Remove skips from doctests (5 of 6) (dask#7864)

dask/diagnostics/profile.py

@@ -35,8 +35,8 @@ class Profiler(Callback):
     22
 
     >>> prof.results        # doctest: +SKIP
-    [('y', (add, 'x', 10), 1435352238.48039, 1435352238.480655, 140285575100160),
-     ('z', (mul, 'y', 2), 1435352238.480657, 1435352238.480803, 140285566707456)]
+    [TaskData(key='y', task=(add, 'x', 10), start_time=..., end_time=..., worker_id=...),
+     TaskData(key='z', task=(mul, 'y', 2), start_time=..., end_time=..., worker_id=...)]
 
     These results can be visualized in a bokeh plot using the ``visualize``
     method. Note that this requires bokeh to be installed.
@@ -45,7 +45,7 @@ class Profiler(Callback):
 
     You can activate the profiler globally
 
-    >>> prof.register()     # doctest: +SKIP
+    >>> prof.register()
 
     If you use the profiler globally you will need to clear out old results
     manually.
@@ -119,7 +119,7 @@ class ResourceProfiler(Callback):
     >>> from operator import add, mul
     >>> from dask.threaded import get
     >>> dsk = {'x': 1, 'y': (add, 'x', 10), 'z': (mul, 'y', 2)}
-    >>> with ResourceProfiler() as prof:  # doctest: +SKIP
+    >>> with ResourceProfiler() as prof:
     ...     get(dsk, 'z')
     22
 
@@ -130,12 +130,12 @@ class ResourceProfiler(Callback):
 
     You can activate the profiler globally
 
-    >>> prof.register()  # doctest: +SKIP
+    >>> prof.register()
 
     If you use the profiler globally you will need to clear out old results
     manually.
 
-    >>> prof.clear()  # doctest: +SKIP
+    >>> prof.clear()
 
     Note that when used as a context manager data will be collected throughout
     the duration of the enclosed block. In contrast, when registered globally
@@ -296,17 +296,18 @@ class CacheProfiler(Callback):
     22
 
     >>> prof.results    # doctest: +SKIP
-    [CacheData('y', (add, 'x', 10), 1, 1435352238.48039, 1435352238.480655),
-     CacheData('z', (mul, 'y', 2), 1, 1435352238.480657, 1435352238.480803)]
+    [CacheData(key='y', task=(add, 'x', 10), metric=1, cache_time=..., free_time=...),
+     CacheData(key='z', task=(mul, 'y', 2), metric=1, cache_time=..., free_time=...)]
 
     The default is to count each task (``metric`` is 1 for all tasks). Other
     functions may used as a metric instead through the ``metric`` keyword. For
     example, the ``nbytes`` function found in ``cachey`` can be used to measure
     the number of bytes in the cache.
 
-    >>> from cachey import nbytes    # doctest: +SKIP
-    >>> with CacheProfiler(metric=nbytes) as prof:  # doctest: +SKIP
+    >>> from cachey import nbytes
+    >>> with CacheProfiler(metric=nbytes) as prof:
     ...     get(dsk, 'z')
+    22
 
     The profiling results can be visualized in a bokeh plot using the
     ``visualize`` method. Note that this requires bokeh to be installed.
@@ -315,7 +316,7 @@ class CacheProfiler(Callback):
 
     You can activate the profiler globally
 
-    >>> prof.register()  # doctest: +SKIP
+    >>> prof.register()
 
     If you use the profiler globally you will need to clear out old results
     manually.