Hi People ! I hope that youāre doing good!
I have a question regarding streamlit, because Iām deploying an app in which you can watch meteoroligcal data, and I want to give to the user the estimated time in which the page takes to reload. I wanted to manage this with a datetime.datetime.now() at the begining of the code, an another one at the end of it, and then substracting them. Iāve try this commands before in local files and it worked good, those datetime objects can be substracted normally as far as i know. I donāt know if this is maybe a streamlit bug, or i need to do something else. If this isnāt a streamlit bug or something I want to apologize for posting the question here. Thanks to everyone!
Hi @juliosimonpedro ,
Do you mind sharing your code snippets ?
It will be easy for others to understand the issue and probably come up with a solution at earliest .
Best
Avra
hi!!! Of course! And thanks a lot!
import streamlit as st
from funciones import abrir_stats, plot, abrirtabla, path, abrir_stats
import matplotlib.pyplot as plt
import pandas as pd
import sqlite3
from datetime import datetime, timedelta, date
import numpy as np
import scipy.stats as scipy
import time
t1=datetime.now()
def tiempo(df):
df["Tiempo Sistema"]=pd.to_datetime(df["Tiempo Sistema"], format="%Y-%m-%d %H:%M:%S")
df.drop_duplicates(subset="Tiempo Sistema", inplace=True)
df.sort_values(by="Tiempo Sistema", ascending=True, inplace=True)
df.set_index("Tiempo Sistema", inplace=True)
return df
def rain(df):
rain=df[df["Rain Rate"]>0]
# print("Mean: {} , min: {}, max: {}".format(rain["Rain Rate"].mean(), rain["Rain Rate"].min(),rain["Rain Rate"].max()))
rain_mean=rain[rain["Rain Rate"]>rain["Rain Rate"].mean()]
return
def ciclo_anom(df):
df["Hora-Min"]=df.index.strftime("%H:%M")
df["Mes"]=df.index.month
cd=df.groupby(["Mes","Hora-Min"]).mean()
std=df.groupby(["Mes","Hora-Min"]).std()
for i in std.columns:
std.rename({i:"std "+i},axis=1,inplace=True)
name={}
for i in cd.columns:
name[i]="CD "+i
cd.rename(name, axis=1, inplace=True)
cd=pd.merge(cd,std,how="inner",left_index=True,right_index=True)
minut=np.arange(0,60,1)
cd.reset_index(inplace=True)
cd["Minutes"]=pd.to_datetime(cd["Hora-Min"], format="%H:%M").dt.minute
vhora=cd["Hora-Min"][cd["Minutes"].isin(minut)].drop_duplicates().array
vhora=np.append(vhora,["23:59"])
cd.drop(["Minutes"],axis=1, inplace=True)
df.reset_index(inplace=True)
merge=pd.merge(df,cd, how="left", left_on=["Mes", "Hora-Min"], right_on=["Mes","Hora-Min"]).drop_duplicates(subset="Tiempo Sistema")
merge.set_index("Tiempo Sistema", inplace=True)
df.set_index("Tiempo Sistema", inplace=True)
v=["Barometer","Outside Humidity", "Outside Temperature", "Solar Radiation", "Wind Speed", "Rain Rate"]
for i in v:
merge["Anomally "+i]=merge[i]-merge["CD "+i]
merge["Lim inf "+i]=merge[i]-merge["std "+i]
merge["Lim sup "+i]=merge[i]+merge["std "+i]
return merge
#Caja para escoger la estaciĆ³n en la pĆ”gina que se desea revisar
estacion=st.sidebar.selectbox("Escoja la estaciĆ³n", ["Oriente", "Turbo"])
#Fechas y tiempos en los cuales el usuario quiere la informaciĆ³n
t1=st.sidebar.date_input("Fecha Inicial", date.today()-timedelta(days=1))
t2=st.sidebar.date_input("Fecha Final", date.today())
tim1=st.sidebar.time_input("Ingrese Tiempo Inicial", datetime.strptime("00:00", "%H:%M").time())
tim2=st.sidebar.time_input("Ingrese Tiempo Final", datetime.strptime("23:59", "%H:%M").time())
#Combinar fecha y hora
dat1=(str(t1)+" "+str(tim1))
dat2=(str(t2)+" "+str(tim2))
#Abrir la tabla de la Database con las fechas segĆŗn la estaciĆ³n que se escoja
if estacion=="Oriente":
est_escog=abrirtabla(path+"/meteoro-oriente.db", "DatosMeteoro",dat1,dat2)
est_escog=tiempo(est_escog)
anom=abrir_stats(path+"/meteoro-oriente.db",est_escog.index.month.drop_duplicates().array)
elif estacion=="Turbo":
est_escog=abrirtabla(path+"/turbodb.db", "2019_2021",dat1,dat2)
est_escog=tiempo(est_escog)
anom=abrir_stats(path+"/turbodb.db",est_escog.index.month.drop_duplicates().array)
#Calcular el ciclo diurno de las variables en el rango escogido, como se ve arriba (FunciĆ³n)
cd_rango=ciclo_anom(est_escog)
# Containers create on horixontal, and columns en vertical
header = st.container()
#Definir tĆrulo de la pĆ”gina
with header:
st.title("PƔgina para ver grƔfica de lluvia")
md="""Esta pĆ”gina estĆ” diseƱada por ahora para comparar las variables Temperatura, Humedad Relativa y PresiĆ³n
con eventos de precipitaciĆ³n :rain_cloud: en las diferentes estaciones
"""
#Print el texto llamado md
st.markdown(md)
#Comando para no tener conflicto al mostrar la gÅafica
st.set_option('deprecation.showPyplotGlobalUse', False)
#Comando para graficar las variables en el rango de tiempo escogido
st.pyplot(plot(dat1,dat2,est_escog,"Rain Rate","Outside Temperature","Outside Humidity",
"Barometer"))
md="""
A continuaciĆ³n se ven las anomalĆas de las variables de la base de datos.
"""
st.markdown(md)
est_escog["Mes"]=est_escog.index.month
est_escog["Hora-Min"]=est_escog.index.strftime("%H:%M")
st.dataframe(est_escog)
est_escog.reset_index(inplace=True)
anomalias=pd.merge(est_escog,anom,left_on=["Mes", "Hora-Min"], right_on=["Mes", "Hora-Min"], how="left")
anomalias.set_index("Tiempo Sistema", inplace=True)
variables=["Outside Temperature","Outside Humidity","Barometer"]
for i in variables:
anomalias["Anomally "+i]=anomalias[i]-anomalias["mean "+i]
est_escog.set_index("Tiempo Sistema", inplace=True)
st.dataframe(anomalias)
#Graficar las anomalĆas en el rango escogido
st.pyplot(plot(dat1,dat2,anomalias,"Rain Rate","Anomally Outside Temperature","Anomally Outside Humidity",
"Anomally Barometer"))
var=st.selectbox("Escoja la variable sobre la cual quiere visualizar el ciclo diurno del rango de fechas escogido",["Outside Temperature","Barometer", "Outside Humidity"])
# st.dataframe(anom[estacion])
def plot_ciclo_diurno(df,variable):
if variable[0]=="B":
color="black"
elif variable=="Outside Temperature":
color="Firebrick"
else:
color="forestgreen"
fig,ax=plt.subplots(figsize=(20,10))
l=[]
for i in df["Hora-Min"].drop_duplicates().sort_values().values:
if i[-2:]=="00" or i[-2:]=="30":
l.append(i)
plt.plot(df["Hora-Min"].drop_duplicates().sort_values(),df.drop_duplicates(subset="Hora-Min").sort_values(by="Hora-Min")[variable],color=color,linewidth=1.5)
plt.fill_between(df["Hora-Min"].drop_duplicates().sort_values(),df.drop_duplicates(subset="Hora-Min").sort_values(by="Hora-Min")["Lim inf "+variable],
df.drop_duplicates(subset="Hora-Min").sort_values(by="Hora-Min")["Lim sup "+variable],color=color,alpha=0.2)
plt.xticks(l,rotation=45)
ax.set_ylabel(variable, size=16)
plt.grid(alpha=0.6)
plt.xlim(["00:00","23:59"])
ax.yaxis.label.set_color(color)
st.pyplot(plot_ciclo_diurno(cd_rango,var))
t2=datetime.now()
t3=t2-t1
st.markdown(f"La pĆ”gina se demorĆ³ haciendo la consulta: {t3}")
hey @juliosimonpedro ,
Thanks for updating with your code.
Unfortunately, I couldnāt reproduce your case with these snippet of code. Possibly some part is missing .
However, if I understood you correctly, you aim to filter the dates between two date ranges, right ?
Best
Avra
@juliosimonpedro you have variable name collisions.
t1=datetime.now()
...
t1=st.sidebar.date_input("Fecha Inicial", date.today()-timedelta(days=1))
...
t2=datetime.now()
suggest you refactor your code
I am able to deploy this script successfully
import streamlit as st
from datetime import datetime
import time
def do_work(time_spent=5):
time.sleep(time_spent)
time_spent = st.number_input("time")
t1 = datetime.now()
# t1 = time.time()
do_work(int(time_spent))
t2 = datetime.now()
# t2 = time.time()
st.write(f"{t2-t1}")
2 Likes
OHH!!! DUMB MEEE jejejeje thanks a lot!
Hey Avraa thanks for replying!
This topic was automatically closed 365 days after the last reply. New replies are no longer allowed.