Class: NumRu::GPhys

Inherits:
Object
  • Object
show all
Defined in:
lib/numru/ganalysis/fitting.rb,
lib/numru/gphys/grib.rb,
lib/numru/ganalysis/eof.rb,
lib/numru/ganalysis/met.rb,
lib/numru/gphys/ep_flux.rb,
lib/numru/gphys/version.rb,
lib/numru/gphys/gphys_io.rb,
lib/numru/ganalysis/log_p.rb,
lib/numru/gphys/gphys_fft.rb,
lib/numru/gphys/gphys_dim_op.rb,
lib/numru/ganalysis/histogram.rb,
lib/numru/gphys/gphys_grib_io.rb,
lib/numru/ganalysis/covariance.rb,
lib/numru/gphys/coordtransform.rb,
lib/numru/gphys/gphys_grads_io.rb,
lib/numru/gphys/gphys_gtool3_io.rb,
lib/numru/gphys/gphys_io_common.rb,
lib/numru/gphys/gphys_netcdf_io.rb,
lib/numru/gphys/gphys_nusdas_io.rb

Overview

GPhys extension with GAnalysis::Fitting

Defined Under Namespace

Modules: EP_Flux, GrADS_IO, GribUtils, Grib_IO, Gtool3_IO, IO, IO_Common, NetCDF_IO, NuSDaS_IO Classes: Grib, GribDim, GribVar

Constant Summary collapse

VERSION = 
"1.4.3.2"
COS_TAPER_SP_FACTOR = 
1.0 / 0.875
BC_SIMPLE =

enum in convol_filter.c

10
BC_CYCLIC =

enum in convol_filter.c

11
BC_TRIM =

enum in convol_filter.c

12
@@fft_forward = 
-1
@@fft_backward = 
1
@@fft_ignore_missing = 
false
@@fft_missing_replace_val = 
nil
@@default_missval =

NC_FILL_DOUBLE/FLOAT ~15*2^119

9.9692099683868690e+36

Class Method Summary collapse

Instance Method Summary collapse

Class Method Details

.fft_ignore_missing(ignore = true, replace_val = nil) ⇒ Object 



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# File 'lib/numru/gphys/gphys_fft.rb', line 331

def self.fft_ignore_missing( ignore=true, replace_val=nil )
  @@fft_ignore_missing = ignore 
  @@fft_missing_replace_val = replace_val
end

Instance Method Details

#bin_mean(dim, len, nminvalid = 1) ⇒ Object

Binning along a dimension (mean)

The values are averaged every “len” grids; unlike running_mean the number of grids is reduced to 1/len. Currently, the only available boundary condition is BC_TRIM.

ARGUMENTS

  • dim (Integer or String) : the dimension

  • len (Integer): length of the bin

  • nminvalid (Integer; optional; defualt=1): Effective only for data with missing. Minimum number of grid points needed for averaging (1 to len).

RETURN VALUE

  • a GPhys



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# File 'lib/numru/gphys/gphys_dim_op.rb', line 113

def bin_mean(dim, len, nminvalid=1)
  dim = dim_index(dim)  # to handle String or negative specification
  GPhys.new( grid.binning(dim, len), data.bin_mean(dim, len, nminvalid) )
end

#bin_sum(dim, len, nminvalid = 1) ⇒ Object

Binning along a dimension (summation)

Similar to bin_mean, but the values are simply summed without averaging

ARGUMENTS

  • dim (Integer or String) : the dimension

  • len (Integer): length of the bin

  • nminvalid (Integer; optional; defualt=1): Effective only for data with missing. Minimum number of grid points needed for averaging (1 to len).

RETURN VALUE

  • a GPhys



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# File 'lib/numru/gphys/gphys_dim_op.rb', line 131

def bin_sum(dim, len, nminvalid=1)
  dim = dim_index(dim)  # to handle String or negative specification
  GPhys.new( grid.binning(dim, len), data.bin_sum(dim, len, nminvalid) ) 
end

#coordtransform(coordmapping, axes_to, *dims) ⇒ Object 



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# File 'lib/numru/gphys/coordtransform.rb', line 41

def coordtransform( coordmapping, axes_to, *dims )

  rankmp = coordmapping.rank

  #< check arguments >
  if axes_to.length != rankmp
    raise ArgumentError,
      "length of axes_to must be equal to the rank of coordmapping"
  end
  if self.rank == rankmp
    dims = (0...rankmp).collect{|i| i}
  elsif self.rank < rankmp
    raise ArgumentError,"rank of coordmapping is greater than self.rank"
  elsif dims.length != rankmp
    raise ArguemntError,
      "# of dimensions speficied is not equal to the rank of coordmapping"
  elsif dims != dims.sort
    raise ArguementErroor,"dims must be in the increasing order"
  end

  #< get grid points >
  vt = coordmapping.map_grid( *dims.collect{|d| axes_to[d].pos.val} )
  x = dims.collect{|d| self.grid.axis(d).pos.val}
  #< prepare the output object >
  axes = (0...self.rank).collect{|i| grid.axis(i)}
  dims.each_with_index{|d,j| axes[d]=axes_to[j]}
  grid_to = Grid.new( *axes )
  vnew = VArray.new( NArray.new( self.data.ntype, *grid_to.shape ),
                    self.data, self.name )

  #< do interpolation (so far only 2D is supported) >
  case dims.length
  when 2
    if !HAVE_NUMRU_SSL2

      p "interpolation without SSL2"
#         raise "Sorry, so far I need SSL2 (ruby-ssl2)"
      self.each_subary_at_dims_with_index( *dims ){ |fxy,idx|

        wgts = Array.new
        idxs = Array.new

        for d in 0..dims.length-1
          wgt = vt[d].dup.fill!(-1.0)
          idx0 = vt[d].dup.to_i.fill!(-1)
          idx1 = idx0.dup.fill!(x[d].length)

          xsort = x[d].sort
          xsortindex = x[d].sort_index
          for i in 0..x[d].length-1
            idx0[ xsort[i] <= vt[d] ] = xsortindex[i]
            idx1[ xsort[-1-i] >= vt[d] ] = xsortindex[-1-i]
          end

          # where idx0=idx1
          wgt[ idx0.eq(idx1) ] = 1.0

          # where vt[d] < x[d].min
          wgt[ idx0 <= -1 ] = 1.0
          idx0[ idx0 <= -1 ] = 0

          # where vt[d] > x[d].max
          wgt[ idx1 >= x[d].length ] = 0.0
          idx1[ idx1 >= x[d].length ] = x[d].length-1

          # normal points
          mask = wgt.eq(-1.0)
          wgt[mask] = (vt[d][mask]-x[d][idx0[mask]])/(x[d][idx1[mask]]-x[d][idx0[mask]])

          wgts.push(wgt)
          idxs[d*2] = idx0
          idxs[d*2+1] = idx1

        end

        case dims.length
#            when 1
#              f =   fxy.data.val[idxs[0]]*(1-wgts[0]) + 
#                    fxy.data.val[idxs[1]]*wgts[0]
#              f = f.to_na if( f.class.to_s == "NArrayMiss" )
        when 2
          lx = fxy.shape[0]
          f =   ( fxy.data.val[idxs[0]+idxs[2]*lx]*(1-wgts[0]) + 
                  fxy.data.val[idxs[1]+idxs[2]*lx]*wgts[0]
                ) * (1-wgts[1]) + 
                ( fxy.data.val[idxs[0]+idxs[3]*lx]*(1-wgts[0]) + 
                  fxy.data.val[idxs[1]+idxs[3]*lx]*wgts[0] 
                ) * wgts[1]
          f = f.to_na if( f.class.to_s == "NArrayMiss" )
        else
          raise "Sorry, #{v.length}D interpolation is yet to be supported"
        end

        if(idx==false)
          vnew[] = f
        else
          vnew[*idx] = f
        end
      }

    else
      ix=iy=0
      m=3
      self.each_subary_at_dims_with_index( *dims ){ |fxy,idx|
        c,xt = SSL2.bicd3(x[0],x[1],fxy.val,m)
        begin
          ix,iy,f = SSL2.bifd3(x[0],x[1],m,c,xt,0,vt[0],ix,0,vt[1],iy)
        rescue
          $stderr.print "Interpolation into", vt[0].inspect, vt[1].inspect
          raise $!
        end
        vnew[*idx] = f
      }
    end
  else
    raise "Sorry, #{v.length}D interpolation is yet to be supported"
  end

  #< finish >
  GPhys.new( grid_to, vnew )
end

#corelation(other, *dims) ⇒ Object Also known as: correlation 



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# File 'lib/numru/ganalysis/covariance.rb', line 93

def corelation(other, *dims)
  GAnalysis.corelation(self, other, *dims)
end

#cos_taper(*dims) ⇒ Object

Spectral factor for the cosine taper. Specta should be multiplied by this.



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# File 'lib/numru/gphys/gphys_fft.rb', line 339

def cos_taper(*dims)
  if dims.length < 1
	raise ArgumentError,'You have to specify one or more dimensions'
  end
  dims.sort!.uniq!
  val = self.data.val
  dims.each{|dim|
	dim = dim_index(dim) if dim.is_a?(String)
	dim += rank if dim < 0
	raise ArgumentError,"dim #{dim} does not exist" if dim<0 || dim>rank
    nx = shape[dim]
	wgt = NArray.float(nx).fill!(1)
    x = 10.0 / nx * (NArray.float(nx).indgen!+0.5) 
	wskl = x.lt(1).where
	wskr = x.gt(9).where
	wgt[wskl] = 0.5*( 1.0 - NMath::cos(Math::PI*x[wskl]) )
	wgt[wskr] = 0.5*( 1.0 - NMath::cos(Math::PI*x[wskr]) )
	wgt.reshape!( *([1]*dim + [nx] + [1]*(rank-dim-1)) )
	val = val*wgt
  }
  to_ret = self.copy
  to_ret.data.val = val
  to_ret
end

#covariance(other, *dims) ⇒ Object 



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# File 'lib/numru/ganalysis/covariance.rb', line 89

def covariance(other, *dims)
  GAnalysis.covariance(self, other, *dims)
end

#detrend(*dims) ⇒ Object 



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# File 'lib/numru/gphys/gphys_fft.rb', line 364

def detrend(*dims)
  if dims.length < 1
	raise ArgumentError,'You have to specify one or more dimensions'
  end
  dims.sort!.uniq!
  val = self.data.val
  dims.each{|dim|
	dim = dim_index(dim) if dim.is_a?(String)
	dim += rank if dim < 0
	raise ArgumentError,"dim #{dim} does not exist" if dim<0 || dim>rank
	if val.is_a?(NArray)
	  x = self.coord(dim).val
	  x.reshape!( *([1]*dim + [x.length] + [1]*(rank-dim-1)) )
	  vmean = val.mean(dim)
	  vxmean = (val*x).mean(dim)
	  xmean = x.mean(dim)
	  x2mean = (x*x).mean(dim)
	  denom = x2mean-xmean**2
	  if denom != 0
 a = (vxmean - vmean*xmean)/denom
 b = (vmean*x2mean - vxmean*xmean)/denom
	  else
 a = 0
 b = vmean
	  end
	elsif val.is_a?(NArrayMiss)
	  x = self.coord(dim).val
	  x.reshape!( *([1]*dim + [x.length] + [1]*(rank-dim-1)) )
	  x = NArrayMiss.to_nam( NArray.new(x.typecode, *val.shape) + x,
 val.get_mask ) 
	  vmean = val.mean(dim)
	  vxmean = (val*x).mean(dim)
	  xmean = x.mean(dim)
	  x2mean = (x*x).mean(dim)
	  denom = x2mean-xmean**2
	  meq0 = denom.eq(0).to_na(0)    # ==0 and not masked
	  mne0 = denom.ne(0).to_na(0)    # !=0 and not masked
      denom.set_mask(mne0)    # only nonzero part will be used to divide:
	  a = (vxmean - vmean*xmean)/denom
	  b = (vmean*x2mean - vxmean*xmean)/denom
	  a[meq0] = 0
	  b[meq0] = vmean[meq0]
	end
	a.newdim!(dim) if !a.is_a?(Numeric)
	b.newdim!(dim) if !b.is_a?(Numeric)
	val = val - a*x-b
  }
  to_ret = self.copy
  to_ret.data.val = val
  to_ret
end

#eof(*args) ⇒ Object 



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# File 'lib/numru/ganalysis/eof.rb', line 229

def eof(*args)
  GAnalysis.eof(self, *args)
end

#fft(backward = false, *dims) ⇒ Object 



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# File 'lib/numru/gphys/gphys_fft.rb', line 416

def fft(backward=false, *dims)
  fftw3 = false
  if defined?(FFTW3)
	fftw3 = true
  elsif !defined?(FFTW)
	raise "Both FFTW3 and FFTW are not installed."
  end
  if backward==true
	dir = @@fft_backward
  elsif !backward
	dir = @@fft_forward
  else
	raise ArgumentError,"1st arg must be true or false (or, equivalenty, nil)"
  end

  # <FFT>

  gfc = self.copy  # make a deep clone
  if fftw3
	val = gfc.data.val
	if @@fft_ignore_missing and val.is_a?(NArrayMiss)
	  if @@fft_missing_replace_val
 val = val.to_na(@@fft_missing_replace_val)
	  else
 val = val.to_na 
	  end
    elsif val.is_a?(NArrayMiss) && val.count_invalid == 0
      val = val.to_na 
	end
	fcoef = FFTW3.fft( val, dir, *dims )
  else
	# --> always FFT for all dimensions
	if dims.length == 0
	  raise ArgumentError,
 "dimension specification is available only if FFTW3 is installed"
	end
	val = gfc.data.val
	if @@fft_ignore_missing and val.is_a?(NArrayMiss)
	  if @@fft_missing_replace_val
 val = val.to_na(@@fft_missing_replace_val)
	  else
 val = val.to_na 
	  end
    elsif val.is_a?(NArrayMiss) && val.count_invalid == 0
      val = val.to_na 
	end
	fcoef = FFTW.fftw( val, dir )
  end
  if dir == @@fft_forward
	if dims.length == 0
	  fcoef = fcoef / fcoef.length       # normalized if forward FT
	else
	  sh = fcoef.shape
	  len = 1
	  dims.each{|d|
 raise ArgumentError, "dimension out of range" if sh[d] == nil
 len *= sh[d]
      }
	  fcoef = fcoef / len
    end
  end
  gfc.data.replace_val( fcoef )

  # <coordinate variables>
  for i in 0...gfc.rank
	if dims.length == 0 || dims.include?(i) || dims.include?(i+rank)
	  __predefined_coord_units_conversion(gfc.coord(i))
	  cv = gfc.coord(i).val
	  n = cv.length
	  clen = (cv.max - cv.min) * n / (n-1)
	  wn = (2*Math::PI/clen) * NArray.new(cv.typecode,cv.length).indgen!
	  if (!backward)
 gfc.coord(i).set_att('origin_in_real_space',cv[0..0])
	  else 
 if ( org = gfc.coord(i).get_att('origin_in_real_space') )
   wn += org[0]
   ###gfc.coord(i).del_att('origin_in_real_space')
 end
	  end
	  gfc.coord(i).replace_val(wn)
	  gfc.coord(i).units = gfc.coord(i).units**(-1)
	  __coord_name_conversion(gfc.coord(i), backward)
	end
  end

  # <fini>
  gfc
end

#fft_deriv(dim) ⇒ Object 



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# File 'lib/numru/gphys/gphys_fft.rb', line 505

def fft_deriv(dim)
  tp = self.data.typecode
  fc = self.fft(false,dim)
  wn = fc.coord(dim)
  k = wn.val.to_type(NArray::Complex)
  n = k.length
  n2a = (n-1)/2
  n2b = [n/2 + 1, n-1].min  # min to avoid error if n=2 (though meaningless)
  kmx = k[-1]+k[1]
  ik = NArray.complex(n)
  ik[0..n2a] = k[0..n2a]*Complex::I
  ik[n2b..-1] = (k[n2b..-1]-kmx) * Complex::I
  dim.times{ik.newdim!(0)}
  (self.rank-dim-1).times{ik.newdim!(-1)}
  fc.replace_val(fc.val*ik)
  deriv = fc.fft(true,dim)
  deriv.units = deriv.units * wn.units
  if tp >= NArray::SCOMPLEX
    deriv
  else
    deriv.real
  end
end

#histogram(opts = Hash.new) ⇒ Object Also known as: histogram1D 



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# File 'lib/numru/ganalysis/histogram.rb', line 155

def histogram(opts=Hash.new)
  GAnalysis.histogram(self, opts)
end

#least_square_fit(functions, ensemble_dims = nil, indep_dims = nil) ⇒ Object 



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# File 'lib/numru/ganalysis/fitting.rb', line 460

def least_square_fit(functions, ensemble_dims=nil, indep_dims=nil)

  #< preparation >

  no_fitting_dims = Array.new
  if ensemble_dims
    ensemble_dims = ensemble_dims.collect{|d| @grid.dim_index(d)}
    no_fitting_dims += ensemble_dims
  end
  if indep_dims
    indep_dims = indep_dims.collect{|d| @grid.dim_index(d)}
    no_fitting_dims += indep_dims
  end
  fitting_dims = (0...rank).collect{|i| i} - no_fitting_dims
  grid_locs = fitting_dims.collect{|d| coord(d).val}
  data = self.val

  #< fitting >
  c, bf, diff = GAnalysis::Fitting.least_square_fit(data, grid_locs, 
                                      functions, ensemble_dims, indep_dims)

  #< make a GPhys of the best fit >

  if !ensemble_dims
    grid = self.grid
  else
    axes = Array.new
    (0...rank).each{|d| 
      axes.push(self.axis(d)) unless ensemble_dims.include?(d)
    }
    grid = Grid.new(*axes)
    shape = bf.shape
    ensemble_dims.sort.reverse_each{|d| shape.delete_at(d)}
    bf = bf.reshape(*shape)
  end

  va = VArray.new(bf, self.data, self.name)
  bf = GPhys.new(grid, va)

  [c, bf, diff]
end

#logp_coord_p2z(pdim = nil) ⇒ Object

Convert the pressure coordinate in self to log-pressure height (after duplicating self)

Return value: a GPhys



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# File 'lib/numru/ganalysis/log_p.rb', line 84

def logp_coord_p2z(pdim=nil)
  pdim = GAnalysis::Met.find_prs_d(self) if !pdim
  p = self.coord(pdim)
  z = GAnalysis::LogP.p2z(p)
  ax = self.axis(pdim).copy
  ax.set_pos(z)
  ax.name = z.name
  grid = self.grid.copy.set_axis(pdim, ax)
  GPhys.new(grid,self.data)
end

#phase_velocity(kdim, fdim, kconv, fconv, kf0_is_c0 = true, no_kfreorder = false) ⇒ Object 



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# File 'lib/numru/gphys/gphys_fft.rb', line 757

def phase_velocity(kdim,fdim,kconv,fconv,kf0_is_c0=true,no_kfreorder=false)
  kax = self.axis(kdim)
  fax = self.axis(fdim)
  kax.pos = kax.pos*kconv if kconv
  fax.pos = fax.pos*fconv if fconv
  cunits = fax.pos.units / kax.pos.units

  f = fax.pos.val
  k = kax.pos.val
  nk = k.length
  nf = f.length
  if no_kfreorder
    k[nk/2+1..-1] = -k[nk/2+1..-1][-1..0]+k[nk/2]
    f[nf/2+1..-1] = -f[nf/2+1..-1][-1..0]+f[nf/2]
  end
  f = -f
  cp = f.newdim(0) / k.newdim(1) #cp[kdim,fdim]
  jf0 = f.eq(0).where[0]  # where f==0
  jk0 = k.eq(0).where[0]  # where k==0
  if kf0_is_c0
    cp[jk0,jf0] = 0.0       # treat k=f=0 as stationary (c=0)
  else
    cp[jk0,jf0] = 1.0/0.0   # not to count k=f=0 component at all (c=infty)
  end

  [cp, cunits]
end

#phase_velocity_binning(kdim, fdim, cbins, kconv = nil, fconv = nil) ⇒ Object 



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# File 'lib/numru/gphys/gphys_fft.rb', line 673

def phase_velocity_binning(kdim, fdim, cbins, kconv=nil, fconv=nil)

  # < process arguments >

  case cbins
  when Hash 
    min = cbins["min"] ||raise(ArgumentError,"a Hash cbins must have 'min'")
    max = cbins["max"] ||raise(ArgumentError,"a Hash cbins must have 'max'")
    int = cbins["int"] ||raise(ArgumentError,"a Hash cbins must have 'int'")
    cbins = Array.new
    eps = int.abs*1e-6   # epsilon to deal with float steps
    (min.to_f..(max.to_f+eps)).step(int){|c| cbins.push(c)}
    cbins = NArray.to_na(cbins)
  when Array
    cbins = NArray.to_na(cbins)
  when NArray
  else
    raise ArgumentError, "cbins must be a Hash or Array or NArray"
  end

  kdim = dim_index(kdim) if kdim.is_a?(String)
  kdim += rank if kdim < 0
  fdim = dim_index(fdim) if fdim.is_a?(String)
  fdim += rank if fdim < 0

  # < sort along wavenumber/freuqency axis >

  pw = self.spect_zero_centering(kdim).spect_one_sided(fdim)

  # < process axes >

  cp, cunits = pw.phase_velocity(kdim,fdim,kconv,fconv,false)

  vcbins = VArray.new(cbins, {"units"=>cunits.to_s, 
                "long_name"=>"phase velocity bounds"}, "cbounds")
  vccent = VArray.new( (cbins[0..-2] + cbins[1..-1])/2, 
                {"units"=>cunits.to_s, "long_name"=>"phase velocity"}, "c")
  axc = Axis.new(true).set_cell(vccent, vcbins).set_pos_to_center
  axes = [axc]   # the first dimension will be "c"
  gr = pw.grid
  (0...pw.rank).each do |d|
    if d!=kdim && d!=fdim
      axes.push(gr.axis(d))
    end
  end
  newgrid = Grid.new(*axes)

  nk = pw.shape[kdim]
  nf = pw.shape[fdim]
  cp.reshape!(nk*nf)

  # < reorder input data >

  dimorder = (0...pw.rank).collect{|i| i}
  dimorder.delete(fdim)
  dimorder.unshift(fdim)
  dimorder.delete(kdim)
  dimorder.unshift(kdim)   # --> [kdim, fdim, the other dims...]
  sh = pw.shape
  reshape = [nk*nf]
  (0...rank).each{|i| reshape.push(sh[i]) if i!=fdim && i!=kdim}
  pwv = pw.val.transpose(*dimorder).reshape(*reshape)  
                           # --> [ combined k&fdim, the other dims...]

  # < binning >

  shc = newgrid.shape
  pwc = NArray.new(pwv.typecode, *shc)    # will have no missing data
  nc = axc.length
  for jc in 0...nc
    w = (cp.gt(cbins[jc]) & cp.lt(cbins[jc+1])).where
    pwc[jc,false] += pwv[w,false].sum(0) if w.length>0
    w = (cp.eq(cbins[jc])).where
    pwc[jc,false] += pwv[w,false].sum(0)/2 if w.length>0  # half from bdry
    w = (cp.eq(cbins[jc+1])).where
    pwc[jc,false] += pwv[w,false].sum(0)/2 if w.length>0  # half from bdry
  end

  vpwc = VArray.new(pwc,pw.data,pw.name)
  gpwc = GPhys.new(newgrid,vpwc)

  gpwc
end

#phase_velocity_binning_iso_norml(kdim, fdim, cmin, cmax, cint, kconv = nil, fconv = nil) ⇒ Object 



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# File 'lib/numru/gphys/gphys_fft.rb', line 665

def phase_velocity_binning_iso_norml(kdim, fdim, cmin, cmax, cint, 
                               kconv=nil, fconv=nil)
  cbins = {"min"=>cmin,"max"=>cmax,"int"=>cint}
  pwc = phase_velocity_binning(kdim, fdim, cbins, kconv, fconv)
  fact = UNumeric[int, pwc.coord(0).units]
  pwc/fact
end

#phase_velocity_filter(xdim, tdim, cmin = nil, cmax = nil, xconv = nil, tconv = nil, remove_xtmean = false) ⇒ Object

Raises:

  • (ArgumentError) 


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# File 'lib/numru/gphys/gphys_fft.rb', line 628

def phase_velocity_filter(xdim, tdim, cmin=nil, cmax=nil, xconv=nil, tconv=nil, remove_xtmean=false)
  raise(ArgumentError,"need at least cmin or cmax") if !(cmin || cmax)


  xdim = dim_index(xdim) if xdim.is_a?(String)
  xdim += rank if xdim < 0
  tdim = dim_index(tdim) if tdim.is_a?(String)
  tdim += rank if tdim < 0
  fc = self.fft(nil,xdim,tdim)
  
  kdim = xdim
  fdim = tdim
  kconv = ( xconv ? 1.0/xconv : nil )
  fconv = ( tconv ? 1.0/tconv : nil )
  cp, = fc.phase_velocity(kdim,fdim,kconv,fconv,!remove_xtmean,true)

  fcv = fc.val
  nk = fc.shape[kdim]
  nf = fc.shape[fdim]
  sel = [true]*fc.rank
  for jf in 0...nf
    for jk in 0...nk
      c = cp[jk,jf]
      if ( cmin && c<cmin or cmax && c>cmax)
        sel[kdim]=jk
        sel[fdim]=jf
        fcv[*sel] = 0.0
      end
    end
  end
  fc.replace_val(fcv)
  gp = fc.fft(true,xdim,tdim)
  gp = gp.real if (self.typecode <= NArray::FLOAT)
  GPhys.new(self.grid_copy, gp.data)
            #^ use the original grid, since units may have changed
end

#rawspect2powerspect(*dims) ⇒ Object 



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# File 'lib/numru/gphys/gphys_fft.rb', line 612

def rawspect2powerspect(*dims)
  # developpers memo: Needs Units conversion.
  factor = nil
  dims.each{|dim|
	ax = self.coord(dim)
	dwn = UNumeric.new( ((ax[-1].val - ax[0].val)/(ax.length - 1)).abs,
   ax.units )
    if !factor
	  factor = dwn**(-1)
	else
	  factor = factor / dwn
	end
  }
  self * factor
end

#running_mean(dim, len_or_wgt = nil, bc = BC_SIMPLE, nminvalid = 1) ⇒ Object

Running mean along a dimension (with optional weight specification).

ARGUMENTS

  • dim (Integer or String) : the dimension

  • len_or_wgt : If Integer, specifies the length; if 1D NArray, specifies the weight (e.g., NArray[1.0, 2.0, 1.0] for the 1-2-1 smooting)

  • bc (Integer; optional) : Speficy one of the folloing:

    • GPhys::BC_SIMPLE (default) : Averaging is trucated at the boundaries (the number of grid points used is reduced near the boundaries). The shape of the object is conserved.

    • GPhys::BC_CYCLIC : Cyclic boundary condition. Shape conserved.

    • GPhys::BC_TRIM : Grids near the boundaries are trimmed to secure the number of grid points to average. Shape not conserved; length along the dim is reduced by (len-1).

  • nminvalid (Integer; optional; defualt=1): This parameter is used only when the data have missing. Minimum number of grid points needed for averaging. Must be from 1 to len.

RETURN VALUE

  • a GPhys

REMARK AND LIMITATION

  • If the length of the running mean is even number, fewer grid points are used from the “left” side; e.g., If len is 6, result is a mean over self..self.

  • Regardless the na_type of self, double is used for avaraging, so:

  • This method does not support complex numbers.



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# File 'lib/numru/gphys/gphys_dim_op.rb', line 46

def running_mean(dim, len_or_wgt=nil, bc=BC_SIMPLE, nminvalid=1)

  #< process arguments >

  dim = dim_index(dim)  # to handle String or negative specification

  case len_or_wgt
  when nil
    raise ArgumentError, "You need to specify the length (Integer) or the weight (1D NArray) as the 2nd argument"
  when Integer
    # len_or_wgt is a length
    len = len_or_wgt  
    wgt = NArray.float(len).fill!(1.0)
  else
    # len_or_wgt is a weight
    wgt = len_or_wgt
    if (!wgt.respond_to?(:rank) || wgt.rank != 1)
      raise ArgumentError, "wgt: expect a 1D NArray(-like obj)"
    end
    len = wgt.length
  end

  #< calc running mean >

  vi = self.val
  if (vi.typecode > NArray::DFLOAT)
    raise("This method supports only real or integer data")
  end 
  if vi.is_a?(NArrayMiss)
    vi, missval = nam2na_missval(vi)
    vo = c_running_mean(vi,dim,wgt,bc,missval,nminvalid)
    vo = NArrayMiss.to_nam(vo, vo.ne(missval) )
  else
    vo = c_running_mean(vi,dim,wgt,bc)
  end

  #< grid >

  if (bc ==  BC_TRIM)
    fst = (len-1)/2    # if odd len/2, if even len/2-1
    lst = -(len/2) - 1 
    grid = self.grid[ *([true]*dim + [fst..lst, false]) ]
  else
    grid = self.grid
  end

  #< result >
  vvo = VArray.new( vo, self.data, self.name )  # Inherit name & attrs
  GPhys.new( grid, vvo )

end

#spect_one_sided(dim) ⇒ Object 



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# File 'lib/numru/gphys/gphys_fft.rb', line 601

def spect_one_sided(dim)
  dim = dim + self.rank if dim<0
  len = self.shape[dim]
  b = self[ *([true]*dim + [0..len/2,false]) ] * 2
  b[*([true]*dim + [0,false])] = b[*([true]*dim + [0,false])] / 2
  if (self.shape[dim] % 2) == 0  # --> even number
    b[*([true]*dim + [-1,false])] = b[*([true]*dim + [-1,false])] / 2
  end
  b
end

#spect_zero_centering(dim) ⇒ Object 



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# File 'lib/numru/gphys/gphys_fft.rb', line 587

def spect_zero_centering(dim)
  dim = dim + self.rank if dim<0
  len = self.shape[dim]
  b = self[ *( [true]*dim + [[(len+1)/2..len-1,0..len/2],false] ) ].copy
  s1 = [true]*dim + [0, false]
  s2 = [true]*dim + [-1, false]
  if (len % 2) == 0   #--> even number
    b[*s1] = b[*s1]/2      # the ends are duplicated --> halved
    b[*s2] = b[*s1]
  end
  b.coord(dim)[0..len/2-1] = -b.coord(dim)[len/2+1..-1].val[-1..0]
  b
end