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Fix merge conflict (CLIMATE-465)
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agoodm committed Dec 2, 2017
2 parents 8b5de9c + da7e5a0 commit 2d30ef8ed998df3cfaba1b53ea17eefae6872a5c
Showing 16 changed files with 466 additions and 11 deletions.
@@ -15,6 +15,32 @@
# specific language governing permissions and limitations
# under the License.

"""
GPM_WRF24_JPDF_comparison.py
This is an example of calculating the joint probability distribution
function of rainfall intensity and duration for the Northern Great
Plains using GPM IMERG data for June/01/2015
In this example:
1. Load the GPM and WRF24 datasets with spatial filter.
2. Load the spatial filter (Bukovsky region mask).
3. Spatially subset the WRF data.
4. Analyze the wet spells.
5. Calculate the joint PDF(JPDF) of spell_duration and peak_rainfall.
6. Visualize the JPDF.
OCW modules demonstrated:
1. datasource/local
2. dataset
3. dataset_processor
4. metrics
5. plotter
"""

from ocw.dataset import Bounds
import ocw.data_source.local as local
import ocw.dataset_processor as dsp
@@ -15,6 +15,32 @@
# specific language governing permissions and limitations
# under the License.

"""
draw_climatology_map_MISR_AOD.py
Use OCW to download an MISR dataset, subset the data, calculate the 16 and 5 year
mean and draw a countour map of the means and the current values.
In this example:
1. Download a dataset from https://dx.doi.org/10.6084/m9.figshare.3753321.v1.
*** Note *** The dataset for this example is not downloaded as part of the example
and must be downloaded to examples directory before running the example.
*** Note *** Depending on the OS on which the example is being run, the download
may remove the - in the filename. Rename the file appropriately.
2. Subset the data set (lat / lon / start date / end date).
3. Calculate the 16, 5 and 1 year mean.
4. Draw a three contour maps using the calculated means and current values.
OCW modules demonstrated:
1. datasource/local
2. dataset
3. dataset_processor
4. plotter
"""

import ocw.dataset as ds
import ocw.data_source.local as local
import ocw.dataset_processor as dsp
@@ -15,6 +15,21 @@
# specific language governing permissions and limitations
# under the License.

"""
esgf_integration_example.py
Use OCW to download an ESGF dataset into the common format of an OCW dataset object.
In this example:
1. Download an ESGF (https://esgf.llnl.gov/) dataset and load it into a OCW dataset object.
OCW modules demonstrated:
1. datasource/esgf
"""

import ocw.data_source.esgf as esgf
from getpass import getpass
import ssl
@@ -15,6 +15,38 @@
# specific language governing permissions and limitations
# under the License.

"""
knmi_to_cru31_full_bias.py
Use OCW to download, evaluate and plot (contour map) a dataset
against a reference dataset and OCW standard metrics (bias).
In this example:
1. Download a netCDF files from a local site.
AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc
2. Load the local files into OCW dataset objects.
3. Interface with the Regional Climate Model Evalutaion Database (https://rcmes.jpl.nasa.gov/)
to load the CRU3.1 Daily-Max Temp dataset (https://rcmes.jpl.nasa.gov/content/cru31).
4. Process each dataset to the same same shape.
5. Temporally rebin the datasets to a single timestep.
6. Spatially regrid the dataset objects to a 1/2 degree grid.
7. Build a bias metric to use for evaluation use the standard OCW metric set.
8. Create an evaluation object using the datasets and metric.
9. Plot the results of the evaluation (contour map).
OCW modules demonstrated:
1. datasource/local
2. datasource/rcmed
3. dataset
4. dataset_processor
5. evaluation
6. metrics
7. plotter
"""

import datetime
import urllib
from os import path
@@ -15,6 +15,38 @@
# specific language governing permissions and limitations
# under the License.

"""
model_ensemble_to_rcmed.py
Use OCW to download, evaluate and plot (contour map) two datasets
against a reference dataset and OCW standard metrics (bias).
In this example:
1. Download two netCDF files from a local site.
AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc
AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax.nc
2. Load the local files into OCW dataset objects.
3. Interface with the Regional Climate Model Evaluation Database (https://rcmes.jpl.nasa.gov/)
to load the CRU3.1 Daily-Max Temp dataset (https://rcmes.jpl.nasa.gov/content/cru31).
4. Temporally rebin the datasets to annual.
5. Spatially regrid the dataset objects to a 1/2 degree grid.
6. Build a bias metric to use for evaluation use the standard OCW metric set.
7. Create an evaluation object using the datasets and metric.
8. Plot the results of the evaluation (contour map).
OCW modules demonstrated:
1. datasource/local
2. datasource/rcmed
3. dataset
4. dataset_processor
5. metrics
6. evaluation
7. plotter
"""

import datetime
import math
import urllib
@@ -15,6 +15,42 @@
# specific language governing permissions and limitations
# under the License.

"""
multi_model_evaluation.py
Use OCW to download, evaluate and plot (contour map) two datasets
against a reference dataset and OCW standard metrics.
In this example:
1. Download two netCDF files from a local site.
AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc
AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc
2. Load the local files into OCW dataset objects.
3. Interface with the Regional Climate Model Evaluation Database (https://rcmes.jpl.nasa.gov/)
to load the CRU3.1 Daily Precipitation dataset (https://rcmes.jpl.nasa.gov/content/cru31).
4. Process each dataset to the same same shape.
a.) Restrict the datasets re: geographic and time boundaries.
b.) Convert the dataset water flux to common units.
c.) Normalize the dataset date / times to monthly.
d.) Spatially regrid each dataset.
5. Calculate the mean annual value for each dataset.
6. Evaluate the datasets against the reference data set and OCW standard metric and plot
a contour map.
OCW modules demonstrated:
1. datasource/local
2. datasource/rcmed
3. dataset
4. dataset_processor
5. metrics
6. evaluation
7. plotter
8. utils
"""

import datetime
import urllib
import numpy as np
@@ -1,3 +1,54 @@
# 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
"""

# Apache OCW lib immports
from ocw.dataset import Dataset, Bounds
import ocw.data_source.local as local
@@ -15,6 +15,27 @@
# specific language governing permissions and limitations
# under the License.

"""
podaac_integration_example.py
Use OCW to download a PODACC dataset, evaluate and plot (contour map).
In this example:
1. Download a remote PO.DAAC (https://podaac.jpl.nasa.gov/) dataset
and read it into an OCW dataset object.
2. Create a temporal STD metric using one of the OCW standard metrics.
3. Evaluate the dataset against the metric and plot a contour map.
OCW modules demonstrated:
1. datasource/podaac_datasource
2. metrics
3. evaluation
4. plotter
"""

import ocw.data_source.podaac_datasource as podaac
import ocw.evaluation as evaluation
import ocw.metrics as metrics
@@ -15,6 +15,35 @@
# specific language governing permissions and limitations
# under the License.

"""
simple_model_to_model_bias.py
Use OCW to download, normalize and evaluate two datasets
against an OCW metric (bias) and plot the results of the
evaluation (contour map).
In this example:
1. Download two netCDF files from a local site.
AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc
AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax.nc
2. Load the local files into OCW dataset objects.
3. Temporally rebin the data anually.
4. Spatially regrid the dataset objects to a 1 degree grid.
5. Build a bias metric to use for evaluation use the standard OCW metric set.
6. Create an evaluation object using the datasets and metric.
7. Plot the results of the evaluation (contour map).
OCW modules demonstrated:
1. datasource/local
2. dataset_processor
3. evaluation
4. metrics
5. plotter
"""

import datetime
from os import path
import sys
@@ -15,6 +15,30 @@
# specific language governing permissions and limitations
# under the License.

"""
simple_model_tstd.py
Use OCW to download, evaluate against a OCW standard
metrics (temporal STD) and plot the results (contour map).
In this example:
1. Download a netCDF files from a local site.
AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc
2. Load the local file into OCW dataset objects.
3. Use the standard OCW metrics to build a metric against which to evaluation (temporal STD).
4. Create an evaluation object of the dataset vs. the metric.
5. Plot the results of the evaluation using a contour map.
OCW modules demonstrated:
1. datasource/local
2. metrics
3. evaluation
4. plotter
"""

from os import path
import urllib

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