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stress_at_work_analysis
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Specify columns to aggregate and save figures as pdfs.

master
junos 2023-05-16 17:05:43 +02:00
parent 118e686491
commit a7446cc34a
1 changed files with 10 additions and 24 deletions
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34
exploration/expl_esm_labels.py
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@ -70,12 +70,9 @@ df_esm_PANAS_daily_means = (
# %% # %%
df_esm_PANAS_summary_participant = ( df_esm_PANAS_summary_participant = (
df_esm_PANAS_daily_means.groupby(["participant_id", "questionnaire_id"]) df_esm_PANAS_daily_means.groupby(["participant_id", "questionnaire_id"])
.agg(["mean", "median", "std"]) .esm_numeric_mean.agg(["mean", "median", "std"])
.reset_index(col_level=1) .reset_index(col_level=1)
) )
df_esm_PANAS_summary_participant.columns = (
df_esm_PANAS_summary_participant.columns.get_level_values(1)
)
df_esm_PANAS_summary_participant[ df_esm_PANAS_summary_participant[
"PANAS_subscale" "PANAS_subscale"
] = df_esm_PANAS_daily_means.questionnaire_id.astype("category").cat.rename_categories( ] = df_esm_PANAS_daily_means.questionnaire_id.astype("category").cat.rename_categories(
@ -87,7 +84,7 @@ fig1 = sns.displot(
data=df_esm_PANAS_summary_participant, x="mean", hue="PANAS_subscale", binwidth=0.2 data=df_esm_PANAS_summary_participant, x="mean", hue="PANAS_subscale", binwidth=0.2
) )
if save_figs: if save_figs:
fig1.figure.savefig("PANAS_mean_participant.png", dpi=300) fig1.figure.savefig("PANAS_mean_participant.pdf", dpi=300)
# %% # %%
sns.displot( sns.displot(
@ -102,7 +99,7 @@ fig2 = sns.displot(
data=df_esm_PANAS_summary_participant, x="std", hue="PANAS_subscale", binwidth=0.05 data=df_esm_PANAS_summary_participant, x="std", hue="PANAS_subscale", binwidth=0.05
) )
if save_figs: if save_figs:
fig2.figure.savefig("PANAS_std_participant.png", dpi=300) fig2.figure.savefig("PANAS_std_participant.pdf", dpi=300)
# %% # %%
df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1] df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]
@ -145,17 +142,14 @@ df_esm_SAM_daily_events = (
# %% # %%
df_esm_SAM_event_summary_participant = ( df_esm_SAM_event_summary_participant = (
df_esm_SAM_daily_events.groupby(["participant_id"]) df_esm_SAM_daily_events.groupby(["participant_id"])
.agg(["mean", "median", "std"]) .SAM_event_ratio.agg(["mean", "median", "std"])
.reset_index(col_level=1) .reset_index(col_level=1)
) )
df_esm_SAM_event_summary_participant.columns = (
df_esm_SAM_event_summary_participant.columns.get_level_values(1)
)
# %% # %%
fig6 = sns.displot(data=df_esm_SAM_event_summary_participant, x="mean", binwidth=0.1) fig6 = sns.displot(data=df_esm_SAM_event_summary_participant, x="mean", binwidth=0.1)
if save_figs: if save_figs:
fig6.figure.savefig("SAM_events_mean_participant.png", dpi=300) fig6.figure.savefig("SAM_events_mean_participant.pdf", dpi=300)
# %% # %%
sns.displot(data=df_esm_SAM_event_summary_participant, x="std", binwidth=0.05) sns.displot(data=df_esm_SAM_event_summary_participant, x="std", binwidth=0.05)
@ -190,12 +184,9 @@ df_esm_SAM_daily_threat_challenge = df_esm_SAM_daily[
# %% # %%
df_esm_SAM_summary_participant = ( df_esm_SAM_summary_participant = (
df_esm_SAM_daily.groupby(["participant_id", "questionnaire_id"]) df_esm_SAM_daily.groupby(["participant_id", "questionnaire_id"])
.agg(["mean", "median", "std"]) .esm_numeric_mean.agg(["mean", "median", "std"])
.reset_index(col_level=1) .reset_index(col_level=1)
) )
df_esm_SAM_summary_participant.columns = (
df_esm_SAM_summary_participant.columns.get_level_values(1)
)
# %% # %%
df_esm_SAM_threat_challenge_summary_participant = df_esm_SAM_summary_participant[ df_esm_SAM_threat_challenge_summary_participant = df_esm_SAM_summary_participant[
@ -226,7 +217,7 @@ fig3 = sns.displot(
binwidth=0.1, binwidth=0.1,
) )
if save_figs: if save_figs:
fig3.figure.savefig("SAM_std_participant.png", dpi=300) fig3.figure.savefig("SAM_std_participant.pdf", dpi=300)
# %% [markdown] # %% [markdown]
# ## Stressfulness of period # ## Stressfulness of period
@ -268,12 +259,9 @@ df_esm_JCQ_daily = (
) )
df_esm_JCQ_summary_participant = ( df_esm_JCQ_summary_participant = (
df_esm_JCQ_daily.groupby(["participant_id", "questionnaire_id"]) df_esm_JCQ_daily.groupby(["participant_id", "questionnaire_id"])
.agg(["mean", "median", "std"]) .esm_score_mean.agg(["mean", "median", "std"])
.reset_index(col_level=1) .reset_index(col_level=1)
) )
df_esm_JCQ_summary_participant.columns = (
df_esm_JCQ_summary_participant.columns.get_level_values(1)
)
df_esm_JCQ_summary_participant[ df_esm_JCQ_summary_participant[
"JCQ_subscale" "JCQ_subscale"
] = df_esm_JCQ_summary_participant.questionnaire_id.astype( ] = df_esm_JCQ_summary_participant.questionnaire_id.astype(
@ -290,7 +278,7 @@ fig4 = sns.displot(
binwidth=0.1, binwidth=0.1,
) )
if save_figs: if save_figs:
fig4.figure.savefig("JCQ_mean_participant.png", dpi=300) fig4.figure.savefig("JCQ_mean_participant.pdf", dpi=300)
# %% # %%
fig5 = sns.displot( fig5 = sns.displot(
@ -300,6 +288,4 @@ fig5 = sns.displot(
binwidth=0.05, binwidth=0.05,
) )
if save_figs: if save_figs:
fig5.figure.savefig("JCQ_std_participant.png", dpi=300) fig5.figure.savefig("JCQ_std_participant.pdf", dpi=300)
# %%