def test_hdf5():
"""Test HDF5 IO
"""
test_file = op.join(tempdir, 'test.hdf5')
x = dict(a=dict(b=np.zeros(3)), c=np.zeros(2, np.complex128),
d=[dict(e=(1, -2., 'hello'))])
write_hdf5(test_file, 1)
assert_equal(read_hdf5(test_file), 1)
assert_raises(IOError, write_hdf5, test_file, x) # file exists
write_hdf5(test_file, x, overwrite=True)
assert_raises(IOError, read_hdf5, test_file + 'FOO') # not found
xx = read_hdf5(test_file)
print(object_diff(x, xx))
assert_true(object_diff(x, xx) == '') # no assert_equal, ugly output
subj_ord = new_subj_ord
'''
# yaml params
with open(yaml_param_file, 'r') as yp:
yaml_params = yaml.load(yp)
subjects = yaml_params['subjects'] # already in npz file
listening_difficulty = np.array(yaml_params['lis_diff'])
spatial_mapping = yaml_params['spatial_mapping']
talker_mapping = yaml_params['talker_mapping']
t_min = yaml_params['t_min']
t_max = yaml_params['t_max']
t_peak = yaml_params['t_peak']
# hdf5 params
params = read_hdf5(param_file)
''' RELEVANT KEYS:
cond_mat: array (384, 4). columns are [attn, spatial, idents, gap]
attns: maintain vs switch
spatials: -30x30, 30x-30, -30x-30, 30x30
idents: MM, FF, MF, FM (male/female talkers)
stim_times: array[0.0, 0.4, 1.2, 1.6, 2.6, 3.]
'''
stim_times = params['stim_times'][0] # gap included already
cond_mat = params['cond_mat']
# figure params
with open(op.join(styledir, 'figure-params.yaml'), 'r') as fp:
fig_params = yaml.load(fp)
signifcol = fig_params['signif_color']
cue = fig_params['cue_color']
msk = fig_params['msk_color']
set_log_level('INFO')
# set configuration
fs = 44100
noise_db = 45 # dB for background noise
stim_db = 65 # dB for stimuli
min_resp_time = 0.1
max_resp_time = 2.0
feedback_dur = 0.5
isi = 0.2
running_total = 0
# you should run stimuli/generate_stimuli first to make the stimuli
# load the result here
stims = read_hdf5(op.join('stimuli', 'equally_spaced_sinewaves.hdf5'))
orig_rms = stims['rms']
freqs = stims['freqs']
fs = stims['fs']
trial_order = stims['trial_order']
num_trials = len(trial_order)
num_freqs = len(freqs)
if num_freqs > 8:
raise RuntimeError('Too many frequencies, not enough buttons.')
# keep only sinusoids, order low-high, convert to list of arrays
wavs = [stims[k] for k in sorted(stims.keys()) if k.startswith('stim_')]
# instructions
instructions = ('You will hear tones at {0} different frequencies. Your job is'
data = []
keydetection = []
for cue in ['NOCUE', 'WITHCUE']:
# for sbj in [2, 4]:
for sbj in [0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 19, 20, 21,
22]:
# identify subject and block number
if sbj <= 9:
subj_num = '40%s' % sbj
else:
subj_num = '4%s' % sbj
work_dir = ('/home/lindsey/Desktop/PythonFiles/Shams/Flashbeeps2.0/%s/Blocks' % cue)
# import data file and relevant variables
for bi in range(1, 16):
blc_num = bi
d = read_hdf5(op.join(work_dir, 'subj%s_block%s.hdf5' %
(subj_num, blc_num)))
presses = d['presses']
played = d['played']
flashed = d['flashed']
flash_timing = d['flipplay_times']
current_block = d['current_block']
condition_type = d['condition_stamp']
control = current_block[::, 3]
wavfile = current_block[::, 1]
number_pressed = [x for x, _ in presses]
raw_RT = [x[1] for x in presses]
timingcheck = trialtiming(control, flash_timing)
if '3' in number_pressed:
keydetection.append((sbj, cue, blc_num, number_pressed.count('3')))
# -*- coding: utf-8 -*-
from os import path as op
import numpy as np
from expyfun import (ExperimentController, visual, assert_version,
get_keyboard_input, decimals_to_binary)
from expyfun.io import read_hdf5, read_wav
feedback = False # should be False for MEG
assert_version('9c4bcea')
p = read_hdf5(op.join(op.dirname(__file__), 'params.hdf5'))
stim_dir = op.join(op.dirname(__file__), p['stim_dir'])
bg_color = [0.2] * 3
fix_color = [0.5] * 3
v_off = 2.
with ExperimentController('standard_setup',
full_screen=False,
stim_db=75,
noise_db=45,
participant='s',
session='0',
output_dir=None,
suppress_resamp=True,
check_rms=None,
version='9c4bcea') as ec:
square = visual.Rectangle(ec, [0, 0, 10, 10], units='deg')
ec.screen_text('expy_standard_setup_test.', wrap=False)
ec.set_background_color(bg_color)
fix = visual.FixationDot(ec, colors=[fix_color, bg_color])
Created on Thu Jul 26 11:35:55 2018
@author: rkmaddox
"""
import numpy as np
from expyfun.io import read_hdf5
from pandas import DataFrame
from statsmodels.formula.api import ols
from statsmodels.stats.anova import anova_lm
from statsmodels.graphics.gofplots import qqplot
from scipy.stats import ttest_rel, ttest_1samp
import matplotlib.pyplot as plt
plt.ion
pc = read_hdf5(
'/home/maddy/Code/data_analysis/Flip_Detect/matrix1.hdf5')['data'] * 100
# sub - cond - snr
snr = np.array([0, -3, -6])
conds = ['0', '90', '180', 'static']
n_sub = pc.shape[0]
# plt.plot(snr, pc[:, 0].T, 'k')
# plt.plot(snr, pc[:, 2].T, 'r')
dpc = pc[:, :3] - pc[:, [3]]
dpc_sem = np.std(dpc, axis=0) / np.sqrt(n_sub)
dpc_mean = dpc.mean(0)
for ci in range(dpc_mean.shape[0]):
fs = 44100
noise_db = 45 # dB for background noise
stim_db = 65 # dB for stimuli
min_resp_time = 0.1
max_resp_time = 2.0
max_wait = np.inf
feedback_dur = 2.0
isi = 0.2
running_total = 0
# make the stimuli if necessary and then load them
fname = 'equally_spaced_sinewaves.hdf5'
if not op.isfile(fname):
from stimuli.plot_generate_simple_stimuli import generate_stimuli
generate_stimuli()
stims = read_hdf5(fname)
orig_rms = stims['rms']
freqs = stims['freqs']
fs = stims['fs']
trial_order = stims['trial_order']
num_trials = len(trial_order)
num_freqs = len(freqs)
if num_freqs > 8:
raise RuntimeError('Too many frequencies, not enough buttons.')
# keep only sinusoids, order low-high, convert to list of arrays
wavs = [stims[k] for k in sorted(stims.keys()) if k.startswith('stim_')]
# instructions
instructions = ('You will hear tones at {0} different frequencies. Your job is'
min_resp_time = 0.1
max_resp_time = 2.0
max_wait = np.inf
feedback_dur = 2.0
isi = 0.2
running_total = 0
# make the stimuli if necessary and then load them
fname = 'equally_spaced_sinewaves.hdf5'
if not op.isfile(fname):
# This sys.path wrangling is only necessary for Sphinx automatic
# documentation building
sys.path.insert(0, os.getcwd())
from generate_simple_stimuli import generate_stimuli
generate_stimuli()
stims = read_hdf5(fname)
orig_rms = stims['rms']
freqs = stims['freqs']
fs = stims['fs']
trial_order = stims['trial_order']
num_trials = len(trial_order)
num_freqs = len(freqs)
if num_freqs > 8:
raise RuntimeError('Too many frequencies, not enough buttons.')
# keep only sinusoids, order low-high, convert to list of arrays
wavs = [stims[k] for k in sorted(stims.keys()) if k.startswith('stim_')]
# instructions
instructions = ('You will hear tones at {0} different frequencies. Your job is'
set_log_level('INFO')
# set configuration
fs = 44100
noise_db = 45 # dB for background noise
stim_db = 65 # dB for stimuli
min_resp_time = 0.1
max_resp_time = 2.0
feedback_dur = 0.5
isi = 0.2
running_total = 0
# you should run stimuli/generate_stimuli first to make the stimuli
# load the result here
stims = read_hdf5(op.join('stimuli', 'equally_spaced_sinewaves.hdf5'))
orig_rms = stims['rms']
freqs = stims['freqs']
fs = stims['fs']
trial_order = stims['trial_order']
num_trials = len(trial_order)
num_freqs = len(freqs)
if num_freqs > 8:
raise RuntimeError('Too many frequencies, not enough buttons.')
# keep only sinusoids, order low-high, convert to list of arrays
wavs = [stims[k] for k in sorted(stims.keys()) if k.startswith('stim_')]
# instructions
instructions = ('You will hear tones at {0} different frequencies. Your job is'
improvements = np.empty((len(subjects), 5))
sems = np.empty((len(subjects), 2))
pvals = np.zeros((len(subjects),))
lapse = np.empty((len(subjects), 1))
thresh = np.empty((len(subjects), 2))
slope = np.empty((len(subjects), 1))
perform_imp = np.empty((len(subjects), 1))
data_resamp = np.empty((len(subjects), 1000, 2, 5))
responses = []
biases = []
d_prime = np.empty((len(subjects), 5))
stem_perimp = np.empty((len(subjects), 1000, 2))
stem_thimp = np.empty((len(subjects), 1000, 2))
for i, s in enumerate(subjects):
data = read_hdf5(path + 'percent_' + s)
responses.append(data['responses'])
thresholds[i] = data['thresholds']
per_cor[i] = data['percent']
ok = data['ok']
params = data['params']
curves[i] = [sigmoid(20 * np.log10(x_plot_fit), lower=0.5, upper=ok[2],
midpt=p[0], slope=ok[1]) for p in params]
perform_imp[i] = data['perform_imp']
lapse[i] = [ok[2]]
slope[i] = [ok[1]]
thresh[i] = [params[0], params[1]]
percent = np.mean(per_cor, 0)
inds = np.argsort(thresholds[:, 0])
# subj-level RT summary
cols = ['subj', 'rt']
rt_summary = slot_hitonly[cols].groupby(cols[:-1]).aggregate('median')
print(rt_summary.describe(), end='\n\n')
# # # # # # # # # # # # # #
# post-hoc: foils by slot #
# # # # # # # # # # # # # #
ph = slot_df.groupby(['lisdiff', 'slot'])['ff'].aggregate('sum').unstack(0)
# ph.plot()
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# post-hoc: correlate switch-minus-maintain AUC against slot 4 AUC #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# hdf5 params
params = read_hdf5(param_file)
cond_mat = params['cond_mat']
df = pd.DataFrame()
df['attn'] = np.array(params['attns'])[cond_mat[:, 0]]
df['spat'] = np.array(params['spatials'])[cond_mat[:, 1]]
df['iden'] = np.array(params['idents'])[cond_mat[:, 2]]
df['lr'] = df['spat'].map(spatial_mapping)
df['mf'] = df['iden'].map(talker_mapping)
# load pupil data
pupil_data = np.load(op.join(datadir, 'capd-pupil-data.npz'))
subj_ord = pupil_data['subjects']
times = pupil_data['t_fit']
data = pupil_data['fits']
t_zero_ix = np.where(np.isclose(times, 0))[0][0]
delta_t = times[-1] - times[t_zero_ix]
assert np.array_equal(subj_ord, np.array(subjects))
