| 1 | import numpy as np |
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| 2 | from netCDF4 import Dataset as NetCDFFile |
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| 3 | import os |
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| 4 | import re |
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| 5 | import numpy.ma as ma |
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| 6 | |
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| 7 | main = 'nc_var_tools.py' |
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| 8 | |
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| 9 | errormsg='ERROR -- error -- ERROR -- error' |
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| 10 | warnmsg='WARNING -- warning -- WARNING -- warning' |
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| 11 | |
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| 12 | fillValue = 1.e20 |
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| 13 | fillValueF = 1.e20 |
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| 14 | fillValueC = '-' |
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| 15 | fillValueI = -99999 |
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| 16 | fillValueI16 = -99999 |
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| 17 | fillValueF64 = 1.e20 |
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| 18 | fillValueI32 = -99999 |
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| 19 | |
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| 20 | def values_line(line, splitv, chars): |
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| 21 | """ Function to retrieve values from a line of ASCII text removing a series of characters |
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| 22 | line= line of ASCII text to work with |
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| 23 | splitv= character to use to split with the line |
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| 24 | chars= list of characters/strings to remove |
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| 25 | >>> values_line('0.0 0.0 1 [ 0 0 ]', ' ', ['[', ']']) |
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| 26 | ['0.0', '0.0', '1', '0', '0'] |
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| 27 | """ |
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| 28 | fname = 'values_line' |
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| 29 | |
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| 30 | vals = line.replace('\n', '') |
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| 31 | for ic in chars: |
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| 32 | vals = vals.replace(ic, '') |
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| 33 | |
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| 34 | vals0 = vals.split(splitv) |
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| 35 | vals = [] |
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| 36 | for val in vals0: |
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| 37 | if not len(val) == 0: vals.append(val) |
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| 38 | |
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| 39 | return vals |
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| 40 | |
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| 41 | def filexist(filen, emsg, fmsg): |
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| 42 | """ Check existence of a file |
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| 43 | filen= file name |
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| 44 | emsg= general error message |
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| 45 | fmsg= message before generic text of inexistence of file |
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| 46 | """ |
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| 47 | if not os.path.isfile(filen): |
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| 48 | print emsg |
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| 49 | print ' ' + fmsg + ' file "' + filen + '" does not exist !!' |
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| 50 | print emsg |
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| 51 | quit(-1) |
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| 52 | |
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| 53 | return |
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| 54 | |
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| 55 | def reduce_spaces(string): |
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| 56 | """ Function to give words of a line of text removing any extra space |
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| 57 | """ |
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| 58 | values = string.replace('\n','').split(' ') |
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| 59 | vals = [] |
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| 60 | for val in values: |
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| 61 | if len(val) > 0: |
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| 62 | vals.append(val) |
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| 63 | |
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| 64 | return vals |
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| 65 | |
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| 66 | def reduce_tabulars(string): |
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| 67 | """ Function to give words of a line of text removing any extra space and tabular separation |
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| 68 | """ |
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| 69 | values = string.replace('\n','').split('\t') |
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| 70 | vals = [] |
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| 71 | for val in values: |
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| 72 | if len(val) > 0: |
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| 73 | vals.append(val) |
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| 74 | |
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| 75 | return vals |
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| 76 | |
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| 77 | def check_arguments(funcname,args,expectargs,char): |
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| 78 | """ Function to check the number of arguments if they are coincident |
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| 79 | check_arguments(funcname,Nargs,args,char) |
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| 80 | funcname= name of the function/program to check |
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| 81 | args= passed arguments |
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| 82 | expectargs= expected arguments |
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| 83 | char= character used to split the arguments |
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| 84 | """ |
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| 85 | |
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| 86 | fname = 'check_arguments' |
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| 87 | |
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| 88 | Nvals = len(args.split(char)) |
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| 89 | Nargs = len(expectargs.split(char)) |
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| 90 | |
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| 91 | if Nvals != Nargs: |
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| 92 | print errormsg |
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| 93 | print ' ' + fname + ': wrong number of arguments:',Nvals," passed to '", \ |
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| 94 | funcname, "' which requires:",Nargs,'!!' |
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| 95 | print ' # given expected _______' |
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| 96 | Nmax = np.max([Nvals, Nargs]) |
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| 97 | for ia in range(Nmax): |
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| 98 | if ia > Nvals-1: |
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| 99 | aval = ' ' |
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| 100 | else: |
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| 101 | aval = args.split(char)[ia] |
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| 102 | if ia > Nargs-1: |
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| 103 | aexp = ' ' |
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| 104 | else: |
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| 105 | aexp = expectargs.split(char)[ia] |
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| 106 | |
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| 107 | print ' ', ia, aval, aexp |
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| 108 | quit(-1) |
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| 109 | |
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| 110 | return |
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| 111 | |
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| 112 | def Str_Bool(val): |
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| 113 | """ Function to transform from a String value to a boolean one |
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| 114 | >>> Str_Bool('True') |
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| 115 | True |
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| 116 | >>> Str_Bool('0') |
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| 117 | False |
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| 118 | >>> Str_Bool('no') |
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| 119 | False |
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| 120 | """ |
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| 121 | |
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| 122 | fname = 'Str_Bool' |
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| 123 | |
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| 124 | if val == 'True' or val == 'true' or val == '1' or val == 'yes' or val == 'T' or val == 't': |
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| 125 | boolv = True |
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| 126 | boolv = True |
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| 127 | elif val == 'False' or val == 'false' or val == '0' or val== 'no' or val == 'F' or val == 'f': |
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| 128 | boolv = False |
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| 129 | else: |
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| 130 | print errormsg |
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| 131 | print ' ' + fname + ": value '" + val + "' not ready!!" |
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| 132 | quit(-1) |
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| 133 | |
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| 134 | return boolv |
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| 135 | |
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| 136 | def Nchar(N,string): |
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| 137 | """ Function to provide 'N' times concatenated 'string' |
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| 138 | N= number of copies |
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| 139 | strin= string to reproduce |
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| 140 | >>> Nchar(4,'#') |
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| 141 | #### |
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| 142 | """ |
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| 143 | fname = 'Nchar' |
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| 144 | |
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| 145 | newstring='' |
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| 146 | for it in range(N): |
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| 147 | newstring=newstring + string |
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| 148 | |
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| 149 | return newstring |
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| 150 | |
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| 151 | def string_dicvals_char(dictionary, string, char): |
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| 152 | """ Function to provide a string taking values from a given [string] where each single character |
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| 153 | coincide with the key of the dictionary [dictionary], separated by a given set of characters [char] |
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| 154 | dictionary= dictionary of the values dim([c]) = {[value]} |
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| 155 | string = [c1][...[cN]] String with the combination of [c]s |
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| 156 | >>> dictvals = {'i': 'dimx', 'j': 'dimy', 'k': 'dimz', 't': 'dimt'} |
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| 157 | >>> string_dicvals_char(dictvals, 'ijkt', ', ') |
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| 158 | dimx, dimy, dimz, dimt |
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| 159 | """ |
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| 160 | fname = 'string_dicvals_char' |
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| 161 | |
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| 162 | Lstring = len(string) |
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| 163 | newstring = '' |
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| 164 | for ic in range(Lstring): |
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| 165 | cc = string[ic:ic+1] |
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| 166 | if not dictionary.has_key(cc): |
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| 167 | print errormsg |
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| 168 | print ' ' + fname + ": dictionary of values does not have character '" +\ |
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| 169 | cc + "' !!" |
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| 170 | print ' it has:',dictionary |
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| 171 | |
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| 172 | if ic == 0: |
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| 173 | newstring = dictionary[cc] |
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| 174 | else: |
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| 175 | newstring = newstring + char + dictionary[cc] |
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| 176 | |
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| 177 | return newstring |
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| 178 | |
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| 179 | def substring(stringv,pos,char): |
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| 180 | """ Function to substitute a character of a given string |
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| 181 | stringv= string to change |
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| 182 | pos= position to change |
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| 183 | char= characters to use as substitution |
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| 184 | >>> substring('0123456',2,'ii') |
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| 185 | 01ii3456 |
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| 186 | """ |
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| 187 | fname = 'substring' |
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| 188 | |
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| 189 | # From: http://stackoverflow.com/questions/1228299/change-one-character-in-a-string-in-python |
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| 190 | newstring = stringv[:pos] + char + stringv[pos+1:] |
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| 191 | |
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| 192 | return newstring |
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| 193 | |
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| 194 | def period_information(idate, edate, totunits): |
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| 195 | """ Function to provide the information of a given period idate, edate (dates in [YYYY][MM][DD][HH][MI][SS] format) |
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| 196 | [idate]= initial date of the period (in [YYYY][MM][DD][HH][MI][SS] format) |
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| 197 | [edate]= end date of the period (in [YYYY][MM][DD][HH][MI][SS] format) |
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| 198 | [totunits]= latest number of entire temporal units to compute: 'year', 'month', 'day', 'hour', 'minute', 'second' |
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| 199 | |
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| 200 | resultant information given as ',' list of: |
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| 201 | [dT]: sign of the temporal increment ('pos', 'neg') |
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| 202 | [idate]: initial date as [YYYY]:[MM]:[DD]:[HH]:[MI]:[SS] |
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| 203 | [edate]: end date as [YYYY]:[MM]:[DD]:[HH]:[MI]:[SS] |
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| 204 | [Nsecs]: total seconds between dates |
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| 205 | [Nyears]: Number of years taken as entire units ([iYYYY]0101000000 to [eYYYY+1]0101000000) |
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| 206 | [ExactYears]: Exact year-dates of the period as a '@' separated list of [YYYY]0101000000 |
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| 207 | [Nmonths]: Number of months taken as entire units ([iYYYY][iMM]01000000 to [eYYYY][eMM+1]01000000) |
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| 208 | [ExactMonths]: Exact mon-dates of the period as a '@' separated list of [YYYY][MM]01000000 |
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| 209 | [Ndays]: Number of days taken as entire units ([iYYYY][iMM][iDD]000000 to [eYYYY][eMM][eDD+1]000000) |
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| 210 | [ExactDays]: Exact day-dates of the period as a '@' separated list of [YYYY][MM][DD]000000 |
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| 211 | [Nhours]: Number of hours taken as entire units ([iYYYY][iMM][iDD][iHH]0000 to [eYYYY][eMM][eDD][eHH+1]0000) |
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| 212 | [ExactHours]: Exact hour-dates of the period as a '@' separated list of [YYYY][MM][DD][HH]0000 |
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| 213 | [Nminutes]: Number of minutes taken as entire units ([iYYYY][iMM][iDD][iHH][iMI]00 to [eYYYY][eMM][eDD][eHH][eMI+1]00) |
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| 214 | [ExactMinutes]: Exact minutes-dates of the period as a '@' separated list of [YYYY][MM][DD][HH][MI]00 |
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| 215 | [Nsecs]: Number of minutes taken as entire units ([iYYYY][iMM][iDD][iHH][iMI][iS] to [eYYYY][eMM][eDD][eHH][eMI][eSE+1]) |
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| 216 | [ExactSeconds]: Exact seconds-dates of the period as a '@' separated list of [YYYY][MM][DD][HH][MI][SS] |
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| 217 | |
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| 218 | >>> period_information( '19760217083110', '19770828000000', 'month') |
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| 219 | pos,1976:02:17:08:31:10,1977:08:28:00:00:00,48180530.0,19,19760201000000@19760301000000@19760401000000@ |
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| 220 | 19760501000000@19760601000000@19760701000000@19760801000000@19760901000000@19761001000000@19761101000000@ |
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| 221 | 19761201000000@19770101000000@19770201000000@19770301000000@19770401000000@19770501000000@19770601000000@ |
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| 222 | 19770701000000@19770801000000@19770901000000 |
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| 223 | """ |
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| 224 | import datetime as dt |
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| 225 | |
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| 226 | fname = 'period_information' |
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| 227 | |
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| 228 | if idate == 'h': |
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| 229 | print fname + '_____________________________________________________________' |
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| 230 | print variables_values.__doc__ |
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| 231 | quit() |
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| 232 | |
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| 233 | expectargs = '[idate],[edate],[totunits]' |
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| 234 | |
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| 235 | check_arguments(fname,str(idate)+','+str(edate)+','+str(totunits),expectargs,',') |
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| 236 | |
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| 237 | # Checking length |
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| 238 | Lidate = len(idate) |
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| 239 | Ledate = len(edate) |
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| 240 | |
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| 241 | if Lidate != Ledate: |
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| 242 | print errormsg |
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| 243 | print ' ' + fname + ': string dates of the period have different length !!' |
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| 244 | print " idate: '" + idate + "' (L=", Lidate, ") edate: '" + edate + \ |
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| 245 | "' (L=", Ledate, ')' |
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| 246 | quit(-1) |
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| 247 | else: |
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| 248 | if Lidate != 14: |
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| 249 | print errormsg |
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| 250 | print ' ' + fname + ": wrong initial date: '" + idate + "' must " + \ |
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| 251 | 'have 14 characters!!' |
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| 252 | print ' and it has:', Lidate |
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| 253 | quit(-1) |
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| 254 | if Ledate != 14: |
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| 255 | print errormsg |
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| 256 | print ' ' + fname + ": wrong end date: '" + edate + "' must " + \ |
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| 257 | 'have 14 characters!!' |
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| 258 | print ' and it has:', Ledate |
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| 259 | quit(-1) |
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| 260 | |
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| 261 | # Checking for right order of dates |
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| 262 | if int(idate) > int(edate): |
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| 263 | print warnmsg |
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| 264 | print ' ' + fname + ": initial date '" + idate + "' after end date '" + \ |
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| 265 | edate + "' !!" |
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| 266 | print " re-sorting them!" |
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| 267 | i1 = edate |
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| 268 | e1 = idate |
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| 269 | idate = i1 |
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| 270 | edate = e1 |
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| 271 | oinf = 'neg' |
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| 272 | else: |
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| 273 | oinf = 'pos' |
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| 274 | |
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| 275 | # Year, month, day, hour, minute, second initial date |
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| 276 | iyrS = idate[0:4] |
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| 277 | imoS = idate[4:6] |
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| 278 | idaS = idate[6:8] |
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| 279 | ihoS = idate[8:10] |
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| 280 | imiS = idate[10:12] |
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| 281 | iseS = idate[12:14] |
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| 282 | |
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| 283 | oinf = oinf + ',' + iyrS+':'+imoS+':'+idaS+':'+ihoS+':'+imiS+':'+iseS |
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| 284 | |
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| 285 | # Year, month, day, hour, minute, second end date |
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| 286 | eyrS = edate[0:4] |
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| 287 | emoS = edate[4:6] |
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| 288 | edaS = edate[6:8] |
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| 289 | ehoS = edate[8:10] |
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| 290 | emiS = edate[10:12] |
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| 291 | eseS = edate[12:14] |
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| 292 | |
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| 293 | oinf = oinf + ',' + eyrS+':'+emoS+':'+edaS+':'+ehoS+':'+emiS+':'+eseS |
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| 294 | |
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| 295 | idateT = dt.datetime(int(iyrS),int(imoS),int(idaS),int(ihoS),int(imiS),int(iseS)) |
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| 296 | edateT = dt.datetime(int(eyrS),int(emoS),int(edaS),int(ehoS),int(emiS),int(eseS)) |
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| 297 | |
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| 298 | # Seconds between dates |
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| 299 | DT = edateT - idateT |
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| 300 | Nsecs = DT.total_seconds() |
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| 301 | |
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| 302 | oinf = oinf + ',' + str(Nsecs) |
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| 303 | |
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| 304 | # Looking for number of years tacking exact beginning of the units [iYYYY]0101000000, [eYYYY+1]0101000000 |
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| 305 | ## |
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| 306 | if totunits == 'year': |
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| 307 | itime = int(iyrS) |
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| 308 | if edate[4:15] != '0101000000': |
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| 309 | etime = int(eyrS)+1 |
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| 310 | else: |
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| 311 | etime = int(eyrS) |
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| 312 | |
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| 313 | itimeT = dt.datetime(itime,1,1,0,0,0) |
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| 314 | |
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| 315 | Nyears = 1 |
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| 316 | ExactYears = itimeT.strftime("%Y%m%d%H%M%S") |
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| 317 | it = int(iyrS) |
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| 318 | while it + 1 <= etime: |
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| 319 | it = it + 1 |
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| 320 | Nyears = Nyears + 1 |
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| 321 | itT = dt.datetime(it,1,1,0,0,0) |
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| 322 | ExactYears = ExactYears + '@' + itT.strftime("%Y%m%d%H%M%S") |
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| 323 | |
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| 324 | oinf = oinf + ',' + str(Nyears) + ',' + ExactYears |
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| 325 | |
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| 326 | # Looking for number of months tacking exact beginning of the units [iYYYY][iMM]01000000, [eYYYY][eMM+1]01000000 |
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| 327 | ## |
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| 328 | if totunits == 'month': |
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| 329 | itime = int(iyrS)*100 + int(imoS) |
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| 330 | if edate[6:15] != '01000000': |
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| 331 | emo1 = int(emoS)+1 |
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| 332 | if emo1 > 13: |
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| 333 | eyr1 = str(int(eyrS) + 1) |
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| 334 | emo1 = 01 |
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| 335 | else: |
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| 336 | eyr1 = eyrS |
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| 337 | |
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| 338 | else: |
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| 339 | eyr1 = eyrS |
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| 340 | emo1 = emoS |
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| 341 | |
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| 342 | etime = int(eyr1)*100 + int(emo1) |
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| 343 | itimeT = dt.datetime(int(iyrS),int(imoS),1,0,0,0) |
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| 344 | |
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| 345 | Nmonths = 1 |
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| 346 | ExactMonths = itimeT.strftime("%Y%m%d%H%M%S") |
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| 347 | it = itime |
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| 348 | while it + 1 <= etime: |
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| 349 | it = it + 1 |
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| 350 | Nmonths = Nmonths + 1 |
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| 351 | ityr = it/100 |
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| 352 | itmo = it - ityr*100 |
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| 353 | if itmo > 12: |
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| 354 | ityr = ityr + 1 |
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| 355 | itmo = 1 |
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| 356 | it = ityr * 100 + itmo |
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| 357 | |
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| 358 | itT = dt.datetime(ityr,itmo,1,0,0,0) |
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| 359 | ExactMonths = ExactMonths + '@' + itT.strftime("%Y%m%d%H%M%S") |
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| 360 | |
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| 361 | oinf = oinf + ',' + str(Nmonths) + ',' + ExactMonths |
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| 362 | |
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| 363 | # Looking for number of days tacking exact beginning of the units [iYYYY][iMM][iDD]000000, [eYYYY][eMM][eDD+1]000000 |
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| 364 | ## |
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| 365 | if totunits == 'day': |
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| 366 | itime = dt.datetime(int(iyrS),int(imoS),int(idaS),0,0,0) |
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| 367 | if edate[8:15] != '000000': |
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| 368 | etime = dt.datetime(int(eyrS), int(emoS), int(edaS)+1,0,0,0) |
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| 369 | else: |
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| 370 | etime = edateT |
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| 371 | |
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| 372 | Ndays = 1 |
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| 373 | ExactDays = itime.strftime("%Y%m%d%H%M%S") |
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| 374 | it = itime |
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| 375 | while it + dt.timedelta(days=1) <= etime: |
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| 376 | it = it + dt.timedelta(days=1) |
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| 377 | Ndays = Ndays + 1 |
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| 378 | ExactDays = ExactDays + '@' + it.strftime("%Y%m%d%H%M%S") |
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| 379 | |
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| 380 | oinf = oinf + ',' + str(Ndays) + ',' + ExactDays |
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| 381 | |
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| 382 | # Looking for number of hours tacking exact beginning of the units [iYYYY][iMM][iDD][iHH]0000, [eYYYY][eMM][eDD][iHH+1]0000 |
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| 383 | ## |
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| 384 | if totunits == 'hour': |
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| 385 | itime = dt.datetime(int(iyrS),int(imoS),int(idaS),int(ihoS),0,0) |
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| 386 | if edate[10:15] != '0000': |
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| 387 | etime = dt.datetime(int(eyrS), int(emoS), int(edaS), int(ehoS)+1,0,0) |
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| 388 | else: |
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| 389 | etime = edateT |
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| 390 | |
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| 391 | Nhours = 1 |
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| 392 | ExactHours = itime.strftime("%Y%m%d%H%M%S") |
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| 393 | it = itime |
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| 394 | while it + dt.timedelta(hours=1) <= etime: |
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| 395 | it = it + dt.timedelta(hours=1) |
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| 396 | Nhours = Nhours + 1 |
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| 397 | ExactHours = ExactHours + '@' + it.strftime("%Y%m%d%H%M%S") |
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| 398 | |
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| 399 | oinf = oinf + ',' + str(Nhours) + ',' + ExactHours |
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| 400 | |
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| 401 | # Looking for number of minutes tacking exact beginning of the units [iYYYY][iMM][iDD][iHH][iMI]00, [eYYYY][eMM][eDD][iHH][iMI+1]00 |
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| 402 | ## |
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| 403 | if totunits == 'minute': |
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| 404 | itime = dt.datetime(int(iyrS),int(imoS),int(idaS),int(ihoS),int(imiS),0) |
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| 405 | if edate[12:15] != '00': |
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| 406 | etime = dt.datetime(int(eyrS), int(emoS), int(edaS), int(ehoS), int(emiS)+1,0) |
|---|
| 407 | else: |
|---|
| 408 | etime = edateT |
|---|
| 409 | |
|---|
| 410 | Nminutes = 1 |
|---|
| 411 | ExactMinutes = itime.strftime("%Y%m%d%H%M%S") |
|---|
| 412 | it = itime |
|---|
| 413 | while it + dt.timedelta(minutes=1) <= etime: |
|---|
| 414 | it = it + dt.timedelta(minutes=1) |
|---|
| 415 | Nminutes = Nminutes + 1 |
|---|
| 416 | ExactMinutes = ExactMinutes + '@' + it.strftime("%Y%m%d%H%M%S") |
|---|
| 417 | |
|---|
| 418 | oinf = oinf + ',' + str(Nminutes) + ',' + ExactMinutes |
|---|
| 419 | |
|---|
| 420 | # Looking for number of seconds tacking exact beginning of the units [iYYYY][iMM][iDD][iHH][iMI][iSE], |
|---|
| 421 | # [eYYYY][eMM][eDD][iHH][iMI][iSE+1] |
|---|
| 422 | ## |
|---|
| 423 | if totunits == 'second': |
|---|
| 424 | itime = dt.datetime(int(iyrS),int(imoS),int(idaS),int(ihoS),int(imiS),int(iseS)) |
|---|
| 425 | print 'Lluis ', edate[12:15], '00' |
|---|
| 426 | if edate[12:15] != '00': |
|---|
| 427 | etime = dt.datetime(int(eyrS), int(emoS), int(edaS), int(ehoS), int(emiS), int(eseS)+1) |
|---|
| 428 | else: |
|---|
| 429 | etime = edateT |
|---|
| 430 | |
|---|
| 431 | Nseconds = 1 |
|---|
| 432 | ExactSeconds = itime.strftime("%Y%m%d%H%M%S") |
|---|
| 433 | it = itime |
|---|
| 434 | while it + dt.timedelta(seconds=1) < etime: |
|---|
| 435 | it = it + dt.timedelta(seconds=1) |
|---|
| 436 | Nseconds = Nseconds + 1 |
|---|
| 437 | ExactSeconds = ExactSeconds + '@' + it.strftime("%Y%m%d%H%M%S") |
|---|
| 438 | |
|---|
| 439 | oinf = oinf + ',' + str(Nseconds) + ',' + ExactSeconds |
|---|
| 440 | |
|---|
| 441 | return oinf |
|---|
| 442 | |
|---|
| 443 | def variables_values(varName): |
|---|
| 444 | """ Function to provide values to plot the different variables values from ASCII file |
|---|
| 445 | 'variables_values.dat' |
|---|
| 446 | variables_values(varName) |
|---|
| 447 | [varName]= name of the variable |
|---|
| 448 | return: [var name], [std name], [minimum], [maximum], |
|---|
| 449 | [long name]('|' for spaces), [units], [color palette] (following: |
|---|
| 450 | http://matplotlib.org/1.3.1/examples/color/colormaps_reference.html) |
|---|
| 451 | [varn]: original name of the variable |
|---|
| 452 | NOTE: It might be better doing it with an external ASII file. But then we |
|---|
| 453 | got an extra dependency... |
|---|
| 454 | >>> variables_values('WRFght') |
|---|
| 455 | ['z', 'geopotential_height', 0.0, 80000.0, 'geopotential|height', 'm2s-2', 'rainbow'] |
|---|
| 456 | """ |
|---|
| 457 | import subprocess as sub |
|---|
| 458 | |
|---|
| 459 | fname='variables_values' |
|---|
| 460 | |
|---|
| 461 | if varName == 'h': |
|---|
| 462 | print fname + '_____________________________________________________________' |
|---|
| 463 | print variables_values.__doc__ |
|---|
| 464 | quit() |
|---|
| 465 | |
|---|
| 466 | # This does not work.... |
|---|
| 467 | # folderins = sub.Popen(["pwd"], stdout=sub.PIPE) |
|---|
| 468 | # folder = list(folderins.communicate())[0].replace('\n','') |
|---|
| 469 | # From http://stackoverflow.com/questions/4934806/how-can-i-find-scripts-directory-with-python |
|---|
| 470 | folder = os.path.dirname(os.path.realpath(__file__)) |
|---|
| 471 | |
|---|
| 472 | infile = folder + '/variables_values.dat' |
|---|
| 473 | |
|---|
| 474 | if not os.path.isfile(infile): |
|---|
| 475 | print errormsg |
|---|
| 476 | print ' ' + fname + ": File '" + infile + "' does not exist !!" |
|---|
| 477 | quit(-1) |
|---|
| 478 | |
|---|
| 479 | # Variable name might come with a statistical surname... |
|---|
| 480 | stats=['min','max','mean','stdv', 'sum'] |
|---|
| 481 | |
|---|
| 482 | # Variables with a statistical section on their name... |
|---|
| 483 | NOstatsvars = ['zmaxth', 'zmax_th', 'lmax_th', 'lmaxth'] |
|---|
| 484 | |
|---|
| 485 | ifst = False |
|---|
| 486 | if not searchInlist(NOstatsvars, varName.lower()): |
|---|
| 487 | for st in stats: |
|---|
| 488 | if varName.find(st) > -1: |
|---|
| 489 | print ' '+ fname + ": varibale '" + varName + "' with a " + \ |
|---|
| 490 | "statistical surname: '",st,"' !!" |
|---|
| 491 | Lst = len(st) |
|---|
| 492 | LvarName = len(varName) |
|---|
| 493 | varn = varName[0:LvarName - Lst] |
|---|
| 494 | ifst = True |
|---|
| 495 | break |
|---|
| 496 | if not ifst: |
|---|
| 497 | varn = varName |
|---|
| 498 | |
|---|
| 499 | ncf = open(infile, 'r') |
|---|
| 500 | |
|---|
| 501 | for line in ncf: |
|---|
| 502 | if line[0:1] != '#': |
|---|
| 503 | values = line.replace('\n','').split(',') |
|---|
| 504 | if len(values) != 8: |
|---|
| 505 | print errormsg |
|---|
| 506 | print "problem in varibale:'", values[0], \ |
|---|
| 507 | 'it should have 8 values and it has',len(values) |
|---|
| 508 | quit(-1) |
|---|
| 509 | |
|---|
| 510 | if varn[0:6] == 'varDIM': |
|---|
| 511 | # Variable from a dimension (all with 'varDIM' prefix) |
|---|
| 512 | Lvarn = len(varn) |
|---|
| 513 | varvals = [varn[6:Lvarn+1], varn[6:Lvarn+1], 0., 1., \ |
|---|
| 514 | "variable|from|size|of|dimension|'" + varn[6:Lvarn+1] + "'", '1', \ |
|---|
| 515 | 'rainbow'] |
|---|
| 516 | else: |
|---|
| 517 | varvals = [values[1].replace(' ',''), values[2].replace(' ',''), \ |
|---|
| 518 | np.float(values[3]), np.float(values[4]),values[5].replace(' ',''),\ |
|---|
| 519 | values[6].replace(' ',''), values[7].replace(' ','')] |
|---|
| 520 | if values[0] == varn: |
|---|
| 521 | ncf.close() |
|---|
| 522 | return varvals |
|---|
| 523 | break |
|---|
| 524 | |
|---|
| 525 | print errormsg |
|---|
| 526 | print ' ' + fname + ": variable '" + varn + "' not defined !!!" |
|---|
| 527 | ncf.close() |
|---|
| 528 | quit(-1) |
|---|
| 529 | |
|---|
| 530 | return |
|---|
| 531 | |
|---|
| 532 | def variables_values_old(varName): |
|---|
| 533 | """ Function to provide values to plot the different variables |
|---|
| 534 | variables_values(varName) |
|---|
| 535 | [varName]= name of the variable |
|---|
| 536 | return: [var name], [std name], [minimum], [maximum], |
|---|
| 537 | [long name]('|' for spaces), [units], [color palette] (following: |
|---|
| 538 | http://matplotlib.org/1.3.1/examples/color/colormaps_reference.html) |
|---|
| 539 | [varn]: original name of the variable |
|---|
| 540 | NOTE: It might be better doing it with an external ASII file. But then we |
|---|
| 541 | got an extra dependency... |
|---|
| 542 | >>> variables_values('WRFght') |
|---|
| 543 | ['z', 'geopotential_height', 0.0, 80000.0, 'geopotential|height', 'm2s-2', 'rainbow'] |
|---|
| 544 | """ |
|---|
| 545 | fname='variables_values' |
|---|
| 546 | |
|---|
| 547 | if varName == 'h': |
|---|
| 548 | print fname + '_____________________________________________________________' |
|---|
| 549 | print variables_values.__doc__ |
|---|
| 550 | quit() |
|---|
| 551 | |
|---|
| 552 | # Variable name might come with a statistical surname... |
|---|
| 553 | stats=['min','max','mean','stdv', 'sum'] |
|---|
| 554 | |
|---|
| 555 | ifst = False |
|---|
| 556 | for st in stats: |
|---|
| 557 | if varName.find(st) > -1: |
|---|
| 558 | print ' '+ fname + ": varibale '" + varName + "' with a statistical "+\ |
|---|
| 559 | " surname: '",st,"' !!" |
|---|
| 560 | Lst = len(st) |
|---|
| 561 | LvarName = len(varName) |
|---|
| 562 | varn = varName[0:LvarName - Lst] |
|---|
| 563 | ifst = True |
|---|
| 564 | break |
|---|
| 565 | if not ifst: |
|---|
| 566 | varn = varName |
|---|
| 567 | |
|---|
| 568 | if varn[0:6] == 'varDIM': |
|---|
| 569 | # Variable from a dimension (all with 'varDIM' prefix) |
|---|
| 570 | Lvarn = len(varn) |
|---|
| 571 | varvals = [varn[6:Lvarn+1], varn[6:Lvarn+1], 0., 1., \ |
|---|
| 572 | "variable|from|size|of|dimension|'" + varn[6:Lvarn+1] + "'", '1', 'rainbox'] |
|---|
| 573 | elif varn == 'a_tht' or varn == 'LA_THT': |
|---|
| 574 | varvals = ['ath', 'total_thermal_plume_cover', 0., 1., \ |
|---|
| 575 | 'total|column|thermal|plume|cover', '1', 'YlGnBu'] |
|---|
| 576 | elif varn == 'acprc' or varn == 'RAINC': |
|---|
| 577 | varvals = ['acprc', 'accumulated_cmulus_precipitation', 0., 3.e4, \ |
|---|
| 578 | 'accumulated|cmulus|precipitation', 'mm', 'Blues'] |
|---|
| 579 | elif varn == 'acprnc' or varn == 'RAINNC': |
|---|
| 580 | varvals = ['acprnc', 'accumulated_non-cmulus_precipitation', 0., 3.e4, \ |
|---|
| 581 | 'accumulated|non-cmulus|precipitation', 'mm', 'Blues'] |
|---|
| 582 | elif varn == 'bils' or varn == 'LBILS': |
|---|
| 583 | varvals = ['bils', 'surface_total_heat_flux', -100., 100., \ |
|---|
| 584 | 'surface|total|heat|flux', 'Wm-2', 'seismic'] |
|---|
| 585 | elif varn == 'landcat' or varn == 'category': |
|---|
| 586 | varvals = ['landcat', 'land_categories', 0., 22., 'land|categories', '1', \ |
|---|
| 587 | 'rainbow'] |
|---|
| 588 | elif varn == 'c' or varn == 'QCLOUD' or varn == 'oliq' or varn == 'OLIQ': |
|---|
| 589 | varvals = ['c', 'condensed_water_mixing_ratio', 0., 3.e-4, \ |
|---|
| 590 | 'condensed|water|mixing|ratio', 'kgkg-1', 'BuPu'] |
|---|
| 591 | elif varn == 'ci' or varn == 'iwcon' or varn == 'LIWCON': |
|---|
| 592 | varvals = ['ci', 'cloud_iced_water_mixing_ratio', 0., 0.0003, \ |
|---|
| 593 | 'cloud|iced|water|mixing|ratio', 'kgkg-1', 'Purples'] |
|---|
| 594 | elif varn == 'cl' or varn == 'lwcon' or varn == 'LLWCON': |
|---|
| 595 | varvals = ['cl', 'cloud_liquidwater_mixing_ratio', 0., 0.0003, \ |
|---|
| 596 | 'cloud|liquid|water|mixing|ratio', 'kgkg-1', 'Blues'] |
|---|
| 597 | elif varn == 'cld' or varn == 'CLDFRA' or varn == 'rneb' or varn == 'lrneb' or \ |
|---|
| 598 | varn == 'LRNEB': |
|---|
| 599 | varvals = ['cld', 'cloud_area_fraction', 0., 1., 'cloud|fraction', '1', \ |
|---|
| 600 | 'gist_gray'] |
|---|
| 601 | elif varn == 'cldc' or varn == 'rnebcon' or varn == 'lrnebcon' or \ |
|---|
| 602 | varn == 'LRNEBCON': |
|---|
| 603 | varvals = ['cldc', 'convective_cloud_area_fraction', 0., 1., \ |
|---|
| 604 | 'convective|cloud|fraction', '1', 'gist_gray'] |
|---|
| 605 | elif varn == 'cldl' or varn == 'rnebls' or varn == 'lrnebls' or varn == 'LRNEBLS': |
|---|
| 606 | varvals = ['cldl', 'large_scale_cloud_area_fraction', 0., 1., \ |
|---|
| 607 | 'large|scale|cloud|fraction', '1', 'gist_gray'] |
|---|
| 608 | elif varn == 'clt' or varn == 'CLT' or varn == 'cldt' or \ |
|---|
| 609 | varn == 'Total cloudiness': |
|---|
| 610 | varvals = ['clt', 'cloud_area_fraction', 0., 1., 'total|cloud|cover', '1', \ |
|---|
| 611 | 'gist_gray'] |
|---|
| 612 | elif varn == 'cll' or varn == 'cldl' or varn == 'LCLDL' or \ |
|---|
| 613 | varn == 'Low-level cloudiness': |
|---|
| 614 | varvals = ['cll', 'low_level_cloud_area_fraction', 0., 1., \ |
|---|
| 615 | 'low|level|(p|>|680|hPa)|cloud|fraction', '1', 'gist_gray'] |
|---|
| 616 | elif varn == 'clm' or varn == 'cldm' or varn == 'LCLDM' or \ |
|---|
| 617 | varn == 'Mid-level cloudiness': |
|---|
| 618 | varvals = ['clm', 'mid_level_cloud_area_fraction', 0., 1., \ |
|---|
| 619 | 'medium|level|(440|<|p|<|680|hPa)|cloud|fraction', '1', 'gist_gray'] |
|---|
| 620 | elif varn == 'clh' or varn == 'cldh' or varn == 'LCLDH' or \ |
|---|
| 621 | varn == 'High-level cloudiness': |
|---|
| 622 | varvals = ['clh', 'high_level_cloud_area_fraction', 0., 1., \ |
|---|
| 623 | 'high|level|(p|<|440|hPa)|cloud|fraction', '1', 'gist_gray'] |
|---|
| 624 | elif varn == 'clmf' or varn == 'fbase' or varn == 'LFBASE': |
|---|
| 625 | varvals = ['clmf', 'cloud_base_max_flux', -0.3, 0.3, 'cloud|base|max|flux', \ |
|---|
| 626 | 'kgm-2s-1', 'seismic'] |
|---|
| 627 | elif varn == 'clp' or varn == 'pbase' or varn == 'LPBASE': |
|---|
| 628 | varvals = ['clp', 'cloud_base_pressure', -0.3, 0.3, 'cloud|base|pressure', \ |
|---|
| 629 | 'Pa', 'Reds'] |
|---|
| 630 | elif varn == 'cpt' or varn == 'ptconv' or varn == 'LPTCONV': |
|---|
| 631 | varvals = ['cpt', 'convective_point', 0., 1., 'convective|point', '1', \ |
|---|
| 632 | 'seismic'] |
|---|
| 633 | elif varn == 'dqajs' or varn == 'LDQAJS': |
|---|
| 634 | varvals = ['dqajs', 'dry_adjustment_water_vapor_tendency', -0.0003, 0.0003, \ |
|---|
| 635 | 'dry|adjustment|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 636 | elif varn == 'dqcon' or varn == 'LDQCON': |
|---|
| 637 | varvals = ['dqcon', 'convective_water_vapor_tendency', -3e-8, 3.e-8, \ |
|---|
| 638 | 'convective|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 639 | elif varn == 'dqdyn' or varn == 'LDQDYN': |
|---|
| 640 | varvals = ['dqdyn', 'dynamics_water_vapor_tendency', -3.e-7, 3.e-7, \ |
|---|
| 641 | 'dynamics|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 642 | elif varn == 'dqeva' or varn == 'LDQEVA': |
|---|
| 643 | varvals = ['dqeva', 'evaporation_water_vapor_tendency', -3.e-6, 3.e-6, \ |
|---|
| 644 | 'evaporation|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 645 | elif varn == 'dqlscst' or varn == 'LDQLSCST': |
|---|
| 646 | varvals = ['dqlscst', 'stratocumulus_water_vapor_tendency', -3.e-7, 3.e-7, \ |
|---|
| 647 | 'stratocumulus|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 648 | elif varn == 'dqlscth' or varn == 'LDQLSCTH': |
|---|
| 649 | varvals = ['dqlscth', 'thermals_water_vapor_tendency', -3.e-7, 3.e-7, \ |
|---|
| 650 | 'thermal|plumes|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 651 | elif varn == 'dqlsc' or varn == 'LDQLSC': |
|---|
| 652 | varvals = ['dqlsc', 'condensation_water_vapor_tendency', -3.e-6, 3.e-6, \ |
|---|
| 653 | 'condensation|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 654 | elif varn == 'dqphy' or varn == 'LDQPHY': |
|---|
| 655 | varvals = ['dqphy', 'physics_water_vapor_tendency', -3.e-7, 3.e-7, \ |
|---|
| 656 | 'physics|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 657 | elif varn == 'dqthe' or varn == 'LDQTHE': |
|---|
| 658 | varvals = ['dqthe', 'thermals_water_vapor_tendency', -3.e-7, 3.e-7, \ |
|---|
| 659 | 'thermal|plumes|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 660 | elif varn == 'dqvdf' or varn == 'LDQVDF': |
|---|
| 661 | varvals = ['dqvdf', 'vertical_difussion_water_vapor_tendency', -3.e-8, 3.e-8,\ |
|---|
| 662 | 'vertical|difussion|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 663 | elif varn == 'dqwak' or varn == 'LDQWAK': |
|---|
| 664 | varvals = ['dqwak', 'wake_water_vapor_tendency', -3.e-7, 3.e-7, \ |
|---|
| 665 | 'wake|water|vapor|tendency', 'kg/kg/s', 'seismic'] |
|---|
| 666 | elif varn == 'dta' or varn == 'tnt' or varn == 'LTNT': |
|---|
| 667 | varvals = ['dta', 'tendency_air_temperature', -3.e-3, 3.e-3, \ |
|---|
| 668 | 'tendency|of|air|temperature', 'K/s', 'seismic'] |
|---|
| 669 | elif varn == 'dtac' or varn == 'tntc' or varn == 'LTNTC': |
|---|
| 670 | varvals = ['dtac', 'moist_convection_tendency_air_temperature', -3.e-3, \ |
|---|
| 671 | 3.e-3, 'moist|convection|tendency|of|air|temperature', 'K/s', 'seismic'] |
|---|
| 672 | elif varn == 'dtar' or varn == 'tntr' or varn == 'LTNTR': |
|---|
| 673 | varvals = ['dtar', 'radiative_heating_tendency_air_temperature', -3.e-3, \ |
|---|
| 674 | 3.e-3, 'radiative|heating|tendency|of|air|temperature', 'K/s', 'seismic'] |
|---|
| 675 | elif varn == 'dtascpbl' or varn == 'tntscpbl' or varn == 'LTNTSCPBL': |
|---|
| 676 | varvals = ['dtascpbl', \ |
|---|
| 677 | 'stratiform_cloud_precipitation_BL_mixing_tendency_air_temperature', \ |
|---|
| 678 | -3.e-6, 3.e-6, \ |
|---|
| 679 | 'stratiform|cloud|precipitation|Boundary|Layer|mixing|tendency|air|' + |
|---|
| 680 | 'temperature', 'K/s', 'seismic'] |
|---|
| 681 | elif varn == 'dtajs' or varn == 'LDTAJS': |
|---|
| 682 | varvals = ['dtajs', 'dry_adjustment_thermal_tendency', -3.e-5, 3.e-5, \ |
|---|
| 683 | 'dry|adjustment|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 684 | elif varn == 'dtcon' or varn == 'LDTCON': |
|---|
| 685 | varvals = ['dtcon', 'convective_thermal_tendency', -3.e-5, 3.e-5, \ |
|---|
| 686 | 'convective|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 687 | elif varn == 'dtdyn' or varn == 'LDTDYN': |
|---|
| 688 | varvals = ['dtdyn', 'dynamics_thermal_tendency', -3.e-4, 3.e-4, \ |
|---|
| 689 | 'dynamics|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 690 | elif varn == 'dteva' or varn == 'LDTEVA': |
|---|
| 691 | varvals = ['dteva', 'evaporation_thermal_tendency', -3.e-3, 3.e-3, \ |
|---|
| 692 | 'evaporation|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 693 | elif varn == 'dtlscst' or varn == 'LDTLSCST': |
|---|
| 694 | varvals = ['dtlscst', 'stratocumulus_thermal_tendency', -3.e-4, 3.e-4, \ |
|---|
| 695 | 'stratocumulus|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 696 | elif varn == 'dtlscth' or varn == 'LDTLSCTH': |
|---|
| 697 | varvals = ['dtlscth', 'thermals_thermal_tendency', -3.e-4, 3.e-4, \ |
|---|
| 698 | 'thermal|plumes|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 699 | elif varn == 'dtlsc' or varn == 'LDTLSC': |
|---|
| 700 | varvals = ['dtlsc', 'condensation_thermal_tendency', -3.e-3, 3.e-3, \ |
|---|
| 701 | 'condensation|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 702 | elif varn == 'dtlwr' or varn == 'LDTLWR': |
|---|
| 703 | varvals = ['dtlwr', 'long_wave_thermal_tendency', -3.e-3, 3.e-3, \ |
|---|
| 704 | 'long|wave|radiation|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 705 | elif varn == 'dtphy' or varn == 'LDTPHY': |
|---|
| 706 | varvals = ['dtphy', 'physics_thermal_tendency', -3.e-4, 3.e-4, \ |
|---|
| 707 | 'physics|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 708 | elif varn == 'dtsw0' or varn == 'LDTSW0': |
|---|
| 709 | varvals = ['dtsw0', 'cloudy_sky_short_wave_thermal_tendency', -3.e-4, 3.e-4, \ |
|---|
| 710 | 'cloudy|sky|short|wave|radiation|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 711 | elif varn == 'dtthe' or varn == 'LDTTHE': |
|---|
| 712 | varvals = ['dtthe', 'thermals_thermal_tendency', -3.e-4, 3.e-4, \ |
|---|
| 713 | 'thermal|plumes|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 714 | elif varn == 'dtvdf' or varn == 'LDTVDF': |
|---|
| 715 | varvals = ['dtvdf', 'vertical_difussion_thermal_tendency', -3.e-5, 3.e-5, \ |
|---|
| 716 | 'vertical|difussion|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 717 | elif varn == 'dtwak' or varn == 'LDTWAK': |
|---|
| 718 | varvals = ['dtwak', 'wake_thermal_tendency', -3.e-4, 3.e-4, \ |
|---|
| 719 | 'wake|thermal|tendency', 'K/s', 'seismic'] |
|---|
| 720 | elif varn == 'ducon' or varn == 'LDUCON': |
|---|
| 721 | varvals = ['ducon', 'convective_eastward_wind_tendency', -3.e-3, 3.e-3, \ |
|---|
| 722 | 'convective|eastward|wind|tendency', 'ms-2', 'seismic'] |
|---|
| 723 | elif varn == 'dudyn' or varn == 'LDUDYN': |
|---|
| 724 | varvals = ['dudyn', 'dynamics_eastward_wind_tendency', -3.e-3, 3.e-3, \ |
|---|
| 725 | 'dynamics|eastward|wind|tendency', 'ms-2', 'seismic'] |
|---|
| 726 | elif varn == 'duvdf' or varn == 'LDUVDF': |
|---|
| 727 | varvals = ['duvdf', 'vertical_difussion_eastward_wind_tendency', -3.e-3, \ |
|---|
| 728 | 3.e-3, 'vertical|difussion|eastward|wind|tendency', 'ms-2', 'seismic'] |
|---|
| 729 | elif varn == 'dvcon' or varn == 'LDVCON': |
|---|
| 730 | varvals = ['dvcon', 'convective_difussion_northward_wind_tendency', -3.e-3, \ |
|---|
| 731 | 3.e-3, 'convective|northward|wind|tendency', 'ms-2', 'seismic'] |
|---|
| 732 | elif varn == 'dvdyn' or varn == 'LDVDYN': |
|---|
| 733 | varvals = ['dvdyn', 'dynamics_northward_wind_tendency', -3.e-3, \ |
|---|
| 734 | 3.e-3, 'dynamics|difussion|northward|wind|tendency', 'ms-2', 'seismic'] |
|---|
| 735 | elif varn == 'dvvdf' or varn == 'LDVVDF': |
|---|
| 736 | varvals = ['dvvdf', 'vertical_difussion_northward_wind_tendency', -3.e-3, \ |
|---|
| 737 | 3.e-3, 'vertical|difussion|northward|wind|tendency', 'ms-2', 'seismic'] |
|---|
| 738 | elif varn == 'etau' or varn == 'ZNU': |
|---|
| 739 | varvals = ['etau', 'etau', 0., 1, 'eta values on half (mass) levels', '-', \ |
|---|
| 740 | 'reds'] |
|---|
| 741 | elif varn == 'evspsbl' or varn == 'LEVAP' or varn == 'evap' or varn == 'SFCEVPde': |
|---|
| 742 | varvals = ['evspsbl', 'water_evaporation_flux', 0., 1.5e-4, \ |
|---|
| 743 | 'water|evaporation|flux', 'kgm-2s-1', 'Blues'] |
|---|
| 744 | elif varn == 'evspsbl' or varn == 'SFCEVPde': |
|---|
| 745 | varvals = ['evspsblac', 'water_evaporation_flux_ac', 0., 1.5e-4, \ |
|---|
| 746 | 'accumulated|water|evaporation|flux', 'kgm-2', 'Blues'] |
|---|
| 747 | elif varn == 'g' or varn == 'QGRAUPEL': |
|---|
| 748 | varvals = ['g', 'grauepl_mixing_ratio', 0., 0.0003, 'graupel|mixing|ratio', \ |
|---|
| 749 | 'kgkg-1', 'Purples'] |
|---|
| 750 | elif varn == 'h2o' or varn == 'LH2O': |
|---|
| 751 | varvals = ['h2o', 'water_mass_fraction', 0., 3.e-2, \ |
|---|
| 752 | 'mass|fraction|of|water', '1', 'Blues'] |
|---|
| 753 | elif varn == 'h' or varn == 'QHAIL': |
|---|
| 754 | varvals = ['h', 'hail_mixing_ratio', 0., 0.0003, 'hail|mixing|ratio', \ |
|---|
| 755 | 'kgkg-1', 'Purples'] |
|---|
| 756 | elif varn == 'hfls' or varn == 'LH' or varn == 'LFLAT' or varn == 'flat': |
|---|
| 757 | varvals = ['hfls', 'surface_upward_latent_heat_flux', -400., 400., \ |
|---|
| 758 | 'upward|latnt|heat|flux|at|the|surface', 'Wm-2', 'seismic'] |
|---|
| 759 | elif varn == 'hfss' or varn == 'LSENS' or varn == 'sens': |
|---|
| 760 | varvals = ['hfss', 'surface_upward_sensible_heat_flux', -150., 150., \ |
|---|
| 761 | 'upward|sensible|heat|flux|at|the|surface', 'Wm-2', 'seismic'] |
|---|
| 762 | elif varn == 'hus' or varn == 'WRFrh' or varn == 'LMDZrh' or varn == 'rhum' or \ |
|---|
| 763 | varn == 'LRHUM': |
|---|
| 764 | varvals = ['hus', 'specific_humidity', 0., 1., 'specific|humidty', '1', \ |
|---|
| 765 | 'BuPu'] |
|---|
| 766 | elif varn == 'huss' or varn == 'WRFrhs' or varn == 'LMDZrhs' or varn == 'rh2m' or\ |
|---|
| 767 | varn == 'LRH2M': |
|---|
| 768 | varvals = ['huss', 'specific_humidity', 0., 1., 'specific|humidty|at|2m', \ |
|---|
| 769 | '1', 'BuPu'] |
|---|
| 770 | elif varn == 'i' or varn == 'QICE': |
|---|
| 771 | varvals = ['i', 'iced_water_mixing_ratio', 0., 0.0003, \ |
|---|
| 772 | 'iced|water|mixing|ratio', 'kgkg-1', 'Purples'] |
|---|
| 773 | elif varn == 'lat' or varn == 'XLAT' or varn == 'XLAT_M' or varn == 'latitude': |
|---|
| 774 | varvals = ['lat', 'latitude', -90., 90., 'latitude', 'degrees North', \ |
|---|
| 775 | 'seismic'] |
|---|
| 776 | elif varn == 'lcl' or varn == 's_lcl' or varn == 'ls_lcl' or varn == 'LS_LCL': |
|---|
| 777 | varvals = ['lcl', 'condensation_level', 0., 2500., 'level|of|condensation', \ |
|---|
| 778 | 'm', 'Greens'] |
|---|
| 779 | elif varn == 'lambdath' or varn == 'lambda_th' or varn == 'LLAMBDA_TH': |
|---|
| 780 | varvals = ['lambdath', 'thermal_plume_vertical_velocity', -30., 30., \ |
|---|
| 781 | 'thermal|plume|vertical|velocity', 'm/s', 'seismic'] |
|---|
| 782 | elif varn == 'lmaxth' or varn == 'LLMAXTH': |
|---|
| 783 | varvals = ['lmaxth', 'upper_level_thermals', 0., 100., 'upper|level|thermals'\ |
|---|
| 784 | , '1', 'Greens'] |
|---|
| 785 | elif varn == 'lon' or varn == 'XLONG' or varn == 'XLONG_M': |
|---|
| 786 | varvals = ['lon', 'longitude', -180., 180., 'longitude', 'degrees East', \ |
|---|
| 787 | 'seismic'] |
|---|
| 788 | elif varn == 'longitude': |
|---|
| 789 | varvals = ['lon', 'longitude', 0., 360., 'longitude', 'degrees East', \ |
|---|
| 790 | 'seismic'] |
|---|
| 791 | elif varn == 'orog' or varn == 'HGT' or varn == 'HGT_M': |
|---|
| 792 | varvals = ['orog', 'orography', 0., 3000., 'surface|altitude', 'm','terrain'] |
|---|
| 793 | elif varn == 'pfc' or varn == 'plfc' or varn == 'LPLFC': |
|---|
| 794 | varvals = ['pfc', 'pressure_free_convection', 100., 1100., \ |
|---|
| 795 | 'pressure|free|convection', 'hPa', 'BuPu'] |
|---|
| 796 | elif varn == 'plcl' or varn == 'LPLCL': |
|---|
| 797 | varvals = ['plcl', 'pressure_lifting_condensation_level', 700., 1100., \ |
|---|
| 798 | 'pressure|lifting|condensation|level', 'hPa', 'BuPu'] |
|---|
| 799 | elif varn == 'pr' or varn == 'RAINTOT' or varn == 'precip' or \ |
|---|
| 800 | varn == 'LPRECIP' or varn == 'Precip Totale liq+sol': |
|---|
| 801 | varvals = ['pr', 'precipitation_flux', 0., 1.e-4, 'precipitation|flux', \ |
|---|
| 802 | 'kgm-2s-1', 'BuPu'] |
|---|
| 803 | elif varn == 'prprof' or varn == 'vprecip' or varn == 'LVPRECIP': |
|---|
| 804 | varvals = ['prprof', 'precipitation_profile', 0., 1.e-3, \ |
|---|
| 805 | 'precipitation|profile', 'kg/m2/s', 'BuPu'] |
|---|
| 806 | elif varn == 'prprofci' or varn == 'pr_con_i' or varn == 'LPR_CON_I': |
|---|
| 807 | varvals = ['prprofci', 'precipitation_profile_convective_i', 0., 1.e-3, \ |
|---|
| 808 | 'precipitation|profile|convective|i', 'kg/m2/s', 'BuPu'] |
|---|
| 809 | elif varn == 'prprofcl' or varn == 'pr_con_l' or varn == 'LPR_CON_L': |
|---|
| 810 | varvals = ['prprofcl', 'precipitation_profile_convective_l', 0., 1.e-3, \ |
|---|
| 811 | 'precipitation|profile|convective|l', 'kg/m2/s', 'BuPu'] |
|---|
| 812 | elif varn == 'prprofli' or varn == 'pr_lsc_i' or varn == 'LPR_LSC_I': |
|---|
| 813 | varvals = ['prprofli', 'precipitation_profile_large_scale_i', 0., 1.e-3, \ |
|---|
| 814 | 'precipitation|profile|large|scale|i', 'kg/m2/s', 'BuPu'] |
|---|
| 815 | elif varn == 'prprofll' or varn == 'pr_lsc_l' or varn == 'LPR_LSC_L': |
|---|
| 816 | varvals = ['prprofll', 'precipitation_profile_large_scale_l', 0., 1.e-3, \ |
|---|
| 817 | 'precipitation|profile|large|scale|l', 'kg/m2/s', 'BuPu'] |
|---|
| 818 | elif varn == 'pracc' or varn == 'ACRAINTOT': |
|---|
| 819 | varvals = ['pracc', 'precipitation_amount', 0., 100., \ |
|---|
| 820 | 'accumulated|precipitation', 'kgm-2', 'BuPu'] |
|---|
| 821 | elif varn == 'prc' or varn == 'LPLUC' or varn == 'pluc' or varn == 'WRFprc' or \ |
|---|
| 822 | varn == 'RAINCde': |
|---|
| 823 | varvals = ['prc', 'convective_precipitation_flux', 0., 2.e-4, \ |
|---|
| 824 | 'convective|precipitation|flux', 'kgm-2s-1', 'Blues'] |
|---|
| 825 | elif varn == 'prci' or varn == 'pr_con_i' or varn == 'LPR_CON_I': |
|---|
| 826 | varvals = ['prci', 'convective_ice_precipitation_flux', 0., 0.003, \ |
|---|
| 827 | 'convective|ice|precipitation|flux', 'kgm-2s-1', 'Purples'] |
|---|
| 828 | elif varn == 'prcl' or varn == 'pr_con_l' or varn == 'LPR_CON_L': |
|---|
| 829 | varvals = ['prcl', 'convective_liquid_precipitation_flux', 0., 0.003, \ |
|---|
| 830 | 'convective|liquid|precipitation|flux', 'kgm-2s-1', 'Blues'] |
|---|
| 831 | elif varn == 'pres' or varn == 'presnivs' or varn == 'pressure' or \ |
|---|
| 832 | varn == 'lpres' or varn == 'LPRES': |
|---|
| 833 | varvals = ['pres', 'air_pressure', 0., 103000., 'air|pressure', 'Pa', \ |
|---|
| 834 | 'Blues'] |
|---|
| 835 | elif varn == 'prls' or varn == 'WRFprls' or varn == 'LPLUL' or varn == 'plul' or \ |
|---|
| 836 | varn == 'RAINNCde': |
|---|
| 837 | varvals = ['prls', 'large_scale_precipitation_flux', 0., 2.e-4, \ |
|---|
| 838 | 'large|scale|precipitation|flux', 'kgm-2s-1', 'Blues'] |
|---|
| 839 | elif varn == 'prsn' or varn == 'SNOW' or varn == 'snow' or varn == 'LSNOW': |
|---|
| 840 | varvals = ['prsn', 'snowfall', 0., 1.e-4, 'snowfall|flux', 'kgm-2s-1', 'BuPu'] |
|---|
| 841 | elif varn == 'prw' or varn == 'WRFprh': |
|---|
| 842 | varvals = ['prw', 'atmosphere_water_vapor_content', 0., 10., \ |
|---|
| 843 | 'water|vapor"path', 'kgm-2', 'Blues'] |
|---|
| 844 | elif varn == 'ps' or varn == 'psfc' or varn =='PSFC' or varn == 'psol' or \ |
|---|
| 845 | varn == 'Surface Pressure': |
|---|
| 846 | varvals=['ps', 'surface_air_pressure', 85000., 105400., 'surface|pressure', \ |
|---|
| 847 | 'hPa', 'cool'] |
|---|
| 848 | elif varn == 'psl' or varn == 'mslp' or varn =='WRFmslp': |
|---|
| 849 | varvals=['psl', 'air_pressure_at_sea_level', 85000., 104000., \ |
|---|
| 850 | 'mean|sea|level|pressure', 'Pa', 'Greens'] |
|---|
| 851 | elif varn == 'qth' or varn == 'q_th' or varn == 'LQ_TH': |
|---|
| 852 | varvals = ['qth', 'thermal_plume_total_water_content', 0., 25., \ |
|---|
| 853 | 'total|water|cotent|in|thermal|plume', 'mm', 'YlOrRd'] |
|---|
| 854 | elif varn == 'r' or varn == 'QVAPOR' or varn == 'ovap' or varn == 'LOVAP': |
|---|
| 855 | varvals = ['r', 'water_mixing_ratio', 0., 0.03, 'water|mixing|ratio', \ |
|---|
| 856 | 'kgkg-1', 'BuPu'] |
|---|
| 857 | elif varn == 'r2' or varn == 'Q2': |
|---|
| 858 | varvals = ['r2', 'water_mixing_ratio_at_2m', 0., 0.03, 'water|mixing|' + \ |
|---|
| 859 | 'ratio|at|2|m','kgkg-1', 'BuPu'] |
|---|
| 860 | elif varn == 'rsds' or varn == 'SWdnSFC' or varn == 'SWdn at surface' or \ |
|---|
| 861 | varn == 'SWDOWN': |
|---|
| 862 | varvals=['rsds', 'surface_downwelling_shortwave_flux_in_air', 0., 1200., \ |
|---|
| 863 | 'downward|SW|surface|radiation', 'Wm-2' ,'Reds'] |
|---|
| 864 | elif varn == 'rsdsacc': |
|---|
| 865 | varvals=['rsdsacc', 'accumulated_surface_downwelling_shortwave_flux_in_air', \ |
|---|
| 866 | 0., 1200., 'accumulated|downward|SW|surface|radiation', 'Wm-2' ,'Reds'] |
|---|
| 867 | elif varn == 'rvor' or varn == 'WRFrvor': |
|---|
| 868 | varvals = ['rvor', 'air_relative_vorticity', -2.5E-3, 2.5E-3, \ |
|---|
| 869 | 'air|relative|vorticity', 's-1', 'seismic'] |
|---|
| 870 | elif varn == 'rvors' or varn == 'WRFrvors': |
|---|
| 871 | varvals = ['rvors', 'surface_air_relative_vorticity', -2.5E-3, 2.5E-3, \ |
|---|
| 872 | 'surface|air|relative|vorticity', 's-1', 'seismic'] |
|---|
| 873 | elif varn == 's' or varn == 'QSNOW': |
|---|
| 874 | varvals = ['s', 'snow_mixing_ratio', 0., 0.0003, 'snow|mixing|ratio', \ |
|---|
| 875 | 'kgkg-1', 'Purples'] |
|---|
| 876 | elif varn == 'stherm' or varn == 'LS_THERM': |
|---|
| 877 | varvals = ['stherm', 'thermals_excess', 0., 0.8, 'thermals|excess', 'K', \ |
|---|
| 878 | 'Reds'] |
|---|
| 879 | elif varn == 'ta' or varn == 'WRFt' or varn == 'temp' or varn == 'LTEMP' or \ |
|---|
| 880 | varn == 'Air temperature': |
|---|
| 881 | varvals = ['ta', 'air_temperature', 195., 320., 'air|temperature', 'K', \ |
|---|
| 882 | 'YlOrRd'] |
|---|
| 883 | elif varn == 'tah' or varn == 'theta' or varn == 'LTHETA': |
|---|
| 884 | varvals = ['tah', 'potential_air_temperature', 195., 320., \ |
|---|
| 885 | 'potential|air|temperature', 'K', 'YlOrRd'] |
|---|
| 886 | elif varn == 'tas' or varn == 'T2' or varn == 't2m' or varn == 'T2M' or \ |
|---|
| 887 | varn == 'Temperature 2m': |
|---|
| 888 | varvals = ['tas', 'air_temperature', 240., 310., 'air|temperature|at|2m', ' \ |
|---|
| 889 | K', 'YlOrRd'] |
|---|
| 890 | elif varn == 'tds' or varn == 'TH2': |
|---|
| 891 | varvals = ['tds', 'air_dew_point_temperature', 240., 310., \ |
|---|
| 892 | 'air|dew|point|temperature|at|2m', 'K', 'YlGnBu'] |
|---|
| 893 | elif varn == 'tke' or varn == 'TKE' or varn == 'tke' or varn == 'LTKE': |
|---|
| 894 | varvals = ['tke', 'turbulent_kinetic_energy', 0., 0.003, \ |
|---|
| 895 | 'turbulent|kinetic|energy', 'm2/s2', 'Reds'] |
|---|
| 896 | elif varn == 'time'or varn == 'time_counter': |
|---|
| 897 | varvals = ['time', 'time', 0., 1000., 'time', \ |
|---|
| 898 | 'hours|since|1949/12/01|00:00:00', 'Reds'] |
|---|
| 899 | elif varn == 'tmla' or varn == 's_pblt' or varn == 'LS_PBLT': |
|---|
| 900 | varvals = ['tmla', 'atmosphere_top_boundary_layer_temperature', 250., 330., \ |
|---|
| 901 | 'atmosphere|top|boundary|layer|temperature', 'K', 'Reds'] |
|---|
| 902 | elif varn == 'ua' or varn == 'vitu' or varn == 'U' or varn == 'Zonal wind' or \ |
|---|
| 903 | varn == 'LVITU': |
|---|
| 904 | varvals = ['ua', 'eastward_wind', -30., 30., 'eastward|wind', 'ms-1', \ |
|---|
| 905 | 'seismic'] |
|---|
| 906 | elif varn == 'uas' or varn == 'u10m' or varn == 'U10' or varn =='Vent zonal 10m': |
|---|
| 907 | varvals = ['uas', 'eastward_wind', -30., 30., 'eastward|2m|wind', \ |
|---|
| 908 | 'ms-1', 'seismic'] |
|---|
| 909 | elif varn == 'va' or varn == 'vitv' or varn == 'V' or varn == 'Meridional wind' \ |
|---|
| 910 | or varn == 'LVITV': |
|---|
| 911 | varvals = ['va', 'northward_wind', -30., 30., 'northward|wind', 'ms-1', \ |
|---|
| 912 | 'seismic'] |
|---|
| 913 | elif varn == 'vas' or varn == 'v10m' or varn == 'V10' or \ |
|---|
| 914 | varn =='Vent meridien 10m': |
|---|
| 915 | varvals = ['vas', 'northward_wind', -30., 30., 'northward|2m|wind', 'ms-1', \ |
|---|
| 916 | 'seismic'] |
|---|
| 917 | elif varn == 'wakedeltaq' or varn == 'wake_deltaq' or varn == 'lwake_deltaq' or \ |
|---|
| 918 | varn == 'LWAKE_DELTAQ': |
|---|
| 919 | varvals = ['wakedeltaq', 'wake_delta_vapor', -0.003, 0.003, \ |
|---|
| 920 | 'wake|delta|mixing|ratio', '-', 'seismic'] |
|---|
| 921 | elif varn == 'wakedeltat' or varn == 'wake_deltat' or varn == 'lwake_deltat' or \ |
|---|
| 922 | varn == 'LWAKE_DELTAT': |
|---|
| 923 | varvals = ['wakedeltat', 'wake_delta_temp', -0.003, 0.003, \ |
|---|
| 924 | 'wake|delta|temperature', '-', 'seismic'] |
|---|
| 925 | elif varn == 'wakeh' or varn == 'wake_h' or varn == 'LWAKE_H': |
|---|
| 926 | varvals = ['wakeh', 'wake_height', 0., 1000., 'height|of|the|wakes', 'm', \ |
|---|
| 927 | 'YlOrRd'] |
|---|
| 928 | elif varn == 'wakeomg' or varn == 'wake_omg' or varn == 'lwake_omg' or \ |
|---|
| 929 | varn == 'LWAKE_OMG': |
|---|
| 930 | varvals = ['wakeomg', 'wake_omega', 0., 3., 'wake|omega', \ |
|---|
| 931 | '-', 'BuGn'] |
|---|
| 932 | elif varn == 'wakes' or varn == 'wake_s' or varn == 'LWAKE_S': |
|---|
| 933 | varvals = ['wakes', 'wake_area_fraction', 0., 0.5, 'wake|spatial|fraction', \ |
|---|
| 934 | '1', 'BuGn'] |
|---|
| 935 | elif varn == 'wa' or varn == 'W' or varn == 'Vertical wind': |
|---|
| 936 | varvals = ['wa', 'upward_wind', -10., 10., 'upward|wind', 'ms-1', \ |
|---|
| 937 | 'seismic'] |
|---|
| 938 | elif varn == 'wap' or varn == 'vitw' or varn == 'LVITW': |
|---|
| 939 | varvals = ['wap', 'upward_wind', -3.e-10, 3.e-10, 'upward|wind', 'mPa-1', \ |
|---|
| 940 | 'seismic'] |
|---|
| 941 | elif varn == 'wss' or varn == 'SPDUV': |
|---|
| 942 | varvals = ['wss', 'air_velocity', 0., 30., 'surface|horizontal|wind|speed', \ |
|---|
| 943 | 'ms-1', 'Reds'] |
|---|
| 944 | # Water budget |
|---|
| 945 | # Water budget de-accumulated |
|---|
| 946 | elif varn == 'ccond' or varn == 'CCOND' or varn == 'ACCCONDde': |
|---|
| 947 | varvals = ['ccond', 'cw_cond', 0., 30., \ |
|---|
| 948 | 'cloud|water|condensation', 'mm', 'Reds'] |
|---|
| 949 | elif varn == 'wbr' or varn == 'ACQVAPORde': |
|---|
| 950 | varvals = ['wbr', 'wbr', 0., 30., 'Water|Budget|water|wapor', 'mm', 'Blues'] |
|---|
| 951 | elif varn == 'diabh' or varn == 'DIABH' or varn == 'ACDIABHde': |
|---|
| 952 | varvals = ['diabh', 'diabh', 0., 30., 'diabatic|heating', 'K', 'Reds'] |
|---|
| 953 | elif varn == 'wbpw' or varn == 'WBPW' or varn == 'WBACPWde': |
|---|
| 954 | varvals = ['wbpw', 'water_budget_pw', 0., 30., 'Water|Budget|water|content',\ |
|---|
| 955 | 'mms-1', 'Reds'] |
|---|
| 956 | elif varn == 'wbf' or varn == 'WBACF' or varn == 'WBACFde': |
|---|
| 957 | varvals = ['wbf', 'water_budget_hfcqv', 0., 30., \ |
|---|
| 958 | 'Water|Budget|horizontal|convergence|of|water|vapour|(+,|' + \ |
|---|
| 959 | 'conv.;|-,|div.)', 'mms-1', 'Reds'] |
|---|
| 960 | elif varn == 'wbfc' or varn == 'WBFC' or varn == 'WBACFCde': |
|---|
| 961 | varvals = ['wbfc', 'water_budget_fc', 0., 30., \ |
|---|
| 962 | 'Water|Budget|horizontal|convergence|of|cloud|(+,|conv.;|-,|' +\ |
|---|
| 963 | 'div.)', 'mms-1', 'Reds'] |
|---|
| 964 | elif varn == 'wbfp' or varn == 'WBFP' or varn == 'WBACFPde': |
|---|
| 965 | varvals = ['wbfp', 'water_budget_cfp', 0., 30., \ |
|---|
| 966 | 'Water|Budget|horizontal|convergence|of|precipitation|(+,|' + \ |
|---|
| 967 | 'conv.;|-,|div.)', 'mms-1', 'Reds'] |
|---|
| 968 | elif varn == 'wbz' or varn == 'WBZ' or varn == 'WBACZde': |
|---|
| 969 | varvals = ['wbz', 'water_budget_z', 0., 30., \ |
|---|
| 970 | 'Water|Budget|vertical|convergence|of|water|vapour|(+,|conv.' +\ |
|---|
| 971 | ';|-,|div.)', 'mms-1', 'Reds'] |
|---|
| 972 | elif varn == 'wbc' or varn == 'WBC' or varn == 'WBACCde': |
|---|
| 973 | varvals = ['wbc', 'water_budget_c', 0., 30., \ |
|---|
| 974 | 'Water|Budget|Cloud|water|species','mms-1', 'Reds'] |
|---|
| 975 | elif varn == 'wbqvd' or varn == 'WBQVD' or varn == 'WBACQVDde': |
|---|
| 976 | varvals = ['wbqvd', 'water_budget_qvd', 0., 30., \ |
|---|
| 977 | 'Water|Budget|water|vapour|divergence', 'mms-1', 'Reds'] |
|---|
| 978 | elif varn == 'wbqvblten' or varn == 'WBQVBLTEN' or varn == 'WBACQVBLTENde': |
|---|
| 979 | varvals = ['wbqvblten', 'water_budget_qv_blten', 0., 30., \ |
|---|
| 980 | 'Water|Budget|QV|tendency|due|to|pbl|parameterization', \ |
|---|
| 981 | 'kg kg-1 s-1', 'Reds'] |
|---|
| 982 | elif varn == 'wbqvcuten' or varn == 'WBQVCUTEN' or varn == 'WBACQVCUTENde': |
|---|
| 983 | varvals = ['wbqvcuten', 'water_budget_qv_cuten', 0., 30., \ |
|---|
| 984 | 'Water|Budget|QV|tendency|due|to|cu|parameterization', \ |
|---|
| 985 | 'kg kg-1 s-1', 'Reds'] |
|---|
| 986 | elif varn == 'wbqvshten' or varn == 'WBQVSHTEN' or varn == 'WBACQVSHTENde': |
|---|
| 987 | varvals = ['wbqvshten', 'water_budget_qv_shten', 0., 30., \ |
|---|
| 988 | 'Water|Budget|QV|tendency|due|to|shallow|cu|parameterization', \ |
|---|
| 989 | 'kg kg-1 s-1', 'Reds'] |
|---|
| 990 | elif varn == 'wbpr' or varn == 'WBP' or varn == 'WBACPde': |
|---|
| 991 | varvals = ['wbpr', 'water_budget_pr', 0., 30., \ |
|---|
| 992 | 'Water|Budget|recipitation', 'mms-1', 'Reds'] |
|---|
| 993 | elif varn == 'wbpw' or varn == 'WBPW' or varn == 'WBACPWde': |
|---|
| 994 | varvals = ['wbpw', 'water_budget_pw', 0., 30., \ |
|---|
| 995 | 'Water|Budget|water|content', 'mms-1', 'Reds'] |
|---|
| 996 | elif varn == 'wbcondt' or varn == 'WBCONDT' or varn == 'WBACCONDTde': |
|---|
| 997 | varvals = ['wbcondt', 'water_budget_condt', 0., 30., \ |
|---|
| 998 | 'Water|Budget|condensation|and|deposition', 'mms-1', 'Reds'] |
|---|
| 999 | elif varn == 'wbqcm' or varn == 'WBQCM' or varn == 'WBACQCMde': |
|---|
| 1000 | varvals = ['wbqcm', 'water_budget_qcm', 0., 30., \ |
|---|
| 1001 | 'Water|Budget|hydrometeor|change|and|convergence', 'mms-1', 'Reds'] |
|---|
| 1002 | elif varn == 'wbsi' or varn == 'WBSI' or varn == 'WBACSIde': |
|---|
| 1003 | varvals = ['wbsi', 'water_budget_si', 0., 30., \ |
|---|
| 1004 | 'Water|Budget|hydrometeor|sink', 'mms-1', 'Reds'] |
|---|
| 1005 | elif varn == 'wbso' or varn == 'WBSO' or varn == 'WBACSOde': |
|---|
| 1006 | varvals = ['wbso', 'water_budget_so', 0., 30., \ |
|---|
| 1007 | 'Water|Budget|hydrometeor|source', 'mms-1', 'Reds'] |
|---|
| 1008 | # Water Budget accumulated |
|---|
| 1009 | elif varn == 'ccondac' or varn == 'ACCCOND': |
|---|
| 1010 | varvals = ['ccondac', 'cw_cond_ac', 0., 30., \ |
|---|
| 1011 | 'accumulated|cloud|water|condensation', 'mm', 'Reds'] |
|---|
| 1012 | elif varn == 'rac' or varn == 'ACQVAPOR': |
|---|
| 1013 | varvals = ['rac', 'ac_r', 0., 30., 'accumualted|water|wapor', 'mm', 'Blues'] |
|---|
| 1014 | elif varn == 'diabhac' or varn == 'ACDIABH': |
|---|
| 1015 | varvals = ['diabhac', 'diabh_ac', 0., 30., 'accumualted|diabatic|heating', \ |
|---|
| 1016 | 'K', 'Reds'] |
|---|
| 1017 | elif varn == 'wbpwac' or varn == 'WBACPW': |
|---|
| 1018 | varvals = ['wbpwac', 'water_budget_pw_ac', 0., 30., \ |
|---|
| 1019 | 'Water|Budget|accumulated|water|content', 'mm', 'Reds'] |
|---|
| 1020 | elif varn == 'wbfac' or varn == 'WBACF': |
|---|
| 1021 | varvals = ['wbfac', 'water_budget_hfcqv_ac', 0., 30., \ |
|---|
| 1022 | 'Water|Budget|accumulated|horizontal|convergence|of|water|vapour|(+,|' + \ |
|---|
| 1023 | 'conv.;|-,|div.)', 'mm', 'Reds'] |
|---|
| 1024 | elif varn == 'wbfcac' or varn == 'WBACFC': |
|---|
| 1025 | varvals = ['wbfcac', 'water_budget_fc_ac', 0., 30., \ |
|---|
| 1026 | 'Water|Budget|accumulated|horizontal|convergence|of|cloud|(+,|conv.;|-,|' +\ |
|---|
| 1027 | 'div.)', 'mm', 'Reds'] |
|---|
| 1028 | elif varn == 'wbfpac' or varn == 'WBACFP': |
|---|
| 1029 | varvals = ['wbfpac', 'water_budget_cfp_ac', 0., 30., \ |
|---|
| 1030 | 'Water|Budget|accumulated|horizontal|convergence|of|precipitation|(+,|' + \ |
|---|
| 1031 | 'conv.;|-,|div.)', 'mm', 'Reds'] |
|---|
| 1032 | elif varn == 'wbzac' or varn == 'WBACZ': |
|---|
| 1033 | varvals = ['wbzac', 'water_budget_z_ac', 0., 30., \ |
|---|
| 1034 | 'Water|Budget|accumulated|vertical|convergence|of|water|vapour|(+,|conv.' +\ |
|---|
| 1035 | ';|-,|div.)', 'mm', 'Reds'] |
|---|
| 1036 | elif varn == 'wbcac' or varn == 'WBACC': |
|---|
| 1037 | varvals = ['wbcac', 'water_budget_c_ac', 0., 30., \ |
|---|
| 1038 | 'Water|Budget|accumulated|Cloud|water|species','mm', 'Reds'] |
|---|
| 1039 | elif varn == 'wbqvdac' or varn == 'WBACQVD': |
|---|
| 1040 | varvals = ['wbqvdac', 'water_budget_qvd_ac', 0., 30., \ |
|---|
| 1041 | 'Water|Budget|accumulated|water|vapour|divergence', 'mm', 'Reds'] |
|---|
| 1042 | elif varn == 'wbqvbltenac' or varn == 'WBACQVBLTEN': |
|---|
| 1043 | varvals = ['wbqvbltenac', 'water_budget_qv_blten_ac', 0., 30., \ |
|---|
| 1044 | 'Water|Budget|accumulated|QV|tendency|due|to|pbl|parameterization', \ |
|---|
| 1045 | 'kg kg-1 s-1', 'Reds'] |
|---|
| 1046 | elif varn == 'wbqvcutenac' or varn == 'WBACQVCUTEN': |
|---|
| 1047 | varvals = ['wbqvcutenac', 'water_budget_qv_cuten_ac', 0., 30., \ |
|---|
| 1048 | 'Water|Budget|accumulated|QV|tendency|due|to|cu|parameterization', \ |
|---|
| 1049 | 'kg kg-1 s-1', 'Reds'] |
|---|
| 1050 | elif varn == 'wbqvshtenac' or varn == 'WBACQVSHTEN': |
|---|
| 1051 | varvals = ['wbqvshtenac', 'water_budget_qv_shten_ac', 0., 30., \ |
|---|
| 1052 | 'Water|Budget|accumulated|QV|tendency|due|to|shallow|cu|parameterization', \ |
|---|
| 1053 | 'kg kg-1 s-1', 'Reds'] |
|---|
| 1054 | elif varn == 'wbprac' or varn == 'WBACP': |
|---|
| 1055 | varvals = ['wbprac', 'water_budget_pr_ac', 0., 30., \ |
|---|
| 1056 | 'Water|Budget|accumulated|precipitation', 'mm', 'Reds'] |
|---|
| 1057 | elif varn == 'wbpwac' or varn == 'WBACPW': |
|---|
| 1058 | varvals = ['wbpwac', 'water_budget_pw_ac', 0., 30., \ |
|---|
| 1059 | 'Water|Budget|accumulated|water|content', 'mm', 'Reds'] |
|---|
| 1060 | elif varn == 'wbcondtac' or varn == 'WBACCONDT': |
|---|
| 1061 | varvals = ['wbcondtac', 'water_budget_condt_ac', 0., 30., \ |
|---|
| 1062 | 'Water|Budget|accumulated|condensation|and|deposition', 'mm', 'Reds'] |
|---|
| 1063 | elif varn == 'wbqcmac' or varn == 'WBACQCM': |
|---|
| 1064 | varvals = ['wbqcmac', 'water_budget_qcm_ac', 0., 30., \ |
|---|
| 1065 | 'Water|Budget|accumulated|hydrometeor|change|and|convergence', 'mm', 'Reds'] |
|---|
| 1066 | elif varn == 'wbsiac' or varn == 'WBACSI': |
|---|
| 1067 | varvals = ['wbsiac', 'water_budget_si_ac', 0., 30., \ |
|---|
| 1068 | 'Water|Budget|accumulated|hydrometeor|sink', 'mm', 'Reds'] |
|---|
| 1069 | elif varn == 'wbsoac' or varn == 'WBACSO': |
|---|
| 1070 | varvals = ['wbsoac', 'water_budget_so_ac', 0., 30., \ |
|---|
| 1071 | 'Water|Budget|accumulated|hydrometeor|source', 'mm', 'Reds'] |
|---|
| 1072 | |
|---|
| 1073 | elif varn == 'xtime' or varn == 'XTIME': |
|---|
| 1074 | varvals = ['xtime', 'time', 0., 1.e5, 'time', \ |
|---|
| 1075 | 'minutes|since|simulation|start', 'Reds'] |
|---|
| 1076 | elif varn == 'x' or varn == 'X': |
|---|
| 1077 | varvals = ['x', 'x', 0., 100., 'x', '-', 'Reds'] |
|---|
| 1078 | elif varn == 'y' or varn == 'Y': |
|---|
| 1079 | varvals = ['y', 'y', 0., 100., 'y', '-', 'Blues'] |
|---|
| 1080 | elif varn == 'z' or varn == 'Z': |
|---|
| 1081 | varvals = ['z', 'z', 0., 100., 'z', '-', 'Greens'] |
|---|
| 1082 | elif varn == 'zg' or varn == 'WRFght' or varn == 'Geopotential height' or \ |
|---|
| 1083 | varn == 'geop' or varn == 'LGEOP': |
|---|
| 1084 | varvals = ['zg', 'geopotential_height', 0., 80000., 'geopotential|height', \ |
|---|
| 1085 | 'm2s-2', 'rainbow'] |
|---|
| 1086 | elif varn == 'zmaxth' or varn == 'zmax_th' or varn == 'LZMAX_TH': |
|---|
| 1087 | varvals = ['zmaxth', 'thermal_plume_height', 0., 4000., \ |
|---|
| 1088 | 'maximum|thermals|plume|height', 'm', 'YlOrRd'] |
|---|
| 1089 | elif varn == 'zmla' or varn == 's_pblh' or varn == 'LS_PBLH': |
|---|
| 1090 | varvals = ['zmla', 'atmosphere_boundary_layer_thickness', 0., 2500., \ |
|---|
| 1091 | 'atmosphere|boundary|layer|thickness', 'm', 'Blues'] |
|---|
| 1092 | else: |
|---|
| 1093 | print errormsg |
|---|
| 1094 | print ' ' + fname + ": variable '" + varn + "' not defined !!!" |
|---|
| 1095 | quit(-1) |
|---|
| 1096 | |
|---|
| 1097 | return varvals |
|---|
| 1098 | |
|---|
| 1099 | def numVector_String(vec,char): |
|---|
| 1100 | """ Function to transform a vector of numbers to a single string [char] separated |
|---|
| 1101 | numVector_String(vec,char) |
|---|
| 1102 | vec= vector with the numerical values |
|---|
| 1103 | char= single character to split the values |
|---|
| 1104 | >>> print numVector_String(np.arange(10),' ') |
|---|
| 1105 | 0 1 2 3 4 5 6 7 8 9 |
|---|
| 1106 | """ |
|---|
| 1107 | fname = 'numVector_String' |
|---|
| 1108 | |
|---|
| 1109 | # if vec == 'h': |
|---|
| 1110 | # print fname + '_____________________________________________________________' |
|---|
| 1111 | # print numVector_String.__doc__ |
|---|
| 1112 | # quit() |
|---|
| 1113 | |
|---|
| 1114 | Nvals = len(vec) |
|---|
| 1115 | |
|---|
| 1116 | string='' |
|---|
| 1117 | for i in range(Nvals): |
|---|
| 1118 | if i == 0: |
|---|
| 1119 | string = str(vec[i]) |
|---|
| 1120 | else: |
|---|
| 1121 | string = string + char + str(vec[i]) |
|---|
| 1122 | |
|---|
| 1123 | return string |
|---|
| 1124 | |
|---|
| 1125 | def index_mat(mat,val): |
|---|
| 1126 | """ Function to provide the coordinates of a given value inside a matrix |
|---|
| 1127 | index_mat(mat,val) |
|---|
| 1128 | mat= matrix with values |
|---|
| 1129 | val= value to search |
|---|
| 1130 | >>> index_mat(np.arange(27).reshape(3,3,3),22) |
|---|
| 1131 | [2 1 1] |
|---|
| 1132 | """ |
|---|
| 1133 | |
|---|
| 1134 | fname = 'index_mat' |
|---|
| 1135 | |
|---|
| 1136 | matshape = mat.shape |
|---|
| 1137 | |
|---|
| 1138 | matlist = list(mat.flatten()) |
|---|
| 1139 | ifound = matlist.index(val) |
|---|
| 1140 | |
|---|
| 1141 | Ndims = len(matshape) |
|---|
| 1142 | valpos = np.zeros((Ndims), dtype=int) |
|---|
| 1143 | baseprevdims = np.zeros((Ndims), dtype=int) |
|---|
| 1144 | |
|---|
| 1145 | for dimid in range(Ndims): |
|---|
| 1146 | baseprevdims[dimid] = np.product(matshape[dimid+1:Ndims]) |
|---|
| 1147 | if dimid == 0: |
|---|
| 1148 | alreadyplaced = 0 |
|---|
| 1149 | else: |
|---|
| 1150 | alreadyplaced = np.sum(baseprevdims[0:dimid]*valpos[0:dimid]) |
|---|
| 1151 | valpos[dimid] = int((ifound - alreadyplaced )/ baseprevdims[dimid]) |
|---|
| 1152 | |
|---|
| 1153 | return valpos |
|---|
| 1154 | |
|---|
| 1155 | def multi_index_mat(mat,val): |
|---|
| 1156 | """ Function to provide the multiple coordinates of a given value inside a matrix |
|---|
| 1157 | index_mat(mat,val) |
|---|
| 1158 | mat= matrix with values |
|---|
| 1159 | val= value to search |
|---|
| 1160 | >>> vals = np.ones((24), dtype=np.float).reshape(2,3,4) |
|---|
| 1161 | vals[:,:,2] = 0. |
|---|
| 1162 | vals[1,:,:] = np.pi |
|---|
| 1163 | vals[:,2,:] = -1. |
|---|
| 1164 | multi_index_mat(vals,1.) |
|---|
| 1165 | [array([0, 0, 0]), array([0, 0, 1]), array([0, 0, 3]), array([0, 1, 0]), array([0, 1, 1]), array([0, 1, 3])] |
|---|
| 1166 | """ |
|---|
| 1167 | fname = 'multi_index_mat' |
|---|
| 1168 | |
|---|
| 1169 | matshape = mat.shape |
|---|
| 1170 | |
|---|
| 1171 | ivalpos = [] |
|---|
| 1172 | matlist = list(mat.flatten()) |
|---|
| 1173 | Lmatlist = len(matlist) |
|---|
| 1174 | |
|---|
| 1175 | val0 = val - val |
|---|
| 1176 | if val != val0: |
|---|
| 1177 | valdiff = val0 |
|---|
| 1178 | else: |
|---|
| 1179 | valdiff = np.ones((1), dtype = type(val)) |
|---|
| 1180 | |
|---|
| 1181 | ifound = 0 |
|---|
| 1182 | while ifound < Lmatlist: |
|---|
| 1183 | if matlist.count(val) == 0: |
|---|
| 1184 | ifound = Lmatlist + 1 |
|---|
| 1185 | else: |
|---|
| 1186 | ifound = matlist.index(val) |
|---|
| 1187 | |
|---|
| 1188 | Ndims = len(matshape) |
|---|
| 1189 | valpos = np.zeros((Ndims), dtype=int) |
|---|
| 1190 | baseprevdims = np.zeros((Ndims), dtype=int) |
|---|
| 1191 | |
|---|
| 1192 | for dimid in range(Ndims): |
|---|
| 1193 | baseprevdims[dimid] = np.product(matshape[dimid+1:Ndims]) |
|---|
| 1194 | if dimid == 0: |
|---|
| 1195 | alreadyplaced = 0 |
|---|
| 1196 | else: |
|---|
| 1197 | alreadyplaced = np.sum(baseprevdims[0:dimid]*valpos[0:dimid]) |
|---|
| 1198 | valpos[dimid] = int((ifound - alreadyplaced )/ baseprevdims[dimid]) |
|---|
| 1199 | matlist[ifound] = valdiff |
|---|
| 1200 | ivalpos.append(valpos) |
|---|
| 1201 | |
|---|
| 1202 | return ivalpos |
|---|
| 1203 | |
|---|
| 1204 | def addfileInfile(origfile,destfile,addfile,addsign): |
|---|
| 1205 | """ Function to add the content of a file [addfile] to another one [origfile] at |
|---|
| 1206 | a given position [addsign] creating a new file called [destfile] |
|---|
| 1207 | origfile= original file |
|---|
| 1208 | destfile= destination file |
|---|
| 1209 | addfile= content of the file to add |
|---|
| 1210 | addsign= sign where to add the file |
|---|
| 1211 | """ |
|---|
| 1212 | fname = 'addfileInfile' |
|---|
| 1213 | |
|---|
| 1214 | if not os.path.isfile(origfile): |
|---|
| 1215 | print errormsg |
|---|
| 1216 | print ' ' + fname + ": original file '" + origfile + "' does not exist !!" |
|---|
| 1217 | quit() |
|---|
| 1218 | |
|---|
| 1219 | if not os.path.isfile(addfile): |
|---|
| 1220 | print errormsg |
|---|
| 1221 | print ' ' + fname + ": adding file '" + addfile + "' does not exist !!" |
|---|
| 1222 | quit() |
|---|
| 1223 | |
|---|
| 1224 | ofile = open(origfile, 'r') |
|---|
| 1225 | |
|---|
| 1226 | # Inspecting flag |
|---|
| 1227 | ## |
|---|
| 1228 | Nfound = 0 |
|---|
| 1229 | for line in ofile: |
|---|
| 1230 | if line == addsign + '\n': Nfound = Nfound + 1 |
|---|
| 1231 | |
|---|
| 1232 | if Nfound == 0: |
|---|
| 1233 | print errormsg |
|---|
| 1234 | print ' ' + fname + ": adding sign '" + addsign + "' not found !!" |
|---|
| 1235 | quit() |
|---|
| 1236 | print ' '+ fname + ': found', Nfound," times sign '" + addsign + "' " |
|---|
| 1237 | |
|---|
| 1238 | ofile.seek(0) |
|---|
| 1239 | dfile = open(destfile, 'w') |
|---|
| 1240 | |
|---|
| 1241 | for line in ofile: |
|---|
| 1242 | if line != addsign + '\n': |
|---|
| 1243 | dfile.write(line) |
|---|
| 1244 | else: |
|---|
| 1245 | afile = open(addfile,'r') |
|---|
| 1246 | for aline in afile: |
|---|
| 1247 | print aline |
|---|
| 1248 | dfile.write(aline) |
|---|
| 1249 | |
|---|
| 1250 | afile.close() |
|---|
| 1251 | |
|---|
| 1252 | ofile.close() |
|---|
| 1253 | dfile.close() |
|---|
| 1254 | |
|---|
| 1255 | print main + " successful writting of file '" + destfile + "' !!" |
|---|
| 1256 | return |
|---|
| 1257 | |
|---|
| 1258 | |
|---|
| 1259 | ####### ###### ##### #### ### ## # |
|---|
| 1260 | |
|---|
| 1261 | def valmodoper(varVal, valuesS): |
|---|
| 1262 | """ Function to run the modification of a variable |
|---|
| 1263 | varVAl= matrix with the values |
|---|
| 1264 | valuesS= [modins],[[modval1],...,[modvalN]] modification instruction, value with which modify |
|---|
| 1265 | [modins]: Modifiers: |
|---|
| 1266 | sumc: add [modval1] |
|---|
| 1267 | subc: remove [modval1] |
|---|
| 1268 | mulc: multiply by [modval1] |
|---|
| 1269 | divc: divide by [modval1] |
|---|
| 1270 | lowthres: if [val] < [modval1]; val = [modval2] |
|---|
| 1271 | upthres: if [val] > [modval1]; val = [modval2] |
|---|
| 1272 | lowthres@oper: if [val] < [modval1]; val = [oper] (operation as [modval2],[modval3]) |
|---|
| 1273 | upthres@oper: if [val] > [modval1]; val = [oper] (operation as [modval2],[modval3]) |
|---|
| 1274 | potc: [val] ** [modval1] |
|---|
| 1275 | """ |
|---|
| 1276 | fname='valmodoper' |
|---|
| 1277 | |
|---|
| 1278 | if valuesS == 'h': |
|---|
| 1279 | print fname + '_____________________________________________________________' |
|---|
| 1280 | print valmodoper.__doc__ |
|---|
| 1281 | quit() |
|---|
| 1282 | |
|---|
| 1283 | valsS = valuesS.split(',') |
|---|
| 1284 | modins = valsS[0] |
|---|
| 1285 | modval = float(valsS[1]) |
|---|
| 1286 | |
|---|
| 1287 | if modins == 'sumc': |
|---|
| 1288 | varVal[:] = varVal[:] + modval |
|---|
| 1289 | elif modins == 'subc': |
|---|
| 1290 | varVal[:] = varVal[:] - modval |
|---|
| 1291 | elif modins == 'mulc': |
|---|
| 1292 | varVal[:] = varVal[:] * modval |
|---|
| 1293 | elif modins == 'divc': |
|---|
| 1294 | varVal[:] = varVal[:] / modval |
|---|
| 1295 | elif modins == 'lowthres': |
|---|
| 1296 | varVal2 = np.where(varVal[:] < float(valsS[1]), float(valsS[2]), varVal[:]) |
|---|
| 1297 | varVal[:] = varVal2 |
|---|
| 1298 | elif modins == 'upthres': |
|---|
| 1299 | varVal2 = np.where(varVal[:] > float(valsS[1]), float(valsS[2]), varVal[:]) |
|---|
| 1300 | varVal[:] = varVal2 |
|---|
| 1301 | elif modins == 'lowthres@oper': |
|---|
| 1302 | if valsS[2] == 'sumc': |
|---|
| 1303 | varVal2 = np.where(varVal[:] < float(valsS[1]), \ |
|---|
| 1304 | varVal[:] + float(valsS[3]), varVal[:]) |
|---|
| 1305 | varVal[:] = varVal2 |
|---|
| 1306 | elif valsS[2] == 'subc': |
|---|
| 1307 | varVal2 = np.where(varVal[:] < float(valsS[1]), \ |
|---|
| 1308 | varVal[:] - float(valsS[3]), varVal[:]) |
|---|
| 1309 | varVal[:] = varVal2 |
|---|
| 1310 | elif valsS[2] == 'mulc': |
|---|
| 1311 | varVal2 = np.where(varVal[:] < float(valsS[1]), \ |
|---|
| 1312 | varVal[:] * float(valsS[3]), varVal[:]) |
|---|
| 1313 | varVal[:] = varVal2 |
|---|
| 1314 | elif valsS[2] == 'divc': |
|---|
| 1315 | varVal2 = np.where(varVal[:] < float(valsS[1]), \ |
|---|
| 1316 | varVal[:] / float(valsS[3]), varVal[:]) |
|---|
| 1317 | varVal[:] = varVal2 |
|---|
| 1318 | elif valsS[2] == 'potc': |
|---|
| 1319 | varVal2 = np.where(varVal[:] < float(valsS[1]), \ |
|---|
| 1320 | varVal[:] ** float(valsS[3]), varVal[:]) |
|---|
| 1321 | varVal[:] = varVal2 |
|---|
| 1322 | else: |
|---|
| 1323 | print errormsg |
|---|
| 1324 | print ' ' + fname + ": Operation to modify values '" + modins + \ |
|---|
| 1325 | "' is not defined !!" |
|---|
| 1326 | quit(-1) |
|---|
| 1327 | elif modins == 'upthres@oper': |
|---|
| 1328 | if valsS[2] == 'sumc': |
|---|
| 1329 | varVal2 = np.where(varVal[:] > float(valsS[1]), \ |
|---|
| 1330 | varVal[:] + float(valsS[3]), varVal[:]) |
|---|
| 1331 | varVal[:] = varVal2 |
|---|
| 1332 | elif valsS[2] == 'subc': |
|---|
| 1333 | varVal2 = np.where(varVal[:] > float(valsS[1]), \ |
|---|
| 1334 | varVal[:] - float(valsS[3]), varVal[:]) |
|---|
| 1335 | varVal[:] = varVal2 |
|---|
| 1336 | elif valsS[2] == 'mulc': |
|---|
| 1337 | varVal2 = np.where(varVal[:] > float(valsS[1]), \ |
|---|
| 1338 | varVal[:] * float(valsS[3]), varVal[:]) |
|---|
| 1339 | varVal[:] = varVal2 |
|---|
| 1340 | elif valsS[2] == 'divc': |
|---|
| 1341 | varVal2 = np.where(varVal[:] > float(valsS[1]), \ |
|---|
| 1342 | varVal[:] / float(valsS[3]), varVal[:]) |
|---|
| 1343 | varVal[:] = varVal2 |
|---|
| 1344 | elif valsS[2] == 'potc': |
|---|
| 1345 | varVal2 = np.where(varVal[:] > float(valsS[1]), \ |
|---|
| 1346 | varVal[:] ** float(valsS[3]), varVal[:]) |
|---|
| 1347 | varVal[:] = varVal2 |
|---|
| 1348 | else: |
|---|
| 1349 | print errormsg |
|---|
| 1350 | print ' ' + fname + ": Operation to modify values '" + modins + \ |
|---|
| 1351 | "' is not defined !!" |
|---|
| 1352 | quit(-1) |
|---|
| 1353 | elif modins == 'potc': |
|---|
| 1354 | varVal[:] = varVal[:] ** modval |
|---|
| 1355 | else: |
|---|
| 1356 | print errormsg |
|---|
| 1357 | print ' ' + fname + ": Operation to modify values '" + modins + \ |
|---|
| 1358 | "' is not defined !!" |
|---|
| 1359 | quit(-1) |
|---|
| 1360 | |
|---|
| 1361 | return varVal |
|---|
| 1362 | |
|---|
| 1363 | def rangedim(end, shape): |
|---|
| 1364 | """Gives the instruction to retrieve values from a dimension of a variable |
|---|
| 1365 | >>> print rangedim(-1, 15) |
|---|
| 1366 | 15 |
|---|
| 1367 | """ |
|---|
| 1368 | if end == -1: |
|---|
| 1369 | return shape |
|---|
| 1370 | else: |
|---|
| 1371 | return end |
|---|
| 1372 | |
|---|
| 1373 | def searchInlist(listname, nameFind): |
|---|
| 1374 | """ Function to search a value within a list |
|---|
| 1375 | listname = list |
|---|
| 1376 | nameFind = value to find |
|---|
| 1377 | >>> searInlist(['1', '2', '3', '5'], '5') |
|---|
| 1378 | True |
|---|
| 1379 | """ |
|---|
| 1380 | for x in listname: |
|---|
| 1381 | if x == nameFind: |
|---|
| 1382 | return True |
|---|
| 1383 | return False |
|---|
| 1384 | |
|---|
| 1385 | def datetimeStr_datetime(StringDT): |
|---|
| 1386 | """ Function to transform a string date ([YYYY]-[MM]-[DD]_[HH]:[MI]:[SS] format) to a date object |
|---|
| 1387 | >>> datetimeStr_datetime('1976-02-17_00:00:00') |
|---|
| 1388 | 1976-02-17 00:00:00 |
|---|
| 1389 | """ |
|---|
| 1390 | import datetime as dt |
|---|
| 1391 | |
|---|
| 1392 | fname = 'datetimeStr_datetime' |
|---|
| 1393 | |
|---|
| 1394 | dateD = np.zeros((3), dtype=int) |
|---|
| 1395 | timeT = np.zeros((3), dtype=int) |
|---|
| 1396 | |
|---|
| 1397 | dateD[0] = int(StringDT[0:4]) |
|---|
| 1398 | dateD[1] = int(StringDT[5:7]) |
|---|
| 1399 | dateD[2] = int(StringDT[8:10]) |
|---|
| 1400 | |
|---|
| 1401 | trefT = StringDT.find(':') |
|---|
| 1402 | if not trefT == -1: |
|---|
| 1403 | # print ' ' + fname + ': refdate with time!' |
|---|
| 1404 | timeT[0] = int(StringDT[11:13]) |
|---|
| 1405 | timeT[1] = int(StringDT[14:16]) |
|---|
| 1406 | timeT[2] = int(StringDT[17:19]) |
|---|
| 1407 | |
|---|
| 1408 | if int(dateD[0]) == 0: |
|---|
| 1409 | print warnmsg |
|---|
| 1410 | print ' ' + fname + ': 0 reference year!! changing to 1' |
|---|
| 1411 | dateD[0] = 1 |
|---|
| 1412 | |
|---|
| 1413 | newdatetime = dt.datetime(dateD[0], dateD[1], dateD[2], timeT[0], timeT[1], timeT[2]) |
|---|
| 1414 | |
|---|
| 1415 | return newdatetime |
|---|
| 1416 | |
|---|
| 1417 | def dateStr_date(StringDate): |
|---|
| 1418 | """ Function to transform a string date ([YYYY]-[MM]-[DD] format) to a date object |
|---|
| 1419 | >>> dateStr_date('1976-02-17') |
|---|
| 1420 | 1976-02-17 |
|---|
| 1421 | """ |
|---|
| 1422 | import datetime as dt |
|---|
| 1423 | |
|---|
| 1424 | dateD = StringDate.split('-') |
|---|
| 1425 | if int(dateD[0]) == 0: |
|---|
| 1426 | print warnmsg |
|---|
| 1427 | print ' dateStr_date: 0 reference year!! changing to 1' |
|---|
| 1428 | dateD[0] = 1 |
|---|
| 1429 | newdate = dt.date(int(dateD[0]), int(dateD[1]), int(dateD[2])) |
|---|
| 1430 | return newdate |
|---|
| 1431 | |
|---|
| 1432 | def timeStr_time(StringDate): |
|---|
| 1433 | """ Function to transform a string date ([HH]:[MI]:[SS] format) to a time object |
|---|
| 1434 | >>> datetimeStr_datetime('04:32:54') |
|---|
| 1435 | 04:32:54 |
|---|
| 1436 | """ |
|---|
| 1437 | import datetime as dt |
|---|
| 1438 | |
|---|
| 1439 | timeT = StringDate.split(':') |
|---|
| 1440 | if len(timeT) == 3: |
|---|
| 1441 | newtime = dt.time(int(timeT[0]), int(timeT[1]), int(timeT[2])) |
|---|
| 1442 | else: |
|---|
| 1443 | newtime = dt.time(int(timeT[0]), int(timeT[1]), 0) |
|---|
| 1444 | |
|---|
| 1445 | return newtime |
|---|
| 1446 | |
|---|
| 1447 | def timeref_datetime(refd, timeval, tu): |
|---|
| 1448 | """ Function to transform from a [timeval] in [tu] units from the time referece [tref] to datetime object |
|---|
| 1449 | refd: time of reference (as datetime object) |
|---|
| 1450 | timeval: time value (as [tu] from [tref]) |
|---|
| 1451 | tu: time units |
|---|
| 1452 | >>> timeref = date(1949,12,1,0,0,0) |
|---|
| 1453 | >>> timeref_datetime(timeref, 229784.36, hours) |
|---|
| 1454 | 1976-02-17 08:21:36 |
|---|
| 1455 | """ |
|---|
| 1456 | import datetime as dt |
|---|
| 1457 | import numpy as np |
|---|
| 1458 | |
|---|
| 1459 | ## Not in timedelta |
|---|
| 1460 | # if tu == 'years': |
|---|
| 1461 | # realdate = refdate + dt.timedelta(years=float(timeval)) |
|---|
| 1462 | # elif tu == 'months': |
|---|
| 1463 | # realdate = refdate + dt.timedelta(months=float(timeval)) |
|---|
| 1464 | if tu == 'weeks': |
|---|
| 1465 | realdate = refd + dt.timedelta(weeks=float(timeval)) |
|---|
| 1466 | elif tu == 'days': |
|---|
| 1467 | realdate = refd + dt.timedelta(days=float(timeval)) |
|---|
| 1468 | elif tu == 'hours': |
|---|
| 1469 | realdate = refd + dt.timedelta(hours=float(timeval)) |
|---|
| 1470 | elif tu == 'minutes': |
|---|
| 1471 | realdate = refd + dt.timedelta(minutes=float(timeval)) |
|---|
| 1472 | elif tu == 'seconds': |
|---|
| 1473 | realdate = refd + dt.timedelta(seconds=float(timeval)) |
|---|
| 1474 | elif tu == 'milliseconds': |
|---|
| 1475 | realdate = refd + dt.timedelta(milliseconds=float(timeval)) |
|---|
| 1476 | else: |
|---|
| 1477 | print errormsg |
|---|
| 1478 | print ' timeref_datetime: time units "' + tu + '" not ready!!!!' |
|---|
| 1479 | quit(-1) |
|---|
| 1480 | |
|---|
| 1481 | return realdate |
|---|
| 1482 | |
|---|
| 1483 | def timeref_datetime_mat(refd, timeval, tu): |
|---|
| 1484 | """ Function to transform from a [timeval] in [tu] units from the time referece [tref] to matrix with: year, day, month, hour, minute, second |
|---|
| 1485 | refd: time of reference (as datetime object) |
|---|
| 1486 | timeval: time value (as [tu] from [tref]) |
|---|
| 1487 | tu: time units |
|---|
| 1488 | >>> timeref = date(1949,12,1,0,0,0) |
|---|
| 1489 | >>> timeref_datetime(timeref, 229784.36, hours) |
|---|
| 1490 | [1976 2 17 8 36 21] |
|---|
| 1491 | """ |
|---|
| 1492 | import datetime as dt |
|---|
| 1493 | import numpy as np |
|---|
| 1494 | |
|---|
| 1495 | |
|---|
| 1496 | realdates = np.zeros(6, dtype=int) |
|---|
| 1497 | ## Not in timedelta |
|---|
| 1498 | # if tu == 'years': |
|---|
| 1499 | # realdate = refdate + dt.timedelta(years=float(timeval)) |
|---|
| 1500 | # elif tu == 'months': |
|---|
| 1501 | # realdate = refdate + dt.timedelta(months=float(timeval)) |
|---|
| 1502 | if tu == 'weeks': |
|---|
| 1503 | realdate = refd + dt.timedelta(weeks=float(timeval)) |
|---|
| 1504 | elif tu == 'days': |
|---|
| 1505 | realdate = refd + dt.timedelta(days=float(timeval)) |
|---|
| 1506 | elif tu == 'hours': |
|---|
| 1507 | realdate = refd + dt.timedelta(hours=float(timeval)) |
|---|
| 1508 | elif tu == 'minutes': |
|---|
| 1509 | realdate = refd + dt.timedelta(minutes=float(timeval)) |
|---|
| 1510 | elif tunits == 'seconds': |
|---|
| 1511 | realdate = refd + dt.timedelta(seconds=float(timeval)) |
|---|
| 1512 | elif tunits == 'milliseconds': |
|---|
| 1513 | realdate = refd + dt.timedelta(milliseconds=float(timeval)) |
|---|
| 1514 | else: |
|---|
| 1515 | print errormsg |
|---|
| 1516 | print ' timeref_datetime: time units "' + tu + '" not ready!!!!' |
|---|
| 1517 | quit(-1) |
|---|
| 1518 | |
|---|
| 1519 | realdates[0] = int(realdate.year) |
|---|
| 1520 | realdates[1] = int(realdate.month) |
|---|
| 1521 | realdates[2] = int(realdate.day) |
|---|
| 1522 | realdates[3] = int(realdate.hour) |
|---|
| 1523 | realdates[4] = int(realdate.second) |
|---|
| 1524 | realdates[5] = int(realdate.minute) |
|---|
| 1525 | |
|---|
| 1526 | return realdates |
|---|
| 1527 | |
|---|
| 1528 | def realdatetime_CFcompilant(times, Srefdate, tunits): |
|---|
| 1529 | """ Function to transform a matrix with real time values ([year, month, day, hour, minute, second]) to a netCDF one |
|---|
| 1530 | times= matrix with times |
|---|
| 1531 | Srefdate= reference date ([YYYY][MM][DD][HH][MI][SS] format) |
|---|
| 1532 | tunits= units of time respect to Srefdate |
|---|
| 1533 | >>> realdatetime_CFcompilant(np.array([ [1976, 2, 17, 8, 20, 0], [1976, 2, 18, 8, 20, 0]], dtype=int), '19491201000000', 'hours') |
|---|
| 1534 | [ 229784.33333333 229808.33333333] |
|---|
| 1535 | """ |
|---|
| 1536 | |
|---|
| 1537 | import datetime as dt |
|---|
| 1538 | yrref=int(Srefdate[0:4]) |
|---|
| 1539 | monref=int(Srefdate[4:6]) |
|---|
| 1540 | dayref=int(Srefdate[6:8]) |
|---|
| 1541 | horref=int(Srefdate[8:10]) |
|---|
| 1542 | minref=int(Srefdate[10:12]) |
|---|
| 1543 | secref=int(Srefdate[12:14]) |
|---|
| 1544 | |
|---|
| 1545 | refdate=dt.datetime(yrref, monref, dayref, horref, minref, secref) |
|---|
| 1546 | |
|---|
| 1547 | dimt=times.shape[0] |
|---|
| 1548 | |
|---|
| 1549 | cfdates = np.zeros((dimt), dtype=np.float64) |
|---|
| 1550 | if tunits == 'weeks': |
|---|
| 1551 | for it in range(dimt): |
|---|
| 1552 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 1553 | cfdates[it] = (cfdate.days + cfdate.seconds/(3600.*24.))/7. |
|---|
| 1554 | elif tunits == 'days': |
|---|
| 1555 | for it in range(dimt): |
|---|
| 1556 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 1557 | cfdates[it] = cfdate.days + cfdate.seconds/(3600.*24.) |
|---|
| 1558 | elif tunits == 'hours': |
|---|
| 1559 | for it in range(dimt): |
|---|
| 1560 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 1561 | cfdates[it] = cfdate.days*24. + cfdate.seconds/3600. |
|---|
| 1562 | elif tunits == 'minutes': |
|---|
| 1563 | for it in range(dimt): |
|---|
| 1564 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 1565 | cfdates[it] = cfdate.days*24.*60. + cfdate.seconds/60. |
|---|
| 1566 | elif tunits == 'seconds': |
|---|
| 1567 | for it in range(dimt): |
|---|
| 1568 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 1569 | cfdates[it] = cfdate.days*24.*3600. + cfdate.seconds |
|---|
| 1570 | elif tunits == 'milliseconds': |
|---|
| 1571 | for it in range(dimt): |
|---|
| 1572 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 1573 | cfdates[it] = cfdate.days*1000.*24.*3600. + cfdate.seconds*1000. |
|---|
| 1574 | elif tunits == 'microseconds': |
|---|
| 1575 | for it in range(dimt): |
|---|
| 1576 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 1577 | cfdates[it] = cfdate.days*1000000.*24.*3600. + cfdate.seconds*1000000. |
|---|
| 1578 | else: |
|---|
| 1579 | print errormsg |
|---|
| 1580 | print ' ' + fname + ': time units "' + tunits + '" is not ready!!!' |
|---|
| 1581 | quit(-1) |
|---|
| 1582 | |
|---|
| 1583 | return cfdates |
|---|
| 1584 | |
|---|
| 1585 | def netCDFdatetime_realdatetime(units, tcalendar, times): |
|---|
| 1586 | """ Function to transfrom from netCDF CF-compilant times to real time |
|---|
| 1587 | """ |
|---|
| 1588 | import datetime as dt |
|---|
| 1589 | |
|---|
| 1590 | txtunits = units.split(' ') |
|---|
| 1591 | tunits = txtunits[0] |
|---|
| 1592 | Srefdate = txtunits[len(txtunits) - 1] |
|---|
| 1593 | |
|---|
| 1594 | # Calendar type |
|---|
| 1595 | ## |
|---|
| 1596 | is360 = False |
|---|
| 1597 | if tcalendar is not None: |
|---|
| 1598 | print ' netCDFdatetime_realdatetime: There is a calendar attribute' |
|---|
| 1599 | if tcalendar == '365_day' or tcalendar == 'noleap': |
|---|
| 1600 | print ' netCDFdatetime_realdatetime: No leap years!' |
|---|
| 1601 | isleapcal = False |
|---|
| 1602 | elif tcalendar == 'proleptic_gregorian' or tcalendar == 'standard' or tcalendar == 'gregorian': |
|---|
| 1603 | isleapcal = True |
|---|
| 1604 | elif tcalendar == '360_day': |
|---|
| 1605 | is360 = True |
|---|
| 1606 | isleapcal = False |
|---|
| 1607 | else: |
|---|
| 1608 | print errormsg |
|---|
| 1609 | print ' netCDFdatetime_realdatetime: Calendar "' + tcalendar + '" not prepared!' |
|---|
| 1610 | quit(-1) |
|---|
| 1611 | |
|---|
| 1612 | # Does reference date contain a time value [YYYY]-[MM]-[DD] [HH]:[MI]:[SS] |
|---|
| 1613 | ## |
|---|
| 1614 | timeval = Srefdate.find(':') |
|---|
| 1615 | |
|---|
| 1616 | if not timeval == -1: |
|---|
| 1617 | print ' netCDFdatetime_realdatetime: refdate with time!' |
|---|
| 1618 | refdate = datetimeStr_datetime(txtunits[len(txtunits) - 2] + '_' + Srefdate) |
|---|
| 1619 | else: |
|---|
| 1620 | refdate = dateStr_date(Srefdate) |
|---|
| 1621 | |
|---|
| 1622 | dimt = len(times) |
|---|
| 1623 | # datetype = type(dt.datetime(1972,02,01)) |
|---|
| 1624 | # realdates = np.array(dimt, datetype) |
|---|
| 1625 | # print realdates |
|---|
| 1626 | |
|---|
| 1627 | ## Not in timedelta |
|---|
| 1628 | # if tunits == 'years': |
|---|
| 1629 | # for it in range(dimt): |
|---|
| 1630 | # realdate = refdate + dt.timedelta(years=float(times[it])) |
|---|
| 1631 | # realdates[it] = int(realdate.year) |
|---|
| 1632 | # elif tunits == 'months': |
|---|
| 1633 | # for it in range(dimt): |
|---|
| 1634 | # realdate = refdate + dt.timedelta(months=float(times[it])) |
|---|
| 1635 | # realdates[it] = int(realdate.year) |
|---|
| 1636 | # realdates = [] |
|---|
| 1637 | realdates = np.zeros((dimt, 6), dtype=int) |
|---|
| 1638 | if tunits == 'weeks': |
|---|
| 1639 | for it in range(dimt): |
|---|
| 1640 | realdate = refdate + dt.timedelta(weeks=float(times[it])) |
|---|
| 1641 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 1642 | elif tunits == 'days': |
|---|
| 1643 | for it in range(dimt): |
|---|
| 1644 | realdate = refdate + dt.timedelta(days=float(times[it])) |
|---|
| 1645 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 1646 | elif tunits == 'hours': |
|---|
| 1647 | for it in range(dimt): |
|---|
| 1648 | realdate = refdate + dt.timedelta(hours=float(times[it])) |
|---|
| 1649 | # if not isleapcal: |
|---|
| 1650 | # Nleapdays = cal.leapdays(int(refdate.year), int(realdate.year)) |
|---|
| 1651 | # realdate = realdate - dt.timedelta(days=Nleapdays) |
|---|
| 1652 | # if is360: |
|---|
| 1653 | # Nyears360 = int(realdate.year) - int(refdate.year) + 1 |
|---|
| 1654 | # realdate = realdate -dt.timedelta(days=Nyears360*5) |
|---|
| 1655 | # realdates[it] = realdate |
|---|
| 1656 | # realdates = refdate + dt.timedelta(hours=float(times)) |
|---|
| 1657 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 1658 | elif tunits == 'minutes': |
|---|
| 1659 | for it in range(dimt): |
|---|
| 1660 | realdate = refdate + dt.timedelta(minutes=float(times[it])) |
|---|
| 1661 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 1662 | elif tunits == 'seconds': |
|---|
| 1663 | for it in range(dimt): |
|---|
| 1664 | realdate = refdate + dt.timedelta(seconds=float(times[it])) |
|---|
| 1665 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 1666 | elif tunits == 'milliseconds': |
|---|
| 1667 | for it in range(dimt): |
|---|
| 1668 | realdate = refdate + dt.timedelta(milliseconds=float(times[it])) |
|---|
| 1669 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 1670 | elif tunits == 'microseconds': |
|---|
| 1671 | for it in range(dimt): |
|---|
| 1672 | realdate = refdate + dt.timedelta(microseconds=float(times[it])) |
|---|
| 1673 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 1674 | else: |
|---|
| 1675 | print errormsg |
|---|
| 1676 | print ' netCDFdatetime_realdatetime: time units "' + tunits + '" is not ready!!!' |
|---|
| 1677 | quit(-1) |
|---|
| 1678 | |
|---|
| 1679 | return realdates |
|---|
| 1680 | |
|---|
| 1681 | def date_juliandate(refyr, TOTsecs): |
|---|
| 1682 | """ Function to transform from a total quantity of seconds since the |
|---|
| 1683 | beginning of a year to 360 days / year calendar |
|---|
| 1684 | TOTsecs= total number of seconds |
|---|
| 1685 | """ |
|---|
| 1686 | fname = 'date_juliandate' |
|---|
| 1687 | |
|---|
| 1688 | secsYear = 3600*24*30*12. |
|---|
| 1689 | secsMonth = 3600*24*30. |
|---|
| 1690 | secsDay = 3600*24. |
|---|
| 1691 | secsHour = 3600. |
|---|
| 1692 | secsMinute = 60. |
|---|
| 1693 | |
|---|
| 1694 | rldate = np.zeros((6), dtype=int) |
|---|
| 1695 | rldate[0] = refyr |
|---|
| 1696 | if TOTsecs > secsYear: |
|---|
| 1697 | rldate[0] = refyr + int(TOTsecs/secsYear) + 1 |
|---|
| 1698 | TOTsecs = TOTsecs - int(TOTsecs/secsYear)*secsYear |
|---|
| 1699 | |
|---|
| 1700 | if np.mod(TOTsecs,secsMonth) == 0: |
|---|
| 1701 | rldate[1] = int(TOTsecs/secsMonth) |
|---|
| 1702 | else: |
|---|
| 1703 | rldate[1] = int(TOTsecs/secsMonth) + 1 |
|---|
| 1704 | TOTsecs = TOTsecs - int(TOTsecs/secsMonth)*secsMonth |
|---|
| 1705 | |
|---|
| 1706 | if np.mod(TOTsecs,secsDay) == 0: |
|---|
| 1707 | rldate[2] = int(TOTsecs/secsDay) |
|---|
| 1708 | else: |
|---|
| 1709 | rldate[2] = int(TOTsecs/secsDay) + 1 |
|---|
| 1710 | TOTsecs = TOTsecs - int(TOTsecs/secsDay)*secsDay |
|---|
| 1711 | |
|---|
| 1712 | if np.mod(TOTsecs,secsHour) == 0: |
|---|
| 1713 | rldate[3] = int(TOTsecs/secsHour) |
|---|
| 1714 | else: |
|---|
| 1715 | rldate[3] = int(TOTsecs/secsHour) + 1 |
|---|
| 1716 | TOTsecs = TOTsecs - int(TOTsecs/secsHour)*secsHour |
|---|
| 1717 | |
|---|
| 1718 | if np.mod(TOTsecs,secsMinute) == 0: |
|---|
| 1719 | rldate[4] = int(TOTsecs/secsMinute) |
|---|
| 1720 | else: |
|---|
| 1721 | rldate[4] = int(TOTsecs/secsMinute) + 1 |
|---|
| 1722 | TOTsecs = TOTsecs - int(TOTsecs/secsMinute)*secsMinute |
|---|
| 1723 | |
|---|
| 1724 | rldate[5] = TOTsecs |
|---|
| 1725 | |
|---|
| 1726 | # print refyr,TOTsecs,':',rldate |
|---|
| 1727 | # quit() |
|---|
| 1728 | |
|---|
| 1729 | return rldate |
|---|
| 1730 | |
|---|
| 1731 | def CFtimes_datetime(ncfile, tname): |
|---|
| 1732 | """ Provide date/time array from a file with a series of netCDF CF-compilant time variable |
|---|
| 1733 | ncfile = netCDF file name |
|---|
| 1734 | tname = name of the variable time in [ncfile] |
|---|
| 1735 | output: |
|---|
| 1736 | array(dimt, 0) = year |
|---|
| 1737 | array(dimt, 1) = month |
|---|
| 1738 | array(dimt, 2) = day |
|---|
| 1739 | array(dimt, 3) = hour |
|---|
| 1740 | array(dimt, 4) = minute |
|---|
| 1741 | array(dimt, 5) = second |
|---|
| 1742 | """ |
|---|
| 1743 | import datetime as dt |
|---|
| 1744 | fname = 'CFtimes_datetime' |
|---|
| 1745 | |
|---|
| 1746 | times = ncfile.variables[tname] |
|---|
| 1747 | timevals = times[:] |
|---|
| 1748 | |
|---|
| 1749 | attvar = times.ncattrs() |
|---|
| 1750 | if not searchInlist(attvar, 'units'): |
|---|
| 1751 | print errormsg |
|---|
| 1752 | print ' ' + fname + ": '" + tname + "' does not have attribute: 'units'" |
|---|
| 1753 | quit(-1) |
|---|
| 1754 | else: |
|---|
| 1755 | units = times.getncattr('units') |
|---|
| 1756 | |
|---|
| 1757 | txtunits = units.split(' ') |
|---|
| 1758 | tunits = txtunits[0] |
|---|
| 1759 | Srefdate = txtunits[len(txtunits) - 1] |
|---|
| 1760 | # Does reference date contain a time value [YYYY]-[MM]-[DD] [HH]:[MI]:[SS] |
|---|
| 1761 | ## |
|---|
| 1762 | timeval = Srefdate.find(':') |
|---|
| 1763 | |
|---|
| 1764 | if not timeval == -1: |
|---|
| 1765 | # print ' refdate with time!' |
|---|
| 1766 | refdate = datetimeStr_datetime(txtunits[len(txtunits) - 2] + '_' + Srefdate) |
|---|
| 1767 | else: |
|---|
| 1768 | refdate = dateStr_date(Srefdate) |
|---|
| 1769 | |
|---|
| 1770 | dimt = len(timevals) |
|---|
| 1771 | realdates = np.zeros((dimt, 6), dtype=int) |
|---|
| 1772 | |
|---|
| 1773 | secsDay=3600*24. |
|---|
| 1774 | |
|---|
| 1775 | # Checking calendar! |
|---|
| 1776 | ## |
|---|
| 1777 | y360 = False |
|---|
| 1778 | daycal360 = ['earth_360d', '360d', '360days', '360_day'] |
|---|
| 1779 | if searchInlist(attvar, 'calendar'): |
|---|
| 1780 | calendar = times.getncattr('calendar') |
|---|
| 1781 | if searchInlist(daycal360,calendar): |
|---|
| 1782 | print warnmsg |
|---|
| 1783 | print ' ' + fname + ': calendar of 12 months of 30 days !!' |
|---|
| 1784 | y360 = True |
|---|
| 1785 | |
|---|
| 1786 | ## Not in timedelta |
|---|
| 1787 | # if tunits == 'years': |
|---|
| 1788 | # for it in range(dimt): |
|---|
| 1789 | # realdate = refdate + dt.timedelta(years=float(times[it])) |
|---|
| 1790 | # realdates[it] = int(realdate.year) |
|---|
| 1791 | # elif tunits == 'months': |
|---|
| 1792 | # for it in range(dimt): |
|---|
| 1793 | # realdate = refdate + dt.timedelta(months=float(times[it])) |
|---|
| 1794 | # realdates[it] = int(realdate.year) |
|---|
| 1795 | if y360: |
|---|
| 1796 | if tunits == 'weeks': |
|---|
| 1797 | for it in range(dimt): |
|---|
| 1798 | deltat = dt.timedelta(weeks=float(times[it])) |
|---|
| 1799 | Tsecs = deltat.days*secsDay + deltat.seconds + deltat.microseconds/1000. |
|---|
| 1800 | realdates[it,:] = date_juliandate(refdate.year,Tsecs) |
|---|
| 1801 | elif tunits == 'days': |
|---|
| 1802 | for it in range(dimt): |
|---|
| 1803 | deltat = dt.timedelta(days=float(times[it])) |
|---|
| 1804 | Tsecs = deltat.days*secsDay + deltat.seconds + deltat.microseconds/1000. |
|---|
| 1805 | realdates[it,:] = date_juliandate(refdate.year,Tsecs) |
|---|
| 1806 | elif tunits == 'hours': |
|---|
| 1807 | for it in range(dimt): |
|---|
| 1808 | realdate = dt.timedelta(hours=float(times[it])) |
|---|
| 1809 | Tsecs = deltat.days*secsDay + deltat.seconds + deltat.microseconds/1000. |
|---|
| 1810 | realdates[it,:] = date_juliandate(refdate.year,Tsecs) |
|---|
| 1811 | elif tunits == 'minutes': |
|---|
| 1812 | for it in range(dimt): |
|---|
| 1813 | realdate = dt.timedelta(minutes=float(times[it])) |
|---|
| 1814 | Tsecs = deltat.days*secsDay + deltat.seconds + deltat.microseconds/1000. |
|---|
| 1815 | realdates[it,:] = date_juliandate(refdate.year,Tsecs) |
|---|
| 1816 | elif tunits == 'seconds': |
|---|
| 1817 | for it in range(dimt): |
|---|
| 1818 | realdate = dt.timedelta(seconds=float(times[it])) |
|---|
| 1819 | Tsecs = deltat.days*secsDay + deltat.seconds + deltat.microseconds/1000. |
|---|
| 1820 | realdates[it,:] = date_juliandate(refdate.year,Tsecs) |
|---|
| 1821 | elif tunits == 'miliseconds': |
|---|
| 1822 | for it in range(dimt): |
|---|
| 1823 | realdate = dt.timedelta(miliseconds=float(times[it])) |
|---|
| 1824 | Tsecs = deltat.days*secsDay + deltat.seconds + deltat.microseconds/1000. |
|---|
| 1825 | realdates[it,:] = date_juliandate(refdate.year,Tsecs) |
|---|
| 1826 | else: |
|---|
| 1827 | print errormsg |
|---|
| 1828 | print ' CFtimes_datetime: time units "' + tunits + '" not ready!!!!' |
|---|
| 1829 | quit(-1) |
|---|
| 1830 | else: |
|---|
| 1831 | if tunits == 'weeks': |
|---|
| 1832 | for it in range(dimt): |
|---|
| 1833 | realdate = refdate + dt.timedelta(weeks=float(times[it])) |
|---|
| 1834 | realdates[it,0] = int(realdate.year) |
|---|
| 1835 | realdates[it,1] = int(realdate.month) |
|---|
| 1836 | realdates[it,2] = int(realdate.day) |
|---|
| 1837 | realdates[it,3] = int(realdate.hour) |
|---|
| 1838 | realdates[it,4] = int(realdate.second) |
|---|
| 1839 | realdates[it,5] = int(realdate.minute) |
|---|
| 1840 | elif tunits == 'days': |
|---|
| 1841 | for it in range(dimt): |
|---|
| 1842 | realdate = refdate + dt.timedelta(days=float(times[it])) |
|---|
| 1843 | realdates[it,0] = int(realdate.year) |
|---|
| 1844 | realdates[it,1] = int(realdate.month) |
|---|
| 1845 | realdates[it,2] = int(realdate.day) |
|---|
| 1846 | realdates[it,3] = int(realdate.hour) |
|---|
| 1847 | realdates[it,4] = int(realdate.second) |
|---|
| 1848 | realdates[it,5] = int(realdate.minute) |
|---|
| 1849 | elif tunits == 'hours': |
|---|
| 1850 | for it in range(dimt): |
|---|
| 1851 | realdate = refdate + dt.timedelta(hours=float(times[it])) |
|---|
| 1852 | realdates[it,0] = int(realdate.year) |
|---|
| 1853 | realdates[it,1] = int(realdate.month) |
|---|
| 1854 | realdates[it,2] = int(realdate.day) |
|---|
| 1855 | realdates[it,3] = int(realdate.hour) |
|---|
| 1856 | realdates[it,4] = int(realdate.second) |
|---|
| 1857 | realdates[it,5] = int(realdate.minute) |
|---|
| 1858 | elif tunits == 'minutes': |
|---|
| 1859 | for it in range(dimt): |
|---|
| 1860 | realdate = refdate + dt.timedelta(minutes=float(times[it])) |
|---|
| 1861 | realdates[it,0] = int(realdate.year) |
|---|
| 1862 | realdates[it,1] = int(realdate.month) |
|---|
| 1863 | realdates[it,2] = int(realdate.day) |
|---|
| 1864 | realdates[it,3] = int(realdate.hour) |
|---|
| 1865 | realdates[it,4] = int(realdate.second) |
|---|
| 1866 | realdates[it,5] = int(realdate.minute) |
|---|
| 1867 | elif tunits == 'seconds': |
|---|
| 1868 | for it in range(dimt): |
|---|
| 1869 | realdate = refdate + dt.timedelta(seconds=float(times[it])) |
|---|
| 1870 | realdates[it,0] = int(realdate.year) |
|---|
| 1871 | realdates[it,1] = int(realdate.month) |
|---|
| 1872 | realdates[it,2] = int(realdate.day) |
|---|
| 1873 | realdates[it,3] = int(realdate.hour) |
|---|
| 1874 | realdates[it,4] = int(realdate.second) |
|---|
| 1875 | realdates[it,5] = int(realdate.minute) |
|---|
| 1876 | elif tunits == 'milliseconds': |
|---|
| 1877 | for it in range(dimt): |
|---|
| 1878 | realdate = refdate + dt.timedelta(milliseconds=float(times[it])) |
|---|
| 1879 | realdates[it,0] = int(realdate.year) |
|---|
| 1880 | realdates[it,1] = int(realdate.month) |
|---|
| 1881 | realdates[it,2] = int(realdate.day) |
|---|
| 1882 | realdates[it,3] = int(realdate.hour) |
|---|
| 1883 | realdates[it,4] = int(realdate.second) |
|---|
| 1884 | realdates[it,5] = int(realdate.minute) |
|---|
| 1885 | else: |
|---|
| 1886 | print errormsg |
|---|
| 1887 | print ' CFtimes_datetime: time units "' + tunits + '" not ready!!!!' |
|---|
| 1888 | quit(-1) |
|---|
| 1889 | |
|---|
| 1890 | return realdates |
|---|
| 1891 | |
|---|
| 1892 | class statsValWeigthed(object): |
|---|
| 1893 | """Weigthed Statistics class providing: |
|---|
| 1894 | vals = values (can be a matrix) |
|---|
| 1895 | wgs = weights (can be a matrix) |
|---|
| 1896 | self.meanv: mean weigthed value |
|---|
| 1897 | self.mean2v: mean quadratic weigthed value |
|---|
| 1898 | self.stdv: weigthed standard deviation |
|---|
| 1899 | self.Nokvalue non None values of a list of values |
|---|
| 1900 | self.meanwgt: mean of the weigths |
|---|
| 1901 | self.mean2wgt: cuadratic mean of the weigths |
|---|
| 1902 | self.stdwgt: standard deviation of the weigths |
|---|
| 1903 | """ |
|---|
| 1904 | |
|---|
| 1905 | def __init__(self, vals, wgs): |
|---|
| 1906 | if vals is None: |
|---|
| 1907 | self.Nv = None |
|---|
| 1908 | self.meanv = None |
|---|
| 1909 | self.mean2v = None |
|---|
| 1910 | self.stdv = None |
|---|
| 1911 | self.Nokvalues = None |
|---|
| 1912 | self.meanwgt = None |
|---|
| 1913 | self.mean2wgt = None |
|---|
| 1914 | self.stdwgt = None |
|---|
| 1915 | else: |
|---|
| 1916 | values = vals.flatten() |
|---|
| 1917 | weights = wgs.flatten() |
|---|
| 1918 | self.Nv=len(values) |
|---|
| 1919 | self.meanv=0. |
|---|
| 1920 | self.mean2v=0. |
|---|
| 1921 | self.stdv=0. |
|---|
| 1922 | self.meanwgt = 0. |
|---|
| 1923 | self.mean2wgt = 0. |
|---|
| 1924 | self.stdwgt = 0. |
|---|
| 1925 | self.Nokvalues = 0 |
|---|
| 1926 | |
|---|
| 1927 | for inum in range(self.Nv): |
|---|
| 1928 | if not values[inum] is None: |
|---|
| 1929 | self.Nokvalues = self.Nokvalues + 1 |
|---|
| 1930 | self.meanv = self.meanv+values[inum]*weights[inum] |
|---|
| 1931 | self.mean2v = self.mean2v+values[inum]*weights[inum]*values[inum] |
|---|
| 1932 | self.meanwgt = self.meanwgt+weights[inum] |
|---|
| 1933 | self.mean2wgt = self.mean2wgt+weights[inum]*weights[inum] |
|---|
| 1934 | |
|---|
| 1935 | self.meanv = self.meanv/float(self.meanwgt) |
|---|
| 1936 | self.mean2v = self.mean2v/float(self.meanwgt) |
|---|
| 1937 | self.stdv = np.sqrt(self.mean2v-self.meanv*self.meanv) |
|---|
| 1938 | self.meanwgt = self.meanwgt/float(self.Nokvalues) |
|---|
| 1939 | self.mean2wgt = self.mean2wgt/float(self.Nokvalues) |
|---|
| 1940 | self.stdwgt = np.sqrt(self.mean2wgt-self.meanwgt*self.meanwgt) |
|---|
| 1941 | |
|---|
| 1942 | return |
|---|
| 1943 | |
|---|
| 1944 | class statsValWeighted_missVal(object): |
|---|
| 1945 | """Weighted Statistics taking into account a missing value class providing: |
|---|
| 1946 | vals = values (can be a matrix) |
|---|
| 1947 | wgs = weights (can be a matrix) |
|---|
| 1948 | missVal= missing value |
|---|
| 1949 | self.meanv: mean weigthed value |
|---|
| 1950 | self.mean2v: mean quadratic weigthed value |
|---|
| 1951 | self.stdv: weigthed standard deviation |
|---|
| 1952 | self.Nokvalue non None values of a list of values |
|---|
| 1953 | self.meanwgt: mean of the weigths |
|---|
| 1954 | self.mean2wgt: cuadratic mean of the weigths |
|---|
| 1955 | self.stdwgt: standard deviation of the weigths |
|---|
| 1956 | self.quantilesv: quantiles of the weighted values |
|---|
| 1957 | """ |
|---|
| 1958 | |
|---|
| 1959 | def __init__(self, vals, wgs, missVal): |
|---|
| 1960 | |
|---|
| 1961 | fname='statsValWeigthed_missVal' |
|---|
| 1962 | if vals is None: |
|---|
| 1963 | self.Nv = None |
|---|
| 1964 | self.meanv = None |
|---|
| 1965 | self.mean2v = None |
|---|
| 1966 | self.stdv = None |
|---|
| 1967 | self.Nokvalues = None |
|---|
| 1968 | self.meanwgt = None |
|---|
| 1969 | self.mean2wgt = None |
|---|
| 1970 | self.stdwgt = None |
|---|
| 1971 | self.quantilesv = None |
|---|
| 1972 | else: |
|---|
| 1973 | Npt = 1 |
|---|
| 1974 | for idim in range(len(vals.shape)): |
|---|
| 1975 | Npt = Npt * vals.shape[idim] |
|---|
| 1976 | if np.sum(vals >= missVal) == Npt: |
|---|
| 1977 | print errormsg |
|---|
| 1978 | print ' ' + fname + ' all values get missing!!' |
|---|
| 1979 | print errormsg |
|---|
| 1980 | quit(-1) |
|---|
| 1981 | vals0 = np.where(vals >= missVal, None, vals) |
|---|
| 1982 | values = vals0.flatten() |
|---|
| 1983 | weights = wgs.flatten() |
|---|
| 1984 | self.Nv=Npt |
|---|
| 1985 | self.meanv=0. |
|---|
| 1986 | self.mean2v=0. |
|---|
| 1987 | self.stdv=0. |
|---|
| 1988 | self.meanwgt = 0. |
|---|
| 1989 | self.mean2wgt = 0. |
|---|
| 1990 | self.stdwgt = 0. |
|---|
| 1991 | self.Nokvalues = 0 |
|---|
| 1992 | |
|---|
| 1993 | valswgt = values |
|---|
| 1994 | for inum in range(self.Nv): |
|---|
| 1995 | if not values[inum] is None: |
|---|
| 1996 | self.Nokvalues = self.Nokvalues + 1 |
|---|
| 1997 | valswgt[inum] = valswgt[inum]*weights[inum] |
|---|
| 1998 | self.meanv = self.meanv+values[inum]*weights[inum] |
|---|
| 1999 | self.mean2v = self.mean2v+values[inum]*weights[inum]*values[inum] |
|---|
| 2000 | self.meanwgt = self.meanwgt+weights[inum] |
|---|
| 2001 | self.mean2wgt = self.mean2wgt+weights[inum]*weights[inum] |
|---|
| 2002 | |
|---|
| 2003 | self.meanv = self.meanv/float(self.meanwgt) |
|---|
| 2004 | self.mean2v = self.mean2v/float(self.meanwgt) |
|---|
| 2005 | self.stdv = np.sqrt(self.mean2v-self.meanv*self.meanv) |
|---|
| 2006 | valswgt = valswgt/np.float(self.meanwgt) |
|---|
| 2007 | self.meanwgt = self.meanwgt/float(self.Nokvalues) |
|---|
| 2008 | self.mean2wgt = self.mean2wgt/float(self.Nokvalues) |
|---|
| 2009 | self.stdwgt = np.sqrt(self.mean2wgt-self.meanwgt*self.meanwgt) |
|---|
| 2010 | valsq=Quantiles(valswgt, 20) |
|---|
| 2011 | self.quantilesv=valsq.quantilesv |
|---|
| 2012 | |
|---|
| 2013 | return |
|---|
| 2014 | |
|---|
| 2015 | class stats2Val(object): |
|---|
| 2016 | """two variables Statistics class providing: |
|---|
| 2017 | vals1 = variable 1 |
|---|
| 2018 | vals2 = variable 2 |
|---|
| 2019 | power = power of the polynomial fitting to apply between both variables |
|---|
| 2020 | self.min[var], self.max[var], self.mean[var], self.mean2[var], self.std[var] of |
|---|
| 2021 | [var] = var1+var2[v1Av2], var1-var2[v1Sv2], var1/var2[v1Dv2], var1*var2[v1Pv2] |
|---|
| 2022 | self.Nokvalues1: number of correct values of variable 1 |
|---|
| 2023 | self.Nokvalues2: number of correct values of variable 2 |
|---|
| 2024 | self.Nokvalues12: number of correct coincident values of variable 1 and variable 2 |
|---|
| 2025 | self.mae=mean(abs(var1-var2)) |
|---|
| 2026 | self.rmse=sqrt((var1-var2)**2) |
|---|
| 2027 | self.correlation (and p-value) |
|---|
| 2028 | self.linRegress: linear regression [trend, intercept, regression coefficient, p_value, standard error] |
|---|
| 2029 | self.polRegress: polinomial Regresion of degree [power] [coef**[power], coef**[power-1], ...., coef**0] |
|---|
| 2030 | """ |
|---|
| 2031 | |
|---|
| 2032 | def __init__(self, vals1, vals2, power): |
|---|
| 2033 | import numpy as np |
|---|
| 2034 | from scipy import stats as sts |
|---|
| 2035 | |
|---|
| 2036 | if vals1 is None: |
|---|
| 2037 | self.Nv = None |
|---|
| 2038 | self.Nokvalues1 = None |
|---|
| 2039 | self.Nokvalues2 = None |
|---|
| 2040 | self.Nokvalues12 = None |
|---|
| 2041 | self.NDvalNone = None |
|---|
| 2042 | self.minv1Av2 = None |
|---|
| 2043 | self.maxv1Av2 = None |
|---|
| 2044 | self.meanv1Av2 = None |
|---|
| 2045 | self.mean2v1Av2 = None |
|---|
| 2046 | self.stdv1Av2 = None |
|---|
| 2047 | self.minv1Sv2 = None |
|---|
| 2048 | self.maxv1Sv2 = None |
|---|
| 2049 | self.meanv1Sv2 = None |
|---|
| 2050 | self.mean2v1Sv2 = None |
|---|
| 2051 | self.stdv1Sv2 = None |
|---|
| 2052 | self.minv1Dv2 = None |
|---|
| 2053 | self.maxv1Dv2 = None |
|---|
| 2054 | self.meanv1Dv2 = None |
|---|
| 2055 | self.mean2v1Dv2 = None |
|---|
| 2056 | self.stdv1Dv2 = None |
|---|
| 2057 | self.minv1Pv2 = None |
|---|
| 2058 | self.maxv1Pv2 = None |
|---|
| 2059 | self.meanv1Pv2 = None |
|---|
| 2060 | self.mean2v1Pv2 = None |
|---|
| 2061 | self.stdv1Pv2 = None |
|---|
| 2062 | self.mae = None |
|---|
| 2063 | self.rmse = None |
|---|
| 2064 | self.corr = None |
|---|
| 2065 | self.linRegress = None |
|---|
| 2066 | self.polRegress = None |
|---|
| 2067 | self.polRegressResidual = None |
|---|
| 2068 | self.polRegressRes = None |
|---|
| 2069 | self.polRegressSingVal = None |
|---|
| 2070 | else: |
|---|
| 2071 | values1 = vals1.flatten() |
|---|
| 2072 | values2 = vals2.flatten() |
|---|
| 2073 | |
|---|
| 2074 | if not len(values1) == len(values2): |
|---|
| 2075 | print errormsg |
|---|
| 2076 | print ' stats2Val: lengths of variables differ!! Lvar1: ', len(values1), ' Lvar2: ',len(values2),' statistics between them can not be computed!' |
|---|
| 2077 | quit(-1) |
|---|
| 2078 | |
|---|
| 2079 | self.Nv=len(values1) |
|---|
| 2080 | self.minv1Av2=10000000000. |
|---|
| 2081 | self.maxv1Av2=-self.minv1Av2 |
|---|
| 2082 | self.meanv1Av2=0. |
|---|
| 2083 | self.mean2v1Av2=0. |
|---|
| 2084 | self.stdv1Av2=0. |
|---|
| 2085 | self.minv1Sv2=self.minv1Av2 |
|---|
| 2086 | self.maxv1Sv2=-self.minv1Av2 |
|---|
| 2087 | self.meanv1Sv2=0. |
|---|
| 2088 | self.mean2v1Sv2=0. |
|---|
| 2089 | self.stdv1Sv2=0. |
|---|
| 2090 | self.minv1Dv2=self.minv1Av2 |
|---|
| 2091 | self.maxv1Dv2=-self.minv1Av2 |
|---|
| 2092 | self.meanv1Dv2=0. |
|---|
| 2093 | self.mean2v1Dv2=0. |
|---|
| 2094 | self.stdv1Dv2=0. |
|---|
| 2095 | self.minv1Pv2=self.minv1Av2 |
|---|
| 2096 | self.maxv1Pv2=-self.minv1Av2 |
|---|
| 2097 | self.meanv1Pv2=0. |
|---|
| 2098 | self.mean2v1Pv2=0. |
|---|
| 2099 | self.stdv1Pv2=0. |
|---|
| 2100 | self.mae = 0. |
|---|
| 2101 | self.rmse = 0. |
|---|
| 2102 | self.corr = np.array([0., 0.]) |
|---|
| 2103 | self.linRegress = np.zeros(5, float) |
|---|
| 2104 | self.polRegress = np.zeros(power+1, float) |
|---|
| 2105 | self.polRegressResidual = 0. |
|---|
| 2106 | self.polRegressSingVal = np.zeros(power+1, float) |
|---|
| 2107 | |
|---|
| 2108 | # v1 [+ / - / / / *] v2 |
|---|
| 2109 | ## |
|---|
| 2110 | self.Nokvalues1 = 0 |
|---|
| 2111 | self.Nokvalues2 = 0 |
|---|
| 2112 | self.Nokvalues12 = 0 |
|---|
| 2113 | self.NDvalNone = 0 |
|---|
| 2114 | for inum in range(self.Nv): |
|---|
| 2115 | if not values1[inum] is None: |
|---|
| 2116 | self.Nokvalues1 = self.Nokvalues1 + 1 |
|---|
| 2117 | if not values2[inum] is None: |
|---|
| 2118 | self.Nokvalues2 = self.Nokvalues2 + 1 |
|---|
| 2119 | if not values1[inum] is None and not values2[inum] is None: |
|---|
| 2120 | self.Nokvalues12 = self.Nokvalues12 + 1 |
|---|
| 2121 | Aval = values1[inum] + values2[inum] |
|---|
| 2122 | Sval = values1[inum] - values2[inum] |
|---|
| 2123 | Pval = values1[inum] * values2[inum] |
|---|
| 2124 | if np.isinf(values1[inum] / values2[inum]) or np.isnan(values1[inum] / values2[inum]): |
|---|
| 2125 | if self.NDvalNone < 1: |
|---|
| 2126 | print warnmsg |
|---|
| 2127 | print ' stats2Val: val1/val2 inf or Nan!!!!' |
|---|
| 2128 | Dval = None |
|---|
| 2129 | self.NDvalNone = self.NDvalNone + 1 |
|---|
| 2130 | else: |
|---|
| 2131 | Dval = values1[inum] / values2[inum] |
|---|
| 2132 | |
|---|
| 2133 | self.mae = self.mae + abs(Sval) |
|---|
| 2134 | self.rmse = self.rmse + Sval**2 |
|---|
| 2135 | |
|---|
| 2136 | if Aval < self.minv1Av2: |
|---|
| 2137 | self.minv1Av2 = Aval |
|---|
| 2138 | if Aval > self.maxv1Av2: |
|---|
| 2139 | self.maxv1Av2 = Aval |
|---|
| 2140 | if Sval < self.minv1Sv2: |
|---|
| 2141 | self.minv1Sv2 = Sval |
|---|
| 2142 | if Sval > self.maxv1Sv2: |
|---|
| 2143 | self.maxv1Sv2 = Sval |
|---|
| 2144 | if not Dval is None and Dval < self.minv1Dv2: |
|---|
| 2145 | self.minv1Dv2 = Dval |
|---|
| 2146 | if not Dval is None and Dval > self.maxv1Dv2: |
|---|
| 2147 | self.maxv1Dv2 = Dval |
|---|
| 2148 | if Pval < self.minv1Pv2: |
|---|
| 2149 | self.minv1Pv2 = Pval |
|---|
| 2150 | if Pval > self.maxv1Pv2: |
|---|
| 2151 | self.maxv1Pv2 = Pval |
|---|
| 2152 | |
|---|
| 2153 | self.meanv1Av2 = self.meanv1Av2+Aval |
|---|
| 2154 | self.mean2v1Av2 = self.mean2v1Av2+Aval*Aval |
|---|
| 2155 | self.meanv1Sv2 = self.meanv1Sv2+Sval |
|---|
| 2156 | self.mean2v1Sv2 = self.mean2v1Sv2+Sval*Sval |
|---|
| 2157 | if not Dval is None: |
|---|
| 2158 | self.meanv1Dv2 = self.meanv1Dv2+Dval |
|---|
| 2159 | self.mean2v1Dv2 = self.mean2v1Dv2+Dval*Dval |
|---|
| 2160 | self.meanv1Pv2 = self.meanv1Pv2+Pval |
|---|
| 2161 | self.mean2v1Pv2 = self.mean2v1Pv2+Pval*Pval |
|---|
| 2162 | |
|---|
| 2163 | ## print 'Nokvalues1: ', self.Nokvalues1, 'Nokvalues2: ', self.Nokvalues2, 'Nokvalues12: ', float(self.Nokvalues12), 'NDvalNone: ',self.NDvalNone |
|---|
| 2164 | self.meanv1Av2 = self.meanv1Av2/float(self.Nokvalues12) |
|---|
| 2165 | self.mean2v1Av2 = self.mean2v1Av2/float(self.Nokvalues12) |
|---|
| 2166 | self.stdv1Av2 = np.sqrt(self.mean2v1Av2-self.meanv1Av2*self.meanv1Av2) |
|---|
| 2167 | self.meanv1Sv2 = self.meanv1Sv2/float(self.Nokvalues12) |
|---|
| 2168 | self.mean2v1Sv2 = self.mean2v1Sv2/float(self.Nokvalues12) |
|---|
| 2169 | self.stdv1Sv2 = np.sqrt(self.mean2v1Sv2-self.meanv1Sv2*self.meanv1Sv2) |
|---|
| 2170 | if self.Nokvalues12 - self.NDvalNone == 0: |
|---|
| 2171 | self.meanv1Dv2 = None |
|---|
| 2172 | self.mean2v1Dv2 = None |
|---|
| 2173 | self.stdv1Dv2 = None |
|---|
| 2174 | print warnmsg |
|---|
| 2175 | print ' stats2Val: all values of val1/val2 are None!' |
|---|
| 2176 | else: |
|---|
| 2177 | self.meanv1Dv2 = self.meanv1Dv2/(float(self.Nokvalues12 - self.NDvalNone)) |
|---|
| 2178 | self.mean2v1Dv2 = self.mean2v1Dv2/(float(self.Nokvalues12 - self.NDvalNone)) |
|---|
| 2179 | self.stdv1Dv2 = np.sqrt(self.mean2v1Dv2-self.meanv1Dv2*self.meanv1Dv2) |
|---|
| 2180 | self.meanv1Pv2 = self.meanv1Pv2/float(self.Nokvalues12) |
|---|
| 2181 | self.mean2v1Pv2 = self.mean2v1Pv2/float(self.Nokvalues12) |
|---|
| 2182 | self.stdv1Pv2 = np.sqrt(self.mean2v1Pv2-self.meanv1Pv2*self.meanv1Pv2) |
|---|
| 2183 | |
|---|
| 2184 | self.mae = self.mae/self.Nokvalues12 |
|---|
| 2185 | self.rmse = np.sqrt(self.rmse/self.Nokvalues12) |
|---|
| 2186 | |
|---|
| 2187 | self.corr = sts.pearsonr(values1, values2) |
|---|
| 2188 | |
|---|
| 2189 | self.linRegress[0], self.linRegress[1], self.linRegress[2], self.linRegress[3], self.linRegress[4] = sts.linregress(values1, values2) |
|---|
| 2190 | |
|---|
| 2191 | polyfitvals=np.polyfit(values1, values2, power, full = True) |
|---|
| 2192 | |
|---|
| 2193 | self.polRegress = polyfitvals[0] |
|---|
| 2194 | self.polRegressRes = polyfitvals[1] |
|---|
| 2195 | self.polRegressSingVal = polyfitvals[3] |
|---|
| 2196 | |
|---|
| 2197 | return |
|---|
| 2198 | |
|---|
| 2199 | class stats2Val_missVal(object): |
|---|
| 2200 | """two variables Statistics taking into account a missing value class providing: |
|---|
| 2201 | vals1 = variable 1 |
|---|
| 2202 | vals2 = variable 2 |
|---|
| 2203 | missVal = missing value |
|---|
| 2204 | power = power of the polynomial fitting to apply between both variables |
|---|
| 2205 | self.min[var], self.max[var], self.mean[var], self.mean2[var], self.std[var] of |
|---|
| 2206 | [var] = var1+var2[v1Av2], var1-var2[v1Sv2], var1/var2[v1Dv2], var1*var2[v1Pv2] |
|---|
| 2207 | self.Nokvalues1: number of correct values of variable 1 |
|---|
| 2208 | self.Nokvalues2: number of correct values of variable 2 |
|---|
| 2209 | self.Nokvalues12: number of correct coincident values of variable 1 and variable 2 |
|---|
| 2210 | self.mae=mean(abs(var1-var2)) |
|---|
| 2211 | self.rmse=sqrt((var1-var2)**2) |
|---|
| 2212 | self.correlation (and p-value) |
|---|
| 2213 | self.linRegress: linear regression [trend, intercept, regression coefficient, p_value, standard error] |
|---|
| 2214 | self.polRegress: polinomial Regresion of degree [power] [coef**[power], coef**[power-1], ...., coef**0] |
|---|
| 2215 | """ |
|---|
| 2216 | |
|---|
| 2217 | def __init__(self, vals1, vals2, power, missVal): |
|---|
| 2218 | import numpy as np |
|---|
| 2219 | from scipy import stats as sts |
|---|
| 2220 | |
|---|
| 2221 | fname='stats2Val_missVal' |
|---|
| 2222 | if vals1 is None: |
|---|
| 2223 | self.Nv = None |
|---|
| 2224 | self.Nokvalues1 = None |
|---|
| 2225 | self.Nokvalues2 = None |
|---|
| 2226 | self.Nokvalues12 = None |
|---|
| 2227 | self.NDvalNone = None |
|---|
| 2228 | self.minv1Av2 = None |
|---|
| 2229 | self.maxv1Av2 = None |
|---|
| 2230 | self.meanv1Av2 = None |
|---|
| 2231 | self.mean2v1Av2 = None |
|---|
| 2232 | self.stdv1Av2 = None |
|---|
| 2233 | self.minv1Sv2 = None |
|---|
| 2234 | self.maxv1Sv2 = None |
|---|
| 2235 | self.meanv1Sv2 = None |
|---|
| 2236 | self.mean2v1Sv2 = None |
|---|
| 2237 | self.stdv1Sv2 = None |
|---|
| 2238 | self.minv1Dv2 = None |
|---|
| 2239 | self.maxv1Dv2 = None |
|---|
| 2240 | self.meanv1Dv2 = None |
|---|
| 2241 | self.mean2v1Dv2 = None |
|---|
| 2242 | self.stdv1Dv2 = None |
|---|
| 2243 | self.minv1Pv2 = None |
|---|
| 2244 | self.maxv1Pv2 = None |
|---|
| 2245 | self.meanv1Pv2 = None |
|---|
| 2246 | self.mean2v1Pv2 = None |
|---|
| 2247 | self.stdv1Pv2 = None |
|---|
| 2248 | self.mae = None |
|---|
| 2249 | self.rmse = None |
|---|
| 2250 | self.corr = None |
|---|
| 2251 | self.linRegress = None |
|---|
| 2252 | self.polRegress = None |
|---|
| 2253 | self.polRegressResidual = None |
|---|
| 2254 | self.polRegressRes = None |
|---|
| 2255 | self.polRegressSingVal = None |
|---|
| 2256 | else: |
|---|
| 2257 | Npt1 = 1 |
|---|
| 2258 | for idim in range(len(vals1.shape)): |
|---|
| 2259 | Npt1 = Npt1 * vals1.shape[idim] |
|---|
| 2260 | if np.sum(vals1 >= missVal) == Npt1: |
|---|
| 2261 | print errormsg |
|---|
| 2262 | print ' ' + fname + ' all values 1 get missing!!' |
|---|
| 2263 | print errormsg |
|---|
| 2264 | quit(-1) |
|---|
| 2265 | Npt2 = 1 |
|---|
| 2266 | for idim in range(len(vals2.shape)): |
|---|
| 2267 | Npt2 = Npt2 * vals2.shape[idim] |
|---|
| 2268 | if np.sum(vals2 >= missVal) == Npt2: |
|---|
| 2269 | print errormsg |
|---|
| 2270 | print ' ' + fname + ' all values 2 get missing!!' |
|---|
| 2271 | print errormsg |
|---|
| 2272 | quit(-1) |
|---|
| 2273 | vals10 = np.where(abs(vals1) >= missVal, None, vals1) |
|---|
| 2274 | vals20 = np.where(abs(vals2) >= missVal, None, vals2) |
|---|
| 2275 | values1 = vals10.flatten() |
|---|
| 2276 | values2 = vals20.flatten() |
|---|
| 2277 | |
|---|
| 2278 | if not len(values1) == len(values2): |
|---|
| 2279 | print errormsg |
|---|
| 2280 | print ' stats2Val: lengths of variables differ!! Lvar1: ', len(values1), ' Lvar2: ',len(values2),' statistics between them can not be computed!' |
|---|
| 2281 | quit(-1) |
|---|
| 2282 | |
|---|
| 2283 | self.Nv=Npt1 |
|---|
| 2284 | self.minv1Av2=10000000000. |
|---|
| 2285 | self.maxv1Av2=-self.minv1Av2 |
|---|
| 2286 | self.meanv1Av2=0. |
|---|
| 2287 | self.mean2v1Av2=0. |
|---|
| 2288 | self.stdv1Av2=0. |
|---|
| 2289 | self.minv1Sv2=self.minv1Av2 |
|---|
| 2290 | self.maxv1Sv2=-self.minv1Av2 |
|---|
| 2291 | self.meanv1Sv2=0. |
|---|
| 2292 | self.mean2v1Sv2=0. |
|---|
| 2293 | self.stdv1Sv2=0. |
|---|
| 2294 | self.minv1Dv2=self.minv1Av2 |
|---|
| 2295 | self.maxv1Dv2=-self.minv1Av2 |
|---|
| 2296 | self.meanv1Dv2=0. |
|---|
| 2297 | self.mean2v1Dv2=0. |
|---|
| 2298 | self.stdv1Dv2=0. |
|---|
| 2299 | self.minv1Pv2=self.minv1Av2 |
|---|
| 2300 | self.maxv1Pv2=-self.minv1Av2 |
|---|
| 2301 | self.meanv1Pv2=0. |
|---|
| 2302 | self.mean2v1Pv2=0. |
|---|
| 2303 | self.stdv1Pv2=0. |
|---|
| 2304 | self.mae = 0. |
|---|
| 2305 | self.rmse = 0. |
|---|
| 2306 | self.corr = np.array([0., 0.]) |
|---|
| 2307 | self.linRegress = np.zeros(5, float) |
|---|
| 2308 | self.polRegress = np.zeros(power+1, float) |
|---|
| 2309 | self.polRegressResidual = 0. |
|---|
| 2310 | self.polRegressSingVal = np.zeros(power+1, float) |
|---|
| 2311 | |
|---|
| 2312 | # v1 [+ / - / / / *] v2 |
|---|
| 2313 | ## |
|---|
| 2314 | self.Nokvalues1 = 0 |
|---|
| 2315 | self.Nokvalues2 = 0 |
|---|
| 2316 | self.Nokvalues12 = 0 |
|---|
| 2317 | self.NDvalNone = 0 |
|---|
| 2318 | for inum in range(self.Nv): |
|---|
| 2319 | if not values1[inum] is None: |
|---|
| 2320 | self.Nokvalues1 = self.Nokvalues1 + 1 |
|---|
| 2321 | if not values2[inum] is None: |
|---|
| 2322 | self.Nokvalues2 = self.Nokvalues2 + 1 |
|---|
| 2323 | if not values1[inum] is None and not values2[inum] is None: |
|---|
| 2324 | self.Nokvalues12 = self.Nokvalues12 + 1 |
|---|
| 2325 | Aval = values1[inum] + values2[inum] |
|---|
| 2326 | Sval = values1[inum] - values2[inum] |
|---|
| 2327 | Pval = values1[inum] * values2[inum] |
|---|
| 2328 | if np.isinf(values1[inum] / values2[inum]) or np.isnan(values1[inum] / values2[inum]): |
|---|
| 2329 | if self.NDvalNone < 1: |
|---|
| 2330 | print warnmsg |
|---|
| 2331 | print ' stats2Val: val1/val2 inf or Nan!!!!' |
|---|
| 2332 | Dval = None |
|---|
| 2333 | self.NDvalNone = self.NDvalNone + 1 |
|---|
| 2334 | else: |
|---|
| 2335 | Dval = values1[inum] / values2[inum] |
|---|
| 2336 | |
|---|
| 2337 | self.mae = self.mae + abs(Sval) |
|---|
| 2338 | self.rmse = self.rmse + Sval**2 |
|---|
| 2339 | |
|---|
| 2340 | if Aval < self.minv1Av2: |
|---|
| 2341 | self.minv1Av2 = Aval |
|---|
| 2342 | if Aval > self.maxv1Av2: |
|---|
| 2343 | self.maxv1Av2 = Aval |
|---|
| 2344 | if Sval < self.minv1Sv2: |
|---|
| 2345 | self.minv1Sv2 = Sval |
|---|
| 2346 | if Sval > self.maxv1Sv2: |
|---|
| 2347 | self.maxv1Sv2 = Sval |
|---|
| 2348 | if not Dval is None and Dval < self.minv1Dv2: |
|---|
| 2349 | self.minv1Dv2 = Dval |
|---|
| 2350 | if not Dval is None and Dval > self.maxv1Dv2: |
|---|
| 2351 | self.maxv1Dv2 = Dval |
|---|
| 2352 | if Pval < self.minv1Pv2: |
|---|
| 2353 | self.minv1Pv2 = Pval |
|---|
| 2354 | if Pval > self.maxv1Pv2: |
|---|
| 2355 | self.maxv1Pv2 = Pval |
|---|
| 2356 | |
|---|
| 2357 | self.meanv1Av2 = self.meanv1Av2+Aval |
|---|
| 2358 | self.mean2v1Av2 = self.mean2v1Av2+Aval*Aval |
|---|
| 2359 | self.meanv1Sv2 = self.meanv1Sv2+Sval |
|---|
| 2360 | self.mean2v1Sv2 = self.mean2v1Sv2+Sval*Sval |
|---|
| 2361 | if not Dval is None: |
|---|
| 2362 | self.meanv1Dv2 = self.meanv1Dv2+Dval |
|---|
| 2363 | self.mean2v1Dv2 = self.mean2v1Dv2+Dval*Dval |
|---|
| 2364 | self.meanv1Pv2 = self.meanv1Pv2+Pval |
|---|
| 2365 | self.mean2v1Pv2 = self.mean2v1Pv2+Pval*Pval |
|---|
| 2366 | |
|---|
| 2367 | ## print 'Nokvalues1: ', self.Nokvalues1, 'Nokvalues2: ', self.Nokvalues2, 'Nokvalues12: ', float(self.Nokvalues12), 'NDvalNone: ',self.NDvalNone |
|---|
| 2368 | self.meanv1Av2 = self.meanv1Av2/float(self.Nokvalues12) |
|---|
| 2369 | self.mean2v1Av2 = self.mean2v1Av2/float(self.Nokvalues12) |
|---|
| 2370 | self.stdv1Av2 = np.sqrt(self.mean2v1Av2-self.meanv1Av2*self.meanv1Av2) |
|---|
| 2371 | self.meanv1Sv2 = self.meanv1Sv2/float(self.Nokvalues12) |
|---|
| 2372 | self.mean2v1Sv2 = self.mean2v1Sv2/float(self.Nokvalues12) |
|---|
| 2373 | self.stdv1Sv2 = np.sqrt(self.mean2v1Sv2-self.meanv1Sv2*self.meanv1Sv2) |
|---|
| 2374 | if self.Nokvalues12 - self.NDvalNone == 0: |
|---|
| 2375 | self.meanv1Dv2 = None |
|---|
| 2376 | self.mean2v1Dv2 = None |
|---|
| 2377 | self.stdv1Dv2 = None |
|---|
| 2378 | print warnmsg |
|---|
| 2379 | print ' stats2Val: all values of val1/val2 are None!' |
|---|
| 2380 | else: |
|---|
| 2381 | self.meanv1Dv2 = self.meanv1Dv2/(float(self.Nokvalues12 - self.NDvalNone)) |
|---|
| 2382 | self.mean2v1Dv2 = self.mean2v1Dv2/(float(self.Nokvalues12 - self.NDvalNone)) |
|---|
| 2383 | self.stdv1Dv2 = np.sqrt(self.mean2v1Dv2-self.meanv1Dv2*self.meanv1Dv2) |
|---|
| 2384 | self.meanv1Pv2 = self.meanv1Pv2/float(self.Nokvalues12) |
|---|
| 2385 | self.mean2v1Pv2 = self.mean2v1Pv2/float(self.Nokvalues12) |
|---|
| 2386 | self.stdv1Pv2 = np.sqrt(self.mean2v1Pv2-self.meanv1Pv2*self.meanv1Pv2) |
|---|
| 2387 | |
|---|
| 2388 | self.mae = self.mae/self.Nokvalues12 |
|---|
| 2389 | self.rmse = np.sqrt(self.rmse/self.Nokvalues12) |
|---|
| 2390 | |
|---|
| 2391 | vals1Nomiss = np.ones(len(values1), dtype=bool) |
|---|
| 2392 | vals2Nomiss = np.ones(len(values1), dtype=bool) |
|---|
| 2393 | |
|---|
| 2394 | for i in range(len(values1)): |
|---|
| 2395 | if values1[i] is None: |
|---|
| 2396 | vals1Nomiss[i] = False |
|---|
| 2397 | |
|---|
| 2398 | for i in range(len(values2)): |
|---|
| 2399 | if values2[i] is None: |
|---|
| 2400 | vals2Nomiss[i] = False |
|---|
| 2401 | |
|---|
| 2402 | v1 = np.array(values1[vals1Nomiss], dtype=float) |
|---|
| 2403 | v2 = np.array(values2[vals2Nomiss], dtype=float) |
|---|
| 2404 | |
|---|
| 2405 | if not v1.shape == v2.shape: |
|---|
| 2406 | print errormsg |
|---|
| 2407 | print ' ' + fname + ': variables without missing values v1: ',v1.shape , ' and v2: ',v2.shape ,' do not have the same shape! ' |
|---|
| 2408 | print errormsg |
|---|
| 2409 | quit(-1) |
|---|
| 2410 | |
|---|
| 2411 | self.corr = sts.pearsonr(v1, v2) |
|---|
| 2412 | |
|---|
| 2413 | self.linRegress[0], self.linRegress[1], self.linRegress[2], self.linRegress[3], self.linRegress[4] = sts.linregress(v1, v2) |
|---|
| 2414 | |
|---|
| 2415 | polyfitvals=np.polyfit(v1, v2, power, full = True) |
|---|
| 2416 | |
|---|
| 2417 | self.polRegress = polyfitvals[0] |
|---|
| 2418 | self.polRegressRes = polyfitvals[1] |
|---|
| 2419 | self.polRegressSingVal = polyfitvals[3] |
|---|
| 2420 | |
|---|
| 2421 | return |
|---|
| 2422 | |
|---|
| 2423 | def mask_2masked(vals1, vals2): |
|---|
| 2424 | """ Function to provide the boolean matrix (in opposite way as it is in the mask) as combination of mask from to masked matrices |
|---|
| 2425 | """ |
|---|
| 2426 | import numpy.ma as ma |
|---|
| 2427 | fname = 'mask_2masked' |
|---|
| 2428 | |
|---|
| 2429 | # if len(vals1.shape) != len(vals2.shape): |
|---|
| 2430 | # print errormsg |
|---|
| 2431 | # print ' ' + fname + ' matrix 1 :', len(vals1.shape), ' and matrix 2 ', len(vals2.shape), ' have different size!' |
|---|
| 2432 | # print errormsg |
|---|
| 2433 | # quit(-1) |
|---|
| 2434 | |
|---|
| 2435 | # for idim in range(len(vals1.shape)): |
|---|
| 2436 | # if vals1.shape[idim] != vals2.shape[idim]: |
|---|
| 2437 | # print errormsg |
|---|
| 2438 | # print ' ' + fname + ' dimension ', idim,' from matrix 1 :', vals1.shape[idim], ' and matrix 2 ', \ |
|---|
| 2439 | # vals2.shape[idim], ' have different size!' |
|---|
| 2440 | # print errormsg |
|---|
| 2441 | # quit(-1) |
|---|
| 2442 | |
|---|
| 2443 | if type(vals1) == type(ma.array(1)): |
|---|
| 2444 | mask1array=np.where(ma.getmaskarray(vals1) == False, True, False) |
|---|
| 2445 | else: |
|---|
| 2446 | mask1array=np.ones(vals1.shape, dtype=bool) |
|---|
| 2447 | |
|---|
| 2448 | if type(vals2) == type(ma.array(1)): |
|---|
| 2449 | mask2array=np.where(ma.getmaskarray(vals2) == False, True, False) |
|---|
| 2450 | else: |
|---|
| 2451 | mask2array=np.ones(vals2.shape, dtype=bool) |
|---|
| 2452 | |
|---|
| 2453 | mask12 = mask1array*mask2array |
|---|
| 2454 | |
|---|
| 2455 | return mask12 |
|---|
| 2456 | |
|---|
| 2457 | def mask_pearsonr(xvals, yvals): |
|---|
| 2458 | """ Function to compute a pearson correlation from mask matrices |
|---|
| 2459 | """ |
|---|
| 2460 | from scipy import stats as sts |
|---|
| 2461 | fillVal = 1.e20 |
|---|
| 2462 | maskxy = mask_2masked(xvals, yvals) |
|---|
| 2463 | |
|---|
| 2464 | if np.sum(maskxy) > 1: |
|---|
| 2465 | pearsonr = sts.pearsonr(xvals[maskxy], yvals[maskxy]) |
|---|
| 2466 | if np.isnan(pearsonr[0]) or np.isnan(pearsonr[1]): |
|---|
| 2467 | pearsonr = ( fillVal, fillVal) |
|---|
| 2468 | else: |
|---|
| 2469 | pearsonr = (fillVal, fillVal) |
|---|
| 2470 | |
|---|
| 2471 | return pearsonr |
|---|
| 2472 | |
|---|
| 2473 | def mask_quantiles(maskmat, Nquants): |
|---|
| 2474 | """ Function to provide the quantiles of a masked array 20 for %5 bins (21 in total) |
|---|
| 2475 | """ |
|---|
| 2476 | import numpy.ma as ma |
|---|
| 2477 | |
|---|
| 2478 | fillValue = 1.e20 |
|---|
| 2479 | |
|---|
| 2480 | sortmat = maskmat.flatten().copy() |
|---|
| 2481 | sortmat.sort() |
|---|
| 2482 | quants = np.zeros(Nquants+1, dtype=type(maskmat[0])) |
|---|
| 2483 | Nv = ma.size(maskmat) |
|---|
| 2484 | NoMask=maskmat.count() |
|---|
| 2485 | |
|---|
| 2486 | if NoMask < Nquants: |
|---|
| 2487 | quants[:] = fillValue |
|---|
| 2488 | else: |
|---|
| 2489 | for iq in range(Nquants): |
|---|
| 2490 | quants[iq] = sortmat[int((NoMask-1)*iq/(Nquants))] |
|---|
| 2491 | |
|---|
| 2492 | quants[Nquants] = sortmat[NoMask-1] |
|---|
| 2493 | |
|---|
| 2494 | return quants |
|---|
| 2495 | |
|---|
| 2496 | def percendone(nvals,tot,percen,msg): |
|---|
| 2497 | """ Function to provide the percentage of an action across the matrix |
|---|
| 2498 | nvals=number of values |
|---|
| 2499 | tot=total number of values |
|---|
| 2500 | percen=percentage frequency for which the message is wanted |
|---|
| 2501 | msg= message |
|---|
| 2502 | """ |
|---|
| 2503 | from sys import stdout |
|---|
| 2504 | |
|---|
| 2505 | num = int(tot * percen/100) |
|---|
| 2506 | if (nvals%num == 0): |
|---|
| 2507 | print '\r ' + msg + '{0:8.3g}'.format(nvals*100./tot) + ' %', |
|---|
| 2508 | stdout.flush() |
|---|
| 2509 | |
|---|
| 2510 | return '' |
|---|
| 2511 | |
|---|
| 2512 | def mask_linregres(vals1, vals2): |
|---|
| 2513 | """ Function to compute a linear regression from masked data |
|---|
| 2514 | vals1: x-values for the regresion |
|---|
| 2515 | vals2: y-values for the regresion |
|---|
| 2516 | """ |
|---|
| 2517 | import numpy.ma as ma |
|---|
| 2518 | |
|---|
| 2519 | fname = 'mask_linregres' |
|---|
| 2520 | |
|---|
| 2521 | missval1 = vals1.get_fill_value() |
|---|
| 2522 | missval2 = vals2.get_fill_value() |
|---|
| 2523 | |
|---|
| 2524 | vals10 = np.where(abs(vals1) >= abs(missval1*0.9), None, vals1) |
|---|
| 2525 | vals20 = np.where(abs(vals2) >= abs(missval2*0.9), None, vals2) |
|---|
| 2526 | |
|---|
| 2527 | values1 = vals10.flatten() |
|---|
| 2528 | values2 = vals20.flatten() |
|---|
| 2529 | |
|---|
| 2530 | vals1Nomiss = np.ones(len(values1), dtype=bool) |
|---|
| 2531 | vals2Nomiss = np.ones(len(values2), dtype=bool) |
|---|
| 2532 | |
|---|
| 2533 | for i in range(len(values1)): |
|---|
| 2534 | if values1[i] is None: |
|---|
| 2535 | vals1Nomiss[i] = False |
|---|
| 2536 | for i in range(len(values2)): |
|---|
| 2537 | if values2[i] is None: |
|---|
| 2538 | vals2Nomiss[i] = False |
|---|
| 2539 | |
|---|
| 2540 | v1 = np.array(values1[vals1Nomiss], dtype=float) |
|---|
| 2541 | v2 = np.array(values2[vals2Nomiss], dtype=float) |
|---|
| 2542 | |
|---|
| 2543 | if len(v1) != len(v2): |
|---|
| 2544 | print errormsg |
|---|
| 2545 | print fname + ': length of masked matrices mat1:',len(v1),'and mat2:',len(v2),'does not match!' |
|---|
| 2546 | print errormsg |
|---|
| 2547 | quit(-1) |
|---|
| 2548 | |
|---|
| 2549 | linregres = np.array(sts.linregress(v1, v2), dtype= np.float64) |
|---|
| 2550 | |
|---|
| 2551 | return linregres |
|---|
| 2552 | |
|---|
| 2553 | def mask_space_stats(maskmat,statsvals,dim): |
|---|
| 2554 | """ Function to give back the multi-dimensional statisitcs of a given masked array |
|---|
| 2555 | maskmat=multidimensional masked array |
|---|
| 2556 | statsvals=[statn]:[values] |
|---|
| 2557 | [statn]: statistics to do: |
|---|
| 2558 | 'quant', quantiles |
|---|
| 2559 | [values]: value for the statistics: Nquantiles |
|---|
| 2560 | dim= dimension to run the statistics |
|---|
| 2561 | """ |
|---|
| 2562 | from sys import stdout |
|---|
| 2563 | |
|---|
| 2564 | fname = 'mask_space_stats' |
|---|
| 2565 | |
|---|
| 2566 | statn=statsvals.split(':')[0] |
|---|
| 2567 | if len(statsvals.split(':')) > 1: |
|---|
| 2568 | values=statsvals.split(':')[1] |
|---|
| 2569 | |
|---|
| 2570 | maskshape = maskmat.shape |
|---|
| 2571 | Ndims = len(maskshape) |
|---|
| 2572 | if statn == 'quant': |
|---|
| 2573 | if len(statsvals.split(':')) == 1: |
|---|
| 2574 | print errormsg |
|---|
| 2575 | print fname + ': statistics "' + statn + '" requires a value!!!' |
|---|
| 2576 | print errormsg |
|---|
| 2577 | quit(-1) |
|---|
| 2578 | Nquants=int(values) |
|---|
| 2579 | if Ndims == 2: |
|---|
| 2580 | if dim == 0: |
|---|
| 2581 | statval = np.ones((21, maskshape[1]), dtype=np.float64)*fillValue |
|---|
| 2582 | for i in range(maskshape[1]): |
|---|
| 2583 | percendone(i, maskshape[1], 5, 'quantiles') |
|---|
| 2584 | statval[:,i] = mask_quantiles(maskmat[:,i],Nquants) |
|---|
| 2585 | if dim == 1: |
|---|
| 2586 | statval = np.ones((21, maskshape[0]), dtype=np.float64)*fillValue |
|---|
| 2587 | for i in range(maskshape[0]): |
|---|
| 2588 | percendone(i, maskshape[0], 5, 'quantiles') |
|---|
| 2589 | statval[:,i] = mask_quantiles(statval[i,:],Nquants) |
|---|
| 2590 | elif Ndims == 3: |
|---|
| 2591 | if dim == 0: |
|---|
| 2592 | statval = np.ones((21, maskshape[1], maskshape[2]), dtype=np.float64)*fillValue |
|---|
| 2593 | for i in range(maskshape[1]): |
|---|
| 2594 | for j in range(maskshape[2]): |
|---|
| 2595 | percendone(i*maskshape[2] + j, maskshape[1]*maskshape[2], 5, 'quantiles') |
|---|
| 2596 | statval[:,i,j] = mask_quantiles(maskmat[:,i,j],Nquants) |
|---|
| 2597 | if dim == 1: |
|---|
| 2598 | statval = np.ones((21, maskshape[0], maskshape[2]), dtype=np.float64)*fillValue |
|---|
| 2599 | for i in range(maskshape[0]): |
|---|
| 2600 | for j in range(maskshape[2]): |
|---|
| 2601 | percendone(i*maskshape[2] + j, maskshape[0]*maskshape[2], 5, 'quantiles') |
|---|
| 2602 | statval[:,i,j] = mask_quantiles(maskmat[i,:,j],Nquants) |
|---|
| 2603 | if dim == 2: |
|---|
| 2604 | statval = np.ones((21, maskshape[0], maskshape[1]), dtype=np.float64)*fillValue |
|---|
| 2605 | for i in range(maskshape[0]): |
|---|
| 2606 | for j in range(maskshape[1]): |
|---|
| 2607 | percendone(i*maskshape[1] + j, maskshape[0]*maskshape[1], 5, 'quantiles') |
|---|
| 2608 | statval[:,i,j] = mask_quantiles(maskmat[i,j,:],Nquants) |
|---|
| 2609 | elif Ndims == 4: |
|---|
| 2610 | if dim == 0: |
|---|
| 2611 | statval = np.ones((21, maskshape[1], maskshape[2], maskshape[3]), dtype=np.float64)*fillValue |
|---|
| 2612 | for i in range(maskshape[1]): |
|---|
| 2613 | for j in range(maskshape[2]): |
|---|
| 2614 | for k in range(maskshape[3]): |
|---|
| 2615 | percendone(i*maskshape[1]*maskshape[2] + j*maskshape[2] + k, maskshape[1]*maskshape[2]*maskshape[3], 5, 'quantiles') |
|---|
| 2616 | statval[:,i,j,k] = mask_quantiles(maskmat[:,i,j,k],Nquants) |
|---|
| 2617 | if dim == 1: |
|---|
| 2618 | statval = np.ones((21, maskshape[0], maskshape[2], maskshape[3]), dtype=np.float64)*fillValue |
|---|
| 2619 | for i in range(maskshape[0]): |
|---|
| 2620 | for j in range(maskshape[2]): |
|---|
| 2621 | for k in range(maskshape[3]): |
|---|
| 2622 | percendone(i*maskshape[0]*maskshape[2] + j*maskshape[2] + k, maskshape[0]*maskshape[2]*maskshape[3], 5, 'quantiles') |
|---|
| 2623 | statval[:,i,j,k] = mask_quantiles(maskmat[i,:,j,k],Nquants) |
|---|
| 2624 | if dim == 2: |
|---|
| 2625 | statval = np.ones((21, maskshape[0], maskshape[1], maskshape[3]), dtype=np.float64)*fillValue |
|---|
| 2626 | for i in range(maskshape[0]): |
|---|
| 2627 | for j in range(maskshape[1]): |
|---|
| 2628 | for k in range(maskshape[3]): |
|---|
| 2629 | percendone(i*maskshape[0]*maskshape[1] + j*maskshape[1] + k, maskshape[0]*maskshape[1]*maskshape[3], 5, 'quantiles') |
|---|
| 2630 | statval[:,i,j,k] = mask_quantiles(maskmat[i,j,:,k],Nquants) |
|---|
| 2631 | if dim == 3: |
|---|
| 2632 | statval = np.ones((21, maskshape[0], maskshape[1], maskshape[2]), dtype=np.float64)*fillValue |
|---|
| 2633 | for i in range(maskshape[0]): |
|---|
| 2634 | for j in range(maskshape[1]): |
|---|
| 2635 | for k in range(maskshape[2]): |
|---|
| 2636 | percendone(i*maskshape[0]*maskshape[1] + j*maskshape[1] + k, maskshape[0]*maskshape[1]*maskshape[2], 5, 'quantiles') |
|---|
| 2637 | statval[:,i,j,k] = mask_quantiles(maskmat[i,j,k,:],Nquants) |
|---|
| 2638 | else: |
|---|
| 2639 | print errormsg |
|---|
| 2640 | print fname + ': size of matrix ', Ndims,'not ready!!!' |
|---|
| 2641 | print errormsg |
|---|
| 2642 | quit(-1) |
|---|
| 2643 | |
|---|
| 2644 | else: |
|---|
| 2645 | print errormsg |
|---|
| 2646 | print fname + ': statistics "' + statn + '" not ready!!!!' |
|---|
| 2647 | print errormsg |
|---|
| 2648 | quit(-1) |
|---|
| 2649 | |
|---|
| 2650 | print stdout.write("\n") |
|---|
| 2651 | |
|---|
| 2652 | return statval |
|---|
| 2653 | |
|---|
| 2654 | class statsVal(object): |
|---|
| 2655 | """Statistics class providing |
|---|
| 2656 | vals = variable |
|---|
| 2657 | self.Nv = number of values |
|---|
| 2658 | self.minv = minimum value |
|---|
| 2659 | self.maxv = maximum value |
|---|
| 2660 | self.meanv = mean value |
|---|
| 2661 | self.mean2v = cuadratic mean value |
|---|
| 2662 | self.stdv = standard deviation value |
|---|
| 2663 | self.Nokvalues = number of correct values of variable |
|---|
| 2664 | self.quantilesv = quantiles (%5 bins) of the variable |
|---|
| 2665 | """ |
|---|
| 2666 | |
|---|
| 2667 | def __init__(self, vals): |
|---|
| 2668 | if vals is None: |
|---|
| 2669 | self.Nv = None |
|---|
| 2670 | self.minv = None |
|---|
| 2671 | self.maxv = None |
|---|
| 2672 | self.meanv = None |
|---|
| 2673 | self.mean2v = None |
|---|
| 2674 | self.stdv = None |
|---|
| 2675 | self.Nokvalues = None |
|---|
| 2676 | self.quantilesv = None |
|---|
| 2677 | else: |
|---|
| 2678 | values = vals.flatten() |
|---|
| 2679 | self.Nv=len(values) |
|---|
| 2680 | self.minv=10000000000. |
|---|
| 2681 | self.maxv=-100000000. |
|---|
| 2682 | self.meanv=0. |
|---|
| 2683 | self.mean2v=0. |
|---|
| 2684 | self.stdv=0. |
|---|
| 2685 | |
|---|
| 2686 | sortedvalues = sorted(values) |
|---|
| 2687 | |
|---|
| 2688 | self.Nokvalues = 0 |
|---|
| 2689 | for inum in range(self.Nv): |
|---|
| 2690 | if not values[inum] is None: |
|---|
| 2691 | self.Nokvalues = self.Nokvalues + 1 |
|---|
| 2692 | if values[inum] < self.minv: |
|---|
| 2693 | self.minv = values[inum] |
|---|
| 2694 | if values[inum] > self.maxv: |
|---|
| 2695 | self.maxv = values[inum] |
|---|
| 2696 | |
|---|
| 2697 | self.meanv = self.meanv+values[inum] |
|---|
| 2698 | self.mean2v = self.mean2v+values[inum]*values[inum] |
|---|
| 2699 | |
|---|
| 2700 | self.meanv = self.meanv/float(self.Nokvalues) |
|---|
| 2701 | self.mean2v = self.mean2v/float(self.Nokvalues) |
|---|
| 2702 | self.stdv = np.sqrt(self.mean2v-self.meanv*self.meanv) |
|---|
| 2703 | self.quantilesv = [] |
|---|
| 2704 | for iq in range(20): |
|---|
| 2705 | self.quantilesv.append(sortedvalues[int((self.Nv-1)*iq/20)]) |
|---|
| 2706 | |
|---|
| 2707 | self.quantilesv.append(sortedvalues[self.Nv-1]) |
|---|
| 2708 | self.medianv = self.quantilesv[10] |
|---|
| 2709 | |
|---|
| 2710 | return |
|---|
| 2711 | |
|---|
| 2712 | class Quantiles(object): |
|---|
| 2713 | """ Class to provide quantiles from a given arrayof values |
|---|
| 2714 | """ |
|---|
| 2715 | |
|---|
| 2716 | def __init__(self, values, Nquants): |
|---|
| 2717 | import numpy.ma as ma |
|---|
| 2718 | |
|---|
| 2719 | if values is None: |
|---|
| 2720 | self.quantilesv = None |
|---|
| 2721 | |
|---|
| 2722 | else: |
|---|
| 2723 | self.quantilesv = [] |
|---|
| 2724 | |
|---|
| 2725 | vals0 = values.flatten() |
|---|
| 2726 | Nvalues = len(vals0) |
|---|
| 2727 | vals = ma.masked_equal(vals0, None) |
|---|
| 2728 | Nvals=len(vals.compressed()) |
|---|
| 2729 | |
|---|
| 2730 | sortedvals = sorted(vals.compressed()) |
|---|
| 2731 | for iq in range(Nquants): |
|---|
| 2732 | self.quantilesv.append(sortedvals[int((Nvals-1)*iq/Nquants)]) |
|---|
| 2733 | |
|---|
| 2734 | self.quantilesv.append(sortedvals[Nvals-1]) |
|---|
| 2735 | |
|---|
| 2736 | return |
|---|
| 2737 | |
|---|
| 2738 | class statsVal_missVal(object): |
|---|
| 2739 | """Statistics class tacking into account a missing value providing |
|---|
| 2740 | vals = variable |
|---|
| 2741 | missval = missing value |
|---|
| 2742 | self.Nv = number of values |
|---|
| 2743 | self.minv = minimum value |
|---|
| 2744 | self.maxv = maximum value |
|---|
| 2745 | self.meanv = mean value |
|---|
| 2746 | self.mean2v = cuadratic mean value |
|---|
| 2747 | self.stdv = standard deviation value |
|---|
| 2748 | self.Nokvalues = number of correct values of variable |
|---|
| 2749 | self.quantilesv = quantiles (%5 bins) of the variable |
|---|
| 2750 | """ |
|---|
| 2751 | |
|---|
| 2752 | def __init__(self, vals, missVal): |
|---|
| 2753 | fname='statsVal_missVal' |
|---|
| 2754 | |
|---|
| 2755 | if vals is None: |
|---|
| 2756 | self.Nv = None |
|---|
| 2757 | self.minv = None |
|---|
| 2758 | self.maxv = None |
|---|
| 2759 | self.meanv = None |
|---|
| 2760 | self.mean2v = None |
|---|
| 2761 | self.stdv = None |
|---|
| 2762 | self.Nokvalues = None |
|---|
| 2763 | self.quantilesv = None |
|---|
| 2764 | else: |
|---|
| 2765 | Npt = 1 |
|---|
| 2766 | for idim in range(len(vals.shape)): |
|---|
| 2767 | Npt = Npt * vals.shape[idim] |
|---|
| 2768 | if np.sum(vals >= missVal) == Npt: |
|---|
| 2769 | print errormsg |
|---|
| 2770 | print ' ' + fname + ' all values get missing!!' |
|---|
| 2771 | print errormsg |
|---|
| 2772 | quit(-1) |
|---|
| 2773 | |
|---|
| 2774 | vals1 = np.where(abs(vals) >= missVal, None, vals) |
|---|
| 2775 | vals0 = np.where(np.isnan(vals1), None, vals1) |
|---|
| 2776 | |
|---|
| 2777 | values = vals0.flatten() |
|---|
| 2778 | self.Nv=Npt |
|---|
| 2779 | self.minv=10000000000. |
|---|
| 2780 | self.maxv=-100000000. |
|---|
| 2781 | self.meanv=0. |
|---|
| 2782 | self.mean2v=0. |
|---|
| 2783 | self.stdv=0. |
|---|
| 2784 | |
|---|
| 2785 | sortedvalues = sorted(values) |
|---|
| 2786 | |
|---|
| 2787 | self.Nokvalues = 0 |
|---|
| 2788 | for inum in range(self.Nv): |
|---|
| 2789 | if not values[inum] is None: |
|---|
| 2790 | self.Nokvalues = self.Nokvalues + 1 |
|---|
| 2791 | if values[inum] < self.minv: |
|---|
| 2792 | self.minv = values[inum] |
|---|
| 2793 | if values[inum] > self.maxv: |
|---|
| 2794 | self.maxv = values[inum] |
|---|
| 2795 | |
|---|
| 2796 | self.meanv = self.meanv+values[inum] |
|---|
| 2797 | self.mean2v = self.mean2v+values[inum]*values[inum] |
|---|
| 2798 | |
|---|
| 2799 | self.meanv = self.meanv/float(self.Nokvalues) |
|---|
| 2800 | self.mean2v = self.mean2v/float(self.Nokvalues) |
|---|
| 2801 | self.stdv = np.sqrt(self.mean2v-self.meanv*self.meanv) |
|---|
| 2802 | self.quantilesv = [] |
|---|
| 2803 | for iq in range(20): |
|---|
| 2804 | self.quantilesv.append(sortedvalues[int((self.Nv-1)*iq/20)]) |
|---|
| 2805 | |
|---|
| 2806 | self.quantilesv.append(sortedvalues[self.Nv-1]) |
|---|
| 2807 | self.medianv = self.quantilesv[10] |
|---|
| 2808 | |
|---|
| 2809 | return |
|---|
| 2810 | |
|---|
| 2811 | def printing_class(classobj): |
|---|
| 2812 | """ Function to print all the values of a given class |
|---|
| 2813 | """ |
|---|
| 2814 | |
|---|
| 2815 | valscls = vars(classobj) |
|---|
| 2816 | for attrcls in valscls: |
|---|
| 2817 | print attrcls, ':', valscls[attrcls] |
|---|
| 2818 | |
|---|
| 2819 | return |
|---|
| 2820 | |
|---|
| 2821 | def fmtprinting_class(classobj): |
|---|
| 2822 | """ Function to print all the values of a given class |
|---|
| 2823 | """ |
|---|
| 2824 | |
|---|
| 2825 | valscls = vars(classobj) |
|---|
| 2826 | for attrcls in valscls: |
|---|
| 2827 | print '@' + attrcls + '@', ':', valscls[attrcls] |
|---|
| 2828 | |
|---|
| 2829 | return |
|---|
| 2830 | |
|---|
| 2831 | def cycl_incr(cyclval,cycle,ninc): |
|---|
| 2832 | """ Function to increment a cyclic value [cyclval] with a cycle [cycle] a given number [ninc] of times |
|---|
| 2833 | >>> cycl_incr(1,4,1) |
|---|
| 2834 | 2 |
|---|
| 2835 | >>> cycl_incr(3,4,1) |
|---|
| 2836 | 0 |
|---|
| 2837 | >>> cycl_incr(1,4,10) |
|---|
| 2838 | 3 |
|---|
| 2839 | """ |
|---|
| 2840 | |
|---|
| 2841 | if ninc >= cycle: |
|---|
| 2842 | print 'cycl_incr: WARNING -- warning -- WARNING -- warning' |
|---|
| 2843 | print ' given increment: ', ninc,' is larger than the cycle !!' |
|---|
| 2844 | ninc = ninc - cycle*int(ninc/cycle) |
|---|
| 2845 | print ' reducing it to: ', ninc |
|---|
| 2846 | |
|---|
| 2847 | val=cyclval + ninc |
|---|
| 2848 | if val >= cycle: |
|---|
| 2849 | val=cyclval + ninc - cycle |
|---|
| 2850 | |
|---|
| 2851 | return val |
|---|
| 2852 | |
|---|
| 2853 | def times_4seasons(tvals, integrity): |
|---|
| 2854 | """ Function to split a time series in matricial date format ([:,year,mon,day,hour,minute,second]) in the four stations DJF,MAM,JJA,SON |
|---|
| 2855 | tvals= matrix withe times as [:,year,mon,day,hour,minute,second] |
|---|
| 2856 | integrity= only give values for entire seasons [True, 3 months for the season], or just split the values by seasons [False] |
|---|
| 2857 | """ |
|---|
| 2858 | fillVal=1.e20 |
|---|
| 2859 | |
|---|
| 2860 | # print tvals |
|---|
| 2861 | |
|---|
| 2862 | dt=tvals.shape[0] |
|---|
| 2863 | seasons=np.ones((dt,4),dtype=bool) |
|---|
| 2864 | seasons=seasons*False |
|---|
| 2865 | |
|---|
| 2866 | monseas=[12,3,6,9] |
|---|
| 2867 | firstseas=False |
|---|
| 2868 | |
|---|
| 2869 | for it in range(dt): |
|---|
| 2870 | if not firstseas: |
|---|
| 2871 | if integrity: |
|---|
| 2872 | for iseas in range(4): |
|---|
| 2873 | if tvals[it,1] == monseas[iseas]: |
|---|
| 2874 | nseas=iseas |
|---|
| 2875 | seasons[it,nseas]=True |
|---|
| 2876 | firstseas=True |
|---|
| 2877 | begseas=it |
|---|
| 2878 | else: |
|---|
| 2879 | for iseas in range(4): |
|---|
| 2880 | for imon in range(3): |
|---|
| 2881 | if tvals[it,1] == cycl_incr(monseas[iseas],12,imon): |
|---|
| 2882 | nseas=iseas |
|---|
| 2883 | seasons[it,nseas]=True |
|---|
| 2884 | firstseas=True |
|---|
| 2885 | else: |
|---|
| 2886 | newseas=cycl_incr(nseas,4,1) |
|---|
| 2887 | if tvals[it,1] == monseas[newseas]: |
|---|
| 2888 | seasons[it,newseas] = True |
|---|
| 2889 | nseas=newseas |
|---|
| 2890 | begseas=it |
|---|
| 2891 | else: |
|---|
| 2892 | seasons[it,nseas] = True |
|---|
| 2893 | |
|---|
| 2894 | endseas = it |
|---|
| 2895 | ## print 'Last season: ',nseas,' beginnig: ',begseas,' ending: ',endseas |
|---|
| 2896 | |
|---|
| 2897 | # Checking integrity of last season (has to have 3 months) |
|---|
| 2898 | ## |
|---|
| 2899 | if integrity: |
|---|
| 2900 | fullseas=True |
|---|
| 2901 | Nmon=np.unique(tvals[begseas:endseas+1,1]) |
|---|
| 2902 | for it in range(begseas,endseas): fullseas=fullseas*seasons[it,nseas] |
|---|
| 2903 | if len(Nmon) < 3 or not fullseas: |
|---|
| 2904 | seasons[begseas:endseas+1,nseas] = False |
|---|
| 2905 | |
|---|
| 2906 | return seasons |
|---|
| 2907 | |
|---|
| 2908 | def realdatetime_CFcompilant(times, Srefdate, tunits): |
|---|
| 2909 | """ Function to transform a matrix with real time values ([year, month, day, hour, minute, second]) to a netCDF one |
|---|
| 2910 | times= matrix with times |
|---|
| 2911 | Srefdate= reference date ([YYYY][MM][DD][HH][MI][SS] format) |
|---|
| 2912 | tunits= units of time respect to Srefdate |
|---|
| 2913 | >>> realdatetime_CFcompilant(np.array([ [1976, 2, 17, 8, 20, 0], [1976, 2, 18, 8, 20, 0]], dtype=int), '19491201000000', 'hours') |
|---|
| 2914 | [ 229784.33333333 229808.33333333] |
|---|
| 2915 | """ |
|---|
| 2916 | |
|---|
| 2917 | import datetime as dt |
|---|
| 2918 | yrref=int(Srefdate[0:4]) |
|---|
| 2919 | monref=int(Srefdate[4:6]) |
|---|
| 2920 | dayref=int(Srefdate[6:8]) |
|---|
| 2921 | horref=int(Srefdate[8:10]) |
|---|
| 2922 | minref=int(Srefdate[10:12]) |
|---|
| 2923 | secref=int(Srefdate[12:14]) |
|---|
| 2924 | |
|---|
| 2925 | refdate=dt.datetime(yrref, monref, dayref, horref, minref, secref) |
|---|
| 2926 | |
|---|
| 2927 | dimt=times.shape[0] |
|---|
| 2928 | |
|---|
| 2929 | cfdates = np.zeros((dimt), dtype=np.float64) |
|---|
| 2930 | if tunits == 'weeks': |
|---|
| 2931 | for it in range(dimt): |
|---|
| 2932 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 2933 | cfdates[it] = (cfdate.days + cfdate.seconds/(3600.*24.))/7. |
|---|
| 2934 | elif tunits == 'days': |
|---|
| 2935 | for it in range(dimt): |
|---|
| 2936 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 2937 | cfdates[it] = cfdate.days + cfdate.seconds/(3600.*24.) |
|---|
| 2938 | elif tunits == 'hours': |
|---|
| 2939 | for it in range(dimt): |
|---|
| 2940 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 2941 | cfdates[it] = cfdate.days*24. + cfdate.seconds/3600. |
|---|
| 2942 | elif tunits == 'minutes': |
|---|
| 2943 | for it in range(dimt): |
|---|
| 2944 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 2945 | cfdates[it] = cfdate.days*24.*60. + cfdate.seconds/60. |
|---|
| 2946 | elif tunits == 'seconds': |
|---|
| 2947 | for it in range(dimt): |
|---|
| 2948 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 2949 | cfdates[it] = cfdate.days*24.*3600. + cfdate.seconds |
|---|
| 2950 | elif tunits == 'milliseconds': |
|---|
| 2951 | for it in range(dimt): |
|---|
| 2952 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 2953 | cfdates[it] = cfdate.days*1000.*24.*3600. + cfdate.seconds*1000. |
|---|
| 2954 | elif tunits == 'microseconds': |
|---|
| 2955 | for it in range(dimt): |
|---|
| 2956 | cfdate = dt.datetime(times[it,0], times[it,1], times[it,2], times[it,3], times[it,4], times[it,5]) - refdate |
|---|
| 2957 | cfdates[it] = cfdate.days*1000000.*24.*3600. + cfdate.seconds*1000000. |
|---|
| 2958 | else: |
|---|
| 2959 | print errormsg |
|---|
| 2960 | print ' ' + fname + ': time units "' + tunits + '" is not ready!!!' |
|---|
| 2961 | quit(-1) |
|---|
| 2962 | |
|---|
| 2963 | return cfdates |
|---|
| 2964 | |
|---|
| 2965 | def realdatetime1_CFcompilant(time, Srefdate, tunits): |
|---|
| 2966 | """ Function to transform a matrix with a real time value ([year, month, day, |
|---|
| 2967 | hour, minute, second]) to a netCDF one |
|---|
| 2968 | time= matrix with time |
|---|
| 2969 | Srefdate= reference date ([YYYY][MM][DD][HH][MI][SS] format) |
|---|
| 2970 | tunits= units of time respect to Srefdate |
|---|
| 2971 | >>> realdatetime1_CFcompilant([1976, 2, 17, 8, 20, 0], '19491201000000', 'hours') |
|---|
| 2972 | 229784.33333333 |
|---|
| 2973 | """ |
|---|
| 2974 | |
|---|
| 2975 | import datetime as dt |
|---|
| 2976 | yrref=int(Srefdate[0:4]) |
|---|
| 2977 | monref=int(Srefdate[4:6]) |
|---|
| 2978 | dayref=int(Srefdate[6:8]) |
|---|
| 2979 | horref=int(Srefdate[8:10]) |
|---|
| 2980 | minref=int(Srefdate[10:12]) |
|---|
| 2981 | secref=int(Srefdate[12:14]) |
|---|
| 2982 | |
|---|
| 2983 | refdate=dt.datetime(yrref, monref, dayref, horref, minref, secref) |
|---|
| 2984 | |
|---|
| 2985 | if tunits == 'weeks': |
|---|
| 2986 | cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5])-refdate |
|---|
| 2987 | cfdates = (cfdate.days + cfdate.seconds/(3600.*24.))/7. |
|---|
| 2988 | elif tunits == 'days': |
|---|
| 2989 | cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate |
|---|
| 2990 | cfdates = cfdate.days + cfdate.seconds/(3600.*24.) |
|---|
| 2991 | elif tunits == 'hours': |
|---|
| 2992 | cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate |
|---|
| 2993 | cfdates = cfdate.days*24. + cfdate.seconds/3600. |
|---|
| 2994 | elif tunits == 'minutes': |
|---|
| 2995 | cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate |
|---|
| 2996 | cfdates = cfdate.days*24.*60. + cfdate.seconds/60. |
|---|
| 2997 | elif tunits == 'seconds': |
|---|
| 2998 | cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate |
|---|
| 2999 | cfdates = cfdate.days*24.*3600. + cfdate.seconds |
|---|
| 3000 | elif tunits == 'milliseconds': |
|---|
| 3001 | cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate |
|---|
| 3002 | cfdates = cfdate.days*1000.*24.*3600. + cfdate.seconds*1000. |
|---|
| 3003 | elif tunits == 'microseconds': |
|---|
| 3004 | cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],times[5]) - refdate |
|---|
| 3005 | cfdates = cfdate.days*1000000.*24.*3600. + cfdate.seconds*1000000. |
|---|
| 3006 | else: |
|---|
| 3007 | print errormsg |
|---|
| 3008 | print ' ' + fname + ': time units "' + tunits + '" is not ready!!!' |
|---|
| 3009 | quit(-1) |
|---|
| 3010 | |
|---|
| 3011 | return cfdates |
|---|
| 3012 | |
|---|
| 3013 | def netCDFdatetime_realdatetime(units, tcalendar, times): |
|---|
| 3014 | """ Function to transfrom from netCDF CF-compilant times to real time |
|---|
| 3015 | [units]= CF time units [tunits] since [YYYY]-[MM]-[HH] [[HH]:[MI]:[SS]] |
|---|
| 3016 | [tcalendar]= time calendar |
|---|
| 3017 | [times]= CF time values |
|---|
| 3018 | """ |
|---|
| 3019 | import datetime as dt |
|---|
| 3020 | |
|---|
| 3021 | txtunits = units.split(' ') |
|---|
| 3022 | tunits = txtunits[0] |
|---|
| 3023 | Srefdate = txtunits[len(txtunits) - 1] |
|---|
| 3024 | |
|---|
| 3025 | # Calendar type |
|---|
| 3026 | ## |
|---|
| 3027 | is360 = False |
|---|
| 3028 | if tcalendar is not None: |
|---|
| 3029 | print ' netCDFdatetime_realdatetime: There is a calendar attribute' |
|---|
| 3030 | if tcalendar == '365_day' or tcalendar == 'noleap': |
|---|
| 3031 | print ' netCDFdatetime_realdatetime: No leap years!' |
|---|
| 3032 | isleapcal = False |
|---|
| 3033 | elif tcalendar == 'proleptic_gregorian' or tcalendar == 'standard' or tcalendar == 'gregorian': |
|---|
| 3034 | isleapcal = True |
|---|
| 3035 | elif tcalendar == '360_day': |
|---|
| 3036 | is360 = True |
|---|
| 3037 | isleapcal = False |
|---|
| 3038 | else: |
|---|
| 3039 | print errormsg |
|---|
| 3040 | print ' netCDFdatetime_realdatetime: Calendar "' + tcalendar + '" not prepared!' |
|---|
| 3041 | quit(-1) |
|---|
| 3042 | |
|---|
| 3043 | # Does reference date contain a time value [YYYY]-[MM]-[DD] [HH]:[MI]:[SS] |
|---|
| 3044 | ## |
|---|
| 3045 | timeval = Srefdate.find(':') |
|---|
| 3046 | |
|---|
| 3047 | if not timeval == -1: |
|---|
| 3048 | print ' netCDFdatetime_realdatetime: refdate with time!' |
|---|
| 3049 | refdate = datetimeStr_datetime(txtunits[len(txtunits) - 2] + '_' + Srefdate) |
|---|
| 3050 | else: |
|---|
| 3051 | refdate = dateStr_date(Srefdate) |
|---|
| 3052 | |
|---|
| 3053 | dimt = len(times) |
|---|
| 3054 | # datetype = type(dt.datetime(1972,02,01)) |
|---|
| 3055 | # realdates = np.array(dimt, datetype) |
|---|
| 3056 | # print realdates |
|---|
| 3057 | |
|---|
| 3058 | ## Not in timedelta |
|---|
| 3059 | # if tunits == 'years': |
|---|
| 3060 | # for it in range(dimt): |
|---|
| 3061 | # realdate = refdate + dt.timedelta(years=float(times[it])) |
|---|
| 3062 | # realdates[it] = int(realdate.year) |
|---|
| 3063 | # elif tunits == 'months': |
|---|
| 3064 | # for it in range(dimt): |
|---|
| 3065 | # realdate = refdate + dt.timedelta(months=float(times[it])) |
|---|
| 3066 | # realdates[it] = int(realdate.year) |
|---|
| 3067 | # realdates = [] |
|---|
| 3068 | realdates = np.zeros((dimt, 6), dtype=int) |
|---|
| 3069 | if tunits == 'weeks': |
|---|
| 3070 | for it in range(dimt): |
|---|
| 3071 | realdate = refdate + dt.timedelta(weeks=float(times[it])) |
|---|
| 3072 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 3073 | elif tunits == 'days': |
|---|
| 3074 | for it in range(dimt): |
|---|
| 3075 | realdate = refdate + dt.timedelta(days=float(times[it])) |
|---|
| 3076 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 3077 | elif tunits == 'hours': |
|---|
| 3078 | for it in range(dimt): |
|---|
| 3079 | realdate = refdate + dt.timedelta(hours=float(times[it])) |
|---|
| 3080 | # if not isleapcal: |
|---|
| 3081 | # Nleapdays = cal.leapdays(int(refdate.year), int(realdate.year)) |
|---|
| 3082 | # realdate = realdate - dt.timedelta(days=Nleapdays) |
|---|
| 3083 | # if is360: |
|---|
| 3084 | # Nyears360 = int(realdate.year) - int(refdate.year) + 1 |
|---|
| 3085 | # realdate = realdate -dt.timedelta(days=Nyears360*5) |
|---|
| 3086 | # realdates[it] = realdate |
|---|
| 3087 | # realdates = refdate + dt.timedelta(hours=float(times)) |
|---|
| 3088 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 3089 | elif tunits == 'minutes': |
|---|
| 3090 | for it in range(dimt): |
|---|
| 3091 | realdate = refdate + dt.timedelta(minutes=float(times[it])) |
|---|
| 3092 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 3093 | elif tunits == 'seconds': |
|---|
| 3094 | for it in range(dimt): |
|---|
| 3095 | realdate = refdate + dt.timedelta(seconds=float(times[it])) |
|---|
| 3096 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 3097 | elif tunits == 'milliseconds': |
|---|
| 3098 | for it in range(dimt): |
|---|
| 3099 | realdate = refdate + dt.timedelta(milliseconds=float(times[it])) |
|---|
| 3100 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 3101 | elif tunits == 'microseconds': |
|---|
| 3102 | for it in range(dimt): |
|---|
| 3103 | realdate = refdate + dt.timedelta(microseconds=float(times[it])) |
|---|
| 3104 | realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second] |
|---|
| 3105 | else: |
|---|
| 3106 | print errormsg |
|---|
| 3107 | print ' netCDFdatetime_realdatetime: time units "' + tunits + '" is not ready!!!' |
|---|
| 3108 | quit(-1) |
|---|
| 3109 | |
|---|
| 3110 | return realdates |
|---|
| 3111 | |
|---|
| 3112 | class cls_yearly_means(object): |
|---|
| 3113 | """ Class to compute the yearly mean of a time series of values |
|---|
| 3114 | tvals=time values (year, month, day, hour, minute, second) format |
|---|
| 3115 | values=time-series of values |
|---|
| 3116 | dateref= reference date [YYYY][MM][DD][HH][MI][SS] format |
|---|
| 3117 | tunits= time units |
|---|
| 3118 | self.dimt = Number of values |
|---|
| 3119 | self.yrv = yearly mean values |
|---|
| 3120 | self.yrt = years of the mean values |
|---|
| 3121 | >>> timesv = netCDFdatetime_realdatetime('days since 1949-12-01 00:00:00', 'standard', np.arange(1129)) |
|---|
| 3122 | >>> valuesv = np.zeros((1129), dtype=np.float) |
|---|
| 3123 | >>> valuesv[0:31] = 0. |
|---|
| 3124 | >>> valuesv[31:394] = 1. |
|---|
| 3125 | >>> valuesv[395:761] = 2. |
|---|
| 3126 | >>> valuesv[761:1127] = 3. |
|---|
| 3127 | >>> valuesv[1127:1129] = 4. |
|---|
| 3128 | |
|---|
| 3129 | >>> yrlmeans = cls_yearly_means(timesv, valuesv, '19491201000000', 'days') |
|---|
| 3130 | >>> print yrlmeans.dimt,yrlmeans.yrv, yrlmeans.yrt |
|---|
| 3131 | 5 [ 0. 1. 2. 3. 4.] [ 1949. 1950. 1951. 1952. 1953.] |
|---|
| 3132 | """ |
|---|
| 3133 | def __init__(self, tvals, values, dateref, tunits): |
|---|
| 3134 | |
|---|
| 3135 | import numpy.ma as ma |
|---|
| 3136 | fillVal=1.e20 |
|---|
| 3137 | fname = 'cls_yearly_means' |
|---|
| 3138 | |
|---|
| 3139 | if tvals is None: |
|---|
| 3140 | self.dimt = None |
|---|
| 3141 | self.yrv = None |
|---|
| 3142 | self.yrt = None |
|---|
| 3143 | |
|---|
| 3144 | elif tvals == 'h': |
|---|
| 3145 | print fname + '_____________________________________________________________' |
|---|
| 3146 | print cls_yearly_means.__doc__ |
|---|
| 3147 | quit() |
|---|
| 3148 | |
|---|
| 3149 | else: |
|---|
| 3150 | dt=len(values) |
|---|
| 3151 | if dt > 0: |
|---|
| 3152 | years = np.unique(tvals[:,0]) |
|---|
| 3153 | Nyrs = len(years) |
|---|
| 3154 | yrmean = np.zeros((Nyrs), dtype=np.float) |
|---|
| 3155 | yrt = np.zeros((Nyrs), dtype=np.float) |
|---|
| 3156 | |
|---|
| 3157 | iiyr = tvals[0,0] |
|---|
| 3158 | yrmean[0] = values[0] |
|---|
| 3159 | yrt[0] = iiyr |
|---|
| 3160 | iit = 0 |
|---|
| 3161 | for iyr in range(Nyrs): |
|---|
| 3162 | nvals = 1 |
|---|
| 3163 | for it in range(iit+1,dt): |
|---|
| 3164 | # print iyr, iiyr, it, tvals[it,0],':',values[it],'->',yrmean[iyr] |
|---|
| 3165 | if tvals[it,0] == iiyr: |
|---|
| 3166 | yrmean[iyr] = yrmean[iyr] + values[it] |
|---|
| 3167 | nvals = nvals + 1 |
|---|
| 3168 | else: |
|---|
| 3169 | yrmean[iyr] = yrmean[iyr] / (nvals*1.) |
|---|
| 3170 | iiyr = tvals[it,0] |
|---|
| 3171 | yrmean[iyr + 1] = values[it] |
|---|
| 3172 | yrt[iyr+1] = iiyr |
|---|
| 3173 | iit = it |
|---|
| 3174 | break |
|---|
| 3175 | |
|---|
| 3176 | yrmean[Nyrs-1] = yrmean[Nyrs-1]/nvals |
|---|
| 3177 | self.dimt = Nyrs |
|---|
| 3178 | self.yrv = yrmean |
|---|
| 3179 | self.yrt = yrt |
|---|
| 3180 | else: |
|---|
| 3181 | print errormsg |
|---|
| 3182 | print ' ' + fname + ': No values passed!' |
|---|
| 3183 | print ' values:', values |
|---|
| 3184 | quit(-1) |
|---|
| 3185 | |
|---|
| 3186 | return |
|---|
| 3187 | |
|---|
| 3188 | class cls_seasonal_means(object): |
|---|
| 3189 | """ Class to compute the seasonal mean of a time series of values |
|---|
| 3190 | tvals=time values (year, month, day, hour, minute, second) format |
|---|
| 3191 | values=time-series of values |
|---|
| 3192 | dateref= reference date [YYYY][MM][DD][HH][MI][SS] format |
|---|
| 3193 | tunits= time units |
|---|
| 3194 | self.dimt = Number of values |
|---|
| 3195 | self.seasv = seasonal mean values |
|---|
| 3196 | self.seast = seasonal time mean values |
|---|
| 3197 | """ |
|---|
| 3198 | def __init__(self, tvals, values, dateref, tunits): |
|---|
| 3199 | |
|---|
| 3200 | import numpy.ma as ma |
|---|
| 3201 | fillVal=1.e20 |
|---|
| 3202 | fname = 'cls_seasonal_means' |
|---|
| 3203 | |
|---|
| 3204 | if tvals is None: |
|---|
| 3205 | self.dimt = None |
|---|
| 3206 | self.seasv = None |
|---|
| 3207 | self.seast = None |
|---|
| 3208 | |
|---|
| 3209 | else: |
|---|
| 3210 | tseasons=times_4seasons(tvals, True) |
|---|
| 3211 | |
|---|
| 3212 | dt=len(values) |
|---|
| 3213 | seasvals=np.ones((dt/12,4), dtype=np.float64)*fillVal |
|---|
| 3214 | seastimes=np.zeros((dt/12,4), dtype=np.float64) |
|---|
| 3215 | dates = realdatetime_CFcompilant(tvals, dateref, tunits) |
|---|
| 3216 | |
|---|
| 3217 | for iseas in range(4): |
|---|
| 3218 | for it in range(dt): |
|---|
| 3219 | if tseasons[it,iseas]: |
|---|
| 3220 | tfirstseas=int(it/11) |
|---|
| 3221 | firstseas=True |
|---|
| 3222 | ## print ' first time-step of season ', iseas, ' is: ',it,' position: ',tfirstseas |
|---|
| 3223 | break |
|---|
| 3224 | for itt in range(it, dt-1,12): |
|---|
| 3225 | seasvals[int(itt/12),iseas]=(values[itt] + values[itt+1] + values[itt+2])/3. |
|---|
| 3226 | seastimes[int(itt/12),iseas]=dates[itt] + (dates[itt+2] - dates[itt])/2. |
|---|
| 3227 | ## print itt, values[itt], values[itt+1], values[itt+2], '--->', seasvals[int(itt/12),iseas] |
|---|
| 3228 | |
|---|
| 3229 | self.dimt = dt/12 |
|---|
| 3230 | self.seasv = ma.masked_equal(seasvals, fillVal) |
|---|
| 3231 | self.seast = ma.masked_equal(seastimes, fillVal) |
|---|
| 3232 | |
|---|
| 3233 | return |
|---|
| 3234 | |
|---|
| 3235 | class cls_seasonal_accums(object): |
|---|
| 3236 | """ Class to compute the seasonal accumulations of a time series of values |
|---|
| 3237 | tvals=time values (year, month, day, hour, minute, second) format |
|---|
| 3238 | values=time-series of values |
|---|
| 3239 | dateref= reference date [YYYY][MM][DD][HH][MI][SS] format |
|---|
| 3240 | tunits= time units |
|---|
| 3241 | self.dimt = Number of values |
|---|
| 3242 | self.seasv = seasonal mean values |
|---|
| 3243 | self.seast = seasonal time mean values |
|---|
| 3244 | """ |
|---|
| 3245 | def __init__(self, tvals, values, dateref, tunits): |
|---|
| 3246 | import numpy.ma as ma |
|---|
| 3247 | fillVal=1.e20 |
|---|
| 3248 | |
|---|
| 3249 | if tvals is None: |
|---|
| 3250 | self.dimt = None |
|---|
| 3251 | self.seasv = None |
|---|
| 3252 | self.seast = None |
|---|
| 3253 | |
|---|
| 3254 | else: |
|---|
| 3255 | tseasons=times_4seasons(tvals, True) |
|---|
| 3256 | |
|---|
| 3257 | dt=len(values) |
|---|
| 3258 | seasvals=np.ones((dt/12,4), dtype=np.float64)*fillVal |
|---|
| 3259 | seastimes=np.zeros((dt/12,4), dtype=np.float64) |
|---|
| 3260 | dates = realdatetime_CFcompilant(tvals, dateref, tunits) |
|---|
| 3261 | |
|---|
| 3262 | for iseas in range(4): |
|---|
| 3263 | for it in range(dt): |
|---|
| 3264 | if tseasons[it,iseas]: |
|---|
| 3265 | tfirstseas=int(it/11) |
|---|
| 3266 | firstseas=True |
|---|
| 3267 | # print ' first time-step of season ', iseas, ' is: ',it,' position: ',tfirstseas |
|---|
| 3268 | break |
|---|
| 3269 | for itt in range(it, dt-1,12): |
|---|
| 3270 | seasvals[int(itt/12),iseas]=values[itt] + values[itt+1] + values[itt+2] |
|---|
| 3271 | seastimes[int(itt/12),iseas]=dates[itt] + (dates[itt+2] - dates[itt])/2. |
|---|
| 3272 | |
|---|
| 3273 | self.dimt = dt/12 |
|---|
| 3274 | self.seasv = ma.masked_equal(seasvals, fillVal) |
|---|
| 3275 | self.seast = ma.masked_equal(seastimes, fillVal) |
|---|
| 3276 | |
|---|
| 3277 | return |
|---|
| 3278 | |
|---|
| 3279 | def seasonal_means(tvals, values): |
|---|
| 3280 | """ Function to compute the seasonal mean of a time series of values |
|---|
| 3281 | tvals=time values (year, month, day, hour, minute, second) format |
|---|
| 3282 | values=time-series of values |
|---|
| 3283 | """ |
|---|
| 3284 | fillVal=1.e20 |
|---|
| 3285 | |
|---|
| 3286 | tseasons=times_4seasons(tvals, True) |
|---|
| 3287 | |
|---|
| 3288 | dt=len(values) |
|---|
| 3289 | seasvals=np.ones((dt/4,4), dtype=np.float64) |
|---|
| 3290 | seasvals=seasvals*fillVal |
|---|
| 3291 | |
|---|
| 3292 | for iseas in range(4): |
|---|
| 3293 | for it in range(dt): |
|---|
| 3294 | if tseasons[it,iseas]: |
|---|
| 3295 | tfirstseas=int(it/11) |
|---|
| 3296 | firstseas=True |
|---|
| 3297 | # print ' first time-step of season ', iseas, ' is: ',it,' position: ',tfirstseas |
|---|
| 3298 | break |
|---|
| 3299 | for itt in range(it, dt-1,12): |
|---|
| 3300 | seasvals[int(itt/12),iseas]=(values[itt] + values[itt+1] + values[itt+2])/3. |
|---|
| 3301 | |
|---|
| 3302 | return seasvals |
|---|
| 3303 | |
|---|
| 3304 | def seasonal_accum(tvals, values): |
|---|
| 3305 | """ Function to compute the seasonal accumulation of a time series of values |
|---|
| 3306 | tvals=time values (year, month, day, hour, minute, second) format |
|---|
| 3307 | values=time-series of values |
|---|
| 3308 | """ |
|---|
| 3309 | fillVal=1.e20 |
|---|
| 3310 | |
|---|
| 3311 | tseasons=times_4seasons(tvals, True) |
|---|
| 3312 | |
|---|
| 3313 | dt=len(values) |
|---|
| 3314 | seasvals=np.ones((dt/4,4), dtype=np.float64) |
|---|
| 3315 | seasvals=seasvals*fillVal |
|---|
| 3316 | |
|---|
| 3317 | for iseas in range(4): |
|---|
| 3318 | for it in range(dt): |
|---|
| 3319 | if tseasons[it,iseas]: |
|---|
| 3320 | tfirstseas=int(it/11) |
|---|
| 3321 | firstseas=True |
|---|
| 3322 | # print ' first time-step of season ', iseas, ' is: ',it,' position: ',tfirstseas |
|---|
| 3323 | break |
|---|
| 3324 | for itt in range(it, dt-1,12): |
|---|
| 3325 | seasvals[int(itt/11),iseas]=values[itt] + values[itt+1] + values[itt+2] |
|---|
| 3326 | |
|---|
| 3327 | return seasvals |
|---|
| 3328 | |
|---|
| 3329 | def load_variable_lastdims(var, prevdims, Nlastdims): |
|---|
| 3330 | """ Function to load the last [Nlastdims] dimensions of a variable [var] at the other dimensions at [prevdims] |
|---|
| 3331 | >>> load_variable_lastdims(np.array(range(5*5*5)).reshape(5,5,5), [1,2], 1) |
|---|
| 3332 | [35 36 37 38 39] |
|---|
| 3333 | """ |
|---|
| 3334 | fname='load_variable_lastdims' |
|---|
| 3335 | |
|---|
| 3336 | dims=var.shape |
|---|
| 3337 | Ndims=len(dims) |
|---|
| 3338 | |
|---|
| 3339 | Nprevdims = len(prevdims) |
|---|
| 3340 | if not Nprevdims + Nlastdims == Ndims: |
|---|
| 3341 | print erromsg |
|---|
| 3342 | print ' ' + fname + ': number of dimensions previous (',Nprevdim,') and last (',Nlastdims, ') does not match with variable size: ',Ndims,' !!!!' |
|---|
| 3343 | print errormsg |
|---|
| 3344 | quit(-1) |
|---|
| 3345 | |
|---|
| 3346 | if Nlastdims > Ndims-1: |
|---|
| 3347 | print errormsg |
|---|
| 3348 | print ' ' + fname + ': number of last dimensions ', Nlastdims,' >= than the number of dimensions of the variable ', Ndims,' !!!!' |
|---|
| 3349 | print errormsg |
|---|
| 3350 | quit(-1) |
|---|
| 3351 | |
|---|
| 3352 | if Ndims == 1: |
|---|
| 3353 | loadvar = var[:] |
|---|
| 3354 | elif Ndims == 2: |
|---|
| 3355 | loadvar = var[prevdims[0], :] |
|---|
| 3356 | elif Ndims == 3: |
|---|
| 3357 | if Nlastdims == 1: |
|---|
| 3358 | loadvar = var[prevdims[0], prevdims[1], :] |
|---|
| 3359 | else: |
|---|
| 3360 | loadvar = var[prevdims[0], :, :] |
|---|
| 3361 | elif Ndims == 4: |
|---|
| 3362 | if Nlastdims == 1: |
|---|
| 3363 | loadvar = var[prevdims[0], prevdims[1], prevdims[2], :] |
|---|
| 3364 | elif Nlastdims == 2: |
|---|
| 3365 | loadvar = var[prevdims[0], prevdims[1], :, :] |
|---|
| 3366 | else: |
|---|
| 3367 | loadvar = var[prevdims[0], :, :, :] |
|---|
| 3368 | elif Ndims == 5: |
|---|
| 3369 | if Nlastdims == 1: |
|---|
| 3370 | loadvar = var[prevdims[0], prevdims[1], prevdims[2], prevdims[3], :] |
|---|
| 3371 | elif Nlastdims == 2: |
|---|
| 3372 | loadvar = var[prevdims[0], prevdims[1], prevdims[2], :, :] |
|---|
| 3373 | elif Nlastdims == 3: |
|---|
| 3374 | loadvar = var[prevdims[0], prevdims[1], :, :, :] |
|---|
| 3375 | else: |
|---|
| 3376 | loadvar = var[prevdims[0], :, :, :, :] |
|---|
| 3377 | elif Ndims == 6: |
|---|
| 3378 | if Nlastdims == 1: |
|---|
| 3379 | loadvar = var[prevdims[0], prevdims[1], prevdims[2], prevdims[3], prevdims[4], :] |
|---|
| 3380 | elif Nlastdims == 2: |
|---|
| 3381 | loadvar = var[prevdims[0], prevdims[1], prevdims[2], prevdims[3], :, :] |
|---|
| 3382 | elif Nlastdims == 3: |
|---|
| 3383 | loadvar = var[prevdims[0], prevdims[1], prevdims[2], :, :, :] |
|---|
| 3384 | elif Nlastdims == 4: |
|---|
| 3385 | loadvar = var[prevdims[0], prevdims[1], :, :, :, :] |
|---|
| 3386 | else: |
|---|
| 3387 | loadvar = var[prevdims[0], :, :, :, :] |
|---|
| 3388 | else: |
|---|
| 3389 | print errormsg |
|---|
| 3390 | print ' ' + fname + ' variable size ', Ndims, ' not ready!!!' |
|---|
| 3391 | print errormsg |
|---|
| 3392 | quit(-1) |
|---|
| 3393 | |
|---|
| 3394 | return loadvar |
|---|
| 3395 | |
|---|
| 3396 | def indices_mask(mask): |
|---|
| 3397 | """ Function to retrieve the indices from a 2D mask |
|---|
| 3398 | mask= 2D mask matrix |
|---|
| 3399 | mat=np.array(range(100), dtype=float).reshape((10,10)) |
|---|
| 3400 | >>> mask1= mat >= 45. |
|---|
| 3401 | >>> mask2= mat <= 55. |
|---|
| 3402 | >>> mask = mask1 * mask2 |
|---|
| 3403 | indices_mask(mask) |
|---|
| 3404 | [[4 5] |
|---|
| 3405 | [4 6] |
|---|
| 3406 | [4 7] |
|---|
| 3407 | [4 8] |
|---|
| 3408 | [4 9] |
|---|
| 3409 | [5 0] |
|---|
| 3410 | [5 1] |
|---|
| 3411 | [5 2] |
|---|
| 3412 | [5 3] |
|---|
| 3413 | [5 4] |
|---|
| 3414 | [5 5]] |
|---|
| 3415 | """ |
|---|
| 3416 | dy=mask.shape[0] |
|---|
| 3417 | dx=mask.shape[1] |
|---|
| 3418 | |
|---|
| 3419 | Nptmask = np.sum(mask) |
|---|
| 3420 | maskindices = np.zeros((Nptmask,2), dtype=int) |
|---|
| 3421 | ij=0 |
|---|
| 3422 | for j in range(dy): |
|---|
| 3423 | for i in range(dx): |
|---|
| 3424 | if mask[j,i] == True: |
|---|
| 3425 | maskindices[ij,0] = j |
|---|
| 3426 | maskindices[ij,1] = i |
|---|
| 3427 | ij = ij + 1 |
|---|
| 3428 | |
|---|
| 3429 | return maskindices |
|---|
| 3430 | |
|---|
| 3431 | def typemod(value, typeval): |
|---|
| 3432 | """ Function to give back a value in a given dtype |
|---|
| 3433 | >>> print(typemod(8.2223, 'np.float64')) |
|---|
| 3434 | <type 'numpy.float64'> |
|---|
| 3435 | >>> print(typemod(8.2223, 'tuple')) |
|---|
| 3436 | <type 'tuple'> |
|---|
| 3437 | """ |
|---|
| 3438 | fname='typemod' |
|---|
| 3439 | |
|---|
| 3440 | if typeval == 'int': |
|---|
| 3441 | return int(value) |
|---|
| 3442 | elif typeval == 'long': |
|---|
| 3443 | return long(value) |
|---|
| 3444 | elif typeval == 'float': |
|---|
| 3445 | return float(value) |
|---|
| 3446 | elif typeval == 'complex': |
|---|
| 3447 | return complex(value) |
|---|
| 3448 | elif typeval == 'str': |
|---|
| 3449 | return str(value) |
|---|
| 3450 | elif typeval == 'bool': |
|---|
| 3451 | return bool(value) |
|---|
| 3452 | elif typeval == 'list': |
|---|
| 3453 | newval = [] |
|---|
| 3454 | newval.append(value) |
|---|
| 3455 | return newval |
|---|
| 3456 | elif typeval == 'dic': |
|---|
| 3457 | newval = {} |
|---|
| 3458 | newval[value] = value |
|---|
| 3459 | return newval |
|---|
| 3460 | elif typeval == 'tuple': |
|---|
| 3461 | newv = [] |
|---|
| 3462 | newv.append(value) |
|---|
| 3463 | newval = tuple(newv) |
|---|
| 3464 | return newval |
|---|
| 3465 | elif typeval == 'np.int8': |
|---|
| 3466 | return np.int8(value) |
|---|
| 3467 | elif typeval == 'np.int16': |
|---|
| 3468 | return np.int16(value) |
|---|
| 3469 | elif typeval == 'np.int32': |
|---|
| 3470 | return np.int32(value) |
|---|
| 3471 | elif typeval == 'np.int64': |
|---|
| 3472 | return np.int64(value) |
|---|
| 3473 | elif typeval == 'np.uint8': |
|---|
| 3474 | return np.uint8(value) |
|---|
| 3475 | elif typeval == 'np.uint16': |
|---|
| 3476 | return np.uint16(value) |
|---|
| 3477 | elif typeval == 'np.np.uint32': |
|---|
| 3478 | return np.uint32(value) |
|---|
| 3479 | elif typeval == 'np.uint64': |
|---|
| 3480 | return np.uint64(value) |
|---|
| 3481 | elif typeval == 'np.float': |
|---|
| 3482 | return np.float(value) |
|---|
| 3483 | elif typeval == 'np.float16': |
|---|
| 3484 | return np.float16(value) |
|---|
| 3485 | elif typeval == 'np.float32': |
|---|
| 3486 | return np.float32(value) |
|---|
| 3487 | elif typeval == 'float32': |
|---|
| 3488 | return np.float32(value) |
|---|
| 3489 | elif typeval == 'np.float64': |
|---|
| 3490 | return np.float64(value) |
|---|
| 3491 | elif typeval == 'float64': |
|---|
| 3492 | return np.float64(value) |
|---|
| 3493 | elif typeval == 'np.complex': |
|---|
| 3494 | return np.complex(value) |
|---|
| 3495 | elif typeval == 'np.complex64': |
|---|
| 3496 | return np.complex64(value) |
|---|
| 3497 | elif typeval == 'np.complex128': |
|---|
| 3498 | return np.complex128(value) |
|---|
| 3499 | else: |
|---|
| 3500 | print errormsg |
|---|
| 3501 | print fname + ': data type "' + typeval + '" is not ready !' |
|---|
| 3502 | print errormsg |
|---|
| 3503 | quit(-1) |
|---|
| 3504 | |
|---|
| 3505 | return |
|---|
| 3506 | |
|---|
| 3507 | def diffdate_units(diffdate, units): |
|---|
| 3508 | """ Function to transform a difference of dates to a certain units |
|---|
| 3509 | diffdate = difference of dates (as timedelta) |
|---|
| 3510 | units = units to transform: 'weeks', 'days', 'hours', 'minutes', 'seconds', 'miliseconds', 'nanoseconds' |
|---|
| 3511 | >>> diffdate_units(dt.datetime(1976, 2, 17, 8, 32, 5) - dt.datetime(1949, 12, 1, 0, 0, 0), 'seconds') |
|---|
| 3512 | 827224325.0 |
|---|
| 3513 | """ |
|---|
| 3514 | fname = 'diffdate_units' |
|---|
| 3515 | |
|---|
| 3516 | if units == 'weeks': |
|---|
| 3517 | diffunits = diffdate.days/7. + diffdate.seconds/(3600.*24.*7.) |
|---|
| 3518 | elif units == 'days': |
|---|
| 3519 | diffunits = diffdate.days + diffdate.seconds/(3600.*24.) |
|---|
| 3520 | elif units == 'hours': |
|---|
| 3521 | diffunits = diffdate.days*24. + diffdate.seconds/(3600.) |
|---|
| 3522 | elif units == 'minutes': |
|---|
| 3523 | diffunits = diffdate.days*24.*60. + diffdate.seconds/(60.) |
|---|
| 3524 | elif units == 'seconds': |
|---|
| 3525 | diffunits = diffdate.days*24.*3600. + diffdate.seconds |
|---|
| 3526 | elif units == 'miliseconds': |
|---|
| 3527 | diffunits = diffdate.days*24.*3600.*1000. + diffdate.seconds*1000. |
|---|
| 3528 | elif units == 'nanoseconds': |
|---|
| 3529 | diffunits = diffdate.days*24.*3600.*1000000. + diffdate.seconds*1000000. |
|---|
| 3530 | else: |
|---|
| 3531 | print errormsg |
|---|
| 3532 | print fname + ': time units "' + units + '" not ready!!!' |
|---|
| 3533 | print errormsg |
|---|
| 3534 | quit(-1) |
|---|
| 3535 | |
|---|
| 3536 | return diffunits |
|---|
| 3537 | |
|---|
| 3538 | def days_month(year,month): |
|---|
| 3539 | """ Function to give the number of days of a month of a given year |
|---|
| 3540 | >>> days_month(1976,2) |
|---|
| 3541 | 29 |
|---|
| 3542 | """ |
|---|
| 3543 | import datetime as dt |
|---|
| 3544 | |
|---|
| 3545 | date1=dt.date(year,month,1) |
|---|
| 3546 | date2=dt.date(year,month+1,1) |
|---|
| 3547 | |
|---|
| 3548 | diffdate = date2-date1 |
|---|
| 3549 | return diffdate.days |
|---|
| 3550 | |
|---|
| 3551 | def mid_period(yr1,mon1,day1,hour1,min1,sec1,yr2,mon2,day2,hour2,min2,sec2): |
|---|
| 3552 | """ Function to give the mid poiint of a period |
|---|
| 3553 | >>> mid_period(1976,2,1,0,0,0,1976,3,1,0,0,0) |
|---|
| 3554 | [1976 2 15 12 0 0] |
|---|
| 3555 | """ |
|---|
| 3556 | import datetime as dt |
|---|
| 3557 | |
|---|
| 3558 | date1=dt.datetime(yr1,mon1,day1,hour1,min1,sec1) |
|---|
| 3559 | date2=dt.datetime(yr2,mon2,day2,hour2,min2,sec2) |
|---|
| 3560 | |
|---|
| 3561 | diffdate = date2-date1 |
|---|
| 3562 | diffseconds = diffdate.days*24*3600 + diffdate.seconds |
|---|
| 3563 | diff2 = dt.timedelta(seconds=diffseconds/2) |
|---|
| 3564 | datenew = date1 + diff2 |
|---|
| 3565 | datenewvals = np.array([datenew.year, datenew.month, datenew.day, datenew.hour, \ |
|---|
| 3566 | datenew.minute, datenew.second]) |
|---|
| 3567 | |
|---|
| 3568 | return datenewvals |
|---|
| 3569 | |
|---|
| 3570 | def days_year(year): |
|---|
| 3571 | """ Function to give the number of days of a year |
|---|
| 3572 | >>> days_year(1976) |
|---|
| 3573 | 366 |
|---|
| 3574 | """ |
|---|
| 3575 | import datetime as dt |
|---|
| 3576 | |
|---|
| 3577 | date1=dt.date(year,1,1) |
|---|
| 3578 | date2=dt.date(year+1,1,1) |
|---|
| 3579 | |
|---|
| 3580 | diffdate = date2-date1 |
|---|
| 3581 | return diffdate.days |
|---|
| 3582 | |
|---|
| 3583 | def days_period(yeari,yearf): |
|---|
| 3584 | """ Function to give the number of days for a period |
|---|
| 3585 | >>> days_period(1976,1980) |
|---|
| 3586 | 1827 |
|---|
| 3587 | """ |
|---|
| 3588 | |
|---|
| 3589 | ndays=0 |
|---|
| 3590 | for iyr in range(yearf-yeari+1): |
|---|
| 3591 | ndays=ndays+days_year(yeari+iyr) |
|---|
| 3592 | |
|---|
| 3593 | return ndays |
|---|
| 3594 | |
|---|
| 3595 | class ysubsetstats(object): |
|---|
| 3596 | """ Class to compute multi-year statistics of a given subset of values |
|---|
| 3597 | values= values to use |
|---|
| 3598 | dates=dates of the values in matrix format ([year], [month], [day], [hour], [minute], [second]) |
|---|
| 3599 | sec=section to use for the period='year', 'month', 'day', 'hour', 'minute', 'second' |
|---|
| 3600 | vali=initial value of the period |
|---|
| 3601 | valf=final value of the period |
|---|
| 3602 | Nq= number of quantiles (20 for 5% bins, it is going to produce 21 values) |
|---|
| 3603 | missval= missing value |
|---|
| 3604 | self.Nvalues = Number of non masked values |
|---|
| 3605 | self.min = minimum non masked value |
|---|
| 3606 | self.max = maximum non masked value |
|---|
| 3607 | self.mean = mean non masked value |
|---|
| 3608 | self.mean2 = quandratic mean non masked value |
|---|
| 3609 | self.stdev = standard deviation non masked value |
|---|
| 3610 | self.quantiles = quantiles non masked value |
|---|
| 3611 | """ |
|---|
| 3612 | def __init__(self, values, dates, sec, vali, valf, Nq, missVal): |
|---|
| 3613 | import numpy.ma as ma |
|---|
| 3614 | |
|---|
| 3615 | fname = 'ysubsetstats' |
|---|
| 3616 | |
|---|
| 3617 | if values is None: |
|---|
| 3618 | self.Nvalues = None |
|---|
| 3619 | self.min = None |
|---|
| 3620 | |
|---|
| 3621 | self.max = None |
|---|
| 3622 | self.mean = None |
|---|
| 3623 | self.mean2 = None |
|---|
| 3624 | self.stdev = None |
|---|
| 3625 | self.quantiles = None |
|---|
| 3626 | |
|---|
| 3627 | else: |
|---|
| 3628 | Nvalues = len(values) |
|---|
| 3629 | Ndates = len(dates) |
|---|
| 3630 | |
|---|
| 3631 | if Nvalues != Ndates: |
|---|
| 3632 | print errormsg |
|---|
| 3633 | print fname + ': number of values ', Nvalues,' does not coincide with the number of dates ',Ndates, '!!!!!!' |
|---|
| 3634 | print errormsg |
|---|
| 3635 | quit(-1) |
|---|
| 3636 | |
|---|
| 3637 | initialsub = np.zeros(Nvalues, dtype=bool) |
|---|
| 3638 | endsub = initialsub.copy() |
|---|
| 3639 | |
|---|
| 3640 | if missVal > 0.: |
|---|
| 3641 | valuesmask = ma.masked_greater_equal(values, missVal*0.9) |
|---|
| 3642 | else: |
|---|
| 3643 | valuesmask = ma.masked_less_equal(values, missVal*0.9) |
|---|
| 3644 | |
|---|
| 3645 | if sec == 'year': |
|---|
| 3646 | isec = 0 |
|---|
| 3647 | elif sec == 'month': |
|---|
| 3648 | isec = 1 |
|---|
| 3649 | elif sec == 'day': |
|---|
| 3650 | isec = 2 |
|---|
| 3651 | elif sec == 'hour': |
|---|
| 3652 | isec = 3 |
|---|
| 3653 | elif sec == 'minute': |
|---|
| 3654 | isec = 4 |
|---|
| 3655 | elif sec == 'second': |
|---|
| 3656 | isec = 5 |
|---|
| 3657 | else: |
|---|
| 3658 | print errormsg |
|---|
| 3659 | print fname + ': sction "' + sec + '" not ready!!!!' |
|---|
| 3660 | print errormsg |
|---|
| 3661 | quit(-1) |
|---|
| 3662 | |
|---|
| 3663 | if vali < valf: |
|---|
| 3664 | timesmaskperi = ma.masked_less(dates[:,isec], vali) |
|---|
| 3665 | timesmaskperf = ma.masked_greater(dates[:,isec], valf) |
|---|
| 3666 | else: |
|---|
| 3667 | timesmaskperi = ma.masked_less(dates[:,isec], valf) |
|---|
| 3668 | timesmaskperf = ma.masked_greater(dates[:,isec], vali) |
|---|
| 3669 | |
|---|
| 3670 | finalmask = valuesmask.mask+timesmaskperi.mask+timesmaskperf.mask |
|---|
| 3671 | finalvalues = ma.array(values, mask=finalmask) |
|---|
| 3672 | |
|---|
| 3673 | finalvalues2 = finalvalues*finalvalues |
|---|
| 3674 | |
|---|
| 3675 | self.Nvalues = finalvalues.count() |
|---|
| 3676 | self.min = finalvalues.min() |
|---|
| 3677 | self.max = finalvalues.max() |
|---|
| 3678 | self.mean = finalvalues.mean() |
|---|
| 3679 | self.mean2 = finalvalues2.mean() |
|---|
| 3680 | self.stdev = finalvalues.std() |
|---|
| 3681 | self.quantiles = mask_quantiles(finalvalues, Nq) |
|---|
| 3682 | |
|---|
| 3683 | return |
|---|
| 3684 | |
|---|
| 3685 | def give_fix1dim_values(mat,iddim,dimval): |
|---|
| 3686 | """ Function to return matrix values when one dimension has been removed at a |
|---|
| 3687 | given value |
|---|
| 3688 | mat = matrix |
|---|
| 3689 | iddim = dimesion to remove (starting from 0 tacking the right most) |
|---|
| 3690 | dimval = value of the dimension |
|---|
| 3691 | |
|---|
| 3692 | >>> matrix = np.array( (range(27)), dtype=np.float).reshape(3,3,3) |
|---|
| 3693 | give_fix1dim_values(matrix,2,2) |
|---|
| 3694 | [[[ 0. 1. 2.] |
|---|
| 3695 | [ 3. 4. 5.] |
|---|
| 3696 | [ 6. 7. 8.]] |
|---|
| 3697 | |
|---|
| 3698 | [[ 9. 10. 11.] |
|---|
| 3699 | [ 12. 13. 14.] |
|---|
| 3700 | [ 15. 16. 17.]]] |
|---|
| 3701 | """ |
|---|
| 3702 | fname='give_fix1dim_values' |
|---|
| 3703 | |
|---|
| 3704 | # Not working ma.squeeze |
|---|
| 3705 | ## import numpy.ma as ma |
|---|
| 3706 | ## print ma.squeeze(mat, axis = (idim,)) |
|---|
| 3707 | |
|---|
| 3708 | matshape = mat.shape |
|---|
| 3709 | mattype = mat.dtype |
|---|
| 3710 | matmask = np.ones( tuple(matshape), dtype=bool) |
|---|
| 3711 | |
|---|
| 3712 | zerodims=np.ones( (6), dtype=int) |
|---|
| 3713 | Ndims = len(matshape) |
|---|
| 3714 | if Ndims > 6: |
|---|
| 3715 | print errmsg |
|---|
| 3716 | print ' ' + fname + ' number of dimensions: ',Ndims,' not ready!!!!!' |
|---|
| 3717 | quit(-1) |
|---|
| 3718 | |
|---|
| 3719 | zerodims[5-Ndims+1:6] = matshape |
|---|
| 3720 | |
|---|
| 3721 | newdims = list(matshape) |
|---|
| 3722 | newdims[Ndims-1-iddim] = matshape[iddim]-1 |
|---|
| 3723 | |
|---|
| 3724 | mask = np.ones(tuple(zerodims), dtype=bool) |
|---|
| 3725 | vals = np.zeros(tuple(zerodims), dtype=mattype) |
|---|
| 3726 | vals[0:zerodims[0],0:zerodims[1],0:zerodims[2],0:zerodims[3],0:zerodims[4], |
|---|
| 3727 | 0:zerodims[5]] = mat |
|---|
| 3728 | |
|---|
| 3729 | for i5 in range(zerodims[5]): |
|---|
| 3730 | for i4 in range(zerodims[4]): |
|---|
| 3731 | for i3 in range(zerodims[3]): |
|---|
| 3732 | for i2 in range(zerodims[2]): |
|---|
| 3733 | for i1 in range(zerodims[1]): |
|---|
| 3734 | for i0 in range(zerodims[0]): |
|---|
| 3735 | # print 'iddim:',iddim,'dimval:',dimval,'-->',i0,i1,i2,i3,i4,i5 |
|---|
| 3736 | if iddim == 5 and dimval == i0: |
|---|
| 3737 | mask[i0,i1,i2,i3,i4,i5] = False |
|---|
| 3738 | elif iddim == 4 and dimval == i1: |
|---|
| 3739 | mask[i0,i1,i2,i3,i4,i5] = False |
|---|
| 3740 | elif iddim == 3 and dimval == i2: |
|---|
| 3741 | mask[i0,i1,i2,i3,i4,i5] = False |
|---|
| 3742 | elif iddim == 2 and dimval == i3: |
|---|
| 3743 | mask[i0,i1,i2,i3,i4,i5] = False |
|---|
| 3744 | elif iddim == 1 and dimval == i4: |
|---|
| 3745 | mask[i0,i1,i2,i3,i4,i5] = False |
|---|
| 3746 | elif iddim == 0 and dimval == i5: |
|---|
| 3747 | mask[i0,i1,i2,i3,i4,i5] = False |
|---|
| 3748 | |
|---|
| 3749 | newmat = vals[mask].reshape(tuple(newdims)) |
|---|
| 3750 | # print 'newmat dim:',iddim,'val:',dimval,':',newdims,'______' |
|---|
| 3751 | # print newmat |
|---|
| 3752 | |
|---|
| 3753 | return newmat |
|---|
| 3754 | |
|---|
| 3755 | def portion_fix1dim_values(mat,iddim,dimval): |
|---|
| 3756 | """ Function to return that portion of the matrix values when one dimension has |
|---|
| 3757 | been removed at a given value |
|---|
| 3758 | mat = matrix |
|---|
| 3759 | iddim = dimesion to remove (starting from 0 tacking the right most) |
|---|
| 3760 | dimval = value of the dimension |
|---|
| 3761 | |
|---|
| 3762 | >>> matrix = np.array( (range(27)), dtype=np.float).reshape(3,3,3) |
|---|
| 3763 | portion_fix1dim_values(matrix,2,2) |
|---|
| 3764 | [[ 18. 19. 20.] |
|---|
| 3765 | [ 21. 22. 23.] |
|---|
| 3766 | [ 24. 25. 26.]] |
|---|
| 3767 | """ |
|---|
| 3768 | fname='portion_fix1dim_values' |
|---|
| 3769 | |
|---|
| 3770 | # Not working ma.squeeze |
|---|
| 3771 | ## import numpy.ma as ma |
|---|
| 3772 | ## print ma.squeeze(mat, axis = (idim,)) |
|---|
| 3773 | |
|---|
| 3774 | matshape = mat.shape |
|---|
| 3775 | mattype = mat.dtype |
|---|
| 3776 | matmask = np.zeros( tuple(matshape), dtype=bool) |
|---|
| 3777 | |
|---|
| 3778 | zerodims=np.ones( (6), dtype=int) |
|---|
| 3779 | Ndims = len(matshape) |
|---|
| 3780 | |
|---|
| 3781 | if Ndims > 6: |
|---|
| 3782 | print errmsg |
|---|
| 3783 | print ' ' + fname + ' number of dimensions: ',Ndims,' not ready!!!!!' |
|---|
| 3784 | quit(-1) |
|---|
| 3785 | |
|---|
| 3786 | zerodims[5-Ndims+1:6] = matshape |
|---|
| 3787 | |
|---|
| 3788 | newdims = [] |
|---|
| 3789 | for idim in range(Ndims): |
|---|
| 3790 | if idim != iddim: |
|---|
| 3791 | newdims.append(matshape[idim]) |
|---|
| 3792 | |
|---|
| 3793 | newdims.reverse() |
|---|
| 3794 | |
|---|
| 3795 | mask = np.zeros(tuple(zerodims), dtype=bool) |
|---|
| 3796 | vals = np.zeros(tuple(zerodims), dtype=mattype) |
|---|
| 3797 | vals[0:zerodims[0],0:zerodims[1],0:zerodims[2],0:zerodims[3],0:zerodims[4], |
|---|
| 3798 | 0:zerodims[5]] = mat |
|---|
| 3799 | |
|---|
| 3800 | for i5 in range(zerodims[5]): |
|---|
| 3801 | for i4 in range(zerodims[4]): |
|---|
| 3802 | for i3 in range(zerodims[3]): |
|---|
| 3803 | for i2 in range(zerodims[2]): |
|---|
| 3804 | for i1 in range(zerodims[1]): |
|---|
| 3805 | for i0 in range(zerodims[0]): |
|---|
| 3806 | # print 'iddim:',iddim,'dimval:',dimval,'-->',i0,i1,i2,i3,i4,i5 |
|---|
| 3807 | if iddim == 5 and dimval == i0: |
|---|
| 3808 | mask[i0,i1,i2,i3,i4,i5] = True |
|---|
| 3809 | elif iddim == 4 and dimval == i1: |
|---|
| 3810 | mask[i0,i1,i2,i3,i4,i5] = True |
|---|
| 3811 | elif iddim == 3 and dimval == i2: |
|---|
| 3812 | mask[i0,i1,i2,i3,i4,i5] = True |
|---|
| 3813 | elif iddim == 2 and dimval == i3: |
|---|
| 3814 | mask[i0,i1,i2,i3,i4,i5] = True |
|---|
| 3815 | elif iddim == 1 and dimval == i4: |
|---|
| 3816 | mask[i0,i1,i2,i3,i4,i5] = True |
|---|
| 3817 | elif iddim == 0 and dimval == i5: |
|---|
| 3818 | mask[i0,i1,i2,i3,i4,i5] = True |
|---|
| 3819 | |
|---|
| 3820 | # print 'newmat dim:',iddim,'val:',dimval,':',newdims,'______' |
|---|
| 3821 | newmat = vals[mask].reshape(tuple(newdims)) |
|---|
| 3822 | # print newmat |
|---|
| 3823 | |
|---|
| 3824 | return newmat |
|---|
| 3825 | |
|---|
| 3826 | def retype(val, vtype): |
|---|
| 3827 | """ Function to transform a value to a given type |
|---|
| 3828 | retype(val, vtype) |
|---|
| 3829 | [val]= value |
|---|
| 3830 | [vtype]= type to transform |
|---|
| 3831 | >>> retype(0, type(True)) |
|---|
| 3832 | False |
|---|
| 3833 | """ |
|---|
| 3834 | |
|---|
| 3835 | fname = 'retype' |
|---|
| 3836 | |
|---|
| 3837 | if val == 'h': |
|---|
| 3838 | print fname + '_____________________________________________________________' |
|---|
| 3839 | print retype.__doc__ |
|---|
| 3840 | quit() |
|---|
| 3841 | |
|---|
| 3842 | if vtype == type(int(1)): |
|---|
| 3843 | newval = int(val) |
|---|
| 3844 | elif vtype == type(float(1)): |
|---|
| 3845 | newval = float(val) |
|---|
| 3846 | # elif vtype == type(float32(1)): |
|---|
| 3847 | # newval = float32(val) |
|---|
| 3848 | elif vtype == type(np.int(1)): |
|---|
| 3849 | typeinf = np.iinfo(np.int) |
|---|
| 3850 | if (abs(np.float64(val)) > typeinf.max): |
|---|
| 3851 | print warnmsg |
|---|
| 3852 | print ' ' + fname + ': value to transform ', val,' overpasses type:', \ |
|---|
| 3853 | vtype,' limits!!' |
|---|
| 3854 | print ' Changing value to kind largest allowed value:', typeinf.max |
|---|
| 3855 | newval = abs(np.float64(val))/np.float64(val) * np.int(typeinf.max) |
|---|
| 3856 | else: |
|---|
| 3857 | newval = np.int(val) |
|---|
| 3858 | elif vtype == type(np.int16(1)): |
|---|
| 3859 | typeinf = np.iinfo(np.int16) |
|---|
| 3860 | if (abs(np.float64(val)) > typeinf.max): |
|---|
| 3861 | print warnmsg |
|---|
| 3862 | print ' ' + fname + ': value to transform ', val,' overpasses type:', \ |
|---|
| 3863 | vtype,' limits!!' |
|---|
| 3864 | print ' Changing value to kind largest allowed value:', typeinf.max |
|---|
| 3865 | newval = abs(np.float64(val))/np.float64(val) * np.int16(typeinf.max) |
|---|
| 3866 | else: |
|---|
| 3867 | newval = np.int16(val) |
|---|
| 3868 | elif vtype == type(np.int32(1)): |
|---|
| 3869 | typeinf = np.iinfo(np.int32) |
|---|
| 3870 | if (np.abs(np.float64(val)) > typeinf.max): |
|---|
| 3871 | print warnmsg |
|---|
| 3872 | print ' ' + fname + ': value to transform ', val,' overpasses type:', \ |
|---|
| 3873 | vtype,' limits!!' |
|---|
| 3874 | print ' Changing value to kind largest allowed value:', typeinf.max |
|---|
| 3875 | newval = abs(np.float64(val))/np.float64(val) * np.int32(typeinf.max) |
|---|
| 3876 | else: |
|---|
| 3877 | newval = np.int32(np.float64(val)) |
|---|
| 3878 | elif vtype == type(np.int64(1)): |
|---|
| 3879 | newval = np.int64(val) |
|---|
| 3880 | elif vtype == type(np.float(1)): |
|---|
| 3881 | newval = np.float(val) |
|---|
| 3882 | # elif vtype == type(np.float16(1)): |
|---|
| 3883 | # newval = np.float16(val) |
|---|
| 3884 | elif vtype == type(np.float32(1)): |
|---|
| 3885 | newval = np.float32(val) |
|---|
| 3886 | elif vtype == type(np.float64(1)): |
|---|
| 3887 | newval = np.float64(val) |
|---|
| 3888 | elif vtype == type(True): |
|---|
| 3889 | if val == 0: |
|---|
| 3890 | newval = False |
|---|
| 3891 | else: |
|---|
| 3892 | newval = True |
|---|
| 3893 | elif vtype == '|S1': |
|---|
| 3894 | newval = str(val) |
|---|
| 3895 | else: |
|---|
| 3896 | print errormsg |
|---|
| 3897 | print ' ' + fname + ': variable type "', vtype, '" not ready!!!' |
|---|
| 3898 | quit(-1) |
|---|
| 3899 | |
|---|
| 3900 | return newval |
|---|
| 3901 | |
|---|
| 3902 | class stats_space2D(object): |
|---|
| 3903 | """spatial statistics for a 2D file: |
|---|
| 3904 | vals= variable ro check (assuming var[t,dy,dx]) |
|---|
| 3905 | self.minv[t]: spatial minimum at each time-step |
|---|
| 3906 | self.maxv[t]: spatial maximum at each time-step |
|---|
| 3907 | self.meanv[t]: spatial mean at each time-step |
|---|
| 3908 | self.mean2v[t]: spatial quadratic mean at each time-step |
|---|
| 3909 | self.stdv[t]: spatial standard deviation at each time-step |
|---|
| 3910 | self.anomv[t]: spatial anomaly from the whole mean at each time-step |
|---|
| 3911 | """ |
|---|
| 3912 | |
|---|
| 3913 | def __init__(self, vals): |
|---|
| 3914 | from scipy import stats as sts |
|---|
| 3915 | fname = 'stats_space2D' |
|---|
| 3916 | |
|---|
| 3917 | if vals1 == 'h': |
|---|
| 3918 | print fname + '_____________________________________________________________' |
|---|
| 3919 | print stats_space2Dvars.__doc__ |
|---|
| 3920 | quit() |
|---|
| 3921 | |
|---|
| 3922 | if vals1 is None: |
|---|
| 3923 | self.minv = None |
|---|
| 3924 | self.maxv = None |
|---|
| 3925 | self.meanv = None |
|---|
| 3926 | self.mean2v = None |
|---|
| 3927 | self.stdv = None |
|---|
| 3928 | else: |
|---|
| 3929 | dimt = vals.shape[0] |
|---|
| 3930 | stats=np.zeros((dimt,5), dtype=np.float) |
|---|
| 3931 | absmean = np.mean(vals) |
|---|
| 3932 | |
|---|
| 3933 | for it in range(dimt): |
|---|
| 3934 | stats[it,0]=np.min(vals[it,:,:]) |
|---|
| 3935 | stats[it,1]=np.max(vals[it,:,:]) |
|---|
| 3936 | stats[it,2]=np.mean(vals[it,:,:]) |
|---|
| 3937 | stats[it,3]=np.mean(vals[it,:,:]*vals[it,:,:]) |
|---|
| 3938 | stats[it,4]=absmean - stats[it,2] |
|---|
| 3939 | |
|---|
| 3940 | stats = np.where(stats > 0.1*fillValue, fillValue, stats) |
|---|
| 3941 | stats = np.where(stats is nan, fillValue, stats) |
|---|
| 3942 | stats = np.where(stats is np.inf, fillValue, stats) |
|---|
| 3943 | stats = np.where(stats is None, fillValue, stats) |
|---|
| 3944 | |
|---|
| 3945 | self.minv=stats[:,0] |
|---|
| 3946 | self.maxv=stats[:,1] |
|---|
| 3947 | self.meanv=stats[:,2] |
|---|
| 3948 | self.mean2v=stats[:,3] |
|---|
| 3949 | self.stdv=np.sqrt(stats[:,3]-stats[:,2]*stats[:,2]) |
|---|
| 3950 | self.anomv=stats[:,4] |
|---|
| 3951 | |
|---|
| 3952 | return |
|---|
| 3953 | |
|---|
| 3954 | class stats_space2Dvars(object): |
|---|
| 3955 | """spatial statistics beween 2 2D files: |
|---|
| 3956 | valsA = variable 1 (assuming var[t,dy,dx]) |
|---|
| 3957 | valsB = variable 2 (assuming var[t,dy,dx]) |
|---|
| 3958 | self.min[t,var], self.max[t,var], self.mean[t,var], self.mean2[t,var], |
|---|
| 3959 | self.std[t,var] |
|---|
| 3960 | [var] = var1+var2[v1Av2], var1-var2[v1Sv2], var1/var2[v1Dv2], var1*var2[v1Pv2] |
|---|
| 3961 | |
|---|
| 3962 | self.mae=mean[t,abs(var1-var2)] |
|---|
| 3963 | self.correlation=correlation[var1,var2] (and p-value) |
|---|
| 3964 | self.bias=[t,mean(var1)-mean(var2)] |
|---|
| 3965 | self.meancorrelation=correlation[mean(var1),mean(var2)] (and p-value) |
|---|
| 3966 | """ |
|---|
| 3967 | |
|---|
| 3968 | def __init__(self, vals1, vals2): |
|---|
| 3969 | from scipy import stats as sts |
|---|
| 3970 | fname = 'stats_space2Dvars' |
|---|
| 3971 | |
|---|
| 3972 | if vals1 == 'h': |
|---|
| 3973 | print fname + '_____________________________________________________________' |
|---|
| 3974 | print stats_space2Dvars.__doc__ |
|---|
| 3975 | quit() |
|---|
| 3976 | |
|---|
| 3977 | if vals1 is None: |
|---|
| 3978 | self.minv1Av2 = None |
|---|
| 3979 | self.maxv1Av2 = None |
|---|
| 3980 | self.meanv1Av2 = None |
|---|
| 3981 | self.mean2v1Av2 = None |
|---|
| 3982 | self.stdv1Av2 = None |
|---|
| 3983 | self.minv1Sv2 = None |
|---|
| 3984 | self.maxv1Sv2 = None |
|---|
| 3985 | self.meanv1Sv2 = None |
|---|
| 3986 | self.mean2v1Sv2 = None |
|---|
| 3987 | self.stdv1Sv2 = None |
|---|
| 3988 | self.minv1Dv2 = None |
|---|
| 3989 | self.maxv1Dv2 = None |
|---|
| 3990 | self.meanv1Dv2 = None |
|---|
| 3991 | self.mean2v1Dv2 = None |
|---|
| 3992 | self.stdv1Dv2 = None |
|---|
| 3993 | self.minv1Pv2 = None |
|---|
| 3994 | self.maxv1Pv2 = None |
|---|
| 3995 | self.meanv1Pv2 = None |
|---|
| 3996 | self.mean2v1Pv2 = None |
|---|
| 3997 | self.stdv1Pv2 = None |
|---|
| 3998 | self.mae = None |
|---|
| 3999 | self.corr = None |
|---|
| 4000 | else: |
|---|
| 4001 | dimt = vals1.shape[0] |
|---|
| 4002 | stats=np.zeros((dimt,26), dtype=np.float) |
|---|
| 4003 | meanvals1 = np.zeros((dimt), dtype=np.float) |
|---|
| 4004 | meanvals2 = np.zeros((dimt), dtype=np.float) |
|---|
| 4005 | |
|---|
| 4006 | for it in range(dimt): |
|---|
| 4007 | v1 = vals1[it,:,:].flatten() |
|---|
| 4008 | v2 = vals2[it,:,:].flatten() |
|---|
| 4009 | |
|---|
| 4010 | # add |
|---|
| 4011 | vs = v1 + v2 |
|---|
| 4012 | stats[it,0] = np.min(vs) |
|---|
| 4013 | stats[it,1] = np.max(vs) |
|---|
| 4014 | stats[it,2] = np.mean(vs) |
|---|
| 4015 | stats[it,3] = np.mean(vs*vs) |
|---|
| 4016 | stats[it,4] = np.sqrt(stats[it,3] - stats[it,2]*stats[it,2]) |
|---|
| 4017 | # sub |
|---|
| 4018 | stats[it,20] = np.mean(np.abs(v1-v2)) |
|---|
| 4019 | vs = v1 - v2 |
|---|
| 4020 | stats[it,5] = np.min(vs) |
|---|
| 4021 | stats[it,6] = np.max(vs) |
|---|
| 4022 | stats[it,7] = np.mean(vs) |
|---|
| 4023 | stats[it,8] = np.mean(vs*vs) |
|---|
| 4024 | stats[it,9] = np.sqrt(stats[it,8] - stats[it,7]*stats[it,7]) |
|---|
| 4025 | |
|---|
| 4026 | # mul |
|---|
| 4027 | vs = v1 * v2 |
|---|
| 4028 | stats[it,10] = np.min(vs) |
|---|
| 4029 | stats[it,11] = np.max(vs) |
|---|
| 4030 | stats[it,12] = np.mean(vs) |
|---|
| 4031 | stats[it,13] = np.mean(vs*vs) |
|---|
| 4032 | stats[it,14] = np.sqrt(stats[it,13] - stats[it,12]*stats[it,12]) |
|---|
| 4033 | # div |
|---|
| 4034 | vs = v1 / v2 |
|---|
| 4035 | stats[it,15] = np.min(vs) |
|---|
| 4036 | stats[it,16] = np.max(vs) |
|---|
| 4037 | stats[it,17] = np.mean(vs) |
|---|
| 4038 | stats[it,18] = np.mean(vs*vs) |
|---|
| 4039 | stats[it,19] = np.sqrt(stats[it,18] - stats[it,17]*stats[it,17]) |
|---|
| 4040 | # corr |
|---|
| 4041 | stats[it,21], stats[it,22] = mask_pearsonr(v1, v2) |
|---|
| 4042 | |
|---|
| 4043 | # Mean values |
|---|
| 4044 | meanvals1[it] = np.mean(v1) |
|---|
| 4045 | meanvals2[it] = np.mean(v2) |
|---|
| 4046 | stats[it,23] = meanvals1[it] - meanvals2[it] |
|---|
| 4047 | |
|---|
| 4048 | stats = np.where(stats > 0.1*fillValue, fillValue, stats) |
|---|
| 4049 | stats = np.where(stats is np.nan, fillValue, stats) |
|---|
| 4050 | stats = np.where(stats is np.inf, fillValue, stats) |
|---|
| 4051 | stats = np.where(stats is None, fillValue, stats) |
|---|
| 4052 | |
|---|
| 4053 | self.minv1Av2 = stats[:,0] |
|---|
| 4054 | self.maxv1Av2 = stats[:,1] |
|---|
| 4055 | self.meanv1Av2 = stats[:,2] |
|---|
| 4056 | self.meanv21Av2 = stats[:,3] |
|---|
| 4057 | self.stdv1Av2 = stats[:,4] |
|---|
| 4058 | |
|---|
| 4059 | self.minv1Sv2 = stats[:,5] |
|---|
| 4060 | self.maxv1Sv2 = stats[:,6] |
|---|
| 4061 | self.meanv1Sv2 = stats[:,7] |
|---|
| 4062 | self.meanv21Sv2 = stats[:,8] |
|---|
| 4063 | self.stdv1Sv2 = stats[:,9] |
|---|
| 4064 | |
|---|
| 4065 | self.minv1Pv2 = stats[:,10] |
|---|
| 4066 | self.maxv1Pv2 = stats[:,11] |
|---|
| 4067 | self.meanv1Pv2 = stats[:,12] |
|---|
| 4068 | self.meanv21Pv2 = stats[:,13] |
|---|
| 4069 | self.stdv1Pv2 = stats[:,14] |
|---|
| 4070 | |
|---|
| 4071 | self.minv1Dv2 = stats[:,15] |
|---|
| 4072 | self.maxv1Dv2 = stats[:,16] |
|---|
| 4073 | self.meanv1Dv2 = stats[:,17] |
|---|
| 4074 | self.meanv21Dv2 = stats[:,18] |
|---|
| 4075 | self.stdv1Dv2 = stats[:,19] |
|---|
| 4076 | |
|---|
| 4077 | self.mae = stats[:,20] |
|---|
| 4078 | self.corr = stats[:,21] |
|---|
| 4079 | self.p_value = stats[:,22] |
|---|
| 4080 | |
|---|
| 4081 | self.bias = stats[:,23] |
|---|
| 4082 | self.meancorr, self.meanp_value = sts.pearsonr(meanvals1, meanvals2) |
|---|
| 4083 | |
|---|
| 4084 | return |
|---|
| 4085 | |
|---|
| 4086 | class stats_time2D(object): |
|---|
| 4087 | """temporal statistics for a 2D file: |
|---|
| 4088 | vals= variable ro check (assuming var[t,dy,dx]) |
|---|
| 4089 | self.minv[t]: temporal minimum at each grid point |
|---|
| 4090 | self.maxv[t]: temporal maximum at each grid point |
|---|
| 4091 | self.meanv[t]: temporal mean at each grid point |
|---|
| 4092 | self.mean2v[t]: temporal quadratic mean at each grid point |
|---|
| 4093 | self.stdv[t]: temporal standard deviation at each grid point |
|---|
| 4094 | self.anomv[t]: temporal anomaly from the whole mean at each grid point |
|---|
| 4095 | """ |
|---|
| 4096 | |
|---|
| 4097 | def __init__(self, vals): |
|---|
| 4098 | from scipy import stats as sts |
|---|
| 4099 | fname = 'stats_time2D' |
|---|
| 4100 | |
|---|
| 4101 | if vals1 == 'h': |
|---|
| 4102 | print fname + '_____________________________________________________________' |
|---|
| 4103 | print stats_time2Dvars.__doc__ |
|---|
| 4104 | quit() |
|---|
| 4105 | |
|---|
| 4106 | if vals1 is None: |
|---|
| 4107 | self.minv = None |
|---|
| 4108 | self.maxv = None |
|---|
| 4109 | self.meanv = None |
|---|
| 4110 | self.mean2v = None |
|---|
| 4111 | self.stdv = None |
|---|
| 4112 | else: |
|---|
| 4113 | dimx = vals.shape[2] |
|---|
| 4114 | dimy = vals.shape[1] |
|---|
| 4115 | stats=np.zeros((dimy,dimx,5), dtype=np.float) |
|---|
| 4116 | absmean = np.mean(vals,axis=0) |
|---|
| 4117 | |
|---|
| 4118 | stats[:,:,0]=np.min(vals, axis=0) |
|---|
| 4119 | stats[:,:,1]=np.max(vals, axis=0) |
|---|
| 4120 | stats[:,:,2]=np.mean(vals, axis=0) |
|---|
| 4121 | stats[:,:,3]=np.mean(vals*vals, axis=0) |
|---|
| 4122 | stats[:,:,4]=absmean - stats[:,:,2] |
|---|
| 4123 | |
|---|
| 4124 | stats = np.where(stats > 0.1*fillValue, fillValue, stats) |
|---|
| 4125 | stats = np.where(stats is np.nan, fillValue, stats) |
|---|
| 4126 | stats = np.where(stats is np.inf, fillValue, stats) |
|---|
| 4127 | stats = np.where(stats is None, fillValue, stats) |
|---|
| 4128 | |
|---|
| 4129 | self.minv=stats[:,:,0] |
|---|
| 4130 | self.maxv=stats[:,:,1] |
|---|
| 4131 | self.meanv=stats[:,:,2] |
|---|
| 4132 | self.mean2v=stats[:,:,3] |
|---|
| 4133 | self.stdv=np.sqrt(stats[:,:,3]-stats[:,:,2]*stats[:,:,2]) |
|---|
| 4134 | self.anomv=stats[:,:,4] |
|---|
| 4135 | |
|---|
| 4136 | return |
|---|
| 4137 | |
|---|
| 4138 | class stats_time2Dvars(object): |
|---|
| 4139 | """temporal statistics beween 2 2D files: |
|---|
| 4140 | valsA = variable 1 (assuming var[t,dy,dx]) |
|---|
| 4141 | valsB = variable 2 (assuming var[t,dy,dx]) |
|---|
| 4142 | self.min[dy,dx,var], self.max[dy,dx,var], self.mean[dy,dx,var], |
|---|
| 4143 | self.mean2[dy,dx,var], self.std[dy,dx,var] |
|---|
| 4144 | [var] = var1+var2[v1Av2], var1-var2[v1Sv2], var1/var2[v1Dv2], var1*var2[v1Pv2] |
|---|
| 4145 | |
|---|
| 4146 | self.mae=mean[dy,dx,abs(var1-var2)] |
|---|
| 4147 | self.correlation=correlation[var1,var2] (and p-value) |
|---|
| 4148 | self.bias=[dy,dx,mean(var1)-mean(var2)] |
|---|
| 4149 | self.meancorrelation=correlation[mean(var1),mean(var2)] (and p-value) |
|---|
| 4150 | """ |
|---|
| 4151 | |
|---|
| 4152 | def __init__(self, vals1, vals2): |
|---|
| 4153 | from scipy import stats as sts |
|---|
| 4154 | fname = 'stats_time2Dvars' |
|---|
| 4155 | |
|---|
| 4156 | if vals1 == 'h': |
|---|
| 4157 | print fname + '_____________________________________________________________' |
|---|
| 4158 | print stats_time2Dvars.__doc__ |
|---|
| 4159 | quit() |
|---|
| 4160 | |
|---|
| 4161 | if vals1 is None: |
|---|
| 4162 | self.minv1Av2 = None |
|---|
| 4163 | self.maxv1Av2 = None |
|---|
| 4164 | self.meanv1Av2 = None |
|---|
| 4165 | self.mean2v1Av2 = None |
|---|
| 4166 | self.stdv1Av2 = None |
|---|
| 4167 | self.minv1Sv2 = None |
|---|
| 4168 | self.maxv1Sv2 = None |
|---|
| 4169 | self.meanv1Sv2 = None |
|---|
| 4170 | self.mean2v1Sv2 = None |
|---|
| 4171 | self.stdv1Sv2 = None |
|---|
| 4172 | self.minv1Dv2 = None |
|---|
| 4173 | self.maxv1Dv2 = None |
|---|
| 4174 | self.meanv1Dv2 = None |
|---|
| 4175 | self.mean2v1Dv2 = None |
|---|
| 4176 | self.stdv1Dv2 = None |
|---|
| 4177 | self.minv1Pv2 = None |
|---|
| 4178 | self.maxv1Pv2 = None |
|---|
| 4179 | self.meanv1Pv2 = None |
|---|
| 4180 | self.mean2v1Pv2 = None |
|---|
| 4181 | self.stdv1Pv2 = None |
|---|
| 4182 | self.mae = None |
|---|
| 4183 | self.corr = None |
|---|
| 4184 | else: |
|---|
| 4185 | dimx = vals1.shape[1] |
|---|
| 4186 | dimy = vals1.shape[1] |
|---|
| 4187 | stats=np.zeros((dimy,dimx,24), dtype=np.float) |
|---|
| 4188 | meanvals1 = np.zeros((dimy,dimx), dtype=np.float) |
|---|
| 4189 | meanvals2 = np.zeros((dimy,dimx), dtype=np.float) |
|---|
| 4190 | # add |
|---|
| 4191 | vs = vals1 + vals2 |
|---|
| 4192 | stats[:,:,0] = np.min(vs,axis=0) |
|---|
| 4193 | stats[:,:,1] = np.max(vs,axis=0) |
|---|
| 4194 | stats[:,:,2] = np.mean(vs,axis=0) |
|---|
| 4195 | stats[:,:,3] = np.mean(vs*vs,axis=0) |
|---|
| 4196 | stats[:,:,4] = np.sqrt(stats[:,:,3] - stats[:,:,2]*stats[:,:,2]) |
|---|
| 4197 | # sub |
|---|
| 4198 | stats[:,:,20] = np.mean(np.abs(vals1-vals2), axis=0) |
|---|
| 4199 | vs = vals1 - vals2 |
|---|
| 4200 | stats[:,:,5] = np.min(vs,axis=0) |
|---|
| 4201 | stats[:,:,6] = np.max(vs,axis=0) |
|---|
| 4202 | stats[:,:,7] = np.mean(vs,axis=0) |
|---|
| 4203 | stats[:,:,8] = np.mean(vs*vs,axis=0) |
|---|
| 4204 | stats[:,:,9] = np.sqrt(stats[:,:,8] - stats[:,:,7]*stats[:,:,7]) |
|---|
| 4205 | # mul |
|---|
| 4206 | vs = vals1 * vals2 |
|---|
| 4207 | stats[:,:,10] = np.min(vs,axis=0) |
|---|
| 4208 | stats[:,:,11] = np.max(vs,axis=0) |
|---|
| 4209 | stats[:,:,12] = np.mean(vs,axis=0) |
|---|
| 4210 | stats[:,:,13] = np.mean(vs*vs,axis=0) |
|---|
| 4211 | stats[:,:,14] = np.sqrt(stats[:,:,13] - stats[:,:,12]*stats[:,:,12]) |
|---|
| 4212 | # div |
|---|
| 4213 | vs = vals1 / vals2 |
|---|
| 4214 | stats[:,:,15] = np.min(vs,axis=0) |
|---|
| 4215 | stats[:,:,16] = np.max(vs,axis=0) |
|---|
| 4216 | stats[:,:,17] = np.mean(vs,axis=0) |
|---|
| 4217 | stats[:,:,18] = np.mean(vs*vs,axis=0) |
|---|
| 4218 | stats[:,:,19] = np.sqrt(stats[:,:,18] - stats[:,:,17]*stats[:,:,17]) |
|---|
| 4219 | |
|---|
| 4220 | |
|---|
| 4221 | # Mean values |
|---|
| 4222 | meanvals1[:,:] = np.mean(vals1,axis=0) |
|---|
| 4223 | meanvals2[:,:] = np.mean(vals2,axis=0) |
|---|
| 4224 | stats[:,:,23] = meanvals1[:,:] - meanvals2[:,:] |
|---|
| 4225 | |
|---|
| 4226 | # corr |
|---|
| 4227 | self.meancorr, self.meanp_value = sts.pearsonr(meanvals1.flatten(), meanvals2.flatten()) |
|---|
| 4228 | |
|---|
| 4229 | for j in range(dimy): |
|---|
| 4230 | for i in range(dimx): |
|---|
| 4231 | stats[j,i,21], stats[j,i,22] = sts.pearsonr(vals1[:,j,i], vals2[:,j,i]) |
|---|
| 4232 | |
|---|
| 4233 | stats = np.where(stats > 0.1*fillValue, fillValue, stats) |
|---|
| 4234 | stats = np.where(stats is np.nan, fillValue, stats) |
|---|
| 4235 | stats = np.where(stats is np.inf, fillValue, stats) |
|---|
| 4236 | stats = np.where(stats is None, fillValue, stats) |
|---|
| 4237 | |
|---|
| 4238 | self.minv1Av2 = stats[:,:,0] |
|---|
| 4239 | self.maxv1Av2 = stats[:,:,1] |
|---|
| 4240 | self.meanv1Av2 = stats[:,:,2] |
|---|
| 4241 | self.meanv21Av2 = stats[:,:,3] |
|---|
| 4242 | self.stdv1Av2 = stats[:,:,4] |
|---|
| 4243 | |
|---|
| 4244 | self.minv1Sv2 = stats[:,:,5] |
|---|
| 4245 | self.maxv1Sv2 = stats[:,:,6] |
|---|
| 4246 | self.meanv1Sv2 = stats[:,:,7] |
|---|
| 4247 | self.meanv21Sv2 = stats[:,:,8] |
|---|
| 4248 | self.stdv1Sv2 = stats[:,:,9] |
|---|
| 4249 | |
|---|
| 4250 | self.minv1Pv2 = stats[:,:,10] |
|---|
| 4251 | self.maxv1Pv2 = stats[:,:,11] |
|---|
| 4252 | self.meanv1Pv2 = stats[:,:,12] |
|---|
| 4253 | self.meanv21Pv2 = stats[:,:,13] |
|---|
| 4254 | self.stdv1Pv2 = stats[:,:,14] |
|---|
| 4255 | |
|---|
| 4256 | self.minv1Dv2 = stats[:,:,15] |
|---|
| 4257 | self.maxv1Dv2 = stats[:,:,16] |
|---|
| 4258 | self.meanv1Dv2 = stats[:,:,17] |
|---|
| 4259 | self.meanv21Dv2 = stats[:,:,18] |
|---|
| 4260 | self.stdv1Dv2 = stats[:,:,19] |
|---|
| 4261 | |
|---|
| 4262 | self.mae = stats[:,:,20] |
|---|
| 4263 | self.corr = stats[:,:,21] |
|---|
| 4264 | self.p_value = stats[:,:,22] |
|---|
| 4265 | |
|---|
| 4266 | self.bias = stats[:,:,23] |
|---|
| 4267 | |
|---|
| 4268 | return |
|---|
| 4269 | |
|---|
| 4270 | #vals1 = np.arange(27).reshape(3,3,3)*1. |
|---|
| 4271 | #vals2 = np.arange(1,28).reshape(3,3,3)*1. |
|---|
| 4272 | |
|---|
| 4273 | #printing_class(stats_time2Dvars(vals1,vals2)) |
|---|
| 4274 | |
|---|
| 4275 | def file_nlines(filen,char): |
|---|
| 4276 | """ Function to provide the number of lines of a file |
|---|
| 4277 | filen= name of the file |
|---|
| 4278 | char= character as no line |
|---|
| 4279 | >>> file_nlines('trajectory.dat','#') |
|---|
| 4280 | 49 |
|---|
| 4281 | """ |
|---|
| 4282 | fname = 'file_nlines' |
|---|
| 4283 | |
|---|
| 4284 | if not os.path.isfile(filen): |
|---|
| 4285 | print errormsg |
|---|
| 4286 | print ' ' + fname + ' file: "' + filen + '" does not exist !!' |
|---|
| 4287 | quit(-1) |
|---|
| 4288 | |
|---|
| 4289 | fo = open(filen,'r') |
|---|
| 4290 | |
|---|
| 4291 | nlines=0 |
|---|
| 4292 | for line in fo: |
|---|
| 4293 | if line[0:1] != char: nlines = nlines + 1 |
|---|
| 4294 | |
|---|
| 4295 | fo.close() |
|---|
| 4296 | |
|---|
| 4297 | return nlines |
|---|
| 4298 | |
|---|
| 4299 | def datetimeStr_conversion(StringDT,typeSi,typeSo): |
|---|
| 4300 | """ Function to transform a string date to an another date object |
|---|
| 4301 | StringDT= string with the date and time |
|---|
| 4302 | typeSi= type of datetime string input |
|---|
| 4303 | typeSo= type of datetime string output |
|---|
| 4304 | [typeSi/o] |
|---|
| 4305 | 'cfTime': [time],[units]; ]time in CF-convention format [units] = [tunits] since [refdate] |
|---|
| 4306 | 'matYmdHMS': numerical vector with [[YYYY], [MM], [DD], [HH], [MI], [SS]] |
|---|
| 4307 | 'YmdHMS': [YYYY][MM][DD][HH][MI][SS] format |
|---|
| 4308 | 'Y-m-d_H:M:S': [YYYY]-[MM]-[DD]_[HH]:[MI]:[SS] format |
|---|
| 4309 | 'Y-m-d H:M:S': [YYYY]-[MM]-[DD] [HH]:[MI]:[SS] format |
|---|
| 4310 | 'Y/m/d H-M-S': [YYYY]/[MM]/[DD] [HH]-[MI]-[SS] format |
|---|
| 4311 | 'WRFdatetime': [Y], [Y], [Y], [Y], '-', [M], [M], '-', [D], [D], '_', [H], |
|---|
| 4312 | [H], ':', [M], [M], ':', [S], [S] |
|---|
| 4313 | >>> datetimeStr_conversion('1976-02-17_08:32:05','Y-m-d_H:M:S','matYmdHMS') |
|---|
| 4314 | [1976 2 17 8 32 5] |
|---|
| 4315 | >>> datetimeStr_conversion(str(137880)+',minutes since 1979-12-01_00:00:00','cfTime','Y/m/d H-M-S') |
|---|
| 4316 | 1980/03/05 18-00-00 |
|---|
| 4317 | """ |
|---|
| 4318 | import datetime as dt |
|---|
| 4319 | |
|---|
| 4320 | fname = 'datetimeStr_conversion' |
|---|
| 4321 | |
|---|
| 4322 | if StringDT[0:1] == 'h': |
|---|
| 4323 | print fname + '_____________________________________________________________' |
|---|
| 4324 | print datetimeStr_conversion.__doc__ |
|---|
| 4325 | quit() |
|---|
| 4326 | |
|---|
| 4327 | if typeSi == 'cfTime': |
|---|
| 4328 | timeval = np.float(StringDT.split(',')[0]) |
|---|
| 4329 | tunits = StringDT.split(',')[1].split(' ')[0] |
|---|
| 4330 | Srefdate = StringDT.split(',')[1].split(' ')[2] |
|---|
| 4331 | |
|---|
| 4332 | # Does reference date contain a time value [YYYY]-[MM]-[DD] [HH]:[MI]:[SS] |
|---|
| 4333 | ## |
|---|
| 4334 | yrref=Srefdate[0:4] |
|---|
| 4335 | monref=Srefdate[5:7] |
|---|
| 4336 | dayref=Srefdate[8:10] |
|---|
| 4337 | |
|---|
| 4338 | trefT = Srefdate.find(':') |
|---|
| 4339 | if not trefT == -1: |
|---|
| 4340 | # print ' ' + fname + ': refdate with time!' |
|---|
| 4341 | horref=Srefdate[11:13] |
|---|
| 4342 | minref=Srefdate[14:16] |
|---|
| 4343 | secref=Srefdate[17:19] |
|---|
| 4344 | refdate = datetimeStr_datetime( yrref + '-' + monref + '-' + dayref + \ |
|---|
| 4345 | '_' + horref + ':' + minref + ':' + secref) |
|---|
| 4346 | else: |
|---|
| 4347 | refdate = datetimeStr_datetime( yrref + '-' + monref + '-' + dayref + \ |
|---|
| 4348 | + '_00:00:00') |
|---|
| 4349 | |
|---|
| 4350 | if tunits == 'weeks': |
|---|
| 4351 | newdate = refdate + dt.timedelta(weeks=float(timeval)) |
|---|
| 4352 | elif tunits == 'days': |
|---|
| 4353 | newdate = refdate + dt.timedelta(days=float(timeval)) |
|---|
| 4354 | elif tunits == 'hours': |
|---|
| 4355 | newdate = refdate + dt.timedelta(hours=float(timeval)) |
|---|
| 4356 | elif tunits == 'minutes': |
|---|
| 4357 | newdate = refdate + dt.timedelta(minutes=float(timeval)) |
|---|
| 4358 | elif tunits == 'seconds': |
|---|
| 4359 | newdate = refdate + dt.timedelta(seconds=float(timeval)) |
|---|
| 4360 | elif tunits == 'milliseconds': |
|---|
| 4361 | newdate = refdate + dt.timedelta(milliseconds=float(timeval)) |
|---|
| 4362 | else: |
|---|
| 4363 | print errormsg |
|---|
| 4364 | print ' timeref_datetime: time units "' + tunits + '" not ready!!!!' |
|---|
| 4365 | quit(-1) |
|---|
| 4366 | |
|---|
| 4367 | yr = newdate.year |
|---|
| 4368 | mo = newdate.month |
|---|
| 4369 | da = newdate.day |
|---|
| 4370 | ho = newdate.hour |
|---|
| 4371 | mi = newdate.minute |
|---|
| 4372 | se = newdate.second |
|---|
| 4373 | elif typeSi == 'matYmdHMS': |
|---|
| 4374 | yr = StringDT[0] |
|---|
| 4375 | mo = StringDT[1] |
|---|
| 4376 | da = StringDT[2] |
|---|
| 4377 | ho = StringDT[3] |
|---|
| 4378 | mi = StringDT[4] |
|---|
| 4379 | se = StringDT[5] |
|---|
| 4380 | elif typeSi == 'YmdHMS': |
|---|
| 4381 | yr = int(StringDT[0:4]) |
|---|
| 4382 | mo = int(StringDT[4:6]) |
|---|
| 4383 | da = int(StringDT[6:8]) |
|---|
| 4384 | ho = int(StringDT[8:10]) |
|---|
| 4385 | mi = int(StringDT[10:12]) |
|---|
| 4386 | se = int(StringDT[12:14]) |
|---|
| 4387 | elif typeSi == 'Y-m-d_H:M:S': |
|---|
| 4388 | dateDT = StringDT.split('_') |
|---|
| 4389 | dateD = dateDT[0].split('-') |
|---|
| 4390 | timeT = dateDT[1].split(':') |
|---|
| 4391 | yr = int(dateD[0]) |
|---|
| 4392 | mo = int(dateD[1]) |
|---|
| 4393 | da = int(dateD[2]) |
|---|
| 4394 | ho = int(timeT[0]) |
|---|
| 4395 | mi = int(timeT[1]) |
|---|
| 4396 | se = int(timeT[2]) |
|---|
| 4397 | elif typeSi == 'Y-m-d H:M:S': |
|---|
| 4398 | dateDT = StringDT.split(' ') |
|---|
| 4399 | dateD = dateDT[0].split('-') |
|---|
| 4400 | timeT = dateDT[1].split(':') |
|---|
| 4401 | yr = int(dateD[0]) |
|---|
| 4402 | mo = int(dateD[1]) |
|---|
| 4403 | da = int(dateD[2]) |
|---|
| 4404 | ho = int(timeT[0]) |
|---|
| 4405 | mi = int(timeT[1]) |
|---|
| 4406 | se = int(timeT[2]) |
|---|
| 4407 | elif typeSi == 'Y/m/d H-M-S': |
|---|
| 4408 | dateDT = StringDT.split(' ') |
|---|
| 4409 | dateD = dateDT[0].split('/') |
|---|
| 4410 | timeT = dateDT[1].split('-') |
|---|
| 4411 | yr = int(dateD[0]) |
|---|
| 4412 | mo = int(dateD[1]) |
|---|
| 4413 | da = int(dateD[2]) |
|---|
| 4414 | ho = int(timeT[0]) |
|---|
| 4415 | mi = int(timeT[1]) |
|---|
| 4416 | se = int(timeT[2]) |
|---|
| 4417 | elif typeSi == 'WRFdatetime': |
|---|
| 4418 | yr = int(StringDT[0])*1000 + int(StringDT[1])*100 + int(StringDT[2])*10 + \ |
|---|
| 4419 | int(StringDT[3]) |
|---|
| 4420 | mo = int(StringDT[5])*10 + int(StringDT[6]) |
|---|
| 4421 | da = int(StringDT[8])*10 + int(StringDT[9]) |
|---|
| 4422 | ho = int(StringDT[11])*10 + int(StringDT[12]) |
|---|
| 4423 | mi = int(StringDT[14])*10 + int(StringDT[15]) |
|---|
| 4424 | se = int(StringDT[17])*10 + int(StringDT[18]) |
|---|
| 4425 | else: |
|---|
| 4426 | print errormsg |
|---|
| 4427 | print ' ' + fname + ': type of String input date "' + typeSi + \ |
|---|
| 4428 | '" not ready !!!!' |
|---|
| 4429 | quit(-1) |
|---|
| 4430 | |
|---|
| 4431 | if typeSo == 'matYmdHMS': |
|---|
| 4432 | dateYmdHMS = np.zeros((6), dtype=int) |
|---|
| 4433 | dateYmdHMS[0] = yr |
|---|
| 4434 | dateYmdHMS[1] = mo |
|---|
| 4435 | dateYmdHMS[2] = da |
|---|
| 4436 | dateYmdHMS[3] = ho |
|---|
| 4437 | dateYmdHMS[4] = mi |
|---|
| 4438 | dateYmdHMS[5] = se |
|---|
| 4439 | elif typeSo == 'YmdHMS': |
|---|
| 4440 | dateYmdHMS = str(yr).zfill(4) + str(mo).zfill(2) + str(da).zfill(2) + \ |
|---|
| 4441 | str(ho).zfill(2) + str(mi).zfill(2) + str(se).zfill(2) |
|---|
| 4442 | elif typeSo == 'Y-m-d_H:M:S': |
|---|
| 4443 | dateYmdHMS = str(yr).zfill(4) + '-' + str(mo).zfill(2) + '-' + \ |
|---|
| 4444 | str(da).zfill(2) + '_' + str(ho).zfill(2) + ':' + str(mi).zfill(2) + ':' + \ |
|---|
| 4445 | str(se).zfill(2) |
|---|
| 4446 | elif typeSo == 'Y-m-d H:M:S': |
|---|
| 4447 | dateYmdHMS = str(yr).zfill(4) + '-' + str(mo).zfill(2) + '-' + \ |
|---|
| 4448 | str(da).zfill(2) + ' ' + str(ho).zfill(2) + ':' + str(mi).zfill(2) + ':' + \ |
|---|
| 4449 | str(se).zfill(2) |
|---|
| 4450 | elif typeSo == 'Y/m/d H-M-S': |
|---|
| 4451 | dateYmdHMS = str(yr).zfill(4) + '/' + str(mo).zfill(2) + '/' + \ |
|---|
| 4452 | str(da).zfill(2) + ' ' + str(ho).zfill(2) + '-' + str(mi).zfill(2) + '-' + \ |
|---|
| 4453 | str(se).zfill(2) |
|---|
| 4454 | elif typeSo == 'WRFdatetime': |
|---|
| 4455 | dateYmdHMS = [] |
|---|
| 4456 | yM = yr/1000 |
|---|
| 4457 | yC = (yr-yM*1000)/100 |
|---|
| 4458 | yD = (yr-yM*1000-yC*100)/10 |
|---|
| 4459 | yU = yr-yM*1000-yC*100-yD*10 |
|---|
| 4460 | |
|---|
| 4461 | mD = mo/10 |
|---|
| 4462 | mU = mo-mD*10 |
|---|
| 4463 | |
|---|
| 4464 | dD = da/10 |
|---|
| 4465 | dU = da-dD*10 |
|---|
| 4466 | |
|---|
| 4467 | hD = ho/10 |
|---|
| 4468 | hU = ho-hD*10 |
|---|
| 4469 | |
|---|
| 4470 | miD = mi/10 |
|---|
| 4471 | miU = mi-miD*10 |
|---|
| 4472 | |
|---|
| 4473 | sD = se/10 |
|---|
| 4474 | sU = se-sD*10 |
|---|
| 4475 | |
|---|
| 4476 | dateYmdHMS.append(str(yM)) |
|---|
| 4477 | dateYmdHMS.append(str(yC)) |
|---|
| 4478 | dateYmdHMS.append(str(yD)) |
|---|
| 4479 | dateYmdHMS.append(str(yU)) |
|---|
| 4480 | dateYmdHMS.append('-') |
|---|
| 4481 | dateYmdHMS.append(str(mD)) |
|---|
| 4482 | dateYmdHMS.append(str(mU)) |
|---|
| 4483 | dateYmdHMS.append('-') |
|---|
| 4484 | dateYmdHMS.append(str(dD)) |
|---|
| 4485 | dateYmdHMS.append(str(dU)) |
|---|
| 4486 | dateYmdHMS.append('_') |
|---|
| 4487 | dateYmdHMS.append(str(hD)) |
|---|
| 4488 | dateYmdHMS.append(str(hU)) |
|---|
| 4489 | dateYmdHMS.append(':') |
|---|
| 4490 | dateYmdHMS.append(str(miD)) |
|---|
| 4491 | dateYmdHMS.append(str(miU)) |
|---|
| 4492 | dateYmdHMS.append(':') |
|---|
| 4493 | dateYmdHMS.append(str(sD)) |
|---|
| 4494 | dateYmdHMS.append(str(sU)) |
|---|
| 4495 | else: |
|---|
| 4496 | print errormsg |
|---|
| 4497 | print ' ' + fname + ': type of output date "' + typeSo + '" not ready !!!!' |
|---|
| 4498 | quit(-1) |
|---|
| 4499 | |
|---|
| 4500 | return dateYmdHMS |
|---|
| 4501 | |
|---|
| 4502 | def table_tex(tablevals, colnames, rownames, of): |
|---|
| 4503 | """ Function to write into a LaTeX tabukar from a table of values |
|---|
| 4504 | tablevals = (ncol nrow) of values |
|---|
| 4505 | colnames = list with ncol labels for the columns |
|---|
| 4506 | rownames = list with nrow labels for the rows |
|---|
| 4507 | of= object ASCII file to write the table |
|---|
| 4508 | """ |
|---|
| 4509 | errormsg = 'ERROR -- error -- ERROR -- error' |
|---|
| 4510 | |
|---|
| 4511 | fname = 'table_tex' |
|---|
| 4512 | |
|---|
| 4513 | Ncol = tablevals.shape[0] |
|---|
| 4514 | Nrow = tablevals.shape[1] |
|---|
| 4515 | |
|---|
| 4516 | if Ncol + 1 != len(colnames): |
|---|
| 4517 | print errormsg |
|---|
| 4518 | print ' ' + fname + ': wrong number of column names!!' |
|---|
| 4519 | print ' data has:', Ncol, 'and you provided:', len(colnames) |
|---|
| 4520 | print ' remember to provide one more for the label of the rows!' |
|---|
| 4521 | print ' you provide:',colnames |
|---|
| 4522 | quit(-1) |
|---|
| 4523 | |
|---|
| 4524 | if Nrow != len(rownames): |
|---|
| 4525 | print errormsg |
|---|
| 4526 | print ' ' + fname + ': wrong number of row names!!' |
|---|
| 4527 | print ' data has:', Nrow, 'and you provided:', len(rownames) |
|---|
| 4528 | print ' you provide:',rownames |
|---|
| 4529 | quit(-1) |
|---|
| 4530 | |
|---|
| 4531 | colcs = '' |
|---|
| 4532 | colns = '' |
|---|
| 4533 | for icol in colnames: |
|---|
| 4534 | colcs = colcs + 'c' |
|---|
| 4535 | if icol == colnames[0]: |
|---|
| 4536 | colns = ' {\\bfseries{' + icol + '}}' |
|---|
| 4537 | else: |
|---|
| 4538 | colns = colns + ' & {\\bfseries{' + icol + '}}' |
|---|
| 4539 | |
|---|
| 4540 | of.write('\n') |
|---|
| 4541 | of.write("%Automatically written file from function '" + fname + "'\n") |
|---|
| 4542 | of.write('\\begin{tabular}{l'+colcs+'}\n') |
|---|
| 4543 | of.write(colns + ' \\\\ \\hline\n') |
|---|
| 4544 | |
|---|
| 4545 | ir = 0 |
|---|
| 4546 | for irow in rownames: |
|---|
| 4547 | rowns = '{\\bfseries{' + irow + '}}' |
|---|
| 4548 | for ic in range(Ncol): |
|---|
| 4549 | rowns = rowns + ' & ' + str(tablevals[ic,ir]) |
|---|
| 4550 | rowns = rowns + ' \\\\' |
|---|
| 4551 | |
|---|
| 4552 | of.write(rowns + '\n') |
|---|
| 4553 | ir = ir + 1 |
|---|
| 4554 | |
|---|
| 4555 | of.write('\\end{tabular}\n') |
|---|
| 4556 | |
|---|
| 4557 | return |
|---|
| 4558 | |
|---|
| 4559 | def radius_dist(dx,dy,ptx,pty): |
|---|
| 4560 | """ Function to generate a matrix with the distance at a given point |
|---|
| 4561 | radius_dist(dx,dy,ptx,pty) |
|---|
| 4562 | [dx/y]: dimension of the matrix |
|---|
| 4563 | [ptx/y]: grid point coordinates of the point |
|---|
| 4564 | >>> radius_dist(3,5,2,2) |
|---|
| 4565 | [[ 1.41421356 1. 1.41421356 2.23606798 3.16227766] |
|---|
| 4566 | [ 1. 0. 1. 2. 3. ] |
|---|
| 4567 | [ 1.41421356 1. 1.41421356 2.23606798 3.16227766]] |
|---|
| 4568 | """ |
|---|
| 4569 | |
|---|
| 4570 | fname = 'radius_dist' |
|---|
| 4571 | |
|---|
| 4572 | if ptx < 0 or ptx > dx-1 or pty < 0 or pty > dy-1: |
|---|
| 4573 | print errormsg |
|---|
| 4574 | print ' ' + fname + ': wrong point coordinates:',dx,',',dy,'for matrix;',dx \ |
|---|
| 4575 | ,'x',dy |
|---|
| 4576 | quit(-1) |
|---|
| 4577 | |
|---|
| 4578 | xdist = np.zeros((dx,dy), dtype=np.float) |
|---|
| 4579 | ydist = np.zeros((dx,dy), dtype=np.float) |
|---|
| 4580 | dist = np.zeros((dx,dy), dtype=np.float) |
|---|
| 4581 | |
|---|
| 4582 | for ix in range(dx): |
|---|
| 4583 | xdist[ix,:] = np.float(ix-ptx) |
|---|
| 4584 | for iy in range(dy): |
|---|
| 4585 | ydist[:,iy] = np.float(iy-pty) |
|---|
| 4586 | |
|---|
| 4587 | dist = np.sqrt(xdist*xdist + ydist*ydist) |
|---|
| 4588 | |
|---|
| 4589 | return dist |
|---|
| 4590 | |
|---|
| 4591 | def lonlat2D(lon,lat): |
|---|
| 4592 | """ Function to return lon, lat 2D matrices from any lon,lat matrix |
|---|
| 4593 | lon= matrix with longitude values |
|---|
| 4594 | lat= matrix with latitude values |
|---|
| 4595 | """ |
|---|
| 4596 | fname = 'lonlat2D' |
|---|
| 4597 | |
|---|
| 4598 | if len(lon.shape) != len(lat.shape): |
|---|
| 4599 | print errormsg |
|---|
| 4600 | print ' ' + fname + ': longitude values with shape:', lon.shape, \ |
|---|
| 4601 | 'is different that latitude values with shape:', lat.shape, '(dif. size) !!' |
|---|
| 4602 | quit(-1) |
|---|
| 4603 | |
|---|
| 4604 | if len(lon.shape) == 3: |
|---|
| 4605 | lonvv = lon[0,:,:] |
|---|
| 4606 | latvv = lat[0,:,:] |
|---|
| 4607 | elif len(lon.shape) == 2: |
|---|
| 4608 | lonvv = lon[:] |
|---|
| 4609 | latvv = lat[:] |
|---|
| 4610 | elif len(lon.shape) == 1: |
|---|
| 4611 | lonlatv = np.meshgrid(lon[:],lat[:]) |
|---|
| 4612 | lonvv = lonlatv[0] |
|---|
| 4613 | latvv = lonlatv[1] |
|---|
| 4614 | |
|---|
| 4615 | return lonvv, latvv |
|---|
| 4616 | |
|---|
| 4617 | |
|---|
| 4618 | def interpolate_locs(locs,coords,kinterp): |
|---|
| 4619 | """ Function to provide interpolate locations on a given axis |
|---|
| 4620 | interpolate_locs(locs,axis,kinterp) |
|---|
| 4621 | locs= locations to interpolate |
|---|
| 4622 | coords= axis values with the reference of coordinates |
|---|
| 4623 | kinterp: kind of interpolation |
|---|
| 4624 | 'lin': linear |
|---|
| 4625 | >>> coordinates = np.arange((10), dtype=np.float) |
|---|
| 4626 | >>> values = np.array([-1.2, 2.4, 5.6, 7.8, 12.0]) |
|---|
| 4627 | >>> interpolate_locs(values,coordinates,'lin') |
|---|
| 4628 | [ -1.2 2.4 5.6 7.8 13. ] |
|---|
| 4629 | >>> coordinates[0] = 0.5 |
|---|
| 4630 | >>> coordinates[2] = 2.5 |
|---|
| 4631 | >>> interpolate_locs(values,coordinates,'lin') |
|---|
| 4632 | [ -3.4 1.93333333 5.6 7.8 13. ] |
|---|
| 4633 | """ |
|---|
| 4634 | |
|---|
| 4635 | fname = 'interpolate_locs' |
|---|
| 4636 | |
|---|
| 4637 | if locs == 'h': |
|---|
| 4638 | print fname + '_____________________________________________________________' |
|---|
| 4639 | print interpolate_locs.__doc__ |
|---|
| 4640 | quit() |
|---|
| 4641 | |
|---|
| 4642 | Nlocs = locs.shape[0] |
|---|
| 4643 | Ncoords = coords.shape[0] |
|---|
| 4644 | |
|---|
| 4645 | dcoords = coords[Ncoords-1] - coords[0] |
|---|
| 4646 | |
|---|
| 4647 | intlocs = np.zeros((Nlocs), dtype=np.float) |
|---|
| 4648 | minc = np.min(coords) |
|---|
| 4649 | maxc = np.max(coords) |
|---|
| 4650 | |
|---|
| 4651 | for iloc in range(Nlocs): |
|---|
| 4652 | for icor in range(Ncoords-1): |
|---|
| 4653 | if locs[iloc] < minc and dcoords > 0.: |
|---|
| 4654 | a = 0. |
|---|
| 4655 | b = 1. / (coords[1] - coords[0]) |
|---|
| 4656 | c = coords[0] |
|---|
| 4657 | elif locs[iloc] > maxc and dcoords > 0.: |
|---|
| 4658 | a = (Ncoords-1)*1. |
|---|
| 4659 | b = 1. / (coords[Ncoords-1] - coords[Ncoords-2]) |
|---|
| 4660 | c = coords[Ncoords-2] |
|---|
| 4661 | elif locs[iloc] < minc and dcoords < 0.: |
|---|
| 4662 | a = (Ncoords-1)*1. |
|---|
| 4663 | b = 1. / (coords[Ncoords-1] - coords[Ncoords-2]) |
|---|
| 4664 | c = coords[Ncoords-2] |
|---|
| 4665 | elif locs[iloc] > maxc and dcoords < 0.: |
|---|
| 4666 | a = 0. |
|---|
| 4667 | b = 1. / (coords[1] - coords[0]) |
|---|
| 4668 | c = coords[0] |
|---|
| 4669 | elif locs[iloc] >= coords[icor] and locs[iloc] < coords[icor+1] and dcoords > 0.: |
|---|
| 4670 | a = icor*1. |
|---|
| 4671 | b = 1. / (coords[icor+1] - coords[icor]) |
|---|
| 4672 | c = coords[icor] |
|---|
| 4673 | print coords[icor], locs[iloc], coords[icor+1], ':', icor, '->', a, b |
|---|
| 4674 | elif locs[iloc] <= coords[icor] and locs[iloc] > coords[icor+1] and dcoords < 0.: |
|---|
| 4675 | a = icor*1. |
|---|
| 4676 | b = 1. / (coords[icor+1] - coords[icor]) |
|---|
| 4677 | c = coords[icor] |
|---|
| 4678 | |
|---|
| 4679 | if kinterp == 'lin': |
|---|
| 4680 | intlocs[iloc] = a + (locs[iloc] - c)*b |
|---|
| 4681 | else: |
|---|
| 4682 | print errormsg |
|---|
| 4683 | print ' ' + fname + ": interpolation kind '" + kinterp + "' not ready !!!!!" |
|---|
| 4684 | quit(-1) |
|---|
| 4685 | |
|---|
| 4686 | return intlocs |
|---|
| 4687 | |
|---|
| 4688 | def vertical_interpolation2D(varb,vart,zorigvals,znewval,kinterp): |
|---|
| 4689 | """ Function to vertically integrate a 3D variable |
|---|
| 4690 | vertical_interpolation2D(varb,vart,zorigvals,znewval) |
|---|
| 4691 | varb= values at the base of the interval of interpolation (2D field) |
|---|
| 4692 | vart= values at the top of the interval of interpolation (2D field) |
|---|
| 4693 | zorigvals= pair of original values (2, 2D field) |
|---|
| 4694 | znewval= new value to interpolate |
|---|
| 4695 | Possible cases: |
|---|
| 4696 | zorigvals[0,:,:] <= znewval < zorigvals[1,:,:] |
|---|
| 4697 | znewval <= zorigvals[0,:,:] < zorigvals[1,:,:] |
|---|
| 4698 | zorigvals[0,:,:] < zorigvals[1,:,:] <= znewval |
|---|
| 4699 | kinterp: kind of interpolation |
|---|
| 4700 | 'lin': linear |
|---|
| 4701 | >>> dx=5 |
|---|
| 4702 | >>> dy=7 |
|---|
| 4703 | >>> vals1 = np.ones((dy,dx), dtype=np.float) |
|---|
| 4704 | >>> vals2 = np.ones((dy,dx), dtype=np.float)*2. |
|---|
| 4705 | |
|---|
| 4706 | >>> zbase = np.zeros((2,dy,dx), dtype=np.float) |
|---|
| 4707 | >>> zbase[0,:,:] = 0.5 |
|---|
| 4708 | >>> zbase[1,:,:] = 1. |
|---|
| 4709 | |
|---|
| 4710 | >>> vertical_interpolation2D(vals1,vals2, zbase, newz,'lin') |
|---|
| 4711 | [[ 1.5 1.5 1.5 1.5 1.5] |
|---|
| 4712 | [ 1.5 1.5 1.5 1.5 1.5] |
|---|
| 4713 | [ 1.5 1.5 1.5 1.5 1.5] |
|---|
| 4714 | [ 1.5 1.5 1.5 1.5 1.5] |
|---|
| 4715 | [ 1.5 1.5 1.5 1.5 1.5] |
|---|
| 4716 | [ 1.5 1.5 1.5 1.5 1.5] |
|---|
| 4717 | [ 1.5 1.5 1.5 1.5 1.5]] |
|---|
| 4718 | """ |
|---|
| 4719 | |
|---|
| 4720 | fname = 'vertical_interpolation2D' |
|---|
| 4721 | |
|---|
| 4722 | if varb == 'h': |
|---|
| 4723 | print fname + '_____________________________________________________________' |
|---|
| 4724 | print vertical_interpolation2D.__doc__ |
|---|
| 4725 | quit() |
|---|
| 4726 | |
|---|
| 4727 | newvar = np.zeros((varb.shape), dtype=np.float) |
|---|
| 4728 | if kinterp == 'lin': |
|---|
| 4729 | ## print ' ' + fname + ' Vertical linear interpolation at',znewval |
|---|
| 4730 | # Standard linear interpolation (y = a + b*incz) |
|---|
| 4731 | # a = zorig[0], b = (vart-varb)/(zorig[1]-zorig[0])), incz = znewval - zorig[0] |
|---|
| 4732 | a = varb |
|---|
| 4733 | b = np.where(zorigvals[1,:,:] == zorigvals[0,:,:], 0., \ |
|---|
| 4734 | (vart - varb)/(zorigvals[1,:,:] - zorigvals[0,:,:])) |
|---|
| 4735 | incz = np.ones((varb.shape), dtype=np.float)*znewval - zorigvals[0,:,:] |
|---|
| 4736 | |
|---|
| 4737 | newvar = a + b*incz |
|---|
| 4738 | # Too code for not be used... but maybe? |
|---|
| 4739 | # dx=varb.shape[1] |
|---|
| 4740 | # dy=varb.shape[0] |
|---|
| 4741 | # for j in range(dy): |
|---|
| 4742 | # for i in range(dx): |
|---|
| 4743 | # if zorigvals[1,j,i] == zorigvals[0,j,i]: print 'equals!', \ |
|---|
| 4744 | # zorigvals[1,j,i], zorigvals[0,j,i],':',newvar[j,i],'@',vart[j,i] |
|---|
| 4745 | # if zorigvals[0,j,i] != zorigvals[0,j,i]: print '0 Nan', \ |
|---|
| 4746 | # zorigvals[0,j,i],':',newvar[j,i],'@',vart[j,i] |
|---|
| 4747 | # if zorigvals[1,j,i] != zorigvals[1,j,i]: print '1 Nan', \ |
|---|
| 4748 | # zorigvals[1,j,i],':',newvar[j,i],'@',vart[j,i] |
|---|
| 4749 | # if zorigvals[0,j,i] is None: print '0 None', zorigvals[0,j,i],':', \ |
|---|
| 4750 | # newvar[j,i],'@',vart[j,i] |
|---|
| 4751 | # if zorigvals[1,j,i] is None: print '1 None', zorigvals[1,j,i],':', \ |
|---|
| 4752 | # newvar[j,i],'@',vart[j,i] |
|---|
| 4753 | else: |
|---|
| 4754 | print errormsg |
|---|
| 4755 | print ' ' + fname + ": interpolation kind '" + kinterp + "' not ready !!!!!" |
|---|
| 4756 | quit(-1) |
|---|
| 4757 | |
|---|
| 4758 | return newvar |
|---|
| 4759 | |
|---|
| 4760 | #dx=5 |
|---|
| 4761 | #dy=7 |
|---|
| 4762 | #vals1 = np.ones((dy,dx), dtype=np.float) |
|---|
| 4763 | #vals2 = np.ones((dy,dx), dtype=np.float)*2. |
|---|
| 4764 | |
|---|
| 4765 | #zbase = np.zeros((2,dy,dx), dtype=np.float) |
|---|
| 4766 | #zbase[0,:,:] = 0.5 |
|---|
| 4767 | #for i in range(dx): |
|---|
| 4768 | # for j in range(dy): |
|---|
| 4769 | # zbase[1,j,i] = np.sqrt((j-dy/2.)**2. + (i-dx/2.)**2.) / \ |
|---|
| 4770 | # np.sqrt((dy/2.)**2.+(dy/2.)**2.) + 1. |
|---|
| 4771 | |
|---|
| 4772 | #zbase[1,:,:] = 1. |
|---|
| 4773 | |
|---|
| 4774 | #newz = 0.75 |
|---|
| 4775 | |
|---|
| 4776 | #print vertical_interpolation2D(vals1,vals2, zbase, newz,'lin') |
|---|
| 4777 | |
|---|
| 4778 | def vertical_interpolation(varb,vart,zorigvals,znewval,kinterp): |
|---|
| 4779 | """ Function to vertically integrate a 1D variable |
|---|
| 4780 | vertical_interpolation(varb,vart,zorigvals,znewval) |
|---|
| 4781 | varb= values at the base of the interval of interpolation |
|---|
| 4782 | vart= values at the top of the interval of interpolation |
|---|
| 4783 | zorigvals= pair of original values (2) |
|---|
| 4784 | znewval= new value to interpolate |
|---|
| 4785 | Possible cases: |
|---|
| 4786 | zorigvals[0,:] <= znewval < zorigvals[1,:] |
|---|
| 4787 | znewval <= zorigvals[0,:] < zorigvals[1,:] |
|---|
| 4788 | zorigvals[0,:] < zorigvals[1,:] <= znewval |
|---|
| 4789 | kinterp: kind of interpolation |
|---|
| 4790 | 'lin': linear |
|---|
| 4791 | >>> dx=5 |
|---|
| 4792 | >>> vals1 = np.ones((dx), dtype=np.float) |
|---|
| 4793 | >>> vals2 = np.ones((dx), dtype=np.float)*2. |
|---|
| 4794 | |
|---|
| 4795 | >>> zbase = np.zeros((2,dx), dtype=np.float) |
|---|
| 4796 | >>> zbase[0,:] = 0.5 |
|---|
| 4797 | >>> for i in range(dx): |
|---|
| 4798 | >>> zbase[1,i] = i + 1. |
|---|
| 4799 | |
|---|
| 4800 | >>> newz = 0.75 |
|---|
| 4801 | >>> vertical_interpolation2D(vals1,vals2, zbase, newz,'lin') |
|---|
| 4802 | [ 1.5 1.16666667 1.1 1.07142857 1.05555556] |
|---|
| 4803 | """ |
|---|
| 4804 | |
|---|
| 4805 | fname = 'vertical_interpolation' |
|---|
| 4806 | |
|---|
| 4807 | if varb == 'h': |
|---|
| 4808 | print fname + '_____________________________________________________________' |
|---|
| 4809 | print vertical_interpolation.__doc__ |
|---|
| 4810 | quit() |
|---|
| 4811 | |
|---|
| 4812 | newvar = np.zeros((varb.shape), dtype=np.float) |
|---|
| 4813 | if kinterp == 'lin': |
|---|
| 4814 | ## print ' ' + fname + ' Vertical linear interpolation at',znewval |
|---|
| 4815 | # Standard linear interpolation (y = a + b*incz) |
|---|
| 4816 | # a = zorig[0], b = (vart-varb)/(zorig[1]-zorig[0])), incz = znewval - zorig[0] |
|---|
| 4817 | a = varb |
|---|
| 4818 | b = np.where(zorigvals[1,:] == zorigvals[0,:], 0., \ |
|---|
| 4819 | (vart - varb)/(zorigvals[1,:] - zorigvals[0,:])) |
|---|
| 4820 | incz = np.ones((varb.shape), dtype=np.float)*znewval - zorigvals[0,:] |
|---|
| 4821 | |
|---|
| 4822 | newvar = a + b*incz |
|---|
| 4823 | else: |
|---|
| 4824 | print errormsg |
|---|
| 4825 | print ' ' + fname + ": interpolation kind '" + kinterp + "' not ready !!!!!" |
|---|
| 4826 | quit(-1) |
|---|
| 4827 | |
|---|
| 4828 | return newvar |
|---|
| 4829 | |
|---|
| 4830 | def interpolate_3D(zorigvs, varv, zintvs, kint): |
|---|
| 4831 | """ Function to interpolate a 3D variable |
|---|
| 4832 | interpolate_3D(zintvs, varv, zorigvals) |
|---|
| 4833 | zorigvs= original 3D z values |
|---|
| 4834 | varv= 3D variable to interpolate |
|---|
| 4835 | zintvs= new series of z values to interpolate |
|---|
| 4836 | kint= kind of interpolation |
|---|
| 4837 | 'lin': linear |
|---|
| 4838 | """ |
|---|
| 4839 | fname = 'interpolate_3D' |
|---|
| 4840 | |
|---|
| 4841 | if zorigvs == 'h': |
|---|
| 4842 | print fname + '_____________________________________________________________' |
|---|
| 4843 | print interpolate_3D.__doc__ |
|---|
| 4844 | quit() |
|---|
| 4845 | |
|---|
| 4846 | Ninv = len(zintvs) |
|---|
| 4847 | dimv = varv.shape |
|---|
| 4848 | |
|---|
| 4849 | # Sense of the original z-variable |
|---|
| 4850 | Lzorig = zorigvs.shape[0] |
|---|
| 4851 | incz = zorigvs[Lzorig-1,0,0] - zorigvs[0,0,0] |
|---|
| 4852 | |
|---|
| 4853 | varin = np.zeros((Ninv,dimv[1],dimv[2]), dtype=np.float) |
|---|
| 4854 | |
|---|
| 4855 | for ilev in range(Ninv): |
|---|
| 4856 | # print ' vertical interpolate level: ',zintvs[ilev] |
|---|
| 4857 | valinf = np.zeros((dimv[1], dimv[2]), dtype=np.float) |
|---|
| 4858 | valsup = np.zeros((dimv[1], dimv[2]), dtype=np.float) |
|---|
| 4859 | zorigv = np.zeros((2,dimv[1], dimv[2]), dtype=np.float) |
|---|
| 4860 | for j in range(valinf.shape[0]): |
|---|
| 4861 | for i in range(valinf.shape[1]): |
|---|
| 4862 | |
|---|
| 4863 | if np.min(zorigvs[:,j,i]) > zintvs[ilev] and incz > 0.: |
|---|
| 4864 | valinf[j,i] = varv[0,j,i] |
|---|
| 4865 | valsup[j,i] = varv[1,j,i] |
|---|
| 4866 | zorigv[0,j,i] = zorigvs[0,j,i] |
|---|
| 4867 | zorigv[1,j,i] = zorigvs[1,j,i] |
|---|
| 4868 | elif np.max(zorigvs[:,j,i]) < zintvs[ilev] and incz < 0.: |
|---|
| 4869 | valinf[j,i] = varv[0,j,i] |
|---|
| 4870 | valsup[j,i] = varv[1,j,i] |
|---|
| 4871 | zorigv[0,j,i] = zorigvs[0,j,i] |
|---|
| 4872 | zorigv[1,j,i] = zorigvs[1,j,i] |
|---|
| 4873 | elif np.max(zorigvs[:,j,i]) < zintvs[ilev] and incz > 0.: |
|---|
| 4874 | valinf[j,i] = varv[Lzorig-2,j,i] |
|---|
| 4875 | valsup[j,i] = varv[Lzorig-1,j,i] |
|---|
| 4876 | zorigv[0,j,i] = zorigvs[Lzorig-2,j,i] |
|---|
| 4877 | zorigv[1,j,i] = zorigvs[Lzorig-1,j,i] |
|---|
| 4878 | elif np.min(zorigvs[:,j,i]) > zintvs[ilev] and incz < 0.: |
|---|
| 4879 | valinf[j,i] = varv[Lzorig-2,j,i] |
|---|
| 4880 | valsup[j,i] = varv[Lzorig-1,j,i] |
|---|
| 4881 | zorigv[0,j,i] = zorigvs[Lzorig-2,j,i] |
|---|
| 4882 | zorigv[1,j,i] = zorigvs[Lzorig-1,j,i] |
|---|
| 4883 | # print 'top: ',i,j,':',zorigv[0,j,i], zintvs[ilev], zorigv[1,j,i] |
|---|
| 4884 | else: |
|---|
| 4885 | for z in range(Lzorig-1): |
|---|
| 4886 | if (zorigvs[z,j,i]-zintvs[ilev])*(zorigvs[z+1,j,i]- \ |
|---|
| 4887 | zintvs[ilev]) <= 0.: |
|---|
| 4888 | valinf[j,i] = varv[z,j,i] |
|---|
| 4889 | valsup[j,i] = varv[z+1,j,i] |
|---|
| 4890 | zorigv[0,j,i] = zorigvs[z,j,i] |
|---|
| 4891 | zorigv[1,j,i] = zorigvs[z+1,j,i] |
|---|
| 4892 | break |
|---|
| 4893 | |
|---|
| 4894 | # print 'zorigvs: ',zorigvs[:,0,0] |
|---|
| 4895 | # print ' valinf:', valinf[0,0] |
|---|
| 4896 | # print ' valsup:', valsup[0,0] |
|---|
| 4897 | # print ' zorigv 0:',zorigv[0,0,0] |
|---|
| 4898 | # print ' zorigv 1:',zorigv[1,0,0] |
|---|
| 4899 | |
|---|
| 4900 | varin[ilev,:,:] = vertical_interpolation2D(valinf,valsup,zorigv, \ |
|---|
| 4901 | zintvs[ilev], kint) |
|---|
| 4902 | # print ' varin:',varin[ilev,0,0] |
|---|
| 4903 | # quit() |
|---|
| 4904 | |
|---|
| 4905 | # quit() |
|---|
| 4906 | return varin |
|---|
| 4907 | |
|---|
| 4908 | def interpolate_2D(zorigvs0, varv0, zdim, zintvs, kint): |
|---|
| 4909 | """ Function to interpolate a 2D variable |
|---|
| 4910 | interpolate_2D(zintvs, varv, zorigvals) |
|---|
| 4911 | zorigvs= original 2D z values |
|---|
| 4912 | varv= 2D variable to interpolate |
|---|
| 4913 | zdim= number of the dimension with the z-values |
|---|
| 4914 | zintvs= new series of z values to interpolate |
|---|
| 4915 | kint= kind of interpolation |
|---|
| 4916 | 'lin': linear |
|---|
| 4917 | """ |
|---|
| 4918 | fname = 'interpolate_2D' |
|---|
| 4919 | |
|---|
| 4920 | if zorigvs0 == 'h': |
|---|
| 4921 | print fname + '_____________________________________________________________' |
|---|
| 4922 | print interpolate_2D.__doc__ |
|---|
| 4923 | quit() |
|---|
| 4924 | |
|---|
| 4925 | Ninv = len(zintvs) |
|---|
| 4926 | if zdim == 0: |
|---|
| 4927 | varv = varv0 |
|---|
| 4928 | zorigvs = zorigvs0 |
|---|
| 4929 | else: |
|---|
| 4930 | varv = np.transpose(varv0) |
|---|
| 4931 | zorigvs = np.transpose(zorigvs0) |
|---|
| 4932 | |
|---|
| 4933 | dimv = varv.shape |
|---|
| 4934 | |
|---|
| 4935 | # Sense of the original z-variable |
|---|
| 4936 | Lzorig = zorigvs.shape[0] |
|---|
| 4937 | incz = zorigvs[Lzorig-1,0] - zorigvs[0,0] |
|---|
| 4938 | |
|---|
| 4939 | varin = np.zeros((Ninv,dimv[1]), dtype=np.float) |
|---|
| 4940 | |
|---|
| 4941 | for ilev in range(Ninv): |
|---|
| 4942 | valinf = np.zeros((dimv[1]), dtype=np.float) |
|---|
| 4943 | valsup = np.zeros((dimv[1]), dtype=np.float) |
|---|
| 4944 | zorigv = np.zeros((2,dimv[1]), dtype=np.float) |
|---|
| 4945 | for i in range(valinf.shape[0]): |
|---|
| 4946 | if np.min(zorigvs[:,i]) > zintvs[ilev] and incz > 0.: |
|---|
| 4947 | valinf[i] = varv[0,i] |
|---|
| 4948 | valsup[i] = varv[1,i] |
|---|
| 4949 | zorigv[0,i] = zorigvs[0,i] |
|---|
| 4950 | zorigv[1,i] = zorigvs[1,i] |
|---|
| 4951 | elif np.max(zorigvs[:,i]) < zintvs[ilev] and incz < 0.: |
|---|
| 4952 | valinf[i] = varv[0,i] |
|---|
| 4953 | valsup[i] = varv[1,i] |
|---|
| 4954 | zorigv[0,i] = zorigvs[0,i] |
|---|
| 4955 | zorigv[1,i] = zorigvs[1,i] |
|---|
| 4956 | elif np.max(zorigvs[:,i]) < zintvs[ilev] and incz > 0.: |
|---|
| 4957 | valinf[i] = varv[Lzorig-2,i] |
|---|
| 4958 | valsup[i] = varv[Lzorig-1,i] |
|---|
| 4959 | zorigv[0,i] = zorigvs[Lzorig-2,i] |
|---|
| 4960 | zorigv[1,i] = zorigvs[Lzorig-1,i] |
|---|
| 4961 | elif np.min(zorigvs[:,i]) > zintvs[ilev] and incz < 0.: |
|---|
| 4962 | valinf[i] = varv[Lzorig-2,i] |
|---|
| 4963 | valsup[i] = varv[Lzorig-1,i] |
|---|
| 4964 | zorigv[0,i] = zorigvs[Lzorig-2,i] |
|---|
| 4965 | zorigv[1,i] = zorigvs[Lzorig-1,i] |
|---|
| 4966 | else: |
|---|
| 4967 | for z in range(Lzorig-1): |
|---|
| 4968 | if (zorigvs[z,i]-zintvs[ilev])*(zorigvs[z+1,i]- \ |
|---|
| 4969 | zintvs[ilev]) <= 0.: |
|---|
| 4970 | valinf[i] = varv[z,i] |
|---|
| 4971 | valsup[i] = varv[z+1,i] |
|---|
| 4972 | zorigv[0,i] = zorigvs[z,i] |
|---|
| 4973 | zorigv[1,i] = zorigvs[z+1,i] |
|---|
| 4974 | break |
|---|
| 4975 | |
|---|
| 4976 | varin[ilev,:] = vertical_interpolation(valinf,valsup,zorigv, \ |
|---|
| 4977 | zintvs[ilev], kint) |
|---|
| 4978 | |
|---|
| 4979 | if zdim == 0: |
|---|
| 4980 | varin0 = varin |
|---|
| 4981 | else: |
|---|
| 4982 | varin0 = np.transpose(varin) |
|---|
| 4983 | |
|---|
| 4984 | return varin0 |
|---|
| 4985 | |
|---|
| 4986 | def list_toVec(listv, kind): |
|---|
| 4987 | """ Function to transform from a list of values to a vector |
|---|
| 4988 | list_toVec(listv, kind) |
|---|
| 4989 | listv= list with the values |
|---|
| 4990 | kind= kind of values |
|---|
| 4991 | >>> list_toVec(['1', 2, '3', 4, 5, '6.9', 7, 9], 'npfloat') |
|---|
| 4992 | [ 1. 2. 3. 4. 5. 6.9 7. 9. ] |
|---|
| 4993 | """ |
|---|
| 4994 | |
|---|
| 4995 | fname = 'list_toVec' |
|---|
| 4996 | |
|---|
| 4997 | if listv == 'h': |
|---|
| 4998 | print fname + '_____________________________________________________________' |
|---|
| 4999 | print list_toVec.__doc__ |
|---|
| 5000 | quit() |
|---|
| 5001 | |
|---|
| 5002 | Nvals = len(listv) |
|---|
| 5003 | |
|---|
| 5004 | if kind == 'npfloat': |
|---|
| 5005 | vecv = np.zeros((Nvals), dtype=np.float) |
|---|
| 5006 | for iv in range(Nvals): |
|---|
| 5007 | vecv[iv] = np.float(listv[iv]) |
|---|
| 5008 | else: |
|---|
| 5009 | print errormsg |
|---|
| 5010 | print ' ' + fname + ': type of value "' + kind + '" not ready!!' |
|---|
| 5011 | quit(-1) |
|---|
| 5012 | |
|---|
| 5013 | return vecv |
|---|
| 5014 | |
|---|
| 5015 | def running_mean(values, run): |
|---|
| 5016 | """ Function to compute a running mean of a series of values |
|---|
| 5017 | running_mean(vals, int) |
|---|
| 5018 | [vals]: series of values |
|---|
| 5019 | [run]: interval to use to compute the running mean |
|---|
| 5020 | NOTE: resultant size would be the same but with None except at [run/2,size(vals)-run/2] |
|---|
| 5021 | >>> running_mean(np.arange(100),10) |
|---|
| 5022 | [None None None None None 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 |
|---|
| 5023 | 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 |
|---|
| 5024 | 30.5 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5 39.5 40.5 41.5 42.5 43.5 44.5 |
|---|
| 5025 | 45.5 46.5 47.5 48.5 49.5 50.5 51.5 52.5 53.5 54.5 55.5 56.5 57.5 58.5 59.5 |
|---|
| 5026 | 60.5 61.5 62.5 63.5 64.5 65.5 66.5 67.5 68.5 69.5 70.5 71.5 72.5 73.5 74.5 |
|---|
| 5027 | 75.5 76.5 77.5 78.5 79.5 80.5 81.5 82.5 83.5 84.5 85.5 86.5 87.5 88.5 89.5 |
|---|
| 5028 | 90.5 91.5 92.5 93.5 None None None None None] |
|---|
| 5029 | """ |
|---|
| 5030 | fname = 'running_mean' |
|---|
| 5031 | |
|---|
| 5032 | if values == 'h': |
|---|
| 5033 | print fname + '_____________________________________________________________' |
|---|
| 5034 | print running_mean.__doc__ |
|---|
| 5035 | quit() |
|---|
| 5036 | |
|---|
| 5037 | runmean = np.ones((len(values)), dtype=np.float)*fillValue |
|---|
| 5038 | |
|---|
| 5039 | for ip in range(run/2,len(values)-run/2): |
|---|
| 5040 | runmean[ip] = np.mean(values[ip-run/2:ip+run/2]) |
|---|
| 5041 | |
|---|
| 5042 | runmean = np.where(runmean == fillValue, None, runmean) |
|---|
| 5043 | |
|---|
| 5044 | return runmean |
|---|
| 5045 | |
|---|
| 5046 | def files_folder(folder,headfile): |
|---|
| 5047 | """ Function to retrieve a list of files from a folder [fold] and head [headfile] |
|---|
| 5048 | >>> files_folder('/media/data2/etudes/WRF_LMDZ/WL_HyMeX_HighRes/medic950116/', |
|---|
| 5049 | 'wrfout_d03') |
|---|
| 5050 | ['wrfout_d03_1995-01-13_02:00:00', 'wrfout_d03_1995-01-13_04:00:00', 'wrfout_d03_1995-01-13_06:00:00', |
|---|
| 5051 | 'wrfout_d03_1995-01-13_06:15:00', 'wrfout_d03_1995-01-13_08:15:00', 'wrfout_d03_1995-01-13_10:15:00', |
|---|
| 5052 | 'wrfout_d03_1995-01-13_12:15:00', 'wrfout_d03_1995-01-13_14:15:00', 'wrfout_d03_1995-01-13_16:15:00', |
|---|
| 5053 | (...) |
|---|
| 5054 | 'wrfout_d03_1995-01-17_18:15:00', 'wrfout_d03_1995-01-17_20:15:00', 'wrfout_d03_1995-01-17_22:15:00'] |
|---|
| 5055 | """ |
|---|
| 5056 | import subprocess as sub |
|---|
| 5057 | fname = 'files_folder' |
|---|
| 5058 | |
|---|
| 5059 | if folder == 'h': |
|---|
| 5060 | print fname + '_____________________________________________________________' |
|---|
| 5061 | print files_folder.__doc__ |
|---|
| 5062 | quit() |
|---|
| 5063 | |
|---|
| 5064 | # ins = folder + "/" + headfile + "*" |
|---|
| 5065 | ins = folder + "/" |
|---|
| 5066 | print 'ins:',ins |
|---|
| 5067 | files = sub.Popen(["/bin/ls","-1",ins], stdout=sub.PIPE) |
|---|
| 5068 | fileslist = files.communicate() |
|---|
| 5069 | listfiles0 = str(fileslist).split('\\n') |
|---|
| 5070 | |
|---|
| 5071 | # Filtering output |
|---|
| 5072 | Lheader=len(headfile) |
|---|
| 5073 | listfiles = [] |
|---|
| 5074 | for ifile in listfiles0: |
|---|
| 5075 | if ifile[0:Lheader] == headfile: |
|---|
| 5076 | listfiles.append(ifile) |
|---|
| 5077 | |
|---|
| 5078 | return listfiles |
|---|
| 5079 | |
|---|
| 5080 | def count_cond(var, val, con): |
|---|
| 5081 | """ Function to count values of a variable which attain a condition |
|---|
| 5082 | [var]= variable |
|---|
| 5083 | [val]= value |
|---|
| 5084 | [con]= statistics/condition |
|---|
| 5085 | 'eq': equal than [val] |
|---|
| 5086 | 'neq': not equal than [val] |
|---|
| 5087 | 'lt': less than [val] |
|---|
| 5088 | 'le': less and equal than [val] |
|---|
| 5089 | 'gt': greater than [val] |
|---|
| 5090 | 'ge': greater and equal than [val] |
|---|
| 5091 | 'in': inside the range [val[0]] <= [var] <= [val[1]] (where [val]=[val1, val2] |
|---|
| 5092 | 'out': inside the range [val[0]] > [var]; [val[1]] < [var] (where [val]=[val1, val2] |
|---|
| 5093 | >>> vals = np.arange(100) |
|---|
| 5094 | >>> count_cond(vals,50,'eq') |
|---|
| 5095 | 1 |
|---|
| 5096 | >>> count_cond(vals,50,'neq') |
|---|
| 5097 | 99 |
|---|
| 5098 | >>> count_cond(vals,50,'lt') |
|---|
| 5099 | 50 |
|---|
| 5100 | >>> count_cond(vals,50,'le') |
|---|
| 5101 | 51 |
|---|
| 5102 | >>> count_cond(vals,50,'gt') |
|---|
| 5103 | 49 |
|---|
| 5104 | >>> count_cond(vals,50,'ge') |
|---|
| 5105 | 50 |
|---|
| 5106 | >>> count_cond(vals,[25,75],'in') |
|---|
| 5107 | 51 |
|---|
| 5108 | >>> count_cond(vals,[25,75],'out') |
|---|
| 5109 | 49 |
|---|
| 5110 | """ |
|---|
| 5111 | fname = 'count_cond' |
|---|
| 5112 | |
|---|
| 5113 | cons = ['eq', 'neq', 'lt', 'le', 'gt', 'ge', 'in', 'out'] |
|---|
| 5114 | |
|---|
| 5115 | if con == 'eq': |
|---|
| 5116 | Nvals = np.sum(var == val) |
|---|
| 5117 | elif con == 'neq': |
|---|
| 5118 | Nvals = np.sum(var != val) |
|---|
| 5119 | elif con == 'lt': |
|---|
| 5120 | Nvals = np.sum(var < val) |
|---|
| 5121 | elif con == 'le': |
|---|
| 5122 | Nvals = np.sum(var <= val) |
|---|
| 5123 | elif con == 'gt': |
|---|
| 5124 | Nvals = np.sum(var > val) |
|---|
| 5125 | elif con == 'ge': |
|---|
| 5126 | Nvals = np.sum(var >= val) |
|---|
| 5127 | elif con == 'in': |
|---|
| 5128 | if len(val) != 2: |
|---|
| 5129 | print errormsg |
|---|
| 5130 | print ' ' + fname + ": for condition '" + con + "' we must have two " + \ |
|---|
| 5131 | " values!!" |
|---|
| 5132 | print ' we have:', val |
|---|
| 5133 | quit(-1) |
|---|
| 5134 | Nvals = np.sum((var >= val[0])*(var <= val[1])) |
|---|
| 5135 | elif con == 'out': |
|---|
| 5136 | if len(val) != 2: |
|---|
| 5137 | print errormsg |
|---|
| 5138 | print ' ' + fname + ": for condition '" + con + "' we must have two " + \ |
|---|
| 5139 | " values!!" |
|---|
| 5140 | print ' we have:', val |
|---|
| 5141 | quit(-1) |
|---|
| 5142 | Nvals = np.sum((var < val[0])+(var > val[1])) |
|---|
| 5143 | else: |
|---|
| 5144 | print errormsg |
|---|
| 5145 | print ' ' + fname + ": condition '" + con + "' not ready!!" |
|---|
| 5146 | print ' only ready:', cons |
|---|
| 5147 | quit(-1) |
|---|
| 5148 | |
|---|
| 5149 | return Nvals |
|---|
| 5150 | |
|---|
| 5151 | def coincident_CFtimes(tvalB, tunitA, tunitB): |
|---|
| 5152 | """ Function to make coincident times for two different sets of CFtimes |
|---|
| 5153 | tvalB= time values B |
|---|
| 5154 | tunitA= time units times A to which we want to make coincidence |
|---|
| 5155 | tunitB= time units times B |
|---|
| 5156 | >>> coincident_CFtimes(np.arange(10),'seconds since 1949-12-01 00:00:00', |
|---|
| 5157 | 'hours since 1949-12-01 00:00:00') |
|---|
| 5158 | [ 0. 3600. 7200. 10800. 14400. 18000. 21600. 25200. 28800. 32400.] |
|---|
| 5159 | >>> coincident_CFtimes(np.arange(10),'seconds since 1949-12-01 00:00:00', |
|---|
| 5160 | 'hours since 1979-12-01 00:00:00') |
|---|
| 5161 | [ 9.46684800e+08 9.46688400e+08 9.46692000e+08 9.46695600e+08 |
|---|
| 5162 | 9.46699200e+08 9.46702800e+08 9.46706400e+08 9.46710000e+08 |
|---|
| 5163 | 9.46713600e+08 9.46717200e+08] |
|---|
| 5164 | """ |
|---|
| 5165 | import datetime as dt |
|---|
| 5166 | fname = 'coincident_CFtimes' |
|---|
| 5167 | |
|---|
| 5168 | trefA = tunitA.split(' ')[2] + ' ' + tunitA.split(' ')[3] |
|---|
| 5169 | trefB = tunitB.split(' ')[2] + ' ' + tunitB.split(' ')[3] |
|---|
| 5170 | tuA = tunitA.split(' ')[0] |
|---|
| 5171 | tuB = tunitB.split(' ')[0] |
|---|
| 5172 | |
|---|
| 5173 | if tuA != tuB: |
|---|
| 5174 | if tuA == 'microseconds': |
|---|
| 5175 | if tuB == 'microseconds': |
|---|
| 5176 | tB = tvalB*1. |
|---|
| 5177 | elif tuB == 'seconds': |
|---|
| 5178 | tB = tvalB*10.e6 |
|---|
| 5179 | elif tuB == 'minutes': |
|---|
| 5180 | tB = tvalB*60.*10.e6 |
|---|
| 5181 | elif tuB == 'hours': |
|---|
| 5182 | tB = tvalB*3600.*10.e6 |
|---|
| 5183 | elif tuB == 'days': |
|---|
| 5184 | tB = tvalB*3600.*24.*10.e6 |
|---|
| 5185 | else: |
|---|
| 5186 | print errormsg |
|---|
| 5187 | print ' ' + fname + ": combination of time untis: '" + tuA + \ |
|---|
| 5188 | "' & '" + tuB + "' not ready !!" |
|---|
| 5189 | quit(-1) |
|---|
| 5190 | elif tuA == 'seconds': |
|---|
| 5191 | if tuB == 'microseconds': |
|---|
| 5192 | tB = tvalB/10.e6 |
|---|
| 5193 | elif tuB == 'seconds': |
|---|
| 5194 | tB = tvalB*1. |
|---|
| 5195 | elif tuB == 'minutes': |
|---|
| 5196 | tB = tvalB*60. |
|---|
| 5197 | elif tuB == 'hours': |
|---|
| 5198 | tB = tvalB*3600. |
|---|
| 5199 | elif tuB == 'days': |
|---|
| 5200 | tB = tvalB*3600.*24. |
|---|
| 5201 | else: |
|---|
| 5202 | print errormsg |
|---|
| 5203 | print ' ' + fname + ": combination of time untis: '" + tuA + \ |
|---|
| 5204 | "' & '" + tuB + "' not ready !!" |
|---|
| 5205 | quit(-1) |
|---|
| 5206 | elif tuA == 'minutes': |
|---|
| 5207 | if tuB == 'microseconds': |
|---|
| 5208 | tB = tvalB/(60.*10.e6) |
|---|
| 5209 | elif tuB == 'seconds': |
|---|
| 5210 | tB = tvalB/60. |
|---|
| 5211 | elif tuB == 'minutes': |
|---|
| 5212 | tB = tvalB*1. |
|---|
| 5213 | elif tuB == 'hours': |
|---|
| 5214 | tB = tvalB*60. |
|---|
| 5215 | elif tuB == 'days': |
|---|
| 5216 | tB = tvalB*60.*24. |
|---|
| 5217 | else: |
|---|
| 5218 | print errormsg |
|---|
| 5219 | print ' ' + fname + ": combination of time untis: '" + tuA + \ |
|---|
| 5220 | "' & '" + tuB + "' not ready !!" |
|---|
| 5221 | quit(-1) |
|---|
| 5222 | elif tuA == 'hours': |
|---|
| 5223 | if tuB == 'microseconds': |
|---|
| 5224 | tB = tvalB/(3600.*10.e6) |
|---|
| 5225 | elif tuB == 'seconds': |
|---|
| 5226 | tB = tvalB/3600. |
|---|
| 5227 | elif tuB == 'minutes': |
|---|
| 5228 | tB = tvalB/60. |
|---|
| 5229 | elif tuB == 'hours': |
|---|
| 5230 | tB = tvalB*1. |
|---|
| 5231 | elif tuB == 'days': |
|---|
| 5232 | tB = tvalB*24. |
|---|
| 5233 | else: |
|---|
| 5234 | print errormsg |
|---|
| 5235 | print ' ' + fname + ": combination of time untis: '" + tuA + \ |
|---|
| 5236 | "' & '" + tuB + "' not ready !!" |
|---|
| 5237 | quit(-1) |
|---|
| 5238 | elif tuA == 'days': |
|---|
| 5239 | if tuB == 'microseconds': |
|---|
| 5240 | tB = tvalB/(24.*3600.*10.e6) |
|---|
| 5241 | elif tuB == 'seconds': |
|---|
| 5242 | tB = tvalB/(24.*3600.) |
|---|
| 5243 | elif tuB == 'minutes': |
|---|
| 5244 | tB = tvalB/(24.*60.) |
|---|
| 5245 | elif tuB == 'hours': |
|---|
| 5246 | tB = tvalB/24. |
|---|
| 5247 | elif tuB == 'days': |
|---|
| 5248 | tB = tvalB*1. |
|---|
| 5249 | else: |
|---|
| 5250 | print errormsg |
|---|
| 5251 | print ' ' + fname + ": combination of time untis: '" + tuA + \ |
|---|
| 5252 | "' & '" + tuB + "' not ready !!" |
|---|
| 5253 | quit(-1) |
|---|
| 5254 | else: |
|---|
| 5255 | print errormsg |
|---|
| 5256 | print ' ' + fname + ": time untis: '" + tuA + "' not ready !!" |
|---|
| 5257 | quit(-1) |
|---|
| 5258 | else: |
|---|
| 5259 | tB = tvalB*1. |
|---|
| 5260 | |
|---|
| 5261 | if trefA != trefB: |
|---|
| 5262 | trefTA = dt.datetime.strptime(trefA, '%Y-%m-%d %H:%M:%S') |
|---|
| 5263 | trefTB = dt.datetime.strptime(trefB, '%Y-%m-%d %H:%M:%S') |
|---|
| 5264 | |
|---|
| 5265 | difft = trefTB - trefTA |
|---|
| 5266 | diffv = difft.days*24.*3600.*10.e6 + difft.seconds*10.e6 + difft.microseconds |
|---|
| 5267 | print ' ' + fname + ': different reference refA:',trefTA,'refB',trefTB |
|---|
| 5268 | print ' difference:',difft,':',diffv,'microseconds' |
|---|
| 5269 | |
|---|
| 5270 | if tuA == 'microseconds': |
|---|
| 5271 | tB = tB + diffv |
|---|
| 5272 | elif tuA == 'seconds': |
|---|
| 5273 | tB = tB + diffv/10.e6 |
|---|
| 5274 | elif tuA == 'minutes': |
|---|
| 5275 | tB = tB + diffv/(60.*10.e6) |
|---|
| 5276 | elif tuA == 'hours': |
|---|
| 5277 | tB = tB + diffv/(3600.*10.e6) |
|---|
| 5278 | elif tuA == 'dayss': |
|---|
| 5279 | tB = tB + diffv/(24.*3600.*10.e6) |
|---|
| 5280 | else: |
|---|
| 5281 | print errormsg |
|---|
| 5282 | print ' ' + fname + ": time untis: '" + tuA + "' not ready !!" |
|---|
| 5283 | quit(-1) |
|---|
| 5284 | |
|---|
| 5285 | return tB |
|---|
| 5286 | |
|---|
| 5287 | def wdismean(pos,val4): |
|---|
| 5288 | """ Function to compute the mean value weighted to its 4 distances |
|---|
| 5289 | pos= x,y position within the 'square' |
|---|
| 5290 | val4= values at the four vertexs (2,2) |
|---|
| 5291 | >>>position = [0.005,0.005] |
|---|
| 5292 | >>>val4 = np.zeros((2,2), dtype=np.float) |
|---|
| 5293 | >>>val4 = np.arange(4).reshape(2,2) |
|---|
| 5294 | 0.035707955234 |
|---|
| 5295 | """ |
|---|
| 5296 | fname = 'wdismean' |
|---|
| 5297 | |
|---|
| 5298 | dist = np.zeros((2,2), dtype=np.float) |
|---|
| 5299 | dist[0,0] = np.sqrt(pos[0]*pos[0]+pos[1]*pos[1]) |
|---|
| 5300 | dist[0,1] = np.sqrt(pos[0]*pos[0]+(1.-pos[1])*(1.-pos[1])) |
|---|
| 5301 | dist[1,0] = np.sqrt((1.-pos[0])*(1.-pos[0])+pos[1]*pos[1]) |
|---|
| 5302 | dist[1,1] = np.sqrt((1.-pos[0])*(1.-pos[0])+(1.-pos[1])*(1.-pos[1])) |
|---|
| 5303 | |
|---|
| 5304 | if np.min(dist) == 0.: |
|---|
| 5305 | pos0 = index_mat(dist, 0.) |
|---|
| 5306 | dist[:,:] = 0. |
|---|
| 5307 | posmean = val4[pos0[0], pos0[1]] |
|---|
| 5308 | else: |
|---|
| 5309 | totdist = np.sum(1./dist) |
|---|
| 5310 | posmean = np.sum(val4.flatten()/dist.flatten())/totdist |
|---|
| 5311 | |
|---|
| 5312 | return posmean |
|---|
| 5313 | |
|---|
| 5314 | def read_ASCIIlist(filen): |
|---|
| 5315 | """ Function to build a list from an ASCII lines file |
|---|
| 5316 | filen= name of the ASCII file |
|---|
| 5317 | """ |
|---|
| 5318 | import os |
|---|
| 5319 | fname = 'read_ASCIIlist' |
|---|
| 5320 | |
|---|
| 5321 | if not os.path.isfile(filen): |
|---|
| 5322 | print errormsg |
|---|
| 5323 | print ' ' + fname + ": ASCII types file '" + filen + "' does not exist !!" |
|---|
| 5324 | quit(-1) |
|---|
| 5325 | |
|---|
| 5326 | of = open(filen, 'r') |
|---|
| 5327 | filevals = [] |
|---|
| 5328 | for linef in of: |
|---|
| 5329 | filevals.append(linef.replace('\n','')) |
|---|
| 5330 | of.close() |
|---|
| 5331 | |
|---|
| 5332 | return filevals |
|---|
| 5333 | |
|---|
| 5334 | def radial_points(angle,dist): |
|---|
| 5335 | """ Function to provide a number of grid point positions for a given angle |
|---|
| 5336 | angle= desired angle (rad) |
|---|
| 5337 | dist= number of grid points |
|---|
| 5338 | >>> radial_points(0.5*np.pi,5) |
|---|
| 5339 | [[ 6.12323400e-17 1.00000000e+00] |
|---|
| 5340 | [ 1.22464680e-16 2.00000000e+00] |
|---|
| 5341 | [ 1.83697020e-16 3.00000000e+00] |
|---|
| 5342 | [ 2.44929360e-16 4.00000000e+00] |
|---|
| 5343 | [ 3.06161700e-16 5.00000000e+00]] |
|---|
| 5344 | """ |
|---|
| 5345 | fname = 'radial_points' |
|---|
| 5346 | radpos = np.zeros((dist,2), dtype=np.float) |
|---|
| 5347 | |
|---|
| 5348 | for ip in range(dist): |
|---|
| 5349 | radpos[ip,0] = (ip+1.)*np.cos(angle) |
|---|
| 5350 | radpos[ip,1] = (ip+1.)*np.sin(angle) |
|---|
| 5351 | |
|---|
| 5352 | return radpos |
|---|
| 5353 | |
|---|
| 5354 | def read_SQradii(filename='SQradii.dat', Npt=-1, maxradii=None): |
|---|
| 5355 | """ Function to read the outcome of the function `squared_radial' |
|---|
| 5356 | filename= Name of the outcome file of the function |
|---|
| 5357 | Npt= Number of points on each grid |
|---|
| 5358 | maxradii= allowed maximum radii |
|---|
| 5359 | 'SQradii.dat': file with the radii and paris of integers after ran squared_radial(1000) |
|---|
| 5360 | """ |
|---|
| 5361 | fname = 'read_SQradii' |
|---|
| 5362 | |
|---|
| 5363 | if not os.path.isfile(filename): |
|---|
| 5364 | print errormsg |
|---|
| 5365 | print ' ' + fname + ": file '" + filename + "' not found!!" |
|---|
| 5366 | quit(-1) |
|---|
| 5367 | |
|---|
| 5368 | infile = open(filename, 'r') |
|---|
| 5369 | |
|---|
| 5370 | gridradii = {} |
|---|
| 5371 | Nradii = {} |
|---|
| 5372 | radii = [] |
|---|
| 5373 | for line in infile: |
|---|
| 5374 | values = values_line(line, ' ', ['[', ']']) |
|---|
| 5375 | rad = np.float(values[0]) |
|---|
| 5376 | # Limited by a given radii |
|---|
| 5377 | if maxradii is not None and rad > maxradii: break |
|---|
| 5378 | Nrad = int(values[2]) |
|---|
| 5379 | couples = [] |
|---|
| 5380 | Nradgood = 0 |
|---|
| 5381 | if Nrad > 1: |
|---|
| 5382 | Ncouples = 0 |
|---|
| 5383 | for ic in range(Nrad): |
|---|
| 5384 | ix = int(values[3 + ic*2]) |
|---|
| 5385 | iy = int(values[3 + ic*2 + 1]) |
|---|
| 5386 | if ix <= Npt and iy <= Npt: |
|---|
| 5387 | couples.append(np.array([ix,iy])) |
|---|
| 5388 | Nradgood = Nradgood + 1 |
|---|
| 5389 | elif ix > Npt and iy > Npt: break |
|---|
| 5390 | else: |
|---|
| 5391 | ix = int(values[3]) |
|---|
| 5392 | iy = int(values[4]) |
|---|
| 5393 | if ix <= Npt and iy <= Npt: |
|---|
| 5394 | couples.append(np.array([ix,iy])) |
|---|
| 5395 | Nradgood = Nradgood + 1 |
|---|
| 5396 | elif ix > Npt and iy > Npt: break |
|---|
| 5397 | |
|---|
| 5398 | if Nradgood > 0: |
|---|
| 5399 | Nradii[rad] = Nradgood |
|---|
| 5400 | finalcouples = np.zeros((Nradgood,2), dtype=int) |
|---|
| 5401 | for ic in range(Nradgood): |
|---|
| 5402 | finalcouples[ic,:] = couples[ic] |
|---|
| 5403 | gridradii[rad] = finalcouples |
|---|
| 5404 | |
|---|
| 5405 | # for rad in sorted(gridradii.keys()): |
|---|
| 5406 | # print rad, gridradii[rad] |
|---|
| 5407 | |
|---|
| 5408 | return gridradii |
|---|
| 5409 | |
|---|
| 5410 | def grid_combinations(x,y): |
|---|
| 5411 | """ Function to provide all the possible grid points combination for a given pair of values |
|---|
| 5412 | x,y= pair of grid points |
|---|
| 5413 | >>>grid_combinations(1,2) |
|---|
| 5414 | [[1, 2], [-1, 2], [-1, -2], [1, -2], [2, 1], [-2, 1], [-2, -1], [2, -1]] |
|---|
| 5415 | """ |
|---|
| 5416 | fname = 'grid_combinations' |
|---|
| 5417 | |
|---|
| 5418 | gridcomb = [] |
|---|
| 5419 | gridcomb.append([x,y]) |
|---|
| 5420 | if x != 0: gridcomb.append([-x,y]) |
|---|
| 5421 | if x != 0 and y != 0: gridcomb.append([-x,-y]) |
|---|
| 5422 | if y != 0: gridcomb.append([x,-y]) |
|---|
| 5423 | |
|---|
| 5424 | if x != y: |
|---|
| 5425 | gridcomb.append([y,x]) |
|---|
| 5426 | if y != 0: gridcomb.append([-y,x]) |
|---|
| 5427 | if x != 0 and y != 0: gridcomb.append([-y,-x]) |
|---|
| 5428 | if x != 0: gridcomb.append([y,-x]) |
|---|
| 5429 | |
|---|
| 5430 | return gridcomb |
|---|
| 5431 | |
|---|
| 5432 | def radii_points(xpos,ypos,Lrad,Nang,dx,dy): |
|---|
| 5433 | """ Function to provide the grid points for radial cross sections, by angle and radii and `squared' radii |
|---|
| 5434 | xpos= x position of the center |
|---|
| 5435 | ypos= y position of the center |
|---|
| 5436 | Lrad= length of the maximum radi in grid points |
|---|
| 5437 | Nang= number of equivalent angles |
|---|
| 5438 | dx= x size of the domain of values |
|---|
| 5439 | dy= y size of the domain of values |
|---|
| 5440 | >>> radpos, SQradpos = radii_points(12,12,1,8,25,25) |
|---|
| 5441 | radpos: |
|---|
| 5442 | [[[ 13. 12. ]] |
|---|
| 5443 | [[ 12.70710678 12.70710678]] |
|---|
| 5444 | [[ 12. 13. ]] |
|---|
| 5445 | [[ 11.29289322 12.70710678]] |
|---|
| 5446 | [[ 11. 12. ]] |
|---|
| 5447 | [[ 11.29289322 11.29289322]] |
|---|
| 5448 | [[ 12. 11. ]] |
|---|
| 5449 | [[ 12.70710678 11.29289322]]] |
|---|
| 5450 | SQradpos: |
|---|
| 5451 | {1.0: [[13, 12], [11, 12], [12, 13], [12, 11]], 1.41421356237: [[13, 13], [11, 13], [11, 11], [13, 11]]} |
|---|
| 5452 | """ |
|---|
| 5453 | fname = 'radii_points' |
|---|
| 5454 | |
|---|
| 5455 | radiipos = np.zeros((Nang, Lrad, 2), dtype = np.float) |
|---|
| 5456 | SQradiipos = {} |
|---|
| 5457 | |
|---|
| 5458 | # Getting the range of radii: |
|---|
| 5459 | pairsSQradii = read_SQradii(Npt=Lrad) |
|---|
| 5460 | SQradii = pairsSQradii.keys() |
|---|
| 5461 | Nradii = len(SQradii) |
|---|
| 5462 | |
|---|
| 5463 | # Angle loop |
|---|
| 5464 | for ia in range(Nang): |
|---|
| 5465 | angle = 2.*np.pi*ia/Nang |
|---|
| 5466 | posangle = np.zeros((Lrad,2), dtype=np.float) |
|---|
| 5467 | |
|---|
| 5468 | # Points at that given angle |
|---|
| 5469 | pangle = radial_points(angle,Lrad) |
|---|
| 5470 | posangle[:,0] = pangle[:,0] + xpos |
|---|
| 5471 | posangle[:,1] = pangle[:,1] + ypos |
|---|
| 5472 | |
|---|
| 5473 | for ip in range(Lrad): |
|---|
| 5474 | iipos = int(posangle[ip,0]) |
|---|
| 5475 | jjpos = int(posangle[ip,1]) |
|---|
| 5476 | |
|---|
| 5477 | # Creation of a matrix with all the grid points at each time-step |
|---|
| 5478 | if iipos >= 0 and iipos < dx-1 and jjpos >= 0 and jjpos < dy-1: |
|---|
| 5479 | radiipos[ia,ip,0] = posangle[ip,0] |
|---|
| 5480 | radiipos[ia,ip,1] = posangle[ip,1] |
|---|
| 5481 | |
|---|
| 5482 | # SQ radii values |
|---|
| 5483 | |
|---|
| 5484 | # Radii loop (avoiding 0) |
|---|
| 5485 | pairswithin = {} |
|---|
| 5486 | for ir in range(1,Nradii): |
|---|
| 5487 | pairs = pairsSQradii[SQradii[ir]] |
|---|
| 5488 | |
|---|
| 5489 | # pairs loop |
|---|
| 5490 | Npairs = len(pairs.flatten())/2 |
|---|
| 5491 | pairsw = [] |
|---|
| 5492 | for ip in range(Npairs): |
|---|
| 5493 | if Npairs == 0: |
|---|
| 5494 | break |
|---|
| 5495 | else: |
|---|
| 5496 | allpairs = grid_combinations(pairs[ip,0],pairs[ip,1]) |
|---|
| 5497 | for iap in range(len(allpairs)): |
|---|
| 5498 | ppos = allpairs[iap] |
|---|
| 5499 | iipos = xpos + ppos[0] |
|---|
| 5500 | jjpos = ypos + ppos[1] |
|---|
| 5501 | |
|---|
| 5502 | # Creation of a matrix with all the grid points at each time-step |
|---|
| 5503 | if iipos >= 0 and iipos < dx and jjpos >= 0 and jjpos < dy: |
|---|
| 5504 | pairsw.append([iipos,jjpos]) |
|---|
| 5505 | |
|---|
| 5506 | SQradiipos[SQradii[ir]] = pairsw |
|---|
| 5507 | |
|---|
| 5508 | return radiipos, SQradiipos |
|---|
| 5509 | |
|---|
| 5510 | def hor_weight_int(SWval, SEval, NWval, NEval, xpos, ypos): |
|---|
| 5511 | """ Function to weighted by distance interpolate a horizontal value from it closest 4 neighbourgs |
|---|
| 5512 | field= a 4 points representing the environment of a given point |
|---|
| 5513 | xpos, ypos: position to which one want to interpolate (relative to 0,0 at the corner SW) |
|---|
| 5514 | >>> hor_weight_int(1., 1., 1., 2., 0.75, 0.75) |
|---|
| 5515 | 1.44888128193 |
|---|
| 5516 | """ |
|---|
| 5517 | fname = 'hor_weight_int' |
|---|
| 5518 | |
|---|
| 5519 | vals = np.array([SWval, SEval, NWval, NEval]) |
|---|
| 5520 | print vals |
|---|
| 5521 | if xpos > 1.: |
|---|
| 5522 | print errormsg |
|---|
| 5523 | print ' ' + fname + ': Wrong position to interpolate! (',xpos,',',ypos, \ |
|---|
| 5524 | ') must be within (1,1) !!' |
|---|
| 5525 | quit(-1) |
|---|
| 5526 | |
|---|
| 5527 | vertexs = np.zeros((4,2), dtype=np.float) |
|---|
| 5528 | vertexs[0,0] = 0. |
|---|
| 5529 | vertexs[0,1] = 0. |
|---|
| 5530 | vertexs[1,0] = 1. |
|---|
| 5531 | vertexs[1,1] = 0. |
|---|
| 5532 | vertexs[2,0] = 0. |
|---|
| 5533 | vertexs[2,1] = 1. |
|---|
| 5534 | vertexs[3,0] = 1. |
|---|
| 5535 | vertexs[3,1] = 1. |
|---|
| 5536 | print vertexs |
|---|
| 5537 | |
|---|
| 5538 | dist = np.sqrt((vertexs[:,0]-xpos)**2 + (vertexs[:,1]-ypos)**2) |
|---|
| 5539 | print dist |
|---|
| 5540 | done = False |
|---|
| 5541 | for id in range(4): |
|---|
| 5542 | if dist[id] == 0.: |
|---|
| 5543 | intval = vals[id] |
|---|
| 5544 | done = True |
|---|
| 5545 | |
|---|
| 5546 | if not done: intval = np.sum(vals/dist)/np.sum(1./dist) |
|---|
| 5547 | |
|---|
| 5548 | return intval |
|---|
| 5549 | |
|---|
| 5550 | def rmNOnum(string): |
|---|
| 5551 | """ Removing from a string all that characters which are not numbers |
|---|
| 5552 | # From: http://stackoverflow.com/questions/4289331/python-extract-numbers-from-a-string |
|---|
| 5553 | """ |
|---|
| 5554 | fname = 'rmNOnum' |
|---|
| 5555 | |
|---|
| 5556 | newstr = str(re.findall("[-+]?\d+[\.]?\d*", string)[0]) |
|---|
| 5557 | |
|---|
| 5558 | return newstr |
|---|
| 5559 | |
|---|
| 5560 | def values_fortran_fmt(lin,fFMT): |
|---|
| 5561 | """ Function to give back the values of an ASCII line according to its fortran printing format |
|---|
| 5562 | lin= ASCII line |
|---|
| 5563 | fFMT= list with the fortran FORMAT formats |
|---|
| 5564 | forline = 'Natchitoches (RGNL) 1 11 0011 ( 31.733, -93.100) ( 28, 157) ( 31.761, -93.113) 41.2 meters' |
|---|
| 5565 | formats = ['A26', 'I2', 'I3', 'A6', 'A2', 'F7.3', 'A1', 'F8.3', 'A3', 'I4', 'A1', 'I4', |
|---|
| 5566 | 'A3', 'F7.3', 'A1', 'F8.3', 'A2', 'F6.1', 'A7'] |
|---|
| 5567 | >>> values_fortran_fmt(forline, formats) |
|---|
| 5568 | ['Natchitoches (RGNL) ', 1, 11, ' 0011 ', ' ( ', 31.733, ', ', -93.1, ') ( ', 28, ', ', 157, ') |
|---|
| 5569 | ( ', 31.761, ', ', -93.113, ') ', 41.2, ' meters'] |
|---|
| 5570 | """ |
|---|
| 5571 | fname = 'values_fortran_fmt' |
|---|
| 5572 | |
|---|
| 5573 | afmts = ['A', 'D', 'F', 'I'] |
|---|
| 5574 | |
|---|
| 5575 | if lin == 'h': |
|---|
| 5576 | print fname + '_____________________________________________________________' |
|---|
| 5577 | print values_fortran_fmt.__doc__ |
|---|
| 5578 | quit() |
|---|
| 5579 | |
|---|
| 5580 | fvalues = [] |
|---|
| 5581 | ichar=0 |
|---|
| 5582 | ival = 0 |
|---|
| 5583 | for ft in fFMT: |
|---|
| 5584 | Lft = len(ft) |
|---|
| 5585 | if ft[0:1] == 'A' or ft[0:1] == 'a': |
|---|
| 5586 | Lfmt = int(ft[1:Lft+1]) |
|---|
| 5587 | fvalues.append(lin[ichar:ichar+Lfmt+1]) |
|---|
| 5588 | ichar = ichar + Lfmt |
|---|
| 5589 | elif ft[0:1] == 'F' or ft[0:1] == 'f': |
|---|
| 5590 | if ft.find('.') != -1: |
|---|
| 5591 | Lft = len(ft.split('.')[0]) |
|---|
| 5592 | Lfmt = int(ft[1:Lft]) |
|---|
| 5593 | fvalues.append(np.float(rmNOnum(lin[ichar:ichar+Lfmt+1]))) |
|---|
| 5594 | ichar = ichar + Lfmt |
|---|
| 5595 | elif ft[0:1] == 'D' or ft[0:1] == 'd': |
|---|
| 5596 | if ft.find('.') != -1: |
|---|
| 5597 | Lft = len(ft.split('.')[0]) |
|---|
| 5598 | Lfmt = int(ft[1:Lft]) |
|---|
| 5599 | fvalues.append(np.float64(rmNOnum(lin[ichar:ichar+Lfmt+1]))) |
|---|
| 5600 | ichar = ichar + Lfmt |
|---|
| 5601 | elif ft[0:1] == 'I' or ft[0:1] == 'i': |
|---|
| 5602 | Lfmt = int(ft[1:Lft+1]) |
|---|
| 5603 | fvalues.append(int(rmNOnum(lin[ichar:ichar+Lfmt+1]))) |
|---|
| 5604 | ichar = ichar + Lfmt |
|---|
| 5605 | elif ft.find('X') != -1 or ft.find('x') != -1: |
|---|
| 5606 | print ' ' + fname + ': skipping space' |
|---|
| 5607 | ichar = ichar + int(rmNOnum(ft)) |
|---|
| 5608 | else: |
|---|
| 5609 | print errormsg |
|---|
| 5610 | print ' ' + fname + ": format '" + ft[0:1] + "' not ready!!" |
|---|
| 5611 | print ' Available formats:',afmts |
|---|
| 5612 | quit(-1) |
|---|
| 5613 | |
|---|
| 5614 | ival = ival + 1 |
|---|
| 5615 | |
|---|
| 5616 | return fvalues |
|---|
| 5617 | |
|---|
| 5618 | def reduce_last_spaces(string): |
|---|
| 5619 | """ Function to reduce the last right spaces from a string |
|---|
| 5620 | string= string to remove the spaces at the righ side of the string |
|---|
| 5621 | >>> reduce_last_spaces('Hola Lluis ') |
|---|
| 5622 | 'Hola Lluis' |
|---|
| 5623 | """ |
|---|
| 5624 | fname = 'reduce_last_spaces' |
|---|
| 5625 | |
|---|
| 5626 | Lstring = len(string) |
|---|
| 5627 | |
|---|
| 5628 | firstspace = -1 |
|---|
| 5629 | for istr in range(Lstring-1,0,-1): |
|---|
| 5630 | if string[istr:istr+1] == ' ': |
|---|
| 5631 | firstspace = istr |
|---|
| 5632 | else: |
|---|
| 5633 | break |
|---|
| 5634 | |
|---|
| 5635 | if firstspace == -1: |
|---|
| 5636 | print errormsg |
|---|
| 5637 | print ' ' + fname + ": string '" + string + "' is not ended by spaces!!" |
|---|
| 5638 | print ' char. number of last character:',ord(string[Lstring:Lstring+1]) |
|---|
| 5639 | quit(-1) |
|---|
| 5640 | |
|---|
| 5641 | newstr = string[0:firstspace] |
|---|
| 5642 | |
|---|
| 5643 | return newstr |
|---|
| 5644 | |
|---|
| 5645 | def squared_radial(Npt): |
|---|
| 5646 | """ Function to provide the series of radii as composite of pairs (x,y) of gid cells |
|---|
| 5647 | Npt= largest amount of grid points on x and y directions |
|---|
| 5648 | >>> squared_radial(5) |
|---|
| 5649 | [[0.0, 0, 0], [1.0, 1, 0], [1.4142135623730951, 1, 1], [2.0, 2, 0], [2.2360679774997898, 2, 1], [2.8284271247461903, 2, 2], [3.0, 3, 0], [3.1622776601683795, 3, 1], [3.6055512754639891, 3, 2], [4.0, 4, 0], [4.1231056256176606, 4, 1], [4.2426406871192848, 3, 3], [4.4721359549995796, 4, 2], [5.0, array([4, 3]), array([5, 0])], [5.0990195135927845, 5, 1], [5.3851648071345037, 5, 2], [5.6568542494923806, 4, 4], [5.8309518948453007, 5, 3], [6.4031242374328485, 5, 4], [7.0710678118654755, 5, 5]] |
|---|
| 5650 | """ |
|---|
| 5651 | fname = 'squared_radial' |
|---|
| 5652 | |
|---|
| 5653 | radii = [] |
|---|
| 5654 | gridradii = {} |
|---|
| 5655 | singradii = [] |
|---|
| 5656 | Nradii = {} |
|---|
| 5657 | |
|---|
| 5658 | # Computing all radii |
|---|
| 5659 | ## |
|---|
| 5660 | for i in range(Npt+1): |
|---|
| 5661 | if np.mod(i,100) == 0: print 'done: ',i |
|---|
| 5662 | for j in range(i+1): |
|---|
| 5663 | # ir = np.float(i) |
|---|
| 5664 | # jr = np.float(j) |
|---|
| 5665 | ir = np.float64(i) |
|---|
| 5666 | jr = np.float64(j) |
|---|
| 5667 | rad = np.sqrt(ir*ir + jr*jr) |
|---|
| 5668 | if not searchInlist(singradii, rad): |
|---|
| 5669 | singradii.append(rad) |
|---|
| 5670 | gridradii[rad] = np.array([i,j], dtype=int) |
|---|
| 5671 | Nradii[rad] = 1 |
|---|
| 5672 | else: |
|---|
| 5673 | # print warnmsg |
|---|
| 5674 | # print ' ' + fname + ': repeated radii',rad,'!!' |
|---|
| 5675 | # print ' Previous values:', gridradii[rad] |
|---|
| 5676 | |
|---|
| 5677 | oldvalues = gridradii[rad] |
|---|
| 5678 | Nradii[rad] = Nradii[rad] + 1 |
|---|
| 5679 | newvalues = np.zeros((Nradii[rad],2), dtype=int) |
|---|
| 5680 | if Nradii[rad] == 2: |
|---|
| 5681 | newvalues[0,:] = oldvalues |
|---|
| 5682 | Nstart = 1 |
|---|
| 5683 | else: |
|---|
| 5684 | Nstart = 0 |
|---|
| 5685 | for Nr in range(Nstart,Nradii[rad]-1): |
|---|
| 5686 | newvalues[Nr,:] = oldvalues[Nr,:] |
|---|
| 5687 | newvalues[Nradii[rad]-1,:] = np.array([i,j], dtype=int) |
|---|
| 5688 | gridradii[rad] = newvalues |
|---|
| 5689 | |
|---|
| 5690 | # Sorting values |
|---|
| 5691 | ## |
|---|
| 5692 | ## Only required to downwrite the output file |
|---|
| 5693 | # radiif = open('SQradii.dat', 'w') |
|---|
| 5694 | # radii = [] |
|---|
| 5695 | # prevrad = 0. |
|---|
| 5696 | # for rad in sorted(gridradii.keys()): |
|---|
| 5697 | # dradii = rad - prevrad |
|---|
| 5698 | ## Check fo repeated values |
|---|
| 5699 | # radii.append([rad, gridradii[rad][0], gridradii[rad][1]]) |
|---|
| 5700 | # radiif.write(str(rad) + ' ' + str(dradii) + ' ' + str(Nradii[rad]) + ' [ '+ \ |
|---|
| 5701 | # numVector_String(gridradii[rad].flatten(), ' ') + ' ] \n') |
|---|
| 5702 | # prevrad = rad.copy() |
|---|
| 5703 | |
|---|
| 5704 | # radiif.close() |
|---|
| 5705 | return gridradii |
|---|
| 5706 | |
|---|
| 5707 | def fillvalue_kind(vartype, fillval0): |
|---|
| 5708 | """ Function to provide the fiven fill_Value for a kind of variable |
|---|
| 5709 | [vartype]= type of the variable |
|---|
| 5710 | [fillval0]= value to take as fill value, 'std' for the standard value |
|---|
| 5711 | Float = 1.e20 |
|---|
| 5712 | Character = '-' |
|---|
| 5713 | Integer = -99999 |
|---|
| 5714 | Integer16 = -99999 |
|---|
| 5715 | Float64 = 1.e20 |
|---|
| 5716 | Integer32 = -99999 |
|---|
| 5717 | """ |
|---|
| 5718 | fname = 'fillvalue_kind' |
|---|
| 5719 | |
|---|
| 5720 | if vartype == type('c'): |
|---|
| 5721 | if fillval0 == 'std': |
|---|
| 5722 | fillval = fillvalueC |
|---|
| 5723 | else: |
|---|
| 5724 | fillval = fillval0 |
|---|
| 5725 | elif vartype == type(int(1)): |
|---|
| 5726 | if fillval0 == 'std': |
|---|
| 5727 | fillval = fillValueI |
|---|
| 5728 | else: |
|---|
| 5729 | fillval = int(fillval0) |
|---|
| 5730 | elif vartype == type(np.int16(1)): |
|---|
| 5731 | if fillval0 == 'std': |
|---|
| 5732 | fillval = np.int(9999999) |
|---|
| 5733 | else: |
|---|
| 5734 | fillval = np.int16(fillval0) |
|---|
| 5735 | elif vartype == type(np.int32(1)): |
|---|
| 5736 | if fillval0 == 'std': |
|---|
| 5737 | fillval = np.int32(fillValueI) |
|---|
| 5738 | else: |
|---|
| 5739 | fillval = np.int32(fillval0) |
|---|
| 5740 | elif vartype == type(np.float(1.)): |
|---|
| 5741 | if fillval0 == 'std': |
|---|
| 5742 | fillval = np.float(fillValueF) |
|---|
| 5743 | else: |
|---|
| 5744 | fillval = np.float(fillval0) |
|---|
| 5745 | elif vartype == type(np.float32(1.)): |
|---|
| 5746 | if fillval0 == 'std': |
|---|
| 5747 | fillval = np.float32(fillValueF) |
|---|
| 5748 | else: |
|---|
| 5749 | fillval = np.float32(fillval0) |
|---|
| 5750 | elif vartype == type(np.float64(1.)): |
|---|
| 5751 | if fillval0 == 'std': |
|---|
| 5752 | fillval = np.float64(fillValueF) |
|---|
| 5753 | else: |
|---|
| 5754 | fillval = np.float64(fillval0) |
|---|
| 5755 | else: |
|---|
| 5756 | print errormsg |
|---|
| 5757 | print ' ' + fname + ': variable type', vartype, 'not ready !!' |
|---|
| 5758 | quit(-1) |
|---|
| 5759 | |
|---|
| 5760 | return fillval |
|---|
| 5761 | |
|---|
| 5762 | def varval_ind(var, ind): |
|---|
| 5763 | """ Function to provide a value from a variable providing a value for each index |
|---|
| 5764 | var= variable |
|---|
| 5765 | ind= list of indices for each dimension of the variable |
|---|
| 5766 | """ |
|---|
| 5767 | fname = 'varval_ind' |
|---|
| 5768 | if len(var.shape) != len(ind): |
|---|
| 5769 | print errormsg |
|---|
| 5770 | print ' ' + fname + ': different shape:', var.shape,'and indices:', ind, '!!' |
|---|
| 5771 | quit(-1) |
|---|
| 5772 | |
|---|
| 5773 | varslice=[] |
|---|
| 5774 | for iv in ind: |
|---|
| 5775 | varslice.append(iv) |
|---|
| 5776 | |
|---|
| 5777 | val = varval_ind[slice(varslice)] |
|---|
| 5778 | |
|---|
| 5779 | return val |
|---|
| 5780 | |
|---|
| 5781 | def CoarselonlatFind(ilon, ilat, lonv, latv, per): |
|---|
| 5782 | """ Function to search a given value from a coarser version of the data |
|---|
| 5783 | ilon, ilat= original 2D matrices with the longitudes and the latitudes |
|---|
| 5784 | lonv, latv= longitude and latitude to find |
|---|
| 5785 | per= period (as fraction over 1) of the fractions of the original grid to use to explore |
|---|
| 5786 | >>> npt=100 |
|---|
| 5787 | >>> lonval0 = np.arange(0.,npt*1.,1.) |
|---|
| 5788 | >>> latval0 = np.arange(0.,npt*1.,1.) |
|---|
| 5789 | >>> lonval = np.zeros((npt,npt), dtype=np.float) |
|---|
| 5790 | >>> latval = np.zeros((npt,npt), dtype=np.float) |
|---|
| 5791 | |
|---|
| 5792 | >>> for i in range(npt): |
|---|
| 5793 | >>> lonval[:,i] = lonval0[i] |
|---|
| 5794 | >>> latval[i,:] = latval0[i] |
|---|
| 5795 | >>> CoarselonlatFind(lonval, latval, 5.25, 5.25, .1) |
|---|
| 5796 | (array([5, 5]), 0.35355339059327379) |
|---|
| 5797 | """ |
|---|
| 5798 | fname = 'CoarselonlatFind' |
|---|
| 5799 | |
|---|
| 5800 | dx = ilon.shape[1] |
|---|
| 5801 | dy = ilon.shape[0] |
|---|
| 5802 | |
|---|
| 5803 | nlon = np.min(ilon) |
|---|
| 5804 | xlon = np.max(ilon) |
|---|
| 5805 | nlat = np.min(ilat) |
|---|
| 5806 | xlat = np.max(ilat) |
|---|
| 5807 | |
|---|
| 5808 | if lonv < nlon or lonv > xlon: |
|---|
| 5809 | print errormsg |
|---|
| 5810 | print ' ' + fname + ': longitude outside data range!!' |
|---|
| 5811 | print ' given value:', lonv,'outside [',nlon,',',xlon,']' |
|---|
| 5812 | quit(-1) |
|---|
| 5813 | if latv < nlat or latv > xlat: |
|---|
| 5814 | print errormsg |
|---|
| 5815 | print ' ' + fname + ': latitude outside data range!!' |
|---|
| 5816 | print ' given value:', latv,'outside [',nlat,',',xlat,']' |
|---|
| 5817 | quit(-1) |
|---|
| 5818 | |
|---|
| 5819 | fracx = int(dx*per) |
|---|
| 5820 | fracy = int(dy*per) |
|---|
| 5821 | |
|---|
| 5822 | fraclon = ilon[::fracy,::fracx] |
|---|
| 5823 | fraclat = ilat[::fracy,::fracx] |
|---|
| 5824 | fracdx = fraclon.shape[1] |
|---|
| 5825 | fracdy = fraclon.shape[0] |
|---|
| 5826 | |
|---|
| 5827 | # print 'fraclon _______' |
|---|
| 5828 | # print fraclon |
|---|
| 5829 | |
|---|
| 5830 | # print 'fraclat _______' |
|---|
| 5831 | # print fraclat |
|---|
| 5832 | |
|---|
| 5833 | # Fraction point |
|---|
| 5834 | difffraclonlat = np.sqrt((fraclon-lonv)**2. + (fraclat-latv)**2.) |
|---|
| 5835 | mindifffracLl = np.min(difffraclonlat) |
|---|
| 5836 | ilatlonfrac = index_mat(difffraclonlat, mindifffracLl) |
|---|
| 5837 | |
|---|
| 5838 | # print 'mindifffracLl:', mindifffracLl, 'ilatlonfrac:', ilatlonfrac |
|---|
| 5839 | # print 'frac lon, lat:', fraclon[ilatlonfrac[0],ilatlonfrac[1]], ',', \ |
|---|
| 5840 | # fraclat[ilatlonfrac[0],ilatlonfrac[1]] |
|---|
| 5841 | # print 'values lon, lat:', lonv, latv |
|---|
| 5842 | |
|---|
| 5843 | # Providing fraction range |
|---|
| 5844 | if fraclon[ilatlonfrac[0],ilatlonfrac[1]] >= lonv and \ |
|---|
| 5845 | fraclat[ilatlonfrac[0],ilatlonfrac[1]] >= latv: |
|---|
| 5846 | # ifracbeg = [ilatlonfrac[0]-1, ilatlonfrac[1]-1] |
|---|
| 5847 | # ifracend = [ilatlonfrac[0], ilatlonfrac[1]] |
|---|
| 5848 | if ilatlonfrac[0] > 0: |
|---|
| 5849 | iybeg = (ilatlonfrac[0]-1)*fracy |
|---|
| 5850 | iyend = ilatlonfrac[0]*fracy+1 |
|---|
| 5851 | else: |
|---|
| 5852 | iybeg = 0 |
|---|
| 5853 | iyend = fracy+1 |
|---|
| 5854 | if ilatlonfrac[1] > 0: |
|---|
| 5855 | ixbeg = (ilatlonfrac[1]-1)*fracx |
|---|
| 5856 | ixend = ilatlonfrac[1]*fracx+1 |
|---|
| 5857 | else: |
|---|
| 5858 | ixbeg = 0 |
|---|
| 5859 | ixend = fracx+1 |
|---|
| 5860 | elif fraclon[ilatlonfrac[0],ilatlonfrac[1]] < lonv and \ |
|---|
| 5861 | fraclat[ilatlonfrac[0],ilatlonfrac[1]] >= latv: |
|---|
| 5862 | # ifracbeg = [ilatlonfrac[0]-1, ilatlonfrac[1]] |
|---|
| 5863 | # ifracend = [ilatlonfrac[0], ilatlonfrac[1]+1] |
|---|
| 5864 | if ilatlonfrac[0] > 0: |
|---|
| 5865 | iybeg = (ilatlonfrac[0]-1)*fracy |
|---|
| 5866 | iyend = ilatlonfrac[0]*fracy+1 |
|---|
| 5867 | else: |
|---|
| 5868 | iybeg = 0 |
|---|
| 5869 | iyend = fracy+1 |
|---|
| 5870 | if ilatlonfrac[1] < fracdx: |
|---|
| 5871 | if ilatlonfrac[1] != 0: |
|---|
| 5872 | ixbeg = (ilatlonfrac[1]-1)*fracx |
|---|
| 5873 | ixend = ilatlonfrac[1]*fracx+1 |
|---|
| 5874 | else: |
|---|
| 5875 | ixbeg = 0 |
|---|
| 5876 | ixend = fracx+1 |
|---|
| 5877 | else: |
|---|
| 5878 | ixbeg = fracdx*fracx |
|---|
| 5879 | ixend = dx+1 |
|---|
| 5880 | elif fraclon[ilatlonfrac[0],ilatlonfrac[1]] < lonv and \ |
|---|
| 5881 | fraclat[ilatlonfrac[0],ilatlonfrac[1]] < latv: |
|---|
| 5882 | # ifracbeg = [ilatlonfrac[0], ilatlonfrac[1]] |
|---|
| 5883 | # ifracend = [ilatlonfrac[0]+1, ilatlonfrac[1]+1] |
|---|
| 5884 | if ilatlonfrac[0] < fracdy: |
|---|
| 5885 | if ilatlonfrac[0] != 0: |
|---|
| 5886 | iybeg = (ilatlonfrac[0]-1)*fracy |
|---|
| 5887 | iyend = ilatlonfrac[0]*fracy+1 |
|---|
| 5888 | else: |
|---|
| 5889 | iybeg = 0 |
|---|
| 5890 | iyend = fracy+1 |
|---|
| 5891 | else: |
|---|
| 5892 | iybeg = fracdy*fracy |
|---|
| 5893 | iyend = dy+1 |
|---|
| 5894 | if ilatlonfrac[1] < fracdx: |
|---|
| 5895 | if ilatlonfrac[1] != 0: |
|---|
| 5896 | ixbeg = (ilatlonfrac[1]-1)*fracx |
|---|
| 5897 | ixend = ilatlonfrac[1]*fracx+1 |
|---|
| 5898 | else: |
|---|
| 5899 | ixbeg = 0 |
|---|
| 5900 | ixend = fracx+1 |
|---|
| 5901 | else: |
|---|
| 5902 | ixbeg = fracdx*fracx |
|---|
| 5903 | ixend = dx+1 |
|---|
| 5904 | elif fraclon[ilatlonfrac[0],ilatlonfrac[1]] >= lonv and \ |
|---|
| 5905 | fraclat[ilatlonfrac[0],ilatlonfrac[1]] < latv: |
|---|
| 5906 | # ifracbeg = [ilatlonfrac[0], ilatlonfrac[1]-1] |
|---|
| 5907 | # ifracend = [ilatlonfrac[0]+1, ilatlonfrac[1]] |
|---|
| 5908 | if ilatlonfrac[0] < fracdy: |
|---|
| 5909 | if ilatlonfrac[0] != 0: |
|---|
| 5910 | iybeg = (ilatlonfrac[0]-1)*fracy |
|---|
| 5911 | iyend = ilatlonfrac[0]*fracy+1 |
|---|
| 5912 | else: |
|---|
| 5913 | iybeg = 0 |
|---|
| 5914 | iyend = fracy+1 |
|---|
| 5915 | else: |
|---|
| 5916 | iybeg = fracdy*fracy |
|---|
| 5917 | iyend = dy+1 |
|---|
| 5918 | if ilatlonfrac[1] > 0: |
|---|
| 5919 | ixbeg = (ilatlonfrac[1]-1)*fracx |
|---|
| 5920 | ixend = ilatlonfrac[1]*fracx+1 |
|---|
| 5921 | else: |
|---|
| 5922 | ixbeg = 0 |
|---|
| 5923 | ixend = fracx+1 |
|---|
| 5924 | |
|---|
| 5925 | # ibeg = [iybeg, ixbeg] |
|---|
| 5926 | # iend = [iyend, ixend] |
|---|
| 5927 | # print 'find window; beginning', ibeg, 'end:', iend |
|---|
| 5928 | lon = ilon[iybeg:iyend,ixbeg:ixend] |
|---|
| 5929 | lat = ilat[iybeg:iyend,ixbeg:ixend] |
|---|
| 5930 | |
|---|
| 5931 | # print 'lon _______' |
|---|
| 5932 | # print lon |
|---|
| 5933 | # print 'lat _______' |
|---|
| 5934 | # print lat |
|---|
| 5935 | |
|---|
| 5936 | # Find point |
|---|
| 5937 | difflonlat = np.sqrt((lon-lonv)**2. + (lat-latv)**2.) |
|---|
| 5938 | mindiffLl = np.min(difflonlat) |
|---|
| 5939 | ilatlon = index_mat(difflonlat, mindiffLl) |
|---|
| 5940 | |
|---|
| 5941 | ilatlon[0] = ilatlon[0] + iybeg |
|---|
| 5942 | ilatlon[1] = ilatlon[1] + ixbeg |
|---|
| 5943 | |
|---|
| 5944 | # print 'mindiffLl:', mindiffLl, 'ilatlon:', ilatlon |
|---|
| 5945 | # print 'lon, lat:', lon[ilatlon[0],ilatlon[1]], ',', lat[ilatlon[0],ilatlon[1]] |
|---|
| 5946 | # quit() |
|---|
| 5947 | |
|---|
| 5948 | return ilatlon, mindiffLl |
|---|
| 5949 | |
|---|
| 5950 | def CoarselonlatFindAll(onc, ilon, ilat, longvs, latvs, per, frac, out): |
|---|
| 5951 | """ Function to search all values from a coarser version of the data |
|---|
| 5952 | onc= netCDF object from an existing file |
|---|
| 5953 | ilon, ilat= original 2D matrices with the longitudes and the latitudes |
|---|
| 5954 | lonv, latv= longitude and latitude to find |
|---|
| 5955 | per= period (as fraction over 1) of the fractions of the original grid to use to explore |
|---|
| 5956 | frac= frequency of points at which syncronize file with calculations |
|---|
| 5957 | out= True/False if outcome is required |
|---|
| 5958 | >>> npt=100 |
|---|
| 5959 | >>> lonval0 = np.arange(0.,npt*1.,1.) |
|---|
| 5960 | >>> latval0 = np.arange(0.,npt*1.,1.) |
|---|
| 5961 | >>> lonval = np.zeros((npt,npt), dtype=np.float) |
|---|
| 5962 | >>> latval = np.zeros((npt,npt), dtype=np.float) |
|---|
| 5963 | >>> lonvalues = np.arange(0.25,10.,0.25) |
|---|
| 5964 | >>> latvalues = np.arange(0.25,10.,0.25) |
|---|
| 5965 | |
|---|
| 5966 | >>> for i in range(npt): |
|---|
| 5967 | >>> lonval[:,i] = lonval0[i] |
|---|
| 5968 | >>> latval[i,:] = latval0[i] |
|---|
| 5969 | >>> CoarselonlatFindAll(onewnc, lonval, latval, lovalues, latvalues, .1, 100) |
|---|
| 5970 | (array([5, 5]), 0.35355339059327379) |
|---|
| 5971 | """ |
|---|
| 5972 | fname = 'CoarselonlatFindAll' |
|---|
| 5973 | |
|---|
| 5974 | dx = ilon.shape[1] |
|---|
| 5975 | dy = ilon.shape[0] |
|---|
| 5976 | |
|---|
| 5977 | nlon = np.min(ilon) |
|---|
| 5978 | xlon = np.max(ilon) |
|---|
| 5979 | nlat = np.min(ilat) |
|---|
| 5980 | xlat = np.max(ilat) |
|---|
| 5981 | |
|---|
| 5982 | Nallpts = len(longvs) |
|---|
| 5983 | |
|---|
| 5984 | fracx = int(dx*per) |
|---|
| 5985 | fracy = int(dy*per) |
|---|
| 5986 | |
|---|
| 5987 | fraclon = ilon[::fracy,::fracx] |
|---|
| 5988 | fraclat = ilat[::fracy,::fracx] |
|---|
| 5989 | fracdx = fraclon.shape[1] |
|---|
| 5990 | fracdy = fraclon.shape[0] |
|---|
| 5991 | |
|---|
| 5992 | # print 'fraclon _______' |
|---|
| 5993 | # print fraclon |
|---|
| 5994 | |
|---|
| 5995 | # print 'fraclat _______' |
|---|
| 5996 | # print fraclat |
|---|
| 5997 | |
|---|
| 5998 | if out: |
|---|
| 5999 | ilatlon = np.zeros((2,Nallpts), dtype=int) |
|---|
| 6000 | mindiffLl = np.zeros((Nallpts), dtype=np.float) |
|---|
| 6001 | |
|---|
| 6002 | iptpos=0 |
|---|
| 6003 | for iv in range(Nallpts): |
|---|
| 6004 | lonv = longvs[iv] |
|---|
| 6005 | latv = latvs[iv] |
|---|
| 6006 | |
|---|
| 6007 | if lonv < nlon or lonv > xlon: |
|---|
| 6008 | print errormsg |
|---|
| 6009 | print ' ' + fname + ': longitude outside data range!!' |
|---|
| 6010 | print ' given value:', lonv,'outside [',nlon,',',xlon,']' |
|---|
| 6011 | quit(-1) |
|---|
| 6012 | if latv < nlat or latv > xlat: |
|---|
| 6013 | print errormsg |
|---|
| 6014 | print ' ' + fname + ': latitude outside data range!!' |
|---|
| 6015 | print ' given value:', latv,'outside [',nlat,',',xlat,']' |
|---|
| 6016 | quit(-1) |
|---|
| 6017 | |
|---|
| 6018 | # Fraction point |
|---|
| 6019 | difffraclonlat = np.sqrt((fraclon-lonv)**2. + (fraclat-latv)**2.) |
|---|
| 6020 | mindifffracLl = np.min(difffraclonlat) |
|---|
| 6021 | ilatlonfrac = index_mat(difffraclonlat, mindifffracLl) |
|---|
| 6022 | |
|---|
| 6023 | # print 'values lon, lat:', lonv, latv |
|---|
| 6024 | # print 'mindifffracLl:', mindifffracLl, 'ilatlonfrac:', ilatlonfrac |
|---|
| 6025 | # print 'frac lon, lat:', fraclon[ilatlonfrac[0],ilatlonfrac[1]], ',', \ |
|---|
| 6026 | # fraclat[ilatlonfrac[0],ilatlonfrac[1]] |
|---|
| 6027 | |
|---|
| 6028 | # Providing fraction range |
|---|
| 6029 | if fraclon[ilatlonfrac[0],ilatlonfrac[1]] >= lonv and \ |
|---|
| 6030 | fraclat[ilatlonfrac[0],ilatlonfrac[1]] >= latv: |
|---|
| 6031 | # ifracbeg = [ilatlonfrac[0]-1, ilatlonfrac[1]-1] |
|---|
| 6032 | # ifracend = [ilatlonfrac[0], ilatlonfrac[1]] |
|---|
| 6033 | if ilatlonfrac[0] > 0: |
|---|
| 6034 | iybeg = (ilatlonfrac[0]-1)*fracy |
|---|
| 6035 | iyend = ilatlonfrac[0]*fracy+1 |
|---|
| 6036 | else: |
|---|
| 6037 | iybeg = 0 |
|---|
| 6038 | iyend = fracy+1 |
|---|
| 6039 | if ilatlonfrac[1] > 0: |
|---|
| 6040 | ixbeg = (ilatlonfrac[1]-1)*fracx |
|---|
| 6041 | ixend = ilatlonfrac[1]*fracx+1 |
|---|
| 6042 | else: |
|---|
| 6043 | ixbeg = 0 |
|---|
| 6044 | ixend = fracx+1 |
|---|
| 6045 | elif fraclon[ilatlonfrac[0],ilatlonfrac[1]] < lonv and \ |
|---|
| 6046 | fraclat[ilatlonfrac[0],ilatlonfrac[1]] >= latv: |
|---|
| 6047 | # ifracbeg = [ilatlonfrac[0]-1, ilatlonfrac[1]] |
|---|
| 6048 | # ifracend = [ilatlonfrac[0], ilatlonfrac[1]+1] |
|---|
| 6049 | if ilatlonfrac[0] > 0: |
|---|
| 6050 | iybeg = (ilatlonfrac[0]-1)*fracy |
|---|
| 6051 | iyend = ilatlonfrac[0]*fracy+1 |
|---|
| 6052 | else: |
|---|
| 6053 | iybeg = 0 |
|---|
| 6054 | iyend = fracy+1 |
|---|
| 6055 | if ilatlonfrac[1] < fracdx: |
|---|
| 6056 | if ilatlonfrac[1] != 0: |
|---|
| 6057 | ixbeg = (ilatlonfrac[1]-1)*fracx |
|---|
| 6058 | ixend = ilatlonfrac[1]*fracx+1 |
|---|
| 6059 | else: |
|---|
| 6060 | ixbeg = 0 |
|---|
| 6061 | ixend = fracx+1 |
|---|
| 6062 | else: |
|---|
| 6063 | ixbeg = fracdx*fracx |
|---|
| 6064 | ixend = dx+1 |
|---|
| 6065 | elif fraclon[ilatlonfrac[0],ilatlonfrac[1]] < lonv and \ |
|---|
| 6066 | fraclat[ilatlonfrac[0],ilatlonfrac[1]] < latv: |
|---|
| 6067 | # ifracbeg = [ilatlonfrac[0], ilatlonfrac[1]] |
|---|
| 6068 | # ifracend = [ilatlonfrac[0]+1, ilatlonfrac[1]+1] |
|---|
| 6069 | if ilatlonfrac[0] < fracdy: |
|---|
| 6070 | if ilatlonfrac[0] != 0: |
|---|
| 6071 | iybeg = (ilatlonfrac[0]-1)*fracy |
|---|
| 6072 | iyend = ilatlonfrac[0]*fracy+1 |
|---|
| 6073 | else: |
|---|
| 6074 | iybeg = 0 |
|---|
| 6075 | iyend = fracy+1 |
|---|
| 6076 | else: |
|---|
| 6077 | iybeg = fracdy*fracy |
|---|
| 6078 | iyend = dy+1 |
|---|
| 6079 | if ilatlonfrac[1] < fracdx: |
|---|
| 6080 | if ilatlonfrac[1] != 0: |
|---|
| 6081 | ixbeg = (ilatlonfrac[1]-1)*fracx |
|---|
| 6082 | ixend = ilatlonfrac[1]*fracx+1 |
|---|
| 6083 | else: |
|---|
| 6084 | ixbeg = 0 |
|---|
| 6085 | ixend = fracx+1 |
|---|
| 6086 | else: |
|---|
| 6087 | ixbeg = fracdx*fracx |
|---|
| 6088 | ixend = dx+1 |
|---|
| 6089 | elif fraclon[ilatlonfrac[0],ilatlonfrac[1]] >= lonv and \ |
|---|
| 6090 | fraclat[ilatlonfrac[0],ilatlonfrac[1]] < latv: |
|---|
| 6091 | # ifracbeg = [ilatlonfrac[0], ilatlonfrac[1]-1] |
|---|
| 6092 | # ifracend = [ilatlonfrac[0]+1, ilatlonfrac[1]] |
|---|
| 6093 | if ilatlonfrac[0] < fracdy: |
|---|
| 6094 | if ilatlonfrac[0] != 0: |
|---|
| 6095 | iybeg = (ilatlonfrac[0]-1)*fracy |
|---|
| 6096 | iyend = ilatlonfrac[0]*fracy+1 |
|---|
| 6097 | else: |
|---|
| 6098 | iybeg = 0 |
|---|
| 6099 | iyend = fracy+1 |
|---|
| 6100 | else: |
|---|
| 6101 | iybeg = fracdy*fracy |
|---|
| 6102 | iyend = dy+1 |
|---|
| 6103 | if ilatlonfrac[1] > 0: |
|---|
| 6104 | ixbeg = (ilatlonfrac[1]-1)*fracx |
|---|
| 6105 | ixend = ilatlonfrac[1]*fracx+1 |
|---|
| 6106 | else: |
|---|
| 6107 | ixbeg = 0 |
|---|
| 6108 | ixend = fracx+1 |
|---|
| 6109 | |
|---|
| 6110 | # ibeg = [iybeg, ixbeg] |
|---|
| 6111 | # iend = [iyend, ixend] |
|---|
| 6112 | # print 'find window; beginning', ibeg, 'end:', iend |
|---|
| 6113 | lon = ilon[iybeg:iyend,ixbeg:ixend] |
|---|
| 6114 | lat = ilat[iybeg:iyend,ixbeg:ixend] |
|---|
| 6115 | # print 'Looking within _______' |
|---|
| 6116 | # print 'lon:',lon[0,0],',',lon[-1,-1] |
|---|
| 6117 | # print 'lat:',lat[0,0],',',lat[-1,-1] |
|---|
| 6118 | |
|---|
| 6119 | # print 'lon _______' |
|---|
| 6120 | # print lon |
|---|
| 6121 | # print 'lat _______' |
|---|
| 6122 | # print lat |
|---|
| 6123 | |
|---|
| 6124 | # Find point |
|---|
| 6125 | difflonlat = np.sqrt((lon-lonv)**2. + (lat-latv)**2.) |
|---|
| 6126 | if out: |
|---|
| 6127 | mindiffLl[iv] = np.min(difflonlat) |
|---|
| 6128 | ilatlon[:,iv] = index_mat(difflonlat, mindiffLl) |
|---|
| 6129 | ilatlon[0,iv] = ilatlon[0,iv] + iybeg |
|---|
| 6130 | ilatlon[1,iv] = ilatlon[1,iv] + ixbeg |
|---|
| 6131 | else: |
|---|
| 6132 | mindiffLl = np.min(difflonlat) |
|---|
| 6133 | ilatlon = index_mat(difflonlat, mindiffLl) |
|---|
| 6134 | ilatlon[0] = ilatlon[0] + iybeg |
|---|
| 6135 | ilatlon[1] = ilatlon[1] + ixbeg |
|---|
| 6136 | |
|---|
| 6137 | # print 'mindiffLl:', mindiffLl, 'ilatlon:', ilatlon |
|---|
| 6138 | # print 'lon, lat:', lon[ilatlon[0],ilatlon[1]], ',', lat[ilatlon[0],ilatlon[1]] |
|---|
| 6139 | # quit() |
|---|
| 6140 | |
|---|
| 6141 | # if mindiffLl == 0. and type(newvar[ilatlon[0],ilatlon[1]]) == type(amsk): |
|---|
| 6142 | # percendone(iv,Ninpts,0.5,'done:') |
|---|
| 6143 | if mindiffLl == 0.: |
|---|
| 6144 | iptpos = iptpos + 1 |
|---|
| 6145 | # Speeding it up! |
|---|
| 6146 | # if mindiffLl > mindiff: |
|---|
| 6147 | # print errormsg |
|---|
| 6148 | # print ' ' + fname + ': for point #', iv,'lon,lat in ' + \ |
|---|
| 6149 | # 'incomplet map:', lonvs[iv], ',', latvs[iv], 'there is ' + \ |
|---|
| 6150 | # 'not a set of lon,lat in the completed map closer than: ', \ |
|---|
| 6151 | # mindiff, '!!' |
|---|
| 6152 | # print ' minimum difference:', mindiffLl |
|---|
| 6153 | # quit(-1) |
|---|
| 6154 | |
|---|
| 6155 | # Speeding it up! |
|---|
| 6156 | # if ilatlon[0] >= 0 and ilatlon[1] >= 0: |
|---|
| 6157 | # newvar[ilatlon[0],ilatlon[1]] = ovar[iv] |
|---|
| 6158 | # newvarin[ilatlon[0],ilatlon[1]] = iv |
|---|
| 6159 | # newvarinpt[iv] = 1 |
|---|
| 6160 | # newvarindiff[iv] = mindiffLl |
|---|
| 6161 | newvar[ilatlon[0],ilatlon[1]] = ovar[iv] |
|---|
| 6162 | newvarin[ilatlon[0],ilatlon[1]] = iv |
|---|
| 6163 | newvarinpt[iv] = 1 |
|---|
| 6164 | newvarindiff[iv] = mindiffLl |
|---|
| 6165 | |
|---|
| 6166 | # print 'Lluis iv:', newvarin[ilatlon[0],ilatlon[1]], \ |
|---|
| 6167 | # 'localized:', newvarinpt[iv], 'values:', \ |
|---|
| 6168 | # newvar[ilatlon[0],ilatlon[1]], 'invalues:', ovar[iv], \ |
|---|
| 6169 | # 'mindist:', newvarindiff[iv], 'point:',ilatlon |
|---|
| 6170 | # else: |
|---|
| 6171 | # print errormsg |
|---|
| 6172 | # print ' ' + fname + ': point iv:', iv, 'at', lonvs[iv], ',', \ |
|---|
| 6173 | # latvs[iv],' not relocated !!' |
|---|
| 6174 | # print ' mindiffl:', mindiffLl, 'ilon:', ilatlon[1], \ |
|---|
| 6175 | # 'ilatlon:', ilatlon[1] |
|---|
| 6176 | # quit(-1) |
|---|
| 6177 | if np.mod(iptpos,fracs) == 0: |
|---|
| 6178 | print 'Lluis syncronizing!' |
|---|
| 6179 | onc.sync() |
|---|
| 6180 | if out: |
|---|
| 6181 | return ilatlon, mindiffLl |
|---|
| 6182 | else: |
|---|
| 6183 | return |
|---|
| 6184 | |
|---|
| 6185 | def lonlatFind(ilon, ilat, lonv, latv): |
|---|
| 6186 | """ Function to search a given value from a lon, lat 2D data |
|---|
| 6187 | ilon, ilat= original 2D matrices with the longitudes and the latitudes |
|---|
| 6188 | lonv, latv= longitude and latitude to find |
|---|
| 6189 | >>> npt=100 |
|---|
| 6190 | >>> lonval0 = np.arange(0.,npt*1.,1.) |
|---|
| 6191 | >>> latval0 = np.arange(0.,npt*1.,1.) |
|---|
| 6192 | >>> lonval = np.zeros((npt,npt), dtype=np.float) |
|---|
| 6193 | >>> latval = np.zeros((npt,npt), dtype=np.float) |
|---|
| 6194 | |
|---|
| 6195 | >>> for i in range(npt): |
|---|
| 6196 | >>> lonval[:,i] = lonval0[i] |
|---|
| 6197 | >>> latval[i,:] = latval0[i] |
|---|
| 6198 | >>> lonlatFind(lonval, latval, 5.25, 5.25) |
|---|
| 6199 | (array([5, 5]), 0.35355339059327379) |
|---|
| 6200 | """ |
|---|
| 6201 | fname = 'lonlatFind' |
|---|
| 6202 | |
|---|
| 6203 | dx = ilon.shape[1] |
|---|
| 6204 | dy = ilon.shape[0] |
|---|
| 6205 | |
|---|
| 6206 | nlon = np.min(ilon) |
|---|
| 6207 | xlon = np.max(ilon) |
|---|
| 6208 | nlat = np.min(ilat) |
|---|
| 6209 | xlat = np.max(ilat) |
|---|
| 6210 | |
|---|
| 6211 | if lonv < nlon or lonv > xlon: |
|---|
| 6212 | print errormsg |
|---|
| 6213 | print ' ' + fname + ': longitude outside data range!!' |
|---|
| 6214 | print ' given value:', lonv,'outside [',nlon,',',xlon,']' |
|---|
| 6215 | quit(-1) |
|---|
| 6216 | if latv < nlat or latv > xlat: |
|---|
| 6217 | print errormsg |
|---|
| 6218 | print ' ' + fname + ': latitude outside data range!!' |
|---|
| 6219 | print ' given value:', latv,'outside [',nlat,',',xlat,']' |
|---|
| 6220 | quit(-1) |
|---|
| 6221 | |
|---|
| 6222 | # Find point |
|---|
| 6223 | difflonlat = np.sqrt((ilon-lonv)**2. + (ilat-latv)**2.) |
|---|
| 6224 | mindiffLl = np.min(difflonlat) |
|---|
| 6225 | ilatlon = index_mat(difflonlat, mindiffLl) |
|---|
| 6226 | |
|---|
| 6227 | # print 'mindiffLl:', mindiffLl, 'ilatlon:', ilatlon |
|---|
| 6228 | # print 'lon, lat:', lon[ilatlon[0],ilatlon[1]], ',', lat[ilatlon[0],ilatlon[1]] |
|---|
| 6229 | |
|---|
| 6230 | return ilatlon, mindiffLl |
|---|
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