This notebook is part of the \(\omega radlib\) documentation: https://docs.wradlib.org.

Copyright (c) \(\omega radlib\) developers. Distributed under the MIT License. See LICENSE.txt for more info.

Multi File OdimH5 reader

Note

The following functionality is deprecated. Please use the xarray backend loaders instead.

This implementation is based on several classes which are described below.

Class Overview

XRadBase

Implements collections.abc.MutableSequence for holding sequential data in the derived classes (eg. sweeps, timeseries, moments).

OdimH5GroupAttributeMixin

Implements properties for XRadMoment, XRadSweep, XRadTimeSeries and XRadVolume to nicely acquire ODIM group metadata, eg. how, what and where groups. Other wanted attributes can be acquired via attrs-property and other (sub-) groups be inspected via groups-property.

OdimH5SweepMetaDataMixin

Implements properties for XRadSweep to nicely acquire ODIM sweep metadata, eg. a1gate, azimuth, nrays, nbins etc.

XRadMoment

Uses OdimH5GroupAttributeMixin to access ODIM metadata. Does not hold any data. Property data fetches the moment as xarray DataArray from the parent XRadSweep.

XRadSweep

Inherits from XRadBase, uses OdimH5GroupAttributeMixin and OdimH5SweepMetaDataMixin. Worker class, where everything happens. Implements methods and properties to retrieve sweep metadata and data. Holds XRadMoments in it’s MutableSequence. Property data is used to load and cache the XRadMoments as combined xarray Dataset. Implements a whole arsenal of other properties to inspect metadata.

XRadSweepOdim:

Inherits from XRadSweep. Accounts for ODIM data layout.

XRadSweepGamic:

Inherits from XRadSweep. Accounts for GAMIC data layout.

XRadTimeSeries

Inherits from XRadBase, holds several XRadSweep objects in it’s MutableSequence. Property data is used to concat and cache the XRadSweeps as xarray Dataset along time dimension. Implements check methods to quickly get information about layout of timeseries data.

XRadVolume

Inherits from XRadBase, holds several XRadTimeSeries objects in it’s MutableSequence. Implements CfRadial2 like root property.

Loading Function

For opening ODIMH5 datafiles wrl.io.open_odim(filename, loader='h5py', **kwargs) can be used.

The user can decide which loader to use (h5py, h5netcdf or netcdf4) to open the files for reading. The output should be the same in any case, although the memory footprint can differ quite substantially. The default loader is netcdf4 if loader isn’t specified.

The datasets are retrieved in further succession via xarray.open_dataset() in combination with either xarray.backends.H5NetCDFStore (for loader h5py and h5netcdf) or xarray.backends.NetCDF4DataStore (for loader netcdf4.

Possible keyword arguments are:

• mask_and_scale bool - If True, apply mask and scale to moment data arrays

• decode_coords bool - If True, decode ODIMH5 coordinates

• decode_times bool - If True, decode times into datetime objects

• chunks int or dict - Data loaded as dask array

• parallel bool - if True, use dask.delayed to load moments in parallel

The user is encouraged to play with the keyword arguments for best alignment with the needs in terms of speed and performance.

import wradlib as wrl
import warnings

# warnings.filterwarnings('ignore')
import matplotlib.pyplot as pl
import numpy as np
import xarray as xr
import os
import glob

try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    pl.ion()

/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

import os
import psutil
import gc

process = psutil.Process(os.getpid())

def memory_usage_psutil():
    # return the memory usage in MB
    rocess = psutil.Process(os.getpid())
    mem = process.memory_full_info().uss / float(1 << 20)
    return mem

def free_memory():
    mem0 = memory_usage_psutil()
    print(gc.collect())
    proc = psutil.Process()
    mem1 = memory_usage_psutil()
    print("Memory freed: {0:.5f} MB".format((mem0 - mem1)))

def check_open_files(full=False):
    proc = psutil.Process()
    print(len(proc.open_files()))
    if full:
        print(proc.open_files())

%%capture
flist = [
    "hdf5/behel/20200207130000.rad.behel.pvol.dbzh.scanz.hdf",
    "hdf5/behel/20200207130000.rad.behel.pvol.rhohv.scanz.hdf",
    "hdf5/behel/20200207130000.rad.behel.pvol.vrad.scanz.hdf",
    "hdf5/behel/20200207130000.rad.behel.pvol.wrad.scanz.hdf",
    "hdf5/behel/20200207130500.rad.behel.pvol.dbzh.scanz.hdf",
    "hdf5/behel/20200207130500.rad.behel.pvol.rhohv.scanz.hdf",
    "hdf5/behel/20200207130500.rad.behel.pvol.vrad.scanz.hdf",
    "hdf5/behel/20200207130500.rad.behel.pvol.wrad.scanz.hdf",
    "hdf5/behel/20200207131000.rad.behel.pvol.dbzh.scanz.hdf",
    "hdf5/behel/20200207131000.rad.behel.pvol.rhohv.scanz.hdf",
    "hdf5/behel/20200207131000.rad.behel.pvol.vrad.scanz.hdf",
    "hdf5/behel/20200207131000.rad.behel.pvol.wrad.scanz.hdf",
    "hdf5/behel/20200207131500.rad.behel.pvol.dbzh.scanz.hdf",
    "hdf5/behel/20200207131500.rad.behel.pvol.rhohv.scanz.hdf",
    "hdf5/behel/20200207131500.rad.behel.pvol.vrad.scanz.hdf",
    "hdf5/behel/20200207131500.rad.behel.pvol.wrad.scanz.hdf",
    "hdf5/behel/20200207132000.rad.behel.pvol.dbzh.scanz.hdf",
    "hdf5/behel/20200207132000.rad.behel.pvol.rhohv.scanz.hdf",
    "hdf5/behel/20200207132000.rad.behel.pvol.vrad.scanz.hdf",
    "hdf5/behel/20200207132000.rad.behel.pvol.wrad.scanz.hdf",
    "hdf5/behel/20200207132500.rad.behel.pvol.dbzh.scanz.hdf",
    "hdf5/behel/20200207132500.rad.behel.pvol.rhohv.scanz.hdf",
    "hdf5/behel/20200207132500.rad.behel.pvol.vrad.scanz.hdf",
    "hdf5/behel/20200207132500.rad.behel.pvol.wrad.scanz.hdf",
    "hdf5/behel/20200207133000.rad.behel.pvol.dbzh.scanz.hdf",
    "hdf5/behel/20200207133000.rad.behel.pvol.rhohv.scanz.hdf",
    "hdf5/behel/20200207133000.rad.behel.pvol.vrad.scanz.hdf",
    "hdf5/behel/20200207133000.rad.behel.pvol.wrad.scanz.hdf",
    "hdf5/behel/20200207133500.rad.behel.pvol.dbzh.scanz.hdf",
    "hdf5/behel/20200207133500.rad.behel.pvol.rhohv.scanz.hdf",
    "hdf5/behel/20200207133500.rad.behel.pvol.vrad.scanz.hdf",
    "hdf5/behel/20200207133500.rad.behel.pvol.wrad.scanz.hdf",
]
f = [wrl.util.get_wradlib_data_file(f) for f in flist]

mem_start = memory_usage_psutil()
print("Current Memory:", mem_start)

Current Memory: 167.31640625

Check open files

check_open_files()

1

Claim Files into class structure

The different files will be opened with h5netcdf, h5py or netcdf4 depending on loader keyword. Only absolutely neccessary metadata (time, elevation) is read from the files to be used for aligning into the structure.

Normally h5py is the most performant loader for ODIM data. But your mileage may vary.

This means that every file is opened once and the filehandle is distributed to XRadSweep. If XRadSweep will be destroyed, the memory will be ready for garbage collection.

Under the hood netcdf4 or h5netcdf will be used to open data as xarray.Dataset depending on the loader-type. All filehandling issues are moved to xarray. No memory holes, no need to track filehandles.

%%time
vol = wrl.io.xarray_depr.open_odim(f, loader="h5py", chunks={})

<timed exec>:1: DeprecatedWarning: open_odim is deprecated as of 1.10 and will be removed in 2.0. Use the appropriate `wradlib.io.open_{engine}_dataset` or `wradlib.io.open_{engine}_mfdataset` function.
Open: 100%|██████████| 32/32 [00:00<00:00, 133.14 Files/s]
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2287: DeprecatedWarning: collect_by_angle is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles = collect_by_angle(sweeps)
Collecting:   0%|          | 0/12 [00:00<?, ? Angles/s]/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
Collecting: 100%|██████████| 12/12 [00:00<00:00, 137.44 Angles/s]

CPU times: user 352 ms, sys: 8.56 ms, total: 361 ms
Wall time: 392 ms

Check open files

check_open_files()

33

Overview type and lenght

print("Volume:", type(vol), len(vol))
print("TimeSeries:", type(vol[0]), len(vol[0]))
print("Sweep:", type(vol[0][0]), len(vol[0][0]))
print("Moment:", type(vol[0][0][0]), vol[0][0][0].quantity)

Volume: <class 'wradlib.io.xarray_depr.XRadVolume'> 12
TimeSeries: <class 'wradlib.io.xarray_depr.XRadTimeSeries'> 8
Sweep: <class 'wradlib.io.xarray_depr.XRadSweepOdim'> 4
Moment: <class 'wradlib.io.xarray_depr.XRadMoment'> DBZH

Overview Contents (repr())

When printing the objects, they tell us a little about themselves and the data they can get access to.

Volume

Here we see, that it is of type wradlib.XRadVolume. It holds 12 sweeps with the shown elevations.

print(vol)

<wradlib.XRadVolume>
Dimension(s): (sweep: 12)
Elevation(s): (25.0, 20.0, 16.0, 13.0, 10.0, 7.5, 5.0, 3.0, 1.8, 0.8, 0.5, 0.3)

TimeSeries

Here we see, that it is of type wradlib.XRadTimeseries. It holds 8 timesteps with a data layout of 360 azimuths by 800 range bins. The elevation is 25.0 deg.

print(vol[0])

<wradlib.XRadTimeSeries>
Dimension(s): (time: 8, azimuth: 360, range: 800)
Elevation(s): (25.0)

Sweep

Here we see, that it is of type wradlib.XRadSweepOdim, which means it is leaded from ODIMH5 standard data. It holds data with layout of 360 azimuths by 800 range bins. The elevation is 25.0 deg. It consists of the radar moments DBZH, RHOHV, VRAD and WRAD.

print(vol[0][0])

<wradlib.XRadSweepOdim>
Dimension(s): (azimuth: 360, range: 800)
Elevation(s): (25.0)
Moment(s): (DBZH, RHOHV, VRAD, WRAD)

Moment

Here we see, that it is of type wradlib.XRadMoment. It holds data with layout of 360 azimuths by 800 range bins. The elevation is 25.0 deg. It is the radar moment DBZH.

print(vol[0][0][0])

<wradlib.XRadMoment>
Dimension(s): (azimuth: 360, range: 800)
Elevation(s): (25.0)
Moment: (DBZH)

Accessing metadata via OdimH5GroupAttributeMixin

You can access underlying metadata for every object. The properties ncpath, ncid, ncfile and filename give information about the location of the metadata. Properties groups and attrs give information about attached subgroups and attributes. how, what and where return the contents of the respective ODIMH5-subgroups if available.

As long as the objects are not deleted the according files are open and the handles can be used to retrieve data from it.

Volume

The OdimH5GroupAttributeMixin access in XRadVolume will retrieve the root-metadata of the first file of the first timeseries, which is the first volume file in most cases.

print("path:", vol.ncpath)
print("  id:", vol.ncid)
print("file:", vol.ncfile)
print("name:", vol.filename)

path: /
  id: <HDF5 group "/" (15 members)>
file: <HDF5 file "20200207130000.rad.behel.pvol.dbzh.scanz.hdf" (mode r)>
name: /home/runner/work/wradlib-notebooks/wradlib-notebooks/wradlib-data/hdf5/behel/20200207130000.rad.behel.pvol.dbzh.scanz.hdf

print(vol.groups)

['dataset1', 'dataset10', 'dataset11', 'dataset12', 'dataset2', 'dataset3', 'dataset4', 'dataset5', 'dataset6', 'dataset7', 'dataset8', 'dataset9', 'how', 'what', 'where']

print(vol.attrs)

{'Conventions': 'ODIM_H5/V2_0'}

print(vol.how)

{'beamwidth': 0.948, 'endepochs': 1581080424, 'highprf': 550, 'lowprf': 0, 'software': 'RAINBOW 5.42.9', 'startepochs': 1581080648, 'system': 'GEMA500', 'wavelength': 5.349}

print(vol.what)

{'date': '20200207', 'object': 'PVOL', 'source': 'WMO:06475,RAD:BX43,PLC:Helchteren,NOD:behel,CTY:605,CMT:behel_scan_200km_dp_dBZ', 'time': '130005', 'version': 'H5rad 2.0'}

print(vol.where)

{'height': 140.0, 'lat': 51.069072, 'lon': 5.4064}

Timeseries

The OdimH5GroupAttributeMixin access in XRadTimeseries will retrieve the group-metadata of the first sweep of the selected timeseries.

ts = vol[0]
print("path:", ts.ncpath)
print("  id:", ts.ncid)
print("file:", ts.ncfile)
print("name:", ts.filename)

path: dataset12
  id: <HDF5 group "/dataset12" (4 members)>
file: <HDF5 file "20200207130000.rad.behel.pvol.dbzh.scanz.hdf" (mode r)>
name: /home/runner/work/wradlib-notebooks/wradlib-notebooks/wradlib-data/hdf5/behel/20200207130000.rad.behel.pvol.dbzh.scanz.hdf

print(ts.groups)

['data1', 'how', 'what', 'where']

print(ts.attrs)

{}

print(ts.how)

{'azangles': '0:1,1:2,2:3,3:4,4:5,5:6,6:7,7:8,8:9,9:10,10:11,11:12,12:13,13:14,14:15,15:16,16:17,17:18,18:19,19:20,20:21,21:22,22:23,23:24,24:25,25:26,26:27,27:28,28:29,29:30,30:31,31:32,32:33,33:34,34:35,35:36,36:37,37:38,38:39,39:40,40:41,41:42,42:43,43:44,44:45,45:46,46:47,47:48,48:49,49:50,50:51,51:52,52:53,53:54,54:55,55:56,56:57,57:58,58:59,59:60,60:61,61:62,62:63,63:64,64:65,65:66,66:67,67:68,68:69,69:70,70:71,71:72,72:73,73:74,74:75,75:76,76:77,77:78,78:79,79:80,80:81,81:82,82:83,83:84,84:85,85:86,86:87,87:88,88:89,89:90,90:91,91:92,92:93,93:94,94:95,95:96,96:97,97:98,98:99,99:100,100:101,101:102,102:103,103:104,104:105,105:106,106:107,107:108,108:109,109:110,110:111,111:112,112:113,113:114,114:115,115:116,116:117,117:118,118:119,119:120,120:121,121:122,122:123,123:124,124:125,125:126,126:127,127:128,128:129,129:130,130:131,131:132,132:133,133:134,134:135,135:136,136:137,137:138,138:139,139:140,140:141,141:142,142:143,143:144,144:145,145:146,146:147,147:148,148:149,149:150,150:151,151:152,152:153,153:154,154:155,155:156,156:157,157:158,158:159,159:160,160:161,161:162,162:163,163:164,164:165,165:166,166:167,167:168,168:169,169:170,170:171,171:172,172:173,173:174,174:175,175:176,176:177,177:178,178:179,179:180,180:181,181:182,182:183,183:184,184:185,185:186,186:187,187:188,188:189,189:190,190:191,191:192,192:193,193:194,194:195,195:196,196:197,197:198,198:199,199:200,200:201,201:202,202:203,203:204,204:205,205:206,206:207,207:208,208:209,209:210,210:211,211:212,212:213,213:214,214:215,215:216,216:217,217:218,218:219,219:220,220:221,221:222,222:223,223:224,224:225,225:226,226:227,227:228,228:229,229:230,230:231,231:232,232:233,233:234,234:235,235:236,236:237,237:238,238:239,239:240,240:241,241:242,242:243,243:244,244:245,245:246,246:247,247:248,248:249,249:250,250:251,251:252,252:253,253:254,254:255,255:256,256:257,257:258,258:259,259:260,260:261,261:262,262:263,263:264,264:265,265:266,266:267,267:268,268:269,269:270,270:271,271:272,272:273,273:274,274:275,275:276,276:277,277:278,278:279,279:280,280:281,281:282,282:283,283:284,284:285,285:286,286:287,287:288,288:289,289:290,290:291,291:292,292:293,293:294,294:295,295:296,296:297,297:298,298:299,299:300,300:301,301:302,302:303,303:304,304:305,305:306,306:307,307:308,308:309,309:310,310:311,311:312,312:313,313:314,314:315,315:316,316:317,317:318,318:319,319:320,320:321,321:322,322:323,323:324,324:325,325:326,326:327,327:328,328:329,329:330,330:331,331:332,332:333,333:334,334:335,335:336,336:337,337:338,338:339,339:340,340:341,341:342,342:343,343:344,344:345,345:346,346:347,347:348,348:349,349:350,350:351,351:352,352:353,353:354,354:355,355:356,356:357,357:358,358:359,359:360,'}

print(ts.what)

{'enddate': '20200207', 'endtime': '130024', 'product': 'SCAN', 'startdate': '20200207', 'starttime': '130005'}

print(ts.where)

{'a1gate': 266, 'elangle': 25.0, 'nbins': 800, 'nrays': 360, 'rscale': 250.0, 'rstart': 0.0}

Sweep

The OdimH5GroupAttributeMixin access in XRadSweep will retrieve the group-metadata of the selected sweep.

swp = vol[0][5]
print("path:", swp.ncpath)
print("  id:", swp.ncid)
print("file:", swp.ncfile)
print("name:", swp.filename)

path: dataset12
  id: <HDF5 group "/dataset12" (4 members)>
file: <HDF5 file "20200207132500.rad.behel.pvol.dbzh.scanz.hdf" (mode r)>
name: /home/runner/work/wradlib-notebooks/wradlib-notebooks/wradlib-data/hdf5/behel/20200207132500.rad.behel.pvol.dbzh.scanz.hdf

print(swp.groups)

['data1', 'how', 'what', 'where']

print(swp.attrs)

{}

print(swp.how)

{'azangles': '0:1,1:2,2:3,3:4,4:5,5:6,6:7,7:8,8:9,9:10,10:11,11:12,12:13,13:14,14:15,15:16,16:17,17:18,18:19,19:20,20:21,21:22,22:23,23:24,24:25,25:26,26:27,27:28,28:29,29:30,30:31,31:32,32:33,33:34,34:35,35:36,36:37,37:38,38:39,39:40,40:41,41:42,42:43,43:44,44:45,45:46,46:47,47:48,48:49,49:50,50:51,51:52,52:53,53:54,54:55,55:56,56:57,57:58,58:59,59:60,60:61,61:62,62:63,63:64,64:65,65:66,66:67,67:68,68:69,69:70,70:71,71:72,72:73,73:74,74:75,75:76,76:77,77:78,78:79,79:80,80:81,81:82,82:83,83:84,84:85,85:86,86:87,87:88,88:89,89:90,90:91,91:92,92:93,93:94,94:95,95:96,96:97,97:98,98:99,99:100,100:101,101:102,102:103,103:104,104:105,105:106,106:107,107:108,108:109,109:110,110:111,111:112,112:113,113:114,114:115,115:116,116:117,117:118,118:119,119:120,120:121,121:122,122:123,123:124,124:125,125:126,126:127,127:128,128:129,129:130,130:131,131:132,132:133,133:134,134:135,135:136,136:137,137:138,138:139,139:140,140:141,141:142,142:143,143:144,144:145,145:146,146:147,147:148,148:149,149:150,150:151,151:152,152:153,153:154,154:155,155:156,156:157,157:158,158:159,159:160,160:161,161:162,162:163,163:164,164:165,165:166,166:167,167:168,168:169,169:170,170:171,171:172,172:173,173:174,174:175,175:176,176:177,177:178,178:179,179:180,180:181,181:182,182:183,183:184,184:185,185:186,186:187,187:188,188:189,189:190,190:191,191:192,192:193,193:194,194:195,195:196,196:197,197:198,198:199,199:200,200:201,201:202,202:203,203:204,204:205,205:206,206:207,207:208,208:209,209:210,210:211,211:212,212:213,213:214,214:215,215:216,216:217,217:218,218:219,219:220,220:221,221:222,222:223,223:224,224:225,225:226,226:227,227:228,228:229,229:230,230:231,231:232,232:233,233:234,234:235,235:236,236:237,237:238,238:239,239:240,240:241,241:242,242:243,243:244,244:245,245:246,246:247,247:248,248:249,249:250,250:251,251:252,252:253,253:254,254:255,255:256,256:257,257:258,258:259,259:260,260:261,261:262,262:263,263:264,264:265,265:266,266:267,267:268,268:269,269:270,270:271,271:272,272:273,273:274,274:275,275:276,276:277,277:278,278:279,279:280,280:281,281:282,282:283,283:284,284:285,285:286,286:287,287:288,288:289,289:290,290:291,291:292,292:293,293:294,294:295,295:296,296:297,297:298,298:299,299:300,300:301,301:302,302:303,303:304,304:305,305:306,306:307,307:308,308:309,309:310,310:311,311:312,312:313,313:314,314:315,315:316,316:317,317:318,318:319,319:320,320:321,321:322,322:323,323:324,324:325,325:326,326:327,327:328,328:329,329:330,330:331,331:332,332:333,333:334,334:335,335:336,336:337,337:338,338:339,339:340,340:341,341:342,342:343,343:344,344:345,345:346,346:347,347:348,348:349,349:350,350:351,351:352,352:353,353:354,354:355,355:356,356:357,357:358,358:359,359:360,'}

print(swp.what)

{'enddate': '20200207', 'endtime': '132523', 'product': 'SCAN', 'startdate': '20200207', 'starttime': '132504'}

print(swp.where)

{'a1gate': 252, 'elangle': 25.0, 'nbins': 800, 'nrays': 360, 'rscale': 250.0, 'rstart': 0.0}

Moment

The OdimH5GroupAttributeMixin access in XRadMoment will retrieve the group-metadata of the selected moment.

mom = vol[0][0][0]
print("path:", mom.ncpath)
print("  id:", mom.ncid)
print("file:", mom.ncfile)
print("name:", mom.filename)

path: dataset12/data1
  id: <HDF5 group "/dataset12/data1" (2 members)>
file: <HDF5 file "20200207130000.rad.behel.pvol.dbzh.scanz.hdf" (mode r)>
name: /home/runner/work/wradlib-notebooks/wradlib-notebooks/wradlib-data/hdf5/behel/20200207130000.rad.behel.pvol.dbzh.scanz.hdf

print(mom.groups)

['data', 'what']

print(mom.attrs)

{}

print(mom.what)

{'gain': 0.5, 'nodata': 255.0, 'offset': -32.0, 'quantity': 'DBZH', 'undetect': 0.0}

CfRadial2 style root object

The XRadVolume object is equipped with a CfRadial2-style root-object, where some information can be retrieved.

vol.root

<xarray.Dataset>
Dimensions:              (sweep: 12)
Dimensions without coordinates: sweep
Data variables:
    volume_number        int64 0
    platform_type        <U5 'fixed'
    instrument_type      <U5 'radar'
    primary_axis         <U6 'axis_z'
    time_coverage_start  <U20 '2020-02-07T13:00:05Z'
    time_coverage_end    <U20 '2020-02-07T13:39:26Z'
    latitude             float64 51.07
    longitude            float64 5.406
    altitude             float64 140.0
    sweep_group_name     (sweep) <U8 'sweep_0' 'sweep_1' ... 'sweep_11'
    sweep_fixed_angle    (sweep) float64 25.0 20.0 16.0 13.0 ... 1.8 0.8 0.5 0.3
Attributes:
    version:          H5rad 2.0
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    Conventions:      ODIM_H5/V2_0

xarray.Dataset

• Dimensions:
  • sweep: 12
• Coordinates: (0)
• Data variables: (11)
  • volume_number
    ()
    int64
    0

    array(0)

  • platform_type
    ()
    <U5
    'fixed'

    array('fixed', dtype='<U5')

  • instrument_type
    ()
    <U5
    'radar'

    array('radar', dtype='<U5')

  • primary_axis
    ()
    <U6
    'axis_z'

    array('axis_z', dtype='<U6')

  • time_coverage_start
    ()
    <U20
    '2020-02-07T13:00:05Z'

    array('2020-02-07T13:00:05Z', dtype='<U20')

  • time_coverage_end
    ()
    <U20
    '2020-02-07T13:39:26Z'

    array('2020-02-07T13:39:26Z', dtype='<U20')

  • latitude
    ()
    float64
    51.07

    array(51.069072)

  • longitude
    ()
    float64
    5.406

    array(5.4064)

  • altitude
    ()
    float64
    140.0

    array(140.)

  • sweep_group_name
    (sweep)
    <U8
    'sweep_0' 'sweep_1' ... 'sweep_11'

    array(['sweep_0', 'sweep_1', 'sweep_2', 'sweep_3', 'sweep_4', 'sweep_5',
           'sweep_6', 'sweep_7', 'sweep_8', 'sweep_9', 'sweep_10', 'sweep_11'],
          dtype='<U8')

  • sweep_fixed_angle
    (sweep)
    float64
    25.0 20.0 16.0 13.0 ... 0.8 0.5 0.3

    array([25. , 20. , 16. , 13. , 10. ,  7.5,  5. ,  3. ,  1.8,  0.8,  0.5,
            0.3])

• Indexes: (0)
• Attributes: (9)

  version :

  H5rad 2.0

  title :

  None

  institution :

  None

  references :

  None

  source :

  None

  history :

  None

  comment :

  im/exported using wradlib

  instrument_name :

  None

  Conventions :

  ODIM_H5/V2_0

Get hold of data using xarray

• The outer class instance XRadVolume does not contain a .data-property because the volume cannot be represented using xarray.

• XRadTimeseries .data works on the sweep level, it can contain one or multiple consecutive sweeps. It will be created on the fly from the XRadSweep .data xarray.Dataset objects via concatenation.

• XRadSweep .data is one single sweep containing multiple radar moments. It is created and cached when first accessed.

• XRadMoment .data is one single moment as xarray DataArray, which is claimed from the parent XRadSweep

Moment

%%time
print("First Access")
mem0 = memory_usage_psutil()
print(vol[-2][0][0].data)
mem1 = memory_usage_psutil()
print("Memory: {} - {}".format(mem0, mem1))
print("Memory added: {0:.5f} MB".format((mem1 - mem0)))

First Access
<xarray.DataArray 'DBZH' (azimuth: 360, range: 800)>
dask.array<_scale_offset_decoding, shape=(360, 800), dtype=float32, chunksize=(360, 800), chunktype=numpy.ndarray>
Coordinates:
    time        datetime64[ns] 2020-02-07T13:03:46
    rtime       (azimuth) datetime64[ns] 2020-02-07T13:03:50.472224256 ... 20...
    elevation   (azimuth) float32 ...
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * range       (range) float32 125.0 375.0 625.0 ... 1.996e+05 1.999e+05
    sweep_mode  <U20 ...
    latitude    float64 ...
    longitude   float64 ...
    altitude    float64 ...
Attributes:
    IMAGE_VERSION:  1.2
    _Undetect:      0.0
    units:          dBZ
    long_name:      Equivalent reflectivity factor H
    standard_name:  radar_equivalent_reflectivity_factor_h
Memory: 192.75390625 - 200.04296875
Memory added: 7.28906 MB
CPU times: user 156 ms, sys: 19.2 ms, total: 176 ms
Wall time: 187 ms

%%time
print("Second Access")
mem0 = memory_usage_psutil()
print(vol[-2][0][0].data)
mem1 = memory_usage_psutil()
print("Memory: {} - {}".format(mem0, mem1))
print("Memory added: {0:.5f} MB".format((mem1 - mem0)))

Second Access
<xarray.DataArray 'DBZH' (azimuth: 360, range: 800)>
dask.array<_scale_offset_decoding, shape=(360, 800), dtype=float32, chunksize=(360, 800), chunktype=numpy.ndarray>
Coordinates:
    time        datetime64[ns] 2020-02-07T13:03:46
    rtime       (azimuth) datetime64[ns] 2020-02-07T13:03:50.472224256 ... 20...
    elevation   (azimuth) float32 ...
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * range       (range) float32 125.0 375.0 625.0 ... 1.996e+05 1.999e+05
    sweep_mode  <U20 ...
    latitude    float64 ...
    longitude   float64 ...
    altitude    float64 ...
Attributes:
    IMAGE_VERSION:  1.2
    _Undetect:      0.0
    units:          dBZ
    long_name:      Equivalent reflectivity factor H
    standard_name:  radar_equivalent_reflectivity_factor_h
Memory: 200.046875 - 200.046875
Memory added: 0.00000 MB
CPU times: user 3.01 ms, sys: 7.47 ms, total: 10.5 ms
Wall time: 11.2 ms

Sweep

%%time
print("First Access")
mem0 = memory_usage_psutil()
print(vol[-1][0].data)
mem1 = memory_usage_psutil()
print("Memory: {} - {}".format(mem0, mem1))
print("Memory added: {0:.5f} MB".format((mem1 - mem0)))

First Access
<xarray.Dataset>
Dimensions:     (azimuth: 360, range: 800)
Coordinates:
    time        datetime64[ns] 2020-02-07T13:04:08
    rtime       (azimuth) datetime64[ns] 2020-02-07T13:04:10.527778816 ... 20...
    elevation   (azimuth) float32 ...
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * range       (range) float32 125.0 375.0 625.0 ... 1.996e+05 1.999e+05
    sweep_mode  <U20 ...
    latitude    float64 ...
    longitude   float64 ...
    altitude    float64 ...
Data variables:
    DBZH        (azimuth, range) float32 dask.array<chunksize=(360, 800), meta=np.ndarray>
    RHOHV       (azimuth, range) float32 dask.array<chunksize=(360, 800), meta=np.ndarray>
    VRAD        (azimuth, range) float32 dask.array<chunksize=(360, 800), meta=np.ndarray>
    WRAD        (azimuth, range) float32 dask.array<chunksize=(360, 800), meta=np.ndarray>
Memory: 200.06640625 - 200.31640625
Memory added: 0.25000 MB
CPU times: user 41.8 ms, sys: 8.04 ms, total: 49.8 ms
Wall time: 52.5 ms

%%time
print("Second Access")
mem0 = memory_usage_psutil()
print(vol[-1][0].data)
mem1 = memory_usage_psutil()
print("Memory: {} - {}".format(mem0, mem1))
print("Memory added: {0:.5f} MB".format((mem1 - mem0)))

Second Access
<xarray.Dataset>
Dimensions:     (azimuth: 360, range: 800)
Coordinates:
    time        datetime64[ns] 2020-02-07T13:04:08
    rtime       (azimuth) datetime64[ns] 2020-02-07T13:04:10.527778816 ... 20...
    elevation   (azimuth) float32 ...
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * range       (range) float32 125.0 375.0 625.0 ... 1.996e+05 1.999e+05
    sweep_mode  <U20 ...
    latitude    float64 ...
    longitude   float64 ...
    altitude    float64 ...
Data variables:
    DBZH        (azimuth, range) float32 dask.array<chunksize=(360, 800), meta=np.ndarray>
    RHOHV       (azimuth, range) float32 dask.array<chunksize=(360, 800), meta=np.ndarray>
    VRAD        (azimuth, range) float32 dask.array<chunksize=(360, 800), meta=np.ndarray>
    WRAD        (azimuth, range) float32 dask.array<chunksize=(360, 800), meta=np.ndarray>
Memory: 200.3203125 - 200.3203125
Memory added: 0.00000 MB
CPU times: user 2.59 ms, sys: 7.82 ms, total: 10.4 ms
Wall time: 11 ms

TimeSeries

%%time
print("First Access")
mem0 = memory_usage_psutil()
print(vol[-1].data)
mem1 = memory_usage_psutil()
print("Memory: {} - {}".format(mem0, mem1))
print("Memory added: {0:.5f} MB".format((mem1 - mem0)))

First Access

Collecting: 100%|██████████| 8/8 [00:00<00:00, 30.14 Timesteps/s]

<xarray.Dataset>
Dimensions:     (time: 8, azimuth: 360, range: 800)
Coordinates:
  * time        (time) datetime64[ns] 2020-02-07T13:04:08 ... 2020-02-07T13:3...
    rtime       (time, azimuth) datetime64[ns] 2020-02-07T13:04:10.527778816 ...
    elevation   (azimuth) float32 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 0.3 0.3 0.3
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * range       (range) float32 125.0 375.0 625.0 ... 1.996e+05 1.999e+05
    sweep_mode  <U20 'azimuth_surveillance'
    latitude    float64 51.07
    longitude   float64 5.406
    altitude    float64 140.0
Data variables:
    DBZH        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 800), meta=np.ndarray>
    RHOHV       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 800), meta=np.ndarray>
    VRAD        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 800), meta=np.ndarray>
    WRAD        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 800), meta=np.ndarray>
Memory: 200.32421875 - 203.2734375
Memory added: 2.94922 MB
CPU times: user 290 ms, sys: 19.8 ms, total: 309 ms
Wall time: 312 ms

%%time
print("Second Access")
mem0 = memory_usage_psutil()
print(vol[-1].data)
mem1 = memory_usage_psutil()
print("Memory: {} - {}".format(mem0, mem1))
print("Memory added: {0:.5f} MB".format((mem1 - mem0)))

Second Access
<xarray.Dataset>
Dimensions:     (time: 8, azimuth: 360, range: 800)
Coordinates:
  * time        (time) datetime64[ns] 2020-02-07T13:04:08 ... 2020-02-07T13:3...
    rtime       (time, azimuth) datetime64[ns] 2020-02-07T13:04:10.527778816 ...
    elevation   (azimuth) float32 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 0.3 0.3 0.3
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * range       (range) float32 125.0 375.0 625.0 ... 1.996e+05 1.999e+05
    sweep_mode  <U20 'azimuth_surveillance'
    latitude    float64 51.07
    longitude   float64 5.406
    altitude    float64 140.0
Data variables:
    DBZH        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 800), meta=np.ndarray>
    RHOHV       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 800), meta=np.ndarray>
    VRAD        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 800), meta=np.ndarray>
    WRAD        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 800), meta=np.ndarray>
Memory: 203.27734375 - 203.27734375
Memory added: 0.00000 MB
CPU times: user 6.29 ms, sys: 4.11 ms, total: 10.4 ms
Wall time: 10.8 ms

Plot Data

Plot Single Sweep

vol[-1].data.pipe(wrl.georef.georeference_dataset).DBZH[0].wradlib.plot()

<matplotlib.collections.QuadMesh at 0x7fab92df8990>

Plot same single sweep from Timeseries

vol[-1].data.DBZH[0].plot()

<matplotlib.collections.QuadMesh at 0x7fab92eaf890>

Exporting Data

Data can be exported to ODIMH5, CfRadial2 and NetCDF4.

ODIMH5

ODIMH5 can only handle one volume not timeseries. So we have to select the timestep which we want to export.

The example shows, how to output the volume to a ODIMH5-file, read it back and check for equality.

vol.to_odim("test_odim.h5", timestep=5)

vol1 = wrl.io.open_odim("test_odim.h5")

/tmp/ipykernel_4260/3074143617.py:1: DeprecatedWarning: open_odim is deprecated as of 1.10 and will be removed in 2.0. Use the appropriate `wradlib.io.open_{engine}_dataset` or `wradlib.io.open_{engine}_mfdataset` function.
  vol1 = wrl.io.open_odim("test_odim.h5")
Open: 100%|██████████| 1/1 [00:00<00:00, 55.53 Files/s]
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2287: DeprecatedWarning: collect_by_angle is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles = collect_by_angle(sweeps)
Collecting:   0%|          | 0/12 [00:00<?, ? Angles/s]/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/wradlib/io/xarray_depr.py:2289: DeprecatedWarning: collect_by_time is deprecated as of 1.10 and will be removed in 2.0. Use xarray BackendEntrypoint based functionality instead.
  angles[i] = collect_by_time(angles[i])
Collecting: 100%|██████████| 12/12 [00:00<00:00, 5032.66 Angles/s]

print(vol[0][5])

<wradlib.XRadSweepOdim>
Dimension(s): (azimuth: 360, range: 800)
Elevation(s): (25.0)
Moment(s): (DBZH, RHOHV, VRAD, WRAD)

print(vol1[0][0])

<wradlib.XRadSweepOdim>
Dimension(s): (azimuth: 360, range: 800)
Elevation(s): (25.0)
Moment(s): (DBZH, RHOHV, VRAD, WRAD)

xr.testing.assert_equal(vol[0][5].data, vol1[0][0].data)

CfRadial2

CfRadial2 can only handle one volume not timeseries. So we have to select the timestep which we want to export.

The example shows, how to output the volume to a CfRadial2-file and read it back. For there is currently no fitting counterpart to open_odim for reading CfRadial2 volumes we resort to wradlib.io.CfRadial reader and compare the underlying numpy arrays. As CfRadial2 data is sorted by time, we have to sort it by azimuth first.

vol.to_cfradial2("test_cfradial2.nc", timestep=5)

vol2 = wrl.io.CfRadial("test_cfradial2.nc", dim0="azimuth")

/tmp/ipykernel_4260/678447143.py:1: FutureWarning: <class 'wradlib.io.xarray_depr.CfRadial'> is deprecated as of 1.10. and will be removed in 2.0. Use xarray backends from `xradar`-package.
  vol2 = wrl.io.CfRadial("test_cfradial2.nc", dim0="azimuth")

np.testing.assert_equal(
    vol[0][5].data.DBZH.values, vol2["sweep_0"].sortby("azimuth").DBZH.values
)

NetCDF4

Using this, the complete volume/timeseries is exported to a NetCDF4 file.

The example shows, how to output the volume to such NetCDF4-file and read it back. For there is currently no fitting counterpart to open_odim for reading these NetCDF4 volumes we resort to xarray.open_dataset reader.

vol.to_netcdf("test_netcdf.nc", timestep=slice(None, None))

Collecting: 100%|██████████| 8/8 [00:00<00:00, 29.50 Timesteps/s]
Collecting: 100%|██████████| 8/8 [00:00<00:00, 24.91 Timesteps/s]
Collecting: 100%|██████████| 8/8 [00:00<00:00, 30.52 Timesteps/s]
Collecting: 100%|██████████| 8/8 [00:00<00:00, 24.96 Timesteps/s]
Collecting: 100%|██████████| 8/8 [00:00<00:00, 19.13 Timesteps/s]
Collecting: 100%|██████████| 8/8 [00:00<00:00, 30.43 Timesteps/s]
Collecting: 100%|██████████| 8/8 [00:00<00:00, 28.41 Timesteps/s]
Collecting: 100%|██████████| 8/8 [00:00<00:00, 20.51 Timesteps/s]
Collecting: 100%|██████████| 8/8 [00:00<00:00, 30.44 Timesteps/s]
Collecting: 100%|██████████| 8/8 [00:00<00:00, 26.69 Timesteps/s]
Collecting: 100%|██████████| 8/8 [00:00<00:00, 34.93 Timesteps/s]

vol3 = xr.open_dataset("test_netcdf.nc", group="sweep_0")

display(vol3)

<xarray.Dataset>
Dimensions:     (time: 8, azimuth: 360, range: 800)
Coordinates:
  * time        (time) datetime64[ns] 2020-02-07T13:00:05 ... 2020-02-07T13:3...
    rtime       (time, azimuth) datetime64[ns] ...
    elevation   (azimuth) float32 ...
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * range       (range) float32 125.0 375.0 625.0 ... 1.996e+05 1.999e+05
    sweep_mode  object ...
    latitude    float64 ...
    longitude   float64 ...
    altitude    float64 ...
Data variables:
    DBZH        (time, azimuth, range) float32 ...
    RHOHV       (time, azimuth, range) float32 ...
    VRAD        (time, azimuth, range) float32 ...
    WRAD        (time, azimuth, range) float32 ...

xarray.Dataset

• Dimensions:
  • time: 8
  • azimuth: 360
  • range: 800
• Coordinates: (9)
  • time
    (time)
    datetime64[ns]
    2020-02-07T13:00:05 ... 2020-02-...

    standard_name :

    time

    array(['2020-02-07T13:00:05.000000000', '2020-02-07T13:05:04.000000000',
           '2020-02-07T13:10:04.000000000', '2020-02-07T13:15:04.000000000',
           '2020-02-07T13:20:04.000000000', '2020-02-07T13:25:04.000000000',
           '2020-02-07T13:30:04.000000000', '2020-02-07T13:35:04.000000000'],
          dtype='datetime64[ns]')

  • rtime
    (time, azimuth)
    datetime64[ns]
    ...

    standard_name :

    time

    [2880 values with dtype=datetime64[ns]]

  • elevation
    (azimuth)
    float32
    ...

    standard_name :

    ray_elevation_angle

    long_name :

    elevation_angle_from_horizontal_plane

    units :

    degrees

    axis :

    radial_elevation_coordinate

    [360 values with dtype=float32]

  • azimuth
    (azimuth)
    float32
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    standard_name :

    ray_azimuth_angle

    long_name :

    azimuth_angle_from_true_north

    units :

    degrees

    axis :

    radial_azimuth_coordinate

    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)

  • range
    (range)
    float32
    125.0 375.0 ... 1.996e+05 1.999e+05

    units :

    meters

    standard_name :

    projection_range_coordinate

    long_name :

    range_to_measurement_volume

    axis :

    radial_range_coordinate

    meters_between_gates :

    250.0

    spacing_is_constant :

    true

    meters_to_center_of_first_gate :

    125.0

    array([1.25000e+02, 3.75000e+02, 6.25000e+02, ..., 1.99375e+05, 1.99625e+05,
           1.99875e+05], dtype=float32)

  • sweep_mode
    ()
    object
    ...

    [1 values with dtype=object]

  • latitude
    ()
    float64
    ...

    [1 values with dtype=float64]

  • longitude
    ()
    float64
    ...

    [1 values with dtype=float64]

  • altitude
    ()
    float64
    ...

    [1 values with dtype=float64]

• Data variables: (4)
  • DBZH
    (time, azimuth, range)
    float32
    ...

    IMAGE_VERSION :

    1.2

    _Undetect :

    0.0

    units :

    dBZ

    long_name :

    Equivalent reflectivity factor H

    standard_name :

    radar_equivalent_reflectivity_factor_h

    [2304000 values with dtype=float32]

  • RHOHV
    (time, azimuth, range)
    float32
    ...

    IMAGE_VERSION :

    1.2

    _Undetect :

    0.0

    units :

    unitless

    long_name :

    Correlation coefficient HV

    standard_name :

    radar_correlation_coefficient_hv

    [2304000 values with dtype=float32]

  • VRAD
    (time, azimuth, range)
    float32
    ...

    IMAGE_VERSION :

    1.2

    _Undetect :

    0.0

    units :

    meters per seconds

    long_name :

    Radial velocity of scatterers away from instrument

    standard_name :

    radial_velocity_of_scatterers_away_from_instrument

    [2304000 values with dtype=float32]

  • WRAD
    (time, azimuth, range)
    float32
    ...

    IMAGE_VERSION :

    1.2

    _Undetect :

    0.0

    units :

    meters per second

    long_name :

    Doppler spectrum width

    standard_name :

    radar_doppler_spectrum_width

    [2304000 values with dtype=float32]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-02-07 13:00:05', '2020-02-07 13:05:04',
                   '2020-02-07 13:10:04', '2020-02-07 13:15:04',
                   '2020-02-07 13:20:04', '2020-02-07 13:25:04',
                   '2020-02-07 13:30:04', '2020-02-07 13:35:04'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • azimuth
    PandasIndex

    PandasIndex(Float64Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,
                    9.5,
                  ...
                  350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5,
                  359.5],
                 dtype='float64', name='azimuth', length=360))

  • range
    PandasIndex

    PandasIndex(Float64Index([   125.0,    375.0,    625.0,    875.0,   1125.0,   1375.0,
                    1625.0,   1875.0,   2125.0,   2375.0,
                  ...
                  197625.0, 197875.0, 198125.0, 198375.0, 198625.0, 198875.0,
                  199125.0, 199375.0, 199625.0, 199875.0],
                 dtype='float64', name='range', length=800))

• Attributes: (0)

xr.testing.assert_equal(vol[0][5].data, vol3.isel(time=5))

Delete object

del mom
del swp
del ts
del vol
del vol1
del vol2
del vol3

Run Garbage Collection

free_memory()

35029
Memory freed: 3.29688 MB

Check Memory

mem_end = memory_usage_psutil()
print("Memory: {} - {}".format(mem_start, mem_end))
print("Memory still in use: {0:.5f} MB".format((mem_end - mem_start)))

Memory: 167.31640625 - 395.125
Memory still in use: 227.80859 MB

Check Open files

No open data files!

check_open_files(True)

3
[popenfile(path='/home/runner/micromamba-root/envs/wradlib-notebooks/share/proj/proj.db', fd=48, position=0, mode='r', flags=688128), popenfile(path='/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', fd=82, position=69642, mode='r', flags=557056), popenfile(path='/home/runner/work/wradlib-notebooks/wradlib-notebooks/notebooks/fileio/test_odim.h5', fd=90, position=0, mode='r', flags=32768)]