numpy

问题 I am trying to write a very simple code which just returns an array of x from 0 to 10 in increments of 0.1, but keep getting error: x = np.arange[[0,10,0.1,np.float]] TypeError: 'builtin_function_or_method' object is not subscriptable This is my code: import numpy as np import matplotlib.pyplot as plt results = {} # creating an empty dictionary f = open (r'D:\Work\Thesis\TEST_figures\telemac_comparison_1', 'w') f.write ('Distance'+'Free surface') result = [[], []] x = np.arange([0,10,0.1,np

问题 I’m basically trying to install Pandas, but to do that I’m supposedly supposed to be installing Numpy. I type this into the terminal: pip install numpy And I get an error which basically says I need Microsoft Visual Studio C++ 14.0. After I downloaded it from the link it provided, I have absolutely no idea how to work with it. I’ve tried to use a YouTube tutorial on how to use Visual Studio, but when they create a project I don’t have any of their options. The only option I have is to create

问题 I know that numpy.random.seed(seed) will output the same numbers if I use the same seed. My question is, does this change over time? If I try to call it again tomorrow, will it still output the same set of random numbers as yesterday? 回答1: The np.random documentation describes the PRNGs used. Apparently, there was a partial switch from MT19937 to PCG64 in the recent past. If you want consistency, you'll need to: fix the PRNG used, and ensure that you're using a local handle (e.g. RandomState

问题 I know that numpy.random.seed(seed) will output the same numbers if I use the same seed. My question is, does this change over time? If I try to call it again tomorrow, will it still output the same set of random numbers as yesterday? 回答1: The np.random documentation describes the PRNGs used. Apparently, there was a partial switch from MT19937 to PCG64 in the recent past. If you want consistency, you'll need to: fix the PRNG used, and ensure that you're using a local handle (e.g. RandomState

问题 I know that numpy.random.seed(seed) will output the same numbers if I use the same seed. My question is, does this change over time? If I try to call it again tomorrow, will it still output the same set of random numbers as yesterday? 回答1: The np.random documentation describes the PRNGs used. Apparently, there was a partial switch from MT19937 to PCG64 in the recent past. If you want consistency, you'll need to: fix the PRNG used, and ensure that you're using a local handle (e.g. RandomState

问题 Suppose I have a Numpy array myAnimation of datatype np.uint8 representing an animation (multiple frames of still 8-bit RGBA images) of shape (y,x,4,k) where y is the height, x is the width, 4 is the number of channels (red, green, blue, alpha), and the k is the number of frames in the animation. Suppose I would like to play back the frames of this NumPy array in PyGame at a specified frame rate (say, 15 frames per second) and have the animation loop. Is this possible to do with Pygame? If so

问题 Suppose I have a Numpy array myAnimation of datatype np.uint8 representing an animation (multiple frames of still 8-bit RGBA images) of shape (y,x,4,k) where y is the height, x is the width, 4 is the number of channels (red, green, blue, alpha), and the k is the number of frames in the animation. Suppose I would like to play back the frames of this NumPy array in PyGame at a specified frame rate (say, 15 frames per second) and have the animation loop. Is this possible to do with Pygame? If so

问题 I have the code shown below, but get the following error: ValueError: could not convert string to float: BRAF Provided that this is a sample of my data ( | is just a separator I added here for demonstration, you can imagine each value in a separate cell in a CSV file): c.401C>T | skin | 23:141905805-141905805 | 9947 | BRAF Could the strings be the issue? How can I read and pass strings in this case? from keras.models import Sequential from keras.layers import Dense from keras.models import

问题 I'm writing a program in which I want to flatten an array, so I used the following code: list_of_lists = [["a","b","c"], ["d","e","f"], ["g","h","i"]] flattened_list = [i for j in list_of_lists for i in j] This results in ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i'] , the desired output. I then found out that using a numpy array, I could've done the same simply by using np.array(((1,2),(3,4),(5,6))).flatten() . I was wondering if there is any downside to always using numpy arrays in the

问题 I would like to update (prepend each one with additional elements) many numpy arrays in a loop, without having to repeat the code for each one. I tried creating a list of all the arrays and looping through the items in that list and updating each one, but that doesn't change the original array. import numpy as np arr01 = [1,2,3] arr02 = [4,5,6] arr99 = [7,8,9] print('initial arr01', arr01) arraylist = [arr01, arr02, arr99] for array in arraylist: array = np.concatenate((np.zeros(3, dtype=int)