File error 1py line 9 in module ar3 numpyconcatenatear1 ar1 file arrayfunction internals line 6 in concatenate typeerror. I have achieved this.
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Only integer scalar arrays can be converted to a scalar index. Only integer scalar arrays can be converted to a scalar index my function. The code is pretty clear below. Only integer scalar arrays can be converted to a scalar index. Only integer scalar arrays can be converted to a scalar index关于这个错误已经第二次遇到了上次遇到错误的时候也就随便搞一搞就解决了但是这次又碰到这个问题然后不知道怎么解决了然后按着正常思路又百度了一番然而没有解决但是依稀上次遇到同样问题时记得也是在处理同样问题的. To me that looks as an inconsistent implementation. Github is home to over 50 million developers working together to host and review code manage projects and build software together.
Hi i am trying to do a slding window on a cube 3d array to get the average over a block of vertical 1d arrays. Only integer scalar arrays can be converted to a scalar index. Only integer scalar arrays can be converted to a scalar index 解决思路. Traceback most recent call last. Only integer scalar arrays can be converted to a scalar index. I am concatenating two one dimensional numpy arrays but i am getting the error.
It is 1 d it is integer and it is scalar. In the following example we are trying to concatenate two arrays using numpys concatenate function the concatenate function concatenating two or more arrays of the same type. What is wrong in the code. Labelarray 2012 valuesofarray nparraylabelarray indices npwherevaluesofarray value0 return arrayindices searchvaluessomevaluesarray2. Only integer scalar arrays can be converted to a scalar index. Dismiss join github today.
It usually can concatenate row wise and column wise. The main problem that i have with this is the fact that median works fine in this scenario. Only integer scalar arrays can be converted to a scalar index with 1d numpy indices array i find this weird since i already checked the array of indices that i have created. Import numpy as np a npones5. Import numpy a numpyarray1 2 3 b numpyarray5 6 numpyconcatenatea b traceback most recent call last.