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copied!<p>Sounds weird but... I can save to csv by using matplotlib.mlab.rec2csv, and then read to ndarray by using numpy.loadtxt. My case is simpler as I already have csv file. Here is an example how it works. </p> <pre><code>>>> a = np.loadtxt( 'name.csv', skiprows=1, delimiter=',', converters = {0: lambda x: 0} ) >>> a array([[ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0.29, 0.29, 0.43, 0.29, 0. ], [ 0. , 0.71, 0.29, 0.57, 0. , 0. ], [ 0. , 1. , 0.57, 0.71, 0. , 0. ], [ 0. , 0.43, 0.29, 0.14, 0.14, 0. ], [ 0. , 1. , 0.43, 0.71, 0. , 0. ], [ 0. , 0.57, 0.57, 0.29, 0.14, 0. ], [ 0. , 1.43, 0.43, 0.86, 0.43, 0. ], [ 0. , 1. , 0.71, 0.57, 0. , 0. ], [ 0. , 1.14, 0.57, 0.29, 0. , 0. ], [ 0. , 1.43, 0.29, 0.71, 0.29, 0.29], [ 0. , 1.14, 0.43, 1. , 0.29, 0.29], [ 0. , 0.43, 1.14, 0.86, 0.43, 0.14], [ 0. , 1.14, 0.86, 0.86, 0.29, 0.29]]) >>> t = a.any( axis = 1 ) >>> t array([False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True], dtype=bool) >>> a.ndim 2 </code></pre> <p>Also in my case I don't need a first column for making a decision. </p>

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