seismic.inventory package

Submodules

seismic.inventory.add_time_corrections module

Add GPS clock time correction csv_data into inventory file to get a modified station xml file

CreationDate:

24/02/2020

Developer:

fei.zhang@ga.gov.au

seismic.inventory.add_time_corrections.add_gpscorrection_into_stationxml(csv_file, input_xml, out_xml=None)[source]

Read in the correction CSV data from a file, get the station metadata node from input_xml file, then add the CSV data into the station xml node to write into out_xml

Parameters
  • csv_file – input csv file with correction data

  • input_xml – input original stationXML file which contains the metadata for the network and station of csv_file

  • out_xml – Directory of the output xml file

Returns

full path of the output xml file

seismic.inventory.add_time_corrections.extract_csvdata(path2xml)[source]

Read the station xml file and extract the csv data to be parsed by pandas

Parameters

path2xml – path_to_stationxml

Returns

csv_str

seismic.inventory.add_time_corrections.get_csv_correction_data(path_csvfile)[source]

Read in the csv data from an input file, get the network_code, station_code, csv_data. Format:

$ head 7D.DE43_clock_correction.csv
net,sta,date,clock_correction
7D,DE43,2012-11-27,1.0398489013215846
7D,DE43,2012-11-28,0.9408504322549281
7D,DE43,2012-11-29,0.8418519631882714
7D,DE43,2012-11-30,0.7428534941216148
7D,DE43,2012-12-01,0.6438550250549583
Parameters

path_csvfile – input csv file in /g/data/ha3/Passive/SHARED_DATA/GPS_Clock/corrections/

Returns

(network_code, station_code, csv_data)

seismic.inventory.engd2stxml module

Engdahl and ISC STN data conversion to FDSN station XML

Creates database of stations from .STN files, curates the data using heuristic rules, and exports new stations to FDSN station XML format network with non-empty, nominal instrument response data.

Cleanup steps applied:

  • Removes “blacklisted” networks that add little value and cause problems due to station code conflicts.

  • Add default station dates where missing.

  • Make “future” station end dates consistent to max Pandas timestamp.

  • Remove records with illegal station codes.

  • Remove duplicate station records.

  • Merge overlapping channel dates for given NET.STAT.CHAN to a single epoch.

seismic.inventory.engd2stxml.cleanup_database(df)[source]

Main cleanup function encompassing the sequential data cleanup steps.

Parameters

df (pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA) – Dataframe of station records to clean up

Returns

Cleaned up dataframe of station records

Return type

pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA

seismic.inventory.engd2stxml.compute_neighboring_station_matrix(df)[source]

Compute sparse matrix representing index of neighboring stations.

Ordering of matrix corresponds to ordering of Dataframe df, which is expected to be sequential integer indexed. For a given station index i, then the non-zero off-diagonal entries in row i of the returned matrix indicate the indices of adjacent, nearby stations.

Parameters

df (pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA) – Dataframe containing station records.

Returns

Sparse binary matrix having non-zero values at indices of neighboring stations.

Return type

scipy.sparse.csr_matrix

seismic.inventory.engd2stxml.latlong_to_cosinedistance(latlong_deg_set1, latlong_deg_set2)[source]

Compute the approximate cosine distance between each station of 2 sets.

Each set is specified as a numpy column vector of [latitude, longitude] positions in degrees.

This function performs an outer product and will produce matrix of size N0 x N1, where N0 is the number of rows in latlong_deg_set1 and N1 is the number of rows in latlong_deg_set2.

Returns numpy.ndarray containing cosines of angles between each pair of stations from the input arguments. If input is 1D, convert to 2D for consistency of matrix orientations.

Parameters
  • latlong_deg_set1 (numpy.array) – First set of numpy column vector of [latitude, longitude] positions in degrees

  • latlong_deg_set2 (numpy.array) – Second set of numpy column vector of [latitude, longitude] positions in degrees

Returns

Array containing cosines of angles between each pair of stations from the input arguments.

Return type

numpy.array

seismic.inventory.engd2stxml.main(iris_xml_file, stations_folder, output_directory, test_mode=False)[source]

Main entry point.

Parameters
  • iris_xml_file (str or pathlib.Path) – Name of IRIS xml file to load (generated by script update_iris_inventory.py)

  • stations_folder (str or pathlib.Path) – Path to folder containing STN files to process

  • output_directory (str or pathlib.Path) – Directory in which output inventory file(s) are saved

seismic.inventory.engd2stxml.merge_overlapping_channel_epochs(df)[source]

Removed overlapping time intervals for a given network.station.channel, as this needs to be unique.

This function expects the input DataFrame to have a sequential integer index.

Parameters

df (pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA) – Dataframe of station records in which to merge overlapping channel dates

seismic.inventory.engd2stxml.populate_default_station_dates(df)[source]

Replace all missing station start and end dates with default values. Replace all missing channel start and end dates with their corresponding station/end dates.

Parameters

df (pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA) – Dataframe in which to fill in missing station start and end dates.

seismic.inventory.engd2stxml.read_eng(fname)[source]

Read Engdahl STN file having the following format of fixed width formatted columns:

AAI   Ambon             BMG, Indonesia, IA-Ne              -3.6870  128.1945      0.0   2005001  2286324  I
AAII                                                       -3.6871  128.1940      0.0   2005001  2286324  I
AAK   Ala Archa         Kyrgyzstan                         42.6390   74.4940      0.0   2005001  2286324  I
ABJI                                                       -7.7957  114.2342      0.0   2005001  2286324  I
APSI                                                       -0.9108  121.6487      0.0   2005001  2286324  I
AS01  Alice Springs Arra                                  -23.6647  133.9508      0.0   2005001  2286324  I
0123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890
         10        20        30        40        50        60        70        80

Each Station Code (first column) might NOT be unique, and network codes are missing here, so all records are placed under ‘GE’ network.

Parameters

fname (str) – STN file name to load

Returns

Pandas Dataframe containing the loaded data in column order of TABLE_COLUMNS.

Return type

pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA

seismic.inventory.engd2stxml.read_isc(fname, use_pickle=False)[source]

Read ISC station inventory having such format and convert to Pandas DataFrame:

109C     32.8892 -117.1100     0.0 2006-06-01 04:11:18 2008-01-04 01:26:30
109C     32.8882 -117.1050   150.0 2008-01-04 01:26:30
             FDSN 109C   TA -- BHZ 2004-05-04 23:00:00 2005-03-03 23:59:59
             FDSN 109C   TA -- LHZ 2004-05-04 23:00:00 2005-03-03 23:59:59
             FDSN 109C   TA -- BHZ 2005-04-11 00:00:00 2006-01-25 22:31:10
             FDSN 109C   TA -- LHZ 2005-04-11 00:00:00 2006-01-25 22:31:10
0123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890
          10        20        30        40        50        60        70        80

The lines starting with a station code are HEADER rows, and provide the station coordinates. The idented lines starting with FDSN provide distinct station, network and channel data for the given station location.

Parameters

fname (str) – STN file name to load

Returns

Pandas Dataframe containing the loaded data in column order of TABLE_COLUMNS.

Return type

pandas.DataFrame conforming to table_format.TABLE_SCHEMA

seismic.inventory.engd2stxml.remove_blacklisted(df)[source]

Remove network codes that are explicitly blacklisted due to QA issues or undesirable overlap with trusted FDSN station codes.

Parameters

df (pandas.DataFrame conforming to table_format.TABLE_SCHEMA) – Dataframe of initially loaded data from STN files

seismic.inventory.engd2stxml.remove_duplicate_stations(df, neighbor_matrix)[source]

Remove stations which are identified as duplicates: * Removes duplicated stations in df based on station code and locality of lat/long coordinates. * Removes duplicated stations based on codes and channel data matching, IRRESPECTIVE of locality

Parameters
  • df (pandas.DataFrame conforming to table_format.TABLE_SCHEMA) – Dataframe containing station records. Is modified during processing.

  • neighbor_matrix (scipy.sparse.csr_matrix) – Sparse binary matrix having non-zero values at indices of neighboring stations.

Returns

Dataframe containing station records with identified duplicates removed.

Return type

pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA

seismic.inventory.engd2stxml.remove_illegal_stationNames(df)[source]

Remove records for station names that do not conform to expected naming convention. Such names can cause problems in downstream station, in particular names with asterisk.

Parameters

df (pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA) – Dataframe containing station records from which illegal station codes should be removed (modified in-place)

seismic.inventory.engd2stxml.reportUnpickleFail(filename)[source]

Standard failure report message when trying to unpickle file.

Parameters

filename (str) – The name of the file that failed to unpickle.

seismic.inventory.engd2stxml.write_portable_inventory(df, fname)[source]

Write the final database to re-usable file formats.

Parameters
  • df (pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA) – Dataframe containing complete inventory of station records.

  • fname (str) – Base filename to export to. Output filename will be appended with file format extensions.

seismic.inventory.fdsnxml_convert module

Helper functions to convert FDSN station XML files to Seiscomp3 SC3ML format.

Can be used as a standalone tool as well:

fdsnxml_convert.py src_path dst_path
seismic.inventory.fdsnxml_convert.sc3_conversion_available()[source]
Check whether conversion to seiscomp3 format is available os this system.

Only works for Python 3, for Python 2 it always returns True (i.e. give it a try).

Returns

True if conversion to sc3ml can be performed on this system, False otherwise.

Return type

bool

seismic.inventory.fdsnxml_convert.toSc3ml(src_path, dst_path, response_fdsnxml=None)[source]

Convert file(s) in src_path from FDSN station XML to SC3ML and emit result(s) to dst_path.

If src_path is a file, dst_path will be treated as a file. If dst_path already exists as a folder, an exception is raised.

If src_path is a folder, dst_path will be treated as a folder. If dst_path already exists as a file, an exception is raised. The src_path directory hierarchy will be walked to find all .xml files, each of which will be converted to a mirrored relative path under dst_path.

Parameters
  • src_path (str or pathlib.Path) – The source path from which to input XML file(s).

  • dst_path (str or pathlib.Path) – The destination path into which converted sc3ml file(s) are output.

  • response_fdsnxml (str or Path) – Path to existing for containing dummy response to insert into records where instrument response is missing.

Raises

OSError, FileNotFoundError, RuntimeError, FileExistsError

seismic.inventory.generate_network_plots module

Load inventory file from hdf5 format and export station location plots to graphics files.

Usage:

python generate_network_plots.py inventory_20190206.h5

where inventory_20190206.h5 here is an example file name. The output folder for the graphics files is inferred from the inventory file name, which would be “inventory_20190206” in this example.

seismic.inventory.inventory_merge module

Merge a master inventory (e.g. from IRIS) with custom inventories.

seismic.inventory.inventory_merge.get_matching_net(search_space, item_to_find)[source]

Find matching network instance

seismic.inventory.inventory_merge.inventory_merge(iris_inv, custom_inv, output_file, test_mode=False)[source]
Merge an IRIS inventory with a custom inventory, filtering out any custom inventory records

that duplicate IRIS records.

Parameters
  • iris_inv (str or Path to file) – Station XML file from which to load IRIS inventory

  • custom_inv (str or Path to file) – Station XML file with custom records to merge with IRIS inventory

  • output_file (str or Path to file) – File name of output file which will contain merged IRIS and custom inventory.

Returns

The merged inventory

Return type

obspy.core.inventory.inventory.Inventory

seismic.inventory.inventory_merge.mean_lat_long(network)[source]

Compute mean of station lat/long coordinates within a network

Parameters

network (obspy.core.inventory.network.Network) – Network on which to compute mean lat/long

Returns

Mean of all station locations in the network

Return type

tuple(float, float)

seismic.inventory.inventory_merge.prune_iris_duplicates(db_other, db_iris)[source]

Prune the records out of db_other which duplicate records that exist in db_iris.

Filtering concepts:

  • Station codes and channel codes are reliable, but network codes and station/channel dates are not.

  • Station lat/lon locations are approximately reliable. Therefore the merge method here matches records with matching station code, channel code and approximate location. For each match, we examine station dates, and discard any records of db_other that overlap with db_iris.

db_iris is not changed by this function.

Parameters
  • db_other (pandas.DataFrame) – DataFrame containing metadata records of custom inventory

  • db_iris (pandas.DataFrame) – DataFrame containing metadata records of IRIS inventory

Returns

DataFrame containing records from db_other which are unique and do not appear in IRIS.

Return type

pandas.DataFrame

seismic.inventory.inventory_split module

Split a station inventory file into a separate file per network.

seismic.inventory.inventory_split.inventory_split(inv_file, output_folder, sc3ml=False)[source]

Split an inventory file (station XML) into separate inventory file per network, stored in folder output_folder.

Parameters
  • inv_file (str or path) – Station inventory to be split. This file is not changed by the script.

  • output_folder (str or path to folder) – Folder where per-network station xml files will be generated.

  • sc3ml (bool, optional) – If True, try to convert output files to sc3ml format if possible using seiscomp3. Defaults to False.

seismic.inventory.inventory_split.split_inventory_by_network(obspy_inv, output_folder, validate=False)[source]

Export a station XML file per network for each network in given obspy Inventory.

Parameters
  • obspy_inv (obspy.core.inventory.inventory.Inventory) – Obspy Inventory containing the networks to export to file.

  • output_folder (str or pathlib.Path) – Folder in which to output the per-network XML files. Will be created if doesn’t yet exist.

  • validate (bool, optional) – Whether to validate the station data on write, defaults to False

seismic.inventory.inventory_util module

Utility functions and constants shared amongst inventory management modules.

class seismic.inventory.inventory_util.Instrument(sensor, response)

Bases: tuple

Create new instance of Instrument(sensor, response)

property response

Alias for field number 1

property sensor

Alias for field number 0

seismic.inventory.inventory_util.extract_unique_sensors_responses(inv, req, show_progress=True, blacklisted_networks=None, test_mode=False)[source]

For the channel codes in the given inventory, determine a nominal instrument response suitable for that code. Note that no attempt is made here to determine an ACTUAL correct response for a given network and station. The only requirement here is to populate a plausible, non-empty response for a given channel code, to placate Seiscomp3 which requires that an instrument response always be present.

Parameters
Returns

Python dict of (obspy.core.inventory.util.Equipment, obspy.core.inventory.response.Response) indexed by str representing channel code

Return type

{str: Instrument(obspy.core.inventory.util.Equipment, obspy.core.inventory.response.Response) } where Instrument is a collections.namedtuple(“Instrument”, [‘sensor’, ‘response’])

seismic.inventory.inventory_util.load_station_xml(inventory_file)[source]

Load a stationxml file

Parameters

inventory_file (str or path) – Name of stationxml file to load

Returns

Station inventory as Obspy Inventory

Return type

obspy.core.inventory.inventory.Inventory

seismic.inventory.inventory_util.obtain_nominal_instrument_response(netcode, statcode, chcode, req)[source]

For given network, station and channel code, find suitable response(s) in IRIS database and return as dict of obspy instrument responses.

Parameters
  • netcode (str) – Network code (may include wildcards)

  • statcode (str) – Station code (may include wildcards)

  • chcode (str) – Channel code (may include wildcards)

  • req (Object conforming to interface of 'requests' library) – Request object to use for URI query

Returns

Dictionary of instrument responses from IRIS for given network(s), station(s) and channel(s).

Return type

dict of {str, Instrument(obspy.core.inventory.util.Equipment, obspy.core.inventory.response.Response)}

seismic.inventory.iris_query module

Helper functions for making and managing web queries to IRIS web service.

seismic.inventory.iris_query.form_channel_request_url(netmask='*', statmask='*', chanmask='*')[source]

Form request URL to download station inventory in stationxml format, down to channel level, with the given filters applied to network codes, station codes and channel codes.

Parameters
  • netmask (str, optional) – Pattern of network codes to match, comma separated with wildcards, defaults to “*”

  • statmask (str, optional) – Pattern of station codes to match, comma separated with wildcards, defaults to “*”

  • chanmask (str, optional) – Pattern of channel codes to match, comma separated with wildcards, defaults to “*”

Returns

Fully formed URL to perform IRIS query and get back FDSN station XML result.

Return type

str

seismic.inventory.iris_query.form_response_request_url(netmask, statmask, chanmask)[source]

Form request URL to download station inventory in stationxml format, down to response level, for the given network, station and channel codes.

Parameters
  • netmask (str, optional) – Pattern of network codes to match, comma separated with wildcards

  • statmask (str, optional) – Pattern of station codes to match, comma separated with wildcards

  • chanmask (str, optional) – Pattern of channel codes to match, comma separated with wildcards

Returns

Fully formed URL to perform IRIS query and get back FDSN station XML result.

Return type

str

seismic.inventory.iris_query.set_text_encoding(resp, quiet=False)[source]

For the given response object, set its encoding from the contents of the text returned from server.

Parameters

resp (requests.Response) – Query response object returned by response.get()

seismic.inventory.pdconvert module

Helper functions for converting between Pandas dataframe and FDSN Inventory, Network, Station and Channel objects.

seismic.inventory.pdconvert.dataframe_to_fdsn_station_xml(inventory_df, nominal_instruments, filename, show_progress=True)[source]

Export dataframe of station metadata to FDSN station xml file

Parameters
  • inventory_df (pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA) – Dataframe containing all the station records to export.

  • nominal_instruments ({str: Instrument(obspy.core.inventory.util.Equipment, obspy.core.inventory.response.Response) }) – Dictionary mapping from channel code to nominal instrument characterization

  • filename (str or path) – Output filename

seismic.inventory.pdconvert.dataframe_to_network(netcode, network_df, instrument_register, progressor=None)[source]

Convert Pandas dataframe with unique network code to obspy Network object.

Parameters
  • netcode (str) – Network code

  • network_df (pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA) – Dataframe containing records for a single network code.

  • instrument_register (dict of {str, Instrument(obspy.core.inventory.util.Equipment, obspy.core.inventory.response.Response)}, optional) – Dictionary of nominal instrument responses indexed by channel code, defaults to None

  • progressor (Callable object receiving incremental update on progress, optional) – Progress bar functor to receive progress updates, defaults to None

Returns

Network object containing the network information from the dataframe

Return type

obspy.core.inventory.network.Network

seismic.inventory.pdconvert.inventory_to_dataframe(inv_object, show_progress=True)[source]

Convert a obspy Inventory object to a Pandas Dataframe.

Parameters
Returns

Pandas Dataframe with sequential integer index and sorted by [NetworkCode, StationCode]. Only populates entries for non-empty channels.

Return type

pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA

seismic.inventory.plotting module

Helper functions for plotting FDSN Network objects.

seismic.inventory.plotting.save_network_local_plots(df, plot_folder, progressor=None, include_stations_list=True)[source]

Save visual map plot per network, saved to file netcode.png.

Parameters
  • df (pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA) – Dataframe of station records to save.

  • plot_folder (str) – Name of output folder

  • progressor (Callable object receiving incremental update on progress, optional) – Progress bar functor to receive progress updates, defaults to None

  • include_stations_list (bool, optional) – If True, also export stationtxt file alongside each png file, defaults to True

seismic.inventory.plotting.save_station_local_plots(df, plot_folder, progressor=None, include_stations_list=True)[source]

Save visual map plot per station, saved to file netcode.stationcode.png.

Parameters
  • df (pandas.DataFrame conforming to seismic.inventory.table_format.TABLE_SCHEMA) – Dataframe of station records to save.

  • plot_folder (str) – Name of output folder

  • progressor (Callable object receiving incremental update on progress, optional) – Progress bar functor to receive progress updates, defaults to None

  • include_stations_list (bool, optional) – If True, also export stationtxt file alongside each png file, defaults to True

seismic.inventory.response module

Description:

Implements a Class for reading/storing responses from a number of sources. The ResponseFactory class is used for attaching ‘bogus’ responses to station inventories that lack them.

References:

CreationDate: 14/02/19

Developer: rakib.hassan@ga.gov.au

Revision History:

LastUpdate: 14/02/19 RH LastUpdate: dd/mm/yyyy Who Optional description

class seismic.inventory.response.ResponseFactory[source]

Bases: object

The ResponseFactory class encapsulates the generation of a collection of named Instrument Response Objects from a variety of sources. Currently it provides the facility to create Response objects from two sources, namely, Poles and Zeroes supplied by the user and from StationXML files generated from RESP files using the PDCC tool (link below). The conversion of RESP files into a corresponding StationXML file, at this stage, must take place externally, because ObsPy lacks that functionality. The intended usage of this class during the creation of an ASDF dataset is as follows:

1. User creates a number of uniquely named Response objects (see associated tests as well) pertaining to different
   channels in a given survey.
2. User fetches these Response objects from an instance of ResponseFactory as needed, while creating ObsPy Channel
   objects, during which Response objects can be passed in as an argument.
3. User builds a hierarchy of channel->station->network inventories, with the appropriate instrument response
   information embedded
4. The master FDSN StaionXML file output after step 3 can then be converted into an SC3ML file (which can be
   ingested by SeisComp3) using the fdsnxml2inv tool.

PDCC tool: https://ds.iris.edu/ds/nodes/dmc/software/downloads/pdcc/

CreateFromInventory(name, obspy_inventory)[source]

Create response from an Inventory

Parameters
CreateFromPAZ(name, pzTransferFunctionType, normFactor, normFreq, stageGain, stageGainFreq, poles, zeros)[source]
CreateFromStationXML(name, respFileName)[source]

Create response from an XML file

Parameters
  • name (str) – Name of the response for later retrieval

  • respFileName (str) – XML file to load

class ResponseFromInventory(source_inventory)[source]

Bases: object

Helper class to get Obspy Response object from an Inventory

Raises

RuntimeError – Raises error if response not found

Constructor

Parameters

source_inventory (obspy.core.inventory.inventory.Inventory) – Inventory from which to extract response

class ResponseFromPAZ(pzTransferFunctionType='LAPLACE (RADIANS/SECOND)', normFactor=80000.0, normFreq=0.01, stageGain=2000.0, stageGainFreq=0.01, poles=[0j], zeros=[0j])[source]

Bases: object

class ResponseFromStationXML(respFileName)[source]

Bases: seismic.inventory.response.ResponseFactory.ResponseFromInventory

Helper class to get Obspy Response object from a station xml file

Constructor

Parameters

respFileName (str) – XML file to load

getResponse(name)[source]

Retrieve response by name

Parameters

name (str) – Name given to response at creation time

Raises

RuntimeError – Raises error if name is not recognized

Returns

The requested response

Return type

obspy.core.inventory.response.Response

seismic.inventory.table_format module

Common format for Pandas Dataframe column ordering of Network, Station and Channel metadata.

seismic.inventory.update_iris_inventory module

Automatically update IRIS-ALL.xml file from IRIS web portal.

Output file is saved as FDSN station xml. Script also generates human readable form as IRIS-ALL.txt.

Example usages:

python update_iris_inventory.py
python update_iris_inventory.py -o outfile.xml
python update_iris_inventory.py --netmask=U* --statmask=K*
python update_iris_inventory.py --netmask=UW,LO --output outfile.xml
seismic.inventory.update_iris_inventory.cleanup(tmp_filename)[source]

Helper function to clean up temporary file on disk.

Parameters

tmp_filename (str) – File name to clean up

seismic.inventory.update_iris_inventory.regenerate_human_readable(iris_data, outfile)[source]

Generate human readable, tabular version of the IRIS database.

Parameters
  • iris_data (str) – String containing result string returned from IRIS query (without data errors).

  • outfile (str) – Output text file name

seismic.inventory.update_iris_inventory.repair_iris_metadata(iris)[source]

Perform text subtitutions to fix known errors in station xml returned from IRIS.

Parameters

iris (requests.models.Response) – Response to IRIS query request containing response text

Returns

The text from the response with known faulty data substituted with fixed data.

Return type

str

seismic.inventory.update_iris_inventory.update_iris_station_xml(req, output_file, options=None)[source]

Pull the latest IRIS complete station inventory (down to station level, not including instrument responses) from IRIS web service and save to file in FDSN station xml format.

Parameters
  • req (Object conforming to interface of 'requests' library) – Request object to use for URI query

  • output_file (str) – Destination file to generate

  • options (Python dict of key-values pairs matching command line options, optional) – Filtering options for network, station and channel codes, defaults to None

Module contents