Densification.... sometimes being dense is a good thing

DanPatterson_Retired

Yup... adding points to poly* features

Already offered... Densify at all license levels?  NO!

My missive .... Densification by Factor  YES!

There is also a Geodesic Densify, but I don't do Geographic in any event, so use esri tools

Why did I reinvent the wheel?  Because the densification basis is to simply provide a sequence of locations, for X and Y then subdivide the intervening location by some factor (hence the ... by Factor)

Here is an example....

a = np.array([[ 20.,&nbsp; 20.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 20.,&nbsp; 30.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 30.,&nbsp; 30.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 30.,&nbsp; 20.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 20.,&nbsp; 20.]])‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍<SPAN class="line-numbers-rows"><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN></SPAN> Then you run some code <SPAN class="keyword token">def</SPAN> <SPAN class="token function">_densify_2D</SPAN><SPAN class="punctuation token">(</SPAN>a<SPAN class="punctuation token">,</SPAN> fact<SPAN class="operator token">=</SPAN><SPAN class="number token">2</SPAN><SPAN class="punctuation token">)</SPAN><SPAN class="punctuation token">:</SPAN> &nbsp;&nbsp;&nbsp; <SPAN class="string token">"""Densify a 2D array using np.interp. &nbsp;&nbsp;&nbsp; """</SPAN> &nbsp;&nbsp;&nbsp; <SPAN class="comment token"># Y = a changed all the y's to a</SPAN> &nbsp;&nbsp;&nbsp; a <SPAN class="operator token">=</SPAN> np<SPAN class="punctuation token">.</SPAN>squeeze<SPAN class="punctuation token">(</SPAN>a<SPAN class="punctuation token">)</SPAN> &nbsp;&nbsp;&nbsp; n_fact <SPAN class="operator token">=</SPAN> len<SPAN class="punctuation token">(</SPAN>a<SPAN class="punctuation token">)</SPAN> <SPAN class="operator token">*</SPAN> fact &nbsp;&nbsp;&nbsp; b <SPAN class="operator token">=</SPAN> np<SPAN class="punctuation token">.</SPAN>arange<SPAN class="punctuation token">(</SPAN><SPAN class="number token">0</SPAN><SPAN class="punctuation token">,</SPAN> n_fact<SPAN class="punctuation token">,</SPAN> fact<SPAN class="punctuation token">)</SPAN> &nbsp;&nbsp;&nbsp; b_new <SPAN class="operator token">=</SPAN> np<SPAN class="punctuation token">.</SPAN>arange<SPAN class="punctuation token">(</SPAN>n_fact <SPAN class="operator token">-</SPAN> <SPAN class="number token">1</SPAN><SPAN class="punctuation token">)</SPAN> &nbsp;&nbsp;&nbsp; c0 <SPAN class="operator token">=</SPAN> np<SPAN class="punctuation token">.</SPAN>interp<SPAN class="punctuation token">(</SPAN>b_new<SPAN class="punctuation token">,</SPAN> b<SPAN class="punctuation token">,</SPAN> a<SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN> <SPAN class="number token">0</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">)</SPAN> &nbsp;&nbsp;&nbsp; c1 <SPAN class="operator token">=</SPAN> np<SPAN class="punctuation token">.</SPAN>interp<SPAN class="punctuation token">(</SPAN>b_new<SPAN class="punctuation token">,</SPAN> b<SPAN class="punctuation token">,</SPAN> a<SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN> <SPAN class="number token">1</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">)</SPAN> &nbsp;&nbsp;&nbsp; n <SPAN class="operator token">=</SPAN> c0<SPAN class="punctuation token">.</SPAN>shape<SPAN class="punctuation token">[</SPAN><SPAN class="number token">0</SPAN><SPAN class="punctuation token">]</SPAN> &nbsp;&nbsp;&nbsp; c <SPAN class="operator token">=</SPAN> np<SPAN class="punctuation token">.</SPAN>zeros<SPAN class="punctuation token">(</SPAN><SPAN class="punctuation token">(</SPAN>n<SPAN class="punctuation token">,</SPAN> <SPAN class="number token">2</SPAN><SPAN class="punctuation token">)</SPAN><SPAN class="punctuation token">)</SPAN> &nbsp;&nbsp;&nbsp; c<SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN> <SPAN class="number token">0</SPAN><SPAN class="punctuation token">]</SPAN> <SPAN class="operator token">=</SPAN> c0 &nbsp;&nbsp;&nbsp; c<SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN> <SPAN class="number token">1</SPAN><SPAN class="punctuation token">]</SPAN> <SPAN class="operator token">=</SPAN> c1 &nbsp;&nbsp;&nbsp; <SPAN class="keyword token">return</SPAN> c‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍<SPAN class="line-numbers-rows"><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN></SPAN>

And you get this. a_dens = _densify_2D(a, fact=2) a_dens array([[ 20.,&nbsp; 20.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 20.,&nbsp; 25.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 20.,&nbsp; 30.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 25.,&nbsp; 30.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 30.,&nbsp; 30.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 30.,&nbsp; 25.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 30.,&nbsp; 20.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 25.,&nbsp; 20.], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [ 20.,&nbsp; 20.]])‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍<SPAN class="line-numbers-rows"><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN></SPAN> a<SPAN class="punctuation token">.</SPAN>shape&nbsp; <SPAN class="comment token"># (49874, 2)</SPAN> <SPAN class="operator token">%</SPAN>timeit _densify_2D<SPAN class="punctuation token">(</SPAN>a<SPAN class="punctuation token">,</SPAN> fact<SPAN class="operator token">=</SPAN><SPAN class="number token">2</SPAN><SPAN class="punctuation token">)</SPAN> <SPAN class="number token">5.25</SPAN> ms ± <SPAN class="number token">323</SPAN> µs per loop &nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="punctuation token">(</SPAN>mean ± std<SPAN class="punctuation token">.</SPAN> dev<SPAN class="punctuation token">.</SPAN> of <SPAN class="number token">7</SPAN> runs<SPAN class="punctuation token">,</SPAN> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">100</SPAN> loops each<SPAN class="punctuation token">)</SPAN><SPAN class="line-numbers-rows"><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN></SPAN>

Where it really shines, is for long features that just need to be densified without any particular requirement for a set spacing.  If is fairly speedy as well.

Hope someone finds this useful, if you don't have the Standard or Advanced license in particular.