CLIMATE-666 - Replace examples with the RCMES script and yaml files - run_RCMES.py, example_package.py and .yaml files replace the old examples.
Project: http://git-wip-us.apache.org/repos/asf/climate/repo Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/7396ffc8 Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/7396ffc8 Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/7396ffc8 Branch: refs/heads/master Commit: 7396ffc82b246c1dcad71661b4884b93ea09bb41 Parents: 82f6651 Author: huikyole <[email protected]> Authored: Tue Sep 22 13:56:14 2015 -0700 Committer: huikyole <[email protected]> Committed: Tue Sep 22 13:56:14 2015 -0700 ---------------------------------------------------------------------- ...ia_prec_DJF_mean_taylor_diagram_to_TRMM.yaml | 45 +++++ ...ordex-AF_tasmax_annual_mean_bias_to_cru.yaml | 46 +++++ ...prec_subregion_annual_cycle_time_series.yaml | 90 +++++++++ ...cap_prec_JJA_mean_taylor_diagram_to_cru.yaml | 44 +++++ ...nterannual_variability_portrait_diagram.yaml | 75 ++++++++ .../old_examples/knmi_to_cru31_full_bias.py | 174 +++++++++++++++++ .../old_examples/model_ensemble_to_rcmed.py | 186 +++++++++++++++++++ examples/old_examples/multi_model_evaluation.py | 151 +++++++++++++++ .../old_examples/multi_model_taylor_diagram.py | 144 ++++++++++++++ .../old_examples/simple_model_to_model_bias.py | 124 +++++++++++++ .../simple_model_to_model_bias_DJF_and_JJA.py | 64 +++++++ examples/old_examples/simple_model_tstd.py | 89 +++++++++ examples/old_examples/subregions.py | 53 ++++++ .../old_examples/subregions_portrait_diagram.py | 139 ++++++++++++++ examples/old_examples/taylor_diagram_example.py | 113 +++++++++++ .../old_examples/time_series_with_regions.py | 141 ++++++++++++++ examples/run_RCMES.py | 7 +- 17 files changed, 1683 insertions(+), 2 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/cmip5_SE_Asia_prec_DJF_mean_taylor_diagram_to_TRMM.yaml ---------------------------------------------------------------------- diff --git a/examples/cmip5_SE_Asia_prec_DJF_mean_taylor_diagram_to_TRMM.yaml b/examples/cmip5_SE_Asia_prec_DJF_mean_taylor_diagram_to_TRMM.yaml new file mode 100644 index 0000000..276e744 --- /dev/null +++ b/examples/cmip5_SE_Asia_prec_DJF_mean_taylor_diagram_to_TRMM.yaml @@ -0,0 +1,45 @@ +workdir: ./ +output_netcdf_filename: cmip5_SE_Asia_prec_DJF_1998-2010.nc + +# (RCMES will temporally subset data between month_start and month_end. If average_each_year is True (False), seasonal mean in each year is (not) calculated and used for metrics calculation.) +time: + maximum_overlap_period: True + start_time: 1981-01-01 + end_time: 2010-12-31 + temporal_resolution: monthly + month_start: 12 + month_end: 2 + average_each_year: False + +space: + min_lat: -15.14 + max_lat: 27.26 + min_lon: 89.26 + max_lon: 146.96 + +regrid: + regrid_on_reference: True + regrid_dlat: 0.50 + regrid_dlon: 0.50 + +datasets: + reference: + data_source: rcmed + data_name: TRMM + dataset_id: 3 + parameter_id: 36 + + targets: + data_source: local + path: ./data/pr_Amon* + variable: pr + +number_of_metrics_and_plots: 1 + +metrics1: Taylor_diagram_spatial_pattern_of_multiyear_climatology + +plots1: + file_name: cmip5_SE_ASIA_prec_DJF_mean_taylor_diagram_to_TRMM + +use_subregions: False + http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/cordex-AF_tasmax_annual_mean_bias_to_cru.yaml ---------------------------------------------------------------------- diff --git a/examples/cordex-AF_tasmax_annual_mean_bias_to_cru.yaml b/examples/cordex-AF_tasmax_annual_mean_bias_to_cru.yaml new file mode 100644 index 0000000..042a9a3 --- /dev/null +++ b/examples/cordex-AF_tasmax_annual_mean_bias_to_cru.yaml @@ -0,0 +1,46 @@ +workdir: ./ +output_netcdf_filename: cordex-AF_CRU_taxmax_monthly_1990-2007.nc + +# (RCMES will temporally subset data between month_start and month_end. If average_each_year is True (False), seasonal mean in each year is (not) calculated and used for metrics calculation.) +time: + maximum_overlap_period: True + start_time: 1990-01-01 + end_time: 2007-12-31 + temporal_resolution: monthly + month_start: 1 + month_end: 12 + average_each_year: False + +space: + min_lat: -45.76 + max_lat: 42.24 + min_lon: -24.64 + max_lon: 60.28 + +regrid: + regrid_on_reference: False + regrid_dlat: 0.44 + regrid_dlon: 0.44 + +datasets: + reference: + data_source: rcmed + data_name: CRU + dataset_id: 10 + parameter_id: 37 + + targets: + data_source: local + path: ./data/AFRICA*tasmax.nc + variable: tasmax + +number_of_metrics_and_plots: 1 + +metrics1: Map_plot_bias_of_multiyear_climatology + +plots1: + file_name: cordex-AF_tasmax_annual_mean_bias_to_cru + subplots_array: !!python/tuple [3,4] + +use_subregions: False + http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/cordex_AF_prec_subregion_annual_cycle_time_series.yaml ---------------------------------------------------------------------- diff --git a/examples/cordex_AF_prec_subregion_annual_cycle_time_series.yaml b/examples/cordex_AF_prec_subregion_annual_cycle_time_series.yaml new file mode 100644 index 0000000..9483cae --- /dev/null +++ b/examples/cordex_AF_prec_subregion_annual_cycle_time_series.yaml @@ -0,0 +1,90 @@ +workdir: ./ +output_netcdf_filename: cordex_AF_prec_monthly_mean_1990-2007.nc + +# (RCMES will temporally subset data between month_start and month_end. If average_each_year is True (False), seasonal mean in each year is (not) calculated and used for metrics calculation.) +time: + maximum_overlap_period: True + start_time: 1998-01-01 + end_time: 2007-12-31 + temporal_resolution: monthly + month_start: 1 + month_end: 12 + average_each_year: False + +space: + min_lat: -45.76 + max_lat: 42.24 + min_lon: -24.64 + max_lon: 60.28 + +regrid: + regrid_on_reference: False + regrid_dlat: 0.44 + regrid_dlon: 0.44 + +datasets: + reference: + data_source: rcmed + data_name: CRU + dataset_id: 10 + parameter_id: 37 + + targets: + data_source: local + path: ./data/AFRICA*pr.nc + variable: pr + +number_of_metrics_and_plots: 1 + +metrics1: Timeseries_plot_subregion_annual_cycle + +plots1: + file_name: cordex_AF_prec_subregion_annual_cycle_time_series + subplots_array: !!python/tuple [7,3] + +use_subregions: True + +subregions: +#subregion name (R01, R02, R03,....) followed by an array of boundaries [south, north, west, east] + R01: + [29.0, 36.5, -10.0, 0.0] + R02: + [29, 37.5, 0, 10] + R03: + [25, 32.5, 10, 20] + R04: + [25, 32.5, 20, 33] + R05: + [12, 20.0, -19.3, -10.2] + R06: + [15, 25.0, 15, 30] + R07: + [7.3, 15, -10, 10] + R08: + [5, 7.3, -10, 10] + R09: + [6.9, 15, 33.9, 40] + R10: + [2.2, 11.8, 44.2, 51.8] + R11: + [0, 10, 10, 25] + R12: + [-10, 0, 10, 25] + R13: + [-15, 0, 30, 40] + R14: + [-27.9, -21.4, 13.6, 20] + R15: + [-35, -27.9, 13.6, 20] + R16: + [-35, -21.4, 20, 35.7] + R17: + [-25.8, -11.7, 43.2, 50.3] + R18: + [25, 35.0, 33, 40] + R19: + [28, 35, 45, 50] + R20: + [13, 20.0, 43, 50] + R21: + [20, 27.5, 50, 58] http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/narccap_prec_JJA_mean_taylor_diagram_to_cru.yaml ---------------------------------------------------------------------- diff --git a/examples/narccap_prec_JJA_mean_taylor_diagram_to_cru.yaml b/examples/narccap_prec_JJA_mean_taylor_diagram_to_cru.yaml new file mode 100644 index 0000000..c6b96cf --- /dev/null +++ b/examples/narccap_prec_JJA_mean_taylor_diagram_to_cru.yaml @@ -0,0 +1,44 @@ +workdir: ./ +output_netcdf_filename: narccap_prec_JJA_1980-2003.nc + +# (RCMES will temporally subset data between month_start and month_end. If average_each_year is True (False), seasonal mean in each year is (not) calculated and used for metrics calculation.) +time: + maximum_overlap_period: False + start_time: 1980-01-01 + end_time: 2003-12-31 + temporal_resolution: monthly + month_start: 6 + month_end: 8 + average_each_year: True + +space: + min_lat: 23.75 + max_lat: 49.75 + min_lon: -125.75 + max_lon: -66.75 + +regrid: + regrid_on_reference: False + regrid_dlat: 0.50 + regrid_dlon: 0.50 + +datasets: + reference: + data_source: rcmed + data_name: CRU + dataset_id: 10 + parameter_id: 37 + + targets: + data_source: local + path: ./data/prec.*ncep.monavg.nc + variable: prec + +number_of_metrics_and_plots: 1 + +metrics1: Taylor_diagram_spatial_pattern_of_multiyear_climatology + +plots1: + file_name: narccap_prec_JJA_mean_taylor_diagram_to_cru + +use_subregions: False http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/narccap_tas_DJF_subregion_interannual_variability_portrait_diagram.yaml ---------------------------------------------------------------------- diff --git a/examples/narccap_tas_DJF_subregion_interannual_variability_portrait_diagram.yaml b/examples/narccap_tas_DJF_subregion_interannual_variability_portrait_diagram.yaml new file mode 100644 index 0000000..de2d98e --- /dev/null +++ b/examples/narccap_tas_DJF_subregion_interannual_variability_portrait_diagram.yaml @@ -0,0 +1,75 @@ +workdir: ./ +output_netcdf_filename: narccap_tas_DJF_mean_mean_1980-2003.nc + +# (RCMES will temporally subset data between month_start and month_end. If average_each_year is True (False), seasonal mean in each year is (not) calculated and used for metrics calculation.) +time: + maximum_overlap_period: False + start_time: 1980-01-01 + end_time: 2003-12-31 + temporal_resolution: monthly + month_start: 12 + month_end: 2 + average_each_year: True + +space: + min_lat: 23.75 + max_lat: 49.75 + min_lon: -125.75 + max_lon: -66.75 + +regrid: + regrid_on_reference: False + regrid_dlat: 0.50 + regrid_dlon: 0.50 + +datasets: + reference: + data_source: rcmed + data_name: CRU + dataset_id: 10 + parameter_id: 38 + + targets: + data_source: local + path: ./data/temp*ncep.monavg.nc + variable: temp + +number_of_metrics_and_plots: 1 + +metrics1: Portrait_diagram_subregion_interannual_variability + +plots1: + file_name: narccap_tas_DJF_subregion_interannual_variability_portrait_diagram + +use_subregions: True + +subregions: +#subregion name (R01, R02, R03,....) followed by an array of boundaries [south, north, west, east] + R01: + [42.75, 49.75, -123.75, -120.25] + R02: + [42.75, 49.75, -119.75, -112.75] + R03: + [37.25, 42.25, -123.75, -117.75] + R04: + [32.25, 37.25, -122.75, -114.75] + R05: + [31.25, 37.25, -113.75, -108.25] + R06: + [31.25, 37.25, -108.25, -99.75] + R07: + [37.25, 43.25, -110.25, -103.75] + R08: + [45.25, 49.25, -99.75, -90.25] + R09: + [34.75, 45.25, -99.75, -90.25] + R10: + [29.75, 34.75, -95.75, -84.75] + R11: + [38.25, 44.75, -89.75, -80.25] + R12: + [38.25, 44.75, -79.75, -70.25] + R13: + [30.75, 38.25, -83.75, -75.25] + R14: + [24.25, 30.75, -83.75, -80.25] http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/knmi_to_cru31_full_bias.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/knmi_to_cru31_full_bias.py b/examples/old_examples/knmi_to_cru31_full_bias.py new file mode 100644 index 0000000..a241442 --- /dev/null +++ b/examples/old_examples/knmi_to_cru31_full_bias.py @@ -0,0 +1,174 @@ +# 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 +# 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.time_range() +# 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(min_lat, max_lat, min_lon, max_lon, start_time, end_time) +knmi_dataset = dsp.subset(new_bounds, knmi_dataset) + +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 use a timedelta > 366 days. I used 999 in this example +knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=999)) +cru31_dataset = dsp.temporal_rebin(cru31_dataset, datetime.timedelta(days=999)) + +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) http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/model_ensemble_to_rcmed.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/model_ensemble_to_rcmed.py b/examples/old_examples/model_ensemble_to_rcmed.py new file mode 100644 index 0000000..1f653a1 --- /dev/null +++ b/examples/old_examples/model_ensemble_to_rcmed.py @@ -0,0 +1,186 @@ +# 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) \ No newline at end of file http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/multi_model_evaluation.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/multi_model_evaluation.py b/examples/old_examples/multi_model_evaluation.py new file mode 100644 index 0000000..8136001 --- /dev/null +++ b/examples/old_examples/multi_model_evaluation.py @@ -0,0 +1,151 @@ +# 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 numpy as np +from os import path + + +#import Apache OCW dependences +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 ocw.utils as utils +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/"; +# Three Local Model Files +FILE_1 = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc" +FILE_2 = "AFRICA_UC-WRF311_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_bias_annual" +#variable that we are analyzing +varName = 'pr' +# 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_MAX, LON_MIN, LON_MAX, START, END) + +#regridding parameters +gridLonStep=0.5 +gridLatStep=0.5 + +#list for all target_datasets +target_datasets =[] +#list for names for all the datasets +allNames =[] + + +# 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) + +if path.exists(FILE_3): + pass +else: + urllib.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="UC")) +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") +# 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") +CRU31 = dsp.water_flux_unit_conversion(CRU31) +CRU31 = dsp.temporal_rebin(CRU31, datetime.timedelta(days=30)) + +for member, each_target_dataset in enumerate(target_datasets): + target_datasets[member] = dsp.subset(EVAL_BOUNDS, target_datasets[member]) + target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[member]) + target_datasets[member] = dsp.temporal_rebin(target_datasets[member], datetime.timedelta(days=30)) + + +""" Spatially Regrid the Dataset Objects to a user defined grid """ +# Using the bounds we will create a new set of lats and lons +print("Regridding datasets") +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) + +#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) + +#find the mean value +#way to get the mean. Note the function exists in util.py +_, CRU31.values = utils.calc_climatology_year(CRU31) +CRU31.values = np.expand_dims(CRU31.values, axis=0) + +for member, each_target_dataset in enumerate(target_datasets): + _,target_datasets[member].values = utils.calc_climatology_year(target_datasets[member]) + target_datasets[member].values = np.expand_dims(target_datasets[member].values, axis=0) + + +for target in target_datasets: + allNames.append(target.name) + +#determine the metrics +mean_bias = metrics.Bias() + +#create the Evaluation object +RCMs_to_CRU_evaluation = evaluation.Evaluation(CRU31, # Reference dataset for the evaluation + # list of target datasets for the evaluation + target_datasets, + # 1 or more metrics to use in the evaluation + [mean_bias]) +RCMs_to_CRU_evaluation.run() + +#extract the relevant data from RCMs_to_CRU_evaluation.results +#the results returns a list (num_target_datasets, num_metrics). See docs for further details +rcm_bias = RCMs_to_CRU_evaluation.results[:][0] +#remove the metric dimension +new_rcm_bias = np.squeeze(np.array(RCMs_to_CRU_evaluation.results)) + +plotter.draw_contour_map(new_rcm_bias, new_lats, new_lons, gridshape=(2, 5),fname=OUTPUT_PLOT, subtitles=allNames, cmap='coolwarm_r') http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/multi_model_taylor_diagram.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/multi_model_taylor_diagram.py b/examples/old_examples/multi_model_taylor_diagram.py new file mode 100644 index 0000000..f91ab3e --- /dev/null +++ b/examples/old_examples/multi_model_taylor_diagram.py @@ -0,0 +1,144 @@ +#Apache OCW lib immports +from ocw.dataset import Dataset, 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 + +from os import path + +# 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, 01, 1) +END = datetime.datetime(2007, 12, 31) +EVAL_BOUNDS = Bounds(LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, 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 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) + +if path.exists(FILE_3): + pass +else: + urllib.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 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: 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, datetime.timedelta(days=30)) + +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], datetime.timedelta(days=30)) + target_datasets[member] = dsp.subset(EVAL_BOUNDS, target_datasets[member]) + +#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_climatology_year(CRU31) +CRU31.values = np.expand_dims(CRU31.values, axis=0) + +#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_climatology_year(target_datasets[member]) + target_datasets[member].values = np.expand_dims(target_datasets[member].values, axis=0) + +allNames =[] + +for target in target_datasets: + allNames.append(target.name) + +#calculate the metrics +pattern_correlation = metrics.PatternCorrelation() +spatial_std_dev = metrics.StdDevRatio() + + +#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 + [spatial_std_dev, pattern_correlation])#, mean_bias,spatial_std_dev_ratio, pattern_correlation]) +RCMs_to_CRU_evaluation.run() + +rcm_std_dev = [results[0] for results in RCMs_to_CRU_evaluation.results] +rcm_pat_cor = [results[1] for results in RCMs_to_CRU_evaluation.results] + +taylor_data = np.array([rcm_std_dev, rcm_pat_cor]).transpose() + +new_taylor_data = np.squeeze(np.array(taylor_data)) + +plotter.draw_taylor_diagram(new_taylor_data, + allNames, + "CRU31", + fname=OUTPUT_PLOT, + fmt='png', + frameon=False) + + + http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/simple_model_to_model_bias.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/simple_model_to_model_bias.py b/examples/old_examples/simple_model_to_model_bias.py new file mode 100644 index 0000000..635e872 --- /dev/null +++ b/examples/old_examples/simple_model_to_model_bias.py @@ -0,0 +1,124 @@ +# 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 +from os import path +import urllib + +import numpy as np + +import ocw.data_source.local as local +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/"; +# 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 = "wrf_bias_compared_to_knmi" + +FILE_1_PATH = path.join('/tmp', FILE_1) +FILE_2_PATH = path.join('/tmp', FILE_2) + +if not path.exists(FILE_1_PATH): + urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1_PATH) +if not path.exists(FILE_2_PATH): + urllib.urlretrieve(FILE_LEADER + FILE_2, FILE_2_PATH) + +""" Step 1: Load Local NetCDF Files into OCW Dataset Objects """ +print("Loading %s into an OCW Dataset Object" % (FILE_1_PATH,)) +knmi_dataset = local.load_file(FILE_1_PATH, "tasmax") +print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,)) + +print("Loading %s into an OCW Dataset Object" % (FILE_2_PATH,)) +wrf_dataset = local.load_file(FILE_2_PATH, "tasmax") +print("WRF_Dataset.values shape: (times, lats, lons) - %s \n" % (wrf_dataset.values.shape,)) + +""" Step 2: Temporally Rebin the Data into an Annual Timestep """ +print("Temporally Rebinning the Datasets to an Annual Timestep") +knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=365)) +wrf_dataset = dsp.temporal_rebin(wrf_dataset, datetime.timedelta(days=365)) +print("KNMI_Dataset.values shape: %s" % (knmi_dataset.values.shape,)) +print("WRF_Dataset.values shape: %s \n\n" % (wrf_dataset.values.shape,)) + +""" Step 3: Spatially Regrid the Dataset Objects to a 1 degree grid """ +# 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() + +# Using the bounds we will create a new set of lats and lons on 1 degree step +new_lons = np.arange(min_lon, max_lon, 1) +new_lats = np.arange(min_lat, max_lat, 1) + +# 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("Final shape of the KNMI_Dataset: \n" + "%s\n" % (knmi_dataset.values.shape, )) +print("Spatially Regridding the WRF_Dataset...") +wrf_dataset = dsp.spatial_regrid(wrf_dataset, new_lats, new_lons) +print("Final shape of the WRF_Dataset: \n" + "%s\n" % (wrf_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, [wrf_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] +print("The results are of type: %s" % type(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 = (4, 5) # 20 Years worth of plots. 20 rows in 1 column +plot_title = "TASMAX Bias of WRF Compared to KNMI (1989 - 2008)" +sub_titles = range(1989, 2009, 1) + +plotter.draw_contour_map(results, lats, lons, fname, + gridshape=gridshape, ptitle=plot_title, + subtitles=sub_titles) http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/simple_model_to_model_bias_DJF_and_JJA.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/simple_model_to_model_bias_DJF_and_JJA.py b/examples/old_examples/simple_model_to_model_bias_DJF_and_JJA.py new file mode 100644 index 0000000..364498a --- /dev/null +++ b/examples/old_examples/simple_model_to_model_bias_DJF_and_JJA.py @@ -0,0 +1,64 @@ +# 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 +from os import path +import urllib + +import numpy as np + +import ocw.data_source.local as local +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 + +# File URL leader +FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/"; +# 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 = "wrf_bias_compared_to_knmi" + +FILE_1_PATH = path.join('/tmp', FILE_1) +FILE_2_PATH = path.join('/tmp', FILE_2) + +if not path.exists(FILE_1_PATH): + urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1_PATH) +if not path.exists(FILE_2_PATH): + urllib.urlretrieve(FILE_LEADER + FILE_2, FILE_2_PATH) + +""" Step 1: Load Local NetCDF Files into OCW Dataset Objects """ +print("Loading %s into an OCW Dataset Object" % (FILE_1_PATH,)) +knmi_dataset = local.load_file(FILE_1_PATH, "tasmax") +print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,)) + +print("Loading %s into an OCW Dataset Object" % (FILE_2_PATH,)) +wrf_dataset = local.load_file(FILE_2_PATH, "tasmax") +print("WRF_Dataset.values shape: (times, lats, lons) - %s \n" % (wrf_dataset.values.shape,)) + +""" Step 2: Calculate seasonal average """ +print("Calculate seasonal average") +knmi_DJF_mean = utils.calc_temporal_mean(dsp.temporal_subset(month_start=12, month_end=2, target_dataset=knmi_dataset)) +wrf_DJF_mean = utils.calc_temporal_mean(dsp.temporal_subset(month_start=12, month_end=2, target_dataset=wrf_dataset)) +print("Seasonally averaged KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_DJF_mean.shape,)) +print("Seasonally averaged wrf_Dataset.values shape: (times, lats, lons) - %s \n" % (wrf_DJF_mean.shape,)) +knmi_JJA_mean = utils.calc_temporal_mean(dsp.temporal_subset(month_start=6, month_end=8, target_dataset=knmi_dataset)) +wrf_JJA_mean = utils.calc_temporal_mean(dsp.temporal_subset(month_start=6, month_end=8, target_dataset=wrf_dataset)) + http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/simple_model_tstd.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/simple_model_tstd.py b/examples/old_examples/simple_model_tstd.py new file mode 100644 index 0000000..4c87813 --- /dev/null +++ b/examples/old_examples/simple_model_tstd.py @@ -0,0 +1,89 @@ +# 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. + +from os import path +import urllib + +import ocw.data_source.local as local +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/"; +# One Local Model Files +FILE_1 = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc" + +# Filename for the output image/plot (without file extension) +OUTPUT_PLOT = "knmi_temporal_std" + +# Download necessary NetCDF file if needed +if path.exists(FILE_1): + pass +else: + urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1) + +""" Step 1: Load Local NetCDF File into OCW Dataset Objects """ +print "Loading %s into an OCW Dataset Object" % (FILE_1,) +# 'tasmax' is variable name of values +knmi_dataset = local.load_file(FILE_1, "tasmax") + +print "KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,) + +# Acessing latittudes and longitudes of netCDF file +lats = knmi_dataset.lats +lons = knmi_dataset.lons + +""" Step 2: Build a Metric to use for Evaluation - Temporal STD for this example """ +# You can build your own metrics, but OCW also ships with some common metrics +print "Setting up a Temporal STD metric to use for evaluation" +std = metrics.TemporalStdDev() + +""" Step 3: 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" +# Temporal STD Metric gets one target dataset then reference dataset should be None +std_evaluation = evaluation.Evaluation(None, [knmi_dataset], [std]) +print "Executing the Evaluation using the object's run() method" +std_evaluation.run() + +""" Step 4: 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 = std_evaluation.unary_results[0][0] +print "The results are of type: %s" % type(results) + +# From the temporal std output I want to make a Contour Map of the region +print "Generating a contour map using ocw.plotter.draw_contour_map()" + +fname = OUTPUT_PLOT +gridshape = (4, 5) # 20 Years worth of plots. 20 rows in 1 column +plot_title = "TASMAX Temporal Standard Deviation (1989 - 2008)" +sub_titles = range(1989, 2009, 1) + +plotter.draw_contour_map(results, lats, lons, fname, + gridshape=gridshape, ptitle=plot_title, + subtitles=sub_titles) http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/subregions.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/subregions.py b/examples/old_examples/subregions.py new file mode 100644 index 0000000..20aaee9 --- /dev/null +++ b/examples/old_examples/subregions.py @@ -0,0 +1,53 @@ +#Apache OCW lib immports +from ocw.dataset import Dataset, 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 + +OUTPUT_PLOT = "subregions" + +# Spatial and temporal configurations +LAT_MIN = -45.0 +LAT_MAX = 42.24 +LON_MIN = -24.0 +LON_MAX = 60.0 +START_SUB = datetime.datetime(2000, 01, 1) +END_SUB = datetime.datetime(2007, 12, 31) + +#regridding parameters +gridLonStep=0.5 +gridLatStep=0.5 + +#Regrid +print("... regrid") +new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep) +new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep) + +list_of_regions = [ + Bounds(-10.0, 0.0, 29.0, 36.5, START_SUB, END_SUB), + Bounds(0.0, 10.0, 29.0, 37.5, START_SUB, END_SUB), + Bounds(10.0, 20.0, 25.0, 32.5, START_SUB, END_SUB), + Bounds(20.0, 33.0, 25.0, 32.5, START_SUB, END_SUB), + Bounds(-19.3,-10.2,12.0, 20.0, START_SUB, END_SUB), + Bounds( 15.0, 30.0, 15.0, 25.0,START_SUB, END_SUB), + Bounds(-10.0, 10.0, 7.3, 15.0, START_SUB, END_SUB), + Bounds(-10.9, 10.0, 5.0, 7.3, START_SUB, END_SUB), + Bounds(33.9, 40.0, 6.9, 15.0, START_SUB, END_SUB), + Bounds(10.0, 25.0, 0.0, 10.0, START_SUB, END_SUB), + Bounds(10.0, 25.0,-10.0, 0.0, START_SUB, END_SUB), + Bounds(30.0, 40.0,-15.0, 0.0, START_SUB, END_SUB), + Bounds(33.0, 40.0, 25.0, 35.0, START_SUB, END_SUB)] + +#for plotting the subregions +plotter.draw_subregions(list_of_regions, new_lats, new_lons, OUTPUT_PLOT, fmt='png') + + + http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/subregions_portrait_diagram.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/subregions_portrait_diagram.py b/examples/old_examples/subregions_portrait_diagram.py new file mode 100644 index 0000000..075de2d --- /dev/null +++ b/examples/old_examples/subregions_portrait_diagram.py @@ -0,0 +1,139 @@ +#Apache OCW lib immports +from ocw.dataset import Dataset, 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 urllib + +# 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, 01, 1) +END = datetime.datetime(2007, 12, 31) +EVAL_BOUNDS = Bounds(LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, 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): + urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1) + +if not path.exists(FILE_2): + urllib.urlretrieve(FILE_LEADER + FILE_2, FILE_2) + +if not path.exists(FILE_3): + urllib.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 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 ...") +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(EVAL_BOUNDS, 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") +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(-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, 35.00)] + +region_list=["R"+str(i+1) for i in xrange(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(new_patcor,allNames, region_list, fname=OUTPUT_PLOT, fmt='png', cmap='coolwarm_r') + + + http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/taylor_diagram_example.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/taylor_diagram_example.py b/examples/old_examples/taylor_diagram_example.py new file mode 100644 index 0000000..b08502e --- /dev/null +++ b/examples/old_examples/taylor_diagram_example.py @@ -0,0 +1,113 @@ +# 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 sys +from os import path +import urllib + +import numpy + +from ocw.dataset import Bounds +import ocw.data_source.local as local +import ocw.dataset_processor as dsp +import ocw.evaluation as evaluation +import ocw.metrics as metrics +import ocw.plotter as plotter + +FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/"; +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" + +# Download some example NetCDF files for the evaluation +################################################################################ +if not path.exists(FILE_1): + urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1) + +if not path.exists(FILE_2): + urllib.urlretrieve(FILE_LEADER + FILE_2, FILE_2) + +# Load the example datasets into OCW Dataset objects. We want to load +# the 'tasmax' variable values. We'll also name the datasets for use +# when plotting. +################################################################################ +knmi_dataset = local.load_file(FILE_1, "tasmax") +wrf_dataset = local.load_file(FILE_2, "tasmax") + +knmi_dataset.name = "knmi" +wrf_dataset.name = "wrf" + +# Date values from loaded datasets might not always fall on reasonable days. +# With monthly data, we could have data falling on the 1st, 15th, or some other +# day of the month. Let's fix that real quick. +################################################################################ +knmi_dataset = dsp.normalize_dataset_datetimes(knmi_dataset, 'monthly') +wrf_dataset = dsp.normalize_dataset_datetimes(wrf_dataset, 'monthly') + +# We're only going to run this evaluation over a years worth of data. We'll +# make a Bounds object and use it to subset our datasets. +################################################################################ +subset = Bounds(-45, 42, -24, 60, datetime.datetime(1989, 1, 1), datetime.datetime(1989, 12, 1)) +knmi_dataset = dsp.subset(subset, knmi_dataset) +wrf_dataset = dsp.subset(subset, wrf_dataset) + +# Temporally re-bin the data into a monthly timestep. +################################################################################ +knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=30)) +wrf_dataset = dsp.temporal_rebin(wrf_dataset, datetime.timedelta(days=30)) + +# Spatially regrid the datasets onto a 1 degree grid. +################################################################################ +# Get the bounds of the reference dataset and use it to create a new +# set of lat/lon values on a 1 degree step +# Using the bounds we will create a new set of lats and lons on 1 degree step +min_lat, max_lat, min_lon, max_lon = knmi_dataset.spatial_boundaries() +new_lons = numpy.arange(min_lon, max_lon, 1) +new_lats = numpy.arange(min_lat, max_lat, 1) + +# Spatially regrid datasets using the new_lats, new_lons numpy arrays +knmi_dataset = dsp.spatial_regrid(knmi_dataset, new_lats, new_lons) +wrf_dataset = dsp.spatial_regrid(wrf_dataset, new_lats, new_lons) + +# Load the metrics that we want to use for the evaluation. +################################################################################ +sstdr = metrics.StdDevRatio() +pc = metrics.PatternCorrelation() + +# Create our new evaluation object. The knmi dataset is the evaluations +# reference dataset. We then provide a list of 1 or more target datasets +# to use for the evaluation. In this case, we only want to use the wrf dataset. +# Then we pass a list of all the metrics that we want to use in the evaluation. +################################################################################ +test_evaluation = evaluation.Evaluation(knmi_dataset, [wrf_dataset], [sstdr, pc]) +test_evaluation.run() + +# Pull our the evaluation results and prepare them for drawing a Taylor diagram. +################################################################################ +spatial_stddev_ratio = test_evaluation.results[0][0] +spatial_correlation = test_evaluation.results[0][1] + +taylor_data = numpy.array([[spatial_stddev_ratio], [spatial_correlation]]).transpose() + +# Draw our taylor diagram! +################################################################################ +plotter.draw_taylor_diagram(taylor_data, + [wrf_dataset.name], + knmi_dataset.name, + fname='taylor_plot', + fmt='png', + frameon=False) http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/old_examples/time_series_with_regions.py ---------------------------------------------------------------------- diff --git a/examples/old_examples/time_series_with_regions.py b/examples/old_examples/time_series_with_regions.py new file mode 100644 index 0000000..1d552a8 --- /dev/null +++ b/examples/old_examples/time_series_with_regions.py @@ -0,0 +1,141 @@ +#Apache OCW lib immports +from ocw.dataset import Dataset, 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 urllib + +# 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" + +LAT_MIN = -45.0 +LAT_MAX = 42.24 +LON_MIN = -24.0 +LON_MAX = 60.0 +START = datetime.datetime(2000, 01, 1) +END = datetime.datetime(2007, 12, 31) + +EVAL_BOUNDS = Bounds(LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END) + +varName = 'pr' +gridLonStep=0.44 +gridLatStep=0.44 + +#needed vars for the script +target_datasets =[] +tSeries =[] +results =[] +labels =[] # could just as easily b the names for each subregion +region_counter = 0 + +# Download necessary NetCDF file if not present +if not path.exists(FILE_1): + urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1) + +if not path.exists(FILE_2): + urllib.urlretrieve(FILE_LEADER + FILE_2, FILE_2) + +if not path.exists(FILE_3): + urllib.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 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") +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(EVAL_BOUNDS, 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") +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 +CRU31.values, CRU31.times = utils.calc_climatology_monthly(CRU31) + +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" + +#append to the target_datasets for final analysis +target_datasets.append(target_datasets_ensemble) + +""" 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)] + +for regions in region_list: + firstTime = True + subset_name = regions[0]+"_CRU31" + #labels.append(subset_name) #for legend, uncomment this line + subset = dsp.subset(list_of_regions[region_counter], CRU31, 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 = dsp.subset(list_of_regions[region_counter],target_datasets[member],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') + results =[] + tSeries =[] + labels =[] + region_counter+=1 + + + http://git-wip-us.apache.org/repos/asf/climate/blob/7396ffc8/examples/run_RCMES.py ---------------------------------------------------------------------- diff --git a/examples/run_RCMES.py b/examples/run_RCMES.py index 33c7cb9..9039486 100644 --- a/examples/run_RCMES.py +++ b/examples/run_RCMES.py @@ -86,9 +86,12 @@ if ref_data_info['data_source'] == 'rcmed': min_lon = np.max([min_lon, ref_dataset.lons.min()]) max_lon = np.min([max_lon, ref_dataset.lons.max()]) bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time) -ref_dataset = dsp.subset(bounds,ref_dataset) + +if ref_dataset.lats.ndim !=2 and ref_dataset.lons.ndim !=2: + ref_dataset = dsp.subset(bounds,ref_dataset) for idata,dataset in enumerate(model_datasets): - model_datasets[idata] = dsp.subset(bounds,dataset) + if dataset.lats.ndim !=2 and dataset.lons.ndim !=2: + model_datasets[idata] = dsp.subset(bounds,dataset) # Temporaly subset both observation and model datasets for the user specified season month_start = time_info['month_start']
