figure added

.xyz

@@ -1,17 +1,20 @@
-15
+18
 
-N     -1.9997737     0.2248998     0.0000000    
-C     -1.8902337    -1.1433002     0.0000000    
-C     -0.8943837     1.0293398     0.0000000    
-C      0.3497463     0.4090598     0.0000000    
-C      0.4321763    -0.9418902     0.0000000    
-N     -0.6604037    -1.7419602     0.0000000    
-N      1.5839363     0.9436498     0.0000000    
-C      2.4005863    -0.1441302     0.0000000    
-N      1.7162263    -1.3147602     0.0000000    
-N     -1.0273937     2.4370398     0.0000000    
-H     -2.7833637    -1.7527702     0.0000000    
-H      3.4806763    -0.0808802     0.0000000    
-H     -1.9629337     2.8682998     0.0000000    
-H     -0.1966537     3.0466298     0.0000000    
-H      2.1045263    -2.2838602     0.0000000    
+C     -2.8804300    -1.2859700    -0.3524300    
+C     -3.8582700    -0.3294800    -0.0640300    
+C     -3.4984000     1.0164400     0.1059900    
+C     -2.1392600     1.3994900    -0.0163500    
+C     -1.1678600     0.4287700    -0.3062000    
+C     -1.5402800    -0.9082700    -0.4730600    
+H     -4.8922100    -0.6407800     0.0258000    
+C     -4.4698000     1.9871600     0.3958400    
+C     -1.7793900     2.7454200     0.1536700    
+H     -0.1241500     0.7030400    -0.4033800    
+H     -0.7881200    -1.6538300    -0.6965000    
+H     -3.1620700    -2.3229000    -0.4828100    
+C     -4.0973800     3.3242000     0.5627200    
+C     -2.7572300     3.7019000     0.4420900    
+H     -5.5135100     1.7128900     0.4930200    
+H     -4.8495400     4.0697500     0.7861700    
+H     -0.7454500     3.0567100     0.0638400    
+H     -2.4755800     4.7388200     0.5724900    

app.py

@@ -5,6 +5,7 @@ import numpy as np
 from Bio.PDB import PDBParser
 import pandas as pd
 import os, sys
+import matplotlib.pyplot as plt
 #sys.path.append(os.getcwd())
 print('importing...')
 from run_gfn import run_gfn2
@@ -54,6 +55,21 @@ def get_qm_atom_features(gfn2_output):
     qm_atom_features['Polarizability'] = pol 
     return qm_atom_features
 
+def get_thermo_data(gfn2_output):
+    thermo_features = {}
+    temperature = gfn2_output["fetchthermo"]["temperature"]
+    entropy = gfn2_output["fetchthermo"]["entropy"]
+    enthalpy = gfn2_output["fetchthermo"]["enthalpy"]
+    cp = gfn2_output["fetchthermo"]["cp"]
+    #atom_list = atom_list.append('Molecule')
+    #charge = charge.append("")
+    #pol = pol.append(gfn2_output["totalpol"])
+    thermo_features['Temperature'] = temperature
+    thermo_features['entropy'] = entropy
+    thermo_features['enthalpy'] = enthalpy
+    thermo_features['cp'] = cp     
+    return thermo_features
+
 
 def get_qm_mol_features(gfn2_output):
     qm_mol_features = {}
@@ -66,6 +82,10 @@ def export_csv(d):
     d.to_csv("qm_atom_features.csv")
     return gr.File.update(value="qm_atom_features.csv", visible=True)
 
+def export_csv_thermo(d):
+    d.to_csv("thermodynamics.csv")
+    return gr.File.update(value="thermodynamics.csv", visible=True)
+
 def get_basic_visualization(input_f,input_format):
     view = py3Dmol.view(width=600, height=400)
     view.setBackgroundColor('white')
@@ -110,15 +130,18 @@ def get_feature_visualization(input_f,input_format, features, xyz):
     view.addModel(input_f, input_format, viewer=(1,0))
     view.addModel(input_f, input_format, viewer=(1,0))
     view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': '#cccccc'}}}, viewer=(0,0))
-    view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': '#cccccc'}}}, viewer=(0,1))
-    view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': '#cccccc'}}}, viewer=(1,0))
-    view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': '#cccccc'}}}, viewer=(0,1))
-    print('features', features)
+    view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': '#cccccc'}, "radius":"0.07"}}, viewer=(0,1))
+    view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': '#cccccc'}, "radius":"0.07"}}, viewer=(1,0))
+    #view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': '#cccccc'}, "radius":"0.5"}}, viewer=(0,1))
+    #print('features', features)
     add_spheres_feature_view(view, features["fetchatomicprops"]["charges"], xyz, (0,1), 1.0, '#4c4e9e')
     add_spheres_feature_view(view, features["fetchatomicprops"]["polarisabilities"], xyz, (1,0), 1.0, '#809BAC')
     add_densities(view, open('dummy_struct_dens.cub', "r").read(), '#F7D7BE', (1,1))
     #view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': '#cccccc'}},'cartoon': {'color': '#4c4e9e', 'alpha':"0.6"}})
     view.zoomTo(viewer=(0,0))
+    view.zoomTo(viewer=(0,1))
+    view.zoomTo(viewer=(1,0))
+    view.zoomTo(viewer=(1,1))
     output = view._make_html().replace("'", '"')
     x = f"""<!DOCTYPE html><html> {output} </html>"""  # do not use ' in this input
     visualization_html = f"""<iframe  style="width: 100%; height:620px" name="result" allow="midi; geolocation; microphone; camera; 
@@ -129,25 +152,76 @@ def get_feature_visualization(input_f,input_format, features, xyz):
     return visualization_html
 
 
+def create_input_files(input_file):
+    input_f = open(input_file.name, "r").read()
+    input_format = input_file.name.split('.')[-1]
+    
+    with open('dummy_struct.'+input_format, "w") as oF:
+        oF.write(input_f)
+    return input_format
+
+def plot_thermo(thermo_data):
+    fig, (ax1,ax2,ax3) = plt.subplots(3, 1, figsize=(8,8))
+    ax1.plot(thermo_data['Temperature'], thermo_data['entropy'], lw=2,color= "#a0210f")
+    ax2.plot(thermo_data['Temperature'], thermo_data['enthalpy'], lw=2, color= "#4c4e9e")
+    ax3.plot(thermo_data['Temperature'], thermo_data['cp'], lw=2, color= "#809BAC")
+    fig.suptitle('Thermodynamics')
+    ax3.set_xlabel('Temperature K')
+    ax1.set_ylabel('Entropy [J/Kmol]')
+    ax2.set_ylabel('Enthalpy [J]')
+    ax3.set_ylabel('Heat capacity [J/Kmol]')
+    fig.tight_layout()
+    #ax1.legend(loc='lower right')
+    return fig
 
 def predict(input_file):
     input_f = open(input_file.name, "r").read()
     input_format = input_file.name.split('.')[-1]
-    
+
     with open('dummy_struct.'+input_format, "w") as oF:
         oF.write(input_f)
     
     input_f2 = open('dummy_struct.'+input_format, "r").read()
-    gfn2_input = ["filename","geom=dummy_struct."+input_format, 'calcdens=1']
+    gfn2_input = ["filename","geom=dummy_struct."+input_format, 'calcdens=1', 'thermo=1']
     gfn2_output = run_gfn2(gfn2_input)
-
+    print(gfn2_output['fetchthermo'].keys())
 
     #qm_mol_features = get_qm_mol_features(gfn2_output, checked_features)
     #basic_visualization_html = get_basic_visualization(input_f,input_format)
-    feature_visualization_html = get_feature_visualization(input_f,input_format, gfn2_output, gfn2_output['xyz'])
+    feature_visualization_html = get_feature_visualization(input_f2,input_format, gfn2_output, gfn2_output['xyz'])
     qm_atom_features = get_qm_atom_features(gfn2_output)
+    thermo_data = get_thermo_data(gfn2_output)
+    plot = plot_thermo(thermo_data)
+    #pd.DataFrame(thermo_data)
+    return feature_visualization_html, pd.DataFrame(qm_atom_features), pd.DataFrame(thermo_data), plot#, pd.DataFrame(thermo_data)#, pd.DataFrame(qm_mol_features, index=[0])
 
-    return feature_visualization_html, pd.DataFrame(qm_atom_features)#, pd.DataFrame(qm_mol_features, index=[0])
+
+def line_plot_fn(input_exe):
+    #input_f = open(input_file.name, "r").read()
+    #input_format = input_file.name.split('.')[-1]
+#
+    #with open('dummy_struct.'+input_format, "w") as oF:
+    #    oF.write(input_f)
+    
+    #input_f2 = open('dummy_struct.'+input_format, "r").read()
+    gfn2_input = ["filename",input_exe, 'thermo=1']
+    gfn2_output = run_gfn2(gfn2_input)
+    print(gfn2_output['fetchthermo'].keys())
+
+    #qm_mol_features = get_qm_mol_features(gfn2_output, checked_features)
+    #basic_visualization_html = get_basic_visualization(input_f,input_format)
+    #feature_visualization_html = get_feature_visualization(input_f2,input_format, gfn2_output, gfn2_output['xyz'])
+    #qm_atom_features = get_qm_atom_features(gfn2_output)
+    thermo_data = get_thermo_data(gfn2_output)
+    pd.DataFrame(thermo_data)
+    return pd.DataFrame(thermo_data)#, pd.DataFrame(qm_mol_features, index=[0])
+
+
+
+#def get_plot_data(input_format):
+#    gfn2_input = ["filename","geom=dummy_struct."+input_format, 'thermo=1']
+#    gfn2_output = run_gfn2(gfn2_input)   
+#    print('plot data output', gfn2_output)
 
 with gr.Blocks() as demo:
     gr.Markdown("# QM property calculation")
@@ -159,28 +233,37 @@ with gr.Blocks() as demo:
     
 
     with gr.Row():
-        input_file = gr.File(label="Structure file for input")
+        input_file = gr.File(label="Structure file for input (xyz format)")
+        input_file2 = input_file
         charge = gr.Textbox(placeholder="Total charge", label="Give the total charge of the input molecule. (Default=0)")
+    
     #checked_features = gr.CheckboxGroup(["Charge", "Polarizability", "Koopman IP", "Electronic Density"], label="QM features", info="Which features shall be calculated?")
     #temperature = gr.Slider(value=300,minimum=0, maximum=1000, label="Temperature for Thermodynamics evaluation in K", step=5)
 
     single_btn = gr.Button(label="Run")
     with gr.Row():
         basic_html = gr.HTML()
-
+        plot = gr.Plot()
+        #gr.LinePlot(get_plot_data(input_format), every=5, x="Date", y="Sales", y_title="Sales ($ millions)", overlay_point=True, width=500, height=500)
     gr.HighlightedText(value=[("Positive Charge","Purple"),("Negative charge","red"),("Polarizability","Light blue"), ("Electronic Densities", "Beige")], color_map={"red":"#a0210f", "Light blue":"#809BAC", "Purple":"#4c4e9e", "Beige":"#F7D7BE"})
 
     with gr.Row():
         Dbutton = gr.Button("Download  calculated atom features")
         csv = gr.File(interactive=False, visible=False)
+        D2button = gr.Button("Download  thermodynamic properties")
+        csv2 = gr.File(interactive=False, visible=False)
+
     with gr.Row():
         df_atom_features = gr.Dataframe()
-        #df_mol_features = gr.Dataframe()
+        df_thermo_props = gr.Dataframe()
 
-    single_btn.click(fn=predict, inputs=[input_file], outputs=[basic_html, df_atom_features])
+    #with gr.Row():
+    #    plot = gr.Plot()
+      
+    single_btn.click(fn=predict, inputs=[input_file], outputs=[basic_html, df_atom_features, df_thermo_props, plot])
     Dbutton.click(export_csv, df_atom_features, csv)
-
-        
+    D2button.click(export_csv_thermo, df_thermo_props, csv2)
+    #demo.load(fn=line_plot_fn, inputs=[input_exe], ouputs=plot)  
 
 
     demo.launch(server_name="0.0.0.0", server_port=7860)

dummy_struct.xyz

@@ -1,17 +1,20 @@
-15
-test structure for adenine
-N         -3.34715        2.34204       -0.00000
-C         -3.23761        0.97384        0.00000
-C         -2.24176        3.14648        0.00000
-C         -0.99763        2.52620       -0.00000
-C         -0.91520        1.17525       -0.00000
-N         -2.00778        0.37518        0.00000
-N          0.23656        3.06079        0.00000
-C          1.05321        1.97301        0.00000
-N          0.36885        0.80238        0.00000
-N         -2.37477        4.55418        0.00000
-H         -4.13074        0.36437       -0.00000
-H          2.13330        2.03626       -0.00000
-H         -3.31031        4.98544       -0.00000
-H         -1.54403        5.16377       -0.00000
-H          0.75715       -0.16672       -0.00000
+18
+
+C         -2.88043       -1.28597       -0.35243
+C         -3.85827       -0.32948       -0.06403
+C         -3.49840        1.01644        0.10599
+C         -2.13926        1.39949       -0.01635
+C         -1.16786        0.42877       -0.30620
+C         -1.54028       -0.90827       -0.47306
+H         -4.89221       -0.64078        0.02580
+C         -4.46980        1.98716        0.39584
+C         -1.77939        2.74542        0.15367
+H         -0.12415        0.70304       -0.40338
+H         -0.78812       -1.65383       -0.69650
+H         -3.16207       -2.32290       -0.48281
+C         -4.09738        3.32420        0.56272
+C         -2.75723        3.70190        0.44209
+H         -5.51351        1.71289        0.49302
+H         -4.84954        4.06975        0.78617
+H         -0.74545        3.05671        0.06384
+H         -2.47558        4.73882        0.57249

qm_atom_features.csv

@@ -1,16 +1,19 @@
 ,atom type,Charge,Polarizability
-0,7,-0.2927107,7.9022415
-1,6,0.1330993,8.2041169
-2,6,0.162086,8.1111222
-3,6,0.0412075,8.3483554
-4,6,0.144987,8.0719558
-5,7,-0.3111943,7.9682014
-6,7,-0.2861547,7.8825044
-7,6,0.0893093,8.1131044
-8,7,-0.0936049,7.1331855
-9,7,-0.2467183,7.6358162
-10,1,0.0567793,2.3388726
-11,1,0.0466292,2.4035132
-12,1,0.1834568,1.7122248
-13,1,0.1898361,1.6868968
-14,1,0.1829925,1.7143291
+0,6,-0.0318051,8.7986948
+1,6,-0.0372131,8.8194373
+2,6,0.0180579,8.6176499
+3,6,0.0180484,8.6176839
+4,6,-0.0371993,8.8193854
+5,6,-0.0318169,8.7987388
+6,1,0.0309827,2.5081171
+7,6,-0.0372016,8.819394
+8,6,-0.0371842,8.8193289
+9,1,0.0309828,2.5081162
+10,1,0.029009,2.5217377
+11,1,0.0290073,2.5217494
+12,6,-0.0318231,8.7987621
+13,6,-0.0318508,8.7988664
+14,1,0.0309837,2.5081098
+15,1,0.0290121,2.5217165
+16,1,0.0309787,2.5081448
+17,1,0.0290316,2.5215795