I've got a three dimensional array in Matlab. The first dimension is time, the second is Humidity, and the third is Temperature. If a Temperature value is < 0, I want every subsequent temperature value to be turned to NaN.

For example if the array is:

```
>> sampl = randn(4,3,2)
sampl(:,:,1) =
0.79487 0.71017 -0.39167
0.51754 -1.3068 0.84166
0.49461 0.74159 0.082784
0.66393 1.4677 0.31467
sampl(:,:,2) =
0.78981 1.3096 1.0434
-0.80122 0.16037 -1.0682
-0.32565 -2.1182 -0.31723
0.28468 0.70708 1.4797
```

What's the most efficient way to turn this into:

```
sampl(:,:,1) =
0.79487 0.71017 NaN
0.51754 NaN NaN
0.49461 NaN NaN
0.66393 NaN NaN
sampl(:,:,2) =
0.78981 1.3096 1.0434
NaN 0.16037 NaN
NaN NaN NaN
NaN NaN NaN
```

Specifically, for a particular slice, we want to process along each column, and as soon as we encounter a negative number in one column, we want that location to be `NaN`

as well as all row locations for that same column that follow this NaN value to also be `NaN`

.

Answer:

Another easy way is to find those locations that are negative in the original matrix, creating another matrix that sets those values to`NaN`

, invoke a `cumsum`

or a cumulative sum along all the rows for each column in each slice of this new matrix, then set the corresponding locations in this `cumsum`

result to `NaN`

in the original matrix to obtain the final result:

```
>> out = sampl;
>> out(out < 0) = NaN;
>> out = cumsum(out);
>> sampl(isnan(out)) = NaN
sampl(:,:,1) =
0.7949 0.7102 NaN
0.5175 NaN NaN
0.4946 NaN NaN
0.6639 NaN NaN
sampl(:,:,2) =
0.7898 1.3096 1.0434
NaN 0.1604 NaN
NaN NaN NaN
NaN NaN NaN
```

The reason why `cumsum`

is useful here is because we would essentially examine each column independently along its rows and keep accumulating over all of the rows for each column which has valid entries **until** we hit a `NaN`

value for a column. After this value, subsequent values in the `cumsum`

would become `NaN`

for each column in each slice independently. As such, after we hit the first `NaN`

in a column, no matter what values we encounter after (`NaN`

or a valid number), the result in the `cumsum`

would still be `NaN`

. This effectively propagates `NaN`

values after we encounter the first negative in a column for your matrix. The last bit is to find those locations in this matrix and set the corresponding locations in the original matrix to `NaN`

, thus giving our result.

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