Skip to content

Commit

Permalink
Replace nose.tools with numpy.testing and assert_true with assert_
Browse files Browse the repository at this point in the history
  • Loading branch information
@arokem
arokem committed Nov 22, 2016
1 parent e75334a commit 46f3829
Show file tree
Hide file tree
Showing 14 changed files with 440 additions and 459 deletions.
65 changes: 32 additions & 33 deletions popeye/tests/test_auditory.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
from matplotlib.pyplot import specgram

import numpy as np
import nose.tools as nt
import numpy.testing as npt
from scipy.signal import chirp

Expand All @@ -13,7 +12,7 @@
from popeye.auditory_stimulus import AuditoryStimulus

def test_auditory_fit():

# stimulus features
lo_freq = 200 # Hz
hi_freq = 12000 # Hz
Expand All @@ -29,63 +28,63 @@ def test_auditory_fit():
verbose = 0
resample_factor = 10
dtype = ctypes.c_double

# sample the time from 0-duration by the fs
time = np.linspace(0,duration,duration*Fs)

# create a chirp stimulus
ch = chirp(time, lo_freq, duration, hi_freq, method='quadratic')
signal = np.concatenate((ch,ch[::-1]))

# instantiate an instance of the Stimulus class
stimulus = AuditoryStimulus(signal, NFFT, Fs, noverlap, resample_factor, dtype, tr_length)

# initialize the gaussian model
model = aud.AuditoryModel(stimulus, utils.double_gamma_hrf)

# generate a random pRF estimate
center_freq = 8910.0
sigma = 567.0
beta = 0.88
baseline = -1.5
hrf_delay = -0.25

# create the "data"
data = model.generate_prediction(center_freq, sigma, beta, baseline, hrf_delay)

# set search grid
c_grid = (lo_freq,hi_freq)
s_grid = (lo_freq,hi_freq)
b_grid = (0.1,1)
bl_grid = (-3,3)
h_grid = (-3.0,3.0)

# set search bounds
c_bound = (lo_freq,hi_freq)
s_bound = (lo_freq,hi_freq)
b_bound = (1e-8,None)
bl_bound = (None,None)
h_bound = (-4.0,4.0)

# loop over each voxel and set up a GaussianFit object
grids = (c_grid, s_grid, b_grid, bl_grid, h_grid,)
bounds = (c_bound, s_bound, b_bound, bl_bound, h_bound,)

# fit the response
fit = aud.AuditoryFit(model, data, grids, bounds, Ns, voxel_index, auto_fit=True, verbose=1)

# assert equivalence
nt.assert_equal(np.round(fit.center_freq), center_freq)
nt.assert_equal(np.round(fit.sigma), sigma)
nt.assert_almost_equal(fit.beta, beta, 2)
nt.assert_almost_equal(fit.baseline, baseline, 2)
nt.assert_almost_equal(fit.hrf_delay, hrf_delay, 2)
npt.assert_equal(np.round(fit.center_freq), center_freq)
npt.assert_equal(np.round(fit.sigma), sigma)
npt.assert_almost_equal(fit.beta, beta, 2)
npt.assert_almost_equal(fit.baseline, baseline, 2)
npt.assert_almost_equal(fit.hrf_delay, hrf_delay, 2)

npt.assert_almost_equal((fit.center_freq0,fit.sigma0,fit.beta0,fit.baseline0,fit.hrf0),
(9050.0, 200.0, 0.32500000000000001, -1.5, 1.5))

def test_auditory_data_upsample():

lo_freq = 200 # Hz
hi_freq = 12000 # Hz
Fs = hi_freq*2 # Hz
Expand All @@ -100,50 +99,50 @@ def test_auditory_data_upsample():
verbose = 0
resample_factor = 10
dtype = ctypes.c_double

# sample the time from 0-duration by the fs
time = np.linspace(0,duration,duration*Fs)

# create a chirp stimulus
ch = chirp(time, lo_freq, duration, hi_freq, method='quadratic')
signal = np.concatenate((ch,ch[::-1]))

# instantiate an instance of the Stimulus class
stimulus = AuditoryStimulus(signal, NFFT, Fs, noverlap, resample_factor, dtype, tr_length)

# initialize the gaussian model
model = aud.AuditoryModel(stimulus, utils.double_gamma_hrf)

# generate a random pRF estimate
center_freq = 5910.0
sigma = 567.0
beta = 0.88
baseline = -1.5
hrf_delay = -0.25

# create the "data"
data = model.generate_prediction(center_freq, sigma, beta, baseline, hrf_delay)
data = data[np.arange(0,len(data),2)]

# set search grid
c_grid = (lo_freq,hi_freq)
s_grid = (lo_freq,hi_freq)
b_grid = (0.1,1)
bl_grid = (-3,3)
h_grid = (-3.0,3.0)

# set search bounds
c_bound = (lo_freq,hi_freq)
s_bound = (lo_freq,hi_freq)
b_bound = (1e-8,None)
bl_bound = (None,None)
h_bound = (-4.0,4.0)

# loop over each voxel and set up a GaussianFit object
grids = (c_grid, s_grid, b_grid, bl_grid, h_grid,)
bounds = (c_bound, s_bound, b_bound, bl_bound, h_bound,)

# fit the response
fit = aud.AuditoryFit(model, data, grids, bounds, Ns, voxel_index, auto_fit=False, verbose=1)
nt.assert_equal(len(fit.data), len(data))

npt.assert_equal(len(fit.data), len(data))
39 changes: 19 additions & 20 deletions popeye/tests/test_auditory_bin.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
from matplotlib.pyplot import specgram

import numpy as np
import nose.tools as nt
import numpy.testing as npt
from scipy.signal import chirp

Expand All @@ -13,7 +12,7 @@
from popeye.auditory_stimulus import AuditoryStimulus

def test_auditory_fit():

# stimulus features
lo_freq = 200 # Hz
hi_freq = 12000 # Hz
Expand All @@ -29,57 +28,57 @@ def test_auditory_fit():
verbose = 0
resample_factor = 10
dtype = ctypes.c_double

# sample the time from 0-duration by the fs
time = np.linspace(0,duration,duration*Fs)

# create a chirp stimulus
ch = chirp(time, lo_freq, duration, hi_freq, method='quadratic')
signal = np.concatenate((ch,ch[::-1]))

# instantiate an instance of the Stimulus class
stimulus = AuditoryStimulus(signal, NFFT, Fs, noverlap, resample_factor, dtype, tr_length)

# initialize the gaussian model
model = aud.AuditoryModel(stimulus, utils.double_gamma_hrf)

# generate a random pRF estimate
center_freq = 8910.0
sigma = 567.0
beta = 0.88
baseline = -1.5
hrf_delay = -0.25

# create the "data"
data = model.generate_prediction(center_freq, sigma, beta, baseline, hrf_delay)

# set search grid
c_grid = (lo_freq,hi_freq)
s_grid = (lo_freq,hi_freq)
b_grid = (0.1,1)
bl_grid = (-3,3)
h_grid = (-3.0,3.0)

# set search bounds
c_bound = (lo_freq,hi_freq)
s_bound = (lo_freq,hi_freq)
b_bound = (1e-8,None)
bl_bound = (None,None)
h_bound = (-4.0,4.0)

# loop over each voxel and set up a GaussianFit object
grids = (c_grid, s_grid, b_grid, bl_grid, h_grid,)
bounds = (c_bound, s_bound, b_bound, bl_bound, h_bound,)

# fit the response
fit = aud.AuditoryFit(model, data, grids, bounds, Ns, voxel_index, auto_fit=True, verbose=1)

# assert equivalence
nt.assert_equal(np.round(fit.center_freq), center_freq)
nt.assert_equal(np.round(fit.sigma), sigma)
nt.assert_almost_equal(fit.beta, beta, 2)
nt.assert_almost_equal(fit.baseline, baseline, 2)
nt.assert_almost_equal(fit.hrf_delay, hrf_delay, 2)
npt.assert_equal(np.round(fit.center_freq), center_freq)
npt.assert_equal(np.round(fit.sigma), sigma)
npt.assert_almost_equal(fit.beta, beta, 2)
npt.assert_almost_equal(fit.baseline, baseline, 2)
npt.assert_almost_equal(fit.hrf_delay, hrf_delay, 2)

npt.assert_almost_equal((fit.center_freq0,fit.sigma0,fit.beta0,fit.baseline0,fit.hrf0),
(9050.0, 200.0, 0.77500000000000002, -1.5, 0.0))
(9050.0, 200.0, 0.77500000000000002, -1.5, 0.0))
37 changes: 18 additions & 19 deletions popeye/tests/test_auditory_bin_pct.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
from matplotlib.pyplot import specgram

import numpy as np
import nose.tools as nt
import numpy.testing as npt
from scipy.signal import chirp

Expand All @@ -13,7 +12,7 @@
from popeye.auditory_stimulus import AuditoryStimulus

def test_auditory_fit():

# stimulus features
lo_freq = 200 # Hz
hi_freq = 12000 # Hz
Expand All @@ -29,53 +28,53 @@ def test_auditory_fit():
verbose = 0
resample_factor = 10
dtype = ctypes.c_double

# sample the time from 0-duration by the fs
time = np.linspace(0,duration,duration*Fs)

# create a chirp stimulus
ch = chirp(time, lo_freq, duration, hi_freq, method='quadratic')
signal = np.concatenate((ch,ch[::-1]))

# instantiate an instance of the Stimulus class
stimulus = AuditoryStimulus(signal, NFFT, Fs, noverlap, resample_factor, dtype, tr_length)

# initialize the gaussian model
model = aud.AuditoryModel(stimulus, utils.double_gamma_hrf)

# generate a random pRF estimate
center_freq = 8910.0
sigma = 567.0
beta = 0.88
hrf_delay = -0.25

# create the "data"
data = model.generate_prediction(center_freq, sigma, beta, hrf_delay)

# set search grid
c_grid = (lo_freq,hi_freq)
s_grid = (lo_freq,hi_freq)
b_grid = (0.1,1)
h_grid = (-3.0,3.0)

# set search bounds
c_bound = (lo_freq,hi_freq)
s_bound = (lo_freq,hi_freq)
b_bound = (1e-8,None)
h_bound = (-4.0,4.0)

# loop over each voxel and set up a GaussianFit object
grids = (c_grid, s_grid, b_grid, h_grid,)
bounds = (c_bound, s_bound, b_bound, h_bound,)

# fit the response
fit = aud.AuditoryFit(model, data, grids, bounds, Ns, voxel_index, auto_fit=True, verbose=1)

# assert equivalence
nt.assert_equal(np.round(fit.center_freq), center_freq)
nt.assert_equal(np.round(fit.sigma), sigma)
nt.assert_almost_equal(fit.beta, beta, 2)
nt.assert_almost_equal(fit.hrf_delay, hrf_delay, 2)
npt.assert_equal(np.round(fit.center_freq), center_freq)
npt.assert_equal(np.round(fit.sigma), sigma)
npt.assert_almost_equal(fit.beta, beta, 2)
npt.assert_almost_equal(fit.hrf_delay, hrf_delay, 2)

npt.assert_almost_equal((fit.center_freq0,fit.sigma0,fit.beta0,fit.hrf0),
(9050.0, 200.0, 0.77500000000000002, 0.0))
(9050.0, 200.0, 0.77500000000000002, 0.0))
Loading

0 comments on commit 46f3829

Please sign in to comment.