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dask / dask-image / 145
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LAST BUILD BRANCH: dependabot/github_actions/actions/checkout-7
DEFAULT BRANCH: main
Ran 30 Sep 2018 07:25AM UTC
Jobs 3
Files 21
Run time 9s
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Use labeled_comprehension directly in more function in ndmeasure (#74)

* Use NumPy's `mean` with `labeled_comprehension`

When computing a label's mean, NumPy's `mean` function can be used
directly with `labeled_comprehension`. As `labeled_comprehension` is
already being used by `mean` under the hood given that is what `sum`
uses. There is no reason not to just use `labeled_comprehension`
directly in `mean` with the function that we would like to compute.
After all each call to the user provided function includes all relevant
values represented by the label. So NumPy's `mean` is accurate in this
case. Should simplify the Dask graph of `mean` and the computation time
needed. Also should benefit any other function using `mean`.

* Use NumPy's `var` with `labeled_comprehension`

When computing a label's variance, NumPy's `var` function can be used
directly with `labeled_comprehension`. As `labeled_comprehension` is
already being used by `variance` under the hood given that is what `sum`
uses. There is no reason not to just use `labeled_comprehension`
directly in `variance` with the function that we would like to compute.
After all each call to the user provided function includes all relevant
values represented by the label. So NumPy's `var` is accurate in this
case. Should simplify the Dask graph of `variance` and the computation
time needed. Also should benefit any other function using `variance`.

* Use NumPy's `std` with `labeled_comprehension`

When computing a label's standard deviation, NumPy's `std` function can
be used directly with `labeled_comprehension`. As
`labeled_comprehension` is already being used by `standard_deviation`
under the hood given that is what `var` uses. There is no reason not to
just use `labeled_comprehension` directly in `standard_deviation` with
the function that we would like to compute.  After all each call to the
user provided function includes all relevant values represented by the
lab... (continued)

216 of 216 branches covered (100.0%)

Branch coverage included in aggregate %.

728 of 728 relevant lines covered (100.0%)

2.99 hits per line

Jobs
ID Job ID Ran Files Coverage
1 145.1 (PYVER="36") 30 Sep 2018 07:25AM UTC 0
100.0
Travis Job 145.1
2 145.2 (PYVER="35") 30 Sep 2018 07:25AM UTC 0
100.0
Travis Job 145.2
3 145.3 (PYVER="27") 30 Sep 2018 07:25AM UTC 0
98.54
Travis Job 145.3
Source Files on build 145
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