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danielballan/CHX image access.ipynb

Created May 3, 2022 11:51
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CHX image access.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "fleet-sherman",
"metadata": {},
"source": [
"The software environment at `jupyter.nsls2.bnl.gov` uses Databroker version 2.0. The main reason is that Databroker 2.0 is *secure-able*; it is at least *possible* to apply access controls to data. (What exactly those access controls *are* is a matter of policy, out of scope for this document.)\n",
"\n",
"But Databroker 2.0 also comes with some nice usability improvements that you can use if you want, especially for imaging workflows. You can also continue to use the older-style usage; we know there are many notebooks that rely on that, and we don't intend to break them!\n",
"\n",
"First we have a new way of getting set up:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "desperate-center",
"metadata": {},
"outputs": [],
"source": [
"from tiled.client import from_profile\n",
"\n",
"c = from_profile(\"chx\", \"dask\")"
]
},
{
"cell_type": "markdown",
"id": "tender-supervisor",
"metadata": {},
"source": [
"Like `db`, this object lets you access all the data from CHX. Unlike `db` it shows a little more useful information right away."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "vocational-tribute",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Catalog {5, 6, 7, 8, 9, 10, 11, 12, 13, 14, ...} ~132660 entries>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c"
]
},
{
"cell_type": "markdown",
"id": "mighty-dryer",
"metadata": {},
"source": [
"Searching on `c` returns another `Catalog` with a subset of the entries. Let's narrow it to results since July 2021."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "planned-november",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Catalog {50247, 50248, 50249, 50250, 50251, 50252, 50253, ...} ~1171 entries>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from databroker.queries import TimeRange\n",
"\n",
"# Let's look at just some recent Runs.\n",
"results = c.search(TimeRange(since=\"2021-07\"))\n",
"results"
]
},
{
"cell_type": "markdown",
"id": "spoken-pilot",
"metadata": {},
"source": [
"Let's pick out a specific Run. This works exactly like with `db`. We can access Runs by (numeric) `scan_id` or (string) `uid`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "blessed-geology",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<BlueskyRun {'primary'} scan_id=50247 uid='f5098dde' 2021-07-01 00:51>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results[50247]"
]
},
{
"cell_type": "markdown",
"id": "surprised-sender",
"metadata": {},
"source": [
"Let's look at what is in here. This is fast; it doesn't actually read the large array data yet."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "wicked-annotation",
"metadata": {},
"outputs": [
{
"data": {
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"</svg>\n",
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".xr-wrap {\n",
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".xr-header {\n",
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".xr-header > div,\n",
".xr-header > ul {\n",
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".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
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"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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".xr-section-item {\n",
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".xr-section-item input {\n",
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".xr-section-summary-in:checked ~ .xr-section-details {\n",
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".xr-array-wrap {\n",
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".xr-array-wrap > label {\n",
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".xr-preview {\n",
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"}\n",
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".xr-array-preview,\n",
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".xr-dim-list {\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (dim_0: 10800, dim_1: 1065, dim_2: 1030, time: 1)\n",
"Coordinates:\n",
" * time (time) float64 1.625e+09\n",
"Dimensions without coordinates: dim_0, dim_1, dim_2\n",
"Data variables:\n",
" eiger1m_single_image (time, dim_0, dim_1, dim_2) float64 dask.array&lt;chunksize=(1, 10, 1065, 1030), meta=np.ndarray&gt;\n",
" eiger1m_single_stats1_total (time) float64 0.0\n",
" eiger1m_single_stats2_total (time) float64 0.0\n",
" eiger1m_single_stats3_total (time) float64 1.638e+14\n",
" eiger1m_single_stats4_total (time) float64 1.638e+14\n",
" eiger1m_single_stats5_total (time) float64 1.638e+14</pre><div class='xr-wrap' hidden><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-dc05bd9d-697b-479e-84b3-4c8c85a2f328' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dc05bd9d-697b-479e-84b3-4c8c85a2f328' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 10800</li><li><span>dim_1</span>: 1065</li><li><span>dim_2</span>: 1030</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a4f4d981-bc1a-4794-a717-d215f6a92639' class='xr-section-summary-in' type='checkbox' checked><label for='section-a4f4d981-bc1a-4794-a717-d215f6a92639' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.625e+09</div><input id='attrs-6792d5be-3d13-4310-a47d-7fcc94f8d5bc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6792d5be-3d13-4310-a47d-7fcc94f8d5bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73be5063-5116-472d-b2f7-c145cdf674e2' class='xr-var-data-in' type='checkbox'><label for='data-73be5063-5116-472d-b2f7-c145cdf674e2' 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([1.625126e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-96cc9791-3902-492b-a3d1-e14ba996663a' class='xr-section-summary-in' type='checkbox' checked><label for='section-96cc9791-3902-492b-a3d1-e14ba996663a' class='xr-section-summary' >Data variables: <span>(6)</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>eiger1m_single_image</span></div><div class='xr-var-dims'>(time, dim_0, dim_1, dim_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10, 1065, 1030), meta=np.ndarray&gt;</div><input id='attrs-c24fe905-da57-49de-802a-f17234b772ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c24fe905-da57-49de-802a-f17234b772ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23c1704a-663a-4c02-b1c8-88273a485ed7' class='xr-var-data-in' type='checkbox'><label for='data-23c1704a-663a-4c02-b1c8-88273a485ed7' 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'><dt><span>object :</span></dt><dd>eiger1m_single</dd></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 88.27 GiB </td> <td> 83.69 MiB </td></tr>\n",
" <tr><th> Shape </th><td> (1, 10800, 1065, 1030) </td> <td> (1, 10, 1065, 1030) </td></tr>\n",
" <tr><th> Count </th><td> 1080 Tasks </td><td> 1080 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
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"\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eiger1m_single_stats1_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-1f37b5fa-c8d8-4667-bbdc-6b1a8e55a92e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f37b5fa-c8d8-4667-bbdc-6b1a8e55a92e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09bac743-a2da-45c5-8cfe-330b2567bcaf' class='xr-var-data-in' type='checkbox'><label for='data-09bac743-a2da-45c5-8cfe-330b2567bcaf' 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'><dt><span>object :</span></dt><dd>eiger1m_single</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eiger1m_single_stats2_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-6757200b-d328-4f4b-8421-597a27c165d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6757200b-d328-4f4b-8421-597a27c165d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b52e507-9664-4654-9602-42443a42909e' class='xr-var-data-in' type='checkbox'><label for='data-7b52e507-9664-4654-9602-42443a42909e' 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'><dt><span>object :</span></dt><dd>eiger1m_single</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eiger1m_single_stats3_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.638e+14</div><input id='attrs-7b765db2-f9cd-4f5c-b618-1e756bfb9333' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b765db2-f9cd-4f5c-b618-1e756bfb9333' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d134eea-bac2-4271-ba69-1680bb4e8d0d' class='xr-var-data-in' type='checkbox'><label for='data-1d134eea-bac2-4271-ba69-1680bb4e8d0d' 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'><dt><span>object :</span></dt><dd>eiger1m_single</dd></dl></div><div class='xr-var-data'><pre>array([1.63822938e+14])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eiger1m_single_stats4_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.638e+14</div><input id='attrs-5c3e6559-748e-4e5e-9a61-e7dcc67cad8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c3e6559-748e-4e5e-9a61-e7dcc67cad8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92d208aa-0994-4521-8696-98e343e8001e' class='xr-var-data-in' type='checkbox'><label for='data-92d208aa-0994-4521-8696-98e343e8001e' 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'><dt><span>object :</span></dt><dd>eiger1m_single</dd></dl></div><div class='xr-var-data'><pre>array([1.63822938e+14])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eiger1m_single_stats5_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.638e+14</div><input id='attrs-88242971-4ad4-4d77-9859-758fd23ee762' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88242971-4ad4-4d77-9859-758fd23ee762' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-208f575d-69ca-4f80-9041-3a372a27214c' class='xr-var-data-in' type='checkbox'><label for='data-208f575d-69ca-4f80-9041-3a372a27214c' 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'><dt><span>object :</span></dt><dd>eiger1m_single</dd></dl></div><div class='xr-var-data'><pre>array([1.63822938e+14])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8fca3ad5-63a5-45c9-a8ec-d41b06b541f6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8fca3ad5-63a5-45c9-a8ec-d41b06b541f6' 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",
"Dimensions: (dim_0: 10800, dim_1: 1065, dim_2: 1030, time: 1)\n",
"Coordinates:\n",
" * time (time) float64 1.625e+09\n",
"Dimensions without coordinates: dim_0, dim_1, dim_2\n",
"Data variables:\n",
" eiger1m_single_image (time, dim_0, dim_1, dim_2) float64 dask.array<chunksize=(1, 10, 1065, 1030), meta=np.ndarray>\n",
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]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = results[50247]['primary'].read() # 'ds' is short for 'dataset'\n",
"ds"
]
},
{
"cell_type": "markdown",
"id": "abroad-alcohol",
"metadata": {},
"source": [
"Let's pull out an image. We can see below its dimensions and size in bytes. This is useful because it gives us a sense of how expensive it would be to load and/or process.\n",
"\n",
"Notice that Area Detector images are always 4D. The first axis the \"Event\" axis. For time series it will have 1 element; for scans it will have more. The second axis represents images within one \"acquisition\". The third and fourth axes are, of course, the dimension on the face of the detector."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "whole-cleaners",
"metadata": {},
"outputs": [
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"Attributes:\n",
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],
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},
"execution_count": 7,
"metadata": {},
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}
],
"source": [
"img = ds['eiger1m_single_image']\n",
"img"
]
},
{
"cell_type": "markdown",
"id": "increasing-danger",
"metadata": {},
"source": [
"Let's slice into the 4D image array `img` to obtain a 2D array (one image frame) that we can visualize."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "unknown-malta",
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;eiger1m_single_image&#x27; (dim_1: 1065, dim_2: 1030)&gt;\n",
"dask.array&lt;getitem, shape=(1065, 1030), dtype=float64, chunksize=(1065, 1030), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" time float64 1.625e+09\n",
"Dimensions without coordinates: dim_1, dim_2\n",
"Attributes:\n",
" object: eiger1m_single</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'eiger1m_single_image'</div><ul class='xr-dim-list'><li><span>dim_1</span>: 1065</li><li><span>dim_2</span>: 1030</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0acefd04-6db4-4f5a-abf6-0b4ff51f2d1e' class='xr-array-in' type='checkbox' checked><label for='section-0acefd04-6db4-4f5a-abf6-0b4ff51f2d1e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1065, 1030), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 8.37 MiB </td> <td> 8.37 MiB </td></tr>\n",
" <tr><th> Shape </th><td> (1065, 1030) </td> <td> (1065, 1030) </td></tr>\n",
" <tr><th> Count </th><td> 1081 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
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"\n",
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"\n",
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" <text x=\"136.056338\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,136.056338,60.000000)\">1065</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></div></li><li class='xr-section-item'><input id='section-a72f4edc-eb2d-4e62-833b-0fbfe873332e' class='xr-section-summary-in' type='checkbox' checked><label for='section-a72f4edc-eb2d-4e62-833b-0fbfe873332e' class='xr-section-summary' >Coordinates: <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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.625e+09</div><input id='attrs-dd65d277-455e-44e6-9e60-ba3ff91ea763' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd65d277-455e-44e6-9e60-ba3ff91ea763' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ff98ded-ca97-4fcd-9d5e-b293fe32d52d' class='xr-var-data-in' type='checkbox'><label for='data-8ff98ded-ca97-4fcd-9d5e-b293fe32d52d' 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(1.6251259e+09)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d34d07ee-e114-4c54-b194-fb1947f15de9' class='xr-section-summary-in' type='checkbox' checked><label for='section-d34d07ee-e114-4c54-b194-fb1947f15de9' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>eiger1m_single</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'eiger1m_single_image' (dim_1: 1065, dim_2: 1030)>\n",
"dask.array<getitem, shape=(1065, 1030), dtype=float64, chunksize=(1065, 1030), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" time float64 1.625e+09\n",
"Dimensions without coordinates: dim_1, dim_2\n",
"Attributes:\n",
" object: eiger1m_single"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame = img[0, 0]\n",
"frame"
]
},
{
"cell_type": "markdown",
"id": "drawn-consideration",
"metadata": {},
"source": [
"If we hand this to matplotlib, matplotlib will cause it to finally be loaded and plot it."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "revised-cannon",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ce76c2303914ae9acaaf0855f1469cc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2b4ff2aec850>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib widget\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.imshow(frame, vmax=1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "possible-sewing",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
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"nbformat_minor": 5
}
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