def draw(path):
data = metadata.load(path)
p_values_pearson = []
p_values_shapiro = []
norm_dist_path = os.path.join(path, "normtest_distribution.png")
if os.path.exists(norm_dist_path):
print("path exists %s, skip" % norm_dist_path)
#return
for srv in data["services"]:
filename = os.path.join(path, srv["filename"])
df = load_timeseries(filename, srv)
columns = []
for c in df.columns:
if (not df[c].isnull().all()) and df[c].var() != 0:
columns.append(c)
df = df[columns]
n = len(columns)
if n == 0:
continue
fig, axis = plt.subplots(n, 2)
fig.set_figheight(n * 4)
fig.set_figwidth(30)
for i, col in enumerate(df.columns):
serie = df[col].dropna()
sns.boxplot(x=serie, ax=axis[i, 0])
statistic_1, p_value_1 = normaltest(serie)
p_values_pearson.append(p_value_1)
statistic_2, p_value_2 = shapiro(serie)
p_values_shapiro.append(p_value_2)
templ = """Pearson's normtest:
statistic: %f
p-value: %E
-> %s
Shapiro-Wilk test for normality:
statistic: %f
p-value: %E
-> %s
"""
outcome_1 = "not normal distributed" if p_value_1 < 0.05 else "normal distributed"
outcome_2 = "not normal distributed" if p_value_2 < 0.05 else "normal distributed"
text = templ % (statistic_1, p_value_1, outcome_1, statistic_2, p_value_2, outcome_2)
axis[i, 1].axis('off')
axis[i, 1].text(0.05, 0.05, text, fontsize=18)
plot_path = os.path.join(path, "%s_normtest.png" % srv["name"])
plt.savefig(plot_path)
print(plot_path)
fig, axis = plt.subplots(2)
fig.set_figheight(8)
measurement = os.path.dirname(os.path.join(path,''))
name = "Distribution of p-value for Pearson's normtest for %s" % measurement
plot = sns.distplot(pd.Series(p_values_pearson, name=name), rug=True, kde=False, norm_hist=False, ax=axis[0])
name = "Distribution of p-value for Shapiro-Wilk's normtest for %s" % measurement
plot = sns.distplot(pd.Series(p_values_shapiro, name=name), rug=True, kde=False, norm_hist=False, ax=axis[1])
fig.savefig(norm_dist_path)
print(norm_dist_path)
评论列表
文章目录
