source: lmdz_wrf/trunk/tools/generic.py @ 1544

# Wrapper for the generic functions written in python from 'generic_tools.py'
# L. Fita, LMD. June 2016
# Python to manage netCDF files.
# From L. Fita work in different places: LMD (France)
# More information at: http://www.xn--llusfb-5va.cat/python/PyNCplot
#
# pyNCplot and its component generic.py comes with ABSOLUTELY NO WARRANTY. 
# This work is licendes under a Creative Commons 
#   Attribution-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-sa/4.0)
#

from optparse import OptionParser
import numpy as np
import datetime as dt
import generic_tools as gen

main = 'generic.py'
errormsg = 'ERROR -- error -- ERROR -- error'
warnmsg = 'WARNING -- warning --WARNING -- warning'
import os
import re

# coincident_CFtimes: Function to make coincident times for two different sets of CFtimes
# count_cond: Function to count values of a variable which attain a condition
# datetimeStr_conversion: Function to transform a string date to an another date object
# grid_combinations: Function to provide all the possible grid points combination for a given pair of values
#   x,y= pair of grid points
# interpolate_locs: Function to provide interpolate locations on a given axis
# PolyArea: Function to compute the area of the polygon following 'Shoelace formula'
# radial_points: Function to provide a number of grid point positions for a given angle
# radius_dist: Function to generate a matrix with the distance at a given point
# rmNOnum: Removing from a string all that characters which are not numbers
# running_mean: Function to compute a running mean of a series of values
# significant_decomposition: Function to decompose a given number by its signifcant potencies
# squared_radial: Function to provide the series of radii as composite of pairs (x,y) of gid cells
#   Npt= largest amount of grid points on x and y directions
# table_tex_file: Function to write into a file a LaTeX tabular from a table of values
# unitsDate: Function to know how many units of time are from a given pair of dates
# variables_values: Function to provide values to plot the different variables values from ASCII file
# wdismean: Function to compute the mean value weighted to its 4 distances
# Character to split passed values

cS = ','
# Character to split serie of values
cV = '@'
# Character for spaces
cE = '!'

# List of available operations
operations=['coincident_CFtimes', 'count_cond', 'datetimeStr_conversion',            \
  'grid_combinations',                                                               \
  'interpolate_locs', 'latex_fig_array', 'list_operations', 'PolyArea',              \
  'radial_points', 'radius_dist',                                                    \
  'rmNOnum', 'running_mean',                                                         \
  'significant_decomposition', 'squared_radial',                                     \
  'table_tex_file', 'unitsDate', 'variables_values', 'wdismean']

hundredvals = '0'
for i in range(1,100): hundredvals = hundredvals + cV + str(i)

vs100 = '0@1@2@3@4@5@6@7@8@9@10@11@12@13@14@15@16@17@18@19@20@21@22@23@24@25@26@27'
vs100 = vs100 + '@28@29@30@31@32@33@34@35@36@37@38@39@40@41@42@43@44@45@46@47@48@49'
vs100 = vs100 + '@50@51@52@53@54@55@56@57@58@59@60@61@62@63@64@65@66@67@68@69@70@71'
va100 = vs100 + '@72@73@74@75@76@77@78@79@80@81@82@83@84@85@86@87@88@89@90@91@92@93'
va100 = vs100 + '@94@95@96@97@98@99'

## e.g. # generic.py -o 'coincident_CFtimes' -S '0@1@2@3@4@5@6@7@8@9,seconds since 1949-12-01 00:00:00,hours since 1949-12-01 00:00:00'
## e.g. # generic.py -o count_cond -S 0@1@2@3@4@5@6@7@8@9,4,le
## e.g. # generic.py -o datetimeStr_conversion -S '1976-02-17_08:32:05,Y-m-d_H:M:S,matYmdHMS'
## e.g. # generic.py -o grid_combinations -S 1,2
## e.g. # generic.py -o interpolate_locs -S -1.2@2.4@5.6@7.8@12.0,0.5@2.5,lin
## e.g. # generic.py -o PolyArea -S -0.5@0.5@0.5@-0.5,0.5@0.5@-0.5@-0.5
## e.g. # generic.py -o radial_points -S 0.785398163397,5
## e.g. # generic.py -o radius_dist -S 3,5,2,2
## e.g. # generic.py -o rmNOnum -S LMD123IPSL
## e.g. # generic.py -o significant_decomposition -S 3.576,-2
## e.g. # generic.py -o table_tex_file -S '5,3,0@5@10@1@6@11@2@7@12@3@8@13@4@9@14,!@a@b@c@d@e,i@ii@iii,table.tex'
## e.g. # generic.py -o unitsDate -S '19490101000000,19760217082932,second'
## e.g. # generic.py -o running_mean -S 0@1@2@3@4@5@6@7@8@9,10
## e.g. # generic.py -o squared_radial -S 3
## e.g. # generic.py -o variables_values -S 'hus'
## e.g. # generic.py -o wdismean -S 0.005@0.005,0.@1.@2.@3.

operationnames = "'" + gen.numVector_String(operations, "', '") + "'"
valuesinf = "'" + cS + "' list of values to use according to the operation ('" + cV +\
  "' for list of values)"

parser = OptionParser()
parser.add_option("-o", "--operation", type='choice', dest="operation", 
  choices=operations, help="operation to make: " + operationnames, metavar="OPER")
parser.add_option("-S", "--valueS (when applicable)", dest="values", 
  help=valuesinf, metavar="VALUES")
(opts, args) = parser.parse_args()

#######    #######
## MAIN
    #######
oper = opts.operation

if oper == 'list_operations':
# From: http://www.diveintopython.net/power_of_introspection/all_together.html
    object = gen
    for opern in operations:
        if  opern != 'list_operations': 
            print opern + '_______ ______ _____ ____ ___ __ _'
            print getattr(object, opern).__doc__

elif oper == 'coincident_CFtimes':
    Nvals = 3
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.coincident_CFtimes.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.coincident_CFtimes.__doc__
            quit(-1)
        vals0 = np.array(vals[0].split(cV), dtype=np.float)
        print gen.coincident_CFtimes(vals0, vals[1], vals[2])

elif oper == 'count_cond':
    Nvals = 3
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.count_cond.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.count_cond.__doc__
            quit(-1)
        vals0 = np.array(vals[0].split(cV), dtype=np.float)
        print gen.count_cond(np.array(vals[0].split(cV), dtype=np.float),           \
          np.float(vals[1]), vals[2])

elif oper == 'datetimeStr_conversion':
    Nvals = 3
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.datetimeStr_conversion.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.datetimeStr_conversion.__doc__
            quit(-1)
        print gen.datetimeStr_conversion(vals[0], vals[1], vals[2])

#'days_period'

elif oper == 'grid_combinations':
    Nvals = 2
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.grid_combinations.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.grid_combinations.__doc__
            quit(-1)

        print gen.grid_combinations(np.int(vals[0]), np.int(vals[1]))

elif oper == 'latex_fig_array':
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.latex_fig_array.__doc__
        print "  NOTE: first argument as existing LaTeX file"
        print "  figs: passing list of figures as '@' separated list"
        print "  caption: using '!' for spaces"
        quit(-1)
    else:
        expectargs = '[latexfile],[figs],[figcaption],[figlabel],[dist],[refsize],'+ \
         '[width],[height],[dorest]'
        gen.check_arguments(oper,opts.values,expectargs,cS)

        objf = open(vals[0], 'a')

        figs = vals[1].split('@')
        caption = vals[2].replace('!',' ')
        gen.latex_fig_array(figs, objf, caption, vals[3], dist=vals[4],              \
          refsize=vals[5], width=vals[6], height=vals[7], dorest=vals[8])
        objf.write('\\end{document}\n')
        objf.close()
 
elif oper == 'interpolate_locs':
    Nvals = 3
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.interpolate_locs.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.interpolate_locs.__doc__
            quit(-1)
        vals0 = np.array(vals[0].split(cV), dtype=np.float)
        vals1 = np.array(vals[1].split(cV), dtype=np.float)

        print gen.interpolate_locs(vals0, vals1, vals[2])

elif oper == 'PolyArea':
    Nvals = 2
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.PolyArea.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.PolyArea.__doc__
            quit(-1)
        xvals = np.array(vals[0].split(cV), dtype=np.float)
        yvals = np.array(vals[1].split(cV), dtype=np.float)

        print gen.PolyArea(xvals, yvals)

elif oper == 'radial_points':
    Nvals = 2
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.radial_points.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.radial_points.__doc__
            quit(-1)
        print gen.radial_points(np.float(vals[0]), int(vals[1]))

elif oper == 'radius_dist':
    Nvals = 1
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.radius_dist.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.radius_dist.__doc__
            quit(-1)
        print gen.radius_dist(int(vals[0]), int(vals[1]), int(vals[2]), int(vals[2]))

elif oper == 'rmNOnum':
    Nvals = 1
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.rmNOnum.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.rmNOnum.__doc__
            quit(-1)
        print gen.rmNOnum(vals[0])

elif oper == 'running_mean':
    Nvals = 2
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.running_mean.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.running_mean.__doc__
            quit(-1)
        print gen.running_mean(np.array(vals[0].split(cV), dtype=np.float), int(vals[1]))

elif oper == 'significant_decomposition':
    Nvals = 2
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.significant_decomposition.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.significant_decomposition.__doc__
            quit(-1)
        print gen.significant_decomposition(np.float(vals[0]), int(vals[1]))

elif oper == 'squared_radial':
    Nvals = 1
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.squared_radial.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.squared_radial.__doc__
            quit(-1)
        print gen.squared_radial(int(vals[0]))

elif oper == 'table_tex_file':
    Nvals = 6
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.table_tex_file.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.table_tex_file.__doc__
            quit(-1)
        vals2 = np.array(vals[2].split(cV),dtype=np.float).reshape(int(vals[0]),     \
          int(vals[1]))
        vals3 = vals[3].replace(cE,' ').split(cV)
        vals4 = vals[4].replace(cE,' ').split(cV)

        print gen.table_tex_file(int(vals[0]), int(vals[1]), vals2, vals3, vals4,    \
          vals[5])

elif oper == 'unitsDate':
    Nvals = 3
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.unitsDate.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.unitsDate.__doc__
            quit(-1)
        print gen.unitsDate(vals[0], vals[1], vals[2])

elif oper == 'variables_values':
    Nvals = 1
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.variables_values.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.variables_values.__doc__
            quit(-1)
        result = gen.variables_values(vals[0])
        print gen.numVector_String(result,':')

elif oper == 'wdismean':
    Nvals = 2
    vals = opts.values.split(cS)
    if vals[0] == 'h':
        print gen.wdismean.__doc__
        quit(-1)
    else:
        if len(vals) != Nvals:
            print errormsg
            print '  ' + main + ": operation '" + oper + "' requires", Nvals, 'and', \
              len(vals), ' has passed!!'
            print gen.wdismean.__doc__
            quit(-1)
        vals0 = np.array(vals[0].split(cV), dtype=np.float)
        vals1 = np.array(vals[1].split(cV), dtype=np.float).reshape(2,2)

        print gen.wdismean(vals0, vals1)