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Streamlit real time

DB CAFE

Dbcafe (토론 | 기여)님의 2024년 8월 26일 (월) 19:08 판
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1 streamlit real time chart[편집]

1.1 Build a real-time dashboard in Python with Tinybird and Dash[편집]

1.1.1 환경 설정 (Python >3.8)[편집]

mkdir python-dashboard
cd python-dashboard
python -mvenv .e
. .e/bin/activate
pip install dash
pip install pandas # Dash utilizes pandas
pip install dash_bootstrap_components # so we can use a Dash Theme
pip install verdin # Tinybird SDK for Python
pip install tinybird-cli
echo -e ".e*\n.tinyb" >> .gitignore
  • Go to tinybird.co/signup and get a free Tinybird account.

1.1.2 TinyBird 인증[편집]

mkdir data-project
cd data-project
tb auth --token <the token you copied>

1.2 샘플[편집]

import streamlit as st
import time
import numpy as np
import pandas as pd

st.set_page_config(
 page_title='Sirkülasyon Testi',
 layout="wide",
 initial_sidebar_state="collapsed",
)

st.sidebar.markdown('''
### Sirkülasyon Testi
Yapılacak değişiklikler programı resetler.
''')

marka = st.sidebar.text_input("Pompa Markası:","Brand")
model = st.sidebar.text_input("Pompa Modeli:","Model")
mod   = st.sidebar.text_input("Çalıştırma Modu:","dP5.0")
zamandamgasi = time.strftime("]_%Y%b%d_%H.%M.%S")
isim = marka+"_"+model+"_["+mod+zamandamgasi

i = 0
t = 0

st.sidebar.write("Otomatik Vana Modunda Deney")
Dint = st.sidebar.number_input("Başlangıç Noktası:",value=15.0)
Dfin = st.sidebar.number_input("Bitiş Noktası:",value=70.0)
ds = st.sidebar.number_input("Adım Aralığı",value=0.25)
dt = st.sidebar.number_input("Adımlar Arası Periyot",value=8)

if st.sidebar.button("Otomatik Modda Kayıt Başlat"):
    kaydet = 1
else:
    kaydet = 0,
    
st.sidebar.write("Manuel Vana Ayarı")
D = st.sidebar.number_input("Vana Açıklığı [0:Kapalı -90:Açık]",value=0.0)
if st.sidebar.button("Ayarla"): 
    yaz(64,[int(D*4)])
    

    
sozluk =[
    ['Date'                   ,"Tarih"                  ,"YMD"       ,""         ],
    ['Time'                   ,"Zaman"                  ,"HMS"       ,""         ],
    ['Temperature'            ,"Sıcaklık"               ,"T"         ,"°C"       ],
    ['Flow_Rate'              ,"Debi"                   ,"Q"         ,"m³/saat"  ],
    ['Valve_Aperture'         ,"Vana Açıklığı"          ,"D"         ,"°"        ],
    ['Static_Pressure'        ,"Durağan Basınç"         ,"P"         ,"Bar"      ],
    ['Suction_Pressure'       ,"Emme Basıncı"           ,"P1"        ,"Bar"      ],
    ['Discharge_Pressure'     ,"Basma Basıncı"          ,"P2"        ,"Bar"      ],
    ['Voltage'                ,"Gerilim"                ,"V"         ,"V"        ],
    ['Current'                ,"Akım"                   ,"I"         ,"A"        ],
    ['Measured_Frequency'     ,"Ölçülen Frekans"        ,"f"         ,"Hz"       ],
    ['Active_Power'           ,"Aktif Güç"              ,"PAP"       ,"W"        ],
    ['Reactive_Power'         ,"Reaktif Güç"            ,"QRP"       ,"VAr"      ],
    ['Apperent_Power'         ,"Görünür Güç"            ,"SAP"       ,"VA"       ],
    ['Phase_Voltage_Angle'    ,"Faz Gerilim Açısı"      ,"PVA"       ,"°"        ],
    ['Phase_Current_Angle'    ,"Faz Akım Açısı"         ,"PCA"       ,"°"        ],
    ['Cos_Phi'                ,"Cos_Phi"                ,"PHI"       ,""         ],
    ['Power_Factor'           ,"Güç Faktörü"            ,"PF"        ,""         ],
    ['Differential_Pressure'  ,"Fark Basıncı"           ,"DP"        ,"Bar"      ],
    ['Head'                   ,"Basma Yüksekliği"       ,"H"         ,"m"        ],
    ['Hydraulic_Power'        ,"Hidrolik Güç"           ,"PHYD"      ,"W"        ],
    ['Efficiency'             ,"Verimlilik"             ,"ETA"       ,"%"        ]
    ]
    
varis = []
for i in sozluk: varis.append(i[0])  #sozluk ilk sütun  (Türkçe için 0 yerine 1. sütun seçilebilir.)
df = pd.DataFrame(columns=varis)
buffy = pd.DataFrame(columns=varis)



col = st.columns(4)
 

###################################### G R A F İ K L E R #############################################

col[0].subheader("Debi & Basma Yüksekliği [m³/h,m]  \n Flow Rate & Head")
QHt = col[0].line_chart()

col[0].subheader("Hidrolik Güç & Verimlilik [W,%] \n Hydraulic Power & eta")
PHYDETAt = col[0].line_chart()

col[1].subheader("Emme & Basma Basınçları [Bar] \n Suction & Discharge Pressure")
P1P2t = col[1].line_chart()

col[1].subheader("Elektrik Güçleri [W, VA, VAr] \n Active, Reactive & Apperent Power" )
PAPQRPSAP = col[1].line_chart()

col[2].subheader("cos(φ) & Güç Faktörü \n cos(φ) & Power Factor")
PHIPFt = col[2].line_chart()

col[2].subheader("Faz Gerilim & Akım Açıları [°] \n Phase Voltage & Current Angles")
PVAPCAt = col[2].line_chart()

###################################### M E T R İ K L E R ##############################################

col[3].write(f"Deney Kimliği: \n {isim}")
YMDt = col[3].empty()
HMSt = col[3].empty()
Tt = col[3].empty()
Dt = col[3].empty()
Pt = col[3].empty()
Vt = col[3].empty()
ft = col[3].empty()

##################################### D E M O ##########################################################

df = pd.read_excel("demo_data.xlsx",index_col=0)

######################################### D Ö N G Ü ####################################################

for i in range(len(df)):
    buff=df.iloc[i]

    with Tt:
        st.metric("Hat Sıcaklığı",f"{buff[2]} °C")
    with YMDt:
        st.metric("Tarih",buff[0])
    with HMSt:
        st.metric("Saat",buff[1])
    with Dt:
        st.metric("Vana Açıklığı",f"{buff[4]} °")
    with Pt:
        st.metric("Durağan Basınç",f"{buff[5]} Bar")
    with Vt:
        st.metric("Gerilim",f"{buff[8]} V")
    with ft:
        st.metric("Ölçülen Frekans",f"{buff[10]} Hz")
    
    QHt.add_rows([[float(buff[3]),float(buff[19])]])
    PHYDETAt.add_rows([[float(buff[20]),float(buff[21])]])
    P1P2t.add_rows([[float(buff[6]),float(buff[7])]])
    PHIPFt.add_rows([[float(buff[16]),float(buff[17])]])
    PAPQRPSAP.add_rows([[float(buff[11]),float(buff[12]),float(buff[13])]])
    PVAPCAt.add_rows([[float(buff[14]),float(buff[15])]])
    
    time.sleep(0.1)

    # if kaydet == 1:
    #     if t%dt==0 and Dint<Dfin:
    #         Dint=Dint+ds
    #         yaz(64,[int(Dint*4)])
    #     elif Dint>=Dfin:
    #         df.to_excel(f"{isim}.xlsx")
    #         kaydet=0

1.3 oracle ash real-time dash-board[편집]

  • matplotlib 이용
pip install streamlit cx_Oracle pandas matplotlib
import cx_Oracle
import pandas as pd
import streamlit as st
import time

# Oracle 데이터베이스 연결 설정
def connect_to_oracle(user, password, dsn):
    try:
        connection = cx_Oracle.connect(user, password, dsn)
        return connection
    except cx_Oracle.DatabaseError as e:
        st.error(f"Database connection error: {e}")
        return None

# ASH 데이터를 가져오는 함수
def fetch_ash_data(connection):
    query = """
    SELECT sample_time,
           session_id,
           session_state,
           event,
           wait_class,
           blocking_session,
           current_obj#,
           current_file#,
           current_block#
    FROM v$active_session_history
    WHERE sample_time > sysdate - INTERVAL '5' MINUTE
    """
    df = pd.read_sql(query, connection)
    return df
import matplotlib.pyplot as plt

# Streamlit 페이지 구성
def main():
    st.title("Oracle ASH Real-time Monitoring")
    
    # Oracle 연결 정보 입력받기
    st.sidebar.header("Database Connection")
    user = st.sidebar.text_input("Username")
    password = st.sidebar.text_input("Password", type="password")
    host = st.sidebar.text_input("Host")
    port = st.sidebar.text_input("Port", value="1521")
    service_name = st.sidebar.text_input("Service Name")
    
    dsn = cx_Oracle.makedsn(host, port, service_name=service_name)
    
    if st.sidebar.button("Connect"):
        connection = connect_to_oracle(user, password, dsn)
        
        if connection:
            st.success("Connected to Oracle Database!")
            
            # 실시간 모니터링 시작
            placeholder = st.empty()
            
            while True:
                # ASH 데이터 가져오기
                df = fetch_ash_data(connection)
                
                # ASH 데이터 프레임 출력
                with placeholder.container():
                    st.subheader("Active Session History Data (Last 5 Minutes)")
                    st.dataframe(df)

                    # 세션 상태 분포 시각화
                    st.subheader("Session State Distribution")
                    session_state_counts = df['SESSION_STATE'].value_counts()
                    st.bar_chart(session_state_counts)
                    
                    # 이벤트별 분포 시각화
                    st.subheader("Wait Events Distribution")
                    event_counts = df['EVENT'].value_counts().head(10)
                    st.bar_chart(event_counts)

                # 주기적으로 데이터 갱신 (5초마다)
                time.sleep(5)
                
if __name__ == "__main__":
    main()


  • echart 이용
pip install streamlit cx_Oracle pandas pyecharts streamlit-echarts

- 오라클 연결은 위와 동일 

from pyecharts.charts import Bar
from pyecharts import options as opts
from streamlit_echarts import st_pyecharts

# 세션 상태 분포 차트 생성
def create_session_state_chart(session_state_counts):
    bar = Bar()
    bar.add_xaxis(session_state_counts.index.tolist())
    bar.add_yaxis("Session State", session_state_counts.values.tolist())
    bar.set_global_opts(title_opts=opts.TitleOpts(title="Session State Distribution"))
    return bar

# 이벤트 분포 차트 생성
def create_event_chart(event_counts):
    bar = Bar()
    bar.add_xaxis(event_counts.index.tolist())
    bar.add_yaxis("Wait Events", event_counts.values.tolist())
    bar.set_global_opts(title_opts=opts.TitleOpts(title="Wait Events Distribution"))
    return bar

# Streamlit 페이지 구성
def main():
    st.title("Oracle ASH Real-time Monitoring with ECharts")
    
    # Oracle 연결 정보 입력받기
    st.sidebar.header("Database Connection")
    user = st.sidebar.text_input("Username")
    password = st.sidebar.text_input("Password", type="password")
    host = st.sidebar.text_input("Host")
    port = st.sidebar.text_input("Port", value="1521")
    service_name = st.sidebar.text_input("Service Name")
    
    dsn = cx_Oracle.makedsn(host, port, service_name=service_name)
    
    if st.sidebar.button("Connect"):
        connection = connect_to_oracle(user, password, dsn)
        
        if connection:
            st.success("Connected to Oracle Database!")
            
            # 실시간 모니터링 시작
            placeholder = st.empty()
            
            while True:
                # ASH 데이터 가져오기
                df = fetch_ash_data(connection)
                
                # ASH 데이터 프레임 출력
                with placeholder.container():
                    st.subheader("Active Session History Data (Last 5 Minutes)")
                    st.dataframe(df)

                    # 세션 상태 분포 시각화
                    st.subheader("Session State Distribution")
                    session_state_counts = df['SESSION_STATE'].value_counts()
                    session_state_chart = create_session_state_chart(session_state_counts)
                    st_pyecharts(session_state_chart)

                    # 이벤트별 분포 시각화
                    st.subheader("Wait Events Distribution")
                    event_counts = df['EVENT'].value_counts().head(10)
                    event_chart = create_event_chart(event_counts)
                    st_pyecharts(event_chart)

                # 주기적으로 데이터 갱신 (5초마다)
                time.sleep(5)
                
if __name__ == "__main__":
    main()

1.4 주식 시황 모니터링[편집]

원본 주소 "http://dbcafe.co.kr/wiki/index.php?title=Streamlit_real_time&oldid=12597"