Glucometro con comidas y estadísticas de las comidas usando el archivo test.csv

funciones_comidas.py

+import pandas as pd
+import requests 
+import scipy.stats as stats
+from datetime import date,datetime
+import numpy as np
+import config
+from urllib.parse import urlparse
+import urllib.request
+import requests
+from PIL import Image
+from PIL.ExifTags import TAGS
+import re
+
+url_api = 'https://nutricion.c3.unam.mx/nd'
+
+
+def readGlucosa(participante, solo_fecha=None):
+
+    url_glucosa = url_api + "/patients/" + str(participante) + "/glucouse/data" 
+    try:
+        df=pd.read_json(url_glucosa, orient='records', convert_dates=False)
+        if solo_fecha:
+            df=df[df['date']==solo_fecha]
+    except:
+        print("no hay datos de glucosa para ", participante)
+        return pd.DataFrame()
+    if not df.empty:
+        df["date_time"] = df["date"] + "-" + df["time"]
+        df["date_time"] = pd.to_datetime(df["date_time"])
+        ordered = df.sort_values(by=["date_time"])
+        ordered = ordered.reset_index(drop=True)
+        return ordered[["date_time", "value"]]
+    else:
+        return df
+    
+def readVisitas(visita):
+
+    visita = 1
+    url_visitas = url_api + "/visits/" + str(visita) + "/patients-data/"
+    datos = requests.get(url_visitas).json()["patient_data"]
+    l1 = [ { cf["controlled_food"]: cf["increment_auc"] for cf in tolerance["tolerance_curve_measure"] } for tolerance in datos ] 
+    df1 = pd.json_normalize(datos)
+    df1.drop("tolerance_curve_measure", inplace=True, axis=1)
+    df1.drop('sample.time_sample_was_taken', inplace=True, axis=1)
+    df1.rename(columns= lambda x: x.split(".")[1] if "." in x else x, inplace=True)
+    return  pd.concat([df1, pd.DataFrame(l1)], axis=1)
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