def setup_experiment(self, info):
circuit = join(get_config("RCX_ROOT"), "positive-behavior-stage1")
self.iface_behavior = self.process.load_circuit(circuit, "RZ6")
self.buffer_signal = self.iface_behavior.get_buffer("signal", "w")
self.buffer_TTL = self.iface_behavior.get_buffer("TTL", "r", block_size=24, src_type="int8", dest_type="int8")
self.buffer_mic = self.iface_behavior.get_buffer("mic", "r")
self.model.data.spout_TTL.fs = self.buffer_TTL.fs
self.model.data.override_TTL.fs = self.buffer_TTL.fs
self.model.data.pump_TTL.fs = self.buffer_TTL.fs
self.model.data.signal_TTL.fs = self.buffer_TTL.fs
self.model.data.free_run_TTL.fs = self.buffer_TTL.fs
self.model.data.microphone.fs = self.buffer_mic.fs
targets = [
self.model.data.spout_TTL,
self.model.data.override_TTL,
self.model.data.pump_TTL,
self.model.data.signal_TTL,
self.model.data.free_run_TTL,
]
self.pipeline_TTL = deinterleave_bits(targets)
self.iface_pump.set_trigger(start="rising", stop="falling")
self.iface_pump.set_direction("infuse")
self.iface_pump.set_volume(0)
def setup_physiology(self):
# Load the circuit
circuit = join(get_config('RCX_ROOT'), 'physiology')
self.iface_physiology = self.process.load_circuit(circuit, 'RZ5')
# Initialize the buffers that will be spooling the data
self.buffer_raw = self.iface_physiology.get_buffer('craw',
'r', src_type='float32', dest_type='float32', channels=CHANNELS,
block_size=1048)
self.buffer_filt = self.iface_physiology.get_buffer('cfilt',
'r', src_type='int16', dest_type='float32', channels=CHANNELS,
block_size=1048)
self.buffer_ts = self.iface_physiology.get_buffer('trig/',
'r', src_type='int32', dest_type='int32', block_size=1)
self.buffer_ts_start = self.iface_physiology.get_buffer('trig/',
'r', src_type='int32', dest_type='int32', block_size=1)
self.buffer_ts_end = self.iface_physiology.get_buffer('trig\\',
'r', src_type='int32', dest_type='int32', block_size=1)
self.buffer_ttl = self.iface_physiology.get_buffer('TTL',
'r', src_type='int8', dest_type='int8', block_size=1)
for i in range(CHANNELS):
name = 'spike{}'.format(i+1)
buffer = self.iface_physiology.get_buffer(name, 'r',
block_size=SPIKE_SNIPPET_SIZE+2)
self.buffer_spikes.append(buffer)
def setup_experiment(self, info):
filename = 'positive-behavior-training-contmask-v4'
circuit = path.join(get_config('RCX_ROOT'), filename)
self.iface_behavior = self.process.load_circuit(circuit, 'RZ6')
self.buffer_masker = self.iface_behavior.get_buffer('masker', 'w')
self.buffer_target = self.iface_behavior.get_buffer('target', 'w')
self.buffer_TTL = self.iface_behavior.get_buffer('TTL', 'r',
block_size=24, src_type='int8', dest_type='int8')
self.buffer_mic = self.iface_behavior.get_buffer('mic', 'r')
self.model.data.spout_TTL.fs = self.buffer_TTL.fs
self.model.data.override_TTL.fs = self.buffer_TTL.fs
self.model.data.pump_TTL.fs = self.buffer_TTL.fs
self.model.data.signal_TTL.fs = self.buffer_TTL.fs
self.model.data.free_run_TTL.fs = self.buffer_TTL.fs
self.model.data.microphone.fs = self.buffer_mic.fs
targets = [self.model.data.spout_TTL,
self.model.data.override_TTL,
self.model.data.pump_TTL,
self.model.data.signal_TTL,
self.model.data.free_run_TTL ]
self.pipeline_TTL = deinterleave_bits(targets)
self.iface_pump.set_trigger(start='rising', stop='falling')
self.iface_pump.set_direction('infuse')
self.iface_pump.set_volume(0)
def profile_experiment(args):
import cProfile
profile_data_file = join(get_config('TEMP_ROOT'), 'profile.dmp')
cProfile.runctx('test_experiment(args)', globals(), {'args': args},
filename=profile_data_file)
# Once experiment is done, print out some statistics
import pstats
p = pstats.Stats(profile_data_file)
p.strip_dirs().sort_stats('cumulative').print_stats(50)
def get_temp_mic_node():
filename = path.join(get_config('TEMP_ROOT'), 'microphone.h5')
log.debug('saving microphone data to %s', filename)
tempfile = tables.openFile(filename, 'a')
# If the file already exists from a prior experiment, then we should
# remove the existing microphone node.
if 'microphone' in tempfile.root:
tempfile.root.microphone._f_remove()
return tempfile.root
def _setup_circuit(self):
# AversiveFMController needs to change the initialization sequence a
# little (i.e. it needs to use different microcode and the microcode
# does not contain int and trial buffers).
circuit = join(get_config('RCX_ROOT'), 'aversive-behavior-FMAM')
self.iface_behavior = self.process.load_circuit(circuit, 'RZ6')
self.buffer_TTL = self.iface_behavior.get_buffer('TTL', 'r',
src_type='int8', dest_type='int8', block_size=24)
self.buffer_contact = self.iface_behavior.get_buffer('contact', 'r',
src_type='int8', dest_type='float32', block_size=24)
self.buffer_spout_start = self.iface_behavior.get_buffer('spout/', 'r',
src_type='int32', block_size=1)
self.buffer_spout_end = self.iface_behavior.get_buffer('spout\\', 'r',
src_type='int32', block_size=1)
def setup_experiment(self, info):
circuit = join(get_config('RCX_ROOT'), 'positive-behavior-v2')
self.iface_behavior = self.process.load_circuit(circuit, 'RZ6')
self.buffer_out = self.iface_behavior.get_buffer('out', 'w')
self.buffer_TTL1 = self.iface_behavior.get_buffer('TTL', 'r',
src_type='int8', dest_type='int8', block_size=24)
self.buffer_TTL2 = self.iface_behavior.get_buffer('TTL2', 'r',
src_type='int8', dest_type='int8', block_size=24)
self.buffer_poke_start = self.iface_behavior.get_buffer('poke_all/',
'r', src_type='int32', dest_type='int32', block_size=1)
self.buffer_poke_end = self.iface_behavior.get_buffer('poke_all\\', 'r',
src_type='int32', dest_type='int32', block_size=1)
# microphone
self.buffer_mic = self.iface_behavior.get_buffer('mic', 'r')
self.model.data.microphone.fs = self.buffer_mic.fs
self.fs_conversion = self.iface_behavior.get_tag('TTL_d')
# Stored in TTL1
self.model.data.spout_TTL.fs = self.buffer_TTL1.fs
self.model.data.poke_TTL.fs = self.buffer_TTL1.fs
self.model.data.signal_TTL.fs = self.buffer_TTL1.fs
self.model.data.reaction_TTL.fs = self.buffer_TTL1.fs
self.model.data.response_TTL.fs = self.buffer_TTL1.fs
self.model.data.reward_TTL.fs = self.buffer_TTL1.fs
# Stored in TTL2
self.model.data.TO_TTL.fs = self.buffer_TTL2.fs
# Timestamp data
self.model.data.trial_epoch.fs = self.iface_behavior.fs
self.model.data.signal_epoch.fs = self.iface_behavior.fs
self.model.data.poke_epoch.fs = self.iface_behavior.fs
self.model.data.all_poke_epoch.fs = self.iface_behavior.fs
self.model.data.response_ts.fs = self.iface_behavior.fs
targets1 = [self.model.data.poke_TTL, self.model.data.spout_TTL,
self.model.data.reaction_TTL, self.model.data.signal_TTL,
self.model.data.response_TTL, self.model.data.reward_TTL, ]
targets2 = [None, self.model.data.TO_TTL]
self.pipeline_TTL1 = deinterleave_bits(targets1)
self.pipeline_TTL2 = deinterleave_bits(targets2)
# Configure the pump
self.iface_pump.set_trigger(start='rising', stop=None)
self.iface_pump.set_direction('infuse')
def _setup_shock(self):
# First, we need to load the circuit we need to control the shocker.
# We currently use DAC channel 12 of the RZ5 to control shock level;
# however, the RZ5 will already have a circuit loaded if we're using it
# for physiology. The physiology circuit is already configured to
# control the shocker. However, if we are not acquiring physiology, we
# need to load a circuit that allows us to control the shocker.
if not self.model.spool_physiology:
circuit = join(get_config('RCX_ROOT'), 'shock-controller')
self.iface_shock = self.process.load_circuit(circuit, 'RZ5')
else:
# This assumes that iface_physiology has already been initialized.
# In the current abstract_experiment_controller, setup_physiology is
# called before setup_experiment. self.physiology_handler is a
# reference to the PhysiologyController object.
self.iface_shock = self.physiology_handler.iface_physiology
import numpy as np
from enable.api import ColorTrait
from chaco.api import AbstractOverlay
from traits.api import Instance, Array, Int
from enable import markers
from cns import get_config
colors = get_config("PAIRED_COLORS_RGB_NORM")
# The keys are values defined by UMS2000. As long as the users has not modified
# the default labels available, they will map to the label in the comment next
# to each entry
cluster_type_marker = {
1: (3, markers.CIRCLE_MARKER), # in process
2: (7, markers.INVERTED_TRIANGLE_MARKER), # good unit
3: (7, markers.TRIANGLE_MARKER), # multi-unit
4: (1, markers.DOT_MARKER), # garbage
5: (3, markers.DOT_MARKER), # needs outlier removal
}
class ExtractedSpikeOverlay(AbstractOverlay):
"""
Supports overlaying the spike times on a multichannel view. The component
must be a subclass of MultiChannelPlot.
clusters
One entry for each timestamp indicating the cluster that event belongs
to
def setup_experiment(self, info):
circuit = path.join(get_config('RCX_ROOT'), 'positive-behavior-contmask-v4')
self.iface_behavior = self.process.load_circuit(circuit, 'RZ6')
self.buffer_target = self.iface_behavior.get_buffer('target', 'w')
self.buffer_masker = self.iface_behavior.get_buffer('masker', 'w')
self.buffer_TTL1 = self.iface_behavior.get_buffer('TTL', 'r',
src_type='int8', dest_type='int8', block_size=24)
self.buffer_TTL2 = self.iface_behavior.get_buffer('TTL2', 'r',
src_type='int8', dest_type='int8', block_size=24)
self.buffer_poke_start = self.iface_behavior.get_buffer('poke_all/',
'r', src_type='int32', dest_type='int32', block_size=1)
self.buffer_poke_end = self.iface_behavior.get_buffer('poke_all\\', 'r',
src_type='int32', dest_type='int32', block_size=1)
# microphone
self.buffer_mic = self.iface_behavior.get_buffer('mic', 'r')
self.model.data.microphone.fs = self.buffer_mic.fs
self.fs_conversion = self.iface_behavior.get_tag('TTL_d')
# Stored in TTL1
self.model.data.spout_TTL.fs = self.buffer_TTL1.fs
self.model.data.poke_TTL.fs = self.buffer_TTL1.fs
self.model.data.signal_TTL.fs = self.buffer_TTL1.fs
self.model.data.reaction_TTL.fs = self.buffer_TTL1.fs
self.model.data.response_TTL.fs = self.buffer_TTL1.fs
self.model.data.reward_TTL.fs = self.buffer_TTL1.fs
# Stored in TTL2
self.model.data.TO_TTL.fs = self.buffer_TTL2.fs
# Timestamp data
self.model.data.trial_epoch.fs = self.iface_behavior.fs
self.model.data.signal_epoch.fs = self.iface_behavior.fs
self.model.data.poke_epoch.fs = self.iface_behavior.fs
self.model.data.all_poke_epoch.fs = self.iface_behavior.fs
self.model.data.response_ts.fs = self.iface_behavior.fs
targets1 = [self.model.data.poke_TTL, self.model.data.spout_TTL,
self.model.data.reaction_TTL, self.model.data.signal_TTL,
self.model.data.response_TTL, self.model.data.reward_TTL, ]
# If the target is set to None, this means that we aren't interested in
# capturing the value of that specific bit. Nothing is stored in the
# first bit of the TTL_2 data; however, we store the timeout TTL in the
# second bit.
targets2 = [None, self.model.data.TO_TTL]
# deinterleave_bits is the Python complement of the RPvds FromBits
# component that breaks down the integer into its individual bits.
# Targets are the destination channel (which map to the underlying HDF5
# array) for each bit. e.g. the value of the first bit gets sent to
# targets1[0] (which is the poke_TTL). deinterleave_bits is a
# "pipeline" function that automatically performs its function each time
# new data arrives and hands the result off to the targets. The targets
# recieve this data and store it in the HDF5 array and notify the GUI
# that new data has arrived and should be plotted.
self.pipeline_TTL1 = deinterleave_bits(targets1)
self.pipeline_TTL2 = deinterleave_bits(targets2)
# Configure the pump
self.iface_pump.set_trigger(start='rising', stop=None)
self.iface_pump.set_direction('infuse')
def trigger_next(self):
# This function is *required* to be called. This basically calls the
# logic (defined in AbstractExperimentController) which clears the
# current value of all parameters and recomputes them (this is important
# in between trials).
self.invalidate_context()
# This must be called before the start of every trial to load (or
# evaluate if the parameter is an expression) the values of each
# parameter. Note this method will also check to see if the value of a
# parameter has changed since the last trial. If so, the corresponding
# set_parametername method will be called with the new value as an
# argument.
self.evaluate_pending_expressions()
# For all variables declared as context=True, you can get the current
# value via self.get_current_value(). This gives the
# abstract_experiment_controller a chance to compute the values of any
# parameters that are defined by expressions first.
repeat_fa = self.get_current_value('repeat_fa')
hw_att = self.get_current_value('hw_att')
go_probability = self.get_current_value('go_probability')
speaker = self.get_current_value('speaker')
if speaker == 'primary':
cal = self.cal_primary
else:
cal = self.cal_secondary
# Determine whether the animal false alarmed by checking the spout and
# nogo data. The last value in the "self.model.data.yes_seq" (which is
# a list) will be True if he went to the spout. The last value in
# self.model.data.nogo_seq will be True if it was a NOGO or NOGO_REPEAT
# trial.
try:
spout = self.model.data.yes_seq[-1]
nogo = self.model.data.nogo_seq[-1]
except IndexError:
spout = False
nogo = False
# First, decide if it's a GO, GO_REMIND, NOGO or NOGO_REPEAT
if len(self.model.data.trial_log) == 0:
# This is the very first trial
ttype = 'GO_REMIND'
settings = self.go_remind
elif self.remind_requested:
# When the user clicks on the "remind" button, it sets the
# remind_requested attribute on the experiment controller to True.
ttype = 'GO_REMIND'
settings = self.go_remind
elif nogo and spout and repeat_fa:
# The animal false alarmed and the user wishes to repeat the trial
# if the animal false alarms
ttype = 'NOGO_REPEAT'
settings = self.nogo_parameters.pop()
elif self.random_generator.uniform() < go_probability:
ttype = 'GO'
settings = self.go_parameters.pop()
else:
ttype = 'NOGO'
settings = self.nogo_parameters.pop()
# Each time we pop() a parameter, it is removed from the list and
# returned
# "unpack" our list into individual variables
F, E, FC, ML, TL, TokenNo, TargetNo = settings
#masker_file = r'E:\programs\ANTJE CMR\CMR\stimuli\M{}{}{}{}.stim'.format(int(F), int(E), int(FC), int(TokenNo))
target_file = path.join(get_config('SOUND_PATH'), 'CMR\stimuli\T{}{}.stim')
target_file = target_file.format(int(FC), int(TargetNo))
#target_file = r'e:\Experimental_Software\sounds\CMR\stimuli\T{}{}.stim'.format(int(FC), int(TargetNo))
#masker = np.fromfile(masker_file, dtype=np.float32)
target = np.fromfile(target_file, dtype=np.float32)
# This method will return the theoretical SPL of the speaker assuming
# you are playing a tone at the specified frequency and voltage (i.e.
# Vrms)
dBSPL_RMS1 = cal.get_spl(frequencies=1e3, voltage=1)
#self.some_trial_variable = dBSPL_RMS1
# Scale waveforms so that we get desired stimulus level assuming 0 dB of
# attenuation
#masker = 10**((ML-dBSPL_RMS1)/20)*masker
target = 10**((TL-dBSPL_RMS1)/20)*target
#stimulus = target + masker
stimulus = target
stimulus = stimulus * 10**(hw_att/20)
# Set the flag in the RPvds circuit that indicates which output to send
# the waveform to
if speaker == 'primary':
self.iface_behavior.set_tag('speaker', 0)
elif speaker == 'secondary':
self.iface_behavior.set_tag('speaker', 1)
self.buffer_target.set(stimulus)
# Be sure that the trial type is added to the list of context variables
# that will be saved to the trial_log file.
self.set_current_value('ttype', ttype)
# Boolean flag in the circuit that indicates whether or not the current
# trial is a go. This ensures that a reward is not delivered on NOGO
# trials.
if ttype.startswith('GO'):
self.iface_behavior.set_tag('go?', 1)
else:
self.iface_behavior.set_tag('go?', 0)
self.set_current_value('target_level', TL)
#self.set_current_value('masker_level',ML)
#self.set_current_value('masker_level',ML)
ML = self.get_current_value('masker_level')
self.set_current_value('TMR',TL-ML)
self.set_current_value('target_number',TargetNo)
#self.set_current_value('masker_number',TokenNo)
#self.set_current_value('masker_envelope',E)
#self.set_current_value('masker_flanker',F)
self.set_current_value('center_frequency',FC)
# This is a "handshake" that lets the RPvds circuit know that we are
# done with preparations for the next trial (e.g. uploading the stimulus
# waveform). The RPvds circuit will not proceed with the next trial
# until it receives a "SoftTrig1" (in TDT parlance)
log.debug('Sending the trial-ready trigger to the RPvds circuit')
self.iface_behavior.trigger(1)
import numpy as np
from enable.api import ColorTrait
from chaco.api import AbstractOverlay
from traits.api import Instance, Array, Int
from enable import markers
from cns import get_config
colors = get_config('PAIRED_COLORS_RGB_NORM')
# The keys are values defined by UMS2000. As long as the users has not modified
# the default labels available, they will map to the label in the comment next
# to each entry
cluster_type_marker = {
1: (3, markers.CIRCLE_MARKER), # in process
2: (7, markers.INVERTED_TRIANGLE_MARKER), # good unit
3: (7, markers.TRIANGLE_MARKER), # multi-unit
4: (1, markers.DOT_MARKER), # garbage
5: (3, markers.DOT_MARKER), # needs outlier removal
}
class ExtractedSpikeOverlay(AbstractOverlay):
'''
Supports overlaying the spike times on a multichannel view. The component
must be a subclass of MultiChannelPlot.
clusters
One entry for each timestamp indicating the cluster that event belongs
to
cluster_ids
List of all cluster IDs
def prepare_experiment(args, store_node, create_child=True):
'''
Given the arguments passed in via the command-line, configure the
Experiment, Controller, Data and Paradigm class accordingly and return an
instance of the model and controller class.
'''
# The HDF5 file that is used for the data
store_file = store_node._v_file
# If the user did not specify a list of parameters that the data should be
# grouped into before analysis (i.e. for computing the hit and false alarm
# fractions), then use the parameters specified via rove as the analysis
# parameters.
if len(args.analyze) == 0:
args.analyze = args.rove[:]
# Load the experiment from the launchers folder. args.type should be the
# name of the corresponding file in the launchers folder (without the .py
# extension)
module = get_experiment(args.type)
# Pull out the classes
paradigm_class = module.Paradigm
experiment_class = module.Experiment
controller_class = module.Controller
data_class = module.Data
node_name = module.node_name
# Create the experiment and data nodes. Hint! This is where you would
# change the default pathname for the experiment if you wished.
if create_child:
name = node_name + '_' + datetime.now().strftime(time_fmt)
exp_node = store_file.createGroup(store_node, name)
else:
exp_node = store_node
# Where the data is stored
data_node = store_file.createGroup(exp_node, 'data')
# Configure the TrialSetting/trial_setting_editor objects to contain the
# parameters we wish to control in the experiment
trial_setting.add_parameters(args.rove, paradigm_class, args.repeats)
if args.att:
# The user wants to specify values in terms of dB attenuation rather
# than a calibrated dB SPL standard. Prepare the calibration
# accordingly.
cal1 = calibration.Attenuation()
cal2 = calibration.Attenuation()
else:
if args.cal is not None:
cal1_filename, cal2_filename = args.cal
else:
cal1_filename = get_config('CAL_PRIMARY')
cal2_filename = get_config('CAL_SECONDARY')
if cal1_filename is None:
raise IOError, 'Unable to find a calibration file for the ' \
'primary speaker'
if cal2_filename is None:
raise IOError, 'Unable to find a calibration file for the ' \
'secondary speaker'
cal1 = calibration.load_mat_cal(cal1_filename, args.equalized)
log.debug('Loaded calibration file %s for primary', cal1_filename)
cal2 = calibration.load_mat_cal(cal2_filename, args.equalized)
log.debug('Loaded calibration file %s for secondary', cal2_filename)
controller_args = {
'cal_primary': cal1,
'cal_secondary': cal2,
'address': args.address,
}
log.debug('store_node: %s', store_node)
log.debug('data_node: %s', data_node)
log.debug('exp_node: %s', exp_node)
# Prepare the classes. This really is a lot of boilerplate to link up
# parameters with paradigms, etc, to facilitate analysis
paradigm = paradigm_class()
data = data_class(store_node=data_node,
save_microphone=args.save_microphone)
data.parameters = args.analyze
model = experiment_class(
store_node=store_node,
experiment_node=exp_node,
data_node=data_node,
data=data,
paradigm=paradigm,
spool_physiology=args.physiology,
)
if args.analyze:
model.plot_index = args.analyze[0]
model.plot_group = args.analyze[1:]
controller = controller_class(**controller_args)
return model, controller
from traits.api import Instance, Int, on_trait_change, \
Dict, HasTraits, Any, List, Str, Enum, Property
from traitsui.api import VGroup, Item, EnumEditor, SetEditor, HGroup
from cns.chaco_exts.helpers import add_default_grids
from chaco.api import DataRange1D, LinearMapper, \
PlotAxis, LogMapper, ArrayDataSource, \
OverlayPlotContainer, LinePlot, ScatterPlot
from traitsui.api import TabularEditor
from traitsui.tabular_adapter import TabularAdapter
from cns import get_config
CHACO_AXES_PADDING = get_config('CHACO_AXES_PADDING')
PAIRED_COLORS_NORM = get_config('PAIRED_COLORS_RGB_NORM')
class ParInfoAdapter(TabularAdapter):
color_map = Dict
parameter_text = Property
def _get_parameter_text(self):
return ', '.join('{}'.format(p) for p in self._get_parameters())
def _get_parameters(self):
return [self.item[p] for p in self.object.parameters]
def _get_bg_color(self):
try:
def ref_cal(duration=1, averages=2, ref_freq=1e3, ref_level=93.8, gain=20,
fft=False, mode='conv'):
'''
Calibrates measuring microphone against a known reference (e.g. a
pistonphone). Typically this is the B&K microphone, but certainly could be
any microphone that is felt to produce a flat frequency response across the
range of frequencies of interest.
TODO: Update this so it incorporates the actual B&K calibration file
(where do we get this?).
level Output of reference in dB SPL
frequency Frequency of reference in Hz
verbose Returns signal and FFT so it can be plotted
'''
circuit_path = join(get_config('RCX_ROOT'), 'play_record.rcx')
circuit = DSPCircuit(circuit_path, 'RZ6')
circuit.start(1)
samples = circuit.convert(duration, 's', 'nPow2')
# Important, rec_delay must be at least 1 or the circuit will not work
circuit.set_tags(play_duration=0, rec_duration=samples, rec_delay=1)
mic_buffer = circuit.get_buffer('mic', 'r')
mic_data = mic_buffer.acquire_samples(1, samples, 1)
print rms(mic_data)
print tone_frequency(circuit.fs, mic_data[0].ravel(), 1e3)
from pylab import plot, show
plot(mic_data[0].ravel()[:5e3])
show()
return
print mic_data.shape
if circuit.get_tag('clipped'):
print 'Clipping occured'
for m in mic_data:
#rms = np.mean(m.ravel()**2)**0.5
magnitude, phase = tone_frequency(circuit.fs, m, 1e3)
print magnitude/dbspltopa(ref_level+gain)
#plot(mic_data[0].ravel())
#show()
return mic_data
#print mic_data.shape
return
# Do the calibration!
#result = tone_power(device, samples, averages=averages, freq=ref_freq,
# fft=fft, mode=mode)
log.debug('Measured %.2f Vrms at %.2f Hz from the pistonphone' % \
(result[0], ref_freq))
sens = result[0] / dbtopa(ref_level)
debug_mesg = 'Using the pistonphone reference of %.2f dB SPL, ' + \
'microphone sensitivity is %.4f Vrms/Pa'
log.debug(debug_mesg % (ref_level, sens))
# Phase is meaningless since we have little control over the pistonphone, so
# we do not return this, just microphone sensitivity.
if fft: return sens, result[-1]
else: return sens
from enable.api import Component, ComponentEditor, Window
from traits.api import HasTraits, Instance, Button, Any
from traitsui.api import Item, Group, View, Controller
from pyface.timer.api import Timer
from chaco.api import (LinearMapper, DataRange1D,
OverlayPlotContainer, PlotAxis)
from chaco.tools.api import ZoomTool, PanTool
from tdt import DSPCircuit
from cns import get_config
from cns.chaco_exts.helpers import add_default_grids
from cns.chaco_exts.tools.window_tool import WindowTool
from cns.channel import FileSnippetChannel
from cns.data.h5_utils import get_temp_file
RCX_ROOT = get_config('RCX_ROOT')
class Demo(HasTraits):
traits_view = View(
Item('handler.button'),
Item('handler.container', editor=ComponentEditor(size=(400,400))),
resizable=True)
class DemoController(Controller):
iface = Any
timer = Any
snippet_source = Any
snippet_store = Any
plot = Any
from traits.api import HasTraits, Float, Enum, Property, cached_property
from traitsui.api import View, Item, VGroup, HGroup
from cns import get_config
from evaluate import Expression
SYRINGE_DATA = get_config('SYRINGE_DATA')
SYRINGE_DEFAULT = get_config('SYRINGE_DEFAULT')
class PumpParadigmMixin(HasTraits):
kw = {'context': True, 'store': 'attribute', 'log': True}
pump_rate = Expression(0.5, label='Pump rate (ml/min)', **kw)
pump_rate_delta = Float(0.025, label='Pump rate delta (ml)', **kw)
pump_syringe = Enum(SYRINGE_DEFAULT, sorted(SYRINGE_DATA.keys()),
label='Syringe', ignore=True, **kw)
pump_syringe_diameter = Property(label='Syringe diameter (mm)',
depends_on='pump_syringe', **kw)
@cached_property
def _get_pump_syringe_diameter(self):
return SYRINGE_DATA[self.pump_syringe]
# Note that we have defined two views here, a simple view and a more
# detailed view. When including this mixin class, you can choose which view
# is used.
detailed_pump_group = VGroup(
HGroup(
'pump_rate',
'pump_rate_delta',
from chaco.tools.api import ZoomTool
from cns.chaco_exts.tools.window_tool import WindowTool
from cns.chaco_exts.helpers import add_default_grids, add_time_axis
from cns.chaco_exts.channel_data_range import ChannelDataRange
from cns.chaco_exts.extremes_multi_channel_plot import ExtremesMultiChannelPlot
from cns.chaco_exts.ttl_plot import TTLPlot
from cns.chaco_exts.epoch_plot import EpochPlot
from cns.chaco_exts.channel_range_tool import MultiChannelRangeTool
from cns.chaco_exts.channel_number_overlay import ChannelNumberOverlay
from cns.chaco_exts.snippet_channel_plot import SnippetChannelPlot
from cns import get_config
from cns.chaco_exts.spike_overlay import SpikeOverlay
from cns.chaco_exts.threshold_overlay import ThresholdOverlay
CHANNELS = get_config('PHYSIOLOGY_CHANNELS')
VOLTAGE_SCALE = 1e3
scale_formatter = lambda x: "{:.2f}".format(x*VOLTAGE_SCALE)
from traitsui.menu import MenuBar, Menu, ActionGroup, Action
def create_menubar():
actions = ActionGroup(
Action(name='Load settings', action='load_settings'),
Action(name='Save settings as', action='saveas_settings'),
Action(name='Restore defaults', action='reset_settings'),
)
menu = Menu(actions, name='&Physiology')
return MenuBar(menu)
def ptt(event_times, trig_times):
from traits.api import (Instance, Any, List, on_trait_change, Enum,
Dict)
from traitsui.api import Controller
from pyface.timer.api import Timer
from cns import get_config
from os.path import join
from cns.pipeline import deinterleave_bits
CHANNELS = get_config('PHYSIOLOGY_CHANNELS')
PHYSIOLOGY_WILDCARD = get_config('PHYSIOLOGY_WILDCARD')
SPIKE_SNIPPET_SIZE = get_config('PHYSIOLOGY_SPIKE_SNIPPET_SIZE')
from .utils import load_instance, dump_instance
class PhysiologyController(Controller):
buffer_ = Any
iface_physiology = Any
buffer_raw = Any
buffer_proc = Any
buffer_ts = Any
buffer_ttl = Any
physiology_ttl_pipeline = Any
buffer_spikes = List(Any)
state = Enum('master', 'client')
process = Instance('tdt.DSPProject')
timer = Instance(Timer)
parent = Any
import tables
from tempfile import mkdtemp
from os import path
from cns import get_config
from traits.api import HasTraits, Instance, List, Any
from cns.channel import (FileMultiChannel, FileChannel, FileSnippetChannel,
FileTimeseries, FileEpoch)
import numpy as np
CHANNELS = get_config('PHYSIOLOGY_CHANNELS')
SNIPPET_SIZE = get_config('PHYSIOLOGY_SPIKE_SNIPPET_SIZE')
class PhysiologyData(HasTraits):
store_node = Any
#############################################################################
# Permanent data
#############################################################################
# Raw (permanent) physiology data that will be stored in the data file that
# is retained at the end of the experiment.
raw = Instance(FileMultiChannel)
sweep = Instance(FileChannel)
ts = Instance(FileTimeseries)
epoch = Instance(FileEpoch)
def _sweep_default(self):
return FileChannel(node=self.store_node, name='sweep', dtype=np.bool,
use_checksum=True)
from experiments.cl_controller_mixin import CLControllerMixin
from experiments.cl_paradigm_mixin import CLParadigmMixin
from experiments.cl_experiment_mixin import CLExperimentMixin
from experiments.aversive_cl_data_mixin import AversiveCLDataMixin
from experiments.pump_controller_mixin import PumpControllerMixin
from experiments.pump_paradigm_mixin import PumpParadigmMixin
from experiments.pump_data_mixin import PumpDataMixin
from cns import get_config
import logging
log = logging.getLogger(__name__)
MAX_VRMS = get_config('MAX_SPEAKER_DAC_VOLTAGE')
class Controller(
CLControllerMixin,
PumpControllerMixin,
AbstractAversiveController):
# Scaling factor used for the waveform. Must call it "dt" because the
# superclass already defines a waveform_sf that is overwritten by the
# superclass trigger_next method.
kw = {'context': True, 'log': True, 'immediate': True}
dt_waveform_sf = Float(np.nan, label='DT scaling factor', **kw)
dt_waveform_error = Bool(False, label='DT waveform error?', **kw)
def initial_setting(self):
return self.nogo_setting()
import os
from os.path import join
from importlib import import_module
from glob import glob
from traits.api import Any, Trait, TraitError
from experiments import trial_setting
from cns import calibration
import logging
log = logging.getLogger(__name__)
from cns import get_config
from datetime import datetime
time_fmt = get_config('TIME_FORMAT')
class ExperimentLauncher(CohortViewHandler):
args = Any
last_paradigm = Trait(None, Any)
def init(self, info):
if not self.load_file(info):
sys.exit()
def launch_experiment(self, info, selected):
'''
Runs specified experiment type. On successful completion of an
experiment, marks the animal as processed and saves the last paradigm
used. If the experiment is launched but not run, changes to the
def _iface_audio_default(self):
circuit = join(get_config('RCX_ROOT'), 'basic_audio')
return self.process.load_circuit(circuit, 'RZ6')
from cns.chaco_exts.ttl_plot import TTLPlot
from cns.chaco_exts.timeseries_plot import TimeseriesPlot
from cns.chaco_exts.epoch_plot import EpochPlot
from cns.chaco_exts.helpers import add_default_grids, add_time_axis
from abstract_experiment import AbstractExperiment
import logging
log = logging.getLogger(__name__)
from traitsui.api import TabularEditor
from traitsui.tabular_adapter import TabularAdapter
from traits.api import *
from cns import get_config
COLORS = get_config('EXPERIMENT_COLORS')
class TrialLogAdapter(TabularAdapter):
# List of tuples (column_name, field )
columns = [ ('P', 'parameter'),
('Time', 'time'),
('Score', 'contact_score'),
]
parameter_width = Float(75)
contact_score_width = Float(50)
reaction_width = Float(25)
response_width = Float(25)
speaker_width = Float(25)
time_width = Float(65)
from traits.api import (HasTraits, Instance, List, Float, on_trait_change)
from traitsui.api import Item, HGroup
from chaco.api import (OverlayPlotContainer, DataRange1D, PlotAxis, ScatterPlot,
LinearMapper, ArrayDataSource, LinePlot)
from chaco.tools.api import ZoomTool
from enable.api import ComponentEditor
import numpy as np
from chaco.function_data_source import FunctionDataSource
from cns import get_config
from .maximum_likelihood import p_yes
SIZE = (400, 400)
CHACO_AXES_PADDING = get_config('CHACO_AXES_PADDING')
numpoints = 500
TRACKS = 2
def _xfunc(low, high):
dx = (high - low) / numpoints
real_low = np.ceil(low/dx) * dx
real_high = np.ceil(high/dx) * dx
return np.linspace(real_low, real_high, numpoints)
class _MLDataSource(FunctionDataSource):
a = Float(0)
m = Float(0)
k = Float(0)
def __getitem__(self, key):
if key not in self._parameters:
raise KeyError, key
return getattr(self, key)
def __len__(self):
return len(self._parameters)
def __iter__(self):
return iter(self._parameters)
from traitsui.api import TabularEditor
from traitsui.tabular_adapter import TabularAdapter
from cns import get_config
color_names = get_config('COLOR_NAMES')
class TrialSettingAdapter(TabularAdapter):
default_value = TrialSetting(ttype='GO')
def _get_bg_color(self):
ttype = self.item.ttype
if ttype == 'NOGO':
return color_names['light red']
elif ttype == 'GO_REMIND':
return color_names['dark green']
elif ttype == 'GO':
return color_names['light green']
trial_setting_editor = TabularEditor(
label='Paradigm'
),
VGroup(
Include('dt_group'),
Include('speaker_group'),
label='Sound'
)
)
class Data(PositiveData, MLDataMixin, PumpDataMixin):
pass
class Experiment(AbstractPositiveExperiment, MLExperimentMixin):
data = Instance(Data, ())
paradigm = Instance(Paradigm, ())
node_name = 'PositiveDTCLExperiment'
if __name__ == '__main__':
import tables
from os.path import join
from cns import get_config
filename = join(get_config('TEMP_ROOT'), 'test_experiment.hd5')
file = tables.openFile(filename, 'w')
from experiments.trial_setting import add_parameters
add_parameters(['test'])
data = Data(store_node=file.root)
experiment = Experiment(data=data)
experiment.configure_traits()
