CLIMATE-934 Fixed error with out of date Bounds constructor.

examples/time_series_with_regions.py

@@ -1,37 +1,59 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+'''
+    Download three netCDF files, process the files to be the same shape,
+    divide the data into 13 subregions and plot a monthly time series for each sub region.
+'''
+
+import sys
+import datetime
+from os import path
+from calendar import monthrange
+import ssl
+
+import numpy as np
+
 # Apache OCW lib immports
-from ocw.dataset import Dataset, Bounds
+from ocw.dataset import Bounds
 import ocw.data_source.local as local
 import ocw.data_source.rcmed as rcmed
 import ocw.dataset_processor as dsp
-import ocw.evaluation as evaluation
-import ocw.metrics as metrics
 import ocw.plotter as plotter
 import ocw.utils as utils
 
-import datetime
-import numpy as np
-import numpy.ma as ma
-from os import path
-import sys
-
 if sys.version_info[0] >= 3:
     from urllib.request import urlretrieve
 else:
     # Not Python 3 - today, it is most likely to be Python 2
     # But note that this might need an update when Python 4
     # might be around one day
     from urllib import urlretrieve
-import ssl
+
 if hasattr(ssl, '_create_unverified_context'):
     ssl._create_default_https_context = ssl._create_unverified_context
 
-
 # File URL leader
-FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/"
+FILE_LEADER = 'http://zipper.jpl.nasa.gov/dist/'
+
 # Three Local Model Files
-FILE_1 = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc"
-FILE_2 = "AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc"
-FILE_3 = "AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc"
+FILE_1 = 'AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc'
+FILE_2 = 'AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc'
+FILE_3 = 'AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc'
 
 LAT_MIN = -45.0
 LAT_MAX = 42.24
@@ -56,106 +78,107 @@
 
 # Download necessary NetCDF file if not present
 if not path.exists(FILE_1):
-    print("Downloading %s" % (FILE_LEADER + FILE_1))
+    print('Downloading %s' % (FILE_LEADER + FILE_1))
     urlretrieve(FILE_LEADER + FILE_1, FILE_1)
 
 if not path.exists(FILE_2):
-    print("Downloading %s" % (FILE_LEADER + FILE_2))
+    print('Downloading %s' % (FILE_LEADER + FILE_2))
     urlretrieve(FILE_LEADER + FILE_2, FILE_2)
 
 if not path.exists(FILE_3):
-    print("Downloading %s" % (FILE_LEADER + FILE_3))
+    print('Downloading %s' % (FILE_LEADER + FILE_3))
     urlretrieve(FILE_LEADER + FILE_3, FILE_3)
 
-""" Step 1: Load Local NetCDF File into OCW Dataset Objects and store in list"""
-target_datasets.append(local.load_file(FILE_1, varName, name="KNMI"))
-target_datasets.append(local.load_file(FILE_2, varName, name="REGCM"))
-target_datasets.append(local.load_file(FILE_3, varName, name="UCT"))
-
+# Step 1: Load Local NetCDF File into OCW Dataset Objects and store in list
+target_datasets.append(local.load_file(FILE_1, varName, name='KNMI'))
+target_datasets.append(local.load_file(FILE_2, varName, name='REGCM'))
+target_datasets.append(local.load_file(FILE_3, varName, name='UCT'))
 
-""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
-print("Working with the rcmed interface to get CRU3.1 Daily Precipitation")
+# Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module
+print('Working with the rcmed interface to get CRU3.1 Daily Precipitation')
 # the dataset_id and the parameter id were determined from
 # https://rcmes.jpl.nasa.gov/content/data-rcmes-database
 CRU31 = rcmed.parameter_dataset(
     10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
 
-
-""" Step 3: Processing datasets so they are the same shape ... """
-print("Processing datasets so they are the same shape")
+# Step 3: Processing datasets so they are the same shape
+print('Processing datasets so they are the same shape')
 CRU31 = dsp.water_flux_unit_conversion(CRU31)
 CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
 
 for member, each_target_dataset in enumerate(target_datasets):
     target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)
-    target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[
-                                                             member])
+    target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[member])
     target_datasets[member] = dsp.normalize_dataset_datetimes(
         target_datasets[member], 'monthly')
 
-print("... spatial regridding")
+print('... spatial regridding')
 new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
 new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
 CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
 
-
 for member, each_target_dataset in enumerate(target_datasets):
     target_datasets[member] = dsp.spatial_regrid(
         target_datasets[member], new_lats, new_lons)
 
-# find climatology monthly for obs and models
+# Find climatology monthly for obs and models.
 CRU31.values, CRU31.times = utils.calc_climatology_monthly(CRU31)
+# Shift the day of the month to the end of the month as matplotlib does not handle
+# the xticks elegantly when the first date is the epoch and tries to determine
+# the start of the xticks based on a value < 1.
+for index, item in enumerate(CRU31.times):
+    CRU31.times[index] = datetime.date(item.year, item.month, monthrange(item.year, item.month)[1])
 
 for member, each_target_dataset in enumerate(target_datasets):
     target_datasets[member].values, target_datasets[
         member].times = utils.calc_climatology_monthly(target_datasets[member])
 
 # make the model ensemble
 target_datasets_ensemble = dsp.ensemble(target_datasets)
-target_datasets_ensemble.name = "ENS"
+target_datasets_ensemble.name = 'ENS'
 
 # append to the target_datasets for final analysis
 target_datasets.append(target_datasets_ensemble)
 
-""" Step 4: Subregion stuff """
+# Step 4: Subregion stuff
 list_of_regions = [
-    Bounds(-10.0, 0.0, 29.0, 36.5),
-    Bounds(0.0, 10.0,  29.0, 37.5),
-    Bounds(10.0, 20.0, 25.0, 32.5),
-    Bounds(20.0, 33.0, 25.0, 32.5),
-    Bounds(-19.3, -10.2, 12.0, 20.0),
-    Bounds(15.0, 30.0, 15.0, 25.0),
-    Bounds(-10.0, 10.0, 7.3, 15.0),
-    Bounds(-10.9, 10.0, 5.0, 7.3),
-    Bounds(33.9, 40.0,  6.9, 15.0),
-    Bounds(10.0, 25.0,  0.0, 10.0),
-    Bounds(10.0, 25.0, -10.0,  0.0),
-    Bounds(30.0, 40.0, -15.0,  0.0),
-    Bounds(33.0, 40.0, 25.0, 35.0)]
-
-region_list = [["R" + str(i + 1)] for i in xrange(13)]
+    Bounds(lat_min=-10.0, lat_max=0.0, lon_min=29.0, lon_max=36.5),
+    Bounds(lat_min=0.0, lat_max=10.0, lon_min=29.0, lon_max=37.5),
+    Bounds(lat_min=10.0, lat_max=20.0, lon_min=25.0, lon_max=32.5),
+    Bounds(lat_min=20.0, lat_max=33.0, lon_min=25.0, lon_max=32.5),
+    Bounds(lat_min=-19.3, lat_max=-10.2, lon_min=12.0, lon_max=20.0),
+    Bounds(lat_min=15.0, lat_max=30.0, lon_min=15.0, lon_max=25.0),
+    Bounds(lat_min=-10.0, lat_max=10.0, lon_min=7.3, lon_max=15.0),
+    Bounds(lat_min=-10.9, lat_max=10.0, lon_min=5.0, lon_max=7.3),
+    Bounds(lat_min=33.9, lat_max=40.0, lon_min=6.9, lon_max=15.0),
+    Bounds(lat_min=10.0, lat_max=25.0, lon_min=0.0, lon_max=10.0),
+    Bounds(lat_min=10.0, lat_max=25.0, lon_min=-10.0, lon_max=0.0),
+    Bounds(lat_min=30.0, lat_max=40.0, lon_min=-15.0, lon_max=0.0),
+    Bounds(lat_min=33.0, lat_max=40.0, lon_min=25.0, lon_max=35.0)]
+
+region_list = [['R' + str(i + 1)] for i in xrange(13)]
 
 for regions in region_list:
     firstTime = True
-    subset_name = regions[0] + "_CRU31"
-    # labels.append(subset_name) #for legend, uncomment this line
+    subset_name = regions[0] + '_CRU31'
+    labels.append(subset_name)
     subset = dsp.subset(CRU31, list_of_regions[region_counter], subset_name)
     tSeries = utils.calc_time_series(subset)
     results.append(tSeries)
     tSeries = []
     firstTime = False
     for member, each_target_dataset in enumerate(target_datasets):
-        subset_name = regions[0] + "_" + target_datasets[member].name
-        # labels.append(subset_name) #for legend, uncomment this line
+        subset_name = regions[0] + '_' + target_datasets[member].name
+        labels.append(subset_name)
         subset = dsp.subset(target_datasets[member],
                             list_of_regions[region_counter],
                             subset_name)
         tSeries = utils.calc_time_series(subset)
         results.append(tSeries)
         tSeries = []
 
-    plotter.draw_time_series(np.array(results), CRU31.times, labels, regions[
-                             0], ptitle=regions[0], fmt='png')
+    plotter.draw_time_series(np.array(results), CRU31.times, labels, regions[0],
+                             label_month=True, ptitle=regions[0], fmt='png')
     results = []
     tSeries = []
     labels = []