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finish reading from hdf5
2023-04-24 13:03:23 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from DataContainer.ReadData import read_hdf5_file"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/*.h5\"\n",
"# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/2023-04-18_0003_Evaporative_Cooling_000.h5\"\n",
"\n",
"filepath = \"//DyLabNAS/Data/Repetition_scan/2023/04/21/0000/*.h5\"\n",
"\n",
"groupList = [\n",
" \"images/MOT_3D_Camera/in_situ_absorption\",\n",
" \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n",
"]\n",
"\n",
"dskey = {\n",
" \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_1\",\n",
" \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_2\",\n",
"}\n",
"\n",
"ds = {\n",
" dskey[groupList[i]]: read_hdf5_file(filepath, groupList[i])\n",
" for i in range(len(groupList))\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (initial_freq: 10, runs: 3, x: 1200, y: 1920)\n",
"Coordinates:\n",
" * initial_freq (initial_freq) float64 100.8 101.0 101.2 ... 102.4 102.6 102.8\n",
" * runs (runs) float64 0.0 1.0 2.0\n",
"Dimensions without coordinates: x, y\n",
"Data variables:\n",
" atoms (initial_freq, runs, x, y) uint16 dask.array&lt;chunksize=(1, 1, 1200, 1920), meta=np.ndarray&gt;\n",
" background (initial_freq, runs, x, y) uint16 dask.array&lt;chunksize=(1, 1, 1200, 1920), meta=np.ndarray&gt;\n",
" dark (initial_freq, runs, x, y) uint16 dask.array&lt;chunksize=(1, 1, 1200, 1920), meta=np.ndarray&gt;\n",
" shotNum (initial_freq, runs) &lt;U2 &#x27;09&#x27; &#x27;19&#x27; &#x27;29&#x27; ... &#x27;05&#x27; &#x27;15&#x27; &#x27;25&#x27;\n",
"Attributes: (12/96)\n",
" TOF_free: 0.02\n",
" abs_img_freq: 110.858\n",
" absorption_imaging_flag: True\n",
" backup_data: True\n",
" blink_off_time: nan\n",
" blink_on_time: nan\n",
" ... ...\n",
" z_offset_img: 0.189\n",
" mot_3d_freq: initial_freq\n",
" initial_freq: [101.67 102.36 102.13 100.98 102.59 10...\n",
" runs: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1...\n",
" scanAxis: [&#x27;initial_freq&#x27;, &#x27;runs&#x27;]\n",
" scanAxisLength: [30. 30.]</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-acbe5b98-dbbf-43d7-87f5-ee6754b80776' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-acbe5b98-dbbf-43d7-87f5-ee6754b80776' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>initial_freq</span>: 10</li><li><span class='xr-has-index'>runs</span>: 3</li><li><span>x</span>: 1200</li><li><span>y</span>: 1920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d41a5553-4533-4366-a384-9aa76c4f40d6' class='xr-section-summary-in' type='checkbox' checked><label for='section-d41a5553-4533-4366-a384-9aa76c4f40d6' class='xr-section-summary' >Coordinates: <span>(2)</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'>initial_freq</span></div><div class='xr-var-dims'>(initial_freq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>100.8 101.0 101.2 ... 102.6 102.8</div><input id='attrs-b4f9e4bb-6fde-4ba3-9ee7-74fbf70bcc53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4f9e4bb-6fde-4ba3-9ee7-74fbf70bcc53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ea213a9-4ac2-498c-9e2e-77c6c959bc61' class='xr-var-data-in' type='checkbox'><label for='data-1ea213a9-4ac2-498c-9e2e-77c6c959bc61' 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([100.75, 100.98, 101.21, 101.44, 101.67, 101.9 , 102.13, 102.36, 102.59,\n",
" 102.82])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-f6057e1a-e9e3-488b-9855-b899a2368bb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6057e1a-e9e3-488b-9855-b899a2368bb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35aa7969-0823-48b5-a8d1-18cba14b0107' class='xr-var-data-in' type='checkbox'><label for='data-35aa7969-0823-48b5-a8d1-18cba14b0107' 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([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df6ae2fc-f153-4a5a-851b-aff75e6b23b2' class='xr-section-summary-in' type='checkbox' checked><label for='section-df6ae2fc-f153-4a5a-851b-aff75e6b23b2' class='xr-section-summary' >Data variables: <span>(4)</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>atoms</span></div><div class='xr-var-dims'>(initial_freq, runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-b1f1b773-4f38-472e-9a5f-7f21a2858d62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1f1b773-4f38-472e-9a5f-7f21a2858d62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb5447dd-1830-45e8-ae90-e234f9ee2cd3' class='xr-var-data-in' type='checkbox'><label for='data-cb5447dd-1830-45e8-ae90-e234f9ee2cd3' 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>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 131.84 MiB </td>\n",
" <td> 4.39 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (10, 3, 1200, 1920) </td>\n",
" <td> (1, 1, 1200, 1920) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 30 chunks in 94 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
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" <td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shotNum</span></div><div class='xr-var-dims'>(initial_freq, runs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;09&#x27; &#x27;19&#x27; &#x27;29&#x27; ... &#x27;05&#x27; &#x27;15&#x27; &#x27;25&#x27;</div><input id='attrs-de262353-e46f-4a6c-b6a3-d9e9970e4262' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de262353-e46f-4a6c-b6a3-d9e9970e4262' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac10f823-c54e-40df-9a90-8d07683d7687' class='xr-var-data-in' type='checkbox'><label for='data-ac10f823-c54e-40df-9a90-8d07683d7687' 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([[&#x27;09&#x27;, &#x27;19&#x27;, &#x27;29&#x27;],\n",
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" [&#x27;01&#x27;, &#x27;11&#x27;, &#x27;21&#x27;],\n",
" [&#x27;04&#x27;, &#x27;14&#x27;, &#x27;24&#x27;],\n",
" [&#x27;05&#x27;, &#x27;15&#x27;, &#x27;25&#x27;]], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3092873-35c9-4329-898a-3fcd2b85306d' class='xr-section-summary-in' type='checkbox' ><label for='section-b3092873-35c9-4329-898a-3fcd2b85306d' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>initial_freq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c9623b14-169b-42ee-8750-004453cc688c' class='xr-index-data-in' type='checkbox'/><label for='index-c9623b14-169b-42ee-8750-004453cc688c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([100.75, 100.98, 101.21, 101.44, 101.67, 101.9, 102.13, 102.36,\n",
" 102.59, 102.82],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;initial_freq&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-71a07080-96b9-48cf-a25a-60adffd90d0b' class='xr-index-data-in' type='checkbox'/><label for='index-71a07080-96b9-48cf-a25a-60adffd90d0b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ed89a77-6588-4d30-955b-a119a8bef42f' class='xr-section-summary-in' type='checkbox' ><label for='section-2ed89a77-6588-4d30-955b-a119a8bef42f' class='xr-section-summary' >Attributes: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.002</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.285</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>0.000155</dd><dt><span>final_freq :</span></dt><dd>104</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd>
" 101.67 102.36 102.13 100.98 102.59 102.82 101.21 101.44 101.9 100.75\n",
" 101.67 102.36 102.13 100.98 102.59 102.82 101.21 101.44 101.9 100.75]</dd><dt><span>runs :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2.\n",
" 2. 2. 2. 2. 2. 2.]</dd><dt><span>scanAxis :</span></dt><dd>[&#x27;initial_freq&#x27;, &#x27;runs&#x27;]</dd><dt><span>scanAxisLength :</span></dt><dd>[30. 30.]</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (initial_freq: 10, runs: 3, x: 1200, y: 1920)\n",
"Coordinates:\n",
" * initial_freq (initial_freq) float64 100.8 101.0 101.2 ... 102.4 102.6 102.8\n",
" * runs (runs) float64 0.0 1.0 2.0\n",
"Dimensions without coordinates: x, y\n",
"Data variables:\n",
" atoms (initial_freq, runs, x, y) uint16 dask.array<chunksize=(1, 1, 1200, 1920), meta=np.ndarray>\n",
" background (initial_freq, runs, x, y) uint16 dask.array<chunksize=(1, 1, 1200, 1920), meta=np.ndarray>\n",
" dark (initial_freq, runs, x, y) uint16 dask.array<chunksize=(1, 1, 1200, 1920), meta=np.ndarray>\n",
" shotNum (initial_freq, runs) <U2 '09' '19' '29' ... '05' '15' '25'\n",
"Attributes: (12/96)\n",
" TOF_free: 0.02\n",
" abs_img_freq: 110.858\n",
" absorption_imaging_flag: True\n",
" backup_data: True\n",
" blink_off_time: nan\n",
" blink_on_time: nan\n",
" ... ...\n",
" z_offset_img: 0.189\n",
" mot_3d_freq: initial_freq\n",
" initial_freq: [101.67 102.36 102.13 100.98 102.59 10...\n",
" runs: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1...\n",
" scanAxis: ['initial_freq', 'runs']\n",
" scanAxisLength: [30. 30.]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds[\"camera_1\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"print(np.append([\"a\"], [\"\"]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": "3.8.10"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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