# Run openEO processes in Shiny apps

If one is new to Shiny in Python, a really good documentation can be found here (opens new window). This documentation is the source for all main interaction in the Shiny app built here.

As any other Shiny app, there will always be an UI and a Server side. Both can be coded in the same script, but they are quite differently set. The UI defines the interface of the application, and therefore one needs to define data inputs, buttons and tabs here. Below, one can see how the "Time Series Analyser" interface has been built.

# Tab2 : Time Series Analyser
ui.nav("Time-Series Analyser", 
# This tab has both a sidebar and panel
    # Define Sidebar Inputs
        # Bounding Box
        ui.input_numeric("w", "xmin (EPSG:4326)", 10.35, min = 0, step = .01),
        ui.input_numeric("s", "ymin (EPSG:4326)", 46.10, min = 0, step = .01),
        ui.input_numeric("e", "xmax (EPSG:4326)", 12.55, min = 0, step = .01),
        ui.input_numeric("n", "ymax (EPSG:4326)", 47.13, min = 0, step = .01),
        # Temporal Filter
        ui.input_date_range("date1date2", "Select timeframe", 
        start = "2019-01-01", end = "2019-12-31", 
        min = "2019-01-01", max = str(date.today()), startview =  "year", weekstart = "1"),

        # Map with bbox
        # Cloud Cover 
        "cloud cover to be considered? (0 to 1 - 0.5 is recommended)", 0.5, 
        min = 0, max = 1, step = .1),

        # Submit Button
        ui.input_action_button("data1", "Submit"),
        # Time Series Plot

Everything in the UI start with defining sidebars, tabs and everything that refers to the layout. This is what is seen by the method ui.panel_sidebar. In the following all inputs (data) are defined for the "Time Series Analyser". Here, the most important is to pay attention to different data types and that each and everyone of them will require a different method. In this dashboard tab, for instance, the input_numeric, input_date_range and input_action_button are used. The other part is then the output items. Here the leaflet interactive map (output_widget) and the output_plot are used. The last one is the one seen before. It refers to the time series plot.

Once some idea is given on how to work with Python's Shiny UI, a lot of questions may come into the mind about how to make these inputs and outputs actually turn into some result. This is where the Server comes into play. The server is again another function, where in this case, the first output to be defined is the leaflet map.

def server(input, output, session):
    # Leaft Map for Time Series
    def map_ts():
      center_y = (input.s() + input.n())/2
      center_x = (input.w() + input.e())/2
      m = L.Map(center=(center_y, center_x), zoom=6)
      rectangle = L.Rectangle(bounds=((input.n(), input.w()), (input.s(), input.e())))
      return m

The function here used for plotting the nice bounding box on an interactive map is L.Rectangle(), being L the name through the ipyleaflet package was imported. It is important to mentioned that a leaflet map has to be defined for each tab, as the coordinates for the bounding box are different for each tab.

After that, the interactions from the first input start being defined. An extent is first defined for the openeo processes:

async def plot_ts():
    with ui.Progress(min=1, max=6) as p:
        # Define the Spatial Extent
        extent = {
          "type": "Polygon",
          "coordinates": [[
            [input.w(), input.n()],
            [input.e(), input.n()],
            [input.e(), input.s()],
            [input.w(), input.s()],
            [input.w(), input.n()]
        p.set(1, message="Local Wrangling")

The output is here defined with a @reactive.event tag, which allows for the inputs to be brought to the server only when the Submit button is hit. A progress bar is also defined, which helps by the fact that the whole process takes a while, and shiny does not naturally give the idea something is running without a progress bar.

After that, the collections are loaded, including the NO2 and the Cloud Cover bands, and the processes above mentioned are run, such as the mask for cloud cover, the interpolation, and the udf. Once the job is sent to backend, the script will wait for it to complete and one can call the files into memory and the rest is standard python and matplotlib usage. The most relevant thing to remember here is that the specific server function one is manipulating, like here, the def map_ts() function, is must have a return call in its end, otherwise no plots will be retrieved from it, obviously enough. Below, one can see how the time series plot is defined, considering the ts_df as a data frame built from the JSONs read as a dictionary in python.

fig, ax = plt.subplots(figsize=(16, 12))
ax.set_title('NO2 Time Series from SENTINEL 5P')
# plt.show()
p.set(6, message="Done")
return fig 

As in a dashboard, one will probably work with rendering plots mainly, that should be resourceful enough to let anyone start playing with openEO and Shiny in python together. If there are any doubts, do not hesitate to reach the developers and consider even creating an issue in this repository. Please be aware of openEO backend related issues that do not concern this dashboard developers.