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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Test for Mohammed
This container was started with
sudo docker run -d -p 433
Step1:  Here are the RadarSat-2 quadpol coherency matrix image directories as created from the Sentinel-1 Toolbox
Step2:  To combine the matrix bands into a single GeoTiff image, we run the python script ingestrs2quad.py
Step3:  Here is an RGB display of the three diagonal matrix elements of the above image (bands 1,6 and 9)
Step4:  To estimate the equivalent number of looks, run the python script enlml.py
Step5:  So the ENL would appear to be about 5.
To run the change sequential change detection on the three images, run the bash script sar_seq_rs2quad.sh. It gathers the three images together and calls the python script sar_seq.py which does the change detection. By choosing a spatial subset (in this case 400x400), the images are clipped and co-registered to the first image. This might be unnecessary if the images are well registered anyway.
If you have a multicore processor you can eneable parallel computation by openeing a terminal window in the container (new terminal) and running
ipcluster start -n 4
Step6:  Here is the change map for the most recent changes
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Python Code:
%matplotlib inline
Explanation: Test for Mohammed
This container was started with
sudo docker run -d -p 433:8888 --name=sar -v /home/mort/imagery/mohammed/Data:/home/imagery mort/sardocker
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ls /home/imagery
Explanation: Here are the RadarSat-2 quadpol coherency matrix image directories as created from the Sentinel-1 Toolbox:
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run /home/ingestrs2quad /home/imagery/RS2_OK82571_PK721079_DK650144_FQ17W_20160403_230258_HH_VV_HV_VH_SLC/
run /home/ingestrs2quad /home/imagery/RS2_OK82571_PK721080_DK650145_FQ17W_20160427_230257_HH_VV_HV_VH_SLC/
run /home/ingestrs2quad /home/imagery/RS2_OK82571_PK721081_DK650146_FQ17W_20160614_230256_HH_VV_HV_VH_SLC/
Explanation: To combine the matrix bands into a single GeoTiff image, we run the python script ingestrs2quad.py:
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run /home/dispms -f /home/imagery/RS2_OK82571_PK721081_DK650146_FQ17W_20160614_230256_HH_VV_HV_VH_SLC/polSAR.tif \
-p [1,6,9]
Explanation: Here is an RGB display of the three diagonal matrix elements of the above image (bands 1,6 and 9):
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run /home/enlml /home/imagery/RS2_OK82571_PK721081_DK650146_FQ17W_20160614_230256_HH_VV_HV_VH_SLC/polSAR.tif
Explanation: To estimate the equivalent number of looks, run the python script enlml.py:
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!/home/sar_seq_rs2quad.sh 20160403 20160427 20160614 [50,50,400,400] 5 0.01
Explanation: So the ENL would appear to be about 5.
To run the change sequential change detection on the three images, run the bash script sar_seq_rs2quad.sh. It gathers the three images together and calls the python script sar_seq.py which does the change detection. By choosing a spatial subset (in this case 400x400), the images are clipped and co-registered to the first image. This might be unnecessary if the images are well registered anyway.
If you have a multicore processor you can eneable parallel computation by openeing a terminal window in the container (new terminal) and running
ipcluster start -n 4
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run /home/dispms \
-f /home/imagery/RS2_OK82571_PK721079_DK650144_FQ17W_20160403_230258_HH_VV_HV_VH_SLC/sarseq(20160403-1-20160614)_cmap.tif -c
Explanation: Here is the change map for the most recent changes:
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