def test_load_data():
# Load/format data
src = os.path.abspath(__file__ + '/../../')+'/demo/demo_data.csv'
raw_data = pd.read_csv(src, skiprows=99, names=['timestamps', 'x', 'y', 'z'], usecols=[0, 1, 2, 3])
raw_data['unix_timestamps'] = pd.to_datetime(raw_data.timestamps, format="%Y-%m-%d %H:%M:%S:%f").values.astype(np.int64) // 10**6
raw_data = raw_data.iloc[:10,:]
# Create an instance of GaitPy
gaitpy = Gaitpy(raw_data,
50,
v_acc_col_name='y',
ts_col_name='unix_timestamps',
v_acc_units='g',
ts_units='ms',
flip=False)
# Run function being tested
obtained_y_accel, obtained_ts = util._load_data(gaitpy, gaitpy.down_sample)
# Confirm expected results
expected_y_accel = pd.Series(np.array([7.138261,7.177487,7.177487,7.215733,6.177209,7.868856,7.676646,5.792788,4.831736,10.713765]), name='y')
pd.testing.assert_series_equal(obtained_y_accel, expected_y_accel)
expected_ts = pd.Series(np.array([1565087150000,1565087150020,1565087150040,1565087150060,1565087150080,
1565087150100,1565087150120,1565087150140,1565087150160,1565087150180]), name='unix_timestamps')
pd.testing.assert_series_equal(obtained_ts, expected_ts)
def extract_features(self,
subject_height,
subject_height_units='centimeter',
sensor_height_ratio=0.53,
result_file=None,
classified_gait=None,
ic_prom=5,
fc_prom=25):
''' Continuous wavelet transform based method of gait feature detection optimization methods
Parameters
----------
subject_height : int or float
Height of the subject. Accepts centimeters by default.
subject_height_units : str
Units of provided subject height. Centimeters by default.
- options: 'centimeter', 'inches', 'meter'
sensor_height_ratio : float
Height of the sensor relative to subject height. Calculated: sensor height / subject height
result_file : str
Optional argument that accepts .csv filepath string to save resulting gait feature dataframe to.
None by default. (ie. myfolder/myfile.csv)
classified_gait : str or pandas.core.frame.DataFrame
Pandas dataframe containing results of gait bout classification procedure (classify_bouts)
OR
File path of .h5 file containing results of gait bout classification procedure (classify_bouts)
ic_prom : int
Prominance of initial contact peak detection
fc_prom : int
Prominance of final contact peak detection
'''
import pandas as pd
import gaitpy.util as util
import warnings
print('\tExtracting features...')
# Load data
y_accel, timestamps = util._load_data(self, self.down_sample)
# If classified gait is provided, load pandas dataframe or h5 file
if classified_gait is not None:
if type(classified_gait) is str:
gait_predictions = pd.read_hdf(classified_gait)
elif type(classified_gait) is pd.core.frame.DataFrame:
gait_predictions = classified_gait
else:
print(
'Unable to load classified gait: Please make sure the data is in the correct format, aborting...'
)
return
# Isolate gait bouts
gait_windows = gait_predictions[gait_predictions['prediction'] ==
1]
if gait_windows.empty:
print(
'The classified_gait data indicates no bouts of gait were detected, aborting...'
)
return
# Concatenate concurrent bouts
gait_bouts = util._concatenate_windows(gait_windows,
window_length=3)
else:
# if classified_gait is not provided, assume entire timeseries is 1 bout of gait
start_time = timestamps[0].astype('datetime64[ms]')
end_time = timestamps.iloc[-1].astype('datetime64[ms]')
gait_bouts = pd.DataFrame(
data={
'start_time': [start_time],
'end_time': [end_time],
'bout_length': [(end_time -
start_time).item().total_seconds()]
})
all_bout_gait_features = pd.DataFrame()
bout_n = 1
# Loop through gait bouts
for row_n, bout in gait_bouts.iterrows():
bout_indices = (
timestamps.astype('datetime64[ms]') >= bout.start_time) & (
timestamps.astype('datetime64[ms]') <= bout.end_time)
bout_data = pd.DataFrame([])
bout_data['y'] = pd.DataFrame(
y_accel.loc[bout_indices].reset_index(drop=True))
bout_data['ts'] = timestamps.loc[bout_indices].reset_index(
drop=True)
if len(bout_data.y) < 15:
warnings.warn('There are too few data points between ' +
str(bout.start_time) + ' and ' +
str(bout.end_time) + ', skipping bout...')
continue
# Run CWT Gait Model IC and FC detection
ic_peaks, fc_peaks = util._cwt(bout_data.y, self.down_sample,
ic_prom, fc_prom)
# Run gait cycle optimization procedure
pd.options.mode.chained_assignment = None
optimized_gait = util._optimization(bout_data['ts'], ic_peaks,
fc_peaks)
if optimized_gait.empty or 1 not in list(
optimized_gait.Gait_Cycle):
continue
# Calculate changes in height of the center of mass
optimized_gait = util._height_change_com(optimized_gait,
bout_data['ts'],
bout_data['y'],
self.down_sample)
# Calculate gait features
sensor_height = util._calculate_sensor_height(
subject_height, subject_height_units, sensor_height_ratio)
gait_features = util._cwt_feature_extraction(
optimized_gait, sensor_height)
# remove center of mass height and gait cycle boolean columns, remove rows with NAs
gait_features.dropna(inplace=True)
gait_features.drop(['CoM_height', 'Gait_Cycle', 'FC_opp_foot'],
axis=1,
inplace=True)
gait_features.insert(0, 'bout_number', bout_n)
gait_features.insert(1, 'bout_length_sec', bout.bout_length)
gait_features.insert(2, 'bout_start_time', bout.start_time)
gait_features.insert(5, 'gait_cycles', len(gait_features))
all_bout_gait_features = all_bout_gait_features.append(
gait_features)
bout_n += 1
all_bout_gait_features.reset_index(drop=True, inplace=True)
all_bout_gait_features.iloc[:,
7:] = all_bout_gait_features.iloc[:,
7:].round(
3)
# Save results
if result_file:
try:
if not result_file.endswith('.csv'):
result_file += '.csv'
all_bout_gait_features.to_csv(result_file,
index=False,
float_format='%.3f')
except:
print(
'Unable to save data: Please make sure your results directory exists, aborting...'
)
return
if all_bout_gait_features.empty:
print(
'\tFeature extraction complete. No gait cycles detected...\n')
else:
print('\tFeature extraction complete!\n')
return all_bout_gait_features
def classify_bouts(self, result_file=None):
""" Gait bout classification using acceleration data in the vertical direction from the lumbar location.
Parameters
----------
result_file : str
Optional argument that accepts .h5 filepath string to save resulting predictions to.
None by default. (ie. myfolder/myfile.h5)
"""
import pickle
import pandas as pd
import os
import deepdish as dd
import gaitpy.util as util
print('\tClassifying bouts of gait...')
# Load model and feature order
model_filename = os.path.dirname(
os.path.realpath(__file__)) + '/model/model.pkl'
features_filename = os.path.dirname(
os.path.realpath(__file__)) + '/model/feature_order.txt'
model = pickle.load(open(model_filename, 'rb'))
feature_order = open(features_filename, 'r').read().splitlines()
model_sample_rate = 50.
# Load data and convert to g
raw_y_accel, ts = util._load_data(self, self.down_sample)
y_accel = raw_y_accel / 9.80665
# Resample data if necessary
if self.down_sample > model_sample_rate:
data, timestamps = util._resample_data(
y_accel, ts,
str(1000. / model_sample_rate) + 'ms')
elif self.down_sample == model_sample_rate:
data = pd.DataFrame({'y': y_accel})
timestamps = pd.DatetimeIndex(ts.astype('datetime64[ms]'))
elif self.down_sample < model_sample_rate:
print(
'Data sample rate too low for bout detection model. Minimum sample rate required: '
+ str(model_sample_rate) + ' hz, aborting...')
return
# Extract signal features from vertical acceleration data
feature_set, start_times_list, end_times_list = util._extract_signal_features(
data, timestamps, model_sample_rate)
feature_set = feature_set[feature_order]
# Predict
try:
pred = model.predict(feature_set)
predictions_df = pd.DataFrame({
'prediction': pred,
'window_start_time': start_times_list,
'window_end_time': end_times_list
})
except:
print(
'Unable to make predictions from signal features, aborting...')
return
# Save predictions to hdf file
if result_file:
try:
if not result_file.endswith('.h5'):
result_file += '.h5'
predictions_dict = {}
predictions_dict['predictions'] = predictions_df
dd.io.save(result_file, predictions_dict)
except:
print(
'Unable to save data: Please make sure your results directory exists, aborting...'
)
return
print('\tBout classification complete!\n')
return predictions_df
def plot_contacts(self, gait_features, result_file=None, show_plot=True):
""" Plot initial and final contacts of lumbar based gait feature extraction
Parameters
----------
gait_features : pandas.DataFrame or str
Pandas dataframe containing results of extract_features function
OR
File path of .csv file containing results of extract_features function
result_file : str
Optional argument that accepts .html filepath string to save resulting gait event plot to.
None by default. (ie. myfolder/myfile.html)
show_plot : bool
Optional boolean argument that specifies whether your plot is displayed. True by default.
"""
from bokeh.plotting import figure, output_file, save, show
from bokeh.models import Legend, Span
import pandas as pd
import gaitpy.util as util
import numpy as np
print('\tPlotting contacts...')
# Load data
y_accel, timestamps = util._load_data(self, self.down_sample)
ts = pd.to_datetime(timestamps, unit='ms')
# Load gait_features
try:
if type(gait_features) is str:
icfc = pd.read_csv(gait_features)
elif type(gait_features) is pd.core.frame.DataFrame:
icfc = gait_features
else:
print(
'Unable to load gait features: Please make sure the gait_features is in the correct format, aborting...'
)
return
except:
print(
'Unable to load gait features: Please make sure you have provided the correct filepath or dataframe, aborting...'
)
return
if icfc.empty:
print('\tGait feature dataframe is empty, aborting...')
return
p = figure(plot_width=1200,
plot_height=600,
x_axis_label='Time',
y_axis_label='m/s^2',
toolbar_location='above',
x_axis_type='datetime')
# Plot vertical axis
p1 = p.line(ts, y_accel, line_width=2, line_color='blue')
# isolate ICs, FCs, and bout start/end times
minima_time = []
minima_signal = []
maxima_time = []
maxima_signal = []
bout_starts = []
bout_ends = []
ics = pd.to_datetime(icfc.IC, unit='ms')
fcs = pd.to_datetime(icfc.FC, unit='ms')
icfc.bout_start_time = icfc.bout_start_time.astype(
np.int64).values // 10**6
bouts = icfc[['bout_number', 'bout_length_sec',
'bout_start_time']].drop_duplicates()
for ic in ics:
minima_time.append(ic)
minima_signal.append(float(y_accel[ts.index[ts == ic]]))
for fc in fcs:
maxima_time.append(fc)
maxima_signal.append(float(y_accel[ts.index[ts == fc]]))
for row, bout in bouts.iterrows():
bout_starts.append(bout.bout_start_time)
bout_ends.append(bout.bout_start_time +
(bout.bout_length_sec * 1000))
# add IC and FCs to plot
p2 = p.circle(minima_time,
minima_signal,
size=15,
color="green",
alpha=0.5)
p3 = p.circle(maxima_time,
maxima_signal,
size=15,
color="darkorange",
alpha=0.5)
# add bout start and end times to plot
for bout_start in bout_starts:
start_bout_line = Span(location=bout_start,
dimension='height',
line_color='green',
line_dash='solid',
line_width=1.5)
p.add_layout(start_bout_line)
for bout_end in bout_ends:
end_bout_line = Span(location=bout_end,
dimension='height',
line_color='red',
line_dash='solid',
line_width=1.5)
p.add_layout(end_bout_line)
# add legend
legend = Legend(items=[("Acceleration", [p1]),
("Initial contact", [p2]),
("Final contact", [p3])],
location=(10, 300))
# format plot
p.add_layout(legend, 'right')
p.xaxis.axis_label_text_font_size = "16pt"
p.yaxis.axis_label_text_font_size = "16pt"
p.axis.major_label_text_font_size = '16pt'
p.title.align = 'center'
p.title.text_font_size = '16pt'
p.xaxis.axis_label_text_font_style = 'normal'
p.yaxis.axis_label_text_font_style = 'normal'
p.xaxis.axis_label_standoff = 5
p.yaxis.axis_label_standoff = 20
p.legend.label_text_font = "arial"
p.legend.label_text_font_size = '16pt'
p.legend.glyph_height = 30
if show_plot:
show(p)
# save plot
if result_file:
try:
if not result_file.endswith('.html'):
result_file += '.html'
output_file(result_file)
save(p)
except:
print(
'Unable to save data: Please make sure your results directory exists, aborting...'
)
return
print('\tPlot complete!\n')
