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Jammy2211 merged 1 commit into from
Aug 10, 2026
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docs: keep marginal summaries as parameter dictionaries #209

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32 changes: 17 additions & 15 deletions notebooks/guides/results/aggregator/samples.ipynb
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Expand Up @@ -463,18 +463,20 @@
"PDF stands for \"Probability Density Function\" and it quantifies probability of each model parameter having values\n",
"that are sampled. It therefore enables error estimation via a process called marginalization.\n",
"\n",
"The median pdf vector is available, which estimates every parameter via 1D marginalization of their PDFs."
"The median pdf vector is available, which estimates every parameter via 1D marginalization of their PDFs.\n",
"\n",
"Because these statistics are computed independently, combining them does not necessarily produce a physically valid\n",
"profile. Requesting a dictionary reports the marginalized parameters without constructing a model instance."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"instance = samples.median_pdf()\n",
"parameter_dict = samples.median_pdf(as_dict=True)\n",
"\n",
"print(\"Median PDF Model Instances: \\n\")\n",
"print(instance, \"\\n\")\n",
"print(instance.galaxies.galaxy.bulge)\n",
"print(\"Median PDF Model Parameters: \\n\")\n",
"print(parameter_dict, \"\\n\")\n",
"print()\n",
"\n",
"vector = samples.median_pdf(as_instance=False)\n",
Expand Down Expand Up @@ -503,12 +505,12 @@
"cell_type": "code",
"metadata": {},
"source": [
"instance_upper_sigma = samples.values_at_upper_sigma(sigma=3.0)\n",
"instance_lower_sigma = samples.values_at_lower_sigma(sigma=3.0)\n",
"parameter_dict_upper_sigma = samples.values_at_upper_sigma(sigma=3.0, as_dict=True)\n",
"parameter_dict_lower_sigma = samples.values_at_lower_sigma(sigma=3.0, as_dict=True)\n",
"\n",
"print(\"Errors Instances: \\n\")\n",
"print(instance_upper_sigma.galaxies.galaxy.bulge, \"\\n\")\n",
"print(instance_lower_sigma.galaxies.galaxy.bulge, \"\\n\")"
"print(\"Marginalized Parameter Bounds: \\n\")\n",
"print(parameter_dict_upper_sigma, \"\\n\")\n",
"print(parameter_dict_lower_sigma, \"\\n\")"
],
"outputs": [],
"execution_count": null
Expand All @@ -524,12 +526,12 @@
"cell_type": "code",
"metadata": {},
"source": [
"instance_upper_values = samples.errors_at_upper_sigma(sigma=3.0)\n",
"instance_lower_values = samples.errors_at_lower_sigma(sigma=3.0)\n",
"parameter_dict_upper_values = samples.errors_at_upper_sigma(sigma=3.0, as_dict=True)\n",
"parameter_dict_lower_values = samples.errors_at_lower_sigma(sigma=3.0, as_dict=True)\n",
"\n",
"print(\"Errors Instances: \\n\")\n",
"print(instance_upper_values.galaxies.galaxy.bulge, \"\\n\")\n",
"print(instance_lower_values.galaxies.galaxy.bulge, \"\\n\")"
"print(\"Marginalized Parameter Errors: \\n\")\n",
"print(parameter_dict_upper_values, \"\\n\")\n",
"print(parameter_dict_lower_values, \"\\n\")"
],
"outputs": [],
"execution_count": null
Expand Down
68 changes: 43 additions & 25 deletions notebooks/guides/results/aggregator/samples_via_aggregator.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -426,18 +426,20 @@
"PDF stands for \"Probability Density Function\" and it quantifies probability of each model parameter having values\n",
"that are sampled. It therefore enables error estimation via a process called marginalization.\n",
"\n",
"The median pdf vector is available, which estimates every parameter via 1D marginalization of their PDFs."
"The median pdf vector is available, which estimates every parameter via 1D marginalization of their PDFs.\n",
"\n",
"Independent marginal statistics need not combine into a physically valid profile, so they are returned as parameter\n",
"dictionaries instead of model instances."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"mp_instances = [samps.median_pdf() for samps in agg.values(\"samples\")]\n",
"mp_dicts = [samps.median_pdf(as_dict=True) for samps in agg.values(\"samples\")]\n",
"\n",
"print(\"Median PDF Model Instances: \\n\")\n",
"print(mp_instances, \"\\n\")\n",
"print(mp_instances[0].galaxies.galaxy.bulge)\n",
"print(\"Median PDF Model Parameters: \\n\")\n",
"print(mp_dicts, \"\\n\")\n",
"print()"
],
"outputs": [],
Expand All @@ -461,24 +463,32 @@
"cell_type": "code",
"metadata": {},
"source": [
"uv3_lists = [samps.values_at_upper_sigma(sigma=3.0) for samps in agg.values(\"samples\")]\n",
"uv3_lists = [\n",
" samps.values_at_upper_sigma(sigma=3.0, as_instance=False)\n",
" for samps in agg.values(\"samples\")\n",
"]\n",
"\n",
"uv3_instances = [\n",
" samps.values_at_upper_sigma(sigma=3.0) for samps in agg.values(\"samples\")\n",
"uv3_dicts = [\n",
" samps.values_at_upper_sigma(sigma=3.0, as_dict=True)\n",
" for samps in agg.values(\"samples\")\n",
"]\n",
"\n",
"lv3_lists = [samps.values_at_lower_sigma(sigma=3.0) for samps in agg.values(\"samples\")]\n",
"lv3_lists = [\n",
" samps.values_at_lower_sigma(sigma=3.0, as_instance=False)\n",
" for samps in agg.values(\"samples\")\n",
"]\n",
"\n",
"lv3_instances = [\n",
" samps.values_at_lower_sigma(sigma=3.0) for samps in agg.values(\"samples\")\n",
"lv3_dicts = [\n",
" samps.values_at_lower_sigma(sigma=3.0, as_dict=True)\n",
" for samps in agg.values(\"samples\")\n",
"]\n",
"\n",
"print(\"Errors Lists: \\n\")\n",
"print(uv3_lists, \"\\n\")\n",
"print(lv3_lists, \"\\n\")\n",
"print(\"Errors Instances: \\n\")\n",
"print(uv3_instances, \"\\n\")\n",
"print(lv3_instances, \"\\n\")"
"print(\"Errors Dictionaries: \\n\")\n",
"print(uv3_dicts, \"\\n\")\n",
"print(lv3_dicts, \"\\n\")"
],
"outputs": [],
"execution_count": null
Expand All @@ -496,23 +506,31 @@
"cell_type": "code",
"metadata": {},
"source": [
"ue3_lists = [samps.errors_at_upper_sigma(sigma=3.0) for samps in agg.values(\"samples\")]\n",
"ue3_lists = [\n",
" samps.errors_at_upper_sigma(sigma=3.0, as_instance=False)\n",
" for samps in agg.values(\"samples\")\n",
"]\n",
"\n",
"# ue3_instances = [\n",
"# samps.errors_at_upper_sigma(sigma=3.0) for samps in agg.values(\"samples\")\n",
"# ]\n",
"ue3_dicts = [\n",
" samps.errors_at_upper_sigma(sigma=3.0, as_dict=True)\n",
" for samps in agg.values(\"samples\")\n",
"]\n",
"\n",
"le3_lists = [samps.errors_at_lower_sigma(sigma=3.0) for samps in agg.values(\"samples\")]\n",
"# le3_instances = [\n",
"# samps.errors_at_lower_sigma(sigma=3.0) for samps in agg.values(\"samples\")\n",
"# ]\n",
"le3_lists = [\n",
" samps.errors_at_lower_sigma(sigma=3.0, as_instance=False)\n",
" for samps in agg.values(\"samples\")\n",
"]\n",
"le3_dicts = [\n",
" samps.errors_at_lower_sigma(sigma=3.0, as_dict=True)\n",
" for samps in agg.values(\"samples\")\n",
"]\n",
"\n",
"print(\"Errors Lists: \\n\")\n",
"print(ue3_lists, \"\\n\")\n",
"print(le3_lists, \"\\n\")\n",
"print(\"Errors Instances: \\n\")\n",
"# print(ue3_instances, \"\\n\")\n",
"# print(le3_instances, \"\\n\")"
"print(\"Errors Dictionaries: \\n\")\n",
"print(ue3_dicts, \"\\n\")\n",
"print(le3_dicts, \"\\n\")"
],
"outputs": [],
"execution_count": null
Expand Down
21 changes: 12 additions & 9 deletions notebooks/guides/results/start_here.ipynb
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Expand Up @@ -577,7 +577,10 @@
" - Deriving errors on derived quantities, such as the Einstein radius.\n",
"\n",
"Below, is an example of how to use the `Samples` object to estimate the mass model parameters which are\n",
"the median of the probability distribution function and its errors at 3 sigma confidence intervals."
"the median of the probability distribution function and its errors at 3 sigma confidence intervals.\n",
"\n",
"These statistics are computed independently for every parameter, so their combined vector is not guaranteed to\n",
"describe one physically valid profile. We therefore request dictionaries instead of constructing model instances."
]
},
{
Expand All @@ -586,18 +589,18 @@
"source": [
"samples = result.samples\n",
"\n",
"median_pdf_instance = samples.median_pdf()\n",
"median_pdf_dict = samples.median_pdf(as_dict=True)\n",
"\n",
"print(\"Median PDF Model Instances: \\n\")\n",
"print(median_pdf_instance.galaxies.galaxy.bulge)\n",
"print(\"Median PDF Model Parameters: \\n\")\n",
"print(median_pdf_dict)\n",
"print()\n",
"\n",
"ue3_instance = samples.values_at_upper_sigma(sigma=3.0)\n",
"le3_instance = samples.values_at_lower_sigma(sigma=3.0)\n",
"ue3_dict = samples.values_at_upper_sigma(sigma=3.0, as_dict=True)\n",
"le3_dict = samples.values_at_lower_sigma(sigma=3.0, as_dict=True)\n",
"\n",
"print(\"Errors Instances: \\n\")\n",
"print(ue3_instance.galaxies.galaxy.bulge, \"\\n\")\n",
"print(le3_instance.galaxies.galaxy.bulge, \"\\n\")"
"print(\"Marginalized Parameter Bounds: \\n\")\n",
"print(ue3_dict, \"\\n\")\n",
"print(le3_dict, \"\\n\")"
],
"outputs": [],
"execution_count": null
Expand Down
30 changes: 16 additions & 14 deletions scripts/guides/results/aggregator/samples.py
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Original file line number Diff line number Diff line change
Expand Up @@ -267,12 +267,14 @@
that are sampled. It therefore enables error estimation via a process called marginalization.

The median pdf vector is available, which estimates every parameter via 1D marginalization of their PDFs.

Because these statistics are computed independently, combining them does not necessarily produce a physically valid
profile. Requesting a dictionary reports the marginalized parameters without constructing a model instance.
"""
instance = samples.median_pdf()
parameter_dict = samples.median_pdf(as_dict=True)

print("Median PDF Model Instances: \n")
print(instance, "\n")
print(instance.galaxies.galaxy.bulge)
print("Median PDF Model Parameters: \n")
print(parameter_dict, "\n")
print()

vector = samples.median_pdf(as_instance=False)
Expand All @@ -290,22 +292,22 @@
By inputting `sigma=3.0` margnialization find the values spanning 99.7% of 1D PDF. Changing this to `sigma=1.0`
would give the errors at the 68.3% confidence limit.
"""
instance_upper_sigma = samples.values_at_upper_sigma(sigma=3.0)
instance_lower_sigma = samples.values_at_lower_sigma(sigma=3.0)
parameter_dict_upper_sigma = samples.values_at_upper_sigma(sigma=3.0, as_dict=True)
parameter_dict_lower_sigma = samples.values_at_lower_sigma(sigma=3.0, as_dict=True)

print("Errors Instances: \n")
print(instance_upper_sigma.galaxies.galaxy.bulge, "\n")
print(instance_lower_sigma.galaxies.galaxy.bulge, "\n")
print("Marginalized Parameter Bounds: \n")
print(parameter_dict_upper_sigma, "\n")
print(parameter_dict_lower_sigma, "\n")

"""
They can also be returned at the values of the parameters at their error values.
"""
instance_upper_values = samples.errors_at_upper_sigma(sigma=3.0)
instance_lower_values = samples.errors_at_lower_sigma(sigma=3.0)
parameter_dict_upper_values = samples.errors_at_upper_sigma(sigma=3.0, as_dict=True)
parameter_dict_lower_values = samples.errors_at_lower_sigma(sigma=3.0, as_dict=True)

print("Errors Instances: \n")
print(instance_upper_values.galaxies.galaxy.bulge, "\n")
print(instance_lower_values.galaxies.galaxy.bulge, "\n")
print("Marginalized Parameter Errors: \n")
print(parameter_dict_upper_values, "\n")
print(parameter_dict_lower_values, "\n")

"""
__Sample Instance__
Expand Down
66 changes: 42 additions & 24 deletions scripts/guides/results/aggregator/samples_via_aggregator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -243,12 +243,14 @@
that are sampled. It therefore enables error estimation via a process called marginalization.

The median pdf vector is available, which estimates every parameter via 1D marginalization of their PDFs.

Independent marginal statistics need not combine into a physically valid profile, so they are returned as parameter
dictionaries instead of model instances.
"""
mp_instances = [samps.median_pdf() for samps in agg.values("samples")]
mp_dicts = [samps.median_pdf(as_dict=True) for samps in agg.values("samples")]

print("Median PDF Model Instances: \n")
print(mp_instances, "\n")
print(mp_instances[0].galaxies.galaxy.bulge)
print("Median PDF Model Parameters: \n")
print(mp_dicts, "\n")
print()

"""
Expand All @@ -261,47 +263,63 @@
By inputting `sigma=3.0` margnialization find the values spanning 99.7% of 1D PDF. Changing this to `sigma=1.0`
would give the errors at the 68.3% confidence limit.
"""
uv3_lists = [samps.values_at_upper_sigma(sigma=3.0) for samps in agg.values("samples")]
uv3_lists = [
samps.values_at_upper_sigma(sigma=3.0, as_instance=False)
for samps in agg.values("samples")
]

uv3_instances = [
samps.values_at_upper_sigma(sigma=3.0) for samps in agg.values("samples")
uv3_dicts = [
samps.values_at_upper_sigma(sigma=3.0, as_dict=True)
for samps in agg.values("samples")
]

lv3_lists = [samps.values_at_lower_sigma(sigma=3.0) for samps in agg.values("samples")]
lv3_lists = [
samps.values_at_lower_sigma(sigma=3.0, as_instance=False)
for samps in agg.values("samples")
]

lv3_instances = [
samps.values_at_lower_sigma(sigma=3.0) for samps in agg.values("samples")
lv3_dicts = [
samps.values_at_lower_sigma(sigma=3.0, as_dict=True)
for samps in agg.values("samples")
]

print("Errors Lists: \n")
print(uv3_lists, "\n")
print(lv3_lists, "\n")
print("Errors Instances: \n")
print(uv3_instances, "\n")
print(lv3_instances, "\n")
print("Errors Dictionaries: \n")
print(uv3_dicts, "\n")
print(lv3_dicts, "\n")

"""
We can compute the upper and lower errors on each parameter at a given sigma limit.

The `ue3` below signifies the upper error at 3 sigma.
"""
ue3_lists = [samps.errors_at_upper_sigma(sigma=3.0) for samps in agg.values("samples")]
ue3_lists = [
samps.errors_at_upper_sigma(sigma=3.0, as_instance=False)
for samps in agg.values("samples")
]

# ue3_instances = [
# samps.errors_at_upper_sigma(sigma=3.0) for samps in agg.values("samples")
# ]
ue3_dicts = [
samps.errors_at_upper_sigma(sigma=3.0, as_dict=True)
for samps in agg.values("samples")
]

le3_lists = [samps.errors_at_lower_sigma(sigma=3.0) for samps in agg.values("samples")]
# le3_instances = [
# samps.errors_at_lower_sigma(sigma=3.0) for samps in agg.values("samples")
# ]
le3_lists = [
samps.errors_at_lower_sigma(sigma=3.0, as_instance=False)
for samps in agg.values("samples")
]
le3_dicts = [
samps.errors_at_lower_sigma(sigma=3.0, as_dict=True)
for samps in agg.values("samples")
]

print("Errors Lists: \n")
print(ue3_lists, "\n")
print(le3_lists, "\n")
print("Errors Instances: \n")
# print(ue3_instances, "\n")
# print(le3_instances, "\n")
print("Errors Dictionaries: \n")
print(ue3_dicts, "\n")
print(le3_dicts, "\n")

"""
__Sample Instance__
Expand Down
19 changes: 11 additions & 8 deletions scripts/guides/results/start_here.py
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Original file line number Diff line number Diff line change
Expand Up @@ -403,21 +403,24 @@

Below, is an example of how to use the `Samples` object to estimate the mass model parameters which are
the median of the probability distribution function and its errors at 3 sigma confidence intervals.

These statistics are computed independently for every parameter, so their combined vector is not guaranteed to
describe one physically valid profile. We therefore request dictionaries instead of constructing model instances.
"""
samples = result.samples

median_pdf_instance = samples.median_pdf()
median_pdf_dict = samples.median_pdf(as_dict=True)

print("Median PDF Model Instances: \n")
print(median_pdf_instance.galaxies.galaxy.bulge)
print("Median PDF Model Parameters: \n")
print(median_pdf_dict)
print()

ue3_instance = samples.values_at_upper_sigma(sigma=3.0)
le3_instance = samples.values_at_lower_sigma(sigma=3.0)
ue3_dict = samples.values_at_upper_sigma(sigma=3.0, as_dict=True)
le3_dict = samples.values_at_lower_sigma(sigma=3.0, as_dict=True)

print("Errors Instances: \n")
print(ue3_instance.galaxies.galaxy.bulge, "\n")
print(le3_instance.galaxies.galaxy.bulge, "\n")
print("Marginalized Parameter Bounds: \n")
print(ue3_dict, "\n")
print(le3_dict, "\n")

"""
__Linear Light Profiles__
Expand Down
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