Difference between revisions of "UCVMC How to process bin data"

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== metadata file ==
 
== metadata file ==
  
meta data contains many fields. Some of the fields are specific to the type
+
Metadata file contains many fields. Some of the fields are specific to the type
of the plot that were made. This is a horizontal slice plot, therefore, it
+
of the plot that were made. These are the fields for a horizontal slice plot.
contains the following metadata
 
  
 
   lon_list
 
   lon_list
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(lon1,lat1) is the lower left corner of the plot and (lon2,lat2) is the upper right
 
(lon1,lat1) is the lower left corner of the plot and (lon2,lat2) is the upper right
corner of the plot. lon_list and lat_list are the ticks of the axis
+
corner of the plot. lon_list and lat_list are the tick values of the axes.
  
 
[http://hypocenter.usc.edu/research/UCVM/cvmh_no_ely_gtl/cvmh_nogtl_vs_0_map_meta.json metadata in json]
 
[http://hypocenter.usc.edu/research/UCVM/cvmh_no_ely_gtl/cvmh_nogtl_vs_0_map_meta.json metadata in json]

Latest revision as of 19:21, 5 July 2018

Binary data can be generated from UCVMC's plotting tool in python or by one of the UCVMC app that is written to work with the UCVMC api.

example plot

This is horizontal Vs slice plot from UCVMC's plot_horizontal_slice.py

CS17.3, no GTL


metadata file

Metadata file contains many fields. Some of the fields are specific to the type of the plot that were made. These are the fields for a horizontal slice plot.

 lon_list
 lat_list
 lat1
 lon1
 lat2
 lon2
 datafile
 num_x
 num_y
 min
 max
 color
 data_type
 outfile
 cvm_selected
 datapoints
 depth

(lon1,lat1) is the lower left corner of the plot and (lon2,lat2) is the upper right corner of the plot. lon_list and lat_list are the tick values of the axes.

metadata in json

binary data file

The data is written out as an array of float32. It can be imported with numpy in python,

        import numpy as np
        fh = open(rawfile, 'r')
        floats = np.fromfile(fh, dtype=np.float32)

and fill into the 2D array as,

         datapoints = np.arange(num_x * num_y,dtype=np.float32).reshape(num_y, num_x)
         i=0
         j=0
         for f in floats:
            datapoints[i][j] = f
            j = j + 1
            if j >= num_x:
                j = 0
                i = i + 1


binary data


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