python - Plotting flaws in Matplotlib -


scatter plot using x,y coordinates proposes plots in matplotlib differ obtained using other programs. example, here results of pca on 2 fit score. same graph using r , same data provides different display…i checked excell , libreoffice : provided same display r. before roaring against matplotlib or report bug, other opinions , check if did things well. flaws?

i checked floats not problem, checked coordinates order similarly,… plot r:

mydata = read.csv("c:/users/anon/desktop/data.txt")  # read csv file summary(mydata) attach(mydata)  plot(mydata)

scatter plot made r enter image description here

same data plotted matplotlib:

import matplotlib.pyplot mpl import numpy np import os # open file pca results , convert float file_data = os.getcwd() + "\\data.txt" f = open(file_data, 'r') data=f.readlines() f.close() x in range(len(data)) :     = data[x]     b = a.split(',')     data[x] = b in xrange(len(data)):     j in xrange(len(data[i])):         data[i][j] = float(data[i][j]) print data[0] x_train = np.mat(data) print "x_train\n",x_train  mpl.scatter(x_train[:, 0], x_train[:, 1], c='white') mpl.show()

scatter plot made matplotlib , results of printing x_train (so can verify data same) enter image description here excell: enter image description here

data: (i cannot put data, please tell me how join *.txt file ~40.5 ko)

0.02753547770433    -0.037999362802379 0.05179194064903    0.0257492713593311 -0.0272928319004863 0.0065143681863637 0.0891355504379135  -0.00801696955147688 0.0946809371499167  -0.00502202338807476 -0.0445799941736001 -0.0435759273767196 -0.333617999778119  -0.204222004815357 -0.127212025425053  -0.110264460064754 -0.0243459270896855 -0.0622273166478512 0.0497080821876597  0.0272080474151131 -0.181221703468915  -0.134945934382777 -0.0699503258694739 -0.0835239795690277

edit: yet exported pca data (from scipy) text file , opened common text file python/matplotlib , r avoid prblms related pca. plots made after handling (and graph before pca looks dome)

edit2: using numpy.loadtxt(), displays r custom method , numpy.loadtxt() provided same data shape, size, type , values, what's mechanism involved? 

x_train numpy.loadtxt() [[ 0.02753548 -0.03799936]  [ 0.05179194  0.02574927]  [-0.02729283  0.00651437]  ...,   [ 0.02670961 -0.00696177]  [ 0.09011859 -0.00661216]  [-0.04406559  0.09285291]]  shape , size (1039l, 2l) 2078  x_train custom-method [[ 0.02753548 -0.03799936]  [ 0.05179194  0.02574927]  [-0.02729283  0.00651437]  ...,   [ 0.02670961 -0.00696177]  [ 0.09011859 -0.00661216]  [-0.04406559  0.09285291]]  shape , size (1039l, 2l) 2078

the problem representing x_train matrix rather 2-dimensional array. means when subset x_train[:, 0], aren't getting 1-dimensional array- getting matrix 1 column (which matplotlib tries scatter). can see printing x_train[:, 0].*

you can fix problem changing line:

x_train = np.mat(data)

to

x_train = np.array(data)

*for example, on data posted, x_train[:, 0] is:

[[ 0.02753548]  [ 0.05179194]  [-0.02729283]  [ 0.08913555]  [ 0.09468094]  [-0.04457999]  [-0.333618  ]  [-0.12721203]  [-0.02434593]  [ 0.04970808]  [-0.1812217 ]  [-0.06995033]]

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