pubget

This document describes pubget version 0.0.8

pubget is a command-line tool for collecting data for biomedical text-mining, and in particular large-scale coordinate-based neuroimaging meta-analysis. It exposes some of the machinery that was used to create the neuroquery dataset, which powers neuroquery.org.

pubget downloads full-text articles from PubMed Central and extracts their text, metadata and stereotactic coordinates. It can also compute TFIDF features for the extracted text, fit NeuroQuery or NeuroSynth, and format its output for use with NiMARE or labelbuddy. It can be extended with plugins.

Besides the command-line interface, pubget's functionality is also exposed through its Python API.

Installation

You can install pubget by running:

pip install pubget

This will install the pubget Python package, as well as the pubget command.

Quick Start

Once pubget is installed, we can download and process biomedical publications so that we can later use them for text-mining or meta-analysis.

pubget run ./pubget_data -q "fMRI[title]"

See pubget run --help for a description of this command. For example, the --n_jobs option allows running some of the steps in parallel.

Usage

The creation of a dataset happens in 3 steps:

• Downloading the articles in bulk from the PMC API.
• Extracting the articles from the bulk download
• Extracting text, stereotactic coordinates and metadata from the articles, and storing this information in CSV files.

Afterwards, some optional steps can also be run, such as:

• Vectorizing the text: transforming it into vectors of TFIDF features.
• Running the same analyses as NeuroSynth or NeuroQuery.
• Preparing the data for use with labelbuddy or NiMARE.

Each of these steps stores its output in a separate directory. Normally, you will run the whole procedure in one command by invoking pubget run. However, separate commands are also provided to run each step separately. Below, we describe each step and its output. Use pubget -h to see a list of all available commands and pubget run -h to see all the options of the main command.

All articles downloaded by pubget come from PubMed Central, and are therefore identified by their PubMed Central ID (pmcid). Note this is not the same as the PubMed ID (pmid). Not all articles in PMC have a pmid.

pubget only downloads articles from the Open Access subset of PMC. The open-access papers are the papers whose license allows downloading their text for text-mining or other reuse (Creative Commons or similar licenses). To restrict search results to the open-access subset on the PMC website (and see the papers that would be downloaded by pubget), select "Open access" in the "article attributes" list.

Step 1: Downloading articles from PMC

This step is executed by the pubget download command. Articles to download can be selected in 2 different ways: by using a query to search the PMC database, or by providing an explicit list of article PMCIDs. To use a list of PMCIDs, we must pass the path to a file containing the IDs as the --pmcids_file parameter. It must contain one ID per line, for example:

8217889
7518235
7500239
7287136
7395771
7154153

Note these must be PubMedCentral IDs, not PubMed IDs. Moreover, Some articles can be viewed on the PubMedCentral website, but are not in the Open Access subset. The publisher of these articles forbids downloading their full text in XML form. pubget filters the list of PMCIDs and only downloads those that are in the Open Access subset. When we use a query instead of a PMCID list, only articles in the Open Access subset are considered.

If we use a query instead, we do not use the --pmcids_file option, but either --query or --query_file. Everything else works in the same way, and the rest of this documentation relies on an example that uses a query.

We must first define our query, with which Pubmed Central will be searched for articles. It can be simple such as fMRI, or more specific such as fMRI[Abstract] AND (2000[PubDate] : 2022[PubDate]). You can build the query using the PMC advanced search interface. For more information see the E-Utilities help. Some examples are provided in the pubget git repository, in docs/example_queries.

The query can be passed either as a string on the command-line with -q or --query or by passing the path of a text file containing the query with -f or --query_file.

If we have an NCBI API key (see details in the E-utilities documentation), we can provide it through the NCBI_API_KEY environment variable or through the --api_key command line argument (the latter has higher precedence).

We must also specify the directory in which all pubget data will be stored. It can be provided either as a command-line argument (as in the examples below), or by exporting the PUBGET_DATA_DIR environment variable. Subdirectories will be created for each different query. In the following we suppose we are storing our data in a directory called pubget_data.

We can thus download all articles with "fMRI" in their title published in 2019 by running:

pubget download -q "fMRI[Title] AND (2019[PubDate] : 2019[PubDate])" pubget_data

Note: writing the query in a file rather than passing it as an argument is more convenient for complex queries, for example those that contain whitespace, newlines or quotes. By storing it in a file we do not need to take care to quote or escape characters that would be interpreted by the shell. In this case we would store our query in a file, say query.txt:

fMRI[Title] AND (2019[PubDate] : 2019[PubDate])

and run

pubget download -f query.txt pubget_data

After running this command, these are the contents of our data directory:

· pubget_data
  └── query_3c0556e22a59e7d200f00ac8219dfd6c
      ├── articlesets
      │   ├── articleset_00000.xml
      │   └── info.json
      └── query.txt

pubget has created a directory for this query, query_3c0556e22a59e7d200f00ac8219dfd6c — in the following we will call it "the query directory". Its name contains the md5 checksum of the query (or PMCID list), which is useful for pubget to reuse the same directory if we run the same query again, but not very helpful for us humans. Therefore, we can use the --alias command-line argument to give this query an alternative name, and pubget will create a symbolic link for us. For example if we run the query above with the added option --alias "fMRI-2019", our pubget_data directory will look like this:

· pubget_data
  ├── fMRI-2019 -> query_3c0556e22a59e7d200f00ac8219dd6c
  └── query_3c0556e22a59e7d200f00ac8219dd6c

If we had used a PMCID list instead of a query, the directory name would start with pmcidList_ instead of query_.

If we used a query it will be stored in query.txt, and if we used a list of PMCIDs, in requested_pmcids.txt, in the query directory.

Inside the query directory, the results of the bulk download are stored in the articlesets subdirectory. The articles themselves are in XML files bundling up to 500 articles called articleset_*.xml. Here there is only one because the search returned less than 500 articles.

Some information about the download is stored in info.json. In particular, is_complete indicates if all articles matching the search have been downloaded. If the download was interrupted, some batches failed to download, or the number of results was limited by using the --n_docs parameter, is_complete will be false and the exit status of the program will be 1. You may want to re-run the command before moving on to the next step if the download is incomplete.

If we run the same query again, only missing batches will be downloaded. If we want to force re-running the search and downloading the whole data we need to remove the articlesets directory.

Step 2: extracting articles from bulk download

This step is executed by the pubget extract_articles command.

Once our download is complete, we extract articles and store each of them in a separate directory. To do so, we pass the articlesets directory created by the pubget download command in step 1:

pubget extract_articles pubget_data/query_3c0556e22a59e7d200f00ac8219dfd6c/articlesets

This creates an articles subdirectory in the query directory, containing the articles. To avoid having a large number of files in a single directory when there are many articles, which can be problematic on some filesystems, the articles are spread over many subdirectories. The names of these subdirectories range from 000 to fff and an article goes in the subdirectory that matches the first 3 hexidecimal digits of the md5 hash of its pmcid.

Our data directory now looks like this (with many articles omitted for conciseness):

· pubget_data
  └── query_3c0556e22a59e7d200f00ac8219dfd6c
      ├── articles
      │   ├── 019
      │   │   └── pmcid_6759467
      │   │       ├── article.xml
      │   │       └── tables
      │   │           └── tables.xml
      │   ├── 01f
      │   │   └── pmcid_6781806
      │   │       ├── article.xml
      │   │       └── tables
      │   │           ├── table_000.csv
      │   │           ├── table_000_info.json
      │   │           ├── table_001.csv
      │   │           ├── table_001_info.json
      │   │           └── tables.xml
      │   ├── ...
      │   └── info.json
      └── articlesets

Note that the subdirectories such as articles/01f can contain one or more articles, even though the examples that appear here only contain one.

Each article directory, such as articles/01f/pmcid_6781806, contains:

• article.xml: the XML file containing the full article in its original format.
• a tables subdirectory, containing:
  • tables.xml: all the article's tables, each provided in 2 formats: its original version, and converted to XHTML using the DocBook stylesheets.
  • For each table, a CSV file containing the extracted data and a JSON file providing information such as the table label, id, caption, and n_header_rows, the number of rows at the start of the CSV that should be treated as part of the table header.

If the download and article extraction were successfully run and we run the same query again, the article extraction is skipped. If we want to force re-running the article extraction we need to remove the articles directory (or the info.json file it contains).

Step 3: extracting data from articles

This step is executed by the pubget extract_data command.

It creates another directory that contains CSV files, containing the text, metadata and coordinates extracted from all the articles.

If we use the --articles_with_coords_only option, only articles in which pubget finds stereotactic coordinates are kept. The name of the resulting directory will reflect that choice.

We pass the path of the articles directory created by pubget extract_articles in the previous step to the pubget extract_data command:

pubget extract_data --articles_with_coords_only pubget_data/query_3c0556e22a59e7d200f00ac8219dfd6c/articles/

Our data directory now contains (ommitting the contents of the previous steps):

· pubget_data
  └── query_3c0556e22a59e7d200f00ac8219dfd6c
      ├── articles
      ├── articlesets
      └── subset_articlesWithCoords_extractedData
          ├── authors.csv
          ├── coordinates.csv
          ├── coordinate_space.csv
          ├── info.json
          ├── links.csv
          ├── neurovault_collections.csv
          ├── neurovault_images.csv
          ├── metadata.csv
          └── text.csv

If we had not used --articles_with_coords_only, the new subdirectory would be named subset_allArticles_extractedData instead.

• metadata.csv contains one row per article, with some metadata: pmcid (PubMed Central ID), pmid (PubMed ID), doi, title, journal, publication_year and license. Note some values may be missing (for example not all articles have a pmid or doi).
• authors.csv contains one row per article per author. Fields are pmcid, surname, given-names.
• text.csv contains one row per article. The first field is the pmcid, and the other fields are title, keywords, abstract, and body, and contain the text extracted from these parts of the article.
• links.csv contains the external links found in the articles. The fields are pmcid, ext-link-type (the type of link, for example "uri", "doi"), and href (usually an URL).
• neurovault_collections.csv and neurovault_images.csv: NeuroVault collection and image IDs that could be extracted from links in the articles, if any.
• coordinates.csv contains one row for each (x, y, z) stereotactic coordinate found in any article. Its fields are the pmcid of the article, the table label and id the coordinates came from, and x, y, z.
• coordinate_space.csv has fields pmcid and coordinate_space. It contains a guess about the stereotactic space coordinates are reported in, based on a heuristic derived from neurosynth. Possible values for the space are the terms used by neurosynth: "MNI", "TAL" (for Talairach space), and "UNKNOWN".

The different files can be joined on the pmcid field.

If all steps up to data extraction were successfully run and we run the same query again, the data extraction is skipped. If we want to force re-running the data extraction we need to remove the corresponding directory (or the info.json file it contains).

Optional step: extracting a new vocabulary

This step is executed by the pubget extract_vocabulary command. When running the full pipeline this step is optional: we must use the --extract_vocabulary option for it to be executed.

It builds a vocabulary of all the words and 2-grams (groups of 2 words) that appear in the downloaded text, and computes their document frequency (the proportion of documents in which a term appears).

pubget extract_vocabulary pubget_data/query_3c0556e22a59e7d200f00ac8219dfd6c/subset_articlesWithCoords_extractedData

The vocabulary is stored in a csv file in a new directory. There is no header and the 2 columns are the term and its document frequency.

· pubget_data
  └── query_3c0556e22a59e7d200f00ac8219dfd6c
      ├── articles
      ├── articlesets
      ├── subset_articlesWithCoords_extractedData
      └── subset_articlesWithCoords_extractedVocabulary
          ├── info.json
          └── vocabulary.csv

When running the whole pipeline (pubget run), if we use the --extract_vocabulary option and do not provide an explicit value for --vocabulary_file, the freshly-extracted vocabulary is used instead of the default neuroquery one for computing TFIDF features (see next step).

Optional step: vectorizing (computing TFIDF features)

This step is executed by the pubget vectorize command. When running the full pipeline this step is optional: we must use the --vectorize_text option for it to be executed. However, if any of the subsequent steps that rely on TFIDF features (NeuroQuery, NeuroSynth or NiMARE steps, see below) are requested, this step is always run and --vectorize_text is ignored. This step is also run whenever we use the --vocabulary_file option.

Some large-scale meta-analysis methods such as NeuroSynth and NeuroQuery rely on TFIDF features to represent articles' text. The last step before we can apply these methods is therefore to extract TFIDF features from the text we obtained in the previous step.

TFIDF features rely on a predefined vocabulary (set of terms or phrases). Each dimension of the feature vector corresponds to a term in the vocabulary and represents the importance of that term in the encoded text. This importance is an increasing function of the term frequency (the number of time the term occurs in the text divided by the length of the text) and a decreasing function of the document frequency (the total number of times the term occurs in the whole corpus or dataset).

To extract the TFIDF features we must therefore choose a vocabulary.

• By default, pubget will download and use the vocabulary used by neuroquery.org.
• If we use the --extract_vocabulary option, a new vocabulary is created from the downloaded text and used for computing TFIDF features (see "extracting a new vocabulary" below).
• If we want to use a different vocabulary we can specify it with the --vocabulary_file option. This file will be parsed as a CSV file with no header, whose first column contains the terms. Other columns are ignored.

We also pass to pubget vectorize the directory containing the text we want to vectorize, created by pubget extract_data in step 3 (here we are using the default vocabulary):

pubget vectorize pubget_data/query_3c0556e22a59e7d200f00ac8219dfd6c/subset_articlesWithCoords_extractedData/

This creates a new directory whose name reflects the data source (whether all articles are kept or only those with coordinates) and the chosen vocabulary (e6f7a7e9c6ebc4fb81118ccabfee8bd7 is the md5 checksum of the contents of the vocabulary file, concatenated with those of the vocabulary mapping file, see "vocabulary mapping" below):

· pubget_data
  └── query_3c0556e22a59e7d200f00ac8219dfd6c
      ├── articles
      ├── articlesets
      ├── subset_articlesWithCoords_extractedData
      └── subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_vectorizedText
          ├── abstract_counts.npz
          ├── abstract_tfidf.npz
          ├── body_counts.npz
          ├── body_tfidf.npz
          ├── feature_names.csv
          ├── info.json
          ├── keywords_counts.npz
          ├── keywords_tfidf.npz
          ├── merged_tfidf.npz
          ├── pmcid.txt
          ├── title_counts.npz
          ├── title_tfidf.npz
          ├── vocabulary.csv
          └── vocabulary.csv_voc_mapping_identity.json

The extracted features are stored in .npz files that can be read for example with scipy.sparse.load_npz.

These files contain matrices of shape (n_docs, n_features), where n_docs is the number of documents and n_features the number of terms in the vocabulary. The pmcid corresponding to each row is found in pmcid.txt, and the term corresponding to each column is found in the first column of feature_names.csv.

feature_names.csv has no header; the first column contains terms and the second one contains their document frequency.

For each article part ("title", "keywords", "abstract" and "body"), we get the counts which hold the raw counts (the number of times each word occurs in that section), and the tfidf which hold the TFIDF features (the counts divided by article length and log document frequency). Moreover, merged_tfidf contains the mean TFIDF computed across all article parts.

If all steps up to vectorization were successfully run and we run the same query again, the vectorization is skipped. If we want to force re-running the vectorization we need to remove the corresponding directory (or the info.json file it contains).

Vocabulary mapping: collapsing synonyms

It is possible to instruct the tokenizer (that extracts words from text) to collapse some pairs of terms that have the same meaning but different spellings, such as "brainstem" and "brain stem".

This is done through a JSON file that contains a mapping of the form {term: replacement}. For example if it contains {"brain stem": "brainstem"}, "brain stem" will be discarded from the vocabulary and every occurrence of "brain stem" will be counted as an occurrence of "brainstem" instead. To be found by pubget, this vocabulary mapping file must be in the same directory as the vocabulary file, and its name must be the vocabulary file's name with _voc_mapping_identity.json appended: for example vocabulary.csv, vocabulary.csv_voc_mapping_identity.json.

When a vocabulary mapping is provided, a shorter vocabulary is therefore created by removing redundant words. The TFIDF and word counts computed by pubget correspond to the shorter vocabulary, which is stored along with its document frequencies in feature_names.csv.

vocabulary.csv contains the document frequencies of the original (full, longer) vocabulary. A vocabulary.csv_voc_mapping_identity.json file is always created by pubget, but if no vocabulary mapping was used, that file contains an empty mapping ({}) and vocabulary.csv and feature_names.csv are identical.

The vocabulary mapping is primarily used by the neuroquery package and its tokenization pipeline, and you can safely ignore this – just remember that the file providing the terms corresponding to the TFIDF features is feature_names.csv.

Optional step: fitting a NeuroQuery encoding model

This step is executed by the pubget fit_neuroquery command. When running the full pipeline it is optional: we must use the --fit_neuroquery option for it to be executed.

In this step, once the TFIDF features and the coordinates have been extracted from downloaded articles, they are used to train a NeuroQuery encoding model — the same type of model that is exposed at neuroquery.org. Details about this model are provided in the NeuroQuery paper and the documentation for the neuroquery package.

Note: for this model to give good results a large dataset is needed, ideally close to 10,000 articles (with coordinates).

We pass the _vectorizedText directory created by pubget vectorize:

pubget fit_neuroquery pubget_data/query_3c0556e22a59e7d200f00ac8219dfd6c/subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_vectorizedText

This creates a directory whose name ends with _neuroqueryModel:

· pubget_data
  └── query_3c0556e22a59e7d200f00ac8219dfd6c
      ├── articles
      ├── articlesets
      ├── subset_articlesWithCoords_extractedData
      ├── subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_neuroqueryModel
      │   ├── app.py
      │   ├── info.json
      │   ├── neuroquery_model
      │   │   ├── corpus_metadata.csv
      │   │   ├── corpus_tfidf.npz
      │   │   ├── mask_img.nii.gz
      │   │   ├── regression
      │   │   │   ├── coef.npy
      │   │   │   ├── intercept.npy
      │   │   │   ├── M.npy
      │   │   │   ├── original_n_features.npy
      │   │   │   ├── residual_var.npy
      │   │   │   └── selected_features.npy
      │   │   ├── smoothing
      │   │   │   ├── smoothing_weight.npy
      │   │   │   └── V.npy
      │   │   ├── vocabulary.csv
      │   │   └── vocabulary.csv_voc_mapping_identity.json
      │   ├── README.md
      │   └── requirements.txt
      └── subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_vectorizedText

You do not need to care about the contents of the neuroquery_model subdirectory, that is data used by the neuroquery package. Just know that it can be used to initialize a neuroquery.NeuroQueryModel with:

from neuroquery import NeuroQueryModel
model = NeuroQueryModel.from_data_dir("neuroquery_model")

The neuroquery documentation provides information and examples on how to use this model.

Visualizing the newly trained model in an interactive web page

It is easy to interact with the model through a small web (Flask) application. From inside the [...]_neuroqueryModel directory, just run pip install -r requirements.txt to install flask, nilearn and neuroquery. Then run flask run and point your web browser to https://localhost:5000: you can play with a local, simplified version of neuroquery.org built with the data we just downloaded.

Optional step: running a NeuroSynth meta-analysis

This step is executed by the pubget fit_neurosynth command. When running the full pipeline it is optional: we must use the --fit_neurosynth option for it to be executed.

In this step, once the TFIDF features and the coordinates have been extracted from downloaded articles, they are used to run meta-analyses using NeuroSynth's "association test" method: a Chi-squared test of independence between voxel activation and term occurrences. See the NeuroSynth paper and neurosynth.org, as well as the neurosynth and NiMARE documentation pages for more information.

We pass the _vectorizedText directory created by pubget vectorize:

pubget fit_neurosynth pubget_data/query_3c0556e22a59e7d200f00ac8219dfd6c/subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_vectorizedText

This creates a directory whose name ends with _neurosynthResults:

· pubget_data
  └── query_3c0556e22a59e7d200f00ac8219dfd6c
      ├── articles
      ├── articlesets
      ├── subset_articlesWithCoords_extractedData
      ├── subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_neurosynthResults
      │   ├── app.py
      │   ├── info.json
      │   ├── metadata.csv
      │   ├── neurosynth_maps
      │   │   ├── aberrant.nii.gz
      │   │   ├── abilities.nii.gz
      │   │   ├── ability.nii.gz
      │   │   └── ...
      │   ├── README.md
      │   ├── requirements.txt
      │   ├── terms.csv
      │   └── tfidf.npz
      └── subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_vectorizedText

The meta-analytic maps for all the terms in the vocabulary can be found in the neurosynth_maps subdirectory.

Visualizing the meta-analytic maps in an interactive web page

It is easy to interact with the NeuroSynth maps through a small web (Flask) application. From inside the [...]_neurosynthResults directory, just run pip install -r requirements.txt to install flask and other dependencies. Then run flask run and point your web browser to https://localhost:5000: you can search for a term and see the corresponding brain map and the documents that mention it.

Optional step: preparing articles for annotation with labelbuddy

This step is executed by the pubget extract_labelbuddy_data command. When running the full pipeline this step is optional: we must use the --labelbuddy or --labelbuddy_batch_size option for it to be executed.

It prepares the articles whose data was extracted for annotation with labelbuddy.

We pass the _extractedData directory created by pubget extract_data:

pubget extract_labelbuddy_data pubget_data/query_3c0556e22a59e7d200f00ac8219dfd6c/subset_articlesWithCoords_extractedData

This creates a directory whose name ends with labelbuddyData containing the batches of documents in JSONL format (in this case there is a single batch):

· pubget_data
  └── query_3c0556e22a59e7d200f00ac8219dfd6c
      ├── articles
      ├── articlesets
      ├── subset_articlesWithCoords_extractedData
      ├── subset_articlesWithCoords_labelbuddyData
      │   ├── batch_info.csv
      │   ├── documents_00001.jsonl
      │   └── info.json
      └── subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_vectorizedText

The documents can be imported into labelbuddy using the GUI or with:

labelbuddy mydb.labelbuddy --import-docs documents_00001.jsonl

See the labelbuddy documentation for details.

The CSV file batch_info.csv provides the location of each article in the .jsonl files: its columns are pmcid, file_name (the name of the .jsonl file containing that article) and line (the line number that contains that article, first line is 0).

Optional step: creating a NiMARE dataset

This step is executed by the pubget extract_nimare_data command. When running the full pipeline this step is optional: we must use the --nimare option for it to be executed.

It creates a NiMARE dataset for the extracted data in JSON format. See the NiMARE documentation for details.

We pass the _vectorizedText directory created by pubget vectorize:

pubget extract_nimare_data pubget_data/query_3c0556e22a59e7d200f00ac8219dfd6c/subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_vectorizedText

The resulting directory contains a nimare_dataset.json file that can be used to initialize a nimare.Dataset.

· pubget_data
  └── query_3c0556e22a59e7d200f00ac8219dfd6c
      ├── articles
      ├── articlesets
      ├── subset_articlesWithCoords_extractedData
      ├── subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_nimareDataset
      │   ├── info.json
      │   └── nimare_dataset.json
      └── subset_articlesWithCoords-voc_e6f7a7e9c6ebc4fb81118ccabfee8bd7_vectorizedText

Using this option requires installing NiMARE, which is not installed by default with pubget. To use this option, install NiMARE separately with

pip install nimare

or install pubget with

pip install "pubget[nimare]"

Full pipeline

We can run all steps in one command by using pubget run.

The full procedure described above could be run by executing:

pubget run -q "fMRI[Title] AND (2019[PubDate] : 2019[PubDate])" \
    --articles_with_coords_only                               \
    pubget_data

(The output directory, pubget_data, could also be provided by exporting the PUBGET_DATA_DIR environment variable instead of passing it on the command line.)

If we also want to apply the optional steps:

pubget run -q "fMRI[Title] AND (2019[PubDate] : 2019[PubDate])" \
    --articles_with_coords_only                               \
    --fit_neuroquery                                          \
    --labelbuddy                                              \
    --nimare                                                  \
    pubget_data

(remember that --nimare requires NiMARE to be installed).

Here also, steps that had already been completed are skipped; we need to remove the corresponding directories if we want to force running these steps again.

See pubget run --help for a description of all options.

Logging

By default pubget commands report their progress by writing to the standard streams. In addition, they can write log files if we provide the --log_dir command-line argument, or if we define the PUBGET_LOG_DIR environment variable (the command-line argument has higher precedence). If this log directory is specified, a new log file with a timestamp is created and all the output is written there as well.

Writing plugins

It is possible to write plugins and define entry points to add functionality that is automatically executed when pubget is run.

The name of the entry point should be pubget.plugin_actions. It must be a function taking no arguments and returning a dictionary with keys pipeline_steps and commands. The corresponding values must be lists of processing step objects, that must implement the interface defined by pubget.PipelineStep and pubget.Command respectively (their types do not need to inherit from these classes).

All steps in pipeline_steps will be run when pubget run is used. All steps in standalone_steps will be added as additional pubget commands; for example if the name of a standalone step is my_plugin, the pubget my_plugin command will become available.

An example plugin that can be used as a template, and more details, are provided in the pubget git repository, in docs/example_plugin.

Contributing

Feedback and contributions are welcome. Development happens at the pubget GitHub repositiory. To install the dependencies required for development, from the directory where you cloned pubget, run:

pip install -e ".[dev]"

The tests can be run with make test_all, or make test_coverage to report test coverage. The documentation can be rendered with make doc. make run_full_pipeline runs the full pubget pipeline on a query returning a realistic number of results (fMRI[title]).

Python API

pubget is mostly intended for use as a command-line tool. However, it is also a Python package and its functionality can be used in Python programs. The Python API closely reflects the command-line programs described above.

The Python API is described on the pubget website.