diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..8a5d951
--- /dev/null
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+
+[[package]]
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+description = "Python/C++ bindings helper module"
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+
+[[package]]
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+[[package]]
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+optional = false
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+platformdirs = ">=3.9.1,<5"
+
+[package.extras]
+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.10,<3.11"
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diff --git a/pyproject.toml b/pyproject.toml
index b76fe2f..dc5a218 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,8 +19,15 @@ name = "pyes"
 version = "2.0.17beta"
 description = ""
 
-[tool.run.ini_options]
-pythonpath = ["src/main/python/", "../libeq/src"]
+[tool.poetry.dependencies]
+python = ">=3.10,<3.11"
+pyinstaller = "~5"
+ppg = {git = "ssh://git@github.com/Kastakin/ppg.git", rev = "main"}
+pandas = "^1.5.0"
+openpyxl = "^3.0.9"
+pyqtgraph = "^0.13.0"
+pyside6-essentials = "~6.4.0"
+numba = "^0.58.1"
 
 [tool.uv.sources]
 libeq = { path = "../libeq", editable = true }
diff --git a/src/main/python/pyes/optimizers/distribution.py b/src/main/python/pyes/optimizers/distribution.py
new file mode 100644
index 0000000..2067a75
--- /dev/null
+++ b/src/main/python/pyes/optimizers/distribution.py
@@ -0,0 +1,1686 @@
+import logging
+from collections import deque
+
+import numpy as np
+import pandas as pd
+from numba import jit, njit, prange
+from numpy.typing import NDArray
+
+
+class Distribution:
+    """
+    Newton-Raphson method to solve iteratively mass balance equations.
+    """
+
+    def __init__(self):
+        # Set limit for underflow/overflow exp
+        self.epsl = 200
+        # Set flag to report that computation is done
+        self.done_flag = False
+
+    def fit(self, data):
+        """
+        Loads the data used to optimize the system.
+
+        :param data: data as given from the GUI.
+        """
+        logging.info("--- START DATA LOADING ---")
+        # Titration or Distribution mode
+        if data["dmode"] == 0:
+            self.distribution = False
+        else:
+            self.distribution = True
+
+        if data["emode"] == 1:
+            self.errors = True
+        else:
+            self.errors = False
+
+        # Charge values of comps
+        self.comp_charge = pd.DataFrame(data["compModel"])["Charge"]
+        # Data relative to the species and solid species
+        species_data: pd.DataFrame = pd.DataFrame(data["speciesModel"])
+        solid_data: pd.DataFrame = pd.DataFrame(data["solidSpeciesModel"])
+        # Data relative to comp concentrations
+        conc_data = pd.DataFrame(data["concModel"])
+
+        if self.distribution:
+            # Independent comp
+            self.ind_comp = data["ind_comp"]
+            # Initial log value
+            initial_log = data["initialLog"]
+            # Final log value
+            final_log = data["finalLog"]
+            # log increments at each point
+            log_increments = data["logInc"]
+
+            # Final log value should be higher than the initial one
+            if initial_log >= final_log:
+                raise Exception("Initial -log[A] should be lower then final -log[A].")
+
+            if log_increments == 0:
+                raise Exception("Increment of -log[A] should be more then zero.")
+            # Create two arrays (log and conc. of independent component)
+            self.ind_comp_logs = np.arange(
+                initial_log, (final_log + log_increments), log_increments
+            )
+            self.ind_comp_c = 10 ** (-self.ind_comp_logs)
+
+            # Calculate the number of points in the interval
+            self.nop = len(self.ind_comp_c)
+        else:
+            self.c_added = conc_data.iloc[:, 1].copy().to_numpy(dtype="float")
+            # Initial volume
+            v0 = data["v0"] * 1e-3
+            # First titration point volume
+            initial_volume = data["initv"] * 1e-3
+            # Volume increment at each point
+            volume_increments = data["vinc"] * 1e-3
+            # number of points
+            self.nop = int(data["nop"])
+
+            if v0 <= 0:
+                raise Exception("Initial volume can't be zero")
+
+            if volume_increments <= 0:
+                raise Exception("Volume increments should be higher then 0.")
+
+            if v0 > initial_volume:
+                raise Exception(
+                    "Initial titration volume should be higer or equal to initial"
+                    " volume."
+                )
+
+            self.v_added = np.array([volume_increments * x for x in range(self.nop)])
+            v1_diff = initial_volume - v0
+            self.v_added += v1_diff
+            v_tot = v0 + self.v_added
+
+        # Check if the number of points in the range of pH is greater then 0
+        if self.nop == 0:
+            raise Exception("Number of points in the -log[A] range shouldn't be 0.")
+
+        # Analytical concentration of each component (including the ones that will be ignored)
+        self.c_tot = conc_data.iloc[:, 0].copy().to_numpy(dtype="float")
+
+        # Check if they are all zero
+        if (self.c_tot == 0).all():
+            raise Exception(
+                "Analytical concentration shouldn't be zero for all components."
+            )
+
+        # Charges of components
+        self.comp_charge = self.comp_charge.copy().to_numpy(dtype="int")
+
+        # Find which components have to be ignored (c0 = 0)
+        ignored_comps = (
+            (self.c_tot == 0) & (np.arange(len(self.c_tot)) != self.ind_comp)
+            if self.distribution
+            else (self.c_tot == 0)
+        )
+
+        # Remove the concentration and charge data relative to those
+        if not self.distribution:
+            self.c_added = np.delete(self.c_added, ignored_comps, 0)
+        self.c_tot = np.delete(self.c_tot, ignored_comps, 0)
+        self.comp_charge = np.delete(self.comp_charge, ignored_comps)
+
+        # get number of effective components
+        self.nc = int(len(conc_data)) - ignored_comps.sum()
+
+        if self.distribution:
+            # for every ignored comp which index is lower
+            # of the designated independent comp
+            # reduce its index by one (they "slide over")
+            self.ind_comp = self.ind_comp - ignored_comps[: self.ind_comp].sum()
+            # Assign total concentrations for each point
+            self.c_tot = np.delete(self.c_tot, self.ind_comp, 0)
+            self.c_tot = np.tile(self.c_tot, [self.nop, 1])
+        else:
+            self.initial_c = self.c_tot
+            self.c_tot = (
+                (np.tile(self.c_tot, [self.nop, 1]) * v0)
+                + (np.tile(self.v_added, [self.nc, 1]).T * self.c_added)
+            ) / np.tile(v_tot, [self.nc, 1]).T
+            self.c_tot[self.c_tot == 0] = -1e-9
+
+        # Store the stoichiometric coefficients for the components
+        # IMPORTANT: each component is considered as a species with logB = 0
+        comp_model = np.identity(self.nc, dtype="int")
+
+        # Ignore the rows relative to the flagged as ignored species and ignored solid species
+        species_not_ignored = species_data.loc[species_data["Ignored"] == False]
+        solid_not_ignored = solid_data.loc[solid_data["Ignored"] == False]
+
+        species_duplicates = species_not_ignored.loc[
+            species_not_ignored.duplicated(subset="Name", keep="first").to_list(),
+            "Name",
+        ]
+        if not species_duplicates.empty:
+            raise Exception(
+                f"Species {', '.join(species_duplicates.to_list())} with indices"
+                f" {', '.join(species_duplicates.index.astype(str).to_list())} appear"
+                " to be duplicates, you can and should remove them to avoid"
+                " ambiguities in the results."
+            )
+
+        solids_duplicates = solid_not_ignored.loc[
+            solid_not_ignored.duplicated(subset="Name", keep="first").to_list(),
+            "Name",
+        ]
+        if not solids_duplicates.empty:
+            raise Exception(
+                f"Solids {', '.join(solids_duplicates.to_list())} with indices"
+                f" {', '.join(solids_duplicates.index.astype(str).to_list())} appear to"
+                " be duplicates, you can and should remove them to avoid ambiguities"
+                " in the results."
+            )
+
+        # Store the stoichiometric coefficients for the species and solid species
+        base_model = species_not_ignored.iloc[:, 8:-1].to_numpy(dtype="int").T
+        solid_model = solid_not_ignored.iloc[:, 8:-1].to_numpy(dtype="int").T
+
+        # Stores log_betas and log_ks of not ignored species
+        base_log_beta = species_not_ignored.iloc[:, 2].to_numpy(dtype="float")
+        base_log_ks = solid_not_ignored.iloc[:, 2].to_numpy(dtype="float")
+
+        # Store comp_names
+        self.comp_names = conc_data.index
+        ignored_comp_names = self.comp_names[ignored_comps]
+        self.comp_names = np.delete(self.comp_names, ignored_comps, 0)
+
+        # Store for each species which component is used to calculate relative percentage.
+        self.species_perc_str = species_not_ignored.iloc[:, -1].to_numpy(dtype="str")
+        self.solid_perc_str = solid_not_ignored.iloc[:, -1].to_numpy(dtype="str")
+
+        # Remove all the species and solid species that have one or more ignored comp with not null coeff.
+        # with their relative betas and component for percentage computation
+        species_to_remove = (base_model[ignored_comps, :] != 0).sum(axis=0) != 0
+        solid_to_remove = (solid_model[ignored_comps, :] != 0).sum(axis=0) != 0
+        base_model = np.delete(base_model, species_to_remove, axis=1)
+        solid_model = np.delete(solid_model, solid_to_remove, axis=1)
+        base_log_beta = np.delete(base_log_beta, species_to_remove, axis=0)
+        base_log_ks = np.delete(base_log_ks, solid_to_remove, axis=0)
+        self.species_perc_str = np.delete(
+            self.species_perc_str, species_to_remove, axis=0
+        )
+        self.solid_perc_str = np.delete(self.solid_perc_str, solid_to_remove, axis=0)
+
+        # Delete the columns for the coeff relative to the ignored components
+        base_model = np.delete(base_model, ignored_comps, axis=0)
+        solid_model = np.delete(solid_model, ignored_comps, axis=0)
+
+        # Transforms the component used to calculate percentages from string to the corresponding index
+        # If any of the species or solid species would use one of the ignored comps
+        # assign the index for computation as if the independent comp
+        # would be used instead (its percent value will be zero)
+        self.species_perc_int, self.solid_perc_int = self._percEncoder(
+            ignored_comp_names
+        )
+
+        # Assemble the models and betas matrix
+        self.model = np.concatenate((comp_model, base_model), axis=1)
+        self.log_beta_ris = np.concatenate(
+            (np.array([0 for _ in range(self.nc)]), base_log_beta), axis=0
+        )
+        self.solid_model = solid_model
+        self.log_ks_ris = base_log_ks
+
+        # Get the number of not-ignored species/solid species
+        self.ns = base_model.shape[1]
+        self.nf = solid_model.shape[1]
+
+        # Number of components and number of species/solids has to be > 0
+        if self.nc <= 0 | (self.ns <= 0 & self.nf <= 0):
+            raise Exception(
+                "Number of components and number of not ignored species should be more"
+                " then zero."
+            )
+
+        if self.errors:
+            self.c0_sigma = conc_data.iloc[:, 2].copy().to_numpy(dtype="float")
+            self.c0_sigma = np.delete(self.c0_sigma, ignored_comps)
+            if self.distribution:
+                self.c0_sigma[self.ind_comp] = 0
+                self.conc_sigma = np.tile(self.c0_sigma, [self.nop, 1])
+            else:
+                self.ct_sigma = conc_data.iloc[:, 3].copy().to_numpy(dtype="float")
+                self.ct_sigma = np.delete(self.ct_sigma, ignored_comps)
+                self.conc_sigma = np.tile(self.c0_sigma, [self.nop, 1]) + (
+                    np.tile(self.v_added, [self.nc, 1]).T * self.ct_sigma
+                )
+
+            self.log_beta_sigma = species_not_ignored.iloc[:, 3].to_numpy(dtype="float")
+            self.log_ks_sigma = solid_not_ignored.iloc[:, 3].to_numpy(dtype="float")
+
+            self.log_beta_sigma = np.delete(
+                self.log_beta_sigma, species_to_remove, axis=0
+            )
+            self.log_ks_sigma = np.delete(self.log_ks_sigma, solid_to_remove, axis=0)
+
+            self.beta_sigma = (
+                self.log_beta_sigma * np.log(10) * (10 ** self.log_beta_ris[self.nc :])
+            )
+
+            self.ks_sigma = self.log_ks_sigma * np.log(10) * (10**self.log_ks_ris)
+
+        # Check the ionic strength mode
+        # Load the required data if so
+        self.imode = data["imode"]
+        if self.imode == 1:
+            # Load reference ionic strength
+            self.species_ris = species_not_ignored.iloc[:, 4].to_numpy(dtype="float")
+            self.solid_ris = solid_not_ignored.iloc[:, 4].to_numpy(dtype="float")
+
+            # Remove ionic strength for species/solids that are ignored
+            self.species_ris = np.delete(self.species_ris, species_to_remove, axis=0)
+            self.solid_ris = np.delete(self.solid_ris, solid_to_remove, axis=0)
+
+            # If ref. ionic strength is not given for one of the species use the reference one
+            self.species_ris = np.where(
+                self.species_ris == 0, data["ris"], self.species_ris
+            )
+            self.solid_ris = np.where(self.solid_ris == 0, data["ris"], self.solid_ris)
+
+            # Add ref. ionic strength for components
+            self.species_ris = np.insert(
+                self.species_ris, 0, [data["ris"] for _ in range(self.nc)]
+            )
+            # Calculate square root of reference ionic strength for species
+            self.species_radqris = np.sqrt(self.species_ris)
+            self.solid_radqris = np.sqrt(self.solid_ris)
+
+            # Load background ions concentration
+            if self.distribution:
+                self.background_c = np.tile(data["cback"], self.nop)
+            else:
+                background_c0 = data["c0back"]
+                background_ct = data["ctback"]
+                self.background_c = np.array(
+                    ((background_c0 * v0) + (background_ct * self.v_added)) / v_tot
+                )
+
+            a = data["a"]
+            self.b = data["b"]
+            c = [data["c0"], data["c1"]]
+            d = [data["d0"], data["d1"]]
+            e = [data["e0"], data["e1"]]
+
+            # Check if default have to be used
+            if (a == 0) & (self.b == 0):
+                a = 0.5
+                self.b = 1.5
+
+            # Compute p* for alla the species
+            species_past = self.model.sum(axis=0) - 1
+            solid_past = self.solid_model.sum(axis=0)
+
+            # Reshape charges into a column vector
+            comp_charge_column = np.reshape(self.comp_charge, (self.nc, 1))
+
+            if self.distribution:
+                # Same vector except missing the charge of the indipendent component, used later in the computation
+                self.comp_charge_no_indipendent = np.delete(
+                    self.comp_charge, self.ind_comp
+                )
+
+            # Compute species charges
+            self.species_charges = (self.model * comp_charge_column).sum(axis=0)
+            self.solid_charges = (self.solid_model * comp_charge_column).sum(axis=0)
+
+            # Compute z* for all the species
+            species_zast = (self.model * (comp_charge_column**2)).sum(axis=0) - (
+                self.species_charges
+            ) ** 2
+            solid_zast = (self.solid_model * (comp_charge_column**2)).sum(axis=0)
+
+            # Compute A/B term of D-H equation
+            self.species_az = a * species_zast
+            self.solid_az = a * solid_zast
+
+            self.species_fib = self.species_radqris / (
+                1 + (self.b * self.species_radqris)
+            )
+            self.solid_fib = self.solid_radqris / (1 + (self.b * self.solid_radqris))
+
+            # For both species and solids sets the Debye-Huckle parameters used to update their defining constants
+            (self.species_cg, self.species_dg, self.species_eg) = self._setDBHParams(
+                species_not_ignored,
+                species_to_remove,
+                species_past,
+                species_zast,
+                c,
+                d,
+                e,
+            )
+
+            (self.solid_cg, self.solid_dg, self.solid_eg) = self._setDBHParams(
+                solid_not_ignored,
+                solid_to_remove,
+                solid_past,
+                solid_zast,
+                c,
+                d,
+                e,
+                solids=True,
+            )
+
+        # Compose species names from the model
+        self.species_names = (
+            list(self.comp_names)
+            + species_not_ignored.iloc[~species_to_remove, 1].to_list()
+        )
+        self.solid_names = solid_not_ignored.iloc[~solid_to_remove, 1].to_list()
+
+        logging.info("--- DATA LOADED ---")
+
+    def predict(self):
+        """
+        Given the loaded data returns species distribution.
+        """
+        # Calculate species distribution
+        # Return formatted species distribution as a nice table
+        logging.info("--- BEGINNING CALCULATION --- ")
+        (
+            species,
+            solid,
+            si,
+            species_sigma,
+            solid_sigma,
+            log_b,
+            log_ks,
+            ionic_strength,
+        ) = self._compute()
+
+        # Set the flag to signal a completed run
+        self.done_flag = True
+
+        # Create the table containing the species/comp. concentration
+        self.species_distribution = pd.DataFrame(
+            species,
+            columns=self.species_names,
+        ).rename_axis(columns="Species Conc. [mol/L]")
+        self.species_distribution = self._setDataframeIndex(self.species_distribution)
+
+        if self.distribution:
+            cans = np.insert(self.c_tot, self.ind_comp, 0, axis=1)
+        else:
+            cans = self.c_tot
+
+        # Compute and create table with percentages of species with respect to component
+        # As defined with the input
+        species_perc_table = self._computePercTable(
+            cans, species, self.model, self.species_perc_int
+        )
+
+        # Percentages are rounded two the second decimal and stored in a dataframe
+        self.species_percentages = (
+            pd.DataFrame(
+                species_perc_table,
+                columns=[self.species_names, self.species_perc_str],
+            )
+            .rename_axis(columns=["Species", r"% relative to comp."])
+            .round(2)
+        )
+        self.species_percentages = self._setDataframeIndex(self.species_percentages)
+
+        if self.nf > 0:
+            # Create the table containing the solid species "concentration"
+            self.solid_distribution = pd.DataFrame(
+                solid, columns=self.solid_names
+            ).rename_axis(columns="Solid Conc. [mol/L]")
+            check = self.solid_distribution.apply(
+                lambda x: pd.Series(
+                    ["*" if i > 0 else "" for i in x],
+                    index=["Prec." + name for name in self.solid_distribution.columns],
+                    dtype=str,
+                ),
+                axis=1,
+            )
+            saturation_index = pd.DataFrame(
+                si, columns=["SI" + name for name in self.solid_names]
+            )
+
+            self.solid_distribution = pd.merge(
+                self.solid_distribution,
+                check,
+                left_index=True,
+                right_index=True,
+                sort=True,
+            )
+
+            self.solid_distribution = pd.merge(
+                self.solid_distribution,
+                saturation_index,
+                left_index=True,
+                right_index=True,
+                sort=True,
+            )
+
+            self.solid_distribution = self.solid_distribution[
+                list(
+                    sum(
+                        zip(
+                            check.columns,
+                            saturation_index,
+                            self.solid_distribution.columns,
+                        ),
+                        (),
+                    )
+                )
+            ]
+            self.solid_distribution = self._setDataframeIndex(self.solid_distribution)
+
+            # Compute solid percentages as for species percentages
+            solid_perc_table = self._computePercTable(
+                cans, solid, self.solid_model, self.solid_perc_int, solids=True
+            )
+
+            self.solid_percentages = (
+                pd.DataFrame(
+                    solid_perc_table,
+                    columns=[self.solid_names, self.solid_perc_str],
+                )
+                .rename_axis(columns=["Solids", r"% relative to comp."])
+                .round(2)
+            )
+            self.solid_percentages = self._setDataframeIndex(self.solid_percentages)
+
+        if self.errors:
+            # For error propagation create the corresponding tables
+            self.species_sigma = pd.DataFrame(
+                species_sigma, columns=self.species_names
+            ).rename_axis(columns="Species Std. Dev. [mol/L]")
+            self.species_sigma = self._setDataframeIndex(self.species_sigma)
+
+            if self.nf > 0:
+                self.solid_sigma = pd.DataFrame(
+                    solid_sigma, columns=self.solid_names
+                ).rename_axis(columns="Solid Std. Dev. [mol]")
+                self.solid_sigma = self._setDataframeIndex(self.solid_sigma)
+
+        # If working at variable ionic strength
+        if self.imode == 1:
+            # Add multi index to the species distribution containing the ionic strength
+            self.species_distribution.insert(0, "I", ionic_strength)
+            self.species_distribution.set_index("I", append=True, inplace=True)
+
+            # Create table containing adjusted LogB for each point
+            self.log_beta = pd.DataFrame(
+                log_b[:, self.nc :],
+                columns=self.species_names[self.nc :],
+            ).rename_axis(columns="Formation Constants")
+            self.log_beta = self._setDataframeIndex(self.log_beta)
+
+            self.log_beta.insert(0, "I", ionic_strength)
+            self.log_beta.set_index("I", append=True, inplace=True)
+
+            if self.nf > 0:
+                self.solid_distribution.insert(0, "I", ionic_strength)
+                self.solid_distribution.set_index("I", append=True, inplace=True)
+                # Create table containing adjusted LogKs for each point
+                self.log_ks = pd.DataFrame(
+                    log_ks,
+                    columns=self.solid_names,
+                ).rename_axis(columns="Solubility Products")
+                self.log_ks = self._setDataframeIndex(self.log_ks)
+
+                self.log_ks.insert(0, "I", ionic_strength)
+                self.log_ks.set_index("I", append=True, inplace=True)
+
+        logging.info("--- CALCULATION TERMINATED ---")
+
+        return True
+
+    def speciesDistribution(self):
+        """
+        Returns the species concentration table.
+        """
+        if self.done_flag:
+            return self.species_distribution
+        else:
+            return False
+
+    def solidDistribution(self):
+        """
+        Returns the solid species concentration table.
+        """
+        if self.done_flag:
+            try:
+                return self.solid_distribution
+            except AttributeError:
+                return pd.DataFrame()
+        else:
+            return False
+
+    def formationConstants(self):
+        """
+        Returns the table containing formation constants and the ionic strength.
+        """
+        if self.done_flag:
+            try:
+                return self.log_beta
+            except AttributeError:
+                return pd.DataFrame()
+        else:
+            return False
+
+    def solubilityProducts(self):
+        """
+        Returns the table containing the LogKps for the solid species present in the model.
+        """
+        if self.done_flag:
+            try:
+                return self.log_ks
+            except AttributeError:
+                return pd.DataFrame()
+        else:
+            return False
+
+    def speciesPercentages(self):
+        """
+        Return percentages of species with respect to the desired component.
+        """
+        if self.done_flag:
+            return self.species_percentages
+        else:
+            return False
+
+    def solidPercentages(self):
+        """
+        Return percentages of solids with respect to the desired component.
+        """
+        if self.done_flag:
+            try:
+                return self.solid_percentages
+            except AttributeError:
+                return pd.DataFrame()
+
+        else:
+            return False
+
+    def speciesSigmas(self):
+        """
+        Return percentages of species with respect to the desired component.
+        """
+        if self.done_flag:
+            try:
+                return self.species_sigma
+            except AttributeError:
+                return pd.DataFrame()
+        else:
+            return False
+
+    def solidSigmas(self):
+        """
+        Return percentages of solids with respect to the desired component.
+        """
+        if self.done_flag:
+            try:
+                return self.solid_sigma
+            except AttributeError:
+                return pd.DataFrame()
+        else:
+            return False
+
+    def parameters(self):
+        """
+        Returns relevant data that was used for the computation
+        """
+        if self.done_flag:
+            species_info = pd.DataFrame(
+                {
+                    "logB": self.log_beta_ris[self.nc :],
+                },
+                index=self.species_names[self.nc :],
+            ).rename_axis(index="Species Names")
+
+            if self.nf > 0:
+                solid_info = pd.DataFrame(
+                    {
+                        "logKs": self.log_ks_ris,
+                    },
+                    index=self.solid_names,
+                ).rename_axis(index="Solid Names")
+            else:
+                solid_info = pd.DataFrame()
+
+            if self.imode == 1:
+                species_info.insert(1, "Ref. I", self.species_ris[self.nc :])
+                species_info.insert(2, "Charge", self.species_charges[self.nc :])
+                species_info.insert(3, "C", self.species_cg[self.nc :])
+                species_info.insert(4, "D", self.species_dg[self.nc :])
+                species_info.insert(5, "E", self.species_eg[self.nc :])
+
+                if self.nf > 0:
+                    solid_info.insert(1, "Ref. I", self.solid_ris)
+                    solid_info.insert(2, "Charge", self.solid_charges)
+                    solid_info.insert(3, "C", self.solid_cg)
+                    solid_info.insert(4, "D", self.solid_dg)
+                    solid_info.insert(5, "E", self.solid_eg)
+
+            if self.errors:
+                species_info.insert(1, "Sigma logB", self.log_beta_sigma)
+
+                if self.nf > 0:
+                    solid_info.insert(1, "Sigma logKs", self.log_ks_sigma)
+
+            comp_info = pd.DataFrame(
+                {
+                    "Charge": self.comp_charge,
+                },
+                index=self.species_names[: self.nc],
+            ).rename_axis(index="Components Names")
+
+            if self.distribution:
+                comp_info["Tot. C."] = np.insert(self.c_tot[0], self.ind_comp, None)
+                if self.errors:
+                    comp_info.insert(2, "Sigma Tot C", self.c0_sigma)
+            else:
+                comp_info["Vessel Conc."] = self.initial_c
+                comp_info["Titrant Conc."] = self.c_added
+                if self.errors:
+                    comp_info.insert(2, "Sigma C0", self.c0_sigma)
+                    comp_info.insert(4, "Sigma cT", self.ct_sigma)
+
+            return species_info, solid_info, comp_info
+        else:
+            return False
+
+    def _compute(self):
+        """
+        Calculate species distribution.
+        """
+        # Initialize array to contain the species concentration
+        # obtained from the calculations
+        for_estimation_c = deque(maxlen=3)
+        results_species_conc = np.zeros(
+            dtype=float, shape=(self.nop, self.ns + self.nc)
+        )
+        results_solid_conc = np.zeros(dtype=float, shape=(self.nop, self.nf))
+        results_solid_si = np.zeros(dtype=float, shape=(self.nop, self.nf))
+        results_species_sigma = np.zeros(
+            dtype=float, shape=(self.nop, self.ns + self.nc)
+        )
+        results_solid_sigma = np.zeros(dtype=float, shape=(self.nop, self.nf))
+        results_log_b = np.zeros(dtype=float, shape=(self.nop, self.ns + self.nc))
+        results_log_ks = np.zeros(dtype=float, shape=(self.nop, self.nf))
+        results_ionic_strength = np.zeros(dtype=float, shape=(self.nop, 1))
+
+        # Cycle over each point of titration
+        for point in range(self.nop):
+            logging.debug("--> OPTIMIZATION POINT: %s", point)
+
+            if self.distribution:
+                c, fixed_c = self._distributionGuess(point, for_estimation_c)
+            else:
+                fixed_c = None
+                c = self._titrationGuess(point, for_estimation_c)
+
+            # Initial guess for solids concentrations should always be zero
+            cp = np.zeros(self.nf)
+
+            logging.debug("INITIAL ESTIMATED FREE C: %s", c)
+            logging.debug("TOTAL C: %s", self.c_tot[point])
+
+            shifts_to_calculate = np.array(
+                [True for _ in range(self.nc)] + [False for _ in range(self.nf)]
+            )
+
+            shifts_to_calculate, shifts_to_skip = self._getComputableShifts(
+                shifts_to_calculate
+            )
+            # Calculate species concentration for aqueous species only
+            (
+                species_conc_calc,
+                solid_conc_calc,
+                log_b,
+                log_ks,
+                ionic_strength,
+            ) = self._newtonRaphson(
+                point,
+                c,
+                cp,
+                self.c_tot[point],
+                fixed_c,
+                shifts_to_calculate,
+                shifts_to_skip,
+                with_solids=False,
+            )
+
+            # Store concentrations before solid precipitation to estimate next points c
+            for_estimation_c.append(species_conc_calc)
+
+            saturation_index_calc = np.zeros(self.nf)
+            adjust_solids = True and (self.nf > 0)
+            while adjust_solids:
+                saturation_index = self._getSaturationIndex(
+                    species_conc_calc[: self.nc], log_ks
+                )
+
+                # Check which solids are to be considered
+                shifts_to_calculate, shifts_to_skip = self._getComputableShifts(
+                    shifts_to_calculate, saturation_index, solid_conc_calc
+                )
+
+                if shifts_to_calculate[-self.nf :].any():
+                    (
+                        species_conc_calc,
+                        solid_conc_calc,
+                        log_b,
+                        log_ks,
+                        ionic_strength,
+                    ) = self._newtonRaphson(
+                        point,
+                        species_conc_calc[: self.nc],
+                        solid_conc_calc,
+                        self.c_tot[point],
+                        fixed_c,
+                        shifts_to_calculate,
+                        shifts_to_skip,
+                        with_solids=True,
+                    )
+                else:
+                    saturation_index_calc = saturation_index
+                    adjust_solids = False
+
+            if self.errors:
+                species_sigma, solid_sigma = self._computeErrors(
+                    species_conc_calc,
+                    solid_conc_calc,
+                    saturation_index_calc,
+                    log_b,
+                    log_ks,
+                    point,
+                )
+            else:
+                species_sigma = np.array([None for _ in range(self.nc + self.ns)])
+                solid_sigma = np.array([None for _ in range(self.nf)])
+
+            # Store calculated species/solid concentration into a vector
+            results_species_conc[point, :] = species_conc_calc
+            results_solid_conc[point, :] = solid_conc_calc
+
+            results_solid_si[point, :] = saturation_index_calc
+            # Store uncertainty for calculated values
+            results_species_sigma[point, :] = species_sigma
+            results_solid_sigma[point, :] = solid_sigma
+            # Store calculated ionic strength
+            results_ionic_strength[point] = ionic_strength
+            # Store calculated LogB/LogKs
+            results_log_b[point, :] = log_b
+            results_log_ks[point, :] = log_ks
+
+        # Stack calculated species concentration/logB/ionic strength in tabular fashion
+        # results_species_conc = np.stack(results_species_conc)
+        # results_solid_conc = np.stack(results_solid_conc)
+        # results_solid_si = np.stack(results_solid_si)
+        # results_species_sigma = np.stack(results_species_sigma)
+        # results_solid_sigma = np.stack(results_solid_sigma)
+        # results_log_b = np.stack(results_log_b)
+        # results_log_ks = np.stack(results_log_ks)
+        # results_ionic_strength = np.stack(results_ionic_strength)
+
+        # Return distribution/logb/ionic strength
+        return (
+            results_species_conc,
+            results_solid_conc,
+            results_solid_si,
+            results_species_sigma,
+            results_solid_sigma,
+            results_log_b,
+            results_log_ks,
+            results_ionic_strength,
+        )
+
+    def _newtonRaphson(
+        self,
+        point,
+        c,
+        cp,
+        c_tot,
+        fixed_c,
+        shifts_to_calculate,
+        shifts_to_skip,
+        with_solids=False,
+    ):
+        np.seterr(all="ignore")
+        iteration = 0
+
+        if not with_solids:
+            # If working with variable ionic strength compute initial guess for species concentration
+            if self.imode == 1:
+                if point == 0:
+                    log_beta, log_ks, _ = self._updateConstants(
+                        c_tot,
+                        self.log_beta_ris,
+                        self.log_ks_ris,
+                        self.comp_charge,
+                        point,
+                        first_guess=True,
+                    )
+                else:
+                    log_beta = self.previous_log_beta
+                    log_ks = self.previous_log_ks
+
+                logging.debug("Estimate of LogB for point %s: %s", point, log_beta)
+
+                c, c_spec = self._damping(point, c, cp, log_beta, c_tot, fixed_c)
+
+                log_beta, log_ks, cis = self._updateConstants(
+                    c_spec,
+                    self.log_beta_ris,
+                    self.log_ks_ris,
+                    self.species_charges,
+                    point,
+                )
+
+                self.previous_log_beta = log_beta
+                self.previous_log_ks = log_ks
+                logging.debug("Updated LogB: %s", log_beta)
+            else:
+                log_beta = self.log_beta_ris
+                log_ks = self.log_ks_ris
+                c, c_spec = self._damping(point, c, cp, log_beta, c_tot, fixed_c)
+                cis = [None]
+        else:
+            if self.imode == 1:
+                c_tot_calc, c_spec = self._speciesConcentration(
+                    c, cp, self.previous_log_beta
+                )
+                log_beta, log_ks, cis = self._updateConstants(
+                    c_spec,
+                    self.log_beta_ris,
+                    self.log_ks_ris,
+                    self.species_charges,
+                    point,
+                )
+
+                self.previous_log_beta = log_beta
+                self.previous_log_ks = log_ks
+            else:
+                log_beta = self.log_beta_ris
+                log_ks = self.log_ks_ris
+                cis = [None]
+            c, c_spec = self._damping(point, c, cp, log_beta, c_tot, fixed_c)
+
+        # Calculate total concentration given the species concentration
+        c_tot_calc, c_spec = self._speciesConcentration(c, cp, log_beta)
+
+        # Calculate saturation index for solid species if any
+        saturation_index = self._getSaturationIndex(c, log_ks)
+
+        # Compute difference between total concentrations and calculated one
+        delta, can_delta, solid_delta = self._computeDelta(
+            c_tot,
+            c_tot_calc,
+            with_solids,
+            saturation_index,
+            shifts_to_calculate[-self.nf :],
+        )
+
+        while iteration < 2000:
+            logging.debug(
+                "-> BEGINNING NEWTON-RAPHSON ITERATION %s ON POINT %s", iteration, point
+            )
+
+            # Compute Jacobian
+            J = self._computeJacobian(
+                c_spec,
+                saturation_index,
+                with_solids,
+                shifts_to_skip[-self.nf :],
+            )
+
+            if self.distribution:
+                # Ignore row and column relative to the independent component
+                J = np.delete(J, self.ind_comp, axis=0)
+                J = np.delete(J, self.ind_comp, axis=1)
+
+            # J, delta = self._scaleMatrix(J, delta)
+
+            # Solve the equations to obtain newton step
+            shifts = np.linalg.solve(J, -delta)
+
+            actual_shifts = np.zeros(self.nc + self.nf)
+            actual_shifts[shifts_to_calculate] = shifts
+            shifts = actual_shifts
+
+            # if self.distribution:
+            #     shifts = np.insert(shifts, self.ind_comp, 0, axis=0)
+
+            logging.debug(
+                "Shifts to be applied to concentrations and precipitates: %s", shifts
+            )
+
+            # if with_solids:
+            #     one_over_del = -shifts / (0.5 * np.append(c, cp))
+            # else:
+            one_over_del = -shifts[: self.nc] / (0.5 * c)
+
+            rev_del = 1 / np.where(one_over_del > 1, one_over_del, 1)
+
+            c = c + rev_del[: self.nc] * shifts[: self.nc]
+
+            # if with_solids:
+            cp = cp + shifts[self.nc :]
+
+            logging.debug("Newton-Raphson updated free concentrations: %s", c)
+            logging.debug("Newton-Raphson updated precipitate concentrations: %s", cp)
+
+            # Calculate total concentration given the updated free/precipitate concentration
+            c_tot_calc, c_spec = self._speciesConcentration(c, cp, log_beta)
+
+            # Compute difference between total concentrations and convergence
+            saturation_index = self._getSaturationIndex(c, log_ks)
+
+            # Compute difference between total concentrations
+            delta, can_delta, solid_delta = self._computeDelta(
+                c_tot,
+                c_tot_calc,
+                with_solids,
+                saturation_index,
+                shifts_to_calculate[-self.nf :],
+            )
+
+            comp_conv_criteria = np.sum(can_delta / c_tot) ** 2
+
+            logging.debug(
+                (
+                    "Convergence for analytical concentrations at Point %s iteration"
+                    " %s: %s"
+                ),
+                point,
+                iteration,
+                comp_conv_criteria,
+            )
+
+            iteration += 1
+            # If convergence criteria is met return check if any solid has to be considered
+            if with_solids:
+                if comp_conv_criteria < 1e-12 and all(
+                    abs(i) <= 1e-9 for i in solid_delta
+                ):
+                    return c_spec, cp, log_beta, log_ks, cis
+            else:
+                if comp_conv_criteria < 1e-12:
+                    return c_spec, cp, log_beta, log_ks, cis
+
+        # If during the first or second run you exceed the iteration limit report it
+        logging.error(
+            "Calculation terminated early, no convergence found at point {} in {}"
+            " iterations".format(point, iteration)
+            + (
+                (
+                    " after solids were considered."
+                    if with_solids
+                    else " before solids were considered."
+                )
+                if self.nf > 0
+                else ""
+            )
+        )
+        raise Exception(
+            "Calculation of species concentration aborted, no convergence found with"
+            " conc {} at point {} in {} iterations".format(
+                str(c_spec), point, iteration
+            )
+            + (
+                (
+                    " after solids were considered."
+                    if with_solids
+                    else " before solids were considered."
+                )
+                if self.nf > 0
+                else ""
+            )
+        )
+
+    def _getComputableShifts(
+        self,
+        shifts_to_calculate: NDArray,
+        saturation_index: NDArray = np.array([]),
+        solid_concentrations: NDArray = np.array([]),
+    ):
+        negative_cp = solid_concentrations < 0
+        supersaturated_solid = saturation_index > 1 + 1e-9
+
+        if negative_cp.any():
+            shifts_to_calculate[-self.nf :] = ~negative_cp
+        elif supersaturated_solid.any():
+            shifts_to_calculate[-self.nf :][np.argmax(saturation_index)] = True
+        else:
+            shifts_to_calculate = np.array(
+                [True for _ in range(self.nc)] + [False for _ in range(self.nf)]
+            )
+
+        if self.distribution:
+            # If calculating distribution of species exclude the indipendent component from the species to consider
+            # shifts_to_calculate = np.delete(shifts_to_calculate, self.ind_comp, axis=0)
+            # shifts_to_skip = np.delete(shifts_to_skip, self.ind_comp, axis=0)
+            shifts_to_calculate[self.ind_comp] = False
+
+        shifts_to_skip = ~shifts_to_calculate
+
+        return shifts_to_calculate, shifts_to_skip
+
+    def _computeDelta(
+        self, c_tot, c_tot_calc, with_solids, saturation_index, cp_to_calculate
+    ):
+        can_delta = c_tot_calc - c_tot
+
+        if with_solids:
+            solid_delta = np.ones(self.nf) - saturation_index
+            solid_delta = solid_delta[cp_to_calculate]
+        else:
+            solid_delta = []
+
+        delta = np.concatenate((can_delta, solid_delta))
+
+        return delta, can_delta, solid_delta
+
+    def _computeJacobian(self, c_spec, saturation_index, with_solids, to_skip):
+        if with_solids:
+            nt = self.nc + self.nf
+            to_skip = np.concatenate(([False for _ in range(self.nc)], to_skip))
+        else:
+            nt = self.nc
+
+        J = numba_jacobian(
+            self.nc,
+            nt,
+            c_spec,
+            saturation_index,
+            self.model,
+            self.solid_model,
+            with_solids,
+        )
+        # print(numba_jacobian.parallel_diagnostics(level=4))
+
+        if with_solids:
+            # Remove rows and columns referring to under-saturated solids
+            J = np.delete(J, to_skip, axis=0)
+            J = np.delete(J, to_skip, axis=1)
+
+        return J
+
+    def _speciesConcentration(self, c, cp, log_beta):
+        """
+        Calculate species concentration it returns c_spec and c_tot_calc:
+        - c_spec[0->nc] = free conc for each component.
+        - c_spec[nc+1->nc+ns] = species concentrations.
+        - c_tot_calc = estimated anaytical concentration.
+        """
+        log_c_spec = self._checkOverUnderFlow(
+            np.sum(np.tile(np.log10(c), (self.ns + self.nc, 1)).T * self.model, axis=0)
+            + log_beta
+        )
+
+        c_spec = 10**log_c_spec
+        logging.debug("Species Concentrations: %s", c_spec)
+
+        # Estimate total concentration given the species concentration
+        c_tot_calc = np.sum(self.model * np.tile(c_spec, (self.nc, 1)), axis=1)
+
+        if self.nf > 0:
+            c_tot_calc += np.sum(self.solid_model * np.tile(cp, (self.nc, 1)), axis=1)
+
+        if self.distribution:
+            # Take out the analytical concentration relative to the independent component
+            c_tot_calc = np.delete(c_tot_calc, self.ind_comp, 0)
+
+        logging.debug("Calculated Total Concentration: %s", c_tot_calc)
+
+        return c_tot_calc, c_spec
+
+    def _distributionGuess(self, point, for_estimation_c):
+        """
+        Return an initial guess for the first newton raphson iteration when dealing with distributions of species
+        """
+        fixed_c = self.ind_comp_c[point]
+        # Initial guess of free concentration (c) is considered as follows:
+        #   - First point as a fraction of the total concentration
+        #   - Second and third points as estimate from previous point
+        #   - Subsequent points are extrapolated as follows
+        if point > 2:
+            lp1 = -np.log10(for_estimation_c[-1][: self.nc])
+            lp2 = -np.log10(for_estimation_c[-2][: self.nc])
+            lp3 = -np.log10(for_estimation_c[-3][: self.nc])
+
+            # If two subsequent points present the same concentration
+            # avoid the issue by using simply the previous point concentration
+            c = np.where(lp2 == lp3, lp1, (lp1 + ((lp1 - lp2) ** 2) / (lp2 - lp3)))
+            # If the extrapolation returns values that would cause under/overflow adjust them accordingly
+            c = self._checkOverUnderFlow(c, d=2)
+
+            if (c < 0).any():
+                c = lp1
+
+            # Convert logs back to concentrations
+            c = 10 ** (-c)
+            # Free C of independent component is set as defined in the settings tab
+            c[self.ind_comp] = fixed_c
+            logging.debug("ESTIMATED C WITH INTERPOLATION")
+        elif point > 0:
+            c = for_estimation_c[-1][: self.nc].copy()
+            c[self.ind_comp] = fixed_c
+            logging.debug("ESTIMATED C FROM PREVIOUS POINT")
+        elif point == 0:
+            c = np.multiply(self.c_tot[0], 0.001)
+            c = np.insert(c, self.ind_comp, fixed_c)
+            logging.debug("ESTIMATED C AS FRACTION TOTAL C")
+        return c, fixed_c
+
+    def _titrationGuess(self, point, for_estimation_c):
+        """
+        Return an initial guess for the first newton raphson iteration when dealing with simulated titrations
+        """
+        # Initial guess of free concentration (c) is considered as follows:
+        #   - First point as a fraction of the total concentration
+        #   - Second and third points as estimate from previous point
+        #   - Subsequent points are extrapolated as follows
+        if point > 2:
+            v = self.v_added[point]
+            v1 = self.v_added[(point - 1)]
+            v2 = self.v_added[(point - 2)]
+            v3 = self.v_added[(point - 3)]
+            lp1 = -np.log10(for_estimation_c[-1][: self.nc])
+            lp2 = -np.log10(for_estimation_c[-2][: self.nc])
+            lp3 = -np.log10(for_estimation_c[-3][: self.nc])
+
+            # If two subsequent points present the same concentration
+            # avoid the issue by using simply the previous point concentration
+            c = np.where(
+                lp2 == lp3,
+                lp1,
+                (
+                    lp1
+                    + (((lp1 - lp2) / (v1 - v2)) ** 2)
+                    * ((v2 - v3) / (lp2 - lp3))
+                    * (v - v1)
+                ),
+            )
+            # If the extrapolation returns values that would cause under/overflow adjust them accordingly
+            c = self._checkOverUnderFlow(c, d=2)
+
+            if (c < 0).any():
+                c = lp1
+
+            # Convert logs back to concentrations
+            c = 10 ** (-c)
+            logging.debug("ESTIMATED C WITH INTERPOLATION")
+        elif point > 0:
+            c = for_estimation_c[(point - 1)][: self.nc]
+            logging.debug("ESTIMATED C FROM PREVIOUS POINT")
+        elif point == 0:
+            c = np.multiply(self.c_tot[0], 0.001)
+            logging.debug("ESTIMATED C AS FRACTION TOTAL C")
+
+        return c
+
+    def _checkOverUnderFlow(self, c, d=1):
+        """
+        Given c check if any of the given log of concentrations would give overflow or underflow errors, adjust accordingly
+        """
+        c = np.where(c > (self.epsl / d), (self.epsl / d), c)
+        c = np.where(c < (-self.epsl / d), (-self.epsl / d), c)
+        return c
+
+    def _setDBHParams(self, species, to_remove, past, zast, c, d, e, solids=False):
+        # Retrive CG/DG/EG for each of the species
+        # Remove values that refers to ignored comps
+        cg = species.iloc[:, 5].to_numpy(dtype="float")
+        dg = species.iloc[:, 6].to_numpy(dtype="float")
+        eg = species.iloc[:, 7].to_numpy(dtype="float")
+        cg = np.delete(cg, to_remove, axis=0)
+        dg = np.delete(dg, to_remove, axis=0)
+        eg = np.delete(eg, to_remove, axis=0)
+
+        if not solids:
+            # If computing solution species adds values for components
+            cg = np.insert(cg, 0, [0 for _ in range(self.nc)])
+            dg = np.insert(dg, 0, [0 for _ in range(self.nc)])
+            eg = np.insert(eg, 0, [0 for _ in range(self.nc)])
+
+        use_reference = (cg == 0) + (dg == 0) + (eg == 0)
+
+        # Compute CG/DG/EG terms of D-H
+        reference_cg = c[0] * past + c[1] * zast
+        reference_dg = d[0] * past + d[1] * zast
+        reference_eg = e[0] * past + e[1] * zast
+
+        cg = np.where(use_reference, reference_cg, cg)
+        dg = np.where(use_reference, reference_dg, dg)
+        eg = np.where(use_reference, reference_eg, eg)
+
+        return cg, dg, eg
+
+    def _damping(self, point, c, cp, log_beta, c_tot, fixed_c):
+        logging.debug("ENTERING DAMP ROUTINE")
+
+        epsilon = 2.5e-1 if point > 0 else 1e-9
+        model = self.model
+        nc = self.nc
+        if self.distribution:
+            nc -= 1
+            model = np.delete(model, self.ind_comp, axis=0)
+            model = np.delete(model, self.ind_comp, axis=1)
+
+        coeff = np.array([0 for _ in range(nc)])
+        a0 = np.max(np.where(model == 0, 1, np.abs(model)), axis=1)
+
+        iteration = 0
+        while True:
+            _, c_spec = self._speciesConcentration(c, cp, log_beta)
+
+            if self.distribution:
+                c_spec = np.delete(c_spec, self.ind_comp)
+
+            c_times_model = np.tile(c_spec, [nc, 1]) * model
+
+            sum_reac = np.where(model > 0, c_times_model, 0).sum(axis=1) + np.where(
+                c_tot < 0, np.abs(c_tot), 0
+            )
+            sum_prod = np.where(c_tot >= 0, c_tot, 0) - np.where(
+                model < 0, c_times_model, 0
+            ).sum(axis=1)
+
+            conv_criteria = (sum_reac - sum_prod) / (sum_reac + sum_prod)
+
+            if all(i < epsilon for i in conv_criteria) or iteration >= 10000:
+                logging.debug("EXITING DAMP ROUTINE")
+                if self.distribution:
+                    c_spec = np.insert(c_spec, self.ind_comp, fixed_c)
+                return c, c_spec
+
+            new_coeff = (
+                0.9
+                - np.where(
+                    (sum_reac > sum_prod), (sum_prod / sum_reac), (sum_reac / sum_prod)
+                )
+                * 0.8
+            )
+
+            if iteration == 0:
+                coeff = new_coeff
+            coeff = np.where(new_coeff > coeff, new_coeff, coeff)
+
+            if self.distribution:
+                c = np.delete(c, self.ind_comp)
+            c = coeff * c * (sum_prod / sum_reac) ** (1 / a0) + (1 - coeff) * c
+            if self.distribution:
+                c = np.insert(c, self.ind_comp, fixed_c)
+
+            iteration += 1
+
+        # # if self.distribution:
+        # #     c_spec = np.insert(c_spec, self.ind_comp, fixed_c)
+        # logging.debug("EXITING DAMP ROUTINE, MAX N ITERATIONS REACHED")
+        # if self.distribution:
+        #     c_spec = np.insert(c_spec, self.ind_comp, fixed_c)
+        # return c, c_spec
+        # raise Exception(
+        #     "Dampening routine couldn't find a solution at point {}".format(point)
+        # )
+
+        # raise Exception(
+        #     "Dampening routine couldn't find a solution at point %s", point
+        # )
+
+    def _getSaturationIndex(self, c, log_ks):
+        if self.nf > 0:
+            saturation_index = np.prod(
+                np.tile(c, [self.nf, 1]).T ** self.solid_model, axis=0
+            ) / (10**log_ks)
+            return saturation_index
+        else:
+            return np.array([])
+
+    def _ionicStr(self, c, charges, point, first_guess):
+        """
+        Calculate ionic strength given concentrations of species and their charges.
+        """
+        if first_guess:
+            ionic_strength = ((c * (charges**2)).sum() / 2) + self.background_c[point]
+        else:
+            ionic_strength = ((c * (charges**2)).sum() + self.background_c[point]) / 2
+
+        return ionic_strength
+
+    def _updateConstants(self, c, log_beta, log_ks, charges, point, first_guess=False):
+        if first_guess and self.distribution:
+            c = np.insert(c, self.ind_comp, 0, axis=0)
+
+        cis = self._ionicStr(c, charges, point, first_guess)
+        logging.debug("Current I: %s %s", cis, ("\tFIRST GUESS" if first_guess else ""))
+        updated_log_b = self._updateLogB(cis, log_beta)
+        updated_log_ks = self._updateLogKs(cis, log_ks)
+        return updated_log_b, updated_log_ks, cis
+
+    def _updateLogKs(self, cis, log_ks):
+        cis = np.tile(cis, self.nf)
+        radqcis = np.sqrt(cis)
+        fib2 = radqcis / (1 + (self.b * radqcis))
+        updated_log_ks = (
+            log_ks
+            + self.solid_az * (fib2 - self.solid_fib)
+            - self.solid_cg * (cis - self.solid_ris)
+            - self.solid_dg * ((cis * radqcis) - (self.solid_ris * self.solid_radqris))
+            - self.solid_eg * ((cis**2) - (self.solid_ris**2))
+        )
+
+        return updated_log_ks
+
+    def _updateLogB(self, cis, log_beta):
+        """
+        Update formation constants of species given the current
+        """
+        cis = np.tile(cis, self.nc + self.ns)
+        radqcis = np.sqrt(cis)
+        fib2 = radqcis / (1 + (self.b * radqcis))
+        updated_log_beta = (
+            log_beta
+            - self.species_az * (fib2 - self.species_fib)
+            + self.species_cg * (cis - self.species_ris)
+            + self.species_dg
+            * ((cis * radqcis) - (self.species_ris * self.species_radqris))
+            + self.species_eg * ((cis**2) - (self.species_ris**2))
+        )
+
+        return updated_log_beta
+
+    def _computePercTable(self, cans, calculated_c, model, percent_to, solids=False):
+        can_to_perc = np.array(
+            [
+                [cans[point, index] for index in percent_to]
+                for point in range(cans.shape[0])
+            ]
+        )
+
+        if not solids:
+            can_to_perc = np.concatenate((cans, can_to_perc), axis=1)
+        adjust_factor = np.array(
+            [
+                model[component, index + (self.nc if not solids else 0)]
+                for index, component in enumerate(percent_to)
+            ]
+        )
+        adjust_factor = np.where(adjust_factor <= 0, 1, adjust_factor)
+        if not solids:
+            adjust_factor = np.concatenate(
+                ([1 for _ in range(self.nc)], adjust_factor), axis=0
+            )
+
+        perc_table = np.where(
+            can_to_perc == 0, 0, (calculated_c * adjust_factor) / can_to_perc
+        )
+        perc_table = perc_table * 100
+
+        return perc_table
+
+    def _percEncoder(self, ignored_comp_names):
+        comp_encoder = dict(zip(self.comp_names, range(self.comp_names.shape[0])))
+        invalid_comp_encoder = dict(
+            zip(ignored_comp_names, range(len(ignored_comp_names)))
+        )
+        default_value = self.ind_comp if self.distribution else 0
+        species_perc_int = self.species_perc_str
+        solid_perc_int = self.solid_perc_str
+        self.species_perc_str = np.concatenate(
+            (self.comp_names, self.species_perc_str), axis=0
+        )
+        for key, value in comp_encoder.items():
+            species_perc_int = np.where(
+                species_perc_int == key, value, species_perc_int
+            )
+            solid_perc_int = np.where(solid_perc_int == key, value, solid_perc_int)
+        for key, value in invalid_comp_encoder.items():
+            species_perc_int = np.where(
+                species_perc_int == key, default_value, species_perc_int
+            )
+            solid_perc_int = np.where(
+                solid_perc_int == key, default_value, solid_perc_int
+            )
+        return species_perc_int.astype(int), solid_perc_int.astype(int)
+
+    def _computeErrors(
+        self, c_all_spec, c_solid, saturation_index, log_b, log_ks, point
+    ):
+        # Get betas from log betas
+        beta = 10 ** log_b[self.nc :]
+        ks = 10**log_ks
+        model = self.model[:, self.nc :]
+        free_c = c_all_spec[: self.nc]
+        c_spec = c_all_spec[self.nc :]
+
+        with_solids = any(c_solid > 0)
+
+        to_skip = np.concatenate(([False for _ in range(self.nc)], c_solid == 0))
+        if with_solids:
+            nt = self.nc + self.nf
+        else:
+            nt = self.nc
+
+        # Define dimension of arrays required
+        M = np.zeros(shape=(nt, nt))
+
+        der_free_beta = np.zeros(shape=(self.nc, self.ns))
+        der_free_tot = np.zeros(shape=(self.nc, self.nc))
+        der_free_ks = np.zeros(shape=(self.nc, self.nf))
+
+        der_solid_beta = np.zeros(shape=(self.nf, self.ns))
+        der_solid_tot = np.zeros(shape=(self.nf, self.nc))
+        der_solid_ks = np.zeros(shape=(self.nf, self.nf))
+
+        b = -model * (c_spec / beta)
+        d = np.identity(self.nc)
+        f = np.zeros(shape=(self.nc, self.nf))
+
+        # Compute common matrix term
+        M[: self.nc, : self.nc] = (
+            (
+                np.tile(c_spec, (self.nc, self.nc, 1))
+                / np.tile(free_c.reshape((self.nc, 1)), (self.nc, 1, self.ns))
+            )
+            * np.tile(model, (self.nc, 1, 1))
+            * np.rot90(np.tile(model, (self.nc, 1, 1)), -1, axes=(0, 1))
+        ).sum(axis=-1)
+        M[: self.nc, : self.nc] += d
+
+        if with_solids:
+            M[: self.nc, self.nc : nt] = self.solid_model
+            M[self.nc : nt, : self.nc] = self.solid_model.T * (
+                np.tile(saturation_index, (self.nc, 1)).T
+                / np.tile(free_c, (nt - self.nc, 1))
+            )
+
+            f = np.concatenate((f, np.diag(saturation_index / ks)), axis=0)
+            b = np.concatenate(
+                (
+                    b,
+                    [[0 for _ in range(self.ns)] for _ in range(self.nf)],
+                )
+            )
+            d = np.concatenate(
+                (
+                    d,
+                    [[0 for _ in range(self.nc)] for _ in range(self.nf)],
+                )
+            )
+
+            der_solid_beta = np.delete(der_solid_beta, c_solid == 0, axis=0)
+            der_solid_tot = np.delete(der_solid_tot, c_solid == 0, axis=0)
+            der_solid_ks = np.delete(der_solid_ks, c_solid == 0, axis=0)
+
+            M = np.delete(M, to_skip, axis=0)
+            M = np.delete(M, to_skip, axis=1)
+
+            b = np.delete(b, to_skip, axis=0)
+
+            d = np.delete(d, to_skip, axis=0)
+
+            f = np.delete(f, to_skip, axis=0)
+
+        if self.distribution:
+            M = np.delete(M, self.ind_comp, axis=0)
+            M = np.delete(M, self.ind_comp, axis=1)
+
+            b = np.delete(b, self.ind_comp, axis=0)
+            d = np.delete(d, self.ind_comp, axis=0)
+            f = np.delete(f, self.ind_comp, axis=0)
+
+            der_free_beta = np.delete(der_free_beta, self.ind_comp, 0)
+            der_free_tot = np.delete(der_free_tot, self.ind_comp, 0)
+            der_free_ks = np.delete(der_free_ks, self.ind_comp, 0)
+
+        # Solve the systems of equations
+        for i in range(self.ns):
+            solution = np.linalg.solve(M, b[:, i])
+            der_free_beta[:, i] = solution[
+                : (self.nc - 1 if self.distribution else self.nc)
+            ]
+            if with_solids:
+                der_solid_beta[:, i] = solution[
+                    (self.nc - 1 if self.distribution else self.nc) :
+                ]
+
+        for r in range(self.nc):
+            solution = np.linalg.solve(M, d[:, r])
+            der_free_tot[:, r] = solution[
+                : (self.nc - 1 if self.distribution else self.nc)
+            ]
+            if with_solids:
+                der_solid_tot[:, r] = solution[
+                    (self.nc - 1 if self.distribution else self.nc) :
+                ]
+
+        if with_solids:
+            for k, skip in enumerate(to_skip[-self.nf :]):
+                if skip:
+                    continue
+                solution = np.linalg.solve(M, f[:, k])
+                der_free_ks[:, k] = solution[
+                    : (self.nc - 1 if self.distribution else self.nc)
+                ]
+                der_solid_ks[:, k] = solution[
+                    (self.nc - 1 if self.distribution else self.nc) :
+                ]
+
+        if with_solids:
+            null_solids_index = np.nonzero(c_solid == 0)[0]
+            if null_solids_index.size:
+                der_solid_beta = np.insert(der_solid_beta, null_solids_index, 0, axis=0)
+                der_solid_tot = np.insert(der_solid_tot, null_solids_index, 0, axis=0)
+                der_solid_ks = np.insert(der_solid_ks, null_solids_index, 0, axis=0)
+
+        if self.distribution:
+            der_free_beta = np.insert(der_free_beta, self.ind_comp, 0, axis=0)
+            der_free_tot = np.insert(der_free_tot, self.ind_comp, 0, axis=0)
+            der_free_ks = np.insert(der_free_ks, self.ind_comp, 0, axis=0)
+
+        # Compute derivatives for the species
+        der_spec_beta = (
+            np.rot90(np.tile(model.T, (self.ns, 1, 1)), -1)
+            * (
+                np.stack(
+                    [np.tile(c_spec, (self.ns, 1)).T for _ in range(self.nc)], axis=-1
+                )
+                / free_c
+            )
+            * np.tile(der_free_beta.T, (self.ns, 1, 1))
+        ).sum(axis=-1) + np.diag(c_spec / beta)
+
+        der_spec_tot = (
+            np.rot90(np.tile(model.T, (self.nc, 1, 1)), -1)
+            * (
+                np.stack(
+                    [np.tile(c_spec, (self.nc, 1)).T for _ in range(self.nc)], axis=-1
+                )
+                / free_c
+            )
+            * np.tile(der_free_tot.T, (self.ns, 1, 1))
+        ).sum(axis=-1)
+
+        der_spec_ks = (
+            np.rot90(np.tile(model.T, (self.nf, 1, 1)), -1)
+            * (
+                np.stack(
+                    [np.tile(c_spec, (self.nf, 1)).T for _ in range(self.nc)], axis=-1
+                )
+                / free_c
+            )
+            * np.tile(der_free_ks.T, (self.ns, 1, 1))
+        ).sum(axis=-1)
+
+        # Calculate uncertanity for components and species given the input
+        comp_sigma = np.sqrt(
+            ((der_free_beta**2) * (self.beta_sigma**2)).sum(axis=1)
+            + ((der_free_tot**2) * (self.conc_sigma[point] ** 2)).sum(axis=1)
+            + ((der_free_ks**2) * (self.ks_sigma**2)).sum(axis=1)
+        )
+
+        species_sigma = np.sqrt(
+            ((der_spec_beta**2) * (self.beta_sigma**2)).sum(axis=1)
+            + ((der_spec_tot**2) * (self.conc_sigma[point] ** 2)).sum(axis=1)
+            + ((der_spec_ks**2) * (self.ks_sigma**2)).sum(axis=1)
+        )
+        species_sigma = np.concatenate((comp_sigma, species_sigma))
+
+        if with_solids:
+            solid_sigma = np.sqrt(
+                ((der_solid_beta**2) * (self.beta_sigma**2)).sum(axis=1)
+                + ((der_solid_tot**2) * (self.conc_sigma[point] ** 2)).sum(axis=1)
+                + ((der_solid_ks**2) * (self.ks_sigma**2)).sum(axis=1)
+            )
+        else:
+            solid_sigma = np.zeros(shape=self.nf)
+
+        return species_sigma, solid_sigma
+
+    def _setDataframeIndex(self, dataframe):
+        if self.distribution:
+            dataframe = dataframe.set_index(self.ind_comp_logs).rename_axis(
+                index="p[" + self.comp_names[self.ind_comp] + "]",
+            )
+        else:
+            dataframe = dataframe.set_index(self.v_added).rename_axis(
+                index="Added Volume [l]",
+            )
+
+        return dataframe
+
+    def _scaleMatrix(self, J: NDArray, delta: NDArray):
+        d1 = np.diag(np.sqrt(J.max(axis=1)))
+        d2 = np.diag(np.sqrt(J.max(axis=0)))
+        # d1 = np.diag(np.sqrt(np.diag(J)))
+        # d2 = np.diag(np.sqrt(np.diag(J)))
+        J = d1 @ J @ d2
+        delta = d1 @ delta
+        return J, delta
+
+
+@njit(parallel=True, cache=True)
+def numba_jacobian(nc, nt, c_spec, saturation_index, model, solid_model, with_solids):
+    J = np.empty(shape=(nt, nt))
+    ns = len(c_spec)
+    # Compute Jacobian
+    # Jacobian for aqueous species
+    for j in prange(nc):
+        for k in prange(nc):
+            val = 0
+            for z in prange(ns):
+                val += model[j, z] * model[k, z] * (c_spec[z] / c_spec[k])
+            J[j, k] = val
+
+    if with_solids:
+        # Jacobian for solid species
+        for j in range(nc):
+            for k in range(nc, nt):
+                J[j, k] = solid_model[j, (k - nc)]
+
+        for j in range(nc, nt):
+            for k in range(nc):
+                J[j, k] = -(solid_model[k, (j - nc)]) * (
+                    saturation_index[(j - nc)] / c_spec[k]
+                )
+    return J