Add light features

Snakefile

@@ -33,6 +33,9 @@ rule all:
         expand("data/processed/{pid}/screen_{day_segment}.csv",
                             pid = config["PIDS"],
                             day_segment = config["SCREEN"]["DAY_SEGMENTS"]),
+        expand("data/processed/{pid}/light_{day_segment}.csv",
+                            pid = config["PIDS"],
+                            day_segment = config["LIGHT"]["DAY_SEGMENTS"]),
         # Reports
         expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),
         expand("reports/figures/{pid}/compliance_heatmap.html", pid=config["PIDS"]),

config.yaml

@@ -72,4 +72,8 @@ SCREEN:
   DAY_SEGMENTS: *day_segments
   METRICS_EVENTS: ["counton", "countunlock", "unlocksperminute"]
   METRICS_DELTAS: ["sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
-  EPISODES: ["unlock"]
+  EPISODES: ["unlock"]
+
+LIGHT:
+  DAY_SEGMENTS: *day_segments
+  METRICS: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]

rules/features.snakefile

@@ -108,3 +108,14 @@ rule screen_metrics:
         "data/processed/{pid}/screen_{day_segment}.csv"
     script:
         "../src/features/screen_metrics.py"
+
+rule light_metrics:
+    input:
+        "data/raw/{pid}/light_with_datetime.csv",
+    params:
+        day_segment = "{day_segment}",
+        metrics = config["LIGHT"]["METRICS"],
+    output:
+        "data/processed/{pid}/light_{day_segment}.csv"
+    script:
+        "../src/features/light_metrics.py"

src/features/light_metrics.py

@@ -0,0 +1,32 @@
+import pandas as pd
+import numpy as np
+
+light_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
+day_segment = snakemake.params["day_segment"]
+metrics = snakemake.params["metrics"]
+
+light_features = pd.DataFrame(columns=["local_date"] + ["light_" + day_segment + "_" + x for x in metrics])
+if not light_data.empty:
+    if day_segment != "daily":
+        light_data =light_data[light_data["local_day_segment"] == day_segment]
+    
+    if not light_data.empty:
+        light_features = pd.DataFrame()
+        if "count" in metrics:
+            light_features["light_" + day_segment + "_count"] = light_data.groupby(["local_date"]).count()["timestamp"]
+        
+        # get light ambient luminance related features
+        if "maxlux" in metrics:
+            light_features["light_" + day_segment + "_maxlux"] = light_data.groupby(["local_date"])["double_light_lux"].max()
+        if "minlux" in metrics:
+            light_features["light_" + day_segment + "_minlux"] = light_data.groupby(["local_date"])["double_light_lux"].min()
+        if "avglux" in metrics:
+            light_features["light_" + day_segment + "_avglux"] = light_data.groupby(["local_date"])["double_light_lux"].mean()
+        if "medianlux" in metrics:
+            light_features["light_" + day_segment + "_medianlux"] = light_data.groupby(["local_date"])["double_light_lux"].median()
+        if "stdlux" in metrics:
+            light_features["light_" + day_segment + "_stdlux"] = light_data.groupby(["local_date"])["double_light_lux"].std()
+        
+        light_features = light_features.fillna(0).reset_index()
+
+light_features.to_csv(snakemake.output[0], index=False)