##### Identifying consecutive masked values in a 3D data array
 Identifying consecutive masked values in a 3D data array chai0404 Programmer named Tim Posts: 12 Threads: 4 Joined: Jan 2020 Reputation: Jan-12-2020, 10:55 PM I have a large 3 dimensional (time, longitude, latitude) input array of daily tmax values. I have masked the values which exceed a certain threshold. I need to find those entries where the mask is True for longer than a specific number of (3) consecutive time steps. The result should be a data array with 0s for the non-consecutive days and numbers corresponding to the length (duration of event) of consecutive elements. Below is some pseudo-code to make myself clearer: events = find_consecutive(input_array, duration=3) input_array = [1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1] events = [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0] I've had a look at scipy nd image but haven't been able to completely figure out how to use it. Any help is appreciated :) Reply chai0404 Programmer named Tim Posts: 12 Threads: 4 Joined: Jan 2020 Reputation: Jan-13-2020, 03:38 AM I have a large 3 dimensional (time, longitude, latitude) input array of daily tmax values. I have masked the values which exceed a certain threshold. I need to find those entries where the mask is True for longer than a specific number of (3) consecutive time steps. The result should be a data array with 0s for the non-consecutive days and numbers corresponding to the length (duration of event) of consecutive elements. Below is some pseudo-code to make myself clearer: events = find_consecutive(input_array, duration=3) input_array = [1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1] events = [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0] I've had a look at scipy nd image but haven't been able to completely figure out how to use it. Any help is appreciated :) Reply Posts: 1,873 Threads: 8 Joined: Jun 2018 Reputation: Jan-13-2020, 08:07 AM (This post was last modified: Jan-13-2020, 08:08 AM by perfringo.) One way of doing it (not that very elegant). On row #6 unpacking is done and _ value is number of groups which is not needed. Same result can be obtained without unpacking as `labels = label(arr)[0]` Rows #6 and 7 can be merged into one but `slices = find_objects(label(arr)[0])` it is not so explicit what is going on. ```import numpy as np from scipy.ndimage.measurements import label, find_objects arr = np.array([1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1]) labels, _ = label(arr) slices = find_objects(labels) for interval in slices: if 3 <= arr[interval].size: arr[interval] = arr[interval].size else: arr[interval] = 0```arr will be: ``Output:[0 0 0 0 0 3 3 3 0 0 0 0 5 5 5 5 5 0 0 0]`` I'm not 'in'-sane. Indeed, I am so far 'out' of sane that you appear a tiny blip on the distant coast of sanity. Bucky Katt, Get Fuzzy Da Bishop: There's a dead bishop on the landing. I don't know who keeps bringing them in here. ....but society is to blame. Reply chai0404 Programmer named Tim Posts: 12 Threads: 4 Joined: Jan 2020 Reputation: Jan-14-2020, 12:30 AM Thanks for your help! I created a function using the code you shared. However, I get the following error - TypeError: list indices must be integers or slices, not tuple ```def consecutive(masked_array): labels, _ = label(masked_array) slices = find_objects(labels) for interval in slices: xr.where(masked_array[interval].size >= 3, masked_array[interval].size, 0) return masked_array```Do you know why this may be? Reply Posts: 1,873 Threads: 8 Joined: Jun 2018 Reputation: Jan-14-2020, 09:10 AM (This post was last modified: Jan-14-2020, 09:10 AM by perfringo.) I am not consider myself as numpy person. However, (I assume that xr.where is obscure np.where) np.where returns a list of indices: ```>>> input_list = [1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1] >>> arr = np.array(input_list) >>> np.where(arr==0) (array([ 1, 2, 3, 4, 8, 9, 11, 17, 18]),) >>> arr array([1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1]) # array unchanged >>> np.where(arr[slice(5, 8, None)].size >= 3, 3, 0) array(3) # we are able to set return value if condition is met >>> arr array([1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1]) # array unchanged```You want to apply new value to slice based on slice length. So you can assign new value to a slice (like in row #7 above): ```>>> for interval in slices: ... arr[interval] = np.where(3 <= arr[interval].size, arr[interval].size, 0) ... >>> arr array([0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0])```For better readability size could be assigned to meaningful name: ```for interval in slices: interval_length = arr[interval].size arr[interval] = np.where(3 <= interval_length, interval_length, 0) ```Performance considaration aside I feel that 'pure' Python conditional expression is more understandable: ```for interval in slices: length = arr[interval].size arr[interval] = length if 3 <= length else 0```With Python 3.8 walrus operator it becomes even more concise: ```for interval in slices: arr[interval] = length if 3 <= (length := arr[interval].size) else 0``` I'm not 'in'-sane. Indeed, I am so far 'out' of sane that you appear a tiny blip on the distant coast of sanity. Bucky Katt, Get Fuzzy Da Bishop: There's a dead bishop on the landing. I don't know who keeps bringing them in here. ....but society is to blame. Reply chai0404 Programmer named Tim Posts: 12 Threads: 4 Joined: Jan 2020 Reputation: Jan-16-2020, 12:27 AM Thanks for the explanations! Reply Posts: 1,873 Threads: 8 Joined: Jun 2018 Reputation: Jan-16-2020, 07:03 AM (Jan-16-2020, 12:27 AM)chai0404 Wrote: Thanks for the explanations! You are welcome. I'm not 'in'-sane. Indeed, I am so far 'out' of sane that you appear a tiny blip on the distant coast of sanity. Bucky Katt, Get Fuzzy Da Bishop: There's a dead bishop on the landing. I don't know who keeps bringing them in here. ....but society is to blame. Reply chai0404 Programmer named Tim Posts: 12 Threads: 4 Joined: Jan 2020 Reputation: Jan-16-2020, 10:35 PM How do I make this work for a 3D array such as the one given below? input_array([[[1, 1, 0, 0, 0, 1, 1, 1, 1, 0], [1, 0, 1, 0, 0, 1, 0, 1, 1, 0], [1, 0, 1, 1, 0, 1, 0, 0, 1, 0]], [[0, 0, 0, 0, 0, 1, 0, 1, 0, 1], [0, 1, 1, 1, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 1, 1, 1, 1, 1]]]) output_array([[[1, 1, 0, 0, 0, 4, 4, 4, 4, 0], [1, 0, 1, 0, 0, 1, 0, 1, 1, 0], [1, 0, 1, 1, 0, 1, 0, 0, 1, 0]], [[0, 0, 0, 0, 0, 1, 0, 1, 0, 1], [0, 3, 3, 3, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 5, 5, 5, 5, 5]]]) I tried this, but it doesn't seem to work - ```def consec(temps): labels, _ = label(temps) # labels the occurrence of 1s and gives it an 'event' number print (label(temps)) slices = find_objects(labels) # find_objects - what does it do? print(slices) new_temps = np.zeros(len(temps)) for i in slices: if temps[i].size >= 3: new_temps[i] = new_temps[i].size else: new_temps[i] = 0 return new_temps``` ```input_array_3D=input_array eventss_3D=np.zeros([len(input_array_3D),len(input_array_3D[0]),len(input_array_3D[0][0])]) # check numpy for a more concise for i in range(len(input_array_3D[0])): for j in range(len(input_array_3D[0][0])): #### getting the timeseries of tmax at each pixel with (i,j) Coordination: input_array_1D=input_array_3D[:,i,j] ##### time series of events for each pixel at (i,j) Coordination eventss = consec(input_array_1D) #### gathering all pixels to gethere in 1 array eventss_3D[:,i,j]=eventss``` Reply Posts: 1,873 Threads: 8 Joined: Jun 2018 Reputation: Jan-17-2020, 05:57 AM Is output example correct? Previously consecutive less than 3 were set to 0, here I observe that in output there are 1 and also 1, 1. I'm not 'in'-sane. Indeed, I am so far 'out' of sane that you appear a tiny blip on the distant coast of sanity. Bucky Katt, Get Fuzzy Da Bishop: There's a dead bishop on the landing. I don't know who keeps bringing them in here. ....but society is to blame. Reply chai0404 Programmer named Tim Posts: 12 Threads: 4 Joined: Jan 2020 Reputation: Jan-19-2020, 10:05 PM Sorry, you're right. It should be: input_array([[[1, 1, 0, 0, 0, 1, 1, 1, 1, 0], [1, 0, 1, 0, 0, 1, 0, 1, 1, 0], [1, 0, 1, 1, 0, 1, 0, 0, 1, 0]], [[0, 0, 0, 0, 0, 1, 0, 1, 0, 1], [0, 1, 1, 1, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 1, 1, 1, 1, 1]]]) output_array([[[0, 0, 0, 0, 0, 4, 4, 4, 4, 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, 3, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 5]]]) Reply

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