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tools.py
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tools.py
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import pandas as pd
import numpy as np
import streamlit as st
from streamlit_extras.app_logo import add_logo
# function
# page config
def page_config(title):
st.set_page_config(page_title=title, page_icon="👀")
hide_st_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
header {visibility: hidden;}
</style>
"""
st.markdown(hide_st_style, unsafe_allow_html=True)
# logo
add_logo("image/logo_cat.png", height=80)
# sidebar
with st.sidebar:
st.info("⬆⬆ Pick a menu above! ⬆⬆")
# banjir
def klasifikasi_banjir(X,scaler_X,model):
# scaling
X_klasifikasi_scaled = scaler_X.transform(X[['height']])
# predict
y_klasifikasi = model.predict(X_klasifikasi_scaled,verbose=0)
y_klasifikasi = np.argmax(y_klasifikasi, axis=1)
# df
y_klasifikasi = pd.DataFrame(y_klasifikasi,columns = ['status_pred'])
df_klasifikasi = X.join(y_klasifikasi)
return df_klasifikasi
def get_X_prediksi(data, date):
data_history = data.loc[data['datetime'] <= date].head(400).sort_values(by=['datetime']).reset_index(drop=True) # 288+108=396, 400>396
data_history['cloudcover_3h (%)'] = data_history['cloudcover (%)'].shift(18)
data_history['humidity_18h (%)'] = data_history['humidity (%)'].shift(108)
data_history['height_diff_18h (cm)'] = data_history['height (cm)'] - data_history['height (cm)'].shift(108)
data_history = data_history.dropna().tail(288).reset_index(drop = True)
data_history = data_history[['datetime',
'height (cm)',
'windgust (kph)',
'cloudcover_3h (%)',
'humidity_18h (%)',
'height_diff_18h (cm)']]
return data_history
def prediksi_banjir(data, date, X, scaler_X, scaler_y, model):
# X
X_prediksi = X.drop(columns=['datetime']).rename(columns={
'height (cm)':'height',
'windgust (kph)':'windgust',
'cloudcover_3h (%)':'cloudcover_3h',
'humidity_18h (%)':'humidity_18h',
'height_diff_18h (cm)':'height_diff_18h'})
# scaling
X_prediksi_scaled = scaler_X.transform(X_prediksi)
# reshape
X_prediksi_scaled = X_prediksi_scaled.reshape(1,288,5)
# predict
y_prediksi = model.predict(X_prediksi_scaled,verbose=0)
# reshape
y_prediksi = y_prediksi.reshape(36,1)
# inverse scaling
y_prediksi_inverse = scaler_y.inverse_transform(y_prediksi)
y_prediksi_inverse = pd.DataFrame(y_prediksi_inverse, columns = ['height']) # y_pred
# DATA FUTURE
data_future = data.loc[data['datetime'] > date].tail(36).sort_values(by=['datetime']).reset_index(drop=True)# 36=step
data_future = data_future[['datetime','height (cm)']].rename(columns = {'height (cm)':'height_true (cm)'})
# DF PRED
if data_future.shape[0] == 36:
df_pred = data_future.join(y_prediksi_inverse)
else:
df_pred = y_prediksi_inverse
return df_pred
def get_info_banjir(y_klasifikasi, y_pred_status):
date = y_klasifikasi['date'][0]
height = y_klasifikasi['height'][0]
status = y_klasifikasi['status_pred'][0]
# Info klasifikasi
st.write("**Prediction info 💬**")
col_title, col_text = st.columns([1,8])
col_title.write('Datetime')
col_text.write(f': {str(date)}')
col_title.write('Height')
col_text.write(f': {str(height.round(2))} cm')
siaga0 = ':green[SIAGA 0]'
siaga1 = ':orange[SIAGA 1]'
siaga2 = ':red[SIAGA 2]'
aman = ":green[[AMAN]]"
waspada = ":orange[[WASPADA]]"
bahaya = ":red[[BAHAYA]]"
# kondisi status
if status == 0:
col_title.write('Status')
col_text.write(f': {siaga0}')
col_title.write("Message")
if (y_pred_status['status_pred']==0).all(): # jika semua siaga 0
col_text.write(f': {aman} Dalam 6 jam kedepan diperkirakan akan tetap berstatus {siaga0}.')
col_text.write('. Tidak akan terjadi banjir.')
elif (y_pred_status['status_pred'] == 1).any() and not (y_pred_status['status_pred'] == 2).any(): # jika ada siaga 1 dan tidak ada siaga 2
t_siaga1_start = (y_pred_status[y_pred_status['status_pred'] == 1].index.min()+1) * 10
col_text.write(f': {waspada} Dalam {t_siaga1_start} menit kedepan diperkirakan akan berstatus {siaga1}.')
col_text.write('. Harap pantau ketinggian air secara berkala.')
elif (y_pred_status['status_pred'] == 2).any(): # jika ada siaga 2
t_siaga2_start = (y_pred_status[y_pred_status['status_pred'] == 2].index.min()+1) * 10
col_text.write(f': {bahaya} Dalam {t_siaga2_start} menit kedepan diperkirakan akan berstatus {siaga2}.')
col_text.write('. Berkemungkinan terjadi banjir, segera lakukan evakuasi.')
else: col_text.write(': -')
elif status == 1:
col_title.write('Status')
col_text.write(f': {siaga1}')
col_title.write("Message")
if (y_pred_status['status_pred']==0).all():
col_text.write(f': {aman} Dalam 10 menit kedepan diperkirakan akan berstatus {siaga0}.')
col_text.write('. Tidak akan terjadi banjir.')
elif (y_pred_status['status_pred']==0).any() and not (y_pred_status['status_pred'] == 2).any():
t_siaga1_end = (y_pred_status[y_pred_status['status_pred'] == 1].index.max()+2) * 10
col_text.write(f': {aman} Dalam {t_siaga1_end} menit kedepan diperkirakan akan berstatus {siaga0}.')
col_text.write('. Tidak akan terjadi banjir.')
elif (y_pred_status['status_pred']==1).all():
col_text.write(f': {waspada} Dalam 6 jam kedepan diperkirakan akan tetap berstatus {siaga1}.')
col_text.write('. Harap pantau ketinggian air secara berkala.')
elif (y_pred_status['status_pred']==2).any():
t_siaga2_start = (y_pred_status[y_pred_status['status_pred'] == 2].index.min()+1) * 10
col_text.write(f': {bahaya} Dalam {t_siaga2_start} menit kedepan diperkirakan akan berstatus {siaga2}.')
col_text.write('. Berkemungkinan terjadi banjir, segera lakukan evakuasi.')
else: col_text.write(': -')
elif status == 2:
col_title.write('Status')
col_text.write(f': {siaga2}')
col_title.write("Message")
if not (y_pred_status['status_pred']==2).any():
col_text.write(f': {waspada} Dalam 10 menit kedepan diperkirakan status {siaga2} akan berakhir.')
col_text.write('. Harap pantau ketinggian air secara berkala.')
elif (y_pred_status['status_pred']==2).any() and not (y_pred_status['status_pred']==2).all():
t_siaga2_end = (y_pred_status[y_pred_status['status_pred'] == 2].index.max()+2) * 10
col_text.write(f': {bahaya} Dalam {t_siaga2_end} menit kedepan diperkirakan masih berstatus {siaga2}.')
col_text.write('. Berkemungkinan terjadi banjir, segera lakukan evakuasi.')
elif (y_pred_status['status_pred']==2).all():
col_text.write(f': {bahaya} Dalam 6 jam kedepan diperkirakan akan tetap berstatus {siaga2}.')
col_text.write('. Berkemungkinan terjadi banjir, segera lakukan evakuasi.')
else: col_text.write(': -')
# gempa
def klasifikasi_gempa(X, scaler_X, model):
X_data=X.iloc[:,:6]
X_scaled = scaler_X.transform(X_data)
X_scaled=X_scaled.reshape(1,1,6)
# predict
y_pred = model.predict(X_scaled, verbose=0)
y_pred = np.argmax(y_pred, axis=1)
# df
df_y_pred = pd.DataFrame(y_pred, columns=['result_pred'])
df_klasifikasi = X.join(df_y_pred)
return df_klasifikasi
def get_info_gempa(data):
data = data.rename(columns = {'aX':'aX (g)',
'aY':'aY (g)',
'aZ':'aZ (g)',
'gX':'gX (deg/s)',
'gY':'gY (deg/s)',
'gZ':'gZ (deg/s)'})
status_pred = data['result_pred'][0]
if status_pred == 1:
st.error("Result: Earthquake")
else:
st.success("Result: Non-Earthquake")
with st.expander("Explore classified data"):
col_ex1,col_ex2 = st.columns([3,1])
col_ex1.dataframe(data)
col_ex2.markdown(":green[0 = Non-Earthquake]")
col_ex2.markdown(":red[1 = Earthquake]")