initial commit

Author: sarangbhagwat

biosteam/evaluation/_model.py

@@ -1,7 +1,8 @@
 # -*- coding: utf-8 -*-
 # BioSTEAM: The Biorefinery Simulation and Techno-Economic Analysis Modules
-# Copyright (C) 2020-2023, Yoel Cortes-Pena <[email protected]>,
-#                          Yalin Li <[email protected]>
+# Copyright (C) 2020-, Yoel Cortes-Pena <[email protected]>,
+#                      Yalin Li <[email protected]>,
+#                      Sarang Bhagwat <[email protected]>
 #
 # This module implements a filtering feature from the stats module of the QSDsan library:
 # QSDsan: Quantitative Sustainable Design for sanitation and resource recovery systems
@@ -14,6 +15,8 @@
 from scipy.optimize import shgo, differential_evolution
 import numpy as np
 import pandas as pd
+from pandas import DataFrame, read_excel
+from chaospy import distributions as shape
 from ._metric import Metric
 from ._feature import MockFeature
 from ._utils import var_indices, var_columns, indices_to_multiindex
@@ -27,7 +30,7 @@
 from .evaluation_tools import load_default_parameters
 import pickle
 
-__all__ = ('Model',)
+__all__ = ('Model', 'EasyInputModel')
 
 def replace_nones(values, replacement):
     for i, j in enumerate(values):
@@ -1268,4 +1271,128 @@ def _info(self, p, m):
     def show(self, p=None, m=None):
         """Return information on p-parameters and m-metrics."""
         print(self._info(p, m))
-    _ipython_display_ = show
+    _ipython_display_ = show
+
+#%% Easier input parameter distributions and load statements for Model objects
+
+def codify(statement):
+    statement = replace_apostrophes(statement)
+    statement = replace_newline(statement)
+    return statement
+
+def replace_newline(statement):
+    statement = statement.replace('\n', ';')
+    return statement
+
+def replace_apostrophes(statement):
+    statement = statement.replace('’', "'").replace('‘', "'").replace('“', '"').replace('”', '"')
+    return statement
+
+def create_function(code, namespace_dict):
+    def wrapper_fn(statement):
+        def f(x):
+            namespace_dict['x'] = x
+            exec(codify(statement), namespace_dict)
+        return f
+    function = wrapper_fn(code)
+    return function
+
+class EasyInputModel(Model):
+    """
+    Create an EasyInputModel object that allows for evaluation over a sample space
+    using the Model class, with input parameter distributions and load functions
+    entered using a spreadsheet file or DataFrame object.
+    
+    Parameters
+    ----------
+    system : System
+        Should reflect the model state.
+    metrics : tuple[Metric]
+        Metrics to be evaluated by model.
+    specification=None : Function, optional
+        Loads specifications once all parameters are set. Specification should 
+        simulate the system as well.
+    params=None : Iterable[Parameter], optional
+        Parameters to sample from.
+    exception_hook : callable(exception, sample)
+        Function called after a failed evaluation. The exception hook should 
+        return either None or metric values given the exception and sample.
+    namespace_dict : dict, optional
+        Dictionary used to update the namespace accessed when executing
+        statements to load values into model parameters.
+        
+    """
+    def __init__(self, system, metrics=None, specification=None, 
+                 parameters=None, retry_evaluation=True, exception_hook='warn',
+                 namespace_dict={}):
+        Model.__init__(self, system=system, metrics=metrics, specification=specification, 
+                     parameters=parameters, retry_evaluation=retry_evaluation, exception_hook=exception_hook)
+        self.namespace_dict = namespace_dict
+        # globals().update(namespace_dict)
+    
+    def load_parameter_distributions(self, distributions,):
+        """
+        Load a list of distributions and statements to load values for user-selected
+        parameters.
+        
+        Parameters
+        ----------
+        distributions : pandas.DataFrame or file path to a spreadsheet of the following format:
+                        Column titles (these must be included, but others may be added for convenience):
+                            'Parameter name': String
+                                Name of the parameter.
+                            'Element': String, optional
+                            'Kind': String, optional
+                            'Units': String, optional
+                            'Baseline': float or int
+                                The baseline value of the parameter.
+                            'Shape': String, one of ['Uniform', 'Triangular']
+                                The shape of the parameter distribution.
+                            'Lower': float or int
+                                The lower value defining the shape of the parameter distribution.
+                            'Midpoint': float or int
+                                The midpoint value defining the shape of a 'Triangular' parameter distribution.
+                            'Upper': float or int
+                                The upper value defining the shape of the parameter distribution.
+                            'Load Statements': String
+                                A statement executed to load the value of the parameter. The value is stored in 
+                                the variable x. A namespace defined in the namespace_dict during EasyInputModel 
+                                initialization may be accessed. 
+                                E.g., to load a value into an example distillation unit D101's light key recovery, 
+                                ensure 'D101' is a key pointing to the D101 unit object in namespace_dict, then 
+                                simply include the load statement: 'D101.Lr = x'.
+                        
+        """
+        
+        df = distributions
+        if type(df) is not DataFrame:
+            df = read_excel(distributions)
+            
+        namespace_dict = self.namespace_dict
+        param = self.parameter
+        
+        for i, row in df.iterrows():
+            name = row['Parameter name']
+            element = row['Element'] # currently only compatible with String elements
+            kind = row['Kind']
+            units = row['Units']
+            baseline = row['Baseline']
+            shape_data = row['Shape']
+            lower, midpoint, upper = row['Lower'], row['Midpoint'], row['Upper']
+            load_statements = row['Load Statements']
+            
+            D = None
+            if shape_data.lower() in ['triangular', 'triangle',]:
+                D = shape.Triangle(lower, midpoint, upper)
+            elif shape_data.lower() in ['uniform',]:
+                if not str(midpoint)=='nan':
+                    raise ValueError(f"The parameter distribution for {name} ({element}) is 'Uniform' but was associated with a given midpoint value.")
+                D = shape.Uniform(lower, upper)
+                
+            param(name=name, 
+                  setter=create_function(load_statements, namespace_dict), 
+                  element=element, 
+                  kind=kind, 
+                  units=units,
+                  baseline=baseline, 
+                  distribution=D)