mas correciones para correr detrás de apache

dinamyc.py

@@ -4,9 +4,9 @@ import plotly.graph_objects as go
 import plotly.express as px
 import requests
 import numpy as np
-+import flask
+import flask
 
-+server = flask.Flask(__name__)
+server = flask.Flask(__name__)
 
 # algunos parámetros globales
 url = 'https://nutricion.c3.unam.mx/nd/'
@@ -88,7 +88,7 @@ external_stylesheets = [
         "rel": "stylesheet",
     },
 ]
-app = Dash(__name__, external_stylesheets=external_stylesheets)
+
 app = Dash(__name__, external_stylesheets=external_stylesheets, server=server, url_base_pathname='/nutri/inventario/')
 app.title = "NutrINDmex. Inventario de datos"
 

estadisticas.py

@@ -5,10 +5,12 @@ import plotly.express as px
 import requests
 import numpy as np
 from plotly.subplots import make_subplots
-import socket
 from sklearn.cluster import KMeans
 from scipy import stats
 from ast import literal_eval
+import flask
+
+server = flask.Flask(__name__)
 
 def datavis(datos):
     medidas = ["imc", "age", "glucose", "hba1c", "ct", "hdl"]    
@@ -49,6 +51,8 @@ def popdist(datos):
     )
     return fig
 
+
+
 def correlaciones(datos, controlado):
 # filtramos solo a los que tienen datos del controlado
     datos_fil = datos.loc[datos[controlado].notnull()]
@@ -76,6 +80,36 @@ def correlaciones(datos, controlado):
     figura.update_layout(showlegend=False)
 
     return figura
+
+def correlaciones_v2(datos, controlado):
+# filtramos solo a los que tienen datos del controlado
+    datos_fil2 = datos.loc[datos[controlado].notnull()]
+
+
+    metricas = ["imc", "age", "glucose", "hba1c"]
+    figura = make_subplots(rows=2, cols=2, shared_xaxes=True, vertical_spacing=0.1, subplot_titles=metricas)
+    lrow = 1
+    lcol = 1
+    for metrica in metricas:
+        datos_sorted = datos_fil2.sort_values(by=metrica, ascending=True)
+        datos_sorted.reset_index(inplace=True)
+        datosfil = datos_sorted[np.abs(stats.zscore(datos_sorted[controlado], nan_policy='omit')) < 3]     
+
+        figura.add_trace(
+            go.Scatter(x=datosfil.index, y=datosfil[controlado], name=metrica, mode='markers'),
+            row=lrow, col=lcol
+        )
+        figura.add_trace(
+            go.Scatter(x=datosfil.index, y=datosfil[controlado].rolling(5).mean(), name="PM(5)"),
+            row=lrow, col=lcol
+        )
+        corr = datos_sorted[[metrica]].corrwith(datos_sorted[controlado]).values[0]
+        figura.add_annotation(row=lrow, col=lcol, text="R = " + "{:.2f}".format(corr))
+        lcol = lcol + 1 if lcol < 2 else 1
+        lrow = lrow + (lcol % 2)
+    figura.update_layout(showlegend=False)
+
+    return figura
     
 def controlado_ppgr(datos):
     datos_fil = datos.loc[datos["Glucosa"].notnull()]
@@ -321,7 +355,8 @@ external_stylesheets = [
         "rel": "stylesheet",
     },
 ]
-app = Dash(__name__, external_stylesheets=external_stylesheets)
+
+app = Dash(__name__, external_stylesheets=external_stylesheets, server=server, url_base_pathname='/nutri/estadisticas/')
 app.title = "NutrIndMex. Análisis de datos."
 
 app.layout = html.Div(children=[
@@ -354,6 +389,11 @@ app.layout = html.Div(children=[
     html.Div(children= [
         html.Div(children= [html.H1("Relación de iAUC/Controlados con diversos índices", className = "header-title2")]),        dcc.Graph(figure=correlaciones(visitas_incluidos, "Glucosa"))
     ]),
+    # correlaciones transpuesta
+    html.Div(children= [
+        html.Div(children= [html.H1("Relación de iAUC/Controlados con diversos índices (traspuesta)", className = "header-title2")]),        dcc.Graph(figure=correlaciones_v2(visitas_incluidos, "Glucosa"))
+    ]),
+
     # areas de tres pacientes
     html.Div(children= [
         html.Div(children= [html.H1("Ejemplos de iAUC de Glucosa", className = "header-title2")]),