Merge pull request #140 from dopplershift/improve-api

Improve API
Unidata · Jul 22, 2017 · 7dd6c7a · 7dd6c7a
2 parents 7ffbe95 + 3ada1e8
commit 7dd6c7a
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Showing 15 changed files with 751 additions and 78 deletions.
diff --git a/.appveyor.yml b/.appveyor.yml
@@ -35,7 +35,7 @@ install:
 build: off
 
 test_script:
-    - cmd: python setup.py test --addopts "-s --junitxml=tests.xml --flake8 --cov=siphon"
+        - cmd: python setup.py test --addopts "-s --junitxml=tests.xml --flake8 --cov=siphon --no-print-logs"
       #- cmd: cd docs
       #- cmd: make html
       #- cmd: cd ..

diff --git a/.prospector.yaml b/.prospector.yaml
@@ -13,4 +13,4 @@ pylint:
 
 pep8:
   options:
-    max-line-length: 90
+    max-line-length: 95
diff --git a/.travis.yml b/.travis.yml
@@ -98,7 +98,7 @@ script:
       fi;
     else
       flake8 --version;
-      python setup.py test --addopts "-s $TEST_OPTS";
+      python setup.py test --addopts "-s $TEST_OPTS --no-print-logs";
     fi
 
 after_script:

diff --git a/examples/Basic_Usage.py b/examples/Basic_Usage.py
@@ -19,4 +19,4 @@
 
 ###########################################
 cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog.xml')
-print(list(cat.catalog_refs.keys()))
+print(list(cat.catalog_refs))
diff --git a/examples/Radar_Server_Level_3.py b/examples/Radar_Server_Level_3.py
@@ -20,7 +20,7 @@
 # First, point to the top-level thredds radar server accessor to find what datasets are
 # available.
 ds = get_radarserver_datasets('http://thredds.ucar.edu/thredds/')
-print(list(ds.keys()))
+print(list(ds))
 
 ###########################################
 # Now create an instance of RadarServer to point to the appropriate

diff --git a/examples/ncss/NCSS_Cartopy_Example.py b/examples/ncss/NCSS_Cartopy_Example.py
@@ -20,7 +20,6 @@
 import numpy as np
 
 from siphon.catalog import TDSCatalog
-from siphon.ncss import NCSS
 
 ###########################################
 # First we construct a `TDSCatalog` instance pointing to our dataset of interest, in
@@ -32,13 +31,9 @@
 print(list(best_gfs.datasets))
 
 ###########################################
-# We pull out this dataset and look at the access urls.
-best_ds = list(best_gfs.datasets.values())[0]
-print(best_ds.access_urls)
-
-###########################################
-# Note the `NetcdfSubset` entry, which we will use with our NCSS class.
-ncss = NCSS(best_ds.access_urls['NetcdfSubset'])
+# We pull out this dataset and get the NCSS access point
+best_ds = best_gfs.datasets[0]
+ncss = best_ds.subset()
 
 ###########################################
 # We can then use the `ncss` object to create a new query object, which

diff --git a/examples/ncss/NCSS_Example.py b/examples/ncss/NCSS_Example.py
@@ -13,7 +13,6 @@
 import matplotlib.pyplot as plt
 
 from siphon.catalog import TDSCatalog
-from siphon.ncss import NCSS
 
 ###########################################
 # First we construct a TDSCatalog instance pointing to our dataset of interest, in
@@ -24,13 +23,9 @@
 print(best_gfs.datasets)
 
 ###########################################
-# We pull out this dataset and look at the access urls.
-best_ds = list(best_gfs.datasets.values())[0]
-print(best_ds.access_urls)
-
-###########################################
-# Note the `NetcdfSubset` entry, which we will use with our NCSS class.
-ncss = NCSS(best_ds.access_urls['NetcdfSubset'])
+# We pull out this dataset and get the NCSS access point
+best_ds = best_gfs.datasets[0]
+ncss = best_ds.subset()
 
 ###########################################
 # We can then use the `ncss` object to create a new query object, which

diff --git a/examples/ncss/NCSS_Timeseries_Examples.py b/examples/ncss/NCSS_Timeseries_Examples.py
@@ -14,7 +14,6 @@
 from netCDF4 import num2date
 
 from siphon.catalog import TDSCatalog
-from siphon.ncss import NCSS
 
 ###########################################
 # First we construct a TDSCatalog instance pointing to our dataset of interest, in
@@ -25,13 +24,9 @@
 print(best_gfs.datasets)
 
 ###########################################
-# We pull out this dataset and look at the access urls.
-best_ds = list(best_gfs.datasets.values())[0]
-print(best_ds.access_urls)
-
-###########################################
-# Note the `NetcdfSubset` entry, which we will use with our NCSS class.
-ncss = NCSS(best_ds.access_urls['NetcdfSubset'])
+# We pull out this dataset and get the NCSS access point
+best_ds = best_gfs.datasets[0]
+ncss = best_ds.subset()
 
 ###########################################
 # We can then use the `ncss` object to create a new query object, which