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prepare release 0.7.0 (#58)
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@OriolAbril
OriolAbril authored Jan 17, 2024
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7 changes: 4 additions & 3 deletions docs/source/changelog.md
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# Change Log

## v0.x.x (Unreleased)
## v0.7.0 (2024 Jan 17)
### New features
* Add support for hashable dimension names {pull}`56`

### Maintenance and fixes

### Documentation
* Fix tests to be compatible with latest xarray {pull}`56`
* Address numpy deprecation warnings {pull}`56`

## v0.6.0 (2023 Jul 11)
### New features
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33 changes: 17 additions & 16 deletions docs/source/tutorials/einops-basics-port.ipynb
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"import numpy\n",
"import xarray\n",
"from xarray_einstats.tutorial import display_np_arrays_as_images\n",
"\n",
"display_np_arrays_as_images()"
]
},
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"Dimensions: (batch: 6, height: 96, width: 96, channel: 3)\n",
"Dimensions without coordinates: batch, height, width, channel\n",
"Data variables:\n",
" ims (batch, height, width, channel) float64 1.0 0.902 ... 1.0 0.8039</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8de6a043-1928-4cf9-a4d5-894879c86774' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8de6a043-1928-4cf9-a4d5-894879c86774' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>batch</span>: 6</li><li><span>height</span>: 96</li><li><span>width</span>: 96</li><li><span>channel</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-99aa168b-0b3b-4d70-b1bf-a4891539b69c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-99aa168b-0b3b-4d70-b1bf-a4891539b69c' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-60c18481-ec75-4cfb-8df4-737da742d80b' class='xr-section-summary-in' type='checkbox' checked><label for='section-60c18481-ec75-4cfb-8df4-737da742d80b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ims</span></div><div class='xr-var-dims'>(batch, height, width, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.902 0.902 ... 1.0 1.0 0.8039</div><input id='attrs-ae4a001f-7c76-482b-96fd-95013ac91a30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae4a001f-7c76-482b-96fd-95013ac91a30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e9c039e-c178-4da6-b7c8-386ffcd06719' class='xr-var-data-in' type='checkbox'><label for='data-7e9c039e-c178-4da6-b7c8-386ffcd06719' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[1. , 0.90196078, 0.90196078],\n",
" ims (batch, height, width, channel) float64 1.0 0.902 ... 1.0 0.8039</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3eec4040-5856-441f-a836-5e03835a0b88' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3eec4040-5856-441f-a836-5e03835a0b88' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>batch</span>: 6</li><li><span>height</span>: 96</li><li><span>width</span>: 96</li><li><span>channel</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9c5bf0d5-07dc-410d-bccd-6bfff020901b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c5bf0d5-07dc-410d-bccd-6bfff020901b' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-acd7bcef-aff7-4348-9b27-01a4199bfc36' class='xr-section-summary-in' type='checkbox' checked><label for='section-acd7bcef-aff7-4348-9b27-01a4199bfc36' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ims</span></div><div class='xr-var-dims'>(batch, height, width, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.902 0.902 ... 1.0 1.0 0.8039</div><input id='attrs-f0841d19-41a9-4c43-8827-96261613a4f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0841d19-41a9-4c43-8827-96261613a4f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e88cda-9cfd-4a98-9234-0151154149e7' class='xr-var-data-in' type='checkbox'><label for='data-f3e88cda-9cfd-4a98-9234-0151154149e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[1. , 0.90196078, 0.90196078],\n",
" [1. , 0.90196078, 0.90196078],\n",
" [1. , 0.90196078, 0.90196078],\n",
" ...,\n",
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" ...,\n",
" [1. , 1. , 0.80392157],\n",
" [1. , 1. , 0.80392157],\n",
" [1. , 1. , 0.80392157]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-59440460-0a4c-49d3-9193-d30ca0db0447' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59440460-0a4c-49d3-9193-d30ca0db0447' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-448a0721-bca8-463c-9b20-dcdd0e3c44d9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-448a0721-bca8-463c-9b20-dcdd0e3c44d9' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
" [1. , 1. , 0.80392157]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0211063-41d1-4cdb-83e8-d05358d2138e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f0211063-41d1-4cdb-83e8-d05358d2138e' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-dea59cb1-db8d-4a0e-9112-3183be97db31' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dea59cb1-db8d-4a0e-9112-3183be97db31' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
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}
],
"source": [
"# the previous is identical to familiar:\n",
"ims.values.mean(axis=0)\n",
"# but is so much more readable"
"# the previous is identical to:\n",
"ims.mean(dim=\"batch\") # as xarray operation\n",
"ims.values.mean(axis=0) # as numpy operation"
]
},
{
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],
"source": [
"# subtract background in each image individually and normalize\n",
"# pay attention to () - this is composition of 0 axis, a dummy axis with 1 element.\n",
"im2 = reduce(ims, 'batch channel', 'max') - ims\n",
"im2 /= reduce(im2, 'batch channel', 'max')\n",
"rearrange(im2, '(batch width) channel').values"
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"name": "stdout",
"output_type": "stream",
"text": [
"Last updated: Tue Jul 11 2023\n",
"Last updated: Wed Jan 17 2024\n",
"\n",
"Python implementation: CPython\n",
"Python version : 3.10.12\n",
"IPython version : 8.14.0\n",
"Python version : 3.11.7\n",
"IPython version : 8.18.1\n",
"\n",
"einops : 0.6.1\n",
"xarray_einstats: 0.6.0\n",
"einops : 0.7.0\n",
"xarray_einstats: 0.7.0\n",
"\n",
"numpy : 1.24.4\n",
"xarray: 2023.6.0\n",
"sys : 3.11.7 | packaged by conda-forge | (main, Dec 15 2023, 08:38:37) [GCC 12.3.0]\n",
"numpy : 1.26.2\n",
"xarray: 2023.12.0\n",
"\n",
"Watermark: 2.4.3\n",
"\n"
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "ArviZ (minimal env)",
"language": "python",
"name": "python3"
"name": "arviz"
},
"language_info": {
"codemirror_mode": {
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
"version": "3.11.7"
}
},
"nbformat": 4,
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