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Circular Imports #1103

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Merged
merged 3 commits into from
Mar 13, 2024
Merged

Circular Imports #1103

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0fb1d15
Move import of BatchedRotCov2D inside function.
j-c-c Mar 7, 2024
e1c9121
Change import in resolution_estimation.py
j-c-c Mar 7, 2024
37552ff
tox
j-c-c Mar 7, 2024
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4 changes: 3 additions & 1 deletion src/aspire/basis/fspca.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
import numpy as np

from aspire.basis import Coef, ComplexCoef, FFBBasis2D, SteerableBasis2D
from aspire.covariance import BatchedRotCov2D
from aspire.operators import BlkDiagMatrix
from aspire.utils import complex_type, fix_signs, real_type

Expand Down Expand Up @@ -147,6 +146,9 @@ def build(self):
self.noise_var = WhiteNoiseEstimator(self.src).estimate()
logger.info(f"Setting noise_var={self.noise_var}")

# Import BatchedRotCov2D here to prevent circular imports.
from aspire.covariance import BatchedRotCov2D

cov2d = BatchedRotCov2D(
src=self.src, basis=self.basis, batch_size=self.batch_size
)
Expand Down
9 changes: 4 additions & 5 deletions src/aspire/utils/resolution_estimation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,8 +8,7 @@
import matplotlib.pyplot as plt
import numpy as np

from aspire.nufft import nufft
from aspire.numeric import fft
from aspire import nufft, numeric
from aspire.utils import grid_2d, grid_3d

logger = logging.getLogger(__name__)
Expand Down Expand Up @@ -152,8 +151,8 @@ def _fft_correlations(self):
]

# Compute centered Fourier transforms.
f1 = fft.centered_fftn(self.a, axes=self._fourier_axes)
f2 = fft.centered_fftn(self.b, axes=self._fourier_axes)
f1 = numeric.fft.centered_fftn(self.a, axes=self._fourier_axes)
f2 = numeric.fft.centered_fftn(self.b, axes=self._fourier_axes)

# Construct an output table of correlations
correlations = np.zeros(
Expand Down Expand Up @@ -232,7 +231,7 @@ def _nufft_correlations(self):
# Note, we want a complex result.
signal = np.vstack((self._a, self._b))
# Compute one large NUFFT for all the signal frames,
f = nufft(signal, fourier_pts, real=False)
f = nufft.nufft(signal, fourier_pts, real=False)
# then unpack as two 1D stacks of the polar grid points, one for _a and _b.
f = f.reshape(self._a.shape[0] + self._b.shape[0], len(r), -1)
f1, f2 = np.vsplit(f, [self._a.shape[0]])
Expand Down