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deploy: e230a9a
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@OriolAbril
OriolAbril committed Oct 1, 2023
1 parent 11ce02b commit 3707a06
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2 changes: 1 addition & 1 deletion ca/.buildinfo
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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: e1af8be91a68430a2b214a3cc5a77e55
config: a450bc8b95b3123f71f5e490bf739cbb
tags: 645f666f9bcd5a90fca523b33c5a78b7
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274 changes: 164 additions & 110 deletions ca/.doctrees/codeautolink-cache.json
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Expand Up @@ -817,6 +817,61 @@
"context": "none",
"resolved_location": "matplotlib.pyplot.figure"
},
{
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],
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},
{
"import_components": [
"arviz",
Expand Down Expand Up @@ -2329,6 +2384,33 @@
"context": "none",
"resolved_location": "matplotlib.pyplot.figure"
},
{
"import_components": [
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"import_components": [
"numpy",
Expand Down Expand Up @@ -3637,17 +3719,6 @@
"context": "none",
"resolved_location": "numpy.arange"
},
{
"import_components": [
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"import_components": [
"pymc",
Expand Down Expand Up @@ -3714,28 +3785,6 @@
"context": "none",
"resolved_location": "pymc.Normal"
},
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"import_components": [
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"resolved_location": "pymc.Deterministic"
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"import_components": [
"pymc",
Expand Down Expand Up @@ -3856,17 +3905,6 @@
{
"source": "with pm.Model(coords=coords) as radon_model:\n floor_idx = pm.ConstantData(\"floor_idx\", srrs_mn.floor, dims=\"obs_id\")\n county_idx = pm.ConstantData(\"county_idx\", srrs_mn.county_code, dims=\"obs_id\")",
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Expand Down Expand Up @@ -3973,17 +4011,6 @@
{
"source": "with radon_model:\n ab_county = pm.Deterministic(\"ab_county\", at.dot(chol, z).T, dims=(\"county\", \"param\"))\n theta = a + ab_county[county_idx, 0] + (b + ab_county[county_idx, 1]) * floor_idx\n\n sigma = pm.Exponential(\"sigma\", 1.0)\n pm.Normal(\"y\", theta, sigma=sigma, observed=srrs_mn.log_radon, dims=\"obs_id\")",
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"import_components": [
"pymc",
Expand Down Expand Up @@ -4093,17 +4120,6 @@
{
"source": "with pm.Model() as linreg_model:\n # use x_ to avoid overwriting x previously defined as np.ndarray\n x_ = pm.MutableData(\"x\", x, dims=\"year\", coords={\"year\": year})\n \n a = pm.Normal(\"a\", 0, 3)\n b = pm.Normal(\"b\", 0, 2)\n sigma = pm.HalfNormal(\"sigma\", 2)\n \n pm.Normal(\"y\", a + b * x_, sigma, observed=y, dims=\"year\")",
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"import_components": [
"pymc",
Expand Down Expand Up @@ -4357,17 +4373,6 @@
{
"source": "with linreg_model:\n pm.set_data({\"x\": x[-1] + x[1:3]}, coords={\"year\": [2022, 2023]})\n pm.sample_posterior_predictive(linreg_idata, predictions=True, extend_inferencedata=True)",
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"import_components": [
"pymc",
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"context": "none",
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"matplotlib",
Expand Down Expand Up @@ -5407,6 +5438,20 @@
"context": "none",
"resolved_location": "matplotlib.gridspec.GridSpec"
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"resolved_location": "matplotlib.figure.Figure.add_subplot"
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"import_components": [
"matplotlib",
Expand All @@ -5420,6 +5465,20 @@
"context": "none",
"resolved_location": "matplotlib.gridspec.GridSpec"
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"matplotlib",
"pyplot",
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"add_subplot"
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"context": "none",
"resolved_location": "matplotlib.figure.Figure.add_subplot"
},
{
"import_components": [
"matplotlib",
Expand All @@ -5433,6 +5492,20 @@
"context": "none",
"resolved_location": "matplotlib.gridspec.GridSpec"
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"matplotlib",
"pyplot",
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"end_lineno": 50,
"context": "none",
"resolved_location": "matplotlib.figure.Figure.add_subplot"
},
{
"import_components": [
"arviz",
Expand All @@ -5457,6 +5530,20 @@
"context": "none",
"resolved_location": "matplotlib.gridspec.GridSpec"
},
{
"import_components": [
"matplotlib",
"pyplot",
"figure",
"()",
"add_subplot"
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"code_str": "fig.add_subplot",
"lineno": 64,
"end_lineno": 64,
"context": "none",
"resolved_location": "matplotlib.figure.Figure.add_subplot"
},
{
"import_components": [
"numpy",
Expand All @@ -5482,17 +5569,6 @@
{
"source": "with pm.Model():\n a_shot = from_posterior(\"a_shot\", post[\"a\"].sel(var=\"shot\").values.flatten())\n a_distance = from_posterior(\"a_distance\", post[\"a\"].sel(var=\"distance\").values.flatten())\n \n a_prior = pm.sample_prior_predictive(2000)",
"names": [
{
"import_components": [
"pymc",
"Model"
],
"code_str": "pm.Model",
"lineno": 1,
"end_lineno": 1,
"context": "none",
"resolved_location": "pymc.Model"
},
{
"import_components": [
"pymc",
Expand Down Expand Up @@ -5531,17 +5607,6 @@
{
"source": "with pm.Model():\n b_shot = from_posterior(\"b_shot\", post[\"b\"].sel(var=\"shot\").values.flatten())\n b_distance = from_posterior(\"b_distance\", post[\"b\"].sel(var=\"distance\").values.flatten())\n \n b_prior = pm.sample_prior_predictive(2000)",
"names": [
{
"import_components": [
"pymc",
"Model"
],
"code_str": "pm.Model",
"lineno": 1,
"end_lineno": 1,
"context": "none",
"resolved_location": "pymc.Model"
},
{
"import_components": [
"pymc",
Expand Down Expand Up @@ -5580,17 +5645,6 @@
{
"source": "with pm.Model():\n c_shot = from_posterior(\"c_shot\", post[\"c\"].sel(var=\"shot\").values.flatten())\n c_distance = from_posterior(\"c_distance\", post[\"c\"].sel(var=\"distance\").values.flatten())\n \n c_prior = pm.sample_prior_predictive(2000)",
"names": [
{
"import_components": [
"pymc",
"Model"
],
"code_str": "pm.Model",
"lineno": 1,
"end_lineno": 1,
"context": "none",
"resolved_location": "pymc.Model"
},
{
"import_components": [
"pymc",
Expand Down
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