Reading PRISM BIL arrays without using GDAL

Since it isn’t always easy to get GDAL working with Conda, I decided to try reading the PRISM BIL file using built in Python modules and NumPy.

The first example I found (Plotting USGS BIL DEMs in Python) provided 95% of the steps needed to get this working using the struct module.  I only needed to change the datatype to a float32/short.  I decided to hardcode the width, height, datatype, number of bands, and nodata value for now, but all of these could/should be read from the HDR file.

import struct

prism_path = 'PRISM_tmean_30yr_normal_4kmM2_annual_bil.bil'
prism_cols = 1405
prism_rows = 621
prism_nodata = -9999

bil_f = open(bil_path, 'rb')
bil_data = bil_f.read()
bil_f.close()

# Unpack binary data into a flat tuple z
s = '<%df' % (int(prism_cols * prism_rows),)
z = struct.unpack(s, bil_data)

prism_array = np.zeros((prism_rows, prism_cols), dtype=np.float32)
for r in range(0, prism_rows):
    for c in range(0, prism_cols):
        prism_array[r][c] = float(z[((prism_cols) * r) + c])
        # prism_array.itemset((r, c), float(z[((prism_cols) * r) + c]))

prism_array[prism_array == prism_nodata] = np.nan

I then realized that NumPy should be able to easily (and quickly) read a simple single band binary array file using fromfile().

import numpy as np

prism_path = 'PRISM_tmean_30yr_normal_4kmM2_annual_bil.bil'
prism_cols = 1405
prism_rows = 621
prism_nodata = -9999

prism_array = np.fromfile(bil_path, dtype=np.float32)
prism_array = prism_array.reshape(prism_rows, prism_cols)
prism_array[prism_array == prism_nodata] = np.nan

Might as well read the width, height, and nodata values from the HDF file, since it is avaiable. You could get the datatype also.

import numpy as np

def read_prism_hdr(hdr_path):
    """Read an ESRI BIL HDR file"""
    with open(hdr_path, 'r') as input_f:
        header_list = input_f.readlines()
    return dict(item.strip().split() for item in header_list)

def read_prism_bil(bil_path):
    """Read an array from ESRI BIL raster file"""
    hdr_dict = read_prism_hdr(bil_path.replace('.bil', '.hdr'))
    # For now, only use NROWS, NCOLS, and NODATA
    # Eventually use NBANDS, BYTEORDER, LAYOUT, PIXELTYPE, NBITS

    prism_array = np.fromfile(bil_path, dtype=np.float32)
    prism_array = prism_array.reshape(
        int(hdr_dict['NROWS']), int(hdr_dict['NCOLS']))
    prism_array[prism_array == float(hdr_dict['NODATA'])] = np.nan
    return prism_array

prism_path = 'PRISM_tmean_30yr_normal_4kmM2_annual_bil.bil'
prism_array = read_prism_bil(prism_path)

Plotting PRISM BIL arrays using GDAL & Matplotlib

Now that the PRISM data is being released in a BIL format, I decided I should update my old post on plotting PRISM arrays. First, I tried using ndimage.imread() and matplotlib.imread(), but neither worked with the BIL format. GDAL can handle it though.

import matplotlib.pyplot as plt
from osgeo import gdal

prism_path = 'PRISM_tmean_30yr_normal_4kmM2_annual_bil.bil'
prism_nodata = -9999

prism_ds = gdal.Open(prism_path)
prism_band = prism_ds.GetRasterBand(1)
prism_array = prism_band.ReadAsArray().astype(np.float32)
# prism_nodata = prism_band.GetNoDataValue()
prism_array[prism_array == prism_nodata] = np.nan
prism_ds = None

plt.imshow(prism_array, cmap='viridis')
plt.show()

I really like the new Viridis colormap!