r files changes

exec/merge_photo_csv.r

@@ -11,9 +11,9 @@ chemin <- "../data/photo_measures/"
 fichiers <- list.files(path = chemin, pattern = "\\.csv$", full.names = TRUE)
 
 liste_data <- lapply(fichiers, read.csv, header = TRUE)
-partial_list_data <- liste_data[3:6]
+#partial_list_data <- liste_data[3:6]
 
-photo <- do.call(rbind, partial_list_data)
+photo <- do.call(rbind, liste_data)
 
 csv_name <- "merged_photo_data.csv"
 csv_path_name <- paste(c(chemin, csv_name), collapse = "")

exec/plot_measures.r

@@ -19,15 +19,16 @@ summary(liste_data)
 print(typeof(liste_data))
 
 # partial_list_data <- liste_data[1:2]
-partial_list_data <- liste_data[3:6]
+partial_list_data <- liste_data[7:8]
 photo <- do.call(rbind, partial_list_data)
 
 max_val_sensor = 254
 photo <- photo %>%
   mutate(across(starts_with("Photo_sensor"), ~ {
-    .x <- sqrt(.x)
+    # .x <- sqrt(.x)
-    # .x <- (.x*-1) + max_val_sensor
+    # max_val_sensor = sqrt(max_val_sensor)
-    # .x <- as.numeric(scale(.x, center = TRUE, scale = TRUE))
+    .x <- (.x*-1) + max_val_sensor
+    .x <- as.numeric(scale(.x, center = TRUE, scale = TRUE))
   }))
 
 photo$time <- as.POSIXct(photo$Epoch)

exec/random_forest_predict.r

@@ -5,19 +5,20 @@ library(ggplot2)
 library(lubridate)
 library(dplyr)
 library(randomForest)
+library(suntools)
 
 setwd("~/Documents/PlatformIO/Projects/Robot_Go_West/arduino-photometrics/exec")
 
 # Load
-solar <- read.csv("../data/solar_pos_data/solar_data_2026-01-05_to_2026-01-10.csv", header=TRUE)
+# solar <- read.csv("../data/solar_pos_data/solar_data_2026-01-13_to_2026-01-16.csv", header=TRUE)
-photo <- read.csv("../data/photo_measures/merged_photo_data.csv", header=TRUE)
+photo <- read.csv("../data/photo_measures/arduino_data_package_auto_20260116_094653.csv", header=TRUE)
 
 # Time type changes
-photo$time <- as.POSIXct(photo$Epoch)
+# photo$time <- as.POSIXct(photo$Epoch)
 
 photo <- photo %>%
   mutate(
-    datetime = as.POSIXct(Epoch, origin = "1970-01-01", tz = "UTC"),
+    datetime = as.POSIXct(Epoch, origin = "1970-01-01", tz = "Europe/Paris"),
     
     jour = as.Date(datetime),
     num_jour = as.numeric(format(datetime, "%j")),
@@ -30,8 +31,6 @@ photo <- photo %>%
     cos_hour = cos(rad_hour)
   )
 
-# Transform data to improve learning during the training phase
-solar$sin_azimut <- sin(solar$azimut)
 
 # Same but normalised values are square root to highlight little light variations
 max_val_sensor = 254
@@ -46,22 +45,33 @@ photo <- photo %>%
 photo <- photo %>%
   select(where(~ !all(is.na(.x))))
 
-# select the nearest time raw of the sun position
-max_timestamp = as.integer(max(photo$Epoch))
-min_timestamp = as.integer(min(photo$Epoch))
-elapsed_time = photo$Epoch[4] - photo$Epoch[3] 
-
-filtered_solar <- solar %>%
-  filter(utime > (min_timestamp - elapsed_time) & 
-           utime < (max_timestamp + elapsed_time))
-
-remove(solar)
 
 
-# merge
+# retreive solar pos
-binded <- bind_cols(filtered_solar, photo)
 
-remove(filtered_solar, photo)
+unix_time <- as.numeric(photo$datetime)
+
+lat <- 44.7912
+lon <- -0.6078
+
+coords <- matrix(c(lon, lat), nrow = 1)
+
+positions <- solarpos(coords, photo$datetime)
+
+df_soleil <- data.frame(
+  timestamp = photo$datetime,
+  utime = unix_time,
+  azimut = positions[, 1],
+  elevation = positions[, 2]
+)
+
+
+# Transform data to improve learning during the training phase
+df_soleil$sin_azimut <- sin(df_soleil$azimut)
+
+binded <- bind_cols(df_soleil, photo)
+
+remove(df_soleil)
 
 # Check elapsed time
 binded$gap_time <- abs(binded$utime - binded$Epoch)

exec/sun_pos.r

@@ -8,8 +8,8 @@ library(lubridate)
 lat <- 44.7912
 lon <- -0.6078
 tz  <- "Europe/Paris"
-d_deb <- "2026-01-05"
+d_deb <- "2026-01-13"
-d_fin <- "2026-01-10"
+d_fin <- "2026-01-16"
 date_debut <- as.POSIXct(d_deb, tz = tz)
 date_fin   <- as.POSIXct(d_fin, tz = tz)
 

src/main.cpp

@@ -15,7 +15,7 @@ uint8_t ledPin = 2, alarm_pin = 19, decTemp = 4, nb_average_measure = 10; // nb
 const uint8_t nbPhotoSensor = 6, nbTempSensor = 1;
 uint8_t analogPin [nbPhotoSensor] = {A0, A1, A2 ,A3 ,A5 ,A6}, schedule = 0, photo_sensor_size = sizeof(uint8_t), temp_sensor_size = sizeof(uint8_t);
 int32_t min_res [nbPhotoSensor] = {128- 30, 160-30, 193-80, 96-10, 323-180, 96-10}; // Manual measurement of personal sensors
-int32_t max_res [nbPhotoSensor] = {2062273, 5554006, 784809, 4755895, 1939035, 289546}; // Manual measurement of personal sensors
+int32_t max_res [nbPhotoSensor] = {2062273-206227, 5554006-555400, 784809-78480, 4755895-475589, 1939035-193903, 289546-28954}; // Manual measurement of personal sensors // less 10 %, there are more résidual light in the measurement working (city context) than the box calibration
 uint32_t time_sec_sum;
 bool winter = true, timestamping = true, photo_sensor = true, temp_sensor = true, awake = true;
 static byte state = true;