I am trying to process mseed data using dataless seed files that are obviously coded in frequency rather than in laplace transform.
Obspy cannot stand with such non laplace dataless files.
I assume that there is somewhere a 2.PI() factor that as to be apply to convert the laplace in freq (if I am right), but where ?
As this laplace/frequency encoding should be tagged somewhere in the dataless file, could it be possible to make obpsy automatically detected and convert the dataless encoding format ?
At least, Does anybody knows how to convert my frequency coded dataless to laplace dataless files ? Is there any standard scripts ?
One other thing you could do for a quick fix is to convert your dataless
to StationXML using IRIS's tool
(https://seiscode.iris.washington.edu/projects/stationxml-converter/wiki) and
then go via..
.. read_inventory(..)
.. stream.attach_response(..)
.. stream.remove_response(..)
essentially going via evalresp.
Trying to use your solution, but I am facing an error...
Traceback (most recent call last):
File "td2a_deconvolution.py", line 33, in <module>
INV=read_inventory("/home/langlami/temp/data-stpaulubaye/SURF.xml")
File "/home/langlami/anaconda/lib/python2.7/site-packages/obspy/station/inventory.py", line 38, in read_inventory
return _readFromPlugin("inventory", path_or_file_object, format=format)[0]
File "/home/langlami/anaconda/lib/python2.7/site-packages/obspy/core/util/base.py", line 321, in _readFromPlugin
raise TypeError('Unknown format for file %s' % filename)
TypeError: Unknown format for file /home/langlami/temp/data-stpaulubaye/SURF.xml
Does the xml file can stand multiple channels ?
Should I add the format when invoking read_inventory fonction ? But which one ?
ObsPy currently uses the StationXML XSD scheme to check if a file is a StationXML file. Many StationXML files out there are strictly speaking not valid StationXML files…
I guess we should also change our format detection algorithm a bit in that regard.
Just force the format (as you already suggested) with