one specific parameter.
+For that the `plotting` module has also a function
+which looks simular to the `S1` and `ST` plots.
+Here we see again large confidence intervals,
+so now we will load results which were calculated with
+a much higher sample number.
+
+```python
+if example == 'A':
+ plotting.heatmaps(result[1])
+ plotting.plot_single_second_order(result[1], 'rad.n')
+```
+
+## Loading results
+We will now load sensitivity results of example A .
+These results were produced with this example and
+a samples number N=1024 and calc_second_order=True
+
+```python
+result_sobol = SobolAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_A.csv')
+)
+result_sobol_2 = SobolAnalyzer.load_second_order_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_second_order_A.csv')
+)
+```
+
+For a better understanding we will only take a
+look at the global class and Electricity goal
+and plot `S1`, `ST` and `S2` in the same window.
+For that we can use the plot function
+with some optional options. This shows how you can easily customize
+these plots, and you can also chang everything
+on the axes of the plots.
+
+```python
+fig = plt.figure(figsize=plt.figaspect(1. / 4.), layout="constrained") # creating one figure
+subfigs = fig.subfigures(1, 3, wspace=0) # creating subfigures for each type of plot
+ax0 = subfigs[0].subplots() # plotting `S1` and `ST`
+plotting.plot_single(
+ result=result_sobol,
+ cal_classes=['global'],
+ goals=['Electricity'],
+ show_plot=False,
+ figs_axes=([subfigs[0]], [ax0]),
+ max_name_len=14
+)
+ax1 = subfigs[1].subplots() # plotting heatmap
+plotting.heatmap(
+ result_sobol_2,
+ cal_class='global',
+ goal='Electricity',
+ ax=ax1,
+ show_plot=False,
+ max_name_len=14
+)
+ax2 = subfigs[2].subplots() # plotting the interactions of one single parameter
+plotting.plot_single_second_order(
+ result=result_sobol_2,
+ para_name='rad.n',
+ show_plot=False,
+ cal_classes=['global'],
+ goals=['Electricity'],
+ figs_axes=([subfigs[2]], [ax2]),
+ max_name_len=14
+)
+plt.show()
+```
+
+Now, what can we see in these results? First, the
+confidence intervals are now much smaller, so that we
+can interpret and connect the different analysis variables.
+`S1` stands for the variance in the objective,
+which is caused by the variation of one parameter
+while all other parameters are constant. The sobol analysis
+variables are normalized with the total variance caused
+by all parameter variations together within
+their bounds. This means that when the parameters had
+no interactions the sum of all `S1` values would
+be 1. `ST` shows the resulting variance of a parameter
+with all his interactions. Let's take a look at the
+interaction between `n` and `G`, which are the highest.
+Here, the `S2` value of the interaction
+between `n` and `G` has a similar value to each difference
+of `S1` and `ST` from these parameters. This show how this
+difference corresponds to the interactions of a parameter.
+All other parameters have only a very small sensitivity.
+These are just some basics, to understand what option you
+have in `AixCaliBuAH`. For more information look up relevant literature.
+
+## Comparison of different Methods
+We will now take a short look at a comparison of the
+`Sobol`, `Fast` and `Morris` method in `AixCaliBuAH`.
+The `SALib` provides more methods, which can
+be implemented here in the future or on your own.
+We already took a look at the `Sobol` method,
+which can compute `S1`, `ST`, and `S2` with their confidence intervals.
+The sobol method needs for that `(2+2k)N` simulations
+where `k` is the number of parameters and `N` is the sample
+number. For variance-based methods `N` should be greater
+than 1000. The sobol method can also compute only `S1` and
+`ST` with calc_second_order=False and (1+k)N simulations.
+The FAST method is another variance-based
+method and only computes `S1` and `ST` with k*N simulations.
+`Sobol` and FAST should show simular results which is the
+case for example B but in example A the FAST method overestimates `ST` in some cases.
+In the right plots, the results for the `Morris` method are shown.
+These are based on the mean of derivatives which
+represents the analysis variables mu. In the estimation
+of the derivatives, only one parameter is changed at a time.
+mu_star is the mean of the absolut values of the derivatives
+and is an approximation of `ST` but
+needs only an `N` of over 100 and `(1+k)N` simulations.
+In this comparison, mu_star shows simular results to `ST`
+of the `Sobol` method, but it is not normalized.
+Last, sigma is computed which is the standard deviation and
+is a sign for a non-linear model or for interactions in the model.
+
+```python
+result_sobol = SobolAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', f'SobolAnalyzer_results_{example}.csv')
+)
+result_fast = FASTAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', f'FASTAnalyzer_results_{example}.csv')
+)
+result_morris = MorrisAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', f'MorrisAnalyzer_results_{example}.csv')
+)
+
+global_class = classes[0]
+fig_comp = plt.figure(figsize=plt.figaspect(1. / 4.), layout="constrained")
+subfigs_comp = fig_comp.subfigures(1, 3, wspace=0)
+ax0_comp = subfigs_comp[0].subplots(3, 1, sharex=True)
+plotting.plot_single(
+ result=result_sobol,
+ cal_classes=[global_class.name],
+ show_plot=False,
+ figs_axes=([subfigs_comp[0]], [ax0_comp])
+)
+subfigs_comp[0].suptitle("Sobol")
+ax1_comp = subfigs_comp[1].subplots(3, 1, sharex=True)
+plotting.plot_single(
+ result=result_fast,
+ cal_classes=[global_class.name],
+ show_plot=False,
+ figs_axes=([subfigs_comp[1]], [ax1_comp])
+)
+subfigs_comp[1].suptitle("FAST")
+ax2_comp = subfigs_comp[2].subplots(3, 1, sharex=True)
+plotting.plot_single(
+ result=result_morris,
+ show_plot=False,
+ cal_classes=[global_class.name],
+ figs_axes=([subfigs_comp[2]], [ax2_comp])
+)
+subfigs_comp[2].suptitle("Morris")
+plt.show()
+```
+
+## Selection of tuner-parameters based on verbose sensitivity results
+We can now also use these verbose sensitivity
+results for a selection of relevant tuner parameters.
+We already saw that our models have interactions,
+so it will be necessary to calibrate them together
+in one calibration class. The calibration class
+global can be used for that because it includes all
+the other specific classes. But in the sensitivity
+results of this class it could be that parameters,
+which are only in one state sensitive can be missed.
+We can use the verbose sensitivity results so
+that a parameter will be selected when it has a
+sensitivity at least in one class and target value
+of the sensitivity results. This is enough that
+the parameter can be calibrated.
+Here, we will use `S1` because it is normalized instead of mu_star,
+and we can set on single threshold for all classes and goals.
+Also, if a parameter is only through interaction sensitive (only in `ST`)
+A real value can't be found and the parameters which interact will
+compensate each others deviation to a real world value.
+This still can happen when we choose with `S1` but it is less likely.
+
+```python
+calibration_class = SobolAnalyzer.select_by_threshold_verbose(classes[0],
+ result=result_sobol,
+ analysis_variable='S1',
+ threshold=0.001,
+ )
+
+print(calibration_class.tuner_paras)
+```
+
+## Time dependent sensitivity
+For analysis porpoises we can also evaluate the time dependent sensitivity
+instead of the sensitivity of larger time intervals. But these results can not yet
+be used to automatically selected tuner parameters for a calibration.
+The function for the time dependent sensitivity is similar to the other one.
+So we can also use the simulations of booth run functions for each other.
+The main difference is that we only need one calibration class from which
+the measured target data is not used and the sensitivity is directly
+calculated for the change of the separate target values and no combined goals.
+In the results, we then get just the additional index time.
+
+```python
+with warnings.catch_warnings():
+ warnings.simplefilter("ignore") # ignoring warnings that are caused by the low sample size
+ result = sen_analyzer.run_time_dependent(
+ cal_class=merged_calibration_classes[0],
+ load_sim_files=True,
+ plot_result=True
+ )
+print(result)
+```
+
+When we use the plot function we can see the sensitivity of each parameter
+changing over time. We can also see that the confidence intervals
+are again large for such a small sample size. When the confidence interval is larger
+than one an info is shown and the confidence interval of the previous
+time step is used to smooth it out for the visualisation.
+Let's load again results which were created with a larger sample size.
+
+## Loading time dependent results
+These results were produced with a samples number `N=1024` and `calc_second_order=True`
+
+```python
+result_sobol_time = SobolAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_time_A.csv')
+)
+result_sobol_2_time = SobolAnalyzer.load_second_order_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_second_order_time_A.csv')
+)
+plotting.plot_time_dependent(result=result_sobol_time, plot_conf=True, show_plot=False)
+```
+
+Now the confidence intervals are smaller and
+only at one time step they are still lager than 1.
+We can also see that the parameter `theCon.G` has the biggest influence.
+So we can take a closer look at this parameter with another plot
+function where all available sensitivity measures are plotted together.
+Second order results are plotted cumulative on top of `S1`.
+This resembles the definition of `ST = S1 + sum(S2_i) + sum(S3_i) + ...`
+
+```python
+plotting.plot_parameter_verbose(parameter='theCon.G',
+ single_result=result_sobol_time,
+ second_order_result=result_sobol_2_time)
+```
+
+At the end we also can create a reproduction
+archive which saves all settings and all created files
+automatically with the reproduction function of ebcpy.
+Not running in jupyter notebook
+
+```python
+file = sen_analyzer.save_for_reproduction(
+ title="SenAnalyzerTest",
+ path=examples_dir.joinpath('testzone'),
+ log_message="This is just an example",
+ remove_saved_files=False,
+ exclude_sim_files=True
+)
+print("ZIP-File to reproduce all this:", file)
+```
diff --git a/examples/docs/e4_calibration_example.md b/examples/docs/e4_calibration_example.md
index cd93f833..e2f5b74e 100644
--- a/examples/docs/e4_calibration_example.md
+++ b/examples/docs/e4_calibration_example.md
@@ -11,6 +11,46 @@ import numpy as np
from aixcalibuha import CalibrationClass, Calibrator, MultipleClassCalibrator
```
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Run an example for a calibration. Make sure you have Dymola installed
+on your device and a working licence. All output data will be stored in
+the current working directory of python. Look at the logs and plots
+to better understand what is happening in the calibration. If you want, you
+can switch the methods to other supported methods or change the framework and
+try the global optimizer of dlib.
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+:param str example:
+ Which example to run, "A" or "B"
+:param int n_cpu:
+ Number of cores to use
+
+Optional, for the fully automated process
+:param ebcpy.simulationapi.SimulationAPI sim_api:
+ Simulation API to simulate the models
+:param list[CalibrationClass] cal_classes:
+ List with multiple CalibrationClass objects for calibration. Goals and
+ TunerParameters have to be set. If only one class is provided (either
+ a list with one entry or a CalibrationClass object) the single-class
+ Calibrator is used.
+:param CalibrationClass framework:
+ See Documentation of ebcpy on available optimization frameworks
+:param str method:
+ See Documentation of ebcpy on available optimization framework methods
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+example = "B"
+n_cpu: int = 1
+sim_api = None
+cal_classes = None
+framework: str = "scipy_differential_evolution"
+method: str = "best1bin"
+```
+
## Setup
Start by loading the simulation api and the calibration classes
diff --git a/examples/docs/e5_automated_process.md b/examples/docs/e5_automated_process.md
index d57ac350..12e7ee37 100644
--- a/examples/docs/e5_automated_process.md
+++ b/examples/docs/e5_automated_process.md
@@ -11,6 +11,25 @@ from examples.e3_sensitivity_analysis_example import run_sensitivity_analysis
from examples.e4_calibration_example import run_calibration
```
+Please define the missing TODOs in the section below according to the docstrings.
+
+```python
+"""
+Arguments of this example:
+
+:param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+:param str example:
+ Whether to use example A (requires windows) or B.
+ Default is "A"
+:param int n_cpu:
+ Number of cores to use
+"""
+examples_dir = "TODO: Add a valid input according to the docstring above"
+example: str = "A"
+n_cpu: int = 1
+```
+
First we run the sensitivity analysis:
```python
diff --git a/examples/e3_1_verbose_sensitivity_analysis_example.py b/examples/e3_1_verbose_sensitivity_analysis_example.py
new file mode 100644
index 00000000..c93795f7
--- /dev/null
+++ b/examples/e3_1_verbose_sensitivity_analysis_example.py
@@ -0,0 +1,470 @@
+# ## Example 3 verbose sensitivity analysis
+# for the analysis of your model and the calibration process
+
+# Goals of this part of the examples:
+# 1. Learn how to execute a verbose sensitivity analysis
+# 2. Learn the meaning of the results and the analysis of your model
+# 3 Learn how to use the results to select tuner-parameters for a calibration
+# 4. Learn other sensitivity methods and compare them
+# 5. Learn how to execute a time dependent sensitivity analysis
+# 6. Learn how to save the results for reproduction
+
+# Start by importing all relevant packages
+import warnings
+import pathlib
+import matplotlib.pyplot as plt
+from aixcalibuha import SobolAnalyzer, FASTAnalyzer, MorrisAnalyzer
+from aixcalibuha.data_types import merge_calibration_classes
+from examples import setup_fmu, setup_calibration_classes
+from aixcalibuha import plotting
+
+
+def run_sensitivity_analysis(
+ examples_dir,
+ example: str = "B",
+ n_cpu: int = 1
+):
+ """
+ Example process of a verbose sensitivity analysis for calibration and analysis porpoises.
+ First, the sensitivity problem is constructed, in this example
+ the `sobol` method is chosen.
+ Afterward, the SenAnalyzer class is instantiated to run the
+ sensitivity analysis in the next step.
+ The result of this analysis is then printed to the user.
+ and tuner-parameters are selected with these results.
+ A comparison between different methods is shown.
+ At the end the option to save a reproduction archive is shown.
+
+ :param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+ e.g. r"LOCAL_PATH_TO\AixCaliBuHA\examples"
+ :param str example:
+ Which example to run, "A" or "B"
+ :param int n_cpu:
+ Number of cores to use
+
+ :return: A list of calibration classes
+ :rtype: list
+ """
+ # ## Setup
+ # Set up the class according to the documentation.
+ # You just have to pass a valid simulation api and
+ # some further settings for the analysis.
+ # Let's first load the necessary simulation api:
+ examples_dir = pathlib.Path(examples_dir)
+ sim_api = setup_fmu(examples_dir=examples_dir, example=example, n_cpu=n_cpu)
+ # To conduct a sensitivity analysis, we need to define calibration classes that
+ # encompass the objectives (goals) for which sensitivity is to be assessed. In this
+ # context, we'll explore distinct calibration classes corresponding to various
+ # states of the models. Initially, we establish a comprehensive calibration class
+ # that spans the entire duration, denoted as `global`. Subsequently, we can
+ # leverage simulations from this global class for other classes targeting
+ # specific time intervals within the overall global range.
+
+ # For the specific states of the models with distinct time intervals, we adopt the
+ # calibration classes from the second example and tailor them for the verbose
+ # sensitivity analysis. Alternatively, custom classes could be created directly
+ # at this stage. Given that we do not require the second return value of the
+ # function (`validation_class`), we opt to ignore it by using the variable
+ # assignment `_` and omit any associated text output of the second example.
+
+ calibration_classes, _ = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=False
+ )
+ merged_calibration_classes = merge_calibration_classes(calibration_classes)
+ merged_calibration_classes[0].name = 'global'
+ # Now, we add the calibration classes for the different states of the system
+ calibration_classes, _ = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=True
+ )
+ merged_calibration_classes.extend(merge_calibration_classes(calibration_classes))
+ # This results in the following calibration classes where we merge the time intervals directly.
+ # We could have also merged them with an option in the `run()` function of the
+ # sensitivity analyzer classes.
+ print("Calibration classes for sensitivity analysis:",
+ [c.name for c in merged_calibration_classes])
+
+ # In our detailed sensitivity analysis, it is essential for all classes to share
+ # identical tuner parameters. This ensures that we can employ the same set of
+ # simulations for calculating sensitivity across various calibration classes.
+ # However, the final class in example B deviates in tuner parameters; hence, we
+ # reset them to align with the tuner parameters of the other classes.
+
+ if example == 'B':
+ merged_calibration_classes[-1].tuner_paras = merged_calibration_classes[0].tuner_paras
+
+ # With the definition of the calibration classes and the loaded `sim_api`,
+ # we now take a look at the different options for sensitivity analysis.
+ # First, we perform the `Sobol` method, which is the most powerful currently
+ # supported method, but is also the most computational demanding one.
+ # Afterward, we will compare the results of the different methods.
+
+ # ## Example of Sobol method
+ # First, we instantiate the Analyzer with the `sim_api` and the number of samples.
+ # For this example we will use a small sample size to reduce the time needed,
+ # which will lead to inaccurate results.
+ # In the comparison of the different methods, we will discuss the required sample size.
+ # We will also define a working directory were all results of the analysis will be stored.
+ # Additionally, we can choose if the samples and corresponding simulation files will be saved.
+ # These files can later be loaded and used for analysis of different calibration classes
+ # without performing new simulations. The simulations during the sensitivity analysis are the
+ # main computational time factor.
+ sen_analyzer = SobolAnalyzer(
+ sim_api=sim_api,
+ num_samples=8,
+ calc_second_order=True,
+ cd=examples_dir.joinpath('testzone', f'verbose_sen_{example}'),
+ save_files=True,
+ savepath_sim=examples_dir.joinpath('testzone', f'verbose_sen_{example}', 'files'),
+ suffix_files='csv'
+ )
+
+ # Now, we run the sensitivity analysis with the verbose option.
+ # With that, we not only get the results for combined target values,
+ # but we also get the results for every target value alone.
+ # Because we defined the first calibration class global
+ # and every calibration class has the same
+ # tuner-parameters, we can use the option `use_first_sim`,
+ # where only the first class will be simulated and the simulation files saved.
+ # These simulations will be used for all other classes.
+
+ # For that the simulations are loaded for each class
+ # and the statistical measure is then evaluated for the
+ # relevant time intervals of each class.
+
+ # When we load simulation files we can use multiprocessing for loading the simulation
+ # and evaluating the statistical measure for each class in their time intervals.
+ # This multiprocessing option is especially
+ # useful for large models and large simulation data,
+ # because only one simulation at a time is stored
+ # in memory for each process.
+ # This can prevent possible memory errors.
+
+ # We disable the automatic plot option here,
+ # but we save all results. Later we can use the plot function
+ # of the plotting module to plot the results.
+
+ result, classes = sen_analyzer.run(calibration_classes=merged_calibration_classes,
+ verbose=True,
+ use_first_sim=True,
+ plot_result=False,
+ save_results=True,
+ n_cpu=n_cpu,
+ scale=False)
+
+ # After running the sensitivity analysis you can see
+ # that the working directory was created and the result
+ # files were saved here. First, the folder "files" was
+ # created as the result of `save_files=True`. In there, the
+ # simulation files are stored in an own folder which
+ # name contains the name of the used calibration class.
+ # Additionally, the corresponding samples are stored.
+ # The simulations are coupled with their samples.
+ # As one results of the analysis, the statistical
+ # measure and corresponding sample are saved for each class.
+ # This information could be used for surrogate-based
+ # calibration, which is currently not implemented in `AixCaliBuHA`.
+ # The main results of the sensitivity analysis are the sensitivity
+ # measures stored in "SobolAnalyzer_results.csv"
+ # and "SobolAnalyzer_results_second_order.csv".
+ # These are also returned from the `run()` function as a tuple of two
+ # dataframes. This is specific to the sobol method, all other
+ # methods only return one dataframe, which is similar to the first return value
+ # of the sobol method with possibly other analysis variables.
+ # Let´s take a look at these results.
+
+ # The first result has as columns the tuner-parameters
+ # and a multi level index with three levels.
+ # The first level defines the calibration class.
+ # The second level defines the Goals (target values). The index `all`
+ # is for the result of the combined target values in the goals.
+ # The last level defines the result of the
+ # sensitivity measure for each class and goal.
+ # These analysis variables are specific for each method.
+ # For their exact meaning I refer to the documentation of the SALib or the literature.
+ # In this example you get a short overview in the comparison later.
+ print("Result of the sensitivity analysis")
+ print('First and total order results of sobol method')
+ print(result[0].to_string())
+ # The second result of the sobol method is for second order sensitive measures.
+ # These describe the interaction between two parameters,
+ # so this dataframe has a fourth index level "Interaction".
+ # In this level, the tuner-parameters are listed again.
+ print('Second order results of sobol method')
+ print(result[1].to_string())
+ # For a better understanding of the results we will now plot them.
+
+ # ## Plotting Sensitivity results
+ # We start with the result which were calculated with the small sample size.
+ # Let's plot the first and total order results. These results
+ # are specific for each single parameter
+ # For each calibration class, a figure is created
+ # which shows for each goal the first order sensitivity `S1`
+ # and the total order sensitivity `ST` combined. For the small
+ # sample size the results have large confidence
+ # intervals, which show that these results are inaccurate as we
+ # noted earlier due to the small sample size.
+ plotting.plot_single(result[0])
+
+ # The plotting of second order results is only useful
+ # and working for more than 2 parameters.
+ # So we only can take a look at them in example A.
+ # If you run example B we will skip the plot of the second order results
+ # and load some sensitivity results of example A.
+
+ # Let's take a look at the second order results `S2` of example A we just created.
+ # This analysis variable shows the interaction of two
+ # parameters, so we can plot them as a heatmap.
+ # We can see that the parameters have no interaction with
+ # themselves what is obvious. Also, we see that the
+ # values for p1,p2 and p2,p1 are the same.
+ # In the heatmap we can't visualize the confidence intervals,
+ # so we will also take a look at the interaction of
+ # one specific parameter.
+ # For that the `plotting` module has also a function
+ # which looks simular to the `S1` and `ST` plots.
+ # Here we see again large confidence intervals,
+ # so now we will load results which were calculated with
+ # a much higher sample number.
+ if example == 'A':
+ plotting.heatmaps(result[1])
+ plotting.plot_single_second_order(result[1], 'rad.n')
+
+ # ## Loading results
+ # We will now load sensitivity results of example A .
+ # These results were produced with this example and
+ # a samples number N=1024 and calc_second_order=True
+
+ result_sobol = SobolAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_A.csv')
+ )
+ result_sobol_2 = SobolAnalyzer.load_second_order_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_second_order_A.csv')
+ )
+ # For a better understanding we will only take a
+ # look at the global class and Electricity goal
+ # and plot `S1`, `ST` and `S2` in the same window.
+ # For that we can use the plot function
+ # with some optional options. This shows how you can easily customize
+ # these plots, and you can also chang everything
+ # on the axes of the plots.
+ fig = plt.figure(figsize=plt.figaspect(1. / 4.), layout="constrained") # creating one figure
+ subfigs = fig.subfigures(1, 3, wspace=0) # creating subfigures for each type of plot
+ ax0 = subfigs[0].subplots() # plotting `S1` and `ST`
+ plotting.plot_single(
+ result=result_sobol,
+ cal_classes=['global'],
+ goals=['Electricity'],
+ show_plot=False,
+ figs_axes=([subfigs[0]], [ax0]),
+ max_name_len=14
+ )
+ ax1 = subfigs[1].subplots() # plotting heatmap
+ plotting.heatmap(
+ result_sobol_2,
+ cal_class='global',
+ goal='Electricity',
+ ax=ax1,
+ show_plot=False,
+ max_name_len=14
+ )
+ ax2 = subfigs[2].subplots() # plotting the interactions of one single parameter
+ plotting.plot_single_second_order(
+ result=result_sobol_2,
+ para_name='rad.n',
+ show_plot=False,
+ cal_classes=['global'],
+ goals=['Electricity'],
+ figs_axes=([subfigs[2]], [ax2]),
+ max_name_len=14
+ )
+ plt.show()
+ # Now, what can we see in these results? First, the
+ # confidence intervals are now much smaller, so that we
+ # can interpret and connect the different analysis variables.
+ # `S1` stands for the variance in the objective,
+ # which is caused by the variation of one parameter
+ # while all other parameters are constant. The sobol analysis
+ # variables are normalized with the total variance caused
+ # by all parameter variations together within
+ # their bounds. This means that when the parameters had
+ # no interactions the sum of all `S1` values would
+ # be 1. `ST` shows the resulting variance of a parameter
+ # with all his interactions. Let's take a look at the
+ # interaction between `n` and `G`, which are the highest.
+ # Here, the `S2` value of the interaction
+ # between `n` and `G` has a similar value to each difference
+ # of `S1` and `ST` from these parameters. This show how this
+ # difference corresponds to the interactions of a parameter.
+ # All other parameters have only a very small sensitivity.
+ # These are just some basics, to understand what option you
+ # have in `AixCaliBuAH`. For more information look up relevant literature.
+
+ # ## Comparison of different Methods
+ # We will now take a short look at a comparison of the
+ # `Sobol`, `Fast` and `Morris` method in `AixCaliBuAH`.
+ # The `SALib` provides more methods, which can
+ # be implemented here in the future or on your own.
+ # We already took a look at the `Sobol` method,
+ # which can compute `S1`, `ST`, and `S2` with their confidence intervals.
+ # The sobol method needs for that `(2+2k)N` simulations
+ # where `k` is the number of parameters and `N` is the sample
+ # number. For variance-based methods `N` should be greater
+ # than 1000. The sobol method can also compute only `S1` and
+ # `ST` with calc_second_order=False and (1+k)N simulations.
+ # The FAST method is another variance-based
+ # method and only computes `S1` and `ST` with k*N simulations.
+ # `Sobol` and FAST should show simular results which is the
+ # case for example B but in example A the FAST method overestimates `ST` in some cases.
+ # In the right plots, the results for the `Morris` method are shown.
+ # These are based on the mean of derivatives which
+ # represents the analysis variables mu. In the estimation
+ # of the derivatives, only one parameter is changed at a time.
+ # mu_star is the mean of the absolut values of the derivatives
+ # and is an approximation of `ST` but
+ # needs only an `N` of over 100 and `(1+k)N` simulations.
+ # In this comparison, mu_star shows simular results to `ST`
+ # of the `Sobol` method, but it is not normalized.
+ # Last, sigma is computed which is the standard deviation and
+ # is a sign for a non-linear model or for interactions in the model.
+ result_sobol = SobolAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', f'SobolAnalyzer_results_{example}.csv')
+ )
+ result_fast = FASTAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', f'FASTAnalyzer_results_{example}.csv')
+ )
+ result_morris = MorrisAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', f'MorrisAnalyzer_results_{example}.csv')
+ )
+
+ global_class = classes[0]
+ fig_comp = plt.figure(figsize=plt.figaspect(1. / 4.), layout="constrained")
+ subfigs_comp = fig_comp.subfigures(1, 3, wspace=0)
+ ax0_comp = subfigs_comp[0].subplots(3, 1, sharex=True)
+ plotting.plot_single(
+ result=result_sobol,
+ cal_classes=[global_class.name],
+ show_plot=False,
+ figs_axes=([subfigs_comp[0]], [ax0_comp])
+ )
+ subfigs_comp[0].suptitle("Sobol")
+ ax1_comp = subfigs_comp[1].subplots(3, 1, sharex=True)
+ plotting.plot_single(
+ result=result_fast,
+ cal_classes=[global_class.name],
+ show_plot=False,
+ figs_axes=([subfigs_comp[1]], [ax1_comp])
+ )
+ subfigs_comp[1].suptitle("FAST")
+ ax2_comp = subfigs_comp[2].subplots(3, 1, sharex=True)
+ plotting.plot_single(
+ result=result_morris,
+ show_plot=False,
+ cal_classes=[global_class.name],
+ figs_axes=([subfigs_comp[2]], [ax2_comp])
+ )
+ subfigs_comp[2].suptitle("Morris")
+ plt.show()
+
+ # ## Selection of tuner-parameters based on verbose sensitivity results
+ # We can now also use these verbose sensitivity
+ # results for a selection of relevant tuner parameters.
+ # We already saw that our models have interactions,
+ # so it will be necessary to calibrate them together
+ # in one calibration class. The calibration class
+ # global can be used for that because it includes all
+ # the other specific classes. But in the sensitivity
+ # results of this class it could be that parameters,
+ # which are only in one state sensitive can be missed.
+ # We can use the verbose sensitivity results so
+ # that a parameter will be selected when it has a
+ # sensitivity at least in one class and target value
+ # of the sensitivity results. This is enough that
+ # the parameter can be calibrated.
+ # Here, we will use `S1` because it is normalized instead of mu_star,
+ # and we can set on single threshold for all classes and goals.
+ # Also, if a parameter is only through interaction sensitive (only in `ST`)
+ # A real value can't be found and the parameters which interact will
+ # compensate each others deviation to a real world value.
+ # This still can happen when we choose with `S1` but it is less likely.
+ calibration_class = SobolAnalyzer.select_by_threshold_verbose(classes[0],
+ result=result_sobol,
+ analysis_variable='S1',
+ threshold=0.001,
+ )
+
+ print(calibration_class.tuner_paras)
+
+ # ## Time dependent sensitivity
+ # For analysis porpoises we can also evaluate the time dependent sensitivity
+ # instead of the sensitivity of larger time intervals. But these results can not yet
+ # be used to automatically selected tuner parameters for a calibration.
+ # The function for the time dependent sensitivity is similar to the other one.
+ # So we can also use the simulations of booth run functions for each other.
+ # The main difference is that we only need one calibration class from which
+ # the measured target data is not used and the sensitivity is directly
+ # calculated for the change of the separate target values and no combined goals.
+ # In the results, we then get just the additional index time.
+
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore") # ignoring warnings that are caused by the low sample size
+ result = sen_analyzer.run_time_dependent(
+ cal_class=merged_calibration_classes[0],
+ load_sim_files=True,
+ plot_result=True
+ )
+ print(result)
+
+ # When we use the plot function we can see the sensitivity of each parameter
+ # changing over time. We can also see that the confidence intervals
+ # are again large for such a small sample size. When the confidence interval is larger
+ # than one an info is shown and the confidence interval of the previous
+ # time step is used to smooth it out for the visualisation.
+ # Let's load again results which were created with a larger sample size.
+
+ # ## Loading time dependent results
+ # These results were produced with a samples number `N=1024` and `calc_second_order=True`
+ result_sobol_time = SobolAnalyzer.load_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_time_A.csv')
+ )
+ result_sobol_2_time = SobolAnalyzer.load_second_order_from_csv(
+ examples_dir.joinpath('data', 'SobolAnalyzer_results_second_order_time_A.csv')
+ )
+ plotting.plot_time_dependent(result=result_sobol_time, plot_conf=True, show_plot=False)
+ # Now the confidence intervals are smaller and
+ # only at one time step they are still lager than 1.
+ # We can also see that the parameter `theCon.G` has the biggest influence.
+ # So we can take a closer look at this parameter with another plot
+ # function where all available sensitivity measures are plotted together.
+ # Second order results are plotted cumulative on top of `S1`.
+ # This resembles the definition of `ST = S1 + sum(S2_i) + sum(S3_i) + ...`
+ plotting.plot_parameter_verbose(parameter='theCon.G',
+ single_result=result_sobol_time,
+ second_order_result=result_sobol_2_time)
+
+ # At the end we also can create a reproduction
+ # archive which saves all settings and all created files
+ # automatically with the reproduction function of ebcpy.
+ # Not running in jupyter notebook
+ file = sen_analyzer.save_for_reproduction(
+ title="SenAnalyzerTest",
+ path=examples_dir.joinpath('testzone'),
+ log_message="This is just an example",
+ remove_saved_files=False,
+ exclude_sim_files=True
+ )
+ print("ZIP-File to reproduce all this:", file)
+
+
+if __name__ == "__main__":
+
+ # Parameters for sen-analysis:
+ EXAMPLE = "A" # Or choose B
+ N_CPU = 2
+
+ # Sensitivity analysis:
+ run_sensitivity_analysis(
+ examples_dir=pathlib.Path(__file__).parent,
+ example=EXAMPLE,
+ n_cpu=N_CPU
+ )
diff --git a/examples/e3_2_verbose_dymola_sensitivity_analysis_example.py b/examples/e3_2_verbose_dymola_sensitivity_analysis_example.py
new file mode 100644
index 00000000..87e8b3f2
--- /dev/null
+++ b/examples/e3_2_verbose_dymola_sensitivity_analysis_example.py
@@ -0,0 +1,119 @@
+# # Example 3 sensitivity analysis with dymola api
+
+# Goals of this part of the examples:
+# 1. Learn how to execute a sensitivity analysis with the dymola api
+#
+import os
+import pathlib
+from examples import setup_dym_api, setup_calibration_classes
+# Import a valid analyzer, e.g. `SobolAnalyzer`
+from aixcalibuha import SobolAnalyzer
+from aixcalibuha.data_types import merge_calibration_classes
+from ebcpy.utils.conversion import convert_tsd_to_modelica_txt
+
+
+def run_sensitivity_analysis(
+ examples_dir,
+ aixlib_mo,
+ example: str = "B",
+ n_cpu: int = 1
+):
+ """
+ Example process of a verbose sensitivity analysis with the dymola_api.
+
+ :param [pathlib.Path, str] examples_dir:
+ Path to the examples folder of AixCaliBuHA
+ :param [pathlib.Path, str] aixlib_mo:
+ Path to the AixLib package.mo file.
+ :param str example:
+ Which example to run, "A" or "B"
+ :param int n_cpu:
+ Number of cores to use
+
+ :return: A list of calibration classes
+ :rtype: list
+ """
+ # ## Setup
+ # Using a dymola api instead of the fmu api
+ examples_dir = pathlib.Path(examples_dir)
+ sim_api = setup_dym_api(examples_dir=examples_dir,
+ aixlib_mo=aixlib_mo,
+ example=example,
+ n_cpu=n_cpu)
+ calibration_classes = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=False
+ )[0]
+ merged_calibration_classes = merge_calibration_classes(calibration_classes)
+ merged_calibration_classes[0].name = 'global'
+ calibration_classes = setup_calibration_classes(
+ examples_dir=examples_dir, example=example, multiple_classes=True
+ )[0]
+ merged_calibration_classes.extend(merge_calibration_classes(calibration_classes))
+
+ if example == 'B':
+ merged_calibration_classes[-1].tuner_paras = merged_calibration_classes[0].tuner_paras
+
+ # ## Example of Sobol method
+ # Set up Sobol analyzer
+ sen_analyzer = SobolAnalyzer(
+ sim_api=sim_api,
+ num_samples=2,
+ calc_second_order=True,
+ cd=examples_dir.joinpath('testzone', f'verbose_sen_dymola_{example}'),
+ save_files=True,
+ load_files=False,
+ savepath_sim=examples_dir.joinpath('testzone', f'verbose_sen_dymola_{example}', 'files'),
+ suffix_files='mat'
+ )
+
+ # The only difference to the fmu example is the handling of inputs.
+ # There we have in the model now a table for the inputs and generate
+ # an input file here. This is only necessary for example A because
+ # example B has no inputs. To generate the input in the correct format,
+ # use the convert_tsd_to_modelica_txt function:
+ if example == "A":
+ table_name = "InputTDryBul"
+ file_name = r"D:\dymola_inputs_A.txt"
+ print(file_name)
+ filepath = convert_tsd_to_modelica_txt(
+ tsd=merged_calibration_classes[0].inputs,
+ table_name=table_name,
+ save_path_file=file_name
+ )
+ # Now we can remove the input from the old fmu calibration classes
+ for cal_class in merged_calibration_classes:
+ cal_class._inputs = None
+ print("Successfully created Dymola input file at", filepath)
+ # run sensitivity analysis
+ result, classes = sen_analyzer.run(calibration_classes=merged_calibration_classes,
+ verbose=True,
+ use_first_sim=True,
+ plot_result=True,
+ save_results=True,
+ n_cpu=2)
+ print("Result of the sensitivity analysis")
+ print('First and total order results of sobol method')
+ print(result[0].to_string())
+ print('Second order results of sobol method')
+ print(result[1].to_string())
+
+ # remove input file
+ if example == "A":
+ os.remove(file_name)
+
+ return classes
+
+
+if __name__ == "__main__":
+
+ # Parameters for sen-analysis:
+ EXAMPLE = "A" # Or choose B
+ N_CPU = 1
+
+ # Sensitivity analysis:
+ run_sensitivity_analysis(
+ examples_dir=pathlib.Path(__file__).parent,
+ aixlib_mo=r"D:\sbg-hst\Repos\AixLib\AixLib\package.mo",
+ example=EXAMPLE,
+ n_cpu=N_CPU
+ )
diff --git a/examples/e3_sensitivity_analysis_example.py b/examples/e3_sensitivity_analysis_example.py
index 62e5c7a0..ebd72925 100644
--- a/examples/e3_sensitivity_analysis_example.py
+++ b/examples/e3_sensitivity_analysis_example.py
@@ -17,7 +17,7 @@ def run_sensitivity_analysis(
Example process of a sensitivity analysis.
First, the sensitivity problem is constructed, in this example
the `morris` method is chosen.
- Afterwards, the sen_analyzer class is instantiated to run the
+ Afterward, the sen_analyzer class is instantiated to run the
sensitivity analysis in the next step.
The result of this analysis is then printed to the user.
The automatic_select function is presented as-well, using a threshold of 1
@@ -44,15 +44,16 @@ def run_sensitivity_analysis(
sen_analyzer = SobolAnalyzer(
sim_api=sim_api,
num_samples=10,
- cd=sim_api.cd,
- analysis_variable='S1'
+ cd=sim_api.cd
)
# Now perform the analysis for the one of the given calibration classes.
calibration_classes = setup_calibration_classes(
examples_dir=examples_dir, example=example
)[0]
- result, classes = sen_analyzer.run(calibration_classes=calibration_classes)
+ result, classes = sen_analyzer.run(calibration_classes=calibration_classes,
+ plot_result=True,
+ save_results=False)
print("Result of the sensitivity analysis")
print(result)
# For each given class, you should see the given tuner parameters
@@ -61,8 +62,9 @@ def run_sensitivity_analysis(
# to remove complexity from our calibration problem:
print("Selecting relevant tuner-parameters using a fixed threshold:")
sen_analyzer.select_by_threshold(calibration_classes=classes,
- result=result,
- threshold=0.01)
+ result=result[0],
+ threshold=0.01,
+ analysis_variable='S1')
for cal_class in classes:
print(f"Class '{cal_class.name}' with parameters:\n{cal_class.tuner_paras}")
# Return the classes and the sim_api to later perform an automated process in example 5
diff --git a/examples/jupyter_notebooks/e1_A_energy_system_analysis.ipynb b/examples/jupyter_notebooks/e1_A_energy_system_analysis.ipynb
index baa87adf..c0879569 100644
--- a/examples/jupyter_notebooks/e1_A_energy_system_analysis.ipynb
+++ b/examples/jupyter_notebooks/e1_A_energy_system_analysis.ipynb
@@ -29,6 +29,18 @@
"outputs": [],
"source": "from ebcpy import DymolaAPI, TimeSeriesData\n"
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nArguments of this example:\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n:param str aixlib_mo:\n Path to the package.mo of the AixLib.\n This example was tested for AixLib version 1.0.0.\n:param str cd:\n Path in which to store the output.\n Default is the examples\\results folder\n:param bool with_plot:\n Show the plot at the end of the script. Default is True.\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\naixlib_mo = \"TODO: Add a valid input according to the docstring above\"\ncd = None\nwith_plot = True\n"
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/examples/jupyter_notebooks/e1_B_energy_system_analysis.ipynb b/examples/jupyter_notebooks/e1_B_energy_system_analysis.ipynb
index 2fb234e1..1569e166 100644
--- a/examples/jupyter_notebooks/e1_B_energy_system_analysis.ipynb
+++ b/examples/jupyter_notebooks/e1_B_energy_system_analysis.ipynb
@@ -12,6 +12,18 @@
"outputs": [],
"source": "import pathlib\nimport matplotlib.pyplot as plt\n"
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nArguments of this example:\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n:param bool with_plot:\n Show the plot at the end of the script. Default is True.\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\nwith_plot = True\n"
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/examples/jupyter_notebooks/e2_A_optimization_problem_definition.ipynb b/examples/jupyter_notebooks/e2_A_optimization_problem_definition.ipynb
index d2235069..907aaca9 100644
--- a/examples/jupyter_notebooks/e2_A_optimization_problem_definition.ipynb
+++ b/examples/jupyter_notebooks/e2_A_optimization_problem_definition.ipynb
@@ -41,6 +41,18 @@
"outputs": [],
"source": "from aixcalibuha import TunerParas, Goals, \\\n CalibrationClass\nfrom aixcalibuha.data_types import merge_calibration_classes\n"
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nArguments of this example:\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n:param str statistical_measure:\n Measure to calculate the scalar of the objective,\n One of the supported methods in\n ebcpy.utils.statistics_analyzer.StatisticsAnalyzer\n e.g. RMSE, MAE, NRMSE\n:param bool multiple_classes:\n If False, all CalibrationClasses will have the\n same name\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\nstatistical_measure = \"NRMSE\"\nmultiple_classes = True\n"
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/examples/jupyter_notebooks/e2_B_optimization_problem_definition.ipynb b/examples/jupyter_notebooks/e2_B_optimization_problem_definition.ipynb
index 99724e5d..c523e409 100644
--- a/examples/jupyter_notebooks/e2_B_optimization_problem_definition.ipynb
+++ b/examples/jupyter_notebooks/e2_B_optimization_problem_definition.ipynb
@@ -41,6 +41,18 @@
"outputs": [],
"source": "from aixcalibuha import TunerParas, Goals, \\\n CalibrationClass\nfrom aixcalibuha.data_types import merge_calibration_classes\n"
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nArguments of this example:\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n:param str statistical_measure:\n Measure to calculate the scalar of the objective,\n One of the supported methods in\n ebcpy.utils.statistics_analyzer.StatisticsAnalyzer\n e.g. RMSE, MAE, NRMSE\n:param bool multiple_classes:\n If False, all CalibrationClasses will have the\n same name\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\nstatistical_measure = \"NRMSE\"\nmultiple_classes = True\n"
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/examples/jupyter_notebooks/e3_1_verbose_sensitivity_analysis_example.ipynb b/examples/jupyter_notebooks/e3_1_verbose_sensitivity_analysis_example.ipynb
new file mode 100644
index 00000000..218bdd7b
--- /dev/null
+++ b/examples/jupyter_notebooks/e3_1_verbose_sensitivity_analysis_example.ipynb
@@ -0,0 +1,337 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Example 3 verbose sensitivity analysis\nfor the analysis of your model and the calibration process\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Goals of this part of the examples:\n1. Learn how to execute a verbose sensitivity analysis\n2. Learn the meaning of the results and the analysis of your model\n3 Learn how to use the results to select tuner-parameters for a calibration\n4. Learn other sensitivity methods and compare them\n5. Learn how to execute a time dependent sensitivity analysis\n6. Learn how to save the results for reproduction\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Start by importing all relevant packages\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "import warnings\nimport pathlib\nimport matplotlib.pyplot as plt\nfrom aixcalibuha import SobolAnalyzer, FASTAnalyzer, MorrisAnalyzer\nfrom aixcalibuha.data_types import merge_calibration_classes\nfrom examples import setup_fmu, setup_calibration_classes\nfrom aixcalibuha import plotting\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nExample process of a verbose sensitivity analysis for calibration and analysis porpoises.\nFirst, the sensitivity problem is constructed, in this example\nthe `sobol` method is chosen.\nAfterward, the SenAnalyzer class is instantiated to run the\nsensitivity analysis in the next step.\nThe result of this analysis is then printed to the user.\nand tuner-parameters are selected with these results.\nA comparison between different methods is shown.\nAt the end the option to save a reproduction archive is shown.\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n e.g. r\"LOCAL_PATH_TO\\AixCaliBuHA\\examples\"\n:param str example:\n Which example to run, \"A\" or \"B\"\n:param int n_cpu:\n Number of cores to use\n\n:return: A list of calibration classes\n:rtype: list\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\nexample: str = \"B\"\nn_cpu: int = 1\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Setup\nSet up the class according to the documentation.\nYou just have to pass a valid simulation api and\nsome further settings for the analysis.\nLet's first load the necessary simulation api:\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "examples_dir = pathlib.Path(examples_dir)\nsim_api = setup_fmu(examples_dir=examples_dir, example=example, n_cpu=n_cpu)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "To conduct a sensitivity analysis, we need to define calibration classes that\nencompass the objectives (goals) for which sensitivity is to be assessed. In this\ncontext, we'll explore distinct calibration classes corresponding to various\nstates of the models. Initially, we establish a comprehensive calibration class\nthat spans the entire duration, denoted as `global`. Subsequently, we can\nleverage simulations from this global class for other classes targeting\nspecific time intervals within the overall global range.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "For the specific states of the models with distinct time intervals, we adopt the\ncalibration classes from the second example and tailor them for the verbose\nsensitivity analysis. Alternatively, custom classes could be created directly\nfunction (`validation_class`), we opt to ignore it by using the variable\nassignment `_` and omit any associated text output of the second example.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "calibration_classes, _ = setup_calibration_classes(\n examples_dir=examples_dir, example=example, multiple_classes=False\n)\nmerged_calibration_classes = merge_calibration_classes(calibration_classes)\nmerged_calibration_classes[0].name = 'global'\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Now, we add the calibration classes for the different states of the system\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "calibration_classes, _ = setup_calibration_classes(\n examples_dir=examples_dir, example=example, multiple_classes=True\n)\nmerged_calibration_classes.extend(merge_calibration_classes(calibration_classes))\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "This results in the following calibration classes where we merge the time intervals directly.\nWe could have also merged them with an option in the `run()` function of the\nsensitivity analyzer classes.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "print(\"Calibration classes for sensitivity analysis:\",\n [c.name for c in merged_calibration_classes])\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "In our detailed sensitivity analysis, it is essential for all classes to share\nidentical tuner parameters. This ensures that we can employ the same set of\nsimulations for calculating sensitivity across various calibration classes.\nHowever, the final class in example B deviates in tuner parameters; hence, we\nreset them to align with the tuner parameters of the other classes.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "if example == 'B':\n merged_calibration_classes[-1].tuner_paras = merged_calibration_classes[0].tuner_paras\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "With the definition of the calibration classes and the loaded `sim_api`,\nwe now take a look at the different options for sensitivity analysis.\nFirst, we perform the `Sobol` method, which is the most powerful currently\nsupported method, but is also the most computational demanding one.\nAfterward, we will compare the results of the different methods.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Example of Sobol method\nFirst, we instantiate the Analyzer with the `sim_api` and the number of samples.\nFor this example we will use a small sample size to reduce the time needed,\nwhich will lead to inaccurate results.\nIn the comparison of the different methods, we will discuss the required sample size.\nWe will also define a working directory were all results of the analysis will be stored.\nAdditionally, we can choose if the samples and corresponding simulation files will be saved.\nThese files can later be loaded and used for analysis of different calibration classes\nwithout performing new simulations. The simulations during the sensitivity analysis are the\nmain computational time factor.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "sen_analyzer = SobolAnalyzer(\n sim_api=sim_api,\n num_samples=8,\n calc_second_order=True,\n cd=examples_dir.joinpath('testzone', f'verbose_sen_{example}'),\n save_files=True,\n savepath_sim=examples_dir.joinpath('testzone', f'verbose_sen_{example}', 'files'),\n suffix_files='csv'\n)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Now, we run the sensitivity analysis with the verbose option.\nWith that, we not only get the results for combined target values,\nbut we also get the results for every target value alone.\nBecause we defined the first calibration class global\nand every calibration class has the same\ntuner-parameters, we can use the option `use_first_sim`,\nwhere only the first class will be simulated and the simulation files saved.\nThese simulations will be used for all other classes.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "For that the simulations are loaded for each class\nand the statistical measure is then evaluated for the\nrelevant time intervals of each class.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "When we load simulation files we can use multiprocessing for loading the simulation\nand evaluating the statistical measure for each class in their time intervals.\nThis multiprocessing option is especially\nuseful for large models and large simulation data,\nbecause only one simulation at a time is stored\nin memory for each process.\nThis can prevent possible memory errors.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "We disable the automatic plot option here,\nbut we save all results. Later we can use the plot function\nof the plotting module to plot the results.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "result, classes = sen_analyzer.run(calibration_classes=merged_calibration_classes,\n verbose=True,\n use_first_sim=True,\n plot_result=False,\n save_results=True,\n n_cpu=n_cpu,\n scale=False)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "After running the sensitivity analysis you can see\nthat the working directory was created and the result\nfiles were saved here. First, the folder \"files\" was\ncreated as the result of `save_files=True`. In there, the\nsimulation files are stored in an own folder which\nname contains the name of the used calibration class.\nAdditionally, the corresponding samples are stored.\nThe simulations are coupled with their samples.\nAs one results of the analysis, the statistical\nmeasure and corresponding sample are saved for each class.\nThis information could be used for surrogate-based\ncalibration, which is currently not implemented in `AixCaliBuHA`.\nThe main results of the sensitivity analysis are the sensitivity\nmeasures stored in \"SobolAnalyzer_results.csv\"\nand \"SobolAnalyzer_results_second_order.csv\".\ndataframes. This is specific to the sobol method, all other\nof the sobol method with possibly other analysis variables.\nLet´s take a look at these results.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "The first result has as columns the tuner-parameters\nand a multi level index with three levels.\nThe first level defines the calibration class.\nThe second level defines the Goals (target values). The index `all`\nis for the result of the combined target values in the goals.\nThe last level defines the result of the\nsensitivity measure for each class and goal.\nThese analysis variables are specific for each method.\nFor their exact meaning I refer to the documentation of the SALib or the literature.\nIn this example you get a short overview in the comparison later.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "print(\"Result of the sensitivity analysis\")\nprint('First and total order results of sobol method')\nprint(result[0].to_string())\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "The second result of the sobol method is for second order sensitive measures.\nThese describe the interaction between two parameters,\nso this dataframe has a fourth index level \"Interaction\".\nIn this level, the tuner-parameters are listed again.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "print('Second order results of sobol method')\nprint(result[1].to_string())\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "For a better understanding of the results we will now plot them.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Plotting Sensitivity results\nWe start with the result which were calculated with the small sample size.\nLet's plot the first and total order results. These results\nare specific for each single parameter\nFor each calibration class, a figure is created\nwhich shows for each goal the first order sensitivity `S1`\nand the total order sensitivity `ST` combined. For the small\nsample size the results have large confidence\nintervals, which show that these results are inaccurate as we\nnoted earlier due to the small sample size.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "plotting.plot_single(result[0])\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "The plotting of second order results is only useful\nand working for more than 2 parameters.\nSo we only can take a look at them in example A.\nIf you run example B we will skip the plot of the second order results\nand load some sensitivity results of example A.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Let's take a look at the second order results `S2` of example A we just created.\nThis analysis variable shows the interaction of two\nparameters, so we can plot them as a heatmap.\nWe can see that the parameters have no interaction with\nthemselves what is obvious. Also, we see that the\nvalues for p1,p2 and p2,p1 are the same.\nIn the heatmap we can't visualize the confidence intervals,\nso we will also take a look at the interaction of\none specific parameter.\nFor that the `plotting` module has also a function\nwhich looks simular to the `S1` and `ST` plots.\nHere we see again large confidence intervals,\nso now we will load results which were calculated with\na much higher sample number.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "if example == 'A':\n plotting.heatmaps(result[1])\n plotting.plot_single_second_order(result[1], 'rad.n')\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Loading results\nWe will now load sensitivity results of example A .\nThese results were produced with this example and\na samples number N=1024 and calc_second_order=True\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "result_sobol = SobolAnalyzer.load_from_csv(\n examples_dir.joinpath('data', 'SobolAnalyzer_results_A.csv')\n)\nresult_sobol_2 = SobolAnalyzer.load_second_order_from_csv(\n examples_dir.joinpath('data', 'SobolAnalyzer_results_second_order_A.csv')\n)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "For a better understanding we will only take a\nlook at the global class and Electricity goal\nand plot `S1`, `ST` and `S2` in the same window.\nFor that we can use the plot function\nwith some optional options. This shows how you can easily customize\nthese plots, and you can also chang everything\non the axes of the plots.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "fig = plt.figure(figsize=plt.figaspect(1. / 4.), layout=\"constrained\") # creating one figure\nsubfigs = fig.subfigures(1, 3, wspace=0) # creating subfigures for each type of plot\nax0 = subfigs[0].subplots() # plotting `S1` and `ST`\nplotting.plot_single(\n result=result_sobol,\n cal_classes=['global'],\n goals=['Electricity'],\n show_plot=False,\n figs_axes=([subfigs[0]], [ax0]),\n max_name_len=14\n)\nax1 = subfigs[1].subplots() # plotting heatmap\nplotting.heatmap(\n result_sobol_2,\n cal_class='global',\n goal='Electricity',\n ax=ax1,\n show_plot=False,\n max_name_len=14\n)\nax2 = subfigs[2].subplots() # plotting the interactions of one single parameter\nplotting.plot_single_second_order(\n result=result_sobol_2,\n para_name='rad.n',\n show_plot=False,\n cal_classes=['global'],\n goals=['Electricity'],\n figs_axes=([subfigs[2]], [ax2]),\n max_name_len=14\n)\nplt.show()\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Now, what can we see in these results? First, the\nconfidence intervals are now much smaller, so that we\ncan interpret and connect the different analysis variables.\n`S1` stands for the variance in the objective,\nwhich is caused by the variation of one parameter\nwhile all other parameters are constant. The sobol analysis\nvariables are normalized with the total variance caused\nby all parameter variations together within\ntheir bounds. This means that when the parameters had\nno interactions the sum of all `S1` values would\nbe 1. `ST` shows the resulting variance of a parameter\nwith all his interactions. Let's take a look at the\ninteraction between `n` and `G`, which are the highest.\nHere, the `S2` value of the interaction\nbetween `n` and `G` has a similar value to each difference\nof `S1` and `ST` from these parameters. This show how this\ndifference corresponds to the interactions of a parameter.\nAll other parameters have only a very small sensitivity.\nThese are just some basics, to understand what option you\nhave in `AixCaliBuAH`. For more information look up relevant literature.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Comparison of different Methods\nWe will now take a short look at a comparison of the\n`Sobol`, `Fast` and `Morris` method in `AixCaliBuAH`.\nThe `SALib` provides more methods, which can\nbe implemented here in the future or on your own.\nWe already took a look at the `Sobol` method,\nwhich can compute `S1`, `ST`, and `S2` with their confidence intervals.\nThe sobol method needs for that `(2+2k)N` simulations\nwhere `k` is the number of parameters and `N` is the sample\nnumber. For variance-based methods `N` should be greater\nthan 1000. The sobol method can also compute only `S1` and\n`ST` with calc_second_order=False and (1+k)N simulations.\nThe FAST method is another variance-based\nmethod and only computes `S1` and `ST` with k*N simulations.\n`Sobol` and FAST should show simular results which is the\ncase for example B but in example A the FAST method overestimates `ST` in some cases.\nIn the right plots, the results for the `Morris` method are shown.\nThese are based on the mean of derivatives which\nrepresents the analysis variables mu. In the estimation\nof the derivatives, only one parameter is changed at a time.\nmu_star is the mean of the absolut values of the derivatives\nand is an approximation of `ST` but\nneeds only an `N` of over 100 and `(1+k)N` simulations.\nIn this comparison, mu_star shows simular results to `ST`\nof the `Sobol` method, but it is not normalized.\nLast, sigma is computed which is the standard deviation and\nis a sign for a non-linear model or for interactions in the model.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "result_sobol = SobolAnalyzer.load_from_csv(\n examples_dir.joinpath('data', f'SobolAnalyzer_results_{example}.csv')\n)\nresult_fast = FASTAnalyzer.load_from_csv(\n examples_dir.joinpath('data', f'FASTAnalyzer_results_{example}.csv')\n)\nresult_morris = MorrisAnalyzer.load_from_csv(\n examples_dir.joinpath('data', f'MorrisAnalyzer_results_{example}.csv')\n)\n\nglobal_class = classes[0]\nfig_comp = plt.figure(figsize=plt.figaspect(1. / 4.), layout=\"constrained\")\nsubfigs_comp = fig_comp.subfigures(1, 3, wspace=0)\nax0_comp = subfigs_comp[0].subplots(3, 1, sharex=True)\nplotting.plot_single(\n result=result_sobol,\n cal_classes=[global_class.name],\n show_plot=False,\n figs_axes=([subfigs_comp[0]], [ax0_comp])\n)\nsubfigs_comp[0].suptitle(\"Sobol\")\nax1_comp = subfigs_comp[1].subplots(3, 1, sharex=True)\nplotting.plot_single(\n result=result_fast,\n cal_classes=[global_class.name],\n show_plot=False,\n figs_axes=([subfigs_comp[1]], [ax1_comp])\n)\nsubfigs_comp[1].suptitle(\"FAST\")\nax2_comp = subfigs_comp[2].subplots(3, 1, sharex=True)\nplotting.plot_single(\n result=result_morris,\n show_plot=False,\n cal_classes=[global_class.name],\n figs_axes=([subfigs_comp[2]], [ax2_comp])\n)\nsubfigs_comp[2].suptitle(\"Morris\")\nplt.show()\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Selection of tuner-parameters based on verbose sensitivity results\nWe can now also use these verbose sensitivity\nresults for a selection of relevant tuner parameters.\nWe already saw that our models have interactions,\nso it will be necessary to calibrate them together\nin one calibration class. The calibration class\nglobal can be used for that because it includes all\nthe other specific classes. But in the sensitivity\nresults of this class it could be that parameters,\nwhich are only in one state sensitive can be missed.\nWe can use the verbose sensitivity results so\nthat a parameter will be selected when it has a\nsensitivity at least in one class and target value\nof the sensitivity results. This is enough that\nthe parameter can be calibrated.\nHere, we will use `S1` because it is normalized instead of mu_star,\nand we can set on single threshold for all classes and goals.\nAlso, if a parameter is only through interaction sensitive (only in `ST`)\nA real value can't be found and the parameters which interact will\ncompensate each others deviation to a real world value.\nThis still can happen when we choose with `S1` but it is less likely.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "calibration_class = SobolAnalyzer.select_by_threshold_verbose(classes[0],\n result=result_sobol,\n analysis_variable='S1',\n threshold=0.001,\n )\n\nprint(calibration_class.tuner_paras)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Time dependent sensitivity\nFor analysis porpoises we can also evaluate the time dependent sensitivity\ninstead of the sensitivity of larger time intervals. But these results can not yet\nbe used to automatically selected tuner parameters for a calibration.\nThe function for the time dependent sensitivity is similar to the other one.\nSo we can also use the simulations of booth run functions for each other.\nThe main difference is that we only need one calibration class from which\nthe measured target data is not used and the sensitivity is directly\ncalculated for the change of the separate target values and no combined goals.\nIn the results, we then get just the additional index time.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\") # ignoring warnings that are caused by the low sample size\n result = sen_analyzer.run_time_dependent(\n cal_class=merged_calibration_classes[0],\n load_sim_files=True,\n plot_result=True\n )\nprint(result)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "When we use the plot function we can see the sensitivity of each parameter\nchanging over time. We can also see that the confidence intervals\nare again large for such a small sample size. When the confidence interval is larger\nthan one an info is shown and the confidence interval of the previous\ntime step is used to smooth it out for the visualisation.\nLet's load again results which were created with a larger sample size.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Loading time dependent results\nThese results were produced with a samples number `N=1024` and `calc_second_order=True`\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "result_sobol_time = SobolAnalyzer.load_from_csv(\n examples_dir.joinpath('data', 'SobolAnalyzer_results_time_A.csv')\n)\nresult_sobol_2_time = SobolAnalyzer.load_second_order_from_csv(\n examples_dir.joinpath('data', 'SobolAnalyzer_results_second_order_time_A.csv')\n)\nplotting.plot_time_dependent(result=result_sobol_time, plot_conf=True, show_plot=False)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Now the confidence intervals are smaller and\nonly at one time step they are still lager than 1.\nWe can also see that the parameter `theCon.G` has the biggest influence.\nSo we can take a closer look at this parameter with another plot\nfunction where all available sensitivity measures are plotted together.\nSecond order results are plotted cumulative on top of `S1`.\nThis resembles the definition of `ST = S1 + sum(S2_i) + sum(S3_i) + ...`\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "plotting.plot_parameter_verbose(parameter='theCon.G',\n single_result=result_sobol_time,\n second_order_result=result_sobol_2_time)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "At the end we also can create a reproduction\narchive which saves all settings and all created files\nautomatically with the reproduction function of ebcpy.\nNot running in jupyter notebook\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "file = sen_analyzer.save_for_reproduction(\n title=\"SenAnalyzerTest\",\n path=examples_dir.joinpath('testzone'),\n log_message=\"This is just an example\",\n remove_saved_files=False,\n exclude_sim_files=True\n)\nprint(\"ZIP-File to reproduce all this:\", file)\n"
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/jupyter_notebooks/e3_2_verbose_dymola_sensitivity_analysis_example.ipynb b/examples/jupyter_notebooks/e3_2_verbose_dymola_sensitivity_analysis_example.ipynb
new file mode 100644
index 00000000..ef7ac32a
--- /dev/null
+++ b/examples/jupyter_notebooks/e3_2_verbose_dymola_sensitivity_analysis_example.ipynb
@@ -0,0 +1,126 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "# Example 3 sensitivity analysis with dymola api\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Goals of this part of the examples:\n1. Learn how to execute a sensitivity analysis with the dymola api\n\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "import os\nimport pathlib\nfrom examples import setup_dym_api, setup_calibration_classes\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Import a valid analyzer, e.g. `SobolAnalyzer`\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "from aixcalibuha import SobolAnalyzer\nfrom aixcalibuha.data_types import merge_calibration_classes\nfrom ebcpy.utils.conversion import convert_tsd_to_modelica_txt\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nExample process of a verbose sensitivity analysis with the dymola_api.\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n:param [pathlib.Path, str] aixlib_mo:\n Path to the AixLib package.mo file.\n:param str example:\n Which example to run, \"A\" or \"B\"\n:param int n_cpu:\n Number of cores to use\n\n:return: A list of calibration classes\n:rtype: list\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\naixlib_mo = \"TODO: Add a valid input according to the docstring above\"\nexample: str = \"B\"\nn_cpu: int = 1\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Setup\nUsing a dymola api instead of the fmu api\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "examples_dir = pathlib.Path(examples_dir)\nsim_api = setup_dym_api(examples_dir=examples_dir,\n aixlib_mo=aixlib_mo,\n example=example,\n n_cpu=n_cpu)\ncalibration_classes = setup_calibration_classes(\n examples_dir=examples_dir, example=example, multiple_classes=False\n)[0]\nmerged_calibration_classes = merge_calibration_classes(calibration_classes)\nmerged_calibration_classes[0].name = 'global'\ncalibration_classes = setup_calibration_classes(\n examples_dir=examples_dir, example=example, multiple_classes=True\n)[0]\nmerged_calibration_classes.extend(merge_calibration_classes(calibration_classes))\n\nif example == 'B':\n merged_calibration_classes[-1].tuner_paras = merged_calibration_classes[0].tuner_paras\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Example of Sobol method\nSet up Sobol analyzer\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "sen_analyzer = SobolAnalyzer(\n sim_api=sim_api,\n num_samples=2,\n calc_second_order=True,\n cd=examples_dir.joinpath('testzone', f'verbose_sen_dymola_{example}'),\n save_files=True,\n load_files=False,\n savepath_sim=examples_dir.joinpath('testzone', f'verbose_sen_dymola_{example}', 'files'),\n suffix_files='mat'\n)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "The only difference to the fmu example is the handling of inputs.\nThere we have in the model now a table for the inputs and generate\nan input file here. This is only necessary for example A because\nexample B has no inputs. To generate the input in the correct format,\nuse the convert_tsd_to_modelica_txt function:\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "if example == \"A\":\n table_name = \"InputTDryBul\"\n file_name = r\"D:\\dymola_inputs_A.txt\"\n print(file_name)\n filepath = convert_tsd_to_modelica_txt(\n tsd=merged_calibration_classes[0].inputs,\n table_name=table_name,\n save_path_file=file_name\n )\n # Now we can remove the input from the old fmu calibration classes\n for cal_class in merged_calibration_classes:\n cal_class._inputs = None\n print(\"Successfully created Dymola input file at\", filepath)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "run sensitivity analysis\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "result, classes = sen_analyzer.run(calibration_classes=merged_calibration_classes,\n verbose=True,\n use_first_sim=True,\n plot_result=True,\n save_results=True,\n n_cpu=2)\nprint(\"Result of the sensitivity analysis\")\nprint('First and total order results of sobol method')\nprint(result[0].to_string())\nprint('Second order results of sobol method')\nprint(result[1].to_string())\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "remove input file\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "if example == \"A\":\n os.remove(file_name)\n"
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/jupyter_notebooks/e3_sensitivity_analysis_example.ipynb b/examples/jupyter_notebooks/e3_sensitivity_analysis_example.ipynb
index 977eb8cb..6b79020d 100644
--- a/examples/jupyter_notebooks/e3_sensitivity_analysis_example.ipynb
+++ b/examples/jupyter_notebooks/e3_sensitivity_analysis_example.ipynb
@@ -17,6 +17,18 @@
"outputs": [],
"source": "from aixcalibuha import SobolAnalyzer\n"
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nExample process of a sensitivity analysis.\nFirst, the sensitivity problem is constructed, in this example\nthe `morris` method is chosen.\nAfterward, the sen_analyzer class is instantiated to run the\nsensitivity analysis in the next step.\nThe result of this analysis is then printed to the user.\nThe automatic_select function is presented as-well, using a threshold of 1\nand the default `mu_star` criterion.\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n:param str example:\n Which example to run, \"A\" or \"B\"\n:param int n_cpu:\n Number of cores to use\n\n:return: A list of calibration classes\n:rtype: list\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\nexample: str = \"B\"\nn_cpu: int = 1\n"
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -27,7 +39,7 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": "from examples import setup_fmu, setup_calibration_classes\nsim_api = setup_fmu(examples_dir=examples_dir, example=example, n_cpu=n_cpu)\n\nsen_analyzer = SobolAnalyzer(\n sim_api=sim_api,\n num_samples=10,\n cd=sim_api.cd,\n analysis_variable='S1'\n )\n"
+ "source": "from examples import setup_fmu, setup_calibration_classes\nsim_api = setup_fmu(examples_dir=examples_dir, example=example, n_cpu=n_cpu)\n\nsen_analyzer = SobolAnalyzer(\n sim_api=sim_api,\n num_samples=10,\n cd=sim_api.cd\n )\n"
},
{
"cell_type": "markdown",
@@ -39,7 +51,7 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": "calibration_classes = setup_calibration_classes(\n examples_dir=examples_dir, example=example\n)[0]\n\nresult, classes = sen_analyzer.run(calibration_classes=calibration_classes)\nprint(\"Result of the sensitivity analysis\")\nprint(result)\n"
+ "source": "calibration_classes = setup_calibration_classes(\n examples_dir=examples_dir, example=example\n)[0]\n\nresult, classes = sen_analyzer.run(calibration_classes=calibration_classes,\n plot_result=True,\n save_results=False)\nprint(\"Result of the sensitivity analysis\")\nprint(result)\n"
},
{
"cell_type": "markdown",
@@ -51,7 +63,7 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": "print(\"Selecting relevant tuner-parameters using a fixed threshold:\")\nsen_analyzer.select_by_threshold(calibration_classes=classes,\n result=result,\n threshold=0.01)\nfor cal_class in classes:\n print(f\"Class '{cal_class.name}' with parameters:\\n{cal_class.tuner_paras}\")\n"
+ "source": "print(\"Selecting relevant tuner-parameters using a fixed threshold:\")\nsen_analyzer.select_by_threshold(calibration_classes=classes,\n result=result[0],\n threshold=0.01,\n analysis_variable='S1')\nfor cal_class in classes:\n print(f\"Class '{cal_class.name}' with parameters:\\n{cal_class.tuner_paras}\")\n"
},
{
"cell_type": "markdown",
diff --git a/examples/jupyter_notebooks/e3_verbose_dymola_sensitivity_analysis_example.ipynb b/examples/jupyter_notebooks/e3_verbose_dymola_sensitivity_analysis_example.ipynb
new file mode 100644
index 00000000..ef7ac32a
--- /dev/null
+++ b/examples/jupyter_notebooks/e3_verbose_dymola_sensitivity_analysis_example.ipynb
@@ -0,0 +1,126 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "# Example 3 sensitivity analysis with dymola api\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Goals of this part of the examples:\n1. Learn how to execute a sensitivity analysis with the dymola api\n\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "import os\nimport pathlib\nfrom examples import setup_dym_api, setup_calibration_classes\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Import a valid analyzer, e.g. `SobolAnalyzer`\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "from aixcalibuha import SobolAnalyzer\nfrom aixcalibuha.data_types import merge_calibration_classes\nfrom ebcpy.utils.conversion import convert_tsd_to_modelica_txt\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nExample process of a verbose sensitivity analysis with the dymola_api.\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n:param [pathlib.Path, str] aixlib_mo:\n Path to the AixLib package.mo file.\n:param str example:\n Which example to run, \"A\" or \"B\"\n:param int n_cpu:\n Number of cores to use\n\n:return: A list of calibration classes\n:rtype: list\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\naixlib_mo = \"TODO: Add a valid input according to the docstring above\"\nexample: str = \"B\"\nn_cpu: int = 1\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Setup\nUsing a dymola api instead of the fmu api\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "examples_dir = pathlib.Path(examples_dir)\nsim_api = setup_dym_api(examples_dir=examples_dir,\n aixlib_mo=aixlib_mo,\n example=example,\n n_cpu=n_cpu)\ncalibration_classes = setup_calibration_classes(\n examples_dir=examples_dir, example=example, multiple_classes=False\n)[0]\nmerged_calibration_classes = merge_calibration_classes(calibration_classes)\nmerged_calibration_classes[0].name = 'global'\ncalibration_classes = setup_calibration_classes(\n examples_dir=examples_dir, example=example, multiple_classes=True\n)[0]\nmerged_calibration_classes.extend(merge_calibration_classes(calibration_classes))\n\nif example == 'B':\n merged_calibration_classes[-1].tuner_paras = merged_calibration_classes[0].tuner_paras\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Example of Sobol method\nSet up Sobol analyzer\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "sen_analyzer = SobolAnalyzer(\n sim_api=sim_api,\n num_samples=2,\n calc_second_order=True,\n cd=examples_dir.joinpath('testzone', f'verbose_sen_dymola_{example}'),\n save_files=True,\n load_files=False,\n savepath_sim=examples_dir.joinpath('testzone', f'verbose_sen_dymola_{example}', 'files'),\n suffix_files='mat'\n)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "The only difference to the fmu example is the handling of inputs.\nThere we have in the model now a table for the inputs and generate\nan input file here. This is only necessary for example A because\nexample B has no inputs. To generate the input in the correct format,\nuse the convert_tsd_to_modelica_txt function:\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "if example == \"A\":\n table_name = \"InputTDryBul\"\n file_name = r\"D:\\dymola_inputs_A.txt\"\n print(file_name)\n filepath = convert_tsd_to_modelica_txt(\n tsd=merged_calibration_classes[0].inputs,\n table_name=table_name,\n save_path_file=file_name\n )\n # Now we can remove the input from the old fmu calibration classes\n for cal_class in merged_calibration_classes:\n cal_class._inputs = None\n print(\"Successfully created Dymola input file at\", filepath)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "run sensitivity analysis\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "result, classes = sen_analyzer.run(calibration_classes=merged_calibration_classes,\n verbose=True,\n use_first_sim=True,\n plot_result=True,\n save_results=True,\n n_cpu=2)\nprint(\"Result of the sensitivity analysis\")\nprint('First and total order results of sobol method')\nprint(result[0].to_string())\nprint('Second order results of sobol method')\nprint(result[1].to_string())\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "remove input file\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "if example == \"A\":\n os.remove(file_name)\n"
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/jupyter_notebooks/e3_verbose_sensitivity_analysis_example.ipynb b/examples/jupyter_notebooks/e3_verbose_sensitivity_analysis_example.ipynb
new file mode 100644
index 00000000..218bdd7b
--- /dev/null
+++ b/examples/jupyter_notebooks/e3_verbose_sensitivity_analysis_example.ipynb
@@ -0,0 +1,337 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Example 3 verbose sensitivity analysis\nfor the analysis of your model and the calibration process\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Goals of this part of the examples:\n1. Learn how to execute a verbose sensitivity analysis\n2. Learn the meaning of the results and the analysis of your model\n3 Learn how to use the results to select tuner-parameters for a calibration\n4. Learn other sensitivity methods and compare them\n5. Learn how to execute a time dependent sensitivity analysis\n6. Learn how to save the results for reproduction\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Start by importing all relevant packages\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "import warnings\nimport pathlib\nimport matplotlib.pyplot as plt\nfrom aixcalibuha import SobolAnalyzer, FASTAnalyzer, MorrisAnalyzer\nfrom aixcalibuha.data_types import merge_calibration_classes\nfrom examples import setup_fmu, setup_calibration_classes\nfrom aixcalibuha import plotting\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nExample process of a verbose sensitivity analysis for calibration and analysis porpoises.\nFirst, the sensitivity problem is constructed, in this example\nthe `sobol` method is chosen.\nAfterward, the SenAnalyzer class is instantiated to run the\nsensitivity analysis in the next step.\nThe result of this analysis is then printed to the user.\nand tuner-parameters are selected with these results.\nA comparison between different methods is shown.\nAt the end the option to save a reproduction archive is shown.\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n e.g. r\"LOCAL_PATH_TO\\AixCaliBuHA\\examples\"\n:param str example:\n Which example to run, \"A\" or \"B\"\n:param int n_cpu:\n Number of cores to use\n\n:return: A list of calibration classes\n:rtype: list\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\nexample: str = \"B\"\nn_cpu: int = 1\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Setup\nSet up the class according to the documentation.\nYou just have to pass a valid simulation api and\nsome further settings for the analysis.\nLet's first load the necessary simulation api:\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "examples_dir = pathlib.Path(examples_dir)\nsim_api = setup_fmu(examples_dir=examples_dir, example=example, n_cpu=n_cpu)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "To conduct a sensitivity analysis, we need to define calibration classes that\nencompass the objectives (goals) for which sensitivity is to be assessed. In this\ncontext, we'll explore distinct calibration classes corresponding to various\nstates of the models. Initially, we establish a comprehensive calibration class\nthat spans the entire duration, denoted as `global`. Subsequently, we can\nleverage simulations from this global class for other classes targeting\nspecific time intervals within the overall global range.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "For the specific states of the models with distinct time intervals, we adopt the\ncalibration classes from the second example and tailor them for the verbose\nsensitivity analysis. Alternatively, custom classes could be created directly\nfunction (`validation_class`), we opt to ignore it by using the variable\nassignment `_` and omit any associated text output of the second example.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "calibration_classes, _ = setup_calibration_classes(\n examples_dir=examples_dir, example=example, multiple_classes=False\n)\nmerged_calibration_classes = merge_calibration_classes(calibration_classes)\nmerged_calibration_classes[0].name = 'global'\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Now, we add the calibration classes for the different states of the system\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "calibration_classes, _ = setup_calibration_classes(\n examples_dir=examples_dir, example=example, multiple_classes=True\n)\nmerged_calibration_classes.extend(merge_calibration_classes(calibration_classes))\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "This results in the following calibration classes where we merge the time intervals directly.\nWe could have also merged them with an option in the `run()` function of the\nsensitivity analyzer classes.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "print(\"Calibration classes for sensitivity analysis:\",\n [c.name for c in merged_calibration_classes])\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "In our detailed sensitivity analysis, it is essential for all classes to share\nidentical tuner parameters. This ensures that we can employ the same set of\nsimulations for calculating sensitivity across various calibration classes.\nHowever, the final class in example B deviates in tuner parameters; hence, we\nreset them to align with the tuner parameters of the other classes.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "if example == 'B':\n merged_calibration_classes[-1].tuner_paras = merged_calibration_classes[0].tuner_paras\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "With the definition of the calibration classes and the loaded `sim_api`,\nwe now take a look at the different options for sensitivity analysis.\nFirst, we perform the `Sobol` method, which is the most powerful currently\nsupported method, but is also the most computational demanding one.\nAfterward, we will compare the results of the different methods.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Example of Sobol method\nFirst, we instantiate the Analyzer with the `sim_api` and the number of samples.\nFor this example we will use a small sample size to reduce the time needed,\nwhich will lead to inaccurate results.\nIn the comparison of the different methods, we will discuss the required sample size.\nWe will also define a working directory were all results of the analysis will be stored.\nAdditionally, we can choose if the samples and corresponding simulation files will be saved.\nThese files can later be loaded and used for analysis of different calibration classes\nwithout performing new simulations. The simulations during the sensitivity analysis are the\nmain computational time factor.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "sen_analyzer = SobolAnalyzer(\n sim_api=sim_api,\n num_samples=8,\n calc_second_order=True,\n cd=examples_dir.joinpath('testzone', f'verbose_sen_{example}'),\n save_files=True,\n savepath_sim=examples_dir.joinpath('testzone', f'verbose_sen_{example}', 'files'),\n suffix_files='csv'\n)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Now, we run the sensitivity analysis with the verbose option.\nWith that, we not only get the results for combined target values,\nbut we also get the results for every target value alone.\nBecause we defined the first calibration class global\nand every calibration class has the same\ntuner-parameters, we can use the option `use_first_sim`,\nwhere only the first class will be simulated and the simulation files saved.\nThese simulations will be used for all other classes.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "For that the simulations are loaded for each class\nand the statistical measure is then evaluated for the\nrelevant time intervals of each class.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "When we load simulation files we can use multiprocessing for loading the simulation\nand evaluating the statistical measure for each class in their time intervals.\nThis multiprocessing option is especially\nuseful for large models and large simulation data,\nbecause only one simulation at a time is stored\nin memory for each process.\nThis can prevent possible memory errors.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "We disable the automatic plot option here,\nbut we save all results. Later we can use the plot function\nof the plotting module to plot the results.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "result, classes = sen_analyzer.run(calibration_classes=merged_calibration_classes,\n verbose=True,\n use_first_sim=True,\n plot_result=False,\n save_results=True,\n n_cpu=n_cpu,\n scale=False)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "After running the sensitivity analysis you can see\nthat the working directory was created and the result\nfiles were saved here. First, the folder \"files\" was\ncreated as the result of `save_files=True`. In there, the\nsimulation files are stored in an own folder which\nname contains the name of the used calibration class.\nAdditionally, the corresponding samples are stored.\nThe simulations are coupled with their samples.\nAs one results of the analysis, the statistical\nmeasure and corresponding sample are saved for each class.\nThis information could be used for surrogate-based\ncalibration, which is currently not implemented in `AixCaliBuHA`.\nThe main results of the sensitivity analysis are the sensitivity\nmeasures stored in \"SobolAnalyzer_results.csv\"\nand \"SobolAnalyzer_results_second_order.csv\".\ndataframes. This is specific to the sobol method, all other\nof the sobol method with possibly other analysis variables.\nLet´s take a look at these results.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "The first result has as columns the tuner-parameters\nand a multi level index with three levels.\nThe first level defines the calibration class.\nThe second level defines the Goals (target values). The index `all`\nis for the result of the combined target values in the goals.\nThe last level defines the result of the\nsensitivity measure for each class and goal.\nThese analysis variables are specific for each method.\nFor their exact meaning I refer to the documentation of the SALib or the literature.\nIn this example you get a short overview in the comparison later.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "print(\"Result of the sensitivity analysis\")\nprint('First and total order results of sobol method')\nprint(result[0].to_string())\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "The second result of the sobol method is for second order sensitive measures.\nThese describe the interaction between two parameters,\nso this dataframe has a fourth index level \"Interaction\".\nIn this level, the tuner-parameters are listed again.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "print('Second order results of sobol method')\nprint(result[1].to_string())\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "For a better understanding of the results we will now plot them.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Plotting Sensitivity results\nWe start with the result which were calculated with the small sample size.\nLet's plot the first and total order results. These results\nare specific for each single parameter\nFor each calibration class, a figure is created\nwhich shows for each goal the first order sensitivity `S1`\nand the total order sensitivity `ST` combined. For the small\nsample size the results have large confidence\nintervals, which show that these results are inaccurate as we\nnoted earlier due to the small sample size.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "plotting.plot_single(result[0])\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "The plotting of second order results is only useful\nand working for more than 2 parameters.\nSo we only can take a look at them in example A.\nIf you run example B we will skip the plot of the second order results\nand load some sensitivity results of example A.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Let's take a look at the second order results `S2` of example A we just created.\nThis analysis variable shows the interaction of two\nparameters, so we can plot them as a heatmap.\nWe can see that the parameters have no interaction with\nthemselves what is obvious. Also, we see that the\nvalues for p1,p2 and p2,p1 are the same.\nIn the heatmap we can't visualize the confidence intervals,\nso we will also take a look at the interaction of\none specific parameter.\nFor that the `plotting` module has also a function\nwhich looks simular to the `S1` and `ST` plots.\nHere we see again large confidence intervals,\nso now we will load results which were calculated with\na much higher sample number.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "if example == 'A':\n plotting.heatmaps(result[1])\n plotting.plot_single_second_order(result[1], 'rad.n')\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Loading results\nWe will now load sensitivity results of example A .\nThese results were produced with this example and\na samples number N=1024 and calc_second_order=True\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "result_sobol = SobolAnalyzer.load_from_csv(\n examples_dir.joinpath('data', 'SobolAnalyzer_results_A.csv')\n)\nresult_sobol_2 = SobolAnalyzer.load_second_order_from_csv(\n examples_dir.joinpath('data', 'SobolAnalyzer_results_second_order_A.csv')\n)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "For a better understanding we will only take a\nlook at the global class and Electricity goal\nand plot `S1`, `ST` and `S2` in the same window.\nFor that we can use the plot function\nwith some optional options. This shows how you can easily customize\nthese plots, and you can also chang everything\non the axes of the plots.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "fig = plt.figure(figsize=plt.figaspect(1. / 4.), layout=\"constrained\") # creating one figure\nsubfigs = fig.subfigures(1, 3, wspace=0) # creating subfigures for each type of plot\nax0 = subfigs[0].subplots() # plotting `S1` and `ST`\nplotting.plot_single(\n result=result_sobol,\n cal_classes=['global'],\n goals=['Electricity'],\n show_plot=False,\n figs_axes=([subfigs[0]], [ax0]),\n max_name_len=14\n)\nax1 = subfigs[1].subplots() # plotting heatmap\nplotting.heatmap(\n result_sobol_2,\n cal_class='global',\n goal='Electricity',\n ax=ax1,\n show_plot=False,\n max_name_len=14\n)\nax2 = subfigs[2].subplots() # plotting the interactions of one single parameter\nplotting.plot_single_second_order(\n result=result_sobol_2,\n para_name='rad.n',\n show_plot=False,\n cal_classes=['global'],\n goals=['Electricity'],\n figs_axes=([subfigs[2]], [ax2]),\n max_name_len=14\n)\nplt.show()\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Now, what can we see in these results? First, the\nconfidence intervals are now much smaller, so that we\ncan interpret and connect the different analysis variables.\n`S1` stands for the variance in the objective,\nwhich is caused by the variation of one parameter\nwhile all other parameters are constant. The sobol analysis\nvariables are normalized with the total variance caused\nby all parameter variations together within\ntheir bounds. This means that when the parameters had\nno interactions the sum of all `S1` values would\nbe 1. `ST` shows the resulting variance of a parameter\nwith all his interactions. Let's take a look at the\ninteraction between `n` and `G`, which are the highest.\nHere, the `S2` value of the interaction\nbetween `n` and `G` has a similar value to each difference\nof `S1` and `ST` from these parameters. This show how this\ndifference corresponds to the interactions of a parameter.\nAll other parameters have only a very small sensitivity.\nThese are just some basics, to understand what option you\nhave in `AixCaliBuAH`. For more information look up relevant literature.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Comparison of different Methods\nWe will now take a short look at a comparison of the\n`Sobol`, `Fast` and `Morris` method in `AixCaliBuAH`.\nThe `SALib` provides more methods, which can\nbe implemented here in the future or on your own.\nWe already took a look at the `Sobol` method,\nwhich can compute `S1`, `ST`, and `S2` with their confidence intervals.\nThe sobol method needs for that `(2+2k)N` simulations\nwhere `k` is the number of parameters and `N` is the sample\nnumber. For variance-based methods `N` should be greater\nthan 1000. The sobol method can also compute only `S1` and\n`ST` with calc_second_order=False and (1+k)N simulations.\nThe FAST method is another variance-based\nmethod and only computes `S1` and `ST` with k*N simulations.\n`Sobol` and FAST should show simular results which is the\ncase for example B but in example A the FAST method overestimates `ST` in some cases.\nIn the right plots, the results for the `Morris` method are shown.\nThese are based on the mean of derivatives which\nrepresents the analysis variables mu. In the estimation\nof the derivatives, only one parameter is changed at a time.\nmu_star is the mean of the absolut values of the derivatives\nand is an approximation of `ST` but\nneeds only an `N` of over 100 and `(1+k)N` simulations.\nIn this comparison, mu_star shows simular results to `ST`\nof the `Sobol` method, but it is not normalized.\nLast, sigma is computed which is the standard deviation and\nis a sign for a non-linear model or for interactions in the model.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "result_sobol = SobolAnalyzer.load_from_csv(\n examples_dir.joinpath('data', f'SobolAnalyzer_results_{example}.csv')\n)\nresult_fast = FASTAnalyzer.load_from_csv(\n examples_dir.joinpath('data', f'FASTAnalyzer_results_{example}.csv')\n)\nresult_morris = MorrisAnalyzer.load_from_csv(\n examples_dir.joinpath('data', f'MorrisAnalyzer_results_{example}.csv')\n)\n\nglobal_class = classes[0]\nfig_comp = plt.figure(figsize=plt.figaspect(1. / 4.), layout=\"constrained\")\nsubfigs_comp = fig_comp.subfigures(1, 3, wspace=0)\nax0_comp = subfigs_comp[0].subplots(3, 1, sharex=True)\nplotting.plot_single(\n result=result_sobol,\n cal_classes=[global_class.name],\n show_plot=False,\n figs_axes=([subfigs_comp[0]], [ax0_comp])\n)\nsubfigs_comp[0].suptitle(\"Sobol\")\nax1_comp = subfigs_comp[1].subplots(3, 1, sharex=True)\nplotting.plot_single(\n result=result_fast,\n cal_classes=[global_class.name],\n show_plot=False,\n figs_axes=([subfigs_comp[1]], [ax1_comp])\n)\nsubfigs_comp[1].suptitle(\"FAST\")\nax2_comp = subfigs_comp[2].subplots(3, 1, sharex=True)\nplotting.plot_single(\n result=result_morris,\n show_plot=False,\n cal_classes=[global_class.name],\n figs_axes=([subfigs_comp[2]], [ax2_comp])\n)\nsubfigs_comp[2].suptitle(\"Morris\")\nplt.show()\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Selection of tuner-parameters based on verbose sensitivity results\nWe can now also use these verbose sensitivity\nresults for a selection of relevant tuner parameters.\nWe already saw that our models have interactions,\nso it will be necessary to calibrate them together\nin one calibration class. The calibration class\nglobal can be used for that because it includes all\nthe other specific classes. But in the sensitivity\nresults of this class it could be that parameters,\nwhich are only in one state sensitive can be missed.\nWe can use the verbose sensitivity results so\nthat a parameter will be selected when it has a\nsensitivity at least in one class and target value\nof the sensitivity results. This is enough that\nthe parameter can be calibrated.\nHere, we will use `S1` because it is normalized instead of mu_star,\nand we can set on single threshold for all classes and goals.\nAlso, if a parameter is only through interaction sensitive (only in `ST`)\nA real value can't be found and the parameters which interact will\ncompensate each others deviation to a real world value.\nThis still can happen when we choose with `S1` but it is less likely.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "calibration_class = SobolAnalyzer.select_by_threshold_verbose(classes[0],\n result=result_sobol,\n analysis_variable='S1',\n threshold=0.001,\n )\n\nprint(calibration_class.tuner_paras)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Time dependent sensitivity\nFor analysis porpoises we can also evaluate the time dependent sensitivity\ninstead of the sensitivity of larger time intervals. But these results can not yet\nbe used to automatically selected tuner parameters for a calibration.\nThe function for the time dependent sensitivity is similar to the other one.\nSo we can also use the simulations of booth run functions for each other.\nThe main difference is that we only need one calibration class from which\nthe measured target data is not used and the sensitivity is directly\ncalculated for the change of the separate target values and no combined goals.\nIn the results, we then get just the additional index time.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\") # ignoring warnings that are caused by the low sample size\n result = sen_analyzer.run_time_dependent(\n cal_class=merged_calibration_classes[0],\n load_sim_files=True,\n plot_result=True\n )\nprint(result)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "When we use the plot function we can see the sensitivity of each parameter\nchanging over time. We can also see that the confidence intervals\nare again large for such a small sample size. When the confidence interval is larger\nthan one an info is shown and the confidence interval of the previous\ntime step is used to smooth it out for the visualisation.\nLet's load again results which were created with a larger sample size.\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "## Loading time dependent results\nThese results were produced with a samples number `N=1024` and `calc_second_order=True`\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "result_sobol_time = SobolAnalyzer.load_from_csv(\n examples_dir.joinpath('data', 'SobolAnalyzer_results_time_A.csv')\n)\nresult_sobol_2_time = SobolAnalyzer.load_second_order_from_csv(\n examples_dir.joinpath('data', 'SobolAnalyzer_results_second_order_time_A.csv')\n)\nplotting.plot_time_dependent(result=result_sobol_time, plot_conf=True, show_plot=False)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Now the confidence intervals are smaller and\nonly at one time step they are still lager than 1.\nWe can also see that the parameter `theCon.G` has the biggest influence.\nSo we can take a closer look at this parameter with another plot\nfunction where all available sensitivity measures are plotted together.\nSecond order results are plotted cumulative on top of `S1`.\nThis resembles the definition of `ST = S1 + sum(S2_i) + sum(S3_i) + ...`\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "plotting.plot_parameter_verbose(parameter='theCon.G',\n single_result=result_sobol_time,\n second_order_result=result_sobol_2_time)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "At the end we also can create a reproduction\narchive which saves all settings and all created files\nautomatically with the reproduction function of ebcpy.\nNot running in jupyter notebook\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "file = sen_analyzer.save_for_reproduction(\n title=\"SenAnalyzerTest\",\n path=examples_dir.joinpath('testzone'),\n log_message=\"This is just an example\",\n remove_saved_files=False,\n exclude_sim_files=True\n)\nprint(\"ZIP-File to reproduce all this:\", file)\n"
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/jupyter_notebooks/e4_calibration_example.ipynb b/examples/jupyter_notebooks/e4_calibration_example.ipynb
index 0480dd66..ae692fd9 100644
--- a/examples/jupyter_notebooks/e4_calibration_example.ipynb
+++ b/examples/jupyter_notebooks/e4_calibration_example.ipynb
@@ -17,6 +17,18 @@
"outputs": [],
"source": "import numpy as np\nfrom aixcalibuha import CalibrationClass, Calibrator, MultipleClassCalibrator\n"
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nRun an example for a calibration. Make sure you have Dymola installed\non your device and a working licence. All output data will be stored in\nthe current working directory of python. Look at the logs and plots\nto better understand what is happening in the calibration. If you want, you\ncan switch the methods to other supported methods or change the framework and\ntry the global optimizer of dlib.\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n:param str example:\n Which example to run, \"A\" or \"B\"\n:param int n_cpu:\n Number of cores to use\n\nOptional, for the fully automated process\n:param ebcpy.simulationapi.SimulationAPI sim_api:\n Simulation API to simulate the models\n:param list[CalibrationClass] cal_classes:\n List with multiple CalibrationClass objects for calibration. Goals and\n TunerParameters have to be set. If only one class is provided (either\n a list with one entry or a CalibrationClass object) the single-class\n Calibrator is used.\n:param CalibrationClass framework:\n See Documentation of ebcpy on available optimization frameworks\n:param str method:\n See Documentation of ebcpy on available optimization framework methods\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\nexample = \"B\"\nn_cpu: int = 1\nsim_api = None\ncal_classes = None\nframework: str = \"scipy_differential_evolution\"\nmethod: str = \"best1bin\"\n"
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/examples/jupyter_notebooks/e5_automated_process.ipynb b/examples/jupyter_notebooks/e5_automated_process.ipynb
index bc850c6c..17130d51 100644
--- a/examples/jupyter_notebooks/e5_automated_process.ipynb
+++ b/examples/jupyter_notebooks/e5_automated_process.ipynb
@@ -17,6 +17,18 @@
"outputs": [],
"source": "from examples.e3_sensitivity_analysis_example import run_sensitivity_analysis\nfrom examples.e4_calibration_example import run_calibration\n"
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "Please define the missing TODOs in the section below according to the docstrings.\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": "\"\"\"\nArguments of this example:\n\n:param [pathlib.Path, str] examples_dir:\n Path to the examples folder of AixCaliBuHA\n:param str example:\n Whether to use example A (requires windows) or B.\n Default is \"A\"\n:param int n_cpu:\n Number of cores to use\n\"\"\"\nexamples_dir = \"TODO: Add a valid input according to the docstring above\"\nexample: str = \"A\"\nn_cpu: int = 1\n"
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/examples/model/AixCaliBuHAExamples.mo b/examples/model/AixCaliBuHAExamples.mo
index 6c30db49..f87ab395 100644
--- a/examples/model/AixCaliBuHAExamples.mo
+++ b/examples/model/AixCaliBuHAExamples.mo
@@ -1,4 +1,4 @@
-within ;
+within ;
package AixCaliBuHAExamples
model HeatPumpSystemCalibration
HeatPumpSystemWithInputs heatPumpSystemWithInputs(
@@ -278,6 +278,253 @@ package AixCaliBuHAExamples
fillPattern = FillPattern.Solid,
points={{-38,64},{68,-2},{-38,-64},{-38,64}})}));
end HeatPumpSystemWithInputs;
+
+ model HeatPumpSystemWithTimTab "Example for a heat pump system"
+ package Medium_sin = AixLib.Media.Water;
+ package Medium_sou = AixLib.Media.Water;
+ AixLib.Fluid.MixingVolumes.MixingVolume vol(
+ energyDynamics=Modelica.Fluid.Types.Dynamics.FixedInitial,
+ redeclare package Medium = Modelica.Media.Air.SimpleAir,
+ V=40,
+ m_flow_nominal=40*6/3600,
+ T_start=293.15)
+ annotation (Placement(transformation(extent={{86,34},{106,54}})));
+ Modelica.Thermal.HeatTransfer.Components.ThermalConductor theCon(G=10000/40)
+ "Thermal conductance with the ambient"
+ annotation (Placement(transformation(extent={{38,54},{58,74}})));
+ Modelica.Thermal.HeatTransfer.Sources.PrescribedHeatFlow preHea
+ "Prescribed heat flow"
+ annotation (Placement(transformation(extent={{38,84},{58,104}})));
+ Modelica.Thermal.HeatTransfer.Components.HeatCapacitor heaCap(C=2*40*1.2*1006)
+ "Heat capacity for furniture and walls"
+ annotation (Placement(transformation(extent={{80,64},{100,84}})));
+ Modelica.Blocks.Sources.CombiTimeTable timTab(
+ extrapolation=Modelica.Blocks.Types.Extrapolation.Periodic,
+ smoothness=Modelica.Blocks.Types.Smoothness.ConstantSegments,
+ table=[-6*3600,0; 8*3600,4000; 18*3600,0])
+ "Time table for internal heat gain"
+ annotation (Placement(transformation(extent={{-2,84},{18,104}})));
+ AixLib.Fluid.HeatExchangers.Radiators.RadiatorEN442_2 rad(
+ energyDynamics=Modelica.Fluid.Types.Dynamics.FixedInitial,
+ T_a_nominal(displayUnit="degC") = 323.15,
+ dp_nominal=0,
+ m_flow_nominal=20000/4180/5,
+ Q_flow_nominal=20000,
+ redeclare package Medium = Medium_sin,
+ T_start=313.15,
+ T_b_nominal=318.15,
+ TAir_nominal=293.15,
+ TRad_nominal=293.15) "Radiator"
+ annotation (Placement(transformation(extent={{40,2},{20,22}})));
+
+ AixLib.Fluid.Sources.Boundary_pT preSou(
+ redeclare package Medium = Medium_sin,
+ nPorts=1,
+ T=313.15)
+ "Source for pressure and to account for thermal expansion of water"
+ annotation (Placement(transformation(extent={{-48,2},{-28,22}})));
+
+ Modelica.Thermal.HeatTransfer.Sources.PrescribedTemperature TOut
+ "Outside temperature"
+ annotation (Placement(transformation(extent={{-2,54},{18,74}})));
+ AixLib.Fluid.Sources.Boundary_pT sou(
+ nPorts=1,
+ redeclare package Medium = Medium_sou,
+ p=200000,
+ T=283.15) "Fluid source on source side"
+ annotation (Placement(transformation(extent={{102,-100},{82,-80}})));
+
+ AixLib.Fluid.Sources.Boundary_pT sin(
+ nPorts=1,
+ redeclare package Medium = Medium_sou,
+ p=200000,
+ T=281.15) "Fluid sink on source side"
+ annotation (Placement(transformation(extent={{-48,-100},{-28,-80}})));
+ AixLib.Systems.HeatPumpSystems.HeatPumpSystem heatPumpSystem(
+ redeclare package Medium_con = Medium_sin,
+ redeclare package Medium_eva = Medium_sou,
+ dataTable=AixLib.DataBase.HeatPump.EN255.Vitocal350BWH113(),
+ use_deFro=false,
+ massDynamics=Modelica.Fluid.Types.Dynamics.DynamicFreeInitial,
+ energyDynamics=Modelica.Fluid.Types.Dynamics.FixedInitial,
+ refIneFre_constant=0.01,
+ dpEva_nominal=0,
+ deltaM_con=0.1,
+ use_opeEnvFroRec=true,
+ tableUpp=[-100,100; 100,100],
+ minIceFac=0,
+ use_chiller=true,
+ calcPel_deFro=100,
+ minRunTime(displayUnit="min"),
+ minLocTime(displayUnit="min"),
+ use_antLeg=false,
+ use_refIne=true,
+ initType=Modelica.Blocks.Types.Init.InitialOutput,
+ minTimeAntLeg(displayUnit="min") = 900,
+ scalingFactor=1,
+ use_tableData=false,
+ dpCon_nominal=0,
+ use_conCap=true,
+ CCon=3000,
+ use_evaCap=true,
+ CEva=3000,
+ Q_flow_nominal=5000,
+ cpEva=4180,
+ cpCon=4180,
+ use_secHeaGen=false,
+ redeclare model TSetToNSet = AixLib.Controls.HeatPump.BaseClasses.OnOffHP
+ ( hys=5),
+ use_sec=true,
+ QCon_nominal=10000,
+ P_el_nominal=2500,
+ redeclare model PerDataHea =
+ AixLib.DataBase.HeatPump.PerformanceData.LookUpTable2D (
+ smoothness=Modelica.Blocks.Types.Smoothness.LinearSegments,
+ dataTable=
+ AixLib.DataBase.HeatPump.EN255.Vitocal350BWH113(
+ tableP_ele=[0,-5.0,0.0,5.0,10.0,15.0; 35,3750,3750,3750,3750,3833;
+ 45,4833,4917,4958,5042,5125; 55,5583,5667,5750,5833,5958; 65,7000,
+ 7125,7250,7417,7583]),
+ printAsserts=false,
+ extrapolation=false),
+ redeclare function HeatingCurveFunction =
+ AixLib.Controls.SetPoints.Functions.HeatingCurveFunction (
+ TDesign=328.15),
+ use_minRunTime=true,
+ use_minLocTime=true,
+ use_runPerHou=true,
+ pre_n_start=true,
+ redeclare AixLib.Fluid.Movers.Data.Pumps.Wilo.Stratos80slash1to12
+ perEva,
+ redeclare AixLib.Fluid.Movers.Data.Pumps.Wilo.Stratos25slash1to4
+ perCon,
+ TCon_nominal=313.15,
+ TCon_start=313.15,
+ TEva_start=283.15,
+ use_revHP=false,
+ VCon=0.004,
+ VEva=0.004)
+ annotation (Placement(transformation(extent={{10,-92},{64,-32}})));
+
+ AixLib.Fluid.Sensors.TemperatureTwoPort
+ senT_a1(
+ redeclare final package Medium = Medium_sin,
+ final m_flow_nominal=1,
+ final transferHeat=true,
+ final allowFlowReversal=true)
+ "Temperature at sink inlet" annotation (
+ Placement(transformation(
+ extent={{10,10},{-10,-10}},
+ rotation=270,
+ origin={88,-20})));
+ Modelica.Blocks.Sources.RealExpression
+ realExpression(y=heatPumpSystem.heatPump.sigBus.PelMea)
+ annotation (
+ Placement(transformation(
+ extent={{-22,-12},{22,12}},
+ rotation=0,
+ origin={80,112})));
+ Modelica.Blocks.Interfaces.RealOutput Pel
+ annotation (Placement(transformation(extent={{120,102},{140,122}})));
+ Modelica.Blocks.Sources.CombiTimeTable InputTDryBul(
+ tableOnFile=true,
+ tableName="InputTDryBul",
+ fileName=Modelica.Utilities.Files.loadResource("D:\dymola_inputs_A.txt"))
+ annotation (Placement(transformation(extent={{-104,0},{-84,20}})));
+ equation
+ connect(theCon.port_b,vol. heatPort) annotation (Line(
+ points={{58,64},{68,64},{68,44},{86,44}},
+ color={191,0,0},
+ smooth=Smooth.None));
+ connect(preHea.port,vol. heatPort) annotation (Line(
+ points={{58,94},{68,94},{68,44},{86,44}},
+ color={191,0,0},
+ smooth=Smooth.None));
+ connect(heaCap.port,vol. heatPort) annotation (Line(
+ points={{90,64},{68,64},{68,44},{86,44}},
+ color={191,0,0},
+ smooth=Smooth.None));
+ connect(timTab.y[1],preHea. Q_flow) annotation (Line(
+ points={{19,94},{38,94}},
+ color={0,0,127},
+ smooth=Smooth.None));
+ connect(rad.heatPortCon,vol. heatPort) annotation (Line(
+ points={{32,19.2},{32,44},{86,44}},
+ color={191,0,0},
+ smooth=Smooth.None));
+ connect(rad.heatPortRad,vol. heatPort) annotation (Line(
+ points={{28,19.2},{28,44},{86,44}},
+ color={191,0,0},
+ smooth=Smooth.None));
+ connect(TOut.port,theCon. port_a) annotation (Line(
+ points={{18,64},{38,64}},
+ color={191,0,0},
+ smooth=Smooth.None));
+ connect(sou.ports[1], heatPumpSystem.port_a2)
+ annotation (Line(points={{82,-90},{64,-90},{64,-83.4286}},
+ color={0,127,255}));
+ connect(sin.ports[1], heatPumpSystem.port_b2) annotation (Line(points={{-28,-90},
+ {14,-90},{14,-83.4286},{10,-83.4286}},
+ color={0,127,255}));
+ connect(senT_a1.port_a, heatPumpSystem.port_b1) annotation (Line(points={{88,-30},
+ {88,-57.7143},{64,-57.7143}}, color={0,127,255}));
+ connect(senT_a1.port_b, rad.port_a) annotation (Line(points={{88,-10},{90,-10},
+ {90,12},{40,12}}, color={0,127,255}));
+ connect(senT_a1.T, heatPumpSystem.TAct) annotation (Line(points={{77,-20},{
+ -4,-20},{-4,-36},{5.95,-36},{5.95,-36.0714}},
+ color={0,0,127}));
+ connect(rad.port_b, heatPumpSystem.port_a1) annotation (Line(points={{20,12},
+ {-12,12},{-12,-57.7143},{10,-57.7143}},color={0,127,255}));
+ connect(rad.port_b, preSou.ports[1])
+ annotation (Line(points={{20,12},{-28,12}}, color={0,127,255}));
+ connect(realExpression.y, Pel)
+ annotation (Line(points={{104.2,112},{130,112}}, color={0,0,127}));
+ connect(InputTDryBul.y[1], TOut.T) annotation (Line(points={{-83,10},{-66,
+ 10},{-66,64},{-4,64}}, color={0,0,127}));
+ connect(InputTDryBul.y[1], heatPumpSystem.T_oda) annotation (Line(points={{
+ -83,10},{-66,10},{-66,-46},{-30,-46},{-30,-45.0714},{5.95,-45.0714}},
+ color={0,0,127}));
+ annotation (Diagram(coordinateSystem(preserveAspectRatio=false, extent={{-120,
+ -120},{120,120}})),
+ experiment(Tolerance=1e-6, StopTime=86400),
+ __Dymola_Commands(file="modelica://AixLib/Resources/Scripts/Dymola/Systems/HeatPumpSystems/Examples/HeatPumpSystem.mos"
+ "Simulate and plot"),
+ Documentation(info="
+ Model for testing the model AixLib.Systems.HeatPumpSystems.HeatPumpSystem.
+
+
+ A simple radiator is used to heat a room. This example is based on
+ the example in
+ AixLib.Fluid.HeatPumps.Examples.ScrollWaterToWater_OneRoomRadiator.
+
+",
+ revisions="
+ -
+ May 22, 2019 by Julian Matthes:
+ Rebuild due to the introducion of the thermal machine partial model
+ (see issue #715)
+
+ -
+ November 26, 2018 by Fabian Wüllhorst:
+ First implementation (see issue #577)
+
+
+"),
+ Icon(coordinateSystem(extent={{-120,-120},{120,120}}), graphics={
+ Ellipse(lineColor = {75,138,73},
+ fillColor={255,255,255},
+ fillPattern = FillPattern.Solid,
+ extent={{-120,-120},{120,120}}),
+ Polygon(lineColor = {0,0,255},
+ fillColor = {75,138,73},
+ pattern = LinePattern.None,
+ fillPattern = FillPattern.Solid,
+ points={{-38,64},{68,-2},{-38,-64},{-38,64}})}));
+ end HeatPumpSystemWithTimTab;
annotation (uses(AixLib(version="1.0.0"), Modelica(version="4.0.0")),
version="1",
conversion(from(version="", script=
diff --git a/pylintrc b/pylintrc
index c4a8917f..0547cae5 100644
--- a/pylintrc
+++ b/pylintrc
@@ -126,7 +126,8 @@ disable=print-statement,
next-method-defined,
dict-items-not-iterating,
dict-keys-not-iterating,
- dict-values-not-iterating
+ dict-values-not-iterating,
+ logging-fstring-interpolation
# Enable the message, report, category or checker with the given id(s). You can
# either give multiple identifier separated by comma (,) or put this option
@@ -148,7 +149,7 @@ evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / stateme
# used to format the message information. See doc for all details
#msg-template=
-# Set the output format. Available formats are text, parseable, colorized, yml
+# Set the output format. Available formats are text, parseable, colorized, json
# and msvs (visual studio).You can also give a reporter class, eg
# mypackage.mymodule.MyReporterClass.
output-format=text
@@ -248,6 +249,7 @@ good-names=i,
MAE,
R2,
MSE,
+ re,
X
_
diff --git a/requirements.txt b/requirements.txt
index 88a3cc92..8ea11bee 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,8 @@
numpy>=1.19.5
matplotlib>=3.3.4
pandas>=1.1.5
-SALib==1.4.5
+SALib>=1.4.6
git+https://github.com/RWTH-EBC/ebcpy.git@master#egg=ebcpy
toml>=0.10.2
-tables>=3.6.1
\ No newline at end of file
+tables>=3.6.1
+GitPython>=3.1.30
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 677035f2..4bfff100 100644
--- a/setup.py
+++ b/setup.py
@@ -11,12 +11,12 @@
'numpy>=1.19.5',
'matplotlib>=3.3.4',
'pandas>=1.3.5',
- 'SALib>=1.3.12',
+ 'SALib>=1.4.6',
'ebcpy>=0.3.0',
'toml>=0.10.2'
]
-__version__ = "0.3.1"
+__version__ = "1.0.0"
setuptools.setup(
name='aixcalibuha',
@@ -39,8 +39,10 @@
'License :: OSI Approved :: MIT License',
'Topic :: Scientific/Engineering',
'Intended Audience :: Science/Research',
- 'Programming Language :: Python :: 3.7',
- 'Programming Language :: Python :: 3.8'
+ 'Programming Language :: Python :: 3.8',
+ 'Programming Language :: Python :: 3.9',
+ 'Programming Language :: Python :: 3.10',
+ 'Programming Language :: Python :: 3.11'
],
keywords=[
'calibration', 'building', 'energy',
diff --git a/tests/test_modelica_cal_sen.py b/tests/test_modelica_cal_sen.py
index ec3bd5b3..943104f8 100644
--- a/tests/test_modelica_cal_sen.py
+++ b/tests/test_modelica_cal_sen.py
@@ -4,13 +4,46 @@
import unittest
import sys
+import os
import pathlib
import shutil
+
+import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from ebcpy import FMU_API, TimeSeriesData
-from aixcalibuha import MorrisAnalyzer, SobolAnalyzer, MultipleClassCalibrator, \
- Calibrator, CalibrationClass, TunerParas, Goals
+from aixcalibuha import MorrisAnalyzer, SobolAnalyzer, FASTAnalyzer, PAWNAnalyzer, \
+ MultipleClassCalibrator, Calibrator, CalibrationClass, TunerParas, Goals, plotting
+
+
+def _set_up():
+ aixcalibuha_dir = pathlib.Path(__file__).parents[1]
+ data_dir = aixcalibuha_dir.joinpath("examples", "data")
+ testzone_dir = aixcalibuha_dir.joinpath("tests", "testzone")
+
+ # As the examples should work, and the cal_class example uses the other examples,
+ # we will test it here:
+ meas_target_data = TimeSeriesData(
+ data_dir.joinpath("PumpAndValve.hdf"), key="examples"
+ )
+ meas_target_data.to_float_index()
+
+ # Setup three variables for different format of setup
+ variable_names = {
+ "TCap": ["TCapacity", "heatCapacitor.T"],
+ "TPipe": {"meas": "TPipe", "sim": "pipe.T"}
+ }
+
+ tuner_paras = TunerParas(names=["speedRamp.duration", "valveRamp.duration"],
+ initial_values=[0.1, 0.1],
+ bounds=[(0.1, 10), (0.1, 10)])
+ # Real "best" values: speedRamp.duration=0.432 and valveRamp.duration=2.5423
+ # Check setup the goals class:
+ goals = Goals(meas_target_data=meas_target_data,
+ variable_names=variable_names,
+ statistical_measure="NRMSE")
+
+ return aixcalibuha_dir, data_dir, testzone_dir, tuner_paras, goals
class TestModelicaCalibrator(unittest.TestCase):
@@ -19,32 +52,8 @@ class TestModelicaCalibrator(unittest.TestCase):
def setUp(self):
"""Called before every test.
Used to setup relevant paths and APIs etc."""
- #%% Define relevant paths
- aixcalibuha_dir = pathlib.Path(__file__).parents[1]
- self.data_dir = aixcalibuha_dir.joinpath("examples", "data")
- self.example_cal_dir = aixcalibuha_dir.joinpath("tests", "testzone")
-
- # As the examples should work, and the cal_class example uses the other examples,
- # we will test it here:
- meas_target_data = TimeSeriesData(
- self.data_dir.joinpath("PumpAndValve.hdf"), key="examples"
- )
- meas_target_data.to_float_index()
-
- # Setup three variables for different format of setup
- variable_names = {
- "TCap": ["TCapacity", "heatCapacitor.T"],
- "TPipe": {"meas": "TPipe", "sim": "pipe.T"}
- }
-
- tuner_paras = TunerParas(names=["speedRamp.duration", "valveRamp.duration"],
- initial_values=[0.1, 0.1],
- bounds=[(0.1, 10), (0.1, 10)])
- # Real "best" values: speedRamp.duration=0.432 and valveRamp.duration=2.5423
- # Check setup the goals class:
- goals = Goals(meas_target_data=meas_target_data,
- variable_names=variable_names,
- statistical_measure="NRMSE")
+ # %% Define relevant paths
+ aixcalibuha_dir, self.data_dir, self.example_cal_dir, tuner_paras, goals = _set_up()
self.calibration_classes = [
CalibrationClass(name="First", start_time=0, stop_time=1,
goals=goals, tuner_paras=tuner_paras),
@@ -87,51 +96,321 @@ def test_mutliple_class_calibration(self):
calibrator.calibrate(framework="scipy_differential_evolution",
method="best1bin")
+ def tearDown(self):
+ """Remove all created folders while calibrating."""
+ try:
+ self.sim_api.close()
+ except AttributeError:
+ pass
+ try:
+ shutil.rmtree(self.example_cal_dir, ignore_errors=True)
+ except FileNotFoundError:
+ pass
+
+
+class TestSenAnalyzer(unittest.TestCase):
+ """Test-class for the SenAnalyzer classes."""
+
+ def setUp(self):
+ """Called before every test.
+ Used to setup relevant paths and APIs etc."""
+ # Define relevant paths
+ aixcalibuha_dir, self.data_dir, self.result_dir, tuner_paras, goals = _set_up()
+ self.calibration_classes = [
+ CalibrationClass(name="global", start_time=0, stop_time=10,
+ goals=goals, tuner_paras=tuner_paras),
+ CalibrationClass(name="heat up", start_time=0, stop_time=1,
+ goals=goals, tuner_paras=tuner_paras),
+ CalibrationClass(name="cool down", start_time=1, stop_time=2,
+ goals=goals, tuner_paras=tuner_paras),
+ CalibrationClass(name="stationary", start_time=2, stop_time=10,
+ goals=goals, tuner_paras=tuner_paras)
+ ]
+
+ # %% Instantiate dymola-api
+ if "win" in sys.platform:
+ model_name = aixcalibuha_dir.joinpath("examples", "model", "PumpAndValve_windows.fmu")
+ else:
+ model_name = aixcalibuha_dir.joinpath("examples", "model", "PumpAndValve_linux.fmu")
+
+ self.sim_api = FMU_API(cd=self.result_dir,
+ model_name=model_name)
+
def test_sa_morris(self):
"""
Function to test the sensitivity analyzer class using morris
+ Used to test all parent functions
"""
# Setup the problem
sen_ana = MorrisAnalyzer(
sim_api=self.sim_api,
- num_samples=1,
+ num_samples=2,
+ cd=self.sim_api.cd.__str__()
+ )
+ sen_result, classes = sen_ana.run(self.calibration_classes,
+ plot_result=False)
+ self._check_sen_run_return(sen_ana, sen_result, classes)
+ with self.assertRaises(ValueError):
+ sen_ana.run(self.calibration_classes,
+ plot_result=False,
+ verbose=True,
+ use_first_sim=True,
+ n_cpu=9999999999)
+ with self.assertRaises(AttributeError):
+ sen_ana.run(self.calibration_classes,
+ plot_result=False,
+ verbose=True,
+ use_first_sim=True,
+ n_cpu=2)
+
+ sen_ana = MorrisAnalyzer(
+ sim_api=self.sim_api,
+ num_samples=2,
cd=self.sim_api.cd,
- analysis_variable='mu_star'
+ save_files=True,
+ )
+ false_calc_classes = self.calibration_classes.copy()
+ false_calc_classes.append(CalibrationClass(
+ name="False", start_time=1, stop_time=11,
+ goals=self.calibration_classes[0].goals,
+ tuner_paras=self.calibration_classes[0].tuner_paras
+ ))
+ with self.assertRaises(ValueError):
+ sen_ana.run(false_calc_classes,
+ plot_result=False,
+ verbose=True,
+ use_first_sim=True,
+ n_cpu=1)
+ sen_ana.run(self.calibration_classes,
+ plot_result=False,
+ verbose=True,
+ use_first_sim=True,
+ n_cpu=1)
+ self._check_sen_run_return(sen_ana, sen_result, classes)
+ sen_ana.run(self.calibration_classes,
+ plot_result=False,
+ verbose=True,
+ use_first_sim=True,
+ n_cpu=2)
+ self._check_sen_run_return(sen_ana, sen_result, classes)
+ os.getlogin = lambda: "test_login"
+ sen_ana.save_for_reproduction(
+ title="SenAnalyzerTest",
+ path=self.result_dir,
+ log_message="This is just an example",
+ remove_saved_files=False,
+ exclude_sim_files=True
)
- self._run_sen_ana(sen_ana)
def test_sa_sobol(self):
"""
Function to test the sensitivity analyzer class using sobol
"""
- # Setup the problem
sen_ana = SobolAnalyzer(
sim_api=self.sim_api,
num_samples=1,
+ cd=self.sim_api.cd
+ )
+ sen_result, classes = sen_ana.run(self.calibration_classes,
+ plot_result=False)
+ self._check_sen_run_return(sen_ana, sen_result, classes)
+
+ def test_sa_fast(self):
+ """
+ Function to test the sensitivity analyzer class using sobol
+ """
+ sen_ana = FASTAnalyzer(
+ sim_api=self.sim_api,
+ num_samples=8,
cd=self.sim_api.cd,
- analysis_variable='S1'
+ M=1
)
- self._run_sen_ana(sen_ana)
+ sen_result, classes = sen_ana.run(self.calibration_classes[0],
+ plot_result=False)
+ self._check_sen_run_return(sen_ana, sen_result, classes)
- def _run_sen_ana(self, sen_ana):
- # Choose initial_values and set boundaries to tuner_parameters
- # Evaluate which tuner_para has influence on what class
- sen_result, classes = sen_ana.run(self.calibration_classes)
- self.assertIsInstance(sen_result, pd.DataFrame)
- self.assertIsInstance(classes, list)
- for _cls in classes:
- self.assertIsInstance(_cls, CalibrationClass)
- classes = sen_ana.select_by_threshold(calibration_classes=classes,
- result=sen_result,
- threshold=0)
+ def test_sa_pawn(self):
+ """
+ Function to test the sensitivity analyzer class using sobol
+ """
+ sen_ana = PAWNAnalyzer(
+ sim_api=self.sim_api,
+ num_samples=2,
+ cd=self.sim_api.cd,
+ sampler="morris"
+ )
+ sen_result, classes = sen_ana.run(self.calibration_classes[0],
+ plot_result=False)
+ self._check_sen_run_return(sen_ana, sen_result, classes)
+ sen_ana = PAWNAnalyzer(
+ sim_api=self.sim_api,
+ num_samples=1,
+ cd=self.sim_api.cd,
+ sampler="sobol"
+ )
+ sen_result, classes = sen_ana.run(self.calibration_classes[0],
+ plot_result=False)
+ self._check_sen_run_return(sen_ana, sen_result, classes)
+ sen_ana = PAWNAnalyzer(
+ sim_api=self.sim_api,
+ num_samples=8,
+ cd=self.sim_api.cd,
+ M=1,
+ sampler="fast"
+ )
+ sen_result, classes = sen_ana.run(self.calibration_classes[0],
+ plot_result=False)
+ self._check_sen_run_return(sen_ana, sen_result, classes)
+
+ def test_select_by_threshold(self):
+ """
+ Function to test the function select_by_threshold of the SenAnalyzer
+ """
+ sen_result = MorrisAnalyzer.load_from_csv(
+ self.data_dir.joinpath("MorrisAnalyzer_results_B.csv"))
+ classes = MorrisAnalyzer.select_by_threshold(calibration_classes=self.calibration_classes,
+ result=sen_result,
+ analysis_variable='mu_star',
+ threshold=0)
self.assertIsInstance(classes, list)
self.assertTrue(len(classes) >= 1)
with self.assertRaises(ValueError):
- sen_ana.select_by_threshold(
+ MorrisAnalyzer.select_by_threshold(
calibration_classes=classes,
result=sen_result,
+ analysis_variable='mu_star',
threshold=np.inf)
+ def test_select_by_threshold_verbose(self):
+ """
+ Function to test the function select_by_threshold of the SenAnalyzer
+ """
+ sen_result = SobolAnalyzer.load_from_csv(
+ self.data_dir.joinpath("SobolAnalyzer_results_B.csv"))
+ cal_class = SobolAnalyzer.select_by_threshold_verbose(
+ calibration_class=self.calibration_classes[0],
+ result=sen_result,
+ analysis_variable="S1",
+ threshold=0,
+ calc_names_for_selection=["heat up", "cool down"]
+ )
+ self.assertIsInstance(cal_class, CalibrationClass)
+ with self.assertRaises(NameError):
+ cal_class = SobolAnalyzer.select_by_threshold_verbose(
+ calibration_class=self.calibration_classes[0],
+ result=sen_result,
+ analysis_variable="S1",
+ threshold=0,
+ calc_names_for_selection=["On", "Off"]
+ )
+ with self.assertRaises(ValueError):
+ SobolAnalyzer.select_by_threshold_verbose(
+ calibration_class=self.calibration_classes[0],
+ result=sen_result,
+ analysis_variable="S1",
+ threshold=np.inf,
+ calc_names_for_selection=["heat up", "cool down"]
+ )
+ with self.assertRaises(NameError):
+ false_cal_class = self.calibration_classes[0]
+ false_cal_class.tuner_paras = TunerParas(names=["false.name", "valveRamp.duration"],
+ initial_values=[0.1, 0.1],
+ bounds=[(0.1, 10), (0.1, 10)])
+ SobolAnalyzer.select_by_threshold_verbose(
+ calibration_class=self.calibration_classes[0],
+ result=sen_result,
+ analysis_variable="S1",
+ threshold=np.inf,
+ calc_names_for_selection=["heat up", "cool down"]
+ )
+
+ def test_plot_single(self):
+ """
+ Function to test the plot function of the SenAnalyzer
+ """
+ sen_result = MorrisAnalyzer.load_from_csv(
+ self.data_dir.joinpath("MorrisAnalyzer_results_B.csv"))
+ plotting.plot_single(result=sen_result,
+ show_plot=False,
+ use_suffix=True)
+
+ def test_plot_sobol_s2(self):
+ """
+ Function to test the second order plot function of the SobolAnalyzer
+ """
+ sen_result = SobolAnalyzer.load_second_order_from_csv(
+ self.data_dir.joinpath("SobolAnalyzer_results_second_order_A.csv"))
+ plotting.plot_second_order(sen_result,
+ use_suffix=True,
+ show_plot=False)
+ plotting.plot_single_second_order(sen_result, "rad.n",
+ show_plot=False)
+ plotting.heatmaps(sen_result,
+ show_plot=False)
+
+ def test_sa_time_dependent(self):
+ """
+ Test time dependent sensitivity analysis and
+ simple plotting on the basis of the sobol method.
+ """
+ sen_ana = SobolAnalyzer(
+ sim_api=self.sim_api,
+ num_samples=1,
+ cd=self.sim_api.cd
+ )
+ sen_result = sen_ana.run_time_dependent(cal_class=self.calibration_classes[0],
+ plot_result=True)
+ self.assertIsInstance(sen_result, tuple)
+ self.assertIsInstance(sen_result[0], pd.DataFrame)
+ self.assertIsInstance(sen_result[1], pd.DataFrame)
+ sen_ana = MorrisAnalyzer(
+ sim_api=self.sim_api,
+ num_samples=2,
+ cd=self.sim_api.cd,
+ save_files=True
+ )
+ sen_result = sen_ana.run_time_dependent(cal_class=self.calibration_classes[0],
+ plot_result=False)
+ self.assertIsInstance(sen_result, pd.DataFrame)
+ sen_result = sen_ana.run_time_dependent(cal_class=self.calibration_classes[0],
+ load_sim_files=True,
+ n_steps=120,
+ plot_result=True)
+ self.assertIsInstance(sen_result, pd.DataFrame)
+ with self.assertRaises(ValueError):
+ sen_result = sen_ana.run_time_dependent(cal_class=self.calibration_classes[0],
+ load_sim_files=True,
+ n_steps=100000000,
+ plot_result=True)
+
+ def test_plot_parameter_verbose(self):
+ """
+ Test the verbose plotting of time dependent sensitivity results of a single parameter
+ """
+ result_sobol_time = SobolAnalyzer.load_from_csv(
+ self.data_dir.joinpath('SobolAnalyzer_results_time_A.csv')
+ )
+ result_sobol_2_time = SobolAnalyzer.load_second_order_from_csv(
+ self.data_dir.joinpath('SobolAnalyzer_results_second_order_time_A.csv')
+ )
+ plotting.plot_parameter_verbose(parameter='G',
+ single_result=result_sobol_time,
+ second_order_result=result_sobol_2_time,
+ use_suffix=True)
+
+ def _check_sen_run_return(self, sen_ana, sen_result, classes):
+ """
+ Check the result of sensitivity_analyzer.run() function
+ """
+ if sen_ana.__class__.__name__ == 'SobolAnalyzer':
+ self.assertIsInstance(sen_result, tuple)
+ self.assertIsInstance(sen_result[1], pd.DataFrame)
+ sen_result = sen_result[0]
+ self.assertIsInstance(sen_result, pd.DataFrame)
+ self.assertIsInstance(classes, list)
+ for _cls in classes:
+ self.assertIsInstance(_cls, CalibrationClass)
+
def tearDown(self):
"""Remove all created folders while calibrating."""
try:
@@ -139,7 +418,7 @@ def tearDown(self):
except AttributeError:
pass
try:
- shutil.rmtree(self.example_cal_dir, ignore_errors=True)
+ shutil.rmtree(self.result_dir, ignore_errors=True)
except FileNotFoundError:
pass