I want to initialise an array that will hold some data. I have created a random matrix (using np.empty) and then multiplied it by np.nan. Is there anything wrong with that? Or is there a better practice that I should stick to?
To further explain my situation: I have data I need to store in an array. Say I have 8 rows of data. The number of elements in each row is not equal, so my matrix row length needs to be as long as the longest row. In other rows, some elements will not be filled. I don't want to use zeros since some of my data might actually be zeros.
I realise I can use some value I know my data will never, but nans is definitely clearer. Just wondering if that can cause any issues later with processing. I realise I need to use nanmax instead of max and so on.
I have created a random matrix (using np.empty) and then multiplied it by np.nan. Is there anything wrong with that? Or is there a better practice that I should stick to?
You can use np.full, for example:
np.full((100, 100), np.nan)
However depending on your needs you could have a look at numpy.ma for masked arrays or scipy.sparse for sparse matrices. It may or may not be suitable, though. Either way you may need to use different functions from the corresponding module instead of the normal numpy ufuncs.
A way I like to do it which probably isn't the best but it's easy to remember is adding a 'nans' method to the numpy object this way:
import numpy as np
def nans(n):
return np.array([np.nan for i in range(n)])
setattr(np,'nans',nans)
and now you can simply use np.nans as if it was the np.zeros:
np.nans(10)
Related
Many functions like in1d and setdiff1d are designed for 1-d array. One workaround to apply these methods on N-dimensional arrays is to make numpy to treat each row (something more high dimensional) as a value.
One approach I found to do so is in this answer Get intersecting rows across two 2D numpy arrays by Joe Kington.
The following code is taken from this answer. The task Joe Kington faced was to detect common rows in two arrays A and B while trying to use in1d.
import numpy as np
A = np.array([[1,4],[2,5],[3,6]])
B = np.array([[1,4],[3,6],[7,8]])
nrows, ncols = A.shape
dtype={'names':['f{}'.format(i) for i in range(ncols)],
'formats':ncols * [A.dtype]}
C = np.intersect1d(A.view(dtype), B.view(dtype))
# This last bit is optional if you're okay with "C" being a structured array...
C = C.view(A.dtype).reshape(-1, ncols)
I am hoping you to help me with any of the following three questions. First, I do not understand the mechanisms behind this method. Can you try to explain it to me?
Second, is there other ways to let numpy treat an subarray as one object?
One more open question: dose Joe's approach have any drawbacks? I mean whether treating rows as a value might cause some problems? Sorry this question is pretty broad.
Try to post what I have learned. The method Joe used is called structured arrays. It will allow users to define what is contained in a single cell/element.
We take a look at the description of the first example the documentation provided.
x = np.array([(1,2.,'Hello'), (2,3.,"World")], ...
dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])
Here we have created a one-dimensional array of length 2. Each element
of this array is a structure that contains three items, a 32-bit
integer, a 32-bit float, and a string of length 10 or less.
Without passing in dtype, however, we will get a 2 by 3 matrix.
With this method, we would be able to let numpy treat a higher dimensional array as an single element with properly set dtype.
Another trick Joe showed is that we don't need to really form a new numpy array to achieve the purpose. We can use the view function (See ndarray.view) to change the way numpy view data. There is a section of Note section in ndarray.view that I think you should take a look before utilizing the method. I have no guarantee that there would not be side effects. The paragraph below is from the note section and seems to call for caution.
For a.view(some_dtype), if some_dtype has a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the behavior of the view cannot be predicted just from the superficial appearance of a (shown by print(a)). It also depends on exactly how a is stored in memory. Therefore if a is C-ordered versus fortran-ordered, versus defined as a slice or transpose, etc., the view may give different results.
Other reference
https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.dtypes.html
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.dtype.html
Let's assume I have an array of phases (from complex numbers)
A = np.angle(np.random.uniform(-1,1,[10,10,10]) + 1j*np.random.uniform(-1,1,[10,10,10]))
I would now like to unwrap this array in ALL dimensions. In the above 3D case I would do
A_unwrapped = np.unwrap(np.unwrap(np.unwrap(A,axis=0), axis=1),axis=2)
While this is still feasible in the 3D case, in case of higher dimensionality, this approach seems a little cumbersome to me. Is there a more efficient way to do this with numpy?
You could use np.apply_over_axes, which is supposed to apply a function over each dimension of an array in turn:
np.apply_over_axes(np.unwrap, A, np.arange(len(A.shape)))
I believe this should do it.
I'm not sure if there is a way to bypass performing the unwrap operation along each axis. Obviously if it acted on individual elements you could use vectorization, but that doesn't seem to be an option here. What you can do that will at least make the code cleaner is create a loop over the dimensions:
for dim in range(len(A.shape)):
A = np.unwrap(A, axis=dim)
You could also repeatedly apply a function that takes the dimension on which to operate as a parameter:
reduce(lambda A, axis: np.unwrap(A, axis=axis), range(len(A.shape)), A)
Remember that in Python 3 reduce needs to be imported from functools.
This must be easy, but I'm very new to pytables. My application has dataset sizes so large they cannot be held in memory, thus I use PyTable CArrays. However, I need to find the maximum element in an array that is not infinity. Naively in numpy I'd do this:
max_element = numpy.max(array[array != numpy.inf])
Obviously that won't work in PyTables without introducing a whole array into memory. I could loop through the CArray in windows that fit in memory, but it'd be surprising to me if there weren't a max/min reduction operation. Is there an elegant mechanism to get the conditional maximum element of that array?
If your CArray is one dimensional, it is probably easier to stick it in a single-column Table. Then you have access to the where() method and can easily evaluate expressions like the following.
from itertools import imap
max(imap(lamdba r: r['col'], tab.where('col != np.inf')))
This works because where() never reads in all the data at once and returns an iterator, which is handed off to map, which is handed off to max. Note that in Python 3, you don't need to import imap() and imap() becomes just the builtin map().
Not using a table means that you need to use the Expr class and do more of the wiring yourself.
Maybe this is a simple issue, but I could not find any information about it so far.
For an optimization in numpy I need an array of functions. The number of functions I need depends on the current object which shall be optimized.
I have already figured out how to create these functions dynamically, but now I would like to store them in an array like this:
myArray = zeros(x)
for i in range(x):
myArray[i] = createFunction(i)
If I run this I get a type mismatch:
float() argument must be a string or a number, not 'function'
Creating the array directly works well:
myArray = array([createFunction(0)...])
But because I don't know the number of functions I need, this is exactly what I want to prevent.
Ah, I get it. You really do mean an array of functions.
The type mismatch error arises because the call to zeros creates an array of floats by default. So your original would work if instead you did myArray = numpy.empty(x, dtype=numpy.object) (note that empty makes more sense than zeros here). The slightly more pythonic version is to use a list comprehension
myArray = numpy.array([createFunction(i) for i in range(x)]).
But you might not need to create a numpy array at all, depending on what you want to do with it:
myArray = [createFunction(i) for i in range(x)]
If you want to avoid the list, it might be better to use numpy.fromfunction along with numpy.vectorize:
myArray = numpy.fromfunction(numpy.vectorize(createFunction),
shape=(x,), dtype=numpy.object)
where (x,) is a tuple giving the shape of the array. The call to vectorize is needed because fromfunction assumes that the function can work on an array of inputs and return an array of scalars, and vectorize converts a function to do exactly that. The dtype=object is needed since otherwise numpy tries to create an array of floats.
Maybe you can use
myArray = array([createFunction(i) for i in range(x)])
If you need an array of functions, is it possible to not use NumPy? NumPy arrays have C-style types and it defaults to float. If you can, just use a standard Python list. But if you absolutely must use NumPy, try defining the array like so:
import numpy as np
a = np.empty([x], dtype=np.dtype(np.object_))
Or however you need it to be with that dtype.
Numpy arrays are homogeneous. That is all elements of a numpy array are of the same type -- python is duck-typed, numpy isn't. This is part of what makes matrix operations on numpy arrays and matrices so fast. However, because of this a data type must be known when the array is first created. Numpy is generally very good at inferring the data type. The problem comes when creating an empty or zeroed array. Since there are no elements to examine numpy must guess the data type. Numpy defaults to numpy.float64 if it isn't given a data type at array creation time. This is a decent choice as numpy is typically used in scientific or engineering areas where floating point numbers are required. This is also why numpy is complaining -- because it can't store your functions as 64-bit floating point numbers.
The quick solution is to let numpy know the data type you want. eg.
myArray = numpy.zeros(x, dtype=numpy.object)
Note that the data type cannot be any class, but must be an instance of numpy.dtype (for advanced use you can create additional dtypes a runtime that numpy can then manipulate). For functions, numpy will store them as numpy.object (which means any generic python object). I do not think you will get any performance benefit from using numpy to store arrays of functions. Perhaps you would be better off creating generator functions and chaining them, converting to a numpy array once you know the result will be a number.
funcs = [createFunction(i) for i in xrange(x)]
def getItemFromEachFunction(i):
return funcs[i]()
arr = numpy.fromfunction(getItemFromEachFunction, (x,))
I have some data represented in a 1300x1341 matrix. I would like to split this matrix in several pieces (e.g. 9) so that I can loop over and process them. The data needs to stay ordered in the sense that x[0,1] stays below (or above if you like) x[0,0] and besides x[1,1].
Just like if you had imaged the data, you could draw 2 vertical and 2 horizontal lines over the image to illustrate the 9 parts.
If I use numpys reshape (eg. matrix.reshape(9,260,745) or any other combination of 9,260,745) it doesn't yield the required structure since the above mentioned ordering is lost...
Did I misunderstand the reshape method or can it be done this way?
What other pythonic/numpy way is there to do this?
Sounds like you need to use numpy.split() which has its documentation here ... or perhaps its sibling numpy.array_split() here. They are for splitting an array into equal subsections without re-arranging the numbers like reshape does,
I haven't tested this but something like:
numpy.array_split(numpy.zeros((1300,1341)), 9)
should do the trick.
reshape, to quote its docs,
Gives a new shape to an array without
changing its data.
In other words, it does not move the array's data around at all -- it just affects the array's dimension. You, on the other hand, seem to require slicing; again quoting:
It is possible to slice and stride
arrays to extract arrays of the same
number of dimensions, but of different
sizes than the original. The slicing
and striding works exactly the same
way it does for lists and tuples
except that they can be applied to
multiple dimensions as well.
So for example thearray[0:260, 0:745] is the "upper leftmost part, thearray[260:520, 0:745] the upper left-of-center part, and so forth. You could have references to the various parts in a list (or dict with appropriate keys) to process them separately.