/
run.py
executable file
·388 lines (330 loc) · 15.3 KB
/
run.py
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# Make sure we use PySide (fixes OS X issue)
import os
os.environ['QT_API'] = 'pyside'
# Set up Traits toolkit.
from traits.etsconfig.etsconfig import ETSConfig
ETSConfig.toolkit = 'qt4'
ETSConfig.company = 'EEGSensor'
# Standard imports
import logging, os, datetime
import numpy as np
# Enthought imports
import traits.api as t
import traitsui.api as tui
from traitsui.api import (View, Label, Item, VGroup, HGroup,
spring, Heading)
from traitsui.qt4.extra.qt_view import QtView
from pyface.api import ImageResource
from pyface.timer.api import do_later
from apptools.preferences.api import Preferences
# Internal imports
from hardware import EEGSensor, SAMPLE_RATE, MAX_HISTORY_LENGTH
from matplotlib.figure import Figure
from mpl import MPLFigureEditor
# We package QDarkStyle for convenience. The most current version is
# at https://pypi.python.org/pypi/QDarkStyle
# or https://github.com/ColinDuquesnoy/QDarkStyleSheet
# This packages is simply for aesthetics.
import qdarkstyle
preferences = Preferences(filename=os.path.join(ETSConfig.get_application_home(True), 'preferences.ini'))
# Custom parameters.
X_WIDTH_S = 6.0
PLOT_STEP = 1
class SensorOperationController(tui.Controller):
""" UI for controlling the hardware. """
model = t.Instance(EEGSensor)
connect = t.Button()
disconnect = t.Button()
def _connect_changed(self):
self.model.connect()
def _disconnect_changed(self):
self.model.disconnect()
traits_view = View(
HGroup(
spring,
VGroup(
HGroup(spring, Heading('EEG Sensor Controls'), spring),
VGroup(Item('com_port', style='simple', enabled_when="not object.connected"),
Item('object.connected', style='readonly'),
# Item('history_length', style='readonly'),
# Item('timeseries_length', style='readonly'),
show_labels=True
),
HGroup(# spring,
Item('controller.connect', enabled_when='not object.connected'),
Item('controller.disconnect', enabled_when='object.connected'),
spring,
show_labels=False),
Label('Last %d points saved to disk on exit.' % MAX_HISTORY_LENGTH),
),
spring,
tui.VGrid(
Heading('Activate Ch:'),
Item('channel_1_on', label='1', enabled_when='not channel_1_enabled'),
Item('channel_2_on', label='2', enabled_when='not channel_2_enabled'),
Item('channel_3_on', label='3', enabled_when='not channel_3_enabled'),
Item('channel_4_on', label='4', enabled_when='not channel_4_enabled'),
Item('channel_5_on', label='5', enabled_when='not channel_5_enabled'),
Item('channel_6_on', label='6', enabled_when='not channel_6_enabled'),
Item('channel_7_on', label='7', enabled_when='not channel_7_enabled'),
Item('channel_8_on', label='8', enabled_when='not channel_8_enabled'),
Heading('Deactivate Ch:'),
Item('channel_1_off', label='1', enabled_when='channel_1_enabled'),
Item('channel_2_off', label='2', enabled_when='channel_2_enabled'),
Item('channel_3_off', label='3', enabled_when='channel_3_enabled'),
Item('channel_4_off', label='4', enabled_when='channel_4_enabled'),
Item('channel_5_off', label='5', enabled_when='channel_5_enabled'),
Item('channel_6_off', label='6', enabled_when='channel_6_enabled'),
Item('channel_7_off', label='7', enabled_when='channel_7_enabled'),
Item('channel_8_off', label='8', enabled_when='channel_8_enabled'),
show_labels=False,
show_border=True,
columns=9,
enabled_when='object.connected'
),
spring,
),
)
from scipy.signal import lfilter
class TimeDomainFilter(t.HasTraits):
""" FIR filter """
b = t.Array()
a = t.Array()
type = t.Enum(['BandPass'])
def apply(self, signal):
if self.type == 'BandPass':
return lfilter(self.b, self.a, signal)
else:
raise NotImplementedError('Filter type %s not implemented' % self.type)
class SensorTimeseriesController(tui.ModelView):
""" UI for a "ganged" timeseries plot. """
model = t.Instance(EEGSensor)
figure = t.Instance(Figure, ())
lines = t.List(t.Any)
axes = t.Any
axes_r = t.Any
filters = t.List(t.Instance(TimeDomainFilter))
y_lim_uv = t.Float(100)
view = View(Item('figure', editor=MPLFigureEditor(),
show_label=False,
springy=True,
full_size=True,),
width=400,
height=700,
resizable=True)
def __init__(self, model=None, **metadata):
""" Set up and initialize the plot. """
tui.ModelView.__init__(self, model=model, **metadata)
axes = self.figure.add_subplot(111)
self.lines = []
y_ticks = []
y_labels = []
y_labels2 = []
for i in range(self.model.timeseries.shape[1] - 1):
line, = axes.plot(self.model.timeseries[::PLOT_STEP, 0],
self.model.timeseries[::PLOT_STEP, i + 1] / self.y_lim_uv + i,
color='mistyrose',
alpha=.75,
linewidth=0.5)
self.lines.append(line)
y_ticks.append(i)
y_labels.append('Ch %d' % (i + 1))
y_labels2.append('Mean: 0.0\nRMS: 0.0')
axes.set_title('EEG Timeseries')
# axes.set_ylabel('Amplitude')
axes.set_xlabel('Time [s]')
axes.set_xlim(0, X_WIDTH_S, auto=False)
axes.set_ylim(-1, self.model.timeseries.shape[1] - 1, auto=False)
axes.set_yticks(y_ticks)
axes.set_yticklabels(y_labels)
self.axes = axes
# Right axes
# self.axes_r = self.figure.add_subplot(1, 1, 1, sharex=axes, frameon=False)
# self.axes_r.yaxis.tick_right()
# self.axes_r.yaxis.set_label_position("right")
# self.axes_r.set_yticks(y_ticks)
# self.axes_r.tick_params(axis='y', which='both', length=0)
# self.axes_r.set_yticklabels(y_labels2, {'size':8})
# self.axes_r.set_ylim(-1, self.model.timeseries.shape[1] - 1, auto=False)
# l/b/r/t
do_later(self.figure.tight_layout) # optimize padding and layout after everything's set up.
@t.on_trait_change('model.data_changed')
def update_plot(self):
""" Update the plot with new data """
if self.model.timeseries.shape[0] < 2:
return
y_labels2 = []
for i, line in enumerate(self.lines):
y = self.model.timeseries[:, i + 1]
nan_mask = np.isnan(y)
y[nan_mask] = 0 # otherwise, a single NAN causes the filtering to fail.
y = y - y.mean()
for filter in self.filters:
# Note that we re-apply the time-domain filter for every single update.
y = filter.apply(y)
y[nan_mask] = np.NAN # put the NAN's back so they're not plotted.
line.set_data(self.model.timeseries[::PLOT_STEP, 0] , # x
y[::PLOT_STEP] / self.y_lim_uv + i # y
)
y_labels2.append('Mean: %0.3f\nRMS: %0.3f' %
(y.mean(), np.sqrt(np.mean(np.square(y))))
)
# self.axes_r.set_yticklabels(y_labels2, {'size':6})
self.axes.set_xlim(max(np.max(self.model.timeseries[:, 0]), X_WIDTH_S) - X_WIDTH_S,
max(np.max(self.model.timeseries[:, 0]), X_WIDTH_S))
self.figure.canvas.draw()
class SensorFFTController(tui.ModelView):
""" UI for spectral plot. """
model = t.Instance(EEGSensor)
figure = t.Instance(Figure, ())
lines = t.List(t.Any)
axes = t.Any
n_fft = t.Int(256)
overlap = t.Float(0.75)
view = View(Item('figure', editor=MPLFigureEditor(),
show_label=False,
springy=True,
full_size=True,),
width=350,
height=450,
resizable=True)
def __init__(self, model=None, **metadata):
""" Setup and initialize the plot. """
tui.ModelView.__init__(self, model=model, **metadata)
axes = self.figure.add_subplot(111)
self.lines = []
for i in range(self.model.timeseries.shape[1] - 1):
line, = axes.plot([0], [1],
color='mistyrose',
alpha=.5)
self.lines.append(line)
axes.set_title('Frequency Content')
axes.set_ylabel(r'Signal Strength ($\mu$V/sqrt(Hz))')
axes.set_xlabel('Frequency (Hz)')
axes.set_xticks([i * 10 for i in range(10)]) # multiples of 10
axes.set_xlim(0, 65, # SAMPLE_RATE / 2,
auto=False)
# axes.set_ylim(-1, self.model.timeseries.shape[1] - 1, auto=False)
# axes.set_yticks(y_ticks)
# axes.set_yticklabels(y_labels)
axes.set_yscale('log')
self.axes = axes
# l/b/r/t
do_later(self.figure.tight_layout) # optimize padding and layout after everything's set up.
def _windowed_fft(self, data, fs):
""" Applies a Hanning window, calculates FFT, and returns one-sided
FFT as well as corresponding frequency vector.
"""
N = len(data)
window = np.hanning(N)
win_pow = np.mean(window ** 2)
windowed_data = np.fft.fft(data * window) / np.sqrt(win_pow)
# freqs = np.linspace(0, 1, N, endpoint=True) * fs
pD = np.abs(windowed_data * np.conjugate(windowed_data) / N ** 2)
freqs = np.fft.fftfreq(N, 1 / float(fs))
f = freqs[:N / 2 ]
pD = pD[:N / 2 ]
pD[1:] = pD[1:] * 2
return pD, f
@t.on_trait_change('model.data_changed')
def update_plot(self):
""" Update the plot with new data """
n_data_pts = self.model.timeseries.shape[0]
if n_data_pts < self.n_fft:
return
if n_data_pts >= 2 * self.n_fft:
n_offset = 2 * self.n_fft
else:
n_offset = self.n_fft
data_to_process = self.model.timeseries[-n_offset:]
hz_per_bin = float(SAMPLE_RATE) / self.n_fft
min_psds = []
max_psds = []
for i, line in enumerate(self.lines):
y = data_to_process[:, i + 1]
nan_mask = np.isnan(y)
y[nan_mask] = 0 # otherwise, a single NAN causes the filtering to fail.
y = y - y.mean()
psd, f = self._windowed_fft(y, SAMPLE_RATE)
psd_per_bin = psd / hz_per_bin
line.set_data(f, # x
np.sqrt(psd_per_bin) # y
)
min_psds.append(psd_per_bin.min())
max_psds.append(psd_per_bin.max())
self.axes.set_ylim(.1,
100) # np.min(min_psds) * .75 + 1e-10, np.max(max_psds) * 1.33)
self.figure.canvas.draw()
class AppHandler(tui.Handler):
def close(self, info, isok):
app = info.object # convenience
app.sensor.disconnect()
file_name = os.path.join(ETSConfig.get_application_home(True),
'sensor_output %s.csv' % str(datetime.datetime.now()).replace(':', '-'))
# make sure directory exists.
if not os.path.exists(ETSConfig.get_application_home(False)):
os.makedirs(ETSConfig.get_application_home(False))
arr = np.array(app.sensor.history)
if not arr.size:
return isok
np.savetxt(file_name,
arr)
msg = 'Output (size %s) saved to %s.' % (str(arr.shape), file_name)
logging.info(msg)
from pyface.api import information
information(info.ui.control, msg, title='Array saved to disk.')
return isok
# def position(self, info):
# """ Maximize the window... """
# ret = tui.Handler.position(self, info)
# info.ui.control.showMaximized()
# return ret
class EEGSensorApp(t.HasTraits):
sensor = t.Instance(EEGSensor)
filters = t.List(t.Instance(TimeDomainFilter))
sensor_operation_controller = t.Instance(SensorOperationController)
def _sensor_operation_controller_default(self):
return SensorOperationController(model=self.sensor)
sensor_timeseries_controller = t.Instance(SensorTimeseriesController)
def _sensor_timeseries_controller_default(self):
return SensorTimeseriesController(model=self.sensor,
filters=self.filters)
sensor_fft_controller = t.Instance(SensorFFTController)
def _sensor_fft_controller_default(self):
return SensorFFTController(model=self.sensor)
traits_view = QtView(
HGroup(
VGroup(
Item('sensor_timeseries_controller', style='custom'),
show_border=True,
show_labels=False
),
VGroup(
Item('sensor_fft_controller', style='custom'),
Item('sensor_operation_controller', style='custom'),
show_border=True,
show_labels=False
),
),
title="EEG Sensor Console",
icon=ImageResource('application'),
# style_sheet_path='dark_style_sheet.qss',
style_sheet=qdarkstyle.load_stylesheet(pyside=True),
resizable=True,
handler=AppHandler(),
)
if __name__ == "__main__":
try:
logging.info('---------- STARTING ---------')
from scipy.io import loadmat
mat = loadmat('bp_filter_coeff.mat')
filters = [TimeDomainFilter(b=mat['bp_filter_coeff']['b'][0, 0].squeeze(),
a=mat['bp_filter_coeff']['a'][0, 0].squeeze()),
TimeDomainFilter(b=mat['bp_filter_coeff']['b_notch'][0, 0].squeeze(),
a=mat['bp_filter_coeff']['a_notch'][0, 0].squeeze()), ]
app = EEGSensorApp(sensor=EEGSensor(preferences=preferences),
filters=filters)
app.configure_traits(id='eeg_main_app')
finally:
preferences.flush()
logging.shutdown()