Kernel: Python 2
ATMS 391 Geophysical Data Analysis
Homework 13: Image manipulation and analysis
(1) Open the satellite data file, and plot it on a basemap.
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Populating the interactive namespace from numpy and matplotlib
(2) Filter out the missing pixels near cloud shields due to the geostationary parallax correction.
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<matplotlib.image.AxesImage at 0x7f99cd5f5f90>
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<matplotlib.image.AxesImage at 0x7f9a42e77950>
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(3) Create a histogram of brightness temperatures in valid regions.
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array([[ 260., 260., 260., ..., 263., 263., 263.],
[ 260., 260., 260., ..., 263., 263., 263.],
[ 264., 264., 264., ..., 270., 269., 269.],
...,
[ 262., 262., 262., ..., 262., 262., 262.],
[ 280., 280., 278., ..., 273., 269., 269.],
[ 280., 280., 278., ..., 273., 271., 271.]], dtype=float32)
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(4) What is the 5th percentile of brightness temperature? The 1st percentile?
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5th percentile: 241
1st percentile: 222
(5) Identify regions of brightness temperature (a) < 210 K (b) < 235 K with a binary mask. Color them with different colors.
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<matplotlib.image.AxesImage at 0x7f9a40675a90>
(6) Count how many objects in the image are below 210 K and 235 K.
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445
(7) Create a histogram and a CDF of the object sizes at 210 and 235 K. Plot them on the same graph with a legend.
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(8) Create a histogram of the minimum values of brightness temperature within each 235 K object.
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