From d85c665e81857ee73a0c6a3944fa6dd617820e88 Mon Sep 17 00:00:00 2001 From: Drew Yang Date: Thu, 13 Mar 2025 17:52:10 -0500 Subject: [PATCH 1/5] =?UTF-8?q?feat:=20=E2=9C=A8=20add=20pre-commit?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .pre-commit-config.yaml | 42 +++++++++++++++++++++++++++++++++++++++++ .vscode/settings.json | 4 ++-- pyproject.toml | 1 + 3 files changed, 45 insertions(+), 2 deletions(-) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..e8f25d213 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,42 @@ +# pip install datajoint[test] +# pre-commit install +# pre-commit run --all-files +# pre-commit autoupdate +# SKIP=flake8 git commit -m "foo" + +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-yaml + - id: check-json + - id: check-toml + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-added-large-files +- repo: https://github.com/codespell-project/codespell + rev: v2.4.1 + hooks: + - id: codespell +- repo: https://github.com/psf/black + rev: 24.2.0 + hooks: + - id: black + args: + - --required-version='24.2.0' + - -check + - -v=datajoint,tests + - --diff +- repo: https://github.com/PyCQA/flake8 + rev: 7.1.2 + hooks: + - id: flake8 + args: + - --ignore=E203,E722,W503 datajoint + - --count + - --max-complexity=62 + - --max-line-length=127 + - --statistics + - --per-file-ignores='datajoint/diagram.py:C901' diff --git a/.vscode/settings.json b/.vscode/settings.json index 00ebd4b97..d2033f21f 100755 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -17,5 +17,5 @@ "[dockercompose]": { "editor.defaultFormatter": "disable" }, - "files.autoSave": "off" -} \ No newline at end of file + //"files.autoSave": "off" +} diff --git a/pyproject.toml b/pyproject.toml index 1eb8c723d..6040be300 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,6 +44,7 @@ classifiers = [ [project.optional-dependencies] test = [ + "pre-commit", "pytest", "pytest-cov", "black==24.2.0", From 7bbf500e8081071a040bca15960849b82f3e75b4 Mon Sep 17 00:00:00 2001 From: Drew Yang Date: Thu, 13 Mar 2025 18:24:32 -0500 Subject: [PATCH 2/5] =?UTF-8?q?feat:=20=E2=9C=A8=20add=20more=20pre-commit?= =?UTF-8?q?=20hooks?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .pre-commit-config.yaml | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e8f25d213..cabd53ffd 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,7 +11,9 @@ repos: rev: v5.0.0 hooks: - id: check-yaml + exclude: 'docs/mkdocs.yaml' # exclude mkdocs.yaml since pymdownx.emoji !! usage - id: check-json + exclude: '(.vscode|.devcontainer)' # exclude these since // was used for comments - id: check-toml - id: trailing-whitespace - id: end-of-file-fixer @@ -20,23 +22,26 @@ repos: rev: v2.4.1 hooks: - id: codespell +- repo: https://github.com/pycqa/isort + rev: 5.12.0 # Use the latest stable version + hooks: + - id: isort + args: + - --profile=black # Optional, makes isort compatible with Black - repo: https://github.com/psf/black - rev: 24.2.0 + rev: 24.2.0 # matching versions in pyproject.toml and github actions hooks: - id: black - args: - - --required-version='24.2.0' - - -check - - -v=datajoint,tests - - --diff + args: ["--check", "-v", "datajoint", "tests", "--diff"] # --required-version is conflicting with pre-commit - repo: https://github.com/PyCQA/flake8 rev: 7.1.2 hooks: - id: flake8 args: - - --ignore=E203,E722,W503 datajoint + - --ignore=E203,E722,W503 - --count - --max-complexity=62 - --max-line-length=127 - --statistics - - --per-file-ignores='datajoint/diagram.py:C901' + - --per-file-ignores=datajoint/diagram.py:C901 + files: datajoint # a lot of files in tests are not compliant From 015dcf6cb9b12ecea3e760fb9bf4d5c8eac48721 Mon Sep 17 00:00:00 2001 From: Drew Yang Date: Thu, 13 Mar 2025 19:11:28 -0500 Subject: [PATCH 3/5] =?UTF-8?q?chore:=20=F0=9F=93=9D=20add=20suggestion=20?= =?UTF-8?q?for=20future=20pre-commit?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .pre-commit-config.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index cabd53ffd..3ba3bdac1 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -45,3 +45,6 @@ repos: - --statistics - --per-file-ignores=datajoint/diagram.py:C901 files: datajoint # a lot of files in tests are not compliant + +## Suggest to add pytest hook that runs unit test | Prerequisite: split unit/integration test +## https://github.com/datajoint/datajoint-python/issues/1211 From f411ed6a90467f07a9095d809ef7157c39529a74 Mon Sep 17 00:00:00 2001 From: Drew Yang Date: Thu, 13 Mar 2025 19:11:49 -0500 Subject: [PATCH 4/5] =?UTF-8?q?fix:=20=F0=9F=90=9B=20clean=20up=20.gitigno?= =?UTF-8?q?re?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 208 +++++++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 180 insertions(+), 28 deletions(-) diff --git a/.gitignore b/.gitignore index 6e1d664ff..f860dfdb2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,35 +1,187 @@ -.ipynb_checkpoints/ -*.json -*/.*.swp -*/.*.swo -*/*.pyc -.DS_Store -__*__ -.idea/ -*.pyc -.python-version -*.egg-info/ +# https://github.com/github/gitignore/blob/main/Python.gitignore + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg MANIFEST -.vagrant/ -dj_local_conf.json -build/ + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ .coverage -./tests/.coverage -./tests/dj-store/* +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: *.log -*.env -docker-compose.yml -notebook -__main__.py -jupyter_custom.js -.eggs -*.code-workspace -docs/site +local_settings.py +db.sqlite3 +db.sqlite3-journal +# Flask stuff: +instance/ +.webassets-cache -!.vscode/settings.json -!.vscode/launch.json -!.devcontainer/devcontainer.json -!.devcontainer/docker-compose.yml +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + +# VS Code +.vscode/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +###################### + +# Mac OS +.DS_Store + +# Datajoint related files +dj_local_conf.json +*.env +!.vscode/launch.json From 9db9d61405908d577883696bfc725b700959b96e Mon Sep 17 00:00:00 2001 From: Drew Yang Date: Thu, 13 Mar 2025 19:13:36 -0500 Subject: [PATCH 5/5] =?UTF-8?q?fix:=20=F0=9F=90=9B=20pre-commit=20auto=20f?= =?UTF-8?q?ix?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .coveragerc | 2 +- .devcontainer/devcontainer.json | 2 +- .devcontainer/docker-compose.yml | 1 - .dockerignore | 2 +- .github/workflows/docs.yaml | 2 +- .vscode/launch.json | 2 +- README.md | 16 +- datajoint/__init__.py | 29 ++- datajoint/admin.py | 6 +- datajoint/attribute_adapter.py | 1 + datajoint/autopopulate.py | 20 +- datajoint/blob.py | 9 +- datajoint/cli.py | 1 + datajoint/condition.py | 16 +- datajoint/connection.py | 16 +- datajoint/declare.py | 10 +- datajoint/dependencies.py | 4 +- datajoint/diagram.py | 12 +- datajoint/expression.py | 21 +- datajoint/external.py | 16 +- datajoint/fetch.py | 12 +- datajoint/hash.py | 2 +- datajoint/heading.py | 21 +- datajoint/jobs.py | 7 +- datajoint/plugin.py | 8 +- datajoint/s3.py | 8 +- datajoint/schemas.py | 23 +-- datajoint/settings.py | 7 +- datajoint/table.py | 34 ++-- datajoint/user_tables.py | 5 +- datajoint/utils.py | 3 +- docker-compose.yaml | 1 - docs/.docker/Dockerfile | 2 +- docs/.docker/pip_requirements.txt | 2 +- docs/.markdownlint.yaml | 6 +- docs/mkdocs.yaml | 8 +- .../.overrides/assets/stylesheets/extra.css | 2 +- docs/src/.overrides/partials/nav.html | 2 +- docs/src/api/make_pages.py | 5 +- docs/src/client/credentials.md | 10 +- docs/src/client/install.md | 62 +++--- docs/src/client/settings.md | 4 +- docs/src/compute/distributed.md | 78 ++++---- docs/src/compute/key-source.md | 20 +- docs/src/compute/make.md | 18 +- docs/src/compute/populate.md | 48 ++--- docs/src/concepts/data-model.md | 86 ++++---- docs/src/concepts/data-pipelines.md | 104 +++++----- docs/src/concepts/principles.md | 80 ++++---- docs/src/concepts/teamwork.md | 50 ++--- docs/src/concepts/terminology.md | 62 +++--- docs/src/design/diagrams.md | 32 +-- docs/src/design/drop.md | 8 +- docs/src/design/integrity.md | 108 +++++----- docs/src/design/normalization.md | 88 ++++----- docs/src/design/recall.md | 4 +- docs/src/design/schema.md | 16 +- docs/src/design/tables/attach.md | 4 +- docs/src/design/tables/attributes.md | 38 ++-- docs/src/design/tables/declare.md | 90 ++++----- docs/src/design/tables/dependencies.md | 100 +++++----- docs/src/design/tables/filepath.md | 18 +- docs/src/design/tables/lookup.md | 6 +- docs/src/design/tables/manual.md | 2 +- docs/src/design/tables/master-part.md | 38 ++-- docs/src/design/tables/primary.md | 92 ++++----- docs/src/design/tables/tiers.md | 26 +-- docs/src/faq.md | 106 +++++----- docs/src/images/added-example-ERD.svg | 2 +- docs/src/images/dimitri-ERD.svg | 2 +- docs/src/images/shapes_pipeline.svg | 2 +- docs/src/images/spawned-classes-ERD.svg | 2 +- docs/src/images/virtual-module-ERD.svg | 2 +- docs/src/index.md | 14 +- docs/src/internal/transpilation.md | 82 ++++---- docs/src/manipulation/delete.md | 6 +- docs/src/manipulation/index.md | 6 +- docs/src/manipulation/insert.md | 32 +-- docs/src/manipulation/transactions.md | 28 +-- docs/src/manipulation/update.md | 24 +-- docs/src/publish-data.md | 32 +-- docs/src/query/aggregation.md | 14 +- docs/src/query/example-schema.md | 4 +- docs/src/query/fetch.md | 50 ++--- docs/src/query/join.md | 6 +- docs/src/query/principles.md | 42 ++-- docs/src/query/project.md | 14 +- docs/src/query/restrict.md | 60 +++--- docs/src/query/union.md | 14 +- docs/src/query/universals.md | 20 +- docs/src/quick-start.md | 24 +-- docs/src/sysadmin/database-admin.md | 186 +++++++++--------- docs/src/sysadmin/external-store.md | 134 ++++++------- images/pipeline.drawio | 2 +- tests/conftest.py | 31 ++- tests/schema.py | 4 +- tests/schema_adapted.py | 8 +- tests/schema_advanced.py | 3 +- tests/schema_aggr_regress.py | 5 +- tests/schema_alter.py | 3 +- tests/schema_external.py | 6 +- tests/schema_privileges.py | 3 +- tests/schema_simple.py | 12 +- tests/schema_university.py | 3 +- tests/schema_uuid.py | 3 +- tests/test_adapted_attributes.py | 9 +- tests/test_admin.py | 4 +- tests/test_aggr_regressions.py | 9 +- tests/test_alter.py | 7 +- tests/test_attach.py | 6 +- tests/test_autopopulate.py | 6 +- tests/test_blob.py | 13 +- tests/test_blob_matlab.py | 3 +- tests/test_bypass_serialization.py | 5 +- tests/test_cascading_delete.py | 4 +- tests/test_cli.py | 2 + tests/test_connection.py | 5 +- tests/test_declare.py | 7 +- tests/test_dependencies.py | 4 +- tests/test_erd.py | 3 +- tests/test_external.py | 11 +- tests/test_external_class.py | 2 + tests/test_fetch.py | 21 +- tests/test_fetch_same.py | 3 +- tests/test_filepath.py | 13 +- tests/test_foreign_keys.py | 1 + tests/test_jobs.py | 9 +- tests/test_json.py | 8 +- tests/test_nan.py | 3 +- tests/test_plugin.py | 6 +- tests/test_privileges.py | 3 + tests/test_reconnection.py | 1 + tests/test_relation.py | 11 +- tests/test_relation_u.py | 4 +- tests/test_relational_operand.py | 11 +- tests/test_s3.py | 8 +- tests/test_schema.py | 7 +- tests/test_schema_keywords.py | 1 + tests/test_settings.py | 6 +- tests/test_tls.py | 3 +- tests/test_university.py | 9 +- tests/test_update1.py | 8 +- tests/test_utils.py | 9 +- tests/test_uuid.py | 9 +- 144 files changed, 1501 insertions(+), 1382 deletions(-) diff --git a/.coveragerc b/.coveragerc index df41fbcc8..9feabe07b 100644 --- a/.coveragerc +++ b/.coveragerc @@ -3,4 +3,4 @@ branch = False source = datajoint [report] -show_missing = True \ No newline at end of file +show_missing = True diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 22f55ede7..3ef7f9b6e 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -47,4 +47,4 @@ } // Uncomment to connect as an existing user other than the container default. More info: https://aka.ms/dev-containers-non-root. // "remoteUser": "devcontainer" -} \ No newline at end of file +} diff --git a/.devcontainer/docker-compose.yml b/.devcontainer/docker-compose.yml index 50efa80b9..71d74e46f 100644 --- a/.devcontainer/docker-compose.yml +++ b/.devcontainer/docker-compose.yml @@ -29,4 +29,3 @@ services: # Overrides default command so things don't shut down after the process ends. command: /bin/sh -c "while sleep 1000; do :; done" - diff --git a/.dockerignore b/.dockerignore index 12e841b46..0c82e343f 100644 --- a/.dockerignore +++ b/.dockerignore @@ -3,4 +3,4 @@ build *.egg-info dist .vscode -__pycache__ \ No newline at end of file +__pycache__ diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml index f5a344445..897e39e5f 100644 --- a/.github/workflows/docs.yaml +++ b/.github/workflows/docs.yaml @@ -16,4 +16,4 @@ jobs: export UPSTREAM_REPO=https://github.com/${GITHUB_REPOSITORY}.git export HOST_UID=$(id -u) docker compose -f docs/docker-compose.yaml up --exit-code-from docs --build - git push origin gh-pages \ No newline at end of file + git push origin gh-pages diff --git a/.vscode/launch.json b/.vscode/launch.json index 2b2502c69..0746b2a85 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -13,4 +13,4 @@ "justMyCode": false } ] -} \ No newline at end of file +} diff --git a/README.md b/README.md index 44277a3e4..916b14587 100644 --- a/README.md +++ b/README.md @@ -5,17 +5,17 @@ # Welcome to DataJoint for Python! -DataJoint for Python is a framework for scientific workflow management based on -relational principles. DataJoint is built on the foundation of the relational data -model and prescribes a consistent method for organizing, populating, computing, and +DataJoint for Python is a framework for scientific workflow management based on +relational principles. DataJoint is built on the foundation of the relational data +model and prescribes a consistent method for organizing, populating, computing, and querying data. -DataJoint was initially developed in 2009 by Dimitri Yatsenko in Andreas Tolias' Lab at -Baylor College of Medicine for the distributed processing and management of large -volumes of data streaming from regular experiments. Starting in 2011, DataJoint has -been available as an open-source project adopted by other labs and improved through +DataJoint was initially developed in 2009 by Dimitri Yatsenko in Andreas Tolias' Lab at +Baylor College of Medicine for the distributed processing and management of large +volumes of data streaming from regular experiments. Starting in 2011, DataJoint has +been available as an open-source project adopted by other labs and improved through contributions from several developers. -Presently, the primary developer of DataJoint open-source software is the company +Presently, the primary developer of DataJoint open-source software is the company DataJoint (https://datajoint.com). ## Data Pipeline Example diff --git a/datajoint/__init__.py b/datajoint/__init__.py index 053db28f2..a7c5e7b2f 100644 --- a/datajoint/__init__.py +++ b/datajoint/__init__.py @@ -55,24 +55,23 @@ "cli", ] -from .logging import logger -from .version import __version__ -from .settings import config -from .connection import conn, Connection -from .schemas import Schema -from .schemas import VirtualModule, list_schemas -from .table import Table, FreeTable -from .user_tables import Manual, Lookup, Imported, Computed, Part -from .expression import Not, AndList, U, Top -from .diagram import Diagram -from .admin import set_password, kill +from . import errors +from .admin import kill, set_password +from .attribute_adapter import AttributeAdapter from .blob import MatCell, MatStruct +from .cli import cli +from .connection import Connection, conn +from .diagram import Diagram +from .errors import DataJointError +from .expression import AndList, Not, Top, U from .fetch import key from .hash import key_hash -from .attribute_adapter import AttributeAdapter -from . import errors -from .errors import DataJointError -from .cli import cli +from .logging import logger +from .schemas import Schema, VirtualModule, list_schemas +from .settings import config +from .table import FreeTable, Table +from .user_tables import Computed, Imported, Lookup, Manual, Part +from .version import __version__ ERD = Di = Diagram # Aliases for Diagram schema = Schema # Aliases for Schema diff --git a/datajoint/admin.py b/datajoint/admin.py index 5e179a19b..e1eb803ec 100644 --- a/datajoint/admin.py +++ b/datajoint/admin.py @@ -1,10 +1,12 @@ -import pymysql +import logging from getpass import getpass + +import pymysql from packaging import version + from .connection import conn from .settings import config from .utils import user_choice -import logging logger = logging.getLogger(__name__.split(".")[0]) diff --git a/datajoint/attribute_adapter.py b/datajoint/attribute_adapter.py index 2814064b3..e062f4c57 100644 --- a/datajoint/attribute_adapter.py +++ b/datajoint/attribute_adapter.py @@ -1,4 +1,5 @@ import re + from .errors import DataJointError, _support_adapted_types from .plugin import type_plugins diff --git a/datajoint/autopopulate.py b/datajoint/autopopulate.py index 6d72b7aa7..d0b0a67c6 100644 --- a/datajoint/autopopulate.py +++ b/datajoint/autopopulate.py @@ -1,18 +1,20 @@ """This module defines class dj.AutoPopulate""" -import logging +import contextlib import datetime -import traceback -import random import inspect -from tqdm import tqdm -from .hash import key_hash -from .expression import QueryExpression, AndList -from .errors import DataJointError, LostConnectionError -import signal +import logging import multiprocessing as mp -import contextlib +import random +import signal +import traceback + import deepdiff +from tqdm import tqdm + +from .errors import DataJointError, LostConnectionError +from .expression import AndList, QueryExpression +from .hash import key_hash # noinspection PyExceptionInherit,PyCallingNonCallable diff --git a/datajoint/blob.py b/datajoint/blob.py index f38525477..82e1c3d18 100644 --- a/datajoint/blob.py +++ b/datajoint/blob.py @@ -3,17 +3,18 @@ compatibility with Matlab-based serialization implemented by mYm. """ -import zlib -from itertools import repeat import collections -from decimal import Decimal import datetime import uuid +import zlib +from decimal import Decimal +from itertools import repeat + import numpy as np + from .errors import DataJointError from .settings import config - deserialize_lookup = { 0: {"dtype": None, "scalar_type": "UNKNOWN"}, 1: {"dtype": None, "scalar_type": "CELL"}, diff --git a/datajoint/cli.py b/datajoint/cli.py index e2432fe6e..3b7e72c25 100644 --- a/datajoint/cli.py +++ b/datajoint/cli.py @@ -1,6 +1,7 @@ import argparse from code import interact from collections import ChainMap + import datajoint as dj diff --git a/datajoint/condition.py b/datajoint/condition.py index de6372c6a..7fbe0c7bc 100644 --- a/datajoint/condition.py +++ b/datajoint/condition.py @@ -1,17 +1,19 @@ """ methods for generating SQL WHERE clauses from datajoint restriction conditions """ -import inspect import collections -import re -import uuid import datetime import decimal +import inspect +import json +import re +import uuid +from dataclasses import dataclass +from typing import List, Union + import numpy import pandas -import json + from .errors import DataJointError -from typing import Union, List -from dataclasses import dataclass JSON_PATTERN = re.compile( r"^(?P\w+)(\.(?P[\w.*\[\]]+))?(:(?P[\w(,\s)]+))?$" @@ -143,7 +145,7 @@ def make_condition(query_expression, condition, columns): condition. :return: an SQL condition string or a boolean value. """ - from .expression import QueryExpression, Aggregation, U + from .expression import Aggregation, QueryExpression, U def prep_value(k, v): """prepare SQL condition""" diff --git a/datajoint/connection.py b/datajoint/connection.py index 5d2fbc27e..6e21b5fef 100644 --- a/datajoint/connection.py +++ b/datajoint/connection.py @@ -3,20 +3,22 @@ the ``conn`` function that provides access to a persistent connection in datajoint. """ +import logging +import pathlib +import re import warnings from contextlib import contextmanager -import pymysql as client -import logging from getpass import getpass -import re -import pathlib -from .settings import config -from . import errors, __version__ -from .dependencies import Dependencies +import pymysql as client + +from . import errors from .blob import pack, unpack +from .dependencies import Dependencies from .hash import uuid_from_buffer from .plugin import connection_plugins +from .settings import config +from .version import __version__ logger = logging.getLogger(__name__.split(".")[0]) query_log_max_length = 300 diff --git a/datajoint/declare.py b/datajoint/declare.py index b1194880f..304476798 100644 --- a/datajoint/declare.py +++ b/datajoint/declare.py @@ -3,13 +3,15 @@ declare the corresponding mysql tables. """ -import re -import pyparsing as pp import logging +import re from hashlib import sha1 -from .errors import DataJointError, _support_filepath_types, FILEPATH_FEATURE_SWITCH + +import pyparsing as pp + from .attribute_adapter import get_adapter from .condition import translate_attribute +from .errors import FILEPATH_FEATURE_SWITCH, DataJointError, _support_filepath_types from .settings import config UUID_DATA_TYPE = "binary(16)" @@ -163,8 +165,8 @@ def compile_foreign_key( :param index_sql: list of INDEX declaration statements, duplicate or redundant indexes are ok. """ # Parse and validate - from .table import Table from .expression import QueryExpression + from .table import Table try: result = foreign_key_parser.parseString(line) diff --git a/datajoint/dependencies.py b/datajoint/dependencies.py index aa1d4c6e1..7496ee600 100644 --- a/datajoint/dependencies.py +++ b/datajoint/dependencies.py @@ -1,7 +1,9 @@ -import networkx as nx import itertools import re from collections import defaultdict + +import networkx as nx + from .errors import DataJointError diff --git a/datajoint/diagram.py b/datajoint/diagram.py index aeced0650..cb3daf4d3 100644 --- a/datajoint/diagram.py +++ b/datajoint/diagram.py @@ -1,14 +1,14 @@ -import networkx as nx import functools +import inspect import io import logging -import inspect -from .table import Table + +import networkx as nx + from .dependencies import topo_sort -from .user_tables import Manual, Imported, Computed, Lookup, Part, _get_tier, _AliasNode from .errors import DataJointError -from .table import lookup_class_name - +from .table import Table, lookup_class_name +from .user_tables import Computed, Imported, Lookup, Manual, Part, _AliasNode, _get_tier try: from matplotlib import pyplot as plt diff --git a/datajoint/expression.py b/datajoint/expression.py index 42be0d59c..dd90087b8 100644 --- a/datajoint/expression.py +++ b/datajoint/expression.py @@ -1,23 +1,24 @@ -from itertools import count -import logging -import inspect import copy +import inspect +import logging import re -from .settings import config -from .errors import DataJointError -from .fetch import Fetch, Fetch1 -from .preview import preview, repr_html +from itertools import count + from .condition import ( AndList, - Top, Not, - make_condition, + PromiscuousOperand, + Top, assert_join_compatibility, extract_column_names, - PromiscuousOperand, + make_condition, translate_attribute, ) from .declare import CONSTANT_LITERALS +from .errors import DataJointError +from .fetch import Fetch, Fetch1 +from .preview import preview, repr_html +from .settings import config logger = logging.getLogger(__name__.split(".")[0]) diff --git a/datajoint/external.py b/datajoint/external.py index 08787ca7f..b3de2ff5d 100644 --- a/datajoint/external.py +++ b/datajoint/external.py @@ -1,15 +1,17 @@ -from pathlib import Path, PurePosixPath, PureWindowsPath +import logging from collections.abc import Mapping +from pathlib import Path, PurePosixPath, PureWindowsPath + from tqdm import tqdm -import logging -from .settings import config + +from . import errors, s3 +from .declare import EXTERNAL_TABLE_ROOT from .errors import DataJointError, MissingExternalFile from .hash import uuid_from_buffer, uuid_from_file -from .table import Table, FreeTable from .heading import Heading -from .declare import EXTERNAL_TABLE_ROOT -from . import s3, errors -from .utils import safe_write, safe_copy +from .settings import config +from .table import FreeTable, Table +from .utils import safe_copy, safe_write logger = logging.getLogger(__name__.split(".")[0]) diff --git a/datajoint/fetch.py b/datajoint/fetch.py index e06af81e4..1c9b811f1 100644 --- a/datajoint/fetch.py +++ b/datajoint/fetch.py @@ -1,13 +1,15 @@ -from functools import partial -from pathlib import Path -import pandas import itertools import json -import numpy as np -import uuid import numbers +import uuid +from functools import partial +from pathlib import Path + +import numpy as np +import pandas from datajoint.condition import Top + from . import blob, hash from .errors import DataJointError from .settings import config diff --git a/datajoint/hash.py b/datajoint/hash.py index 67ec103ae..f58c65732 100644 --- a/datajoint/hash.py +++ b/datajoint/hash.py @@ -1,6 +1,6 @@ import hashlib -import uuid import io +import uuid from pathlib import Path diff --git a/datajoint/heading.py b/datajoint/heading.py index f765f8356..c81b5a61a 100644 --- a/datajoint/heading.py +++ b/datajoint/heading.py @@ -1,18 +1,19 @@ -import numpy as np -from collections import namedtuple, defaultdict -from itertools import chain -import re import logging -from .errors import DataJointError, _support_filepath_types, FILEPATH_FEATURE_SWITCH +import re +from collections import defaultdict, namedtuple +from itertools import chain + +import numpy as np + +from .attribute_adapter import AttributeAdapter, get_adapter from .declare import ( - UUID_DATA_TYPE, - SPECIAL_TYPES, - TYPE_PATTERN, EXTERNAL_TYPES, NATIVE_TYPES, + SPECIAL_TYPES, + TYPE_PATTERN, + UUID_DATA_TYPE, ) -from .attribute_adapter import get_adapter, AttributeAdapter - +from .errors import FILEPATH_FEATURE_SWITCH, DataJointError, _support_filepath_types logger = logging.getLogger(__name__.split(".")[0]) diff --git a/datajoint/jobs.py b/datajoint/jobs.py index 2763d51f4..d6b31e13e 100644 --- a/datajoint/jobs.py +++ b/datajoint/jobs.py @@ -1,10 +1,11 @@ import os -from .hash import key_hash import platform -from .table import Table -from .settings import config + from .errors import DuplicateError +from .hash import key_hash from .heading import Heading +from .settings import config +from .table import Table ERROR_MESSAGE_LENGTH = 2047 TRUNCATION_APPENDIX = "...truncated" diff --git a/datajoint/plugin.py b/datajoint/plugin.py index 48dce6561..8cb668092 100644 --- a/datajoint/plugin.py +++ b/datajoint/plugin.py @@ -1,9 +1,11 @@ -from .settings import config -import pkg_resources +import logging from pathlib import Path + +import pkg_resources from cryptography.exceptions import InvalidSignature from otumat import hash_pkg, verify -import logging + +from .settings import config logger = logging.getLogger(__name__.split(".")[0]) diff --git a/datajoint/s3.py b/datajoint/s3.py index 66f8e2c95..98dc75708 100644 --- a/datajoint/s3.py +++ b/datajoint/s3.py @@ -2,12 +2,14 @@ AWS S3 operations """ +import logging +import uuid from io import BytesIO +from pathlib import Path + import minio # https://docs.minio.io/docs/python-client-api-reference import urllib3 -import uuid -import logging -from pathlib import Path + from . import errors logger = logging.getLogger(__name__.split(".")[0]) diff --git a/datajoint/schemas.py b/datajoint/schemas.py index 7ea40724f..8cb7a3668 100644 --- a/datajoint/schemas.py +++ b/datajoint/schemas.py @@ -1,19 +1,20 @@ -import warnings -import logging -import inspect -import re import collections +import inspect import itertools +import logging +import re +import types +import warnings + from .connection import conn -from .settings import config -from .errors import DataJointError, AccessError -from .jobs import JobTable +from .errors import AccessError, DataJointError from .external import ExternalMapping from .heading import Heading -from .utils import user_choice, to_camel_case -from .user_tables import Part, Computed, Imported, Manual, Lookup, _get_tier -from .table import lookup_class_name, Log, FreeTable -import types +from .jobs import JobTable +from .settings import config +from .table import FreeTable, Log, lookup_class_name +from .user_tables import Computed, Imported, Lookup, Manual, Part, _get_tier +from .utils import to_camel_case, user_choice logger = logging.getLogger(__name__.split(".")[0]) diff --git a/datajoint/settings.py b/datajoint/settings.py index f1c300029..30b206f99 100644 --- a/datajoint/settings.py +++ b/datajoint/settings.py @@ -2,13 +2,14 @@ Settings for DataJoint """ -from contextlib import contextmanager +import collections import json +import logging import os import pprint -import logging -import collections +from contextlib import contextmanager from enum import Enum + from .errors import DataJointError LOCALCONFIG = "dj_local_conf.json" diff --git a/datajoint/table.py b/datajoint/table.py index db9eaffa1..ff51170ed 100644 --- a/datajoint/table.py +++ b/datajoint/table.py @@ -1,30 +1,32 @@ import collections -import itertools +import csv import inspect -import platform -import numpy as np -import pandas +import itertools +import json import logging -import uuid -import csv +import platform import re -import json +import uuid from pathlib import Path -from .settings import config -from .declare import declare, alter -from .condition import make_condition -from .expression import QueryExpression +from typing import Union + +import numpy as np +import pandas + from . import blob -from .utils import user_choice, get_master, is_camel_case -from .heading import Heading +from .condition import make_condition +from .declare import alter, declare from .errors import ( - DuplicateError, AccessError, DataJointError, - UnknownAttributeError, + DuplicateError, IntegrityError, + UnknownAttributeError, ) -from typing import Union +from .expression import QueryExpression +from .heading import Heading +from .settings import config +from .utils import get_master, is_camel_case, user_choice from .version import __version__ as version logger = logging.getLogger(__name__.split(".")[0]) diff --git a/datajoint/user_tables.py b/datajoint/user_tables.py index 36bdb9ca6..9c2e79d34 100644 --- a/datajoint/user_tables.py +++ b/datajoint/user_tables.py @@ -3,10 +3,11 @@ """ import re -from .table import Table + from .autopopulate import AutoPopulate -from .utils import from_camel_case, ClassProperty from .errors import DataJointError +from .table import Table +from .utils import ClassProperty, from_camel_case _base_regexp = r"[a-z][a-z0-9]*(_[a-z][a-z0-9]*)*" diff --git a/datajoint/utils.py b/datajoint/utils.py index 1aae610d8..1d89d5272 100644 --- a/datajoint/utils.py +++ b/datajoint/utils.py @@ -1,8 +1,9 @@ """General-purpose utilities""" import re -from pathlib import Path import shutil +from pathlib import Path + from .errors import DataJointError diff --git a/docker-compose.yaml b/docker-compose.yaml index 9088dc533..9e413bdb3 100644 --- a/docker-compose.yaml +++ b/docker-compose.yaml @@ -76,4 +76,3 @@ services: pip install -q -e ".[test]" pip freeze | grep datajoint pytest --cov-report term-missing --cov=datajoint tests - diff --git a/docs/.docker/Dockerfile b/docs/.docker/Dockerfile index e7e1d90a7..7fadd8b83 100644 --- a/docs/.docker/Dockerfile +++ b/docs/.docker/Dockerfile @@ -12,4 +12,4 @@ RUN \ COPY --chown=anaconda:anaconda ./${PACKAGE} /main/${PACKAGE} COPY --chown=anaconda:anaconda ./docs/mkdocs.yaml /main/docs/mkdocs.yaml COPY --chown=anaconda:anaconda ./docs/src /main/docs/src -COPY --chown=anaconda:anaconda ./CHANGELOG.md /main/docs/src/changelog.md \ No newline at end of file +COPY --chown=anaconda:anaconda ./CHANGELOG.md /main/docs/src/changelog.md diff --git a/docs/.docker/pip_requirements.txt b/docs/.docker/pip_requirements.txt index aab686ff7..111ecb946 100644 --- a/docs/.docker/pip_requirements.txt +++ b/docs/.docker/pip_requirements.txt @@ -8,4 +8,4 @@ mkdocs-gen-files mkdocs-literate-nav mkdocs-exclude-search mkdocs-jupyter -mkdocs-section-index \ No newline at end of file +mkdocs-section-index diff --git a/docs/.markdownlint.yaml b/docs/.markdownlint.yaml index 593bb1d73..a045a4962 100644 --- a/docs/.markdownlint.yaml +++ b/docs/.markdownlint.yaml @@ -1,19 +1,19 @@ # https://github.com/DavidAnson/markdownlint # https://github.com/DavidAnson/markdownlint/blob/main/doc/Rules.md MD009: false # permit trailing spaces -MD013: +MD013: line_length: "88" # Line length limits tables: false # disable for tables headings: false # disable for headings code_blocks: false # disable for code blocks MD030: false # Number of spaces after a list MD033: # HTML elements allowed - allowed_elements: + allowed_elements: - "div" - "span" - "a" - "br" - - "sup" + - "sup" - "figure" MD034: false # Bare URLs OK MD031: false # Spacing w/code blocks. Conflicts with `??? Note` and code tab styling diff --git a/docs/mkdocs.yaml b/docs/mkdocs.yaml index 52193bdca..ecd1ec6e2 100644 --- a/docs/mkdocs.yaml +++ b/docs/mkdocs.yaml @@ -6,17 +6,17 @@ repo_name: datajoint/datajoint-python nav: - DataJoint Python: index.md - Quick Start Guide: quick-start.md - - Concepts: + - Concepts: - Principles: concepts/principles.md - Data Model: concepts/data-model.md - Data Pipelines: concepts/data-pipelines.md - Teamwork: concepts/teamwork.md - Terminology: concepts/terminology.md - - System Administration: + - System Administration: - Database Administration: sysadmin/database-admin.md - Bulk Storage Systems: sysadmin/bulk-storage.md - External Store: sysadmin/external-store.md - - Client Configuration: + - Client Configuration: - Install: client/install.md - Credentials: client/credentials.md - Settings: client/settings.md @@ -38,7 +38,7 @@ nav: - Indexes: design/tables/indexes.md - Master-Part Relationships: design/tables/master-part.md - Schema Diagrams: design/diagrams.md - - Entity Normalization: design/normalization.md + - Entity Normalization: design/normalization.md - Data Integrity: design/integrity.md - Schema Recall: design/recall.md - Schema Drop: design/drop.md diff --git a/docs/src/.overrides/assets/stylesheets/extra.css b/docs/src/.overrides/assets/stylesheets/extra.css index c468acbe9..ca0cca378 100644 --- a/docs/src/.overrides/assets/stylesheets/extra.css +++ b/docs/src/.overrides/assets/stylesheets/extra.css @@ -102,4 +102,4 @@ html a[title="YouTube"].md-social__link svg { [data-md-color-scheme="slate"] .jupyter-wrapper .Table Td { color: var(--dj-black) -} \ No newline at end of file +} diff --git a/docs/src/.overrides/partials/nav.html b/docs/src/.overrides/partials/nav.html index 5c090954d..019d79726 100644 --- a/docs/src/.overrides/partials/nav.html +++ b/docs/src/.overrides/partials/nav.html @@ -30,4 +30,4 @@ {% include "partials/nav-item.html" %} {% endfor %} - \ No newline at end of file + diff --git a/docs/src/api/make_pages.py b/docs/src/api/make_pages.py index 87673d14e..72c1fc326 100644 --- a/docs/src/api/make_pages.py +++ b/docs/src/api/make_pages.py @@ -1,8 +1,9 @@ """Generate the api pages and navigation.""" -import mkdocs_gen_files -from pathlib import Path import os +from pathlib import Path + +import mkdocs_gen_files package = os.getenv("PACKAGE") nav = mkdocs_gen_files.Nav() diff --git a/docs/src/client/credentials.md b/docs/src/client/credentials.md index 4f56fbef3..bac54a6cf 100644 --- a/docs/src/client/credentials.md +++ b/docs/src/client/credentials.md @@ -18,20 +18,20 @@ dj.config['database.password'] = "haha not my real password" Skip setting the password to make DataJoint prompt to enter the password every time. -You may save the configuration in the local work directory with +You may save the configuration in the local work directory with `dj.config.save_local()` or for all your projects in `dj.config.save_global()`. -Configuration changes should be made through the `dj.config` interface; the config file +Configuration changes should be made through the `dj.config` interface; the config file should not be modified directly by the user. -You may leave the user or the password as `None`, in which case you will be prompted to +You may leave the user or the password as `None`, in which case you will be prompted to enter them manually for every session. Setting the password as an empty string allows access without a password. -Note that the system environment variables `DJ_HOST`, `DJ_USER`, and `DJ_PASS` will +Note that the system environment variables `DJ_HOST`, `DJ_USER`, and `DJ_PASS` will overwrite the settings in the config file. You can use them to set the connection credentials instead of config files. -To change the password, the `dj.set_password` function will walk you through the +To change the password, the `dj.set_password` function will walk you through the process: ```python diff --git a/docs/src/client/install.md b/docs/src/client/install.md index 5133bf64f..d9684f302 100644 --- a/docs/src/client/install.md +++ b/docs/src/client/install.md @@ -15,10 +15,10 @@ pip3 install --upgrade datajoint ## DataJoint Python Windows Install Guide -This document outlines the steps necessary to install DataJoint on Windows for use in +This document outlines the steps necessary to install DataJoint on Windows for use in connecting to a remote server hosting a DataJoint database. -Some limited discussion of installing MySQL is discussed in `MySQL for Windows`, but is -not covered in-depth since this is an uncommon usage scenario and not strictly required +Some limited discussion of installing MySQL is discussed in `MySQL for Windows`, but is +not covered in-depth since this is an uncommon usage scenario and not strictly required to connect to DataJoint pipelines. ### Quick steps @@ -32,7 +32,7 @@ Quick install steps for advanced users are as follows: For ERD drawing support: -- Install Graphviz for Windows and ensure it is in `PATH` (64 bit builds currently +- Install Graphviz for Windows and ensure it is in `PATH` (64 bit builds currently tested; URL below.) ```bash pip install pydotplus matplotlib @@ -50,12 +50,12 @@ The latest 64 bit 3.x version, currently 3.6.3, is available from the [Python si From here run the installer to install Python. -For a single-user machine, the regular installation process is sufficient - be sure to +For a single-user machine, the regular installation process is sufficient - be sure to select the `Add Python to PATH` option: ![install-python-simple](../images/install-python-simple.png){: style="align:left"} -For a shared machine, run the installer as administrator (right-click, run as +For a shared machine, run the installer as administrator (right-click, run as administrator) and select the advanced installation. Be sure to select options as follows: @@ -64,57 +64,57 @@ Be sure to select options as follows: ### Step 2: verify installation -To verify the Python installation and make sure that your system is ready to install +To verify the Python installation and make sure that your system is ready to install DataJoint, open a command window by entering `cmd` into the Windows search bar: ![install-cmd-prompt](../images/install-cmd-prompt.png){: style="align:left"} -From here `python` and the Python package manager `pip` can be verified by running +From here `python` and the Python package manager `pip` can be verified by running `python -V` and `pip -V`, respectively: ![install-verify-python](../images/install-verify-python.png){: style="align:left"} -If you receive the error message that either `pip` or `python` is not a recognized -command, please uninstall Python and ensure that the option to add Python to the `PATH` +If you receive the error message that either `pip` or `python` is not a recognized +command, please uninstall Python and ensure that the option to add Python to the `PATH` variable was properly configured. ### Step 3: install DataJoint -DataJoint (and other Python modules) can be easily installed using the `pip` Python -package manager which is installed as a part of Python and was verified in the previous +DataJoint (and other Python modules) can be easily installed using the `pip` Python +package manager which is installed as a part of Python and was verified in the previous step. To install DataJoint simply run `pip install datajoint`: ![install-datajoint-1](../images/install-datajoint-1.png){: style="align:left"} -This will proceed to install DataJoint, along with several other required packages from +This will proceed to install DataJoint, along with several other required packages from the PIP repository. When finished, a summary of the activity should be presented: ![install-datajoint-2](../images/install-datajoint-2.png){: style="align:left"} -Note: You can find out more about the packages installed here and many other freely -available open source packages via [pypi](https://pypi.python.org/pypi), the Python +Note: You can find out more about the packages installed here and many other freely +available open source packages via [pypi](https://pypi.python.org/pypi), the Python package index site. ### (Optional) step 4: install packages for ERD support -To draw diagrams of your DataJoint schema, the following additional steps should be +To draw diagrams of your DataJoint schema, the following additional steps should be followed. #### Install Graphviz -DataJoint currently utilizes [Graphviz](http://graphviz.org) to generate the ERD +DataJoint currently utilizes [Graphviz](http://graphviz.org) to generate the ERD visualizations. -Although a Windows version of Graphviz is available from the main site, it is an older +Although a Windows version of Graphviz is available from the main site, it is an older and out of date 32-bit version. The recommended pre-release builds of the 64 bit version are available here: https://ci.appveyor.com/project/ellson/graphviz-pl238 -More specifically, the build artifacts from the `Win64; Configuration: Release` are -recommended, available +More specifically, the build artifacts from the `Win64; Configuration: Release` are +recommended, available [here](https://ci.appveyor.com/api/buildjobs/hlkclpfhf6gnakjq/artifacts/build%2FGraphviz-install.exe). This is a regular Windows installer executable, and will present a dialog when starting: @@ -135,23 +135,23 @@ Once installed, Graphviz can be verified from a fresh command window as follows: ![install-verify-graphviz](../images/install-verify-graphviz.png){: style="align:left"} -If you receive the error message that the `dot` program is not a recognized command, +If you receive the error message that the `dot` program is not a recognized command, please uninstall Graphviz and ensure that the option to add Python to the PATH variable was properly configured. -Important: in some cases, running the `dot -c` command in a command prompt is required +Important: in some cases, running the `dot -c` command in a command prompt is required to properly initialize the Graphviz installation. #### Install PyDotPlus -The PyDotPlus library links the Graphviz installation to DataJoint and is easily +The PyDotPlus library links the Graphviz installation to DataJoint and is easily installed via `pip`: ![install-pydotplus](../images/install-pydotplus.png){: style="align:left"} #### Install Matplotlib -The Matplotlib library provides useful plotting utilities which are also used by +The Matplotlib library provides useful plotting utilities which are also used by DataJoint's `Diagram` drawing facility. The package is easily installed via `pip`: @@ -167,30 +167,30 @@ you to create and share documents that contain live code, equations, visualizations and narrative text. ''' -Although not a part of DataJoint, Jupyter Notebook can be a very useful tool for +Although not a part of DataJoint, Jupyter Notebook can be a very useful tool for building and interacting with DataJoint pipelines. It is easily installed from `pip` as well: ![install-jupyter-1](../images/install-jupyter-1.png){: style="align:left"} ![install-jupyter-2](../images/install-jupyter-2.png){: style="align:left"} -Once installed, Jupyter Notebook can be started via the `jupyter notebook` command, +Once installed, Jupyter Notebook can be started via the `jupyter notebook` command, which should now be on your path: ![install-verify-jupyter](../images/install-verify-jupyter.png){: style="align:left"} -By default Jupyter Notebook will start a local private web server session from the +By default Jupyter Notebook will start a local private web server session from the directory where it was started and start a web browser session connected to the session. ![install-run-jupyter-1](../images/install-run-jupyter-1.png){: style="align:left"} ![install-run-jupyter-2](../images/install-run-jupyter-2.png){: style="align:left"} -You now should be able to use the notebook viewer to navigate the filesystem and to +You now should be able to use the notebook viewer to navigate the filesystem and to create new project folders and interactive Jupyter/Python/DataJoint notebooks. ### Git for Windows -The [Git](https://git-scm.com/) version control system is not a part of DataJoint but +The [Git](https://git-scm.com/) version control system is not a part of DataJoint but is recommended for interacting with the broader Python/Git/GitHub sharing ecosystem. The Git for Windows installer is available from https://git-scm.com/download/win. @@ -203,7 +203,7 @@ The default settings should be sufficient and correct in most cases. For hosting pipelines locally, the MySQL server package is required. -MySQL for windows can be installed via the installers available from the +MySQL for windows can be installed via the installers available from the [MySQL website](https://dev.mysql.com/downloads/windows/). -Please note that although DataJoint should be fully compatible with a Windows MySQL +Please note that although DataJoint should be fully compatible with a Windows MySQL server installation, this mode of operation is not tested by the DataJoint team. diff --git a/docs/src/client/settings.md b/docs/src/client/settings.md index 64932bb69..cb9a69fff 100644 --- a/docs/src/client/settings.md +++ b/docs/src/client/settings.md @@ -2,10 +2,10 @@ If you are not using DataJoint on your own, or are setting up a DataJoint system for other users, some additional configuration options may be required -to support [TLS](#tls-configuration) or +to support [TLS](#tls-configuration) or [external storage](../sysadmin/external-store.md). ## TLS Configuration -Starting with v0.12, DataJoint will by default use TLS if it is available. TLS can be +Starting with v0.12, DataJoint will by default use TLS if it is available. TLS can be forced on or off with the boolean `dj.config['database.use_tls']`. diff --git a/docs/src/compute/distributed.md b/docs/src/compute/distributed.md index 69fc193f3..68c31f093 100644 --- a/docs/src/compute/distributed.md +++ b/docs/src/compute/distributed.md @@ -2,35 +2,35 @@ ## Job reservations -Running `populate` on the same table on multiple computers will causes them to attempt +Running `populate` on the same table on multiple computers will causes them to attempt to compute the same data all at once. This will not corrupt the data since DataJoint will reject any duplication. -One solution could be to cause the different computing nodes to populate the tables in +One solution could be to cause the different computing nodes to populate the tables in random order. This would reduce some collisions but not completely prevent them. -To allow efficient distributed computing, DataJoint provides a built-in job reservation +To allow efficient distributed computing, DataJoint provides a built-in job reservation process. -When `dj.Computed` tables are auto-populated using job reservation, a record of each -ongoing computation is kept in a schema-wide `jobs` table, which is used internally by +When `dj.Computed` tables are auto-populated using job reservation, a record of each +ongoing computation is kept in a schema-wide `jobs` table, which is used internally by DataJoint to coordinate the auto-population effort among multiple computing processes. -Job reservations are activated by setting the keyword argument `reserve_jobs=True` in +Job reservations are activated by setting the keyword argument `reserve_jobs=True` in `populate` calls. -With job management enabled, the `make` method of each table class will also consult -the `jobs` table for reserved jobs as part of determining the next record to compute -and will create an entry in the `jobs` table as part of the attempt to compute the +With job management enabled, the `make` method of each table class will also consult +the `jobs` table for reserved jobs as part of determining the next record to compute +and will create an entry in the `jobs` table as part of the attempt to compute the resulting record for that key. If the operation is a success, the record is removed. -In the event of failure, the job reservation entry is updated to indicate the details +In the event of failure, the job reservation entry is updated to indicate the details of failure. -Using this simple mechanism, multiple processes can participate in the auto-population -effort without duplicating computational effort, and any errors encountered during the -course of the computation can be individually inspected to determine the cause of the +Using this simple mechanism, multiple processes can participate in the auto-population +effort without duplicating computational effort, and any errors encountered during the +course of the computation can be individually inspected to determine the cause of the issue. -As part of DataJoint, the jobs table can be queried using native DataJoint syntax. For +As part of DataJoint, the jobs table can be queried using native DataJoint syntax. For example, to list the jobs currently being run: ```python @@ -42,18 +42,18 @@ __job_results e4da3b7fbbce23 reserved datajoint@localhos loc (2 tuples) ``` -The above output shows that a record for the `JobResults` table is currently reserved -for computation, along with various related details of the reservation, such as the -MySQL connection ID, client user and host, process ID on the remote system, timestamp, +The above output shows that a record for the `JobResults` table is currently reserved +for computation, along with various related details of the reservation, such as the +MySQL connection ID, client user and host, process ID on the remote system, timestamp, and the key for the record that the job is using for its computation. -Since DataJoint table keys can be of varying types, the key is stored in a binary +Since DataJoint table keys can be of varying types, the key is stored in a binary format to allow the table to store arbitrary types of record key data. The subsequent sections will discuss querying the jobs table for key data. -As mentioned above, jobs encountering errors during computation will leave their record +As mentioned above, jobs encountering errors during computation will leave their record reservations in place, and update the reservation record with details of the error. -For example, if a Python process is interrupted via the keyboard, a KeyboardError will +For example, if a Python process is interrupted via the keyboard, a KeyboardError will be logged to the database as follows: ```python @@ -65,12 +65,12 @@ __job_results 3416a75f4cea91 error KeyboardInterr datajoint@localhos local (1 tuples) ``` -By leaving the job reservation record in place, the error can be inspected, and if +By leaving the job reservation record in place, the error can be inspected, and if necessary the corresponding `dj.Computed` update logic can be corrected. From there the jobs entry can be cleared, and the computation can then be resumed. -In the meantime, the presence of the job reservation will prevent this particular +In the meantime, the presence of the job reservation will prevent this particular record from being processed during subsequent auto-population calls. -Inspecting the job record for failure details can proceed much like any other DataJoint +Inspecting the job record for failure details can proceed much like any other DataJoint query. For example, given the above table, errors can be inspected as follows: @@ -92,11 +92,11 @@ Out[3]: ('timestamp', datetime.datetime(2017, 9, 4, 15, 3, 53))])] ``` -This particular error occurred when processing the record with ID `2`, resulted from a +This particular error occurred when processing the record with ID `2`, resulted from a `KeyboardInterrupt`, and has no additional error trace. -After any system or code errors have been resolved, the table can simply be cleaned of +After any system or code errors have been resolved, the table can simply be cleaned of errors and the computation rerun. For example: @@ -105,10 +105,10 @@ For example: In [4]: (schema.jobs & 'status="error"' ).delete() ``` -In some cases, it may be preferable to inspect the jobs table records using populate +In some cases, it may be preferable to inspect the jobs table records using populate keys. -Since job keys are hashed and stored as a blob in the jobs table to support the varying -types of keys, we need to query using the key hash instead of simply using the raw key +Since job keys are hashed and stored as a blob in the jobs table to support the varying +types of keys, we need to query using the key hash instead of simply using the raw key data. This can be done by using `dj.key_hash` to convert the key as follows: @@ -117,17 +117,17 @@ This can be done by using `dj.key_hash` to convert the key as follows: In [4]: jk = {'table_name': JobResults.table_name, 'key_hash' : dj.key_hash({'id': 2})} In [5]: schema.jobs & jk -Out[5]: -*table_name *key_hash status key error_message error_stac user host pid connection_id timestamp +Out[5]: +*table_name *key_hash status key error_message error_stac user host pid connection_id timestamp +------------+ +------------+ +--------+ +--------+ +------------+ +--------+ +------------+ +-------+ +--------+ +------------+ +------------+ __job_results c81e728d9d4c2f error =BLOB= KeyboardInterr =BLOB= datajoint@localhost localhost 15571 59 2017-09-04 14: (Total: 1) -In [6]: (schema.jobs & jk).delete() +In [6]: (schema.jobs & jk).delete() In [7]: schema.jobs & jk -Out[7]: -*table_name *key_hash status key error_message error_stac user host pid connection_id timestamp +Out[7]: +*table_name *key_hash status key error_message error_stac user host pid connection_id timestamp +------------+ +----------+ +--------+ +--------+ +------------+ +--------+ +------+ +------+ +-----+ +------------+ +-----------+ (Total: 0) @@ -135,11 +135,11 @@ Out[7]: ## Managing connections -The DataJoint method `dj.kill` allows for viewing and termination of database +The DataJoint method `dj.kill` allows for viewing and termination of database connections. Restrictive conditions can be used to identify specific connections. -Restrictions are specified as strings and can involve any of the attributes of -`information_schema.processlist`: `ID`, `USER`, `HOST`, `DB`, `COMMAND`, `TIME`, +Restrictions are specified as strings and can involve any of the attributes of +`information_schema.processlist`: `ID`, `USER`, `HOST`, `DB`, `COMMAND`, `TIME`, `STATE`, and `INFO`. Examples: @@ -147,9 +147,9 @@ Examples: `dj.kill('HOST LIKE "%compute%"')` lists only connections from hosts containing "compute". `dj.kill('TIME > 600')` lists only connections older than 10 minutes. -A list of connections meeting the restriction conditions (if present) are presented to -the user, along with the option to kill processes. By default, output is ordered by -ascending connection ID. To change the output order of dj.kill(), an additional +A list of connections meeting the restriction conditions (if present) are presented to +the user, along with the option to kill processes. By default, output is ordered by +ascending connection ID. To change the output order of dj.kill(), an additional order_by argument can be provided. For example, to sort the output by hostname in descending order: diff --git a/docs/src/compute/key-source.md b/docs/src/compute/key-source.md index 36757098a..76796ec0c 100644 --- a/docs/src/compute/key-source.md +++ b/docs/src/compute/key-source.md @@ -2,40 +2,40 @@ ## Default key source -**Key source** refers to the set of primary key values over which +**Key source** refers to the set of primary key values over which [autopopulate](./populate.md) iterates, calling the `make` method at each iteration. Each `key` from the key source is passed to the table's `make` call. -By default, the key source for a table is the [join](../query/join.md) of its primary +By default, the key source for a table is the [join](../query/join.md) of its primary [dependencies](../design/tables/dependencies.md). For example, consider a schema with three tables. -The `Stimulus` table contains one attribute `stimulus_type` with one of two values, +The `Stimulus` table contains one attribute `stimulus_type` with one of two values, "Visual" or "Auditory". -The `Modality` table contains one attribute `modality` with one of three values, "EEG", +The `Modality` table contains one attribute `modality` with one of three values, "EEG", "fMRI", and "PET". The `Protocol` table has primary dependencies on both the `Stimulus` and `Modality` tables. -The key source for `Protocol` will then be all six combinations of `stimulus_type` and +The key source for `Protocol` will then be all six combinations of `stimulus_type` and `modality` as shown in the figure below. ![Combination of stimulus_type and modality](../images/key_source_combination.png){: style="align:center"} ## Custom key source -A custom key source can be configured by setting the `key_source` property within a +A custom key source can be configured by setting the `key_source` property within a table class, after the `definition` string. Any [query object](../query/fetch.md) can be used as the key source. In most cases the new key source will be some alteration of the default key source. -Custom key sources often involve restriction to limit the key source to only relevant +Custom key sources often involve restriction to limit the key source to only relevant entities. Other designs may involve using only one of a table's primary dependencies. -In the example below, the `EEG` table depends on the `Recording` table that lists all +In the example below, the `EEG` table depends on the `Recording` table that lists all recording sessions. -However, the `populate` method of `EEG` should only ingest recordings where the +However, the `populate` method of `EEG` should only ingest recordings where the `recording_type` is `EEG`. -Setting a custom key source prevents the `populate` call from iterating over recordings +Setting a custom key source prevents the `populate` call from iterating over recordings of the wrong type. ```python diff --git a/docs/src/compute/make.md b/docs/src/compute/make.md index 8992728ed..c67711079 100644 --- a/docs/src/compute/make.md +++ b/docs/src/compute/make.md @@ -1,25 +1,25 @@ # Transactions in Make Each call of the [make](../compute/make.md) method is enclosed in a transaction. -DataJoint users do not need to explicitly manage transactions but must be aware of +DataJoint users do not need to explicitly manage transactions but must be aware of their use. Transactions produce two effects: -First, the state of the database appears stable within the `make` call throughout the +First, the state of the database appears stable within the `make` call throughout the transaction: -two executions of the same query will yield identical results within the same `make` +two executions of the same query will yield identical results within the same `make` call. -Second, any changes to the database (inserts) produced by the `make` method will not +Second, any changes to the database (inserts) produced by the `make` method will not become visible to other processes until the `make` call completes execution. -If the `make` method raises an exception, all changes made so far will be discarded and +If the `make` method raises an exception, all changes made so far will be discarded and will never become visible to other processes. -Transactions are particularly important in maintaining -[group integrity](../design/integrity.md#group-integrity) with +Transactions are particularly important in maintaining +[group integrity](../design/integrity.md#group-integrity) with [master-part relationships](../design/tables/master-part.md). -The `make` call of a master table first inserts the master entity and then inserts all +The `make` call of a master table first inserts the master entity and then inserts all the matching part entities in the part tables. -None of the entities become visible to other processes until the entire `make` call +None of the entities become visible to other processes until the entire `make` call completes, at which point they all become visible. diff --git a/docs/src/compute/populate.md b/docs/src/compute/populate.md index acec3a9ae..76fc62aee 100644 --- a/docs/src/compute/populate.md +++ b/docs/src/compute/populate.md @@ -1,17 +1,17 @@ # Auto-populate -Auto-populated tables are used to define, execute, and coordinate computations in a +Auto-populated tables are used to define, execute, and coordinate computations in a DataJoint pipeline. Tables in the initial portions of the pipeline are populated from outside the pipeline. -In subsequent steps, computations are performed automatically by the DataJoint pipeline +In subsequent steps, computations are performed automatically by the DataJoint pipeline in auto-populated tables. -Computed tables belong to one of the two auto-populated +Computed tables belong to one of the two auto-populated [data tiers](../design/tables/tiers.md): `dj.Imported` and `dj.Computed`. -DataJoint does not enforce the distinction between imported and computed tables: the +DataJoint does not enforce the distinction between imported and computed tables: the difference is purely semantic, a convention for developers to follow. -If populating a table requires access to external files such as raw storage that is not +If populating a table requires access to external files such as raw storage that is not part of the database, the table is designated as **imported**. Otherwise it is **computed**. @@ -21,14 +21,14 @@ Their data definition follows the same [definition syntax](../design/tables/decl ## Make -For auto-populated tables, data should never be entered using +For auto-populated tables, data should never be entered using [insert](../manipulation/insert.md) directly. Instead these tables must define the callback method `make(self, key)`. The `insert` method then can only be called on `self` inside this callback method. -Imagine that there is a table `test.Image` that contains 2D grayscale images in its +Imagine that there is a table `test.Image` that contains 2D grayscale images in its `image` attribute. -Let us define the computed table, `test.FilteredImage` that filters the image in some +Let us define the computed table, `test.FilteredImage` that filters the image in some way and saves the result in its `filtered_image` attribute. The class will be defined as follows. @@ -49,25 +49,25 @@ class FilteredImage(dj.Computed): self.insert1(key) ``` -The `make` method receives one argument: the dict `key` containing the primary key -value of an element of [key source](key-source.md) to be worked on. +The `make` method receives one argument: the dict `key` containing the primary key +value of an element of [key source](key-source.md) to be worked on. -The key represents the partially filled entity, usually already containing the +The key represents the partially filled entity, usually already containing the [primary key](../design/tables/primary.md) attributes of the key source. The `make` callback does three things: -1. [Fetches](../query/fetch.md) data from tables upstream in the pipeline using the +1. [Fetches](../query/fetch.md) data from tables upstream in the pipeline using the `key` for [restriction](../query/restrict.md). 2. Computes and adds any missing attributes to the fields already in `key`. 3. Inserts the entire entity into `self`. -`make` may populate multiple entities in one call when `key` does not specify the +`make` may populate multiple entities in one call when `key` does not specify the entire primary key of the populated table. ## Populate -The inherited `populate` method of `dj.Imported` and `dj.Computed` automatically calls +The inherited `populate` method of `dj.Imported` and `dj.Computed` automatically calls `make` for every key for which the auto-populated table is missing data. The `FilteredImage` table can be populated as @@ -76,30 +76,30 @@ The `FilteredImage` table can be populated as FilteredImage.populate() ``` -The progress of long-running calls to `populate()` in datajoint-python can be +The progress of long-running calls to `populate()` in datajoint-python can be visualized by adding the `display_progress=True` argument to the populate call. Note that it is not necessary to specify which data needs to be computed. -DataJoint will call `make`, one-by-one, for every key in `Image` for which +DataJoint will call `make`, one-by-one, for every key in `Image` for which `FilteredImage` has not yet been computed. Chains of auto-populated tables form computational pipelines in DataJoint. ## Populate options -The `populate` method accepts a number of optional arguments that provide more features +The `populate` method accepts a number of optional arguments that provide more features and allow greater control over the method's behavior. -- `restrictions` - A list of restrictions, restricting as +- `restrictions` - A list of restrictions, restricting as `(tab.key_source & AndList(restrictions)) - tab.proj()`. Here `target` is the table to be populated, usually `tab` itself. -- `suppress_errors` - If `True`, encountering an error will cancel the current `make` +- `suppress_errors` - If `True`, encountering an error will cancel the current `make` call, log the error, and continue to the next `make` call. - Error messages will be logged in the job reservation table (if `reserve_jobs` is + Error messages will be logged in the job reservation table (if `reserve_jobs` is `True`) and returned as a list. See also `return_exception_objects` and `reserve_jobs`. Defaults to `False`. -- `return_exception_objects` - If `True`, error objects are returned instead of error +- `return_exception_objects` - If `True`, error objects are returned instead of error messages. This applies only when `suppress_errors` is `True`. Defaults to `False`. @@ -118,10 +118,10 @@ call, log the error, and continue to the next `make` call. ## Progress -The method `table.progress` reports how many `key_source` entries have been populated +The method `table.progress` reports how many `key_source` entries have been populated and how many remain. Two optional parameters allow more advanced use of the method. -A parameter of restriction conditions can be provided, specifying which entities to +A parameter of restriction conditions can be provided, specifying which entities to consider. -A Boolean parameter `display` (default is `True`) allows disabling the output, such +A Boolean parameter `display` (default is `True`) allows disabling the output, such that the numbers of remaining and total entities are returned but not printed. diff --git a/docs/src/concepts/data-model.md b/docs/src/concepts/data-model.md index 71220e168..9754e2392 100644 --- a/docs/src/concepts/data-model.md +++ b/docs/src/concepts/data-model.md @@ -2,106 +2,106 @@ ## What is a data model? -A **data model** refers to a conceptual framework for thinking about data and about +A **data model** refers to a conceptual framework for thinking about data and about operations on data. -A data model defines the mental toolbox of the data scientist; it has less to do with -the architecture of the data systems, although architectures are often intertwined with +A data model defines the mental toolbox of the data scientist; it has less to do with +the architecture of the data systems, although architectures are often intertwined with data models. -Among the most familiar data models are those based on files and folders: data of any -kind are lumped together into binary strings called **files**, files are collected into +Among the most familiar data models are those based on files and folders: data of any +kind are lumped together into binary strings called **files**, files are collected into folders, and folders can be nested within other folders to create a folder hierarchy. Another family of data models are various **tabular models**. -For example, items in CSV files are listed in rows, and the attributes of each item are +For example, items in CSV files are listed in rows, and the attributes of each item are stored in columns. -Various **spreadsheet** models allow forming dependencies between cells and groups of +Various **spreadsheet** models allow forming dependencies between cells and groups of cells, including complex calculations. -The **object data model** is common in programming, where data are represented as +The **object data model** is common in programming, where data are represented as objects in memory with properties and methods for transformations of such data. ## Relational data model The **relational model** is a way of thinking about data as sets and operations on sets. -Formalized almost a half-century ago -([Codd, 1969](https://dl.acm.org/citation.cfm?doid=362384.362685)), the relational data -model provides the most rigorous approach to structured data storage and the most +Formalized almost a half-century ago +([Codd, 1969](https://dl.acm.org/citation.cfm?doid=362384.362685)), the relational data +model provides the most rigorous approach to structured data storage and the most precise approach to data querying. -The model is defined by the principles of data representation, domain constraints, -uniqueness constraints, referential constraints, and declarative queries as summarized +The model is defined by the principles of data representation, domain constraints, +uniqueness constraints, referential constraints, and declarative queries as summarized below. ### Core principles of the relational data model **Data representation** Data are represented and manipulated in the form of relations. - A relation is a set (i.e. an unordered collection) of entities of values for each of + A relation is a set (i.e. an unordered collection) of entities of values for each of the respective named attributes of the relation. - Base relations represent stored data while derived relations are formed from base + Base relations represent stored data while derived relations are formed from base relations through query expressions. - A collection of base relations with their attributes, domain constraints, uniqueness + A collection of base relations with their attributes, domain constraints, uniqueness constraints, and referential constraints is called a schema. **Domain constraints** - Attribute values are drawn from corresponding attribute domains, i.e. predefined sets + Attribute values are drawn from corresponding attribute domains, i.e. predefined sets of values. - Attribute domains may not include relations, which keeps the data model flat, i.e. + Attribute domains may not include relations, which keeps the data model flat, i.e. free of nested structures. **Uniqueness constraints** Entities within relations are addressed by values of their attributes. - To identify and relate data elements, uniqueness constraints are imposed on subsets + To identify and relate data elements, uniqueness constraints are imposed on subsets of attributes. Such subsets are then referred to as keys. One key in a relation is designated as the primary key used for referencing its elements. **Referential constraints** - Associations among data are established by means of referential constraints with the + Associations among data are established by means of referential constraints with the help of foreign keys. - A referential constraint on relation A referencing relation B allows only those + A referential constraint on relation A referencing relation B allows only those entities in A whose foreign key attributes match the key attributes of an entity in B. **Declarative queries** - Data queries are formulated through declarative, as opposed to imperative, + Data queries are formulated through declarative, as opposed to imperative, specifications of sought results. - This means that query expressions convey the logic for the result rather than the + This means that query expressions convey the logic for the result rather than the procedure for obtaining it. - Formal languages for query expressions include relational algebra, relational + Formal languages for query expressions include relational algebra, relational calculus, and SQL. -The relational model has many advantages over both hierarchical file systems and -tabular models for maintaining data integrity and providing flexible access to +The relational model has many advantages over both hierarchical file systems and +tabular models for maintaining data integrity and providing flexible access to interesting subsets of the data. -Popular implementations of the relational data model rely on the Structured Query +Popular implementations of the relational data model rely on the Structured Query Language (SQL). -SQL comprises distinct sublanguages for schema definition, data manipulation, and data +SQL comprises distinct sublanguages for schema definition, data manipulation, and data queries. -SQL thoroughly dominates in the space of relational databases and is often conflated +SQL thoroughly dominates in the space of relational databases and is often conflated with the relational data model in casual discourse. -Various terminologies are used to describe related concepts from the relational data +Various terminologies are used to describe related concepts from the relational data model. -Similar to spreadsheets, relations are often visualized as tables with *attributes* +Similar to spreadsheets, relations are often visualized as tables with *attributes* corresponding to *columns* and *entities* corresponding to *rows*. In particular, SQL uses the terms *table*, *column*, and *row*. ## DataJoint is a refinement of the relational data model -DataJoint is a conceptual refinement of the relational data model offering a more -expressive and rigorous framework for database programming +DataJoint is a conceptual refinement of the relational data model offering a more +expressive and rigorous framework for database programming ([Yatsenko et al., 2018](https://arxiv.org/abs/1807.11104)). -The DataJoint model facilitates clear conceptual modeling, efficient schema design, and +The DataJoint model facilitates clear conceptual modeling, efficient schema design, and precise and flexible data queries. -The model has emerged over a decade of continuous development of complex data pipelines -for neuroscience experiments +The model has emerged over a decade of continuous development of complex data pipelines +for neuroscience experiments ([Yatsenko et al., 2015](https://www.biorxiv.org/content/early/2015/11/14/031658)). -DataJoint has allowed researchers with no prior knowledge of databases to collaborate -effectively on common data pipelines sustaining data integrity and supporting flexible +DataJoint has allowed researchers with no prior knowledge of databases to collaborate +effectively on common data pipelines sustaining data integrity and supporting flexible access. DataJoint is currently implemented as client libraries in MATLAB and Python. -These libraries work by transpiling DataJoint queries into SQL before passing them on -to conventional relational database systems that serve as the backend, in combination +These libraries work by transpiling DataJoint queries into SQL before passing them on +to conventional relational database systems that serve as the backend, in combination with bulk storage systems for storing large contiguous data objects. DataJoint comprises: @@ -109,9 +109,9 @@ DataJoint comprises: - a schema [definition](../design/tables/declare.md) language - a data [manipulation](../manipulation/index.md) language - a data [query](../query/principles.md) language -- a [diagramming](../design/diagrams.md) notation for visualizing relationships between +- a [diagramming](../design/diagrams.md) notation for visualizing relationships between modeled entities -The key refinement of DataJoint over other relational data models and their -implementations is DataJoint's support of +The key refinement of DataJoint over other relational data models and their +implementations is DataJoint's support of [entity normalization](../design/normalization.md). diff --git a/docs/src/concepts/data-pipelines.md b/docs/src/concepts/data-pipelines.md index 998ad372a..6dffcdae4 100644 --- a/docs/src/concepts/data-pipelines.md +++ b/docs/src/concepts/data-pipelines.md @@ -2,22 +2,22 @@ ## What is a data pipeline? -A scientific **data pipeline** is a collection of processes and systems for organizing -the data, computations, and workflows used by a research group as they jointly perform +A scientific **data pipeline** is a collection of processes and systems for organizing +the data, computations, and workflows used by a research group as they jointly perform complex sequences of data acquisition, processing, and analysis. A variety of tools can be used for supporting shared data pipelines: Data repositories Research teams set up a shared **data repository**. - This minimal data management tool allows depositing and retrieving data and managing + This minimal data management tool allows depositing and retrieving data and managing user access. - For example, this may include a collection of files with standard naming conventions + For example, this may include a collection of files with standard naming conventions organized into folders and sub-folders. - Or a data repository might reside on the cloud, for example in a collection of S3 + Or a data repository might reside on the cloud, for example in a collection of S3 buckets. - This image of data management -- where files are warehoused and retrieved from a - hierarchically-organized system of folders -- is an approach that is likely familiar + This image of data management -- where files are warehoused and retrieved from a + hierarchically-organized system of folders -- is an approach that is likely familiar to most scientists. Database systems @@ -31,7 +31,7 @@ Database systems However, usually the more advanced concepts involved in building and using relational databases fall under the specific expertise of data scientists. Data pipelines - **Data pipeline** frameworks may include all the features of a database system along + **Data pipeline** frameworks may include all the features of a database system along with additional functionality: 1. Integrating computations to perform analyses and manage intermediate results in a principled way. @@ -48,35 +48,35 @@ Major features of data management frameworks: data repositories, databases, and ## What is DataJoint? -DataJoint is a free open-source framework for creating scientific data pipelines +DataJoint is a free open-source framework for creating scientific data pipelines directly from MATLAB or Python (or any mixture of the two). -The data are stored in a language-independent way that allows interoperability between +The data are stored in a language-independent way that allows interoperability between MATLAB and Python, with additional languages in the works. -DataJoint pipelines become the central tool in the operations of data-intensive labs or -consortia as they organize participants with different roles and skills around a common +DataJoint pipelines become the central tool in the operations of data-intensive labs or +consortia as they organize participants with different roles and skills around a common framework. -In DataJoint, a data pipeline is a sequence of steps (more generally, a directed +In DataJoint, a data pipeline is a sequence of steps (more generally, a directed acyclic graph) with integrated data storage at each step. -The pipeline may have some nodes requiring manual data entry or import from external -sources, some that read from raw data files, and some that perform computations on data +The pipeline may have some nodes requiring manual data entry or import from external +sources, some that read from raw data files, and some that perform computations on data stored in other database nodes. -In a typical scenario, experimenters and acquisition instruments feed data into nodes -at the head of the pipeline, while downstream nodes perform automated computations for +In a typical scenario, experimenters and acquisition instruments feed data into nodes +at the head of the pipeline, while downstream nodes perform automated computations for data processing and analysis. -For example, this is the pipeline for a simple mouse experiment involving calcium +For example, this is the pipeline for a simple mouse experiment involving calcium imaging in mice. ![A data pipeline](../images/pipeline.png){: style="width:250px; align:center"} -In this example, the experimenter first enters information about a mouse, then enters -information about each imaging session in that mouse, and then each scan performed in +In this example, the experimenter first enters information about a mouse, then enters +information about each imaging session in that mouse, and then each scan performed in each imaging session. -Next the automated portion of the pipeline takes over to import the raw imaging data, -perform image alignment to compensate for motion, image segmentation to identify cells +Next the automated portion of the pipeline takes over to import the raw imaging data, +perform image alignment to compensate for motion, image segmentation to identify cells in the images, and extraction of calcium traces. -Finally, the receptive field (RF) computation is performed by relating the calcium +Finally, the receptive field (RF) computation is performed by relating the calcium signals to the visual stimulus information. ## How DataJoint works @@ -88,49 +88,49 @@ Conceptual overview of DataJoint operation. ![DataJoint operation](../images/how-it-works.png){: style="align:center"} DataJoint provides a simple and powerful data model, which is detailed more formally in [Yatsenko D, Walker EY, Tolias AS (2018). DataJoint: A Simpler Relational Data Model.](https://arxiv.org/abs/1807.11104). -Put most generally, a "data model" defines how to think about data and the operations +Put most generally, a "data model" defines how to think about data and the operations that can be performed on them. -DataJoint's model is a refinement of the relational data model: all nodes in the -pipeline are simple tables storing data, tables are related by their shared attributes, +DataJoint's model is a refinement of the relational data model: all nodes in the +pipeline are simple tables storing data, tables are related by their shared attributes, and query operations can combine the contents of multiple tables. -DataJoint enforces specific constraints on the relationships between tables that help +DataJoint enforces specific constraints on the relationships between tables that help maintain data integrity and enable flexible access. -DataJoint uses a succinct data definition language, a powerful data query language, and +DataJoint uses a succinct data definition language, a powerful data query language, and expressive visualizations of the pipeline. -A well-defined and principled approach to data organization and computation enables +A well-defined and principled approach to data organization and computation enables teams of scientists to work together efficiently. The data become immediately available to all participants with appropriate access privileges. -Some of the "participants" may be computational agents that perform processing and -analysis, and so DataJoint features a built-in distributed job management process to +Some of the "participants" may be computational agents that perform processing and +analysis, and so DataJoint features a built-in distributed job management process to allow distributing analysis between any number of computers. -From a practical point of view, the back-end data architecture may vary depending on +From a practical point of view, the back-end data architecture may vary depending on project requirements. -Typically, the data architecture includes a relational database server (e.g. MySQL) and +Typically, the data architecture includes a relational database server (e.g. MySQL) and a bulk data storage system (e.g. [AWS S3](https://aws.amazon.com/s3/) or a filesystem). -However, users need not interact with the database directly, but via MATLAB or Python +However, users need not interact with the database directly, but via MATLAB or Python objects that are each associated with an individual table in the database. -One of the main advantages of this approach is that DataJoint clearly separates the -data model facing the user from the data architecture implementing data management and -computing. DataJoint works well in combination with good code sharing (e.g. with -[git](https://git-scm.com/)) and environment sharing (e.g. with +One of the main advantages of this approach is that DataJoint clearly separates the +data model facing the user from the data architecture implementing data management and +computing. DataJoint works well in combination with good code sharing (e.g. with +[git](https://git-scm.com/)) and environment sharing (e.g. with [Docker](https://www.docker.com/)). -DataJoint is designed for quick prototyping and continuous exploration as experimental +DataJoint is designed for quick prototyping and continuous exploration as experimental designs change or evolve. -New analysis methods can be added or removed at any time, and the structure of the -workflow itself can change over time, for example as new data acquisition methods are +New analysis methods can be added or removed at any time, and the structure of the +workflow itself can change over time, for example as new data acquisition methods are developed. -With DataJoint, data sharing and publishing is no longer a separate step at the end of +With DataJoint, data sharing and publishing is no longer a separate step at the end of the project. -Instead data sharing is an inherent feature of the process: to share data with other -collaborators or to publish the data to the world, one only needs to set the access +Instead data sharing is an inherent feature of the process: to share data with other +collaborators or to publish the data to the world, one only needs to set the access privileges. ## Real-life example -The [Mesoscale Activity Project](https://www.simonsfoundation.org/funded-project/%20multi-regional-neuronal-dynamics-of-memory-guided-flexible-behavior/) +The [Mesoscale Activity Project](https://www.simonsfoundation.org/funded-project/%20multi-regional-neuronal-dynamics-of-memory-guided-flexible-behavior/) (MAP) is a collaborative project between four neuroscience labs. MAP uses DataJoint for data acquisition, processing, analysis, interfaces, and external sharing. @@ -139,19 +139,19 @@ The DataJoint pipeline for the MAP project. ![A data pipeline for the MAP project](../images/map-dataflow.png){: style="align:center"} The pipeline is hosted in the cloud through [Amazon Web Services](https://aws.amazon.com/) (AWS). -MAP data scientists at the Janelia Research Campus and Baylor College of Medicine +MAP data scientists at the Janelia Research Campus and Baylor College of Medicine defined the data pipeline. -Experimental scientists enter manual data directly into the pipeline using the +Experimental scientists enter manual data directly into the pipeline using the [Helium web interface](https://github.com/mattbdean/Helium). The raw data are preprocessed using the DataJoint client libraries in MATLAB and Python; -the preprocessed data are ingested into the pipeline while the bulky and raw data are -shared using [Globus](https://globus.org) transfer through the -[PETREL](https://www.alcf.anl.gov/petrel) storage servers provided by the Argonne +the preprocessed data are ingested into the pipeline while the bulky and raw data are +shared using [Globus](https://globus.org) transfer through the +[PETREL](https://www.alcf.anl.gov/petrel) storage servers provided by the Argonne National Lab. -Data are made immediately available for exploration and analysis to collaborating labs, +Data are made immediately available for exploration and analysis to collaborating labs, and the analysis results are also immediately shared. Analysis data may be visualized through web interfaces. -Intermediate results may be exported into the [NWB](https://nwb.org) format for sharing +Intermediate results may be exported into the [NWB](https://nwb.org) format for sharing with external groups. ## Summary of DataJoint features diff --git a/docs/src/concepts/principles.md b/docs/src/concepts/principles.md index 94070f59d..2bf491590 100644 --- a/docs/src/concepts/principles.md +++ b/docs/src/concepts/principles.md @@ -2,23 +2,23 @@ ## Theoretical Foundations -*DataJoint Core* implements a systematic framework for the joint management of -structured scientific data and its associated computations. -The framework builds on the theoretical foundations of the +*DataJoint Core* implements a systematic framework for the joint management of +structured scientific data and its associated computations. +The framework builds on the theoretical foundations of the [Relational Model](https://en.wikipedia.org/wiki/Relational_model) and the [Entity-Relationship Model](https://en.wikipedia.org/wiki/Entity%E2%80%93relationship_model), -introducing a number of critical clarifications for the effective use of databases as -scientific data pipelines. -Notably, DataJoint introduces the concept of *computational dependencies* as a native +introducing a number of critical clarifications for the effective use of databases as +scientific data pipelines. +Notably, DataJoint introduces the concept of *computational dependencies* as a native first-class citizen of the data model. -This integration of data structure and computation into a single model, defines a new +This integration of data structure and computation into a single model, defines a new class of *computational scientific databases*. -This page defines the key principles of this model without attachment to a specific -implementation while a more complete description of the model can be found in +This page defines the key principles of this model without attachment to a specific +implementation while a more complete description of the model can be found in [Yatsenko et al, 2018](https://doi.org/10.48550/arXiv.1807.11104). -DataJoint developers are developing these principles into an +DataJoint developers are developing these principles into an [open standard](https://en.wikipedia.org/wiki/Open_standard) to allow multiple alternative implementations. @@ -28,18 +28,18 @@ alternative implementations. DataJoint uses only one data structure in all its operations—the *entity set*. -1. All data are represented in the form of *entity sets*, i.e. an ordered collection of -*entities*. -2. All entities of an entity set belong to the same well-defined entity class and have -the same set of named attributes. -3. Attributes in an entity set has a *data type* (or *domain*), representing the set of +1. All data are represented in the form of *entity sets*, i.e. an ordered collection of +*entities*. +2. All entities of an entity set belong to the same well-defined entity class and have +the same set of named attributes. +3. Attributes in an entity set has a *data type* (or *domain*), representing the set of its valid values. -4. Each entity in an entity set provides the *attribute values* for all of the +4. Each entity in an entity set provides the *attribute values* for all of the attributes of its entity class. -5. Each entity set has a *primary key*, *i.e.* a subset of attributes that, jointly, +5. Each entity set has a *primary key*, *i.e.* a subset of attributes that, jointly, uniquely identify any entity in the set. -These formal terms have more common (even if less precise) variants: +These formal terms have more common (even if less precise) variants: | formal | common | |:-:|:--:| @@ -54,14 +54,14 @@ A collection of *stored tables* make up a *database*. DataJoint introduces a streamlined syntax for defining a stored table. -Each line in the definition defines an attribute with its name, data type, an optional +Each line in the definition defines an attribute with its name, data type, an optional default value, and an optional comment in the format: ```python name [=default] : type [# comment] ``` -Primary attributes come first and are separated from the rest of the attributes with +Primary attributes come first and are separated from the rest of the attributes with the divider `---`. For example, the following code defines the entity set for entities of class `Employee`: @@ -72,13 +72,13 @@ employee_id : int ssn = null : int # optional social security number date_of_birth : date gender : enum('male', 'female', 'other') -home_address="" : varchar(1000) +home_address="" : varchar(1000) primary_phone="" : varchar(12) ``` ### Data Tiers -Stored tables are designated into one of four *tiers* indicating how their data +Stored tables are designated into one of four *tiers* indicating how their data originates. | table tier | data origin | @@ -92,36 +92,36 @@ originates. ### Data Normalization -A collection of data is considered normalized when organized into a collection of -entity sets, where each entity set represents a well-defined entity class with all its -attributes applicable to each entity in the set and the same primary key identifying +A collection of data is considered normalized when organized into a collection of +entity sets, where each entity set represents a well-defined entity class with all its +attributes applicable to each entity in the set and the same primary key identifying -The normalization procedure often includes splitting data from one table into several -tables, one for each proper entity set. +The normalization procedure often includes splitting data from one table into several +tables, one for each proper entity set. ### Databases and Schemas -Stored tables are named and grouped into namespaces called *schemas*. -A collection of schemas make up a *database*. -A *database* has a globally unique address or name. -A *schema* has a unique name within its database. -Within a *connection* to a particular database, a stored table is identified as +Stored tables are named and grouped into namespaces called *schemas*. +A collection of schemas make up a *database*. +A *database* has a globally unique address or name. +A *schema* has a unique name within its database. +Within a *connection* to a particular database, a stored table is identified as `schema.Table`. A schema typically groups tables that are logically related. -## Dependencies +## Dependencies -Entity sets can form referential dependencies that express and +Entity sets can form referential dependencies that express and -### Diagramming +### Diagramming ## Data integrity ### Entity integrity -*Entity integrity* is the guarantee made by the data management process of the 1:1 -mapping between real-world entities and their digital representations. -In practice, entity integrity is ensured when it is made clear +*Entity integrity* is the guarantee made by the data management process of the 1:1 +mapping between real-world entities and their digital representations. +In practice, entity integrity is ensured when it is made clear ### Referential integrity @@ -129,8 +129,8 @@ In practice, entity integrity is ensured when it is made clear ## Data manipulations -## Data queries +## Data queries -### Query Operators +### Query Operators ## Pipeline computations diff --git a/docs/src/concepts/teamwork.md b/docs/src/concepts/teamwork.md index 46bd9e3a9..c25394441 100644 --- a/docs/src/concepts/teamwork.md +++ b/docs/src/concepts/teamwork.md @@ -2,7 +2,7 @@ ## Data management in a science project -Science labs organize their projects as a sequence of activities of experiment design, +Science labs organize their projects as a sequence of activities of experiment design, data acquisition, and processing and analysis.
@@ -10,22 +10,22 @@ data acquisition, and processing and analysis.
Workflow and dataflow in a common findings-centered approach to data science in a science lab.
-Many labs lack a uniform data management strategy that would span longitudinally across +Many labs lack a uniform data management strategy that would span longitudinally across the entire project lifecycle as well as laterally across different projects. -Prior to publishing their findings, the research team may need to publish the data to +Prior to publishing their findings, the research team may need to publish the data to support their findings. -Without a data management system, this requires custom repackaging of the data to -conform to the [FAIR principles](https://www.nature.com/articles/sdata201618) for +Without a data management system, this requires custom repackaging of the data to +conform to the [FAIR principles](https://www.nature.com/articles/sdata201618) for scientific data management. ## Data-centric project organization -DataJoint is designed to support a data-centric approach to large science projects in -which data are viewed as a principal output of the research project and are managed +DataJoint is designed to support a data-centric approach to large science projects in +which data are viewed as a principal output of the research project and are managed systematically throughout in a single framework through the entire process. -This approach requires formulating a general data science plan and upfront investment +This approach requires formulating a general data science plan and upfront investment for setting up resources and processes and training the teams. The team uses DataJoint to build data pipelines to support multiple projects. @@ -34,7 +34,7 @@ The team uses DataJoint to build data pipelines to support multiple projects.
Workflow and dataflow in a data pipeline-centered approach.
-Data pipelines support project data across their entire lifecycle, including the +Data pipelines support project data across their entire lifecycle, including the following functions - experiment design @@ -46,13 +46,13 @@ following functions - visualization of analysis results - export for sharing and publishing -Through all these activities, all these data are made accessible to all authorized -participants and distributed computations can be done in parallel without compromising +Through all these activities, all these data are made accessible to all authorized +participants and distributed computations can be done in parallel without compromising data integrity. ## Team roles -The adoption of a uniform data management framework allows separation of roles and +The adoption of a uniform data management framework allows separation of roles and division of labor among team members, leading to greater efficiency and better scaling.
@@ -63,38 +63,38 @@ division of labor among team members, leading to greater efficiency and better s Scientists design and conduct experiments, collecting data. - They interact with the data pipeline through graphical user interfaces designed by + They interact with the data pipeline through graphical user interfaces designed by others. They understand what analysis is used to test their hypotheses. Data scientists - have the domain expertise and select and implement the processing and analysis + have the domain expertise and select and implement the processing and analysis methods for experimental data. - Data scientists are in charge of defining and managing the data pipeline using - DataJoint's data model, but they may not know the details of the underlying + Data scientists are in charge of defining and managing the data pipeline using + DataJoint's data model, but they may not know the details of the underlying architecture. - They interact with the pipeline using client programming interfaces directly from + They interact with the pipeline using client programming interfaces directly from languages such as MATLAB and Python. - The bulk of this manual is written for working data scientists, except for System + The bulk of this manual is written for working data scientists, except for System Administration. Data engineers work with the data scientists to support the data pipeline. - They rely on their understanding of the DataJoint data model to configure and - administer the required IT resources such as database servers, data storage + They rely on their understanding of the DataJoint data model to configure and + administer the required IT resources such as database servers, data storage servers, networks, cloud instances, [Globus](https://globus.org) endpoints, etc. - Data engineers can provide general solutions such as web hosting, data publishing, + Data engineers can provide general solutions such as web hosting, data publishing, interfaces, exports and imports. - The System Administration section of this tutorial contains materials helpful in + The System Administration section of this tutorial contains materials helpful in accomplishing these tasks. -DataJoint is designed to delineate a clean boundary between **data science** and **data +DataJoint is designed to delineate a clean boundary between **data science** and **data engineering**. -This allows data scientists to use the same uniform data model for data pipelines +This allows data scientists to use the same uniform data model for data pipelines backed by a variety of information technologies. -This delineation also enables economies of scale as a single data engineering team can +This delineation also enables economies of scale as a single data engineering team can support a wide spectrum of science projects. diff --git a/docs/src/concepts/terminology.md b/docs/src/concepts/terminology.md index 4502274d6..0fdc41e96 100644 --- a/docs/src/concepts/terminology.md +++ b/docs/src/concepts/terminology.md @@ -2,32 +2,32 @@ # Terminology -DataJoint introduces a principled data model, which is described in detail in +DataJoint introduces a principled data model, which is described in detail in [Yatsenko et al., 2018](https://arxiv.org/abs/1807.11104). -This data model is a conceptual refinement of the Relational Data Model and also draws +This data model is a conceptual refinement of the Relational Data Model and also draws on the Entity-Relationship Model (ERM). The Relational Data Model was inspired by the concepts of relations in Set Theory. -When the formal relational data model was formulated, it introduced additional +When the formal relational data model was formulated, it introduced additional terminology (e.g. *relation*, *attribute*, *tuple*, *domain*). -Practical programming languages such as SQL do not precisely follow the relational data -model and introduce other terms to approximate relational concepts (e.g. *table*, +Practical programming languages such as SQL do not precisely follow the relational data +model and introduce other terms to approximate relational concepts (e.g. *table*, *column*, *row*, *datatype*). -Subsequent data models (e.g. ERM) refined the relational data model and introduced -their own terminology to describe analogous concepts (e.g. *entity set*, +Subsequent data models (e.g. ERM) refined the relational data model and introduced +their own terminology to describe analogous concepts (e.g. *entity set*, *relationship set*, *attribute set*). -As a result, similar concepts may be described using different sets of terminologies, +As a result, similar concepts may be described using different sets of terminologies, depending on the context and the speaker's background. -For example, what is known as a **relation** in the formal relational model is called a -**table** in SQL; the analogous concept in ERM and DataJoint is called an **entity +For example, what is known as a **relation** in the formal relational model is called a +**table** in SQL; the analogous concept in ERM and DataJoint is called an **entity set**. -The DataJoint documentation follows the terminology defined in -[Yatsenko et al, 2018](https://arxiv.org/abs/1807.11104), except *entity set* is +The DataJoint documentation follows the terminology defined in +[Yatsenko et al, 2018](https://arxiv.org/abs/1807.11104), except *entity set* is replaced with the more colloquial *table* or *query result* in most cases. -The table below summarizes the terms used for similar concepts across the related data +The table below summarizes the terms used for similar concepts across the related data models. Data model terminology @@ -51,65 +51,65 @@ DataJoint users do not interact with it directly. A **DataJoint schema** is - a database on the database server containing tables with data *and* - - a collection of classes (in MATLAB or Python) associated with the database, one + - a collection of classes (in MATLAB or Python) associated with the database, one class for each table. -In MATLAB, the collection of classes is organized as a **package**, i.e. a file folder +In MATLAB, the collection of classes is organized as a **package**, i.e. a file folder starting with a `+`. -In Python, the collection of classes is any set of classes decorated with the +In Python, the collection of classes is any set of classes decorated with the appropriate `schema` object. -Very commonly classes for tables in one database are organized as a distinct Python +Very commonly classes for tables in one database are organized as a distinct Python module. Thus, typical DataJoint projects have one module per database. However, this organization is up to the user's discretion. ## Base tables -**Base tables** are tables stored in the database, and are often referred to simply as +**Base tables** are tables stored in the database, and are often referred to simply as *tables* in DataJoint. -Base tables are distinguished from **derived tables**, which result from relational +Base tables are distinguished from **derived tables**, which result from relational [operators](../query/operators.md). ## Relvars and relation values -Early versions of the DataJoint documentation referred to the relation objects as +Early versions of the DataJoint documentation referred to the relation objects as [relvars](https://en.wikipedia.org/wiki/Relvar). -This term emphasizes the fact that relational variables and expressions do not contain -actual data but are rather symbolic representations of data to be retrieved from the +This term emphasizes the fact that relational variables and expressions do not contain +actual data but are rather symbolic representations of data to be retrieved from the database. The specific value of a relvar would then be referred to as the **relation value**. The value of a relvar can change with changes in the state of the database. -The more recent iteration of the documentation has grown less pedantic and more often +The more recent iteration of the documentation has grown less pedantic and more often uses the term *table* instead. ## Metadata The vocabulary of DataJoint does not include this term. -In data science, the term **metadata** commonly means "data about the data" rather than +In data science, the term **metadata** commonly means "data about the data" rather than the data themselves. -For example, metadata could include data sizes, timestamps, data types, indexes, +For example, metadata could include data sizes, timestamps, data types, indexes, keywords. -In contrast, neuroscientists often use the term to refer to conditions and annotations +In contrast, neuroscientists often use the term to refer to conditions and annotations about experiments. -This distinction arose when such information was stored separately from experimental +This distinction arose when such information was stored separately from experimental recordings, such as in physical notebooks. -Such "metadata" are used to search and to classify the data and are in fact an integral +Such "metadata" are used to search and to classify the data and are in fact an integral part of the *actual* data. In DataJoint, all data other than blobs can be used in searches and categorization. -These fields may originate from manual annotations, preprocessing, or analyses just as +These fields may originate from manual annotations, preprocessing, or analyses just as easily as from recordings or behavioral performance. -Since "metadata" in the neuroscience sense are not distinguished from any other data in +Since "metadata" in the neuroscience sense are not distinguished from any other data in a pipeline, DataJoint avoids the term entirely. Instead, DataJoint differentiates data into [data tiers](../design/tables/tiers.md). ## Glossary -We've taken careful consideration to use consistent terminology. +We've taken careful consideration to use consistent terminology. diff --git a/docs/src/design/diagrams.md b/docs/src/design/diagrams.md index ecfca6934..826f78926 100644 --- a/docs/src/design/diagrams.md +++ b/docs/src/design/diagrams.md @@ -1,10 +1,10 @@ # Diagrams Diagrams are a great way to visualize the pipeline and understand the flow -of data. DataJoint diagrams are based on **entity relationship diagram** (ERD). -Objects of type `dj.Diagram` allow visualizing portions of the data pipeline in +of data. DataJoint diagrams are based on **entity relationship diagram** (ERD). +Objects of type `dj.Diagram` allow visualizing portions of the data pipeline in graphical form. -Tables are depicted as nodes and [dependencies](./tables/dependencies.md) as directed +Tables are depicted as nodes and [dependencies](./tables/dependencies.md) as directed edges between them. The `draw` method plots the graph. @@ -25,19 +25,19 @@ DataJoint uses the following conventions: - Computed=red circle - Part=black text The names of [part tables](./tables/master-part.md) are indicated in a smaller font. -- [Dependencies](./tables/dependencies.md) are indicated as edges in the graph and +- [Dependencies](./tables/dependencies.md) are indicated as edges in the graph and always directed downward, forming a **directed acyclic graph**. - Foreign keys contained within the primary key are indicated as solid lines. This means that the referenced table becomes part of the primary key of the dependent table. - Foreign keys that are outside the primary key are indicated by dashed lines. -- If the primary key of the dependent table has no other attributes besides the foreign +- If the primary key of the dependent table has no other attributes besides the foreign key, the foreign key is a thick solid line, indicating a 1:{0,1} relationship. -- Foreign keys made without renaming the foreign key attributes are in black whereas +- Foreign keys made without renaming the foreign key attributes are in black whereas foreign keys that rename the attributes are indicated in red. ## Diagramming an entire schema -To plot the Diagram for an entire schema, an Diagram object can be initialized with the +To plot the Diagram for an entire schema, an Diagram object can be initialized with the schema object (which is normally used to decorate table objects) ```python @@ -46,7 +46,7 @@ schema = dj.Schema('my_database') dj.Diagram(schema).draw() ``` -or alternatively an object that has the schema object as an attribute, such as the +or alternatively an object that has the schema object as an attribute, such as the module defining a schema: ```python @@ -55,14 +55,14 @@ import seq # import the sequence module defining the seq database dj.Diagram(seq).draw() # draw the Diagram ``` -Note that calling the `.draw()` method is not necessary when working in a Jupyter +Note that calling the `.draw()` method is not necessary when working in a Jupyter notebook. -You can simply let the object display itself, for example by entering `dj.Diagram(seq)` +You can simply let the object display itself, for example by entering `dj.Diagram(seq)` in a notebook cell. -The Diagram will automatically render in the notebook by calling its `_repr_html_` +The Diagram will automatically render in the notebook by calling its `_repr_html_` method. -A Diagram displayed without `.draw()` will be rendered as an SVG, and hovering the -mouse over a table will reveal a compact version of the output of the `.describe()` +A Diagram displayed without `.draw()` will be rendered as an SVG, and hovering the +mouse over a table will reveal a compact version of the output of the `.describe()` method. ### Initializing with a single table @@ -73,12 +73,12 @@ A `dj.Diagram` object can be initialized with a single table. dj.Diagram(seq.Genome).draw() ``` -A single node makes a rather boring graph but ERDs can be added together or subtracted +A single node makes a rather boring graph but ERDs can be added together or subtracted from each other using graph algebra. ### Adding diagrams together -However two graphs can be added, resulting in new graph containing the union of the +However two graphs can be added, resulting in new graph containing the union of the sets of nodes from the two original graphs. The corresponding foreign keys will be automatically @@ -89,7 +89,7 @@ The corresponding foreign keys will be automatically ### Expanding diagrams upstream and downstream -Adding a number to an Diagram object adds nodes downstream in the pipeline while +Adding a number to an Diagram object adds nodes downstream in the pipeline while subtracting a number from Diagram object adds nodes upstream in the pipeline. Examples: diff --git a/docs/src/design/drop.md b/docs/src/design/drop.md index 7b63930cb..35a9ac513 100644 --- a/docs/src/design/drop.md +++ b/docs/src/design/drop.md @@ -1,12 +1,12 @@ # Drop -The `drop` method completely removes a table from the database, including its +The `drop` method completely removes a table from the database, including its definition. It also removes all dependent tables, recursively. -DataJoint will first display the tables being dropped and the number of entities in +DataJoint will first display the tables being dropped and the number of entities in each before prompting the user for confirmation to proceed. -The `drop` method is often used during initial design to allow altered table +The `drop` method is often used during initial design to allow altered table definitions to take effect. ```python @@ -16,7 +16,7 @@ Person.drop() ## Dropping part tables -A [part table](../design/tables/master-part.md) is usually removed as a consequence of +A [part table](../design/tables/master-part.md) is usually removed as a consequence of calling `drop` on its master table. To enforce this workflow, calling `drop` directly on a part table produces an error. In some cases, it may be necessary to override this behavior. diff --git a/docs/src/design/integrity.md b/docs/src/design/integrity.md index 56416e4d7..8c1f93376 100644 --- a/docs/src/design/integrity.md +++ b/docs/src/design/integrity.md @@ -1,103 +1,103 @@ # Data Integrity -The term **data integrity** describes guarantees made by the data management process -that prevent errors and corruption in data due to technical failures and human errors +The term **data integrity** describes guarantees made by the data management process +that prevent errors and corruption in data due to technical failures and human errors arising in the course of continuous use by multiple agents. -DataJoint pipelines respect the following forms of data integrity: **entity -integrity**, **referential integrity**, and **group integrity** as described in more +DataJoint pipelines respect the following forms of data integrity: **entity +integrity**, **referential integrity**, and **group integrity** as described in more detail below. ## Entity integrity -In a proper relational design, each table represents a collection of discrete +In a proper relational design, each table represents a collection of discrete real-world entities of some kind. -**Entity integrity** is the guarantee made by the data management process that entities +**Entity integrity** is the guarantee made by the data management process that entities from the real world are reliably and uniquely represented in the database system. -Entity integrity states that the data management process must prevent duplicate +Entity integrity states that the data management process must prevent duplicate representations or misidentification of entities. -DataJoint enforces entity integrity through the use of +DataJoint enforces entity integrity through the use of [primary keys](./tables/primary.md). -Entity integrity breaks down when a process allows data pertaining to the same +Entity integrity breaks down when a process allows data pertaining to the same real-world entity to be entered into the database system multiple times. For example, a school database system may use unique ID numbers to distinguish students. -Suppose the system automatically generates an ID number each time a student record is -entered into the database without checking whether a record already exists for that +Suppose the system automatically generates an ID number each time a student record is +entered into the database without checking whether a record already exists for that student. -Such a system violates entity integrity, because the same student may be assigned +Such a system violates entity integrity, because the same student may be assigned multiple ID numbers. -The ID numbers succeed in uniquely identifying each student record but fail to do so +The ID numbers succeed in uniquely identifying each student record but fail to do so for the actual students. Note that a database cannot guarantee or enforce entity integrity by itself. -Entity integrity is a property of the entire data management process as a whole, -including institutional practices and user actions in addition to database +Entity integrity is a property of the entire data management process as a whole, +including institutional practices and user actions in addition to database configurations. ## Referential integrity -**Referential integrity** is the guarantee made by the data management process that -related data across the database remain present, correctly associated, and mutually +**Referential integrity** is the guarantee made by the data management process that +related data across the database remain present, correctly associated, and mutually consistent. -Guaranteeing referential integrity means enforcing the constraint that no entity can +Guaranteeing referential integrity means enforcing the constraint that no entity can exist in the database without all the other entities on which it depends. -Referential integrity cannot exist without entity integrity: references to entity +Referential integrity cannot exist without entity integrity: references to entity cannot be validated if the identity of the entity itself is not guaranteed. -Referential integrity fails when a data management process allows new data to be +Referential integrity fails when a data management process allows new data to be entered that refers to other data missing from the database. -For example, assume that each electrophysiology recording must refer to the mouse +For example, assume that each electrophysiology recording must refer to the mouse subject used during data collection. -Perhaps an experimenter attempts to insert ephys data into the database that refers to +Perhaps an experimenter attempts to insert ephys data into the database that refers to a nonexistent mouse, due to a misspelling. -A system guaranteeing referential integrity, such as DataJoint, will refuse the +A system guaranteeing referential integrity, such as DataJoint, will refuse the erroneous data. Enforcement of referential integrity does not stop with data ingest. -[Deleting](../manipulation/delete.md) data in DataJoint also deletes any dependent +[Deleting](../manipulation/delete.md) data in DataJoint also deletes any dependent downstream data. Such cascading deletions are necessary to maintain referential integrity. -Consider the deletion of a mouse subject without the deletion of the experimental +Consider the deletion of a mouse subject without the deletion of the experimental sessions involving that mouse. -A database that allows such deletion will break referential integrity, as the +A database that allows such deletion will break referential integrity, as the experimental sessions for the removed mouse depend on missing data. -Any data management process that allows data to be deleted with no consideration of +Any data management process that allows data to be deleted with no consideration of dependent data cannot maintain referential integrity. -[Updating](../manipulation/update.md) data already present in a database system also +[Updating](../manipulation/update.md) data already present in a database system also jeopardizes referential integrity. -For this reason, the DataJoint workflow does not include updates to entities once they +For this reason, the DataJoint workflow does not include updates to entities once they have been ingested into a pipeline. -Allowing updates to upstream entities would break the referential integrity of any +Allowing updates to upstream entities would break the referential integrity of any dependent data downstream. -For example, permitting a user to change the name of a mouse subject would invalidate -any experimental sessions that used that mouse, presuming the mouse name was part of +For example, permitting a user to change the name of a mouse subject would invalidate +any experimental sessions that used that mouse, presuming the mouse name was part of the primary key. -The proper way to change data in DataJoint is to delete the existing entities and to +The proper way to change data in DataJoint is to delete the existing entities and to insert corrected ones, preserving referential integrity. ## Group integrity -**Group integrity** denotes the guarantee made by the data management process that +**Group integrity** denotes the guarantee made by the data management process that entities composed of multiple parts always appear in their complete form. -Group integrity in DataJoint is formalized through +Group integrity in DataJoint is formalized through [master-part](./tables/master-part.md) relationships. -The master-part relationship has important implications for dependencies, because a -downstream entity depending on a master entity set may be considered to depend on the +The master-part relationship has important implications for dependencies, because a +downstream entity depending on a master entity set may be considered to depend on the parts as well. ## Relationships -In DataJoint, the term **relationship** is used rather generally to describe the -effects of particular configurations of [dependencies](./tables/dependencies.md) +In DataJoint, the term **relationship** is used rather generally to describe the +effects of particular configurations of [dependencies](./tables/dependencies.md) between multiple entity sets. -It is often useful to classify relationships as one-to-one, many-to-one, one-to-many, +It is often useful to classify relationships as one-to-one, many-to-one, one-to-many, and many-to-many. -In a **one-to-one relationship**, each entity in a downstream table has exactly one +In a **one-to-one relationship**, each entity in a downstream table has exactly one corresponding entity in the upstream table. -A dependency of an entity set containing the death dates of mice on an entity set -describing the mice themselves would obviously be a one-to-one relationship, as in the +A dependency of an entity set containing the death dates of mice on an entity set +describing the mice themselves would obviously be a one-to-one relationship, as in the example below. ```python @@ -120,9 +120,9 @@ death_date : datetime ![doc_1-1](../images/doc_1-1.png){: style="align:center"} -In a **one-to-many relationship**, multiple entities in a downstream table may depend +In a **one-to-many relationship**, multiple entities in a downstream table may depend on the same entity in the upstream table. -The example below shows a table containing individual channel data from multi-channel +The example below shows a table containing individual channel data from multi-channel recordings, representing a one-to-many relationship. ```python @@ -147,13 +147,13 @@ channel_data : longblob ``` ![doc_1-many](../images/doc_1-many.png){: style="align:center"} -In a **many-to-one relationship**, each entity in a table is associated with multiple +In a **many-to-one relationship**, each entity in a table is associated with multiple entities from another table. -Many-to-one relationships between two tables are usually established using a separate +Many-to-one relationships between two tables are usually established using a separate membership table. -The example below includes a table of mouse subjects, a table of subject groups, and a +The example below includes a table of mouse subjects, a table of subject groups, and a membership [part table](./tables/master-part.md) listing the subjects in each group. -A many-to-one relationship exists between the `Mouse` table and the `SubjectGroup` +A many-to-one relationship exists between the `Mouse` table and the `SubjectGroup` table, with is expressed through entities in `GroupMember`. ```python @@ -182,13 +182,13 @@ class GroupMember(dj.Part): ![doc_many-1](../images/doc_many-1.png){: style="align:center"} -In a **many-to-many relationship**, multiple entities in one table may each relate to +In a **many-to-many relationship**, multiple entities in one table may each relate to multiple entities in another upstream table. -Many-to-many relationships between two tables are usually established using a separate +Many-to-many relationships between two tables are usually established using a separate association table. -Each entity in the association table links one entity from each of the two upstream +Each entity in the association table links one entity from each of the two upstream tables it depends on. -The below example of a many-to-many relationship contains a table of recording +The below example of a many-to-many relationship contains a table of recording modalities and a table of multimodal recording sessions. Entities in a third table represent the modes used for each session. @@ -214,5 +214,5 @@ class SessionMode(dj.Part): ![doc_many-many](../images/doc_many-many.png){: style="align:center"} -The types of relationships between entity sets are expressed in the +The types of relationships between entity sets are expressed in the [Diagram](diagrams.md) of a schema. diff --git a/docs/src/design/normalization.md b/docs/src/design/normalization.md index 0166f4a0a..000028396 100644 --- a/docs/src/design/normalization.md +++ b/docs/src/design/normalization.md @@ -1,117 +1,117 @@ # Entity Normalization DataJoint uses a uniform way of representing any data. -It does so in the form of **entity sets**, unordered collections of entities of the +It does so in the form of **entity sets**, unordered collections of entities of the same type. -The term **entity normalization** describes the commitment to represent all data as +The term **entity normalization** describes the commitment to represent all data as well-formed entity sets. -Entity normalization is a conceptual refinement of the -[relational data model](../concepts/data-model.md) and is the central principle of the +Entity normalization is a conceptual refinement of the +[relational data model](../concepts/data-model.md) and is the central principle of the DataJoint model ([Yatsenko et al., 2018](https://arxiv.org/abs/1807.11104)). -Entity normalization leads to clear and logical database designs and to easily +Entity normalization leads to clear and logical database designs and to easily comprehensible data queries. -Entity sets are a type of **relation** -(from the [relational data model](../concepts/data-model.md)) and are often visualized +Entity sets are a type of **relation** +(from the [relational data model](../concepts/data-model.md)) and are often visualized as **tables**. -Hence the terms **relation**, **entity set**, and **table** can be used interchangeably +Hence the terms **relation**, **entity set**, and **table** can be used interchangeably when entity normalization is assumed. ## Criteria of a well-formed entity set -1. All elements of an entity set belong to the same well-defined and readily identified +1. All elements of an entity set belong to the same well-defined and readily identified **entity type** from the model world. -2. All attributes of an entity set are applicable directly to each of its elements, +2. All attributes of an entity set are applicable directly to each of its elements, although some attribute values may be missing (set to null). -3. All elements of an entity set must be distinguishable form each other by the same +3. All elements of an entity set must be distinguishable form each other by the same primary key. 4. Primary key attribute values cannot be missing, i.e. set to null. -5. All elements of an entity set participate in the same types of relationships with +5. All elements of an entity set participate in the same types of relationships with other entity sets. ## Entity normalization in schema design -Entity normalization applies to schema design in that the designer is responsible for -the identification of the essential entity types in their model world and of the +Entity normalization applies to schema design in that the designer is responsible for +the identification of the essential entity types in their model world and of the dependencies among the entity types. -The term entity normalization may also apply to a procedure for refactoring a schema +The term entity normalization may also apply to a procedure for refactoring a schema design that does not meet the above criteria into one that does. -In some cases, this may require breaking up some entity sets into multiple entity sets, +In some cases, this may require breaking up some entity sets into multiple entity sets, which may cause some entities to be represented across multiple entity sets. In other cases, this may require converting attributes into their own entity sets. -Technically speaking, entity normalization entails compliance with the -[Boyce-Codd normal form](https://en.wikipedia.org/wiki/Boyce%E2%80%93Codd_normal_form) -while lacking the representational power for the applicability of more complex normal +Technically speaking, entity normalization entails compliance with the +[Boyce-Codd normal form](https://en.wikipedia.org/wiki/Boyce%E2%80%93Codd_normal_form) +while lacking the representational power for the applicability of more complex normal forms ([Kent, 1983](https://dl.acm.org/citation.cfm?id=358054)). -Adherence to entity normalization prevents redundancies in storage and data +Adherence to entity normalization prevents redundancies in storage and data manipulation anomalies. -The same criteria originally motivated the formulation of the classical relational +The same criteria originally motivated the formulation of the classical relational normal forms. ## Entity normalization in data queries Entity normalization applies to data queries as well. -DataJoint's [query operators](../query/operators.md) are designed to preserve the +DataJoint's [query operators](../query/operators.md) are designed to preserve the entity normalization of their inputs. -For example, the outputs of operators [restriction](../query/restrict.md), -[proj](../query/project.md), and [aggr](../query/aggregation.md) retain the same entity +For example, the outputs of operators [restriction](../query/restrict.md), +[proj](../query/project.md), and [aggr](../query/aggregation.md) retain the same entity type as the (first) input. -The [join](../query/join.md) operator produces a new entity type comprising the pairing +The [join](../query/join.md) operator produces a new entity type comprising the pairing of the entity types of its inputs. -[Universal sets](../query/universals.md) explicitly introduce virtual entity sets when +[Universal sets](../query/universals.md) explicitly introduce virtual entity sets when necessary to accomplish a query. ## Examples of poor normalization -Design choices lacking entity normalization may lead to data inconsistencies or +Design choices lacking entity normalization may lead to data inconsistencies or anomalies. -Below are several examples of poorly normalized designs and their normalized +Below are several examples of poorly normalized designs and their normalized alternatives. ### Indirect attributes All attributes should apply to the entity itself. Avoid attributes that actually apply to one of the entity's other attributes. -For example, consider the table `Author` with attributes `author_name`, `institution`, +For example, consider the table `Author` with attributes `author_name`, `institution`, and `institution_address`. -The attribute `institution_address` should really be held in a separate `Institution` +The attribute `institution_address` should really be held in a separate `Institution` table that `Author` depends on. ### Repeated attributes Avoid tables with repeated attributes of the same category. -A better solution is to create a separate table that depends on the first (often a -[part table](../design/tables/master-part.md)), with multiple individual entities +A better solution is to create a separate table that depends on the first (often a +[part table](../design/tables/master-part.md)), with multiple individual entities rather than repeated attributes. -For example, consider the table `Protocol` that includes the attributes `equipment1`, +For example, consider the table `Protocol` that includes the attributes `equipment1`, `equipment2`, and `equipment3`. -A better design would be to create a `ProtocolEquipment` table that links each entity -in `Protocol` with multiple entities in `Equipment` through +A better design would be to create a `ProtocolEquipment` table that links each entity +in `Protocol` with multiple entities in `Equipment` through [dependencies](../design/tables/dependencies.md). ### Attributes that do not apply to all entities All attributes should be relevant to every entity in a table. -Attributes that apply only to a subset of entities in a table likely belong in a +Attributes that apply only to a subset of entities in a table likely belong in a separate table containing only that subset of entities. -For example, a table `Protocol` should include the attribute `stimulus` only if all +For example, a table `Protocol` should include the attribute `stimulus` only if all experiment protocols include stimulation. -If the not all entities in `Protocol` involve stimulation, then the `stimulus` +If the not all entities in `Protocol` involve stimulation, then the `stimulus` attribute should be moved to a part table that has `Protocol` as its master. Only protocols using stimulation will have an entry in this part table. ### Transient attributes Attributes should be relevant to all entities in a table at all times. -Attributes that do not apply to all entities should be moved to another dependent table +Attributes that do not apply to all entities should be moved to another dependent table containing only the appropriate entities. -This principle also applies to attributes that have not yet become meaningful for some +This principle also applies to attributes that have not yet become meaningful for some entities or that will not remain meaningful indefinitely. -For example, consider the table `Mouse` with attributes `birth_date` and `death_date`, +For example, consider the table `Mouse` with attributes `birth_date` and `death_date`, where `death_date` is set to `NULL` for living mice. -Since the `death_date` attribute is not meaningful for mice that are still living, -the proper design would include a separate table `DeceasedMouse` that depends on +Since the `death_date` attribute is not meaningful for mice that are still living, +the proper design would include a separate table `DeceasedMouse` that depends on `Mouse`. -`DeceasedMouse` would only contain entities for dead mice, which improves integrity and +`DeceasedMouse` would only contain entities for dead mice, which improves integrity and averts the need for [updates](../manipulation/update.md). diff --git a/docs/src/design/recall.md b/docs/src/design/recall.md index a53daf7b3..56226cabd 100644 --- a/docs/src/design/recall.md +++ b/docs/src/design/recall.md @@ -113,7 +113,7 @@ Student - StudentMajor ### Creating a virtual module -Virtual modules provide a way to access the classes corresponding to tables in a +Virtual modules provide a way to access the classes corresponding to tables in a DataJoint schema without having to create local files. `spawn_missing_classes` creates the new classes in the local namespace. @@ -124,7 +124,7 @@ equivalent to the Python command: import university as uni ``` -We can mimic this import without having access to `university.py` using the +We can mimic this import without having access to `university.py` using the `VirtualModule` class object: ```python diff --git a/docs/src/design/schema.md b/docs/src/design/schema.md index d09a174a9..94bf6cdcc 100644 --- a/docs/src/design/schema.md +++ b/docs/src/design/schema.md @@ -6,7 +6,7 @@ On the database server, related tables are grouped into a named collection calle This grouping organizes the data and allows control of user access. A database server may contain multiple schemas each containing a subset of the tables. A single pipeline may comprise multiple schemas. -Tables are defined within a schema, so a schema must be created before the creation of +Tables are defined within a schema, so a schema must be created before the creation of any tables. By convention, the `datajoint` package is imported as `dj`. @@ -21,29 +21,29 @@ schema = dj.Schema('alice_experiment') This statement creates the database schema `alice_experiment` on the server. -The returned object `schema` will then serve as a decorator for DataJoint classes, as +The returned object `schema` will then serve as a decorator for DataJoint classes, as described in [table declaration syntax](./tables/declare.md). It is a common practice to have a separate Python module for each schema. -Therefore, each such module has only one `dj.Schema` object defined and is usually +Therefore, each such module has only one `dj.Schema` object defined and is usually named `schema`. -The `dj.Schema` constructor can take a number of optional parameters after the schema +The `dj.Schema` constructor can take a number of optional parameters after the schema name. - `context` - Dictionary for looking up foreign key references. Defaults to `None` to use local context. - `connection` - Specifies the DataJoint connection object. Defaults to `dj.conn()`. -- `create_schema` - When `False`, the schema object will not create a schema on the +- `create_schema` - When `False`, the schema object will not create a schema on the database and will raise an error if one does not already exist. Defaults to `True`. -- `create_tables` - When `False`, the schema object will not create tables on the +- `create_tables` - When `False`, the schema object will not create tables on the database and will raise errors when accessing missing tables. Defaults to `True`. ## Working with existing data -See the chapter [recall](recall.md) for how to work with data in -existing pipelines, including accessing a pipeline from one language when the pipeline +See the chapter [recall](recall.md) for how to work with data in +existing pipelines, including accessing a pipeline from one language when the pipeline was developed using another. diff --git a/docs/src/design/tables/attach.md b/docs/src/design/tables/attach.md index c22748d0a..c4950ffdf 100644 --- a/docs/src/design/tables/attach.md +++ b/docs/src/design/tables/attach.md @@ -4,8 +4,8 @@ ### Configuration & Usage -Corresponding to issue -[#480](https://github.com/datajoint/datajoint-python/issues/480), +Corresponding to issue +[#480](https://github.com/datajoint/datajoint-python/issues/480), the `attach` attribute type allows users to `attach` files into DataJoint schemas as DataJoint-managed files. This is in contrast to traditional `blobs` which are encodings of programming language data structures such as arrays. diff --git a/docs/src/design/tables/attributes.md b/docs/src/design/tables/attributes.md index f3033b218..0c2e7a8f9 100644 --- a/docs/src/design/tables/attributes.md +++ b/docs/src/design/tables/attributes.md @@ -1,7 +1,7 @@ # Datatypes DataJoint supports the following datatypes. -To conserve database resources, use the smallest and most restrictive datatype +To conserve database resources, use the smallest and most restrictive datatype sufficient for your data. This also ensures that only valid data are entered into the pipeline. @@ -14,11 +14,11 @@ This also ensures that only valid data are entered into the pipeline. - `int`: a 32-bit integer number, ranging from -2,147,483,648 to 2,147,483,647. - `int unsigned`: a 32-bit positive integer, ranging from 0 to 4,294,967,295. - `enum`: one of several explicitly enumerated values specified as strings. - Use this datatype instead of text strings to avoid spelling variations and to save + Use this datatype instead of text strings to avoid spelling variations and to save storage space. - For example, the datatype for an anesthesia attribute could be + For example, the datatype for an anesthesia attribute could be `enum("urethane", "isoflurane", "fentanyl")`. - Do not use enums in primary keys due to the difficulty of changing their definitions + Do not use enums in primary keys due to the difficulty of changing their definitions consistently in multiple tables. - `date`: date as `'YYYY-MM-DD'`. @@ -28,9 +28,9 @@ This also ensures that only valid data are entered into the pipeline. The default value may be set to `CURRENT_TIMESTAMP`. Unlike `datetime`, a `timestamp` value will be adjusted to the local time zone. -- `char(N)`: a character string up to *N* characters (but always takes the entire *N* +- `char(N)`: a character string up to *N* characters (but always takes the entire *N* bytes to store). -- `varchar(N)`: a text string of arbitrary length up to *N* characters that takes +- `varchar(N)`: a text string of arbitrary length up to *N* characters that takes *M+1* or *M+2* bytes of storage, where *M* is the actual length of each stored string. - `float`: a single-precision floating-point number. Takes 4 bytes. @@ -38,20 +38,20 @@ bytes to store). - `double`: a double-precision floating-point number. Takes 8 bytes. - Because equality comparisons are error-prone, neither `float` nor `double` should be + Because equality comparisons are error-prone, neither `float` nor `double` should be used in primary keys. -- `decimal(N,F)`: a fixed-point number with *N* total decimal digits and *F* +- `decimal(N,F)`: a fixed-point number with *N* total decimal digits and *F* fractional digits. - This datatype is well suited to represent numbers whose magnitude is well defined - and does not warrant the use of floating-point representation or requires precise + This datatype is well suited to represent numbers whose magnitude is well defined + and does not warrant the use of floating-point representation or requires precise decimal representations (e.g. dollars and cents). - Because of its well-defined precision, `decimal` values can be used in equality + Because of its well-defined precision, `decimal` values can be used in equality comparison and be included in primary keys. -- `longblob`: arbitrary numeric array (e.g. matrix, image, structure), up to 4 +- `longblob`: arbitrary numeric array (e.g. matrix, image, structure), up to 4 [GiB](http://en.wikipedia.org/wiki/Gibibyte) in size. Numeric arrays are compatible between MATLAB and Python (NumPy). - The `longblob` and other `blob` datatypes can be configured to store data + The `longblob` and other `blob` datatypes can be configured to store data [externally](../../sysadmin/external-store.md) by using the `blob@store` syntax. ## Less common (but supported) datatypes @@ -59,11 +59,11 @@ fractional digits. - `decimal(N,F) unsigned`: same as `decimal`, but limited to nonnegative values. - `mediumint` a 24-bit integer number, ranging from -8,388,608 to 8,388,607. - `mediumint unsigned`: a 24-bit positive integer, ranging from 0 to 16,777,216. -- `mediumblob`: arbitrary numeric array, up to 16 +- `mediumblob`: arbitrary numeric array, up to 16 [MiB](http://en.wikipedia.org/wiki/Mibibyte) -- `blob`: arbitrary numeric array, up to 64 +- `blob`: arbitrary numeric array, up to 64 [KiB](http://en.wikipedia.org/wiki/Kibibyte) -- `tinyblob`: arbitrary numeric array, up to 256 bytes (actually smaller due to header +- `tinyblob`: arbitrary numeric array, up to 256 bytes (actually smaller due to header info). ## Special DataJoint-only datatypes @@ -71,10 +71,10 @@ info). These types abstract certain kinds of non-database data to facilitate use together with DataJoint. -- `attach`: a [file attachment](attach.md) similar to email attachments facillitating +- `attach`: a [file attachment](attach.md) similar to email attachments facillitating sending/receiving an opaque data file to/from a DataJoint pipeline. -- `filepath@store`: a [filepath](filepath.md) used to link non-DataJoint managed files +- `filepath@store`: a [filepath](filepath.md) used to link non-DataJoint managed files into a DataJoint pipeline. ## Datatypes not (yet) supported @@ -84,5 +84,5 @@ into a DataJoint pipeline. - `longtext` - `bit` -For additional information about these datatypes, see +For additional information about these datatypes, see http://dev.mysql.com/doc/refman/5.6/en/data-types.html diff --git a/docs/src/design/tables/declare.md b/docs/src/design/tables/declare.md index 565469ff7..d4fb070a2 100644 --- a/docs/src/design/tables/declare.md +++ b/docs/src/design/tables/declare.md @@ -4,18 +4,18 @@ ### Classes represent tables -To make it easy to work with tables in MATLAB and Python, DataJoint programs create a +To make it easy to work with tables in MATLAB and Python, DataJoint programs create a separate class for each table. -Computer programmers refer to this concept as +Computer programmers refer to this concept as [object-relational mapping](https://en.wikipedia.org/wiki/Object-relational_mapping). -For example, the class `experiment.Subject` in the DataJoint client language may +For example, the class `experiment.Subject` in the DataJoint client language may correspond to the table called `subject` on the database server. -Users never need to see the database directly; they only interact with data in the +Users never need to see the database directly; they only interact with data in the database by creating and interacting with DataJoint classes. #### Data tiers -The table class must inherit from one of the following superclasses to indicate its +The table class must inherit from one of the following superclasses to indicate its data tier: `dj.Lookup`, `dj.Manual`, `dj.Imported`, `dj.Computed`, or `dj.Part`. See [tiers](tiers.md) and [master-part](./master-part.md). @@ -23,7 +23,7 @@ See [tiers](tiers.md) and [master-part](./master-part.md). To define a DataJoint table in Python: -1. Define a class inheriting from the appropriate DataJoint class: `dj.Lookup`, +1. Define a class inheriting from the appropriate DataJoint class: `dj.Lookup`, `dj.Manual`, `dj.Imported` or `dj.Computed`. 2. Decorate the class with the schema object (see [schema](../schema.md)) @@ -46,39 +46,39 @@ class Person(dj.Manual): ''' ``` -The `@schema` decorator uses the class name and the data tier to check whether an +The `@schema` decorator uses the class name and the data tier to check whether an appropriate table exists on the database. -If a table does not already exist, the decorator creates one on the database using the +If a table does not already exist, the decorator creates one on the database using the definition property. -The decorator attaches the information about the table to the class, and then returns +The decorator attaches the information about the table to the class, and then returns the class. -The class will become usable after you define the `definition` property as described in +The class will become usable after you define the `definition` property as described in [Table definition](#table-definition). #### DataJoint classes in Python -DataJoint for Python is implemented through the use of classes providing access to the +DataJoint for Python is implemented through the use of classes providing access to the actual tables stored on the database. -Since only a single table exists on the database for any class, interactions with all +Since only a single table exists on the database for any class, interactions with all instances of the class are equivalent. -As such, most methods can be called on the classes themselves rather than on an object, +As such, most methods can be called on the classes themselves rather than on an object, for convenience. -Whether calling a DataJoint method on a class or on an instance, the result will only +Whether calling a DataJoint method on a class or on an instance, the result will only depend on or apply to the corresponding table. -All of the basic functionality of DataJoint is built to operate on the classes +All of the basic functionality of DataJoint is built to operate on the classes themselves, even when called on an instance. -For example, calling `Person.insert(...)` (on the class) and `Person.insert(...)` (on -an instance) both have the identical effect of inserting data into the table on the +For example, calling `Person.insert(...)` (on the class) and `Person.insert(...)` (on +an instance) both have the identical effect of inserting data into the table on the database server. -DataJoint does not prevent a user from working with instances, but the workflow is +DataJoint does not prevent a user from working with instances, but the workflow is complete without the need for instantiation. -It is up to the user whether to implement additional functionality as class methods or +It is up to the user whether to implement additional functionality as class methods or methods called on instances. ### Valid class names -Note that in both MATLAB and Python, the class names must follow the CamelCase compound +Note that in both MATLAB and Python, the class names must follow the CamelCase compound word notation: - start with a capital letter and @@ -94,18 +94,18 @@ Invalid class names: ## Table Definition -DataJoint models data as sets of **entities** with shared **attributes**, often +DataJoint models data as sets of **entities** with shared **attributes**, often visualized as tables with rows and columns. Each row represents a single entity and the values of all of its attributes. -Each column represents a single attribute with a name and a datatype, applicable to +Each column represents a single attribute with a name and a datatype, applicable to entity in the table. -Unlike rows in a spreadsheet, entities in DataJoint don't have names or numbers: they +Unlike rows in a spreadsheet, entities in DataJoint don't have names or numbers: they can only be identified by the values of their attributes. -Defining a table means defining the names and datatypes of the attributes as well as +Defining a table means defining the names and datatypes of the attributes as well as the constraints to be applied to those attributes. Both MATLAB and Python use the same syntax define tables. -For example, the following code in defines the table `User`, that contains users of the +For example, the following code in defines the table `User`, that contains users of the database: The table definition is contained in the `definition` property of the class. @@ -123,27 +123,27 @@ class User(dj.Manual): """ ``` -This defines the class `User` that creates the table in the database and provides all +This defines the class `User` that creates the table in the database and provides all its data manipulation functionality. ### Table creation on the database server Users do not need to do anything special to have a table created in the database. Tables are created at the time of class definition. -In fact, table creation on the database is one of the jobs performed by the decorator +In fact, table creation on the database is one of the jobs performed by the decorator `@schema` of the class. ### Changing the definition of an existing table Once the table is created in the database, the definition string has no further effect. -In other words, changing the definition string in the class of an existing table will +In other words, changing the definition string in the class of an existing table will not actually update the table definition. To change the table definition, one must first [drop](../drop.md) the existing table. -This means that all the data will be lost, and the new definition will be applied to +This means that all the data will be lost, and the new definition will be applied to create the new empty table. -Therefore, in the initial phases of designing a DataJoint pipeline, it is common to -experiment with variations of the design before populating it with substantial amounts +Therefore, in the initial phases of designing a DataJoint pipeline, it is common to +experiment with variations of the design before populating it with substantial amounts of data. It is possible to modify a table without dropping it. @@ -151,9 +151,9 @@ This topic is covered separately. ### Reverse-engineering the table definition -DataJoint objects provide the `describe` method, which displays the table definition +DataJoint objects provide the `describe` method, which displays the table definition used to define the table when it was created in the database. -This definition may differ from the definition string of the class if the definition +This definition may differ from the definition string of the class if the definition string has been edited after creation of the table. Examples @@ -169,13 +169,13 @@ Each line can be one of the following: - The optional first line starting with a `#` provides a description of the table's purpose. It may also be thought of as the table's long title. -- A new attribute definition in any of the following forms (see +- A new attribute definition in any of the following forms (see [Attributes](./attributes.md) for valid datatypes): ``name : datatype`` ``name : datatype # comment`` ``name = default : datatype`` ``name = default : datatype # comment`` -- The divider `---` (at least three hyphens) separating primary key attributes above +- The divider `---` (at least three hyphens) separating primary key attributes above from secondary attributes below. - A foreign key in the format `-> ReferencedTable`. (See [Dependencies](dependencies.md).) @@ -190,7 +190,7 @@ full_name : varchar(255) start_date : date # date when joined the lab ``` -This will define the table with attributes `username`, `full_name`, and `start_date`, +This will define the table with attributes `username`, `full_name`, and `start_date`, in which `username` is the [primary key](primary.md). ### Attribute names @@ -205,31 +205,31 @@ Valid attribute names Invalid attribute names `firstName`, `first name`, `2photon_scan`, `two-photon_scan`, `TwoPhotonScan` -Ideally, attribute names should be unique across all tables that are likely to be used +Ideally, attribute names should be unique across all tables that are likely to be used in queries together. -For example, tables often have attributes representing the start times of sessions, +For example, tables often have attributes representing the start times of sessions, recordings, etc. -Such attributes must be uniquely named in each table, such as `session_start_time` or +Such attributes must be uniquely named in each table, such as `session_start_time` or `recording_start_time`. ### Default values Secondary attributes can be given default values. -A default value will be used for an attribute if no other value is given at the time +A default value will be used for an attribute if no other value is given at the time the entity is [inserted](../../manipulation/insert.md) into the table. Generally, default values are numerical values or character strings. -Default values for dates must be given as strings as well, contained within quotes +Default values for dates must be given as strings as well, contained within quotes (with the exception of `CURRENT_TIMESTAMP`). Note that default values can only be used when inserting as a mapping. -Primary key attributes cannot have default values (with the exceptions of +Primary key attributes cannot have default values (with the exceptions of `auto_increment` and `CURRENT_TIMESTAMP` attributes; see [primary-key](primary.md)). An attribute with a default value of `NULL` is called a **nullable attribute**. -A nullable attribute can be thought of as applying to all entities in a table but +A nullable attribute can be thought of as applying to all entities in a table but having an optional *value* that may be absent in some entities. -Nullable attributes should *not* be used to indicate that an attribute is inapplicable +Nullable attributes should *not* be used to indicate that an attribute is inapplicable to some entities in a table (see [normalization](../normalization.md)). -Nullable attributes should be used sparingly to indicate optional rather than +Nullable attributes should be used sparingly to indicate optional rather than inapplicable attributes that still apply to all entities in the table. `NULL` is a special literal value and does not need to be enclosed in quotes. diff --git a/docs/src/design/tables/dependencies.md b/docs/src/design/tables/dependencies.md index 3af77c38b..e06278ee8 100644 --- a/docs/src/design/tables/dependencies.md +++ b/docs/src/design/tables/dependencies.md @@ -3,23 +3,23 @@ ## Understanding dependencies A schema contains collections of tables of related data. -Accordingly, entities in one table often derive some of their meaning or context from +Accordingly, entities in one table often derive some of their meaning or context from entities in other tables. -A **foreign key** defines a **dependency** of entities in one table on entities in +A **foreign key** defines a **dependency** of entities in one table on entities in another within a schema. -In more complex designs, dependencies can even exist between entities in tables from +In more complex designs, dependencies can even exist between entities in tables from different schemas. -Dependencies play a functional role in DataJoint and do not simply label the structure +Dependencies play a functional role in DataJoint and do not simply label the structure of a pipeline. -Dependencies provide entities in one table with access to data in another table and +Dependencies provide entities in one table with access to data in another table and establish certain constraints on entities containing a foreign key. -A DataJoint pipeline, including the dependency relationships established by foreign +A DataJoint pipeline, including the dependency relationships established by foreign keys, can be visualized as a graph with nodes and edges. -The diagram of such a graph is called the **entity relationship diagram** or +The diagram of such a graph is called the **entity relationship diagram** or [Diagram](../diagrams.md). The nodes of the graph are tables and the edges connecting them are foreign keys. -The edges are directed and the overall graph is a **directed acyclic graph**, a graph +The edges are directed and the overall graph is a **directed acyclic graph**, a graph with no loops. For example, the Diagram below is the pipeline for multipatching experiments @@ -27,27 +27,27 @@ For example, the Diagram below is the pipeline for multipatching experiments ![mp-diagram](../../images/mp-diagram.png){: style="align:center"} The graph defines the direction of the workflow. -The tables at the top of the flow need to be populated first, followed by those tables -one step below and so forth until the last table is populated at the bottom of the +The tables at the top of the flow need to be populated first, followed by those tables +one step below and so forth until the last table is populated at the bottom of the pipeline. -The top of the pipeline tends to be dominated by lookup tables (gray stars) and manual +The top of the pipeline tends to be dominated by lookup tables (gray stars) and manual tables (green squares). -The middle has many imported tables (blue triangles), and the bottom has computed +The middle has many imported tables (blue triangles), and the bottom has computed tables (red stars). ## Defining a dependency -Foreign keys are defined with arrows `->` in the [table definition](declare.md), +Foreign keys are defined with arrows `->` in the [table definition](declare.md), pointing to another table. A foreign key may be defined as part of the [primary-key](primary.md). In the Diagram, foreign keys from the primary key are shown as solid lines. -This means that the primary key of the referenced table becomes part of the primary key +This means that the primary key of the referenced table becomes part of the primary key of the new table. A foreign key outside the primary key is indicated by dashed line in the ERD. -For example, the following definition for the table `mp.Slice` has three foreign keys, +For example, the following definition for the table `mp.Slice` has three foreign keys, including one within the primary key. ```python @@ -82,7 +82,7 @@ experimenter : varchar(20) # person who performed this experiment ``` This displayed heading reflects the actual attributes in the table. -The foreign keys have been replaced by the primary key attributes of the referenced +The foreign keys have been replaced by the primary key attributes of the referenced tables, including their data types and comments. ## How dependencies work @@ -91,46 +91,46 @@ The foreign key `-> A` in the definition of table `B` has the following effects: 1. The primary key attributes of `A` are made part of `B`'s definition. 2. A referential constraint is created in `B` with reference to `A`. -3. If one does not already exist, an index is created to speed up searches in `B` for +3. If one does not already exist, an index is created to speed up searches in `B` for matches to `A`. (The reverse search is already fast because it uses the primary key of `A`.) -A referential constraint means that an entity in `B` cannot exist without a matching +A referential constraint means that an entity in `B` cannot exist without a matching entity in `A`. -**Matching** means attributes in `B` that correspond to the primary key of `A` must +**Matching** means attributes in `B` that correspond to the primary key of `A` must have the same values. -An attempt to insert an entity into `B` that does not have a matching counterpart in +An attempt to insert an entity into `B` that does not have a matching counterpart in `A` will fail. -Conversely, deleting an entity from `A` that has matching entities in `B` will result -in the deletion of those matching entities and so forth, recursively, downstream in the +Conversely, deleting an entity from `A` that has matching entities in `B` will result +in the deletion of those matching entities and so forth, recursively, downstream in the pipeline. When `B` references `A` with a foreign key, one can say that `B` **depends** on `A`. -In DataJoint terms, `B` is the **dependent table** and `A` is the **referenced table** +In DataJoint terms, `B` is the **dependent table** and `A` is the **referenced table** with respect to the foreign key from `B` to `A`. -Note to those already familiar with the theory of relational databases: The usage of -the words "depends" and "dependency" here should not be confused with the unrelated +Note to those already familiar with the theory of relational databases: The usage of +the words "depends" and "dependency" here should not be confused with the unrelated concept of *functional dependencies* that is used to define normal forms. ## Referential integrity -Dependencies enforce the desired property of databases known as +Dependencies enforce the desired property of databases known as **referential integrity**. -Referential integrity is the guarantee made by the data management process that related +Referential integrity is the guarantee made by the data management process that related data across the database remain present, correctly associated, and mutually consistent. -Guaranteeing referential integrity means enforcing the constraint that no entity can +Guaranteeing referential integrity means enforcing the constraint that no entity can exist in the database without all the other entities on which it depends. -An entity in table `B` depends on an entity in table `A` when they belong to them or +An entity in table `B` depends on an entity in table `A` when they belong to them or are computed from them. ## Dependencies with renamed attributes -In most cases, a dependency includes the primary key attributes of the referenced table +In most cases, a dependency includes the primary key attributes of the referenced table as they appear in its table definition. -Sometimes it can be helpful to choose a new name for a foreign key attribute that +Sometimes it can be helpful to choose a new name for a foreign key attribute that better fits the context of the dependent table. -DataJoint provides the following [projection](../../query/project.md) syntax to rename +DataJoint provides the following [projection](../../query/project.md) syntax to rename the primary key attributes when they are included in the new table. The dependency @@ -139,22 +139,22 @@ The dependency -> Table.project(new_attr='old_attr') ``` -renames the primary key attribute `old_attr` of `Table` as `new_attr` before +renames the primary key attribute `old_attr` of `Table` as `new_attr` before integrating it into the table definition. Any additional primary key attributes will retain their original names. -For example, the table `Experiment` may depend on table `User` but rename the `user` +For example, the table `Experiment` may depend on table `User` but rename the `user` attribute into `operator` as follows: ```python -> User.proj(operator='user') ``` -In the above example, an entity in the dependent table depends on exactly one entity in +In the above example, an entity in the dependent table depends on exactly one entity in the referenced table. Sometimes entities may depend on multiple entities from the same table. -Such a design requires a way to distinguish between dependent attributes having the +Such a design requires a way to distinguish between dependent attributes having the same name in the reference table. -For example, a table for `Synapse` may reference the table `Cell` twice as +For example, a table for `Synapse` may reference the table `Cell` twice as `presynaptic` and `postsynaptic`. The table definition may appear as @@ -166,16 +166,16 @@ The table definition may appear as connection_strength : double # (pA) peak synaptic current ``` -If the primary key of `Cell` is (`animal_id`, `slice_id`, `cell_id`), then the primary -key of `Synapse` resulting from the above definition will be (`animal_id`, `slice_id`, +If the primary key of `Cell` is (`animal_id`, `slice_id`, `cell_id`), then the primary +key of `Synapse` resulting from the above definition will be (`animal_id`, `slice_id`, `presynaptic`, `postsynaptic`). -Projection always returns all of the primary key attributes of a table, so `animal_id` +Projection always returns all of the primary key attributes of a table, so `animal_id` and `slice_id` are included, with their original names. -Note that the design of the `Synapse` table above imposes the constraint that the +Note that the design of the `Synapse` table above imposes the constraint that the synapse can only be found between cells in the same animal and in the same slice. -Allowing representation of synapses between cells from different slices requires the +Allowing representation of synapses between cells from different slices requires the renamimg of `slice_id` as well: ```python @@ -186,19 +186,19 @@ renamimg of `slice_id` as well: connection_strength : double # (pA) peak synaptic current ``` -In this case, the primary key of `Synapse` will be (`animal_id`, `presynaptic_slice`, +In this case, the primary key of `Synapse` will be (`animal_id`, `presynaptic_slice`, `presynaptic_cell`, `postsynaptic_slice`, `postsynaptic_cell`). -This primary key still imposes the constraint that synapses can only form between cells +This primary key still imposes the constraint that synapses can only form between cells within the same animal but now allows connecting cells across different slices. -In the Diagram, renamed foreign keys are shown as red lines with an additional dot node +In the Diagram, renamed foreign keys are shown as red lines with an additional dot node in the middle to indicate that a renaming took place. ## Foreign key options Note: Foreign key options are currently in development. -Foreign keys allow the additional options `nullable` and `unique`, which can be +Foreign keys allow the additional options `nullable` and `unique`, which can be inserted in square brackets following the arrow. For example, in the following table definition @@ -209,9 +209,9 @@ rig_id : char(4) # experimental rig -> Person ``` -each rig belongs to a person, but the table definition does not prevent one person +each rig belongs to a person, but the table definition does not prevent one person owning multiple rigs. -With the `unique` option, a person may only appear once in the entire table, which +With the `unique` option, a person may only appear once in the entire table, which means that no one person can own more than one rig. ```python @@ -220,7 +220,7 @@ rig_id : char(4) # experimental rig -> [unique] Person ``` -With the `nullable` option, a rig may not belong to anyone, in which case the foreign +With the `nullable` option, a rig may not belong to anyone, in which case the foreign key attributes for `Person` are set to `NULL`: ```python @@ -229,7 +229,7 @@ rig_id : char(4) # experimental rig -> [nullable] Person ``` -Finally with both `unique` and `nullable`, a rig may or may not be owned by anyone and +Finally with both `unique` and `nullable`, a rig may or may not be owned by anyone and each person may own up to one rig. ```python diff --git a/docs/src/design/tables/filepath.md b/docs/src/design/tables/filepath.md index 166c42133..05e9ca744 100644 --- a/docs/src/design/tables/filepath.md +++ b/docs/src/design/tables/filepath.md @@ -1,13 +1,13 @@ # Filepath Datatype -Note: Filepath Datatype is available as a preview feature in DataJoint Python v0.12. -This means that the feature is required to be explicitly enabled. To do so, make sure +Note: Filepath Datatype is available as a preview feature in DataJoint Python v0.12. +This means that the feature is required to be explicitly enabled. To do so, make sure to set the environment variable `FILEPATH_FEATURE_SWITCH=TRUE` prior to use. ## Configuration & Usage -Corresponding to issue -[#481](https://github.com/datajoint/datajoint-python/issues/481), +Corresponding to issue +[#481](https://github.com/datajoint/datajoint-python/issues/481), the `filepath` attribute type links DataJoint records to files already managed outside of DataJoint. This can aid in sharing data with other systems such as allowing an image viewer application to @@ -16,7 +16,7 @@ tables to reference data which reside outside of DataJoint pipelines. To define a table using the `filepath` datatype, an existing DataJoint -[store](../../sysadmin/external-store.md) should be created and then referenced in the +[store](../../sysadmin/external-store.md) should be created and then referenced in the new table definition. For example, given a simple store: ```python @@ -38,7 +38,7 @@ class ScanImages(dj.Manual): -> Session image_id: int --- - image_path: filepath@data + image_path: filepath@data """ ``` @@ -79,9 +79,9 @@ as follows: ### Disable Fetch Verification -Note: Skipping the checksum is not recommended as it ensures file integrity i.e. -downloaded files are not corrupted. With S3 stores, most of the time to complete a -`.fetch()` is from the file download itself as opposed to evaluating the checksum. This +Note: Skipping the checksum is not recommended as it ensures file integrity i.e. +downloaded files are not corrupted. With S3 stores, most of the time to complete a +`.fetch()` is from the file download itself as opposed to evaluating the checksum. This option will primarily benefit `filepath` usage connected to a local `file` store. To disable checksums you can set a threshold in bytes diff --git a/docs/src/design/tables/lookup.md b/docs/src/design/tables/lookup.md index 216c15ab4..79b2c67ba 100644 --- a/docs/src/design/tables/lookup.md +++ b/docs/src/design/tables/lookup.md @@ -1,16 +1,16 @@ # Lookup Tables -Lookup tables contain basic facts that are not specific to an experiment and are fairly +Lookup tables contain basic facts that are not specific to an experiment and are fairly persistent. Their contents are typically small. In GUIs, lookup tables are often used for drop-down menus or radio buttons. In computed tables, they are often used to specify alternative methods for computations. Lookup tables are commonly populated from their `contents` property. In a [diagram](../diagrams.md) they are shown in gray. -The decision of which tables are lookup tables and which are manual can be somewhat +The decision of which tables are lookup tables and which are manual can be somewhat arbitrary. -The table below is declared as a lookup table with its contents property provided to +The table below is declared as a lookup table with its contents property provided to generate entities. ```python diff --git a/docs/src/design/tables/manual.md b/docs/src/design/tables/manual.md index 3c3532d62..d97b6ce52 100644 --- a/docs/src/design/tables/manual.md +++ b/docs/src/design/tables/manual.md @@ -3,7 +3,7 @@ Manual tables are populated during experiments through a variety of interfaces. Not all manual information is entered by typing. Automated software can enter it directly into the database. -What makes a manual table manual is that it does not perform any computations within +What makes a manual table manual is that it does not perform any computations within the DataJoint pipeline. The following code defines three manual tables `Animal`, `Session`, and `Scan`: diff --git a/docs/src/design/tables/master-part.md b/docs/src/design/tables/master-part.md index eacce084b..629bfb8ab 100644 --- a/docs/src/design/tables/master-part.md +++ b/docs/src/design/tables/master-part.md @@ -1,8 +1,8 @@ # Master-Part Relationship -Often an entity in one table is inseparably associated with a group of entities in +Often an entity in one table is inseparably associated with a group of entities in another, forming a **master-part** relationship. -The master-part relationship ensures that all parts of a complex representation appear +The master-part relationship ensures that all parts of a complex representation appear together or not at all. This has become one of the most powerful data integrity principles in DataJoint. @@ -10,7 +10,7 @@ As an example, imagine segmenting an image to identify regions of interest. The resulting segmentation is inseparable from the ROIs that it produces. In this case, the two tables might be called `Segmentation` and `Segmentation.ROI`. -In Python, the master-part relationship is expressed by making the part a nested class +In Python, the master-part relationship is expressed by making the part a nested class of the master. The part is subclassed from `dj.Part` and does not need the `@schema` decorator. @@ -41,45 +41,45 @@ class Segmentation(dj.Computed): ## Populating -Master-part relationships can form in any data tier, but DataJoint observes them more +Master-part relationships can form in any data tier, but DataJoint observes them more strictly for auto-populated tables. -To populate both the master `Segmentation` and the part `Segmentation.ROI`, it is +To populate both the master `Segmentation` and the part `Segmentation.ROI`, it is sufficient to call the `populate` method of the master: ```python Segmentation.populate() ``` -Note that the entities in the master and the matching entities in the part are inserted -within a single `make` call of the master, which means that they are a processed inside -a single transactions: either all are inserted and committed or the entire transaction +Note that the entities in the master and the matching entities in the part are inserted +within a single `make` call of the master, which means that they are a processed inside +a single transactions: either all are inserted and committed or the entire transaction is rolled back. This ensures that partial results never appear in the database. -For example, imagine that a segmentation is performed, but an error occurs halfway +For example, imagine that a segmentation is performed, but an error occurs halfway through inserting the results. -If this situation were allowed to persist, then it might appear that 20 ROIs were +If this situation were allowed to persist, then it might appear that 20 ROIs were detected where 45 had actually been found. ## Deleting -To delete from a master-part pair, one should never delete from the part tables +To delete from a master-part pair, one should never delete from the part tables directly. The only valid method to delete from a part table is to delete the master. -This has been an unenforced rule, but upcoming versions of DataJoint will prohibit +This has been an unenforced rule, but upcoming versions of DataJoint will prohibit direct deletes from the master table. -DataJoint's [delete](../../manipulation/delete.md) operation is also enclosed in a +DataJoint's [delete](../../manipulation/delete.md) operation is also enclosed in a transaction. -Together, the rules of master-part relationships ensure a key aspect of data integrity: -results of computations involving multiple components and steps appear in their +Together, the rules of master-part relationships ensure a key aspect of data integrity: +results of computations involving multiple components and steps appear in their entirety or not at all. ## Multiple parts The master-part relationship cannot be chained or nested. DataJoint does not allow part tables of other part tables per se. -However, it is common to have a master table with multiple part tables that depend on +However, it is common to have a master table with multiple part tables that depend on each other. For example: @@ -105,8 +105,8 @@ response: longblob # response of a channel """ ``` -Conceptually, one or more channels belongs to an electrode, and one or more electrodes +Conceptually, one or more channels belongs to an electrode, and one or more electrodes belong to an array. -This example assumes that information about an array's response (which consists -ultimately of the responses of multiple electrodes each consisting of multiple channel +This example assumes that information about an array's response (which consists +ultimately of the responses of multiple electrodes each consisting of multiple channel responses) including it's electrodes and channels are entered together. diff --git a/docs/src/design/tables/primary.md b/docs/src/design/tables/primary.md index 723f369e9..fc4f5b8e0 100644 --- a/docs/src/design/tables/primary.md +++ b/docs/src/design/tables/primary.md @@ -7,39 +7,39 @@ DataJoint does not answer questions of the type "What is the 10th element of thi Instead, entities are distinguished by the values of their attributes. Furthermore, the entire entity is not required for identification. In each table, a subset of its attributes are designated to be the **primary key**. -Attributes in the primary key alone are sufficient to differentiate any entity from any +Attributes in the primary key alone are sufficient to differentiate any entity from any other within the table. -Each table must have exactly one -[primary key](http://en.wikipedia.org/wiki/Primary_key): a subset of its attributes +Each table must have exactly one +[primary key](http://en.wikipedia.org/wiki/Primary_key): a subset of its attributes that uniquely identify each entity in the table. -The database uses the primary key to prevent duplicate entries, to relate data across +The database uses the primary key to prevent duplicate entries, to relate data across tables, and to accelerate data queries. The choice of the primary key will determine how you identify entities. Therefore, make the primary key **short**, **expressive**, and **persistent**. For example, mice in our lab are assigned unique IDs. -The mouse ID number `animal_id` of type `smallint` can serve as the primary key for the +The mouse ID number `animal_id` of type `smallint` can serve as the primary key for the table `Mice`. -An experiment performed on a mouse may be identified in the table `Experiments` by two +An experiment performed on a mouse may be identified in the table `Experiments` by two attributes: `animal_id` and `experiment_number`. -DataJoint takes the concept of primary keys somewhat more seriously than other models +DataJoint takes the concept of primary keys somewhat more seriously than other models and query languages. -Even **table expressions**, i.e. those tables produced through operations on other +Even **table expressions**, i.e. those tables produced through operations on other tables, have a well-defined primary key. -All operators on tables are designed in such a way that the results always have a +All operators on tables are designed in such a way that the results always have a well-defined primary key. -In all representations of tables in DataJoint, the primary key attributes are always -listed before other attributes and highlighted for emphasis (e.g. in a **bold** font or +In all representations of tables in DataJoint, the primary key attributes are always +listed before other attributes and highlighted for emphasis (e.g. in a **bold** font or marked with an asterisk \*) ## Defining a primary key -In table declarations, the primary key attributes always come first and are separated +In table declarations, the primary key attributes always come first and are separated from the other attributes with a line containing at least three hyphens. -For example, the following is the definition of a table containing database users where +For example, the following is the definition of a table containing database users where `username` is the primary key. ```python @@ -53,60 +53,60 @@ role : enum('admin', 'contributor', 'viewer') ## Entity integrity -The primary key defines and enforces the desired property of databases known as +The primary key defines and enforces the desired property of databases known as [entity integrity](../integrity.md). -**Entity integrity** ensures that there is a one-to-one and unambiguous mapping between +**Entity integrity** ensures that there is a one-to-one and unambiguous mapping between real-world entities and their representations in the database system. -The data management process must prevent any duplication or misidentification of +The data management process must prevent any duplication or misidentification of entities. To enforce entity integrity, DataJoint implements several rules: - Every table must have a primary key. -- Primary key attributes cannot have default values (with the exception of +- Primary key attributes cannot have default values (with the exception of `auto_increment` and `CURRENT_TIMESTAMP`; see below). -- Operators on tables are defined with respect to the primary key and preserve a +- Operators on tables are defined with respect to the primary key and preserve a primary key in their results. ## Datatypes in primary keys -All integer types, dates, timestamps, and short character strings make good primary key +All integer types, dates, timestamps, and short character strings make good primary key attributes. -Character strings are somewhat less suitable because they can be long and because they +Character strings are somewhat less suitable because they can be long and because they may have invisible trailing spaces. -Floating-point numbers should be avoided because rounding errors may lead to +Floating-point numbers should be avoided because rounding errors may lead to misidentification of entities. -Enums are okay as long as they do not need to be modified after +Enums are okay as long as they do not need to be modified after [dependencies](dependencies.md) are already created referencing the table. Finally, DataJoint does not support blob types in primary keys. The primary key may be composite, i.e. comprising several attributes. -In DataJoint, hierarchical designs often produce tables whose primary keys comprise +In DataJoint, hierarchical designs often produce tables whose primary keys comprise many attributes. ## Choosing primary key attributes -A primary key comprising real-world attributes is a good choice when such real-world +A primary key comprising real-world attributes is a good choice when such real-world attributes are already properly and permanently assigned. -Whatever characteristics are used to uniquely identify the actual entities can be used +Whatever characteristics are used to uniquely identify the actual entities can be used to identify their representations in the database. -If there are no attributes that could readily serve as a primary key, an artificial +If there are no attributes that could readily serve as a primary key, an artificial attribute may be created solely for the purpose of distinguishing entities. -In such cases, the primary key created for management in the database must also be used +In such cases, the primary key created for management in the database must also be used to uniquely identify the entities themselves. -If the primary key resides only in the database while entities remain indistinguishable +If the primary key resides only in the database while entities remain indistinguishable in the real world, then the process cannot ensure entity integrity. -When a primary key is created as part of data management rather than based on -real-world attributes, an institutional process must ensure the uniqueness and +When a primary key is created as part of data management rather than based on +real-world attributes, an institutional process must ensure the uniqueness and permanence of such an identifier. -For example, the U.S. government assigns every worker an identifying attribute, the +For example, the U.S. government assigns every worker an identifying attribute, the social security number. -However, the government must go to great lengths to ensure that this primary key is -assigned exactly once, by checking against other less convenient candidate keys (i.e. +However, the government must go to great lengths to ensure that this primary key is +assigned exactly once, by checking against other less convenient candidate keys (i.e. the combination of name, parents' names, date of birth, place of birth, etc.). -Just like the SSN, well managed primary keys tend to get institutionalized and find +Just like the SSN, well managed primary keys tend to get institutionalized and find multiple uses. Your lab must maintain a system for uniquely identifying important entities. @@ -116,31 +116,31 @@ Use these as the primary keys in the corresponding tables in your DataJoint data ### Using hashes as primary keys Some tables include too many attributes in their primary keys. -For example, the stimulus condition in a psychophysics experiment may have a dozen -parameters such that a change in any one of them makes a different valid stimulus +For example, the stimulus condition in a psychophysics experiment may have a dozen +parameters such that a change in any one of them makes a different valid stimulus condition. -In such a case, all the attributes would need to be included in the primary key to +In such a case, all the attributes would need to be included in the primary key to ensure entity integrity. However, long primary keys make it difficult to reference individual entities. To be most useful, primary keys need to be relatively short. -This problem is effectively solved through the use of a hash of all the identifying +This problem is effectively solved through the use of a hash of all the identifying attributes as the primary key. For example, MD5 or SHA-1 hash algorithms can be used for this purpose. To keep their representations human-readable, they may be encoded in base-64 ASCII. -For example, the 128-bit MD5 hash can be represented by 21 base-64 ASCII characters, -but for many applications, taking the first 8 to 12 characters is sufficient to avoid +For example, the 128-bit MD5 hash can be represented by 21 base-64 ASCII characters, +but for many applications, taking the first 8 to 12 characters is sufficient to avoid collisions. ### `auto_increment` Some entities are created by the very action of being entered into the database. The action of entering them into the database gives them their identity. -It is impossible to duplicate them since entering the same thing twice still means +It is impossible to duplicate them since entering the same thing twice still means creating two distinct entities. In such cases, the use of an auto-incremented primary key is warranted. -These are declared by adding the word `auto_increment` after the data type in the +These are declared by adding the word `auto_increment` after the data type in the declaration. The datatype must be an integer. Then the database will assign incrementing numbers at each insert. @@ -155,14 +155,14 @@ entry_text : varchar(4000) entry_time = CURRENT_TIMESTAMP : timestamp(3) # automatic timestamp with millisecond precision ``` -DataJoint passes `auto_increment` behavior to the underlying MySQL and therefore it has -the same limitation: it can only be used for tables with a single attribute in the +DataJoint passes `auto_increment` behavior to the underlying MySQL and therefore it has +the same limitation: it can only be used for tables with a single attribute in the primary key. -If you need to auto-increment an attribute in a composite primary key, you will need to +If you need to auto-increment an attribute in a composite primary key, you will need to do so programmatically within a transaction to avoid collisions. -For example, let’s say that you want to auto-increment `scan_idx` in a table called +For example, let’s say that you want to auto-increment `scan_idx` in a table called `Scan` whose primary key is `(animal_id, session, scan_idx)`. You must already have the values for `animal_id` and `session` in the dictionary `key`. Then you can do the following: diff --git a/docs/src/design/tables/tiers.md b/docs/src/design/tables/tiers.md index b127d6992..2cf1f9428 100644 --- a/docs/src/design/tables/tiers.md +++ b/docs/src/design/tables/tiers.md @@ -1,6 +1,6 @@ # Data Tiers -DataJoint assigns all tables to one of the following data tiers that differentiate how +DataJoint assigns all tables to one of the following data tiers that differentiate how the data originate. ## Table tiers @@ -15,26 +15,26 @@ the data originate. Table data tiers indicate to database administrators how valuable the data are. Manual data are the most valuable, as re-entry may be tedious or impossible. Computed data are safe to delete, as the data can always be recomputed from within DataJoint. -Imported data are safer than manual data but less safe than computed data because of +Imported data are safer than manual data but less safe than computed data because of dependency on external data sources. -With these considerations, database administrators may opt not to back up computed +With these considerations, database administrators may opt not to back up computed data, for example, or to back up imported data less frequently than manual data. The data tier of a table is specified by the superclass of its class. -For example, the User class in [definitions](declare.md) uses the `dj.Manual` +For example, the User class in [definitions](declare.md) uses the `dj.Manual` superclass. Therefore, the corresponding User table on the database would be of the Manual tier. -Furthermore, the classes for **imported** and **computed** tables have additional -capabilities for automated processing as described in +Furthermore, the classes for **imported** and **computed** tables have additional +capabilities for automated processing as described in [Auto-populate](../../compute/populate.md). ## Internal conventions for naming tables -On the server side, DataJoint uses a naming scheme to generate a table name +On the server side, DataJoint uses a naming scheme to generate a table name corresponding to a given class. The naming scheme includes prefixes specifying each table's data tier. -First, the name of the class is converted from `CamelCase` to `snake_case` +First, the name of the class is converted from `CamelCase` to `snake_case` ([separation by underscores](https://en.wikipedia.org/wiki/Snake_case)). Then the name is prefixed according to the data tier. @@ -45,16 +45,16 @@ Then the name is prefixed according to the data tier. For example: -The table for the class `StructuralScan` subclassing `dj.Manual` will be named +The table for the class `StructuralScan` subclassing `dj.Manual` will be named `structural_scan`. -The table for the class `SpatialFilter` subclassing `dj.Lookup` will be named +The table for the class `SpatialFilter` subclassing `dj.Lookup` will be named `#spatial_filter`. Again, the internal table names including prefixes are used only on the server side. -These are never visible to the user, and DataJoint users do not need to know these +These are never visible to the user, and DataJoint users do not need to know these conventions -However, database administrators may use these naming patterns to set backup policies +However, database administrators may use these naming patterns to set backup policies or to restrict access based on data tiers. ## Part tables @@ -64,5 +64,5 @@ Instead, they share the same tier as their master table. The prefix for part tables also differs from the other tiers. They are prefixed by the name of their master table, separated by two underscores. -For example, the table for the class `Channel(dj.Part)` with the master +For example, the table for the class `Channel(dj.Part)` with the master `Ephys(dj.Imported)` will be named `_ephys__channel`. diff --git a/docs/src/faq.md b/docs/src/faq.md index a3d5fd92d..e82d67588 100644 --- a/docs/src/faq.md +++ b/docs/src/faq.md @@ -4,16 +4,16 @@ It is common to enter data during experiments using a graphical user interface. -1. [DataJoint LabBook](https://github.com/datajoint/datajoint-labbook) is an open +1. [DataJoint LabBook](https://github.com/datajoint/datajoint-labbook) is an open source project for data entry. -2. The DataJoint Works platform is set up as a fully managed service to host and +2. The DataJoint Works platform is set up as a fully managed service to host and execute data pipelines. ## Does DataJoint support other programming languages? -DataJoint [Python](https://datajoint.com/docs/core/datajoint-python/) and -[Matlab](https://datajoint.com/docs/core/datajoint-matlab/) APIs are both actively +DataJoint [Python](https://datajoint.com/docs/core/datajoint-python/) and +[Matlab](https://datajoint.com/docs/core/datajoint-matlab/) APIs are both actively supported. Previous projects implemented some DataJoint features in [Julia](https://github.com/BrainCOGS/neuronex_workshop_2018/tree/julia/julia) and [Rust](https://github.com/datajoint/datajoint-core). DataJoint's data model and data @@ -79,113 +79,113 @@ DataJoint counterpart. ## Where is my data? -New users often ask this question thinking of passive **data repositories** -- -collections of files and folders and a separate collection of metadata -- information +New users often ask this question thinking of passive **data repositories** -- +collections of files and folders and a separate collection of metadata -- information about how the files were collected and what they contain. -Let's address metadata first, since the answer there is easy: Everything goes in the +Let's address metadata first, since the answer there is easy: Everything goes in the database! -Any information about the experiment that would normally be stored in a lab notebook, +Any information about the experiment that would normally be stored in a lab notebook, in an Excel spreadsheet, or in a Word document is entered into tables in the database. These tables can accommodate numbers, strings, dates, or numerical arrays. -The entry of metadata can be manual, or it can be an automated part of data acquisition -(in this case the acquisition software itself is modified to enter information directly +The entry of metadata can be manual, or it can be an automated part of data acquisition +(in this case the acquisition software itself is modified to enter information directly into the database). Depending on their size and contents, raw data files can be stored in a number of ways. -In the simplest and most common scenario, raw data continue to be stored in either a +In the simplest and most common scenario, raw data continue to be stored in either a local filesystem or in the cloud as collections of files and folders. -The paths to these files are entered in the database (again, either manually or by +The paths to these files are entered in the database (again, either manually or by automated processes). This is the point at which the notion of a **data pipeline** begins. -Below these "manual tables" that contain metadata and file paths are a series of tables -that load raw data from these files, process it in some way, and insert derived or +Below these "manual tables" that contain metadata and file paths are a series of tables +that load raw data from these files, process it in some way, and insert derived or summarized data directly into the database. -For example, in an imaging application, the very large raw .TIFF stacks would reside on -the filesystem, but the extracted fluorescent trace timeseries for each cell in the +For example, in an imaging application, the very large raw .TIFF stacks would reside on +the filesystem, but the extracted fluorescent trace timeseries for each cell in the image would be stored as a numerical array directly in the database. -Or the raw video used for animal tracking might be stored in a standard video format on -the filesystem, but the computed X/Y positions of the animal would be stored in the +Or the raw video used for animal tracking might be stored in a standard video format on +the filesystem, but the computed X/Y positions of the animal would be stored in the database. -Storing these intermediate computations in the database makes them easily available for +Storing these intermediate computations in the database makes them easily available for downstream analyses and queries. ## Do I have to manually enter all my data into the database? -No! While some of the data will be manually entered (the same way that it would be -manually recorded in a lab notebook), the advantage of DataJoint is that standard -downstream processing steps can be run automatically on all new data with a single +No! While some of the data will be manually entered (the same way that it would be +manually recorded in a lab notebook), the advantage of DataJoint is that standard +downstream processing steps can be run automatically on all new data with a single command. This is where the notion of a **data pipeline** comes into play. -When the workflow of cleaning and processing the data, extracting important features, -and performing basic analyses is all implemented in a DataJoint pipeline, minimal +When the workflow of cleaning and processing the data, extracting important features, +and performing basic analyses is all implemented in a DataJoint pipeline, minimal effort is required to analyze newly-collected data. -Depending on the size of the raw files and the complexity of analysis, useful results +Depending on the size of the raw files and the complexity of analysis, useful results may be available in a matter of minutes or hours. -Because these results are stored in the database, they can be made available to anyone +Because these results are stored in the database, they can be made available to anyone who is given access credentials for additional downstream analyses. ## Won't the database get too big if all my data are there? Typically, this is not a problem. -If you find that your database is getting larger than a few dozen TB, DataJoint -provides transparent solutions for storing very large chunks of data (larger than the 4 +If you find that your database is getting larger than a few dozen TB, DataJoint +provides transparent solutions for storing very large chunks of data (larger than the 4 GB that can be natively stored as a LONGBLOB in MySQL). -However, in many scenarios even long time series or images can be stored directly in +However, in many scenarios even long time series or images can be stored directly in the database with little effect on performance. ## Why not just process the data and save them back to a file? There are two main advantages to storing results in the database. The first is data integrity. -Because the relationships between data are enforced by the structure of the database, -DataJoint ensures that the metadata in the upstream nodes always correctly describes +Because the relationships between data are enforced by the structure of the database, +DataJoint ensures that the metadata in the upstream nodes always correctly describes the computed results downstream in the pipeline. -If a specific experimental session is deleted, for example, all the data extracted from -that session are automatically removed as well, so there is no chance of "orphaned" +If a specific experimental session is deleted, for example, all the data extracted from +that session are automatically removed as well, so there is no chance of "orphaned" data. Likewise, the database ensures that computations are atomic. -This means that any computation performed on a dataset is performed in an all-or-none +This means that any computation performed on a dataset is performed in an all-or-none fashion. Either all of the data are processed and inserted, or none at all. This ensures that there are no incomplete data. -Neither of these important features of data integrity can be guaranteed by a file +Neither of these important features of data integrity can be guaranteed by a file system. -The second advantage of storing intermediate results in a data pipeline is flexible +The second advantage of storing intermediate results in a data pipeline is flexible access. -Accessing arbitrarily complex subsets of the data can be achieved with DataJoint's +Accessing arbitrarily complex subsets of the data can be achieved with DataJoint's flexible query language. -When data are stored in files, collecting the desired data requires trawling through -the file hierarchy, finding and loading the files of interest, and selecting the +When data are stored in files, collecting the desired data requires trawling through +the file hierarchy, finding and loading the files of interest, and selecting the interesting parts of the data. This brings us to the final important question: ## How do I get my data out? -This is the fun part. See [queries](query/operators.md) for details of the DataJoint +This is the fun part. See [queries](query/operators.md) for details of the DataJoint query language directly from MATLAB and Python. ## Interfaces -Multiple interfaces may be used to get the data into and out of the pipeline. +Multiple interfaces may be used to get the data into and out of the pipeline. -Some labs use third-party GUI applications such as -[HeidiSQL](https://www.heidisql.com/) and -[Navicat](https://www.navicat.com/), for example. These applications allow entering +Some labs use third-party GUI applications such as +[HeidiSQL](https://www.heidisql.com/) and +[Navicat](https://www.navicat.com/), for example. These applications allow entering and editing data in tables similarly to spreadsheets. -The Helium Application (https://mattbdean.github.io/Helium/ and -https://github.com/mattbdean/Helium) is web application for browsing DataJoint -pipelines and entering new data. -Matt Dean develops and maintains Helium under the direction of members of Karel +The Helium Application (https://mattbdean.github.io/Helium/ and +https://github.com/mattbdean/Helium) is web application for browsing DataJoint +pipelines and entering new data. +Matt Dean develops and maintains Helium under the direction of members of Karel Svoboda's lab at Janelia Research Campus and Vathes LLC. -Data may also be imported or synchronized into a DataJoint pipeline from existing LIMS -(laboratory information management systems). -For example, the [International Brain Lab](https://internationalbrainlab.com) -synchronizes data from an [Alyx database](https://github.com/cortex-lab/alyx). +Data may also be imported or synchronized into a DataJoint pipeline from existing LIMS +(laboratory information management systems). +For example, the [International Brain Lab](https://internationalbrainlab.com) +synchronizes data from an [Alyx database](https://github.com/cortex-lab/alyx). For implementation details, see https://github.com/int-brain-lab/IBL-pipeline. -Other labs (e.g. Sinz Lab) have developed GUI interfaces using the Flask web framework +Other labs (e.g. Sinz Lab) have developed GUI interfaces using the Flask web framework in Python. diff --git a/docs/src/images/added-example-ERD.svg b/docs/src/images/added-example-ERD.svg index 7603f2c2c..0884853f4 100644 --- a/docs/src/images/added-example-ERD.svg +++ b/docs/src/images/added-example-ERD.svg @@ -204,4 +204,4 @@ points                - \ No newline at end of file + diff --git a/docs/src/images/dimitri-ERD.svg b/docs/src/images/dimitri-ERD.svg index 5c805f8ed..590b30887 100644 --- a/docs/src/images/dimitri-ERD.svg +++ b/docs/src/images/dimitri-ERD.svg @@ -114,4 +114,4 @@ - \ No newline at end of file + diff --git a/docs/src/images/shapes_pipeline.svg b/docs/src/images/shapes_pipeline.svg index 203b7b47c..14572c4ce 100644 --- a/docs/src/images/shapes_pipeline.svg +++ b/docs/src/images/shapes_pipeline.svg @@ -33,4 +33,4 @@ shape_width           - \ No newline at end of file + diff --git a/docs/src/images/spawned-classes-ERD.svg b/docs/src/images/spawned-classes-ERD.svg index 65fbd7ccd..313841e81 100644 --- a/docs/src/images/spawned-classes-ERD.svg +++ b/docs/src/images/spawned-classes-ERD.svg @@ -144,4 +144,4 @@ - \ No newline at end of file + diff --git a/docs/src/images/virtual-module-ERD.svg b/docs/src/images/virtual-module-ERD.svg index 69d98ae2a..28eb0c481 100644 --- a/docs/src/images/virtual-module-ERD.svg +++ b/docs/src/images/virtual-module-ERD.svg @@ -144,4 +144,4 @@ - \ No newline at end of file + diff --git a/docs/src/index.md b/docs/src/index.md index 70332d4e1..8c5f8fcb1 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -1,14 +1,14 @@ # Welcome to DataJoint for Python! -DataJoint for Python is a framework for scientific workflow management based on -relational principles. DataJoint is built on the foundation of the relational data -model and prescribes a consistent method for organizing, populating, computing, and +DataJoint for Python is a framework for scientific workflow management based on +relational principles. DataJoint is built on the foundation of the relational data +model and prescribes a consistent method for organizing, populating, computing, and querying data. -DataJoint was initially developed in 2009 by Dimitri Yatsenko in Andreas Tolias' Lab at -Baylor College of Medicine for the distributed processing and management of large -volumes of data streaming from regular experiments. Starting in 2011, DataJoint has -been available as an open-source project adopted by other labs and improved through +DataJoint was initially developed in 2009 by Dimitri Yatsenko in Andreas Tolias' Lab at +Baylor College of Medicine for the distributed processing and management of large +volumes of data streaming from regular experiments. Starting in 2011, DataJoint has +been available as an open-source project adopted by other labs and improved through contributions from several developers. Presently, the primary developer of DataJoint open-source software is the company [DataJoint](https://datajoint.com){:target="_blank"}. diff --git a/docs/src/internal/transpilation.md b/docs/src/internal/transpilation.md index a2ff1d0c4..cc02380c0 100644 --- a/docs/src/internal/transpilation.md +++ b/docs/src/internal/transpilation.md @@ -1,9 +1,9 @@ # Transpiler Design -This section contains the information and reasoning that went into the design of the +This section contains the information and reasoning that went into the design of the DataJoint-to-SQL transpiler. -MySQL appears to differ from standard SQL by the sequence of evaluating the clauses of +MySQL appears to differ from standard SQL by the sequence of evaluating the clauses of the SELECT statement. ``` @@ -13,11 +13,11 @@ MySQL: FROM > WHERE > SELECT > GROUP BY > HAVING -Moving `SELECT` to an earlier phase allows the `GROUP BY` and `HAVING` clauses to use +Moving `SELECT` to an earlier phase allows the `GROUP BY` and `HAVING` clauses to use alias column names created by the `SELECT` clause. -The current implementation targets the MySQL implementation where table column aliases +The current implementation targets the MySQL implementation where table column aliases can be used in `HAVING`. -If postgres or CockroachDB cannot be coerced to work this way, restrictions of +If postgres or CockroachDB cannot be coerced to work this way, restrictions of aggregations will have to be updated accordingly. ## QueryExpression @@ -29,29 +29,29 @@ Property `heading` describes all attributes. Operator `proj` creates a new heading. -Property `restriction` contains the `AndList` of conditions. Operator `&` creates a new +Property `restriction` contains the `AndList` of conditions. Operator `&` creates a new restriction appending the new condition to the input's restriction. -Property `support` represents the `FROM` clause and contains a list of either +Property `support` represents the `FROM` clause and contains a list of either `QueryExpression` objects or table names in the case of base queries. The joint operator `*` adds new elements to the `support` attribute. -At least one element must be present in `support`. Multiple elements in `support` +At least one element must be present in `support`. Multiple elements in `support` indicate a join. -From the user's perspective `QueryExpression` objects are immutable: once created they +From the user's perspective `QueryExpression` objects are immutable: once created they cannot be modified. All operators derive new objects. ### Alias attributes -`proj` can create an alias attribute by renaming an existing attribute or calculating a +`proj` can create an alias attribute by renaming an existing attribute or calculating a new attribute. Alias attributes are the primary reason why subqueries are sometimes required. ### Subqueries -Projections, restrictions, and joins do not necessarily trigger new subqueries: the -resulting `QueryExpression` object simply merges the properties of its inputs into +Projections, restrictions, and joins do not necessarily trigger new subqueries: the +resulting `QueryExpression` object simply merges the properties of its inputs into self: `heading`, `restriction`, and `support`. The input object is treated as a subquery in the following cases: @@ -59,59 +59,59 @@ The input object is treated as a subquery in the following cases: 1. A restriction is applied that uses alias attributes in the heading 1. A projection uses an alias attribute to create a new alias attribute. 1. A join is performed on an alias attribute. -1. An Aggregation is used a restriction. +1. An Aggregation is used a restriction. An error arises if -1. If a restriction or a projection attempts to use attributes not in the current +1. If a restriction or a projection attempts to use attributes not in the current heading. 2. If attempting to join on attributes that are not join-compatible 3. If attempting to restrict by a non-join-compatible expression -A subquery is created by creating a new `QueryExpression` object (or a subclass object) +A subquery is created by creating a new `QueryExpression` object (or a subclass object) with its `support` pointing to the input object. ### Join compatibility The join is always natural (i.e. *equijoin* on the namesake attributes). -**Before version 0.13:** As of version `0.12.*` and earlier, two query expressions were -considered join-compatible if their namesake attributes were the primary key of at -least one of the input expressions. This rule was easiest to implement but does not +**Before version 0.13:** As of version `0.12.*` and earlier, two query expressions were +considered join-compatible if their namesake attributes were the primary key of at +least one of the input expressions. This rule was easiest to implement but does not provide best semantics. -**Version 0.13:** In version `0.13.*`, two query expressions are considered -join-compatible if their namesake attributes are either in the primary key or in a +**Version 0.13:** In version `0.13.*`, two query expressions are considered +join-compatible if their namesake attributes are either in the primary key or in a foreign key in both input expressions. **Future (potentially version 0.14+):** -This compatibility requirement will be further restricted to require that the namesake -attributes ultimately derive from the same primary key attribute by being passed down +This compatibility requirement will be further restricted to require that the namesake +attributes ultimately derive from the same primary key attribute by being passed down through foreign keys. -The same join compatibility rules apply when restricting one query expression with +The same join compatibility rules apply when restricting one query expression with another. ### Join mechanics Any restriction applied to the inputs of a join can be applied to its output. -Therefore, those inputs that are not turned into queries donate their supports, +Therefore, those inputs that are not turned into queries donate their supports, restrictions, and projections to the join itself. ## Table -`Table` is a subclass of `QueryExpression` implementing table manipulation methods such +`Table` is a subclass of `QueryExpression` implementing table manipulation methods such as `insert`, `insert1`, `delete`, `update1`, and `drop`. -The restriction operator `&` applied to a `Table` preserves its class identity so that +The restriction operator `&` applied to a `Table` preserves its class identity so that the result remains of type `Table`. -However, `proj` converts the result into a `QueryExpression` object. This may produce a +However, `proj` converts the result into a `QueryExpression` object. This may produce a base query that is not an instance of Table. ## Aggregation `Aggregation` is a subclass of `QueryExpression`. -Its main input is the *aggregating* query expression and it takes an additional second +Its main input is the *aggregating* query expression and it takes an additional second input — the *aggregated* query expression. The SQL equivalent of aggregation is @@ -121,15 +121,15 @@ The SQL equivalent of aggregation is 1. followed by a projection. The projection works the same as `.proj` with respect to the first input. -With respect to the second input, the projection part of aggregation allows only -calculated attributes that use aggregating functions (*eg* `SUM`, `AVG`, `COUNT`) -applied to the attributes of the aggregated (second) input and non-aggregating +With respect to the second input, the projection part of aggregation allows only +calculated attributes that use aggregating functions (*eg* `SUM`, `AVG`, `COUNT`) +applied to the attributes of the aggregated (second) input and non-aggregating functions on the attribute of the aggregating (first) input. `Aggregation` supports all the same operators as `QueryExpression` except: -1. `restriction` turns into a `HAVING` clause instead of a `WHERE` clause. This allows -applying any valid restriction without making a subquery (at least for MySQL). +1. `restriction` turns into a `HAVING` clause instead of a `WHERE` clause. This allows +applying any valid restriction without making a subquery (at least for MySQL). Therefore, restricting an `Aggregation` object never results in a subquery. 2. In joins, aggregation always turns into a subquery. @@ -142,16 +142,16 @@ A `Union` object results from the `+` operator on two `QueryExpression` objects. Its `support` property contains the list of expressions (at least two) to unify. Thus the `+` operator on unions simply merges their supports, making a bigger union. -The `Union` operator performs an OUTER JOIN of its inputs provided that the inputs have -the same primary key and no secondary attributes in common. +The `Union` operator performs an OUTER JOIN of its inputs provided that the inputs have +the same primary key and no secondary attributes in common. Union treats all its inputs as subqueries except for unrestricted Union objects. ## Universal Sets `dj.U` -`dj.U` is a special operand in query expressions that allows performing special -operations. By itself, it can never form a query and is not a subclass of -`QueryExpression`. Other query expressions are modified through participation in +`dj.U` is a special operand in query expressions that allows performing special +operations. By itself, it can never form a query and is not a subclass of +`QueryExpression`. Other query expressions are modified through participation in operations with `dj.U`. ### Aggregating by `dj.U` @@ -162,9 +162,9 @@ operations with `dj.U`. ## Query "Backprojection" -Once a QueryExpression is used in a `fetch` operation or becomes a subquery in another +Once a QueryExpression is used in a `fetch` operation or becomes a subquery in another query, it can project out all unnecessary attributes from its own inputs, recursively. This is implemented by the `finalize` method. -This simplification produces much leaner queries resulting in improved query -performance in version 0.13, especially on complex queries with blob data, compensating +This simplification produces much leaner queries resulting in improved query +performance in version 0.13, especially on complex queries with blob data, compensating for MySQL's deficiencies in query optimization. diff --git a/docs/src/manipulation/delete.md b/docs/src/manipulation/delete.md index 83eb0125b..4e34c69ce 100644 --- a/docs/src/manipulation/delete.md +++ b/docs/src/manipulation/delete.md @@ -1,9 +1,9 @@ # Delete -The `delete` method deletes entities from a table and all dependent entries in +The `delete` method deletes entities from a table and all dependent entries in dependent tables. -Delete is often used in conjunction with the [restriction](../query/restrict.md) +Delete is often used in conjunction with the [restriction](../query/restrict.md) operator to define the subset of entities to delete. Delete is performed as an atomic transaction so that partial deletes never occur. @@ -22,7 +22,7 @@ tuning.VonMises.delete() ## Deleting from part tables -Entities in a [part table](../design/tables/master-part.md) are usually removed as a +Entities in a [part table](../design/tables/master-part.md) are usually removed as a consequence of deleting the master table. To enforce this workflow, calling `delete` directly on a part table produces an error. diff --git a/docs/src/manipulation/index.md b/docs/src/manipulation/index.md index a1c2b3c4c..295195778 100644 --- a/docs/src/manipulation/index.md +++ b/docs/src/manipulation/index.md @@ -1,9 +1,9 @@ # Data Manipulation -Data **manipulation** operations change the state of the data stored in the database +Data **manipulation** operations change the state of the data stored in the database without modifying the structure of the stored data. -These operations include [insert](insert.md), [delete](delete.md), and +These operations include [insert](insert.md), [delete](delete.md), and [update](update.md). -Data manipulation operations in DataJoint respect the +Data manipulation operations in DataJoint respect the [integrity](../design/integrity.md) constraints. diff --git a/docs/src/manipulation/insert.md b/docs/src/manipulation/insert.md index c710e38a4..c64e55f17 100644 --- a/docs/src/manipulation/insert.md +++ b/docs/src/manipulation/insert.md @@ -3,7 +3,7 @@ The `insert` method of DataJoint table objects inserts entities into the table. In Python there is a separate method `insert1` to insert one entity at a time. -The entity may have the form of a Python dictionary with key names matching the +The entity may have the form of a Python dictionary with key names matching the attribute names in the table. ```python @@ -13,18 +13,18 @@ lab.Person.insert1( last_name='Cooper')) ``` -The entity also may take the form of a sequence of values in the same order as the +The entity also may take the form of a sequence of values in the same order as the attributes in the table. ```python lab.Person.insert1(['alice', 'Alice', 'Cooper']) ``` -Additionally, the entity may be inserted as a +Additionally, the entity may be inserted as a [NumPy record array](https://docs.scipy.org/doc/numpy/reference/generated/numpy.record.html#numpy.record) or [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html). -The `insert` method accepts a sequence or a generator of multiple entities and is used +The `insert` method accepts a sequence or a generator of multiple entities and is used to insert multiple entities at once. ```python @@ -51,37 +51,37 @@ Several optional parameters can be used with `insert`: ## Batched inserts -Inserting a set of entities in a single `insert` differs from inserting the same set of +Inserting a set of entities in a single `insert` differs from inserting the same set of entities one-by-one in a `for` loop in two ways: 1. Network overhead is reduced. Network overhead can be tens of milliseconds per query. - Inserting 1000 entities in a single `insert` call may save a few seconds over + Inserting 1000 entities in a single `insert` call may save a few seconds over inserting them individually. 2. The insert is performed as an all-or-nothing transaction. - If even one insert fails because it violates any constraint, then none of the + If even one insert fails because it violates any constraint, then none of the entities in the set are inserted. -However, inserting too many entities in a single query may run against buffer size or +However, inserting too many entities in a single query may run against buffer size or packet size limits of the database server. -Due to these limitations, performing inserts of very large numbers of entities should +Due to these limitations, performing inserts of very large numbers of entities should be broken up into moderately sized batches, such as a few hundred at a time. ## Server-side inserts Data inserted into a table often come from other tables already present on the database server. -In such cases, data can be [fetched](../query/fetch.md) from the first table and then -inserted into another table, but this results in transfers back and forth between the +In such cases, data can be [fetched](../query/fetch.md) from the first table and then +inserted into another table, but this results in transfers back and forth between the database and the local system. -Instead, data can be inserted from one table into another without transfers between the +Instead, data can be inserted from one table into another without transfers between the database and the local system using [queries](../query/principles.md). -In the example below, a new schema has been created in preparation for phase two of a +In the example below, a new schema has been created in preparation for phase two of a project. -Experimental protocols from the first phase of the project will be reused in the second +Experimental protocols from the first phase of the project will be reused in the second phase. -Since the entities are already present on the database in the `Protocol` table of the -`phase_one` schema, we can perform a server-side insert into `phase_two.Protocol` +Since the entities are already present on the database in the `Protocol` table of the +`phase_one` schema, we can perform a server-side insert into `phase_two.Protocol` without fetching a local copy. ```python diff --git a/docs/src/manipulation/transactions.md b/docs/src/manipulation/transactions.md index fa4f4294b..4b05fc528 100644 --- a/docs/src/manipulation/transactions.md +++ b/docs/src/manipulation/transactions.md @@ -1,26 +1,26 @@ # Transactions -In some cases, a sequence of several operations must be performed as a single -operation: -interrupting the sequence of such operations halfway would leave the data in an invalid -state. -While the sequence is in progress, other processes accessing the database will not see +In some cases, a sequence of several operations must be performed as a single +operation: +interrupting the sequence of such operations halfway would leave the data in an invalid +state. +While the sequence is in progress, other processes accessing the database will not see the partial results until the transaction is complete. -The sequence make include [data queries](../query/principles.md) and +The sequence make include [data queries](../query/principles.md) and [manipulations](index.md). -In such cases, the sequence of operations may be enclosed in a transaction. +In such cases, the sequence of operations may be enclosed in a transaction. -Transactions are formed using the `transaction` property of the connection object. +Transactions are formed using the `transaction` property of the connection object. The connection object may be obtained from any table object. -The `transaction` property can then be used as a context manager in Python's `with` +The `transaction` property can then be used as a context manager in Python's `with` statement. -For example, the following code inserts matching entries for the master table `Session` +For example, the following code inserts matching entries for the master table `Session` and its part table `Session.Experimenter`. ```python -# get the connection object +# get the connection object connection = Session.connection # insert Session and Session.Experimenter entries in a transaction @@ -30,7 +30,7 @@ with connection.transaction: Session.Experimenter.insert1({**key, 'experimenter': username}) ``` -Here, to external observers, both inserts will take effect together upon exiting from +Here, to external observers, both inserts will take effect together upon exiting from the `with` block or will not have any effect at all. -For example, if the second insert fails due to an error, the first insert will be -rolled back. +For example, if the second insert fails due to an error, the first insert will be +rolled back. diff --git a/docs/src/manipulation/update.md b/docs/src/manipulation/update.md index 1ea18c3f8..7faa7cb87 100644 --- a/docs/src/manipulation/update.md +++ b/docs/src/manipulation/update.md @@ -1,34 +1,34 @@ # Cautious Update -In database programming, the **update** operation refers to modifying the values of +In database programming, the **update** operation refers to modifying the values of individual attributes in an entity within a table without replacing the entire entity. -Such an in-place update mechanism is not part of DataJoint's data manipulation model, -because it circumvents data +Such an in-place update mechanism is not part of DataJoint's data manipulation model, +because it circumvents data [dependency constraints](../design/integrity.md#referential-integrity). This is not to say that data cannot be changed once they are part of a pipeline. -In DataJoint, data is changed by replacing entire entities rather than by updating the +In DataJoint, data is changed by replacing entire entities rather than by updating the values of their attributes. -The process of deleting existing entities and inserting new entities with corrected -values ensures the [integrity](../design/integrity.md) of the data throughout the +The process of deleting existing entities and inserting new entities with corrected +values ensures the [integrity](../design/integrity.md) of the data throughout the pipeline. -This approach applies specifically to automated tables +This approach applies specifically to automated tables (see [Auto-populated tables](../compute/populate.md)). However, manual tables are often edited outside DataJoint through other interfaces. -It is up to the user's discretion to allow updates in manual tables, and the user must +It is up to the user's discretion to allow updates in manual tables, and the user must be cognizant of the fact that updates will not trigger re-computation of dependent data. ## Usage -For some cases, it becomes necessary to deliberately correct existing values where a +For some cases, it becomes necessary to deliberately correct existing values where a user has chosen to accept the above responsibility despite the caution. -The `update1` method accomplishes this if the record already exists. Note that updates +The `update1` method accomplishes this if the record already exists. Note that updates to primary key values are not allowed. -The method should only be used to fix problems, and not as part of a regular workflow. -When updating an entry, make sure that any information stored in dependent tables that +The method should only be used to fix problems, and not as part of a regular workflow. +When updating an entry, make sure that any information stored in dependent tables that depends on the update values is properly updated as well. ## Examples diff --git a/docs/src/publish-data.md b/docs/src/publish-data.md index e68a2843a..774cf0456 100644 --- a/docs/src/publish-data.md +++ b/docs/src/publish-data.md @@ -1,34 +1,34 @@ # Publishing Data -DataJoint is a framework for building data pipelines that support rigorous flow of -structured data between experimenters, data scientists, and computing agents *during* -data acquisition and processing within a centralized project. -Publishing final datasets for the outside world may require additional steps and +DataJoint is a framework for building data pipelines that support rigorous flow of +structured data between experimenters, data scientists, and computing agents *during* +data acquisition and processing within a centralized project. +Publishing final datasets for the outside world may require additional steps and conversion. ## Provide access to a DataJoint server -One approach for publishing data is to grant public access to an existing pipeline. -Then public users will be able to query the data pipelines using DataJoint's query +One approach for publishing data is to grant public access to an existing pipeline. +Then public users will be able to query the data pipelines using DataJoint's query language and output interfaces just like any other users of the pipeline. -For security, this may require synchronizing the data onto a separate read-only public -server. +For security, this may require synchronizing the data onto a separate read-only public +server. ## Containerizing as a DataJoint pipeline -Containerization platforms such as [Docker](https://www.docker.com/) allow convenient -distribution of environments including database services and data. -It is convenient to publish DataJoint pipelines as a docker container that deploys the +Containerization platforms such as [Docker](https://www.docker.com/) allow convenient +distribution of environments including database services and data. +It is convenient to publish DataJoint pipelines as a docker container that deploys the populated DataJoint pipeline. -One example of publishing a DataJoint pipeline as a docker container is +One example of publishing a DataJoint pipeline as a docker container is > Sinz, F., Ecker, A.S., Fahey, P., Walker, E., Cobos, E., Froudarakis, E., Yatsenko, D., Pitkow, Z., Reimer, J. and Tolias, A., 2018. Stimulus domain transfer in recurrent models for large scale cortical population prediction on video. In Advances in Neural Information Processing Systems (pp. 7198-7209). https://www.biorxiv.org/content/early/2018/10/25/452672 The code and the data can be found at https://github.com/sinzlab/Sinz2018_NIPS ## Exporting into a collection of files -Another option for publishing and archiving data is to export the data from the +Another option for publishing and archiving data is to export the data from the DataJoint pipeline into a collection of files. -DataJoint provides features for exporting and importing sections of the pipeline. -Several ongoing projects are implementing the capability to export from DataJoint -pipelines into [Neurodata Without Borders](https://www.nwb.org/) files. +DataJoint provides features for exporting and importing sections of the pipeline. +Several ongoing projects are implementing the capability to export from DataJoint +pipelines into [Neurodata Without Borders](https://www.nwb.org/) files. diff --git a/docs/src/query/aggregation.md b/docs/src/query/aggregation.md index 06c16c9c9..e47fd0b33 100644 --- a/docs/src/query/aggregation.md +++ b/docs/src/query/aggregation.md @@ -1,21 +1,21 @@ # Aggr -**Aggregation**, performed with the `aggr` operator, is a special form of `proj` with +**Aggregation**, performed with the `aggr` operator, is a special form of `proj` with the additional feature of allowing aggregation calculations on another table. It has the form `tab.aggr(other, ...)` where `other` is another table. Without the argument `other`, `aggr` and `proj` are exactly equivalent. -Aggregation allows adding calculated attributes to each entity in `tab` based on -aggregation functions over attributes in the +Aggregation allows adding calculated attributes to each entity in `tab` based on +aggregation functions over attributes in the [matching](./operators.md#matching-entities) entities of `other`. -Aggregation functions include `count`, `sum`, `min`, `max`, `avg`, `std`, `variance`, +Aggregation functions include `count`, `sum`, `min`, `max`, `avg`, `std`, `variance`, and others. -Aggregation functions can only be used in the definitions of new attributes within the +Aggregation functions can only be used in the definitions of new attributes within the `aggr` operator. -As with `proj`, the output of `aggr` has the same entity class, the same primary key, +As with `proj`, the output of `aggr` has the same entity class, the same primary key, and the same number of elements as `tab`. -Primary key attributes are always included in the output and may be renamed, just like +Primary key attributes are always included in the output and may be renamed, just like in `proj`. ## Examples diff --git a/docs/src/query/example-schema.md b/docs/src/query/example-schema.md index 18b960e0a..063e36574 100644 --- a/docs/src/query/example-schema.md +++ b/docs/src/query/example-schema.md @@ -4,10 +4,10 @@ The example schema below contains data for a university enrollment system. Information about students, departments, courses, etc. are organized in multiple tables. Warning: - Empty primary keys, such as in the `CurrentTerm` table, are not yet supported by + Empty primary keys, such as in the `CurrentTerm` table, are not yet supported by DataJoint. This feature will become available in a future release. - See [Issue #113](https://github.com/datajoint/datajoint-python/issues/113) for more + See [Issue #113](https://github.com/datajoint/datajoint-python/issues/113) for more information. ```python diff --git a/docs/src/query/fetch.md b/docs/src/query/fetch.md index f78b07dd2..105d70084 100644 --- a/docs/src/query/fetch.md +++ b/docs/src/query/fetch.md @@ -2,24 +2,24 @@ Data queries in DataJoint comprise two distinct steps: -1. Construct the `query` object to represent the required data using tables and +1. Construct the `query` object to represent the required data using tables and [operators](operators.md). -2. Fetch the data from `query` into the workspace of the host language -- described in +2. Fetch the data from `query` into the workspace of the host language -- described in this section. -Note that entities returned by `fetch` methods are not guaranteed to be sorted in any +Note that entities returned by `fetch` methods are not guaranteed to be sorted in any particular order unless specifically requested. -Furthermore, the order is not guaranteed to be the same in any two queries, and the -contents of two identical queries may change between two sequential invocations unless +Furthermore, the order is not guaranteed to be the same in any two queries, and the +contents of two identical queries may change between two sequential invocations unless they are wrapped in a transaction. Therefore, if you wish to fetch matching pairs of attributes, do so in one `fetch` call. -The examples below are based on the [example schema](example-schema.md) for this part +The examples below are based on the [example schema](example-schema.md) for this part of the documentation. ## Entire table -The following statement retrieves the entire table as a NumPy +The following statement retrieves the entire table as a NumPy [recarray](https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html). ```python @@ -32,10 +32,10 @@ To retrieve the data as a list of `dict`: data = query.fetch(as_dict=True) ``` -In some cases, the amount of data returned by fetch can be quite large; in these cases -it can be useful to use the `size_on_disk` attribute to determine if running a bare +In some cases, the amount of data returned by fetch can be quite large; in these cases +it can be useful to use the `size_on_disk` attribute to determine if running a bare fetch would be wise. -Please note that it is only currently possible to query the size of entire tables +Please note that it is only currently possible to query the size of entire tables stored directly in the database at this time. ## As separate variables @@ -52,7 +52,7 @@ keydict = tab.fetch1("KEY") # single key dict when tab has exactly one entity keylist = tab.fetch("KEY") # list of key dictionaries [{}, ...] ``` -`KEY` can also used when returning attribute values as separate variables, such that +`KEY` can also used when returning attribute values as separate variables, such that one of the returned variables contains the entire primary keys. ## Sorting and limiting the results @@ -63,7 +63,7 @@ To sort the result, use the `order_by` keyword argument. # ascending order: data = query.fetch(order_by='name') # descending order: -data = query.fetch(order_by='name desc') +data = query.fetch(order_by='name desc') # by name first, year second: data = query.fetch(order_by=('name desc', 'year')) # sort by the primary key: @@ -72,25 +72,25 @@ data = query.fetch(order_by='KEY') data = query.fetch(order_by=('name', 'KEY desc')) ``` -The `order_by` argument can be a string specifying the attribute to sort by. By default -the sort is in ascending order. Use `'attr desc'` to sort in descending order by -attribute `attr`. The value can also be a sequence of strings, in which case, the sort +The `order_by` argument can be a string specifying the attribute to sort by. By default +the sort is in ascending order. Use `'attr desc'` to sort in descending order by +attribute `attr`. The value can also be a sequence of strings, in which case, the sort performed on all the attributes jointly in the order specified. -The special attribute name `'KEY'` represents the primary key attributes in order that +The special attribute name `'KEY'` represents the primary key attributes in order that they appear in the index. Otherwise, this name can be used as any other argument. -If an attribute happens to be a SQL reserved word, it needs to be enclosed in +If an attribute happens to be a SQL reserved word, it needs to be enclosed in backquotes. For example: ```python data = query.fetch(order_by='`select` desc') ``` -The `order_by` value is eventually passed to the `ORDER BY` +The `order_by` value is eventually passed to the `ORDER BY` [clause](https://dev.mysql.com/doc/refman/5.7/en/order-by-optimization.html). -Similarly, the `limit` and `offset` arguments can be used to limit the result to a +Similarly, the `limit` and `offset` arguments can be used to limit the result to a subset of entities. For example, one could do the following: @@ -99,14 +99,14 @@ For example, one could do the following: data = query.fetch(order_by='name', limit=10, offset=5) ``` -Note that an `offset` cannot be used without specifying a `limit` as well. +Note that an `offset` cannot be used without specifying a `limit` as well. ## Usage with Pandas -The [pandas library](http://pandas.pydata.org/) is a popular library for data analysis +The [pandas library](http://pandas.pydata.org/) is a popular library for data analysis in Python which can easily be used with DataJoint query results. -Since the records returned by `fetch()` are contained within a `numpy.recarray`, they -can be easily converted to `pandas.DataFrame` objects by passing them into the +Since the records returned by `fetch()` are contained within a `numpy.recarray`, they +can be easily converted to `pandas.DataFrame` objects by passing them into the `pandas.DataFrame` constructor. For example: @@ -115,12 +115,12 @@ import pandas as pd frame = pd.DataFrame(tab.fetch()) ``` -Calling `fetch()` with the argument `format="frame"` returns results as +Calling `fetch()` with the argument `format="frame"` returns results as `pandas.DataFrame` objects indexed by the table's primary key attributes. ```python frame = tab.fetch(format="frame") ``` -Returning results as a `DataFrame` is not possible when fetching a particular subset of +Returning results as a `DataFrame` is not possible when fetching a particular subset of attributes or when `as_dict` is set to `True`. diff --git a/docs/src/query/join.md b/docs/src/query/join.md index c5a6ec371..d0ab0eae0 100644 --- a/docs/src/query/join.md +++ b/docs/src/query/join.md @@ -12,12 +12,12 @@ The result contains all matching combinations of entities from both arguments. ### Examples of joins -Example 1 : When the operands have no common attributes, the result is the cross +Example 1 : When the operands have no common attributes, the result is the cross product -- all combinations of entities. ![join-example1](../images/join-example1.png){: style="width:464px; align:center"} -Example 2 : When the operands have common attributes, only entities with matching +Example 2 : When the operands have common attributes, only entities with matching values are kept. ![join-example2](../images/join-example2.png){: style="width:689px; align:center"} @@ -28,7 +28,7 @@ Example 3 : Joining on secondary attribute. ### Properties of join -1. When `A` and `B` have the same attributes, the join `A * B` becomes equivalent to +1. When `A` and `B` have the same attributes, the join `A * B` becomes equivalent to the set intersection `A` ∩ `B`. Hence, DataJoint does not need a separate intersection operator. diff --git a/docs/src/query/principles.md b/docs/src/query/principles.md index 633f12b97..9b9fd284d 100644 --- a/docs/src/query/principles.md +++ b/docs/src/query/principles.md @@ -1,35 +1,35 @@ # Query Principles **Data queries** retrieve data from the database. -A data query is performed with the help of a **query object**, which is a symbolic +A data query is performed with the help of a **query object**, which is a symbolic representation of the query that does not in itself contain any actual data. -The simplest query object is an instance of a **table class**, representing the +The simplest query object is an instance of a **table class**, representing the contents of an entire table. -For example, if `experiment.Session` is a DataJoint table class, you can create a query +For example, if `experiment.Session` is a DataJoint table class, you can create a query object to retrieve its entire contents as follows: ```python query = experiment.Session() ``` -More generally, a query object may be formed as a **query expression** constructed by +More generally, a query object may be formed as a **query expression** constructed by applying [operators](operators.md) to other query objects. -For example, the following query retrieves information about all experiments and scans +For example, the following query retrieves information about all experiments and scans for mouse 102 (excluding experiments with no scans): ```python query = experiment.Session * experiment.Scan & 'animal_id = 102' ``` -Note that for brevity, query operators can be applied directly to class objects rather -than instance objects so that `experiment.Session` may be used in place of +Note that for brevity, query operators can be applied directly to class objects rather +than instance objects so that `experiment.Session` may be used in place of `experiment.Session()`. -You can preview the contents of the query in Python, Jupyter Notebook, or MATLAB by +You can preview the contents of the query in Python, Jupyter Notebook, or MATLAB by simply displaying the object. -In the image below, the object `query` is first defined as a restriction of the table +In the image below, the object `query` is first defined as a restriction of the table `EEG` by values of the attribute `eeg_sample_rate` greater than 1000 Hz. Displaying the object gives a preview of the entities that will be returned by `query`. Note that this preview only lists a few of the entities that will be returned. @@ -39,30 +39,30 @@ Also, the preview does not contain any data for attributes of datatype `blob`. Defining a query object and previewing the entities returned by the query. -Once the desired query object is formed, the query can be executed using its +Once the desired query object is formed, the query can be executed using its [fetch](fetch.md) methods. -To **fetch** means to transfer the data represented by the query object from the +To **fetch** means to transfer the data represented by the query object from the database server into the workspace of the host language. ```python s = query.fetch() ``` -Here fetching from the `query` object produces the NumPy record array `s` of the +Here fetching from the `query` object produces the NumPy record array `s` of the queried data. ## Checking for returned entities -The preview of the query object shown above displayed only a few of the entities -returned by the query but also displayed the total number of entities that would be +The preview of the query object shown above displayed only a few of the entities +returned by the query but also displayed the total number of entities that would be returned. -It can be useful to know the number of entities returned by a query, or even whether a +It can be useful to know the number of entities returned by a query, or even whether a query will return any entities at all, without having to fetch all the data themselves. -The `bool` function applied to a query object evaluates to `True` if the query returns +The `bool` function applied to a query object evaluates to `True` if the query returns any entities and to `False` if the query result is empty. -The `len` function applied to a query object determines the number of entities returned +The `len` function applied to a query object determines the number of entities returned by the query. ```python @@ -72,10 +72,10 @@ n = len(Session & 'session_date >= "2018-01-01"') ## Normalization in queries -Query objects adhere to entity [entity normalization](../design/normalization.md) just +Query objects adhere to entity [entity normalization](../design/normalization.md) just like the stored tables do. -The result of a query is a well-defined entity set with an readily identifiable entity -class and designated primary attributes that jointly distinguish any two entities from +The result of a query is a well-defined entity set with an readily identifiable entity +class and designated primary attributes that jointly distinguish any two entities from each other. -The query [operators](operators.md) are designed to keep the result normalized even in +The query [operators](operators.md) are designed to keep the result normalized even in complex query expressions. diff --git a/docs/src/query/project.md b/docs/src/query/project.md index 151f77300..99e5749c7 100644 --- a/docs/src/query/project.md +++ b/docs/src/query/project.md @@ -1,6 +1,6 @@ # Proj -The `proj` operator represents **projection** and is used to select attributes +The `proj` operator represents **projection** and is used to select attributes (columns) from a table, to rename them, or to create new calculated attributes. ## Simple projection @@ -8,7 +8,7 @@ The `proj` operator represents **projection** and is used to select attributes The simple projection selects a subset of attributes of the original table. However, the primary key attributes are always included. -Using the [example schema](example-schema.md), let table `department` have attributes +Using the [example schema](example-schema.md), let table `department` have attributes **dept**, *dept_name*, *dept_address*, and *dept_phone*. The primary key attribute is in bold. @@ -16,7 +16,7 @@ Then `department.proj()` will have attribute **dept**. `department.proj('dept')` will have attribute **dept**. -`department.proj('dept_name', 'dept_phone')` will have attributes **dept**, +`department.proj('dept_name', 'dept_phone')` will have attributes **dept**, *dept_name*, and *dept_phone*. ## Renaming @@ -27,7 +27,7 @@ Any attribute can be renamed, including primary key attributes. This is done using keyword arguments: `tab.proj(new_attr='old_attr')` -For example, let table `tab` have attributes **mouse**, **session**, *session_date*, +For example, let table `tab` have attributes **mouse**, **session**, *session_date*, *stimulus*, and *behavior*. The primary key attributes are in bold. @@ -52,11 +52,11 @@ yields all ordered pairs of all cells in each slice. ## Calculations -In addition to selecting or renaming attributes, `proj` can compute new attributes from +In addition to selecting or renaming attributes, `proj` can compute new attributes from existing ones. For example, let `tab` have attributes `mouse`, `scan`, `surface_z`, and `scan_z`. -To obtain the new attribute `depth` computed as `scan_z - surface_z` and then to +To obtain the new attribute `depth` computed as `scan_z - surface_z` and then to restrict to `depth > 500`: ```python @@ -64,5 +64,5 @@ tab.proj(depth='scan_z-surface_z') & 'depth > 500' ``` Calculations are passed to SQL and are not parsed by DataJoint. -For available functions, you may refer to the +For available functions, you may refer to the [MySQL documentation](https://dev.mysql.com/doc/refman/8.0/en/functions.html). diff --git a/docs/src/query/restrict.md b/docs/src/query/restrict.md index 0cb3cc29b..74d396183 100644 --- a/docs/src/query/restrict.md +++ b/docs/src/query/restrict.md @@ -2,9 +2,9 @@ ## Restriction operators `&` and `-` -The restriction operator `A & cond` selects the subset of entities from `A` that meet +The restriction operator `A & cond` selects the subset of entities from `A` that meet the condition `cond`. -The exclusion operator `A - cond` selects the complement of restriction, i.e. the +The exclusion operator `A - cond` selects the complement of restriction, i.e. the subset of entities from `A` that do not meet the condition `cond`. Restriction and exclusion. @@ -22,17 +22,17 @@ The condition `cond` may be one of the following: + a `Not` object + a query expression -As the restriction and exclusion operators are complementary, queries can be +As the restriction and exclusion operators are complementary, queries can be constructed using both operators that will return the same results. For example, the queries `A & cond` and `A - Not(cond)` will return the same entities. ## Restriction by a table -When restricting table `A` with another table, written `A & B`, the two tables must be +When restricting table `A` with another table, written `A & B`, the two tables must be **join-compatible** (see `join-compatible` in [Operators](./operators.md)). -The result will contain all entities from `A` for which there exist a matching entity +The result will contain all entities from `A` for which there exist a matching entity in `B`. -Exclusion of table `A` with table `B`, or `A - B`, will contain all entities from `A` +Exclusion of table `A` with table `B`, or `A - B`, will contain all entities from `A` for which there are no matching entities in `B`. Restriction by another table. @@ -45,9 +45,9 @@ Exclusion by another table. ### Restriction by a table with no common attributes -Restriction of table `A` with another table `B` having none of the same attributes as +Restriction of table `A` with another table `B` having none of the same attributes as `A` will simply return all entities in `A`, unless `B` is empty as described below. -Exclusion of table `A` with `B` having no common attributes will return no entities, +Exclusion of table `A` with `B` having no common attributes will return no entities, unless `B` is empty as described below. Restriction by a table having no common attributes. @@ -60,7 +60,7 @@ Exclusion by a table having no common attributes. ### Restriction by an empty table -Restriction of table `A` with an empty table will return no entities regardless of +Restriction of table `A` with an empty table will return no entities regardless of whether there are any matching attributes. Exclusion of table `A` with an empty table will return all entities in `A`. @@ -75,33 +75,33 @@ Exclusion by an empty table. ## Restriction by a mapping A key-value mapping may be used as an operand in restriction. -For each key that is an attribute in `A`, the paired value is treated as part of an +For each key that is an attribute in `A`, the paired value is treated as part of an equality condition. Any key-value pairs without corresponding attributes in `A` are ignored. -Restriction by an empty mapping or by a mapping with no keys matching the attributes in +Restriction by an empty mapping or by a mapping with no keys matching the attributes in `A` will return all the entities in `A`. Exclusion by an empty mapping or by a mapping with no matches will return no entities. -For example, let's say that table `Session` has the attribute `session_date` of +For example, let's say that table `Session` has the attribute `session_date` of [datatype](../design/tables/attributes.md) `datetime`. -You are interested in sessions from January 1st, 2018, so you write the following +You are interested in sessions from January 1st, 2018, so you write the following restriction query using a mapping. ```python Session & {'session_date': "2018-01-01"} ``` -Our mapping contains a typo omitting the final `e` from `session_date`, so no keys in +Our mapping contains a typo omitting the final `e` from `session_date`, so no keys in our mapping will match any attribute in `Session`. As such, our query will return all of the entities of `Session`. ## Restriction by a string -Restriction can be performed when `cond` is an explicit condition on attribute values, +Restriction can be performed when `cond` is an explicit condition on attribute values, expressed as a string. Such conditions may include arithmetic operations, functions, range tests, etc. -Restriction of table `A` by a string containing an attribute not found in table `A` +Restriction of table `A` by a string containing an attribute not found in table `A` produces an error. ```python @@ -129,12 +129,12 @@ cond_frame = pd.DataFrame( data={'first_name': ['Aaron'], 'last_name': ['Aaronson']}) ``` -When `cond` is a collection of conditions, the conditions are applied by logical +When `cond` is a collection of conditions, the conditions are applied by logical disjunction (logical OR). -Thus, restriction of table `A` by a collection will return all entities in `A` that +Thus, restriction of table `A` by a collection will return all entities in `A` that meet *any* of the conditions in the collection. -For example, if you restrict the `Student` table by a collection containing two -conditions, one for a first and one for a last name, your query will return any +For example, if you restrict the `Student` table by a collection containing two +conditions, one for a first and one for a last name, your query will return any students with a matching first name *or* a matching last name. ```python @@ -157,34 +157,34 @@ Exclusion of table `A` by an empty collection returns all the entities of `A`. ## Restriction by an `AndList` The special function `dj.AndList` represents logical conjunction (logical AND). -Restriction of table `A` by an `AndList` will return all entities in `A` that meet +Restriction of table `A` by an `AndList` will return all entities in `A` that meet *all* of the conditions in the list. `A & dj.AndList([c1, c2, c3])` is equivalent to `A & c1 & c2 & c3`. -Usually, it is more convenient to simply write out all of the conditions, as +Usually, it is more convenient to simply write out all of the conditions, as `A & c1 & c2 & c3`. -However, when a list of conditions has already been generated, the list can simply be +However, when a list of conditions has already been generated, the list can simply be passed as the argument to `dj.AndList`. -Restriction of table `A` by an empty `AndList`, as in `A & dj.AndList([])`, will return +Restriction of table `A` by an empty `AndList`, as in `A & dj.AndList([])`, will return all of the entities in `A`. Exclusion by an empty `AndList` will return no entities. ## Restriction by a `Not` object -The special function `dj.Not` represents logical negation, such that `A & dj.Not(cond)` +The special function `dj.Not` represents logical negation, such that `A & dj.Not(cond)` is equivalent to `A - cond`. ## Restriction by a query -Restriction by a query object is a generalization of restriction by a table (which is -also a query object), because DataJoint queries always produce well-defined entity +Restriction by a query object is a generalization of restriction by a table (which is +also a query object), because DataJoint queries always produce well-defined entity sets, as described in [entity normalization](../design/normalization.md). -As such, restriction by queries follows the same behavior as restriction by tables +As such, restriction by queries follows the same behavior as restriction by tables described above. -The example below creates a query object corresponding to all the sessions performed by +The example below creates a query object corresponding to all the sessions performed by the user Alice. -The `Experiment` table is then restricted by the query object, returning all the +The `Experiment` table is then restricted by the query object, returning all the experiments that are part of sessions performed by Alice. ```python diff --git a/docs/src/query/union.md b/docs/src/query/union.md index 385cc2a17..71f0fa687 100644 --- a/docs/src/query/union.md +++ b/docs/src/query/union.md @@ -1,20 +1,20 @@ # Union -The union operator is not yet implemented -- this page serves as the specification for +The union operator is not yet implemented -- this page serves as the specification for the upcoming implementation. Union is rarely needed in practice. ## Union operator `+` The result of the union operator `A + B` contains all the entities from both operands. -[Entity normalization](../design/normalization.md) requires that the operands in a -union both belong to the same entity type with the same primary key using homologous +[Entity normalization](../design/normalization.md) requires that the operands in a +union both belong to the same entity type with the same primary key using homologous attributes. In the absence of any secondary attributes, the result of a union is the simple set union. -When secondary attributes are present, they must have the same names and datatypes in +When secondary attributes are present, they must have the same names and datatypes in both operands. -The two operands must also be **disjoint**, without any duplicate primary key values +The two operands must also be **disjoint**, without any duplicate primary key values across both inputs. These requirements prevent ambiguity of attribute values and preserve entity identity. @@ -27,9 +27,9 @@ These requirements prevent ambiguity of attribute values and preserve entity ide Otherwise, an error will be raised. 4. The result `A + B` will have the same primary key as `A` and `B`. 5. The result `A + B` will have all the non-key attributes from both `A` and `B`. -6. For entities that are found in both `A` and `B` (based on the primary key), the +6. For entities that are found in both `A` and `B` (based on the primary key), the secondary attributes will be filled from the corresponding entities in `A` and `B`. -7. For entities that are only found in either `A` or `B`, the other operand's secondary +7. For entities that are only found in either `A` or `B`, the other operand's secondary attributes will filled with null values. ## Examples of union diff --git a/docs/src/query/universals.md b/docs/src/query/universals.md index 15a4bb442..a9f12dd96 100644 --- a/docs/src/query/universals.md +++ b/docs/src/query/universals.md @@ -1,25 +1,25 @@ # Universal Sets -All [query operators](operators.md) are designed to preserve the entity types of their +All [query operators](operators.md) are designed to preserve the entity types of their inputs. -However, some queries require creating a new entity type that is not represented by any +However, some queries require creating a new entity type that is not represented by any stored tables. This means that a new entity type must be explicitly defined as part of the query. Universal sets fulfill this role. -**Universal sets** are used in DataJoint to define virtual tables with arbitrary +**Universal sets** are used in DataJoint to define virtual tables with arbitrary primary key structures for use in query expressions. -A universal set, defined using class `dj.U`, denotes the set of all possible entities +A universal set, defined using class `dj.U`, denotes the set of all possible entities with given attributes of any possible datatype. Universal sets allow query expressions using virtual tables when no suitable base table exists. -Attributes of universal sets are allowed to be matched to any namesake attributes, even +Attributes of universal sets are allowed to be matched to any namesake attributes, even those that do not come from the same initial source. -For example, you may like to query the university database for the complete list of +For example, you may like to query the university database for the complete list of students' home cities, along with the number of students from each city. -The [schema](example-schema.md) for the university database does not have a table for +The [schema](example-schema.md) for the university database does not have a table for cities and states. -A virtual table can fill the role of the nonexistent base table, allowing queries that +A virtual table can fill the role of the nonexistent base table, allowing queries that would not be possible otherwise. ```python @@ -36,9 +36,9 @@ U('home_state').aggr(Student, n="count(*)") U().aggr(Student, n="count(*)") ``` -The result of aggregation on a universal set is restricted to the entities with matches +The result of aggregation on a universal set is restricted to the entities with matches in the aggregated table, such as `Student` in the example above. -In other words, `X.aggr(A, ...)` is interpreted as `(X & A).aggr(A, ...)` for universal +In other words, `X.aggr(A, ...)` is interpreted as `(X & A).aggr(A, ...)` for universal set `X`. All attributes of a universal set are considered primary. diff --git a/docs/src/quick-start.md b/docs/src/quick-start.md index 65a5df433..056e2f6c6 100644 --- a/docs/src/quick-start.md +++ b/docs/src/quick-start.md @@ -57,7 +57,7 @@ Next, please install DataJoint via one of the following: - Ensure you have [pip](https://pip.pypa.io/en/stable/installation/) installed. - Install [graphviz](https://graphviz.org/download/#linux) pre-requisite for diagram visualization. - + To install: ```bash @@ -88,7 +88,7 @@ Next, please install DataJoint via one of the following: dj.config["database.password"] = "{password}" ``` - These configuration settings can be saved either locally or system-wide using one + These configuration settings can be saved either locally or system-wide using one of the following commands: ```python dj.config.save_local() @@ -108,9 +108,9 @@ Next, please install DataJoint via one of the following: } ``` - These settings will be loaded whenever a Python instance is launched from this - directory. To configure settings globally, save a similar file as - `.datajoint_config.json` in your home directory. A local config, if present, will + These settings will be loaded whenever a Python instance is launched from this + directory. To configure settings globally, save a similar file as + `.datajoint_config.json` in your home directory. A local config, if present, will take precedent over global settings. ## Data Pipeline Definition @@ -240,16 +240,16 @@ Let's add data for a rectangle: Rectangle.insert1(dict(shape_id=1, shape_height=2, shape_width=4)) ``` -Given the following [table definition](./design/tables/declare.md), we can insert data -as tuples, dicts, pandas dataframes, or pathlib `Path` relative paths to local CSV +Given the following [table definition](./design/tables/declare.md), we can insert data +as tuples, dicts, pandas dataframes, or pathlib `Path` relative paths to local CSV files. -```python +```python mouse_id: int # unique mouse id --- dob: date # mouse date of birth sex: enum('M', 'F', 'U') # sex of mouse - Male, Female, or Unknown -``` +``` === "Tuple" @@ -289,16 +289,16 @@ sex: enum('M', 'F', 'U') # sex of mouse - Male, Female, or Unknown === "CSV" Given the following CSV in the current working directory as `mice.csv` - + ```console mouse_id,dob,sex 1,2016-11-19,M 2,2016-11-20,U 5,2016-12-25,F ``` - + We can import as follows: - + ```python from pathlib import Path mouse.insert(Path('./mice.csv')) diff --git a/docs/src/sysadmin/database-admin.md b/docs/src/sysadmin/database-admin.md index 64bf92cd8..63f3afb7b 100644 --- a/docs/src/sysadmin/database-admin.md +++ b/docs/src/sysadmin/database-admin.md @@ -2,51 +2,51 @@ ## Hosting -Let’s say a person, a lab, or a multi-lab consortium decide to use DataJoint as their +Let’s say a person, a lab, or a multi-lab consortium decide to use DataJoint as their data pipeline platform. What IT resources and support will be required? -DataJoint uses a MySQL-compatible database server such as MySQL, MariaDB, Percona -Server, or Amazon Aurora to store the structured data used for all relational +DataJoint uses a MySQL-compatible database server such as MySQL, MariaDB, Percona +Server, or Amazon Aurora to store the structured data used for all relational operations. -Large blocks of data associated with these records such as multidimensional numeric -arrays (signals, images, scans, movies, etc) can be stored within the database or +Large blocks of data associated with these records such as multidimensional numeric +arrays (signals, images, scans, movies, etc) can be stored within the database or stored in additionally configured [bulk storage](../client/stores.md). -The first decisions you need to make are where this server will be hosted and how it +The first decisions you need to make are where this server will be hosted and how it will be administered. -The server may be hosted on your personal computer, on a dedicated machine in your lab, +The server may be hosted on your personal computer, on a dedicated machine in your lab, or in a cloud-based database service. ### Cloud hosting -Increasingly, many teams make use of cloud-hosted database services, which allow great +Increasingly, many teams make use of cloud-hosted database services, which allow great flexibility and easy administration of the database server. A cloud hosting option will be provided through https://works.datajoint.com. -DataJoint Works simplifies the setup for labs that wish to host their data pipelines in +DataJoint Works simplifies the setup for labs that wish to host their data pipelines in the cloud and allows sharing pipelines between multiple groups and locations. -Being an open-source solution, other cloud services such as Amazon RDS can also be used +Being an open-source solution, other cloud services such as Amazon RDS can also be used in this role, albeit with less DataJoint-centric customization. ### Self hosting -In the most basic configuration, the relational database management system (database +In the most basic configuration, the relational database management system (database server) is installed on an individual user's personal computer. -To support a group of users, a specialized machine can be configured as a dedicated +To support a group of users, a specialized machine can be configured as a dedicated database server. -This server can be accessed by multiple DataJoint clients to query the data and perform -computations. +This server can be accessed by multiple DataJoint clients to query the data and perform +computations. -For larger groups and multi-site collaborations with heavy workloads, the database +For larger groups and multi-site collaborations with heavy workloads, the database server cluster may be configured in the cloud or on premises. -The following section provides some basic guidelines for these configurations here and +The following section provides some basic guidelines for these configurations here and in the subsequent sections of the documentation. ### General server / hardware support requirements -The following table lists some likely scenarios for DataJoint database server +The following table lists some likely scenarios for DataJoint database server deployments and some reasonable estimates of the required computer hardware. -The required IT/systems support needed to ensure smooth operations in the absence of +The required IT/systems support needed to ensure smooth operations in the absence of local database expertise is also listed. #### IT infrastructures @@ -63,88 +63,88 @@ local database expertise is also listed. ### Hardware considerations -As in any computer system, CPU, RAM memory, disk storage, and network speed are +As in any computer system, CPU, RAM memory, disk storage, and network speed are important components of performance. The relational database component of DataJoint is no exception to this rule. -This section discusses the various factors relating to selecting a server for your +This section discusses the various factors relating to selecting a server for your DataJoint pipelines. #### CPU -CPU speed and parallelism (number of cores/threads) will impact the speed of queries +CPU speed and parallelism (number of cores/threads) will impact the speed of queries and the number of simultaneous queries which can be efficiently supported by the system. -It is a good rule of thumb to have enough cores to support the number of active users +It is a good rule of thumb to have enough cores to support the number of active users and background tasks you expect to have running during a typical 'busy' day of usage. -For example, a team of 10 people might want to have 8 cores to support a few active +For example, a team of 10 people might want to have 8 cores to support a few active queries and background tasks. #### RAM -The amount of RAM will impact the amount of DataJoint data kept in memory, allowing for -faster querying of data since the data can be searched and returned to the user without +The amount of RAM will impact the amount of DataJoint data kept in memory, allowing for +faster querying of data since the data can be searched and returned to the user without needing to access the slower disk drives. -It is a good idea to get enough memory to fully store the more important and frequently -accessed portions of your dataset with room to spare, especially if in-database blob +It is a good idea to get enough memory to fully store the more important and frequently +accessed portions of your dataset with room to spare, especially if in-database blob storage is used instead of external [bulk storage](bulk-storage.md). #### Disk -The disk storage for a DataJoint database server should have fast random access, -ideally with flash-based storage to eliminate the rotational delay of mechanical hard +The disk storage for a DataJoint database server should have fast random access, +ideally with flash-based storage to eliminate the rotational delay of mechanical hard drives. #### Networking -When network connections are used, network speed and latency are important to ensure -that large query results can be quickly transferred across the network and that delays +When network connections are used, network speed and latency are important to ensure +that large query results can be quickly transferred across the network and that delays due to data entry/query round-trip have minimal impact on the runtime of the program. #### General recommendations DataJoint datasets can consist of many thousands or even millions of records. -Generally speaking one would want to make sure that the relational database system has -sufficient CPU speed and parallelism to support a typical number of concurrent users +Generally speaking one would want to make sure that the relational database system has +sufficient CPU speed and parallelism to support a typical number of concurrent users and to execute searches quickly. -The system should have enough RAM to store the primary key values of commonly used +The system should have enough RAM to store the primary key values of commonly used tables and operating system caches. -Disk storage should be fast enough to support quick loading of and searching through +Disk storage should be fast enough to support quick loading of and searching through the data. -Lastly, network bandwidth must be sufficient to support transferring user records +Lastly, network bandwidth must be sufficient to support transferring user records quickly. ### Large-scale installations -Database replication may be beneficial if system downtime or precise database +Database replication may be beneficial if system downtime or precise database responsiveness is a concern -Replication can allow for easier coordination of maintenance activities, faster -recovery in the event of system problems, and distribution of the database workload +Replication can allow for easier coordination of maintenance activities, faster +recovery in the event of system problems, and distribution of the database workload across server machines to increase throughput and responsiveness. #### Multi-master replication Multi-master replication configurations allow for all replicas to be used in a read/ write fashion, with the workload being distributed among all machines. -However, multi-master replication is also more complicated, requiring front-end -machines to distribute the workload, similar performance characteristics on all -replicas to prevent bottlenecks, and redundant network connections to ensure the +However, multi-master replication is also more complicated, requiring front-end +machines to distribute the workload, similar performance characteristics on all +replicas to prevent bottlenecks, and redundant network connections to ensure the replicated machines are always in sync. ### Recommendations -It is usually best to go with the simplest solution which can suit the requirements of -the installation, adjusting workloads where possible and adding complexity only as +It is usually best to go with the simplest solution which can suit the requirements of +the installation, adjusting workloads where possible and adding complexity only as needs dictate. -Resource requirements of course depend on the data collection and processing needs of -the given pipeline, but there are general size guidelines that can inform any system +Resource requirements of course depend on the data collection and processing needs of +the given pipeline, but there are general size guidelines that can inform any system configuration decisions. -A reasonably powerful workstation or small server should support the needs of a small +A reasonably powerful workstation or small server should support the needs of a small group (2-10 users). -A medium or large server should support the needs of a larger user community (10-30 +A medium or large server should support the needs of a larger user community (10-30 users). -A replicated or distributed setup of 2 or more medium or large servers may be required +A replicated or distributed setup of 2 or more medium or large servers may be required in larger cases. -These requirements can be reduced through the use of external or cloud storage, which +These requirements can be reduced through the use of external or cloud storage, which is discussed in the subsequent section. | Usage Scenario | DataJoint Database Computer | Hardware Recommendation | @@ -171,7 +171,7 @@ CREATE USER 'alice'@'%' IDENTIFIED BY 'alices-secret-password'; Existing users can be listed using the following SQL: ```mysql -SELECT user, host from mysql.user; +SELECT user, host from mysql.user; ``` Teams that use DataJoint typically divide their data into schemas @@ -242,8 +242,8 @@ to alice. ## Backups and Recovery -Backing up your DataJoint installation is critical to ensuring that your work is safe -and can be continued in the event of system failures, and several mechanisms are +Backing up your DataJoint installation is critical to ensuring that your work is safe +and can be continued in the event of system failures, and several mechanisms are available to use. Much like your live installation, your backup will consist of two portions: @@ -251,35 +251,35 @@ Much like your live installation, your backup will consist of two portions: - Backup of the Relational Data - Backup of optional external bulk storage -This section primarily deals with backup of the relational data since most of the -optional bulk storage options use "regular" flat-files for storage and can be backed up +This section primarily deals with backup of the relational data since most of the +optional bulk storage options use "regular" flat-files for storage and can be backed up via any "normal" disk backup regime. There are many options to backup MySQL; subsequent sections discuss a few options. ### Cloud hosted backups -In the case of cloud-hosted options, many cloud vendors provide automated backup of +In the case of cloud-hosted options, many cloud vendors provide automated backup of your data, and some facility for downloading such backups externally. -Due to the wide variety of cloud-specific options, discussion of these options falls +Due to the wide variety of cloud-specific options, discussion of these options falls outside of the scope of this documentation. -However, since the cloud server is also a MySQL server, other options listed here may +However, since the cloud server is also a MySQL server, other options listed here may work for your situation. ### Disk-based backup -The simplest option for many cases is to perform a disk-level backup of your MySQL +The simplest option for many cases is to perform a disk-level backup of your MySQL installation using standard disk backup tools. -It should be noted that all database activity should be stopped for the duration of the +It should be noted that all database activity should be stopped for the duration of the backup to prevent errors with the backed up data. This can be done in one of two ways: - Stopping the MySQL server program - Using database locks -These methods are required since MySQL data operations can be ongoing in the background +These methods are required since MySQL data operations can be ongoing in the background even when no user activity is ongoing. -To use a database lock to perform a backup, the following commands can be used as the +To use a database lock to perform a backup, the following commands can be used as the MySQL administrator: ```mysql @@ -287,78 +287,78 @@ FLUSH TABLES WITH READ LOCK; UNLOCK TABLES; ``` -The backup should be performed between the issuing of these two commands, ensuring the +The backup should be performed between the issuing of these two commands, ensuring the database data is consistent on disk when it is backed up. ### MySQLDump -Disk based backups may not be feasible for every installation, or a database may +Disk based backups may not be feasible for every installation, or a database may require constant activity such that stopping it for backups is not feasible. -In such cases, the simplest option is +In such cases, the simplest option is [MySQLDump](https://dev.mysql.com/doc/mysql-backup-excerpt/8.0/en/using-mysqldump.html), a command line tool that prints the contents of your database contents in SQL form. -This tool is generally acceptable for most cases and is especially well suited for +This tool is generally acceptable for most cases and is especially well suited for smaller installations due to its simplicity and ease of use. -For larger installations, the lower speed of MySQLDump can be a limitation, since it -has to convert the database contents to and from SQL rather than dealing with the +For larger installations, the lower speed of MySQLDump can be a limitation, since it +has to convert the database contents to and from SQL rather than dealing with the database files directly. -Additionally, since backups are performed within a transaction, the backup will be -valid up to the time the backup began rather than to its completion, which can make -ensuring that the latest data are fully backed up more difficult as the time it takes +Additionally, since backups are performed within a transaction, the backup will be +valid up to the time the backup began rather than to its completion, which can make +ensuring that the latest data are fully backed up more difficult as the time it takes to run a backup grows. ### Percona XTraBackup -The Percona `xtrabackup` tool provides near-realtime backup capability of a MySQL -installation, with extended support for replicated databases, and is a good tool for +The Percona `xtrabackup` tool provides near-realtime backup capability of a MySQL +installation, with extended support for replicated databases, and is a good tool for backing up larger databases. -However, this tool requires local disk access as well as reasonably fast backup media, -since it builds an ongoing transaction log in real time to ensure that backups are +However, this tool requires local disk access as well as reasonably fast backup media, +since it builds an ongoing transaction log in real time to ensure that backups are valid up to the point of their completion. This strategy fails if it cannot keep up with the write speed of the database. -Further, the backups it generates are in binary format and include incomplete database +Further, the backups it generates are in binary format and include incomplete database transactions, which require careful attention to detail when restoring. -As such, this solution is recommended only for advanced use cases or larger databases +As such, this solution is recommended only for advanced use cases or larger databases where limitations of the other solutions may apply. ### Locking and DDL issues -One important thing to note is that at the time of writing, MySQL's transactional -system is not `data definition language` aware, meaning that changes to table +One important thing to note is that at the time of writing, MySQL's transactional +system is not `data definition language` aware, meaning that changes to table structures occurring during some backup schemes can result in corrupted backup copies. -If schema changes will be occurring during your backup window, it is a good idea to -ensure that appropriate locking mechanisms are used to prevent these changes during +If schema changes will be occurring during your backup window, it is a good idea to +ensure that appropriate locking mechanisms are used to prevent these changes during critical steps of the backup process. -However, on busy installations which cannot be stopped, the use of locks in many backup -utilities may cause issues if your programs expect to write data to the database during +However, on busy installations which cannot be stopped, the use of locks in many backup +utilities may cause issues if your programs expect to write data to the database during the backup window. -In such cases it might make sense to review the given backup tools for locking related -options or to use other mechanisms such as replicas or alternate backup tools to +In such cases it might make sense to review the given backup tools for locking related +options or to use other mechanisms such as replicas or alternate backup tools to prevent interaction of the database. ### Replication and snapshots for backup -Larger databases consisting of many Terabytes of data may take many hours or even days -to backup and restore, and so downtime resulting from system failure can create major +Larger databases consisting of many Terabytes of data may take many hours or even days +to backup and restore, and so downtime resulting from system failure can create major impacts to ongoing work. -While not backup tools per-se, use of MySQL replication and disk snapshots +While not backup tools per-se, use of MySQL replication and disk snapshots can be useful to assist in reducing the downtime resulting from a full database outage. -Replicas can be configured so that one copy of the data is immediately online in the +Replicas can be configured so that one copy of the data is immediately online in the event of server crash. -When a server fails in this case, users and programs simply restart and point to the +When a server fails in this case, users and programs simply restart and point to the new server before resuming work. -Replicas can also reduce the system load generated by regular backup procedures, since +Replicas can also reduce the system load generated by regular backup procedures, since they can be backed up instead of the main server. -Additionally they can allow more flexibility in a given backup scheme, such as allowing +Additionally they can allow more flexibility in a given backup scheme, such as allowing for disk snapshots on a busy system that would not otherwise be able to be stopped. -A replica copy can be stopped temporarily and then resumed while a disk snapshot or +A replica copy can be stopped temporarily and then resumed while a disk snapshot or other backup operation occurs. diff --git a/docs/src/sysadmin/external-store.md b/docs/src/sysadmin/external-store.md index 301270043..dbcdc169d 100644 --- a/docs/src/sysadmin/external-store.md +++ b/docs/src/sysadmin/external-store.md @@ -1,28 +1,28 @@ # External Store DataJoint organizes most of its data in a relational database. -Relational databases excel at representing relationships between entities and storing +Relational databases excel at representing relationships between entities and storing structured data. -However, relational databases are not particularly well-suited for storing large +However, relational databases are not particularly well-suited for storing large continuous chunks of data such as images, signals, and movies. -An attribute of type `longblob` can contain an object up to 4 GiB in size (after -compression) but storing many such large objects may hamper the performance of queries +An attribute of type `longblob` can contain an object up to 4 GiB in size (after +compression) but storing many such large objects may hamper the performance of queries on the entire table. -A good rule of thumb is that objects over 10 MiB in size should not be put in the +A good rule of thumb is that objects over 10 MiB in size should not be put in the relational database. -In addition, storing data in cloud-hosted relational databases (e.g. AWS RDS) may be +In addition, storing data in cloud-hosted relational databases (e.g. AWS RDS) may be more expensive than in cloud-hosted simple storage systems (e.g. AWS S3). -DataJoint allows the use of `external` storage to store large data objects within its +DataJoint allows the use of `external` storage to store large data objects within its relational framework but outside of the main database. -Defining an externally-stored attribute is used using the notation `blob@storename` -(see also: [definition syntax](../design/tables/declare.md)) and works the same way as -a `longblob` attribute from the users perspective. However, its data are stored in an +Defining an externally-stored attribute is used using the notation `blob@storename` +(see also: [definition syntax](../design/tables/declare.md)) and works the same way as +a `longblob` attribute from the users perspective. However, its data are stored in an external storage system rather than in the relational database. -Various systems can play the role of external storage, including a shared file system -accessible to all team members with access to these objects or a cloud storage +Various systems can play the role of external storage, including a shared file system +accessible to all team members with access to these objects or a cloud storage solutions such as AWS S3. For example, the following table stores motion-aligned two-photon movies. @@ -34,12 +34,12 @@ For example, the following table stores motion-aligned two-photon movies. aligned_movie : blob@external # motion-aligned movie in 'external' store ``` -All [insert](../manipulation/insert.md) and [fetch](../query/fetch.md) operations work -identically for `external` attributes as they do for `blob` attributes, with the same +All [insert](../manipulation/insert.md) and [fetch](../query/fetch.md) operations work +identically for `external` attributes as they do for `blob` attributes, with the same serialization protocol. Similar to `blobs`, `external` attributes cannot be used in restriction conditions. -Multiple external storage configurations may be used simultaneously with the +Multiple external storage configurations may be used simultaneously with the `@storename` portion of the attribute definition determining the storage location. ```python @@ -51,12 +51,12 @@ aligned_movie : blob@external-raw # motion-aligned movie in 'external-raw' sto ## Principles of operation -External storage is organized to emulate individual attribute values in the relational +External storage is organized to emulate individual attribute values in the relational database. -DataJoint organizes external storage to preserve the same data integrity principles as +DataJoint organizes external storage to preserve the same data integrity principles as in relational storage. -1. The external storage locations are specified in the DataJoint connection +1. The external storage locations are specified in the DataJoint connection configuration with one specification for each store. ```python @@ -76,30 +76,30 @@ configuration with one specification for each store. dj.config['cache'] = '/net/djcache' ``` -2. Each schema corresponds to a dedicated folder at the storage location with the same +2. Each schema corresponds to a dedicated folder at the storage location with the same name as the database schema. -3. Stored objects are identified by the [SHA-256](https://en.wikipedia.org/wiki/SHA-2) +3. Stored objects are identified by the [SHA-256](https://en.wikipedia.org/wiki/SHA-2) hashes (in web-safe base-64 ASCII) of their serialized contents. - This scheme allows for the same object—used multiple times in the same schema—to be + This scheme allows for the same object—used multiple times in the same schema—to be stored only once. -4. In the `external-raw` storage, the objects are saved as files with the hash as the +4. In the `external-raw` storage, the objects are saved as files with the hash as the filename. -5. In the `external` storage, external files are stored in a directory layout -corresponding to the hash of the filename. By default, this corresponds to the first 2 -characters of the hash, followed by the second 2 characters of the hash, followed by +5. In the `external` storage, external files are stored in a directory layout +corresponding to the hash of the filename. By default, this corresponds to the first 2 +characters of the hash, followed by the second 2 characters of the hash, followed by the actual file. -6. Each database schema has an auxiliary table named `~external_` for each +6. Each database schema has an auxiliary table named `~external_` for each configured external store. It is automatically created the first time external storage is used. - The primary key of `~external_` is the hash of the data (for blobs and + The primary key of `~external_` is the hash of the data (for blobs and attachments) or of the relative paths to the files for filepath-based storage. - Other attributes are the `count` of references by tables in the schema, the `size` - of the object in bytes, and the `timestamp` of the last event (creation, update, or + Other attributes are the `count` of references by tables in the schema, the `size` + of the object in bytes, and the `timestamp` of the last event (creation, update, or deletion). Below are sample entries in `~external_`. @@ -108,75 +108,75 @@ configured external store. | -- | -- | -- | -- | -- | | 1GEqtEU6JYEOLS4sZHeHDxWQ3JJfLlH VZio1ga25vd2 | 1039536788 | NULL | NULL | 2017-06-07 23:14:01 | - The fields `filepath` and `contents_hash` relate to the - [filepath](../design/tables/filepath.md) datatype, which will be discussed + The fields `filepath` and `contents_hash` relate to the + [filepath](../design/tables/filepath.md) datatype, which will be discussed separately. -7. Attributes of type `@` are declared as renamed -[foreign keys](../design/tables/dependencies.md) referencing the +7. Attributes of type `@` are declared as renamed +[foreign keys](../design/tables/dependencies.md) referencing the `~external_` table (but are not shown as such to the user). -8. The [insert](../manipulation/insert.md) operation encodes and hashes the blob data. -If an external object is not present in storage for the same hash, the object is saved -and if the save operation is successful, corresponding entities in table +8. The [insert](../manipulation/insert.md) operation encodes and hashes the blob data. +If an external object is not present in storage for the same hash, the object is saved +and if the save operation is successful, corresponding entities in table `~external_` for that store are created. -9. The [delete](../manipulation/delete.md) operation first deletes the foreign key -reference in the target table. The external table entry and actual external object is +9. The [delete](../manipulation/delete.md) operation first deletes the foreign key +reference in the target table. The external table entry and actual external object is not actually deleted at this time (`soft-delete`). 10. The [fetch](../query/fetch.md) operation uses the hash values to find the data. - In order to prevent excessive network overhead, a special external store named + In order to prevent excessive network overhead, a special external store named `cache` can be configured. - If the `cache` is enabled, the `fetch` operation need not access + If the `cache` is enabled, the `fetch` operation need not access `~external_` directly. - Instead `fetch` will retrieve the cached object without downloading directly from + Instead `fetch` will retrieve the cached object without downloading directly from the `real` external store. 11. Cleanup is performed regularly when the database is in light use or off-line. 12. DataJoint never removes objects from the local `cache` folder. - The `cache` folder may just be periodically emptied entirely or based on file + The `cache` folder may just be periodically emptied entirely or based on file access date. - If dedicated `cache` folders are maintained for each schema, then a special - procedure will be provided to remove all objects that are no longer listed in + If dedicated `cache` folders are maintained for each schema, then a special + procedure will be provided to remove all objects that are no longer listed in `~external_`. -Data removal from external storage is separated from the delete operations to ensure -that data are not lost in race conditions between inserts and deletes of the same -objects, especially in cases of transactional processing or in processes that are +Data removal from external storage is separated from the delete operations to ensure +that data are not lost in race conditions between inserts and deletes of the same +objects, especially in cases of transactional processing or in processes that are likely to get terminated. -The cleanup steps are performed in a separate process when the risks of race conditions +The cleanup steps are performed in a separate process when the risks of race conditions are minimal. -The process performing the cleanups must be isolated to prevent interruptions resulting +The process performing the cleanups must be isolated to prevent interruptions resulting in loss of data integrity. ## Configuration The following steps must be performed to enable external storage: -1. Assign external location settings for each storage as shown in the +1. Assign external location settings for each storage as shown in the [Step 1](#principles-of-operation) example above. Use `dj.config` for configuration. - - `protocol` [`s3`, `file`] Specifies whether `s3` or `file` external storage is + - `protocol` [`s3`, `file`] Specifies whether `s3` or `file` external storage is desired. - - `endpoint` [`s3`] Specifies the remote endpoint to the external data for all + - `endpoint` [`s3`] Specifies the remote endpoint to the external data for all schemas as well as the target port. - `bucket` [`s3`] Specifies the appropriate `s3` bucket organization. - - `location` [`s3`, `file`] Specifies the subdirectory within the root or bucket of - store to preserve data. External objects are thus stored remotely with the following - path structure: + - `location` [`s3`, `file`] Specifies the subdirectory within the root or bucket of + store to preserve data. External objects are thus stored remotely with the following + path structure: `////`. - - `access_key` [`s3`] Specifies the access key credentials for accessing the external + - `access_key` [`s3`] Specifies the access key credentials for accessing the external location. - - `secret_key` [`s3`] Specifies the secret key credentials for accessing the external + - `secret_key` [`s3`] Specifies the secret key credentials for accessing the external location. - - `secure` [`s3`] Optional specification to establish secure external storage + - `secure` [`s3`] Optional specification to establish secure external storage connection with TLS (aka SSL, HTTPS). Defaults to `False`. 2. Optionally, for each schema specify the `cache` folder for local fetch cache. - This is done by saving the path in the `cache` key of the DataJoint configuration + This is done by saving the path in the `cache` key of the DataJoint configuration dictionary: ```python @@ -185,17 +185,17 @@ The following steps must be performed to enable external storage: ## Cleanup -Deletion of records containing externally stored blobs is a `soft-delete` which only +Deletion of records containing externally stored blobs is a `soft-delete` which only removes the database-side records from the database. -To cleanup the external tracking table or the actual external files, a separate process +To cleanup the external tracking table or the actual external files, a separate process is provided as follows. To remove only the tracking entries in the external table, call `delete` -on the `~external_` table for the external configuration with the argument +on the `~external_` table for the external configuration with the argument `delete_external_files=False`. -Note: Currently, cleanup operations on a schema's external table are not 100% - transaction safe and so must be run when there is no write activity occurring +Note: Currently, cleanup operations on a schema's external table are not 100% + transaction safe and so must be run when there is no write activity occurring in tables which use a given schema / external store pairing. ```python @@ -211,7 +211,7 @@ schema.external['external_raw'].delete(delete_external_files=True) ``` Note: Setting `delete_external_files=True` will always attempt to delete - the underlying data file, and so should not typically be used with + the underlying data file, and so should not typically be used with the `filepath` datatype. ## Migration between DataJoint v0.11 and v0.12 @@ -252,7 +252,7 @@ to upgrade to DataJoint v0.12, the following process should be followed: 4. Adjust your external storage configuration (in `datajoint.config`) to the new v0.12 configuration format (see above). - 5. Migrate external tracking tables for each schema to use the new format. For + 5. Migrate external tracking tables for each schema to use the new format. For instance in Python: ```python @@ -262,7 +262,7 @@ to upgrade to DataJoint v0.12, the following process should be followed: migrate.migrate_dj011_external_blob_storage_to_dj012(db_schema_name, external_store) ``` - 6. Verify pipeline functionality after this process has completed. For instance in + 6. Verify pipeline functionality after this process has completed. For instance in Python: ```python diff --git a/images/pipeline.drawio b/images/pipeline.drawio index 470c3d884..11e0ceaaf 100644 --- a/images/pipeline.drawio +++ b/images/pipeline.drawio @@ -1 +1 @@ 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 diff --git a/tests/conftest.py b/tests/conftest.py index 3569863f9..88d55e32f 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,30 +1,27 @@ -import datajoint as dj -from packaging import version -from typing import Dict, List +import json import os +import shutil from os import environ, remove -import minio -import urllib3 +from pathlib import Path +from typing import Dict, List + import certifi -import shutil -import pytest +import minio import networkx as nx -import json -from pathlib import Path +import pytest +import urllib3 +from packaging import version + +import datajoint as dj from datajoint import errors from datajoint.errors import ( ADAPTED_TYPE_SWITCH, FILEPATH_FEATURE_SWITCH, DataJointError, ) -from . import ( - schema, - schema_simple, - schema_advanced, - schema_adapted, - schema_external, - schema_uuid as schema_uuid_module, -) + +from . import schema, schema_adapted, schema_advanced, schema_external, schema_simple +from . import schema_uuid as schema_uuid_module @pytest.fixture(scope="session") diff --git a/tests/schema.py b/tests/schema.py index 68cbb30f4..2b7977465 100644 --- a/tests/schema.py +++ b/tests/schema.py @@ -2,10 +2,12 @@ Sample schema with realistic tables for testing """ +import inspect import random + import numpy as np + import datajoint as dj -import inspect class TTest(dj.Lookup): diff --git a/tests/schema_adapted.py b/tests/schema_adapted.py index ab9a02e76..06e28c3d1 100644 --- a/tests/schema_adapted.py +++ b/tests/schema_adapted.py @@ -1,9 +1,11 @@ -import datajoint as dj import inspect -import networkx as nx import json -from pathlib import Path import tempfile +from pathlib import Path + +import networkx as nx + +import datajoint as dj class GraphAdapter(dj.AttributeAdapter): diff --git a/tests/schema_advanced.py b/tests/schema_advanced.py index 6a35cb34a..10ce5b9e9 100644 --- a/tests/schema_advanced.py +++ b/tests/schema_advanced.py @@ -1,6 +1,7 @@ -import datajoint as dj import inspect +import datajoint as dj + class Person(dj.Manual): definition = """ diff --git a/tests/schema_aggr_regress.py b/tests/schema_aggr_regress.py index 9b85bfffb..7a07b1075 100644 --- a/tests/schema_aggr_regress.py +++ b/tests/schema_aggr_regress.py @@ -1,6 +1,7 @@ -import datajoint as dj -import itertools import inspect +import itertools + +import datajoint as dj class R(dj.Lookup): diff --git a/tests/schema_alter.py b/tests/schema_alter.py index d607bc7c4..b86f6c7ec 100644 --- a/tests/schema_alter.py +++ b/tests/schema_alter.py @@ -1,6 +1,7 @@ -import datajoint as dj import inspect +import datajoint as dj + class Experiment(dj.Imported): original_definition = """ # information about experiments diff --git a/tests/schema_external.py b/tests/schema_external.py index ce51af9c5..a9e86964f 100644 --- a/tests/schema_external.py +++ b/tests/schema_external.py @@ -2,11 +2,13 @@ A schema for testing external attributes """ -import tempfile import inspect -import datajoint as dj +import tempfile + import numpy as np +import datajoint as dj + class Simple(dj.Manual): definition = """ diff --git a/tests/schema_privileges.py b/tests/schema_privileges.py index b53d6b264..a580d80d6 100644 --- a/tests/schema_privileges.py +++ b/tests/schema_privileges.py @@ -1,6 +1,7 @@ -import datajoint as dj import inspect +import datajoint as dj + class Parent(dj.Lookup): definition = """ diff --git a/tests/schema_simple.py b/tests/schema_simple.py index f3e591382..5e5137db5 100644 --- a/tests/schema_simple.py +++ b/tests/schema_simple.py @@ -2,15 +2,17 @@ A simple, abstract schema to test relational algebra """ -import random -import datajoint as dj -import itertools import hashlib +import inspect +import itertools +import random import uuid +from datetime import date, timedelta + import faker import numpy as np -from datetime import date, timedelta -import inspect + +import datajoint as dj class SelectPK(dj.Lookup): diff --git a/tests/schema_university.py b/tests/schema_university.py index c569dbcbf..ac5ba4867 100644 --- a/tests/schema_university.py +++ b/tests/schema_university.py @@ -1,6 +1,7 @@ -import datajoint as dj import inspect +import datajoint as dj + class Student(dj.Manual): definition = """ diff --git a/tests/schema_uuid.py b/tests/schema_uuid.py index 00b45ee78..4e295bc86 100644 --- a/tests/schema_uuid.py +++ b/tests/schema_uuid.py @@ -1,5 +1,6 @@ -import uuid import inspect +import uuid + import datajoint as dj top_level_namespace_id = uuid.UUID("00000000-0000-0000-0000-000000000000") diff --git a/tests/test_adapted_attributes.py b/tests/test_adapted_attributes.py index 714da8a69..ffd137795 100644 --- a/tests/test_adapted_attributes.py +++ b/tests/test_adapted_attributes.py @@ -1,9 +1,12 @@ import os -import pytest import tempfile -import datajoint as dj -import networkx as nx from itertools import zip_longest + +import networkx as nx +import pytest + +import datajoint as dj + from . import schema_adapted from .schema_adapted import Connectivity, Layout diff --git a/tests/test_admin.py b/tests/test_admin.py index 43b418f80..b7fa15a33 100644 --- a/tests/test_admin.py +++ b/tests/test_admin.py @@ -2,11 +2,13 @@ Collection of test cases to test admin module. """ -import datajoint as dj import os + import pymysql import pytest +import datajoint as dj + @pytest.fixture() def user_alice(db_creds_root) -> dict: diff --git a/tests/test_aggr_regressions.py b/tests/test_aggr_regressions.py index 7cc5119ea..ea740cd39 100644 --- a/tests/test_aggr_regressions.py +++ b/tests/test_aggr_regressions.py @@ -2,11 +2,14 @@ Regression tests for issues 386, 449, 484, and 558 — all related to processing complex aggregations and projections. """ +import uuid + import pytest + import datajoint as dj -import uuid -from .schema_uuid import Topic, Item, top_level_namespace_id -from .schema_aggr_regress import R, Q, S, A, B, X, LOCALS_AGGR_REGRESS + +from .schema_aggr_regress import LOCALS_AGGR_REGRESS, A, B, Q, R, S, X +from .schema_uuid import Item, Topic, top_level_namespace_id @pytest.fixture(scope="function") diff --git a/tests/test_alter.py b/tests/test_alter.py index 5146d6266..375d31d55 100644 --- a/tests/test_alter.py +++ b/tests/test_alter.py @@ -1,8 +1,11 @@ -import pytest import re + +import pytest + import datajoint as dj + from . import schema as schema_any_module -from .schema_alter import Experiment, Parent, LOCALS_ALTER +from .schema_alter import LOCALS_ALTER, Experiment, Parent COMBINED_CONTEXT = { **schema_any_module.LOCALS_ANY, diff --git a/tests/test_attach.py b/tests/test_attach.py index b3ecea04e..362db6933 100644 --- a/tests/test_attach.py +++ b/tests/test_attach.py @@ -1,6 +1,8 @@ -import pytest -from pathlib import Path import os +from pathlib import Path + +import pytest + from .schema_external import Attach diff --git a/tests/test_autopopulate.py b/tests/test_autopopulate.py index 580fb406a..899d90d9e 100644 --- a/tests/test_autopopulate.py +++ b/tests/test_autopopulate.py @@ -1,7 +1,9 @@ +import pymysql import pytest -from datajoint import DataJointError + import datajoint as dj -import pymysql +from datajoint import DataJointError + from . import schema diff --git a/tests/test_blob.py b/tests/test_blob.py index 6c5a6f5a1..7c790db75 100644 --- a/tests/test_blob.py +++ b/tests/test_blob.py @@ -1,13 +1,16 @@ -import pytest -import datajoint as dj import timeit -import numpy as np import uuid -from decimal import Decimal from datetime import datetime -from datajoint.blob import pack, unpack +from decimal import Decimal + +import numpy as np +import pytest from numpy.testing import assert_array_equal from pytest import approx + +import datajoint as dj +from datajoint.blob import pack, unpack + from .schema import Longblob diff --git a/tests/test_blob_matlab.py b/tests/test_blob_matlab.py index 17a6ac651..081841fb4 100644 --- a/tests/test_blob_matlab.py +++ b/tests/test_blob_matlab.py @@ -1,8 +1,9 @@ import numpy as np import pytest +from numpy.testing import assert_array_equal + import datajoint as dj from datajoint.blob import pack, unpack -from numpy.testing import assert_array_equal class Blob(dj.Manual): diff --git a/tests/test_bypass_serialization.py b/tests/test_bypass_serialization.py index 90fc35090..da7f0b0e3 100644 --- a/tests/test_bypass_serialization.py +++ b/tests/test_bypass_serialization.py @@ -1,8 +1,9 @@ -import pytest -import datajoint as dj import numpy as np +import pytest from numpy.testing import assert_array_equal +import datajoint as dj + test_blob = np.array([1, 2, 3]) diff --git a/tests/test_cascading_delete.py b/tests/test_cascading_delete.py index b4adb31c2..71216fcb2 100644 --- a/tests/test_cascading_delete.py +++ b/tests/test_cascading_delete.py @@ -1,7 +1,9 @@ import pytest + import datajoint as dj -from .schema_simple import A, B, D, E, G, L, Website, Profile + from .schema import ComplexChild, ComplexParent +from .schema_simple import A, B, D, E, G, L, Profile, Website @pytest.fixture diff --git a/tests/test_cli.py b/tests/test_cli.py index decfbca01..be0faf64d 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -4,7 +4,9 @@ import json import subprocess + import pytest + import datajoint as dj diff --git a/tests/test_connection.py b/tests/test_connection.py index 497255753..db301d9af 100644 --- a/tests/test_connection.py +++ b/tests/test_connection.py @@ -2,11 +2,12 @@ Collection of test cases to test connection module. """ -import datajoint as dj -from datajoint import DataJointError import numpy as np import pytest +import datajoint as dj +from datajoint import DataJointError + class Subjects(dj.Manual): definition = """ diff --git a/tests/test_declare.py b/tests/test_declare.py index 6e66f4c81..828021939 100644 --- a/tests/test_declare.py +++ b/tests/test_declare.py @@ -1,10 +1,13 @@ +import inspect + import pytest -from .schema import * + import datajoint as dj -import inspect from datajoint.declare import declare from datajoint.settings import config +from .schema import * + @pytest.fixture(scope="function") def enable_add_hidden_timestamp(): diff --git a/tests/test_dependencies.py b/tests/test_dependencies.py index 9eb7fc22f..3be4a21dc 100644 --- a/tests/test_dependencies.py +++ b/tests/test_dependencies.py @@ -1,6 +1,7 @@ -from datajoint import errors from pytest import raises +from datajoint import errors + def test_nullable_dependency(thing_tables): """test nullable unique foreign key""" @@ -21,6 +22,7 @@ def test_nullable_dependency(thing_tables): def test_topo_sort(): import networkx as nx + import datajoint as dj graph = nx.DiGraph( diff --git a/tests/test_erd.py b/tests/test_erd.py index 1cdd936b0..e2344cf8a 100644 --- a/tests/test_erd.py +++ b/tests/test_erd.py @@ -1,6 +1,7 @@ import datajoint as dj -from .schema_simple import LOCALS_SIMPLE, A, B, D, E, G, L, OutfitLaunch + from .schema_advanced import * +from .schema_simple import LOCALS_SIMPLE, A, B, D, E, G, L, OutfitLaunch def test_decorator(schema_simp): diff --git a/tests/test_external.py b/tests/test_external.py index 1e212b7d9..10021c0aa 100644 --- a/tests/test_external.py +++ b/tests/test_external.py @@ -1,10 +1,13 @@ +import os + import numpy as np from numpy.testing import assert_array_equal -from datajoint.external import ExternalTable -from datajoint.blob import pack, unpack + import datajoint as dj -from .schema_external import SimpleRemote, Simple -import os +from datajoint.blob import pack, unpack +from datajoint.external import ExternalTable + +from .schema_external import Simple, SimpleRemote def test_external_put(schema_ext, mock_stores, mock_cache): diff --git a/tests/test_external_class.py b/tests/test_external_class.py index 15136a944..84597e52f 100644 --- a/tests/test_external_class.py +++ b/tests/test_external_class.py @@ -1,5 +1,7 @@ from numpy.testing import assert_almost_equal + import datajoint as dj + from . import schema_external diff --git a/tests/test_fetch.py b/tests/test_fetch.py index 7a3cf5a11..7df767028 100644 --- a/tests/test_fetch.py +++ b/tests/test_fetch.py @@ -1,16 +1,19 @@ -import pytest -from typing import List -from operator import itemgetter +import decimal +import io import itertools +import logging +import os +import warnings +from operator import itemgetter +from typing import List + import numpy as np -import decimal import pandas -import warnings -from . import schema +import pytest + import datajoint as dj -import os -import logging -import io + +from . import schema def test_getattribute(subject): diff --git a/tests/test_fetch_same.py b/tests/test_fetch_same.py index 32d041347..0c136b097 100644 --- a/tests/test_fetch_same.py +++ b/tests/test_fetch_same.py @@ -1,5 +1,6 @@ -import pytest import numpy as np +import pytest + import datajoint as dj diff --git a/tests/test_filepath.py b/tests/test_filepath.py index 54478e476..cc3db2cc2 100644 --- a/tests/test_filepath.py +++ b/tests/test_filepath.py @@ -1,11 +1,14 @@ -import pytest -import datajoint as dj +import io +import logging import os -from pathlib import Path import random +from pathlib import Path + +import pytest + +import datajoint as dj + from .schema_external import Filepath, FilepathS3 -import logging -import io def test_path_match(schema_ext, enable_filepath_feature, minio_client, store="repo"): diff --git a/tests/test_foreign_keys.py b/tests/test_foreign_keys.py index 18daa952a..b271c6c1f 100644 --- a/tests/test_foreign_keys.py +++ b/tests/test_foreign_keys.py @@ -1,4 +1,5 @@ from datajoint.declare import declare + from .schema_advanced import * diff --git a/tests/test_jobs.py b/tests/test_jobs.py index 9d1d4636b..dc363076d 100644 --- a/tests/test_jobs.py +++ b/tests/test_jobs.py @@ -1,9 +1,12 @@ -import pytest -from . import schema -from datajoint.jobs import ERROR_MESSAGE_LENGTH, TRUNCATION_APPENDIX import random import string + +import pytest + import datajoint as dj +from datajoint.jobs import ERROR_MESSAGE_LENGTH, TRUNCATION_APPENDIX + +from . import schema def test_reserve_job(subject, schema_any): diff --git a/tests/test_json.py b/tests/test_json.py index 53016505c..0a819b99e 100644 --- a/tests/test_json.py +++ b/tests/test_json.py @@ -1,10 +1,12 @@ -import pytest import inspect -from datajoint.declare import declare -import datajoint as dj + import numpy as np +import pytest from packaging.version import Version +import datajoint as dj +from datajoint.declare import declare + if Version(dj.conn().query("select @@version;").fetchone()[0]) < Version("8.0.0"): pytest.skip("These tests require MySQL >= v8.0.0", allow_module_level=True) diff --git a/tests/test_nan.py b/tests/test_nan.py index 68a28079c..25e4e332b 100644 --- a/tests/test_nan.py +++ b/tests/test_nan.py @@ -1,7 +1,8 @@ import numpy as np -import datajoint as dj import pytest +import datajoint as dj + class NanTest(dj.Manual): definition = """ diff --git a/tests/test_plugin.py b/tests/test_plugin.py index 65864525d..7fd9aff22 100644 --- a/tests/test_plugin.py +++ b/tests/test_plugin.py @@ -1,8 +1,10 @@ +from os import path + +import pkg_resources import pytest + import datajoint.errors as djerr import datajoint.plugin as p -import pkg_resources -from os import path @pytest.mark.skip(reason="marked for deprecation") diff --git a/tests/test_privileges.py b/tests/test_privileges.py index 57880081c..2bf67a386 100644 --- a/tests/test_privileges.py +++ b/tests/test_privileges.py @@ -1,6 +1,9 @@ import os + import pytest + import datajoint as dj + from . import schema, schema_privileges namespace = locals() diff --git a/tests/test_reconnection.py b/tests/test_reconnection.py index 5eea4af11..99357aae0 100644 --- a/tests/test_reconnection.py +++ b/tests/test_reconnection.py @@ -3,6 +3,7 @@ """ import pytest + import datajoint as dj from datajoint import DataJointError diff --git a/tests/test_relation.py b/tests/test_relation.py index 169ffc29a..565e1eafa 100644 --- a/tests/test_relation.py +++ b/tests/test_relation.py @@ -1,11 +1,14 @@ -import pytest -from inspect import getmembers import re -import pandas +from inspect import getmembers +from unittest.mock import patch + import numpy as np +import pandas +import pytest + import datajoint as dj from datajoint.table import Table -from unittest.mock import patch + from . import schema diff --git a/tests/test_relation_u.py b/tests/test_relation_u.py index dbb3b6737..59cee0249 100644 --- a/tests/test_relation_u.py +++ b/tests/test_relation_u.py @@ -1,6 +1,8 @@ import pytest -import datajoint as dj from pytest import raises + +import datajoint as dj + from .schema import * from .schema_simple import * diff --git a/tests/test_relational_operand.py b/tests/test_relational_operand.py index f2c16e9cd..2dbea672e 100644 --- a/tests/test_relational_operand.py +++ b/tests/test_relational_operand.py @@ -1,13 +1,16 @@ -import pytest +import datetime import random import string -import pandas -import datetime + import numpy as np +import pandas +import pytest + import datajoint as dj from datajoint.errors import DataJointError -from .schema_simple import * + from .schema import * +from .schema_simple import * @pytest.fixture diff --git a/tests/test_s3.py b/tests/test_s3.py index b5babdd8b..970310ca8 100644 --- a/tests/test_s3.py +++ b/tests/test_s3.py @@ -1,9 +1,11 @@ import pytest -from .schema_external import SimpleRemote +from minio import Minio + +from datajoint.blob import pack from datajoint.errors import DataJointError from datajoint.hash import uuid_from_buffer -from datajoint.blob import pack -from minio import Minio + +from .schema_external import SimpleRemote def test_connection(http_client, minio_client, s3_creds): diff --git a/tests/test_schema.py b/tests/test_schema.py index 257de221c..fb3cfa752 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -1,8 +1,11 @@ +import inspect import types +from inspect import getmembers + import pytest -import inspect + import datajoint as dj -from inspect import getmembers + from . import schema diff --git a/tests/test_schema_keywords.py b/tests/test_schema_keywords.py index 22ed1c2a0..ebb5a899b 100644 --- a/tests/test_schema_keywords.py +++ b/tests/test_schema_keywords.py @@ -1,4 +1,5 @@ import pytest + import datajoint as dj diff --git a/tests/test_settings.py b/tests/test_settings.py index b937d5ad3..4eb8be539 100644 --- a/tests/test_settings.py +++ b/tests/test_settings.py @@ -1,10 +1,12 @@ +import os import pprint import random import string + import pytest -from datajoint import DataJointError, settings + import datajoint as dj -import os +from datajoint import DataJointError, settings __author__ = "Fabian Sinz" diff --git a/tests/test_tls.py b/tests/test_tls.py index 22558af5b..6c2effc43 100644 --- a/tests/test_tls.py +++ b/tests/test_tls.py @@ -1,7 +1,8 @@ import pytest -import datajoint as dj from pymysql.err import OperationalError +import datajoint as dj + def test_secure_connection(db_creds_test, connection_test): result = ( diff --git a/tests/test_university.py b/tests/test_university.py index 800ee7cdf..24f01dd4c 100644 --- a/tests/test_university.py +++ b/tests/test_university.py @@ -1,10 +1,13 @@ -import pytest import hashlib from pathlib import Path -from datajoint import DataJointError + +import pytest + import datajoint as dj -from .schema_university import * +from datajoint import DataJointError + from . import schema_university +from .schema_university import * def _hash4(table): diff --git a/tests/test_update1.py b/tests/test_update1.py index f29d2ab0e..ff53466d4 100644 --- a/tests/test_update1.py +++ b/tests/test_update1.py @@ -1,8 +1,10 @@ -import pytest import os -import numpy as np -from pathlib import Path import tempfile +from pathlib import Path + +import numpy as np +import pytest + import datajoint as dj from datajoint import DataJointError diff --git a/tests/test_utils.py b/tests/test_utils.py index 88fb355d0..2781554a1 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,14 +2,11 @@ Collection of test cases to test core module. """ -from datajoint import DataJointError -from datajoint.utils import ( - from_camel_case, - to_camel_case, - is_camel_case, -) import pytest +from datajoint import DataJointError +from datajoint.utils import from_camel_case, is_camel_case, to_camel_case + def test_is_camel_case(): assert is_camel_case("AllGroups") diff --git a/tests/test_uuid.py b/tests/test_uuid.py index d99aa6c4c..4392e4769 100644 --- a/tests/test_uuid.py +++ b/tests/test_uuid.py @@ -1,9 +1,12 @@ -import pytest import uuid -from .schema_uuid import Basic, Item, Topic -from datajoint import DataJointError from itertools import count +import pytest + +from datajoint import DataJointError + +from .schema_uuid import Basic, Item, Topic + def test_uuid(schema_uuid): """test inserting and fetching of UUID attributes and restricting by UUID attributes"""