Naryn allows efficient access and analysis of medical records that are maintained in a custom database.
Naryn can work under R (as a package) or Python (as a module). The vast majority of the functions and the concepts are shared between the two implementations, yet certain differences still exist and are summarized in a table below. Code examples and function names in this document are presented for R but they can equally run in Python with the interface changes as to the table.
Naryn allows accessing the data that resides in tracks where each track holds certain type of medical data such as patients’ diagnoses or their hemoglobin level at certain points of time. The track files can be aggregated from one or more directories. Before the tracks can be accessed, Naryn needs to establish connection to the directories, also referred as db dirs. Call emr_db.connect function to establish the access to the tracks in the db_dirs. To establish a connection using emr_db.connect, Naryn requires to specify at-least one db dir. Optionally, emr_db.connect accepts additional db dirs which can also contain additional tracks. In a case where 2 or more db dirs contain the same track name (namespace collision), the track will be taken from the db dir which was passed last in the order of connections. For example, if we have 2 db dirs /db1 and /db2 which both contain a track named track1, the call emr_db.connect(c('/db1', '/db2')) will result with Naryn using track1 from /db2. As you might expect the overriding is consistent not only for the track’s data itself, but also for any other Naryn entity using or pointing to the track.
Even though all db directories may contain track files, their designation is different. All the db dirs except for the last dir in the order of connections are mainly read-only. The directory which was connected last in the order, is termed user dir, and is intended to store volatile data like the results of intermediate calculations. New tracks can be created only in the db dir which was last in the order of connections, using emr_track.import or emr_track.create. In order to write tracks to a db dir which is not last in the connection order, you must explicitly pass the path to the required db dir, and this should be done for a well justified reason.
A track may be marked as read-only to prevent its accidental deletion or modification. Use emr_track.readonly to set or get read-only property of the track. A newly created tracks is always writable. If you wish to mark it as “read-only”, please do it in a separate call.
emr_db.connect supports two modes of work - ‘load on demand’ and ‘pre-load’. In ‘load on demand’ mode tracks are loaded into memory only when they are accessed. Tracks stay in the memory up until R sessions ends or the package is unloaded (Python: since modules cannot be forced to unload, db_unload is introduced).
In ‘pre-load’ mode, all the tracks are pre-loaded into memory making subsequent track access significantly faster. As loaded tracks reside in shared memory, other R sessions running on the same machine may also enjoy significant run-time boost. On the flip side, pre-loading all the tracks prolongs the execution of emr_db.connect and requires enough memory to accommodate all the data.
Choosing between the two modes depends on the specific needs. While load_on_demand=TRUE seems to be a solid default choice, in an environment where there are frequent short-living R sessions, each accessing a track, one might opt for running a “daemon” - an additional permanent R session. The daemon would pre-load all the tracks in advance and stay alive thus boosting the run-time of the later emerging sessions.
Naryn caches certain data on the disk to maintain fast run-times. In particular two files (.naryn and .ids) are created in any database, and another file called .logical_tracks is created in global databases.
.naryn file contains a list of all tracks in the current root directory and their last modification dates. This file spares a full root directory rescan when emr_db.connect is called. The recorded modification dates allow to efficiently synchronize the track changes induced by synchronously running R sessions.
.logical_tracks implements the same mechanism for logical tracks, which store their properties (source and values) under a folder called logical.
.ids file contains available ids that are used to run certain types of track expression iterators (see below). The source of these ids comes from a `patients.dob} (i.e. Date Of Birth) track, which must be present in the global root directory before these iterators may be utilized.
Various functions such as emr_track.import modify these files according to the changes that DB undergoes (addition / removal / modification of tracks). Thus manual (outside of Naryn) modification, replacement, addition or deletion of track files cause the cache files to go out of sync. Various problems might arise as a consequence, such as run-time errors, out-dated data from modified tracks and sub-optimal run-time performance.
Manual modifications of the database files can still be performed, yet they must be ratified by running emr_db.reload.
Naryn creates files and directories with a umask of 007 (except for read-only tracks), which means that files and directories would have permissions of 660 (rw-rw----) and 770 (rwxrwx---) respectively. This means that in order to access a database that someone outside the group created, the file and folder permissions need to be changed first.
Each track is stored in a binary file with .nrtrack file extension. One of the two internal formats, dense or sparse, is automatically selected during the track creation. The choice of the exact format is based on the optimal run-time performance.
Track is a data structure that stores a set of records of (id, time, ref, numeric value) type. For example, hemoglobin level of patients can be stored in this way, where id would be the id of the patient and time would indicate the moment when the blood test was made. Another track can contain the code of the laboratory which carried out the test. If the times of the records from the two tracks match, one would conclude which lab performed the given test.
Time resolution is always in hours. It might happen that two different blood tests are carried out by two different labs for the same patient at the same hour. Assuming that each lab has certain bias due to different equipment used, the reads of the hemoglobin might come out different. Since both of the tests are carried out at exactly the same hour it will be impossible later to link each result to the lab that performed it.
In those cases when two or more values share identical id and time Naryn requires them to use then different ref (references). A reference is an integer number in the range of [-1, 254], which when no time collision occurs is normally set to -1. However, in cases of ambiguity it can give additional resolution to the time. In our blood example the results of the first lab could have been recorded with ref = 0 and the second lab would do it with ref = 1. This way the two hemoglobin readings could later be separated and correctly linked to their originating labs.
Tracks store numerical values assigned to the patients and times. The numerical data however can have different meaning and hence impose different set of operations to be applied to it. Laboratory codes, diagnosis codes, binary information such as date of birth or doctor visits are one type of data which we call categorical. Another type of data indicate usually the readings of different instruments such as the heartbeat rate or glucose level. This type of data is called quantitative.
The operations that can be applied to both of these types can be very different. One might want to search for the specific diagnosis code, yet it makes little sense to search for the very specific heartbeat rate, say “68”. On contrary heartbeat rate readings from different times can be averaged or a mean value might be calculated - something that has no meaning in case of categorical data.
During the track creation one must specify the type of the track: categorical or quantitative. Various operations that can be later applied to the track are bound to the track type.
In addition to the physical tracks which are stored in the binary files, naryn supports a concept of a logical track which is an alias to a physical track. For example, assume we have a track called lab.103 which contains hemoglobin levels of patients. It would be more convenient to refer to it explicitly by hemoglobin instead of remembering the lab code. Logical tracks do exactly this, we can create a logical track called hemoglobin which refers to the physical lab.103:
emr_track.logical.create("hemoglobin", "lab.103")
emr_extract("hemoglobin")You can also use logical tracks to create an alias for specific values from a categorical track. For example, suppose we have a track called diagnosis.250 which contains the diagnosis times of ICD code 250 (“250.*”), with the values being the sub-diagnosis (e.g. 1 for 250.1 and 4 for 250.4). Logical tracks allow us to create an alias for a specific sub-diagnosis value and then refer to it as a regular track:
emr_track.logical.create("dx.250.1_4", "diagnosis.250", values = c(1,4))
emr_extract("dx.250.1_4")Under the hood logical tracks are implemented using the virtual tracks mechanism (see below), but unlike virtual tracks - they are part of the database and are persistent between sessions. You can delete a logical track by calling emr_track.logical.rm and list them using emr_track.logical.ls.
In addition to numeric data a track may store arbitrary meta-data such as description, source, etc. The meta-data is stored in the form of name-value pairs or attributes where the value is a character string.
Though not officially enforced attributes are intended to store relatively short character strings. Please use track variables to store data in any other format.
A single attribute can be retrieved, added, modified or deleted using emr_track.attr.get and emr_track.attr.set functions. Bulk access to more than one attribute is facilitated by emr_track.attr.export function.
Track names which attributes values match a pattern can be retrieved using emr_track.ls, emr_track.global.ls and emr_track.user.ls functions.
Track statistics, results of time-consuming per-track calculations, historical data and any other data in arbitrary format can be stored in a track’s supplementary data in the form of track variables. Track variable can be retrieved, added, modified or deleted using emr_track.var.get, emr_track.var.set and emr_track.var.rm functions. List of track variables can be retrieved using emr_track.var.ls function.
Note: track variables created in R are not visible in Python and vice versa.
Though both track attributes and track variables can be used to store meta-data of a track, there are a few important differences between the two that are summed up in the following table:
| Track Attributes | Track Variables | |
|---|---|---|
| Optimal use case | Track properties as short, non-empty character strings (description, source, …) | Arbitrary data associated with the track |
| Value type | Character string | Arbitrary |
| Single value retrieval | emr_track.attr.get |
emr_track.var.get |
| Bulk value retrieval | emr_track.attr.export |
— |
| Single value modification | emr_track.attr.set |
emr_track.var.set |
| Object names retrieval | emr_track.attr.export |
emr_track.var.ls |
| Object removal | emr_track.attr.rm |
emr_track.var.rm |
| Search by value | R: emr_track.ls, emr_track.global.ls, emr_track.user.ls
|
— |
| R vs. Python compatibility | Yes | No |
The analysis of data often involves dividing the data to train and test sets. Naryn allows to subset the data via emr_db.subset function. emr_db.subset accepts a list of ids or samples the ids randomly. These ids constitute the subset. The ids that are not in the subset are skipped by all the iterators, filters and various functions.
One may think of a subset as an additional layer, a “viewport”, that filters out some of the ids. Some lower-level functions such as emr_track.info or emr_track.unique ignore the subsets. Same applies to percentile.* functions of the virtual tracks.
Track expression allows to retrieve numerical data that is recorded in the tracks. Track expressions are widely used in various functions (emr_screen, emr_extract, emr_dist, …).
Track expression is a character string that closely resembles a valid R/Python expression. Just like any other R/Python expression it may include conditions, function calls and variables defined beforehand. "1 > 2", "mean(1:10)" and "myvar < 17" are all valid track expressions. Unlike regular R/Python expressions track expression might also contain track names and / or virtual track names.
To understand how the track expression allows the access to the tracks we must explain how the track expression gets evaluated.
Every track expression is accompanied by an iterator that produces a set of id-time points of (id, time, ref) type. For each each iterator point the track expression is evaluated. The value of the track expression "mean(1:10)" is constant regardless the iterator point. However the track expression might contain a track name mytrack, like: "mytrack * 3". Naryn recognizes then that mytrack is not a regular R/Python variable but rather a track name. A new run-time track variable named mytrack is added then to R environment (or Python module local dictionary). For each iterator point this variable is assigned the value of the track that matches (id, time, ref) (or NaN if no matching value exists in the track). Once mytrack is assigned the corresponding value, the track expression is evaluated in R/Python.
To boost the performance of the track expression evaluation, run-time track variables are actually defined as vectors in R rather than scalars. The result of the evaluation is expected to be also a vector of a similar size. One should always keep in his mind the vectorial notation and write the track expressions accordingly.
For example, at first glance a track expression "min(mytrack, 10)" seems to be perfectly fine. However the evaluation of this expression produces always a scalar, i.e. a single number even if mytrack is actually a vector. The way to correct the specific track expression so that it works on vectors, is to use pmin function instead of min.
Python
Similarly to R, a track variable in Python is not a scalar but rather an instance of numpy.ndarray. The evaluation of a track expression must therefore produce a numpy.ndarray as well. Various operations on numpy arrays indeed work the same way as with scalars, however logical operations require different syntax. For instance:
will produce an error given that mytrack1 and mytrack2 are numpy arrays. The correct way to write the expression is:
One may coerce the track variable to behave like a scalar: by setting emr_eval.buf.size option to 1 (see Appendix for more details). Beware though that this might take its heavy toll on run-time.
If the track expression contains a track (or virtual track) name, then the values from the track are fetched one-by-one into the identically named R variable based on id, time and ref of the iterator point. If however ref of the iterator point equals to -1, we treat it as a “wildcard”: matching is required then only for id and time.
“Wildcard” reference in the iterator might create a new issue: more than one track value might match then a single iterator point. In this case the value placed in the track variable (e.g. mytrack) depends on the type of the track. If the track is categorical the track variable is set to -1, otherwise it is set to the average of all matching values.
So far we have shown that in some situations mytrack variable can be set to the average of the matching track values. But what if we do not want to average the values but rather pick up the maximal, minimal or median value? What if we want to use the percentile of a track value rather than the value itself? And maybe we even want to alter the time of the iterator point: shift it or expand to a time window and by that look at the different set of track values? For instance: given an iterator point we might want to know what was the maximal level of glucose during the last year that preceded the time of the point.
This is where virtual tracks come in use.
Virtual track is a named set of rules that describe how the track should be proceeded, and how the time of the iterator point should be modified. Virtual tracks are created by emr_vtrack.create function:
emr_vtrack.create("annual_glucose",
src = "glucose_track", func = "quantile",
param = 0.5, time.shift = c(-year(), 0)
)This call creates a new virtual track named annual_glucose based on the underlying physical source track glucose_track. For each iterator point with time T we look at values of glucose_track in the time window of [T-365*24,T], i.e. one year prior to T. We calculate then the median over the values (func="quantile", param=0.5).
There is a rich set of various functions besides “quantile” that can be applied to the track values. Some of these functions can be used only with categorical tracks, other ones - only with quantitative tracks and some functions can be applied to both types of the track. Please refer the documentation of emr_vtrack.create.
Once a virtual track is created it can be used in a track expression:
emr_extract("annual_glucose", iterator = list(year(), "patients.dob"))This would give us a median of an annual glucose level in year-steps starting from the patient’s birthday. (This example makes use of an Extended Beat Iterator that would be explained later.)
Let’s expand our example further and ignore in our calculations the glucose readings that had been made within a week after steroids had been prescribed. We can use an additional filter parameter to do that.
emr_filter.create("steroids_filter", "steroids_track", time.shift=c(-week(), 0))
emr_vtrack.create("annual_glucose",
src = "glucose_track", func = "quantile",
param = 0.5, time.shift = c(-year(), 0), filter = "!steroids_filter"
)
emr_extract("annual_glucose", iterator = list(year(), "date_of_birth_track"))Filter is applied to the ID-Time points of the source track (e.g. glucose_track in our example). The virtual track function (quantile, …) is applied then only to the points that pass the filter. The concept of filters is explained extensively in a separate chapter.
Virtual tracks allow also to remap the patient ids. This is done via id.map parameter which accepts a data frame that defines the id mapping. Remapping ids might be useful if family ties are explored. For example, instead of glucose level of the patient we are interested to check the glucose level of one of his family members.
So far we have discussed the track expressions and how they are evaluated given the iterator point. In this section we will show how the iterator points are generated.
An iterator is defined via iterator parameter. There are a few types of iterators such as track iterator, beat iterator, etc. The type determines which points are generated by the iterator. The information about each type is listed below.
Iterator is always accompanied by four additional parameters: stime, etime, keepref and filter. stime and etime bind the time scope of the iterator: the points that the iterator generates lie always within these boundaries. The effect of keepref=TRUE depends on the iterator type. However for all the iterator types if keepref=FALSE the reference of all the iterator points is set to -1. filter parameter sets the iterator filter which is discussed thoroughly later in the document in a separate chapter.
Track iterator returns the points (including the reference) from the specified track. Track name is specified as a string.
If keepref=FALSE the reference of each point is set to -1.
Example:
# Returns the level of glucose one hour after the insulin shot was made
emr_vtrack.create("glucose", "glucose_track", func="avg", time.shift=1)
emr_extract("glucose", iterator="insulin_shot_track")Id-Time points iterator generates points from an id-time points table (see: Appendix). If keepref=FALSE the reference of each point is set to -1.
Example:
# Returns the level of glucose one hour after the insulin shot was made
emr_vtrack.create("glucose", "glucose_track", func="avg", time.shift=1)
r <- emr_extract("insulin_shot_track") # <-- implicit iterator is used here
emr_extract("glucose", iterator=r)Ids iterator generates points with ids taken from an ids table (see: Appendix) and times that run from stime to etime with a step of 1.
If keepref=TRUE for each id-time pair the iterator generates 255 points with references running from 0 to 254. If keepref=FALSE only one point is generated for the given id and time, and its reference is set to -1.
Example:
# Returns the level of glucose for each hour in year 2016 for ids 2 and 5
stime <- emr_date2time(1, 1, 2016, 0)
etime <- emr_date2time(31, 12, 2016, 23)
emr_extract("glucose", iterator=data.frame(id=c(2,5)), stime=stime, etime=etime)Time intervals iterator generates points for all the ids that appear in ‘patients.dob’ track with times taken from a time intervals table (see: Appendix). Each time starts at the beginning of the time interval and runs to the end of it with a step of 1. That being said the points that lie outside of [stime, etime] range are skipped.
If keepref=TRUE for each id-time pair the iterator generates 255 points with references running from 0 to 254. If keepref=FALSE only one point is generated for the given id and time, and its reference is set to -1.
Example:
# Returns the level of hangover for all patients the next day after New Year Eve
# for the years 2015 and 2016
stime1 <- emr_date2time(1, 1, 2015, 0)
etime1 <- emr_date2time(1, 1, 2015, 23)
stime2 <- emr_date2time(1, 1, 2016, 0)
etime2 <- emr_date2time(1, 1, 2016, 23)
emr_extract("alcohol_level_track", iterator=data.frame(stime=c(stime1, stime2),
etime=c(etime1, etime2)))Id-Time intervals iterator generates for each id points that cover ['stime', 'etime'] time range as specified in id-time intervals table (see: Appendix). Each time starts at the beginning of the time interval and runs to the end of it with a step of 1. That being said the points that lie outside of [stime, etime] range are skipped.
If keepref=TRUE for each id-time pair the iterator generates 255 points with references running from 0 to 254. If keepref=FALSE only one point is generated for the given id and time, and its reference is set to -1.
Beat Iterator generates a “time beat” at the given period for each id that appear in ‘patients.dob’ track. The period is given always in hours.
Example:
emr_extract("glucose_track", iterator=10, stime=1000, etime=2000)This will create a beat iterator with a period of 10 hours starting at stime up until etime is reached. If, for example, stime equals 1000 then the beat iterator will create for each id iterator points at times: 1000, 1010, 1020, …
If keepref=TRUE for each id-time pair the iterator generates 255 points with references running from 0 to 254. If keepref=FALSE only one point is generated for the given id and time, and its reference is set to -1.
Extended beat iterator is as its name suggests a variation on the beat iterator. It works by the same principle of creating time points with the given period however instead of basing the times count on stime it accepts an additional parameter - a track or a Id-Time Points table - that instructs what should be the initial time point for each of the ids. The two parameters (period and mapping) should come in a list. Each id is required to appear only once and if a certain id does not appear at all, it is skipped by the iterator.
Anyhow points that lie outside of [stime, etime] range are not generated.
Example:
# Returns the maximal weight of patients at one year span starting from their birthdays
emr_vtrack.create("weight", "weight_track", func = "max", time.shift = c(0, year()))
emr_extract("weight", iterator = list(year(), "birthday_track"), stime = 1000, etime = 2000)periodic iterator goes over every year/month. You can use it by running emr_monthly_iterator or emr_yearly_iterator.
Example:
iter <- emr_yearly_iterator(emr_date2time(1, 1, 2002), emr_date2time(1, 1, 2017))
emr_extract("dense_track", iterator = iter, stime = 1, etime = 3)
iter <- emr_monthly_iterator(emr_date2time(1, 1, 2002), n = 15)
emr_extract("dense_track", iterator = iter, stime = 1, etime = 3)The iterator is set implicitly if its value remains NULL (which is the default). In that case the track expression is analyzed and searched for track names. If all the track variables or virtual track variables point to the same track, this track is used as a source for a track iterator. If more then one track appears in the track expression, an error message is printed out notifying ambiguity.
During the evaluation of a track expression one can access a specially defined variable named EMR_TIME (Python: TIME). This variable contains a vector (numpy.ndarray in Python) of current iterator times. The length of the vector matches the length of the track variable (which is a vector too).
Note that some values in EMR_TIME might be set 0. Skip those intervals and the values of the track variables at the corresponding indices.
# Returns times of the current iterator as a day of month
emr_extract("emr_time2dayofmonth(EMR_TIME)", iterator = "sparse_track")Filter is used to approve / reject an ID-Time point. It can be applied to an iterator, in which case the iterator points are required to be approved by the filter before they are passed further to the track expression. Filter may also be used by a virtual track. In this case the virtual track function (see func parameter of emr_vtrack.create) is applied only to the points from the source track (src parameter) that pass the filter.
Filter has a form of a logical expression consisting of named or unnamed elementary filters (the “building bricks” of the filter) connected with the logical operators: &, |, ! (and, or and not in Python) and brackets ().
Suppose we are interested in hemoglobin levels of patients who were prescribed either drugX or drugY but not drugZ within a time window of one week before the test. Assume that drugX, drugY and drugZ are residing each in its separate track. Without filters we would need to call emr_extract four times, store potentially huge data frame results in the memory and finally merge the tables within R while caring about time windows. With filters we can do it much easier:
emr_filter.create("filterX", "drugX", time.shift = c(week(), 0))
emr_filter.create("filterY", "drugY", time.shift = c(week(), 0))
emr_filter.create("filterZ", "drugZ", time.shift = c(week(), 0))
emr_extract("hemoglobin", filter = "(filterX | filterY) & !filterZ")We can further expand the example above by specifying the ‘operator’ argument on filter creation. If we wish to extract, the same information as before, but in this case we are interested only in patients which have an hemoglobin level of at least 16 (in addition to our drug treatment requirements). Under the same assumptions in the previous example, our code would look like:
emr_filter.create("filterX", "drugX", time.shift = c(week(), 0))
emr_filter.create("filterY", "drugY", time.shift = c(week(), 0))
emr_filter.create("filterZ", "drugZ", time.shift = c(week(), 0))
emr_filter.create("hemoglobin_gt_16", "hemoglobin", val=16, operator=">")
emr_extract("hemoglobin", filter = "(filterX | filterY) & !filterZ & hemoglobin_gt_16")Python
Filter with logical conditions will use Python’s notation like:
Each call to emr_filter.create creates a named elementary filter (or simply: named filter) with a unique name. The named filter can then be used in filter parameter of an iterator and be combined with other named filters using the logical operators.
In our previous example we created three named filters based on three tracks. If time window was not required, we could have used the names of the tracks directly in the filter, like: filter = "(drugX | drugY) & !drugZ".
In addition to track names other types of objects can be used within the filter. These are: Id-Time Points Table, Ids Table, Time Intervals Table and Id-Time Intervals Table (see Appendix for the format of these tables). When used in the filter the object should be constructed in advanced and be referred by its name. “In place” construction (aka: filter = "data.frame(...)" is not allowed.
The ID-Time Point embeds within itself a reference value. Named filters allow to specify whether the reference should be used for matching or not. When keepref=TRUE is set within emr_filter.create, the candidate point’s reference is matched with the filter’s reference. Otherwise the references are ignored.
It is important to remember that references are always ignored when any object but a named filter is used within a filter. For instance, if filter = "drug" and drug is a name of a track (and not a name of a named filter), then the references will be ignored during the matching. To ensure the filter matches the references of drug track, one must define a named filter with keepref=TRUE parameter:
emr_filter.create("drug_filter", "drug", keepref=TRUE)
emr_extract(my.track.expression, filter="drug_filter", keepref=TRUE)Various functions in the library such as emr_quantiles make use of pseudo-random number generator. Each time the function is invoked a unique series of random numbers is issued. Hence two identical calls might produce different results. To guarantee reproducible results call set.seed (Python: seed) before invoking the function.
Note: R and Python implementations of Naryn use different pseudo-random number generator algorithms. Sadly it means that the result achieved in R cannot be reproducible in Python if random is used, even if identical seed is shared between the two platforms.
To boost the run time performance various functions in the library support multitasking mode, i.e. parallel computation by several concurrent processes. Multitasking is not invoked immediately: approximately 0.3 seconds from the function launch the actual progress is measured and total run-time is estimated. If the estimated run-time exceeds the limit (currently: 2 seconds), multitasking kicks in.
The number of processes launched in the multitasking mode depends on the total run-time estimation (longer run-time will use more processes) and the values of emr_min.processes and emr_max.processes R options. In any case the number of processes never exceeds the number of CPU cores available.
Multitasking can significantly boost the performance however it utilizes more CPU. When CPU utilization is the priority it is advisable to switch off multitasking by setting emr_multitasking R option to FALSE.
In addition to increased CPU usage multitasking might also alter the behavior of functions that return ID-Time points such as emr_extract and emr_screen. When multitasking is not invoked these functions return the results always sorted by ID, time and reference. In multitasking mode however the result might come out unsorted. Moreover subsequent calls might return results reshuffled differently. One might use sort parameter in these functions to ensure the points come out sorted. Please bear in mind that sorting the results takes its toll especially on particularly large data frames. That’s why by default sort is set to FALSE.
| R | Python | |
|---|---|---|
| Naming Conventions (except for virtual track ‘func’, which stays unchanged) |
emr_xxx.yyy.zzz |
xxx_yyy_zzz |
| Variables | Defined in .naryn environment: EMR_GROOTEMR_UROOT
|
Defined in module’s environment:_GROOT_UROOT
|
| Run-time Variables (available only during track expression evaluation) | EMR_TIME |
TIME |
| Package / Module Options | Controlled via standard options mechanism: options(emr_xxx.yyy=zzz) getOption("emr_xxx.yyy")
|
Controlled by module’s CONFIG variable: CONFIG['xxx_yyy']=zzz CONFIG['xxx_yyy']
|
| Data Types (used as function parameters) |
data.frame list vector of strings vector of numerics NULL
|
pandas.DataFrame list list of strings numpy.ndarray of numerics None
|
| Data Types (return value) |
data.frame list vector of strings vector of numerics, no labels vector of numerics, with labels NULL
|
pandas.DataFrame dict numpy.ndarray of objects (strings) numpy.ndarray of numerics pandas.DataFrame with two columns (label, numeric) None
|
| Database Management | Database is unloaded when the package is detached. |
db_unload() must be called explicitly to unload the database. |
| Setting seed for random number generator. Note: R and Python use different random generators, results are therefore not reproducible between them. |
set.seed |
seed |
| Track Variables | Variables saved in Python are not visible in R. | Variables saved in R are not visible in Python. |
| Setting Track Variables |
emr_track.set creates a directory named .trackname.var
|
track_set creates a directory named .trackname.pyvar
|
| Named Filters and Virtual Tracks | Named filters and virtuals tracks may be saved along with the rest of R’s environment. |
filter_export, filter_import, vtrack_export, vtrack_import must be explicitly called to save / restore named filters or virtual tracks. |
| Pattern Matching |
emr_track.ls, emr_track.global.ls, emr_track.user.ls, emr_track.var.ls, emr_filter.ls accept pattern matching parameters. Return: vector of strings that match the pattern. |
track_ls, track_global_ls, track_user_ls, track_var_ls, filter_ls do not support pattern matching. Return: numpy.ndarray of objects (strings) that contains all the objects (tracks, …) |
| Time shift parameter (used in various functions) |
time.shift is a numeric or a vector of two numerics. |
time_shift is a numeric or a list of two numerics. |
| Calculating Distribution |
emr_dist returns N-dimensional vector with labels (dimension names) |
dist return N-dimensional numpy.ndarray without labels. |
| Calculating Correlation Statistics |
emr_cor: For N-dimensional binning the returned value r may be addressed as: r$cor[bin1,...,binN,i,j], where i and j are indices of cor.exprs. |
cor: For N-dimensional binning the returned value r may be addressed as: r['cor'][bin1,...,binN,i,j], where i and j are indices of cor_exprs. |
| Others |
emr_annotate |
Not implemented, use pandas.DataFrame.merge or pandas.merge_sorted instead. |
Naryn supports the following options. The options can be set/examined via R’s options and getOption.
(Use CONFIG['option_name'] to control the module options in Python. Please mind as well Python’s name convention: R’s emr_xxx.yyy option will change its name to xxx_yyy.)
| Option | Default Value | Description |
|---|---|---|
emr_multitasking |
TRUE |
Should the multitasking be allowed? |
emr_min.processes |
8 |
Minimal number of processes launched when multitasking is invoked. |
emr_max.processes |
20 |
Maximal number of processes launched when multitasking is invoked. |
emr_max.data.size |
10000000 |
Maximal size of data sets (rows of a data frame, length of a vector, …) stored in memory. Prevents excessive memory usage. |
emr_eval.buf.size |
1000 |
Size of the track expression evaluation buffer. |
emr_warning.itr.no.filter.size |
100000 |
Threshold above which “beat iterator used without filter” warning is issued. |
Id-Time Points table is a data frame having two first columns named ‘id’ and ‘time’. References might be specified by a third column named ‘ref’. If ‘ref’ column is missing or named differently references are set to -1. Additional columns, if presented, are ignored.
Id-Time Values table is an extension of Id-Time Points table with an additional column named ‘value’. Additional columns, if presented, are ignored.
Ids table is a data frame having the first column named ‘id’. Each id must appear only once. Additional columns of the data frame, if presented, are ignored.