add nwp viewing page

requirements.txt

@@ -12,4 +12,8 @@ uvicorn==0.23.2
 geopandas==0.14.2
 garrett-streamlit-auth0==0.9
 pydantic==2.5.3
+xarray==2023.5.0
+fsspec==2024.2.0
+s3fs==2024.2.0
+ocf_blosc2==0.0.4
 

src/main.py

@@ -29,6 +29,7 @@
 from tables.raw import make_raw_table
 from tables.summary import make_recent_summary_stats, make_forecast_horizon_table
 from users import user_page
+from nwp_page import nwp_page
 
 st.get_option("theme.primaryColor")
 st.set_page_config(layout="centered", page_title="OCF Dashboard")
@@ -268,7 +269,8 @@ def metric_page():
         "PV Site Forecast": pvsite_forecast_page,
         "Sites Toolbox": sites_toolbox_page,
         "API Users": user_page,
+        "NWP": nwp_page
     }
 
-    demo_name = st.sidebar.selectbox("Choose a page", page_names_to_funcs.keys())
+    demo_name = st.sidebar.selectbox("Choose a page", page_names_to_funcs.keys(), 6)
     page_names_to_funcs[demo_name]()

src/nwp_page.py

@@ -0,0 +1,140 @@
+import plotly.graph_objects as go
+import streamlit as st
+import xarray as xr
+
+
+@st.cache_data(ttl=3600)
+def get_data(zarr_file):
+    ds = xr.open_dataset(zarr_file)
+    print("Loading")
+    ds = ds.load()
+    return ds
+
+
+def nwp_page():
+    """Main page for pvsite forecast"""
+
+    st.markdown(
+        f'<h1 style="color:#63BCAF;font-size:48px;">{"NWP"}</h1>',
+        unsafe_allow_html=True,
+    )
+
+    # text input box
+    st.write("Enter the zarr file you want to explore")
+    zarr_file = st.text_input(
+        "Zarr File", "s3://nowcasting-nwp-development/data-national/latest.zarr"
+    )
+
+    # open zarr file
+    ds = get_data(zarr_file)
+    st.write(f"Data variables are {list(ds.variables)}")
+
+    # option if to image or time series plot
+    st.sidebar.write("Select the type of plot you want to view")
+    plot_type = st.sidebar.selectbox("Plot Type", ["Image", "Time Series"])
+
+    # select the channel you want to view
+    st.sidebar.write("Select the channel you want to view")
+    if plot_type == "Image":
+
+        # select one item from a list
+        channels = st.sidebar.selectbox("Channels", list(ds.variable.values), 0)
+    else:
+        channels = st.sidebar.multiselect(
+            "Channels", list(ds.variable.values), list(ds.variable.values)[0]
+        )
+
+    d_one_channel = ds.sel(variable=channels)
+
+    variable_name = list(d_one_channel.variables)[0]
+
+    if plot_type == "Image":
+
+        # select which step you want
+        init_time = d_one_channel.init_time.values
+        st.sidebar.write(f"Select the step you want to view, the init time is {init_time}")
+        steps = list(d_one_channel.step.values)
+        step_idx = st.sidebar.selectbox("Step", range(0, len(steps)), 0)
+
+        step = steps[step_idx]
+        d_one_channel_one_step = d_one_channel.sel(step=step)
+
+        # get values
+        if "ECMWF_NW-INDIA" in d_one_channel_one_step.variables:
+            values = d_one_channel_one_step["ECMWF_NW-INDIA"]
+        elif "ECMWF_UK" in d_one_channel_one_step.variables:
+            values = d_one_channel_one_step["ECMWF_UK"]
+        elif "UKV" in d_one_channel_one_step.variables:
+            values = d_one_channel_one_step["UKV"]
+
+        else:
+            values = d_one_channel_one_step
+
+        # reduce dimensions
+        if len(values.shape) == 3:
+            values = values[0, :, :].values
+        elif len(values.shape) == 4:
+            values = values[0, 0, :, :].values
+
+        # create heat map / image
+        print("Making heat map")
+        fig = go.Figure(
+            data=go.Heatmap(
+                z=values,
+                x=d_one_channel_one_step.longitude.values,
+                y=d_one_channel_one_step.latitude.values,
+                colorscale="Viridis",
+            )
+        )
+
+        # add title
+        fig.update_layout(
+            title=f"NWP {channels} at {init_time} + {step}",
+            xaxis_title="Longitude",
+            yaxis_title="Latitude",
+        )
+        # make figure bigger
+        fig.update_layout(
+            autosize=False,
+            width=1000,
+            height=1000,
+        )
+        st.plotly_chart(fig, theme="streamlit", height=2000)
+
+    else:
+        print("Average over latitude and longitude")
+        if "latitude" in d_one_channel.dims and "longitude" in d_one_channel.dims:
+            df = d_one_channel.mean(dim=["latitude", "longitude"])
+        else:
+            df = d_one_channel.mean(dim=["x", "y"])
+
+        print("Convert to dataframe")
+        df = df.to_dataframe()
+        df.reset_index(inplace=True)
+
+        print("Creat time index")
+        df["time"] = df["init_time"] + df["step"]
+        print(df)
+        df = df[["time", "variable", variable_name]]
+
+        print("pivot on time")
+        df = df.pivot("time", columns="variable")
+        df.columns = [c[1] for c in df.columns]
+
+        print("Making plot")
+        fig = go.Figure()
+        for c in df.columns:
+            fig.add_trace(go.Scatter(x=df.index, y=df[c], name=c))
+        # add title
+        fig.update_layout(
+            title=f"NWP Time Series {channels}",
+            xaxis_title="Time",
+            yaxis_title="Value",
+        )
+        # make figure bigger
+        fig.update_layout(
+            autosize=False,
+            width=1000,
+            height=500,
+        )
+        st.plotly_chart(fig, theme="streamlit", height=800)