Stochastic Gradient Descent (SGD) example and functionality

.github/workflows/tests.yml

@@ -65,13 +65,9 @@ jobs:
       - name: Install test dependencies
         run: |
           pip install .[test]
-      - name: Lint with flake8
+      - name: Lint with ruff
         run: |
-          pip install flake8
-          # stop the build if there are Python syntax errors or undefined names
-          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+          ruff check .
       - name: Check types with MyPy
         run: |
           mypy src/mpol --pretty

.gitignore

@@ -144,4 +144,6 @@ plotsdir
 runs
 
 # hatch-generated version file
-src/mpol/mpol_version.py
+src/mpol/mpol_version.py
+
+.ruff_cache

CONTRIBUTORS.md

@@ -10,3 +10,4 @@ Contributors
 * Hannah Grzybowski, `@hgrzy`
 * Mary Ogborn
 * Tyler Quinn, `@trq5014`
+* Kristin Hopley

docs/_static/baselines/src/print_conversions.py

@@ -1,3 +1,8 @@
+import csv
+
+import numpy as np
+from mpol.constants import c_ms
+
 import argparse
 
 parser = argparse.ArgumentParser(
@@ -6,11 +11,6 @@
 parser.add_argument("outfile", help="Destination to save CSV table.")
 args = parser.parse_args()
 
-import csv
-
-import numpy as np
-
-from mpol.constants import c_ms
 
 header = ["baseline", "100 GHz (Band 3)", "230 GHz (Band 6)", "340 GHz (Band 7)"]
 
@@ -20,18 +20,18 @@
 
 def format_baseline(baseline_m):
     if baseline_m < 1e3:
-        return "{:.0f} m".format(baseline_m)
+        return f"{baseline_m:.0f} m"
     elif baseline_m < 1e6:
-        return "{:.0f} km".format(baseline_m * 1e-3)
+        return f"{baseline_m * 1e-3:.0f} km"
 
 
 def format_lambda(lam):
     if lam < 1e3:
-        return "{:.0f}".format(lam) + " :math:`\lambda`"
+        return f"{lam:.0f}" + r" :math:`\lambda`"
     elif lam < 1e6:
-        return "{:.0f}".format(lam * 1e-3) + " :math:`\mathrm{k}\lambda`"
+        return f"{lam * 1e-3:.0f}" + r" :math:`\mathrm{k}\lambda`"
     else:
-        return "{:.0f}".format(lam * 1e-6) + " :math:`\mathrm{M}\lambda`"
+        return f"{lam * 1e-6:.0f}" + r" :math:`\mathrm{M}\lambda`"
 
 
 data = []

docs/_static/fftshift/src/plot.py

@@ -1,19 +1,19 @@
-import argparse
-
-parser = argparse.ArgumentParser(description="Create the fftshift plot")
-parser.add_argument("outfile", help="Destination to save plot.")
-args = parser.parse_args()
-
 import matplotlib.pyplot as plt
 import numpy as np
 from astropy.io import fits
 from astropy.utils.data import download_file
 from matplotlib import patches
 from matplotlib.colors import LogNorm
 from matplotlib.gridspec import GridSpec
-
 from mpol import coordinates
 
+import argparse
+
+parser = argparse.ArgumentParser(description="Create the fftshift plot")
+parser.add_argument("outfile", help="Destination to save plot.")
+args = parser.parse_args()
+
+
 fname = download_file(
     "https://zenodo.org/record/4711811/files/logo_cont.fits",
     cache=True,

docs/changelog.md

@@ -3,7 +3,11 @@
 # Changelog
 
 ## v0.3.0
-
+- removed explicit type declarations in base MPoL modules. Previously, core representations were set to be in `float64` or `complex128`. Now, core MPoL representations (e.g., {class}`mpol.images.BaseCube`) will follow the [default tensor type](https://pytorch.org/docs/stable/generated/torch.set_default_tensor_type.html), which is commonly `torch.float32`. If you want your model to run fully in `float32` or `complex64`, then be sure that your data is also in these formats, since otherwise PyTorch will promote downstream tensors as needed. Fully `float32` or `complex64` models should be able to run on Apple MPS [#254](https://github.com/MPoL-dev/MPoL/issues/254) 
+- added {meth}`mpol.utils.convolve_packed_cube` method to convolve a 3D packed image cube with a 2D Gaussian. You can specify major axis, minor axis, and rotation angle.
+- added {meth}`mpol.utils.uv_gaussian_taper` to calculate a Gaussian tapering window in the visibility plane.
+- added the `vis_ext_Mlam` instance attribute to {class}`mpol.coordinates.GridCoords` for convenience plotting of visibility grids with axes labels in units of M$\lambda$.
+- Updated [MPoL-dev/examples](https://github.com/MPoL-dev/examples) with Stochastic Gradient Descent Example.
 - Standardized nomenclature of {class}`mpol.coordinates.GridCoords` and {class}`mpol.fourier.FourierCube` to use `sky_cube` for a normal image and `ground_cube` for a normal visibility cube (rather than `sky_` for visibility quantities). Routines use `packed_cube` instead of `cube` internally to be clear when packed format is preferred.
 - Modified {class}`mpol.coordinates.GridCoords` object to use cached properties [#187](https://github.com/MPoL-dev/MPoL/pull/187).
 - Changed the base spatial frequency unit from k$\lambda$ to $\lambda$, addressing [#223](https://github.com/MPoL-dev/MPoL/issues/223). This will affect most users data-reading routines!

docs/conf.py

@@ -1,4 +1,3 @@
-import os
 
 # -- Project information -----------------------------------------------------
 from pkg_resources import DistributionNotFound, get_distribution
@@ -46,7 +45,7 @@
 autodoc_mock_imports = ["torch", "torchvision"]
 autodoc_member_order = "bysource"
 # https://github.com/sphinx-doc/sphinx/issues/9709
-# bug that if we set this here, we can't list individual members in the 
+# bug that if we set this here, we can't list individual members in the
 # actual API doc
 # autodoc_default_options = {"members": None}
 

pyproject.toml

@@ -36,7 +36,6 @@ dev = [
     "mypy",
     "frank>=1.2.1",
     "sphinx>=7.2.0",
-    "sphinx-autodoc2",
     "jupytext",
     "ipython!=8.7.0", # broken version for syntax higlight https://github.com/spatialaudio/nbsphinx/issues/687
     "nbsphinx",
@@ -51,7 +50,8 @@ dev = [
     "asdf",
     "pyro-ppl",
     "arviz[all]",
-    "visread>=0.0.4"
+    "visread>=0.0.4",
+    "ruff"
 ]
 test = [
     "pytest",
@@ -62,6 +62,7 @@ test = [
     "mypy",
     "visread>=0.0.4",
     "frank>=1.2.1",
+    "ruff"
 ]
 
 [project.urls]
@@ -105,4 +106,18 @@ module = [
     "MPoL.precomposed",
     "MPoL.utils"
     ]
-disallow_untyped_defs = true
+disallow_untyped_defs = true
+
+[tool.ruff]
+target-version = "py310"
+line-length = 88
+# will enable after sorting module locations
+# select = ["F", "I", "E", "W", "YTT", "B", "Q", "PLE", "PLR", "PLW", "UP"]
+lint.ignore = [
+    "E741",    # Allow ambiguous variable names
+    "PLR0911", # Allow many return statements
+    "PLR0913", # Allow many arguments to functions
+    "PLR0915", # Allow many statements
+    "PLR2004", # Allow magic numbers in comparisons
+]
+exclude = []

src/mpol/__init__.py

@@ -1,2 +1 @@
-from mpol.mpol_version import __version__
 zenodo_record = 10064221

src/mpol/coordinates.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
-from functools import cached_property
 
+from functools import cached_property
 from typing import Any
 
 import numpy as np
@@ -10,7 +10,7 @@
 
 import mpol.constants as const
 from mpol.exceptions import CellSizeError
-from mpol.utils import get_max_spatial_freq, get_maximum_cell_size
+from mpol.utils import get_maximum_cell_size
 
 
 class GridCoords:
@@ -79,6 +79,7 @@ class GridCoords:
     :ivar vis_ext: length-4 list of (left, right, bottom, top) expected by routines
         like ``matplotlib.pyplot.imshow`` in the ``extent`` parameter assuming
         ``origin='lower'``. Units of [:math:`\lambda`]
+    :ivar vis_ext_Mlam: like vis_ext, but in units of [:math:`\mathrm{M}\lambda`].
     """
 
     def __init__(self, cell_size: float, npix: int):
@@ -205,16 +206,18 @@ def vis_ext(self) -> list[float]:
             self.u_bin_max,
             self.v_bin_min,
             self.v_bin_max,
-        ]  # [kλ]
+        ]  # [λ]
+
+    @property
+    def vis_ext_Mlam(self) -> list[float]:
+        return [1e-6 * edge for edge in self.vis_ext]
 
-    # --------------------------------------------------------------------------
-    # Non-identical u & v properties
-    # --------------------------------------------------------------------------
     @cached_property
     def ground_u_centers_2D(self) -> npt.NDArray[np.floating[Any]]:
         # only useful for plotting a sky_vis
         # uu increasing, no fftshift
-        # tile replicates the 1D u_centers array to a 2D array the size of the full UV grid
+        # tile replicates the 1D u_centers array to a 2D array the size of the full
+        # UV grid
         return np.tile(self.u_centers, (self.npix_u, 1))
 
     @cached_property
@@ -304,10 +307,10 @@ def check_data_fit(
 
         Parameters
         ----------
-        uu : :class:`torch.Tensor` of `torch.double`
+        uu : :class:`torch.Tensor`
             u spatial frequency coordinates.
             Units of [:math:`\lambda`]
-        vv : :class:`torch.Tensor` of `torch.double`
+        vv : :class:`torch.Tensor`
             v spatial frequency coordinates.
             Units of [:math:`\lambda`]
 
@@ -354,6 +357,6 @@ def __eq__(self, other: Any) -> bool:
             # don't attempt to compare against different types
             return NotImplemented
 
-        # GridCoords objects are considered equal if they have the same cell_size and npix, since
-        # all other attributes are derived from these two core properties.
+        # GridCoords objects are considered equal if they have the same cell_size and
+        # npix, since all other attributes are derived from these two core properties.
         return bool(self.cell_size == other.cell_size and self.npix == other.npix)

src/mpol/crossval.py

@@ -2,7 +2,6 @@
 
 import copy
 import logging
-from collections import defaultdict
 from typing import Any
 
 import numpy as np
@@ -11,11 +10,9 @@
 from numpy.typing import NDArray
 
 from mpol.datasets import Dartboard, GriddedDataset
-from mpol.precomposed import GriddedNet
+
 # from mpol.training import TrainTest, train_to_dirty_image
 # from mpol.training import TrainTest, train_to_dirty_image
-from mpol.plot import split_diagnostics_fig
-from mpol.utils import loglinspace
 
 
 # class CrossValidate:
@@ -59,7 +56,8 @@
 #         Number of k-folds to use in cross-validation
 #     split_method : str, default='dartboard'
 #         Method to split full dataset into train/test subsets
-#     dartboard_q_edges, dartboard_phi_edges : list of float, default=None, unit=[klambda]
+#     dartboard_q_edges, dartboard_phi_edges : list of float, default=None, 
+#   unit=[klambda]
 #         Radial and azimuthal bin edges of the cells used to split the dataset
 #         if `split_method`==`dartboard` (see `datasets.Dartboard`)
 #     split_diag_fig : bool, default=False

src/mpol/datasets.py

@@ -7,9 +7,8 @@
 from numpy import floating, integer
 from numpy.typing import ArrayLike, NDArray
 
-from mpol.coordinates import GridCoords
-
 from mpol import utils
+from mpol.coordinates import GridCoords
 
 
 class GriddedDataset(torch.nn.Module):
@@ -20,7 +19,7 @@ class GriddedDataset(torch.nn.Module):
         If providing this, cannot provide ``cell_size`` or ``npix``.
     vis_gridded : :class:`torch.Tensor` of :class:`torch.complex128`
         the gridded visibility data stored in a "packed" format (pre-shifted for fft)
-    weight_gridded : :class:`torch.Tensor` of :class:`torch.double`
+    weight_gridded : :class:`torch.Tensor` 
         the weights corresponding to the gridded visibility data,
         also in a packed format
     mask : :class:`torch.Tensor` of :class:`torch.bool`