├── .gitignore ├── config_files ├── example.config └── example2.config ├── example.config ├── read_calib.py ├── validatecop.py ├── COPparamsFs.py ├── read_datfile.py ├── wiicop.svg ├── README.md ├── GetCOPparams.py ├── WiiCopFunctions.py ├── hyperellipsoid.py ├── wiicop.py ├── wiicop_v1.py └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | # ignore the __pycache__ directory 2 | /__pycache__/* 3 | # any test files 4 | test*.py 5 | -------------------------------------------------------------------------------- /config_files/example.config: -------------------------------------------------------------------------------- 1 | # Example config file 2 | [study info] 3 | study_name = June's study 4 | study_dir = /home/kevin/studydir 5 | 6 | [factors] 7 | # acq_time is compulsory and can have an integer value(s) for acquisition time in seconds or 'inf' 8 | # 'inf' means stop acquisition manually 9 | acq_time = 3 10 | side = right,left 11 | trials = trial1,trial2,trial3 12 | 13 | 14 | -------------------------------------------------------------------------------- /config_files/example2.config: -------------------------------------------------------------------------------- 1 | # Example config file 2 | [study info] 3 | study_name = researcher's study 4 | study_dir = /home/researchers/studydir 5 | 6 | [factors] 7 | # acq_time is compulsory and can have an integer value(s) for acquisition time in seconds of 'inf' 8 | # for manual stopping of the acquisition 9 | side = right,left 10 | trials = trial1,trial2,trial3 11 | acq_time = 100 12 | 13 | -------------------------------------------------------------------------------- /example.config: -------------------------------------------------------------------------------- 1 | # Example config for study 2 | [study info] 3 | study_name = example study 4 | study_dir = /home/researchers/Documents/wiistudies/example/ 5 | 6 | [factors] 7 | # acq_time is compulsory and can have an integer value(s) for acquisition time in seconds of 'inf' 8 | # for manual stopping of the acquisition 9 | group = year1,year4 10 | trials = trial1,trial2,trial3 11 | acq_time = 40 12 | 13 | -------------------------------------------------------------------------------- /read_calib.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # to read calibration data and store it as a readable text file 3 | import tkinter.filedialog as tk_fd 4 | import pickle 5 | import numpy as np 6 | import sys 7 | 8 | 9 | dfile = tk_fd.askopenfilename(title = 'Get pickled data file',filetypes=[('Data files', '*.dat'), ('All files','*')]) 10 | if not dfile: 11 | print('No file chosen') 12 | sys.exit(0) 13 | with open(dfile,'rb') as fptr: 14 | dc = pickle.load(fptr, fix_imports=False) 15 | print(dc) 16 | -------------------------------------------------------------------------------- /validatecop.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # to test cop parameters 3 | 4 | from hyperellipsoid import hyperellipsoid 5 | import numpy as np 6 | import math 7 | import COPparamsFs as cp 8 | import matplotlib.pyplot as plt 9 | 10 | def pol2cart(rho, phi): 11 | x = rho * np.cos(phi) 12 | y = rho * np.sin(phi) 13 | return(x, y) 14 | 15 | test_hyp = False 16 | 17 | 18 | if test_hyp: 19 | # test hyperellipsoid 20 | cnt = 0 21 | trls = 1000 22 | for ia in range(0,trls): 23 | # create test data 24 | coords = np.random.normal(0,1,(100,2)) 25 | area, axes, angles, center, rot = hyperellipsoid(coords, show=False) 26 | # get new data point 27 | x = np.random.normal(0,1,1) 28 | y = np.random.normal(0,1,1) 29 | # test if in ellipse centred at (0,0) and whose axes are aligned to x & y axes 30 | if x**2/axes[0]**2 + y**2/axes[1]**2 <= 1: 31 | cnt += 1 32 | print(cnt/trls) 33 | 34 | # test path length 35 | nsamp = 2 36 | # create data with known path length - length = 1 at each section 37 | # create uniform random angles 38 | angs = np.random.rand(nsamp) * 2 * math.pi 39 | uvecs = np.transpose(np.array(pol2cart(1,angs))) 40 | lgths = np.linalg.norm(uvecs, axis=1) 41 | # lghts should all = 1... 42 | print(lgths) 43 | # get cumulative sum 44 | tstdat = np.cumsum(uvecs, axis = 0) 45 | # plt.plot(tstdat[:,0],tstdat[:,1],'ro-') 46 | # plt.axes().set_aspect('equal') 47 | # plt.show() 48 | out = cp.pathl(tstdat) 49 | print(out) 50 | -------------------------------------------------------------------------------- /COPparamsFs.py: -------------------------------------------------------------------------------- 1 | # file to store functions that calculate COP parameters 2 | # All functions need to input a cop data file, being a numpy array with 3 3 | # columns: x-data, y-data, time (seconds) 4 | 5 | # IMPORTS 6 | import numpy as np 7 | from scipy import signal 8 | from scipy.interpolate import interp1d 9 | # Assumes hyperellipsoid.py is in same directory. Written by 'Marcos Duarte. 10 | # https://github.com/demotu/BMC' 11 | from hyperellipsoid import hyperellipsoid 12 | 13 | def to_sing(arr): 14 | # converts array to an nd array with one singleton dimension 15 | arr = np.reshape(arr,(arr.size,-1)) 16 | return arr 17 | 18 | def nsamp(cop_dat): 19 | # get number of samples 20 | return cop_dat.shape[0] 21 | 22 | def samp_rate(cop_dat): 23 | # get sample rate 24 | bg = cop_dat[0,-1] 25 | lst = cop_dat[-1,-1] 26 | tme = lst - bg 27 | nsmp = cop_dat.shape[0] - 1 28 | return nsmp/tme 29 | 30 | def PI95(cop_dat): 31 | # get area of 95% prediction ellipse 32 | area, axes, angles, center, R = hyperellipsoid(cop_dat[:,(0,1)], units='mm', show=False) 33 | return area 34 | 35 | def resamp(cop_dat): 36 | # resample data to even sample points using same average sample rate 37 | # resample data 38 | t = np.linspace(cop_dat[0,2], cop_dat[-1,2], num=nsamp(cop_dat), endpoint=True) 39 | f_cor = interp1d(cop_dat[:,2], cop_dat[:,0], kind='linear') 40 | cor = f_cor(t) 41 | f_sag = interp1d(cop_dat[:,2], cop_dat[:,1], kind='linear') 42 | sag = f_sag(t) 43 | # convert all to nd arrays 44 | cor = to_sing(cor) 45 | sag = to_sing(sag) 46 | t = to_sing(t) 47 | return np.concatenate((cor,sag,t),axis=1) 48 | 49 | def bfilt(cop_dat, cutoff, order): 50 | # Butterworth filter 51 | b,a = signal.butter(order, cutoff) 52 | # filter coronal 53 | cor_lp = signal.filtfilt(b, a, cop_dat[:,0]) 54 | # filter sagittal 55 | sag_lp = signal.filtfilt(b, a, cop_dat[:,1]) 56 | return np.concatenate((to_sing(cor_lp),to_sing(sag_lp),to_sing(cop_dat[:,2])),axis=1) 57 | 58 | def pathl(cop_dat): 59 | # to calculate COP path length 60 | delt = np.diff(cop_dat[:,(0,1)], axis = 0) 61 | sqs = np.square(delt) 62 | sum_s = np.add(sqs[:,0],sqs[:,1]) 63 | lgths = np.sqrt(sum_s) 64 | return np.sum(lgths) 65 | -------------------------------------------------------------------------------- /read_datfile.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # to read a pickled file into memory 3 | import tkinter.filedialog as tk_fd 4 | import pickle 5 | import numpy as np 6 | import sys 7 | import matplotlib.pyplot as plt 8 | from scipy import signal 9 | from scipy.interpolate import interp1d 10 | import math 11 | 12 | doplots = True 13 | savecsv = False 14 | 15 | dfile = tk_fd.askopenfilename(title = 'Get pickled data file',filetypes=[('Data files', '*.dat'), ('All files','*')]) 16 | if not dfile: 17 | print('No file chosen') 18 | sys.exit(0) 19 | with open(dfile,'rb') as fptr: 20 | dc = pickle.load(fptr, fix_imports=False) 21 | # convert time data into seconds 22 | #t_dat = dc['timedat'].astype(int) 23 | t_dat = dc['timedat'] 24 | # print(t_dat) 25 | # print(t_dat[:,1]/1000000) 26 | t_dat = t_dat[:,0] + t_dat[:,1]/1000000 27 | # reshape t_dat so that it is an nd array with one singleton dimension 28 | t_dat = np.reshape(t_dat,(t_dat.size,-1)) 29 | # subtract the first time value from all 30 | t_dat = np.subtract(t_dat,t_dat[0]) 31 | # add time data to cop data 32 | bb_dat = np.concatenate((dc['cop'],t_dat), axis=1) 33 | n_samp = bb_dat.shape[0] 34 | # add raw sensor data 35 | bb_dat = np.concatenate((bb_dat,dc['rawsens']), axis=1) 36 | # save each numpy array as a CSV file... 37 | if savecsv: 38 | fname = tk_fd.asksaveasfilename(title='Choose file name to save BB data', filetypes=[('CSV files', '.csv'), ('all files', '.*')]) 39 | if fname: 40 | np.savetxt(fname,bb_dat, delimiter=',', header='copX,copY,time,TopR,BotR,TopL,BotL', comments='') 41 | # plot statokinesiogram - coronal plane using time as x-axis 42 | if doplots: 43 | 44 | # resample data 45 | t = np.linspace(bb_dat[0,2], bb_dat[-1,2], num=n_samp, endpoint=True) 46 | f_cor = interp1d(bb_dat[:,2], bb_dat[:,0], kind='linear') 47 | cor = f_cor(t) 48 | f_sag = interp1d(bb_dat[:,2], bb_dat[:,1], kind='linear') 49 | sag = f_sag(t) 50 | 51 | # Butterworth filter 52 | order = 4 53 | # -3dB cutoff freq proportion of Nyquist freq (half sampling freq) 54 | cutoff = 2/3 55 | b,a = signal.butter(order, cutoff) 56 | # filter coronal 57 | cor_lp = signal.filtfilt(b, a, cor) 58 | print(len(cor)) 59 | print(len(cor_lp)) 60 | # filter sagittal 61 | sag_lp = signal.filtfilt(b, a, sag) 62 | 63 | # plot coronal 64 | fg, (ax1, ax2) = plt.subplots(1, 2, sharey=True) 65 | # resampled or raw 66 | ax1.plot(bb_dat[:,2],bb_dat[:,0],'bo-') 67 | # ax1.plot(t,cor,'bo-') 68 | ax1.set_xlabel('time (secs)') 69 | ax1.set_ylabel('coronal plane') 70 | ax1.grid() 71 | # filtered 72 | ax2.plot(t,cor_lp,'bo-') 73 | ax2.set_xlabel('time (secs)') 74 | ax2.grid() 75 | plt.show() 76 | 77 | # plot sagittal 78 | fg, (ax1, ax2) = plt.subplots(1, 2, sharey=True) 79 | # resampled or raw 80 | ax1.plot(bb_dat[:,2],bb_dat[:,1],'bo-') 81 | # ax1.plot(t,sag,'bo-') 82 | ax1.set_xlabel('time (secs)') 83 | ax1.set_ylabel('coronal plane') 84 | ax1.grid() 85 | # filtered 86 | ax2.plot(t,sag_lp,'bo-') 87 | ax2.set_xlabel('time (secs)') 88 | ax2.grid() 89 | plt.show() 90 | -------------------------------------------------------------------------------- /wiicop.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 19 | 21 | 39 | 41 | 42 | 44 | image/svg+xml 45 | 47 | 48 | 49 | 50 | 51 | 56 | 59 | 65 | 75 | 80 | 90 | 95 | 104 | 113 | 114 | 115 | 116 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # wiicop.py 2 | 3 | Python 3 software for obtaining centre of pressure (cop) data from the Nintendo Wii board. Works on linux computers only. Also obtains subject code and experiment factors such as group (case/control), acquisition time etc. This is used by students and researchers at the British School of Osteopathy, London [website](http://www.bso.ac.uk/). 4 | 5 | #UPDATE 6 | 7 | New version of `wiicop.py` increases the sample rate (on a Intel Pentium P6200 dual core 2.13GHz) of 10Hz to around 65Hz. It also saves the data in a CSV file instead of a python data file. The CSV file has three columns: cop x value (coronal plane), cop y value (sagittal plane), and time (seconds). It also updates the cop screen display to 20Hz. This makes it achieve the sample rate standards recommended by Scoppa et al (Scoppa, F.; Capra, R.; Gallamini, M. & Shiffer, R. Clinical stabilometry standardization: basic definitions-acquisition interval-sampling frequency Gait & posture, Elsevier, 2013, 37, 290-292). This means that `GetCOPparams.py` will need ammending to work with the new data files. 8 | 9 | #HOW TO USE 10 | 1. Follow all the install instructions below 11 | 12 | 2. Via a terminal, cd to the wiicp.py directory and run wiicop.py: 13 | `./wiicop.py` 14 | 15 | 3. You then calibrate the board for that session. Apply calibration weights, as accurately as possible, to the centre of the board. You can configure which weights to use by editing the top of 'wiicop.py'. The weight options are defined in a dictionary `pd.Series`, numbered from 0. Choose also the units. 16 | 17 | `# Pandas series to define calibration weights` 18 | 19 | `calib_wgts = pd.Series({0:5,1:9,2:16.5})` 20 | 21 | `# calibration units ('Kgs' or 'lbs')` 22 | 23 | `calib_units = 'Kgs'` 24 | 25 | The calibration will check that all the sensors are obtaining 95% of readings that are greater than zero. If insufficiently heavy calibration weights are used, or the surface is uneven, one of the sensors will be floating in the air and not taking readings. The program will terminate if that is the case. The program fits a linear model to the data from each sensor and saves the details in the session directory. 26 | 27 | 4. If set up correctly, it should ask via the command line to choose a study. Then you create a name for the session. A name will be suggested but check one hasn't already got the same name (i.e. if you've done another session the same morning) 28 | 29 | 5. You will then be able to choose study factors for the study. E.g. 'before' or 'after', 'treatment' or 'control' etc. 30 | 31 | 6. A window will then pop up displaying the centre of pressure as a green dot. To start recording data, press the spacebar. If you have configured it to do a timed acquisition, it will stop after the time is up. Otherwise you can stop the acquisition by pressing the spacebar again. 32 | 33 | 7. You will be asked if you want to get another aquisition. If you choose no, the session will terminate. 34 | 35 | 8. You can use `GetCOPparams.py` to read the COP and the calibration data. Currently it calculates area of 95% prediction ellipse (thanks to Marcos Duarte for `hyperellipsoid.py`. https://github.com/demotu/BMC), path length and path velocity. 36 | 37 | 38 | 39 | #INSTALL INSTRUCTIONS 40 | 41 | 42 | 1. Go to [here](https://github.com/dvdhrm/xwiimote) to download the zip file containing the xwiimote software and unzip it in a suitable directory. Or clone using git. 43 | 44 | 2. Go to [here](https://github.com/dvdhrm/xwiimote-bindings) to download the zip file containing the xwiimote bindings and unzip it another directory. Or clone using git. 45 | 46 | 3. Install the following dependencies (ubuntu based distributions) via: 47 | 48 | `sudo apt-get install libudev-dev libncurses5-dev libncursesw5-dev automake autoconf autogen libtool swig python3-dev python3-tk python3-pip` 49 | 50 | 4. Install python modules: 51 | 52 | `sudo pip3 install pyudev pandas matplotlib scipy` 53 | 54 | 5. Compile and install xwiimote library. 55 | Change (cd) to xwiimote directory (xwiimote-master), then run: 56 | 57 | `sudo ./autogen.sh` 58 | 59 | `sudo ./configure` 60 | 61 | `sudo make` 62 | 63 | `sudo make install` 64 | 65 | 66 | 6. Create the xwiimote configure file in /etc/ld.so.comf.d: 67 | 68 | `cd /etc/ld.so.conf.d` 69 | 70 | `sudo nano xwiimote.conf` 71 | 72 | And add the following line to this file: 73 | 74 | `/usr/local/lib` 75 | 76 | and save: Ctrl O, Ctrl X. Next, reload library cache using the following: 77 | 78 | `sudo ldconfig` 79 | 80 | 7. Test xwiimote libraries are installed and can connect Wiiboard by connecting Wiiboard via Bluetooth and running: 81 | 82 | `sudo xwiishow` 83 | 84 | and follow instructions. 85 | 86 | 8. Compile and install xwiimote python bindings. Change (cd) to xwiimote-bindings directory and run following: 87 | 88 | `sudo ./autogen.sh` 89 | 90 | `sudo ./configure PYTHON=/usr/bin/python3` 91 | 92 | `sudo make` 93 | 94 | `sudo make install` 95 | 96 | 97 | 9. Add user to input group to allow user (‘usersname’) to run xwiimote software: 98 | 99 | `sudo usermod -a -G input usersname` 100 | 101 | then log out and log back in again 102 | 103 | 10. Create a directory to put the wiiboard software into, e.g. ~/Wiiboard. Copy wiicop.py and WiiCopFunctions.py into this directory. Then add this directory to your python path by adding this line to your ~/.bashrc file: 104 | 105 | `export PYTHONPATH="$PYTHONPATH:/home/yourusername/path/to/wiicopdir"` 106 | 107 | 11. In same directory create a directory called config_files to put study configuration files for each study. These need to be amended – see below. 108 | 109 | 12. Make sure the python file 'wiicop.py' is executable. 110 | 111 | Either: Right click on it, select 'Properties', select 'Permissions' tab and tick the 'Allow executing file as program' tickbox. 112 | 113 | Or: In a terminal, cd to directory of wiicop.py and run: 114 | 115 | `chmod +x wiicop.py` 116 | 117 | 12. Create a study directory and a sub directory for each study. Amend config file for each study to point to the right directory 118 | 119 | 120 | #NOTES: 121 | In Linux Mint 18 Cinnamon edition the default bluetooth (Blueberry) tool doesn't seem to work. Uninstalling it and installing Blueman seems to work: 122 | 123 | sudo apt-get remove --purge blueberry 124 | sudo apt-get install blueman 125 | 126 | Then reboot 127 | 128 | 129 | 130 | 131 | 132 | -------------------------------------------------------------------------------- /GetCOPparams.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # software to convert raw centre of pressure (COP) data into various COP based measures of balance 3 | 4 | # IMPORTS 5 | # ~~~~~~~ 6 | import tkinter.filedialog as tk_fd 7 | import sys 8 | import os 9 | import re 10 | import pickle 11 | import numpy as np 12 | import pandas as pd 13 | import configparser 14 | import matplotlib.pyplot as plt 15 | import matplotlib 16 | import COPparamsFs as cp 17 | # %matplotlib inline 18 | #import pdb; pdb.set_trace() 19 | 20 | 21 | # INITIALISE 22 | # set regular expression to find calibration file 23 | cal_re = "calib.*dat" 24 | # set regular expression to find cop data file 25 | cop_re = "subj.*.dat" 26 | # specify list of cop parameters 27 | cop_params = ['pred_ellipse','path_length','velocity'] 28 | # string that signifies subject code 29 | sbjstr = 'subj' 30 | # Display stabilogram flag 31 | disps_f = False 32 | # Save stabilogram flag 33 | saves_f = True 34 | # name of image directory 35 | imdir = "stabilograms" 36 | # Balance board dimensions width and length in mm (Leach, J.M., Mancini, M., Peterka, R.J., Hayes, 37 | # T.L. and Horak, F.B., 2014. Validating and calibrating the Nintendo Wii 38 | # balance board to derive reliable center of pressure measures. Sensors, 39 | # 14(10), pp.18244-18267.) 40 | BB_Y = 238 41 | BB_X = 433 42 | # filter parameters... 43 | # cutoff frequency as proportion of Nyquist 44 | cutoff = 2/3 45 | # order of Butterworth filter 46 | order = 4 47 | 48 | # set up if plots flagged 49 | if disps_f or saves_f: 50 | matplotlib.rcParams['toolbar'] = 'None' 51 | # create empty list to store stuff to plot 52 | plt_lst = [] 53 | # create figure and axis 54 | fig,ax = plt.subplots(1) 55 | fig.canvas.set_window_title('Stabilogram') 56 | 57 | # setup regular expression objects 58 | cal_re_o = re.compile(cal_re) 59 | cop_re_o = re.compile(cop_re) 60 | 61 | # create empty pandas dataframes to store calibration and cop data 62 | cal_df=pd.DataFrame(columns=['session','sensor','slope','slope.se','r.coef','p-val']) 63 | # change to $XDG_RUNTIME_DIR/gvfs where samba mounts its shares 64 | # gvfs_pth = os.environ['XDG_RUNTIME_DIR']+'/gvfs/' 65 | # os.chdir(os.path.dirname(gvfs_pth)) 66 | # Get user to choose config file 67 | config_file = tk_fd.askopenfilename(title = 'Get config file for study',filetypes=[('Data files', '*.config'), ('All files','*')]) 68 | # move to directory above config file 69 | os.chdir(os.path.dirname(config_file)) 70 | os.chdir("..") 71 | # read selected config file 72 | config = configparser.ConfigParser() 73 | config.read(config_file) 74 | # get info list of factors 75 | fct_lst = config.options('factors') 76 | cop_df=pd.DataFrame(columns=['session','subj'] + fct_lst + cop_params) 77 | 78 | 79 | # SEARCH THROUGH CHOSEN DIRECTORY STRUCTURE 80 | # for data and calibration files 81 | seshd = tk_fd.askdirectory(title = 'Open study directory containing sessions') 82 | if not seshd: 83 | sys.exit() 84 | for root, dirs, files in os.walk(seshd): 85 | # for each directory 86 | if len(files) > 0: 87 | 88 | # look for calibration file using reg expression 89 | c_lst = list(filter(cal_re_o.match,files)) 90 | if c_lst: 91 | # if list c_lst is not empty.. 92 | # should contain only one calibration file 93 | assert len(c_lst)==1, "more than one calibration file in directory: {}".format(root) 94 | # open calibration file 95 | c_pth = os.path.join(root,c_lst[0]) 96 | with open(c_pth,'rb') as fptr: 97 | tmp = pickle.load(fptr, fix_imports=False) 98 | 99 | # Store 1 row of calibration data... 100 | cal_dat = tmp['details'] 101 | nxt_cal = len(cal_df.index) 102 | s_ind = 0 103 | for sns in cal_dat.keys(): 104 | # for each sensor.. 105 | # create empty row 106 | cal_df.loc[nxt_cal+s_ind] = None 107 | cal_df.ix[nxt_cal+s_ind,'session'] = os.path.basename(root) 108 | cal_df.ix[nxt_cal+s_ind,'sensor'] = sns 109 | cal_df.ix[nxt_cal+s_ind,'slope'] = cal_dat[sns]['m'] 110 | cal_df.ix[nxt_cal+s_ind,'slope.se'] = cal_dat[sns]['se'] 111 | cal_df.ix[nxt_cal+s_ind,'r.coef'] = cal_dat[sns]['r'] 112 | cal_df.ix[nxt_cal+s_ind,'p-val'] = cal_dat[sns]['p'] 113 | s_ind+=1 114 | 115 | # look for cop data file using reg expression 116 | d_lst = list(filter(cop_re_o.match,files)) 117 | if d_lst: 118 | for fi in d_lst: 119 | # for each data file.. 120 | 121 | # store row of study metadata... 122 | nxt_cop = len(cop_df.index) 123 | # create empty row 124 | cop_df.loc[nxt_cop] = None 125 | # strip extension 126 | prts = fi.split('.') 127 | # save levels as list 128 | lev_lst = prts[0].split('_') 129 | # get subject code 130 | tmp = [s for s in lev_lst if sbjstr in s] 131 | scode = tmp[0].strip(sbjstr) 132 | # convert to int then back to string with 3 leading zeros 133 | scode = int(scode) 134 | scode = str(scode).zfill(3) 135 | cop_df.ix[nxt_cop,'subj'] = scode 136 | # get session 137 | cop_df.ix[nxt_cop,'session'] = os.path.basename(root) 138 | # read study metadata into df 139 | for fct_i in fct_lst: 140 | for lev in lev_lst: 141 | if lev in config['factors'][fct_i]: 142 | cop_df.ix[nxt_cop,fct_i] = lev 143 | 144 | 145 | # read COP data 146 | d_pth = os.path.join(root,fi) 147 | with open(d_pth,'rb') as fptr: 148 | pkl = pickle.load(fptr) 149 | # convert time data into seconds 150 | t_dat = pkl['timedat'] 151 | t_dat = t_dat[:,0] + t_dat[:,1]/1000000 152 | # reshape t_dat so that it is an nd array with one singleton dimension 153 | t_dat = np.reshape(t_dat,(t_dat.size,-1)) 154 | # add time data to cop data 155 | cop_dat = np.concatenate((pkl['cop'],t_dat), axis=1) 156 | 157 | # Preprocess COP data 158 | # resample to even sample points 159 | cop_dat_r = cp.resamp(cop_dat) 160 | # low pass filtering 161 | cop_dat_f = cp.bfilt(cop_dat_r, cutoff, order) 162 | # # TEST *** 163 | # # plot coronal 164 | # fg, (ax1, ax2) = plt.subplots(1, 2, sharey=True) 165 | # # resampled or raw 166 | # ax1.plot(cop_dat[:,2],cop_dat[:,0],'bo-') 167 | # # ax1.plot(t,cor,'bo-') 168 | # ax1.set_xlabel('time (secs)') 169 | # ax1.set_ylabel('coronal plane') 170 | # ax1.set_title('Raw data') 171 | # ax1.grid() 172 | # # resampled and filtered 173 | # ax2.plot(cop_dat_f[:,2],cop_dat_f[:,0],'bo-') 174 | # ax2.set_xlabel('time (secs)') 175 | # ax2.set_title('Resampled and filtered') 176 | # ax2.grid() 177 | # mng = plt.get_current_fig_manager() 178 | # mng.resize(*mng.window.maxsize()) 179 | # plt.show() 180 | # # *** 181 | 182 | # Get COP parameters... 183 | # 95% Prediction interval 184 | cop_df.ix[nxt_cop,'pred_ellipse'] = cp.PI95(cop_dat_f) 185 | # path length 186 | pl = cp.pathl(cop_dat) 187 | cop_df.ix[nxt_cop,'path_length'] = pl 188 | # velocity 189 | import pdb; pdb.set_trace() 190 | cop_df.ix[nxt_cop,'velocity'] = pl/int(cop_df.ix[nxt_cop,'acq_time']) 191 | 192 | # store data for plotting if flagged 193 | if disps_f or saves_f: 194 | # create a dictionary of relevant study factors 195 | std_fct = {} 196 | std_fct['subj'] = scode 197 | for fct_i in fct_lst: 198 | for lev in lev_lst: 199 | if lev in config['factors'][fct_i]: 200 | std_fct[fct_i] = lev 201 | plt_lst.append([[cop_dat, std_fct]]) 202 | 203 | # Plot stabilograms (see Scoppa2013) if flagged 204 | if disps_f or saves_f: 205 | if saves_f: 206 | # create image directory if it doesn't exist in current working directory 207 | imdirpth = os.path.join(os.getcwd(),imdir) 208 | if not(os.path.isdir(imdirpth)): 209 | os.mkdir(imdirpth) 210 | ax.axis([-BB_X/2, BB_X/2, -BB_Y/2, BB_Y/2]) 211 | ax.grid() 212 | plot1 = True 213 | plt.ion() 214 | for ia in plt_lst: 215 | if plot1: 216 | line_h, = ax.plot(ia[0][0][:,0], ia[0][0][:,1],'b-') 217 | plot1 = False 218 | else: 219 | line_h.set_xdata(ia[0][0][:,0]) 220 | line_h.set_ydata(ia[0][0][:,1]) 221 | plt.title('Subject: '+ia[0][1]['subj']) 222 | # get factors string 223 | fct_str = "" 224 | for ib in fct_lst: 225 | fct_str = fct_str + ib + ": " + ia[0][1][ib] + ", " 226 | fct_str = fct_str.rstrip(", ") 227 | txt_h = plt.text(-200,100,fct_str, fontsize=14) 228 | if disps_f: 229 | plt.show() 230 | plt.pause(0.5) 231 | if saves_f: 232 | # create file name 233 | im_file = ".png" 234 | for ib in fct_lst: 235 | im_file = ia[0][1][ib] + im_file 236 | im_file = "subj" + ia[0][1]['subj'] + "_" + im_file 237 | plt.savefig(os.path.join(os.getcwd(),imdir,im_file)) 238 | txt_h.remove() 239 | 240 | 241 | # create results directory if it doesn't exist 242 | res_dir = os.path.join(seshd,'results') 243 | if not(os.path.isdir(res_dir)): 244 | os.mkdir(res_dir) 245 | # write calibration results 246 | calib_file = os.path.join(res_dir,'calib_results.csv') 247 | cal_df.to_csv(calib_file) 248 | # write study results 249 | study_file = os.path.join(res_dir,'study_results.csv') 250 | cop_df.to_csv(study_file) 251 | -------------------------------------------------------------------------------- /WiiCopFunctions.py: -------------------------------------------------------------------------------- 1 | # file to store functions for use by Wiicop.py 2 | 3 | import pyudev 4 | import xwiimote 5 | import errno 6 | import select 7 | import sys 8 | import os 9 | import numpy as np 10 | import tkinter as tk 11 | import tkinter.font as font 12 | import string 13 | 14 | # function to test if string is a valid subject code 15 | def validcode(testnm): 16 | valid_chars='{}{}'.format(string.ascii_letters,string.digits) 17 | valid = True 18 | for c in testnm: 19 | if c not in valid_chars: 20 | valid = False 21 | break 22 | return valid 23 | 24 | # function to obtain acquisition info: subject code, study factor levels, acquisition time 25 | def get_acq_info(s_config): 26 | # function to obtain acquisition info: subject code, study factor levels, acquisition time 27 | # input: s_config: config object for study 28 | # output: a dictionary with 2 compulsory keys: 'subject_code' and 'acq_time' and other keys being 29 | # the names of factors and they're associated chosen levels. 30 | # get subject code 31 | s_c = input('Enter subject code: ') 32 | while not validcode(s_c): 33 | print('\nOnly letters and numbers for subject codes\n') 34 | s_c = input('Enter subject code: ') 35 | fact_choices = {'subject_code':s_c} 36 | # get names of factors as a list 37 | fct_names = [] 38 | for factor in s_config['factors']: 39 | fct_names.append(factor) 40 | # check if 'acq_time' is in list of factors 41 | if not 'acq_time' in fct_names: 42 | raise ValueError('Message from get_acq_info: There is no acq_time factor in the config file~~') 43 | # get factor levels 44 | for factor in s_config['factors']: 45 | levs = s_config['factors'][factor].split(',') 46 | if len(levs)==1: 47 | fact_choices.update({factor:levs[0]}) 48 | continue 49 | tit_str = 'Choose level for {}'.format(factor) 50 | chc = txtmenu(tit_str,levs) 51 | fact_choices.update({factor:levs[chc]}) 52 | return fact_choices 53 | 54 | # returns device object for balance board 55 | def connectBB(): 56 | # returns sys path for balance board using evdev module 57 | context = pyudev.Context() 58 | # get list of devices matching devtype = 'balanceboard' 59 | devices = pyudev.Enumerator(context) 60 | tst = devices.match_attribute('devtype', 'balanceboard') 61 | # create empty list to store balance board info 62 | bboards = [] 63 | # populate list 64 | for dfg in tst: 65 | bboards.append(dfg) 66 | # test how many bboards are connected - should be only one 67 | if len(bboards) == 0: 68 | print('No Wii balance boards found') 69 | return None 70 | if len(bboards) > 1: 71 | print('More than one Wii balance boards found: exiting') 72 | return None 73 | print('\nBalance board found!\n') 74 | # return the first in the list 75 | bb = bboards[0] 76 | return bb 77 | 78 | # function to get data from BB and process it function 'func' 79 | def procBBdata(bb, func, *args): 80 | # function to get data from BB and process it function 'func' 81 | # returns sens_dat that is an NX4 numpy array of raw sensor readings 82 | # each row a single data acquisition 83 | n_s = 4 84 | bbdev = xwiimote.iface(bb.sys_path) 85 | p = select.poll() 86 | p.register(bbdev.get_fd(), select.POLLIN) 87 | # open bb device 88 | bbdev.open(xwiimote.IFACE_BALANCE_BOARD) 89 | # create xwiimote event structure 90 | revt = xwiimote.event() 91 | # create numpy array to store data from board 92 | tmp_dat = np.empty((1,n_s)) 93 | # creat another to accumulate all data 94 | sens_dat = np.empty((0,n_s)) 95 | go_flg = True 96 | try: 97 | while go_flg: 98 | # waits here until event occurs 99 | polls = p.poll() 100 | for fd, evt in polls: 101 | try: 102 | bbdev.dispatch(revt) 103 | # revt.get_abs() takes an integer argument: 104 | # 0 - top right when power button pointing towards you 105 | # 1 - bottom right 106 | # 2 - top left 107 | # 3 - bottom left 108 | for i_s in range(n_s): 109 | tmp_dat[0,i_s] = revt.get_abs(i_s)[0] 110 | # function that does something with sens_dat. Functions 111 | # called by procBBdata must have parameters: 112 | # go_flg, tmp_dat, sens_dat 113 | go_flg, sens_dat = func(go_flg, tmp_dat, sens_dat, *args) 114 | except IOError as e: 115 | # do nothing if resource unavailable 116 | if e.errno != errno.EAGAIN: 117 | print(e) 118 | p.unregister(bbdev.get_fd()) 119 | except KeyboardInterrupt: 120 | pass 121 | # cleaning 122 | bbdev.close(xwiimote.IFACE_BALANCE_BOARD) 123 | p.unregister(bbdev.get_fd()) 124 | return sens_dat 125 | 126 | # function to calculate COP 127 | def calcCOP(indat,cal_mod,BB_X, BB_Y): 128 | # inputs: 'indat' a 1 X N_S numpy array of sensor measurements 129 | # 'cal_mod': a 2 X N_S numpy array, 1st row are scale values, 2nd row are offsets 130 | # BB_X, BB_Y - size of balance boad in mm 131 | cal_dat = indat * cal_mod[0,:] 132 | cal_dat += cal_mod[1,:] 133 | cal_dat = cal_dat.flatten() 134 | cop_x = BB_X/2*(cal_dat[0]+cal_dat[1]- (cal_dat[2]+cal_dat[3])) / (cal_dat[0]+cal_dat[1]+cal_dat[2]+cal_dat[3]) 135 | cop_y = BB_Y/2*(cal_dat[0]+cal_dat[2]- (cal_dat[1]+cal_dat[3])) / (cal_dat[0]+cal_dat[1]+cal_dat[2]+cal_dat[3]) 136 | return np.array([cop_x, cop_y]) 137 | 138 | # function to get n_samp samples from the wii board 139 | def getnsamp(go_flg, tmp_dat, sens_dat, n_samp): 140 | # function to get n_samp samples from the wii board 141 | # functions called by procBBdata must have parameters: 142 | # go_flg, tmp_dat, sens_dat 143 | # if number of obtained samples less than n_samp.. 144 | if sens_dat.shape[0] < n_samp: 145 | # get sensor data row 146 | sens_dat = np.vstack((sens_dat,tmp_dat)) 147 | else: 148 | # flag end of data acquisition 149 | go_flg = False 150 | return go_flg, sens_dat 151 | 152 | # funtion to get choice from a list 153 | def txtmenu(tit_str,opt_lst): 154 | # funtion to get choice from a list 155 | print('\n'+tit_str+':') 156 | print('~'*len(tit_str)) 157 | v_chs = set(range(1,len(opt_lst)+1)) 158 | for i_opts in range(len(opt_lst)): 159 | print(" "+str(i_opts+1)+") "+opt_lst[i_opts]) 160 | inp = input("? ") 161 | inp_invalid = True 162 | while inp_invalid: 163 | try: 164 | ans = int(inp) 165 | if ans not in v_chs: 166 | raise Exception('not valid choice, try again') 167 | inp_invalid = False 168 | except ValueError: 169 | print("Input should be a number, try again") 170 | inp = input("? ") 171 | except Exception as err: 172 | print(err) 173 | inp = input("? ") 174 | 175 | return ans-1 176 | 177 | # function that returns a list of directories 178 | def listdirs(strt_dir): 179 | # function that returns a list of directories 180 | dir_lst = [] 181 | for entry in os.scandir(strt_dir): 182 | if entry.is_dir(): 183 | dir_lst.append(entry.name) 184 | return dir_lst 185 | 186 | # function to take list of previous session names and a default name for new 187 | # session and return users choice of new session name 188 | def get_sessionname(prev_lst,def_name): 189 | # function to take list of previous session names and a default name for new 190 | # session and return users choice of new session name 191 | # INITIALISE parameters 192 | # background colour 193 | bcol = 'linen' 194 | # font size 195 | f_sz = 11 196 | # base height (px) 197 | b_hgt = 182 198 | # height of listbox in lines 199 | lst_hgt = max(1,len(prev_lst)) 200 | # window size 201 | w_hgt = round(b_hgt + f_sz*4/3*lst_hgt) 202 | # window title 203 | wnd_tit = 'Name new session' 204 | # FUNCTION definitions 205 | # function to read entry box when ok button clicked and exit 206 | def click_ok(): 207 | s_name = ent.get() 208 | usr_chose.set(True) 209 | root.destroy() 210 | # function to read entry box when return pressed and exit 211 | def name_ent(event): 212 | s_name = ent.get() 213 | usr_chose.set(True) 214 | root.destroy() 215 | # function to read double-clicked line in listbox and write into entrybox 216 | def old2new(event): 217 | # get selected item from list 218 | sel_item = prev_lst[lbox.curselection()[0]] 219 | # write it to entry box 220 | s_name.set(sel_item) 221 | def val_entry(): 222 | return False 223 | # CREATE window 224 | # create root 225 | root = tk.Tk() 226 | # flag to indicate choice has been made (i.e. window not closed). Must use 227 | # tkinter varables as can't use python global vars. These must be created after 'root = Tk()' 228 | usr_chose = tk.BooleanVar() 229 | usr_chose.set(False) 230 | # set title of dialogue 231 | root.title(wnd_tit) 232 | # set height of window 233 | root.geometry('260x{}'.format(w_hgt)) 234 | # set font size for widgets 235 | customFont = font.Font(size=f_sz) 236 | # create listbox 237 | lbox = tk.Listbox(root, height=lst_hgt, font=customFont, bg=bcol) 238 | # populate listbox 239 | cnt = 1 240 | for i_lst in prev_lst: 241 | lbox.insert(cnt,i_lst) 242 | cnt += 1 243 | # bind listbox to return keypress to call old2new 244 | lbox.bind("",old2new) 245 | # bind listbox to double click to call old2new 246 | lbox.bind("",old2new) 247 | # label for listbox 248 | lbox_lab = tk.Label(root, text = 'Previous sessions', font=customFont) 249 | # create string var for entry box with default name 250 | s_name = tk.StringVar() 251 | s_name.set(def_name) 252 | # Entry box to get name of session 253 | # TO DO validate entry here 254 | ent = tk.Entry(root, font=customFont, textvariable=s_name, bg=bcol) 255 | # set focus to entry box 256 | ent.focus_set() 257 | # bind entry box widget to return keypress 258 | ent.bind("",name_ent) 259 | # label for entry box 260 | ebox_lab = tk.Label(root, text = 'New session', font=customFont) 261 | # ok button to record entry 262 | butt = tk.Button(root, font=customFont, text = 'OK', command=click_ok) 263 | # assemble widgets 264 | lbox_lab.pack(pady=(5,0)) 265 | lbox.pack(padx=(15)) 266 | ebox_lab.pack(pady=(25,0)) 267 | ent.pack(padx=(15)) 268 | butt.pack(pady=(20,0)) 269 | root.mainloop() 270 | # if user doesn't close window return what's in entry box 271 | if usr_chose.get(): 272 | return(s_name.get()) 273 | else: 274 | return(None) 275 | -------------------------------------------------------------------------------- /hyperellipsoid.py: -------------------------------------------------------------------------------- 1 | """Prediction hyperellipsoid for multivariate data.""" 2 | 3 | from __future__ import division, print_function 4 | import numpy as np 5 | 6 | __author__ = 'Marcos Duarte, https://github.com/demotu/BMC' 7 | __version__ = "1.0.3" 8 | __license__ = "MIT" 9 | 10 | 11 | def hyperellipsoid(P, y=None, z=None, pvalue=.95, units=None, show=True, ax=None): 12 | """ 13 | Prediction hyperellipsoid for multivariate data. 14 | 15 | The hyperellipsoid is a prediction interval for a sample of a multivariate 16 | random variable and is such that there is pvalue*100% of probability that a 17 | new observation will be contained inside the hyperellipsoid [1]_. 18 | The hyperellipsoid is also a tolerance region such that the average or 19 | expected value of the proportion of the population contained in this region 20 | is exactly pvalue*100% (called Type 2 tolerance region by Chew (1966) [1]_). 21 | 22 | The directions and lengths of the semi-axes are found, respectively, as the 23 | eigenvectors and eigenvalues of the covariance matrix of the data using 24 | the concept of principal components analysis (PCA) [2]_ or singular value 25 | decomposition (SVD) [3]_ and the length of the semi-axes are adjusted to 26 | account for the necessary prediction probability. 27 | 28 | The volume of the hyperellipsoid is calculated with the same equation for 29 | the volume of a n-dimensional ball [4]_ with the radius replaced by the 30 | semi-axes of the hyperellipsoid. 31 | 32 | This function calculates the prediction hyperellipsoid for the data, 33 | which is considered a (finite) sample of a multivariate random variable 34 | with normal distribution (i.e., the F distribution is used and not 35 | the approximation by the chi-square distribution). 36 | 37 | Parameters 38 | ---------- 39 | P : 1-D or 2-D array_like 40 | For a 1-D array, P is the abscissa values of the [x,y] or [x,y,z] data. 41 | For a 2-D array, P is the joined values of the multivariate data. 42 | The shape of the 2-D array should be (n, p) where n is the number of 43 | observations (rows) and p the number of dimensions (columns). 44 | y : 1-D array_like, optional (default = None) 45 | Ordinate values of the [x, y] or [x, y, z] data. 46 | z : 1-D array_like, optional (default = None) 47 | Ordinate values of the [x, y] or [x, y, z] data. 48 | pvalue : float, optional (default = .95) 49 | Desired prediction probability of the hyperellipsoid. 50 | units : str, optional (default = None) 51 | Units of the input data. 52 | show : bool, optional (default = True) 53 | True (1) plots data in a matplotlib figure, False (0) to not plot. 54 | Only the results for p=2 (ellipse) or p=3 (ellipsoid) will be plotted. 55 | ax : a matplotlib.axes.Axes instance (default = None) 56 | 57 | Returns 58 | ------- 59 | hypervolume : float 60 | Hypervolume (e.g., area of the ellipse or volume of the ellipsoid). 61 | axes : 1-D array 62 | Lengths of the semi-axes hyperellipsoid (largest first). 63 | angles : 1-D array 64 | Angles of the semi-axes hyperellipsoid (only for 2D or 3D data). 65 | For the ellipsoid (3D data), the angles are the Euler angles 66 | calculated in the XYZ sequence. 67 | center : 1-D array 68 | Centroid of the hyperellipsoid. 69 | rotation : 2-D array 70 | Rotation matrix for hyperellipsoid semi-axes (only for 2D or 3D data). 71 | 72 | References 73 | ---------- 74 | .. [1] http://www.jstor.org/stable/2282774 75 | .. [2] http://en.wikipedia.org/wiki/Principal_component_analysis 76 | .. [3] http://en.wikipedia.org/wiki/Singular_value_decomposition 77 | .. [4] http://en.wikipedia.org/wiki/Volume_of_an_n-ball 78 | 79 | Examples 80 | -------- 81 | >>> from hyperellipsoid import hyperellipsoid 82 | >>> y = np.cumsum(np.random.randn(3000)) / 50 83 | >>> x = np.cumsum(np.random.randn(3000)) / 100 84 | >>> area, axes, angles, center, R = hyperellipsoid(x, y, units='cm') 85 | >>> print('Area =', area) 86 | >>> print('Semi-axes =', axes) 87 | >>> print('Angles =', angles) 88 | >>> print('Center =', center) 89 | >>> print('Rotation matrix =\n', R) 90 | 91 | >>> P = np.random.randn(1000, 3) 92 | >>> P[:, 2] = P[:, 2] + P[:, 1]*.5 93 | >>> P[:, 1] = P[:, 1] + P[:, 0]*.5 94 | >>> volume, axes, angles, center, R = hyperellipsoid(P, units='cm') 95 | """ 96 | 97 | from scipy.stats import f as F 98 | from scipy.special import gamma 99 | 100 | P = np.array(P, ndmin=2, dtype=float) 101 | if P.shape[0] == 1: 102 | P = P.T 103 | if y is not None: 104 | y = np.array(y, copy=False, ndmin=2, dtype=float) 105 | if y.shape[0] == 1: 106 | y = y.T 107 | P = np.concatenate((P, y), axis=1) 108 | if z is not None: 109 | z = np.array(z, copy=False, ndmin=2, dtype=float) 110 | if z.shape[0] == 1: 111 | z = z.T 112 | P = np.concatenate((P, z), axis=1) 113 | # covariance matrix 114 | cov = np.cov(P, rowvar=0) 115 | # singular value decomposition 116 | U, s, Vt = np.linalg.svd(cov) 117 | p, n = s.size, P.shape[0] 118 | # F percent point function 119 | fppf = F.ppf(pvalue, p, n-p)*(n-1)*p*(n+1)/n/(n-p) 120 | # semi-axes (largest first) 121 | saxes = np.sqrt(s*fppf) 122 | hypervolume = np.pi**(p/2)/gamma(p/2+1)*np.prod(saxes) 123 | # rotation matrix 124 | if p == 2 or p == 3: 125 | R = Vt 126 | if s.size == 2: 127 | angles = np.array([np.rad2deg(np.arctan2(R[1, 0], R[0, 0])), 128 | 90-np.rad2deg(np.arctan2(R[1, 0], -R[0, 0]))]) 129 | else: 130 | angles = rotXYZ(R, unit='deg') 131 | # centroid of the hyperellipsoid 132 | center = np.mean(P, axis=0) 133 | else: 134 | R, angles = None, None 135 | 136 | if show and (p == 2 or p == 3): 137 | _plot(P, hypervolume, saxes, center, R, pvalue, units, ax) 138 | 139 | return hypervolume, saxes, angles, center, R 140 | 141 | 142 | def _plot(P, hypervolume, saxes, center, R, pvalue, units, ax): 143 | """Plot results of the hyperellipsoid function, see its help.""" 144 | 145 | try: 146 | import matplotlib.pyplot as plt 147 | except ImportError: 148 | print('matplotlib is not available.') 149 | else: 150 | # code based on https://github.com/minillinim/ellipsoid: 151 | # parametric equations 152 | u = np.linspace(0, 2*np.pi, 100) 153 | if saxes.size == 2: 154 | x = saxes[0]*np.cos(u) 155 | y = saxes[1]*np.sin(u) 156 | # rotate data 157 | for i in range(len(x)): 158 | [x[i], y[i]] = np.dot([x[i], y[i]], R) + center 159 | else: 160 | v = np.linspace(0, np.pi, 100) 161 | x = saxes[0]*np.outer(np.cos(u), np.sin(v)) 162 | y = saxes[1]*np.outer(np.sin(u), np.sin(v)) 163 | z = saxes[2]*np.outer(np.ones_like(u), np.cos(v)) 164 | # rotate data 165 | for i in range(len(x)): 166 | for j in range(len(x)): 167 | [x[i,j],y[i,j],z[i,j]] = np.dot([x[i,j],y[i,j],z[i,j]], R) + center 168 | 169 | if saxes.size == 2: 170 | if ax is None: 171 | fig, ax = plt.subplots(1, 1, figsize=(5, 5)) 172 | # plot raw data 173 | ax.plot(P[:, 0], P[:, 1], '.-', color=[0, 0, 1, .5]) 174 | # plot ellipse 175 | ax.plot(x, y, color=[0, 1, 0, 1], linewidth=2) 176 | # plot axes 177 | for i in range(saxes.size): 178 | # rotate axes 179 | a = np.dot(np.diag(saxes)[i], R).reshape(2, 1) 180 | # points for the axes extremities 181 | a = np.dot(a, np.array([-1, 1], ndmin=2))+center.reshape(2, 1) 182 | ax.plot(a[0], a[1], color=[1, 0, 0, .6], linewidth=2) 183 | ax.text(a[0, 1], a[1, 1], '%d' % (i + 1), 184 | fontsize=20, color='r') 185 | plt.axis('equal') 186 | plt.grid() 187 | title = r'Prediction ellipse (p=%4.2f): Area=' % pvalue 188 | if units is not None: 189 | units2 = ' [%s]' % units 190 | units = units + r'$^2$' 191 | title = title + r'%.2f %s' % (hypervolume, units) 192 | else: 193 | units2 = '' 194 | title = title + r'%.2f' % hypervolume 195 | else: 196 | from mpl_toolkits.mplot3d import Axes3D 197 | if ax is None: 198 | fig = plt.figure(figsize=(7, 7)) 199 | ax = fig.add_axes([0, 0, 1, 1], projection='3d') 200 | ax.view_init(20, 30) 201 | # plot raw data 202 | ax.plot(P[:, 0], P[:, 1], P[:, 2], '.-', color=[0, 0, 1, .4]) 203 | # plot ellipsoid 204 | ax.plot_surface(x, y, z, rstride=5, cstride=5, color=[0, 1, 0, .1], 205 | linewidth=1, edgecolor=[.1, .9, .1, .4]) 206 | # ax.plot_wireframe(x, y, z, color=[0, 1, 0, .5], linewidth=1) 207 | # rstride=3, cstride=3, edgecolor=[0, 1, 0, .5]) 208 | # plot axes 209 | for i in range(saxes.size): 210 | # rotate axes 211 | a = np.dot(np.diag(saxes)[i], R).reshape(3, 1) 212 | # points for the axes extremities 213 | a = np.dot(a, np.array([-1, 1], ndmin=2))+center.reshape(3, 1) 214 | ax.plot(a[0], a[1], a[2], color=[1, 0, 0, .6], linewidth=2) 215 | ax.text(a[0, 1], a[1, 1], a[2, 1], '%d' % (i+1), 216 | fontsize=20, color='r') 217 | lims = [np.min([P.min(), x.min(), y.min(), z.min()]), 218 | np.max([P.max(), x.max(), y.max(), z.max()])] 219 | ax.set_xlim(lims) 220 | ax.set_ylim(lims) 221 | ax.set_zlim(lims) 222 | title = r'Prediction ellipsoid (p=%4.2f): Volume=' % pvalue 223 | if units is not None: 224 | units2 = ' [%s]' % units 225 | units = units + r'$^3$' 226 | title = title + r'%.2f %s' % (hypervolume, units) 227 | else: 228 | units2 = '' 229 | title = title + r'%.2f' % hypervolume 230 | ax.set_zlabel('Z' + units2, fontsize=18) 231 | 232 | ax.set_xlabel('X' + units2, fontsize=18) 233 | ax.set_ylabel('Y' + units2, fontsize=18) 234 | plt.title(title) 235 | plt.show() 236 | 237 | return ax 238 | 239 | 240 | def rotXYZ(R, unit='deg'): 241 | """ Compute Euler angles from matrix R using XYZ sequence.""" 242 | 243 | angles = np.zeros(3) 244 | angles[0] = np.arctan2(R[2, 1], R[2, 2]) 245 | angles[1] = np.arctan2(-R[2, 0], np.sqrt(R[0, 0]**2 + R[1, 0]**2)) 246 | angles[2] = np.arctan2(R[1, 0], R[0, 0]) 247 | 248 | if unit[:3].lower() == 'deg': # convert from rad to degree 249 | angles = np.rad2deg(angles) 250 | 251 | return angles 252 | -------------------------------------------------------------------------------- /wiicop.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # to test speed of wii board data acquisition 3 | 4 | import os 5 | import sys 6 | from subprocess import run 7 | import time 8 | import threading 9 | from queue import Queue 10 | import errno 11 | import numpy as np 12 | import pandas as pd 13 | import pickle 14 | from scipy import stats 15 | import pyudev 16 | import xwiimote 17 | import select 18 | import configparser 19 | from WiiCopFunctions import connectBB, calcCOP, procBBdata, txtmenu,\ 20 | get_sessionname, listdirs, get_acq_info, getnsamp, validcode 21 | from datetime import datetime 22 | import matplotlib as mpl 23 | import matplotlib.pyplot as plt 24 | from matplotlib.animation import FuncAnimation 25 | 26 | # user defined options 27 | # ~~~~~~~~~~~~~~~~~~~~ 28 | # sample size for calibration 29 | smp_size = 50 30 | # outlier z-score threshold defined here 31 | out_thresh = 3 32 | # percentage zeros %age max limit defined here 33 | maxpcnt = 5 34 | # Pandas series to define calibration weights 35 | # calib_wgts = pd.Series({0:5,1:10,2:18}) 36 | calib_wgts = pd.Series({0:8,1:12,2:16}) 37 | # calibration units ('Kgs' or 'lbs') 38 | # calib_units = 'lbs' 39 | calib_units = 'Kgs' 40 | # set the time interval for FuncAnimation (milliseconds) 41 | anim_interval = 50 42 | 43 | # constants 44 | # ~~~~~~~~~ 45 | # dictionary for enumeration of sensors 46 | SENS_DCT = {0:'Top right',1:'Bottom right',2:'Top left',3:'Bottom left'} 47 | # number of sensors 48 | N_S = 4 49 | # lbs to kgs conversion factor 50 | LBS2KGS = 0.453592 51 | # Balance board dimensions width and length in mm (Leach, J.M., Mancini, M., Peterka, R.J., Hayes, 52 | # T.L. and Horak, F.B., 2014. Validating and calibrating the Nintendo Wii 53 | # balance board to derive reliable center of pressure measures. Sensors, 54 | # 14(10), pp.18244-18267.) 55 | BB_Y = 238 56 | BB_X = 433 57 | 58 | 59 | # FUNCTION DEFINITIONS 60 | def aqc_name(acq_info): 61 | # This is returns a string for labelling acquisition and for file name 62 | tmp = acq_info.copy() 63 | afn = 'subj' 64 | afn = afn+tmp.pop('subject_code') 65 | acqt = tmp.pop('acq_time') 66 | for iks in tmp.values(): 67 | afn = afn+'_'+iks 68 | if acqt!='inf': 69 | afn = afn+'_t'+acqt 70 | else: 71 | afn = afn+'_tmanual' 72 | return afn 73 | 74 | 75 | # CLASS DEFINITIONS 76 | # create a class based on threading.thread 77 | class wii_thread(threading.Thread): 78 | def __init__ (self,bb,cal_mod,BB_X,BB_Y): 79 | threading.Thread.__init__(self) 80 | self.runflag = True 81 | self.storeflag = False 82 | self.n_s = 4 83 | self.bbdev = xwiimote.iface(bb.sys_path) 84 | self.p = select.poll() 85 | self.p.register(self.bbdev.get_fd(), select.POLLIN) 86 | # open bb device 87 | self.bbdev.open(xwiimote.IFACE_BALANCE_BOARD) 88 | # create xwiimote event structure 89 | self.revt = xwiimote.event() 90 | # create numpy array to store data from board 91 | self.tmp_dat = np.empty((1,self.n_s)) 92 | self.cop = np.empty((1,2)) 93 | self.cop_dat = np.empty((0,2)) 94 | self.cal_mod = cal_mod 95 | self.BB_X = BB_X 96 | self.BB_Y = BB_Y 97 | 98 | def run(self): 99 | lock.acquire() 100 | runflag = self.runflag 101 | lock.release() 102 | while runflag: 103 | polls = self.p.poll() 104 | try: 105 | self.bbdev.dispatch(self.revt) 106 | tdat = self.revt.get_time() 107 | for i_s in range(self.n_s): 108 | self.tmp_dat[0,i_s] = self.revt.get_abs(i_s)[0] 109 | self.cop = calcCOP(self.tmp_dat,self.cal_mod,self.BB_X,self.BB_Y) 110 | lock.acquire() 111 | storeflag = self.storeflag 112 | lock.release() 113 | if storeflag: 114 | wii_q.put(np.concatenate((self.cop,np.array(tdat)))) 115 | except IOError as e: 116 | # do nothing if resource unavailable 117 | if e.errno != errno.EAGAIN: 118 | print(e) 119 | self.p.unregister(self.bbdev.get_fd()) 120 | lock.acquire() 121 | runflag = self.runflag 122 | lock.release() 123 | # close down BB interface 124 | self.bbdev.close(xwiimote.IFACE_BALANCE_BOARD) 125 | self.p.unregister(self.bbdev.get_fd()) 126 | 127 | 128 | class plot_cop: 129 | 'object to implement plotting cop data in animation loop' 130 | 131 | def __init__(self,aqc_info,BB_X,BB_Y): 132 | self.acq_info = aqc_info 133 | # Initial instructions 134 | self.text_start = 'Press Spacebar to start recording' 135 | self.text_stop = 'Press Spacebar to stop recording' 136 | # remove toolbar 137 | mpl.rcParams['toolbar'] = 'None' 138 | # Create new figure and an axes which fills it... 139 | # set figure width in inches 140 | fig_width = 12 141 | # set fig ratio based on size of bboard rectange whose corners are sensors 142 | self.fig = plt.figure(figsize=(fig_width, fig_width*BB_Y/BB_X)) 143 | self.fig.canvas.set_window_title(aqc_name(self.acq_info)) 144 | # frameon determines whether background of frame will be drawn 145 | ax = self.fig.add_axes([0, 0, 1, 1], frameon=False) 146 | ax.set_xlim(-BB_X/2, BB_X/2), ax.set_xticks([]) 147 | ax.set_ylim(-BB_Y/2, BB_Y/2), ax.set_yticks([]) 148 | # create a scatter object at initial position 0,0 149 | self.scat = ax.scatter(0, 0, s=200, lw=0.5, facecolors='green') 150 | # create text box 151 | self.text_h = ax.text(0.02, 0.98, self.text_start, verticalalignment='top',horizontalalignment='left', 152 | transform=ax.transAxes, fontsize=12, bbox=dict(facecolor='white'), gid = 'notrec') 153 | # create timer object 154 | if self.acq_info['acq_time'] != 'inf': 155 | acq_time_ms = int(self.acq_info['acq_time'])*1000 156 | self.acq_timer = self.fig.canvas.new_timer(interval=acq_time_ms) 157 | self.acq_timer.add_callback(self.t_event) 158 | # attach keypress event handler to figure canvas 159 | self.fig.canvas.mpl_connect('key_press_event', self.onkeypress) 160 | 161 | def animate(self,cop_i): 162 | # plot COP 163 | lock.acquire() 164 | cop = thd.cop 165 | lock.release() 166 | self.scat.set_offsets(cop) 167 | 168 | # Keypress event handler 169 | def onkeypress(self,evt): 170 | if evt.key==' ': 171 | # spacebar pressed 172 | if self.text_h.get_gid()=='notrec': 173 | # start recording data... 174 | # change colour of dot 175 | self.scat.set_facecolors('red') 176 | plt.draw() 177 | # set gid to recording to flag recording state 178 | self.text_h.set_gid('rec') 179 | if self.acq_info['acq_time'] != 'inf': 180 | # timed acquisition - start timer 181 | self.acq_timer.start() 182 | self.text_h.set_text('Timed acquisition') 183 | else: 184 | # manual acq 185 | # change instructions 186 | self.text_h.set_text(self.text_stop) 187 | # set thread to store data in queue 188 | lock.acquire() 189 | thd.storeflag = True 190 | lock.release() 191 | 192 | elif self.text_h.get_gid()=='rec': 193 | # stop recording 194 | if self.acq_info['acq_time'] != 'inf': 195 | # timed acq - do nothing 196 | pass 197 | else: 198 | # recording data, manual acq 199 | lock.acquire() 200 | thd.storeflag = False 201 | thd.runflag = False 202 | lock.release() 203 | plt.close() 204 | else: 205 | print('error in onkeypress - unrecognised text_h gid') 206 | 207 | # callback function for timer 208 | def t_event(self): 209 | # stop thread queuing data and stop it running 210 | lock.acquire() 211 | thd.storeflag = False 212 | thd.runflag = False 213 | lock.release() 214 | self.acq_timer.remove_callback(self.t_event) 215 | plt.close() 216 | 217 | 218 | # ~~~~~~~~~~~~~~~ 219 | # MAIN ROUTINE 220 | # ~~~~~~~~~~~~~~~ 221 | 222 | # to suppress the annoying warning 223 | import warnings 224 | warnings.filterwarnings('ignore') 225 | # clear terminal 226 | run('clear') 227 | 228 | # connect to balance board and exit if none connected 229 | bb = connectBB() 230 | if bb==None: 231 | time.sleep(5) 232 | print('Exiting') 233 | sys.exit() 234 | 235 | # SELECT STUDY 236 | # ~~~~~~~~~~~~ 237 | # select study and read config file 238 | # get directories 239 | script_dir = os.path.dirname(os.path.realpath(__file__)) 240 | config_dir = os.path.join(script_dir,'config_files') 241 | 242 | # Get list of config files in config_dir 243 | config_files_t = os.listdir(config_dir) 244 | config_files = [x for x in config_files_t if '.config' in x] 245 | 246 | # use config parser to read each config file for names of study 247 | s_names = list() 248 | config_tmp = configparser.ConfigParser() 249 | for i_f in config_files: 250 | cf = os.path.join(config_dir,i_f) 251 | config_tmp.read(cf) 252 | s_names.append(config_tmp['study info']['study_name']) 253 | 254 | # user interface to get user selection 255 | del(config_tmp) 256 | chc = txtmenu('Select study',s_names) 257 | 258 | # read selected config file 259 | config = configparser.ConfigParser() 260 | config.read(os.path.join(config_dir,config_files[chc])) 261 | 262 | 263 | # SETUP SESSION 264 | # ~~~~~~~~~~~~~ 265 | # get path of study directory 266 | std_dir = config['study info']['study_dir'] 267 | 268 | # get path of session directory... 269 | # read all names of all sessions in study directory 270 | prev_sesh = listdirs(std_dir) 271 | 272 | # default name for session 273 | tmnow = datetime.now() 274 | def_name = tmnow.strftime("%b_%d_%Y_%p") 275 | s_dir_nm = get_sessionname(prev_sesh,def_name) 276 | 277 | # check if session dir already exists 278 | if s_dir_nm in prev_sesh: 279 | print('Session already exists. Start again and choose another name') 280 | time.sleep(5) 281 | sys.exit() 282 | 283 | # session path 284 | sesh_path = os.path.join(std_dir,s_dir_nm) 285 | # create directory 286 | os.mkdir(sesh_path,mode=0o775) 287 | 288 | 289 | # CALIBRATE BOARD 290 | # ~~~~~~~~~~~~~~~ 291 | # preallocate array for mean of sensor readings for each calibration weight 292 | n_calib = len(calib_wgts) 293 | sens_mean = np.empty([N_S,n_calib]) 294 | print('\n\nStarting calibration sequence...\nApply weights as close as possible to the centre...\n') 295 | for i_ws in range(n_calib): 296 | print('Apply',str(calib_wgts[i_ws]),calib_units,'to balance board\n') 297 | input_str = input('Press return when ready...\n\n') 298 | # read data 299 | sens_dat = procBBdata(bb, getnsamp, smp_size) 300 | # print(sens_dat) 301 | # for each sensor... 302 | for i_s in range(N_S): 303 | sens_dat1 = sens_dat[:,i_s] 304 | # print out percentage of readings == 0 305 | prctzero = sum(sens_dat1==0)/smp_size*100 306 | if prctzero > 0: 307 | print('Warning: percentage zeros for {0} sensor = {1:.2f}%'.format(SENS_DCT[i_s],prctzero)) 308 | # detect if all values are for sensor are zero 309 | if prctzero > maxpcnt: 310 | print('Error: percentage zeros for {0} sensor exceeds maximum ({1:.2f}%).'.format(SENS_DCT[i_s],prctzero)) 311 | print('Use heavier weight or move board to another location.') 312 | print('Exiting') 313 | time.sleep(5) 314 | sys.exit() 315 | else: 316 | # get zscores for sensor 317 | zscrs = stats.zscore(sens_dat1) 318 | # replace those outside threshold with nans 319 | sens_dat1[np.absolute(zscrs) > out_thresh] = np.nan 320 | # get mean excluding nans. 321 | sens_mean[i_s,i_ws] = np.nanmean(sens_dat1) 322 | 323 | # For each sensor get a linear model to calibrate data... 324 | # create dictionary to store results to file and array for model parameters 325 | # 'm'-slopes,'c'-intercepts, 'p'-p-values, 'r'- r values, 'se' -standard errors 326 | # cal_mod row zero = slopes, row 1 = intercepts. Each col represents a sensor 327 | # Calibration weights are divided by number of sensors 328 | cal_mod = np.empty([2,N_S]) 329 | cal_dat = dict() 330 | dc = dict() 331 | for i_s in range(N_S): 332 | cal_m, cal_c, cal_r, cal_p, cal_se = stats.linregress(sens_mean[i_s,:],calib_wgts.values/N_S) 333 | 334 | # store results to dictionary 335 | dc.update({'m':cal_m}) 336 | dc.update({'c':cal_c}) 337 | dc.update({'r':cal_r}) 338 | dc.update({'p':cal_p}) 339 | dc.update({'se':cal_se}) 340 | 341 | # store model parameters to an array 342 | cal_mod[0,i_s] = cal_m 343 | cal_mod[1,i_s] = cal_c 344 | cal_dat.update({SENS_DCT[i_s]:dc}) 345 | 346 | # save calibration data in session directory 347 | calib_dat = {'model':cal_mod, 'details':cal_dat} 348 | cfn = os.path.join(sesh_path,'calibration_dat') 349 | with open(cfn,'wb') as fptr: 350 | pickle.dump(calib_dat,fptr) 351 | print('Remove calibration weights from balance board\n\n') 352 | 353 | # #TEST overide calibration 354 | # cal_mod = np.array([[0.01776906,0.01645395,0.02366412,0.02252513],[ 0.39208467,-0.7261971,-0.05245845,-3.55288195]]) 355 | 356 | # GET SERIES OF ACQUISITIONS 357 | loop_flag = True 358 | 359 | # set up objects for data acquisition 360 | lock = threading.Lock() 361 | wii_q = Queue(maxsize=0) 362 | 363 | 364 | while loop_flag: 365 | 366 | # Get acquisition info 367 | # {'group': 'case', 'acq_time': 'inf', 'subject_code': '121', 'epoch': 'before'} 368 | acq_info = get_acq_info(config) 369 | 370 | # create plot_cop instance 371 | pltcop_obj = plot_cop(acq_info,BB_X,BB_Y) 372 | thd = wii_thread(bb,cal_mod,BB_X,BB_Y) 373 | 374 | # Start thread 375 | thd.start() 376 | 377 | # PLOT ANIMATION - interval can't be too small or it gives an attribute error 378 | animation = FuncAnimation(pltcop_obj.fig, pltcop_obj.animate,interval=anim_interval) 379 | plt.show() 380 | 381 | thd.join() 382 | 383 | # get data from queue and write to file 384 | acq_data = np.empty((0,4)) 385 | while not(wii_q.empty()): 386 | acq_data = np.vstack((acq_data,wii_q.get())) 387 | # convert 3rd (secs) & 4th (microsecs) col to 1 col of secs 388 | acq_data[:,2] = acq_data[:,2] + acq_data[:,3]/1000000 389 | # convert to pandas dataframe 390 | acq_dat_df = pd.DataFrame(data=acq_data[:,(0,1,2)],columns=('copx','copy','time')) 391 | # subtract the time of the first reading from the others - start a time=o 392 | acq_dat_df['time'] = acq_dat_df['time'] - acq_dat_df.ix[0,'time'] 393 | # write to file... 394 | # get save file name... 395 | sfn = aqc_name(acq_info)+'.csv' 396 | sfn = os.path.join(sesh_path,sfn) 397 | print(sfn) 398 | acq_dat_df.to_csv(sfn,index=False) 399 | 400 | # ask user if they wish to do another acquisition 401 | chc = input('Get another acquisition? (y/n)\n') 402 | if chc in ['y','Y']: 403 | pass 404 | # clear terminal 405 | #run('clear') 406 | else: 407 | loop_flag = False 408 | # clear terminal 409 | # run('clear') 410 | 411 | # END OF ACQUISITION LOOP 412 | -------------------------------------------------------------------------------- /wiicop_v1.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # To test a single loop of the acquisition series loop. No timer functionality to begin with 3 | 4 | import pyudev 5 | import xwiimote 6 | import errno 7 | import select 8 | import sys 9 | import os 10 | import time 11 | from subprocess import run 12 | import numpy as np 13 | from scipy import stats 14 | import pandas as pd 15 | import pickle 16 | import configparser 17 | from WiiCopFunctions import * 18 | # from WiiCopFunctions import (get_sessionname, txtmenu, listdirs, 19 | # get_acq_info, connectBB, calcCOP, procBBdata, getnsamp, validcode) 20 | from datetime import datetime 21 | import matplotlib as mpl 22 | import matplotlib.pyplot as plt 23 | from matplotlib.animation import FuncAnimation 24 | 25 | # user defined options 26 | # ~~~~~~~~~~~~~~~~~~~~ 27 | # sample size for calibration 28 | smp_size = 50 29 | # outlier z-score threshold defined here 30 | out_thresh = 3 31 | # percentage zeros %age max limit defined here 32 | maxpcnt = 5 33 | # Pandas series to define calibration weights 34 | # calib_wgts = pd.Series({0:5,1:10,2:18}) 35 | calib_wgts = pd.Series({0:5,1:9,2:16.5}) 36 | # calibration units ('Kgs' or 'lbs') 37 | # calib_units = 'lbs' 38 | calib_units = 'Kgs' 39 | # set the time interval for FuncAnimation (milliseconds) 40 | anim_interval = 100 41 | 42 | # constants 43 | # ~~~~~~~~~ 44 | # dictionary for enumeration of sensors 45 | SENS_DCT = {0:'Top right',1:'Bottom right',2:'Top left',3:'Bottom left'} 46 | # number of sensors 47 | N_S = 4 48 | # lbs to kgs conversion factor 49 | LBS2KGS = 0.453592 50 | # Balance board dimensions width and length in mm (Leach, J.M., Mancini, M., Peterka, R.J., Hayes, 51 | # T.L. and Horak, F.B., 2014. Validating and calibrating the Nintendo Wii 52 | # balance board to derive reliable center of pressure measures. Sensors, 53 | # 14(10), pp.18244-18267.) 54 | BB_Y = 238 55 | BB_X = 433 56 | 57 | # define acquisition object 58 | class acq_object: 59 | 'object to implement plotting data in animation loop, storage and saving data' 60 | 61 | # This function initializes the necessary variables to calculate the COP (center of pressure) 62 | # aqc_info: dictionary of acquisition specific info 63 | # sesh_path: path to session directory 64 | # bb: a pyudev device object for balance board 65 | # cal_mod: calibration model 66 | def __init__(self,aqc_info,sesh_path,bb,cal_mod): 67 | 68 | # create numpy array to store data initially from board 69 | self.tmp_dat = np.empty((1,N_S)) 70 | self.sens_dat = np.empty((0,N_S)) 71 | self.time_dat = np.empty((0,2)) 72 | self.cop_dat = np.empty((0,2)) 73 | 74 | # store session path 75 | self.sesh_path = sesh_path 76 | 77 | # create var to store start and end time 78 | save_start = 0 79 | save_end = 0 80 | 81 | # set save data to file and array flag 82 | self.savedatf = False 83 | self.storedat = False 84 | 85 | # input acquisition info 86 | self.acq_info = aqc_info 87 | 88 | #initializing wii and poll objects 89 | self.p_obj = select.poll() 90 | self.bbdev = xwiimote.iface(bb.sys_path) 91 | 92 | # register bbdev to pollong object 93 | self.p_obj.register(self.bbdev.get_fd(), select.POLLIN) 94 | # open bb device 95 | self.bbdev.open(xwiimote.IFACE_BALANCE_BOARD) 96 | 97 | # event structure 98 | self.revt = xwiimote.event() 99 | # calibration model 100 | self.cal_mod = cal_mod 101 | 102 | 103 | def animate(self,cop_i): 104 | polls = self.p_obj.poll() 105 | for fd, evt in polls: 106 | try: 107 | self.bbdev.dispatch(self.revt) 108 | 109 | # read each sensor 110 | for i_s in range(N_S): 111 | # get the 'x' data from the Absolute Motion Payload for each sensor 112 | self.tmp_dat[0,i_s] = self.revt.get_abs(i_s)[0] 113 | 114 | # get COP (center of pressure) 115 | cop_p = calcCOP(self.tmp_dat,self.cal_mod,BB_X,BB_Y) 116 | 117 | # plot COP 118 | scat.set_offsets(cop_p) 119 | 120 | if self.storedat: 121 | 122 | # save data in arrays... 123 | self.cop_dat = np.vstack((self.cop_dat,cop_p)) 124 | self.time_dat = np.vstack((self.time_dat,np.array(self.revt.get_time()))) 125 | self.sens_dat = np.vstack((self.sens_dat,self.tmp_dat)) 126 | 127 | except IOError as e: 128 | 129 | # if resource unavailable do nothing 130 | if e.errno != errno.EAGAIN: 131 | print(e) 132 | 133 | # This is called to put data in dictionary & save as a pickled binary file 134 | def save_bbdat(self): 135 | 136 | bbdat = {'rawsens':self.sens_dat,'timedat':self.time_dat,'cop':self.cop_dat} 137 | # get save file name... 138 | sfn = self.aqc_name()+'.dat' 139 | sfn = os.path.join(self.sesh_path,sfn) 140 | with open(sfn,'wb') as fptr: 141 | pickle.dump(bbdat,fptr) 142 | 143 | # This is returns a string for labelling acquisition and for file name 144 | def aqc_name(self): 145 | 146 | tmp = self.acq_info.copy() 147 | afn = 'subj' 148 | afn = afn+tmp.pop('subject_code') 149 | acqt = tmp.pop('acq_time') 150 | for iks in tmp.values(): 151 | afn = afn+'_'+iks 152 | if acqt!='inf': 153 | afn = afn+'_'+acqt 154 | return afn 155 | 156 | # This closes the device 157 | def shutdown(self): 158 | 159 | self.bbdev.close(xwiimote.IFACE_BALANCE_BOARD) 160 | self.p_obj.unregister(self.bbdev.get_fd()) 161 | 162 | 163 | # ~~~~~~~~~~~~~~~ 164 | # MAIN ROUTINE 165 | # ~~~~~~~~~~~~~~~ 166 | 167 | # to suppress the annoying warning 168 | import warnings 169 | warnings.filterwarnings('ignore') 170 | # clear terminal 171 | run('clear') 172 | 173 | # connect to balance board and exit if none connected 174 | bb = connectBB() 175 | if bb==None: 176 | time.sleep(5) 177 | print('Exiting') 178 | sys.exit() 179 | 180 | 181 | # SELECT STUDY 182 | # ~~~~~~~~~~~~ 183 | # select study and read config file 184 | # get directories 185 | script_dir = os.path.dirname(os.path.realpath(__file__)) 186 | config_dir = os.path.join(script_dir,'config_files') 187 | 188 | # Get list of config files in config_dir 189 | config_files_t = os.listdir(config_dir) 190 | config_files = [x for x in config_files_t if '.config' in x] 191 | 192 | # use config parser to read each config file for names of study 193 | s_names = list() 194 | config_tmp = configparser.ConfigParser() 195 | for i_f in config_files: 196 | cf = os.path.join(config_dir,i_f) 197 | config_tmp.read(cf) 198 | s_names.append(config_tmp['study info']['study_name']) 199 | 200 | # user interface to get user selection 201 | del(config_tmp) 202 | chc = txtmenu('Select study',s_names) 203 | 204 | # read selected config file 205 | config = configparser.ConfigParser() 206 | config.read(os.path.join(config_dir,config_files[chc])) 207 | 208 | 209 | # SETUP SESSION 210 | # ~~~~~~~~~~~~~ 211 | # get path of study directory 212 | std_dir = config['study info']['study_dir'] 213 | 214 | # get path of session directory... 215 | # read all names of all sessions in study directory 216 | prev_sesh = listdirs(std_dir) 217 | 218 | # default name for session 219 | tmnow = datetime.now() 220 | def_name = tmnow.strftime("%b_%d_%Y_%p") 221 | s_dir_nm = get_sessionname(prev_sesh,def_name) 222 | 223 | # check if session dir already exists 224 | if s_dir_nm in prev_sesh: 225 | print('Session already exists. Start again and choose another name') 226 | time.sleep(5) 227 | sys.exit() 228 | 229 | # session path 230 | sesh_path = os.path.join(std_dir,s_dir_nm) 231 | # create directory 232 | os.mkdir(sesh_path,mode=0o775) 233 | 234 | 235 | # CALIBRATE BOARD 236 | # ~~~~~~~~~~~~~~~ 237 | # preallocate array for mean of sensor readings for each calibration weight 238 | n_calib = len(calib_wgts) 239 | sens_mean = np.empty([N_S,n_calib]) 240 | print('\n\nStarting calibration sequence...\nApply weights as close as possible to the centre...\n') 241 | for i_ws in range(n_calib): 242 | print('Apply',str(calib_wgts[i_ws]),calib_units,'to balance board\n') 243 | input_str = input('Press return when ready...\n\n') 244 | # read data 245 | sens_dat = procBBdata(bb, getnsamp, smp_size) 246 | # print(sens_dat) 247 | # for each sensor... 248 | for i_s in range(N_S): 249 | sens_dat1 = sens_dat[:,i_s] 250 | # print out percentage of readings == 0 251 | prctzero = sum(sens_dat1==0)/smp_size*100 252 | if prctzero > 0: 253 | print('Warning: percentage zeros for {0} sensor = {1:.2f}%'.format(SENS_DCT[i_s],prctzero)) 254 | # detect if all values are for sensor are zero 255 | if prctzero > maxpcnt: 256 | print('Error: percentage zeros for {0} sensor exceeds maximum ({1:.2f}%).'.format(SENS_DCT[i_s],prctzero)) 257 | print('Use heavier weight or move board to another location.') 258 | print('Exiting') 259 | time.sleep(5) 260 | sys.exit() 261 | else: 262 | # get zscores for sensor 263 | zscrs = stats.zscore(sens_dat1) 264 | # replace those outside threshold with nans 265 | sens_dat1[np.absolute(zscrs) > out_thresh] = np.nan 266 | # get mean excluding nans. 267 | sens_mean[i_s,i_ws] = np.nanmean(sens_dat1) 268 | 269 | # For each sensor get a linear model to calibrate data... 270 | # create dictionary to store results to file and array for model parameters 271 | # 'm'-slopes,'c'-intercepts, 'p'-p-values, 'r'- r values, 'se' -standard errors 272 | # cal_mod row zero = slopes, row 1 = intercepts. Each col represents a sensor 273 | # Calibration weights are divided by number of sensors 274 | cal_mod = np.empty([2,N_S]) 275 | cal_dat = dict() 276 | dc = dict() 277 | for i_s in range(N_S): 278 | cal_m, cal_c, cal_r, cal_p, cal_se = stats.linregress(sens_mean[i_s,:],calib_wgts.values/N_S) 279 | 280 | # store results to dictionary 281 | dc.update({'m':cal_m}) 282 | dc.update({'c':cal_c}) 283 | dc.update({'r':cal_r}) 284 | dc.update({'p':cal_p}) 285 | dc.update({'se':cal_se}) 286 | 287 | # store model parameters to an array 288 | cal_mod[0,i_s] = cal_m 289 | cal_mod[1,i_s] = cal_c 290 | cal_dat.update({SENS_DCT[i_s]:dc}) 291 | 292 | # save calibration data in session directory 293 | calib_dat = {'model':cal_mod, 'details':cal_dat} 294 | cfn = os.path.join(sesh_path,'calibration_dat') 295 | with open(cfn,'wb') as fptr: 296 | pickle.dump(calib_dat,fptr) 297 | print('Remove calibration weights from balance board\n\n') 298 | 299 | ##TEST overide calibration 300 | #cal_mod = np.array([[0.01776906,0.01645395,0.02366412,0.02252513],[ 0.39208467,-0.7261971,-0.05245845,-3.55288195]]) 301 | 302 | 303 | # GET SERIES OF ACQUISITIONS 304 | loop_flag = True 305 | 306 | while loop_flag: 307 | 308 | # Get acquisition info 309 | # {'group': 'case', 'acq_time': 'inf', 'subject_code': '121', 'epoch': 'before'} 310 | acq_info = get_acq_info(config) 311 | 312 | # get acquisition time in milliseconds if timed 313 | if acq_info['acq_time'] != 'inf': 314 | acq_time_ms = int(acq_info['acq_time'])*1000 315 | 316 | # create acq_object instance 317 | acqobj = acq_object(acq_info,sesh_path,bb,cal_mod) 318 | 319 | # CREATE WINDOW 320 | # Initial instructions 321 | text_start = 'Press Spacebar to start recording' 322 | text_stop = 'Press Spacebar to stop recording' 323 | 324 | # remove toolbar 325 | mpl.rcParams['toolbar'] = 'None' 326 | 327 | # Create new figure and an axes which fills it... 328 | # set figure width in inches 329 | fig_width = 12 330 | 331 | # set fig ratio based on size of bboard rectange whose corners are sensors 332 | fig = plt.figure(figsize=(fig_width, fig_width*BB_Y/BB_X)) 333 | fig.canvas.set_window_title(acqobj.aqc_name()) 334 | 335 | # frameon determines whether background of frame will be drawn 336 | ax = fig.add_axes([0, 0, 1, 1], frameon=False) 337 | ax.set_xlim(-BB_X/2, BB_X/2), ax.set_xticks([]) 338 | ax.set_ylim(-BB_Y/2, BB_Y/2), ax.set_yticks([]) 339 | 340 | # create a scatter object at initial position 0,0 341 | cop_x = 0 342 | cop_y = 0 343 | scat = ax.scatter(cop_x, cop_x, s=200, lw=0.5, facecolors='green') 344 | 345 | # create text box 346 | text_h = ax.text(0.02, 0.98, text_start, verticalalignment='top',horizontalalignment='left', 347 | transform=ax.transAxes, fontsize=12, bbox=dict(facecolor='white'), gid = 'notrec') 348 | 349 | # DEFINING KEYPRESS EVENT HANDLER 350 | def onkeypress(evt): 351 | if evt.key==' ': 352 | 353 | # spacebar pressed 354 | if text_h.get_gid()=='notrec': 355 | # not recording data... 356 | # change colour of dot 357 | scat.set_facecolors('red') 358 | plt.draw() 359 | # set gid to recording to flag recording state 360 | text_h.set_gid('rec') 361 | if acq_info['acq_time'] != 'inf': 362 | # timed acquisition - start timer 363 | acq_timer.start() 364 | text_h.set_text('Timed acquisition') 365 | # set acquisition object to store data in array 366 | acqobj.storedat = True 367 | else: 368 | # manual acq 369 | # change instructions 370 | text_h.set_text(text_stop) 371 | # set acquisition object to store data in array 372 | acqobj.storedat = True 373 | 374 | elif text_h.get_gid()=='rec': 375 | 376 | if acq_info['acq_time'] != 'inf': 377 | # timed acq - do nothing 378 | pass 379 | else: 380 | # recording data, manual acq 381 | acqobj.storedat = False 382 | acqobj.savedatf = True 383 | plt.close() 384 | else: 385 | print('error in onkeypress - unrecognised text_h gid') 386 | 387 | def t_event(): 388 | # callback function for timer 389 | acqobj.storedat = False 390 | acqobj.savedatf = True 391 | acq_timer.remove_callback(t_event) 392 | plt.close() 393 | 394 | # create timer object 395 | if acq_info['acq_time'] != 'inf': 396 | acq_timer = fig.canvas.new_timer(interval=acq_time_ms) 397 | acq_timer.add_callback(t_event) 398 | 399 | # attach keypress event handler to figure canvas 400 | cid = fig.canvas.mpl_connect('key_press_event', onkeypress) 401 | 402 | # PLOT ANIMATION - interval can't be too small or it gives an attribute error 403 | animation = FuncAnimation(fig, acqobj.animate, interval=anim_interval) 404 | plt.show() 405 | 406 | # save data if flag = True 407 | print(acqobj.savedatf) 408 | if acqobj.savedatf: 409 | acqobj.save_bbdat() 410 | print('\nSession {}:'.format(s_dir_nm)) 411 | print(' Data has been saved in file {}\n'.format(acqobj.aqc_name()+'.dat')) 412 | else: 413 | print('\nNo data has been saved in this acquisition\n') 414 | 415 | # shutdown devices 416 | acqobj.shutdown() 417 | 418 | # ask user if they wish to do another acquisition 419 | chc = input('Get another acquisition? (y/n)\n') 420 | if chc in ['y','Y']: 421 | pass 422 | # clear terminal 423 | #run('clear') 424 | else: 425 | loop_flag = False 426 | # clear terminal 427 | # run('clear') 428 | 429 | # END OF ACQUISITION LOOP 430 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------