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feat: add argument seed to topaz normalize #234

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feat: add argument seed to topaz normalize #234

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3 changes: 2 additions & 1 deletion topaz/commands/normalize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,7 @@ def add_arguments(parser=None):
parser.add_argument('--format', dest='format_', default='mrc', help='image format(s) to write. choices are mrc, tiff, and png. images can be written in multiple formats by specifying each in a comma separated list, e.g. mrc,png would write mrc and png format images (default: mrc)')

parser.add_argument('-v', '--verbose', action='store_true', help='verbose output')
parser.add_argument('--seed', default=0, type=int, help='seed to be used in the normalize process. default is 0 (random seed)')

return parser

Expand All @@ -54,7 +55,7 @@ def main(args):
num_workers = 0 if use_cuda else args.num_workers

normalize_images(args.files, args.destdir, num_workers, args.scale, args.affine, args.niters, args.alpha, args.beta,
args.sample, args.metadata, formats, use_cuda, args.verbose)
args.sample, args.metadata, formats, use_cuda, args.verbose, seed=args.seed)


if __name__ == '__main__':
Expand Down
14 changes: 8 additions & 6 deletions topaz/stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@ def calculate_pi(expected_num_particles, radius, total_pixels, dims=2):


def normalize(x, alpha=900, beta=1, num_iters=100, sample=1
, method='gmm', use_cuda=False, verbose=False):
, method='gmm', use_cuda=False, verbose=False, seed=0):
if method == 'affine':
mu = x.mean()
std = x.std()
Expand All @@ -51,11 +51,12 @@ def normalize(x, alpha=900, beta=1, num_iters=100, sample=1
# fit the parameters of the model
x_sample = x
scale = 1
randomGenerator = np.random.RandomState(None if seed == 0 else seed)
if sample > 1:
# estimate parameters using sample from x
n = int(np.round(x.size/sample))
scale = x.size/n
x_sample = np.random.choice(x.ravel(), size=n, replace=False)
x_sample = randomGenerator.choice(x.ravel(), size=n, replace=False)

mu, std, pi, logp, mus, stds, pis, logps = norm_fit(x_sample, alpha=alpha, beta=beta
, scale=scale
Expand Down Expand Up @@ -281,7 +282,7 @@ def gmm_fit_numpy(x, pi=0.5, alpha=0.5, beta=0.5, tol=1e-3, num_iters=50, verbos

class Normalize:
def __init__(self, dest, scale, affine, num_iters, alpha, beta
, sample, metadata, formats, use_cuda):
, sample, metadata, formats, use_cuda, seed=0):
self.dest = dest
self.scale = scale
self.affine = affine
Expand All @@ -292,6 +293,7 @@ def __init__(self, dest, scale, affine, num_iters, alpha, beta
self.metadata = metadata
self.formats = formats
self.use_cuda = use_cuda
self.seed = seed

def __call__(self, path):
# load the image
Expand All @@ -313,7 +315,7 @@ def __call__(self, path):
if self.affine:
method = 'affine'
x,metadata = normalize(x, alpha=self.alpha, beta=self.beta, num_iters=self.num_iters,
method=method, sample=self.sample, use_cuda=self.use_cuda)
method=method, sample=self.sample, use_cuda=self.use_cuda, seed=self.seed)

# save the image and the metadata
name,_ = os.path.splitext(os.path.basename(path))
Expand All @@ -336,12 +338,12 @@ def __call__(self, path):


def normalize_images(paths:List[str], dest:str, num_workers:int, scale:int, affine:bool, niters:int, alpha:float,
beta:float, sample:int, metadata:bool, formats:List[str], use_cuda:bool, verbose:bool):
beta:float, sample:int, metadata:bool, formats:List[str], use_cuda:bool, verbose:bool, seed:int):
if not os.path.exists(dest):
os.makedirs(dest)

process = Normalize(dest, scale, affine, niters, alpha, beta,
sample, metadata, formats, use_cuda)
sample, metadata, formats, use_cuda, seed=seed)

if num_workers > 1:
pool = mp.Pool(num_workers)
Expand Down