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Created June 13, 2024 00:13
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2024_06_12_test_ooi_compute_Sv.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# Import packages\n",
"from pathlib import Path\n",
"import itertools as it\n",
"import datetime as dt\n",
"from dateutil import parser as dtparser\n",
"from tqdm import tqdm\n",
"\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 8 workers with 3.5 GiB RAM each\n",
"client = Client(n_workers=8)\n",
"client.dashboard_link"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# Grab URLs of RAW Files between 2017-08-21 07:00 - 2017-08-31 07:00;\n",
"# ten days worth of data\n",
"\n",
"fs = fsspec.filesystem('https')\n",
"\n",
"ooi_raw_url = (\n",
" \"https://rawdata.oceanobservatories.org/files/\"\n",
" \"CE04OSPS/PC01B/ZPLSCB102_10.33.10.143/2017/08\"\n",
")\n",
"\n",
"def in_range(raw_file: str, start: dt.datetime, end: dt.datetime) -> bool:\n",
" \"\"\"Check if file url is in datetime range\"\"\"\n",
" file_name = Path(raw_file).name\n",
" file_datetime = dtparser.parse(file_name, fuzzy=True)\n",
" return file_datetime >= start and file_datetime <= end\n",
"\n",
"start_datetime = dt.datetime(2017, 8, 21, 7, 0)\n",
"end_datetime = dt.datetime(2017, 8, 31, 7, 0)\n",
"\n",
"desired_day_urls = [f\"{ooi_raw_url}/{day}\" for day in range(start_datetime.day, end_datetime.day + 1)]\n",
"all_raw_file_urls = it.chain.from_iterable([fs.glob(f\"{day_url}/*.raw\") for day_url in desired_day_urls])\n",
"desired_raw_file_urls = list(filter(\n",
" lambda raw_file: in_range(\n",
" raw_file, \n",
" start_datetime-dt.timedelta(hours=3), # 3 hour buffer to select files\n",
" end_datetime+dt.timedelta(hours=3)\n",
" ), \n",
" all_raw_file_urls\n",
"))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of OOI Raw Files: 154\n"
]
}
],
"source": [
"print(\"Number of OOI Raw Files:\", len(desired_raw_file_urls))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 1%|▏ | 2/154 [00:09<11:46, 4.65s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 2%|▏ | 3/154 [00:13<11:38, 4.63s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 6%|▌ | 9/154 [00:40<10:54, 4.52s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 16%|█▌ | 24/154 [01:48<09:54, 4.57s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 16%|█▌ | 25/154 [01:52<09:49, 4.57s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 19%|█▉ | 30/154 [02:13<08:46, 4.25s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 27%|██▋ | 42/154 [03:04<07:27, 4.00s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 61%|██████ | 94/154 [06:54<04:30, 4.50s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 62%|██████▏ | 95/154 [06:59<04:27, 4.54s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 68%|██████▊ | 105/154 [07:43<03:38, 4.46s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 79%|███████▉ | 122/154 [08:58<02:21, 4.42s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 80%|███████▉ | 123/154 [09:03<02:19, 4.50s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 82%|████████▏ | 126/154 [09:16<02:05, 4.50s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 82%|████████▏ | 127/154 [09:21<02:02, 4.54s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 86%|████████▋ | 133/154 [09:47<01:28, 4.21s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 88%|████████▊ | 135/154 [09:56<01:24, 4.46s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 90%|████████▉ | 138/154 [10:10<01:12, 4.54s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 93%|█████████▎| 143/154 [10:32<00:48, 4.43s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
" 94%|█████████▎| 144/154 [10:37<00:44, 4.46s/it]/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/duck_array_ops.py:216: RuntimeWarning: invalid value encountered in cast\n",
" return data.astype(dtype, **kwargs)\n",
"100%|██████████| 154/154 [11:20<00:00, 4.42s/it]\n"
]
}
],
"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",
"for raw_file_url in tqdm(desired_raw_file_urls):\n",
" open_and_save(\n",
" raw_file=raw_file_url,\n",
" sonar_model='ek60',\n",
" use_swap=True,\n",
" save_path=Path('convertallfiles')\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 154/154 [00:13<00:00, 11.77it/s]\n"
]
}
],
"source": [
"# Combine Echodata Objects\n",
"ed_list = []\n",
"for converted_file in tqdm(sorted(Path('convertallfiles').glob(\"*.zarr\"))):\n",
" ed_list.append(\n",
" ep.open_converted(\n",
" converted_raw_path=converted_file,\n",
" chunks={}\n",
" )\n",
" )\n",
"ed_combined = ep.combine_echodata(ed_list)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/xarray/core/dataset.py:2548: SerializationWarning: saving variable None with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
" return to_zarr( # type: ignore[call-overload,misc]\n",
"/home/exouser/miniforge3/envs/echopype/lib/python3.9/site-packages/dask/array/chunk.py:278: RuntimeWarning: invalid value encountered in cast\n",
" return x.astype(astype_dtype, **kwargs)\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": 16,
"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": 17,
"metadata": {},
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;backscatter_r&#x27; (channel: 3, ping_time: 881758,\n",
" range_sample: 1072)&gt; Size: 23GB\n",
"dask.array&lt;open_dataset-backscatter_r, shape=(3, 881758, 1072), dtype=float64, chunksize=(3, 3886, 1072), 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] 7MB 2017-08-21T04:57:17.329287 ....\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>: 881758</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-5efb2fcb-1c8c-4705-91b8-e845206a1afb' class='xr-array-in' type='checkbox' checked><label for='section-5efb2fcb-1c8c-4705-91b8-e845206a1afb' 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, 3886, 1072), 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",
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" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 21.13 GiB </td>\n",
" <td> 95.35 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3, 881758, 1072) </td>\n",
" <td> (3, 3886, 1072) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 227 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",
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"</table></div></div></li><li class='xr-section-item'><input id='section-ba9e1936-0d17-48c6-9fbe-af820f6ffa90' class='xr-section-summary-in' type='checkbox' checked><label for='section-ba9e1936-0d17-48c6-9fbe-af820f6ffa90' 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-1fd1b69a-e122-47da-b463-21234192d382' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fd1b69a-e122-47da-b463-21234192d382' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aeba2220-ab8d-4d1f-9b6e-c800e34590fc' class='xr-var-data-in' type='checkbox'><label for='data-aeba2220-ab8d-4d1f-9b6e-c800e34590fc' 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'>2017-08-21T04:57:17.329287 ... 2...</div><input id='attrs-52cc2940-0bd7-46d8-a8c3-da61c1089373' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52cc2940-0bd7-46d8-a8c3-da61c1089373' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eba99767-6c10-4dcf-b7ca-86e0c6528845' class='xr-var-data-in' type='checkbox'><label for='data-eba99767-6c10-4dcf-b7ca-86e0c6528845' 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;2017-08-21T04:57:17.329287000&#x27;, &#x27;2017-08-21T04:57:18.332344000&#x27;,\n",
" &#x27;2017-08-21T04:57:19.335403000&#x27;, ..., &#x27;2017-08-31T11:33:30.733850000&#x27;,\n",
" &#x27;2017-08-31T11:33:31.735909000&#x27;, &#x27;2017-08-31T11:33:32.738966000&#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-2be1780f-3200-41bb-9658-cc268d18eb0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2be1780f-3200-41bb-9658-cc268d18eb0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac5ff25f-c25b-43e5-9ca0-950b3967bc7c' class='xr-var-data-in' type='checkbox'><label for='data-ac5ff25f-c25b-43e5-9ca0-950b3967bc7c' 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-d4e91f2f-2629-4298-909d-440cf7f50562' class='xr-section-summary-in' type='checkbox' ><label for='section-d4e91f2f-2629-4298-909d-440cf7f50562' 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-33284410-cc73-429f-b0e3-57518fb0e74e' class='xr-index-data-in' type='checkbox'/><label for='index-33284410-cc73-429f-b0e3-57518fb0e74e' 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-d4eb7cec-e9d9-40c6-8932-5ae401c7526f' class='xr-index-data-in' type='checkbox'/><label for='index-d4eb7cec-e9d9-40c6-8932-5ae401c7526f' 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;2017-08-21 04:57:17.329287&#x27;, &#x27;2017-08-21 04:57:18.332344&#x27;,\n",
" &#x27;2017-08-21 04:57:19.335403&#x27;, &#x27;2017-08-21 04:57:20.338461&#x27;,\n",
" &#x27;2017-08-21 04:57:21.341518&#x27;, &#x27;2017-08-21 04:57:22.344574&#x27;,\n",
" &#x27;2017-08-21 04:57:23.347631&#x27;, &#x27;2017-08-21 04:57:24.350689&#x27;,\n",
" &#x27;2017-08-21 04:57:25.353746&#x27;, &#x27;2017-08-21 04:57:26.356804&#x27;,\n",
" ...\n",
" &#x27;2017-08-31 11:33:23.712448&#x27;, &#x27;2017-08-31 11:33:24.715508&#x27;,\n",
" &#x27;2017-08-31 11:33:25.718563&#x27;, &#x27;2017-08-31 11:33:26.721621&#x27;,\n",
" &#x27;2017-08-31 11:33:27.724678&#x27;, &#x27;2017-08-31 11:33:28.727736&#x27;,\n",
" &#x27;2017-08-31 11:33:29.730793&#x27;, &#x27;2017-08-31 11:33:30.733850&#x27;,\n",
" &#x27;2017-08-31 11:33:31.735909&#x27;, &#x27;2017-08-31 11:33:32.738966&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;ping_time&#x27;, length=881758, 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-2e5f061b-c221-45fc-a384-8f7e5fda4cb1' class='xr-index-data-in' type='checkbox'/><label for='index-2e5f061b-c221-45fc-a384-8f7e5fda4cb1' 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-846cb664-8d33-47fd-9e0f-81efb34b49c8' class='xr-section-summary-in' type='checkbox' checked><label for='section-846cb664-8d33-47fd-9e0f-81efb34b49c8' 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: 881758,\n",
" range_sample: 1072)> Size: 23GB\n",
"dask.array<open_dataset-backscatter_r, shape=(3, 881758, 1072), dtype=float64, chunksize=(3, 3886, 1072), 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] 7MB 2017-08-21T04:57:17.329287 ....\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"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed_combined[\"Sonar/Beam_group1\"][\"backscatter_r\"]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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 47.35 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": 19,
"metadata": {},
"outputs": [
{
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"dask.array&lt;open_dataset-Sv, shape=(3, 881758, 1072), dtype=float64, chunksize=(3, 1000, 1072), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
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" * ping_time (ping_time) datetime64[ns] 7MB 2017-08-21T04:57:17.329287 ....\n",
" * range_sample (range_sample) int64 9kB 0 1 2 3 4 ... 1068 1069 1070 1071\n",
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" long_name: Volume backscattering strength (Sv re 1 m-1)\n",
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"</table></div></div></li><li class='xr-section-item'><input id='section-d91f32ad-837d-4ce1-8b9d-d2ec7cd5c35b' class='xr-section-summary-in' type='checkbox' checked><label for='section-d91f32ad-837d-4ce1-8b9d-d2ec7cd5c35b' 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-6ce70780-e241-4c3f-a81e-ac9af40007a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ce70780-e241-4c3f-a81e-ac9af40007a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa5c5d4c-5693-45c6-ac35-7f0c443164e1' class='xr-var-data-in' type='checkbox'><label for='data-aa5c5d4c-5693-45c6-ac35-7f0c443164e1' 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'>2017-08-21T04:57:17.329287 ... 2...</div><input id='attrs-2171bd9b-8f76-4f18-8e66-a9b31f33a973' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2171bd9b-8f76-4f18-8e66-a9b31f33a973' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f67b588-e062-48e1-8fd5-89c0350042ef' class='xr-var-data-in' type='checkbox'><label for='data-6f67b588-e062-48e1-8fd5-89c0350042ef' 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;2017-08-21T04:57:17.329287000&#x27;, &#x27;2017-08-21T04:57:18.332344000&#x27;,\n",
" &#x27;2017-08-21T04:57:19.335403000&#x27;, ..., &#x27;2017-08-31T11:33:30.733850000&#x27;,\n",
" &#x27;2017-08-31T11:33:31.735909000&#x27;, &#x27;2017-08-31T11:33:32.738966000&#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-3cb6a080-e262-45b3-bc76-68e14848c699' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cb6a080-e262-45b3-bc76-68e14848c699' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0960e433-6905-429e-b3bc-916dc4205c7b' class='xr-var-data-in' type='checkbox'><label for='data-0960e433-6905-429e-b3bc-916dc4205c7b' 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-4a6c79a8-9d9b-45fc-8293-3197a464dd5f' class='xr-section-summary-in' type='checkbox' ><label for='section-4a6c79a8-9d9b-45fc-8293-3197a464dd5f' 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-3bf899fc-11eb-4626-8253-0cc2cb86f619' class='xr-index-data-in' type='checkbox'/><label for='index-3bf899fc-11eb-4626-8253-0cc2cb86f619' 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-e69a4353-9159-4ae7-b5e5-019e3d3f56f6' class='xr-index-data-in' type='checkbox'/><label for='index-e69a4353-9159-4ae7-b5e5-019e3d3f56f6' 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;2017-08-21 04:57:17.329287&#x27;, &#x27;2017-08-21 04:57:18.332344&#x27;,\n",
" &#x27;2017-08-21 04:57:19.335403&#x27;, &#x27;2017-08-21 04:57:20.338461&#x27;,\n",
" &#x27;2017-08-21 04:57:21.341518&#x27;, &#x27;2017-08-21 04:57:22.344574&#x27;,\n",
" &#x27;2017-08-21 04:57:23.347631&#x27;, &#x27;2017-08-21 04:57:24.350689&#x27;,\n",
" &#x27;2017-08-21 04:57:25.353746&#x27;, &#x27;2017-08-21 04:57:26.356804&#x27;,\n",
" ...\n",
" &#x27;2017-08-31 11:33:23.712448&#x27;, &#x27;2017-08-31 11:33:24.715508&#x27;,\n",
" &#x27;2017-08-31 11:33:25.718563&#x27;, &#x27;2017-08-31 11:33:26.721621&#x27;,\n",
" &#x27;2017-08-31 11:33:27.724678&#x27;, &#x27;2017-08-31 11:33:28.727736&#x27;,\n",
" &#x27;2017-08-31 11:33:29.730793&#x27;, &#x27;2017-08-31 11:33:30.733850&#x27;,\n",
" &#x27;2017-08-31 11:33:31.735909&#x27;, &#x27;2017-08-31 11:33:32.738966&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;ping_time&#x27;, length=881758, 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-1bb3c722-6d60-4427-b49c-6e2df7f2e422' class='xr-index-data-in' type='checkbox'/><label for='index-1bb3c722-6d60-4427-b49c-6e2df7f2e422' 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-66739ff0-e5d0-4a51-864f-60742c1e30b2' class='xr-section-summary-in' type='checkbox' checked><label for='section-66739ff0-e5d0-4a51-864f-60742c1e30b2' 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: 881758, range_sample: 1072)> Size: 23GB\n",
"dask.array<open_dataset-Sv, shape=(3, 881758, 1072), dtype=float64, chunksize=(3, 1000, 1072), 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] 7MB 2017-08-21T04:57:17.329287 ....\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": 19,
"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": 20,
"metadata": {},
"outputs": [
{
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" </tr>\n",
" </table>\n",
"\n",
" </details>\n",
"</div>\n",
" \n",
" </div>\n",
" </div>\n",
"</div>"
],
"text/plain": [
"HighLevelGraph with 2 layers.\n",
"<dask.highlevelgraph.HighLevelGraph object at 0x7ff4404d71c0>\n",
" 0. original-open_dataset-Sv-187f8a92abec0415ed05601f097a961b\n",
" 1. open_dataset-Sv-187f8a92abec0415ed05601f097a961b"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_Sv_combined[\"Sv\"].data.dask"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "echopype",
"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": "3.9.19"
}
},
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"nbformat_minor": 2
}
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