Modified Plot Basis function to work with ndims

apps/Analytical.py

@@ -112,6 +112,7 @@
                 ]),
                 html.Br(),
                 html.Br(),
+                dcc.Store(id='ndims'),
                 dbc.Row(
                     dbc.Col(
                         html.Div(id='param_add', children=[])))
@@ -657,6 +658,7 @@ def UpdateInputField(value):
 
 @app.callback(
     Output('ParamObjects', 'data'),
+    Output('ndims','data'),
     [
         Input('AP_button', 'n_clicks'),
         Input({'type': 'params', 'index': dash.dependencies.ALL}, 'value'),
@@ -700,7 +702,7 @@ def ParamListUpload(n_clicks, shape_parameter_A, shape_parameter_B, distribution
                                      shape_parameter_B=shape_parameter_B[j], lower=min[j], upper=max[j], order=order[j])
 
             param_list.append(param)
-    return jsonpickle.encode(param_list)
+    return jsonpickle.encode(param_list),len(param_list)
 
 
 ###################################################################
@@ -832,6 +834,21 @@ def Set_Polynomial(parameters, basis, method):
     myPoly = eq.Poly(parameters=parameters, basis=basis, method=method)
     return myPoly
 
+###################################################################
+# Callback for automatic selection of solver method based on basis selection
+###################################################################
+
+@app.callback(
+    Output('solver_method', 'value'),
+    [Input('drop_basis', 'value')],
+    prevent_initial_call=True
+)
+def SetMethod(drop_basis):
+    if drop_basis == 'total-order':
+        return 'least-squares'
+    else:
+        return 'numerical-integration'
+
 
 
 ###################################################################
@@ -846,7 +863,8 @@ def Set_Polynomial(parameters, basis, method):
      ],
     [
         Input('CC_button', 'n_clicks'),
-        Input('ParamObjects', 'data')],
+        Input('ParamObjects', 'data'),
+        Input('ndims','data')],
     [
         State('AP_button', 'n_clicks'),
         State('drop_basis', 'value'),
@@ -857,7 +875,7 @@ def Set_Polynomial(parameters, basis, method):
     ],
     prevent_initial_call=True
 )
-def OutputCardinality(n_clicks, param_obj, params_click, basis_select, q_val, levels, growth_rule, solver_method):
+def OutputCardinality(n_clicks, param_obj,ndims,params_click, basis_select, q_val, levels, growth_rule, solver_method):
     if n_clicks != 0:
         if basis_select is None:
             return 'Error...',None,True,'No basis value selected'
@@ -871,25 +889,12 @@ def OutputCardinality(n_clicks, param_obj, params_click, basis_select, q_val, le
             mybasis = Set_Basis(basis_val=basis_select, order=basis_ord, level=levels, q_val=q_val, growth_rule=growth_rule)
             myPoly = eq.Poly(parameters=param_data, basis=mybasis, method=solver_method)
 
+
             return [mybasis.get_cardinality(), jsonpickle.encode(myPoly),False,None]
     else:
         raise PreventUpdate
 
 
-###################################################################
-# Callback for automatic selection of solver method based on basis selection
-###################################################################
-
-@app.callback(
-    Output('solver_method', 'value'),
-    [Input('drop_basis', 'value')],
-    prevent_initial_call=True
-)
-def SetMethod(drop_basis):
-    if drop_basis == 'total-order':
-        return 'least-squares'
-    else:
-        return 'numerical-integration'
 
 ###################################################################
 # Plotting Function: To plot basis 1D/2D/3D
@@ -901,9 +906,10 @@ def SetMethod(drop_basis):
     [
         Input('PolyObject', 'data'),
         Input('AP_button', 'n_clicks'),
+        Input('ndims','data')
     ]
 )
-def PlotBasis(poly, n_clicks):
+def PlotBasis(poly, n_clicks,ndims):
     if poly is not None:
         myPoly = jsonpickle.decode(poly)
         DOE = myPoly.get_points()
@@ -915,15 +921,15 @@ def PlotBasis(poly, n_clicks):
         fig.update_xaxes(color='black', linecolor='black', showline=True, tickcolor='black', ticks='outside')
         fig.update_yaxes(color='black', linecolor='black', showline=True, tickcolor='black', ticks='outside',
                          zerolinecolor='lightgrey')
-        if n_clicks == 1:
+        if ndims == 1:
             fig.add_trace(go.Scatter(x=DOE, y=DOE, mode='markers',marker=dict(size=5, color="rgb(144, 238, 144)", opacity=0.6,
                                                line=dict(color='rgb(0,0,0)', width=1))))
             return fig
-        elif n_clicks == 2:
+        elif ndims == 2:
             fig.add_trace(go.Scatter(x=DOE[:, 0], y=DOE[:, 1],mode='markers',marker=dict(size=5, color="rgb(144, 238, 144)", opacity=0.6,
                                                line=dict(color='rgb(0,0,0)', width=1))))
             return fig
-        elif n_clicks>=3:
+        elif ndims>=3:
             fig.update_layout(dict(margin={'t': 0, 'r': 0, 'l': 0, 'b': 0, 'pad': 10}, autosize=True,
                       scene=dict(
                           aspectmode='cube',
@@ -991,7 +997,6 @@ def f(op):
             for i in range(n_clicks):
                 exec(x[i])
             return ne.evaluate(expr)
-
         myPoly.set_model(f)
         values = myPoly.get_mean_and_variance()
         mean = values[0]
@@ -1087,12 +1092,13 @@ def ToggleCheck(n_clicks):
         Input('CU_button', 'n_clicks'),
         Input('True_vals', 'data'),
         Input('AP_button','n_clicks'),
-        Input('toggle','value')
+        Input('toggle','value'),
+        Input('ndims','data')
     ],
     prevent_initial_call=True
 )
-def Plot_poly_3D(ModelSet, n_clicks, true_vals, param_num, dims):
-    if n_clicks > 0:
+def Plot_poly_3D(ModelSet, n_clicks, true_vals, param_num, dims,ndims):
+    if ModelSet is not None:
         if dims:
             if param_num==2:
                 layout = dict(margin={'t': 0, 'r': 0, 'l': 0, 'b': 0, 'pad': 10}, autosize=True,
@@ -1162,7 +1168,7 @@ def Plot_poly_3D(ModelSet, n_clicks, true_vals, param_num, dims):
             fig.add_trace(go.Scattergl(x=DOE[:,0], y=y_true.flatten(), mode='markers', name='Training samples',
                                        marker=dict(color='rgb(135,206,250)', size=15, opacity=0.5,
                                                    line=dict(color='rgb(0,0,0)', width=1))))
-            
+
             return fig
 
     else: