return 1
if 0.001 < pvalue < 0.01:
return 2
if pvalue < 0.001:
return 3
print(ns)
print(s)
ind = ["Gaussian", "Hessian", "Laplacian", "Ilastik", "MitoSegNet"]
# ilastik vs mitonet
significance_bar(pos_y=1.1, pos_x=[3, 4], bar_y=0.03, p=star_counter(p_il), y_dist=0.03, distance=0.11)
# laplacian vs mitonet
significance_bar(pos_y=1.2, pos_x=[2, 4], bar_y=0.03, p=star_counter(p_la), y_dist=0.03, distance=0.11)
# hessian vs mitonet
significance_bar(pos_y=1.3, pos_x=[1, 4], bar_y=0.03, p=star_counter(p_he), y_dist=0.03, distance=0.11)
# gaussian vs mitonet
significance_bar(pos_y=1.4, pos_x=[0, 4], bar_y=0.03, p=star_counter(p_ga), y_dist=0.03, distance=0.11)
# creating stacked bar graph
p1 = plt.bar(ind, ns)
s = [1-i for i in ns]
p2 = plt.bar(ind, s, bottom=ns)
#p2 = plt.bar(ind, s1, bottom=ns, color="rosybrown")
def start_analysis():
desc = descriptor.get()
stat_val = stat_value.get()
tab1_name = table1_name.get()
tab2_name = table2_name.get()
ylab = stat_val + " " + desc.lower()
ylab_size = 34
xlab = [table1_name.get(), table2_name.get()]
new_tab1_name = tab1_name + " normality test p-value"
new_tab2_name = tab2_name + " normality test p-value"
stat_frame = pd.DataFrame(columns=[
new_tab1_name, new_tab2_name, "Hypothesis test",
"Hypothesis test p-value", "Effect size", "N"
])
norm_p, dataframe, pval_l, eff_siz_l, hyp_test, max_vals = self.get_stats(
desc, table_path1.get(), table_path2.get(), tab1_name,
tab2_name, stat_val)
if norm_p != False:
# table with p-values and effect sizes
########
stat_frame[new_tab1_name] = [norm_p[0]]
stat_frame[new_tab2_name] = [norm_p[1]]
stat_frame["Hypothesis test"] = [hyp_test]
stat_frame["Hypothesis test p-value"] = pval_l
stat_frame["Effect size"] = eff_siz_l
stat_frame["N"] = [len(dataframe)]
########
increase = 0
for index, row in stat_frame.iterrows():
if row["Hypothesis test p-value"] > 0.05:
p = 0
elif 0.01 < row["Hypothesis test p-value"] < 0.05:
p = 1
elif 0.001 < row["Hypothesis test p-value"] < 0.01:
p = 2
else:
p = 3
max_bar = np.max(max_vals)
x1 = 0
x2 = 1
significance_bar(pos_y=max_bar + 0.1 * max_bar + increase,
pos_x=[x1, x2],
bar_y=max_bar * 0.05,
p=p,
y_dist=max_bar * 0.02,
distance=0.05)
increase += max_bar * 0.1
# select plot
plot = sb.boxplot(data=dataframe, color="white", fliersize=0)
sb.swarmplot(data=dataframe, color="black", size=8)
# label the y axis
plt.ylabel(ylab, fontsize=ylab_size)
# label the x axis
plt.xticks(list(range(len(xlab))), xlab)
# determine fontsize of x and y ticks
plot.tick_params(axis="x", labelsize=28)
plot.tick_params(axis="y", labelsize=28)
plt.show()
# print p-values based on chi-square test
for seg in observed_values:
if seg != "MitoSegNet":
f_obs = [observed_values["MitoSegNet"].tolist(), observed_values[seg].tolist()]
pval = chi2_contingency(f_obs, correction=False)[1]
print(seg, pval)
ind = ["MitoSegNet", "Pretrained\nFiji U-Net", "Ilastik", "Gaussian", "Hessian", "Laplacian"]
dist = 0.1
bar_y = 0.03
significance_bar(pos_y=1.1, pos_x=[0, 1], bar_y=bar_y, p=2, y_dist=bar_y, distance=dist)
significance_bar(pos_y=1.2, pos_x=[0, 2], bar_y=bar_y, p=2, y_dist=bar_y, distance=dist)
significance_bar(pos_y=1.3, pos_x=[0, 3], bar_y=bar_y, p=3, y_dist=bar_y, distance=dist)
significance_bar(pos_y=1.4, pos_x=[0, 4], bar_y=bar_y, p=3, y_dist=bar_y, distance=dist)
significance_bar(pos_y=1.5, pos_x=[0, 5], bar_y=bar_y, p=3, y_dist=bar_y, distance=dist)
# creating stacked bar graph
#"""
p1 = plt.bar(ind, ns, color="white", edgecolor="black")
p2 = plt.bar(ind, s, bottom=ns, color="black", edgecolor="black")
plt.legend((p1[0], p2[0]), ('p>0.05', 'p<0.05'), prop={"size": 20}, bbox_to_anchor=(1, 0.5))
#"""
# possiblity to create stacked bar graph showing ns and three different alpha categories
elif 0.01 < row["Hypothesis test p-value"] < 0.05:
p = 1
elif 0.001 < row["Hypothesis test p-value"] < 0.01:
p = 2
else:
p = 3
max_bar = np.max(max_vals)
x1= file_list.index(row["Data 1"])
x2 = file_list.index(row["Data 2"])
significance_bar(pos_y=max_bar+0.1*max_bar+increase, pos_x=[x1, x2], bar_y=max_bar*0.05, p=p, y_dist=max_bar*0.02,
distance=0.05)
increase+=max_bar*0.1
# select plot
plot = sb.boxplot(data=dataframe, color="white", fliersize=0)
sb.swarmplot(data=dataframe, color="black", size=8)
# label the y axis
plt.ylabel(ylab, fontsize=ylab_size)
# label the x axis
plt.xticks(list(range(len(xlab))), xlab)
# determine fontsize of x and y ticks
all_data["MitoSegNet"].tolist(),
all_data["Finetuned\nFiji U-Net"].tolist()
])
print(dt)
#dt.to_excel("soc_posthoc.xlsx")
# check effect sizes of segmentations against msn segmentation
for seg in seg_list:
if seg != "MitoSegNet":
print(seg, cohens_d(all_data[seg], all_data["MitoSegNet"]))
bar_y = 0.03
significance_bar(pos_y=2.1,
pos_x=[0, 2],
bar_y=bar_y,
p=3,
y_dist=bar_y,
distance=0.1)
significance_bar(pos_y=2.2,
pos_x=[0, 3],
bar_y=bar_y,
p=3,
y_dist=bar_y,
distance=0.1)
significance_bar(pos_y=2.3,
pos_x=[0, 4],
bar_y=bar_y,
p=1,
y_dist=bar_y,
distance=0.1)
significance_bar(pos_y=2.4,
print("\n")
print(cohens_d(all_data["Gaussian"], all_data["MitoSegNet"]))
print(cohens_d(all_data["Hessian"], all_data["MitoSegNet"]))
print(cohens_d(all_data["Laplacian"], all_data["MitoSegNet"]))
print(cohens_d(all_data["Ilastik"], all_data["MitoSegNet"]))
print(cohens_d(all_data["Finetuned\nFiji U-Net"], all_data["MitoSegNet"]))
#print(cohens_d(all_data["Fiji U-Net"], all_data["MitoSegNet"]))
pos_y_start = 11
dist_bar_y = 0.2
if p_f < 0.05:
significance_bar(pos_y=pos_y_start,
pos_x=[0, 1],
bar_y=dist_bar_y,
p=star_counter(p_f),
y_dist=dist_bar_y,
distance=0.11)
if p_i < 0.05:
significance_bar(pos_y=pos_y_start + 0.5,
pos_x=[0, 2],
bar_y=dist_bar_y,
p=star_counter(p_i),
y_dist=dist_bar_y,
distance=0.11)
if p_g < 0.05:
significance_bar(pos_y=pos_y_start + 1,
pos_x=[0, 3],
bar_y=dist_bar_y,
p=star_counter(p_g),
y_dist=dist_bar_y,
all_data["Finetuned\nFiji U-Net"] = u_l_pt
#all_data["Fiji U-Net"] = u_l
#all_data.to_csv(path + "/dice_coefficient_table.csv")
x = [0, 1]
y = [1, 1]
# pos_y and pos_x determine position of bar, p sets the number of asterisks, y_dist sets y distance of the asterisk to
# bar, and distance sets the distance between two or more asterisks
pd = 0.08
significance_bar(pos_y=1,
pos_x=[0, 2],
bar_y=0.02,
p=2,
y_dist=0.02,
distance=pd)
significance_bar(pos_y=1.05,
pos_x=[0, 3],
bar_y=0.02,
p=3,
y_dist=0.02,
distance=pd)
significance_bar(pos_y=1.1,
pos_x=[0, 4],
bar_y=0.02,
p=1,
y_dist=0.02,
distance=pd)
significance_bar(pos_y=1.15,
cohens_d = np.abs(np.average(data1) - np.average(data2)) / p_std
return cohens_d
print("\n")
print(cohens_d(mitosegnet, hess))
print(cohens_d(mitosegnet, gauss))
print(cohens_d(mitosegnet, lap))
print(cohens_d(mitosegnet, il))
significance_bar(pos_y=2.9,
pos_x=[0, 4],
bar_y=0.1,
p=3,
y_dist=0.1,
distance=0.11)
significance_bar(pos_y=2.6,
pos_x=[1, 4],
bar_y=0.1,
p=3,
y_dist=0.1,
distance=0.11)
significance_bar(pos_y=2.3,
pos_x=[2, 4],
bar_y=0.1,
p=3,
y_dist=0.1,
distance=0.11)
significance_bar(pos_y=2,
])
print(dt)
#dt.to_excel("ed_posthoc.xlsx")
# check effect sizes of segmentations against msn segmentation
for seg in seg_list:
if seg != "MitoSegNet":
print(seg, cohens_d(all_data[seg], all_data["MitoSegNet"]))
pos_y_start = 6
dist_bar_y = 0.2
significance_bar(pos_y=pos_y_start + 0.5,
pos_x=[0, 2],
bar_y=dist_bar_y,
p=1,
y_dist=dist_bar_y,
distance=0.11)
significance_bar(pos_y=pos_y_start + 1.3,
pos_x=[0, 3],
bar_y=dist_bar_y,
p=3,
y_dist=dist_bar_y,
distance=0.11)
significance_bar(pos_y=pos_y_start + 2.1,
pos_x=[0, 4],
bar_y=dist_bar_y,
p=1,
y_dist=dist_bar_y,
distance=0.11)
significance_bar(pos_y=pos_y_start + 3.5,
all_data["Laplacian"]
])
#print(dt)
#dt.to_excel("dc_posthoc.xlsx")
#print(cohens_d(all_data["MitoSegNet"], all_data["Ilastik"]))
# pos_y and pos_x determine position of bar, p sets the number of asterisks, y_dist sets y distance of the asterisk to
# bar, and distance sets the distance between two or more asterisks
dist = 0.08
bar_y = 0.02
significance_bar(pos_y=1,
pos_x=[0, 2],
bar_y=bar_y,
p=2,
y_dist=bar_y,
distance=dist)
significance_bar(pos_y=1.05,
pos_x=[0, 3],
bar_y=bar_y,
p=3,
y_dist=bar_y,
distance=dist)
significance_bar(pos_y=1.1,
pos_x=[0, 4],
bar_y=bar_y,
p=1,
y_dist=bar_y,
distance=dist)
significance_bar(pos_y=1.15,
ga_l.append(i_u_g)
h_l.append(i_u_h)
la_l.append(i_u_l)
il_l.append(i_u_il)
u_l_1.append(i_u_m)
all_data["Gaussian"] = ga_l
all_data["Hessian"] = h_l
all_data["Laplacian"] = la_l
all_data["Ilastik"] = il_l
all_data["MitoSegNet"] = u_l_1
significance_bar(pos_y=1,
pos_x=[0, 4],
bar_y=0.03,
p=1,
y_dist=0.02,
distance=0.1)
sb.boxplot(data=all_data, color="skyblue") #.set(ylabel="Dice coefficient")
plt.ylabel("Intersection over Union", size=18)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
print(normaltest(all_data["Gaussian"])[1])
print(normaltest(all_data["Hessian"])[1])
print(normaltest(all_data["Laplacian"])[1])
print(normaltest(all_data["Ilastik"])[1])
print(normaltest(all_data["MitoSegNet"])[1])
