Using ppx¶
Configuration¶
By default, ppx will download project files in the .ppx
directory under
the current user’s home directory (~/.ppx
on Linux and MacOS). There
are several ways to specify different data directories:
Change the ppx data directory for all future Python sessions by setting the
PPX_DATA_DIR
environment variable to your preferred directory.Change the ppx data directory for a Python session using the
ppx.set_data_dir()
function.Specify a data directory for a project using the
local
argument:>>> import ppx >>> proj = ppx.find_project("PXD000001", local="my/data/dir")
Why does ppx set a default data directory? We found that this makes it easier to reuse the same proteomics data files in multiple tasks that we’re working on.
As of ppx v1.3.0, cloud paths can also be used as the data directory. This
allows you to stream downloaded files to AWS S3, Google Cloud Storage, or Azure
Blob Storage. To use a cloud storage provider, simply set the data directory to
a cloud URI, such as s3://my-data-bucket/ppx
using any of the methods
above. Please note that you’ll also need to setup credentials for your cloud
provider—see the CloudPathLib documentation for details.
Examples¶
To begin, we first import the ppx package:
>>> import ppx
We can now find a project using its ProteomeXchange or MassIVE identifier. Note that ppx currently only supports projects hosted on PRIDE and MassIVE. For this example, we’ll use a project from PRIDE:
>>> proj = ppx.find_project("PXD000001")
Here, proj
is a is PrideProject
object with
methods that let us explore the available files and download files that we
select. Let’s retrieve a list of all of the files associated with this project
on PRIDE:
>>> remote_files = proj.remote_files()
>>> print(remote_files)
['F063721.dat', 'F063721.dat-mztab.txt', 'PRIDE_Exp_Complete_Ac_22134.xml.gz', 'PRIDE_Exp_mzData_Ac_22134.xml.gz', 'PXD000001_mztab.txt', 'README.txt', 'TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML', 'TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzXML', 'TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML', 'TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw', 'erwinia_carotovora.fasta', 'generated/PRIDE_Exp_Complete_Ac_22134.pride.mgf.gz', 'generated/PRIDE_Exp_Complete_Ac_22134.pride.mztab.gz']
Alternatively, we can glob for specific files of interest:
>>> mzml_files = proj.remote_files("*.mzML")
>>> print(mzml_files)
['TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML']
Once we’ve determined what file we desire to download, we can download them to our local data directory. In this case, that is ~/.ppx/PXD000001:
>>> downloaded = proj.download("F063721.dat-mztab.txt")
>>> print(downloaded)
[PosixPath('/Users/wfondrie/.ppx/PXD000001/F063721.dat-mztab.txt')]
Once we’ve downloaded files, ppx no longer needs an internet connection to retrieve a project’s local data. However, you will need to specify the repository in which the project data resides. If we start a new Python session, we can find our previous files easily:
>>> import ppx
>>> proj = ppx.find_project("PXD000001", repo="PRIDE")
>>> local_files = proj.local_files()
>>> print(local_files)
[PosixPath('/Users/wfondrie/.ppx/PXD000001/F063721.dat-mztab.txt')]
For more details about the available methods for a project, see our Python API
documentation for the PrideProject
and
MassiveProject
classes.
Using Cloud Storage¶
We use CloudPathlib to power support for AWS S3, Google Cloud Storage, and Azure Blob Storage. To use a cloud storage provider, create the bucket for ppx to use and set it as the ppx data directory.
For example using AWS S3, we can save the files of a project to an S3 bucket:
>>> proj = ppx.find_project("PXD000001", local="s3://my-bucket/PXD000001")
>>> proj.download("README.txt")
[S3Path('s3://my-bucket/PXD000001/README.txt')]
CloudPathLib then provides methods to download files from S3 when you need them:
>>> readme_on_s3 = proj.local_files("README.txt")[0]
>>> readme_on_s3.download_to("README.txt")
PosixPath(README.txt)