Select days to analyse

Co-authored-by: JulioV <juliovhz@gmail.com>

Snakefile

@@ -4,9 +4,18 @@ include: "rules/preprocessing.snakefile"
 include: "rules/features.snakefile"
 include: "rules/models.snakefile"
 include: "rules/reports.snakefile"
+include: "rules/mystudy.snakefile" # You can add snakfiles with rules tailored to your project
 
 rule all:
     input:
+        # My study (this is an example of a rule created specifically for a study)
+        expand("data/interim/{pid}/days_to_analyse_{days_before_surgery}_{days_in_hospital}_{days_after_discharge}.csv",
+                            pid=config["PIDS"],
+                            days_before_surgery = config["METRICS_FOR_ANALYSIS"]["DAYS_BEFORE_SURGERY"],
+                            days_after_discharge= config["METRICS_FOR_ANALYSIS"]["DAYS_AFTER_DISCHARGE"],
+                            days_in_hospital= config["METRICS_FOR_ANALYSIS"]["DAYS_IN_HOSPITAL"]),
+
+        # Feature extraction
         expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),
         expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["FITBIT_TABLE"]),
         expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),
@@ -64,7 +73,7 @@ rule all:
         expand("data/processed/metrics_for_population_model/{source}_{day_segment}.csv",
                                 source = config["METRICS_FOR_ANALYSIS"]["SOURCES"],
                                 day_segment = config["METRICS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
-        # Reports
+        # Vizualisations
         expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),
         expand("reports/figures/{pid}/compliance_heatmap.html", pid=config["PIDS"]),
         expand("reports/figures/{pid}/battery_consumption_rates_barchart.html", pid=config["PIDS"]),

config.yaml

@@ -130,4 +130,7 @@ METRICS_FOR_ANALYSIS:
   PHONE_METRICS: [accelerometer, applications_foreground, battery, call_incoming, call_missed, call_outgoing, google_activity_recognition, light, location_barnett, screen, sms_received, sms_sent]
   FITBIT_METRICS: [fitbit_heartrate, fitbit_step]
   PHONE_FITBIT_METRICS: "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile
-  DROP_VALID_SENSED_DAYS: True
+  DROP_VALID_SENSED_DAYS: True
+  DAYS_BEFORE_SURGERY: 15
+  DAYS_AFTER_DISCHARGE: 7
+  DAYS_IN_HOSPITAL: F

rules/mystudy.snakefile

@@ -0,0 +1,12 @@
+rule days_to_analyse:
+    input:
+        participant_info = "data/external/participant_info.csv",
+        pid_file = "data/external/{pid}"
+    params:
+        days_before_surgery = config["METRICS_FOR_ANALYSIS"]["DAYS_BEFORE_SURGERY"],
+        days_after_discharge = config["METRICS_FOR_ANALYSIS"]["DAYS_AFTER_DISCHARGE"],
+        days_in_hospital= config["METRICS_FOR_ANALYSIS"]["DAYS_IN_HOSPITAL"]
+    output:
+        "data/interim/{pid}/days_to_analyse_{days_before_surgery}_{days_in_hospital}_{days_after_discharge}.csv"
+    script:
+        "../src/models/select_days_to_analyse.py"

src/models/select_days_to_analyse.py

@@ -0,0 +1,34 @@
+import pandas as pd
+from datetime import timedelta
+
+def appendDaysInRange(days_to_analyse, start_date, end_date):
+    num_of_days = (end_date - start_date).days
+    for day in range(num_of_days + 1):
+            days_to_analyse = days_to_analyse.append({"days_to_analyse": start_date + timedelta(days = day)}, ignore_index=True)
+    return days_to_analyse
+
+days_before_surgery = snakemake.params["days_before_surgery"]
+days_in_hospital = snakemake.params["days_in_hospital"]
+days_after_discharge = snakemake.params["days_after_discharge"]
+
+participant_info = pd.read_csv(snakemake.input["participant_info"], parse_dates=["surgery_date", "discharge_date"])
+with open(snakemake.input["pid_file"], encoding="ISO-8859-1") as external_file:
+    pid_file_content = external_file.readlines()
+device_ids = pid_file_content[0].strip().split(",")
+
+days_to_analyse = pd.DataFrame(columns = ["days_to_analyse"])
+participant_info = participant_info[participant_info["device_id"].isin(device_ids)]
+try:
+    surgery_date, discharge_date = participant_info["surgery_date"].iloc[0].date(), participant_info["discharge_date"].iloc[0].date()
+except:
+    pass
+else:
+    start_date = surgery_date - timedelta(days = days_before_surgery)
+    end_date = discharge_date + timedelta(days = days_after_discharge)
+
+    days_to_analyse = appendDaysInRange(days_to_analyse, start_date, surgery_date - timedelta(days = 1))
+    if days_in_hospital == "T":
+        days_to_analyse = appendDaysInRange(days_to_analyse, surgery_date, discharge_date)
+    days_to_analyse = appendDaysInRange(days_to_analyse, discharge_date + timedelta(days = 1), end_date)
+    
+days_to_analyse.to_csv(snakemake.output[0], index=False)