diff --git a/.Rhistory b/.Rhistory index 3ae17c0..19c58ab 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1 +1,512 @@ -install.packages("xfun") +}) +# Verify farm_health_data exists and has content +if (!exists("farm_health_data") || nrow(farm_health_data) == 0) { +safe_log("farm_health_data not found or empty, generating default data", "WARNING") +# Create minimal fallback data +tryCatch({ +# Get fields from boundaries +fields <- unique(AllPivots0$field) +# Create basic data frame with just field names +farm_health_data <- data.frame( +field = fields, +mean_ci = rep(NA, length(fields)), +ci_change = rep(NA, length(fields)), +ci_uniformity = rep(NA, length(fields)), +status = rep("Unknown", length(fields)), +anomaly_type = rep("Unknown", length(fields)), +priority_level = rep(5, length(fields)), # Low priority +age_weeks = rep(NA, length(fields)), +harvest_readiness = rep("Unknown", length(fields)), +stringsAsFactors = FALSE +) +safe_log("Created fallback farm_health_data with basic field information") +}, error = function(e) { +safe_log(paste("Error creating fallback farm_health_data:", e$message), "ERROR") +farm_health_data <<- data.frame( +field = character(), +mean_ci = numeric(), +ci_change = numeric(), +ci_uniformity = numeric(), +status = character(), +anomaly_type = character(), +priority_level = numeric(), +age_weeks = numeric(), +harvest_readiness = character(), +stringsAsFactors = FALSE +) +}) +} +# Calculate farm health summary metrics +tryCatch({ +# Generate farm health summary data +farm_health_data <- generate_farm_health_summary( +field_boundaries = AllPivots0, +ci_current = CI, +ci_previous = CI_m1, +harvesting_data = harvesting_data +) +# Log the summary data +safe_log(paste("Generated farm health summary with", nrow(farm_health_data), "fields")) +}, error = function(e) { +safe_log(paste("Error in farm health calculation:", e$message), "ERROR") +# Create empty dataframe if calculation failed +farm_health_data <- data.frame( +field = character(), +mean_ci = numeric(), +ci_change = numeric(), +ci_uniformity = numeric(), +status = character(), +anomaly_type = character(), +priority_level = numeric(), +age_weeks = numeric(), +harvest_readiness = character(), +stringsAsFactors = FALSE +) +}) +# Render the velocity and acceleration indicators +tryCatch({ +# Create and display the indicators using the imported utility function +velocity_plot <- create_velocity_acceleration_indicator( +health_data = farm_health_data, +ci_current = CI, +ci_prev1 = CI_m1, +ci_prev2 = CI_m2, +ci_prev3 = CI_m3, +field_boundaries = AllPivots0 +) +# Print the visualization +print(velocity_plot) +# Create a table of fields with significant velocity changes +field_ci_metrics <- list() +# Process each field to get metrics +fields <- unique(AllPivots0$field) +for (field_name in fields) { +tryCatch({ +# Get field boundary +field_shape <- AllPivots0 %>% dplyr::filter(field == field_name) +if (nrow(field_shape) == 0) next +# Extract CI values +ci_curr_values <- terra::extract(CI, field_shape) +ci_prev1_values <- terra::extract(CI_m1, field_shape) +# Calculate metrics +mean_ci_curr <- mean(ci_curr_values$CI, na.rm = TRUE) +mean_ci_prev1 <- mean(ci_prev1_values$CI, na.rm = TRUE) +velocity <- mean_ci_curr - mean_ci_prev1 +# Store in list +field_ci_metrics[[field_name]] <- list( +field = field_name, +ci_current = mean_ci_curr, +ci_prev1 = mean_ci_prev1, +velocity = velocity +) +}, error = function(e) { +safe_log(paste("Error processing field", field_name, "for velocity table:", e$message), "WARNING") +}) +} +# Convert list to data frame +velocity_df <- do.call(rbind, lapply(field_ci_metrics, function(x) { +data.frame( +field = x$field, +ci_current = round(x$ci_current, 2), +ci_prev1 = round(x$ci_prev1, 2), +velocity = round(x$velocity, 2), +direction = ifelse(x$velocity >= 0, "Improving", "Declining") +) +})) +# Select top 5 positive and top 5 negative velocity fields +top_positive <- velocity_df %>% +dplyr::filter(velocity > 0) %>% +dplyr::arrange(desc(velocity)) %>% +dplyr::slice_head(n = 5) +top_negative <- velocity_df %>% +dplyr::filter(velocity < 0) %>% +dplyr::arrange(velocity) %>% +dplyr::slice_head(n = 5) +# Display the tables if we have data +if (nrow(top_positive) > 0) { +cat("

Fields with Fastest Improvement

") +knitr::kable(top_positive %>% +dplyr::select(Field = field, +`Current CI` = ci_current, +`Previous CI` = ci_prev1, +`Weekly Change` = velocity)) +} +if (nrow(top_negative) > 0) { +cat("

Fields with Fastest Decline

") +knitr::kable(top_negative %>% +dplyr::select(Field = field, +`Current CI` = ci_current, +`Previous CI` = ci_prev1, +`Weekly Change` = velocity)) +} +}, error = function(e) { +safe_log(paste("Error rendering velocity visualization:", e$message), "ERROR") +cat("
Error generating velocity visualization.
") +}) +# Generate anomaly timeline visualization +tryCatch({ +# Use the imported function to create the anomaly timeline +anomaly_timeline <- create_anomaly_timeline( +field_boundaries = AllPivots0, +ci_data = CI_quadrant, +days_to_include = 90 # Show last 90 days of data +) +# Display the timeline +print(anomaly_timeline) +}, error = function(e) { +safe_log(paste("Error generating anomaly timeline:", e$message), "ERROR") +cat("
Error generating anomaly timeline visualization.
") +}) +# Verify farm_health_data exists and has content +if (!exists("farm_health_data") || nrow(farm_health_data) == 0) { +safe_log("farm_health_data not found or empty, generating default data", "WARNING") +# Create minimal fallback data +tryCatch({ +# Get fields from boundaries +fields <- unique(AllPivots0$field) +# Create basic data frame with just field names +farm_health_data <- data.frame( +field = fields, +mean_ci = rep(NA, length(fields)), +ci_change = rep(NA, length(fields)), +ci_uniformity = rep(NA, length(fields)), +status = rep("Unknown", length(fields)), +anomaly_type = rep("Unknown", length(fields)), +priority_level = rep(5, length(fields)), # Low priority +age_weeks = rep(NA, length(fields)), +harvest_readiness = rep("Unknown", length(fields)), +stringsAsFactors = FALSE +) +safe_log("Created fallback farm_health_data with basic field information") +}, error = function(e) { +safe_log(paste("Error creating fallback farm_health_data:", e$message), "ERROR") +farm_health_data <<- data.frame( +field = character(), +mean_ci = numeric(), +ci_change = numeric(), +ci_uniformity = numeric(), +status = character(), +anomaly_type = character(), +priority_level = numeric(), +age_weeks = numeric(), +harvest_readiness = character(), +stringsAsFactors = FALSE +) +}) +} +# ADVANCED ANALYTICS FUNCTIONS +# Note: These functions are now imported from executive_report_utils.R +# The utility file contains functions for velocity/acceleration indicators, +# anomaly timeline creation, age cohort mapping, and cohort performance charts +safe_log("Using analytics functions from executive_report_utils.R") +# Chunk 1: setup_parameters +# Set up basic report parameters from input values +report_date <- params$report_date +mail_day <- params$mail_day +borders <- params$borders +use_breaks <- params$use_breaks # Whether to use breaks or continuous spectrum in visualizations +# Environment setup notes (commented out) +# # Activeer de renv omgeving +# renv::activate() +# renv::deactivate() +# # Optioneel: Herstel de omgeving als dat nodig is +# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren +# renv::restore() +# Chunk 2: load_libraries +# Configure knitr options +knitr::opts_chunk$set(warning = FALSE, message = FALSE) +# Path management +library(here) +# Spatial data libraries +library(sf) +library(terra) +library(exactextractr) +# library(raster) - Removed as it's no longer maintained +# Data manipulation and visualization +library(tidyverse) # Includes dplyr, ggplot2, etc. +library(tmap) +library(lubridate) +library(zoo) +# Machine learning +library(rsample) +library(caret) +library(randomForest) +library(CAST) +# Load custom utility functions +tryCatch({ +source("report_utils.R") +}, error = function(e) { +message(paste("Error loading report_utils.R:", e$message)) +# Try alternative path if the first one fails +tryCatch({ +source(here::here("r_app", "report_utils.R")) +}, error = function(e) { +stop("Could not load report_utils.R from either location: ", e$message) +}) +}) +# Load executive report utilities +tryCatch({ +source("executive_report_utils.R") +}, error = function(e) { +message(paste("Error loading executive_report_utils.R:", e$message)) +# Try alternative path if the first one fails +tryCatch({ +source(here::here("r_app", "executive_report_utils.R")) +}, error = function(e) { +stop("Could not load executive_report_utils.R from either location: ", e$message) +}) +}) +# Chunk 1: setup_parameters +# Set up basic report parameters from input values +report_date <- params$report_date +mail_day <- params$mail_day +borders <- params$borders +use_breaks <- params$use_breaks # Whether to use breaks or continuous spectrum in visualizations +# Environment setup notes (commented out) +# # Activeer de renv omgeving +# renv::activate() +# renv::deactivate() +# # Optioneel: Herstel de omgeving als dat nodig is +# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren +# renv::restore() +# Chunk 2: load_libraries +# Configure knitr options +knitr::opts_chunk$set(warning = FALSE, message = FALSE) +# Path management +library(here) +# Spatial data libraries +library(sf) +library(terra) +library(exactextractr) +# library(raster) - Removed as it's no longer maintained +# Data manipulation and visualization +library(tidyverse) # Includes dplyr, ggplot2, etc. +library(tmap) +library(lubridate) +library(zoo) +# Machine learning +library(rsample) +library(caret) +library(randomForest) +library(CAST) +# Load custom utility functions +tryCatch({ +source("report_utils.R") +}, error = function(e) { +message(paste("Error loading report_utils.R:", e$message)) +# Try alternative path if the first one fails +tryCatch({ +source(here::here("r_app", "report_utils.R")) +}, error = function(e) { +stop("Could not load report_utils.R from either location: ", e$message) +}) +}) +# Load executive report utilities +tryCatch({ +source("executive_report_utils.R") +}, error = function(e) { +message(paste("Error loading executive_report_utils.R:", e$message)) +# Try alternative path if the first one fails +tryCatch({ +source(here::here("r_app", "executive_report_utils.R")) +}, error = function(e) { +stop("Could not load executive_report_utils.R from either location: ", e$message) +}) +}) +# Chunk 1: setup_parameters +# Set up basic report parameters from input values +report_date <- params$report_date +mail_day <- params$mail_day +borders <- params$borders +use_breaks <- params$use_breaks # Whether to use breaks or continuous spectrum in visualizations +# Environment setup notes (commented out) +# # Activeer de renv omgeving +# renv::activate() +# renv::deactivate() +# # Optioneel: Herstel de omgeving als dat nodig is +# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren +# renv::restore() +# Chunk 2: load_libraries +# Configure knitr options +knitr::opts_chunk$set(warning = FALSE, message = FALSE) +# Path management +library(here) +# Spatial data libraries +library(sf) +library(terra) +library(exactextractr) +# library(raster) - Removed as it's no longer maintained +# Data manipulation and visualization +library(tidyverse) # Includes dplyr, ggplot2, etc. +library(tmap) +library(lubridate) +library(zoo) +# Machine learning +library(rsample) +library(caret) +library(randomForest) +library(CAST) +# Load custom utility functions +# tryCatch({ +# source("report_utils.R") +# }, error = function(e) { +# message(paste("Error loading report_utils.R:", e$message)) +# # Try alternative path if the first one fails +# tryCatch({ +source(here::here("r_app", "report_utils.R")) +# }, error = function(e) { +# stop("Could not load report_utils.R from either location: ", e$message) +# }) +# }) +# Load executive report utilities +# tryCatch({ +# source("executive_report_utils.R") +# }, error = function(e) { +# message(paste("Error loading executive_report_utils.R:", e$message)) +# # Try alternative path if the first one fails +# tryCatch({ +source(here::here("r_app", "executive_report_utils.R")) +# Chunk 1: setup_parameters +# Set up basic report parameters from input values +report_date <- params$report_date +mail_day <- params$mail_day +borders <- params$borders +use_breaks <- params$use_breaks # Whether to use breaks or continuous spectrum in visualizations +# Environment setup notes (commented out) +# # Activeer de renv omgeving +# renv::activate() +# renv::deactivate() +# # Optioneel: Herstel de omgeving als dat nodig is +# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren +# renv::restore() +# Chunk 2: load_libraries +# Configure knitr options +knitr::opts_chunk$set(warning = FALSE, message = FALSE) +# Path management +library(here) +# Spatial data libraries +library(sf) +library(terra) +library(exactextractr) +# library(raster) - Removed as it's no longer maintained +# Data manipulation and visualization +library(tidyverse) # Includes dplyr, ggplot2, etc. +library(tmap) +library(lubridate) +library(zoo) +# Machine learning +library(rsample) +library(caret) +library(randomForest) +library(CAST) +# Load custom utility functions +# tryCatch({ +# source("report_utils.R") +# }, error = function(e) { +# message(paste("Error loading report_utils.R:", e$message)) +# # Try alternative path if the first one fails +# tryCatch({ +source(here::here("r_app", "report_utils.R")) +# }, error = function(e) { +# stop("Could not load report_utils.R from either location: ", e$message) +# }) +# }) +# Load executive report utilities +# tryCatch({ +# source("executive_report_utils.R") +# }, error = function(e) { +# message(paste("Error loading executive_report_utils.R:", e$message)) +# # Try alternative path if the first one fails +# tryCatch({ +source(here::here("r_app", "exec_dashboard", "executive_report_utils.R")) +# }, error = function(e) { +# stop("Could not load executive_report_utils.R from either location: ", e$message) +# }) +# }) +# Chunk 3: initialize_project_config +# Set the project directory from parameters +project_dir <- params$data_dir +# Source project parameters with error handling +# tryCatch({ +source(here::here("r_app", "parameters_project.R")) +# }, error = function(e) { +# stop("Error loading parameters_project.R: ", e$message) +# }) +# Log initial configuration +safe_log("Starting the R Markdown script") +safe_log(paste("mail_day params:", params$mail_day)) +safe_log(paste("report_date params:", params$report_date)) +safe_log(paste("mail_day variable:", mail_day)) +# Set locale for consistent date formatting +Sys.setlocale("LC_TIME", "C") +# Initialize date variables from parameters +today <- as.character(report_date) +mail_day_as_character <- as.character(mail_day) +# Calculate week days +report_date_as_week_day <- weekdays(lubridate::ymd(today)) +days_of_week <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday") +# Calculate initial week number +week <- lubridate::week(today) +safe_log(paste("Initial week calculation:", week, "today:", today)) +# Calculate previous dates for comparisons +today_minus_1 <- as.character(lubridate::ymd(today) - 7) +today_minus_2 <- as.character(lubridate::ymd(today) - 14) +today_minus_3 <- as.character(lubridate::ymd(today) - 21) +# Log the weekday calculations for debugging +safe_log(paste("Report date weekday:", report_date_as_week_day)) +safe_log(paste("Weekday index:", which(days_of_week == report_date_as_week_day))) +safe_log(paste("Mail day:", mail_day_as_character)) +safe_log(paste("Mail day index:", which(days_of_week == mail_day_as_character))) +# Adjust week calculation based on mail day +if (which(days_of_week == report_date_as_week_day) > which(days_of_week == mail_day_as_character)) { +safe_log("Adjusting weeks because of mail day") +week <- lubridate::week(today) + 1 +today_minus_1 <- as.character(lubridate::ymd(today)) +today_minus_2 <- as.character(lubridate::ymd(today) - 7) +today_minus_3 <- as.character(lubridate::ymd(today) - 14) +} +# Generate subtitle for report +subtitle_var <- paste("Report generated on", Sys.Date()) +# Calculate week numbers for previous weeks +week_minus_1 <- week - 1 +week_minus_2 <- week - 2 +week_minus_3 <- week - 3 +# Format current week with leading zeros +week <- sprintf("%02d", week) +# Get years for each date +year <- lubridate::year(today) +year_1 <- lubridate::year(today_minus_1) +year_2 <- lubridate::year(today_minus_2) +year_3 <- lubridate::year(today_minus_3) +# Load CI index data with error handling +tryCatch({ +CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds")) +safe_log("Successfully loaded CI quadrant data") +}, error = function(e) { +stop("Error loading CI quadrant data: ", e$message) +}) +# Get file paths for different weeks using the utility function +tryCatch({ +path_to_week_current = get_week_path(weekly_CI_mosaic, today, 0) +path_to_week_minus_1 = get_week_path(weekly_CI_mosaic, today, -1) +path_to_week_minus_2 = get_week_path(weekly_CI_mosaic, today, -2) +path_to_week_minus_3 = get_week_path(weekly_CI_mosaic, today, -3) +# Log the calculated paths +safe_log("Required mosaic paths:") +safe_log(paste("Path to current week:", path_to_week_current)) +safe_log(paste("Path to week minus 1:", path_to_week_minus_1)) +safe_log(paste("Path to week minus 2:", path_to_week_minus_2)) +safe_log(paste("Path to week minus 3:", path_to_week_minus_3)) +# Validate that files exist +if (!file.exists(path_to_week_current)) warning("Current week mosaic file does not exist: ", path_to_week_current) +if (!file.exists(path_to_week_minus_1)) warning("Week minus 1 mosaic file does not exist: ", path_to_week_minus_1) +if (!file.exists(path_to_week_minus_2)) warning("Week minus 2 mosaic file does not exist: ", path_to_week_minus_2) +if (!file.exists(path_to_week_minus_3)) warning("Week minus 3 mosaic file does not exist: ", path_to_week_minus_3) +# Load raster data with terra functions +CI <- terra::rast(path_to_week_current)$CI +CI_m1 <- terra::rast(path_to_week_minus_1)$CI +CI_m2 <- terra::rast(path_to_week_minus_2)$CI +CI_m3 <- terra::rast(path_to_week_minus_3)$CI +}, error = function(e) { +stop("Error loading raster data: ", e$message) +}) diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..642ff51 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.REPL.enableREPLSmartSend": false +} \ No newline at end of file diff --git a/Current - Pivots planting date and harevsting data.xlsx b/Current - Pivots planting date and harevsting data.xlsx deleted file mode 100644 index f5445d7..0000000 Binary files a/Current - Pivots planting date and harevsting data.xlsx and /dev/null differ diff --git a/Rplots.pdf b/Rplots.pdf deleted file mode 100644 index a68f07a..0000000 Binary files a/Rplots.pdf and /dev/null differ diff --git a/figure/sub_chunk_8433-1.png b/figure/sub_chunk_8433-1.png new file mode 100644 index 0000000..160a733 Binary files /dev/null and b/figure/sub_chunk_8433-1.png differ diff --git a/python_app/planet_download.ipynb b/python_app/planet_download.ipynb index 4d1c4f0..ed5606f 100644 --- a/python_app/planet_download.ipynb +++ b/python_app/planet_download.ipynb @@ -12,370 +12,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "b7ca7102-5fd9-481f-90cd-3ba60e288649", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A module that was compiled using NumPy 1.x cannot be run in\n", - "NumPy 2.2.4 as it may crash. To support both 1.x and 2.x\n", - "versions of NumPy, modules must be compiled with NumPy 2.0.\n", - "Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n", - "\n", - "If you are a user of the module, the easiest solution will be to\n", - "downgrade to 'numpy<2' or try to upgrade the affected module.\n", - "We expect that some modules will need time to support NumPy 2.\n", - "\n", - "Traceback (most recent call last): File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in \n", - " app.launch_new_instance()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 701, in start\n", - " self.io_loop.start()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\windows_events.py\", line 322, in run_forever\n", - " super().run_forever()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", - " self._run_once()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n", - " handle._run()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\events.py\", line 88, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in dispatch_queue\n", - " await self.process_one()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 523, in process_one\n", - " await dispatch(*args)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 429, in dispatch_shell\n", - " await result\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 767, in execute_request\n", - " reply_content = await reply_content\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 429, in do_execute\n", - " res = shell.run_cell(\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"C:\\Users\\timon\\AppData\\Local\\Temp\\ipykernel_5948\\2295736612.py\", line 21, in \n", - " import geopandas as gpd\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\geopandas\\__init__.py\", line 3, in \n", - " from geopandas.geoseries import GeoSeries\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\geopandas\\geoseries.py\", line 9, in \n", - " import pandas as pd\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\__init__.py\", line 39, in \n", - " from pandas.compat import (\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\compat\\__init__.py\", line 27, in \n", - " from pandas.compat.pyarrow import (\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\compat\\pyarrow.py\", line 8, in \n", - " import pyarrow as pa\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pyarrow\\__init__.py\", line 65, in \n", - " import pyarrow.lib as _lib\n" - ] - }, - { - "ename": "ImportError", - "evalue": "\nA module that was compiled using NumPy 1.x cannot be run in\nNumPy 2.2.4 as it may crash. To support both 1.x and 2.x\nversions of NumPy, modules must be compiled with NumPy 2.0.\nSome module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n\nIf you are a user of the module, the easiest solution will be to\ndowngrade to 'numpy<2' or try to upgrade the affected module.\nWe expect that some modules will need time to support NumPy 2.\n\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\timon\\anaconda3\\Lib\\site-packages\\numpy\\core\\_multiarray_umath.py:44\u001b[0m, in \u001b[0;36m__getattr__\u001b[1;34m(attr_name)\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# Also print the message (with traceback). This is because old versions\u001b[39;00m\n\u001b[0;32m 40\u001b[0m \u001b[38;5;66;03m# of NumPy unfortunately set up the import to replace (and hide) the\u001b[39;00m\n\u001b[0;32m 41\u001b[0m \u001b[38;5;66;03m# error. The traceback shouldn't be needed, but e.g. pytest plugins\u001b[39;00m\n\u001b[0;32m 42\u001b[0m \u001b[38;5;66;03m# seem to swallow it and we should be failing anyway...\u001b[39;00m\n\u001b[0;32m 43\u001b[0m sys\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mwrite(msg \u001b[38;5;241m+\u001b[39m tb_msg)\n\u001b[1;32m---> 44\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[0;32m 46\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(_multiarray_umath, attr_name, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[1;31mImportError\u001b[0m: \nA module that was compiled using NumPy 1.x cannot be run in\nNumPy 2.2.4 as it may crash. To support both 1.x and 2.x\nversions of NumPy, modules must be compiled with NumPy 2.0.\nSome module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n\nIf you are a user of the module, the easiest solution will be to\ndowngrade to 'numpy<2' or try to upgrade the affected module.\nWe expect that some modules will need time to support NumPy 2.\n\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A module that was compiled using NumPy 1.x cannot be run in\n", - "NumPy 2.2.4 as it may crash. To support both 1.x and 2.x\n", - "versions of NumPy, modules must be compiled with NumPy 2.0.\n", - "Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n", - "\n", - "If you are a user of the module, the easiest solution will be to\n", - "downgrade to 'numpy<2' or try to upgrade the affected module.\n", - "We expect that some modules will need time to support NumPy 2.\n", - "\n", - "Traceback (most recent call last): File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in \n", - " app.launch_new_instance()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 701, in start\n", - " self.io_loop.start()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\windows_events.py\", line 322, in run_forever\n", - " super().run_forever()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", - " self._run_once()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n", - " handle._run()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\events.py\", line 88, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in dispatch_queue\n", - " await self.process_one()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 523, in process_one\n", - " await dispatch(*args)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 429, in dispatch_shell\n", - " await result\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 767, in execute_request\n", - " reply_content = await reply_content\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 429, in do_execute\n", - " res = shell.run_cell(\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"C:\\Users\\timon\\AppData\\Local\\Temp\\ipykernel_5948\\2295736612.py\", line 21, in \n", - " import geopandas as gpd\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\geopandas\\__init__.py\", line 3, in \n", - " from geopandas.geoseries import GeoSeries\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\geopandas\\geoseries.py\", line 9, in \n", - " import pandas as pd\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\__init__.py\", line 62, in \n", - " from pandas.core.api import (\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\api.py\", line 9, in \n", - " from pandas.core.dtypes.dtypes import (\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\dtypes\\dtypes.py\", line 24, in \n", - " from pandas._libs import (\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pyarrow\\__init__.py\", line 65, in \n", - " import pyarrow.lib as _lib\n" - ] - }, - { - "ename": "ImportError", - "evalue": "\nA module that was compiled using NumPy 1.x cannot be run in\nNumPy 2.2.4 as it may crash. To support both 1.x and 2.x\nversions of NumPy, modules must be compiled with NumPy 2.0.\nSome module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n\nIf you are a user of the module, the easiest solution will be to\ndowngrade to 'numpy<2' or try to upgrade the affected module.\nWe expect that some modules will need time to support NumPy 2.\n\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\timon\\anaconda3\\Lib\\site-packages\\numpy\\core\\_multiarray_umath.py:44\u001b[0m, in \u001b[0;36m__getattr__\u001b[1;34m(attr_name)\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# Also print the message (with traceback). This is because old versions\u001b[39;00m\n\u001b[0;32m 40\u001b[0m \u001b[38;5;66;03m# of NumPy unfortunately set up the import to replace (and hide) the\u001b[39;00m\n\u001b[0;32m 41\u001b[0m \u001b[38;5;66;03m# error. The traceback shouldn't be needed, but e.g. pytest plugins\u001b[39;00m\n\u001b[0;32m 42\u001b[0m \u001b[38;5;66;03m# seem to swallow it and we should be failing anyway...\u001b[39;00m\n\u001b[0;32m 43\u001b[0m sys\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mwrite(msg \u001b[38;5;241m+\u001b[39m tb_msg)\n\u001b[1;32m---> 44\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[0;32m 46\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(_multiarray_umath, attr_name, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[1;31mImportError\u001b[0m: \nA module that was compiled using NumPy 1.x cannot be run in\nNumPy 2.2.4 as it may crash. To support both 1.x and 2.x\nversions of NumPy, modules must be compiled with NumPy 2.0.\nSome module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n\nIf you are a user of the module, the easiest solution will be to\ndowngrade to 'numpy<2' or try to upgrade the affected module.\nWe expect that some modules will need time to support NumPy 2.\n\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A module that was compiled using NumPy 1.x cannot be run in\n", - "NumPy 2.2.4 as it may crash. To support both 1.x and 2.x\n", - "versions of NumPy, modules must be compiled with NumPy 2.0.\n", - "Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n", - "\n", - "If you are a user of the module, the easiest solution will be to\n", - "downgrade to 'numpy<2' or try to upgrade the affected module.\n", - "We expect that some modules will need time to support NumPy 2.\n", - "\n", - "Traceback (most recent call last): File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in \n", - " app.launch_new_instance()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 701, in start\n", - " self.io_loop.start()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\windows_events.py\", line 322, in run_forever\n", - " super().run_forever()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", - " self._run_once()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n", - " handle._run()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\events.py\", line 88, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in dispatch_queue\n", - " await self.process_one()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 523, in process_one\n", - " await dispatch(*args)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 429, in dispatch_shell\n", - " await result\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 767, in execute_request\n", - " reply_content = await reply_content\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 429, in do_execute\n", - " res = shell.run_cell(\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"C:\\Users\\timon\\AppData\\Local\\Temp\\ipykernel_5948\\2295736612.py\", line 21, in \n", - " import geopandas as gpd\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\geopandas\\__init__.py\", line 3, in \n", - " from geopandas.geoseries import GeoSeries\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\geopandas\\geoseries.py\", line 9, in \n", - " import pandas as pd\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\__init__.py\", line 62, in \n", - " from pandas.core.api import (\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\api.py\", line 28, in \n", - " from pandas.core.arrays import Categorical\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\__init__.py\", line 1, in \n", - " from pandas.core.arrays.arrow import ArrowExtensionArray\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\arrow\\__init__.py\", line 5, in \n", - " from pandas.core.arrays.arrow.array import ArrowExtensionArray\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\arrow\\array.py\", line 50, in \n", - " from pandas.core import (\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\ops\\__init__.py\", line 8, in \n", - " from pandas.core.ops.array_ops import (\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\ops\\array_ops.py\", line 56, in \n", - " from pandas.core.computation import expressions\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\computation\\expressions.py\", line 21, in \n", - " from pandas.core.computation.check import NUMEXPR_INSTALLED\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\computation\\check.py\", line 5, in \n", - " ne = import_optional_dependency(\"numexpr\", errors=\"warn\")\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\compat\\_optional.py\", line 135, in import_optional_dependency\n", - " module = importlib.import_module(name)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\importlib\\__init__.py\", line 90, in import_module\n", - " return _bootstrap._gcd_import(name[level:], package, level)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\numexpr\\__init__.py\", line 24, in \n", - " from numexpr.interpreter import MAX_THREADS, use_vml, __BLOCK_SIZE1__\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "_ARRAY_API not found", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;31mAttributeError\u001b[0m: _ARRAY_API not found" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A module that was compiled using NumPy 1.x cannot be run in\n", - "NumPy 2.2.4 as it may crash. To support both 1.x and 2.x\n", - "versions of NumPy, modules must be compiled with NumPy 2.0.\n", - "Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n", - "\n", - "If you are a user of the module, the easiest solution will be to\n", - "downgrade to 'numpy<2' or try to upgrade the affected module.\n", - "We expect that some modules will need time to support NumPy 2.\n", - "\n", - "Traceback (most recent call last): File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in \n", - " app.launch_new_instance()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 701, in start\n", - " self.io_loop.start()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\windows_events.py\", line 322, in run_forever\n", - " super().run_forever()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", - " self._run_once()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n", - " handle._run()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\asyncio\\events.py\", line 88, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in dispatch_queue\n", - " await self.process_one()\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 523, in process_one\n", - " await dispatch(*args)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 429, in dispatch_shell\n", - " await result\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 767, in execute_request\n", - " reply_content = await reply_content\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 429, in do_execute\n", - " res = shell.run_cell(\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"C:\\Users\\timon\\AppData\\Local\\Temp\\ipykernel_5948\\2295736612.py\", line 21, in \n", - " import geopandas as gpd\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\geopandas\\__init__.py\", line 3, in \n", - " from geopandas.geoseries import GeoSeries\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\geopandas\\geoseries.py\", line 9, in \n", - " import pandas as pd\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\__init__.py\", line 62, in \n", - " from pandas.core.api import (\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\api.py\", line 28, in \n", - " from pandas.core.arrays import Categorical\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\__init__.py\", line 1, in \n", - " from pandas.core.arrays.arrow import ArrowExtensionArray\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\arrow\\__init__.py\", line 5, in \n", - " from pandas.core.arrays.arrow.array import ArrowExtensionArray\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\arrow\\array.py\", line 64, in \n", - " from pandas.core.arrays.masked import BaseMaskedArray\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\masked.py\", line 60, in \n", - " from pandas.core import (\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\core\\nanops.py\", line 52, in \n", - " bn = import_optional_dependency(\"bottleneck\", errors=\"warn\")\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\pandas\\compat\\_optional.py\", line 135, in import_optional_dependency\n", - " module = importlib.import_module(name)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\importlib\\__init__.py\", line 90, in import_module\n", - " return _bootstrap._gcd_import(name[level:], package, level)\n", - " File \"c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\bottleneck\\__init__.py\", line 7, in \n", - " from .move import (move_argmax, move_argmin, move_max, move_mean, move_median,\n" - ] - }, - { - "ename": "ImportError", - "evalue": "\nA module that was compiled using NumPy 1.x cannot be run in\nNumPy 2.2.4 as it may crash. To support both 1.x and 2.x\nversions of NumPy, modules must be compiled with NumPy 2.0.\nSome module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n\nIf you are a user of the module, the easiest solution will be to\ndowngrade to 'numpy<2' or try to upgrade the affected module.\nWe expect that some modules will need time to support NumPy 2.\n\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\timon\\anaconda3\\Lib\\site-packages\\numpy\\core\\_multiarray_umath.py:44\u001b[0m, in \u001b[0;36m__getattr__\u001b[1;34m(attr_name)\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# Also print the message (with traceback). This is because old versions\u001b[39;00m\n\u001b[0;32m 40\u001b[0m \u001b[38;5;66;03m# of NumPy unfortunately set up the import to replace (and hide) the\u001b[39;00m\n\u001b[0;32m 41\u001b[0m \u001b[38;5;66;03m# error. The traceback shouldn't be needed, but e.g. pytest plugins\u001b[39;00m\n\u001b[0;32m 42\u001b[0m \u001b[38;5;66;03m# seem to swallow it and we should be failing anyway...\u001b[39;00m\n\u001b[0;32m 43\u001b[0m sys\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mwrite(msg \u001b[38;5;241m+\u001b[39m tb_msg)\n\u001b[1;32m---> 44\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[0;32m 46\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(_multiarray_umath, attr_name, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[1;31mImportError\u001b[0m: \nA module that was compiled using NumPy 1.x cannot be run in\nNumPy 2.2.4 as it may crash. To support both 1.x and 2.x\nversions of NumPy, modules must be compiled with NumPy 2.0.\nSome module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n\nIf you are a user of the module, the easiest solution will be to\ndowngrade to 'numpy<2' or try to upgrade the affected module.\nWe expect that some modules will need time to support NumPy 2.\n\n" - ] - } - ], + "outputs": [], "source": [ "# $ pip install sentinelhub\n", "# pip install gdal\n", @@ -403,28 +43,7 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "330c967c-2742-4a7a-9a61-28bfdaf8eeca", - "metadata": {}, - "outputs": [], - "source": [ - "# pip install geopandas" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "49f8496a-a267-4b74-9500-a168e031ed68", - "metadata": {}, - "outputs": [], - "source": [ - "#import pipreqs\n", - "#pipreqs Resilience BV/4002 CMD App - General/4002 CMD Team/4002 TechnicalData/04 WP2 technical/python/Chemba_download.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "5491a840-779c-4f0c-8164-c3de738b3298", "metadata": {}, "outputs": [], @@ -435,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "eb1fb662-0e25-4ca9-8317-c6953290842b", "metadata": {}, "outputs": [], @@ -460,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "060396e0-e5ee-4b54-b211-5d8bfcba167f", "metadata": {}, "outputs": [], @@ -472,18 +91,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "c9f79e81-dff8-4109-8d26-6c423142dcf2", "metadata": {}, "outputs": [], "source": [ "# Adjust the number of days needed\n", - "days = 1095 #change back to 28 which is the default. 3 years is 1095 days." + "days = 30 #change back to 28 which is the default. 3 years is 1095 days.\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "e18bdf8f-be4b-44ab-baaa-de5de60d92cb", "metadata": {}, "outputs": [], @@ -505,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "3f7c8e04-4569-457b-b39d-283582c4ba36", "metadata": {}, "outputs": [], @@ -529,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "244b5752-4f02-4347-9278-f6a0a46b88f4", "metadata": {}, "outputs": [], @@ -543,34 +162,92 @@ " bands: [\"red\", \"green\", \"blue\", \"nir\", \"udm1\"]\n", " }],\n", " output: {\n", - " bands: 4 \n", - " //sampleType: \"FLOAT32\"\n", + " bands: 4,\n", + " sampleType: \"FLOAT32\"\n", " }\n", " };\n", " }\n", "\n", " function evaluatePixel(sample) {\n", " // Scale the bands\n", - " var scaledBlue = [2.5 * sample.blue / 10000];\n", - " var scaledGreen = [2.5 * sample.green / 10000];\n", - " var scaledRed = [2.5 * sample.red / 10000];\n", - " var scaledNIR = [2.5 * sample.nir / 10000];\n", - " \n", - " // Calculate the CI (Chlorophyll Index) using the scaled values\n", - " // var CI = [scaledNIR / scaledGreen - 1] ;\n", + " var scaledBlue = 2.5 * sample.blue / 10000;\n", + " var scaledGreen = 2.5 * sample.green / 10000;\n", + " var scaledRed = 2.5 * sample.red / 10000;\n", + " var scaledNIR = 2.5 * sample.nir / 10000;\n", + " \n", + " // Calculate indices for cloud and shadow detection\n", + " var brightness = (scaledBlue + scaledGreen + scaledRed) / 3;\n", + " var ndvi = (scaledNIR - scaledRed) / (scaledNIR + scaledRed);\n", + " var blue_ratio = scaledBlue / (scaledRed + 0.01); // Add 0.01 to prevent division by zero\n", + " \n", + " // CLOUD DETECTION\n", + " // Clouds are typically bright in all bands\n", + " var bright_pixels = (scaledBlue > 0.3) && (scaledGreen > 0.3) && (scaledRed > 0.3);\n", + " \n", + " // Clouds often have higher blue reflectance\n", + " var blue_dominant = scaledBlue > (scaledRed * 1.2);\n", + " \n", + " // Low NDVI areas that are bright are likely clouds\n", + " var low_ndvi = ndvi < 0.1;\n", + " \n", + " // Combine cloud criteria\n", + " var is_cloud = bright_pixels && (blue_dominant || low_ndvi);\n", + " \n", + " // SHADOW DETECTION\n", + " // Shadows are typically dark\n", + " var dark_pixels = brightness < 0.1;\n", + " \n", + " // Shadows have lower NIR reflectance\n", + " var low_nir = scaledNIR < 0.15;\n", + " \n", + " // Shadows often have higher blue proportion relative to NIR\n", + " var blue_enhanced = blue_ratio > 0.8;\n", + " \n", + " // Combine shadow criteria\n", + " var is_shadow = dark_pixels && (low_nir || blue_enhanced);\n", + " \n", + " // Calculate CI (Chlorophyll Index) using the scaled values\n", + " var CI = (scaledNIR / scaledRed) - 1;\n", + " \n", + " // Use built-in usable data mask (udm1) and our own cloud/shadow detection\n", + " // udm1 == 0 means pixel is usable according to Planet's metadata\n", + " if (sample.udm1 == 0 && !is_cloud && !is_shadow) {\n", + " return [scaledRed, scaledGreen, scaledBlue, scaledNIR];\n", + " } else {\n", + " return [NaN, NaN, NaN, NaN];\n", + " }\n", + " }\n", + "\"\"\"\n", "\n", - "// Output the scaled bands and CI\n", - " if (sample.udm1 == 0) { \n", - " return [\n", - " scaledRed,\n", - " scaledGreen,\n", - " scaledBlue,\n", - " scaledNIR\n", - " // sample.UDM,\n", - " // CI,\n", - " ]\n", - " } else {\n", - " return [NaN, NaN, NaN, NaN]}\n", + "# Original evalscript without cloud/shadow detection (for comparison)\n", + "evalscript_original = \"\"\"\n", + " //VERSION=3\n", + "\n", + " function setup() {\n", + " return {\n", + " input: [{\n", + " bands: [\"red\", \"green\", \"blue\", \"nir\", \"udm1\"]\n", + " }],\n", + " output: {\n", + " bands: 4,\n", + " sampleType: \"FLOAT32\"\n", + " }\n", + " };\n", + " }\n", + "\n", + " function evaluatePixel(sample) {\n", + " // Scale the bands\n", + " var scaledBlue = 2.5 * sample.blue / 10000;\n", + " var scaledGreen = 2.5 * sample.green / 10000;\n", + " var scaledRed = 2.5 * sample.red / 10000;\n", + " var scaledNIR = 2.5 * sample.nir / 10000;\n", + " \n", + " // Only use udm1 mask (Planet's usable data mask)\n", + " if (sample.udm1 == 0) {\n", + " return [scaledRed, scaledGreen, scaledBlue, scaledNIR];\n", + " } else {\n", + " return [NaN, NaN, NaN, NaN];\n", + " }\n", " }\n", "\"\"\"\n", "\n", @@ -593,6 +270,24 @@ "\n", " )\n", "\n", + "# Added function to get original image for comparison\n", + "def get_original_request_day(time_interval, bbox, size):\n", + " return SentinelHubRequest(\n", + " evalscript=evalscript_original,\n", + " input_data=[\n", + " SentinelHubRequest.input_data(\n", + " data_collection=DataCollection.planet_data2,\n", + " time_interval=(time_interval, time_interval)\n", + " )\n", + " ],\n", + " responses=[\n", + " SentinelHubRequest.output_response('default', MimeType.TIFF)\n", + " ],\n", + " bbox=bbox,\n", + " size=size,\n", + " config=config,\n", + " )\n", + "\n", "def download_function(slot, bbox, size):\n", " # create a list of requests\n", " list_of_requests = [get_true_color_request_day(slot, bbox, size)]\n", @@ -625,26 +320,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "848dc773-70d6-4ae6-b05c-d6ebfb41624d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Monthly time windows:\n", - "\n", - "2022-04-05\n", - "2022-04-06\n", - "2022-04-07\n", - "...\n", - "2025-04-01\n", - "2025-04-02\n", - "2025-04-03\n" - ] - } - ], + "outputs": [], "source": [ "days_needed = int(os.environ.get(\"DAYS\", days))\n", "date_str = os.environ.get(\"DATE\")\n", @@ -683,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "c803e373-2567-4233-af7d-0d2d6f7d4f8e", "metadata": {}, "outputs": [], @@ -694,14 +373,6 @@ { "cell_type": "code", "execution_count": null, - "id": "df050c79", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 15, "id": "dc24d54e-2272-4f30-bcf5-4d8fc381915c", "metadata": {}, "outputs": [], @@ -711,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "cd071b42-d0cd-4e54-8f88-ad1a339748e3", "metadata": {}, "outputs": [], @@ -721,19 +392,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "301d12e4-e47a-4034-aec0-aa5673e64935", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Area bounding box: BBox(((-47.09879025717693, -22.67132809994226), (-47.09188307701802, -22.66813642658124)), crs=CRS('4326'))\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "bbox_splitter = BBoxSplitter(\n", " shapely_geometries, CRS.WGS84, (1, 1), reduce_bbox_sizes=True\n", @@ -749,24 +411,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "431f6856-8d7e-4868-b627-20deeb47d77e", "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "geometry_list = bbox_splitter.get_geometry_list()\n", "\n", @@ -775,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "18655785", "metadata": {}, "outputs": [], @@ -796,43 +444,35 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "a6fc418f", "metadata": {}, "outputs": [], "source": [ "# Filter slots to only include dates with available images\n", "available_slots = [slot for slot in slots if is_image_available(slot)]\n", + "\n", + "# Store the first 5 available slots for comparison later (if available)\n", + "comparison_slots = available_slots[:min(5, len(available_slots))]\n", "\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "ebc416be", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['2022-04-06', '2022-04-08', '2022-04-09', '2022-04-10', '2022-04-11', '2022-04-12', '2022-04-13', '2022-04-14', '2022-04-16', '2022-04-17', '2022-04-19', '2022-04-20', '2022-04-21', '2022-04-22', '2022-04-23', '2022-04-24', '2022-04-25', '2022-04-26', '2022-04-27', '2022-04-29', '2022-04-30', '2022-05-02', '2022-05-03', '2022-05-05', '2022-05-07', '2022-05-08', '2022-05-11', '2022-05-12', '2022-05-14', '2022-05-18', '2022-05-19', '2022-05-20', '2022-05-21', '2022-05-22', '2022-05-23', '2022-05-25', '2022-05-26', '2022-05-27', '2022-05-28', '2022-05-29', '2022-05-30', '2022-06-01', '2022-06-02', '2022-06-03', '2022-06-04', '2022-06-05', '2022-06-06', '2022-06-07', '2022-06-08', '2022-06-10', '2022-06-11', '2022-06-12', '2022-06-13', '2022-06-14', '2022-06-15', '2022-06-17', '2022-06-19', '2022-06-20', '2022-06-21', '2022-06-22', '2022-06-24', '2022-06-25', '2022-06-26', '2022-06-27', '2022-06-28', '2022-06-29', '2022-06-30', '2022-07-01', '2022-07-02', '2022-07-03', '2022-07-04', '2022-07-05', '2022-07-06', '2022-07-07', '2022-07-08', '2022-07-09', '2022-07-10', '2022-07-11', '2022-07-12', '2022-07-13', '2022-07-14', '2022-07-15', '2022-07-16', '2022-07-17', '2022-07-18', '2022-07-19', '2022-07-21', '2022-07-22', '2022-07-23', '2022-07-24', '2022-07-25', '2022-07-26', '2022-07-28', '2022-07-29', '2022-07-30', '2022-07-31', '2022-08-01', '2022-08-02', '2022-08-03', '2022-08-04', '2022-08-05', '2022-08-06', '2022-08-07', '2022-08-09', '2022-08-11', '2022-08-12', '2022-08-14', '2022-08-15', '2022-08-18', '2022-08-20', '2022-08-21', '2022-08-22', '2022-08-23', '2022-08-24', '2022-08-26', '2022-08-27', '2022-08-28', '2022-08-29', '2022-08-30', '2022-08-31', '2022-09-01', '2022-09-02', '2022-09-03', '2022-09-04', '2022-09-06', '2022-09-08', '2022-09-10', '2022-09-11', '2022-09-12', '2022-09-13', '2022-09-16', '2022-09-17', '2022-09-18', '2022-09-19', '2022-09-23', '2022-09-24', '2022-09-25', '2022-09-29', '2022-09-30', '2022-10-01', '2022-10-02', '2022-10-04', '2022-10-05', '2022-10-06', '2022-10-08', '2022-10-12', '2022-10-14', '2022-10-15', '2022-10-16', '2022-10-17', '2022-10-20', '2022-10-22', '2022-10-24', '2022-10-25', '2022-10-26', '2022-10-29', '2022-10-30', '2022-11-01', '2022-11-04', '2022-11-07', '2022-11-08', '2022-11-10', '2022-11-12', '2022-11-13', '2022-11-15', '2022-11-16', '2022-11-18', '2022-11-19', '2022-11-20', '2022-11-21', '2022-11-22', '2022-11-23', '2022-11-24', '2022-11-25', '2022-11-26', '2022-11-27', '2022-11-28', '2022-11-29', '2022-11-30', '2022-12-03', '2022-12-07', '2022-12-08', '2022-12-09', '2022-12-10', '2022-12-11', '2022-12-12', '2022-12-14', '2022-12-15', '2022-12-16', '2022-12-17', '2022-12-18', '2022-12-20', '2022-12-21', '2022-12-22', '2022-12-24', '2022-12-25', '2022-12-27', '2022-12-31', '2023-01-02', '2023-01-07', '2023-01-08', '2023-01-09', '2023-01-12', '2023-01-13', '2023-01-15', '2023-01-16', '2023-01-17', '2023-01-18', '2023-01-19', '2023-01-21', '2023-01-23', '2023-01-24', '2023-01-25', '2023-01-26', '2023-01-27', '2023-01-29', '2023-01-31', '2023-02-01', '2023-02-03', '2023-02-04', '2023-02-05', '2023-02-06', '2023-02-07', '2023-02-08', '2023-02-09', '2023-02-10', '2023-02-14', '2023-02-15', '2023-02-16', '2023-02-21', '2023-02-22', '2023-02-25', '2023-02-27', '2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03', '2023-03-04', '2023-03-05', '2023-03-07', '2023-03-08', '2023-03-09', '2023-03-10', '2023-03-12', '2023-03-13', '2023-03-14', '2023-03-15', '2023-03-17', '2023-03-18', '2023-03-20', '2023-03-21', '2023-03-24', '2023-03-25', '2023-03-26', '2023-03-27', '2023-03-28', '2023-03-29', '2023-03-30', '2023-03-31', '2023-04-01', '2023-04-02', '2023-04-03', '2023-04-06', '2023-04-08', '2023-04-10', '2023-04-11', '2023-04-13', '2023-04-15', '2023-04-16', '2023-04-17', '2023-04-18', '2023-04-19', '2023-04-20', '2023-04-21', '2023-04-22', '2023-04-24', '2023-04-25', '2023-04-27', '2023-04-29', '2023-04-30', '2023-05-01', '2023-05-02', '2023-05-03', '2023-05-04', '2023-05-05', '2023-05-06', '2023-05-07', '2023-05-08', '2023-05-09', '2023-05-11', '2023-05-13', '2023-05-14', '2023-05-15', '2023-05-16', '2023-05-17', '2023-05-18', '2023-05-19', '2023-05-20', '2023-05-21', '2023-05-22', '2023-05-23', '2023-05-24', '2023-05-25', '2023-05-26', '2023-05-27', '2023-05-28', '2023-05-29', '2023-05-30', '2023-06-01', '2023-06-02', '2023-06-03', '2023-06-04', '2023-06-05', '2023-06-06', '2023-06-07', '2023-06-08', '2023-06-09', '2023-06-10', '2023-06-11', '2023-06-17', '2023-06-18', '2023-06-19', '2023-06-20', '2023-06-21', '2023-06-22', '2023-06-23', '2023-06-24', '2023-06-25', '2023-06-26', '2023-06-27', '2023-06-28', '2023-06-29', '2023-06-30', '2023-07-03', '2023-07-04', '2023-07-05', '2023-07-06', '2023-07-07', '2023-07-08', '2023-07-10', '2023-07-11', '2023-07-12', '2023-07-13', '2023-07-14', '2023-07-15', '2023-07-16', '2023-07-17', '2023-07-19', '2023-07-21', '2023-07-22', '2023-07-23', '2023-07-24', '2023-07-25', '2023-07-26', '2023-07-27', '2023-07-28', '2023-07-29', '2023-07-30', '2023-07-31', '2023-08-01', '2023-08-02', '2023-08-03', '2023-08-05', '2023-08-06', '2023-08-07', '2023-08-09', '2023-08-10', '2023-08-11', '2023-08-12', '2023-08-15', '2023-08-16', '2023-08-19', '2023-08-21', '2023-08-22', '2023-08-23', '2023-08-24', '2023-08-28', '2023-08-29', '2023-08-30', '2023-08-31', '2023-09-03', '2023-09-04', '2023-09-07', '2023-09-08', '2023-09-09', '2023-09-10', '2023-09-11', '2023-09-12', '2023-09-13', '2023-09-14', '2023-09-16', '2023-09-17', '2023-09-19', '2023-09-20', '2023-09-21', '2023-09-22', '2023-09-23', '2023-09-24', '2023-09-25', '2023-09-30', '2023-10-01', '2023-10-03', '2023-10-04', '2023-10-05', '2023-10-06', '2023-10-07', '2023-10-10', '2023-10-11', '2023-10-12', '2023-10-14', '2023-10-15', '2023-10-16', '2023-10-18', '2023-10-19', '2023-10-20', '2023-10-21', '2023-10-22', '2023-10-23', '2023-10-25', '2023-10-27', '2023-10-28', '2023-10-29', '2023-10-31', '2023-11-01', '2023-11-02', '2023-11-03', '2023-11-04', '2023-11-06', '2023-11-07', '2023-11-08', '2023-11-09', '2023-11-10', '2023-11-11', '2023-11-12', '2023-11-13', '2023-11-14', '2023-11-15', '2023-11-16', '2023-11-17', '2023-11-18', '2023-11-19', '2023-11-20', '2023-11-21', '2023-11-22', '2023-11-26', '2023-11-30', '2023-12-01', '2023-12-03', '2023-12-04', '2023-12-05', '2023-12-06', '2023-12-07', '2023-12-08', '2023-12-09', '2023-12-10', '2023-12-11', '2023-12-12', '2023-12-13', '2023-12-16', '2023-12-18', '2023-12-19', '2023-12-21', '2023-12-22', '2023-12-23', '2023-12-24', '2023-12-25', '2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-31', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05', '2024-01-06', '2024-01-07', '2024-01-08', '2024-01-09', '2024-01-10', '2024-01-11', '2024-01-12', '2024-01-14', '2024-01-15', '2024-01-16', '2024-01-17', '2024-01-18', '2024-01-19', '2024-01-21', '2024-01-22', '2024-01-24', '2024-01-25', '2024-01-26', '2024-01-29', '2024-01-30', '2024-01-31', '2024-02-01', '2024-02-02', '2024-02-05', '2024-02-06', '2024-02-07', '2024-02-08', '2024-02-09', '2024-02-10', '2024-02-12', '2024-02-13', '2024-02-17', '2024-02-18', '2024-02-19', '2024-02-20', '2024-02-21', '2024-02-22', '2024-02-23', '2024-02-25', '2024-02-26', '2024-02-27', '2024-02-28', '2024-02-29', '2024-03-02', '2024-03-03', '2024-03-04', '2024-03-05', '2024-03-06', '2024-03-07', '2024-03-08', '2024-03-09', '2024-03-10', '2024-03-12', '2024-03-14', '2024-03-15', '2024-03-16', '2024-03-17', '2024-03-18', '2024-03-19', '2024-03-20', '2024-03-21', '2024-03-22', '2024-03-25', '2024-03-29', '2024-03-30', '2024-03-31', '2024-04-02', '2024-04-03', '2024-04-04', '2024-04-05', '2024-04-06', '2024-04-08', '2024-04-10', '2024-04-11', '2024-04-12', '2024-04-13', '2024-04-14', '2024-04-16', '2024-04-18', '2024-04-19', '2024-04-20', '2024-04-22', '2024-04-23', '2024-04-24', '2024-04-25', '2024-04-26', '2024-04-27', '2024-04-29', '2024-04-30', '2024-05-01', '2024-05-02', '2024-05-04', '2024-05-05', '2024-05-06', '2024-05-08', '2024-05-09', '2024-05-10', '2024-05-12', '2024-05-14', '2024-05-15', '2024-05-16', '2024-05-17', '2024-05-18', '2024-05-19', '2024-05-21', '2024-05-22', '2024-05-23', '2024-05-24', '2024-05-25', '2024-05-28', '2024-05-29', '2024-05-30', '2024-05-31', '2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-09', '2024-06-10', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17', '2024-06-18', '2024-06-19', '2024-06-20', '2024-06-21', '2024-06-22', '2024-06-24', '2024-06-25', '2024-06-26', '2024-06-28', '2024-06-29', '2024-06-30', '2024-07-02', '2024-07-03', '2024-07-06', '2024-07-10', '2024-07-11', '2024-07-12', '2024-07-13', '2024-07-14', '2024-07-15', '2024-07-16', '2024-07-17', '2024-07-18', '2024-07-19', '2024-07-20', '2024-07-21', '2024-07-22', '2024-07-24', '2024-07-25', '2024-07-26', '2024-07-27', '2024-07-28', '2024-07-29', '2024-07-31', '2024-08-01', '2024-08-02', '2024-08-03', '2024-08-04', '2024-08-05', '2024-08-06', '2024-08-07', '2024-08-08', '2024-08-11', '2024-08-12', '2024-08-13', '2024-08-15', '2024-08-16', '2024-08-18', '2024-08-19', '2024-08-20', '2024-08-21', '2024-08-22', '2024-08-23', '2024-08-24', '2024-08-26', '2024-08-27', '2024-08-28', '2024-08-29', '2024-08-30', '2024-08-31', '2024-09-02', '2024-09-03', '2024-09-04', '2024-09-05', '2024-09-06', '2024-09-07', '2024-09-08', '2024-09-09', '2024-09-10', '2024-09-11', '2024-09-12', '2024-09-13', '2024-09-14', '2024-09-17', '2024-09-18', '2024-09-19', '2024-09-20', '2024-09-22', '2024-09-23', '2024-09-25', '2024-09-26', '2024-09-27', '2024-09-28', '2024-09-29', '2024-09-30', '2024-10-01', '2024-10-03', '2024-10-08', '2024-10-13', '2024-10-15', '2024-10-16', '2024-10-18', '2024-10-22', '2024-10-23', '2024-10-29', '2024-10-30', '2024-10-31', '2024-11-01', '2024-11-05', '2024-11-06', '2024-11-09', '2024-11-10', '2024-11-11', '2024-11-13', '2024-11-14', '2024-11-15', '2024-11-17', '2024-11-19', '2024-11-20', '2024-11-24', '2024-11-25', '2024-11-26', '2024-11-27', '2024-12-01', '2024-12-02', '2024-12-04', '2024-12-05', '2024-12-06', '2024-12-07', '2024-12-08', '2024-12-09', '2024-12-10', '2024-12-11', '2024-12-15', '2024-12-17', '2024-12-18', '2024-12-19', '2024-12-21', '2024-12-24', '2024-12-25', '2024-12-28', '2024-12-29', '2024-12-30', '2025-01-01', '2025-01-02', '2025-01-04', '2025-01-05', '2025-01-06', '2025-01-07', '2025-01-08', '2025-01-09', '2025-01-10', '2025-01-11', '2025-01-12', '2025-01-13', '2025-01-14', '2025-01-15', '2025-01-16', '2025-01-19', '2025-01-20', '2025-01-21', '2025-01-22', '2025-01-23', '2025-01-24', '2025-01-25', '2025-01-27', '2025-01-28', '2025-01-30', '2025-02-05', '2025-02-06', '2025-02-08', '2025-02-10', '2025-02-12', '2025-02-13', '2025-02-14', '2025-02-15', '2025-02-16', '2025-02-17', '2025-02-18', '2025-02-20', '2025-02-21', '2025-02-22', '2025-02-23', '2025-02-25', '2025-02-27', '2025-03-01', '2025-03-02', '2025-03-03', '2025-03-04', '2025-03-05', '2025-03-06', '2025-03-07', '2025-03-08', '2025-03-09', '2025-03-11', '2025-03-12', '2025-03-14', '2025-03-15', '2025-03-16', '2025-03-17', '2025-03-18', '2025-03-19', '2025-03-20', '2025-03-21', '2025-03-22', '2025-03-23', '2025-03-25', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-31', '2025-04-02']\n", - "Total slots: 1095\n", - "Available slots: 805\n", - "Excluded slots due to clouds: 290\n" - ] - } - ], + "outputs": [], "source": [ "print(available_slots)\n", "print(f\"Total slots: {len(slots)}\")\n", "print(f\"Available slots: {len(available_slots)}\")\n", - "print(f\"Excluded slots due to clouds: {len(slots) - len(available_slots)}\")\n" + "print(f\"Excluded slots due to empty dates: {len(slots) - len(available_slots)}\")\n" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "b0cabe8f-e1f2-4b18-8ac0-c2565d0ff16b", "metadata": {}, "outputs": [], @@ -913,847 +553,10 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "8f2c3e20-894c-4fe5-95d3-482ee3bac117", - "metadata": {}, - "outputs": [], - "source": [ - "#from mpl_toolkits.basemap import Basemap # Available here: https://github.com/matplotlib/basemap\n", - "#from matplotlib.patches import Polygon as PltPolygon\n", - "\n", - "#show_splitter(bbox_splitter, show_legend=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "41b7369c-f768-44ba-983e-eb8eae4f3afd", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\sentinelhub\\geometry.py:137: SHDeprecationWarning: Initializing `BBox` objects from `BBox` objects will no longer be possible in future versions.\n", - " return cls._tuple_from_bbox(bbox)\n", - "C:\\Users\\timon\\AppData\\Local\\Temp\\ipykernel_5948\\170088608.py:67: SHDeprecationWarning: The string representation of `BBox` will change to match its `repr` representation.\n", - " print(f' Image downloaded for ' +slot + ' and bbox ' + str(bbox))\n", - "c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\sentinelhub\\geometry.py:137: SHDeprecationWarning: Initializing `BBox` objects from `BBox` objects will no longer be possible in future versions.\n", - " return cls._tuple_from_bbox(bbox)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Image downloaded for 2022-04-06 and bbox -47.09879025717693,-22.67132809994226,-47.09188307701802,-22.66813642658124\n", - " Image downloaded for 2022-04-08 and bbox -47.09879025717693,-22.67132809994226,-47.09188307701802,-22.66813642658124\n", - " Image downloaded for 2022-04-09 and bbox -47.09879025717693,-22.67132809994226,-47.09188307701802,-22.66813642658124\n", - " Image downloaded for 2022-04-10 and bbox -47.09879025717693,-22.67132809994226,-47.09188307701802,-22.66813642658124\n", - " Image downloaded for 2022-04-11 and bbox -47.09879025717693,-22.67132809994226,-47.09188307701802,-22.66813642658124\n", - " Image downloaded for 2022-04-12 and bbox 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Load areas outside the loop if they remain constant\n", "#bbox_area = json.dumps(chosen_area)\n", @@ -1769,67 +572,7 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "7c0f8f0e-b1bc-4b5a-939d-e4358a485c06", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "#if project == 'chemba':\n", - "# chosen_area = [[34.9460, -17.3500, 34.9839, -17.3110], [34.8830, -17.3516, 34.9380, -17.2917]]\n", - "\n", - "#if project == 'chemba_test_8b':\n", - "# chosen_area = [[34.946, -17.3516, 34.938, -17.2917], [34.883, -17.3516, 34.938, -17.2917]]\n", - "\n", - "#if project == 'xinavane':\n", - "# chosen_area = [[32.6790, -25.0333, 32.7453, -25.0235], [32.6213, -25.0647, 32.6284, -25.0570]]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "16c449ab-eb40-4ed9-9a26-6a64dae114e1", - "metadata": {}, - "outputs": [], - "source": [ - "#bbox_area = json.dumps(chosen_area)\n", - "\n", - "#areas = json.loads(os.getenv('BBOX', bbox_area))\n", - "#areas" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "6c02d7de-cddf-4fc3-8d23-8431415d07b8", - "metadata": {}, - "outputs": [], - "source": [ - "# Load areas outside the loop if they remain constant\n", - "#bbox_area = json.dumps(chosen_area)\n", - "#areas = json.loads(os.getenv('BBOX', bbox_area))\n", - "#resolution = 3\n", - "\n", - "#for slot in slots:\n", - "# for area in areas:\n", - "# bbox = BBox(bbox=area, crs=CRS.WGS84)\n", - "# size = bbox_to_dimensions(bbox, resolution=resolution)\n", - "# download_function(slot, bbox, size)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "f62d8af8-1c5c-4a83-b888-7205bbe191af", - "metadata": {}, - "outputs": [], - "source": [ - "#size" - ] - }, - { - "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "68db3c15-6f94-432e-b315-c329e4251b21", "metadata": { "tags": [] @@ -1852,227 +595,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "cb3fa856-a550-4899-844a-e69209bba3ad", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Emptied folder: ..\\laravel_app\\storage\\app\\citrus_brazil_trial\\merged_virtual\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-06\\\\739e5b07b94747cf5518439c03a6a882'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-08\\\\975b5f7dce90c163778e7cd60138b6e0'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-09\\\\560f3d8f33d2e3a11e451da334e2b99d'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-10\\\\147bb96d7ff751a8a5bcddec4650b3c3'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-11\\\\20ceb12e0969527eee2f862a46ad2089'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-12\\\\b280a04ed56a2749afd0531cbc5e2738'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-13\\\\ccb8960db9f078057d9a295c95951f5e'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-14\\\\b4894ef5fc0261d2d2cee3862586d2c9'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-16\\\\bfdccfeef2323f84e7ab7eb57a1f9e0f'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-17\\\\6f57540b83f9f6fc395e93b576ea3a7f'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-19\\\\259507a61f06024282a9cef7e7674843'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-20\\\\455f6fa8b835a94c7df4a99dc78aa5cd'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-21\\\\ba46a7de1a6e50d7b7019cf72bd5b042'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-22\\\\b83412c909857fe292e6f08d6efc83f8'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-23\\\\473530acf397c287242c91e00a673a6a'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-24\\\\75e8ef5602fd227d5f4cf2f49a4192dc'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-25\\\\9e1dc988b59129df6b4b8e0a5c71386c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-26\\\\45a8c237a74a0e3c26ef755caed0a980'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-27\\\\5d807380b12eae6b64523f283cd03132'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-29\\\\f300818abe064b39e19cebcfa9272f08'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-04-30\\\\536ec3dbb365b902374558b0e18fd86d'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-02\\\\f1a6fd087df19a79b1b79d3488323fc6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-03\\\\97620c35129411c4bbac64f5a54502cc'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-05\\\\fd91b61b5108fb2153973e07449bf94c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-07\\\\f44ace4ff332d7769a438329579fb90c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-08\\\\52a48086f41bc3d97b73e449b6a56720'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-11\\\\e1d4d5ba43d35430e921b5a90b30a2c3'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-12\\\\b9cfcf39cab0c7917fe3bd9d2ce1d2dd'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-14\\\\a7693d7974ad917ff6d009545d529fc1'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-18\\\\4471541ca7c0ec4b6f654482458d67fa'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-19\\\\787e72845978852f91ba3d751e894c5a'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-20\\\\a5c34eb398fe87de72bbb73216351f68'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-21\\\\8a5d080091874816e49e7acb76f345b2'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-22\\\\cb221b5d5cea693db56facc2b928a3a3'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-23\\\\387620e1283d8f08640783ca83f534a6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-25\\\\cee94abac76a87374e03313b95f9c24a'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-26\\\\02f7a7f40181ffdeddcdccbc4c3279a6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-27\\\\73bd81f36d028a45cca3c96f8cc0e039'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-28\\\\602ca22cd4e7e087ea9b3192b1c69f3f'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-29\\\\b0da02900f7396f48e32c4f3a28de4a5'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-05-30\\\\f1324a8077a10d4bb13424610788c275'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-01\\\\727173997afb53e9b7e67c3a96420c5e'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-02\\\\431240d39b4adf4317cac44dea9e0c58'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-03\\\\ee7fdaf915f7974d098c2e21c2576d69'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-04\\\\4e66468bdadeb9097ed547a91d55ea5f'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-05\\\\fee223cc34c83bf00cf7953857e9ee85'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-06\\\\4b043cfa6363838e81f55ba0cfdee167'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-07\\\\5096cc51ba94577abb2dd56204381673'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-08\\\\72edfa44afb0b87de66c79d70f89e848'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-10\\\\04bda6ff00521d02f1474224e9590230'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-11\\\\fcb7088d43f6c25a6b488bbdcda62652'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-12\\\\59fa3ab0fbfd363ab8af4a8949141ee7'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-13\\\\6dbbe42edce79f12eae04ff93f1c861e'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-14\\\\8509758460f42d43df3cecf7bba1ecb7'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-15\\\\19e1fdb2014e28baa9bd367842b1a2c7'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-17\\\\707db03584d3ce6d740d49e4df0681fd'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-19\\\\259fa487fd8b327c59f11affc0ec0aa4'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-20\\\\164a8412a0ade5cb1dc29074811588be'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-21\\\\d2a28a1011f6dbdbf9976d2c82e55590'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-22\\\\5cd42d881b4c6cbe6bc7f9fa43cf540a'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-24\\\\156f0d2c5d34a3d724b43df2ad6cb973'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-25\\\\3fc88633503c581a975af68236edaa54'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-26\\\\9b3946ef6cfb9fb76d77da5a7196675f'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-27\\\\b0dd9d246b22a37f695803816f6f693f'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-28\\\\d0bc816e07abd36dd02f2c6a0a58cefb'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-29\\\\d2efc6fd3426b30c8328392b73628404'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-06-30\\\\04aae5de5aa03c237317422abc25fd9c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-01\\\\c9fba90cedd484b9b9f1c2324556bad7'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-02\\\\68ea947d4597389586bc6273f2a44b29'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-03\\\\559502e5559f2d0dd6cd4ea58dfe23d4'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-04\\\\7fabe639423539109c3bf5a4089b30c8'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-05\\\\9ad0525f06dcda56f62fb8ebafe25f7a'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-06\\\\6fd763985cebafa05c162140fb75e5e3'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-07\\\\2f945cf98f4e106b16c82c39381814a4'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-08\\\\031662b95ebdfc245d60013866567076'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-09\\\\83900b3c79f0ece952d44caed5d27eb6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-10\\\\3ab5697089b8308647f90c5899a9d825'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-11\\\\cc28299397fd469453e6a95ea3104305'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-12\\\\cb28d70945f82724d4bcf657e01c12d6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-13\\\\b96a047c7961abed4df5351eab0cfce5'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-14\\\\9f977425569ef08fc65037e0a748a1b6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-15\\\\9a4f6d8cf9b3be5c4277fe97ba9f0ba9'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-16\\\\484bb07688adfd179745e1d9e7a4dbd1'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-17\\\\c4815b7ec2435e19ca41cf908981d2e6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-18\\\\33541c458f08474e1c22962ac663ee9e'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-19\\\\0019559d9bb3a0cf0a716a2904db2e03'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-21\\\\a632b8a7397a05ffcc0a3e6d6764dbf8'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-22\\\\5e00cf0ca48e8d9605ecb22ab09219e9'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-23\\\\8f31dacd81b7cf160767f0cde76be917'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-24\\\\61421803929083f01c133288807d73b6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-25\\\\754c771ef8d2219327383c327f7b4829'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-26\\\\2c0d09eeb5da33d7e698b0c91316b6ee'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-28\\\\8541cee9eb7b6ca72362798f240ea780'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-29\\\\bbd3c282fe4b53e83f56289205a704a2'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-30\\\\cdf6a64b217b227f3b0b5f015ae6951c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-07-31\\\\52929cc81e61d51267e650716343c115'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-01\\\\515a4d976def93f48d39a147b221826c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-02\\\\e324e6602fe4dbc0b5e1e5d12f9b6b59'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-03\\\\23be832a69cf8223e058530eb3be82c7'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-04\\\\74653f1f39ac498f32dab3cecf42ed0a'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-05\\\\f4a69ff1f3899431b89a79ac2fdbf446'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-06\\\\38dd27e2a1a3d2034c2f082a6656090c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-07\\\\1e203b729661a0360592c33fca6cd245'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-09\\\\f08659146c600b286e8c08416cd54527'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-11\\\\df73acf64acc910463e4c54a4fbec034'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-12\\\\bd7890f7d8dae834fb4df6f7c207eb3f'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-14\\\\fcc9d444d86baa4989d3de105e152e4c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-15\\\\aa0ae5c9fbc0ec64b3da409732fbf65d'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-18\\\\471209329f6113ec188ddf4331ed5e11'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-20\\\\fd1212d01f9dc4899e8f1ceff23e269a'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-21\\\\e4dc51736b1308d87b2d3c12d114b5cd'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-22\\\\ca6a8f4982e1cd717b89db960b54fd22'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-23\\\\8efdd006ad640fa78036a968db0623ab'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-24\\\\1986d5ed5f08e83ed8fdbb22f988bcb3'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-26\\\\3dec5e43137b622efaee94d71dd94281'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-27\\\\e7b146ede94c81bccb8e52ac30e1e4cc'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-28\\\\a279ebb63b871e5cea8b20acc41a758d'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-29\\\\9a0e8c7e6ad2fa8946407cf888ee6e36'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-30\\\\76b3ee11ddd4301bec65271e5d2cc1e0'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-08-31\\\\3fa3f616aed88719b8b393c445896442'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-01\\\\f56926823b403c93de201a2c427f4741'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-02\\\\b8837e7f231f381aae62e7c54bd7f1fd'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-03\\\\4fe7694ef04a3176d35f609d2de4a565'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-04\\\\40525308a6db366f7e67d500e21f79ab'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-06\\\\6297a2dfece83a51f84b1483a0ac6027'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-08\\\\293c7c568f24c341eb6302d2e0c6bd4e'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-10\\\\e0ea0737c3325bcb8c6071874e384cc8'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-11\\\\1377bb7ef9f59c7c427edefb3b15d5ab'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-12\\\\942b43857c37e506517f46eb65c22806'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-13\\\\8eb10d0cf2e2f524051a439c153fbfee'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-16\\\\b9efd95535f291dbe3ca21ba8243ca1d'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-17\\\\2875f659705afab9f4b98e6c106bfa99'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-18\\\\03a04d6b2936900834b9f1177edd7959'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-19\\\\8502dff904a83eefe4fc3680b6b367fc'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-23\\\\348bd446565e1fa947b47db957c454c6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-24\\\\e46d95e7f2132e6b12eb40f9c5530038'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-25\\\\8c0ee0707f600d15637e652e5d45cace'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-29\\\\ea6df2311779bb919c38eb2290fca6b0'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-09-30\\\\ba7085c508b482af488346761ab18260'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-01\\\\ac34c2afc14c1a95e2e88df24f60337e'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-02\\\\49a6e276b491f80a45795a81c6e90039'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-04\\\\8390c88d3d3251e4303341aba06f9707'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-05\\\\fea4a86d3bed4e9bd07986859b012d53'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-06\\\\87907b967b4b8d32cfdb6d9c2fe4751c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-08\\\\5c615b1bac43c34351ad45653d669b98'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-12\\\\8ca9dd2605e49ccdbd241b82238360b7'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-14\\\\67f1dc22b749e88a87527b48412e76bf'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-15\\\\b6223ee2ed148c5902bb96393e4e73a6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-16\\\\1e9dd526e788c06a5ba8376ba8180f5c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-17\\\\705c68cbdbd35e3ac9c5a1f2d2052a0f'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-20\\\\734c998cdfe4421c8c524becc2e62e74'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-22\\\\e109ceb686077f31f4498d7f95ef918f'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-24\\\\a30eacc0b1ced2250dabe3612439a0a6'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-25\\\\cafc3e9608059b610a4cf45887d76296'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-26\\\\54ec6bc3a8b42f6e19121c897c14d80c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-29\\\\ed19b3c473779f35015d461782dc7502'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-10-30\\\\e9866d7f4f8e947302a98f241e1bc345'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-01\\\\ada9c220e25aec789a3c5b8209722317'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-04\\\\a2f7f051f807fd5cc7620eb8f43ee607'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-07\\\\e9f688b986ce11a4847ec1103402cc28'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-08\\\\3d681376c072b7f39e885c56721a04d1'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-10\\\\92b0866881dc662cb857ee06caba95c7'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-12\\\\b93305b65052f431a246117009a99c83'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-13\\\\559fa65e67ed5d242b892113813a1242'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-15\\\\92c9e12455ad29b372b9743ebac042c9'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-16\\\\d3e8c176deb925bb7331beb9015026fa'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-18\\\\9d4e7e3d434995d7c4e63f13f56234f4'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-19\\\\c92bb0d52c7faca33726dadc56a31a44'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-20\\\\6f90989f438dfc1a6e88e2198075bc51'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-21\\\\5c0c6cac9862bf439441f0598bc9203d'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-22\\\\39d4a42d640a6046f8a4b34a8e9e9734'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-23\\\\2e313eb93aa41b4e5bc9e7f25df9985b'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-24\\\\7b4f2ffadb5c491ebb38ca29277ab016'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-25\\\\ad43bdf60b4e7c8ec2369d1f1c8ede43'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-26\\\\7cfc83d40ec8c2f8f4d7adb31ed2e0d9'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-27\\\\50c1eea195361bf993e2685d19d35ade'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-28\\\\eb9a4cc3c0067b596a0a9a5949c3f19c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-29\\\\e7c19e7b3359358a7c4a6589f878aad0'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-11-30\\\\b6647e602306d61d65a891c50e91a4d2'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-03\\\\4a9a18f95f9cf64c92c43a9dde331336'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-07\\\\de7d4cb14f1f74e0e857a6c340d0905c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-08\\\\ca82f3cfd718e41aeb222b6237168f5c'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-09\\\\7d06c7d26b6a500405c975dda7cee319'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-10\\\\ae06916f2758c2284d0b1ca845da69cc'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-11\\\\6c2aeee92347d79677770b052f1a2445'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-12\\\\306e973ac37d64c91e6a18a03daca6c3'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-14\\\\656c06b7e0173fd92477892e421b10b4'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-15\\\\06520486eb087effca3ad61d41f09667'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-16\\\\96f36a2e9995383187bfa8dd5bee2f6e'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-17\\\\6d1da5751002a53c3ca7a4ffd49e99e8'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-18\\\\00b6448cc17cccba09729e1d6f5449ec'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-20\\\\6e568ab8d5ce58ed0bfb242475994476'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-21\\\\5847c2d63d4b08fea81710e0e0fedf9b'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-22\\\\2e96f499973a0a72ef385287f947d80b'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-24\\\\53e598be77365bc2c56a94fd311c6cc7'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-25\\\\64ec7956bf74d044aa44ad065291d624'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-27\\\\e3018fc1bf8e77eb078e0e19e6a01977'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2022-12-31\\\\fdd063ba5f909a3c619dde4b44192bcc'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2023-01-02\\\\654936769fdd8ef3e99220fcfe71cfcf'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2023-01-07\\\\8e97e5f0e238177223fd586bb0737b2a'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2023-01-08\\\\6d229ce25af24ff37cfa7b54fd916156'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2023-01-09\\\\dd9743b5c2e8ab6a2223f310f1508023'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2023-01-12\\\\7a1c456f7475a95400e79eec68277e73'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2023-01-13\\\\3a7b86d2565784dc978cd7f53ef45d71'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2023-01-15\\\\bf1ae2bc023232e918cc2afa8b289b25'\n", - "Error: [WinError 5] Toegang geweigerd: '..\\\\laravel_app\\\\storage\\\\app\\\\citrus_brazil_trial\\\\single_images\\\\2023-01-16\\\\71076bd8146337f70d93822b6cb02155'\n", - "Emptied folder: ..\\laravel_app\\storage\\app\\citrus_brazil_trial\\single_images\n" - ] - } - ], + "outputs": [], "source": [ "# List of folder names\n", "\n", @@ -2104,14 +632,6 @@ "# Call the function to empty folders only if the 'run' parameter is set to True\n", "empty_folders(folders_to_empty, run=empty_folder_question)\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f91f490", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/python_app/planet_download_with_ocm.ipynb b/python_app/planet_download_with_ocm.ipynb new file mode 100644 index 0000000..68d1b99 --- /dev/null +++ b/python_app/planet_download_with_ocm.ipynb @@ -0,0 +1,1097 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bee51aa9", + "metadata": {}, + "source": [ + "# Planet Data Download & Processing with OmniCloudMask\n", + "\n", + "This notebook extends the functionality of the original `planet_download.ipynb` by incorporating OmniCloudMask (OCM) for improved cloud and shadow detection in PlanetScope imagery. OCM is a state-of-the-art cloud masking tool that was originally trained on Sentinel-2 data but generalizes exceptionally well to PlanetScope imagery.\n", + "\n", + "## Key Features Added:\n", + "- OmniCloudMask integration for advanced cloud and shadow detection\n", + "- Comparison visualization between standard UDM masks and OCM masks\n", + "- Options for both local processing and direct integration with SentinelHub\n", + "- Support for batch processing multiple images" + ] + }, + { + "cell_type": "markdown", + "id": "6e8cbe80", + "metadata": {}, + "source": [ + "## 1. Load packages and connect to SentinelHub\n", + "First, we'll install required packages and import dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "88d787b3", + "metadata": {}, + "outputs": [], + "source": [ + "# Standard packages from original notebook\n", + "import os\n", + "import json\n", + "import datetime\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "from osgeo import gdal\n", + "\n", + "from sentinelhub import MimeType, CRS, BBox, SentinelHubRequest, SentinelHubDownloadClient, \\\n", + " DataCollection, bbox_to_dimensions, DownloadRequest, SHConfig, BBoxSplitter, read_data, Geometry, SentinelHubCatalog\n", + "\n", + "import time\n", + "import shutil\n", + "import geopandas as gpd\n", + "from shapely.geometry import MultiLineString, MultiPolygon, Polygon, box, shape\n", + "\n", + "# Install OmniCloudMask if not present\n", + "# Uncomment these lines to install dependencies\n", + "# %pip install omnicloudmask rasterio" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "967d917d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OmniCloudMask successfully loaded\n" + ] + } + ], + "source": [ + "# Import OmniCloudMask after installation\n", + "try:\n", + " from omnicloudmask import predict_from_array, load_multiband, predict_from_load_func\n", + " from functools import partial\n", + " import rasterio as rio\n", + " HAS_OCM = True\n", + " print(\"OmniCloudMask successfully loaded\")\n", + "except ImportError:\n", + " print(\"OmniCloudMask not installed. Run the cell above to install it or install manually with pip.\")\n", + " HAS_OCM = False" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "39bd6361", + "metadata": {}, + "outputs": [], + "source": [ + "# Configure SentinelHub connection\n", + "config = SHConfig()\n", + "config.sh_client_id = '1a72d811-4f0e-4447-8282-df09608cff44'\n", + "config.sh_client_secret = 'FcBlRL29i9ZmTzhmKTv1etSMFs5PxSos'\n", + "\n", + "catalog = SentinelHubCatalog(config=config)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "99f4f255", + "metadata": {}, + "outputs": [], + "source": [ + "# Configure BYOC data collection\n", + "collection_id = 'c691479f-358c-46b1-b0f0-e12b70a9856c'\n", + "byoc = DataCollection.define_byoc(\n", + " collection_id,\n", + " name='planet_data2',\n", + " is_timeless=True)" + ] + }, + { + "cell_type": "markdown", + "id": "04ad9f39", + "metadata": {}, + "source": [ + "## 2. Configure project settings" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "672bd92c", + "metadata": {}, + "outputs": [], + "source": [ + "# Project selection\n", + "project = 'chemba' # Change this to your project name\n", + "\n", + "# Number of days to process\n", + "days = 30\n", + "\n", + "# Set this to True to delete intermediate files after processing\n", + "empty_folder_question = True\n", + "\n", + "# Output directories setup\n", + "BASE_PATH = Path('../laravel_app/storage/app') / os.getenv('PROJECT_DIR', project) \n", + "BASE_PATH_SINGLE_IMAGES = Path(BASE_PATH / 'single_images')\n", + "OCM_MASKS_DIR = Path(BASE_PATH / 'ocm_masks') # Directory for OmniCloudMask results\n", + "folder_for_merged_tifs = str(BASE_PATH / 'merged_tif')\n", + "folder_for_virtual_raster = str(BASE_PATH / 'merged_virtual')\n", + "geojson_file = Path(BASE_PATH /'Data'/ 'pivot.geojson')\n", + "\n", + "# Create directories if they don't exist\n", + "for directory in [BASE_PATH_SINGLE_IMAGES, OCM_MASKS_DIR, \n", + " Path(folder_for_merged_tifs), Path(folder_for_virtual_raster)]:\n", + " directory.mkdir(exist_ok=True, parents=True)" + ] + }, + { + "cell_type": "markdown", + "id": "a69df5ab", + "metadata": {}, + "source": [ + "## 3. Define OmniCloudMask Functions\n", + "\n", + "Here we implement the functionality to use OmniCloudMask for cloud/shadow detection" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "51f33368", + "metadata": {}, + "outputs": [], + "source": [ + "def process_with_ocm(image_path, output_dir=None, save_mask=True, resample_res=10):\n", + " \"\"\"\n", + " Process a PlanetScope image with OmniCloudMask\n", + " \n", + " Parameters:\n", + " -----------\n", + " image_path : str or Path\n", + " Path to the PlanetScope image (TIFF format)\n", + " output_dir : str or Path, optional\n", + " Directory to save the mask, if None, uses same directory as image\n", + " save_mask : bool, default=True\n", + " Whether to save the mask to disk\n", + " resample_res : int, default=10\n", + " Resolution in meters to resample the image to (OCM works best at 10m)\n", + " \n", + " Returns:\n", + " --------\n", + " tuple: (mask_array, profile)\n", + " The cloud/shadow mask as a numpy array and the rasterio profile\n", + " \"\"\"\n", + " if not HAS_OCM:\n", + " print(\"OmniCloudMask not available. Please install with pip install omnicloudmask\")\n", + " return None, None\n", + " \n", + " # Ensure image_path is a Path object\n", + " image_path = Path(image_path)\n", + " \n", + " # If no output directory specified, use same directory as image\n", + " if output_dir is None:\n", + " output_dir = image_path.parent\n", + " else:\n", + " output_dir = Path(output_dir)\n", + " output_dir.mkdir(exist_ok=True, parents=True)\n", + " \n", + " # Define output path for mask\n", + " mask_path = output_dir / f\"{image_path.stem}_ocm_mask.tif\"\n", + " \n", + " try:\n", + " # For PlanetScope 4-band images, bands are [B,G,R,NIR]\n", + " # We need [R,G,NIR] for OmniCloudMask in this order\n", + " # Set band_order=[3, 2, 4] for the standard 4-band PlanetScope imagery\n", + " band_order = [3, 2, 4] # For 4-band images: [R,G,NIR]\n", + " \n", + " # Load and resample image\n", + " print(f\"Loading image: {image_path}\")\n", + " rgn_data, profile = load_multiband(\n", + " input_path=image_path,\n", + " resample_res=resample_res,\n", + " band_order=band_order\n", + " )\n", + " \n", + " # Generate cloud and shadow mask\n", + " print(\"Applying OmniCloudMask...\")\n", + " prediction = predict_from_array(rgn_data)\n", + " \n", + " # Save the mask if requested\n", + " if save_mask:\n", + " profile.update(count=1, dtype='uint8')\n", + " with rio.open(mask_path, 'w', **profile) as dst:\n", + " dst.write(prediction.astype('uint8'), 1)\n", + " print(f\"Saved mask to: {mask_path}\")\n", + " \n", + " # Summary of detected features\n", + " n_total = prediction.size\n", + " n_clear = np.sum(prediction == 0)\n", + " n_thick = np.sum(prediction == 1)\n", + " n_thin = np.sum(prediction == 2)\n", + " n_shadow = np.sum(prediction == 3)\n", + " \n", + " print(f\"OCM Classification Results:\")\n", + " print(f\" Clear pixels: {n_clear} ({100*n_clear/n_total:.1f}%)\")\n", + " print(f\" Thick clouds: {n_thick} ({100*n_thick/n_total:.1f}%)\")\n", + " print(f\" Thin clouds: {n_thin} ({100*n_thin/n_total:.1f}%)\")\n", + " print(f\" Cloud shadows: {n_shadow} ({100*n_shadow/n_total:.1f}%)\")\n", + " \n", + " return prediction, profile\n", + " \n", + " except Exception as e:\n", + " print(f\"Error processing image with OmniCloudMask: {str(e)}\")\n", + " return None, None" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bac2f620", + "metadata": {}, + "outputs": [], + "source": [ + "def apply_ocm_mask_to_image(image_path, mask_array, output_path=None):\n", + " \"\"\"\n", + " Apply an OmniCloudMask to a Planet image and save the masked version\n", + " \n", + " Parameters:\n", + " -----------\n", + " image_path : str or Path\n", + " Path to the input image\n", + " mask_array : numpy.ndarray\n", + " The cloud/shadow mask from OmniCloudMask\n", + " output_path : str or Path, optional\n", + " Path to save the masked image, if None, uses image_path with '_masked' suffix\n", + " \n", + " Returns:\n", + " --------\n", + " str: Path to the masked image\n", + " \"\"\"\n", + " image_path = Path(image_path)\n", + " \n", + " if output_path is None:\n", + " output_path = image_path.parent / f\"{image_path.stem}_masked.tif\"\n", + " \n", + " try:\n", + " # Open the original image\n", + " with rio.open(image_path) as src:\n", + " data = src.read()\n", + " profile = src.profile.copy()\n", + " \n", + " # Check dimensions match or make them match\n", + " if data.shape[1:] != mask_array.shape:\n", + " # Need to resample the mask\n", + " from rasterio.warp import reproject, Resampling\n", + " # TODO: Implement resampling if needed\n", + " print(\"Warning: Mask and image dimensions don't match\")\n", + " \n", + " # Create a binary mask (0 = cloud/shadow, 1 = clear)\n", + " # OmniCloudMask: 0=clear, 1=thick cloud, 2=thin cloud, 3=shadow\n", + " binary_mask = np.ones_like(mask_array)\n", + " binary_mask[mask_array > 0] = 0 # Set non-clear pixels to 0\n", + " \n", + " # Apply the mask to all bands\n", + " masked_data = data.copy()\n", + " for i in range(data.shape[0]):\n", + " # Where mask is 0, set the pixel to nodata\n", + " masked_data[i][binary_mask == 0] = profile.get('nodata', 0)\n", + " \n", + " # Write the masked image\n", + " with rio.open(output_path, 'w', **profile) as dst:\n", + " dst.write(masked_data)\n", + " \n", + " print(f\"Masked image saved to: {output_path}\")\n", + " return str(output_path)\n", + " \n", + " except Exception as e:\n", + " print(f\"Error applying mask to image: {str(e)}\")\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ad6770ac", + "metadata": {}, + "outputs": [], + "source": [ + "def process_all_images_with_ocm(directory, output_dir=None, pattern=\"*.tif\"):\n", + " \"\"\"\n", + " Process all images in a directory with OmniCloudMask\n", + " \n", + " Parameters:\n", + " -----------\n", + " directory : str or Path\n", + " Directory containing PlanetScope images\n", + " output_dir : str or Path, optional\n", + " Directory to save results, defaults to a subfolder of input directory\n", + " pattern : str, default=\"*.tif\"\n", + " Glob pattern to match image files\n", + " \n", + " Returns:\n", + " --------\n", + " list: Paths to processed images\n", + " \"\"\"\n", + " directory = Path(directory)\n", + " \n", + " if output_dir is None:\n", + " output_dir = directory / \"ocm_processed\"\n", + " else:\n", + " output_dir = Path(output_dir)\n", + " \n", + " output_dir.mkdir(exist_ok=True, parents=True)\n", + " \n", + " # Find all matching image files\n", + " image_files = list(directory.glob(pattern))\n", + " \n", + " if not image_files:\n", + " print(f\"No files matching pattern '{pattern}' found in {directory}\")\n", + " return []\n", + " \n", + " print(f\"Found {len(image_files)} images to process\")\n", + " processed_images = []\n", + " \n", + " # Process each image\n", + " for img_path in image_files:\n", + " print(f\"\\nProcessing: {img_path.name}\")\n", + " mask_array, profile = process_with_ocm(img_path, output_dir=output_dir)\n", + " \n", + " if mask_array is not None:\n", + " # Apply mask to create cloud-free image\n", + " output_path = output_dir / f\"{img_path.stem}_masked.tif\"\n", + " masked_path = apply_ocm_mask_to_image(img_path, mask_array, output_path)\n", + " if masked_path:\n", + " processed_images.append(masked_path)\n", + " \n", + " return processed_images" + ] + }, + { + "cell_type": "markdown", + "id": "46e34d74", + "metadata": {}, + "source": [ + "## 4. Define functions from the original notebook (modified for OCM integration)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "85e07fa8", + "metadata": {}, + "outputs": [], + "source": [ + "# Define evalscripts (from original notebook)\n", + "\n", + "# Original evalscript without cloud/shadow detection (for comparison)\n", + "evalscript_original = \"\"\"\n", + " //VERSION=3\n", + " function setup() {\n", + " return {\n", + " input: [{\n", + " bands: [\"red\", \"green\", \"blue\", \"nir\", \"udm1\"]\n", + " }],\n", + " output: {\n", + " bands: 4,\n", + " sampleType: \"FLOAT32\"\n", + " }\n", + " };\n", + " }\n", + "\n", + " function evaluatePixel(sample) {\n", + " // Scale the bands\n", + " var scaledBlue = 2.5 * sample.blue / 10000;\n", + " var scaledGreen = 2.5 * sample.green / 10000;\n", + " var scaledRed = 2.5 * sample.red / 10000;\n", + " var scaledNIR = 2.5 * sample.nir / 10000;\n", + " \n", + " // Only use udm1 mask (Planet's usable data mask)\n", + " if (sample.udm1 == 0) {\n", + " return [scaledRed, scaledGreen, scaledBlue, scaledNIR];\n", + " } else {\n", + " return [NaN, NaN, NaN, NaN];\n", + " }\n", + " }\n", + "\"\"\"\n", + "\n", + "# Placeholder for code to be replaced by OCM-processed imagery later\n", + "evalscript_true_color = evalscript_original" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9dee95dd", + "metadata": {}, + "outputs": [], + "source": [ + "def get_true_color_request_day(time_interval, bbox, size):\n", + " \"\"\"Request with original evalscript (will be replaced by OCM results later)\"\"\"\n", + " return SentinelHubRequest(\n", + " evalscript=evalscript_true_color,\n", + " input_data=[\n", + " SentinelHubRequest.input_data(\n", + " data_collection=DataCollection.planet_data2,\n", + " time_interval=(time_interval, time_interval)\n", + " )\n", + " ],\n", + " responses=[\n", + " SentinelHubRequest.output_response('default', MimeType.TIFF)\n", + " ],\n", + " bbox=bbox,\n", + " size=size,\n", + " config=config,\n", + " data_folder=str(BASE_PATH_SINGLE_IMAGES / time_interval),\n", + " )\n", + "\n", + "def get_original_request_day(time_interval, bbox, size):\n", + " \"\"\"Request with Planet UDM-only mask (for comparison)\"\"\"\n", + " return SentinelHubRequest(\n", + " evalscript=evalscript_original,\n", + " input_data=[\n", + " SentinelHubRequest.input_data(\n", + " data_collection=DataCollection.planet_data2,\n", + " time_interval=(time_interval, time_interval)\n", + " )\n", + " ],\n", + " responses=[\n", + " SentinelHubRequest.output_response('default', MimeType.TIFF)\n", + " ],\n", + " bbox=bbox,\n", + " size=size,\n", + " config=config,\n", + " )\n", + "\n", + "def download_function(slot, bbox, size):\n", + " \"\"\"Download imagery for a given date and bbox\"\"\"\n", + " list_of_requests = [get_true_color_request_day(slot, bbox, size)]\n", + " list_of_requests = [request.download_list[0] for request in list_of_requests]\n", + " data = SentinelHubDownloadClient(config=config).download(list_of_requests, max_threads=15)\n", + " print(f'Image downloaded for {slot} and bbox {str(bbox)}')\n", + " time.sleep(.1)\n", + " \n", + "def merge_files(slot):\n", + " \"\"\"Merge downloaded tiles into a single image\"\"\"\n", + " # Get all response.tiff files\n", + " slot_folder = Path(BASE_PATH_SINGLE_IMAGES / slot)\n", + " if not slot_folder.exists():\n", + " raise ValueError(f\"Folder not found: {slot_folder}\")\n", + " \n", + " file_list = [f\"{x}/response.tiff\" for x in slot_folder.iterdir() if Path(f\"{x}/response.tiff\").exists()]\n", + " \n", + " if not file_list:\n", + " raise ValueError(f\"No response.tiff files found in {slot_folder}\")\n", + " \n", + " print(f\"Found {len(file_list)} files to merge\")\n", + " \n", + " folder_for_merged_tifs = str(BASE_PATH / 'merged_tif' / f\"{slot}.tif\")\n", + " folder_for_virtual_raster = str(BASE_PATH / 'merged_virtual' / f\"merged{slot}.vrt\")\n", + " \n", + " # Make sure parent directories exist\n", + " Path(folder_for_merged_tifs).parent.mkdir(exist_ok=True, parents=True)\n", + " Path(folder_for_virtual_raster).parent.mkdir(exist_ok=True, parents=True)\n", + "\n", + " try:\n", + " # Create a virtual raster\n", + " print(f\"Building VRT from {len(file_list)} files\")\n", + " vrt_all = gdal.BuildVRT(folder_for_virtual_raster, file_list)\n", + " \n", + " if vrt_all is None:\n", + " raise ValueError(f\"Failed to create virtual raster: {folder_for_virtual_raster}\")\n", + " \n", + " # Write VRT to disk\n", + " vrt_all.FlushCache()\n", + " \n", + " # Convert to GeoTIFF\n", + " print(f\"Translating VRT to GeoTIFF: {folder_for_merged_tifs}\")\n", + " result = gdal.Translate(\n", + " folder_for_merged_tifs,\n", + " folder_for_virtual_raster,\n", + " xRes=10,\n", + " yRes=10,\n", + " resampleAlg=\"bilinear\" # or \"nearest\" if you prefer\n", + " )\n", + " \n", + " if result is None:\n", + " raise ValueError(f\"Failed to translate VRT to GeoTIFF: {folder_for_merged_tifs}\")\n", + " \n", + " # Make sure the file was created\n", + " if not Path(folder_for_merged_tifs).exists():\n", + " raise ValueError(f\"Output GeoTIFF file was not created: {folder_for_merged_tifs}\")\n", + " \n", + " return folder_for_merged_tifs\n", + " except Exception as e:\n", + " print(f\"Error during merging: {str(e)}\")\n", + " # If we have individual files but merging failed, return the first one as a fallback\n", + " if file_list:\n", + " print(f\"Returning first file as fallback: {file_list[0]}\")\n", + " return file_list[0]\n", + " raise" + ] + }, + { + "cell_type": "markdown", + "id": "d21a132b", + "metadata": {}, + "source": [ + "## 5. Setup date ranges and test data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c00fc762", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time windows to process:\n", + "\n", + "2025-04-17\n", + "2025-04-18\n", + "2025-04-19\n", + "...\n", + "2025-05-14\n", + "2025-05-15\n", + "2025-05-16\n" + ] + } + ], + "source": [ + "# Configure date ranges (from original notebook)\n", + "days_needed = int(os.environ.get(\"DAYS\", days))\n", + "date_str = os.environ.get(\"DATE\")\n", + "\n", + "if date_str:\n", + " end = datetime.datetime.strptime(date_str, \"%Y-%m-%d\").date()\n", + "else:\n", + " end = datetime.date.today() \n", + "\n", + "start = end - datetime.timedelta(days=days_needed - 1)\n", + "slots = [(start + datetime.timedelta(days=i)).strftime('%Y-%m-%d') for i in range(days_needed)]\n", + "\n", + "print('Time windows to process:\\n')\n", + "if len(slots) > 10:\n", + " for slot in slots[:3]:\n", + " print(slot)\n", + " print(\"...\")\n", + " for slot in slots[-3:]:\n", + " print(slot)\n", + "else:\n", + " for slot in slots:\n", + " print(slot)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "8947de86", + "metadata": {}, + "outputs": [], + "source": [ + "# For testing, use a specific date with known clouds/shadows\n", + "# Comment this out to process all dates defined above\n", + "slots = ['2024-10-22'] # Change to a date with clouds/shadows in your area" + ] + }, + { + "cell_type": "markdown", + "id": "ede9e761", + "metadata": {}, + "source": [ + "## 6. Load geospatial data and prepare for processing" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "485e5fa1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Area bounding box: BBox(((-47.09879025717693, -22.67132809994226), (-47.09188307701802, -22.66813642658124)), crs=CRS('4326'))\n" + ] + } + ], + "source": [ + "# Load field boundaries and prepare bounding boxes\n", + "geo_json = gpd.read_file(str(geojson_file))\n", + "geometries = [Geometry(geometry, crs=CRS.WGS84) for geometry in geo_json.geometry]\n", + "shapely_geometries = [geometry.geometry for geometry in geometries]\n", + "\n", + "# Split area into manageable bounding boxes\n", + "bbox_splitter = BBoxSplitter(\n", + " shapely_geometries, CRS.WGS84, (1, 1), reduce_bbox_sizes=True\n", + ")\n", + "print(\"Area bounding box:\", bbox_splitter.get_area_bbox().__repr__())\n", + "bbox_list = bbox_splitter.get_bbox_list()\n", + "info_list = bbox_splitter.get_info_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0eb2ccf1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['2024-12-30']\n", + "Total slots: 1\n", + "Available slots: 1\n", + "Excluded slots due to empty dates: 0\n" + ] + } + ], + "source": [ + "# Function to check if images are available for each date\n", + "def is_image_available(date):\n", + " for bbox in bbox_list:\n", + " search_iterator = catalog.search(\n", + " collection=byoc,\n", + " bbox=bbox,\n", + " time=(date, date)\n", + " )\n", + " if len(list(search_iterator)) > 0:\n", + " return True\n", + " return False\n", + "\n", + "# Filter slots to only include dates with available images\n", + "available_slots = [slot for slot in slots if is_image_available(slot)]\n", + "comparison_slots = available_slots[:min(5, len(available_slots))]\n", + "\n", + "print(available_slots)\n", + "print(f\"Total slots: {len(slots)}\")\n", + "print(f\"Available slots: {len(available_slots)}\")\n", + "print(f\"Excluded slots due to empty dates: {len(slots) - len(available_slots)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d628f797", + "metadata": {}, + "source": [ + "## 7. Download and process images" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8966f944", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Downloading images for date: 2024-12-30\n", + " Processing bbox 1/1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\sentinelhub\\geometry.py:137: SHDeprecationWarning: Initializing `BBox` objects from `BBox` objects will no longer be possible in future versions.\n", + " return cls._tuple_from_bbox(bbox)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image downloaded for 2024-12-30 and bbox -47.09879025717693,-22.67132809994226,-47.09188307701802,-22.66813642658124\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\timon\\AppData\\Local\\Temp\\ipykernel_25312\\3091203660.py:43: SHDeprecationWarning: The string representation of `BBox` will change to match its `repr` representation.\n", + " print(f'Image downloaded for {slot} and bbox {str(bbox)}')\n" + ] + } + ], + "source": [ + "# Download images\n", + "resolution = 10 # Using 10m resolution for better OmniCloudMask results\n", + "\n", + "for slot in available_slots:\n", + " print(f\"\\nDownloading images for date: {slot}\")\n", + " \n", + " for i, bbox in enumerate(bbox_list):\n", + " bbox_obj = BBox(bbox=bbox, crs=CRS.WGS84)\n", + " size = bbox_to_dimensions(bbox_obj, resolution=resolution)\n", + " print(f\" Processing bbox {i+1}/{len(bbox_list)}\")\n", + " download_function(slot, bbox_obj, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "43a8b55e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\sentinelhub\\geometry.py:137: SHDeprecationWarning: Initializing `BBox` objects from `BBox` objects will no longer be possible in future versions.\n", + " return cls._tuple_from_bbox(bbox)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image downloaded for 2024-12-30 and bbox -47.09879025717693,-22.67132809994226,-47.09188307701802,-22.66813642658124\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\timon\\AppData\\Local\\Temp\\ipykernel_25312\\3091203660.py:43: SHDeprecationWarning: The string representation of `BBox` will change to match its `repr` representation.\n", + " print(f'Image downloaded for {slot} and bbox {str(bbox)}')\n" + ] + } + ], + "source": [ + "resolution = 3\n", + "\n", + "for slot in available_slots:\n", + " for bbox in bbox_list:\n", + " bbox = BBox(bbox=bbox, crs=CRS.WGS84)\n", + " size = bbox_to_dimensions(bbox, resolution=resolution)\n", + " download_function(slot, bbox, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "f15f04f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 2 files to merge\n", + "Building VRT from 2 files\n", + "Translating VRT to GeoTIFF: ..\\laravel_app\\storage\\app\\citrus_brazil_trial\\merged_tif\\2024-12-30.tif\n", + "Error during merging: Failed to translate VRT to GeoTIFF: ..\\laravel_app\\storage\\app\\citrus_brazil_trial\\merged_tif\\2024-12-30.tif\n", + "Returning first file as fallback: ..\\laravel_app\\storage\\app\\citrus_brazil_trial\\single_images\\2024-12-30\\0aeb88ec276c5a05278127eb769d73ec/response.tiff\n" + ] + } + ], + "source": [ + "for slot in available_slots:\n", + " merge_files(slot)" + ] + }, + { + "cell_type": "markdown", + "id": "ee0ae99e", + "metadata": {}, + "source": [ + "## 8. Clean up intermediate files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fe25a4d", + "metadata": {}, + "outputs": [], + "source": [ + "# Clean up intermediate files if requested\n", + "folders_to_empty = [BASE_PATH / 'merged_virtual', BASE_PATH_SINGLE_IMAGES]\n", + "\n", + "def empty_folders(folders, run=True):\n", + " if not run:\n", + " print(\"Skipping empty_folders function.\")\n", + " return\n", + " \n", + " for folder in folders:\n", + " try:\n", + " for filename in os.listdir(folder):\n", + " file_path = os.path.join(folder, filename)\n", + " try:\n", + " if os.path.isfile(file_path):\n", + " os.unlink(file_path)\n", + " elif os.path.isdir(file_path):\n", + " shutil.rmtree(file_path)\n", + " except Exception as e:\n", + " print(f\"Error: {e}\")\n", + " print(f\"Emptied folder: {folder}\")\n", + " except OSError as e:\n", + " print(f\"Error: {e}\")\n", + "\n", + "# Call the function to empty folders only if requested\n", + "empty_folders(folders_to_empty, run=False) # Change to True if you want to clean up" + ] + }, + { + "cell_type": "markdown", + "id": "25638297", + "metadata": {}, + "source": [ + "## 9. Visualize and compare cloud masks" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "7d3a73e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing ..\\laravel_app\\storage\\app\\chemba\\merged_tif\\2024-10-22.tif with c:\\\\Users\\\\timon\\\\Resilience BV\\\\4020 SCane ESA DEMO - Documenten\\\\General\\\\4020 SCDEMO Team\\\\4020 TechnicalData\\\\WP3\\\\smartcane\\\\python_app\\\\planet_ocm_processor.py...\n", + "Input image: ..\\laravel_app\\storage\\app\\chemba\\merged_tif\\2024-10-22.tif\n", + "Output directory: ..\\laravel_app\\storage\\app\\chemba\\ocm_masks\n", + "--- Running gdalinfo for 2024-10-22.tif ---\n", + "--- gdalinfo STDOUT ---\n", + "Driver: GTiff/GeoTIFF\n", + "Files: ..\\laravel_app\\storage\\app\\chemba\\merged_tif\\2024-10-22.tif\n", + "Size is 3605, 2162\n", + "Coordinate System is:\n", + "GEOGCRS[\"WGS 84\",\n", + " ENSEMBLE[\"World Geodetic System 1984 ensemble\",\n", + " MEMBER[\"World Geodetic System 1984 (Transit)\"],\n", + " MEMBER[\"World Geodetic System 1984 (G730)\"],\n", + " MEMBER[\"World Geodetic System 1984 (G873)\"],\n", + " MEMBER[\"World Geodetic System 1984 (G1150)\"],\n", + " MEMBER[\"World Geodetic System 1984 (G1674)\"],\n", + " MEMBER[\"World Geodetic System 1984 (G1762)\"],\n", + " MEMBER[\"World Geodetic System 1984 (G2139)\"],\n", + " ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n", + " LENGTHUNIT[\"metre\",1]],\n", + " ENSEMBLEACCURACY[2.0]],\n", + " PRIMEM[\"Greenwich\",0,\n", + " ANGLEUNIT[\"degree\",0.0174532925199433]],\n", + " CS[ellipsoidal,2],\n", + " AXIS[\"geodetic latitude (Lat)\",north,\n", + " ORDER[1],\n", + " ANGLEUNIT[\"degree\",0.0174532925199433]],\n", + " AXIS[\"geodetic longitude (Lon)\",east,\n", + " ORDER[2],\n", + " ANGLEUNIT[\"degree\",0.0174532925199433]],\n", + " USAGE[\n", + " SCOPE[\"Horizontal component of 3D system.\"],\n", + " AREA[\"World.\"],\n", + " BBOX[-90,-180,90,180]],\n", + " ID[\"EPSG\",4326]]\n", + "Data axis to CRS axis mapping: 2,1\n", + "Origin = (34.883117060422094,-17.291731714592061)\n", + "Pixel Size = (0.000027942347249,-0.000027653607237)\n", + "Metadata:\n", + " AREA_OR_POINT=Area\n", + "Image Structure Metadata:\n", + " INTERLEAVE=PIXEL\n", + "Corner Coordinates:\n", + "Upper Left ( 34.8831171, -17.2917317) ( 34d52'59.22\"E, 17d17'30.23\"S)\n", + "Lower Left ( 34.8831171, -17.3515188) ( 34d52'59.22\"E, 17d21' 5.47\"S)\n", + "Upper Right ( 34.9838492, -17.2917317) ( 34d59' 1.86\"E, 17d17'30.23\"S)\n", + "Lower Right ( 34.9838492, -17.3515188) ( 34d59' 1.86\"E, 17d21' 5.47\"S)\n", + "Center ( 34.9334831, -17.3216253) ( 34d56' 0.54\"E, 17d19'17.85\"S)\n", + "Band 1 Block=3605x1 Type=Byte, ColorInterp=Gray\n", + "Band 2 Block=3605x1 Type=Byte, ColorInterp=Undefined\n", + "Band 3 Block=3605x1 Type=Byte, ColorInterp=Undefined\n", + "Band 4 Block=3605x1 Type=Byte, ColorInterp=Undefined\n", + "\n", + "--- Attempting to run OCM processor for 2024-10-22.tif ---\n", + "--- Script STDOUT ---\n", + "--- Starting OCM processing for 2024-10-22.tif ---\n", + "Input 3m image (absolute): C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\merged_tif\\2024-10-22.tif\n", + "Output base directory (absolute): C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\n", + "Intermediate 10m image path: C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\\intermediate_ocm_files\\2024-10-22_10m.tif\n", + "Resampling C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\merged_tif\\2024-10-22.tif to (10, 10)m resolution -> C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\\intermediate_ocm_files\\2024-10-22_10m.tif\n", + "Reprojected raster saved to: C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\\intermediate_ocm_files\\2024-10-22_10m_reprojected.tif\n", + "Successfully resampled image saved to: C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\\intermediate_ocm_files\\2024-10-22_10m.tif\n", + "Loading 10m image for OCM: C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\\intermediate_ocm_files\\2024-10-22_10m.tif\n", + "Applying OmniCloudMask...\n", + "Error processing 10m image with OmniCloudMask: Source shape (1, 1, 673, 1078) is inconsistent with given indexes 1\n", + "OCM processing failed. Exiting.\n", + "\n", + "--- Script STDERR ---\n", + "c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\omnicloudmask\\cloud_mask.py:145: UserWarning: Significant no-data areas detected. Adjusting patch size to 336px and overlap to 168px to minimize no-data patches.\n", + " warnings.warn(\n", + "\n", + "Successfully processed 2024-10-22.tif with c:\\\\Users\\\\timon\\\\Resilience BV\\\\4020 SCane ESA DEMO - Documenten\\\\General\\\\4020 SCDEMO Team\\\\4020 TechnicalData\\\\WP3\\\\smartcane\\\\python_app\\\\planet_ocm_processor.py\n", + "--- Script STDOUT ---\n", + "--- Starting OCM processing for 2024-10-22.tif ---\n", + "Input 3m image (absolute): C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\merged_tif\\2024-10-22.tif\n", + "Output base directory (absolute): C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\n", + "Intermediate 10m image path: C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\\intermediate_ocm_files\\2024-10-22_10m.tif\n", + "Resampling C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\merged_tif\\2024-10-22.tif to (10, 10)m resolution -> C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\\intermediate_ocm_files\\2024-10-22_10m.tif\n", + "Reprojected raster saved to: C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\\intermediate_ocm_files\\2024-10-22_10m_reprojected.tif\n", + "Successfully resampled image saved to: C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\\intermediate_ocm_files\\2024-10-22_10m.tif\n", + "Loading 10m image for OCM: C:\\Users\\timon\\Resilience BV\\4020 SCane ESA DEMO - Documenten\\General\\4020 SCDEMO Team\\4020 TechnicalData\\WP3\\smartcane\\laravel_app\\storage\\app\\chemba\\ocm_masks\\intermediate_ocm_files\\2024-10-22_10m.tif\n", + "Applying OmniCloudMask...\n", + "Error processing 10m image with OmniCloudMask: Source shape (1, 1, 673, 1078) is inconsistent with given indexes 1\n", + "OCM processing failed. Exiting.\n", + "\n", + "--- Script STDERR ---\n", + "c:\\Users\\timon\\anaconda3\\Lib\\site-packages\\omnicloudmask\\cloud_mask.py:145: UserWarning: Significant no-data areas detected. Adjusting patch size to 336px and overlap to 168px to minimize no-data patches.\n", + " warnings.warn(\n", + "\n", + "Successfully processed 2024-10-22.tif with c:\\\\Users\\\\timon\\\\Resilience BV\\\\4020 SCane ESA DEMO - Documenten\\\\General\\\\4020 SCDEMO Team\\\\4020 TechnicalData\\\\WP3\\\\smartcane\\\\python_app\\\\planet_ocm_processor.py\n" + ] + } + ], + "source": [ + "import subprocess\n", + "import sys # Added for more detailed error printing\n", + "\n", + "# Path to the Python script\n", + "script_path = r\"c:\\\\Users\\\\timon\\\\Resilience BV\\\\4020 SCane ESA DEMO - Documenten\\\\General\\\\4020 SCDEMO Team\\\\4020 TechnicalData\\\\WP3\\\\smartcane\\\\python_app\\\\planet_ocm_processor.py\"\n", + "\n", + "# Directory containing the recently downloaded images (merged TIFFs)\n", + "images_dir = BASE_PATH / 'merged_tif'\n", + "\n", + "# Output directory for OCM processor (defined in cell 8)\n", + "# OCM_MASKS_DIR should be defined earlier in your notebook, e.g.,\n", + "# OCM_MASKS_DIR = Path(BASE_PATH / 'ocm_masks')\n", + "# OCM_MASKS_DIR.mkdir(exist_ok=True, parents=True) # Ensure it exists\n", + "available_slots = [\"2024-10-22\"] # Change this to the available slots you want to process\n", + "# Run the script for each available slot (date)\n", + "for slot in available_slots:\n", + " image_file = images_dir / f\"{slot}.tif\"\n", + " if image_file.exists():\n", + " print(f\"Processing {image_file} with {script_path}...\")\n", + " print(f\"Input image: {str(image_file)}\")\n", + " print(f\"Output directory: {str(OCM_MASKS_DIR)}\")\n", + " \n", + " try:\n", + " # Run gdalinfo to inspect the image before processing\n", + " print(f\"--- Running gdalinfo for {image_file.name} ---\")\n", + " gdalinfo_result = subprocess.run(\n", + " [\"gdalinfo\", str(image_file)],\n", + " capture_output=True,\n", + " text=True,\n", + " check=True\n", + " )\n", + " print(\"--- gdalinfo STDOUT ---\")\n", + " print(gdalinfo_result.stdout)\n", + " if gdalinfo_result.stderr:\n", + " print(\"--- gdalinfo STDERR ---\")\n", + " print(gdalinfo_result.stderr)\n", + " except subprocess.CalledProcessError as e:\n", + " print(f\"gdalinfo failed for {image_file.name}:\")\n", + " print(\"--- gdalinfo STDOUT ---\")\n", + " print(e.stdout)\n", + " print(\"--- gdalinfo STDERR ---\")\n", + " print(e.stderr)\n", + " # Decide if you want to continue to the next image or stop\n", + " # continue \n", + " except FileNotFoundError:\n", + " print(\"Error: gdalinfo command not found. Make sure GDAL is installed and in your system's PATH.\")\n", + " # Decide if you want to continue or stop\n", + " # break # or continue\n", + " \n", + " print(f\"--- Attempting to run OCM processor for {image_file.name} ---\")\n", + " try:\n", + " # Run the script, passing the image file and OCM_MASKS_DIR as arguments\n", + " process_result = subprocess.run(\n", + " [sys.executable, str(script_path), str(image_file), str(OCM_MASKS_DIR)], \n", + " capture_output=True, # Capture stdout and stderr\n", + " text=True, # Decode output as text\n", + " check=False # Do not raise an exception for non-zero exit codes, we'll check manually\n", + " )\n", + " \n", + " # Print the output from the script\n", + " print(\"--- Script STDOUT ---\")\n", + " print(process_result.stdout)\n", + " \n", + " if process_result.stderr:\n", + " print(\"--- Script STDERR ---\")\n", + " print(process_result.stderr)\n", + " \n", + " if process_result.returncode != 0:\n", + " print(f\"Error: Script {script_path} failed for {image_file.name} with exit code {process_result.returncode}\")\n", + " else:\n", + " print(f\"Successfully processed {image_file.name} with {script_path}\")\n", + " \n", + " except subprocess.CalledProcessError as e:\n", + " # This block will be executed if check=True and the script returns a non-zero exit code\n", + " print(f\"Error running script {script_path} for {image_file.name}:\")\n", + " print(\"--- Script STDOUT ---\")\n", + " print(e.stdout) # stdout from the script\n", + " print(\"--- Script STDERR ---\")\n", + " print(e.stderr) # stderr from the script (this will contain the GDAL error)\n", + " except Exception as e:\n", + " print(f\"An unexpected error occurred while trying to run {script_path} for {image_file.name}: {e}\")\n", + " \n", + " else:\n", + " print(f\"Image file not found: {image_file}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "7cb00e6a", + "metadata": {}, + "source": [ + "## 10. Understanding OmniCloudMask Results\n", + "\n", + "OmniCloudMask produces a classified raster with these values:\n", + "- **0 = Clear**: No clouds or shadows detected\n", + "- **1 = Thick Cloud**: Dense clouds that completely obscure the ground\n", + "- **2 = Thin Cloud**: Semi-transparent clouds or haze\n", + "- **3 = Shadow**: Cloud shadows on the ground\n", + "\n", + "The masked images have had all non-zero classes (clouds and shadows) removed, which provides cleaner data for analysis of crop conditions. This can significantly improve the accuracy of vegetation indices and other agricultural metrics derived from the imagery.\n", + "\n", + "For more information about OmniCloudMask, visit:\n", + "- GitHub repository: https://github.com/DPIRD-DMA/OmniCloudMask\n", + "- Paper: https://www.sciencedirect.com/science/article/pii/S0034425725000987" + ] + }, + { + "cell_type": "markdown", + "id": "2837be37", + "metadata": {}, + "source": [ + "### 9a. Upsample OCM mask to 3x3m and apply to original high-res image\n", + "\n", + "This step ensures that the OCM cloud/shadow mask (generated at 10x10m) is upsampled to match the original 3x3m PlanetScope image, so the final masked output preserves the native resolution for downstream analysis." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python_app/planet_ocm_processor.py b/python_app/planet_ocm_processor.py new file mode 100644 index 0000000..ed434f2 --- /dev/null +++ b/python_app/planet_ocm_processor.py @@ -0,0 +1,319 @@ +import os +import argparse +import numpy as np +from pathlib import Path +from osgeo import gdal +import rasterio as rio +from rasterio.enums import Resampling +from rasterio.warp import reproject +from osgeo import osr + +# Attempt to import OmniCloudMask and set a flag +try: + from omnicloudmask import predict_from_array, load_multiband + HAS_OCM = True +except ImportError: + HAS_OCM = False + +def calculate_utm_zone_and_hemisphere(longitude, latitude): + """ + Calculate the UTM zone and hemisphere based on longitude and latitude. + """ + utm_zone = int((longitude + 180) / 6) + 1 + is_southern = latitude < 0 + return utm_zone, is_southern + +def reproject_to_projected_crs(input_path, output_path): + """ + Reprojects a raster to a projected coordinate system (e.g., UTM). + """ + input_ds = gdal.Open(str(input_path)) + if not input_ds: + raise ValueError(f"Failed to open input raster: {input_path}") + + # Get the source spatial reference + source_srs = osr.SpatialReference() + source_srs.ImportFromWkt(input_ds.GetProjection()) + + # Get the geographic coordinates of the image's center + geo_transform = input_ds.GetGeoTransform() + width = input_ds.RasterXSize + height = input_ds.RasterYSize + center_x = geo_transform[0] + (width / 2) * geo_transform[1] + center_y = geo_transform[3] + (height / 2) * geo_transform[5] + + # Calculate the UTM zone and hemisphere dynamically + utm_zone, is_southern = calculate_utm_zone_and_hemisphere(center_x, center_y) + + # Define the target spatial reference + target_srs = osr.SpatialReference() + target_srs.SetWellKnownGeogCS("WGS84") + target_srs.SetUTM(utm_zone, is_southern) + + # Create the warp options + warp_options = gdal.WarpOptions( + dstSRS=target_srs.ExportToWkt(), + format="GTiff" + ) + + # Perform the reprojection + gdal.Warp(str(output_path), input_ds, options=warp_options) + input_ds = None # Close the dataset + print(f"Reprojected raster saved to: {output_path}") + return output_path + +def resample_image(input_path, output_path, resolution=(10, 10), resample_alg="bilinear"): + """ + Resamples a raster to a specified resolution using gdal.Translate. + """ + print(f"Resampling {input_path} to {resolution}m resolution -> {output_path}") + + # Reproject the input image to a projected CRS + reprojected_path = str(Path(output_path).with_name(f"{Path(output_path).stem}_reprojected.tif")) + reproject_to_projected_crs(input_path, reprojected_path) + + # Open the reprojected dataset + input_ds = gdal.Open(reprojected_path) + if not input_ds: + raise ValueError(f"Failed to open reprojected raster: {reprojected_path}") + + # Perform the resampling + result = gdal.Translate( + str(output_path), + input_ds, + xRes=resolution[0], + yRes=resolution[1], + resampleAlg=resample_alg + ) + input_ds = None # Explicitly dereference the GDAL dataset + if result is None: + raise ValueError(f"Failed to resample image to {output_path}") + print(f"Successfully resampled image saved to: {output_path}") + return output_path + +def run_ocm_on_image(image_path_10m, ocm_output_dir, save_mask=True): + """ + Processes a 10m resolution image with OmniCloudMask. + Adapted from process_with_ocm in the notebook. + """ + if not HAS_OCM: + print("OmniCloudMask not available. Please install with: pip install omnicloudmask") + return None, None + + image_path_10m = Path(image_path_10m) + ocm_output_dir = Path(ocm_output_dir) + ocm_output_dir.mkdir(exist_ok=True, parents=True) + + mask_10m_path = ocm_output_dir / f"{image_path_10m.stem}_ocm_mask_10m.tif" + + try: + # Open the image to check dimensions + with rio.open(image_path_10m) as src: + width, height = src.width, src.height + + # Check if the image is too small for OmniCloudMask + if width < 50 or height < 50: + print(f"Warning: Image {image_path_10m} is too small for OmniCloudMask (width: {width}, height: {height}). Skipping.") + return None, None + + # PlanetScope 4-band images are typically [B,G,R,NIR] + # OCM expects [R,G,NIR] for its default model. + # Band numbers for load_multiband are 1-based. + # If original is B(1),G(2),R(3),NIR(4), then R=3, G=2, NIR=4 + band_order = [3, 2, 4] + + print(f"Loading 10m image for OCM: {image_path_10m}") + # load_multiband resamples if resample_res is different from source, + # but here image_path_10m is already 10m. + # We pass resample_res=None to use the image's own resolution. + rgn_data, profile = load_multiband( + input_path=str(image_path_10m), + resample_res=10, # Explicitly set target resolution for OCM + band_order=band_order + ) + + print("Applying OmniCloudMask...") + prediction = predict_from_array(rgn_data) + + if save_mask: + profile.update(count=1, dtype='uint8') + with rio.open(mask_10m_path, 'w', **profile) as dst: + dst.write(prediction.astype('uint8'), 1) + print(f"Saved 10m OCM mask to: {mask_10m_path}") + + # Summary (optional, can be removed for cleaner script output) + n_total = prediction.size + n_clear = np.sum(prediction == 0) + n_thick = np.sum(prediction == 1) + n_thin = np.sum(prediction == 2) + n_shadow = np.sum(prediction == 3) + print(f" OCM: Clear: {100*n_clear/n_total:.1f}%, Thick: {100*n_thick/n_total:.1f}%, Thin: {100*n_thin/n_total:.1f}%, Shadow: {100*n_shadow/n_total:.1f}%") + + return str(mask_10m_path), profile + except Exception as e: + print(f"Error processing 10m image with OmniCloudMask: {str(e)}") + return None, None + + +def upsample_mask_to_3m(mask_10m_path, target_3m_image_path, output_3m_mask_path): + """ + Upsamples a 10m OCM mask to match the 3m target image. + Adapted from upsample_mask_to_highres in the notebook. + """ + print(f"Upsampling 10m mask {mask_10m_path} to 3m, referencing {target_3m_image_path}") + with rio.open(mask_10m_path) as src_mask, rio.open(target_3m_image_path) as src_img_3m: + mask_data_10m = src_mask.read(1) + + img_shape_3m = (src_img_3m.height, src_img_3m.width) + img_transform_3m = src_img_3m.transform + img_crs_3m = src_img_3m.crs + + upsampled_mask_3m_data = np.zeros(img_shape_3m, dtype=mask_data_10m.dtype) + + reproject( + source=mask_data_10m, + destination=upsampled_mask_3m_data, + src_transform=src_mask.transform, + src_crs=src_mask.crs, + dst_transform=img_transform_3m, + dst_crs=img_crs_3m, + resampling=Resampling.nearest + ) + + profile_3m_mask = src_img_3m.profile.copy() + profile_3m_mask.update({ + 'count': 1, + 'dtype': upsampled_mask_3m_data.dtype + }) + + with rio.open(output_3m_mask_path, 'w', **profile_3m_mask) as dst: + dst.write(upsampled_mask_3m_data, 1) + print(f"Upsampled 3m OCM mask saved to: {output_3m_mask_path}") + return str(output_3m_mask_path) + + +def apply_3m_mask_to_3m_image(image_3m_path, mask_3m_path, final_masked_output_path): + """ + Applies an upsampled 3m OCM mask to the original 3m image. + Adapted from apply_upsampled_mask_to_highres in the notebook. + """ + print(f"Applying 3m mask {mask_3m_path} to 3m image {image_3m_path}") + image_3m_path = Path(image_3m_path) + mask_3m_path = Path(mask_3m_path) + final_masked_output_path = Path(final_masked_output_path) + final_masked_output_path.parent.mkdir(parents=True, exist_ok=True) + + try: + with rio.open(image_3m_path) as src_img_3m, rio.open(mask_3m_path) as src_mask_3m: + img_data_3m = src_img_3m.read() + img_profile_3m = src_img_3m.profile.copy() + mask_data_3m = src_mask_3m.read(1) + + if img_data_3m.shape[1:] != mask_data_3m.shape: + print(f"Warning: 3m image shape {img_data_3m.shape[1:]} and 3m mask shape {mask_data_3m.shape} do not match.") + # This should ideally not happen if upsampling was correct. + + # OCM: 0=clear, 1=thick cloud, 2=thin cloud, 3=shadow + # We want to mask out (set to nodata) pixels where OCM is > 0 + binary_mask = np.ones_like(mask_data_3m, dtype=np.uint8) + binary_mask[mask_data_3m > 0] = 0 # 0 for cloud/shadow, 1 for clear + + masked_img_data_3m = img_data_3m.copy() + nodata_val = img_profile_3m.get('nodata', 0) # Use existing nodata or 0 + + for i in range(img_profile_3m['count']): + masked_img_data_3m[i][binary_mask == 0] = nodata_val + + # Ensure dtype of profile matches data to be written + # If original image was float, but nodata is int (0), rasterio might complain + # It's safer to use the original image's dtype for the output. + img_profile_3m.update(dtype=img_data_3m.dtype) + + with rio.open(final_masked_output_path, 'w', **img_profile_3m) as dst: + dst.write(masked_img_data_3m) + + print(f"Final masked 3m image saved to: {final_masked_output_path}") + return str(final_masked_output_path) + + except Exception as e: + print(f"Error applying 3m mask to 3m image: {str(e)}") + return None + + +def main(): + parser = argparse.ArgumentParser(description="Process PlanetScope 3m imagery with OmniCloudMask.") + parser.add_argument("input_3m_image", type=str, help="Path to the input merged 3m PlanetScope GeoTIFF image.") + parser.add_argument("output_dir", type=str, help="Directory to save processed files (10m image, masks, final 3m masked image).") + + args = parser.parse_args() + + try: + # Resolve paths to absolute paths immediately + input_3m_path = Path(args.input_3m_image).resolve(strict=True) + # output_base_dir is the directory where outputs will be saved. + # It should exist when the script is called (created by the notebook). + output_base_dir = Path(args.output_dir).resolve(strict=True) + except FileNotFoundError as e: + print(f"Error: Path resolution failed. Input image or output base directory may not exist or is not accessible: {e}") + return + except Exception as e: + print(f"Error resolving paths: {e}") + return + + # The check for input_3m_path.exists() is now covered by resolve(strict=True) + + # Define intermediate and final file paths using absolute base paths + intermediate_dir = output_base_dir / "intermediate_ocm_files" + intermediate_dir.mkdir(parents=True, exist_ok=True) + + image_10m_path = intermediate_dir / f"{input_3m_path.stem}_10m.tif" + # OCM mask (10m) will be saved inside run_ocm_on_image, in a subdir of intermediate_dir + ocm_mask_output_dir = intermediate_dir / "ocm_10m_mask_output" + + # Upsampled OCM mask (3m) + mask_3m_upsampled_path = intermediate_dir / f"{input_3m_path.stem}_ocm_mask_3m_upsampled.tif" + + # Final masked image (3m) + final_masked_3m_path = output_base_dir / f"{input_3m_path.stem}_ocm_masked_3m.tif" + + print(f"--- Starting OCM processing for {input_3m_path.name} ---") + print(f"Input 3m image (absolute): {input_3m_path}") + print(f"Output base directory (absolute): {output_base_dir}") + print(f"Intermediate 10m image path: {image_10m_path}") + + # 1. Resample 3m input to 10m for OCM + try: + resample_image(input_3m_path, image_10m_path, resolution=(10, 10)) + except Exception as e: + print(f"Failed to resample to 10m: {e}") + return + + # 2. Run OCM on the 10m image + mask_10m_generated_path, _ = run_ocm_on_image(image_10m_path, ocm_mask_output_dir) + if not mask_10m_generated_path: + print("OCM processing failed. Exiting.") + return + + # 3. Upsample the 10m OCM mask to 3m + try: + upsample_mask_to_3m(mask_10m_generated_path, input_3m_path, mask_3m_upsampled_path) + except Exception as e: + print(f"Failed to upsample 10m OCM mask to 3m: {e}") + return + + # 4. Apply the 3m upsampled mask to the original 3m image + try: + apply_3m_mask_to_3m_image(input_3m_path, mask_3m_upsampled_path, final_masked_3m_path) + except Exception as e: + print(f"Failed to apply 3m mask to 3m image: {e}") + return + + print(f"--- Successfully completed OCM processing for {input_3m_path.name} ---") + print(f"Final 3m masked output: {final_masked_3m_path}") + +if __name__ == "__main__": + if not HAS_OCM: + print("OmniCloudMask library is not installed. Please install it to run this script.") + print("You can typically install it using: pip install omnicloudmask") + else: + main() \ No newline at end of file diff --git a/r_app/CI_report_dashboard_planet.Rmd b/r_app/CI_report_dashboard_planet.Rmd index 71de512..cb59c18 100644 --- a/r_app/CI_report_dashboard_planet.Rmd +++ b/r_app/CI_report_dashboard_planet.Rmd @@ -2,18 +2,17 @@ params: ref: "word-styles-reference-var1.docx" output_file: CI_report.docx - report_date: "2024-08-28" - data_dir: "Chemba" + report_date: "2024-07-18" + data_dir: "chemba" mail_day: "Wednesday" borders: TRUE - use_breaks: FALSE output: # html_document: # toc: yes # df_print: paged word_document: reference_docx: !expr file.path("word-styles-reference-var1.docx") - toc: yes + toc: no editor_options: chunk_output_type: console --- @@ -22,8 +21,7 @@ editor_options: # Set up basic report parameters from input values report_date <- params$report_date mail_day <- params$mail_day -borders <- params$borders -use_breaks <- params$use_breaks # Whether to use breaks or continuous spectrum in visualizations +borders <- params$borders # Environment setup notes (commented out) # # Activeer de renv omgeving @@ -38,26 +36,21 @@ use_breaks <- params$use_breaks # Whether to use breaks or continuous spectrum # Configure knitr options knitr::opts_chunk$set(warning = FALSE, message = FALSE) -# Path management -library(here) - -# Spatial data libraries -library(sf) -library(terra) -library(exactextractr) -# library(raster) - Removed as it's no longer maintained - -# Data manipulation and visualization -library(tidyverse) # Includes dplyr, ggplot2, etc. -library(tmap) -library(lubridate) -library(zoo) - -# Machine learning -library(rsample) -library(caret) -library(randomForest) -library(CAST) +# Load all packages at once with suppressPackageStartupMessages +suppressPackageStartupMessages({ + library(here) + library(sf) + library(terra) + library(exactextractr) + library(tidyverse) + library(tmap) + library(lubridate) + library(zoo) + library(rsample) + library(caret) + library(randomForest) + library(CAST) +}) # Load custom utility functions tryCatch({ @@ -127,9 +120,6 @@ if (which(days_of_week == report_date_as_week_day) > which(days_of_week == mail_ today_minus_3 <- as.character(lubridate::ymd(today) - 14) } -# Generate subtitle for report -subtitle_var <- paste("Report generated on", Sys.Date()) - # Calculate week numbers for previous weeks week_minus_1 <- week - 1 week_minus_2 <- week - 2 @@ -217,7 +207,95 @@ tryCatch({ }) ``` -`r subtitle_var` +```{r create_front_page_variables, include=FALSE} +# Create variables for the front page +farm_name <- stringr::str_to_title(gsub("_", " ", project_dir)) + +# Format dates for display +report_date_formatted <- format(as.Date(report_date), "%B %d, %Y") +current_year <- format(Sys.Date(), "%Y") + +# Get total field count and area if available +tryCatch({ + total_fields <- length(unique(AllPivots0$field)) + total_area_ha <- round(sum(sf::st_area(AllPivots0)) / 10000, 1) # Convert to hectares +}, error = function(e) { + total_fields <- "N/A" + total_area_ha <- "N/A" +}) +``` + +--- +title: "" +--- + +```{=openxml} + + + + + + + + + + + SUGARCANE CROP MONITORING REPORT + + +``` + +
+ +**`r farm_name`** + +**Chlorophyll Index Analysis** + +Report Date: **`r report_date_formatted`** + +--- + +
+ +
+ +## Report Summary + +**Farm Location:** `r farm_name` +**Report Period:** Week `r week` of `r current_year` +**Data Source:** Planet Labs Satellite Imagery +**Analysis Type:** Chlorophyll Index (CI) Monitoring + +**Field Coverage:** +- Total Fields Monitored: `r total_fields` +- Total Area: `r total_area_ha` hectares + +**Report Generated:** `r format(Sys.Date(), "%B %d, %Y at %H:%M")` + +--- + +## About This Report + +This automated report provides weekly analysis of sugarcane crop health using satellite-derived Chlorophyll Index (CI) measurements. The analysis helps identify: + +- Field-level crop health variations +- Weekly changes in crop vigor +- Areas requiring agricultural attention +- Growth patterns across different field sections + +**Key Features:** +- High-resolution satellite imagery analysis +- Week-over-week change detection +- Individual field performance metrics +- Actionable insights for crop management + +
+ +\pagebreak + + + + \pagebreak # Explanation of the Report @@ -253,34 +331,104 @@ CI values typically range from 0 (bare soil or severely stressed vegetation) to Use these insights to identify areas that may need irrigation, fertilization, or other interventions, and to track the effectiveness of your management practices over time. \pagebreak -# Chlorophyll Index (CI) Overview Map - Current Week -```{r render_ci_overview_map, echo=FALSE, fig.height=6.8, fig.width=9, message=FALSE, warning=FALSE} -# Create overview chlorophyll index map +# RGB Satellite Image - Current Week (if available) +```{r render_rgb_map, echo=FALSE, fig.height=6.9, fig.width=9, message=FALSE, warning=FALSE} +# Check if RGB bands are available and create RGB map tryCatch({ - # Base shape - map <- tmap::tm_shape(CI, unit = "m") + # Load the full raster to check available bands + full_raster <- terra::rast(path_to_week_current) + available_bands <- names(full_raster) - # Add raster layer with either breaks or continuous spectrum based on parameter - if (use_breaks) { - map <- map + tmap::tm_raster(breaks = c(0,0.5,1,2,3,4,5,6,7,Inf), - palette = "RdYlGn", - midpoint = NA, - legend.is.portrait = FALSE, - title = "Chlorophyll Index (CI)") - } else { - map <- map + tmap::tm_raster(palette = "RdYlGn", - style = "cont", - midpoint = NA, - legend.is.portrait = FALSE, - title = "Chlorophyll Index (CI)") + # Check if RGB bands are available (look for red, green, blue or similar naming) + rgb_bands_available <- any(grepl("red|Red|RED", available_bands, ignore.case = TRUE)) && + any(grepl("green|Green|GREEN", available_bands, ignore.case = TRUE)) && + any(grepl("blue|Blue|BLUE", available_bands, ignore.case = TRUE)) + + # Alternative check for numbered bands that might be RGB (e.g., band_1, band_2, band_3) + if (!rgb_bands_available && length(available_bands) >= 3) { + # Check if we have at least 3 bands that could potentially be RGB + potential_rgb_bands <- grep("band_[1-3]|B[1-3]|[1-3]", available_bands, ignore.case = TRUE) + rgb_bands_available <- length(potential_rgb_bands) >= 3 } + if (rgb_bands_available) { + safe_log("RGB bands detected - creating RGB visualization") + + # Try to extract RGB bands (prioritize named bands first) + red_band <- NULL + green_band <- NULL + blue_band <- NULL + + # Look for named RGB bands first + red_candidates <- grep("red|Red|RED", available_bands, ignore.case = TRUE, value = TRUE) + green_candidates <- grep("green|Green|GREEN", available_bands, ignore.case = TRUE, value = TRUE) + blue_candidates <- grep("blue|Blue|BLUE", available_bands, ignore.case = TRUE, value = TRUE) + + if (length(red_candidates) > 0) red_band <- red_candidates[1] + if (length(green_candidates) > 0) green_band <- green_candidates[1] + if (length(blue_candidates) > 0) blue_band <- blue_candidates[1] + + # Fallback to numbered bands if named bands not found + if (is.null(red_band) || is.null(green_band) || is.null(blue_band)) { + if (length(available_bands) >= 3) { + # Assume first 3 bands are RGB (common convention) + red_band <- available_bands[1] + green_band <- available_bands[2] + blue_band <- available_bands[3] + } + } + + if (!is.null(red_band) && !is.null(green_band) && !is.null(blue_band)) { + # Extract RGB bands + rgb_raster <- c(full_raster[[red_band]], full_raster[[green_band]], full_raster[[blue_band]]) + names(rgb_raster) <- c("red", "green", "blue") + + # Create RGB map + map <- tmap::tm_shape(rgb_raster, unit = "m") + + tmap::tm_rgb() + + tmap::tm_scalebar(position = c("right", "bottom"), text.color = "white") + + tmap::tm_compass(position = c("right", "bottom"), text.color = "white") + + tmap::tm_shape(AllPivots0) + + tmap::tm_borders(col = "white", lwd = 2) + + tmap::tm_text("sub_field", size = 0.6, col = "white") + + tmap::tm_layout(main.title = paste0("RGB Satellite Image - Week ", week), + main.title.size = 0.8, + main.title.color = "black") + + # Print the map + print(map) + + safe_log("RGB map created successfully") + } else { + safe_log("Could not identify RGB bands despite detection", "WARNING") + cat("RGB bands detected but could not be properly identified. Skipping RGB visualization.\n") + } + } else { + safe_log("No RGB bands available in the current week mosaic") + cat("**Note:** RGB satellite imagery is not available for this week. Only spectral index data is available.\n\n") + } +}, error = function(e) { + safe_log(paste("Error creating RGB map:", e$message), "ERROR") + cat("**Note:** Could not create RGB visualization for this week.\n\n") +}) +``` + +\pagebreak +# Chlorophyll Index (CI) Overview Map - Current Week +```{r render_ci_overview_map, echo=FALSE, fig.height=6.9, fig.width=9, message=FALSE, warning=FALSE} +# Create overview chlorophyll index map +tryCatch({ # Base shape + map <- tmap::tm_shape(CI, unit = "m") # Add raster layer with continuous spectrum (fixed scale 1-8 for consistent comparison) + map <- map + tmap::tm_raster(col.scale = tm_scale_continuous(values = "brewer.rd_yl_gn", + limits = c(1, 8)), + col.legend = tm_legend(title = "Chlorophyll Index (CI)", + orientation = "landscape", + position = tm_pos_out("center", "bottom"))) + # Complete the map with layout and other elements - map <- map + tmap::tm_layout(legend.outside = TRUE, - legend.outside.position = "bottom", - legend.show = TRUE) + - tmap::tm_scale_bar(position = tm_pos_out("right", "bottom"), text.color = "black") + - tmap::tm_compass(position = tm_pos_out("right", "bottom"), text.color = "black") + + map <- map + + tmap::tm_scalebar(position = c("right", "bottom"), text.color = "black") + + tmap::tm_compass(position = c("right", "bottom"), text.color = "black") + tmap::tm_shape(AllPivots0) + tmap::tm_borders(col = "black") + tmap::tm_text("sub_field", size = 0.6, col = "black") @@ -293,36 +441,24 @@ tryCatch({ text(1, 1, "Error creating CI overview map", cex=1.5) }) ``` -\newpage +\pagebreak # Weekly Chlorophyll Index Difference Map -```{r render_ci_difference_map, echo=FALSE, fig.height=6.8, fig.width=9, message=FALSE, warning=FALSE} +```{r render_ci_difference_map, echo=FALSE, fig.height=6.9, fig.width=9, message=FALSE, warning=FALSE} # Create chlorophyll index difference map -tryCatch({ - # Base shape - map <- tmap::tm_shape(last_week_dif_raster_abs, unit = "m") - - # Add raster layer with either breaks or continuous spectrum based on parameter - if (use_breaks) { - map <- map + tmap::tm_raster(breaks = c(-3,-2,-1,0,1,2,3), - palette = "RdYlGn", - midpoint = 0, - legend.is.portrait = FALSE, - title = "Chlorophyll Index (CI) Change") - } else { - map <- map + tmap::tm_raster(palette = "RdYlGn", - style = "cont", - midpoint = 0, - legend.is.portrait = FALSE, - title = "Chlorophyll Index (CI) Change") - } +tryCatch({ # Base shape + map <- tmap::tm_shape(last_week_dif_raster_abs, unit = "m") # Add raster layer with continuous spectrum (centered at 0 for difference maps, fixed scale) + map <- map + tmap::tm_raster(col.scale = tm_scale_continuous(values = "brewer.rd_yl_gn", + midpoint = 0, + limits = c(-3, 3)), + col.legend = tm_legend(title = "Chlorophyll Index (CI) Change", + orientation = "landscape", + position = tm_pos_out("center", "bottom"))) # Complete the map with layout and other elements - map <- map + tmap::tm_layout(legend.outside = TRUE, - legend.outside.position = "bottom", - legend.show = TRUE) + - tmap::tm_scale_bar(position = tm_pos_out("right", "bottom"), text.color = "black") + - tmap::tm_compass(position = tm_pos_out("right", "bottom"), text.color = "black") + + map <- map + + tmap::tm_scalebar(position = c("right", "bottom"), text.color = "black") + + tmap::tm_compass(position = c("right", "bottom"), text.color = "black") + tmap::tm_shape(AllPivots0) + tmap::tm_borders(col = "black") + tmap::tm_text("sub_field", size = 0.6, col = "black") @@ -335,8 +471,8 @@ tryCatch({ text(1, 1, "Error creating CI difference map", cex=1.5) }) ``` -\newpage -\newpage +\pagebreak + ```{r generate_field_visualizations, eval=TRUE, fig.height=3.8, fig.width=10, message=FALSE,echo=FALSE, warning=FALSE, include=TRUE, results='asis'} # Generate detailed visualizations for each field @@ -347,7 +483,7 @@ tryCatch({ dplyr::summarise(.groups = 'drop') # Generate plots for each field - purrr::walk(AllPivots_merged$field, function(field_name) { + purrr::walk(AllPivots_merged$field[1:5], function(field_name) { tryCatch({ cat("\n") # Add an empty line for better spacing @@ -358,14 +494,12 @@ tryCatch({ current_ci = CI, ci_minus_1 = CI_m1, ci_minus_2 = CI_m2, - last_week_diff = last_week_dif_raster_abs, - three_week_diff = three_week_dif_raster_abs, + last_week_diff = last_week_dif_raster_abs, three_week_diff = three_week_dif_raster_abs, harvesting_data = harvesting_data, week = week, week_minus_1 = week_minus_1, week_minus_2 = week_minus_2, week_minus_3 = week_minus_3, - use_breaks = use_breaks, borders = borders ) @@ -513,7 +647,7 @@ tryCatch({ # Predict yields for the current season (focus on mature fields over 300 days) pred_rf_current_season <- prepare_predictions(stats::predict(model_ffs_rf, newdata = prediction_yields), prediction_yields) %>% - dplyr::filter(Age_days > 300) %>% + dplyr::filter(Age_days > 1) %>% dplyr::mutate(CI_per_day = round(total_CI / Age_days, 1)) safe_log("Successfully completed yield prediction calculations") @@ -565,3 +699,7 @@ tryCatch({ }) ``` + + +\pagebreak + diff --git a/r_app/CI_report_dashboard_planet_files/figure-html/ci_diff_kaart-1.png b/r_app/CI_report_dashboard_planet_files/figure-html/ci_diff_kaart-1.png deleted file mode 100644 index 10c8573..0000000 Binary files a/r_app/CI_report_dashboard_planet_files/figure-html/ci_diff_kaart-1.png and /dev/null differ diff --git a/r_app/CI_report_dashboard_planet_files/figure-html/ci_overzicht_kaart-1.png b/r_app/CI_report_dashboard_planet_files/figure-html/ci_overzicht_kaart-1.png deleted file mode 100644 index c28951a..0000000 Binary files a/r_app/CI_report_dashboard_planet_files/figure-html/ci_overzicht_kaart-1.png and /dev/null differ diff --git a/r_app/CI_report_executive_summary.Rmd b/r_app/CI_report_executive_summary.Rmd index 1e21152..6341f6c 100644 --- a/r_app/CI_report_executive_summary.Rmd +++ b/r_app/CI_report_executive_summary.Rmd @@ -2,8 +2,8 @@ params: ref: "word-styles-reference-var1.docx" output_file: CI_report.docx - report_date: "2024-08-28" - data_dir: "Chemba" + report_date: "2025-06-16" + data_dir: "simba" mail_day: "Wednesday" borders: TRUE use_breaks: FALSE @@ -60,17 +60,32 @@ library(randomForest) library(CAST) # Load custom utility functions -tryCatch({ - source("report_utils.R") -}, error = function(e) { - message(paste("Error loading report_utils.R:", e$message)) - # Try alternative path if the first one fails - tryCatch({ +# tryCatch({ +# source("report_utils.R") +# }, error = function(e) { +# message(paste("Error loading report_utils.R:", e$message)) +# # Try alternative path if the first one fails +# tryCatch({ source(here::here("r_app", "report_utils.R")) - }, error = function(e) { - stop("Could not load report_utils.R from either location: ", e$message) - }) -}) +# }, error = function(e) { +# stop("Could not load report_utils.R from either location: ", e$message) +# }) +# }) + +# Load executive report utilities +# tryCatch({ +# source("executive_report_utils.R") +# }, error = function(e) { +# message(paste("Error loading executive_report_utils.R:", e$message)) +# # Try alternative path if the first one fails +# tryCatch({ + source(here::here("r_app","exec_dashboard", "executive_report_utils.R")) +# }, error = function(e) { +# stop("Could not load executive_report_utils.R from either location: ", e$message) +# }) +# }) + +safe_log("Successfully loaded utility functions") ``` ```{r initialize_project_config, message=FALSE, warning=FALSE, include=FALSE} @@ -489,975 +504,218 @@ tryCatch({ }) ``` -```{r executive_summary_functions, message=FALSE, warning=FALSE, include=FALSE} -# EXECUTIVE SUMMARY HELPER FUNCTIONS - -#' Generate a summary of farm health status -#' -#' @param field_boundaries Field boundaries spatial data (sf object) -#' @param ci_current Current CI raster -#' @param ci_previous Previous week's CI raster -#' @param harvesting_data Data frame with harvesting information -#' @return A data frame with farm status summary metrics -#' -generate_farm_health_summary <- function(field_boundaries, ci_current, ci_previous, harvesting_data) { - # Generate a summary data frame of farm health by field - tryCatch({ - # Get unique field names - fields <- unique(field_boundaries$field) - - # Initialize result dataframe - summary_data <- data.frame( - field = character(), - mean_ci = numeric(), - ci_change = numeric(), - ci_uniformity = numeric(), - status = character(), - anomaly_type = character(), - priority_level = numeric(), - age_weeks = numeric(), - harvest_readiness = character(), - stringsAsFactors = FALSE - ) - - # Process each field with better error handling - for (field_name in fields) { - tryCatch({ - # Get field boundary - field_shape <- field_boundaries %>% dplyr::filter(field == field_name) - - # Skip if field shape is empty - if (nrow(field_shape) == 0) { - safe_log(paste("Empty field shape for", field_name), "WARNING") - next - } - - # Get field age from harvesting data - field_age_data <- harvesting_data %>% - dplyr::filter(field == field_name) %>% - dplyr::arrange(desc(season_start)) %>% - dplyr::slice(1) - - # Default age if not available - field_age_weeks <- if (nrow(field_age_data) > 0 && !is.na(field_age_data$age)) { - field_age_data$age - } else { - 10 # Default age - } - - # Extract CI values for this field using extract instead of crop/mask to avoid pointer issues - # This is more robust than the crop+mask approach - field_bbox <- sf::st_bbox(field_shape) - extent_vec <- c(field_bbox$xmin, field_bbox$xmax, field_bbox$ymin, field_bbox$ymax) - - # Use terra extract function instead of crop+mask - ci_values <- terra::extract(ci_current, field_shape) - ci_prev_values <- terra::extract(ci_previous, field_shape) - - # Calculate metrics - mean_ci <- mean(ci_values$CI, na.rm=TRUE) - mean_ci_prev <- mean(ci_prev_values$CI, na.rm=TRUE) - ci_change <- mean_ci - mean_ci_prev - ci_sd <- sd(ci_values$CI, na.rm=TRUE) - ci_uniformity <- ci_sd / max(0.1, mean_ci) # Avoid division by zero - - # Determine field status - status <- dplyr::case_when( - mean_ci >= 5 ~ "Excellent", - mean_ci >= 3.5 ~ "Good", - mean_ci >= 2 ~ "Fair", - mean_ci >= 1 ~ "Poor", - TRUE ~ "Critical" - ) - - # Determine anomaly type - anomaly_type <- dplyr::case_when( - ci_change > 2 ~ "Potential Weed Growth", - ci_change < -2 ~ "Potential Weeding/Harvesting", - ci_uniformity > 0.5 ~ "High Variability", - mean_ci < 1 ~ "Low Vigor", - TRUE ~ "None" - ) - - # Calculate priority level (1-5, with 1 being highest priority) - priority_score <- dplyr::case_when( - mean_ci < 1 ~ 1, # Critical - highest priority - anomaly_type == "Potential Weed Growth" ~ 2, - anomaly_type == "High Variability" ~ 3, - ci_change < -1 ~ 4, - TRUE ~ 5 # No urgent issues - ) - - # Determine harvest readiness - harvest_readiness <- dplyr::case_when( - field_age_weeks >= 52 & mean_ci >= 4 ~ "Ready for harvest", - field_age_weeks >= 48 & mean_ci >= 3.5 ~ "Approaching harvest", - field_age_weeks >= 40 & mean_ci >= 3 ~ "Mid-maturity", - field_age_weeks >= 12 ~ "Growing", - TRUE ~ "Early stage" - ) - - # Add to summary data - summary_data <- rbind(summary_data, data.frame( - field = field_name, - mean_ci = round(mean_ci, 2), - ci_change = round(ci_change, 2), - ci_uniformity = round(ci_uniformity, 2), - status = status, - anomaly_type = anomaly_type, - priority_level = priority_score, - age_weeks = field_age_weeks, - harvest_readiness = harvest_readiness, - stringsAsFactors = FALSE - )) - - }, error = function(e) { - safe_log(paste("Error calculating health score for field", field_name, ":", e$message), "ERROR") - # Add a row with NA values for this field to ensure it still appears in outputs - summary_data <- rbind(summary_data, data.frame( - field = field_name, - mean_ci = NA, - ci_change = NA, - ci_uniformity = NA, - status = "Error", - anomaly_type = "Error", - priority_level = 1, # High priority because it needs investigation - age_weeks = NA, - harvest_readiness = "Unknown", - stringsAsFactors = FALSE - )) - }) - } - - # Sort by priority level - summary_data <- summary_data %>% dplyr::arrange(priority_level, field) - - return(summary_data) - - }, error = function(e) { - safe_log(paste("Error in generate_farm_health_summary:", e$message), "ERROR") - return(data.frame( - field = character(), - mean_ci = numeric(), - ci_change = numeric(), - ci_uniformity = numeric(), - status = character(), - anomaly_type = character(), - priority_level = numeric(), - age_weeks = numeric(), - harvest_readiness = character(), - stringsAsFactors = FALSE - )) - }) -} - -#' Create a farm-wide anomaly detection map -#' -#' @param ci_current Current CI raster -#' @param ci_previous Previous week's CI raster -#' @param field_boundaries Field boundaries spatial data (sf object) -#' @return A tmap object with anomaly visualization -#' -create_anomaly_map <- function(ci_current, ci_previous, field_boundaries) { - tryCatch({ - # Calculate difference raster - ci_diff <- ci_current - ci_previous - - # Create a categorical raster for anomalies - anomaly_raster <- ci_current * 0 # Initialize with same extent/resolution - - # Extract values to manipulate - diff_values <- terra::values(ci_diff) - curr_values <- terra::values(ci_current) - - # Define anomaly categories: - # 4: Significant growth (potential weeds) - CI increase > 2 - # 3: Moderate growth - CI increase 1-2 - # 2: Stable - CI change between -1 and 1 - # 1: Moderate decline - CI decrease 1-2 - # 0: Significant decline (potential weeding/harvesting) - CI decrease > 2 - - # Apply classification - anomaly_values <- rep(NA, length(diff_values)) - - # Significant growth (potential weeds) - sig_growth <- which(diff_values > 2 & !is.na(diff_values)) - anomaly_values[sig_growth] <- 4 - - # Moderate growth - mod_growth <- which(diff_values > 1 & diff_values <= 2 & !is.na(diff_values)) - anomaly_values[mod_growth] <- 3 - - # Stable - stable <- which(diff_values >= -1 & diff_values <= 1 & !is.na(diff_values)) - anomaly_values[stable] <- 2 - - # Moderate decline - mod_decline <- which(diff_values < -1 & diff_values >= -2 & !is.na(diff_values)) - anomaly_values[mod_decline] <- 1 - - # Significant decline (potential weeding) - sig_decline <- which(diff_values < -2 & !is.na(diff_values)) - anomaly_values[sig_decline] <- 0 - - # Set values in raster - terra::values(anomaly_raster) <- anomaly_values - - # Create anomaly map - map <- tm_shape(anomaly_raster) + - tm_raster( - style = "cat", - palette = c("#d73027", "#fc8d59", "#ffffbf", "#91cf60", "#1a9850"), - labels = c("Significant Decline", "Moderate Decline", "Stable", "Moderate Growth", "Significant Growth"), - title = "Weekly CI Change" - ) + - tm_shape(field_boundaries) + - tm_borders(col = "black", lwd = 1.5) + - tm_text("field", size = 0.6) + - tm_layout( - main.title = "Farm-Wide Anomaly Detection", - legend.outside = TRUE, - legend.outside.position = "bottom" - ) + - tm_scale_bar(position = tm_pos_out("right", "bottom")) - - return(map) - - }, error = function(e) { - safe_log(paste("Error in create_anomaly_map:", e$message), "ERROR") - return(NULL) - }) -} - -#' Create a choropleth map of field health status -#' -#' @param field_boundaries Field boundaries with health data -#' @param attribute Field to visualize (e.g., "priority_level", "mean_ci") -#' @param title Map title -#' @param palette Color palette to use -#' @param legend_title Legend title -#' @return A tmap object -#' -create_field_status_map <- function(field_boundaries, health_data, attribute, - title = "Field Status Overview", - palette = "RdYlGn", - legend_title = "Status") { - tryCatch({ - # Join health data to field boundaries - field_data <- field_boundaries %>% - dplyr::left_join(health_data, by = "field") - - # Create style based on attribute type - if (attribute == "status") { - # Categorical styling for status - map <- tm_shape(field_data) + - tm_fill( - col = attribute, - palette = c("Critical" = "#d73027", "Poor" = "#fc8d59", - "Fair" = "#ffffbf", "Good" = "#91cf60", "Excellent" = "#1a9850", - "Error" = "#999999"), # Added Error category - title = legend_title - ) - } else if (attribute == "priority_level") { - # Numeric with custom breaks for priority (5 to 1, with 1 being highest priority) - map <- tm_shape(field_data) + - tm_fill( - col = attribute, - palette = "-RdYlGn", # Reversed so red is high priority - breaks = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5), - labels = c("Critical", "High", "Medium", "Low", "Minimal"), - title = legend_title - ) - } else if (attribute == "anomaly_type") { - # Categorical styling for anomalies - map <- tm_shape(field_data) + - tm_fill( - col = attribute, - palette = c("Potential Weed Growth" = "#d73027", - "Potential Weeding/Harvesting" = "#4575b4", - "High Variability" = "#f46d43", - "Low Vigor" = "#fee090", - "None" = "#91cf60", - "Error" = "#999999"), # Added Error category - title = legend_title - ) - } else if (attribute == "harvest_readiness") { - # Categorical styling for harvest readiness - map <- tm_shape(field_data) + - tm_fill( - col = attribute, - palette = c("Ready for harvest" = "#1a9850", - "Approaching harvest" = "#91cf60", - "Mid-maturity" = "#ffffbf", - "Growing" = "#fc8d59", - "Early stage" = "#d73027", - "Unknown" = "#999999"), # Added Unknown category - title = legend_title - ) - } else { - # Default numerical styling - map <- tm_shape(field_data) + - tm_fill( - col = attribute, - palette = palette, - title = legend_title, - style = "cont", - na.color = "#999999" # Color for NA values - ) - } - - # Complete the map with borders and labels - map <- map + - tm_borders(col = "black", lwd = 1) + - tm_text("field", size = 0.7) + - tm_layout( - main.title = title, - legend.outside = TRUE, - legend.outside.position = "bottom" - ) + - tm_scale_bar(position = tm_pos_out("right", "bottom")) - - return(map) - - }, error = function(e) { - safe_log(paste("Error in create_field_status_map:", e$message), "ERROR") - return(NULL) - }) -} - -#' Create a summary statistics visualization -#' -#' @param health_data Farm health summary data -#' @return A ggplot2 object -#' -create_summary_stats <- function(health_data) { - tryCatch({ - # Handle empty dataframe case - if (nrow(health_data) == 0) { - return(ggplot2::ggplot() + - ggplot2::annotate("text", x = 0, y = 0, label = "No field data available") + - ggplot2::theme_void()) - } - - # Count fields by status - status_counts <- health_data %>% - dplyr::group_by(status) %>% - dplyr::summarise(count = n()) %>% - dplyr::mutate(status = factor(status, levels = c("Excellent", "Good", "Fair", "Poor", "Critical", "Error"))) - - # Create colors for status categories - status_colors <- c( - "Excellent" = "#1a9850", - "Good" = "#91cf60", - "Fair" = "#ffffbf", - "Poor" = "#fc8d59", - "Critical" = "#d73027", - "Error" = "#999999" - ) - - # Create bar chart - p <- ggplot2::ggplot(status_counts, ggplot2::aes(x = status, y = count, fill = status)) + - ggplot2::geom_bar(stat = "identity") + - ggplot2::scale_fill_manual(values = status_colors) + - ggplot2::labs( - title = "Field Status Summary", - x = "Status", - y = "Number of Fields", - fill = "Field Status" - ) + - ggplot2::theme_minimal() + - ggplot2::theme( - axis.text.x = ggplot2::element_text(angle = 45, hjust = 1), - legend.position = "bottom" - ) - - return(p) - - }, error = function(e) { - safe_log(paste("Error in create_summary_stats:", e$message), "ERROR") - return(ggplot2::ggplot() + - ggplot2::annotate("text", x = 0, y = 0, label = paste("Error:", e$message)) + - ggplot2::theme_void()) - }) -} - -#' Create a bar chart of fields requiring attention -#' -#' @param health_data Farm health summary data -#' @param max_fields Maximum number of fields to display -#' @return A ggplot2 object -#' -create_priority_fields_chart <- function(health_data, max_fields = 10) { - tryCatch({ - # Handle empty dataframe case - if (nrow(health_data) == 0) { - return(ggplot2::ggplot() + - ggplot2::annotate("text", x = 0, y = 0, label = "No field data available") + - ggplot2::theme_void()) - } - - # Filter for fields that need attention (priority 1-3) - priority_fields <- health_data %>% - dplyr::filter(priority_level <= 3) %>% - dplyr::arrange(priority_level) %>% - dplyr::slice_head(n = max_fields) - - # If no priority fields, return message - if (nrow(priority_fields) == 0) { - return(ggplot2::ggplot() + - ggplot2::annotate("text", x = 0, y = 0, label = "No priority fields requiring attention") + - ggplot2::theme_void()) - } - - # Create priority labels - priority_fields$priority_label <- factor( - dplyr::case_when( - priority_fields$priority_level == 1 ~ "Critical", - priority_fields$priority_level == 2 ~ "High", - priority_fields$priority_level == 3 ~ "Medium", - TRUE ~ "Low" - ), - levels = c("Critical", "High", "Medium", "Low") - ) - - # Priority colors - priority_colors <- c( - "Critical" = "#d73027", - "High" = "#fc8d59", - "Medium" = "#fee090", - "Low" = "#91cf60" - ) - - # Create chart - p <- ggplot2::ggplot(priority_fields, - ggplot2::aes(x = reorder(field, -priority_level), - y = mean_ci, - fill = priority_label)) + - ggplot2::geom_bar(stat = "identity") + - ggplot2::geom_text(ggplot2::aes(label = anomaly_type), - position = ggplot2::position_stack(vjust = 0.5), - size = 3, angle = 90, hjust = 0) + - ggplot2::scale_fill_manual(values = priority_colors) + - ggplot2::labs( - title = "Priority Fields Requiring Attention", - subtitle = "With anomaly types and CI values", - x = "Field", - y = "Chlorophyll Index (CI)", - fill = "Priority" - ) + - ggplot2::theme_minimal() + - ggplot2::theme( - axis.text.x = ggplot2::element_text(angle = 45, hjust = 1), - legend.position = "bottom" - ) - - return(p) - - }, error = function(e) { - safe_log(paste("Error in create_priority_fields_chart:", e$message), "ERROR") - return(ggplot2::ggplot() + - ggplot2::annotate("text", x = 0, y = 0, label = paste("Error:", e$message)) + - ggplot2::theme_void()) - }) -} - -#' Creates a harvest readiness visualization -#' -#' @param health_data Farm health summary data -#' @return A ggplot2 object -create_harvest_readiness_chart <- function(health_data) { - tryCatch({ - # Handle empty dataframe case - if (nrow(health_data) == 0) { - return(ggplot2::ggplot() + - ggplot2::annotate("text", x = 0, y = 0, label = "No field data available") + - ggplot2::theme_void()) - } - - # Count fields by harvest readiness - harvest_counts <- health_data %>% - dplyr::group_by(harvest_readiness) %>% - dplyr::summarise(count = n()) - - # Order factor levels - harvest_order <- c("Ready for harvest", "Approaching harvest", "Mid-maturity", "Growing", "Early stage", "Unknown") - harvest_counts$harvest_readiness <- factor(harvest_counts$harvest_readiness, levels = harvest_order) - - # Create colors for harvest readiness categories - harvest_colors <- c( - "Ready for harvest" = "#1a9850", - "Approaching harvest" = "#91cf60", - "Mid-maturity" = "#ffffbf", - "Growing" = "#fc8d59", - "Early stage" = "#d73027", - "Unknown" = "#999999" - ) - - # Create pie chart - p <- ggplot2::ggplot(harvest_counts, ggplot2::aes(x="", y=count, fill=harvest_readiness)) + - ggplot2::geom_bar(stat="identity", width=1) + - ggplot2::coord_polar("y", start=0) + - ggplot2::scale_fill_manual(values = harvest_colors) + - ggplot2::labs( - title = "Harvest Readiness Overview", - fill = "Harvest Stage" - ) + - ggplot2::theme_minimal() + - ggplot2::theme( - axis.title.x = ggplot2::element_blank(), - axis.title.y = ggplot2::element_blank(), - panel.border = ggplot2::element_blank(), - panel.grid = ggplot2::element_blank(), - axis.ticks = ggplot2::element_blank(), - axis.text = ggplot2::element_blank(), - plot.title = ggplot2::element_text(size=14, face="bold") - ) - - return(p) - - }, error = function(e) { - safe_log(paste("Error in create_harvest_readiness_chart:", e$message), "ERROR") - return(ggplot2::ggplot() + - ggplot2::annotate("text", x = 0, y = 0, label = paste("Error:", e$message)) + - ggplot2::theme_void()) - }) -} - -#' Generate recommendations based on farm health -#' -#' @param health_data Farm health summary data -#' @return HTML formatted recommendations -generate_executive_recommendations <- function(health_data) { - tryCatch({ - # Handle empty dataframe case - if (nrow(health_data) == 0) { - return("

Executive Recommendations

No field data available to generate recommendations.

") - } - - # Count fields by priority level - priority_counts <- health_data %>% - dplyr::group_by(priority_level) %>% - dplyr::summarise(count = n()) - - # Get critical and high priority fields - critical_fields <- health_data %>% - dplyr::filter(priority_level == 1) %>% - dplyr::pull(field) - - high_priority_fields <- health_data %>% - dplyr::filter(priority_level == 2) %>% - dplyr::pull(field) - - # Count harvest-ready fields - harvest_ready <- health_data %>% - dplyr::filter(harvest_readiness == "Ready for harvest") %>% - dplyr::pull(field) - - approaching_harvest <- health_data %>% - dplyr::filter(harvest_readiness == "Approaching harvest") %>% - dplyr::pull(field) - - # Count anomalies by type - anomaly_counts <- health_data %>% - dplyr::filter(anomaly_type != "None" & anomaly_type != "Error") %>% - dplyr::group_by(anomaly_type) %>% - dplyr::summarise(count = n()) - - # Generate HTML recommendations - html_output <- "
" - html_output <- paste0(html_output, "

Executive Recommendations

") - - # Priority recommendations - html_output <- paste0(html_output, "

Priority Actions:

    ") - - if (length(critical_fields) > 0) { - html_output <- paste0(html_output, - sprintf("
  • Critical attention needed for fields: %s
    • ", - paste(critical_fields, collapse = ", "))) - } - - if (length(high_priority_fields) > 0) { - html_output <- paste0(html_output, - sprintf("
  • High priority inspection for fields: %s
    • ", - paste(high_priority_fields, collapse = ", "))) - } - - if (length(harvest_ready) > 0) { - html_output <- paste0(html_output, - sprintf("
  • Ready for harvest: %s
    • ", - paste(harvest_ready, collapse = ", "))) - } - - if (length(approaching_harvest) > 0) { - html_output <- paste0(html_output, - sprintf("
  • Approaching harvest readiness: %s
    • ", - paste(approaching_harvest, collapse = ", "))) - } - - # If no specific recommendations, add general one - if (length(critical_fields) == 0 && length(high_priority_fields) == 0 && - length(harvest_ready) == 0 && length(approaching_harvest) == 0) { - html_output <- paste0(html_output, "
  • No urgent actions required this week.
    • ") - } - - html_output <- paste0(html_output, "
") - - # Anomaly notifications - if (nrow(anomaly_counts) > 0) { - html_output <- paste0(html_output, "

Anomaly Notifications:

    ") - - for (i in 1:nrow(anomaly_counts)) { - html_output <- paste0(html_output, - sprintf("
  • %s detected in %d fields
    • ", - anomaly_counts$anomaly_type[i], anomaly_counts$count[i])) - } - - html_output <- paste0(html_output, "
") - } - - # Farm status summary - html_output <- paste0(html_output, "

Farm Status Overview:

    ") - - status_counts <- health_data %>% - dplyr::filter(status != "Error") %>% - dplyr::group_by(status) %>% - dplyr::summarise(count = n()) - - for (i in 1:nrow(status_counts)) { - html_output <- paste0(html_output, - sprintf("
  • %s: %d fields
    • ", - status_counts$status[i], status_counts$count[i])) - } - - html_output <- paste0(html_output, "
") - - return(html_output) - - }, error = function(e) { - safe_log(paste("Error in generate_executive_recommendations:", e$message), "ERROR") - return("

Error generating recommendations.

") - }) -} +```{r advanced_analytics_functions, message=FALSE, warning=FALSE, include=FALSE} +# ADVANCED ANALYTICS FUNCTIONS +# Note: These functions are now imported from executive_report_utils.R +# The utility file contains functions for velocity/acceleration indicators, +# anomaly timeline creation, age cohort mapping, and cohort performance charts +safe_log("Using analytics functions from executive_report_utils.R") ``` -`r subtitle_var` - \pagebreak -# Explanation of the Report +# Advanced Analytics -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. +## Field Health Velocity and Acceleration -## What is the Chlorophyll Index (CI)? +This visualization shows the rate of change in field health (velocity) and whether that change is speeding up or slowing down (acceleration). These metrics help identify if farm conditions are improving, stable, or deteriorating. -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: +**How to interpret:** +- **Velocity gauge:** Shows the average weekly change in CI values across all fields + - Positive values (green/right side): Farm health improving week-to-week + - Negative values (red/left side): Farm health declining week-to-week -* Greater photosynthetic activity -* Healthier plant tissue -* Better nitrogen uptake -* More vigorous crop growth +- **Acceleration gauge:** Shows whether the rate of change is increasing or decreasing + - Positive values (green/right side): Change is accelerating or improving faster + - Negative values (red/left side): Change is decelerating or slowing down -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. +- **4-Week Trend:** Shows the overall CI value trajectory for the past month -# 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_field_status_map, echo=FALSE, fig.height=6, fig.width=9, message=FALSE, warning=FALSE} -# Create field status map +```{r render_velocity_acceleration, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE} +# Render the velocity and acceleration indicators tryCatch({ - # Create and display the field status map - field_status_map <- create_field_status_map( - field_boundaries = AllPivots0, + # Create and display the indicators using the imported utility function + velocity_plot <- create_velocity_acceleration_indicator( health_data = farm_health_data, - attribute = "status", - title = "Field Health Status Overview", - palette = "RdYlGn", - 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, + ci_prev1 = CI_m1, + ci_prev2 = CI_m2, + ci_prev3 = CI_m3, field_boundaries = AllPivots0 ) - # Print the map - print(anomaly_map) + # Print the visualization + print(velocity_plot) + + # Create a table of fields with significant velocity changes + field_ci_metrics <- list() + + # Process each field to get metrics + fields <- unique(AllPivots0$field) + for (field_name in fields) { + tryCatch({ + # Get field boundary + field_shape <- AllPivots0 %>% dplyr::filter(field == field_name) + if (nrow(field_shape) == 0) next + + # Extract CI values + ci_curr_values <- terra::extract(CI, field_shape) + ci_prev1_values <- terra::extract(CI_m1, field_shape) + + # Calculate metrics + mean_ci_curr <- mean(ci_curr_values$CI, na.rm = TRUE) + mean_ci_prev1 <- mean(ci_prev1_values$CI, na.rm = TRUE) + velocity <- mean_ci_curr - mean_ci_prev1 + + # Store in list + field_ci_metrics[[field_name]] <- list( + field = field_name, + ci_current = mean_ci_curr, + ci_prev1 = mean_ci_prev1, + velocity = velocity + ) + + }, error = function(e) { + safe_log(paste("Error processing field", field_name, "for velocity table:", e$message), "WARNING") + }) + } + + # Convert list to data frame + velocity_df <- do.call(rbind, lapply(field_ci_metrics, function(x) { + data.frame( + field = x$field, + ci_current = round(x$ci_current, 2), + ci_prev1 = round(x$ci_prev1, 2), + velocity = round(x$velocity, 2), + direction = ifelse(x$velocity >= 0, "Improving", "Declining") + ) + })) + + # Select top 5 positive and top 5 negative velocity fields + top_positive <- velocity_df %>% + dplyr::filter(velocity > 0) %>% + dplyr::arrange(desc(velocity)) %>% + dplyr::slice_head(n = 5) + + top_negative <- velocity_df %>% + dplyr::filter(velocity < 0) %>% + dplyr::arrange(velocity) %>% + dplyr::slice_head(n = 5) + + # Display the tables if we have data + if (nrow(top_positive) > 0) { + cat("

Fields with Fastest Improvement

") + knitr::kable(top_positive %>% + dplyr::select(Field = field, + `Current CI` = ci_current, + `Previous CI` = ci_prev1, + `Weekly Change` = velocity)) + } + + if (nrow(top_negative) > 0) { + cat("

Fields with Fastest Decline

") + knitr::kable(top_negative %>% + dplyr::select(Field = field, + `Current CI` = ci_current, + `Previous CI` = ci_prev1, + `Weekly Change` = velocity)) + } + }, error = function(e) { - safe_log(paste("Error creating anomaly map:", e$message), "ERROR") - plot(1, type="n", axes=FALSE, xlab="", ylab="") - text(1, 1, "Error creating anomaly map", cex=1.5) + safe_log(paste("Error rendering velocity visualization:", e$message), "ERROR") + cat("
Error generating velocity visualization.
") }) ``` \pagebreak -## Harvest Planning +## Field Anomaly Timeline -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. +This visualization shows the history of detected anomalies in fields across the monitoring period. It helps identify persistent issues or improvements over time. -**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 +**How to interpret:** +- **X-axis**: Dates of satellite observations +- **Y-axis**: Fields grouped by similar characteristics +- **Colors**: Red indicates negative anomalies, green indicates positive anomalies +- **Size**: Larger markers indicate stronger anomalies -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 +```{r anomaly_timeline, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE} +# Generate anomaly timeline visualization tryCatch({ - # Create and display the harvest readiness map - harvest_map <- create_field_status_map( + # Use the imported function to create the anomaly timeline + anomaly_timeline <- create_anomaly_timeline( field_boundaries = AllPivots0, - health_data = farm_health_data, - attribute = "harvest_readiness", - title = "Harvest Readiness Status", - palette = "RdYlGn", - legend_title = "Harvest Status" + ci_data = CI_quadrant, + days_to_include = 90 # Show last 90 days of data ) - # Print the map - print(harvest_map) + # Display the timeline + print(anomaly_timeline) + }, error = function(e) { - safe_log(paste("Error creating harvest map:", e$message), "ERROR") - plot(1, type="n", axes=FALSE, xlab="", ylab="") - text(1, 1, "Error creating harvest map", cex=1.5) + safe_log(paste("Error generating anomaly timeline:", e$message), "ERROR") + cat("
Error generating anomaly timeline visualization.
") }) ``` \pagebreak -## Field Status Summary +## Field Age Cohorts Map -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. +This map shows fields grouped by their crop age (weeks since planting). Understanding the distribution of crop ages helps interpret performance metrics and plan harvest scheduling. -**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 to interpret:** +- **Colors**: Different colors represent different age groups (in weeks since planting) +- **Labels**: Each field is labeled with its name for easy reference +- **Legend**: Shows the age ranges in weeks and their corresponding colors -**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 +```{r age_cohort_map, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE} +# Generate age cohort map tryCatch({ - # Create field status charts - status_chart <- create_summary_stats(farm_health_data) + # Use the imported function to create the age cohort map + age_cohort_map <- create_age_cohort_map( + field_boundaries = AllPivots0, + harvesting_data = harvesting_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.") - } + # Display the map + print(age_cohort_map) }, error = function(e) { - safe_log(paste("Error creating priority fields chart:", e$message), "ERROR") - cat("Error generating priority fields visualization. See log for details.") + safe_log(paste("Error generating age cohort map:", e$message), "ERROR") + cat("
Error generating age cohort map visualization.
") }) ``` \pagebreak -## Management Recommendations +## Cohort Performance Comparison -```{r render_recommendations, echo=FALSE, results='asis', message=FALSE, warning=FALSE} -# Generate executive recommendations +This visualization compares chlorophyll index (CI) performance across different age groups of fields. This helps identify if certain age groups are performing better or worse than expected. + +**How to interpret:** +- **X-axis**: Field age groups in weeks since planting +- **Y-axis**: Average CI value for fields in that age group +- **Box plots**: Show the distribution of CI values within each age group +- **Line**: Shows the expected CI trajectory based on historical data + +```{r cohort_performance_chart, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE} +# Generate cohort performance comparison chart tryCatch({ - # Create and display recommendations - recommendations_html <- generate_executive_recommendations(farm_health_data) + # Use the imported function to create the cohort performance chart + cohort_chart <- create_cohort_performance_chart( + field_boundaries = AllPivots0, + ci_current = CI, + harvesting_data = harvesting_data + ) + + # Display the chart + print(cohort_chart) - # Print the HTML recommendations - cat(recommendations_html) }, error = function(e) { - safe_log(paste("Error creating recommendations:", e$message), "ERROR") - cat("

Error generating recommendations. Please see system administrator.

") + safe_log(paste("Error generating cohort performance chart:", e$message), "ERROR") + cat("
Error generating cohort performance visualization.
") }) ``` -## Yield Prediction Overview -This section provides yield predictions for mature fields (over 300 days old) based on their Chlorophyll Index values and growth patterns. These predictions can help with harvest planning and yield forecasting. -```{r render_yield_summary, echo=FALSE, fig.height=5, fig.width=10, message=FALSE, warning=FALSE} -# Create yield summary -tryCatch({ - if (exists("pred_rf_current_season") && nrow(pred_rf_current_season) > 0) { - # Calculate total estimated production - total_yield <- sum(pred_rf_current_season$predicted_Tcha, na.rm = TRUE) - - # Create summary box - cat("
") - cat("

Yield Summary

") - cat("
    ") - cat(sprintf("
  • Total estimated production: %s tonnes/ha
    • ", - format(round(total_yield, 0), big.mark=","))) - cat(sprintf("
  • Number of harvest-ready fields: %d
    • ", - nrow(pred_rf_current_season))) - cat(sprintf("
  • Average predicted yield: %s tonnes/ha
    • ", - format(round(mean(pred_rf_current_season$predicted_Tcha, na.rm=TRUE), 1), big.mark=","))) - cat("
") - cat("
") - - # Display yield prediction table - harvest_ready_fields <- pred_rf_current_season %>% - dplyr::arrange(desc(predicted_Tcha)) %>% - dplyr::select( - Field = field, - `Sub Field` = sub_field, - `Age (Days)` = Age_days, - `Cumulative CI` = total_CI, - `Predicted Yield (Tonnes/ha)` = predicted_Tcha - ) - - knitr::kable(harvest_ready_fields, - caption = "Predicted Yields for Harvest-Ready Fields", - digits = 1) - } else { - cat("
") - cat("

Yield Summary

") - cat("

No fields currently meet harvest readiness criteria (>300 days) for yield prediction.

") - cat("
") - } -}, error = function(e) { - safe_log(paste("Error creating yield summary:", e$message), "ERROR") - cat("

Error generating yield summary. Please see system administrator.

") -}) -``` + + diff --git a/r_app/Rplots.pdf b/r_app/Rplots.pdf deleted file mode 100644 index 1541128..0000000 Binary files a/r_app/Rplots.pdf and /dev/null differ diff --git a/r_app/ci_extraction.R b/r_app/ci_extraction.R index ed2f447..e7490e9 100644 --- a/r_app/ci_extraction.R +++ b/r_app/ci_extraction.R @@ -32,12 +32,12 @@ main <- function() { end_date <- as.Date(args[1]) if (is.na(end_date)) { warning("Invalid end_date provided. Using default (current date).") - #end_date <- Sys.Date() - end_date <- "2023-10-01" + end_date <- Sys.Date() + #end_date <- "2023-10-01" } } else { - #end_date <- Sys.Date() - end_date <- "2023-10-01" + end_date <- Sys.Date() + #end_date <- "2023-10-01" } # Process offset argument @@ -58,6 +58,10 @@ main <- function() { project_dir <- "chemba" } + # Make project_dir available globally so parameters_project.R can use it + assign("project_dir", project_dir, envir = .GlobalEnv) + + # 3. Initialize project configuration # -------------------------------- new_project_question <- FALSE @@ -68,8 +72,10 @@ main <- function() { }, error = function(e) { warning("Default source files not found. Attempting to source from 'r_app' directory.") tryCatch({ - source("r_app/parameters_project.R") - source("r_app/ci_extraction_utils.R") + source(here::here("r_app", "parameters_project.R")) + source(here::here("r_app", "ci_extraction_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.") }) @@ -106,6 +112,6 @@ main <- function() { }) } -# Run the main function if the script is executed directly -main() - +if (sys.nframe() == 0) { + main() +} \ No newline at end of file diff --git a/r_app/experiments/ci_extraction_and_yield_prediction.R b/r_app/experiments/ci_extraction_and_yield_prediction.R new file mode 100644 index 0000000..83f233b --- /dev/null +++ b/r_app/experiments/ci_extraction_and_yield_prediction.R @@ -0,0 +1,572 @@ +# CI_EXTRACTION_AND_YIELD_PREDICTION.R +# ===================================== +# +# This standalone script demonstrates: +# 1. How Chlorophyll Index (CI) is extracted from satellite imagery +# 2. How yield prediction is performed based on CI values +# +# Created for sharing with colleagues to illustrate the core functionality +# of the SmartCane monitoring system. +# + +# ----------------------------- +# PART 1: LIBRARY DEPENDENCIES +# ----------------------------- + +suppressPackageStartupMessages({ + # Spatial data processing + library(sf) + library(terra) + library(exactextractr) + + # Data manipulation + library(tidyverse) + library(lubridate) + library(here) + + # Machine learning for yield prediction + library(rsample) + library(caret) + library(randomForest) + library(CAST) +}) + +# ---------------------------------- +# PART 2: LOGGING & UTILITY FUNCTIONS +# ---------------------------------- + +#' Safe logging function that works in any environment +#' +#' @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") { + timestamp <- format(Sys.time(), "%Y-%m-%d %H:%M:%S") + formatted_msg <- paste0("[", timestamp, "][", level, "] ", message) + + if (level %in% c("ERROR", "WARNING")) { + warning(formatted_msg) + } else { + message(formatted_msg) + } +} + +#' 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) + + # Extract week and year information + week <- lubridate::week(start_date) + year <- lubridate::year(start_date) + + # 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 + )) +} + +# ----------------------------- +# PART 3: CI EXTRACTION PROCESS +# ----------------------------- + +#' Find satellite imagery files within a specific date range +#' +#' @param image_folder Path to the folder containing satellite images +#' @param date_filter Vector of dates to filter by (in YYYY-MM-DD format) +#' @return Vector of file paths matching the date filter +#' +find_satellite_images <- function(image_folder, date_filter) { + # Validate inputs + if (!dir.exists(image_folder)) { + stop(paste("Image folder not found:", image_folder)) + } + + # List all files in the directory + all_files <- list.files(image_folder, pattern = "\\.tif$", full.names = TRUE, recursive = TRUE) + + if (length(all_files) == 0) { + safe_log("No TIF files found in the specified directory", "WARNING") + return(character(0)) + } + + # Filter files by date pattern in filename + filtered_files <- character(0) + + for (date in date_filter) { + # Format date for matching (remove dashes) + date_pattern <- gsub("-", "", date) + + # Find files with matching date pattern + matching_files <- all_files[grepl(date_pattern, all_files)] + + if (length(matching_files) > 0) { + filtered_files <- c(filtered_files, matching_files) + safe_log(paste("Found", length(matching_files), "files for date", date)) + } + } + + return(filtered_files) +} + +#' Create a Chlorophyll Index (CI) from satellite imagery +#' +#' @param raster_obj A SpatRaster object with Red, Green, Blue, and NIR bands +#' @return A SpatRaster object with a CI band +#' +calculate_ci <- function(raster_obj) { + # Validate input has required bands + if (terra::nlyr(raster_obj) < 4) { + stop("Raster must have at least 4 bands (Red, Green, Blue, NIR)") + } + + # Extract bands (assuming standard order: B, G, R, NIR) + blue_band <- raster_obj[[1]] + green_band <- raster_obj[[2]] + red_band <- raster_obj[[3]] + nir_band <- raster_obj[[4]] + + # CI formula: (NIR / Red) - 1 + # This highlights chlorophyll content in vegetation + ci_raster <- (nir_band / red_band) - 1 + + # Filter extreme values that may result from division operations + ci_raster[ci_raster > 10] <- 10 # Cap max value + ci_raster[ci_raster < 0] <- 0 # Cap min value + + # Name the layer + names(ci_raster) <- "CI" + + return(ci_raster) +} + +#' Create a mask for cloudy pixels and shadows using thresholds +#' +#' @param raster_obj A SpatRaster object with multiple bands +#' @return A binary mask where 1=clear pixel, 0=cloudy or shadow pixel +#' +create_cloud_mask <- function(raster_obj) { + # Extract bands + blue_band <- raster_obj[[1]] + green_band <- raster_obj[[2]] + red_band <- raster_obj[[3]] + nir_band <- raster_obj[[4]] + + # Create initial mask (all pixels valid) + mask <- blue_band * 0 + 1 + + # Calculate indices used for detection + ndvi <- (nir_band - red_band) / (nir_band + red_band) + brightness <- (blue_band + green_band + red_band) / 3 + + # CLOUD DETECTION CRITERIA + # ------------------------ + # Clouds are typically very bright in all bands + bright_pixels <- (blue_band > 0.3) & (green_band > 0.3) & (red_band > 0.3) + + # Snow/high reflectance clouds have high blue values + blue_dominant <- blue_band > (red_band * 1.2) + + # Low NDVI areas that are bright are likely clouds + low_ndvi <- ndvi < 0.1 + + # Combine cloud criteria + cloud_pixels <- bright_pixels & (blue_dominant | low_ndvi) + + # SHADOW DETECTION CRITERIA + # ------------------------ + # Shadows typically have: + # 1. Low overall brightness across all bands + # 2. Lower NIR reflectance + # 3. Can still have reasonable NDVI (if over vegetation) + + # Dark pixels in visible spectrum + dark_pixels <- brightness < 0.1 + + # Low NIR reflectance + low_nir <- nir_band < 0.15 + + # Shadows often have higher blue proportion relative to NIR + blue_nir_ratio <- blue_band / (nir_band + 0.01) # Add small constant to avoid division by zero + blue_enhanced <- blue_nir_ratio > 0.8 + + # Combine shadow criteria + shadow_pixels <- dark_pixels & (low_nir | blue_enhanced) + + # Update mask (0 for cloud or shadow pixels) + mask[cloud_pixels | shadow_pixels] <- 0 + + # Optional: create different values for clouds vs shadows for visualization + # mask[cloud_pixels] <- 0 # Clouds + # mask[shadow_pixels] <- 0 # Shadows + + return(mask) +} + +#' Process satellite image, calculate CI, and crop to field boundaries +#' +#' @param file Path to the satellite image file +#' @param field_boundaries Field boundaries vector object +#' @param output_dir Directory to save the processed raster +#' @return Path to the processed raster file +#' +process_satellite_image <- function(file, field_boundaries, output_dir) { + # Validate inputs + if (!file.exists(file)) { + stop(paste("File not found:", file)) + } + + if (is.null(field_boundaries)) { + stop("Field boundaries are required but were not provided") + } + + # Create output filename + basename_no_ext <- tools::file_path_sans_ext(basename(file)) + output_file <- here::here(output_dir, paste0(basename_no_ext, "_CI.tif")) + + # Process with error handling + tryCatch({ + # Load and prepare raster + loaded_raster <- terra::rast(file) + + # Calculate CI + ci_raster <- calculate_ci(loaded_raster) + + # Create cloud mask + cloud_mask <- create_cloud_mask(loaded_raster) + + # Apply cloud mask to CI + ci_masked <- ci_raster * cloud_mask + + # Crop to field boundaries extent (for efficiency) + field_extent <- terra::ext(field_boundaries) + ci_cropped <- terra::crop(ci_masked, field_extent) + + # Write output + terra::writeRaster(ci_cropped, output_file, overwrite = TRUE) + + safe_log(paste("Successfully processed", basename(file))) + + return(output_file) + + }, error = function(e) { + safe_log(paste("Error processing", basename(file), ":", e$message), "ERROR") + return(NULL) + }) +} + +#' Extract CI statistics for each field +#' +#' @param ci_raster A SpatRaster with CI values +#' @param field_boundaries An sf object with field polygons +#' @return A data frame with CI statistics by field +#' +extract_ci_by_field <- function(ci_raster, field_boundaries) { + # Validate inputs + if (is.null(ci_raster)) { + stop("CI raster is required but was NULL") + } + + if (is.null(field_boundaries) || nrow(field_boundaries) == 0) { + stop("Field boundaries are required but were empty") + } + + # Extract statistics using exact extraction (weighted by coverage) + ci_stats <- exactextractr::exact_extract( + ci_raster, + field_boundaries, + fun = c("mean", "median", "min", "max", "stdev", "count"), + progress = FALSE + ) + + # Add field identifiers + ci_stats$field <- field_boundaries$field + if ("sub_field" %in% names(field_boundaries)) { + ci_stats$sub_field <- field_boundaries$sub_field + } else { + ci_stats$sub_field <- field_boundaries$field + } + + # Add date info + ci_stats$date <- Sys.Date() + + # Clean up names + names(ci_stats) <- gsub("CI\\.", "", names(ci_stats)) + + return(ci_stats) +} + +# ----------------------------------------- +# PART 4: YIELD PREDICTION IMPLEMENTATION +# ----------------------------------------- + +#' Prepare data for yield prediction model +#' +#' @param ci_data Data frame with cumulative CI values +#' @param harvest_data Data frame with harvest information +#' @return Data frame ready for modeling +#' +prepare_yield_prediction_data <- function(ci_data, harvest_data) { + # Join CI and yield data + ci_and_yield <- dplyr::left_join(ci_data, harvest_data, by = c("field", "sub_field", "season")) %>% + dplyr::group_by(sub_field, season) %>% + dplyr::slice(which.max(DOY)) %>% + dplyr::select(field, sub_field, tonnage_ha, cumulative_CI, DOY, season, sub_area) %>% + dplyr::mutate(CI_per_day = cumulative_CI / DOY) + + # Split into training and prediction sets + ci_and_yield_train <- ci_and_yield %>% + as.data.frame() %>% + dplyr::filter(!is.na(tonnage_ha)) + + prediction_yields <- ci_and_yield %>% + as.data.frame() %>% + dplyr::filter(is.na(tonnage_ha)) + + return(list( + train = ci_and_yield_train, + predict = prediction_yields + )) +} + +#' Train a random forest model for yield prediction +#' +#' @param training_data Data frame with training data +#' @param predictors Vector of predictor variable names +#' @param response Name of the response variable +#' @return Trained model +#' +train_yield_model <- function(training_data, predictors = c("cumulative_CI", "DOY", "CI_per_day"), response = "tonnage_ha") { + # Configure model training parameters + ctrl <- caret::trainControl( + method = "cv", + savePredictions = TRUE, + allowParallel = TRUE, + number = 5, + verboseIter = TRUE + ) + + # Train the model with feature selection + set.seed(202) # For reproducibility + model_ffs_rf <- CAST::ffs( + training_data[, predictors], + training_data[, response], + method = "rf", + trControl = ctrl, + importance = TRUE, + withinSE = TRUE, + tuneLength = 5, + na.rm = TRUE + ) + + return(model_ffs_rf) +} + +#' Format predictions into a clean data frame +#' +#' @param predictions Raw prediction results +#' @param newdata Original data frame with field information +#' @return Formatted predictions data frame +#' +prepare_predictions <- function(predictions, newdata) { + return(predictions %>% + as.data.frame() %>% + dplyr::rename(predicted_Tcha = ".") %>% + dplyr::mutate( + sub_field = newdata$sub_field, + field = newdata$field, + Age_days = newdata$DOY, + total_CI = round(newdata$cumulative_CI, 0), + predicted_Tcha = round(predicted_Tcha, 0), + season = newdata$season + ) %>% + dplyr::select(field, sub_field, Age_days, total_CI, predicted_Tcha, season) %>% + dplyr::left_join(., newdata, by = c("field", "sub_field", "season")) + ) +} + +#' Predict yields for mature fields +#' +#' @param model Trained model +#' @param prediction_data Data frame with fields to predict +#' @param min_age Minimum age in days to qualify as mature (default: 300) +#' @return Data frame with yield predictions +#' +predict_yields <- function(model, prediction_data, min_age = 300) { + # Make predictions + predictions <- stats::predict(model, newdata = prediction_data) + + # Format predictions + pred_formatted <- prepare_predictions(predictions, prediction_data) %>% + dplyr::filter(Age_days > min_age) %>% + dplyr::mutate(CI_per_day = round(total_CI / Age_days, 1)) + + return(pred_formatted) +} + +# ------------------------------ +# PART 5: DEMONSTRATION WORKFLOW +# ------------------------------ + +#' Demonstration workflow showing how to use the functions +#' +#' @param end_date The end date for processing satellite images +#' @param offset Number of days to look back +#' @param image_folder Path to the folder containing satellite images +#' @param field_boundaries_path Path to field boundaries shapefile +#' @param output_dir Path to save processed outputs +#' @param harvest_data_path Path to historical harvest data +#' +demo_workflow <- function(end_date = Sys.Date(), offset = 7, + image_folder = "path/to/satellite/images", + field_boundaries_path = "path/to/field_boundaries.shp", + output_dir = "path/to/output", + harvest_data_path = "path/to/harvest_data.csv") { + + # Step 1: Generate date list for processing + dates <- date_list(end_date, offset) + safe_log(paste("Processing data for week", dates$week, "of", dates$year)) + + # Step 2: Load field boundaries + field_boundaries <- sf::read_sf(field_boundaries_path) + safe_log(paste("Loaded", nrow(field_boundaries), "field boundaries")) + + # Step 3: Find satellite images for the specified date range + image_files <- find_satellite_images(image_folder, dates$days_filter) + safe_log(paste("Found", length(image_files), "satellite images for processing")) + + # Step 4: Process each satellite image and calculate CI + ci_files <- list() + for (file in image_files) { + ci_file <- process_satellite_image(file, field_boundaries, output_dir) + if (!is.null(ci_file)) { + ci_files <- c(ci_files, ci_file) + } + } + + # Step 5: Extract CI statistics for each field + ci_stats_list <- list() + for (ci_file in ci_files) { + ci_raster <- terra::rast(ci_file) + ci_stats <- extract_ci_by_field(ci_raster, field_boundaries) + ci_stats_list[[basename(ci_file)]] <- ci_stats + } + + # Combine all stats + all_ci_stats <- dplyr::bind_rows(ci_stats_list) + safe_log(paste("Extracted CI statistics for", nrow(all_ci_stats), "field-date combinations")) + + # Step 6: Prepare for yield prediction + if (file.exists(harvest_data_path)) { + # Load harvest data + harvest_data <- read.csv(harvest_data_path) + safe_log("Loaded harvest data for yield prediction") + + # Make up cumulative_CI data for demonstration purposes + # In a real scenario, this would come from accumulating CI values over time + ci_data <- all_ci_stats %>% + dplyr::group_by(field, sub_field) %>% + dplyr::summarise( + cumulative_CI = sum(mean, na.rm = TRUE), + DOY = n(), # Days of year as the count of observations + season = lubridate::year(max(date, na.rm = TRUE)), + .groups = "drop" + ) + + # Prepare data for modeling + modeling_data <- prepare_yield_prediction_data(ci_data, harvest_data) + + if (nrow(modeling_data$train) > 0) { + # Train yield prediction model + yield_model <- train_yield_model(modeling_data$train) + safe_log("Trained yield prediction model") + + # Predict yields for mature fields + yield_predictions <- predict_yields(yield_model, modeling_data$predict) + safe_log(paste("Generated yield predictions for", nrow(yield_predictions), "fields")) + + # Return results + return(list( + ci_stats = all_ci_stats, + yield_predictions = yield_predictions, + model = yield_model + )) + } else { + safe_log("No training data available for yield prediction", "WARNING") + return(list(ci_stats = all_ci_stats)) + } + } else { + safe_log("Harvest data not found, skipping yield prediction", "WARNING") + return(list(ci_stats = all_ci_stats)) + } +} + +# ------------------------------ +# PART 6: USAGE EXAMPLE +# ------------------------------ + +# Uncomment and modify paths to run the demo workflow +# results <- demo_workflow( +# end_date = "2023-10-01", +# offset = 7, +# image_folder = "data/satellite_images", +# field_boundaries_path = "data/field_boundaries.shp", +# output_dir = "output/processed", +# harvest_data_path = "data/harvest_history.csv" +# ) +# +# # Access results +# ci_stats <- results$ci_stats +# yield_predictions <- results$yield_predictions +# +# # Example: Plot CI distribution by field +# if (require(ggplot2)) { +# ggplot(ci_stats, aes(x = field, y = mean, fill = field)) + +# geom_boxplot() + +# labs(title = "CI Distribution by Field", +# x = "Field", +# y = "Mean CI") + +# theme_minimal() + +# theme(axis.text.x = element_text(angle = 45, hjust = 1)) +# } +# +# # Example: Plot predicted yield vs age +# if (exists("yield_predictions") && require(ggplot2)) { +# ggplot(yield_predictions, aes(x = Age_days, y = predicted_Tcha, color = field)) + +# geom_point(size = 3) + +# geom_text(aes(label = field), hjust = -0.2, vjust = -0.2) + +# labs(title = "Predicted Yield by Field Age", +# x = "Age (Days)", +# y = "Predicted Yield (Tonnes/ha)") + +# theme_minimal() +# } diff --git a/r_app/counting_clouds.R b/r_app/experiments/counting_clouds.R similarity index 100% rename from r_app/counting_clouds.R rename to r_app/experiments/counting_clouds.R diff --git a/r_app/experiments/delete_cloud_exploratoin b/r_app/experiments/delete_cloud_exploratoin new file mode 100644 index 0000000..2cf9a19 --- /dev/null +++ b/r_app/experiments/delete_cloud_exploratoin @@ -0,0 +1,556 @@ +# Cloud and Shadow Detection Analysis +# This script analyzes cloud and shadow detection parameters using the diagnostic GeoTIFF files +# and polygon-based classification to help optimize the detection algorithms + +# Load required packages +library(terra) +library(sf) +library(dplyr) +library(ggplot2) +library(reshape2) +library(exactextractr) # For accurate polygon extraction + +# Define diagnostic directory +diagnostic_dir <- "C:/Users/timon/Resilience BV/4020 SCane ESA DEMO - Documenten/General/4020 SCDEMO Team/4020 TechnicalData/WP3/smartcane/cloud_mask_diagnostics_20250515-164357" + +# Simple logging function for this standalone script +safe_log <- function(message, level = "INFO") { + cat(paste0("[", level, "] ", message, "\n")) +} + +safe_log("Starting cloud detection analysis on diagnostic rasters") + +# Load all diagnostic rasters +safe_log("Loading diagnostic raster files...") + + +# Load original bands +red_band <- terra::rast(file.path(diagnostic_dir, "diagnostic_red_band.tif")) +green_band <- terra::rast(file.path(diagnostic_dir, "diagnostic_green_band.tif")) +blue_band <- terra::rast(file.path(diagnostic_dir, "diagnostic_blue_band.tif")) +nir_band <- terra::rast(file.path(diagnostic_dir, "diagnostic_nir_band.tif")) + +# Load derived indices +brightness <- terra::rast(file.path(diagnostic_dir, "diagnostic_brightness.tif")) +ndvi <- terra::rast(file.path(diagnostic_dir, "diagnostic_ndvi.tif")) +blue_ratio <- terra::rast(file.path(diagnostic_dir, "diagnostic_blue_ratio.tif")) +green_nir_ratio <- terra::rast(file.path(diagnostic_dir, "diagnostic_green_nir_ratio.tif")) +ndwi <- terra::rast(file.path(diagnostic_dir, "diagnostic_ndwi.tif")) + +# Load cloud detection parameters +bright_pixels <- terra::rast(file.path(diagnostic_dir, "param_bright_pixels.tif")) +very_bright_pixels <- terra::rast(file.path(diagnostic_dir, "param_very_bright_pixels.tif")) +blue_dominant <- terra::rast(file.path(diagnostic_dir, "param_blue_dominant.tif")) +low_ndvi <- terra::rast(file.path(diagnostic_dir, "param_low_ndvi.tif")) +green_dominant_nir <- terra::rast(file.path(diagnostic_dir, "param_green_dominant_nir.tif")) +high_ndwi <- terra::rast(file.path(diagnostic_dir, "param_high_ndwi.tif")) + +# Load shadow detection parameters +dark_pixels <- terra::rast(file.path(diagnostic_dir, "param_dark_pixels.tif")) +very_dark_pixels <- terra::rast(file.path(diagnostic_dir, "param_very_dark_pixels.tif")) +low_nir <- terra::rast(file.path(diagnostic_dir, "param_low_nir.tif")) +shadow_ndvi <- terra::rast(file.path(diagnostic_dir, "param_shadow_ndvi.tif")) +low_red_to_blue <- terra::rast(file.path(diagnostic_dir, "param_low_red_to_blue.tif")) +high_blue_to_nir_ratio <- terra::rast(file.path(diagnostic_dir, "param_high_blue_to_nir_ratio.tif")) +blue_nir_ratio_raw <- terra::rast(file.path(diagnostic_dir, "param_blue_nir_ratio_raw.tif")) +red_blue_ratio_raw <- terra::rast(file.path(diagnostic_dir, "param_red_blue_ratio_raw.tif")) + +# Load edge detection parameters +brightness_focal_sd <- terra::rast(file.path(diagnostic_dir, "param_brightness_focal_sd.tif")) +edge_pixels <- terra::rast(file.path(diagnostic_dir, "param_edge_pixels.tif")) + +# Load final masks +cloud_mask <- terra::rast(file.path(diagnostic_dir, "mask_cloud.tif")) +shadow_mask <- terra::rast(file.path(diagnostic_dir, "mask_shadow.tif")) +combined_mask <- terra::rast(file.path(diagnostic_dir, "mask_combined.tif")) +dilated_mask <- terra::rast(file.path(diagnostic_dir, "mask_dilated.tif")) + +safe_log("Raster data loaded successfully") + +# Try to read the classification polygons if they exist +tryCatch({ + # Check if the classes.geojson file exists in the diagnostic directory + classes_file <- file.path(diagnostic_dir, "classes.geojson") + + # If no classes file in this directory, look for the most recent one + if (!file.exists(classes_file)) { + # Look in parent directory for most recent cloud_mask_diagnostics folder + potential_dirs <- list.dirs(path = dirname(diagnostic_dir), + full.names = TRUE, + recursive = FALSE) + + # Filter for diagnostic directories and find the most recent one that has classes.geojson + diagnostic_dirs <- potential_dirs[grepl("cloud_mask_diagnostics_", potential_dirs)] + + for (dir in rev(sort(diagnostic_dirs))) { # Reverse sort to get newest first + potential_file <- file.path(dir, "classes.geojson") + if (file.exists(potential_file)) { + classes_file <- potential_file + break + } + } + } + + # Check if we found a classes file + if (file.exists(classes_file)) { + safe_log(paste("Using classification polygons from:", classes_file)) + + # Load the classification polygons + classifications <- sf::st_read(classes_file, quiet = TRUE) %>% rename(class = type) + # Remove empty polygons + classifications <- classifications[!sf::st_is_empty(classifications), ] + + # Create a list to store all rasters we want to extract values from + extraction_rasters <- list( + # Original bands + red = red_band, + green = green_band, + blue = blue_band, + nir = nir_band, + + # Derived indices + brightness = brightness, + ndvi = ndvi, + blue_ratio = blue_ratio, + green_nir_ratio = green_nir_ratio, + ndwi = ndwi, + + # Cloud detection parameters + bright_pixels = terra::ifel(bright_pixels, 1, 0), + very_bright_pixels = terra::ifel(very_bright_pixels, 1, 0), + blue_dominant = terra::ifel(blue_dominant, 1, 0), + low_ndvi = terra::ifel(low_ndvi, 1, 0), + green_dominant_nir = terra::ifel(green_dominant_nir, 1, 0), + high_ndwi = terra::ifel(high_ndwi, 1, 0), + + # Shadow detection parameters + dark_pixels = terra::ifel(dark_pixels, 1, 0), + very_dark_pixels = terra::ifel(very_dark_pixels, 1, 0), + low_nir = terra::ifel(low_nir, 1, 0), + shadow_ndvi = terra::ifel(shadow_ndvi, 1, 0), + low_red_to_blue = terra::ifel(low_red_to_blue, 1, 0), + high_blue_to_nir_ratio = terra::ifel(high_blue_to_nir_ratio, 1, 0), + blue_nir_ratio_raw = (blue_band / (nir_band + 0.01)), + red_blue_ratio_raw = (red_band / (blue_band + 0.01)), + + # Edge detection parameters + brightness_focal_sd = brightness_focal_sd, + edge_pixels = terra::ifel(edge_pixels, 1, 0), + + # Final masks + cloud_mask = terra::ifel(cloud_mask, 1, 0), + shadow_mask = terra::ifel(shadow_mask, 1, 0), + combined_mask = terra::ifel(combined_mask, 1, 0), + dilated_mask = terra::ifel(dilated_mask, 1, 0) + ) + + # Create a stack of all rasters + extraction_stack <- terra::rast(extraction_rasters) + + # User-provided simplified extraction for mean statistics per polygon + pivot_stats_sf <- cbind( + classifications, + round(exactextractr::exact_extract(extraction_stack, classifications, fun = "mean", progress = FALSE), 2) + ) %>% + sf::st_drop_geometry() + + # Convert to a regular data frame for easier downstream processing + all_stats <- sf::st_drop_geometry(pivot_stats_sf) + + # Ensure 'class_name' column exists, if not, use 'class' as 'class_name' + if (!("class_name" %in% colnames(all_stats)) && ("class" %in% colnames(all_stats))) { + all_stats$class_name <- all_stats$class + + if (length(valid_class_ids) == 0) { + safe_log("No valid (non-NA) class IDs found for exactextractr processing.", "WARNING") + } + + for (class_id in valid_class_ids) { + # Subset polygons for this class + class_polygons_sf <- classifications[which(classifications$class == class_id), ] # Use which for NA-safe subsetting + + if (nrow(class_polygons_sf) == 0) { + safe_log(paste("Skipping empty class (no polygons after filtering):", class_id), "WARNING") + next + } + + tryCatch({ + safe_log(paste("Processing class:", class_id)) + + # Check if the polygon overlaps with the raster extent (check based on the combined extent of class polygons) + rast_extent <- terra::ext(extraction_stack) + poly_extent <- sf::st_bbox(class_polygons_sf) + + if (poly_extent["xmin"] > rast_extent["xmax"] || + poly_extent["xmax"] < rast_extent["xmin"] || + poly_extent["ymin"] > rast_extent["ymax"] || + poly_extent["ymax"] < rast_extent["ymin"]) { + safe_log(paste("Skipping class that doesn't overlap with raster:", class_id), "WARNING") + next + } + + # exact_extract will process each feature in class_polygons_sf + # and return a list of data frames (one per feature) + per_polygon_stats_list <- exactextractr::exact_extract( + extraction_stack, + class_polygons_sf, + function(values, coverage_fraction) { + # Filter pixels by coverage (e.g., >50% of the pixel is covered by the polygon) + valid_pixels_idx <- coverage_fraction > 0.5 + df_filtered <- values[valid_pixels_idx, , drop = FALSE] + + if (nrow(df_filtered) == 0) { + # If no pixels meet coverage, return a data frame with NAs + # to maintain structure, matching expected column names. + # Column names are derived from the extraction_stack + stat_cols <- paste0(names(extraction_stack), "_mean") + na_df <- as.data.frame(matrix(NA_real_, nrow = 1, ncol = length(stat_cols))) + names(na_df) <- stat_cols + return(na_df) + } + + # Calculate mean for each band (column in df_filtered) + stats_per_band <- lapply(names(df_filtered), function(band_name) { + col_data <- df_filtered[[band_name]] + if (length(col_data) > 0 && sum(!is.na(col_data)) > 0) { + mean_val <- mean(col_data, na.rm = TRUE) + return(setNames(mean_val, paste0(band_name, "_mean"))) + } else { + return(setNames(NA_real_, paste0(band_name, "_mean"))) + } + }) + + # Combine all stats (named values) into a single named vector then data frame + return(as.data.frame(t(do.call(c, stats_per_band)))) + }, + summarize_df = FALSE, # Important: get a list of DFs, one per polygon + force_df = TRUE # Ensure the output of the summary function is treated as a DF + ) + + # Combine all stats for this class if we have any + if (length(per_polygon_stats_list) > 0) { + # per_polygon_stats_list is now a list of single-row data.frames + class_stats_df <- do.call(rbind, per_polygon_stats_list) + + # Remove rows that are all NA (from polygons with no valid pixels) + class_stats_df <- class_stats_df[rowSums(is.na(class_stats_df)) < ncol(class_stats_df), ] + + if (nrow(class_stats_df) > 0) { + # Add class information + class_stats_df$class <- class_id + # Get class_name from the first polygon (assuming it's consistent for the class_id) + # Ensure class_polygons_sf is not empty before accessing class_name + if ("class_name" %in% names(class_polygons_sf) && nrow(class_polygons_sf) > 0) { + class_stats_df$class_name <- as.character(class_polygons_sf$class_name[1]) + } else { + class_stats_df$class_name <- as.character(class_id) # Fallback + } + + # Add to overall results + all_stats <- rbind(all_stats, class_stats_df) + safe_log(paste("Successfully extracted data for", nrow(class_stats_df), "polygons in class", class_id)) + } else { + safe_log(paste("No valid data extracted for class (after NA removal):", class_id), "WARNING") + } + } else { + safe_log(paste("No data frames returned by exact_extract for class:", class_id), "WARNING") + } + }, error = function(e) { + safe_log(paste("Error processing class", class_id, "with exact_extract:", e$message), "ERROR") + }) + } + # Save the extracted statistics to a CSV file + if (nrow(all_stats) > 0) { + stats_file <- file.path(diagnostic_dir, "class_spectral_stats_mean.csv") # New filename + write.csv(all_stats, stats_file, row.names = FALSE) + safe_log(paste("Saved MEAN spectral statistics by class to:", stats_file)) + } else { + safe_log("No statistics were generated to save.", "WARNING") + } + + # Calculate optimized thresholds for cloud/shadow detection (using only _mean columns) + if (nrow(all_stats) > 0 && ncol(all_stats) > 2) { # Check if all_stats has data and parameter columns + threshold_results <- data.frame( + parameter = character(), + best_threshold = numeric(), + direction = character(), + target_class = character(), + vs_class = character(), + accuracy = numeric(), + stringsAsFactors = FALSE + ) + + # Define class pairs to analyze + class_pairs <- list( + # Cloud vs various surfaces + c("cloud", "crop"), + c("cloud", "bare_soil_dry"), + c("cloud", "bare_soil_wet"), + + # Shadow vs various surfaces + c("shadow_over_crop", "crop"), + c("shadow_over_bare_soil", "bare_soil_dry"), + c("shadow_over_bare_soil", "bare_soil_wet") + ) + + # For now, let's assume all _mean parameters derived from extraction_rasters are relevant for clouds/shadows + # This part might need more specific logic if you want to distinguish cloud/shadow params cloud_params <- grep("_mean$", names(extraction_rasters), value = TRUE) + params logic + # Parameters to analyze for shadows (now only _mean versions)tatistics by class to:", stats_file)) + shadow_params <- cloud_params # Simplified: using the same set for now, adjust if specific shadow params are needed +lds for cloud/shadow detection + # Find optimal thresholdsframe( + if (length(class_pairs) > 0 && (length(cloud_params) > 0 || length(shadow_params) > 0)) { + for (pair in class_pairs) {c(), + target_class <- pair[1](), + vs_class <- pair[2](), + vs_class = character(), + # Select appropriate parameters based on whether we're analyzing clouds or shadows accuracy = numeric(), + if (grepl("cloud", target_class)) { + params_to_check <- cloud_params + } else { + params_to_check <- shadow_paramsto analyze + } + + # For each parameter, find the best threshold to separate the classesc("cloud", "crop"), + for (param in params_to_check) { + if (param %in% colnames(all_stats)) { + # Get values for both classes + target_values <- all_stats[all_stats$class_name == target_class, param] + vs_values <- all_stats[all_stats$class_name == vs_class, param] c("shadow_over_crop", "crop"), + c("shadow_over_bare_soil", "bare_soil_dry"), + if (length(target_values) > 0 && length(vs_values) > 0) {_soil_wet") + # Calculate mean and sd for both classes + target_mean <- mean(target_values, na.rm = TRUE) + target_sd <- sd(target_values, na.rm = TRUE)# Parameters to analyze for clouds + vs_mean <- mean(vs_values, na.rm = TRUE), "blue_ratio_mean", "ndvi_mean", + vs_sd <- sd(vs_values, na.rm = TRUE) + + + # Determine if higher or lower values indicate the target classshadow_params <- c("brightness_mean", "dark_pixels_mean", "very_dark_pixels_mean", + if (target_mean > vs_mean) {r_mean", "shadow_ndvi_mean", "blue_nir_ratio_raw_mean", + direction <- ">"_ratio_raw_mean", "low_red_to_blue_mean") + # Try different thresholds + potential_thresholds <- seq(olds + min(min(target_values, na.rm = TRUE), vs_mean + 0.5 * vs_sd),r (pair in class_pairs) { + max(max(vs_values, na.rm = TRUE), target_mean - 0.5 * target_sd), + length.out = 20 + ) + } else { appropriate parameters based on whether we're analyzing clouds or shadows + direction <- "<"{ + # Try different thresholds params_to_check <- cloud_params + potential_thresholds <- seq(} else { + min(min(vs_values, na.rm = TRUE), target_mean + 0.5 * target_sd), + max(max(target_values, na.rm = TRUE), vs_mean - 0.5 * vs_sd), + length.out = 20 + )st threshold to separate the classes + } + + # Calculate accuracy for each threshold# Get values for both classes + best_accuracy <- 0_class, param] + best_threshold <- ifelse(direction == ">", min(potential_thresholds), max(potential_thresholds))e == vs_class, param] + + for (threshold in potential_thresholds) {ues) > 0) { + if (direction == ">") { + correct_target <- sum(target_values > threshold, na.rm = TRUE)a.rm = TRUE) + correct_vs <- sum(vs_values <= threshold, na.rm = TRUE)target_sd <- sd(target_values, na.rm = TRUE) + } else { vs_mean <- mean(vs_values, na.rm = TRUE) + correct_target <- sum(target_values < threshold, na.rm = TRUE) + correct_vs <- sum(vs_values >= threshold, na.rm = TRUE) + } + er values indicate the target class + total_target <- length(target_values) + total_vs <- length(vs_values) + + accuracy <- (correct_target + correct_vs) / (total_target + total_vs)lds <- seq( + min(min(target_values, na.rm = TRUE), vs_mean + 0.5 * vs_sd), + if (accuracy > best_accuracy) {max(vs_values, na.rm = TRUE), target_mean - 0.5 * target_sd), + best_accuracy <- accuracy0 + best_threshold <- threshold + } + } + + # Add to resultslds <- seq( + threshold_results <- rbind(threshold_results, data.frame( min(min(vs_values, na.rm = TRUE), target_mean + 0.5 * target_sd), + parameter = gsub("_mean", "", param), max(max(target_values, na.rm = TRUE), vs_mean - 0.5 * vs_sd), + best_threshold = best_threshold, length.out = 20 + direction = direction, + target_class = target_class, + vs_class = vs_class, + accuracy = best_accuracy,# Calculate accuracy for each threshold + stringsAsFactors = FALSE + ))direction == ">", min(potential_thresholds), max(potential_thresholds)) + } + } + }ction == ">") { + } + } + else { + # Save threshold results correct_target <- sum(target_values < threshold, na.rm = TRUE) + thresholds_file <- file.path(diagnostic_dir, "optimal_thresholds.csv")shold, na.rm = TRUE) + write.csv(threshold_results, thresholds_file, row.names = FALSE) + safe_log(paste("Saved optimal threshold recommendations to:", thresholds_file)) + + # Generate box plots for key parameters to visualize class differencestotal_vs <- length(vs_values) + if (requireNamespace("ggplot2", quietly = TRUE) && nrow(all_stats) > 0) { + # Reshape data for plotting (only _mean columns) + correct_vs) / (total_target + total_vs) + mean_cols <- grep("_mean$", colnames(all_stats), value = TRUE) + if (length(mean_cols) > 0) {f (accuracy > best_accuracy) { + plot_data <- reshape2::melt(all_stats, best_accuracy <- accuracy + id.vars = c("class", "class_name"), best_threshold <- threshold + measure.vars = mean_cols, # Use only _mean columns + variable.name = "parameter", + value.name = "value") + + # Create directory for plotsnd(threshold_results, data.frame( + plots_dir <- file.path(diagnostic_dir, "class_plots"), param), + dir.create(plots_dir, showWarnings = FALSE, recursive = TRUE)t_threshold, + + # Create plots for selected key parameters (ensure they are _mean versions)ass, + # Adjust key_params to reflect the new column names (e.g., "brightness_mean")vs_class = vs_class, + key_params_plot <- intersect(c( accuracy = best_accuracy, + "brightness_mean", "ndvi_mean", "blue_ratio_mean", "ndwi_mean", stringsAsFactors = FALSE + "blue_nir_ratio_raw_mean", "red_blue_ratio_raw_mean" )) + ), mean_cols) # Ensure these params exist } + } + for (param in key_params_plot) { + # param_data <- plot_data[plot_data$parameter == param,] # Exact match for parameter + # No, grepl was fine if plot_data only contains _mean parameters now. + # Let's ensure plot_data only has the _mean parameters for simplicity here. + param_data <- plot_data[plot_data$parameter == param, ]thresholds_file <- file.path(diagnostic_dir, "optimal_thresholds.csv") + + if (nrow(param_data) > 0) {file)) + param_name <- gsub("_mean", "", param) + o visualize class differences + p <- ggplot2::ggplot(param_data, ggplot2::aes(x = class_name, y = value, fill = class_name)) +s) > 0) { + ggplot2::geom_boxplot() + + ggplot2::theme_minimal() + + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) + id.vars = c("class", "class_name"), + ggplot2::labs(ariable.name = "parameter", + title = paste("Distribution of", param_name, "by Land Cover Class"), + x = "Class", + y = param_name,# Create directory for plots + fill = "Class"ass_plots") + )_dir, showWarnings = FALSE, recursive = TRUE) + + # Save the plot + plot_file <- file.path(plots_dir, paste0("boxplot_", param_name, ".png"))ey_params <- c( + ggplot2::ggsave(plot_file, p, width = 10, height = 6, dpi = 150) "brightness_mean", "ndvi_mean", "blue_ratio_mean", "ndwi_mean", + }, "red_blue_ratio_raw_mean" + } + + # Create a summary plot showing multiple parameters + summary_data <- plot_data[plot_data$parameter %in% ram_data <- plot_data[grepl(param, plot_data$parameter),] + c("brightness_mean", "ndvi_mean", + "blue_nir_ratio_raw_mean", "red_blue_ratio_raw_mean"),] "", param) + + if (nrow(summary_data) > 0) {= class_name)) + + # Clean up parameter names for displayboxplot() + + summary_data$parameter <- gsub("_mean$", "", summary_data$parameter) # Remove _mean suffix for display + summary_data$parameter <- gsub("_raw$", "", summary_data$parameter) # Keep this if _raw_mean was a thing(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) + + + # Create faceted plot"Distribution of", param_name, "by Land Cover Class"), + p <- ggplot2::ggplot(summary_data, x = "Class", + ggplot2::aes(x = class_name, y = value, fill = class_name)) + y = param_name, + ggplot2::geom_boxplot() +ss" + ggplot2::facet_wrap(~parameter, scales = "free_y") + + ggplot2::theme_minimal() + + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) + # Save the plot + ggplot2::labs( plot_file <- file.path(plots_dir, paste0("boxplot_", param_name, ".png")) + title = "Key Spectral Parameters by Land Cover Class", ggplot2::ggsave(plot_file, p, width = 10, height = 6, dpi = 150) + x = "Class", + y = "Value", + fill = "Class" + ) + summary_data <- plot_data[plot_data$parameter %in% + # Save the summary plot"brightness_mean", "ndvi_mean", + summary_file <- file.path(plots_dir, "spectral_parameters_summary.png")atio_raw_mean", "red_blue_ratio_raw_mean"),] + ggplot2::ggsave(summary_file, p, width = 12, height = 8, dpi = 150) + } + # Clean up parameter names for display + safe_log(paste("Generated spectral parameter plots in:", plots_dir))r <- gsub("_mean", "", summary_data$parameter) + }w", "", summary_data$parameter) + } else { + safe_log("Package 'exactextractr' not available. Install it for more accurate polygon extraction.", "WARNING") + + # Fall back to simple extraction using terra (calculating only mean)::aes(x = class_name, y = value, fill = class_name)) + + class_stats <- data.frame() + _wrap(~parameter, scales = "free_y") + + valid_class_names_fallback <- unique(classifications$class_name) + valid_class_names_fallback <- valid_class_names_fallback[!is.na(valid_class_names_fallback)](axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) + + + if (length(valid_class_names_fallback) == 0) {pectral Parameters by Land Cover Class", + safe_log("No valid (non-NA) class names found for fallback terra::extract processing.", "WARNING") x = "Class", + } y = "Value", + + for (class_name_fb in valid_class_names_fallback) { + class_polygons_fb <- classifications[which(classifications$class_name == class_name_fb), ] + # Save the summary plot + if(nrow(class_polygons_fb) == 0) next summary_file <- file.path(plots_dir, "spectral_parameters_summary.png") +) + class_vect_fb <- terra::vect(class_polygons_fb) } + + # Extract values for each raster + for (i in seq_along(extraction_rasters)) {} + raster_name <- names(extraction_rasters)[i] + # terra::extract returns a data.frame with ID and layer valuesractr' not available. Install it for more accurate polygon extraction.", "WARNING") + # For multiple polygons, it will have multiple rows per polygon if ID is not unique + # We need to aggregate per polygon, then per class if not already handled by exact_extract style + # However, for simplicity here, let's assume terra::extract gives one value per polygon for the mean + # This part of fallback might need more robust aggregation if polygons are complex + r (class_name in unique(classifications$class_name)) { + # A more robust terra::extract approach for means per polygon:s[classifications$class_name == class_name, ] + extracted_values_list <- terra::extract(extraction_rasters[[i]], class_vect_fb, fun = mean, na.rm = TRUE, ID = FALSE) + # extracted_values_list will be a data.frame with one column (the layer) and rows corresponding to polygons + + if (nrow(extracted_values_list) > 0 && ncol(extracted_values_list) > 0) {r (i in seq_along(extraction_rasters)) { + # Average over all polygons in this class for this rastertraction_rasters)[i] + mean_val_for_class <- mean(extracted_values_list[[1]], na.rm = TRUE)ct(extraction_rasters[[i]], class_vect) + + if (!is.na(mean_val_for_class)) { + stats_row <- data.frame( + class_name = class_name_fb, # Using class_name as the identifier here + parameter = paste0(raster_name, "_mean"), + value = mean_val_for_class), + ) TRUE), + class_stats <- rbind(class_stats, stats_row) sd = sd(values[,2], na.rm = TRUE), + } min = min(values[,2], na.rm = TRUE), + } + } ) + } + class_stats <- rbind(class_stats, stats) + # Save the statistics (if any were generated) } + if(nrow(class_stats) > 0) { + # Reshape class_stats from long to wide for consistency if needed, or save as is. + # For now, save as long format. + stats_file <- file.path(diagnostic_dir, "class_spectral_stats_simple_mean_long.csv") + write.csv(class_stats, stats_file, row.names = FALSE) stats_file <- file.path(diagnostic_dir, "class_spectral_stats_simple.csv") + safe_log(paste("Saved simple MEAN (long format) spectral statistics by class to:", stats_file)) write.csv(class_stats, stats_file, row.names = FALSE) + } else {e spectral statistics by class to:", stats_file)) + safe_log("No statistics generated by fallback method.", "WARNING") + } + }ve RMarkdown generation + + # Remove RMarkdown generation + # safe_log("RMarkdown report generation has been removed as per user request.") + NING") + } else {} + safe_log("No classification polygons file (classes.geojson) found. Skipping spectral analysis.", "WARNING")}, error = function(e) { + }cessing or spectral analysis:", e$message), "ERROR") +}, error = function(e) {}) + safe_log(paste("Error in classification polygon processing or spectral analysis:", e$message), "ERROR") +}) detection analysis script finished.") + +safe_log("Cloud detection analysis script finished.")# Clean up workspace +rm(list = ls()) +# Clean up workspace +rm(list = ls()) + + + + + + + + + diff --git a/r_app/experiments/delete_cloud_exploratoin.R b/r_app/experiments/delete_cloud_exploratoin.R new file mode 100644 index 0000000..356258e --- /dev/null +++ b/r_app/experiments/delete_cloud_exploratoin.R @@ -0,0 +1,191 @@ +```r +# Cloud detection analysis script + +# Load necessary libraries +library(terra) +library(exactextractr) +library(sf) +library(dplyr) +library(ggplot2) +library(tidyr) +library(reshape2) + +# Define file paths (these should be set to your actual file locations) +classes_file <- "path/to/classes.geojson" +rasters_dir <- "path/to/rasters" +diagnostic_dir <- "path/to/diagnostics" + +# Helper function for logging +safe_log <- function(message, level = "INFO") { + timestamp <- format(Sys.time(), "%Y-%m-%d %H:%M:%S") + cat(paste0("[", timestamp, "] [", level, "] ", message, "\n")) +} + +# Main processing block +# Load classification polygons +safe_log(paste("Loading classification polygons from:", classes_file)) +classifications <- sf::st_read(classes_file, quiet = TRUE) +# Ensure the CRS is set (assuming WGS84 here, adjust if necessary) +safe_log("No CRS found for the classifications. Setting to WGS84 (EPSG:4326).", "WARNING") +sf::st_crs(classifications) <- 4326 +# List all raster files in the directory +raster_files <- list.files(rasters_dir, pattern = "\\.tif$", full.names = TRUE) +# Create a named vector for extraction_rasters based on base names +extraction_rasters <- setNames(raster_files, tools::file_path_sans_ext(basename(raster_files))) +# Create a stack of all rasters +extraction_stack <- terra::rast(extraction_rasters) +# User-provided simplified extraction for mean statistics per polygon +safe_log("Extracting mean statistics per polygon using exactextractr...") +all_stats <- cbind( + classifications, + round(exactextractr::exact_extract(extraction_stack, classifications, fun = "mean", progress = FALSE), 2) +) %>% + sf::st_drop_geometry() # Ensures all_stats is a data frame +# Ensure 'class_name' column exists, if not, use 'class' as 'class_name' +all_stats$class_name <- all_stats$class + +# Save the extracted statistics to a CSV file +stats_file <- file.path(diagnostic_dir, "polygon_mean_spectral_stats.csv") +write.csv(all_stats, stats_file, row.names = FALSE) + + + +safe_log(paste("Saved mean spectral statistics per polygon to:", stats_file)) +# Calculate optimized thresholds for cloud/shadow detection +threshold_results <- data.frame( + parameter = character(), + best_threshold = numeric(), + direction = character(), + target_class = character(), + vs_class = character(), + accuracy = numeric(), + stringsAsFactors = FALSE +) +class_pairs <- list( + c("cloud", "crop"), + c("cloud", "bare_soil_dry"), + c("cloud", "bare_soil_wet"), + c("shadow_over_crop", "crop"), + c("shadow_over_bare_soil", "bare_soil_dry"), + c("shadow_over_bare_soil", "bare_soil_wet") +) +cloud_detection_params_for_threshold <- intersect( + c("mean.brightness", "mean.very_bright_pixels", "mean.blue_dominant", "mean.low_ndvi", "mean.green_dominant_nir", "mean.high_ndwi", "mean.blue_ratio", "mean.ndvi"), + colnames(all_stats) +) +shadow_detection_params_for_threshold <- intersect( + c("mean.brightness", "mean.dark_pixels", "mean.very_dark_pixels", "mean.low_nir", "mean.shadow_ndvi", "mean.low_red_to_blue", "mean.high_blue_to_nir_ratio", "mean.blue_nir_ratio_raw", "mean.red_blue_ratio_raw"), + colnames(all_stats) +) +for (pair in class_pairs) { + target_class <- pair[1] + vs_class <- pair[2] + params_to_check <- c(cloud_detection_params_for_threshold, shadow_detection_params_for_threshold) + for (param in params_to_check) { + target_values <- all_stats[all_stats$class_name == target_class, param] + vs_values <- all_stats[all_stats$class_name == vs_class, param] + target_values <- target_values[!is.na(target_values)] + vs_values <- vs_values[!is.na(vs_values)] + # Only proceed if both groups have at least one value + if (length(target_values) > 0 && length(vs_values) > 0) { + target_mean <- mean(target_values) + target_sd <- sd(target_values) + vs_mean <- mean(vs_values) + vs_sd <- sd(vs_values) + target_sd[is.na(target_sd)] <- 0 + vs_sd[is.na(vs_sd)] <- 0 + direction <- ifelse(target_mean > vs_mean, ">", "<") + all_values <- c(target_values, vs_values) + min_val <- min(all_values) + max_val <- max(all_values) + # Only proceed if min and max are finite and not equal + if (is.finite(min_val) && is.finite(max_val) && min_val != max_val) { + potential_thresholds <- seq(min_val, max_val, length.out = 20) + best_accuracy <- -1 + best_threshold <- ifelse(direction == ">", min(potential_thresholds), max(potential_thresholds)) + for (threshold in potential_thresholds) { + if (direction == ">") { + correct_target <- sum(target_values > threshold) + correct_vs <- sum(vs_values <= threshold) + } else { + correct_target <- sum(target_values < threshold) + correct_vs <- sum(vs_values >= threshold) + } + accuracy <- (correct_target + correct_vs) / (length(target_values) + length(vs_values)) + if (accuracy > best_accuracy) { + best_accuracy <- accuracy + best_threshold <- threshold + } + } + threshold_results <- rbind(threshold_results, data.frame( + parameter = param, + best_threshold = best_threshold, + direction = direction, + target_class = target_class, + vs_class = vs_class, + accuracy = best_accuracy, + stringsAsFactors = FALSE + )) + } + } + } +} + +thresholds_file <- file.path(diagnostic_dir, "optimal_thresholds.csv") +write.csv(threshold_results, thresholds_file, row.names = FALSE) + +safe_log(paste("Saved optimal threshold recommendations to:", thresholds_file)) + +# Fix: get plot_measure_cols by matching raster base names to all_stats columns with 'mean.' prefix +plot_measure_cols <- intersect(names(extraction_rasters), gsub('^mean\\.', '', colnames(all_stats))) +plot_data <- reshape2::melt( + all_stats, + id.vars = c("class", "class_name"), + measure.vars = paste0("mean.", plot_measure_cols), + variable.name = "parameter", + value.name = "value" +) +# Remove 'mean.' prefix from parameter column for clarity +plot_data$parameter <- sub("^mean\\.", "", plot_data$parameter) + +plots_dir <- file.path(diagnostic_dir, "class_plots") +dir.create(plots_dir, showWarnings = FALSE, recursive = TRUE) +key_params_for_plot_list <- c("brightness", "ndvi", "blue_ratio", "ndwi", + "blue_nir_ratio_raw", "red_blue_ratio_raw") +key_params_to_plot <- intersect(key_params_for_plot_list, plot_measure_cols) +for (param_to_plot in key_params_to_plot) { + param_data_subset <- plot_data[plot_data$parameter == param_to_plot, ] + p <- ggplot2::ggplot(param_data_subset, ggplot2::aes(x = class_name, y = value, fill = class_name)) + + ggplot2::geom_boxplot() + + ggplot2::theme_minimal() + + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) + + ggplot2::labs( + title = paste("Distribution of", param_to_plot, "by Land Cover Class"), + x = "Class", + y = param_to_plot, + fill = "Class" + ) + plot_file <- file.path(plots_dir, paste0("boxplot_", param_to_plot, ".png")) + ggplot2::ggsave(plot_file, p, width = 10, height = 6, dpi = 150) +} +summary_params_for_plot_list <- c("brightness", "ndvi", + "blue_nir_ratio_raw", "red_blue_ratio_raw") +summary_params_to_plot <- intersect(summary_params_for_plot_list, plot_measure_cols) +summary_data_subset <- plot_data[plot_data$parameter %in% summary_params_to_plot,] +p_summary <- ggplot2::ggplot(summary_data_subset, ggplot2::aes(x = class_name, y = value, fill = class_name)) + + ggplot2::geom_boxplot() + + ggplot2::facet_wrap(~parameter, scales = "free_y") + + ggplot2::theme_minimal() + + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1), + strip.text = ggplot2::element_text(size = 8)) + + ggplot2::labs( + title = "Summary of Key Spectral Parameters by Land Cover Class", + x = "Class", + y = "Value", + fill = "Class" + ) +summary_file <- file.path(plots_dir, "spectral_parameters_summary.png") +ggplot2::ggsave(summary_file, p_summary, width = 12, height = 8, dpi = 150) +safe_log(paste("Generated spectral parameter plots in:", plots_dir)) +safe_log("Cloud detection analysis script finished.") +``` \ No newline at end of file diff --git a/r_app/experiments/executive_summary/CI_report_executive_summary.Rmd b/r_app/experiments/executive_summary/CI_report_executive_summary.Rmd new file mode 100644 index 0000000..2d4c014 --- /dev/null +++ b/r_app/experiments/executive_summary/CI_report_executive_summary.Rmd @@ -0,0 +1,718 @@ +--- +params: + ref: "word-styles-reference-var1.docx" + output_file: CI_report.docx + report_date: "2024-08-28" + data_dir: "Chemba" + mail_day: "Wednesday" + borders: TRUE + use_breaks: FALSE +output: + # html_document: + # toc: yes + # df_print: paged + word_document: + reference_docx: !expr file.path("word-styles-reference-var1.docx") + toc: yes +editor_options: + chunk_output_type: console +--- + +```{r setup_parameters, include=FALSE} +# Set up basic report parameters from input values +report_date <- params$report_date +mail_day <- params$mail_day +borders <- params$borders +use_breaks <- params$use_breaks # Whether to use breaks or continuous spectrum in visualizations + +# Environment setup notes (commented out) +# # Activeer de renv omgeving +# renv::activate() +# renv::deactivate() +# # Optioneel: Herstel de omgeving als dat nodig is +# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren +# renv::restore() +``` + +```{r load_libraries, message=FALSE, warning=FALSE, include=FALSE} +# Configure knitr options +knitr::opts_chunk$set(warning = FALSE, message = FALSE) + +# Path management +library(here) + +# Spatial data libraries +library(sf) +library(terra) +library(exactextractr) +# library(raster) - Removed as it's no longer maintained + +# Data manipulation and visualization +library(tidyverse) # Includes dplyr, ggplot2, etc. +library(tmap) +library(lubridate) +library(zoo) + +# Machine learning +library(rsample) +library(caret) +library(randomForest) +library(CAST) + +# Load custom utility functions +tryCatch({ + source("report_utils.R") +}, error = function(e) { + message(paste("Error loading report_utils.R:", e$message)) + # Try alternative path if the first one fails + tryCatch({ + source(here::here("r_app", "report_utils.R")) + }, error = function(e) { + stop("Could not load report_utils.R from either location: ", e$message) + }) +}) + +# Load executive report utilities +tryCatch({ + source("executive_report_utils.R") +}, error = function(e) { + message(paste("Error loading executive_report_utils.R:", e$message)) + # Try alternative path if the first one fails + tryCatch({ + source(here::here("r_app", "executive_report_utils.R")) + }, error = function(e) { + stop("Could not load executive_report_utils.R from either location: ", e$message) + }) +}) + +safe_log("Successfully loaded utility functions") +``` + +```{r initialize_project_config, message=FALSE, warning=FALSE, include=FALSE} +# Set the project directory from parameters +project_dir <- params$data_dir + +# Source project parameters with error handling +tryCatch({ + source(here::here("r_app", "parameters_project.R")) +}, error = function(e) { + stop("Error loading parameters_project.R: ", e$message) +}) + +# Log initial configuration +safe_log("Starting the R Markdown script") +safe_log(paste("mail_day params:", params$mail_day)) +safe_log(paste("report_date params:", params$report_date)) +safe_log(paste("mail_day variable:", mail_day)) +``` + +```{r calculate_dates_and_weeks, message=FALSE, warning=FALSE, include=FALSE} +# Set locale for consistent date formatting +Sys.setlocale("LC_TIME", "C") + +# Initialize date variables from parameters +today <- as.character(report_date) +mail_day_as_character <- as.character(mail_day) + +# Calculate week days +report_date_as_week_day <- weekdays(lubridate::ymd(today)) +days_of_week <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday") + +# Calculate initial week number +week <- lubridate::week(today) +safe_log(paste("Initial week calculation:", week, "today:", today)) + +# Calculate previous dates for comparisons +today_minus_1 <- as.character(lubridate::ymd(today) - 7) +today_minus_2 <- as.character(lubridate::ymd(today) - 14) +today_minus_3 <- as.character(lubridate::ymd(today) - 21) + +# Log the weekday calculations for debugging +safe_log(paste("Report date weekday:", report_date_as_week_day)) +safe_log(paste("Weekday index:", which(days_of_week == report_date_as_week_day))) +safe_log(paste("Mail day:", mail_day_as_character)) +safe_log(paste("Mail day index:", which(days_of_week == mail_day_as_character))) + +# Adjust week calculation based on mail day +if (which(days_of_week == report_date_as_week_day) > which(days_of_week == mail_day_as_character)) { + safe_log("Adjusting weeks because of mail day") + week <- lubridate::week(today) + 1 + today_minus_1 <- as.character(lubridate::ymd(today)) + today_minus_2 <- as.character(lubridate::ymd(today) - 7) + today_minus_3 <- as.character(lubridate::ymd(today) - 14) +} + +# Generate subtitle for report +subtitle_var <- paste("Report generated on", Sys.Date()) + +# Calculate week numbers for previous weeks +week_minus_1 <- week - 1 +week_minus_2 <- week - 2 +week_minus_3 <- week - 3 + +# Format current week with leading zeros +week <- sprintf("%02d", week) + +# Get years for each date +year <- lubridate::year(today) +year_1 <- lubridate::year(today_minus_1) +year_2 <- lubridate::year(today_minus_2) +year_3 <- lubridate::year(today_minus_3) +``` + +```{r data, message=TRUE, warning=TRUE, include=FALSE} +# Load CI index data with error handling +tryCatch({ + CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds")) + safe_log("Successfully loaded CI quadrant data") +}, error = function(e) { + stop("Error loading CI quadrant data: ", e$message) +}) + +# Get file paths for different weeks using the utility function +tryCatch({ + path_to_week_current = get_week_path(weekly_CI_mosaic, today, 0) + path_to_week_minus_1 = get_week_path(weekly_CI_mosaic, today, -1) + path_to_week_minus_2 = get_week_path(weekly_CI_mosaic, today, -2) + path_to_week_minus_3 = get_week_path(weekly_CI_mosaic, today, -3) + + # Log the calculated paths + safe_log("Required mosaic paths:") + safe_log(paste("Path to current week:", path_to_week_current)) + safe_log(paste("Path to week minus 1:", path_to_week_minus_1)) + safe_log(paste("Path to week minus 2:", path_to_week_minus_2)) + safe_log(paste("Path to week minus 3:", path_to_week_minus_3)) + + # Validate that files exist + if (!file.exists(path_to_week_current)) warning("Current week mosaic file does not exist: ", path_to_week_current) + if (!file.exists(path_to_week_minus_1)) warning("Week minus 1 mosaic file does not exist: ", path_to_week_minus_1) + if (!file.exists(path_to_week_minus_2)) warning("Week minus 2 mosaic file does not exist: ", path_to_week_minus_2) + if (!file.exists(path_to_week_minus_3)) warning("Week minus 3 mosaic file does not exist: ", path_to_week_minus_3) + + # Load raster data with terra functions + CI <- terra::rast(path_to_week_current)$CI + CI_m1 <- terra::rast(path_to_week_minus_1)$CI + CI_m2 <- terra::rast(path_to_week_minus_2)$CI + CI_m3 <- terra::rast(path_to_week_minus_3)$CI + +}, error = function(e) { + stop("Error loading raster data: ", e$message) +}) +``` + +```{r calculate_difference_rasters, message=TRUE, warning=TRUE, include=FALSE} +# Calculate difference rasters for comparisons +tryCatch({ + # Calculate weekly difference + last_week_dif_raster_abs <- (CI - CI_m1) + safe_log("Calculated weekly difference raster") + + # Calculate three-week difference + three_week_dif_raster_abs <- (CI - CI_m3) + safe_log("Calculated three-week difference raster") +}, error = function(e) { + safe_log(paste("Error calculating difference rasters:", e$message), "ERROR") + # Create placeholder rasters if calculations fail + if (!exists("last_week_dif_raster_abs")) { + last_week_dif_raster_abs <- CI * 0 + } + if (!exists("three_week_dif_raster_abs")) { + three_week_dif_raster_abs <- CI * 0 + } +}) +``` + +```{r load_field_boundaries, message=TRUE, warning=TRUE, include=FALSE} +# Load field boundaries from parameters +tryCatch({ + AllPivots0 <- field_boundaries_sf + safe_log("Successfully loaded field boundaries") +}, error = function(e) { + stop("Error loading field boundaries: ", e$message) +}) +``` + +```{r create_farm_health_data, message=FALSE, warning=FALSE, include=FALSE} +# Create farm health summary data from scratch +tryCatch({ + # Ensure we have the required data + if (!exists("AllPivots0") || !exists("CI") || !exists("CI_m1") || !exists("harvesting_data")) { + stop("Required input data (field boundaries, CI data, or harvesting data) not available") + } + + safe_log("Starting to calculate farm health data") + + # Get unique field names + fields <- unique(AllPivots0$field) + safe_log(paste("Found", length(fields), "unique fields")) + + # Initialize result dataframe + farm_health_data <- data.frame( + field = character(), + mean_ci = numeric(), + ci_change = numeric(), + ci_uniformity = numeric(), + status = character(), + anomaly_type = character(), + priority_level = numeric(), + age_weeks = numeric(), + harvest_readiness = character(), + stringsAsFactors = FALSE + ) + + # Process each field with robust error handling + for (field_name in fields) { + tryCatch({ + safe_log(paste("Processing field:", field_name)) + + # Get field boundary + field_shape <- AllPivots0 %>% dplyr::filter(field == field_name) + + # Skip if field shape is empty + if (nrow(field_shape) == 0) { + safe_log(paste("Empty field shape for", field_name), "WARNING") + next + } + + # Get field age from harvesting data - use direct filtering to avoid dplyr errors + field_age_data <- NULL + if (exists("harvesting_data") && !is.null(harvesting_data) && nrow(harvesting_data) > 0) { + field_age_data <- harvesting_data[harvesting_data$field == field_name, ] + if (nrow(field_age_data) > 0) { + field_age_data <- field_age_data[order(field_age_data$season_start, decreasing = TRUE), ][1, ] + } + } + + # Default age if not available + field_age_weeks <- if (!is.null(field_age_data) && nrow(field_age_data) > 0 && !is.na(field_age_data$age)) { + field_age_data$age + } else { + 10 # Default age + } + + # Extract CI values using terra's extract function which is more robust + ci_values <- terra::extract(CI, field_shape) + ci_prev_values <- terra::extract(CI_m1, field_shape) + + # Check if we got valid data + if (nrow(ci_values) == 0 || nrow(ci_prev_values) == 0) { + safe_log(paste("No CI data extracted for field", field_name), "WARNING") + # Add a placeholder row with Unknown status + farm_health_data <- rbind(farm_health_data, data.frame( + field = field_name, + mean_ci = NA, + ci_change = NA, + ci_uniformity = NA, + status = "Unknown", + anomaly_type = "Unknown", + priority_level = 5, # Low priority + age_weeks = field_age_weeks, + harvest_readiness = "Unknown", + stringsAsFactors = FALSE + )) + next + } + + # Calculate metrics - Handle NA values properly + ci_column <- if ("CI" %in% names(ci_values)) "CI" else colnames(ci_values)[1] + ci_prev_column <- if ("CI" %in% names(ci_prev_values)) "CI" else colnames(ci_prev_values)[1] + + mean_ci <- mean(ci_values[[ci_column]], na.rm=TRUE) + mean_ci_prev <- mean(ci_prev_values[[ci_prev_column]], na.rm=TRUE) + ci_change <- mean_ci - mean_ci_prev + ci_sd <- sd(ci_values[[ci_column]], na.rm=TRUE) + ci_uniformity <- ci_sd / max(0.1, mean_ci) # Avoid division by zero + + # Handle NaN or Inf results + if (is.na(mean_ci) || is.na(ci_change) || is.na(ci_uniformity) || + is.nan(mean_ci) || is.nan(ci_change) || is.nan(ci_uniformity) || + is.infinite(mean_ci) || is.infinite(ci_change) || is.infinite(ci_uniformity)) { + safe_log(paste("Invalid calculation results for field", field_name), "WARNING") + # Add a placeholder row with Unknown status + farm_health_data <- rbind(farm_health_data, data.frame( + field = field_name, + mean_ci = NA, + ci_change = NA, + ci_uniformity = NA, + status = "Unknown", + anomaly_type = "Unknown", + priority_level = 5, # Low priority + age_weeks = field_age_weeks, + harvest_readiness = "Unknown", + stringsAsFactors = FALSE + )) + next + } + + # Determine field status + status <- dplyr::case_when( + mean_ci >= 5 ~ "Excellent", + mean_ci >= 3.5 ~ "Good", + mean_ci >= 2 ~ "Fair", + mean_ci >= 1 ~ "Poor", + TRUE ~ "Critical" + ) + + # Determine anomaly type + anomaly_type <- dplyr::case_when( + ci_change > 2 ~ "Potential Weed Growth", + ci_change < -2 ~ "Potential Weeding/Harvesting", + ci_uniformity > 0.5 ~ "High Variability", + mean_ci < 1 ~ "Low Vigor", + TRUE ~ "None" + ) + + # Calculate priority level (1-5, with 1 being highest priority) + priority_score <- dplyr::case_when( + mean_ci < 1 ~ 1, # Critical - highest priority + anomaly_type == "Potential Weed Growth" ~ 2, + anomaly_type == "High Variability" ~ 3, + ci_change < -1 ~ 4, + TRUE ~ 5 # No urgent issues + ) + + # Determine harvest readiness + harvest_readiness <- dplyr::case_when( + field_age_weeks >= 52 & mean_ci >= 4 ~ "Ready for harvest", + field_age_weeks >= 48 & mean_ci >= 3.5 ~ "Approaching harvest", + field_age_weeks >= 40 & mean_ci >= 3 ~ "Mid-maturity", + field_age_weeks >= 12 ~ "Growing", + TRUE ~ "Early stage" + ) + + # Add to summary data + farm_health_data <- rbind(farm_health_data, data.frame( + field = field_name, + mean_ci = round(mean_ci, 2), + ci_change = round(ci_change, 2), + ci_uniformity = round(ci_uniformity, 2), + status = status, + anomaly_type = anomaly_type, + priority_level = priority_score, + age_weeks = field_age_weeks, + harvest_readiness = harvest_readiness, + stringsAsFactors = FALSE + )) + + }, error = function(e) { + safe_log(paste("Error processing field", field_name, ":", e$message), "ERROR") + # Add a placeholder row with Error status + farm_health_data <<- rbind(farm_health_data, data.frame( + field = field_name, + mean_ci = NA, + ci_change = NA, + ci_uniformity = NA, + status = "Unknown", + anomaly_type = "Unknown", + priority_level = 5, # Low priority since we don't know the status + age_weeks = NA, + harvest_readiness = "Unknown", + stringsAsFactors = FALSE + )) + }) + } + + # Make sure we have data for all fields + if (nrow(farm_health_data) == 0) { + safe_log("No farm health data was created", "ERROR") + stop("Failed to create farm health data") + } + + # Sort by priority level + farm_health_data <- farm_health_data %>% dplyr::arrange(priority_level, field) + + safe_log(paste("Successfully created farm health data for", nrow(farm_health_data), "fields")) + +}, error = function(e) { + safe_log(paste("Error creating farm health data:", e$message), "ERROR") + # Create an empty dataframe that can be filled by the verification chunk +}) +``` + +```{r verify_farm_health_data, message=FALSE, warning=FALSE, include=FALSE} +# Verify farm_health_data exists and has content +if (!exists("farm_health_data") || nrow(farm_health_data) == 0) { + safe_log("farm_health_data not found or empty, generating default data", "WARNING") + + # Create minimal fallback data + tryCatch({ + # Get fields from boundaries + fields <- unique(AllPivots0$field) + + # Create basic data frame with just field names + farm_health_data <- data.frame( + field = fields, + mean_ci = rep(NA, length(fields)), + ci_change = rep(NA, length(fields)), + ci_uniformity = rep(NA, length(fields)), + status = rep("Unknown", length(fields)), + anomaly_type = rep("Unknown", length(fields)), + priority_level = rep(5, length(fields)), # Low priority + age_weeks = rep(NA, length(fields)), + harvest_readiness = rep("Unknown", length(fields)), + stringsAsFactors = FALSE + ) + + safe_log("Created fallback farm_health_data with basic field information") + }, error = function(e) { + safe_log(paste("Error creating fallback farm_health_data:", e$message), "ERROR") + farm_health_data <<- data.frame( + field = character(), + mean_ci = numeric(), + ci_change = numeric(), + ci_uniformity = numeric(), + status = character(), + anomaly_type = character(), + priority_level = numeric(), + age_weeks = numeric(), + harvest_readiness = character(), + stringsAsFactors = FALSE + ) + }) +} +``` + +```{r calculate_farm_health, message=FALSE, warning=FALSE, include=FALSE} +# Calculate farm health summary metrics +tryCatch({ + # Generate farm health summary data + farm_health_data <- generate_farm_health_summary( + field_boundaries = AllPivots0, + ci_current = CI, + ci_previous = CI_m1, + harvesting_data = harvesting_data + ) + + # Log the summary data + safe_log(paste("Generated farm health summary with", nrow(farm_health_data), "fields")) + +}, error = function(e) { + safe_log(paste("Error in farm health calculation:", e$message), "ERROR") + # Create empty dataframe if calculation failed + farm_health_data <- data.frame( + field = character(), + mean_ci = numeric(), + ci_change = numeric(), + ci_uniformity = numeric(), + status = character(), + anomaly_type = character(), + priority_level = numeric(), + age_weeks = numeric(), + harvest_readiness = character(), + stringsAsFactors = FALSE + ) +}) +``` + +```{r advanced_analytics_functions, message=FALSE, warning=FALSE, include=FALSE} +# ADVANCED ANALYTICS FUNCTIONS +# Note: These functions are now imported from executive_report_utils.R +# The utility file contains functions for velocity/acceleration indicators, +# anomaly timeline creation, age cohort mapping, and cohort performance charts +safe_log("Using analytics functions from executive_report_utils.R") +``` + +\pagebreak +# Advanced Analytics + +## Field Health Velocity and Acceleration + +This visualization shows the rate of change in field health (velocity) and whether that change is speeding up or slowing down (acceleration). These metrics help identify if farm conditions are improving, stable, or deteriorating. + +**How to interpret:** +- **Velocity gauge:** Shows the average weekly change in CI values across all fields + - Positive values (green/right side): Farm health improving week-to-week + - Negative values (red/left side): Farm health declining week-to-week + +- **Acceleration gauge:** Shows whether the rate of change is increasing or decreasing + - Positive values (green/right side): Change is accelerating or improving faster + - Negative values (red/left side): Change is decelerating or slowing down + +- **4-Week Trend:** Shows the overall CI value trajectory for the past month + +```{r render_velocity_acceleration, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE} +# Render the velocity and acceleration indicators +tryCatch({ + # Create and display the indicators using the imported utility function + velocity_plot <- create_velocity_acceleration_indicator( + health_data = farm_health_data, + ci_current = CI, + ci_prev1 = CI_m1, + ci_prev2 = CI_m2, + ci_prev3 = CI_m3, + field_boundaries = AllPivots0 + ) + + # Print the visualization + print(velocity_plot) + + # Create a table of fields with significant velocity changes + field_ci_metrics <- list() + + # Process each field to get metrics + fields <- unique(AllPivots0$field) + for (field_name in fields) { + tryCatch({ + # Get field boundary + field_shape <- AllPivots0 %>% dplyr::filter(field == field_name) + if (nrow(field_shape) == 0) next + + # Extract CI values + ci_curr_values <- terra::extract(CI, field_shape) + ci_prev1_values <- terra::extract(CI_m1, field_shape) + + # Calculate metrics + mean_ci_curr <- mean(ci_curr_values$CI, na.rm = TRUE) + mean_ci_prev1 <- mean(ci_prev1_values$CI, na.rm = TRUE) + velocity <- mean_ci_curr - mean_ci_prev1 + + # Store in list + field_ci_metrics[[field_name]] <- list( + field = field_name, + ci_current = mean_ci_curr, + ci_prev1 = mean_ci_prev1, + velocity = velocity + ) + + }, error = function(e) { + safe_log(paste("Error processing field", field_name, "for velocity table:", e$message), "WARNING") + }) + } + + # Convert list to data frame + velocity_df <- do.call(rbind, lapply(field_ci_metrics, function(x) { + data.frame( + field = x$field, + ci_current = round(x$ci_current, 2), + ci_prev1 = round(x$ci_prev1, 2), + velocity = round(x$velocity, 2), + direction = ifelse(x$velocity >= 0, "Improving", "Declining") + ) + })) + + # Select top 5 positive and top 5 negative velocity fields + top_positive <- velocity_df %>% + dplyr::filter(velocity > 0) %>% + dplyr::arrange(desc(velocity)) %>% + dplyr::slice_head(n = 5) + + top_negative <- velocity_df %>% + dplyr::filter(velocity < 0) %>% + dplyr::arrange(velocity) %>% + dplyr::slice_head(n = 5) + + # Display the tables if we have data + if (nrow(top_positive) > 0) { + cat("

Fields with Fastest Improvement

") + knitr::kable(top_positive %>% + dplyr::select(Field = field, + `Current CI` = ci_current, + `Previous CI` = ci_prev1, + `Weekly Change` = velocity)) + } + + if (nrow(top_negative) > 0) { + cat("

Fields with Fastest Decline

") + knitr::kable(top_negative %>% + dplyr::select(Field = field, + `Current CI` = ci_current, + `Previous CI` = ci_prev1, + `Weekly Change` = velocity)) + } + +}, error = function(e) { + safe_log(paste("Error rendering velocity visualization:", e$message), "ERROR") + cat("
Error generating velocity visualization.
") +}) +``` + +\pagebreak +## Field Anomaly Timeline + +This visualization shows the history of detected anomalies in fields across the monitoring period. It helps identify persistent issues or improvements over time. + +**How to interpret:** +- **X-axis**: Dates of satellite observations +- **Y-axis**: Fields grouped by similar characteristics +- **Colors**: Red indicates negative anomalies, green indicates positive anomalies +- **Size**: Larger markers indicate stronger anomalies + +```{r anomaly_timeline, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE} +# Generate anomaly timeline visualization +tryCatch({ + # Use the imported function to create the anomaly timeline + anomaly_timeline <- create_anomaly_timeline( + field_boundaries = AllPivots0, + ci_data = CI_quadrant, + days_to_include = 90 # Show last 90 days of data + ) + + # Display the timeline + print(anomaly_timeline) + +}, error = function(e) { + safe_log(paste("Error generating anomaly timeline:", e$message), "ERROR") + cat("
Error generating anomaly timeline visualization.
") +}) +``` + +\pagebreak +## Field Age Cohorts Map + +This map shows fields grouped by their crop age (weeks since planting). Understanding the distribution of crop ages helps interpret performance metrics and plan harvest scheduling. + +**How to interpret:** +- **Colors**: Different colors represent different age groups (in weeks since planting) +- **Labels**: Each field is labeled with its name for easy reference +- **Legend**: Shows the age ranges in weeks and their corresponding colors + +```{r age_cohort_map, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE} +# Generate age cohort map +tryCatch({ + # Use the imported function to create the age cohort map + age_cohort_map <- create_age_cohort_map( + field_boundaries = AllPivots0, + harvesting_data = harvesting_data + ) + + # Display the map + print(age_cohort_map) + +}, error = function(e) { + safe_log(paste("Error generating age cohort map:", e$message), "ERROR") + cat("
Error generating age cohort map visualization.
") +}) +``` + +\pagebreak +## Cohort Performance Comparison + +This visualization compares chlorophyll index (CI) performance across different age groups of fields. This helps identify if certain age groups are performing better or worse than expected. + +**How to interpret:** +- **X-axis**: Field age groups in weeks since planting +- **Y-axis**: Average CI value for fields in that age group +- **Box plots**: Show the distribution of CI values within each age group +- **Line**: Shows the expected CI trajectory based on historical data + +```{r cohort_performance_chart, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE} +# Generate cohort performance comparison chart +tryCatch({ + # Use the imported function to create the cohort performance chart + cohort_chart <- create_cohort_performance_chart( + field_boundaries = AllPivots0, + ci_current = CI, + harvesting_data = harvesting_data + ) + + # Display the chart + print(cohort_chart) + +}, error = function(e) { + safe_log(paste("Error generating cohort performance chart:", e$message), "ERROR") + cat("
Error generating cohort performance visualization.
") +}) +``` + + + diff --git a/r_app/experiments/executive_summary/CI_report_executive_summary_v2.Rmd b/r_app/experiments/executive_summary/CI_report_executive_summary_v2.Rmd new file mode 100644 index 0000000..6e33804 --- /dev/null +++ b/r_app/experiments/executive_summary/CI_report_executive_summary_v2.Rmd @@ -0,0 +1,1531 @@ +--- +params: + ref: "word-styles-reference-var1.docx" + output_file: CI_report.docx + report_date: "2025-06-16" + data_dir: "simba" + mail_day: "Wednesday" + borders: TRUE + use_breaks: FALSE +output: + # html_document: + # toc: yes + # df_print: paged + word_document: + reference_docx: !expr file.path("word-styles-reference-var1.docx") + toc: yes +editor_options: + chunk_output_type: console +--- + +```{r setup_parameters, include=FALSE} +# Set up basic report parameters from input values +report_date <- params$report_date +mail_day <- params$mail_day +borders <- params$borders +use_breaks <- params$use_breaks # Whether to use breaks or continuous spectrum in visualizations + +# Environment setup notes (commented out) +# # Activeer de renv omgeving +# renv::activate() +# renv::deactivate() +# # Optioneel: Herstel de omgeving als dat nodig is +# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren +# renv::restore() +``` + +```{r load_libraries, message=FALSE, warning=FALSE, include=FALSE} +# Configure knitr options +knitr::opts_chunk$set(warning = FALSE, message = FALSE) + +# Path management +library(here) + +# Spatial data libraries +library(sf) +library(terra) +library(exactextractr) +# library(raster) - Removed as it's no longer maintained + +# Data manipulation and visualization +library(tidyverse) # Includes dplyr, ggplot2, etc. +library(tmap) +library(lubridate) +library(zoo) + +# Machine learning +library(rsample) +library(caret) +library(randomForest) +library(CAST) + +# Load custom utility functions +# tryCatch({ +# source("report_utils.R") +# }, error = function(e) { +# message(paste("Error loading report_utils.R:", e$message)) +# # Try alternative path if the first one fails +# tryCatch({ + source(here::here("r_app", "report_utils.R")) +# }, error = function(e) { +# stop("Could not load report_utils.R from either location: ", e$message) +# }) +# }) + +# Load executive report utilities +# tryCatch({ +# source("executive_report_utils.R") +# }, error = function(e) { +# message(paste("Error loading executive_report_utils.R:", e$message)) +# # Try alternative path if the first one fails +# tryCatch({ + source(here::here("r_app", "exec_dashboard", "executive_report_utils.R")) +# }, error = function(e) { +# stop("Could not load executive_report_utils.R from either location: ", e$message) +# }) +# }) +``` + +```{r initialize_project_config, message=FALSE, warning=FALSE, include=FALSE} +# Set the project directory from parameters +project_dir <- params$data_dir + +# Source project parameters with error handling +# tryCatch({ + source(here::here("r_app", "parameters_project.R")) +# }, error = function(e) { +# stop("Error loading parameters_project.R: ", e$message) +# }) + +# Log initial configuration +safe_log("Starting the R Markdown script") +safe_log(paste("mail_day params:", params$mail_day)) +safe_log(paste("report_date params:", params$report_date)) +safe_log(paste("mail_day variable:", mail_day)) +``` + +```{r calculate_dates_and_weeks, message=FALSE, warning=FALSE, include=FALSE} +# Set locale for consistent date formatting +Sys.setlocale("LC_TIME", "C") + +# Initialize date variables from parameters +today <- as.character(report_date) +mail_day_as_character <- as.character(mail_day) + +# Calculate week days +report_date_as_week_day <- weekdays(lubridate::ymd(today)) +days_of_week <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday") + +# Calculate initial week number +week <- lubridate::week(today) +safe_log(paste("Initial week calculation:", week, "today:", today)) + +# Calculate previous dates for comparisons +today_minus_1 <- as.character(lubridate::ymd(today) - 7) +today_minus_2 <- as.character(lubridate::ymd(today) - 14) +today_minus_3 <- as.character(lubridate::ymd(today) - 21) + +# Log the weekday calculations for debugging +safe_log(paste("Report date weekday:", report_date_as_week_day)) +safe_log(paste("Weekday index:", which(days_of_week == report_date_as_week_day))) +safe_log(paste("Mail day:", mail_day_as_character)) +safe_log(paste("Mail day index:", which(days_of_week == mail_day_as_character))) + +# Adjust week calculation based on mail day +if (which(days_of_week == report_date_as_week_day) > which(days_of_week == mail_day_as_character)) { + safe_log("Adjusting weeks because of mail day") + week <- lubridate::week(today) + 1 + today_minus_1 <- as.character(lubridate::ymd(today)) + today_minus_2 <- as.character(lubridate::ymd(today) - 7) + today_minus_3 <- as.character(lubridate::ymd(today) - 14) +} + +# Generate subtitle for report +subtitle_var <- paste("Report generated on", Sys.Date()) + +# Calculate week numbers for previous weeks +week_minus_1 <- week - 1 +week_minus_2 <- week - 2 +week_minus_3 <- week - 3 + +# Format current week with leading zeros +week <- sprintf("%02d", week) + +# Get years for each date +year <- lubridate::year(today) +year_1 <- lubridate::year(today_minus_1) +year_2 <- lubridate::year(today_minus_2) +year_3 <- lubridate::year(today_minus_3) +``` + +```{r data, message=TRUE, warning=TRUE, include=FALSE} +# Load CI index data with error handling +tryCatch({ + CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds")) + safe_log("Successfully loaded CI quadrant data") +}, error = function(e) { + stop("Error loading CI quadrant data: ", e$message) +}) + +# Get file paths for different weeks using the utility function +tryCatch({ + path_to_week_current = get_week_path(weekly_CI_mosaic, today, 0) + path_to_week_minus_1 = get_week_path(weekly_CI_mosaic, today, -1) + path_to_week_minus_2 = get_week_path(weekly_CI_mosaic, today, -2) + path_to_week_minus_3 = get_week_path(weekly_CI_mosaic, today, -3) + + # Log the calculated paths + safe_log("Required mosaic paths:") + safe_log(paste("Path to current week:", path_to_week_current)) + safe_log(paste("Path to week minus 1:", path_to_week_minus_1)) + safe_log(paste("Path to week minus 2:", path_to_week_minus_2)) + safe_log(paste("Path to week minus 3:", path_to_week_minus_3)) + + # Validate that files exist + if (!file.exists(path_to_week_current)) warning("Current week mosaic file does not exist: ", path_to_week_current) + if (!file.exists(path_to_week_minus_1)) warning("Week minus 1 mosaic file does not exist: ", path_to_week_minus_1) + if (!file.exists(path_to_week_minus_2)) warning("Week minus 2 mosaic file does not exist: ", path_to_week_minus_2) + if (!file.exists(path_to_week_minus_3)) warning("Week minus 3 mosaic file does not exist: ", path_to_week_minus_3) + + # Load raster data with terra functions + CI <- terra::rast(path_to_week_current)$CI + CI_m1 <- terra::rast(path_to_week_minus_1)$CI + CI_m2 <- terra::rast(path_to_week_minus_2)$CI + CI_m3 <- terra::rast(path_to_week_minus_3)$CI + +}, error = function(e) { + stop("Error loading raster data: ", e$message) +}) +``` + +```{r calculate_difference_rasters, message=TRUE, warning=TRUE, include=FALSE} +# Calculate difference rasters for comparisons +tryCatch({ + # Calculate weekly difference + last_week_dif_raster_abs <- (CI - CI_m1) + safe_log("Calculated weekly difference raster") + + # Calculate three-week difference + three_week_dif_raster_abs <- (CI - CI_m3) + safe_log("Calculated three-week difference raster") +}, error = function(e) { + safe_log(paste("Error calculating difference rasters:", e$message), "ERROR") + # Create placeholder rasters if calculations fail + if (!exists("last_week_dif_raster_abs")) { + last_week_dif_raster_abs <- CI * 0 + } + if (!exists("three_week_dif_raster_abs")) { + three_week_dif_raster_abs <- CI * 0 + } +}) +``` + +```{r load_field_boundaries, message=TRUE, warning=TRUE, include=FALSE} +# Load field boundaries from parameters +tryCatch({ + AllPivots0 <- field_boundaries_sf + safe_log("Successfully loaded field boundaries") +}, error = function(e) { + stop("Error loading field boundaries: ", e$message) +}) +``` + +```{r create_farm_health_data, message=FALSE, warning=FALSE, include=FALSE} +# Create farm health summary data from scratch +tryCatch({ + # Ensure we have the required data + if (!exists("AllPivots0") || !exists("CI") || !exists("CI_m1") || !exists("harvesting_data")) { + stop("Required input data (field boundaries, CI data, or harvesting data) not available") + } + + safe_log("Starting to calculate farm health data") + + # Get unique field names + fields <- unique(AllPivots0$field) + safe_log(paste("Found", length(fields), "unique fields")) + + # Initialize result dataframe + farm_health_data <- data.frame( + field = character(), + mean_ci = numeric(), + ci_change = numeric(), + ci_uniformity = numeric(), + status = character(), + anomaly_type = character(), + priority_level = numeric(), + age_weeks = numeric(), + harvest_readiness = character(), + stringsAsFactors = FALSE + ) + + # Process each field with robust error handling + for (field_name in fields) { + tryCatch({ + safe_log(paste("Processing field:", field_name)) + + # Get field boundary + field_shape <- AllPivots0 %>% dplyr::filter(field == field_name) + + # Skip if field shape is empty + if (nrow(field_shape) == 0) { + safe_log(paste("Empty field shape for", field_name), "WARNING") + next + } + + # Get field age from harvesting data - use direct filtering to avoid dplyr errors + field_age_data <- NULL + if (exists("harvesting_data") && !is.null(harvesting_data) && nrow(harvesting_data) > 0) { + field_age_data <- harvesting_data[harvesting_data$field == field_name, ] + if (nrow(field_age_data) > 0) { + field_age_data <- field_age_data[order(field_age_data$season_start, decreasing = TRUE), ][1, ] + } + } + + # Default age if not available + field_age_weeks <- if (!is.null(field_age_data) && nrow(field_age_data) > 0 && !is.na(field_age_data$age)) { + field_age_data$age + } else { + 10 # Default age + } + + # Extract CI values using terra's extract function which is more robust + ci_values <- terra::extract(CI, field_shape) + ci_prev_values <- terra::extract(CI_m1, field_shape) + + # Check if we got valid data + if (nrow(ci_values) == 0 || nrow(ci_prev_values) == 0) { + safe_log(paste("No CI data extracted for field", field_name), "WARNING") + # Add a placeholder row with Unknown status + farm_health_data <- rbind(farm_health_data, data.frame( + field = field_name, + mean_ci = NA, + ci_change = NA, + ci_uniformity = NA, + status = "Unknown", + anomaly_type = "Unknown", + priority_level = 5, # Low priority + age_weeks = field_age_weeks, + harvest_readiness = "Unknown", + stringsAsFactors = FALSE + )) + next + } + + # Calculate metrics - Handle NA values properly + ci_column <- if ("CI" %in% names(ci_values)) "CI" else colnames(ci_values)[1] + ci_prev_column <- if ("CI" %in% names(ci_prev_values)) "CI" else colnames(ci_prev_values)[1] + + mean_ci <- mean(ci_values[[ci_column]], na.rm=TRUE) + mean_ci_prev <- mean(ci_prev_values[[ci_prev_column]], na.rm=TRUE) + ci_change <- mean_ci - mean_ci_prev + ci_sd <- sd(ci_values[[ci_column]], na.rm=TRUE) + ci_uniformity <- ci_sd / max(0.1, mean_ci) # Avoid division by zero + + # Handle NaN or Inf results + if (is.na(mean_ci) || is.na(ci_change) || is.na(ci_uniformity) || + is.nan(mean_ci) || is.nan(ci_change) || is.nan(ci_uniformity) || + is.infinite(mean_ci) || is.infinite(ci_change) || is.infinite(ci_uniformity)) { + safe_log(paste("Invalid calculation results for field", field_name), "WARNING") + # Add a placeholder row with Unknown status + farm_health_data <- rbind(farm_health_data, data.frame( + field = field_name, + mean_ci = NA, + ci_change = NA, + ci_uniformity = NA, + status = "Unknown", + anomaly_type = "Unknown", + priority_level = 5, # Low priority + age_weeks = field_age_weeks, + harvest_readiness = "Unknown", + stringsAsFactors = FALSE + )) + next + } + + # Determine field status + status <- dplyr::case_when( + mean_ci >= 5 ~ "Excellent", + mean_ci >= 3.5 ~ "Good", + mean_ci >= 2 ~ "Fair", + mean_ci >= 1 ~ "Poor", + TRUE ~ "Critical" + ) + + # Determine anomaly type + anomaly_type <- dplyr::case_when( + ci_change > 2 ~ "Potential Weed Growth", + ci_change < -2 ~ "Potential Weeding/Harvesting", + ci_uniformity > 0.5 ~ "High Variability", + mean_ci < 1 ~ "Low Vigor", + TRUE ~ "None" + ) + + # Calculate priority level (1-5, with 1 being highest priority) + priority_score <- dplyr::case_when( + mean_ci < 1 ~ 1, # Critical - highest priority + anomaly_type == "Potential Weed Growth" ~ 2, + anomaly_type == "High Variability" ~ 3, + ci_change < -1 ~ 4, + TRUE ~ 5 # No urgent issues + ) + + # Determine harvest readiness + harvest_readiness <- dplyr::case_when( + field_age_weeks >= 52 & mean_ci >= 4 ~ "Ready for harvest", + field_age_weeks >= 48 & mean_ci >= 3.5 ~ "Approaching harvest", + field_age_weeks >= 40 & mean_ci >= 3 ~ "Mid-maturity", + field_age_weeks >= 12 ~ "Growing", + TRUE ~ "Early stage" + ) + + # Add to summary data + farm_health_data <- rbind(farm_health_data, data.frame( + field = field_name, + mean_ci = round(mean_ci, 2), + ci_change = round(ci_change, 2), + ci_uniformity = round(ci_uniformity, 2), + status = status, + anomaly_type = anomaly_type, + priority_level = priority_score, + age_weeks = field_age_weeks, + harvest_readiness = harvest_readiness, + stringsAsFactors = FALSE + )) + + }, error = function(e) { + safe_log(paste("Error processing field", field_name, ":", e$message), "ERROR") + # Add a placeholder row with Error status + farm_health_data <<- rbind(farm_health_data, data.frame( + field = field_name, + mean_ci = NA, + ci_change = NA, + ci_uniformity = NA, + status = "Unknown", + anomaly_type = "Unknown", + priority_level = 5, # Low priority since we don't know the status + age_weeks = NA, + harvest_readiness = "Unknown", + stringsAsFactors = FALSE + )) + }) + } + + # Make sure we have data for all fields + if (nrow(farm_health_data) == 0) { + safe_log("No farm health data was created", "ERROR") + stop("Failed to create farm health data") + } + + # Sort by priority level + farm_health_data <- farm_health_data %>% dplyr::arrange(priority_level, field) + + safe_log(paste("Successfully created farm health data for", nrow(farm_health_data), "fields")) + +}, error = function(e) { + safe_log(paste("Error creating farm health data:", e$message), "ERROR") + # Create an empty dataframe that can be filled by the verification chunk +}) +``` + +```{r verify_farm_health_data, message=FALSE, warning=FALSE, include=FALSE} +# Verify farm_health_data exists and has content +if (!exists("farm_health_data") || nrow(farm_health_data) == 0) { + safe_log("farm_health_data not found or empty, generating default data", "WARNING") + + # Create minimal fallback data + tryCatch({ + # Get fields from boundaries + fields <- unique(AllPivots0$field) + + # Create basic data frame with just field names + farm_health_data <- data.frame( + field = fields, + mean_ci = rep(NA, length(fields)), + ci_change = rep(NA, length(fields)), + ci_uniformity = rep(NA, length(fields)), + status = rep("Unknown", length(fields)), + anomaly_type = rep("Unknown", length(fields)), + priority_level = rep(5, length(fields)), # Low priority + age_weeks = rep(NA, length(fields)), + harvest_readiness = rep("Unknown", length(fields)), + stringsAsFactors = FALSE + ) + + safe_log("Created fallback farm_health_data with basic field information") + }, error = function(e) { + safe_log(paste("Error creating fallback farm_health_data:", e$message), "ERROR") + farm_health_data <<- data.frame( + field = character(), + mean_ci = numeric(), + ci_change = numeric(), + ci_uniformity = numeric(), + status = character(), + anomaly_type = character(), + priority_level = numeric(), + age_weeks = numeric(), + harvest_readiness = character(), + stringsAsFactors = FALSE + ) + }) +} +``` + +```{r calculate_farm_health, message=FALSE, warning=FALSE, include=FALSE} +# Calculate farm health summary metrics +tryCatch({ + # Generate farm health summary data + farm_health_data <- generate_farm_health_summary( + field_boundaries = AllPivots0, + ci_current = CI, + ci_previous = CI_m1, + harvesting_data = harvesting_data + ) + + # Log the summary data + safe_log(paste("Generated farm health summary with", nrow(farm_health_data), "fields")) + +}, error = function(e) { + safe_log(paste("Error in farm health calculation:", e$message), "ERROR") + # Create empty dataframe if calculation failed + farm_health_data <- data.frame( + field = character(), + mean_ci = numeric(), + ci_change = numeric(), + ci_uniformity = numeric(), + status = character(), + anomaly_type = character(), + priority_level = numeric(), + age_weeks = numeric(), + harvest_readiness = character(), + stringsAsFactors = FALSE + ) +}) +``` + +```{r executive_summary_functions, message=FALSE, warning=FALSE, include=FALSE} +# EXECUTIVE SUMMARY HELPER FUNCTIONS + +#' Generate a summary of farm health status +#' +#' @param field_boundaries Field boundaries spatial data (sf object) +#' @param ci_current Current CI raster +#' @param ci_previous Previous week's CI raster +#' @param harvesting_data Data frame with harvesting information +#' @return A data frame with farm status summary metrics +#' +generate_farm_health_summary <- function(field_boundaries, ci_current, ci_previous, harvesting_data) { + # Generate a summary data frame of farm health by field + tryCatch({ + # Get unique field names + fields <- unique(field_boundaries$field) + + # Initialize result dataframe + summary_data <- data.frame( + field = character(), + mean_ci = numeric(), + ci_change = numeric(), + ci_uniformity = numeric(), + status = character(), + anomaly_type = character(), + priority_level = numeric(), + age_weeks = numeric(), + harvest_readiness = character(), + stringsAsFactors = FALSE + ) + + # Process each field with better error handling + for (field_name in fields) { + tryCatch({ + # Get field boundary + field_shape <- field_boundaries %>% dplyr::filter(field == field_name) + + # Skip if field shape is empty + if (nrow(field_shape) == 0) { + safe_log(paste("Empty field shape for", field_name), "WARNING") + next + } + + # Get field age from harvesting data + field_age_data <- harvesting_data %>% + dplyr::filter(field == field_name) %>% + dplyr::arrange(desc(season_start)) %>% + dplyr::slice(1) + + # Default age if not available + field_age_weeks <- if (nrow(field_age_data) > 0 && !is.na(field_age_data$age)) { + field_age_data$age + } else { + 10 # Default age + } + + # Extract CI values for this field using extract instead of crop/mask to avoid pointer issues + # This is more robust than the crop+mask approach + field_bbox <- sf::st_bbox(field_shape) + extent_vec <- c(field_bbox$xmin, field_bbox$xmax, field_bbox$ymin, field_bbox$ymax) + + # Use terra extract function instead of crop+mask + ci_values <- terra::extract(ci_current, field_shape) + ci_prev_values <- terra::extract(ci_previous, field_shape) + + # Calculate metrics + mean_ci <- mean(ci_values$CI, na.rm=TRUE) + mean_ci_prev <- mean(ci_prev_values$CI, na.rm=TRUE) + ci_change <- mean_ci - mean_ci_prev + ci_sd <- sd(ci_values$CI, na.rm=TRUE) + ci_uniformity <- ci_sd / max(0.1, mean_ci) # Avoid division by zero + + # Determine field status + status <- dplyr::case_when( + mean_ci >= 5 ~ "Excellent", + mean_ci >= 3.5 ~ "Good", + mean_ci >= 2 ~ "Fair", + mean_ci >= 1 ~ "Poor", + TRUE ~ "Critical" + ) + + # Determine anomaly type + anomaly_type <- dplyr::case_when( + ci_change > 2 ~ "Potential Weed Growth", + ci_change < -2 ~ "Potential Weeding/Harvesting", + ci_uniformity > 0.5 ~ "High Variability", + mean_ci < 1 ~ "Low Vigor", + TRUE ~ "None" + ) + + # Calculate priority level (1-5, with 1 being highest priority) + priority_score <- dplyr::case_when( + mean_ci < 1 ~ 1, # Critical - highest priority + anomaly_type == "Potential Weed Growth" ~ 2, + anomaly_type == "High Variability" ~ 3, + ci_change < -1 ~ 4, + TRUE ~ 5 # No urgent issues + ) + + # Determine harvest readiness + harvest_readiness <- dplyr::case_when( + field_age_weeks >= 52 & mean_ci >= 4 ~ "Ready for harvest", + field_age_weeks >= 48 & mean_ci >= 3.5 ~ "Approaching harvest", + field_age_weeks >= 40 & mean_ci >= 3 ~ "Mid-maturity", + field_age_weeks >= 12 ~ "Growing", + TRUE ~ "Early stage" + ) + + # Add to summary data + summary_data <- rbind(summary_data, data.frame( + field = field_name, + mean_ci = round(mean_ci, 2), + ci_change = round(ci_change, 2), + ci_uniformity = round(ci_uniformity, 2), + status = status, + anomaly_type = anomaly_type, + priority_level = priority_score, + age_weeks = field_age_weeks, + harvest_readiness = harvest_readiness, + stringsAsFactors = FALSE + )) + + }, error = function(e) { + safe_log(paste("Error calculating health score for field", field_name, ":", e$message), "ERROR") + # Add a row with NA values for this field to ensure it still appears in outputs + summary_data <- rbind(summary_data, data.frame( + field = field_name, + mean_ci = NA, + ci_change = NA, + ci_uniformity = NA, + status = "Error", + anomaly_type = "Error", + priority_level = 1, # High priority because it needs investigation + age_weeks = NA, + harvest_readiness = "Unknown", + stringsAsFactors = FALSE + )) + }) + } + + # Sort by priority level + summary_data <- summary_data %>% dplyr::arrange(priority_level, field) + + return(summary_data) + + }, error = function(e) { + safe_log(paste("Error in generate_farm_health_summary:", e$message), "ERROR") + return(data.frame( + field = character(), + mean_ci = numeric(), + ci_change = numeric(), + ci_uniformity = numeric(), + status = character(), + anomaly_type = character(), + priority_level = numeric(), + age_weeks = numeric(), + harvest_readiness = character(), + stringsAsFactors = FALSE + )) }) +} + +#' Create a raster-based CI map showing pixel values +#' +#' @param ci_raster CI raster data +#' @param field_boundaries Field boundaries spatial data (sf object) for overlay +#' @param title Map title +#' @param legend_title Legend title +#' @return A tmap object with raster visualization +#' +create_ci_raster_map <- function(ci_raster, field_boundaries, + title = "Chlorophyll Index (CI) - Pixel Values", + legend_title = "CI Value") { + tryCatch({ + # Create raster map + map <- tm_shape(ci_raster) + + tm_raster( + palette = "RdYlGn", + title = legend_title, + style = "cont" + # n = 10, + # alpha = 0.8 + ) + + # Add field boundaries as overlay + tm_shape(field_boundaries) + + tm_borders(col = "black", lwd = 0.5, alpha = 0.7) + + tm_text("field", size = 0.6, col = "black", alpha = 0.8) + + tm_layout( + main.title = title, + legend.outside = TRUE, + legend.outside.position = "bottom" + ) + + tm_scale_bar(position = tm_pos_out("right", "bottom")) + + return(map) + + }, error = function(e) { + safe_log(paste("Error in create_ci_raster_map:", e$message), "ERROR") + return(NULL) + }) +} + +#' Create a farm-wide anomaly detection map +#' +#' @param ci_current Current CI raster +#' @param ci_previous Previous week's CI raster +#' @param field_boundaries Field boundaries spatial data (sf object) +#' @return A tmap object with anomaly visualization +#' +create_anomaly_map <- function(ci_current, ci_previous, field_boundaries) { + tryCatch({ + # Calculate difference raster + ci_diff <- ci_current - ci_previous + + # Create a categorical raster for anomalies + anomaly_raster <- ci_current * 0 # Initialize with same extent/resolution + + # Extract values to manipulate + diff_values <- terra::values(ci_diff) + curr_values <- terra::values(ci_current) + + # Define anomaly categories: + # 4: Significant growth (potential weeds) - CI increase > 2 + # 3: Moderate growth - CI increase 1-2 + # 2: Stable - CI change between -1 and 1 + # 1: Moderate decline - CI decrease 1-2 + # 0: Significant decline (potential weeding/harvesting) - CI decrease > 2 + + # Apply classification + anomaly_values <- rep(NA, length(diff_values)) + + # Significant growth (potential weeds) + sig_growth <- which(diff_values > 2 & !is.na(diff_values)) + anomaly_values[sig_growth] <- 4 + + # Moderate growth + mod_growth <- which(diff_values > 1 & diff_values <= 2 & !is.na(diff_values)) + anomaly_values[mod_growth] <- 3 + + # Stable + stable <- which(diff_values >= -1 & diff_values <= 1 & !is.na(diff_values)) + anomaly_values[stable] <- 2 + + # Moderate decline + mod_decline <- which(diff_values < -1 & diff_values >= -2 & !is.na(diff_values)) + anomaly_values[mod_decline] <- 1 + + # Significant decline (potential weeding) + sig_decline <- which(diff_values < -2 & !is.na(diff_values)) + anomaly_values[sig_decline] <- 0 + + # Set values in raster + terra::values(anomaly_raster) <- anomaly_values + + # Create anomaly map + map <- tm_shape(anomaly_raster) + + tm_raster( + style = "cat", + palette = c("#d73027", "#fc8d59", "#ffffbf", "#91cf60", "#1a9850"), + labels = c("Significant Decline", "Moderate Decline", "Stable", "Moderate Growth", "Significant Growth"), + title = "Weekly CI Change" + ) + + tm_shape(field_boundaries) + + tm_borders(col = "black", lwd = 1.5) + + tm_text("field", size = 0.6) + + tm_layout( + main.title = "Farm-Wide Anomaly Detection", + legend.outside = TRUE, + legend.outside.position = "bottom" + ) + + tm_scale_bar(position = tm_pos_out("right", "bottom")) + + return(map) + + }, error = function(e) { + safe_log(paste("Error in create_anomaly_map:", e$message), "ERROR") + return(NULL) + }) +} + +#' Create a choropleth map of field health status +#' +#' @param field_boundaries Field boundaries with health data +#' @param attribute Field to visualize (e.g., "priority_level", "mean_ci") +#' @param title Map title +#' @param palette Color palette to use +#' @param legend_title Legend title +#' @return A tmap object +#' +create_field_status_map <- function(field_boundaries, health_data, attribute, + title = "Field Status Overview", + legend_title = "Status") { + tryCatch({ + # Join health data to field boundaries + field_data <- field_boundaries %>% + dplyr::left_join(health_data, by = "field") + + # Create style based on attribute type + if (attribute == "status") { + # Categorical styling for status + map <- tm_shape(field_data) + + tm_fill( + col = attribute, + palette = c("Excellent" = "#1a9850", "Good" = "#91cf60", + "Fair" = "#ffffbf", "Poor" = "#fc8d59", "Critical" = "#d73027", + "Error" = "#999999"), # Added Error category + title = legend_title + ) + } else if (attribute == "priority_level") { + # Numeric with custom breaks for priority (5 to 1, with 1 being highest priority) + map <- tm_shape(field_data) + + tm_fill( + col = attribute, + palette = "-RdYlGn", # Reversed so red is high priority + breaks = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5), + labels = c("Critical", "High", "Medium", "Low", "Minimal"), + title = legend_title + ) + } else if (attribute == "anomaly_type") { + # Categorical styling for anomalies + map <- tm_shape(field_data) + + tm_fill( + col = attribute, + palette = c("Potential Weed Growth" = "#d73027", + "Potential Weeding/Harvesting" = "#4575b4", + "High Variability" = "#f46d43", + "Low Vigor" = "#fee090", + "None" = "#91cf60", + "Error" = "#999999"), # Added Error category + title = legend_title + ) + } else if (attribute == "harvest_readiness") { + # Categorical styling for harvest readiness + map <- tm_shape(field_data) + + tm_fill( + col = attribute, + palette = c("Ready for harvest" = "#1a9850", + "Approaching harvest" = "#91cf60", + "Mid-maturity" = "#ffffbf", + "Growing" = "#fc8d59", + "Early stage" = "#d73027", "Unknown" = "#999999"), # Added Unknown category + title = legend_title + ) + } else { + # Default numerical styling + map <- tm_shape(field_data) + + tm_fill( + col = attribute, + palette = "RdYlGn", + title = legend_title, + style = "cont", + na.color = "#999999" # Color for NA values + ) + } + + # Complete the map with borders and labels + map <- map + + tm_borders(col = "black", lwd = 1) + + tm_text("field", size = 0.7) + + tm_layout( + main.title = title, + legend.outside = TRUE, + legend.outside.position = "bottom" + ) + + tm_scale_bar(position = tm_pos_out("right", "bottom")) + + return(map) + + }, error = function(e) { + safe_log(paste("Error in create_field_status_map:", e$message), "ERROR") + return(NULL) + }) +} + +#' Create a summary statistics visualization +#' +#' @param health_data Farm health summary data +#' @return A ggplot2 object +#' +create_summary_stats <- function(health_data) { + tryCatch({ + # Handle empty dataframe case + if (nrow(health_data) == 0) { + return(ggplot2::ggplot() + + ggplot2::annotate("text", x = 0, y = 0, label = "No field data available") + + ggplot2::theme_void()) + } + + # Count fields by status + status_counts <- health_data %>% + dplyr::group_by(status) %>% + dplyr::summarise(count = n()) %>% + dplyr::mutate(status = factor(status, levels = c("Excellent", "Good", "Fair", "Poor", "Critical", "Error"))) + + # Create colors for status categories + status_colors <- c( + "Excellent" = "#1a9850", + "Good" = "#91cf60", + "Fair" = "#ffffbf", + "Poor" = "#fc8d59", + "Critical" = "#d73027", + "Error" = "#999999" + ) + + # Create bar chart + p <- ggplot2::ggplot(status_counts, ggplot2::aes(x = status, y = count, fill = status)) + + ggplot2::geom_bar(stat = "identity") + + ggplot2::scale_fill_manual(values = status_colors) + + ggplot2::labs( + title = "Field Status Summary", + x = "Status", + y = "Number of Fields", + fill = "Field Status" + ) + + ggplot2::theme_minimal() + + ggplot2::theme( + axis.text.x = ggplot2::element_text(angle = 45, hjust = 1), + legend.position = "bottom" + ) + + return(p) + + }, error = function(e) { + safe_log(paste("Error in create_summary_stats:", e$message), "ERROR") + return(ggplot2::ggplot() + + ggplot2::annotate("text", x = 0, y = 0, label = paste("Error:", e$message)) + + ggplot2::theme_void()) + }) +} + +#' Create a bar chart of fields requiring attention +#' +#' @param health_data Farm health summary data +#' @param max_fields Maximum number of fields to display +#' @return A ggplot2 object +#' +create_priority_fields_chart <- function(health_data, max_fields = 10) { + tryCatch({ + # Handle empty dataframe case + if (nrow(health_data) == 0) { + return(ggplot2::ggplot() + + ggplot2::annotate("text", x = 0, y = 0, label = "No field data available") + + ggplot2::theme_void()) + } + + # Filter for fields that need attention (priority 1-3) + priority_fields <- health_data %>% + dplyr::filter(priority_level <= 3) %>% + dplyr::arrange(priority_level) %>% + dplyr::slice_head(n = max_fields) + + # If no priority fields, return message + if (nrow(priority_fields) == 0) { + return(ggplot2::ggplot() + + ggplot2::annotate("text", x = 0, y = 0, label = "No priority fields requiring attention") + + ggplot2::theme_void()) + } + + # Create priority labels + priority_fields$priority_label <- factor( + dplyr::case_when( + priority_fields$priority_level == 1 ~ "Critical", + priority_fields$priority_level == 2 ~ "High", + priority_fields$priority_level == 3 ~ "Medium", + TRUE ~ "Low" + ), + levels = c("Critical", "High", "Medium", "Low") + ) + + # Priority colors + priority_colors <- c( + "Critical" = "#d73027", + "High" = "#fc8d59", + "Medium" = "#fee090", + "Low" = "#91cf60" + ) + + # Create chart + p <- ggplot2::ggplot(priority_fields, + ggplot2::aes(x = reorder(field, -priority_level), + y = mean_ci, + fill = priority_label)) + + ggplot2::geom_bar(stat = "identity") + + ggplot2::geom_text(ggplot2::aes(label = anomaly_type), + position = ggplot2::position_stack(vjust = 0.5), + size = 3, angle = 90, hjust = 0) + + ggplot2::scale_fill_manual(values = priority_colors) + + ggplot2::labs( + title = "Priority Fields Requiring Attention", + subtitle = "With anomaly types and CI values", + x = "Field", + y = "Chlorophyll Index (CI)", + fill = "Priority" + ) + + ggplot2::theme_minimal() + + ggplot2::theme( + axis.text.x = ggplot2::element_text(angle = 45, hjust = 1), + legend.position = "bottom" + ) + + return(p) + + }, error = function(e) { + safe_log(paste("Error in create_priority_fields_chart:", e$message), "ERROR") + return(ggplot2::ggplot() + + ggplot2::annotate("text", x = 0, y = 0, label = paste("Error:", e$message)) + + ggplot2::theme_void()) + }) +} + +#' Creates a harvest readiness visualization +#' +#' @param health_data Farm health summary data +#' @return A ggplot2 object +create_harvest_readiness_chart <- function(health_data) { + tryCatch({ + # Handle empty dataframe case + if (nrow(health_data) == 0) { + return(ggplot2::ggplot() + + ggplot2::annotate("text", x = 0, y = 0, label = "No field data available") + + ggplot2::theme_void()) + } + + # Count fields by harvest readiness + harvest_counts <- health_data %>% + dplyr::group_by(harvest_readiness) %>% + dplyr::summarise(count = n()) + + # Order factor levels + harvest_order <- c("Ready for harvest", "Approaching harvest", "Mid-maturity", "Growing", "Early stage", "Unknown") + harvest_counts$harvest_readiness <- factor(harvest_counts$harvest_readiness, levels = harvest_order) + + # Create colors for harvest readiness categories + harvest_colors <- c( + "Ready for harvest" = "#1a9850", + "Approaching harvest" = "#91cf60", + "Mid-maturity" = "#ffffbf", + "Growing" = "#fc8d59", + "Early stage" = "#d73027", + "Unknown" = "#999999" + ) + + # Create pie chart + p <- ggplot2::ggplot(harvest_counts, ggplot2::aes(x="", y=count, fill=harvest_readiness)) + + ggplot2::geom_bar(stat="identity", width=1) + + ggplot2::coord_polar("y", start=0) + + ggplot2::scale_fill_manual(values = harvest_colors) + + ggplot2::labs( + title = "Harvest Readiness Overview", + fill = "Harvest Stage" + ) + + ggplot2::theme_minimal() + + ggplot2::theme( + axis.title.x = ggplot2::element_blank(), + axis.title.y = ggplot2::element_blank(), + panel.border = ggplot2::element_blank(), + panel.grid = ggplot2::element_blank(), + axis.ticks = ggplot2::element_blank(), + axis.text = ggplot2::element_blank(), + plot.title = ggplot2::element_text(size=14, face="bold") + ) + + return(p) + + }, error = function(e) { + safe_log(paste("Error in create_harvest_readiness_chart:", e$message), "ERROR") + return(ggplot2::ggplot() + + ggplot2::annotate("text", x = 0, y = 0, label = paste("Error:", e$message)) + + ggplot2::theme_void()) + }) +} + +#' Generate recommendations based on farm health +#' +#' @param health_data Farm health summary data +#' @return HTML formatted recommendations +generate_executive_recommendations <- function(health_data) { + tryCatch({ + # Handle empty dataframe case + if (nrow(health_data) == 0) { + return("

Executive Recommendations

No field data available to generate recommendations.

") + } + + # Count fields by priority level + priority_counts <- health_data %>% + dplyr::group_by(priority_level) %>% + dplyr::summarise(count = n()) + + # Get critical and high priority fields + critical_fields <- health_data %>% + dplyr::filter(priority_level == 1) %>% + dplyr::pull(field) + + high_priority_fields <- health_data %>% + dplyr::filter(priority_level == 2) %>% + dplyr::pull(field) + + # Count harvest-ready fields + harvest_ready <- health_data %>% + dplyr::filter(harvest_readiness == "Ready for harvest") %>% + dplyr::pull(field) + + approaching_harvest <- health_data %>% + dplyr::filter(harvest_readiness == "Approaching harvest") %>% + dplyr::pull(field) + + # Count anomalies by type + anomaly_counts <- health_data %>% + dplyr::filter(anomaly_type != "None" & anomaly_type != "Error") %>% + dplyr::group_by(anomaly_type) %>% + dplyr::summarise(count = n()) + + # Generate HTML recommendations + html_output <- "
" + html_output <- paste0(html_output, "

Executive Recommendations

") + + # Priority recommendations + html_output <- paste0(html_output, "

Priority Actions:

    ") + + if (length(critical_fields) > 0) { + html_output <- paste0(html_output, + sprintf("
  • Critical attention needed for fields: %s
    • ", + paste(critical_fields, collapse = ", "))) + } + + if (length(high_priority_fields) > 0) { + html_output <- paste0(html_output, + sprintf("
  • High priority inspection for fields: %s
    • ", + paste(high_priority_fields, collapse = ", "))) + } + + if (length(harvest_ready) > 0) { + html_output <- paste0(html_output, + sprintf("
  • Ready for harvest: %s
    • ", + paste(harvest_ready, collapse = ", "))) + } + + if (length(approaching_harvest) > 0) { + html_output <- paste0(html_output, + sprintf("
  • Approaching harvest readiness: %s
    • ", + paste(approaching_harvest, collapse = ", "))) + } + + # If no specific recommendations, add general one + if (length(critical_fields) == 0 && length(high_priority_fields) == 0 && + length(harvest_ready) == 0 && length(approaching_harvest) == 0) { + html_output <- paste0(html_output, "
  • No urgent actions required this week.
    • ") + } + + html_output <- paste0(html_output, "
") + + # Anomaly notifications + if (nrow(anomaly_counts) > 0) { + html_output <- paste0(html_output, "

Anomaly Notifications:

    ") + + for (i in 1:nrow(anomaly_counts)) { + html_output <- paste0(html_output, + sprintf("
  • %s detected in %d fields
    • ", + anomaly_counts$anomaly_type[i], anomaly_counts$count[i])) + } + + html_output <- paste0(html_output, "
") + } + + # Farm status summary + html_output <- paste0(html_output, "

Farm Status Overview:

    ") + + status_counts <- health_data %>% + dplyr::filter(status != "Error") %>% + dplyr::group_by(status) %>% + dplyr::summarise(count = n()) + + for (i in 1:nrow(status_counts)) { + html_output <- paste0(html_output, + sprintf("
  • %s: %d fields
    • ", + status_counts$status[i], status_counts$count[i])) + } + + html_output <- paste0(html_output, "
") + + return(html_output) + + }, error = function(e) { + safe_log(paste("Error in generate_executive_recommendations:", e$message), "ERROR") + return("

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("
") + cat("

Yield Summary

") + cat("
    ") + cat(sprintf("
  • Total estimated production: %s tonnes/ha
    • ", + format(round(total_yield, 0), big.mark=","))) + cat(sprintf("
  • Number of harvest-ready fields: %d
    • ", + nrow(pred_rf_current_season))) + cat(sprintf("
  • Average predicted yield: %s tonnes/ha
    • ", + format(round(mean(pred_rf_current_season$predicted_Tcha, na.rm=TRUE), 1), big.mark=","))) + cat("
") + cat("
") + + # Display yield prediction table + harvest_ready_fields <- pred_rf_current_season %>% + dplyr::arrange(desc(predicted_Tcha)) %>% + dplyr::select( + Field = field, + `Sub Field` = sub_field, + `Age (Days)` = Age_days, + `Cumulative CI` = total_CI, + `Predicted Yield (Tonnes/ha)` = predicted_Tcha + ) + + knitr::kable(harvest_ready_fields, + caption = "Predicted Yields for Harvest-Ready Fields", + digits = 1) + } else { + cat("
") + cat("

Yield Summary

") + cat("

No fields currently meet harvest readiness criteria (>300 days) for yield prediction.

") + cat("
") + } +}, error = function(e) { + safe_log(paste("Error creating yield summary:", e$message), "ERROR") + 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/max_ci_field.r b/r_app/experiments/max_ci_field.r similarity index 100% rename from r_app/max_ci_field.r rename to r_app/experiments/max_ci_field.r 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")) +cat(paste(" Optimal CI:", round(quad_optimal_ci, 4), "\n")) + + diff --git a/r_app/plot_testing.R b/r_app/experiments/plot_testing.R similarity index 100% rename from r_app/plot_testing.R rename to r_app/experiments/plot_testing.R diff --git a/r_app/run_tests.R b/r_app/experiments/run_tests.R similarity index 100% rename from r_app/run_tests.R rename to r_app/experiments/run_tests.R 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/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 d55dd1e..189f4f4 100644 --- a/r_app/interpolate_growth_model.R +++ b/r_app/interpolate_growth_model.R @@ -32,15 +32,20 @@ main <- function() { 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("ci_extraction_utils.R") + source("growth_model_utils.R") }, error = function(e) { warning("Default source files not found. Attempting to source from 'r_app' directory.") tryCatch({ - source("r_app/parameters_project.R") - source("r_app/ci_extraction_utils.R") + 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.") }) @@ -92,5 +97,6 @@ main <- function() { }) } -# Run the main function if the script is executed directly -main() +if (sys.nframe() == 0) { + main() +} diff --git a/r_app/mosaic_creation.R b/r_app/mosaic_creation.R index ed20ba2..7f92d9f 100644 --- a/r_app/mosaic_creation.R +++ b/r_app/mosaic_creation.R @@ -28,19 +28,31 @@ main <- function() { # Capture command line arguments args <- commandArgs(trailingOnly = TRUE) + # 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) + } + + # Make project_dir available globally so parameters_project.R can use it + assign("project_dir", project_dir, envir = .GlobalEnv) + # 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 <- "2023-10-01" # Default date for testing + 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 <- "2023-10-01" # Default date for testing - message("No end_date provided. Using current date:", format(end_date)) + 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 @@ -56,25 +68,20 @@ main <- function() { message("No offset provided. Using default:", offset, "days") } - # 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) - } + # 3. Initialize project configuration # -------------------------------- tryCatch({ source("parameters_project.R") - source("ci_extraction_utils.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("r_app/parameters_project.R") - source("r_app/ci_extraction_utils.R") + 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.") }) @@ -82,8 +89,8 @@ main <- function() { # 4. Generate date range for processing # --------------------------------- - dates <- date_list(end_date, offset) - log_message(paste("Processing data for week", dates$week, "of", dates$year)) + dates <- date_list(end_date, offset) + safe_log(paste("Processing data for week", dates$week, "of", dates$year)) # Create output filename file_name_tif <- if (length(args) >= 4 && !is.na(args[4])) { @@ -92,7 +99,7 @@ main <- function() { paste0("week_", sprintf("%02d", dates$week), "_", dates$year, ".tif") } - log_message(paste("Output will be saved as:", file_name_tif)) + safe_log(paste("Output will be saved as:", file_name_tif)) # 5. Create weekly mosaic using the function from utils # ------------------------------------------------- @@ -107,5 +114,6 @@ main <- function() { ) } -# Run the main function if the script is executed directly -main() +if (sys.nframe() == 0) { + main() +} diff --git a/r_app/mosaic_creation_utils.R b/r_app/mosaic_creation_utils.R index 2e92030..5891cc6 100644 --- a/r_app/mosaic_creation_utils.R +++ b/r_app/mosaic_creation_utils.R @@ -3,8 +3,7 @@ # Utility functions for creating weekly mosaics from daily satellite imagery. # These functions support cloud cover assessment, date handling, and mosaic creation. -#' Safe logging function that works whether log_message exists or not -#' +#' Safe logging function #' @param message The message to log #' @param level The log level (default: "INFO") #' @return NULL (used for side effects) @@ -158,34 +157,46 @@ count_cloud_coverage <- function(vrt_list, field_boundaries) { 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) %>% + total_pix_area <- terra::rast(vrt_list[1]) |> + terra::subset(1) |> + terra::setValues(1) |> + terra::crop(field_boundaries, mask = TRUE) |> terra::global(fun = "notNA") - # Extract layer 1 from all rasters (for cloud detection) - layer_5_list <- purrr::map(vrt_list, function(file) { - terra::rast(file) %>% terra::subset(1) - }) %>% terra::rast() + # 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$missing_pixels_percentage[i] < 45) + } - # Calculate percentage of missing pixels (clouds) - missing_pixels_count <- terra::global(layer_5_list, fun = "notNA") %>% - dplyr::mutate( - total_pixels = total_pix_area$notNA, - missing_pixels_percentage = round(100 - ((notNA / total_pix_area$notNA) * 100)), - thres_5perc = as.integer(missing_pixels_percentage < 5), - thres_40perc = as.integer(missing_pixels_percentage < 45) - ) + # Store processed rasters and cloud masks as attributes + attr(missing_pixels_df, "cloud_masks") <- cloud_masks + attr(missing_pixels_df, "processed_rasters") <- processed_rasters # Log results safe_log(paste( "Cloud cover assessment completed for", length(vrt_list), "files.", - sum(missing_pixels_count$thres_5perc), "files with <5% cloud cover,", - sum(missing_pixels_count$thres_40perc), "files with <45% cloud cover" + sum(missing_pixels_df$thres_5perc), "files with <5% cloud cover,", + sum(missing_pixels_df$thres_40perc), "files with <45% cloud cover" )) - - return(missing_pixels_count) + return(missing_pixels_df) }, error = function(e) { warning("Error in cloud coverage calculation: ", e$message) return(NULL) @@ -209,8 +220,8 @@ create_mosaic <- function(vrt_list, missing_pixels_count, field_boundaries = NUL safe_log("No images available for this period, creating empty mosaic", "WARNING") - x <- terra::rast(raster_files_final[1]) %>% - terra::setValues(0) %>% + x <- terra::rast(raster_files_final[1]) |> + terra::setValues(0) |> terra::crop(field_boundaries, mask = TRUE) names(x) <- c("Red", "Green", "Blue", "NIR", "CI") @@ -227,40 +238,111 @@ create_mosaic <- function(vrt_list, missing_pixels_count, field_boundaries = NUL return(x) } - # 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)) + # 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") - # Create mosaic based on available cloud-free images - if (sum(missing_pixels_count$thres_5perc) > 1) { - safe_log("Creating max composite from multiple cloud-free images (<5% clouds)") + if (!is.null(processed_rasters) && length(processed_rasters) > 0) { + safe_log("Using cloud-masked rasters for mosaic creation") - cloudy_rasters_list <- vrt_list[index_5perc] - rsrc <- terra::sprc(cloudy_rasters_list) - x <- terra::mosaic(rsrc, fun = "max") - - } else if (sum(missing_pixels_count$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]]) + # 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 { - safe_log("No cloud-free images available, using all images", "WARNING") + # Fall back to original behavior if no cloud-masked rasters available + safe_log("No cloud-masked rasters available, using original images", "WARNING") - rsrc <- terra::sprc(vrt_list) - x <- terra::mosaic(rsrc, fun = "max") + # 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 @@ -288,18 +370,51 @@ save_mosaic <- function(mosaic_raster, output_dir, file_name, plot_result = FALS # 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 diff --git a/r_app/report_utils.R b/r_app/report_utils.R index 76a5a94..8ed131b 100644 --- a/r_app/report_utils.R +++ b/r_app/report_utils.R @@ -55,10 +55,9 @@ subchunkify <- function(g, fig_height=7, fig_width=5) { #' @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) -#' @param use_breaks Whether to use breaks or continuous spectrum for the raster (default: TRUE) #' @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, use_breaks = TRUE){ +create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week, age, borders = FALSE){ # Input validation if (missing(pivot_raster) || is.null(pivot_raster)) { stop("pivot_raster is required") @@ -75,34 +74,17 @@ create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = 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 either breaks or continuous spectrum based on parameter - if (use_breaks) { - map <- map + 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, - title = "CI") - } else { - map <- map + tm_raster(palette = "RdYlGn", - legend.is.portrait = legend_is_portrait, - style = "cont", # Use continuous spectrum - title = "CI") - } - - # Add layout elements + # 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, - legend.show = show_legend, - legend.position = c("left", "bottom"), - # legend.width = 0.5, - # legend.height = 0.5, - # legend.text.size = 0.8, - # legend.title.size = 0.9, - legend.outside = FALSE) + main.title.size = 0.7) # Add borders if requested if (borders) { @@ -128,10 +110,9 @@ create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = #' @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) -#' @param use_breaks Whether to use breaks or continuous spectrum for the raster (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, use_breaks = TRUE){ +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){ # Input validation if (missing(pivot_raster) || is.null(pivot_raster)) { stop("pivot_raster is required") @@ -148,35 +129,18 @@ create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_lege 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 either breaks or continuous spectrum based on parameter - if (use_breaks) { - map <- map + tm_raster(breaks = c(-3,-2,-1,0,1,2,3), - palette = "RdYlGn", - legend.is.portrait = legend_is_portrait, - midpoint = 0, - title = "CI difference") - } else { - map <- map + tm_raster(palette = "RdYlGn", - legend.is.portrait = legend_is_portrait, - style = "cont", # Use continuous spectrum - midpoint = 0, - title = "CI difference") - } - - # Add layout elements + # 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, - legend.show = show_legend, - legend.position = c("left", "bottom"), - # legend.width = 0.5, - # legend.height = 0.5, - # legend.text.size = 0.8, - # legend.title.size = 0.9, - legend.outside = FALSE) + main.title.size = 0.7) # Add borders if requested if (borders) { @@ -205,7 +169,6 @@ create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_lege #' @param week_minus_1 Previous week number #' @param week_minus_2 Two weeks ago week number #' @param week_minus_3 Three weeks ago week number -#' @param use_breaks Whether to use discrete breaks or continuous spectrum (default: TRUE) #' @param borders Whether to display field borders (default: TRUE) #' @return NULL (adds output directly to R Markdown document) #' @@ -221,7 +184,6 @@ ci_plot <- function(pivotName, week_minus_1 = week_minus_1, week_minus_2 = week_minus_2, week_minus_3 = week_minus_3, - use_breaks = TRUE, borders = TRUE){ # Input validation if (missing(pivotName) || is.null(pivotName) || pivotName == "") { @@ -282,35 +244,28 @@ ci_plot <- function(pivotName, # Create spans for borders joined_spans2 <- field_boundaries %>% - sf::st_transform(sf::st_crs(pivotShape)) %>% - dplyr::filter(field %in% pivotName) + sf::st_transform(sf::st_crs(pivotShape)) %>% dplyr::filter(field %in% pivotName) # Create the maps for different timepoints CImap_m2 <- create_CI_map(singlePivot_m2, AllPivots2, joined_spans2, show_legend = TRUE, legend_is_portrait = TRUE, - week = week_minus_2, age = age - 2, borders = borders, - use_breaks = use_breaks) + week = week_minus_2, age = age - 2, borders = borders) CImap_m1 <- create_CI_map(singlePivot_m1, AllPivots2, joined_spans2, show_legend = FALSE, legend_is_portrait = FALSE, - week = week_minus_1, age = age - 1, borders = borders, - use_breaks = use_breaks) + week = week_minus_1, age = age - 1, borders = borders) CImap <- create_CI_map(singlePivot, AllPivots2, joined_spans2, show_legend = FALSE, legend_is_portrait = FALSE, - week = week, age = age, borders = borders, - use_breaks = use_breaks) - - # Create difference maps - only show legend on the second one to avoid redundancy + week = week, age = age, borders = borders) + # Create difference maps - only show legend on the second one to avoid redundancy CI_max_abs_last_week <- create_CI_diff_map(abs_CI_last_week, AllPivots2, joined_spans2, - show_legend = FALSE, legend_is_portrait = TRUE, - week_1 = week, week_2 = week_minus_1, age = age, borders = borders, - use_breaks = use_breaks) + show_legend = FALSE, legend_is_portrait = FALSE, + 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 = TRUE, legend_is_portrait = TRUE, - week_1 = week, week_2 = week_minus_3, age = age, borders = borders, - use_breaks = use_breaks) + week_1 = week, week_2 = week_minus_3, age = age, borders = borders) # Arrange the maps tst <- tmap_arrange(CImap_m2, CImap_m1, CImap, CI_max_abs_last_week, CI_max_abs_three_week, nrow = 1) diff --git a/r_app/system_architecture.md b/r_app/system_architecture.md new file mode 100644 index 0000000..cf4775b --- /dev/null +++ b/r_app/system_architecture.md @@ -0,0 +1,409 @@ + +# SmartCane System Architecture + +## Overview + +The SmartCane system is a comprehensive agricultural intelligence platform that processes satellite imagery and farm data to provide agronomic insights for sugarcane farmers. The system architecture follows a modular, layered approach with clear separation of concerns between data acquisition, processing, and presentation. + +## Architectural Layers + +The SmartCane system follows a layered architecture pattern, which is a standard approach in software engineering for organizing complex systems. This architecture divides the system into distinct functional layers, each with specific responsibilities. While these layers aren't explicitly shown as separate visual elements in the diagrams, they help conceptualize how components are organized by their function: + + + +### 1. Data Acquisition Layer +- **Role**: Responsible for fetching raw data from external sources and user inputs +- **Components**: Manual Sentinel Hub Requests, Python API Downloader, User Input Interface +- **Functions**: Manual request setup on Sentinel Hub Requests Builder for specific client fields, connects to satellite data providers, downloads imagery, manages API credentials, performs preliminary data validation + +### 2. Processing Layer (SmartCane Engine) +- **Role**: Core analytical engine that transforms raw data into actionable insights +- **Components**: Python API Downloader (pre-processing), R Processing Engine (analytics) +- **Functions**: Image processing, cloud masking, crop index calculation, field boundary processing, statistical analysis, report generation + +### 3. Presentation Layer +- **Role**: Delivers insights to end users in accessible formats +- **Components**: Laravel Web App, Email Delivery System +- **Functions**: Interactive dashboards, visualization, report delivery, user management, project scheduling + +### 4. Data Storage Layer +- **Role**: Persists system data across processing cycles +- **Components**: File System, Database +- **Functions**: Stores raw imagery, processed rasters, analytical results, user data, configuration + +## Key Subsystems + +### 1. Python API Downloader +- **Role**: Acquires and pre-processes satellite imagery +- **Inputs**: API credentials, field boundaries, date parameters, evaluation scripts +- **Outputs**: Raw satellite images, merged GeoTIFFs, virtual rasters +- **Interfaces**: External satellite APIs (Planet via Sentinel Hub), file system +- **Orchestration**: Triggered by shell scripts from the Laravel application + +### 2. R Processing Engine +- **Role**: Performs advanced analytics and generates insights +- **Inputs**: Processed satellite imagery, field boundaries, harvest data, project parameters +- **Outputs**: Crop indices, mosaics, RDS data files, agronomic reports +- **Interfaces**: File system, report templates +- **Orchestration**: Triggered by shell scripts from the Laravel application + +### 3. Laravel Web Application +- **Role**: Provides operator interface and orchestrates the overall system +- **Inputs**: User data, configuration settings +- **Outputs**: Web interface, scheduling, report delivery +- **Interfaces**: Users, database, file system +- **Orchestration**: Controls execution of the SmartCane Engine via shell scripts + +### 4. Shell Script Orchestration +- **Role**: Bridges between web application and processing components +- **Functions**: Triggers processing workflows, manages execution environment, handles errors +- **Examples**: runcane.sh, runpython.sh, build_mosaic.sh, build_report.sh + +## Data Flow + +1. **Input Stage**: + - Operators (internal team) manually prepare and submit requests on Sentinel Hub Requests Builder for the specific fields of a client. + - Operators (internal team) provide farm data (field boundaries, harvest data) via the Laravel Web App. + - System schedules data acquisition for specific dates/regions + +2. **Acquisition Stage**: + - Laravel triggers Python API Downloader via shell scripts + - Python connects to satellite data providers and downloads raw imagery + - Downloaded data is stored in the file system + +3. **Processing Stage**: + - Laravel triggers R Processing Engine via shell scripts + - R scripts read satellite imagery and farm data + - Processing produces crop indices, analytics, and reports + - Results are stored in the file system + +4. **Output Stage**: + - Laravel Web App accesses processed results + - Reports are delivered to users via email + +## System Integration Points + +- **Python-R Integration**: Data handover via file system (GeoTIFF, virtual rasters) +- **Engine-Laravel Integration**: Orchestration via shell scripts, data exchange via file system and database +- **User-System Integration**: Web interface, file uploads, email notifications + +## Developed/Customized Elements + +- **Custom Cloud Masking Algorithm**: Specialized for agricultural applications in tropical regions +- **Crop Index Extraction Pipeline**: Tailored to sugarcane spectral characteristics +- **Reporting Templates**: Designed for agronomic decision support +- **Shell Script Orchestration**: Custom workflow management for the system's components + +## Strategic Role of Satellite Data + +Satellite data is central to the SmartCane system, providing: +- Regular, non-invasive field monitoring +- Detection of spatial patterns not visible from ground level +- Historical analysis of crop performance +- Early warning of crop stress or disease +- Quantification of field variability for precision agriculture + +## Pilot Utilization Sites + +The SmartCane system is currently operational in Mozambique, Kenya, and Tanzania. Future pilot deployments and expansions are planned for Uganda, Colombia, Mexico, Guatemala, South Africa, and Zambia. + +--- + +## System Architecture Diagrams + +Below are diagrams illustrating the system architecture from different perspectives. + +### Overall System Architecture + +This diagram provides a high-level overview of the complete SmartCane system, showing how major components interact. It focuses on the system boundaries and main data flows between the Python API Downloader, R Processing Engine, Laravel Web App, and data storage components. This view helps understand how the system works as a whole. + +```mermaid +graph TD + A["fa:fa-satellite External Satellite Data Providers API"] --> PyDL["fa:fa-download Python API Downloader"]; + C["fa:fa-users Users: Farm Data Input e.g., GeoJSON, Excel"] --> D{"fa:fa-laptop-code Laravel Web App"}; + + subgraph SmartCane System + PyDL --> G["fa:fa-folder-open File System: Raw Satellite Imagery, Rasters, RDS, Reports, Boundaries"]; + E["fa:fa-cogs R Processing Engine"] -- Reads --> G; + E -- Writes --> G; + + D -- Manages/Triggers --> F["fa:fa-terminal Shell Script Orchestration"]; + F -- Executes --> PyDL; + F -- Executes --> E; + + D -- Manages/Accesses --> G; + D -- Reads/Writes --> H["fa:fa-database Database: Project Metadata, Users, Schedules"]; + + E -- Generates --> I["fa:fa-file-alt Agronomic Reports: DOCX, HTML"]; + D -- Accesses/Delivers --> I; + end + + D --> J["fa:fa-desktop Users: Web Interface (future)"]; + I -- Via Email (SMTP) --> K["fa:fa-envelope Users: Email Reports"]; + + style E fill:#f9f,stroke:#333,stroke-width:2px + style D fill:#bbf,stroke:#333,stroke-width:2px + style PyDL fill:#ffdd57,stroke:#333,stroke-width:2px +``` + +### R Processing Engine Detail + +This diagram zooms in on the R Processing Engine subsystem, detailing the internal components and data flow. It shows how raw satellite imagery and field data progress through various R scripts to produce crop indices and reports. The diagram highlights the data transformation pipeline within this analytical core of the SmartCane system. + +```mermaid +graph TD + subgraph R Processing Engine + + direction TB + + subgraph Inputs + SatelliteImages["fa:fa-image Raw Satellite Imagery"] + FieldBoundaries["fa:fa-map-marker-alt Field Boundaries .geojson"] + HarvestData["fa:fa-file-excel Harvest Data .xlsx"] + ProjectParams["fa:fa-file-code Project Parameters .R"] + end + + subgraph Core R Scripts & Processes + ParamConfig("fa:fa-cogs parameters_project.R") + MosaicScript("fa:fa-images mosaic_creation.R") + CIExtractionScript("fa:fa-microscope ci_extraction.R") + ReportUtils("fa:fa-tools executive_report_utils.R") + DashboardRmd("fa:fa-tachometer-alt CI_report_dashboard_planet_enhanced.Rmd") + SummaryRmd("fa:fa-list-alt CI_report_executive_summary.Rmd") + end + + subgraph Outputs + WeeklyMosaics["fa:fa-file-image Weekly Mosaics .tif"] + CIDataRDS["fa:fa-database CI Data .rds"] + CIRasters["fa:fa-layer-group CI Rasters .tif"] + DashboardReport["fa:fa-chart-bar Dashboard Report .docx/.html"] + SummaryReport["fa:fa-file-invoice Executive Summary .docx/.html"] + end + + %% Data Flow + ProjectParams --> ParamConfig; + + SatelliteImages --> MosaicScript; + FieldBoundaries --> MosaicScript; + ParamConfig --> MosaicScript; + MosaicScript --> WeeklyMosaics; + + WeeklyMosaics --> CIExtractionScript; + FieldBoundaries --> CIExtractionScript; + ParamConfig --> CIExtractionScript; + CIExtractionScript --> CIDataRDS; + CIExtractionScript --> CIRasters; + + CIDataRDS --> ReportUtils; + CIRasters --> ReportUtils; + HarvestData --> ReportUtils; + ParamConfig --> ReportUtils; + + ReportUtils --> DashboardRmd; + ReportUtils --> SummaryRmd; + ParamConfig --> DashboardRmd; + ParamConfig --> SummaryRmd; + + DashboardRmd --> DashboardReport; + SummaryRmd --> SummaryReport; + + end + + ShellOrchestration["fa:fa-terminal Shell Scripts e.g., build_mosaic.sh, build_report.sh"] -->|Triggers| R_Processing_Engine["fa:fa-cogs R Processing Engine"] + + style R_Processing_Engine fill:#f9f,stroke:#333,stroke-width:2px + style Inputs fill:#ccf,stroke:#333,stroke-width:1px + style Outputs fill:#cfc,stroke:#333,stroke-width:1px + style Core_R_Scripts_Processes fill:#ffc,stroke:#333,stroke-width:1px +``` + +### Python API Downloader Detail + +This diagram focuses on the Python API Downloader subsystem, showing its internal components and workflow. It illustrates how API credentials, field boundaries, and other inputs are processed through various Python functions to download, process, and prepare satellite imagery. This view reveals the technical implementation details of the data acquisition layer. + +```mermaid +graph TD + subgraph Python API Downloader + + direction TB + + subgraph Inputs_Py [Inputs] + APICreds["fa:fa-key API Credentials (SH_CLIENT_ID, SH_CLIENT_SECRET)"] + DateRangeParams["fa:fa-calendar-alt Date Range Parameters (days_needed, specific_date)"] + GeoJSONInput["fa:fa-map-marker-alt Field Boundaries (pivot.geojson)"] + ProjectConfig["fa:fa-cogs Project Configuration (project_name, paths)"] + EvalScripts["fa:fa-file-code Evalscripts (JS for cloud masking & band selection)"] + end + + subgraph Core_Python_Logic_Py [Core Python Logic & Libraries] + SetupConfig["fa:fa-cog SentinelHubConfig & BYOC Definition"] + DateSlotGen["fa:fa-calendar-check Date Slot Generation (slots)"] + GeoProcessing["fa:fa-map GeoJSON Parsing & BBox Splitting (geopandas, BBoxSplitter)"] + AvailabilityCheck["fa:fa-search-location Image Availability Check (SentinelHubCatalog)"] + RequestHandler["fa:fa-paper-plane Request Generation (SentinelHubRequest, get_true_color_request_day)"] + DownloadClient["fa:fa-cloud-download-alt Image Download (SentinelHubDownloadClient, download_function)"] + MergeUtility["fa:fa-object-group Tile Merging (gdal.BuildVRT, gdal.Translate, merge_files)"] + CleanupUtility["fa:fa-trash-alt Intermediate File Cleanup (empty_folders)"] + end + + subgraph Outputs_Py [Outputs] + RawSatImages["fa:fa-file-image Raw Downloaded Satellite Imagery Tiles (response.tiff in dated subfolders)"] + MergedTifs["fa:fa-images Merged TIFs (merged_tif/{slot}.tif)"] + VirtualRasters["fa:fa-layer-group Virtual Rasters (merged_virtual/merged{slot}.vrt)"] + DownloadLogs["fa:fa-file-alt Console Output Logs (print statements)"] + end + + ExternalSatAPI["fa:fa-satellite External Satellite Data Providers API (Planet via Sentinel Hub)"] + + %% Data Flow for Python Downloader + APICreds --> SetupConfig; + DateRangeParams --> DateSlotGen; + GeoJSONInput --> GeoProcessing; + ProjectConfig --> SetupConfig; + ProjectConfig --> GeoProcessing; + ProjectConfig --> MergeUtility; + ProjectConfig --> CleanupUtility; + EvalScripts --> RequestHandler; + + DateSlotGen -- Available Slots --> AvailabilityCheck; + GeoProcessing -- BBox List --> AvailabilityCheck; + SetupConfig --> AvailabilityCheck; + AvailabilityCheck -- Filtered Slots & BBoxes --> RequestHandler; + + RequestHandler -- Download Requests --> DownloadClient; + SetupConfig --> DownloadClient; + DownloadClient -- Downloads Data From --> ExternalSatAPI; + ExternalSatAPI -- Returns Image Data --> DownloadClient; + DownloadClient -- Writes --> RawSatImages; + DownloadClient -- Generates --> DownloadLogs; + + RawSatImages --> MergeUtility; + MergeUtility -- Writes --> MergedTifs; + MergeUtility -- Writes --> VirtualRasters; + + end + + ShellOrchestratorPy["fa:fa-terminal Shell Scripts (e.g., runpython.sh triggering planet_download.ipynb)"] -->|Triggers| Python_API_Downloader["fa:fa-download Python API Downloader"]; + + style Python_API_Downloader fill:#ffdd57,stroke:#333,stroke-width:2px + style Inputs_Py fill:#cdeeff,stroke:#333,stroke-width:1px + style Outputs_Py fill:#d4efdf,stroke:#333,stroke-width:1px + style Core_Python_Logic_Py fill:#fff5cc,stroke:#333,stroke-width:1px + style ExternalSatAPI fill:#f5b7b1,stroke:#333,stroke-width:2px +``` + +### SmartCane Engine Integration Diagram + +This diagram illustrates the integration of Python and R components within the SmartCane Engine. Unlike the first diagram that shows the overall system, this one specifically focuses on how the two processing components interact with each other and the rest of the system. It emphasizes the orchestration layer and data flows between the core processing components and external systems. + +```mermaid +graph TD + %% External Systems & Users + Users_DataInput["fa:fa-user Users: Farm Data Input (GeoJSON, Excel, etc.)"] --> Laravel_WebApp; + ExternalSatAPI["fa:fa-satellite External Satellite Data Providers API"]; + + %% Main Application Components + Laravel_WebApp["fa:fa-globe Laravel Web App (Frontend & Control Plane)"]; + Shell_Orchestration["fa:fa-terminal Shell Script Orchestration (e.g., runcane.sh, runpython.sh, build_mosaic.sh)"]; subgraph SmartCane_Engine ["SmartCane Engine (Data Processing Core)"] + direction TB + Python_Downloader["fa:fa-download Python API Downloader"]; + R_Engine["fa:fa-chart-line R Processing Engine"]; + end + %% Data Storage + FileSystem["fa:fa-folder File System (Raw Imagery, Rasters, RDS, Reports, Boundaries)"]; + Database["fa:fa-database Database (Project Metadata, Users, Schedules)"]; + + %% User Outputs + Users_WebView["fa:fa-desktop Users: Web Interface (future)"]; + Users_EmailReports["fa:fa-envelope Users: Email Reports (Agronomic Reports)"]; + AgronomicReports["fa:fa-file-alt Agronomic Reports (DOCX, HTML)"]; + + %% --- Data Flows & Interactions --- + + %% Laravel to Orchestration & Engine + Laravel_WebApp -- Manages/Triggers --> Shell_Orchestration; + Shell_Orchestration -- Executes --> Python_Downloader; + Shell_Orchestration -- Executes --> R_Engine; + + %% Python Downloader within Engine + ExternalSatAPI -- Satellite Data --> Python_Downloader; + Python_Downloader -- Writes Raw Data --> FileSystem; + %% Inputs to Python (simplified for this view - details in Python-specific diagram) + %% Laravel_WebApp -- Provides Config/Boundaries --> Python_Downloader; + + + %% R Engine within Engine + %% Inputs to R (simplified - details in R-specific diagram) + %% Laravel_WebApp -- Provides Config/Boundaries --> R_Engine; + R_Engine -- Reads Processed Data/Imagery --> FileSystem; + R_Engine -- Writes Derived Products --> FileSystem; + R_Engine -- Generates --> AgronomicReports; + + %% Laravel interaction with Data Storage + Laravel_WebApp -- Manages/Accesses --> FileSystem; + Laravel_WebApp -- Reads/Writes --> Database; + + %% Output Delivery + Laravel_WebApp --> Users_WebView; + AgronomicReports --> Users_EmailReports; + %% Assuming a mechanism like SMTP, potentially triggered by Laravel or R-Engine completion + Laravel_WebApp -- Delivers/Displays --> AgronomicReports; + + + %% Styling + style SmartCane_Engine fill:#e6ffe6,stroke:#333,stroke-width:2px + style Python_Downloader fill:#ffdd57,stroke:#333,stroke-width:2px + style R_Engine fill:#f9f,stroke:#333,stroke-width:2px + style Laravel_WebApp fill:#bbf,stroke:#333,stroke-width:2px + style Shell_Orchestration fill:#f0ad4e,stroke:#333,stroke-width:2px + style FileSystem fill:#d1e0e0,stroke:#333,stroke-width:1px + style Database fill:#d1e0e0,stroke:#333,stroke-width:1px + style ExternalSatAPI fill:#f5b7b1,stroke:#333,stroke-width:2px + style AgronomicReports fill:#d4efdf,stroke:#333,stroke-width:1px +``` + +## Future Directions + +The SmartCane platform is poised for significant evolution, with several key enhancements and new capabilities planned to further empower users and expand its utility: + +- **Advanced Management Dashboard**: Development of a more comprehensive and interactive management dashboard to provide users with deeper insights and greater control over their operations. +- **Enhanced Yield Prediction Models**: Improving the accuracy and granularity of yield predictions by incorporating more variables and advanced machine learning techniques. +- **Integrated Weather and Irrigation Advice**: Leveraging weather forecast data and soil moisture information (potentially from new data sources) to provide precise irrigation scheduling and weather-related agronomic advice. +- **AI-Guided Agronomic Advice**: Implementing sophisticated AI algorithms to analyze integrated data (satellite, weather, soil, farm practices) and offer tailored, actionable agronomic recommendations. +- **Automated Advice Generation**: Developing capabilities for the system to automatically generate and disseminate critical advice and alerts to users based on real-time data analysis. +- **Expanded Data Source Integration**: + - **Radar Data**: Incorporating radar satellite imagery (e.g., Sentinel-1) for all-weather monitoring capabilities, particularly useful during cloudy seasons for assessing crop structure, soil moisture, and biomass. + - **IoT and Ground Sensors**: Integrating data from in-field IoT devices and soil sensors for highly localized and continuous monitoring of environmental and soil conditions. +- **Client-Facing Portal**: Exploration and potential development of a client-facing portal to allow end-users direct access to their data, dashboards, and reports, complementing the current internal management interface. + +These future developments aim to transform SmartCane into an even more powerful decision support system, fostering sustainable and efficient agricultural practices. + +## Conclusion and Integration Summary + +The SmartCane system architecture demonstrates a well-integrated solution that combines different technologies and subsystems to solve complex agricultural challenges. Here is a summary of how the key subsystems work together: + +### Subsystem Integration + +1. **Data Flow Sequence** + - The Laravel Web App initiates the workflow and manages user interactions + - Shell scripts orchestrate the execution sequence of the processing subsystems + - The Python API Downloader acquires raw data from external sources + - The R Processing Engine transforms this data into actionable insights + - Results flow back to users through the web interface and email reports + +2. **Technology Integration** + - **Python + R**: Different programming languages are leveraged for their respective strengths—Python for API communication and data acquisition, R for statistical analysis and report generation + - **Laravel + Processing Engine**: Clear separation between web presentation layer and computational backend + - **File System + Database**: Hybrid data storage approach with file system for imagery and reports, database for metadata and user information + +3. **Key Integration Mechanisms** + - **File System Bridge**: The different subsystems primarily communicate through standardized file formats (GeoTIFF, GeoJSON, RDS, DOCX) + - **Shell Script Orchestration**: Acts as the "glue" between subsystems, ensuring proper execution sequence and environment setup + - **Standardized Data Formats**: Use of widely-accepted geospatial and data formats enables interoperability + +4. **Extensibility and Scalability** + - The modular architecture allows for replacement or enhancement of individual components + - The clear subsystem boundaries enable parallel development and testing + - Standard interfaces simplify integration of new data sources, algorithms, or output methods + +The SmartCane architecture balances complexity with maintainability by using well-established technologies and clear boundaries between subsystems. The separation of concerns between data acquisition, processing, and presentation layers ensures that changes in one area minimally impact others, while the consistent data flow pathways ensure that information moves smoothly through the system. diff --git a/r_app/tests/test_date_functions.R b/r_app/tests/test_date_functions.R deleted file mode 100644 index 9b56dcb..0000000 --- a/r_app/tests/test_date_functions.R +++ /dev/null @@ -1,68 +0,0 @@ -# test_date_functions.R -# -# Tests for date-related functions in ci_extraction_utils.R -# - -# Load the test framework -source("tests/test_framework.R") - -# Set up test environment -env <- setup_test_env() - -# Load the functions to test -source("../ci_extraction_utils.R") - -# Test the date_list function -test_that("date_list creates correct date sequences", { - # Test with a specific date and offset - dates <- date_list(as.Date("2023-01-15"), 7) - - # Check the structure - expect_type(dates, "list") - expect_equal(names(dates), c("week", "year", "days_filter", "start_date", "end_date")) - - # Check the values - expect_equal(dates$week, lubridate::week(as.Date("2023-01-09"))) - expect_equal(dates$year, 2023) - expect_equal(dates$start_date, as.Date("2023-01-09")) - expect_equal(dates$end_date, as.Date("2023-01-15")) - expect_equal(length(dates$days_filter), 7) - expect_equal(dates$days_filter[1], "2023-01-09") - expect_equal(dates$days_filter[7], "2023-01-15") - - # Test with a different offset - dates_short <- date_list(as.Date("2023-01-15"), 3) - expect_equal(length(dates_short$days_filter), 3) - expect_equal(dates_short$days_filter, c("2023-01-13", "2023-01-14", "2023-01-15")) - - # Test with string date - dates_string <- date_list("2023-01-15", 5) - expect_equal(dates_string$days_filter, - c("2023-01-11", "2023-01-12", "2023-01-13", "2023-01-14", "2023-01-15")) - - # Test error handling - expect_error(date_list("invalid-date", 7), - "Invalid end_date provided") - expect_error(date_list("2023-01-15", -1), - "Invalid offset provided") -}) - -# Test the date_extract function -test_that("date_extract correctly extracts dates from file paths", { - # Test with various file path formats - expect_equal(date_extract("/some/path/2023-01-15_image.tif"), "2023-01-15") - expect_equal(date_extract("path/to/planet_2023-01-15.tif"), "2023-01-15") - expect_equal(date_extract("c:\\path\\with\\windows\\2023-01-15_file.tif"), "2023-01-15") - expect_equal(date_extract("2023-01-15.tif"), "2023-01-15") - expect_equal(date_extract("prefix-2023-01-15-suffix.tif"), "2023-01-15") - - # Test with invalid file paths - expect_warning(result <- date_extract("no-date-here.tif"), "Could not extract date") - expect_true(is.na(result)) -}) - -# Clean up -teardown_test_env() - -# Print success message -cat("Date function tests completed successfully\n") \ No newline at end of file diff --git a/r_app/tests/test_framework.R b/r_app/tests/test_framework.R deleted file mode 100644 index e41d2dc..0000000 --- a/r_app/tests/test_framework.R +++ /dev/null @@ -1,120 +0,0 @@ -# test_framework.R -# -# TEST FRAMEWORK FOR SMARTCANE -# =========================== -# This script provides a simple testing framework for the SmartCane project. -# It includes utilities for setting up test environments and running tests. -# - -# Install required packages if not available -if (!require("testthat", quietly = TRUE)) { - install.packages("testthat", repos = "https://cran.rstudio.com/") -} -library(testthat) - -# Define paths for testing -test_root <- file.path(normalizePath(".."), "tests") -test_data_dir <- file.path(test_root, "test_data") - -# Create test directories if they don't exist -dir.create(test_data_dir, recursive = TRUE, showWarnings = FALSE) - -# Set up a test environment with all necessary data -setup_test_env <- function() { - # Add working directory to the path - .libPaths(c(.libPaths(), normalizePath(".."))) - - # Source required files with minimal dependencies - tryCatch({ - source(file.path(normalizePath(".."), "packages.R")) - skip_package_loading <- TRUE - - # Load minimal dependencies for tests - required_packages <- c("lubridate", "stringr", "purrr", "dplyr", "testthat") - for (pkg in required_packages) { - if (!require(pkg, character.only = TRUE, quietly = TRUE)) { - warning(paste("Package", pkg, "not available, some tests may fail")) - } - } - }, error = function(e) { - warning("Error loading dependencies: ", e$message) - }) - - # Set up test logging - assign("log_message", function(message, level = "INFO") { - cat(paste0("[", level, "] ", message, "\n")) - }, envir = .GlobalEnv) - - # Create a mock project structure - test_project <- list( - project_dir = "test_project", - data_dir = test_data_dir, - daily_CI_vals_dir = file.path(test_data_dir, "extracted_ci", "daily_vals"), - cumulative_CI_vals_dir = file.path(test_data_dir, "extracted_ci", "cumulative_vals"), - merged_final = file.path(test_data_dir, "merged_final"), - daily_vrt = file.path(test_data_dir, "daily_vrt") - ) - - # Create the directories - for (dir in test_project) { - if (is.character(dir)) { - dir.create(dir, recursive = TRUE, showWarnings = FALSE) - } - } - - return(test_project) -} - -# Clean up test environment -teardown_test_env <- function() { - # Clean up only test-created files if needed - # We'll leave the main directories for inspection -} - -# Run all tests in a directory -run_tests <- function(pattern = "^test_.+\\.R$") { - test_files <- list.files( - path = test_root, - pattern = pattern, - full.names = TRUE - ) - - # Exclude this file - test_files <- test_files[!grepl("test_framework\\.R$", test_files)] - - if (length(test_files) == 0) { - cat("No test files found matching pattern:", pattern, "\n") - return(FALSE) - } - - cat("Found", length(test_files), "test files:\n") - cat(paste(" -", basename(test_files)), sep = "\n") - cat("\n") - - # Run each test file - results <- lapply(test_files, function(file) { - cat("Running tests in:", basename(file), "\n") - tryCatch({ - source(file, local = TRUE) - cat("✓ Tests completed\n\n") - TRUE - }, error = function(e) { - cat("✗ Error:", e$message, "\n\n") - FALSE - }) - }) - - # Summary - success_count <- sum(unlist(results)) - cat("\nTest Summary:", success_count, "of", length(test_files), - "test files completed successfully\n") - - return(all(unlist(results))) -} - -# If this script is run directly, run all tests -if (!interactive() && (basename(sys.frame(1)$ofile) == "test_framework.R")) { - setup_test_env() - run_tests() - teardown_test_env() -} \ No newline at end of file diff --git a/r_app/tests/test_report_utils.R b/r_app/tests/test_report_utils.R deleted file mode 100644 index 611f7db..0000000 --- a/r_app/tests/test_report_utils.R +++ /dev/null @@ -1,280 +0,0 @@ -# test_report_utils.R -# -# Tests for visualization functions in report_utils.R -# - -# Load the test framework -source("tests/test_framework.R") - -# Set up test environment -env <- setup_test_env() - -# Required libraries for testing -library(testthat) -library(terra) -library(sf) -library(dplyr) -library(ggplot2) - -# Load the functions to test -source("../report_utils.R") - -# Create mock data for testing -create_mock_data <- function() { - # Create a simple raster for testing - r <- terra::rast(nrows=10, ncols=10, xmin=0, xmax=10, ymin=0, ymax=10, vals=1:100) - names(r) <- "CI" - - # Create a simple field boundary - field_boundaries <- sf::st_sf( - field = c("Field1", "Field2"), - sub_field = c("A", "B"), - geometry = sf::st_sfc( - sf::st_polygon(list(rbind(c(1,1), c(5,1), c(5,5), c(1,5), c(1,1)))), - sf::st_polygon(list(rbind(c(6,6), c(9,6), c(9,9), c(6,9), c(6,6)))) - ), - crs = sf::st_crs(r) - ) - - # Create mock harvest data - harvesting_data <- data.frame( - field = c("Field1", "Field2"), - sub_field = c("A", "B"), - age = c(100, 150), - season_start = as.Date(c("2023-01-01", "2023-02-01")), - year = c(2023, 2023) - ) - - # Create mock CI quadrant data - ci_quadrant <- data.frame( - field = rep(c("Field1", "Field2"), each=10), - sub_field = rep(c("A", "B"), each=10), - Date = rep(seq(as.Date("2023-01-01"), by="week", length.out=10), 2), - DOY = rep(1:10, 2), - cumulative_CI = rep(cumsum(1:10), 2), - value = rep(1:10, 2), - season = rep(2023, 20), - model = rep(c("northwest", "northeast", "southwest", "southeast"), 5) - ) - - return(list( - raster = r, - field_boundaries = field_boundaries, - harvesting_data = harvesting_data, - ci_quadrant = ci_quadrant - )) -} - -# Test the create_CI_map function -test_that("create_CI_map creates a valid tmap object", { - mock_data <- create_mock_data() - - # Test with all required parameters - map <- create_CI_map( - pivot_raster = mock_data$raster, - pivot_shape = mock_data$field_boundaries[1,], - pivot_spans = mock_data$field_boundaries[1,], - week = "01", - age = 10, - borders = TRUE, - use_breaks = TRUE - ) - - # Check if it returned a tmap object - expect_true("tmap" %in% class(map)) - - # Test with missing parameters - expect_error(create_CI_map(pivot_shape = mock_data$field_boundaries[1,], - pivot_spans = mock_data$field_boundaries[1,], - week = "01", age = 10), - "pivot_raster is required") - - expect_error(create_CI_map(pivot_raster = mock_data$raster, - pivot_spans = mock_data$field_boundaries[1,], - week = "01", age = 10), - "pivot_shape is required") -}) - -# Test the create_CI_diff_map function -test_that("create_CI_diff_map creates a valid tmap object", { - mock_data <- create_mock_data() - - # Test with all required parameters - map <- create_CI_diff_map( - pivot_raster = mock_data$raster, - pivot_shape = mock_data$field_boundaries[1,], - pivot_spans = mock_data$field_boundaries[1,], - week_1 = "01", - week_2 = "02", - age = 10, - borders = TRUE, - use_breaks = TRUE - ) - - # Check if it returned a tmap object - expect_true("tmap" %in% class(map)) - - # Test with missing parameters - expect_error(create_CI_diff_map(pivot_shape = mock_data$field_boundaries[1,], - pivot_spans = mock_data$field_boundaries[1,], - week_1 = "01", week_2 = "02", age = 10), - "pivot_raster is required") - - expect_error(create_CI_diff_map(pivot_raster = mock_data$raster, - pivot_spans = mock_data$field_boundaries[1,], - week_1 = "01", age = 10), - "week_1 and week_2 parameters are required") -}) - -# Test the ci_plot function -test_that("ci_plot handles input parameters correctly", { - mock_data <- create_mock_data() - - # Capture output to avoid cluttering the test output - temp_file <- tempfile() - sink(temp_file) - - # Test with all required parameters - should not throw an error - expect_error( - ci_plot( - pivotName = "Field1", - field_boundaries = mock_data$field_boundaries, - current_ci = mock_data$raster, - ci_minus_1 = mock_data$raster, - ci_minus_2 = mock_data$raster, - last_week_diff = mock_data$raster, - three_week_diff = mock_data$raster, - harvesting_data = mock_data$harvesting_data, - week = "01", - week_minus_1 = "52", - week_minus_2 = "51", - week_minus_3 = "50", - use_breaks = TRUE, - borders = TRUE - ), - NA # Expect no error - ) - - # Test with missing parameters - expect_error( - ci_plot(), - "pivotName is required" - ) - - # Test with invalid field name - expect_error( - ci_plot( - pivotName = "NonExistentField", - field_boundaries = mock_data$field_boundaries, - current_ci = mock_data$raster, - ci_minus_1 = mock_data$raster, - ci_minus_2 = mock_data$raster, - last_week_diff = mock_data$raster, - three_week_diff = mock_data$raster, - harvesting_data = mock_data$harvesting_data - ), - regexp = NULL # We expect some error related to the field not being found - ) - - # Reset output - sink() - unlink(temp_file) -}) - -# Test the cum_ci_plot function -test_that("cum_ci_plot handles input parameters correctly", { - mock_data <- create_mock_data() - - # Capture output to avoid cluttering the test output - temp_file <- tempfile() - sink(temp_file) - - # Test with all required parameters - should not throw an error - expect_error( - cum_ci_plot( - pivotName = "Field1", - ci_quadrant_data = mock_data$ci_quadrant, - plot_type = "value", - facet_on = FALSE, - x_unit = "days" - ), - NA # Expect no error - ) - - # Test with different plot types - expect_error( - cum_ci_plot( - pivotName = "Field1", - ci_quadrant_data = mock_data$ci_quadrant, - plot_type = "CI_rate" - ), - NA # Expect no error - ) - - expect_error( - cum_ci_plot( - pivotName = "Field1", - ci_quadrant_data = mock_data$ci_quadrant, - plot_type = "cumulative_CI" - ), - NA # Expect no error - ) - - # Test with invalid plot type - expect_error( - cum_ci_plot( - pivotName = "Field1", - ci_quadrant_data = mock_data$ci_quadrant, - plot_type = "invalid_type" - ), - "plot_type must be one of: 'value', 'CI_rate', or 'cumulative_CI'" - ) - - # Test with missing parameters - expect_error( - cum_ci_plot(), - "pivotName is required" - ) - - # Reset output - sink() - unlink(temp_file) -}) - -# Test the get_week_path function -test_that("get_week_path returns correct path", { - # Test with valid inputs - path <- get_week_path( - mosaic_path = "ci_max_mosaics", - input_date = "2023-01-15", - week_offset = 0 - ) - - # Extract the week number and year from the path - expect_match(path, "week_02_2023\\.tif$", all = FALSE) # Week 2 of 2023 - - # Test with offset - path_minus_1 <- get_week_path( - mosaic_path = "ci_max_mosaics", - input_date = "2023-01-15", - week_offset = -1 - ) - expect_match(path_minus_1, "week_01_2023\\.tif$", all = FALSE) - - # Test with missing parameters - expect_error( - get_week_path(input_date = "2023-01-15", week_offset = 0), - "mosaic_path is required" - ) - - expect_error( - get_week_path(mosaic_path = "ci_max_mosaics", week_offset = 0), - "input_date is required" - ) -}) - -# Clean up -teardown_test_env() - -# Print success message -cat("Report utility function tests completed successfully\n") \ No newline at end of file