From a84d5114d5048e76324091eed238d1ccef606b5b Mon Sep 17 00:00:00 2001 From: Jeremy Kubica <104161096+jeremykubica@users.noreply.github.com> Date: Thu, 5 Dec 2024 11:31:47 -0500 Subject: [PATCH 01/10] Add a vectorized trajectory prediction function --- src/kbmod/trajectory_utils.py | 49 ++++++++++++++++++++++++++++++++++ tests/test_trajectory_utils.py | 24 +++++++++++++++++ 2 files changed, 73 insertions(+) diff --git a/src/kbmod/trajectory_utils.py b/src/kbmod/trajectory_utils.py index 9163964e7..423febad7 100644 --- a/src/kbmod/trajectory_utils.py +++ b/src/kbmod/trajectory_utils.py @@ -19,6 +19,55 @@ from kbmod.search import Trajectory +def predict_pixel_locations(times, x0, vx, centered=True, as_int=True): + """A vectorized Python implementation of the logic to predict the + pixel locations from a starting pixel and a velocity. + + Parameters + ---------- + times : list-like + The length T list of zero-shifted times. + x0 : list-like + The length R list of starting pixel locations. + vx : list-like + The length R list of pixel velocities (in pixels per day) for each + trajectory. + centered : `bool` + Shift the prediction to be at the center of the pixel + (e.g. xp = x0 + vx * time + 0.5f). + Default = True. + as_int : `bool` + Return the predictions as integers. + Default = True. + + Returns + ------- + pos : `numpy.ndarray` + A R x T matrix where R is the number of trajectories (length of x0 and vx) + and T is the number of times. + """ + # Make sure everything is a numpy array. + times = np.asarray(times) + x0 = np.asarray(x0) + vx = np.asarray(vx) + + # Check the arrays are the same length. + if len(x0) != len(vx): + raise ValueError(f"x0 and vx must be same size. Found {len(x0)} vs {len(vx)}") + + # Compute the (floating point) predicted pixel position. + pos = vx[:, np.newaxis] * times[np.newaxis, :] + x0[:, np.newaxis] + if centered: + pos = pos + 0.5 + + # If returned as int, we do not use an explicit floor in order to stay + # consistent with the existing implementation. + if as_int: + pos = pos.astype(int) + + return pos + + def make_trajectory_from_ra_dec(ra, dec, v_ra, v_dec, wcs): """Create a trajectory object from (RA, dec) information. diff --git a/tests/test_trajectory_utils.py b/tests/test_trajectory_utils.py index 53a5c84d0..896e35806 100644 --- a/tests/test_trajectory_utils.py +++ b/tests/test_trajectory_utils.py @@ -8,6 +8,30 @@ class test_trajectory_utils(unittest.TestCase): + def test_predict_pixel_locations(self): + x0 = [0.0, 1.0, 2.0] + vx = [1.0, 0.0, -1.0] + times = [0, 1, 2, 3] + + # When centering and using an integer, the last point for x0=2.0 vx=-1.0 + # is at -0.5 which is rounded to 0.0 because we are using int truncation + # instead of an explicit floor for consistency. + pos = predict_pixel_locations(times, x0, vx, centered=True, as_int=True) + expected = np.array([[0, 1, 2, 3], [1, 1, 1, 1], [2, 1, 0, 0]]) + self.assertTrue(np.array_equal(pos, expected)) + + pos = predict_pixel_locations(times, x0, vx, centered=False, as_int=True) + expected = np.array([[0, 1, 2, 3], [1, 1, 1, 1], [2, 1, 0, -1]]) + self.assertTrue(np.array_equal(pos, expected)) + + pos = predict_pixel_locations(times, x0, vx, centered=True, as_int=False) + expected = np.array([[0.5, 1.5, 2.5, 3.5], [1.5, 1.5, 1.5, 1.5], [2.5, 1.5, 0.5, -0.5]]) + self.assertTrue(np.array_equal(pos, expected)) + + pos = predict_pixel_locations(times, x0, vx, centered=False, as_int=False) + expected = np.array([[0.0, 1.0, 2.0, 3.0], [1.0, 1.0, 1.0, 1.0], [2.0, 1.0, 0.0, -1.0]]) + self.assertTrue(np.array_equal(pos, expected)) + def test_predict_skypos(self): # Create a fake WCS with a known pointing. my_wcs = WCS(naxis=2) From 04c07ce006aa16f902c733af08ed1a9543c012a1 Mon Sep 17 00:00:00 2001 From: Jeremy Kubica <104161096+jeremykubica@users.noreply.github.com> Date: Thu, 12 Dec 2024 15:00:45 -0500 Subject: [PATCH 02/10] Avoid crash when updating likelihoods of empty Results --- src/kbmod/results.py | 5 ++++- tests/test_results.py | 3 +++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/src/kbmod/results.py b/src/kbmod/results.py index 15b6ebac6..f45460d4f 100644 --- a/src/kbmod/results.py +++ b/src/kbmod/results.py @@ -331,12 +331,15 @@ def _update_likelihood(self): ------ Raises an IndexError if the necessary columns are missing. """ + num_rows = len(self.table) + if num_rows == 0: + return # Nothing to do for an empty table + if "psi_curve" not in self.table.colnames: raise IndexError("Missing column 'phi_curve'. Use add_psi_phi_data()") if "phi_curve" not in self.table.colnames: raise IndexError("Missing column 'phi_curve'. Use add_psi_phi_data()") - num_rows = len(self.table) num_times = len(self.table["phi_curve"][0]) if "obs_valid" in self.table.colnames: phi_sum = (self.table["phi_curve"] * self.table["obs_valid"]).sum(axis=1) diff --git a/tests/test_results.py b/tests/test_results.py index 56bc73d73..21a5babfd 100644 --- a/tests/test_results.py +++ b/tests/test_results.py @@ -53,6 +53,9 @@ def test_empty(self): self.assertEqual(len(table.colnames), 7) self.assertEqual(table.get_num_times(), 0) + # Check that we don't crash on updating the likelihoods. + table._update_likelihood() + def test_from_trajectories(self): table = Results.from_trajectories(self.trj_list) self.assertEqual(len(table), self.num_entries) From 034f307f47c8be9c8f59f1464daa40cdacc68057 Mon Sep 17 00:00:00 2001 From: Jeremy Kubica <104161096+jeremykubica@users.noreply.github.com> Date: Fri, 13 Dec 2024 10:19:50 -0500 Subject: [PATCH 03/10] Implement SigmaG filtering in JAX --- pyproject.toml | 1 + src/kbmod/filters/sigma_g_filter.py | 98 ++++++++++++++++++----------- tests/test_sigma_g_filter.py | 36 ++++++++++- 3 files changed, 95 insertions(+), 40 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 97311fe5b..5ad3bb598 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,6 +34,7 @@ dynamic = ["version"] dependencies = [ "astropy>=5.1", "astroquery>=0.4.6", + "jax", "joblib>=1.4", "matplotlib>=3.5", "numpy<2.0", diff --git a/src/kbmod/filters/sigma_g_filter.py b/src/kbmod/filters/sigma_g_filter.py index c862cddfe..6ab42d487 100644 --- a/src/kbmod/filters/sigma_g_filter.py +++ b/src/kbmod/filters/sigma_g_filter.py @@ -5,6 +5,9 @@ by Smotherman et. al. 2021 """ +from functools import partial +from jax import jit, vmap +import jax.numpy as jnp import logging import numpy as np from scipy.special import erfinv @@ -12,9 +15,50 @@ from kbmod.results import Results from kbmod.search import DebugTimer + logger = logging.getLogger(__name__) +def sigma_g_jax(data, low_bnd, high_bnd, n_sigma, coeff, clip_negative): + """The core function for performing a sigma filtering on a series of data points + with clipped_negative. These are typically likelihoods for KBMOD. + + Parameters + ---------- + data : `numpy.ndarray` + A length T matrix of data points for filtering. + low_bnd : `float` + The lower bound of the interval to use to estimate the standard deviation. + high_bnd : `float` + The upper bound of the interval to use to estimate the standard deviation. + n_sigma : `float` + The number of standard deviations to use for the bound. + coeff : `float` + The precomputed coefficient based on the given bounds. + clip_negative : `bool` + A Boolean indicating whether to use negative values when computing + standard deviation. + + Returns + ------- + index_valid : `numpy.ndarray` + A length T array of Booleans indicating if each point is valid (True) + or has been filtered (False). + """ + # Compute the percentiles for this array of values. If we are clipping the negatives then only + # use the positive points. + masked_data = jnp.where((not clip_negative) | (data > 0.0), data, jnp.nan) + lower_per, median, upper_per = jnp.nanpercentile(masked_data, jnp.array([low_bnd, 50, high_bnd])) + + # Compute the bounds for each row, enforcing a minimum gap in case all the + # points are identical (upper_per == lower_per). + delta = upper_per - lower_per + nSigmaG = n_sigma * coeff * jnp.where(delta > 1e-8, delta, 1e-8) + + index_valid = jnp.isfinite(data) & (data <= median + nSigmaG) & (data >= median - nSigmaG) + return index_valid + + class SigmaGClipping: """This class contains the basic information for performing SigmaG clipping. @@ -41,9 +85,21 @@ def __init__(self, low_bnd=25, high_bnd=75, n_sigma=2, clip_negative=False): self.low_bnd = low_bnd self.high_bnd = high_bnd - self.clip_negative = clip_negative self.n_sigma = n_sigma self.coeff = SigmaGClipping.find_sigma_g_coeff(low_bnd, high_bnd) + self.clip_negative = clip_negative + + # Create compiled vmapped functions that applies the Sigma G filtering + # with the given parameters. + base_fn = partial( + sigma_g_jax, + low_bnd=self.low_bnd, + high_bnd=self.high_bnd, + n_sigma=self.n_sigma, + coeff=self.coeff, + clip_negative=self.clip_negative, + ) + self.sigma_g_jax_fn = vmap(jit(base_fn)) @staticmethod def find_sigma_g_coeff(low_bnd, high_bnd): @@ -107,15 +163,7 @@ def compute_clipped_sigma_g(self, lh): sigmaG = self.coeff * delta nSigmaG = self.n_sigma * sigmaG - # Its unclear why we only filter zeros for one of the two cases, but leaving the logic in - # to stay consistent with the original code. - if self.clip_negative: - good_index = np.where( - np.logical_and(lh != 0, np.logical_and(lh > median - nSigmaG, lh < median + nSigmaG)) - )[0] - else: - good_index = np.where(np.logical_and(lh > median - nSigmaG, lh < median + nSigmaG))[0] - + good_index = np.where(np.logical_and(lh > median - nSigmaG, lh < median + nSigmaG))[0] return good_index def compute_clipped_sigma_g_matrix(self, lh): @@ -134,33 +182,8 @@ def compute_clipped_sigma_g_matrix(self, lh): A N x T matrix of Booleans indicating if each point is valid (True) or has been filtered (False). """ - if self.clip_negative: - # We mask out the values less than zero so they are not used in the median computation. - masked_lh = np.copy(lh) - masked_lh[lh <= 0] = np.nan - lower_per, median, upper_per = np.nanpercentile( - masked_lh, [self.low_bnd, 50, self.high_bnd], axis=1 - ) - else: - lower_per, median, upper_per = np.nanpercentile(lh, [self.low_bnd, 50, self.high_bnd], axis=1) - - # Compute the bounds for each row, enforcing a minimum gap in case all the - # points are identical (upper_per == lower_per). - delta = upper_per - lower_per - delta[delta < 1e-8] = 1e-8 - nSigmaG = self.n_sigma * self.coeff * delta - - num_cols = lh.shape[1] - lower_bnd = np.repeat(np.array([median - nSigmaG]).T, num_cols, axis=1) - upper_bnd = np.repeat(np.array([median + nSigmaG]).T, num_cols, axis=1) - - # Its unclear why we only filter zeros for one of the two cases, but leaving the logic in - # to stay consistent with the original code. - if self.clip_negative: - index_valid = np.isfinite(lh) & (lh != 0) & (lh < upper_bnd) & (lh > lower_bnd) - else: - index_valid = np.isfinite(lh) & (lh < upper_bnd) & (lh > lower_bnd) - return index_valid + inds_valid = self.sigma_g_jax_fn(jnp.asarray(lh)) + return inds_valid def apply_clipped_sigma_g(clipper, result_data): @@ -183,4 +206,3 @@ def apply_clipped_sigma_g(clipper, result_data): obs_valid = clipper.compute_clipped_sigma_g_matrix(lh) result_data.update_obs_valid(obs_valid) filter_timer.stop() - return diff --git a/tests/test_sigma_g_filter.py b/tests/test_sigma_g_filter.py index edddcffe0..031e4e147 100644 --- a/tests/test_sigma_g_filter.py +++ b/tests/test_sigma_g_filter.py @@ -84,7 +84,7 @@ def test_sigma_g_negative_clipping(self): params = SigmaGClipping(clip_negative=True) result = params.compute_clipped_sigma_g(lh) for i in range(num_points): - self.assertEqual(i in result, i > 2 and i != 5 and i != 14) + self.assertEqual(i in result, i > 2 and i != 14) def test_sigma_g_all_negative_clipping(self): num_points = 10 @@ -106,10 +106,11 @@ def test_sigma_g_clipping_matrix_negative_clipping(self): expected = np.array( [ [True] * num_points, - [False, False, False, True, True, False] + [True] * (num_points - 6), + [False, False, False] + [True] * (num_points - 3), [False] * num_points, ] ) + sigma_g = SigmaGClipping(clip_negative=True) # Surpress the warning we get from encountering a row of all NaNs. @@ -144,6 +145,37 @@ def test_apply_clipped_sigma_g_results(self): for j in range(i, num_times): self.assertTrue(valid[j]) + def test_sigmag_parity(self): + """Test that we get the same results when using the batch and the non-batch methods.""" + num_tests = 20 + + # Run the test with differing numbers of points and with/without clipping. + for num_obs in [10, 20, 50]: + for clipped in [True, False]: + for num_extreme in [0, 1, 2, 3]: + with self.subTest( + num_obs_used=num_obs, use_clipped=clipped, num_extreme_used=num_extreme + ): + # Generate the data from a fixed random seed (same for every subtest). + rng = np.random.default_rng(100) + data = 10.0 * rng.random((num_tests, num_obs)) - 0.5 + + # Add extreme values for each row. + for row in range(num_tests): + for ext_num in range(num_extreme): + idx = int(num_obs * rng.random()) + data[row, idx] = 100.0 * rng.random() - 50.0 + + clipper = SigmaGClipping(25, 75, clip_negative=clipped) + + batch_res = clipper.compute_clipped_sigma_g_matrix(data) + for row in range(num_tests): + # Compute the individual results (as indices) and convert + # those into a vector of bools for comparison. + ind_res = clipper.compute_clipped_sigma_g(data[row]) + ind_bools = [(idx in ind_res) for idx in range(num_obs)] + self.assertTrue(np.array_equal(batch_res[row], ind_bools)) + def test_sigmag_computation(self): self.assertAlmostEqual(SigmaGClipping.find_sigma_g_coeff(25.0, 75.0), 0.7413, delta=0.001) self.assertRaises(ValueError, SigmaGClipping.find_sigma_g_coeff, -1.0, 75.0) From 189ebb482a1a31cc45373599926479d86cf5202d Mon Sep 17 00:00:00 2001 From: Jeremy Kubica <104161096+jeremykubica@users.noreply.github.com> Date: Fri, 13 Dec 2024 10:31:36 -0500 Subject: [PATCH 04/10] Update sigma_g_filter.py --- src/kbmod/filters/sigma_g_filter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/kbmod/filters/sigma_g_filter.py b/src/kbmod/filters/sigma_g_filter.py index 6ab42d487..87cdd67ea 100644 --- a/src/kbmod/filters/sigma_g_filter.py +++ b/src/kbmod/filters/sigma_g_filter.py @@ -182,7 +182,7 @@ def compute_clipped_sigma_g_matrix(self, lh): A N x T matrix of Booleans indicating if each point is valid (True) or has been filtered (False). """ - inds_valid = self.sigma_g_jax_fn(jnp.asarray(lh)) + inds_valid = self.sigma_g_jax_fn(jnp.asarray(lh)).block_until_ready() return inds_valid From 7791cfa226add40aeae08f60282ff594cb746c04 Mon Sep 17 00:00:00 2001 From: Jeremy Kubica <104161096+jeremykubica@users.noreply.github.com> Date: Mon, 16 Dec 2024 09:44:15 -0500 Subject: [PATCH 05/10] Remove subtract_template --- src/kbmod/search/layered_image.cpp | 17 ---------- src/kbmod/search/layered_image.h | 3 -- src/kbmod/search/pydocs/layered_image_docs.h | 4 --- tests/test_layered_image.py | 33 -------------------- 4 files changed, 57 deletions(-) diff --git a/src/kbmod/search/layered_image.cpp b/src/kbmod/search/layered_image.cpp index e6f7eab51..2f7e9372c 100644 --- a/src/kbmod/search/layered_image.cpp +++ b/src/kbmod/search/layered_image.cpp @@ -119,22 +119,6 @@ void LayeredImage::apply_mask(int flags) { variance.apply_mask(flags, mask); } -void LayeredImage::subtract_template(RawImage& sub_template) { - assert_sizes_equal(sub_template.get_width(), width, "template width"); - assert_sizes_equal(sub_template.get_height(), height, "template height"); - const uint64_t num_pixels = get_npixels(); - - logging::getLogger("kbmod.search.layered_image")->debug("Subtracting template image."); - - float* sci_pixels = science.data(); - float* tem_pixels = sub_template.data(); - for (uint64_t i = 0; i < num_pixels; ++i) { - if (pixel_value_valid(sci_pixels[i]) && pixel_value_valid(tem_pixels[i])) { - sci_pixels[i] -= tem_pixels[i]; - } - } -} - void LayeredImage::set_science(RawImage& im) { assert_sizes_equal(im.get_width(), width, "science layer width"); assert_sizes_equal(im.get_height(), height, "science layer height"); @@ -268,7 +252,6 @@ static void layered_image_bindings(py::module& m) { }) .def("binarize_mask", &li::binarize_mask, pydocs::DOC_LayeredImage_binarize_mask) .def("apply_mask", &li::apply_mask, pydocs::DOC_LayeredImage_apply_mask) - .def("sub_template", &li::subtract_template, pydocs::DOC_LayeredImage_sub_template) .def("get_science", &li::get_science, py::return_value_policy::reference_internal, pydocs::DOC_LayeredImage_get_science) .def("get_mask", &li::get_mask, py::return_value_policy::reference_internal, diff --git a/src/kbmod/search/layered_image.h b/src/kbmod/search/layered_image.h index 5b5edf156..186950142 100644 --- a/src/kbmod/search/layered_image.h +++ b/src/kbmod/search/layered_image.h @@ -68,9 +68,6 @@ class LayeredImage { void binarize_mask(int flags_to_keep); void apply_mask(int flags); - // Subtracts a template image from the science layer. - void subtract_template(RawImage& sub_template); - // Setter functions for the individual layers. void set_science(RawImage& im); void set_mask(RawImage& im); diff --git a/src/kbmod/search/pydocs/layered_image_docs.h b/src/kbmod/search/pydocs/layered_image_docs.h index 02ce08ba0..c706d1283 100644 --- a/src/kbmod/search/pydocs/layered_image_docs.h +++ b/src/kbmod/search/pydocs/layered_image_docs.h @@ -66,10 +66,6 @@ static const auto DOC_LayeredImage_apply_mask = R"doc( call binarize_mask() first. )doc"; -static const auto DOC_LayeredImage_sub_template = R"doc( - Subtract given image template - )doc"; - static const auto DOC_LayeredImage_get_science = R"doc( Returns the science layer raw_image. )doc"; diff --git a/tests/test_layered_image.py b/tests/test_layered_image.py index e2b44bcb6..5571b9e1a 100644 --- a/tests/test_layered_image.py +++ b/tests/test_layered_image.py @@ -284,39 +284,6 @@ def test_psi_and_phi_image(self): else: self.assertFalse(pixel_value_valid(phi.get_pixel(y, x))) - def test_subtract_template(self): - sci = self.image.get_science() - sci.mask_pixel(7, 10) - sci.mask_pixel(7, 11) - sci.set_pixel(7, 12, math.nan) - sci.set_pixel(7, 13, np.nan) - old_sci = RawImage(sci.image.copy()) # Make a copy. - - template = RawImage(self.image.get_width(), self.image.get_height()) - template.set_all(0.0) - for h in range(sci.height): - for w in range(4, sci.width): - template.set_pixel(h, w, 0.01 * h) - self.image.sub_template(template) - - for y in range(sci.height): - for x in range(sci.width): - if y == 7 and (x >= 10 and x <= 13): - self.assertFalse(sci.pixel_has_data(y, x)) - elif x < 4: - val1 = old_sci.get_pixel(y, x) - val2 = sci.get_pixel(y, x) - self.assertEqual(val1, val2) - else: - val1 = old_sci.get_pixel(y, x) - 0.01 * y - val2 = sci.get_pixel(y, x) - self.assertAlmostEqual(val1, val2, delta=1e-5) - - # Test that we fail when the template size does not match. - template2 = RawImage(self.image.get_width(), self.image.get_height() + 1) - template2.set_all(0.0) - self.assertRaises(RuntimeError, self.image.sub_template, template2) - if __name__ == "__main__": unittest.main() From 32db854574206505f81a0665a9219cd62e622ed1 Mon Sep 17 00:00:00 2001 From: Jeremy Kubica <104161096+jeremykubica@users.noreply.github.com> Date: Mon, 16 Dec 2024 09:46:44 -0500 Subject: [PATCH 06/10] Remove compute_fraction_masked --- src/kbmod/search/layered_image.cpp | 15 --------------- src/kbmod/search/layered_image.h | 3 --- src/kbmod/search/pydocs/layered_image_docs.h | 10 ---------- tests/test_layered_image.py | 14 -------------- 4 files changed, 42 deletions(-) diff --git a/src/kbmod/search/layered_image.cpp b/src/kbmod/search/layered_image.cpp index 2f7e9372c..19affd537 100644 --- a/src/kbmod/search/layered_image.cpp +++ b/src/kbmod/search/layered_image.cpp @@ -196,19 +196,6 @@ RawImage LayeredImage::generate_phi_image() { return result; } -double LayeredImage::compute_fraction_masked() const { - double masked_count = 0.0; - double total_count = 0.0; - - for (int j = 0; j < height; ++j) { - for (int i = 0; i < width; ++i) { - if (!science_pixel_has_data({j, i})) masked_count += 1.0; - total_count++; - } - } - return masked_count / total_count; -} - #ifdef Py_PYTHON_H static void layered_image_bindings(py::module& m) { using li = search::LayeredImage; @@ -268,8 +255,6 @@ static void layered_image_bindings(py::module& m) { .def("get_npixels", &li::get_npixels, pydocs::DOC_LayeredImage_get_npixels) .def("get_obstime", &li::get_obstime, pydocs::DOC_LayeredImage_get_obstime) .def("set_obstime", &li::set_obstime, pydocs::DOC_LayeredImage_set_obstime) - .def("compute_fraction_masked", &li::compute_fraction_masked, - pydocs::DOC_LayeredImage_compute_fraction_masked) .def("generate_psi_image", &li::generate_psi_image, pydocs::DOC_LayeredImage_generate_psi_image) .def("generate_phi_image", &li::generate_phi_image, pydocs::DOC_LayeredImage_generate_phi_image); } diff --git a/src/kbmod/search/layered_image.h b/src/kbmod/search/layered_image.h index 186950142..570448541 100644 --- a/src/kbmod/search/layered_image.h +++ b/src/kbmod/search/layered_image.h @@ -83,9 +83,6 @@ class LayeredImage { RawImage generate_psi_image(); RawImage generate_phi_image(); - // Debugging and statistics functions. - double compute_fraction_masked() const; - private: unsigned width; unsigned height; diff --git a/src/kbmod/search/pydocs/layered_image_docs.h b/src/kbmod/search/pydocs/layered_image_docs.h index c706d1283..0936157be 100644 --- a/src/kbmod/search/pydocs/layered_image_docs.h +++ b/src/kbmod/search/pydocs/layered_image_docs.h @@ -218,16 +218,6 @@ static const auto DOC_LayeredImage_generate_phi_image = R"doc( A ``RawImage`` of the same dimensions as the ``LayeredImage``. )doc"; -static const auto DOC_LayeredImage_compute_fraction_masked = R"doc( - Computes the fraction of pixels in the layered image that are masked. - This can be used for debugging purposes. - - Returns - ------- - value : `double` - The fraction of pixels in the science image that are masked. - )doc"; - } // namespace pydocs #endif /* LAYEREDIMAGE_DOCS */ diff --git a/tests/test_layered_image.py b/tests/test_layered_image.py index 5571b9e1a..e17a43f18 100644 --- a/tests/test_layered_image.py +++ b/tests/test_layered_image.py @@ -181,20 +181,6 @@ def test_mask_pixel(self): self.assertEqual(pixel_value_valid(pix_val), expected) self.assertEqual(self.image.science_pixel_has_data(y, x), expected) - def test_compute_fraction_masked(self): - total_pixels = self.width * self.height - - # Mask 50 pixels - for y in range(0, 10): - for x in range(0, 5): - self.image.mask_pixel(y, x) - self.assertAlmostEqual(self.image.compute_fraction_masked(), 50.0 / total_pixels) - - # Mask another 25 pixels. - for x in range(3, 28): - self.image.mask_pixel(12, x) - self.assertAlmostEqual(self.image.compute_fraction_masked(), 75.0 / total_pixels) - def test_binarize_mask(self): # Mask out a range of pixels. mask = self.image.get_mask() From 1efe4337ce47bcfdb85b4174deee1893b270796c Mon Sep 17 00:00:00 2001 From: Jeremy Kubica <104161096+jeremykubica@users.noreply.github.com> Date: Mon, 16 Dec 2024 11:06:43 -0500 Subject: [PATCH 07/10] Upgrade to numpy 2.0 --- include/eigen | 2 +- lib/pybind11 | 2 +- pyproject.toml | 26 ++++++++++++-------------- 3 files changed, 14 insertions(+), 16 deletions(-) diff --git a/include/eigen b/include/eigen index 6d829e766..c01ff4531 160000 --- a/include/eigen +++ b/include/eigen @@ -1 +1 @@ -Subproject commit 6d829e766ff1b1ab867d93631163cbc63ed5798f +Subproject commit c01ff45312582b2ea896ee307a49165ca4790332 diff --git a/lib/pybind11 b/lib/pybind11 index 3efe9d4cb..741d86f2e 160000 --- a/lib/pybind11 +++ b/lib/pybind11 @@ -1 +1 @@ -Subproject commit 3efe9d4cb5d7314faee722205e560b8b932aed9e +Subproject commit 741d86f2e3527b667ba85d273a5eea19a0978ef5 diff --git a/pyproject.toml b/pyproject.toml index 5ad3bb598..55077fa27 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,16 +32,17 @@ classifiers = [ ] dynamic = ["version"] dependencies = [ - "astropy>=5.1", - "astroquery>=0.4.6", + "astropy>=6.1", + "astroquery>=0.4.7", + "dask[array]>=2024.4.1", # Needed for astropy library dependency "jax", - "joblib>=1.4", - "matplotlib>=3.5", - "numpy<2.0", - "pandas>=1.5.1", + "jaxlib", + "matplotlib>=3.9", + "numpy>=2.0", + "pandas>=2.2", # Needed for scikit_learn "reproject", - "scipy>=1.9.2", - "scikit_learn>=1.0.0", + "scipy>=1.13", + "scikit_learn>=1.5.0", "koffi>=0.1.1", "jplephem", "PyYAML>=6.0" @@ -55,15 +56,12 @@ Changelog = "https://epyc.astro.washington.edu/~kbmod/project_details/release_no [project.optional-dependencies] analysis = [ - "tensorflow>=2.9", - "matplotlib>=3.6.1", + "ephem>=4.1", "ipywidgets>=8.0", - "ephem>=4.1" ] docs = [ - "sphinx", - # https://github.com/spatialaudio/nbsphinx/issues/655 - "sphinx-gallery<0.11", + "sphinx>=8.0", + "sphinx-gallery", "sphinx-codeautolink", "sphinx-design", "numpydoc", From 0e08c1fcae0f40145d68e13ffb94c4ab3e61e421 Mon Sep 17 00:00:00 2001 From: Jeremy Kubica <104161096+jeremykubica@users.noreply.github.com> Date: Mon, 16 Dec 2024 11:53:50 -0500 Subject: [PATCH 08/10] Remove duplicate function --- src/kbmod/file_utils.py | 70 ----------------------------------------- 1 file changed, 70 deletions(-) delete mode 100644 src/kbmod/file_utils.py diff --git a/src/kbmod/file_utils.py b/src/kbmod/file_utils.py deleted file mode 100644 index fcc1fdde9..000000000 --- a/src/kbmod/file_utils.py +++ /dev/null @@ -1,70 +0,0 @@ -"""A collection of utility functions for working with files in KBMOD.""" - -import csv -import re -from collections import OrderedDict -from itertools import product -from math import copysign -from pathlib import Path - -import astropy.units as u -import numpy as np -from astropy.coordinates import * -from astropy.io import fits -from astropy.time import Time - -import kbmod.search as kb -from kbmod.search import LayeredImage -from kbmod.trajectory_utils import trajectory_from_np_object - - -def load_deccam_layered_image(filename, psf): - """Load a layered image from the legacy deccam format. - - Parameters - ---------- - filename : `str` - The name of the file to load. - psf : `PSF` - The PSF to use for the image. - - Returns - ------- - img : `LayeredImage` - The loaded image. - - Raises - ------ - Raises a ``FileNotFoundError`` if the file does not exist. - Raises a ``ValueError`` if any of the validation checks fail. - """ - if not Path(filename).is_file(): - raise FileNotFoundError(f"{filename} not found") - - img = None - with fits.open(filename) as hdul: - if len(hdul) < 4: - raise ValueError("Not enough extensions for legacy deccam format") - - # Extract the obstime trying from a few keys and a few extensions. - obstime = -1.0 - for key, ext in product(["MJD", "DATE-AVG", "MJD-OBS"], [0, 1]): - if key in hdul[ext].header: - value = hdul[ext].header[key] - if type(value) is float: - obstime = value - break - if type(value) is str: - timesys = hdul[ext].header.get("TIMESYS", "UTC").lower() - obstime = Time(value, scale=timesys).mjd - break - - img = LayeredImage( - hdul[1].data.astype(np.float32), # Science - hdul[3].data.astype(np.float32), # Variance - hdul[2].data.astype(np.float32), # Mask - psf, - obstime, - ) - - return img From 8f88b3e9a7582dd4750f16a86482e4fc41069b04 Mon Sep 17 00:00:00 2001 From: Jeremy Kubica <104161096+jeremykubica@users.noreply.github.com> Date: Mon, 16 Dec 2024 15:31:40 -0500 Subject: [PATCH 09/10] Remove unused make_global_mask() function --- src/kbmod/search/image_stack.cpp | 28 ---------------------- src/kbmod/search/image_stack.h | 3 --- src/kbmod/search/pydocs/image_stack_docs.h | 21 ---------------- tests/test_image_stack.py | 17 ------------- 4 files changed, 69 deletions(-) diff --git a/src/kbmod/search/image_stack.cpp b/src/kbmod/search/image_stack.cpp index ea64ce36e..c8e13b5b0 100644 --- a/src/kbmod/search/image_stack.cpp +++ b/src/kbmod/search/image_stack.cpp @@ -144,33 +144,6 @@ void ImageStack::clear_from_gpu() { data_on_gpu = false; } -RawImage ImageStack::make_global_mask(int flags, int threshold) { - uint64_t npixels = get_npixels(); - - // Start with an empty global mask. - RawImage global_mask = RawImage(width, height); - global_mask.set_all(0.0); - - // For each pixel count the number of images where it is masked. - std::vector counts(npixels, 0); - for (unsigned int img = 0; img < images.size(); ++img) { - auto imgMask = images[img].get_mask().get_image().reshaped(); - - // Count the number of times a pixel has any of the given flags - for (uint64_t pixel = 0; pixel < npixels; ++pixel) { - if ((flags & static_cast(imgMask[pixel])) != 0) counts[pixel]++; - } - } - - // Set all pixels below threshold to 0 and all above to 1 - auto global_m = global_mask.get_image().reshaped(); - for (uint64_t p = 0; p < npixels; ++p) { - global_m[p] = counts[p] < threshold ? 0.0 : 1.0; - } - - return global_mask; -} - #ifdef Py_PYTHON_H static void image_stack_bindings(py::module& m) { using is = search::ImageStack; @@ -194,7 +167,6 @@ static void image_stack_bindings(py::module& m) { .def("build_zeroed_times", &is::build_zeroed_times, pydocs::DOC_ImageStack_build_zeroed_times) .def("sort_by_time", &is::sort_by_time, pydocs::DOC_ImageStack_sort_by_time) .def("img_count", &is::img_count, pydocs::DOC_ImageStack_img_count) - .def("make_global_mask", &is::make_global_mask, pydocs::DOC_ImageStack_make_global_mask) .def("convolve_psf", &is::convolve_psf, pydocs::DOC_ImageStack_convolve_psf) .def("get_width", &is::get_width, pydocs::DOC_ImageStack_get_width) .def("get_height", &is::get_height, pydocs::DOC_ImageStack_get_height) diff --git a/src/kbmod/search/image_stack.h b/src/kbmod/search/image_stack.h index 20c59abc4..0eb5310d2 100644 --- a/src/kbmod/search/image_stack.h +++ b/src/kbmod/search/image_stack.h @@ -47,9 +47,6 @@ class ImageStack { void convolve_psf(); - // Make and return a global mask. - RawImage make_global_mask(int flags, int threshold); - virtual ~ImageStack(); // Functions to handle transfering data to/from GPU. diff --git a/src/kbmod/search/pydocs/image_stack_docs.h b/src/kbmod/search/pydocs/image_stack_docs.h index b6f8344e5..a500175c3 100644 --- a/src/kbmod/search/pydocs/image_stack_docs.h +++ b/src/kbmod/search/pydocs/image_stack_docs.h @@ -130,27 +130,6 @@ static const auto DOC_ImageStack_sort_by_time = R"doc( Raises a ``RuntimeError`` if the input image is the data is currently on GPU. )doc"; -static const auto DOC_ImageStack_make_global_mask = R"doc( - Create a new global mask from a set of flags and a threshold. - The global mask marks a pixel as masked if and only if it is masked - by one of the given flags in at least ``threshold`` individual images. - The returned mask is binary. - - Parameters - ---------- - flags : `int` - A bit mask of mask flags to use when counting. - threshold : `int` - The minimum number of images in which a pixel must be masked to be - part of the global mask. - - Returns - ------- - global_mask : `RawImage` - A RawImage containing the global mask with 1 for masked pixels - and 0 for unmasked pixels. - )doc"; - static const auto DOC_ImageStack_convolve_psf = R"doc( Convolves each image (science and variance layers) with the PSF stored in the LayeredImage object. diff --git a/tests/test_image_stack.py b/tests/test_image_stack.py index a3eb346da..6ccade166 100644 --- a/tests/test_image_stack.py +++ b/tests/test_image_stack.py @@ -98,23 +98,6 @@ def test_times(self): for i in range(self.num_images): self.assertEqual(times[i], 2.0 * i) - def test_make_global_mask(self): - # Mask a single point in each image with flag=2. - for i in range(self.num_images): - self.images[i].get_mask().set_pixel(5, 5, 2) - - # Apply the global mask for flag=1 and a threshold of the bit set - # in at least one mask. Note this ignores flag=2 for the count. - mask = self.im_stack.make_global_mask(1, 1) - - # Check that the correct pixels are masked - for y in range(self.im_stack.get_height()): - for x in range(self.im_stack.get_width()): - if y == 10 and x >= 10 and x <= 10 + (self.num_images - 1): - self.assertEqual(mask.get_pixel(y, x), 1) - else: - self.assertEqual(mask.get_pixel(y, x), 0) - # WOW, this is the first test that caught the fact that interpolated_add # called add, and that add had flipped i and j by accident. 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