![]() ![]() You can easily convert all the masked pixels to an arbitrary value (e.g. Result = np.ma.mean(ndvi_array, axis=0) # (n rows by n cols array)įurthermore, if there are pixels where all the bands have a value of 0, you will end up with masked pixels, represented with a - when you print the array. Ndvi_array = np.ma.array(ndvi_array, mask=(ndvi_array = 0)) # masked array The only differences is that you mask the array to ignore zeros and calculate the mean using np.ma.mean() rather than np.mean(). Following the same principle from the code above, you can use a masked numpy array to accomplish this. I see you are trying to ignore zeros when calculating the mean. Result = ndvi_an(axis=0) # (n rows by n cols array) Ndvi_array = np.stack(ndvi_array) # (n bands by n rows by n cols array) Ndvi_array = # list to store all the bandsĭata = ndvi_stack.GetRasterBand(band).ReadAsArray().astype('float') # (n rows by n cols array) Ndvi_stack = gdal.Open('NDVI_stack/ndvi_stack.tif') One way to accomplish this is to create a 3D numpy array using np.stack() and then calculating the mean by specifying the axis. However you likely want to get a mean value for each set of values in the same pixel in your ndvi_stack image. Use the numpy stack() function to join two or more arrays into one.In your code, you are calculating a mean value for each band.In this example, the concatenate() function joins elements of two arrays along an existing axis while the stack() function joins the two arrays along a new axis. ] Code language: JSON / JSON with Comments ( json ) array()Ĭ = np.concatenate((a,b)) # return 1-D arrayĭ = np.stack((a,b)) # return 2-D array print(c) The following example illustrates the difference between stack() and concatenate() functions: a = np. ( 2, 2, 2) Code language: Python ( python ) NumPy stack() vs. Print(c.shape) Code language: Python ( python ) The result is a 3D array: import numpy as np The following example uses the stack() function to join elements of two 2D arrays. ] Code language: Python ( python ) 2) Using numpy stack() function to join 2D arrays The following example uses the stack() function to join two 1D arrays horizontally by using axis 1: import numpy as np Print(c) Code language: Python ( python ) The following example uses the stack() function to join two 1D arrays: import numpy as np 1) Using stack() function to join 1D arrays ![]() Let’s take some examples of using the stack() function. By default, the axis is zero which joins the input arrays vertically.īesides the stack() function, NumPy also has vstack() function that joins two or more arrays vertically and hstack() function that joins two or more arrays horizontally. The axis parameter specifies the axis in the result array along which the function stacks the input arrays. In this syntax, the (a1, a2, …) is a sequence of arrays with ndarray type or array-like objects. ![]() The following shows the syntax of the stack() function: numpy.stack((a1,a2.),axis= 0) Code language: Python ( python ) Unlike the concatenate() function, the stack() function joins 1D arrays to be one 2D array and joins 2D arrays to be one 3D array. The stack() function two or more arrays into a single array. Introduction to the NumPy stack() function Summary: in this tutorial, you’ll learn how to use the NumPy stack() function to join two or more arrays into a single array. ![]()
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