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Refactor SN Ia e2e test to use oversampled opsim #132

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merged 7 commits into from
Sep 27, 2024
Merged

Refactor SN Ia e2e test to use oversampled opsim #132

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868404a
overampled_observations fixture
hombit Sep 16, 2024
6c557c6
Change oversampled_observations to cover a year
hombit Sep 16, 2024
2fba726
Use oversampled opsim in Ia test
hombit Sep 25, 2024
dcaf0da
Refactor test_snia_end2end with oversampled opsim
hombit Sep 26, 2024
35a0f3c
snia e2e: Restore original redshift range
hombit Sep 26, 2024
39fea63
Make suggested changes to the PR
jeremykubica Sep 27, 2024
a8013a5
Clear notebook output
jeremykubica Sep 27, 2024
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208 changes: 148 additions & 60 deletions docs/notebooks/test_snia.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,35 +3,50 @@
{
"cell_type": "code",
"execution_count": null,
"id": "1bd86f47-7061-4aff-a7e0-be226219b139",
"metadata": {},
"id": "f2dc23f31b3601e1",
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"end_time": "2024-09-26T15:38:20.170140Z",
"start_time": "2024-09-26T15:38:20.167668Z"
}
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
"# %load_ext autoreload\n",
"# %autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4bf6fe10-59dc-439e-9371-c0cc15755f62",
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"id": "4e459a387df01a7c",
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"end_time": "2024-09-26T15:38:20.177116Z",
"start_time": "2024-09-26T15:38:20.174746Z"
}
},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"import tdastro\n",
"\n",
"sys.path.append(\n",
" str((Path(tdastro.__file__).parent / \"..\" / \"..\" / \"tests\" / \"tdastro\" / \"sources\").resolve())\n",
")"
"test_path = Path(tdastro.__file__).parent.parent.parent / \"tests\" / \"tdastro\"\n",
"\n",
"sys.path.append(str(test_path.resolve()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "817f5d23-925d-4ef7-bc22-ad110020e57d",
"metadata": {},
"id": "824593e5f3032eb6",
"metadata": {
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"end_time": "2024-09-26T15:38:20.667800Z",
"start_time": "2024-09-26T15:38:20.233254Z"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
Expand All @@ -42,30 +57,48 @@
{
"cell_type": "code",
"execution_count": null,
"id": "c87c4488-9368-4a0a-8400-1596bb15492a",
"metadata": {},
"id": "3742a30a04a1b62c",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:20.797258Z",
"start_time": "2024-09-26T15:38:20.672792Z"
}
},
"outputs": [],
"source": [
"from test_snia import test_snia_end2end"
"import conftest\n",
"from sources.test_snia import test_snia_end2end"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3285d471-c454-4c7a-b7cb-b8a79ab7ec98",
"metadata": {},
"id": "58a2bc03f3a3a3a0",
"metadata": {
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"end_time": "2024-09-26T15:38:47.963561Z",
"start_time": "2024-09-26T15:38:20.802799Z"
}
},
"outputs": [],
"source": [
"res = test_snia_end2end(\n",
" None, opsim_db_file=None, opsim=False, nsample=10, return_result=True, phase_rest=np.linspace(-15, 45, 20)\n",
"res, passbands = test_snia_end2end(\n",
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My preference would be not to use test_snia_end2end here and instead repeat the code in the notebook. I know code repetition is usually considered bad, but I think the code of interest to the notebook user will be what is in that function. Leaving the code out of the notebook doesn't provide the user all the information.

Alternatively, if we really want to keep code in sync, we could break out modules into new files.

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We have planned to update notebook to show how model is defined with more text descriptions by breaking the end2end test into multiple steps. Right now I prefer to keep it this way so I don't need to change the same thing in two different places when experimenting.

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I agree with this, but I've just keep the same NB structure as it was before. I'd keep these changes out of the scope of this PR or ask someone to help me commiting/PRing these changes

" conftest.oversampled_observations(conftest.opsim_shorten(conftest.test_data_dir())),\n",
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There are a few different approaches we could use to access the opsim_shorten here without having to change the pytest fixtures in the conftest.py file.

  1. [My preference] Move the directory names to the top level __init__.py file (or a new paths.py file in the base directory):
_TDASTRO_BASE_DIR = os.path.dirname(__file__)
_TDASTRO_TEST_DIR = os.path.join(_TDASTRO_BASE_DIR, "tests", "tdastro")
_TDASTRO_TEST_DATA_DIR = os.path.join(_TDASTRO_TEST_DIR, "data")

Then both the notebooks and conftest.py use these.

We would need to include a cell to do:

base_opsim = OpSim.from_db(opsim_shorten)
oversampled_observations = oversample_opsim(
        base_opsim,
        pointing=(0.0, 0.0),
        search_radius=180.0,
        delta_t=0.01,
        time_range=(61406.0, 61771.0),
        bands=None,
        strategy="darkest_sky",
    )

but I think it would be good to explicitly include that in the notebook. It allows the user to see where you are pulling the data and how you are transforming it.

  1. [Not prefered] I think in notebooks it is fair to just provide a path like
test_opsim_filename = "../../tests/tdastro/data/opsim_shorten.db")

It's not as robust as the approach above, but that is okay since users are expected to interact with the notebooks.

" passbands_dir=conftest.passbands_dir(conftest.test_data_dir()),\n",
" nsample=20,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c60e849f-8529-4714-92e1-345182a12a68",
"metadata": {},
"id": "5c4b0574aec01df4",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:47.971555Z",
"start_time": "2024-09-26T15:38:47.969424Z"
}
},
"outputs": [],
"source": [
"hostmass = [x[\"parameter_values\"][\"hostmass\"] for x in res]\n",
Expand All @@ -80,8 +113,13 @@
{
"cell_type": "code",
"execution_count": null,
"id": "57607e1e-e18a-4408-a7b9-ec400c72c9a8",
"metadata": {},
"id": "aaefb2f29d444cf2",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:48.053565Z",
"start_time": "2024-09-26T15:38:47.976849Z"
}
},
"outputs": [],
"source": [
"plt.hist(hostmass)"
Expand All @@ -90,8 +128,13 @@
{
"cell_type": "code",
"execution_count": null,
"id": "561c6c8c-84d9-484d-9759-56b020a018d6",
"metadata": {},
"id": "b86d82fe1a518d5a",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:48.225753Z",
"start_time": "2024-09-26T15:38:48.093211Z"
}
},
"outputs": [],
"source": [
"plt.hist(x1)\n",
Expand All @@ -101,8 +144,13 @@
{
"cell_type": "code",
"execution_count": null,
"id": "8addd896-8f21-4813-8053-b75debf114b2",
"metadata": {},
"id": "edc24806f5752b25",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:48.299469Z",
"start_time": "2024-09-26T15:38:48.236483Z"
}
},
"outputs": [],
"source": [
"plt.hist(c)"
Expand All @@ -111,8 +159,13 @@
{
"cell_type": "code",
"execution_count": null,
"id": "bd1bbdd5-f236-4e6d-84f3-161ab6cca4fd",
"metadata": {},
"id": "35ab73410bd98ac1",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:48.352012Z",
"start_time": "2024-09-26T15:38:48.305324Z"
}
},
"outputs": [],
"source": [
"plt.scatter(hostmass, x1)"
Expand All @@ -121,8 +174,13 @@
{
"cell_type": "code",
"execution_count": null,
"id": "151bd30a-26f4-40eb-ba99-611f82d61edf",
"metadata": {},
"id": "2b56df734bccfe1b",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:48.411671Z",
"start_time": "2024-09-26T15:38:48.362721Z"
}
},
"outputs": [],
"source": [
"plt.hist(x0)"
Expand All @@ -131,8 +189,13 @@
{
"cell_type": "code",
"execution_count": null,
"id": "3b9e3896-c530-419c-8bb0-a9c1984eae0d",
"metadata": {},
"id": "1140a75e38023d80",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:48.467349Z",
"start_time": "2024-09-26T15:38:48.417896Z"
}
},
"outputs": [],
"source": [
"plt.hist(z)"
Expand All @@ -141,8 +204,13 @@
{
"cell_type": "code",
"execution_count": null,
"id": "b31893a6-be21-4c69-8e6c-4cfec316d309",
"metadata": {},
"id": "75b49a8b4f8fa1b4",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:48.583009Z",
"start_time": "2024-09-26T15:38:48.498730Z"
}
},
"outputs": [],
"source": [
"# cosmo = FlatLambdaCDM(H0=73, Om0=0.3)\n",
Expand Down Expand Up @@ -170,8 +238,13 @@
{
"cell_type": "code",
"execution_count": null,
"id": "d7443d9e-7bbd-4087-b31e-8a4030c32b16",
"metadata": {},
"id": "98996d76d56cb7b9",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:48.700933Z",
"start_time": "2024-09-26T15:38:48.618682Z"
}
},
"outputs": [],
"source": [
"plt.scatter(z, mb)\n",
Expand All @@ -183,8 +256,13 @@
{
"cell_type": "code",
"execution_count": null,
"id": "494d4400-c7ef-453d-bfa3-4abea973997e",
"metadata": {},
"id": "320e5ddcec3fcdd",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:48.751128Z",
"start_time": "2024-09-26T15:38:48.706426Z"
}
},
"outputs": [],
"source": [
"plt.scatter(hostmass, mu - distmod)\n",
Expand All @@ -194,8 +272,13 @@
{
"cell_type": "code",
"execution_count": null,
"id": "55a3c72f-a231-4108-b518-779e37502cd7",
"metadata": {},
"id": "4dce3f1ba8792c87",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:48.887113Z",
"start_time": "2024-09-26T15:38:48.757687Z"
}
},
"outputs": [],
"source": [
"for i in range(0, 3):\n",
Expand All @@ -204,7 +287,7 @@
" except Exception:\n",
" continue\n",
" saltpars = {\"x0\": x0[i], \"x1\": x1[i], \"c\": c[i], \"z\": z[i], \"t0\": t0[i]}\n",
" model = sncosmo.Model(\"salt2-h17\")\n",
" model = sncosmo.Model(\"salt3\")\n",
" model.update(saltpars)\n",
" print(saltpars)\n",
" print(model.parameters)\n",
Expand All @@ -217,41 +300,46 @@
{
"cell_type": "code",
"execution_count": null,
"id": "acdcbb01-ead0-4a20-b479-3324ccd7e562",
"metadata": {},
"id": "6e1d0698de0bfaaa",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:49.434973Z",
"start_time": "2024-09-26T15:38:48.893241Z"
}
},
"outputs": [],
"source": [
"for i in range(0, 3):\n",
" phase_obs = res[i][\"phase_rest\"] * (1 + z[i])\n",
" times = res[i][\"times\"]\n",
" colors = \"gr\"\n",
" for color, f in zip(\"gr\", colors):\n",
" plt.plot(\n",
" times, res[i][\"bandfluxes\"][\"LSST_\" + f], \"-\", marker=\"o\", label=f, color=color, alpha=0.6, lw=2\n",
" )\n",
" colors = [\"red\", \"brown\"]\n",
" for f, color in zip(\"ri\", colors):\n",
" band_name = f\"LSST_{f}\"\n",
" plt.plot(times, res[i][\"bandfluxes\"][band_name], \"-\", label=f, color=color, alpha=0.6, lw=2)\n",
" saltpars = {\"x0\": x0[i], \"x1\": x1[i], \"c\": c[i], \"z\": z[i], \"t0\": t0[i]}\n",
" model = sncosmo.Model(\"salt2-h17\")\n",
" model = sncosmo.Model(\"salt3\")\n",
" model.update(saltpars)\n",
" print(saltpars)\n",
" flux = model.bandflux(\"lsst\" + f, times, zpsys=\"ab\", zp=8.9 + 2.5 * 9) # -48.6)\n",
" sncosmo_band = sncosmo.Bandpass(\n",
" *passbands.passbands[band_name].processed_transmission_table.T, name=band_name\n",
" )\n",
" flux = model.bandflux(sncosmo_band, times, zpsys=\"ab\", zp=8.9 + 2.5 * 9) # -48.6)\n",
" plt.plot(times, flux, \"--\", label=f, color=color)\n",
" plt.xlabel(\"MJD\")\n",
" plt.ylabel(\"Flux, nJy\")\n",
" plt.legend()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d95053a7-1e5c-4d36-8579-82b3dd8b9cbb",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc680d32-cc9c-428f-90c4-944649999d9f",
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-26T15:38:49.443348Z",
"start_time": "2024-09-26T15:38:49.441986Z"
}
},
"outputs": [],
"source": []
}
Expand Down
13 changes: 13 additions & 0 deletions tests/tdastro/astro_utils/test_opsim.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -285,3 +285,16 @@ def test_oversample_opsim(opsim_shorten):
oversampled["skyBrightness"].unique().size >= oversampled["filter"].unique().size
), "there should be at least as many skyBrightness values as bands"
assert oversampled["skyBrightness"].isna().sum() == 0, "skyBrightness has NaN values"


def test_fixture_oversampled_observations(oversampled_observations):
"""Test the fixture oversampled_observations."""
assert len(oversampled_observations) == 36_500
assert set(oversampled_observations["filter"]) == {"g", "r"}
assert oversampled_observations["skyBrightness"].isna().sum() == 0
assert oversampled_observations["skyBrightness"].unique().size >= 2
assert np.all(oversampled_observations["observationStartMJD"] >= 61406.0)
assert np.all(oversampled_observations["observationStartMJD"] <= 61771.0)
np.testing.assert_allclose(oversampled_observations["fieldRA"], 0.0)
np.testing.assert_allclose(oversampled_observations["fieldDec"], 0.0)
np.testing.assert_allclose(np.diff(oversampled_observations["observationStartMJD"]), 0.01)
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