scipy

问题 Suppose I have the vertices of a polygon and they are all oriented CCW. I wish to generate n equidistance points along the boundary of this polygon. Does anyone know of any existing package that does this, and if not, an algorithm one can use? I am working in Python. For example, here is what I would like if the polygon in question is a rectangle: enter image description here 回答1: shapely : import shapely.geometry as sg import shapely.affinity as sa import matplotlib.pyplot as P import numpy

问题 Suppose I have the vertices of a polygon and they are all oriented CCW. I wish to generate n equidistance points along the boundary of this polygon. Does anyone know of any existing package that does this, and if not, an algorithm one can use? I am working in Python. For example, here is what I would like if the polygon in question is a rectangle: enter image description here 回答1: shapely : import shapely.geometry as sg import shapely.affinity as sa import matplotlib.pyplot as P import numpy

问题 Suppose I have the vertices of a polygon and they are all oriented CCW. I wish to generate n equidistance points along the boundary of this polygon. Does anyone know of any existing package that does this, and if not, an algorithm one can use? I am working in Python. For example, here is what I would like if the polygon in question is a rectangle: enter image description here 回答1: shapely : import shapely.geometry as sg import shapely.affinity as sa import matplotlib.pyplot as P import numpy

问题 Hi I want to add error bars to the histogram within this code.I have seen few post about it but I didn't find them helpful.this code produce random numbers with Gaussian distribution and a kernel estimation apply to it.I need to have errorbars to estimate how much the histogram is inaccurate with changing the bandwidth from random import * import numpy as np from matplotlib.pyplot import* from matplotlib import* import scipy.stats as stats def hist_with_kde(data, bandwidth = 0.3): #set number

问题 I can not get the color leaves from the scipy dendrogram dictionary. As stated in the documentation and in this github issue, the color_list key in the dendrogram dictionary refers to the links, not the leaves. It would be nice to have another key referring to the leaves, sometimes you need this for coloring other types of graphics, such as this scatter plot in the example below. import numpy as np import matplotlib.pyplot as plt from scipy.cluster.hierarchy import linkage, dendrogram # DATA

问题 I noticed that both scipy and sklearn have a cosine similarity/cosine distance functions. I wanted to test the speed for each on pairs of vectors: setup1 = "import numpy as np; arrs1 = [np.random.rand(400) for _ in range(60)];arrs2 = [np.random.rand(400) for _ in range(60)]" setup2 = "import numpy as np; arrs1 = [np.random.rand(400) for _ in range(60)];arrs2 = [np.random.rand(400) for _ in range(60)]" import1 = "from sklearn.metrics.pairwise import cosine_similarity" stmt1 = "[float(cosine

问题 One of my algorithms performs automatic peak detection based on a Gaussian function, and then later determines the the edges based either on a multiplier (user setting) of the sigma or the 'full width at half maximum'. In the scenario where a user specified that he/she wants the peak limited at 2 Sigma, the algorithm takes -/+ 2*sigma from the peak center (mu). However, I noticed that the sigma returned by curve_fit can be negative, which is something that has been noticed before as can be

问题 As a minimal reproducible example, suppose I have the following multivariate normal distribution: import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from scipy.stats import multivariate_normal, gaussian_kde # Choose mean vector and variance-covariance matrix mu = np.array([0, 0]) sigma = np.array([[2, 0], [0, 3]]) # Create surface plot data x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) X, Y = np.meshgrid(x, y) rv = multivariate_normal(mean=mu,

问题 As a minimal reproducible example, suppose I have the following multivariate normal distribution: import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from scipy.stats import multivariate_normal, gaussian_kde # Choose mean vector and variance-covariance matrix mu = np.array([0, 0]) sigma = np.array([[2, 0], [0, 3]]) # Create surface plot data x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) X, Y = np.meshgrid(x, y) rv = multivariate_normal(mean=mu,

问题 As a minimal reproducible example, suppose I have the following multivariate normal distribution: import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from scipy.stats import multivariate_normal, gaussian_kde # Choose mean vector and variance-covariance matrix mu = np.array([0, 0]) sigma = np.array([[2, 0], [0, 3]]) # Create surface plot data x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) X, Y = np.meshgrid(x, y) rv = multivariate_normal(mean=mu,