<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><font class="Apple-style-span" size="4">On Apr 4, 2013, at 12:42 PM, James Stroud wrote:</font><div><blockquote type="cite"><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><font class="Apple-style-span" size="4">So, even if numpy-to-flex conversion catches wrongly [1] typed arrays, users still need to know which numpy dtypes to use for which flex constructors. Here is what I have determined:</font></div></div></blockquote><div><font class="Apple-style-span" size="4"><br></font></div><font class="Apple-style-span" size="4">I realized that it is possible that unqualified specifiers like "numpy.float", etc, may have different meanings on different systems. Therefore, I think a better (i.e. more explicit) mapping is<br></font><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><ul><li><font class="Apple-style-span" size="4"><b>flex.bool</b> : numpy.bool8</font></li><li><font class="Apple-style-span" size="4"><b>flex.int</b> : numpy.int32</font></li><li><font class="Apple-style-span" size="4"><b>flex.long</b> : numpy.int64</font></li><li><font class="Apple-style-span" size="4"><b>flex.float</b> : numpy.float32</font></li><li><font class="Apple-style-span" size="4">f<b>lex.double</b> : numpy.float64</font></li></ul></div></div></div><font class="Apple-style-span" size="4">James</font></body></html>