python (scipy) TypeError

[chrischris201444 at gmail.com]

Τη Δευτέρα, 3 Οκτωβρίου 2016 - 7:17:03 μ.μ. UTC+3, ο χρήστης chrischr... at gmail.com έγραψε:
> 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()

yes you are corrent now i dont have error,i have coloring from -3 to 50 with step 1.. the -3 number is the -3 from my range ?