numpy

问题 Why np.inf > 1e999 returns False? I'm using Python 3.7 回答1: The notation 1e999 results in a float -- and this float is larger than the maximum possible value. So 1e999 becomes inf . A quick test in Ipython: In [11]: 1e999 == np.inf Out[11]: True 来源： https://stackoverflow.com/questions/63585238/python-np-inf-not-larger-than-number

问题 This question already has answers here : Vectorized way of calculating row-wise dot product two matrices with Scipy (5 answers) Closed 4 years ago . We are currently working on a python project and have to vectorize a lot due to performance constraints. We end up with the following calculation: We have two numpy arrays of shape (20,6) and want to calculate the pairwise dot product of the rows, i.e. we should obtain a (20,1) matrix in the end, where each row is the scalar obtained by the

问题 I am currently using xarray to make probability maps. I want to use a statistical assessment like a “counting” exercise. Meaning, for all data points in NEU count how many times both variables jointly exceed their threshold. That means 1th percentile of the precipitation data and 99th percentile of temperature data. Then the probability (P) of join occurrence is simply the number of joint exceedances divided by the number of data points in your dataset. <xarray.Dataset> Dimensions: (latitude:

问题 I am currently using xarray to make probability maps. I want to use a statistical assessment like a “counting” exercise. Meaning, for all data points in NEU count how many times both variables jointly exceed their threshold. That means 1th percentile of the precipitation data and 99th percentile of temperature data. Then the probability (P) of join occurrence is simply the number of joint exceedances divided by the number of data points in your dataset. <xarray.Dataset> Dimensions: (latitude:

问题 I have a numpy array of numbers and i want to create a boolean array of the same size and dimensions that says whether or not that element lies between 2 numbers. For example: a=np.array([[1,2,3],[4,5,6],[7,8,9]]) I know if I write, print a>3 I get an array that has the first three elements "False" and the rest "True" np.array([[False,False,False],[True,True,True],[True,True,True]]) But i want to get a boolean array where the conditions are such that a>3 and a<8 Is there a way to do this

问题 I have a dataset like x = 3,4,6,77,3 y = 8,5,2,5,5 labels = "null","exit","power","smile","null" Then I use from matplotlib import pyplot as plt plt.scatter(x,y) colorbar = plt.colorbar(labels) plt.show() to make a scatter plot, but cannot make colorbar showing labels as its colors. How to get this? 回答1: I'm not sure, if it's a good idea to do that for scatter plots in general (you have the same description for different data points, maybe just use some legend here?), but I guess a specific

问题 I have 2 arrays x and y with shapes (2, 3, 3) , respectively, (3, 3) . I want to compute the dot product z with shape (2, 3) in the following way: x = np.array([[[a111, a121, a131], [a211, a221, a231], [a311, a321, a331]], [[a112, a122, a132], [a212, a222, a232], [a312, a322, a332]]]) y = np.array([[b11, b12, b13], [b21, b22, b23], [b31, b32, b33]]) z = np.array([[a111*b11+a121*b12+a131*b13, a211*b21+a221*b22+a231*b23, a311*b31+a321*b32+a331*b33], [a112*b11+a122*b12+a132*b13, a212*b21+a222

问题 I'm not sure if this is a bug or if I'm doing something wrong. I've got the following code: r_div = 200 r_max = 1.4 numMax=.84 lowerBin = int((numMax - .2)/(r_max/r_div)) upperBin = int((numMax + .2)/(r_max/r_div)) k =np.arange((r_max/r_div)*lowerBin,(r_max/r_div)*(upperBin+1),r_max/r_div) When I run np.shape(k), I get (59). Now, if I change the upper limit by one in the last line: k =np.arange((r_max/r_div)*lowerBin,(r_max/r_div)*(upperBin),r_max/r_div) and run np.shape(k) again, it gives me

问题 I'm not sure if this is a bug or if I'm doing something wrong. I've got the following code: r_div = 200 r_max = 1.4 numMax=.84 lowerBin = int((numMax - .2)/(r_max/r_div)) upperBin = int((numMax + .2)/(r_max/r_div)) k =np.arange((r_max/r_div)*lowerBin,(r_max/r_div)*(upperBin+1),r_max/r_div) When I run np.shape(k), I get (59). Now, if I change the upper limit by one in the last line: k =np.arange((r_max/r_div)*lowerBin,(r_max/r_div)*(upperBin),r_max/r_div) and run np.shape(k) again, it gives me

问题 Let's say I have this array x: x = array([1, 2, 3, 4, 5, 6, 7, 8]) x.shape = (8,1) I want to reshape it to become array([[1, 3, 5, 7], [2, 4, 6, 8]]) this is a reshape(2, 4) on x but in the straight forward way: y = x.reshape(2,4) y becomes array([[1, 2, 3, 4], [5, 6, 7, 8]]) and that's not what I want. Is there a way to transform the array in that specific way? 回答1: In[4]: x.reshape(4, 2).T Out[4]: array([[1, 3, 5, 7], [2, 4, 6, 8]]) 回答2: The easiest way to do this is to specify the order