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Writing and subsequently reading a cfradial version of the pyart radar object takes a very long time. In benchmark testing, this averaged near 35-40% of the total hodograph processing time.
The pyart.retrieve.vad_browning can evidently be run using sweeps extracted from the raw/native pyart radar object (initial data format is MSG31 binary), simply by converting radar_1sweep = ncrad.extract_sweeps([idx]) to radar_1sweep = radar.extract_sweeps([idx]), and removing the write_cfradial and read_cfradial calls. A try/except block must be added though, to handle sweeps in which not insufficient data is available which otherwise would result in a ValueError.
One minor item of note: somewhat inexplicably, there are subtle/minor differences in the data when comparing what's in the original radar object and what's read in by pyart.io.read_cfradial. This differences are clearly introduced either by write_cfradial or read_cfradial. Regardless, the differences are slight (see images below), but probably not enough to justify the significant time increase to utilize cfradial data.
Example of differences caused entirely by pyart.io.write_cfradial and/or pyart.io.read_cfradial:
Hodograph using cfradial data:
Hodograph using original pyart.io.read object:
The text was updated successfully, but these errors were encountered:
lcarlaw
changed the title
Very long processing times in hodo_plot.py due to cfradial write and read. Solution, plus possible bug in pyart code?
Long processing times in hodo_plot.py due to cfradial write and read. Solution, plus possible bug in pyart code?
Aug 4, 2024
Writing and subsequently reading a cfradial version of the pyart radar object takes a very long time. In benchmark testing, this averaged near 35-40% of the total hodograph processing time.
The
pyart.retrieve.vad_browning
can evidently be run using sweeps extracted from the raw/native pyart radar object (initial data format is MSG31 binary), simply by convertingradar_1sweep = ncrad.extract_sweeps([idx])
toradar_1sweep = radar.extract_sweeps([idx])
, and removing thewrite_cfradial
andread_cfradial
calls. A try/except block must be added though, to handle sweeps in which not insufficient data is available which otherwise would result in aValueError
.One minor item of note: somewhat inexplicably, there are subtle/minor differences in the data when comparing what's in the original radar object and what's read in by
pyart.io.read_cfradial
. This differences are clearly introduced either bywrite_cfradial
orread_cfradial
. Regardless, the differences are slight (see images below), but probably not enough to justify the significant time increase to utilize cfradial data.Example of differences caused entirely by
pyart.io.write_cfradial
and/orpyart.io.read_cfradial
:Hodograph using cfradial data:
Hodograph using original pyart.io.read object:
The text was updated successfully, but these errors were encountered: