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Robert Elder commited on May 6, 2025

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aae0aac

1 Parent(s): e33154d

fixing quantity module errors

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Files changed (8) hide show

color3_module/templates/color3_index.html +3 -1
exposure3_module/templates/exposure3_index.html +3 -1
qrf_functions.py +4 -4
qrf_train.py +1 -1
quantity_functions.py +29 -23
quantity_module/quantity.py +19 -9
quantity_module/templates/quantity_index.html +3 -1
quantity_module/templates/quantity_report.html +2 -2

color3_module/templates/color3_index.html CHANGED Viewed

@@ -24,7 +24,9 @@
 <p> For details on how to use the CHRIS color additive module, please click the information icons next to each section header and read the
   <a href="{{url_for('.static', filename='COU.html')}}"> Context of Use (CoU)</a>, which includes limitations of use. Answers to frequently asked questions can be
-  found <a href="{{url_for('.static', filename='FAQ.html')}}"> here</a>. For a history of updates, please see the <a href="{{url_for('.static', filename='Changelog.html')}}"> changelog</a>. </p>
 <body>

 <p> For details on how to use the CHRIS color additive module, please click the information icons next to each section header and read the
   <a href="{{url_for('.static', filename='COU.html')}}"> Context of Use (CoU)</a>, which includes limitations of use. Answers to frequently asked questions can be
+  found <a href="{{url_for('.static', filename='FAQ.html')}}"> here</a>. For a history of updates, please see the <a href="{{url_for('.static', filename='Changelog.html')}}"> changelog</a>.
+  <!--For reference and disclaimer information, please see the <a href="{{url_for('.static', filename='RST.html')}}"> RST information</a> page.-->
+</p>
 <body>

exposure3_module/templates/exposure3_index.html CHANGED Viewed

@@ -26,7 +26,9 @@
 <p> For details on how to use the CHRIS bulk chemical module, please click the information icons next to each section header and read the
 <a href="{{url_for('.static', filename='exposure_COU.html')}}"> context of use (COU)</a>, which includes limitations of use.
-For a history of updates, please see the <a href="{{url_for('.static', filename='Changelog.html')}}"> changelog</a>. </p>
 <body>

 <p> For details on how to use the CHRIS bulk chemical module, please click the information icons next to each section header and read the
 <a href="{{url_for('.static', filename='exposure_COU.html')}}"> context of use (COU)</a>, which includes limitations of use.
+For a history of updates, please see the <a href="{{url_for('.static', filename='Changelog.html')}}"> changelog</a>.
+<!--For reference and disclaimer information, please see the <a href="{{url_for('.static', filename='RST.html')}}"> RST information</a> page.-->
+</p>
 <body>

qrf_functions.py CHANGED Viewed

@@ -9,8 +9,8 @@ import mordred.descriptors
 import rdkit
 from rdkit import Chem
-def QRF_Ceramic(density, polytg, quantiles=[0.03,0.5,0.97]):
-    with open(f'qrf_model_bundle_37.pkl','rb') as f:
         reg, imp, scaler_X, sub_desc_list = pickle.load(f)
     df_X = pd.read_excel('qrf_x.xlsx')
     df_y = pd.read_excel('qrf_y.xlsx')
@@ -34,8 +34,8 @@ def QRF_Ceramic(density, polytg, quantiles=[0.03,0.5,0.97]):
     return D_pred, domain_extrap
-def QRF_Apply(density, polytg, smiles, quantiles=[0.03,0.5,0.97]):
-    with open(f'qrf_model_bundle_37.pkl','rb') as f:
         reg, imp, scaler_X, sub_desc_list = pickle.load(f)
     # get list of descriptors to calculate
     solute_desc_list = sub_desc_list.copy()

 import rdkit
 from rdkit import Chem
+def QRF_Ceramic(density, polytg, quantiles=[0.03,0.5,0.97], T=37):
+    with open(f'qrf_model_bundle_{int(T)}.pkl','rb') as f:
         reg, imp, scaler_X, sub_desc_list = pickle.load(f)
     df_X = pd.read_excel('qrf_x.xlsx')
     df_y = pd.read_excel('qrf_y.xlsx')
     return D_pred, domain_extrap
+def QRF_Apply(density, polytg, smiles, quantiles=[0.03,0.5,0.97], T=37):
+    with open(f'qrf_model_bundle_{int(T)}.pkl','rb') as f:
         reg, imp, scaler_X, sub_desc_list = pickle.load(f)
     # get list of descriptors to calculate
     solute_desc_list = sub_desc_list.copy()

qrf_train.py CHANGED Viewed

@@ -5,7 +5,7 @@ import sklearn
 import sklearn.impute
 from quantile_forest import RandomForestQuantileRegressor
-T_target = 37
 T_cut = 5
 qhiv, qlov = 0.97, 0.03
 state = 12345

 import sklearn.impute
 from quantile_forest import RandomForestQuantileRegressor
+T_target = 50
 T_cut = 5
 qhiv, qlov = 0.97, 0.03
 state = 12345

quantity_functions.py CHANGED Viewed

@@ -200,7 +200,7 @@ 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,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)
     """
@@ -230,33 +230,39 @@ def get_D_dists(w,T,Polymer_Tg,Solvent_Name,Solvent_MW,Solute_MW,CHRIS_category,
     lnD_D0 = c*w/(1+(c-1)*w) * np.log(DWCs/Ds)
     ## distribution of D_CHRIS
     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:
-        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:

     cs = cs[cs>0]
     return cs
+def get_D_dists(w,T,Polymer_Tg,Solvent_Name,Solvent_MW,Solute_MW,CHRIS_category,rng,N=100000,return_DCs=False,input_Ds=None):
     """
     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)
     """
     lnD_D0 = c*w/(1+(c-1)*w) * np.log(DWCs/Ds)
     ## distribution of D_CHRIS
     if not use_new:
+        if input_Ds is None:
+            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:
+            D_list = input_Ds
+    else:
+        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]
+            #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]))
+        else:
+            D_list = input_Ds
     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/quantity.py CHANGED Viewed

@@ -36,6 +36,7 @@ def exp_post():
     Polymer_Tg = float(request.form['Polymer_Tg'])  ## NOTE Tg is provided in C
     T = float(request.form['T'])
     if T<Polymer_Tg:
         return render_template('quantity_temperatureError.html')
@@ -107,23 +108,32 @@ def exp_post():
     if polymer == 'Other polymer':
         use_qrf = True
-    ## TODO implement total quantity prediction with QRF
-    if use_qrf and False:
         method = 'qrf'
         if is_ceramic:
-            diff,domain_extrap = QRF_Ceramic(PolymerDensity, Polymer_Tg, quantiles=[0.03,0.5,0.97])
         else:
-            diff,domain_extrap = QRF_Apply(PolymerDensity, Polymer_Tg, smiles, quantiles=[0.03,0.5,0.97])
-        diff = diff[2] # upper bound
         if domain_extrap:
             # outside training domain, default to Wilke-Chang
-            diff = Piecewise(MW, params[None])
             method = 'qrf/wc'
     else:
         ## use categories
-        ## TODO what is the correct worst-case for a generic polymer? G2?
         CHRIS_category = categories[pIndex]
-        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 0:
@@ -133,7 +143,7 @@ def exp_post():
             print('M_expt, PolymerVolume, SurfaceArea, SolventVolume, ExtractionTime*3600, K_expt')
             print(M_expt, PolymerVolume, SurfaceArea, SolventVolume, ExtractionTime*3600, K_expt)
             print(np.nanquantile(M0_pred, [0.05,0.5,0.95]))
-        if CHRIS_category:
             method = 'category'
         else:
             method = 'wc'

     Polymer_Tg = float(request.form['Polymer_Tg'])  ## NOTE Tg is provided in C
     T = float(request.form['T'])
+    rng = np.random.Generator(np.random.PCG64(seed=12345))
     if T<Polymer_Tg:
         return render_template('quantity_temperatureError.html')
     if polymer == 'Other polymer':
         use_qrf = True
+    if use_qrf:
         method = 'qrf'
+        quantiles = list(np.linspace(0,1,101))
         if is_ceramic:
+            diff,domain_extrap = QRF_Ceramic(PolymerDensity, Polymer_Tg, quantiles=quantiles, T=T)
         else:
+            diff,domain_extrap = QRF_Apply(PolymerDensity, Polymer_Tg, smiles, quantiles=quantiles, T=T)
+        #diff = diff[2] # upper bound
         if domain_extrap:
             # outside training domain, default to Wilke-Chang
+            #diff = Piecewise(MW, params[None])
+            D_dist_noswell, D_dist_swell = get_D_dists(Swelling_wtfrac, T+273.15, Polymer_Tg+273.15, SolventName, SolventMW, SoluteMW, 'G2', 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)
             method = 'qrf/wc'
+        else:
+            ## TODO implement total quantity prediction with QRF
+            D_dist_noswell, D_dist_swell = get_D_dists(Swelling_wtfrac, T+273.15, Polymer_Tg+273.15, SolventName, SolventMW, SoluteMW, 'G2', rng, return_DCs=False, N=N_sample, input_Ds=diff)
+            M0_pred = get_M_dist(D_dist_swell, M_expt, PolymerVolume, SurfaceArea, SolventVolume, ExtractionTime*3600, K_expt=K_expt)
     else:
         ## use categories
         CHRIS_category = categories[pIndex]
+        CHRIS_flag = None
+        if CHRIS_category is None:
+            ## worst-case for a generic polymer --> G2
+            CHRIS_flag = 'wc'
+            CHRIS_category = 'G2'
         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 0:
             print('M_expt, PolymerVolume, SurfaceArea, SolventVolume, ExtractionTime*3600, K_expt')
             print(M_expt, PolymerVolume, SurfaceArea, SolventVolume, ExtractionTime*3600, K_expt)
             print(np.nanquantile(M0_pred, [0.05,0.5,0.95]))
+        if CHRIS_flag is None:
             method = 'category'
         else:
             method = 'wc'

quantity_module/templates/quantity_index.html CHANGED Viewed

@@ -29,7 +29,9 @@
 <p> For details on how to use the CHRIS total quantity module, please click the information icons next to each section header and read the
 <a href="{{url_for('.static', filename='exposure_COU.html')}}"> context of use (COU)</a>, which includes limitations of use.
-For a history of updates, please see the <a href="{{url_for('.static', filename='Changelog.html')}}"> changelog</a>. </p>
 <body>

 <p> For details on how to use the CHRIS total quantity module, please click the information icons next to each section header and read the
 <a href="{{url_for('.static', filename='exposure_COU.html')}}"> context of use (COU)</a>, which includes limitations of use.
+For a history of updates, please see the <a href="{{url_for('.static', filename='Changelog.html')}}"> changelog</a>. <!--For reference and disclaimer information,
+please see the <a href="{{url_for('.static', filename='RST.html')}}"> RST information</a> page.-->
+</p>
 <body>

quantity_module/templates/quantity_report.html CHANGED Viewed

@@ -77,11 +77,11 @@
 {% if methods[0]=="category" %}
 <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 = {{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 = {{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 = {{M0}} {{units}}. </u>
 {% endif %}
 <p>

 {% if methods[0]=="category" %}
 <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 a glassy polymer assumed) 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 = {{M0}} {{units}}. </u>
 {% elif methods[0]=="qrf/wc" %}
+<u> Modeling extraction from the polymer (with worst-case diffusion in a glassy polymer assumed because this system is outside the model training domain) estimates the total quantity = {{M0}} {{units}}. </u>
 {% endif %}
 <p>