Explore barometer and temperature data.

Add docstrings to models.

config/environment.yml

@@ -12,7 +12,7 @@ dependencies:
   - mypy
   - nodejs
   - pandas
-  - psycopg2
+  - psycopg2 >= 2.9.1
   - python-dotenv
   - pytz
   - seaborn

config/models.py

@@ -166,12 +166,43 @@ class Application(Base, AWAREsensor):
 
 
 class Barometer(Base, AWAREsensor):
+    """
+    Contains the barometer sensor data.
+
+    Attributes
+    ----------
+    double_values_0: float
+        The ambient air pressure in mbar (hPa)
+    accuracy: int
+        Sensor’s accuracy level, either 1, 2, or 3 (see [SensorManager](https://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH))
+    """
+
     double_values_0 = Column(Float, nullable=False)
     accuracy = Column(SmallInteger, nullable=True)
     label = Column(String, nullable=True)
 
 
 class BarometerSensor(Base, AWAREsensor):
+    """
+    Contains the barometer sensor capabilities.
+
+    Attributes
+    ----------
+    double_sensor_maximum_range: float
+        Maximum sensor value possible
+    double_sensor_minimum_delay: float
+        Minimum sampling delay in microseconds
+    sensor_name: str
+    double_sensor_power_ma: float
+        Sensor’s power drain in mA
+    double_sensor_resolution: float
+        Sensor’s resolution in sensor’s units
+    sensor_type: str
+    sensor_vendor: str
+        Sensor’s manufacturer
+    sensor_version: str
+    """
+
     __tablename__ = "barometer_sensor"
     # Since this table is not really important,
     # I will leave all columns as nullable. (nullable=True by default.)
@@ -257,6 +288,19 @@ class Imperfection(Base):
 
 
 class LightSensor(Base, AWAREsensor):
+    """
+    Contains the light sensor data.
+    Note: Even though this table is named light_sensor, it actually contains what AWARE calls light data
+        (rather than the data about the sensor's capabilities). Cf. Barometer(Sensor) and Temperature(Sensor).
+
+    Attributes
+    ----------
+    double_light_lux: float
+        The ambient luminance in lux units
+    accuracy: int
+        Sensor’s accuracy level, either 1, 2, or 3 (see [SensorManager](https://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH))
+    """
+
     __tablename__ = "light_sensor"
     double_light_lux = Column(Float, nullable=False)
     accuracy = Column(Integer, nullable=True)
@@ -376,12 +420,43 @@ class SMS(Base, AWAREsensor):
 
 
 class Temperature(Base, AWAREsensor):
+    """
+    Contains the temperature sensor data.
+
+    Attributes
+    ----------
+    temperature_celsius: float
+        Measured temperature in °C
+    accuracy: int
+        Sensor’s accuracy level, either 1, 2, or 3 (see [SensorManager](https://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH))
+    """
+
     temperature_celsius = Column(Float, nullable=False)
     accuracy = Column(SmallInteger, nullable=True)
     label = Column(String, nullable=True)
 
 
 class TemperatureSensor(Base, AWAREsensor):
+    """
+    Contains the temperature sensor capabilities.
+
+    Attributes
+    ----------
+    double_sensor_maximum_range: float
+        Maximum sensor value possible
+    double_sensor_minimum_delay: float
+        Minimum sampling delay in microseconds
+    sensor_name: str
+    double_sensor_power_ma: float
+        Sensor’s power drain in mA
+    double_sensor_resolution: float
+        Sensor’s resolution in sensor’s units
+    sensor_type: str
+    sensor_vendor: str
+        Sensor’s manufacturer
+    sensor_version: str
+    """
+
     # I left all of these nullable,
     # as we haven't seen any data from this sensor anyway.
     __tablename__ = "temperature_sensor"

exploration/expl_ambient.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.12.0
+#       jupytext_version: 1.13.0
 #   kernelspec:
 #     display_name: straw2analysis
 #     language: python
@@ -21,7 +21,6 @@ import sys
 
 import seaborn as sns
 from pytz import timezone
-from tabulate import tabulate
 
 nb_dir = os.path.split(os.getcwd())[0]
 if nb_dir not in sys.path:
@@ -103,7 +102,7 @@ df_light_nokia.loc[df_light_nokia["double_light_lux"] == 0, ["datetime_lj"]]
 # Zeroes are present during the day. It does happens when the sensor is physically blocked.
 
 # %% [markdown]
-# # Differences between participants
+# ## Differences between participants
 
 # %%
 df_light_participants = (
@@ -188,9 +187,52 @@ barometer_sensor_cols.remove("timestamp")
 barometer_sensor_cols.remove("device_id")
 print(df_barometer_sensor_samsung.drop_duplicates(subset=barometer_sensor_cols))
 
-# %%
+# %% [markdown]
-## Barometer data
+# ## Barometer data
 
 # %%
-df_barometer_sensor_samsung = get_ambient_data(["samsung_0000002"], "barometer")
+df_barometer_samsung = get_ambient_data(["samsung_0000002"], "barometer")
-print(df_barometer_sensor_samsung)
+print(df_barometer_samsung)
+
+# %%
+df_barometer_inactive = get_ambient_data(participants_inactive_usernames, "barometer")
+
+# %%
+df_barometer_inactive.accuracy.value_counts()
+
+# %%
+df_barometer_inactive.participant_id.nunique()
+
+# %%
+df_barometer_inactive.double_values_0.describe()
+
+# %% [markdown]
+# From [Wikipedia](https://en.wikipedia.org/wiki/Atmospheric_pressure#Mean_sea-level_pressure):
+#
+# > The lowest measurable sea-level pressure is found at the centers of tropical cyclones and tornadoes, with a record low of 870 mbar (87 kPa; 26 inHg).
+
+# %%
+df_barometer_inactive[df_barometer_inactive["double_values_0"] < 870]
+
+# %%
+sns.displot(
+    data=df_barometer_inactive[df_barometer_inactive["double_values_0"] > 870],
+    x="double_values_0",
+    binwidth=10,
+    height=8,
+)
+
+# %% [markdown]
+# # Temperature data
+
+# %% [markdown]
+# ## Temperature sensor
+
+# %% [markdown]
+# This table is empty.
+
+# %% [markdown]
+# ## Temperature data
+
+# %% [markdown]
+# This table is empty.

features/ambient.py

@@ -2,7 +2,14 @@ from collections.abc import Collection
 
 import pandas as pd
 
-from config.models import Barometer, BarometerSensor, LightSensor, Participant, Temperature, TemperatureSensor
+from config.models import (
+    Barometer,
+    BarometerSensor,
+    LightSensor,
+    Participant,
+    Temperature,
+    TemperatureSensor,
+)
 from setup import db_engine, session
 
 
@@ -25,36 +32,33 @@ def get_ambient_data(usernames: Collection, sensor=None) -> pd.DataFrame:
         A dataframe of ambient sensor data.
     """
     if sensor == "barometer":
-        query_ambient = (
+        query_ambient = session.query(Barometer, Participant.username).filter(
-            session.query(Barometer, Participant.username)
+            Participant.id == Barometer.participant_id
-            .filter(Participant.id == Barometer.participant_id)
         )
     elif sensor == "barometer_sensor":
-        query_ambient = (
+        query_ambient = session.query(BarometerSensor, Participant.username).filter(
-            session.query(BarometerSensor, Participant.username)
+            Participant.id == BarometerSensor.participant_id
-                .filter(Participant.id == BarometerSensor.participant_id)
         )
     elif sensor == "light":
-        query_ambient = (
+        query_ambient = session.query(LightSensor, Participant.username).filter(
-            session.query(LightSensor, Participant.username)
+            Participant.id == LightSensor.participant_id
-            .filter(Participant.id == LightSensor.participant_id)
         )
     # Note that LightSensor and its light_sensor table are incorrectly named.
     # In this table, we actually find light data, i.e. double_light_lux, the ambient luminance in lux,
     # and NOT light sensor data (its range, dela, resolution, vendor etc.) as the name suggests.
     # We do not have light sensor data saved in the database.
     elif sensor == "temperature":
-        query_ambient = (
+        query_ambient = session.query(Temperature, Participant.username).filter(
-            session.query(Temperature, Participant.username)
+            Participant.id == Temperature.participant_id
-            .filter(Participant.id == Temperature.participant_id)
         )
     elif sensor == "temperature_sensor":
-        query_ambient = (
+        query_ambient = session.query(TemperatureSensor, Participant.username).filter(
-            session.query(TemperatureSensor, Participant.username)
+            Participant.id == TemperatureSensor.participant_id
-            .filter(Participant.id == TemperatureSensor.participant_id)
         )
     else:
-        raise KeyError("Specify one of the ambient sensors: barometer, barometer_sensor, light, temperature, or temperature_sensor.")
+        raise KeyError(
+            "Specify one of the ambient sensors: barometer, barometer_sensor, light, temperature, or temperature_sensor."
+        )
 
     query_ambient = query_ambient.filter(Participant.username.in_(usernames))
     with db_engine.connect() as connection: