diff --git a/src/visualization/compliance_heatmap.py b/src/visualization/compliance_heatmap.py
index b1d8595c..9e1f413e 100644
--- a/src/visualization/compliance_heatmap.py
+++ b/src/visualization/compliance_heatmap.py
@@ -8,7 +8,7 @@ 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())
+        compliance_matrix.append(date_bins["has_row"].tolist())
     return compliance_matrix
 
 def getComplianceHeatmap(dates, compliance_matrix, pid, output_path, bin_size):
@@ -18,7 +18,7 @@ def getComplianceHeatmap(dates, compliance_matrix, pid, output_path, bin_size):
     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)"]]))
+                                     colorscale='Viridis'))
     plot.update_layout(title="Five minutes has_row heatmap for " + pid)
     pio.write_html(plot, file=output_path, auto_open=False)
 
@@ -28,6 +28,10 @@ sensors_dates = []
 sensors_five_minutes_row_is = pd.DataFrame()
 for sensor_path in snakemake.input:
     sensor_data = pd.read_csv(sensor_path)
+    
+    # check if the sensor is off
+    if sensor_data.empty:
+        continue
 
     # create a dataframe contains 2 columns: local_date_time, has_row
     sensor_data["has_row"] = [1]*sensor_data.shape[0]
@@ -43,6 +47,7 @@ for sensor_path in snakemake.input:
     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())
+    sensor_five_minutes_row_is["has_row"] = (sensor_five_minutes_row_is["has_row"]>0).astype(int)
     # merge current sensor with previous sensors
     if sensors_five_minutes_row_is.empty:
         sensors_five_minutes_row_is = sensor_five_minutes_row_is
diff --git a/src/visualization/heatmap_rows.py b/src/visualization/heatmap_rows.py
index 956351ed..2e8b8e0b 100644
--- a/src/visualization/heatmap_rows.py
+++ b/src/visualization/heatmap_rows.py
@@ -25,30 +25,36 @@ sensor_data = pd.read_csv(snakemake.input[0])
 sensor_name = snakemake.params["table"]
 pid = snakemake.params["pid"]
 
-start_date = sensor_data["local_date"][0]
-end_date = sensor_data.at[sensor_data.index[-1],"local_date"]
+# check if we have sensor data
+if sensor_data.empty:
+    empty_html = open(snakemake.output[0], "w")
+    empty_html.write("There is no "+ sensor_name + " data for "+pid)
+    empty_html.close()
+else:
+    start_date = sensor_data["local_date"][0]
+    end_date = sensor_data.at[sensor_data.index[-1],"local_date"]
 
-# Make local hour double digit
-sensor_data["local_hour"] = sensor_data["local_hour"].map("{0:0=2d}".format)
+    # Make local hour double digit
+    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()
+    # 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)])
+    # 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")
+    # 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())
+    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()
+    # 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])
+    # Create heatmap
+    hourly_row_count = getComplianceMatrix(dates, resampled_hourly_bins)
+    getHourlyRowCountHeatmap(dates, hourly_row_count, sensor_name, pid, snakemake.output[0])