我有一个名为sig2z的函数，我想在dask数组中应用：
def sig2z(da, zr, zi, nvar=None, dim=None, coord=None):
"""
Interpolate variables on \sigma coordinates onto z coordinates.
Parameters
----------
da : `dask.array`
The data on sigma coordinates to be interpolated
zr : `dask.array`
The depths corresponding to sigma layers
zi : `numpy.array`
The depths which to interpolate the data on
nvar : str (optional)
Name of the variable. Only necessary when the variable is
horizontal velocity.
Returns
-------
dai : `dask.array`
The data interpolated onto a spatial uniform z coordinate
"""
if np.diff(zi)[0] < 0. or zi.max() <= 0.:
raise ValueError("The values in `zi` should be postive and increasing.")
if np.any(np.absolute(zr[0]) < np.absolute(zr[-1])):
raise ValueError("`zr` should have the deepest depth at index 0.")
if zr.shape != da.shape[-3:]:
raise ValueError("`zr` should have the same "
"spatial dimensions as `da`.")
if dim == None:
dim = da.dims
if coord == None:
coord = da.coords
N = da.shape
nzi = len(zi)
if len(N) == 4:
dai = np.empty((N[0],nzi,N[-2],N[-1]))
elif len(N) == 3:
dai = np.empty((nzi,N[-2],N[-1]))
else:
raise ValueError("The data should at least have three dimensions")
dai[:] = np.nan
zi = -zi[::-1] # ROMS has deepest level at index=0
if nvar=='u': # u variables
zl = .5*(zr.shift(eta_rho=-1, xi_rho=-1)
+ zr.shift(eta_rho=-1)
)
elif nvar=='v': # v variables
zl = .5*(zr.shift(xi_rho=-1)
+ zr.shift(eta_rho=-1, xi_rho=-1)
)
else:
zl = zr
for i in range(N[-1]):
for j in range(N[-2]):
# only bother for sufficiently deep regions
if zl[:,j,i].min() < -1e2:
# only interp on z above topo
ind = np.argwhere(zi >= zl[:,j,i].copy().min())
if len(N) == 4:
for s in range(N[0]):
dai[s,:len(ind),j,i] = _interpolate(da[s,:,j,i].copy(),
zl[:,j,i].copy(),
zi[int(ind[0]):]
)
else:
dai[:len(ind),j,i] = _interpolate(da[:,j,i].copy(),
zl[:,j,i].copy(),
zi[int(ind[0]):]
)
return xr.DataArray(dai, dims=dim, coords=coord)
这在xarray.DataArray上运行正常但是当我将它应用于dask.array时，我收到以下错误：
test = dsar.map_blocks(sig2z, w[0].data,
zr.chunk({'eta_rho':1,'xi_rho':1}).data, zi,
dim, coord,
chunks=dai[0].chunks, dtype=dai.dtype
).compute()
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-29-d81bad2f4486> in <module>()
----> 1 test.compute()
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/dask/base.py in compute(self, **kwargs)
95 Extra keywords to forward to the scheduler ``get`` function.
96 """
---> 97 (result,) = compute(self, traverse=False, **kwargs)
98 return result
99
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/dask/base.py in compute(*args, **kwargs)
202 dsk = collections_to_dsk(variables, optimize_graph, **kwargs)
203 keys = [var._keys() for var in variables]
--> 204 results = get(dsk, keys, **kwargs)
205
206 results_iter = iter(results)
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/dask/threaded.py in get(dsk, result, cache, num_workers, **kwargs)
73 results = get_async(pool.apply_async, len(pool._pool), dsk, result,
74 cache=cache, get_id=_thread_get_id,
---> 75 pack_exception=pack_exception, **kwargs)
76
77 # Cleanup pools associated to dead threads
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/dask/local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)
519 _execute_task(task, data) # Re-execute locally
520 else:
--> 521 raise_exception(exc, tb)
522 res, worker_id = loads(res_info)
523 state['cache'][key] = res
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/dask/compatibility.py in reraise(exc, tb)
58 if exc.__traceback__ is not tb:
59 raise exc.with_traceback(tb)
---> 60 raise exc
61
62 else:
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
288 try:
289 task, data = loads(task_info)
--> 290 result = _execute_task(task, data)
291 id = get_id()
292 result = dumps((result, id))
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/dask/local.py in _execute_task(arg, cache, dsk)
269 func, args = arg[0], arg[1:]
270 args2 = [_execute_task(a, cache) for a in args]
--> 271 return func(*args2)
272 elif not ishashable(arg):
273 return arg
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/dask/array/core.py in getarray(a, b, lock)
63 c = a[b]
64 if type(c) != np.ndarray:
---> 65 c = np.asarray(c)
66 finally:
67 if lock:
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
529
530 """
--> 531 return array(a, dtype, copy=False, order=order)
532
533
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/xarray/core/indexing.py in __array__(self, dtype)
425
426 def __array__(self, dtype=None):
--> 427 self._ensure_cached()
428 return np.asarray(self.array, dtype=dtype)
429
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/xarray/core/indexing.py in _ensure_cached(self)
422 def _ensure_cached(self):
423 if not isinstance(self.array, np.ndarray):
--> 424 self.array = np.asarray(self.array)
425
426 def __array__(self, dtype=None):
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
529
530 """
--> 531 return array(a, dtype, copy=False, order=order)
532
533
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/xarray/core/indexing.py in __array__(self, dtype)
406
407 def __array__(self, dtype=None):
--> 408 return np.asarray(self.array, dtype=dtype)
409
410 def __getitem__(self, key):
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
529
530 """
--> 531 return array(a, dtype, copy=False, order=order)
532
533
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/xarray/core/indexing.py in __array__(self, dtype)
373 def __array__(self, dtype=None):
374 array = orthogonally_indexable(self.array)
--> 375 return np.asarray(array[self.key], dtype=None)
376
377 def __getitem__(self, key):
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
529
530 """
--> 531 return array(a, dtype, copy=False, order=order)
532
533
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/xarray/core/indexing.py in __array__(self, dtype)
373 def __array__(self, dtype=None):
374 array = orthogonally_indexable(self.array)
--> 375 return np.asarray(array[self.key], dtype=None)
376
377 def __getitem__(self, key):
/home/takaya/.conda/envs/arab/lib/python3.6/site-packages/xarray/backends/netCDF4_.py in __getitem__(self, key)
58 with self.datastore.ensure_open(autoclose=True):
59 try:
---> 60 data = getitem(self.get_array(), key)
61 except IndexError:
62 # Catch IndexError in netCDF4 and return a more informative
netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable.__getitem__ (netCDF4/_netCDF4.c:39743)()
netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable._get (netCDF4/_netCDF4.c:49835)()
RuntimeError: Resource temporarily unavailable
有人可以告诉我为什么我收到此错误？提前谢谢。
答案 0 :(得分：0)
pid号码，打开文件描述符，内存是有限的资源。
当errno.EAGAIN发生时
[EAGAIN] The system-imposed limit on the total number of processes under
execution would be exceeded. This limit is configuration-dependent.
[EAGAIN] The system-imposed limit MAXUPRC () on the total number of processes
under execution by a single user would be exceeded.
要更轻松地重现错误，您可以在程序开头添加：
import resource
resource.setrlimit(resource.RLIMIT_NPROC, (20, 20))
问题可能是所有子进程都处于活动状态，因为您没有调用p.stdin.close()，并且当重定向到管道时，gnuplot的stdin可能会被完全缓冲，即gnuplot个进程可能会卡住等待输入。和/或您的应用程序使用了太多的文件描述符（文件描述符在Python 2.7上默认由子进程继承）而不释放它们。
如果输入不依赖于输出且输入的大小有限，则使用.communicate()：
from subprocess import Popen, PIPE, STDOUT
p = Popen("gnuplot", stdin=PIPE, stdout=PIPE, stderr=PIPE,
close_fds=True, # to avoid running out of file descriptors
bufsize=-1, # fully buffered (use zero (default) if no p.communicate())
universal_newlines=True) # translate newlines, encode/decode text
out, err = p.communicate("\n".join(['set terminal gif;', contents]))
.communicate()写入所有输入并读取所有输出（同时，因此没有死锁）然后关闭p.stdin，p.stdout，p.stderr（即使输入很小而且gnuplot＆＃39; s完全缓冲; EOF刷新缓冲区）并等待进程完成（没有僵尸）。
Popen在polls exit status of all known subprocesses的构造函数中调用_cleanup()，即使您不会调用p.wait()，也不应该有很多僵尸进程（死了，但状态未见。）