add function plot_measurementchain_and_welloperation()

first step to include plots for wells with
EC (or TEMP) together with well operation data (i.e. Q per day) for each of the wells 9,10 and 13 equipped with EC chains. In addition to @chsprenger plot for each well I also added the total wellfield production rate.

to do:
- add to automated workflow
- add 📖
- improve style

R/plot_measurementchain_and_welloperation.R

@@ -0,0 +1,94 @@
+plot_measurementchain_and_well_operation <- function(mc_dat,
+                                                     well_op_data_meta,
+                                                     brunnen_nr = 9,
+                                                     para = "Leitfaehigkeit",
+                                                     y_label = "elektr. Leitf\u00E4higkeit (\u00B5S/cm)") {
+
+
+  stopifnot("")
+
+
+# plot time series Brunnen 9
+selection <- mc_dat %>% 
+  dplyr::filter(.data[["parameter"]] ==  para, 
+                .data[["brunnen_nummer"]] == brunnen_nr)
+
+
+n_sensors <- length(unique(selection$einbau_sensor_muGOK))
+
+custom_palette <- RColorBrewer::brewer.pal(n_sensors, 
+                                           "Dark2")
+
+p_well <- ggplot2::ggplot(selection,
+                          ggplot2::aes(x = datum_uhrzeit,
+                                       y = messwert,
+                                       group = einbau_sensor_muGOK,
+                                       color = as.factor(einbau_sensor_muGOK))) +
+  ggplot2::geom_line() +
+  ggplot2::scale_color_manual(values = custom_palette) + 
+  ggplot2::labs(x="", y = y_label, color = "Sensor [muGOK]") +
+  ggplot2::theme_bw() +
+  #ggplot2::xlim(as.Date("2023-05-10"), as.Date("2024-03-31")) +
+  #ggplot2::ylim(500,3000) +
+  ggplot2::theme(legend.position = "top",
+                 axis.text.x = ggplot2::element_blank()) +  
+  ggplot2::guides(color = ggplot2::guide_legend(ncol = n_sensors)) 
+
+#p_well
+
+dat_well <- well_op_data_meta %>% dplyr::filter(.data$brunnen_nummer == brunnen_nr)
+
+sum_well <- dat_well %>%
+  dplyr::group_by(.data$bwb_datum) %>% 
+  dplyr::summarise(n = dplyr::n(), 
+            total_q = sum(.data$menge_summe_m3, na.rm = TRUE) )
+
+sum_well$ma7 <- zoo::rollmean(sum_well$total_q, k = 7, fill = NA, align = "right")
+sum_well$ma10 <- zoo::rollmean(sum_well$total_q, k = 10, fill = NA, align = "right")
+
+plot_q_well <- ggplot2::ggplot(sum_well, ggplot2::aes(x = as.Date(bwb_datum), y = total_q)) +
+  ggplot2::geom_bar(stat = "identity", width=1, color = "blue") +
+  ggplot2::labs(x="", y = sprintf("Q, Brunnen %2d (m3/d)", brunnen_nr)) +
+  ggplot2::theme_bw() +
+  ggplot2::theme(axis.text.x = ggplot2::element_blank())
+  # ggplot2::scale_x_date(date_breaks = "1 month", date_labels = "%b %Y") +
+  # ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 0, 
+  #                                                    vjust = 0.5, 
+  #                                                    hjust = 1)) #+
+  #ggplot2::xlim(as.Date("2023-05-10"), as.Date("2024-03-31")) 
+
+
+sum_wellfield <- well_op_data_meta %>%
+  dplyr::group_by(.data$bwb_datum) %>% 
+  dplyr::summarise(n = dplyr::n(), 
+                   total_q = sum(.data$menge_summe_m3, na.rm = TRUE) )
+
+sum_wellfield$ma7 <- zoo::rollmean(sum_well$total_q, k = 7, fill = NA, align = "right")
+sum_wellfield$ma10 <- zoo::rollmean(sum_well$total_q, k = 10, fill = NA, align = "right")
+
+plot_q_wellfield <- ggplot2::ggplot(sum_wellfield, ggplot2::aes(x = as.Date(bwb_datum), y = total_q)) +
+  ggplot2::geom_bar(stat = "identity", width=1, color = "blue") +
+  ggplot2::labs(x="Zeit", y = "Q, Brunnenfeld K-Galerie (m3/d)") +
+  ggplot2::theme_bw() +
+  ggplot2::scale_x_date(date_breaks = "1 month", date_labels = "%b %Y") +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 0, 
+                                                     vjust = 0.5, 
+                                                     hjust = 1))
+
+
+combined_plot <- cowplot::plot_grid(p_well, 
+                                    plot_q_well,
+                                    plot_q_wellfield, 
+                                    ncol = 1, align = 'v')
+
+combined_plot_with_title <- cowplot::ggdraw() +
+  cowplot::draw_plot(combined_plot, 0, 0, 1, 1) +
+  cowplot::draw_label(sprintf("Brunnen %2d", brunnen_nr), x = 0.2, y = 0.8, size = 12, hjust = 0.5)
+
+combined_plot_with_title
+
+}
+
+plot_measurementchain_and_well_operation(mc_dat = mc_dat, 
+                                         well_op_data_meta = well_op_data_meta, 
+                                         brunnen_nr = 9)

vignettes/measurement-chains.Rmd

@@ -94,7 +94,10 @@ paths <- kwb.utils::resolve(list(
   export_dir = "<local_dir>/export", 
  
   # KWB cloud directory to which data in "export_dir" is uploaded
-  upload_dir = "projects/GeoSalz/Monitoring/messketten"
+  upload_dir = "projects/GeoSalz/Monitoring/messketten",
+  
+  # KWB cloud directory with latest BWB well operation data
+  well_operation = "<upload_dir>/BWB_Brunnen_Prozessdaten"
 ))
 
 # Print all paths
@@ -374,3 +377,64 @@ if (kwb.utils::isTryError(result)) {
   )
 }
 ```
+
+### Download Well Operation Data from Cloud 
+
+```{r download_well_operation_data_from_cloud}
+
+mc_dat <- mc_data %>%  
+  dplyr::left_join(metadata[,c("sensor_id", "einbau_sensor_muGOK")], by = "sensor_id") %>% 
+  dplyr::left_join(mc_files %>% dplyr::select(sftp_path, galerie, brunnen_nummer),
+                   by = c(file = "sftp_path"))
+
+
+well_op_file <- kwb.nextcloud::list_files(path = paths$well_operation,
+                                     full_info = TRUE,
+                                     ) %>% 
+  dplyr::filter(lastmodified == max(lastmodified))
+
+
+file <- well_op_file$file[1]
+tdir <- fs::path_norm(paths$download_dir)
+
+xlsx_file <- kwb.nextcloud::download_files(hrefs = well_op_file$href,
+                                           target_dir = tdir)
+
+well_op_data <- readxl::read_xlsx(path = xlsx_file) %>% 
+  janitor::clean_names() %>%
+  dplyr::filter(.data$menge_summe_m3 < 2000)
+
+
+
+separate_name_der_messstelle_gms <- function(string) {
+  
+tibble::tibble(
+  wasserwerk = stringr::str_sub(string, 1L, 3L),
+  galerie = stringr::str_sub(string, 4L, 4L) %>% toupper(),
+  brunnen_nummer = stringr::str_sub(string, 5L, 9L) %>%
+    stringr::str_remove_all(pattern = "-") %>% 
+    as.integer(),
+  unbekannter_buchstabe = stringr::str_sub(string, 10L, 10L) %>%
+    stringr::str_remove_all(pattern = "-") %>% 
+    as.character(),
+  brunnen_baujahr = stringr::str_sub(string, 12L, 15L) %>%
+    stringr::str_remove_all(pattern = "-") %>% 
+    as.integer(),
+  brunnen_bauart = stringr::str_sub(string, 16L, 16L) %>%
+    stringr::str_remove_all(pattern = "-") %>% 
+    as.character()
+  )
+}
+
+
+well_op_data_meta <- well_op_data %>% 
+  dplyr::bind_cols(separate_name_der_messstelle_gms(well_op_data$name_der_messstelle_gms))
+
+
+```
+
+
+### Make combined EC and well operation plot
+
+
+