## Author: AS
def errormess(text,printvar=None):
    print text
    if printvar: print printvar 
    exit()
    return

## Author: AS
def adjust_length (tab, zelen):
    from numpy import ones
    if tab is None:
        outtab = ones(zelen) * -999999
    else:
        if zelen != len(tab):
            print "not enough or too much values... setting same values all variables"
            outtab = ones(zelen) * tab[0]
        else:
            outtab = tab
    return outtab

## Author: AS
def getname(var=False,winds=False,anomaly=False):
    if var and winds:     basename = var + '_UV'
    elif var:             basename = var
    elif winds:           basename = 'UV'
    else:                 errormess("please set at least winds or var",printvar=nc.variables)
    if anomaly:           basename = 'd' + basename
    return basename

## Author: AS
def localtime(utc,lon):
    ltst = utc + lon / 15.
    ltst = int (ltst * 10) / 10.
    ltst = ltst % 24
    return ltst

## Author: AS
def whatkindfile (nc):
    if 'controle' in nc.variables:   typefile = 'gcm'
    elif 'phisinit' in nc.variables: typefile = 'gcm' 
    elif 'vert' in nc.variables:     typefile = 'mesoapi'
    elif 'U' in nc.variables:        typefile = 'meso'
    elif 'HGT_M' in nc.variables:    typefile = 'geo'
    #else:                            errormess("whatkindfile: typefile not supported.")
    else:                            typefile = 'gcm' # for lslin-ed files from gcm
    return typefile

## Author: AS
def getfield (nc,var):
    ## this allows to get much faster (than simply referring to nc.variables[var])
    import numpy as np
    dimension = len(nc.variables[var].dimensions)
    #print "   Opening variable with", dimension, "dimensions ..."
    if dimension == 2:    field = nc.variables[var][:,:]
    elif dimension == 3:  field = nc.variables[var][:,:,:]
    elif dimension == 4:  field = nc.variables[var][:,:,:,:]
    # if there are NaNs in the ncdf, they should be loaded as a masked array which will be
    # recasted as a regular array later in reducefield
    if (np.isnan(np.sum(field)) and (type(field).__name__ not in 'MaskedArray')):
       print "Warning: netcdf as nan values but is not loaded as a Masked Array."
       print "recasting array type"
       out=np.ma.masked_invalid(field)
       out.set_fill_value([np.NaN])
    else:
       out=field
    return out

## Author: AS + TN + AC
def reducefield (input,d4=None,d3=None,d2=None,d1=None,yint=False,alt=None):
    ### we do it the reverse way to be compliant with netcdf "t z y x" or "t y x" or "y x"
    ### it would be actually better to name d4 d3 d2 d1 as t z y x
    import numpy as np
    from mymath import max,mean
    dimension = np.array(input).ndim
    shape = np.array(input).shape
    #print 'd1,d2,d3,d4: ',d1,d2,d3,d4
    print 'IN  REDUCEFIELD dim,shape: ',dimension,shape
    output = input
    error = False
    #### this is needed to cope the case where d4,d3,d2,d1 are single integers and not arrays
    if d4 is not None and not isinstance(d4, np.ndarray): d4=[d4]
    if d3 is not None and not isinstance(d3, np.ndarray): d3=[d3]
    if d2 is not None and not isinstance(d2, np.ndarray): d2=[d2]
    if d1 is not None and not isinstance(d1, np.ndarray): d1=[d1]
    ### now the main part
    if dimension == 2:
        if   d2 >= shape[0]: error = True
        elif d1 >= shape[1]: error = True
        elif d1 is not None and d2 is not None:  output = mean(input[d2,:],axis=0); output = mean(output[d1],axis=0)
        elif d1 is not None:         output = mean(input[:,d1],axis=1)
        elif d2 is not None:         output = mean(input[d2,:],axis=0)
    elif dimension == 3:
        if   max(d4) >= shape[0]: error = True
        elif max(d2) >= shape[1]: error = True
        elif max(d1) >= shape[2]: error = True
        elif d4 is not None and d2 is not None and d1 is not None:  
            output = mean(input[d4,:,:],axis=0); output = mean(output[d2,:],axis=0); output = mean(output[d1],axis=0)
        elif d4 is not None and d2 is not None:    output = mean(input[d4,:,:],axis=0); output=mean(output[d2,:],axis=0)
        elif d4 is not None and d1 is not None:    output = mean(input[d4,:,:],axis=0); output=mean(output[:,d1],axis=1)
        elif d2 is not None and d1 is not None:    output = mean(input[:,d2,:],axis=1); output=mean(output[:,d1],axis=1)
        elif d1 is not None:                       output = mean(input[:,:,d1],axis=2)
        elif d2 is not None:                       output = mean(input[:,d2,:],axis=1)
        elif d4 is not None:                       output = mean(input[d4,:,:],axis=0)
    elif dimension == 4:
        if   max(d4) >= shape[0]: error = True
        elif max(d3) >= shape[1]: error = True
        elif max(d2) >= shape[2]: error = True
        elif max(d1) >= shape[3]: error = True
        elif d4 is not None and d3 is not None and d2 is not None and d1 is not None:
             output = mean(input[d4,:,:,:],axis=0)
             output = reduce_zaxis(output[d3,:,:],ax=0,yint=yint,vert=alt,indice=d3)
             output = mean(output[d2,:],axis=0)
             output = mean(output[d1],axis=0)
        elif d4 is not None and d3 is not None and d2 is not None: 
             output = mean(input[d4,:,:,:],axis=0)
             output = reduce_zaxis(output[d3,:,:],ax=0,yint=yint,vert=alt,indice=d3)
             output = mean(output[d2,:],axis=0)
        elif d4 is not None and d3 is not None and d1 is not None: 
             output = mean(input[d4,:,:,:],axis=0)
             output = reduce_zaxis(output[d3,:,:],ax=0,yint=yint,vert=alt,indice=d3)
             output = mean(output[:,d1],axis=1)
        elif d4 is not None and d2 is not None and d1 is not None: 
             output = mean(input[d4,:,:,:],axis=0)
             output = mean(output[:,d2,:],axis=1)
             output = mean(output[:,d1],axis=1)
        elif d3 is not None and d2 is not None and d1 is not None: 
             output = reduce_zaxis(input[:,d3,:,:],ax=1,yint=yint,vert=alt,indice=d3) 
             output = mean(output[:,d2,:],axis=1)
             output = mean(output[:,d1],axis=1) 
        elif d4 is not None and d3 is not None:  
             output = mean(input[d4,:,:,:],axis=0) 
             output = reduce_zaxis(output[d3,:,:],ax=0,yint=yint,vert=alt,indice=d3)
        elif d4 is not None and d2 is not None:  
             output = mean(input[d4,:,:,:],axis=0) 
             output = mean(output[:,d2,:],axis=1)
        elif d4 is not None and d1 is not None:  
             output = mean(input[d4,:,:,:],axis=0) 
             output = mean(output[:,:,d1],axis=2)
        elif d3 is not None and d2 is not None:  
             output = reduce_zaxis(input[:,d3,:,:],ax=1,yint=yint,vert=alt,indice=d3)
             output = mean(output[:,d2,:],axis=1)
        elif d3 is not None and d1 is not None:  
             output = reduce_zaxis(input[:,d3,:,:],ax=1,yint=yint,vert=alt,indice=d3)
             output = mean(output[:,:,d1],axis=0)
        elif d2 is not None and d1 is not None:  
             output = mean(input[:,:,d2,:],axis=2)
             output = mean(output[:,:,d1],axis=2)
        elif d1 is not None:        output = mean(input[:,:,:,d1],axis=3)
        elif d2 is not None:        output = mean(input[:,:,d2,:],axis=2)
        elif d3 is not None:        output = reduce_zaxis(input[:,d3,:,:],ax=0,yint=yint,vert=alt,indice=d3)
        elif d4 is not None:        output = mean(input[d4,:,:,:],axis=0)
    dimension = np.array(output).ndim
    shape = np.array(output).shape
    print 'OUT REDUCEFIELD dim,shape: ',dimension,shape
    return output, error

## Author: AC + AS
def reduce_zaxis (input,ax=None,yint=False,vert=None,indice=None):
    from mymath import max,mean
    from scipy import integrate
    if yint and vert is not None and indice is not None:
       if type(input).__name__=='MaskedArray':
          input.set_fill_value([np.NaN])
          output = integrate.trapz(input.filled(),x=vert[indice],axis=ax)
       else:
          output = integrate.trapz(input,x=vert[indice],axis=ax)
    else:
       output = mean(input,axis=ax)
    return output

## Author: AS + TN
def definesubplot ( numplot, fig ):
    from matplotlib.pyplot import rcParams
    rcParams['font.size'] = 12. ## default (important for multiple calls)
    if numplot <= 0:
        subv = 99999
        subh = 99999
    elif numplot == 1: 
        subv = 99999
        subh = 99999
    elif numplot == 2:
        subv = 1
        subh = 2
        fig.subplots_adjust(wspace = 0.35)
        rcParams['font.size'] = int( rcParams['font.size'] * 3. / 4. )
    elif numplot == 3:
        subv = 2
        subh = 2
        fig.subplots_adjust(wspace = 0.5)
        rcParams['font.size'] = int( rcParams['font.size'] * 1. / 2. )
    elif   numplot == 4:
        subv = 2
        subh = 2
        fig.subplots_adjust(wspace = 0.3, hspace = 0.3)
        rcParams['font.size'] = int( rcParams['font.size'] * 2. / 3. )
    elif numplot <= 6:
        subv = 2
        subh = 3
        fig.subplots_adjust(wspace = 0.4, hspace = 0.0)
        rcParams['font.size'] = int( rcParams['font.size'] * 1. / 2. )
    elif numplot <= 8:
        subv = 2
        subh = 4
        fig.subplots_adjust(wspace = 0.3, hspace = 0.3)
        rcParams['font.size'] = int( rcParams['font.size'] * 1. / 2. )
    elif numplot <= 9:
        subv = 3
        subh = 3
        fig.subplots_adjust(wspace = 0.3, hspace = 0.3)
        rcParams['font.size'] = int( rcParams['font.size'] * 1. / 2. )
    elif numplot <= 12:
        subv = 3
        subh = 4
        fig.subplots_adjust(wspace = 0.1, hspace = 0.1)
        rcParams['font.size'] = int( rcParams['font.size'] * 1. / 2. )
    elif numplot <= 16:
        subv = 4
        subh = 4
        fig.subplots_adjust(wspace = 0.3, hspace = 0.3)
        rcParams['font.size'] = int( rcParams['font.size'] * 1. / 2. )
    else:
        print "number of plot supported: 1 to 16"
        exit()
    return subv,subh

## Author: AS
def getstralt(nc,nvert):
    typefile = whatkindfile(nc)
    if typefile is 'meso':                      
        stralt = "_lvl" + str(nvert)
    elif typefile is 'mesoapi':
        zelevel = int(nc.variables['vert'][nvert])
        if abs(zelevel) < 10000.:   strheight=str(zelevel)+"m"
        else:                       strheight=str(int(zelevel/1000.))+"km"
        if 'altitude'       in nc.dimensions:   stralt = "_"+strheight+"-AMR"
        elif 'altitude_abg' in nc.dimensions:   stralt = "_"+strheight+"-ALS"
        elif 'bottom_top'   in nc.dimensions:   stralt = "_"+strheight
        elif 'pressure'     in nc.dimensions:   stralt = "_"+str(zelevel)+"Pa"
        else:                                   stralt = ""
    else:
        stralt = ""
    return stralt

## Author: AS
def getlschar ( namefile ):
    from netCDF4 import Dataset
    from timestuff import sol2ls
    from numpy import array
    nc  = Dataset(namefile)
    zetime = None
    if 'Times' in nc.variables: 
        zetime = nc.variables['Times'][0]
        shape = array(nc.variables['Times']).shape
        if shape[0] < 2: zetime = None
    if zetime is not None \
       and 'vert' not in nc.variables:
        #### strangely enough this does not work for api or ncrcat results!
        zetimestart = getattr(nc, 'START_DATE')
        zeday = int(zetime[8]+zetime[9]) - int(zetimestart[8]+zetimestart[9])
        if zeday < 0:    lschar=""  ## might have crossed a month... fix soon
        else:            lschar="_Ls"+str( int( 10. * sol2ls ( getattr( nc, 'JULDAY' ) + zeday ) ) / 10. )
        ###
        zetime2 = nc.variables['Times'][1]
        one  = int(zetime[11]+zetime[12]) + int(zetime[14]+zetime[15])/37.
        next = int(zetime2[11]+zetime2[12]) + int(zetime2[14]+zetime2[15])/37. 
        zehour    = one
        zehourin  = abs ( next - one )
    else:
        lschar=""
        zehour = 0
        zehourin = 1  
    return lschar, zehour, zehourin

## Author: AS
def getprefix (nc):
    prefix = 'LMD_MMM_'
    prefix = prefix + 'd'+str(getattr(nc,'GRID_ID'))+'_'
    prefix = prefix + str(int(getattr(nc,'DX')/1000.))+'km_'
    return prefix

## Author: AS
def getproj (nc):
    typefile = whatkindfile(nc)
    if typefile in ['mesoapi','meso','geo']:
        ### (il faudrait passer CEN_LON dans la projection ?)
        map_proj = getattr(nc, 'MAP_PROJ')
        cen_lat  = getattr(nc, 'CEN_LAT')
        if map_proj == 2:
            if cen_lat > 10.:    
                proj="npstere"
                #print "NP stereographic polar domain" 
            else:            
                proj="spstere"
                #print "SP stereographic polar domain"
        elif map_proj == 1: 
            #print "lambert projection domain" 
            proj="lcc"
        elif map_proj == 3: 
            #print "mercator projection"
            proj="merc"
        else:
            proj="merc"
    elif typefile in ['gcm']:  proj="cyl"    ## pb avec les autres (de trace derriere la sphere ?)
    else:                      proj="ortho"
    return proj    

## Author: AS
def ptitle (name):
    from matplotlib.pyplot import title
    title(name)
    print name

## Author: AS
def polarinterv (lon2d,lat2d):
    import numpy as np
    wlon = [np.min(lon2d),np.max(lon2d)]
    ind = np.array(lat2d).shape[0] / 2  ## to get a good boundlat and to get the pole
    wlat = [np.min(lat2d[ind,:]),np.max(lat2d[ind,:])]
    return [wlon,wlat]

## Author: AS
def simplinterv (lon2d,lat2d):
    import numpy as np
    return [[np.min(lon2d),np.max(lon2d)],[np.min(lat2d),np.max(lat2d)]]

## Author: AS
def wrfinterv (lon2d,lat2d):
    nx = len(lon2d[0,:])-1
    ny = len(lon2d[:,0])-1
    lon1 = lon2d[0,0] 
    lon2 = lon2d[nx,ny] 
    lat1 = lat2d[0,0] 
    lat2 = lat2d[nx,ny] 
    if abs(0.5*(lat1+lat2)) > 60.:  wider = 0.5 * (abs(lon1)+abs(lon2)) * 0.1
    else:                           wider = 0.
    if lon1 < lon2:  wlon = [lon1, lon2 + wider]  
    else:            wlon = [lon2, lon1 + wider]
    if lat1 < lat2:  wlat = [lat1, lat2]
    else:            wlat = [lat2, lat1]
    return [wlon,wlat]

## Author: AS
def makeplotres (filename,res=None,pad_inches_value=0.25,folder='',disp=True,ext='png',erase=False):
    import  matplotlib.pyplot as plt
    from os import system 
    addstr = ""
    if res is not None:
        res = int(res)
        addstr = "_"+str(res)
    name = filename+addstr+"."+ext
    if folder != '':      name = folder+'/'+name
    plt.savefig(name,dpi=res,bbox_inches='tight',pad_inches=pad_inches_value)
    if disp:              display(name)
    if ext in ['eps','ps','svg']:   system("tar czvf "+name+".tar.gz "+name+" ; rm -f "+name)
    if erase:   system("mv "+name+" to_be_erased")		
    return

## Author: AS
def dumpbdy (field,n,stag=None):
    nx = len(field[0,:])-1
    ny = len(field[:,0])-1
    if stag == 'U': nx = nx-1
    if stag == 'V': ny = ny-1
    return field[n:ny-n,n:nx-n]

## Author: AS
def getcoorddef ( nc ):   
    import numpy as np
    ## getcoord2d for predefined types
    typefile = whatkindfile(nc)
    if typefile in ['mesoapi','meso']:
        [lon2d,lat2d] = getcoord2d(nc)
        lon2d = dumpbdy(lon2d,6)
        lat2d = dumpbdy(lat2d,6)
    elif typefile in ['gcm']: 
        [lon2d,lat2d] = getcoord2d(nc,nlat="latitude",nlon="longitude",is1d=True)
    elif typefile in ['geo']:
        [lon2d,lat2d] = getcoord2d(nc,nlat='XLAT_M',nlon='XLONG_M')
    return lon2d,lat2d    

## Author: AS
def getcoord2d (nc,nlat='XLAT',nlon='XLONG',is1d=False):
    import numpy as np
    if is1d:
        lat = nc.variables[nlat][:]
        lon = nc.variables[nlon][:]
        [lon2d,lat2d] = np.meshgrid(lon,lat)
    else:
        lat = nc.variables[nlat][0,:,:]
        lon = nc.variables[nlon][0,:,:]
        [lon2d,lat2d] = [lon,lat]
    return lon2d,lat2d

## Author: AS
def smooth (field, coeff):
	## actually blur_image could work with different coeff on x and y
	if coeff > 1:	result = blur_image(field,int(coeff))
	else:		result = field
	return result

## FROM COOKBOOK http://www.scipy.org/Cookbook/SignalSmooth
def gauss_kern(size, sizey=None):
	import numpy as np
    	# Returns a normalized 2D gauss kernel array for convolutions
    	size = int(size)
    	if not sizey:
	        sizey = size
	else:
	        sizey = int(sizey)
	x, y = np.mgrid[-size:size+1, -sizey:sizey+1]
	g = np.exp(-(x**2/float(size)+y**2/float(sizey)))
	return g / g.sum()

## FROM COOKBOOK http://www.scipy.org/Cookbook/SignalSmooth
def blur_image(im, n, ny=None) :
	from scipy.signal import convolve
	# blurs the image by convolving with a gaussian kernel of typical size n. 
	# The optional keyword argument ny allows for a different size in the y direction.
    	g = gauss_kern(n, sizey=ny)
    	improc = convolve(im, g, mode='same')
    	return improc

## Author: AS
def getwinddef (nc):    
    ## getwinds for predefined types
    typefile = whatkindfile(nc)
    ###
    if typefile is 'mesoapi':    [uchar,vchar] = ['Um','Vm']
    elif typefile is 'gcm':      [uchar,vchar] = ['u','v']
    elif typefile is 'meso':     [uchar,vchar] = ['U','V']
    else:                        [uchar,vchar] = ['not found','not found']
    ###
    if typefile in ['meso']:     metwind = False ## geometrical (wrt grid) 
    else:                        metwind = True  ## meteorological (zon/mer)
    if metwind is False:         print "Not using meteorological winds. You trust numerical grid as being (x,y)"
    ###
    return uchar,vchar,metwind

## Author: AS
def vectorfield (u, v, x, y, stride=3, scale=15., factor=250., color='black', csmooth=1, key=True):
    ## scale regle la reference du vecteur
    ## factor regle toutes les longueurs (dont la reference). l'AUGMENTER pour raccourcir les vecteurs.
    import  matplotlib.pyplot               as plt
    import  numpy                           as np
    posx = np.min(x) - np.std(x) / 10.
    posy = np.min(y) - np.std(y) / 10.
    u = smooth(u,csmooth)
    v = smooth(v,csmooth)
    widthvec = 0.003 #0.005 #0.003
    q = plt.quiver( x[::stride,::stride],\
                    y[::stride,::stride],\
                    u[::stride,::stride],\
                    v[::stride,::stride],\
                    angles='xy',color=color,pivot='middle',\
                    scale=factor,width=widthvec )
    if color in ['white','yellow']:     kcolor='black'
    else:                               kcolor=color
    if key: p = plt.quiverkey(q,posx,posy,scale,\
                   str(int(scale)),coordinates='data',color=kcolor,labelpos='S',labelsep = 0.03)
    return 

## Author: AS
def display (name):
    from os import system
    system("display "+name+" > /dev/null 2> /dev/null &")
    return name

## Author: AS
def findstep (wlon):
    steplon = int((wlon[1]-wlon[0])/4.)  #3
    step = 120.
    while step > steplon and step > 15. :       step = step / 2.
    if step <= 15.:
        while step > steplon and step > 5.  :   step = step - 5.
    if step <= 5.:
        while step > steplon and step > 1.  :   step = step - 1.
    if step <= 1.:
        step = 1. 
    return step

## Author: AS
def define_proj (char,wlon,wlat,back=None,blat=None):
    from    mpl_toolkits.basemap            import Basemap
    import  numpy                           as np
    import  matplotlib                      as mpl
    from mymath import max
    meanlon = 0.5*(wlon[0]+wlon[1])
    meanlat = 0.5*(wlat[0]+wlat[1])
    if blat is None:
        if   wlat[0] >= 80.:   blat =  40. 
        elif wlat[1] <= -80.:  blat = -40.
        elif wlat[1] >= 0.:    blat = wlat[0] 
        elif wlat[0] <= 0.:    blat = wlat[1]
    #print "blat ", blat
    h = 50.  ## en km
    radius = 3397200.
    if   char == "cyl":     m = Basemap(rsphere=radius,projection='cyl',\
                              llcrnrlat=wlat[0],urcrnrlat=wlat[1],llcrnrlon=wlon[0],urcrnrlon=wlon[1])
    elif char == "moll":    m = Basemap(rsphere=radius,projection='moll',lon_0=meanlon)
    elif char == "ortho":   m = Basemap(rsphere=radius,projection='ortho',lon_0=meanlon,lat_0=meanlat)
    elif char == "lcc":     m = Basemap(rsphere=radius,projection='lcc',lat_1=meanlat,lat_0=meanlat,lon_0=meanlon,\
                              llcrnrlat=wlat[0],urcrnrlat=wlat[1],llcrnrlon=wlon[0],urcrnrlon=wlon[1])
    elif char == "npstere": m = Basemap(rsphere=radius,projection='npstere', boundinglat=blat, lon_0=0.)
    elif char == "spstere": m = Basemap(rsphere=radius,projection='spstere', boundinglat=blat, lon_0=180.)
    elif char == "nplaea":  m = Basemap(rsphere=radius,projection='nplaea', boundinglat=wlat[0], lon_0=meanlon)
    elif char == "laea":    m = Basemap(rsphere=radius,projection='laea',lon_0=meanlon,lat_0=meanlat,lat_ts=meanlat,\
                              llcrnrlat=wlat[0],urcrnrlat=wlat[1],llcrnrlon=wlon[0],urcrnrlon=wlon[1])
    elif char == "nsper":   m = Basemap(rsphere=radius,projection='nsper',lon_0=meanlon,lat_0=meanlat,satellite_height=h*1000.)
    elif char == "merc":    m = Basemap(rsphere=radius,projection='merc',lat_ts=0.,\
                              llcrnrlat=wlat[0],urcrnrlat=wlat[1],llcrnrlon=wlon[0],urcrnrlon=wlon[1])
    fontsizemer = int(mpl.rcParams['font.size']*3./4.)
    if char in ["cyl","lcc","merc","nsper","laea"]:   step = findstep(wlon)
    else:                                             step = 10.
    steplon = step*2.
    #if back in ["geolocal"]:                          
    #    step = np.min([5.,step])
    #    steplon = step
    m.drawmeridians(np.r_[-180.:180.:steplon], labels=[0,0,0,1], color='grey', fontsize=fontsizemer)
    m.drawparallels(np.r_[-90.:90.:step], labels=[1,0,0,0], color='grey', fontsize=fontsizemer)
    if back: m.warpimage(marsmap(back),scale=0.75)
            #if not back:
            #    if not var:                                        back = "mola"    ## if no var:         draw mola
            #    elif typefile in ['mesoapi','meso','geo'] \
            #       and proj not in ['merc','lcc','nsper','laea']:  back = "molabw"  ## if var but meso:   draw molabw
            #    else:                                              pass             ## else:              draw None
    return m

## Author: AS
#### test temporaire
def putpoints (map,plot):
    #### from http://www.scipy.org/Cookbook/Matplotlib/Maps
    # lat/lon coordinates of five cities.
    lats = [18.4]
    lons = [-134.0]
    points=['Olympus Mons']
    # compute the native map projection coordinates for cities.
    x,y = map(lons,lats)
    # plot filled circles at the locations of the cities.
    map.plot(x,y,'bo')
    # plot the names of those five cities.
    wherept = 0 #1000 #50000
    for name,xpt,ypt in zip(points,x,y):
       plot.text(xpt+wherept,ypt+wherept,name)
    ## le nom ne s'affiche pas...
    return

## Author: AS
def calculate_bounds(field,vmin=None,vmax=None):
    import numpy as np
    from mymath import max,min,mean
    ind = np.where(field < 9e+35)
    fieldcalc = field[ ind ] # la syntaxe compacte ne marche si field est un tuple
    ###
    dev = np.std(fieldcalc)*3.0
    ###
    if vmin is None:
        zevmin = mean(fieldcalc) - dev
    else:             zevmin = vmin
    ###
    if vmax is None:  zevmax = mean(fieldcalc) + dev
    else:             zevmax = vmax
    if vmin == vmax:
                      zevmin = mean(fieldcalc) - dev  ### for continuity
                      zevmax = mean(fieldcalc) + dev  ### for continuity            
    ###
    if zevmin < 0. and min(fieldcalc) > 0.: zevmin = 0.
    print "BOUNDS field ", min(fieldcalc), max(fieldcalc)
    print "BOUNDS adopted ", zevmin, zevmax
    return zevmin, zevmax

## Author: AS
def bounds(what_I_plot,zevmin,zevmax):
    from mymath import max,min,mean
    ### might be convenient to add the missing value in arguments
    #what_I_plot[ what_I_plot < zevmin ] = zevmin#*(1. + 1.e-7)
    if zevmin < 0: what_I_plot[ what_I_plot < zevmin*(1. - 1.e-7) ] = zevmin*(1. - 1.e-7)
    else:          what_I_plot[ what_I_plot < zevmin*(1. + 1.e-7) ] = zevmin*(1. + 1.e-7)
    print "NEW MIN ", min(what_I_plot)
    what_I_plot[ what_I_plot > 9e+35  ] = -9e+35
    what_I_plot[ what_I_plot > zevmax ] = zevmax
    print "NEW MAX ", max(what_I_plot)
    return what_I_plot

## Author: AS
def nolow(what_I_plot):
    from mymath import max,min
    lim = 0.15*0.5*(abs(max(what_I_plot))+abs(min(what_I_plot)))
    print "NO PLOT BELOW VALUE ", lim
    what_I_plot [ abs(what_I_plot) < lim ] = 1.e40 
    return what_I_plot

## Author: AS
def zoomset (wlon,wlat,zoom):
    dlon = abs(wlon[1]-wlon[0])/2.
    dlat = abs(wlat[1]-wlat[0])/2.
    [wlon,wlat] = [ [wlon[0]+zoom*dlon/100.,wlon[1]-zoom*dlon/100.],\
                    [wlat[0]+zoom*dlat/100.,wlat[1]-zoom*dlat/100.] ]
    print "ZOOM %",zoom,wlon,wlat
    return wlon,wlat

## Author: AS
def fmtvar (whichvar="def"):
    fmtvar    =     { \
             "tk":           "%.0f",\
             "T_NADIR_DAY":  "%.0f",\
             "T_NADIR_NIT":  "%.0f",\
             "tpot":         "%.0f",\
             "TSURF":        "%.0f",\
             "def":          "%.1e",\
             "PTOT":         "%.0f",\
             "HGT":          "%.1e",\
             "USTM":         "%.2f",\
             "HFX":          "%.0f",\
             "ICETOT":       "%.1e",\
             "TAU_ICE":      "%.2f",\
             "VMR_ICE":      "%.1e",\
             "MTOT":         "%.1f",\
             "anomaly":      "%.1f",\
             "W":            "%.1f",\
             "WMAX_TH":      "%.1f",\
             "QSURFICE":     "%.0f",\
             "Um":           "%.0f",\
             "ALBBARE":      "%.2f",\
             "TAU":          "%.1f",\
             "CO2":          "%.2f",\
             #### T.N.
             "TEMP":         "%.0f",\
             "VMR_H2OICE":   "%.0f",\
             "VMR_H2OVAP":   "%.0f",\
             "TAUTES":       "%.2f",\
             "TAUTESAP":     "%.2f",\

                    }
    if whichvar not in fmtvar:
        whichvar = "def"
    return fmtvar[whichvar]

## Author: AS
####################################################################################################################
### Colorbars http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps?action=AttachFile&do=get&target=colormaps3.png
def defcolorb (whichone="def"):
    whichcolorb =    { \
             "def":          "spectral",\
             "HGT":          "spectral",\
             "tk":           "gist_heat",\
             "TSURF":        "RdBu_r",\
             "QH2O":         "PuBu",\
             "USTM":         "YlOrRd",\
             #"T_nadir_nit":  "RdBu_r",\
             #"T_nadir_day":  "RdBu_r",\
             "HFX":          "RdYlBu",\
             "ICETOT":       "YlGnBu_r",\
             #"MTOT":         "PuBu",\
             "CCNQ":         "YlOrBr",\
             "CCNN":         "YlOrBr",\
             "TEMP":         "Jet",\
             "TAU_ICE":      "Blues",\
             "VMR_ICE":      "Blues",\
             "W":            "jet",\
             "WMAX_TH":      "spectral",\
             "anomaly":      "RdBu_r",\
             "QSURFICE":     "hot_r",\
             "ALBBARE":      "spectral",\
             "TAU":          "YlOrBr_r",\
             "CO2":          "YlOrBr_r",\
             #### T.N.
             "MTOT":         "Jet",\
             "H2O_ICE_S":    "RdBu",\
             "VMR_H2OICE":   "PuBu",\
             "VMR_H2OVAP":   "PuBu",\
                     }
#W --> spectral ou jet
#spectral BrBG RdBu_r
    #print "predefined colorbars"
    if whichone not in whichcolorb:
        whichone = "def"
    return whichcolorb[whichone]

## Author: AS
def definecolorvec (whichone="def"):
        whichcolor =    { \
                "def":          "black",\
                "vis":          "yellow",\
                "vishires":     "yellow",\
                "molabw":       "yellow",\
                "mola":         "black",\
                "gist_heat":    "white",\
                "hot":          "tk",\
                "gist_rainbow": "black",\
                "spectral":     "black",\
                "gray":         "red",\
                "PuBu":         "black",\
                        }
        if whichone not in whichcolor:
                whichone = "def"
        return whichcolor[whichone]

## Author: AS
def marsmap (whichone="vishires"):
        from os import uname
        mymachine = uname()[1]
        ### not sure about speed-up with this method... looks the same
        if "lmd.jussieu.fr" in mymachine: domain = "/u/aslmd/WWW/maps/"
        else:                             domain = "http://www.lmd.jussieu.fr/~aslmd/maps/"
	whichlink = 	{ \
		#"vis":		"http://maps.jpl.nasa.gov/pix/mar0kuu2.jpg",\
		#"vishires":	"http://www.lmd.jussieu.fr/~aslmd/maps/MarsMap_2500x1250.jpg",\
                #"geolocal":    "http://dl.dropbox.com/u/11078310/geolocal.jpg",\
		#"mola":	"http://www.lns.cornell.edu/~seb/celestia/mars-mola-2k.jpg",\
		#"molabw":	"http://dl.dropbox.com/u/11078310/MarsElevation_2500x1250.jpg",\
                "vis":         domain+"mar0kuu2.jpg",\
                "vishires":    domain+"MarsMap_2500x1250.jpg",\
                "geolocal":    domain+"geolocal.jpg",\
                "mola":        domain+"mars-mola-2k.jpg",\
                "molabw":      domain+"MarsElevation_2500x1250.jpg",\
                "clouds":      "http://www.johnstonsarchive.net/spaceart/marswcloudmap.jpg",\
                "jupiter":     "http://www.mmedia.is/~bjj/data/jupiter_css/jupiter_css.jpg",\
                "jupiter_voy": "http://www.mmedia.is/~bjj/data/jupiter/jupiter_vgr2.jpg",\
                "bw":          "http://users.info.unicaen.fr/~karczma/TEACH/InfoGeo/Images/Planets/EarthElevation_2500x1250.jpg",\
                "contrast":    "http://users.info.unicaen.fr/~karczma/TEACH/InfoGeo/Images/Planets/EarthMapAtmos_2500x1250.jpg",\
                "nice":        "http://users.info.unicaen.fr/~karczma/TEACH/InfoGeo/Images/Planets/earthmap1k.jpg",\
                "blue":        "http://eoimages.gsfc.nasa.gov/ve/2430/land_ocean_ice_2048.jpg",\
                "blueclouds":  "http://eoimages.gsfc.nasa.gov/ve/2431/land_ocean_ice_cloud_2048.jpg",\
                "justclouds":  "http://eoimages.gsfc.nasa.gov/ve/2432/cloud_combined_2048.jpg",\
			}
        ### see http://www.mmedia.is/~bjj/planetary_maps.html
	if whichone not in whichlink: 
		print "marsmap: choice not defined... you'll get the default one... "
		whichone = "vishires"  
        return whichlink[whichone]

#def earthmap (whichone):
#	if   whichone == "contrast":	whichlink="http://users.info.unicaen.fr/~karczma/TEACH/InfoGeo/Images/Planets/EarthMapAtmos_2500x1250.jpg"
#	elif whichone == "bw":		whichlink="http://users.info.unicaen.fr/~karczma/TEACH/InfoGeo/Images/Planets/EarthElevation_2500x1250.jpg"
#	elif whichone == "nice":	whichlink="http://users.info.unicaen.fr/~karczma/TEACH/InfoGeo/Images/Planets/earthmap1k.jpg"
#	return whichlink

## Author: AS
def latinterv (area="Whole"):
    list =    { \
        "Europe":                [[ 20., 80.],[- 50.,  50.]],\
        "Central_America":       [[-10., 40.],[ 230., 300.]],\
        "Africa":                [[-20., 50.],[- 50.,  50.]],\
        "Whole":                 [[-90., 90.],[-180., 180.]],\
        "Southern_Hemisphere":   [[-90., 60.],[-180., 180.]],\
        "Northern_Hemisphere":   [[-60., 90.],[-180., 180.]],\
        "Tharsis":               [[-30., 60.],[-170.,- 10.]],\
        "Whole_No_High":         [[-60., 60.],[-180., 180.]],\
        "Chryse":                [[-60., 60.],[- 60.,  60.]],\
        "North_Pole":            [[ 50., 90.],[-180., 180.]],\
        "Close_North_Pole":      [[ 75., 90.],[-180., 180.]],\
        "Far_South_Pole":        [[-90.,-40.],[-180., 180.]],\
        "South_Pole":            [[-90.,-50.],[-180., 180.]],\
        "Close_South_Pole":      [[-90.,-75.],[-180., 180.]],\
              }
    if area not in list:   area = "Whole"
    [olat,olon] = list[area]
    return olon,olat

## Author: TN
def separatenames (name):
  from numpy import concatenate
  # look for comas in the input name to separate different names (files, variables,etc ..)
  if name is None:
     names = None
  else:
    names = []
    stop = 0
    currentname = name
    while stop == 0:
      indexvir = currentname.find(',')
      if indexvir == -1:
        stop = 1
        name1 = currentname
      else:
        name1 = currentname[0:indexvir]
      names = concatenate((names,[name1]))
      currentname = currentname[indexvir+1:len(currentname)]
  return names

## Author: TN [old]
def getopmatrix (kind,n):
  import numpy as np
  # compute matrix of operations between files
  # the matrix is 'number of files'-square
  # 1: difference (row minus column), 2: add
  # not 0 in diag : just plot
  if n == 1:
    opm = np.eye(1)
  elif kind == 'basic':
    opm = np.eye(n)
  elif kind == 'difference1': # show differences with 1st file
    opm = np.zeros((n,n))
    opm[0,:] = 1
    opm[0,0] = 0
  elif kind == 'difference2': # show differences with 1st file AND show 1st file
    opm = np.zeros((n,n))
    opm[0,:] = 1
  else:
    opm = np.eye(n)
  return opm
 
## Author: TN [old] 
def checkcoherence (nfiles,nlat,nlon,ntime):
  if (nfiles > 1):
     if (nlat > 1):
        errormess("what you asked is not possible !")
  return 1

## Author: TN
def readslices(saxis):
  from numpy import empty
  if saxis == None:
     zesaxis = None
  else:
     zesaxis = empty((len(saxis),2))
     for i in range(len(saxis)):
        a = separatenames(saxis[i])
        if len(a) == 1:
           zesaxis[i,:] = float(a[0])
        else:
           zesaxis[i,0] = float(a[0])
           zesaxis[i,1] = float(a[1])
           
  return zesaxis

## Author: TN
def  getsindex(saxis,index,axis):
# input  : all the desired slices and the good index
# output : all indexes to be taken into account for reducing field
  import numpy as np
  if ( np.array(axis).ndim == 2):
      axis = axis[:,0]
  if saxis is None:
      zeindex = None
  else:
      aaa = int(np.argmin(abs(saxis[index,0] - axis)))
      bbb = int(np.argmin(abs(saxis[index,1] - axis)))
      [imin,imax] = np.sort(np.array([aaa,bbb]))
      zeindex = np.array(range(imax-imin+1))+imin
      # because -180 and 180 are the same point in longitude,
      # we get rid of one for averaging purposes.
      if axis[imin] == -180 and axis[imax] == 180:
         zeindex = zeindex[0:len(zeindex)-1]
         print "INFO: whole longitude averaging asked, so last point is not taken into account."
  return zeindex
     
## Author: TN
def define_axis(lon,lat,vert,time,indexlon,indexlat,indexvert,indextime,what_I_plot,dim0,vertmode):
# Purpose of define_axis is to find x and y axis scales in a smart way
# x axis priority: 1/time 2/lon 3/lat 4/vertical
# To be improved !!!...
   from numpy import array,swapaxes
   x = None
   y = None
   count = 0
   what_I_plot = array(what_I_plot)
   shape = what_I_plot.shape
   if indextime is None:
      print "AXIS is time"
      x = time
      count = count+1
   if indexlon is None:
      print "AXIS is lon"
      if count == 0: x = lon
      else: y = lon
      count = count+1
   if indexlat is None:
      print "AXIS is lat"
      if count == 0: x = lat
      else: y = lat
      count = count+1
   if indexvert is None and dim0 is 4:
      print "AXIS is vert"
      if vertmode == 0: # vertical axis is as is (GCM grid)
         if count == 0: x=range(len(vert))
         else: y=range(len(vert))
         count = count+1
      else: # vertical axis is in kms
         if count == 0: x = vert
         else: y = vert
         count = count+1
   x = array(x)
   y = array(y)
   print "CHECK: what_I_plot.shape", what_I_plot.shape
   print "CHECK: x.shape, y.shape", x.shape, y.shape
   if len(shape) == 1:
      print shape[0]
      if shape[0] != len(x):
         print "WARNING HERE !!!"
         x = y
   elif len(shape) == 2:
      print shape[1], len(y), shape[0], len(x)
      if shape[1] == len(y) and shape[0] == len(x) and shape[0] != shape[1]:
         what_I_plot = swapaxes(what_I_plot,0,1)
         print "INFO: swapaxes", what_I_plot.shape, shape
         #x0 = x
         #x = y
         #y = x0
   #print "define_axis", x, y
   return what_I_plot,x,y

# Author: TN + AS
def determineplot(slon, slat, svert, stime):
    nlon = 1 # number of longitudinal slices -- 1 is None
    nlat = 1
    nvert = 1
    ntime = 1
    nslices = 1
    if slon is not None:
        nslices = nslices*len(slon)
        nlon = len(slon)
    if slat is not None:
        nslices = nslices*len(slat)
        nlat = len(slat)
    if svert is not None:
        nslices = nslices*len(svert)
        nvert = len(svert)
    if stime is not None:
        nslices = nslices*len(stime)
        ntime = len(stime)
    #else:
    #    nslices = 2  

    mapmode = 0
    if slon is None and slat is None:
       mapmode = 1 # in this case we plot a map, with the given projection

    return nlon, nlat, nvert, ntime, mapmode, nslices