Add support to read fitbit data from csv files

config.yaml

@@ -31,7 +31,7 @@ PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files
 SENSOR_DATA:
   PHONE:
     SOURCE: 
-      TYPE: DATABASE
+      TYPE: DATABASE # Phone only supports DATABASE for now
       DATABASE_GROUP: *database_group
       DEVICE_ID_COLUMN: device_id # column name
     TIMEZONE: 
@@ -39,7 +39,7 @@ SENSOR_DATA:
       VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
   FITBIT:
     SOURCE: 
-      TYPE: DATABASE # DATABASE or CSV_FILES (set each FITBIT_SENSOR TABLE attribute accordingly)
+      TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path)
       DATABASE_GROUP: *database_group
       DEVICE_ID_COLUMN: device_id # column name
     TIMEZONE: 

rules/preprocessing.smk

@@ -40,7 +40,8 @@ rule download_phone_data:
 
 rule download_fitbit_data:
     input:
-        "data/external/participant_files/{pid}.yaml"
+        participant_file = "data/external/participant_files/{pid}.yaml",
+        input_file = "" if config["SENSOR_DATA"]["FITBIT"]["SOURCE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]["CSV"][str(wildcards.fitbit_data_type).upper()] 
     params:
         source = config["SENSOR_DATA"]["FITBIT"]["SOURCE"],
         sensor = "fitbit_" + "{sensor}",

src/data/download_fitbit_data.R

@@ -6,7 +6,8 @@ library(stringr)
 library(yaml)
 
 
-participant_file <- snakemake@input[[1]]
+participant_file <- snakemake@input[["participant_file"]]
+input_file <- snakemake@input[["input_file"]]
 source <- snakemake@params[["source"]]
 sensor <- snakemake@params[["sensor"]]
 table <- snakemake@params[["table"]]
@@ -25,16 +26,20 @@ if(source$TYPE == "DATABASE"){
   query <- paste0("SELECT * FROM ", table, " WHERE ",source$DEVICE_ID_COLUMN," IN ('", paste0(device_ids, collapse = "','"), "')")
   sensor_data <- dbGetQuery(dbEngine, query)
   dbDisconnect(dbEngine)
-  sensor_data <- sensor_data %>%
+} else if(source$TYPE == "FILES"){
+  sensor_data <- read_csv_chunked(input_file, callback = DataFrameCallback$new(function(x, pos) subset(x,x[[source$DEVICE_ID_COLUMN]] %in% device_ids)), progress = T, chunk_size = 50000)
+  if(is.null(sensor_data)) # emtpy file
+    sensor_data <- read.csv(input_file)
+}
+
+sensor_data <- sensor_data %>%
   rename(device_id = source$DEVICE_ID_COLUMN) %>% 
   mutate(device_id = unified_device_id) # Unify device_id
 
-  if(FALSE) # For MoSHI use, we didn't split fitbit sensors into different tables
+if(FALSE) # For MoSHI use, we didn't split fitbit sensors into different tables
   sensor_data <- sensor_data %>% filter(fitbit_data_type == str_split(sensor, "_", simplify = TRUE)[[2]])
 
-  # Droping duplicates on all columns except for _id or id
-  sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id"))))
+# Droping duplicates on all columns except for _id or id
+sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id"))))
 
-  write_csv(sensor_data, sensor_file)
-
-}
+write_csv(sensor_data, sensor_file)

src/data/fitbit_parse_calories.py

@@ -38,8 +38,8 @@ if table_format == "JSON":
     json_raw = pd.read_csv(snakemake.input[0])
     summary, intraday = parseCaloriesData(json_raw)
 elif table_format == "CSV":
-    summary = pd.read_csv(snakemake.input[0])
-    intraday = pd.read_csv(snakemake.input[1])
+    summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
+    intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
 
 if summary.shape[0] > 0:
     summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6

src/data/fitbit_parse_heartrate.py

@@ -125,8 +125,8 @@ if table_format == "JSON":
     json_raw = pd.read_csv(snakemake.input[0])
     summary, intraday = parseHeartrateData(json_raw)
 elif table_format == "CSV":
-    summary = pd.read_csv(snakemake.input[0])
-    intraday = pd.read_csv(snakemake.input[1])
+    summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
+    intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
 
 if summary.shape[0] > 0:
     summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6

src/data/fitbit_parse_sleep.py

@@ -212,8 +212,8 @@ if table_format == "JSON":
     json_raw = pd.read_csv(snakemake.input[0])
     summary, intraday = parseSleepData(json_raw)
 elif table_format == "CSV":
-    summary = pd.read_csv(snakemake.input[0])
-    intraday = pd.read_csv(snakemake.input[1])
+    summary = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
+    intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
 
 if summary.shape[0] > 0:
     summary["start_timestamp"] = summary["local_start_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6

src/data/fitbit_parse_steps.py

@@ -41,8 +41,8 @@ if table_format == "JSON":
     json_raw = pd.read_csv(snakemake.input[0])
     summary, intraday = parseStepsData(json_raw)
 elif table_format == "CSV":
-    summary = pd.read_csv(snakemake.input[0])
-    intraday = pd.read_csv(snakemake.input[1])
+    summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
+    intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
 
 if summary.shape[0] > 0:
     summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6