This comes up time and again. ....I have 'X' rasters for an area and I need to determine Y over time....

Here is an example.... ....calculating percentiles....

Cell Statistics... is the obvious choice. But you don't have the Spatial Analyst extension? Doesn't matter. All the tools that you need are already provided if you are using current versions of ArcMap or ArcGIS PRO.

Do some work

**(1) Reading the raster files**

`import numpy as np`

import arcpy

arrs = []

folder = r"C:\Data\rasters" # ---- specify a folder containing all the rasters ----

arcpy.env.workspace = folder

rasters = arcpy.ListRasters("*", "TIF")

for raster in rasters:

arrs.append(arcpy.RasterToNumPyArray(raster))

a = np.array(arrs) # ---- the master array ----

Once the rasters are all converted to arrays, they were appended to a list. A master array was created in line 9.

As an example, 31 rasters in tif format were read in. These are small rasters, but they can be as large as you can process. You are now ready to calculate values for them using numpy.

**(2) Getting the statistics**

`median = np.median(a, axis=0) # ---- pick a statistic ----`

array([[ 59., 54., 36., ..., 57., 46., 46.],

[ 43., 45., 59., ..., 38., 51., 51.],

[ 35., 50., 57., ..., 47., 55., 65.],

...,

[ 43., 52., 40., ..., 56., 62., 60.],

[ 45., 57., 39., ..., 45., 44., 48.],

[ 49., 57., 50., ..., 56., 50., 58.]]

mins = np.min(a, axis=0)

array([[1, 3, 0, ..., 6, 4, 5],

[1, 5, 6, ..., 6, 7, 2],

[3, 2, 4, ..., 2, 5, 4],

...,

[4, 0, 1, ..., 3, 4, 6],

[0, 0, 0, ..., 5, 1, 0],

[4, 1, 1, ..., 0, 3, 7]])

# ---- name a statistic, with or without nodata values, it can be done ----

Now when you are done your calculations you will probably want to make a raster so you can bask in the beauty of your efforts.

**(3) Reading the raster information and saving the result**

`rast = arcpy.Raster(r01) # ---- read the first raster we loaded ----`

rast = arcpy.Raster(r01) # ---- simple

dir(rast) # ---- get raster information... some snipping here ----

[..., 'bandCount', 'catalogPath', 'compressionType', 'extent', 'format', 'hasRAT',

'height', 'isInteger', 'isTemporary', 'maximum', 'mean', 'meanCellHeight',

'meanCellWidth', 'minimum', 'name', 'noDataValue', 'path', 'pixelType', 'save',

'spatialReference', 'standardDeviation', 'uncompressedSize', 'width']

# ---- Save the result out to a new raster ------

r01 = rasters[1] # --- rasters have the same extent and cell size

rast = arcpy.Raster(r01)

lower_left = rast.extent.lowerLeft # ---- needed to produce output

cell_size = rast.meanCellHeight # ---- we will use this for x and y

f = r"c:\temp\result.tif"

r = arcpy.NumPyArrayToRaster(a, lower_left, cell_size, cell_size)

r.save(f)

**(4) Saving and reloading the arrays**

Now, the nice thing about arrays is that you can save a 3D to a file for reloading later (or any dimension for that matter).

If you have a 3D array like we have, this is kind of like a big 'zip'.

`np.save(r"c:\temp\myarray.npy", a) # ---- 'a' is the multi dimensional array`

a.shape # ---- 31 days, 100 x 100 cells

(31, 100, 100)

# ---- reload it

im_back = np.load(r"c:\temp\myarray.npy")

im_back.shape

(31, 100, 100)

# ---- check to see if it is good

np.all(a == im_back) # ---- some numpy magic ;)

True

That's about all. Not covered here is nodata cells (easy, you can assign one) and statistics for nodata (easy, every stat has a nodata equivalent).

**Try your skills with the attached *.npy file.**

Nice. Gets a bit trickier when dealing large rasters ;-)

What's your preferred method of handling NoData? I usually upcast to float and use np.nan<statistic>, I've tried masked arrays but found them quite slow.