Re: [Numpy-discussion] loop through values in a array and find maximum as looping
It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks if I understand correctly it's a minimum.reduce numpy a = np.concatenate((np.arange(5)[::-1], np.arange(5)))*np.ones((4,3,1)) np.minimum.reduce(a, axis=2) array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) a.T.shape (10, 3, 4) python with iterable reduce(np.maximum, a.T) array([[ 4., 4., 4., 4.], [ 4., 4., 4., 4.], [ 4., 4., 4., 4.]]) reduce(np.minimum, a.T) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
Hi Olivier, No that does not seem to do anything am I missing another step whereever b is greater than a replace b with a? thanks On Wed, Dec 7, 2011 at 11:55 AM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
thanks for responding Josef but that is not really what I am looking for, I have a multidimensional array and if the next array has any values greater than what is in my first array I want to replace them. The data are contained in netcdf files. I can achieve what I want if I combine all of my arrays using numpy concatenate and then using the command numpy.max(myarray, axis=0) but because I have so many arrays I end up with a memory error so I need to find a way to get the maximum while looping. On Wed, Dec 7, 2011 at 12:36 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks if I understand correctly it's a minimum.reduce numpy a = np.concatenate((np.arange(5)[::-1], np.arange(5)))*np.ones((4,3,1)) np.minimum.reduce(a, axis=2) array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) a.T.shape (10, 3, 4) python with iterable reduce(np.maximum, a.T) array([[ 4., 4., 4., 4.], [ 4., 4., 4., 4.], [ 4., 4., 4., 4.]]) reduce(np.minimum, a.T) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
Weird, it worked for me (with a and b two 1d numpy arrays). Anyway, Josef's solution is probably much more efficient (especially if you can put all your arrays into a single tensor). -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com Hi Olivier, No that does not seem to do anything am I missing another step whereever b is greater than a replace b with a? thanks On Wed, Dec 7, 2011 at 11:55 AM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
If you need to do them one after the other, numpy.maximum(a, b) will do it (it won't work in-place on 'a' though, it'll make a new copy). -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com thanks for responding Josef but that is not really what I am looking for, I have a multidimensional array and if the next array has any values greater than what is in my first array I want to replace them. The data are contained in netcdf files. I can achieve what I want if I combine all of my arrays using numpy concatenate and then using the command numpy.max(myarray, axis=0) but because I have so many arrays I end up with a memory error so I need to find a way to get the maximum while looping. On Wed, Dec 7, 2011 at 12:36 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks if I understand correctly it's a minimum.reduce numpy a = np.concatenate((np.arange(5)[::-1], np.arange(5)))*np.ones((4,3,1)) np.minimum.reduce(a, axis=2) array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) a.T.shape (10, 3, 4) python with iterable reduce(np.maximum, a.T) array([[ 4., 4., 4., 4.], [ 4., 4., 4., 4.], [ 4., 4., 4., 4.]]) reduce(np.minimum, a.T) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
I think you want np.maximum(a, b, out=a) - Nathaniel On Dec 6, 2011 9:04 PM, questions anon questions.a...@gmail.com wrote: thanks for responding Josef but that is not really what I am looking for, I have a multidimensional array and if the next array has any values greater than what is in my first array I want to replace them. The data are contained in netcdf files. I can achieve what I want if I combine all of my arrays using numpy concatenate and then using the command numpy.max(myarray, axis=0) but because I have so many arrays I end up with a memory error so I need to find a way to get the maximum while looping. On Wed, Dec 7, 2011 at 12:36 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks if I understand correctly it's a minimum.reduce numpy a = np.concatenate((np.arange(5)[::-1], np.arange(5)))*np.ones((4,3,1)) np.minimum.reduce(a, axis=2) array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) a.T.shape (10, 3, 4) python with iterable reduce(np.maximum, a.T) array([[ 4., 4., 4., 4.], [ 4., 4., 4., 4.], [ 4., 4., 4., 4.]]) reduce(np.minimum, a.T) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
Thanks, I didn't know you could specify the out array :) (to the OP: my initial suggestion, although probably not very efficient, seems to work with 2D arrays too, so I have no idea why it didn't work for you -- but Nathaniel's one seems to be the ideal one anyway). -=- Olivier 2011/12/6 Nathaniel Smith n...@pobox.com I think you want np.maximum(a, b, out=a) - Nathaniel On Dec 6, 2011 9:04 PM, questions anon questions.a...@gmail.com wrote: thanks for responding Josef but that is not really what I am looking for, I have a multidimensional array and if the next array has any values greater than what is in my first array I want to replace them. The data are contained in netcdf files. I can achieve what I want if I combine all of my arrays using numpy concatenate and then using the command numpy.max(myarray, axis=0) but because I have so many arrays I end up with a memory error so I need to find a way to get the maximum while looping. On Wed, Dec 7, 2011 at 12:36 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks if I understand correctly it's a minimum.reduce numpy a = np.concatenate((np.arange(5)[::-1], np.arange(5)))*np.ones((4,3,1)) np.minimum.reduce(a, axis=2) array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) a.T.shape (10, 3, 4) python with iterable reduce(np.maximum, a.T) array([[ 4., 4., 4., 4.], [ 4., 4., 4., 4.], [ 4., 4., 4., 4.]]) reduce(np.minimum, a.T) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
thanks for all of your help, that does look appropriate but I am not sure how to loop it over thousands of files. I need to keep the first array to compare with but replace any greater values as I loop through each array comparing back to the same array. does that make sense? On Wed, Dec 7, 2011 at 1:12 PM, Olivier Delalleau sh...@keba.be wrote: Thanks, I didn't know you could specify the out array :) (to the OP: my initial suggestion, although probably not very efficient, seems to work with 2D arrays too, so I have no idea why it didn't work for you -- but Nathaniel's one seems to be the ideal one anyway). -=- Olivier 2011/12/6 Nathaniel Smith n...@pobox.com I think you want np.maximum(a, b, out=a) - Nathaniel On Dec 6, 2011 9:04 PM, questions anon questions.a...@gmail.com wrote: thanks for responding Josef but that is not really what I am looking for, I have a multidimensional array and if the next array has any values greater than what is in my first array I want to replace them. The data are contained in netcdf files. I can achieve what I want if I combine all of my arrays using numpy concatenate and then using the command numpy.max(myarray, axis=0) but because I have so many arrays I end up with a memory error so I need to find a way to get the maximum while looping. On Wed, Dec 7, 2011 at 12:36 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks if I understand correctly it's a minimum.reduce numpy a = np.concatenate((np.arange(5)[::-1], np.arange(5)))*np.ones((4,3,1)) np.minimum.reduce(a, axis=2) array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) a.T.shape (10, 3, 4) python with iterable reduce(np.maximum, a.T) array([[ 4., 4., 4., 4.], [ 4., 4., 4., 4.], [ 4., 4., 4., 4.]]) reduce(np.minimum, a.T) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
The out=a keyword will ensure your first array will keep being updated. So you can do something like: a = my_list_of_arrays[0] for b in my_list_of_arrays[1:]: numpy.maximum(a, b, out=a) -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com thanks for all of your help, that does look appropriate but I am not sure how to loop it over thousands of files. I need to keep the first array to compare with but replace any greater values as I loop through each array comparing back to the same array. does that make sense? On Wed, Dec 7, 2011 at 1:12 PM, Olivier Delalleau sh...@keba.be wrote: Thanks, I didn't know you could specify the out array :) (to the OP: my initial suggestion, although probably not very efficient, seems to work with 2D arrays too, so I have no idea why it didn't work for you -- but Nathaniel's one seems to be the ideal one anyway). -=- Olivier 2011/12/6 Nathaniel Smith n...@pobox.com I think you want np.maximum(a, b, out=a) - Nathaniel On Dec 6, 2011 9:04 PM, questions anon questions.a...@gmail.com wrote: thanks for responding Josef but that is not really what I am looking for, I have a multidimensional array and if the next array has any values greater than what is in my first array I want to replace them. The data are contained in netcdf files. I can achieve what I want if I combine all of my arrays using numpy concatenate and then using the command numpy.max(myarray, axis=0) but because I have so many arrays I end up with a memory error so I need to find a way to get the maximum while looping. On Wed, Dec 7, 2011 at 12:36 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks if I understand correctly it's a minimum.reduce numpy a = np.concatenate((np.arange(5)[::-1], np.arange(5)))*np.ones((4,3,1)) np.minimum.reduce(a, axis=2) array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) a.T.shape (10, 3, 4) python with iterable reduce(np.maximum, a.T) array([[ 4., 4., 4., 4.], [ 4., 4., 4., 4.], [ 4., 4., 4., 4.]]) reduce(np.minimum, a.T) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
On Tue, Dec 6, 2011 at 9:36 PM, Olivier Delalleau sh...@keba.be wrote: The out=a keyword will ensure your first array will keep being updated. So you can do something like: a = my_list_of_arrays[0] for b in my_list_of_arrays[1:]: numpy.maximum(a, b, out=a) I didn't think of the out argument which makes it more efficient, but in my example I used Python's reduce which takes an iterable and not one huge array. Josef -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com thanks for all of your help, that does look appropriate but I am not sure how to loop it over thousands of files. I need to keep the first array to compare with but replace any greater values as I loop through each array comparing back to the same array. does that make sense? On Wed, Dec 7, 2011 at 1:12 PM, Olivier Delalleau sh...@keba.be wrote: Thanks, I didn't know you could specify the out array :) (to the OP: my initial suggestion, although probably not very efficient, seems to work with 2D arrays too, so I have no idea why it didn't work for you -- but Nathaniel's one seems to be the ideal one anyway). -=- Olivier 2011/12/6 Nathaniel Smith n...@pobox.com I think you want np.maximum(a, b, out=a) - Nathaniel On Dec 6, 2011 9:04 PM, questions anon questions.a...@gmail.com wrote: thanks for responding Josef but that is not really what I am looking for, I have a multidimensional array and if the next array has any values greater than what is in my first array I want to replace them. The data are contained in netcdf files. I can achieve what I want if I combine all of my arrays using numpy concatenate and then using the command numpy.max(myarray, axis=0) but because I have so many arrays I end up with a memory error so I need to find a way to get the maximum while looping. On Wed, Dec 7, 2011 at 12:36 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks if I understand correctly it's a minimum.reduce numpy a = np.concatenate((np.arange(5)[::-1], np.arange(5)))*np.ones((4,3,1)) np.minimum.reduce(a, axis=2) array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) a.T.shape (10, 3, 4) python with iterable reduce(np.maximum, a.T) array([[ 4., 4., 4., 4.], [ 4., 4., 4., 4.], [ 4., 4., 4., 4.]]) reduce(np.minimum, a.T) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
Is 'a' a regular numpy array or something fancier? -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com thanks again my only problem though is that the out=a in the loop does not seem to replace my a= outside the loop so my final a is whatever I started with for a. Not sure what I am doing wrong whether it is something with the loop or with the command. On Wed, Dec 7, 2011 at 1:44 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 9:36 PM, Olivier Delalleau sh...@keba.be wrote: The out=a keyword will ensure your first array will keep being updated. So you can do something like: a = my_list_of_arrays[0] for b in my_list_of_arrays[1:]: numpy.maximum(a, b, out=a) I didn't think of the out argument which makes it more efficient, but in my example I used Python's reduce which takes an iterable and not one huge array. Josef -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com thanks for all of your help, that does look appropriate but I am not sure how to loop it over thousands of files. I need to keep the first array to compare with but replace any greater values as I loop through each array comparing back to the same array. does that make sense? On Wed, Dec 7, 2011 at 1:12 PM, Olivier Delalleau sh...@keba.be wrote: Thanks, I didn't know you could specify the out array :) (to the OP: my initial suggestion, although probably not very efficient, seems to work with 2D arrays too, so I have no idea why it didn't work for you -- but Nathaniel's one seems to be the ideal one anyway). -=- Olivier 2011/12/6 Nathaniel Smith n...@pobox.com I think you want np.maximum(a, b, out=a) - Nathaniel On Dec 6, 2011 9:04 PM, questions anon questions.a...@gmail.com wrote: thanks for responding Josef but that is not really what I am looking for, I have a multidimensional array and if the next array has any values greater than what is in my first array I want to replace them. The data are contained in netcdf files. I can achieve what I want if I combine all of my arrays using numpy concatenate and then using the command numpy.max(myarray, axis=0) but because I have so many arrays I end up with a memory error so I need to find a way to get the maximum while looping. On Wed, Dec 7, 2011 at 12:36 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks if I understand correctly it's a minimum.reduce numpy a = np.concatenate((np.arange(5)[::-1], np.arange(5)))*np.ones((4,3,1)) np.minimum.reduce(a, axis=2) array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) a.T.shape (10, 3, 4) python with iterable reduce(np.maximum, a.T) array([[ 4., 4., 4., 4.], [ 4., 4., 4., 4.], [ 4., 4., 4., 4.]]) reduce(np.minimum, a.T) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
I *think* it may work better if you replace the last 3 lines in your loop by: a=all_TSFC[0] if len(all_TSFC) 1: N.maximum(a, TSFC, out=a) Not 100% sure that would work though, as I'm not entirely confident I understand your code. -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com Something fancier I think, I am able to compare the result with my previous method so I can easily see I am doing something wrong. see code below: all_TSFC=[] for (path, dirs, files) in os.walk(MainFolder): for dir in dirs: print dir path=path+'/' for ncfile in files: if ncfile[-3:]=='.nc': print dealing with ncfiles:, ncfile ncfile=os.path.join(path,ncfile) ncfile=Dataset(ncfile, 'r+', 'NETCDF4') TSFC=ncfile.variables['T_SFC'][:] fillvalue=ncfile.variables['T_SFC']._FillValue TSFC=MA.masked_values(TSFC, fillvalue) ncfile.close() all_TSFC.append(TSFC) a=TSFC[0] for b in TSFC[1:]: N.maximum(a,b,out=a) big_array=N.ma.concatenate(all_TSFC) Max=big_array.max(axis=0) print max is, Max,a is, a On Wed, Dec 7, 2011 at 2:34 PM, Olivier Delalleau sh...@keba.be wrote: Is 'a' a regular numpy array or something fancier? -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com thanks again my only problem though is that the out=a in the loop does not seem to replace my a= outside the loop so my final a is whatever I started with for a. Not sure what I am doing wrong whether it is something with the loop or with the command. On Wed, Dec 7, 2011 at 1:44 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 9:36 PM, Olivier Delalleau sh...@keba.be wrote: The out=a keyword will ensure your first array will keep being updated. So you can do something like: a = my_list_of_arrays[0] for b in my_list_of_arrays[1:]: numpy.maximum(a, b, out=a) I didn't think of the out argument which makes it more efficient, but in my example I used Python's reduce which takes an iterable and not one huge array. Josef -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com thanks for all of your help, that does look appropriate but I am not sure how to loop it over thousands of files. I need to keep the first array to compare with but replace any greater values as I loop through each array comparing back to the same array. does that make sense? On Wed, Dec 7, 2011 at 1:12 PM, Olivier Delalleau sh...@keba.be wrote: Thanks, I didn't know you could specify the out array :) (to the OP: my initial suggestion, although probably not very efficient, seems to work with 2D arrays too, so I have no idea why it didn't work for you -- but Nathaniel's one seems to be the ideal one anyway). -=- Olivier 2011/12/6 Nathaniel Smith n...@pobox.com I think you want np.maximum(a, b, out=a) - Nathaniel On Dec 6, 2011 9:04 PM, questions anon questions.a...@gmail.com wrote: thanks for responding Josef but that is not really what I am looking for, I have a multidimensional array and if the next array has any values greater than what is in my first array I want to replace them. The data are contained in netcdf files. I can achieve what I want if I combine all of my arrays using numpy concatenate and then using the command numpy.max(myarray, axis=0) but because I have so many arrays I end up with a memory error so I need to find a way to get the maximum while looping. On Wed, Dec 7, 2011 at 12:36 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of many (1s) of arrays. I need to loop through many multidimentional arrays and if a value is larger (in the same place as the previous array) then I would like that value to replace it. e.g. a=[1,1,2,2 11,2,2 1,1,2,2] b=[1,1,3,2 2,1,0,0 1,1,2,0] where ba replace with value in b, so the new a should be : a=[1,1,3,2] 2,1,2,2 1,1,2,2] and then keep looping through many arrays and replace whenever value is larger. I have tried numpy.putmask but that results in TypeError: putmask() argument 1 must be numpy.ndarray, not list Any other ideas? Thanks if I understand correctly it's a minimum.reduce numpy a = np.concatenate((np.arange(5)[::-1], np.arange(5)))*np.ones((4,3,1)) np.minimum.reduce(a, axis=2) array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) a.T.shape (10, 3, 4) python with
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
On 07.12.2011, at 5:07AM, Olivier Delalleau wrote: I *think* it may work better if you replace the last 3 lines in your loop by: a=all_TSFC[0] if len(all_TSFC) 1: N.maximum(a, TSFC, out=a) Not 100% sure that would work though, as I'm not entirely confident I understand your code. -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com Something fancier I think, I am able to compare the result with my previous method so I can easily see I am doing something wrong. see code below: all_TSFC=[] for (path, dirs, files) in os.walk(MainFolder): for dir in dirs: print dir path=path+'/' for ncfile in files: if ncfile[-3:]=='.nc': print dealing with ncfiles:, ncfile ncfile=os.path.join(path,ncfile) ncfile=Dataset(ncfile, 'r+', 'NETCDF4') TSFC=ncfile.variables['T_SFC'][:] fillvalue=ncfile.variables['T_SFC']._FillValue TSFC=MA.masked_values(TSFC, fillvalue) ncfile.close() all_TSFC.append(TSFC) a=TSFC[0] for b in TSFC[1:]: N.maximum(a,b,out=a) I also understood TSFC is already the array you want to work on, so above you'd just take a slice and overwrite the result in the next file iteration anyway. Iterating over the list all_TSFC should be correct, but I understood you don't want to load the entire input into memory in you working code. Then you can simply skip the list, just need to take care of initial conditions - something like the following should do: path=path+'/' a = None for ncfile in files: if ncfile[-3:]=='.nc': print dealing with ncfiles:, ncfile ncfile=os.path.join(path,ncfile) ncfile=Dataset(ncfile, 'r+', 'NETCDF4') TSFC=ncfile.variables['T_SFC'][:] fillvalue=ncfile.variables['T_SFC']._FillValue TSFC=MA.masked_values(TSFC, fillvalue) ncfile.close() if not is instance(a,N.ndarray): a=TSFC else: N.maximum(a, TSFC, out=a) HTH, Derek big_array=N.ma.concatenate(all_TSFC) Max=big_array.max(axis=0) print max is, Max,a is, a ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
On 07/12/2011, at 1:49 PM, questions anon wrote: fillvalue=ncfile.variables['T_SFC']._FillValue TSFC=MA.masked_values(TSFC, fillvalue) You can probably also eliminate the above two lines from your code. TSFC=ncfile.variables['T_SFC'][:] If your NetCDF files are properly structured, the above line will give you a masked array. If you really need to put a fill value in to go to a non-masked array, better to do this just once after the maximums have been determined. Tim ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
sorry the 'all_TSFC' is for my other check of maximum using concatenate and N.max, I know that works so I am comparing it to this method. The only reason I need another method is for memory error issues. I like the code I have written so far as it makes sense to me. I can't get the extra examples I have been given to work and that is most likely because I don't understand them, these are the errors I get : Traceback (most recent call last): File d:\plot_summarystats\test_plot_remove_memoryerror_max.py, line 46, in module N.maximum(a,TSFC,out=a) ValueError: non-broadcastable output operand with shape (106,193) doesn't match the broadcast shape (721,106,193) and Traceback (most recent call last): File d:\plot_summarystats\test_plot_remove_memoryerror_max.py, line 45, in module if not instance(a, N.ndarray): NameError: name 'instance' is not defined On Wed, Dec 7, 2011 at 3:07 PM, Olivier Delalleau sh...@keba.be wrote: I *think* it may work better if you replace the last 3 lines in your loop by: a=all_TSFC[0] if len(all_TSFC) 1: N.maximum(a, TSFC, out=a) Not 100% sure that would work though, as I'm not entirely confident I understand your code. -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com Something fancier I think, I am able to compare the result with my previous method so I can easily see I am doing something wrong. see code below: all_TSFC=[] for (path, dirs, files) in os.walk(MainFolder): for dir in dirs: print dir path=path+'/' for ncfile in files: if ncfile[-3:]=='.nc': print dealing with ncfiles:, ncfile ncfile=os.path.join(path,ncfile) ncfile=Dataset(ncfile, 'r+', 'NETCDF4') TSFC=ncfile.variables['T_SFC'][:] fillvalue=ncfile.variables['T_SFC']._FillValue TSFC=MA.masked_values(TSFC, fillvalue) ncfile.close() all_TSFC.append(TSFC) a=TSFC[0] for b in TSFC[1:]: N.maximum(a,b,out=a) big_array=N.ma.concatenate(all_TSFC) Max=big_array.max(axis=0) print max is, Max,a is, a On Wed, Dec 7, 2011 at 2:34 PM, Olivier Delalleau sh...@keba.be wrote: Is 'a' a regular numpy array or something fancier? -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com thanks again my only problem though is that the out=a in the loop does not seem to replace my a= outside the loop so my final a is whatever I started with for a. Not sure what I am doing wrong whether it is something with the loop or with the command. On Wed, Dec 7, 2011 at 1:44 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 9:36 PM, Olivier Delalleau sh...@keba.be wrote: The out=a keyword will ensure your first array will keep being updated. So you can do something like: a = my_list_of_arrays[0] for b in my_list_of_arrays[1:]: numpy.maximum(a, b, out=a) I didn't think of the out argument which makes it more efficient, but in my example I used Python's reduce which takes an iterable and not one huge array. Josef -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com thanks for all of your help, that does look appropriate but I am not sure how to loop it over thousands of files. I need to keep the first array to compare with but replace any greater values as I loop through each array comparing back to the same array. does that make sense? On Wed, Dec 7, 2011 at 1:12 PM, Olivier Delalleau sh...@keba.be wrote: Thanks, I didn't know you could specify the out array :) (to the OP: my initial suggestion, although probably not very efficient, seems to work with 2D arrays too, so I have no idea why it didn't work for you -- but Nathaniel's one seems to be the ideal one anyway). -=- Olivier 2011/12/6 Nathaniel Smith n...@pobox.com I think you want np.maximum(a, b, out=a) - Nathaniel On Dec 6, 2011 9:04 PM, questions anon questions.a...@gmail.com wrote: thanks for responding Josef but that is not really what I am looking for, I have a multidimensional array and if the next array has any values greater than what is in my first array I want to replace them. The data are contained in netcdf files. I can achieve what I want if I combine all of my arrays using numpy concatenate and then using the command numpy.max(myarray, axis=0) but because I have so many arrays I end up with a memory error so I need to find a way to get the maximum while looping. On Wed, Dec 7, 2011 at 12:36 PM, josef.p...@gmail.com wrote: On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau sh...@keba.be wrote: It may not be the most efficient way to do this, but you can do: mask = b a a[mask] = b[mask] -=- Olivier 2011/12/6 questions anon questions.a...@gmail.com I would like to produce an array with the maximum values out of
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
On 07.12.2011, at 5:54AM, questions anon wrote: sorry the 'all_TSFC' is for my other check of maximum using concatenate and N.max, I know that works so I am comparing it to this method. The only reason I need another method is for memory error issues. I like the code I have written so far as it makes sense to me. I can't get the extra examples I have been given to work and that is most likely because I don't understand them, these are the errors I get : Traceback (most recent call last): File d:\plot_summarystats\test_plot_remove_memoryerror_max.py, line 46, in module N.maximum(a,TSFC,out=a) ValueError: non-broadcastable output operand with shape (106,193) doesn't match the broadcast shape (721,106,193) and OK, then it seems we did not indeed grasp the entire scope of the problem - since you have initialised a from the previous array TSFC (not from TSFC[0]?!), this can only mean the arrays read in come in different shapes? I don't quite understand how the previous version did not raise an error then; but if you only want the (106,193)-subarray you have indeed to keep the loop for b in TSFC[:]: N.maximum(a,b,out=a) But you would have to find some way to distinguish between ndim=2 and ndim=3 input, if really both can occur... Traceback (most recent call last): File d:\plot_summarystats\test_plot_remove_memoryerror_max.py, line 45, in module if not instance(a, N.ndarray): NameError: name 'instance' is not defined Sorry, typing error (or devious auto-correct?) - this should be 'isinstance()' Cheers, Derek ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] loop through values in a array and find maximum as looping
thanks for all your responses. I think I have FINALLY worked it out with all of your help. I just assigned one array from one ncfile to a at the beginning of my code and then ran the loop and it worked!! sorry for all the questions but I learn so much playing and getting ideas from others. Thanks again. code below for anyone else that needs to do the same. onefile=Dataset(E:/01/IDZ00026_T_SFC.nc, 'r+', 'NETCDF4') oneTSFC=onefile.variables['T_SFC'][:] a=oneTSFC[0] for (path, dirs, files) in os.walk(MainFolder): for dir in dirs: print dir path=path+'/' for ncfile in files: if ncfile[-3:]=='.nc': ncfile=os.path.join(path,ncfile) ncfile=Dataset(ncfile, 'r+', 'NETCDF4') TSFC=ncfile.variables['T_SFC'][:] ncfile.close() for b in TSFC[:]: N.maximum(a,b, out=a) print a On Wed, Dec 7, 2011 at 4:11 PM, Derek Homeier de...@astro.physik.uni-goettingen.de wrote: On 07.12.2011, at 5:54AM, questions anon wrote: sorry the 'all_TSFC' is for my other check of maximum using concatenate and N.max, I know that works so I am comparing it to this method. The only reason I need another method is for memory error issues. I like the code I have written so far as it makes sense to me. I can't get the extra examples I have been given to work and that is most likely because I don't understand them, these are the errors I get : Traceback (most recent call last): File d:\plot_summarystats\test_plot_remove_memoryerror_max.py, line 46, in module N.maximum(a,TSFC,out=a) ValueError: non-broadcastable output operand with shape (106,193) doesn't match the broadcast shape (721,106,193) and OK, then it seems we did not indeed grasp the entire scope of the problem - since you have initialised a from the previous array TSFC (not from TSFC[0]?!), this can only mean the arrays read in come in different shapes? I don't quite understand how the previous version did not raise an error then; but if you only want the (106,193)-subarray you have indeed to keep the loop for b in TSFC[:]: N.maximum(a,b,out=a) But you would have to find some way to distinguish between ndim=2 and ndim=3 input, if really both can occur... Traceback (most recent call last): File d:\plot_summarystats\test_plot_remove_memoryerror_max.py, line 45, in module if not instance(a, N.ndarray): NameError: name 'instance' is not defined Sorry, typing error (or devious auto-correct?) - this should be 'isinstance()' Cheers, Derek ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
