cobertura de protocolos en multiplot

dinamyc.py

@@ -2,6 +2,7 @@ import pandas as pd
 from dash import Dash, Input, Output, dcc, html
 from dash import Dash, Input, Output, dcc, html
 import plotly.graph_objects as go
 import plotly.graph_objects as go
 import plotly.express as px
 import plotly.express as px
+from plotly.subplots import make_subplots
 import requests
 import requests
 import numpy as np
 import numpy as np
 import flask
 import flask
@@ -395,26 +396,30 @@ def update_datos_por_paciente(etapa, fuente):
     Output("cobertura_protocolo","figure"),
     Output("cobertura_protocolo","figure"),
     Input("select_vis_protocolo", "value")
     Input("select_vis_protocolo", "value")
 )
 )
-def update_por_protocol(visita):
+def update_por_protocolo(visita):
+    lcol = 1
+    lrow = 1
     df1 = visitas_datos[visita-1]
     df1 = visitas_datos[visita-1]
     protocolos = {
     protocolos = {
-        "pyhsiological" : ["pass", "pad", "ipaq", "stress", "hadsa", "hadsd"],
+        "pyhsiological" : ["pas", "pad", "ipaq", "stress", "hadsa", "hadsd"],
         "tolerance_curves" : ["Pan", "Gelatina", "Glucosa"],
         "tolerance_curves" : ["Pan", "Gelatina", "Glucosa"],
         "corporal_composition" : ["mmkg", "mmekg", "mgkg", "mlgkg", "percent_mg", "gv"],
         "corporal_composition" : ["mmkg", "mmekg", "mgkg", "mlgkg", "percent_mg", "gv"],
         "biochemical" :["hba1c", "glucose", "ct", "tag", "ldl", "hdl", "pcr","alt", "ast","homa", "insuline" ],
         "biochemical" :["hba1c", "glucose", "ct", "tag", "ldl", "hdl", "pcr","alt", "ast","homa", "insuline" ],
-        "antropometric" : ["imc", "weight", "waist_circunference"]
-    }
-    
-    cuentas = 100 *  df1.replace("",np.nan).count() / len(df1)
-    figura = {
-        "data": [
-            {
-                "x": cuentas.index,
-                "y": cuentas,
-                "type": "bar",
-            }
-        ],
+        "antropometric" : ["imc", "weight", "waist_circumference"]
     }
     }
+    figura = make_subplots(rows=2, cols=3, vertical_spacing=0.25)
+    for protocolo in protocolos.keys():
+        columnas = list(set(df1.columns).intersection(set(protocolos[protocolo])))
+        cuentas = 100 * df1[columnas].replace("",np.nan).count() / len(df1)
+        figura.add_trace(
+            go.Bar(x=cuentas.index, y=cuentas),
+            row = lrow,
+            col = lcol,
+        )
+        lcol = lcol + 1
+        if lcol > 3:
+            lcol = 1
+            lrow = lrow + 1
     return figura
     return figura
 
 
 @app.callback(
 @app.callback(