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Grab the CMAC DOD via ACT I/O

import numpy as np
import xarray as xr
import netCDF4 as nc

import pyart
import act

## You are using the Python ARM Radar Toolkit (Py-ART), an open source
## library for working with weather radar data. Py-ART is partly
## supported by the U.S. Department of Energy as part of the Atmospheric
## Radiation Measurement (ARM) Climate Research Facility, an Office of
## Science user facility.
##
## If you use this software to prepare a publication, please cite:
##
##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119

Grab the CMAC DOD via ACT I/O#

# Use ACT to query the ARM DOD API and initiate the CMAC DoD
dims = {'time' : 1, 'range' : 668, 'sweep' : 8, 'string_length' : 192}
dod = act.io.create_obj_from_arm_dod('xprecipradarcmacppi.c1', dims, version='2.0', fill_value=-9999)

dod

<xarray.Dataset>
Dimensions:                                   (time: 1, range: 668, sweep: 8,
                                               string_length: 192)
Coordinates:
  * time                                      (time) int64 0
  * range                                     (range) int64 0 1 2 ... 666 667
Dimensions without coordinates: sweep, string_length
Data variables: (12/48)
    azimuth                                   (time) int64 -9999
    elevation                                 (time) int64 -9999
    DBZ                                       (time, range) int64 -9999 ... -...
    VEL                                       (time, range) int64 -9999 ... -...
    WIDTH                                     (time, range) int64 -9999 ... -...
    ZDR                                       (time, range) int64 -9999 ... -...
    ...                                        ...
    time_coverage_end                         (string_length) int64 -9999 ......
    time_reference                            (string_length) int64 -9999 ......
    volume_number                             int64 -9999
    latitude                                  int64 -9999
    longitude                                 int64 -9999
    altitude                                  int64 -9999
Attributes: (12/25)
    command_line:          
    Conventions:           ARM-1.3 CF/Radial instrument_parameters
    process_version:       
    dod_version:           
    input_datastreams:     
    site_id:               
    ...                    ...
    translator:            https://www.arm.gov/capabilities/instruments/xprec...
    mentors:               https://www.arm.gov/connect-with-arm/organization/...
    source:                Colorado State University's X-Band Precipitation R...
    input_datastream:      xprecipradarS2.00
    field_names:           DBZ, VEL, WIDTH, ZDR, PHIDP, RHOHV, NCP, DBZhv, cb...
    history:

Create the Encodings for NetCDF output#

encodings = {
             'time'  : {'dtype' : np.double, '_FillValue' : -9999.0},
             'range' : {'dtype' : np.single, '_FillValue' : -9999.0},
             'azimuth' : {'dtype' : np.single, '_FillValue' : -9999.0},
             'elevation' : {'dtype' : np.single, '_FillValue' : -9999.0},
             'DBZ' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'VEL' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'WIDTH' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'ZDR' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'PHIDP' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'RHOHV' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'NCP' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'DBZhv' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'cbb_flag' : {'dtype' : np.double, '_FillValue' : -9999.0},
             'sounding_temperature' : {'dtype' : np.single, '_FillValue' : -9999.0},
             'height' : {'dtype' : np.single, '_FillValue' : -9999.0},
             'signal_to_noise_ratio' : {'dtype' : np.single, '_FillValue' : -32768.0},
             'velocity_texture' : {'dtype' : np.double, '_FillValue' : -9999.0}, 
             'gate_id' : {'dtype' : 'int16', '_FillValue' : -9999},
             'simulated_velocity' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'corrected_velocity' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'unfolded_differential_phase' : {'dtype' : np.double, '_FillValue' : -32768.0}, 
             'corrected_differential_phase' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'filtered_corrected_differential_phase' : {'dtype' : np.double, '_FillValue' : -32768.0},
             'corrected_specific_diff_phase' : {'dtype' : np.double, '_FillValue' : -9999.0},
             'filtered_corrected_specific_diff_phase' : {'dtype' : np.double, '_FillValue' : -9999.0},
             'corrected_differential_reflectivity' : {'dtype' : np.double, '_FillValue' : 1e20},
             'corrected_reflectivity' : {'dtype' : np.double, '_FillValue' : 1e20},
             'height_over_iso0' : {'dtype' : np.single, '_FillValue' : -9999},
             'specific_attenuation' : {'dtype' : np.double, '_FillValue' : 1e20},
             'path_integrated_attenuation' : {'dtype' : np.double, '_FillValue' : 1e20},
             'specific_differential_attenuation' : {'dtype' : np.double, '_FillValue' : 1e20},
             'path_integrated_differential_attenuation' : {'dtype' : np.double, '_FillValue' : 1e20},
             'rain_rate_A' : {'dtype' : np.double, '_FillValue' : 1e20}, 
             'snow_rate_ws2012' : {'dtype' : np.double ,'_FillValue' : 1e20},
             'snow_rate_ws88diw' : {'dtype' : np.double, '_FillValue' : 1e20},
             'snow_rate_m2009_1' : {'dtype' : np.double, '_FillValue' : 1e20},
             'snow_rate_m2009_2' : {'dtype' : np.double, '_FillValue' : 1e20},
             'sweep_number' : {'dtype' : 'int16', '_FillValue' : -9999},
             'fixed_angle' : {'dtype' : np.single, '_FillValue' : -9999.0},
             'sweep_start_ray_index' : {'dtype' : 'int16', '_FillValue' : -9999},
             'sweep_end_ray_index' : {'dtype' : 'int16', '_FillValue' : -9999},
             'sweep_mode' : {'dtype' : np.str_},
             'nyquist_velocity' : {'dtype' : np.double, '_FillValue' : -9999.0},
             'time_coverage_start' : {'dtype' : np.str_}, 
             'time_coverage_end' : {'dtype' : np.str_},
             'time_reference' : {'dtype' : np.str_},
             'volume_number' : {'dtype' : 'int16', '_FillValue' : -9999},
             'latitude' : {'dtype' : np.double, '_FillValue' : -9999.0},
             'longitude' : {'dtype' : np.double, '_FillValue' : -9999.0},
             'altitude' : {'dtype' : np.double, '_FillValue' : -9999.0}
            }

joe = xr.Dataset()

nmask = dod['sounding_temperature'].where('time' + 'range' == '-9999', encodings['sounding_temperature']['_FillValue'])
nmask = dod['sounding_temperature'].where('time' + 'range' == -9999, encodings['sounding_temperature']['_FillValue'])

nmask

<xarray.DataArray 'sounding_temperature' (time: 1, range: 668)>
array([[-9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
...
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999., -9999., -9999., -9999., -9999.,
        -9999., -9999., -9999., -9999.]])
Coordinates:
  * time     (time) int64 0
  * range    (range) int64 0 1 2 3 4 5 6 7 8 ... 660 661 662 663 664 665 666 667
Attributes:
    long_name:      Interpolated profile
    units:          degC
    standard_name:  interpolated_profile
    missing_value:  -9999

Modify the Datatype and Fill Values for the Initial DOD#

# Create a blank DataSet to hold all the converted values
new_dod = xr.Dataset()
# Add the attributes in
new_dod.attrs = dod.attrs

new_dod

<xarray.Dataset>
Dimensions:  ()
Data variables:
    *empty*
Attributes: (12/25)
    command_line:          
    Conventions:           ARM-1.3 CF/Radial instrument_parameters
    process_version:       
    dod_version:           
    input_datastreams:     
    site_id:               
    ...                    ...
    translator:            https://www.arm.gov/capabilities/instruments/xprec...
    mentors:               https://www.arm.gov/connect-with-arm/organization/...
    source:                Colorado State University's X-Band Precipitation R...
    input_datastream:      xprecipradarS2.00
    field_names:           DBZ, VEL, WIDTH, ZDR, PHIDP, RHOHV, NCP, DBZhv, cb...
    history:               
# Loop over all the variables; update the FillValue and Data Type
for var in encodings:
    print(var, dod[var].dims)
    # Check if the DataArray is multidimensional (can't have 3 dimensions in this DOD)
    if len(dod[var].dims) == 2:
        # Character variables won't have a FillValue
        if encodings[var]['dtype'] != np.str_:
            # Convert all values within the DataArray to the correct Fill Value
            # NOTE: xr.where(condition, value_when_condition_is_not_met); so if every index is MVC, check for opposite
            mask = dod[var].where(dod[var].dims[0] + dod[var].dims[1] == -9999, encodings[var]['_FillValue'])
            # Append to the empty DataSet; make sure to change the data type
            new_dod[var] = mask.astype(encodings[var]['dtype'])
            # Delete _FillValue attribute; setting on write
            if '_FillValue' in new_dod[var].attrs:
                del new_dod[var].attrs['_FillValue']
        else:
            new_dod[var] = dod[var].astype(encodings[var]['dtype'])
    # most of the dimensions are one dimensional
    elif len(dod[var].dims) == 1:
        # Don't want repeating range values
        if var == 'range' or var == 'time':
            new_dod[var] = dod[var].astype(encodings[var]['dtype'])
            if '_FillValue' in new_dod[var].attrs:
                del new_dod[var].attrs['_FillValue']
        else:
            # Character variables won't have a FillValue
            if encodings[var]['dtype'] != np.str_:
                # Convert all values within the DataArray to the correct Fill Value
                mask = dod[var].where(dod[var].dims[0] == -9999, encodings[var]['_FillValue'])
                # Append to the empty DataSet; make sure to change the data type
                new_dod[var] = mask.astype(encodings[var]['dtype'])
                if '_FillValue' in new_dod[var].attrs:
                    del new_dod[var].attrs['_FillValue']
            else:
                new_dod[var] = dod[var].astype(encodings[var]['dtype'])
    # For the three variables without dimensions
    else:
        new_dod[var] = dod[var].astype(encodings[var]['dtype'])
        if '_FillValue' in new_dod[var].attrs:
            del new_dod[var].attrs['_FillValue']

time ('time',)
range ('range',)
azimuth ('time',)
elevation ('time',)
DBZ ('time', 'range')
VEL ('time', 'range')
WIDTH ('time', 'range')
ZDR ('time', 'range')
PHIDP ('time', 'range')
RHOHV ('time', 'range')
NCP ('time', 'range')
DBZhv ('time', 'range')
cbb_flag ('time', 'range')
sounding_temperature ('time', 'range')
height ('time', 'range')
signal_to_noise_ratio ('time', 'range')
velocity_texture ('time', 'range')
gate_id ('time', 'range')
simulated_velocity ('time', 'range')
corrected_velocity ('time', 'range')
unfolded_differential_phase ('time', 'range')
corrected_differential_phase ('time', 'range')
filtered_corrected_differential_phase ('time', 'range')
corrected_specific_diff_phase ('time', 'range')
filtered_corrected_specific_diff_phase ('time', 'range')
corrected_differential_reflectivity ('time', 'range')
corrected_reflectivity ('time', 'range')
height_over_iso0 ('time', 'range')
specific_attenuation ('time', 'range')
path_integrated_attenuation ('time', 'range')
specific_differential_attenuation ('time', 'range')
path_integrated_differential_attenuation ('time', 'range')
rain_rate_A ('time', 'range')
snow_rate_ws2012 ('time', 'range')
snow_rate_ws88diw ('time', 'range')
snow_rate_m2009_1 ('time', 'range')
snow_rate_m2009_2 ('time', 'range')
sweep_number ('sweep',)
fixed_angle ('sweep',)
sweep_start_ray_index ('sweep',)
sweep_end_ray_index ('sweep',)
sweep_mode ('sweep', 'string_length')
nyquist_velocity ('time',)
time_coverage_start ('string_length',)
time_coverage_end ('string_length',)
time_reference ('string_length',)
volume_number ()
latitude ()
longitude ()
altitude ()

new_dod

<xarray.Dataset>
Dimensions:                                   (time: 1, range: 668, sweep: 8,
                                               string_length: 192)
Coordinates:
  * time                                      (time) int64 0
  * range                                     (range) int64 0 1 2 ... 666 667
Dimensions without coordinates: sweep, string_length
Data variables: (12/48)
    azimuth                                   (time) float32 -9.999e+03
    elevation                                 (time) float32 -9.999e+03
    DBZ                                       (time, range) float64 -3.277e+0...
    VEL                                       (time, range) float64 -3.277e+0...
    WIDTH                                     (time, range) float64 -3.277e+0...
    ZDR                                       (time, range) float64 -3.277e+0...
    ...                                        ...
    time_coverage_end                         (string_length) <U21 '-9999' .....
    time_reference                            (string_length) <U21 '-9999' .....
    volume_number                             int16 -9999
    latitude                                  float64 -9.999e+03
    longitude                                 float64 -9.999e+03
    altitude                                  float64 -9.999e+03
Attributes: (12/25)
    command_line:          
    Conventions:           ARM-1.3 CF/Radial instrument_parameters
    process_version:       
    dod_version:           
    input_datastreams:     
    site_id:               
    ...                    ...
    translator:            https://www.arm.gov/capabilities/instruments/xprec...
    mentors:               https://www.arm.gov/connect-with-arm/organization/...
    source:                Colorado State University's X-Band Precipitation R...
    input_datastream:      xprecipradarS2.00
    field_names:           DBZ, VEL, WIDTH, ZDR, PHIDP, RHOHV, NCP, DBZhv, cb...
    history:               
# Fix some meta data issues
del new_dod['sounding_temperature'].attrs['missing_value']
del new_dod['height'].attrs['missing_value']
del new_dod['velocity_texture'].attrs['missing_value']

Write the File / Read It Back in to Test#

new_dod.to_netcdf('sail_cmac_dod.nc', encoding=encodings)

test = xr.open_dataset('sail_cmac_dod.nc', mask_and_scale=False)

test

<xarray.Dataset>
Dimensions:                                   (time: 1, range: 668, sweep: 8,
                                               string_length: 192)
Coordinates:
  * time                                      (time) datetime64[ns] 1970-01-01
  * range                                     (range) float32 0.0 1.0 ... 667.0
    azimuth                                   (time) float32 ...
    elevation                                 (time) float32 ...
Dimensions without coordinates: sweep, string_length
Data variables: (12/46)
    DBZ                                       (time, range) float64 ...
    VEL                                       (time, range) float64 ...
    WIDTH                                     (time, range) float64 ...
    ZDR                                       (time, range) float64 ...
    PHIDP                                     (time, range) float64 ...
    RHOHV                                     (time, range) float64 ...
    ...                                        ...
    time_coverage_end                         (string_length) object ...
    time_reference                            (string_length) object ...
    volume_number                             int16 ...
    latitude                                  float64 ...
    longitude                                 float64 ...
    altitude                                  float64 ...
Attributes: (12/25)
    command_line:          
    Conventions:           ARM-1.3 CF/Radial instrument_parameters
    process_version:       
    dod_version:           
    input_datastreams:     
    site_id:               
    ...                    ...
    translator:            https://www.arm.gov/capabilities/instruments/xprec...
    mentors:               https://www.arm.gov/connect-with-arm/organization/...
    source:                Colorado State University's X-Band Precipitation R...
    input_datastream:      xprecipradarS2.00
    field_names:           DBZ, VEL, WIDTH, ZDR, PHIDP, RHOHV, NCP, DBZhv, cb...
    history:               
test.DBZ.data

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        -32768., -32768., -32768., -32768., -32768., -32768., -32768.,
        -32768., -32768., -32768., -32768., -32768., -32768., -32768.,
        -32768., -32768., -32768., -32768., -32768., -32768., -32768.,
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dod_v2 = nc.Dataset('/Users/jrobrien/ARM/data/CSU-XPrecipRadar/dod/cmac_dod_v2.nc')

dod_v2

<class 'netCDF4._netCDF4.Dataset'>
root group (NETCDF4 data model, file format HDF5):
    command_line: 
    Conventions: ARM-1.3 CF/Radial instrument_parameters
    process_version: 
    dod_version: 
    input_datastreams: 
    site_id: 
    platform_id: 
    facility_id: 
    data_level: 
    location_description: 
    datastream: 
    institution: U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Climate Research Facility
    references: See XPRECIPRADAR Instrument Handbook
    doi: 10.5439/1883164
    comment: This is highly experimental and initial data. There are many known and unknown issues. Please do not use before contacting the Translator responsible scollis@anl.gov
    attributions: This data is collected by the ARM Climate Research facility. Radar system is operated by the radar engineering team radar@arm.gov and the data is processed by the precipitation radar products team. LP code courtesy of Scott Giangrande BNL.
    vap_name: cmac
    known_issues: False phidp jumps in insect regions. Still uses old Giangrande code. Issues with some snow below melting layer.
    developers: Robert Jackson, ANL. Zachary Sherman, ANL. Maxwell Grover, ANL. Joseph O'Brien, ANL.
    translator: https://www.arm.gov/capabilities/instruments/xprecipradar
    mentors: https://www.arm.gov/connect-with-arm/organization/instrument-mentors/list#xprecipradar
    source: Colorado State University's X-Band Precipitation Radar (XPRECIPRADAR) (DOI: 10.5439/1844501)
    input_datastream: xprecipradarS2.00
    field_names: DBZ, VEL, WIDTH, ZDR, PHIDP, RHOHV, NCP, DBZhv, cbb_flag, sounding_temperature, height, signal_to_noise_ratio, velocity_texture, gate_id, simulated_velocity, corrected_velocity, unfolded_differential_phase, corrected_differential_phase, filtered_corrected_differential_phase, corrected_specific_diff_phase, filtered_corrected_specific_diff_phase, corrected_differential_reflectivity, corrected_reflectivity, height_over_iso0, specific_attenuation, path_integrated_attenuation, specific_differential_attenuation, path_integrated_differential_attenuation, rain_rate_A, snow_rate_ws2012, snow_rate_ws88diw, snow_rate_m2009_1, snow_rate_m2009_2
    history: 
    dimensions(sizes): time(1), range(668), sweep(8), string_length(192)
    variables(dimensions): int64 time(time), int64 range(range), float64 DBZ(time, range), float64 VEL(time, range), float64 WIDTH(time, range), float64 ZDR(time, range), float64 PHIDP(time, range), float64 RHOHV(time, range), float64 NCP(time, range), float64 DBZhv(time, range), float64 sounding_temperature(time, range), float64 height(time, range), float64 signal_to_noise_ratio(time, range), float64 velocity_texture(time, range), float64 corrected_velocity(time, range), float64 unfolded_differential_phase(time, range), float64 corrected_differential_phase(time, range), float64 filtered_corrected_differential_phase(time, range), float64 corrected_specific_diff_phase(time, range), float64 filtered_corrected_specific_diff_phase(time, range), float64 corrected_differential_reflectivity(time, range), float64 corrected_reflectivity(time, range), float64 specific_attenuation(time, range), float64 path_integrated_attenuation(time, range), float64 specific_differential_attenuation(time, range), float64 path_integrated_differential_attenuation(time, range), float64 rain_rate_A(time, range), float64 snow_rate_ws2012(time, range), float64 snow_rate_ws88diw(time, range), float64 snow_rate_m2009_1(time, range), float64 snow_rate_m2009_2(time, range), float64 sweep_number(sweep), float64 fixed_angle(sweep), float64 sweep_start_ray_index(sweep), float64 sweep_end_ray_index(sweep), <class 'str'> sweep_mode(sweep, string_length), <class 'str'> time_coverage_start(string_length), <class 'str'> time_coverage_end(string_length), <class 'str'> time_reference(string_length), float64 azimuth(time), float64 elevation(time), float64 cbb_flag(time, range), float64 gate_id(time, range), float64 simulated_velocity(time, range), float64 height_over_iso0(time, range), float64 nyquist_velocity(time), float64 volume_number(), float64 latitude(), float64 longitude(), float64 altitude()
    groups:
Contents