No, there are no technical reasons why a numpy-like package does not exist for Node.js and, more generally, JavaScript. There are two main obstacles preventing Node.js and JavaScript from achieving more mind share in the data science and numeric computing communities. The first obstacle is community. While the JavaScript community is huge, the subset of … Read more
In the end, the most simple thing (calculating the coefficient with a riemann sum) was the most portable/efficient/robust way to solve my problem: import numpy as np def cn(n): c = y*np.exp(-1j*2*n*np.pi*time/period) return c.sum()/c.size def f(x, Nh): f = np.array([2*cn(i)*np.exp(1j*2*i*np.pi*x/period) for i in range(1,Nh+1)]) return f.sum() y2 = np.array([f(t,50).real for t in time]) plot(time, y) … Read more
You have to keep a reference to your Wrapper while any numpy array exists. Easiest way to achieve this, is to save this reference in a attribute of the ctype-buffer: class MyWrapper(object): def __init__(self, n=10): # buffer allocated by external library self.size = n self.addr = libc.malloc(C.sizeof(C.c_int) * n) def __del__(self): # buffer freed by … Read more
The astype method works well: >>> numpy.float64(‘6374.345407799015’).astype(str) ‘6374.345407799015’
This is how numpy uses advanced indexing to broadcast array shapes. When you pass a 0 for the first index, and y for the last index, numpy will broadcast the 0 to be the same shape as y. The following equivalence holds: x[0,:,:,y] == x[(0, 0, 0),:,:,y]. here is an example import numpy as np … Read more
Probably there’s something wrong with the input values for X and/or T. The function from the question works ok: import numpy as np from math import e def sigmoid(X, T): return 1.0 / (1.0 + np.exp(-1.0 * np.dot(X, T))) X = np.array([[1, 2, 3], [5, 0, 0]]) T = np.array([[1, 2], [1, 1], [4, 4]]) … Read more
One way could be to build a graph using NetworkX and obtain the adjacency matrix directly as a dataframe with nx.to_pandas_adjacency. To account for the co-occurrences of the edges in the graph, we can create a nx.MultiGraph, which allows for multiple edges connecting the same pair of nodes: import networkx as nx G = nx.from_edgelist(pair_array, … Read more
What you are looking for is the numpy.typing.NDArray class: https://numpy.org/doc/stable/reference/typing.html#numpy.typing.NDArray numpy.typing.NDArray[A] is an alias for numpy.ndarray[Any, numpy.dtype[A]]: import numpy as np import numpy.typing as npt a: npt.NDArray[np.complex64] = np.zeros((3, 3), dtype=np.complex64) # reveal_type(a) # -> numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]]] print(a) prints [[0.+0.j 0.+0.j 0.+0.j] [0.+0.j 0.+0.j 0.+0.j] [0.+0.j 0.+0.j 0.+0.j]] Note that even though you annotate … Read more
You may have a NAN, INF, or NINF floating around somewhere. Try this: np.isfinite(diff_images).all() np.isfinite(b_0).all() If one or both of those returns False, that’s likely the cause of the runtime error.
You are looking for masked arrays. Here’s an example. import numpy.ma as ma a = ma.array([1, 2, None], mask = [0, 0, 1]) print “average =”, ma.average(a) From the numpy docs linked above, “The numpy.ma module provides a nearly work-alike replacement for numpy that supports data arrays with masks.”