{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using CF-Xarray to easily analyze CMORized and non-CMORized output\n",
"\n",
"This notebook demonstrates how to use CF attributes in datasets to write code that generalizes across datasets that follow different naming conventions.\n",
"\n",
"It makes a simple depth plot of zonal velocity at (0°N, 140°W) and uses the [cf_xarray](https://cf-xarray.readthedocs.io/en/latest/) package to take advantage of CF attributes. For example, the zonal velocity variable (named `UVEL` or `uo` depending on output) is extracted using the CF standard name `sea_water_x_velocity`. The associated latitude, longitude variables (`ULAT`, `lat`, `yh`; `ULONG`, `lon`, `xh`) are extracted using cf_xarray logic for identifying appropriate latitude and longitude variables.\n",
"\n",
"There are some outstanding issues:\n",
"- CESM-POP2 does not output a nice complete set of CF attributes for each variable. This will need to be added as a post-processing step.\n",
"- indexing on a curvilinear lat-lon grid is hard, but xarray will make this more convenient soon.\n",
"- indexing along longitudes that could be 0→360 or -180→180 is also hard, but we can fix this when xarray exposes more indexing functionality."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"matplotlib : 3.3.2\n",
"numpy : 1.19.2\n",
"intake : 0.6.0\n",
"cf_xarray : 0.4.1.dev31+g7a8c620\n",
"distributed : 2.30.0\n",
"pint_xarray : 0.2\n",
"pop_tools : 2020.9.14\n",
"xarray : 0.16.3.dev150+g37522e991\n",
"intake_esm : 2020.8.15\n",
"dask_jobqueue: 0.7.1\n",
"\n"
]
}
],
"source": [
"%load_ext watermark\n",
"\n",
"import cf_xarray as cfxr\n",
"import dask_jobqueue\n",
"import distributed\n",
"import intake\n",
"import intake_esm\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pint_xarray\n",
"import xarray as xr\n",
"from cf_xarray.units import units # loads some UDUNITS definitions\n",
"\n",
"import pop_tools\n",
"\n",
"xr.set_options(keep_attrs=True)\n",
"\n",
"%watermark -iv"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"cluster = dask_jobqueue.PBSCluster(\n",
" cores=1, # The number of cores you want\n",
" memory='10GB', # Amount of memory\n",
" processes=1, # How many processes\n",
" queue='casper', # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)\n",
" local_directory='$TMPDIR', # Use your local directory\n",
" resource_spec='select=1:ncpus=1:mem=10GB', # Specify resources\n",
" project='ncgd0048', # Input your project ID here\n",
" walltime='02:00:00', # Amount of wall time\n",
" interface='ib0', # Interface to use\n",
")\n",
"\n",
"cluster.scale(6)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
This dataset was created on 2018-08-09 at 18:18:26.3
cell_methods :
cell_methods = time: mean ==> the variable values are averaged over the time interval between the previous time coordinate and the current one. cell_methods absent ==> the variable values are at the time given by the current time coordinate.
native gx1v7 displaced pole grid (384x320 latxlon)
grid_label :
gn
initialization_index :
1
institution :
National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA
institution_id :
NCAR
license :
CMIP6 model data produced by <The National Center for Atmospheric Research> is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
mip_era :
CMIP6
model_doi_url :
https://doi.org/10.5065/D67H1H0V
nominal_resolution :
100 km
parent_activity_id :
CMIP
parent_experiment_id :
piControl-spinup
parent_mip_era :
CMIP6
parent_source_id :
CESM2
parent_time_units :
days since 0001-01-01 00:00:00
parent_variant_label :
r1i1p1f1
physics_index :
1
product :
model-output
realization_index :
1
realm :
ocean
source :
CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)
source_id :
CESM2
source_type :
AOGCM BGC AER
sub_experiment :
none
sub_experiment_id :
none
table_id :
Omon
tracking_id :
hdl:21.14100/10d78d6e-34a5-44d7-a2c4-47708f7e098e
variable_id :
uo
variant_info :
CMIP6 CESM2 piControl experiment with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)
"
],
"text/plain": [
"\n",
", 'meter / second')>\n",
"Coordinates:\n",
" * xq (xq) float64 -286.3 -285.7 -285.0 -284.3 ... 71.0 71.67 72.33 73.0\n",
" * yh (yh) float64 -79.2 -79.08 -78.95 -78.82 ... 87.55 87.64 87.71 87.74\n",
" * zl (zl) float64 1.25 3.75 6.251 ... 5.366e+03 5.619e+03 5.873e+03\n",
" * time (time) object 0001-01-16 12:00:00 ... 0058-12-16 12:00:00\n",
"Attributes:\n",
" long_name: Sea Water X Velocity\n",
" cell_methods: zl:mean yh:mean xq:point time: mean\n",
" time_avg_info: average_T1,average_T2,average_DT\n",
" standard_name: sea_water_x_velocity\n",
" interp_method: none"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mom6zs.cf[\"sea_water_x_velocity\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare x velocity across three simulations\n",
"\n",
"We will now make a simple plot. There are `UnitStrippedWarnings` because xarray is pulling out numpy arrays from the pint arrays. This will get fixed."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.8/site-packages/numpy/core/_asarray.py:83: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n",
" return array(a, dtype, copy=False, order=order)\n",
"/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.8/site-packages/numpy/core/_asarray.py:83: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n",
" return array(a, dtype, copy=False, order=order)\n",
"/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.8/site-packages/numpy/core/_asarray.py:83: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n",
" return array(a, dtype, copy=False, order=order)\n",
"/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.8/site-packages/numpy/core/_asarray.py:83: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n",
" return array(a, dtype, copy=False, order=order)\n",
"/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.8/site-packages/numpy/core/_asarray.py:83: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n",
" return array(a, dtype, copy=False, order=order)\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 402
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def plot_euc(ds, **plot_kwargs):\n",
" if \"nlat\" in ds.dims and \"nlat\" not in ds.coords:\n",
" ds = set_coords(ds)\n",
"\n",
" # gets UVEL in CESM-POP, \"uo\" in CESM-MOM6, \"uo\" in CMOR\n",
" u = ds.cf[\"sea_water_x_velocity\"]\n",
"\n",
" # find the z-coordinate name: \"z_t\" in CESM-POP, \"lev\" in CMOR, \"zl\" in CESM-MOM6\n",
" Zname = u.cf.coordinates[\"vertical\"][0]\n",
"\n",
" # Make sure we are using SI units\n",
" # convert velocity to m/s\n",
" # convert the Z-coordinate to m\n",
" u = u.pint.to(\"m/s\").pint.to({Zname: \"m\"})\n",
"\n",
" # --- find (0, -140)\n",
" lat = u.cf[\"latitude\"] # gets possibly 2D latitude variable\n",
" lon = u.cf[\"longitude\"]\n",
"\n",
" # TODO: Xarray will provide a better solution here in ≈ 6 months\n",
" # right now, we need to know\n",
" # Unfortunately 220*units.degress_east == -140*degrees_east is False\n",
" # If not, we could avoid this if condition\n",
" if bool(np.all(lon.data > 0)):\n",
" lon_target = 220 * units.degrees_east\n",
" else:\n",
" lon_target = -140 * units.degrees_east\n",
" lat_target = 0 * units.degrees_north\n",
"\n",
" if lon.ndim == 2:\n",
" # find index corresponding to (0, -140)\n",
" # TODO: we need to extract magnitudes (pure numpy arrays, no units)\n",
" ilat, ilon = np.unravel_index(\n",
" np.argmin(\n",
" (lat - lat_target).data.magnitude ** 2 + (lon - lon_target).data.magnitude ** 2\n",
" ),\n",
" lon.shape,\n",
" )\n",
" # TODO: .isel(X=ilon, Y=ilat) does not work\n",
" # because for the CMOR dataset, \"lon\" and \"nlon\" both have axis=\"X\"\n",
" u_sub = u.cf.isel({\"X\": ilon, \"Y\": ilat})\n",
" else:\n",
" u_sub = u.cf.sel({\"X\": lon_target, \"Y\": lat_target}, method=\"nearest\")\n",
"\n",
" # now plot the last year for demonstration\n",
" # All datasets here use \"time\" for the time axis,\n",
" # so using .cf.mean(\"time\") is really just for demo purposes\n",
" (\n",
" u_sub.cf.sel(vertical=slice(500)) # top 500m\n",
" .cf.isel(time=slice(-12, None))\n",
" .cf.mean(\"time\")\n",
" .cf.plot(**plot_kwargs) # puts 'Z' on the y-axis automatically and\n",
" # makes sure values increase downward (since attrs[\"positive\"] == \"down\")\n",
" )\n",
"\n",
"\n",
"plot_euc(cmor, label=\"CMOR-POP\")\n",
"plot_euc(cesm, label=\"CESM-POP\")\n",
"plot_euc(mom6zs, label=\"CESM-MOM6-z*\")\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Automatic guessing of coordinates and axes variables.\n",
"\n",
"`cf_xarray` copies over some heuristics from metpy to enable automatically detecting axes and coordinate variables. Calling this function will return a new Dataset or DataArray with new attributes set. You will want to save the return value."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I think 'z_t' is of type 'Z'. It matched re.compile('(z|nav_lev|gdep|lv_|[o]*lev|bottom_top|sigma|h(ei)?ght|altitude|depth|isobaric|pres|isotherm)[a-z_]*[0-9]*')\n",
"I think 'z_t_150m' is of type 'Z'. It matched re.compile('(z|nav_lev|gdep|lv_|[o]*lev|bottom_top|sigma|h(ei)?ght|altitude|depth|isobaric|pres|isotherm)[a-z_]*[0-9]*')\n",
"I think 'z_w' is of type 'Z'. It matched re.compile('(z|nav_lev|gdep|lv_|[o]*lev|bottom_top|sigma|h(ei)?ght|altitude|depth|isobaric|pres|isotherm)[a-z_]*[0-9]*')\n",
"I think 'z_w_top' is of type 'Z'. It matched re.compile('(z|nav_lev|gdep|lv_|[o]*lev|bottom_top|sigma|h(ei)?ght|altitude|depth|isobaric|pres|isotherm)[a-z_]*[0-9]*')\n",
"I think 'z_w_bot' is of type 'Z'. It matched re.compile('(z|nav_lev|gdep|lv_|[o]*lev|bottom_top|sigma|h(ei)?ght|altitude|depth|isobaric|pres|isotherm)[a-z_]*[0-9]*')\n",
"I think 'lat_aux_grid' is of type 'latitude'. It matched re.compile('y?(nav_lat|lat|gphi)[a-z0-9]*')\n",
"I think 'time' is of type 'time'. It has a datetime-like type.\n",
"I think 'nlat' is of type 'Y'. It matched re.compile('y|j|nlat|nj')\n",
"I think 'nlon' is of type 'X'. It matched re.compile('x|i|nlon|ni')\n"
]
},
{
"data": {
"text/html": [
"
This dataset was created on 2018-08-09 at 18:18:26.3
cell_methods :
cell_methods = time: mean ==> the variable values are averaged over the time interval between the previous time coordinate and the current one. cell_methods absent ==> the variable values are at the time given by the current time coordinate.
"
],
"text/plain": [
"\n",
"Dimensions: (moc_comp: 3, transport_comp: 5, transport_reg: 2, z_t: 60, z_w: 60, nlat: 384, nlon: 320, time: 24000, d2: 2, z_t_150m: 15, z_w_top: 60, z_w_bot: 60, lat_aux_grid: 395, moc_z: 61)\n",
"Coordinates: (12/14)\n",
" * z_t (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
" * z_t_150m (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04\n",
" * z_w (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
" * z_w_top (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
" * z_w_bot (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
" * lat_aux_grid (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0\n",
" ... ...\n",
" ULAT (nlat, nlon) float64 [degrees_N] dask.array\n",
" ... ...\n",
" salinity_factor float64 -0.00347\n",
" sflux_factor float64 0.1\n",
" nsurface_t float64 8.61e+04\n",
" nsurface_u float64 8.297e+04\n",
" time_bound (time, d2) object dask.array\n",
" UVEL (time, z_t, nlat, nlon) float32 [cm/s] dask.array...\n",
"Attributes:\n",
" title: b.e21.B1850.f09_g17.CMIP6-piControl.001\n",
" history: none\n",
" Conventions: CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-cu...\n",
" time_period_freq: month_1\n",
" model_doi_url: https://doi.org/10.5065/D67H1H0V\n",
" contents: Diagnostic and Prognostic Variables\n",
" source: CCSM POP2, the CCSM Ocean Component\n",
" revision: $Id: tavg.F90 89644 2018-08-04 14:26:01Z klindsay $\n",
" calendar: All years have exactly 365 days.\n",
" start_time: This dataset was created on 2018-08-09 at 18:18:26.3\n",
" cell_methods: cell_methods = time: mean ==> the variable values are ..."
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cesm.cf.guess_coord_axis(verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I think 'lat' is of type 'latitude'. It matched re.compile('y?(nav_lat|lat|gphi)[a-z0-9]*')\n",
"I think 'lev' is of type 'Z'. It matched re.compile('(z|nav_lev|gdep|lv_|[o]*lev|bottom_top|sigma|h(ei)?ght|altitude|depth|isobaric|pres|isotherm)[a-z_]*[0-9]*')\n",
"I think 'lon' is of type 'longitude'. It matched re.compile('x?(nav_lon|lon|glam)[a-z0-9]*')\n",
"I think 'nlat' is of type 'Y'. It matched re.compile('y|j|nlat|nj')\n",
"I think 'nlon' is of type 'X'. It matched re.compile('x|i|nlon|ni')\n",
"I think 'time' is of type 'time'. It has a datetime-like type.\n",
"I think 'time_bnds' is of type 'time'. It matched re.compile('\\\\bt\\\\b|(time|min|hour|day|week|month|year)[0-9]*')\n",
"I think 'time_bnds' is of type 'T'. It matched re.compile('\\\\bt\\\\b|(time|min|hour|day|week|month|year)[0-9]*')\n",
"I think 'lat_bnds' is of type 'latitude'. It matched re.compile('y?(nav_lat|lat|gphi)[a-z0-9]*')\n",
"I think 'lon_bnds' is of type 'longitude'. It matched re.compile('x?(nav_lon|lon|glam)[a-z0-9]*')\n",
"I think 'lev_bnds' is of type 'Z'. It matched re.compile('(z|nav_lev|gdep|lv_|[o]*lev|bottom_top|sigma|h(ei)?ght|altitude|depth|isobaric|pres|isotherm)[a-z_]*[0-9]*')\n"
]
},
{
"data": {
"text/html": [
"
native gx1v7 displaced pole grid (384x320 latxlon)
grid_label :
gn
initialization_index :
1
institution :
National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA
institution_id :
NCAR
license :
CMIP6 model data produced by <The National Center for Atmospheric Research> is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
mip_era :
CMIP6
model_doi_url :
https://doi.org/10.5065/D67H1H0V
nominal_resolution :
100 km
parent_activity_id :
CMIP
parent_experiment_id :
piControl-spinup
parent_mip_era :
CMIP6
parent_source_id :
CESM2
parent_time_units :
days since 0001-01-01 00:00:00
parent_variant_label :
r1i1p1f1
physics_index :
1
product :
model-output
realization_index :
1
realm :
ocean
source :
CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)
source_id :
CESM2
source_type :
AOGCM BGC AER
sub_experiment :
none
sub_experiment_id :
none
table_id :
Omon
tracking_id :
hdl:21.14100/10d78d6e-34a5-44d7-a2c4-47708f7e098e
variable_id :
uo
variant_info :
CMIP6 CESM2 piControl experiment with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)