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From whiteh...@apache.org
Subject [2/3] climate git commit: - Wrote DSP subset function more succintly - Update DS print statement in _validate_inputs - Update Bounds __init__ logic
Date Thu, 23 Apr 2015 20:48:58 GMT
- Wrote DSP subset function more succintly
- Update DS print statement in _validate_inputs
- Update Bounds __init__ logic


Project: http://git-wip-us.apache.org/repos/asf/climate/repo
Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/446e827b
Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/446e827b
Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/446e827b

Branch: refs/heads/master
Commit: 446e827b823e66e3d15d224a7ad60479e2d4a13d
Parents: 126631d
Author: Kim Whitehall <kim.d.whitehall@jpl.nasa.gov>
Authored: Thu Apr 23 13:10:44 2015 -0700
Committer: Kim Whitehall <kim.d.whitehall@jpl.nasa.gov>
Committed: Thu Apr 23 13:10:44 2015 -0700

----------------------------------------------------------------------
 ocw/dataset.py           | 16 ++++-----
 ocw/dataset_processor.py | 82 +++++++++++++++++++++----------------------
 2 files changed, 49 insertions(+), 49 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/climate/blob/446e827b/ocw/dataset.py
----------------------------------------------------------------------
diff --git a/ocw/dataset.py b/ocw/dataset.py
index b5358f9..d231f0e 100644
--- a/ocw/dataset.py
+++ b/ocw/dataset.py
@@ -185,7 +185,7 @@ class Dataset:
         # Finally check that the Values array conforms to the proper shape
         if value_dim == 2 and values.shape != (lat_count, lon_count):
             err_msg = """Value Array must be of shape (times, lats, lons).
-    Expected shape (%s, %s, %s) but received (%s, %s, %s)""" % (lat_count,
+    Expected shape (%s, %s) but received (%s, %s)""" % (lat_count,
                                                                 lon_count,
                                                                 values.shape[0],
                                                                 values.shape[1])
@@ -270,16 +270,16 @@ class Bounds(object):
         self._lon_min = float(lon_min)
         self._lon_max = float(lon_max)
 
-        if not start:
-            self._start = None 
-        else:
+        if start:
             self._start = start
-
-        if not end:
-            self._end = None 
         else:
-            self._end = end
+            self._start = None
 
+        if end:
+            self._end = end
+        else:
+            self._end = None
+       
     @property
     def lat_min(self):
         return self._lat_min

http://git-wip-us.apache.org/repos/asf/climate/blob/446e827b/ocw/dataset_processor.py
----------------------------------------------------------------------
diff --git a/ocw/dataset_processor.py b/ocw/dataset_processor.py
index 76da817..f600429 100644
--- a/ocw/dataset_processor.py
+++ b/ocw/dataset_processor.py
@@ -183,48 +183,48 @@ def subset(subregion, target_dataset, subregion_name=None):
     if not subregion_name:
         subregion_name = target_dataset.name
 
-    # Build new dataset with subset information
+   # Slice the values array with our calculated slice indices
     if target_dataset.values.ndim == 2:
-        return ds.Dataset(
-            # Slice the lats array with our calculated slice indices
-            target_dataset.lats[dataset_slices["lat_start"]: 
-                                dataset_slices["lat_end"] + 1],
-            # Slice the lons array with our calculated slice indices
-            target_dataset.lons[dataset_slices["lon_start"]: 
-                                dataset_slices["lon_end"] + 1],
-            # Slice the times array with our calculated slice indices
-            target_dataset.times[dataset_slices["time_start"]: 
-                                dataset_slices["time_end"]+ 1],
-            # Slice the values array with our calculated slice indices
-            target_dataset.values[
-                dataset_slices["lat_start"]:dataset_slices["lat_end"] + 1,
-                dataset_slices["lon_start"]:dataset_slices["lon_end"] + 1],
-            variable=target_dataset.variable,
-            units=target_dataset.units,
-            name=subregion_name,
-            origin=target_dataset.origin
-        )
-    if target_dataset.values.ndim == 3:
-        return ds.Dataset(
-            # Slice the lats array with our calculated slice indices
-            target_dataset.lats[dataset_slices["lat_start"]: 
-                                dataset_slices["lat_end"] + 1],
-            # Slice the lons array with our calculated slice indices
-            target_dataset.lons[dataset_slices["lon_start"]: 
-                                dataset_slices["lon_end"] + 1],
-            # Slice the times array with our calculated slice indices
-            target_dataset.times[dataset_slices["time_start"]: 
-                                dataset_slices["time_end"]+ 1],
-            # Slice the values array with our calculated slice indices
-            target_dataset.values[
-                dataset_slices["time_start"]:dataset_slices["time_end"] + 1,
-                dataset_slices["lat_start"]:dataset_slices["lat_end"] + 1,
-                dataset_slices["lon_start"]:dataset_slices["lon_end"] + 1],
-            variable=target_dataset.variable,
-            units=target_dataset.units,
-            name=subregion_name,
-            origin=target_dataset.origin
-        )
+        subset_values = ma.zeros([len(target_dataset.values[
+            dataset_slices["lat_start"]:dataset_slices["lat_end"]]), 
+            len(target_dataset.values[
+                dataset_slices["lon_start"]:dataset_slices["lon_end"]])])
+
+        subset_values = target_dataset.values[
+            dataset_slices["lat_start"]:dataset_slices["lat_end"] + 1,
+            dataset_slices["lon_start"]:dataset_slices["lon_end"] + 1]
+
+    elif target_dataset.values.ndim == 3:
+        subset_values = ma.zeros([len(target_dataset.values[
+            dataset_slices["time_start"]:dataset_slices["time_end"]]),
+            len(target_dataset.values[
+                dataset_slices["lat_start"]:dataset_slices["lat_end"]]), 
+            len(target_dataset.values[
+                dataset_slices["lon_start"]:dataset_slices["lon_end"]])])
+        
+        subset_values = target_dataset.values[
+            dataset_slices["time_start"]:dataset_slices["time_end"] + 1,
+            dataset_slices["lat_start"]:dataset_slices["lat_end"] + 1,
+            dataset_slices["lon_start"]:dataset_slices["lon_end"] + 1]
+            
+    # Build new dataset with subset information
+    return ds.Dataset(
+        # Slice the lats array with our calculated slice indices
+        target_dataset.lats[dataset_slices["lat_start"]: 
+                            dataset_slices["lat_end"] + 1],
+        # Slice the lons array with our calculated slice indices
+        target_dataset.lons[dataset_slices["lon_start"]: 
+                            dataset_slices["lon_end"] + 1],
+        # Slice the times array with our calculated slice indices
+        target_dataset.times[dataset_slices["time_start"]: 
+                            dataset_slices["time_end"]+ 1],
+        # Slice the values array with our calculated slice indices
+        subset_values,
+        variable=target_dataset.variable,
+        units=target_dataset.units,
+        name=subregion_name,
+        origin=target_dataset.origin
+    )
 
 
 def safe_subset(subregion, target_dataset, subregion_name=None):


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