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🔖 bump to v2024.10.0
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@perillaroc
perillaroc committed Oct 28, 2024
1 parent 2a03173 commit 19196a2
Showing 1 changed file with 158 additions and 16 deletions.
174 changes: 158 additions & 16 deletions reki/format/grads/field.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
def load_field_from_file(
file_path: Union[str, Path],
parameter: str,
level_type: str = None,
level_type: Optional[str] = None,
level: Union[int, float, list] = None,
level_dim: Optional[str] = None,
latitude_direction: Literal["degree_north", "degree_south"] = "degree_north",
Expand All @@ -27,13 +27,18 @@ def load_field_from_file(
Parameters
----------
file_path
parameter
level_type
CTL description file path.
parameter : str
parameter name
level_type : Optional[str]
type of level
* pl / ml
* index
* single
* None
level
level : Union[int, float, list]
level value
level_dim
latitude_direction
* degree_north
Expand All @@ -49,35 +54,172 @@ def load_field_from_file(
Examples
--------
Load GRAPES GFS GMF postvar data:
Get CMA-GFS GMF modelvar data file path.
>>> from reki.data_finder import find_local_file
>>> from reki.format.grads import load_field_from_file
>>> postvar_file_path = find_local_file(
... "grapes_gfs_gmf/bin/postvar_ctl",
... start_time="2021080200",
>>> modelvar_file_path = find_local_file(
... "cma_gfs_gmf/bin/modelvar_ctl",
... start_time="2024102600",
... forecast_time="36h"
... )
>>> modelvar_file_path
PosixPath('/g3/COMMONDATA/OPER/CEMC/GFS_GMF/Fcst-long/2024102600/model.ctl_2024102600_036')
Load a single level field:
>>> field = load_field_from_file(
... postvar_file_path,
... parameter="t",
... level_type="pl",
... level=850
... modelvar_file_path,
... parameter="ts",
... level_type="single",
... )
>>> field
<xarray.DataArray 'ts' (latitude: 1671, longitude: 2501)> Size: 17MB
array([[276.65912, 276.66272, 276.66632, ..., 265.26233, 265.36075,
265.45914],
[271.72498, 271.74908, 272.00296, ..., 258.56754, 258.8194 ,
265.57312],
[271.73434, 271.7566 , 272.02072, ..., 259.0332 , 258.90613,
265.68716],
...,
[302.55112, 302.55115, 302.55112, ..., 302.75116, 302.75116,
302.75116],
[302.5451 , 302.5451 , 302.5451 , ..., 302.74515, 302.74515,
302.74515],
[302.53912, 302.53912, 302.53912, ..., 302.73914, 302.73914,
302.73914]], dtype=float32)
Coordinates:
* latitude (latitude) float64 13kB 60.1 60.07 60.04 ... 10.06 10.03 10.0
* longitude (longitude) float64 20kB 70.0 70.03 70.06 ... 144.9 145.0 145.0
level int64 8B 0
valid_time datetime64[ns] 8B ...
Attributes:
description: surface temperature
Load a multi level field:
Load GRAPES TYM postvar data. Set ``forecast_time`` option because all data files of one cycle use one CTL file.
>>> field = load_field_from_file(
... modelvar_file_path,
... parameter="pi",
... level_type="ml",
... level=[1,2,3,4]
... )
>>> field
<xarray.DataArray 'pi' (ml: 4, latitude: 1441, longitude: 2880)> Size: 66MB
array([[[0.9994781 , 0.99947816, 0.9994783 , ..., 0.99947804,
0.9994781 , 0.99947816],
[0.9994006 , 0.99940056, 0.9994005 , ..., 0.99940073,
0.9994006 , 0.9994006 ],
[0.99941564, 0.9994156 , 0.9994156 , ..., 0.99941593,
0.9994159 , 0.99941576],
...,
[0.9025082 , 0.90250385, 0.90249956, ..., 0.902521 ,
0.9025168 , 0.9025125 ],
[0.9023999 , 0.90239733, 0.9023947 , ..., 0.90240735,
0.9024049 , 0.90240246],
[0.90238315, 0.90238225, 0.90238154, ..., 0.9023854 ,
0.9023846 , 0.9023839 ]],
[[0.9987454 , 0.99874544, 0.99874544, ..., 0.9987453 ,
0.9987453 , 0.9987454 ],
[0.9986675 , 0.9986674 , 0.99866736, ..., 0.9986676 ,
0.99866754, 0.9986675 ],
[0.99867845, 0.9986784 , 0.9986783 , ..., 0.99867874,
0.9986786 , 0.99867857],
...
[0.90097326, 0.90096897, 0.9009646 , ..., 0.90098614,
0.90098184, 0.90097755],
[0.9008647 , 0.9008622 , 0.90085965, ..., 0.9008723 ,
0.9008698 , 0.9008672 ],
[0.9008468 , 0.900846 , 0.9008453 , ..., 0.90084904,
0.90084827, 0.90084755]],
[[0.99662673, 0.9966268 , 0.99662685, ..., 0.9966266 ,
0.9966266 , 0.9966267 ],
[0.996547 , 0.9965469 , 0.9965469 , ..., 0.9965471 ,
0.99654704, 0.99654704],
[0.9965461 , 0.99654603, 0.99654603, ..., 0.99654627,
0.9965462 , 0.99654615],
...,
[0.8998071 , 0.89980286, 0.8997985 , ..., 0.8998199 ,
0.8998157 , 0.89981145],
[0.89969814, 0.8996955 , 0.899693 , ..., 0.89970565,
0.89970315, 0.89970064],
[0.8996787 , 0.89967805, 0.8996773 , ..., 0.89968103,
0.89968026, 0.8996795 ]]], dtype=float32)
Coordinates:
* latitude (latitude) float64 12kB 90.0 89.88 89.75 ... -89.88 -90.0
* longitude (longitude) float64 23kB 0.0 0.125 0.25 ... 359.6 359.8 359.9
* ml (ml) float64 32B 1.0 2.0 3.0 4.0
valid_time datetime64[ns] 8B ...
start_time datetime64[ns] 8B ...
forecast_time timedelta64[ns] 8B 1 days 12:00:00
Attributes:
description: PI
Get CMA-MESO postvar data file path.
>>> postvar_file_path = find_local_file(
... "grapes_tym/bin/postvar_ctl",
... start_time="2021080200",
... "cma_meso_3km/bin/postvar_ctl",
... start_time="2024102600",
... )
>>> postvar_file_path
PosixPath('/g3/COMMONDATA/OPER/CEMC/MESO_3KM/Fcst-main/2024102600/postvar.ctl_202410260000000')
Load a field. Set ``forecast_time`` option because all data files of one cycle use one CTL file.
>>> field = load_field_from_file(
... postvar_file_path,
... parameter="t",
... level_type="pl",
... forecast_time="24h",
... level=850
... )
>>> field
<xarray.DataArray 't' (pl: 26, latitude: 1671, longitude: 2501)> Size: 435MB
array([[[275.32828, 275.32657, 275.3065 , ..., 260.2067 , 260.09564,
259.9529 ],
[275.34738, 275.35904, 275.31445, ..., 260.0906 , 259.94995,
259.89807],
[275.40707, 275.39713, 275.38556, ..., 260.17816, 259.95865,
259.75906],
...,
[299.74286, 299.75177, 299.7522 , ..., 300.81787, 300.75705,
300.7689 ],
[299.73053, 299.7287 , 299.73077, ..., 300.78677, 300.80746,
300.80475],
[299.73132, 299.7241 , 299.72073, ..., 300.7902 , 300.8348 ,
300.81497]],
[[274.36758, 274.35873, 274.33942, ..., 258.95673, 258.8462 ,
258.70416],
[274.3802 , 274.38016, 274.39774, ..., 258.84058, 258.70056,
258.64957],
[274.4292 , 274.4326 , 274.42468, ..., 258.92773, 258.70926,
258.51123],
...
[221.9119 , 221.9043 , 221.90732, ..., 220.80681, 220.79927,
220.79141],
[221.9006 , 221.89711, 221.89825, ..., 220.78723, 220.77835,
220.76967],
[221.88934, 221.88995, 221.89085, ..., 220.76501, 220.7564 ,
220.74776]],
[[218.22253, 218.22556, 218.22949, ..., 225.40901, 225.4072 ,
225.40222],
[218.22551, 218.2267 , 218.22943, ..., 225.07826, 225.0852 ,
225.40283],
[218.2302 , 218.23157, 218.23302, ..., 225.08052, 225.0856 ,
225.39835],
...,
[234.57108, 234.70776, 234.7125 , ..., 232.78271, 232.81221,
232.84262],
[234.57117, 234.64745, 234.65125, ..., 232.78842, 232.81712,
232.84677],
[234.57133, 234.57503, 234.57837, ..., 232.82465, 232.83774,
232.85094]]], dtype=float32)
Coordinates:
* latitude (latitude) float64 13kB 60.1 60.07 60.04 ... 10.06 10.03 10.0
* longitude (longitude) float64 20kB 70.0 70.03 70.06 ... 144.9 145.0 145.0
* pl (pl) float64 208B 1e+03 975.0 950.0 925.0 ... 30.0 20.0 10.0
valid_time datetime64[ns] 8B ...
Attributes:
description: temperature
"""
if isinstance(forecast_time, str):
forecast_time = pd.to_timedelta(forecast_time)
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