pymseed - a Python package to read and write miniSEED formatted data

The pymseed package supports reading and writing of miniSEED formatted data. Both miniSEED version 2 (defined in the SEED standard) and miniSEED version 3 are supported.

The package uses the C-language libmseed for most of the data format and manipulation work.

Installation

The releases should be installed directly from PyPI with, for example, pip install pymseed.

If using numpy features use optional dependency "numpy" or install it independently e.g. pip install pymseed[numpy].

For package development use optional dependency "dev" for needed dependencies e.g. pip install pymseed[dev].

Example usage

Working programs for a variety of use cases can be found in the examples directory of the repository.

Read a file and print details from each record:

from pymseed import MS3Record, TimeFormat

input_file = "examples/example_data.mseed"

for msr in MS3Record.from_file(input_file):
    # Print values directly
    print(f'   SourceID: {msr.sourceid}, record length {msr.reclen}')
    print(f' Start Time: {msr.starttime_str(timeformat=TimeFormat.ISOMONTHDAY_SPACE_Z)}')
    print(f'    Samples: {msr.samplecnt}')

    # Alternatively, use the library print function
    msr.print()

Read a file into a trace list and print the list:

from pymseed import MS3TraceList

traces = MS3TraceList.from_file("examples/example_data.mseed")

# Print the trace list using the library print function
traces.print(details=1, gaps=True)

# Alternatively, traverse the data structures and print each trace ID and segment
for traceid in traces:
    print(traceid)

    for segment in traceid:
        print('  ', segment)

Simple example of writing multiple channels of data:

import math
from pymseed import MS3TraceList, timestr2nstime

# Generate sinusoid data, starting at 0, 45, and 90 degrees
data0 = list(map(lambda x: int(math.sin(math.radians(x)) * 500), range(0, 500)))
data1 = list(map(lambda x: int(math.sin(math.radians(x)) * 500), range(45, 500 + 45)))
data2 = list(map(lambda x: int(math.sin(math.radians(x)) * 500), range(90, 500 + 90)))

traces = MS3TraceList()

output_file = "output.mseed"
sample_rate = 40.0
starttime = timestr2nstime("2024-01-01T15:13:55.123456789Z")
format_version = 2
max_record_length = 512

# Add generated data to the trace list
traces.add_data(sourceid="FDSN:XX_TEST__B_S_1",
                data_samples=data0, sample_type='i',
                sample_rate=sample_rate, starttime=starttime)

traces.add_data(sourceid="FDSN:XX_TEST__B_S_2",
                data_samples=data1, sample_type='i',
                sample_rate=sample_rate, starttime=starttime)

traces.add_data(sourceid="FDSN:XX_TEST__B_S_3",
                data_samples=data2, sample_type='i',
                sample_rate=sample_rate, starttime=starttime)

traces.to_file(output_file,
               format_version=format_version,
               max_record_length=max_record_length)

Converting between Source IDs and NSLC codes

miniSEED 3 and FDSN Source Identifiers use a single string (for example FDSN:IU_COLA_00_B_H_Z) to identify a unique time series channel. Classic SEED-style identifiers split the same information into network, station, location, and channel (NSLC) codes. SourceIDs are a superset of SEED v2 codes, all SEED codes can be represented as SourceIDs, but not all SourceIDs will fit into SEED codes.

The utility methods nslc2sourceid() and sourceid2nslc() support mapping between these identifier systems:

from pymseed import nslc2sourceid, sourceid2nslc

# NSLC (four strings) → FDSN source ID
sid = nslc2sourceid("IU", "COLA", "00", "BHZ")
# 'FDSN:IU_COLA_00_B_H_Z'

# Blank location codes are represented as an empty strings
sid2 = nslc2sourceid("XX", "TEST", "", "BHZ")
# 'FDSN:XX_TEST__B_H_Z'

# Source ID → (network, station, location, channel)
net, sta, loc, chan = sourceid2nslc("FDSN:IU_COLA_00_B_H_Z")
assert (net, sta, loc, chan) == ("IU", "COLA", "00", "BHZ")

# To accommodate practical identifier conversion `sourceid2nslc()` does not
# strictly require field lengths for SEED v2 conformance, instead converting
# fields to their most SEED-like form.  For example single character
# Source ID fields of band, source and subsource are collapsed to a SEED
# channel (B_H_Z -> BHZ); but if the codes cannot form a SEED channel
# they are left in the "extended channel" form of Source IDs.  Furthermore,
# larger-than-SEED network, station, and location codes are not truncated
# to fit SEED v2 fields.  For example:

nslc = sourceid2nslc("FDSN:NETWORK_STATION_LOCATION_G_SR_1")
assert nslc == ('NETWORK', 'STATION', 'LOCATION', 'G_SR_1')

Invalid source IDs raise ValueError from sourceid2nslc(); invalid NSLC combinations raise ValueError from nslc2sourceid().

Threaded usage

The pymseed package is safe to use with threads as long as the threads are not sharing data structures, e.g. a MS3TraceList.

The underlying libmseed library uses thread-local storage for logging, allowing each thread to have its own logging configuration.

When using threads, call configure_logging() in each thread to initialize the logging registry for that thread. This can be done either explicitly at the start of the thread function or as an initializer for thread pools:

from concurrent.futures import ThreadPoolExecutor
from pymseed import configure_logging, MS3TraceList

def process_file(filename):
    traces = MS3TraceList.from_file(filename)
    # ... process traces ...
    return len(traces)

# A list of files to process (silly example)
file_list = ["examples/example_data.mseed",
             "examples/example_data.mseed"]

# Using initializer to configure logging for each worker thread
with ThreadPoolExecutor(max_workers=4, initializer=configure_logging) as executor:
    results = executor.map(process_file, file_list)

Package design rationale

The package functionality and exposed API are designed to support the most common use cases of reading and writing miniSEED data using libmseed. Extensions of data handling beyond the functionality of the library are out-of-scope for this package. Furthermore, the naming of functions, classes, arguments, etc. often follows the naming used in the library in order to reference their fundamentals at the C level if needed; even though this leaves some names distinctly non-Pythonic.

In a nutshell, the goal of this package is to provide just enough of a Python layer to libmseed to handle the most common cases of miniSEED data without needing to know any of the C-level details.

License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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BLAKE2b-256	`9476376c78d255d37b57c96ccfca8cf963f9b0e45395c1d034a4b0c887e53a5c`

Algorithm	Hash digest
SHA256	`f5e476ff1105e21cd58059e1cab236a08d3e68489f5d988618142b75f91619e7`
MD5	`dee9577e1bd0df56e33100b9c46ff485`
BLAKE2b-256	`4cb29f1143784f7b51430146300b213fb27d693759d8d4246ee2cdb8c7a35539`

Algorithm	Hash digest
SHA256	`51c48d64e3ef13148f942cf4f73436f155a05757ec6b31fa0bde51bb49c0b0cb`
MD5	`9d03172587befff757e3a4fe1dc3f212`
BLAKE2b-256	`baf3d3457c3f5994dbe6809a41ea86bb442d59c151deed5753dc4d3329aa4372`

Algorithm	Hash digest
SHA256	`4fe2a58f777853c372e37d1739fa1f566bbb667c08fad92c272dccbccaaf2523`
MD5	`20484673fb123768621c46bd03f2071e`
BLAKE2b-256	`1db83d8a9de77eaa2b66fe4583f95917f6cedaf8f7f032e823a87b21022b4616`

pymseed 0.9.5

pymseed - a Python package to read and write miniSEED formatted data

Installation

Example usage

Converting between Source IDs and NSLC codes

Threaded usage

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1 maintainer

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Algorithm	Hash digest
SHA256	`40e7d837286546dd384eaef28c1dd9d393e6c7b8676b47899146192a210aaeef`
MD5	`14c6cab9a559292caec73b266a7bca0e`
BLAKE2b-256	`e3903c9ec603b43f12a248ba20cadf99aa3de1cdaf5da78a33319d2b426ccd93`

Algorithm	Hash digest
SHA256	`bd0578475f5a7f69e87a7f98c5ebaefdaa2c870c0299220f6a642ed1b7bb44bb`
MD5	`b47042d02ebdb38c6a3edb6cb942c865`
BLAKE2b-256	`d9af6f151c10353e101f31fe36eb462b53afa74561f07d775677432a0b38087f`

Algorithm	Hash digest
SHA256	`7cec5e10abbdbb6ce32b82f71700ea0dcce8e170ea532ee4ab1aa99ef5a0bb42`
MD5	`18ecde029535a1c3b119df3caa367408`
BLAKE2b-256	`9529ec51a3d99427d310ea1b8944b6437a54dc0dcd5791b9e7a7d84bfbaab77e`

Algorithm	Hash digest
SHA256	`055a92233211d58630fc9555ba98d9e64480eb812454c89e03a8b5ea56973e50`
MD5	`2981c61d6c65a2f8c9ecff9b3e627bed`
BLAKE2b-256	`c9a6a2866ae2dc6b9a36774ad8e015a381124967c069761dd1551e4e6681e047`

Algorithm	Hash digest
SHA256	`63be364cea6a1abbb1deb846057cbe710a21c91aba2cc48620c9bdcfb1cac90b`
MD5	`bc0110583ec11b535f12d7e30920ffa0`
BLAKE2b-256	`a9ebec894f0207fec17df618ed3c3a1a483b2c42dd19c58e44e43b2c012acce6`

Algorithm	Hash digest
SHA256	`e50334ddcd64c06d7c0f6978566dc8f47f3be5381e12bcccff03b424a4171e86`
MD5	`4ba53f62033a975d6b4e245a28d5a62a`
BLAKE2b-256	`85429b96d48c66bf335df6e4c36cb5fa7153e2bbf1ddf6bc0ae8ad2fbf1f9644`

Algorithm	Hash digest
SHA256	`137ffb0371f45e757e705583419a32bd394ef65e2b96e06bd1832ab4ac673157`
MD5	`6fefe106ba897aafa060688c7fa93b70`
BLAKE2b-256	`cea043d2645a9816e86b527d75624ccf73649fa9cd7445f039dd0ad2368e3b22`

Algorithm	Hash digest
SHA256	`ab2334afc2966a68bc3555d940392b93ee52c79ea62225b6b32c019f6070d73a`
MD5	`dab09f32e13ae9de2ddafec1305ed382`
BLAKE2b-256	`076a9375fe3cc4f22126852b6bd93c6262d3543b578ff8fd829093c721c8da57`

Algorithm	Hash digest
SHA256	`d8dda40ec8407d46cc8bba296eb600770584c277c5a6349bbc2cbee616fecb32`
MD5	`349f0cce7884b8b27665d472267539f2`
BLAKE2b-256	`0bcf82d719031251d87a7dc80ddbbc0a38672d1831395e36199e4e1a9a833688`

Algorithm	Hash digest
SHA256	`96d57a5ebcb3a4620db455d82e4c79e28eab342a57ee50f84ffe81d289623b57`
MD5	`a815184b655041856ae7efc9a3a1ed6d`
BLAKE2b-256	`fcf0d58b8c2ef079e8e3f340e1d976d5b733b0c1ceb223ab2dbb498ae9a3657d`