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figures_for_paper.py
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figures_for_paper.py
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# from analyze_data import *
from show_results import *
from pylab import *
import statsmodels.formula.api as smf
from statsmodels.formula.api import ols
import statsmodels.api as sm
# first load the data
df = load_data()
# clean the data, get only relevant columns and information
df_clean, relevant_keys, stat_keys, stat_names = get_relevant_data(df)
def stars(p):
if p < 0.0001:
return "****"
elif (p < 0.001):
return "***"
elif (p < 0.01):
return "**"
elif (p < 0.05):
return "*"
else:
return "-"
def condition_plot(a, b, x, a_str, b_str, x_str, df_, x_name):
x_stop_0 = x[b == 0].dropna()
x_stop_1 = x[b == 1].dropna()
x_priming_0 = x[a == 0].dropna()
x_priming_1 = x[a == 1].dropna()
stop_stat, stop_p = stats.ttest_ind(x_stop_0, x_stop_1)
priming_stat, priming_p = stats.ttest_ind(x_priming_0, x_priming_1)
if x_name == 'T':
ab_lbl = ['NO-STOP', 'STOP']
x_data = []
x_data.append(x_stop_0)
x_data.append(x_stop_1)
n_x = 2
x_cond = np.array([1, 2])
else:
ab_lbl = ['NO-STOP/NO-PRIMING', 'NO-STOP/PRIMING', 'STOP/NO-PRIMING', 'STOP/PRIMING']
x_data = []
x_data.append(x[(a == 0) & (b == 0)])
x_data.append(x[(a == 1) & (b == 0)])
x_data.append(x[(a == 0) & (b == 1)])
x_data.append(x[(a == 1) & (b == 1)])
n_x = 4
x_cond = np.array([1, 2, 3, 4])
fig = figure()
ax = fig.add_subplot(111)
bp = ax.boxplot(x_data)
params = {
'axes.labelsize': 24,
'text.fontsize': 24,
'legend.fontsize': 24,
'xtick.labelsize': 24,
'ytick.labelsize': 24,
'text.usetex': True,
'figure.figsize': [20, 10]
}
rcParams.update(params)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.tick_params(axis='x', direction='out')
ax.tick_params(axis='y', length=0)
ax.grid(axis='y', color="0.9", linestyle='-', linewidth=1)
ax.set_axisbelow(True)
fig.subplots_adjust(left=0.2)
ax.set_xticklabels(ab_lbl)
ax.set_ylabel('$%s$' % x_name)
# y_max = np.max(np.concatenate(x_data))
# y_min = np.min(np.concatenate(x_data))
#
# ax.annotate("", xy=(1.5, y_max), xycoords='data',
# xytext=(3.5, y_max), textcoords='data',
# arrowprops=dict(arrowstyle="-", ec='#111111',
# connectionstyle="bar,fraction=0.2"))
#
#
# ax.text(2.5, y_max + abs(y_max - y_min) * 0.1, stars(stop_p),
# horizontalalignment='center',
# verticalalignment='center')
# #
#
# print('Stop: No: %2.2f \\pm %2.2f, N=%d, Yes: %2.2f \\pm %2.2f, N=%d' %
# (np.mean(x_stop_0), np.std(x_stop_0), len(x_stop_0),
# np.mean(x_stop_1), np.std(x_stop_1), len(x_stop_1)))
# print('Priming: No: %2.2f \\pm %2.2f, N=%d, Yes: %2.2f \\pm %2.2f, N=%d' %
# (np.mean(x_priming_0), np.std(x_priming_0), len(x_priming_0),
# np.mean(x_priming_1), np.std(x_priming_1), len(x_priming_1)))
# print()
#
# stop_stat, stop_p = stats.ttest_ind(x_stop_0, x_stop_1)
# priming_stat, priming_p = stats.ttest_ind(x_priming_0, x_priming_1)
#
#
#
# x_avg = np.zeros([n_x])
# x_std = np.zeros([n_x])
# for i_d, x_d in enumerate(x_data):
# x_avg[i_d] = np.mean(x_d)
# x_std[i_d] = np.std(x_d) / np.sqrt(len(x_d))
#
# data = x_avg
# xlocations = x_cond
# error = x_std
# labels = ab_lbl
#
# # df_['ab'] = df_.apply(lambda row: row[a_str] + row[b_str] * 2, axis=1)
# # df_na = df_[[x_str, 'ab']].dropna()
# # mc = MultiComparison(df_na[x_str], df_na['ab'])
# # result = mc.tukeyhsd()
# # print(result.summary())
# # print(mc.groupsunique)
#
# xlocations = np.array(range(len(data))) + 0.5
# width = 0.5
# the_linewidth = 10
# bar(xlocations, data, yerr=error, width=width, ecolor='black')
#
#
# # if stop_p < 0.05:
# # plot([xlocations[0] + 0.25, xlocations[1] + 0.25], [max(data)+max(error) * 1.2, max(data)+max(error) * 1.2],
# # '-', color='black', linewidth=the_linewidth)
# # plot([xlocations[2] + 0.25, xlocations[3] + 0.25], [max(data) + max(error) * 1.2, max(data) + max(error) * 1.2],
# # '-', color='black', linewidth=the_linewidth)
# # plot([(xlocations[0] + 0.25 + xlocations[1] + 0.25) / 2.0,
# # (xlocations[0] + 0.25 + xlocations[1] + 0.25) / 2.0,],
# # [max(data) + max(error) * 1.2, max(data) + max(error) * 1.7],
# # '-', color='black', linewidth=the_linewidth)
# # plot([(xlocations[2] + 0.25 + xlocations[3] + 0.25) / 2.0,
# # (xlocations[2] + 0.25 + xlocations[3] + 0.25) / 2.0, ],
# # [max(data) + max(error) * 1.2, max(data) + max(error) * 1.7],
# # '-', color='black', linewidth=the_linewidth)
# # plot([(xlocations[0] + 0.25 + xlocations[1] + 0.25) / 2.0,
# # (xlocations[2] + 0.25 + xlocations[3] + 0.25) / 2.0, ],
# # [max(data) + max(error) * 1.7, max(data) + max(error) * 1.7],
# # '-', color='black', linewidth=the_linewidth)
# # if stop_p < 0.001:
# # text(np.mean(xlocations) + 0.25, max(data) + max(error) * 2.0, '***')
# # elif stop_p < 0.01:
# # text(np.mean(xlocations) + 0.25, max(data) + max(error) * 2.0, '**')
# # elif stop_p < 0.05:
# # text(np.mean(xlocations) + 0.25, max(data) + max(error) * 2.0, '*')
# #
# # if priming_p < 0.05:
# # plot([xlocations[0] + 0.35, xlocations[2] + 0.15], [max(data) + max(error) * 1.2, max(data) + max(error) * 1.2],
# # '-', color='black', linewidth=the_linewidth)
# # plot([xlocations[1] + 0.35, xlocations[3] + 0.15], [max(data) + max(error) * 1.5, max(data) + max(error) * 1.5],
# # '-', color='black', linewidth=the_linewidth)
# # plot([(xlocations[0] + 0.25 + xlocations[2] + 0.25) / 2.0,
# # (xlocations[0] + 0.25 + xlocations[2] + 0.25) / 2.0, ],
# # [max(data) + max(error) * 1.2, max(data) + max(error) * 2.0],
# # '-', color='black', linewidth=the_linewidth)
# # plot([(xlocations[1] + 0.25 + xlocations[3] + 0.25) / 2.0,
# # (xlocations[1] + 0.25 + xlocations[3] + 0.25) / 2.0, ],
# # [max(data) + max(error) * 1.5, max(data) + max(error) * 2.0],
# # '-', color='black', linewidth=the_linewidth)
# # plot([(xlocations[0] + 0.25 + xlocations[2] + 0.25) / 2.0,
# # (xlocations[1] + 0.25 + xlocations[3] + 0.25) / 2.0, ],
# # [max(data) + max(error) * 2.0, max(data) + max(error) * 2.0],
# # '-', color='black', linewidth=the_linewidth)
# # if priming_p < 0.001:
# # text(np.mean(xlocations) + 0.25, max(data) + max(error) * 2.3, '***')
# # elif priming_p < 0.01:
# # text(np.mean(xlocations) + 0.25, max(data) + max(error) * 2.3, '**')
# # elif priming_p < 0.05:
# # text(np.mean(xlocations) + 0.25, max(data) + max(error) * 2.3, '*')
#
# # yticks(range(0, 8))
# # xticks(xlocations + width / 2, labels)
# plt.xticks([], [])
#
# xlim(0, xlocations[-1] + width * 2)
# title_str = '%s, p_stop=%2.6f, p_priming=%2.6f' % (x_str, stop_p, priming_p)
# # title(title_str)
# # gca().get_xaxis().tick_bottom()
# # gca().get_yaxis().tick_left()
# # axis([xlocations[0], xlocations[-1] + 0.5, 0, max(data) + max(error) * 4.0])
# # y_label = '$%s$' % x_name
# # ylabel(y_label, usetex=True, fontsize=48)
# plt.tick_params(axis='both', which='major', labelsize=48)
# show()
#
# # print(x_avg, x_std)
# # plt.bar(x_cond, x_avg)
# # plt.errorbar(x_cond, x_avg, yerr=x_std, fmt='.', ecolor='black')
# # plt.xticks(x_cond, ab_lbl)
# # plt.title(x_str)
# # plt.axis([0,5, 0, 1.0])
plt.show()
def analyze_conditions(df_clean, stat_keys, stat_names):
a_str = 'condition_framing'
b_str = 'condition_stop'
c_str = 'gender'
for i_stat, x_str in enumerate(stat_keys):
x_name = stat_names[i_stat]
print('=============', x_str, '==================')
x = df_clean[x_str].dropna()
print('%s: %2.2f \\pm %2.2f, N=%d' % (x_str, np.mean(x), np.std(x), len(x)))
t_stat, p_value = stats.ttest_1samp(x, 0)
print('T-score: %2.3f, p_value: %2.3f' % (t_stat, p_value))
data = df_clean[[a_str, b_str, x_str]].dropna()
a = data[a_str]
b = data[b_str]
x = data[x_str]
formula = '%s ~ C(%s) * C(%s)' % (x_str, a_str, b_str)
est = smf.ols(formula=formula, data=data).fit()
print(est.summary())
print('F(%d, %d) = %2.2f, p=%2.3f' % (est.df_model, est.df_resid, est.fvalue, est.f_pvalue))
print('====== anova ======')
moore_lm = ols(formula=formula, data=data).fit()
table = sm.stats.anova_lm(moore_lm, typ=2) # Type 2 ANOVA DataFrame
print(table)
print('====== end ======')
# # formula = '%s ~ C(%s) * C(%s) * C(%s)' % (x_str, a_str, b_str, c_str)
# model = ols(formula, data).fit()
# aov_table = anova_lm(model, typ=2)
# eta_squared(aov_table)
# omega_squared(aov_table)
# print(aov_table)
# fig = interaction_plot(a, b, x, colors=['red', 'blue'], markers=['D', '^'], ms=10)
# plt.title(x_str)
# plt.show()
formula = '%s ~ C(%s)' % (x_str, a_str)
est = smf.ols(formula=formula, data=data).fit()
print(est.summary())
# # formula = '%s ~ C(%s) * C(%s) * C(%s)' % (x_str, a_str, b_str, c_str)
# model = ols(formula, data).fit()
# aov_table = anova_lm(model, typ=2)
# eta_squared(aov_table)
# omega_squared(aov_table)
# print(aov_table)
formula = '%s ~ C(%s)' % (x_str, b_str)
est = smf.ols(formula=formula, data=data).fit()
print(est.summary())
# model = ols(formula, data).fit()
# aov_table = anova_lm(model, typ=2)
# eta_squared(aov_table)
# omega_squared(aov_table)
# print(aov_table)
# the_data = x
# the_group = a + 2 * b
# f_value, p_value = stats.f_oneway(the_data, the_group)
# print(f_value, p_value)
# mc = MultiComparison(the_data, the_group)
# result = mc.tukeyhsd()
# print(result.summary())
# print(mc.groupsunique)
# result.plot_simultaneous()
# plt.show()
condition_plot(a, b, x, a_str, b_str, x_str, data, x_name)
# except:
# plt.show()
# get 2x2 conditions
# https://www.marsja.se/three-ways-to-carry-out-2-way-anova-with-python/
df_clean = renormalize_entropies(df_clean, stat_keys)
analyze_conditions(df_clean, stat_keys, stat_names)