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[REF] Changes model module -> metrics module #325

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Jun 6, 2019
Merged

[REF] Changes model module -> metrics module #325

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5aec006
Changes model module -> metrics module
jbteves Jun 3, 2019
a594872
Updates API documentation
jbteves Jun 3, 2019
4d13bc6
Renames fit->kundu_fit
jbteves Jun 3, 2019
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12 changes: 6 additions & 6 deletions docs/api.rst
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Expand Up @@ -80,23 +80,23 @@ API
.. _calibration_ref:


:mod:`tedana.model`: Computing TE-dependence metrics
:mod:`tedana.metrics`: Computing TE-dependence metrics
----------------------------------------------------

.. automodule:: tedana.model
.. automodule:: tedana.metrics
:no-members:
:no-inherited-members:

.. autosummary:: tedana.model
.. autosummary:: tedana.metrics
:toctree: generated/
:template: function.rst

tedana.model.dependence_metrics
tedana.model.kundu_metrics
tedana.metrics.dependence_metrics
tedana.metrics.kundu_metrics

:template: module.rst

tedana.model.fit
tedana.metrics.fit

.. currentmodule:: tedana

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4 changes: 2 additions & 2 deletions tedana/decomposition/pca.py
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Expand Up @@ -7,7 +7,7 @@
from scipy import stats
from sklearn.decomposition import PCA

from tedana import model, utils, io
from tedana import metrics, utils, io
from tedana.decomposition._utils import eimask
from tedana.stats import computefeats2
from tedana.selection import kundu_tedpca
Expand Down Expand Up @@ -201,7 +201,7 @@ def tedpca(data_cat, data_oc, combmode, mask, t2s, t2sG,

# Normalize each component's time series
vTmixN = stats.zscore(comp_ts, axis=0)
comptable, _, _, _ = model.dependence_metrics(
comptable, _, _, _ = metrics.dependence_metrics(
data_cat, data_oc, comp_ts, eimum, t2s, tes, ref_img,
reindex=False, mmixN=vTmixN, algorithm=None,
label='mepca_', out_dir=out_dir, verbose=verbose)
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2 changes: 1 addition & 1 deletion tedana/model/__init__.py → tedana/metrics/__init__.py
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@@ -1,7 +1,7 @@
# emacs: -*- mode: python-mode; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:

from .fit import (
from .kundu_fit import (
dependence_metrics, kundu_metrics, get_coeffs, computefeats2
)

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0 tedana/model/fit.py → tedana/metrics/kundu_fit.py
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16 changes: 8 additions & 8 deletions tedana/tests/test_model_fit_dependence_metrics.py
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@@ -1,11 +1,11 @@
"""
Tests for tedana.model.fit
Tests for tedana.metrics.fit
"""

import numpy as np
import pytest

from tedana.model import fit
from tedana.metrics import kundu_fit


def test_break_dependence_metrics():
Expand All @@ -27,7 +27,7 @@ def test_break_dependence_metrics():
# Shape of catd is wrong
catd = np.empty((n_samples+1, n_echos, n_vols))
with pytest.raises(ValueError) as e_info:
fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
kundu_fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
t2s=t2s, tes=tes, ref_img=ref_img,
reindex=False, mmixN=None, algorithm='kundu_v3')
assert str(e_info.value) == ('First dimensions (number of samples) of '
Expand All @@ -40,7 +40,7 @@ def test_break_dependence_metrics():
catd = np.empty((n_samples, n_echos, n_vols))
t2s = np.empty((n_samples+1, n_vols))
with pytest.raises(ValueError) as e_info:
fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
kundu_fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
t2s=t2s, tes=tes, ref_img=ref_img,
reindex=False, mmixN=None, algorithm='kundu_v3')
assert str(e_info.value) == ('First dimensions (number of samples) of '
Expand All @@ -53,7 +53,7 @@ def test_break_dependence_metrics():
t2s = np.empty((n_samples, n_vols))
tsoc = np.empty((n_samples+1, n_vols))
with pytest.raises(ValueError) as e_info:
fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
kundu_fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
t2s=t2s, tes=tes, ref_img=ref_img,
reindex=False, mmixN=None, algorithm='kundu_v3')
assert str(e_info.value) == ('First dimensions (number of samples) of '
Expand All @@ -66,7 +66,7 @@ def test_break_dependence_metrics():
catd = np.empty((n_samples, n_echos+1, n_vols))
tsoc = np.empty((n_samples, n_vols))
with pytest.raises(ValueError) as e_info:
fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
kundu_fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
t2s=t2s, tes=tes, ref_img=ref_img,
reindex=False, mmixN=None, algorithm='kundu_v3')
assert str(e_info.value) == ('Second dimension of catd ({0}) does not '
Expand All @@ -77,7 +77,7 @@ def test_break_dependence_metrics():
# Shape of catd is wrong
catd = np.empty((n_samples, n_echos, n_vols+1))
with pytest.raises(ValueError) as e_info:
fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
kundu_fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
t2s=t2s, tes=tes, ref_img=ref_img,
reindex=False, mmixN=None, algorithm='kundu_v3')
assert str(e_info.value) == ('Number of volumes in catd '
Expand All @@ -90,7 +90,7 @@ def test_break_dependence_metrics():
catd = np.empty((n_samples, n_echos, n_vols))
t2s = np.empty((n_samples, n_vols+1))
with pytest.raises(ValueError) as e_info:
fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
kundu_fit.dependence_metrics(catd=catd, tsoc=tsoc, mmix=mmix, mask=mask,
t2s=t2s, tes=tes, ref_img=ref_img,
reindex=False, mmixN=None, algorithm='kundu_v3')
assert str(e_info.value) == ('Number of volumes in catd ({0}) '
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4 changes: 2 additions & 2 deletions tedana/tests/test_model_kundu_metrics.py
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Expand Up @@ -6,7 +6,7 @@
import numpy as np
import pandas as pd

from tedana.model import fit
from tedana.metrics import kundu_fit


def test_smoke_kundu_metrics():
Expand Down Expand Up @@ -34,5 +34,5 @@ def test_smoke_kundu_metrics():
metric_maps['Br_R2_clmaps'] = np.random.randint(low=0, high=2,
size=(n_voxels, n_comps))

comptable = fit.kundu_metrics(comptable, metric_maps)
comptable = kundu_fit.kundu_metrics(comptable, metric_maps)
assert comptable is not None
4 changes: 2 additions & 2 deletions tedana/viz.py
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Expand Up @@ -9,7 +9,7 @@
matplotlib.use('AGG')
import matplotlib.pyplot as plt

from tedana import model
from tedana import metrics
from tedana.utils import get_spectrum

LGR = logging.getLogger(__name__)
Expand Down Expand Up @@ -77,7 +77,7 @@ def write_comp_figs(ts, mask, comptable, mmix, ref_img, out_dir,
LGR.warning('Provided colormap is not recognized, proceeding with default')
png_cmap = 'coolwarm'
# regenerate the beta images
ts_B = model.get_coeffs(ts, mmix, mask)
ts_B = metrics.get_coeffs(ts, mmix, mask)
ts_B = ts_B.reshape(ref_img.shape[:3] + ts_B.shape[1:])
# trim edges from ts_B array
ts_B = trim_edge_zeros(ts_B)
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10 changes: 5 additions & 5 deletions tedana/workflows/tedana.py
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Expand Up @@ -20,7 +20,7 @@
from scipy import stats
from nilearn.masking import compute_epi_mask

from tedana import (decay, combine, decomposition, io, model, selection, utils,
from tedana import (decay, combine, decomposition, io, metrics, selection, utils,
viz)
import tedana.gscontrol as gsc
from tedana.workflows.parser_utils import is_valid_file
Expand Down Expand Up @@ -403,23 +403,23 @@ def tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None,
# Estimate betas and compute selection metrics for mixing matrix
# generated from dimensionally reduced data using full data (i.e., data
# with thermal noise)
comptable, metric_maps, betas, mmix = model.dependence_metrics(
comptable, metric_maps, betas, mmix = metrics.dependence_metrics(
catd, data_oc, mmix_orig, mask, t2s, tes,
ref_img, reindex=True, label='meica_', out_dir=out_dir,
algorithm='kundu_v2', verbose=verbose)
np.savetxt(op.join(out_dir, 'meica_mix.1D'), mmix)

comptable = model.kundu_metrics(comptable, metric_maps)
comptable = metrics.kundu_metrics(comptable, metric_maps)
comptable = selection.kundu_selection_v2(comptable, n_echos, n_vols)
else:
LGR.info('Using supplied mixing matrix from ICA')
mmix_orig = np.loadtxt(op.join(out_dir, 'meica_mix.1D'))
comptable, metric_maps, betas, mmix = model.dependence_metrics(
comptable, metric_maps, betas, mmix = metrics.dependence_metrics(
catd, data_oc, mmix_orig, mask, t2s, tes,
ref_img, label='meica_', out_dir=out_dir,
algorithm='kundu_v2', verbose=verbose)
if ctab is None:
comptable = model.kundu_metrics(comptable, metric_maps)
comptable = metrics.kundu_metrics(comptable, metric_maps)
comptable = selection.kundu_selection_v2(comptable, n_echos, n_vols)
else:
comptable = pd.read_csv(ctab, sep='\t', index_col='component')
Expand Down