<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 12kB\n",
"Dimensions: (sample: 100, school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
- " * sample (sample) object MultiIndex\n",
- " * chain (sample) int64 3 1 1 3 2 3 3 3 3 1 0 0 ... 2 0 2 2 3 2 2 3 2 0 3 2\n",
- " * draw (sample) int64 214 202 176 487 371 27 ... 207 411 170 102 70 385\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * sample (sample) object 800B MultiIndex\n",
+ " * chain (sample) int64 800B 1 2 0 1 2 1 3 3 2 2 1 ... 1 2 1 0 0 1 0 2 1 0 0\n",
+ " * draw (sample) int64 800B 203 316 58 22 372 214 ... 460 136 37 476 368\n",
"Data variables:\n",
- " mu (sample) float64 10.14 0.2007 12.31 0.4654 ... -5.88 12.93 4.57\n",
- " theta (school, sample) float64 10.01 24.78 14.16 ... -4.128 14.04 3.417\n",
- " tau (sample) float64 3.008 19.69 1.974 3.611 ... 2.457 1.675 2.081\n",
- " log_tau (sample) float64 1.101 2.98 0.6802 1.284 ... 0.8991 0.5161 0.733\n",
- "Attributes: (6)
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
sample
(sample)
object
MultiIndex
array([(3, 214), (1, 202), (1, 176), (3, 487), (2, 371), (3, 27), (3, 311),\n",
- " (3, 158), (3, 368), (1, 492), (0, 44), (0, 437), (1, 80), (1, 434),\n",
- " (3, 331), (0, 492), (3, 149), (2, 151), (3, 404), (1, 162), (1, 284),\n",
- " (3, 379), (1, 164), (1, 102), (0, 326), (2, 176), (0, 64), (0, 162),\n",
- " (2, 439), (3, 39), (2, 366), (2, 37), (3, 319), (3, 81), (2, 406),\n",
- " (0, 194), (2, 226), (1, 311), (2, 19), (3, 150), (3, 71), (3, 66),\n",
- " (1, 391), (0, 240), (2, 490), (0, 418), (2, 243), (3, 61), (2, 488),\n",
- " (2, 267), (2, 466), (3, 335), (0, 309), (1, 125), (0, 135), (2, 293),\n",
- " (2, 75), (0, 268), (3, 248), (1, 4), (2, 320), (3, 98), (2, 408),\n",
- " (3, 257), (2, 452), (1, 37), (2, 97), (2, 472), (1, 367), (0, 182),\n",
- " (2, 94), (3, 479), (0, 383), (0, 220), (3, 180), (1, 244), (0, 497),\n",
- " (1, 372), (1, 184), (0, 69), (3, 159), (1, 167), (1, 410), (2, 63),\n",
- " (0, 138), (1, 170), (0, 269), (3, 429), (2, 69), (0, 399), (2, 395),\n",
- " (2, 155), (3, 449), (2, 104), (2, 207), (3, 411), (2, 170), (0, 102),\n",
- " (3, 70), (2, 385)], dtype=object)
chain
(sample)
int64
3 1 1 3 2 3 3 3 ... 3 2 2 3 2 0 3 2
array([3, 1, 1, 3, 2, 3, 3, 3, 3, 1, 0, 0, 1, 1, 3, 0, 3, 2, 3, 1, 1, 3, 1, 1,\n",
- " 0, 2, 0, 0, 2, 3, 2, 2, 3, 3, 2, 0, 2, 1, 2, 3, 3, 3, 1, 0, 2, 0, 2, 3,\n",
- " 2, 2, 2, 3, 0, 1, 0, 2, 2, 0, 3, 1, 2, 3, 2, 3, 2, 1, 2, 2, 1, 0, 2, 3,\n",
- " 0, 0, 3, 1, 0, 1, 1, 0, 3, 1, 1, 2, 0, 1, 0, 3, 2, 0, 2, 2, 3, 2, 2, 3,\n",
- " 2, 0, 3, 2])
draw
(sample)
int64
214 202 176 487 ... 170 102 70 385
array([214, 202, 176, 487, 371, 27, 311, 158, 368, 492, 44, 437, 80, 434,\n",
- " 331, 492, 149, 151, 404, 162, 284, 379, 164, 102, 326, 176, 64, 162,\n",
- " 439, 39, 366, 37, 319, 81, 406, 194, 226, 311, 19, 150, 71, 66,\n",
- " 391, 240, 490, 418, 243, 61, 488, 267, 466, 335, 309, 125, 135, 293,\n",
- " 75, 268, 248, 4, 320, 98, 408, 257, 452, 37, 97, 472, 367, 182,\n",
- " 94, 479, 383, 220, 180, 244, 497, 372, 184, 69, 159, 167, 410, 63,\n",
- " 138, 170, 269, 429, 69, 399, 395, 155, 449, 104, 207, 411, 170, 102,\n",
- " 70, 385])
- created_at :
- 2022-10-13T14:37:37.315398
- arviz_version :
- 0.13.0.dev0
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
"
],
"text/plain": [
- "\n",
+ " Size: 12kB\n",
"Dimensions: (sample: 100, school: 8)\n",
"Coordinates:\n",
- " * school (school) <xarray.DataArray 'school' (school: 8)>\n",
+ "<xarray.DataArray 'school' (school: 8)> Size: 512B\n",
"'Choate' 'Deerfield' 'Phillips Andover' ... "St. Paul's" 'Mt. Hermon'\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
"
+ " dtype='object', name='school'))
"
],
"text/plain": [
- "\n",
+ " Size: 512B\n",
"'Choate' 'Deerfield' 'Phillips Andover' ... \"St. Paul's\" 'Mt. Hermon'\n",
"Coordinates:\n",
- " * school (school) \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -6029,6 +6108,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -6079,7 +6159,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -6087,7 +6167,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -6099,6 +6180,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -6361,20 +6446,20 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 93kB\n",
"Dimensions: (chain: 2, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 2\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 16B 0 2\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " mu (chain, draw) float64 7.872 3.385 9.1 7.304 ... 2.871 4.096 1.776\n",
- " theta (chain, draw, school) float64 12.32 9.905 14.95 ... 2.363 -2.968\n",
- " tau (chain, draw) float64 4.726 3.909 4.844 1.857 ... 4.09 2.72 1.917\n",
- " log_tau (chain, draw) float64 1.553 1.363 1.578 ... 1.408 1.001 0.6508\n",
- "Attributes: (6)
- created_at :
- 2022-10-13T14:37:37.315398
- arviz_version :
- 0.13.0.dev0
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -6472,6 +6557,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -6522,7 +6608,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -6530,7 +6616,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -6542,6 +6629,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -6804,28 +6895,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 69kB\n",
"Dimensions: (chain: 2, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 2\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 16B 0 2\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 2
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 2], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 64kB ...\n",
+ "Attributes: (4)
- chain: 2
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 2], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -6860,6 +6951,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -6910,7 +7002,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -6918,7 +7010,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -6930,6 +7023,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -7192,28 +7289,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 69kB\n",
"Dimensions: (chain: 2, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 2\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 16B 0 2\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 2
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 2], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 64kB ...\n",
+ "Attributes: (4)
- chain: 2
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 2], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -7248,6 +7345,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -7298,7 +7396,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -7306,7 +7404,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -7318,6 +7417,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -7580,36 +7683,36 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 125kB\n",
"Dimensions: (chain: 2, draw: 500)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 2\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
+ " * chain (chain) int64 16B 0 2\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
"Data variables: (12/16)\n",
- " max_energy_error (chain, draw) float64 ...\n",
- " energy_error (chain, draw) float64 ...\n",
- " lp (chain, draw) float64 ...\n",
- " index_in_trajectory (chain, draw) int64 ...\n",
- " acceptance_rate (chain, draw) float64 ...\n",
- " diverging (chain, draw) bool ...\n",
+ " max_energy_error (chain, draw) float64 8kB ...\n",
+ " energy_error (chain, draw) float64 8kB ...\n",
+ " lp (chain, draw) float64 8kB ...\n",
+ " index_in_trajectory (chain, draw) int64 8kB ...\n",
+ " acceptance_rate (chain, draw) float64 8kB ...\n",
+ " diverging (chain, draw) bool 1kB ...\n",
" ... ...\n",
- " smallest_eigval (chain, draw) float64 ...\n",
- " step_size_bar (chain, draw) float64 ...\n",
- " step_size (chain, draw) float64 ...\n",
- " energy (chain, draw) float64 ...\n",
- " tree_depth (chain, draw) int64 ...\n",
- " perf_counter_diff (chain, draw) float64 ...\n",
- "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[1000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[1000 values with dtype=float64]
lp
(chain, draw)
float64
...
[1000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[1000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[1000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[1000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[1000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[1000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[1000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[1000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[1000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[1000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[1000 values with dtype=float64]
energy
(chain, draw)
float64
...
[1000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[1000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[1000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 2], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " smallest_eigval (chain, draw) float64 8kB ...\n",
+ " step_size_bar (chain, draw) float64 8kB ...\n",
+ " step_size (chain, draw) float64 8kB ...\n",
+ " energy (chain, draw) float64 8kB ...\n",
+ " tree_depth (chain, draw) int64 8kB ...\n",
+ " perf_counter_diff (chain, draw) float64 8kB ...\n",
+ "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[1000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[1000 values with dtype=float64]
lp
(chain, draw)
float64
...
[1000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[1000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[1000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[1000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[1000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[1000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[1000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[1000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[1000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[1000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[1000 values with dtype=float64]
energy
(chain, draw)
float64
...
[1000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[1000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[1000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 2], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
+ " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -7644,6 +7747,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -7694,7 +7798,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -7702,7 +7806,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -7714,6 +7819,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -7976,30 +8085,30 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 45kB\n",
"Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " tau (chain, draw) float64 ...\n",
- " theta (chain, draw, school) float64 ...\n",
- " mu (chain, draw) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " tau (chain, draw) float64 4kB ...\n",
+ " theta (chain, draw, school) float64 32kB ...\n",
+ " mu (chain, draw) float64 4kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.602116
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.602116
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -8034,6 +8143,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -8084,7 +8194,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -8092,7 +8202,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -8104,6 +8215,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -8366,28 +8481,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 37kB\n",
"Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 32kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -8422,6 +8537,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -8472,7 +8588,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -8480,7 +8596,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -8492,6 +8609,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -8754,23 +8875,23 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " obs (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -8805,6 +8926,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -8855,7 +8977,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -8863,7 +8985,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -8875,6 +8998,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -9137,16 +9264,16 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " scores (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " scores (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
@@ -9457,7 +9584,8 @@
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
- ".xr-attrs dt, dd {\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
@@ -9506,7 +9634,7 @@
"\t> constant_data"
]
},
- "execution_count": 12,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -9526,7 +9654,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -9540,8 +9668,8 @@
" \n",
" \n",
" - \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
"
\n",
"
\n",
@@ -9576,6 +9704,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -9626,7 +9755,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -9634,7 +9763,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -9646,6 +9776,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -9908,22 +10042,22 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 145kB\n",
"Dimensions: (chain: 4, draw: 400, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 100 101 102 103 104 105 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 3kB 100 101 102 103 104 105 ... 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " mu (chain, draw) float64 11.7 8.118 -5.88 -7.149 ... 1.767 3.486 3.404\n",
- " theta (chain, draw, school) float64 14.23 9.72 9.195 ... 6.762 1.295\n",
- " tau (chain, draw) float64 4.289 2.765 2.457 1.719 ... 2.741 2.932 4.461\n",
- " log_tau (chain, draw) float64 1.456 1.017 0.8991 ... 1.008 1.076 1.495\n",
- "Attributes: (6)
- created_at :
- 2022-10-13T14:37:37.315398
- arviz_version :
- 0.13.0.dev0
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -10002,6 +10136,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -10052,7 +10187,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -10060,7 +10195,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -10072,6 +10208,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -10334,28 +10474,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 106kB\n",
"Dimensions: (chain: 4, draw: 400, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 100 101 102 103 104 105 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 3kB 100 101 102 103 104 105 ... 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 400
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
+ " obs (chain, draw, school) float64 102kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 400
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=400))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=400))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -10390,6 +10530,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -10440,7 +10581,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -10448,7 +10589,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -10460,6 +10602,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -10722,28 +10868,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 106kB\n",
"Dimensions: (chain: 4, draw: 400, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 100 101 102 103 104 105 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 3kB 100 101 102 103 104 105 ... 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 400
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
+ " obs (chain, draw, school) float64 102kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 400
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=400))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=400))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -10778,6 +10924,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -10828,7 +10975,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -10836,7 +10983,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -10848,6 +10996,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -11110,36 +11262,36 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 197kB\n",
"Dimensions: (chain: 4, draw: 400)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 100 101 102 103 104 ... 496 497 498 499\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 3kB 100 101 102 103 ... 496 497 498 499\n",
"Data variables: (12/16)\n",
- " max_energy_error (chain, draw) float64 ...\n",
- " energy_error (chain, draw) float64 ...\n",
- " lp (chain, draw) float64 ...\n",
- " index_in_trajectory (chain, draw) int64 ...\n",
- " acceptance_rate (chain, draw) float64 ...\n",
- " diverging (chain, draw) bool ...\n",
+ " max_energy_error (chain, draw) float64 13kB ...\n",
+ " energy_error (chain, draw) float64 13kB ...\n",
+ " lp (chain, draw) float64 13kB ...\n",
+ " index_in_trajectory (chain, draw) int64 13kB ...\n",
+ " acceptance_rate (chain, draw) float64 13kB ...\n",
+ " diverging (chain, draw) bool 2kB ...\n",
" ... ...\n",
- " smallest_eigval (chain, draw) float64 ...\n",
- " step_size_bar (chain, draw) float64 ...\n",
- " step_size (chain, draw) float64 ...\n",
- " energy (chain, draw) float64 ...\n",
- " tree_depth (chain, draw) int64 ...\n",
- " perf_counter_diff (chain, draw) float64 ...\n",
- "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[1600 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[1600 values with dtype=float64]
lp
(chain, draw)
float64
...
[1600 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[1600 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[1600 values with dtype=float64]
diverging
(chain, draw)
bool
...
[1600 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[1600 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[1600 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[1600 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[1600 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[1600 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[1600 values with dtype=float64]
step_size
(chain, draw)
float64
...
[1600 values with dtype=float64]
energy
(chain, draw)
float64
...
[1600 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[1600 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[1600 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
+ " smallest_eigval (chain, draw) float64 13kB ...\n",
+ " step_size_bar (chain, draw) float64 13kB ...\n",
+ " step_size (chain, draw) float64 13kB ...\n",
+ " energy (chain, draw) float64 13kB ...\n",
+ " tree_depth (chain, draw) int64 13kB ...\n",
+ " perf_counter_diff (chain, draw) float64 13kB ...\n",
+ "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[1600 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[1600 values with dtype=float64]
lp
(chain, draw)
float64
...
[1600 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[1600 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[1600 values with dtype=float64]
diverging
(chain, draw)
bool
...
[1600 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[1600 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[1600 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[1600 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[1600 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[1600 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[1600 values with dtype=float64]
step_size
(chain, draw)
float64
...
[1600 values with dtype=float64]
energy
(chain, draw)
float64
...
[1600 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[1600 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[1600 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=400))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
+ " dtype='int64', name='draw', length=400))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -11174,6 +11326,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -11224,7 +11377,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -11232,7 +11385,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -11244,6 +11398,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -11506,30 +11664,30 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 36kB\n",
"Dimensions: (chain: 1, draw: 400, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 100 101 102 103 104 105 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 3kB 100 101 102 103 104 105 ... 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " tau (chain, draw) float64 ...\n",
- " theta (chain, draw, school) float64 ...\n",
- " mu (chain, draw) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 400
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
+ " tau (chain, draw) float64 3kB ...\n",
+ " theta (chain, draw, school) float64 26kB ...\n",
+ " mu (chain, draw) float64 3kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 400
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=400))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=400))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.602116
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.602116
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -11564,6 +11722,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -11614,7 +11773,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -11622,7 +11781,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -11634,6 +11794,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -11896,28 +12060,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 29kB\n",
"Dimensions: (chain: 1, draw: 400, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 100 101 102 103 104 105 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 3kB 100 101 102 103 104 105 ... 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 400
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
+ " obs (chain, draw, school) float64 26kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 400
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=400))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=400))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -11952,6 +12116,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -12002,7 +12167,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -12010,7 +12175,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -12022,6 +12188,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -12284,23 +12454,23 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " obs (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -12335,6 +12505,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -12385,7 +12556,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -12393,7 +12564,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -12405,6 +12577,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -12667,16 +12843,16 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " scores (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " scores (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
@@ -12987,7 +13163,8 @@
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
- ".xr-attrs dt, dd {\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
@@ -13036,7 +13213,7 @@
"\t> constant_data"
]
},
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -13054,7 +13231,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -13068,8 +13245,8 @@
" \n",
" \n",
" - \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
"
\n",
"
\n",
@@ -13104,6 +13281,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -13154,7 +13332,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -13162,7 +13340,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -13174,6 +13353,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -13436,22 +13619,22 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 145kB\n",
"Dimensions: (chain: 4, draw: 400, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 100 101 102 103 104 105 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 3kB 100 101 102 103 104 105 ... 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " mu (chain, draw) float64 11.7 8.118 -5.88 -7.149 ... 1.767 3.486 3.404\n",
- " theta (chain, draw, school) float64 14.23 9.72 9.195 ... 6.762 1.295\n",
- " tau (chain, draw) float64 4.289 2.765 2.457 1.719 ... 2.741 2.932 4.461\n",
- " log_tau (chain, draw) float64 1.456 1.017 0.8991 ... 1.008 1.076 1.495\n",
- "Attributes: (6)
- created_at :
- 2022-10-13T14:37:37.315398
- arviz_version :
- 0.13.0.dev0
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -13530,6 +13713,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -13580,7 +13764,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -13588,7 +13772,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -13600,6 +13785,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -13862,28 +14051,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 133kB\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 128kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -13918,6 +14107,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -13968,7 +14158,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -13976,7 +14166,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -13988,6 +14179,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -14250,28 +14445,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 133kB\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 128kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -14306,6 +14501,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -14356,7 +14552,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -14364,7 +14560,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -14376,6 +14573,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -14638,36 +14839,36 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 246kB\n",
"Dimensions: (chain: 4, draw: 500)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
"Data variables: (12/16)\n",
- " max_energy_error (chain, draw) float64 ...\n",
- " energy_error (chain, draw) float64 ...\n",
- " lp (chain, draw) float64 ...\n",
- " index_in_trajectory (chain, draw) int64 ...\n",
- " acceptance_rate (chain, draw) float64 ...\n",
- " diverging (chain, draw) bool ...\n",
+ " max_energy_error (chain, draw) float64 16kB ...\n",
+ " energy_error (chain, draw) float64 16kB ...\n",
+ " lp (chain, draw) float64 16kB ...\n",
+ " index_in_trajectory (chain, draw) int64 16kB ...\n",
+ " acceptance_rate (chain, draw) float64 16kB ...\n",
+ " diverging (chain, draw) bool 2kB ...\n",
" ... ...\n",
- " smallest_eigval (chain, draw) float64 ...\n",
- " step_size_bar (chain, draw) float64 ...\n",
- " step_size (chain, draw) float64 ...\n",
- " energy (chain, draw) float64 ...\n",
- " tree_depth (chain, draw) int64 ...\n",
- " perf_counter_diff (chain, draw) float64 ...\n",
- "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " smallest_eigval (chain, draw) float64 16kB ...\n",
+ " step_size_bar (chain, draw) float64 16kB ...\n",
+ " step_size (chain, draw) float64 16kB ...\n",
+ " energy (chain, draw) float64 16kB ...\n",
+ " tree_depth (chain, draw) int64 16kB ...\n",
+ " perf_counter_diff (chain, draw) float64 16kB ...\n",
+ "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
+ " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -14702,6 +14903,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -14752,7 +14954,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -14760,7 +14962,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -14772,6 +14975,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -15034,30 +15241,30 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 45kB\n",
"Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " tau (chain, draw) float64 ...\n",
- " theta (chain, draw, school) float64 ...\n",
- " mu (chain, draw) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " tau (chain, draw) float64 4kB ...\n",
+ " theta (chain, draw, school) float64 32kB ...\n",
+ " mu (chain, draw) float64 4kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.602116
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.602116
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -15092,6 +15299,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -15142,7 +15350,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -15150,7 +15358,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -15162,6 +15371,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -15424,28 +15637,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 37kB\n",
"Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 32kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -15480,6 +15693,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -15530,7 +15744,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -15538,7 +15752,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -15550,6 +15765,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -15812,23 +16031,23 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " obs (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -15863,6 +16082,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -15913,7 +16133,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -15921,7 +16141,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -15933,6 +16154,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -16195,16 +16420,16 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " scores (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " scores (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
@@ -16515,7 +16740,8 @@
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
- ".xr-attrs dt, dd {\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
@@ -16564,7 +16790,7 @@
"\t> constant_data"
]
},
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -16584,7 +16810,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -16621,6 +16847,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -16671,7 +16898,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -16679,7 +16906,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -16691,6 +16919,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -16953,25 +17185,25 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 32B\n",
"Dimensions: ()\n",
"Data variables:\n",
- " mu float64 4.486\n",
- " theta float64 4.912\n",
- " tau float64 4.124\n",
- " log_tau float64 1.173
"
+ " mu float64 8B 4.486\n",
+ " theta float64 8B 4.912\n",
+ " tau float64 8B 4.124\n",
+ " log_tau float64 8B 1.173
"
],
"text/plain": [
- "\n",
+ " Size: 32B\n",
"Dimensions: ()\n",
"Data variables:\n",
- " mu float64 4.486\n",
- " theta float64 4.912\n",
- " tau float64 4.124\n",
- " log_tau float64 1.173"
+ " mu float64 8B 4.486\n",
+ " theta float64 8B 4.912\n",
+ " tau float64 8B 4.124\n",
+ " log_tau float64 8B 1.173"
]
},
- "execution_count": 15,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -16991,7 +17223,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -17028,6 +17260,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -17078,7 +17311,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -17086,7 +17319,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -17098,6 +17332,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -17360,33 +17598,33 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 600B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " mu float64 4.486\n",
- " theta (school) float64 6.46 5.028 3.938 4.872 3.667 3.975 6.581 4.772\n",
- " tau float64 4.124\n",
- " log_tau float64 1.173
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " mu float64 8B 4.486\n",
+ " theta (school) float64 64B 6.46 5.028 3.938 4.872 3.667 3.975 6.581 4.772\n",
+ " tau float64 8B 4.124\n",
+ " log_tau float64 8B 1.173
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
"
+ " dtype='object', name='school'))
"
],
"text/plain": [
- "\n",
+ " Size: 600B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 1MB\n",
"Dimensions: (chain: 4, draw: 500, school: 8, school_bis: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
- " * school_bis (school_bis) <U16 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * school_bis (school_bis) <U16 512B 'Choate' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " mu (chain, draw) float64 7.872 3.385 9.1 ... 3.486 3.404\n",
- " theta (chain, draw, school) float64 12.32 9.905 ... 6.762 1.295\n",
- " tau (chain, draw) float64 4.726 3.909 4.844 ... 2.932 4.461\n",
- " log_tau (chain, draw) float64 1.553 1.363 1.578 ... 1.076 1.495\n",
- " mlogtau (chain, draw) float64 nan nan nan ... 1.494 1.496 1.511\n",
- " theta_school_diff (chain, draw, school, school_bis) float64 0.0 ... 0.0\n",
- "Attributes: (6)
- chain: 4
- draw: 500
- school: 8
- school_bis: 8
chain
(chain)
int64
0 1 2 3
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
school_bis
(school_bis)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
- created_at :
- 2022-10-13T14:37:37.315398
- arviz_version :
- 0.13.0.dev0
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
"
],
"text/plain": [
- "\n",
+ " Size: 1MB\n",
"Dimensions: (chain: 4, draw: 500, school: 8, school_bis: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
- " * school (school) <xarray.DataArray 'theta_school_diff' (chain: 4, draw: 500)>\n",
+ "<xarray.DataArray 'theta_school_diff' (chain: 4, draw: 500)> Size: 16kB\n",
"2.415 2.156 -0.04943 1.228 3.384 9.662 ... -1.656 -0.4021 1.524 -3.372 -6.305\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
- " school <U16 'Choate'\n",
- " school_bis <U16 'Deerfield'
2.415 2.156 -0.04943 1.228 3.384 ... -0.4021 1.524 -3.372 -6.305
array([[ 2.41531869, 2.15629954, -0.04942665, ..., -1.568983 ,\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " school <U16 64B 'Choate'\n",
+ " school_bis <U16 64B 'Deerfield'
2.415 2.156 -0.04943 1.228 3.384 ... -0.4021 1.524 -3.372 -6.305
array([[ 2.41531869, 2.15629954, -0.04942665, ..., -1.568983 ,\n",
" 3.49509445, -0.75245938],\n",
" [-1.36296658, 3.18605183, -3.57582959, ..., -0.03288082,\n",
" 0.81613882, 0.93268411],\n",
" [11.85464717, -5.43221857, 0.85662645, ..., -1.62004592,\n",
" 3.99854315, -0.87783417],\n",
" [ 2.95365309, 2.07818191, 4.51853032, ..., 1.5236138 ,\n",
- " -3.37150005, -6.30547202]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " -3.37150005, -6.30547202]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
"
+ " dtype='int64', name='draw', length=500))
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],
"text/plain": [
- "\n",
+ " Size: 16kB\n",
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"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
- " school <xarray.DataArray 'theta_school_diff' (chain: 4, draw: 500,\n",
- " pairwise_school_diff: 3)>\n",
+ " pairwise_school_diff: 3)> Size: 48kB\n",
"2.415 -6.741 -1.84 2.156 -3.474 3.784 ... -2.619 6.923 -6.305 1.667 -6.641\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
- " school (pairwise_school_diff) <U16 'Choate' 'Hotchkiss' 'Mt. Hermon'\n",
- " school_bis (pairwise_school_diff) <U16 'Deerfield' 'Choate' 'Lawrenceville'\n",
- "Dimensions without coordinates: pairwise_school_diff
2.415 -6.741 -1.84 2.156 -3.474 ... -2.619 6.923 -6.305 1.667 -6.641
array([[[ 2.41531869, -6.74108399, -1.84042946],\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " school (pairwise_school_diff) <U16 192B 'Choate' ... 'Mt. Hermon'\n",
+ " school_bis (pairwise_school_diff) <U16 192B 'Deerfield' ... 'Lawrenceville'\n",
+ "Dimensions without coordinates: pairwise_school_diff
2.415 -6.741 -1.84 2.156 -3.474 ... -2.619 6.923 -6.305 1.667 -6.641
array([[[ 2.41531869, -6.74108399, -1.84042946],\n",
" [ 2.15629954, -3.47410767, 3.78363613],\n",
" [ -0.04942665, 4.28447846, 0.62431982],\n",
" ...,\n",
@@ -18803,24 +19059,24 @@
" ...,\n",
" [ 1.5236138 , -0.88482747, -7.33031773],\n",
" [ -3.37150005, -2.61944346, 6.92332351],\n",
- " [ -6.30547202, 1.66679103, -6.64140953]]])
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(chain)
int64
0 1 2 3
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(pairwise_school_diff)
<U16
'Choate' 'Hotchkiss' 'Mt. Hermon'
array(['Choate', 'Hotchkiss', 'Mt. Hermon'], dtype='<U16')
school_bis
(pairwise_school_diff)
<U16
'Deerfield' ... 'Lawrenceville'
array(['Deerfield', 'Choate', 'Lawrenceville'], dtype='<U16')
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " [ -6.30547202, 1.66679103, -6.64140953]]])
chain
(chain)
int64
0 1 2 3
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(pairwise_school_diff)
<U16
'Choate' 'Hotchkiss' 'Mt. Hermon'
array(['Choate', 'Hotchkiss', 'Mt. Hermon'], dtype='<U16')
school_bis
(pairwise_school_diff)
<U16
'Deerfield' ... 'Lawrenceville'
array(['Deerfield', 'Choate', 'Lawrenceville'], dtype='<U16')
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
"
+ " dtype='int64', name='draw', length=500))
"
],
"text/plain": [
"\n",
+ " pairwise_school_diff: 3)> Size: 48kB\n",
"2.415 -6.741 -1.84 2.156 -3.474 3.784 ... -2.619 6.923 -6.305 1.667 -6.641\n",
"Coordinates:\n",
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- " * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
- " school (pairwise_school_diff) <xarray.DataArray 'theta_school_diff' (chain: 4, draw: 500, school: 3,\n",
- " school_bis: 3)>\n",
+ " school_bis: 3)> Size: 144kB\n",
"2.415 0.0 -4.581 -4.326 -6.741 -11.32 ... 1.667 -6.077 -5.203 1.102 -6.641\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Hotchkiss' 'Mt. Hermon'\n",
- " * school_bis (school_bis) <U16 'Deerfield' 'Choate' 'Lawrenceville'
2.415 0.0 -4.581 -4.326 -6.741 ... 1.667 -6.077 -5.203 1.102 -6.641
array([[[[ 2.41531869, 0. , -4.58110972],\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 192B 'Choate' 'Hotchkiss' 'Mt. Hermon'\n",
+ " * school_bis (school_bis) <U16 192B 'Deerfield' 'Choate' 'Lawrenceville'
2.415 0.0 -4.581 -4.326 -6.741 ... 1.667 -6.077 -5.203 1.102 -6.641
array([[[[ 2.41531869, 0. , -4.58110972],\n",
" [ -4.3257653 , -6.74108399, -11.3221937 ],\n",
" [ 5.15599894, 2.74068026, -1.84042946]],\n",
"\n",
@@ -19258,23 +19520,23 @@
"\n",
" [[ -6.30547202, 0. , -7.74350435],\n",
" [ -4.63868099, 1.66679103, -6.07671332],\n",
- " [ -5.2033772 , 1.10209482, -6.64140953]]]])
chain
(chain)
int64
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draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' 'Hotchkiss' 'Mt. Hermon'
array(['Choate', 'Hotchkiss', 'Mt. Hermon'], dtype='<U16')
school_bis
(school_bis)
<U16
'Deerfield' ... 'Lawrenceville'
array(['Deerfield', 'Choate', 'Lawrenceville'], dtype='<U16')
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " [ -5.2033772 , 1.10209482, -6.64140953]]]])
chain
(chain)
int64
0 1 2 3
draw
(draw)
int64
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array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' 'Hotchkiss' 'Mt. Hermon'
array(['Choate', 'Hotchkiss', 'Mt. Hermon'], dtype='<U16')
school_bis
(school_bis)
<U16
'Deerfield' ... 'Lawrenceville'
array(['Deerfield', 'Choate', 'Lawrenceville'], dtype='<U16')
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Hotchkiss', 'Mt. Hermon'], dtype='object', name='school'))
PandasIndex
PandasIndex(Index(['Deerfield', 'Choate', 'Lawrenceville'], dtype='object', name='school_bis'))
"
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Hotchkiss', 'Mt. Hermon'], dtype='object', name='school'))
PandasIndex
PandasIndex(Index(['Deerfield', 'Choate', 'Lawrenceville'], dtype='object', name='school_bis'))
"
],
"text/plain": [
"\n",
+ " school_bis: 3)> Size: 144kB\n",
"2.415 0.0 -4.581 -4.326 -6.741 -11.32 ... 1.667 -6.077 -5.203 1.102 -6.641\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -19408,6 +19670,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -19458,7 +19721,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -19466,7 +19729,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -19478,6 +19742,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -19740,26 +20008,26 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 1MB\n",
"Dimensions: (chain: 4, draw: 500, school: 8, school_bis: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
- " * school_bis (school_bis) <U16 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * school_bis (school_bis) <U16 512B 'Choate' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " mu (chain, draw) float64 7.872 3.385 9.1 ... 3.486 3.404\n",
- " theta (chain, draw, school) float64 12.32 9.905 ... 6.762 1.295\n",
- " tau (chain, draw) float64 4.726 3.909 4.844 ... 2.932 4.461\n",
- " log_tau (chain, draw) float64 1.553 1.363 1.578 ... 1.076 1.495\n",
- " mlogtau (chain, draw) float64 nan nan nan ... 1.494 1.496 1.511\n",
- " theta_school_diff (chain, draw, school, school_bis) float64 0.0 ... 0.0\n",
- "Attributes: (6)
- chain: 4
- draw: 500
- school: 8
- school_bis: 8
chain
(chain)
int64
0 1 2 3
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
school_bis
(school_bis)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
- \n",
+ " dtype='object', name='school_bis'))
- created_at :
- 2022-10-13T14:37:37.315398
- arviz_version :
- 0.13.0.dev0
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -19887,6 +20155,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -19937,7 +20206,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -19945,7 +20214,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -19957,6 +20227,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -20219,28 +20493,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 133kB\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 128kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
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\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -20275,6 +20549,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -20325,7 +20600,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -20333,7 +20608,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -20345,6 +20621,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -20607,15 +20887,15 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 36kB\n",
"Dimensions: (chain: 4, draw: 500, new_school: 2)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
- " * new_school (new_school) <U13 'Essex College' 'Moordale'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * new_school (new_school) <U13 104B 'Essex College' 'Moordale'\n",
"Data variables:\n",
- " obs (chain, draw, new_school) float64 2.041 -2.556 ... -0.2822\n",
- "Attributes: (2)
- chain: 4
- draw: 500
- new_school: 2
obs
(chain, draw, new_school)
float64
2.041 -2.556 ... -1.015 -0.2822
array([[[ 2.04091912, -2.55566503],\n",
+ " obs (chain, draw, new_school) float64 32kB 2.041 -2.556 ... -0.2822\n",
+ "Attributes: (2)
- chain: 4
- draw: 500
- new_school: 2
obs
(chain, draw, new_school)
float64
2.041 -2.556 ... -1.015 -0.2822
array([[[ 2.04091912, -2.55566503],\n",
" [ 0.41809885, -0.56776961],\n",
" [-0.45264929, -0.21559716],\n",
" ...,\n",
@@ -20645,17 +20925,17 @@
" ...,\n",
" [ 1.57846099, 0.24653314],\n",
" [ 0.64302486, 1.42710376],\n",
- " [-1.01529472, -0.28215614]]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " [-1.01529472, -0.28215614]]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Essex College', 'Moordale'], dtype='object', name='new_school'))
- created_at :
- 2023-12-28T12:47:21.311677
- arviz_version :
- 0.16.1
\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Essex College', 'Moordale'], dtype='object', name='new_school'))
- created_at :
- 2024-09-28T19:22:37.147191+00:00
- arviz_version :
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\n",
" \n",
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" \n",
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+ " \n",
+ " \n",
" \n",
" \n",
"
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@@ -20690,6 +20970,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -20740,7 +21021,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -20748,7 +21029,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -20760,6 +21042,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -21022,28 +21308,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 133kB\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 128kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -21078,6 +21364,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -21128,7 +21415,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -21136,7 +21423,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -21148,6 +21436,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -21410,36 +21702,36 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 246kB\n",
"Dimensions: (chain: 4, draw: 500)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
"Data variables: (12/16)\n",
- " max_energy_error (chain, draw) float64 ...\n",
- " energy_error (chain, draw) float64 ...\n",
- " lp (chain, draw) float64 ...\n",
- " index_in_trajectory (chain, draw) int64 ...\n",
- " acceptance_rate (chain, draw) float64 ...\n",
- " diverging (chain, draw) bool ...\n",
+ " max_energy_error (chain, draw) float64 16kB ...\n",
+ " energy_error (chain, draw) float64 16kB ...\n",
+ " lp (chain, draw) float64 16kB ...\n",
+ " index_in_trajectory (chain, draw) int64 16kB ...\n",
+ " acceptance_rate (chain, draw) float64 16kB ...\n",
+ " diverging (chain, draw) bool 2kB ...\n",
" ... ...\n",
- " smallest_eigval (chain, draw) float64 ...\n",
- " step_size_bar (chain, draw) float64 ...\n",
- " step_size (chain, draw) float64 ...\n",
- " energy (chain, draw) float64 ...\n",
- " tree_depth (chain, draw) int64 ...\n",
- " perf_counter_diff (chain, draw) float64 ...\n",
- "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " smallest_eigval (chain, draw) float64 16kB ...\n",
+ " step_size_bar (chain, draw) float64 16kB ...\n",
+ " step_size (chain, draw) float64 16kB ...\n",
+ " energy (chain, draw) float64 16kB ...\n",
+ " tree_depth (chain, draw) int64 16kB ...\n",
+ " perf_counter_diff (chain, draw) float64 16kB ...\n",
+ "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
+ " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -21474,6 +21766,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -21524,7 +21817,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -21532,7 +21825,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -21544,6 +21838,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -21806,30 +22104,30 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 45kB\n",
"Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " tau (chain, draw) float64 ...\n",
- " theta (chain, draw, school) float64 ...\n",
- " mu (chain, draw) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " tau (chain, draw) float64 4kB ...\n",
+ " theta (chain, draw, school) float64 32kB ...\n",
+ " mu (chain, draw) float64 4kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.602116
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.602116
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -21864,6 +22162,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -21914,7 +22213,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -21922,7 +22221,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -21934,6 +22234,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -22196,28 +22500,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 37kB\n",
"Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 32kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -22252,6 +22556,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -22302,7 +22607,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -22310,7 +22615,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -22322,6 +22628,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -22584,23 +22894,23 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " obs (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -22635,6 +22945,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -22685,7 +22996,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -22693,7 +23004,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -22705,6 +23017,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -22967,16 +23283,16 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " scores (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " scores (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
@@ -23287,7 +23603,8 @@
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
- ".xr-attrs dt, dd {\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
@@ -23337,7 +23654,7 @@
"\t> constant_data"
]
},
- "execution_count": 25,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -23368,7 +23685,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -23382,8 +23699,8 @@
" \n",
" \n",
" - \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
"
\n",
"
\n",
@@ -23418,6 +23735,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -23468,7 +23786,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -23476,7 +23794,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -23488,6 +23807,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -23750,21 +24073,21 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 309kB\n",
"Dimensions: (draw: 500, school: 8, school_bis: 8)\n",
"Coordinates:\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
- " * school_bis (school_bis) <U16 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * school_bis (school_bis) <U16 512B 'Choate' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " mu (draw) float64 5.974 5.096 7.177 ... 3.284 4.739 3.146\n",
- " theta (draw, school) float64 9.519 5.554 6.118 ... 5.595 3.773\n",
- " tau (draw) float64 4.068 3.156 3.603 ... 2.725 3.225 2.979\n",
- " log_tau (draw) float64 1.322 1.118 1.234 ... 0.958 1.035 0.9508\n",
- " mlogtau (draw) float64 nan nan nan nan ... 0.993 1.002 1.01 1.021\n",
- " theta_school_diff (draw, school, school_bis) float64 0.0 3.965 ... 0.0
- draw: 500
- school: 8
- school_bis: 8
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
school_bis
(school_bis)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
- \n",
+ " dtype='object', name='school_bis'))
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -24025,6 +24348,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -24075,7 +24399,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -24083,7 +24407,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -24095,6 +24420,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -24357,28 +24686,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 133kB\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 128kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -24413,6 +24742,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -24463,7 +24793,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -24471,7 +24801,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -24483,6 +24814,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -24745,15 +25080,15 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 36kB\n",
"Dimensions: (chain: 4, draw: 500, new_school: 2)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
- " * new_school (new_school) <U13 'Essex College' 'Moordale'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * new_school (new_school) <U13 104B 'Essex College' 'Moordale'\n",
"Data variables:\n",
- " obs (chain, draw, new_school) float64 2.041 -2.556 ... -0.2822\n",
- "Attributes: (2)
- chain: 4
- draw: 500
- new_school: 2
obs
(chain, draw, new_school)
float64
2.041 -2.556 ... -1.015 -0.2822
array([[[ 2.04091912, -2.55566503],\n",
+ " obs (chain, draw, new_school) float64 32kB 2.041 -2.556 ... -0.2822\n",
+ "Attributes: (2)
- chain: 4
- draw: 500
- new_school: 2
obs
(chain, draw, new_school)
float64
2.041 -2.556 ... -1.015 -0.2822
array([[[ 2.04091912, -2.55566503],\n",
" [ 0.41809885, -0.56776961],\n",
" [-0.45264929, -0.21559716],\n",
" ...,\n",
@@ -24783,17 +25118,17 @@
" ...,\n",
" [ 1.57846099, 0.24653314],\n",
" [ 0.64302486, 1.42710376],\n",
- " [-1.01529472, -0.28215614]]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " [-1.01529472, -0.28215614]]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Essex College', 'Moordale'], dtype='object', name='new_school'))
- created_at :
- 2023-12-28T12:47:21.311677
- arviz_version :
- 0.16.1
\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Essex College', 'Moordale'], dtype='object', name='new_school'))
- created_at :
- 2024-09-28T19:22:37.147191+00:00
- arviz_version :
- 0.20.0
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -24828,6 +25163,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -24878,7 +25214,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -24886,7 +25222,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -24898,6 +25235,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -25160,28 +25501,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 133kB\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 128kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -25216,6 +25557,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -25266,7 +25608,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -25274,7 +25616,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -25286,6 +25629,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -25548,36 +25895,36 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 246kB\n",
"Dimensions: (chain: 4, draw: 500)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
"Data variables: (12/16)\n",
- " max_energy_error (chain, draw) float64 ...\n",
- " energy_error (chain, draw) float64 ...\n",
- " lp (chain, draw) float64 ...\n",
- " index_in_trajectory (chain, draw) int64 ...\n",
- " acceptance_rate (chain, draw) float64 ...\n",
- " diverging (chain, draw) bool ...\n",
+ " max_energy_error (chain, draw) float64 16kB ...\n",
+ " energy_error (chain, draw) float64 16kB ...\n",
+ " lp (chain, draw) float64 16kB ...\n",
+ " index_in_trajectory (chain, draw) int64 16kB ...\n",
+ " acceptance_rate (chain, draw) float64 16kB ...\n",
+ " diverging (chain, draw) bool 2kB ...\n",
" ... ...\n",
- " smallest_eigval (chain, draw) float64 ...\n",
- " step_size_bar (chain, draw) float64 ...\n",
- " step_size (chain, draw) float64 ...\n",
- " energy (chain, draw) float64 ...\n",
- " tree_depth (chain, draw) int64 ...\n",
- " perf_counter_diff (chain, draw) float64 ...\n",
- "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " smallest_eigval (chain, draw) float64 16kB ...\n",
+ " step_size_bar (chain, draw) float64 16kB ...\n",
+ " step_size (chain, draw) float64 16kB ...\n",
+ " energy (chain, draw) float64 16kB ...\n",
+ " tree_depth (chain, draw) int64 16kB ...\n",
+ " perf_counter_diff (chain, draw) float64 16kB ...\n",
+ "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
+ " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -25612,6 +25959,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -25662,7 +26010,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -25670,7 +26018,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -25682,6 +26031,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -25944,16 +26297,16 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 45kB\n",
"Dimensions: (draw: 500, school: 8)\n",
"Coordinates:\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " tau (draw) float64 1.941 3.388 4.208 5.687 ... 0.8353 0.06893 2.145\n",
- " theta (draw, school) float64 4.866 4.59 -0.7404 ... 3.33 -2.031 6.045\n",
- " mu (draw) float64 3.903 3.915 -1.751 2.595 ... -2.294 0.7908 2.869
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -26092,6 +26445,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -26142,7 +26496,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -26150,7 +26504,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -26162,6 +26517,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -26424,28 +26783,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 37kB\n",
"Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 32kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -26480,6 +26839,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -26530,7 +26890,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -26538,7 +26898,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -26550,6 +26911,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -26812,23 +27177,23 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " obs (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -26863,6 +27228,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -26913,7 +27279,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -26921,7 +27287,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -26933,6 +27300,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -27195,16 +27566,16 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " scores (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " scores (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
@@ -27515,7 +27886,8 @@
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
- ".xr-attrs dt, dd {\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
@@ -27565,7 +27937,7 @@
"\t> constant_data"
]
},
- "execution_count": 30,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -27590,7 +27962,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -27604,8 +27976,8 @@
" \n",
" \n",
" - \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
"
\n",
"
\n",
@@ -27640,6 +28012,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -27690,7 +28063,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -27698,7 +28071,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -27710,6 +28084,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -27972,247 +28350,145 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
- "Dimensions: (draw: 500, school: 8, school_bis: 8)\n",
+ "<xarray.Dataset> Size: 1MB\n",
+ "Dimensions: (chain: 4, draw: 500, school: 8, school_bis: 8)\n",
"Coordinates:\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
- " * school_bis (school_bis) <U16 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * school_bis (school_bis) <U16 512B 'Choate' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " mu (draw) float64 8.974 8.096 10.18 ... 6.284 7.739 6.146\n",
- " theta (draw, school) float64 12.52 8.554 9.118 ... 8.595 6.773\n",
- " tau (draw) float64 7.068 6.156 6.603 ... 5.725 6.225 5.979\n",
- " log_tau (draw) float64 4.322 4.118 4.234 ... 3.958 4.035 3.951\n",
- " mlogtau (draw) float64 nan nan nan nan ... 3.993 4.002 4.01 4.021\n",
- " theta_school_diff (draw, school, school_bis) float64 3.0 6.965 ... 3.0
- draw: 500
- school: 8
- school_bis: 8
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
school_bis
(school_bis)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
mu
(draw)
float64
8.974 8.096 10.18 ... 7.739 6.146
array([ 8.97368017, 8.09614549, 10.17713997, 9.87741417, 9.88441083,\n",
- " 7.84086372, 10.01708358, 9.30316861, 7.51620587, 6.51435901,\n",
- " 11.02470906, 8.95045866, 8.02167729, 7.3671087 , 6.83334779,\n",
- " 6.94213036, 7.16589421, 5.12301607, 4.95301774, 5.87799146,\n",
- " 5.45097497, 7.6973103 , 9.23496838, 8.71981672, 11.43315988,\n",
- " 10.8214925 , 9.96952817, 10.02978194, 8.6004881 , 10.09120336,\n",
- " 10.01093531, 9.77268374, 9.2905079 , 7.5431412 , 7.94294994,\n",
- " 7.97649353, 8.42971457, 8.22441946, 8.12380029, 7.85319167,\n",
- " 8.59925422, 8.28412171, 6.44179875, 5.54282744, 5.04706891,\n",
- " 6.51007274, 5.81257858, 5.98355207, 4.25352519, 6.0289464 ,\n",
- " 7.29796057, 8.26610036, 6.37041698, 7.18994019, 7.21989074,\n",
- " 8.10601394, 5.9274154 , 8.19747242, 6.38401204, 7.82853361,\n",
- " 7.79157776, 9.29972209, 8.28340737, 8.80484852, 3.43976964,\n",
- " 4.13816338, 4.99660115, 7.53854082, 7.18764282, 8.08607123,\n",
- " 7.82755518, 7.87705035, 8.13060706, 8.65131506, 8.12875312,\n",
- " 8.10175967, 6.70935631, 7.91147853, 6.95385959, 5.84575154,\n",
- " 5.26131669, 4.91153602, 7.78462284, 6.65852601, 7.68021586,\n",
- " 6.64944805, 6.59396976, 8.08703284, 7.93070605, 8.88752838,\n",
- " 8.69143312, 11.2534381 , 10.61786813, 8.51614066, 7.81312486,\n",
- " 9.60340997, 8.03224433, 10.28815644, 9.27653186, 5.20050788,\n",
- "...\n",
- " 7.83215454, 7.31025742, 8.52342996, 10.47556492, 11.40733007,\n",
- " 10.91724981, 9.58490944, 9.89924272, 8.94222017, 6.43319604,\n",
- " 9.60243616, 9.36189187, 6.80598967, 7.22733614, 8.1172315 ,\n",
- " 8.12510871, 9.61519371, 6.65237379, 8.72495477, 7.6349722 ,\n",
- " 4.86071336, 6.70878444, 7.03215053, 7.68583976, 7.32610419,\n",
- " 4.51856954, 9.97837558, 7.53510668, 9.49720081, 9.01585584,\n",
- " 7.64320497, 10.53733721, 7.99320792, 9.80154945, 6.83870421,\n",
- " 7.83408989, 6.9818728 , 6.87105982, 8.07985906, 8.24314344,\n",
- " 7.85720007, 8.26432426, 9.12469169, 8.66073344, 9.60622546,\n",
- " 9.0544301 , 8.01085876, 7.07245337, 7.41033219, 7.1244903 ,\n",
- " 5.82800074, 6.35555163, 8.12740508, 6.62961373, 7.97251047,\n",
- " 4.35997506, 6.07773379, 6.5442542 , 5.56527828, 5.79055045,\n",
- " 6.96084845, 8.01102 , 6.70448752, 8.42014382, 7.30528838,\n",
- " 6.03493602, 6.08845156, 6.75605787, 6.52525218, 6.2562104 ,\n",
- " 6.58446059, 7.07887414, 7.09665291, 7.62351667, 6.76542611,\n",
- " 6.52521781, 8.1456518 , 7.95544984, 7.59747767, 7.05346634,\n",
- " 5.92800398, 4.35454107, 7.06265637, 7.02687012, 8.4344199 ,\n",
- " 6.70135365, 7.66555525, 4.63401616, 6.88774047, 6.90969401,\n",
- " 6.68654685, 5.51199123, 5.86022208, 7.22286784, 6.97624991,\n",
- " 7.75407113, 6.53404815, 6.28390709, 7.73875823, 6.14562567])
theta
(draw, school)
float64
12.52 8.554 9.118 ... 8.595 6.773
array([[12.5190006 , 8.5538375 , 9.11797152, ..., 11.26897288,\n",
- " 11.28328517, 11.03034163],\n",
- " [10.48141224, 9.98433356, 9.07419118, ..., 6.63953594,\n",
- " 10.90751667, 9.09286812],\n",
- " [10.32773875, 9.89026362, 10.05512777, ..., 11.31300687,\n",
- " 10.04794125, 9.97572556],\n",
- " ...,\n",
- " [ 6.047783 , 6.47235698, 7.45528476, ..., 7.09130004,\n",
- " 8.36576861, 4.85789959],\n",
- " [ 9.82337688, 8.58880778, 5.78970216, ..., 4.8740546 ,\n",
- " 6.13647791, 9.70457258],\n",
- " [ 6.34116028, 8.09193064, 1.4151514 , ..., 5.60257005,\n",
- " 8.59492911, 6.77311163]])
tau
(draw)
float64
7.068 6.156 6.603 ... 6.225 5.979
array([ 7.06777672, 6.15578323, 6.60290338, 8.78674782, 7.99791629,\n",
- " 8.93827284, 9.25265185, 7.72455418, 9.78584927, 9.49886649,\n",
- " 9.39464286, 10.96962525, 9.792903 , 11.80778665, 8.38340175,\n",
- " 10.90427878, 10.57656998, 8.9664143 , 8.98953719, 10.8490396 ,\n",
- " 10.54993238, 8.17961082, 8.4652292 , 7.99943296, 7.44016139,\n",
- " 7.3115449 , 7.32348229, 6.6495615 , 6.217016 , 5.68928529,\n",
- " 6.06877757, 5.47965636, 4.88171151, 5.78420116, 5.62295691,\n",
- " 5.69791747, 6.5159523 , 6.09278051, 4.89036139, 5.09688512,\n",
- " 5.49215524, 5.72371072, 6.49030974, 6.53160641, 5.96398495,\n",
- " 6.21665436, 7.09169266, 9.39026772, 8.03330529, 7.97603609,\n",
- " 7.77784331, 7.36531215, 7.4601659 , 5.80507495, 5.3501381 ,\n",
- " 6.095213 , 6.50675464, 6.12710161, 5.79361256, 4.86652857,\n",
- " 7.30979058, 6.42740763, 7.74104485, 7.41679668, 7.82857123,\n",
- " 6.81436024, 7.03065647, 6.79255865, 6.65891116, 5.76910642,\n",
- " 5.22663935, 4.5741797 , 4.34328882, 4.92134566, 4.70012019,\n",
- " 4.88589882, 5.58078539, 6.25028148, 6.51568834, 5.44678037,\n",
- " 5.56186234, 7.65392084, 6.51925728, 5.29815742, 6.63063287,\n",
- " 4.8630152 , 5.16440822, 5.649998 , 5.90568389, 5.91782833,\n",
- " 5.18914897, 5.59549741, 6.31528782, 6.8608286 , 8.61453817,\n",
- " 8.50407556, 9.90790968, 10.87946071, 10.353364 , 10.04856825,\n",
- "...\n",
- " 6.42652914, 6.57338204, 6.74770977, 5.88104615, 6.56005932,\n",
- " 5.34044104, 5.87780539, 6.27653872, 6.82292255, 7.72303119,\n",
- " 7.40799976, 7.99298655, 7.76961762, 9.29895696, 7.44599886,\n",
- " 11.46419054, 8.37063054, 11.21373564, 8.57406191, 6.19423088,\n",
- " 7.13873245, 8.31248957, 9.23281937, 6.22694466, 7.30018798,\n",
- " 8.88784769, 6.93389001, 6.96732384, 5.77207946, 6.80397644,\n",
- " 6.25831244, 6.77610036, 7.46025825, 6.33782052, 7.0974327 ,\n",
- " 6.04840039, 5.79897829, 5.42587791, 5.38386316, 5.39282826,\n",
- " 5.96949488, 6.19170637, 4.83911656, 4.75128774, 4.94500911,\n",
- " 4.85281446, 4.80988697, 4.81275746, 5.39425682, 4.65065357,\n",
- " 4.89625809, 5.6089216 , 5.55957386, 7.76142826, 6.68749536,\n",
- " 7.08084387, 7.24416674, 6.34649285, 5.6608757 , 6.9892499 ,\n",
- " 6.62772503, 6.32206395, 5.60515095, 5.26993893, 5.15716903,\n",
- " 5.18656049, 6.55071147, 5.63647986, 5.97383805, 4.56236948,\n",
- " 5.87050116, 5.58342585, 5.66041767, 5.04853393, 5.5992427 ,\n",
- " 7.02498566, 6.23537501, 6.31062732, 6.4664859 , 8.77580729,\n",
- " 7.99415149, 8.72156943, 6.82769794, 6.95974907, 7.85488577,\n",
- " 8.40903182, 8.54289633, 5.8141554 , 6.07008713, 8.5777204 ,\n",
- " 8.80977649, 7.76253058, 6.03482558, 8.26768483, 7.46827381,\n",
- " 6.88216991, 5.73418374, 5.72470382, 6.22504062, 5.97903687])
log_tau
(draw)
float64
4.322 4.118 4.234 ... 4.035 3.951
array([4.32212556, 4.11764856, 4.23430516, 4.52712483, 4.57180336,\n",
- " 4.59710111, 4.76888052, 4.47739993, 4.48501664, 4.43572025,\n",
- " 4.60170582, 4.63659171, 4.81292151, 4.99935121, 4.60076885,\n",
- " 4.83411829, 4.76503459, 4.6225159 , 4.49361353, 4.91946337,\n",
- " 4.86287935, 4.5951552 , 4.59465596, 4.55712956, 4.38504505,\n",
- " 4.42312395, 4.41873094, 4.14388028, 4.01633582, 3.83147614,\n",
- " 3.87888469, 3.85543821, 3.60054467, 3.92276128, 3.79141052,\n",
- " 3.749707 , 3.88844695, 3.94007953, 3.58870299, 3.65533775,\n",
- " 3.85676477, 3.89525469, 4.05448166, 4.10475804, 4.03333584,\n",
- " 4.10569129, 4.31572942, 4.62596931, 4.45548446, 4.51836969,\n",
- " 4.50332905, 4.37313078, 4.39671947, 3.9999761 , 3.83446174,\n",
- " 4.08750122, 4.19208694, 4.09224322, 3.96974108, 3.5647696 ,\n",
- " 4.36678009, 4.06524726, 4.33866268, 4.28663071, 4.46052959,\n",
- " 4.24912767, 4.28475128, 4.29676047, 4.24253936, 3.87752469,\n",
- " 3.69056442, 3.38785849, 3.2509575 , 3.53294362, 3.45310091,\n",
- " 3.48162201, 3.74580352, 3.83137577, 3.95496117, 3.75435586,\n",
- " 3.72876676, 4.10436436, 3.96749644, 3.72609674, 3.86470599,\n",
- " 3.5363501 , 3.63629029, 3.89929707, 3.95499136, 3.97458604,\n",
- " 3.65407765, 3.7109385 , 3.84869173, 3.94788434, 4.49051811,\n",
- " 4.16734508, 4.52528834, 4.65168588, 4.49136407, 4.56663388,\n",
- "...\n",
- " 4.04930386, 4.16284491, 4.2876382 , 3.96473001, 4.1665937 ,\n",
- " 3.81266219, 3.98534418, 4.131541 , 4.11553434, 4.52492898,\n",
- " 4.35239702, 4.54272234, 4.45051093, 4.48478808, 4.23224911,\n",
- " 4.87260057, 4.4580245 , 4.82822227, 4.43352967, 4.06545374,\n",
- " 4.29893155, 4.44350617, 4.35318041, 3.98140379, 4.13339888,\n",
- " 4.51068474, 4.1459622 , 4.12826635, 3.84799164, 4.07020212,\n",
- " 3.97887093, 4.01753226, 4.17841632, 3.98409605, 4.1166347 ,\n",
- " 3.94616877, 3.8293701 , 3.75817912, 3.76175016, 3.61564367,\n",
- " 3.8085938 , 3.85779852, 3.5392893 , 3.48652763, 3.55109029,\n",
- " 3.50195708, 3.48497505, 3.4984326 , 3.6623651 , 3.3897953 ,\n",
- " 3.48961253, 3.80520122, 3.70128035, 4.10832107, 4.10037509,\n",
- " 4.10587526, 4.19483734, 3.85241875, 3.77324695, 3.99495856,\n",
- " 3.98078574, 3.88856338, 3.73855622, 3.6734914 , 3.64531916,\n",
- " 3.65450283, 4.08323331, 3.77993352, 3.81227438, 3.39179354,\n",
- " 3.75881012, 3.76865767, 3.85020295, 3.61642445, 3.82759113,\n",
- " 4.13230688, 3.95640152, 3.97045308, 4.01824782, 4.33490267,\n",
- " 4.29329024, 4.45188135, 4.29971941, 4.28329117, 4.44397147,\n",
- " 4.60702321, 4.51286532, 3.99354128, 4.08536704, 4.52038248,\n",
- " 4.4752091 , 4.20395751, 3.9201547 , 4.51400454, 4.33073743,\n",
- " 4.19930214, 3.95451375, 3.95802908, 4.03454781, 3.95077052])
mlogtau
(draw)
float64
nan nan nan ... 4.002 4.01 4.021
array([ nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, 4.30977551,\n",
- " 4.31339958, 4.31850923, 4.32175752, 4.31121454, 4.29646771,\n",
- " 4.28627571, 4.27473984, 4.2670367 , 4.25673119, 4.23931218,\n",
- " 4.23461367, 4.22318678, 4.2137016 , 4.19944719, 4.1966424 ,\n",
- " 4.18494259, 4.17533693, 4.16882182, 4.16380033, 4.14296156,\n",
- " 4.11951526, 4.09536933, 4.06849536, 4.04801164, 4.02937276,\n",
- " 4.01054272, 3.99708417, 3.99083408, 3.98960659, 3.98806418,\n",
- " 3.98506182, 3.99004034, 3.99737938, 3.99344609, 3.994912 ,\n",
- " 3.99064486, 3.98560173, 3.98478608, 3.99211184, 3.99849681,\n",
- " 3.99444307, 3.99075674, 3.98664095, 3.98350347, 3.99264712,\n",
- " 3.99388019, 3.99807137, 3.9985857 , 3.99930329, 4.00026858,\n",
- "...\n",
- " 4.24079808, 4.23145719, 4.22714048, 4.22026642, 4.21389765,\n",
- " 4.20199003, 4.19228985, 4.18344793, 4.17838413, 4.17990264,\n",
- " 4.18186296, 4.18432244, 4.18262747, 4.18369914, 4.18777546,\n",
- " 4.20025858, 4.19932353, 4.20698224, 4.20424792, 4.19810419,\n",
- " 4.19308824, 4.19353773, 4.19349187, 4.18467999, 4.18420958,\n",
- " 4.18986859, 4.18993768, 4.18966549, 4.18596696, 4.18510645,\n",
- " 4.17984098, 4.17863544, 4.18347456, 4.18234616, 4.17901141,\n",
- " 4.17103034, 4.1672182 , 4.16313152, 4.15988649, 4.15308363,\n",
- " 4.15601855, 4.14669802, 4.13584688, 4.12187329, 4.11068578,\n",
- " 4.09437974, 4.08307464, 4.07388501, 4.06683324, 4.05605268,\n",
- " 4.04485886, 4.03770598, 4.02597883, 4.02885065, 4.02752628,\n",
- " 4.03339054, 4.0375804 , 4.03199796, 4.02515221, 4.0145528 ,\n",
- " 4.00712057, 3.99403739, 3.9797983 , 3.96357237, 3.95183377,\n",
- " 3.92747181, 3.91997599, 3.89901021, 3.88658511, 3.8731119 ,\n",
- " 3.86230948, 3.84881251, 3.83875296, 3.83145337, 3.82533721,\n",
- " 3.81776966, 3.81397844, 3.81082218, 3.8142273 , 3.81952131,\n",
- " 3.8258097 , 3.83449668, 3.83692274, 3.84290665, 3.84945338,\n",
- " 3.86267047, 3.87634037, 3.88104762, 3.88751995, 3.90561473,\n",
- " 3.91894704, 3.92587022, 3.93348752, 3.95403706, 3.96963001,\n",
- " 3.98357691, 3.99296768, 4.00215961, 4.00960326, 4.02082277])
theta_school_diff
(draw, school, school_bis)
float64
3.0 6.965 6.401 ... 4.171 1.178 3.0
array([[[ 3.00000000e+00, 6.96516309e+00, 6.40102907e+00, ...,\n",
- " 4.25002772e+00, 4.23571543e+00, 4.48865897e+00],\n",
- " [-9.65163094e-01, 3.00000000e+00, 2.43586598e+00, ...,\n",
- " 2.84864624e-01, 2.70552336e-01, 5.23495875e-01],\n",
- " [-4.01029074e-01, 3.56413402e+00, 3.00000000e+00, ...,\n",
- " 8.48998643e-01, 8.34686355e-01, 1.08762989e+00],\n",
+ " mu (chain, draw) float64 16kB 10.87 6.385 ... 6.486 6.404\n",
+ " theta (chain, draw, school) float64 128kB 15.32 12.91 ... 4.295\n",
+ " tau (chain, draw) float64 16kB 7.726 6.909 ... 5.932 7.461\n",
+ " log_tau (chain, draw) float64 16kB 4.553 4.363 ... 4.076 4.495\n",
+ " mlogtau (chain, draw) float64 16kB nan nan nan ... 4.496 4.511\n",
+ " theta_school_diff (chain, draw, school, school_bis) float64 1MB 3.0 ... 3.0\n",
+ "Attributes: (6)
- chain: 4
- draw: 500
- school: 8
- school_bis: 8
chain
(chain)
int64
0 1 2 3
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
school_bis
(school_bis)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " [ 6.53251469, 5.00890089, 3.51080602, ..., 7.70724881,\n",
+ " 6.07331446, 0.37693108],\n",
+ " [ 7.1827505 , 10.55425055, 7.45603444, ..., 4.5289581 ,\n",
+ " 4.09609826, 11.45228161],\n",
+ " [ 3.19295578, 9.4984278 , 2.10557598, ..., 10.93646013,\n",
+ " 9.76245459, 4.2950506 ]]])
tau
(chain, draw)
float64
7.726 6.909 7.844 ... 5.932 7.461
array([[7.72574006, 6.90899361, 7.8440252 , ..., 4.89383828, 8.92006245,\n",
+ " 7.32589567],\n",
+ " [4.97083011, 5.04902913, 5.12376496, ..., 5.17459002, 4.32755081,\n",
+ " 4.21199466],\n",
+ " [6.50127689, 5.89324317, 7.27328591, ..., 7.08978031, 5.72016996,\n",
+ " 4.91701121],\n",
+ " [9.07325982, 6.77186702, 6.17053747, ..., 5.74060666, 5.93237926,\n",
+ " 7.46124596]])
log_tau
(chain, draw)
float64
4.553 4.363 4.578 ... 4.076 4.495
array([[4.55302418, 4.36327995, 4.57774603, ..., 3.6386056 , 4.778347 ,\n",
+ " 4.46461921],\n",
+ " [3.67845483, 3.71736608, 3.75319044, ..., 3.77684015, 3.28333575,\n",
+ " 3.19226749],\n",
+ " [4.25312773, 4.06237808, 4.45238307, ..., 4.40849125, 4.00069436,\n",
+ " 3.65076731],\n",
+ " [4.8038955 , 4.32757011, 4.15390112, ..., 4.00817931, 4.07581413,\n",
+ " 4.49542809]])
mlogtau
(chain, draw)
float64
nan nan nan ... 4.494 4.496 4.511
array([[ nan, nan, nan, ..., 4.16476321, 4.19559572,\n",
+ " 4.22597193],\n",
+ " [ nan, nan, nan, ..., 3.12348971, 3.131342 ,\n",
+ " 3.13737294],\n",
+ " [ nan, nan, nan, ..., 4.22653292, 4.21500516,\n",
+ " 4.20868681],\n",
+ " [ nan, nan, nan, ..., 4.4938526 , 4.49647017,\n",
+ " 4.5112594 ]])
theta_school_diff
(chain, draw, school, school_bis)
float64
3.0 5.415 0.3691 ... -2.467 3.0
array([[[[ 3.00000000e+00, 5.41531869e+00, 3.69070082e-01, ...,\n",
+ " -1.58110972e+00, 2.12262624e+00, 2.59319742e-01],\n",
+ " [ 5.84681314e-01, 3.00000000e+00, -2.04624860e+00, ...,\n",
+ " -3.99642840e+00, -2.92692441e-01, -2.15599894e+00],\n",
+ " [ 5.63092992e+00, 8.04624860e+00, 3.00000000e+00, ...,\n",
+ " 1.04982020e+00, 4.75355616e+00, 2.89024966e+00],\n",
+ " ...,\n",
+ " [ 7.58110972e+00, 9.99642840e+00, 4.95017980e+00, ...,\n",
+ " 3.00000000e+00, 6.70373596e+00, 4.84042946e+00],\n",
+ " [ 3.87737376e+00, 6.29269244e+00, 1.24644384e+00, ...,\n",
+ " -7.03735961e-01, 3.00000000e+00, 1.13669350e+00],\n",
+ " [ 5.74068026e+00, 8.15599894e+00, 3.10975034e+00, ...,\n",
+ " 1.15957054e+00, 4.86330650e+00, 3.00000000e+00]],\n",
+ "\n",
+ " [[ 3.00000000e+00, 5.15629954e+00, 1.11463600e+01, ...,\n",
+ " 1.18925351e+01, 4.23040040e+00, 8.10889901e+00],\n",
+ " [ 8.43700462e-01, 3.00000000e+00, 8.99006042e+00, ...,\n",
+ " 9.73623560e+00, 2.07410086e+00, 5.95259947e+00],\n",
+ " [-5.14635996e+00, -2.99006042e+00, 3.00000000e+00, ...,\n",
+ " 3.74617518e+00, -3.91595956e+00, -3.74609480e-02],\n",
+ "...\n",
+ " [ 3.46207603e-01, -3.02529245e+00, 7.29236668e-02, ...,\n",
+ " 3.00000000e+00, 3.43285984e+00, -3.92332351e+00],\n",
+ " [-8.66522389e-02, -3.45815229e+00, -3.59936175e-01, ...,\n",
+ " 2.56714016e+00, 3.00000000e+00, -4.35618335e+00],\n",
+ " [ 7.26953111e+00, 3.89803106e+00, 6.99624717e+00, ...,\n",
+ " 9.92332351e+00, 1.03561833e+01, 3.00000000e+00]],\n",
+ "\n",
+ " [[ 3.00000000e+00, -3.30547202e+00, 4.08737981e+00, ...,\n",
+ " -4.74350435e+00, -3.56949881e+00, 1.89790518e+00],\n",
+ " [ 9.30547202e+00, 3.00000000e+00, 1.03928518e+01, ...,\n",
+ " 1.56196766e+00, 2.73597321e+00, 8.20337720e+00],\n",
+ " [ 1.91262019e+00, -4.39285182e+00, 3.00000000e+00, ...,\n",
+ " -5.83088416e+00, -4.65687862e+00, 8.10525376e-01],\n",
+ " ...,\n",
+ " [ 1.07435044e+01, 4.43803234e+00, 1.18308842e+01, ...,\n",
+ " 3.00000000e+00, 4.17400554e+00, 9.64140953e+00],\n",
+ " [ 9.56949881e+00, 3.26402679e+00, 1.06568786e+01, ...,\n",
+ " 1.82599446e+00, 3.00000000e+00, 8.46740399e+00],\n",
+ " [ 4.10209482e+00, -2.20337720e+00, 5.18947462e+00, ...,\n",
+ " -3.64140953e+00, -2.46740399e+00, 3.00000000e+00]]]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='object', name='school'))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school_bis'))
\n",
+ " dtype='object', name='school_bis'))
- created_at :
- 2022-10-13T14:37:37.315398
- arviz_version :
- 0.13.0.dev0
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -28247,6 +28523,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -28297,7 +28574,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -28305,7 +28582,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -28317,6 +28595,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -28579,28 +28861,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 133kB\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 128kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -28635,6 +28917,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -28685,7 +28968,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -28693,7 +28976,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -28705,6 +28989,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -28967,15 +29255,15 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 36kB\n",
"Dimensions: (chain: 4, draw: 500, new_school: 2)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
- " * new_school (new_school) <U13 'Essex College' 'Moordale'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * new_school (new_school) <U13 104B 'Essex College' 'Moordale'\n",
"Data variables:\n",
- " obs (chain, draw, new_school) float64 2.041 -2.556 ... -0.2822\n",
- "Attributes: (2)
- chain: 4
- draw: 500
- new_school: 2
obs
(chain, draw, new_school)
float64
2.041 -2.556 ... -1.015 -0.2822
array([[[ 2.04091912, -2.55566503],\n",
+ " obs (chain, draw, new_school) float64 32kB 2.041 -2.556 ... -0.2822\n",
+ "Attributes: (2)
- chain: 4
- draw: 500
- new_school: 2
obs
(chain, draw, new_school)
float64
2.041 -2.556 ... -1.015 -0.2822
array([[[ 2.04091912, -2.55566503],\n",
" [ 0.41809885, -0.56776961],\n",
" [-0.45264929, -0.21559716],\n",
" ...,\n",
@@ -29005,17 +29293,17 @@
" ...,\n",
" [ 1.57846099, 0.24653314],\n",
" [ 0.64302486, 1.42710376],\n",
- " [-1.01529472, -0.28215614]]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " [-1.01529472, -0.28215614]]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Essex College', 'Moordale'], dtype='object', name='new_school'))
- created_at :
- 2023-12-28T12:47:21.311677
- arviz_version :
- 0.16.1
\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Essex College', 'Moordale'], dtype='object', name='new_school'))
- created_at :
- 2024-09-28T19:22:37.147191+00:00
- arviz_version :
- 0.20.0
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -29050,6 +29338,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -29100,7 +29389,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -29108,7 +29397,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -29120,6 +29410,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -29382,28 +29676,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 133kB\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 128kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -29438,6 +29732,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -29488,7 +29783,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -29496,7 +29791,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -29508,6 +29804,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -29770,36 +30070,36 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 246kB\n",
"Dimensions: (chain: 4, draw: 500)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
"Data variables: (12/16)\n",
- " max_energy_error (chain, draw) float64 ...\n",
- " energy_error (chain, draw) float64 ...\n",
- " lp (chain, draw) float64 ...\n",
- " index_in_trajectory (chain, draw) int64 ...\n",
- " acceptance_rate (chain, draw) float64 ...\n",
- " diverging (chain, draw) bool ...\n",
+ " max_energy_error (chain, draw) float64 16kB ...\n",
+ " energy_error (chain, draw) float64 16kB ...\n",
+ " lp (chain, draw) float64 16kB ...\n",
+ " index_in_trajectory (chain, draw) int64 16kB ...\n",
+ " acceptance_rate (chain, draw) float64 16kB ...\n",
+ " diverging (chain, draw) bool 2kB ...\n",
" ... ...\n",
- " smallest_eigval (chain, draw) float64 ...\n",
- " step_size_bar (chain, draw) float64 ...\n",
- " step_size (chain, draw) float64 ...\n",
- " energy (chain, draw) float64 ...\n",
- " tree_depth (chain, draw) int64 ...\n",
- " perf_counter_diff (chain, draw) float64 ...\n",
- "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " smallest_eigval (chain, draw) float64 16kB ...\n",
+ " step_size_bar (chain, draw) float64 16kB ...\n",
+ " step_size (chain, draw) float64 16kB ...\n",
+ " energy (chain, draw) float64 16kB ...\n",
+ " tree_depth (chain, draw) int64 16kB ...\n",
+ " perf_counter_diff (chain, draw) float64 16kB ...\n",
+ "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
+ " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -29834,6 +30134,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -29884,7 +30185,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -29892,7 +30193,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -29904,6 +30206,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -30166,120 +30472,34 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
- "Dimensions: (draw: 500, school: 8)\n",
+ "<xarray.Dataset> Size: 45kB\n",
+ "Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " tau (draw) float64 1.941 3.388 4.208 5.687 ... 0.8353 0.06893 2.145\n",
- " theta (draw, school) float64 4.866 4.59 -0.7404 ... 3.33 -2.031 6.045\n",
- " mu (draw) float64 3.903 3.915 -1.751 2.595 ... -2.294 0.7908 2.869
tau
(draw)
float64
1.941 3.388 4.208 ... 0.06893 2.145
array([1.94060202e+00, 3.38786056e+00, 4.20819481e+00, 5.68742111e+00,\n",
- " 4.82244460e+00, 7.84969782e+00, 2.41850082e+00, 1.11683428e+00,\n",
- " 5.10984626e-01, 1.67640274e+00, 8.37550751e+00, 3.55750200e+01,\n",
- " 1.87888479e+01, 7.38007966e+01, 7.19904857e+00, 2.97111678e+00,\n",
- " 2.38009906e+00, 8.61688371e-01, 1.82005508e+00, 7.05099843e-01,\n",
- " 3.73935694e+00, 8.73489254e+00, 1.39668221e+00, 2.17078500e+01,\n",
- " 9.59674138e+00, 3.15703268e+00, 3.33847615e+00, 3.51144898e+01,\n",
- " 6.80634203e+00, 4.11871168e+00, 4.93255227e+00, 4.83826812e+00,\n",
- " 2.72760625e+01, 5.24910941e+00, 2.85667668e+00, 4.28176247e+00,\n",
- " 2.28124585e+01, 1.34945279e+01, 9.68820812e+00, 1.07728226e+01,\n",
- " 1.20481030e+02, 3.31906378e-01, 4.63523234e-01, 1.26739022e+00,\n",
- " 2.51130780e+03, 5.07713155e+00, 9.28824918e-01, 9.14991935e+00,\n",
- " 4.66414362e+01, 8.84758086e+01, 3.00341165e-01, 7.30464820e-01,\n",
- " 3.40976947e+01, 2.99629266e+01, 2.26765592e+00, 1.78358355e+01,\n",
- " 3.30808448e+01, 3.88184972e+00, 8.84199075e-01, 1.51343747e+01,\n",
- " 2.65672354e+00, 1.31077618e+00, 2.25382327e+01, 1.22813964e+02,\n",
- " 7.85565515e+00, 7.41863308e-01, 9.49848204e+00, 1.41059847e+02,\n",
- " 9.25655368e-01, 1.78766382e+00, 2.62553715e+00, 8.58588175e-01,\n",
- " 1.74600321e+01, 4.62398480e+00, 1.01280222e+00, 5.74512287e+00,\n",
- " 1.26181092e+01, 4.56117182e+00, 1.03129481e+01, 1.05807520e+02,\n",
- "...\n",
- " 4.44939392e+00, 6.07065997e+01, 3.09150476e+00, 1.07895462e+00,\n",
- " 3.40577271e+01, 8.87566932e+03, 5.14275783e-01, 2.34873432e+00,\n",
- " 4.28166200e+00, 6.11436072e+00, 7.02201974e+00, 9.11145054e-02,\n",
- " 5.06762347e+00, 5.54001194e+00, 1.65166669e+00, 4.62314520e+00,\n",
- " 2.79342562e+01, 8.20769981e+00, 1.34952820e+01, 8.67771773e-01,\n",
- " 2.67941929e+00, 5.78094132e+00, 4.08853487e+01, 1.21540351e+01,\n",
- " 6.21499874e+00, 2.05937430e+01, 5.18603595e+00, 5.93430042e+00,\n",
- " 1.27090430e+01, 9.37798481e+00, 3.80986538e+00, 1.14897162e+03,\n",
- " 6.66454695e+01, 1.68318555e+01, 6.50185117e+01, 1.77443210e+00,\n",
- " 1.98821067e+01, 1.48732781e+00, 5.32400098e+01, 8.90298404e-01,\n",
- " 9.74456150e+00, 3.17898046e+00, 4.64437308e+00, 1.67900077e+00,\n",
- " 2.11625865e+00, 5.88562332e-01, 8.72761717e+00, 3.23788264e+01,\n",
- " 2.62725682e+00, 2.48389110e+00, 5.78998327e+00, 4.86828624e+00,\n",
- " 8.96244600e+00, 9.85486905e-01, 4.35475195e+00, 8.21724160e+00,\n",
- " 3.67358130e+00, 3.19700555e-01, 4.32539022e+00, 7.87027890e-01,\n",
- " 2.16835786e+00, 3.34426424e+00, 1.67688831e+01, 9.15246738e-01,\n",
- " 5.68705452e+00, 4.86622906e+00, 2.32790878e+00, 3.16238422e+01,\n",
- " 8.20289530e+00, 1.59128252e+03, 1.70394780e+00, 2.42565587e+00,\n",
- " 7.62767038e+00, 2.41576900e+01, 4.25452773e+01, 1.21193875e+00,\n",
- " 1.02716549e+01, 8.35319808e-01, 6.89347686e-02, 2.14488132e+00])
theta
(draw, school)
float64
4.866 4.59 -0.7404 ... -2.031 6.045
array([[ 4.86620856, 4.58956728, -0.74044057, ..., 3.9610381 ,\n",
- " 2.65493249, 6.01863257],\n",
- " [ 4.37914099, 5.96929909, -2.97367789, ..., 11.03055872,\n",
- " 5.05284821, 2.67610001],\n",
- " [-1.19602373, -9.83468642, 2.28396607, ..., -1.73761977,\n",
- " 8.42950283, 2.36634401],\n",
- " ...,\n",
- " [-2.81086618, -3.98468829, -2.08099901, ..., -2.53012815,\n",
- " -2.05930505, -3.19172734],\n",
- " [ 0.81207434, 0.79427128, 0.83181532, ..., 0.79266084,\n",
- " 0.86104331, 0.7793056 ],\n",
- " [ 3.57736989, 3.53795807, 5.30449351, ..., 3.3301618 ,\n",
- " -2.03071509, 6.04538653]])
mu
(draw)
float64
3.903 3.915 -1.751 ... 0.7908 2.869
array([ 3.90253102e+00, 3.91500660e+00, -1.75114302e+00, 2.59485538e+00,\n",
- " -4.44296321e-01, 7.61000307e+00, -1.04548271e+01, -6.00723208e+00,\n",
- " -2.99616365e+00, 4.75484334e+00, -7.53597289e-01, -4.49206911e+00,\n",
- " 1.22966674e+00, -1.17350445e+00, -1.14598007e+00, -1.10634181e+01,\n",
- " 6.31763132e+00, 1.73772124e+00, -4.91208423e-01, -2.75385285e+00,\n",
- " -3.41248265e+00, 1.42264349e+00, 4.11819735e-01, 1.73585996e-01,\n",
- " -5.07312782e+00, -9.99257696e+00, -6.56537987e+00, -6.24410186e+00,\n",
- " 1.30989418e+00, 1.92730225e+00, 9.19697020e+00, 8.53506427e+00,\n",
- " 3.39755738e+00, -4.93707846e+00, -7.33345648e+00, 2.98252256e+00,\n",
- " -3.18159106e-01, 2.27959142e+00, -4.62958545e+00, -9.70160468e-02,\n",
- " -1.60798088e+00, -6.00729427e+00, 8.37985516e+00, 9.52705402e-01,\n",
- " 1.47243794e+00, -4.65377145e+00, 7.28348474e-01, -3.06384730e+00,\n",
- " -6.15183994e+00, -1.41063529e+00, 5.31880456e+00, 4.28382637e-01,\n",
- " -8.23432363e+00, 1.22762109e+00, -8.29777595e-01, 4.21780345e+00,\n",
- " 5.62271511e-01, -1.17786709e+00, 8.09037873e+00, -6.71477871e-01,\n",
- " -6.73172600e+00, -7.64168460e-01, -9.25141462e-02, -1.39886914e+00,\n",
- " 3.37072301e+00, 6.22566150e+00, 6.39989161e+00, -7.42547579e+00,\n",
- " 8.30312575e+00, -1.07413010e+00, -1.79272549e+00, 1.01766431e+01,\n",
- " -2.62649315e+00, -6.23298070e+00, -3.11080725e+00, 4.74329549e+00,\n",
- " -5.28479613e+00, 3.33562049e+00, -2.64374091e+00, -5.36198855e+00,\n",
- "...\n",
- " 4.71858610e+00, -2.38511285e+00, 6.85363619e-01, 7.86083664e+00,\n",
- " 8.58371336e-01, -1.22509704e+00, -2.96950232e+00, 5.94273132e+00,\n",
- " -2.96290955e+00, 5.30842670e+00, -2.93315882e+00, 2.00138807e+00,\n",
- " -5.38929306e+00, -3.79404004e+00, 9.41113416e+00, -4.29479242e+00,\n",
- " -4.87319690e+00, -1.32639504e+00, -4.40725483e+00, 2.48578744e+00,\n",
- " -2.74719435e+00, 4.19794394e+00, 3.37369715e+00, 4.68554077e+00,\n",
- " 2.98601993e+00, -9.56558363e+00, -4.80824536e-01, 2.32683687e+00,\n",
- " 1.84240293e+00, 4.31505480e+00, 1.78131745e+00, 4.74830962e+00,\n",
- " -2.51044292e-01, -6.19005739e+00, -6.80007397e+00, 5.41635224e-01,\n",
- " 2.53050533e+00, -1.22188016e+00, 5.05691442e+00, 2.03910664e+00,\n",
- " -5.22800335e-02, -1.12511498e+00, 6.59945576e+00, -3.58516561e+00,\n",
- " 7.22804145e+00, 4.59556418e+00, -4.82300404e+00, 4.70726322e+00,\n",
- " -3.32578499e+00, 3.15320672e+00, 1.14604995e+01, -4.35587322e+00,\n",
- " 7.06014478e+00, 2.34213481e+00, -1.31820085e+00, 3.72632528e+00,\n",
- " -9.31774491e-01, -5.03118301e+00, 2.59806644e+00, -2.02997029e+00,\n",
- " -7.30940496e+00, -2.53759146e+00, -3.66411956e+00, 9.38270535e+00,\n",
- " 3.79591101e+00, -5.59892859e-01, 1.18231368e+01, 1.26943270e+01,\n",
- " 5.77152605e+00, -7.33882918e+00, 2.08163637e+00, -4.50230861e+00,\n",
- " -1.39653308e-01, -1.04822114e-01, 2.08910055e+00, -6.44041200e+00,\n",
- " -6.60626614e-01, -2.29356629e+00, 7.90828077e-01, 2.86879752e+00])
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " tau (chain, draw) float64 4kB 1.941 3.388 4.208 ... 0.06893 2.145\n",
+ " theta (chain, draw, school) float64 32kB 4.866 4.59 ... -2.031 6.045\n",
+ " mu (chain, draw) float64 4kB 3.903 3.915 -1.751 ... 0.7908 2.869\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
tau
(chain, draw)
float64
1.941 3.388 4.208 ... 0.06893 2.145
array([[1.940602, 3.387861, 4.208195, ..., 0.83532 , 0.068935, 2.144881]])
theta
(chain, draw, school)
float64
4.866 4.59 -0.7404 ... -2.031 6.045
array([[[ 4.866209, 4.589567, ..., 2.654932, 6.018633],\n",
+ " [ 4.379141, 5.969299, ..., 5.052848, 2.6761 ],\n",
+ " ...,\n",
+ " [ 0.812074, 0.794271, ..., 0.861043, 0.779306],\n",
+ " [ 3.57737 , 3.537958, ..., -2.030715, 6.045387]]])
mu
(chain, draw)
float64
3.903 3.915 -1.751 ... 0.7908 2.869
array([[ 3.902531, 3.915007, -1.751143, ..., -2.293566, 0.790828, 2.868798]])
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.602116
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -30314,6 +30534,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -30364,7 +30585,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -30372,7 +30593,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -30384,6 +30606,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -30646,28 +30872,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 37kB\n",
"Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 32kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -30702,6 +30928,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -30752,7 +30979,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -30760,7 +30987,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -30772,6 +31000,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -31034,23 +31266,23 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " obs (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -31085,6 +31317,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -31135,7 +31368,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -31143,7 +31376,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -31155,6 +31389,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -31417,16 +31655,16 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " scores (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " scores (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
@@ -31737,7 +31975,8 @@
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
- ".xr-attrs dt, dd {\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
@@ -31787,7 +32026,7 @@
"\t> constant_data"
]
},
- "execution_count": 31,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -31806,7 +32045,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -31820,8 +32059,8 @@
" \n",
" \n",
" - \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
"
\n",
"
\n",
@@ -31856,6 +32095,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -31906,7 +32146,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -31914,7 +32154,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -31926,6 +32167,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -32188,247 +32433,119 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
- "Dimensions: (draw: 500, school: 8, school_bis: 8)\n",
+ "<xarray.Dataset> Size: 1MB\n",
+ "Dimensions: (chain: 4, draw: 500, school: 8, school_bis: 8)\n",
"Coordinates:\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
- " * school_bis (school_bis) <U16 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " * school_bis (school_bis) <U16 512B 'Choate' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " mu (draw) float64 5.974 5.096 7.177 ... 3.284 4.739 3.146\n",
- " theta (draw, school) float64 9.519 5.554 6.118 ... 5.595 3.773\n",
- " tau (draw) float64 4.068 3.156 3.603 ... 2.725 3.225 2.979\n",
- " log_tau (draw) float64 1.322 1.118 1.234 ... 0.958 1.035 0.9508\n",
- " mlogtau (draw) float64 nan nan nan nan ... 0.993 1.002 1.01 1.021\n",
- " theta_school_diff (draw, school, school_bis) float64 0.0 3.965 ... 0.0
- draw: 500
- school: 8
- school_bis: 8
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
school_bis
(school_bis)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
mu
(draw)
float64
5.974 5.096 7.177 ... 4.739 3.146
array([ 5.97368017, 5.09614549, 7.17713997, 6.87741417, 6.88441083,\n",
- " 4.84086372, 7.01708358, 6.30316861, 4.51620587, 3.51435901,\n",
- " 8.02470906, 5.95045866, 5.02167729, 4.3671087 , 3.83334779,\n",
- " 3.94213036, 4.16589421, 2.12301607, 1.95301774, 2.87799146,\n",
- " 2.45097497, 4.6973103 , 6.23496838, 5.71981672, 8.43315988,\n",
- " 7.8214925 , 6.96952817, 7.02978194, 5.6004881 , 7.09120336,\n",
- " 7.01093531, 6.77268374, 6.2905079 , 4.5431412 , 4.94294994,\n",
- " 4.97649353, 5.42971457, 5.22441946, 5.12380029, 4.85319167,\n",
- " 5.59925422, 5.28412171, 3.44179875, 2.54282744, 2.04706891,\n",
- " 3.51007274, 2.81257858, 2.98355207, 1.25352519, 3.0289464 ,\n",
- " 4.29796057, 5.26610036, 3.37041698, 4.18994019, 4.21989074,\n",
- " 5.10601394, 2.9274154 , 5.19747242, 3.38401204, 4.82853361,\n",
- " 4.79157776, 6.29972209, 5.28340737, 5.80484852, 0.43976964,\n",
- " 1.13816338, 1.99660115, 4.53854082, 4.18764282, 5.08607123,\n",
- " 4.82755518, 4.87705035, 5.13060706, 5.65131506, 5.12875312,\n",
- " 5.10175967, 3.70935631, 4.91147853, 3.95385959, 2.84575154,\n",
- " 2.26131669, 1.91153602, 4.78462284, 3.65852601, 4.68021586,\n",
- " 3.64944805, 3.59396976, 5.08703284, 4.93070605, 5.88752838,\n",
- " 5.69143312, 8.2534381 , 7.61786813, 5.51614066, 4.81312486,\n",
- " 6.60340997, 5.03224433, 7.28815644, 6.27653186, 2.20050788,\n",
- "...\n",
- " 4.83215454, 4.31025742, 5.52342996, 7.47556492, 8.40733007,\n",
- " 7.91724981, 6.58490944, 6.89924272, 5.94222017, 3.43319604,\n",
- " 6.60243616, 6.36189187, 3.80598967, 4.22733614, 5.1172315 ,\n",
- " 5.12510871, 6.61519371, 3.65237379, 5.72495477, 4.6349722 ,\n",
- " 1.86071336, 3.70878444, 4.03215053, 4.68583976, 4.32610419,\n",
- " 1.51856954, 6.97837558, 4.53510668, 6.49720081, 6.01585584,\n",
- " 4.64320497, 7.53733721, 4.99320792, 6.80154945, 3.83870421,\n",
- " 4.83408989, 3.9818728 , 3.87105982, 5.07985906, 5.24314344,\n",
- " 4.85720007, 5.26432426, 6.12469169, 5.66073344, 6.60622546,\n",
- " 6.0544301 , 5.01085876, 4.07245337, 4.41033219, 4.1244903 ,\n",
- " 2.82800074, 3.35555163, 5.12740508, 3.62961373, 4.97251047,\n",
- " 1.35997506, 3.07773379, 3.5442542 , 2.56527828, 2.79055045,\n",
- " 3.96084845, 5.01102 , 3.70448752, 5.42014382, 4.30528838,\n",
- " 3.03493602, 3.08845156, 3.75605787, 3.52525218, 3.2562104 ,\n",
- " 3.58446059, 4.07887414, 4.09665291, 4.62351667, 3.76542611,\n",
- " 3.52521781, 5.1456518 , 4.95544984, 4.59747767, 4.05346634,\n",
- " 2.92800398, 1.35454107, 4.06265637, 4.02687012, 5.4344199 ,\n",
- " 3.70135365, 4.66555525, 1.63401616, 3.88774047, 3.90969401,\n",
- " 3.68654685, 2.51199123, 2.86022208, 4.22286784, 3.97624991,\n",
- " 4.75407113, 3.53404815, 3.28390709, 4.73875823, 3.14562567])
theta
(draw, school)
float64
9.519 5.554 6.118 ... 5.595 3.773
array([[ 9.5190006 , 5.5538375 , 6.11797152, ..., 8.26897288,\n",
- " 8.28328517, 8.03034163],\n",
- " [ 7.48141224, 6.98433356, 6.07419118, ..., 3.63953594,\n",
- " 7.90751667, 6.09286812],\n",
- " [ 7.32773875, 6.89026362, 7.05512777, ..., 8.31300687,\n",
- " 7.04794125, 6.97572556],\n",
- " ...,\n",
- " [ 3.047783 , 3.47235698, 4.45528476, ..., 4.09130004,\n",
- " 5.36576861, 1.85789959],\n",
- " [ 6.82337688, 5.58880778, 2.78970216, ..., 1.8740546 ,\n",
- " 3.13647791, 6.70457258],\n",
- " [ 3.34116028, 5.09193064, -1.5848486 , ..., 2.60257005,\n",
- " 5.59492911, 3.77311163]])
tau
(draw)
float64
4.068 3.156 3.603 ... 3.225 2.979
array([4.06777672, 3.15578323, 3.60290338, 5.78674782, 4.99791629,\n",
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- " 6.39464286, 7.96962525, 6.792903 , 8.80778665, 5.38340175,\n",
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- " 7.54993238, 5.17961082, 5.4652292 , 4.99943296, 4.44016139,\n",
- " 4.3115449 , 4.32348229, 3.6495615 , 3.217016 , 2.68928529,\n",
- " 3.06877757, 2.47965636, 1.88171151, 2.78420116, 2.62295691,\n",
- " 2.69791747, 3.5159523 , 3.09278051, 1.89036139, 2.09688512,\n",
- " 2.49215524, 2.72371072, 3.49030974, 3.53160641, 2.96398495,\n",
- " 3.21665436, 4.09169266, 6.39026772, 5.03330529, 4.97603609,\n",
- " 4.77784331, 4.36531215, 4.4601659 , 2.80507495, 2.3501381 ,\n",
- " 3.095213 , 3.50675464, 3.12710161, 2.79361256, 1.86652857,\n",
- " 4.30979058, 3.42740763, 4.74104485, 4.41679668, 4.82857123,\n",
- " 3.81436024, 4.03065647, 3.79255865, 3.65891116, 2.76910642,\n",
- " 2.22663935, 1.5741797 , 1.34328882, 1.92134566, 1.70012019,\n",
- " 1.88589882, 2.58078539, 3.25028148, 3.51568834, 2.44678037,\n",
- " 2.56186234, 4.65392084, 3.51925728, 2.29815742, 3.63063287,\n",
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- " 5.50407556, 6.90790968, 7.87946071, 7.353364 , 7.04856825,\n",
- "...\n",
- " 3.42652914, 3.57338204, 3.74770977, 2.88104615, 3.56005932,\n",
- " 2.34044104, 2.87780539, 3.27653872, 3.82292255, 4.72303119,\n",
- " 4.40799976, 4.99298655, 4.76961762, 6.29895696, 4.44599886,\n",
- " 8.46419054, 5.37063054, 8.21373564, 5.57406191, 3.19423088,\n",
- " 4.13873245, 5.31248957, 6.23281937, 3.22694466, 4.30018798,\n",
- " 5.88784769, 3.93389001, 3.96732384, 2.77207946, 3.80397644,\n",
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- " 3.04840039, 2.79897829, 2.42587791, 2.38386316, 2.39282826,\n",
- " 2.96949488, 3.19170637, 1.83911656, 1.75128774, 1.94500911,\n",
- " 1.85281446, 1.80988697, 1.81275746, 2.39425682, 1.65065357,\n",
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- " 3.62772503, 3.32206395, 2.60515095, 2.26993893, 2.15716903,\n",
- " 2.18656049, 3.55071147, 2.63647986, 2.97383805, 1.56236948,\n",
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- " 4.02498566, 3.23537501, 3.31062732, 3.4664859 , 5.77580729,\n",
- " 4.99415149, 5.72156943, 3.82769794, 3.95974907, 4.85488577,\n",
- " 5.40903182, 5.54289633, 2.8141554 , 3.07008713, 5.5777204 ,\n",
- " 5.80977649, 4.76253058, 3.03482558, 5.26768483, 4.46827381,\n",
- " 3.88216991, 2.73418374, 2.72470382, 3.22504062, 2.97903687])
log_tau
(draw)
float64
1.322 1.118 1.234 ... 1.035 0.9508
array([1.32212556, 1.11764856, 1.23430516, 1.52712483, 1.57180336,\n",
- " 1.59710111, 1.76888052, 1.47739993, 1.48501664, 1.43572025,\n",
- " 1.60170582, 1.63659171, 1.81292151, 1.99935121, 1.60076885,\n",
- " 1.83411829, 1.76503459, 1.6225159 , 1.49361353, 1.91946337,\n",
- " 1.86287935, 1.5951552 , 1.59465596, 1.55712956, 1.38504505,\n",
- " 1.42312395, 1.41873094, 1.14388028, 1.01633582, 0.83147614,\n",
- " 0.87888469, 0.85543821, 0.60054467, 0.92276128, 0.79141052,\n",
- " 0.749707 , 0.88844695, 0.94007953, 0.58870299, 0.65533775,\n",
- " 0.85676477, 0.89525469, 1.05448166, 1.10475804, 1.03333584,\n",
- " 1.10569129, 1.31572942, 1.62596931, 1.45548446, 1.51836969,\n",
- " 1.50332905, 1.37313078, 1.39671947, 0.9999761 , 0.83446174,\n",
- " 1.08750122, 1.19208694, 1.09224322, 0.96974108, 0.5647696 ,\n",
- " 1.36678009, 1.06524726, 1.33866268, 1.28663071, 1.46052959,\n",
- " 1.24912767, 1.28475128, 1.29676047, 1.24253936, 0.87752469,\n",
- " 0.69056442, 0.38785849, 0.2509575 , 0.53294362, 0.45310091,\n",
- " 0.48162201, 0.74580352, 0.83137577, 0.95496117, 0.75435586,\n",
- " 0.72876676, 1.10436436, 0.96749644, 0.72609674, 0.86470599,\n",
- " 0.5363501 , 0.63629029, 0.89929707, 0.95499136, 0.97458604,\n",
- " 0.65407765, 0.7109385 , 0.84869173, 0.94788434, 1.49051811,\n",
- " 1.16734508, 1.52528834, 1.65168588, 1.49136407, 1.56663388,\n",
- "...\n",
- " 1.04930386, 1.16284491, 1.2876382 , 0.96473001, 1.1665937 ,\n",
- " 0.81266219, 0.98534418, 1.131541 , 1.11553434, 1.52492898,\n",
- " 1.35239702, 1.54272234, 1.45051093, 1.48478808, 1.23224911,\n",
- " 1.87260057, 1.4580245 , 1.82822227, 1.43352967, 1.06545374,\n",
- " 1.29893155, 1.44350617, 1.35318041, 0.98140379, 1.13339888,\n",
- " 1.51068474, 1.1459622 , 1.12826635, 0.84799164, 1.07020212,\n",
- " 0.97887093, 1.01753226, 1.17841632, 0.98409605, 1.1166347 ,\n",
- " 0.94616877, 0.8293701 , 0.75817912, 0.76175016, 0.61564367,\n",
- " 0.8085938 , 0.85779852, 0.5392893 , 0.48652763, 0.55109029,\n",
- " 0.50195708, 0.48497505, 0.4984326 , 0.6623651 , 0.3897953 ,\n",
- " 0.48961253, 0.80520122, 0.70128035, 1.10832107, 1.10037509,\n",
- " 1.10587526, 1.19483734, 0.85241875, 0.77324695, 0.99495856,\n",
- " 0.98078574, 0.88856338, 0.73855622, 0.6734914 , 0.64531916,\n",
- " 0.65450283, 1.08323331, 0.77993352, 0.81227438, 0.39179354,\n",
- " 0.75881012, 0.76865767, 0.85020295, 0.61642445, 0.82759113,\n",
- " 1.13230688, 0.95640152, 0.97045308, 1.01824782, 1.33490267,\n",
- " 1.29329024, 1.45188135, 1.29971941, 1.28329117, 1.44397147,\n",
- " 1.60702321, 1.51286532, 0.99354128, 1.08536704, 1.52038248,\n",
- " 1.4752091 , 1.20395751, 0.9201547 , 1.51400454, 1.33073743,\n",
- " 1.19930214, 0.95451375, 0.95802908, 1.03454781, 0.95077052])
mlogtau
(draw)
float64
nan nan nan ... 1.002 1.01 1.021
array([ nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, 1.30977551,\n",
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- " 1.23461367, 1.22318678, 1.2137016 , 1.19944719, 1.1966424 ,\n",
- " 1.18494259, 1.17533693, 1.16882182, 1.16380033, 1.14296156,\n",
- " 1.11951526, 1.09536933, 1.06849536, 1.04801164, 1.02937276,\n",
- " 1.01054272, 0.99708417, 0.99083408, 0.98960659, 0.98806418,\n",
- " 0.98506182, 0.99004034, 0.99737938, 0.99344609, 0.994912 ,\n",
- " 0.99064486, 0.98560173, 0.98478608, 0.99211184, 0.99849681,\n",
- " 0.99444307, 0.99075674, 0.98664095, 0.98350347, 0.99264712,\n",
- " 0.99388019, 0.99807137, 0.9985857 , 0.99930329, 1.00026858,\n",
- "...\n",
- " 1.24079808, 1.23145719, 1.22714048, 1.22026642, 1.21389765,\n",
- " 1.20199003, 1.19228985, 1.18344793, 1.17838413, 1.17990264,\n",
- " 1.18186296, 1.18432244, 1.18262747, 1.18369914, 1.18777546,\n",
- " 1.20025858, 1.19932353, 1.20698224, 1.20424792, 1.19810419,\n",
- " 1.19308824, 1.19353773, 1.19349187, 1.18467999, 1.18420958,\n",
- " 1.18986859, 1.18993768, 1.18966549, 1.18596696, 1.18510645,\n",
- " 1.17984098, 1.17863544, 1.18347456, 1.18234616, 1.17901141,\n",
- " 1.17103034, 1.1672182 , 1.16313152, 1.15988649, 1.15308363,\n",
- " 1.15601855, 1.14669802, 1.13584688, 1.12187329, 1.11068578,\n",
- " 1.09437974, 1.08307464, 1.07388501, 1.06683324, 1.05605268,\n",
- " 1.04485886, 1.03770598, 1.02597883, 1.02885065, 1.02752628,\n",
- " 1.03339054, 1.0375804 , 1.03199796, 1.02515221, 1.0145528 ,\n",
- " 1.00712057, 0.99403739, 0.9797983 , 0.96357237, 0.95183377,\n",
- " 0.92747181, 0.91997599, 0.89901021, 0.88658511, 0.8731119 ,\n",
- " 0.86230948, 0.84881251, 0.83875296, 0.83145337, 0.82533721,\n",
- " 0.81776966, 0.81397844, 0.81082218, 0.8142273 , 0.81952131,\n",
- " 0.8258097 , 0.83449668, 0.83692274, 0.84290665, 0.84945338,\n",
- " 0.86267047, 0.87634037, 0.88104762, 0.88751995, 0.90561473,\n",
- " 0.91894704, 0.92587022, 0.93348752, 0.95403706, 0.96963001,\n",
- " 0.98357691, 0.99296768, 1.00215961, 1.00960326, 1.02082277])
theta_school_diff
(draw, school, school_bis)
float64
0.0 3.965 3.401 ... -1.822 0.0
array([[[ 0. , 3.96516309, 3.40102907, ..., 1.25002772,\n",
- " 1.23571543, 1.48865897],\n",
- " [-3.96516309, 0. , -0.56413402, ..., -2.71513538,\n",
- " -2.72944766, -2.47650413],\n",
- " [-3.40102907, 0.56413402, 0. , ..., -2.15100136,\n",
- " -2.16531365, -1.91237011],\n",
+ " mu (chain, draw) float64 16kB 7.872 3.385 ... 3.486 3.404\n",
+ " theta (chain, draw, school) float64 128kB 12.32 9.905 ... 1.295\n",
+ " tau (chain, draw) float64 16kB 4.726 3.909 ... 2.932 4.461\n",
+ " log_tau (chain, draw) float64 16kB 1.553 1.363 ... 1.076 1.495\n",
+ " mlogtau (chain, draw) float64 16kB nan nan nan ... 1.496 1.511\n",
+ " theta_school_diff (chain, draw, school, school_bis) float64 1MB 0.0 ... 0.0\n",
+ "Attributes: (6)
- chain: 4
- draw: 500
- school: 8
- school_bis: 8
chain
(chain)
int64
0 1 2 3
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
school_bis
(school_bis)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " [ 5.677651, 1.679108, ..., 1.280615, 7.627658],\n",
+ " [ 1.625447, 2.503281, ..., 2.362756, -2.967994]],\n",
+ "\n",
+ " [[ 8.344508, 5.390855, ..., 12.46814 , 12.607797],\n",
+ " [ 8.93115 , 6.852969, ..., 7.013971, 5.136297],\n",
+ " ...,\n",
+ " [ 4.182751, 7.554251, ..., 1.096098, 8.452282],\n",
+ " [ 0.192956, 6.498428, ..., 6.762455, 1.295051]]])
tau
(chain, draw)
float64
4.726 3.909 4.844 ... 2.932 4.461
array([[4.72574 , 3.908994, 4.844025, ..., 1.893838, 5.920062, 4.325896],\n",
+ " [1.97083 , 2.049029, 2.123765, ..., 2.17459 , 1.327551, 1.211995],\n",
+ " [3.501277, 2.893243, 4.273286, ..., 4.08978 , 2.72017 , 1.917011],\n",
+ " [6.07326 , 3.771867, 3.170537, ..., 2.740607, 2.932379, 4.461246]])
log_tau
(chain, draw)
float64
1.553 1.363 1.578 ... 1.076 1.495
array([[1.55302418, 1.36327995, 1.57774603, ..., 0.6386056 , 1.778347 ,\n",
+ " 1.46461921],\n",
+ " [0.67845483, 0.71736608, 0.75319044, ..., 0.77684015, 0.28333575,\n",
+ " 0.19226749],\n",
+ " [1.25312773, 1.06237808, 1.45238307, ..., 1.40849125, 1.00069436,\n",
+ " 0.65076731],\n",
+ " [1.8038955 , 1.32757011, 1.15390112, ..., 1.00817931, 1.07581413,\n",
+ " 1.49542809]])
mlogtau
(chain, draw)
float64
nan nan nan ... 1.494 1.496 1.511
array([[ nan, nan, nan, ..., 1.16476321, 1.19559572,\n",
+ " 1.22597193],\n",
+ " [ nan, nan, nan, ..., 0.12348971, 0.131342 ,\n",
+ " 0.13737294],\n",
+ " [ nan, nan, nan, ..., 1.22653292, 1.21500516,\n",
+ " 1.20868681],\n",
+ " [ nan, nan, nan, ..., 1.4938526 , 1.49647017,\n",
+ " 1.5112594 ]])
theta_school_diff
(chain, draw, school, school_bis)
float64
0.0 2.415 -2.631 ... -5.467 0.0
array([[[[ 0.00000000e+00, 2.41531869e+00, -2.63092992e+00, ...,\n",
+ " -4.58110972e+00, -8.77373755e-01, -2.74068026e+00],\n",
+ " [-2.41531869e+00, 0.00000000e+00, -5.04624860e+00, ...,\n",
+ " -6.99642840e+00, -3.29269244e+00, -5.15599894e+00],\n",
+ " [ 2.63092992e+00, 5.04624860e+00, 0.00000000e+00, ...,\n",
+ " -1.95017980e+00, 1.75355616e+00, -1.09750340e-01],\n",
+ " ...,\n",
+ " [ 4.58110972e+00, 6.99642840e+00, 1.95017980e+00, ...,\n",
+ " 0.00000000e+00, 3.70373596e+00, 1.84042946e+00],\n",
+ " [ 8.77373755e-01, 3.29269244e+00, -1.75355616e+00, ...,\n",
+ " -3.70373596e+00, 0.00000000e+00, -1.86330650e+00],\n",
+ " [ 2.74068026e+00, 5.15599894e+00, 1.09750340e-01, ...,\n",
+ " -1.84042946e+00, 1.86330650e+00, 0.00000000e+00]],\n",
+ "\n",
+ " [[ 0.00000000e+00, 2.15629954e+00, 8.14635996e+00, ...,\n",
+ " 8.89253514e+00, 1.23040040e+00, 5.10889901e+00],\n",
+ " [-2.15629954e+00, 0.00000000e+00, 5.99006042e+00, ...,\n",
+ " 6.73623560e+00, -9.25899137e-01, 2.95259947e+00],\n",
+ " [-8.14635996e+00, -5.99006042e+00, 0.00000000e+00, ...,\n",
+ " 7.46175179e-01, -6.91595956e+00, -3.03746095e+00],\n",
+ "...\n",
+ " [-2.65379240e+00, -6.02529245e+00, -2.92707633e+00, ...,\n",
+ " 0.00000000e+00, 4.32859842e-01, -6.92332351e+00],\n",
+ " [-3.08665224e+00, -6.45815229e+00, -3.35993618e+00, ...,\n",
+ " -4.32859842e-01, 0.00000000e+00, -7.35618335e+00],\n",
+ " [ 4.26953111e+00, 8.98031057e-01, 3.99624717e+00, ...,\n",
+ " 6.92332351e+00, 7.35618335e+00, 0.00000000e+00]],\n",
+ "\n",
+ " [[ 0.00000000e+00, -6.30547202e+00, 1.08737981e+00, ...,\n",
+ " -7.74350435e+00, -6.56949881e+00, -1.10209482e+00],\n",
+ " [ 6.30547202e+00, 0.00000000e+00, 7.39285182e+00, ...,\n",
+ " -1.43803234e+00, -2.64026792e-01, 5.20337720e+00],\n",
+ " [-1.08737981e+00, -7.39285182e+00, 0.00000000e+00, ...,\n",
+ " -8.83088416e+00, -7.65687862e+00, -2.18947462e+00],\n",
+ " ...,\n",
+ " [ 7.74350435e+00, 1.43803234e+00, 8.83088416e+00, ...,\n",
+ " 0.00000000e+00, 1.17400554e+00, 6.64140953e+00],\n",
+ " [ 6.56949881e+00, 2.64026792e-01, 7.65687862e+00, ...,\n",
+ " -1.17400554e+00, 0.00000000e+00, 5.46740399e+00],\n",
+ " [ 1.10209482e+00, -5.20337720e+00, 2.18947462e+00, ...,\n",
+ " -6.64140953e+00, -5.46740399e+00, 0.00000000e+00]]]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='object', name='school'))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school_bis'))
\n",
+ " dtype='object', name='school_bis'))
- created_at :
- 2022-10-13T14:37:37.315398
- arviz_version :
- 0.13.0.dev0
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -32463,6 +32580,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -32513,7 +32631,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -32521,7 +32639,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -32533,6 +32652,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -32795,32 +32918,32 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 133kB\n",
"Dimensions: (chain: 4, draw: 500, school: 8, Upper: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
- " upper (Upper) <U16 'CHOATE' 'DEERFIELD' ... "ST. PAUL'S" 'MT. HERMON'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " upper (Upper) <U16 512B 'CHOATE' 'DEERFIELD' ... 'MT. HERMON'\n",
"Dimensions without coordinates: Upper\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
- Upper: 8
chain
(chain)
int64
0 1 2 3
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
upper
(Upper)
<U16
'CHOATE' ... 'MT. HERMON'
array(['CHOATE', 'DEERFIELD', 'PHILLIPS ANDOVER', 'PHILLIPS EXETER',\n",
+ " obs (chain, draw, school) float64 128kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
- Upper: 8
chain
(chain)
int64
0 1 2 3
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
upper
(Upper)
<U16
'CHOATE' ... 'MT. HERMON'
array(['CHOATE', 'DEERFIELD', 'PHILLIPS ANDOVER', 'PHILLIPS EXETER',\n",
" 'HOTCHKISS', 'LAWRENCEVILLE', "ST. PAUL'S", 'MT. HERMON'],\n",
- " dtype='<U16')
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " dtype='<U16')
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:41.460544
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -32855,6 +32978,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -32905,7 +33029,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -32913,7 +33037,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -32925,6 +33050,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -33187,15 +33316,15 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 36kB\n",
"Dimensions: (chain: 4, draw: 500, new_school: 2)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
- " * new_school (new_school) <U13 'Essex College' 'Moordale'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * new_school (new_school) <U13 104B 'Essex College' 'Moordale'\n",
"Data variables:\n",
- " obs (chain, draw, new_school) float64 2.041 -2.556 ... -0.2822\n",
- "Attributes: (2)
- chain: 4
- draw: 500
- new_school: 2
obs
(chain, draw, new_school)
float64
2.041 -2.556 ... -1.015 -0.2822
array([[[ 2.04091912, -2.55566503],\n",
+ " obs (chain, draw, new_school) float64 32kB 2.041 -2.556 ... -0.2822\n",
+ "Attributes: (2)
- chain: 4
- draw: 500
- new_school: 2
obs
(chain, draw, new_school)
float64
2.041 -2.556 ... -1.015 -0.2822
array([[[ 2.04091912, -2.55566503],\n",
" [ 0.41809885, -0.56776961],\n",
" [-0.45264929, -0.21559716],\n",
" ...,\n",
@@ -33225,17 +33354,17 @@
" ...,\n",
" [ 1.57846099, 0.24653314],\n",
" [ 0.64302486, 1.42710376],\n",
- " [-1.01529472, -0.28215614]]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " [-1.01529472, -0.28215614]]])
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Essex College', 'Moordale'], dtype='object', name='new_school'))
- created_at :
- 2023-12-28T12:47:21.311677
- arviz_version :
- 0.16.1
\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Essex College', 'Moordale'], dtype='object', name='new_school'))
- created_at :
- 2024-09-28T19:22:37.147191+00:00
- arviz_version :
- 0.20.0
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -33270,6 +33399,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -33320,7 +33450,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -33328,7 +33458,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -33340,6 +33471,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -33602,28 +33737,28 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 133kB\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " obs (chain, draw, school) float64 128kB ...\n",
+ "Attributes: (4)
- chain: 4
- draw: 500
- school: 8
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.487399
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -33658,6 +33793,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -33708,7 +33844,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -33716,7 +33852,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -33728,6 +33865,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -33990,36 +34131,36 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 246kB\n",
"Dimensions: (chain: 4, draw: 500)\n",
"Coordinates:\n",
- " * chain (chain) int64 0 1 2 3\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
"Data variables: (12/16)\n",
- " max_energy_error (chain, draw) float64 ...\n",
- " energy_error (chain, draw) float64 ...\n",
- " lp (chain, draw) float64 ...\n",
- " index_in_trajectory (chain, draw) int64 ...\n",
- " acceptance_rate (chain, draw) float64 ...\n",
- " diverging (chain, draw) bool ...\n",
+ " max_energy_error (chain, draw) float64 16kB ...\n",
+ " energy_error (chain, draw) float64 16kB ...\n",
+ " lp (chain, draw) float64 16kB ...\n",
+ " index_in_trajectory (chain, draw) int64 16kB ...\n",
+ " acceptance_rate (chain, draw) float64 16kB ...\n",
+ " diverging (chain, draw) bool 2kB ...\n",
" ... ...\n",
- " smallest_eigval (chain, draw) float64 ...\n",
- " step_size_bar (chain, draw) float64 ...\n",
- " step_size (chain, draw) float64 ...\n",
- " energy (chain, draw) float64 ...\n",
- " tree_depth (chain, draw) int64 ...\n",
- " perf_counter_diff (chain, draw) float64 ...\n",
- "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " smallest_eigval (chain, draw) float64 16kB ...\n",
+ " step_size_bar (chain, draw) float64 16kB ...\n",
+ " step_size (chain, draw) float64 16kB ...\n",
+ " energy (chain, draw) float64 16kB ...\n",
+ " tree_depth (chain, draw) int64 16kB ...\n",
+ " perf_counter_diff (chain, draw) float64 16kB ...\n",
+ "Attributes: (6)
max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
+ " dtype='int64', name='draw', length=500))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:37.324929
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
- sampling_time :
- 7.480114936828613
- tuning_steps :
- 1000
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -34054,6 +34195,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -34104,7 +34246,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -34112,7 +34254,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -34124,6 +34267,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -34386,120 +34533,34 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
- "Dimensions: (draw: 500, school: 8)\n",
+ "<xarray.Dataset> Size: 45kB\n",
+ "Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " tau (draw) float64 1.941 3.388 4.208 5.687 ... 0.8353 0.06893 2.145\n",
- " theta (draw, school) float64 4.866 4.59 -0.7404 ... 3.33 -2.031 6.045\n",
- " mu (draw) float64 3.903 3.915 -1.751 2.595 ... -2.294 0.7908 2.869
tau
(draw)
float64
1.941 3.388 4.208 ... 0.06893 2.145
array([1.94060202e+00, 3.38786056e+00, 4.20819481e+00, 5.68742111e+00,\n",
- " 4.82244460e+00, 7.84969782e+00, 2.41850082e+00, 1.11683428e+00,\n",
- " 5.10984626e-01, 1.67640274e+00, 8.37550751e+00, 3.55750200e+01,\n",
- " 1.87888479e+01, 7.38007966e+01, 7.19904857e+00, 2.97111678e+00,\n",
- " 2.38009906e+00, 8.61688371e-01, 1.82005508e+00, 7.05099843e-01,\n",
- " 3.73935694e+00, 8.73489254e+00, 1.39668221e+00, 2.17078500e+01,\n",
- " 9.59674138e+00, 3.15703268e+00, 3.33847615e+00, 3.51144898e+01,\n",
- " 6.80634203e+00, 4.11871168e+00, 4.93255227e+00, 4.83826812e+00,\n",
- " 2.72760625e+01, 5.24910941e+00, 2.85667668e+00, 4.28176247e+00,\n",
- " 2.28124585e+01, 1.34945279e+01, 9.68820812e+00, 1.07728226e+01,\n",
- " 1.20481030e+02, 3.31906378e-01, 4.63523234e-01, 1.26739022e+00,\n",
- " 2.51130780e+03, 5.07713155e+00, 9.28824918e-01, 9.14991935e+00,\n",
- " 4.66414362e+01, 8.84758086e+01, 3.00341165e-01, 7.30464820e-01,\n",
- " 3.40976947e+01, 2.99629266e+01, 2.26765592e+00, 1.78358355e+01,\n",
- " 3.30808448e+01, 3.88184972e+00, 8.84199075e-01, 1.51343747e+01,\n",
- " 2.65672354e+00, 1.31077618e+00, 2.25382327e+01, 1.22813964e+02,\n",
- " 7.85565515e+00, 7.41863308e-01, 9.49848204e+00, 1.41059847e+02,\n",
- " 9.25655368e-01, 1.78766382e+00, 2.62553715e+00, 8.58588175e-01,\n",
- " 1.74600321e+01, 4.62398480e+00, 1.01280222e+00, 5.74512287e+00,\n",
- " 1.26181092e+01, 4.56117182e+00, 1.03129481e+01, 1.05807520e+02,\n",
- "...\n",
- " 4.44939392e+00, 6.07065997e+01, 3.09150476e+00, 1.07895462e+00,\n",
- " 3.40577271e+01, 8.87566932e+03, 5.14275783e-01, 2.34873432e+00,\n",
- " 4.28166200e+00, 6.11436072e+00, 7.02201974e+00, 9.11145054e-02,\n",
- " 5.06762347e+00, 5.54001194e+00, 1.65166669e+00, 4.62314520e+00,\n",
- " 2.79342562e+01, 8.20769981e+00, 1.34952820e+01, 8.67771773e-01,\n",
- " 2.67941929e+00, 5.78094132e+00, 4.08853487e+01, 1.21540351e+01,\n",
- " 6.21499874e+00, 2.05937430e+01, 5.18603595e+00, 5.93430042e+00,\n",
- " 1.27090430e+01, 9.37798481e+00, 3.80986538e+00, 1.14897162e+03,\n",
- " 6.66454695e+01, 1.68318555e+01, 6.50185117e+01, 1.77443210e+00,\n",
- " 1.98821067e+01, 1.48732781e+00, 5.32400098e+01, 8.90298404e-01,\n",
- " 9.74456150e+00, 3.17898046e+00, 4.64437308e+00, 1.67900077e+00,\n",
- " 2.11625865e+00, 5.88562332e-01, 8.72761717e+00, 3.23788264e+01,\n",
- " 2.62725682e+00, 2.48389110e+00, 5.78998327e+00, 4.86828624e+00,\n",
- " 8.96244600e+00, 9.85486905e-01, 4.35475195e+00, 8.21724160e+00,\n",
- " 3.67358130e+00, 3.19700555e-01, 4.32539022e+00, 7.87027890e-01,\n",
- " 2.16835786e+00, 3.34426424e+00, 1.67688831e+01, 9.15246738e-01,\n",
- " 5.68705452e+00, 4.86622906e+00, 2.32790878e+00, 3.16238422e+01,\n",
- " 8.20289530e+00, 1.59128252e+03, 1.70394780e+00, 2.42565587e+00,\n",
- " 7.62767038e+00, 2.41576900e+01, 4.25452773e+01, 1.21193875e+00,\n",
- " 1.02716549e+01, 8.35319808e-01, 6.89347686e-02, 2.14488132e+00])
theta
(draw, school)
float64
4.866 4.59 -0.7404 ... -2.031 6.045
array([[ 4.86620856, 4.58956728, -0.74044057, ..., 3.9610381 ,\n",
- " 2.65493249, 6.01863257],\n",
- " [ 4.37914099, 5.96929909, -2.97367789, ..., 11.03055872,\n",
- " 5.05284821, 2.67610001],\n",
- " [-1.19602373, -9.83468642, 2.28396607, ..., -1.73761977,\n",
- " 8.42950283, 2.36634401],\n",
- " ...,\n",
- " [-2.81086618, -3.98468829, -2.08099901, ..., -2.53012815,\n",
- " -2.05930505, -3.19172734],\n",
- " [ 0.81207434, 0.79427128, 0.83181532, ..., 0.79266084,\n",
- " 0.86104331, 0.7793056 ],\n",
- " [ 3.57736989, 3.53795807, 5.30449351, ..., 3.3301618 ,\n",
- " -2.03071509, 6.04538653]])
mu
(draw)
float64
3.903 3.915 -1.751 ... 0.7908 2.869
array([ 3.90253102e+00, 3.91500660e+00, -1.75114302e+00, 2.59485538e+00,\n",
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- " 1.22966674e+00, -1.17350445e+00, -1.14598007e+00, -1.10634181e+01,\n",
- " 6.31763132e+00, 1.73772124e+00, -4.91208423e-01, -2.75385285e+00,\n",
- " -3.41248265e+00, 1.42264349e+00, 4.11819735e-01, 1.73585996e-01,\n",
- " -5.07312782e+00, -9.99257696e+00, -6.56537987e+00, -6.24410186e+00,\n",
- " 1.30989418e+00, 1.92730225e+00, 9.19697020e+00, 8.53506427e+00,\n",
- " 3.39755738e+00, -4.93707846e+00, -7.33345648e+00, 2.98252256e+00,\n",
- " -3.18159106e-01, 2.27959142e+00, -4.62958545e+00, -9.70160468e-02,\n",
- " -1.60798088e+00, -6.00729427e+00, 8.37985516e+00, 9.52705402e-01,\n",
- " 1.47243794e+00, -4.65377145e+00, 7.28348474e-01, -3.06384730e+00,\n",
- " -6.15183994e+00, -1.41063529e+00, 5.31880456e+00, 4.28382637e-01,\n",
- " -8.23432363e+00, 1.22762109e+00, -8.29777595e-01, 4.21780345e+00,\n",
- " 5.62271511e-01, -1.17786709e+00, 8.09037873e+00, -6.71477871e-01,\n",
- " -6.73172600e+00, -7.64168460e-01, -9.25141462e-02, -1.39886914e+00,\n",
- " 3.37072301e+00, 6.22566150e+00, 6.39989161e+00, -7.42547579e+00,\n",
- " 8.30312575e+00, -1.07413010e+00, -1.79272549e+00, 1.01766431e+01,\n",
- " -2.62649315e+00, -6.23298070e+00, -3.11080725e+00, 4.74329549e+00,\n",
- " -5.28479613e+00, 3.33562049e+00, -2.64374091e+00, -5.36198855e+00,\n",
- "...\n",
- " 4.71858610e+00, -2.38511285e+00, 6.85363619e-01, 7.86083664e+00,\n",
- " 8.58371336e-01, -1.22509704e+00, -2.96950232e+00, 5.94273132e+00,\n",
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- " -7.30940496e+00, -2.53759146e+00, -3.66411956e+00, 9.38270535e+00,\n",
- " 3.79591101e+00, -5.59892859e-01, 1.18231368e+01, 1.26943270e+01,\n",
- " 5.77152605e+00, -7.33882918e+00, 2.08163637e+00, -4.50230861e+00,\n",
- " -1.39653308e-01, -1.04822114e-01, 2.08910055e+00, -6.44041200e+00,\n",
- " -6.60626614e-01, -2.29356629e+00, 7.90828077e-01, 2.86879752e+00])
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " tau (chain, draw) float64 4kB 1.941 3.388 4.208 ... 0.06893 2.145\n",
+ " theta (chain, draw, school) float64 32kB 4.866 4.59 ... -2.031 6.045\n",
+ " mu (chain, draw) float64 4kB 3.903 3.915 -1.751 ... 0.7908 2.869\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
tau
(chain, draw)
float64
1.941 3.388 4.208 ... 0.06893 2.145
array([[1.940602, 3.387861, 4.208195, ..., 0.83532 , 0.068935, 2.144881]])
theta
(chain, draw, school)
float64
4.866 4.59 -0.7404 ... -2.031 6.045
array([[[ 4.866209, 4.589567, ..., 2.654932, 6.018633],\n",
+ " [ 4.379141, 5.969299, ..., 5.052848, 2.6761 ],\n",
+ " ...,\n",
+ " [ 0.812074, 0.794271, ..., 0.861043, 0.779306],\n",
+ " [ 3.57737 , 3.537958, ..., -2.030715, 6.045387]]])
mu
(chain, draw)
float64
3.903 3.915 -1.751 ... 0.7908 2.869
array([[ 3.902531, 3.915007, -1.751143, ..., -2.293566, 0.790828, 2.868798]])
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.602116
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -34534,6 +34595,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -34584,7 +34646,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -34592,7 +34654,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -34604,6 +34667,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -34866,32 +34933,32 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 37kB\n",
"Dimensions: (chain: 1, draw: 500, school: 8, Upper: 8)\n",
"Coordinates:\n",
- " * chain (chain) int64 0\n",
- " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
- " upper (Upper) <U16 'CHOATE' 'DEERFIELD' ... "ST. PAUL'S" 'MT. HERMON'\n",
+ " * chain (chain) int64 8B 0\n",
+ " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " upper (Upper) <U16 512B 'CHOATE' 'DEERFIELD' ... 'MT. HERMON'\n",
"Dimensions without coordinates: Upper\n",
"Data variables:\n",
- " obs (chain, draw, school) float64 ...\n",
- "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
- Upper: 8
chain
(chain)
int64
0
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
- " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
upper
(Upper)
<U16
'CHOATE' ... 'MT. HERMON'
array(['CHOATE', 'DEERFIELD', 'PHILLIPS ANDOVER', 'PHILLIPS EXETER',\n",
+ " obs (chain, draw, school) float64 32kB ...\n",
+ "Attributes: (4)
- chain: 1
- draw: 500
- school: 8
- Upper: 8
chain
(chain)
int64
0
draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " 'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
upper
(Upper)
<U16
'CHOATE' ... 'MT. HERMON'
array(['CHOATE', 'DEERFIELD', 'PHILLIPS ANDOVER', 'PHILLIPS EXETER',\n",
" 'HOTCHKISS', 'LAWRENCEVILLE', "ST. PAUL'S", 'MT. HERMON'],\n",
- " dtype='<U16')
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " dtype='<U16')
PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
- " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " dtype='int64', name='draw', length=500))
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.604969
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -34926,6 +34993,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -34976,7 +35044,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -34984,7 +35052,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -34996,6 +35065,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -35258,27 +35331,27 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 1kB\n",
"Dimensions: (school: 8, Upper: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
- " upper (Upper) <U16 'CHOATE' 'DEERFIELD' ... "ST. PAUL'S" 'MT. HERMON'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
+ " upper (Upper) <U16 512B 'CHOATE' 'DEERFIELD' ... 'MT. HERMON'\n",
"Dimensions without coordinates: Upper\n",
"Data variables:\n",
- " obs (school) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
- "Attributes: (4)
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.606375
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
- " \n",
- " \n",
+ " \n",
+ " \n",
" \n",
" \n",
"
\n",
@@ -35313,6 +35386,7 @@
"}\n",
"\n",
"html[theme=dark],\n",
+ "html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -35363,7 +35437,7 @@
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
@@ -35371,7 +35445,8 @@
"}\n",
"\n",
".xr-section-item input {\n",
- " display: none;\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
@@ -35383,6 +35458,10 @@
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
@@ -35645,16 +35724,16 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "
<xarray.Dataset>\n",
+ "<xarray.Dataset> Size: 576B\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
- " * school (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
+ " * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'\n",
"Data variables:\n",
- " scores (school) float64 ...\n",
- "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+ " scores (school) float64 64B ...\n",
+ "Attributes: (4)
PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
" 'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],\n",
- " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
+ " dtype='object', name='school'))
- arviz_version :
- 0.13.0.dev0
- created_at :
- 2022-10-13T14:37:26.607471
- inference_library :
- pymc
- inference_library_version :
- 4.2.2
\n",
" \n",
" \n",
" \n",
@@ -35965,7 +36044,8 @@
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
- ".xr-attrs dt, dd {\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
@@ -36015,7 +36095,7 @@
"\t> constant_data"
]
},
- "execution_count": 39,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -36056,7 +36136,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.11.8"
},
"varInspector": {
"cols": {