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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
*** 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
from __future__ import print_function
import numpy as np
import as ma
import ocw.data_source.local as local
import ocw.dataset as ds
import ocw.dataset_processor as dsp
import ocw.plotter as plotter
# data source:
# is publicly available.
dataset = local.load_file('',
# Subset the data for East Asia.
Bounds = ds.Bounds(lat_min=20, lat_max=57.7, lon_min=90, lon_max=150)
dataset = dsp.subset(dataset, Bounds)
# The original dataset includes nonabsorbing AOD values between March 2000 and February 2015.
# dsp.temporal_subset will extract data in September-October-November.
dataset_SON = dsp.temporal_subset(
dataset, month_start=9, month_end=11, average_each_year=True)
ny, nx = dataset_SON.values.shape[1:]
# multi-year mean aod
clim_aod = ma.zeros([3, ny, nx])
clim_aod[0, :] = ma.mean(dataset_SON.values, axis=0) # 16-year mean
clim_aod[1, :] = ma.mean(dataset_SON.values[-5:, :],
axis=0) # the last 5-year mean
clim_aod[2, :] = dataset_SON.values[-1, :] # the last year's value
# plot clim_aod (3 subplots)
plotter.draw_contour_map(clim_aod, dataset_SON.lats, dataset_SON.lons,
gridshape=[1, 3], subtitles=['2000-2015: 16 years', '2011-2015: 5 years', '2015: 1 year'],
clevs=np.arange(21) * 0.02)