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Update app.py
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app.py
CHANGED
@@ -1,3 +1,4 @@
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#%%
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from matplotlib.pyplot import title
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import tensorflow as tf
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@@ -19,6 +20,7 @@ mylist = [1]
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df = pd.DataFrame(columns=["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)"])
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
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os.environ["CUDA_VISIBLE_DEVICES"] = ""
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if ("0" == ""):
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uri = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip"
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@@ -30,7 +32,6 @@ if ("0" == ""):
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st.dataframe(df)
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if ("0" != ""):
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backlogmax = 4
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today = datetime.date.today()
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ayear = int(today.strftime("%Y"))-0
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@@ -38,7 +39,7 @@ if ("0" != ""):
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amonthday = int(today.strftime("%d"))
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adf = pd.DataFrame(columns=["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)"])
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for i in range(ayear-
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alink = ("https://data.weather.gov.hk/weatherAPI/opendata/opendata.php?dataType=CLMTEMP&year={}&rformat=csv&station=HKO").format(str(i))
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df = pd.read_csv(alink, skiprows=[0,1,2], skipfooter=3, engine='python', on_bad_lines='skip')
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@@ -168,7 +169,7 @@ val_data = features.loc[train_split:]
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start = past + future
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end = start + train_split
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x_train = train_data[[i for i in range(
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y_train = features.iloc[start:end][[1]]
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sequence_length = int(past / step)
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@@ -176,7 +177,7 @@ x_end = len(val_data) - past - future
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label_start = train_split + past + future
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x_val = val_data.iloc[:x_end][[i for i in range(
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y_val = features.iloc[label_start:][[1]]
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dataset_val = keras.preprocessing.timeseries_dataset_from_array(
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@@ -226,4 +227,4 @@ def plot():
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fig = plot()
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st.pyplot(fig)
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# %%
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#%%
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from matplotlib.pyplot import title
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import tensorflow as tf
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df = pd.DataFrame(columns=["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)"])
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
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os.environ["CUDA_VISIBLE_DEVICES"] = ""
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mybacklogmax = 4
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if ("0" == ""):
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uri = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip"
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st.dataframe(df)
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if ("0" != ""):
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today = datetime.date.today()
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ayear = int(today.strftime("%Y"))-0
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amonthday = int(today.strftime("%d"))
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adf = pd.DataFrame(columns=["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)"])
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for i in range((ayear-mybacklogmax),ayear,1):
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alink = ("https://data.weather.gov.hk/weatherAPI/opendata/opendata.php?dataType=CLMTEMP&year={}&rformat=csv&station=HKO").format(str(i))
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df = pd.read_csv(alink, skiprows=[0,1,2], skipfooter=3, engine='python', on_bad_lines='skip')
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start = past + future
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end = start + train_split
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x_train = train_data[[i for i in range(mybacklogmax)]].values
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y_train = features.iloc[start:end][[1]]
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sequence_length = int(past / step)
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label_start = train_split + past + future
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x_val = val_data.iloc[:x_end][[i for i in range(mybacklogmax)]].values
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y_val = features.iloc[label_start:][[1]]
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dataset_val = keras.preprocessing.timeseries_dataset_from_array(
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fig = plot()
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st.pyplot(fig)
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# %%
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