""" Description: Getting started: Original Author: Erica Morrill Department of Biomedical Engineering Lerner Research Institute Cleveland Clinic Cleveland, OH morrile2@ccf.org """ import xml.etree.ElementTree as ET import os import Tkinter as tk import pandas import tkFileDialog import matplotlib.pyplot as plt from matplotlib.ticker import MaxNLocator import numpy as np import time import math import ConfigParser import statsmodels.formula.api as sm class FileSelectionApp(tk.Tk): """Application to display all of the trials in a list""" def __init__(self): tk.Tk.__init__(self) home = os.path.expanduser('~') for dirname, subdirList, fileList in os.walk(home): for dir in subdirList: if "MULTIS_test" in dir and "studies" not in dirname: multis_dir = dirname + '/' + dir try: multis_dir except NameError: multis_dir = tkFileDialog.askdirectory(title="Open MULTIS trials directory") self.directory = multis_dir self.getSubjects() self.title('Select Files') self.masterdf = pandas.DataFrame(data=None) self.columnconfigure(0, weight=1) self.var = [] self.i = 0 col = 0 for item in self.subFiles: self.var.append(tk.IntVar()) c = tk.Checkbutton(self, text=item[0:-2], variable=self.var[self.i]) c.grid(column = col, row=self.i%10, sticky='w') self.i +=1 col = self.i/10 self.minsize(200, 100/(self.i)) tk.Button(self, text="Okay", command=self.checkBoxes).grid(row=1, column =self.i/10+1, sticky='ens') tk.Button(self, text="Select All", command=self.SelectAll).grid(row=0, column=self.i/10+1, sticky='ens') def yview(self, *args): apply(self.yview, args) def checkBoxes(self): s_time = time.time() locations = ['LA_A', 'LA_P', 'UA_A', 'UA_P', 'LL_A','LL_P', 'UL_A', 'UL_P'] # locations = ['LA_A'] #Used for testing for loc in locations: count = 0 self.df = pandas.DataFrame(data=None) self.location = loc for bb in self.var: if bb.get() == 1: xml = self.dir[count] + '/Configuration/' + os.path.split(self.dir[count])[1] + '.xml' # self.saveDemographicData(xml) self.saveData(xml, count) count += 1 #Build Function to create report self.avg = self.df.mean() self.stdDev = self.df.std() self.masterdf.to_csv('/home/morrile2/Documents/Multis/studies/Indentation/dat/' + '002_MasterList_indentation.csv') print("Elapsed Time: %f seconds" %float((time.time()-s_time))) self.quit() def saveDemographicData(self, xml_name): doc = ET.parse(xml_name) root = doc.getroot() subjData = root.find("Subject_Data") Anatomical = subjData.find("Anatomical_Measurements") for child in Anatomical: if child.attrib['type'] == 'Cluster': for cl in child.findall("Cluster"): if cl._children[0].text == 'Length': length = float(cl._children[1].text) elif cl._children[0].text == 'Distal Circumference': circ_d = float(cl._children[1].text) elif cl._children[0].text == 'Central Circumference': circ_c = float(cl._children[1].text) elif cl._children[0].text == 'Proximal Circumference': circ_p = float(cl._children[1].text) if child.tag == 'Upper_Arm': column_names = ['dc (cm)', 'cc (cm)', 'pc (cm)'] self.df_UA = pandas.DataFrame([[circ_d, circ_c, circ_p]], columns=column_names) elif child.tag == 'Lower_Arm': column_names = ['dc (cm)', 'cc (cm)', 'pc (cm)'] self.df_LA = pandas.DataFrame([[circ_d, circ_c, circ_p]], columns=column_names) elif child.tag == 'Upper_Leg': column_names = ['dc (cm)', 'cc (cm)', 'pc (cm)'] self.df_UL = pandas.DataFrame([[circ_d, circ_c, circ_p]], columns=column_names) elif child.tag == 'Lower_Leg': column_names = ['dc (cm)', 'cc (cm)', 'pc (cm)'] self.df_LL = pandas.DataFrame([[circ_d, circ_c, circ_p]], columns=column_names) def lin_fit(self, x, y): try: '''Fits a linear fit of the form mx to the data''' A = np.vstack([x]).T m, _, _, _ = np.linalg.lstsq(A, y) y_fit = m * x y_bar = np.average(y) SS_tot = np.sum((y - y_bar) ** 2) SS_res = np.sum((y - y_fit) ** 2) R_sqr = 1 - SS_res / SS_tot RMS = np.sqrt(np.average((y - y_fit) ** 2)) return m[0], R_sqr, y_fit, RMS except: return np.nan, np.nan, np.nan, np.nan def poly_fit(self, x, y): try: data = pandas.DataFrame(data={'x':x, 'y':y}) olsres2 = sm.ols(formula='y~x+I(x**2)-1', data=data).fit() y_fit = olsres2.predict(data) y_bar = np.average(y) SS_tot = np.sum((y - y_bar) ** 2) SS_res = np.sum((y - y_fit) ** 2) R_sqr = 1 - SS_res / SS_tot RMS = np.sqrt(np.average((y - y_fit) ** 2)) return R_sqr, y_fit, RMS except: return np.nan, np.nan, np.nan def saveData(self, subj_xml, count): # print(self.dir[count]) files = self.getThickFiles(self.location, self.dir[count]) # print(len(files)) subj_xml = ET.parse(subj_xml) root = subj_xml.getroot() demo = root.find('Subject_Data').find("Demographics") age = (demo.find("Age").text) gender = (demo.find("Gender").get("sel")) ethnicity = demo.find("Ethnicity").get("sel") race = demo.find("Race").get("sel") H_M = root.find("Subject_Data").find("Height_and_Mass") height = float(H_M.find("Height").find("Magnitude").text) mass = float(H_M.find("Mass").find("Magnitude").text) BMI = mass/((height/100)**2) activity = (root.find("Subject_Data").find("Activity_Level").find("Lifestyle").get("sel")) if len(files) > 0: for xml_name in files: skin = [] fat = [] muscle = [] Fx = [] Fy = [] Fz = [] total = [] time = [] doc = ET.parse(xml_name) root = doc.getroot() subj = root.find('Subject') src = subj.find('Source') locs = src.findall("Frame") for loc in locs: thick = loc.find("Thickness") force = loc.find("Forces") Fx.append(float(force.find("Fx").text)) Fy.append(float(force.find("Fy").text)) Fz.append(float(force.find("Fz").text)) skin.append(float(thick.find("Skin").text)) fat.append(float(thick.find("Fat").text)) muscle.append(float(thick.find("Muscle").text)) total.append(skin[-1] + fat[-1] + muscle[-1]) time.append(float(loc.find("Time").attrib["value"])) if 'C_' in os.path.split(xml_name)[1]: config = ConfigParser.RawConfigParser() if not config.read(os.path.join(os.path.join(self.dir[count], 'Configuration'), os.path.split(xml_name)[1][0:25] + '_State.cfg')): raise IOError, "Cannot load configuration file... Check path." probe = config.get('6-DOF Load', 'Description') if probe[1:-1] == "9L4 Ultrasound": probe_area = 45*15 # in mm elif probe[1:-1] == "14L5 Ultrasound": probe_area = 45*8 # in mm Fx = np.array(Fx) Fy = np.array(Fy) Fz = np.array(Fz) force_mag = np.sqrt((Fx-Fx[0])**2+(Fy-Fy[0])**2+(Fz-Fz[0])**2) column_names = ['TotalStrain'] df_data = pandas.DataFrame([[(total[0]-total[-1])/total[0]]], columns=column_names) if min(force_mag) > 2: print(os.path.split(xml_name), min(force_mag)) force_mag = force_mag/probe_area total = np.array(total) time = np.array(time) R_sqr_poly, y_poly_fit, RMS_poly = self.poly_fit(-(total-total[0]), force_mag) stiff_t, R_sqr, y_lin_fit, RMS_lin = self.lin_fit(-(total-total[0]), force_mag) EMod_t = stiff_t * total[0] # if R_sqr_poly - R_sqr < 0.05: # plt.scatter(-(total-total[0]), force_mag) # plt.plot(-(total-total[0]), y_poly_fit, 'g') # plt.plot(-(total-total[0]), y_lin_fit, 'r') # plt.ylabel('Pressure (MPa)') # plt.xlabel('Displacement (mm)') # plt.savefig('/home/morrile2/Documents/Multis/studies/Indentation/dat/Linear Plots/'+os.path.split(xml_name)[1][:-3]+'png') # plt.close() ind_rate, R_sqr_2, y_lin_fit_2, RMS_lin_2 = self.lin_fit(time-time[0], (total[0]-total)) column_names = ['Thickness', 'Total_Stiff', 'R_squared', 'R_squared_poly', 'Diff rsquared', 'RMS_lin', 'RMS_poly', 'Diff RMS', 'Total_EMod', 'Rate'] df_cent_stiff = pandas.DataFrame([[total[0], stiff_t, R_sqr, R_sqr_poly, R_sqr_poly-R_sqr, RMS_lin, RMS_poly, RMS_lin-RMS_poly, EMod_t, ind_rate*1000]], columns=column_names) column_names = ['SubID', 'Location', 'Age', 'Gender', 'BMI', 'ActivityLevel', 'Race', 'Ethnicity'] df_demo = pandas.DataFrame( [[xml_name[-40:-37], self.location[0:4], age, gender, BMI, activity, race, ethnicity]], columns=column_names) self.masterdf = self.masterdf.append(pandas.concat([df_demo, df_data, df_cent_stiff], axis=1), ignore_index=True) else: print("Error", os.path.split(xml_name)[1]) def SelectAll(self): for bb in self.var: bb.set(1) def getSubjects(self): self.subFiles = [] self.dir = [] for dirname,subdirList,fileList in os.walk(self.directory): for file in fileList: if "TA_inclusion.xml" in file: self.dir.append(os.path.split(os.path.split(dirname)[0])[0]) self.subFiles.append(os.path.split(os.path.split(os.path.split(dirname)[0])[0])[1]) sortedTrials = sorted(zip(self.subFiles, self.dir)) self.subFiles, self.dir = zip(*sortedTrials) def getThickFiles(self, location, directory): loc_files = [] thick_xml_list = self.parseXMLinclusion(location, os.path.join(os.path.join(os.path.join(directory,'TissueThickness'), 'UltrasoundManual'), os.path.split(directory)[1]+"_TA_inclusion.xml")) for th_xml in thick_xml_list: loc_files.append(os.path.join(directory, 'TissueThickness', 'UltrasoundManual', th_xml)) return loc_files def parseXMLinclusion(self, location, inclusion): doc = ET.parse(inclusion) root = doc.getroot() thickness_xmls = [] for child in root: for subChild in child: try: XML = subChild.attrib["Indentation"] if XML != "None" and XML[-28:-27] == "I" and XML[-34:-30] == location: thickness_xmls.append(XML) except: continue return thickness_xmls if __name__ == "__main__": app = FileSelectionApp() app.mainloop()