Merge remote-tracking branch 'origin/main' into fix_lm_grid

.github/workflows/ci.yml

@@ -39,31 +39,37 @@ jobs:
           fetch-depth: 0
 
       - name: Prepare mamba installation
-        if: matrix.install-method == 'mamba'
+        if: matrix.install-method == 'mamba' &&  contains(github.event.pull_request.labels.*.name, 'documentation-only') == false
         env:
           PYTHON_VERSION: ${{ matrix.python-version }}
         run: |
           # setup correct python version
           sed -i -e "s/- python.*/- python=$PYTHON_VERSION/g" environment.yml
 
       - name: mamba setup
-        if: matrix.install-method == 'mamba'
+        if: matrix.install-method == 'mamba' &&  contains(github.event.pull_request.labels.*.name, 'documentation-only') == false
         uses: mamba-org/setup-micromamba@v1
+        with:
+          environment-file: environment.yml
+          cache-downloads: true
 
       - name: Install dependencies
+        env:
+          PYTHON_VERSION: ${{ matrix.python-version }}
         run: |
           python --version
           pip install pytest-cov restructuredtext-lint pytest-xdist 'coverage!=6.3.0'
-          pip install .[all]
+          pip install -e .[all]
           pip freeze
+          pip list
 
       - name: List installed package versions (conda)
         if: matrix.environment-type == 'mamba'
         run: micromamba list
 
       - name: Tests
         run: |
-          py.test --cov --cov-report=xml
+          pytest --cov --cov-report=xml
 
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v4

docs/changes/31.maintenance.rst

@@ -1 +1,2 @@
 - use observation class to pass sampling options to the fits writer
+- include writer in tests

docs/changes/33.feature.rst

@@ -0,0 +1,3 @@
+Changes to `vis_loop` function in `visibility.py`:
+- add a an optional tqdm progress bar to get a visual confirmation the calculation is still running
+- add optional `batch_size` parameter to control memory consumption

docs/changes/35.maintenance.rst

@@ -0,0 +1 @@
+- switch from setup.py to pyproject.toml

environment.yml

@@ -1,14 +1,18 @@
----
+# A conda environment with all useful packages for pyvisgen developers
 name: pyvisgen
 channels:
     - pytorch
     - defaults
     - conda-forge
 dependencies:
   - python
+  - astropy<=6.1.0
   - pytorch
   - numpy
+  - matplotlib
   - pip
-  - pip:
-    - towncrier
-    - -e .
+  - towncrier
+  - jupyter
+  - pytest
+  - pytest-cov
+  - pytest-runner

pyproject.toml

@@ -0,0 +1,94 @@
+[build-system]
+requires = ["setuptools>=64", "setuptools_scm[toml]>=8"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "pyvisgen"
+version = "0.2.0"
+description = "Simulate radio interferometer observations and visibility generation with the RIME formalism."
+readme = "README.md"
+authors = [
+    {name = "Kevin Schmidt, Felix Geyer, Stefan Fröse"},
+]
+maintainers = [
+    {name = "Kevin Schmidt", email = "[email protected]"},
+    {name = "Felix Geyer", email = "[email protected]"},
+]
+license = {text = "MIT"}
+classifiers = [
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Topic :: Scientific/Engineering :: Astronomy",
+    "Topic :: Scientific/Engineering :: Physics",
+    "Topic :: Scientific/Engineering :: Information Analysis",
+    "Development Status :: 4 - Beta",
+]
+
+requires-python = ">=3.10"
+
+dependencies = [
+    "numpy",
+    "astropy<=6.1.0",
+    "torch",
+    "matplotlib",
+    "ipython",
+    "scipy",
+    "pandas",
+    "toml",
+    "pytest",
+    "pytest-cov",
+    "jupyter",
+    "astroplan",
+    "torch",
+    "tqdm",
+    "numexpr",
+    "click",
+    "h5py",
+    "natsort",
+    "pre-commit",
+]
+
+[project.scripts]
+    pyvisgen_create_dataset = "pyvisgen.simulation.scripts.create_dataset:main"
+
+[tool.setuptools.packages.find]
+    where = ["pyvisgen"]
+
+[tool.towncrier]
+    package = "ctapipe"
+    directory = "docs/changes"
+    filename = "CHANGES.rst"
+    template = "docs/changes/template.rst"
+    # let towncrier create proper links to the merged PR
+    issue_format = "`#{issue} <https://github.com/cta-observatory/ctapipe/pull/{issue}>`__"
+
+    [tool.towncrier.fragment.feature]
+        name = "New Features"
+        showcontent = true
+
+    [tool.towncrier.fragment.bugfix]
+        name = "Bug Fixes"
+        showcontent = true
+
+    [tool.towncrier.fragment.api]
+        name = "API Changes"
+        showcontent = true
+
+    [tool.towncrier.fragment.datamodel]
+        name = "Data Model Changes"
+        showcontent = true
+
+    [tool.towncrier.fragment.optimization]
+        name = "Refactoring and Optimization"
+        showcontent = true
+
+    [tool.towncrier.fragment.maintenance]
+        name = "Maintenance"
+        showcontent = true
+
+    [[tool.towncrier.section]]
+        name = ""
+        path = ""

pyvisgen/fits/writer.py

@@ -39,7 +39,7 @@ def create_vis_hdu(data, obs, layout="vlba", source_name="sim-source-0"):
     ra = obs.ra.cpu().numpy().item()
     dec = obs.dec.cpu().numpy().item()
     freq = obs.ref_frequency.cpu().numpy().item()
-    freq_d = obs.bandwidths.cpu().numpy().item()
+    freq_d = obs.bandwidths[0].cpu().numpy().item()
 
     ws = wcs.WCS(naxis=7)
     ws.wcs.crpix = [1, 1, 1, 1, 1, 1, 1]

pyvisgen/simulation/visibility.py

@@ -4,6 +4,8 @@
 
 import pyvisgen.simulation.scan as scan
 
+from tqdm import tqdm
+
 
 @dataclass
 class Visibilities:
@@ -37,7 +39,7 @@ def add(self, visibilities):
         ]
 
 
-def vis_loop(obs, SI, num_threads=10, noisy=True, mode="full"):
+def vis_loop(obs, SI, num_threads=10, noisy=True, mode="full", batch_size=100, show_progress=False):
     torch.set_num_threads(num_threads)
     torch._dynamo.config.suppress_errors = True
 
@@ -93,7 +95,12 @@ def vis_loop(obs, SI, num_threads=10, noisy=True, mode="full"):
     else:
         raise ValueError("Unsupported mode!")
 
-    for p in torch.arange(bas[:].shape[1]).split(1000):
+    batches = torch.arange(bas[:].shape[1]).split(batch_size)
+
+    if show_progress:
+        batches = tqdm(batches)
+
+    for p in batches:
         bas_p = bas[:][:, p]
 
         int_values = torch.cat(

setup.py

@@ -13,7 +13,6 @@
     packages=find_packages(),
     install_requires=[
         "numpy",
-        "astropy",
         "matplotlib",
         "ipython",
         "scipy",

tests/test_simulation.py

@@ -30,6 +30,7 @@ def test_create_sampling_rc():
 def test_vis_loop():
     import torch
 
+    import pyvisgen.fits.writer as writer
     from pyvisgen.simulation.data_set import create_observation
     from pyvisgen.simulation.visibility import vis_loop
     from pyvisgen.utils.data import load_bundles, open_bundles
@@ -57,3 +58,9 @@ def test_vis_loop():
     # num_vis_calc = vis_data.base_num[vis_data.date == vis_data.date[0]].shape[0]
     # dunno what's going on here
     # assert num_vis_theory == num_vis_calc
+    #
+
+    out_path = Path(conf["out_path_fits"])
+    out = out_path / Path("vis_0.fits")
+    hdu_list = writer.create_hdu_list(vis_data, obs)
+    hdu_list.writeto(out, overwrite=True)