Overview of using apps#
What are apps?#
Apps are ready-made “functions” that you can apply to your data without needing to know all the technical details. They are easy to use, even if you’re not an expert programmer. Multiple apps can be naturally composed into “pipelines”, which are fully equipped for robust and reproducible application to data. In fact, apps and app pipelines can be applied to a single, or thousands, of data file(s) without writing loops or conditionals.
Apps have several key features, they:
trap errors
check the validity of input data
can be used by themselves or combined into a “composed” app (aka pipeline)
automatically track the relationship between an input data record and its output record
How do I start to use apps?#
Three top-level functions are very useful:
available_apps()identifies what apps are installed (see Displaying installed apps)app_help()shows what a given app can do (see Getting help on an app)get_app()returns an app instance for you to use (see Getting an app)
Two other crucial concepts concern:
Types of apps#
There are 3 types of apps:
loaders (by convention, names starts with
load_<data type>)writers (by convention, names starts with
write_<data type>)generic (no naming convention)
As their names imply, loaders load, writers write and generic apps do other operations on data.
Composability#
Most cogent3 apps are “composable”, meaning that multiple apps can be combined into a single function by addition. For example, say we have an app (fit_model) that performs a molecular evolutionary analysis on an alignment, and another app (extract_stats) that gets the statistics from the result. We could perform these steps sequentially as follows
fitted = fit_model(alignment)
stats = extract_stats(fitted)
Composability allows us to simplify this as follows
app = fit_model + extract_stats
stats = app(fitted)
We can have many more apps in a composed function than just the two shown here.
Composability rules#
There are rules around app composition, starting with app types. Loaders and writers are special cases. If included, a loader must always be first, e.g.
app = a_loader + a_generic
If included, a writer must always be last, e.g.
app = a_generic + a_writer
Changing the order for either of the above will result in a TypeError.
The next constraint on app composition are the input and output types of the apps involved. Specifically, apps define the type of input they work on and the type of output they produce. For two apps to be composed, the output (or return) type of app on the left (e.g. a_loader) must overlap with the input type of the app on the right (e.g. a_generic). If they don’t match, a TypeError is raised.
An example#
I illustrate the general approach for a simple example – extracting third codon positions. As I’m defining a writer, I also need to define the destination (a directory in this case) where it will write to.
from cogent3 import get_app, open_data_store
out_dstore = open_data_store(path_to_dir, suffix="fa", mode="w")
loader = get_app("load_aligned", format="fasta", moltype="dna")
cpos3 = get_app("take_codon_positions", 3)
writer = get_app("write_seqs", out_dstore, format="fasta")
There are two ways in which I can apply the three above apps to data:
1. Using apps sequentially like functions#
data = loader("data/primate_brca1.fasta")
just3rd = cpos3(data)
m = writer(just3rd)
The resulting alignment just3rd will be written into the out_dstore directory in fasta format with the same filename as the original data ("primate_brca1.fasta").
Note
m is a DataMember of out_dstore.
2. Composing several apps into a multi-step “process”#
We can make this simpler by creating a single composed function.
process = loader + cpos3 + writer
m = process("data/primate_brca1.fasta")
Applying a process to multiple data records#
To apply a composed function to multiple files requires a data store. Using open_data_store() we identify all data files in a directory that we want to analyse, in the following case, all fasta file in the data directory. process can be then applied to all records in the data store without having to loop.
dstore = open_data_store("data", suffix="fasta", mode="r")
result = process.apply_to(dstore)
Note
result is out_dstore.
Other important features#
The settings and data analysed will be logged#
A log file will be written into the same data store as the output. The log includes information on the conditions under which the analysis was run and fingerprint all input and output files.
out_dstore.summary_logs
| time | name | python version | who | command | composable |
|---|---|---|---|---|---|
| 2024-12-19 06:34:25 | logs/load_aligned-take_codon_positions-write_seqs-e58dbc40.log | 3.12.8 | runner | /home/runner/work/cogent3.github.io/cogent3.github.io/.venv/lib/python3.12/site-packages/ipykernel_launcher.py -f /tmp/tmptmyzg0kr.json --HistoryManager.hist_file=:memory: | load_aligned(moltype='dna', format='fasta') +take_codon_positions(positions=(3,), fourfold_degenerate=False, gc='Standard',moltype='dna') +write_seqs(data_store=DataStoreDirectory(source=/home/runner/work/cogent3.github.io/cogent3.github.io/c3org/doc/doc/tmpto34iuuv,mode=Mode.w, suffix=fa, limit=None, verbose=False), id_from_source= |
1 rows x 6 columns
Failures are recorded#
Any “failures” (see The NotCompleted object) are saved. The data store class provides methods for interrogating those. First, a general summary of the output data store indicates we have 6 records that did not complete.
out_dstore.describe
| record type | number |
|---|---|
| completed | 8 |
| not_completed | 6 |
| logs | 1 |
3 rows x 2 columns
These occur for this example primarily because some of the files contain sequences that are not aligned
out_dstore.summary_not_completed
| type | origin | message | num | source |
|---|---|---|---|---|
| ERROR | load_aligned | "ValueError: Input se... 'E', 'E', 'L', 'L']" | 6 | refseqs.fasta, inseqs_protein.fasta, ... |
1 rows x 5 columns
You can track progress#
result = process.apply_to(dstore, show_progress=True)
You can do parallel computation#
result = process.apply_to(dstore, parallel=True)
By default, this will use all available processors on your machine. (See Parallel computations for more details plus how to take advantage of multiple machines using MPI.)
All of the above#
process.apply_to(dstore, parallel=True, show_progress=True)