| # 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. |
| |
| import datetime |
| import math |
| import urllib |
| from os import path |
| |
| import numpy as np |
| |
| import ocw.data_source.local as local |
| import ocw.data_source.rcmed as rcmed |
| from ocw.dataset import Bounds as Bounds |
| import ocw.dataset_processor as dsp |
| import ocw.evaluation as evaluation |
| import ocw.metrics as metrics |
| import ocw.plotter as plotter |
| |
| # File URL leader |
| FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/" |
| # This way we can easily adjust the time span of the retrievals |
| YEARS = 1 |
| # Two Local Model Files |
| FILE_1 = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc" |
| FILE_2 = "AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax.nc" |
| # Filename for the output image/plot (without file extension) |
| OUTPUT_PLOT = "tasmax_africa_bias_annual" |
| |
| # Download necessary NetCDF file if not present |
| if path.exists(FILE_1): |
| pass |
| else: |
| urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1) |
| |
| if path.exists(FILE_2): |
| pass |
| else: |
| urllib.urlretrieve(FILE_LEADER + FILE_2, FILE_2) |
| |
| |
| """ Step 1: Load Local NetCDF File into OCW Dataset Objects """ |
| # Load local knmi model data |
| knmi_dataset = local.load_file(FILE_1, "tasmax") |
| knmi_dataset.name = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax" |
| |
| wrf311_dataset = local.load_file(FILE_2, "tasmax") |
| wrf311_dataset.name = "AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax" |
| |
| |
| |
| """ Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """ |
| print("Working with the rcmed interface to get CRU3.1 Daily-Max Temp") |
| metadata = rcmed.get_parameters_metadata() |
| |
| cru_31 = [m for m in metadata if m['parameter_id'] == "39"][0] |
| |
| """ The RCMED API uses the following function to query, subset and return the |
| raw data from the database: |
| |
| rcmed.parameter_dataset(dataset_id, parameter_id, min_lat, max_lat, min_lon, |
| max_lon, start_time, end_time) |
| |
| The first two required params are in the cru_31 variable we defined earlier |
| """ |
| # Must cast to int since the rcmed api requires ints |
| dataset_id = int(cru_31['dataset_id']) |
| parameter_id = int(cru_31['parameter_id']) |
| |
| # The spatial_boundaries() function returns the spatial extent of the dataset |
| min_lat, max_lat, min_lon, max_lon = wrf311_dataset.spatial_boundaries() |
| |
| # There is a boundry alignment issue with the datasets. To mitigate this |
| # we will use the math.floor() and math.ceil() functions to shrink the |
| # boundries slighty. |
| min_lat = math.ceil(min_lat) |
| max_lat = math.floor(max_lat) |
| min_lon = math.ceil(min_lon) |
| max_lon = math.floor(max_lon) |
| |
| print("Calculating the Maximum Overlap in Time for the datasets") |
| |
| cru_start = datetime.datetime.strptime(cru_31['start_date'], "%Y-%m-%d") |
| cru_end = datetime.datetime.strptime(cru_31['end_date'], "%Y-%m-%d") |
| knmi_start, knmi_end = knmi_dataset.time_range() |
| # Set the Time Range to be the year 1989 |
| start_time = datetime.datetime(1989,1,1) |
| end_time = datetime.datetime(1989,12,1) |
| |
| print("Time Range is: %s to %s" % (start_time.strftime("%Y-%m-%d"), |
| end_time.strftime("%Y-%m-%d"))) |
| |
| print("Fetching data from RCMED...") |
| cru31_dataset = rcmed.parameter_dataset(dataset_id, |
| parameter_id, |
| min_lat, |
| max_lat, |
| min_lon, |
| max_lon, |
| start_time, |
| end_time) |
| |
| """ Step 3: Resample Datasets so they are the same shape """ |
| |
| print("Temporally Rebinning the Datasets to an Annual Timestep") |
| # To run annual temporal Rebinning use a timedelta of 360 days. |
| knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=360)) |
| wrf311_dataset = dsp.temporal_rebin(wrf311_dataset, datetime.timedelta(days=360)) |
| cru31_dataset = dsp.temporal_rebin(cru31_dataset, datetime.timedelta(days=360)) |
| |
| # Running Temporal Rebin early helps negate the issue of datasets being on different |
| # days of the month (1st vs. 15th) |
| # Create a Bounds object to use for subsetting |
| new_bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time) |
| |
| # Subset our model datasets so they are the same size |
| knmi_dataset = dsp.subset(new_bounds, knmi_dataset) |
| wrf311_dataset = dsp.subset(new_bounds, wrf311_dataset) |
| |
| """ Spatially Regrid the Dataset Objects to a 1/2 degree grid """ |
| # Using the bounds we will create a new set of lats and lons on 1/2 degree step |
| new_lons = np.arange(min_lon, max_lon, 0.5) |
| new_lats = np.arange(min_lat, max_lat, 0.5) |
| |
| # Spatially regrid datasets using the new_lats, new_lons numpy arrays |
| knmi_dataset = dsp.spatial_regrid(knmi_dataset, new_lats, new_lons) |
| wrf311_dataset = dsp.spatial_regrid(wrf311_dataset, new_lats, new_lons) |
| cru31_dataset = dsp.spatial_regrid(cru31_dataset, new_lats, new_lons) |
| |
| # Generate an ensemble dataset from knmi and wrf models |
| ensemble_dataset = dsp.ensemble([knmi_dataset, wrf311_dataset]) |
| |
| """ Step 4: Build a Metric to use for Evaluation - Bias for this example """ |
| print("Setting up a Bias metric to use for evaluation") |
| bias = metrics.Bias() |
| |
| """ Step 5: Create an Evaluation Object using Datasets and our Metric """ |
| # The Evaluation Class Signature is: |
| # Evaluation(reference, targets, metrics, subregions=None) |
| # Evaluation can take in multiple targets and metrics, so we need to convert |
| # our examples into Python lists. Evaluation will iterate over the lists |
| print("Making the Evaluation definition") |
| bias_evaluation = evaluation.Evaluation(cru31_dataset, |
| [knmi_dataset, wrf311_dataset, ensemble_dataset], |
| [bias]) |
| print("Executing the Evaluation using the object's run() method") |
| bias_evaluation.run() |
| |
| """ Step 6: Make a Plot from the Evaluation.results """ |
| # The Evaluation.results are a set of nested lists to support many different |
| # possible Evaluation scenarios. |
| # |
| # The Evaluation results docs say: |
| # The shape of results is (num_target_datasets, num_metrics) if no subregion |
| # Accessing the actual results when we have used 3 datasets and 1 metric is |
| # done this way: |
| print("Accessing the Results of the Evaluation run") |
| results = bias_evaluation.results |
| |
| # From the bias output I want to make a Contour Map of the region |
| print("Generating a contour map using ocw.plotter.draw_contour_map()") |
| |
| lats = new_lats |
| lons = new_lons |
| fname = OUTPUT_PLOT |
| gridshape = (3, 1) # Using a 3 x 1 since we have a 1 year of data for 3 models |
| plotnames = ["KNMI", "WRF311", "ENSEMBLE"] |
| for i, result in enumerate(results): |
| plot_title = "TASMAX Bias of CRU 3.1 vs. %s (%s - %s)" % (plotnames[i], start_time.strftime("%Y/%d/%m"), end_time.strftime("%Y/%d/%m")) |
| output_file = "%s_%s" % (fname, plotnames[i].lower()) |
| print "creating %s" % (output_file,) |
| plotter.draw_contour_map(result[0], lats, lons, output_file, |
| gridshape=gridshape, ptitle=plot_title) |