Update app.py

app.py

@@ -16,18 +16,53 @@ if ("0" == "mycustom"):
 	os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
 	os.environ["CUDA_VISIBLE_DEVICES"] = ""
 
-uri = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip"
-zip_path = keras.utils.get_file(origin=uri, fname="jena_climate_2009_2016.csv.zip")
-zip_file = ZipFile(zip_path)
-zip_file.extractall()
-csv_path = "jena_climate_2009_2016.csv"
-df = pd.read_csv(csv_path)
+if ("0" == "mycustom"):
+	uri = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip"
+	zip_path = keras.utils.get_file(origin=uri, fname="jena_climate_2009_2016.csv.zip")
+	zip_file = ZipFile(zip_path)
+	zip_file.extractall()
+	csv_path = "jena_climate_2009_2016.csv"
+	df = pd.read_csv(csv_path)
 
-if ("0" != "mycustom"):
+if ("0" == "mycustom"):
 	mybacklogmax = 10000
 	df = df.head(n=mybacklogmax)
 	st.dataframe(df)
 
+if ("0" == "mycustom"):
+	myfields = [0, 1, 5, 7, 8, 10, 11]
+	myfields = [1]
+	mytitles = ["Date Time","p (mbar)","T (degC)","Tpot (K)","Tdew (degC)","rh (%)","VPmax (mbar)","VPact (mbar)","VPdef (mbar)","sh (g/kg)","H2OC (mmol/mol)","rho (g/m**3)","wv (m/s)","max. wv (m/s)","wd (deg)"]
+
+	mybacklogmax = 10
+
+	atoday = datetime.date.today()
+
+	ayear = int(atoday.strftime("%Y"))-0
+	amonth = int(atoday.strftime("%m"))
+	amonthday = int(atoday.strftime("%d"))
+
+	csvString = ""
+	csvString += (",").join(mytitles)
+	adf = pd.DataFrame(columns=mytitles)
+	for i in range((ayear-mybacklogmax),ayear,1):
+		alink = ("https://data.weather.gov.hk/weatherAPI/opendata/opendata.php?dataType=CLMTEMP&year={}&rformat=csv&station=HKO").format(str(i))
+		df = pd.read_csv(alink, skiprows=[0,1,2], skipfooter=3, engine='python', on_bad_lines='skip')
+
+		df = df.reset_index()  # make sure indexes pair with number of rows
+		for index, row in df.iterrows():
+			if (row[2]!=amonth) or (row[3]!=amonthday):
+				continue
+
+			adate = ("{:02d}.{:02d}.{} 00:00:00").format(row[3], row[2], row[1])
+			csvString += '\n'+(",").join([adate,"",str(row[4]),"","","","","","","","","","","",""])
+			st.write(row[0],adate)
+			adf = adf.append({"Date Time":adate,"T (degC)":(row[4]),}, ignore_index=True)
+			break
+	adf = pd.read_csv(StringIO(csvString), sep=",")
+	df = adf
+	st.dataframe(df)
+
 #%%
 
 title = "Timeseries forecasting for weather prediction"
@@ -85,18 +120,29 @@ learning_rate = 0.001
 batch_size = 256
 epochs = 10
 
+if ("0" != "mycustom"):
+	past = 10
+	future = 1
+	batch_size = 1
 
 def normalize(data, train_split):
     data_mean = data[:train_split].mean(axis=0)
     data_std = data[:train_split].std(axis=0)
     return (data - data_mean) / data_std
 
-
 print(
     "The selected parameters are:",
     ", ".join([titles[i] for i in [0, 1, 5, 7, 8, 10, 11]]),
 )
 selected_features = [feature_keys[i] for i in [0, 1, 5, 7, 8, 10, 11]]
+
+if ("0" != "mycustom"):
+	print(
+	    "The selected parameters are:",
+	    ", ".join([titles[i] for i in myfields]),
+	)
+	selected_features = [feature_keys[i] for i in myfields]
+
 features = df[selected_features]
 features.index = df[date_time_key]
 features.head()
@@ -119,6 +165,10 @@ learning_rate = 0.001
 batch_size = 256
 epochs = 10
 
+if ("0" != "mycustom"):
+	past = 10
+	future = 1
+	batch_size = 1
 
 def normalize(data, train_split):
     data_mean = data[:train_split].mean(axis=0)
@@ -129,6 +179,14 @@ print(
     ", ".join([titles[i] for i in [0, 1, 5, 7, 8, 10, 11]]),
 )
 selected_features = [feature_keys[i] for i in [0, 1, 5, 7, 8, 10, 11]]
+
+if ("0" != "mycustom"):
+	print(
+	    "The selected parameters are:",
+	    ", ".join([titles[i] for i in myfields]),
+	)
+	selected_features = [feature_keys[i] for i in myfields]
+
 features = df[selected_features]
 features.index = df[date_time_key]
 features.head()