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ctuguinay/2024_06_13_test_ooi_compute_Sv.ipynb

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2024_06_13_test_ooi_compute_Sv.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Import packages\n",
"from pathlib import Path\n",
"\n",
"from tqdm import tqdm\n",
"import fsspec\n",
"from dask.distributed import Client\n",
"\n",
"import echopype as ep"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'http://127.0.0.1:8787/status'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Set 32 workers with ~8 GiB RAM each\n",
"client = Client(n_workers=64)\n",
"client.dashboard_link"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Grab all URLs of RAW Files in the month 2017-08\n",
"fs = fsspec.filesystem('https')\n",
"ooi_raw_url = (\n",
" \"https://rawdata.oceanobservatories.org/files/\"\n",
" \"CE04OSPS/PC01B/ZPLSCB102_10.33.10.143/2017/\"\n",
")\n",
"desired_raw_file_urls = fs.glob(f\"{ooi_raw_url}/08/*/*.raw\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of OOI Raw Files: 326\n"
]
}
],
"source": [
"print(\"Number of OOI Raw Files:\", len(desired_raw_file_urls))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Save Echodata objects locally\n",
"def open_and_save(raw_file, sonar_model, use_swap, save_path, open_kwargs={}):\n",
" try:\n",
" ed = ep.open_raw(raw_file=raw_file, sonar_model=sonar_model, use_swap=use_swap, **open_kwargs)\n",
" ed.to_zarr(save_path, overwrite=True, compute=True)\n",
" except Exception as e:\n",
" print(\"Error with Exception: \", e)\n",
"\n",
"futures = []\n",
"for raw_file_url in tqdm(desired_raw_file_urls):\n",
" future = client.submit(\n",
" open_and_save,\n",
" retries=3,\n",
" raw_file=raw_file_url,\n",
" sonar_model='ek60',\n",
" use_swap=True,\n",
" save_path=Path('convertallfiles')\n",
" )\n",
" futures.append(future)\n",
"client.gather(futures)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 326/326 [00:00<00:00, 4851.62it/s]\n"
]
}
],
"source": [
"# Combine Echodata Objects\n",
"ed_future_list = []\n",
"for converted_file in tqdm(sorted(Path('convertallfiles').glob(\"*.zarr\"))):\n",
" ed_future = client.submit( \n",
" ep.open_converted,\n",
" retries=2,\n",
" converted_raw_path=converted_file,\n",
" chunks={}\n",
" )\n",
" ed_future_list.append(ed_future)\n",
"ed_list = client.gather(ed_future_list)\n",
"ed_combined = ep.combine_echodata(ed_list)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/distributed/client.py:3164: UserWarning: Sending large graph of size 16.19 MiB.\n",
"This may cause some slowdown.\n",
"Consider scattering data ahead of time and using futures.\n",
" warnings.warn(\n"
]
}
],
"source": [
"# Save combined Echodata object to Zarr\n",
"ed_combined.to_zarr(\"ed_combined.zarr\", overwrite=True, compute=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Read Combined Echodata object from Zarr: Will be loaded as Lazy-Loaded\n",
"ed_combined = ep.open_converted(\"ed_combined.zarr\", chunks={})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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".xr-section-summary-in:checked ~ .xr-section-details {\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;backscatter_r&#x27; (channel: 3, ping_time: 2047332,\n",
" range_sample: 1072)&gt; Size: 53GB\n",
"dask.array&lt;open_dataset-backscatter_r, shape=(3, 2047332, 1072), dtype=float64, chunksize=(3, 3983, 1046), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * channel (channel) &lt;U39 468B &#x27;GPT 38 kHz 00907208dd13 5-1 OOI.38|20...\n",
" * ping_time (ping_time) datetime64[ns] 16MB 2017-08-01T00:00:00.061951 ...\n",
" * range_sample (range_sample) int64 9kB 0 1 2 3 4 ... 1068 1069 1070 1071\n",
"Attributes:\n",
" long_name: Raw backscatter measurements (real part)\n",
" units: dB</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'backscatter_r'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>channel</span>: 3</li><li><span class='xr-has-index'>ping_time</span>: 2047332</li><li><span class='xr-has-index'>range_sample</span>: 1072</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-22f7298a-1a2c-4569-8c9e-df3ca2a12852' class='xr-array-in' type='checkbox' checked><label for='section-22f7298a-1a2c-4569-8c9e-df3ca2a12852' 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=(3, 3983, 1046), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
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" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
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" <th> Bytes </th>\n",
" <td> 49.06 GiB </td>\n",
" <td> 95.36 MiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3, 2047332, 1072) </td>\n",
" <td> (3, 3983, 1046) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1030 chunks in 2 graph layers </td>\n",
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"</table></div></div></li><li class='xr-section-item'><input id='section-c8293283-bd81-40a8-a08b-9726bb83c77b' class='xr-section-summary-in' type='checkbox' checked><label for='section-c8293283-bd81-40a8-a08b-9726bb83c77b' class='xr-section-summary' >Coordinates: <span>(3)</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'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>&lt;U39</div><div class='xr-var-preview xr-preview'>&#x27;GPT 38 kHz 00907208dd13 5-1 OO...</div><input id='attrs-9186c50d-7fcc-43f0-aeb6-a3b66bf9440a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9186c50d-7fcc-43f0-aeb6-a3b66bf9440a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6edf2211-b4d1-4a1c-8eee-3f64839f8d3d' class='xr-var-data-in' type='checkbox'><label for='data-6edf2211-b4d1-4a1c-8eee-3f64839f8d3d' 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>long_name :</span></dt><dd>Vendor channel ID</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;GPT 38 kHz 00907208dd13 5-1 OOI.38|200&#x27;,\n",
" &#x27;GPT 120 kHz 00907208a0b1 3-1 ES120-7CD&#x27;,\n",
" &#x27;GPT 200 kHz 00907208dd13 5-2 OOI38|200&#x27;], dtype=&#x27;&lt;U39&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ping_time</span></div><div class='xr-var-dims'>(ping_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-08-01T00:00:00.061951 ... 2...</div><input id='attrs-4a6ca042-5562-4a1b-9b8d-974a135b6122' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a6ca042-5562-4a1b-9b8d-974a135b6122' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a91d8e1-abd3-44c2-b48e-9771c32d9ad5' class='xr-var-data-in' type='checkbox'><label for='data-5a91d8e1-abd3-44c2-b48e-9771c32d9ad5' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Timestamp of each ping</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-08-01T00:00:00.061951000&#x27;, &#x27;2017-08-01T00:00:02.072065000&#x27;,\n",
" &#x27;2017-08-01T00:00:03.075123000&#x27;, ..., &#x27;2017-08-31T23:54:57.333338000&#x27;,\n",
" &#x27;2017-08-31T23:54:58.336395000&#x27;, &#x27;2017-08-31T23:54:59.339453000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_sample</span></div><div class='xr-var-dims'>(range_sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1068 1069 1070 1071</div><input id='attrs-6b8355ba-1a27-421a-a617-28344d59719d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b8355ba-1a27-421a-a617-28344d59719d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-306a151a-7318-477d-b5fb-4d47446f968a' class='xr-var-data-in' type='checkbox'><label for='data-306a151a-7318-477d-b5fb-4d47446f968a' 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>long_name :</span></dt><dd>Along-range sample number, base 0</dd></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 1069, 1070, 1071])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-92743e87-23f6-4788-b9f6-a99a4f8c8261' class='xr-section-summary-in' type='checkbox' ><label for='section-92743e87-23f6-4788-b9f6-a99a4f8c8261' class='xr-section-summary' >Indexes: <span>(3)</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>channel</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bbd73bb8-14fb-46ac-b245-aefa9f56743e' class='xr-index-data-in' type='checkbox'/><label for='index-bbd73bb8-14fb-46ac-b245-aefa9f56743e' 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(Index([&#x27;GPT 38 kHz 00907208dd13 5-1 OOI.38|200&#x27;,\n",
" &#x27;GPT 120 kHz 00907208a0b1 3-1 ES120-7CD&#x27;,\n",
" &#x27;GPT 200 kHz 00907208dd13 5-2 OOI38|200&#x27;],\n",
" dtype=&#x27;object&#x27;, name=&#x27;channel&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ping_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-38e5ffbe-586d-4cd7-b033-36a064ca6972' class='xr-index-data-in' type='checkbox'/><label for='index-38e5ffbe-586d-4cd7-b033-36a064ca6972' 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(DatetimeIndex([&#x27;2017-08-01 00:00:00.061951&#x27;, &#x27;2017-08-01 00:00:02.072065&#x27;,\n",
" &#x27;2017-08-01 00:00:03.075123&#x27;, &#x27;2017-08-01 00:00:04.078180&#x27;,\n",
" &#x27;2017-08-01 00:00:05.081238&#x27;, &#x27;2017-08-01 00:00:06.084293&#x27;,\n",
" &#x27;2017-08-01 00:00:07.087351&#x27;, &#x27;2017-08-01 00:00:08.090408&#x27;,\n",
" &#x27;2017-08-01 00:00:09.093466&#x27;, &#x27;2017-08-01 00:00:10.096523&#x27;,\n",
" ...\n",
" &#x27;2017-08-31 23:54:50.311937&#x27;, &#x27;2017-08-31 23:54:51.314995&#x27;,\n",
" &#x27;2017-08-31 23:54:52.318050&#x27;, &#x27;2017-08-31 23:54:53.321108&#x27;,\n",
" &#x27;2017-08-31 23:54:54.324165&#x27;, &#x27;2017-08-31 23:54:55.327223&#x27;,\n",
" &#x27;2017-08-31 23:54:56.330280&#x27;, &#x27;2017-08-31 23:54:57.333338&#x27;,\n",
" &#x27;2017-08-31 23:54:58.336395&#x27;, &#x27;2017-08-31 23:54:59.339453&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;ping_time&#x27;, length=2047332, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_sample</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b3c498a5-6a17-4dba-9095-d56c674bb2b7' class='xr-index-data-in' type='checkbox'/><label for='index-b3c498a5-6a17-4dba-9095-d56c674bb2b7' 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(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071],\n",
" dtype=&#x27;int64&#x27;, name=&#x27;range_sample&#x27;, length=1072))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a41c278-38b2-4982-a51a-f737f1c18352' class='xr-section-summary-in' type='checkbox' checked><label for='section-1a41c278-38b2-4982-a51a-f737f1c18352' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Raw backscatter measurements (real part)</dd><dt><span>units :</span></dt><dd>dB</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'backscatter_r' (channel: 3, ping_time: 2047332,\n",
" range_sample: 1072)> Size: 53GB\n",
"dask.array<open_dataset-backscatter_r, shape=(3, 2047332, 1072), dtype=float64, chunksize=(3, 3983, 1046), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * channel (channel) <U39 468B 'GPT 38 kHz 00907208dd13 5-1 OOI.38|20...\n",
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"Attributes:\n",
" long_name: Raw backscatter measurements (real part)\n",
" units: dB"
]
},
"execution_count": 9,
"metadata": {},
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],
"source": [
"ed_combined[\"Sonar/Beam_group1\"][\"backscatter_r\"]"
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},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/distributed/client.py:3164: UserWarning: Sending large graph of size 111.73 MiB.\n",
"This may cause some slowdown.\n",
"Consider scattering data ahead of time and using futures.\n",
" warnings.warn(\n"
]
}
],
"source": [
"# Compute Volume Backscattering using a Swap Zarr files and chunking every 1000th ping\n",
"# time index\n",
"ds_Sv_combined = ep.calibrate.compute_Sv(\n",
" ed_combined,\n",
" use_swap=True,\n",
" chunk_dict={\"ping_time\": 1000}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
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".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Sv&#x27; (channel: 3, ping_time: 2047332, range_sample: 1072)&gt; Size: 53GB\n",
"dask.array&lt;open_dataset-Sv, shape=(3, 2047332, 1072), dtype=float64, chunksize=(3, 1000, 1046), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * channel (channel) &lt;U39 468B &#x27;GPT 38 kHz 00907208dd13 5-1 OOI.38|20...\n",
" * ping_time (ping_time) datetime64[ns] 16MB 2017-08-01T00:00:00.061951 ...\n",
" * range_sample (range_sample) int64 9kB 0 1 2 3 4 ... 1068 1069 1070 1071\n",
"Attributes:\n",
" long_name: Volume backscattering strength (Sv re 1 m-1)\n",
" units: dB</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Sv'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>channel</span>: 3</li><li><span class='xr-has-index'>ping_time</span>: 2047332</li><li><span class='xr-has-index'>range_sample</span>: 1072</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-25f3e8b3-f01f-4ed8-b2d2-b10b69184af6' class='xr-array-in' type='checkbox' checked><label for='section-25f3e8b3-f01f-4ed8-b2d2-b10b69184af6' 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=(3, 1000, 1046), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 49.06 GiB </td>\n",
" <td> 23.94 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3, 2047332, 1072) </td>\n",
" <td> (3, 1000, 1046) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 4096 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"100\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
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"\n",
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" <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">3</text>\n",
"</svg>\n",
" </td>\n",
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"</table></div></div></li><li class='xr-section-item'><input id='section-15b7fb26-de9b-4f51-af95-e19382adb025' class='xr-section-summary-in' type='checkbox' checked><label for='section-15b7fb26-de9b-4f51-af95-e19382adb025' class='xr-section-summary' >Coordinates: <span>(3)</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'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>&lt;U39</div><div class='xr-var-preview xr-preview'>&#x27;GPT 38 kHz 00907208dd13 5-1 OO...</div><input id='attrs-5c71f1ac-e806-4adb-ba0a-1bd6226a6d46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c71f1ac-e806-4adb-ba0a-1bd6226a6d46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30cd9c05-4d38-4924-9f4f-25291f9ec58f' class='xr-var-data-in' type='checkbox'><label for='data-30cd9c05-4d38-4924-9f4f-25291f9ec58f' 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>long_name :</span></dt><dd>Vendor channel ID</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;GPT 38 kHz 00907208dd13 5-1 OOI.38|200&#x27;,\n",
" &#x27;GPT 120 kHz 00907208a0b1 3-1 ES120-7CD&#x27;,\n",
" &#x27;GPT 200 kHz 00907208dd13 5-2 OOI38|200&#x27;], dtype=&#x27;&lt;U39&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ping_time</span></div><div class='xr-var-dims'>(ping_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-08-01T00:00:00.061951 ... 2...</div><input id='attrs-631185b6-ccd0-4549-be7a-5b4ef1a290fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-631185b6-ccd0-4549-be7a-5b4ef1a290fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15423b55-598e-4a8f-8c09-e730c31c78ae' class='xr-var-data-in' type='checkbox'><label for='data-15423b55-598e-4a8f-8c09-e730c31c78ae' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Timestamp of each ping</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-08-01T00:00:00.061951000&#x27;, &#x27;2017-08-01T00:00:02.072065000&#x27;,\n",
" &#x27;2017-08-01T00:00:03.075123000&#x27;, ..., &#x27;2017-08-31T23:54:57.333338000&#x27;,\n",
" &#x27;2017-08-31T23:54:58.336395000&#x27;, &#x27;2017-08-31T23:54:59.339453000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_sample</span></div><div class='xr-var-dims'>(range_sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1068 1069 1070 1071</div><input id='attrs-b8ae214f-4d4a-4e8b-a0ec-7b2c6dd925f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8ae214f-4d4a-4e8b-a0ec-7b2c6dd925f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6795d5b0-5c94-45d3-870b-f89500298685' class='xr-var-data-in' type='checkbox'><label for='data-6795d5b0-5c94-45d3-870b-f89500298685' 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>long_name :</span></dt><dd>Along-range sample number, base 0</dd></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 1069, 1070, 1071])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b5d4d27-fb2d-4eba-a573-3daebefa5cbc' class='xr-section-summary-in' type='checkbox' ><label for='section-5b5d4d27-fb2d-4eba-a573-3daebefa5cbc' class='xr-section-summary' >Indexes: <span>(3)</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>channel</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4b993f0c-6d9a-419d-af99-3a97ccf2a046' class='xr-index-data-in' type='checkbox'/><label for='index-4b993f0c-6d9a-419d-af99-3a97ccf2a046' 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(Index([&#x27;GPT 38 kHz 00907208dd13 5-1 OOI.38|200&#x27;,\n",
" &#x27;GPT 120 kHz 00907208a0b1 3-1 ES120-7CD&#x27;,\n",
" &#x27;GPT 200 kHz 00907208dd13 5-2 OOI38|200&#x27;],\n",
" dtype=&#x27;object&#x27;, name=&#x27;channel&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ping_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0896ac98-119a-4cf9-963a-85ac1a31ac69' class='xr-index-data-in' type='checkbox'/><label for='index-0896ac98-119a-4cf9-963a-85ac1a31ac69' 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(DatetimeIndex([&#x27;2017-08-01 00:00:00.061951&#x27;, &#x27;2017-08-01 00:00:02.072065&#x27;,\n",
" &#x27;2017-08-01 00:00:03.075123&#x27;, &#x27;2017-08-01 00:00:04.078180&#x27;,\n",
" &#x27;2017-08-01 00:00:05.081238&#x27;, &#x27;2017-08-01 00:00:06.084293&#x27;,\n",
" &#x27;2017-08-01 00:00:07.087351&#x27;, &#x27;2017-08-01 00:00:08.090408&#x27;,\n",
" &#x27;2017-08-01 00:00:09.093466&#x27;, &#x27;2017-08-01 00:00:10.096523&#x27;,\n",
" ...\n",
" &#x27;2017-08-31 23:54:50.311937&#x27;, &#x27;2017-08-31 23:54:51.314995&#x27;,\n",
" &#x27;2017-08-31 23:54:52.318050&#x27;, &#x27;2017-08-31 23:54:53.321108&#x27;,\n",
" &#x27;2017-08-31 23:54:54.324165&#x27;, &#x27;2017-08-31 23:54:55.327223&#x27;,\n",
" &#x27;2017-08-31 23:54:56.330280&#x27;, &#x27;2017-08-31 23:54:57.333338&#x27;,\n",
" &#x27;2017-08-31 23:54:58.336395&#x27;, &#x27;2017-08-31 23:54:59.339453&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;ping_time&#x27;, length=2047332, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_sample</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-73291add-60c8-442d-9bc0-9d90f1c92b36' class='xr-index-data-in' type='checkbox'/><label for='index-73291add-60c8-442d-9bc0-9d90f1c92b36' 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(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071],\n",
" dtype=&#x27;int64&#x27;, name=&#x27;range_sample&#x27;, length=1072))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-92f70b6b-321b-4867-9379-76400e40f966' class='xr-section-summary-in' type='checkbox' checked><label for='section-92f70b6b-321b-4867-9379-76400e40f966' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volume backscattering strength (Sv re 1 m-1)</dd><dt><span>units :</span></dt><dd>dB</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'Sv' (channel: 3, ping_time: 2047332, range_sample: 1072)> Size: 53GB\n",
"dask.array<open_dataset-Sv, shape=(3, 2047332, 1072), dtype=float64, chunksize=(3, 1000, 1046), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * channel (channel) <U39 468B 'GPT 38 kHz 00907208dd13 5-1 OOI.38|20...\n",
" * ping_time (ping_time) datetime64[ns] 16MB 2017-08-01T00:00:00.061951 ...\n",
" * range_sample (range_sample) int64 9kB 0 1 2 3 4 ... 1068 1069 1070 1071\n",
"Attributes:\n",
" long_name: Volume backscattering strength (Sv re 1 m-1)\n",
" units: dB"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Sv should be a Dask Array with a tiny tiny task graph\n",
"# (the tasks should entail opening a Zarr store)\n",
"ds_Sv_combined[\"Sv\"]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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" <h3 style=\"margin-bottom: 0px;\">HighLevelGraph</h3>\n",
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" HighLevelGraph with 2 layers and 4097 keys from all layers.\n",
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],
"text/plain": [
"HighLevelGraph with 2 layers.\n",
"<dask.highlevelgraph.HighLevelGraph object at 0x70145c8120a0>\n",
" 0. original-open_dataset-Sv-31a2da23558141428be0435eee0f0bcc\n",
" 1. open_dataset-Sv-31a2da23558141428be0435eee0f0bcc"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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]
}
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