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stress_at_work_analysis
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Explore between-participants differences in light.

communication
junos 2021-07-23 18:50:17 +02:00
parent dbb59c4aef
commit 9580533c14
1 changed files with 64 additions and 0 deletions
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exploration/expl_light.py
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@ -102,3 +102,67 @@ df_light_nokia.loc[df_light_nokia["double_light_lux"] == 0, ["datetime_lj"]]
# %% [markdown] # %% [markdown]
# Zeroes are present during the day. It does happens when the sensor is physically blocked. # Zeroes are present during the day. It does happens when the sensor is physically blocked.
# %% [markdown]
# # Differences between participants
# %%
df_light_participants = (
df_light_inactive[["participant_id", "device_id", "double_light_lux"]]
.groupby(["participant_id", "device_id"])
.agg(["mean", "median", "std", "min", "max"])
.reset_index(col_level=1)
)
df_light_participants.columns = df_light_participants.columns.get_level_values(1)
# %%
df_light_participants[df_light_participants["min"] > 0]
# %%
df_light_inactive[
df_light_inactive["device_id"] == "3188b03e-8b6f-45da-894e-769eed81bbda"
].shape
# %% [markdown]
# This was a Lenovo Vibe K6, but the small range of values is due to a reinstallation shortly after the first (unsuccessful) installation.
# %%
sns.displot(data=df_light_participants, x="mean", binwidth=0.1, log_scale=(True, False))
# %%
sns.displot(data=df_light_participants, x="max", binwidth=0.1, log_scale=(True, False))
# %% [markdown]
# Variability in means is probably due to variability in maxima.
# %%
histogram_median = sns.displot(
data=df_light_participants, x="median", binwidth=50, log_scale=(False, False)
)
# %%
df_light_participants[df_light_participants["median"] > 1e4]
# %% [markdown]
# This was a Cubot KingKong Mini 2 phone.
# %%
histogram_median = sns.displot(
data=df_light_participants, x="median", binwidth=50, log_scale=(False, False)
)
histogram_median.set(xlim=(0, 600))
# %% [markdown]
# Other medians are much more similar.
# %%
df_light_participants["std_rel"] = (
df_light_participants["std"] / df_light_participants["max"]
)
# %%
sns.displot(data=df_light_participants, x="std_rel", binwidth=0.005)
# %% [markdown]
# Relative variability is homogeneous.
#
# This means that light data needs to be standardized. Min/max standardization would probably fit best.