<p dir="auto"><em>Original ticket <a href="http://projects.scipy.org/scipy/ticket/985" rel="nofollow">http://projects.scipy.org/scipy/ticket/985</a> on 2009-08-06 by trac user rubert, assigned to unknown.</em></p>
<p dir="auto">I assume the 1-D hilbert transform function in the signal module was intended to work like its Matlab equivalent.  That is, if I hilbert transform an N-D array it will only do it along the zeroth dimension.</p>
<p dir="auto">The hilbert transform was returning zeros for me when I attempted to use it on two-D arrays.  This patch fixes that.  The problem was with the arguments passed to ifft.</p>
<p dir="auto"><em>Original ticket <a href="http://projects.scipy.org/scipy/ticket/1642" rel="nofollow">http://projects.scipy.org/scipy/ticket/1642</a> on 2012-04-14 by <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/rgommers/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/rgommers">@rgommers</a>, assigned to unknown.</em></p>
<p dir="auto">This warning was showing up by default for a long time. The cause seems to be that the optional nxest/nyest parameters to surfit, which are determined in fitpack.pyf, are too small if kx=ky=1.</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="    &gt;&gt;&gt; x = [1,1,1,2,2,2,4,4,4]
    &gt;&gt;&gt; y = [1,2,3,1,2,3,1,2,3]
    &gt;&gt;&gt; z = array([0,7,8,3,4,7,1,3,4])
    &gt;&gt;&gt; lut = SmoothBivariateSpline(x, y, z, kx=1, ky=1, s=0)
/Users/rgommers/Code/scipy/scipy/interpolate/fitpack2.py:613: UserWarning: 
The required storage space exceeds the available storage space: nxest
or nyest too small, or s too small.
The weighted least-squares spline corresponds to the current set of
knots.
  warnings.warn(message)"><pre class="notranslate"><code class="notranslate">    &gt;&gt;&gt; x = [1,1,1,2,2,2,4,4,4]
    &gt;&gt;&gt; y = [1,2,3,1,2,3,1,2,3]
    &gt;&gt;&gt; z = array([0,7,8,3,4,7,1,3,4])
    &gt;&gt;&gt; lut = SmoothBivariateSpline(x, y, z, kx=1, ky=1, s=0)
/Users/rgommers/Code/scipy/scipy/interpolate/fitpack2.py:613: UserWarning: 
The required storage space exceeds the available storage space: nxest
or nyest too small, or s too small.
The weighted least-squares spline corresponds to the current set of
knots.
  warnings.warn(message)
</code></pre></div>
<p dir="auto">This needs some investigation. I'll filter out the warning for now.</p>
<p dir="auto">A likely related problem:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="In [3]: %paste
        x = [1,1,1,2,2,2,3,3,3]
        y = [1,2,3,1,2,3,1,2,3]
        z = [0,7,8,3,4,7,1,3,4]
        s = 0.1
        tx = [1+s,3-s]
        ty = [1+s,3-s]
        lut = LSQBivariateSpline(x,y,z,tx,ty,kx=1,ky=1)
## -- End pasted text --
/Users/rgommers/Code/scipy/scipy/interpolate/fitpack2.py:684: UserWarning: 
The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank deficient
system (deficiency=7). If deficiency is large, the results may be
inaccurate. Deficiency may strongly depend on the value of eps.
  warnings.warn(message)"><pre class="notranslate"><code class="notranslate">In [3]: %paste
        x = [1,1,1,2,2,2,3,3,3]
        y = [1,2,3,1,2,3,1,2,3]
        z = [0,7,8,3,4,7,1,3,4]
        s = 0.1
        tx = [1+s,3-s]
        ty = [1+s,3-s]
        lut = LSQBivariateSpline(x,y,z,tx,ty,kx=1,ky=1)
## -- End pasted text --
/Users/rgommers/Code/scipy/scipy/interpolate/fitpack2.py:684: UserWarning: 
The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank deficient
system (deficiency=7). If deficiency is large, the results may be
inaccurate. Deficiency may strongly depend on the value of eps.
  warnings.warn(message)
</code></pre></div>