| # 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 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 |
| 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/" |
| # This way we can easily adjust the time span of the retrievals |
| YEARS = 3 |
| # Two Local Model Files |
| MODEL = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc" |
| # Filename for the output image/plot (without file extension) |
| OUTPUT_PLOT = "cru_31_tmax_knmi_africa_bias_full" |
| |
| # Download necessary NetCDF file if not present |
| if path.exists(MODEL): |
| pass |
| else: |
| urllib.urlretrieve(FILE_LEADER + MODEL, MODEL) |
| |
| """ Step 1: Load Local NetCDF File into OCW Dataset Objects """ |
| print("Loading %s into an OCW Dataset Object" % (MODEL,)) |
| knmi_dataset = local.load_file(MODEL, "tasmax") |
| print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,)) |
| |
| """ 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']) |
| |
| print("We are going to use the Model to constrain the Spatial Domain") |
| # The spatial_boundaries() function returns the spatial extent of the dataset |
| print("The KNMI_Dataset spatial bounds (min_lat, max_lat, min_lon, max_lon) are: \n" |
| "%s\n" % (knmi_dataset.spatial_boundaries(), )) |
| print("The KNMI_Dataset spatial resolution (lat_resolution, lon_resolution) is: \n" |
| "%s\n\n" % (knmi_dataset.spatial_resolution(), )) |
| min_lat, max_lat, min_lon, max_lon = knmi_dataset.spatial_boundaries() |
| |
| 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.temporal_boundaries() |
| # Grab the Max Start Time |
| start_time = max([cru_start, knmi_start]) |
| # Grab the Min End Time |
| end_time = min([cru_end, knmi_end]) |
| print("Overlap computed to be: %s to %s" % (start_time.strftime("%Y-%m-%d"), |
| end_time.strftime("%Y-%m-%d"))) |
| print("We are going to grab the first %s year(s) of data" % YEARS) |
| end_time = datetime.datetime(start_time.year + YEARS, start_time.month, start_time.day) |
| print("Final Overlap 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("CRU31_Dataset.values shape: (times, lats, lons) - %s" % (cru31_dataset.values.shape,)) |
| print("KNMI_Dataset.values shape: (times, lats, lons) - %s" % (knmi_dataset.values.shape,)) |
| print("Our two datasets have a mis-match in time. We will subset on time to %s years\n" % YEARS) |
| |
| # Create a Bounds object to use for subsetting |
| new_bounds = Bounds(lat_min=min_lat, lat_max=max_lat, lon_min=min_lon, lon_max=max_lon, start=start_time, end=end_time) |
| knmi_dataset = dsp.subset(knmi_dataset, new_bounds) |
| |
| print("CRU31_Dataset.values shape: (times, lats, lons) - %s" % (cru31_dataset.values.shape,)) |
| print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,)) |
| |
| print("Temporally Rebinning the Datasets to a Single Timestep") |
| # To run FULL temporal Rebinning, |
| knmi_dataset = dsp.temporal_rebin(knmi_dataset, temporal_resolution='full') |
| cru31_dataset = dsp.temporal_rebin(cru31_dataset, temporal_resolution='full') |
| |
| print("KNMI_Dataset.values shape: %s" % (knmi_dataset.values.shape,)) |
| print("CRU31_Dataset.values shape: %s \n\n" % (cru31_dataset.values.shape,)) |
| |
| """ 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 0.5 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 |
| print("Spatially Regridding the KNMI_Dataset...") |
| knmi_dataset = dsp.spatial_regrid(knmi_dataset, new_lats, new_lons) |
| print("Spatially Regridding the CRU31_Dataset...") |
| cru31_dataset = dsp.spatial_regrid(cru31_dataset, new_lats, new_lons) |
| print("Final shape of the KNMI_Dataset:%s" % (knmi_dataset.values.shape, )) |
| print("Final shape of the CRU31_Dataset:%s" % (cru31_dataset.values.shape, )) |
| |
| """ Step 4: Build a Metric to use for Evaluation - Bias for this example """ |
| # You can build your own metrics, but OCW also ships with some common metrics |
| 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(knmi_dataset, [cru31_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_metrics, num_target_datasets) if no subregion |
| # Accessing the actual results when we have used 1 metric and 1 dataset is |
| # done this way: |
| print("Accessing the Results of the Evaluation run") |
| results = bias_evaluation.results[0][0,:] |
| |
| # 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 = (1, 1) # Using a 1 x 1 since we have a single Bias for the full time range |
| plot_title = "TASMAX Bias of KNMI Compared to CRU 3.1 (%s - %s)" % (start_time.strftime("%Y/%d/%m"), end_time.strftime("%Y/%d/%m")) |
| sub_titles = ["Full Temporal Range"] |
| |
| plotter.draw_contour_map(results, lats, lons, fname, |
| gridshape=gridshape, ptitle=plot_title, |
| subtitles=sub_titles) |