Merge pull request #216 from esheldon/test-for-speed

mdet_tests/test_mdet_regression.py

@@ -265,7 +265,7 @@ def test_mdet_regression(fname, write=False):
                     ),
                 }
             else:
-                assert col in ["shear"]
+                assert col in ["shear", "shear_bands"]
 
 
 if __name__ == "__main__":

ngmix/jacobian/jacobian.py

@@ -318,8 +318,8 @@ def _finish_init(self, row0, col0, dvdrow, dvdcol, dudrow, dudcol):
 
     def __repr__(self):
         fmt = (
-            'row0: %-10.5g col0: %-10.5g dvdrow: %-10.5g '
-            'dvdcol: %-10.5g dudrow: %-10.5g dudcol: %-10.5g'
+            'ngmix.Jacobian(row=%r, col=%r, dvdrow=%r, '
+            'dvdcol=%r, dudrow=%r, dudcol=%r)'
         )
         return fmt % (self.row0,
                       self.col0,

ngmix/metacal/metacal.py

@@ -6,6 +6,7 @@
 """
 import copy
 import logging
+from functools import lru_cache
 import numpy as np
 from ..gexceptions import GMixRangeError, BootPSFFailure
 from ..shape import Shape
@@ -20,6 +21,14 @@
 logger = logging.getLogger(__name__)
 
 
+@lru_cache(maxsize=128)
+def _cached_galsim_stuff(img, wcs_repr, xinterp):
+    import galsim
+    image = galsim.Image(np.array(img), wcs=eval(wcs_repr))
+    image_int = galsim.InterpolatedImage(image, x_interpolant=xinterp)
+    return image, image_int
+
+
 class MetacalDilatePSF(object):
     """
     Create manipulated images for use in metacalibration
@@ -355,22 +364,24 @@ def _set_data(self):
         # these would share data with the original numpy arrays, make copies
         # to be sure they don't get modified
         #
-        self.image = galsim.Image(obs.image.copy(),
-                                  wcs=self.get_wcs())
-
-        self.psf_image = galsim.Image(obs.psf.image.copy(),
-                                      wcs=self.get_psf_wcs())
+        image, image_int = _cached_galsim_stuff(
+            tuple(tuple(ii) for ii in obs.image.copy()),
+            repr(self.get_wcs()),
+            self.interp,
+        )
+        self.image = image
+        self.image_int = image_int
 
-        # interpolated psf image
-        psf_int = galsim.InterpolatedImage(self.psf_image,
-                                           x_interpolant=self.interp)
+        psf_image, psf_int = _cached_galsim_stuff(
+            tuple(tuple(ii) for ii in obs.psf.image.copy()),
+            repr(self.get_psf_wcs()),
+            self.interp,
+        )
+        self.psf_image = psf_image
 
         # this can be used to deconvolve the psf from the galaxy image
         psf_int_inv = galsim.Deconvolve(psf_int)
 
-        self.image_int = galsim.InterpolatedImage(self.image,
-                                                  x_interpolant=self.interp)
-
         # deconvolved galaxy image, psf+pixel removed
         self.image_int_nopsf = galsim.Convolve(self.image_int,
                                                psf_int_inv)

ngmix/prepsfmom.py

@@ -1,4 +1,5 @@
 import logging
+import functools
 
 import numpy as np
 import scipy.fft as fft
@@ -84,14 +85,14 @@ def _meas(self, obs, psf_obs, return_kernels):
         eff_pad_factor = target_dim / obs.image.shape[0]
 
         # pad image, psf and weight map, get FFTs, apply cen_phases
-        kim, im_row, im_col = _zero_pad_and_compute_fft(
+        kim, im_row, im_col = _zero_pad_and_compute_fft_cached(
             obs.image, obs.jacobian.row0, obs.jacobian.col0, target_dim,
             self.ap_rad,
         )
         fft_dim = kim.shape[0]
 
         if psf_obs is not None:
-            kpsf_im, psf_im_row, psf_im_col = _zero_pad_and_compute_fft(
+            kpsf_im, psf_im_row, psf_im_col = _zero_pad_and_compute_fft_cached(
                 psf_obs.image,
                 psf_obs.jacobian.row0, psf_obs.jacobian.col0,
                 target_dim,
@@ -128,17 +129,17 @@ def _meas(self, obs, psf_obs, return_kernels):
         # now build the kernels
         if self.kernel == "ksigma":
             kernels = _ksigma_kernels(
-                target_dim,
-                self.fwhm,
-                obs.jacobian.dvdrow, obs.jacobian.dvdcol,
-                obs.jacobian.dudrow, obs.jacobian.dudcol,
+                int(target_dim),
+                float(self.fwhm),
+                float(obs.jacobian.dvdrow), float(obs.jacobian.dvdcol),
+                float(obs.jacobian.dudrow), float(obs.jacobian.dudcol),
             )
         elif self.kernel in ["gauss", "pgauss"]:
             kernels = _gauss_kernels(
-                target_dim,
-                self.fwhm,
-                obs.jacobian.dvdrow, obs.jacobian.dvdcol,
-                obs.jacobian.dudrow, obs.jacobian.dudcol,
+                int(target_dim),
+                float(self.fwhm),
+                float(obs.jacobian.dvdrow), float(obs.jacobian.dvdcol),
+                float(obs.jacobian.dudrow), float(obs.jacobian.dudcol),
             )
         else:
             raise ValueError(
@@ -365,7 +366,7 @@ def _compute_cen_phase_shift(cen_row, cen_col, dim, msk=None):
         return np.cos(kcen) + 1j*np.sin(kcen)
 
 
-def _zero_pad_and_compute_fft(im, cen_row, cen_col, target_dim, ap_rad):
+def _zero_pad_and_compute_fft_impl(im, cen_row, cen_col, target_dim, ap_rad):
     """zero pad and compute the FFT
 
     Returns the fft, cen_row in the padded image, and cen_col in the padded image.
@@ -382,6 +383,30 @@ def _zero_pad_and_compute_fft(im, cen_row, cen_col, target_dim, ap_rad):
     return kpim, pad_cen_row, pad_cen_col
 
 
+# see https://stackoverflow.com/a/52332109 for how this works
+@functools.lru_cache(maxsize=128)
+def _zero_pad_and_compute_fft_cached_impl(
+    im_tuple, cen_row, cen_col, target_dim, ap_rad
+):
+    return _zero_pad_and_compute_fft_impl(
+        np.array(im_tuple), cen_row, cen_col, target_dim, ap_rad
+    )
+
+
+@functools.wraps(_zero_pad_and_compute_fft_impl)
+def _zero_pad_and_compute_fft_cached(im, cen_row, cen_col, target_dim, ap_rad):
+    return _zero_pad_and_compute_fft_cached_impl(
+        tuple(tuple(ii) for ii in im),
+        float(cen_row), float(cen_col), int(target_dim), float(ap_rad)
+    )
+
+
+_zero_pad_and_compute_fft_cached.cache_info \
+    = _zero_pad_and_compute_fft_cached_impl.cache_info
+_zero_pad_and_compute_fft_cached.cache_clear \
+    = _zero_pad_and_compute_fft_cached_impl.cache_clear
+
+
 def _deconvolve_im_psf_inplace(kim, kpsf_im, max_amp, min_psf_frac=1e-5):
     """deconvolve the PSF from an image in place.
 
@@ -398,6 +423,7 @@ def _deconvolve_im_psf_inplace(kim, kpsf_im, max_amp, min_psf_frac=1e-5):
     return kim, kpsf_im, msk
 
 
+@functools.lru_cache(maxsize=128)
 def _ksigma_kernels(
     dim,
     kernel_size,
@@ -505,6 +531,7 @@ def _ksigma_kernels(
     )
 
 
+@functools.lru_cache(maxsize=128)
 def _gauss_kernels(
     dim,
     kernel_size,

ngmix/tests/_galsim_sims.py

@@ -3,13 +3,12 @@
 import galsim
 
 
-def _get_obs(rng, set_noise_image=False, noise=1.0e-6):
+def _get_obs(rng, set_noise_image=False, noise=1.0e-6, psf_fwhm=0.9, n=None):
 
     psf_noise = 1.0e-6
 
     scale = 0.263
 
-    psf_fwhm = 0.9
     gal_fwhm = 0.7
 
     psf = galsim.Gaussian(fwhm=psf_fwhm)
@@ -18,7 +17,10 @@ def _get_obs(rng, set_noise_image=False, noise=1.0e-6):
     obj = galsim.Convolve(psf, obj0)
 
     psf_im = psf.drawImage(scale=scale).array
-    im = obj.drawImage(scale=scale).array
+    if n is not None:
+        im = obj.drawImage(scale=scale, nx=n, ny=n).array
+    else:
+        im = obj.drawImage(scale=scale).array
 
     psf_im += rng.normal(scale=psf_noise, size=psf_im.shape)
     im += rng.normal(scale=noise, size=im.shape)

ngmix/tests/test_metacal_cache.py

@@ -0,0 +1,41 @@
+import time
+import numpy as np
+import ngmix
+import ngmix.metacal.metacal
+from ._galsim_sims import _get_obs
+from ..metacal.metacal import _cached_galsim_stuff
+
+
+def test_metacal_cache():
+    # first warm up numba
+    rng = np.random.RandomState(seed=100)
+    obs = _get_obs(rng, noise=0.005, set_noise_image=True, psf_fwhm=0.8, n=300)
+    t0 = time.time()
+    ngmix.metacal.get_all_metacal(obs, rng=rng, types=["noshear"])
+    t0 = time.time() - t0
+    print("first time: %r seconds" % t0, flush=True)
+    print(_cached_galsim_stuff.cache_info(), flush=True)
+
+    # now cache it
+    rng = np.random.RandomState(seed=10)
+    obs = _get_obs(rng, noise=0.005, set_noise_image=True, n=300)
+    t1 = time.time()
+    ngmix.metacal.get_all_metacal(obs, rng=rng, types=["noshear"])
+    t1 = time.time() - t1
+    print("second time: %r seconds" % t1, flush=True)
+    print(_cached_galsim_stuff.cache_info(), flush=True)
+
+    # now use cache
+    rng = np.random.RandomState(seed=10)
+    obs = _get_obs(rng, noise=0.005, set_noise_image=True, n=300)
+    t2 = time.time()
+    ngmix.metacal.get_all_metacal(obs, rng=rng, types=["noshear"])
+    t2 = time.time() - t2
+    print("third time: %r seconds (< %r?)" % (t2, t1*0.7), flush=True)
+    print(_cached_galsim_stuff.cache_info(), flush=True)
+
+    # numba should be slower always but we do not care how much
+    assert t1 < t0
+
+    # we expect roughly 30% gains
+    assert t2 < t1*0.7