You need to strip off the trailing ‘;’ from the lines. A possible workaround if you know you have 5 columns is: np.loadtxt(‘test.csv’, delimiter=”;”, usecols=range(5)) Or, use genfromtext instead which handles missing values np.genfromtxt(‘test.csv’, delimiter=”;”)[:,:-1]
You are looking for is: if a is None: a = something else The problem is that, by using the == operator, if the input element a is a numpy array, numpy will try to perform an element wise comparison and tell you that you cannot compare it. For a a numpy array, a == … Read more
The ctypes attribute of ndarray can interact with the ctypes module, for example, ndarray.ctypes.data is the data address of the array, you can cast it to a float * pointer, and then pass the pointer to the C function. import numpy as np from cffi import FFI ffi = FFI() ffi.cdef(“void copy(float *in, float *out, … Read more
You can see your current line width with numpy.get_printoptions()[‘linewidth’] and set it with numpy.set_printoptions(linewidth=160) Automatically set printing width If you’d like the terminal width to be set automatically, you can have Python execute a startup script. So create a file ~/.python_startup.py or whatever you want to call it, with this inside it: # Set the … Read more
you can use multi dimension index: import numpy as np wall = np.zeros((10,10),dtype=np.int) block = np.arange(1,7).reshape(2,3) x = 2 y = 3 wall[x:x+block.shape[0], y:y+block.shape[1]] = block the output is: >>> wall array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, … Read more
There are several things going on every time you make a call to scipy.interpolate.griddata: First, a call to sp.spatial.qhull.Delaunay is made to triangulate the irregular grid coordinates. Then, for each point in the new grid, the triangulation is searched to find in which triangle (actually, in which simplex, which in your 3D case will be … Read more
Use .argsort() it returns an numpy.array of indices that sort the given numpy.array. You call it as a function or as a method on your array. For example, suppose you have import numpy as np arr = np.array([[-0.30565392, -0.96605562], [ 0.85331367, -2.62963495], [ 0.87839643, -0.28283675], [ 0.72676698, 0.93213482], [-0.52007354, 0.27752806], [-0.08701666, 0.22764316], [-1.78897817, 0.50737573], [ … Read more
Yes you can use numpy.s_: Example: >>> a = np.arange(10).reshape(2, 5) >>> >>> m = np.s_[0:2, 3:4] >>> >>> a[m] array([[3], [8]]) And in this case: my_slice = np.s_[cpix[1]-50:cpix[1]+50, cpix[0]-50:cpix[0]+50] a1 = array1[my_slice] a2 = array2[my_slice] a3 = array3[my_slice] You can also use numpy.r_ in order to translates slice objects to concatenation along the first … Read more
By adding the axis argument, NumPy looks at the rows and columns individually. When it’s not given, the array a is flattened into a single 1D array. axis=0 means that the operation is performed down the columns of a 2D array a in turn. For example np.argmin(a, axis=0) returns the index of the minimum value … Read more
numpy.argmax only returns the index of the first occurrence. You could apply argmax to a reversed view of the array: import numpy as np a = np.array([0,0,4,4,4,4,2,2,2,2]) b = a[::-1] i = len(b) – np.argmax(b) – 1 i # 5 a[i:] # array([4, 2, 2, 2, 2]) Note numpy doesn’t copy the array but instead … Read more