1d:T69f,# Alternate version with wind rose at each time step + scatter plot of methane concentration time series ```r faceted_time_series_plot2( sensor_concentrations, sensor_coords, wind_data, start_time, end_time, output_dt, text_size = 12 ) ``` ## Arguments - `sensor_concentrations`: Data frame. Output from a sensor simulation function, which must include a column named "Group.1" which contains the timestamps (e.g., "YYYY-MM-DD HH:MM:SS") and a column "Sensor_1" for the sensor concentration values. - `sensor_coords`: A data frame or matrix containing sensor locations. - `wind_data`: A list containing wind data with components u and v - `start_time`: POSIXct. Start time of the simulation. - `end_time`: POSIXct. End time of the simulation. - `output_dt`: Integer. Desired time resolution (in seconds) for the final output of concentrations. - `text_size`: Default at 12. ## Returns A ggplot object with faceted time series plots of methane concentrations and wind rose data. ## Examples ```r set.seed(123) sim_dt <- 10 puff_dt <- 10 output_dt <- 60 start_time <- "2024-01-01 12:00:00" end_time <- "2024-01-01 13:00:00" emission_rate <- 3.5 wind_data <- data.frame( wind_u = runif(3601, min = -3, max = 0.7), wind_v = runif(3601, min = -3, max = 1.5) ) source_coords <- c(0, 0, 2.5) sensor_coords <- matrix(c(-6.525403221327715e-15, -35.52264, 2.01775), ncol = 3, byrow = TRUE) out <- simulate_sensor_mode( start_time, end_time, source_coords, emission_rate, wind_data, sensor_coords, sim_dt, puff_dt, output_dt, puff_duration = 1200 ) faceted_time_series_plot2(out, sensor_coords, wind_data, as.POSIXct(start_time), as.POSIXct(end_time), output_dt ) ```1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.57084 14.446 5.70302 14.2222 5.70302 14.0299C5.70302 13.8576 5.69679 13.4019 5.69323 12.797C3.67661 13.235 3.25112 11.825 3.25112 11.825C2.92132 10.9874 2.44599 10.7644 2.44599 10.7644C1.78773 10.3149 2.49584 10.3238 2.49584 10.3238C3.2235

faceted_time_series_plot2 function