ISPAQ is a Python client that allows seismic data scientists and instrumentation operators to run data quality metrics on their own workstation, using much of same code as used in EarthScope's (formerly IRIS) MUSTANG data quality web service. It can be installed on Linux and macOS.
Users have the ability to create personalized preference files that list combinations of station specifiers and statistical metrics of interest, such that they can be run repeatedly over data from many different time periods. Alternatively, single station specifiers and metrics can be specified on the command line for simple runs or for use in shell scripting.
ISPAQ offers the option for access to FDSN Web Services to retrieve seismic data and metadata directly from selected data centers supporting the FDSN protocol. Users also have the option to read local miniSEED files and metadata on their own workstations and construct on-the-spot data quality analyses on that data.
Output is optionally in CSV format or written to an internal SQLite database for tabular metrics. In addition, Probability Density Functions (PDF) can be plotted to PNG image files. The PDF plots also include the New High Noise Model and the New Low Noise Model curves Peterson,1993 and the minimum, maximum, and mode PDF statistics curves.
The business logic for MUSTANG metrics is emulated through ObsPy and custom Python code and the core calculations are performed using the same R packages as used by MUSTANG.
[EarthScope] (https://www.earthscope.org) (formerly IRIS) has developed a comprehensive quality assurance system called MUSTANG.
The MUSTANG system was built to operate at EarthScope and is not generally portable. However, the key MUSTANG component is the Metric Calculators and these are publicly available. While the results of MUSTANG calculations are stored in a database and provided to users via web services, ISPAQ is intended to carry out the process of calculating these metrics locally on the user's workstation. This has the benefit of allowing users to generate just-in-time metrics on data of their choosing, whether stored an FDSN data center or on the user's own data store.
EarthScope has over 40 MUSTANG metrics algorithms, most written in R, that are now available in the CRAN (Comprehensive R Archive Network) repository under the name IRISMustangMetrics. ISPAQ comes with the latest version of these packages available in CRAN and ISPAQ has an update capability to allow users to seamlessly upgrade these R packages as new releases become available.
ISPAQ contains business logic similar to MUSTANG, such that the computed metrics produced are identical (or very similar) to the results you will see in MUSTANG. The end result is a lightweight and portable version of MUSTANG that users are free to leverage on their own hardware.
Questions or comments can be directed to the EarthScope Quality Assurance Group at dmc_qa@iris.washington.edu.
ISPAQ is distributed through GitHub, via EarthScope's public repository (EarthScope). You will use a git
client command to get a copy of the latest stable release. In addition, you will use the miniconda
python package manager to create a customized Python environment designed to run ISPAQ properly. This will
include a localized installation of ObsPy and R.
If running macOS, Xcode command line tools should be installed. Check for existence and install if missing:
xcode-select --install
Follow the steps below to begin running ISPAQ.
You must first have git installed your system. This is a commonly used source code management system
and serves well as a mode of software distribution as it is easy to capture updates. See the
Git Home Page to begin installation of git before proceeding further.
After you have git installed, you will download the ISPAQ distribution into a directory of your choosing from GitHub by opening a text terminal and typing:
git clone https://github.com/EarthScope/ispaq.git
This will produce a copy of this code distribution in the directory you have chosen. When new ispaq versions become available, you can update ISPAQ by typing:
cd ispaq
git pull origin master
Anaconda is quickly becoming the defacto package manager for scientific applications written python or R. Miniconda is a trimmed down version of Anaconda that contains the bare necessities without loading a large list of data science packages up front. With miniconda, you can set up a custom python environment with just the packages you need to run ISPAQ.
Proceed to the Miniconda web site to find the installer for your
operating system before proceeding with the instructions below. If you can run conda from the command
line, then you know you have it successfully installed.
By setting up a conda virtual environment, we assure that our ISPAQ installation is entirely separate from any other installed software.
You will go into the ispaq directory that you created with git, update miniconda, then create an
environment specially for ispaq. You have to activate the ISPAQ environment whenever you
perform installs, updates, or run ISPAQ.
Note: If you are upgrading from ISPAQ 2.0 to ISPAQ 3.0+, you should create a new ispaq environment.
Instructions for Linux or macOS (Intel chip)
cd ispaq #top level directory
conda update conda
conda env remove --name ispaq #if you are upgrading from an existing ISPAQ 2.0 installation to ISPAQ 3.0
conda create --name ispaq -c conda-forge python=3.8 obspy=1.4.0
conda activate ispaq
conda install -c conda-forge --file ispaq-conda-install.txt
Instructions for macOS (Apple M1 or M2 chip):
cd ispaq
conda update conda
conda env remove --name ispaq
CONDA_SUBDIR=osx-64 conda create --name ispaq -c conda-forge python=3.8 obspy=1.4.0
conda activate ispaq
CONDA_SUBDIR=osx-64 conda install -c conda-forge --file ispaq-conda-install.txt
See what is installed in our (ispaq) environment with:
conda list
Now install the EarthScope R packages for ISPAQ using the -I option:
python run_ispaq.py -I #downloads latest packages from CRAN (https://cran.r-project.org)
Or alternatively, install the EarthScope R packages from local files:
R CMD INSTALL seismicRoll_1.1.4.tar.gz
R CMD INSTALL IRISSeismic_1.6.6.tar.gz
R CMD INSTALL IRISMustangMetrics_2.4.5.tar.gz
You should run ./run_ispaq.py -U after you update ISPAQ minor versions to verify that you have both the
required minimum versions of anaconda packages and the most recent EarthScope R packages.
Note: If you are using macOS and see the error: "'math.h' file not found" when compiling seismicRoll, then it is likely that your command line tools are missing. Try running
xcode-select --install.
Every time you use ISPAQ you must ensure that you are running in the proper Anaconda environment. If you followed the instructions above you only need to type:
cd ispaq
conda activate ispaq
after which your prompt should begin with (ispaq) . You run ispaq using the run_ispaq.py
python script. The example below shows how to get ISPAQ to show the help display. A leading ./
is used to indicate that the script is in the current directory.
A list of command-line options is available with the --help flag:
(ispaq) bash-3.2$ python run_ispaq.py -h
usage: run_ispaq.py [-h] [-P PREFERENCES_FILE] [-M METRICS] [-S STATIONS]
[--starttime STARTTIME] [--endtime ENDTIME]
[--dataselect_url DATASELECT_URL] [--station_url STATION_URL]
[--event_url EVENT_URL] [--resp_dir RESP_DIR]
[--output OUTPUT] [--db_name DB_NAME] [--csv_dir CSV_DIR]
[--psd_dir PSD_DIR] [--pdf_dir PDF_DIR] [--pdf_type PDF_TYPE]
[--pdf_interval PDF_INTERVAL] [--plot_include PLOT_INCLUDE]
[--sncl_format SNCL_FORMAT] [--sds_files] [--sigfigs SIGFIGS]
[--log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}] [-A] [-V]
[-I] [-U] [-L]
ISPAQ version 3.1.0
single arguments:
-h, --help show this help message and exit
-A, --append append to TRANSCRIPT file rather than overwriting
-V, --version show program's version number and exit
-I, --install-r install CRAN EarthScope Mustang packages, and exit
-U, --update-r check for and install newer CRAN EarthScope Mustang packages
and/or update required conda packages, and exit
-L, --list-metrics list names of available metrics and exit
arguments for running metrics:
-P PREFERENCES_FILE, --preferences-file PREFERENCES_FILE
path to preference file, default=./preference_files/default.txt
-M METRICS, --metrics METRICS single Metrics alias as defined in preference file, or one or
more metric names in a comma-separated list, required
-S STATIONS, --stations STATIONS
single Station_SNCLs alias as defined in preference file, or
one or more SNCL[Q] in a comma-separated list, required.
notes: SNCL[Q] refers to Station.Network.Channel.Location.(optional)Quality
If using wildcarding, enclose in quotation marks
--starttime STARTTIME starttime in ObsPy UTCDateTime format, required for webservice requests
and defaults to earliest data file for local data
examples: YYYY-MM-DD, YYYYMMDD, YYYY-DDD, YYYYDDD[THH:MM:SS]
--endtime ENDTIME endtime in ObsPy UTCDateTime format, default=starttime + 1 day;
if starttime is also not specified then it defaults to the latest data
file for local data
examples: YYYY-MM-DD, YYYYMMDD, YYYY-DDD, YYYYDDD[THH:MM:SS]
optional arguments for overriding preference file entries:
--dataselect_url DATASELECT_URL FDSN webservice or path to directory with miniSEED files
--station_url STATION_URL FDSN webservice or path to stationXML file
--event_url EVENT_URL FDSN webservice or path to QuakeML file
--resp_dir RESP_DIR path to directory with RESP files
--output OUTPUT write metrics to csv file (csv) or sqlite database file (db). Options: csv, db
--db_name DB_NAME name of sqlite database file, if output=csv
--csv_dir CSV_DIR directory to write generated metrics .csv files, if output=csv
--psd_dir PSD_DIR directory to write/read existing PSD .csv files, if output=csv
--pdf_dir PDF_DIR directory to write generated PDF files
--pdf_type PDF_TYPE output format of generated PDFs - text and/or plot
--pdf_interval PDF_INTERVAL time span for PDFs - daily and/or aggregated over the entire span
--plot_include PLOT_INCLUDE PDF plot graphics options - legend, colorbar, and/or fixed_yaxis_limits,
or none
--sncl_format SNCL_FORMAT format of SNCL aliases and miniSEED file names
examples:"N.S.L.C","S.N.L.C"
where N=network code, S=station code, L=location code, C=channel code
--sds_files if set, ISPAQ will look for local data files with Seiscomp SDS naming format
NET.STA.LOC.CHAN.TYPE.YEAR.DAY where TYPE=D
--sigfigs SIGFIGS number of significant figures used for output columns named "value"
other arguments:
--log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}
log level printed to console, default="INFO"
If no preference file is specified and the default file ./preference_files/default.txt cannot be found:
--csv_dir, pdf_dir, and psd_dir default to "."
--sncl_format defaults to "N.S.C.L"
--sigfigs defaults to "6"
--pdf_type defaults to "plot,text"
--pdf_interval defaults to "aggregated"
--plot_include defaults to "colorbar,legend"
For those that prefer to run ISPAQ as a package, you can use the following invocation (using help example):
(ispaq) $ python -m ispaq.ispaq --help
When calculating metrics, valid arguments for -M and -S are required and must be provided.
If -P is not provided, ISPAQ uses the default preference file located at ispaq/preference_files/default.txt.
However, all entries in the preference file can be overridden by command-line options.
If --log-level is not specified, the default log-level is INFO.
When --starttime is invoked without --endtime, metrics are run for a single day. Metrics that are defined
as day-long metrics (24 hour windows, see metrics documentation at
MUSTANG) will be calculated for the time period
00:00:00-23:59:59.9999. An endtime of YYYY-DD-MM is interpreted as YYYY-DD-MM 00:00:00 so that
e.g., --starttime=2016-01-01 --endtime=2016-01-02 will also calculate one day of metrics. When an end time
greater than one day is requested, metrics will be calculated by cycling through multiple single days to produce
a measurement for each day. Additionally, and only if using local data files, you can run metrics without specifying
a start time. In this case, ISPAQ will use a start time corresponding to the earliest file found that matches the
requested Station_SNCLs. If end time is also not specified, ISPAQ will use an end time corresponding to the latest file
found that matches the requested Station_SNCLs.
The ISPAQ system is designed to be configurable through the use of preference files.
These are usually located in the preference_files/ directory. Not surprisingly, the default
preference file is preference_files/default.txt. This file is self describing
with the following comments in the header:
# Preferences fall into five categories:
# * Metrics -- aliases for user defined combinations of metrics (Use with -M)
# * Station_SNCLs -- aliases for user defined combinations of SNCL patterns (Use with -S)
# SNCL patterns are station names formatted as Network.Station.Location.Channel
# wildcards * and ? are allowed. SNCL pattern format can be modified
# using the Preferences sncl_format.
# * Data_Access -- FDSN web services or local files
# * Preferences -- additional user preferences
# * PDF_Preferences -- preferences specific to PDF calculation
#
# This file is in a very simple format. After each category heading, all lines containing a colon
# will be interpreted as key:value and made available to ISPAQ.
#
Metric aliases can be any of one of the predefined options or any user-created alias_name: metric combination,
where metric can be a single metric name or a comma separated list of valid metric names. Aliases cannot be
combinations of other aliases.
Example: myMetrics: num_gaps, sample_mean, cross_talk.
Station_SNCL aliases are user created alias_name: Network.Station.Location.Channel[.Quality] combinations, where [ ] denotes an optional element.
Station_SNCLs can be comma separated lists. * or ? wildcards can be used in any of the network, station, location, channel, or quality elements.
Example: "myStations: IU.ANMO.10.BHZ.M, IU.*.00.BH?.M, IU.ANMO.*.?HZ, II.PFO.??.*. By default, aliases are formatted
as Network.Station.Location.Channel[.Quality]. This format pattern can be modified using the sncl_formatentry discussed below.
Note: the use of the quality code is optional and is not fully utilized in this version of ISPAQ. Specifying a quality code will not guarantee that ISPAQ retrieves data with only that quality code; instead data will be of whatever quality the specified web services (or local data) provides. This is a known issue and will be addressed in a future release.
Note: the PDF metric will use the quality code specified, if there is one, as it retrieves PSDs. If no quality code is specified in the station SNCL, then it will look for any and all quality codes that might exist for that SNCL.
Note: When directly specifying a SNCL pattern on the command line, SNCLs containing wildcards should be enclosed by quotes to avoid a possible error of unrecognized arguments.
Data_Access has four entries describing where to find data, metadata, events, and optionally response files.
dataselect_url:should indicate a miniSEED data resource as one of the FDSN web service aliases used by ObsPy (e.g.IRIS), the EarthScope PH5 web service alias 'IRISPH5', an explicit URL pointing to an FDSN web service domain (e.g.http://service.iris.edu), or a file path to a directory containing miniSEED files (See: "Using Local Data Files", below).
NOTE: When data is missing and it is marked as percent_availability=0, the quality code to assign to the target must be inferred. To do this, the current logic is to assign quality "M" for EarthScope (fdsnws) derived data, and quality "D" for all other data (IRISPH5, local data, or any other webservice). We are aware that this is too simplistic to truly capture the range of possible quality codes, and have it on our radar to improve with a later release.
-
station_url:should indicate a metadata location as an FDSN web service alias, the EarthScope PH5 web service alias 'IRISPH5', an explicit URL, or a path to a file containing metadata in StationXML format (schema). For web services, this should point to the same place asdataselect_url(e.g.http://service.iris.edu). For local metadata, StationXML is read at the channel level and any response information is ignored. Local instrument response (if used) is expected to be in RESP file format and specified in theresp_direntry (see below). If neither webservices or StationXML is available for metadata, thestation_urlentry should be left unspecified (blank). In this case, metrics that do not require metadata will still be calculated. Metrics that do require metadata information (cross_talk, polarity_check, orientation_check, transfer_function) will not be calculated and will return a log message stating "No available waveforms".If you are starting from a dataless SEED metadata file, you can create StationXML from this using the FDSN StationXML-SEED Converter.
-
event_url:should indicate an event catalog resource as an FDSN web service alias (e.g.USGS), an explicit URL (e.g.https://earthquake.usgs.gov), or a path to a file containing event information in QuakeML format (schema). Only web service providers that can output text format can be used at this time. This entry will only be used by metrics that require event information in order to be calculated (cross_talk, polarity_check, orientation_check). -
resp_dir:should be unspecified or absent if local response files are not used. The default behavior is to retrieve response information from the EarthScope web service Evalresp. To use local instrument responses instead of Evalresp, this parameter should indicate a path to a directory containing response files in RESP format. Local response files are expected to be namedRESP.network.station.location.channelorRESP.station.network.location.channel. Filenames with extension.txtare also acceptable. E.g.,RESP.IU.CASY.00.BH1, RESP.CASY.IU.00.BH1, RESP.IU.CASY.00.BH1.txt.Response information is only needed when generating PSD derived metrics, PDF plots, or the transfer_function metric.
If you are starting from a dataless SEED, you can create RESP files using rdseed.
Preferences has eight entries describing ispaq output.
-
output:either 'db' (write to SQLite database) or 'csv' (write to CSV files) -
db_name:if writing to a database (output=db), the name of the database -
csv_dir:of writing to CSV (output=csv), directory path for output of generated metric text files (CSV). If the directory does not exist, then it attempts to create that directory. -
psd_dir:should be followed by a directory path for writing and reading PSD csv files. If the directory does not exist, then it defaults to the current working directory. PSD csv files generated by the 'psd_corrected' metric will be written to a directory structure within 'psd_dir' based on network code and station code ('psd_dir'/NET/STA) -
pdf_dir:should be followed by a directory path for output of PDF csv and png files. These files will be written to a directory structure within 'pdf_dir' based on network code and station code ('pdf_dir'/NET/STA). -
sigfigs:should indicate the number of significant figures used for output columns named "value". Default is 6. -
sncl_format:should be the format of sncl aliases and miniSEED file names, must be some combination of period separatedN=network,S=station,L=location,C=channel (e.g.,N.S.L.C, S.N.L.C). If nosncl_formatexists, it defaults toN.S.L.C. -
sds_files:if set to 'True', ISPAQ will look for files using the SeisComp SDS file naming convention with type='D', (e.g. NET.STA.LOC.CHAN.D.YEAR.DAY) when using local data files
PDF_Preferences has three entries describing PDF output.
-
pdf_type:should be followed by either "text","plot", or "text,plot".
"text" will output PDF information in a csv format file with frequency, power, and hits columns, or to a database.
"plot" will output a PDF plot in a png format file.
"text,plot" will output both. -
pdf_interval:should be followed by either "daily","aggregate", or "daily,aggregate".
"daily" will calculate separate PDFs for each day between the starttime and endtime.
"aggregate" will calculate one PDF spanning the starttime to endtime span.
"daily,aggregate" will calculate both. -
plot_include:should be followed by any of "legend","colorbar","fixed_yaxis_limits".
"legend" will include the legend for the minimum/maximum/mode PDF statistics curves.
"colorbar" will include the histogram legend for the PDF.
"fixed_axis_limits" will plot the PDF with y-axis limits of -25 to -225 dB (if not specified, the y-axis limits are determined by the data).
"legend,colorbar,fixed_axis_limits" will create a PDF plot with all three features.
Any of these preference file entries can be overridden by command-line arguments:
-M "Metric name/alias", -S "Station_SNCL", --dataselect_url, --station_url, --event_url, --resp_dir,
--output, --db_name, --csv_output_dir, --plot_output_dir, --sigfigs, --sncl_format,--pdf_type,
--pdf_interval, --plot_include, --sncl_format, --sigfigs
More information about using local files can be found below in the section "Using Local Data Files".
ISPAQ will always create a log file named ISPAQ_TRANSCRIPT.log to record actions taken
and messages generated during processing.
In addition, the metric calculations will write to either .csv files or to a SQLite database, depending on the
output option selected.
Results of most metrics calculations will be written to .csv files using the following naming scheme:
MetricAlias_Station_SNCLAlias_startdate__businessLogic.csv
when a single day is specified on the command line or
MetricAlias_Station_SNCLAlias_startdate_enddate_businessLogic.csv
when multiple days are specified from the command line. End date in this context is inclusive of that day.
If specifying metrics and station_SNCLs from the command line instead of using preference file aliases, the metric name and station_SNCL[Q] will be used instead of the MetricAlias and Station_SNCLAlias in the output file name. In addition, any instances of command-line wildcards "*" or "?" will be replaced with the letter "x" in the output file name.
businessLogic corresponds to which script is invoked:
| businessLogic | ISPAQ script | metrics |
|---|---|---|
| simpleMetrics | simple_metrics.py | most metrics |
| SNRMetrics | SNR_metrics.py | sample_snr |
| PSDMetrics | PSD_metrics.py, PDF_aggregator.py | pct_above_nhnm, pct_below_nlnm, dead_channel_{lin,gsn}, psd_corrected, pdf |
| crossTalkMetrics | crossTalk_metrics.py | cross_talk |
| pressureCorrelationMetrics | pressureCorrelation_metrics.py | pressure_effects |
| crossCorrelationMetrics | crossCorrelation_metrics.py | polarity_check |
| orientationCheckMetrics | orientationCheck_metrics.py | orientation_check |
| transferMetrics | transferFunction_metrics.py | transfer_function |
The metric alias psdPdf in the default preference file (or any user defined set with metric 'psd_corrected') will generate corrected PSDs in files named:
S.N.C.L.Q_startdate_PSDcorrected.csv
The metric alias psdPdf in the default preference file (or any user defined set with metric 'pdf') will generate PDFs in files named:
S.N.C.L.Q_startdate_PDF.csv (for daily PDF text)S.N.C.L.Q_startdate_enddate_PDF.csv (for aggregate PDF text)S.N.C.L.Q_startdate_PDF.png (for daily PDF plot)S.N.C.L.Q_startdate_enddate_PDF.png (for aggregate PDF plot)
Note: The metric 'pdf' requires that corrected PSDs exist. If using
output'csv' thenS.N.C.L.Q_startdate_PSDcorrected.csv files must exist in thepsd_dirspecified directory.
If you run the metric 'pdf' alone and see the warning 'No PSD files found', then try running metric 'psd_corrected' first to generate the PSD files. You will also see the warning 'No PSD files found' if there is no data available for that day. These two metrics can be run simulataneously, as it will calculate the PSDs before calculating the PDFs.
Using the 'db' output option will write to a SQLite database with the filename supplied in the db_name field. All metrics values,
except for any .png PSD or PDFs that may be generated, will be inserted into the database. Tables within the datbase correspond to the
metric name. For example:
sqlite> .tables
amplifier_saturation max_range sample_mean
calibration_signal max_stalta sample_median
clock_locked missing_padded_data sample_min
cross_talk num_gaps sample_rate_channel
dead_channel_gsn num_overlaps sample_rate_resp
dead_channel_lin num_spikes sample_rms
digital_filter_charging orientation_check sample_snr
digitizer_clipping pct_above_nhnm sample_unique
event_begin pct_below_nlnm spikes
event_end pdf suspect_time_tag
event_in_progress percent_availability telemetry_sync_error
glitches polarity_check timing_correction
max_gap psd_corrected timing_quality
max_overlap sample_max
The majority of tables (metrics) will have the same set of columns. These include:
target - the network.station.location.channel.quality code that the measurement corresponds to
value - value of measurement
start - start time of the measurement
end - end time of the measurement
lddate - the load date, when the measurement was inserted (or updated) in the table
In addition to those fields, these metrics have other columns as well:
- polarity_check:
snclq2 - transfer_function:
gain_ratio,phase_diff,ms_coherence - orientation_check:
azimuth_R,backAzimuth,azimuth_Y_obs,azimuth_X_obs,azimuth_Y_meta,azimuth_X_meta,max_Czr,max_C_zr,magnitude - psd_corrected:
frequency,power - pdf:
frequency,power,hits
Note: transfer_function, orientation_check, psd_corrected, and pdf metrics all lack the
valuecolumn.
The metric 'pdf' requires that corrected PSDs exist. If using output 'db' then the PSDs must exist in the database specified by db_name.
If you run the metric 'pdf' and see the warning 'Unable to access PSD values', then try running metric 'psd_corrected'
first to generate the PSD values. These two metrics can be run simulataneously, as it will calculate the PSDs before calculating the PDFs. You will also see the warning 'Unable to access PSD values' if there is no data available for that day,
or 'Unable to access table psd_corrected' if no the table does not exist, which may indicate that no PSDs have been calculated and added to the database
yet.
For those using QuARG, a utility produced by EarthScope for generating quality assurance reports, it is possible
to have QuARG read metrics from your local ISPAQ SQLite database rather than from the MUSTANG web services. Simply point the metric source in the QuARG preference file to the database file produced by ISPAQ and it will use your local metric values.
Examples of how to access and use the metrics are included as jupyter notebooks in the EXAMPLES/ directory. For more information on how to navigate a SQLite database, see https://sqlite.org/cli.html. Given your ispaq environment is activated, you should be able to run the jupyter notebooks if you have installed the conda environment using the provided ispaq-conda-install.txt file. But if you are having trouble you can go through installation steps here: https://jupyter.org/install.
Example invocations are found in the EXAMPLES section and at the end of this README.
You can modify the information printed to the console by modifying the --log-level.
To see detailed progress information use --log-level DEBUG. To hide everything other
than an outright crash use --log-level CRITICAL. If --log-level is not invoked, the default is
to print information at the INFO level. The other available levels are WARNING and ERROR.
The following example demonstrates what you should see. Note: Please ignore the warning message from matplotlib. It will only occur on first use.
(ispaq) $ run_ispaq.py -M basicStats -S basicStats --starttime 2010-04-20 --log-level INFO
2017-05-26 13:58:12 - INFO - Running ISPAQ version 1.0.0 on Fri May 26 13:58:12 2017
~/miniconda2/envs/ispaq/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is
building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
2017-05-26 13:58:22 - INFO - Calculating simple metrics for 3 SNCLs on 2010-04-20
2017-05-26 13:58:22 - INFO - 000 Calculating simple metrics for IU.ANMO.00.BH1
2017-05-26 13:58:24 - INFO - 001 Calculating simple metrics for IU.ANMO.00.BH2
2017-05-26 13:58:25 - INFO - 002 Calculating simple metrics for IU.ANMO.00.BHZ
2017-05-26 13:58:26 - INFO - Writing simple metrics to basicStats_basicStats_2010-04-20__simpleMetrics.csv
2017-05-26 13:58:26 - INFO - ALL FINISHED!
Additional information about running ISPAQ on the command line can be found by invoking run_ispaq.py --help.
Local data files should be in miniSEED format and organized in network-station-channel-day files. By default, ISPAQ recognizes the following file naming convention:
Network.Station.Location.Channel.Year.JulianDay.Quality
where Quality is optional (e.g., TA.P19K..BHZ.2016.214.M or TA.P19K..BHZ.2016.214).
This naming convention can be modified by using the sncl_format entry in the preferences file or
the --sncl_format option on the command line. sncl_format allows you to specify a different order for
Network.Station.Location.Channel, although all these elements must be present in the file name.
For example, sncl_format S.N.L.C will change the file naming convention that ISPAQ uses to:
Station.Network.Location.Channel.Year.JulianDay.Quality
where Quality is again optional (e.g. P19K.TA..BHZ.2016.214.M or P19K.TA..BHZ.2016.214).
If the sds_files parameter is set either as a command line flag (--sds_files) or set to 'True' in the preference
file, ISPAQ will look for files using the Seiscomp SDS file name format with type='D', e.g.,
Network.Station.Location.Channel.D.Year.JulianDay
The sncl_format and optional quality code are also honored when using sds_format.
ISPAQ will search for miniSEED files in the directory specified by dataselect_url in the preferences file or
--dataselect_url on the command line. Furthermore, it will recursively follow that directory structure and
look for miniSEED files in directories contained within the dataselect_url directory. If more than one file name
is found that matches the same requested network, station, location, channel, year, and julian day, then the metrics
will be run on the first file that is found. To request all data files, use preference file Station_SNCL alias:
*.*.*.*, or -S "*.*.*.*" from the command line". Wildcarding every element is strongly discouraged when using
FDSN webservices instead of local files.
Note: All data is expected to be in the day file that matches its timestamp; if records do not break on the UTC day boundary, data that is not in the correct day file will not be used in the metrics calculation. This can lead to cases where, for example, a gap is calculated at the start of a day when the data for that time period is in the previous day file.
If your miniSEED files are not already split on UTC day boundaries, one tool that can be used for this task is the
dataselect command-line tool available at https://github.com/EarthScope/dataselect.
Follow the releases link in the README to download the latest
version of the source code. The following example reads the input miniSEED files, splits the records on day
boundaries, and writes to files named network.station.location.channel.year.julianday.quality.
Example: dataselect -Sd -A %n.%s.%l.%c.%Y.%j.%q inputfiles
The command-line argument -U, --update-r can be used to check CRAN for newer IRISSeismic, seismicRoll, and
IRISMustangMetrics R packages.
(ispaq) bash-3.2$ ./run_ispaq.py -U
2021-11-16 12:06:09 - INFO - Running ISPAQ version 3.0.0 on Tue Nov 16 12:06:09 2021
2021-11-16 12:06:11 - INFO - Checking for recommended conda packages...
2021-11-16 12:06:11 - INFO - Required conda packages found
2021-11-16 12:06:11 - INFO - Checking for EarthScope R package updates...
package installed CRAN upgrade
0 seismicRoll 1.1.4 1.1.4 False
1 IRISSeismic 1.6.3 1.6.3 False
2 IRISMustangMetrics 2.4.4 2.4.4 False
2021-11-16 12:06:15 - INFO - No CRAN packages need updating.
Alternatively, the command-line argument `-I`, `--install-r` will install the CRAN packages regardless of what
version is already installed
If a newer CRAN package does exist, the -U option will then automatically download the package from CRAN and
install it. ISPAQ code can be updated using git pull origin master. Sometimes it is necessary to update the ISPAQ
python code in conjunction with the CRAN code.
The command-line argument -L will list the names of available metrics.
-
amplifier_saturation: The number of times that the 'Amplifier saturation detected' bit in the 'dq_flags' byte is set within a miniSEED file. This data quality flag is set by some dataloggers in the fixed section of the miniSEED header. The flag was intended to indicate that the preamp is being overdriven, but the exact meaning is datalogger-specific. Documentation
-
calibration_signal: The number of times that the 'Calibration signals present' bit in the 'act_flags' byte is set within a miniSEED file. A value of 1 indicates that a calibration signal was being sent to that channel. Documentation
-
clock_locked: The number of times that the 'Clock locked' bit in the 'io_flags' byte is set within a miniSEED file. This clock flag is set to 1 by some dataloggers in the fixed section of the miniSEED header to indicate that its GPS has locked with enough satellites to obtain a time/position fix. Documentation
-
cross_talk: The correlation coefficient of channel pairs from the same sensor. Data windows are defined by seismic events. Correlation coefficients near 1 may indicate cross-talk between those channels. Documentation
-
dead_channel_gsn: A boolean measurement providing a TRUE or FALSE indication that the median PSD values of channel exhibit an average 5dB deviation below the NLNM in the 4 to 8s period band as measured using a McNamara PDF noise matrix. The TRUE condition is indicated with a numeric representation of '1' and the FALSE condition represented as a '0'. Documentation
- channels = [BCDHLM][HX].
-
dead_channel_lin: Dead channel metric - linear fit. This metric is calculated from the mean of all the PSDs generated (typically 47 for a 24 hour period). Values of the PSD mean curve over the band linLoPeriod:linHiPeriod are fit to a linear curve by a least squares linear regression of PSD mean ~ log(period). The dead_channel_lin metric is the standard deviation of the fit residuals of this regression. Lower numbers indicate a better fit and a higher likelihood that the mean PSD is linear - an indication that the sensor is not returning expected seismic energy. Documentation
- channels = [BCDHM][HX].
-
digital_filter_charging: The number of times that the 'A digital filter may be charging' bit in the 'dq_flags' byte is set within a miniSEED file. Data samples acquired while a datalogger is loading filter parameters - such as after a reboot - may contain a transient. Documentation
-
digitizer_clipping: The number of times that the 'Digitizer clipping detected' bit in the 'dq_flags' byte is set within a miniSEED file. This flag indicates that the input voltage has exceeded the maximum range of the ADC. Documentation
-
event_begin: The number of times that the 'Beginning of an event, station trigger' bit in the 'act_flags' byte is set within a miniSEED file. This metric can be used to quickly identify data days that may have events. It may also indicate when trigger parameters need adjusting at a station. Documentation
-
event_end: The number of times that the 'End of an event, station detrigger' bit in the 'act_flags' byte is set within a miniSEED file. This metric can be used to quickly identify data days that may have events. It may also indicate when trigger parameters need adjusting at a station. Documentation
-
event_in_progress: The number of times that the 'Event in progress' bit in the 'act_flags' byte is set within a miniSEED file. This metric can be used to quickly identify data days that may have events. It may also indicate when trigger parameters need adjusting at a station. Documentation
-
glitches: The number of times that the 'Glitches detected' bit in the 'dq_flags' byte is set within a miniSEED file. This metric can be used to identify data with large filled values that data users may need to handle in a way that they don't affect their research outcomes. Documentation
-
max_gap: Indicates the size of the largest gap encountered within a 24-hour window. Documentation
-
max_overlap: Indicates the size of the largest overlap in seconds encountered within a 24-hour window. Documentation
-
max_range: This metric calculates the difference between the largest and smallest sample value in a 5 minute rolling window and returns the largest value encountered within a 24-hour timespan. Documentation
-
max_stalta: The STALTAMetric function calculates the maximum of STA/LTA of the incoming seismic signal over a 24 hour period. In order to reduce computation time of the rolling averages, the averaging window is advanced in 1/2 second increments. Documentation
- channels = [BHCDES][HPLX].
-
missing_padded_data: The number of times that the 'Missing/padded data present' bit in the 'dq_flags' byte is set within a miniSEED file. This metric can be used to identify data with padded values that data users may need to handle in a way that they don't affect their research outcomes. Documentation
-
num_gaps: This metric reports the number of gaps encountered within a 24-hour window. Documentation
-
num_overlaps: This metric reports the number of overlaps encountered in a 24-hour window. Documentation
-
num_spikes: This metric uses a rolling Hampel filter, a median absolute deviation (MAD) test, to find outliers in a timeseries. The number of discrete spikes is determined after adjacent outliers have been combined into individual spikes. NOTE: not to be confused with the spikes metric, which is an SOH flag only. Documentation
- channels = [BH][HX].
-
orientation_check: Determine channel orientations by rotating horizontal channels until the resulting radial component maximizes cross-correlation with the Hilbert transform of the vertical component. This metric uses Rayleigh waves from large, shallow events. Documentation
- channels = [BCHLM][HX].
-
pct_above_nhnm: Percent above New High Noise Model. Percentage of Probability Density Function values that are above the New High Noise Model. This value is calculated over the entire time period. Documentation
- channels = [BCDHM][HX].
-
pct_below_nlnm: Percent below New Low Noise Model. Percentage of Probability Density Function values that are below the New Low Noise Model. This value is calculated over the entire time period. Documentation
- channels = [BCDHM][HX].
-
pdf: Probability density function plots and/or text output (controlled by PDF_Preferences in the preference file; or by
--pdf_type,--pdf_interval,--plot_includeon the command line). You must have local PSD files written in the format produced by the 'psd_corrected' metric (below) or run it concurrently with 'psd_corrected'. These files should be in a directory specified by thepsd_direntry in the preference file or by--psd_diron the command line, or in the database specified bydb_name. Reference: Ambient Noise Levels in the Continental United States, McNamara and Buland, 2004 -
percent_availability: The portion of data available for each day is represented as a percentage. 100% data available means full coverage of data for the reported start and end time. Documentation
NOTE: percent_availability will only be calculated for target-days that have metadata. If metadata is available but no data can be retrieved, then it will be marked as percent_availability=0. In this case, the quality code associated with that target cannot be determined from the data itself and must be inferred. ISPAQ will currently mark data from
EarthScope(fdsnws) as quality "M" and data from all other sources as "D". We are aware that this may not be able to capture the complexity of possible quality codes and will work on improving the logic in a future release.
-
polarity_check: The signed cross-correlation peak value based on the cross-correlation of two neighboring station channels in proximity to a large earthquake signal. A negative peak close to -1.0 can indicate reversed polarity. Documentation
- channels = [BCFHLM][HX].
-
pressure_effects: The correlation coefficient of a seismic channel and an LDO pressure channel. Large correlation coefficients may indicate the presence of atmospheric effects in the seismic data. Documentation
- channels = LH., LDO
-
psd_corrected: Power spectral density values, corrected for instrument response, in text format (starttime, endtime, frequency, power). Documentation
- channels = .[HLGNPYXD].
-
sample_max: This metric reports largest amplitude value in counts encountered within a 24-hour window. Documentation
-
sample_mean: This metric reports the average amplitude value in counts over a 24-hour window. This mean is one measure of the central tendency of the amplitudes that is calculated from every amplitude value present in the time series. The mean value itself may not occur as an amplitude value in the times series. Documentation
-
sample_median: This metric reports the middle amplitude value in counts of sorted amplitude values from a 24-hour window. This median is one measure of the central tendency of the amplitudes in a time series when values are arranged in sorted order. The median value itself always occurs as an amplitude value in the times series. Documentation
-
sample_min: This metric reports smallest amplitude value in counts encountered within a 24-hour window. Documentation
-
sample_rate_channel: A boolean measurement that returns 0 if miniSEED and channel sample rates agree within 1%, or 1 if they disagree. Documentation
-
sample_rate_resp: A boolean measurement that returns 0 if miniSEED and response-derived sample rates agree within 15%, or 1 if they disagree. Response-derived sample rates assume that the high-frequency amplitude rolloff is ~85% of the Nyquist frequency. Documentation
-
sample_rms: Displays the RMS variance of trace amplitudes within a 24-hour window. Documentation
-
sample_snr: A ratio of the RMS variance calculated from data 30 seconds before and 30 seconds following the predicted first-arriving P phase. Documentation
- channels = .[HLGNPYX].
-
sample_unique: This metric reports the number (count) of unique values in data trace over a 24-hour window. Documentation
-
spikes: The number of times that the 'Spikes detected' bit in the 'dq_flags' byte is set within a miniSEED file. This data quality flag is set by some dataloggers in the fixed section of the miniSEED header when short-duration spikes have been detected in the data. Because spikes have shorter duration than the natural period of most seismic sensors, spikes often indicate a problem introduced at or after the datalogger. Documentation
-
suspect_time_tag: The number of times that the 'Time tag is questionable' bit in the 'dq_flags' byte is set within a miniSEED file. This metric can be used to identify stations with GPS locking problems and data days with potential timing issues. Documentation
-
telemetry_sync_error: The number of times that the 'Telemetry synchronization error' bit in the 'dq_flags' byte is set within a miniSEED file. This metric can be searched to determine which stations may have telemetry problems or to identify or omit gappy data from a data request. Documentation
-
timing_correction: The number of times that the 'Time correction applied' bit in the 'act_flags' byte is set within a miniSEED file. This clock quality flag is set by the network operator in the fixed section of the miniSEED header when a timing correction stored in field 16 of the miniSEED fixed header has been applied to the data's original time stamp. A value of 0 means that no timing correction has been applied. Documentation
-
timing_quality: Daily average of the SEED timing quality stored in miniSEED blockette 1001. This value is vendor specific and expressed as a percentage of maximum accuracy. Percentage is NULL if not present in the miniSEED. Documentation
-
transfer_function: Transfer function metric consisting of the gain ratio, phase difference and magnitude squared of two co-located sensors. Documentation
- channels = [BCFHLM][HX].
Access to restricted data from ISPAQ can be managed with a .netrc file that has valid credentials. To set up ISPAQ for use with a .netrc file:
cd ispaq
conda activate ispaq
touch $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
touch $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh
Edit the $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh as follows:
#!/bin/sh
export IrisClient_netrc='path-to-netrc-file'
where 'path-to-netrc-file' is the file path for your .netrc.
Edit the $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh as follows:
#!/bin/sh
unset IrisClient_netrc
Then you'll need to re-install the CRAN IRISSeismic package:
conda deactivate
conda activate ispaq
Rscript -e 'Sys.getenv("IrisClient_netrc")' # verify that your .netrc file path is correct
./run_ispaq.py -I
Note: not using
-Pin the command line is the same as specifying-P preference_files/default.txt
cd ispaq # top-level directory
conda activate ispaq
./run_ispaq.py -M basicStats -S basicStats --starttime 2010-100 # starttime specified as julian day
./run_ispaq.py -M stateOfHealth -S ANMO --starttime 2013-01-05 # starttime specified as calendar day
./run_ispaq.py -M gaps -S ANMO --starttime 2011-01-01 --endtime 2011-01-08
./run_ispaq.py -M psdPdf -S psdPdf --starttime 2013-06-01 --endtime 2013-06-04
./run_ispaq.py -M sample_mean -S II.KAPI.00.BHZ --starttime 2013-01-05 --dataselect_url ./test_data --station_url ./test_data/II.KAPI_station.xml --output csv --csv_dir ./test_out
./run_ispaq.py -M psd_corrected,pdf -S II.KAPI.00.BHZ --starttime 2013-01-05 --endtime 2013-01-08 --dataselect_url ./test_data --station_url ./test_data/II.KAPI_station.xml --output csv --psd_dir ./test_out/PSDs --pdf_dir ./test_out/PDFs --pdf_type plot --pdf_interval aggregated
./run_ispaq.py -M basicStats -S basicStats --starttime 2010-100 --output db --db_name ispaq_example.db
To view values in sqlite database:
sqlite3 ispaq_example.db
At sqlite prompt:
.tables
select * from sample_mean;
select * from sample_max;
Ctrl + D to exit sqlite