testm

python/docs/source/tutorial/data/CO2_C6.csv

@@ -0,0 +1,18 @@
+kind,T,P,x1,y1
+bubble,303.15,20.24,0.2385,0.9721
+bubble,303.15,30.36,0.3698,0.9768
+bubble,303.15,39.55,0.5063,0.9815
+bubble,303.15,50.95,0.7078,0.9842
+bubble,303.15,57.83,0.843,0.9855
+bubble,303.15,64.68,0.941,0.9884
+bubble,303.15,67.46,0.9656,0.9909
+bubble,315.15,20.84,0.2168,0.956
+bubble,315.15,30.4,0.3322,0.966
+bubble,315.15,40.52,0.4446,0.9748
+bubble,315.15,50.66,0.5809,0.9753
+bubble,315.15,60.29,0.718,0.9753
+bubble,315.15,69.11,0.8457,0.9764
+bubble,315.15,72.89,0.8928,0.9773
+bubble,315.15,75.94,0.9252,0.9777
+bubble,315.15,77.5,0.9424,0.9768
+bubble,315.15,78.66,0.952,0.9756

python/tests/conftest.py

@@ -0,0 +1,24 @@
+import os
+
+import pandas as pd
+
+import pathlib
+
+import pytest
+
+
+PATH = pathlib.Path(os.path.abspath(os.path.dirname(__file__)))
+DATA_PATH = PATH / "datasets"
+
+
+@pytest.fixture
+def data_path():
+    return DATA_PATH.joinpath
+
+
+@pytest.fixture
+def data_co2_c6_pxy(data_path):
+    tc = [304.1, 504.0]
+    pc = [73.75, 30.12]
+    w = [0.4, 0.299]
+    return pd.read_csv(data_path("co2_c6.csv")), tc, pc, w

python/tests/datasets/co2_c6.csv

@@ -0,0 +1,18 @@
+kind,T,P,x1,y1
+bubble,303.15,20.24,0.2385,0.9721
+bubble,303.15,30.36,0.3698,0.9768
+bubble,303.15,39.55,0.5063,0.9815
+bubble,303.15,50.95,0.7078,0.9842
+bubble,303.15,57.83,0.843,0.9855
+bubble,303.15,64.68,0.941,0.9884
+bubble,303.15,67.46,0.9656,0.9909
+bubble,315.15,20.84,0.2168,0.956
+bubble,315.15,30.4,0.3322,0.966
+bubble,315.15,40.52,0.4446,0.9748
+bubble,315.15,50.66,0.5809,0.9753
+bubble,315.15,60.29,0.718,0.9753
+bubble,315.15,69.11,0.8457,0.9764
+bubble,315.15,72.89,0.8928,0.9773
+bubble,315.15,75.94,0.9252,0.9777
+bubble,315.15,77.5,0.9424,0.9768
+bubble,315.15,78.66,0.952,0.9756

python/tests/fitting/test_fitting.py

@@ -0,0 +1,24 @@
+import pytest
+import yaeos
+import yaeos.fitting
+import yaeos.fitting.model_setters
+
+
+def test_fit_co2_c6_pxy(data_co2_c6_pxy):
+    data, tc, pc, w = data_co2_c6_pxy
+
+    mr0 = yaeos.QMR(kij=[[0, 0], [0, 0]], lij=[[0, 0], [0, 0]])
+    model = yaeos.PengRobinson76(tc, pc, w, mixrule=mr0)
+
+    fit_kij = yaeos.fitting.model_setters.fit_kij_lij
+    args = (model, True, False)
+
+    problem = yaeos.fitting.BinaryFitter(
+        model_setter=fit_kij, model_setter_args=args, data=data
+    )
+
+    x0 = [0.0]
+
+    problem.fit(x0, bounds=None)
+
+    assert (abs(problem.solution.x[0] - 0.11) < 1e-1)

python/yaeos/fitting/core.py

@@ -9,7 +9,32 @@
 
 
 class BinaryFitter:
-    """Binary Fitter."""
+    """BinaryFitter class.
+
+    This class is used to fit binary interaction parameters to experimental
+    data. The objective function is defined as the sum of the squared errors
+    between the experimental data and the model predictions.
+
+    Parameters
+    ----------
+    model_setter : callable
+        A function that returns a model object. The function should take the
+        optimization parameters as the first argument and any other arguments
+        as the following arguments.
+    model_setter_args : tuple
+        A tuple with the arguments to pass to the model_setter function.
+    data : pandas.DataFrame
+        A DataFrame with the experimental data.
+        The DataFrame should have the following columns:
+        - kind: str, the kind of data point
+                (bubble, dew, liquid-liquid, PT, critical)
+        - x1: float, the mole fraction of component 1
+        - y1: float, the mole fraction of component 1
+        - T: float, the temperature in K
+        - P: float, the pressure in bar
+    verbose : bool, optional
+        If True, print the objective function value and the optimization
+    """
 
     def __init__(self, model_setter, model_setter_args, data, verbose=False):
         self._get_model = model_setter
@@ -21,18 +46,19 @@ def __init__(self, model_setter, model_setter_args, data, verbose=False):
     def objective_function(self, x_values):
         """
         Objective function to minimize when fitting interaction parameters.
+
+        Parameters
+        ----------
+        x_values : array-like
+            The interaction parameters to fit.
         """
         model = self._get_model(x_values, *self._get_model_args)
         data = self.data
 
-        # ==============================================================
-        # Calculate the critical line and remove the NaN values
-        # --------------------------------------------------------------
+        # =====================================================================
+        # Calculate the critical line starting from the heavy component
+        # ---------------------------------------------------------------------
         cl = model.critical_line(z0=[0, 1], zi=[1, 0], a0=1e-2, s=1e-2)
-        msk = ~np.isnan(cl["T"])
-        cl["T"] = cl["T"][msk]
-        cl["P"] = cl["P"][msk]
-        cl["a"] = cl["a"][msk]
 
         err = 0
 
@@ -41,7 +67,12 @@ def objective_function(self, x_values):
             y = [row["y1"], 1 - row["y1"]]
             t = row["T"]
             p = row["P"]
-            w = row["weight"]
+
+            try:
+                w = row["weight"]
+            except KeyError:
+                w = 1
+
             error_i = 0
 
             # =================================================================
@@ -97,6 +128,10 @@ def objective_function(self, x_values):
                 error_i += distances[nearest]
 
             err += error_i * w
+
+        # =====================================================================
+        # Normalize the error and save the valuation
+        # ---------------------------------------------------------------------
         err = err / len(data)
 
         self.evaluations["fobj"].append(err)
@@ -107,8 +142,31 @@ def objective_function(self, x_values):
         return err
 
     def fit(self, x0, bounds, method="Nelder-Mead"):
-        """Fit the model to the data."""
+        """Fit the model to the data.
+
+        Fit the model to the data using the objective function defined in
+        the objective_function method. The optimization is performed using
+        the scipy.optimize.minimize function.
+        The optimization result is stored in the `.solution` property. Which
+
+        Parameters
+        ----------
+        x0 : array-like
+            Initial guess for the fitting parameters.
+        bounds : array-like
+            Bounds for the fitting parameters.
+        method : str, optional
+            The optimization method to use. Default is 'Nelder-Mead'.
+
+        Returns
+        -------
+        None
+        """
         sol = minimize(
             self.objective_function, x0=x0, bounds=bounds, method=method
         )
         self._solution = sol
+
+    @property
+    def solution(self):
+        return self._solution

python/yaeos/fitting/model_setters.py

@@ -7,10 +7,8 @@
 import numpy as np
 
 
-def fit_kij_lij(x, args):
+def fit_kij_lij(x, model, fit_kij, fit_lij):
     """Set the kij and/or lij parameter of the model."""
-    model = args[0]
-    fit_kij, fit_lij = args[1:]
 
     mr = model.mixrule