Reading zipped ESRI BIL files with Python

Question

I have precipitation data from the PRISM Climate Group which are now offered in .bil format (ESRI BIL, I think) and I'd like to be able to read these datasets with Python.

I've installed the spectral package, but the open_image() method returns an error:

def ReadBilFile(bil):
    import spectral as sp
    b = sp.open_image(bil)
ReadBilFile(r'G:\truncated\ppt\1950\PRISM_ppt_stable_4kmM2_1950_bil.bil')

IOError: Unable to determine file type or type not supported.

The documentation for spectral clearly says that it supports BIL files, can anyone shed any light on what's happening here? I am also open to using GDAL, which supposedly supports the similar/equivalent ESRI EHdr format, but I can't find any good code snipets to get started.

Answer 1

Ok, I'm sorry to post a question and then answer it myself so quickly, but I found a nice set of course slides from Utah State University that has a lecture on opening raster image data with GDAL. For the record, here is the code I used to open the PRISM Climate Group datasets (which are in the EHdr format).

import gdal

def ReadBilFile(bil):

    gdal.GetDriverByName('EHdr').Register()
    img = gdal.Open(bil)
    band = img.GetRasterBand(1)
    data = band.ReadAsArray()
    return data

if __name__ == '__main__':
    a = ReadBilFile(r'G:\truncated\ppt\1950\PRISM_ppt_stable_4kmM2_1950_bil.bil')
    print a[44, 565]

EDIT 5/27/2014

I've built upon my answer above and wanted to share it here since the documentation seems to be lacking. I now have a class with one main method that reads the BIL file as an array and returns some key attributes.

import gdal
import gdalconst

class BilFile(object):

    def __init__(self, bil_file):
        self.bil_file = bil_file
        self.hdr_file = bil_file.split('.')[0]+'.hdr'

    def get_array(self, mask=None):
        self.nodatavalue, self.data = None, None
        gdal.GetDriverByName('EHdr').Register()
        img = gdal.Open(self.bil_file, gdalconst.GA_ReadOnly)
        band = img.GetRasterBand(1)
        self.nodatavalue = band.GetNoDataValue()
        self.ncol = img.RasterXSize
        self.nrow = img.RasterYSize
        geotransform = img.GetGeoTransform()
        self.originX = geotransform[0]
        self.originY = geotransform[3]
        self.pixelWidth = geotransform[1]
        self.pixelHeight = geotransform[5]
        self.data = band.ReadAsArray()
        self.data = np.ma.masked_where(self.data==self.nodatavalue, self.data)
        if mask is not None:
            self.data = np.ma.masked_where(mask==True, self.data)
        return self.nodatavalue, self.data

I call this class using the following function where I use GDAL's vsizip function to read the BIL file directly from a zip file.

import prism
def getPrecipData(years=None):
    grid_pnts = prism.getGridPointsFromTxt()
    flrd_pnts = np.array(pd.read_csv(r'D:\truncated\PrismGridPointsFlrd.csv').grid_code)
    mask = prism.makeGridMask(grid_pnts, grid_codes=flrd_pnts)
    for year in years:
        bil = r'/vsizip/G:\truncated\PRISM_ppt_stable_4kmM2_{0}_all_bil.zip\PRISM_ppt_stable_4kmM2_{0}_bil.bil'.format(year)
        b = prism.BilFile(bil)
        nodatavalue, data = b.get_array(mask=mask)
        data *= mm_to_in
        b.write_to_csv(data, 'PrismPrecip_{}.txt'.format(year))
    return

# Get datasets
years = range(1950, 2011, 5)
getPrecipData(years=years)

Answer 2

You've already figured out a good solution to reading the file so this answer is just in regard to shedding light on the error you encountered.

The problem is that the spectral package does not support the Esri multiband raster format. BIL (Band Interleaved by Line) is not a specific file format; rather, it is a data interleave scheme (like BIP and BSQ), which can be used in many file formats. The spectral package does support BIL for the file formats that it recognizes (e.g., ENVI, Erdas LAN) but the Esri raster header is not one of those.

Answer 3

It's now 2017 and there is a slightly better option. The package rasterio supports bil files.

>>>import rasterio
>>>tmean = rasterio.open('PRISM_tmean_stable_4kmD1_20060101_bil.bil')
>>>tmean.affine
Affine(0.041666666667, 0.0, -125.0208333333335,
       0.0, -0.041666666667, 49.9375000000025)
>>> tmean.crs
CRS({'init': 'epsg:4269'})
>>> tmean.width
1405
>>> tmean.height
621
>>> tmean.read().shape
(1, 621, 1405)