No field data available to generate recommendations.
Error generating recommendations.
") + }) +} +``` + +`r subtitle_var` + +\pagebreak +# Explanation of the Report + +This report provides a detailed analysis of your sugarcane fields based on satellite imagery, helping you monitor crop health and development throughout the growing season. The data is processed weekly to give you timely insights for optimal farm management decisions. + +## What is the Chlorophyll Index (CI)? + +The **Chlorophyll Index (CI)** is a vegetation index that measures the relative amount of chlorophyll in plant leaves. Chlorophyll is the green pigment responsible for photosynthesis in plants. Higher CI values indicate: + +* Greater photosynthetic activity +* Healthier plant tissue +* Better nitrogen uptake +* More vigorous crop growth + +CI values typically range from 0 (bare soil or severely stressed vegetation) to 7+ (very healthy, dense vegetation). For sugarcane, values between 3-7 generally indicate good crop health, depending on the growth stage. + +# Executive Dashboard + +## Farm Health Status + +The map below shows the overall health status of all fields based on current Chlorophyll Index values. This provides a quick overview of which areas of your farm are performing well and which might need intervention. + +**How it works:** Field health status is determined by the average Chlorophyll Index (CI) value across each field: +- **Excellent** (dark green): CI ≥ 5.0 +- **Good** (light green): CI 3.5-4.99 +- **Fair** (yellow): CI 2.0-3.49 +- **Poor** (orange): CI 1.0-1.99 +- **Critical** (red): CI < 1.0 + +Fields with higher CI values indicate better crop vigor and photosynthetic activity, which typically correlate with healthier plants. + +```{r render_ci_continuous_map, echo=FALSE, fig.height=6, fig.width=9, message=FALSE, warning=FALSE} +# Create CI continuous raster map +tryCatch({ + # Create and display the CI raster map with pixel values + ci_raster_map <- create_ci_raster_map( + ci_raster = CI, + field_boundaries = AllPivots0, + title = "Chlorophyll Index (CI) - Pixel Values", + legend_title = "CI Value" + ) + + # Print the map + print(ci_raster_map) +}, error = function(e) { + safe_log(paste("Error creating CI raster map:", e$message), "ERROR") + plot(1, type="n", axes=FALSE, xlab="", ylab="") + text(1, 1, "Error creating CI raster map", cex=1.5) +}) +``` + +```{r render_field_status_map, echo=FALSE, fig.height=6, fig.width=9, message=FALSE, warning=FALSE} +# Create field status map +tryCatch({ + # Create and display the field status map + field_status_map <- create_field_status_map( + field_boundaries = AllPivots0, + health_data = farm_health_data, + attribute = "status", + title = "Field Health Status Overview", + legend_title = "Health Status" + ) + + # Print the map + print(field_status_map) +}, error = function(e) { + safe_log(paste("Error creating field status map:", e$message), "ERROR") + plot(1, type="n", axes=FALSE, xlab="", ylab="") + text(1, 1, "Error creating field status map", cex=1.5) +}) +``` + +## Management Priorities + +This map highlights which fields require priority management attention based on current health indicators and trends. Fields in red require immediate attention, while green fields are performing well with minimal intervention needed. + +**How it works:** Priority levels are calculated based on a combination of factors: +- **Critical Priority** (dark red): Fields with CI < 1.0 or critical health issues +- **High Priority** (red): Fields with potential weed growth (CI increase > 2) +- **Medium Priority** (orange): Fields with high internal variability +- **Low Priority** (light green): Fields with moderate decline in CI +- **Minimal Priority** (dark green): Stable, healthy fields + +The priority algorithm considers both absolute CI values and week-to-week changes to identify fields that need immediate management attention. + +```{r render_priority_map, echo=FALSE, fig.height=6, fig.width=9, message=FALSE, warning=FALSE} +# Create priority management map +tryCatch({ + # Fix the priority mapping so red = high priority, green = low priority + # Reverse the priority levels before mapping (1=critical becomes 5, 5=minimal becomes 1) + farm_health_data$display_priority <- 6 - farm_health_data$priority_level + + # Create and display the priority map with corrected priority levels + priority_map <- tm_shape(AllPivots0 %>% dplyr::left_join(farm_health_data, by = "field")) + + tm_fill( + col = "display_priority", + palette = "-RdYlGn", # Now properly oriented: red = high priority, green = low priority + breaks = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5), + labels = c("Minimal", "Low", "Medium", "High", "Critical"), + title = "Priority Level" + ) + + tm_borders(col = "black", lwd = 1) + + tm_text("field", size = 0.7) + + tm_layout( + main.title = "Field Management Priority", + legend.outside = TRUE, + legend.outside.position = "bottom" + ) + + tm_scale_bar(position = tm_pos_out("right", "bottom")) + + # Print the map + print(priority_map) +}, error = function(e) { + safe_log(paste("Error creating priority map:", e$message), "ERROR") + plot(1, type="n", axes=FALSE, xlab="", ylab="") + text(1, 1, "Error creating priority map", cex=1.5) +}) +``` + +\pagebreak +## Crop Anomaly Detection + +The map below highlights potential anomalies in your fields that may require investigation. Areas with sudden changes in CI values could indicate weeding activities, rapid weed growth, or other management interventions. + +**How it works:** This map compares current week's CI values with those from the previous week: +- **Significant Growth** (dark green): CI increase > 2 units (potential weed growth) +- **Moderate Growth** (light green): CI increase of 1-2 units +- **Stable** (yellow): CI change between -1 and +1 units +- **Moderate Decline** (orange): CI decrease of 1-2 units +- **Significant Decline** (red): CI decrease > 2 units (potential weeding/harvesting activities) + +Areas with significant growth (dark green) may indicate rapid weed growth that requires monitoring, while significant declines (red) often indicate recent management activities like weeding or harvesting. + +```{r render_anomaly_map, echo=FALSE, fig.height=6, fig.width=9, message=FALSE, warning=FALSE} +# Create anomaly detection map +tryCatch({ + # Create and display the anomaly map + anomaly_map <- create_anomaly_map( + ci_current = CI, + ci_previous = CI_m1, + field_boundaries = AllPivots0 + ) + + # Print the map + print(anomaly_map) +}, error = function(e) { + safe_log(paste("Error creating anomaly map:", e$message), "ERROR") + plot(1, type="n", axes=FALSE, xlab="", ylab="") + text(1, 1, "Error creating anomaly map", cex=1.5) +}) +``` + +\pagebreak +## Harvest Planning + +This map shows the harvest readiness status of all fields, helping you plan harvest operations and logistics. Fields in dark green are ready for harvest, while those in yellow through red are at earlier growth stages. + +**How it works:** Harvest readiness is determined by combining field age and CI values: +- **Ready for harvest** (dark green): Fields ≥52 weeks old with CI ≥4.0 +- **Approaching harvest** (light green): Fields ≥48 weeks old with CI ≥3.5 +- **Mid-maturity** (yellow): Fields ≥40 weeks old with CI ≥3.0 +- **Growing** (orange): Fields ≥12 weeks old +- **Early stage** (red): Fields <12 weeks old + +This classification helps prioritize harvesting operations and logistical planning by identifying fields that are at optimal maturity for maximum sugar content. + +```{r render_harvest_map, echo=FALSE, fig.height=6, fig.width=9, message=FALSE, warning=FALSE} +# Create harvest planning map +tryCatch({ + # Create and display the harvest readiness map + harvest_map <- create_field_status_map( + field_boundaries = AllPivots0, + health_data = farm_health_data, + attribute = "harvest_readiness", + title = "Harvest Readiness Status", + legend_title = "Harvest Status" + ) + + # Print the map + print(harvest_map) +}, error = function(e) { + safe_log(paste("Error creating harvest map:", e$message), "ERROR") + plot(1, type="n", axes=FALSE, xlab="", ylab="") + text(1, 1, "Error creating harvest map", cex=1.5) +}) +``` + +\pagebreak +## Field Status Summary + +The charts below provide an overview of your farm's health and harvest readiness status, showing the distribution of fields across different health categories and maturity stages. + +**How the Field Status Chart works:** This bar chart displays the count of fields in each health status category, based on the same CI thresholds described in the Farm Health Status section: +- **Excellent** (dark green): CI ≥ 5.0 +- **Good** (light green): CI 3.5-4.99 +- **Fair** (yellow): CI 2.0-3.49 +- **Poor** (orange): CI 1.0-1.99 +- **Critical** (red): CI < 1.0 + +**How the Harvest Readiness Chart works:** This pie chart shows the distribution of fields by harvest readiness, allowing you to see at a glance how many fields are in each stage of development. Fields are categorized based on both age and CI values as described in the Harvest Planning section above. + +```{r render_status_charts, echo=FALSE, fig.height=5, fig.width=10, message=FALSE, warning=FALSE} +# Create field status summary visualization +tryCatch({ + # Create field status charts + status_chart <- create_summary_stats(farm_health_data) + + # Print the chart + print(status_chart) + + # Create a second row with harvest readiness chart + harvest_chart <- create_harvest_readiness_chart(farm_health_data) + + # Print the chart + print(harvest_chart) +}, error = function(e) { + safe_log(paste("Error creating status summary charts:", e$message), "ERROR") + plot(1, type="n", axes=FALSE, xlab="", ylab="") + text(1, 1, "Error creating status summary charts", cex=1.5) +}) +``` + +## Priority Fields Requiring Attention + +The chart below highlights fields that require immediate management attention based on their health scores and anomaly detection. These should be prioritized for field inspections. + +**How it works:** This chart shows fields with priority levels 1-3 (critical, high, and medium): +- Fields are ordered by priority level, with the most critical fields on the left +- Bar height represents the Chlorophyll Index (CI) value +- Bar colors indicate priority level: red (critical), orange (high), yellow (medium) +- Text labels show the detected anomaly type for each field + +The table below the chart provides detailed metrics for these priority fields, including CI values, weekly changes, anomaly types, and harvest status. Only fields requiring management attention (priority levels 1-3) are included. + +```{r render_priority_fields_chart, echo=FALSE, fig.height=5, fig.width=10, message=FALSE, warning=FALSE} +# Create priority fields chart +tryCatch({ + # Create and display priority fields chart + priority_chart <- create_priority_fields_chart(farm_health_data) + + # Print the chart + print(priority_chart) + + # Create a table of priority fields + priority_table <- farm_health_data %>% + dplyr::filter(priority_level <= 3) %>% + dplyr::arrange(priority_level, field) %>% + dplyr::select( + Field = field, + Status = status, + `CI Value` = mean_ci, + `Weekly Change` = ci_change, + `Anomaly Type` = anomaly_type, + `Age (Weeks)` = age_weeks, + `Harvest Status` = harvest_readiness + ) + + # Display the table if there are priority fields + if (nrow(priority_table) > 0) { + knitr::kable(priority_table, caption = "Priority Fields Requiring Management Attention") + } else { + cat("No priority fields requiring immediate attention this week.") + } + +}, error = function(e) { + safe_log(paste("Error creating priority fields chart:", e$message), "ERROR") + cat("Error generating priority fields visualization. See log for details.") +}) +``` + +\pagebreak +## Management Recommendations + +```{r render_recommendations, echo=FALSE, results='asis', message=FALSE, warning=FALSE} +# Generate executive recommendations +tryCatch({ + # Create and display recommendations + recommendations_html <- generate_executive_recommendations(farm_health_data) + + # Print the HTML recommendations + cat(recommendations_html) +}, error = function(e) { + safe_log(paste("Error creating recommendations:", e$message), "ERROR") + cat("Error generating recommendations. Please see system administrator.
") +}) +``` + +## Yield Prediction Overview + +This section provides yield predictions for mature fields (over 300 days old) based on their Chlorophyll Index values and growth patterns. These predictions can help with harvest planning and yield forecasting. + +```{r render_yield_summary, echo=FALSE, fig.height=5, fig.width=10, message=FALSE, warning=FALSE} +# Create yield summary +tryCatch({ + if (exists("pred_rf_current_season") && nrow(pred_rf_current_season) > 0) { + # Calculate total estimated production + total_yield <- sum(pred_rf_current_season$predicted_Tcha, na.rm = TRUE) + + # Create summary box + cat("No fields currently meet harvest readiness criteria (>300 days) for yield prediction.
") + cat("Error generating yield summary. Please see system administrator.
") +}) +``` + diff --git a/r_app/experiments/executive_summary/executive_report_utils.R b/r_app/experiments/executive_summary/executive_report_utils.R new file mode 100644 index 0000000..398249c --- /dev/null +++ b/r_app/experiments/executive_summary/executive_report_utils.R @@ -0,0 +1,437 @@ +# EXECUTIVE REPORT UTILITIES +# This file contains functions for creating advanced visualizations for the executive summary report + +#' Create a velocity and acceleration indicator for CI change +#' +#' @param health_data Current farm health data +#' @param ci_current Current CI raster +#' @param ci_prev1 CI raster from 1 week ago +#' @param ci_prev2 CI raster from 2 weeks ago +#' @param ci_prev3 CI raster from 3 weeks ago +#' @param field_boundaries Field boundaries spatial data (sf object) +#' @return A ggplot2 object with velocity and acceleration gauges +#' +create_velocity_acceleration_indicator <- function(health_data, ci_current, ci_prev1, ci_prev2, ci_prev3, field_boundaries) { + tryCatch({ + # Calculate farm-wide metrics for multiple weeks + mean_ci_current <- mean(health_data$mean_ci, na.rm = TRUE) + + # Calculate previous week metrics + # Extract CI values for previous weeks + field_ci_metrics <- data.frame(field = character(), + week_current = numeric(), + week_minus_1 = numeric(), + week_minus_2 = numeric(), + week_minus_3 = numeric(), + stringsAsFactors = FALSE) + + # Process each field + fields <- unique(field_boundaries$field) + for (field_name in fields) { + tryCatch({ + # Get field boundary + field_shape <- field_boundaries %>% dplyr::filter(field == field_name) + if (nrow(field_shape) == 0) next + + # Extract CI values for all weeks + ci_curr_values <- terra::extract(ci_current, field_shape) + ci_prev1_values <- terra::extract(ci_prev1, field_shape) + ci_prev2_values <- terra::extract(ci_prev2, field_shape) + ci_prev3_values <- terra::extract(ci_prev3, field_shape) + + # Calculate mean CI for each week + mean_ci_curr <- mean(ci_curr_values$CI, na.rm = TRUE) + mean_ci_prev1 <- mean(ci_prev1_values$CI, na.rm = TRUE) + mean_ci_prev2 <- mean(ci_prev2_values$CI, na.rm = TRUE) + mean_ci_prev3 <- mean(ci_prev3_values$CI, na.rm = TRUE) + + # Add to metrics table + field_ci_metrics <- rbind(field_ci_metrics, data.frame( + field = field_name, + week_current = mean_ci_curr, + week_minus_1 = mean_ci_prev1, + week_minus_2 = mean_ci_prev2, + week_minus_3 = mean_ci_prev3, + stringsAsFactors = FALSE + )) + }, error = function(e) { + message(paste("Error processing field", field_name, "for velocity indicator:", e$message)) + }) + } + + # Calculate farm-wide averages + farm_avg <- colMeans(field_ci_metrics[, c("week_current", "week_minus_1", "week_minus_2", "week_minus_3")], na.rm = TRUE) + + # Calculate velocity (rate of change) - current week compared to last week + velocity <- farm_avg["week_current"] - farm_avg["week_minus_1"] + + # Calculate previous velocity (last week compared to two weeks ago) + prev_velocity <- farm_avg["week_minus_1"] - farm_avg["week_minus_2"] + + # Calculate acceleration (change in velocity) + acceleration <- velocity - prev_velocity + + # Prepare data for velocity gauge + velocity_data <- data.frame( + label = "Weekly CI Change", + value = velocity + ) + + # Prepare data for acceleration gauge + acceleration_data <- data.frame( + label = "Change Acceleration", + value = acceleration + ) + + # Create velocity trend data + trend_data <- data.frame( + week = c(-3, -2, -1, 0), + ci_value = c(farm_avg["week_minus_3"], farm_avg["week_minus_2"], + farm_avg["week_minus_1"], farm_avg["week_current"]) + ) + + # Create layout grid for the visualizations + layout_matrix <- matrix(c(1, 1, 2, 2, 3, 3), nrow = 2, byrow = TRUE) + + # Create velocity gauge + velocity_gauge <- ggplot2::ggplot(velocity_data, ggplot2::aes(x = 0, y = 0)) + + ggplot2::geom_arc_bar(ggplot2::aes( + x0 = 0, y0 = 0, + r0 = 0.5, r = 1, + start = -pi/2, end = pi/2, + fill = "background" + ), fill = "#f0f0f0") + + ggplot2::geom_arc_bar(ggplot2::aes( + x0 = 0, y0 = 0, + r0 = 0.5, r = 1, + start = -pi/2, + end = -pi/2 + (pi * (0.5 + (velocity / 2))), # Scale to range -1 to +1 + fill = "velocity" + ), fill = ifelse(velocity >= 0, "#1a9850", "#d73027")) + + ggplot2::geom_text(ggplot2::aes(label = sprintf("%.2f", velocity)), + size = 8, fontface = "bold") + + ggplot2::geom_text(ggplot2::aes(label = "Velocity"), y = -0.3, size = 4) + + ggplot2::coord_fixed() + + ggplot2::theme_void() + + ggplot2::scale_fill_manual(values = c("background" = "#f0f0f0", "velocity" = "steelblue"), + guide = "none") + + ggplot2::annotate("text", x = -0.85, y = 0, label = "Declining", + angle = 90, size = 3.5) + + ggplot2::annotate("text", x = 0.85, y = 0, label = "Improving", + angle = -90, size = 3.5) + + ggplot2::labs(title = "Farm Health Velocity", + subtitle = "CI change per week") + + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5, size = 14, face = "bold"), + plot.subtitle = ggplot2::element_text(hjust = 0.5, size = 12)) + + # Create acceleration gauge + acceleration_gauge <- ggplot2::ggplot(acceleration_data, ggplot2::aes(x = 0, y = 0)) + + ggplot2::geom_arc_bar(ggplot2::aes( + x0 = 0, y0 = 0, + r0 = 0.5, r = 1, + start = -pi/2, end = pi/2, + fill = "background" + ), fill = "#f0f0f0") + + ggplot2::geom_arc_bar(ggplot2::aes( + x0 = 0, y0 = 0, + r0 = 0.5, r = 1, + start = -pi/2, + end = -pi/2 + (pi * (0.5 + (acceleration / 1))), # Scale to range -0.5 to +0.5 + fill = "acceleration" + ), fill = ifelse(acceleration >= 0, "#1a9850", "#d73027")) + + ggplot2::geom_text(ggplot2::aes(label = sprintf("%.2f", acceleration)), + size = 8, fontface = "bold") + + ggplot2::geom_text(ggplot2::aes(label = "Acceleration"), y = -0.3, size = 4) + + ggplot2::coord_fixed() + + ggplot2::theme_void() + + ggplot2::scale_fill_manual(values = c("background" = "#f0f0f0", "acceleration" = "steelblue"), + guide = "none") + + ggplot2::annotate("text", x = -0.85, y = 0, label = "Slowing", + angle = 90, size = 3.5) + + ggplot2::annotate("text", x = 0.85, y = 0, label = "Accelerating", + angle = -90, size = 3.5) + + ggplot2::labs(title = "Change Acceleration", + subtitle = "Increasing or decreasing trend") + + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5, size = 14, face = "bold"), + plot.subtitle = ggplot2::element_text(hjust = 0.5, size = 12)) + + # Create trend chart + trend_chart <- ggplot2::ggplot(trend_data, ggplot2::aes(x = week, y = ci_value)) + + ggplot2::geom_line(size = 1.5, color = "steelblue") + + ggplot2::geom_point(size = 3, color = "steelblue") + + ggplot2::geom_hline(yintercept = trend_data$ci_value[1], linetype = "dashed", color = "gray50") + + ggplot2::labs( + title = "4-Week CI Trend", + x = "Weeks from current", + y = "Average CI Value" + ) + + ggplot2::theme_minimal() + + ggplot2::scale_x_continuous(breaks = c(-3, -2, -1, 0)) + + # Create table of top velocity changes + field_ci_metrics$velocity <- field_ci_metrics$week_current - field_ci_metrics$week_minus_1 + top_velocity_fields <- field_ci_metrics %>% + dplyr::arrange(desc(abs(velocity))) %>% + dplyr::slice_head(n = 5) %>% + dplyr::select(field, velocity) %>% + dplyr::mutate(direction = ifelse(velocity >= 0, "Improving", "Declining")) + + # Combine into multi-panel figure + main_plot <- gridExtra::grid.arrange( + gridExtra::grid.arrange(velocity_gauge, acceleration_gauge, ncol = 2), + trend_chart, + heights = c(1.5, 1), + nrow = 2 + ) + + return(main_plot) + + }, error = function(e) { + message(paste("Error in create_velocity_acceleration_indicator:", e$message)) + return(ggplot2::ggplot() + + ggplot2::annotate("text", x = 0, y = 0, label = paste("Error creating velocity indicator:", e$message)) + + ggplot2::theme_void()) + }) +} + +#' Generate a field health score based on CI values and trends +#' +#' @param ci_current Current CI raster +#' @param ci_change CI change raster +#' @param field_age_weeks Field age in weeks +#' @return List containing score, status, and component scores +#' +generate_field_health_score <- function(ci_current, ci_change, field_age_weeks) { + # Get mean CI value for the field + mean_ci <- terra::global(ci_current, "mean", na.rm=TRUE)[[1]] + + # Get mean CI change + mean_change <- terra::global(ci_change, "mean", na.rm=TRUE)[[1]] + + # Get CI uniformity (coefficient of variation) + ci_sd <- terra::global(ci_current, "sd", na.rm=TRUE)[[1]] + ci_uniformity <- ifelse(mean_ci > 0, ci_sd / mean_ci, 1) + + # Calculate base score from current CI (scale 0-5) + # Adjusted for crop age - expectations increase with age + expected_ci <- min(5, field_age_weeks / 10) # Simple linear model + ci_score <- max(0, min(5, 5 - 2 * abs(mean_ci - expected_ci))) + + # Add points for positive change (scale 0-3) + change_score <- max(0, min(3, 1 + mean_change)) + + # Add points for uniformity (scale 0-2) + uniformity_score <- max(0, min(2, 2 * (1 - ci_uniformity))) + + # Calculate total score (0-10) + total_score <- ci_score + change_score + uniformity_score + + # Create status label + status <- dplyr::case_when( + total_score >= 8 ~ "Excellent", + total_score >= 6 ~ "Good", + total_score >= 4 ~ "Fair", + total_score >= 2 ~ "Needs Attention", + TRUE ~ "Critical" + ) + + # Return results + return(list( + score = round(total_score, 1), + status = status, + components = list( + ci = round(ci_score, 1), + change = round(change_score, 1), + uniformity = round(uniformity_score, 1) + ) + )) +} + +#' Create an irrigation recommendation map +#' +#' @param ci_current Current CI raster +#' @param ci_change CI change raster +#' @param field_shape Field boundary shape +#' @param title Map title +#' @return A tmap object with irrigation recommendations +#' +create_irrigation_map <- function(ci_current, ci_change, field_shape, title = "Irrigation Priority Zones") { + # Create a new raster for irrigation recommendations + irrigation_priority <- ci_current * 0 + + # Extract values for processing + ci_values <- terra::values(ci_current) + change_values <- terra::values(ci_change) + + # Create priority zones: + # 3 = High priority (low CI, negative trend) + # 2 = Medium priority (low CI but stable, or good CI with negative trend) + # 1 = Low priority (watch, good CI with slight decline) + # 0 = No action needed (good CI, stable/positive trend) + priority_values <- rep(NA, length(ci_values)) + + # High priority: Low CI (< 2) and negative change (< 0) + high_priority <- which(ci_values < 2 & change_values < 0 & !is.na(ci_values) & !is.na(change_values)) + priority_values[high_priority] <- 3 + + # Medium priority: Low CI (< 2) with stable/positive change, or moderate CI (2-4) with significant negative change (< -1) + medium_priority <- which( + (ci_values < 2 & change_values >= 0 & !is.na(ci_values) & !is.na(change_values)) | + (ci_values >= 2 & ci_values < 4 & change_values < -1 & !is.na(ci_values) & !is.na(change_values)) + ) + priority_values[medium_priority] <- 2 + + # Low priority (watch): Moderate/good CI (>= 2) with mild negative change (-1 to 0) + low_priority <- which( + ci_values >= 2 & change_values < 0 & change_values >= -1 & !is.na(ci_values) & !is.na(change_values) + ) + priority_values[low_priority] <- 1 + + # No action needed: Good CI (>= 2) with stable/positive change (>= 0) + no_action <- which(ci_values >= 2 & change_values >= 0 & !is.na(ci_values) & !is.na(change_values)) + priority_values[no_action] <- 0 + + # Set values in the irrigation priority raster + terra::values(irrigation_priority) <- priority_values + + # Create the map + tm_shape(irrigation_priority) + + tm_raster( + style = "cat", + palette = c("#1a9850", "#91cf60", "#fc8d59", "#d73027"), + labels = c("No Action", "Watch", "Medium Priority", "High Priority"), + title = "Irrigation Need" + ) + + tm_shape(field_shape) + + tm_borders(lwd = 2) + + tm_layout( + main.title = title, + legend.outside = FALSE, + legend.position = c("left", "bottom") + ) +} + +#' Simple mock function to get weather data for a field +#' In a real implementation, this would fetch data from a weather API +#' +#' @param start_date Start date for weather data +#' @param end_date End date for weather data +#' @param lat Latitude of the field center +#' @param lon Longitude of the field center +#' @return A data frame of weather data +#' +get_weather_data <- function(start_date, end_date, lat = -16.1, lon = 34.7) { + # This is a mock implementation - in production, you'd replace with actual API call + # to a service like OpenWeatherMap, NOAA, or other weather data provider + + # Create date sequence + dates <- seq.Date(from = as.Date(start_date), to = as.Date(end_date), by = "day") + n_days <- length(dates) + + # Generate some random but realistic weather data with seasonal patterns + # More rain in summer, less in winter (Southern hemisphere) + month_nums <- as.numeric(format(dates, "%m")) + + # Simplified seasonal patterns - adjust for your local climate + is_rainy_season <- month_nums %in% c(11, 12, 1, 2, 3, 4) + + # Generate rainfall - more in rainy season, occasional heavy rainfall + rainfall <- numeric(n_days) + rainfall[is_rainy_season] <- pmax(0, rnorm(sum(is_rainy_season), mean = 4, sd = 8)) + rainfall[!is_rainy_season] <- pmax(0, rnorm(sum(!is_rainy_season), mean = 0.5, sd = 2)) + + # Add some rare heavy rainfall events + heavy_rain_days <- sample(which(is_rainy_season), size = max(1, round(sum(is_rainy_season) * 0.1))) + rainfall[heavy_rain_days] <- rainfall[heavy_rain_days] + runif(length(heavy_rain_days), 20, 50) + + # Generate temperatures - seasonal variation + temp_mean <- 18 + 8 * sin((month_nums - 1) * pi/6) # Peak in January (month 1) + temp_max <- temp_mean + rnorm(n_days, mean = 5, sd = 1) + temp_min <- temp_mean - rnorm(n_days, mean = 5, sd = 1) + + # Create weather data frame + weather_data <- data.frame( + date = dates, + rainfall_mm = round(rainfall, 1), + temp_max_c = round(temp_max, 1), + temp_min_c = round(temp_min, 1), + temp_mean_c = round((temp_max + temp_min) / 2, 1) + ) + + return(weather_data) +} + +#' Creates a weather summary visualization integrated with CI data +#' +#' @param pivotName Name of the pivot field +#' @param ci_data CI quadrant data +#' @param days_to_show Number of days of weather to show +#' @return ggplot object +#' +create_weather_ci_plot <- function(pivotName, ci_data = CI_quadrant, days_to_show = 30) { + # Get field data + field_data <- ci_data %>% + dplyr::filter(field == pivotName) %>% + dplyr::arrange(Date) %>% + dplyr::filter(!is.na(value)) + + if (nrow(field_data) == 0) { + return(ggplot() + + annotate("text", x = 0, y = 0, label = "No data available") + + theme_void()) + } + + # Get the latest date and 30 days before + latest_date <- max(field_data$Date, na.rm = TRUE) + start_date <- latest_date - days_to_show + + # Filter for recent data only + recent_field_data <- field_data %>% + dplyr::filter(Date >= start_date) + + # Get center point coordinates for the field (would be calculated from geometry in production) + # This is mocked for simplicity + lat <- -16.1 # Mock latitude + lon <- 34.7 # Mock longitude + + # Get weather data + weather_data <- get_weather_data(start_date, latest_date, lat, lon) + + # Aggregate CI data to daily mean across subfields if needed + daily_ci <- recent_field_data %>% + dplyr::group_by(Date) %>% + dplyr::summarize(mean_ci = mean(value, na.rm = TRUE)) + + # Create combined plot with dual y-axis + g <- ggplot() + + # Rainfall as bars + geom_col(data = weather_data, aes(x = date, y = rainfall_mm), + fill = "#1565C0", alpha = 0.7, width = 0.7) + + # CI as a line + geom_line(data = daily_ci, aes(x = Date, y = mean_ci * 10), + color = "#2E7D32", size = 1) + + geom_point(data = daily_ci, aes(x = Date, y = mean_ci * 10), + color = "#2E7D32", size = 2) + + # Temperature range as ribbon + geom_ribbon(data = weather_data, + aes(x = date, ymin = temp_min_c, ymax = temp_max_c), + fill = "#FF9800", alpha = 0.2) + + # Primary y-axis (rainfall) + scale_y_continuous( + name = "Rainfall (mm)", + sec.axis = sec_axis(~./10, name = "Chlorophyll Index & Temperature (°C)") + ) + + labs( + title = paste("Field", pivotName, "- Weather and CI Relationship"), + subtitle = paste("Last", days_to_show, "days"), + x = "Date" + ) + + theme_minimal() + + theme( + axis.title.y.left = element_text(color = "#1565C0"), + axis.title.y.right = element_text(color = "#2E7D32"), + legend.position = "bottom" + ) + + return(g) +} \ No newline at end of file diff --git a/r_app/experiments/max_ci_field.r b/r_app/experiments/max_ci_field.r new file mode 100644 index 0000000..9a72636 --- /dev/null +++ b/r_app/experiments/max_ci_field.r @@ -0,0 +1,54 @@ +library(sf) +library(terra) +library(tidyverse) +library(lubridate) +library(exactextractr) +library(readxl) + + +# Vang alle command line argumenten op +args <- commandArgs(trailingOnly = TRUE) + +# Controleer of er ten minste één argument is doorgegeven +if (length(args) == 0) { + stop("Geen argumenten doorgegeven aan het script") +} + +# Converteer het eerste argument naar een numerieke waarde +end_date <- as.Date(args[1]) +if (is.na(end_date)) { + end_date <- lubridate::dmy("28-08-2024") +} + +offset <- as.numeric(args[2]) +# Controleer of weeks_ago een geldig getal is +if (is.na(offset)) { + # stop("Het argument is geen geldig getal") + offset <- 7 +} + +week <- week(end_date) +# Converteer het tweede argument naar een string waarde +project_dir <- as.character(args[3]) + +# Controleer of data_dir een geldige waarde is +if (!is.character(project_dir)) { + project_dir <- "chemba" +} +new_project_question = FALSE + +source("parameters_project.R") +source("ci_extraction_utils.R") + + + +raster_files <- list.files(merged_final,full.names = T, pattern = ".tif") + +# Load all raster files +rasters <- lapply(raster_files, function(file) { + r <- rast(file) + r[[which(names(r) == "CI")]] # Select the CI band +}) +# Calculate the maximum CI per pixel +max_ci_raster <- do.call(terra::app, c(rasters, fun = mean, na.rm = TRUE)) +plot(max_ci_raster) diff --git a/r_app/experiments/mosaic_creation_fixed.R b/r_app/experiments/mosaic_creation_fixed.R new file mode 100644 index 0000000..e69de29 diff --git a/r_app/experiments/optimal_ci_analysis.R b/r_app/experiments/optimal_ci_analysis.R new file mode 100644 index 0000000..d353274 --- /dev/null +++ b/r_app/experiments/optimal_ci_analysis.R @@ -0,0 +1,421 @@ +# Optimal CI Analysis - Day 30 CI values with quadratic fitting +# Author: SmartCane Analysis Team +# Date: 2025-06-12 + +# Load required libraries +library(ggplot2) +library(dplyr) +library(readr) + +# Set file path +rds_file_path <- "C:/Users/timon/Resilience BV/4020 SCane ESA DEMO - Documenten/General/4020 SCDEMO Team/4020 TechnicalData/WP3/smartcane/laravel_app/storage/app/chemba/Data/extracted_ci/cumulative_vals/All_pivots_Cumulative_CI_quadrant_year_v2.rds" + +# Check if file exists +if (!file.exists(rds_file_path)) { + stop("RDS file not found at specified path: ", rds_file_path) +} + +# Load the data +cat("Loading RDS file...\n") +ci_data <- readRDS(rds_file_path) + +# Display structure of the data to understand it better +cat("Data structure:\n") +str(ci_data) +cat("\nFirst few rows:\n") +head(ci_data) +cat("\nColumn names:\n") +print(colnames(ci_data)) + +# Filter data based on requirements +cat("\nApplying data filters...\n") + +# 1. Filter out models that don't reach at least DOY 300 +model_doy_max <- ci_data %>% + group_by(model) %>% + summarise(max_doy = max(DOY, na.rm = TRUE), .groups = 'drop') + +valid_models <- model_doy_max %>% + filter(max_doy >= 300) %>% + pull(model) + +cat(paste("Models before filtering:", n_distinct(ci_data$model), "\n")) +cat(paste("Models reaching at least DOY 300:", length(valid_models), "\n")) + +ci_data <- ci_data %>% + filter(model %in% valid_models) + +# 2. Apply IQR filtering per DOY (remove outliers outside IQR) +cat("Applying IQR filtering per DOY...\n") +original_rows <- nrow(ci_data) + +ci_data <- ci_data %>% + group_by(DOY) %>% + mutate( + Q1 = quantile(FitData, 0.25, na.rm = TRUE), + Q3 = quantile(FitData, 0.75, na.rm = TRUE), + IQR = Q3 - Q1, + lower_bound = Q1 - 1.5 * IQR, + upper_bound = Q3 + 1.5 * IQR + ) %>% + filter(FitData >= lower_bound & FitData <= upper_bound) %>% + select(-Q1, -Q3, -IQR, -lower_bound, -upper_bound) %>% + ungroup() + +filtered_rows <- nrow(ci_data) +cat(paste("Rows before IQR filtering:", original_rows, "\n")) +cat(paste("Rows after IQR filtering:", filtered_rows, "\n")) +cat(paste("Removed", original_rows - filtered_rows, "outliers (", + round(100 * (original_rows - filtered_rows) / original_rows, 1), "%)\n")) + +# Check what day values are available after filtering +if ("DOY" %in% colnames(ci_data)) { + cat("\nUnique DOY values after filtering:\n") + print(sort(unique(ci_data$DOY))) +} else { + cat("\nNo 'DOY' column found. Available columns:\n") + print(colnames(ci_data)) +} + +# Extract CI values at day 30 for each field +cat("\nExtracting day 30 CI values...\n") + +# Set column names based on known structure +day_col <- "DOY" +ci_col <- "FitData" +field_col <- "model" + +# Try different possible field column name combinations +if ("field" %in% colnames(ci_data)) { + field_col <- "field" +} else if ("Field" %in% colnames(ci_data)) { + field_col <- "Field" +} else if ("pivot" %in% colnames(ci_data)) { + field_col <- "pivot" +} else if ("Pivot" %in% colnames(ci_data)) { + field_col <- "Pivot" +} else if ("field_id" %in% colnames(ci_data)) { + field_col <- "field_id" +} else if ("Field_ID" %in% colnames(ci_data)) { + field_col <- "Field_ID" +} + +# Check if we found the required columns +if (!("DOY" %in% colnames(ci_data))) { + stop("DOY column not found in data") +} +if (!("FitData" %in% colnames(ci_data))) { + stop("FitData column not found in data") +} +if (is.null(field_col)) { + cat("Could not automatically identify field column. Please check column names.\n") + cat("Available columns: ", paste(colnames(ci_data), collapse = ", "), "\n") + stop("Manual field column identification required") +} + +cat(paste("Using columns - DOY:", day_col, "Field:", field_col, "FitData:", ci_col, "\n")) + +# Extract day 30 data +day_30_data <- ci_data %>% + filter(DOY %% 30 == 0) %>% + select(field = !!sym(field_col), ci = !!sym(ci_col), DOY) %>% + na.omit() %>% + arrange(field) + +cat(paste("Found", nrow(day_30_data), "fields with day 30 CI values\n")) + +if (nrow(day_30_data) == 0) { + stop("No data found for day 30. Check if day 30 exists in the dataset.") +} + +# Display summary of day 30 data +cat("\nSummary of day 30 CI values:\n") +print(summary(day_30_data)) + +# Add field index for plotting (assuming fields represent some spatial or sequential order) +#day_30_data$field_index <- 1:nrow(day_30_data) + +# Create scatter plot +p1 <- ggplot(day_30_data, aes(x = DOY, y = ci)) + + geom_point(color = "blue", size = 3, alpha = 0.7) + + labs( + title = "CI Values at Day 30 for All Fields", + x = "Day of Year (DOY)", + y = "CI Value", + subtitle = paste("Total fields:", nrow(day_30_data)) + ) + + theme_minimal() + + theme( + plot.title = element_text(size = 14, face = "bold"), + axis.title = element_text(size = 12), + axis.text = element_text(size = 10) + ) + +print(p1) + +# Try multiple curve fitting approaches +cat("\nTrying different curve fitting approaches...\n") + +# Aggregate data by DOY (take mean CI for each DOY to reduce noise) +aggregated_data <- day_30_data %>% + group_by(DOY) %>% + summarise( + mean_ci = mean(ci, na.rm = TRUE), + median_ci = median(ci, na.rm = TRUE), + count = n(), + .groups = 'drop' + ) %>% + filter(count >= 5) # Only keep DOY values with sufficient data points + +cat(paste("Aggregated to", nrow(aggregated_data), "DOY points with sufficient data\n")) + +# Create prediction data for smooth curves +x_smooth <- seq(min(aggregated_data$DOY), max(aggregated_data$DOY), length.out = 100) + +# 1. Quadratic model (as requested) +cat("\n1. Fitting quadratic model...\n") +quad_model <- lm(mean_ci ~ poly(DOY, 2, raw = TRUE), data = aggregated_data) +quad_pred <- predict(quad_model, newdata = data.frame(DOY = x_smooth)) +quad_r2 <- summary(quad_model)$r.squared +cat(paste("Quadratic R² =", round(quad_r2, 3), "\n")) + +# 2. Logistic growth model: y = K / (1 + exp(-r*(x-x0))) - biologically realistic +cat("\n2. Fitting logistic growth model...\n") +tryCatch({ + # Estimate starting values + K_start <- max(aggregated_data$mean_ci) * 1.1 # Carrying capacity + r_start <- 0.05 # Growth rate + x0_start <- mean(aggregated_data$DOY) # Inflection point + + logistic_model <- nls(mean_ci ~ K / (1 + exp(-r * (DOY - x0))), + data = aggregated_data, + start = list(K = K_start, r = r_start, x0 = x0_start), + control = nls.control(maxiter = 1000)) + + logistic_pred <- predict(logistic_model, newdata = data.frame(DOY = x_smooth)) + logistic_r2 <- 1 - sum(residuals(logistic_model)^2) / sum((aggregated_data$mean_ci - mean(aggregated_data$mean_ci))^2) + cat(paste("Logistic growth R² =", round(logistic_r2, 3), "\n")) +}, error = function(e) { + cat("Logistic growth model failed to converge\n") + logistic_model <- NULL + logistic_pred <- NULL + logistic_r2 <- NA +}) + +# 3. Beta function model: good for crop growth curves with clear peak +cat("\n3. Fitting Beta function model...\n") +tryCatch({ + # Normalize DOY to 0-1 range for Beta function + doy_min <- min(aggregated_data$DOY) + doy_max <- max(aggregated_data$DOY) + aggregated_data$doy_norm <- (aggregated_data$DOY - doy_min) / (doy_max - doy_min) + + # Beta function: y = a * (x^(p-1)) * ((1-x)^(q-1)) + c + beta_model <- nls(mean_ci ~ a * (doy_norm^(p-1)) * ((1-doy_norm)^(q-1)) + c, + data = aggregated_data, + start = list(a = max(aggregated_data$mean_ci) * 20, p = 2, q = 3, c = min(aggregated_data$mean_ci)), + control = nls.control(maxiter = 1000)) + + # Predict on normalized scale then convert back + x_smooth_norm <- (x_smooth - doy_min) / (doy_max - doy_min) + beta_pred <- predict(beta_model, newdata = data.frame(doy_norm = x_smooth_norm)) + beta_r2 <- 1 - sum(residuals(beta_model)^2) / sum((aggregated_data$mean_ci - mean(aggregated_data$mean_ci))^2) + cat(paste("Beta function R² =", round(beta_r2, 3), "\n")) +}, error = function(e) { + cat("Beta function model failed to converge\n") + beta_model <- NULL + beta_pred <- NULL + beta_r2 <- NA +}) + +# 4. Gaussian (normal) curve: good for symmetric growth patterns +cat("\n4. Fitting Gaussian curve...\n") +tryCatch({ + # Gaussian: y = a * exp(-((x-mu)^2)/(2*sigma^2)) + c + gaussian_model <- nls(mean_ci ~ a * exp(-((DOY - mu)^2)/(2 * sigma^2)) + c, + data = aggregated_data, + start = list(a = max(aggregated_data$mean_ci), + mu = aggregated_data$DOY[which.max(aggregated_data$mean_ci)], + sigma = 50, + c = min(aggregated_data$mean_ci)), + control = nls.control(maxiter = 1000)) + + gaussian_pred <- predict(gaussian_model, newdata = data.frame(DOY = x_smooth)) + gaussian_r2 <- 1 - sum(residuals(gaussian_model)^2) / sum((aggregated_data$mean_ci - mean(aggregated_data$mean_ci))^2) + cat(paste("Gaussian R² =", round(gaussian_r2, 3), "\n")) +}, error = function(e) { + cat("Gaussian model failed to converge\n") + gaussian_model <- NULL + gaussian_pred <- NULL + gaussian_r2 <- NA +}) + +# 5. LOESS (local regression) - for comparison +cat("\n5. Fitting LOESS smoothing...\n") +loess_model <- loess(mean_ci ~ DOY, data = aggregated_data, span = 0.5) +loess_pred <- predict(loess_model, newdata = data.frame(DOY = x_smooth)) +loess_r2 <- 1 - sum(residuals(loess_model)^2) / sum((aggregated_data$mean_ci - mean(aggregated_data$mean_ci))^2) +cat(paste("LOESS R² =", round(loess_r2, 3), "\n")) + +# Calculate confidence intervals for both models +cat("\nCalculating confidence intervals...\n") + +# Function to calculate confidence intervals using residual-based method +calculate_ci <- function(model, data, newdata, alpha = 0.5) { + # Get model predictions + pred_vals <- predict(model, newdata = newdata) + + # For parametric models (lm), use built-in prediction intervals + if(class(model)[1] == "lm") { + pred_intervals <- predict(model, newdata = newdata, interval = "confidence", level = 1 - alpha) + return(list( + lower = pred_intervals[, "lwr"], + upper = pred_intervals[, "upr"] + )) + } + + # For LOESS, calculate confidence intervals using residual bootstrap + if(class(model)[1] == "loess") { + # Calculate residuals from the original model + fitted_vals <- fitted(model) + residuals_vals <- residuals(model) + residual_sd <- sd(residuals_vals, na.rm = TRUE) + + # Use normal approximation for confidence intervals + # For 50% CI, use 67% quantile (approximately 0.67 standard deviations) + margin <- qnorm(1 - alpha/2) * residual_sd + + return(list( + lower = pred_vals - margin, + upper = pred_vals + margin + )) + } + + # Fallback method + residual_sd <- sd(residuals(model), na.rm = TRUE) + margin <- qnorm(1 - alpha/2) * residual_sd + return(list( + lower = pred_vals - margin, + upper = pred_vals + margin + )) +}# Calculate CIs for quadratic model using aggregated data (same as model fitting) +quad_ci <- calculate_ci(quad_model, aggregated_data, data.frame(DOY = x_smooth)) + +# Calculate CIs for LOESS model using aggregated data (same as model fitting) +loess_ci <- calculate_ci(loess_model, aggregated_data, data.frame(DOY = x_smooth)) + +# Create separate plots for LOESS and Quadratic models +cat("\nCreating LOESS plot with confidence intervals...\n") + +# LOESS plot +p_loess <- ggplot(day_30_data, aes(x = DOY, y = ci)) + + geom_point(color = "lightblue", size = 1.5, alpha = 0.4) + + geom_point(data = aggregated_data, aes(x = DOY, y = mean_ci), + color = "darkblue", size = 3, alpha = 0.8) + + geom_ribbon(data = data.frame(DOY = x_smooth, + lower = loess_ci$lower, + upper = loess_ci$upper), + aes(x = DOY, ymin = lower, ymax = upper), + alpha = 0.3, fill = "purple", inherit.aes = FALSE) + + geom_line(data = data.frame(DOY = x_smooth, loess = loess_pred), + aes(x = DOY, y = loess), + color = "purple", size = 1.5) + + labs( + title = "LOESS Model - CI Values Over Growing Season", + x = "Day of Year (DOY)", + y = "CI Value", + subtitle = paste("LOESS R² =", round(loess_r2, 3), "| 50% Confidence Interval") + ) + + theme_minimal() + + theme( + plot.title = element_text(size = 14, face = "bold"), + axis.title = element_text(size = 12), + axis.text = element_text(size = 10) + ) + +# Find optimal point for LOESS +loess_max_idx <- which.max(loess_pred) +loess_optimal_doy <- x_smooth[loess_max_idx] +loess_optimal_ci <- loess_pred[loess_max_idx] + +p_loess <- p_loess + + geom_point(aes(x = loess_optimal_doy, y = loess_optimal_ci), + color = "red", size = 5, shape = 8) + + annotate("text", + x = loess_optimal_doy + 30, + y = loess_optimal_ci, + label = paste("Optimal: DOY", round(loess_optimal_doy, 1), "\nCI =", round(loess_optimal_ci, 3)), + color = "red", size = 4, fontface = "bold") + +print(p_loess) + +cat("\nCreating Quadratic plot with confidence intervals...\n") + +# Quadratic plot +p_quadratic <- ggplot(day_30_data, aes(x = DOY, y = ci)) + + geom_point(color = "lightcoral", size = 1.5, alpha = 0.4) + + geom_point(data = aggregated_data, aes(x = DOY, y = mean_ci), + color = "darkred", size = 3, alpha = 0.8) + + geom_ribbon(data = data.frame(DOY = x_smooth, + lower = quad_ci$lower, + upper = quad_ci$upper), + aes(x = DOY, ymin = lower, ymax = upper), + alpha = 0.3, fill = "red", inherit.aes = FALSE) + + geom_line(data = data.frame(DOY = x_smooth, quadratic = quad_pred), + aes(x = DOY, y = quadratic), + color = "red", size = 1.5) + + labs( + title = "Quadratic Model - CI Values Over Growing Season", + x = "Day of Year (DOY)", + y = "CI Value", + subtitle = paste("Quadratic R² =", round(quad_r2, 3), "| 50% Confidence Interval") + ) + + theme_minimal() + + theme( + plot.title = element_text(size = 14, face = "bold"), + axis.title = element_text(size = 12), + axis.text = element_text(size = 10) + ) + +# Find optimal point for Quadratic +quad_coeffs <- coef(quad_model) +a <- quad_coeffs[3] +b <- quad_coeffs[2] + +if(a != 0) { + quad_optimal_doy <- -b / (2*a) + doy_range <- range(aggregated_data$DOY) + if(quad_optimal_doy >= doy_range[1] && quad_optimal_doy <= doy_range[2]) { + quad_optimal_ci <- predict(quad_model, newdata = data.frame(DOY = quad_optimal_doy)) + } else { + quad_optimal_doy <- x_smooth[which.max(quad_pred)] + quad_optimal_ci <- max(quad_pred) + } +} else { + quad_optimal_doy <- x_smooth[which.max(quad_pred)] + quad_optimal_ci <- max(quad_pred) +} + +p_quadratic <- p_quadratic + + geom_point(aes(x = quad_optimal_doy, y = quad_optimal_ci), + color = "darkred", size = 5, shape = 8) + + annotate("text", + x = quad_optimal_doy + 30, + y = quad_optimal_ci, + label = paste("Optimal: DOY", round(quad_optimal_doy, 1), "\nCI =", round(quad_optimal_ci, 3)), + color = "darkred", size = 4, fontface = "bold") + +print(p_quadratic) +print(p_loess) + +# Print results summary +cat("\n=== RESULTS SUMMARY ===\n") +cat(paste("LOESS Model - R² =", round(loess_r2, 3), "\n")) +cat(paste(" Optimal DOY:", round(loess_optimal_doy, 1), "\n")) +cat(paste(" Optimal CI:", round(loess_optimal_ci, 4), "\n\n")) + +cat(paste("Quadratic Model - R² =", round(quad_r2, 3), "\n")) +cat(paste(" Optimal DOY:", round(quad_optimal_doy, 1), "\n")) 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+library(here) +library(sf) +library(tidyverse) +library(tmap) +library(lubridate) +library(exactextractr) +library(zoo) +library(raster) +library(terra) +library(rsample) +library(caret) +library(randomForest) +library(CAST) + +project_dir <- "chemba" +source(here("r_app", "parameters_project.R")) +cumulative_CI_vals_dir <- "C:/Users/timon/Resilience BV/4020 SCane ESA DEMO - Documenten/General/4020 SCDEMO Team/4020 TechnicalData/WP3/smartcane/laravel_app/storage/app/chemba/Data/extracted_ci/cumulative_vals" +CI_quadrant <- readRDS(here(cumulative_CI_vals_dir,"All_pivots_Cumulative_CI_quadrant_year_v2.rds"))# %>% + +cum_ci_plot <- function(pivotName, plot_type = "value", facet_on = TRUE, x_axis = "days") { + + # pivotName = "3a13" + data_ci <- CI_quadrant %>% filter(field == pivotName) + + if (nrow(data_ci) == 0) { + return(cum_ci_plot2(pivotName)) # Return an empty data frame if no data is found + } + + data_ci2 <- data_ci %>% + mutate(CI_rate = cumulative_CI / DOY, + week = week(Date), + week_from_doy = DOY / 7) %>% + group_by(field) %>% + mutate(mean_CIrate_rolling_10_days = rollapplyr(CI_rate, width = 10, FUN = mean, partial = TRUE), + mean_rolling_10_days = rollapplyr(value, width = 10, FUN = mean, partial = TRUE)) + + data_ci2 <- data_ci2 %>% mutate(season = as.factor(season)) + + date_preperation_perfect_pivot <- data_ci2 %>% + group_by(season) %>% + summarise(min_date = min(Date), + max_date = max(Date), + days = max_date - min_date) + + unique_seasons <- sort(unique(date_preperation_perfect_pivot$season), decreasing = TRUE)[1:3] + + # Determine the y aesthetic based on the plot type + y_aesthetic <- switch(plot_type, + "CI_rate" = "mean_CIrate_rolling_10_days", + "cumulative_CI" = "cumulative_CI", + "value" = "mean_rolling_10_days") + + y_label <- switch(plot_type, + "CI_rate" = "10-Day Rolling Mean CI Rate (cumulative CI / age)", + "cumulative_CI" = "Cumulative CI", + "value" = "10-Day Rolling Mean CI") + + if (facet_on) { + g <- ggplot(data = data_ci2 %>% filter(season %in% unique_seasons)) + + facet_wrap(~season, scales = "free_x") + + geom_line(aes_string(x = "Date", y = y_aesthetic, col = "sub_field", group = "sub_field")) + + labs(title = paste("Plot of", y_label, "for Pivot", pivotName), + color = "Field Name", + y = y_label) + + scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y") + + theme_minimal() + + theme(axis.text.x = element_text(angle = 60, hjust = 1), + legend.justification = c(1, 0), legend.position = c(1, 0), + legend.title = element_text(size = 8), + legend.text = element_text(size = 8)) + + guides(color = guide_legend(nrow = 2, byrow = TRUE)) + } else if (x_axis == "weeks") { + g <- ggplot(data = data_ci2 %>% filter(season %in% unique_seasons)) + + facet_wrap(~sub_field, nrow=1) + + geom_line(aes_string(x = "week_from_doy", y = y_aesthetic, col = "season", group = "season")) + + labs(title = paste("Plot of", y_label, "for Pivot", pivotName), + color = "Season", + y = y_label, + x = "Week of Year") + + theme_minimal() + + theme(axis.text.x = element_text(angle = 60, hjust = 1), + legend.justification = c(1, 0), legend.position = c(1, 0), + legend.title = element_text(size = 8), + legend.text = element_text(size = 8)) + + guides(color = guide_legend(nrow = 2, byrow = TRUE)) + } else { + g <- ggplot(data = data_ci2 %>% filter(season %in% unique_seasons)) + + facet_wrap(~sub_field, nrow=1) + + geom_line(aes_string(x = "DOY", y = y_aesthetic, col = "season", group = "season")) + + labs(title = paste("Plot of", y_label, "for Pivot", pivotName), + color = "Season", + y = y_label, + x = "Age of Crop (Days)") + + theme_minimal() + + theme(axis.text.x = element_text(angle = 60, hjust = 1), + legend.justification = c(1, 0), legend.position = c(1, 0), + legend.title = element_text(size = 8), + legend.text = element_text(size = 8)) + + guides(color = guide_legend(nrow = 2, byrow = TRUE)) + } + + return(g) +} + +# facet_on FALSE +g1 <- cum_ci_plot("1.16", "value", FALSE, "days") +g2 <- cum_ci_plot("3a13", "cumulative_CI", FALSE, "days") +g3 <- cum_ci_plot("3a13", "CI_rate", FALSE, "days") +g7 <- cum_ci_plot("3a13", "value", FALSE, "weeks") +g8 <- cum_ci_plot("3a13", "cumulative_CI", FALSE, "weeks") +g9 <- cum_ci_plot("3a13", "CI_rate", FALSE, "weeks") + +# facet_on TRUE +g4 <- cum_ci_plot("3a13", "value", TRUE, "days") +g5 <- cum_ci_plot("3a13", "cumulative_CI", TRUE, "days") +g6 <- cum_ci_plot("3a13", "CI_rate", TRUE, "days") + diff --git a/r_app/experiments/plotting_ci_all_field_dates.R b/r_app/experiments/plotting_ci_all_field_dates.R new file mode 100644 index 0000000..8e7259e --- /dev/null +++ b/r_app/experiments/plotting_ci_all_field_dates.R @@ -0,0 +1,83 @@ +library(ggplot2) +library(dplyr) +library(sf) +library(terra) + +# Define the file paths +raster_dir <- "C:/Users/timon/Resilience BV/4020 SCane ESA DEMO - Documenten/General/4020 SCDEMO Team/4020 TechnicalData/WP3/smartcane/laravel_app/storage/app/chemba/merged_final_tif" +polygon_path <- "C:/Users/timon/Resilience BV/4020 SCane ESA DEMO - Documenten/General/4020 SCDEMO Team/4020 TechnicalData/WP3/smartcane/r_app/experiments/pivot.geojson" + +# Load the polygon +polygon <- st_read(polygon_path) + +# Filter the polygon for the specific field +polygon_filtered <- subset(polygon, field == "1.1") + +# List all raster files in the directory +raster_files <- list.files(raster_dir, pattern = "\\.tif$", full.names = FALSE) +raster_file <- raster_files[1] + +# Initialize an empty list to store CI values +ci_values <- list() + +# Define a function to process each raster file +process_raster_file <- function(raster_file) { + # Extract the date from the file name (assuming the date is in YYYYMMDD format) + date <- as.Date(substr(raster_file, 1, 10), format = "%Y-%m-%d") + + # Load the raster + raster_data <- terra::rast(file.path(raster_dir, raster_file)) + + # Crop the raster using the filtered polygon + cropped_raster <- terra::crop(raster_data, polygon_filtered) + + # Extract the CI band (assuming the CI band is the first band) + ci_band <- cropped_raster$CI + + # Extract the CI values from the CI band + return(data.frame(Date = date, CI = terra::values(ci_band)) %>% + filter(!is.na(CI)) %>% + mutate(Polygon = unique(polygon_filtered$field))) +} + +# Use walk to apply the function to each raster file +ci_values <- purrr::map(raster_files, process_raster_file) + +# Combine all CI values into a single data frame +ci_values_df <- do.call(rbind, ci_values) + +head(ci_values_df) + +# Plot the first raster +plot(terra::rast(file.path(raster_dir, raster_files[1]))[[1]]) + +# Calculate the mean CI value +ci_stats <- ci_values_df %>% + group_by(Date, Polygon) %>% + summarize( + mean_ci = mean(CI, na.rm = TRUE), + q5 = quantile(CI, 0.05, na.rm = TRUE), + q25 = quantile(CI, 0.25, na.rm = TRUE), + q50 = quantile(CI, 0.50, na.rm = TRUE), + q75 = quantile(CI, 0.75, na.rm = TRUE), + q95 = quantile(CI, 0.95, na.rm = TRUE), + cv = sd(CI, na.rm = TRUE) / mean(CI, na.rm = TRUE) * 100, + .groups = "drop" + ) + +# Plot the mean CI value over time +ggplot(ci_stats, aes(x = Date, y = mean_ci)) + + geom_line() + + geom_ribbon(aes(ymin = q25, ymax = q75), alpha = 0.2) + + labs(title = "Mean CI value over time", + x = "Date", + y = "Mean CI") + + theme_minimal() + +# Plot the coefficient of variation over time +ggplot(ci_stats, aes(x = Date, y = cv)) + + geom_line() + + labs(title = "Coefficient of variation over time", + x = "Date", + y = "CV (%)") + + theme_minimal() diff --git a/r_app/experiments/run_tests.R b/r_app/experiments/run_tests.R new file mode 100644 index 0000000..65a776d --- /dev/null +++ b/r_app/experiments/run_tests.R @@ -0,0 +1,38 @@ +# run_tests.R +# +# TEST RUNNER FOR SMARTCANE +# ======================= +# This script runs all tests for the SmartCane project. +# Usage: Rscript run_tests.R [test_pattern] +# - test_pattern: Optional regex pattern to match test files (default: all test_*.R files) +# + +# Process command line arguments +args <- commandArgs(trailingOnly = TRUE) +test_pattern <- if (length(args) > 0) args[1] else "^test_.+\\.R$" + +# Set working directory to script location +script_dir <- dirname(normalizePath(sys.frame(1)$ofile)) +setwd(script_dir) + +# Source test framework +source("tests/test_framework.R") + +# Set up test environment +env <- setup_test_env() + +# Run tests +cat("Running tests with pattern:", test_pattern, "\n\n") +success <- run_tests(test_pattern) + +# Clean up +teardown_test_env() + +# Exit with appropriate status code +if (success) { + cat("\n✓ All tests passed successfully!\n") + quit(save = "no", status = 0) +} else { + cat("\n✗ Some tests failed.\n") + quit(save = "no", status = 1) +} \ No newline at end of file diff --git a/r_app/utils_3.R b/r_app/experiments/utils_3.R similarity index 100% rename from r_app/utils_3.R rename to r_app/experiments/utils_3.R diff --git a/r_app/growth_model_utils.R b/r_app/growth_model_utils.R new file mode 100644 index 0000000..e297e36 --- /dev/null +++ b/r_app/growth_model_utils.R @@ -0,0 +1,233 @@ +# filepath: c:\Users\timon\Resilience BV\4020 SCane ESA DEMO - Documenten\General\4020 SCDEMO Team\4020 TechnicalData\WP3\smartcane\r_app\growth_model_utils.R +# +# GROWTH_MODEL_UTILS.R +# =================== +# Utility functions for growth model interpolation and manipulation. +# These functions support the creation of continuous growth models from point measurements. + +#' Safe logging function that works whether log_message exists or not +#' +#' @param message The message to log +#' @param level The log level (default: "INFO") +#' @return NULL (used for side effects) +#' +safe_log <- function(message, level = "INFO") { + if (exists("log_message")) { + log_message(message, level) + } else { + if (level %in% c("ERROR", "WARNING")) { + warning(message) + } else { + message(message) + } + } +} + +#' Load and prepare the combined CI data +#' +#' @param data_dir Directory containing the combined CI data +#' @return Long-format dataframe with CI values by date +#' +load_combined_ci_data <- function(data_dir) { + file_path <- here::here(data_dir, "combined_CI_data.rds") + + if (!file.exists(file_path)) { + stop(paste("Combined CI data file not found:", file_path)) + } + + safe_log(paste("Loading CI data from:", file_path)) + + # Load and transform the data to long format + pivot_stats <- readRDS(file_path) %>% + dplyr::ungroup() %>% + dplyr::group_by(field, sub_field) %>% + dplyr::summarise(dplyr::across(everything(), ~ first(stats::na.omit(.))), .groups = "drop") + + pivot_stats_long <- pivot_stats %>% + tidyr::gather("Date", value, -field, -sub_field) %>% + dplyr::mutate(Date = lubridate::ymd(Date)) %>% + tidyr::drop_na(c("value", "Date")) %>% + dplyr::mutate(value = as.numeric(value)) %>% + dplyr::filter_all(dplyr::all_vars(!is.infinite(.))) %>% + dplyr::distinct() + + safe_log(paste("Loaded", nrow(pivot_stats_long), "CI data points")) + + return(pivot_stats_long) +} + +#' Extract and interpolate CI data for a specific field and season +#' +#' @param field_name Name of the field or sub-field +#' @param harvesting_data Dataframe with harvesting information +#' @param field_CI_data Dataframe with CI measurements +#' @param season Year of the growing season +#' @return Dataframe with interpolated daily CI values +#' +extract_CI_data <- function(field_name, harvesting_data, field_CI_data, season) { + # Filter harvesting data for the given season and field name + filtered_harvesting_data <- harvesting_data %>% + dplyr::filter(year == season, sub_field == field_name) + + if (nrow(filtered_harvesting_data) == 0) { + safe_log(paste("No harvesting data found for field:", field_name, "in season:", season), "WARNING") + return(data.frame()) + } + + # Filter field CI data for the given field name + filtered_field_CI_data <- field_CI_data %>% + dplyr::filter(sub_field == field_name) + + # Return an empty data frame if no CI data is found + if (nrow(filtered_field_CI_data) == 0) { + safe_log(paste("No CI data found for field:", field_name, "in season:", season), "WARNING") + return(data.frame()) + } + + # Log season dates + season_start <- filtered_harvesting_data$season_start[1] + season_end <- filtered_harvesting_data$season_end[1] + ci_date_range <- paste(format(min(filtered_field_CI_data$Date), "%Y-%m-%d"), + "to", + format(max(filtered_field_CI_data$Date), "%Y-%m-%d")) + + # Create a linear interpolation function for the CI data + tryCatch({ + ApproxFun <- stats::approxfun(x = filtered_field_CI_data$Date, y = filtered_field_CI_data$value) + Dates <- seq.Date(min(filtered_field_CI_data$Date), max(filtered_field_CI_data$Date), by = 1) + LinearFit <- ApproxFun(Dates) + + # Combine interpolated data with the original CI data + CI <- data.frame(Date = Dates, FitData = LinearFit) %>% + dplyr::left_join(filtered_field_CI_data, by = "Date") %>% + dplyr::filter(Date > filtered_harvesting_data$season_start & Date < filtered_harvesting_data$season_end) + + # If CI is empty after filtering, return an empty dataframe + if (nrow(CI) == 0) { + safe_log(paste0("No CI data within season dates for field: ", field_name, + " (Season: ", season, ", dates: ", + format(season_start, "%Y-%m-%d"), " to ", + format(season_end, "%Y-%m-%d"), + "). Available CI data range: ", ci_date_range), + "WARNING") + return(data.frame()) + } + + # Add additional columns + CI <- CI %>% + dplyr::mutate( + DOY = seq(1, n(), 1), + model = paste0("Data", season, " : ", field_name), + season = season, + subField = field_name + ) + + # Log successful interpolation + safe_log(paste0("Successfully interpolated CI data for field: ", field_name, + " (Season: ", season, ", dates: ", + format(season_start, "%Y-%m-%d"), " to ", + format(season_end, "%Y-%m-%d"), + "). ", nrow(CI), " data points created.")) + + return(CI) + }, error = function(e) { + safe_log(paste0("Error interpolating CI data for field ", field_name, + " in season ", season, + " (", format(season_start, "%Y-%m-%d"), " to ", + format(season_end, "%Y-%m-%d"), + "): ", e$message), "ERROR") + return(data.frame()) + }) +} + +#' Generate interpolated CI data for all fields and seasons +#' +#' @param years Vector of years to process +#' @param harvesting_data Dataframe with harvesting information +#' @param ci_data Long-format dataframe with CI measurements +#' @return Dataframe with interpolated daily CI values for all fields/seasons +#' +generate_interpolated_ci_data <- function(years, harvesting_data, ci_data) { + safe_log("Starting CI data interpolation for all fields") + + # Process each year + result <- purrr::map_df(years, function(yr) { + safe_log(paste("Processing year:", yr)) + + # Get the fields harvested in this year with valid season start dates + sub_fields <- harvesting_data %>% + dplyr::filter(year == yr, !is.na(season_start)) %>% + dplyr::pull(sub_field) + + if (length(sub_fields) == 0) { + safe_log(paste("No fields with valid season data for year:", yr), "WARNING") + return(data.frame()) + } + + # Filter sub_fields to only include those with value data in ci_data + valid_sub_fields <- sub_fields %>% + purrr::keep(~ any(ci_data$sub_field == .x)) + + if (length(valid_sub_fields) == 0) { + safe_log(paste("No fields with CI data for year:", yr), "WARNING") + return(data.frame()) + } + + # Extract and interpolate data for each valid field + safe_log(paste("Processing", length(valid_sub_fields), "fields for year:", yr)) + + result <- purrr::map(valid_sub_fields, ~ extract_CI_data(.x, + harvesting_data = harvesting_data, + field_CI_data = ci_data, + season = yr)) %>% + purrr::list_rbind() + + safe_log(paste("Generated", nrow(result), "interpolated data points for year:", yr)) + return(result) + }) + + safe_log(paste("Total interpolated data points:", nrow(result))) + return(result) +} + +#' Calculate growth metrics for interpolated CI data +#' +#' @param interpolated_data Dataframe with interpolated CI values +#' @return Dataframe with added growth metrics (CI_per_day and cumulative_CI) +#' +calculate_growth_metrics <- function(interpolated_data) { + if (nrow(interpolated_data) == 0) { + safe_log("No data provided to calculate growth metrics", "WARNING") + return(interpolated_data) + } + + result <- interpolated_data %>% + dplyr::group_by(model) %>% + dplyr::mutate( + CI_per_day = FitData - dplyr::lag(FitData), + cumulative_CI = cumsum(FitData) + ) + + return(result) +} + +#' Save interpolated growth model data +#' +#' @param data Dataframe with interpolated growth data +#' @param output_dir Directory to save the output +#' @param file_name Filename for the output (default: "All_pivots_Cumulative_CI_quadrant_year_v2.rds") +#' @return Path to the saved file +#' +save_growth_model <- function(data, output_dir, file_name = "All_pivots_Cumulative_CI_quadrant_year_v2.rds") { + # Create output directory if it doesn't exist + dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) + + # Create full file path + file_path <- here::here(output_dir, file_name) + + # Save the data + saveRDS(data, file_path) + + safe_log(paste("Interpolated CI data saved to:", file_path)) + return(file_path) +} \ No newline at end of file diff --git a/r_app/installPackages.R b/r_app/installPackages.R index 75b5e9e..c8d1a2f 100644 --- a/r_app/installPackages.R +++ b/r_app/installPackages.R @@ -1,15 +1,40 @@ -install.packages('CAST') -install.packages("packages/CIprep_0.1.4.tar.gz",repos=NULL, type="source") -install.packages('caret') -install.packages('exactextractr') -install.packages('googledrive') -install.packages('here') -install.packages('lubridate') -install.packages('raster') -install.packages('readxl') -install.packages('rsample') -install.packages('sf') -install.packages('terra') -install.packages('tidyverse') -install.packages('tmap') -install.packages('zoo') +# Install required packages for SmartCane project +# This script installs all packages needed to run the CI report dashboard + +# List of required packages +required_packages <- c( + # Core packages + "here", "tidyverse", "sf", "terra", "tmap", "lubridate", + + # Additional data manipulation + "zoo", "readxl", "knitr", "rmarkdown", "dplyr", "purrr", "stringr", + + # Spatial analysis + "exactextractr", + + # Machine learning and statistics + "rsample", "caret", "randomForest", "CAST" +) + +# Function to install missing packages +install_if_missing <- function(pkg) { + if (!requireNamespace(pkg, quietly = TRUE)) { + message(paste("Installing package:", pkg)) + install.packages(pkg, repos = "https://cloud.r-project.org") + } else { + message(paste("Package already installed:", pkg)) + } +} + +# Install missing packages +for (pkg in required_packages) { + install_if_missing(pkg) +} + +# Load core packages to verify installation +library(here) +library(tidyverse) +library(sf) +library(terra) + +message("All required packages have been installed!") diff --git a/r_app/interpolate_growth_model.R b/r_app/interpolate_growth_model.R index 2813f1e..189f4f4 100644 --- a/r_app/interpolate_growth_model.R +++ b/r_app/interpolate_growth_model.R @@ -1,124 +1,102 @@ -library(sf) -library(terra) -library(tidyverse) -library(lubridate) -library(exactextractr) +# filepath: c:\Users\timon\Resilience BV\4020 SCane ESA DEMO - Documenten\General\4020 SCDEMO Team\4020 TechnicalData\WP3\smartcane\r_app\interpolate_growth_model.R +# +# INTERPOLATE_GROWTH_MODEL.R +# ========================= +# This script interpolates CI (Chlorophyll Index) values between measurement dates +# to create a continuous growth model. It generates daily values and cumulative +# CI statistics for each field. +# +# Usage: Rscript interpolate_growth_model.R [project_dir] +# - project_dir: Project directory name (e.g., "chemba") +# -# Vang alle command line argumenten op -args <- commandArgs(trailingOnly = TRUE) - -# Converteer het tweede argument naar een string waarde -project_dir <- as.character(args[1]) - -# Controleer of data_dir een geldige waarde is -if (!is.character(project_dir)) { - project_dir <- "chemba" -} - -source("parameters_project.R") -source("ci_extraction_utils.R") -# Check if the file exists -file_path <- here(cumulative_CI_vals_dir, "combined_CI_data.rds") -pivot_stats2 <- data.frame() -if (file.exists(file_path)) { - pivot_stats2 <- readRDS(file_path) %>% - ungroup() %>% - group_by(field, sub_field) %>% - summarise(across(everything(), ~ first(na.omit(.))), .groups = "drop") -} -head(pivot_stats2) - #%>% drop_na(pivot_quadrant) - -# gather data into long format for easier calculation and visualisation -pivot_stats_long <- pivot_stats2 %>% - tidyr::gather("Date", value, -field, -sub_field ) %>% - mutate(#Date = right(Date, 8), - Date = lubridate::ymd(Date) - ) %>% - drop_na(c("value","Date")) %>% - mutate(value = as.numeric(value))%>% - filter_all(all_vars(!is.infinite(.))) %>% - # rename(field = pivot_quadrant, - # sub_field = field) %>% - unique() - -years <- harvesting_data %>% - filter(!is.na(season_start)) %>% - distinct(year) %>% - pull(year) - -extract_CI_data <- function(field_names, harvesting_data, field_CI_data, season) { - # Filter harvesting data for the given season and field names - filtered_harvesting_data <- harvesting_data %>% - filter(year == season, sub_field %in% field_names) - - # Filter field CI data for the given field names - filtered_field_CI_data <- field_CI_data %>% - filter(sub_field %in% field_names) - - # Return an empty data frame if no CI data is found - if (nrow(filtered_field_CI_data) == 0) { - return(data.frame()) - } - - - # Create a linear interpolation function for the CI data - ApproxFun <- approxfun(x = filtered_field_CI_data$Date, y = filtered_field_CI_data$value) - Dates <- seq.Date(ymd(min(filtered_field_CI_data$Date)), ymd(max(filtered_field_CI_data$Date)), by = 1) - LinearFit <- ApproxFun(Dates) - # Combine interpolated data with the original CI data - - CI <- data.frame(Date = Dates, FitData = LinearFit) %>% - left_join(., filtered_field_CI_data, by = "Date") %>% - filter(Date > filtered_harvesting_data$season_start & Date < filtered_harvesting_data$season_end) - - # If CI is empty after filtering, return an empty dataframe - if (nrow(CI) == 0) { - message ('CI empty after filtering') - return(data.frame()) - } - - # Add additional columns if data exists - CI <- CI %>% - mutate(DOY = seq(1, n(), 1), - model = paste0("Data", season, " : ", field_names), - season = season, - subField = field_names) - - return(CI) -} - -message(harvesting_data) - -CI_all <- map_df(years, function(yr) { - # yr = 2021 - message(yr) - - # Get the fields harvested in this year - sub_fields <- harvesting_data %>% - filter(year == yr) %>% - filter(!is.na(season_start)) %>% - pull(sub_field) - - - # Filter sub_fields to only include those with value data in pivot_stats_long - valid_sub_fields <- sub_fields %>% - keep(~ any(pivot_stats_long$sub_field == .x)) - - # Extract data for each valid field - map(valid_sub_fields, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = yr)) %>% - list_rbind() +# 1. Load required packages +# ----------------------- +suppressPackageStartupMessages({ + library(tidyverse) + library(lubridate) + library(here) }) -# it will crash here if CI_all is empty and will not overwrite the rds rendering growth_model.R useless -# if(nrow(CI_all) > 0){ - CI_all <- CI_all %>% - group_by(model) %>% - mutate(CI_per_day = FitData - lag(FitData), cumulative_CI = cumsum(FitData)) -# } +# 2. Main function to handle interpolation +# ------------------------------------- +main <- function() { + # Process command line arguments + args <- commandArgs(trailingOnly = TRUE) + + # Get project directory from arguments or use default + if (length(args) >= 1 && !is.na(args[1])) { + project_dir <- as.character(args[1]) + } else { + project_dir <- "chemba" + message("No project_dir provided. Using default:", project_dir) + } + + # Make project_dir available globally so parameters_project.R can use it + assign("project_dir", project_dir, envir = .GlobalEnv) + + # Initialize project configuration and load utility functions + tryCatch({ + source("parameters_project.R") + source("growth_model_utils.R") + }, error = function(e) { + warning("Default source files not found. Attempting to source from 'r_app' directory.") + tryCatch({ + source(here::here("r_app", "parameters_project.R")) + source(here::here("r_app", "growth_model_utils.R")) + warning(paste("Successfully sourced files from 'r_app' directory.")) + }, error = function(e) { + stop("Failed to source required files from both default and 'r_app' directories.") + }) + }) + + log_message("Starting CI growth model interpolation") + + # Load and process the data + tryCatch({ + # Load the combined CI data + CI_data <- load_combined_ci_data(cumulative_CI_vals_dir) + + # Validate harvesting data + if (is.null(harvesting_data) || nrow(harvesting_data) == 0) { + stop("No harvesting data available") + } + + # Get the years from harvesting data + years <- harvesting_data %>% + filter(!is.na(season_start)) %>% + distinct(year) %>% + pull(year) + + log_message(paste("Processing data for years:", paste(years, collapse = ", "))) + + # Generate interpolated CI data for each year and field + CI_all <- generate_interpolated_ci_data(years, harvesting_data, CI_data) + + # Calculate growth metrics and save the results + if (nrow(CI_all) > 0) { + # Add daily and cumulative metrics + CI_all_with_metrics <- calculate_growth_metrics(CI_all) + + # Save the processed data + save_growth_model( + CI_all_with_metrics, + cumulative_CI_vals_dir, + "All_pivots_Cumulative_CI_quadrant_year_v2.rds" + ) + } else { + log_message("No CI data was generated after interpolation", level = "WARNING") + } + + log_message("Growth model interpolation completed successfully") + + }, error = function(e) { + log_message(paste("Error in growth model interpolation:", e$message), level = "ERROR") + stop(e$message) + }) +} - -saveRDS(CI_all, here(cumulative_CI_vals_dir,"All_pivots_Cumulative_CI_quadrant_year_v2.rds")) -message('rds saved') - +if (sys.nframe() == 0) { + main() +} diff --git a/r_app/mosaic_creation.R b/r_app/mosaic_creation.R index 85a3d18..7f92d9f 100644 --- a/r_app/mosaic_creation.R +++ b/r_app/mosaic_creation.R @@ -1,137 +1,119 @@ -library(sf) -library(terra) -library(tidyverse) -library(lubridate) +# filepath: c:\Users\timon\Resilience BV\4020 SCane ESA DEMO - Documenten\General\4020 SCDEMO Team\4020 TechnicalData\WP3\smartcane\r_app\mosaic_creation.R +# +# MOSAIC_CREATION.R +# =============== +# This script creates weekly mosaics from daily satellite imagery. +# It handles command-line arguments and initiates the mosaic creation process. +# +# Usage: Rscript mosaic_creation.R [end_date] [offset] [project_dir] [file_name] +# - end_date: End date for processing (YYYY-MM-DD format) +# - offset: Number of days to look back from end_date +# - project_dir: Project directory name (e.g., "chemba") +# - file_name: Optional custom output file name +# -# Vang alle command line argumenten op -args <- commandArgs(trailingOnly = TRUE) +# 1. Load required packages +# ----------------------- +suppressPackageStartupMessages({ + library(sf) + library(terra) + library(tidyverse) + library(lubridate) + library(here) +}) -# Controleer of er ten minste één argument is doorgegeven -if (length(args) == 0) { - stop("Geen argumenten doorgegeven aan het script") -} - -# Converteer het eerste argument naar een numerieke waarde -end_date <- as.Date(args[1]) - - -offset <- as.numeric(args[2]) -# Controleer of weeks_ago een geldig getal is -if (is.na(offset)) { - # stop("Het argument is geen geldig getal") - offset <- 7 -} - -# Converteer het tweede argument naar een string waarde -project_dir <- as.character(args[3]) - -# Controleer of data_dir een geldige waarde is -if (!is.character(project_dir)) { - project_dir <- "chemba" -} - -source("parameters_project.R") -source("mosaic_creation_utils.R") - -week <- week(end_date) - -dates <- date_list(end_date, offset) - -file_name_tif <- as.character(args[4]) -if (is.na(file_name_tif)) { - file_name_tif <- paste0("week_", sprintf("%02d", dates$week), "_", dates$year, ".tif") -} - -print(dates) -print(file_name_tif) -#load pivot geojson -# pivot_sf_q <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(pivot, pivot_quadrant) %>% vect() - -vrt_files <- list.files(here(daily_vrt),full.names = T) -vrt_list <- map(dates$days_filter, ~ vrt_files[grepl(pattern = .x, x = vrt_files)]) %>% - compact() %>% - flatten_chr() - -raster_files_final <- list.files(merged_final,full.names = T, pattern = ".tif") - -if (length(vrt_list) > 0) { - print("vrt list made, preparing mosaic creation by counting cloud cover") +# 2. Process command line arguments and run mosaic creation +# ------------------------------------------------------ +main <- function() { + # Capture command line arguments + args <- commandArgs(trailingOnly = TRUE) - total_pix_area <- - rast(vrt_list[1]) %>% terra::subset(1) %>% setValues(1) %>% - crop(field_boundaries, mask = TRUE) %>% - global(., fun = "notNA") #%>% - - layer_5_list <- purrr::map(vrt_list, function(vrt_list) { - rast(vrt_list[1]) %>% terra::subset(1) - }) %>% rast() - - missing_pixels_count <- - layer_5_list %>% global(., fun = "notNA") %>% - mutate( - total_pixels = total_pix_area$notNA, - missing_pixels_percentage = round(100 - (( - notNA / total_pix_area$notNA - ) * 100)), - thres_5perc = as.integer(missing_pixels_percentage < 5), - thres_40perc = as.integer(missing_pixels_percentage < 45) - ) - - index_5perc <- which(missing_pixels_count$thres_5perc == max(missing_pixels_count$thres_5perc)) - index_40perc <- which(missing_pixels_count$thres_40perc == max(missing_pixels_count$thres_40perc)) - - ## Create mosaic - - if (sum(missing_pixels_count$thres_5perc) > 1) { - message("More than 1 raster without clouds (<5%), max composite made") - - cloudy_rasters_list <- vrt_list[index_5perc] - - rsrc <- sprc(cloudy_rasters_list) - x <- terra::mosaic(rsrc, fun = "max") - names(x) <- c("Red", "Green", "Blue", "NIR", "CI") - - } else if (sum(missing_pixels_count$thres_5perc) == 1) { - message("Only 1 raster without clouds (<5%)") - - x <- rast(vrt_list[index_5perc[1]]) - names(x) <- c("Red", "Green", "Blue", "NIR", "CI") - - } else if (sum(missing_pixels_count$thres_40perc) > 1) { - message("More than 1 image contains clouds, composite made of <40% cloud cover images") - - cloudy_rasters_list <- vrt_list[index_40perc] - - rsrc <- sprc(cloudy_rasters_list) - x <- mosaic(rsrc, fun = "max") - names(x) <- c("Red", "Green", "Blue", "NIR", "CI") - - } else if (sum(missing_pixels_count$thres_40perc) == 1) { - message("Only 1 image available but contains clouds") - - x <- rast(vrt_list[index_40perc[1]]) - names(x) <- c("Red", "Green", "Blue", "NIR", "CI") - - } else if (sum(missing_pixels_count$thres_40perc) == 0) { - message("No cloud free images available, all images combined") - - rsrc <- sprc(vrt_list) - x <- mosaic(rsrc, fun = "max") - names(x) <- c("Red", "Green", "Blue", "NIR", "CI") - + # Process project_dir argument with default + if (length(args) >= 3 && !is.na(args[3])) { + project_dir <- as.character(args[3]) + } else { + # Default project directory + project_dir <- "chemba" + message("No project_dir provided. Using default:", project_dir) } -} else{ - message("No images available this week, empty mosaic created") + # Make project_dir available globally so parameters_project.R can use it + assign("project_dir", project_dir, envir = .GlobalEnv) - x <- rast(raster_files_final[1]) %>% setValues(0) %>% - crop(field_boundaries, mask = TRUE) + # Process end_date argument with default + if (length(args) >= 1 && !is.na(args[1])) { + end_date <- as.Date(args[1]) + if (is.na(end_date)) { + message("Invalid end_date provided. Using current date.") + end_date <- Sys.Date() + #end_date <- "2024-08-25" # Default date for testing + } + } else { + # Default to current date if no argument is provided + end_date <- Sys.Date() + #end_date <- "2024-08-25" # Default date for testing + message("No end_date provided. Using current date: ", format(end_date)) + } + + # Process offset argument with default + if (length(args) >= 2 && !is.na(args[2])) { + offset <- as.numeric(args[2]) + if (is.na(offset) || offset <= 0) { + message("Invalid offset provided. Using default (7 days).") + offset <- 7 + } + } else { + # Default to 7 days if no argument is provided + offset <- 7 + message("No offset provided. Using default:", offset, "days") + } - names(x) <- c("Red", "Green", "Blue", "NIR", "CI") -} -plot(x$CI, main = paste("CI map ", dates$week)) -plotRGB(x, main = paste("RGB map ", dates$week)) -file_path_tif <- here(weekly_CI_mosaic ,file_name_tif) -writeRaster(x, file_path_tif, overwrite=TRUE) -message("Raster written/made at: ", file_path_tif) + + # 3. Initialize project configuration + # -------------------------------- + tryCatch({ + source("parameters_project.R") + source("mosaic_creation_utils.R") + safe_log(paste("Successfully sourced files from default directory.")) + }, error = function(e) { + warning("Default source files not found. Attempting to source from 'r_app' directory.") + tryCatch({ + source(here::here("r_app", "parameters_project.R")) + source(here::here("r_app", "mosaic_creation_utils.R")) + warning(paste("Successfully sourced files from 'r_app' directory.")) + }, error = function(e) { + stop("Failed to source required files from both default and 'r_app' directories.") + }) + }) + + # 4. Generate date range for processing + # --------------------------------- + dates <- date_list(end_date, offset) + safe_log(paste("Processing data for week", dates$week, "of", dates$year)) + + # Create output filename + file_name_tif <- if (length(args) >= 4 && !is.na(args[4])) { + as.character(args[4]) + } else { + paste0("week_", sprintf("%02d", dates$week), "_", dates$year, ".tif") + } + + safe_log(paste("Output will be saved as:", file_name_tif)) + + # 5. Create weekly mosaic using the function from utils + # ------------------------------------------------- + create_weekly_mosaic( + dates = dates, + field_boundaries = field_boundaries, + daily_vrt_dir = daily_vrt, + merged_final_dir = merged_final, + output_dir = weekly_CI_mosaic, + file_name_tif = file_name_tif, + create_plots = TRUE + ) +} + +if (sys.nframe() == 0) { + main() +} diff --git a/r_app/mosaic_creation_utils.R b/r_app/mosaic_creation_utils.R index 68aadb8..5891cc6 100644 --- a/r_app/mosaic_creation_utils.R +++ b/r_app/mosaic_creation_utils.R @@ -1,13 +1,424 @@ -# utils for mosaic creation +# MOSAIC_CREATION_UTILS.R +# ====================== +# Utility functions for creating weekly mosaics from daily satellite imagery. +# These functions support cloud cover assessment, date handling, and mosaic creation. -date_list <- function(end_date, offset){ - offset <- as.numeric(offset) - 1 - end_date <- as.Date(end_date) +#' Safe logging function +#' @param message The message to log +#' @param level The log level (default: "INFO") +#' @return NULL (used for side effects) +#' +safe_log <- function(message, level = "INFO") { + if (exists("log_message")) { + log_message(message, level) + } else { + if (level %in% c("ERROR", "WARNING")) { + warning(message) + } else { + message(message) + } + } +} + +#' Generate a sequence of dates for processing +#' +#' @param end_date The end date for the sequence (Date object) +#' @param offset Number of days to look back from end_date +#' @return A list containing week number, year, and a sequence of dates for filtering +#' +date_list <- function(end_date, offset) { + # Input validation + if (!lubridate::is.Date(end_date)) { + end_date <- as.Date(end_date) + if (is.na(end_date)) { + stop("Invalid end_date provided. Expected a Date object or a string convertible to Date.") + } + } + + offset <- as.numeric(offset) + if (is.na(offset) || offset < 1) { + stop("Invalid offset provided. Expected a positive number.") + } + + # Calculate date range + offset <- offset - 1 # Adjust offset to include end_date start_date <- end_date - lubridate::days(offset) - week <- week(start_date) - year <- year(start_date) - days_filter <- seq(from = start_date, to = end_date, by = "day") + # Extract week and year information + week <- lubridate::week(start_date) + year <- lubridate::year(start_date) - return(list("week" = week, "year" = year, "days_filter" = days_filter)) + # Generate sequence of dates + days_filter <- seq(from = start_date, to = end_date, by = "day") + days_filter <- format(days_filter, "%Y-%m-%d") # Format for consistent filtering + + # Log the date range + safe_log(paste("Date range generated from", start_date, "to", end_date)) + + return(list( + "week" = week, + "year" = year, + "days_filter" = days_filter, + "start_date" = start_date, + "end_date" = end_date + )) +} + +#' Create a weekly mosaic from available VRT files +#' +#' @param dates List from date_list() with date range info +#' @param field_boundaries Field boundaries for image cropping +#' @param daily_vrt_dir Directory containing VRT files +#' @param merged_final_dir Directory with merged final rasters +#' @param output_dir Output directory for weekly mosaics +#' @param file_name_tif Output filename for the mosaic +#' @param create_plots Whether to create visualization plots (default: TRUE) +#' @return The file path of the saved mosaic +#' +create_weekly_mosaic <- function(dates, field_boundaries, daily_vrt_dir, + merged_final_dir, output_dir, file_name_tif, + create_plots = TRUE) { + # Find VRT files for the specified date range + vrt_list <- find_vrt_files(daily_vrt_dir, dates) + + # Find final raster files for fallback + raster_files_final <- list.files(merged_final_dir, full.names = TRUE, pattern = "\\.tif$") + + # Process the mosaic if VRT files are available + if (length(vrt_list) > 0) { + safe_log("VRT list created, assessing cloud cover for mosaic creation") + + # Calculate cloud cover statistics + missing_pixels_count <- count_cloud_coverage(vrt_list, field_boundaries) + + # Create mosaic based on cloud cover assessment + mosaic <- create_mosaic(vrt_list, missing_pixels_count, field_boundaries, raster_files_final) + + } else { + safe_log("No VRT files available for the date range, creating empty mosaic", "WARNING") + + # Create empty mosaic if no files are available + if (length(raster_files_final) == 0) { + stop("No VRT files or final raster files available to create mosaic") + } + + mosaic <- terra::rast(raster_files_final[1]) %>% + terra::setValues(0) %>% + terra::crop(field_boundaries, mask = TRUE) + + names(mosaic) <- c("Red", "Green", "Blue", "NIR", "CI") + } + + # Save the mosaic + file_path <- save_mosaic(mosaic, output_dir, file_name_tif, create_plots) + + safe_log(paste("Weekly mosaic processing completed for week", dates$week)) + + return(file_path) +} + +#' Find VRT files within a date range +#' +#' @param vrt_directory Directory containing VRT files +#' @param dates List from date_list() function containing days_filter +#' @return Character vector of VRT file paths +#' +find_vrt_files <- function(vrt_directory, dates) { + # Get all VRT files in directory + vrt_files <- list.files(here::here(vrt_directory), full.names = TRUE) + + if (length(vrt_files) == 0) { + warning("No VRT files found in directory: ", vrt_directory) + return(character(0)) + } + + # Filter files by dates + vrt_list <- purrr::map(dates$days_filter, ~ vrt_files[grepl(pattern = .x, x = vrt_files)]) %>% + purrr::compact() %>% + purrr::flatten_chr() + + # Log results + safe_log(paste("Found", length(vrt_list), "VRT files for the date range")) + + return(vrt_list) +} + +#' Count missing pixels (clouds) in rasters +#' +#' @param vrt_list List of VRT files to analyze +#' @param field_boundaries Field boundaries vector for masking +#' @return Data frame with cloud coverage statistics +#' +count_cloud_coverage <- function(vrt_list, field_boundaries) { + if (length(vrt_list) == 0) { + warning("No VRT files provided for cloud coverage calculation") + return(NULL) + } + + tryCatch({ + # Calculate total pixel area using the first VRT file + total_pix_area <- terra::rast(vrt_list[1]) |> + terra::subset(1) |> + terra::setValues(1) |> + terra::crop(field_boundaries, mask = TRUE) |> + terra::global(fun = "notNA") + + # Process each raster to detect clouds and shadows + processed_rasters <- list() + cloud_masks <- list() + + # Create data frame for missing pixels count + missing_pixels_df <- data.frame( + filename = vrt_list, + notNA = numeric(length(vrt_list)), + total_pixels = numeric(length(vrt_list)), + missing_pixels_percentage = numeric(length(vrt_list)), + thres_5perc = numeric(length(vrt_list)), + thres_40perc = numeric(length(vrt_list)) + ) + # Fill in the data frame with missing pixel statistics + for (i in seq_along(processed_rasters)) { + notna_count <- terra::global(processed_rasters[[i]][[1]], fun = "notNA")$notNA + missing_pixels_df$notNA[i] <- notna_count + missing_pixels_df$total_pixels[i] <- total_pix_area$notNA + missing_pixels_df$missing_pixels_percentage[i] <- round(100 - ((notna_count / total_pix_area$notNA) * 100)) + missing_pixels_df$thres_5perc[i] <- as.integer(missing_pixels_df$missing_pixels_percentage[i] < 5) + missing_pixels_df$thres_40perc[i] <- as.integer(missing_pixels_df$missing_pixels_percentage[i] < 45) + } + + # Store processed rasters and cloud masks as attributes + attr(missing_pixels_df, "cloud_masks") <- cloud_masks + attr(missing_pixels_df, "processed_rasters") <- processed_rasters + + # Log results + safe_log(paste( + "Cloud cover assessment completed for", length(vrt_list), "files.", + sum(missing_pixels_df$thres_5perc), "files with <5% cloud cover,", + sum(missing_pixels_df$thres_40perc), "files with <45% cloud cover" + )) + return(missing_pixels_df) + }, error = function(e) { + warning("Error in cloud coverage calculation: ", e$message) + return(NULL) + }) +} + +#' Create a mosaic from VRT files based on cloud coverage +#' +#' @param vrt_list List of VRT files to create mosaic from +#' @param missing_pixels_count Cloud coverage statistics from count_cloud_coverage() +#' @param field_boundaries Field boundaries vector for masking (optional) +#' @param raster_files_final List of processed raster files to use as fallback +#' @return A SpatRaster object with the mosaic +#' +create_mosaic <- function(vrt_list, missing_pixels_count, field_boundaries = NULL, raster_files_final = NULL) { + # If no VRT files, create an empty mosaic + if (length(vrt_list) == 0) { + if (length(raster_files_final) == 0 || is.null(field_boundaries)) { + stop("No VRT files available and no fallback raster files or field boundaries provided") + } + + safe_log("No images available for this period, creating empty mosaic", "WARNING") + + x <- terra::rast(raster_files_final[1]) |> + terra::setValues(0) |> + terra::crop(field_boundaries, mask = TRUE) + + names(x) <- c("Red", "Green", "Blue", "NIR", "CI") + return(x) + } + + # If missing pixel count was not calculated, use all files + if (is.null(missing_pixels_count)) { + safe_log("No cloud coverage data available, using all images", "WARNING") + + rsrc <- terra::sprc(vrt_list) + x <- terra::mosaic(rsrc, fun = "max") + names(x) <- c("Red", "Green", "Blue", "NIR", "CI") + return(x) + } + + # Check if we have processed rasters from cloud detection + processed_rasters <- attr(missing_pixels_count, "processed_rasters") + cloud_masks <- attr(missing_pixels_count, "cloud_masks") + + if (!is.null(processed_rasters) && length(processed_rasters) > 0) { + safe_log("Using cloud-masked rasters for mosaic creation") + + # Determine best rasters to use based on cloud coverage + index_5perc <- which(missing_pixels_count$thres_5perc == max(missing_pixels_count$thres_5perc)) + index_40perc <- which(missing_pixels_count$thres_40perc == max(missing_pixels_count$thres_40perc)) + + # Create mosaic based on available cloud-free images + if (sum(missing_pixels_count$thres_5perc) > 1) { + safe_log("Creating max composite from multiple cloud-free images (<5% clouds)") + + # Use the cloud-masked rasters instead of original files + cloudy_rasters_list <- processed_rasters[index_5perc] + rsrc <- terra::sprc(cloudy_rasters_list) + x <- terra::mosaic(rsrc, fun = "max") + + # Also create a composite mask showing where data is valid + mask_list <- cloud_masks[index_5perc] + mask_rsrc <- terra::sprc(mask_list) + mask_composite <- terra::mosaic(mask_rsrc, fun = "max") + attr(x, "cloud_mask") <- mask_composite + + } else if (sum(missing_pixels_count$thres_5perc) == 1) { + safe_log("Using single cloud-free image (<5% clouds)") + + # Use the cloud-masked raster + x <- processed_rasters[[index_5perc[1]]] + attr(x, "cloud_mask") <- cloud_masks[[index_5perc[1]]] + + } else if (sum(missing_pixels_count$thres_40perc) > 1) { + safe_log("Creating max composite from partially cloudy images (<40% clouds)", "WARNING") + + # Use the cloud-masked rasters + cloudy_rasters_list <- processed_rasters[index_40perc] + rsrc <- terra::sprc(cloudy_rasters_list) + x <- terra::mosaic(rsrc, fun = "max") + + # Also create a composite mask + mask_list <- cloud_masks[index_40perc] + mask_rsrc <- terra::sprc(mask_list) + mask_composite <- terra::mosaic(mask_rsrc, fun = "max") + attr(x, "cloud_mask") <- mask_composite + + } else if (sum(missing_pixels_count$thres_40perc) == 1) { + safe_log("Using single partially cloudy image (<40% clouds)", "WARNING") + + # Use the cloud-masked raster + x <- processed_rasters[[index_40perc[1]]] + attr(x, "cloud_mask") <- cloud_masks[[index_40perc[1]]] + + } else { + safe_log("No cloud-free images available, using all cloud-masked images", "WARNING") + + # Use all cloud-masked rasters + rsrc <- terra::sprc(processed_rasters) + x <- terra::mosaic(rsrc, fun = "max") + + # Also create a composite mask + mask_rsrc <- terra::sprc(cloud_masks) + mask_composite <- terra::mosaic(mask_rsrc, fun = "max") + attr(x, "cloud_mask") <- mask_composite + } + } else { + # Fall back to original behavior if no cloud-masked rasters available + safe_log("No cloud-masked rasters available, using original images", "WARNING") + + # Determine best rasters to use based on cloud coverage + index_5perc <- which(missing_pixels_count$thres_5perc == max(missing_pixels_count$thres_5perc)) + index_40perc <- which(missing_pixels_count$thres_40perc == max(missing_pixels_count$thres_40perc)) + + # Create mosaic based on available cloud-free images + if (sum(missing_pixels_count$thres_5perc) > 1) { + safe_log("Creating max composite from multiple cloud-free images (<5% clouds)") + + cloudy_rasters_list <- vrt_list[index_5perc] + rsrc <- terra::sprc(cloudy_rasters_list) + x <- terra::mosaic(rsrc, fun = "max") + + } else if (sum(missing_pixels_count$thres_5perc) == 1) { + safe_log("Using single cloud-free image (<5% clouds)") + + x <- terra::rast(vrt_list[index_5perc[1]]) + + } else if (sum(missing_pixels_count$thres_40perc) > 1) { + safe_log("Creating max composite from partially cloudy images (<40% clouds)", "WARNING") + + cloudy_rasters_list <- vrt_list[index_40perc] + rsrc <- terra::sprc(cloudy_rasters_list) + x <- terra::mosaic(rsrc, fun = "max") + + } else if (sum(missing_pixels_count$thres_40perc) == 1) { + safe_log("Using single partially cloudy image (<40% clouds)", "WARNING") + + x <- terra::rast(vrt_list[index_40perc[1]]) + + } else { + safe_log("No cloud-free images available, using all images", "WARNING") + + rsrc <- terra::sprc(vrt_list) + x <- terra::mosaic(rsrc, fun = "max") + } + } + + # Set consistent layer names + names(x) <- c("Red", "Green", "Blue", "NIR", "CI") + return(x) +} + +#' Save a mosaic raster to disk +#' +#' @param mosaic_raster A SpatRaster object to save +#' @param output_dir Directory to save the output +#' @param file_name Filename for the output raster +#' @param plot_result Whether to create visualizations (default: FALSE) +#' @return The file path of the saved raster +#' +save_mosaic <- function(mosaic_raster, output_dir, file_name, plot_result = FALSE) { + # Validate input + if (is.null(mosaic_raster)) { + stop("No mosaic raster provided to save") + } + + # Create output directory if it doesn't exist + dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) + + # Create full file path + file_path <- here::here(output_dir, file_name) + + # Get cloud mask if it exists + cloud_mask <- attr(mosaic_raster, "cloud_mask") + + # Save raster + terra::writeRaster(mosaic_raster, file_path, overwrite = TRUE) + + # Save cloud mask if available + if (!is.null(cloud_mask)) { + # Create mask filename by adding _mask before extension + mask_file_name <- gsub("\\.(tif|TIF)$", "_mask.\\1", file_name) + mask_file_path <- here::here(output_dir, mask_file_name) + + # Save the mask + terra::writeRaster(cloud_mask, mask_file_path, overwrite = TRUE) + safe_log(paste("Cloud/shadow mask saved to:", mask_file_path)) + } + + # Create plots if requested + if (plot_result) { + # Plot the CI band + if ("CI" %in% names(mosaic_raster)) { + terra::plot(mosaic_raster$CI, main = paste("CI map", file_name)) + } + + # Plot RGB image + if (all(c("Red", "Green", "Blue") %in% names(mosaic_raster))) { + terra::plotRGB(mosaic_raster, main = paste("RGB map", file_name)) + } + + # Plot cloud mask if available + if (!is.null(cloud_mask)) { + terra::plot(cloud_mask, main = paste("Cloud/shadow mask", file_name), + col = c("red", "green")) + } + + # If we have both RGB and cloud mask, create a side-by-side comparison + if (all(c("Red", "Green", "Blue") %in% names(mosaic_raster)) && !is.null(cloud_mask)) { + old_par <- par(mfrow = c(1, 2)) + terra::plotRGB(mosaic_raster, main = "RGB Image") + + # Create a colored mask for visualization (red = cloud/shadow, green = clear) + mask_plot <- cloud_mask + terra::plot(mask_plot, main = "Cloud/Shadow Mask", col = c("red", "green")) + par(old_par) + } + } + + # Log save completion + safe_log(paste("Mosaic saved to:", file_path)) + + return(file_path) } diff --git a/r_app/packages.R b/r_app/packages.R new file mode 100644 index 0000000..d6534fa --- /dev/null +++ b/r_app/packages.R @@ -0,0 +1,117 @@ +# packages.R +# +# PACKAGE MANAGEMENT FOR SMARTCANE +# =============================== +# This script centralizes all package dependencies for the SmartCane project. +# It installs missing packages and loads all required libraries. +# + +#' Check and install packages if needed +#' +#' @param pkg_list List of packages to check and install +#' @param install_missing Whether to install missing packages +#' @return Vector of packages that couldn't be installed (if any) +#' +check_and_install_packages <- function(pkg_list, install_missing = TRUE) { + # Check which packages are already installed + is_installed <- pkg_list %in% rownames(installed.packages()) + missing_pkgs <- pkg_list[!is_installed] + + # Install missing packages if requested + failed_pkgs <- character(0) + if (length(missing_pkgs) > 0) { + if (install_missing) { + message("Installing ", length(missing_pkgs), " missing packages...") + for (pkg in missing_pkgs) { + tryCatch({ + install.packages(pkg, repos = "https://cran.rstudio.com/", dependencies = TRUE) + message(" Installed: ", pkg) + }, error = function(e) { + warning("Failed to install package: ", pkg) + warning("Error: ", e$message) + failed_pkgs <<- c(failed_pkgs, pkg) + }) + } + } else { + message("The following packages are required but not installed:") + message(paste(missing_pkgs, collapse = ", ")) + failed_pkgs <- missing_pkgs + } + } else { + message("All required packages are already installed.") + } + + return(failed_pkgs) +} + +#' Load all required packages for SmartCane project +#' +#' @param verbose Whether to show messages during loading +#' @return Logical indicating success (TRUE if all packages loaded) +#' +load_smartcane_packages <- function(verbose = FALSE) { + # Define all required packages + required_packages <- c( + # Geospatial packages + "sf", # Simple Features for spatial vector data + "terra", # Raster data processing + "exactextractr", # Fast extraction from rasters + "tmap", # Thematic mapping for spatial visualization + + # Data manipulation + "tidyverse", # Collection of data manipulation packages + "lubridate", # Date manipulation + "readxl", # Excel file reading + "stringr", # String manipulation + "purrr", # Functional programming tools + "zoo", # Time series processing with rolling functions + + # Visualization + "ggplot2", # Advanced plotting + "leaflet", # Interactive maps + "plotly", # Interactive plots + + # Machine learning and statistics + "caret", # Classification and regression training + "rsample", # Data sampling for modeling + "randomForest", # Random forest implementation + "CAST", # Feature selection for spatial data + + # Project management + "here", # Path handling + + # Document generation + "knitr", # Dynamic report generation + "rmarkdown" # R Markdown processing + ) + + # Check and install missing packages + failed_pkgs <- check_and_install_packages(required_packages) + + # Load all installed packages + success <- TRUE + for (pkg in setdiff(required_packages, failed_pkgs)) { + tryCatch({ + if (verbose) message("Loading package: ", pkg) + suppressPackageStartupMessages(library(pkg, character.only = TRUE)) + }, error = function(e) { + warning("Failed to load package: ", pkg) + warning("Error: ", e$message) + success <- FALSE + }) + } + + # Report any issues + if (length(failed_pkgs) > 0) { + warning("The following packages could not be installed: ", + paste(failed_pkgs, collapse = ", ")) + success <- FALSE + } + + return(success) +} + +# Run the loading function if the script is sourced directly +if (!exists("skip_package_loading") || !skip_package_loading) { + load_smartcane_packages() +} \ No newline at end of file diff --git a/r_app/parameters_project.R b/r_app/parameters_project.R index 6751c9b..1db88f5 100644 --- a/r_app/parameters_project.R +++ b/r_app/parameters_project.R @@ -1,82 +1,225 @@ -library(here) -library('readxl') -#chemba +# filepath: c:\Users\timon\Resilience BV\4020 SCane ESA DEMO - Documenten\General\4020 SCDEMO Team\4020 TechnicalData\WP3\smartcane\r_app\parameters_project.R +# +# PARAMETERS_PROJECT.R +# ==================== +# This script defines project parameters, directory structures, and loads field boundaries. +# It establishes all the necessary paths and creates required directories for the SmartCane project. -laravel_storage_dir <- here("laravel_app/storage/app", project_dir) -reports_dir <- here(laravel_storage_dir, "reports") -log_dir <- here(laravel_storage_dir, "logs") -planet_tif_folder <- here(laravel_storage_dir, "merged_tif") -merged_final <- here(laravel_storage_dir, "merged_final_tif") -planet_tif_folder <- here(laravel_storage_dir, "merged_tif") -merged_final <- here(laravel_storage_dir, "merged_final_tif") -data_dir <- here(laravel_storage_dir, "Data") -extracted_CI_dir <- here(data_dir, "extracted_ci") -daily_CI_vals_dir <- here(extracted_CI_dir, "daily_vals") -cumulative_CI_vals_dir <- here(extracted_CI_dir, "cumulative_vals") -weekly_CI_mosaic <- here(laravel_storage_dir, "weekly_mosaic") -daily_vrt <- here(data_dir, "vrt") -harvest_dir <- here(data_dir, "HarvestData") +# 1. Load required libraries +# ------------------------- +suppressPackageStartupMessages({ + library(here) + library(readxl) + library(sf) + library(dplyr) + library(tidyr) +}) -dir.create(here(laravel_storage_dir), showWarnings = FALSE) -dir.create(here(reports_dir), showWarnings = FALSE) -dir.create(here(data_dir), showWarnings = FALSE) -dir.create(here(log_dir), showWarnings = FALSE) -dir.create(here(extracted_CI_dir), showWarnings = FALSE) -dir.create(here(daily_CI_vals_dir), showWarnings = FALSE) -dir.create(here(cumulative_CI_vals_dir), showWarnings = FALSE) -dir.create(here(weekly_CI_mosaic),showWarnings = FALSE) -dir.create(here(daily_vrt), showWarnings = FALSE) -dir.create(merged_final,showWarnings = FALSE) -dir.create(harvest_dir,showWarnings = FALSE) - -field_boundaries_sf <- st_read(here(data_dir, "pivot.geojson"), crs = 4326) - -names(field_boundaries_sf) <- c("field", "sub_field", "geometry") - -field_boundaries <- field_boundaries_sf %>% terra::vect() - -harvesting_data <- read_excel(here(data_dir, "harvest.xlsx")) %>% - dplyr::select( - c( - "field", - "sub_field", - "year", - "season_start", - "season_end", - "age", - "sub_area", - "tonnage_ha" - ) - ) %>% - mutate( - field = as.character(field), - sub_field = as.character(sub_field), - year = as.numeric(year), - season_start = as.Date(season_start, format="%d/%m/%Y"), - season_end = as.Date(season_end, format="%d/%m/%Y"), - age = as.numeric(age), - sub_area = as.character(sub_area), - tonnage_ha = as.numeric(tonnage_ha) - ) %>% - mutate( - season_end = case_when(season_end > Sys.Date() ~ Sys.Date(), - is.na(season_end) ~ Sys.Date(), - TRUE ~ season_end), - age = round(as.numeric(season_end - season_start) / 7, 0) +# 2. Define project directory structure +# ----------------------------------- +setup_project_directories <- function(project_dir) { + # Base directories + laravel_storage_dir <- here("laravel_app/storage/app", project_dir) + + # Main subdirectories + dirs <- list( + reports = here(laravel_storage_dir, "reports"), + logs = here(laravel_storage_dir, "logs"), + data = here(laravel_storage_dir, "Data"), + tif = list( + merged = here(laravel_storage_dir, "merged_tif"), + final = here(laravel_storage_dir, "merged_final_tif") + ), + weekly_mosaic = here(laravel_storage_dir, "weekly_mosaic"), + extracted_ci = list( + base = here(laravel_storage_dir, "Data/extracted_ci"), + daily = here(laravel_storage_dir, "Data/extracted_ci/daily_vals"), + cumulative = here(laravel_storage_dir, "Data/extracted_ci/cumulative_vals") + ), + vrt = here(laravel_storage_dir, "Data/vrt"), + harvest = here(laravel_storage_dir, "Data/HarvestData") ) - -log_file <- here(log_dir, paste0(format(Sys.Date(), "%Y%m%d"), ".log")) - - - - - -# Create a logging function -log_message <- function(message) { - cat(message, "\n", file = log_file, append = TRUE) + + # Create all directories + for (dir_path in unlist(dirs)) { + dir.create(dir_path, showWarnings = FALSE, recursive = TRUE) + } + + # Return directory structure for use in other functions + return(list( + laravel_storage_dir = laravel_storage_dir, + reports_dir = dirs$reports, + log_dir = dirs$logs, + data_dir = dirs$data, + planet_tif_folder = dirs$tif$merged, + merged_final = dirs$tif$final, + daily_CI_vals_dir = dirs$extracted_ci$daily, + cumulative_CI_vals_dir = dirs$extracted_ci$cumulative, + weekly_CI_mosaic = dirs$weekly_mosaic, + daily_vrt = dirs$vrt, + harvest_dir = dirs$harvest, + extracted_CI_dir = dirs$extracted_ci$base + )) } - -log_head <- function(list) { - log_message(paste(capture.output(str(head(list))), collapse = "\n")) +# 3. Load field boundaries +# ---------------------- +load_field_boundaries <- function(data_dir) { + field_boundaries_path <- here(data_dir, "pivot.geojson") + + if (!file.exists(field_boundaries_path)) { + stop(paste("Field boundaries file not found at path:", field_boundaries_path)) + } + + tryCatch({ + field_boundaries_sf <- st_read(field_boundaries_path, crs = 4326, quiet = TRUE) + names(field_boundaries_sf) <- c("field", "sub_field", "geometry") + field_boundaries <- terra::vect(field_boundaries_sf) + + return(list( + field_boundaries_sf = field_boundaries_sf, + field_boundaries = field_boundaries + )) + }, error = function(e) { + stop(paste("Error loading field boundaries:", e$message)) + }) +} + +# 4. Load harvesting data +# --------------------- +load_harvesting_data <- function(data_dir) { + harvest_file <- here(data_dir, "harvest.xlsx") + + if (!file.exists(harvest_file)) { + warning(paste("Harvest data file not found at path:", harvest_file)) + return(NULL) + } + + tryCatch({ + harvesting_data <- read_excel(harvest_file) %>% + dplyr::select( + c( + "field", + "sub_field", + "year", + "season_start", + "season_end", + "age", + "sub_area", + "tonnage_ha" + ) + ) %>% + mutate( + field = as.character(field), + sub_field = as.character(sub_field), + year = as.numeric(year), + season_start = as.Date(season_start, format="%d/%m/%Y"), + season_end = as.Date(season_end, format="%d/%m/%Y"), + age = as.numeric(age), + sub_area = as.character(sub_area), + tonnage_ha = as.numeric(tonnage_ha) + ) %>% + mutate( + season_end = case_when( + season_end > Sys.Date() ~ Sys.Date(), + is.na(season_end) ~ Sys.Date(), + TRUE ~ season_end + ), + age = round(as.numeric(season_end - season_start) / 7, 0) + ) + + return(harvesting_data) + }, error = function(e) { + warning(paste("Error loading harvesting data:", e$message)) + return(NULL) + }) +} + +# 5. Define logging functions globally first +# --------------------------------------- +# Create a simple default log function in case setup_logging hasn't been called yet +log_message <- function(message, level = "INFO") { + timestamp <- format(Sys.time(), "%Y-%m-%d %H:%M:%S") + formatted_message <- paste0("[", level, "] ", timestamp, " - ", message) + cat(formatted_message, "\n") +} + +log_head <- function(list, level = "INFO") { + log_message(paste(capture.output(str(head(list))), collapse = "\n"), level) +} + +# 6. Set up full logging system with file output +# ------------------------------------------- +setup_logging <- function(log_dir) { + log_file <- here(log_dir, paste0(format(Sys.Date(), "%Y%m%d"), ".log")) + + # Create enhanced log functions + log_message <- function(message, level = "INFO") { + timestamp <- format(Sys.time(), "%Y-%m-%d %H:%M:%S") + formatted_message <- paste0("[", level, "] ", timestamp, " - ", message) + cat(formatted_message, "\n", file = log_file, append = TRUE) + + # Also print to console for debugging + if (level %in% c("ERROR", "WARNING")) { + cat(formatted_message, "\n") + } + } + + log_head <- function(list, level = "INFO") { + log_message(paste(capture.output(str(head(list))), collapse = "\n"), level) + } + + # Update the global functions with the enhanced versions + assign("log_message", log_message, envir = .GlobalEnv) + assign("log_head", log_head, envir = .GlobalEnv) + + return(list( + log_file = log_file, + log_message = log_message, + log_head = log_head + )) +} + +# 7. Initialize the project +# ---------------------- +# Export project directories and settings +initialize_project <- function(project_dir) { + # Set up directory structure + dirs <- setup_project_directories(project_dir) + + # Set up logging + logging <- setup_logging(dirs$log_dir) + + # Load field boundaries + boundaries <- load_field_boundaries(dirs$data_dir) + + # Load harvesting data + harvesting_data <- load_harvesting_data(dirs$data_dir) + + # Return all initialized components + return(c( + dirs, + list( + logging = logging, + field_boundaries = boundaries$field_boundaries, + field_boundaries_sf = boundaries$field_boundaries_sf, + harvesting_data = harvesting_data + ) + )) +} + +# When script is sourced, initialize with the global project_dir variable if it exists +if (exists("project_dir")) { + # Now we can safely log before initialization + log_message(paste("Initializing project with directory:", project_dir)) + + project_config <- initialize_project(project_dir) + + # Expose all variables to the global environment + list2env(project_config, envir = .GlobalEnv) + + # Log project initialization completion + log_message(paste("Project initialized with directory:", project_dir)) +} else { + warning("project_dir variable not found. Please set project_dir before sourcing parameters_project.R") } diff --git a/r_app/report_utils.R b/r_app/report_utils.R index 065d782..930ec3c 100644 --- a/r_app/report_utils.R +++ b/r_app/report_utils.R @@ -1,3 +1,34 @@ +# REPORT_UTILS.R +# ============= +# Utility functions for generating SmartCane reports with visualizations. +# These functions support the creation of maps, charts and report elements +# for the CI_report_dashboard_planet.Rmd document. + +#' Safe logging function that works whether log_message exists or not +#' +#' @param message The message to log +#' @param level The log level (default: "INFO") +#' @return NULL (used for side effects) +#' +safe_log <- function(message, level = "INFO") { + if (exists("log_message")) { + log_message(message, level) + } else { + if (level %in% c("ERROR", "WARNING")) { + warning(message) + } else { + message(message) + } + } +} + +#' Creates a sub-chunk for use within RMarkdown documents +#' +#' @param g A ggplot object to render in the sub-chunk +#' @param fig_height Height of the figure in inches +#' @param fig_width Width of the figure in inches +#' @return NULL (writes chunk directly to output) +#' subchunkify <- function(g, fig_height=7, fig_width=5) { g_deparsed <- paste0(deparse( function() {g} @@ -14,12 +45,48 @@ subchunkify <- function(g, fig_height=7, fig_width=5) { cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE)) } +#' Creates a Chlorophyll Index map for a pivot +#' +#' @param pivot_raster The raster data containing CI values +#' @param pivot_shape The shape of the pivot field +#' @param pivot_spans Additional boundary data for the field +#' @param show_legend Whether to show the legend (default: FALSE) +#' @param legend_is_portrait Whether to show the legend in portrait orientation (default: FALSE) +#' @param week Week number to display in the title +#' @param age Age of the crop in weeks +#' @param borders Whether to display field borders (default: FALSE) +#' @return A tmap object with the CI map +#' create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week, age, borders = FALSE){ - map <- tm_shape(pivot_raster, unit = "m") + - tm_raster(breaks = c(0,0.5,1,2,3,4,5,6,7,Inf), palette = "RdYlGn",legend.is.portrait = legend_is_portrait ,midpoint = NA) + - tm_layout(main.title = paste0("\nMax CI week ", week,"\n", age, " weeks old"), - main.title.size = 0.7, legend.show = show_legend) + # Input validation + if (missing(pivot_raster) || is.null(pivot_raster)) { + stop("pivot_raster is required") + } + if (missing(pivot_shape) || is.null(pivot_shape)) { + stop("pivot_shape is required") + } + if (missing(pivot_spans) || is.null(pivot_spans)) { + stop("pivot_spans is required") + } + if (missing(week) || is.null(week)) { + stop("week parameter is required") + } + if (missing(age) || is.null(age)) { + stop("age parameter is required") + } + # Create the base map + map <- tm_shape(pivot_raster, unit = "m") # Add raster with continuous spectrum (fixed scale 1-8 for consistent comparison) + map <- map + tm_raster(col.scale = tm_scale_continuous(values = "brewer.rd_yl_gn", + limits = c(1, 8)), + col.legend = tm_legend(title = "CI", + orientation = if(legend_is_portrait) "portrait" else "landscape", + show = show_legend, + position = c("left", "bottom"))) + # Add layout elements + map <- map + tm_layout(main.title = paste0("Max CI week ", week,"\n", age, " weeks old"), + main.title.size = 0.7) + # Add borders if requested if (borders) { map <- map + tm_shape(pivot_shape) + @@ -32,12 +99,50 @@ create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = return(map) } +#' Creates a Chlorophyll Index difference map between two weeks +#' +#' @param pivot_raster The raster data containing CI difference values +#' @param pivot_shape The shape of the pivot field +#' @param pivot_spans Additional boundary data for the field +#' @param show_legend Whether to show the legend (default: FALSE) +#' @param legend_is_portrait Whether to show the legend in portrait orientation (default: FALSE) +#' @param week_1 First week number for comparison +#' @param week_2 Second week number for comparison +#' @param age Age of the crop in weeks +#' @param borders Whether to display field borders (default: TRUE) +#' @return A tmap object with the CI difference map +#' create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week_1, week_2, age, borders = TRUE){ - map <- tm_shape(pivot_raster, unit = "m") + - tm_raster(breaks = c(-3,-2,-1,0,1,2,3), palette = "RdYlGn",legend.is.portrait = legend_is_portrait, midpoint = 0, title = "CI difference") + - tm_layout(main.title = paste0("CI change week ", week_1, " - week ", week_2, "\n", age, " weeks old"), - main.title.size = 0.7, legend.show = show_legend) + # Input validation + if (missing(pivot_raster) || is.null(pivot_raster)) { + stop("pivot_raster is required") + } + if (missing(pivot_shape) || is.null(pivot_shape)) { + stop("pivot_shape is required") + } + if (missing(pivot_spans) || is.null(pivot_spans)) { + stop("pivot_spans is required") + } + if (missing(week_1) || is.null(week_1) || missing(week_2) || is.null(week_2)) { + stop("week_1 and week_2 parameters are required") + } + if (missing(age) || is.null(age)) { + stop("age parameter is required") + } + # Create the base map + map <- tm_shape(pivot_raster, unit = "m") # Add raster with continuous spectrum (centered at 0 for difference maps, fixed scale) + map <- map + tm_raster(col.scale = tm_scale_continuous(values = "brewer.rd_yl_gn", + midpoint = 0, + limits = c(-3, 3)), + col.legend = tm_legend(title = "CI difference", + orientation = if(legend_is_portrait) "portrait" else "landscape", + show = show_legend, + position = c("left", "bottom"))) + # Add layout elements + map <- map + tm_layout(main.title = paste0("CI change week ", week_1, " - week ", week_2, "\n", age, " weeks old"), + main.title.size = 0.7) + # Add borders if requested if (borders) { map <- map + tm_shape(pivot_shape) + @@ -50,186 +155,336 @@ create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_lege return(map) } -ci_plot <- function(pivotName){ - # pivotName = "1.1" - pivotShape <- AllPivots0 %>% terra::subset(field %in% pivotName) %>% st_transform(crs(CI)) - age <- harvesting_data %>% dplyr::filter(field %in% pivotName) %>% sort("year") %>% tail(., 1) %>% dplyr::select(age) %>% unique() %>% pull() %>% round() - - AllPivots2 <- AllPivots0 %>% dplyr::filter(field %in% pivotName) - - singlePivot <- CI %>% crop(., pivotShape) %>% mask(., pivotShape) - - singlePivot_m1 <- CI_m1 %>% crop(., pivotShape) %>% mask(., pivotShape) - singlePivot_m2 <- CI_m2 %>% crop(., pivotShape) %>% mask(., pivotShape) - # singlePivot_m3 <- CI_m3 %>% crop(., pivotShape) %>% mask(., pivotShape) - - abs_CI_last_week <- last_week_dif_raster_abs %>% crop(., pivotShape) %>% mask(., pivotShape) - abs_CI_three_week <- three_week_dif_raster_abs %>% crop(., pivotShape) %>% mask(., pivotShape) - - planting_date <- harvesting_data %>% dplyr::filter(field %in% pivotName) %>% ungroup() %>% dplyr::select(season_start) %>% unique() - - joined_spans2 <- AllPivots0 %>% st_transform(crs(pivotShape)) %>% dplyr::filter(field %in% pivotName) #%>% unique() %>% st_crop(., pivotShape) - - CImap_m2 <- create_CI_map(singlePivot_m2, AllPivots2, joined_spans2, show_legend= T, legend_is_portrait = T, week = week_minus_2, age = age -2, borders = borders) - CImap_m1 <- create_CI_map(singlePivot_m1, AllPivots2, joined_spans2, show_legend= F, legend_is_portrait = F, week = week_minus_1, age = age -1, borders = borders) - CImap <- create_CI_map(singlePivot, AllPivots2, joined_spans2, show_legend= F, legend_is_portrait = F, week = week, age = age, borders = borders) - - - CI_max_abs_last_week <- create_CI_diff_map(abs_CI_last_week,AllPivots2, joined_spans2, show_legend = T, legend_is_portrait = T, week_1 = week, week_2 = week_minus_1, age = age, borders = borders) - CI_max_abs_three_week <- create_CI_diff_map(abs_CI_three_week, AllPivots2, joined_spans2, show_legend = T, legend_is_portrait = T, week_1 = week, week_2 = week_minus_3, age = age, borders = borders) - - tst <- tmap_arrange(CImap_m2, CImap_m1, CImap,CI_max_abs_last_week, CI_max_abs_three_week, nrow = 1) - - cat(paste("## Field", pivotName, "-", age, "weeks after planting/harvest", "\n")) - # cat("\n") - # cat('