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Commenting out heavy computational cells in macronutrients notebook
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@rmshkv
rmshkv committed Jun 3, 2024
1 parent 8216b15 commit f697aec
Showing 1 changed file with 62 additions and 88 deletions.
150 changes: 62 additions & 88 deletions notebooks/ocn-macronuts.ipynb
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]
},
{
"cell_type": "code",
"execution_count": null,
"id": "02d46a8a-7de9-4ed9-a672-f692856a799e",
"cell_type": "raw",
"id": "21b76c34-1205-4c05-8480-8c4303ce0947",
"metadata": {},
"outputs": [],
"source": [
"ds_annual = year_mean(ds)\n",
"ds_annual"
]
},
{
"cell_type": "markdown",
"id": "0915979c-58e5-4217-83ff-e5bb7a93a0b2",
"cell_type": "raw",
"id": "63b605f1-3171-45fe-a2a0-83df48c5d308",
"metadata": {},
"source": [
"Note that our time coordinate is now called `year` instead, and has only years now. We can select specific years to plot:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "044ce3e1-394d-471a-a8e6-5e99ce72f087",
"cell_type": "raw",
"id": "4e9186e0-f42f-4906-96fd-c72c6579884d",
"metadata": {},
"outputs": [],
"source": [
"ds_annual['NO3'].sel(year=2010).isel(z_t=0).plot()"
]
},
{
"cell_type": "markdown",
"id": "79c5d1a6-8987-4d23-82ef-e4e9b0e35227",
"cell_type": "raw",
"id": "4d5135be-7e07-45f5-86bf-022db9ab48ee",
"metadata": {},
"source": [
"### Let's make a nicer-looking map"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85d669d3-24e7-4626-b862-20123b0f9816",
"cell_type": "raw",
"id": "3de10a7a-3036-429b-b79f-cd14c80d83bb",
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(8,6))\n",
"\n",
Expand All @@ -374,8 +368,8 @@
]
},
{
"cell_type": "markdown",
"id": "b741fef7-406c-4186-bf26-479c03ca8fae",
"cell_type": "raw",
"id": "21b6b012-cc2f-43e2-9aa7-10558d9c83b7",
"metadata": {},
"source": [
"## Compare long-term mean to World Ocean Atlas 2018\n",
Expand All @@ -391,11 +385,9 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7d11915-3fe0-4037-8cba-cce3929cb7c9",
"cell_type": "raw",
"id": "6301b55e-bdfb-4587-bdc0-9a4250469386",
"metadata": {},
"outputs": [],
"source": [
"woa_file_path = 's3://pythia/ocean-bgc/obs/WOA2018_POPgrid.nc'\n",
"\n",
Expand All @@ -407,42 +399,36 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e2adc2d-ab7c-4e85-b3fe-d17480e5e90d",
"cell_type": "raw",
"id": "f4c321db-9498-4e47-856c-a195cd957a13",
"metadata": {},
"outputs": [],
"source": [
"ds_mean = ds_annual.mean(\"year\").compute()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36254386-83a7-4e53-a221-9316b54e762c",
"cell_type": "raw",
"id": "c270fb67-a0dc-4422-aa2b-c087262cdf80",
"metadata": {},
"outputs": [],
"source": [
"NO3_diff = ds_mean.NO3 - ds_woa.NO3\n",
"PO4_diff = ds_mean.PO4 - ds_woa.PO4\n",
"SiO3_diff = ds_mean.SiO3 - ds_woa.SiO3"
]
},
{
"cell_type": "markdown",
"id": "71607513-6627-4425-b360-8e83a3038e0a",
"cell_type": "raw",
"id": "80ae3f57-db24-43ed-a276-57d99743426a",
"metadata": {},
"source": [
"### Surface comparison\n",
"We choose to set up a dictionary with some parameters for each plot we want to make, to cut down on repetition in the actual plotting code block. This could be condensed even further, but there's a tradeoff between conciseness and readability! We specify the variables we want to plot (in this case different nutrients) and things like the colormaps and normalization. In addition to plotting each nutrient from the modeled data and observations, we also plot the bias, which is the difference between the two datasets. This helps us see how the model differs from observations.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9621fecf-9123-4534-ac8d-85fb24c9a50a",
"cell_type": "raw",
"id": "6d73c7e6-d114-4877-add9-954c9003e555",
"metadata": {},
"outputs": [],
"source": [
"ds_dict_surf = {'CESMNO3': {'title': 'CESM surface NO$_3$',\n",
" 'label': 'NO$_3$ (mmol m$^{-3}$)',\n",
Expand Down Expand Up @@ -494,19 +480,17 @@
]
},
{
"cell_type": "markdown",
"id": "9a240b2d-69fb-465a-871b-16948706490f",
"cell_type": "raw",
"id": "c7f315eb-b00e-46e1-900d-ec897d7b10f7",
"metadata": {},
"source": [
"Here we pull from the above dictionary to actually make the plots."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8bf55132-8eee-40e0-81aa-f7485ea31361",
"cell_type": "raw",
"id": "d5f515d7-9ab5-4c55-bdca-bc3885e7269f",
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(18,10))\n",
"\n",
Expand All @@ -527,27 +511,25 @@
]
},
{
"cell_type": "markdown",
"id": "2103fda4-e9a9-4ae9-bb3d-37dc728dc99b",
"cell_type": "raw",
"id": "e24ad4f6-f703-40be-85df-7b33f4d0a760",
"metadata": {},
"source": [
"### Comparison at 100m"
]
},
{
"cell_type": "markdown",
"id": "9428e5cb-d301-4e2a-bdf4-35140408390c",
"cell_type": "raw",
"id": "73cc42f7-ddb8-4ded-b3b8-0f01616c62f6",
"metadata": {},
"source": [
"Similar to above, but at a depth of 100m rather than at the surface."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6744ba94-b999-41dd-b8da-ecd88b33cb56",
"cell_type": "raw",
"id": "cd28703f-82c9-4c76-af79-4f1469a3b4f5",
"metadata": {},
"outputs": [],
"source": [
"ds_dict_100m = {'CESMNO3': {'title': 'CESM 100m NO$_3$',\n",
" 'label': 'NO$_3$ (mmol m$^{-3}$)',\n",
Expand Down Expand Up @@ -599,11 +581,9 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9780941b-7fe9-4db7-9618-c1692a36c787",
"cell_type": "raw",
"id": "ed52d30f-9b42-4583-890e-a1dc15b4efb2",
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(18,10))\n",
"\n",
Expand All @@ -623,27 +603,25 @@
]
},
{
"cell_type": "markdown",
"id": "63d10d8b-09c6-41b4-97d4-2913b1d9872e",
"cell_type": "raw",
"id": "287cbdd6-82b9-4b22-b79c-ec98e723d06f",
"metadata": {},
"source": [
"## Global mean macronutrient profiles"
]
},
{
"cell_type": "markdown",
"id": "30e9b004-6c9f-4768-a1f7-893686fd9419",
"cell_type": "raw",
"id": "e16ab1e8-0f9c-4bd1-897b-3d046b4bcb1a",
"metadata": {},
"source": [
" Let's write a function to take a global mean of the variables we're interested in, so that we can look at some depth profiles rather than maps. Also remember that we already took a mean over the whole time range (and the WOA dataset already had this mean taken), so this is a mean in time as well. Like the above maps, we also plot a bias panel to directly compare the difference between the datasets."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a4d8ed0-b740-4c70-9478-544153e3ad35",
"cell_type": "raw",
"id": "fa908ee6-3f4a-4352-b977-93fe95527233",
"metadata": {},
"outputs": [],
"source": [
"def global_mean(ds, ds_grid, compute_vars, include_ms=False):\n",
" \"\"\"\n",
Expand Down Expand Up @@ -677,39 +655,33 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "04bf0b7e-d2d1-4ec3-b630-63496058754e",
"cell_type": "raw",
"id": "a7540610-7135-4c7f-807e-6bead0f2547d",
"metadata": {},
"outputs": [],
"source": [
"ds_glb = global_mean(ds_mean, ds_grid, ['NO3','PO4','SiO3']).compute()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f53e0d97-d55a-471f-aaa0-54fa36350a2a",
"cell_type": "raw",
"id": "11726462-0ff0-48fa-ba30-a5a33b5cc28a",
"metadata": {},
"outputs": [],
"source": [
"ds_glb_woa = global_mean(ds_woa, ds_grid, ['NO3','PO4','SiO3']).compute()"
]
},
{
"cell_type": "markdown",
"id": "03bdf16c-6e83-418a-a896-5ba2599d8cbd",
"cell_type": "raw",
"id": "5ec97a98-8ed6-4ff2-9f43-9ebca68834d3",
"metadata": {},
"source": [
"Rather than setting up a dictionary of parameters, here we choose to make the plots inline since there aren't as many."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69c80aff-2e77-4566-a570-fc4034411e96",
"cell_type": "raw",
"id": "08533f63-af14-4e65-aca5-d7278cb7eb4f",
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(6,10))\n",
"\n",
Expand Down Expand Up @@ -777,43 +749,41 @@
]
},
{
"cell_type": "markdown",
"id": "49ec424d-e651-48df-ae02-b99fa2802efa",
"cell_type": "raw",
"id": "fe4bf978-0bf6-4ce9-9d3b-6534ced341d9",
"metadata": {},
"source": [
"And close the Dask cluster we spun up at the beginning."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cbbc29ed-b235-4c8f-8d76-50ce3d576ecb",
"cell_type": "raw",
"id": "3503dd15-89bf-4a8f-86f1-958d32dd70c1",
"metadata": {},
"outputs": [],
"source": [
"cluster.close()"
]
},
{
"cell_type": "markdown",
"id": "74857cbe-daae-40a6-b19c-10e7048d6a4d",
"cell_type": "raw",
"id": "b4db0ee7-b64b-434b-92bd-7295ebb12c4c",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "0fa03354-c521-44e3-9ed3-634fbb5141d1",
"cell_type": "raw",
"id": "b27f59b1-8edf-4748-9fbc-90d4291f0802",
"metadata": {},
"source": [
"## Summary\n",
"You've learned how to plot and evaluate the distribution of some key ocean nutrients in CESM output."
]
},
{
"cell_type": "markdown",
"id": "6ffae2b4-18ff-4e05-8ce7-b9baf77b4516",
"cell_type": "raw",
"id": "2db28b89-d481-421e-8bc4-80e028e374d9",
"metadata": {},
"source": [
"## Resources and references\n",
Expand All @@ -825,6 +795,10 @@
}
],
"metadata": {
"kernelspec": {
"display_name": "",
"name": ""
},
"language_info": {
"name": "python"
}
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