# Dash This page is not responding

**URL:** <https://community.plotly.com/t/dash-this-page-is-not-responding/72440>\
**Category:** Dash Python\
**Tags:** question\
**Created:** [February 8, 2023, 4:02pm UTC](https://community.plotly.com/t/dash-this-page-is-not-responding/72440 "2023-02-08T16:02:08Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![tararu](https://avatars.discourse-cdn.com/v4/letter/t/3bc359/32.png) [@tararu](https://community.plotly.com/u/tararu)\
**Post date:** [February 8, 2023, 4:02pm UTC](https://community.plotly.com/t/dash-this-page-is-not-responding/72440/1 "2023-02-08T16:02:08Z")

</div>

Hi,  
I have a dash app that implement a predefined class inside it. the dash is designed to produce plots depending on specific paymasters: type-picker: F, M or R, which in return determines the data-picker and plot-picker.  
once these values are chosen, the class test\_stat is called and the method stat\_test is applied to the chosen data (.csv) performing specific statistical tests, such as chi square and AD test, which are then passed on to the last callback to produce graphs.  
Without dash, the Program works perfectly. With dahs however, i used to have many errors which i tried to correct. the latest update is i have no error codes, however, the app is just frozen, can’t interact with the bottoms and stays there without displaying anything and i only get the message ’ This page is not responding’.  
I can’t figure out what is the problem. Is it because i am using a predefined class inside the app? (the class takes some time to do the calculations, but not much), or i am doing something wrong with the callbacks?  
Any help is appreciated  
Thanks  
below is the code:

> data\_type = [‘F’, ‘M’, ‘R’]  
> data\_dict = data\_namer(names\_list[:-2])[0]
> 
> def data\_picker(data\_type):  
> return data\_dict[data\_type]
> 
> app = Dash()  
> app.layout = html.Div(id = ‘main-div’,  
> children=[  
> html.H4(‘Raw data Statistical tests’),  
> html.P(‘Chose data type:’),  
> dcc.RadioItems(data\_type, value = data\_type[0], id = ‘type-picker’),  
> html.P(‘Chose plot:’),  
> dcc.RadioItems( id=‘plot-picker’), #value = ‘chi’,  
> html.P(‘Chose data:’),  
> dcc.Dropdown(id = ‘data-picker’, multi = True),  
> dcc.Graph(id = ‘plot’) ]

> ```
> )
> 
> ```
> 
> @app.callback(Output(‘plot-picker’,‘options’), [Input(‘type-picker’, ‘value’)])  
> def plot\_picker(type\_pick):  
> if type\_pick == ‘R’:  
> return [‘chi’, ‘ks’, ‘ks\_D\_below’, ‘ad’, ‘chi\_row’, ‘ad\_row’]  
> if type\_pick == ‘M’:  
> return [‘chi’, ‘ks’, ‘ks\_1’, ‘ad’]  
> if type\_pick == ‘F’:  
> return [‘chi’, ‘ks’, ‘ks\_1’, ‘ad’]
> 
> @app.callback(Output(‘data-picker’,‘options’), [Input(‘type-picker’, ‘value’)])  
> def data\_picker(type\_pick):  
> return data\_dict[type\_pick]
> 
> @app.callback(Output(‘plot’,‘figure’),  
> [Input(‘type-picker’, ‘value’), Input(‘data-picker’, ‘options’), Input(‘plot-picker’, ‘options’)])  
> def plot\_data(type\_pick, data\_pick, plot\_pick):
> 
> ```
> if type_pick == 'R':
> #['chi', 'ks', 'ks_D_below', 'ad', 'chi_row', 'ad_row']
> #chi test
> if plot_pick == 'chi':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> chi_1 = test_1.chi_1
> trace = [go.Scatter(x = np.arange(1, len(chi_1)+1)/len(chi_1), y = sorted([x[0] for x in chi_1]), mode ='lines+markers', name='chi_'+name)]
> data.append(trace)
> layout = go.Layout(title='chi_square for {}, #tests: {} ({}_test)'.format(name, len(test_1.header), type_pick))
> fig_chi = go.Figure(data, layout)
> fig_chi.add_hline(y=np.mean([x[0] for x in chi_1]), line_width=1, line_dash="dash", line_color="red", annotation_text='mean_'+name, annotation_position="bottom right")
> fig_chi.add_hline(y=np.median([x[0] for x in chi_1]), line_width=1, line_dash="dash", line_color="orange", annotation_text='median_'+name, annotation_position="bottom left")
> fig_chi.update_xaxes(title_text = 'Individual Tests')
> fig_chi.update_yaxes(title_text = 'p_value')
> return fig_chi
>             
> elif plot_pick == 'chi_row':
> data_row = []   
> for data_ in data_pick: 
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> chi_1_row = test_1.chi_1_row
> trace= [go.Scatter(x = np.arange(1, len(chi_1_row)+1)/len(chi_1_row), y = sorted([x[0] for x in chi_1_row]), mode ='lines+markers', name='chi_'+name)]
> data_row.append(trace)
> layout_row = go.Layout(title='chi_square for{}, #tests: {} ({}_test)'.format(name, len(test_1.header), type_pick))
> fig_chi_row = go.Figure(data_row, layout_row)
> fig_chi_row.add_hline(y=np.mean([x[0] for x in chi_1_row]), line_width=1, line_dash="dash", line_color="red", annotation_text='mean_'+name, annotation_position="bottom right")
> fig_chi_row.add_hline(y=np.median([x[0] for x in chi_1_row]), line_width=1, line_dash="dash", line_color="orange", annotation_text='median_'+name, annotation_position="bottom left")
> fig_chi_row.update_xaxes(title_text = 'Individual Tests')
> fig_chi_row.update_yaxes(title_text = 'p_value')
> return fig_chi_row
> 
> #ks test
> elif plot_pick == 'ks_D_below':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> ks_1_below = [(x[0][0], x[1]) for x in test_1.ks if x[0][0] <= x[0][1]]
> ks_1_list = []
> for h in test_1.header:
> ks_ = []
> for x in ks_1_below:
> if x[1] == h:
> ks_.append(x[0])
> ks_1_list.append(ks_)
> header_1 =test_1.header
> y_1 = [len(x) for x in ks_1_list]
> trace= [go.Bar(x = header_1, y =y_1,text = y_1,)]
> data.append(trace)
> layout_1 = go.Layout(title='Number of rows in each of {} tests (for {} {}_row test) that their respective D_value is leass than D_critical for that specific test. <br>Mean = {}, min = {}, max = {}'.format(len(test_1.header),name, type_pick, np.mean(y_1), min(y_1), max(y_1)))
> fig_1 = go.Figure(trace, layout_1)
> fig_1.update_xaxes(title_text = 'Individual Tests')
> fig_1.update_yaxes(title_text = 'D_value')
> return fig_1
> 
> elif plot_pick == 'ks':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> ks_1 = test_1.ks_1
> D_crit_1 = test_1.D_crit_1  
>             
> # df_1 = pd.DataFrame({'x': np.arange(1, len(ks_1)+1)/len(ks_1), 'y1': sorted([x[0][0] for x in ks_1])})
> # df_1_c = pd.DataFrame({'x': np.arange(1, len(ks_1)+1)/len(ks_1), 'y2': test_1.D_critical[0]})
> 
> # fig_1 = intersection_point(df_1, df_1_c, type_pick)
> # fig_1.update_xaxes(title_text = 'Individual Tests')
> # fig_1.update_yaxes(title_text = 'D_value')
> # layout_1 = go.Layout(title='ks_test D_critical for {} for alpha = [0.01], #tests: {} for {}_test'.format(name, len(test_1.header), type_pick))
> # fig_1.layout = layout_1
> # self.ks = fig_1
> trace = [go.Scatter(x = np.arange(1, len(ks_1)+1)/len(ks_1), y = sorted([x[0][0] for x in ks_1]), mode ='lines+markers', name='KS_'+name)]
> data.append(trace)
> layout = go.Layout(title='ks_test D_value for {}, #tests: {} ({}_test)]'.format(name, len(test_1.header), type_pick))
> fig_ks = go.Figure(data, layout)
> fig_ks.update_xaxes(title_text = 'Individual Tests')
> fig_ks.update_yaxes(title_text = 'D_value')
>         
> return fig_ks
>         
>         
> #AD test
> elif plot_pick == 'ad_row':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> ad_1_row = test_1.ad_1_row 
> len_1 = len([x[0] for x in test_1.ad_row if 0.01<x[1]<0.99])
> trace = [go.Scatter(x = np.arange(1, len(ad_1_row)+1)/len(ad_1_row), y = sorted([x[1] for x in ad_1_row]), mode ='lines+markers', name='AD_'+name+' /{}'.format(len_1))]
> data.append(trace)
> layout = go.Layout(title='AD_test_row plot for {}, #tests: {} ({}_test)'.format(name, len(test_1.header), type_pick))
> fig_ad_row = go.Figure(data, layout)
> fig_ad_row.add_hline(y=np.mean([x[1] for x in ad_1_row]), line_width=1, line_dash="dash", line_color="red", annotation_text='mean_'+name, annotation_position="bottom right")
> fig_ad_row.add_hline(y=np.median([x[1] for x in ad_1_row]), line_width=1, line_dash="dash", line_color="orange", annotation_text='median_'+name, annotation_position="top right")
> fig_ad_row.update_xaxes(title_text = 'Individual rows')
> fig_ad_row.update_yaxes(title_text = '1-p')
> return fig_ad_row
>     
> #collective
> elif plot_pick == 'ad':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> ad_1 = test_1.ad_1
> p_tot_1 = test_1.p_tot
> trace = [go.Scatter(x = np.arange(1, len(p_tot_1)+1)/len(p_tot_1), y = sorted(p_tot_1), mode ='lines+markers', name='P_'+name+', AD = {}'.format(round(ad_1[1], 3)))]
> data.append(trace)
> layout = go.Layout(title='AD_test plot for {}, #tests: {} ({}_test)'.format(name, len(test_1.header), type_pick))
> fig_ad = go.Figure(data, layout)
> fig_ad.update_xaxes(title_text = 'Individual tests')
> fig_ad.update_yaxes(title_text = 'p_value (chi2)')
> return fig_ad
> 
> elif type_pick == 'F':
> #['chi', 'ks', 'ks_1', 'ad']
> #Chi
> if plot_pick == 'chi':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> chi_1 = test_1.chi_1
> data = [go.Scatter(x = np.arange(1, len(chi_1)+1)/len(chi_1), y = sorted([x[0] for x in chi_1]), mode ='lines+markers', name='chi_'+name)]
> layout = go.Layout(title='chi_square for {}, #tests: {} ({}_test)'.format(self.name_1, len(test_1.header),self.type))
> fig_chi = go.Figure(data, layout)
> fig_chi.add_hline(y=np.mean([x[0] for x in chi_1]), line_width=1, line_dash="dash", line_color="red", annotation_text='mean_'+'{}'.format(self.name_1), annotation_position="bottom right")
> fig_chi.add_hline(y=np.median([x[0] for x in chi_1]), line_width=1, line_dash="dash", line_color="orange", annotation_text='median_'+'{}'.format(self.name_1), annotation_position="bottom left")
> fig_chi.update_xaxes(title_text = 'Individual Tests')
> fig_chi.update_yaxes(title_text = 'p_value')
> return fig_chi
> 
> #KS
> elif plot_pick == 'ks_1':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> ks_1 = test_1.ks_1
> D_crit_1 = test_1.D_crit_1  
>     
> df_1 = pd.DataFrame({'x': np.arange(1, len(ks_1)+1)/len(ks_1), 'y1': sorted([x[0][0] for x in ks_1])})
> df_1_c = pd.DataFrame({'x': np.arange(1, len(ks_1)+1)/len(ks_1), 'y2': test_1.D_critical[0]})
> 
> fig_1 = intersection_point(df_1, df_1_c, type_pick)
> fig_1.update_xaxes(title_text = 'Individual Tests')
> fig_1.update_yaxes(title_text = 'D_value')
> layout_1 = go.Layout(title='ks_test D_critical for {} for alpha = [0.01], #tests: {} for {}_test'.format(name, len(test_1.header), type_pick))
> fig_1.layout = layout_1
> return fig_1
>     
> elif plot_pick == 'ks':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> ks_1 = test_1.ks_1
> D_crit_1 = test_1.D_crit_1 
> data = [go.Scatter(x = np.arange(1, len(ks_1)+1)/len(ks_1), y = sorted([x[0][0] for x in ks_1]), mode ='lines+markers', name='KS_'+name)]
> layout = go.Layout(title='ks_test D_value for {}, #tests: {} ({}_test)]'.format(name, len(test_1.header), type_pick))
> fig_ks = go.Figure(data, layout)
> fig_ks.update_xaxes(title_text = 'Individual Tests')
> fig_ks.update_yaxes(title_text = 'D_value')
> fig_ks.add_hline(y=test_1.D_critical[0], line_width=1, line_dash="dash", line_color="red", annotation_text='0.01_'+name, annotation_position="bottom right")
> fig_ks.add_hline(y=test_1.D_critical[1], line_width=1, line_dash="dash", line_color="orange", annotation_text='0.05_'+name, annotation_position="bottom left")
> return fig_ks
>     
> #AD
> elif plot_pick == 'ad':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> ad_1 = test_1.ad_1
> data = [go.Scatter(x = np.arange(1, len(ad_1)+1)/len(ad_1), y = [ad_1[1]], mode ='lines+markers', name='AD_'+name+' AD = {}'.format(ad_1[1]))]
> layout = go.Layout(title='AD plot for {}, #tests: {} ({}_test)'.format(name, len(test_1.header), type_pick))
> fig_ad = go.Figure(data, layout)
> fig_ad.update_xaxes(title_text = 'Individual tests')
> fig_ad.update_yaxes(title_text = '1-p')
> return fig_ad
>     
>     
> elif type_pick == 'M': 
> #['chi', 'ks', 'ks_1', 'ad']
> #chi
> if plot_pick == 'chi':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()
> chi_1 = test_1.chi_1
> trace = [go.Scatter(x = np.arange(1, len(chi_1)+1)/len(chi_1), y = sorted([x[0] for x in chi_1]), mode ='lines+markers', name='chi_'+name)]
> data.append(trace)
> layout = go.Layout(title='chi_square for {}, #tests: {} ({}_test)'.format(name, len(test_1.header),type_pick))
> fig_chi = go.Figure(data, layout)
> fig_chi.add_hline(y=np.mean([x[0] for x in chi_1]), line_width=1, line_dash="dash", line_color="red", annotation_text='mean_'+name, annotation_position="bottom right")
> fig_chi.add_hline(y=np.median([x[0] for x in chi_1]), line_width=1, line_dash="dash", line_color="orange", annotation_text='median_'+name, annotation_position="bottom left")
> fig_chi.update_xaxes(title_text = 'Individual Tests')
> fig_chi.update_yaxes(title_text = 'p_value')
> fig_chi.update_xaxes(title_text = 'Individual n rows for each m test (total of nxm points)')
> return fig_chi
> 
> #KS_plot     
> elif plot_pick == 'ks':
> data = []
> for data_ in data_pick:
> path = data_ + '//' + type_pick.lower()+ '_test'
> name = data_
> test_1=test_stat(path, type_pick.lower())
> test_1.stat_test()      
> ks_1 = test_1.ks_1
> D_crit_1 = test_1.D_crit_1  
>             
> df_1 = pd.DataFrame({'x': np.arange(1, len(ks_1)+1)/len(ks_1), 'y1': sorted([x[0][0] for x in ks_1])})
> df_1_c = pd.DataFrame({'x': np.arange(1, len(ks_1)+1)/len(ks_1), 'y2': test_1.D_critical[0]})
> 
> fig_1 = intersection_point(df_1, df_1_c, type_pick)
> fig_1.update_xaxes(title_text = 'Individual Tests')
> fig_1.update_yaxes(title_text = 'D_value')
> layout_1 = go.Layout(title='ks_test D_critical for {} for alpha = [0.01], #tests: {} for {}_test'.format(name, len(test_1.header), type_pick))
> fig_1.layout = layout_1
> return fig_1
> 
> # data = [go.Scatter(x = np.arange(1, len(ks_1)+1)/len(ks_1), y = sorted([x[0][0] for x in ks_1]), mode ='lines+markers', name='KS_'+'{}'.format(self.name_1))]
> # layout = go.Layout(title='ks_test D_value for {}, #tests: {} ({}_test)]'.format(self.name_1, len(test_1.header), self.type))
> # fig_ks = go.Figure(data, layout)
> # fig_ks.up
> 
> ```

---

<div class="post-metadata">

**Author:** ![stu](https://sea2.discourse-cdn.com/flex024/user_avatar/community.plotly.com/stu/32/18439_2.png) [@stu](https://community.plotly.com/u/stu)\
**Post date:** [February 8, 2023, 4:22pm UTC](https://community.plotly.com/t/dash-this-page-is-not-responding/72440/2 "2023-02-08T16:22:07Z")

</div>

Hi @tararu ,

> [@tararu](#):
>
> [State(‘plot-picker’, ‘options’)],

=\>  
`State('plot-picker', 'value')`

‘options’ will pass in a list.

---

<div class="post-metadata">

**Author:** ![tararu](https://avatars.discourse-cdn.com/v4/letter/t/3bc359/32.png) [@tararu](https://community.plotly.com/u/tararu)\
**Post date:** [February 8, 2023, 5:43pm UTC](https://community.plotly.com/t/dash-this-page-is-not-responding/72440/3 "2023-02-08T17:43:00Z")

</div>

Thank you for your reply. Actually the State dcc is commented out, i am not using it in the code. I removed form the code above. sorry for the confusion.