# Romulo plot ## Romulo for CML ``` python sel_time = {"time": slice("2019-07-17", "2019-07-27")} cml = open_cml_sample().sel(**sel_time) att = cml["tsl_avg"] - cml["rsl_avg"] att ``` 
<xarray.DataArray (cml_id: 126, sublink_id: 6, time: 1052)> Size: 6MB
57.5 57.6 57.6 57.6 57.6 57.7 57.7 57.7 57.7 ... nan nan nan nan nan nan nan nan
Coordinates: (10)
Attributes:
    units:    dBm
The OpenSense standard defines three dimensions. However, Romulo plots are two-dimensional. For this reason, we need to reduce the data from three dimensions to two. This can be done by combining the `cml_id` and `sublink_id` values to create a single dimension. Data is sorted by length because, for equal frequency and polarization, *A**r**a**i**n*β€„βˆβ€„*l**e**n**g**t**h*. ------------------------------------------------------------------------ ### romulo_plot_cml ``` python def romulo_plot_cml( cml:DataArray, # CML data to plot title:str=None, # Title for the plot fig:Figure=None, ax:Axes=None, figsize:tuple=(16, 9), # Figure size, only used if fig or ax are None vmax:float | None=None, # Maximum value for colormap )->tuple: ``` *Plot an overview of the CML data sorted by length. Negative values, which are not phisically possible are shown in red.* ``` python romulo_plot_cml(att, "Average attenuation"); ``` ![](20_plot.romulo_files/figure-commonmark/cell-4-output-1.png) In order to plot negative data (for example RSL) you can do: ``` python romulo_plot_cml(-cml["rsl_avg"], "Inverted Average Received Signal Level"); ``` ![](20_plot.romulo_files/figure-commonmark/cell-5-output-1.png) ## Romulo for gauges ``` python gauges = open_gauge_sample().sel(**sel_time) ``` ------------------------------------------------------------------------ ### romulo_plot_gauges ``` python def romulo_plot_gauges( gauges:DataArray, # Gauge data to plot fig:Figure=None, ax:Axes=None, figsize:tuple=(12, 2), # Figure size, only used if fig or ax are None vmax:float=4, # Maximum value for colormap )->tuple: ``` *Plot data as a heatmap with grid lines separating each gauge.* The following error will be displayed if we try to call the function using the source data.
Code ``` python try: romulo_plot_gauges(gauges["rainfall_amount"]); except IndexError as e: print(e) ```
Irregular time index detected: pcolormesh/imshow stretch cells across gaps, producing misleading plots. Please regularize the time index before plotting: - e.g. `da.reindex(time=pd.date_range(..., freq='5min'))`: inserts NaN at missing steps. Use when timestamps already align to a fixed grid/sampling rate but some are absent. - e.g. `da.resample(time='5min').sum(min_count=1)`: aggregates values into regular bins. Use when timestamps don't fall on a fixed grid/sampling rate. Let’s therefore regularise the dataset and plot the results. As our sampling rate is regular (every five minutes), we can use reindexing. In the plot, red values indicate invalid data (negative rain), while black values show missing data. ``` python regularized = gauges.reindex(time=pd.date_range(gauges.time.min().values, gauges.time.max().values, freq="5min")) romulo_plot_gauges(regularized["rainfall_amount"]); ``` ![](20_plot.romulo_files/figure-commonmark/cell-9-output-1.png) Or only a single gauge ``` python romulo_plot_gauges(regularized["rainfall_amount"].isel(id=slice(0, 1)), figsize=(12, 0.5)); ``` ![](20_plot.romulo_files/figure-commonmark/cell-10-output-1.png) ## CML and Gauges together ------------------------------------------------------------------------ ### romulo_plot ``` python def romulo_plot( cml:DataArray, # CML raw or derived data in OpenSense standard gauges:DataArray, # Rain gauges data as 2D data array title:str | None=None, figsize:tuple=(16, 9), ratio:int=4, # Ratio of the CML plot vs Gauges plot cml_vmax:float | None=None, # Maximum value for CML colormap gauges_vmax:float=4, # Maximum value for Gauges colormap )->tuple: ``` *Call self as a function.* ``` python romulo_plot(att, regularized["rainfall_amount"], title="Attenuation and rainfall in Douala"); ``` ![](20_plot.romulo_files/figure-commonmark/cell-12-output-1.png)