hello
i try to follow some tutorial but i have that error :
Traceback (most recent call last):
File "C:\Python27\test\test\earth.py", line 42, in <module>
slope_array = np.ones_like(data_array) * nodataval
TypeError: unsupported operand type(s) for *: 'int' and 'NoneType'
first i define that lines of code :
data_array = raster2array(filename)
nodataval = getNoDataValue(filename)
and the line with error :
slope_array = np.ones_like(data_array) * nodataval
how can i fix this error ?i change type field ?
on the four prints:
print(resolution)
print(nodataval)
print(type(data_array))
print(data_array.shape)
i take that exports :
{'east-west': 0.0002777777777777778, 'north-south': 0.0002777777777777778}
None
<type 'numpy.ndarray'>
(3601, 3601)
the full code :
from __future__ import division
from osgeo import gdal
from matplotlib.colors import ListedColormap
from matplotlib import colors
import sys
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import math
filename = 'dem.tif'
def getResolution(rasterfn):
raster = gdal.Open(rasterfn)
geotransform = raster.GetGeoTransform()
res = {"east-west": abs(geotransform[1]),
"north-south": abs(geotransform[5])}
return res
def raster2array(rasterfn):
raster = gdal.Open(rasterfn)
band = raster.GetRasterBand(1)
return band.ReadAsArray()
def getNoDataValue(rasterfn):
raster = gdal.Open(rasterfn)
band = raster.GetRasterBand(1)
return band.GetNoDataValue()
data_array = raster2array(filename)
nodataval = getNoDataValue(filename)
resolution = getResolution(filename)
print(resolution)
print(nodataval)
print(type(data_array))
print(data_array.shape)
num_rows = data_array.shape[0]
num_cols = data_array.shape[1]
slope_array = np.ones_like(data_array) * nodataval
aspect_array = np.ones_like(data_array) * nodataval
for i in range(1, num_rows - 1):
for j in range(1, num_cols - 1):
a = data_array[i - 1][j - 1]
b = data_array[i - 1][j]
c = data_array[i - 1][j + 1]
d = data_array[i][j - 1]
e = data_array[i][j]
f = data_array[i][j + 1]
g = data_array[i + 1][j - 1]
h = data_array[i + 1][j]
q = data_array[i + 1][j + 1]
vals = [a, b, c, d, e, f, g, h, q]
if nodataval in vals:
all_present = False
else:
all_present = True
if all_present == True:
dz_dx = (c + (2 * f) + q - a - (2 * d) - g) / (8 *
resolution['east-west'])
dz_dy = (g + (2 * h) + q - a - (2 * b) - c) / (8 *
resolution['north-south'])
dz_dx_sq = math.pow(dz_dx, 2)
dz_dy_sq = math.pow(dz_dy, 2)
rise_run = math.sqrt(dz_dx_sq + dz_dy_sq)
slope_array[i][j] = math.atan(rise_run) * 57.29578
aspect = math.atan2(dz_dy, (-1 * dz_dx)) * 57.29578
if aspect < 0:
aspect_array[i][j] = 90 - aspect
elif aspect > 90:
aspect_array[i][j] = 360 - aspect + 90
else:
aspect_array[i][j] = 90 - aspect
hist, bins = np.histogram(slope_array, bins=100, range=(0,
np.amax(slope_array)))
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.xlabel('Slope (degrees)')
plt.ylabel('Frequency')
plt.show()
color_map = ListedColormap(['white', 'darkgreen', 'green', 'limegreen', 'lime',
'greenyellow', 'yellow', 'gold',
'orange', 'orangered', 'red'])
# range begins at negative value so that missing values are white
color_bounds = list(range(-3, math.ceil(np.amax(slope_array)), 1))
color_norm = colors.BoundaryNorm(color_bounds, color_map.N)
#Create the plot and colorbar
img = plt.imshow(slope_array, cmap = color_map, norm = color_norm)
cbar = plt.colorbar(img, cmap = color_map, norm = color_norm,
boundaries = color_bounds, ticks = color_bounds)
#Show the visualization
plt.axis('off')
plt.title("Slope (degrees)")
plt.show()
plt.close()
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