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import xarray as xr
import atmPy
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%matplotlib inline
plt.style.use('hagen_default')
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ceil = atmPy.data_archives.arm.read_ceilometer_nc('/Volumes/HTelg_4TB_Backup/arm_data/OLI/ceilometer/oliceilM1.b1.20170522.000013.nc')
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fname = '/Volumes/HTelg_4TB_Backup/arm_data/OLI/kazr/olikazrgeM1.a1.20170522.190520.nc'
data = xr.open_dataset(fname)
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data.reflectivity.plot()
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ts = atmPy.general.timeseries.TimeSeries_2D(data.reflectivity.to_pandas())
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f,a,pc,cb = ts.plot()
a.set_ylim((0,3000))
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snr = data.snr_copol.to_pandas()
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reflectivity = data.reflectivity.to_pandas()
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from matplotlib.colors import LogNorm
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import inspect
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fname = '/Volumes/HTelg_4TB_Backup/arm_data/OLI/kazr/olikazrgeM1.a1.20170522.190520.nc'
kazr = atmPy.data_archives.arm.read_kazr_nc(fname)
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out = kazr.zoom_time('2017-05-22 19:05:20', '2017-05-22 19:15:00')
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out.reflectivity.plot(snr_max=10)
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type(kazr.reflectivity).__name__
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inspect.getmro(kazr.reflectivity)
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fname = '/Volumes/HTelg_4TB_Backup/arm_data/OLI/kazr/olikazrgeM1.a1.20170522.190520.nc'
kazr = atmPy.data_archives.arm.read_kazr_nc(fname)
ceil = atmPy.data_archives.arm.read_ceilometer_nc('/Volumes/HTelg_4TB_Backup/arm_data/OLI/ceilometer/oliceilM1.b1.20170522.000013.nc')
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f,a = plt.subplots()
kazr.reflectivity.plot(ax = a, snr_max=10)
ceil.cloudbase.plot(ax = a)
a.set_ylim((0,1100))
a.set_xlim(kazr.reflectivity.get_timespan())
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