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prepare patch release (#2269)
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Diff for: .gitignore

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.installed.cfg
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*.egg
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.ipynb_checkpoints
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.virtual_documents
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# PyInstaller
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# Usually these files are written by a python script from a template

Diff for: CHANGELOG.md

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# Change Log
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## v0.x.x Unreleased
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### New features
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## v0.16.1 (2023 Jul 18)
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### Maintenance and fixes
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### Deprecation
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- Fix Numba deprecation errors and incorrect nopython usage ([2268](https://github.com/arviz-devs/arviz/pull/2268))
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### Documentation
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- Rerun Numba notebook
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## v0.16.0 (2023 Jul 13)
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Diff for: arviz/__init__.py

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# pylint: disable=wildcard-import,invalid-name,wrong-import-position
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"""ArviZ is a library for exploratory analysis of Bayesian models."""
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__version__ = "0.17.0.dev0"
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__version__ = "0.16.1"
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import logging
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import os

Diff for: doc/source/user_guide/Numba.ipynb

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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2.61 s ± 569 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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"140 ms ± 2.59 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.54 ms ± 383 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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"1.03 ms ± 44.3 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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]
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}
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],
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"That is almost 300 times faster!! Let's compare this to NumPy"
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"That is almost 150 times faster!! Let's compare this to NumPy"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8.68 ms ± 435 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
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"1.79 ms ± 124 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"False"
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]
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},
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"execution_count": 17,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"182 ms ± 66.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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"57.8 ms ± 1.02 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"10.4 ms ± 2.97 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
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"462 µs ± 16.8 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"True"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"19.9 ms ± 1.37 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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"7.18 ms ± 359 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3.97 ms ± 574 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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"359 µs ± 62.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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}
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],
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,

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