I have a 3D data cube and I am trying to make a plot of the first axis at a specific value of the other two axes. The goal is to make a velocity plot at given coordinates in the sky.
I have tried to create an 1D array from the 3D array by putting in my values for the last two axes. This is what I have tried
achan=50
dchan = 200
lmcdata[:][achan][dchan] #this array has three axes, vchan, achan, dchan.
I am expecting an array of size 120 as there are 120 velocity channels that make up the vchan axis. When trying the code above I keep getting an array of size 655 which is the number of entries for the dchan axis.
Python slicing works from left to right. In this case, lmcdata[:] is returning the whole lmcdata list. So, lmcdata[:][achan][dchan] is equivalent to just lmcdata[achan][dchan].
For higher level indexing and slicing tasks like this, I highly recommend the numpy package. You will be able to slice lmcdata as expected after turning it into a numpy array: lmcdata = np.asarray(lmcdata).
Related
New Here. I have a 100x100x100 array. I want to select only the first 10x10x10 values in the array, then average across first two dimensions for a 100x10 array. Then I need to pull out 20 of the values from the 100x10 and plot those 20 numbers on a scatterplot. Any help?
I have a numpy array of shape (3,12,7,5). I would like to have the sum of all slices along the first axis of this array.
data = np.random.randint(low=0, high=8000, size=3*12*7*5).reshape(3,12,7,5)
data[0,...].sum()
data[1,...].sum()
data[2,...].sum()
np.array((data[0,...].sum(), data[1,...].sum(), data[2,...].sum()))
First, I thought this should be possible using np.sum(data, axis=...) but it is not.
How do I perform this calculation in a single shot. What is the numpy magic?
For a generic ndarray, you could reshape into a 2D array, keeping the number of elements along the first axis same and merging all of the remaining axes as the second axis and finally sum along that axis, like so -
data.reshape(data.shape[0],-1).sum(axis=1)
For a 4D array, you could include the axes along which the summation is to be performed. So, to solve our case, we would have -
data.sum(axis=(1,2,3))
This could be extended to make it work for generic ndarrays by creating a tuple of appropriate axis IDs and thus avoid reshaping, like so -
data.sum(axis=tuple(np.arange(1,data.ndim)))
I have several N-dimensional arrays of different shapes and want to combine them into a new (N+1)-dimensional array, where the new axis has a length corresponding to the number of initial N-d arrays.
This answer is sufficient if the original arrays are all the same shape; however, it does not work if they have different shapes.
I don't really want to reshape the arrays to a congruent size and fill with empty elements due to the subsequent analysis I need to perform on the final array.
Specifically, I have four 4D arrays. One of the things I want to do with the resulting 5D array is plot parts of the four arrays on the same matplotlib figure. Obviously I could plot each one separately, however soon I will have more than four 4D arrays and am looking for a dynamic solution.
While I was writing this, Sven gave the same answer in the comments...
Put the arrays in a python list in the following manner:
5d_list = []
5d_list.append(4D_array_1)
5d_list.append(4D_array_2)
...
Then you can unpack them:
for 4d_array in 5d_list:
#plot 4d array on figure
I am trying to create a histogram in python using matplotlib.pyplot.hist.
I have an array of data that varies, however when put my code into python the histogram is returned with values in all bins equal to each other, or equal to zero which is not correct.
The histogram should look the the line graph above it with bins roughly the same height and in the same shape as the graph above.
The line graph above the histogram is there to illustrate what my data looks like and to show that my data does vary.
My data array is called spectrumnoise and is just a function I have created against an array x
x=np.arange[0.1,20.1,0.1]
The code I am using to create the histogram and the line graph above it is
import matplotlib.pylot as mpl
mpl.plot(x,spectrumnoise)
mpl.hist(spectrumnoise,bins=50,histtype='step')
mpl.show()
I have also tried using
mpl.hist((x,spectrumnoise),bins=50,histtype=step)
I have also changed the number of bins countless times to see if that helps an normalising the histogram function but nothing works.
Image of the output of the code can be seen here
The problem is that spectrumnoise is a list of arrays, not a numpy.ndarray. When you hand hist a list of arrays as its first argument, it treats each element as a separate dataset to plot. All the bins have the same height because each 'dataset' in the list has only one value in it!
From the hist docstring:
Multiple data can be provided via x as a list of datasets
of potentially different length ([x0, x1, ...]), or as
a 2-D ndarray in which each column is a dataset.
Try converting spectrumnoise to a 1D array:
pp.hist(np.vstack(spectrumnoise),50)
As an aside, looking at your code there's absolutely no reason to convert your data to lists in the first place. What you ought to do is operate directly on slices in your array, e.g.:
data[20:40] += y1
I have a 3D array created using the numpy mgrid command so that each element has a certain value and the indexes retain the spatial information. For example, if one summed over the z-axis (3rd dimension) then the the resultant 2D array could be used in matplotlib with the function imshow() to obtain an image with different binned pixel values.
My question is: How can I obtain the index values for each element in this grid (a,b,c)?
I need to use the index values to calculate the relative angle of each point to the origin of the grid. (eg. theta=sin-1(sqrt(x^2+y^2)/sqrt(x^2+y^2+z^2))
Maybe this can be translated to another 3D grid where each element is the array [a,b,c]?
I'm not exactly clear on your meaning, but if you are looking for 3d arrays that contain the indices x, y, and z, then the following may suit your needs; assume your data is held in a 3D array called "abc":
import numpy as nm
x,y,z = nm.mgrid[[slice(dm) for dm in abc.shape]]