I have an image that I converted to a numpy array using OpenCV. I want to copy the numpy array's printed output and assign it to a different variable. The issue is the resulting printed out numpy array appears to be thousands of lines long arranged vertically. Below is just a tiny snippet screen shot:
My question is: is there a way I can print out the numpy array so that it prints horizontally instead? Or is there a way to convert my numpy array into a short unique identifier like using bitwise or something.
Related
So I want to convert this list:
[[[1,2]],[[1,2],[3,4]]]
to a numpy array.
However it gives me:
[list([[1,2]]),list([[1,2],[3,4]])]
The list is not rectangular, it wont work
I currently have data where each row has a text passage and a numpy float array.
As far as I know, the it's not efficient to save these two datatypes into one data format (correct me if I am wrong). So I am going to save them separately, with another column of ints that will be used to map the two datasets together when I want to join them again.
I have having trouble figuring out how to append a column of ints next to the float arrays (if anyone has a solution to that I would love to hear it) and then save the numpy array.
But then I realized I can just save the float arrays as is with numpy.save without the extra int column if I can get a confirmation that numpy.save and numpy.load will never change the order of the arrays.
That way I can just append the loaded numpy float arrays to the pandas dataframe as is.
Logically, I don't see any reason why the order of the rows would change, but perhaps there's some optimization compression that I am unaware of.
Would numpy.save or numpy.load ever change the order of a numpy array of float arrays?
The order will not change by the numpy save / load. You are saving the numpy object as is. An array is an ordered object.
Note: if you want to save multiple data arrays to the same file, you can use np.savez.
>>> np.savez('out.npz', f=array_of_floats, s=array_of_strings)
You can retrieve back each with the following:
>>> data = np.load('out.npz')
>>> array_of_floats = data['f']
>>> array_of_strings = data['s']
This question is different from this one: How to print the full NumPy array, without truncation?
In that question, the user wanted to know how to print the full array without truncation. I can print the array without truncation just fine. My problem is that only a small portion of the screen width is used. When trying to inspect large adjacency matrices, it's impossible to inspect them when the rows unnecessarily wrap.
I'm asking this question here because it always takes me hours to find the solution, and I want to disambiguate it from that answer post above.
For example:
import networkx as nx
import numpy as np
np.set_printoptions(threshold=np.inf)
graph = nx.gnm_random_graph(20, 20, 1)
nx.to_numpy_matrix(graph)
This output displays as:
Just gonna post NaN's comment as the answer:
Use np.set_printoptions(linewidth=n) where n has to do with the number of characters (not array elements) per line. So in your case n=100 should do the trick.
I have two text files that have matrices written in them(not numpy matrices, so its a list of lists). These matrices are written in string format, so the text file looks like this :
[[1,2,3],[3,4,5],[6,7,8]],[[3,3,3],[5,6,7],.....
I want to read this matrix back from the text file using python. I can't read using numpy as it gives ValueError: could not convert string to float
Is there anyway to do this? Would it be easier if I just wrote the matrix as a numpy matrix in the first place(I need to change code of a previous program for that, and was just wondering if there was a python way of loading matrices when it was stored as a string in a text file)?
You could make use of the ast module:
import ast
strArray = "[[1,2,3],[3,4,5],[6,7,8]]"
# evaluates the array in string format and converts it to a python array object
array = ast.literal_eval(strArray)
note:
For multiple nested arrays like you have, literal_eval will most likely convert the string into a tuple with nested arrays as elements. Just keep that in mind as you use this module.
I am currently writing some code which is supposed to perform FFT on a set of data. I have a python list of points and I can easily create a time list. When I run fft(datalist), I get the 'TypeError: 'numpy.ndarray' object is not callable' error. I think (but please correct me) the issue is that the list is one dimension and they have no attachment to time at all by using that one line of code above. My question is, do I have to input a two dimensional array with time and data points? or am I completely wrong and have to rethink?
Thanks, Mike
Edit - forgot to add some code. The t=time. Could it be because the number of entries in the array isnt equal to 2^n where N is an integer?
sample_rate=10.00
t=r_[0:191.6:1/sample_rate]
S = fft([mylist])
print S
The Numpy and SciPy fft functions are looking to have numpy arrays as input, not native python lists. Also they work just fine with lengths that are not powers of two. You probably just need to cast your list as an array before passing it to the fft.
From your example code above try:
from numpy.fftpack import fft
from numpy import array
""" However you generate your list goes here """
S = fft(array([mylist]))