updates to quantity module

functions.py

@@ -13,6 +13,12 @@ def SigFigs(number, n):
     if number == 0: return number
     return round(number, n - int(math.floor(math.log10(math.fabs(number)))) - 1)
 
+def HtmlNumber(number,n):
+    number_str = f'{number:.{n}e}'
+    base, exponent = number_str.split('e')
+    html_output = f'{base} × 10<sup>{exponent[0] if exponent[0]=="-" else ""}{int(exponent[1:]):d}</sup>'
+    return html_output
+
 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
@@ -85,6 +91,8 @@ def CdfPlot(vals, units=None):
     xlabel = 'Total quantity' if units is None else f'Total quantity ({units})'
     fig, ax = plt.subplots(figsize=(6, 4))
     ax.ecdf(vals, c='b', lw=3)
+    q50 = np.nanquantile(vals,0.5)
+    ax.plot([q50,q50],[0,0.5],'k--',[q50],[0.5],'ko',[ax.get_xlim()[0],q50],[0.5,0.5],'k--',lw=2,ms=10)
     ax.set_xscale('log')
     ax.set(
         xlabel=xlabel,

quantity_functions.py

@@ -18,8 +18,8 @@ from functions import PowerLaw, Piringer
 ### TODO arbitrary T
 
 ## sampling parameters
-N_sample = int(1e5)
-rng = np.random.Generator(np.random.PCG64(seed=12345))
+N_sample = int(1e6)
+#rng = np.random.Generator(np.random.PCG64(seed=12345))
 
 ## list of solvents to include
 solvents = sorted(['ethanol', 'isopropanol', 'acetonitrile', 'toluene', 'dichloromethane', 'hexane'])
@@ -28,6 +28,9 @@ solvents = sorted(['ethanol', 'isopropanol', 'acetonitrile', 'toluene', 'dichlor
 T_cut = 20
 MW_cut = 20
 
+use_new = True
+T_cut_new = 0.5 
+
 #### read data files
 # CHRIS bounds
 #with open('quantity_module/data/bounds.pkl', 'rb') as fp:
@@ -35,11 +38,15 @@ MW_cut = 20
 #with open('quantity_module/data/bounds-50.pkl', 'rb') as fp:
 #    params_dict_ub_50, params_dict_lb_50, params_dict_ub_band_50, params_dict_lb_band_50, params_dict_q95_50, params_dict_q50_50, params_dict_q05_50 = pickle.load(fp)
 # CHRIS parameter distributions
-param_dists = {}
-with open('quantity_module/data/param_distribution_37.json','r') as fp:
-    param_dists[37] = json.load(fp)
-with open('quantity_module/data/param_distribution_50.json','r') as fp:
-    param_dists[50] = json.load(fp)
+if not use_new:
+    param_dists = {}
+    with open('quantity_module/data/param_distribution_37.json','r') as fp:
+        param_dists[37] = json.load(fp)
+    with open('quantity_module/data/param_distribution_50.json','r') as fp:
+        param_dists[50] = json.load(fp)
+else:
+    with open('quantity_module/data/param_distribution_allT.json','r') as fp:
+        param_dists = json.load(fp)
 # other stuff
 df_visc = pd.read_excel('quantity_module/data/solvent-viscosity.xlsx')
 df_desc = pd.read_excel(f'quantity_module/data/data-descriptors-mordred-numconfs51.xlsx', usecols=['Solute_InChIKey', 'Vabc','VMcGowan'])
@@ -47,19 +54,29 @@ df_desc = pd.read_excel(f'quantity_module/data/data-descriptors-mordred-numconfs
 #df2 = pd.read_excel('quantity_module/data/db-D.xlsx', sheet_name=1) ## XXX?
 #df2['CHRIS Class'] = df2['CHRIS Class'].fillna('none')
 #df2['New Class'] = df2['New Class'].fillna('none')
-## clean data
-df_final_37 = pd.read_excel('quantity_module/data/db-D-interp-37-clean.xlsx')
-df_final_50 = pd.read_excel('quantity_module/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'))
+if not use_new:
+    ## clean data
+    df_final_37 = pd.read_excel('quantity_module/data/db-D-interp-37-clean.xlsx')
+    df_final_50 = pd.read_excel('quantity_module/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('quantity_module/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
@@ -100,30 +117,43 @@ def get_WC(T,solv,V):
     return D_WC, eta, params['MW']
 
 #### add Wilke-Chang
-## 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
+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(mol):
     calc = mordred.Calculator([mordred.descriptors.VdwVolumeABC, mordred.descriptors.McGowanVolume])
@@ -170,14 +200,17 @@ def get_c_dist(T,Tg,MW):
     cs = cs[cs>0]
     return cs
 
-def get_D_dists(w,T,Polymer_Tg,Solvent_Name,Solvent_MW,Solute_MW,CHRIS_category,N=10000,return_DCs=False):
+def get_D_dists(w,T,Polymer_Tg,Solvent_Name,Solvent_MW,Solute_MW,CHRIS_category,rng,N=100000,return_DCs=False):
     """
     D_dist_noswell, D_dist_swell, (c, Ds, DWCs, DCs) = get_D_dists(Swelling_wtfrac,T,Polymer_Tg,Solvent_Name,Solvent_MW,Solute_MW,CHRIS_category,return_DCs=True,N=N)
     """
-    if np.abs(T-310)<2:
-        df_final_T = df_final_37
-    if np.abs(T-323)<2:
-        df_final_T = df_final_50
+    if not use_new:
+        if np.abs(T-310)<2:
+            df_final_T = df_final_37
+        if np.abs(T-323)<2:
+            df_final_T = df_final_50
+    else:
+        df_final_T = df_final.loc[np.abs(df_final['T']-T)<T_cut_new]
     #Solvent_MW = solvmws[Solvent_Name]
     cs = get_c_dist(T,Polymer_Tg,Solvent_MW)
     if Solute_MW < 50:
@@ -196,17 +229,34 @@ def get_D_dists(w,T,Polymer_Tg,Solvent_Name,Solvent_MW,Solute_MW,CHRIS_category,
     c = rng.choice(cs, N)
     lnD_D0 = c*w/(1+(c-1)*w) * np.log(DWCs/Ds)
     ## distribution of D_CHRIS
-    if Solute_MW > 50:
-        params = param_dists[T-273.15][f'{CHRIS_category}_hi']
-    else:
-        params = param_dists[T-273.15][f'{CHRIS_category}_lo']
-    if params[0] == 'pir':
-        A_list = params[1:]
-        D_list = [Piringer(Solute_MW, Ai, T) for Ai in A_list]
+    if not use_new:
+        if Solute_MW > 50:
+            params = param_dists[T-273.15][f'{CHRIS_category}_hi']
+        else:
+            params = param_dists[T-273.15][f'{CHRIS_category}_lo']
+        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 = np.exp([PowerLaw(Solute_MW, Ai, Ball) for Ai in A_list])
     else:
-        Ball = params[1]
-        A_list = params[2:]
-        D_list = np.exp([PowerLaw(Solute_MW, Ai, Ball) for Ai in A_list])
+        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]
+        #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 += list(np.exp([PowerLaw(Solute_MW, Ai, Ball) for Ai in A_list]))
     D_dist_noswell = rng.choice(D_list, N)
     D_dist_swell = np.exp(np.log(D_dist_noswell)+lnD_D0)
     if return_DCs:

quantity_module/data/bounds.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:832efc7ec94168572bd7e6c33fddfeb61974ecbb0b9b5cc88165042943fe11c1
-size 4415

quantity_module/data/copy-data.sh

@@ -1,11 +1,18 @@
+#!/bin/bash
+
 cp /Users/robert.elder/doc/experiments/01-diffusion/data/solvent-viscosity.xlsx .
 cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/descs/data-descriptors-mordred-numconfs51.xlsx .
-cp /Users/robert.elder/doc/experiments/08-solubility/data/db-properties-polymer.xlsx .
-cp /Users/robert.elder/doc/experiments/01-diffusion/data/db-D.xlsx .
-cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/bounds.pkl .
-cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/bounds-50.pkl .
-cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/param_distribution_37.json .
-cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/param_distribution_50.json .
-cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/db-D-interp-37-clean.xlsx .
-cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/db-D-interp-50-clean.xlsx .
+cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/param_distribution_allT.json .
+cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/db-D-interp-allT-clean.xlsx .
 cp /Users/robert.elder/doc/experiments/01-diffusion/data/vrentas-duda-params.xlsx .
+
+if 0;
+    cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/param_distribution_37.json .
+    cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/param_distribution_50.json .
+    cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/db-D-interp-37-clean.xlsx .
+    cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/db-D-interp-50-clean.xlsx .
+    #cp /Users/robert.elder/doc/experiments/08-solubility/data/db-properties-polymer.xlsx .
+    #cp /Users/robert.elder/doc/experiments/01-diffusion/data/db-D.xlsx .
+    #cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/bounds.pkl .
+    #cp /Users/robert.elder/doc/experiments/01-diffusion/data/tmp-plots/bounds-99/bounds-50.pkl .
+fi

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

@@ -1,3 +1,3 @@
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quantity_module/data/db-D-interp-50-clean.xlsx

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quantity_module/data/{bounds-50.pkl → db-D-interp-allT-clean.xlsx}

@@ -1,3 +1,3 @@
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quantity_module/data/db-D.xlsx

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quantity_module/data/db-properties-polymer.xlsx

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quantity_module/quantity.py

@@ -2,7 +2,7 @@ import sys,numbers
 import numpy as np
 import pandas as pd
 from flask import render_template, request
-from functions import SigFigs, Piringer, WilkeChang, CdfPlot
+from functions import SigFigs, HtmlNumber, Piringer, WilkeChang, CdfPlot
 from functions import Piecewise, PowerLaw
 from qrf_functions import QRF_Apply, QRF_Ceramic
 from . import blueprint
@@ -123,9 +123,10 @@ def exp_post():
         ## use categories
         ## TODO what is the correct worst-case for a generic polymer? G2?
         CHRIS_category = categories[pIndex]
-        D_dist_noswell, D_dist_swell = get_D_dists(Swelling_wtfrac, T+273.15, Polymer_Tg+273.15, SolventName, SolventMW, SoluteMW, CHRIS_category, return_DCs=False, N=N_sample)
+        rng = np.random.Generator(np.random.PCG64(seed=12345))
+        D_dist_noswell, D_dist_swell = get_D_dists(Swelling_wtfrac, T+273.15, Polymer_Tg+273.15, SolventName, SolventMW, SoluteMW, CHRIS_category, rng, return_DCs=False, N=N_sample)
         M0_pred = get_M_dist(D_dist_swell, M_expt, PolymerVolume, SurfaceArea, SolventVolume, ExtractionTime*3600, K_expt=K_expt)
-        if 1:
+        if 0:
             print('Swelling_wtfrac, T+273.15, Polymer_Tg+273.15, SolventName, SolventMW, SoluteMW, CHRIS_category')
             print(Swelling_wtfrac, T+273.15, Polymer_Tg+273.15, SolventName, SolventMW, SoluteMW, CHRIS_category)
             print(np.nanquantile(D_dist_swell, [0.05,0.5,0.95]))
@@ -138,33 +139,21 @@ def exp_post():
             method = 'wc'
 
     # Generate the rate plot using matplotlib
-    pngImageB64String = CdfPlot(M0_pred[~np.isnan(M0_pred)], units=units)
-#    release = SheetRelease(amount, vol, area, time, diff)
-#
-#    MOS = TTC / release
-#
-#    # Generate the rate plot using matplotlib
-#    #tarray = np.arange(1., 31., 1.)
-#    #rates = SheetRates(amount, vol, area, tarray, diff)
-#    #pngImageB64String = RatePlot(tarray, rates)
-#
-#    # Generate cumulative release at given times
-#    tlist = [1.]
-#    if len(request.form.getlist('exposure30d')):
-#        tlist.append(30.)
-#    if len(request.form.getlist('exposureuser')):
-#        tlist.append(float(request.form['exposureuserbox']))
-#    release_list = [SheetRelease(amount, vol, area, t*24, diff) for t in tlist]
-#    #print(release_list, file=sys.stderr)
-#    frac_list = [str(SigFigs(r/amount*100,2))+'%' for r in release_list]
-#    release_list = [SigFigs(r,2) for r in release_list]
-#    df_rel = pd.DataFrame(np.transpose([tlist,release_list,frac_list]),columns=['Exposure duration (days)',f'Cumulative exposure ({units})','Cumulative exposure (%)'])
-#    df_rel['Exposure duration (days)'] = df_rel['Exposure duration (days)'].astype(float)
-#    release_table = df_rel.sort_values(by='Exposure duration (days)').to_html(index=False)
-#
-#    release = SigFigs(release, 2)
-#    MOS = SigFigs(MOS, 2)
-#    diff = SigFigs(diff, 2)
+    #pngImageB64String = CdfPlot(M0_pred[~np.isnan(M0_pred)], units=units)
+
+    # table of percentiles
+    #vals = np.quantile(M0_pred, [0.05,0.25,0.5,0.75,0.95])
+    #vals = [SigFigs(v,6) for v in np.quantile(M0_pred, [0.05,0.25,0.5,0.75,0.95])]
+    vals = np.quantile(M0_pred, [0.05,0.25,0.5,0.75,0.95])
+    pcts = np.round(vals/vals[2]*100,1)
+    vals = [HtmlNumber(v,3) for v in vals]
+    #df_table = pd.DataFrame(data=np.array([[r'5<sup>th</sup>', r'25<sup>th</sup>', r'50<sup>th</sup> (median)', r'75<sup>th</sup>', r'95<sup>th</sup>'], vals]).T, columns=[r'Percentile', r'\( M_0 \)'])
+    df_table = pd.DataFrame(data=np.array([[r'5<sup>th</sup>', r'25<sup>th</sup>', r'50<sup>th</sup> (median)', r'75<sup>th</sup>', r'95<sup>th</sup>'], vals, pcts]).T, columns=[r'Percentile', r'\( M_0 \)'+f' ({units})', r'\( M_0 \) (% median)'])
+    #df_table[r'\( M_0 \)'] = df_table[r'\( M_0 \)'].astype(float)
+    #table = df_table.to_html(index=False, escape=False, justify='center')
+    #table = df_table.style.set_properties(subset=['\( M_0 \)'], **{'text-align': 'right'}).format(lambda x: f"{float(x):.3f}", subset='\( M_0 \)').set_table_attributes('border="1"').hide(axis='index').to_html(index=False, escape=False, justify='center')
+    table = df_table.style.set_properties(subset=[f'\( M_0 \) ({units})', r'\( M_0 \) (% median)'], **{'text-align': 'right'}).set_table_attributes('border="1"').hide(axis='index').to_html(index=False, escape=False, justify='center')
+    #print(table)
 
     tau = np.nanquantile(D_dist_swell,0.5) * (ExtractionTime*3600) / (PolymerVolume/SurfaceArea)**2
 
@@ -172,9 +161,9 @@ def exp_post():
     tau_out = SigFigs(tau,6)
 
     return render_template('quantity_report.html', show_properties=show_properties, polymers=polymers, pIndex=pIndex, 
-                           area=SurfaceArea, vol=PolymerVolume, units=units, M=M_expt, M0=M0_out, time=ExtractionTime, image=pngImageB64String,
+                           area=SurfaceArea, vol=PolymerVolume, units=units, M=M_expt, M0=M0_out, time=ExtractionTime, 
                            solventvol=SolventVolume, solventname=SolventName, swelling=Swelling_percent, K=K_expt, T=T, tau=tau_out,
-                           chemName=chemName, MW=MW, LogP=LogP, rho=rho, mp=mp, iupac=iupac, cas=cas, smiles=smiles, molImage=molImage,
+                           chemName=chemName, MW=MW, LogP=LogP, rho=rho, mp=mp, iupac=iupac, cas=cas, smiles=smiles, molImage=molImage, table=table,
                            LogP_origin=LogP_origin, rho_origin=rho_origin, mp_origin=mp_origin, ceramic=is_ceramic, methods=[method,Polymer_Tg,PolymerDensity],
                            mass=mass, density=PolymerDensity)
 

quantity_module/templates/quantity_index.html

@@ -11,6 +11,12 @@
   <script src="{{ url_for('static',filename='bootstrap.min.js') }}" integrity="sha384-VHvPCCyXqtD5DqJeNxl2dtTyhF78xXNXdkwX1CZeRusQfRKp+tA7hAShOK/B/fQ2" crossorigin="anonymous"></script>
   <link rel= "stylesheet" type= "text/css" href= "{{ url_for('static',filename='styles.css') }}">
 
+  <style>
+    table, th, td {
+      border: 1px solid black;
+      text-align: left;
+    }
+  </style>
 </head>
 
 <img src="{{ url_for('static',filename='images/FDAlogo.png') }}" style="float: left;" height="100"/>
@@ -31,22 +37,33 @@ For a history of updates, please see the <a href="{{url_for('.static', filename=
 
 <!-- Chemical input section -->
 
-<h3> Chemical identity <button type="button" class="Info_btn" data-toggle="modal" data-target="#LeachModal" >&#9432;</button> </h3>
-
-  Identifier type: <select name="IDtype">
-    <option value="CAS" selected>CAS</option>
-    <option value="SMILES" >SMILES</option>
-    <option value="common" >Common name</option>
-  </select> <br>
-
-  Identifier: <input name="chemName" id="chemName" type="text" value="" required><br>
-
-  Extracted amount: <input name="amount" id="amount" value="1.0" step="any" min="0.000001" type="number" required> 
-  Amount units: <select name="units">
-    <option value="mg" selected>mg</option>
-    <option value="µg">µg</option>
-  </select>
-  <br>
+<table id="table1" align="left" style="float: none;">
+  <tr><td colspan="2"><h4> Chemical identity <button type="button" class="Info_btn" data-toggle="modal" data-target="#LeachModal" >&#9432;</button> </h4></td></tr>
+
+  <tr>
+      <th> Identifier type </th> 
+        <td> <select name="IDtype">
+             <option value="CAS" selected>CAS</option>
+             <option value="SMILES" >SMILES</option>
+             <option value="common" >Common name</option>
+             </select> 
+        </td>
+  </tr>
+  <tr>
+        <th>Identifier</th> 
+        <td><input name="chemName" id="chemName" type="text" value="" required></td>
+  </tr>
+  </tr>
+        <th>Extracted amount</th> 
+        <td><input name="amount" id="amount" value="1.0" step="any" min="0.000001" type="number" required></td>
+  </tr>
+  </tr>
+        <th> Amount units</th> 
+        <td><select name="units">
+            <option value="mg" selected>mg</option>
+            <option value="µg">µg</option>
+            </select>
+        </td>
 
   <!-- Modal -->
   <div id="LeachModal" class="modal fade" role="dialog">
@@ -77,17 +94,29 @@ For a history of updates, please see the <a href="{{url_for('.static', filename=
 
 <!-- Polymer matrix input section -->
 
-<h3> Polymer matrix <button type="button" class="Info_btn" data-toggle="modal" data-target="#PolymerModal">&#9432;</button> </h3>
-Matrix: <select name="polymer" id="polymer">
-  <option value="{{polymers[0]}}" selected>{{polymers[0]}}</option>
-  {% for polymer in polymers[1:] %}
-    <option value="{{polymer}}">{{polymer}}</option>
-  {% endfor %}
-</select> <br>
-Mass (g): <input name="mass" id="mass" step="any" value="1.0" min="0.000001" type="number" required> <br>
-Density (g/cm<sup>3</sup>): <input name="density" id="density" step="any" value="1.0" min="0.01" type="number" required><br>
-Glass transition temperature (&deg;C): <input name="Polymer_Tg" id="Polymer_Tg" step="any" min="-273.15" max="500" value="0" type="number" required><br>
-</span>
+<tr><td colspan="2"><h4> Polymer matrix <button type="button" class="Info_btn" data-toggle="modal" data-target="#PolymerModal">&#9432;</button> </td></tr></h4>
+  <tr>
+      <th>Matrix</th>
+          <td><select name="polymer" id="polymer" style="max-width:20em;">
+              <option value="{{polymers[0]}}" selected>{{polymers[0]}}</option>
+              {% for polymer in polymers[1:] %}
+                <option value="{{polymer}}">{{polymer}}</option>
+              {% endfor %}
+              </select>
+          </td>
+  </tr>
+  <tr>
+      <th>Mass (g)</th>
+      <td><input name="mass" id="mass" step="any" value="1.0" min="0.000001" type="number" required> </td>
+  </tr>
+  <tr>
+      <th>Density (g/cm<sup>3</sup>)</th>
+      <td><input name="density" id="density" step="any" value="1.0" min="0.01" type="number" required></td>
+  </tr>
+  <tr>
+      <th>Glass transition temperature (&deg;C)</th>
+      <td><input name="Polymer_Tg" id="Polymer_Tg" step="any" min="-273.15" max="500" value="0" type="number" required></td>
+  </tr>
 
 <!-- Modal -->
 <div id="PolymerModal" class="modal fade" role="dialog">
@@ -115,19 +144,21 @@ Glass transition temperature (&deg;C): <input name="Polymer_Tg" id="Polymer_Tg"
 
 <!-- Extraction parameters input section -->
 
-  <h3> Extraction parameters  <button type=button class="Info_btn" data-toggle="modal" data-target="#ExtractionModal">&#9432;</button> </h3>
-  Device surface area (cm<sup>2</sup>): <input name="area" id="area" step="any" value="5.0" min="0.001" type="number" required><br>
-  Duration (hours):                     <input name="time" id="time" step="any" value="24.0" min="0.001" type="number" required><br>
-  Temperature (&deg;C):                 <input name="T" id="T" step="any" value="50.0" min="-273.15" type="number" required><br>
-  Solvent: <select name="solventname" id="solventname">
-    <option value="{{solvents[0]}}" selected>{{solvents[0]}}</option>
-    {% for solvent in solvents[1:] %}
-      <option value="{{solvent}}">{{solvent}}</option>
-    {% endfor %}
-  </select> <br>
-  Solvent volume (mL):                  <input name="solventvol" id="solventvol" step="any" value="10.0" min="0.001" type="number" required><br>
-  Swelling amount (wt%):                <input name="swelling" id="swelling" step="any" value="10.0" max="100.0" min="0.0" type="number" required><br>
-  Partition coefficient:                <input name="K" id="K" step="any" value="10.0" min="0.00001" type="number" required><br>
+  <tr><td colspan="2"><h4> Extraction parameters  <button type=button class="Info_btn" data-toggle="modal" data-target="#ExtractionModal">&#9432;</button></td></tr> </h4>
+      <tr><th>Device surface area (cm<sup>2</sup>)</th><td> <input name="area" id="area" step="any" value="5.0" min="0.001" type="number" required></td></tr>
+      <tr><th>Duration (hours)</th><td>                     <input name="time" id="time" step="any" value="24.0" min="0.001" type="number" required></td></tr>
+      <tr><th>Temperature (&deg;C)</th><td>                 <input name="T" id="T" step="any" value="50.0" min="20" max="75" type="number" required></td></tr>
+      <tr><th>Solvent</th>
+        <td> <select name="solventname" id="solventname">
+        <option value="{{solvents[0]}}" selected>{{solvents[0]}}</option>
+            {% for solvent in solvents[1:] %}
+              <option value="{{solvent}}">{{solvent}}</option>
+            {% endfor %}
+        </select> </td></tr>
+      <tr><th>Solvent volume (mL)</th><td>                  <input name="solventvol" id="solventvol" step="any" value="10.0" min="0.001" type="number" required></td></tr>
+      <tr><th>Swelling amount (wt%)</th><td>                <input name="swelling" id="swelling" step="any" value="10.0" max="100.0" min="0.0" type="number" required></td></tr>
+      <tr><th>Partition coefficient</th><td>                <input name="K" id="K" step="any" value="10.0" min="0.00001" type="number" required></td></tr>
+  </table>
 
 <!-- Modal -->
 <div id="ExtractionModal" class="modal fade" role="dialog">
@@ -160,7 +191,7 @@ Glass transition temperature (&deg;C): <input name="Polymer_Tg" id="Polymer_Tg"
 <p><button type="submit">Estimate total quantity</button></p>
 
 <!--
-  <h3> Exposure assessment <button type=button class="Info_btn" data-toggle="modal" data-target="#ExposureModal">&#9432;</button> </h3>
+  <h4> Exposure assessment <button type=button class="Info_btn" data-toggle="modal" data-target="#ExposureModal">&#9432;</button> </h4>
   Cumulative exposure duration(s): 
   <input type="checkbox" id="exposure1d" name="exposure1d" value="exposure1d" checked=True, disabled=True>
   <label for="exposure1d">1 day</label>

quantity_module/templates/quantity_report.html

@@ -75,13 +75,13 @@
 
 <p>
 {% if methods[0]=="category" %}
-<u> Modeling extraction from {{polymers[pIndex]}} estimates the total quantity \( M_0 \) = {{M0}} {{units}}. </u>
+<u> Modeling extraction from {{polymers[pIndex]}} estimates the total quantity = {{M0}} {{units}}. </u>
 {% elif methods[0]=="wc" %}
-<u> Modeling extraction from the polymer (with worst-case diffusion in water assumed because no T<sub>g</sub> was specified) estimates the total quantity \( M_0 \) = {{M0}} {{units}}. </u>
+<u> Modeling extraction from the polymer (with worst-case diffusion in water assumed because no T<sub>g</sub> was specified) estimates the total quantity = {{M0}} {{units}}. </u>
 {% elif methods[0]=="qrf" %}
-<u> Modeling extraction from the polymer (with density = {{methods[2]}} g/cm<sup>3</sup> and T<sub>g</sub> = {{methods[1]}} &deg;C) estimates the total quantity \( M_0 \) = {{M0}} {{units}}. </u>
+<u> Modeling extraction from the polymer (with density = {{methods[2]}} g/cm<sup>3</sup> and T<sub>g</sub> = {{methods[1]}} &deg;C) estimates the total quantity = {{M0}} {{units}}. </u>
 {% elif methods[0]=="qrf/wc" %}
-<u> Modeling extraction from the polymer (with worst-case diffusion in water assumed because this system is outside the model training domain) estimates the total quantity \( M_0 \) = {{M0}} {{units}}. </u>
+<u> Modeling extraction from the polymer (with worst-case diffusion in water assumed because this system is outside the model training domain) estimates the total quantity = {{M0}} {{units}}. </u>
 {% endif %}
 <p>
 
@@ -111,18 +111,23 @@ where \( \tau= D t A^2 / V^2 \) and \( A \) and \( V \) are the surface area and
 It can be helpful to examine the cumulative amount released over various exposure durations. For example, the 30-day cumulative exposure may be compared to the TSL<sub>&le;30 d</sub> as recommended in ISO 10993-17:2023. Note that this calculation assumes a single device is used for the entire duration. If multiple devices are used concurrently or sequentially (e.g., daily replacement), this should be accounted for during toxicological risk assessment.
 </p>
 
+  (1.+alpha)*(1.-np.exp(T/alpha**2.)*sp.special.erfc(np.sqrt(T)/alpha))
 <div>
 {{ release_table | safe }}
 </div> -->
 
 <p>
-This estimate was derived using the solution to the conservative plane sheet model for mass release, \( M \):
+This estimate was derived using the solutions to the conservative plane sheet model for mass release, \( M \):
 
 \[
-\frac{M}{M_0} = \frac{1}{1+1/\Psi}\left[1-\sum^\infty_{n=1} \frac{2\Psi(1+\Psi)}{1+\Psi+\Psi^2q_n^2}\exp\left(-\tau q_n^2\right)\right]
+\frac{M(\tau)}{M_0} = \left\{
+  \begin{array}{cr}
+  (1+\Psi) \left[1-\exp\left( \frac{\tau}{\alpha^2} \right) \mathop{\rm erfc} \left( \frac{\tau^{0.5}}{\Psi} \right) \right] & \tau \leq 0.05 \\
+  \frac{1}{1+1/\Psi}\left[1-\sum^\infty_{n=1} \frac{2\Psi(1+\Psi)}{1+\Psi+\Psi^2q_n^2}\exp\left(-\tau q_n^2\right)\right] & \tau > 0.05
+      \end{array} \right.
 \]
 
-where \( \tau= D t A^2 / V_p^2 \), \( A \) and \( V_p \) are the surface area and volume of the polymer matrix, respectively, \( D \) is a conservative estimate of the diffusion coefficient of the extractable within the polymer matrix, and \( t \) is time. The quantity \( \Psi = V_s/V_p K \), where \( V_s \) is the solvent volume and \( K \) is the polymer-solvent partition coefficient for the extractable. \( M_0 \) is total quantity initially contained in the polymer.  Based on the input provided, the calculation used the following values:
+where \( \tau= D t A^2 / V_p^2 \), \( A \) and \( V_p \) are the surface area and volume of the polymer matrix, respectively, \( D \) is a conservative estimate of the diffusion coefficient of the extractable within the polymer matrix, and \( t \) is time. The quantity \( \Psi = V_s/V_p K \), where \( V_s \) is the solvent volume and \( K \) is the polymer-solvent partition coefficient for the extractable. \( q_n \) are the roots of \( \tan x + \Psi x \). \( M_0 \) is total quantity initially contained in the polymer.  Based on the input provided, the calculation used the following values:
 </p>
 
 <p>
@@ -132,14 +137,16 @@ Duration \( t \) = {{time}} h <br>
 Temperature = {{T}} &deg;C <br>
 Solvent = {{solventname}} <br>
 Solvent volume \( V_s \) = {{solventvol}} cm<sup>3</sup> <br>
-Polymer volume \( V_p \) = {{vol}} cm<sup>3</sup> (based on polymer mass = {{mass}} g and density = {{density}} g/cm<sup>3</sup> <br>
-Partition coefficient = {{K}} <br>
+Polymer volume \( V_p \) = {{vol}} cm<sup>3</sup> (based on polymer mass = {{mass}} g and density = {{density}} g/cm<sup>3</sup>) <br>
+Partition coefficient  \( K \)= {{K}} <br>
 Swelling = {{swelling}} wt% (used to estimate \( D \))<br>
 </p>
 
-<p>The total quantity reported above is the median of this predicted distribution:</p>
+<p>The total quantity reported above is the median of the distribution of predicted amounts. Additional percentiles are provided here for informational purposes:</p>
 
-<p><img src="{{ image }}"/></p>
+<div>
+{{ table | safe }}
+</div>
 
 <p>The progress of the extraction can be expressed through the dimensionless time \( \tau = Dt/L^2 \), where \( D \) is the solute diffusivity in the polymer, \( t \) is the extraction duration, and \( L = V_p/A \) is the characteristic length scale. For your extraction, \( \tau \) = {{tau}}. Extractions with \( \tau \) &geq; 0.1 result in more accurate estimates of the total quantity, and when \( \tau \) &geq; 1.0 the extracted amount may be used directly as the total quantity.</p>