From 4c07ccccf214120615b32810aa71845a16aeef61 Mon Sep 17 00:00:00 2001 From: junos Date: Fri, 2 Jul 2021 16:02:55 +0200 Subject: [PATCH] Prepare figures for publication. --- statistical_analysis/adherence.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/statistical_analysis/adherence.py b/statistical_analysis/adherence.py index 613fff2..eab2b16 100644 --- a/statistical_analysis/adherence.py +++ b/statistical_analysis/adherence.py @@ -203,7 +203,7 @@ df_session_counts_time_diff = df_session_counts_time_completed.join( # %% [markdown] # Now, select only the daytime EMAs of interest. Discard the differences between *different day* EMAs. -# %% +# %% tags=[] time_workday_completed_less_than_1_day = ( (df_session_counts_time_diff.time == "daytime") # Only take daytime EMAs. & ~( @@ -221,9 +221,9 @@ df_session_workday = df_session_workday.assign( ) # %% -g1 = sns.displot(df_session_workday["time_diff_minutes"], binwidth=5, height=5) +g1 = sns.displot(df_session_workday["time_diff_minutes"], binwidth=5, height=5, aspect=1.5, color="#28827C") g1.set_axis_labels("Time difference [min]", "Session count") -# g1.savefig("WorkdayEMAtimeDiff.pdf") +#g1.savefig("WorkdayEMAtimeDiff.pdf") # %% [markdown] # There are some sessions that are really close together. By design, none should be closer than 30 min. Let's take a look at those. @@ -278,9 +278,9 @@ df_mean_daytime_interval = df_session_workday.groupby("participant_id").median() df_mean_daytime_interval.describe() # %% -g2 = sns.displot(df_mean_daytime_interval.time_diff_minutes, binwidth=5, height=5) +g2 = sns.displot(df_mean_daytime_interval.time_diff_minutes, binwidth=5, height=5, aspect=1.5, color="#28827C") g2.set_axis_labels("Median time difference [min]", "Participant count") -# g2.savefig("WorkdayEMAtimeDiffMedianParticip.pdf") +#g2.savefig("WorkdayEMAtimeDiffMedianParticip.pdf") # %% df_adherence = df_adherence.merge( @@ -303,7 +303,7 @@ df_count_daytime_per_participant = df_session_workday.groupby( df_count_daytime_per_participant["time"].describe() # %% -sns.displot(df_count_daytime_per_participant.time, binwidth=1, height=5) +sns.displot(df_count_daytime_per_participant.time, binwidth=1, height=5, aspect=1.5, color="#28827C") # %% [markdown] # ## Evening EMA @@ -334,9 +334,10 @@ s_evening_completed_ratio = ( s_evening_completed_ratio.describe() # %% -g3 = sns.displot(s_evening_completed_ratio - 0.001, binwidth=0.05, height=5) +g3 = sns.displot(s_evening_completed_ratio - 0.001, binwidth=0.05, height=5, aspect=1.5, color="#28827C") g3.set_axis_labels("Ratio of days with the evening EMA filled out", "Participant count") -# g3.savefig("EveningEMAratioParticip.pdf") +g3.set(xlim=(1.01,0.59)) +#g3.savefig("EveningEMAratioParticip.pdf") # %% df_adherence = df_adherence.merge(