[351] | 1 | # Python script to transfomr ASCII LIDAR outputs to netCDF |
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[352] | 2 | ## g.e. # TS_ASCII_netCDF.py -f //media/ExtDiskD/bkup/ciclad/etudes/WL_HyMeX/iop15/wrf/run/control/stations_20121018000000-20121022000000/h0001.d01.TS -s 20121018000000 |
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[351] | 3 | |
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| 4 | import os |
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| 5 | from optparse import OptionParser |
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| 6 | import numpy as np |
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| 7 | from netCDF4 import Dataset as NetCDFFile |
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[928] | 8 | import re |
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[351] | 9 | |
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| 10 | main = 'TS_ASCII_netCDF.py' |
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| 11 | errormsg='ERROR -- error -- ERROR -- error' |
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| 12 | warnmsg='WARNING -- warning -- WARNING -- warning' |
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| 13 | |
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| 14 | fillValue = 1.e20 |
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| 15 | |
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[928] | 16 | def searchInlist(listname, nameFind): |
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| 17 | """ Function to search a value within a list |
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| 18 | listname = list |
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| 19 | nameFind = value to find |
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| 20 | >>> searInlist(['1', '2', '3', '5'], '5') |
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| 21 | True |
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| 22 | """ |
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| 23 | for x in listname: |
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| 24 | if x == nameFind: |
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| 25 | return True |
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| 26 | return False |
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| 27 | |
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| 28 | def reduce_spaces(string): |
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| 29 | """ Function to give words of a line of text removing any extra space |
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| 30 | """ |
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| 31 | values = string.replace('\n','').split(' ') |
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| 32 | vals = [] |
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| 33 | for val in values: |
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| 34 | if len(val) > 0: |
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| 35 | vals.append(val) |
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| 36 | |
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| 37 | return vals |
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| 38 | |
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| 39 | def reduce_last_spaces(string): |
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| 40 | """ Function to reduce the last right spaces from a string |
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| 41 | string= string to remove the spaces at the righ side of the string |
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| 42 | >>> reduce_last_spaces('Hola Lluis ') |
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| 43 | 'Hola Lluis' |
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| 44 | """ |
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| 45 | fname = 'reduce_last_spaces' |
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| 46 | |
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| 47 | Lstring = len(string) |
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| 48 | |
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| 49 | firstspace = -1 |
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| 50 | for istr in range(Lstring-1,0,-1): |
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| 51 | if string[istr:istr+1] == ' ': |
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| 52 | firstspace = istr |
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| 53 | else: |
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| 54 | break |
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| 55 | |
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| 56 | if firstspace == -1: |
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| 57 | print errormsg |
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| 58 | print ' ' + fname + ": string '" + string + "' is not ended by spaces!!" |
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| 59 | print ' char. number of last character:',ord(string[Lstring:Lstring+1]) |
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| 60 | quit(-1) |
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| 61 | |
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| 62 | newstr = string[0:firstspace] |
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| 63 | |
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| 64 | return newstr |
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| 65 | |
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| 66 | def set_attribute(ncv, attrname, attrvalue): |
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| 67 | """ Sets a value of an attribute of a netCDF variable. Removes previous attribute value if exists |
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| 68 | ncv = object netcdf variable |
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| 69 | attrname = name of the attribute |
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| 70 | attrvalue = value of the attribute |
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| 71 | """ |
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| 72 | |
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| 73 | attvar = ncv.ncattrs() |
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| 74 | if searchInlist(attvar, attrname): |
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| 75 | attr = ncv.delncattr(attrname) |
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| 76 | |
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| 77 | attr = ncv.setncattr(attrname, attrvalue) |
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| 78 | |
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| 79 | return attr |
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| 80 | |
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| 81 | def rmNOnum(string): |
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| 82 | """ Removing from a string all that characters which are not numbers |
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| 83 | # From: http://stackoverflow.com/questions/4289331/python-extract-numbers-from-a-string |
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| 84 | """ |
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| 85 | fname = 'rmNOnum' |
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| 86 | |
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| 87 | newstr = str(re.findall("[-+]?\d+[\.]?\d*", string)[0]) |
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| 88 | |
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| 89 | return newstr |
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| 90 | |
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| 91 | def basicvardef(varobj, vstname, vlname, vunits): |
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| 92 | """ Function to give the basic attributes to a variable |
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| 93 | varobj= netCDF variable object |
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| 94 | vstname= standard name of the variable |
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| 95 | vlname= long name of the variable |
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| 96 | vunits= units of the variable |
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| 97 | """ |
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| 98 | attr = varobj.setncattr('standard_name', vstname) |
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| 99 | attr = varobj.setncattr('long_name', vlname) |
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| 100 | attr = varobj.setncattr('units', vunits) |
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| 101 | |
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| 102 | return |
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| 103 | |
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| 104 | def values_fortran_fmt(lin,fFMT): |
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| 105 | """ Function to give back the values of an ASCII line according to its fortran printing format |
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| 106 | lin= ASCII line |
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| 107 | fFMT= list with the fortran FORMAT formats |
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| 108 | forline = 'Natchitoches (RGNL) 1 11 0011 ( 31.733, -93.100) ( 28, 157) ( 31.761, -93.113) 41.2 meters' |
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| 109 | formats = ['A26', 'I2', 'I3', 'A6', 'A2', 'F7.3', 'A1', 'F8.3', 'A3', 'I4', 'A1', 'I4', |
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| 110 | 'A3', 'F7.3', 'A1', 'F8.3', 'A2', 'F6.1', 'A7'] |
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| 111 | >>> values_fortran_fmt(forline, formats) |
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| 112 | ['Natchitoches (RGNL) ', 1, 11, ' 0011 ', ' ( ', 31.733, ', ', -93.1, ') ( ', 28, ', ', 157, ') |
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| 113 | ( ', 31.761, ', ', -93.113, ') ', 41.2, ' meters'] |
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| 114 | """ |
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| 115 | fname = 'values_fortran_fmt' |
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| 116 | |
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| 117 | afmts = ['A', 'D', 'F', 'I'] |
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| 118 | |
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| 119 | if lin == 'h': |
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| 120 | print fname + '_____________________________________________________________' |
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| 121 | print values_fortran_fmt.__doc__ |
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| 122 | quit() |
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| 123 | |
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| 124 | fvalues = [] |
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| 125 | ichar=0 |
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| 126 | ival = 0 |
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| 127 | for ft in fFMT: |
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| 128 | Lft = len(ft) |
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| 129 | if ft[0:1] == 'A' or ft[0:1] == 'a': |
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| 130 | Lfmt = int(ft[1:Lft+1]) |
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| 131 | fvalues.append(lin[ichar:ichar+Lfmt+1]) |
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| 132 | ichar = ichar + Lfmt |
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| 133 | elif ft[0:1] == 'F' or ft[0:1] == 'f': |
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| 134 | if ft.find('.') != -1: |
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| 135 | Lft = len(ft.split('.')[0]) |
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| 136 | Lfmt = int(ft[1:Lft]) |
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| 137 | fvalues.append(np.float(rmNOnum(lin[ichar:ichar+Lfmt+1]))) |
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| 138 | ichar = ichar + Lfmt |
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| 139 | elif ft[0:1] == 'D' or ft[0:1] == 'd': |
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| 140 | if ft.find('.') != -1: |
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| 141 | Lft = len(ft.split('.')[0]) |
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| 142 | Lfmt = int(ft[1:Lft]) |
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| 143 | fvalues.append(np.float64(rmNOnum(lin[ichar:ichar+Lfmt+1]))) |
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| 144 | ichar = ichar + Lfmt |
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| 145 | elif ft[0:1] == 'I' or ft[0:1] == 'i': |
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| 146 | Lfmt = int(ft[1:Lft+1]) |
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| 147 | fvalues.append(int(rmNOnum(lin[ichar:ichar+Lfmt+1]))) |
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| 148 | ichar = ichar + Lfmt |
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| 149 | elif ft.find('X') != -1 or ft.find('x') != -1: |
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| 150 | print ' ' + fname + ': skipping space' |
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| 151 | ichar = ichar + int(rmNOnum(ft)) |
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| 152 | else: |
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| 153 | print errormsg |
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| 154 | print ' ' + fname + ": format '" + ft[0:1] + "' not ready!!" |
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| 155 | print ' Available formats:',afmts |
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| 156 | quit(-1) |
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| 157 | |
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| 158 | ival = ival + 1 |
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| 159 | |
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| 160 | return fvalues |
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| 161 | |
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| 162 | |
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[566] | 163 | def ts_header(ln): |
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| 164 | """ Function to get the values of the header of the *.TS files |
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| 165 | line=ASCII lines with the header of the TS file |
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| 166 | getting the line format from WRFV3.3 'EMCORE' in file 'share/wrf_timeseries.F' |
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| 167 | """ |
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| 168 | fname = 'ts_header' |
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| 169 | |
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[569] | 170 | fmts=['A26', 'I2', 'I3', 'A6', 'A2', 'F7.3', 'A1', 'F8.3', 'A3', 'I4', 'A1', 'I4',\ |
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[566] | 171 | 'A3', 'F7.3', 'A1', 'F8.3', 'A2', 'F6.1', 'A7'] |
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| 172 | |
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[928] | 173 | headervalues = values_fortran_fmt(ln,fmts) |
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[566] | 174 | |
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| 175 | return headervalues |
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| 176 | |
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[928] | 177 | def variables_values(varName): |
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| 178 | """ Function to provide values to plot the different variables values from ASCII file |
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| 179 | 'variables_values.dat' |
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| 180 | variables_values(varName) |
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| 181 | [varName]= name of the variable |
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| 182 | return: [var name], [std name], [minimum], [maximum], |
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| 183 | [long name]('|' for spaces), [units], [color palette] (following: |
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| 184 | http://matplotlib.org/1.3.1/examples/color/colormaps_reference.html) |
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| 185 | [varn]: original name of the variable |
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| 186 | NOTE: It might be better doing it with an external ASII file. But then we |
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| 187 | got an extra dependency... |
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| 188 | >>> variables_values('WRFght') |
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| 189 | ['z', 'geopotential_height', 0.0, 80000.0, 'geopotential|height', 'm2s-2', 'rainbow'] |
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| 190 | """ |
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| 191 | import subprocess as sub |
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| 192 | |
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| 193 | fname='variables_values' |
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| 194 | |
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| 195 | if varName == 'h': |
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| 196 | print fname + '_____________________________________________________________' |
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| 197 | print variables_values.__doc__ |
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| 198 | quit() |
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| 199 | |
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| 200 | # This does not work.... |
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| 201 | # folderins = sub.Popen(["pwd"], stdout=sub.PIPE) |
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| 202 | # folder = list(folderins.communicate())[0].replace('\n','') |
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| 203 | # From http://stackoverflow.com/questions/4934806/how-can-i-find-scripts-directory-with-python |
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| 204 | folder = os.path.dirname(os.path.realpath(__file__)) |
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| 205 | |
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| 206 | infile = folder + '/variables_values.dat' |
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| 207 | |
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| 208 | if not os.path.isfile(infile): |
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| 209 | print errormsg |
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| 210 | print ' ' + fname + ": File '" + infile + "' does not exist !!" |
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| 211 | quit(-1) |
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| 212 | |
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| 213 | # Variable name might come with a statistical surname... |
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| 214 | stats=['min','max','mean','stdv', 'sum'] |
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| 215 | |
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| 216 | # Variables with a statistical section on their name... |
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| 217 | NOstatsvars = ['zmaxth', 'zmax_th', 'lmax_th', 'lmaxth'] |
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| 218 | |
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| 219 | ifst = False |
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| 220 | if not searchInlist(NOstatsvars, varName.lower()): |
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| 221 | for st in stats: |
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| 222 | if varName.find(st) > -1: |
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| 223 | print ' '+ fname + ": varibale '" + varName + "' with a " + \ |
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| 224 | "statistical surname: '",st,"' !!" |
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| 225 | Lst = len(st) |
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| 226 | LvarName = len(varName) |
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| 227 | varn = varName[0:LvarName - Lst] |
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| 228 | ifst = True |
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| 229 | break |
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| 230 | if not ifst: |
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| 231 | varn = varName |
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| 232 | |
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| 233 | ncf = open(infile, 'r') |
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| 234 | |
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| 235 | for line in ncf: |
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| 236 | if line[0:1] != '#': |
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| 237 | values = line.replace('\n','').split(',') |
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| 238 | if len(values) != 8: |
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| 239 | print errormsg |
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| 240 | print "problem in varibale:'", values[0], \ |
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| 241 | 'it should have 8 values and it has',len(values) |
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| 242 | quit(-1) |
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| 243 | |
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| 244 | if varn[0:6] == 'varDIM': |
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| 245 | # Variable from a dimension (all with 'varDIM' prefix) |
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| 246 | Lvarn = len(varn) |
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| 247 | varvals = [varn[6:Lvarn+1], varn[6:Lvarn+1], 0., 1., \ |
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| 248 | "variable|from|size|of|dimension|'" + varn[6:Lvarn+1] + "'", '1', \ |
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| 249 | 'rainbow'] |
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| 250 | else: |
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| 251 | varvals = [values[1].replace(' ',''), values[2].replace(' ',''), \ |
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| 252 | np.float(values[3]), np.float(values[4]),values[5].replace(' ',''),\ |
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| 253 | values[6].replace(' ',''), values[7].replace(' ','')] |
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| 254 | if values[0] == varn: |
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| 255 | ncf.close() |
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| 256 | return varvals |
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| 257 | break |
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| 258 | |
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| 259 | print errormsg |
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| 260 | print ' ' + fname + ": variable '" + varn + "' not defined !!!" |
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| 261 | ncf.close() |
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| 262 | quit(-1) |
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| 263 | |
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| 264 | return |
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| 265 | |
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[351] | 266 | ####### ###### ##### #### ### ## # |
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| 267 | |
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| 268 | parser = OptionParser() |
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| 269 | parser.add_option("-f", "--TS_file", dest="lfile", |
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[352] | 270 | help="Time Series ASCII text file to use", metavar="FILE") |
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| 271 | parser.add_option("-s", "--SimulationStartTime", dest="stime", |
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| 272 | help="Starting time of the simulation ([YYYY][MM][DD][HH][MI][SS] format)", metavar="DATE") |
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[351] | 273 | |
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| 274 | (opts, args) = parser.parse_args() |
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| 275 | |
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| 276 | |
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| 277 | tsvn = ['t', 'q', 'u', 'v', 'psfc', 'glw', 'gsw', 'hfx', 'lh', 'tsk', 'tslb1', 'rainc', 'rainnc', 'clw'] |
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| 278 | |
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| 279 | tsvln = ['2 m Temperature', '2 m vapor mixing ratio', '10 m U wind (earth-relative)', '10 m V wind (earth-relative)', 'surface pressure', 'downward longwave radiation flux at the ground (downward is positive)', 'net shortwave radiation flux at the ground (downward is positive)', 'surface sensible heat flux (upward is positive)', 'surface latent heat flux (upward is positive)', 'skin temperature', 'top soil layer temperature', 'rainfall from a cumulus scheme', 'rainfall from an explicit scheme', 'total column-integrated water vapor and cloud variables'] |
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| 280 | |
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[352] | 281 | tsvu = ['K', 'kg/kg', 'm/s', 'm/s', 'Pa', 'W/m2', 'W/m2', 'W/m2', 'W/m2', 'K', 'K', 'mm', 'mm', '1'] |
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[351] | 282 | |
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[928] | 283 | |
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[351] | 284 | ####### ####### |
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| 285 | ## MAIN |
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| 286 | ####### |
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| 287 | |
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| 288 | ofile = 'ts.nc' |
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| 289 | Ntsvariables = len(tsvn) |
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| 290 | |
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| 291 | if not os.path.isfile(opts.lfile): |
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| 292 | print errormsg |
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[352] | 293 | print ' ' + main + ': Time-Series ASCII text file "' + opts.lfile + \ |
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| 294 | '" does not exist !!' |
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[351] | 295 | print errormsg |
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| 296 | quit() |
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| 297 | |
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[352] | 298 | if opts.stime is None: |
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| 299 | print errormsg |
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| 300 | print ' ' + main + ': No initial date/time of the simulation is provided!' |
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| 301 | quit(-1) |
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| 302 | else: |
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| 303 | stime = opts.stime |
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| 304 | refdate = stime[0:4] + '-' + stime[4:6] + '-' + stime[6:8] + ' ' + stime[8:10] + \ |
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| 305 | ':' + stime[10:12] + ':' + stime[12:14] |
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| 306 | |
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[351] | 307 | objlfile = open(opts.lfile, 'r') |
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| 308 | |
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| 309 | objofile = NetCDFFile(ofile, 'w') |
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| 310 | |
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| 311 | # Creation of dimensions |
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| 312 | ## |
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| 313 | objofile.createDimension('time',None) |
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| 314 | |
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[928] | 315 | set_attribute(objofile, 'author', 'Lluis Fita Borrell') |
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| 316 | set_attribute(objofile, 'institution', 'Laboratoire Meteorologique Dynamique') |
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| 317 | set_attribute(objofile, 'university', 'University Pierre et Marie Curie') |
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| 318 | set_attribute(objofile, 'center', 'Centre national de la recherche scientifique') |
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| 319 | set_attribute(objofile, 'country', 'France') |
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| 320 | set_attribute(objofile, 'city', 'Paris') |
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| 321 | set_attribute(objofile, 'script', 'TS_ASCII_netCFD.py') |
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| 322 | set_attribute(objofile, 'version', '1.0') |
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[351] | 323 | |
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| 324 | time_step = [] |
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| 325 | psfc = [] |
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| 326 | rainc = [] |
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| 327 | rainnc = [] |
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| 328 | drydens = [] |
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| 329 | |
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| 330 | tsvals = {} |
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| 331 | |
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| 332 | iline=0 |
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| 333 | itz = 0 |
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| 334 | for line in objlfile: |
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[928] | 335 | values = reduce_spaces(line) |
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[351] | 336 | # print iline, values[0], dimz, Searchdimz |
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| 337 | # Writting general information |
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| 338 | if iline == 0: |
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| 339 | newvar = objofile.createVariable('station','c') |
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[566] | 340 | valueshead = ts_header(line) |
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[459] | 341 | |
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[928] | 342 | set_attribute(newvar, 'name', reduce_last_spaces(valueshead[0])) |
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| 343 | set_attribute(newvar, 'acronym',valueshead[3].replace(' ','')) |
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[459] | 344 | |
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[928] | 345 | set_attribute(newvar, 'real_lon', valueshead[5]) |
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| 346 | set_attribute(newvar, 'real_lat', valueshead[7]) |
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[459] | 347 | |
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[928] | 348 | set_attribute(newvar, 'x_grid_point', valueshead[9]) |
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| 349 | set_attribute(newvar, 'y_grid_point', valueshead[11]) |
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[459] | 350 | |
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[928] | 351 | set_attribute(newvar, 'model_lon', valueshead[13]) |
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| 352 | set_attribute(newvar, 'model_lat', valueshead[15]) |
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| 353 | set_attribute(newvar, 'model_height', valueshead[17]) |
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[352] | 354 | simstarttime = refdate |
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[351] | 355 | else: |
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| 356 | tsvals[itz] = values |
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[352] | 357 | time_step.append(np.float(values[1])) |
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| 358 | itz = itz + 1 |
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[351] | 359 | iline = iline + 1 |
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| 360 | |
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| 361 | dimt = len(time_step) |
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| 362 | |
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[352] | 363 | print ' Found:',dimt,'time steps' |
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[351] | 364 | objlfile.close() |
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| 365 | |
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| 366 | time_stepv = np.zeros((dimt), dtype=np.float) |
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[352] | 367 | tsvaluesv = np.zeros( (dimt,Ntsvariables), dtype= np.float) |
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[351] | 368 | |
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[353] | 369 | pracc = np.zeros((dimt), dtype=np.float) |
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| 370 | |
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[351] | 371 | itz = 0 |
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| 372 | for it in range(dimt): |
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| 373 | time_stepv[it] = np.float(time_step[it]) |
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| 374 | |
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[352] | 375 | for iv in range(Ntsvariables): |
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| 376 | tsvaluesv[it,iv] = np.float(tsvals[itz][iv+5]) |
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[351] | 377 | |
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[353] | 378 | pracc[it] = np.float(tsvals[it][16]) + np.float(tsvals[it][17]) |
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[352] | 379 | itz = itz + 1 |
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[353] | 380 | |
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| 381 | # time |
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[352] | 382 | newvar = objofile.createVariable('time','f8',('time')) |
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[504] | 383 | newvar[:] = time_stepv*3600. |
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[928] | 384 | newattr = basicvardef(newvar, 'time', 'time', 'seconds since ' + \ |
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[351] | 385 | simstarttime.replace('_',' ')) |
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[928] | 386 | set_attribute(newvar, 'calendar', 'standard') |
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[351] | 387 | |
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[619] | 388 | dt = time_stepv[1] - time_stepv[0] |
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| 389 | |
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[352] | 390 | # time-series variables |
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[351] | 391 | for iv in range(Ntsvariables): |
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[619] | 392 | if tsvn[iv] == 't' or tsvn[iv] == 'u' or tsvn[iv] == 'v' or tsvn[iv] == 'q': |
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| 393 | varname, stdname, minvar, maxvar, longname, unitsvar, cbarvar = \ |
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[928] | 394 | variables_values('TS' + tsvn[iv]) |
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[619] | 395 | tsu = unitsvar |
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| 396 | else: |
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| 397 | varname, stdname, minvar, maxvar, longname, unitsvar, cbarvar = \ |
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[928] | 398 | variables_values(tsvn[iv]) |
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[619] | 399 | tsu = tsvu[iv] |
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| 400 | |
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| 401 | newvar = objofile.createVariable(varname, 'f4', ('time')) |
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[352] | 402 | newvar[:] = tsvaluesv[:,iv] |
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[351] | 403 | |
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[928] | 404 | newattr = basicvardef(newvar, stdname, longname.replace('|',' '), tsu) |
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| 405 | newattr = set_attribute(newvar, 'wrfTSname', tsvn[iv]) |
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| 406 | newattr = set_attribute(newvar, 'wrfTSdesc', tsvln[iv]) |
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[619] | 407 | |
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[353] | 408 | # Extra vars |
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| 409 | |
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| 410 | # pr |
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| 411 | varvals = np.zeros((dimt), dtype=np.float) |
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| 412 | varvals[1:dimt] = pracc[1:dimt] - pracc[0:dimt-1] |
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[619] | 413 | varname, stdname, minvar, maxvar, longname, unitsvar, cbarvar = \ |
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[928] | 414 | variables_values('RAINTOT') |
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[353] | 415 | |
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[619] | 416 | newvar = objofile.createVariable(varname, 'f4', ('time')) |
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| 417 | newvar[:] = varvals / dt |
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[928] | 418 | newattr = basicvardef(newvar, stdname, longname.replace('|',' '), unitsvar ) |
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[353] | 419 | |
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[351] | 420 | objofile.sync() |
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| 421 | objofile.close() |
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| 422 | |
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[352] | 423 | print 'Successfull generation of Time-Series netCDF file "' + ofile + '" !!!!!' |
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