From 1c8cbe07bbbc536e01bd26ab980daa07759bddbe Mon Sep 17 00:00:00 2001
From: Padma Venkatraman <69484711+padma18-vb@users.noreply.github.com>
Date: Fri, 28 Jul 2023 15:52:12 -0700
Subject: [PATCH 1/7] Declaration of intent :-)
---
README.rst | 1 +
1 file changed, 1 insertion(+)
diff --git a/README.rst b/README.rst
index 480504947..f3142c360 100644
--- a/README.rst
+++ b/README.rst
@@ -26,6 +26,7 @@ How to contribute to the repository?
Check out our step-by-step instructions how you can contribute code to this project Contributing_ Guidelines.
Are you new to coding and contributing to a software development project? No worries! We help you out! Write us on the LSST Slack channel #sl-pipeline.
+Note: `sim-pipeline` is written in object-oriented python. What fun!
How can I participate in LSST strong lensing projects?
------------------------------------------------------
From 07c7e02ab2114fa47f23eb62386a7f208fd97cf3 Mon Sep 17 00:00:00 2001
From: Padma Venkatraman <69484711+padma18-vb@users.noreply.github.com>
Date: Thu, 5 Oct 2023 10:20:58 -0700
Subject: [PATCH 2/7] Added skeleton for OM10-cosmoDC2 systems
---
sim_pipeline/Deflectors/om10lens.py | 18 ++++++++++++++++++
sim_pipeline/Sources/cosmoDC2AGN.py | 16 ++++++++++++++++
2 files changed, 34 insertions(+)
create mode 100644 sim_pipeline/Deflectors/om10lens.py
create mode 100644 sim_pipeline/Sources/cosmoDC2AGN.py
diff --git a/sim_pipeline/Deflectors/om10lens.py b/sim_pipeline/Deflectors/om10lens.py
new file mode 100644
index 000000000..82a47d454
--- /dev/null
+++ b/sim_pipeline/Deflectors/om10lens.py
@@ -0,0 +1,18 @@
+from sim_pipeline.Deflectors.deflector_base import DeflectorBase
+
+
+class OM10Lens(DeflectorBase):
+ def __init__(self, deflector_table, kwargs_cut, cosmo, sky_area):
+ """
+ :param deflector_table: csv file containing lens properties
+ :param kwargs_cut: cuts to impose on lens properties
+ :param cosmo: cosmology used
+ :param sky_area:
+ """
+ super().__init__(deflector_table, kwargs_cut, cosmo, sky_area)
+
+ def deflector_number(self):
+ return super().deflector_number()
+
+ def draw_deflector(self):
+ return super().draw_deflector()
diff --git a/sim_pipeline/Sources/cosmoDC2AGN.py b/sim_pipeline/Sources/cosmoDC2AGN.py
new file mode 100644
index 000000000..924c2f00b
--- /dev/null
+++ b/sim_pipeline/Sources/cosmoDC2AGN.py
@@ -0,0 +1,16 @@
+import pandas as pd
+from sim_pipeline.Sources.source_base import SourceBase
+
+
+class cosmoDC2AGN(SourceBase):
+ def __init__(self, source_file, cosmo, sky_area):
+ """
+ :param source_file: csv file containing source properties
+ :param cosmo: cosmology used
+ :param sky_area:
+ """
+ super().__init__(cosmo, sky_area)
+ self.sources = pd.read_csv(source_file)
+
+ def source_number(self):
+ return super().source_number()
From 4efbeac28d768f49e371f1893cd249336a211e2a Mon Sep 17 00:00:00 2001
From: Padma Venkatraman <69484711+padma18-vb@users.noreply.github.com>
Date: Fri, 27 Oct 2023 09:08:38 -0700
Subject: [PATCH 3/7] formatting errors fixed
---
data/OM10/deflector_dict.csv | 2134 +++++
data/OM10/final_test_sample.csv | 2134 +++++
data/OM10/source_dict.csv | 2134 +++++
.../galaxy_galaxy_lensing_tutorial.ipynb | 27 +-
.../om10agn_galaxy_lensing_tutorial.ipynb | 8229 +++++++++++++++++
sim_pipeline/Deflectors/om10.py | 53 +
sim_pipeline/Deflectors/om10lens.py | 18 -
sim_pipeline/Sources/cosmoDC2AGN.py | 13 +-
sim_pipeline/om10_lens.py | 556 ++
sim_pipeline/om10_lens_pop.py | 53 +
10 files changed, 15330 insertions(+), 21 deletions(-)
create mode 100644 data/OM10/deflector_dict.csv
create mode 100644 data/OM10/final_test_sample.csv
create mode 100644 data/OM10/source_dict.csv
create mode 100644 notebooks/om10agn_galaxy_lensing_tutorial.ipynb
create mode 100644 sim_pipeline/Deflectors/om10.py
delete mode 100644 sim_pipeline/Deflectors/om10lens.py
create mode 100644 sim_pipeline/om10_lens.py
create mode 100644 sim_pipeline/om10_lens_pop.py
diff --git a/data/OM10/deflector_dict.csv b/data/OM10/deflector_dict.csv
new file mode 100644
index 000000000..09e68d6ef
--- /dev/null
+++ b/data/OM10/deflector_dict.csv
@@ -0,0 +1,2134 @@
+,NIMG,ZLENS,VELDISP,ELLIP,PHIE,GAMMA,PHIG,ZSRC,XSRC,YSRC,MAGI_IN,MAGI,IMSEP,XIMG,YIMG,MAG,DELAY,DD,DDLUM,ABMAG_I,APMAG_I,KCORR,DS,DDS,DSLUM,ABMAGI_IN,ABMAGI_IN3,ABMAGI_IN4
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diff --git a/data/OM10/final_test_sample.csv b/data/OM10/final_test_sample.csv
new file mode 100644
index 000000000..9b394d271
--- /dev/null
+++ b/data/OM10/final_test_sample.csv
@@ -0,0 +1,2134 @@
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diff --git a/data/OM10/source_dict.csv b/data/OM10/source_dict.csv
new file mode 100644
index 000000000..1ef2d7e6a
--- /dev/null
+++ b/data/OM10/source_dict.csv
@@ -0,0 +1,2134 @@
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diff --git a/notebooks/galaxy_galaxy_lensing_tutorial.ipynb b/notebooks/galaxy_galaxy_lensing_tutorial.ipynb
index b6c563e24..1e6de2ee2 100644
--- a/notebooks/galaxy_galaxy_lensing_tutorial.ipynb
+++ b/notebooks/galaxy_galaxy_lensing_tutorial.ipynb
@@ -71,7 +71,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2023-07-10T22:02:02.025669Z",
@@ -82,7 +82,30 @@
},
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m/Users/padmavenkatraman/Documents/StrongLensing/sim-pipeline/notebooks/galaxy_galaxy_lensing_tutorial.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 11\u001b[0m kwargs_source_cut \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39mband\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mg\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mband_max\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m28\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mz_min\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m0.1\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mz_max\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m5.0\u001b[39m}\n\u001b[1;32m 13\u001b[0m \u001b[39m# run skypy pipeline and make galaxy-galaxy population class using GalaxyGalaxyLensPop\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m gg_lens_pop \u001b[39m=\u001b[39m GalaxyGalaxyLensPop(\n\u001b[1;32m 15\u001b[0m deflector_type\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mall-galaxies\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 16\u001b[0m source_type\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mgalaxies\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 17\u001b[0m kwargs_deflector_cut\u001b[39m=\u001b[39;49mkwargs_deflector_cut,\n\u001b[1;32m 18\u001b[0m kwargs_source_cut\u001b[39m=\u001b[39;49mkwargs_source_cut,\n\u001b[1;32m 19\u001b[0m kwargs_mass2light\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m 20\u001b[0m skypy_config\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m 21\u001b[0m sky_area\u001b[39m=\u001b[39;49msky_area,\n\u001b[1;32m 22\u001b[0m cosmo\u001b[39m=\u001b[39;49mcosmo,\n\u001b[1;32m 23\u001b[0m )\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/sim-pipeline/sim_pipeline/galaxy_galaxy_lens_pop.py:47\u001b[0m, in \u001b[0;36mGalaxyGalaxyLensPop.__init__\u001b[0;34m(self, deflector_type, source_type, kwargs_deflector_cut, kwargs_source_cut, kwargs_mass2light, skypy_config, sky_area, filters, cosmo)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(sky_area, cosmo)\n\u001b[1;32m 44\u001b[0m \u001b[39mif\u001b[39;00m deflector_type \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39melliptical\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mall-galaxies\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39mor\u001b[39;00m source_type \u001b[39min\u001b[39;00m [\n\u001b[1;32m 45\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mgalaxies\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 46\u001b[0m ]:\n\u001b[0;32m---> 47\u001b[0m pipeline \u001b[39m=\u001b[39m SkyPyPipeline(\n\u001b[1;32m 48\u001b[0m skypy_config\u001b[39m=\u001b[39;49mskypy_config,\n\u001b[1;32m 49\u001b[0m sky_area\u001b[39m=\u001b[39;49msky_area,\n\u001b[1;32m 50\u001b[0m filters\u001b[39m=\u001b[39;49mfilters,\n\u001b[1;32m 51\u001b[0m cosmo\u001b[39m=\u001b[39;49mcosmo,\n\u001b[1;32m 52\u001b[0m )\n\u001b[1;32m 53\u001b[0m \u001b[39mif\u001b[39;00m kwargs_deflector_cut \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 54\u001b[0m kwargs_deflector_cut \u001b[39m=\u001b[39m {}\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/sim-pipeline/sim_pipeline/Pipelines/skypy_pipeline.py:71\u001b[0m, in \u001b[0;36mSkyPyPipeline.__init__\u001b[0;34m(self, skypy_config, sky_area, filters, cosmo)\u001b[0m\n\u001b[1;32m 68\u001b[0m tmp_file\u001b[39m.\u001b[39mwrite(content)\n\u001b[1;32m 70\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pipeline \u001b[39m=\u001b[39m Pipeline\u001b[39m.\u001b[39mread(tmp_file\u001b[39m.\u001b[39mname)\n\u001b[0;32m---> 71\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_pipeline\u001b[39m.\u001b[39;49mexecute()\n\u001b[1;32m 73\u001b[0m \u001b[39m# Remove the temporary file after the pipeline has been executed\u001b[39;00m\n\u001b[1;32m 74\u001b[0m os\u001b[39m.\u001b[39mremove(tmp_file\u001b[39m.\u001b[39mname)\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/silver/lib/python3.11/site-packages/skypy/pipeline/_pipeline.py:185\u001b[0m, in \u001b[0;36mPipeline.execute\u001b[0;34m(self, parameters)\u001b[0m\n\u001b[1;32m 182\u001b[0m names \u001b[39m=\u001b[39m [n\u001b[39m.\u001b[39mstrip() \u001b[39mfor\u001b[39;00m n \u001b[39min\u001b[39;00m column\u001b[39m.\u001b[39msplit(\u001b[39m'\u001b[39m\u001b[39m,\u001b[39m\u001b[39m'\u001b[39m)]\n\u001b[1;32m 183\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(names) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 184\u001b[0m \u001b[39m# Multi-column assignment\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m t \u001b[39m=\u001b[39m Table(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mevaluate(settings), names\u001b[39m=\u001b[39mnames)\n\u001b[1;32m 186\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate[table]\u001b[39m.\u001b[39madd_columns(t\u001b[39m.\u001b[39mcolumns)\n\u001b[1;32m 187\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 188\u001b[0m \u001b[39m# Single column assignment\u001b[39;00m\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/silver/lib/python3.11/site-packages/skypy/pipeline/_pipeline.py:262\u001b[0m, in \u001b[0;36mPipeline.evaluate\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[39mreturn\u001b[39;00m {k: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mevaluate(v) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m value\u001b[39m.\u001b[39mitems()}\n\u001b[1;32m 260\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, Item):\n\u001b[1;32m 261\u001b[0m \u001b[39m# evaluate item\u001b[39;00m\n\u001b[0;32m--> 262\u001b[0m \u001b[39mreturn\u001b[39;00m value\u001b[39m.\u001b[39;49mevaluate(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m 263\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 264\u001b[0m \u001b[39m# everything else return unchanged\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[39mreturn\u001b[39;00m value\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/silver/lib/python3.11/site-packages/skypy/pipeline/_items.py:93\u001b[0m, in \u001b[0;36mCall.evaluate\u001b[0;34m(self, pipeline)\u001b[0m\n\u001b[1;32m 91\u001b[0m kwargs \u001b[39m=\u001b[39m pipeline\u001b[39m.\u001b[39mevaluate(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkwargs)\n\u001b[1;32m 92\u001b[0m log\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCalling \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunction\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 93\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfunction(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/silver/lib/python3.11/site-packages/skypy/utils/photometry.py:228\u001b[0m, in \u001b[0;36mSpectrumTemplates.apparent_magnitudes\u001b[0;34m(self, coefficients, redshift, filters, cosmology, stellar_mass, resolution)\u001b[0m\n\u001b[1;32m 226\u001b[0m distmod \u001b[39m=\u001b[39m cosmology\u001b[39m.\u001b[39mdistmod(redshift)\u001b[39m.\u001b[39mvalue\n\u001b[1;32m 227\u001b[0m mass_modulus \u001b[39m=\u001b[39m \u001b[39m-\u001b[39m\u001b[39m2.5\u001b[39m\u001b[39m*\u001b[39mnp\u001b[39m.\u001b[39mlog10(stellar_mass) \u001b[39mif\u001b[39;00m stellar_mass \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39m0\u001b[39m\n\u001b[0;32m--> 228\u001b[0m m \u001b[39m=\u001b[39m mag_ab(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwavelength, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtemplates, filters, redshift\u001b[39m=\u001b[39;49mredshift,\n\u001b[1;32m 229\u001b[0m coefficients\u001b[39m=\u001b[39;49mcoefficients, distmod\u001b[39m=\u001b[39;49mdistmod, interpolate\u001b[39m=\u001b[39;49mresolution)\n\u001b[1;32m 230\u001b[0m \u001b[39mreturn\u001b[39;00m (m\u001b[39m.\u001b[39mT \u001b[39m+\u001b[39m mass_modulus)\u001b[39m.\u001b[39mT\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/silver/lib/python3.11/site-packages/skypy/utils/photometry.py:120\u001b[0m, in \u001b[0;36mmag_ab\u001b[0;34m(wavelength, spectrum, filters, redshift, coefficients, distmod, interpolate)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[39mfor\u001b[39;00m j, f \u001b[39min\u001b[39;00m np\u001b[39m.\u001b[39mndenumerate(filters):\n\u001b[1;32m 118\u001b[0m \u001b[39m# create a shifted filter for redshift\u001b[39;00m\n\u001b[1;32m 119\u001b[0m fs \u001b[39m=\u001b[39m f\u001b[39m.\u001b[39mcreate_shifted(z)\n\u001b[0;32m--> 120\u001b[0m m[i\u001b[39m+\u001b[39m(\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m,)\u001b[39m+\u001b[39mj] \u001b[39m=\u001b[39m fs\u001b[39m.\u001b[39;49mget_ab_maggies(spectrum, wavelength)\n\u001b[1;32m 122\u001b[0m \u001b[39m# if interpolating, compute the full set of redshifts\u001b[39;00m\n\u001b[1;32m 123\u001b[0m \u001b[39mif\u001b[39;00m interpolate:\n\u001b[1;32m 124\u001b[0m \u001b[39m# diy interpolation keeps memory use to a minimum\u001b[39;00m\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/silver/lib/python3.11/site-packages/speclite/filters.py:1085\u001b[0m, in \u001b[0;36mFilterResponse.get_ab_maggies\u001b[0;34m(self, spectrum, wavelength, axis)\u001b[0m\n\u001b[1;32m 1081\u001b[0m convolution \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconvolve_with_function(\n\u001b[1;32m 1082\u001b[0m spectrum, photon_weighted\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, units\u001b[39m=\u001b[39mdefault_flux_unit)\n\u001b[1;32m 1083\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 1084\u001b[0m \u001b[39m# Allow interpolation since this is a convenience method.\u001b[39;00m\n\u001b[0;32m-> 1085\u001b[0m convolution \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconvolve_with_array(\n\u001b[1;32m 1086\u001b[0m wavelength, spectrum, photon_weighted\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m 1087\u001b[0m interpolate\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, axis\u001b[39m=\u001b[39;49maxis, units\u001b[39m=\u001b[39;49mdefault_flux_unit)\n\u001b[1;32m 1088\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1089\u001b[0m \u001b[39mreturn\u001b[39;00m convolution\u001b[39m.\u001b[39mvalue \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mab_zeropoint\u001b[39m.\u001b[39mvalue\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/silver/lib/python3.11/site-packages/speclite/filters.py:1041\u001b[0m, in \u001b[0;36mFilterResponse.convolve_with_array\u001b[0;34m(self, wavelength, values, photon_weighted, interpolate, axis, units, method)\u001b[0m\n\u001b[1;32m 995\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Convolve this response with a tabulated function of wavelength.\u001b[39;00m\n\u001b[1;32m 996\u001b[0m \n\u001b[1;32m 997\u001b[0m \u001b[39mThis is a convenience method that creates a temporary\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[39m axis integrated out.\u001b[39;00m\n\u001b[1;32m 1038\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1039\u001b[0m convolution \u001b[39m=\u001b[39m FilterConvolution(\n\u001b[1;32m 1040\u001b[0m \u001b[39mself\u001b[39m, wavelength, photon_weighted, interpolate, units)\n\u001b[0;32m-> 1041\u001b[0m \u001b[39mreturn\u001b[39;00m convolution(values, axis, method)\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/silver/lib/python3.11/site-packages/speclite/filters.py:1441\u001b[0m, in \u001b[0;36mFilterConvolution.__call__\u001b[0;34m(self, values, axis, method, plot)\u001b[0m\n\u001b[1;32m 1438\u001b[0m \u001b[39m# Check that input units are compatible with expected units\u001b[39;00m\n\u001b[1;32m 1439\u001b[0m \u001b[39m# and initialize array of values without units.\u001b[39;00m\n\u001b[1;32m 1440\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1441\u001b[0m values_no_units \u001b[39m=\u001b[39m values\u001b[39m.\u001b[39;49mto(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minput_units)\u001b[39m.\u001b[39mvalue\n\u001b[1;32m 1442\u001b[0m input_has_units \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 1443\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m:\n\u001b[1;32m 1444\u001b[0m \u001b[39m# Input values have no units, so we assume that they are in\u001b[39;00m\n\u001b[1;32m 1445\u001b[0m \u001b[39m# self.input_units if these are defined, or else that the caller\u001b[39;00m\n\u001b[1;32m 1446\u001b[0m \u001b[39m# knows what they are doing.\u001b[39;00m\n",
+ "File \u001b[0;32m~/Documents/StrongLensing/silver/lib/python3.11/site-packages/astropy/units/quantity.py:905\u001b[0m, in \u001b[0;36mQuantity.to\u001b[0;34m(self, unit, equivalencies, copy)\u001b[0m\n\u001b[1;32m 902\u001b[0m result[name] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m[name]\u001b[39m.\u001b[39m_to_value(unit, equivalencies)\n\u001b[1;32m 903\u001b[0m \u001b[39mreturn\u001b[39;00m result\n\u001b[0;32m--> 905\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mto\u001b[39m(\u001b[39mself\u001b[39m, unit, equivalencies\u001b[39m=\u001b[39m[], copy\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[1;32m 906\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 907\u001b[0m \u001b[39m Return a new `~astropy.units.Quantity` object with the specified unit.\u001b[39;00m\n\u001b[1;32m 908\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 930\u001b[0m \u001b[39m to_value : get the numerical value in a given unit.\u001b[39;00m\n\u001b[1;32m 931\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m 932\u001b[0m \u001b[39m# We don't use `to_value` below since we always want to make a copy\u001b[39;00m\n\u001b[1;32m 933\u001b[0m \u001b[39m# and don't want to slow down this method (esp. the scalar case).\u001b[39;00m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
"source": [
"# define a cosmology\n",
"cosmo = FlatLambdaCDM(H0=70, Om0=0.3)\n",
diff --git a/notebooks/om10agn_galaxy_lensing_tutorial.ipynb b/notebooks/om10agn_galaxy_lensing_tutorial.ipynb
new file mode 100644
index 000000000..ad11a1e97
--- /dev/null
+++ b/notebooks/om10agn_galaxy_lensing_tutorial.ipynb
@@ -0,0 +1,8229 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from astropy.cosmology import FlatLambdaCDM\n",
+ "from astropy.units import Quantity\n",
+ "from sim_pipeline.galaxy_galaxy_lens_pop import GalaxyGalaxyLensPop\n",
+ "from sim_pipeline.Plots.galaxy_galaxy_plots import GalaxyGalaxyLensingPlots\n",
+ "import numpy as np\n",
+ "import corner\n",
+ "import pandas as pd\n",
+ "import lenstronomy.Util.constants as const"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Initial Data Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ " \n",
+ " | \n",
+ " om10M_i | \n",
+ " om10z | \n",
+ " index | \n",
+ " Min Distance | \n",
+ " cosmodc2_index | \n",
+ " sersic_bulge | \n",
+ " size_minor_bulge_true | \n",
+ " dec_true | \n",
+ " size_disk_true | \n",
+ " size_minor_disk_true | \n",
+ " ... | \n",
+ " q | \n",
+ " size_true_kpc | \n",
+ " size_true_log | \n",
+ " size_true_kpc_log | \n",
+ " bulge_to_total_ratio_i_log | \n",
+ " stellar_mass_log | \n",
+ " stellar_mass_bulge_log | \n",
+ " stellar_mass_disk_log | \n",
+ " halo_mass_log | \n",
+ " bin | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " -25.008095 | \n",
+ " 2.65 | \n",
+ " 0 | \n",
+ " 0.107426 | \n",
+ " 9917.0 | \n",
+ " 4.0 | \n",
+ " 0.462818 | \n",
+ " -28.381324 | \n",
+ " 1.002705 | \n",
+ " 0.193438 | \n",
+ " ... | \n",
+ " 0.710240 | \n",
+ " 5.284734 | \n",
+ " -0.185995 | \n",
+ " 0.723023 | \n",
+ " 0.000000 | \n",
+ " 11.073158 | \n",
+ " 11.073158 | \n",
+ " -0.570010 | \n",
+ " 12.320147 | \n",
+ " 2.5 - 3.0 | \n",
+ "
\n",
+ " \n",
+ " 14 | \n",
+ " -25.095188 | \n",
+ " 2.45 | \n",
+ " 18 | \n",
+ " 0.210879 | \n",
+ " 9917.0 | \n",
+ " 4.0 | \n",
+ " 0.462818 | \n",
+ " -28.381324 | \n",
+ " 1.002705 | \n",
+ " 0.193438 | \n",
+ " ... | \n",
+ " 0.710240 | \n",
+ " 5.284734 | \n",
+ " -0.185995 | \n",
+ " 0.723023 | \n",
+ " 0.000000 | \n",
+ " 11.073158 | \n",
+ " 11.073158 | \n",
+ " -0.570010 | \n",
+ " 12.320147 | \n",
+ " 2.5 - 3.0 | \n",
+ "
\n",
+ " \n",
+ " 26 | \n",
+ " -25.061939 | \n",
+ " 2.59 | \n",
+ " 31 | \n",
+ " 0.134481 | \n",
+ " 9917.0 | \n",
+ " 4.0 | \n",
+ " 0.462818 | \n",
+ " -28.381324 | \n",
+ " 1.002705 | \n",
+ " 0.193438 | \n",
+ " ... | \n",
+ " 0.710240 | \n",
+ " 5.284734 | \n",
+ " -0.185995 | \n",
+ " 0.723023 | \n",
+ " 0.000000 | \n",
+ " 11.073158 | \n",
+ " 11.073158 | \n",
+ " -0.570010 | \n",
+ " 12.320147 | \n",
+ " 2.5 - 3.0 | \n",
+ "
\n",
+ " \n",
+ " 61 | \n",
+ " -25.102290 | \n",
+ " 2.54 | \n",
+ " 83 | \n",
+ " 0.178529 | \n",
+ " 9917.0 | \n",
+ " 4.0 | \n",
+ " 0.462818 | \n",
+ " -28.381324 | \n",
+ " 1.002705 | \n",
+ " 0.193438 | \n",
+ " ... | \n",
+ " 0.710240 | \n",
+ " 5.284734 | \n",
+ " -0.185995 | \n",
+ " 0.723023 | \n",
+ " 0.000000 | \n",
+ " 11.073158 | \n",
+ " 11.073158 | \n",
+ " -0.570010 | \n",
+ " 12.320147 | \n",
+ " 2.5 - 3.0 | \n",
+ "
\n",
+ " \n",
+ " 130 | \n",
+ " -24.940274 | \n",
+ " 2.39 | \n",
+ " 170 | \n",
+ " 0.188864 | \n",
+ " 9917.0 | \n",
+ " 4.0 | \n",
+ " 0.462818 | \n",
+ " -28.381324 | \n",
+ " 1.002705 | \n",
+ " 0.193438 | \n",
+ " ... | \n",
+ " 0.710240 | \n",
+ " 5.284734 | \n",
+ " -0.185995 | \n",
+ " 0.723023 | \n",
+ " 0.000000 | \n",
+ " 11.073158 | \n",
+ " 11.073158 | \n",
+ " -0.570010 | \n",
+ " 12.320147 | \n",
+ " 2.5 - 3.0 | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 2108 | \n",
+ " -21.901879 | \n",
+ " 2.13 | \n",
+ " 2783 | \n",
+ " 0.013457 | \n",
+ " 8100.0 | \n",
+ " 4.0 | \n",
+ " 0.398532 | \n",
+ " -27.833679 | \n",
+ " 0.812260 | \n",
+ " 0.656106 | \n",
+ " ... | \n",
+ " 0.992910 | \n",
+ " 3.363527 | \n",
+ " -0.396446 | \n",
+ " 0.526795 | \n",
+ " -0.000006 | \n",
+ " 10.177324 | \n",
+ " 10.177323 | \n",
+ " 3.767176 | \n",
+ " 11.880815 | \n",
+ " 2.0 - 2.5 | \n",
+ "
\n",
+ " \n",
+ " 2120 | \n",
+ " -21.472621 | \n",
+ " 1.90 | \n",
+ " 2799 | \n",
+ " 0.018944 | \n",
+ " 6624.0 | \n",
+ " 4.0 | \n",
+ " 0.271402 | \n",
+ " -28.352587 | \n",
+ " 0.664376 | \n",
+ " 0.164489 | \n",
+ " ... | \n",
+ " 0.687045 | \n",
+ " 3.347413 | \n",
+ " -0.403372 | \n",
+ " 0.524709 | \n",
+ " 0.000000 | \n",
+ " 10.712845 | \n",
+ " 10.712845 | \n",
+ " 0.042562 | \n",
+ " 12.473480 | \n",
+ " 1.5 - 2.0 | \n",
+ "
\n",
+ " \n",
+ " 2124 | \n",
+ " -22.033031 | \n",
+ " 2.55 | \n",
+ " 2804 | \n",
+ " 0.040030 | \n",
+ " 9859.0 | \n",
+ " 4.0 | \n",
+ " 0.362162 | \n",
+ " -27.568521 | \n",
+ " 0.935928 | \n",
+ " 0.688451 | \n",
+ " ... | \n",
+ " 0.888890 | \n",
+ " 3.316291 | \n",
+ " -0.389209 | \n",
+ " 0.520653 | \n",
+ " -0.002433 | \n",
+ " 10.115864 | \n",
+ " 10.115813 | \n",
+ " 6.189734 | \n",
+ " 12.087527 | \n",
+ " 2.5 - 3.0 | \n",
+ "
\n",
+ " \n",
+ " 2126 | \n",
+ " -22.863294 | \n",
+ " 2.46 | \n",
+ " 2807 | \n",
+ " 0.024685 | \n",
+ " 10030.0 | \n",
+ " 4.0 | \n",
+ " 0.406656 | \n",
+ " -27.798642 | \n",
+ " 0.702246 | \n",
+ " 0.670270 | \n",
+ " ... | \n",
+ " 0.998194 | \n",
+ " 3.329499 | \n",
+ " -0.389987 | \n",
+ " 0.522379 | \n",
+ " 0.000000 | \n",
+ " 10.674628 | \n",
+ " 10.674628 | \n",
+ " 0.901244 | \n",
+ " 12.315252 | \n",
+ " 2.0 - 2.5 | \n",
+ "
\n",
+ " \n",
+ " 2130 | \n",
+ " -21.824196 | \n",
+ " 2.35 | \n",
+ " 2812 | \n",
+ " 0.024528 | \n",
+ " 6973.0 | \n",
+ " 4.0 | \n",
+ " 0.310991 | \n",
+ " -27.853446 | \n",
+ " 0.535504 | \n",
+ " 0.451001 | \n",
+ " ... | \n",
+ " 0.995392 | \n",
+ " 2.575279 | \n",
+ " -0.505246 | \n",
+ " 0.410824 | \n",
+ " 0.000000 | \n",
+ " 10.740470 | \n",
+ " 10.740470 | \n",
+ " -0.431206 | \n",
+ " 12.296244 | \n",
+ " 2.0 - 2.5 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
2133 rows × 89 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " om10M_i om10z index Min Distance cosmodc2_index sersic_bulge \\\n",
+ "0 -25.008095 2.65 0 0.107426 9917.0 4.0 \n",
+ "14 -25.095188 2.45 18 0.210879 9917.0 4.0 \n",
+ "26 -25.061939 2.59 31 0.134481 9917.0 4.0 \n",
+ "61 -25.102290 2.54 83 0.178529 9917.0 4.0 \n",
+ "130 -24.940274 2.39 170 0.188864 9917.0 4.0 \n",
+ "... ... ... ... ... ... ... \n",
+ "2108 -21.901879 2.13 2783 0.013457 8100.0 4.0 \n",
+ "2120 -21.472621 1.90 2799 0.018944 6624.0 4.0 \n",
+ "2124 -22.033031 2.55 2804 0.040030 9859.0 4.0 \n",
+ "2126 -22.863294 2.46 2807 0.024685 10030.0 4.0 \n",
+ "2130 -21.824196 2.35 2812 0.024528 6973.0 4.0 \n",
+ "\n",
+ " size_minor_bulge_true dec_true size_disk_true size_minor_disk_true \\\n",
+ "0 0.462818 -28.381324 1.002705 0.193438 \n",
+ "14 0.462818 -28.381324 1.002705 0.193438 \n",
+ "26 0.462818 -28.381324 1.002705 0.193438 \n",
+ "61 0.462818 -28.381324 1.002705 0.193438 \n",
+ "130 0.462818 -28.381324 1.002705 0.193438 \n",
+ "... ... ... ... ... \n",
+ "2108 0.398532 -27.833679 0.812260 0.656106 \n",
+ "2120 0.271402 -28.352587 0.664376 0.164489 \n",
+ "2124 0.362162 -27.568521 0.935928 0.688451 \n",
+ "2126 0.406656 -27.798642 0.702246 0.670270 \n",
+ "2130 0.310991 -27.853446 0.535504 0.451001 \n",
+ "\n",
+ " ... q size_true_kpc size_true_log size_true_kpc_log \\\n",
+ "0 ... 0.710240 5.284734 -0.185995 0.723023 \n",
+ "14 ... 0.710240 5.284734 -0.185995 0.723023 \n",
+ "26 ... 0.710240 5.284734 -0.185995 0.723023 \n",
+ "61 ... 0.710240 5.284734 -0.185995 0.723023 \n",
+ "130 ... 0.710240 5.284734 -0.185995 0.723023 \n",
+ "... ... ... ... ... ... \n",
+ "2108 ... 0.992910 3.363527 -0.396446 0.526795 \n",
+ "2120 ... 0.687045 3.347413 -0.403372 0.524709 \n",
+ "2124 ... 0.888890 3.316291 -0.389209 0.520653 \n",
+ "2126 ... 0.998194 3.329499 -0.389987 0.522379 \n",
+ "2130 ... 0.995392 2.575279 -0.505246 0.410824 \n",
+ "\n",
+ " bulge_to_total_ratio_i_log stellar_mass_log stellar_mass_bulge_log \\\n",
+ "0 0.000000 11.073158 11.073158 \n",
+ "14 0.000000 11.073158 11.073158 \n",
+ "26 0.000000 11.073158 11.073158 \n",
+ "61 0.000000 11.073158 11.073158 \n",
+ "130 0.000000 11.073158 11.073158 \n",
+ "... ... ... ... \n",
+ "2108 -0.000006 10.177324 10.177323 \n",
+ "2120 0.000000 10.712845 10.712845 \n",
+ "2124 -0.002433 10.115864 10.115813 \n",
+ "2126 0.000000 10.674628 10.674628 \n",
+ "2130 0.000000 10.740470 10.740470 \n",
+ "\n",
+ " stellar_mass_disk_log halo_mass_log bin \n",
+ "0 -0.570010 12.320147 2.5 - 3.0 \n",
+ "14 -0.570010 12.320147 2.5 - 3.0 \n",
+ "26 -0.570010 12.320147 2.5 - 3.0 \n",
+ "61 -0.570010 12.320147 2.5 - 3.0 \n",
+ "130 -0.570010 12.320147 2.5 - 3.0 \n",
+ "... ... ... ... \n",
+ "2108 3.767176 11.880815 2.0 - 2.5 \n",
+ "2120 0.042562 12.473480 1.5 - 2.0 \n",
+ "2124 6.189734 12.087527 2.5 - 3.0 \n",
+ "2126 0.901244 12.315252 2.0 - 2.5 \n",
+ "2130 -0.431206 12.296244 2.0 - 2.5 \n",
+ "\n",
+ "[2133 rows x 89 columns]"
+ ]
+ },
+ "execution_count": 72,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = pd.read_csv(\"../data/OM10/final_test_sample.csv\", index_col=0)\n",
+ "pd.read_csv(\"../data/OM10/source_dict.csv\", index_col=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lens_cols = [\"om\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "om10M_i\n",
+ "om10z\n",
+ "index\n",
+ "Min Distance\n",
+ "cosmodc2_index\n",
+ "sersic_bulge\n",
+ "size_minor_bulge_true\n",
+ "dec_true\n",
+ "size_disk_true\n",
+ "size_minor_disk_true\n",
+ "size_true\n",
+ "size_bulge_true\n",
+ "mag_i\n",
+ "ra_true\n",
+ "mag_true_i\n",
+ "sersic_disk\n",
+ "magnification\n",
+ "size_minor_true\n",
+ "galaxy_id\n",
+ "tau_i\n",
+ "sf_g\n",
+ "sf_z\n",
+ "sf_r\n",
+ "sf_i\n",
+ "tau_y\n",
+ "seed\n",
+ "tau_g\n",
+ "sf_u\n",
+ "tau_r\n",
+ "tau_z\n",
+ "sf_y\n",
+ "M_i\n",
+ "tau_u\n",
+ "flux_g\n",
+ "flux_u_noMW\n",
+ "dec\n",
+ "ra\n",
+ "flux_r_noMW\n",
+ "flux_u\n",
+ "flux_r\n",
+ "flux_g_noMW\n",
+ "flux_z\n",
+ "flux_z_noMW\n",
+ "redshift\n",
+ "flux_y\n",
+ "flux_i\n",
+ "flux_i_noMW\n",
+ "is_variable\n",
+ "is_pointsource\n",
+ "flux_y_noMW\n",
+ "ellipticity_1_disk_true\n",
+ "ellipticity_2_bulge_true\n",
+ "ellipticity_2_true\n",
+ "ellipticity_2_disk_true\n",
+ "ellipticity_1_true\n",
+ "ellipticity_2_true_dc2\n",
+ "ellipticity_1_bulge_true\n",
+ "ellipticity_disk_true\n",
+ "ellipticity_true\n",
+ "ellipticity_1_bulge_true_dc2\n",
+ "ellipticity_bulge_true\n",
+ "ellipticity_1_true_dc2\n",
+ "ellipticity_1_disk_true_dc2\n",
+ "ellipticity_2_bulge_true_dc2\n",
+ "ellipticity_2_disk_true_dc2\n",
+ "Mag_true_i_lsst_z0\n",
+ "bulge_to_total_ratio_i\n",
+ "stellar_mass_bulge\n",
+ "halo_mass\n",
+ "stellar_mass\n",
+ "stellar_mass_disk\n",
+ "phi_disk\n",
+ "q_disk\n",
+ "phi_bulge\n",
+ "q_bulge\n",
+ "phi\n",
+ "q\n",
+ "size_true_kpc\n",
+ "size_true_log\n",
+ "size_true_kpc_log\n",
+ "bulge_to_total_ratio_i_log\n",
+ "stellar_mass_log\n",
+ "stellar_mass_bulge_log\n",
+ "stellar_mass_disk_log\n",
+ "halo_mass_log\n",
+ "bin\n",
+ "NIMG\n",
+ "ZLENS\n",
+ "VELDISP\n",
+ "ELLIP\n",
+ "PHIE\n",
+ "GAMMA\n",
+ "PHIG\n",
+ "ZSRC\n",
+ "XSRC\n",
+ "YSRC\n",
+ "MAGI_IN\n",
+ "MAGI\n",
+ "IMSEP\n",
+ "XIMG\n",
+ "YIMG\n",
+ "MAG\n",
+ "DELAY\n",
+ "DD\n",
+ "DDLUM\n",
+ "ABMAG_I\n",
+ "APMAG_I\n",
+ "KCORR\n",
+ "DS\n",
+ "DDS\n",
+ "DSLUM\n",
+ "ABMAGI_IN\n",
+ "ABMAGI_IN3\n",
+ "ABMAGI_IN4\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in data.columns:\n",
+ " print(i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "theta_E = (4 * np.pi * (data[\"VELDISP\"] ** 2) * data[\"DDS\"]) / ((3e5) ** 2 * data[\"DS\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.1, 0.9, 'RMSE = 0.06')"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "