--- a
+++ b/sisfall_clean.py
@@ -0,0 +1,565 @@
+import pandas as pd
+import numpy as np
+import warnings
+import time
+warnings.filterwarnings('ignore')
+import matplotlib
+matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+
+path = '/Users/mattjohnson/Desktop/Python2018/sisfall/SubjectDataFrames/acm_SA'
+
+subjectList = []
+
+firstIndex = 1
+lastIndex = 4
+
+print('*',firstIndex, 'to', lastIndex-1, '...')
+
+for i in range(firstIndex, lastIndex):
+    data = pd.read_csv(path + str(i).zfill(2) + '.csv')
+    data = data.drop('Unnamed: 0', axis=1)
+    df = data[['x1', 'y1', 'z1', 'x2', 'y2', 'z2', 'activity', 'subject', 'trial']]
+    subjectList.append(df)
+
+# Codes for ADLs
+dailies = ['D01', 'D02', 'D03', 'D04', 'D05', 'D06', 'D07', 'D08', 'D09', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15',
+           'D16', 'D17', 'D18', 'D19']
+# Codes for Falls
+falls = ['F01', 'F02', 'F03', 'F04', 'F05', 'F06', 'F07', 'F08', 'F09', 'F10', 'F11', 'F12', 'F13', 'F14', 'F15']
+
+# Lists to hold dataframes sorted by activity (ADLs and Falls)
+adl_list = []
+fall_list = []
+# Iterate through subject data and sort into ADLs and Falls
+for s in subjectList:
+    for d in dailies:
+        tempdf = s[s['activity'] == d]
+        adl_list.append(tempdf)
+
+    for f in falls:
+        tempdf = s[s['activity'] == f]
+        fall_list.append(tempdf)
+
+
+# Titles for fall activities
+fall_titles = ['Fall forward while walking caused by a slip', 'Fall backward while walking caused by a slip',
+    'Lateral fall while walking caused by a slip', 'Fall forward while walking caused by a trip',
+    'Fall forward while jogging caused by a trip', 'Vertical fall while walking caused by fainting',
+    'Fall while walking, with use of hands in a table to dampen fall, caused by fainting',
+    'Fall forward when trying to get up', 'Lateral fall when trying to get up',
+    'Fall forward when trying to sit down', 'Fall backward when trying to sit down', 'Lateral fall when trying to sit down',
+    'Fall forward while sitting, caused by fainting or falling asleep',
+    'Fall backward while sitting, caused by fainting or falling asleep',
+    'Lateral fall while sitting, caused by fainting or falling asleep']
+# Titles for ADLs
+adl_titles = ['Walking slowly', 'Walking quickly', 'Jogging slowly', 'Jogging quickly', 'Walking upstairs and downstairs slowly',
+    'Walking upstairs and downstairs quickly','Slowly sit in a half height chair, wait a moment, and up slowly',
+    'Quickly sit in a half height chair, wait a moment, and up quickly',
+    'Slowly sit in a low height chair, wait a moment, and up slowly','Quickly sit in a low height chair, wait a moment, and up quickly',
+    'Sitting a moment, trying to get up, and collapse into a chair',
+    'Sitting a moment, lying slowly, wait a moment, and sit again','Sitting a moment, lying quickly, wait a moment, and sit again',
+    'Being on oneís back change to lateral position, wait a moment, and change to oneís back',
+    'Standing, slowly bending at knees, and getting up', 'Standing, slowly bending without bending knees, and getting up',
+    'Standing, get into a car, remain seated and get out of the car','Stumble while walking',
+    'Gently jump without falling (trying to reach a high object)']
+
+
+from scipy.signal import butter, lfilter, freqz
+
+# Filter requirements.
+order = 4
+fs = 200.0  # sample rate, Hz
+cutoff = 5.0  # desired cutoff frequency of the filter, Hz
+
+
+# From??????
+def butter_lowpass(cutoff, fs, order=5):
+    nyq = 0.5 * fs
+    normal_cutoff = cutoff / nyq
+    b, a = butter(order, normal_cutoff, btype='low', analog=False)
+    return b, a
+
+
+def butter_lowpass_filter(data, cutoff, fs, order=5):
+    b, a = butter_lowpass(cutoff, fs, order=order)
+    y = lfilter(b, a, data)
+    return y
+
+
+# Codes for trials
+trials = ['R01', 'R02', 'R03', 'R04', 'R05']
+horiz_std_mag_THRESHOLD = 170  # Set threshold for Horizontal Standard Deviation Magnitude
+horiz_vec_mag_THRESHOLD = 400  # Set threshold for Horizontal Sum Vector Magnitude
+vector_mag_THRESHOLD = 900  # Set threshold for Sum Vector Magnitude
+gyro_horiz_std_THRESHOLD = 170
+
+# Lists to hold prepared fall and ADL dataframes
+fall_df_list = []
+adl_df_list = []
+# Lists to hold sliding windows of 1.25s with a 50% overlap
+windowList_adl = []
+windowList_fall = []
+
+windowHighlights_adl = []
+falseAlarms = []
+
+
+# Method that takes in a dataframe and kind=['f', 'a'] and stores sliding windows of 1.25s
+# with a 50% overlap
+def sliding_window(dataframe, kind):
+    k = 0
+    for i in range(0, len(dataframe) - 256, 128):
+        w = 256  # Size of sliding window (256 points at 200Hz = 1.25 seconds)
+        w1 = dataframe.iloc[i:i + w][:]
+
+        if ((w1['horiz_std_mag9'].max() >= horiz_std_mag_THRESHOLD) * 1 + (
+                w1['horiz_vector_mag9'].max() >= horiz_vec_mag_THRESHOLD) * 1
+                + (w1['vm'].max() >= vector_mag_THRESHOLD) * 1 + (
+                        w1['gyro_horiz_std_mag'].max() >= gyro_horiz_std_THRESHOLD) * 1 >= 3):
+            w1['Fall'] = 1
+
+            if kind == 'a':
+                falseAlarms.append(k)
+                print('k:', k, 'i:', i, '\n')
+                print('False Alarm: hstd9:', w1['horiz_std_mag9'].max(), 'hvm9:', w1['horiz_vector_mag9'].max())
+                print('\tvm:', w1['vm'].max(), 'hvm9:', 'ghstd:', w1['gyro_horiz_std_mag'].max())
+                windowHighlights_adl.append(1)
+        else:
+            w1['Fall'] = 0
+            if kind == 'a':
+                windowHighlights_adl.append(0)
+
+        if kind == 'f':  # If fall
+            windowList_fall.append(w1)
+        else:  # If ADL
+            windowList_adl.append(w1)
+        k += 1
+
+
+# Method that takes in a kind=['f', 'a'] and prepares respective data
+def prepare_data(kind):
+    if kind == 'f':  # If fall
+        putList, takeList = fall_df_list, fall_list
+    else:  # If ADL
+        putList, takeList = adl_df_list, adl_list
+
+    start = time.time()  # Timer for testing
+
+    for i in range(0, len(takeList)):  # Iterate through the takeList (fall_list or adl_list)
+
+        my_df = takeList[i].copy()  # copy dataframe from list
+        new_df = pd.DataFrame()  # placeholder
+
+        for trial in trials:  # Iterate through trials (1-5)
+
+            # Get relevant trial data
+            trial_df = my_df[my_df['trial'] == trial]
+
+            tempdf = pd.DataFrame()  # dataframe for putting into filter
+            # Low Pass Buttersworth Filter and remove bias
+            tempdf['ax'], tempdf['ay'], tempdf['az'] = trial_df['x1'], trial_df['y1'], trial_df['z1']
+            tempdf = tempdf.reset_index(drop=True)
+            tempdf['fx'] = pd.Series(butter_lowpass_filter(trial_df['x1'], cutoff, fs, order))
+            tempdf['fy'] = pd.Series(butter_lowpass_filter(trial_df['y1'], cutoff, fs, order))
+            tempdf['fz'] = pd.Series(butter_lowpass_filter(trial_df['z1'], cutoff, fs, order))
+            tempdf['bx'] = tempdf['fx'].diff()
+            tempdf['by'] = tempdf['fy'].diff()
+            tempdf['bz'] = tempdf['fz'].diff()
+
+            tempdf = tempdf.reset_index(drop=True)
+            trial_df = trial_df.reset_index(drop=True)
+            tempdf['gx'], tempdf['gy'], tempdf['gz'] = trial_df['x2'], trial_df['y2'], trial_df['z2']
+
+            # Rolling averages
+            tempdf['y_roll'] = pd.Series(tempdf['by'].rolling(200).mean())
+            tempdf['fy_roll'] = pd.Series(tempdf['fy'].rolling(200).mean())
+            tempdf['gy_roll'] = pd.Series(tempdf['by'].rolling(200).mean())
+
+            # Rolling standard deviations
+            tempdf['bx_std'] = tempdf['bx'].rolling(200).std()
+            tempdf['by_std'] = tempdf['by'].rolling(200).std()
+            tempdf['bz_std'] = tempdf['bz'].rolling(200).std()
+            tempdf['fx_std'] = tempdf['fx'].rolling(200).std()
+            tempdf['fy_std'] = tempdf['fy'].rolling(200).std()
+            tempdf['fz_std'] = tempdf['fz'].rolling(200).std()
+            tempdf['gx_std'] = tempdf['fx'].rolling(200).std()
+            tempdf['gy_std'] = tempdf['fy'].rolling(200).std()
+            tempdf['gz_std'] = tempdf['fz'].rolling(200).std()
+
+
+            # Integral stuff
+            tempdf['xsum'] = pd.expanding_sum(((abs(tempdf['ax']).rolling(2).sum() / 2) * (1 / 200)).fillna(0))
+            tempdf['ysum'] = pd.expanding_sum(((abs(tempdf['ay']).rolling(2).sum() / 2) * (1 / 200)).fillna(0))
+            tempdf['zsum'] = pd.expanding_sum(((abs(tempdf['az']).rolling(2).sum() / 2) * (1 / 200)).fillna(0))
+            tempdf['time'] = 1 / 200
+            tempdf['time'] = pd.expanding_sum(tempdf['time'])
+            # C10 Signal Magnitude Area
+            tempdf['SigMagArea'] = (tempdf['xsum'] + tempdf['ysum'] + tempdf['zsum']) / tempdf['time']
+            # C11
+            tempdf['HorizSigMagArea'] = (tempdf['xsum'] + tempdf['zsum']) / tempdf['time']
+            # Sum vector magnitude
+            tempdf['vm'] = np.sqrt(tempdf['fx'] ** 2 + tempdf['fy'] ** 2 + tempdf['fz'] ** 2)
+            # Maximum peak to peak acceleration amplitude
+            tempdf['Amax'] = (tempdf['vm'].rolling(200).max())
+            tempdf['Amin'] = (tempdf['vm'].rolling(200).min())
+            # C3
+            tempdf['peak_diff'] = tempdf['Amax'] - tempdf['Amin']
+            # Angle from horizontal to z-axis
+            tempdf['angle_from_horiz'] = np.arctan2(np.sqrt(tempdf['fx'] ** 2 + tempdf['fz'] ** 2), -tempdf['fy']) * 180 / np.pi
+            tempdf['angle_std'] = pd.rolling_std(tempdf['angle_from_horiz'], 200)
+
+            # had to make versions of this to put into sliding window, will change once I
+            # confirm they're the same as the others below
+            tempdf['horiz_std_mag9'] = np.sqrt(tempdf['fx_std'] ** 2 + tempdf['fz_std'] ** 2)
+            tempdf['horiz_vector_mag9'] = np.sqrt(tempdf['fx'] ** 2 + tempdf['fz'] ** 2)
+            tempdf['std_mag9'] = np.sqrt(tempdf['fx_std'] ** 2 + tempdf['fy_std'] ** 2 + tempdf['fz_std'] ** 2)
+            tempdf['diff_std_mag9'] = np.sqrt(tempdf['bx_std'] ** 2 + tempdf['by_std'] ** 2 + tempdf['bz_std'] ** 2)
+            tempdf['horiz_mag2'] = np.sqrt(tempdf['bx'] ** 2 + tempdf['bz'] ** 2)
+            tempdf['horiz_std_mag2'] = np.sqrt(tempdf['bx_std'] ** 2 + tempdf['bz_std'] ** 2)
+            tempdf['vector_mag2'] = np.sqrt(tempdf['bx'] ** 2 + tempdf['by'] ** 2 + tempdf['bz'] ** 2)
+
+            tempdf['gyro_horiz_std_mag'] = np.sqrt(tempdf['gx_std'] ** 2 + tempdf['gz_std'] ** 2)
+            tempdf['gyro_vector_mag'] = np.sqrt(tempdf['gx'] ** 2 + tempdf['gy'] ** 2 + tempdf['gz'] ** 2)
+            tempdf['gyro_horiz_mag'] = np.sqrt(tempdf['gx'] ** 2 + tempdf['gz'] ** 2)
+            tempdf['gyro_std_mag'] = np.sqrt(tempdf['gx_std'] ** 2 + tempdf['gy_std'] ** 2 + tempdf['gz_std'] ** 2)
+
+            tempdf = pd.concat(
+                [tempdf.reset_index(drop=True), trial_df[['activity', 'subject', 'trial']].reset_index(drop=True)],
+                axis=1)
+            new_df = pd.concat([new_df.reset_index(drop=True), tempdf])
+
+            sliding_window(tempdf, kind)
+
+        # differential vector mag
+        new_df['vector_mag'] = np.sqrt(new_df['fx'] ** 2 + new_df['fy'] ** 2 + new_df['fz'] ** 2)
+        # C2
+        new_df['horiz_mag'] = np.sqrt(new_df['fx'] ** 2 + new_df['fz'] ** 2)
+        #
+        new_df['vert'] = new_df['by'] - new_df['y_roll']
+        new_df['vert2'] = new_df['ay'] - new_df['y_roll']
+        new_df['vert3'] = new_df['fy'] - new_df['fy_roll']
+        # C9
+        new_df['std_mag2'] = np.sqrt(new_df['bx_std'] ** 2 + new_df['by_std'] ** 2 + new_df['bz_std'] ** 2)
+
+        putList.append(new_df.fillna(0))
+
+    print('Completed... It took', time.time() - start, 'seconds.')
+
+
+prepare_data('f') # Prepare Fall Data
+prepare_data('a') # Prepare ADL Data
+
+print('ADL dataframes:',len(adl_df_list), '\t\tFall dataframes:', len(fall_df_list))
+print('ADL windows:', len(windowList_adl), '\t\tFall windows:', len(windowList_fall))
+
+wList_f = windowList_fall[:]     # Copy of fall window list
+wList_a = windowList_adl[:]
+
+# putting together falls and adls
+fall_df = pd.concat(fall_df_list)
+adl_df = pd.concat(adl_df_list)
+all_df = pd.concat([fall_df, adl_df]).fillna(0) # Filling nulls with zeroes
+
+horiz_std_mag_THRESHOLD = 155  # Set threshold for Horizontal Standard Deviation Magnitude
+horiz_vec_mag_THRESHOLD = 400  # Set threshold for Horizontal Sum Vector Magnitude
+vector_mag_THRESHOLD = 750  # Set threshold for Sum Vector Magnitude
+gyro_horiz_std_THRESHOLD = 150
+belowThresh = 0  # Keep track of windows below thresholds
+aboveThresh = 0  # Keep track of windows above thresholds
+i = 0  # placeholder
+lastActInd = 0  # Last activity index
+lastTrialNum = 0  # Last trial number
+fallTrialList = []  #
+listInFallTrialList = []  #
+missedFalls = 0
+
+for window in wList_f:  # Iterate through fall windows
+    windNum = i  # Set window index
+    respWindNum = i % 22  # Set respective window index (0-21)
+    activityIndex = int(i / (22 * 5))  # Calculate activity index (0-13/14?)
+    trialNum = (int(i / 22)) % 5  # Calculate trial number (0-4)
+
+    # Custom setting for thresholds, currently need to pass 2/3 thresholds to pass
+    if ((window['horiz_std_mag9'].max() >= horiz_std_mag_THRESHOLD) * 1 + (
+            window['horiz_vector_mag9'].max() >= horiz_vec_mag_THRESHOLD) * 1
+            + (window['vm'].max() >= vector_mag_THRESHOLD) * 1 + (
+                    window['gyro_horiz_std_mag'].max() >= gyro_horiz_std_THRESHOLD) * 1 >= 1):
+
+        aboveThresh += 1
+
+        if (activityIndex == lastActInd):
+            if (trialNum == lastTrialNum):
+                listInFallTrialList.append(respWindNum)
+                lastTrialNum = trialNum
+                lastActInd = activityIndex
+            else:
+                fallTrialList.append(listInFallTrialList)
+                listInFallTrialList = []
+                listInFallTrialList.append(respWindNum)
+                lastActInd = activityIndex
+                diff = (trialNum - lastTrialNum)
+                absdiff = diff % 5
+
+                if absdiff > 1:
+                    for j in range(absdiff - 1):
+                        fallTrialList.append([])
+                        missedFalls += 1
+
+                lastTrialNum = trialNum
+        else:
+            diff = (trialNum - lastTrialNum)
+            absdiff = diff % 5
+            activity_diff = (activityIndex - lastActInd)
+
+            fallTrialList.append(listInFallTrialList)
+            if (activity_diff > 0) & (trialNum != 0):
+                for k in range(3 - absdiff):
+                    fallTrialList.append([])
+                    missedFalls += 1
+            if absdiff > 1:
+                for j in range(absdiff - 1):
+                    fallTrialList.append([])
+                    missedFalls += 1
+            listInFallTrialList = []
+            listInFallTrialList.append(respWindNum)
+            lastTrialNum = trialNum
+            lastActInd = activityIndex
+    else:
+        belowThresh += 1
+    i += 1
+
+fallTrialList.append(listInFallTrialList)
+
+print('below:', belowThresh)
+print('above:', aboveThresh)
+
+
+FTL = fallTrialList[:]
+print('len fallTrialList:', len(FTL))
+print('missed falls:', missedFalls)
+print('false alarms:', len(falseAlarms), ':', falseAlarms)
+
+
+fall_windows = pd.concat(wList_f)
+adl_windows = pd.concat(wList_a)
+all_windows = pd.concat([fall_windows, adl_windows])
+
+
+print('COMPLETED')
+
+
+
+# Titles for fall activities
+fall_titles = ['Fall forward while walking caused by a slip', 'Fall backward while walking caused by a slip',
+    'Lateral fall while walking caused by a slip', 'Fall forward while walking caused by a trip',
+    'Fall forward while jogging caused by a trip', 'Vertical fall while walking caused by fainting',
+    'Fall while walking, with use of hands in a table to dampen fall, caused by fainting',
+    'Fall forward when trying to get up', 'Lateral fall when trying to get up',
+    'Fall forward when trying to sit down', 'Fall backward when trying to sit down', 'Lateral fall when trying to sit down',
+    'Fall forward while sitting, caused by fainting or falling asleep',
+    'Fall backward while sitting, caused by fainting or falling asleep',
+    'Lateral fall while sitting, caused by fainting or falling asleep']
+# Titles for ADLs
+adl_titles = ['Walking slowly', 'Walking quickly', 'Jogging slowly', 'Jogging quickly', 'Walking upstairs and downstairs slowly',
+    'Walking upstairs and downstairs quickly','Slowly sit in a half height chair, wait a moment, and up slowly',
+    'Quickly sit in a half height chair, wait a moment, and up quickly',
+    'Slowly sit in a low height chair, wait a moment, and up slowly','Quickly sit in a low height chair, wait a moment, and up quickly',
+    'Sitting a moment, trying to get up, and collapse into a chair',
+    'Sitting a moment, lying slowly, wait a moment, and sit again','Sitting a moment, lying quickly, wait a moment, and sit again',
+    'Being on oneís back change to lateral position, wait a moment, and change to oneís back',
+    'Standing, slowly bending at knees, and getting up', 'Standing, slowly bending without bending knees, and getting up',
+    'Standing, get into a car, remain seated and get out of the car','Stumble while walking',
+    'Gently jump without falling (trying to reach a high object)']
+
+dailies = ['D01', 'D02', 'D03', 'D04', 'D05', 'D06', 'D07', 'D08', 'D09', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15',
+           'D16', 'D17', 'D18', 'D19']
+falls = ['F01', 'F02', 'F03', 'F04', 'F05', 'F06', 'F07', 'F08', 'F09', 'F10', 'F11', 'F12', 'F13', 'F14', 'F15']
+
+fs = 200.0  # frequency samplet
+subjectCodes = ['SA01', 'SA02', 'SA03', 'SA04']
+
+
+def get_trial_time(index, kind):
+    r = 5
+    if kind == 'f':
+        n = 3000.0
+        t = np.linspace(0, 15.0, n, endpoint=False)
+    else:
+        if index in list(range(0, 4)):
+            T = 100  # seconds
+        elif index in [4, 5, 16]:
+            T = 25
+        else:
+            T = 12
+
+        n = int(T * fs)  # total number of samples
+        t = np.linspace(0, T, n, endpoint=False)
+
+        if index <= 3:
+            r = 1
+
+    return t, int(n), int(r)
+
+
+## index w <-- activity = F[w+1], eg. index=4 gives a df that contains activity F05
+## This makes sense since data_list[0] contains activity F01
+
+def plot_trials(index, subjectIndex, kind):
+    if kind == 'f':
+        correctList, correctTitles, correctCodes = fall_df_list, fall_titles, falls
+    else:
+        correctList, correctTitles, correctCodes = adl_df_list, adl_titles, dailies
+
+    new_df = correctList[index + 15 * subjectIndex]
+    t, n, r = get_trial_time(index, kind)
+
+    T = int(n / fs)
+    l = list(range(0, int(1000 * T), int(1000 * 0.625)))
+    l = np.array(l) / 1000
+    xcoords = list(l)
+
+    plt.figure(figsize=(15, 2 * r))
+
+    for i in range(0, r):
+        curr_df = new_df[i * n:i * n + n]
+        if len(curr_df) != 0:
+            if len(curr_df) != n:
+                curr_df = curr_df.append(curr_df.iloc[len(curr_df) - 1])
+            plt.subplot(r, 1, i + 1)
+            plt.plot(t, curr_df['fx'], 'b-', label='x')
+            plt.plot(t, curr_df['fy'], 'r-', label='y')
+            plt.plot(t, curr_df['fz'], 'y-', label='z')
+            for xc in xcoords:
+                plt.axvline(x=xc)
+            plt.grid()
+            labs = list(range(0, len(xcoords) - 2))
+            plt.xticks(xcoords, labs)
+            plt.legend()
+            plt.ylabel('trial' + str(i + 1))
+            if i == 0:
+                plt.title(subjectCodes[subjectIndex] + ' - ' + correctCodes[index] + ':' + correctTitles[index])
+
+        if kind == 'f':
+            tempA = FTL[5 * index + i + 75 * subjectIndex]
+            if len(tempA) > 0:
+                shadeStart = tempA[0]
+                shadeFin = tempA[len(tempA) - 1] + 2
+                plt.axvspan(shadeStart * .625, shadeFin * .625, color='red', alpha=0.5)
+
+    plt.subplots_adjust(hspace=0.35)
+    plt.show()
+
+
+def plot_one_from_each(index, kind):
+    if kind == 'f':
+        correctList, correctTitles, correctCodes = fall_df_list, fall_titles, falls
+    else:
+        correctList, correctTitles, correctCodes = adl_df_list, adl_titles, dailies
+
+    plt.figure(figsize=(15, 10))
+
+    for i in range(0, 5):
+        if (index + i + 1) == len(correctList): return
+        new_df = correctList[index + i]
+        t, n, r = get_trial_time(index, kind)
+
+        try:
+            curr_df = new_df[0:n]
+            if len(curr_df) != n:
+                curr_df = curr_df.append(curr_df.iloc[len(curr_df) - 1])
+            plt.subplot(5, 1, i + 1)
+            plt.plot(t, curr_df['bx'], 'b-', label='x')
+            plt.plot(t, curr_df['by'], 'r-', label='y')
+            plt.plot(t, curr_df['bz'], 'y-', label='z')
+            plt.grid()
+            plt.legend()
+            plt.ylabel('Acc')
+            plt.title(correctCodes[index + i] + ' ' + correctTitles[index + i])
+        except:
+            print('')
+
+    plt.subplots_adjust(hspace=0.4)
+    plt.show()
+
+
+def plot_feats(index, tri, kind):
+    if kind == 'f':
+        correctList, correctTitles, correctCodes = fall_df_list, fall_titles, falls
+    else:
+        correctList, correctTitles, correctCodes = adl_df_list, adl_titles, dailies
+
+    t, n, r = get_trial_time(index, kind)
+
+    new_df = correctList[index]
+    curr_df = new_df[tri * n:tri * n + n]
+
+    feat_list = ['vector_mag', 'vector_mag2', 'horiz_mag', 'vert', 'std_mag9', 'horiz_std_mag9',
+                 'peak_diff', 'HorizSigMagArea', 'angle_from_horiz', 'gyro_horiz_std_mag']
+    colour_list = ['b-', 'r-', 'k-', 'c-', 'C2', 'C4', 'C1', 'C5', 'C6', 'C7']
+
+    x = len(feat_list)
+    plt.figure(figsize=(15, 2 * x))
+
+    for i, feat, colour in zip(range(0, x), feat_list, colour_list):
+        plt.subplot(x, 1, i + 1)
+        plt.plot(t, curr_df[feat], colour, label=feat)
+        plt.grid()
+        plt.legend()
+        plt.ylabel(feat)
+        if i == 0: plt.title(correctCodes[index] + ' ' + correctTitles[index])
+
+    plt.subplots_adjust(hspace=0.35)
+    plt.show()
+
+
+def plot_trial(index, tri, kind):
+    if kind == 'f':
+        correctList, correctTitles, correctCodes = fall_df_list, fall_titles, falls
+    else:
+        correctList, correctTitles, correctCodes = adl_df_list, adl_titles, dailies
+
+    new_df = correctList[index]
+
+    t, n, r = get_trial_time(index, kind)
+    plt.figure(figsize=(15, 24))
+
+    xtypes = ['ax', 'fx', 'bx', 'gx']
+    ytypes = ['ay', 'fy', 'by', 'gy']
+    ztypes = ['az', 'fz', 'bz', 'gz']
+    ylabs = ['raw acc (m/s^2)', 'filtered', 'filt differential', 'gyro']
+    for i in range(0, len(xtypes)):
+        curr_df = new_df[tri * n:tri * n + n]
+        if len(curr_df) != n:
+            curr_df = curr_df.append(curr_df.iloc[len(curr_df) - 1])
+        plt.subplot(12, 1, i + 1)
+        plt.plot(t, curr_df[xtypes[i]], 'b-', label='x')
+        plt.plot(t, curr_df[ytypes[i]], 'r-', label='y')
+        plt.plot(t, curr_df[ztypes[i]], 'y-', label='z')
+        plt.grid()
+        plt.legend()
+        plt.ylabel(ylabs[i])
+        if i == 0: plt.title(correctCodes[index] + ' ' + correctTitles[index])
+
+    curr_df = correctList[index][0:int(n)]
+    if len(curr_df) != n:
+        curr_df = curr_df.append(curr_df.iloc[len(curr_df) - 1])
+
+    feat_list = ['vector_mag', 'vector_mag2', 'horiz_mag', 'vert', 'std_mag9', 'horiz_std_mag9',
+                 'peak_diff', 'HorizSigMagArea', 'angle_from_horiz', 'gyro_horiz_std_mag']
+    colour_list = ['b-', 'r-', 'k-', 'c-', 'C2', 'C4', 'C1', 'C5', 'C6', 'C7']
+
+    x = len(feat_list) + len(xtypes)
+    plt.figure(figsize=(15, 2 * x))
+
+    for i, feat, colour in zip(range(0, x), feat_list, colour_list):
+        plt.subplot(12, 1, i + 1)
+        plt.plot(t, curr_df[feat], colour, label=feat)
+        plt.grid()
+        plt.legend()
+        plt.ylabel(feat)
+        if i == 0: plt.title(correctCodes[index] + ' ' + correctTitles[index])
+
+    plt.subplots_adjust(hspace=0.35)
+    plt.show()
+