Refactor row heatmap and add all sensors compliance

Snakefile

@@ -12,7 +12,6 @@ rule all:
                             sms_type = config["COM_SMS"]["SMS_TYPES"],
                             day_segment = config["COM_SMS"]["DAY_SEGMENTS"],
                             metric = config["COM_SMS"]["METRICS"]),
-        expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),
         expand("data/processed/{pid}/com_call_{call_type}_{segment}_{metric}.csv",
                             pid=config["PIDS"], 
                             call_type = config["COM_CALL"]["CALL_TYPE_MISSED"],
@@ -23,6 +22,9 @@ rule all:
                             call_type = config["COM_CALL"]["CALL_TYPE_TAKEN"],
                             segment = config["COM_CALL"]["DAY_SEGMENTS"],
                             metric = config["COM_CALL"]["METRICS_TAKEN"]),
+        # 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"], sensor=config["SENSORS"]),
 
 # --- Packrat Rules --- #
 ## Taken from https://github.com/lachlandeer/snakemake-econ-r

rules/reports.snakefile

@@ -7,4 +7,14 @@ rule heatmap_rows:
     output:
         "reports/figures/{pid}/{sensor}_heatmap_rows.html"
     script:
-        "../src/visualization/heatmap_rows.py"
+        "../src/visualization/heatmap_rows.py"
+
+rule compliance_heatmap:
+    input:
+        expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["SENSORS"])
+    params:
+        pid = "{pid}"
+    output:
+        "reports/figures/{pid}/compliance_heatmap.html"
+    script:
+        "../src/visualization/compliance_heatmap.py"

src/visualization/compliance_heatmap.py

@@ -0,0 +1,65 @@
+import pandas as pd
+import numpy as np
+import plotly.io as pio
+import plotly.graph_objects as go
+import datetime
+
+def getComplianceMatrix(dates, compliance_bins):
+    compliance_matrix = []
+    for date in dates:
+        date_bins = compliance_bins[compliance_bins["local_date"] == date]
+        compliance_matrix.append(((date_bins["has_row"]>0).astype(int)).tolist())
+    return compliance_matrix
+
+def getComplianceHeatmap(dates, compliance_matrix, pid, output_path, bin_size):
+    bins_per_hour = int(60 / bin_size)
+    x_axis_labels = ["{0:0=2d}".format(x // bins_per_hour) + ":" + \
+                    "{0:0=2d}".format(x % bins_per_hour * bin_size) for x in range(24 * bins_per_hour)]
+    plot = go.Figure(data=go.Heatmap(z=compliance_matrix,
+                                     x=x_axis_labels,
+                                     y=dates,
+                                     colorscale=[[0, "rgb(255, 255, 255)"],[1, "rgb(120, 120, 120)"]]))
+    plot.update_layout(title="Five minutes has_row heatmap for " + pid)
+    pio.write_html(plot, file=output_path, auto_open=False)
+
+# get current patient id
+pid = snakemake.params["pid"]
+sensors_dates = []
+sensors_five_minutes_row_is = pd.DataFrame()
+for sensor_path in snakemake.input:
+    sensor_data = pd.read_csv(sensor_path)
+
+    # create a dataframe contains 2 columns: local_date_time, has_row
+    sensor_data["has_row"] = [1]*sensor_data.shape[0]
+    sensor_data["local_date_time"] = pd.to_datetime(sensor_data["local_date_time"])
+    sensed_bins = sensor_data[["local_date_time", "has_row"]]
+
+    # get the first date and the last date of current sensor
+    start_date = datetime.datetime.combine(sensed_bins["local_date_time"][0].date(), datetime.time(0,0,0))
+    end_date = datetime.datetime.combine(sensed_bins["local_date_time"][sensed_bins.shape[0]-1].date(), datetime.time(23,59,59))
+
+    # add the above datetime with has_row=0 to our dataframe
+    sensed_bins.loc[sensed_bins.shape[0], :] = [start_date, 0]
+    sensed_bins.loc[sensed_bins.shape[0], :] = [end_date, 0]
+    # get bins with 5 min
+    sensor_five_minutes_row_is = pd.DataFrame(sensed_bins.resample("5T", on="local_date_time")["has_row"].sum())
+    # merge current sensor with previous sensors
+    if sensors_five_minutes_row_is.empty:
+        sensors_five_minutes_row_is = sensor_five_minutes_row_is
+    else:
+        sensors_five_minutes_row_is = pd.concat([sensors_five_minutes_row_is, sensor_five_minutes_row_is]).groupby("local_date_time").sum()
+
+
+sensors_five_minutes_row_is.reset_index(inplace=True)
+# resample again to impute missing dates
+sensors_five_minutes_row_is_successive = pd.DataFrame(sensors_five_minutes_row_is.resample("5T", on="local_date_time")["has_row"].sum())
+
+# get sorted date list
+sensors_five_minutes_row_is_successive.reset_index(inplace=True)
+sensors_five_minutes_row_is_successive["local_date"] = sensors_five_minutes_row_is_successive["local_date_time"].apply(lambda x: x.date())
+dates = list(set(sensors_five_minutes_row_is_successive["local_date"]))
+dates.sort()
+compliance_matrix = getComplianceMatrix(dates, sensors_five_minutes_row_is_successive)
+
+# get heatmap
+getComplianceHeatmap(dates, compliance_matrix, pid, snakemake.output[0], 5)

src/visualization/heatmap_rows.py

@@ -1,35 +1,54 @@
 import pandas as pd
-import numpy as np
 import plotly.io as pio
 import plotly.graph_objects as go
+import datetime
 
-def getHourlyRowCount(dates, sensor_data):
+def getComplianceMatrix(dates, compliance_bins):
-    hourly_row_count = []
+    compliance_matrix = []
     for date in dates:
-        num_rows = []
+        date_bins = compliance_bins[compliance_bins["local_date"] == date]["count"].tolist()
-        daily_rows = sensor_data[sensor_data["local_date"] == date]
+        compliance_matrix.append(date_bins)
-        for hour in range(24):
+    return compliance_matrix
-            hourly_rows = daily_rows[daily_rows["local_hour"] == hour]
+
-            num_rows.append(hourly_rows.shape[0])
-        hourly_row_count.append(num_rows)
-    return hourly_row_count
 
 def getHourlyRowCountHeatmap(dates, hourly_row_count, sensor_name, pid, output_path):
-    plot = go.Figure(data=go.Heatmap(z=hourly_row_count,x=[x for x in range(24)],y=dates,colorscale='Viridis'))
+    plot = go.Figure(data=go.Heatmap(z=hourly_row_count,
-    plot.update_layout(title="Hourly row count heatmap for " + pid + " for sensor " + sensor_name)
+                                     x=[x for x in range(24)],
+                                     y=[datetime.datetime.strftime(date, '%Y/%m/%d') for date in dates],
+                                     colorscale='Viridis'))
+    plot.update_layout(title="Hourly row count heatmap for " + pid + " and sensor " + sensor_name)
     pio.write_html(plot, file=output_path, auto_open=False)
 
 
 
 sensor_data = pd.read_csv(snakemake.input[0])
-# get current sensor name
 sensor_name = snakemake.params["table"]
-# get current patient id
 pid = snakemake.params["pid"]
-# get sorted date list
+
-dates = list(set(sensor_data["local_date"]))
+start_date = sensor_data["local_date"][0]
-dates.sort()
+end_date = sensor_data.at[sensor_data.index[-1],"local_date"]
-# get num of rows per hour per day
+
-hourly_row_count = getHourlyRowCount(dates, sensor_data)
+# Make local hour double digit
-# get heatmap
+sensor_data["local_hour"] = sensor_data["local_hour"].map("{0:0=2d}".format)
+
+# Group and count by local_date and local_hour
+sensor_data_hourly_bins = sensor_data.groupby(["local_date","local_hour"]).agg(count=("timestamp","count")).reset_index()
+
+# Add first and last day boundaries for resampling
+sensor_data_hourly_bins = sensor_data_hourly_bins.append([pd.Series([start_date, "00", 0], sensor_data_hourly_bins.columns),
+                                                          pd.Series([end_date, "23", 0], sensor_data_hourly_bins.columns)])
+
+# Rebuild local date hour for resampling
+sensor_data_hourly_bins["local_date_hour"] = pd.to_datetime(sensor_data_hourly_bins["local_date"] + \
+                                             " " + sensor_data_hourly_bins["local_hour"] + ":00:00")
+
+resampled_hourly_bins = pd.DataFrame(sensor_data_hourly_bins.resample("1H", on="local_date_hour")["count"].sum())
+
+# Extract list of dates for creating the heatmap
+resampled_hourly_bins.reset_index(inplace=True)
+resampled_hourly_bins["local_date"] = resampled_hourly_bins["local_date_hour"].dt.date
+dates = resampled_hourly_bins["local_date"].drop_duplicates().tolist()
+
+# Create heatmap
+hourly_row_count = getComplianceMatrix(dates, resampled_hourly_bins)
 getHourlyRowCountHeatmap(dates, hourly_row_count, sensor_name, pid, snakemake.output[0])