Added fix for issues from xarray 0.9, which was caused by xarray no longer including default dimensions in to coordinate mappings. Updated documentation and notebooks. Modified documentation URL in setup. Dropping requirement for numpy 1.9

README.md

@@ -11,5 +11,5 @@ Installation via conda:
 
 Documentation is available at:
 
-http://wrf-python.readthedocs.io
+http://wrf-python.rtfd.org
 

conda_recipe/LICENSE

@@ -0,0 +1,32 @@
+PLEASE READ THIS SOFTWARE LICENSE ("LICENSE") CAREFULLY BEFORE USING THE 
+SOFTWARE. BY USING THE SOFTWARE, YOU ARE AGREEING TO BE BOUND BY ALL OF THE 
+TERMS OF THIS LICENSE. IF YOU DO NOT AGREE TO THE TERMS OF THIS LICENSE, 
+DO NOT USE THE SOFTWARE.
+
+Copyright © 2016 the University Corporation for Atmospheric Research ("UCAR"). 
+All rights reserved. Developed by NCAR's Computational and Information Systems 
+Laboratory, UCAR, www2.cisl.ucar.edu.
+
+Redistribution and use of the Software in source and binary forms, with or 
+without modification, is permitted provided that the following conditions 
+are met:
+
+- Neither the names of NCAR's Computational and Information Systems Laboratory, 
+  the University Corporation for Atmospheric Research, nor the names of its 
+  sponsors or contributors may be used to endorse or promote products derived 
+  from this Software without specific prior written permission.
+
+- Redistributions of source code must retain the above copyright notices, this 
+  list of conditions, and the disclaimer below.
+
+- Redistributions in binary form must reproduce the above copyright notice, this 
+  list of conditions, and the disclaimer below in the documentation and/or other 
+  materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 
+IMPLIED, INCLUDING, BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 
+CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, INDIRECT, 
+INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES OR OTHER LIABILITY, 
+WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR 
+IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE SOFTWARE.

conda_recipe/meta.yaml

@@ -12,41 +12,32 @@ source:
 build:
   number: 1
   detect_binary_files_with_prefix: true
-  skip: True  # [win32 or np>=112 or (win and py>=35)]
+  skip: True  # [win32 or (win and py>=35)]
 
 requirements:
   build:
-    - pip
+    - setuptools
     - numpy x.x
     - wrapt
     - m2w64-toolchain  # [win]
     - gcc  # [unix]
-    - libgcc  # [unix]
     - python
-    - vc 9  # [win and py27]
-    - vc 10  # [win and py34]
-    - vc 14  # [win and py>=35]
   run:
     - numpy x.x
     - python
     - wrapt 
     - m2w64-toolchain  # [win]
-    - gcc  # [unix]
+    - libgfortran  # [unix]
     - libgcc  # [unix]
-    - xarray
-    - vc 9  # [win and py27]
-    - vc 10  # [win and py34]
-    - vc 14  # [win and py>=35]
 
 test:
-  requires:
-    - nose
   imports:
     - wrf 
 
 about:
   home: https://github.com/NCAR/wrf-python
-  license: "NCL Source Code"
+  license: "UCAR"
+  license_file: '{{ environ["RECIPE_DIR"] }}/LICENSE' 
   summary: "Diagnostic and interpolation routines for WRF-ARW data."
 
 extra:

doc/source/basic_usage.rst

@@ -36,25 +36,24 @@ Result:
 .. code-block:: none
 
     <xarray.DataArray u'slp' (south_north: 1059, west_east: 1799)>
-    array([[ 1012.22033691,  1012.29815674,  1012.24786377, ...,
-         1010.13201904,  1009.93231201,  1010.06707764],
-       [ 1012.43286133,  1012.44476318,  1012.33666992, ...,
-         1010.1072998 ,  1010.10845947,  1010.04760742],
-       [ 1012.39544678,  1012.38085938,  1012.41705322, ...,
-         1010.22937012,  1010.05596924,  1010.02679443],
-       ..., 
-        [ 1009.0423584 ,  1009.06921387,  1008.98779297, ...,
-         1019.19281006,  1019.14434814,  1019.1105957 ],
-       [ 1009.22485352,  1009.07513428,  1008.98638916, ...,
-         1019.07189941,  1019.04266357,  1019.0612793 ],
-       [ 1009.18896484,  1009.1071167 ,  1008.97979736, ...,
-         1018.91778564,  1018.95684814,  1019.04748535]], dtype=float32) 
+    array([[ 1012.220337,  1012.298157,  1012.247864, ...,  1010.132019,
+             1009.932312,  1010.067078],
+           [ 1012.432861,  1012.444763,  1012.33667 , ...,  1010.1073  ,
+             1010.108459,  1010.047607],
+           [ 1012.395447,  1012.380859,  1012.417053, ...,  1010.22937 ,
+             1010.055969,  1010.026794],
+           ..., 
+           [ 1009.042358,  1009.069214,  1008.987793, ...,  1019.19281 ,
+             1019.144348,  1019.110596],
+           [ 1009.224854,  1009.075134,  1008.986389, ...,  1019.071899,
+             1019.042664,  1019.061279],
+           [ 1009.188965,  1009.107117,  1008.979797, ...,  1018.917786,
+             1018.956848,  1019.047485]], dtype=float32)
     Coordinates:
-        XLONG        (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
-        XLAT         (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
-        Time         datetime64[ns] 2016-10-07
-      * south_north  (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
-      * west_east    (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
+        XLONG    (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
+        XLAT     (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
+        Time     datetime64[ns] 2016-10-07
+    Dimensions without coordinates: south_north, west_east
     Attributes:
         FieldType: 104
         MemoryOrder: XY
@@ -62,10 +61,10 @@ Result:
         units: hPa
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
+
     
 .. _extract_ncvars:
 
@@ -94,26 +93,35 @@ Result:
 .. code-block:: none    
 
     <xarray.DataArray u'P' (bottom_top: 50, south_north: 1059, west_east: 1799)>
-    array([[[  1.21753906e+03,   1.22532031e+03,   1.22030469e+03, ...,
-           1.00760156e+03,   9.87640625e+02,   1.00111719e+03],
-        [  1.23877344e+03,   1.24004688e+03,   1.22926562e+03, ...,
-           1.00519531e+03,   1.00529688e+03,   9.99171875e+02],
-        [  1.23503906e+03,   1.23367188e+03,   1.23731250e+03, ...,
-           1.01739844e+03,   1.00005469e+03,   9.97093750e+02],
-        ..., 
-        [  1.77978516e+00,   1.77050781e+00,   1.79003906e+00, ...,
-           4.22949219e+00,   4.25659180e+00,   4.13647461e+00],
-        [  1.73291016e+00,   1.76879883e+00,   1.77978516e+00, ...,
-           4.24047852e+00,   4.24707031e+00,   4.13549805e+00],
-        [  1.71533203e+00,   1.65722656e+00,   1.67480469e+00, ...,
-           4.06884766e+00,   4.03637695e+00,   4.04785156e+00]]], dtype=float32)
+    array([[[  1.217539e+03,   1.225320e+03, ...,   9.876406e+02,   1.001117e+03],
+            [  1.238773e+03,   1.240047e+03, ...,   1.005297e+03,   9.991719e+02],
+            ..., 
+            [  9.208594e+02,   9.059141e+02, ...,   1.902922e+03,   1.904805e+03],
+            [  9.172734e+02,   9.091094e+02, ...,   1.894375e+03,   1.903422e+03]],
+    
+           [[  1.219562e+03,   1.210273e+03, ...,   9.973984e+02,   9.907891e+02],
+            [  1.224578e+03,   1.223508e+03, ...,   9.985547e+02,   9.921172e+02],
+            ..., 
+            [  9.012734e+02,   9.052031e+02, ...,   1.897766e+03,   1.894500e+03],
+            [  9.137500e+02,   9.071719e+02, ...,   1.893273e+03,   1.893664e+03]],
+    
+           ..., 
+           [[  7.233154e+00,   7.224121e+00, ...,   3.627930e+00,   3.613770e+00],
+            [  7.226318e+00,   7.358154e+00, ...,   3.725098e+00,   3.634033e+00],
+            ..., 
+            [  5.354248e+00,   5.406006e+00, ...,   1.282715e+01,   1.264844e+01],
+            [  5.295410e+00,   5.177490e+00, ...,   1.256274e+01,   1.257642e+01]],
+    
+           [[  2.362061e+00,   2.376221e+00, ...,   1.151367e+00,   1.156982e+00],
+            [  2.342529e+00,   2.403809e+00, ...,   1.198486e+00,   1.155273e+00],
+            ..., 
+            [  1.732910e+00,   1.768799e+00, ...,   4.247070e+00,   4.135498e+00],
+            [  1.715332e+00,   1.657227e+00, ...,   4.036377e+00,   4.047852e+00]]], dtype=float32)
     Coordinates:
-        XLONG        (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
-        XLAT         (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
-        Time         datetime64[ns] 2016-10-07
-      * bottom_top   (bottom_top) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
-      * south_north  (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
-      * west_east    (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
+        XLONG    (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
+        XLAT     (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
+        Time     datetime64[ns] 2016-10-07
+    Dimensions without coordinates: bottom_top, south_north, west_east
     Attributes:
         FieldType: 104
         MemoryOrder: XYZ
@@ -121,10 +129,9 @@ Result:
         units: Pa
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
            
                     
 Disabling xarray and metadata
@@ -267,10 +274,8 @@ Result:
         XLONG        (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
         XLAT         (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
       * Time         (Time) datetime64[ns] 2016-10-07 2016-10-07 2016-10-07
-      * bottom_top   (bottom_top) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
-      * south_north  (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
-      * west_east    (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
         datetime     (Time) datetime64[ns] 2016-10-07T00:00:00 ...
+    Dimensions without coordinates: bottom_top, south_north, west_east
     Attributes:
         FieldType: 104
         MemoryOrder: XYZ
@@ -278,10 +283,9 @@ Result:
         units: Pa
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
         
 
 Combining Multiple Files Using the 'join' Method
@@ -326,28 +330,19 @@ Result:
 .. code-block:: none
 
     <xarray.DataArray u'P' (file: 3, bottom_top: 50, south_north: 1059, west_east: 1799)>
-    array([[[[  1.21753906e+03,   1.22532031e+03,   1.22030469e+03, ...,
-            1.00760156e+03,   9.87640625e+02,   1.00111719e+03],
-         [  1.23877344e+03,   1.24004688e+03,   1.22926562e+03, ...,
-            1.00519531e+03,   1.00529688e+03,   9.99171875e+02],
-         [  1.23503906e+03,   1.23367188e+03,   1.23731250e+03, ...,
-            1.01739844e+03,   1.00005469e+03,   9.97093750e+02],
-         ..., 
-         [  1.77978516e+00,   1.77050781e+00,   1.79003906e+00, ...,
-            4.22949219e+00,   4.25659180e+00,   4.13647461e+00],
-         [  1.73291016e+00,   1.76879883e+00,   1.77978516e+00, ...,
-            4.24047852e+00,   4.24707031e+00,   4.13549805e+00],
-         [  1.71533203e+00,   1.65722656e+00,   1.67480469e+00, ...,
-            4.06884766e+00,   4.03637695e+00,   4.04785156e+00]]]], dtype=float32)
+    array([[[[  1.217539e+03, ...,   1.001117e+03],
+             ..., 
+             [  9.172734e+02, ...,   1.903422e+03]],
+            ..., 
+            [[  2.362061e+00, ...,   1.156982e+00],
+             ..., 
+             [  1.715332e+00, ...,   4.047852e+00]]]], dtype=float32)
     Coordinates:
-        XLONG        (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
-        XLAT         (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
-      * bottom_top   (bottom_top) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
-      * south_north  (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
-      * west_east    (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
-      * file         (file) int64 0 1 2
-        datetime     (file) datetime64[ns] 2016-10-07T00:00:00 ...
-        Time         int64 0
+        XLONG     (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
+        XLAT      (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
+      * file      (file) int64 0 1 2
+        datetime  (file) datetime64[ns] 2016-10-07 ...
+    Dimensions without coordinates: bottom_top, south_north, west_east
     Attributes:
         FieldType: 104
         MemoryOrder: XYZ
@@ -355,10 +350,9 @@ Result:
         units: Pa
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
     
                         
 Note how the 'Time' dimension was replaced with the 'file' dimension, due to the 
@@ -388,28 +382,20 @@ Result
 .. code-block:: none
 
     <xarray.DataArray u'P' (file: 3, Time: 1, bottom_top: 50, south_north: 1059, west_east: 1799)>
-    array([[[[[  1.21753906e+03,   1.22532031e+03,   1.22030469e+03, ...,
-             1.00760156e+03,   9.87640625e+02,   1.00111719e+03],
-          [  1.23877344e+03,   1.24004688e+03,   1.22926562e+03, ...,
-             1.00519531e+03,   1.00529688e+03,   9.99171875e+02],
-          [  1.23503906e+03,   1.23367188e+03,   1.23731250e+03, ...,
-             1.01739844e+03,   1.00005469e+03,   9.97093750e+02],
+    array([[[[[  1.217539e+03, ...,   1.001117e+03],
           ..., 
-          [  1.77978516e+00,   1.77050781e+00,   1.79003906e+00, ...,
-             4.22949219e+00,   4.25659180e+00,   4.13647461e+00],
-          [  1.73291016e+00,   1.76879883e+00,   1.77978516e+00, ...,
-             4.24047852e+00,   4.24707031e+00,   4.13549805e+00],
-          [  1.71533203e+00,   1.65722656e+00,   1.67480469e+00, ...,
-             4.06884766e+00,   4.03637695e+00,   4.04785156e+00]]]]], dtype=float32)
+          [  9.172734e+02, ...,   1.903422e+03]],
+
+         ..., 
+         [[  2.362061e+00, ...,   1.156982e+00],
+          ..., 
+          [  1.715332e+00, ...,   4.047852e+00]]]]], dtype=float32)
     Coordinates:
-        XLONG        (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
-        XLAT         (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
-      * bottom_top   (bottom_top) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
-      * south_north  (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
-      * west_east    (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
-      * file         (file) int64 0 1 2
-        datetime     (file, Time) datetime64[ns] 2016-10-07T00:00:00 ...
-      * Time         (Time) int64 0
+        XLONG     (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
+        XLAT      (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
+      * file      (file) int64 0 1 2
+        datetime  (file, Time) datetime64[ns] 2016-10-07 2016-10-07 2016-10-07
+    Dimensions without coordinates: Time, bottom_top, south_north, west_east
     Attributes:
         FieldType: 104
         MemoryOrder: XYZ
@@ -417,10 +403,9 @@ Result
         units: Pa
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
 
                    
 Dictionaries of WRF File Sequences
@@ -459,28 +444,21 @@ Result:
 .. code-block:: none
 
     <xarray.DataArray 'P' (key_0: 2, Time: 2, bottom_top: 50, south_north: 1059, west_east: 1799)>
-    array([[[[[  1.21753906e+03,   1.22532031e+03,   1.22030469e+03, ...,
-             1.00760156e+03,   9.87640625e+02,   1.00111719e+03],
-          [  1.23877344e+03,   1.24004688e+03,   1.22926562e+03, ...,
-             1.00519531e+03,   1.00529688e+03,   9.99171875e+02],
-          [  1.23503906e+03,   1.23367188e+03,   1.23731250e+03, ...,
-             1.01739844e+03,   1.00005469e+03,   9.97093750e+02],
-          ..., 
-          [  1.77978516e+00,   1.77050781e+00,   1.79003906e+00, ...,
-             4.22949219e+00,   4.25659180e+00,   4.13647461e+00],
-          [  1.73291016e+00,   1.76879883e+00,   1.77978516e+00, ...,
-             4.24047852e+00,   4.24707031e+00,   4.13549805e+00],
-          [  1.71533203e+00,   1.65722656e+00,   1.67480469e+00, ...,
-             4.06884766e+00,   4.03637695e+00,   4.04785156e+00]]]]], dtype=float32)
+    array([[[[[  1.217539e+03, ...,   1.001117e+03],
+              ..., 
+              [  9.172734e+02, ...,   1.903422e+03]],
+    
+             ..., 
+             [[  2.362061e+00, ...,   1.156982e+00],
+              ..., 
+              [  1.715332e+00, ...,   4.047852e+00]]]]], dtype=float32)
     Coordinates:
-        XLONG        (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
-        XLAT         (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
-      * Time         (Time) datetime64[ns] 2016-10-07T00:00:00 ...
-      * bottom_top   (bottom_top) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
-      * south_north  (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
-      * west_east    (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
-        datetime     (Time) datetime64[ns] 2016-10-07T00:00:00 ...
-      * key_0        (key_0) <U6 u'ens1' u'ens2'
+        XLONG     (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
+        XLAT      (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
+      * Time      (Time) datetime64[ns] 2016-10-07 ...
+        datetime  (Time) datetime64[ns] 2016-10-07 ...
+      * key_0     (key_0) <U6 u'label1' u'label2'
+    Dimensions without coordinates: bottom_top, south_north, west_east
     Attributes:
         FieldType: 104
         MemoryOrder: XYZ
@@ -488,10 +466,9 @@ Result:
         units: Pa
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
                         
 Interpolation Routines
 --------------------------
@@ -542,17 +519,15 @@ Result:
         XLONG        (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
         XLAT         (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
         Time         datetime64[ns] 2016-10-07
-      * south_north  (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
-      * west_east    (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
+    Dimensions without coordinates: south_north, west_east
     Attributes:
         FieldType: 104
         units: m
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
         level: 500 hPa
         missing_value: 9.96920996839e+36
         _FillValue: 9.96920996839e+36
@@ -623,17 +598,16 @@ Result:
         Time      datetime64[ns] 2016-10-07
         xy_loc    (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
       * vertical  (vertical) float32 0.0 261.828 523.656 785.484 1047.31 1309.14 ...
-      * idx       (idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
+    Dimensions without coordinates: idx
     Attributes:
         FieldType: 104
         description: pressure
         units: hPa
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
         orientation: (0.0, 529.0) to (1797.0, 529.0)
         missing_value: 9.96920996839e+36
         _FillValue: 9.96920996839e+36
@@ -689,17 +663,16 @@ Result:
         Time      datetime64[ns] 2016-10-07
         xy_loc    (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
       * vertical  (vertical) float32 0.0 261.828 523.656 785.484 1047.31 1309.14 ...
-      * idx       (idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
+    Dimensions without coordinates: idx
     Attributes:
         FieldType: 104
         description: pressure
         units: hPa
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
         orientation: (0.0, 529.0) to (1797.0, 529.0) ; center=CoordPair(x=899.0, y=529.0) ; angle=90.0
         missing_value: 9.96920996839e+36
         _FillValue: 9.96920996839e+36
@@ -760,17 +733,16 @@ Result:
         Time      datetime64[ns] 2016-10-07
         xy_loc    (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
       * vertical  (vertical) float32 0.0 261.828 523.656 785.484 1047.31 1309.14 ...
-      * idx       (idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
+    Dimensions without coordinates: idx
     Attributes:
         FieldType: 104
         description: pressure
         units: hPa
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
         orientation: (0.0, 529.0) to (1797.0, 529.0)
         missing_value: 9.96920996839e+36
         _FillValue: 9.96920996839e+36
@@ -827,17 +799,16 @@ Result:
         Time      datetime64[ns] 2016-10-07
         xy_loc    (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
       * vertical  (vertical) float32 1000.0 2000.0 3000.0
-      * idx       (idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
+    Dimensions without coordinates: idx
     Attributes:
         FieldType: 104
         description: pressure
         units: hPa
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
         orientation: (0.0, 529.0) to (1797.0, 529.0)
         missing_value: 9.96920996839e+36
         _FillValue: 9.96920996839e+36
@@ -893,17 +864,16 @@ Result:
     Coordinates:
         Time      datetime64[ns] 2016-10-07
         xy_loc    (line_idx) object CoordPair(x=899.0, y=0.0, lat=24.3645858765, lon=-97.5) ...
-      * line_idx  (line_idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
+    Dimensions without coordinates: line_idx
     Attributes:
         FieldType: 104
         description: TEMP at 2 M
         units: K
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
         orientation: (899.0, 0.0) to (899.0, 1057.0) 
 
 
@@ -943,17 +913,16 @@ Result:
     Coordinates:
         Time      datetime64[ns] 2016-10-07
         xy_loc    (line_idx) object CoordPair(x=899.0, y=0.0, lat=24.3645858765, lon=-97.5) ...
-      * line_idx  (line_idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
+    Dimensions without coordinates: line_idx
     Attributes:
         FieldType: 104
         description: TEMP at 2 M
         units: K
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
         orientation: (899.0, 0.0) to (899.0, 1057.0) ; center=CoordPair(x=899, y=529) ; angle=0.0 
 
         
@@ -1001,17 +970,16 @@ Result:
     Coordinates:
         Time      datetime64[ns] 2016-10-07
         xy_loc    (line_idx) object CoordPair(x=899.0, y=0.0, lat=24.3645858765, lon=-97.5) ...
-      * line_idx  (line_idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
+    Dimensions without coordinates: line_idx
     Attributes:
         FieldType: 104
         description: TEMP at 2 M
         units: K
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
         orientation: (899.0, 0.0) to (899.0, 1057.0)    
             
 
@@ -1067,9 +1035,8 @@ Result:
         XLONG         (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
         XLAT          (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
         Time          datetime64[ns] 2016-10-07
-      * south_north   (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
-      * west_east     (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
       * interp_level  (interp_level) int64 200 300 500 1000
+    Dimensions without coordinates: south_north, west_east
     Attributes:
         FieldType: 104
         MemoryOrder: XYZ
@@ -1077,10 +1044,9 @@ Result:
         units: K
         stagger: 
         coordinates: XLONG XLAT
-        projection: LambertConformal(bottom_left=(21.138123, -122.71953), 
-                    top_right=(47.843636, -60.901367), stand_lon=-97.5, 
-                    moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5, 
-                    pole_lat=90.0, pole_lon=0.0)
+        projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147, 
+                                     truelat1=38.5, truelat2=38.5, pole_lat=90.0, 
+                                     pole_lon=0.0)
         vert_interp_type: eth
 
             
@@ -1122,7 +1088,7 @@ Result:
     Coordinates:
       * lat_lon   (lat_lon) <U3 u'lat' u'lon'
         xy_coord  object CoordPair(x=400, y=200)
-        idx       int64 0
+    Dimensions without coordinates: idx
         
         
     <xarray.DataArray u'xy' (x_y: 2)>
@@ -1130,7 +1096,7 @@ Result:
     Coordinates:
         latlon_coord  object CoordPair(lat=28.5581640822, lon=-112.678276173)
       * x_y           (x_y) <U1 u'x' u'y'
-        idx           int64 0
+    Dimensions without coordinates: idx
     
     
 Example With Multiple Coordinates
@@ -1163,7 +1129,7 @@ Result:
     Coordinates:
       * lat_lon   (lat_lon) <U3 u'lat' u'lon'
         xy_coord  (idx) object CoordPair(x=400, y=200) CoordPair(x=105, y=205)
-      * idx       (idx) int64 0 1
+    Dimensions without coordinates: idx
         
         
     <xarray.DataArray u'xy' (x_y: 2, idx: 2)>
@@ -1172,7 +1138,7 @@ Result:
     Coordinates:
         latlon_coord  (idx) object CoordPair(lat=28.5581640822, lon=-112.678276173) ...
       * x_y           (x_y) <U1 u'x' u'y'
-      * idx           (idx) int64 0 1
+    Dimensions without coordinates: idx
 
 
 Mapping Helper Routines

doc/source/index.rst

@@ -3,15 +3,28 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
+.. meta::
+   :description: Diagnostic and interpolation routines for WRF ARW data
+   :keywords: wrf, python, wrf-python, weather research and forecasting,
+              weather research and forecasting model, model, weather,
+              numerical weather prediction, model, matplotlib, cartopy,
+              wrf-arw, arw, ncar, ucar, cisl, ncl, ncar command language,
+              national center for atmospheric research, 
+              university corporation for atmospheric research,
+              pynio, pyngl, interpolation
 
 wrf-python
 ===========
 
+A collection of diagnostic and interpolation routines for use with output from 
+the Weather Research and Forecasting (WRF-ARW) Model.
 
-wrf-python is a package to help users working with the Weather 
-Research and Forecasting Model (WRF-ARW) output.  wrf-python includes over 
-thirty diagnostics calculations along with various interpolation routines. When
-coupled with either matplotlib or PyNGL, users can create plots like this:
+This package provides over 30 diagnostic calculations, 
+several interpolation routines, and utilities to help with plotting 
+via cartopy, basemap, or PyNGL. The functionality is similar to what is 
+provided by the NCL WRF package. 
+
+When coupled with either matplotlib or PyNGL, users can create plots like this:
 
 .. image:: _static/images/matthew.png    
    :scale: 100%
@@ -33,7 +46,6 @@ Documentation
    ./license
 
 
-
 Indices and tables
 ==================
 

doc/source/new.rst

@@ -30,3 +30,14 @@ v1.0b1
   to find a workaround before the next release. Windows users should use 
   Python 2.7 or Python 3.4 for now.
   
+  
+v1.0b2
+----------
+
+- Beta release 2.
+- xarray 0.9 no longer includes default index dimensions in the coordinate 
+  mappings.  This was causing a crash in the routines that cause a reduction
+  in dimension shape, mainly the interpolation routines.  This has been 
+  fixed.
+- Documentation updated to show the new output from xarray.
+

doc/source/plot.rst

@@ -40,9 +40,9 @@ Plotting a Two-dimensional Field
     import cartopy.crs as crs
     from cartopy.feature import NaturalEarthFeature
     
-    from wrf import (to_np, getvar, smooth2d, get_cartopy, latlon_coords,
-                    cartopy_xlim, cartopy_ylim)
+    from wrf import to_np, getvar, smooth2d, get_cartopy, cartopy_xlim, cartopy_ylim
     
+    # Open the NetCDF file
     ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
     
     # Get the sea level pressure
@@ -51,16 +51,15 @@ Plotting a Two-dimensional Field
     # Smooth the sea level pressure since it tends to be noisy near the mountains
     smooth_slp = smooth2d(slp, 3)
     
-    # Get the latitude and longitude coordinate arrays
-    lats, lons = latlon_coords(smooth_slp)
-    
-    # The WrfProj.cartopy() method returns a cartopy.crs projection object
-    cart_proj = get_cartopy(smooth_slp)
+    # Get the latitude and longitude points
+    lats, lons = latlon_coords(slp)
     
-    # Create a figure that's 10x10
-    fig = plt.figure(figsize=(10,10))
+    # Get the cartopy mapping object
+    cart_proj = get_cartopy(slp)
     
-    # Get the GeoAxes set to the projection used by WRF
+    # Create a figure
+    fig = plt.figure(figsize=(12,9))
+    # Set the GeoAxes to the projection used by WRF
     ax = plt.axes(projection=cart_proj)
     
     # Download and add the states and coastlines
@@ -70,19 +69,18 @@ Plotting a Two-dimensional Field
     ax.coastlines('50m', linewidth=0.8)
     
     # Make the contour outlines and filled contours for the smoothed sea level pressure.
-    plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp), 10, colors="black", 
-                transform=crs.PlateCarree())
-    plt.contourf(to_np(lons), to_np(lats), to_np(smooth_slp), 10, transform=crs.PlateCarree())
+    plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp), 10, colors="black", transform=crs.PlateCarree())
+    plt.contourf(to_np(lons), to_np(lats), to_np(smooth_slp), 10, transform=crs.PlateCarree(), cmap=get_cmap("jet"))
     
     # Add a color bar
-    plt.colorbar(ax=ax, shrink=.47)
+    plt.colorbar(ax=ax, shrink=.62)
     
-    # Set the map limits
+    # Set the map limits.  Not really necessary, but used for demonstration.
     ax.set_xlim(cartopy_xlim(smooth_slp))
     ax.set_ylim(cartopy_ylim(smooth_slp))
     
     # Add the gridlines
-    ax.gridlines()
+    ax.gridlines(color="black", linestyle="dotted")
 
     plt.title("Sea Level Pressure (hPa)")
 
@@ -104,9 +102,9 @@ Horizontal Interpolation to a Pressure Level
     import cartopy.crs as crs
     from cartopy.feature import NaturalEarthFeature
     
-    from wrf import (getvar, interplevel, to_np, get_cartopy, 
-                    latlon_coords, cartopy_xlim, cartopy_ylim)
+    from wrf import getvar, interplevel, to_np, latlon_coords, get_cartopy, cartopy_xlim, cartopy_ylim
     
+    # Open the NetCDF file
     ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
     
     # Extract the pressure, geopotential height, and wind variables
@@ -125,12 +123,12 @@ Horizontal Interpolation to a Pressure Level
     # Get the lat/lon coordinates
     lats, lons = latlon_coords(ht_500)
     
-    # Get the cartopy map projection information
+    # Get the map projection information
     cart_proj = get_cartopy(ht_500)
     
     # Create the figure
-    fig = plt.figure(figsize=(10,10))
-    ax = plt.axes([0.1,0.1,0.8,0.8], projection=cart_proj)
+    fig = plt.figure(figsize=(12,9))
+    ax = plt.axes(projection=cart_proj)
     
     # Download and add the states and coastlines
     states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
@@ -138,13 +136,13 @@ Horizontal Interpolation to a Pressure Level
     ax.add_feature(states, linewidth=0.5)
     ax.coastlines('50m', linewidth=0.8)
     
-    # Create the 500 hPa geopotential height contours
+    # Add the 500 hPa geopotential height contours
     levels = np.arange(520., 580., 6.)
     contours = plt.contour(to_np(lons), to_np(lats), to_np(ht_500), levels=levels, colors="black", 
                 transform=crs.PlateCarree())
     plt.clabel(contours, inline=1, fontsize=10, fmt="%i")
     
-    # Wind Speed contours
+    # Add the wind speed contours
     levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120]
     wspd_contours = plt.contourf(to_np(lons), to_np(lats), to_np(wspd_500), levels=levels,
                                  cmap=get_cmap("rainbow"), 
@@ -186,19 +184,18 @@ plot, see :ref:`cross_example`.
     import cartopy.feature as cfeature
     from netCDF4 import Dataset
     
-    from wrf import (getvar, to_np, vertcross, smooth2d, CoordPair, GeoBounds, 
-                    latlon_coords, get_cartopy, cartopy_xlim, cartopy_ylim)
+    from wrf import (getvar, to_np, vertcross, smooth2d, CoordPair, GeoBounds, get_cartopy, 
+                     latlon_coords, cartopy_xlim, cartopy_ylim)
     
-    # Open the output NetCDF file
-    filename = "wrfout_d01_2016-10-07_00_00_00"
-    ncfile = DataSet(filename)
+    # Open the NetCDF file
+    ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
     
     # Get the WRF variables
     slp = getvar(ncfile, "slp")
     smooth_slp = smooth2d(slp, 3)
     ctt = getvar(ncfile, "ctt")
-    height = getvar(ncfile, "z")
-    dbz = getvar(ncfile, "dbz")
+    z = getvar(ncfile, "z", timeidx=0)
+    dbz = getvar(ncfile, "dbz", timeidx=0)
     Z = 10**(dbz/10.)
     wspd =  getvar(ncfile, "wspd_wdir", units="kt")[0,:]
     
@@ -206,53 +203,47 @@ plot, see :ref:`cross_example`.
     start_point = CoordPair(lat=26.76, lon=-80.0)
     end_point = CoordPair(lat=26.76, lon=-77.8)
     
-    # Compute the vertical cross-section interpolation.  Also, include the 
-    # lat/lon points along the cross-section in the metadata by setting 
-    # latlon to True.
-    z_cross = vertcross(Z, height, wrfin=ncfile, start_point=start_point, 
-                        end_point=end_point, latlon=True, meta=True)
-    wspd_cross = vertcross(wspd, height, wrfin=ncfile, start_point=start_point, 
-                           end_point=end_point, latlon=True, meta=True)
+    # Compute the vertical cross-section interpolation.  Also, include the lat/lon points along the cross-section 
+    # in the metadata by setting latlon to True.
+    z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
+    wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
     dbz_cross = 10.0 * np.log10(z_cross)
     
-    # Get the latitude and longitude coordinate arrays
+    # Get the lat/lon points
     lats, lons = latlon_coords(slp)
     
     # Get the cartopy projection object
     cart_proj = get_cartopy(slp)
     
-    # Create the figure which will have 3 subplots, one on the left, and 
-    # two vertically stacked on the right.
-    fig = plt.figure(figsize=(7,5))
+    # Create a figure that will have 3 subplots
+    fig = plt.figure(figsize=(10,7))
     ax_ctt = fig.add_subplot(1,2,1,projection=cart_proj)
     ax_wspd = fig.add_subplot(2,2,2)
     ax_dbz = fig.add_subplot(2,2,4)
     
     # Download and create the states, land, and oceans using cartopy features
     states = cfeature.NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
-                                          name='admin_1_states_provinces_shp')
+                                 name='admin_1_states_provinces_shp')
     land = cfeature.NaturalEarthFeature(category='physical', name='land', scale='50m', 
                                         facecolor=cfeature.COLORS['land'])
     ocean = cfeature.NaturalEarthFeature(category='physical', name='ocean', scale='50m', 
                                          facecolor=cfeature.COLORS['water'])
     
-    # Make the ctt contours.
+    # Make the pressure contours
     contour_levels = [960, 965, 970, 975, 980, 990]
-    c1 = ax_ctt.contour(to_np(lons), to_np(lats), to_np(smooth_slp), levels=contour_levels, 
-                colors="white", transform=crs.PlateCarree(), zorder=3, linewidths=1.0)
+    c1 = ax_ctt.contour(lons, lats, to_np(smooth_slp), levels=contour_levels, colors="white", 
+                transform=crs.PlateCarree(), zorder=3, linewidths=1.0)
     
     # Create the filled cloud top temperature contours
     contour_levels = [-80.0, -70.0, -60, -50, -40, -30, -20, -10, 0, 10]
-    ctt_contours = ax_ctt.contourf(lons, lats, to_np(ctt), contour_levels, 
-                                   cmap=get_cmap("Greys"), transform=crs.PlateCarree(), 
-                                   zorder=2)
+    ctt_contours = ax_ctt.contourf(to_np(lons), to_np(lats), to_np(ctt), contour_levels, cmap=get_cmap("Greys"),
+                 transform=crs.PlateCarree(), zorder=2)
     
-    # Draw the yellow cross section line
-    ax_ctt.plot([start_point.lon, end_point.lon], [start_point.lat, end_point.lat], 
-                color="yellow", marker="o", transform=crs.PlateCarree(), zorder=3)
+    ax_ctt.plot([start_point.lon, end_point.lon], [start_point.lat, end_point.lat], color="yellow", 
+                marker="o", transform=crs.PlateCarree(), zorder=3)
     
-    # Create the label bar for cloud top temperature
-    cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.5)
+    # Create the color bar for cloud top temperature
+    cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.60)
     cb_ctt.ax.tick_params(labelsize=5)
     
     # Draw the oceans, land, and states
@@ -265,21 +256,21 @@ plot, see :ref:`cross_example`.
                            CoordPair(lat=30.0, lon=-72.0))
     ax_ctt.set_xlim(cartopy_xlim(ctt, geobounds=hur_bounds))
     ax_ctt.set_ylim(cartopy_ylim(ctt, geobounds=hur_bounds))
-    ax_ctt.gridlines()
+    ax_ctt.gridlines(color="white", linestyle="dotted")
     
-    # Make the contour plot for wspd
-    wspd_contours = ax_wspd.contourf(to_np(wspd_cross))
+    # Make the contour plot for wind speed
+    wspd_contours = ax_wspd.contourf(to_np(wspd_cross), cmap=get_cmap("jet"))
     # Add the color bar
     cb_wspd = fig.colorbar(wspd_contours, ax=ax_wspd)
     cb_wspd.ax.tick_params(labelsize=5)
     
     # Make the contour plot for dbz
     levels = [5 + 5*n for n in range(15)]
-    dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels)
+    dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels, cmap=get_cmap("jet"))
     cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz)
     cb_dbz.ax.tick_params(labelsize=5)
     
-    # Set the x-ticks to use latitude and longitude labels.
+    # Set the x-ticks to use latitude and longitude labels
     coord_pairs = to_np(dbz_cross.coords["xy_loc"])
     x_ticks = np.arange(coord_pairs.shape[0])
     x_labels = [pair.latlon_str() for pair in to_np(coord_pairs)]
@@ -288,7 +279,7 @@ plot, see :ref:`cross_example`.
     ax_dbz.set_xticks(x_ticks[::20])
     ax_dbz.set_xticklabels(x_labels[::20], rotation=45, fontsize=4) 
     
-    # Set the y-ticks to be height.
+    # Set the y-ticks to be height
     vert_vals = to_np(dbz_cross.coords["vertical"])
     v_ticks = np.arange(vert_vals.shape[0])
     ax_wspd.set_yticks(v_ticks[::20])
@@ -301,7 +292,7 @@ plot, see :ref:`cross_example`.
     ax_wspd.set_ylabel("Height (m)", fontsize=5)
     ax_dbz.set_ylabel("Height (m)", fontsize=5)
     
-    # Add titles
+    # Add a title
     ax_ctt.set_title("Cloud Top Temperature (degC)", {"fontsize" : 7})
     ax_wspd.set_title("Cross-Section of Wind Speed (kt)", {"fontsize" : 7})
     ax_dbz.set_title("Cross-Section of Reflectivity (dBZ)", {"fontsize" : 7})
@@ -333,25 +324,27 @@ Plotting a Two-Dimensional Field
     from matplotlib.cm import get_cmap
     from mpl_toolkits.basemap import Basemap
     
-    from wrf import to_np, getvar, smooth2d, latlon_coords, get_basemap
+    from wrf import to_np, getvar, smooth2d, get_basemap, latlon_coords
     
+    # Open the NetCDF file
     ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
     
     # Get the sea level pressure
     slp = getvar(ncfile, "slp")
     
-    # Smooth the sea level pressure since it tends to be noisey near the mountains
+    # Smooth the sea level pressure since it tends to be noisy near the mountains
     smooth_slp = smooth2d(slp, 3)
     
-    # Get the latitude and longitude coordinates
+    # Get the latitude and longitude points
     lats, lons = latlon_coords(slp)
     
-    # Get the basemap projection object
+    # Get the basemap object
     bm = get_basemap(slp)
     
-    # Create a figure that's 10x10
-    fig = plt.figure(figsize=(10,10))
+    # Create a figure
+    fig = plt.figure(figsize=(12,9))
     
+    # Add geographic outlines
     bm.drawcoastlines(linewidth=0.25)
     bm.drawstates(linewidth=0.25)
     bm.drawcountries(linewidth=0.25)
@@ -362,12 +355,13 @@ Plotting a Two-Dimensional Field
     
     # Draw the contours and filled contours
     bm.contour(x, y, to_np(smooth_slp), 10, colors="black")
-    bm.contourf(x, y, to_np(smooth_slp), 10)
+    bm.contourf(x, y, to_np(smooth_slp), 10, cmap=get_cmap("jet"))
     
     # Add a color bar
-    plt.colorbar(shrink=.47)
+    plt.colorbar(shrink=.62)
     
     plt.title("Sea Level Pressure (hPa)")
+    
     plt.show()
     
 
@@ -387,6 +381,7 @@ Horizontal Interpolation to a Pressure Level
     
     from wrf import getvar, interplevel, to_np, get_basemap, latlon_coords
     
+    # Open the NetCDF file
     ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
     
     # Extract the pressure, geopotential height, and wind variables
@@ -405,27 +400,28 @@ Horizontal Interpolation to a Pressure Level
     # Get the lat/lon coordinates
     lats, lons = latlon_coords(ht_500)
     
-    # Get the basemap projection object
+    # Get the basemap object
     bm = get_basemap(ht_500)
     
     # Create the figure
-    fig = plt.figure(figsize=(8,8))
-    ax = plt.axes([0.1,0.1,0.8,0.8])
+    fig = plt.figure(figsize=(12,9))
+    ax = plt.axes()
     
-    # Get the x and y coordinates
+    # Convert the lat/lon coordinates to x/y coordinates in the projection space
     x, y = bm(to_np(lons), to_np(lats))
     
-    # Create the 500 hPa geopotential height contours
+    # Add the 500 hPa geopotential height contours
     levels = np.arange(520., 580., 6.)
     contours = bm.contour(x, y, to_np(ht_500), levels=levels, colors="black")
     plt.clabel(contours, inline=1, fontsize=10, fmt="%i")
     
-    # Wind Speed contours
+    # Add the wind speed contours
     levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120]
     wspd_contours = bm.contourf(x, y, to_np(wspd_500), levels=levels,
                                  cmap=get_cmap("rainbow"))
     plt.colorbar(wspd_contours, ax=ax, orientation="horizontal", pad=.05)
     
+    # Add the geographic boundaries
     bm.drawcoastlines(linewidth=0.25)
     bm.drawstates(linewidth=0.25)
     bm.drawcountries(linewidth=0.25)
@@ -438,6 +434,7 @@ Horizontal Interpolation to a Pressure Level
     
     plt.show()
     
+    
 Panel Plots from the Front Page
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -458,9 +455,8 @@ plot, see :ref:`cross_example`.
     
     from wrf import getvar, to_np, vertcross, smooth2d, CoordPair, get_basemap, latlon_coords
     
-    # Open the output NetCDF file
-    filename = "/Users/ladwig/Documents/wrf_files/wrfout_d01_2016-10-07_00_00_00"
-    ncfile = Dataset(filename)
+    # Open the NetCDF file
+    ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
     
     # Get the WRF variables
     slp = getvar(ncfile, "slp")
@@ -475,29 +471,28 @@ plot, see :ref:`cross_example`.
     start_point = CoordPair(lat=26.76, lon=-80.0)
     end_point = CoordPair(lat=26.76, lon=-77.8)
     
-    # Compute the vertical cross-section interpolations.  Also, include the lat/lon points along the cross-section 
-    # in the metadata by setting latlon to True.
+    # Compute the vertical cross-section interpolation.  Also, include the lat/lon points along the cross-section in 
+    # the metadata by setting latlon to True.
     z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
     wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
     dbz_cross = 10.0 * np.log10(z_cross)
     
-    # Extract the latitude and longitude coordinate arrays
+    # Get the latitude and longitude points
     lats, lons = latlon_coords(slp)
     
-    # Create a figure that will have 3 subplots.  One on the left, 
-    # two vertically stacked on the right.
-    fig = plt.figure(figsize=(8,5))
+    # Create the figure that will have 3 subplots
+    fig = plt.figure(figsize=(10,7))
     ax_ctt = fig.add_subplot(1,2,1)
     ax_wspd = fig.add_subplot(2,2,2)
     ax_dbz = fig.add_subplot(2,2,4)
     
-    # Get the basemap projection class
+    # Get the basemap object
     bm = get_basemap(slp)
     
-    # Get the x, y values
+    # Convert the lat/lon points in to x/y points in the projection space
     x, y = bm(to_np(lons), to_np(lats))
     
-    # Make the pressure contours.
+    # Make the pressure contours
     contour_levels = [960, 965, 970, 975, 980, 990]
     c1 = bm.contour(x, y, to_np(smooth_slp), levels=contour_levels, colors="white", 
                     zorder=3, linewidths=1.0, ax=ax_ctt)
@@ -511,12 +506,11 @@ plot, see :ref:`cross_example`.
     bm.plot([point_x[0], point_x[1]], [point_y[0], point_y[1]], color="yellow", 
                 marker="o", zorder=3, ax=ax_ctt)
     
-    # Create the label bar for cloud top temperature
-    cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.68)
+    # Create the color bar for cloud top temperature
+    cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.60)
     cb_ctt.ax.tick_params(labelsize=5)
     
-    # Draw the oceans, land, and states.  Use the same colors as the cartopy
-    # example
+    # Draw the oceans, land, and states
     bm.drawcoastlines(linewidth=0.25, ax=ax_ctt)
     bm.drawstates(linewidth=0.25, ax=ax_ctt)
     bm.drawcountries(linewidth=0.25, ax=ax_ctt)
@@ -526,13 +520,12 @@ plot, see :ref:`cross_example`.
     
     # Draw Parallels
     parallels = np.arange(np.amin(lats), 30., 2.5)
-    bm.drawparallels(parallels, ax=ax_ctt)
+    bm.drawparallels(parallels, ax=ax_ctt, color="white")
     
     merids = np.arange(-85.0, -72.0, 2.5)
-    bm.drawmeridians(merids, ax=ax_ctt)
+    bm.drawmeridians(merids, ax=ax_ctt, color="white")
     
-    # Get the x and y coordinate ranges for the cropped region around the 
-    # hurricane
+    # Crop the image to the hurricane region
     x_start, y_start = bm(-85.0, np.amin(lats))
     x_end, y_end = bm(-72.0, 30.0)
     
@@ -540,14 +533,14 @@ plot, see :ref:`cross_example`.
     ax_ctt.set_ylim([y_start, y_end])
     
     # Make the contour plot for wspd
-    wspd_contours = ax_wspd.contourf(to_np(wspd_cross))
+    wspd_contours = ax_wspd.contourf(to_np(wspd_cross), cmap=get_cmap("jet"))
     # Add the color bar
     cb_wspd = fig.colorbar(wspd_contours, ax=ax_wspd)
     cb_wspd.ax.tick_params(labelsize=5)
     
     # Make the contour plot for dbz
     levels = [5 + 5*n for n in range(15)]
-    dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels)
+    dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels, cmap=get_cmap("jet"))
     cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz)
     cb_dbz.ax.tick_params(labelsize=5)
     
@@ -573,7 +566,7 @@ plot, see :ref:`cross_example`.
     ax_wspd.set_ylabel("Height (m)", fontsize=5)
     ax_dbz.set_ylabel("Height (m)", fontsize=5)
     
-    # Add a title
+    # Add titles
     ax_ctt.set_title("Cloud Top Temperature (degC)", {"fontsize" : 7})
     ax_wspd.set_title("Cross-Section of Wind Speed (kt)", {"fontsize" : 7})
     ax_dbz.set_title("Cross-Section of Reflectivity (dBZ)", {"fontsize" : 7})
@@ -598,34 +591,34 @@ plotted using the standard matplotlib API.
     import numpy as np
     import matplotlib.pyplot as plt
     from matplotlib.cm import get_cmap
+    import cartopy.crs as crs
+    from cartopy.feature import NaturalEarthFeature
     from netCDF4 import Dataset
     
     from wrf import to_np, getvar, CoordPair, vertcross
     
-    # Open the output NetCDF file with PyNIO
+    # Open the NetCDF file
     filename = "wrfout_d01_2016-10-07_00_00_00"
     ncfile = Dataset(filename)
     
-    # Extract pressure and model height
+    # Extract the model height and wind speed
     z = getvar(ncfile, "z")
     wspd =  getvar(ncfile, "uvmet_wspd_wdir", units="kt")[0,:]
     
-    # Define the start point and end point for the cross section using 
-    # latitude and longitude coordinates.
+    # Create the start point and end point for the cross section
     start_point = CoordPair(lat=26.76, lon=-80.0)
     end_point = CoordPair(lat=26.76, lon=-77.8)
     
-    # Compute the vertical cross-section interpolation.  Also, include the lat/lon 
-    # points along the cross-section in the metadata by setting latlon to True.
-    wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, 
-                           end_point=end_point, latlon=True, meta=True)
+    # Compute the vertical cross-section interpolation.  Also, include the lat/lon points along the cross-section.
+    wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
     
     # Create the figure
-    fig = plt.figure(figsize=(10,5))
-    ax = plt.axes([0.1,0.1,0.8,0.8])
+    fig = plt.figure(figsize=(12,6))
+    ax = plt.axes()
     
     # Make the contour plot
-    wspd_contours = ax.contourf(to_np(wspd_cross))
+    wspd_contours = ax.contourf(to_np(wspd_cross), cmap=get_cmap("jet"))
+    
     # Add the color bar
     plt.colorbar(wspd_contours, ax=ax)
     

requirements.txt

@@ -1,2 +1,2 @@
-numpy>=1.9.0
+numpy>=1.10
 wrapt>=1.10

setup.py

@@ -55,7 +55,7 @@ numpy.distutils.core.setup(
                         "GitHub Repository:\n\n"
                         "https://github.com/NCAR/wrf-python\n\n"
                         "Documentation:\n\n"
-                        "https://wrf-python.readthedocs.io\n"),
+                        "https://wrf-python.rtfd.org\n"),
     url = "https://github.com/NCAR/wrf-python",
     keywords = ["python", "wrf-python", "wrf", "forecast", "model",
                 "weather research and forecasting", "interpolation", 

src/wrf/version.py

@@ -1,2 +1,2 @@
-__version__ = "1.0b1"
+__version__ = "1.0b2"
 

test/ipynb/SLS_Example.ipynb

@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from __future__ import (absolute_import, division, print_function, unicode_literals)\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.cm import get_cmap\n",
+    "import cartopy.crs as crs\n",
+    "import cartopy.feature as cfeature\n",
+    "from netCDF4 import Dataset as nc\n",
+    "\n",
+    "from wrf import to_np, getvar, smooth2d\n",
+    "\n",
+    "# Open the output NetCDF with netcdf-python\n",
+    "filename = \"/Users/ladwig/Documents/wrf_files/wrfout_d01_2016-10-07_00_00_00\"\n",
+    "ncfile = nc(filename)\n",
+    "\n",
+    "# Get sea level pressure and cloud top temperature\n",
+    "slp = getvar(ncfile, \"slp\")\n",
+    "ctt = getvar(ncfile, \"ctt\")\n",
+    "\n",
+    "print (ctt)\n",
+    "\n",
+    "# Smooth the SLP\n",
+    "smooth_slp = smooth2d(slp, 3)\n",
+    "\n",
+    "# Extract the latitude and longitude coordinate arrays as regular numpy array instead of xarray.DataArray\n",
+    "lons = ctt.coords[\"XLONG\"]\n",
+    "lats = ctt.coords[\"XLAT\"]\n",
+    "\n",
+    "# Get the cartopy projection class\n",
+    "wrf_proj = slp.attrs[\"projection\"]\n",
+    "cart_proj = wrf_proj.cartopy()\n",
+    "\n",
+    "# Create the figure\n",
+    "fig = plt.figure(figsize=(8,8))\n",
+    "ax = plt.axes([0.1,0.1,0.8,0.8], projection=cart_proj)\n",
+    "\n",
+    "# Download and create the states, land, and oceans using cartopy features\n",
+    "states = cfeature.NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',\n",
+    "                             name='admin_1_states_provinces_shp')\n",
+    "land = cfeature.NaturalEarthFeature(category='physical', name='land', scale='50m', \n",
+    "                                    facecolor=cfeature.COLORS['land'])\n",
+    "ocean = cfeature.NaturalEarthFeature(category='physical', name='ocean', scale='50m', \n",
+    "                                     facecolor=cfeature.COLORS['water'])\n",
+    "\n",
+    "# Make the pressure contours.\n",
+    "#contour_levels = [960, 962, 965, 967, 970, 975, 980, 985, 990, 995]\n",
+    "#c1 = plt.contour(lons, lats, to_np(smooth_slp), levels=contour_levels, colors=\"white\", \n",
+    "#            transform=crs.PlateCarree(), zorder=3, linewidth=1.3)\n",
+    "\n",
+    "# Add pressure contour labels\n",
+    "#plt.clabel(c1, contour_levels, inline=True, fmt='%1.1f', fontsize=8)\n",
+    "\n",
+    "# Create the filled cloud top temperature contours\n",
+    "#contour_levels = [-80, -70, -60, -50, -40, -30, -20, -10, 0, 10, 20, 30]\n",
+    "contour_levels = np.arange(-80.0, 10.0, 10.0)\n",
+    "plt.contourf(to_np(lons), to_np(lats), to_np(ctt), contour_levels, cmap=get_cmap(\"Greys\"),\n",
+    "             transform=crs.PlateCarree(), zorder=2)\n",
+    "\n",
+    "# Create the label bar for cloud top temperature\n",
+    "cb2 = plt.colorbar(ax=ax, shrink=.80)\n",
+    "\n",
+    "# Draw the oceans, land, and states\n",
+    "ax.add_feature(ocean)\n",
+    "ax.add_feature(land)\n",
+    "ax.add_feature(states, linewidth=.5, edgecolor=\"black\")\n",
+    "\n",
+    "# Crop the domain to the region around the hurricane\n",
+    "ax.set_extent([-85., -75.0, np.amin(lats), 30.0], crs=crs.PlateCarree())\n",
+    "# Set the map limits\n",
+    "#ax.set_xlim(wrf_proj.cartopy_xlim())\n",
+    "#ax.set_ylim(wrf_proj.cartopy_ylim())\n",
+    "\n",
+    "ax.gridlines(crs=crs.PlateCarree(), draw_labels=False)\n",
+    "\n",
+    "# Add the title and show the image\n",
+    "#plt.title(\"Hurricane Matthew Cloud Top Temperature (degC) and Sea Level Pressure (hPa)\")\n",
+    "#plt.title(\"Hurricane Matthew Cloud Top Temperature (degC)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

test/ipynb/WRF_Workshop_Demo.ipynb

@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -81,24 +81,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "ename": "NIOError",
-     "evalue": "Unable to open file",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNIOError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-9e6cb63ecf8c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"wrfout_d01_2010-06-13_21-00-00\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mpynio_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\".nc\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mncfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpynio_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Alternative using netCDF4-python (for reference)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/ladwig/miniconda2/lib/python2.7/site-packages/PyNIO/Nio.pyc\u001b[0m in \u001b[0;36mopen_file\u001b[0;34m(filename, mode, options, history, format)\u001b[0m\n\u001b[1;32m    733\u001b[0m     \u001b[0mmask_above_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_option_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_Nio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moption_defaults\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'MaskAboveValue'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    734\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 735\u001b[0;31m     \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Nio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    736\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    737\u001b[0m     \u001b[0mfile_proxy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_proxy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'str'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m__del__\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m__del__\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcreate_variable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_variable\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcreate_group\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_group\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNIOError\u001b[0m: Unable to open file"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",