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Adding progress bars for permutations #60

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merged 1 commit into from
Jan 15, 2019
Merged

Adding progress bars for permutations #60

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15 changes: 9 additions & 6 deletions nimare/meta/cbma/ale.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
import warnings
from time import time
import multiprocessing as mp
from tqdm.auto import tqdm

import numpy as np
import nibabel as nib
Expand Down Expand Up @@ -267,9 +268,10 @@ def _run_ale(self, ma_maps, prefix='', n_cores=4):
iter_hist_bins = [hist_bins] * self.n_iters
params = zip(iter_dfs, iter_ijks, iter_null_dists, iter_hist_bins,
iter_conns)
pool = mp.Pool(n_cores)
perm_results = pool.map(self._perm, params)
pool.close()

with mp.Pool(n_cores) as p:
perm_results = list(tqdm(p.imap(self._perm, params), total=self.n_iters))

perm_max_values, perm_clust_sizes = zip(*perm_results)

percentile = 100 * (1 - self.clust_thresh)
Expand Down Expand Up @@ -515,9 +517,10 @@ def fit(self, ids, voxel_thresh=0.001, n_iters=10000, n_cores=4):
# Define parameters
iter_dfs = [iter_df] * self.n_iters
params = zip(iter_dfs, iter_ijks)
pool = mp.Pool(n_cores)
perm_scale_values = pool.map(self._perm, params)
pool.close()

with mp.Pool(n_cores) as p:
perm_scale_values = list(tqdm(p.imap(self._perm, params), total=self.n_iters))

perm_scale_values = np.stack(perm_scale_values)

p_values, z_values = self._scale_to_p(ale_values, perm_scale_values,
Expand Down
18 changes: 9 additions & 9 deletions nimare/meta/cbma/mkda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
import warnings
import multiprocessing as mp

from tqdm.auto import tqdm
import numpy as np
import nibabel as nib
from scipy import ndimage, special
Expand Down Expand Up @@ -85,9 +86,9 @@ def fit(self, ids, voxel_thresh=0.01, q=0.05, n_iters=1000, n_cores=4):
iter_dfs = [iter_df] * n_iters
params = zip(iter_ijks, iter_dfs, iter_wv, iter_conn)

pool = mp.Pool(n_cores)
perm_results = pool.map(self._perm, params)
pool.close()
with mp.Pool(n_cores) as p:
perm_results = list(tqdm(p.imap(self._perm, params), total=self.n_iters))

perm_max_values, perm_clust_sizes = zip(*perm_results)

percentile = 100 * (1 - q)
Expand Down Expand Up @@ -251,7 +252,6 @@ def fit(self, ids, ids2=None, voxel_thresh=0.01, q=0.05, corr='FWE',
'specificity_chi2': pFgA_chi2_vals}

if corr == 'FWE':
pool = mp.Pool(n_cores)
iter_dfs = [red_coords.copy()] * n_iters
null_ijk = np.vstack(np.where(self.mask.get_data())).T
rand_idx = np.random.choice(null_ijk.shape[0],
Expand All @@ -260,8 +260,9 @@ def fit(self, ids, ids2=None, voxel_thresh=0.01, q=0.05, corr='FWE',
iter_ijks = np.split(rand_ijk, rand_ijk.shape[1], axis=1)

params = zip(iter_dfs, iter_ijks, range(n_iters))
perm_results = pool.map(self._perm, params)
pool.close()

with mp.Pool(n_cores) as p:
perm_results = list(tqdm(p.imap(self._perm, params), total=self.n_iters))
pAgF_null_chi2_dist, pFgA_null_chi2_dist = zip(*perm_results)

# pAgF_FWE
Expand Down Expand Up @@ -392,9 +393,8 @@ def fit(self, ids, q=0.05, n_iters=10000, n_cores=4):
iter_dfs = [iter_df] * n_iters
params = zip(iter_ijks, iter_dfs)

pool = mp.Pool(n_cores)
perm_max_values = pool.map(self._perm, params)
pool.close()
with mp.Pool(n_cores) as p:
perm_max_values = list(tqdm(p.imap(self._perm, params), total=self.n_iters))

percentile = 100 * (1 - q)

Expand Down