diff --git a/.gitignore b/.gitignore
index dfa38d910f..6a2f23cc96 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,6 +17,7 @@ tags
# Sphinx
_build
+docs/jupyter_execute
# Merge tool
*.orig
diff --git a/.gitmodules b/.gitmodules
deleted file mode 100644
index b19939f4dc..0000000000
--- a/.gitmodules
+++ /dev/null
@@ -1,4 +0,0 @@
-[submodule "docs/source/pymc-examples"]
- path = docs/source/pymc-examples
- url = https://github.com/pymc-devs/pymc-examples.git
- branch = main
diff --git a/conda-envs/environment-dev-py37.yml b/conda-envs/environment-dev-py37.yml
index 0054d7788a..7263fb4245 100644
--- a/conda-envs/environment-dev-py37.yml
+++ b/conda-envs/environment-dev-py37.yml
@@ -23,9 +23,8 @@ dependencies:
- python-graphviz
- python=3.7
- scipy>1.4.1
-- sphinx-autobuild>=0.7
+- sphinx-copybutton
- sphinx-notfound-page
-- sphinx-panels
- sphinx>=1.5
- typing-extensions
- pip:
diff --git a/conda-envs/environment-dev-py38.yml b/conda-envs/environment-dev-py38.yml
index 63d7fa1fd1..21858097ce 100644
--- a/conda-envs/environment-dev-py38.yml
+++ b/conda-envs/environment-dev-py38.yml
@@ -23,9 +23,8 @@ dependencies:
- python-graphviz
- python=3.8
- scipy>1.4.1
-- sphinx-autobuild>=0.7
+- sphinx-copybutton
- sphinx-notfound-page
-- sphinx-panels
- sphinx>=1.5
- typing-extensions>=3.7.4
- pip:
diff --git a/conda-envs/environment-dev-py39.yml b/conda-envs/environment-dev-py39.yml
index b9ba50f49e..0b007a5aa2 100644
--- a/conda-envs/environment-dev-py39.yml
+++ b/conda-envs/environment-dev-py39.yml
@@ -23,9 +23,8 @@ dependencies:
- python-graphviz
- python=3.9
- scipy>1.4.1
-- sphinx-autobuild>=0.7
+- sphinx-copybutton
- sphinx-notfound-page
-- sphinx-panels
- sphinx>=1.5
- typing-extensions>=3.7.4
- pip:
diff --git a/conda-envs/windows-environment-dev-py38.yml b/conda-envs/windows-environment-dev-py38.yml
index 13f173df5d..513fe2e2af 100644
--- a/conda-envs/windows-environment-dev-py38.yml
+++ b/conda-envs/windows-environment-dev-py38.yml
@@ -27,8 +27,8 @@ dependencies:
- pytest-cov>=2.5
- pytest>=3.0
- sphinx-autobuild>=0.7
+- sphinx-copybutton
- sphinx-notfound-page
-- sphinx-panels
- sphinx>=1.5
- watermark
- pip:
diff --git a/docs/logos/sponsors/numfocus.png b/docs/logos/sponsors/numfocus.png
new file mode 100644
index 0000000000..adf2de2257
Binary files /dev/null and b/docs/logos/sponsors/numfocus.png differ
diff --git a/docs/pymc-labs-logo.png b/docs/logos/sponsors/pymc-labs.png
similarity index 100%
rename from docs/pymc-labs-logo.png
rename to docs/logos/sponsors/pymc-labs.png
diff --git a/docs/pymc_logo.jpg b/docs/pymc_logo.jpg
deleted file mode 100644
index 28dcdfc437..0000000000
Binary files a/docs/pymc_logo.jpg and /dev/null differ
diff --git a/docs/source/Advanced_usage_of_Aesara_in_PyMC.rst b/docs/source/Advanced_usage_of_Aesara_in_PyMC.rst
index 2172e43980..7da1aa7e11 100644
--- a/docs/source/Advanced_usage_of_Aesara_in_PyMC.rst
+++ b/docs/source/Advanced_usage_of_Aesara_in_PyMC.rst
@@ -1,5 +1,5 @@
:orphan:
-
+(Advanced_usage_of_Aesara_in_PyMC)=
..
_referenced in docs/source/notebooks/table_of_contents_tutorials.js
diff --git a/docs/source/_templates/footer.html b/docs/source/_templates/footer.html
new file mode 100644
index 0000000000..93083ce2cc
--- /dev/null
+++ b/docs/source/_templates/footer.html
@@ -0,0 +1,9 @@
+
diff --git a/docs/source/_templates/layout.html b/docs/source/_templates/layout.html
index fbcc5b278e..0f4ea8c215 100644
--- a/docs/source/_templates/layout.html
+++ b/docs/source/_templates/layout.html
@@ -2,25 +2,25 @@
{% block docs_sidebar %}
- {% if pagename != 'index' %}
- {{ super() }}
- {% endif %}
+{% if pagename != 'index' %}
+{{ super() }}
+{% endif %}
{% endblock %}
{% block docs_toc %}
- {% if pagename != 'index' %}
- {{ super() }}
- {% endif %}
+{% if pagename != 'index' %}
+{{ super() }}
+{% endif %}
{% endblock %}
{% block docs_main %}
- {% if pagename == 'index' %}
-
PyMC allows you to write down models using an intuitive syntax to describe a data generating - process.
-Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate - inference — including minibatch-ADVI for scaling to large datasets — or using - Gaussian processes to build Bayesian nonparametric models.
-PyMC is licensed under the Apache License, V2.
-Please choose from the following:
-See Google Scholar for a continuously updated list of papers citing PyMC.
-PyMC is a non-profit project under NumFOCUS umbrella. - If you value PyMC and want to support its development, consider - donating to the project or - read our support PyMC page. -
- - -\n", + " | mean | \n", + "sd | \n", + "hdi_3% | \n", + "hdi_97% | \n", + "mcse_mean | \n", + "mcse_sd | \n", + "ess_mean | \n", + "ess_sd | \n", + "ess_bulk | \n", + "ess_tail | \n", + "r_hat | \n", + "
---|---|---|---|---|---|---|---|---|---|---|---|
alpha | \n", + "0.96 | \n", + "0.11 | \n", + "0.75 | \n", + "1.16 | \n", + "0.00 | \n", + "0.0 | \n", + "9813.79 | \n", + "9781.97 | \n", + "9816.05 | \n", + "6783.11 | \n", + "1.0 | \n", + "
beta[0] | \n", + "1.10 | \n", + "0.12 | \n", + "0.89 | \n", + "1.33 | \n", + "0.00 | \n", + "0.0 | \n", + "8841.92 | \n", + "8797.67 | \n", + "8856.21 | \n", + "7109.65 | \n", + "1.0 | \n", + "
beta[1] | \n", + "2.99 | \n", + "0.53 | \n", + "1.95 | \n", + "3.95 | \n", + "0.01 | \n", + "0.0 | \n", + "7878.01 | \n", + "7765.26 | \n", + "7880.25 | \n", + "6515.70 | \n", + "1.0 | \n", + "
sigma | \n", + "1.07 | \n", + "0.08 | \n", + "0.92 | \n", + "1.21 | \n", + "0.00 | \n", + "0.0 | \n", + "8651.16 | \n", + "8475.93 | \n", + "8901.69 | \n", + "6633.66 | \n", + "1.0 | \n", + "
<xarray.Dataset>\n", + "Dimensions: (chain: 2, disasters_missing_dim_0: 2, draw: 10000)\n", + "Coordinates:\n", + " * chain (chain) int64 0 1\n", + " * draw (draw) int64 0 1 2 3 4 ... 9995 9996 9997 9998 9999\n", + " * disasters_missing_dim_0 (disasters_missing_dim_0) int64 0 1\n", + "Data variables:\n", + " switchpoint (chain, draw) int64 1891 1891 1891 ... 1892 1891\n", + " disasters_missing (chain, draw, disasters_missing_dim_0) int64 7 ....\n", + " early_rate (chain, draw) float64 3.025 3.076 ... 3.307 3.005\n", + " late_rate (chain, draw) float64 0.877 0.8663 ... 0.802 0.9272\n", + "Attributes:\n", + " created_at: 2021-02-08T06:29:28.922616\n", + " arviz_version: 0.11.0\n", + " inference_library: pymc3\n", + " inference_library_version: 3.11.0\n", + " sampling_time: 33.77551817893982\n", + " tuning_steps: 1000
array([0, 1])
array([ 0, 1, 2, ..., 9997, 9998, 9999])
array([0, 1])
array([[1891, 1891, 1891, ..., 1889, 1889, 1889],\n", + " [1892, 1886, 1889, ..., 1893, 1892, 1891]])
array([[[7, 0],\n", + " [6, 0],\n", + " [5, 1],\n", + " ...,\n", + " [1, 0],\n", + " [2, 1],\n", + " [0, 1]],\n", + "\n", + " [[3, 1],\n", + " [3, 1],\n", + " [2, 0],\n", + " ...,\n", + " [5, 1],\n", + " [5, 2],\n", + " [3, 0]]])
array([[3.02490339, 3.07599078, 3.56514373, ..., 2.61447178, 2.61447178,\n", + " 3.44918562],\n", + " [3.27857005, 3.03935203, 3.30460144, ..., 3.16269593, 3.30703433,\n", + " 3.00495012]])
array([[0.8769546 , 0.86634116, 0.95610952, ..., 0.826213 , 0.826213 ,\n", + " 0.91611314],\n", + " [0.9603648 , 1.01766597, 0.92037428, ..., 0.82312568, 0.80196379,\n", + " 0.92723499]])
<xarray.Dataset>\n", + "Dimensions: (chain: 2, disasters_dim_0: 111, draw: 10000)\n", + "Coordinates:\n", + " * chain (chain) int64 0 1\n", + " * draw (draw) int64 0 1 2 3 4 5 ... 9994 9995 9996 9997 9998 9999\n", + " * disasters_dim_0 (disasters_dim_0) int64 0 1 2 3 4 5 ... 106 107 108 109 110\n", + "Data variables:\n", + " disasters (chain, draw, disasters_dim_0) float64 -1.775 ... -1.003\n", + "Attributes:\n", + " created_at: 2021-02-08T06:29:30.682570\n", + " arviz_version: 0.11.0\n", + " inference_library: pymc3\n", + " inference_library_version: 3.11.0
array([0, 1])
array([ 0, 1, 2, ..., 9997, 9998, 9999])
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,\n", + " 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,\n", + " 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,\n", + " 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,\n", + " 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,\n", + " 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,\n", + " 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110])
array([[[-1.77544061, -2.27799936, -1.77544061, ..., -1.00825466,\n", + " -0.8769546 , -1.00825466],\n", + " [-1.75953639, -2.24534725, -1.75953639, ..., -1.00981766,\n", + " -0.86634116, -1.00981766],\n", + " [-1.65838008, -1.99661362, -1.65838008, ..., -1.00099233,\n", + " -0.95610952, -1.00099233],\n", + " ...,\n", + " [-1.94827729, -2.59665312, -1.94827729, ..., -1.01711567,\n", + " -0.826213 , -1.01711567],\n", + " [-1.94827729, -2.59665312, -1.94827729, ..., -1.01711567,\n", + " -0.826213 , -1.01711567],\n", + " [-1.67468685, -2.04598661, -1.67468685, ..., -1.00372855,\n", + " -0.91611314, -1.00372855]],\n", + "\n", + " [[-1.70699441, -2.12902496, -1.70699441, ..., -1.00080687,\n", + " -0.9603648 , -1.00080687],\n", + " [-1.77082848, -2.26862205, -1.77082848, ..., -1.00015423,\n", + " -1.01766597, -1.00015423],\n", + " [-1.70139178, -2.11551382, -1.70139178, ..., -1.00334915,\n", + " -0.92037428, -1.00334915],\n", + " ...,\n", + " [-1.73505054, -2.19306364, -1.73505054, ..., -1.01777206,\n", + " -0.82312568, -1.01777206],\n", + " [-1.7008809 , -2.114267 , -1.7008809 , ..., -1.02265561,\n", + " -0.80196379, -1.02265561],\n", + " [-1.78196007, -2.29113702, -1.78196007, ..., -1.00278324,\n", + " -0.92723499, -1.00278324]]])
<xarray.Dataset>\n", + "Dimensions: (accept_dim_0: 2, accepted_dim_0: 2, chain: 2, draw: 10000, scaling_dim_0: 2)\n", + "Coordinates:\n", + " * chain (chain) int64 0 1\n", + " * draw (draw) int64 0 1 2 3 4 5 ... 9995 9996 9997 9998 9999\n", + " * accepted_dim_0 (accepted_dim_0) int64 0 1\n", + " * scaling_dim_0 (scaling_dim_0) int64 0 1\n", + " * accept_dim_0 (accept_dim_0) int64 0 1\n", + "Data variables:\n", + " perf_counter_start (chain, draw) float64 481.6 481.6 481.6 ... 498.6 498.6\n", + " accepted (chain, draw, accepted_dim_0) bool False True ... True\n", + " diverging (chain, draw) bool False False False ... False False\n", + " step_size (chain, draw) float64 0.8683 0.8683 ... 1.078 1.078\n", + " tree_size (chain, draw) float64 3.0 1.0 3.0 3.0 ... 3.0 3.0 3.0\n", + " step_size_bar (chain, draw) float64 1.135 1.135 1.135 ... 1.112 1.112\n", + " energy (chain, draw) float64 180.8 177.0 178.7 ... 179.2 176.4\n", + " depth (chain, draw) int64 2 1 2 2 1 2 2 2 ... 2 1 2 2 1 2 2 2\n", + " scaling (chain, draw, scaling_dim_0) float64 1.464 ... 2.358\n", + " process_time_diff (chain, draw) float64 0.000612 0.00028 ... 0.000477\n", + " lp (chain, draw) float64 -177.8 -177.0 ... -179.1 -175.6\n", + " perf_counter_diff (chain, draw) float64 0.0006293 0.0002809 ... 0.0004771\n", + " accept (chain, draw, accept_dim_0) float64 0.3994 ... 2.969\n", + " energy_error (chain, draw) float64 -0.005702 -0.01118 ... -0.2271\n", + " max_energy_error (chain, draw) float64 1.129 -0.01118 ... 0.174 -0.2271\n", + " mean_tree_accept (chain, draw) float64 0.7362 1.0 ... 0.9566 0.9841\n", + "Attributes:\n", + " created_at: 2021-02-08T06:29:28.933359\n", + " arviz_version: 0.11.0\n", + " inference_library: pymc3\n", + " inference_library_version: 3.11.0\n", + " sampling_time: 33.77551817893982\n", + " tuning_steps: 1000
array([0, 1])
array([ 0, 1, 2, ..., 9997, 9998, 9999])
array([0, 1])
array([0, 1])
array([0, 1])
array([[481.62788461, 481.6291168 , 481.62996704, ..., 495.36697623,\n", + " 495.36820486, 495.36928442],\n", + " [484.98033663, 484.98164721, 484.98357028, ..., 498.62839408,\n", + " 498.62960144, 498.63077901]])
array([[[False, True],\n", + " [ True, False],\n", + " [ True, False],\n", + " ...,\n", + " [False, False],\n", + " [ True, False],\n", + " [ True, False]],\n", + "\n", + " [[False, True],\n", + " [False, True],\n", + " [ True, True],\n", + " ...,\n", + " [ True, True],\n", + " [ True, True],\n", + " [ True, True]]])
array([[False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False]])
array([[0.86829042, 0.86829042, 0.86829042, ..., 0.86829042, 0.86829042,\n", + " 0.86829042],\n", + " [1.0782656 , 1.0782656 , 1.0782656 , ..., 1.0782656 , 1.0782656 ,\n", + " 1.0782656 ]])
array([[3., 1., 3., ..., 1., 1., 3.],\n", + " [3., 3., 3., ..., 3., 3., 3.]])
array([[1.13544036, 1.13544036, 1.13544036, ..., 1.13544036, 1.13544036,\n", + " 1.13544036],\n", + " [1.11179432, 1.11179432, 1.11179432, ..., 1.11179432, 1.11179432,\n", + " 1.11179432]])
array([[180.84583537, 177.02264999, 178.74471241, ..., 177.91502384,\n", + " 179.90576987, 177.29210912],\n", + " [177.80556826, 177.9962228 , 178.65102567, ..., 179.71597262,\n", + " 179.20684298, 176.38309815]])
array([[2, 1, 2, ..., 1, 1, 2],\n", + " [2, 2, 2, ..., 2, 2, 2]])
array([[[1.4641 , 2.662 ],\n", + " [1.4641 , 2.662 ],\n", + " [1.4641 , 2.662 ],\n", + " ...,\n", + " [1.4641 , 2.662 ],\n", + " [1.4641 , 2.662 ],\n", + " [1.4641 , 2.662 ]],\n", + "\n", + " [[1.331 , 2.35794769],\n", + " [1.331 , 2.35794769],\n", + " [1.331 , 2.35794769],\n", + " ...,\n", + " [1.331 , 2.35794769],\n", + " [1.331 , 2.35794769],\n", + " [1.331 , 2.35794769]]])
array([[0.000612, 0.00028 , 0.000512, ..., 0.00048 , 0.000462, 0.000682],\n", + " [0.000659, 0.000737, 0.000566, ..., 0.000642, 0.000668, 0.000477]])
array([[-177.84833139, -176.96998605, -177.62208958, ..., -177.33637597,\n", + " -178.41132849, -176.00996052],\n", + " [-177.04631713, -177.91064831, -176.3128248 , ..., -178.69018227,\n", + " -179.11188422, -175.55994681]])
array([[0.00062935, 0.00028094, 0.00051247, ..., 0.00048048, 0.00046108,\n", + " 0.00102844],\n", + " [0.00066692, 0.00077655, 0.00056549, ..., 0.00066893, 0.00068386,\n", + " 0.00047712]])
array([[[0.39939657, 1.09440673],\n", + " [2.31412349, 0.720068 ],\n", + " [1.68987729, 0.05915239],\n", + " ...,\n", + " [0. , 0.89222621],\n", + " [0.34131396, 0.18869271],\n", + " [2.92985378, 0.28010575]],\n", + "\n", + " [[0.27690895, 0.63139657],\n", + " [0.03377025, 0.43825176],\n", + " [2.89674824, 1.64828846],\n", + " ...,\n", + " [0.82312568, 1. ],\n", + " [0.41156284, 2.70066777],\n", + " [5.68686693, 2.96930198]]])
array([[-0.00570162, -0.01118174, 0.30225096, ..., -0.01023484,\n", + " 0. , -0.42615453],\n", + " [ 0.04211059, 0.01095353, 0.05170346, ..., 0. ,\n", + " 0.17401783, -0.22711861]])
array([[ 1.12866425, -0.01118174, 0.7498719 , ..., -0.01023484,\n", + " 0.35802402, -0.42615453],\n", + " [ 0.35504171, 0.02885821, 0.82411198, ..., 0.35797759,\n", + " 0.17401783, -0.22711861]])
array([[0.7361729 , 1. , 0.70539919, ..., 1. , 0.69905628,\n", + " 1. ],\n", + " [0.84416844, 0.98705606, 0.70683012, ..., 0.70433264, 0.95660635,\n", + " 0.98413996]])
<xarray.Dataset>\n", + "Dimensions: (disasters_dim_0: 111)\n", + "Coordinates:\n", + " * disasters_dim_0 (disasters_dim_0) int64 0 1 2 3 4 5 ... 106 107 108 109 110\n", + "Data variables:\n", + " disasters (disasters_dim_0) float64 4.0 5.0 4.0 0.0 ... 1.0 0.0 1.0\n", + "Attributes:\n", + " created_at: 2021-02-08T06:29:30.683682\n", + " arviz_version: 0.11.0\n", + " inference_library: pymc3\n", + " inference_library_version: 3.11.0
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,\n", + " 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,\n", + " 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,\n", + " 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,\n", + " 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,\n", + " 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,\n", + " 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110])
array([ 4., 5., 4., 0., 1., 4., 3., 4., 0., 6., 3., 3., 4.,\n", + " 0., 2., 6., 3., 3., 5., 4., 5., 3., 1., 4., 4., 1.,\n", + " 5., 5., 3., 4., 2., 5., 2., 2., 3., 4., 2., 1., 3.,\n", + " nan, 2., 1., 1., 1., 1., 3., 0., 0., 1., 0., 1., 1.,\n", + " 0., 0., 3., 1., 0., 3., 2., 2., 0., 1., 1., 1., 0.,\n", + " 1., 0., 1., 0., 0., 0., 2., 1., 0., 0., 0., 1., 1.,\n", + " 0., 2., 3., 3., 1., nan, 2., 1., 1., 1., 1., 2., 4.,\n", + " 2., 0., 0., 1., 4., 0., 0., 0., 1., 0., 0., 0., 0.,\n", + " 0., 1., 0., 0., 1., 0., 1.])
\n", + " | rank | \n", + "loo | \n", + "p_loo | \n", + "d_loo | \n", + "weight | \n", + "se | \n", + "dse | \n", + "warning | \n", + "loo_scale | \n", + "
---|---|---|---|---|---|---|---|---|---|
pooled | \n", + "0 | \n", + "-30.569563 | \n", + "0.680583 | \n", + "0.000000 | \n", + "1.0 | \n", + "1.105191 | \n", + "0.0000 | \n", + "False | \n", + "log | \n", + "
hierarchical | \n", + "1 | \n", + "-30.754275 | \n", + "1.113869 | \n", + "0.184711 | \n", + "0.0 | \n", + "1.045108 | \n", + "0.2397 | \n", + "False | \n", + "log | \n", + "
\n", + " | mean | \n", + "sd | \n", + "hdi_3% | \n", + "hdi_97% | \n", + "mcse_mean | \n", + "mcse_sd | \n", + "ess_mean | \n", + "ess_sd | \n", + "ess_bulk | \n", + "ess_tail | \n", + "r_hat | \n", + "
---|---|---|---|---|---|---|---|---|---|---|---|
betas[0] | \n", + "0.09 | \n", + "0.11 | \n", + "-0.1 | \n", + "0.30 | \n", + "0.0 | \n", + "0.0 | \n", + "1621.30 | \n", + "1304.44 | \n", + "1622.59 | \n", + "1219.60 | \n", + "1.0 | \n", + "
betas[1] | \n", + "1.07 | \n", + "0.13 | \n", + "0.8 | \n", + "1.29 | \n", + "0.0 | \n", + "0.0 | \n", + "1733.47 | \n", + "1682.04 | \n", + "1736.02 | \n", + "1377.74 | \n", + "1.0 | \n", + "