Explore low levels of luminance.
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@ -6,7 +6,7 @@
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.11.2
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# jupytext_version: 1.11.4
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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@ -14,10 +14,13 @@
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# ---
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# %%
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# %matplotlib inline
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import datetime
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import os
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import sys
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import seaborn as sns
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from pytz import timezone
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from tabulate import tabulate
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nb_dir = os.path.split(os.getcwd())[0]
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@ -26,9 +29,14 @@ if nb_dir not in sys.path:
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import participants.query_db
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TZ_LJ = timezone("Europe/Ljubljana")
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# %%
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from features.light import *
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# %% [markdown]
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# # Basic characteristics
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# %%
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df_light_nokia = get_light_data(["nokia_0000003"])
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print(df_light_nokia)
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@ -40,6 +48,17 @@ df_light_inactive = get_light_data(participants_inactive_usernames)
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# %%
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df_light_inactive.accuracy.value_counts()
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# %% [markdown]
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# From [SensorManager](https://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH):
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#
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# ```java
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# public static final int SENSOR_STATUS_ACCURACY_HIGH
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# ```
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#
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# This sensor is reporting data with maximum accuracy
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#
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# Constant Value: 3 (0x00000003)
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# %%
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df_light_inactive.double_light_lux.describe()
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@ -71,3 +90,15 @@ df_light_low = df_light_inactive[df_light_inactive["double_light_lux"] <= 10]
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sns.displot(data=df_light_low, x="double_light_lux", binwidth=0.5, height=8)
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# %%
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df_light_very_low = df_light_low[df_light_low["double_light_lux"] < 0.5]
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df_light_very_low.double_light_lux.value_counts()
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# %%
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df_light_nokia["datetime_lj"] = df_light_nokia["timestamp"].apply(
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lambda x: datetime.datetime.fromtimestamp(x / 1000.0, tz=TZ_LJ)
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)
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df_light_nokia.loc[df_light_nokia["double_light_lux"] == 0, ["datetime_lj"]]
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# %% [markdown]
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# Zeroes are present during the day. It does happens when the sensor is physically blocked.
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