python的scipy学习笔记

Scripy

scipy是世界上著名的python开源科学计算库，建立在Numpy之上。它增加的功能包括数值积分、最优化、统计和一些专用函数

from scipy import io as spio

import numpy as np

a = np.ones((3,3))

a

array([[ 1.,  1.,  1.],       [ 1.,  1.,  1.],       [ 1.,  1.,  1.]])

#保存文件spio.savemat('C:/file.mat',{'a':a})

help(spio.savemat)

Help on function savemat in module scipy.io.matlab.mio:savemat(file_name, mdict, appendmat=True, format='5', long_field_names=False, do_compression=False, oned_as='row')    Save a dictionary of names and arrays into a MATLAB-style .mat file.    This saves the array objects in the given dictionary to a MATLAB-    style .mat file.    Parameters    ----------    file_name : str or file-like object        Name of the .mat file (.mat extension not needed if ``appendmat ==        True``).        Can also pass open file_like object.    mdict : dict        Dictionary from which to save matfile variables.    appendmat : bool, optional        True (the default) to append the .mat extension to the end of the        given filename, if not already present.    format : {'5', '4'}, string, optional        '5' (the default) for MATLAB 5 and up (to 7.2),        '4' for MATLAB 4 .mat files    long_field_names : bool, optional        False (the default) - maximum field name length in a structure is        31 characters which is the documented maximum length.        True - maximum field name length in a structure is 63 characters        which works for MATLAB 7.6+    do_compression : bool, optional        Whether or not to compress matrices on write.  Default is False.    oned_as : {'row', 'column'}, optional        If 'column', write 1-D numpy arrays as column vectors.        If 'row', write 1-D numpy arrays as row vectors.    See also    --------    mio4.MatFile4Writer    mio5.MatFile5Writer

#导入文件data = spio.loadmat('C:/file.mat',struct_as_record = True)

2统计

• 2.1分析随机数

import scipy.stats as stats

#生成正太分布随机数generated = stats.norm.rvs(size = 900)

generated

array([ -8.57702008e-03,   9.34395671e-01,   1.07440042e+00,         1.00175527e+00,   4.05260109e-01,  -1.71532834e-01,         1.28612758e+00,  -1.26120658e+00,   3.67144688e-01,        -1.04442344e-01,   7.90235505e-03,  -6.67001721e-03,         6.13195998e-01,  -2.16338293e+00,   2.09860328e+00,         6.33194981e-01,   2.03136868e-01,  -9.29293599e-01,        -8.56928963e-01,  -2.44011626e-01,  -5.31689867e-02,        -1.24476129e+00,   7.60883971e-01,   7.95282986e-01,         9.46875135e-01,   7.90638736e-01,  -1.12043692e+00,        -1.18010216e+00,  -1.82637092e+00,  -7.70707320e-01,        -8.85168815e-01,  -2.13382386e+00,  -4.13410828e-01,         2.30710582e-01,   1.18079290e+00,   6.64199532e-01,        -2.32247961e-01,   5.71861235e-01,   5.32447953e-01,        -1.85822781e+00,  -2.17234207e+00,  -1.13766408e-01,         4.87043355e-01,  -3.37903417e-01,  -8.73254019e-01,         1.24012883e+00,   1.05969170e+00,   1.54033334e+00,        -1.81938673e+00,  -9.46099331e-01,  -2.28434412e-01,         4.95264428e-02,  -9.16084090e-02,   4.97175574e-01,        -1.83193959e-01,   6.79238063e-01,   9.82022503e-01,        -3.41959691e-01,   1.95898942e-01,  -8.77840956e-01,         9.37490022e-01,   1.40194800e+00,   3.82318544e-01,         4.94116866e-01,   1.40500546e-01,  -1.42585746e+00,        -6.47472425e-01,   7.61179569e-01,   1.90284641e+00,        -1.92007455e-01,  -8.24959110e-01,  -1.36508135e+00,        -9.87497779e-01,  -7.87154834e-01,   6.48851502e-01,         3.45757066e-01,   4.97401786e-01,  -1.37838741e+00,         6.68326453e-01,  -7.45281035e-03,  -6.87896715e-01,        -4.42433853e-02,   1.35577281e-01,   9.90372050e-01,        -7.94591967e-01,   1.17244876e+00,   7.21593628e-01,        -2.18188146e+00,  -1.74321542e+00,  -1.20665810e-01,        -7.85089763e-01,  -8.03934846e-01,   3.52067648e-01,         4.51641541e-02,   2.02415887e+00,  -2.22211424e-01,         9.77893602e-01,   1.42809239e+00,   8.70386832e-02,       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  2.36966855e-01,  -1.49680419e+00,  -7.08836864e-01,        -4.19104102e-02,   1.87883890e+00,   2.75181315e-01,        -4.81536936e-01,  -8.53264700e-01,  -1.24714958e+00,        -2.27455324e-02,   1.26401894e+00,  -2.03742759e+00,         9.72867134e-01,  -1.15709932e+00,  -2.33857205e+00,        -2.53761054e-01,   2.91258529e-01,   3.61303111e-01,         1.78690632e-01,   1.04381188e+00,   1.78092894e+00,         3.57799516e-01,   6.11381856e-01,  -2.01523172e+00,        -4.91435049e-01,   5.74343371e-01,  -2.20917620e-01,         3.32183536e-01,  -5.43338847e-01,   6.52536212e-01,        -9.48194392e-01,  -2.04130001e-01,  -8.95863300e-01,        -1.19701053e+00,  -3.20675198e-01,   2.28599076e-01,        -6.65408390e-01,   8.37657901e-01,  -1.47516197e+00,         5.72013090e-01,  -8.25868503e-01,   5.84025301e-01,         4.31123037e-01,   1.17358979e-01,  -1.92581247e+00,         2.62441213e+00,   3.77998770e-01,   8.71679264e-01,         1.20366495e+00,  -1.73494283e+00,   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 7.86674775e-01,  -6.68301615e-01,        -5.50772522e-01,   1.58535485e+00,  -1.01111619e+00,         3.33193490e-01,   4.39118905e-01,   5.95421702e-01,         1.50134262e+00,  -6.40652826e-02,  -1.09377506e+00,         5.02173790e-01,   1.32249126e+00,  -1.06941610e+00,         1.25382693e+00,   9.63443808e-01,   1.61550512e+00,         1.31008518e+00,  -6.74169207e-01,   6.22607733e-01,         1.47181251e-01,  -1.11788690e-01,   1.55871336e+00,         3.86184012e-01,  -2.28916679e-02,   1.45092103e-01,        -1.79990883e+00,   7.09320259e-02,   1.25312230e+00,        -6.19137946e-02,  -2.97767642e-01,   1.40083810e+00,        -1.10577069e+00,  -1.57777324e-01,   1.96763574e-01,        -8.58558958e-01,  -4.22699979e-01,   3.90939223e-01,         7.57377976e-01,   5.19619757e-02,  -3.30396914e-01,         1.96870445e-02,   1.70082337e+00,  -1.37289839e+00,        -9.44965154e-01,   1.85226672e+00,   2.69731751e+00,        -9.80726602e-01,  -1.28915219e+00,  -3.57541740e-01,        -4.94145141e-01,  -1.33312920e+00,   1.36852763e+00,        -6.81853148e-01,  -2.39890217e+00,  -2.41959762e+00,        -1.67907271e+00,  -3.16777074e-01,   9.56808268e-01,         8.98617440e-01,  -1.46148042e+00,  -9.24668170e-01,        -1.36675357e+00,  -1.12352457e+00,   7.15689398e-01,        -2.65214757e-01,   4.51890526e-01,   2.34159122e-01,         8.76335430e-01,   1.00100771e+00,   6.37132053e-02,        -6.05090330e-01,   1.64640288e+00,  -1.50775894e+00,        -7.53724612e-01,  -7.76229274e-02,   5.01969943e-01,        -1.16238031e+00,   7.50194978e-02,   1.40422003e-01,        -2.36880822e-01,   3.33647054e+00,  -1.09270742e-01,         1.68183326e+00,  -1.93483715e+00,   1.40400691e+00,        -6.28588116e-01,  -4.82263615e-01,  -1.20235572e+00,        -1.13838155e+00,  -1.23334300e+00,   5.93611680e-01,        -8.62545233e-01,   1.01932831e-01,  -8.95226500e-01,        -3.24949964e-03,  -6.81216156e-01,   2.32232790e-01,        -1.13576872e+00,  -3.75214336e-01,   1.28826020e+00,         1.46169691e+00,  -1.97541723e+00,  -8.00728422e-01,        -3.57387583e-01,   1.10629523e+00,  -2.17961563e-01,        -9.53136241e-01,  -3.47280636e-01,  -7.40469374e-01,        -9.45060015e-01,  -9.40056093e-01,   3.42817751e-01,         2.24696211e-01,   1.00577615e-02,   7.04295085e-01,        -5.83266564e-01,  -1.30427491e+00,  -1.18325232e+00,        -9.13554701e-01,   2.14500470e+00,   1.09738357e+00,         1.65965044e+00,   1.03117883e+00,  -6.12318060e-01,         2.99601766e+00,   2.11151281e+00,   1.37336352e+00,        -7.73590684e-01,  -1.63544052e+00,   1.41375580e+00,        -1.74511625e-01,  -6.28548750e-01,  -3.05603123e-01,        -2.88737168e-01,   1.61016512e-01,   4.91164673e-02,         7.74071852e-01,   1.68639602e+00,  -1.60406469e+00,         6.87715078e-01,  -1.01669330e+00,  -1.35847514e+00,         7.64383376e-01,  -8.33303760e-01,  -6.61535440e-01,        -1.88951496e-01,   1.36734117e-01,   1.07180271e-01,        -1.22431584e+00,  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-2.83373331e-02,   7.74386707e-01,         2.65801026e-01,   1.69668918e+00,   1.11934156e+00,        -4.03075669e-01,  -2.51044329e-01,   8.23889389e-01,        -7.47709947e-01,   2.74301772e-01,   4.94666432e-01,        -7.74144113e-01,   2.16429711e+00,  -1.75448275e+00,         5.02684401e-01,   2.00349500e+00,  -3.96458097e-02,         1.19668669e+00,   8.86858913e-01,  -2.28335441e-01,         4.13119337e-02,   1.73747576e+00,   2.88713065e-02,        -2.37174582e-02,   1.60602837e-01,   1.02044838e+00,        -2.40466341e-01,  -4.98814702e-01,   2.05244767e+00,         7.61024346e-02,  -6.24932942e-01,  -1.24369105e+00,        -1.13372369e+00,  -9.42983450e-01,  -1.77087632e+00,        -1.03294302e+00,   1.11491657e+00,   1.54228229e-01,         7.79870463e-01,   6.62207531e-01,  -6.91837306e-01,        -2.11363920e-01,   1.13814050e+00,   5.84399242e-01,         7.46063428e-01,  -9.93511224e-01,   1.13624666e+00,        -8.36308453e-01,   7.06332956e-01,   3.29638437e-02,         8.34887370e-01,   2.34153866e-02,   1.60080801e+00,        -2.95040992e-01,  -6.37435034e-01,   8.41548527e-01,         6.61707902e-01,   2.44292385e-01,   3.45380767e-01,         9.48698235e-01,   1.03537437e+00,   5.26750798e-01,        -7.23441939e-01,   2.20939016e-01,   6.14280580e-01,         1.24638406e+00,  -3.89840794e-01,   2.77281358e-01,         2.03263721e-01,   3.56064449e-01,   2.52976328e+00,        -7.62992277e-01,   3.62450963e-01,   2.11087839e-01,         1.28319871e-01,   5.67062659e-02,   6.85595525e-01,         6.22716970e-01,   7.43024402e-01,  -1.02184558e+00,        -8.25634598e-01,   8.62951863e-01,   1.48746363e+00,         1.11532104e-01,   1.17891125e+00,   4.86530555e-01,         2.86076984e-01,   1.90781185e+00,  -1.15526337e+00,         9.19066637e-01,  -1.97165984e+00,   1.15480226e-01,         1.08673373e+00,  -6.38360011e-02,  -9.51268654e-01,         1.75232288e+00,   1.00470663e+00,   3.17986825e-01,         4.87140605e-01,   6.07403540e-01,   5.08142527e-02,         6.53316576e-02,  -3.75113569e-01,  -1.00665663e+00,         2.29468961e-01,   3.77438844e-01,  -8.24845273e-01,        -5.48875638e-01,   8.93670570e-01,  -1.75281391e+00,         1.64839202e+00,  -4.61249113e-01,   4.15285846e-01,        -1.22318954e+00,  -2.93356084e-01,   1.19868669e+00,        -1.60617155e+00,   8.68654907e-01,  -1.56371912e+00,        -1.24653754e+00,   3.35944947e-01,   9.53491951e-01,         1.18748423e+00,  -1.66209561e+00,   3.95659778e-01,        -7.99829084e-01,   4.99823822e-01,  -1.84405651e+00,        -7.60457279e-01,   4.37015860e-01,   4.73556878e-01,        -1.05959427e+00,  -1.22525309e+00,  -2.02163797e+00,         7.88709452e-01,  -2.09055740e-01,   8.40744105e-02,        -2.55810754e+00,  -1.14107178e-01,   1.06346320e+00,         1.23562750e+00,   3.13387131e-01,  -1.94647421e+00,         4.06034452e-01,   2.71193863e-01,   1.42017267e-01,         5.27550797e-03,   6.24576153e-01,  -1.60264457e+00,        -8.85434929e-01,  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  7.63597286e-01,  -1.42894460e-03,   4.81969568e-01,        -5.87603941e-01,   9.39222136e-03,  -3.05536086e-01,         1.64381411e+00,  -9.11753445e-01,  -5.46896937e-01,        -2.51088319e-01,  -4.31253151e-01,  -1.63267616e+00,        -7.42853581e-01,  -1.04335111e+00,   1.36604275e-01,         6.86008503e-01,  -2.89364506e+00,  -1.87319784e-01,         1.68670771e+00,   4.86112815e-01,  -2.15190488e-01,         3.89619864e-01,  -1.49314281e+00,  -7.79986305e-01,        -1.96106408e-01,   6.05759978e-02,   1.09481267e+00,        -8.69498436e-01,  -1.13672055e+00,  -8.89960199e-02,        -2.09224375e+00,  -7.90270603e-01,   1.23380872e+00,        -3.09047830e-02,  -1.57026181e+00,  -1.08800818e+00,        -7.34747075e-01,  -7.06408683e-01,   9.45412393e-01,         2.29660391e+00,   8.68736225e-01,   8.73884652e-01,        -8.18886650e-01,  -7.88239837e-01,  -1.59553882e-01,        -6.90625490e-01,   4.66742848e-01,   1.47781061e+00,        -1.71867492e+00,   2.76318832e-01,  -2.65222632e-01])

#用正太分布拟合生成的数据，得到均值和标准差Mean,std = stats.norm.fit(generated)

print 'Mean =',Mean,'std = ',std

Mean = -0.0397782005748 std =  1.02362777523

偏度描述的是概率分布的偏斜程度，该检验值返回两个值，其中第二个值是p-value，即观察到是数据服从正太分布的概率，取值为0-1

stats.skewtest(generated)

SkewtestResult(statistic=-0.28148367699151844, pvalue=0.7783394460550499)

#峰度检验stats.kurtosistest(generated)

KurtosistestResult(statistic=-0.44522204637017299, pvalue=0.65615929600597189)

#正态性检验stats.normaltest(generated)

NormaltestResult(statistic=0.27745573098670995, pvalue=0.870464879759689)

使用scipy我们很方便得到数据所在区域中某百分比处的数值

#得到95%处的数值score-at-percentilestats.scoreatpercentile(generated,95)

1.6176908986583227

#同样，可以反过来得到数值所在的百分比percentile-of-scorestats.percentileofscore(generated,1)

84.333333333333343

使用matplot绘制生成数据的分布直方图

import matplotlib.pyplot as plt

plt.hist(generated)

(array([   5.,   22.,   75.,  179.,  189.,  214.,  143.,   54.,   14.,    5.]), array([-3.23172914, -2.57490917, -1.9180892 , -1.26126924, -0.60444927,         0.0523707 ,  0.70919067,  1.36601063,  2.0228306 ,  2.67965057,         3.33647054]), <a list of 10 Patch objects>)

plt.show()

以后还会增加。。。