scipy

问题 I am using scipy.optimize.curve_fit to fit a curve to some data i have. The curves, for the most part, seem to fit very well. For some reason, pcov = inf when i print it off. What i really need is to calculate the error associated with the parameters i'm fitting, and am not sure how exactly to do this even if it does give me the covariance matrix. The model being fit to is: def intensity(x,R_out,R_in,K_in,K_out,a,b,c): K_in,K_out = abs(0.0),abs(K_out) if x<=R_in: return 2*R_out*(K_out*np.sqrt

问题 I am trying to find the fastest way to perform the following pairwise distance calculation in Python. I want to use the distances to rank a list_of_objects by their similarity. Each item in the list_of_objects is characterised by four measurements a, b, c, d, which are made on very different scales e.g.: object_1 = [0.2, 4.5, 198, 0.003] object_2 = [0.3, 2.0, 999, 0.001] object_3 = [0.1, 9.2, 321, 0.023] list_of_objects = [object_1, object_2, object_3] The aim is to get a pairwise distance

问题 I am trying to find the fastest way to perform the following pairwise distance calculation in Python. I want to use the distances to rank a list_of_objects by their similarity. Each item in the list_of_objects is characterised by four measurements a, b, c, d, which are made on very different scales e.g.: object_1 = [0.2, 4.5, 198, 0.003] object_2 = [0.3, 2.0, 999, 0.001] object_3 = [0.1, 9.2, 321, 0.023] list_of_objects = [object_1, object_2, object_3] The aim is to get a pairwise distance

问题 I am trying to find the fastest way to perform the following pairwise distance calculation in Python. I want to use the distances to rank a list_of_objects by their similarity. Each item in the list_of_objects is characterised by four measurements a, b, c, d, which are made on very different scales e.g.: object_1 = [0.2, 4.5, 198, 0.003] object_2 = [0.3, 2.0, 999, 0.001] object_3 = [0.1, 9.2, 321, 0.023] list_of_objects = [object_1, object_2, object_3] The aim is to get a pairwise distance

问题 I am trying to find the fastest way to perform the following pairwise distance calculation in Python. I want to use the distances to rank a list_of_objects by their similarity. Each item in the list_of_objects is characterised by four measurements a, b, c, d, which are made on very different scales e.g.: object_1 = [0.2, 4.5, 198, 0.003] object_2 = [0.3, 2.0, 999, 0.001] object_3 = [0.1, 9.2, 321, 0.023] list_of_objects = [object_1, object_2, object_3] The aim is to get a pairwise distance

问题 I have a cable I am dropping from moving vehicle onto the ground. Using a camera system I estimate the location where the rope touches the ground in realtime. Movement of the vehicle and inaccuracy in the estimation of the location result in a point-cloud of touchdown locations. From this point cloud, I'd like to obain the most likely path of the cable lying on the ground. I'd like to achieve this in real-time, and I'd like the fit to be updated according to new data. The frequency of new

问题 I have a cable I am dropping from moving vehicle onto the ground. Using a camera system I estimate the location where the rope touches the ground in realtime. Movement of the vehicle and inaccuracy in the estimation of the location result in a point-cloud of touchdown locations. From this point cloud, I'd like to obain the most likely path of the cable lying on the ground. I'd like to achieve this in real-time, and I'd like the fit to be updated according to new data. The frequency of new

问题 I have developed Bicubic interpolation for demonstration to some undergraduate students using Python Programming language. The methodology is as explained in wikipedia, The code is working fine except the results I am getting are slightly different than what is obtained when using scipy library. The interpolation code is shown below in the function bicubic_interpolation . import numpy as np import matplotlib.pyplot as plt from mpl_toolkits import mplot3d from scipy import interpolate import

问题 I have a dataframe as follows. I need to do ANOVA on this between three conditions. The dataframe looks like: data0 = pd.DataFrame({'Names': ['CTA15', 'CTA15', 'AC007', 'AC007', 'AC007','AC007'], 'value': [22, 22, 2, 2, 2,5], 'condition':['NON', 'NON', 'YES', 'YES', 'RE','RE']}) I need to do ANOVA test between YES and NON, NON and RE and YES and RE, conditions from conditions for Names. I know I could do it like this, NON=df.query('condition =="NON"and Names=="CTA15"') no=df.value YES=df

问题 I make following Python Code to calculate center and size of Gaussian-like distribution basis of moment method. But, I can't make the code to calculate the angle of gaussian. Please look at pictures. First Picture is original data. Second picture is reconstruct data from the result of moment method. But, second picture is insufficient reconstruction. Because, original data is inclined distribution. I have to, I think, calculate the angle of axis for Gaussian-like distribution. To assume that