Merge pull request #985 from HERA-Team/new-release

Prepare for new release with updated hera-sim

.github/workflows/publish.yaml

@@ -23,11 +23,15 @@ jobs:
           python-version: ${{ env.PYTHON }}
 
       - name: Install build
-        run: pip install build
+        run: pip install build twine
 
       - name: Build a binary wheel and a source tarball
         run: |
           python -m build
+
+      - name: Twine Check
+        run: twine check dist/*
+
       - name: Publish to PyPI
         if: startsWith(github.event.ref, 'refs/tags')
         uses: pypa/[email protected]

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ exclude: 'hera_cal/data/'
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.6.0
+  rev: v5.0.0
   hooks:
   - id: trailing-whitespace
   - id: check-added-large-files
@@ -22,7 +22,7 @@ repos:
 
 - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
-  rev: v0.5.0
+  rev: v0.9.1
   hooks:
     # Run the linter.
     - id: ruff

hera_cal/io.py

@@ -1762,8 +1762,8 @@ def get_file_times(filepaths, filetype='uvh5'):
             assert AIPY, "you need aipy to use the miriad filetype"
             uv = aipy.miriad.UV(f)
             # get integration time
-            int_time = uv['inttime'] / (units.si.day.in_units(units.si.s))
-            int_time_rad = uv['inttime'] * 2 * np.pi / (units.si.sday.in_units(units.si.s))
+            int_time = uv['inttime'] / (units.si.day.to(units.si.s))
+            int_time_rad = uv['inttime'] * 2 * np.pi / (units.si.sday.to(units.si.s))
             # get start and stop, add half an integration
             start_lst = uv['lst'] + int_time_rad / 2.0
             start_time = uv['time'] + int_time / 2.0

hera_cal/noise.py

@@ -92,7 +92,7 @@ def predict_noise_variance_from_autos(bl, data, dt=None, df=None, nsamples=None,
         Noise variance predicted on baseline bl in units of data squared.
     '''
     if dt is None:
-        dt = infer_dt(data.times_by_bl, bl) * units.si.day.in_units(units.si.s)
+        dt = infer_dt(data.times_by_bl, bl) * units.si.day.to(units.si.s)
     if df is None:
         assert (len(data.freqs) > 1)  # cannot infer channel width if only one channel is present
         df = np.median(np.ediff1d(data.freqs))
@@ -102,7 +102,7 @@ def predict_noise_variance_from_autos(bl, data, dt=None, df=None, nsamples=None,
         auto_bl1, auto_bl2 = (bl[0], bl[0], join_pol(ap1, ap1)), (bl[1], bl[1], join_pol(ap2, ap2))
     else:
         auto_bl1, auto_bl2 = (auto_ant, auto_ant, join_pol(ap1, ap1)), (auto_ant, auto_ant, join_pol(ap2, ap2))
-    
+
     var = np.abs(data[auto_bl1] * data[auto_bl2] / dt / df)
     if nsamples is not None:
         return var / nsamples[bl]

hera_cal/tests/mock_uvdata.py

@@ -173,7 +173,7 @@ def add_noise_to_uvd(uvd, autos: bool = False):
     hd = io.to_HERAData(uvd)
 
     data, flags, nsamples = hd.read()
-    dt = (data.times[1] - data.times[0]) * units.si.day.in_units(units.si.s)
+    dt = (data.times[1] - data.times[0]) * units.si.day.to(units.si.s)
     df = data.freqs[1] - data.freqs[0]
 
     for bl in data.bls():

pyproject.toml

@@ -7,6 +7,61 @@ write_to = "hera_cal/_version.py"
 parentdir_prefix_version = "hera_cal-"
 fallback_version  = "0.0.0"
 
+[project]
+name = "hera-calibration"
+description = "A collection of calibration routines to run on the HERA instrument."
+readme = "README.md"
+authors = [
+  {name="HERA Team"},
+]
+license = {text="BSD"}
+classifiers = [
+  "Development Status :: 4 - Beta",
+  "Intended Audience :: Science/Research",
+  "License :: OSI Approved",
+  "License :: OSI Approved :: MIT License",
+  "Natural Language :: English",
+  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3 :: Only",
+  "Topic :: Scientific/Engineering :: Astronomy",
+  "Topic :: Scientific/Engineering :: Physics",
+]
+dynamic=["version"]
+requires-python = ">=3.9"
+dependencies = [
+    'numpy>=1.10',
+    "scipy>=1.9.0",
+    "h5py",
+    "hdf5plugin",
+    "astropy",
+    "astropy-healpix",
+    "pyuvdata>=3.1.3",
+    "linsolve",
+    "hera_qm>=2.2.0",
+    "scikit-learn",
+    "hera-filters",
+    "line_profiler",
+    "aipy",
+    "rich",
+    "jax",
+    "jaxlib",
+    "optax",
+    "attrs",
+    "frozendict",
+    "toml",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest",
+    "pre-commit",
+    "pytest-cov",
+    "hera_sim>=4.3.0",
+    "pytest-xdist",
+    "hypothesis",
+    "ruff",
+]
+
 [tool.pytest]
 addopts = "--ignore=scripts --cov=hera_cal --cov-config=.coveragerc"
 
@@ -58,11 +113,16 @@ select = [
     # "UP",   # pyupgrade
 ]
 
+exclude = [
+    "scripts/notebooks/*.ipynb",  # For now, ignore notebooks when linting.
+]
+
 ignore = [
     "E402",
     "E501",
     "W291",
     "W293",
+    "A005",   # module io shadows python stdlib
     # Following Rulesets we really don't want
     "AIR",    # airflow - unused library
     "ASYNC",  # async  -- unused library

scripts/notebooks/omnical_convergence.ipynb

@@ -204,15 +204,15 @@
     "        k = kde.gaussian_kde(res)\n",
     "        xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]\n",
     "        zi = k(np.vstack([xi.flatten(), yi.flatten()]))\n",
-    "        plt.xlabel('$\\Delta$g, real')\n",
-    "        plt.ylabel('$\\Delta$g, imag')\n",
+    "        plt.xlabel(r'$\\Delta$g, real')\n",
+    "        plt.ylabel(r'$\\Delta$g, imag')\n",
     "        plt.xlim(-lim,lim); plt.ylim(-lim,lim)\n",
     "        ax.grid(b=True, which='major', color='k', linestyle='-')\n",
     "        \n",
     "        ax2 = plt.subplot(122, sharey=ax)\n",
     "        plt.grid()\n",
     "        _ = plt.contour(xi, yi, zi.reshape(xi.shape), alpha=.5)\n",
-    "        plt.xlabel('$\\Delta$g, real')\n",
+    "        plt.xlabel(r'$\\Delta$g, real')\n",
     "        plt.xlim(-lim,lim)\n",
     "        plt.setp(ax2.get_yticklabels(), visible=False)\n",
     "        ax2.grid(b=True, which='major', color='k', linestyle='-')"
@@ -657,7 +657,7 @@
     "    err = [np.sqrt(np.average(np.abs(r)**2) / r.size) for r in res]\n",
     "    color = 'rgbk'[i]\n",
     "    _ = plt.semilogy(ants, avg, color+'.', label=label+' <$x-x_0$>')\n",
-    "    _ = plt.semilogy(ants, err, color+'x', label=label+' $\\sigma$')\n",
+    "    _ = plt.semilogy(ants, err, color+'x', label=label+r' $\\sigma$')\n",
     "plt.ylabel('Residual Deviation')\n",
     "plt.xlabel('Antenna Index')\n",
     "plt.legend()\n",
@@ -764,7 +764,7 @@
     "for i in range(4):\n",
     "    sol = sols[i]\n",
     "    if i == 0:\n",
-    "        label1, label2 = '<$x-x_0$>', '$\\sigma$'\n",
+    "        label1, label2 = '<$x-x_0$>', r'$\\sigma$'\n",
     "    else:\n",
     "        label1, label2 = '', ''\n",
     "    sol_degen = info.remove_degen(antpos, sol, degen_sol=gains)\n",

scripts/notebooks/running_smooth_cal.ipynb

@@ -216,7 +216,7 @@
     "plt.subplot(121)\n",
     "plt.imshow(np.angle(original_gains[ant]) / (~cs.flag_grids[ant]), cmap='inferno', aspect='auto', extent=extent)\n",
     "plt.colorbar(label='Phase (radians)')\n",
-    "plt.title('Unfiltered: ' + str(ant));\n",
+    "plt.title('Unfiltered: ' + str(ant))\n",
     "plt.xlabel('Frequency (MHz)')\n",
     "plt.ylabel('JD -' + str(int(np.floor(cs.time_grid[0]))))\n",
     "\n",