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Per #1954, ci-run-unit add unit tests to demonstrate the newly added …
…semilatlon functionality. Still need to update the docs.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,103 @@ | ||
| from __future__ import print_function | ||
|
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||
| import os | ||
| import sys | ||
| import numpy as np | ||
| import datetime as dt | ||
| from netCDF4 import Dataset,chartostring | ||
|
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| ########################################### | ||
|
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| ## | ||
| ## input file specified on the command line | ||
| ## load the data into the numpy array | ||
| ## | ||
|
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| if len(sys.argv) == 4: | ||
| # Read the input file as the first argument | ||
| input_file = os.path.expandvars(sys.argv[1]) | ||
| var_name = sys.argv[2] | ||
| axis = sys.argv[3] | ||
|
|
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| try: | ||
| # Print some output to verify that this script ran | ||
| print("Input File: " + repr(input_file)) | ||
| print("Variable: " + repr(var_name)) | ||
| print("Axis: " + repr(axis)) | ||
|
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| # Read input file | ||
| f = Dataset(input_file, 'r') | ||
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| data = np.float64(f.variables[var_name][0,:,:,:]) | ||
| data[data > 1.0e30] = np.nan | ||
|
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| pvals = list(np.float64(f.variables["pressure"][:])) | ||
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| if axis == "lon": | ||
| met_data = np.nanmean(data[::-1], axis=1).copy() | ||
| elif axis == "lat": | ||
| met_data = np.nanmean(data[::-1], axis=2).transpose().copy() | ||
| elif axis == "latlon": | ||
| met_data = np.nanmean(data[::-1], axis=1).copy() | ||
| else: | ||
| print("ERROR: Unsupported axis type: " + axis) | ||
| sys.exit(1) | ||
|
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| print("Data Shape: " + repr(met_data.shape)) | ||
| print("Data Type: " + repr(met_data.dtype)) | ||
| except NameError: | ||
| print("Trouble reading data from input file") | ||
| else: | ||
| print("Must specify exactly one input file, variable name, and summary axis (lat, lon, latlon).") | ||
| sys.exit(1) | ||
|
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| ########################################### | ||
|
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| ## | ||
| ## create the metadata dictionary | ||
| ## | ||
|
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| init = dt.datetime.strptime(getattr(f, "START_DATE"), "%Y-%m-%d_%H:%M:%S") | ||
| valid_ref = dt.datetime.strptime(getattr(f.variables["Time"], "units"), "hours since %Y-%m-%d %H:%M:%S") | ||
| add_hours = float(f.variables["Time"][:]) | ||
| valid = valid_ref + dt.timedelta(hours=add_hours) | ||
| lead, rem = divmod((valid-init).total_seconds(), 3600) | ||
| accum = "00" | ||
|
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| # Use the first column of lats | ||
|
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| if axis == "lon": | ||
| lats = list() | ||
| lons = list(np.float64(f.variables["XLONG"][0,0,:])) | ||
| elif axis == "lat": | ||
| lats = list(np.float64(f.variables["XLAT"][0,:,0])) | ||
| lons = list() | ||
| elif axis == "latlon": | ||
| lats = list(np.float64(f.variables["XLONG"][0,0,:])) | ||
| lons = list(np.float64(f.variables["XLAT"][0,0,:])) | ||
|
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| levels = list(pvals) | ||
| times = list() | ||
|
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| attrs = { | ||
| 'valid': valid.strftime("%Y%m%d_%H%M%S"), | ||
| 'init': init.strftime("%Y%m%d_%H%M%S"), | ||
| 'lead': str(int(lead)), | ||
| 'accum': accum, | ||
|
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| 'name': var_name, | ||
| 'long_name': str(getattr(f.variables[var_name], "description")), | ||
| 'level': axis + "_mean", | ||
| 'units': str(getattr(f.variables[var_name], "units")), | ||
|
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| 'grid': { | ||
| 'type' : "SemiLatLon", | ||
| 'name' : axis + "_mean", | ||
| 'lats' : lats, | ||
| 'lons' : lons, | ||
| 'levels' : levels, | ||
| 'times' : times | ||
| } | ||
| } | ||
|
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| print("Attributes: " + repr(attrs)) |