""" 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 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" in : # multis_dir = dirname + '/' + dir # # try: # multis_dir # except NameError: # multis_dir = tkFileDialog.askdirectory(title="Open MULTIS trials directory") multis_dir = '../dat/MULTIS_test' 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 for item in self.subFiles: self.var.append(tk.IntVar()) c = tk.Checkbutton(self, text=item, variable=self.var[self.i]) c.grid(column = 0, row=self.i, sticky='w') self.i +=1 tk.Button(self, text="Okay", command=self.checkBoxes).grid(row=1, column =1, sticky='ens') tk.Button(self, text="Select All", command=self.SelectAll).grid(row=0, column=1, sticky='ens') def yview(self, *args): apply(self.yview, args) def make_summary(self, segment, seg_name): df2 = segment[['Segment', 'Location', 'Skin_Proximal', 'Fat_Proximal', 'Muscle_Proximal']] df2['Loc2'] = ['P']*len(df2) df2.columns = ['Segment', 'Location','Skin', 'Fat', 'Muscle', 'Loc2'] df3=segment[['Segment', 'Location', 'Skin_Central', 'Fat_Central', 'Muscle_Central']] df3['Loc2'] = ['M']*len(df3) df3.columns = ['Segment', 'Location','Skin', 'Fat', 'Muscle', 'Loc2'] df4 = segment[['Segment', 'Location', 'Skin_Distal', 'Fat_Distal', 'Muscle_Distal']] df4['Loc2'] = ['D'] * len(df4) df4.columns = ['Segment', 'Location', 'Skin', 'Fat', 'Muscle', 'Loc2'] # df_mean = df2.append(df3.append(df4)).groupby(['Location', 'Loc2']).mean() # df_std_pos = df2.append(df3.append(df4)).groupby(['Location', 'Loc2']).std() df_mean = df3.groupby(['Segment','Location', 'Loc2']).mean() df_std = df3.groupby(['Segment', 'Location', 'Loc2']).std() # df_std_neg = np.zeros(df_std_pos._values.shape) # print(df_std_pos._values.shape, df_std_neg.shape) # print(np.vstack(([df_std_neg], [df_std_pos]))) def stylize_axes(ax): ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.xaxis.set_tick_params(top='off', direction='out', width=1) ax.yaxis.set_tick_params(right='off', direction='out', width=1) fig, ax = plt.subplots(figsize=(4,3)) title = seg_name + " Skin" tissue = "Skin" error = np.array([np.vstack(([np.zeros(df_std[tissue]._values.shape)], [np.array(df_std[tissue]._values)]))]) df_mean[tissue].plot(kind='bar', width = 1.0, yerr = error) # plt.title(title) # plt.ylabel("Thickness (mm)") plt.xlabel("") plt.ylim([0, 3]) plt.xticks(np.arange(4), ("A", "L", "M", "P"), rotation='horizontal') plt.yticks([0, 1, 2, 3], ("0", "1", "2", "3")) fig.subplots_adjust(bottom=0.2) stylize_axes(ax) plt.savefig('../doc/Figures/' + title + '.png', format='png', dpi = 150) plt.close() fig, ax = plt.subplots(figsize=(4,3)) title = seg_name + " Fat" tissue = "Fat" error = np.array([np.vstack(([np.zeros(df_std[tissue]._values.shape)], [np.array(df_std[tissue]._values)]))]) df_mean[tissue].plot(kind='bar', width=1.0, yerr=error) # plt.title(title) # plt.ylabel("Thickness (mm)") plt.xlabel("") plt.ylim([0, 30]) plt.xticks(np.arange(4), ("A", "L", "M", "P"), rotation='horizontal') # plt.yticks([0, 10, 20, 30], ("0", "10", "20", "30")) fig.subplots_adjust(bottom=0.2) stylize_axes(ax) plt.savefig('../doc/Figures/' + title + '.png', format='png', dpi = 150) plt.close() fig, ax = plt.subplots(figsize=(4,3)) title = seg_name + " Muscle" tissue = "Muscle" error = np.array([np.vstack(([np.zeros(df_std[tissue]._values.shape)], [np.array(df_std[tissue]._values)]))]) df_mean[tissue].plot(kind='bar', width=1.0, yerr=error) # plt.title(title) # plt.ylabel("Thickness (mm)") plt.xlabel("") plt.ylim([0, 70]) plt.xticks(np.arange(4), ("A", "L", "M", "P"), rotation='horizontal') # plt.yticks([0, 10, 20, 30, 40, 50, 60, 70], ("0", "", "20", "", "40", "", "60", "")) fig.subplots_adjust(bottom=0.2) stylize_axes(ax) plt.savefig('../doc/Figures/' + title + '.png', format='png', dpi = 150) plt.close() def checkBoxes(self): s_time = time.time() locations = ['LA_A', 'LA_L', 'LA_P', 'LA_M', 'UA_A', 'UA_L', 'UA_P', 'UA_M', 'LL_A', 'LL_L', 'LL_P', 'LL_M', 'UL_A', 'UL_L', 'UL_P', 'UL_M'] # 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(count) count += 1 #Build Function to create report self.avg = self.df.mean() self.stdDev = self.df.std() self.make_summary(self.masterdf[(self.masterdf['Segment'] == "UA")], "Upper Arm") self.make_summary(self.masterdf[(self.masterdf['Segment'] == "LA")], "Lower Arm") self.make_summary(self.masterdf[(self.masterdf['Segment'] == "UL")], "Upper Leg") self.make_summary(self.masterdf[(self.masterdf['Segment'] == "LL")], "Lower Leg") plt.show() self.masterdf.to_csv('../sol/' + 'Thickness_data_raw.csv') # Check for missing data missing_prox = self.masterdf["Total_Proximal"].isnull() missing_cent = self.masterdf["Total_Central"].isnull() missing_dist = self.masterdf["Total_Distal"].isnull() print("Missing proximal: ") print(self.masterdf[["SubID", "Segment", "Location"]][missing_prox]) print("Missing central: ") print(self.masterdf[["SubID", "Segment", "Location"]][missing_cent]) print("Missing Distal: ") print(self.masterdf[["SubID", "Segment", "Location"]][missing_dist]) print("Number of locations missing data", len(self.masterdf[missing_prox])+len(self.masterdf[missing_cent])+len(self.masterdf[missing_dist])) 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 = ['pc (cm)', 'cc (cm)', 'dc (cm)'] self.df_UA = pandas.DataFrame([[circ_p, circ_c, circ_d]], columns=column_names) elif child.tag == 'Lower_Arm': column_names = ['pc (cm)', 'cc (cm)', 'dc (cm)'] self.df_LA = pandas.DataFrame([[circ_p, circ_c, circ_d]], columns=column_names) elif child.tag == 'Upper_Leg': column_names = ['pc (cm)', 'cc (cm)', 'dc (cm)'] self.df_UL = pandas.DataFrame([[circ_p, circ_c, circ_d]], columns=column_names) elif child.tag == 'Lower_Leg': column_names = ['pc (cm)', 'cc (cm)', 'dc (cm)'] self.df_LL = pandas.DataFrame([[circ_p, circ_c, circ_d]], columns=column_names) def saveData(self, count): # print(self.dir[count]) files = self.getThickFiles(self.location, self.dir[count]) for xml_name in files: # xml_name = self.dir[count]+'/'+self.subFiles[count] doc = ET.parse(xml_name) root = doc.getroot() subj = root.find('Subject') src = subj.find('Source') loc = src.find("Frame") thick = loc.find("Thickness") force = loc.find("Forces") f_mag = math.sqrt((float(force.find("Fx").text))**2 + (float(force.find("Fy").text))**2 + (float(force.find("Fz").text))**2) if f_mag > 2 and subj.attrib["ID"] != 'MULTIS001-1' and subj.attrib["ID"] != 'MULTIS002-1' and subj.attrib["ID"] != 'MULTIS004-1' and subj.attrib["ID"] != 'MULTIS005-1': print('Min force over 2N', xml_name, 'Force = %.2f' % f_mag) skin = (float(thick.find("Skin").text)) fat = (float(thick.find("Fat").text)) muscle = (float(thick.find("Muscle").text)) if skin != np.nan and fat != np.nan and muscle != np.nan: total = skin+fat+muscle else: total = np.nan if 'P_' in os.path.split(xml_name)[1]: column_names = ['Skin_Proximal', 'Fat_Proximal', 'Muscle_Proximal', 'Total_Proximal'] df_prox = pandas.DataFrame([[skin, fat, muscle, total]], columns=column_names) # print(df_prox) debug = "p" elif 'C_' in os.path.split(xml_name)[1]: column_names = ['Skin_Central', 'Fat_Central', 'Muscle_Central', 'Total_Central'] df_cent = pandas.DataFrame([[skin, fat, muscle, total]], columns=column_names) # print(df_cent) elif 'D_' in os.path.split(xml_name)[1]: column_names = ['Skin_Distal', 'Fat_Distal', 'Muscle_Distal', 'Total_Distal'] df_dist = pandas.DataFrame([[skin, fat, muscle, total]], columns=column_names) # print(df_dist) debug = 'd' else: print("Error", os.path.split(xml_name)[1]) column_names = ['SubID', 'Segment', 'Location'] df_demo = pandas.DataFrame([[xml_name[-40:-37], self.location[0:2], self.location[3]]], columns=column_names) if self.location[0:2] == 'UA': df_circ = self.df_UA elif self.location[0:2] == 'LA': df_circ = self.df_LA elif self.location[0:2] == 'UL': df_circ = self.df_UL elif self.location[0:2] == 'LL': df_circ = self.df_LL try: df_prox except NameError: column_names = ['Skin_Proximal', 'Fat_Proximal', 'Muscle_Proximal', 'Total_Proximal'] df_prox = pandas.DataFrame([[np.nan, np.nan, np.nan, np.nan]], columns=column_names) try: df_cent except NameError: column_names = ['Skin_Central', 'Fat_Central', 'Muscle_Central', 'Total_Central'] df_cent = pandas.DataFrame([[np.nan, np.nan, np.nan, np.nan]], columns=column_names) try: df_dist except NameError: column_names = ['Skin_Distal', 'Fat_Distal', 'Muscle_Distal', 'Total_Distal'] df_dist = pandas.DataFrame([[np.nan, np.nan, np.nan, np.nan]], columns=column_names) self.df = self.df.append(pandas.concat([df_prox, df_cent, df_dist], axis=1), ignore_index=True) self.masterdf = self.masterdf.append(pandas.concat([df_demo, df_circ, df_prox, df_cent, df_dist], axis=1), ignore_index=True) 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(dirname) self.subFiles.append(os.path.split(dirname)[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(directory,os.path.split(directory)[1]+"_TA_inclusion.xml")) for th_xml in thick_xml_list: loc_files.append(os.path.join(directory, 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: XML = subChild.attrib["Anatomical"] if XML != "None" and XML[-28:-27] == "A" and XML[-34:-30] == location: thickness_xmls.append(XML) return thickness_xmls if __name__ == "__main__": app = FileSelectionApp() app.mainloop()