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199 lines
5.5 KiB
Markdown
199 lines
5.5 KiB
Markdown
# SCIentific PYthon 简介
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**`Ipython`** 提供了一个很好的解释器界面。
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**`Matplotlib`** 提供了一个类似 **`Matlab`** 的画图工具。
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**`Numpy`** 提供了 `ndarray` 对象，可以进行快速的向量化计算。
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**`Scipy`** 是 **`Python`** 中进行科学计算的一个第三方库，以 **`Numpy`** 为基础。
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**`Pandas`** 是处理时间序列数据的第三方库，提供一个类似 **`R`** 语言的环境。
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**`StatsModels`** 是一个统计库，着重于统计模型。
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**`Scikits`** 以 **`Scipy`** 为基础，提供如 **`scikits-learn` 机器学习**和**`scikits-image` 图像处理**等高级用法。
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## Scipy
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**`Scipy`** 由不同科学计算领域的子模块组成：
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| 子模块 | 描述 |
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| --- | --- |
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| `cluster` | 聚类算法 |
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| `constants` | 物理数学常数 |
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| `fftpack` | 快速傅里叶变换 |
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| `integrate` | 积分和常微分方程求解 |
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| `interpolate` | 插值 |
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| `io` | 输入输出 |
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| `linalg` | 线性代数 |
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| `odr` | 正交距离回归 |
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| `optimize` | 优化和求根 |
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| `signal` | 信号处理 |
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| `sparse` | 稀疏矩阵 |
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| `spatial` | 空间数据结构和算法 |
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| `special` | 特殊方程 |
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| `stats` | 统计分布和函数 |
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| `weave` | C/C++ 积分 |
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在使用 **`Scipy`** 之前，为了方便，假定这些基础的模块已经被导入：
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In [1]:
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```py
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import numpy as np
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import scipy as sp
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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```
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使用 **Scipy** 中的子模块时，需要分别导入：
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In [2]:
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```py
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from scipy import linalg, optimize
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```
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对于一些常用的函数，这些在子模块中的函数可以在 `scipy` 命名空间中调用。另一方面，由于 **`Scipy`** 以 **`Numpy`** 为基础，因此很多基础的 **`Numpy`** 函数可以在`scipy` 命名空间中直接调用。
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我们可以使用 `numpy` 中的 `info` 函数来查看函数的文档：
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In [3]:
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```py
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np.info(optimize.fmin)
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```
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```py
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fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None,
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full_output=0, disp=1, retall=0, callback=None)
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Minimize a function using the downhill simplex algorithm.
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This algorithm only uses function values, not derivatives or second
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derivatives.
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Parameters
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----------
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func : callable func(x,*args)
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The objective function to be minimized.
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x0 : ndarray
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Initial guess.
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args : tuple, optional
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Extra arguments passed to func, i.e. ``f(x,*args)``.
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callback : callable, optional
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Called after each iteration, as callback(xk), where xk is the
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current parameter vector.
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xtol : float, optional
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Relative error in xopt acceptable for convergence.
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ftol : number, optional
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Relative error in func(xopt) acceptable for convergence.
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maxiter : int, optional
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Maximum number of iterations to perform.
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maxfun : number, optional
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Maximum number of function evaluations to make.
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full_output : bool, optional
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Set to True if fopt and warnflag outputs are desired.
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disp : bool, optional
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Set to True to print convergence messages.
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retall : bool, optional
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Set to True to return list of solutions at each iteration.
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Returns
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-------
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xopt : ndarray
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Parameter that minimizes function.
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fopt : float
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Value of function at minimum: ``fopt = func(xopt)``.
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iter : int
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Number of iterations performed.
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funcalls : int
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Number of function calls made.
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warnflag : int
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1 : Maximum number of function evaluations made.
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2 : Maximum number of iterations reached.
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allvecs : list
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Solution at each iteration.
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See also
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--------
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minimize: Interface to minimization algorithms for multivariate
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functions. See the 'Nelder-Mead' `method` in particular.
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Notes
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-----
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Uses a Nelder-Mead simplex algorithm to find the minimum of function of
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one or more variables.
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This algorithm has a long history of successful use in applications.
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But it will usually be slower than an algorithm that uses first or
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second derivative information. In practice it can have poor
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performance in high-dimensional problems and is not robust to
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minimizing complicated functions. Additionally, there currently is no
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complete theory describing when the algorithm will successfully
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converge to the minimum, or how fast it will if it does.
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References
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----------
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.. [1] Nelder, J.A. and Mead, R. (1965), "A simplex method for function
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minimization", The Computer Journal, 7, pp. 308-313
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.. [2] Wright, M.H. (1996), "Direct Search Methods: Once Scorned, Now
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Respectable", in Numerical Analysis 1995, Proceedings of the
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1995 Dundee Biennial Conference in Numerical Analysis, D.F.
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Griffiths and G.A. Watson (Eds.), Addison Wesley Longman,
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Harlow, UK, pp. 191-208.
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```
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可以用 `lookfor` 来查询特定关键词相关的函数：
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In [4]:
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```py
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np.lookfor("resize array")
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```
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```py
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Search results for 'resize array'
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---------------------------------
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numpy.chararray.resize
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Change shape and size of array in-place.
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numpy.ma.resize
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Return a new masked array with the specified size and shape.
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numpy.oldnumeric.ma.resize
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The original array's total size can be any size.
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numpy.resize
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Return a new array with the specified shape.
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numpy.chararray
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chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,
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numpy.memmap
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Create a memory-map to an array stored in a *binary* file on disk.
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numpy.ma.mvoid.resize
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.. warning::
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```
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还可以指定查找的模块：
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In [5]:
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```py
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np.lookfor("remove path", module="os")
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```
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```py
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Search results for 'remove path'
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--------------------------------
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os.removedirs
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removedirs(path)
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os.walk
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Directory tree generator.
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``` |