Update app.py

app.py

@@ -262,7 +262,7 @@ def labres(column_changer):
     asc_abn_outliers = zscore_df[(zscore_df['people'].str.contains('Cyto'))&(zscore_df['year']==int(df.year.max()))]
     
     ref_outlier = df[(df['people'].str.contains('Cytol')) & (df['year'] ==  int(df.year.max()))]
-    thy_outlier = df[(df['people'].str.contains('Cytop')) & (df['year'] ==  int(df.year.max()))]
+    #thy_outlier = df[(df['people'].str.contains('Cytop')) & (df['year'] ==  int(df.year.max()))]
     
     # ASCUS & ABNORMAL:
     
@@ -270,7 +270,7 @@ def labres(column_changer):
     lab_comment_4 = ""
     if column_changer == col_options[1]:
         avg_zscore = zscore_eval.get_group('KGYC')['ASC-US/ASC-H ratio(%)'].mean()
-        treshold_1 = 90.0 - avg_zscore*1
+        treshold_1 = 90.0 - avg_zscore*1 #Ezeket a küszöbértékeket a lab átlagból és a referencia értékből számolja, így a kenézyre 83/17% jött ki attention zone-ra
         treshold_2 = 10.0 + avg_zscore*1
         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 {treshold_1:.2f}% / {treshold_2:.2f}%.* <br><br>"
     if column_changer == col_options[2]:
@@ -468,11 +468,11 @@ def lab_management(year_slider, column_changer):
         return (asclsil_bg* ref_vline * lab_vline * scatter_plot).opts(shared_axes=False)
     elif column_changer == 'ASC-US/ASC-H ratio(%)' :
         scatter_plot = scatterdata[scatterdata.people.str.contains('Cyto', na=False)].hvplot.scatter(x=column_changer, y='people', color='black', size=sdot_size,height= sheight,
-                                                        xlim=(35,100), xticks = [min_x,lab,ref, max_x], rot=45, grid=True, title=column_changer,
+                                                        xlim=(17,100), xticks = [min_x,lab,ref, max_x], rot=45, grid=True, title=column_changer,
                                                         xformatter='%.1f',ylabel='',xlabel='', tools = [hover_cp]).opts(fontsize = pl_title,fontscale=f_scale_lab,shared_axes=False,toolbar=None, default_tools = [])
-        asc_us_goal = hv.VSpan(75, 100).opts(shared_axes=False,toolbar=None, default_tools = [])
-        asc_us_bdln = hv.VSpan(65,75).opts(shared_axes=False,toolbar=None, default_tools = [])
-        asc_us_att = hv.VSpan(35, 65).opts(shared_axes=False,toolbar=None, default_tools = [])
+        asc_us_goal = hv.VSpan(90, 100).opts(shared_axes=False,toolbar=None, default_tools = []) #Ezt esetleg meg lehet változtatni a labor tresholdra, de akkor túlságosan összetolódna a graph
+        asc_us_bdln = hv.VSpan(83,90).opts(shared_axes=False,toolbar=None, default_tools = [])
+        asc_us_att = hv.VSpan(17, 83).opts(shared_axes=False,toolbar=None, default_tools = [])
         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)
@@ -611,7 +611,7 @@ def ascus_rate_overview_ct(ct_select):
         
         return pn.pane.Markdown(f"""
         ## ASC-US/ASC-H ratio Overview:
-        **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>
+        **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 82.85%/17.15%.*<br><br>
         **Result:** {ct_comment} {ct_comment_2}
         """, sizing_mode ="stretch_width", styles={'font-size': fpx})
     else: