scipy

1D Wasserstein distance in Python

由 十年热恋 提交于 2020-12-13 05:23:13
问题 The formula below is a special case of the Wasserstein distance/optimal transport when the source and target distributions, x and y (also called marginal distributions) are 1D, that is, are vectors. where F^{-1} are inverse probability distribution functions of the cumulative distributions of the marginals u and v , derived from real data called x and y , both generated from the normal distribution: import numpy as np from numpy.random import randn import scipy.stats as ss n = 100 x = randn(n

How to fit a non linear function with python?

由 爷，独闯天下 提交于 2020-12-13 04:33:45
问题 I have the following code written in R to estimate three coefficients (a, b and c): y <- c(120, 125, 158, 300, 350, 390, 2800, 5900, 7790) t <- 1:9 fit <- nls(y ~ a * (((b + c)^2/b) * exp(-(b + c) * t))/(1 + (c/b) * exp(-(b + c) * t))^2, start = list(a = 17933, b = 0.01, c = 0.31)) and i get this result > summary(fit ) Formula: y ~ a * (((b + c)^2/b) * exp(-(b + c) * t))/(1 + (c/b) * exp(-(b + c) * t))^2 Parameters: Estimate Std. Error t value Pr(>|t|) a 2.501e+04 2.031e+03 12.312 1.75e-05 **

Understanding `leafsize` in scipy.spatial.KDTree

由 自闭症网瘾萝莉.ら 提交于 2020-12-13 03:38:16
问题 Problem statement: I have 150k points in a 3D space with their coordinates stored in a matrix with dimension [150k, 3] in mm. I want to find all the neighbors of a given point p that are within a radius r . And I want to do that in the most accurate way. How should I choose my leafsize parameter ? from scipy.spatial import KDTree import numpy as np pts = np.random.rand(150000,3) T1 = KDTree(pts, leafsize=20) T2 = KDTree(pts, leafsize=1) neighbors1= T1.query_ball_point((0.3,0.2,0.1), r=2.0)

Understanding `leafsize` in scipy.spatial.KDTree

由 一笑奈何 提交于 2020-12-13 03:34:11
问题 Problem statement: I have 150k points in a 3D space with their coordinates stored in a matrix with dimension [150k, 3] in mm. I want to find all the neighbors of a given point p that are within a radius r . And I want to do that in the most accurate way. How should I choose my leafsize parameter ? from scipy.spatial import KDTree import numpy as np pts = np.random.rand(150000,3) T1 = KDTree(pts, leafsize=20) T2 = KDTree(pts, leafsize=1) neighbors1= T1.query_ball_point((0.3,0.2,0.1), r=2.0)

Python - Integrating a function and plotting results

由 我们两清 提交于 2020-12-13 03:21:30
问题 I'm trying to solve Bernoulli's beam equation numerically and plotting the results. First derivative of the equation is the slope and the second derivative is the deflection. I approached the problem step-by-step, first plot the function and then integrate it and plot the integration results on the same diagram. My code so far is bellow. I suspect the problem lies in the fact that the integrate.quad returns a single value and I'm trying to get multiple values from it. Does anyone know how to

How to fit the best probability distribution model to my data in python?

由 点点圈 提交于 2020-12-12 05:33:40
问题 i have about 20,000 rows of data like this,, Id | value 1 30 2 3 3 22 .. n 27 I did statistics to my data,, the average value 33.85, median 30.99, min 2.8, max 206, 95% confidence interval 0.21.. So most values around 33, and there are some outliers (a little).. So it seems like a distribution with long tail. I am new to both distribution and python,, i tried class fitter https://pypi.org/project/fitter/ to try many distribution from Scipy package,, and loglaplace distribution showed the

How to fit the best probability distribution model to my data in python?

由 人走茶凉 提交于 2020-12-12 05:31:09
问题 i have about 20,000 rows of data like this,, Id | value 1 30 2 3 3 22 .. n 27 I did statistics to my data,, the average value 33.85, median 30.99, min 2.8, max 206, 95% confidence interval 0.21.. So most values around 33, and there are some outliers (a little).. So it seems like a distribution with long tail. I am new to both distribution and python,, i tried class fitter https://pypi.org/project/fitter/ to try many distribution from Scipy package,, and loglaplace distribution showed the

How to fit the best probability distribution model to my data in python?

由 耗尽温柔 提交于 2020-12-12 05:30:15
问题 i have about 20,000 rows of data like this,, Id | value 1 30 2 3 3 22 .. n 27 I did statistics to my data,, the average value 33.85, median 30.99, min 2.8, max 206, 95% confidence interval 0.21.. So most values around 33, and there are some outliers (a little).. So it seems like a distribution with long tail. I am new to both distribution and python,, i tried class fitter https://pypi.org/project/fitter/ to try many distribution from Scipy package,, and loglaplace distribution showed the

Python实现音乐的剪辑

由 ⅰ亾dé卋堺 提交于 2020-12-12 01:44:47
一、读取音频文件 from scipy.io import wavfile import numpy as np like = wavfile.read( ' ./嘤嘤嘤.wav ' ) print (like) 　　结果： 　 　　图片是三维的ndarray，视频是四维的由[[img],[img],[img]]+音频组成 　　 这里读取音频文件使用的scipy，scipy四个高端科学计算端。 　　 音频结果将返回一个tuple。第一维参数是采样频率，单位为秒；第二维数据是一个ndarray表示歌曲，如果第二维的ndarray只有一个数据表示单声道，两个数据表示立体声。很明显这里是个立体声。 　　所以，通过控制第二维数据就能对歌曲进行裁剪。 　　 继续音频裁剪就是： 　　　　like[1][ start_s*44100 : end_s*44100 ] 　　　　对like这个元组第二维数据进行裁剪，所以是like[1];第二维数据中是对音乐数据切分。 start_s表示你想裁剪音频的起始时间；同理end_s表示你裁剪音频的结束时间。乘44100 是因为每秒需要进行44100次采样。 　 对音频进行裁剪，并保存到本地，使用wavfile中的write： wavfile.write( ' like2.wav ' ,44100,like[1][30*44100:45*44100]) 　

Python安装cvxpy包的解决方案

由 浪尽此生 提交于 2020-12-08 07:44:57
背景 工作需要用到cvxpy库求解线性规划问题（cvxpy是解决凸优化问题的），发现安装不了，找了很多教程，发现都不是很靠谱，还把我自己的scipy包搞崩溃，很多之前的from scipy import stats这类都出错。现在把我成功安装的经验分享一下。 现象 安装cvxpy过程中需要scs等包，不会安装成功，你再去安装csc等包时会报没有Microsoft Visual C++ 14.0 （或者C++Build等错误 ）。 解决办法： 1、 下载 安装C++或者组件后再安装cvxpy相关包。 (我本人是WIN7没有安装成功) 2、下载第三方python包 https://www.lfd.uci.edu/~gohlke/pythonlibs/ 下载cvxpy相关的包（scs、cvxopt、ecos、multiprocess、scipy、cvxpy）到本地安装 # 在命令行输入下面命令 XXX为相应包名（连带后缀） pip install xxx . whl (我本人在安装scipy时把原来的搞崩溃了。 import scipy.stats 时的各种无法导入库（模块) 3、个人解决办法： 按照 scipy、cvxopt、scs、ecos、multiprocess、cvxpy 顺序依次（顺序错了可能会导致安装不成功）安装。 先 在线 安装 pip install ***
订阅 scipy