Load, manipulate and save R&S waveform files
A few more paragraphs explaining purposes and features.
Install from pypi.org:
$ pip install RsWaveform
You need at least Python 3.7.
import RsWaveform
import RsWaveform
filename = "tests/data/dummy.wv"
wv = RsWaveform.RsWaveform(file=filename)
# default loader + saver will be used which is currently a ".wv" type
# This is the same as wv = RsWaveform.RsWaveform(load=RsWaveform.wv.Load, save=RsWaveform.wv.Save, file=filename)
wv.data[0]
>> array([0.2 + 0.4j, 0.6 + 0.8j])
wv.meta[0]
>> {
'type': 'SMU-WV',
'copyright': 'Rohde & Schwarz',
'comment': 'Test waveform file',
'clock': 100000000.0,
'marker': {'marker list 1': [[0, 1], [32, 0], [63, 0]],},
'control_length': None,
'control_list': {},
'level offs': (2.220459, 0.0),
'date': datetime.datetime(2023, 1, 5, 10, 3, 52),
'control length': 2,
'encryption_flag': False,
'center_frequency': 1000000000.0,
'scalingfactor': 1
}
import RsWaveform
import numpy as np
import datetime
wv = RsWaveform.RsWaveform() # default loader + saver will be used which is currently a ".wv" type
# This is the same as wv = RsWaveform.RsWaveform(load=RsWaveform.wv.Load, save=RsWaveform.wv.Save)
wv.data[0] = np.ones((2,)) + 1j * np.zeros((1,))
# Set values as dict
wv.meta[0].update({
'type': 'SMU-WV',
'copyright': 'Rohde & Schwarz',
'level offs': (2.220459, 0.0),
'date': datetime.datetime.now(),
'clock': 100000000.0,
'control length': 2,
})
# or use the meta data properties
wv.meta[0].comment = 'Test waveform file'
wv.meta[0].marker.update({'marker list 1': [[0, 1], [32, 0], [63, 0]]})
# save to file
wv.save(r"someFileName.wv")
import RsWaveform
filename = "tests/data/dummy.iqw"
iqw = RsWaveform.Iqw(
file=filename) # default loader + saver will be used which is
# currently a ".iqw" type
# This is the same as iqw = RsWaveform.Iqw(load=RsWaveform.iqw.Load,
# save=RsWaveform.iqw.Save, file=filename)
iqw.data[0]
>> array([0.2 + 0.4j, 0.6 + 0.8j])
import RsWaveform
import numpy as np
iqw = RsWaveform.Iqw() # default loader + saver will be used which is
# currently a ".iqw" type
# This is the same as iqw = RsWaveform.Iqw(load=RsWaveform.iqw.Load,
# save=RsWaveform.iqw.Save)
iqw.data[0] = np.ones((2,)) + 1j * np.zeros((1,))
iqw.save(r"someFileName.iqw") # save to file
import RsWaveform
import datetime
filename = "tests/data/dummy.iq.tar"
iqtar = RsWaveform.IqTar(file=filename)
# default loader + saver will be used which is
# currently a ".iqw" type
# This is the same as iqtar = RsWaveform.IqTar(load=RsWaveform.iqtar.Load,
# save=RsWaveform.iqtar.Save, file=filename)
iqtar.data[0]
>> array([0.2 + 0.4j, 0.6 + 0.8j])
iqtar.meta[0]
>> {
'clock': 10000.0,
'scalingfactor': 1.0,
'datatype': 'float32',
'format': 'complex',
'name': 'Python iq.tar Writer (iqdata.py)',
'comment': 'RS WaveForm, TheAE-RA',
"datetime": datetime.datetime(2023, 3, 1, 10, 19, 37, 43312),
}
import RsWaveform
import datetime
iqtar = RsWaveform.IqTar()
# default loader + saver will be used which is
# currently a ".iqw" type
# This is the same as iqtar = RsWaveform.IqTar(load=RsWaveform.iqtar.Load,
# save=RsWaveform.iqtar.Save, file=filename)
iqtar.data[0] = np.ones((2,)) + 1j * np.zeros((1,))
# Set values as dict
iqtar.meta[0] = {
'clock': 10000.0,
'scalingfactor': 1.0,
'datatype': 'float32',
'format': 'complex',
'name': 'Python iq.tar Writer (iqdata.py)',
"datetime": datetime.datetime.now(),
}
# or use the meta data properties
iqtar.meta[0].comment = 'RS WaveForm, TheAE-RA'
# save to file
iqtar.save("somefilename.iq.tar")
The RsWaveform package provides also these convenience functions for digital signal processing
- normalize
- calculate_peak - output as dB
- calculate_rms - output as dB
- calculate_par - output as dB
- convert_to_db - amplitude based
You can use them as following
import RsWaveform
import numpy as np
data = RsWaveform.normalize(np.ones((2,)) + 1j * np.zeros((1,)))
RsWaveform.calculate_peak(data)
>> 0.0
RsWaveform.calculate_rms(data)
>> 0.0
RsWaveform.calculate_par(data)
>> 0.0
- Author: Carsten Sauerbrey ([email protected])
- Author: Daniela Rossetto ([email protected])
We welcome any contributions, enhancements, and bug-fixes. Open an issue on Github and submit a pull request.
In case you finished the work on your branch, create a pull request, describe (optionally) your changes in the pull request and set at least one of the authors mentioned above as code reviewer. The closed branch should be deleted after merge.
Before approving a pull request, check for and discuss:
- Repetitive (copy & paste) code -> Could this be refactored/moved to a function?
- Stale / commented out functional code -> Could these artifacts be deleted?
- Duplication of existing functionality -> Could existing code already solve the adressed problem?
- Unused imports -> Could these imports be cleaned up?`
- File locations don't mirror their logical connection to a feature -> Could they be grouped within a logical unit (e.g. folder)?
- Outdated / needlessly complex python functionality -> Could this be solved by more modern python language features (e.g. itertools)?
- Is there a test for your functionality? -> add new test or modify an existing test.