| /// This is the implementation of the first 2 parts of the wonderful 4-parts | |
| /// posts "Understanding Parser Combinators" of | |
| /// [F# for fun and profit](https://fsharpforfunandprofit.com/): | |
| module ParserLib1 = | |
| open System | |
| /// Type that represents Success/Failure in parsing | |
| type ParseResult<'a> = |
| #!/bin/sh | |
| # Function: | |
| # print $output_num sections by order, | |
| # each have $sec_num adjacent records | |
| # from file $inp | |
| inp=$1 | |
| output_num=$2 | |
| sec_num=$3 |
| rc=%{$reset_color%} | |
| cy=$fg_bold[cyan] | |
| bl=$fg_bold[blue] | |
| re=$fg_bold[red] | |
| wh=%{$fg_bold[white]%} | |
| ye=$fg_bold[yellow] | |
| gr=$fg_bold[green] | |
| ma=$fg_bold[magenta] | |
| ZSH_THEME_GIT_PROMPT_PREFIX="git(" |
Python 语言最明显的特征之一是用缩进量表示从属关系，没有采用 Java, C 用括号定义作用域的自由书写风格。 所以 Python 对代码格式的要求十分严格，社区发展出了完整的编码规范和丰富的检查和修复工具，并能够整合到几乎所有主流编辑器中。
编码规范在提升代码质量和可读性、降低维护成本等方面发挥了核心作用，但如果没有相应的工具链帮助其落地，很难真正产生效果，本文第3、4节专门论述如何搭建可用的工具链。
Python 社区不鼓励对编码规范做人工审查，因为人工审查存在如下问题：
| #!/bin/bash | |
| # 使用方法： | |
| # * 提取文件 `ycz6502.csv` 中所有包含的日期，保存到各自的csv文件中：`./filecutter.sh ycz6502.csv` | |
| # * 提取文件 `ycz6502.csv` 中指定日期（2017年6月4日）的数据到csv文件中：./filecutter.sh ycz6502.csv 20170604 | |
| sourcefile=$(basename -s .csv $1) | |
| if [[ $# -gt 1 ]]; then | |
| csvgrep -c time -m $2 $1 > $sourcefile-$2.csv | |
| echo "$sourcefile-$2.csv created" |
| --- | |
| title: "二次型系统的参数估计和异常检测" | |
| output: html_notebook | |
| --- | |
| 二次型系统（[Quadratic form](https://en.wikipedia.org/wiki/Quadratic_form)）是只包含二次项，不包含常数和一次项的单变量或者多变量系统，例如下面分别是包含1, 2 和 3个特征变量的二次型系统： | |
| $$ | |
| y = a x^2 \\ | |
| y = a x^2 + b xy + c y^2 \\ | |
| y = a x^2 + b y^2 + c z^2 + d xy + e xz + f yz |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import pywt | |
| original = pywt.data.camera() | |
| noiseSigma = 16.0 | |
| image = original + np.random.normal(0, noiseSigma, size=original.shape) |
Let $$ \begin{align} ss_{xy} = \sum(x_i - \bar x) (y_i - \bar y) \tag 1 \end{align} $$
| import pandas as pd | |
| import numpy as np | |
| # based on [Pandas Pivot Table Explained](http://pbpython.com/pandas-pivot-table-explained.html) | |
| # basic pivot table function | |
| df = pd.read_excel('./sales-funnel.xlsx') | |
| df['Status'] = df['Status'].astype('category') | |
| df['Status'].cat.set_categories(["won", "pending", "presented", "declined"], inplace=True) |
| def meta = ''' | |
| { | |
| "img": "../../../../static/img/treeImg/project.png", | |
| "name": "财务预算", | |
| "id": "1510126562947", | |
| "open": true, | |
| "level": 0, | |
| "select": false, | |
| "dictoryList": [ | |
| { |