How can I find maximum along multiple axes of a multidimensional numpy array?

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I have a numpy array of these dimensions

data.shape (categories, models, types, events, days) -> (10, 11, 50, 100, 14)

Now, I want to find the maximum of the 14 days for all events for each of the 11 models. But I am not sure how to do it in the numpy way. I am not sure if this is correct.

modelmax = 

nmodels = 11



for modelcount in range(nmodels):

    modelmax.append(np.max(data[0][modelcount][:], axis=2))

As an example, for the 100 events:

np.max(data, axis=4)[0][0][0])



[ 3.9264417   3.3029506   3.0707457   3.6646023   1.7508441   3.1634364

  6.195052    1.5353022   1.8033538   1.4508389   1.3882699   2.0849068

  3.654939    6.6364765   3.92829     6.6467876   1.5442419   4.639682

  9.361191    5.261462    1.7438816   5.6970205   2.4356377   1.6073244

  2.6177561   6.886767    3.890399    2.8880894   1.9826577   1.0888597

  4.3763924   3.8597727   1.790302    1.0277777   6.270729    9.311213

  2.318774    2.9298437   1.139397    0.9598383   3.0489902   1.6736581

  1.3983868   2.0979824   4.169757    1.0739225   1.5311266   1.4676268

  1.726325    1.8057758   2.226462    2.6197987   4.49518     2.3042605

  5.7164993   1.182242    1.5107205   2.2920077   2.205539    1.4702082

  2.154468    2.0641963   4.9628353   1.9987459   2.1360166   1.7073958

  1.943267    7.5767093   1.3124634   2.2648168   1.1504744   3.210688

  2.6720855   2.998225    4.365262    3.5410352  10.765423    4.6292825

  3.1789696   0.92157686  1.663245    1.5835482   3.1070056   1.6918416

  8.086268    3.7994847   2.4314868   1.6471033   1.1688241   1.7820593

  3.3509188   1.3092748   3.7915008   1.018912    3.2404447   1.596657

  2.0869658   2.6753283   2.1096318   8.786542  ]

I have also tried

np.max(dryflow[0][:], axis=3)

But these multidimensional indices are leaving me confused.

edited Nov 7 at 22:09

asked Nov 7 at 22:01

maximusdooku

1,39021343

And what about categories and types?
– Chiel
Nov 7 at 22:12

@Chiel I am trying to reduce the complexity of the problem for understanding purposes. But if I could have the daily maximums for category, model, type and event, that would be swell....I am just very confused regarding the multi-dims, and I am probably not relaying what I don't understand...
– maximusdooku
Nov 7 at 22:16

Eventually, I will take the log of the 14 day maximums and find the overall maximum and minimum for every type...Hope it explains a bit..
– maximusdooku
Nov 7 at 22:18

In numpy it is better to use [0,0,0,0] style of indexing rather than [0][0][0][0]. Sometimes they produce the same thing, but sometimes the differences give problems. Also with arrays [:] does nothing for you.
– hpaulj
Nov 7 at 22:20

@hpaulj Thanks! I didn't know there was any difference. BTW, can you expand a little bit on [:] doing nothing..
– maximusdooku
Nov 7 at 22:23

|
show 2 more comments

up vote
0
down vote

favorite

I have a numpy array of these dimensions

data.shape (categories, models, types, events, days) -> (10, 11, 50, 100, 14)

Now, I want to find the maximum of the 14 days for all events for each of the 11 models. But I am not sure how to do it in the numpy way. I am not sure if this is correct.

modelmax = 

nmodels = 11



for modelcount in range(nmodels):

    modelmax.append(np.max(data[0][modelcount][:], axis=2))

As an example, for the 100 events:

np.max(data, axis=4)[0][0][0])



[ 3.9264417   3.3029506   3.0707457   3.6646023   1.7508441   3.1634364

  6.195052    1.5353022   1.8033538   1.4508389   1.3882699   2.0849068

  3.654939    6.6364765   3.92829     6.6467876   1.5442419   4.639682

  9.361191    5.261462    1.7438816   5.6970205   2.4356377   1.6073244

  2.6177561   6.886767    3.890399    2.8880894   1.9826577   1.0888597

  4.3763924   3.8597727   1.790302    1.0277777   6.270729    9.311213

  2.318774    2.9298437   1.139397    0.9598383   3.0489902   1.6736581

  1.3983868   2.0979824   4.169757    1.0739225   1.5311266   1.4676268

  1.726325    1.8057758   2.226462    2.6197987   4.49518     2.3042605

  5.7164993   1.182242    1.5107205   2.2920077   2.205539    1.4702082

  2.154468    2.0641963   4.9628353   1.9987459   2.1360166   1.7073958

  1.943267    7.5767093   1.3124634   2.2648168   1.1504744   3.210688

  2.6720855   2.998225    4.365262    3.5410352  10.765423    4.6292825

  3.1789696   0.92157686  1.663245    1.5835482   3.1070056   1.6918416

  8.086268    3.7994847   2.4314868   1.6471033   1.1688241   1.7820593

  3.3509188   1.3092748   3.7915008   1.018912    3.2404447   1.596657

  2.0869658   2.6753283   2.1096318   8.786542  ]

I have also tried

np.max(dryflow[0][:], axis=3)

But these multidimensional indices are leaving me confused.

edited Nov 7 at 22:09

asked Nov 7 at 22:01

maximusdooku

1,39021343

And what about categories and types?
– Chiel
Nov 7 at 22:12

@Chiel I am trying to reduce the complexity of the problem for understanding purposes. But if I could have the daily maximums for category, model, type and event, that would be swell....I am just very confused regarding the multi-dims, and I am probably not relaying what I don't understand...
– maximusdooku
Nov 7 at 22:16

Eventually, I will take the log of the 14 day maximums and find the overall maximum and minimum for every type...Hope it explains a bit..
– maximusdooku
Nov 7 at 22:18

In numpy it is better to use [0,0,0,0] style of indexing rather than [0][0][0][0]. Sometimes they produce the same thing, but sometimes the differences give problems. Also with arrays [:] does nothing for you.
– hpaulj
Nov 7 at 22:20

@hpaulj Thanks! I didn't know there was any difference. BTW, can you expand a little bit on [:] doing nothing..
– maximusdooku
Nov 7 at 22:23

|
show 2 more comments

up vote
0
down vote

favorite

I have a numpy array of these dimensions

data.shape (categories, models, types, events, days) -> (10, 11, 50, 100, 14)

Now, I want to find the maximum of the 14 days for all events for each of the 11 models. But I am not sure how to do it in the numpy way. I am not sure if this is correct.

modelmax = 

nmodels = 11



for modelcount in range(nmodels):

    modelmax.append(np.max(data[0][modelcount][:], axis=2))

As an example, for the 100 events:

np.max(data, axis=4)[0][0][0])



[ 3.9264417   3.3029506   3.0707457   3.6646023   1.7508441   3.1634364

  6.195052    1.5353022   1.8033538   1.4508389   1.3882699   2.0849068

  3.654939    6.6364765   3.92829     6.6467876   1.5442419   4.639682

  9.361191    5.261462    1.7438816   5.6970205   2.4356377   1.6073244

  2.6177561   6.886767    3.890399    2.8880894   1.9826577   1.0888597

  4.3763924   3.8597727   1.790302    1.0277777   6.270729    9.311213

  2.318774    2.9298437   1.139397    0.9598383   3.0489902   1.6736581

  1.3983868   2.0979824   4.169757    1.0739225   1.5311266   1.4676268

  1.726325    1.8057758   2.226462    2.6197987   4.49518     2.3042605

  5.7164993   1.182242    1.5107205   2.2920077   2.205539    1.4702082

  2.154468    2.0641963   4.9628353   1.9987459   2.1360166   1.7073958

  1.943267    7.5767093   1.3124634   2.2648168   1.1504744   3.210688

  2.6720855   2.998225    4.365262    3.5410352  10.765423    4.6292825

  3.1789696   0.92157686  1.663245    1.5835482   3.1070056   1.6918416

  8.086268    3.7994847   2.4314868   1.6471033   1.1688241   1.7820593

  3.3509188   1.3092748   3.7915008   1.018912    3.2404447   1.596657

  2.0869658   2.6753283   2.1096318   8.786542  ]

I have also tried

np.max(dryflow[0][:], axis=3)

But these multidimensional indices are leaving me confused.

edited Nov 7 at 22:09

asked Nov 7 at 22:01

maximusdooku

1,39021343

I have a numpy array of these dimensions

data.shape (categories, models, types, events, days) -> (10, 11, 50, 100, 14)

Now, I want to find the maximum of the 14 days for all events for each of the 11 models. But I am not sure how to do it in the numpy way. I am not sure if this is correct.

modelmax = 

nmodels = 11



for modelcount in range(nmodels):

    modelmax.append(np.max(data[0][modelcount][:], axis=2))

As an example, for the 100 events:

np.max(data, axis=4)[0][0][0])



[ 3.9264417   3.3029506   3.0707457   3.6646023   1.7508441   3.1634364

  6.195052    1.5353022   1.8033538   1.4508389   1.3882699   2.0849068

  3.654939    6.6364765   3.92829     6.6467876   1.5442419   4.639682

  9.361191    5.261462    1.7438816   5.6970205   2.4356377   1.6073244

  2.6177561   6.886767    3.890399    2.8880894   1.9826577   1.0888597

  4.3763924   3.8597727   1.790302    1.0277777   6.270729    9.311213

  2.318774    2.9298437   1.139397    0.9598383   3.0489902   1.6736581

  1.3983868   2.0979824   4.169757    1.0739225   1.5311266   1.4676268

  1.726325    1.8057758   2.226462    2.6197987   4.49518     2.3042605

  5.7164993   1.182242    1.5107205   2.2920077   2.205539    1.4702082

  2.154468    2.0641963   4.9628353   1.9987459   2.1360166   1.7073958

  1.943267    7.5767093   1.3124634   2.2648168   1.1504744   3.210688

  2.6720855   2.998225    4.365262    3.5410352  10.765423    4.6292825

  3.1789696   0.92157686  1.663245    1.5835482   3.1070056   1.6918416

  8.086268    3.7994847   2.4314868   1.6471033   1.1688241   1.7820593

  3.3509188   1.3092748   3.7915008   1.018912    3.2404447   1.596657

  2.0869658   2.6753283   2.1096318   8.786542  ]

I have also tried

np.max(dryflow[0][:], axis=3)

But these multidimensional indices are leaving me confused.

python numpy

edited Nov 7 at 22:09

asked Nov 7 at 22:01

maximusdooku

1,39021343

edited Nov 7 at 22:09

asked Nov 7 at 22:01

maximusdooku

1,39021343

edited Nov 7 at 22:09

asked Nov 7 at 22:01

maximusdooku

1,39021343

asked Nov 7 at 22:01

maximusdooku

1,39021343

asked Nov 7 at 22:01

maximusdooku

1,39021343

And what about categories and types?
– Chiel
Nov 7 at 22:12

@Chiel I am trying to reduce the complexity of the problem for understanding purposes. But if I could have the daily maximums for category, model, type and event, that would be swell....I am just very confused regarding the multi-dims, and I am probably not relaying what I don't understand...
– maximusdooku
Nov 7 at 22:16

Eventually, I will take the log of the 14 day maximums and find the overall maximum and minimum for every type...Hope it explains a bit..
– maximusdooku
Nov 7 at 22:18

In numpy it is better to use [0,0,0,0] style of indexing rather than [0][0][0][0]. Sometimes they produce the same thing, but sometimes the differences give problems. Also with arrays [:] does nothing for you.
– hpaulj
Nov 7 at 22:20

@hpaulj Thanks! I didn't know there was any difference. BTW, can you expand a little bit on [:] doing nothing..
– maximusdooku
Nov 7 at 22:23

|
show 2 more comments

And what about categories and types?
– Chiel
Nov 7 at 22:12

@Chiel I am trying to reduce the complexity of the problem for understanding purposes. But if I could have the daily maximums for category, model, type and event, that would be swell....I am just very confused regarding the multi-dims, and I am probably not relaying what I don't understand...
– maximusdooku
Nov 7 at 22:16

Eventually, I will take the log of the 14 day maximums and find the overall maximum and minimum for every type...Hope it explains a bit..
– maximusdooku
Nov 7 at 22:18

In numpy it is better to use [0,0,0,0] style of indexing rather than [0][0][0][0]. Sometimes they produce the same thing, but sometimes the differences give problems. Also with arrays [:] does nothing for you.
– hpaulj
Nov 7 at 22:20

@hpaulj Thanks! I didn't know there was any difference. BTW, can you expand a little bit on [:] doing nothing..
– maximusdooku
Nov 7 at 22:23

And what about categories and types?
– Chiel
Nov 7 at 22:12

@Chiel I am trying to reduce the complexity of the problem for understanding purposes. But if I could have the daily maximums for category, model, type and event, that would be swell....I am just very confused regarding the multi-dims, and I am probably not relaying what I don't understand...
– maximusdooku
Nov 7 at 22:16

Eventually, I will take the log of the 14 day maximums and find the overall maximum and minimum for every type...Hope it explains a bit..
– maximusdooku
Nov 7 at 22:18

In numpy it is better to use [0,0,0,0] style of indexing rather than [0][0][0][0]. Sometimes they produce the same thing, but sometimes the differences give problems. Also with arrays [:] does nothing for you.
– hpaulj
Nov 7 at 22:20

@hpaulj Thanks! I didn't know there was any difference. BTW, can you expand a little bit on [:] doing nothing..
– maximusdooku
Nov 7 at 22:23

|
show 2 more comments

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