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add example netcdf (small)
1 parent cf01bbc commit d15e2ec

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‎.gitignore‎

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# Generated example fixture files
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examples/fixtures/*.nc
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examples/_temp_data
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*-checkpoint*

‎examples/docs_l2_examples.ipynb‎

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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 637 ms, sys: 109 ms, total: 746 ms\n",
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"Wall time: 2.37 s\n"
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"CPU times: user 610 ms, sys: 89.3 ms, total: 700 ms\n",
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"Wall time: 3.25 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"# bounding_box = (lon_min, lat_min, lon_max, lat_max)\n",
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"import point_collocation as pc\n",
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"plan = pc.plan(\n",
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" df_points[0:100], # -82.7375, 27.3835\t\n",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"granules 1-20 of 24 processed, 95 points matched, 00:02:55\n",
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"granules 21-24 of 24 processed, 25 points matched, 00:03:43\n",
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"CPU times: user 2min 42s, sys: 6.3 s, total: 2min 49s\n",
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"Wall time: 3min 43s\n"
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"granules 1-1 of 24 processed, 1 points matched, 00:00:07\n",
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"granules 2-2 of 24 processed, 1 points matched, 00:00:10\n",
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"granules 3-3 of 24 processed, 9 points matched, 00:00:24\n",
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"granules 4-4 of 24 processed, 8 points matched, 00:00:37\n",
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"granules 5-5 of 24 processed, 8 points matched, 00:00:51\n",
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"granules 6-6 of 24 processed, 5 points matched, 00:00:58\n",
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"granules 7-7 of 24 processed, 3 points matched, 00:01:03\n",
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"granules 8-8 of 24 processed, 10 points matched, 00:01:18\n",
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"granules 9-9 of 24 processed, 1 points matched, 00:01:23\n",
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"granules 10-10 of 24 processed, 1 points matched, 00:01:25\n",
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"granules 11-11 of 24 processed, 2 points matched, 00:01:29\n",
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"granules 12-12 of 24 processed, 1 points matched, 00:01:33\n",
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"granules 13-13 of 24 processed, 1 points matched, 00:01:35\n",
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"granules 14-14 of 24 processed, 1 points matched, 00:01:39\n",
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"granules 15-15 of 24 processed, 9 points matched, 00:01:51\n",
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"granules 16-16 of 24 processed, 8 points matched, 00:02:06\n",
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"granules 17-17 of 24 processed, 8 points matched, 00:02:17\n",
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"granules 18-18 of 24 processed, 6 points matched, 00:02:27\n",
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"granules 19-19 of 24 processed, 3 points matched, 00:02:35\n",
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"granules 20-20 of 24 processed, 9 points matched, 00:02:49\n",
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"granules 21-21 of 24 processed, 9 points matched, 00:03:03\n",
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"granules 22-22 of 24 processed, 9 points matched, 00:03:18\n",
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"granules 23-23 of 24 processed, 6 points matched, 00:03:30\n",
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"granules 24-24 of 24 processed, 1 points matched, 00:03:36\n",
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"CPU times: user 2min 43s, sys: 6.29 s, total: 2min 49s\n",
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"Wall time: 3min 37s\n"
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]
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},
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{
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],
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"source": [
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"%%time\n",
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"res = pc.matchup(plan, geometry=\"swath\", variables=[\"Rrs\"])\n",
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"res = pc.matchup(plan, geometry=\"swath\", variables=[\"Rrs\"], batch_size=1)\n",
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"res"
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]
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},
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a3bc3394-7a19-4a22-a3ee-75a2418d328b",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎examples/docs_l3_examples.ipynb‎

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"name": "stdout",
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"text": [
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"CPU times: user 82.9 ms, sys: 9.51 ms, total: 92.4 ms\n",
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"Wall time: 2.04 s\n"
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"CPU times: user 89.4 ms, sys: 937 μs, total: 90.3 ms\n",
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"Wall time: 1.87 s\n"
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}
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"granules 1-1 of 18 processed, 1 points matched, 00:00:28\n",
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"granules 2-2 of 18 processed, 9 points matched, 00:03:51\n",
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"granules 3-3 of 18 processed, 8 points matched, 00:06:53\n",
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"CPU times: user 9min 41s, sys: 8.33 s, total: 9min 49s\n",
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"Wall time: 9min 54s\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mres = pc.matchup(plan[0:100], geometry=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mgrid\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m, variables = [\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mavw\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m], batch_size=1, spatial_method=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mxoak\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2572\u001b[39m, in \u001b[36mInteractiveShell.run_cell_magic\u001b[39m\u001b[34m(self, magic_name, line, cell)\u001b[39m\n\u001b[32m 2570\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.builtin_trap:\n\u001b[32m 2571\u001b[39m args = (magic_arg_s, cell)\n\u001b[32m-> \u001b[39m\u001b[32m2572\u001b[39m result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2574\u001b[39m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[32m 2575\u001b[39m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[32m 2576\u001b[39m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[32m 2577\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
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"\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1447\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m 1445\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m interrupt_occured:\n\u001b[32m 1446\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exit_on_interrupt \u001b[38;5;129;01mand\u001b[39;00m captured_exception:\n\u001b[32m-> \u001b[39m\u001b[32m1447\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m captured_exception\n\u001b[32m 1448\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m 1449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
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"\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1411\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m 1409\u001b[39m st = clock2()\n\u001b[32m 1410\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1411\u001b[39m \u001b[43mexec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_ns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1412\u001b[39m out = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1413\u001b[39m \u001b[38;5;66;03m# multi-line %%time case\u001b[39;00m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m<timed exec>:1\u001b[39m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/engine.py:205\u001b[39m, in \u001b[36mmatchup\u001b[39m\u001b[34m(plan, geometry, variables, open_method, spatial_method, open_dataset_kwargs, silent, batch_size, save_dir, granule_range)\u001b[39m\n\u001b[32m 203\u001b[39m effective_vars: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m] = variables \u001b[38;5;28;01mif\u001b[39;00m variables \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m plan.variables\n\u001b[32m 204\u001b[39m effective_kwargs = {\u001b[33m\"\u001b[39m\u001b[33mchunks\u001b[39m\u001b[33m\"\u001b[39m: {}, **(open_dataset_kwargs \u001b[38;5;129;01mor\u001b[39;00m {})}\n\u001b[32m--> \u001b[39m\u001b[32m205\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_execute_plan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 206\u001b[39m \u001b[43m \u001b[49m\u001b[43mplan\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 207\u001b[39m \u001b[43m \u001b[49m\u001b[43mgeometry\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgeometry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 208\u001b[39m \u001b[43m \u001b[49m\u001b[43mopen_method\u001b[49m\u001b[43m=\u001b[49m\u001b[43mopen_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 209\u001b[39m \u001b[43m \u001b[49m\u001b[43mspatial_method\u001b[49m\u001b[43m=\u001b[49m\u001b[43mspatial_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 210\u001b[39m \u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m=\u001b[49m\u001b[43meffective_vars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 211\u001b[39m \u001b[43m \u001b[49m\u001b[43msilent\u001b[49m\u001b[43m=\u001b[49m\u001b[43msilent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 212\u001b[39m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 213\u001b[39m \u001b[43m \u001b[49m\u001b[43msave_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43msave_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 214\u001b[39m \u001b[43m \u001b[49m\u001b[43mgranule_range\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgranule_range\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 215\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43meffective_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 216\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/engine.py:537\u001b[39m, in \u001b[36m_execute_plan\u001b[39m\u001b[34m(plan, geometry, open_method, spatial_method, variables, silent, batch_size, save_dir, granule_range, **open_dataset_kwargs)\u001b[39m\n\u001b[32m 535\u001b[39m _extract_nearest(ds, row, variables, lon_name, lat_name)\n\u001b[32m 536\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m537\u001b[39m \u001b[43m_extract_xoak\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlon_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlat_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 539\u001b[39m output_rows.append(row)\n\u001b[32m 540\u001b[39m batch_rows.append(row)\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/engine.py:676\u001b[39m, in \u001b[36m_extract_xoak\u001b[39m\u001b[34m(ds, row, variables, lon_name, lat_name)\u001b[39m\n\u001b[32m 673\u001b[39m ds_work[lon_name] = xr.DataArray(lon_2d, dims=lat_dims)\n\u001b[32m 675\u001b[39m \u001b[38;5;66;03m# Build the NDPointIndex using the sklearn k-d tree adapter.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m676\u001b[39m indexed_ds = \u001b[43mds_work\u001b[49m\u001b[43m.\u001b[49m\u001b[43mset_xindex\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 677\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mlat_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlon_name\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 678\u001b[39m \u001b[43m \u001b[49m\u001b[43mxr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mindexes\u001b[49m\u001b[43m.\u001b[49m\u001b[43mNDPointIndex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 679\u001b[39m \u001b[43m \u001b[49m\u001b[43mtree_adapter_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mSklearnKDTreeAdapter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 680\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 682\u001b[39m \u001b[38;5;66;03m# Build the target selection (one query point).\u001b[39;00m\n\u001b[32m 683\u001b[39m target = xr.Dataset(\n\u001b[32m 684\u001b[39m {\n\u001b[32m 685\u001b[39m lat_name: xr.DataArray([row[\u001b[33m\"\u001b[39m\u001b[33mlat\u001b[39m\u001b[33m\"\u001b[39m]]),\n\u001b[32m 686\u001b[39m lon_name: xr.DataArray([row[\u001b[33m\"\u001b[39m\u001b[33mlon\u001b[39m\u001b[33m\"\u001b[39m]]),\n\u001b[32m 687\u001b[39m }\n\u001b[32m 688\u001b[39m )\n",
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"\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataset.py:5019\u001b[39m, in \u001b[36mDataset.set_xindex\u001b[39m\u001b[34m(self, coord_names, index_cls, **options)\u001b[39m\n\u001b[32m 5013\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 5014\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mthose coordinates already have an index: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mindexed_coords\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 5015\u001b[39m )\n\u001b[32m 5017\u001b[39m coord_vars = {name: \u001b[38;5;28mself\u001b[39m._variables[name] \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m coord_names}\n\u001b[32m-> \u001b[39m\u001b[32m5019\u001b[39m index = \u001b[43mindex_cls\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfrom_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoord_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 5021\u001b[39m new_coord_vars = index.create_variables(coord_vars)\n\u001b[32m 5023\u001b[39m \u001b[38;5;66;03m# special case for setting a pandas multi-index from level coordinates\u001b[39;00m\n\u001b[32m 5024\u001b[39m \u001b[38;5;66;03m# TODO: remove it once we depreciate pandas multi-index dimension (tuple\u001b[39;00m\n\u001b[32m 5025\u001b[39m \u001b[38;5;66;03m# elements) coordinate\u001b[39;00m\n",
689-
"\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/indexes/nd_point_index.py:276\u001b[39m, in \u001b[36mNDPointIndex.from_variables\u001b[39m\u001b[34m(cls, variables, options)\u001b[39m\n\u001b[32m 271\u001b[39m tree_adapter_cls = ScipyKDTreeAdapter\n\u001b[32m 273\u001b[39m points = get_points(variables.values())\n\u001b[32m 275\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(\n\u001b[32m--> \u001b[39m\u001b[32m276\u001b[39m \u001b[43mtree_adapter_cls\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mopts\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m 277\u001b[39m coord_names=\u001b[38;5;28mtuple\u001b[39m(variables),\n\u001b[32m 278\u001b[39m dims=var0.dims,\n\u001b[32m 279\u001b[39m shape=var0.shape,\n\u001b[32m 280\u001b[39m )\n",
690-
"\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xoak/tree_adapters.py:47\u001b[39m, in \u001b[36mSklearnKDTreeAdapter.__init__\u001b[39m\u001b[34m(self, points, options)\u001b[39m\n\u001b[32m 44\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, points: np.ndarray, options: Mapping[\u001b[38;5;28mstr\u001b[39m, Any]):\n\u001b[32m 45\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mneighbors\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m KDTree\n\u001b[32m---> \u001b[39m\u001b[32m47\u001b[39m \u001b[38;5;28mself\u001b[39m._kdtree = \u001b[43mKDTree\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n",
691-
"\u001b[31mKeyboardInterrupt\u001b[39m: "
666+
"granules 1-1 of 18 processed, 1 points matched, 00:00:06\n",
667+
"granules 2-2 of 18 processed, 9 points matched, 00:00:07\n",
668+
"granules 3-3 of 18 processed, 8 points matched, 00:00:08\n",
669+
"granules 4-4 of 18 processed, 8 points matched, 00:00:08\n",
670+
"granules 5-5 of 18 processed, 10 points matched, 00:00:09\n",
671+
"granules 6-6 of 18 processed, 1 points matched, 00:00:09\n",
672+
"granules 7-7 of 18 processed, 2 points matched, 00:00:10\n",
673+
"granules 8-8 of 18 processed, 1 points matched, 00:00:10\n",
674+
"granules 9-9 of 18 processed, 1 points matched, 00:00:11\n",
675+
"granules 10-10 of 18 processed, 9 points matched, 00:00:12\n",
676+
"granules 11-11 of 18 processed, 8 points matched, 00:00:12\n",
677+
"granules 12-12 of 18 processed, 8 points matched, 00:00:13\n",
678+
"granules 13-13 of 18 processed, 6 points matched, 00:00:13\n",
679+
"granules 14-14 of 18 processed, 3 points matched, 00:00:14\n",
680+
"granules 15-15 of 18 processed, 9 points matched, 00:00:15\n",
681+
"granules 16-16 of 18 processed, 9 points matched, 00:00:15\n",
682+
"granules 17-17 of 18 processed, 6 points matched, 00:00:16\n",
683+
"granules 18-18 of 18 processed, 1 points matched, 00:00:16\n",
684+
"CPU times: user 4.66 s, sys: 483 ms, total: 5.14 s\n",
685+
"Wall time: 17.5 s\n"
692686
]
693687
}
694688
],

‎examples/fixtures/make-earthaccess-fixtures.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -69,7 +69,7 @@
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lon=slice(-162, -160),
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lat=slice(2, 0)
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)
72-
ds.to_netcdf("/home/jovyan/earthaccess_matchup/examples/fixtures/pace_l3_sample.nc")
72+
ds.to_netcdf("/home/jovyan/point-collocation/examples/fixtures/pace_l3_sample.nc")
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import earthaccess
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import xarray as xr
@@ -85,4 +85,4 @@
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number_of_reflective_bands=slice(0,10),
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number_of_lines=slice(0,10),
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pixels_per_line=slice(0,10))
88-
dt_small.to_netcdf("/home/jovyan/earthaccess_matchup/examples/fixtures/pace_l2_sample.nc")
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dt_small.to_netcdf("/home/jovyan/point-collocation/examples/fixtures/pace_l2_sample.nc")

‎examples/fixtures/pace_l2_sample.nc‎

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‎examples/fixtures/pace_l3_sample.nc‎

363 KB
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