scipy

Python图像处理

守給你的承諾、 提交于 2020-07-25 18:31:44
作者|Garima Singh 编译|VK 来源|Git Connected 以前照相从来没有那么容易。现在你只需要一部手机。拍照是免费的,如果我们不考虑手机的费用的话。就在上一代人之前,业余艺术家和真正的艺术家如果拍照非常昂贵,并且每张照片的成本也不是免费的。 我们拍照是为了及时保存伟大的时刻,被保存的记忆随时准备在未来被"打开"。 就像腌制东西一样,我们要注意正确的防腐剂。当然,手机也为我们提供了一系列的图像处理软件,但是一旦我们需要处理大量的照片,我们就需要其他的工具。这时,编程和Python就派上用场了。Python及其模块如Numpy、Scipy、Matplotlib和其他特殊模块提供了各种各样的函数,能够处理大量图片。 为了向你提供必要的知识,本章的Python教程将处理基本的图像处理和操作。为此,我们使用模块NumPy、Matplotlib和SciPy。 我们从scipy包misc开始。 # 以下行仅在Python notebook中需要: %matplotlib inline from scipy import misc ascent = misc.ascent() import matplotlib.pyplot as plt plt.gray() plt.imshow(ascent) plt.show() 除了图像之外,我们还可以看到带有刻度的轴

第6天:Python 模块和包

◇◆丶佛笑我妖孽 提交于 2020-07-24 10:50:33
模块与包是任何大型程序的核心,就连 Python 安装程序本身也是一个包。 重点涉及有关模块和包的常用编程技术,例如如何组织包、把大型模块分割成多个文件、创建命名空间包。同时,也给出了让你自定义导入语句的秘籍。 先给大家解释一下模块、包、库之间的概念: 模块(module)其实就是 py 文件,里面定义了一些函数、类、变量等 包(package)是多个模块的聚合体形成的文件夹,里面可以是多个 py 文件,也可以嵌套文件夹 库是参考其他编程语言的说法,是指完成一定功能的代码集合,在 Python 中的形式就是模块和包 模块其实一个 py 文件,用来封装一组功能;包是将一类模块归集到一起,比模块的概念更大一些;库就是由其它程序员封装好的功能组,一般比包的概念更大一些。 下面我们来分开介绍: 模块 由上面的内容我们得知模块就是一个 py 文件,这个文本文件中存储着一组功能,方面我们再次使用的时候,提高代码的复用率。我们成这一个的一个 py 文件为 Python 模块(Module)。其他 Python 脚本中,通过 import 载入定义好的 Python 模块。 定义和调用 Python 模块 我们先来看如何定义一个 Python 模块。 定义一个 hello.py 模块,内容如下: def sayhello( ): print("Hello World!") 通常我们使用

Extract interpolated values from a 2D array based on a large set of xy points

不问归期 提交于 2020-07-22 04:55:50
问题 I have a reasonably large 1000 x 4000 pixel xr.DataArray returned from an OpenDataCube query, and a large set (> 200,000) of xy point values. I need to sample the array to return a value under each xy point, and return interpolated values (e.g. if the point lands halfway between a 0 and a 1.0 pixel, the value returned should be 0.5 ). xr.interp lets me easily sample interpolated values, but it returns a huge matrix of every combination of all the x and y values, rather than just the values

How to get value of area under multiple peaks

二次信任 提交于 2020-07-22 04:02:06
问题 I have some data from a bioanalyzer which gives me time (x-axis) and absorbance values (y-axis). The time is every .05 seconds and its from 32s to 138 so you can imagine how many data points I have. I've created a graph using plotly and matplotlib, just so that I have more libraries to work with to find a solution, so a solution in either library is ok! What I'm trying to do is make my script find the area under each peak and return my value. def create_plot(sheet_name): sample = book.sheet

Goodness of fit test for Weibull distribution in python

给你一囗甜甜゛ 提交于 2020-07-21 03:32:42
问题 I have some data that I have to test to see if it comes from a Weibull distribution with unknown parameters. In R I could use https://cran.r-project.org/web/packages/KScorrect/index.html but I can't find anything in Python. Using scipy.stats I can fit parameters with: scipy.stats.weibull_min.fit(values) However in order to turn this into a test I think I need to perform some Monte-Carlo simulation (e.g. https://en.m.wikipedia.org/wiki/Lilliefors_test) I am not sure what to do exactly. How can

Goodness of fit test for Weibull distribution in python

ぃ、小莉子 提交于 2020-07-21 03:31:47
问题 I have some data that I have to test to see if it comes from a Weibull distribution with unknown parameters. In R I could use https://cran.r-project.org/web/packages/KScorrect/index.html but I can't find anything in Python. Using scipy.stats I can fit parameters with: scipy.stats.weibull_min.fit(values) However in order to turn this into a test I think I need to perform some Monte-Carlo simulation (e.g. https://en.m.wikipedia.org/wiki/Lilliefors_test) I am not sure what to do exactly. How can

Reproduce Matlab's SVD in python

核能气质少年 提交于 2020-07-21 03:14:02
问题 I'm trying to reproduce some large project that was written in Matlab, using python. I managed to reproduce most of the results, but I have a problem specifically with SVD decomposition. (I'm looking only on the last, V, part.) In Matlab: [~, ~, V] = svd([4.719, -17.257, -11.5392; -17.2575, 63.9545, 40.5581; -11.5392, 40.5581, 31.3256]); This gets me the following V: -0.2216 0.0241 -0.9748 0.8081 -0.5549 -0.1974 0.5457 0.8316 -0.1035 in numpy: np.linalg.svd(np.array([[4.71993, -17.2575, -11

Reproduce Matlab's SVD in python

十年热恋 提交于 2020-07-21 03:13:10
问题 I'm trying to reproduce some large project that was written in Matlab, using python. I managed to reproduce most of the results, but I have a problem specifically with SVD decomposition. (I'm looking only on the last, V, part.) In Matlab: [~, ~, V] = svd([4.719, -17.257, -11.5392; -17.2575, 63.9545, 40.5581; -11.5392, 40.5581, 31.3256]); This gets me the following V: -0.2216 0.0241 -0.9748 0.8081 -0.5549 -0.1974 0.5457 0.8316 -0.1035 in numpy: np.linalg.svd(np.array([[4.71993, -17.2575, -11

Can I use a machine learning model as the objective function in an optimization problem?

半城伤御伤魂 提交于 2020-07-18 16:00:18
问题 The bounty expires in 7 hours . Answers to this question are eligible for a +50 reputation bounty. Neel is looking for an answer from a reputable source . I have a data set for which I use Sklearn Decision Tree regression machine learning package to build a model for prediction purposes. Subsequently, I am trying to utilize scipy.optimize package to solve for the minimized solution based on a given constraint. However, I am not sure if I can take the decision tree model as the objective

Can I use a machine learning model as the objective function in an optimization problem?

…衆ロ難τιáo~ 提交于 2020-07-18 15:59:26
问题 The bounty expires in 7 hours . Answers to this question are eligible for a +50 reputation bounty. Neel is looking for an answer from a reputable source . I have a data set for which I use Sklearn Decision Tree regression machine learning package to build a model for prediction purposes. Subsequently, I am trying to utilize scipy.optimize package to solve for the minimized solution based on a given constraint. However, I am not sure if I can take the decision tree model as the objective