Add an option to print figures and set font sizes.
parent
1bdb334c42
commit
9e87b1f176
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@ -6,7 +6,7 @@
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# extension: .py
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# extension: .py
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# format_name: percent
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# format_name: percent
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# format_version: '1.3'
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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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# kernelspec:
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# display_name: straw2analysis
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# display_name: straw2analysis
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# language: python
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# language: python
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@ -14,6 +14,7 @@
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# ---
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# ---
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# %%
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# %%
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# %matplotlib inline
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import os
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import os
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import sys
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import sys
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@ -6,7 +6,7 @@
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# extension: .py
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# extension: .py
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# format_name: percent
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# format_name: percent
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# format_version: '1.3'
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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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# kernelspec:
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# display_name: straw2analysis
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# display_name: straw2analysis
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# language: python
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# language: python
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@ -14,12 +14,12 @@
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# ---
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# ---
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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 datetime
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# %%
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import os
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import os
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import sys
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import sys
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import matplotlib.pyplot as plt
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import pandas as pd
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import pandas as pd
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import seaborn as sns
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import seaborn as sns
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import statsmodels.api as sm
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import statsmodels.api as sm
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@ -31,6 +31,24 @@ if nb_dir not in sys.path:
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import participants.query_db
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import participants.query_db
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from features.esm import *
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from features.esm import *
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# %%
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SAVE_FIGS = True
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FIG_HEIGHT = 5
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FIG_ASPECT = 1.6
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FIG_COLOUR = "#28827C"
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SMALL_SIZE = 10
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MEDIUM_SIZE = 12
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BIGGER_SIZE = 14
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plt.rc("font", size=SMALL_SIZE) # controls default text sizes
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plt.rc("axes", titlesize=SMALL_SIZE) # fontsize of the axes title
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plt.rc("axes", labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
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plt.rc("xtick", labelsize=SMALL_SIZE) # fontsize of the tick labels
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plt.rc("ytick", labelsize=SMALL_SIZE) # fontsize of the tick labels
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plt.rc("legend", fontsize=SMALL_SIZE) # legend fontsize
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plt.rc("figure", titlesize=BIGGER_SIZE) # fontsize of the figure title
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# %%
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# %%
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baseline_si = pd.read_csv("E:/STRAWbaseline/results-survey637813.csv")
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baseline_si = pd.read_csv("E:/STRAWbaseline/results-survey637813.csv")
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baseline_be_1 = pd.read_csv("E:/STRAWbaseline/results-survey358134.csv")
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baseline_be_1 = pd.read_csv("E:/STRAWbaseline/results-survey358134.csv")
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@ -130,7 +148,7 @@ df_adherence.describe()
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df_adherence[["gender", "startlanguage"]].value_counts()
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df_adherence[["gender", "startlanguage"]].value_counts()
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# %%
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# %%
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sns.displot(df_adherence["finished_sessions"], binwidth=5, height=5)
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sns.displot(df_adherence["finished_sessions"], binwidth=5, height=FIG_HEIGHT)
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# %%
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# %%
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lm_adherence = smf.ols(
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lm_adherence = smf.ols(
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@ -224,12 +242,14 @@ df_session_workday = df_session_workday.assign(
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g1 = sns.displot(
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g1 = sns.displot(
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df_session_workday["time_diff_minutes"],
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df_session_workday["time_diff_minutes"],
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binwidth=5,
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binwidth=5,
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height=5,
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height=FIG_HEIGHT,
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aspect=1.5,
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aspect=FIG_ASPECT,
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color="#28827C",
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color=FIG_COLOUR,
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)
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)
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g1.set_axis_labels("Time difference [min]", "Session count")
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g1.set_axis_labels("Time difference [min]", "Session count")
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# g1.savefig("WorkdayEMAtimeDiff.pdf")
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g1.set(xlim=(0, 570))
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if SAVE_FIGS:
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g1.savefig("WorkdayEMAtimeDiff.pdf")
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# %% [markdown]
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# %% [markdown]
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# 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.
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# 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.
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@ -296,12 +316,13 @@ df_mean_daytime_interval.describe()
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g2 = sns.displot(
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g2 = sns.displot(
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df_mean_daytime_interval.time_diff_minutes,
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df_mean_daytime_interval.time_diff_minutes,
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binwidth=5,
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binwidth=5,
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height=5,
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height=FIG_HEIGHT,
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aspect=1.5,
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aspect=FIG_ASPECT,
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color="#28827C",
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color=FIG_COLOUR,
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)
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)
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g2.set_axis_labels("Median time difference [min]", "Participant count")
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g2.set_axis_labels("Median time difference [min]", "Participant count")
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# g2.savefig("WorkdayEMAtimeDiffMedianParticip.pdf")
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if SAVE_FIGS:
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g2.savefig("WorkdayEMAtimeDiffMedianParticip.pdf")
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# %%
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# %%
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df_adherence = df_adherence.merge(
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df_adherence = df_adherence.merge(
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@ -327,9 +348,9 @@ df_count_daytime_per_participant["time"].describe()
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sns.displot(
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sns.displot(
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df_count_daytime_per_participant.time,
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df_count_daytime_per_participant.time,
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binwidth=1,
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binwidth=1,
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height=5,
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height=FIG_HEIGHT,
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aspect=1.5,
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aspect=FIG_ASPECT,
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color="#28827C",
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color=FIG_COLOUR,
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)
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)
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# %% [markdown]
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# %% [markdown]
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@ -364,13 +385,14 @@ s_evening_completed_ratio.describe()
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g3 = sns.displot(
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g3 = sns.displot(
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s_evening_completed_ratio - 0.001,
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s_evening_completed_ratio - 0.001,
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binwidth=0.05,
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binwidth=0.05,
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height=5,
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height=FIG_HEIGHT,
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aspect=1.5,
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aspect=FIG_ASPECT,
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color="#28827C",
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color=FIG_COLOUR,
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)
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)
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g3.set_axis_labels("Ratio of days with the evening EMA filled out", "Participant count")
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g3.set_axis_labels("Ratio of days with the evening EMA filled out", "Participant count")
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g3.set(xlim=(1.01, 0.59))
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g3.set(xlim=(1.01, 0.59))
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# g3.savefig("EveningEMAratioParticip.pdf")
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if SAVE_FIGS:
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g3.savefig("EveningEMAratioParticip.pdf")
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# %%
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# %%
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df_adherence = df_adherence.merge(
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df_adherence = df_adherence.merge(
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@ -386,5 +408,3 @@ lr_ols_evening_ratio = smf.ols(
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)
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)
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ls_result_evening_ratio = lr_ols_evening_ratio.fit()
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ls_result_evening_ratio = lr_ols_evening_ratio.fit()
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ls_result_evening_ratio.summary()
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ls_result_evening_ratio.summary()
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# %%
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