ndindex | A Python library for manipulating indices of ndarrays
kandi X-RAY | ndindex Summary
kandi X-RAY | ndindex Summary
A Python library for manipulating indices of ndarrays. The documentation for ndindex can be found at ndindex is a library that allows representing and manipulating objects that can be valid indices to numpy arrays, i.e., slices, integers, ellipses, None, integer and boolean arrays, and tuples thereof. The goals of the library are.
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ndindex Examples and Code Snippets
Lines of Code : 16License : Permissive (MIT)
Copy>>> from ndindex import *
>>> Slice(None, 10).reduce()
Slice(0, 10, 1)
>>> import numpy as np
>>> len(Slice(2, 10, 3))
3
>>> len(np.arange(10)[2:10:3])
3
>>> Tuple(0, slice(0, 10)).newshape((10, Community Discussions
Trending Discussions on ndindex
QUESTION
Jacobian of matrix with respect to itself
I am implementing an in-house automatic differentiation module using only native functions of NumPy, and for any kind of matrix operations, constructing a 4D array from a 2D array like the one in the picture seems to show up in different places.
My current approach is quite simple: if I'm given a k-by-d matrix called a, I am doing something like
ANSWER
Answered 2021-Jun-10 at 04:48If all elements are either 0 or 1 (as I believe your picture shows), then:
QUESTION
Physical Background
I'm working on a function that calculates some metrics for each vertical profile in an up to four dimensional temperature field (time, longitude, latitude, pressure as height measure). I have a working function that takes the pressure and temperature at a single location and returns the metrics (tropopause information). I want to wrap it with a function that applies it to every vertical profile in the data passed.
Technical Description of the Problem
I want my function to apply another function to every 1D array corresponding to the last dimension in my N-dimensional array, where N <= 4. So I need an efficient loop over all dimensions but the last one without knowing the number of dimensions beforehand.
Why I Open a New Question
I am aware of several questions (e.g., iterating over some dimensions of a ndarray, Iterating over the last dimensions of a numpy array, Iterating over 3D numpy using one dimension as iterator remaining dimensions in the loop, Iterating over a numpy matrix with unknown dimension) asking how to iterate over a specific dimension or how to iterate over an array with unknown dimensions. The combination of these two problems is new as far as I know. Using numpy.nditer for example I haven't found out how to exclude just the last dimension regardless of the number of dimensions left.
EDIT
I tried to do a minimal, reproducible example:
...ANSWER
Answered 2021-Jun-07 at 11:09I've used @hpaulj 's reshape approach several times. It means the loop can iterate the whole array by 1d slices.
Simplified the function and data to have something to test.
QUESTION
I have an array A with the shape (3,3) which can be thought of as the sliding window view of an unkown array with the shape (5,). I want to compute the inverse of windowing the array with the shape (5,). The adjoint operation of this will be summation. What I mean is that I want to accumulate the values in each corresponding window with the related position in the array with the shape (5,). Ofcourse, my expected output of this inverse function and the input A are not related and are just ordinary arrays. I have two examples which I hope explains this better.
ANSWER
Answered 2021-Apr-07 at 05:34IIUC the problem proposed here is equivalent to rotate matrix A by -45 degrees and sum row-wise (at least for the 2D version). For a better understanding of what I mean by rotating the matrix, see this post.
QUESTION
I have 2d array and dataframe df with x and y coordinates, I want to map values of 2D array to corresponding x and y coordinates in the dataframe in a new column
Array
...ANSWER
Answered 2021-Feb-15 at 12:39Code:
QUESTION
I am writing an implementation of Bridson's Poisson disc sampling (https://www.cs.ubc.ca/~rbridson/docs/bridson-siggraph07-poissondisk.pdf) for Python.
A feature of this sampling is that there is a set minimal distance between samples, and it avoids clustering. The neighbour lookup for new candidate points exploits this feature, and uses a background grid to speed up searches.
The background grid consists of a boolean grid M that stores True for non-empty cells and False otherwise, and a grid of points P that stores the exact coordinates. Both of them are implemented as n-dimensional numpy arrays.
The grid size cellsize is selected in such a way that there is at most one sample in each grid cell, and then you only have to check a few of the nearest rows and columns.
For now, I use the following procedure for checking if the point p is close to any of the existing points:
ANSWER
Answered 2021-Feb-06 at 09:22The dimensionality makes it a bit tricky. In the end, the overhead for preparing the sliced into an array was well worth it. Maybe a further optimization would be possible if you knew the dimensions beforehands. It shouldn't be too much of a difference. As an interesting part, this made the M matrix almost redundant - it is only used to check if the point itself is worth the try since the rest of the multiplication is sped up drastically by the slice.
Update:I re-introduced the M check as per comment and also used the np.square instead of np.power as op mentioned.
Time after the np.power > np.square transition:
QUESTION
I'm pulling in a 4000 wide x 2000 high flat map of the earth (src_filename) and have pre-calculated the pixel relocation x, y values which are stored in a numpy.ndarray (transform_array, 2000, 2000, 2).
The relocated pixels are supposed to be copied from a srcimg_array to a 2000x2000 polarimg_array which gets saved out to an image.
I have a for loop that iterates through the transform_array, reads the location value in each location, and then copies pixel RGB value from the source to the polar array - ie: transform_array [0, 0] contains [1414, 1500] which means the source image color values at [0, 0] get copied to [1414, 1500] in the polarimg_array, [0, 1] value gets copied to [1413, 1500], etc.
This process takes about 30 seconds per image and I'm trying to speed that process up.
How can this be accomplished using Python? (ie: not C or Cython or the like).
...ANSWER
Answered 2021-Jan-30 at 19:48First, like sai mentioned in his comment if you want to relocate pixels from srcimg_array to polarimg_array based on transform_array like you explained in your question:
I have a for loop that iterates through the transform_array, reads the location value in each location, and then copies pixel RGB value from the source to the polar array - ie: transform_array [0, 0] contains [1414, 1500] which means the source image color values at [0, 0] get copied to [1414, 1500] in the polarimg_array, [0, 1] value gets copied to [1413, 1500], etc.
Then you would use for loop like this:
QUESTION
I'm trying to do a grid search over a model I've trained. So, producing a mesh, then predicting that mesh with the model to find a maximum. I'm producing the mesh with:
...ANSWER
Answered 2020-Oct-21 at 10:34Since you're working with tuples, and numpy supports tuple indexing, let's start with that.
Effectively, you want to do your slicing like a[:, 0, 0, 0, 0]. But your index is a tuple, and you're attempting something like a[:, (0,0,0,0)] - this gives you four hyperplanes along the second dimension instead. Your indexing should be more like a[(:,0,0,0,0)] - but this gives a syntax error.
So the solution would be to use the slice built-in.
QUESTION
Suppose that two integer arrays min and max are given and they have equal shape. How to generate all Numpy arrays such that min[indices] <= ar[indices] <= max[indices] for all indices in np.ndindex(shape)? I have looked at the Numpy array creation routines but none of them seem to do what I want. I considered also starting with the min array and looping over its indices, adding 1 until the corresponding entry in max was reached, but I want to know if Numpy provides methods to do this more cleanly. As an example, if
ANSWER
Answered 2020-Oct-02 at 18:48This will work,
Also since range and itertools.product both returns a generator it's memory efficient (O(1) space).
QUESTION
In Python when we want to iterate over a matrix with an arbitrary dimension, we can use this line of code:
...ANSWER
Answered 2020-Aug-21 at 13:09In nd4j, we have a NDIndexIterator that allows you to iterate over the coordinates.
Here is the example:
QUESTION
Hello I am triying to plot a list of colors in the following format:
...ANSWER
Answered 2020-Jul-10 at 14:14Given that descriptor is a 2-d array consisting of [r,g,b] values for each point:
You're giving all 3 coordinates for each point for all 3 x,y,z parameters in scatter() function. Instead of
descriptor[0], descriptor[0], descriptor[0]you need to accessdescriptor[:,0], descriptor[:,1], descriptor[:,2]to user r,g, and b values respectively for x,y and z coordinates.Matplotlib accepts 2-d array of tuples of r,g,b values as color, but they must be in range of [0,1], so
color = descirptor/255
This is my code:
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Install ndindex
You can use ndindex like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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