/
QCExeExample.py
3053 lines (2624 loc) · 112 KB
/
QCExeExample.py
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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
#!/usr/bin/env python
# coding: utf-8
#import pandas for dataframes, import seaborn for graphs, import csv, import os for file handling
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning) #suppress futurewarnings from matplotlib
import pandas as pd
import seaborn as sns
import os
from scipy import stats as st
import csv
from csv import writer
from sklearn.linear_model import LinearRegression
import numpy as np
from sklearn.datasets import make_regression
import webbrowser
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
sns.set_theme(style="darkgrid")
sns.set(font_scale = 1.5)
global df
csv_name = 'ExampleData.csv'#INPUT csv name here
df = pd.read_csv(csv_name) #import csv
list_outliers = {} #global list of outliers for later use
study_name = "STUDY_NAME" #INPUT study name
from datetime import date
date = date.today().strftime("%m/%d/%Y") #INPUT date updated (mm/dd/yyyy)
# In[ ]:
#drop unneeded columns - FOR OTHER STUDIES, UNCOMMENT
# col_name=""
#--------------------------------------------------------------------------------------------------------------------
# with open('') as f: #INPUT variables_to_remove here (ensure file in local folder, or insert path)
# col_name = f.read()
# col_toRemove = col_name.split(", ")
# for col in col_toRemove:
# if col in df.columns:
# del df[col]
#drop unneeded columns ---> FOR USE WITH PAUL'S OUTPUTS ONLY, COMMENT OUT FOR OTHER STUDIES
for col in df.columns:
if (('GlobalEfficiency' in col) or ('MaximizedModularity' in col)
or ('MeanClusteringCoeff' in col) or ('MeanTotalStrength'in col)
or ('NetworkCharacteristic' in col) or ('TotalStrength' in col)
or ('dummyrest' in col) or ('session_id' in col) or ('subject_id' in col)
or ('dummy_rest' in col) or ('file_name' in col) or ('1back' in col)
or ('acq_id' in col) or ('anatomical_zstat1' in col)):
del df[col]
#filter id columns to include only integers
#Skip id filtering
# for id in df['id0'].dropna():
# if(len(id)>3):
# idnum=id[-3:]
# df['id0']=df['id0'].replace(id,idnum)
# In[ ]:
#create csv file for data
def write(var, col, slope, intercept, r_value, p_value, std_err, slope2=-1, intercept2=-1, r_value2=-1,
p_value2=-1, std_err2=-1, flag = False):
fname = str(var) + "_data.csv" #csv name
if(flag):
#clear csv file to update values if first time called
f = open(fname, "w+")
f.close()
title = [] #placeholder for title row
if(not isinstance(slope2,np.ndarray) and slope2 == -1): #if second set of data not given
equation = "y="+str(slope)+"*x+"+str(intercept)
values = [col,slope,intercept,r_value**2,p_value,std_err,equation]
title = ["Variable","Slope","Intercept","R^2","P_value","Std_err", "Equation"]
else: #if second set of data is given
equation = "y="+str(slope)+"*x+"+str(intercept)
equation2 = "y="+str(slope2)+"*x+"+str(intercept2)
if(var == 'MLR'): #MLR title and values are different than the rest
values = ["",col,slope,intercept,r_value**2,equation,
"",col,slope2,intercept2,r_value2**2,equation2]
title = ["Male","Variable","Slope (Age, BMI)","Intercept","R^2", "Equation",
"Female","Variable","Slope (Age, BMI)","Intercept","R^2", "Equation"]
else:
values = ["",col,slope,intercept,r_value**2,p_value,std_err,equation,
"",col,slope2,intercept2,r_value2**2,p_value2,std_err2,equation2]
title = ["Male","Variable","Slope","Intercept","R^2","p_value","Std_err", "Equation",
"Female","Variable","Slope","Intercept","R^2","p_value","Std_err", "Equation"]
with open(fname, 'a', newline='') as f_object: #open in append mode
writer_object = writer(f_object)
if(flag): #If first row, add titles
writer_object.writerow(title)
writer_object.writerow(values)
f_object.close()
# In[ ]:
#SAY
#create Age and Sex/BMI and BMIbySex graphs
plt.rcParams.update({'font.size': 30})
def createGraph(col,AgeSex):
try:
std_line = df[col].std()
#create std lines, palette for colors, legend
std1 = pd.DataFrame(np.full((len(df['id0']),1),std_line+df[col].mean()),columns = ['value'])
std1 = pd.concat([df['id0'],std1],axis = 1)
std2 = pd.DataFrame(np.full((len(df['id0']),1),-1*std_line+df[col].mean()),columns = ['value'])
std2 = pd.concat([df['id0'],std2],axis = 1)
std3 = pd.DataFrame(np.full((len(df['id0']),1),2*std_line+df[col].mean()),columns = ['value'])
std3 = pd.concat([df['id0'],std3],axis = 1)
std4 = pd.DataFrame(np.full((len(df['id0']),1),-2*std_line+df[col].mean()),columns = ['value'])
std4 = pd.concat([df['id0'],std4],axis = 1)
d_std = pd.concat([std1,std2,std3,std4],axis= 0,keys=['std1', 'std2','std3','std4']).reset_index()
d_std = d_std.rename(columns={'level_0': 'line', 'level_1': 'i'})
pal = ['magenta','magenta','cyan','cyan']
ax = sns.lineplot(data = d_std,x = 'id0',y = 'value',hue = 'line', palette = pal, legend = False)
ax = sns.scatterplot(x='id0',y=col, data=df)
ax.set_xlabel('id0',fontsize=14)
ax.set_ylabel(col, fontsize=14)
ax.set_xticklabels(df['id0'],rotation=270)
fig = ax.get_figure()
fig.legend(labels=["+\u03C3","-\u03C3","+2\u03C3","-2\u03C3"])
fig.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)
fig.set_size_inches(8,6)
if(AgeSex):
fig.savefig(col+'.png') #save each plot in local folder
plt.close(fig)
if(AgeSex):
#variable over age, linear regression
ax = sns.jointplot(x='Age',y=col, data=df,kind="reg", truncate=False,
color="m", height=7)
# ax.set_xticklabels(df['Age'],rotation=270)
fig1 = ax.fig
fig1.savefig(col+'_Age.png') #save each plot in local folder
plt.close(fig1)
#variable over sex, linear regression
pal = dict(Male="#6495ED", Female="#F08080")
ax = sns.lmplot(x='Age',y=col, col="Sex", hue="Sex", data=df,
palette=pal, y_jitter=.02, truncate=False)
#ax.set_xticklabels(df['Sex'],rotation=270)
fig2 = ax.fig
fig2.set_size_inches(12,6)
fig2.savefig(col+'_Sex.png') #save each plot in local folder
plt.close(fig2)
else:
ax = sns.jointplot(x='BMI',y=col, data=df,kind="reg", truncate=False,
color="g", height=7)
# ax.set_xticklabels(df['Age'],rotation=270)
fig3 = ax.fig
fig3.set_size_inches(8,6)
fig3.savefig(col+'_BMI.png') #save each plot in local folder
plt.close(fig3)
#variable over BMI by sex, linear regression
pal = dict(Male="#6495ED", Female="#F08080")
ax = sns.lmplot(x='BMI',y=col, col="Sex", hue="Sex", data=df,
palette=pal, y_jitter=.02, truncate=False)
#ax.set_xticklabels(df['Sex'],rotation=270)
fig2 = ax.fig
fig2.set_size_inches(12,6)
fig2.savefig(col+'_BxS.png') #save each plot in local folder
plt.close(fig2)
except TypeError:
print('Graph not generated for: '+col)
except ValueError:
print("value: "+col)
# In[ ]:
#SAY
#create Behvaioral Data graphs
def createBehavioralGraph(col,cols):
for var in cols:
try:
#variable over age, linear regression
ax = sns.jointplot(x=str(var),y=col, data=df,kind="reg", truncate=False,
color="m", height=7)
fig1 = ax.fig
fig1.set_size_inches(8,6)
fig1.savefig(col+'_'+var+'.png') #save each plot in local folder
plt.close(fig1)
#variable over sex, linear regression
pal = dict(Male="#6495ED", Female="#F08080")
ax = sns.lmplot(x=str(var),y=col, col="Sex", hue="Sex", data=df,
palette=pal, y_jitter=.02, truncate=False)
fig2 = ax.fig
fig2.set_size_inches(12,6)
fig2.savefig(col+'_'+var+'_Sex.png') #save each plot in local folder
plt.close(fig2)
except TypeError:
print('Graph not generated for: '+col)
except ValueError:
print("value: "+col)
# In[ ]:
#3D Graph Generation
def create3DGraph(col, r_value1, r_value2, mlr1, mlr2):
try:
#Male
min1 = df_temp_Male['Age'].min()
min2 = df_temp_Male['BMI'].min()
max1 = df_temp_Male['Age'].max()
max2 = df_temp_Male['BMI'].max()
fig = plt.figure()
fig.set_size_inches(12, 6)
ax = fig.add_subplot(121,projection='3d')
ax.scatter(df_temp_Male['Age'],df_temp_Male['BMI'],df_temp_Male[col],color = 'blue')
ax.set_title(col + ":\n Male (R^2 = "+str(round(r_value1,5))+")")
ax.set_xlabel("Age")
ax.set_ylabel("BMI")
ax.set_zlabel(col)
ax.view_init(30,60)
#graph MLR (3D Regression)
line1 = np.linspace(min1, max1, 30)
line2 = np.linspace(min2, max2, 30)
xx_pred, yy_pred = np.meshgrid(line1, line2)
model = np.array([xx_pred.flatten(), yy_pred.flatten()]).T
predicted = mlr1.predict(model)
ax.plot_trisurf(xx_pred.flatten(), yy_pred.flatten(), predicted, color=(0,0,0,.3), edgecolor=(0,0,0,.2))
#Female
min1 = df_temp_Female['Age'].min()
min2 = df_temp_Female['BMI'].min()
max1 = df_temp_Female['Age'].max()
max2 = df_temp_Female['BMI'].max()
ax = fig.add_subplot(122,projection='3d')
ax.scatter(df_temp_Female['Age'],df_temp_Female['BMI'],df_temp_Female[col],color = 'red')
ax.set_title(col + ":\n Female (R^2 = "+str(round(r_value2,5))+")")
ax.set_xlabel("Age")
ax.set_ylabel("BMI")
ax.set_zlabel(col)
ax.view_init(30,60)
#graph MLR (3D Regression)
line1 = np.linspace(min1, max1, 30) # range of porosity values
line2 = np.linspace(min2, max2, 30) # range of brittleness values
xx_pred, yy_pred = np.meshgrid(line1, line2)
model = np.array([xx_pred.flatten(), yy_pred.flatten()]).T
predicted2 = mlr2.predict(model)
ax.plot_trisurf(xx_pred.flatten(), yy_pred.flatten(), predicted2, color=(0,0,0,.3), edgecolor=(0,0,0,.2))
plt.savefig(col+'_3D.png') #save each plot in local folder
plt.close(fig)
except ValueError:
print(col)
except TypeError:
print(col+" TypeError")
# In[ ]:
first = True
#Make data tables and graphs, graph only if significant p-value
df_temp = df.copy(deep=True)
for i,col in enumerate(df.columns.drop(['id0','id0.1','id0.2','id0.3','id0.4','id0.5','id0.6','id0.7','run_id','Age','Sex', 'BMI', 'datetime'])):
#create temporary dataframes for regression equations
graphedAgeSex = False #Flag to check if graph has already been generated
graphedBMI = False #Flag to check if graph has already been generated
AgeSex = True #Boolean for whether Age and Sex graphs or BMI and BxS graphs should be generated
df_temp_Age = df_temp[df_temp['Age'].notna()]
df_temp_Age = df_temp_Age[df_temp_Age[col].notna()]
df_temp_Sex = df_temp[df_temp['Sex'].notna()]
df_temp_Male = df_temp_Sex.copy(deep=True)
df_temp_Male.drop(df_temp_Male.loc[df_temp_Male['Sex']=='Female'].index, inplace=True)
df_temp_Male = df_temp_Male[df_temp_Male[col].notna()]
df_temp_Female = df_temp_Sex.copy(deep=True)
df_temp_Female.drop(df_temp_Female.loc[df_temp_Female['Sex']=='Male'].index, inplace=True)
df_temp_Female = df_temp_Female[df_temp_Female[col].notna()]
df_temp_BMI = df_temp[df_temp['BMI'].notna()]
df_temp_BMI = df_temp_BMI[df_temp_BMI[col].notna()]
df_temp_MaleMLR = df_temp_Male.copy(deep=True)
df_temp_MaleMLR = df_temp_MaleMLR[df_temp_MaleMLR['Age'].notna()]
df_temp_MaleMLR = df_temp_MaleMLR[df_temp_MaleMLR['BMI'].notna()]
df_temp_FemaleMLR = df_temp_Female.copy(deep=True)
df_temp_FemaleMLR = df_temp_FemaleMLR[df_temp_FemaleMLR['Age'].notna()]
df_temp_FemaleMLR = df_temp_FemaleMLR[df_temp_FemaleMLR['BMI'].notna()]
#Age
if(df_temp_Age.size != 0):
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp_Age['Age'],df_temp_Age[col])
write('Age', col,slope, intercept, r_value, p_value, std_err,flag = first)
if(p_value<.05 and not graphedAgeSex):
createGraph(col,AgeSex)
graphedAgeSex = True
#Sex
if(df_temp_Male.size != 0 and df_temp_Female.size != 0 ):
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp_Male['Age'],df_temp_Male[col])
slope2, intercept2, r_value2, p_value2, std_err2 = st.linregress(df_temp_Female['Age'],df_temp_Female[col])
write('Sex', col, slope, intercept, r_value, p_value, std_err,
slope2, intercept2, r_value2, p_value2, std_err2,flag = first)
if((p_value<.05 or p_value2<.05) and not graphedAgeSex):
createGraph(col, AgeSex)
graphedAgeSex = True
#BMI
if(df_temp_BMI.size != 0):
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp_BMI['BMI'],df_temp_BMI[col])
write('BMI', col,slope, intercept, r_value, p_value, std_err,flag = first)
if(p_value<.05 and not graphedBMI):
createGraph(col, not AgeSex)
graphedBMI = True
#BxS
if(df_temp_Male.size != 0 and df_temp_Female.size != 0 ):
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp_Male['BMI'],df_temp_Male[col])
slope2, intercept2, r_value2, p_value2, std_err2 = st.linregress(df_temp_Female['BMI'],df_temp_Female[col])
write('BxS', col, slope, intercept, r_value, p_value, std_err,
slope2, intercept2, r_value2, p_value2, std_err2,flag = first)
if((p_value<.05 or p_value2<.05) and not graphedBMI):
createGraph(col, not AgeSex)
graphedBMI = True
#Multiple Linear Regression, split by sex
try:
#Male
mlr1 = LinearRegression()
mlr1.fit(df_temp_MaleMLR[['Age', 'BMI']], df_temp_MaleMLR[col])
intercept = mlr1.intercept_
slope = mlr1.coef_
r_value = mlr1.score(df_temp_MaleMLR[['Age', 'BMI']],df_temp_MaleMLR[col])
#Female
mlr2 = LinearRegression()
mlr2.fit(df_temp_FemaleMLR[['Age', 'BMI']], df_temp_FemaleMLR[col])
intercept2 = mlr2.intercept_
slope2 = mlr2.coef_
r_value2 = mlr2.score(df_temp_FemaleMLR[['Age', 'BMI']],df_temp_FemaleMLR[col])
write('MLR', col, slope, intercept, r_value, 0, 0, slope2, intercept2, r_value2, flag = first) #0,0 for p_value, stdErr
if(r_value>0.1 or r_value2>0.1): #filter graph generation by R^2>0.1
create3DGraph(col, r_value, r_value2, mlr1, mlr2)
except ValueError:
print(col)
first = False
# In[ ]:
first = True
#Make data tables for measurements
for i,col in enumerate(df.columns.drop(['id0','id0.1','id0.2','id0.3','id0.4','id0.5','id0.6','id0.7','run_id','Age','Sex', 'BMI', 'datetime'])):
#create temporary dataframes for regression equations
df_temp = df.copy(deep=True)
df_temp = df_temp[df_temp[col].notna()]
df_temp = df_temp[df_temp['peakvo2_ml_gxt'].notna()]
df_temp = df_temp[df_temp['fft_4step_t1'].notna()]
df_temp = df_temp[df_temp['fft_4step_t2'].notna()]
df_temp = df_temp[df_temp['fft_stair_ds_tester1'].notna()]
df_temp = df_temp[df_temp['fft_stair_us_tester1'].notna()]
df_temp_Sex = df_temp[df_temp['Sex'].notna()]
df_temp_Male = df_temp_Sex.copy(deep=True)
df_temp_Male.drop(df_temp_Male.loc[df_temp_Male['Sex']=='Female'].index, inplace=True)
df_temp_Female = df_temp_Sex.copy(deep=True)
df_temp_Female.drop(df_temp_Female.loc[df_temp_Female['Sex']=='Male'].index, inplace=True)
cols = []
if(df_temp.size != 0):
#vo2
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp['peakvo2_ml_gxt'], df_temp[col])
write('V_O2', col,slope, intercept, r_value, p_value, std_err,flag = first)
if(p_value<.05):
cols.append('peakvo2_ml_gxt')
#four1
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp['fft_4step_t1'],df_temp[col])
write('four1', col,slope, intercept, r_value, p_value, std_err,flag = first)
if(p_value<.05 ):
cols.append('fft_4step_t1')
#four2
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp['fft_4step_t2'],df_temp[col])
write('four2', col,slope, intercept, r_value, p_value, std_err,flag = first)
if(p_value<.05 ):
cols.append('fft_4step_t2')
#stairDown
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp['fft_stair_ds_tester1'],df_temp[col])
write('stairDown', col,slope, intercept, r_value, p_value, std_err,flag = first)
if(p_value<.05 ):
cols.append('fft_stair_ds_tester1')
#stairUp
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp['fft_stair_us_tester1'],df_temp[col])
write('stairUp', col,slope, intercept, r_value, p_value, std_err,flag = first)
if(p_value<.05 ):
cols.append('fft_stair_us_tester1')
if(df_temp_Male.size != 0 and df_temp_Female.size != 0 ):
#vo2
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp_Male['peakvo2_ml_gxt'],df_temp_Male[col])
slope2, intercept2, r_value2, p_value2, std_err2 = st.linregress(df_temp_Female['peakvo2_ml_gxt'],df_temp_Female[col])
write('V_O2_Sex', col, slope, intercept, r_value, p_value, std_err,
slope2, intercept2, r_value2, p_value2, std_err2,flag = first)
if (p_value<.05 or p_value2<.05) and 'peakvo2_ml_gxt' not in cols:
cols.append('peakvo2_ml_gxt')
#four1
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp_Male['fft_4step_t1'],df_temp_Male[col])
slope2, intercept2, r_value2, p_value2, std_err2 = st.linregress(df_temp_Female['fft_4step_t1'],df_temp_Female[col])
write('four1_Sex', col, slope, intercept, r_value, p_value, std_err,
slope2, intercept2, r_value2, p_value2, std_err2,flag = first)
if(p_value<.05 or p_value2<.05) and 'fft_4step_t1' not in cols:
cols.append('fft_4step_t1')
#four2
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp_Male['fft_4step_t2'],df_temp_Male[col])
slope2, intercept2, r_value2, p_value2, std_err2 = st.linregress(df_temp_Female['fft_4step_t2'],df_temp_Female[col])
write('four2_Sex', col, slope, intercept, r_value, p_value, std_err,
slope2, intercept2, r_value2, p_value2, std_err2,flag = first)
if(p_value<.05 or p_value2<.05) and 'fft_4step_t2' not in cols:
cols.append('fft_4step_t2')
#stairDown
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp_Male['fft_stair_ds_tester1'],df_temp_Male[col])
slope2, intercept2, r_value2, p_value2, std_err2 = st.linregress(df_temp_Female['fft_stair_ds_tester1'],df_temp_Female[col])
write('stairDown_Sex', col, slope, intercept, r_value, p_value, std_err,
slope2, intercept2, r_value2, p_value2, std_err2,flag = first)
if(p_value<.05 or p_value2<.05) and 'fft_stair_ds_tester1' not in cols:
cols.append('fft_stair_ds_tester1')
#stairUp
slope, intercept, r_value, p_value, std_err = st.linregress(df_temp_Male['fft_stair_us_tester1'],df_temp_Male[col])
slope2, intercept2, r_value2, p_value2, std_err2 = st.linregress(df_temp_Female['fft_stair_us_tester1'],df_temp_Female[col])
write('stairUp_Sex', col, slope, intercept, r_value, p_value, std_err,
slope2, intercept2, r_value2, p_value2, std_err2, flag = first)
if(p_value<.05 or p_value2<.05) and 'fft_stair_us_tester1' not in cols:
cols.append('fft_stair_us_tester1')
createBehavioralGraph(col,cols)
first = False
# In[ ]:
#create descriptions for different variables using keywords, return corresponding string
def desc(name):
if('snrd' in name):
return 'Dietrich’s SNR using air background as reference:<span style="color: green"> ↑</span>'
elif('snr' in name or 'tsnr' in name):
return 'Signal-to-Noise ratio:<span style="color: green"> ↑</span>'
elif('cnr' in name):
return 'Contrast-to-noise ratio:<span style="color: green"> ↑</span>'
elif('fwhm' in name):
return 'Full-width half maximum estimations:<span style="color: green"> ↓</span>'
elif('qi2' in name):
return 'Goodness of fit of a noise model into the background noise: <span style="color: green">↓</span>'
elif('cjv' in name):
return 'Coefficient of joint variation:<span style="color: green"> ↓</span>'
elif(name.startswith('efc')):
return """Entropy focus criterion:
<span style="color: green"> ↓ more uniform distribution, less noisy</span>"""
elif('fber' in name):
return 'Foreground-background energy ratio:<span style="color: green"> ↑</span>'
elif('qi1' in name):
return 'Segmentation using mathematical morphology:<span style="color: green"> ↓</span>'
elif('inu' in name):
return 'Intensity non-uniformity estimate measurements:<span style="color: green"> ~1</span>'
elif('pve' in name):
return 'Partial volume errors:<span style="color: green"> ↓</span>'
elif('wm2max' in name):
return 'White-matter to maximum intensity ratio:<span style="color: green"> [0.6, 0.8]</span>'
elif('icv' in name):
return 'Intracranial volume fractions: <span style="color: green"> “should move within a normative range”</span>'
elif('rpve' in name):
return 'Residual partial volume errors:<span style="color: green"> ↓</span>'
elif('fd' in name):
return 'Framewise displacement:<span style="color: green"> ↓</span>'
elif('dvars' in name):
return 'Temporal derivative of timecourses RMS variance over voxels:<span style="color: green"> ↓</span>'
elif('gsr' in name):
return 'Ghost-to-signal Ratio:<span style="color: green"> ↓</span>'
elif('gcor' in name):
return 'Global correlation :<span style="color: green"> ↓</span>'
elif('spikes' in name):
return 'High frequency and global intensity :<span style="color: green"> ↓ (lessvolumes to remove if filtering)</span>'
elif('aor' in name):
return 'AFNI’s outlier ratio: mean fraction of outliers per fMRI volume'
elif('aqi' in name):
return 'AFNI’s quality index: mean quality index'
elif('coregCrossCorr' in name):
return 'Cross correlation:<span style="color: green"> ↑</span>'
elif('CoregJaccard' in name):
return 'Jaccard index:<span style="color: green"> ↑</span>'
elif('CoregDice' in name):
return 'Dice index:<span style="color: green"> ↑</span>'
elif('CoregCoverage' in name):
return 'Coverage index:<span style="color: green"> ↑</span>'
elif('regCrossCorr' in name):
return 'Cross correlation:<span style="color: green"> ↑</span>'
elif('regJaccard' in name):
return 'Jaccard index:<span style="color: green"> ↑</span>'
elif('regDice' in name):
return 'Dice index:<span style="color: green"> ↑</span>'
elif('regCoverage' in name):
return 'Coverage index:<span style="color: green"> ↑</span>'
elif('normCrossCorr' in name):
return 'Cross correlation:<span style="color: green"> ↑</span>'
elif('normJaccard' in name):
return 'Jaccard index:<span style="color: green"> ↑</span>'
elif('normDice' in name):
return 'Dice index:<span style="color: green"> ↑</span>'
elif('normCoverage' in name):
return 'Coverage index:<span style="color: green"> ↑</span>'
elif('relMeanRMSMotion' in name):
return 'Mean value of RMS motion:<span style="color: green"> ↓</span>'
elif('relMaxRMSMotion' in name):
return 'Maximum value of RMS motion:<span style="color: green"> ↓</span>'
elif('nSpikesFD' in name):
return 'Number of spikes per FD:<span style="color: green"> ↓</span>'
elif('nspikesDV' in name):
return 'Number of spikes per DV:<span style="color: green"> ↓</span>'
elif('pctSpikesDV' in name):
return 'Percentage of spikes per DV:<span style="color: green"> ↓</span>'
elif('pctSpikesFD' in name):
return 'Percentage of spikes per DV:<span style="color: green"> ↓/span>'
elif('meanDV' in name):
return 'Mean DVARS:<span style="color: green"> ↓</span>'
elif('motionDVCorrInit' in name):
return 'Correlation of RMS and DVARS before regression:<span style="color: green"> ↓</span>'
elif('motionDVCorrFinal' in name):
return 'Correlation of RMS and DVARS after regression :<span style="color: green"> ↓ lower than init</span>'
elif('nNuisanceParameters' in name):
return 'Total number of nuisance Parameters in addition to custom regressors:<span style="color: green"> ↓</span> (confound regression model-dependent)'
elif('nVolCensored' in name):
return 'Total number of volume(s) censored:<span style="color: green"> ↓</span>'
elif('estimatedLostTemporalDOF' in name):
return 'Total degree of freedom lost:<span style="color: green"> ↓</span>'
elif('mean_fd' in name):
return 'Mean framewise displacement:<span style="color: green"> ↓</span>'
elif('max_fd' in name):
return 'Maximum framewise displacement:<span style="color: green"> ↓</span>'
elif('max_translation' in name):
return '<span style="color: green"> ↓</span>'
elif('max_rotation' in name):
return '<span style="color: green"> ↓</span>'
elif('max_rel_translation' in name):
return 'Maxima of derivative of max_translation:<span style="color: green"> ↓</span>'
elif('max_rel_rotation' in name):
return 'Maxima of derivative of max_rotation:<span style="color: green"> ↓</span>'
elif('t1_dice_distance' in name):
return '<span style="color: green"> ↑</span>'
elif('mni_dice_distance' in name):
return '<span style="color: green"> ↑</span>'
elif('raw_incoherence_index ' in name):
return '<span style="color: green"> ↓</span>'
elif('raw_coherence_index ' in name):
return '<span style="color: green"> ↑</span>'
elif('t1_incoherence_index ' in name):
return '<span style="color: green"> ↓</span>'
elif('t1_coherence_index ' in name):
return '<span style="color: green"> ↑</span>'
elif('num_bad_slices' in name):
return '<span style="color: green"> ↓</span>'
elif('raw_dimension' in name):
return 'Should match protocol field of view'
elif('raw_voxel_size' in name):
return 'Should match protocol resolution'
elif('raw_max_b' in name):
return 'Should match protocol maximum b'
elif('raw_neighbor_corr' in name):
return 'Neighboring DWI Correlation (NDC)'
elif('raw_num_directions' in name):
return 'Should match protocol number of directions for dwi scan'
elif('t1_dimension' in name):
return 'Preprocessed space field of view'
elif('t1_voxel_size' in name):
return 'Preprocessed space resolution controlled by --output_resolution value'
if('t1_max_b' in name):
return 'Equal to raw_max_b'
elif('t1_neighbor_corr' in name):
return 'Equal to raw_neighbor_corr'
elif('t1_num_directions' in name):
return 'Equal to raw_num_directions'
else:
return ""
# In[ ]:
def mean(name):
return str(df[name].mean())
# In[12]:
def median(name):
return str(df[name].median())
# In[13]:
def std(name):
return str(df[name].std())
# In[14]:
def rnge(name):
return str(df[name].max() - df[name].min())
# In[15]:
def outliers(name):
flag = False
outs = ""
mean_ = float(mean(name))
std_ = float(std(name))
for i in df.index:
if((mean_-2*std_)>df[name][i] or df[name][i]>(mean_+2*std_)):
if(flag):
outs += ", "+ str(df['id0'][i])
if df['id0'][i] in list_outliers:
list_outliers[df['id0'][i]].append(name)
else:
list_outliers[df['id0'][i]] = [name]
else:
outs += str(df['id0'][i])
flag = True
if df['id0'][i] in list_outliers:
list_outliers[df['id0'][i]].append(name)
else:
list_outliers[df['id0'][i]] = [name]
return outs
# In[16]:
def get_outliers():
output = ""
#remove nan key to sort keys in ascending order
removed = 0;
for key in list_outliers.keys():
if type(key)==float:
removed=key
break
#add nan key entry to return string
output += "<div class = outliers><br><h2>"+str(removed) + "</h2><h3> (" +str(len(list_outliers.get(removed)))+" occurences):</h3><br>"
flag = False
for item in list_outliers.get(removed):
if flag:
output += ", "+ str(item)
else:
output += str(item)
flag = True
outliers = {k: list_outliers[k] for k in list_outliers if type(k)==str}
for key in sorted(outliers):
output += "<div class = outliers><br><h2>"+str(key) + "</h2><h3> (" +str(len(list_outliers.get(key)))+" occurences):</h3><br>"
flag = False
for item in outliers.get(key):
if flag:
output += ", "+ str(item)
else:
output += str(item)
flag = True
output += "</div>"
output += "</div>"
return output
# In[ ]:
#List of graph tags
tags = ["Sex","Age","BMI","BxS","_3D"]
# In[ ]:
#CSS - In-document styling sheet for each site
#dropdown menu from https://www.w3schools.com/howto/howto_css_dropdown.asp
css = """ body{
margin:0;
}
.row {
display: flex;
}
.column {
flex: 50%;
padding: 5px;
}
table, th, td {
border: 1px solid black;
border-collapse: collapse;
}
.image1 {
display: flex;
justify-content: center;
}
.navbar {
overflow: hidden;
background-color: #13294b;
}
.navbar a {
float: left;
font-size: 16px;
color: white;
text-align: center;
padding: 14px 16px;
text-decoration: none;
}
.active{
background-color: #E84A27;
}
.dropdown {
float: left;
overflow: hidden;
}
.dropdown .dropbtn {
font-size: 16px;
border: none;
outline: none;
color: white;
padding: 14px 16px;
background-color: #13294b;
font-family: inherit;
margin: 0;
}
.navbar a:hover, .dropdown:hover .dropbtn {
background-color: #E8E9EA;
}
.dropdown-content {
display: none;
position: absolute;
background-color: #13294b;
min-width: 160px;
box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
z-index: 1;
}
.dropdown-content a {
float: none;
color: white;
padding: 12px 16px;
text-decoration: none;
display: block;
text-align: left;
}
.dropdown-content a:hover {
background-color: #E8E9EA;
}
.dropdown:hover .dropdown-content {
display: block;
}
#searchbar{
padding:13px;
border-radius: 10px;
}
input[type=text] {
width: 7%;
-webkit-transition: width 0.15s ease-in-out;
transition: width 0.15s ease-in-out;
}
input[type=text]:focus {
width: 15%;
}
"""
# In[ ]:
list_outliers.clear()
main_name = study_name+"QCGraphs.html"
f = open(main_name,'w') #create QCGraphs in local folder
grphs = ""
for filename in os.listdir(os.getcwd()):
if filename.endswith(".png") and filename != "Illini_icon.png" and filename != "heatmap_total.png" and filename != "heatmap_95.png":
#descriptive stats
cont = True
name = filename[:len(filename) - 4]
#Check if Age and Sex Graphs exist; if not, should be in BMI page
if not(os.path.exists(name+"_Age.png") or os.path.exists(name+"_Sex.png")):
continue
exclude = ["_Age", "_Sex","_BMI", "_BxS", "_peakvo2_ml_gxt", "_fft_4step_t1", "_fft_4step_t2", "_fft_stair_ds_tester1", "_fft_stair_us_tester1" ]
#Check if png should not be on this page
for item in exclude:
if (item in name):
cont = False
if not cont:
continue
_stats = """<table style="width:50% ">
<tr>
<th colspan="2">"""+name+"""</th>
</tr>
<tr>
<td>Mean</td>
<td>"""+mean(name)+"""</td>
</tr>
<tr>
<td>Median</td>
<td>"""+median(name)+"""</td>
</tr>
<tr>
<td>Std.</td>
<td>"""+std(name)+"""</td>
</tr>
<tr>
<td>Range</td>
<td>"""+rnge(name)+"""</td>
</tr>
</table>"""
#Insert sets of 3 graphs in template to ensure graph stays with corresponding age and sex graphs
grphs += "<div class=row>"
grphs += ("<div class=column><h2 style='text-align:center'>"+name+"_Age</h2><div class='image1'><img src="+ name
+"_Age.png>\n</div></i></p></div>")
grphs+= ("<div class=column><h2 style='text-align:center'>"+name+"</h2><div class='image1'><img src="+ filename+
" >\n</div><br><p font-style=italic align=center><i>"+_stats+
"<br><font size='+2'>" +str(desc(name))+
"</font><br>Outliers: "+outliers(name)+"</i></p></div>")
grphs += ("<div class=column><h2 style='text-align:center'>"+name+"_Sex</h2><div class='image1'><img src="+ name +
"_Sex.png>\n</div></i></p></div>")
grphs += "</div>"
else:
continue
#linked stylesheet for caret symbol for dropdown
code = """<html><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
<style>"""+css+"""</style>
<meta name="robots" content="noindex">
<head>
<div class="navbar">
<a class="active" href="""+study_name+"""QCGraphs.html>QC Graphs</a>
<a href="""+study_name+"""nbs_qsiGraphs.html> NBS QSI </a>
<a href="""+study_name+"""nbs_RSFC.html> NBS RSFC</a>
<a href="""+study_name+"""func_restGraphs.html> Functional Resting-State</a>
<a href="""+study_name+"""diff_qcGraphs.html> Diffusion QC </a>
<a href="""+study_name+"""t1wGraphs.html> T1 Weighted </a>
<a href="""+study_name+"""t2wGraphs.html> T2 Weighted </a>
<div class="dropdown">
<button class="dropbtn">Behavioral Graphs
<i class="fa fa-caret-down"></i>
</button>
<div class="dropdown-content">
<a href="""+study_name+"""vo2Graphs.html> Peak VO2 </a>
<a href="""+study_name+"""fourStep1Graphs.html> FourStep 1 </a>
<a href="""+study_name+"""fourStep2Graphs.html> FourStep 2 </a>
<a href="""+study_name+"""stairDownGraphs.html> Stair-Down </a>
<a href="""+study_name+"""stairUpGraphs.html> Stair-Up </a>
</div>
</div>
<a href="""+study_name+"""outliers.html>Outliers</a>
<a href="""+study_name+"""data.html>Data</a>
<a href="""+study_name+"""about.html>About</a>
<img src="Illini_icon.png" width = 3.5% style="float:right">
<input id="searchbar" onkeyup="Search_var()" type="text" name="search" placeholder="Search...">
</div>
</head>
"""
f.write(code)
#insert graphs
code2 = """
<body style="background-color:#f0f0f0">
<div style = "text-align: center; vertical-align: middle;">
<h1>Last Updated: """+date+"""</h1>
</div>
"""+grphs+"""</body>
<script>
function Search_var() {
let input = document.getElementById('searchbar').value
input=input.toLowerCase();
let x = document.getElementsByClassName('row');
for (i = 0; i < x.length; i++) {
if (!x[i].innerHTML.toLowerCase().includes(input)) {
x[i].style.display="none";
}
else {
x[i].style.display="";
}
}
}
</script>
</html>"""
f.write(code2)
f.close()
#open html file
QCfile = 'file:///'+os.getcwd()+'/' + main_name
webbrowser.open_new_tab(QCfile)
# In[ ]:
# BMI_name = study_name+"BMIGraphs.html"
# f = open(BMI_name,'w') #create BMIGraphs in local folder
# grphs = ""
# for filename in os.listdir(os.getcwd()):
# if filename.endswith(".png") and filename != "Illini_icon.png" and filename != "heatmap_total.png" and filename != "heatmap_95.png":
# #descriptive stats
# name = filename[:len(filename) - 4]
# #Check if Age and Sex Graphs exist; if not, should be in BMI page
# if not(os.path.exists(name+"_BMI.png") or os.path.exists(name+"_BxS.png")):
# continue
# exclude = ["_Age", "_Sex","_BMI", "_BxS", "_peakvo2_ml_gxt", "_fft_4step_t1", "fft_4step_t2", "_fft_stair_ds_tester1", "_fft_stair_us_tester1" ]
# #Check if png should not be on this page
# for item in exclude:
# if (item in name):
# cont = False
# if not cont:
# continue
# _stats = """<table style="width:50% ">
# <tr>
# <th colspan="2">"""+name+"""</th>
# </tr>
# <tr>
# <td>Mean</td>
# <td>"""+mean(name)+"""</td>
# </tr>
# <tr>
# <td>Median</td>
# <td>"""+median(name)+"""</td>
# </tr>
# <tr>
# <td>Std.</td>
# <td>"""+std(name)+"""</td>
# </tr>