I have a few functions that return an array of data corresponding to parameters ranges. Example: for a 2d array a, the a_{ij} value corresponds to the parameter set (param1_i, param2_j). How do I return the result and keep the parameter-value correspondence? Calling the function for each and every of param1_i, para2_j and returning on...
during preparing data for numpy calculate ,i curious about way to contruct myarray.shape => (2,18,18) from d1.shape => (18,18) d2.shape => (18,18) i try to use numpy command hstack([[d1],[d2]]) but it looks not work!! ...
i 'm just finish labs meeting with my advisor, previous code is written in matlab and it run offline mode not realtime mode, so i decide to convert to python+numpy (in offline version) but after labs meeting, my advisor raise issue about speed of realtime recognition, so i have doubt about speed of python+numpy to do this project. or bet...
Hi Everyone, Is there a way to append a row to a NumPy rec.array()? For example, x1=np.array([1,2,3,4]) x2=np.array(['a','dd','xyz','12']) x3=np.array([1.1,2,3,4]) r = np.core.records.fromarrays([x1,x2,x3],names='a,b,c') append(r,(5,'cc',43.0),axis=0) The easiest way would to extract all the column as nd.array() types, add the sepa...
I'd like to use principal component analysis (PCA) for dimensionality reduction. Does numpy or scipy already have it, or do I have to roll my own using numpy.linalg.eigh? I don't just want to use singular value decomposition (SVD) because my input data are quite high-dimensional (~460 dimensions), so I think SVD will be slower than com...
I am a beginner and got an issue, really head around now. Here is the code: n=3 #time step #f, v and r are arrays,eg [3,4,5] #r,v,f all have initial array which is when n=0 def force(): r=position() f=r*2 return f def position(n): v=velocity(n) for i in range(n): #This part may wrong... r=v*i ...
After doing some processing on an audio or image array, it needs to be normalized within a range before it can be written back to a file. This can be done like so: # Normalize audio channels to between -1.0 and +1.0 audio[:,0] = audio[:,0]/abs(audio[:,0]).max() audio[:,1] = audio[:,1]/abs(audio[:,1]).max() # Normalize image to between...
Hi,I have post the similar question before,however,I think I may have misinterpreted my question,so may I just post my origin code here,and looking for someone can help me,I am really stuck now..thanks alot. from numpy import * import math as M #initial condition All in SI unit G=6.673*10**-11 #Gravitational constant ms=1.9889*10**3...
How do I convert a django QuerySet to numpy record array? PS: I know you can iterate and construct it and but is there any other cleaner solution? ...
I want to write a data string to a numpy array. Pseudo Code: d=numpy.zeros(10,dtype=numpy.character) d[1:6]='hello' Example result: d= array(['', 'h', 'e', 'l', 'l', 'o', '', '', '', ''], dtype='|S1') How can this be done with numpy most naturally and efficiently? I don't want for loops, generators, or anything iterative,...
i try to use create Cell Array in Numpy, Anyone have an Information ? ...
I'm trying to compute the laplacian of a 2d field A using scipy.ndimage.convolve. stencil = numpy.array([[0, 1, 0],[1, -4, 1], [0, 1, 0]]) scipy.ndimage.convolve(A, stencil, mode='wrap') This doesn't seem to give me the right answer though. Any ideas where I'm going wrong, or are there better ways of computing the laplacian in numpy? ...
I have several (10 or so) CSV-formatted data sets. Each column of a data set represents one aspect of a running system (available RAM, CPU usage, open TCP connections, and so forth). Each row contains the values for these columns at one moment in time. The data sets were captured during individual runs of the same test. The number of r...
I'm trying to use fancy indexing instead of looping to speed up a function in Numpy. To the best of my knowledge, I've implemented the fancy indexing version correctly. The problem is that the two functions (loop and fancy-indexed) do not return the same result. I'm not sure why. It's worth pointing out that the functions do return the s...
In pure python you can grow matrices column by column pretty easily: data = [] for i in something: newColumn = getColumnDataAsList(i) data.append(newColumn) numpy's array doesn't have the append function. The hstack function doesn't work on zero sized arrays, thus the following won't work: data = numpy.array([]) for i in some...
>>> import numpy as np >>> a = np.array(['zero', 'one', 'two', 'three']) >>> a[1] = 'thirteen' >>> print a ['zero' 'thirt' 'two' 'three'] >>> As you can see, the second element has been truncated to the maximum number of characters in the original array. Is it possible to workaround this problem? ...
Suppose I make two recarrays with the same dtype and stack them: >>> import numpy as np >>> dt = [('foo', int), ('bar', float)] >>> a = np.empty(2, dtype=dt).view(np.recarray) >>> b = np.empty(3, dtype=dt).view(np.recarray) >>> c = np.hstack((a,b)) Although a and b are recarrays, c is not: >>> c.foo Traceback (most recent call last):...
As in this example http://stackoverflow.com/questions/259451/how-to-extract-frequency-information-from-an-input-audio-stream-using-portaudio I'm curious about portaudio and numpy... I'm not 100% sure about fft, how can I pass numpy a chunk and get back three values from -1.0 to 1.0 for bass, mid and treble ? I don't mind if this just...
In numpy there is a function that makes arrays print prettier. set_printoptions(suppress = True) In other words, instead of this: array([[ 0.00000000e+00, -3.55271368e-16, 0.00000000e+00, 1.74443793e-16, 9.68149172e-17], [ 5.08273978e-17, -4.42527959e-16, 1.57859836e-17, 1.35982590e-16, 5.599181...
hi all, how can I import an array to python (numpy) from a file and that way the file must be written. For example, a matrix to and from that file type (extention). thanks for any response ...