examples/multi_model_taylor_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.

 """
     multi_model_taylor_diagram.py

     Use OCW to download, normalize and evaluate three datasets
     against a reference dataset and OCW standard metrics
     drawing a Taylor diagram of the results of the evaluation.

     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. Process each dataset to the same same shape.
         a.) Restrict the datasets re: geographic and time boundaries.
         b.) Temporally rebin the data (monthly).
         c.) Spatially regrid each dataset.
     4.  Extract the metrics used for the evaluation and evaluate
         against a reference dataset and standard OCW metrics.
     5.  Draw evaluation results Taylor diagram.

     OCW modules demonstrated:

     1. datasource/local
     2. dataset
     3. dataset_processor
     4. evaluation
     5. metrics
     6. plotter
     7. 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 = "pr_africa_taylor"

 # 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 = []
 ref_datasets = []

 # 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="REGM3"))
 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: Resample Datasets so they are the same shape.
 print("Resampling datasets ...")
 print("... on units")
 CRU31 = dsp.water_flux_unit_conversion(CRU31)
 print("... temporal")
 CRU31 = dsp.temporal_rebin(CRU31, temporal_resolution='monthly')

 for member, each_target_dataset in enumerate(target_datasets):
     target_datasets[member] =\
         dsp.water_flux_unit_conversion(target_datasets[member])
     target_datasets[member] =\
         dsp.temporal_rebin(target_datasets[member], temporal_resolution='monthly')
     target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)

 # Regrid
 print("... regrid")
 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 mean values
 # way to get the mean. Note the function exists in util.py as def
 # calc_climatology_year(dataset):
 CRU31.values = utils.calc_temporal_mean(CRU31)

 # 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 member, each_target_dataset in enumerate(target_datasets):
     target_datasets[member].values =\
         utils.calc_temporal_mean(target_datasets[member])

 allNames = []

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

 # calculate the metrics
 taylor_diagram = metrics.SpatialPatternTaylorDiagram()


 # 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
                                                [taylor_diagram])
 RCMs_to_CRU_evaluation.run()

 taylor_data = RCMs_to_CRU_evaluation.results[0]

 plotter.draw_taylor_diagram(taylor_data,
                             allNames,
                             "CRU31",
                             fname=OUTPUT_PLOT,
                             fmt='png',
                             frameon=False)
	# 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.

	"""
	multi_model_taylor_diagram.py

	Use OCW to download, normalize and evaluate three datasets
	against a reference dataset and OCW standard metrics
	drawing a Taylor diagram of the results of the evaluation.

	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. Process each dataset to the same same shape.
	a.) Restrict the datasets re: geographic and time boundaries.
	b.) Temporally rebin the data (monthly).
	c.) Spatially regrid each dataset.
	4. Extract the metrics used for the evaluation and evaluate
	against a reference dataset and standard OCW metrics.
	5. Draw evaluation results Taylor diagram.

	OCW modules demonstrated:

	1. datasource/local
	2. dataset
	3. dataset_processor
	4. evaluation
	5. metrics
	6. plotter
	7. 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 = "pr_africa_taylor"

	# 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 = []
	ref_datasets = []

	# 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="REGM3"))
	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: Resample Datasets so they are the same shape.
	print("Resampling datasets ...")
	print("... on units")
	CRU31 = dsp.water_flux_unit_conversion(CRU31)
	print("... temporal")
	CRU31 = dsp.temporal_rebin(CRU31, temporal_resolution='monthly')

	for member, each_target_dataset in enumerate(target_datasets):
	target_datasets[member] =\
	dsp.water_flux_unit_conversion(target_datasets[member])
	target_datasets[member] =\
	dsp.temporal_rebin(target_datasets[member], temporal_resolution='monthly')
	target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)

	# Regrid
	print("... regrid")
	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 mean values
	# way to get the mean. Note the function exists in util.py as def
	# calc_climatology_year(dataset):
	CRU31.values = utils.calc_temporal_mean(CRU31)

	# 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 member, each_target_dataset in enumerate(target_datasets):
	target_datasets[member].values =\
	utils.calc_temporal_mean(target_datasets[member])

	allNames = []

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

	# calculate the metrics
	taylor_diagram = metrics.SpatialPatternTaylorDiagram()


	# 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
	[taylor_diagram])
	RCMs_to_CRU_evaluation.run()

	taylor_data = RCMs_to_CRU_evaluation.results[0]

	plotter.draw_taylor_diagram(taylor_data,
	allNames,
	"CRU31",
	fname=OUTPUT_PLOT,
	fmt='png',
	frameon=False)