initial upload

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.xlsx filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,18 @@
+# Use Python base image
+FROM python:3.12
+
+# Set working directory
+WORKDIR /app
+
+# Copy current files into container
+COPY . /app
+
+# Install Flask
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose default port
+EXPOSE 7860
+
+# Run with Gunicorn
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "CHRIS:app"]
+

Kps_function/Kps_model/coeffs.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6732a08b936cc768bafc6aa80883c6a85ae232ec101288d0383db616e5f476d
+size 24413

Kps_function/Kps_model/meta.json

@@ -0,0 +1,114 @@
+{
+  "features": [
+    "c",
+    "x",
+    "xy2",
+    "y_m",
+    "y2_m",
+    "y3_m"
+  ],
+  "taus": [
+    0.01,
+    0.02,
+    0.03,
+    0.04,
+    0.05,
+    0.060000000000000005,
+    0.06999999999999999,
+    0.08,
+    0.09,
+    0.09999999999999999,
+    0.11,
+    0.12,
+    0.13,
+    0.14,
+    0.15000000000000002,
+    0.16,
+    0.17,
+    0.18000000000000002,
+    0.19,
+    0.2,
+    0.21000000000000002,
+    0.22,
+    0.23,
+    0.24000000000000002,
+    0.25,
+    0.26,
+    0.27,
+    0.28,
+    0.29000000000000004,
+    0.3,
+    0.31,
+    0.32,
+    0.33,
+    0.34,
+    0.35000000000000003,
+    0.36000000000000004,
+    0.37,
+    0.38,
+    0.39,
+    0.4,
+    0.41000000000000003,
+    0.42000000000000004,
+    0.43,
+    0.44,
+    0.45,
+    0.46,
+    0.47000000000000003,
+    0.48000000000000004,
+    0.49,
+    0.5,
+    0.51,
+    0.52,
+    0.53,
+    0.54,
+    0.55,
+    0.56,
+    0.5700000000000001,
+    0.5800000000000001,
+    0.59,
+    0.6,
+    0.61,
+    0.62,
+    0.63,
+    0.64,
+    0.65,
+    0.66,
+    0.67,
+    0.68,
+    0.6900000000000001,
+    0.7000000000000001,
+    0.7100000000000001,
+    0.72,
+    0.73,
+    0.74,
+    0.75,
+    0.76,
+    0.77,
+    0.78,
+    0.79,
+    0.8,
+    0.81,
+    0.8200000000000001,
+    0.8300000000000001,
+    0.8400000000000001,
+    0.85,
+    0.86,
+    0.87,
+    0.88,
+    0.89,
+    0.9,
+    0.91,
+    0.92,
+    0.93,
+    0.9400000000000001,
+    0.9500000000000001,
+    0.9600000000000001,
+    0.97,
+    0.98,
+    0.99
+  ],
+  "has_intercept": true,
+  "library": "statsmodels.QuantReg",
+  "notes": "Save preprocessing steps separately if any (scaling, transforms)."
+}

Kps_function/read_Kps_model.py

@@ -0,0 +1,201 @@
+import json
+import numpy as np
+from pathlib import Path
+
+class QuantileGridFromCoeffs:
+    def __init__(self, export_dir):
+        self.export_dir = Path(export_dir)
+        meta = json.loads((self.export_dir / "meta.json").read_text())
+        self.features = meta["features"]
+        self.taus = np.array(meta["taus"], dtype=float)
+        self.has_intercept = meta.get("has_intercept", False)
+
+        # load coefficients
+        coeffs = np.load(self.export_dir / "coeffs.npz")
+        # build a matrix shape (n_taus, n_coef)
+        coefs = []
+        for t in self.taus:
+            key = f"tau_{t}"
+            if key not in coeffs:
+                # try rounding formatting
+                found = [k for k in coeffs.files if k.startswith("tau_") and abs(float(k.split("_")[1]) - t) < 1e-12]
+                if not found:
+                    raise KeyError(f"Coefficient for tau={t} not found in {coeffs.files}")
+                key = found[0]
+            coefs.append(coeffs[key])
+        self.coef_matrix = np.vstack(coefs)  # shape (m_taus, n_coef)
+
+    def _create_polynomial_features(self, X):
+        """
+        Create polynomial features for interaction terms.
+
+        Parameters:
+            X (array): Array with columns [x, y]
+
+        Returns:
+            Design matrix with polynomial features
+        """
+        x = X[:, 0]
+        y = X[:, 1]
+
+        A = 2
+
+        # Create design matrix with polynomial features up to the specified degree
+        features = []
+
+        # Constant term (intercept)
+        if 'c' in self.features:
+            features.append(np.ones_like(x))
+        # Linear terms
+        if 'x' in self.features:
+            features.append(x)
+        if 'y' in self.features:
+            features.append(y)
+        if 'y_m' in self.features:
+            features.append(y-A)
+        if 'y_p' in self.features:
+            features.append(y+A)
+        # Interaction terms
+        if 'xy' in self.features:
+            features.append(x * y)  
+        if 'xy_m' in self.features:
+            features.append(x * (y-A))  
+        if 'xy_p' in self.features:
+            features.append(x * (y+A))  
+        if 'xy2' in self.features:
+            features.append(x * y**2)  
+        if 'xy2_m' in self.features:
+            features.append(x * (y-A)**2)  
+        if 'xy2_p' in self.features:
+            features.append(x * (y+A)**2)  
+        if 'x2y' in self.features:
+            features.append(x**2 * y)  
+        if 'xy3' in self.features:
+            features.append(x * y**3)  
+        if 'xy4' in self.features:
+            features.append(x * y**4)  
+        if 'xy3_m' in self.features:
+            features.append(x * (y-A)**3)  
+        if 'xy3_p' in self.features:
+            features.append(x * (y+A)**3)  
+        if 'x3y' in self.features:
+            features.append(x**3 * y)  
+        # Higher order terms
+        if 'x2' in self.features:
+            features.append(x**2)
+        if 'x3' in self.features:
+            features.append(x**3)
+        if 'y2' in self.features:
+            features.append(y**2)
+        if 'y3' in self.features:
+            features.append(y**3)
+        if 'y4' in self.features:
+            features.append(y**4)
+        if 'y2_m' in self.features:
+            features.append((y-A)**2)
+        if 'y3_m' in self.features:
+            features.append((y-A)**3)
+        if 'y4_m' in self.features:
+            features.append((y-A)**4)
+        if 'y2_p' in self.features:
+            features.append((y+A)**2)
+        if 'y3_p' in self.features:
+            features.append((y+A)**3)
+        if 'y4_p' in self.features:
+            features.append((y+A)**4)
+        return np.column_stack(features)
+
+    def predict_quantiles(self, X_new):
+        """
+        Return Q (n_points, m_taus) predicted quantiles.
+        """
+        X_new = np.asarray(X_new, dtype=float)
+        Xd = self._create_polynomial_features(X_new)  # shape (n, p)
+        # matrix multiply: (m_taus, p) @ (p, n) -> (m_taus, n) then transpose
+        Q = (self.coef_matrix @ Xd.T).T
+        # optionally enforce monotonicity in tau
+        Q_sorted = np.sort(Q, axis=1)
+        return self.taus, Q_sorted
+
+    def predict_tau(self, X_new, tau_star):
+        taus, Q = self.predict_quantiles(X_new)
+        # vectorized interpolation (same approach as earlier)
+        # implement interpolation between nearest taus
+        import numpy as np
+        t0_idx = np.searchsorted(taus, tau_star, side='right') - 1
+        # for simplicity assume scalar tau_star
+        j = int(np.clip(t0_idx, 0, len(taus)-2))
+        t0, t1 = taus[j], taus[j+1]
+        q0, q1 = Q[:, j], Q[:, j+1]
+        w = (tau_star - t0) / (t1 - t0)
+        return q0 + w * (q1 - q0)
+
+    def sample(self, X_new, n_samples=1, rng=None):
+        """
+        Draw samples from the approximate conditional distribution at X_new
+        using inverse-CDF sampling based on the saved quantile grid.
+
+        Parameters
+        ----------
+        X_new : array-like, shape (n_points, 2)
+            New points [x, y] in your domain (e.g., LogP, PolarityIndex).
+        n_samples : int
+            Number of samples per point.
+        rng : None, int, or np.random.Generator
+            Random seed or Generator for reproducibility.
+
+        Returns
+        -------
+        samples : ndarray, shape (n_points, n_samples)
+            Samples drawn from the interpolated quantile function.
+        """
+        if n_samples < 1:
+            raise ValueError("n_samples must be >= 1")
+
+        # Setup RNG
+        if isinstance(rng, np.random.Generator):
+            gen = rng
+        else:
+            gen = np.random.default_rng(rng)
+
+        X_new = np.asarray(X_new, dtype=float)
+        if X_new.ndim != 2 or X_new.shape[1] != 2:
+            raise ValueError("X_new must be a 2D array with exactly two columns [x, y]")
+
+        # Get quantile grid predictions: Q has shape (n_points, n_taus)
+        taus, Q = self.predict_quantiles(X_new)
+        taus = np.asarray(taus, dtype=float)
+        Q = np.asarray(Q, dtype=float)
+
+        n_points, m = Q.shape
+        if m < 2:
+            raise RuntimeError("Need at least two taus to sample with interpolation.")
+
+        # Sample u in the supported tau range of the grid
+        u = gen.uniform(taus[0], taus[-1], size=(n_points, n_samples))
+
+        # For each u, find interval [taus[j], taus[j+1]]
+        j = np.searchsorted(taus, u, side="right") - 1
+        j = np.clip(j, 0, m - 2)
+
+        # Gather endpoints
+        t0 = taus[j]
+        t1 = taus[j + 1]
+
+        row_idx = np.arange(n_points)[:, None]
+        q0 = Q[row_idx, j]
+        q1 = Q[row_idx, j + 1]
+
+        # Linear interpolation
+        w = (u - t0) / (t1 - t0)
+        samples = q0 + w * (q1 - q0)
+
+        return samples
+
+## read saved model
+#model = QuantileGridFromCoeffs(export_dir='Kps_model')
+## example points: [(LogP, Polarity_Index), ...]
+#X_new = np.array([[2.34665198, 10.2], ])
+## sample the distribution at each X
+#samples = model.sample(X_new, n_samples=50, rng=0)
+#print(samples[0])

assets/style.css

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+input:disabled {
+    color: transparent !important;
+}
+
+/* Hide dropdown selected text */
+.Select.is-disabled .Select-value-label {
+    color: transparent !important;
+}

data/Kps-experiment.xlsx

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+size 95371

data/Tissue_Diffusion_Data_v4.xlsx

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data/data-descriptors-mordred-numconfs51.xlsx

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data/db-D-interp-37-clean.xlsx

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data/db-D-interp-50-clean.xlsx

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data/db-D-interp-allT-clean.xlsx

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data/db-Kpt-corrected.xlsx

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data/db-polymer-properties-and-categories.xlsx

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data/extreme-solutes-universe.xlsx

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data/functions.py

@@ -0,0 +1,303 @@
+import json,pickle
+import numbers
+import numpy as np
+import pandas as pd
+import scipy as sp
+from scipy.optimize import bisect
+import scipy.special
+
+import rdkit
+from rdkit.Chem import AllChem as Chem
+import chemicals
+
+import mordred
+import mordred.descriptors
+
+def Piringer(Mw, Ap, T=310.):
+
+    # Semi-empirical model for D(Mw) given polymer property Ap- Toxicol. Sci. 2019, 172 (1), 201–212.
+    if Mw > 1100.: # if molecule is greater than 1100 g/mol, default to that value as worst case
+        Mw = 1100.
+    return 1e4 * np.exp(Ap - 0.1351 * Mw ** (2. / 3.) + 0.003 * Mw - 10454. / T)
+    
+def PowerLaw(Mw, A, B):
+
+    logMw = np.log(Mw)
+    logD = A+logMw*B
+    return np.exp(logD)
+    
+def Polymers():
+
+    PolyData = pd.read_csv('data/polymer_names_noglasses.tsv', sep='\t')
+    polymers = np.array(list(PolyData['Polymer_Name']))
+    categories = np.array(list(PolyData['New Class']))
+    #polymers = np.array(list(PolyData['Polymer_Name']) + ['Other polymer'])
+    #categories = np.array(list(PolyData['New Class']) + [None])
+    
+    return polymers, categories
+
+# Get polymers
+polymers, categories = Polymers()
+
+# Get solutes
+soluteData = pd.read_excel('data/soluteData.xlsx')
+nSolutes = len(soluteData)
+
+## list of solvents to include, all semi-polar and non-polar solvents in ISO 10993-18:2020 Table D.1 (except DMSO, which is not used in practice)
+df_visc = pd.read_excel('data/solventData.xlsx')
+solvents = df_visc["Solvent_Name"].tolist()
+
+## sampling parameters
+N_sample = int(1e5)
+#rng = np.random.Generator(np.random.PCG64(seed=12345))
+
+## c distribution parameters
+T_cut = 20
+MW_cut = 20
+
+use_new = True
+T_cut_new = 0.5
+
+#### read data files
+# CHRIS parameter distributions
+if not use_new:
+    param_dists = {}
+    with open('data/param_distribution_37.json','r') as fp:
+        param_dists[37] = json.load(fp)
+    with open('data/param_distribution_50.json','r') as fp:
+        param_dists[50] = json.load(fp)
+else:
+    with open('data/param_distribution_allT.json','r') as fp:
+        param_dists = json.load(fp)
+# other stuff
+
+df_desc = pd.read_excel(f'data/data-descriptors-mordred-numconfs51.xlsx', usecols=['Solute_InChIKey', 'Vabc','VMcGowan'])
+if not use_new:
+    ## clean data
+    df_final_37 = pd.read_excel('data/db-D-interp-37-clean.xlsx')
+    df_final_50 = pd.read_excel('data/db-D-interp-50-clean.xlsx')
+    # convert all T to K
+    df_final_37['T'] = df_final_37['T'] + 273.15
+    df_final_37['Polymer_Tg'] = df_final_37['Polymer_Tg'] + 273.15
+    df_final_37['Polymer_Tm'] = df_final_37['Polymer_Tm'] + 273.15
+    df_final_50['T'] = df_final_50['T'] + 273.15
+    df_final_50['Polymer_Tg'] = df_final_50['Polymer_Tg'] + 273.15
+    df_final_50['Polymer_Tm'] = df_final_50['Polymer_Tm'] + 273.15
+    # add volumes
+    df_final_37 = pd.merge(df_final_37, df_desc[['Solute_InChIKey', 'Vabc', 'VMcGowan']], how='left', on='Solute_InChIKey', suffixes=('', '_dupe'))
+    df_final_50 = pd.merge(df_final_50, df_desc[['Solute_InChIKey', 'Vabc', 'VMcGowan']], how='left', on='Solute_InChIKey', suffixes=('', '_dupe'))
+else:
+    ## clean data
+    df_final = pd.read_excel('data/db-D-interp-allT-clean.xlsx')
+    # convert all T to K
+    df_final['T'] = df_final['T'] + 273.15
+    df_final['Polymer_Tg'] = df_final['Polymer_Tg'] + 273.15
+    df_final['Polymer_Tm'] = df_final['Polymer_Tm'] + 273.15
+    # add volumes
+    df_final = pd.merge(df_final, df_desc[['Solute_InChIKey', 'Vabc', 'VMcGowan']], how='left', on='Solute_InChIKey', suffixes=('', '_dupe'))
+
+#### solvent-specific viscosity
+# add MW
+Solvent_MWs = {solv:df_visc.loc[df_visc['Solvent_Name']==solv,'MW'].iloc[0] for solv in solvents}
+#Solvent_Densities = {solv:string2density(solv)[0] for solv in solvents}
+Solvent_Densities = {solv:df_visc.loc[df_visc['Solvent_Name']==solv,'density'].iloc[0] for solv in solvents}
+Solvent_PIs = {solv:df_visc.loc[df_visc['Solvent_Name']==solv,'polarity index'].iloc[0] for solv in solvents}
+# linear relation to estimate Vabc when it fails for a molecule
+Vabc = df_desc['Vabc']
+Vmcg = df_desc['VMcGowan']
+m = ~pd.isna(Vabc)
+popt_V = np.polyfit(Vmcg[m], Vabc[m], 1)
+
+def get_WC(T,solv,V):
+    params = df_visc[df_visc['Solvent_Name']==solv].iloc[0]
+    if params['Equation'] == '10^A(1/T-1/B)':
+        eta = 10**(params['A']*(1/T-1/params['B']))
+    elif params['Equation'] == 'A*exp(B/T)':
+        eta = params['A']*np.exp(params['B']/T)
+    elif params['Equation'] == 'E*exp(A+B/(T/298.15)+C/(T/298.15)^2+D/(T/298.15)^3)':
+        eta = params['E']*np.exp(params['A'] + params['B']/(T/298.15) + params['C']/(T/298.15)**2 + params['D']/(T/298.15)**3)
+    elif params['Equation'] == 'A*exp(-0.01*B*(T-298.15))':
+        eta = params['A']*np.exp(-0.01*params['B']*(T-298.15))
+    elif params['Equation'] == 'A+BT/1+CT+DT^2':
+        eta = (params['A']+params['B']*T) / (1 + params['C']*T + params['D']*T**2)
+    elif params['Equation'] == 'A+B/T+C/T^2+D/T^3':
+        eta = params['A'] + params['B']/T + params['C']/T**2 + params['D']/T**3
+    elif params['Equation'] == 'A*298.15/T':
+        eta = params['A'] * 298.15/T
+    elif params['Equation'] == 'A*T+B':
+        eta = params['A'] * T + params['A']
+    else:
+        eta = np.nan
+    D_WC = 7.4e-8*(params['MW']*params['WC_assoc_param'])**0.5*(T)/eta/V**0.6
+    return D_WC, eta, params['MW']
+
+#### add Wilke-Chang
+if not use_new:
+    ## 50 C
+    # estimate Vabc for those with nan values
+    m = pd.isna(df_final_50['Vabc'])
+    v = np.polyval(popt_V, df_final_50['VMcGowan'][m])
+    df_final_50.loc[m, 'Vabc'] = v
+    T = df_final_50['T']
+    V = df_final_50['Vabc']
+    for solv in solvents:
+        D_WC, eta, MW_solvent = get_WC(T, solv, V)
+        df_final_50[f'eta_{solv}'] = eta
+        df_final_50[f'D_WC_{solv}'] = D_WC
+        df_final_50[f'MW_solvent_{solv}'] = MW_solvent
+    ## 37 C
+    # estimate Vabc for those with nan values
+    m = pd.isna(df_final_37['Vabc'])
+    v = np.polyval(popt_V, df_final_37['VMcGowan'][m])
+    df_final_37.loc[m, 'Vabc'] = v
+    T = df_final_37['T']
+    V = df_final_37['Vabc']
+    for solv in solvents:
+        D_WC, eta, MW_solvent = get_WC(T, solv, V)
+        df_final_37[f'eta_{solv}'] = eta
+        df_final_37[f'D_WC_{solv}'] = D_WC
+        df_final_37[f'MW_solvent_{solv}'] = MW_solvent
+else:
+    # estimate Vabc for those with nan values
+    m = pd.isna(df_final['Vabc'])
+    v = np.polyval(popt_V, df_final['VMcGowan'][m])
+    df_final.loc[m, 'Vabc'] = v
+    T = df_final['T']
+    V = df_final['Vabc']
+    for solv in solvents:
+        D_WC, eta, MW_solvent = get_WC(T, solv, V)
+        df_final[f'eta_{solv}'] = eta
+        df_final[f'D_WC_{solv}'] = D_WC
+        df_final[f'MW_solvent_{solv}'] = MW_solvent
+
+def get_V(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    calc = mordred.Calculator([mordred.descriptors.VdwVolumeABC, mordred.descriptors.McGowanVolume])
+    Vabc,Vmcg = list(calc(mol).values())
+    if not isinstance(Vabc, numbers.Number):
+        Vabc = np.polyval(popt_V, Vmcg)
+    return Vabc
+
+#### Vrentas-Duda setup
+df_vd_solv = pd.read_excel('data/vrentas-duda-params.xlsx', sheet_name='Solutes')
+df_vd_solv.drop_duplicates(keep='first', inplace=True, ignore_index=True) # drop exact duplicates
+df_vd_poly = pd.read_excel('data/vrentas-duda-params.xlsx', sheet_name='Polymers')
+df_vd_poly.drop_duplicates(keep='first', inplace=True, ignore_index=True) # drop exact duplicates
+df_props = pd.read_excel('data/db-polymer-properties-and-categories.xlsx')
+df_vd_poly = pd.merge(df_vd_poly, df_props[['Polymer_Name','Polymer_Tg','Polymer_Tm', 'CHRIS Class', 'New Class']], how='left', on='Polymer_Name')
+df_vd_poly['New Class'] = df_vd_poly['New Class'].fillna('none')
+df_vd_poly['CHRIS Class'] = df_vd_poly['CHRIS Class'].fillna('none')
+
+## Calculate c
+dfs_vd_allT = []
+for T in np.arange(100,800,20):
+    fV_polyT = (df_vd_poly['K12']*(df_vd_poly['K22-Tg2']+T))
+    #fV_polyT[fV_polyT<0.025] = 0.025
+    for solvname in set(df_vd_solv['Solute_Name']):
+        df_sol = df_vd_solv[df_vd_solv['Solute_Name']==solvname]
+        for row in df_sol.iterrows():
+            row = row[1]
+            fV_sol = (row['K11']*(row['K21-Tg1']+T))
+            c_sol = fV_sol / fV_polyT
+            df_vd_allT = pd.concat([row]*len(df_vd_poly), axis=1, ignore_index=True).T
+            df_vd_allT = pd.concat([df_vd_allT, df_vd_poly], axis=1)
+            df_vd_allT['c'] = c_sol
+            df_vd_allT['T'] = T
+            dfs_vd_allT.append(df_vd_allT)
+df_vd_allT = pd.concat(dfs_vd_allT, ignore_index=True)
+df_vd_allT['T-Tg'] = df_vd_allT['T']-df_vd_allT['Tg2']
+df_vd_allT['T-Tg1'] = df_vd_allT['T']-df_vd_allT['Tg1']
+
+def get_c_dist(T,Tg,MW):
+    m = (~pd.isna(df_vd_allT['c'])) & (np.abs(df_vd_allT['T-Tg']-max(T_cut,T-Tg))<T_cut) & (np.abs(df_vd_allT['M1']-MW)<MW_cut)
+    cs = df_vd_allT.loc[m, 'c']
+    cs = np.array(cs)
+    cs = cs[~np.isnan(cs)]
+    cs = cs[cs>0]
+    return cs
+
+def get_c_dist_cat(T,CHRIS_category,MW):
+    m = (~pd.isna(df_vd_allT['c'])) & (df_vd_allT['T-Tg']>0) & (np.abs(df_vd_allT['T']-T)<T_cut) & (np.abs(df_vd_allT['M1']-MW)<MW_cut) & (df_vd_allT['New Class']==CHRIS_category)
+    cs = df_vd_allT.loc[m, 'c']
+    cs = np.array(cs)
+    cs = cs[~np.isnan(cs)]
+    cs = cs[cs>0]
+    return cs
+
+def get_D_Extract(w,T,Polymer_Tg,Solvent_Name,Solvent_MW,Solute_MW,Solute_Vabc,CHRIS_category,N=10000,return_DCs=False,input_Ds=None):
+       
+    df_final_T = df_final.loc[np.abs(df_final['T']-T)<T_cut_new]
+    if (T <= Polymer_Tg) or (input_Ds is not None):
+        cs = get_c_dist(T,Polymer_Tg,Solvent_MW)
+    else:
+        cs = get_c_dist_cat(T,CHRIS_category,Solvent_MW)
+    if not len(cs):
+        cs = get_c_dist(T,Polymer_Tg,Solvent_MW)
+    rng = np.random.Generator(np.random.PCG64(seed=12345))
+    c = rng.choice(cs, N)
+    if Solute_Vabc is None:
+        if Solute_MW < 50:
+            m50 = df_final_T['MW']<=50
+        else:
+            m50 = df_final_T['MW']>50
+        ## within cutoffs, with at least N closest (by sorting, separating at MW = 50)
+        m2 = (np.abs(df_final_T['Polymer_Tg']-Polymer_Tg)<T_cut) & (np.abs(df_final_T['MW']-Solute_MW)<MW_cut) & m50
+        if m2.sum()<25:
+            vT = df_final_T.loc[m50,'Polymer_Tg']-Polymer_Tg; vM = df_final_T.loc[m50,'MW']-Solute_MW; m3 = pd.concat([np.abs(vT), np.abs(vM)], axis=1).sort_values(by=['Polymer_Tg', 'MW']).index[1:26]
+            m2 = list(set(m2.index[m2]).union(set(m3)))
+        if return_DCs:
+            Ds,DWCs,DCs = rng.choice([df_final_T.loc[m2,'D'], df_final_T.loc[m2,f'D_WC_{Solvent_Name}'], df_final_T.loc[m2,f'D_CHRIS_q50']], N, axis=1)
+        else:
+            Ds,DWCs = rng.choice([df_final_T.loc[m2,'D'], df_final_T.loc[m2,f'D_WC_{Solvent_Name}']], N, axis=1)
+    else:
+        DWCs, eta, MW_solvent = get_WC(T, Solvent_Name, Solute_Vabc)
+    ## distribution of D_CHRIS
+    if input_Ds is None:
+        if Solute_MW > 50:
+            subkey = f'{CHRIS_category}_hi'
+        else:
+            subkey = f'{CHRIS_category}_lo'
+        allparams = [param_dists[Ti][subkey] for Ti in param_dists if T+T_cut_new >= int(Ti)+273.15 >= T-T_cut_new]
+        D_list = []
+        for params in allparams:
+            if params[0] == 'pir':
+                A_list = params[1:]
+                D_list += [Piringer(Solute_MW, Ai, T) for Ai in A_list]
+            else:
+                Ball = params[1]
+                A_list = params[2:]
+                D_list += [PowerLaw(Solute_MW, Ai, Ball) for Ai in A_list]
+    else:
+        D_list = input_Ds
+    D_dist_noswell = rng.choice(D_list, N)
+    if Solute_Vabc is None:
+        lnD_D0 = c*w/(1+(c-1)*w) * np.log(DWCs/Ds)
+    else:
+        lnD_D0 = c*w/(1+(c-1)*w) * np.log(DWCs/D_dist_noswell)
+    D_dist_swell = np.exp(np.log(D_dist_noswell)+lnD_D0)
+    if return_DCs:
+        return D_dist_noswell, D_dist_swell, (c, Ds, DWCs, DCs)
+    else:
+        return D_dist_swell
+
+def get_D_CHRIS(Solute_MW,CHRIS_category,N=10000):
+    
+    T = 310.15
+    rng = np.random.Generator(np.random.PCG64(seed=12345))
+    if Solute_MW > 50:
+        subkey = f'{CHRIS_category}_hi'
+    else:
+        subkey = f'{CHRIS_category}_lo'
+    allparams = [param_dists[Ti][subkey] for Ti in param_dists if T+T_cut_new >= int(Ti)+273.15 >= T-T_cut_new]
+    D_list = []
+    for params in allparams:
+        if params[0] == 'pir':
+            A_list = params[1:]
+            D_list += [Piringer(Solute_MW, Ai, T) for Ai in A_list]
+        else:
+            Ball = params[1]
+            A_list = params[2:]
+            D_list += [PowerLaw(Solute_MW, Ai, Ball) for Ai in A_list]
+
+    return rng.choice(D_list, N)
+    

data/param_distribution_37.json

@@ -0,0 +1 @@
+{"R1_hi": ["pow", -1.7210813178252082, -5.316806497783963, -5.4494932012881385, -5.768182223826546, -5.571599248471992, -5.332462794946952, -7.7161800923227855, -6.7846218883178455, -6.076436830393357, -5.937324027930641, -5.4290991858647075, -8.06074734080945, -7.5509946441655025, -7.493319728112297, -6.143393011163276, -5.744119873303038, -5.713536449930954, -5.245794412301276, -7.238997768936623, -5.246358505784474, -7.01323922285557, -6.732878249465795, -7.137122775103951, -6.995280165475592, -6.353291780114349, -6.60469811090624, -6.855747987914241, -7.488719438432026, -3.686551067727473, -4.906329498045797, -6.299715462690976, -6.092875266113934, -4.449148280201202, -4.043683172093038, -5.59407597774206, -6.761372264473744, -6.5340270263058375, -5.28826387782572, -5.399291242888079, -5.362353646239544, -5.264893285811262, -4.975886197021524, -6.728235603941291, -4.40329100075691, -5.42668007116478, -3.4735286648330526, -5.604917835117476, -4.205308139781945, -3.117079553113051, 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data/param_distribution_50.json

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data/polymer_names.tsv

@@ -0,0 +1,54 @@
+Polymer_Name	Long_Name	Additional_Names	New Class	Class ID	Simpler_Name
+ABS	Acrylonitrile butadiene styrene		G1	6	Acrylonitrile butadiene styrene
+PIB	Butyl rubber	Polyisobutylene	P2	3	Butyl rubber
+Epoxy	Epoxy resin		R2	1	Epoxy resin
+EPDM	Ethylene-propylene-diene monomer rubber		R2	1	Ethylene-propylene-diene monomer rubber
+HPIB	Halobutyl rubber (chlorobutyl/bromobutyl)		R2	1	Halobutyl rubber chlorobutyl/bromobutyl
+PI	Natural rubber	Polyisoprene	R2	1	Natural rubber
+NBR	Nitrile rubber	Acrylonitrile-butadiene rubber	R2	1	Nitrile rubber
+PEHMA	Poly(2-ethylhexyl methacrylate)		P1	2	Poly2-ethylhexyl methacrylate
+PAN	Polyacrylonitrile		G1	6	Polyacrylonitrile
+Nylon	Polyamide	Nylon	P4	5	Polyamide
+PBD	Polybutadiene		R2	1	Polybutadiene
+PBD-H	Polybutadiene (hydrogenated)		R2	1	Polybutadiene hydrogenated
+PBT	Poly(butylene terephthalate)		G1	6	Polybutylene terephthalate
+PC	Polycarbonate		G2	7	Polycarbonate
+PCP	Polychloroprene	Neoprene	R2	1	Polychloroprene
+ECO	Polyepichlorohydrin		R2	1	Polyepichlorohydrin
+PEEK	Poly(ether ether ketone)		G1	6	Polyether ether ketone
+PEI	Poly(ether imide)		G1	6	Polyether imide
+PEBAX	Poly(ether-block-amide)	PEBAX	P1	2	Polyether-block-amide
+HDPE	Polyethylene (density > 0.94 g/cc)		P3	4	Polyethylene density > 0.94 g/cc
+LDPE	Polyethylene (density <= 0.94 g/cc)		P1	2	Polyethylene density <= 0.94 g/cc
+PEN	Poly(ethylene naphthalate)		G2	7	Polyethylene naphthalate
+PET	Poly(ethylene terephthalate)		G2	7	Polyethylene terephthalate
+ECTFE	Poly(ethylene-co-chlorotrifluoroethylene)		P3	4	Polyethylene-co-chlorotrifluoroethylene
+EPP	Poly(ethylene-co-propylene)		P1	2	Polyethylene-co-propylene
+EVA	Poly(ethylene-co-vinyl acetate)		P1	2	Polyethylene-co-vinyl acetate
+EVOH	Poly(ethylene-co-vinyl alcohol)		P3	4	Polyethylene-co-vinyl alcohol
+PMMA	Poly(methyl methacrylate)		G2	7	Polymethyl methacrylate
+PEMA	Poly(ethyl methacrylate)		P4	5	Polyethyl methacrylate
+PBMA	Poly(butyl methacrylate)		P1	2	Polybutyl methacrylate
+PMA	Poly(methyl acrylate)		P2	3	Polymethyl acrylate
+PEA	Poly(ethyl acrylate)		P1	2	Polyethyl acrylate
+PBA	Poly(butyl acrylate)		R2	1	Polybutyl acrylate
+PECA	Poly(ethyl cyanoacrylate)		G2	7	Polyethyl cyanoacrylate
+PBCA	Poly(butyl cyanoacrylate)		G2	7	Polybutyl cyanoacrylate
+PHCA	Poly(hexyl cyanoacrylate)		G1	6	Polyhexyl cyanoacrylate
+POM	Polyoxymethylene		P3	4	Polyoxymethylene
+PP	Polypropylene		P2	3	Polypropylene
+PS	Polystyrene		G1	6	Polystyrene
+PSF	Polysulfone		G1	6	Polysulfone
+PTFE	Poly(tetrafluoroethylene)	Teflon	P2	3	Polytetrafluoroethylene
+FEP	Fluorinated ethylene propylene	"Teflon FEP, poly(tetrafluoroethylene-co-hexafluoropropylene)"	P2	3	Fluorinated ethylene propylene
+PTFE-PP	Poly(tetrafluoroethylene-co-propylene)	AFLAS	R2	1	Polytetrafluoroethylene-co-propylene
+PU	Polyurethane		P1	2	Polyurethane
+PVAc	Poly(vinyl acetate)		P3	4	Polyvinyl acetate
+pPVC	Poly(vinyl chloride) (plasticized)		R2	1	Polyvinyl chloride plasticized
+uPVC	Poly(vinyl chloride) (unplasticized)		G1	6	Polyvinyl chloride unplasticized
+PVDF	Poly(vinylidene difluoride)		P3	4	Polyvinylidene difluoride
+PVDF-HFP	Poly(vinylidene fluoride-co-hexafluoropropylene)		P1	2	Polyvinylidene fluoride-co-hexafluoropropylene
+PVDF-HFP-PTFE	Poly(vinylidene fluoride-co-hexafluoropropylene-co-tetrafluoroethylene)		P1	2	Polyvinylidene fluoride-co-hexafluoropropylene-co-tetrafluoroethylene
+Siloxane	Silicones	"poly(dimethyl siloxane), poly(methyl propyl siloxane), poly(methyl octyl siloxane), poly(methyl tetradecyl siloxane), poly(butyl ester siloxane), poly(methyl phenyl siloxane), poly[methyl(trifluoropropyl)siloxane]"	R1	0	Silicones
+SBR	Styrene-butadiene rubber		R2	1	Styrene-butadiene rubber
+TPE	Thermoplastic elastomers		R2	1	Thermoplastic elastomers

data/polymer_names_noglasses.tsv

@@ -0,0 +1,39 @@
+Polymer_Name	Long_Name	Additional_Names	New Class	Class ID	Simpler_Name
+PIB	Butyl rubber	Polyisobutylene	P2	3	Butyl rubber
+Epoxy	Epoxy resin		R2	1	Epoxy resin
+EPDM	Ethylene-propylene-diene monomer rubber		R2	1	Ethylene-propylene-diene monomer rubber
+HPIB	Halobutyl rubber (chlorobutyl/bromobutyl)		R2	1	Halobutyl rubber chlorobutyl/bromobutyl
+PI	Natural rubber	Polyisoprene	R2	1	Natural rubber
+NBR	Nitrile rubber	Acrylonitrile-butadiene rubber	R2	1	Nitrile rubber
+PEHMA	Poly(2-ethylhexyl methacrylate)		P1	2	Poly2-ethylhexyl methacrylate
+Nylon	Polyamide	Nylon	P4	5	Polyamide
+PBD	Polybutadiene		R2	1	Polybutadiene
+PBD-H	Polybutadiene (hydrogenated)		R2	1	Polybutadiene hydrogenated
+PCP	Polychloroprene	Neoprene	R2	1	Polychloroprene
+ECO	Polyepichlorohydrin		R2	1	Polyepichlorohydrin
+PEBAX	Poly(ether-block-amide)	PEBAX	P1	2	Polyether-block-amide
+HDPE	Polyethylene (density > 0.94 g/cc)		P3	4	Polyethylene density > 0.94 g/cc
+LDPE	Polyethylene (density <= 0.94 g/cc)		P1	2	Polyethylene density <= 0.94 g/cc
+ECTFE	Poly(ethylene-co-chlorotrifluoroethylene)		P3	4	Polyethylene-co-chlorotrifluoroethylene
+EPP	Poly(ethylene-co-propylene)		P1	2	Polyethylene-co-propylene
+EVA	Poly(ethylene-co-vinyl acetate)		P1	2	Polyethylene-co-vinyl acetate
+EVOH	Poly(ethylene-co-vinyl alcohol)		P3	4	Polyethylene-co-vinyl alcohol
+PEMA	Poly(ethyl methacrylate)		P4	5	Polyethyl methacrylate
+PBMA	Poly(butyl methacrylate)		P1	2	Polybutyl methacrylate
+PMA	Poly(methyl acrylate)		P2	3	Polymethyl acrylate
+PEA	Poly(ethyl acrylate)		P1	2	Polyethyl acrylate
+PBA	Poly(butyl acrylate)		R2	1	Polybutyl acrylate
+POM	Polyoxymethylene		P3	4	Polyoxymethylene
+PP	Polypropylene		P2	3	Polypropylene
+PTFE	Poly(tetrafluoroethylene)	Teflon	P2	3	Polytetrafluoroethylene
+FEP	Fluorinated ethylene propylene	"Teflon FEP, poly(tetrafluoroethylene-co-hexafluoropropylene)"	P2	3	Fluorinated ethylene propylene
+PTFE-PP	Poly(tetrafluoroethylene-co-propylene)	AFLAS	R2	1	Polytetrafluoroethylene-co-propylene
+PU	Polyurethane		P1	2	Polyurethane
+PVAc	Poly(vinyl acetate)		P3	4	Polyvinyl acetate
+pPVC	Poly(vinyl chloride) (plasticized)		R2	1	Polyvinyl chloride plasticized
+PVDF	Poly(vinylidene difluoride)		P3	4	Polyvinylidene difluoride
+PVDF-HFP	Poly(vinylidene fluoride-co-hexafluoropropylene)		P1	2	Polyvinylidene fluoride-co-hexafluoropropylene
+PVDF-HFP-PTFE	Poly(vinylidene fluoride-co-hexafluoropropylene-co-tetrafluoroethylene)		P1	2	Polyvinylidene fluoride-co-hexafluoropropylene-co-tetrafluoroethylene
+Siloxane	Silicones	"poly(dimethyl siloxane), poly(methyl propyl siloxane), poly(methyl octyl siloxane), poly(methyl tetradecyl siloxane), poly(butyl ester siloxane), poly(methyl phenyl siloxane), poly[methyl(trifluoropropyl)siloxane]"	R1	0	Silicones
+SBR	Styrene-butadiene rubber		R2	1	Styrene-butadiene rubber
+TPE	Thermoplastic elastomers		R2	1	Thermoplastic elastomers

data/soluteData.xlsx

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data/solvent-viscosity.xlsx

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data/solventData.xlsx

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data/vrentas-duda-params.xlsx

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data/~$db-Kpt-corrected.xlsx

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+size 165

fast_bisect/README

@@ -0,0 +1,6 @@
+# 1. if needed, edit the user-defined function in fast_bisect.py to what you want
+
+# 2. compile the Cython code
+python fast_bisect.py build_ext --inplace
+
+# 3. example usage of compiled cython code shown in fast_bisect_example.py

fast_bisect/fast_bisect.py

@@ -0,0 +1,7 @@
+# setup.py
+from setuptools import setup
+from Cython.Build import cythonize
+
+setup(
+    ext_modules = cythonize("fast_bisect.pyx"),
+)

fast_bisect/fast_bisect.pyx

@@ -0,0 +1,28 @@
+from scipy.optimize.cython_optimize cimport bisect
+
+# import math from Cython
+from libc cimport math
+
+# user-defined struct for extra parameters
+ctypedef struct test_params:
+    double alpha
+
+# user-defined function
+## NOTE put your function here using Cython math
+cdef double f(double x, void *args) noexcept:
+    cdef test_params *myargs = <test_params *> args
+    return math.tan(x) + myargs.alpha * x
+    #return myargs.C0 - math.exp(-(x - myargs.C1))
+
+# Cython wrapper function for bisect
+cdef double bisect_wrapper(dict args, double a, double b, double xtol, double rtol, int maxiter):
+    # Cython automatically casts dictionary to struct
+    cdef test_params myargs = args
+    return bisect(f, a, b, <test_params *> &myargs, xtol, rtol, maxiter, NULL)
+
+# Python function
+#def cbisect(args=myargs, a=XLO, b=XHI, xtol=XTOL, rtol=RTOL, maxiter=MITR):
+def cbisect(args, a, b, xtol=1e-8, rtol=1e-15, maxiter=100):
+    '''Calls Cython wrapper from Python.'''
+    return bisect_wrapper(args, a, b, xtol, rtol, maxiter)
+

fast_bisect/fast_bisect_example.py

@@ -0,0 +1,16 @@
+import numpy as np
+import scipy as sp
+import scipy.optimize
+from fast_bisect import cbisect
+
+myargs = {'alpha': 1.0}
+
+def f(x,args):
+    return np.tan(x)-args['alpha']*x
+
+%timeit sp.optimize.bisect(f, a=0.5, b=2.0, args=myargs, xtol=1e-8, rtol=1e-15, maxiter=100)
+# 10.2 μs ± 124 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
+
+%timeit cbisect(a=0.5, b=2.0, args=myargs, xtol=1e-8, rtol=1e-15, maxiter=100)
+# 241 ns ± 0.411 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
+

main.py

@@ -0,0 +1,823 @@
+from dash import Dash, html, dcc, Input, Output, State
+import dash_bootstrap_components as dbc
+import plotly.graph_objects as go
+import pandas as pd
+from data.functions import *
+from transportEqs import *
+import numpy as np
+from Kps_function.read_Kps_model import *
+import os
+
+app = Dash(
+    __name__,
+    assets_folder=os.path.join(os.path.dirname(__file__), "assets")
+)
+
+def build_figure(dataframe: pd.DataFrame) -> go.Figure:
+    fig = go.Figure()
+
+    # Condition 1
+    fig.add_bar(
+        y=dataframe["Condition 1"],
+        x=dataframe["CASRN"],
+        name="Condition 1",
+        orientation="v",
+        error_y=dict(
+            type="data",
+            symmetric=False,
+            array=dataframe["Cond1_err_plus"],  # +x direction
+            arrayminus=dataframe["Cond1_err_minus"],  # -x direction
+            visible=True,
+        ),
+        marker=dict(color="red"),
+    )
+
+    # Condition 2
+    fig.add_bar(
+        y=dataframe["Condition 2"],
+        x=dataframe["CASRN"],
+        name="Condition 2",
+        orientation="v",
+        error_y=dict(
+            type="data",
+            symmetric=False,
+            array=dataframe["Cond2_err_plus"],
+            arrayminus=dataframe["Cond2_err_minus"],
+            visible=True,
+        ),
+        marker=dict(color="blue"),
+    )
+
+    #fig.add_hline(
+    #    y=0.9,
+    #    line_width=3,
+    #    line_color="green",
+    #    line_dash="longdash"
+    #)
+
+    # --- Compute data-driven x-range (include error bars) ---
+    x1 = dataframe["Condition 1"].to_numpy(dtype=float)
+    x2 = dataframe["Condition 2"].to_numpy(dtype=float)
+
+    # --- Compute upper bounds including error bars ---
+    x1_max = x1 - dataframe["Cond1_err_minus"].to_numpy(dtype=float)
+    x2_max = x2 - dataframe["Cond2_err_minus"].to_numpy(dtype=float)
+
+    # Piecewise maxima across the two conditions (array length = number of CASRN)
+    piecewise_max = np.maximum(x1_max, x2_max)
+
+    # Minimum of the piecewise maxima array (ignoring NaNs)
+    min_of_piecewise_max = np.nanmin(piecewise_max)
+
+    # Fallback if everything is NaN or non-finite
+    if not np.isfinite(min_of_piecewise_max):
+        min_of_piecewise_max = 1.0
+
+    # Minimum y = 3 orders of magnitude below that
+    y_min = float(min_of_piecewise_max * 1e-1)
+
+    # Ensure strictly positive for log scale
+    y_min = float(np.maximum(y_min, np.nextafter(0.0, 1.0)))
+
+    fig.update_layout(
+        barmode="group",
+        legend=dict(
+            orientation="h",
+            x=0.5,
+            xanchor="center",
+            y=1.08,
+            font=dict(size=18)  # ← legend text size
+        ),
+        yaxis=dict(
+            title=dict(
+                text=r"Fraction released",
+                font=dict(size=18)  # ← axis label size
+            ),
+            type="log",
+            range=[np.log10(y_min), np.log10(1.)]
+        ),
+        xaxis=dict(
+            title=dict(
+                text=r"Boundary chemicals (CAS)",
+                standoff=0,  # adjust: try 0–10 depending on how close you want it
+                font = dict(size=18)  # ← axis label size
+            )
+        ),
+        margin=dict(l=50, r=50, t=20, b=20),
+        autosize=False,
+        height=600,
+        width=800,
+    )
+
+    fig.update_xaxes(tickfont=dict(size=14))
+    fig.update_yaxes(tickfont=dict(size=14))
+
+    return fig
+
+# --------------------------------------------------------------------
+# Layout
+# --------------------------------------------------------------------
+app.layout = html.Div(
+    style={
+        "fontFamily": "Arial, Helvetica, sans-serif",
+        "padding": "15px",
+    },
+    children=[
+        html.H1("Extraction condition comparison tool",
+            style={
+            "textAlign": "center",
+            "marginBottom": "20px",
+            "fontSize": "36px",
+            },
+        ),
+        html.Div(
+            style={
+                "display": "flex",
+                "gap": "40px",
+            },
+            children=[
+                # LEFT: Form / Controls
+                html.Div(
+                    style={
+                        "flex": "0 0 40%",
+                        "maxWidth": "500px",
+                    },
+                    children=[
+                        # Polymer component section
+                        html.H3(
+                            "Polymer component",
+                            style={
+                                "textDecoration": "underline",
+                                "fontSize": "18px",
+                                "marginBottom": "10px",
+                            },
+                        ),
+                        html.Div(
+                            style={
+                                "display": "grid",
+                                "gridTemplateColumns": "1fr 1fr",
+                                "columnGap": "20px",
+                                "rowGap": "10px",
+                                "marginBottom": "10px",
+                            },
+                            children=[
+                                # Left column
+                                html.Div(
+                                    children=[
+                                        html.Div("Matrix"),
+                                        dcc.Dropdown(
+                                            id="matrix",
+                                            options=[{"label": x, "value": x} for x in polymers],
+                                            value=polymers[25],  # default selection (optional)
+                                            clearable=False,
+                                            style={"width": "100%"}
+                                        ),
+                                        html.Div("Tg (°C)", style={"marginTop": "10px"}),
+                                        dcc.Input(
+                                            id="tg",
+                                            type="number",
+                                            placeholder="0.0",
+                                            value="0.0",
+                                            style={"width": "100%"},
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "Crystallinity (%)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="crystal",
+                                            type="number",
+                                            placeholder="0.0",
+                                            value="0.0",
+                                            style={"width": "100%"},
+                                            required=True,
+                                        ),
+                                    ]
+                                ),
+                                # Right column
+                                html.Div(
+                                    children=[
+                                        html.Div("Density (g/cm³)"),
+                                        dcc.Input(
+                                            id="density",
+                                            type="number",
+                                            placeholder="1.0",
+                                            value="1.0",
+                                            style={"width": "100%"},
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "Volume (cm³)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="polymer_volume",
+                                            type="number",
+                                            placeholder="1.0",
+                                            value="1.0",
+                                            style={"width": "100%"},
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "Surface area (cm²)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="surface_area",
+                                            type="number",
+                                            placeholder="30.0",
+                                            value="30.0",
+                                            style={"width": "100%"},
+                                            required=True,
+                                        ),
+                                    ]
+                                ),
+                            ],
+                        ),
+
+                        # Conditions row
+                        html.Div(
+                            style={
+                                "display": "grid",
+                                "gridTemplateColumns": "1fr 1fr",
+                                "columnGap": "30px",
+                            },
+                            children=[
+                                # Condition 1
+                                html.Div(
+                                    children=[
+                                        html.H3(
+                                            "Condition #1",
+                                            style={
+                                                "textDecoration": "underline",
+                                                "fontSize": "18px",
+                                                "marginBottom": "10px",
+                                            },
+                                        ),
+                                        html.Div(
+                                            [
+                                                html.Div(
+                                                    dcc.RadioItems(
+                                                        id="condition1_mode",
+                                                        options=[
+                                                            {
+                                                                "label": "In vitro",
+                                                                "value": "in_vitro",
+                                                            },
+                                                            {
+                                                                "label": "In vivo (conservative)",
+                                                                "value": "in_vivo_conservative",
+                                                            },
+                                                            {
+                                                                "label": "In vivo (tissue)",
+                                                                "value": "in_vivo_tissue",
+                                                            },
+                                                        ],
+                                                        value="in_vitro",
+                                                        labelStyle={
+                                                            "display": "inline-block"
+                                                        },
+                                                    ),
+                                                    style={"marginBottom": "10px"},
+                                                ),
+                                            ],
+                                        ),
+                                        html.Div("Solvent"),
+                                        dcc.Dropdown(
+                                            id="c1_solvent",
+                                            options=[{"label": x, "value": x} for x in solvents],
+                                            value=solvents[9],  # default selection (optional)
+                                            clearable=False,
+                                            disabled=True,
+                                            style={"width": "100%"}
+                                        ),
+                                        html.Div(
+                                            "Volume (cm³)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="c1_volume",
+                                            type="number",
+                                            placeholder="10.0",
+                                            value="10.0",
+                                            style={"width": "100%"},
+                                            disabled=True,
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "Swelling ratio (m/m)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="c1_swelling",
+                                            type="number",
+                                            placeholder="1.0",
+                                            value="1.0",
+                                            style={"width": "100%"},
+                                            disabled=True,
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "Extraction T (°C)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="c1_temp",
+                                            type="number",
+                                            placeholder="50.0",
+                                            value="50.0",
+                                            style={"width": "100%"},
+                                            disabled=True,
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "Extraction time (h)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="c1_time",
+                                            type="number",
+                                            placeholder="24.0",
+                                            value="24.0",
+                                            style={"width": "100%"},
+                                            #disabled=True,
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "# of iterations",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="c1_iterations",
+                                            type="number",
+                                            placeholder="1",
+                                            value="1",
+                                            style={"width": "100%"},
+                                            disabled=True,
+                                            required=True,
+                                        ),
+                                    ]
+                                ),
+                                # Condition 2
+                                html.Div(
+                                    children=[
+                                        html.H3(
+                                            "Condition #2",
+                                            style={
+                                                "textDecoration": "underline",
+                                                "fontSize": "18px",
+                                                "marginBottom": "10px",
+                                            },
+                                        ),
+                                        html.Div(
+                                            [
+                                                html.Div(
+                                                    dcc.RadioItems(
+                                                        id="condition2_mode",
+                                                        options=[
+                                                            {
+                                                                "label": "In vitro",
+                                                                "value": "in_vitro",
+                                                            },
+                                                            {
+                                                                "label": "In vivo (conservative)",
+                                                                "value": "in_vivo_conservative",
+                                                            },
+                                                            {
+                                                                "label": "In vivo (tissue)",
+                                                                "value": "in_vivo_tissue",
+                                                            },
+                                                        ],
+                                                        value="in_vivo_conservative",
+                                                        labelStyle={
+                                                            "display": "inline-block"
+                                                        },
+                                                    ),
+                                                    style={"marginBottom": "10px"},
+                                                ),
+                                            ],
+                                        ),
+                                        html.Div("Solvent"),
+                                        dcc.Dropdown(
+                                            id="c2_solvent",
+                                            options=[{"label": x, "value": x} for x in solvents],
+                                            value=solvents[2],  # default selection (optional)
+                                            clearable=False,
+                                            disabled=True,
+                                            style={"width": "100%"}
+                                        ),
+                                        html.Div(
+                                            "Volume (cm³)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="c2_volume",
+                                            type="number",
+                                            placeholder="10.0",
+                                            value="10.0",
+                                            style={"width": "100%"},
+                                            disabled=True,
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "Swelling ratio (m/m)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="c2_swelling",
+                                            type="number",
+                                            placeholder="1.0",
+                                            value="1.0",
+                                            style={"width": "100%"},
+                                            disabled=True,
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "Extraction T (°C)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="c2_temp",
+                                            type="number",
+                                            placeholder="50.0",
+                                            value="50.0",
+                                            style={"width": "100%"},
+                                            disabled=True,
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "Extraction time (h)",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="c2_time",
+                                            type="number",
+                                            placeholder="24.0",
+                                            value="24.0",
+                                            style={"width": "100%"},
+                                            #disabled=True,
+                                            required=True,
+                                        ),
+                                        html.Div(
+                                            "# of iterations",
+                                            style={"marginTop": "10px"},
+                                        ),
+                                        dcc.Input(
+                                            id="c2_iterations",
+                                            type="number",
+                                            placeholder="1",
+                                            value="1",
+                                            style={"width": "100%"},
+                                            disabled=True,
+                                            required=True,
+                                        ),
+                                    ]
+                                ),
+                            ],
+                        ),
+
+                        # Calculate button
+                        html.Div(
+                            style={
+                                "marginTop": "10px",
+                            },
+                            children=[
+                                # ---- Header on its own line ----
+                                html.H3(
+                                    "Monte Carlo",
+                                    style={
+                                        "textDecoration": "underline",
+                                        "fontSize": "18px",
+                                        "marginBottom": "15px",
+                                    },
+                                ),
+
+                                # ---- Two-column layout: Samples/Input | Calculate ----
+                                html.Div(
+                                    style={
+                                        "display": "grid",
+                                        "gridTemplateColumns": "1fr 1fr",
+                                        "columnGap": "20px",
+                                        "marginTop": "10px",
+                                    },
+                                    children=[
+
+                                        # -------- Column 1: Samples label + input --------
+                                        html.Div(
+                                            style={"display": "flex", "flexDirection": "column",
+                                                   },
+                                            children=[
+                                                html.Div("Samples"),
+                                                dcc.Input(
+                                                    id="samples",
+                                                    type="number",
+                                                    placeholder="1000",
+                                                    value="1000",
+                                                    required=True,
+                                                ),
+                                            ],
+                                        ),
+
+                                        # -------- Column 2: Calculate button --------
+                                        html.Div(
+                                            style={"textAlign": "center"},
+                                            children=[
+                                                html.Button(
+                                                    "Calculate",
+                                                    id="calculate",
+                                                    n_clicks=0,
+                                                    style={
+                                                        "padding": "8px 25px",
+                                                        "border": "1px solid #333",
+                                                        "borderRadius": "3px",
+                                                        "backgroundColor": "white",
+                                                        "cursor": "pointer",
+                                                    },
+                                                )
+                                            ],
+                                        ),
+                                    ],
+                                ),
+                            ],
+                        )
+                    ],
+                ),
+
+                # RIGHT: Bar Chart
+                html.Div(
+                    style={
+                        "flex": "1",
+                        "display": "flex",
+                        "justifyContent": "center",
+                    },
+                    children=[
+                        dcc.Loading(
+                            id="mm0_loading",
+                            type="default",  # still works with custom_spinner
+                            # This box defines the exact area of the graph and overlay
+                            parent_style={
+                                "position": "relative",
+                                "width": "800px",  # match figure layout.width
+                                "height": "600px",  # match figure layout.height
+                            },
+                            # This is the semi-transparent veil over that 600x800 box
+                            overlay_style={
+                                "backgroundColor": "rgba(255, 255, 255, 0.5)",
+                                "display": "flex",
+                                "justifyContent": "center",
+                                "alignItems": "center",
+                            },
+                            custom_spinner=html.Div(
+                                [
+                                    html.H2("Processing"),
+                                    dbc.Spinner(color="danger"),
+                                ],
+                                style={"textAlign": "center"},
+                            ),
+                            children=dcc.Graph(
+                                id="mm0_chart",
+                                config={"displayModeBar": False},
+                                # Graph fills the 600x800 parent
+                                style={
+                                    "width": "800px",
+                                    "height": "600px",
+                                },
+                            ),
+                        ),
+                    ],
+                ),
+            ],
+        ),
+    ],
+)
+
+
+# --------------------------------------------------------------------
+# Callback placeholder:
+# Wire this up to your actual calculation (update df + figure)
+# --------------------------------------------------------------------
+@app.callback(
+    [
+        Output("c1_solvent", "disabled"),
+        Output("c1_volume", "disabled"),
+        Output("c1_swelling", "disabled"),
+        Output("c1_temp", "disabled"),
+        #Output("c1_time", "disabled"),
+        Output("c1_iterations", "disabled"),
+    ],
+    Input("condition1_mode", "value"),
+)
+def toggle_condition1_inputs(mode):
+    disable = (mode != "in_vitro")
+    return [disable] * 5
+
+@app.callback(
+    [
+        Output("c2_solvent", "disabled"),
+        Output("c2_volume", "disabled"),
+        Output("c2_swelling", "disabled"),
+        Output("c2_temp", "disabled"),
+        #Output("c2_time", "disabled"),
+        Output("c2_iterations", "disabled"),
+    ],
+    Input("condition2_mode", "value"),
+)
+
+def toggle_condition2_inputs(mode):
+    disable = (mode != "in_vitro")
+    return [disable] * 5
+
+@app.callback(
+    Output("mm0_chart", "figure"),
+    Input("calculate", "n_clicks"),
+    State("matrix", "value"),
+    State("tg", "value"),
+    State("crystal", "value"),
+    State("density", "value"),
+    State("polymer_volume", "value"),
+    State("surface_area", "value"),
+    # Condition 1
+    State("condition1_mode", "value"),
+    State("c1_solvent", "value"),
+    State("c1_volume", "value"),
+    State("c1_swelling", "value"),
+    State("c1_temp", "value"),
+    State("c1_time", "value"),
+    State("c1_iterations", "value"),
+    # Condition 2
+    State("condition2_mode", "value"),
+    State("c2_solvent", "value"),
+    State("c2_volume", "value"),
+    State("c2_swelling", "value"),
+    State("c2_temp", "value"),
+    State("c2_time", "value"),
+    State("c2_iterations", "value"),
+    #prevent_initial_call=True,
+    State("samples", "value"),
+)
+
+def update_chart(n_clicks,
+                 matrix, tg, crystal, density, polymer_volume, surface_area,
+                 c1_mode, c1_solvent, c1_volume, c1_swelling,
+                 c1_temp, c1_time, c1_iter,
+                 c2_mode, c2_solvent, c2_volume,
+                 c2_swelling, c2_temp, c2_time, c2_iter,samples):
+
+    # TODO: replace with logic that uses the form inputs
+    # For now, simply return the same figure when "Calculate" is clicked.
+
+    df = pd.DataFrame()
+    df['CASRN'] = soluteData['CASRN'].to_numpy()
+
+    N = int(samples)
+    pindex = where(polymers == matrix)[0][0]
+    CHRIS_category = categories[pindex]
+    Polymer_Density = float(density)
+    Polymer_Tg = float(tg) + 273.15
+    Polymer_X = float(crystal)/100.
+
+    L = float(polymer_volume)/float(surface_area)
+
+    # Condition 1
+    ExtractionTime = float(c1_time) * 3600.
+    if c1_mode == 'in_vivo_conservative':
+        medians, lowers, uppers = ConservativeMonteCarlo(soluteData, CHRIS_category, L, ExtractionTime, N)
+
+    elif c1_mode == 'in_vivo_tissue':
+        medians, lowers, uppers = TissueMonteCarlo(soluteData, CHRIS_category, L, ExtractionTime, N)
+
+    else:
+        Solvent_Name = c1_solvent
+        Solvent_MW = Solvent_MWs[Solvent_Name]
+        Solvent_Density = Solvent_Densities[Solvent_Name]
+        Solvent_PI = Solvent_PIs[Solvent_Name]
+        ExtractionT = float(c1_temp) + 273.15
+        Ms_M0 = float(c1_swelling)
+        Swell = 1.+(Ms_M0-1.)*Polymer_Density/Solvent_Density
+        w = (Ms_M0 - 1.)/(Ms_M0-Polymer_X)
+        Iterations = float(c1_iter)
+        Lbath = float(c1_volume) / float(surface_area)
+
+        medians, lowers, uppers = ExtractMonteCarlo(soluteData, Solvent_PI, w, ExtractionT, Polymer_Tg, Solvent_Name, Solvent_MW,
+                                                    ExtractionTime, Swell, Iterations, CHRIS_category, L, Lbath, N)
+
+    df['Condition 1'] = medians
+    df['Cond1_err_plus'] = uppers-medians
+    df['Cond1_err_minus'] = medians-lowers
+
+    # Condition 2
+    ExtractionTime = float(c2_time) * 3600.
+    if c2_mode == 'in_vivo_conservative':
+        medians, lowers, uppers = ConservativeMonteCarlo(soluteData, CHRIS_category, L, ExtractionTime, N)
+
+    elif c2_mode == 'in_vivo_tissue':
+        medians, lowers, uppers = TissueMonteCarlo(soluteData, CHRIS_category, L, ExtractionTime, N)
+
+    else:
+        Solvent_Name = c2_solvent
+        Solvent_MW = Solvent_MWs[Solvent_Name]
+        Solvent_Density = Solvent_Densities[Solvent_Name]
+        Solvent_PI = Solvent_PIs[Solvent_Name]
+        ExtractionT = float(c2_temp) + 273.15
+        Ms_M0 = float(c2_swelling)
+        Swell = 1. + (Ms_M0 - 1.) * Polymer_Density / Solvent_Density
+        w = (Ms_M0 - 1.) / (Ms_M0 - Polymer_X)
+        Iterations = float(c2_iter)
+        Lbath = float(c2_volume) / float(surface_area)
+
+        medians, lowers, uppers = ExtractMonteCarlo(soluteData, Solvent_PI, w, ExtractionT, Polymer_Tg, Solvent_Name, Solvent_MW,
+                                                    ExtractionTime, Swell, Iterations, CHRIS_category, L, Lbath, N)
+
+    df['Condition 2'] = medians
+    df['Cond2_err_plus'] = uppers - medians
+    df['Cond2_err_minus'] = medians - lowers
+
+    return build_figure(df)
+
+def ConservativeMonteCarlo(soluteData, CHRIS_category, L, time, N):
+
+    nSolutes = len(soluteData)
+
+    medians = zeros(nSolutes)
+    lowers = zeros(nSolutes)
+    uppers = zeros(nSolutes)
+
+    for iSolute in range(nSolutes):
+
+        Solute_MW = soluteData['MW_new'].iloc[iSolute]
+        D_CHRIS = get_D_CHRIS(Solute_MW, CHRIS_category, N=N)
+        tau = D_CHRIS * time / L ** 2. # fixed 1 day
+        mass = Conservative(tau)
+
+        medians[iSolute] = percentile(mass, 50)
+        lowers[iSolute] = percentile(mass, 25)
+        uppers[iSolute] = percentile(mass, 75)
+
+    return medians, lowers, uppers
+
+def TissueMonteCarlo(soluteData, CHRIS_category, L, time, N):
+
+    nSolutes = len(soluteData)
+
+    medians = zeros(nSolutes)
+    lowers = zeros(nSolutes)
+    uppers = zeros(nSolutes)
+
+    for iSolute in range(nSolutes):
+
+        Solute_MW = soluteData['MW_new'].iloc[iSolute]
+        Solute_logP = soluteData['LogP_new'].iloc[iSolute]
+
+        D_CHRIS = get_D_CHRIS(Solute_MW, CHRIS_category, N=N)
+        tau = D_CHRIS * time / L ** 2.
+
+        Dt = get_Dt(N)
+        Kpt = get_Kpt(Solute_logP, N)
+        beta = (1. / Kpt) * sqrt(Dt / D_CHRIS)
+
+        mass = SolubilityLimited(beta, tau)
+
+        medians[iSolute] = percentile(mass, 50)
+        lowers[iSolute] = percentile(mass, 25)
+        uppers[iSolute] = percentile(mass, 75)
+
+    return medians, lowers, uppers
+
+def ExtractMonteCarlo(soluteData, Solvent_PI, w, ExtractionT, Polymer_Tg, Solvent_Name, Solvent_MW,
+                      ExtractionTime, Swell, Iterations, CHRIS_category, L, Lbath, N):
+
+    nSolutes = len(soluteData)
+
+    medians = zeros(nSolutes)
+    lowers = zeros(nSolutes)
+    uppers = zeros(nSolutes)
+
+    for iSolute in range(nSolutes):
+
+        Solute_MW = soluteData['MW_new'].iloc[iSolute]
+        Solute_logP = soluteData['LogP_new'].iloc[iSolute]
+        Solute_Vabc = soluteData['Vabc'].iloc[iSolute]
+
+        D_Extract = get_D_Extract(w, ExtractionT, Polymer_Tg, Solvent_Name, Solvent_MW, Solute_MW, Solute_Vabc,
+                                  CHRIS_category, N=N)
+        tau = D_Extract * ExtractionTime / L ** 2.
+
+        ToKPS = array([[Solute_logP, Solvent_PI]])
+        model = QuantileGridFromCoeffs(export_dir='Kps_function/Kps_model')
+        Kps = 10**model.sample(ToKPS, n_samples=N)[0]
+        alpha = Lbath / L / Kps
+
+        massExtraction = zeros(N)
+        for i in range(N):
+            massExtraction[i] = Extraction(tau[i], alpha[i], Kps[i], Swell, Iterations)
+
+        medians[iSolute] = percentile(massExtraction, 50)
+        lowers[iSolute] = percentile(massExtraction, 25)
+        uppers[iSolute] = percentile(massExtraction, 75)
+
+    return medians, lowers, uppers
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

requirements.txt

@@ -0,0 +1,25 @@
+flask
+gunicorn
+nltk==3.9.1
+pandas==2.2.3
+PubChemPy==1.0.4
+CIRpy==1.0.2
+chemicals==1.3.3
+urllib3==2.2.3
+requests==2.32.3
+selenium==4.32.0
+chromedriver-binary==138.0.7158.0.0
+pillow==11.0.0
+rdkit==2024.3.5
+dill==0.3.8
+scikit-learn==1.6.1
+padelpy==0.1.16
+Flask==3.1.0
+matplotlib==3.9.2
+scipy==1.14.1
+quantile-forest==1.4.0
+mordred==1.2.0
+beautifulsoup4==4.12.3
+setuptools==72.1.0
+openpyxl==3.1.5
+

transportEqs.py

@@ -0,0 +1,156 @@
+from numpy import *
+import pandas as pd
+
+def Conservative(tau):
+    release = 2. * sqrt(tau / pi) * (tau <= 0.2) + \
+              (1. - (8. / (pi ** 2.)) * exp(-tau * pi ** 2. / 4.)) * (tau > 0.2)
+
+    return release
+
+from scipy.special import erfc
+def SolubilityLimited(beta, tau, nmax=100):
+    result = 0.
+    for n in range(1, nmax + 1):
+        result = result + ((1. - beta) / (1. + beta)) ** (n - 1.) * (
+                    exp(-n ** 2. / tau) - (n * sqrt(pi) / sqrt(tau)) * erfc(n / sqrt(tau)))
+
+    return ((2. * beta) / (1. + beta)) * sqrt(tau / pi) * (1. - (2. * beta / (1. + beta)) * result)
+
+
+# finite bath model functions
+from fast_bisect.fast_bisect import cbisect
+
+def PlaneSheetFiniteBathMassC(tau, alpha, nterms):
+    Minfty = 1. / (1. + 1. / (alpha))
+
+    qn = zeros((nterms))
+    for j in range(nterms):
+        solved = False
+        eps = 1e-8
+        f = lambda x: tan(x) + alpha * x
+        myargs = {'alpha': alpha}
+        rts = cbisect(a=pi / 2. + j * pi + eps, b=pi * (1. + j) - eps, args=myargs)
+        # print(rts)
+        solved = True
+        qn[j] = rts
+
+    result = 1.
+    for j in range(nterms):
+        result = result - (2. * alpha * (1. + alpha)) * exp(-tau * qn[j] ** 2.) / (
+                    1. + alpha + alpha ** 2. * qn[j] ** 2.)
+    result = Minfty * result
+
+    return result
+
+
+def PlaneSheetFiniteBathMassApprox(tau, alpha):
+    Minfty = 1. / (1. + 1. / (alpha))
+
+    # T = minimum(tau/alpha**2.,100)
+    # result = (tau/alpha**2.<100.)* Minfty*(1.+alpha)*(1.-exp(T)*erfc(sqrt(tau)/alpha))
+    # result = result + (tau/alpha**2.>=100.)* \
+    #   Minfty*(1.+alpha)*(1.-alpha/(sqrt(pi)*sqrt(tau))+alpha**3./(2.*sqrt(pi)*(tau)**1.5)-3.*alpha**5./(4.*sqrt(pi)*(tau)**2.5))
+
+    T = tau / alpha ** 2.
+    if T < 100.:
+        result = Minfty * (1. + alpha) * (1. - exp(T) * erfc(sqrt(tau) / alpha))
+    else:
+        result = Minfty * (1. + alpha) * (1. - alpha / (sqrt(pi) * sqrt(tau)) + alpha ** 3. / (
+                    2. * sqrt(pi) * (tau) ** 1.5) - 3. * alpha ** 5. / (4. * sqrt(pi) * (tau) ** 2.5))
+
+    return result
+
+def PlaneSheetAnalytical(tau, alpha, nterms=10):
+    # result = (tau>0.1) * PlaneSheetFiniteBathMassC(tau,alpha,nterms) + \
+    #        (tau<=0.1) * PlaneSheetFiniteBathMassApprox(tau,alpha)
+
+    if tau > 0.1:
+        result = PlaneSheetFiniteBathMassC(tau, alpha, nterms)
+    else:
+        result = PlaneSheetFiniteBathMassApprox(tau, alpha)
+
+    return result
+
+def multiEquilSwell(alpha, Kps, swell, iterations):
+
+    # alpha is initial before swelling
+    result = 1.-(Kps*(swell+alpha)/(Kps*(swell+alpha)+swell-1.))*(swell/(swell+alpha))**iterations
+
+    return result
+
+def multiELSwell(tau, alpha, Kps, swell, iterations):
+
+    resultEq = multiEquilSwell(alpha, Kps, swell, iterations)
+    resultKinetic = Conservative(tau*iterations)
+
+    #result = (resultEq < resultKinetic) * resultEq + (resultEq >= resultKinetic) * resultKinetic
+
+    if resultEq < resultKinetic:
+        result = resultEq
+    else:
+        result = resultKinetic
+
+    return result
+
+def Extraction(tau, alpha, Kps, swell, iterations):
+
+    tauSpec = tau*iterations
+    alphaSpec = (alpha*iterations/swell)-(swell-1)/swell/Kps # for a single iteration
+
+    #result = (tauSpec > 1.) * multiELSwell(tau, alpha, Kps, swell, iterations) + (tauSpec <= 1.) * PlaneSheetAnalytical(tauSpec,alphaSpec)
+
+    if tauSpec > 1.:
+        result = multiELSwell(tau, alpha, Kps, swell, iterations)
+    else:
+        result = PlaneSheetAnalytical(tauSpec,alphaSpec)
+    #result = PlaneSheetAnalytical(tauSpec,alphaSpec)
+
+    return result
+
+# Kps data - switch out when Robert give me the updated one
+Kpsdata = pd.read_excel('data/Kps-experiment.xlsx')
+KpsData = Kpsdata["Kps"].to_numpy()
+
+# Kpt data
+KptData = pd.read_excel('data/db-Kpt-corrected.xlsx')
+
+def get_Kpt(KowTarget, N):
+    rng = random.Generator(random.PCG64(seed=12345))
+
+    if KowTarget >= 1.8:
+        Kdata = KptData[KptData["LogKow"].ge(1.8)]
+        Kpt = Kdata["LogKpt"].to_numpy()
+        Kpt = Kpt[~isnan(Kpt)]
+
+    else:
+        Kdata = KptData[KptData["LogKow"].lt(1.8)]
+        Kpt = Kdata["LogKpt"].to_numpy()
+        Kow = Kdata["LogKow"].to_numpy()
+        KptNew = Kpt[~isnan(Kpt)]
+        KowNew = Kow[~isnan(Kpt)]
+
+        Kpt = KptNew + 1.03 * (KowTarget - KowNew)
+
+    return 10 ** rng.choice(Kpt, N)
+
+# Dt data
+DTdata = pd.read_excel('data/Tissue_Diffusion_Data_v4.xlsx')
+
+barrier = DTdata['Barrier Tissue      (Yes/No)']
+DT = DTdata['Diffusion Coefficient (cm2/s)']
+
+DTbarrier = DT[barrier == 'Yes'].to_numpy()
+DTnonbarrier = DT[barrier == 'No'].to_numpy()
+
+def get_Dt(N):
+    rng = random.Generator(random.PCG64(seed=12345))
+
+    return rng.choice(DTbarrier, N)
+
+Kpsdata = pd.read_excel('data/Kps-experiment.xlsx')
+KpsData = Kpsdata["Kps"].to_numpy()
+
+def get_Kps(N):
+    rng = random.Generator(random.PCG64(seed=12345))
+
+    return rng.choice(KpsData, N)