examples/subregions_portrait_diagram.py - climate - Git at Google

 # 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.

 """
     subregions_portrait_diagram.py

     Use OCW to download, normalize, evaluate and plot (portrait diagram)
     three local datasets against a reference dataset.

     In this example:

     1. Download three netCDF files from a local site.
         AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc
         AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc
         AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc
     2. Load the local files into OCW dataset objects.
     3. Interface with the Regional Climate Model Evaluation Database (https://rcmes.jpl.nasa.gov/)
        to load the CRU3.1 Daily Precipitation dataset (https://rcmes.jpl.nasa.gov/content/cru31).
     4. Process each dataset to the same same shape.
         a.) Restrict the datasets re: geographic and time boundaries.
         b.) Convert the dataset water flux to common units.
         c.) Normalize the dataset date / times to monthly.
         d.) Spatially regrid each dataset.
     5.  Calculate the mean annual value for each dataset.
     6.  Separate each dataset into 13 subregions.
     7.  Extract the metrics used for the evaluation and evaluate
         against a reference dataset.
     8.  Create a portrait diagram of the results of the evaluation.

     OCW modules demonstrated:

     1. datasource/local
     2. datasource/rcmed
     3. dataset
     4. dataset_processor
     5. metrics
     6. evaluation
     7. plotter
     8. utils

 """
 from __future__ import print_function

 import datetime
 import ssl
 import sys
 from os import path

 import numpy as np

 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
 from ocw.dataset import Bounds

 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

 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/'
 # 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'
 # Filename for the output image/plot (without file extension)
 OUTPUT_PLOT = 'portrait_diagram'

 # Spatial and temporal configurations
 LAT_MIN = -45.0
 LAT_MAX = 42.24
 LON_MIN = -24.0
 LON_MAX = 60.0
 START = datetime.datetime(2000, 1, 1)
 END = datetime.datetime(2007, 12, 31)
 EVAL_BOUNDS = Bounds(lat_min=LAT_MIN, lat_max=LAT_MAX, lon_min=LON_MIN,
                      lon_max=LON_MAX, start=START, end=END)

 # variable that we are analyzing
 varName = 'pr'

 # regridding parameters
 gridLonStep = 0.5
 gridLatStep = 0.5

 # some vars for this evaluation
 target_datasets_ensemble = []
 target_datasets = []
 allNames = []

 # Download necessary NetCDF file if not present
 if not path.exists(FILE_1):
     urlretrieve(FILE_LEADER + FILE_1, FILE_1)

 if not path.exists(FILE_2):
     urlretrieve(FILE_LEADER + FILE_2, FILE_2)

 if not path.exists(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 2: Fetch an OCW Dataset Object from the data_source.rcmed module
 print('Working with the rcmed interface to get CRU3.1 Monthly Mean 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 ...')
 CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
 print('... on units')
 CRU31 = dsp.water_flux_unit_conversion(CRU31)

 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.normalize_dataset_datetimes(
         target_datasets[member], 'monthly')

 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 the total annual mean. Note the function exists in util.py as def
 # calc_climatology_year(dataset):
 _, CRU31.values = utils.calc_climatology_year(CRU31)

 for member, each_target_dataset in enumerate(target_datasets):
     _, target_datasets[member].values = \
         utils.calc_climatology_year(target_datasets[member])

 # make the model ensemble
 target_datasets_ensemble = dsp.ensemble(target_datasets)
 target_datasets_ensemble.name = 'ENS'

 # append to the target_datasets for final analysis
 target_datasets.append(target_datasets_ensemble)

 for target in target_datasets:
     allNames.append(target.name)

 list_of_regions = [
     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=35.00)]

 region_list = ['R' + str(i + 1) for i in range(13)]

 # metrics
 pattern_correlation = metrics.PatternCorrelation()

 # create the Evaluation object
 RCMs_to_CRU_evaluation = evaluation.Evaluation(CRU31,  # Reference dataset for the evaluation
                                                # 1 or more target datasets for
                                                # the evaluation
                                                target_datasets,
                                                # 1 or more metrics to use in
                                                # the evaluation
                                                [pattern_correlation],
                                                # list of subregion Bounds
                                                # Objects
                                                list_of_regions)
 RCMs_to_CRU_evaluation.run()

 new_patcor = np.squeeze(np.array(RCMs_to_CRU_evaluation.results), axis=1)

 plotter.draw_portrait_diagram(np.transpose(
     new_patcor), allNames, region_list, fname=OUTPUT_PLOT, fmt='png', cmap='coolwarm_r')
	# 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.

	"""
	subregions_portrait_diagram.py

	Use OCW to download, normalize, evaluate and plot (portrait diagram)
	three local datasets against a reference dataset.

	In this example:

	1. Download three netCDF files from a local site.
	AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc
	AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc
	AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc
	2. Load the local files into OCW dataset objects.
	3. Interface with the Regional Climate Model Evaluation Database (https://rcmes.jpl.nasa.gov/)
	to load the CRU3.1 Daily Precipitation dataset (https://rcmes.jpl.nasa.gov/content/cru31).
	4. Process each dataset to the same same shape.
	a.) Restrict the datasets re: geographic and time boundaries.
	b.) Convert the dataset water flux to common units.
	c.) Normalize the dataset date / times to monthly.
	d.) Spatially regrid each dataset.
	5. Calculate the mean annual value for each dataset.
	6. Separate each dataset into 13 subregions.
	7. Extract the metrics used for the evaluation and evaluate
	against a reference dataset.
	8. Create a portrait diagram of the results of the evaluation.

	OCW modules demonstrated:

	1. datasource/local
	2. datasource/rcmed
	3. dataset
	4. dataset_processor
	5. metrics
	6. evaluation
	7. plotter
	8. utils

	"""
	from __future__ import print_function

	import datetime
	import ssl
	import sys
	from os import path

	import numpy as np

	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
	from ocw.dataset import Bounds

	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

	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/'
	# 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'
	# Filename for the output image/plot (without file extension)
	OUTPUT_PLOT = 'portrait_diagram'

	# Spatial and temporal configurations
	LAT_MIN = -45.0
	LAT_MAX = 42.24
	LON_MIN = -24.0
	LON_MAX = 60.0
	START = datetime.datetime(2000, 1, 1)
	END = datetime.datetime(2007, 12, 31)
	EVAL_BOUNDS = Bounds(lat_min=LAT_MIN, lat_max=LAT_MAX, lon_min=LON_MIN,
	lon_max=LON_MAX, start=START, end=END)

	# variable that we are analyzing
	varName = 'pr'

	# regridding parameters
	gridLonStep = 0.5
	gridLatStep = 0.5

	# some vars for this evaluation
	target_datasets_ensemble = []
	target_datasets = []
	allNames = []

	# Download necessary NetCDF file if not present
	if not path.exists(FILE_1):
	urlretrieve(FILE_LEADER + FILE_1, FILE_1)

	if not path.exists(FILE_2):
	urlretrieve(FILE_LEADER + FILE_2, FILE_2)

	if not path.exists(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 2: Fetch an OCW Dataset Object from the data_source.rcmed module
	print('Working with the rcmed interface to get CRU3.1 Monthly Mean 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 ...')
	CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
	print('... on units')
	CRU31 = dsp.water_flux_unit_conversion(CRU31)

	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.normalize_dataset_datetimes(
	target_datasets[member], 'monthly')

	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 the total annual mean. Note the function exists in util.py as def
	# calc_climatology_year(dataset):
	_, CRU31.values = utils.calc_climatology_year(CRU31)

	for member, each_target_dataset in enumerate(target_datasets):
	_, target_datasets[member].values = \
	utils.calc_climatology_year(target_datasets[member])

	# make the model ensemble
	target_datasets_ensemble = dsp.ensemble(target_datasets)
	target_datasets_ensemble.name = 'ENS'

	# append to the target_datasets for final analysis
	target_datasets.append(target_datasets_ensemble)

	for target in target_datasets:
	allNames.append(target.name)

	list_of_regions = [
	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=35.00)]

	region_list = ['R' + str(i + 1) for i in range(13)]

	# metrics
	pattern_correlation = metrics.PatternCorrelation()

	# create the Evaluation object
	RCMs_to_CRU_evaluation = evaluation.Evaluation(CRU31, # Reference dataset for the evaluation
	# 1 or more target datasets for
	# the evaluation
	target_datasets,
	# 1 or more metrics to use in
	# the evaluation
	[pattern_correlation],
	# list of subregion Bounds
	# Objects
	list_of_regions)
	RCMs_to_CRU_evaluation.run()

	new_patcor = np.squeeze(np.array(RCMs_to_CRU_evaluation.results), axis=1)

	plotter.draw_portrait_diagram(np.transpose(
	new_patcor), allNames, region_list, fname=OUTPUT_PLOT, fmt='png', cmap='coolwarm_r')