CODEWITHSUNDEEP
pythongdalno-data
ustroetz
ustroetz

Reputation: 6312

GDAL ReadAsArray does not ignore NoData Value

I am trying to read the band of a TIFF as an array. The problem is GDAL does not ignore the NoData values. Is there a way to tell GDAL to ignore it?

When I compute statistics, GDAL ignores the NoData values.

import os, sys, gdal, ogr, numpy
from gdalconst import *

# register all of the drivers
gdal.AllRegister()

# open the image
ds = gdal.Open('test_slope.tif', GA_ReadOnly)

# get image size
rows = ds.RasterYSize
cols = ds.RasterXSize
bands = ds.RasterCount

# Set NoData Value
band = ds.GetRasterBand(1)
ndv = -3.4028230607371e+38
band.SetNoDataValue(ndv)

# Get Statistics
stats = band.ComputeStatistics(0)
print stats

# read in band as array
bandList = []
band.GetNoDataValue()
data = band.ReadAsArray(0, 0, cols, rows)
print data


>>> 
[0.0, 126.59918975830078, 25.757117870945123, 15.399812314100501]
[[ -3.40282306e+38  -3.40282306e+38  -3.40282306e+38 ...,  -3.40282306e+38
   -3.40282306e+38  -3.40282306e+38]
 [ -3.40282306e+38  -3.40282306e+38  -3.40282306e+38 ...,  -3.40282306e+38
   -3.40282306e+38  -3.40282306e+38]
 [ -3.40282306e+38  -3.40282306e+38  -3.40282306e+38 ...,  -3.40282306e+38
   -3.40282306e+38  -3.40282306e+38]
 ..., 
 [ -3.40282306e+38  -3.40282306e+38  -3.40282306e+38 ...,  -3.40282306e+38
   -3.40282306e+38  -3.40282306e+38]
 [ -3.40282306e+38  -3.40282306e+38  -3.40282306e+38 ...,  -3.40282306e+38
   -3.40282306e+38  -3.40282306e+38]
 [ -3.40282306e+38  -3.40282306e+38  -3.40282306e+38 ...,  -3.40282306e+38
   -3.40282306e+38  -3.40282306e+38]]
>>>

Upvotes: 4

Views: 7752

Answers (1)

abudis
abudis

Reputation: 2881

I believe you could create a numpy.MaskedArray from your numpy.ndarray:

import numpy as np
import numpy.ma as ma

ndv = -3.40282306e+38

data = np.array([[0.0, 126.59918975830078, 25.757117870945123, 15.399812314100501],
                [-3.40282306e+38, -3.40282306e+38, -3.40282306e+38, -3.40282306e+38]])

masked_data = ma.masked_where(data == ndv, data)
print masked_data

Result:

[[0.0 126.599189758 25.7571178709 15.3998123141]
[-- -- -- --]]

In numpy, a combination of a numpy.ndarray and a mask is used to allow for handling of missing data.

Upvotes: 4

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