Helper Function to determine resolution of coordinates about podpac HOT 11 CLOSED

This code might be useful: https://github.com/creare-com/pydem/blob/master/pydem/utils.py#L145

from podpac.

This might also be useful: https://pyproj4.github.io/pyproj/stable/api/crs/crs.html

from podpac.

Left to-do

• Finish Unit Tests
• Check for units

from podpac.

• cKDTree with boxsize wraparound implementation for stacked NearestNeighbor
• UTM projection handling

from podpac.

For coords.horizontal_resolution(units='m', type='nominal'), let's talk about the different types

types =

• "nominal" <-- this is wrong but cheap to calculate. Give a 'nominal' resolution over the entire domain
• "summary" <-- This is still approximate, gives the mean and standard deviation to each point
• "full" <-- min distance between every point (dx, dy, basically)
• "full_exact" <-- uses brute-force for the stacked coordinates

This is a proposal, feel free to propose something better.

from podpac.

Implemented 3 of the 4 types:

• "nominal" <-- this is wrong but cheap to calculate. Give a 'nominal' resolution over the entire domain
• "summary" <--- Returns average and std.dev for each point.
• "full" <--- returns the exact distance between every point, or every dx,dy if coordinates are unstacked

from podpac.

9851b1c

from podpac.

e05a662

from podpac.

Wrote some code to address the problem of lat/lon evaluating to different distances based upon the other dimension's value. However, I realized once passing in non-square unstacked coordinates that I failed to account for the difference resolution changing for each differing value of the other dimension.

Let our unstacked coordinate system be a $M$ by $N$ matrix, where $M$ is the number of rows (in grid squares), and $N$ is the number of columns. See Blender visualization:

Previously, we were returning two arrays of size $M$ and size $N$. This meant we were returning either a single row/column or a selection of each column. For example:

Another example:

Instead, we want to return this for latitude:

So, for latitude we want to return a $N$ by $M$ matrix.

For longitude:

So, for longitude we want to return a $M$ by $N$ matrix.

For the "summary" type resolution, we can simply average/compute std.dev of all of these values.

from podpac.

The $M+1$ or $N+1$ implementation is not possible due to the fact we don't store the values in the +1 dimension in our coords.

from podpac.

closed by #505

from podpac.