dev

Author: davidhassell

cf/data/collapse/dask_collapse.py

@@ -10,7 +10,7 @@
 from operator import mul
 
 import numpy as np
-from cfdm.data.dask_utils import cfdm_asanyarray
+from cfdm.data.dask_utils import cfdm_to_memory
 from dask.array import chunk
 from dask.array.core import _concatenate2
 from dask.array.reductions import divide, numel
@@ -276,9 +276,9 @@ def cf_mean_chunk(
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
     if weights is not None:
-        weights = cfdm_asanyarray(weights)
+        weights = cfdm_to_memory(weights)
 
     # N, sum
     d = cf_sum_chunk(x, weights=weights, dtype=dtype, **kwargs)
@@ -401,7 +401,7 @@ def cf_max_chunk(x, dtype=None, computing_meta=False, **kwargs):
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
 
     return {
         "max": chunk.max(x, **kwargs),
@@ -555,7 +555,7 @@ def cf_min_chunk(x, dtype=None, computing_meta=False, **kwargs):
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
 
     return {
         "min": chunk.min(x, **kwargs),
@@ -662,7 +662,7 @@ def cf_range_chunk(x, dtype=None, computing_meta=False, **kwargs):
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
 
     # N, max
     d = cf_max_chunk(x, **kwargs)
@@ -779,7 +779,7 @@ def cf_rms_chunk(x, weights=None, dtype="f8", computing_meta=False, **kwargs):
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
 
     return cf_mean_chunk(
         np.multiply(x, x, dtype=dtype), weights=weights, dtype=dtype, **kwargs
@@ -857,7 +857,7 @@ def cf_sample_size_chunk(x, dtype="i8", computing_meta=False, **kwargs):
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
 
     if np.ma.isMA(x):
         N = chunk.sum(np.ones_like(x, dtype=dtype), **kwargs)
@@ -985,10 +985,10 @@ def cf_sum_chunk(
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
 
     if weights is not None:
-        weights = cfdm_asanyarray(weights)
+        weights = cfdm_to_memory(weights)
         if check_weights:
             w_min = weights.min()
             if w_min <= 0:
@@ -1107,9 +1107,9 @@ def cf_sum_of_weights_chunk(
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
     if weights is not None:
-        weights = cfdm_asanyarray(weights)
+        weights = cfdm_to_memory(weights)
 
     # N
     d = cf_sample_size_chunk(x, **kwargs)
@@ -1152,9 +1152,9 @@ def cf_sum_of_weights2_chunk(
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
     if weights is not None:
-        weights = cfdm_asanyarray(weights)
+        weights = cfdm_to_memory(weights)
 
     # N
     d = cf_sample_size_chunk(x, **kwargs)
@@ -1193,7 +1193,7 @@ def cf_unique_chunk(x, dtype=None, computing_meta=False, **kwargs):
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
 
     return {"unique": np.unique(x)}
 
@@ -1298,11 +1298,11 @@ def cf_var_chunk(
     if computing_meta:
         return x
 
-    x = cfdm_asanyarray(x)
+    x = cfdm_to_memory(x)
 
     weighted = weights is not None
     if weighted:
-        weights = cfdm_asanyarray(weights)
+        weights = cfdm_to_memory(weights)
 
     # N, V1, sum
     d = cf_mean_chunk(x, weights=weights, dtype=dtype, **kwargs)

cf/data/dask_regrid.py

@@ -1,7 +1,7 @@
 """Regridding functions used within a dask graph."""
 
 import numpy as np
-from cfdm.data.dask_utils import cfdm_asanyarray
+from cfdm.data.dask_utils import cfdm_to_memory
 
 
 def regrid(
@@ -175,12 +175,12 @@ def regrid(
     """
     weights, dst_mask = weights_dst_mask
 
-    a = cfdm_asanyarray(a)
+    a = cfdm_to_memory(a)
     if dst_mask is not None:
-        dst_mask = cfdm_asanyarray(dst_mask)
+        dst_mask = cfdm_to_memory(dst_mask)
 
     if ref_src_mask is not None:
-        ref_src_mask = cfdm_asanyarray(ref_src_mask)
+        ref_src_mask = cfdm_to_memory(ref_src_mask)
 
     # ----------------------------------------------------------------
     # Reshape the array into a form suitable for the regridding dot

cf/data/dask_utils.py

@@ -8,7 +8,7 @@
 from functools import partial
 
 import numpy as np
-from cfdm.data.dask_utils import cfdm_asanyarray
+from cfdm.data.dask_utils import cfdm_to_memory
 from scipy.ndimage import convolve1d
 
 from ..cfdatetime import dt, dt2rt, rt2dt
@@ -38,8 +38,8 @@ def cf_contains(a, value):
             value.
 
     """
-    a = cfdm_asanyarray(a)
-    value = cfdm_asanyarray(value)
+    a = cfdm_to_memory(a)
+    value = cfdm_to_memory(value)
     return np.array(value in a).reshape((1,) * a.ndim)
 
 
@@ -73,7 +73,7 @@ def cf_convolve1d(a, window=None, axis=-1, origin=0):
             Convolved float array with same shape as input.
 
     """
-    a = cfdm_asanyarray(a)
+    a = cfdm_to_memory(a)
 
     # Cast to float to ensure that NaNs can be stored
     if a.dtype != float:
@@ -155,7 +155,7 @@ def cf_percentile(a, q, axis, method, keepdims=False, mtol=1):
     """
     from math import prod
 
-    a = cfdm_asanyarray(a)
+    a = cfdm_to_memory(a)
 
     if np.ma.isMA(a) and not np.ma.is_masked(a):
         # Masked array with no masked elements
@@ -274,7 +274,7 @@ def cf_YMDhms(a, attr):
     array([1, 2])
 
     """
-    a = cfdm_asanyarray(a)
+    a = cfdm_to_memory(a)
     return _array_getattr(a, attr=attr)
 
 
@@ -307,7 +307,7 @@ def cf_rt2dt(a, units):
      cftime.DatetimeGregorian(2000, 1, 2, 0, 0, 0, 0, has_year_zero=False)]
 
     """
-    a = cfdm_asanyarray(a)
+    a = cfdm_to_memory(a)
 
     if not units.iscalendartime:
         return rt2dt(a, units_in=units)
@@ -363,7 +363,7 @@ def cf_dt2rt(a, units):
     [365 366]
 
     """
-    a = cfdm_asanyarray(a)
+    a = cfdm_to_memory(a)
     return dt2rt(a, units_out=units, units_in=None)
 
 
@@ -404,7 +404,7 @@ def cf_units(a, from_units, to_units):
     [1000. 2000.]
 
     """
-    a = cfdm_asanyarray(a)
+    a = cfdm_to_memory(a)
     return Units.conform(
         a, from_units=from_units, to_units=to_units, inplace=False
     )
@@ -428,7 +428,7 @@ def cf_is_masked(a):
             values.
 
     """
-    a = cfdm_asanyarray(a)
+    a = cfdm_to_memory(a)
     out = np.ma.is_masked(a)
     return np.array(out).reshape((1,) * a.ndim)
 
@@ -461,5 +461,5 @@ def cf_filled(a, fill_value=None):
     [[-999    2    3]]
 
     """
-    a = cfdm_asanyarray(a)
+    a = cfdm_to_memory(a)
     return np.ma.filled(a, fill_value=fill_value)