From b0ffe9927dde3d7ba570694deb7f31b3f9f507d7 Mon Sep 17 00:00:00 2001 From: hlingchen Date: Tue, 24 Oct 2023 03:06:09 +0000 Subject: [PATCH] deploy: 3f85f99387dbc0108c263ddd3fb8c7e97af996fd --- _sources/chapters/data-analytics/scipy-basic.ipynb | 8 ++++---- chapters/data-analytics/scipy-basic.html | 8 ++++---- searchindex.js | 2 +- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/_sources/chapters/data-analytics/scipy-basic.ipynb b/_sources/chapters/data-analytics/scipy-basic.ipynb index 70b5cc7..f450723 100644 --- a/_sources/chapters/data-analytics/scipy-basic.ipynb +++ b/_sources/chapters/data-analytics/scipy-basic.ipynb @@ -15,7 +15,7 @@ "id": "lZMyAdqhL9hY" }, "source": [ - "[SciPy](https://www.scipy.org/) is the core library for scientific computing in Python. It provides many user-friendly and efficient numerical routines, such as numerical integration, interpolation, optimization, linear algebra, and **statistics**. These routines are composed as task-specific sub-modules in `SciPy`, such as `scipy.cluster` for vector quantization/ Kmeans, `scipy.linalg` for linear algebra routines. All `SciPy` sub-modules depend on `NumPy`, but are mostly independent of each other. \n", + "[SciPy](https://www.scipy.org/) is the core library for scientific computing in Python. It provides many user-friendly and efficient numerical routines, such as numerical integration, interpolation, optimization, linear algebra, and **statistics**. These routines are composed as task-specific subpackages in `SciPy`, such as `scipy.cluster` for vector quantization/ Kmeans, `scipy.linalg` for linear algebra routines. All `SciPy` subpackages depend on `NumPy`, but are mostly independent of each other. \n", "\n", "`scipy.stats` module contains a large number of summary and frequency statistics, probability distributions, correlation functions, statistical tests, kernel density estimation, quasi-Monte Carlo functionality, and so on. \n", "\n", @@ -23,7 +23,7 @@ "* `scipy.stats`: Statistics, Distributions, Statistical Tests and Correlations\n", "* Extreme Value Analysis\n", "\n", - "The standard way of importing NumPy and one SciPy sub-module is:" + "The standard way of importing NumPy and one SciPy sub-package is:" ] }, { @@ -938,7 +938,7 @@ "\\end{align}\n", "$\n", "\n", - "where $\\mu, \\sigma, \\xi$ are respectively GEV's location `loc`, scale `scale`, and shape `c` parameters. Obviously, there are no explicit solutions to these equations when we plugin the sample L-moments. Luckily, we could resort to the function solver `scipy.optimize.fsolve` in the optimization sub-module of SciPy to get numerical solutions." + "where $\\mu, \\sigma, \\xi$ are respectively GEV's location `loc`, scale `scale`, and shape `c` parameters. Obviously, there are no explicit solutions to these equations when we plugin the sample L-moments. Luckily, we could resort to the function solver `scipy.optimize.fsolve` in the optimization sub-package of SciPy to get numerical solutions." ] }, { @@ -1326,7 +1326,7 @@ "source": [ "## References\n", "+ This tutorial was edited based on [Python Statistics Fundamentals](https://realpython.com/python-statistics/), [Scipy Lecture Notes](https://scipy-lectures.org/), [royalosyin's guide to carry out EVA](https://github.com/royalosyin/A-Beginner-Guide-to-Carry-out-Extreme-Value-Analysis-with-Codes-in-Python) and [OpenHydrology's lmoments3 repository](https://github.com/OpenHydrology/lmoments3).\n", - "+ Only the `scipy.stats` sub-module is introduced here. If you wish to get a quick glimpse on other sub-modules of SciPy, you could refer to [scipy-lectures](https://scipy-lectures.org/intro/scipy.html)." + "+ Only the `scipy.stats` sub-package is introduced here. If you wish to get a quick glimpse on other subpackages of SciPy, you could refer to [scipy-lectures](https://scipy-lectures.org/intro/scipy.html)." ] } ], diff --git a/chapters/data-analytics/scipy-basic.html b/chapters/data-analytics/scipy-basic.html index 2115dcd..3109139 100644 --- a/chapters/data-analytics/scipy-basic.html +++ b/chapters/data-analytics/scipy-basic.html @@ -426,14 +426,14 @@

Contents

4.1. SciPy tutorial#

-

SciPy is the core library for scientific computing in Python. It provides many user-friendly and efficient numerical routines, such as numerical integration, interpolation, optimization, linear algebra, and statistics. These routines are composed as task-specific sub-modules in SciPy, such as scipy.cluster for vector quantization/ Kmeans, scipy.linalg for linear algebra routines. All SciPy sub-modules depend on NumPy, but are mostly independent of each other.

+

SciPy is the core library for scientific computing in Python. It provides many user-friendly and efficient numerical routines, such as numerical integration, interpolation, optimization, linear algebra, and statistics. These routines are composed as task-specific subpackages in SciPy, such as scipy.cluster for vector quantization/ Kmeans, scipy.linalg for linear algebra routines. All SciPy subpackages depend on NumPy, but are mostly independent of each other.

scipy.stats module contains a large number of summary and frequency statistics, probability distributions, correlation functions, statistical tests, kernel density estimation, quasi-Monte Carlo functionality, and so on.

In this tutorial, we will cover:

-

The standard way of importing NumPy and one SciPy sub-module is:

+

The standard way of importing NumPy and one SciPy sub-package is:

import numpy as np
@@ -972,7 +972,7 @@ 
4.1.5.2. Fit distribution (parameter est
 \frac{\lambda_3}{\lambda_2} & =\frac{2(1-3^{-\xi})}{1-2^{-\xi}}-3
 \end{align}
 \)

-
where \(\mu, \sigma, \xi\) are respectively GEV’s location loc, scale scale, and shape c parameters. Obviously, there are no explicit solutions to these equations when we plugin the sample L-moments. Luckily, we could resort to the function solver scipy.optimize.fsolve in the optimization sub-module of SciPy to get numerical solutions.

+
where \(\mu, \sigma, \xi\) are respectively GEV’s location loc, scale scale, and shape c parameters. Obviously, there are no explicit solutions to these equations when we plugin the sample L-moments. Luckily, we could resort to the function solver scipy.optimize.fsolve in the optimization sub-package of SciPy to get numerical solutions.

 

 

 
# Estimate GEV parameters using the function solver
@@ -1246,7 +1246,7 @@ 
4.1.5.4. Estimate extreme values for spe
 
4.1.6. References#
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