1 | #! /usr/bin/python |
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2 | # ====================================================================== |
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3 | # *** TOOL diamlr.py *** |
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4 | # Postprocessing of intermediate NEMO model output for |
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5 | # multiple-linear-regression analysis (diamlr) |
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6 | # ====================================================================== |
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7 | # History : ! 2019 (S. Mueller) |
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8 | # ---------------------------------------------------------------------- |
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9 | import sys |
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10 | # ---------------------------------------------------------------------- |
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11 | # NEMO/TOOLS 4.0 , NEMO Consortium (2019) |
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12 | # $Id$ |
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13 | # Software governed by the CeCILL license (see ./LICENSE) |
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14 | # ---------------------------------------------------------------------- |
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15 | |
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16 | # ---------------------------------------------------------------------- |
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17 | # *** SUBROUTINE get_args *** |
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18 | # Parse command line arguments |
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19 | # ---------------------------------------------------------------------- |
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20 | def get_args(): |
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21 | from argparse import ArgumentParser |
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22 | import re |
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23 | |
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24 | # Set up command-line argument parser |
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25 | parser = ArgumentParser( |
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26 | description='Postprocessing of intermediate NEMO model output'+ |
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27 | ' for multiple-linear-regression analysis (diamlr)') |
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28 | parser.add_argument('--file_scalar', help= |
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29 | 'Filename of scalar intermediate NEMO model'+ |
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30 | ' output for multiple-linear-regression'+ |
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31 | ' analysis') |
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32 | parser.add_argument('--file_grid', help= |
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33 | 'Filename of gridded intermediate NEMO model'+ |
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34 | ' output for multiple-linear-regression'+ |
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35 | ' analysis') |
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36 | parser.add_argument('--regressors', nargs='+', required=False, |
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37 | help='Optional list of regressors to include'+ |
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38 | ' in analysis; if omitted, all available'+ |
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39 | ' regressors will be included') |
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40 | args = parser.parse_args() |
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41 | return args |
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42 | |
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43 | # ---------------------------------------------------------------------- |
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44 | # *** SUBROUTINE main *** |
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45 | # Finalisation of multiple-linear-regression analysis |
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46 | # ---------------------------------------------------------------------- |
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47 | def main(): |
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48 | from netCDF4 import Dataset as nc |
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49 | import re |
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50 | import numpy as np |
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51 | from os.path import basename, splitext |
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52 | from time import strftime, localtime |
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53 | |
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54 | # Get command-line arguments |
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55 | args = get_args() |
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56 | |
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57 | # Get filenames/locations of intermdiate diamlr output |
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58 | fn_scalar = args.file_scalar |
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59 | fn_grid = args.file_grid |
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60 | |
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61 | # Open regressor-regressor scalar-product data set |
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62 | f = nc(fn_scalar, 'r') |
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63 | |
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64 | # Detect available regressors; reduce list of regressors according |
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65 | # to list of selected regressors (if specified) |
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66 | regs = {} |
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67 | vn_re = re.compile('(diamlr_r[0-9]{3})\.(diamlr_r[0-9]{3})') |
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68 | for vn in f.variables.keys(): |
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69 | vn_match = vn_re.match(vn) |
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70 | if (vn_match): |
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71 | reg1 = vn_match.group(1) |
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72 | reg2 = vn_match.group(2) |
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73 | if not args.regressors or reg1 in args.regressors: |
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74 | regs[vn_match.group(1)] = True |
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75 | if not args.regressors or reg2 in args.regressors: |
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76 | regs[vn_match.group(2)] = True |
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77 | regs = regs.keys() |
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78 | regs.sort() |
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79 | |
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80 | print('Compile and invert matrix of regressor-regressor scalar'+ |
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81 | 'products ...') |
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82 | |
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83 | # Set up square matrix, XX, of regressor-regressor scalar products |
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84 | xx = np.matrix(np.zeros((len(regs), len(regs)))) |
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85 | for i1 in range(len(regs)): |
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86 | vn1 = regs[i1] |
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87 | for i2 in range(regs.index(vn1)+1): |
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88 | vn2 = regs[i2] |
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89 | if f.variables[vn1+'.'+vn2]: |
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90 | xx_sum = np.sum(f.variables[vn1+'.'+vn2][:]) |
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91 | else: |
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92 | xx_sum = np.sum(f.variables[vn2+'.'+vn1][:]) |
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93 | xx[i1,i2] = xx_sum |
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94 | if i1 != i2: |
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95 | xx[i2,i1] = xx_sum |
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96 | |
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97 | # Close regressor-regressor scalar-product data set |
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98 | f.close() |
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99 | |
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100 | # Compute inverse matrix, XX^-1; convert matrix to an array to |
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101 | # enable the dot-product computation of XX with a large array below |
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102 | ixx = np.array(xx**-1) |
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103 | |
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104 | print(' ... done') |
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105 | |
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106 | # Open field-regressor scalar-product data set |
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107 | f = nc(fn_grid, 'r') |
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108 | |
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109 | # Detect analysed fields |
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110 | flds = {} |
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111 | vn_re = re.compile('(diamlr_f[0-9]{3})\.(diamlr_r[0-9]{3})') |
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112 | for vn in f.variables.keys(): |
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113 | vn_match = vn_re.match(vn) |
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114 | if (vn_match): |
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115 | if vn_match.group(2) in regs: |
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116 | flds[vn_match.group(1)] = True |
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117 | flds = flds.keys() |
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118 | flds.sort() |
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119 | |
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120 | # Open and prepare output file, incl. replication of |
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121 | # domain-decomposition metadata (if present) from the |
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122 | # field-regressor data set |
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123 | fn_out = './'+basename(fn_grid) |
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124 | if fn_out.find('diamlr') > 0: |
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125 | fn_out = fn_out.replace('diamlr', 'diamlr_coeffs') |
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126 | else: |
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127 | fn_parts = splitext(basename(fn_grid)) |
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128 | fn_out = fn_parts[0]+'_diamlr_coeffs'+fn_parts[1] |
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129 | nc_out = nc(fn_out, 'w', format='NETCDF4', clobber=False) |
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130 | nc_atts = { |
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131 | 'name' : fn_out, |
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132 | 'description' : 'Multiple-linear-regression analysis output', |
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133 | 'title' : 'Multiple-linear-regression analysis output', |
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134 | 'timeStamp' : strftime('%Y-%m-%d %H:%M:%S %Z', localtime())} |
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135 | for nc_att in f.ncattrs(): |
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136 | if nc_att in ['ibegin', 'jbegin', |
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137 | 'ni', 'nj'] or nc_att.startswith('DOMAIN'): |
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138 | nc_atts[nc_att] = f.getncattr(nc_att) |
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139 | nc_out.setncatts(nc_atts) |
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140 | for nc_dimname in f.dimensions.keys(): |
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141 | nc_dim = f.dimensions[nc_dimname] |
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142 | if nc_dim.isunlimited(): |
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143 | nc_out.createDimension(nc_dim.name) |
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144 | else: |
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145 | nc_out.createDimension(nc_dim.name, nc_dim.size) |
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146 | |
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147 | # Read in fields of scalar products of model diagnostics and |
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148 | # regressors and compute the regression coefficients for the current |
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149 | # field; add resulting fields to output file |
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150 | for fld in flds: |
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151 | print('Completing analysis for field '+fld+' ...') |
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152 | xy = np.array([]) |
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153 | for reg in regs: |
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154 | if xy.size == 0: |
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155 | xy = np.sum(f.variables[fld+'.'+reg][:], |
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156 | axis=0)[np.newaxis,:] |
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157 | else: |
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158 | xy = np.r_[xy, np.sum(f.variables[fld+'.'+reg][:], |
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159 | axis=0)[np.newaxis,:]] |
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160 | b=np.reshape(np.dot(ixx, np.reshape(xy,(len(xy),xy[0,:].size))), |
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161 | (xy.shape)) |
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162 | print(' ... done') |
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163 | |
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164 | for reg in regs: |
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165 | nr = regs.index(reg) |
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166 | nc_gridvar = f.variables[fld+'.'+reg] |
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167 | name = nc_gridvar.name.split('.') |
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168 | nc_var = nc_out.createVariable(name[0]+'-'+name[1], |
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169 | nc_gridvar.datatype, |
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170 | nc_gridvar.dimensions) |
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171 | for nc_att in nc_gridvar.ncattrs(): |
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172 | if nc_att in ['_FillValue', 'missing_value']: |
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173 | nc_var.setncattr(nc_att, nc_gridvar.getncattr( |
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174 | nc_att)) |
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175 | name = nc_gridvar.getncattr('standard_name').split('.') |
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176 | nc_var.setncattr( |
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177 | 'standard_name', name[0]+' regressed on '+name[1]) |
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178 | nc_var[0,:] = b[nr,:].data |
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179 | |
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180 | # Close output file; close field-regressor scalar-product data set |
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181 | nc_out.close() |
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182 | f.close() |
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183 | |
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184 | # ---------------------------------------------------------------------- |
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185 | # *** main PROGRAM *** |
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186 | # ---------------------------------------------------------------------- |
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187 | if __name__ == "__main__": |
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188 | |
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189 | main() |
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