Update app.py

app.py

@@ -99,7 +99,7 @@ select_style = """
 mobile_style = {'font-size': f'{fpx}', 'padding': '40px'}
 first_year = df['year'].nsmallest(2).iloc[-1]
 
-col_options = ['ASC/LSIL', 'ASC-US/ASC-H ratio(%)','Thyroid AUS Rate(%)','Abnormal Rate(%)','Referral Rate(%)']
+col_options = ['ASC/LSIL', 'ASC-US/ASC-H ratio(%)','Abnormal Rate(%)','Referral Rate(%)']
 column_changer = pn.widgets.Select(name='Select QA Parameter', options=col_options, styles = mobile_style, width = 400, height=150, margin = 0,stylesheets= [select_style])
 year_slider = pn.widgets.Select(name='Select Year', value = int(df.year.max()),options=list(range(int(first_year), int(df.year.max())+1)), styles = mobile_style, width = 400, height=150, margin = 0,stylesheets= [select_style])
 
@@ -215,7 +215,7 @@ vertical_legend_indiv =vertical_legend_indiv.hide(axis='rows')
 vertical_legend_indiv= pn.pane.DataFrame(vertical_legend_indiv, index= False)
 vertical_legend_indiv= pn.Column("## Legend", vertical_legend_indiv)
 
-zcol = [col_options[0],col_options[1],col_options[3]]
+zcol = [col_options[0],col_options[1],col_options[2]]
 zscore_base = df[zcol]
 new_row = [0.75, 12.5, 1.5]
 zscore_base.iloc[1] = new_row
@@ -255,8 +255,8 @@ def find_last_decreasing_element(lst):
             return lst[i-1]
     return lst[len(lst)-1]
 def labres(column_changer):
-    lab_zscore = zscore_eval.get_group('MEDSERV')
-    abs_results = df[df['people'] == 'MEDSERV']
+    lab_zscore = zscore_eval.get_group('KGYC')
+    abs_results = df[df['people'] == 'KGYC']
     
     
     asc_abn_outliers = zscore_df[(zscore_df['people'].str.contains('Cyto'))&(zscore_df['year']==int(df.year.max()))]
@@ -269,11 +269,11 @@ def labres(column_changer):
     lab_comment_3= f"No outliers regarding {column_changer}"
     lab_comment_4 = ""
     if column_changer == col_options[1]:
-        avg_zscore = zscore_eval.get_group('MEDSERV')['ASC-US/ASC-H ratio(%)'].mean()
+        avg_zscore = zscore_eval.get_group('KGYC')['ASC-US/ASC-H ratio(%)'].mean()
         descript = f"**Description:** *ASC-US/ASC-H ratio literature reference value 90% / 10%. Given our average laboratory Z score of {avg_zscore:.2f} the attention threshold is at 65% / 35%.*<br><br>"
-    if column_changer == col_options[3]:
+    if column_changer == col_options[2]:
         descript = "**Description:** *Abnormal Rate represents the relative ratio of ASC, SIL and AGC diagnoses. Alterations ( Z Score: <-2) means a level of insecurity that requires attention.*<br><br>"
-    if column_changer == col_options[1] or column_changer == col_options[3]:
+    if column_changer == col_options[1] or column_changer == col_options[2]:
         
         lab_value = lab_zscore[column_changer].iloc[len(lab_zscore)-1]
         
@@ -336,35 +336,35 @@ def labres(column_changer):
         lab_comment_5= ""
         lab_comment_6= ""
     # Thyroid Rate(%):
-    elif column_changer == col_options[2]:
-        descript = "**Description:** *Thyroid AUS Rate signifies the ratio of AUS (TBS 3) cytology reports. More than 15% requires attention.* <br><br>"
-        thyroid_value = abs_results[column_changer].iloc[len(abs_results)-1]
+    #elif column_changer == col_options[2]:
+        #descript = "**Description:** *Thyroid AUS Rate signifies the ratio of AUS (TBS 3) cytology reports. More than 15% requires attention.* <br><br>"
+        #thyroid_value = abs_results[column_changer].iloc[len(abs_results)-1]
         
-        if thyroid_value < 10:
-            lab_comment = f"Latest record of laboratory result is within laboratory goal zone. {thyroid_value:.2f}%"
-        elif 10 < thyroid_value < 15:
-            lab_comment = f"Latest record of laboratory result is in laboratory borderline zone. {thyroid_value:.2f}%"
-        else:
-            lab_comment = f"Latest laboratory result value is {thyroid_value:.2f}%, performance follow up and global laboratory practice revision is advised."
-        if (thyroid_value > 10) & (abs_results[column_changer].iloc[len(abs_results)-1] > abs_results[column_changer].iloc[len(abs_results)-2]):
-            increasing_tendency = find_values_more_than_previous(lab_zscore[column_changer].to_list())
-            lab_comment_2 = f"with tendencious decrease in performance since year {increasing_tendency[0]+int(first_year)}."
-        else:
-            lab_comment_2 = "with no relevant tendency or no tendency at all in decrease of performance. "
-        if (thy_outlier[column_changer] > 15).any():
-            thy_outly = thy_outlier[thy_outlier[column_changer] > 15]['people'].to_list()
-            lab_comment_3 = f"Outliers regarding {column_changer}: **{thy_outly}**,"
-            for o in thy_outly:
-                if (len(df.groupby(['people']).get_group(o))>1) & (df.groupby(['people']).get_group(o)[column_changer].iloc[len(df.groupby(['people']).get_group(o))-1] >= df.groupby(['people']).get_group(o)[column_changer].iloc[len(df.groupby(['people']).get_group(o))-2]):
-                    first_out = find_values_more_than_previous(df.groupby(['people']).get_group(o)[column_changer].to_list())
-                    if len(first_out) > 2:
-                        lab_comment_4.append(f"**{o}** with tendencious decrease since **{find_first_decrease_in_performance(first_out)+int(first_year)}**")
-                    else:
-                        lab_comment_4.append(f"**{o}** with tendencious decrease compared to last registered result in**{zscore_eval.get_group(o)['year'].iloc[first_out[0]]}**")
-        lab_comment_5= ""
-        lab_comment_6= ""
+        #if thyroid_value < 10:
+        #    lab_comment = f"Latest record of laboratory result is within laboratory goal zone. {thyroid_value:.2f}%"
+        #elif 10 < thyroid_value < 15:
+        #    lab_comment = f"Latest record of laboratory result is in laboratory borderline zone. {thyroid_value:.2f}%"
+        #else:
+        #    lab_comment = f"Latest laboratory result value is {thyroid_value:.2f}%, performance follow up and global laboratory practice revision is advised."
+        #if (thyroid_value > 10) & (abs_results[column_changer].iloc[len(abs_results)-1] > abs_results[column_changer].iloc[len(abs_results)-2]):
+        #    increasing_tendency = find_values_more_than_previous(lab_zscore[column_changer].to_list())
+        #    lab_comment_2 = f"with tendencious decrease in performance since year {increasing_tendency[0]+int(first_year)}."
+        #else:
+        #    lab_comment_2 = "with no relevant tendency or no tendency at all in decrease of performance. "
+        #if (thy_outlier[column_changer] > 15).any():
+        #    thy_outly = thy_outlier[thy_outlier[column_changer] > 15]['people'].to_list()
+        #    lab_comment_3 = f"Outliers regarding {column_changer}: **{thy_outly}**,"
+        #    for o in thy_outly:
+        #        if (len(df.groupby(['people']).get_group(o))>1) & (df.groupby(['people']).get_group(o)[column_changer].iloc[len(df.groupby(['people']).get_group(o))-1] >= df.groupby(['people']).get_group(o)[column_changer].iloc[len(df.groupby(['people']).get_group(o))-2]):
+        #            first_out = find_values_more_than_previous(df.groupby(['people']).get_group(o)[column_changer].to_list())
+        #            if len(first_out) > 2:
+        #                lab_comment_4.append(f"**{o}** with tendencious decrease since **{find_first_decrease_in_performance(first_out)+int(first_year)}**")
+        #            else:
+        #                lab_comment_4.append(f"**{o}** with tendencious decrease compared to last registered result in**{zscore_eval.get_group(o)['year'].iloc[first_out[0]]}**")
+        #lab_comment_5= ""
+        #lab_comment_6= ""
     # Referral Rate(%)
-    elif column_changer == col_options[4]:
+    elif column_changer == col_options[3]:
         descript = "**Description:** *Referral Rate signifies the ratio the Cytologist consults cases. < 5 % and > 15% requires attention.*<br><br>"
         referral_value = abs_results['Referral Rate(%)'].iloc[len(abs_results)-1]
         
@@ -420,7 +420,7 @@ inter_labres = pn.bind(labres, column_changer)
 
 
 def lab_management(year_slider, column_changer):
-    scatterdata = df[(df.year == year_slider)&(df.people.str.contains('MED|Cyto'))].sort_values(by="people", \
+    scatterdata = df[(df.year == year_slider)&(df.people.str.contains('KGYC|Cyto'))].sort_values(by="people", \
                     ascending=False,key=natsort_keygen()).reset_index(drop=True)
     ref = df[column_changer].iloc[0]
     lab = scatterdata[column_changer].iloc[0]
@@ -474,19 +474,19 @@ def lab_management(year_slider, column_changer):
         ascus_bg = (asc_us_goal.opts(color='#ACFFA0', alpha = 0.75)* asc_us_att.opts(color='#B34D93', alpha = 0.9)*\
                    asc_us_bdln.opts(color='#6AB35F', alpha = 0.75))
         return (ascus_bg* ref_vline * lab_vline*scatter_plot).opts(shared_axes=False)
-    elif column_changer =='Thyroid AUS Rate(%)':
-        scatter_plot = scatterdata[scatterdata.people.str.contains('Cytop', na=False)].hvplot.scatter(x=column_changer, y='people', color='black', size=sdot_size,height= sheight,
-                                                        xlim=(0,30), xticks = [0,lab,10,15,30], grid=True, title=column_changer,
-                                                        xformatter='%.0f',ylabel='',xlabel='',tools = [hover_cp]).opts(fontsize = pl_title,fontscale=f_scale_lab,shared_axes=False,toolbar=None, default_tools = [])
-        
-        aus_goal = hv.VSpan(0, 10).opts(shared_axes=False,toolbar=None, default_tools = [])
-        aus_bdln = hv.VSpan(10,15).opts(shared_axes=False,toolbar=None, default_tools = [])
-        aus_att = hv.VSpan(15, 30).opts(shared_axes=False,toolbar=None, default_tools = [])
-        aus_bg = (aus_goal.opts(color='#ACFFA0', alpha = 0.75)* aus_att.opts(color='#B34D93', alpha = 0.9)*\
-                   aus_bdln.opts(color='#6AB35F', alpha = 0.75))
-    
-        return (aus_bg* lab_vline * scatter_plot).opts(shared_axes=False)
-    
+    #elif column_changer =='Thyroid AUS Rate(%)':
+    #    scatter_plot = scatterdata[scatterdata.people.str.contains('Cytop', na=False)].hvplot.scatter(x=column_changer, y='people', color='black', size=sdot_size,height= sheight,
+    #                                                    xlim=(0,30), xticks = [0,lab,10,15,30], grid=True, title=column_changer,
+    #                                                    xformatter='%.0f',ylabel='',xlabel='',tools = [hover_cp]).opts(fontsize = pl_title,fontscale=f_scale_lab,shared_axes=False,toolbar=None, default_tools = [])
+    #    
+    #    aus_goal = hv.VSpan(0, 10).opts(shared_axes=False,toolbar=None, default_tools = [])
+    #    aus_bdln = hv.VSpan(10,15).opts(shared_axes=False,toolbar=None, default_tools = [])
+    #    aus_att = hv.VSpan(15, 30).opts(shared_axes=False,toolbar=None, default_tools = [])
+    #    aus_bg = (aus_goal.opts(color='#ACFFA0', alpha = 0.75)* aus_att.opts(color='#B34D93', alpha = 0.9)*\
+    #               aus_bdln.opts(color='#6AB35F', alpha = 0.75))
+    #
+    #    return (aus_bg* lab_vline * scatter_plot).opts(shared_axes=False)
+    #
     elif column_changer == 'Abnormal Rate(%)':
         scatter_plot = scatterdata[scatterdata.people.str.contains('Cytol', na=False)].hvplot.scatter(x=column_changer, y='people', color='black', size=sdot_size,height= sheight,
                                                         xlim=(0,max_x_abn), xticks = [0,lab,ref,3, max_x_abn], grid=True, title=column_changer,
@@ -514,10 +514,10 @@ def lab_management(year_slider, column_changer):
         return (ref_bg * lab_vline * scatter_plot).opts(toolbar='above', default_tools=[]).opts(shared_axes=False)
 
 def add_no_cases_lab(year_slider):
-    scatterdata = df[(df.year == year_slider)&(df.people.str.contains('MED'))].sort_values(by='year').reset_index(drop=True)
+    scatterdata = df[(df.year == year_slider)&(df.people.str.contains('KGYC'))].sort_values(by='year').reset_index(drop=True)
     gyncase_no = scatterdata['Number'].iloc[0]
-    thycase_no = scatterdata['ThyAC Number'].iloc[0]
-    return pn.pane.Markdown(f""" **Gynecology Cases: {gyncase_no}<br> Thyroid FNA Cases: {int(thycase_no)}**""",
+    #thycase_no = scatterdata['ThyAC Number'].iloc[0]
+    return pn.pane.Markdown(f""" **Case number: {gyncase_no}** """,
                            styles = {'font-size':fpx})
 interactive_no_lab = pn.bind(add_no_cases_lab, year_slider)
 interactive_func = pn.bind(lab_management, year_slider, column_changer)
@@ -590,7 +590,7 @@ def ascus_rate_overview_ct(ct_select):
         last_year = ct_result['year'].iloc[-1]
         second_last_result = ct_result['ASC-US/ASC-H ratio(%)'].iloc[-2]
         second_last_year = ct_result['year'].iloc[-2] 
-        avg_zscore = zscore_eval.get_group('MEDSERV')['ASC-US/ASC-H ratio(%)'].mean()
+        avg_zscore = zscore_eval.get_group('KGYC')['ASC-US/ASC-H ratio(%)'].mean()
         if 75 < last_result <= 100:
             ct_comment = f"Your latest result from **{last_year}** is within the laboratory goal zone **{last_result:.0f}%/{(100-last_result):.0f}%**."
             ct_comment_2 = ""
@@ -832,7 +832,7 @@ def ascus_rate_overview_cp(cp_select):
         last_year = ct_result['year'].iloc[-1]
         second_last_result = ct_result['ASC-US/ASC-H ratio(%)'].iloc[-2]
         second_last_year = ct_result['year'].iloc[-2] 
-        avg_zscore = zscore_eval.get_group('MEDSERV')['ASC-US/ASC-H ratio(%)'].mean()
+        avg_zscore = zscore_eval.get_group('KGYC')['ASC-US/ASC-H ratio(%)'].mean()
         if 75 < last_result <= 100:
             ct_comment = f"Your latest result from **{last_year}** is within the laboratory goal zone **{last_result:.0f}%/{(100-last_result):.0f}%**."
             ct_comment_2 = ""
@@ -908,47 +908,47 @@ inter_asclsil_cp = pn.bind(asclsil_rate_overview_cp, cp_select)
 # In[57]:
 
 
-def thyroid_rate_overview_cp(cp_select):
-    ct_result = df[df['people'] == cp_select][['Thyroid AUS Rate(%)','year']]
-    if ct_result['Thyroid AUS Rate(%)'].sum() > 0.0:
-        
-        if len(ct_result) > 1:
-            last_result = ct_result['Thyroid AUS Rate(%)'].iloc[-1]
-            second_last_result = ct_result['Thyroid AUS Rate(%)'].iloc[-2]
-            last_year = ct_result['year'].iloc[-1]
-            second_last_year = ct_result['year'].iloc[-2]
-            if 0 < last_result < 10:
-                ct_comment = f"Your latest result from **{last_year}** is within the laboratory goal zone **{last_result:.1f}%**."
-                ct_comment_2 = ""
-            elif 10 <last_result < 15:
-                ct_comment = f"Your latest record from **{last_year}** is in the laboratory borderline zone **{last_result:.1f}%**."
-                if last_result > second_last_result:
-                    ct_comment_2 = f"A decrease in the thyroid AUS ratio can be observed compared to your previous record **({second_last_result:.1f}%)** from **{second_last_year}**."
-                else:
-                    ct_comment_2 =""
-            elif last_result > 15:
-                ct_comment = f"Your latest registered result from **{last_year}** is **{last_result:.1f}%**, your thyroid AUS ratio is too high. You use the AUS (TBS 3) category too much. "
-                if last_result < second_last_result:
-                    ct_comment_2 = f"You performed better compared to your previous record **({second_last_result:.1f}%)** from **{second_last_year}**, but please consult with an experienced cytopathologist."
-                else:
-                    ct_comment_2 = f"<br>Please consult with an experienced cytopathologist about your result since your thyroid AUS ratio worsened compared to your previous record **({second_last_result:.1f}%)** from **{second_last_year}**."
-
-
-
-            return pn.pane.Markdown(f"""
-                    ## Thyroid AUS Rate(%) Overview:
-                     *Thyroid AUS Rate signifies the ratio of TBS 3 cytology reports. More than 15% requires attention.* <br><br>
-                     {ct_comment} {ct_comment_2}
-                    """, sizing_mode ="stretch_width", styles = {'font-size':fpx})
-        else:
-            return pn.pane.Markdown(""" 
-            ## Not enough data to calculate parameters. 
-            """, styles = {'font-size':fpx})
-    else:
-        return pn.pane.Markdown(""" 
-            ## Current cytopathologist doesn't examine thyroid FNA cases. 
-            """, styles = {'font-size':fpx})
-inter_thyroid_cp = pn.bind(thyroid_rate_overview_cp, cp_select)
+#def thyroid_rate_overview_cp(cp_select):
+#    ct_result = df[df['people'] == cp_select][['Thyroid AUS Rate(%)','year']]
+#    if ct_result['Thyroid AUS Rate(%)'].sum() > 0.0:
+ #       
+  #      if len(ct_result) > 1:
+   #         last_result = ct_result['Thyroid AUS Rate(%)'].iloc[-1]
+    #        second_last_result = ct_result['Thyroid AUS Rate(%)'].iloc[-2]
+     #       last_year = ct_result['year'].iloc[-1]
+      #      second_last_year = ct_result['year'].iloc[-2]
+       #     if 0 < last_result < 10:
+        #        ct_comment = f"Your latest result from **{last_year}** is within the laboratory goal zone **{last_result:.1f}%**."
+         #       ct_comment_2 = ""
+          #  elif 10 <last_result < 15:
+           #     ct_comment = f"Your latest record from **{last_year}** is in the laboratory borderline zone **{last_result:.1f}%**."
+            #    if last_result > second_last_result:
+             #       ct_comment_2 = f"A decrease in the thyroid AUS ratio can be observed compared to your previous record **({second_last_result:.1f}%)** from **{second_last_year}**."
+              #  else:
+               #     ct_comment_2 =""
+            #elif last_result > 15:
+             #   ct_comment = f"Your latest registered result from **{last_year}** is **{last_result:.1f}%**, your thyroid AUS ratio is too high. You use the AUS (TBS 3) category too much. "
+              #  if last_result < second_last_result:
+               #     ct_comment_2 = f"You performed better compared to your previous record **({second_last_result:.1f}%)** from **{second_last_year}**, but please consult with an experienced cytopathologist."
+                #else:
+                 #   ct_comment_2 = f"<br>Please consult with an experienced cytopathologist about your result since your thyroid AUS ratio worsened compared to your previous record **({second_last_result:.1f}%)** from **{second_last_year}**."
+#
+#
+#
+ #           return pn.pane.Markdown(f"""
+  #                  ## Thyroid AUS Rate(%) Overview:
+   #                  *Thyroid AUS Rate signifies the ratio of TBS 3 cytology reports. More than 15% requires attention.* <br><br>
+    #                 {ct_comment} {ct_comment_2}
+     #               """, sizing_mode ="stretch_width", styles = {'font-size':fpx})
+      #  else:
+       #     return pn.pane.Markdown(""" 
+        #    ## Not enough data to calculate parameters. 
+         #   """, styles = {'font-size':fpx})
+    #else:
+     #   return pn.pane.Markdown(""" 
+      #      ## Current cytopathologist doesn't examine thyroid FNA cases. 
+       #     """, styles = {'font-size':fpx})
+#inter_thyroid_cp = pn.bind(thyroid_rate_overview_cp, cp_select)
 # In[12]:
 
 
@@ -999,28 +999,28 @@ def update_scatter(year_slider,cp_select):
         ascus_bg = (asc_us_goal.opts(color='#ACFFA0', alpha = 0.75)*asc_us_bdln.opts(color='#6AB35F', alpha = 0.9)*\
                     asc_us_att.opts(color='#B34D93', alpha = 0.9))
 
-        if pd.isna(scatterdata['Thyroid AUS Rate(%)'].iloc[0]):
-            return ((asclsil_bg* ref_lsil_vline * lab_lsil_vline * scatter_lsil).opts(toolbar='above') +\
-                    (ascus_bg* ref_ascus_vline * lab_ascus_vline*scatter_ascus).opts(toolbar='above')).cols(1).opts(shared_axes=False)
-        else:
-            ref_aus = df['Thyroid AUS Rate(%)'].iloc[0]
-            lab_aus = labdata['Thyroid AUS Rate(%)'].iloc[0]
-            max_x_aus = labdata['Thyroid AUS Rate(%)'].max()
-            tooltips_aus = f"""<div><span style="font-size: {fpx};font-weight: bold">@{{Thyroid AUS Rate(%)}} </span></div>"""
-            hover_aus = HoverTool(tooltips=tooltips_aus)
-            scatter_aus = scatterdata.iloc[0:1].hvplot.scatter(x='Thyroid AUS Rate(%)', y='people', color='black', size=sdot_person,height= 160,
-                                                            xlim=(0,30), xticks = [0,lab_aus,10,15,30], yaxis = 'bare',grid=True, title='Thyroid AUS Rate(%)',
-                                                            xformatter='%.1f',xlabel ='', ylabel='', tools = [hover_aus]).opts(fontsize = pl_title,fontscale=f_scale, shared_axes=False,toolbar=None, default_tools = [])
-            lab_aus_vline = hv.VLine(x=lab_aus).opts(color='#5B91D1', line_dash ='dashed', alpha=1).opts(shared_axes=False,toolbar=None, default_tools = [])
-            aus_goal = hv.VSpan(0, 10).opts(shared_axes=False,toolbar=None, default_tools = [])
-            aus_bdln = hv.VSpan(10,15).opts(shared_axes=False,toolbar=None, default_tools = [])
-            aus_att = hv.VSpan(15, 30).opts(shared_axes=False,toolbar=None, default_tools = [])
-            aus_bg = (aus_goal.opts(color='#ACFFA0', alpha = 0.75)* aus_att.opts(color='#B34D93', alpha = 0.9)*\
-                       aus_bdln.opts(color='#6AB35F', alpha = 0.75))
-
-            return ((asclsil_bg* ref_lsil_vline * lab_lsil_vline * scatter_lsil).opts(toolbar='above') +\
-                    (ascus_bg* ref_ascus_vline * lab_ascus_vline*scatter_ascus).opts(toolbar='above')+\
-                   (aus_bg * lab_aus_vline * scatter_aus)).cols(1).opts(shared_axes=False)
+        #if pd.isna(scatterdata['Thyroid AUS Rate(%)'].iloc[0]):
+        #    return ((asclsil_bg* ref_lsil_vline * lab_lsil_vline * scatter_lsil).opts(toolbar='above') +\
+        #            (ascus_bg* ref_ascus_vline * lab_ascus_vline*scatter_ascus).opts(toolbar='above')).cols(1).opts(shared_axes=False)
+        #else:
+        #    ref_aus = df['Thyroid AUS Rate(%)'].iloc[0]
+        #    lab_aus = labdata['Thyroid AUS Rate(%)'].iloc[0]
+        #    max_x_aus = labdata['Thyroid AUS Rate(%)'].max()
+        #    tooltips_aus = f"""<div><span style="font-size: {fpx};font-weight: bold">@{{Thyroid AUS Rate(%)}} </span></div>"""
+        #    hover_aus = HoverTool(tooltips=tooltips_aus)
+        #    scatter_aus = scatterdata.iloc[0:1].hvplot.scatter(x='Thyroid AUS Rate(%)', y='people', color='black', size=sdot_person,height= 160,
+        #                                                    xlim=(0,30), xticks = [0,lab_aus,10,15,30], yaxis = 'bare',grid=True, title='Thyroid AUS Rate(%)',
+        #                                                    xformatter='%.1f',xlabel ='', ylabel='', tools = [hover_aus]).opts(fontsize = pl_title,fontscale=f_scale, shared_axes=False,toolbar=None, default_tools = [])
+        #    lab_aus_vline = hv.VLine(x=lab_aus).opts(color='#5B91D1', line_dash ='dashed', alpha=1).opts(shared_axes=False,toolbar=None, default_tools = [])
+        #    aus_goal = hv.VSpan(0, 10).opts(shared_axes=False,toolbar=None, default_tools = [])
+        #    aus_bdln = hv.VSpan(10,15).opts(shared_axes=False,toolbar=None, default_tools = [])
+        #    aus_att = hv.VSpan(15, 30).opts(shared_axes=False,toolbar=None, default_tools = [])
+        #    aus_bg = (aus_goal.opts(color='#ACFFA0', alpha = 0.75)* aus_att.opts(color='#B34D93', alpha = 0.9)*\
+        #               aus_bdln.opts(color='#6AB35F', alpha = 0.75))
+
+        #    return ((asclsil_bg* ref_lsil_vline * lab_lsil_vline * scatter_lsil).opts(toolbar='above') +\
+        #            (ascus_bg* ref_ascus_vline * lab_ascus_vline*scatter_ascus).opts(toolbar='above')+\
+        #           (aus_bg * lab_aus_vline * scatter_aus)).cols(1).opts(shared_axes=False)
             
     else:
         return pn.pane.Markdown("# N/A", styles={'font-size':'20px'})
@@ -1031,11 +1031,11 @@ def add_no_cases_cp(year_slider,cp_select):
     scatterdata = df[(df.year == year_slider)&(df['people'] == cp_select)]
     if len(scatterdata) > 0:
         case_no = scatterdata['Number'].iloc[0]
-        if pd.isna(scatterdata['Thyroid AUS Rate(%)'].iloc[0]):
-            return pn.pane.Markdown(f'# Gyn Cases examined: {case_no}')
-        else:
-            ac_no =scatterdata['ThyAC Number'].iloc[0]
-            return pn.pane.Markdown(f'# Gyn Cases examined: {case_no} <br><br> Thyroid AC Cases examined: {int(ac_no)}')
+        #if pd.isna(scatterdata['Thyroid AUS Rate(%)'].iloc[0]):
+        #    return pn.pane.Markdown(f'# Gyn Cases examined: {case_no}')
+        #else:
+        #    ac_no =scatterdata['ThyAC Number'].iloc[0]
+        return pn.pane.Markdown(f'# Gyn Cases examined: {case_no}')
        
             
             
@@ -1048,7 +1048,7 @@ interactive_no_cp = pn.bind(add_no_cases_cp,year_slider,cp_select)
 cp_performance = pn.Tabs(('Performance results',pn.Column(pn.Row(cp_select,year_slider),interactive_no_cp,\
                                                           pn.Row(pn.Column(pn.pane.Markdown("<br><br>"),vertical_legend_indiv),interactive_qa, height = 600 )\
                                                           ,pn.Column(pn.pane.Markdown("<br><br>"),modal_btn)))\
-                         ,('Results overview',pn.Column(cp_select, inter_asclsil_cp, inter_ascus_cp, inter_thyroid_cp)),styles = {'font-size': '20px'})
+                         ,('Results overview',pn.Column(cp_select, inter_asclsil_cp, inter_ascus_cp)),styles = {'font-size': '20px'})
 
 
 
@@ -1068,7 +1068,7 @@ cp_performance = pn.Tabs(('Performance results',pn.Column(pn.Row(cp_select,year_
 
 
 results = df[df['year'] != 2017]
-med_ref_distribution = pd.concat([results[results.people.str.contains('MEDSERV')],results.iloc[[0]] ])[['ASC/LSIL','ASC-US/ASC-H ratio(%)', 'Abnormal Rate(%)','Referral Rate(%)']]
+med_ref_distribution = pd.concat([results[results.people.str.contains('KGYC')],results.iloc[[0]] ])[['ASC/LSIL','ASC-US/ASC-H ratio(%)', 'Abnormal Rate(%)','Referral Rate(%)']]
 med_ref_distribution.loc[len(med_ref_distribution)] = {'ASC/LSIL':0.75,'ASC-US/ASC-H ratio(%)':12.5, 'Abnormal Rate(%)':1.5}
 
 
@@ -1110,7 +1110,7 @@ def hook_abnorm(plot, element):
 
 
 def add_norm_curves(column_changer,year_slider):
-    if column_changer == 'Thyroid AUS Rate(%)' or column_changer == 'Referral Rate(%)':
+    if column_changer == 'Referral Rate(%)':
         return None
     else:    
         mu= med_ref_distribution.iloc[-2][column_changer]