Module pyl4c.data.fixtures
Commonly used global constants. New users should change
ANCILLARY_DATA_PATHS if it doesn't match their file system.
Source for EASE-Grid 2.0 parameters:
<https://nsidc.org/ease/ease-grid-projection-gt>
NOTE: EASE-Grid 2.0 parameters do not match exactly those specified at NSIDC because, in fact, they must be 1000 or 9000, depending on the grid size, in order to get the right number of rows and columns in the output image. Grid resolution is only a whole number for the polar (north or south hemisphere) grids.
ANCILLARY_DATA_PATHS is loaded from a file,
pyl4c/data/files/ancillary_file_paths.yaml, which describes the location on
the local file system of certain ancillary files (and naming conventions
within those files):
smap_l4c_ancillary_data_file_path: "SPL4C_Vv4040_SMAP_L4_C.Ancillary.h5"
smap_l4c_1km_ancillary_data_lc_path: "MCD12Q1_M01_lc_dom_uint8"
smap_l4c_9km_ancillary_data_lc_path: "MOD12Q1_M09_lc_dom_uint8"
smap_l4c_1km_ancillary_data_x_coord_path: "SMAP_L4_C_LON_14616_x_34704_M01_flt32"
smap_l4c_1km_ancillary_data_y_coord_path: "SMAP_L4_C_LAT_14616_x_34704_M01_flt32"
smap_l4c_9km_ancillary_data_x_coord_path: "SMAP_L4_C_LON_1624_x_3856_M09_flt32"
smap_l4c_9km_ancillary_data_y_coord_path: "SMAP_L4_C_LAT_1624_x_3856_M09_flt32"
smap_l4c_9km_pft_subgrid_counts_CONUS: "SMAP_L4C_Vv4040_1km_subgrid_PFT_counts_CONUS.h5"
smap_l4c_9km_sparse_col_index: "MCD12Q1_M09land_col.uint16"
smap_l4c_9km_sparse_row_index: "MCD12Q1_M09land_row.uint16"
transcom_netcdf_path: "CarbonTracker_TransCom_and_other_regions.nc"
Functions
def parameter_mapped(name,
pft_array,
bplut=OrderedDict([('_version', '4'), ('LUE', array([[ nan, 1.71, 1.38, 1.71, 1.19, 1.95, 1.68, 2.53, 3.6 , nan]])), ('CUE', array([[ nan, 0.7 , 0.57, 0.79, 0.78, 0.65, 0.6 , 0.72, 0.71, nan]])), ('tmin', array([[ nan, 235., 230., 259., 260., 259., 251., 246., 266., nan], [ nan, 309., 303., 301., 284., 304., 302., 314., 319., nan]])), ('vpd', array([[ nan, 0.000e+00, 1.500e+01, 8.690e+02, 1.500e+03, 4.000e+00, 0.000e+00, 2.280e+02, 1.500e+03, nan], [ nan, 4.169e+03, 7.000e+03, 3.452e+03, 5.401e+03, 4.282e+03, 4.229e+03, 4.516e+03, 7.000e+03, nan]])), ('smrz', array([[ nan, -30., 18., -30., -26., 22., -30., -15., 10., nan], [ nan, 74., 26., 49., 87., 72., 76., 30., 68., nan]])), ('smsf', array([[ nan, -50., -46., -9., -50., -15., -3., -42., -49., nan], [ nan, 46., 59., 43., 53., 48., 51., 41., 30., nan]])), ('ft', array([[ nan, 0.58, 0.36, 0.55, 0.9 , 0.91, 0.85, 0.77, 1. , nan], [ nan, 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , nan]])), ('tsoil', array([[ nan, 265.04, 477.83, 238.81, 267.26, 292.82, 232.24, 263.97, 329.63, nan]])), ('decay_rates', array([[ nan, 0.027 , 0.028 , 0.028 , 0.03 , 0.015 , 0.025 , 0.019 , 0.035 , nan], [ nan, 0.0108 , 0.0112 , 0.0112 , 0.012 , 0.006 , 0.01 , 0.0076 , 0.014 , nan], [ nan, 0.000251, 0.00026 , 0.00026 , 0.000279, 0.00014 , 0.000233, 0.000177, 0.000326, nan]])), ('f_metabolic', array([[ nan, 0.49, 0.71, 0.67, 0.67, 0.62, 0.76, 0.78, 0.78, nan]])), ('f_structural', array([[ nan, 0.3 , 0.3 , 0.7 , 0.3 , 0.35, 0.55, 0.5 , 0.8 , nan]]))]))-
Expand source code
def parameter_mapped(name, pft_array, bplut = BPLUT): ''' Given a BPLUT parameter and a PFT array, returns an array with the corresponding parameter values for each PFT code. Parameters ---------- name : str The name of the BPLUT parameter pft_array : numpy.ndarray Array of any size or shape with numeric elements corresponding to PFT codes bplut : dict (Optional) A collection of BPLUT parameters Returns ------- numpy.ndarray Array of same size, shape as pft_array but with parameter values in place of PFT codes ''' param = bplut[name] # Basically, index the <name> array, in PFT order, by PFT numeric codes return np.asarray(param)[:,np.ravel(pft_array)].reshape(pft_array.shape)Given a BPLUT parameter and a PFT array, returns an array with the corresponding parameter values for each PFT code.
Parameters
name:str- The name of the BPLUT parameter
pft_array:numpy.ndarray- Array of any size or shape with numeric elements corresponding to PFT codes
bplut:dict- (Optional) A collection of BPLUT parameters
Returns
numpy.ndarray- Array of same size, shape as pft_array but with parameter values in place of PFT codes
def restore_bplut(csv_file_path, version_id=None)-
Expand source code
def restore_bplut(csv_file_path, version_id = None): ''' Translates a BPLUT CSV file to a Python internal representation (OrderedDict instance). Parameters ---------- csv_file_path : str File path to the CSV representation of the BPLUT version_id : str (Optional) Version identifier for the BPLUT Returns ------- OrderedDict ''' contents = [] with open(csv_file_path, 'r') as stream: reader = csv.DictReader( filter(lambda row: row[0] != '#', stream), fieldnames = BPLUT_HEADER) for row in reader: contents.append(row) result = BPLUT.copy() result['_version'] = 'UNKNOWN' if version_id is None else version_id result['LUE'] = np.array([[ contents[p-1]['LUEmax'] if p in range(1, 9) else np.nan for p in range(0, 10) ]], dtype = np.float32).round(4) result['CUE'] = np.array([[ (1 - float(contents[p-1]['fraut'])) if p in range(1, 9) else np.nan for p in range(0, 10) ]], dtype = np.float32).round(4) result['tmin'] = np.array([ [contents[p-1]['Tmin_min_K'] if p in range(1, 9) else np.nan for p in range(0, 10)], [contents[p-1]['Tmin_max_K'] if p in range(1, 9) else np.nan for p in range(0, 10)], ], dtype = np.float32).round(1) result['vpd'] = np.array([ [contents[p-1]['VPD_min_Pa'] if p in range(1, 9) else np.nan for p in range(0, 10)], [contents[p-1]['VPD_max_Pa'] if p in range(1, 9) else np.nan for p in range(0, 10)], ], dtype = np.float32).round(2) result['smrz'] = np.array([ [contents[p-1]['SMrz_min'] if p in range(1, 9) else np.nan for p in range(0, 10)], [contents[p-1]['SMrz_max'] if p in range(1, 9) else np.nan for p in range(0, 10)], ], dtype = np.float32).round(1) result['smsf'] = np.array([ [contents[p-1]['SMtop_min'] if p in range(1, 9) else np.nan for p in range(0, 10)], [contents[p-1]['SMtop_max'] if p in range(1, 9) else np.nan for p in range(0, 10)], ], dtype = np.float32).round(1) result['ft'] = np.array([ [contents[p-1]['FT_min'] if p in range(1, 9) else np.nan for p in range(0, 10)], [contents[p-1]['FT_max'] if p in range(1, 9) else np.nan for p in range(0, 10)], ], dtype = np.float32).round(3) result['tsoil'] = np.array([[ contents[p-1]['Tsoil_beta0'] if p in range(1, 9) else np.nan for p in range(0, 10) ]], dtype = np.float32).round(1) result['f_metabolic'] = np.array([[ contents[p-1]['fmet'] if p in range(1, 9) else np.nan for p in range(0, 10) ]], dtype = np.float32).round(3) result['f_structural'] = np.array([[ contents[p-1]['fstr'] if p in range(1, 9) else np.nan for p in range(0, 10) ]], dtype = np.float32).round(1) result['decay_rates'] = np.array([ [contents[p-1]['kopt'] if p in range(1, 9) else np.nan for p in range(0, 10)], [contents[p-1]['kstr'] if p in range(1, 9) else np.nan for p in range(0, 10)], [contents[p-1]['kslw'] if p in range(1, 9) else np.nan for p in range(0, 10)], ], dtype = np.float32).round(3) # The "kstr" and "kslw" values are really the fraction of kopt # assigned to the second and third pools result['decay_rates'][1,:] = np.multiply( result['decay_rates'][0,:], result['decay_rates'][1,:]) result['decay_rates'][2,:] = np.multiply( result['decay_rates'][0,:], result['decay_rates'][2,:]) return resultTranslates a BPLUT CSV file to a Python internal representation (OrderedDict instance).
Parameters
csv_file_path:str- File path to the CSV representation of the BPLUT
version_id:str- (Optional) Version identifier for the BPLUT
Returns
OrderedDict
def restore_bplut_flat(csv_file_path, version_id=None)-
Expand source code
def restore_bplut_flat(csv_file_path, version_id = None): ''' Translates a BPLUT CSV file to a Python internal representation (OrderedDict instance). Compare to `restore_bplut()`, this version sets parameter values as flat lists instead of n-dimensional NumPy arrays. Parameters ---------- csv_file_path : str File path to the CSV representation of the BPLUT version_id : str (Optional) Version identifier for the BPLUT Returns ------- OrderedDict ''' params = restore_bplut(csv_file_path, version_id) result = dict() for key, value in params.items(): if key not in ('tmin', 'vpd', 'smsf', 'smrz', 'ft'): result[key] = value continue for i, array in enumerate(value.tolist()): result[f'{key}{i}'] = np.array(array).reshape((1,len(array))) return OrderedDict(result)Translates a BPLUT CSV file to a Python internal representation (OrderedDict instance). Compare to
restore_bplut(), this version sets parameter values as flat lists instead of n-dimensional NumPy arrays.Parameters
csv_file_path:str- File path to the CSV representation of the BPLUT
version_id:str- (Optional) Version identifier for the BPLUT
Returns
OrderedDict