tprdb-utilities 0.4.0

A toolkit for accessing and working with data from the CRITT Translation Process Research Database (TPR-DB).

tprdb-utilities

A Python toolkit for downloading and reading data tables from the CRITT Translation Process Research Database (TPR-DB).

Three functions cover the full workflow:

Function	What it does
fetch_TPRDB_tables	Downloads study tables from the CRITT API and saves them to a local directory structure
read_TPRDB_tables	Reads those tables from a local clone into a single pandas.DataFrame
prep_parallel_texts	Builds segment-aligned bitext and tritext DataFrames ready for MT evaluation

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Installation

# pip
pip install tprdb-utilities

# uv
uv add tprdb-utilities

# poetry
poetry add tprdb-utilities

---

Quick Start

1 — Download data (fetcher)

Public study (no credentials needed):

from tprdb_utilities import fetch_TPRDB_tables

fetch_TPRDB_tables(
    path="/path/to/local/data",
    studies=["DG21"],
    extensions=["ss", "st"],
    public=True,
)

Private study (requires your TPR-DB username and API token):

from tprdb_utilities import fetch_TPRDB_tables

fetch_TPRDB_tables(
    path="/path/to/local/data",
    studies=["MYSTUDY"],
    extensions=["st"],
    public=False,
    username="myTPRDBusername",   # case-sensitive, must match your account
    token="my-api-token",
)

After downloading, the function always prints a summary like this:

DG21 [ss]: Done fetching (^_^)
DG21 [st]: Done fetching (^_^)

=== fetch_TPRDB_tables Summary ===
StudyID  : DG21
Clone dir: /path/to/local/data/tprdb-mothership-clone
User dir : PUBLIC

Extension  Status            Time
---------  ----------------  ------
ss         Downloaded        1.23s
st         Downloaded        0.98s

To read these files with read_TPRDB_tables:
  path      = "/path/to/local/data/tprdb-mothership-clone"
  user      = "PUBLIC"
  studies   = ["DG21"]

Copy those argument values directly into read_TPRDB_tables.

Subsequent calls are bandwidth-efficient. When files for an extension are already present, fetch_TPRDB_tables sends the X-Client-Tables-Timestamp header (sourced from the studySummary.xml bundled with the study). The server returns 304 Not Modified when nothing has changed, so no data is transferred. The summary will reflect the outcome:

Extension  Status            Time
---------  ----------------  ------
ss         Up to date (304)  0.21s
st         Updated           1.05s

---

2 — Read data (reader)

Use the path and user values printed by fetch_TPRDB_tables at the end of its summary output.

Public study (user="PUBLIC"):

from tprdb_utilities import read_TPRDB_tables

df = read_TPRDB_tables(
    studies=["DG21", "AR22"],
    extension="st",
    path="/path/to/local/data/tprdb-mothership-clone",
    user="PUBLIC",
)

Private study (user="<your TPR-DB username>"):

from tprdb_utilities import read_TPRDB_tables

df = read_TPRDB_tables(
    studies=["MYSTUDY"],
    extension="st",
    path="/path/to/local/data/tprdb-mothership-clone",
    user="USER_DIRECTORY_NAME",
)

---

3 — Transform data (transformer)

Once you have the SG, ST, and TT tables loaded, prep_parallel_texts aligns translation segments across participants and builds parallel-text DataFrames suitable for automatic MT evaluation with tools like BLEU or COMET.

from tprdb_utilities import read_TPRDB_tables, prep_parallel_texts

path = "/path/to/local/data/tprdb-mothership-clone"

sg = read_TPRDB_tables(["RUC17"], "sg", path)
st = read_TPRDB_tables(["RUC17"], "st", path)
tt = read_TPRDB_tables(["RUC17"], "tt", path)

parallel_texts = prep_parallel_texts(sg, st, tt)

The return value is a dictionary. Keys follow two patterns:

Key pattern	Contains
"ST_{part}"	Bitext — source text + one participant's translations
"ST_{p1}_{p2}"	Tritext — source text + two participants' translations

# Bitext: source text aligned with P01's translations
parallel_texts["ST_P01"]
# Study  Task  Text  STseg  String_ST                   String_P01
# RUC17  P     4     1      Developing countries are …  发展中国家不愿 …
# …

# Tritext: source text aligned with P01's and P02's translations
parallel_texts["ST_P01_P02"]
# Study  Task  Text  STseg  String_ST                   String_P01       String_P02
# RUC17  P     4     1      Developing countries are …  发展中国家不愿 …  虽然我们可以 …
# …

# Extract just the text columns for evaluation
bitext = parallel_texts["ST_P01"][["String_ST", "String_P01"]]
tritext = parallel_texts["ST_P01_P02"][["String_ST", "String_P01", "String_P02"]]

By default both bitexts and tritexts are produced. To generate only one kind:

# Bitexts only
parallel_texts = prep_parallel_texts(sg, st, tt, prep_tritexts=False)

# Tritexts only
parallel_texts = prep_parallel_texts(sg, st, tt, prep_bitexts=False)

Tritext DataFrames contain only source segments that both participants translated (inner join on study, task, text, and segment number). Merged segments that could not be split are included in bitexts (with the component source strings concatenated) but excluded from tritexts.

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Directory Structure

fetch_TPRDB_tables creates the following layout under path:

<path>/
└── tprdb-mothership-clone/
    ├── PUBLIC/                  ← public studies
    │   └── <StudyID>/
    │       ├── studySummary.xml
    │       └── Tables/
    │           ├── session1.st
    │           └── ...
    └── <username>/             ← private studies
        └── <StudyID>/
            ├── studySummary.xml
            └── Tables/
                ├── session1.st
                └── ...

Each zip response bundles a studySummary.xml file alongside the table files. fetch_TPRDB_tables places it in the <StudyID>/ directory (one level above Tables/) and uses it on subsequent calls to detect whether the server data has changed.

read_TPRDB_tables expects this exact layout, so the two functions are designed to work together seamlessly.

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Supported Table Extensions

ag, au, ex, fd, fu, hc, hs, kd, ku, pu, sg, ss, st, tt

License

MIT — see LICENSE.