Data Editing


MsPASS uses an idea that was been a part of seismic reflection from the earliest days of digital processing of seismic signals commonly called trace editing. Trace editing means removing “bad” data from subsequent processing where the meaning of “bad” is based on some computed metric or human editing using a graphical user interface. In all cases, the decision of “good” or “bad” is not grey but a black and white (boolean) decision. We implement this concept in MsPASS through a boolean live attribute that is a component of all MsPASS data objects (i.e. both atomic data TimeSeries and Seismogram as well as ensembles of same). Any MsPASS data object can be “killed” by calling it’s kill method. Algorithms can and always should test the attribute live to verify the data object has data that can be viewed as valid. No algorithm should ever assume data marked dead (live set False) can be used for any further processing.

In MsPASS we abstract the process of defining “trace editing” through a standardized API. In this section of the manual we describe how to use the generic metrics that are part of MsPASS. We also show how users can add custom metrics by either computing custom Metadata or running an inline computation on the sample data.

MsPASS Trace Editing Algorithms

Simple Comparison operators - example for starters

All the algorithms currently implemented in MsPASS use another model borrowed from seismic reflection processing. That is, the editing is driven by tests of the values of one or more “header” (As noted elsewhere MsPASS data defined a generalized “header” we call Metadata) attributes. That means all standard numeric comparison operators (in python the >, <, <=, >=, ==, and != operators).

Before defining the abstraction and the common API it is, perhaps, more instructive to give an example. Suppose we wanted to exclude all data from processing that have an estimated bandwidth less than 12 dB (2 octaves). The following is an example serial job looping over an entire dataset defined by Seismogram objects:

# ... Preceding code: import commands and creating database handle db ...

editor = MetadataLT("bandwidth",12.0)
query = {}   # Change to a MongoDB query to process data subset
cursor = db.wf_Seismogram.find(query)
for doc in cursor:
  d = db.read_data(doc)
  d = arrival_snr_QC(d)
  d = editor(d)

We made this example as simple as possible by using all defaults for the function arrival_snr_QC. That function has many options and depends upon a detail that the example assumes was previously computed; the Metadata attribute “Parrival” is assumed to have been set to a time within the time interval spanned by each datum. The key step for the concepts of this section is the creation of the python class MetadataLT in the first line. The name is meant to be mnemonic for using a Metadata test with the “Less Than (FORTRAN LT or python <)” operation using the value stored in Metadata with the key “bandwidth”. We directly call the MetadataLT object, which is equivalent to calling the``kill_if_true`` method, to implement the test. That method will return a version of d marked dead if the value of “bandwidth” is less than 12.0.

For completeness here is a parallel version of that same script using dask:

editor = MetadataLT("bandwidth", 12.0)
query = {}
seisbag = db.read_distributed_data(db, query)
seisbag =
seisbag =

where we also removed the comments and omit the (required to do anything) call to seisbag.compute() because this example would always be most appropriate as a section inside a workflow.

All Simple Comparison Operators

All the simple comparison operators using tests against a Metadata single attribute Have the same, common API with the following elements:

  1. All operators are implemented as a subclass of an (abstract) base class called Executioner.

  2. All operators have and one and only one constructor.

  3. All the constructors of this set have have two required, positional argument. The first is the Metadata key whose value is to compared. The second (argument 1 starting from 0) must contain the (constant) value for the comparison. In the example above the key is “bandwidth” and the test value is 12.0.

  4. All operators have an optional verbose boolean argument. When set True all kills will generate an informational error log entry with a detailed message on why the datum was killed. The default, in all cases, verbose is False because trace editing of large data sets can produce bloated error logs when done in verbose mode. We judged the possibility of filling the file system and crashing a bigger issue than losing a kill record. Needless to say, if understanding reasons for why individual data are killed is important you should turn on verbose.

  5. All operators have names of the form MetadataXX with Metadata a fixed string and XX the Fortranish name for the corresponding comparison operation. For instance, in the example above we used MetadataLT which means the comparison used is the python < operator that FORTRAN would call .LT..

  6. All operators implement the (required) method kill_if_true. As the name implies that method kills the datum if the conditional defined in construction of the object resolves true. For our example above that means if x is the value extracted from Metadata and a is the boundary set as arg1 in the constructor, the datum is killed if x<a. This method always returns a copy of the data passed to it marked dead or alive depending on the outcome of the test. That is essential for use of the function in a map operator like our parallel example above. All operators will throw a MsPASSError exception if the data received is not one of the data objects known to MsPASS. All implementations also silently do nothing if the input is already marked dead.

  7. As a convenience the base class implements the __call__ method. In this case the __call__ method can be thought of as shorthand for the kill_if_true method. We used this feature in the example above by using the form d = editor(d) that is a shorthand for the more explicit but verbose d = editor.kill_if_true(d).

The following is a table of the names of all the simple comparison functions using the common API. In each cell x is the value extracted from Metadata and a is the boundary value for the comparison test. We emphasize that in all cases the kill_if_true method, or the function form, kills the datum if the test shown resolves as True.

Table 6 Simple Metadata-based Edit Classes

Class name

Kill Test


x > a


x >= a


x == a


x != a


x < a


x <= a

The constructors for all simple comparison testers have this the following, common signature:

def __init__(self, key, value, verbose=False):

where key is the Metadata key used to fetch the data for x in the table above and value is the value assigned to a.

The verbose flag is a common argument for all the MsPASS metadata-based testers. Normally (default verbose=False) kills are done silently. When set true all kills will generate an Informational elog entry with a detailed message giving the details of why the datum was killed.

Existence Tests

Unlike classical header implementations that have fixed slots that always have data in them, Metadata is open-ended. That means data for a particular key may or may not exist. We thus supply two existence classes. The class names are MetadataDefined and MetadataUndefined. The kill_if_true methods for these each kill a datum if a key loaded in on construction exists or does not exist respectively. Both have constructors with this signature:

def __init__(self, key, verbose=False):

where key is the Metadata key that is to be tested by the kill_if_true method. verbose is as noted above for the simple comparison testers.

MetadataUndefined is a particularly important editor to prefilter data prior to running one or more processing functions. If a function requires one or more metadata attributes MetadataUndefined can be used to filter out all data that would cause that algorithm to fail anyway.

Interval Comparison

Another common test for editing data is filtering data defined by a range of values. A type example is P wave receiver functions that commonly only use data with epicentral distances between about 30 and 100 degrees. Another would be the size of some amplitude metric defined by a range of positive values. A way to accomplish that within a workflow without using multiple simple comparison operators is to apply an interval filter that kills data outside a specified range.

There are two complications in defining a range test. First, there are two mirror-image tests: is the value to be tested inside an interval or outside the interval. The second is should the test be inclusive of the edges? i.e. should the tests be <= or just < (similarly >= or >)? That could have been done with nine different classes for all the possible combinations of the three boolean variables it takes to define all the possibilities or a FiringSquad instance as describd below. As a convenience we implemented a single class called MetadataInterval with three boolean values defined in the constructor. The constructor has this signature:

def __init__(self, key, lower_endpoint, upper_endpoint,
  use_lower_edge=True, use_upper_edge=True, kill_if_outside=True, verbose=False):

The three booleans (use_upper_edge, use_lower_edge, and kill_if_outside) determine how equality with the edges is handled and if the test is “inside” or “outside” the specified range. These are defined in the table below noting that in the table a=lower_endpoint and b=upper_endpoint. In all cases True means if the test is true the datum will be marked dead.

Table 7 MetadataInterval operators




Kill test




a <= x <= b




a < x <= b




a <= x < b




a < x < b




x <= a and x >= b




x < a and x >= b




x <= a and x > b




x < a and x > b

Defining Multiple Editors

The final Metadata-based editor in MsPASS was given the name FiringSquad. Although the name is admittedly a bit tongue-in-cheek, the imagery the name provokes describes the function well: a datum facing a firing squad is facing multiple executioner who may or may not kill you. This class has a signature similar to the other Metadata-based editors:

class FiringSquad(Executioner):

Meaning it inherits the base class Executioner and requires a custom implementation of the kill_if_true method. It can be used alone in a workflow exactly like the single test functions described above. A FiringSquad is simply a way to apply multiple Metadata tests in a single function call to the kill_if_true method.

As with MetadataInterval the constructor is different and has this signature:

def __init__(self, executioner_list, verbose=False):

where executioner_list is expected to be any iterable container made up only of python classes that are subclasses of Executioner. (All the classes covered in this document are subclasses of Executioner.) Verbose has the same meaning as described above with an important exception. It is not global but refers only to errors internal to FiringSquad. Any testers needing a verbose option enabled will need to have the verbose option specified during their construction.

When the kill_if_true method is called for this class the list of executioners are called in order defined by the list. The victim cannot be hit by more than one bullet. Once a datum is killed the kill_if_true method returns the body and drops further tests.

A feature of a FiringSquad not enabled in any of the other classes described in this document is that it implements operator +=. The += operator can be used is to append an additional test to an existing FiringSquad. e.g. suppose we had a workflow that creates a FiringSquad associated with the symbol squad. The following example creates a <= test against the Metadata key “mad_snr” and adds it to the list of test in squad:

maddog = MetadataLE("mad_snr",4.0)
squad += maddog

Finally, note it is possible to have recursive FiringSquad tests. That is, a FiringSquad can itself contain another FiringSquad as one of the tests set in the executioner_list passed on construction.

How to Implement an Extension

Before considering developing an extension editor consider seriously if what you need can be accomplished with one of two alternatives:

  1. Can the test be cast into a composite FiringSquad with the right components.

  2. If you need to compute some nonstandard quantity from the sample data ask yourself if the result can be reduced to a small set of numbers that can be saved as Metadata with nonstandard keys? If so, you can focus on the unique calculations and have the code post the results to Metadata. There is a high probability you can then use one or more of the classes described above to apply the needed test.

Anyone familiar with a basic understanding of inheritance in an object-oriented language in general and python in particular will recognize our implementation of all the classes described above as textbook applications of inheritance. A custom extension that can plug into this same class structure must do two things:

  1. The class declaration must declare it to be a subclass of Executioner.

  2. The class MUST implement a custom kill_if_true method.

In addition, almost any implementation will require a base constructor (i.e. the line def __init__(self,...args..):) defining internal parameters that define the boundaries of the kill test. The kill_if_true method would normally use “self” parameters set by the constructor.

Some key points about extensions:

  • Our examples all use attributes fetched from Metadata. That is NOT a requirement. Many algorithms are possible that would compute a test directly from sample data. Be warned, however, that different MsPASS data objects all have fundamentally different sample data organizations. Hence, a class that handles sample data would require a test for the unique data to which it could be applied.

  • Our examples are dogmatic in requiring the data be MsPASS data objects. That is required because the implementation uses the kill method that the caller can be assured is part of the data object received. Extensions that can plug in cleanly (e.g. as a member of a FiringSquad) should do the same test for MsPASS data objects. That test is standardized in the base class method input_is_valid. Use of that method in extensions is strongly advised to avoid unexpected aborts.

  • Consider implementing the verbose option as described here for consistency.

  • As with many things like this the best way to see how to build is an extension is to use the class implementations described above as examples.