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import streamlit as st
import pandas as pd
import numpy as np
astations = [
["hongkongobservatory","HKO",22.3022566,114.1722662,"Hong Kong Observatory"],
["kingspark","KP",22.3115408,114.1685675,"Observatory Meteorological Station, King's Park"],
]
astationcolumns = ['akey','astationcode','alatitude','alongitude','atitle',]
acontainer1 = st.empty()
acontainer2 = st.empty()
acontainer3 = st.empty()
acontainer4 = st.empty()
def asubmit(aparam):
import datetime
from io import StringIO
adf2 = aparam["adataframe"]
aselecteditem = adf2.loc[adf2["atitle"]==aparam["aselected"], "astationcode"]
aselectedrow = adf2[adf2["atitle"]==aparam["aselected"]]
aselectedindex = adf2.index[adf2["atitle"]==aparam["aselected"]].tolist()
adf3 = pd.DataFrame(
[
[
aselectedrow["alatitude"][aselectedindex[0]],
aselectedrow["alongitude"][aselectedindex[0]],
]
],
columns=['lat', 'lon']
)
aparam["acontainer"].map(adf3)
abacklogmax = 10
atoday = datetime.date.today()
ayear = int(atoday.strftime("%Y"))-0
amonth = int(atoday.strftime("%m"))
amonthday = int(atoday.strftime("%d"))
atitles = aparam["acolumntitles"]
csvString = ""
csvString += (",").join(atitles)
adf3 = pd.DataFrame(columns=atitles)
for i in range((ayear-abacklogmax),ayear,1):
alink = ("https://data.weather.gov.hk/weatherAPI/opendata/opendata.php?dataType=CLMTEMP&year={}&rformat=csv&station={}").format(str(i),aselecteditem.values[0])
adf4 = pd.read_csv(alink, skiprows=[0,1,2], skipfooter=3, engine='python', on_bad_lines='skip')
adf4 = adf4.reset_index() # make sure indexes pair with number of rows
for index, row in adf4.iterrows():
if (row[2]!=amonth) or (row[3]!=amonthday):
continue
adate = ("{}{:02d}{:02d}").format(row[1], row[2], row[3])
csvString += '\n'+(",").join([adate,str(row[4])])
st.write(row[0],adate)
adf3 = adf3.append({"Date":adate,"Celsius":(row[4]),}, ignore_index=True)
break
adf3 = pd.read_csv(StringIO(csvString), sep=",")
aparam["acontainer"].dataframe(adf3)
if ("0" == "mycustom"):
import matplotlib.pyplot as plt
xpoints = np.array(adf3['Date'])
ypoints = np.array(adf3['Celsius'])
plt.plot(xpoints, ypoints, 1)
plt.xlabel('Date')
plt.ylabel('Celsius')
plt.show()
from sklearn.linear_model import LinearRegression
# Creating a Linear Regression model on our data
lin = LinearRegression()
# lin.fit(adf3[['Date']].astype('float').astype('int32'), adf3['Celsius'])
lin.fit(pd.to_numeric(adf3[['Date']]), adf3['Celsius'])
# Creating a plot
ax = adf3.plot.scatter(x='Date', y='Celsius', alpha=.1)
ax.plot(adf3['Date'], lin.predict(adf3[['Date']]), c='r')
# lin.score(adf3[['Date']], adf3['Celsius'])
# st.write(lin.predict(np.array([[20230223]])))
st.write(lin.predict(np.array([['Date']], dtype='int32')))
adf = pd.DataFrame(
astations,
columns=astationcolumns
)
aoption = acontainer1.selectbox(
'Which station?',
adf['atitle']
)
if acontainer2.button("Submit") == True:
asubmit(aparam={"aselected":aoption,"acontainer":acontainer3,"adataframe":adf,"acolumntitles":['Date','Celsius'],})
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