From 410c75707d2b764dd99de5a2c1cc62c46eabd5ca Mon Sep 17 00:00:00 2001 From: ddelange <14880945+ddelange@users.noreply.github.com> Date: Fri, 19 Jul 2024 23:19:41 +0200 Subject: [PATCH] Fix CI using uv instead of micromamba --- .github/workflows/pythonpackage.yml | 221 +- .github/workflows/wheel-universal.yml | 2 +- .readthedocs.yaml | 5 +- .releash.py | 6 +- asv.conf.json | 105 - ci/04-run-test-suite.sh | 2 +- ci/conda-base-minimal.yml | 7 - ci/conda-env-extra.yml | 1 - ci/conda-env-nightlies.yml | 1 - ci/conda-env-notebooks.yml | 2 - ci/conda-env.yml | 58 - dodo.py | 4 +- misc/conda/aplus/bld.bat | 8 - misc/conda/aplus/build.sh | 9 - misc/conda/aplus/meta.yaml | 60 - misc/conda/attrdict/bld.bat | 8 - misc/conda/attrdict/build.sh | 9 - misc/conda/attrdict/meta.yaml | 65 - misc/conda/cachetools/bld.bat | 8 - misc/conda/cachetools/build.sh | 9 - misc/conda/cachetools/meta.yaml | 61 - misc/conda/jprops/bld.bat | 8 - misc/conda/jprops/build.sh | 9 - misc/conda/jprops/meta.yaml | 60 - 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a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -5,104 +5,75 @@ on: branches: - master pull_request: - branches: - - master - - docs schedule: - cron: "0 2 * * 1-5" -# to make bash use ~/.bashrc, see https://github.com/marketplace/actions/setup-miniconda#example-5-custom-installer -defaults: - run: - shell: bash -l {0} - concurrency: group: ${{ github.workflow }}-${{ github.ref || github.run_id }} cancel-in-progress: ${{ github.event_name == 'pull_request' }} +defaults: + run: + shell: bash + jobs: main: - env: - CACHE_NUMBER_COMPILE: 6 - MAMBA_ROOT_PREFIX: /dev/shm/mambaroot runs-on: ${{ matrix.os }} strategy: fail-fast: false - max-parallel: 12 matrix: - os: [ubuntu-latest, windows-latest, macOS-12] - python-version: [3.8, 3.9, "3.10", "3.11", "3.12"] + os: [ubuntu-latest, windows-latest, macos-14] + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] steps: - - name: disk space - if: ${{ (github.event.pull_request.head.repo.full_name == 'vaexio/vaex') && (matrix.os == 'ubuntu-latest') }} - run: | - df -h - - uses: maxim-lobanov/setup-xcode@v1 - # alternative would be to upgrade tapi to 1100.0.11, we can possibly remove this in the future - if: matrix.os == 'macOS-12' - with: - xcode-version: "13.2.1" - uses: actions/checkout@v2 with: submodules: true - - uses: actions/checkout@v2 - if: ${{ (github.event.pull_request.head.repo.full_name == 'vaexio/vaex') && (matrix.os == 'ubuntu-latest') }} - with: - repository: vaexio/vaex-enterprise - token: ${{ secrets.PAT_PULL_ENTERPRISE }} - path: vaex-enterprise - - name: install micromamba - uses: mamba-org/setup-micromamba@v1 + + - uses: actions/setup-python@v5 with: - micromamba-version: 1.5.8-0 - environment-name: vaex-dev - environment-file: ci/conda-env.yml - create-args: >- - python=${{ matrix.python-version }} + python-version: ${{ matrix.python-version }} + + - run: pip install --upgrade pip setuptools wheel uv + + - name: Create virtual environment + run: | + uv venv ~/.venv + echo "VIRTUAL_ENV=$HOME/.venv" >> $GITHUB_ENV + echo "$HOME/.venv/bin" >> $GITHUB_PATH + + # - uses: actions/checkout@v2 + # if: ${{ (github.event.pull_request.head.repo.full_name == 'vaexio/vaex') && (matrix.os == 'ubuntu-latest') }} + # with: + # repository: vaexio/vaex-enterprise + # token: ${{ secrets.PAT_PULL_ENTERPRISE }} + # path: vaex-enterprise + - name: Extra non-windows installs if: matrix.os != 'windows-latest' run: | echo "not needed for now" - # - name: Extra windows installs - # if: matrix.os == 'windows-latest' - # run: | - # # not sure why, but the conda env is strange (old dask) if we put this constraint in the conda solver - # # also might solve scipy issues - # micromamba update scipy ipython dask - # micromamba install scipy=1.7.1 - # - name: free disk space - # if: matrix.os == 'ubuntu-latest' - # run: | - # sudo swapoff -a - # sudo rm -f /swapfile - # sudo apt clean - # docker rmi $(docker image ls -aq) - # df -h + + - name: Setup Graphviz + uses: ts-graphviz/setup-graphviz@v2 + + - name: Setup PCRE (Nix-only) + if: matrix.os != 'windows-latest' + run: sudo -E bash bin/install_pcre.sh + - name: Install OpenMP runtime (Mac-only) - if: matrix.os == 'macOS-12' - run: | - brew install libomp - - name: Cache compiled binaries - # this fails for this combination, leading to binaries filled with 0's - if: matrix.os != 'macOS-12' - id: cache-compiled-binaries - uses: actions/cache@v2 - with: - path: | - packages/vaex-core/build/lib*/vaex/vaexfast* - packages/vaex-core/build/lib*/vaex/super* - key: ${{ runner.OS }}-${{ matrix.python-version }}-${{ hashFiles('packages/vaex-core/src/*') }}-${{ env.CACHE_NUMBER_COMPILE }} - - name: Fix cache timestamp - run: | - touch packages/vaex-core/build/lib*/vaex/vaexfast* || echo "ok to fail" - touch packages/vaex-core/build/lib*/vaex/super* || echo "ok to fail" - ls -alh packages/vaex-core/build/lib*/vaex/ || echo "ok to fail" + if: startswith(matrix.os, 'macos') + run: brew install libomp + + - name: Copy dll (Windows-only) + if: (matrix.os == 'windows-latest') + uses: ./ci/actions/windll + - name: Install vaex - run: | - pip install myst_parser - pip install -e . -v - # ./ci/03-install-vaex.sh micromamba + run: uv pip install .[ci] + + - run: uv pip freeze + # - name: Install vaex-enterprise # if: ${{ (github.event.pull_request.head.repo.full_name == 'vaexio/vaex') && (matrix.os == 'ubuntu-latest') }} # run: | @@ -110,26 +81,7 @@ jobs: # micromamba install -c conda-forge distributed # pip install ray # pip install -e . - - name: Copy dll - if: (matrix.os == 'windows-latest') - uses: ./ci/actions/windll - - name: Build Python packages - run: | - mkdir dist - (cd packages/vaex-core && python setup.py bdist_wheel); cp packages/vaex-core/dist/* dist - (cd packages/vaex-graphql && python setup.py bdist_wheel); cp packages/vaex-graphql/dist/* dist - (cd packages/vaex-jupyter && python setup.py bdist_wheel); cp packages/vaex-jupyter/dist/* dist - (cd packages/vaex-ml && python setup.py bdist_wheel); cp packages/vaex-ml/dist/* dist - (cd packages/vaex-viz && python setup.py bdist_wheel); cp packages/vaex-viz/dist/* dist - (cd packages/vaex-astro && python setup.py bdist_wheel); cp packages/vaex-astro/dist/* dist - (cd packages/vaex-hdf5 && python setup.py bdist_wheel); cp packages/vaex-hdf5/dist/* dist - (cd packages/vaex-server && python setup.py bdist_wheel); cp packages/vaex-server/dist/* dist - (cd packages/vaex-meta && python setup.py bdist_wheel); cp packages/vaex-server/dist/* dist - - name: Upload builds - uses: actions/upload-artifact@v2 - with: - name: dist-${{ github.run_number }}-${{ matrix.python-version }}-${{ matrix.os }} - path: ./dist + # - name: Lint with flake8 # run: | # echo "soon" @@ -138,32 +90,30 @@ jobs: # # flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide # # flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Install pytest-asyncio correct version - if: matrix.python-version != '3.10' - run: | - # 3.10 doesn't seem to work - pip install "pytest-asyncio<0.14" + - name: Test with pytest run: | ./ci/04-run-test-suite.sh + - name: Test with pytest (with cache on) - if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-latest' + if: matrix.python-version == '3.12' && matrix.os == 'ubuntu-latest' run: | VAEX_CACHE_RESULTS=1 ./ci/04-run-test-suite.sh + - name: Check ml spec - # no catboost for py39 and py37, xgboost fails on 3.6 - if: matrix.python-version != '3.9' && matrix.os != 'windows-latest' run: | python -m vaex.ml.spec packages/vaex-ml/vaex/ml/spec_new.json diff packages/vaex-ml/vaex/ml/spec_new.json packages/vaex-ml/vaex/ml/spec.json + # - name: Test with pytest (vaex-enterprise) # if: ${{ (github.event.pull_request.head.repo.full_name == 'vaexio/vaex') && (matrix.os == 'ubuntu-latest') }} # run: | # py.test vaex-enterprise/tests --timeout=1000 + - name: Test notebooks if: matrix.os != 'windows-latest' - run: | - ./ci/05-run-notebooks.sh + run: ./ci/05-run-notebooks.sh + - name: Authenticate Google Cloud Platform if: ${{ (github.event.pull_request.head.repo.full_name == 'vaexio/vaex') && !((matrix.os == 'windows-latest')) }} uses: google-github-actions/setup-gcloud@v0 @@ -171,74 +121,17 @@ jobs: project_id: ${{ secrets.GCP_PROJECT_ID_VAEX }} service_account_key: ${{ secrets.GCP_SA_KEY_VAEX }} export_default_credentials: true + - name: Test vaex-contrib # do not run in a PR from someone else, skip windows - if: ${{ (github.event.pull_request.head.repo.full_name == 'vaexio/vaex') && !((matrix.os == 'windows-latest')) }} + if: ${{ (github.event.pull_request.head.repo.full_name == 'vaexio/vaex') && (matrix.os != 'windows-latest') }} env: PROJECT_ID: ${{ secrets.GCP_PROJECT_ID_VAEX }} - run: | - pip install -e 'packages/vaex-contrib[gcp]' - ./ci/06-run-contrib-tests.sh - - install: - runs-on: ${{ matrix.os }} - needs: [main] - - strategy: - fail-fast: false - matrix: - os: [ubuntu-latest, windows-latest, macOS-12] - python-version: [3.8, 3.9] - - steps: - - uses: actions/checkout@v2 - - uses: actions/download-artifact@v2 - with: - name: dist-${{ github.run_number }}-${{ matrix.python-version }}-${{ matrix.os }} - path: ./dist - - - name: Install micromamba - uses: mamba-org/setup-micromamba@v1 - with: - micromamba-version: 1.5.6-0 - environment-name: vaex-test - environment-file: ci/conda-base-minimal.yml - create-args: >- - python=${{ matrix.python-version }} - pcre - rich - # it shouldn't be needed to use pcre, maybe we should make the wheels similar to how we make distributions - - name: Cache pip - uses: actions/cache@v2 - env: - # Increase this value to reset cache - CACHE_NUMBER: 0 - with: - path: | - ~/.cache/pip - key: - pip-${{ runner.os }}-${{ matrix.python-version }}-${{ env.CACHE_NUMBER }} - - - name: Install core packages - run: | - pip install -vv dist/vaex*core* dist/vaex*hdf5* - - - name: Test core import - run: | - python -c "import vaex; df = vaex.example()" - - - name: Install all packages - run: | - pip install -vv dist/* - - - name: Test import - run: | - python -c "import vaex; df = vaex.example()" + run: ./ci/06-run-contrib-tests.sh - name: Test comand line run: | vaex convert ~/.vaex/data/helmi-dezeeuw-2000-FeH-v2-10percent.hdf5 test.parquet - pip install rich VAEX_PROGRESS_TYPE=rich vaex convert ~/.vaex/data/helmi-dezeeuw-2000-FeH-v2-10percent.hdf5 test.parquet vaex settings yaml vaex settings md @@ -246,7 +139,7 @@ jobs: - name: Test server if: matrix.os != 'windows-latest' run: | - vaex server --add-example --port 9999& + vaex server --add-example --port 9999 & - name: Wait for Vaex server if: matrix.os != 'windows-latest' diff --git a/.github/workflows/wheel-universal.yml b/.github/workflows/wheel-universal.yml index 3be4f23408..48fb563c30 100644 --- a/.github/workflows/wheel-universal.yml +++ b/.github/workflows/wheel-universal.yml @@ -43,7 +43,7 @@ jobs: - name: Build vaex (meta) run: | - (cp README.md packages/vaex-meta/ && cd packages/vaex-meta && python setup.py sdist bdist_wheel); cp packages/vaex-meta/dist/* dist + (cp README.md packages/vaex/ && cd packages/vaex && python setup.py sdist bdist_wheel); cp packages/vaex/dist/* dist - name: Publish a Python distribution to PyPI if: startsWith(github.ref, 'refs/tags/meta') diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 471f074fe8..4341992e1f 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -33,4 +33,7 @@ submodules: # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html python: install: - - requirements: requirements_rtd.txt + - method: pip + path: . + extra_requirements: + - ci diff --git a/.releash.py b/.releash.py index 1aa9745f3e..5d52b0c6b9 100644 --- a/.releash.py +++ b/.releash.py @@ -10,7 +10,7 @@ core.version_source = version_core core.version_targets.append(VersionTarget(core, '{path}/vaex/core/_version.py')) core.version_targets.append(VersionTargetReplace(core, [ - 'packages/vaex-meta/setup.py', + 'packages/vaex/setup.py', ])) @@ -32,7 +32,7 @@ def add_version_replace(package): def add_meta_version_replace(package): # always bump the meta package package.version_targets.append(VersionTargetReplace(package, [ - 'packages/vaex-meta/setup.py', + 'packages/vaex/setup.py', ], pattern='{name}(?P[^0-9]*)' + str(package.version_source), )) add_version_replace(core) @@ -43,7 +43,7 @@ def add_meta_version_replace(package): #core.release_targets.append(gitpush) core.release_targets.append(ReleaseTargetCondaForge(core, '../feedstocks/vaex-core-feedstock')) -packages = ['vaex-core', 'vaex-meta', 'vaex-viz', 'vaex-hdf5', 'vaex-server', 'vaex-astro', 'vaex-jupyter', 'vaex-ml', 'vaex-graphql', 'vaex-contrib'] +packages = ['vaex-core', 'vaex', 'vaex-viz', 'vaex-hdf5', 'vaex-server', 'vaex-astro', 'vaex-jupyter', 'vaex-ml', 'vaex-graphql', 'vaex-contrib'] names = [k[5:] for k in packages[1:]] for name in names: diff --git a/asv.conf.json b/asv.conf.json deleted file mode 100644 index c0496bfffc..0000000000 --- a/asv.conf.json +++ /dev/null @@ -1,105 +0,0 @@ -{ - "version": 1, - "project": "vaex", - "project_url": "https://vaex.io/", - "repo": ".", - "environment_type": "conda", - "show_commit_url": "http://github.com/vaexio/vaex/commit/", - "env_dir": ".asv/env", - "results_dir": ".asv/results", - "html_dir": ".asv/html", - "conda_channels": [ - "conda-forge", - "defaults" - ], - "pythons": [ - "3.7" - ], - "matrix": { - "setuptools": [ - "42.0.2" - ], - "certifi": [ - "2019.6.16" - ], - "pip": [], - "numpy": [], - "scipy": [], - "pyqt": [], - "matplotlib": [], - "pyopengl": [], - "h5py": [], - "numexpr": [], - "astropy": [], - "tornado": [], - "cython": [], - "runipy": [], - "pandas": [], - "pytest": [], - "numba": [], - "pyarrow": [ - "0.15.1" - ], - "graphviz": [], - "python-graphviz": [], - "pcre": [], - "catboost": [], - "libcxx": [ - "9.0.0" - ], - "notebook": [], - "scikit-learn": [], - "lightgbm": [], - "py-xgboost": [], - "bqplot": [ - "0.12.6" - ], - "ipyleaflet": [ - "0.12.3" - ], - "ipympl": [ - "0.5.6" - ], - "ipyvolume": [ - "0.5.2" - ], - "graphene": [ - "2.1.8" - ], - "graphene-tornado": [ - "2.6.1" - ], - "pip+python-dateutil": [ - "2.8.0" - ], - "pip+aplus": [], - "pip+futures": [ - "3.1.1" - ], - "pip+future": [ - "0.18.2" - ], - "pip+pyyaml": [], - "pip+cachetools": [], - "pip+progressbar2": [], - "pip+psutil": [ - "5.6.7" - ], - "pip+pillow": [], - "pip+requests": [], - "pip+s3fs": [ - "0.2.2" - ], - "pip+ipyvuetify": [] - }, - "install_timeout": 1800, - "install_command": [ - "in-dir={env_dir} python -mpip install {wheel_file}", - "in-dir={env_dir} pip install {build_dir} --verbose" - ], - "build_command": [ - "in-dir={build_dir}/packages/vaex-core python setup.py build", - "PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}/packages/vaex-core" - ], - "build_cache_size": 5 -} diff --git a/ci/04-run-test-suite.sh b/ci/04-run-test-suite.sh index 504d1a5a13..77f54f8bec 100755 --- a/ci/04-run-test-suite.sh +++ b/ci/04-run-test-suite.sh @@ -10,7 +10,7 @@ export VAEX_SERVER_OVERRIDE='{"dataframe.vaex.io":"dataframe-dev.vaex.io"}' pytest tests\ packages/vaex-core/vaex/datatype_test.py\ packages/vaex-core/vaex/file/\ - packages/vaex-core/vaex/test/dataset.py::TestDataset\ + vaex.test.dataset::TestDataset\ --doctest-modules\ packages/vaex-core/vaex/datatype.py\ packages/vaex-core/vaex/utils.py\ diff --git a/ci/conda-base-minimal.yml b/ci/conda-base-minimal.yml deleted file mode 100644 index d80a0e2f94..0000000000 --- a/ci/conda-base-minimal.yml +++ /dev/null @@ -1,7 +0,0 @@ -name: vaex-dev -channels: - - conda-forge -dependencies: -- numpy<1.21 -- pandas -- scipy diff --git a/ci/conda-env-extra.yml b/ci/conda-env-extra.yml deleted file mode 100644 index d8e29b3e7f..0000000000 --- a/ci/conda-env-extra.yml +++ /dev/null @@ -1 +0,0 @@ -catboost diff --git a/ci/conda-env-nightlies.yml b/ci/conda-env-nightlies.yml deleted file mode 100644 index 19d8363071..0000000000 --- a/ci/conda-env-nightlies.yml +++ /dev/null @@ -1 +0,0 @@ -pyarrow diff --git a/ci/conda-env-notebooks.yml b/ci/conda-env-notebooks.yml deleted file mode 100644 index 66c0b11ef2..0000000000 --- a/ci/conda-env-notebooks.yml +++ /dev/null @@ -1,2 +0,0 @@ -plotly -ipyvolume==0.6.0a6 diff --git a/ci/conda-env.yml b/ci/conda-env.yml deleted file mode 100644 index 10b0f18fca..0000000000 --- a/ci/conda-env.yml +++ /dev/null @@ -1,58 +0,0 @@ -name: vaex-dev -channels: - - msys2 - - conda-forge - - numba/label/dev -dependencies: -- aplus -- astropy -- blake3 -- cython -- cachetools -- catboost -- dask<2024.2.0 -- diskcache -- filelock -- fsspec<2022.2.0 -- gcsfs -- geopandas -- graphviz -- joblib<1.3 # 1.3 is broken for py36 -- h5py -- httpx # for testing with starlette/fastapi -- ipyvolume=0.6.0a6 -- matplotlib-base -- nest-asyncio<1.5.2 -- notebook -- numba -- numpy<1.26.4 -- pandas -- pcre -- pip==20.3.4 -- plotly -- python-annoy -- pyarrow>=5.0.0 -- pyqt -- pytest -- pytest-asyncio -- pytest-mpl -- pytest-timeout -- python-graphviz -- py-xgboost -- pyyaml -- rich -- s3fs>=2021.8.0 -- scikit-learn -- scipy -- tabulate -- tornado -- uvicorn<0.16 -- xarray<2022.6.0 -# currently not using this, since the test that requires this is flakey -# - myst-parser<0.18 # 0.18 breaks our test, missing main -- pytz # for some reason, pip has trouble resolving this, and the libraries below -- python-utils -- progressbar2 -- zipp<3.16.0 -- pip: - - lightgbm>=4.0.0 \ No newline at end of file diff --git a/dodo.py b/dodo.py index 2dccf0c355..7e71a921e4 100644 --- a/dodo.py +++ b/dodo.py @@ -23,7 +23,7 @@ def action(targets): return { 'actions': [action], 'targets': ["binder/requirements.txt"], - 'file_dep': ['packages/vaex-meta/vaex/meta/_version.py'] + 'file_dep': ['packages/vaex/vaex/meta/_version.py'] } @@ -35,6 +35,6 @@ def action(targets): return { 'actions': [action], - 'targets': ["packages/vaex-meta/README.md"], + 'targets': ["packages/vaex/README.md"], 'file_dep': ['README.md'] } diff --git a/misc/conda/aplus/bld.bat b/misc/conda/aplus/bld.bat deleted file mode 100644 index 87b1481d74..0000000000 --- a/misc/conda/aplus/bld.bat +++ /dev/null @@ -1,8 +0,0 @@ -"%PYTHON%" setup.py install -if errorlevel 1 exit 1 - -:: Add more build steps here, if they are necessary. - -:: See -:: http://docs.continuum.io/conda/build.html -:: for a list of environment variables that are set during the build process. diff --git a/misc/conda/aplus/build.sh b/misc/conda/aplus/build.sh deleted file mode 100644 index 4d7fc032b8..0000000000 --- a/misc/conda/aplus/build.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -$PYTHON setup.py install - -# Add more build steps here, if they are necessary. - -# See -# http://docs.continuum.io/conda/build.html -# for a list of environment variables that are set during the build process. diff --git a/misc/conda/aplus/meta.yaml b/misc/conda/aplus/meta.yaml deleted file mode 100644 index 66682292f2..0000000000 --- a/misc/conda/aplus/meta.yaml +++ /dev/null @@ -1,60 +0,0 @@ -package: - name: aplus - version: "0.11.0" - -source: - fn: aplus-0.11.0.tar.gz - url: https://files.pythonhosted.org/packages/9a/25/58f13028bc43c2d219e32402995275ed577cb02da7e43876a4020727c66a/aplus-0.11.0.tar.gz - md5: cb3009b753154aea6bbf3c6088b2a509 -# patches: - # List any patch files here - # - fix.patch - -# build: - # noarch_python: True - # preserve_egg_dir: True - # entry_points: - # Put any entry points (scripts to be generated automatically) here. The - # syntax is module:function. For example - # - # - aplus = aplus:main - # - # Would create an entry point called aplus that calls aplus.main() - - - # If this is a new build for the same version, increment the build - # number. If you do not include this key, it defaults to 0. - # number: 1 - -requirements: - build: - - python - - run: - - python - -test: - # Python imports - imports: - - aplus - - # commands: - # You can put test commands to be run here. Use this to test that the - # entry points work. - - - # You can also put a file called run_test.py in the recipe that will be run - # at test time. - - # requires: - # Put any additional test requirements here. For example - # - nose - -about: - #home: The package home page - license: UNKNOWN - summary: 'UNKNOWN' - -# See -# http://docs.continuum.io/conda/build.html for -# more information about meta.yaml diff --git a/misc/conda/attrdict/bld.bat b/misc/conda/attrdict/bld.bat deleted file mode 100644 index 87b1481d74..0000000000 --- a/misc/conda/attrdict/bld.bat +++ /dev/null @@ -1,8 +0,0 @@ -"%PYTHON%" setup.py install -if errorlevel 1 exit 1 - -:: Add more build steps here, if they are necessary. - -:: See -:: http://docs.continuum.io/conda/build.html -:: for a list of environment variables that are set during the build process. diff --git a/misc/conda/attrdict/build.sh b/misc/conda/attrdict/build.sh deleted file mode 100644 index 4d7fc032b8..0000000000 --- a/misc/conda/attrdict/build.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -$PYTHON setup.py install - -# Add more build steps here, if they are necessary. - -# See -# http://docs.continuum.io/conda/build.html -# for a list of environment variables that are set during the build process. diff --git a/misc/conda/attrdict/meta.yaml b/misc/conda/attrdict/meta.yaml deleted file mode 100644 index 09ad147433..0000000000 --- a/misc/conda/attrdict/meta.yaml +++ /dev/null @@ -1,65 +0,0 @@ -package: - name: attrdict - version: "2.0.0" - -source: - fn: attrdict-2.0.0.tar.gz - url: https://files.pythonhosted.org/packages/35/bb/bac3e42ae04bc082c28cd8186bfb5b50fb240a4f7419f876c683125ccc8b/attrdict-2.0.0.tar.gz - md5: 8a7c1a4e737fe9e2b2b8844c0f7746f8 -# patches: - # List any patch files here - # - fix.patch - -# build: - # noarch_python: True - # preserve_egg_dir: True - # entry_points: - # Put any entry points (scripts to be generated automatically) here. The - # syntax is module:function. For example - # - # - attrdict = attrdict:main - # - # Would create an entry point called attrdict that calls attrdict.main() - - - # If this is a new build for the same version, increment the build - # number. If you do not include this key, it defaults to 0. - # number: 1 - -requirements: - build: - - python - - setuptools - - six - - run: - - python - - six - -test: - # Python imports - imports: - - attrdict - - # commands: - # You can put test commands to be run here. Use this to test that the - # entry points work. - - - # You can also put a file called run_test.py in the recipe that will be run - # at test time. - - requires: - - coverage - - nose >=1.0 - # Put any additional test requirements here. For example - # - nose - -about: - home: https://github.com/bcj/AttrDict - license: MIT License - summary: 'A dict with attribute-style access' - -# See -# http://docs.continuum.io/conda/build.html for -# more information about meta.yaml diff --git a/misc/conda/cachetools/bld.bat b/misc/conda/cachetools/bld.bat deleted file mode 100644 index 87b1481d74..0000000000 --- a/misc/conda/cachetools/bld.bat +++ /dev/null @@ -1,8 +0,0 @@ -"%PYTHON%" setup.py install -if errorlevel 1 exit 1 - -:: Add more build steps here, if they are necessary. - -:: See -:: http://docs.continuum.io/conda/build.html -:: for a list of environment variables that are set during the build process. diff --git a/misc/conda/cachetools/build.sh b/misc/conda/cachetools/build.sh deleted file mode 100644 index 4d7fc032b8..0000000000 --- a/misc/conda/cachetools/build.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -$PYTHON setup.py install - -# Add more build steps here, if they are necessary. - -# See -# http://docs.continuum.io/conda/build.html -# for a list of environment variables that are set during the build process. diff --git a/misc/conda/cachetools/meta.yaml b/misc/conda/cachetools/meta.yaml deleted file mode 100644 index f4520bc9d2..0000000000 --- a/misc/conda/cachetools/meta.yaml +++ /dev/null @@ -1,61 +0,0 @@ -package: - name: cachetools - version: "1.1.6" - -source: - fn: cachetools-1.1.6.tar.gz - url: https://files.pythonhosted.org/packages/ba/00/b0ec69e21142cd838b2383a7881cf18368e35847cb66f908c8f25bcbaafc/cachetools-1.1.6.tar.gz - md5: 387d7f34effd9335ae55bd0762e77bfa -# patches: - # List any patch files here - # - fix.patch - -# build: - # noarch_python: True - # preserve_egg_dir: True - # entry_points: - # Put any entry points (scripts to be generated automatically) here. The - # syntax is module:function. For example - # - # - cachetools = cachetools:main - # - # Would create an entry point called cachetools that calls cachetools.main() - - - # If this is a new build for the same version, increment the build - # number. If you do not include this key, it defaults to 0. - # number: 1 - -requirements: - build: - - python - - setuptools - - run: - - python - -test: - # Python imports - imports: - - cachetools - - # commands: - # You can put test commands to be run here. Use this to test that the - # entry points work. - - - # You can also put a file called run_test.py in the recipe that will be run - # at test time. - - # requires: - # Put any additional test requirements here. For example - # - nose - -about: - home: https://github.com/tkem/cachetools - license: MIT License - summary: 'Extensible memoizing collections and decorators' - -# See -# http://docs.continuum.io/conda/build.html for -# more information about meta.yaml diff --git a/misc/conda/jprops/bld.bat b/misc/conda/jprops/bld.bat deleted file mode 100644 index 87b1481d74..0000000000 --- a/misc/conda/jprops/bld.bat +++ /dev/null @@ -1,8 +0,0 @@ -"%PYTHON%" setup.py install -if errorlevel 1 exit 1 - -:: Add more build steps here, if they are necessary. - -:: See -:: http://docs.continuum.io/conda/build.html -:: for a list of environment variables that are set during the build process. diff --git a/misc/conda/jprops/build.sh b/misc/conda/jprops/build.sh deleted file mode 100644 index 4d7fc032b8..0000000000 --- a/misc/conda/jprops/build.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -$PYTHON setup.py install - -# Add more build steps here, if they are necessary. - -# See -# http://docs.continuum.io/conda/build.html -# for a list of environment variables that are set during the build process. diff --git a/misc/conda/jprops/meta.yaml b/misc/conda/jprops/meta.yaml deleted file mode 100644 index f868524422..0000000000 --- a/misc/conda/jprops/meta.yaml +++ /dev/null @@ -1,60 +0,0 @@ -package: - name: jprops - version: "1.0" - -source: - fn: jprops-1.0.tar.gz - url: https://files.pythonhosted.org/packages/1f/57/42e0088e3607b2d157bb039ba5c19536010f3d505871573cb28020e1e1d6/jprops-1.0.tar.gz - md5: d150084738d03d8046418a0f79879821 -# patches: - # List any patch files here - # - fix.patch - -# build: - # noarch_python: True - # preserve_egg_dir: True - # entry_points: - # Put any entry points (scripts to be generated automatically) here. The - # syntax is module:function. For example - # - # - jprops = jprops:main - # - # Would create an entry point called jprops that calls jprops.main() - - - # If this is a new build for the same version, increment the build - # number. If you do not include this key, it defaults to 0. - # number: 1 - -requirements: - build: - - python - - setuptools - - run: - - python - -# test: - # Python imports - # imports: - - # commands: - # You can put test commands to be run here. Use this to test that the - # entry points work. - - - # You can also put a file called run_test.py in the recipe that will be run - # at test time. - - # requires: - # Put any additional test requirements here. For example - # - nose - -about: - home: http://mgood.github.com/jprops/ - license: BSD - summary: 'Parser for Java .properties files' - -# See -# http://docs.continuum.io/conda/build.html for -# more information about meta.yaml diff --git a/misc/conda/kapteyn/bld.bat b/misc/conda/kapteyn/bld.bat deleted file mode 100644 index 87b1481d74..0000000000 --- a/misc/conda/kapteyn/bld.bat +++ /dev/null @@ -1,8 +0,0 @@ -"%PYTHON%" setup.py install -if errorlevel 1 exit 1 - -:: Add more build steps here, if they are necessary. - -:: See -:: http://docs.continuum.io/conda/build.html -:: for a list of environment variables that are set during the build process. diff --git a/misc/conda/kapteyn/build.sh b/misc/conda/kapteyn/build.sh deleted file mode 100644 index 4d7fc032b8..0000000000 --- a/misc/conda/kapteyn/build.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -$PYTHON setup.py install - -# Add more build steps here, if they are necessary. - -# See -# http://docs.continuum.io/conda/build.html -# for a list of environment variables that are set during the build process. diff --git a/misc/conda/kapteyn/meta.yaml b/misc/conda/kapteyn/meta.yaml deleted file mode 100644 index 03e176960e..0000000000 --- a/misc/conda/kapteyn/meta.yaml +++ /dev/null @@ -1,64 +0,0 @@ -package: - name: kapteyn - version: "2.3" - -source: - fn: kapteyn-2.3.tar.gz - url: https://files.pythonhosted.org/packages/0a/c3/4ceffdeb87918ade3dc9649e100d3b0be0fdc38b5105324b17686a3e0e48/kapteyn-2.3.tar.gz - md5: 9abb373995d1b683936c48485a41c2b4 -# patches: - # List any patch files here - # - fix.patch - -# build: - # noarch_python: True - # preserve_egg_dir: True - # entry_points: - # Put any entry points (scripts to be generated automatically) here. The - # syntax is module:function. For example - # - # - kapteyn = kapteyn:main - # - # Would create an entry point called kapteyn that calls kapteyn.main() - - - # If this is a new build for the same version, increment the build - # number. If you do not include this key, it defaults to 0. - # number: 1 - -requirements: - build: - - python - - setuptools - - six - - numpy - - cython - run: - - python - - numpy - - six -test: - # Python imports - imports: - - kapteyn - - kapteyn.wcs - # commands: - # You can put test commands to be run here. Use this to test that the - # entry points work. - - - # You can also put a file called run_test.py in the recipe that will be run - # at test time. - - # requires: - # Put any additional test requirements here. For example - # - nose - -about: - home: http://www.astro.rug.nl/software/kapteyn/ - license: BSD License or GNU Library or Lesser General Public License (LGPL) - summary: 'Kapteyn Package: Python modules for astronomical applications' - -# See -# http://docs.continuum.io/conda/build.html for -# more information about meta.yaml diff --git a/misc/conda/layeredconfig/bld.bat b/misc/conda/layeredconfig/bld.bat deleted file mode 100644 index 87b1481d74..0000000000 --- a/misc/conda/layeredconfig/bld.bat +++ /dev/null @@ -1,8 +0,0 @@ -"%PYTHON%" setup.py install -if errorlevel 1 exit 1 - -:: Add more build steps here, if they are necessary. - -:: See -:: http://docs.continuum.io/conda/build.html -:: for a list of environment variables that are set during the build process. diff --git a/misc/conda/layeredconfig/build.sh b/misc/conda/layeredconfig/build.sh deleted file mode 100644 index 4d7fc032b8..0000000000 --- a/misc/conda/layeredconfig/build.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -$PYTHON setup.py install - -# Add more build steps here, if they are necessary. - -# See -# http://docs.continuum.io/conda/build.html -# for a list of environment variables that are set during the build process. diff --git a/misc/conda/layeredconfig/meta.yaml b/misc/conda/layeredconfig/meta.yaml deleted file mode 100644 index 6bbaefb55c..0000000000 --- a/misc/conda/layeredconfig/meta.yaml +++ /dev/null @@ -1,65 +0,0 @@ -package: - name: layeredconfig - version: "0.1.0" - -source: - fn: layeredconfig-0.1.0.tar.gz - url: https://files.pythonhosted.org/packages/d8/a3/9818f77bda2e9c1a490f353d6f9bd5a1d301e20c7b7172600b0a69829291/layeredconfig-0.1.0.tar.gz - md5: c19b10f505fa39f4656ac64072c5619d -# patches: - # List any patch files here - # - fix.patch - -# build: - # noarch_python: True - # preserve_egg_dir: True - # entry_points: - # Put any entry points (scripts to be generated automatically) here. The - # syntax is module:function. For example - # - # - layeredconfig = layeredconfig:main - # - # Would create an entry point called layeredconfig that calls layeredconfig.main() - - - # If this is a new build for the same version, increment the build - # number. If you do not include this key, it defaults to 0. - # number: 1 - -requirements: - build: - - python - - setuptools - - six - - pyyaml - - run: - - python - - six - - pyyaml - - requests -test: - # Python imports - imports: - - layeredconfig - - # commands: - # You can put test commands to be run here. Use this to test that the - # entry points work. - - - # You can also put a file called run_test.py in the recipe that will be run - # at test time. - - # requires: - # Put any additional test requirements here. For example - # - nose - -about: - home: https://github.com/staffanm/layeredconfig - license: BSD License - summary: 'Manages configuration coming from config files, environment variables, command line arguments, code defaults or other sources' - -# See -# http://docs.continuum.io/conda/build.html for -# more information about meta.yaml diff --git a/misc/conda/progressbar2/bld.bat b/misc/conda/progressbar2/bld.bat deleted file mode 100644 index 87b1481d74..0000000000 --- a/misc/conda/progressbar2/bld.bat +++ /dev/null @@ -1,8 +0,0 @@ -"%PYTHON%" setup.py install -if errorlevel 1 exit 1 - -:: Add more build steps here, if they are necessary. - -:: See -:: http://docs.continuum.io/conda/build.html -:: for a list of environment variables that are set during the build process. diff --git a/misc/conda/progressbar2/build.sh b/misc/conda/progressbar2/build.sh deleted file mode 100644 index 4d7fc032b8..0000000000 --- a/misc/conda/progressbar2/build.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -$PYTHON setup.py install - -# Add more build steps here, if they are necessary. - -# See -# http://docs.continuum.io/conda/build.html -# for a list of environment variables that are set during the build process. diff --git a/misc/conda/progressbar2/meta.yaml b/misc/conda/progressbar2/meta.yaml deleted file mode 100644 index 9680ebbda1..0000000000 --- a/misc/conda/progressbar2/meta.yaml +++ /dev/null @@ -1,61 +0,0 @@ -package: - name: progressbar2 - version: "3.6.0" - -source: - fn: progressbar2-3.6.0.tar.gz - url: https://files.pythonhosted.org/packages/c0/06/b85e78d16a802ed3d7b100762f45a377bb57ee71cb66b3d8b0aec98f4039/progressbar2-3.6.0.tar.gz - md5: cb839d723cf5747fdd159050dd8b612f -# patches: - # List any patch files here - # - fix.patch - -# build: - # noarch_python: True - # preserve_egg_dir: True - # entry_points: - # Put any entry points (scripts to be generated automatically) here. The - # syntax is module:function. For example - # - # - progressbar2 = progressbar2:main - # - # Would create an entry point called progressbar2 that calls progressbar2.main() - - - # If this is a new build for the same version, increment the build - # number. If you do not include this key, it defaults to 0. - # number: 1 - -requirements: - build: - - python - - setuptools - - run: - - python - -test: - # Python imports - imports: - - progressbar - - # commands: - # You can put test commands to be run here. Use this to test that the - # entry points work. - - - # You can also put a file called run_test.py in the recipe that will be run - # at test time. - - # requires: - # Put any additional test requirements here. For example - # - nose - -about: - home: https://github.com/WoLpH/python-progressbar - license: BSD License - summary: 'A Python Progressbar library to provide visual (yet text based) progress to\nlong running operations.' - -# See -# http://docs.continuum.io/conda/build.html for -# more information about meta.yaml diff --git a/misc/conda/vaex/bld.bat b/misc/conda/vaex/bld.bat deleted file mode 100644 index 87b1481d74..0000000000 --- a/misc/conda/vaex/bld.bat +++ /dev/null @@ -1,8 +0,0 @@ -"%PYTHON%" setup.py install -if errorlevel 1 exit 1 - -:: Add more build steps here, if they are necessary. - -:: See -:: http://docs.continuum.io/conda/build.html -:: for a list of environment variables that are set during the build process. diff --git a/misc/conda/vaex/build.sh b/misc/conda/vaex/build.sh deleted file mode 100644 index 2d466c4513..0000000000 --- a/misc/conda/vaex/build.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -$PYTHON setup.py install --single-version-externally-managed --record=record.txt - -# Add more build steps here, if they are necessary. - -# See -# http://docs.continuum.io/conda/build.html -# for a list of environment variables that are set during the build process. diff --git a/misc/conda/vaex/meta.yaml b/misc/conda/vaex/meta.yaml deleted file mode 100644 index 97b47c7a37..0000000000 --- a/misc/conda/vaex/meta.yaml +++ /dev/null @@ -1,117 +0,0 @@ -package: - name: vaex - version: "1.0.0b2" - -source: - fn: vaex-1.0.0b2.tar.gz - url: https://pypi.python.org/packages/c5/f2/4956c264012df7cd936b8b5deba675f30ec830c707f55345e9ecba1eec36/vaex-1.0.0b2.tar.gz - md5: 012070a8a6ffe1de8c2bbfadf8958ba7 -# patches: - # List any patch files here - # - fix.patch - -build: - script: python setup.py install --single-version-externally-managed --record record.txt - osx_is_app: True - # noarch_python: True - # preserve_egg_dir: True - entry_points: - # Put any entry points (scripts to be generated automatically) here. The - # syntax is module:function. For example - # - # - vaex = vaex:main - # - # Would create an entry point called vaex that calls vaex.main() - - - vaex=vaex.ui.main:main - - # If this is a new build for the same version, increment the build - # number. If you do not include this key, it defaults to 0. - # number: 1 - - -requirements: - build: - - python - - pyopengl >=3.1.0 - - numpy >=1.7.2 - - scipy >=0.1 - - astropy >=0.3 - - h5py >=2.2.1 - - jprops - - matplotlib >=1.3.1 - - numexpr - - psutil >=1.2.1 - - aplus - - tornado >4.1 - - futures - - future >=0.15.2 - - pyyaml - - cachetools - - progressbar2 - - attrdict - - pandas - - pillow - - run: - - python - - pyopengl >=3.1.0 - - numpy >=1.7.2 - - scipy >=0.1 - - astropy >=0.3 - - h5py >=2.2.1 - - jprops - - matplotlib >=1.3.1 - - numexpr - - psutil >=1.2.1 - - aplus - - tornado >4.1 - - futures - - future >=0.15.2 - - pyyaml - - cachetools - - progressbar2 - - attrdict - - pandas - - pillow - - python.app # [osx] - -test: - # Python imports - imports: - - vaex - - vaex.file - - vaex.misc - - vaex.test - - vaex.ui - - vaex.ui.icons - - vaex.ui.plugin - - commands: - # You can put test commands to be run here. Use this to test that the - # entry points work. - - #- vaex --help - - # You can also put a file called run_test.py in the recipe that will be run - # at test time. - - # requires: - # Put any additional test requirements here. For example - # - nose -app: - entry: vaex - summary: 'Vaex is a graphical tool to visualize and explore large tabular datasets.' - icon: vaex32.png - type: desk - - -about: - home: https://www.astro.rug.nl/~breddels/vaex - license: MIT -# license_file: LICENSE - summary: 'Vaex is a graphical tool and library to visualize and explore large tabular datasets.' - -# See -# http://docs.continuum.io/conda/build.html for -# more information about meta.yaml diff --git a/packages/vaex-astro/setup.py b/packages/vaex-astro/setup.py index 26006116e3..8ae1e3ade4 100644 --- a/packages/vaex-astro/setup.py +++ b/packages/vaex-astro/setup.py @@ -12,7 +12,7 @@ license = 'MIT' version = version.__version__ url = 'https://www.github.com/maartenbreddels/vaex' -install_requires_astro = ['vaex-core>=4.5.0,<5', 'astropy'] +install_requires_astro = ['vaex-core~=4.5', 'astropy'] setup( name=name + '-astro', diff --git a/packages/vaex-contrib/setup.py b/packages/vaex-contrib/setup.py index 61edc62354..4121424de4 100644 --- a/packages/vaex-contrib/setup.py +++ b/packages/vaex-contrib/setup.py @@ -10,7 +10,7 @@ license = 'MIT' version = version.__version__ url = 'https://www.github.com/vaexio/vaex' -install_requires_contrib = ['vaex-core>=4.0.0,<5'] +install_requires_contrib = ['vaex-core~=4.0'] setup(name=name + '-contrib', version=version, @@ -18,7 +18,7 @@ url=url, install_requires=install_requires_contrib, extras_require={ - 'gcp': ['google-cloud-bigquery', 'google-cloud-bigquery-storage'], + 'all': ['google-cloud-bigquery', 'google-cloud-bigquery-storage'], }, license=license, packages=['vaex.contrib', 'vaex.contrib.io'], diff --git a/packages/vaex-core/setup.py b/packages/vaex-core/setup.py index cba3be7472..ecd664227a 100644 --- a/packages/vaex-core/setup.py +++ b/packages/vaex-core/setup.py @@ -23,20 +23,16 @@ license = 'MIT' version = version.__version__ url = 'https://www.github.com/maartenbreddels/vaex' -# TODO: can we do without requests and progressbar2? # TODO: after python2 supports frops, future and futures can also be dropped -# TODO: would be nice to have astropy only as dep in vaex-astro setup_requires = ["numpy~=1.16"] -install_requires_core = ["numpy~=1.16", "aplus", "tabulate>=0.8.3", - "future>=0.15.2", "pyyaml", "progressbar2", - "requests", "six", "cloudpickle", "pandas", "dask!=2022.4.0", - "nest-asyncio>=1.3.3", "pyarrow>=5.0.0", "frozendict!=2.2.0", +install_requires_core = ["numpy~=1.16", "aplus", "tabulate>=0.8.3", "dask!=2022.4.0", + "future>=0.15.2", "pyyaml", "six", "cloudpickle", "pandas", + "nest_asyncio>=1.3.3", "pyarrow>=5.0.0", "frozendict!=2.2.0", "blake3", "filelock", "pydantic>=1.8.0", "rich", ] -if sys.version_info[0] == 2: - install_requires_core.append("futures>=2.2.0") -install_requires_viz = ["matplotlib>=1.3.1", ] -install_requires_astro = ["kapteyn"] +extras_require_core = { + 'all': ["gcsfs>=0.6.2", "s3fs", "ipyvolume", "diskcache", "fsspec", "h5py", "httpx", "aplus", "psutil", "graphviz"], +} if "MACOSX_DEPLOYMENT_TARGET" not in os.environ: os.environ["MACOSX_DEPLOYMENT_TARGET"] = "10.9" @@ -191,9 +187,7 @@ def __str__(self): "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", ], - extras_require={ - 'all': ["gcsfs>=0.6.2", "s3fs"] - }, + extras_require=extras_require_core, entry_points={ 'console_scripts': ['vaex = vaex.__main__:main'], 'gui_scripts': ['vaexgui = vaex.__main__:main'], # sometimes in osx, you need to run with this diff --git a/packages/vaex-core/vaex/test/__init__.py b/packages/vaex-core/vaex/test/__init__.py deleted file mode 100644 index 76ea15a988..0000000000 --- a/packages/vaex-core/vaex/test/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -__author__ = 'maartenbreddels' -import unittest - -def load_tests(loader, tests, pattern): - import vaex.test.dataset - import vaex.test.ui - import vaex.test.misc - import vaex.test.plot - import vaex.test.cmodule - print(loader, tests, pattern) - for module in [vaex.test.dataset, vaex.test.ui, vaex.test.misc, vaex.test.cmodule, vaex.test.plot]: - tests.addTests(unittest.TestLoader().loadTestsFromModule(module)) - return tests - - diff --git a/packages/vaex-core/vaex/test/__main__.py b/packages/vaex-core/vaex/test/__main__.py deleted file mode 100644 index 3b6bb267a4..0000000000 --- a/packages/vaex-core/vaex/test/__main__.py +++ /dev/null @@ -1,10 +0,0 @@ -__author__ = 'maartenbreddels' -import unittest -import vaex.test.dataset -def main(args=None): - if args is None or len(args) == 1: - args = ["vaex", "vaex.test"] - unittest.main(argv=args) - -if __name__ == "__main__": - main() diff --git a/packages/vaex-core/vaex/test/all.py b/packages/vaex-core/vaex/test/all.py deleted file mode 100644 index 096371b0a8..0000000000 --- a/packages/vaex-core/vaex/test/all.py +++ /dev/null @@ -1 +0,0 @@ -__author__ = 'maartenbreddels' diff --git a/packages/vaex-core/vaex/test/cmodule.py b/packages/vaex-core/vaex/test/cmodule.py deleted file mode 100644 index 700de3b53f..0000000000 --- a/packages/vaex-core/vaex/test/cmodule.py +++ /dev/null @@ -1,135 +0,0 @@ -# -*- coding: utf-8 -*- -import vaex.dataset as dataset -import numpy as np -import unittest -import vaex as vx -import vaex.vaexfast - -class TestStatisticNd(unittest.TestCase): - def test_add(self): - x = np.arange(10, dtype=np.float64) - grid = np.zeros((10,2), dtype=np.float64) - w = x * 1 - w[2] = np.nan - #grid[...,0] = np.inf - #grid[...,1] = -np.inf - vaex.vaexfast.statisticNd([x], w, grid, [0.], [10.], 0) - print(grid) - - grid0 = np.zeros((1,), dtype=np.float64) - vaex.vaexfast.statisticNd([], w, grid0, [], [], 0) - print(grid0) - - def test_2(self): - x = np.arange(10, dtype=np.float64) + 10 - grid = np.zeros((2), dtype=np.float64) - grid[...,0] = np.inf - grid[...,1] = -np.inf - w = x * 1 - w[2] = np.nan - print(np.nansum(w)) - #grid[...,0] = np.inf - #grid[...,1] = -np.inf - vaex.vaexfast.statisticNd([], w, grid, [], [], 2) - print(grid) - - def test_edges(self): - #grid = np.zeros((10+3,1), dtype=np.float64) - grid = np.zeros((10,1), dtype=np.float64) - x = np.arange(10, dtype=np.float64) - x[0] = np.nan - vaex.vaexfast.statisticNd([x], None, grid, [4.], [6.], 0, False) - print(grid.T) - grid = np.zeros((10+3,1), dtype=np.float64) - x = np.arange(10, dtype=np.float64) - x[0] = np.nan - vaex.vaexfast.statisticNd([x], None, grid, [4.], [6.], 0, True) - print(x) - print(grid.T) - self.assertEqual(sum(grid), len(x)) - self.assertEqual(grid[-1], 4) - self.assertEqual(grid[1], 3) - self.assertEqual(grid[0], 1) - - - grid = np.zeros((10,10,1), dtype=np.float64) - x = np.arange(10, dtype=np.float64) - y = np.arange(10, dtype=np.float64) - x[0] = np.nan - y[-1] = np.nan - y[-2] = np.nan - x[1] = np.nan - y[1] = np.nan - vaex.vaexfast.statisticNd([x, y], None, grid, [4., 3.], [6., 7.], 0, True) - print(grid[...,0]) - print(grid.shape) - - self.assertEqual(np.sum(grid), len(x)) - self.assertEqual(grid[0, 0], 1) # both are nan - self.assertEqual(grid[0, 1], 1) # this is where x[0] is nan - self.assertEqual(grid[-1, 0], 2) # this is where y[-1] and y[-2] are both nan - - - def test_find_edges(self): - grid = np.zeros((10+3,1), dtype=np.float64) - x = np.arange(10, dtype=np.float64) - #vaex.vaexfast.statisticNd([x], None, grid, [-0.5], [10.-0.5], 0, True) - vaex.vaexfast.statisticNd([x], None, grid, [0.], [10.], 0, True) - print(grid.T) - #return - c = np.cumsum(grid[1:], axis=0) - print(c.T, c.shape) - c = c.reshape(( -1)) - print(c, c.shape) - values = np.array(4.5) - print(values.T, values.shape) - edges = np.zeros((2), dtype=np.int64) - vaex.vaexfast.grid_find_edges(c, values, edges) - print(edges) - - - -class TestGridInterpolate(unittest.TestCase): - def test_interpolate(self): - x = np.array([[0., 1.]]) - y = np.array([2.]) - vaex.vaexfast.grid_interpolate(x, y, 0.5) - self.assertEqual(y[0], 0.5) - - x = np.array([[0.5, 0.5, 0.5, 0.5, 0.5, 1,1,1,1,1]]) - - vaex.vaexfast.grid_interpolate(x, y, 0.5) - self.assertEqual(y[0], 1./4) - - x = np.array([[0, 0.5, 0.75, 1]]) - - vaex.vaexfast.grid_interpolate(x, y, 0.5) - self.assertEqual(y[0], 1./3) - - vaex.vaexfast.grid_interpolate(x, y, 0.75) - self.assertEqual(y[0], 2./3) - - vaex.vaexfast.grid_interpolate(x, y, 0.5+0.25/2) - self.assertEqual(y[0], 0.5) - - - x = np.array([[0, 0.5, 0.75, 1], [0.5, 0.5, 0.75, 1], [0, 0.5, 1.0, 1]]) - y = np.array([2., 2., 2.]) - vaex.vaexfast.grid_interpolate(x, y, 0.5) - np.testing.assert_array_almost_equal(y, np.array([1/3., 1./3/2, 1./3.]) ) - - - x = np.array([[0, 0.5, 0.75, 1], [0.5, 0.5, 0.75, 1], [0, 0.5, 1.0, 1]]) - vaex.vaexfast.grid_interpolate(x, y, 0.75) - np.testing.assert_array_almost_equal(y, np.array([2 / 3., 2./3, 1./2])) - - - print("#######") - x = np.array([[0, 0.5, 0.75, 1], [0.5, 0.5, 0.75, 1], [0, 0.5, 1.0, 1]]) - vaex.vaexfast.grid_interpolate(x, y, 1.0) - np.testing.assert_array_almost_equal(y, np.array([1., 1, 5./6])) - -if __name__ == '__main__': - unittest.main() - - diff --git a/packages/vaex-core/vaex/test/dataset.py b/packages/vaex-core/vaex/test/dataset.py deleted file mode 100644 index acfd5a9cff..0000000000 --- a/packages/vaex-core/vaex/test/dataset.py +++ /dev/null @@ -1,1722 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import print_function -import os -import vaex.dataset as dataset -import numpy as np -import unittest -import vaex as vx -import tempfile -import vaex.server.tornado_server -import astropy.io.fits -import astropy.units -import pandas as pd -import vaex.execution -import contextlib - - -basedir = os.path.dirname(__file__) - - -@contextlib.contextmanager -def small_buffer(ds, size=3): - if ds.is_local(): - previous = ds.executor.chunk_size - ds.executor.chunk_size = size - ds._invalidate_caches() - try: - yield - finally: - ds.executor.chunk_size = previous - else: - yield # for remote dfs we don't support this ... or should we? - - -# these need to be global for pickling -def function_upper(x): - return np.array(x.upper()) -import vaex.serialize -@vaex.serialize.register -class Multiply: - def __init__(self, scale=0): self.scale = scale - @classmethod - def state_from(cls, state, trusted=True): - return cls(scale=state) - def state_get(self): return self.scale - def __call__(self, x): return x * self.scale - - -vx.set_log_level_exception() -#vx.set_log_level_off() -#vx.set_log_level_debug() - -def from_scalars(**kwargs): - return vx.from_arrays(**{k:np.array([v]) for k, v in kwargs.items()}) - -class CallbackCounter(object): - def __init__(self, return_value=None): - self.counter = 0 - self.return_value = return_value - self.last_args = None - self.last_kwargs = None - - def __call__(self, *args, **kwargs): - self.counter += 1 - self.last_args = args - self.last_kwargs = kwargs - return self.return_value - -class TestDataset(unittest.TestCase): - def setUp(self): - - columns = {} - - # x is non-c - # same as np.arange(10, dtype=">f8")., but with strides == 16, instead of 8 - use_filtering = True - if use_filtering: - self.zero_index = 2 - self.x = self.x_all = x = np.arange(-2, 40, dtype=">f8").reshape((-1,21)).T.copy()[:,0] - self.y = y = x ** 2 - self.ints = np.arange(-2,19, dtype="i8") - self.ints[1] = 2**62+1 - self.ints[2] = -2**62+1 - self.ints[3] = -2**62-1 - self.ints[1+10] = 2**62+1 - self.ints[2+10] = -2**62+1 - self.ints[3+10] = -2**62-1 - columns["x"] = x - columns["y"] = y - mo = m = np.arange(-2, 40, dtype=">f8").reshape((-1,21)).T.copy()[:,0] - ma_value = 77777 - m[-1+10+2] = ma_value - m[-1+20] = ma_value - else: - self.x = x = np.arange(20, dtype=">f8").reshape((-1,10)).T.copy()[:,0] - self.y = y = x ** 2 - self.ints = np.arange(10, dtype="i8") - self.ints[0] = 2**62+1 - self.ints[1] = -2**62+1 - self.ints[2] = -2**62-1 - columns["x"] = x - columns["y"] = y - m = x.copy() - ma_value = 77777 - m[-1] = ma_value - self.m = m = np.ma.array(m, mask=m==ma_value) - self.mi = mi = np.ma.array(m.data.astype(np.int64), mask=m.data==ma_value, fill_value=88888) - columns["m"] = m - columns["mi"] = mi - columns["ints"] = self.ints - - columns["f"] = np.arange(len(self.x), dtype=np.float64) - - name = np.array(list(map(lambda x: str(x) + "bla" + ('_' * int(x)), self.x)), dtype='U') #, dtype=np.string_) - columns["name"] = np.array(name) - columns["name_arrow"] = vaex.string_column(name) - - self.dataset = vaex.from_dict(columns) - self.dataset.set_variable("t", 1.) - self.dataset.add_virtual_column("z", "x+t*y") - self.dataset.units["x"] = astropy.units.Unit("km") - self.dataset.units["y"] = astropy.units.Unit("km/s") - self.dataset.units["t"] = astropy.units.Unit("s") - self.dataset.ucds["x"] = "some;ucd" - self.names = self.dataset.get_column_names() - if use_filtering: - self.dataset.select('(x >= 0) & (x < 10)', name=vaex.dataframe.FILTER_SELECTION_NAME) - self.x = x = self.x[2:12] - self.y = y = self.y[2:12] - self.m = m = self.m[2:12] - self.ints = ints = self.ints[2:12] - - # TODO; better virtual and variables support - # TODO: this is a copy since concatenated dfs do not yet support - # global selections - - # a 'deep' copy - self.dataset_no_filter = vaex.from_items(*self.dataset.to_items(virtual=False, strings=True)) - self.dataset_no_filter.add_virtual_column("z", "x+t*y") - self.dataset_no_filter.set_variable("t", 1.) - - - #self.jobsManager = dataset.JobsManager() - - x = np.array([0., 1]) - y = np.array([-1., 1]) - self.datasetxy = vx.dataframe.DataFrameLocal() - self.datasetxy.add_column("x", x) - self.datasetxy.add_column("y", y) - - x1 = np.array([1., 3]) - x2 = np.array([2., 3, 4,]) - x3 = np.array([5.]) - self.x_concat = np.concatenate((x1, x2, x3)) - - dataset1 = vx.dataframe.DataFrameLocal() - dataset2 = vx.dataframe.DataFrameLocal() - dataset3 = vx.dataframe.DataFrameLocal() - dataset1.add_column("x", x1) - dataset2.add_column("x", x2) - dataset3.add_column("x", x3) - # dataset3.add_column("y", x3**2) - self.dataset_concat = vaex.concat([dataset1, dataset2, dataset3]) - - self.dataset_concat_dup = vaex.concat([self.dataset_no_filter, self.dataset_no_filter, self.dataset_no_filter]) - self.dataset_local = self.dataset - self.datasetxy_local = self.datasetxy - self.dataset_concat_local = self.dataset_concat - self.dataset_concat_dup_local = self.dataset_concat_dup - - np.random.seed(0) # fix seed so that test never fails randomly - - self.df = self.dataset.to_pandas_df() - - def test_function(self): - def multiply(factor=2): - def f(x): - return x*factor - return f - ds = self.dataset - f = ds.add_function('mul2', multiply(2)) - ds['x2'] = f(ds.x) - self.assertEqual((self.x * 2).tolist(), ds.evaluate('x2').tolist()) - ds.state_get() - - - def test_apply(self): - ds_copy = self.dataset.copy() - ds = self.dataset - with small_buffer(ds, 2): - upper = ds.apply(function_upper, arguments=[ds['name']]) - ds['NAME'] = upper - - name = ds.evaluate('NAME') - self.assertEqual(name[0], u'0.0BLA') - ds_copy.state_set(ds.state_get()) - name = ds_copy.evaluate('NAME') - self.assertEqual(name[0], u'0.0BLA') - - ds['a1'] = ds.apply(lambda x: x+1, arguments=['x']) - ds['a2'] = ds.apply(lambda x: x+2, arguments=['x']) - assert (ds['a1']+1).evaluate().tolist() == ds['a2'].evaluate().tolist() - - - def test_filter(self): - ds = self.dataset - if ds.is_local(): # remote doesn't have a cache - ds._invalidate_selection_cache() - with small_buffer(ds): - ds1 = ds.copy() - ds1.select(ds1.x > 4, name=vaex.dataframe.FILTER_SELECTION_NAME, mode='and') - ds1._invalidate_caches() - - ds2 = ds[ds.x > 4] - ds1.x.evaluate() - # self.assertEqual(ds1.x.evaluate().tolist(), ds2.x.evaluate().tolist()) - - ds2.select(ds.x < 6) - x = ds2.x.evaluate(selection=True) - self.assertEqual(x.tolist(), [5]) - # print("=" * 70) - - def test_default_selection(self): - ds = self.dataset - ds._invalidate_selection_cache() - with small_buffer(ds): - indices = ds._filtered_range_to_unfiltered_indices(0, 2) - self.assertEqual(indices.tolist(), [self.zero_index+0, self.zero_index+1]) - - ds = ds[ds.x > 2] - indices = ds._filtered_range_to_unfiltered_indices(0, 2) - assert indices.tolist() == [self.zero_index+3, self.zero_index+4] - - x = ds.x.evaluate(0, 2) - indices = ds._filtered_range_to_unfiltered_indices(0, 2) - assert len(x) == 2 - assert x[0] == 3 - - x = ds.x.evaluate(4, 7) - indices = ds._filtered_range_to_unfiltered_indices(4, 7) - assert len(x) == 3 - assert x[0] == 3+4 - - def test_unique(self): - ds = vaex.from_arrays(x=np.array([2,2,1,0,1,1,2])) - with small_buffer(ds): - classes = ds.unique('x') - assert np.sort(classes).tolist() == [0, 1, 2] - - def test_amuse(self): - ds = vx.open(os.path.join(basedir, "files", "default_amuse_plummer.hdf5")) - self.assertGreater(len(ds), 0) - self.assertGreater(len(ds.get_column_names()), 0) - self.assertIsNotNone(ds.unit("x")) - self.assertIsNotNone(ds.unit("vx")) - self.assertIsNotNone(ds.unit("mass")) - ds.close() - - def test_masked_array_output(self): - fn = tempfile.mktemp(".hdf5") - print(fn) - self.dataset.sort('x').export_hdf5(fn) - output = vaex.open(fn) - self.assertEqual(self.dataset.sum("m"), output.sum("m")) - - table = self.dataset.to_astropy_table() - fn = tempfile.mktemp(".vot") - print(fn) - from astropy.io.votable import from_table, writeto - votable = from_table(table) - writeto(votable, fn) - output = vaex.open(fn) - self.assertEqual(self.dataset.sum("m"), output.sum("m")) - - def test_formats(self): - return # TODO: not workign ATM because of fits + strings - ds_fits = vx.open(os.path.join(basedir, "files", "gaia-small-fits-basic.fits")) - ds_fits_plus = vx.open(os.path.join(basedir, "files", "gaia-small-fits-plus.fits")) - ds_colfits = vx.open(os.path.join(basedir, "files", "gaia-small-colfits-basic.fits")) - ds_colfits_plus = vx.open(os.path.join(basedir, "files", "gaia-small-colfits-plus.fits")) - ds_vot = vx.open(os.path.join(basedir, "files", "gaia-small-votable.vot")) - # FIXME: the votable gives issues - dslist = [ds_fits, ds_fits_plus, ds_colfits, ds_colfits_plus]#, ds_vot] - for ds1 in dslist: - path_hdf5 = tempfile.mktemp(".hdf5") - ds1.export_hdf5(path_hdf5) - ds2 = vx.open(path_hdf5) - diff, missing, types, meta = ds1.compare(ds2) - self.assertEqual(diff, [], "difference between %s and %s" % (ds1.path, ds2.path)) - self.assertEqual(missing, [], "missing columns %s and %s" % (ds1.path, ds2.path)) - self.assertEqual(meta, [], "meta mismatch between columns %s and %s" % (ds1.path, ds2.path)) - - path_fits = tempfile.mktemp(".fits") - ds1.export_fits(path_fits) - ds2 = vx.open(path_fits) - diff, missing, types, meta = ds1.compare(ds2) - self.assertEqual(diff, [], "difference between %s and %s" % (ds1.path, ds2.path)) - self.assertEqual(missing, [], "missing columns %s and %s" % (ds1.path, ds2.path)) - self.assertEqual(meta, [], "meta mismatch between columns %s and %s" % (ds1.path, ds2.path)) - - if 0: - N = len(dslist) - for i in range(N): - for j in range(i+1, N): - ds1 = dslist[i] - ds2 = dslist[j] - diff, missing, types, meta = ds1.compare(ds2) - self.assertEqual(diff, [], "difference between %s and %s" % (ds1.path, ds2.path)) - self.assertEqual(missing, [], "missing columns %s and %s" % (ds1.path, ds2.path)) - self.assertEqual(meta, [], "meta mismatch between columns %s and %s" % (ds1.path, ds2.path)) - - def test_to(self): - def test_equal(ds1, ds2, units=True, ucds=True, description=True, descriptions=True, skip=[]): - if description: - self.assertEqual(ds1.description, ds2.description) - for name in ds1.get_column_names(virtual=False): - if name in skip: - continue - self.assertIn(name, ds2.get_column_names(virtual=False)) - np.testing.assert_array_equal(ds1.evaluate(name), ds2.evaluate(name), err_msg='mismatch in ' +name) - if units: - self.assertEqual(ds1.units.get(name), ds2.units.get(name)) - if ucds: - self.assertEqual(ds1.ucds.get(name), ds2.ucds.get(name)) - if descriptions: - self.assertEqual(ds1.descriptions.get(name), ds2.descriptions.get(name)) - - # as numpy dict - ds2 = vx.from_arrays(**self.dataset.to_dict()) - test_equal(self.dataset, ds2, ucds=False, units=False, description=False, descriptions=False) - - # as pandas - ds2 = vx.from_pandas(self.dataset.to_pandas_df(array_type='numpy')) - # skip masked arrays, pandas doesn't understand that, converts it to nan, so we can't compare - test_equal(self.dataset, ds2, ucds=False, units=False, description=False, descriptions=False, skip=['m', 'mi']) - - df = self.dataset.to_pandas_df(index_name="name", array_type='numpy') - ds2 = vx.from_pandas(df, index_name="name", copy_index=True) - test_equal(self.dataset, ds2, ucds=False, units=False, description=False, descriptions=False, skip=['m', 'mi']) - - ds2 = vx.from_pandas(self.dataset.to_pandas_df(index_name="name", array_type='numpy')) - assert "name" not in ds2.get_column_names() - - # as astropy table - ds2 = vx.from_astropy_table(self.dataset.to_astropy_table()) - test_equal(self.dataset, ds2) - - # as arrow table - ds2 = vx.from_arrow_table(self.dataset.to_arrow_table()) - test_equal(self.dataset, ds2, ucds=False, units=False, description=False, descriptions=False, ) - - # return a copy - ds2 = self.dataset.to_copy(virtual=True) - assert "z" not in ds2.columns - assert "z" in ds2.virtual_columns - test_equal(self.dataset, ds2) - - def test_add_column(self): - columns = self.dataset.get_column_names() - self.dataset.add_column("x", self.dataset.data.x) - self.assertSequenceEqual(columns, self.dataset.get_column_names()) - self.dataset.add_column("extra", self.dataset.data.x) - extra = self.dataset.evaluate("extra") - np.testing.assert_array_almost_equal(extra, self.dataset.data.x[self.zero_index:self.zero_index+10]) - with self.assertRaises(ValueError): - self.dataset.add_column("unequal", self.dataset.data.x[:10]) - with self.assertRaises(ValueError): - self.dataset.add_column("unequal", self.dataset.data.x[:11]) - - - def test_rename_column(self): - self.dataset.rename("x", "xx") - self.assertNotIn("x", self.dataset.columns) - self.assertNotIn("x", self.dataset.column_names) - self.assertNotIn("x", self.dataset.units) - self.assertNotIn("x", self.dataset.ucds) - self.assertIn("xx", self.dataset.columns) - self.assertIn("xx", self.dataset.column_names) - self.assertIn("xx", self.dataset.units) - self.assertIn("xx", self.dataset.ucds) - - def test_csv(self): - separator = "," - fn = tempfile.mktemp(".csv") - #print(fn) - with open(fn, "w") as f: - print(separator.join(["x", "y", "m", "mi", "name", "name_arrow", "ints", "f"]), file=f) - values = [self.dataset.evaluate(k) for k in 'x y m mi name name_arrow ints f'.split()] - for x, y, m, mi, name, name_arrow, i, f_ in zip(*values):#zip(self.x, self.y, self.dataset.data.m, self.dataset.data.mi, self.dataset.data.name, self.dataset.data.ints, self.dataset.data.f): - print(separator.join(map(str, [x, y, m, mi, name, name_arrow, i, f_])), file=f) - ds = vx.from_csv(fn, index_col=False, copy_index=True) - changes = self.dataset.compare(ds, report_difference=True) - diff = changes[0] - #print(diff) - self.assertEqual(changes[0], [], "changes in dataset") - self.assertEqual(changes[1], ['index'], "mssing columns") - - def test_ascii(self): - for seperator in " \t,": - for use_header in [True, False]: - #print(">>>", repr(seperator), use_header) - fn = tempfile.mktemp("asc") - with open(fn, "w") as f: - if use_header: - print(seperator.join(["x", "y"]), file=f) - for x, y, name in zip(self.x, self.y, self.dataset.data.name): - print(seperator.join(map(repr, [x, y])), file=f) - #with open(fn) as f: - # print(f.read()) - sep = seperator - if seperator == " ": - sep = None - if use_header: - ds = vx.from_ascii(fn, seperator=sep) - else: - ds = vx.from_ascii(fn, seperator=seperator, names="x y".split()) - - np.testing.assert_array_almost_equal(ds.data.x, self.x) - np.testing.assert_array_almost_equal(ds.data.y, self.y) - #np.testing.assert_array_equal(ds.data.names, self.dataset.data.name) - #if seperator == ",": - # df = pd.read_csv(fn) - # ds = vx.from_pandas(df) - # np.testing.assert_array_almost_equal(ds.data.x, self.x) - # np.testing.assert_array_almost_equal(ds.data.y, self.y) - #np.testing.assert_array_equal(ds.data.names, self.dataset.data.name) - - def tearDown(self): - pass - - def test_mixed_endian(self): - - x = np.arange(10., dtype=">f8") - y = np.arange(10, dtype=" 5', name='test') - ds.add_virtual_column('xx', 'x**2') - fmul = ds.add_function('fmul', mul) - ds['mul'] = fmul(ds.x) - count = ds.count('x', selection='test') - sum = ds.sum('xx', selection='test') - summul = ds.sum('mul', selection='test') - ds.state_write(statefile) - copy.state_load(statefile) - self.assertEqual(count, copy.count('x', selection='test')) - self.assertEqual(sum, copy.sum('xx', selection='test')) - self.assertEqual(summul, copy.sum('3*x', selection='test')) - self.assertEqual(summul, copy.sum('mul', selection='test')) - - def test_strings(self): - # TODO: concatenated dfs with strings of different length - self.assertEqual(["x", "y", "m", "mi", "ints", "f"], self.dataset.get_column_names(virtual=False, strings=False)) - - names = ["x", "y", "m", "mi", "ints", "f", "name", "name_arrow", "z"] - self.assertEqual(names, self.dataset.get_column_names()) - - if self.dataset.is_local(): - # check if strings are exported - path_hdf5 = tempfile.mktemp(".hdf5") - self.dataset.export_hdf5(path_hdf5) - exported_dataset = vx.open(path_hdf5) - self.assertEqual(names, exported_dataset.get_column_names()) - - path_arrow = tempfile.mktemp(".arrow") - self.dataset.export_arrow(path_arrow) - exported_dataset = vx.open(path_arrow) - self.assertEqual(names, exported_dataset.get_column_names(strings=True)) - - # for fits we do not support arrow like strings - # TODO: get back support for SXX (string/binary) format - self.dataset.drop("name_arrow", inplace=True) - self.dataset.drop("name", inplace=True) - names.remove("name_arrow") - names.remove("name") - path_fits = tempfile.mktemp(".fits") - self.dataset.export_fits(path_fits) - exported_dataset = vx.open(path_fits) - self.assertEqual(names, exported_dataset.get_column_names(strings=True)) - - path_fits_astropy = tempfile.mktemp(".fits") - with astropy.io.fits.open(path_fits) as fitsfile: - # make sure astropy can read the data - bla = fitsfile[1].data - try: - fitsfile.writeto(path_fits_astropy) - finally: - os.remove(path_fits_astropy) - - - def histogram_cumulative(self): - - self.dataset("x").histogram() - - def test_units(self): - assert self.dataset.unit("x") == astropy.units.km - assert self.dataset.unit("y") == astropy.units.km/astropy.units.second - assert self.dataset.unit("t") == astropy.units.second - assert self.dataset.unit("z") == astropy.units.km - assert self.dataset.unit("x+y") == None - - def test_dtype(self): - self.assertEqual(self.dataset.data_type("x"), np.dtype(">f8")) - self.assertEqual(self.dataset.data_type("f"), np.float64) - self.assertEqual(self.dataset.data_type("x*f"), np.float64) - - def test_byte_size(self): - arrow_size = self.dataset.columns['name_arrow'].nbytes +\ - self.dataset.columns['name'].nbytes - self.assertEqual(self.dataset.byte_size(), (8*6 + 2)*len(self.dataset) + arrow_size) - self.dataset.select("x < 1") - self.assertEqual(self.dataset.byte_size(selection=True), 8*6 + 2 + arrow_size) - - def test_ucd_find(self): - self.dataset.ucds["x"] = "a;b;c" - self.dataset.ucds["y"] = "b;c;d" - self.dataset.ucds["z"] = "b;c;d" - self.assertEqual(self.dataset.ucd_find("a"), "x") - self.assertEqual(self.dataset.ucd_find("b"), "x") - self.assertEqual(self.dataset.ucd_find("^b"), "y") - self.assertEqual(self.dataset.ucd_find("c"), "x") - self.assertEqual(self.dataset.ucd_find("d"), "y") - - self.assertEqual(self.dataset.ucd_find("b;c"), "x") - self.assertEqual(self.dataset.ucd_find("^b;c"), "y") - - def test_data_access(self): - assert (all(self.dataset.data.x == self.dataset.columns["x"])) - - - def test_not_implemented(self): - subspace = vaex.legacy.Subspace(self.dataset, ["x", "y"], self.dataset.executor, False) - with self.assertRaises(NotImplementedError): - subspace.minmax() - with self.assertRaises(NotImplementedError): - subspace.mean() - with self.assertRaises(NotImplementedError): - subspace.var() - with self.assertRaises(NotImplementedError): - subspace.sum() - with self.assertRaises(NotImplementedError): - subspace.histogram([]) - with self.assertRaises(NotImplementedError): - subspace.limits_sigma() - - def test_length(self): - self.assertEqual(len(self.dataset), 10) - - def t_est_length_mask(self): - self.dataset._set_mask(self.dataset.columns['x'] < 5) - self.assertEqual(self.dataset.length(selection=True), 5) - - def test_evaluate(self): - for t in [2, 3]: - self.dataset.set_variable("t", t) - x = self.dataset.evaluate("x") - y = self.dataset.evaluate("y") - z = self.dataset.evaluate("z") - z_test = x + t * y - np.testing.assert_array_almost_equal(z, z_test) - x = self.dataset.evaluate("x", selection="x < 4") - self.assertEqual(x.tolist(), x[:4].tolist()) - - - def test_invalid_expression(self): - with self.assertRaises(SyntaxError): - self.dataset.validate_expression("x/") - with self.assertRaises(NameError): - self.dataset.validate_expression("hoeba(x)") - with self.assertRaises(NameError): - self.dataset.validate_expression("x()") - self.dataset.validate_expression("sin(x)+tan(y)") - with self.assertRaises((KeyError, NameError)): # TODO: should we have just one error type? - self.dataset.validate_expression("doesnotexist") - self.dataset.validate_expression("x / y * z + x - x - -x") - self.dataset.validate_expression("x < 0") - self.dataset.validate_expression("x <= 0") - self.dataset.validate_expression("x > 0") - self.dataset.validate_expression("x >= 0") - self.dataset.validate_expression("x == 0") - self.dataset.validate_expression("x != 0") - - def test_evaluate_nested(self): - self.dataset.add_virtual_column("z2", "-z") - self.dataset.add_virtual_column("z3", "z+z2") - zeros = self.dataset.evaluate("z3") - np.testing.assert_array_almost_equal(zeros, np.zeros(len(self.dataset))) - - - - def test_count(self): - self.dataset.select("x < 5") - ds = self.dataset[self.dataset.x < 5] - df = self.df[self.df.x < 5] - np.testing.assert_array_almost_equal(self.dataset.count("x", selection=None), 10) - np.testing.assert_array_almost_equal(self.dataset.count("x", selection=True), 5) - np.testing.assert_array_almost_equal(self.dataset.x.count(selection=True), 5) - np.testing.assert_array_almost_equal(self.dataset['x'].count(), 10) - np.testing.assert_array_almost_equal(self.df['x'].count(), 10) - np.testing.assert_array_almost_equal(ds['x'].count(), 5) - np.testing.assert_array_almost_equal(df['x'].count(), 5) - - self.dataset.select("x >= 5") - ds = self.dataset[self.dataset.x >= 5] - df = self.df[self.df.x >= 5] - np.testing.assert_array_almost_equal(self.dataset.count("m", selection=None), 9) - np.testing.assert_array_almost_equal(self.dataset.count("m", selection=True), 4) - np.testing.assert_array_almost_equal(self.dataset['m'].count(), 9) - np.testing.assert_array_almost_equal(self.df['m'].count(), 9) - np.testing.assert_array_almost_equal(ds['m'].count(), 4) - np.testing.assert_array_almost_equal(df['m'].count(), 4) - - # convert to float - x = self.x_all * 1. # keep a local version, this is actually using mutation of that - # which is not really supported (caching and hashing will get confused) - self.dataset_local.columns["x"] = x #x[self.zero_index:self.zero_index + 10] - x[self.zero_index] = np.nan - if self.dataset.is_local(): - self.dataset._invalidate_caches() - else: - if hasattr(self, 'webserver1'): - self.webserver1.dfs[0]._invalidate_caches() - self.webserver2.dfs[0]._invalidate_caches() - self.dataset_local._invalidate_caches() - - self.df = self.dataset_local.to_pandas_df() - self.dataset.select("x < 5") - ds = self.dataset[self.dataset.x < 5] - df = self.df[self.df.x < 5] - # import pdb - # pdb.set_trace() - np.testing.assert_array_almost_equal(self.dataset.count("x", selection=None), 9) - np.testing.assert_array_almost_equal(self.dataset['x'].count(), 9) - np.testing.assert_array_almost_equal(self.df['x'].count(), 9) - np.testing.assert_array_almost_equal(self.dataset.count("x", selection=True), 4) - np.testing.assert_array_almost_equal(ds['x'].count(), 4) - np.testing.assert_array_almost_equal(df['x'].count(), 4) - np.testing.assert_array_almost_equal(self.dataset.count("y", selection=None), 9) # this is because of the filter x<10 - np.testing.assert_array_almost_equal(self.dataset_no_filter.count("y", selection=None), 10) - np.testing.assert_array_almost_equal(self.dataset['y'].count(), 9) - np.testing.assert_array_almost_equal(self.dataset_no_filter['y'].count(), 10) - np.testing.assert_array_almost_equal(self.df['y'].count(), 9) - np.testing.assert_array_almost_equal(self.dataset.count("y", selection=True), 4) - np.testing.assert_array_almost_equal(ds['y'].count(), 4) - np.testing.assert_array_almost_equal(df['y'].count(), 4) - np.testing.assert_array_almost_equal(self.dataset.count(selection=None), 9) - np.testing.assert_array_almost_equal(self.dataset_no_filter.count(selection=None), 10) - np.testing.assert_array_almost_equal(self.dataset.count(), 9) - np.testing.assert_array_almost_equal(self.dataset_no_filter.count(), 10) - #np.testing.assert_array_almost_equal(self.df.count(), 9) # TODO: this is different in pandas - # we modified the data.. so actually this should be 4.. - np.testing.assert_array_almost_equal(self.dataset.count(selection=True), 4) - np.testing.assert_array_almost_equal(ds.count(), 4) - np.testing.assert_array_almost_equal(self.dataset.count("*", selection=None), 9) - np.testing.assert_array_almost_equal(self.dataset_no_filter.count("*", selection=None), 10) - np.testing.assert_array_almost_equal(self.dataset.count(), 9) - np.testing.assert_array_almost_equal(self.dataset_no_filter.count(), 10) - np.testing.assert_array_almost_equal(self.dataset.count("*", selection=True), 4) - np.testing.assert_array_almost_equal(ds.count(), 4) - - task = self.dataset.count("x", selection=True, delay=True) - self.dataset.execute() - np.testing.assert_array_almost_equal(task.get(), 4) - - - np.testing.assert_array_almost_equal(self.dataset.count("x", selection=None, binby=["x"], limits=[0, 10], shape=1), [9]) - np.testing.assert_array_almost_equal(self.dataset.count("x", selection=True, binby=["x"], limits=[0, 10], shape=1), [4]) - np.testing.assert_array_almost_equal(self.dataset.count("*", selection=None, binby=["x"], limits=[0, 10], shape=1), [9]) - np.testing.assert_array_almost_equal(self.dataset.count("*", selection=True, binby=["x"], limits=[0, 10], shape=1), [4]) - np.testing.assert_array_almost_equal(self.dataset .count("*", selection=None, binby=["y"], limits=[0, 9**2+1], shape=1), [9]) - np.testing.assert_array_almost_equal(self.dataset_no_filter.count("*", selection=None, binby=["y"], limits=[0, 9**2+1], shape=1), [10]) - np.testing.assert_array_almost_equal(self.dataset.count("*", selection=True, binby=["y"], limits=[0, 9**2+1], shape=1), [4]) - np.testing.assert_array_almost_equal(self.dataset.count("x", selection=None, binby=["y"], limits=[0, 9**2+1], shape=1), [9]) - np.testing.assert_array_almost_equal(self.dataset.count("x", selection=True, binby=["y"], limits=[0, 9**2+1], shape=1), [4]) - - np.testing.assert_array_almost_equal(self.dataset.count("x", selection=None, binby=["x"], limits=[0, 10], shape=2), [4, 5]) - np.testing.assert_array_almost_equal(self.dataset.count("x", selection=True, binby=["x"], limits=[0, 10], shape=2), [4, 0]) - - ds = self.dataset - a = ds.count("x", binby="y", limits=[0, 100], shape=2) - ds.select("(y >= 0) & (y < 50)") - b = ds.count("x", selection=True) - ds.select("(y >= 50) & (y < 100)") - c = ds.count("x", selection=True) - np.testing.assert_array_almost_equal(a, [b, c]) - - ds = self.dataset[(self.dataset.y >= 0) & (self.dataset.y < 50)] - b = ds.count('x') - ds = self.dataset[(self.dataset.y >= 50) & (self.dataset.y < 100)] - c = ds.count('x') - np.testing.assert_array_almost_equal(a, [b, c]) - - - df = self.df[(self.df.y >= 0) & (self.df.y < 50)] - b = df['x'].count() - df = self.df[(self.df.y >= 50) & (self.df.y < 100)] - c = df['x'].count() - np.testing.assert_array_almost_equal(a, [b, c]) - - - def test_cov(self): - # convert to float - x = self.dataset_local.columns["x"][:10] = self.dataset_local.columns["x"][:10] * 1. - y = self.y - def cov(*args): - return np.cov(args, bias=1) - self.dataset.select("x < 5") - - - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=None), cov(x, y)) - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=True), cov(x[:5], y[:5])) - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=[False, True]), [cov(x, y), cov(x[:5], y[:5])]) - - #self.dataset.columns["x"][0] = np.nan - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=None), cov(x, y)) - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=True), cov(x[:5], y[:5])) - - task = self.dataset.cov("x", "y", selection=True, delay=True) - self.dataset.execute() - np.testing.assert_array_almost_equal(task.get(), cov(x[:5], y[:5])) - - - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=None, binby=["x"], limits=[0, 10], shape=1), [cov(x, y)]) - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=True, binby=["x"], limits=[0, 10], shape=1), [cov(x[:5], y[:5])]) - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=None, binby=["y"], limits=[0, 9**2+1], shape=1), [cov(x, y)]) - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=True, binby=["y"], limits=[0, 9**2+1], shape=1), [cov(x[:5], y[:5])]) - - nan22 = [[np.nan, np.nan], [np.nan, np.nan]] - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=None, binby=["x"], limits=[0, 10], shape=2), [cov(x[:5], y[:5]), cov(x[5:], y[5:])]) - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=True, binby=["x"], limits=[0, 10], shape=2), [cov(x[:5], y[:5]), nan22]) - - i = 7 - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=None, binby=["y"], limits=[0, 9**2+1], shape=2), [cov(x[:i], y[:i]), cov(x[i:], y[i:])]) - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=True, binby=["y"], limits=[0, 9**2+1], shape=2), [cov(x[:5], y[:5]), nan22]) - - i = 5 - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=None, binby=["x"], limits=[0, 10], shape=2), [cov(x[:i], y[:i]), cov(x[i:], y[i:])]) - np.testing.assert_array_almost_equal(self.dataset.cov("x", "y", selection=True, binby=["x"], limits=[0, 10], shape=2), [cov(x[:i], y[:i]), nan22]) - - # include 3rd varialble - self.dataset.add_virtual_column("z", "x*y") - z = self.dataset.evaluate("z") - np.testing.assert_array_almost_equal(self.dataset.cov(["x", "y", "z"], selection=None), cov(x, y, z)) - - nan33 = [[np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan]] - np.testing.assert_array_almost_equal(self.dataset.cov(["x", "y", "z"], selection=None, binby=["x"], limits=[0, 10], shape=2), [cov(x[:5], y[:5], z[:5]), cov(x[5:], y[5:], z[5:])]) - np.testing.assert_array_almost_equal(self.dataset.cov(["x", "y", "z"], selection=True, binby=["x"], limits=[0, 10], shape=2), [cov(x[:5], y[:5], z[:5]), nan33]) - - i = 7 - np.testing.assert_array_almost_equal(self.dataset.cov(["x", "y", "z"], selection=None, binby=["y"], limits=[0, 9**2+1], shape=2), [cov(x[:i], y[:i], z[:i]), cov(x[i:], y[i:], z[i:])]) - np.testing.assert_array_almost_equal(self.dataset.cov(["x", "y", "z"], selection=True, binby=["y"], limits=[0, 9**2+1], shape=2), [cov(x[:5], y[:5], z[:5]), nan33]) - - i = 5 - np.testing.assert_array_almost_equal(self.dataset.cov(["x", "y", "z"], selection=None, binby=["x"], limits=[0, 10], shape=2), [cov(x[:i], y[:i], z[:i]), cov(x[i:], y[i:], z[i:])]) - np.testing.assert_array_almost_equal(self.dataset.cov(["x", "y", "z"], selection=True, binby=["x"], limits=[0, 10], shape=2), [cov(x[:i], y[:i], z[:i]), nan33]) - - # including nan - n = np.arange(-self.zero_index, 20.-1) - n[self.zero_index+1] = np.nan - self.dataset_local.add_column('n', n) - assert not np.any(np.isnan(self.dataset.cov("x", "n"))) - - - def test_covar(self): - # convert to float - x = self.dataset_local.columns["x"][:10] = self.dataset_local.columns["x"][:10] * 1. - y = self.y - def covar(x, y): - mask = np.isfinite(x * y) - #w = np.isfinite(x * y) * 1.0 - x = x[mask] - y = y[mask] - return np.cov([x, y], bias=1)[1,0] - self.dataset.select("x < 5") - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=None), covar(x, y)) - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=True), covar(x[:5], y[:5])) - - #self.dataset.columns["x"][0] = np.nan - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=None), covar(x, y)) - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=True), covar(x[:5], y[:5])) - - task = self.dataset.covar("x", "y", selection=True, delay=True) - self.dataset.execute() - np.testing.assert_array_almost_equal(task.get(), covar(x[:5], y[:5])) - - - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=None, binby=["x"], limits=[0, 10], shape=1), [covar(x, y)]) - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=True, binby=["x"], limits=[0, 10], shape=1), [covar(x[:5], y[:5])]) - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=None, binby=["y"], limits=[0, 9**2+1], shape=1), [covar(x, y)]) - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=True, binby=["y"], limits=[0, 9**2+1], shape=1), [covar(x[:5], y[:5])]) - - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=None, binby=["x"], limits=[0, 10], shape=2), [covar(x[:5], y[:5]), covar(x[5:], y[5:])]) - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=True, binby=["x"], limits=[0, 10], shape=2), [covar(x[:5], y[:5]), np.nan]) - - i = 7 - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=None, binby=["y"], limits=[0, 9**2+1], shape=2), [covar(x[:i], y[:i]), covar(x[i:], y[i:])]) - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=True, binby=["y"], limits=[0, 9**2+1], shape=2), [covar(x[:5], y[:5]), np.nan]) - - i = 5 - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=None, binby=["x"], limits=[0, 10], shape=2), [covar(x[:i], y[:i]), covar(x[i:], y[i:])]) - np.testing.assert_array_almost_equal(self.dataset.covar("x", "y", selection=True, binby=["x"], limits=[0, 10], shape=2), [covar(x[:i], y[:i]), np.nan]) - - - def test_percentile(self): - - ds = vx.example() - #ds.median_approx('z', binby=['x'], limits=[-10, 10], shape=16) - #ds.median_approx('z', binby=['x', 'y'], limits=[-10, 10], shape=4) - #m = ds.median_approx('z+x/10', binby=['x'], limits=[-10, 10], shape=32, percentile_shape=128*10 , percentile_limits=[-10,10]) - m = ds.median_approx('z+x/10', binby=['x'], limits=[6.875000, 7.500000], shape=1, percentile_shape=128*10 , percentile_limits=[-10,10]) - mc = ds.median_approx("z+x/10", selection='(x > 6.875000) & (x <= 7.500000)', percentile_shape=128*10 , percentile_limits=[-10,10]) - - - #print(m, m[32-5], mc) - print(m, mc) - - return - dsodd = vx.from_arrays(x=np.arange(3)) # 0,1,2 - dseven = vx.from_arrays(x=np.arange(4)) # 0,1,2,3 - self.dataset.select("x < 5") - o = 0#10/30/2. - - #x = dsodd.data.x - ds = dsodd - #ds = dseven - x = ds.data.x - print("median", np.median(x)) - for offset in [-0.99, -0.5, 0.0]:#[0:1]: - print() - print("offset", offset) - limits = [0+offset, x.max()+1+offset] - print(">>>", ds.percentile_approx("x", selection=None, percentile_limits=limits, percentile_shape=len(x)),) - #np.testing.assert_array_almost_equal( - # ds.percentile_approx("x", selection=None, percentile_limits=limits, percentile_shape=4), - # np.median(x), decimal=2) - #return - - - np.testing.assert_array_almost_equal( - self.dataset.percentile_approx("x", selection=None, percentile_limits=[0-o, 10-o], percentile_shape=100), - np.median(self.x), decimal=1) - np.testing.assert_array_almost_equal( - self.dataset.percentile_approx("x", selection=None, percentile_limits=[0-o, 10-o], percentile_shape=1000), - np.median(self.x), decimal=2) - np.testing.assert_array_almost_equal( - self.dataset.percentile_approx(["x", "y"], selection=None, percentile_shape=10000), - [np.median(self.x), np.median(self.y)], - decimal=3) - return - np.testing.assert_array_almost_equal(self.dataset.percentile_approx("x", selection=True), np.median(self.x[:5])) - - # convert to float - x = self.dataset.columns["x"] = self.dataset.columns["x"] * 1. - y = self.y - self.dataset.columns["x"][0] = np.nan - np.testing.assert_array_almost_equal(self.dataset.sum("x", selection=None), np.nansum(x)) - np.testing.assert_array_almost_equal(self.dataset.sum("x", selection=True), np.nansum(x[:5])) - - task = self.dataset.sum("x", selection=True, delay=True) - self.dataset.execute() - np.testing.assert_array_almost_equal(task.get(), np.nansum(x[:5])) - - - np.testing.assert_array_almost_equal(self.dataset.sum("x", selection=None, binby=["x"], limits=[0, 10], shape=1), [np.nansum(x)]) - np.testing.assert_array_almost_equal(self.dataset.sum("x", selection=True, binby=["x"], limits=[0, 10], shape=1), [np.nansum(x[:5])]) - np.testing.assert_array_almost_equal(self.dataset.sum("x", selection=None, binby=["y"], limits=[0, 9**2+1], shape=1), [np.nansum(x)]) - np.testing.assert_array_almost_equal(self.dataset.sum("x", selection=True, binby=["y"], limits=[0, 9**2+1], shape=1), [np.nansum(x[:5])]) - - np.testing.assert_array_almost_equal(self.dataset.sum("x", selection=None, binby=["x"], limits=[0, 10], shape=2), [np.nansum(x[:5]), np.nansum(x[5:])]) - np.testing.assert_array_almost_equal(self.dataset.sum("x", selection=True, binby=["x"], limits=[0, 10], shape=2), [np.nansum(x[:5]), 0]) - - i = 7 - np.testing.assert_array_almost_equal(self.dataset.sum("x", selection=None, binby=["y"], limits=[0, 9**2+1], shape=2), [np.nansum(x[:i]), np.nansum(x[i:])]) - np.testing.assert_array_almost_equal(self.dataset.sum("x", selection=True, binby=["y"], limits=[0, 9**2+1], shape=2), [np.nansum(x[:5]), 0]) - - i = 5 - np.testing.assert_array_almost_equal(self.dataset.sum("y", selection=None, binby=["x"], limits=[0, 10], shape=2), [np.nansum(y[:i]), np.nansum(y[i:])]) - np.testing.assert_array_almost_equal(self.dataset.sum("y", selection=True, binby=["x"], limits=[0, 10], shape=2), [np.nansum(y[:5]), 0]) - - def test_concat(self): - dc = self.dataset_concat_dup - self.assertEqual(len(self.dataset_concat_dup), len(self.dataset)*3) - - - self.assertEqual(self.dataset_concat.get_column_names(), ["x"]) - N = len(self.x_concat) - # try out every possible slice - for i1 in range(N-1): - for i2 in range(i1+1,N): - #print "***", i1, i2 - a = self.dataset_concat.columns["x"][i1:i2] - b = self.x_concat[i1:i2] - #print a, b - np.testing.assert_array_almost_equal(a, b) - - ar1 = np.zeros((10, 2)) - ar2 = np.zeros((20)) - arrays = [ar1, ar2] - N = len(arrays) - dfs = [vx.dataframe.DataFrameLocal() for i in range(N)] - for dataset, array in zip(dfs, arrays): - dataset.add_column("x", array) - with self.assertRaises(ValueError): - dataset_concat = vaex.concat(dfs) - - - ar1 = np.zeros((10)) - ar2 = np.zeros((20)) - arrays = [ar1, ar2] - N = len(arrays) - dfs = [vx.dataframe.DataFrameLocal() for i in range(N)] - for dataset, array in zip(dfs, arrays): - dataset.add_column("x", array) - dataset_concat = vaex.concat(dfs) - - - dataset_concat1 = vaex.concat(dfs) - dataset_concat2 = vaex.concat(dfs) - # TODO: is it really a performance penalty if we don't 'collapse' multiple concats - # self.assertEqual(len(dataset_concat1.concat(dataset_concat2).dataset.datasets), 4) - # self.assertEqual(len(dataset_concat1.concat(dfs[0]).dataset.datasets), 3) - # self.assertEqual(len(dfs[0].concat(dataset_concat1).dataset.datasets), 3) - # self.assertEqual(len(dfs[0].concat(dfs[0]).dataset.datasets), 2) - - def test_export_concat(self): - x1 = np.arange(1000, dtype=np.float32) - x2 = np.arange(100, dtype=np.float32) - self.x_concat = np.concatenate((x1, x2)) - - dataset1 = vx.dataframe.DataFrameLocal() - dataset2 = vx.dataframe.DataFrameLocal() - dataset1.add_column("x", x1) - dataset2.add_column("x", x2) - - self.dataset_concat = vaex.concat([dataset1, dataset2]) - - path_hdf5 = tempfile.mktemp(".hdf5") - self.dataset_concat.export_hdf5(path_hdf5) - - def test_export_sorted(self): - self.dataset.add_column("s", 100-self.dataset.data.x) - path_hdf5 = tempfile.mktemp(".hdf5") - self.dataset.sort("s").export_hdf5(path_hdf5) - ds2 = vaex.open(path_hdf5) - np.testing.assert_array_equal(self.dataset.data.x[self.zero_index:self.zero_index+10], ds2.data.x[::-1]) - - def test_export_sorted_arrow(self): - self.dataset.add_column("s", 100-self.dataset.data.x) - path_arrow = tempfile.mktemp(".arrow") - self.dataset.sort("s").export_arrow(path_arrow) - ds2 = vaex.open(path_arrow) - np.testing.assert_array_equal(self.dataset.data.x[self.zero_index:self.zero_index+10], np.array(ds2.data.x)[::-1]) - - def test_export(self): - if vx.utils.osname == "windows": - # we hit https://github.com/h5py/h5py/issues/2346 on windows - # possible due to changes in GHA? - return - path = path_hdf5 = tempfile.mktemp(".hdf5") - path_fits = tempfile.mktemp(".fits") - path_fits_astropy = tempfile.mktemp(".fits") - - for dataset in [self.dataset_concat_dup, self.dataset]: - for fraction in [1, 0.5]: - dataset.set_active_fraction(fraction) - dataset.select("x > 3") - dataset.select("x > 2", name="named") - length = len(dataset) - for column_names in [["x", "y", "z"], ["x"], ["y"], ["z"], dataset.get_column_names()]: - for byteorder in "<=>": - for shuffle in [False, True]: - for export in [dataset.__class__.export_fits, dataset.__class__.export_hdf5, dataset.__class__.export_arrow, dataset.__class__.export_parquet]: #if byteorder == ">" else [dataset.export_hdf5]: - # print (">>>", dataset, path, column_names, byteorder, shuffle, selection, fraction, dataset.length_unfiltered())#, virtual) - #byteorder = "<" - if export == dataset.__class__.export_fits and byteorder != ">": - #print("skip", export == dataset.export_fits, byteorder != ">", byteorder) - continue # fits only does big endian - if export == dataset.__class__.export_fits and byteorder == ">": - continue # arrow only little endian - if vx.utils.osname == "windows" and export == dataset.export_hdf5 and byteorder == ">": - #print("skip", vx.utils.osname) - continue # TODO: IS this a bug for h5py on win32?, leads to an open file - # same issue on windows for arrow, closing the mmapped file does not help - # for the moment we create a new temp file - path_arrow = tempfile.mktemp(".arrow") - path_parquet = tempfile.mktemp(".parquet") - #print dataset.length_unfiltered() - #print len(dataset) - df = dataset[column_names] - if shuffle: - df = df.shuffle() - if export == df.__class__.export_hdf5: - path = path_hdf5 - export(df, path, byteorder=byteorder, progress=False) - elif export == df.__class__.export_arrow: - path = path_arrow - export(df, path, progress=False) - elif export == df.__class__.export_parquet: - path = path_parquet - export(df, path, progress=False) - else: - path = path_fits - export(df, path, progress=False) - with astropy.io.fits.open(path) as fitsfile: - # make sure astropy can read the data - bla = fitsfile[1].data - try: - fitsfile.writeto(path_fits_astropy) - finally: - os.remove(path_fits_astropy) - if path.endswith('arrow') or path.endswith('parquet'): - compare = vx.open(path) - else: - compare = vx.open(path) - if column_names is None: - column_names = ["x", "y", "m", "mi", "ints", "f", "z", "name", "name_arrow"] - self.assertEqual(compare.get_column_names(), column_names) - def make_masked(ar): - if export == dataset.export_fits: # for fits the missing values will be filled in with nan - if ar.dtype.kind == "f": - nanmask = np.isnan(ar) - if np.any(nanmask): - ar = np.ma.array(ar, mask=nanmask) - return ar - - for column_name in column_names: - values = dataset.evaluate(column_name, filtered=False) - values = dataset.evaluate(column_name, array_type="numpy") - if not shuffle: - dtype = np.array(compare.columns[column_name][:]).dtype # we don't want any casting - compare_values = compare.columns[column_name][:] - if isinstance(compare_values, vaex.column.Column): - compare_values = compare_values.to_numpy() - np.testing.assert_array_equal(compare_values, values[:length].astype(dtype)) - compare.close() - #os.remove(path) - - # self.dataset_concat_dup references self.dataset, so set it's active_fraction to 1 again - dataset.set_active_fraction(1) - - def test_export_cmdline(self): - import vaex.convert - path_arrow = tempfile.mktemp(".arrow") - path_hdf5 = tempfile.mktemp(".hdf5") - dataset = self.dataset - dataset.export(path_arrow) - name = "vaex convert" - #print(path_fits) - assert vaex.convert.main([name, "--no-progress", "-q", path_arrow, path_hdf5]) == 0 - - def test_fraction(self): - counter_selection = CallbackCounter() - counter_current_row = CallbackCounter() - self.dataset.signal_pick.connect(counter_current_row) - self.dataset.signal_selection_changed.connect(counter_selection) - - self.dataset.set_active_fraction(1.0) # this shouldn't trigger - self.assertEqual(counter_selection.counter, 0) - self.assertEqual(counter_current_row.counter, 0) - length = len(self.dataset) - self.dataset.set_active_fraction(0.1) # this should trigger - self.assertEqual(counter_selection.counter, 1) - self.assertEqual(counter_current_row.counter, 1) - - # test for event and the effect of the length - # the active_fraction only applies to the underlying length, which is 20 - self.dataset.set_active_fraction(0.25) - self.assertEqual(counter_selection.counter, 2) - self.assertEqual(counter_current_row.counter, 2) - self.assertEqual(length/2 - self.zero_index, len(self.dataset)) - - self.dataset.select("x > 5") - self.assertEqual(counter_selection.counter, 3) - self.assertEqual(counter_current_row.counter, 2) - self.assertTrue(self.dataset.has_selection()) - self.dataset.set_active_fraction(0.25) # nothing should happen, still the same - self.assertTrue(self.dataset.has_selection()) - self.dataset.set_active_fraction(0.4999) - self.assertFalse(self.dataset.has_selection()) - - self.dataset.set_current_row(1) - self.assertTrue(self.dataset.has_current_row()) - self.dataset.set_active_fraction(0.25) - self.assertFalse(self.dataset.has_current_row()) - - if self.dataset.is_local(): # this part doesn't work for remote dfs - for dataset in [self.dataset, self.dataset_concat]: - dataset.set_active_fraction(1.0) - x = dataset.columns["x"][:] * 1. # make a copy - dataset.set_active_fraction(0.25) - length = len(dataset) - a = x[:length] - b = dataset.columns["x"][:len(dataset)] - np.testing.assert_array_almost_equal(a, b) - self.assertLess(length, dataset.length_original()) - - # TODO: test if statistics and histogram work on the active_fraction - self.dataset.set_active_fraction(1) - total = self.dataset["x"].sum() - self.dataset.set_active_fraction(0.25) - total_half = self.dataset["x"].sum() - self.assertLess(total_half, total) - - limits = [(-100, 100)] - self.dataset.set_active_fraction(1) - total = self.dataset["x"].count(limits=limits).sum() - self.dataset.set_active_fraction(0.25) - total_half = self.dataset["x"].count(limits=limits).sum() - self.assertLess(total_half, total) - - - def test_current_row(self): - counter_current_row = CallbackCounter() - self.dataset.signal_pick.connect(counter_current_row) - self.dataset.set_current_row(0) - self.assertEqual(counter_current_row.counter, 1) - - with self.assertRaises(IndexError): - self.dataset.set_current_row(-1) - with self.assertRaises(IndexError): - self.dataset.set_current_row(len(self.dataset)) - - - def t_not_needed_est_current(self): - for dataset in [self.dataset, self.dataset_concat]: - for i in range(len(dataset)): - dataset.set_current_row(i) - values = dataset("x", "x**2").current() - value = dataset.columns["x"][:][i] - self.assertEqual([value, value**2], values) - - - def test_dropna(self): - ds = self.dataset - ds.select_non_missing(column_names=['m']) - self.assertEqual(ds.count(selection=True), 9) - ds.select_non_missing(drop_masked=False, column_names=['m']) - self.assertEqual(ds.count(selection=True), 10) - - self.dataset_local.data.x[self.zero_index] = np.nan - ds.select_non_missing(column_names=['x']) - self.assertEqual(ds.count(selection=True), 9) - ds.select_non_missing(drop_nan=False, column_names=['x']) - if ds.is_local(): - self.assertEqual(ds.count(selection=True), 10) - else: - # TODO: on the server, the filter selection gets re-executed (x < 10) - # causing it to skip the nan anyway, find a good way to test this? - self.assertEqual(ds.count(selection=True), 9) - - ds.select_non_missing() - self.assertEqual(ds.count(selection=True), 8) - ds.select_non_missing(drop_masked=False) - self.assertEqual(ds.count(selection=True), 9) - ds.select_non_missing(drop_nan=False) - if ds.is_local(): - self.assertEqual(ds.count(selection=True), 9) - else: - # TODO: same as above - self.assertEqual(ds.count(selection=True), 8) - - - def test_selection_in_handler(self): - self.dataset.select("x > 5") - # in the handler, we should know there is not selection - def check(*ignore): - self.assertFalse(self.dataset.has_selection()) - self.dataset.signal_selection_changed.connect(check) - self.dataset.select_nothing() - - - - def test_nearest(self): - index, distance, (value,) = self.dataset("x").nearest([3]) - self.assertEqual(index, 3 + self.zero_index) - self.assertEqual(distance, 0) - self.assertEqual(value, 3) - - index, distance, (value,) = self.dataset("x").nearest([3.7]) - self.assertEqual(index, 4 + self.zero_index) - self.assertAlmostEqual(distance, 0.3) - self.assertEqual(value, 4) - - self.dataset.select("x > 5") - index, distance, (value,) = self.dataset("x").selected().nearest([3.7]) - self.assertEqual(index, 6 + self.zero_index) - self.assertEqual(distance, 2.3) - self.assertEqual(value, 6) - - - def test_select_circle(self): - # Circular selection - self.dataset.select_circle('x', 'y', 0.5, 0.5, 1, name='circ') - # Assert - np.testing.assert_equal(2, self.dataset.count(selection='circ')) - - def test_select_ellipse(self): - # Ellipse election - self.dataset.select_ellipse('x', 'y', 3, 10, 2, 15, -10, name='elli') - # Assert - np.testing.assert_equal(3, self.dataset.count(selection='elli')) - - -# allow multiple python versions on one machine to run the test -import sys -test_port = 29110 + sys.version_info[0] * 10 + sys.version_info[1] - -#class A:#class estDatasetRemote(TestDataset): -class TestDatasetRemote(TestDataset): -#class A: - use_websocket = True - - @classmethod - def setUpClass(cls): - global test_port - cls.webserver = vaex.server.WebServer(dfs=[], port=test_port, cache_byte_size=0).tornado_server - #print "serving" - cls.webserver.serve_threaded() - #print "getting server object" - scheme = "ws" if cls.use_websocket else "http" - cls.server = vx.server("%s://localhost:%d" % (scheme, test_port)) - test_port += 1 - - - @classmethod - def tearDownClass(cls): - cls.server.close() - cls.webserver.stop_serving() - - - def setUp(self): - # run all tests from TestDataset, but now served at the server - super(TestDatasetRemote, self).setUp() - # for the webserver we don't support filters on top of filters - # so the server always uses the full dataset - # self.dataset_no_filter.name = 'dataset' - # self.dataset = self.dataset_no_filter - - self.dataset_local = self.dataset - self.datasetxy_local = self.datasetxy - self.dataset_concat_local = self.dataset_concat - self.dataset_concat_dup_local = self.dataset_concat_dup - - dfs = [self.dataset_local, self.datasetxy_local, self.dataset_concat_local, self.dataset_concat_dup_local] - #print "get dfs" - self.webserver.set_dfs(dfs) - dfs = self.server.dfs(as_dict=True) - #print "got it", dfs - - self.dataset = dfs["dataset"] - self.datasetxy = dfs["datasetxy"] - self.dataset_concat = dfs["dataset_concat"] - self.dataset_concat_dup = dfs["dataset_concat_dup"] - #print "all done" - - - - def tearDown(self): - TestDataset.tearDown(self) - #print "stop serving" - - def test_to(self): - pass # not supported - - def test_amuse(self): - pass # no need - - def test_ascii(self): - pass # no need - - def test_csv(self): - pass # no need - def test_export(self): - pass # we can't export atm - - def test_concat(self): - pass # doesn't make sense to test this for remote - - def test_data_access(self): - pass - - def test_byte_size(self): - pass # we don't know the selection's length for dataset remote.. - - def test_add_column(self): - pass # can't add column to remove objects - - def test_rename_column(self): - pass # TODO: we cannot do that now - - def test_masked_array_output(self): - pass # cannot test exporting - - def test_export_sorted(self): - pass # cannot test exporting - - def test_formats(self): - pass # cannot test exporting - - def test_default_selection(self): - pass # uses local information - - #def test_selection(self): - # pass - - #def test_count(self): - # pass - #def test_sum(self): - # pass - #def test_cov(self): - # pass - #def test_correlation(self): - # pass - #def test_covar(self): - # pass - #def test_mean(self): - # pass - #def test_minmax(self): - # pass - #def test_var_and_std(self): - # pass - #def test_limits(self): - # pass - -# import vaex.distributed -#class A:#class T_estDatasetDistributed(unittest.TestCase): -#class TestDatasetDistributed(unittest.TestCase): -class TestDatasetDistributed(TestDatasetRemote): - - use_websocket = False - - def setUp(self): - TestDataset.setUp(self) - global test_port - # self.dataset_local = self.dataset = dataframe.DataFrameLocal("dataset") - self.dataset_local = self.dataset - - dfs = [self.dataset] - dfs_copy = [k.copy() for k in dfs] # otherwise we share the selection cache - for ds in dfs_copy: - ds.name = self.dataset_local.name - dfs = [self.dataset] - self.webserver1 = vaex.server.WebServer(dfs=dfs, port=test_port).tornado_server - self.webserver1.serve_threaded() - test_port += 1 - self.webserver2 = vaex.server.WebServer(dfs=dfs_copy, port=test_port).tornado_server - self.webserver2.serve_threaded() - test_port += 1 - - scheme = "ws" if self.use_websocket else "http" - self.server1 = vx.server("%s://localhost:%d" % (scheme, test_port-2)) - self.server2 = vx.server("%s://localhost:%d" % (scheme, test_port-1)) - test_port += 1 - dfs1 = self.server1.dfs(as_dict=True) - dfs2 = self.server2.dfs(as_dict=True) - self.dfs = [dfs1["dataset"], dfs2["dataset"]] - self.dataset = vaex.distributed.DatasetDistributed(self.dfs) - - - - def tearDown(self): - #TestDataset.tearDown(self) - #print "stop serving" - self.webserver1.stop_serving() - self.webserver2.stop_serving() - - def test_histogram(self): - #print self.dataset, self.dataset.__call__ - #print self.dataset.subspace("x") - #self.dataset_local.set_active_range(5, 10) - counts = self.dataset("x").histogram([[0,10]], size=10) - #import pdb - #pdb.set_trace() - self.assertTrue(all(counts == 1), "counts is %r" % counts) - return - - sums = self.dataset("x").histogram([[0,10]], size=10, weight="y") - assert(all(sums == self.y)) - - self.dataset.select("x < 5") - mask = self.x < 5 - - counts = self.dataset("x").selected().histogram([[0,10]], size=10) - mod_counts = counts * 1. - mod_counts[~mask] = 0 - assert(all(counts == mod_counts)) - - mod_sums = self.y * 1. - mod_sums[~mask] = 0 - sums = self.dataset("x").selected().histogram([[0,10]], size=10, weight="y") - assert(all(sums == mod_sums)) - - - x = np.array([0, 1, 0, 1]) - y = np.array([0, 0, 1, 1]) - dataset = vx.from_arrays(x=x, y=y) - counts = dataset("x", "y").histogram([[0.,2.], [0.,2.]], size=2) - assert(np.all(counts == 1)) - - x = np.array([0, 1, 0, 1, 0, 1, 0, 1]) - y = np.array([0, 0, 1, 1, 0, 0, 1, 1]) - z = np.array([0, 0, 0, 0, 1, 1, 1, 1]) - dataset = vx.from_arrays(x=x, y=y, z=z) - counts = dataset("x", "y", "z").histogram([[0.,2.], [0.,2.], [0.,2.]], size=2) - assert(np.all(counts == 1)) - - x = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]) - y = np.array([0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]) - z = np.array([0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1]) - w = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,]) - dataset = vx.from_arrays(x=x, y=y, z=z, w=w) - counts = dataset("x", "y", "z", "w").histogram([[0.,2.], [0.,2.], [0.,2.], [0.,2.]], size=2) - assert(np.all(counts == 1)) - return - -#class TestDatasetRemotePlain(TestDatasetRemote): -# use_websocket = False -""" - - - -class T_stWebServer(unittest.TestCase): - def setUp(self): - self.dataset = dataframe.DataFrameLocal() - - self.x = x = np.arange(10) - self.y = y = x ** 2 - self.dataset.add_column("x", x) - self.dataset.add_column("y", y) - - self.webserver = vaex.server.WebServer(dfs=[self.dataset], port=test_port).tornado_server - self.webserver.serve_threaded() - self.server = vx.server("http://localhost:%d" % test_port) - self.dataset_remote = self.server.dfs()[0] - - def tearDown(self): - self.webserver.stop_serving() - - def test_list(self): - dfs = self.server.dfs() - self.assertTrue(len(dfs) == 1) - dataset_remote = dfs[0] - self.assertEqual(dataset_remote.name, self.dataset.name) - self.assertEqual(dataset_remote.get_column_names(), self.dataset.get_column_names()) - self.assertEqual(len(dataset_remote), len(self.dataset)) - - def test_minmax(self): - self.assertEqual(self.dataset_remote("x", "y").minmax().tolist(), self.dataset("x", "y").minmax().tolist()) - - def test_var(self): - self.assertEqual(self.dataset_remote("x", "y").var().tolist(), self.dataset("x", "y").var().tolist()) - - def test_histogram(self): - grid1 = self.dataset("x").bounded().gridded(32).grid - grid2 = self.dataset_remote("x").bounded().gridded(32).grid - self.assertEqual(grid1.tolist(), grid2.tolist()) - -""" - -if __name__ == '__main__': - unittest.main() diff --git a/packages/vaex-core/vaex/test/expresso.py b/packages/vaex-core/vaex/test/expresso.py deleted file mode 100644 index 18b67babef..0000000000 --- a/packages/vaex-core/vaex/test/expresso.py +++ /dev/null @@ -1,39 +0,0 @@ -import vaex.dataset as dataset -import unittest -import vaex.expresso - - -class TestExpresso(unittest.TestCase): - #def setUp(self): - def test_ops(self): - for binop in "+ - * / // % & | ^".split(): - vaex.expresso.validate_expression('a %s b' % binop, ['a', 'b']) - for binop in "+ - * / // % & | ^".split(): - with self.assertRaises(NameError): - vaex.expresso.validate_expression('a %s b' % binop, ['a']) - vaex.expresso.validate_expression('a %s b' % binop, ['b']) - def test_vars(self): - with self.assertRaises(NameError): - vaex.expresso.validate_expression('a+b', ['a']) - vaex.expresso.validate_expression('a+b', ['a', 'b']) - - def test_functions(self): - with self.assertRaises(NameError): - vaex.expresso.validate_expression("f(a, b)", ['a'], ['f']) - with self.assertRaises(NameError): - vaex.expresso.validate_expression("f(a, b)", ['a', 'b'], ['g']) - vaex.expresso.validate_expression("f(a, b)", ['a', 'b'], ['f']) - - def test_subsript(self): - vaex.expresso.validate_expression("a[0]", 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COMMENT The VOTable describes the metadata of the table contained COMMENT in the following BINTABLE extension. COMMENT Such a BINTABLE extension can be used on its own as a perfectly COMMENT good table, but the information from this HDU may provide some COMMENT useful additional metadata. COMMENT There is one following BINTABLE. NTABLE = 1 / Number of following BINTABLE HDUs END - - - - - - -SELECT * - - -FROM gaiadr1.gaia_source -JOIN gaiadr1.tmass_best_neighbour -ON gaiadr1.gaia_source.source_id = gaiadr1.tmass_best_neighbour.source_id -JOIN gaiadr1.tmass_original_valid -ON gaiadr1.tmass_best_neighbour.tmass_oid = gaiadr1.tmass_original_valid.tmass_oid - -WHERE - -gaiadr1.gaia_source.ra < 0.1 and -gaiadr1.gaia_source.dec < 0.1 and -gaiadr1.gaia_source.ra > -0.1 and -gaiadr1.gaia_source.dec > -0.1 - - -If you use public Gaia DR1 data in your paper, please take note of our guide on how to acknowledge and cite Gaia DR1: http://gaia.esac.esa.int/documentation/GDR1/Miscellaneous/sec_credit_and_citation_instructions.html - - - - -1490181948554O - - - -All Gaia data processed by the Data Processing and Analysis Consortium -comes tagged with a solution identifier. This is a numeric field -attached to each table row that can be used to unequivocally identify -the version of all the subsystems that where used in the generation of -the data as well as the input data used. It is mainly for internal DPAC -use but is included in the published data releases to enable end users -to examine the provenance of processed data products. To decode a given -solution ID visit - - - -Unique identifier of the Gaia source, the attribute corresponds to -GaiaSource.sourceId - - - -Random index which can be used to select smaller subsets of the data -that are still representative. The column contains a random permutation -of the numbers from 0 to N-1, where N is the number of rows. - -The random index can be useful for validation (testing on 10 different -random subsets), visualization (displaying 1% of the data), and -statistical exploration of the data, without the need to download all -the data. - - - -Reference epoch to which the astrometic source parameters are referred, -expressed as a Julian Year in TCB. - - -J2000 right ascension with respect to the ICRS - - -Standard error \sigma_{\alpha *} \equiv \sigma_\alpha\cos\delta of the -right ascension of the source in ICRS at the reference epoch refEpoch. - - -J2000 declination with respect to the ICRS - - -Standard error \sigma_\delta of the declination of the source in ICRS at -the reference epoch refEpoch - - -Absolute barycentric stellar parallax \varpi of the soure at the -reference epoch refEpoch - - -Standard error \sigma_\varpi of the stellar parallax at the reference -epoch refEpoch - - -Proper motion in right ascension \mu_{\alpha *} of the source in ICRS at -the reference epoch refEpoch. This is the projection of the proper -motion vector in the direction of increasing right ascension. - - -Standard error \sigma_{\mu\alpha *} of the proper motion vector in right -ascension at the reference epoch refEpoch - - -Proper motion in declination \mu_\delta of the source at the reference -epoch refEpoch. This is the projection of the proper motion vector in -the direction of increasing declination. - - -Standard error \sigma_{\mu\delta} of the proper motion in declination at -the reference epoch refEpoch - - -Correlation between right ascension and declination, in dimensionless -units [-1:+1] - - -Correlation between right ascension and parallax, in dimensionless units -[-1:+1] - - -Correlation between right ascension and proper motion in right -ascension, in dimensionless units [-1:+1] - - -Correlation between right ascension and proper motion in declination, in -dimensionless units [-1:+1] - - -Correlation between declination and parallax, in dimensionless units -[-1:+1] - - -Correlation between declination and proper motion in right ascension, in -dimensionless units [-1:+1] - - -Correlation between declination and proper motion in declination, in -dimensionless units [-1:+1] - - -Correlation between parallax and proper motion in right ascension, in -dimensionless units [-1:+1] - - -Correlation between parallax and proper motion in declination, in -dimensionless units [-1:+1] - - -Correlation between proper motion in right ascension and proper motion -in declination, in dimensionless units [-1:+1] - - -Total number of AL observations (= CCD transits) used in the astrometric -solution of the source, independent of their weight. Note that some -observations may be strongly downweighted (see astrometricNBadObsAl). - - - -Total number of AC observations (= CCD transits) used in the astrometric -solution of the source, independent of their weight. Note that some -observations may be strongly downweighted (see astrometricNBadObsAc). -Nearly all sources having G <13 will have AC observations from 2d -windows, while fainter than that limit only \sim1% of stars (the -so–called ‘calibration faint stars’) are assigned 2d windows resulting -in AC observations. - - - -Number of AL observations (= CCD transits) that were not strongly -downweighted in the astrometric solution of the source. Strongly -downweighted observations (with downweighting factor w<0.2) are instead -counted in astrometricNBadObsAl. The sum of astrometricNGoodObsAl and -astrometricNBadObsAl equals astrometricNObsAl, the total number of AL -observations used in the astrometric solution of the source. - - - -Number of AC observations (= CCD transits) that were not strongly -downweighted in the astrometric solution of the source. Strongly -downweighted observations (with downweighting factor w<0.2) are instead -counted in astrometricNBadObsAc. The sum of astrometricNGoodObsAc and -astrometricNBadObsAc equals astrometricNObsAc, the total number of AC -observations used in the astrometric solution of the source. - - - -Number of AL observations (= CCD transits) that were strongly -downweighted in the astrometric solution of the source, and therefore -contributed little to the determination of the astrometric parameters. -An observation is considered to be strongly downweighted if its -downweighting factor w<0.2, which means that the absolute value of the -astrometric residual exceeds 4.83 times the total uncertainty of the -observation, calculated as the quadratic sum of the centroiding -uncertainty, excess source noise, and excess attitude noise. - - - -Number of AC observations (= CCD transits) that were strongly -downweighted in the astrometric solution of the source, and therefore -contributed little to the determination of the astrometric parameters. -An observation is considered to be strongly downweighted if its -downweighting factor w<0.2, which means that the absolute value of the -astrometric residual exceeds 4.83 times the total uncertainty of the -observation, calculated as the quadratic sum of the centroiding -uncertainty, excess source noise, and excess attitude noise. - - - -In the TGAS solution \tt astrometricDeltaQ (\Delta Q) indicates the -discrepancy between the Hipparcos proper motion and the TGAS proper -motion. A large value of \tt deltaQ could indicate non-linear motion -(e.g. in a binary). - -The precise definition is - -\Delta Q = -\begin{bmatrix} -\Delta\mu_{\alpha *} & \Delta\mu_{\delta} -\end{bmatrix} -\left(\vec{C}_\text{pm,\,T}+\vec{C}_\text{pm,\,H}\right)^{-1} -\begin{bmatrix} -\Delta\mu_{\alpha *} \\ \Delta\mu_{\delta} -\end{bmatrix} - -where \Delta\mu_{\alpha *} = \mu_{\alpha *,\rm T}-\mu_{\alpha *,\rm H}, -\Delta\mu_{\delta} = \mu_{\delta,\rm T}-\mu_{\delta,\rm H}, with T and H -indicating values from the Gaia DR1 (TGAS) solution and Hipparcos -catalogue. \vec{C}_\text{pm,\,T} and \vec{C}_\text{pm,\,H} are the -corresponding 2\times 2 covariance matrices. - -In order to compute \Delta Q the two sets of proper motions must use the -same reference frame and the same reference epoch. Thus, the proper -motion components as given in the Hipparcos catalogue were rotated to -the Gaia DR1 reference frame, and then propagated to the Gaia reference -epoch. - - -This is the excess noise \epsilon_i of the source. It measures the -disagreement, expressed as an angle, between the observations of a -source and the best-fitting standard astrometric model (using five -astrometric parameters). The assumed observational noise in each -observation is quadratically increased by \epsilon_i in order to -statistically match the residuals in the astrometric solution. A value -of 0 signifies that the source is astrometrically well-behaved, i.e.  -that the residuals of the fit statistically agree with the assumed -observational noise. A positive value signifies that the residuals are -statistically larger than expected. - -The significance of \epsilon_i is given by \tt -astrometricExcessNoiseSig (D). If D\le 2 then \epsilon_i is probably not -significant, and the source may be astrometrically well-behaved even if -\epsilon_i is large. - -The excess noise \epsilon_i may absorb all kinds of modelling errors -that are not accounted for by the observational noise (image centroiding -error) or the excess attitude noise. Such modelling errors include LSF -and PSF calibration errors, geometric instrument calibration errors, and -part of the high-frequency attitude noise. These modelling errors are -particularly important in the early data releases, but should decrease -as the astrometric modelling of the instrument and attitude improves -over the years. - -Additionally, sources that deviate from the standard five-parameter -astrometric model (e.g. unresolved binaries, exoplanet systems, etc.) -may have positive \epsilon_i. Given the many other possible -contributions to the excess noise, the user must study the empirical -distributions of \epsilon_i and D to make sensible cutoffs before -filtering out sources for their particular application. - -In Gaia DR1, the excess source noise has the same interpretation as -described above for both the primary (TGAS) and secondary data sets. It -measures the disagreement between the five-parameter model and the -observations, augmented by the different priors used. Thus, in TGAS the -excess noise may be increased if the proper motion seen during the 14 -months of Gaia observations are not in agreement with the proper motion -inferred from the Tycho-2/Gaia comparison. In the secondary solution the -excess noise may be increased if the Gaia observations indicate a proper -motion or parallax several times larger than the prior uncertainty. - -The excess source noise is further explained in Sects. 3.6 and 5.1.2 of -Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -A dimensionless measure (D) of the significance of the calculated \tt -astrometricExcessNoise (\epsilon_i). A value D>2 indicates that the -given \epsilon_i is probably significant. - -For good fits in the limit of a large number of observations, D should -be zero in half of the cases and approximately follow the positive half -of a normal distribution with zero mean and unit standard deviation for -the other half. Consequently, D is expected to be greater than 2 for -only a few percent of the sources with well-behaved astrometric -solutions. - -In the early data releases \epsilon_i will however include instrument -and attitude modelling errors that are statistically significant and -could result in large values of \epsilon_i and D. The user must study -the empirical distributions of these statistics and make sensible -cutoffs before filtering out sources for their particular application. - -The excess noise significance is further explained in Sect. 5.1.2 of -Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Flag indicating if this source was used as a primary source (\tt true) -or secondary source (\tt false). Only primary sources contribute to the -estimation of attitude, calibration, and global parameters. The -estimation of source parameters is otherwise done in exactly the same -way for primary and secondary sources. - - -Relegation factor of the source calculated as per Eq. (118) in Lindegren -et al. (2012) used for the primary selection process. - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Mean astrometric weight of the source in the AL direction. - -The mean astrometric weight of the source is calculated as per Eq. (119) -in Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Mean astrometric weight of the source in the AC direction - -The mean astrometric weight of the source is calculated as per Eq. (119) -in Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Type of prior used in the astrometric solution: - -- 0: No prior used - -- 1: Galaxy Bayesian Prior for parallax and proper motion - -- 2: Galaxy Bayesian Prior for parallax and proper motion relaxed by - factor 10 - -- 3: Hipparcos prior for position - -- 4: Hipparcos prior for position and proper motion - -- 5: Tycho2 prior for position - -- 6: Quasar prior for proper motion - -The Galaxy Bayesian Prior is defined in , where it is denoted -\sigma_{\varpi,F90} (for the parallax) and -\sigma_{\mu,F90}={\cal R}\sigma_{\varpi,F90}, with {\cal -R}=10 yr^{-1} (for proper motion). The Galaxy Bayesian Prior relaxed by -a factor 10 is 10\sigma_{\varpi,F90} and 10\sigma_{\mu,F90}, -respectively. - -For Gaia DR1 the only types of priors used are 2 (for the secondary data -set), 3 (for the Hipparcos subset of the primary data set), or 5 (for -the non-Hipparcos subset of the primary data set). Type 6 was used for -internal calibration purposes and alignment of the reference frame, but -the corresponding astrometric results are in general not published. - - - -This field indicates the number of observations (detection transits) -that have been matched to a given source during the last internal -crossmatch revision. - - - -During data processing, this source happened to been duplicated and one -source only has been kept. This may indicate observational, -cross-matching or processing problems, or stellar multiplicity, and -probable astrometric or photometric problems in all cases. In DR1, for -close doubles with separations below some 2 arcsec, truncated windows -have not been processed, neither in astrometry and photometry. The -transmitted window is centred on the brighter part of the acquired -window, so the brighter component has a better chance to be selected, -even when processing the fainter transit. If more than two images are -contained in a window, the result of the image parameter determination -is un-predictable in the sense that it might refer to either (or -neither) image, and no consistency is assured. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -Number of observations (CCD transits) that contributed to the G mean -flux and mean flux error. - - - -Mean flux in the G-band. - - -Error on the mean flux in the G-band. - - -Mean magnitude in the G band. This is computed from the G-band mean flux -applying the magnitude zero-point in the Vega scale. - - -Flag indicating if variability was identified in the photometric G band: - -- source not processed and/or exported to catalogue - -- Source not identified as variable - -- source identified and processed as variable, see tables - PhotVariableSummary, PhotVariableTimeSeriesGfov, - PhotVariableTimeSeriesGfovStatisticalParameters, and Cepheid or - Rrlyrae for more details. - -Note that for this data release only a small subset of (variable) -sources was processed and/or exported, so for many (known) variable -sources this flag is set to “NOT AVAILABLEâ€. No “CONSTANT†sources were -exported either. - - -Galactic Longitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Galactic Latitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Ecliptic Longitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Ecliptic Latitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Incremental unique numeric identifier (increasing with declination). -This is the only field which was not in the original 2MASS catalogue, -but was added for cross-match purposes. - - - -The unique source identifier in the original External catalogue. - - -Angular distance between a Gaia source and its nearest neighbour in the -External Catalogue - - -Number of sources in the External Catalogue which match the Gaia source -within position errors. -The identifiers of all the neighbours can be found in the Neighbourhood -table. - - - -Number of other Gaia sources that have as best-neighbour the same -External Catalogue source. -In case there are no other Gaia sources with the same best-neighbour in -the external catalogue, the number of mates is equal to zero. -Given the Gaia high angular resolution, it will happen that what appears -as a single object in an external catalogue will be resolved by Gaia and -as such will be the best-match of more than one Gaia object. - - - -The best-match to a Gaia source in an external catalogue is the source -in the external catalogue that has the highest probability to be the -best-match. -As the probability is based on positional and density properties, it -could happen that there is more than one source in the external -catalogue with the same probability. -Even if a single best-match is always chosen, this field tells the user -if there were more “best†neighbours. Those neighbours can be found in -the Neighbourhood table. - - - -This flag is set to 0 if Gaia proper motions were not available and were -thus not used in the XMatch. -This flag is set to 1 if Gaia proper motions were available and were -thus used in the XMatch (for the First Gaia release the TGASS -sub-sample. - - - -Photometric quality flag. Three character flag, one character per band -[JHKs], that provides a summary of the net quality of the default -photometry in each band. -The value for ph_qual is set for a band according to the precedence of -the table below. For example, a source that is tested and meets the -conditions for category “X†is not tested for subsequent qualities. - -- “X†- There is a detection at this location, but no valid brightness - estimate can be extracted using any algorithm. Default magnitude is - null. - -- “U†- Upper limit on magnitude. Source is not detected in this band, - or it is detected, but not resolved in a consistent fashion with - other bands. A value of ph_qual=“U†does not necessarily mean that - there is no flux detected in this band at the location. - -- “F†- This category includes sources where a reliable estimate of - the photometric error could not be determined. The uncertainties - reported for these sources in [jhk]_msigcom are flags and have - numeric values >8.0. - -- “E†- This category includes detections where the goodness-of-fit - quality of the profile-fit photometry was very poor, or detections - where psf fit photometry did not converge and an aperture magnitude - is reported, or detections where the number of frames was too small - in relation to the number of frames in which a detection was - geometrically possible. - -- “A†- Detections in any brightness regime where valid measurements - were made with [jhk]_snr>10 AND [jhk]_cmsig<0.10857. - -- “B†- Detections in any brightness regime where valid measurements - were made with [jhk]_snr>7 AND [jhk]_cmsig<0.15510. - -- “C†- Detections in any brightness regime where valid measurements - were made with [jhk]_snr>5 AND [jhk]_cmsig<0.21714. - -- “D†- Detections in any brightness regime where valid measurements - were made with no [jhk]_snr or [jhk]_cmsig requirement. - - -Sexagesimal, equatorial position-based source name in the form: -hmmssss+ddmmsss[ABC...] The prefix “2MASS J†in not explicitely listed -in the designation. - - -Semi-major axis length of the one sigma position uncertainty ellipse - - -Semi-minor axis length of the one sigma position uncertainty ellipse - - -Position angle on the sky of the semi-major axis of the position -uncertainty ellipse (East of North) - - - -Default J-band magnitude. -In case of a source not detected in the J-band, it is an upper limit and -the corresponding total photometric uncertainty is NULL. -In case of a source detected in the J-band and with no useful brightness -estimate, it is set to NULL. - - -Total photometric uncertainty for the default J-band magnitude. This -column is NULL if the default magnitude is a 95% confidence upper limit -(i.e. the source is not detected, or inconsistently deblended in the -J-band). - - -Default H-band magnitude. -In case of a source not detected in the H-band, it is an upper limit and -the corresponding total photometric uncertainty is NULL. -In case of a source detected in the H-band and with no useful brightness -estimate, it is set to NULL. - - -Total photometric uncertainty for the default H-band magnitude. This -column is NULL if the default magnitude is a 95% confidence upper limit -(i.e. the source is not detected, or inconsistently deblended in the -H-band). - - -Default Ks-band magnitude. -In case of a source not detected in the Ks-band, it is an upper limit -and the corresponding total photometric uncertainty is NULL. -In case of a source detected in the Ks-band and with no useful -brightness estimate, it is set to NULL. - - -Total photometric uncertainty for the default Ks-band magnitude. This -column is NULL if the default magnitude is a 95% confidence upper limit -(i.e. the source is not detected, or inconsistently deblended in the -Ks-band. - - -Unique identification number of the record in the XSC (2MASS Extended -Source Catalogue) that corresponds to this point source. Can be NULL if -no corresponding extended source is present in the XSC. - - - -The Julian Date of the source measurement accurate to +30 seconds. This -value is extrapolated from the start time of the Survey scan using the -difference between the declinations of the source and the first row in -the Tile divided by the scanning rate of the telescope (approximately -5sec). The scanning rate of the two 2MASS telescopes was slightly -different because of the need to optimize the dithering of images on the -arrays. - - -
-
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XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / 8-bit bytes NAXIS = 2 / 2-dimensional table NAXIS1 = 11336 / width of single row in bytes NAXIS2 = 1 / single-row table PCOUNT = 0 / size of special data area GCOUNT = 1 / one data group TFIELDS = 77 / number of columns EXTNAME = 'gaia-small.vot' / table name TTYPE1 = 'solution_id' / label for column 1 TFORM1 = '26K ' / format for column 1 TDIM1 = '(1,26) ' / dimensions for column 1 TUCD1 = 'meta.id;meta.version' TTYPE2 = 'source_id' / label for column 2 TFORM2 = '26K ' / format for column 2 TDIM2 = '(1,26) ' / dimensions for column 2 TUCD2 = 'meta.id;meta.main' TTYPE3 = 'random_index' / label for column 3 TFORM3 = '26K ' / format for column 3 TDIM3 = '(1,26) ' / dimensions for column 3 TUCD3 = 'meta.code' TTYPE4 = 'ref_epoch' / label for column 4 TFORM4 = '26D ' / format for column 4 TDIM4 = '(1,26) ' / dimensions for column 4 TUNIT4 = 'Time[Julian Years]' / units for column 4 TUCD4 = 'meta.ref;time.epoch' TTYPE5 = 'ra ' / label for column 5 TFORM5 = '26D ' / format for column 5 TDIM5 = '(1,26) ' / dimensions for column 5 TUNIT5 = 'Angle[deg]' / units for column 5 TCOMM5 = 'J2000 right ascension with respect to the ICRS' TUCD5 = 'pos.eq.ra;meta.main' TUTYP5 = 'Char.SpatialAxis.Coverage.Location.Coord.Position2D.Value2.C1' TTYPE6 = 'ra_error' / label for column 6 TFORM6 = '26D ' / format for column 6 TDIM6 = '(1,26) ' / dimensions for column 6 TUNIT6 = 'Angle[mas]' / units for column 6 TUCD6 = 'stat.error;pos.eq.ra' TTYPE7 = 'dec ' / label for column 7 TFORM7 = '26D ' / format for column 7 TDIM7 = '(1,26) ' / dimensions for column 7 TUNIT7 = 'Angle[deg]' / units for column 7 TCOMM7 = 'J2000 declination with respect to the ICRS' TUCD7 = 'pos.eq.dec;meta.main' TUTYP7 = 'Char.SpatialAxis.Coverage.Location.Coord.Position2D.Value2.C2' TTYPE8 = 'dec_error' / label for column 8 TFORM8 = '26D ' / format for column 8 TDIM8 = '(1,26) ' / dimensions for column 8 TUNIT8 = 'Angle[mas]' / units for column 8 TUCD8 = 'stat.error;pos.eq.dec' TTYPE9 = 'parallax' / label for column 9 TFORM9 = '26D ' / format for column 9 TDIM9 = '(1,26) ' / dimensions for column 9 TUNIT9 = 'Angle[mas]' / units for column 9 TUCD9 = 'pos.parallax' TTYPE10 = 'parallax_error' / label for column 10 TFORM10 = '26D ' / format for column 10 TDIM10 = '(1,26) ' / dimensions for column 10 TUNIT10 = 'Angle[mas]' / units for column 10 TUCD10 = 'stat.error;pos.parallax' TTYPE11 = 'pmra ' / label for column 11 TFORM11 = '26D ' / format for column 11 TDIM11 = '(1,26) ' / dimensions for column 11 TUNIT11 = 'Angular Velocity[mas/year]' / units for column 11 TUCD11 = 'pos.pm;pos.eq.ra' TTYPE12 = 'pmra_error' / label for column 12 TFORM12 = '26D ' / format for column 12 TDIM12 = '(1,26) ' / dimensions for column 12 TUNIT12 = 'Angular Velocity[mas/year]' / units for column 12 TUCD12 = 'stat.error;pos.pm;pos.eq.ra' TTYPE13 = 'pmdec ' / label for column 13 TFORM13 = '26D ' / format for column 13 TDIM13 = '(1,26) ' / dimensions for column 13 TUNIT13 = 'Angular Velocity[mas/year]' / units for column 13 TUCD13 = 'pos.pm;pos.eq.dec' TTYPE14 = 'pmdec_error' / label for column 14 TFORM14 = '26D ' / format for column 14 TDIM14 = '(1,26) ' / dimensions for column 14 TUNIT14 = 'Angular Velocity[mas/year]' / units for column 14 TUCD14 = 'stat.error;pos.pm;pos.eq.dec' TTYPE15 = 'ra_dec_corr' / label for column 15 TFORM15 = '26E ' / format for column 15 TDIM15 = '(1,26) ' / dimensions for column 15 TUNIT15 = 'Dimensionless[see description]' / units for column 15 TUCD15 = 'stat.correlation' TTYPE16 = 'ra_parallax_corr' / label for column 16 TFORM16 = '26E ' / format for column 16 TDIM16 = '(1,26) ' / dimensions for column 16 TUNIT16 = 'Dimensionless[see description]' / units for column 16 TUCD16 = 'stat.correlation' TTYPE17 = 'ra_pmra_corr' / label for column 17 TFORM17 = '26E ' / format for column 17 TDIM17 = '(1,26) ' / dimensions for column 17 TUNIT17 = 'Dimensionless[see description]' / units for column 17 TUCD17 = 'stat.correlation' TTYPE18 = 'ra_pmdec_corr' / label for column 18 TFORM18 = '26E ' / format for column 18 TDIM18 = '(1,26) ' / dimensions for column 18 TUNIT18 = 'Dimensionless[see description]' / units for column 18 TUCD18 = 'stat.correlation' TTYPE19 = 'dec_parallax_corr' / label for column 19 TFORM19 = '26E ' / format for column 19 TDIM19 = '(1,26) ' / dimensions for column 19 TUNIT19 = 'Dimensionless[see description]' / units for column 19 TUCD19 = 'stat.correlation' TTYPE20 = 'dec_pmra_corr' / label for column 20 TFORM20 = '26E ' / format for column 20 TDIM20 = '(1,26) ' / dimensions for column 20 TUNIT20 = 'Dimensionless[see description]' / units for column 20 TUCD20 = 'stat.correlation' TTYPE21 = 'dec_pmdec_corr' / label for column 21 TFORM21 = '26E ' / format for column 21 TDIM21 = '(1,26) ' / dimensions for column 21 TUNIT21 = 'Dimensionless[see description]' / units for column 21 TUCD21 = 'stat.correlation' TTYPE22 = 'parallax_pmra_corr' / label for column 22 TFORM22 = '26E ' / format for column 22 TDIM22 = '(1,26) ' / dimensions for column 22 TUNIT22 = 'Dimensionless[see description]' / units for column 22 TUCD22 = 'stat.correlation' TTYPE23 = 'parallax_pmdec_corr' / label for column 23 TFORM23 = '26E ' / format for column 23 TDIM23 = '(1,26) ' / dimensions for column 23 TUNIT23 = 'Dimensionless[see description]' / units for column 23 TUCD23 = 'stat.correlation' TTYPE24 = 'pmra_pmdec_corr' / label for column 24 TFORM24 = '26E ' / format for column 24 TDIM24 = '(1,26) ' / dimensions for column 24 TUNIT24 = 'Dimensionless[see description]' / units for column 24 TUCD24 = 'stat.correlation' TTYPE25 = 'astrometric_n_obs_al' / label for column 25 TFORM25 = '26J ' / format for column 25 TDIM25 = '(1,26) ' / dimensions for column 25 TUCD25 = 'meta.number' TTYPE26 = 'astrometric_n_obs_ac' / label for column 26 TFORM26 = '26J ' / format for column 26 TDIM26 = '(1,26) ' / dimensions for column 26 TUCD26 = 'meta.number' TTYPE27 = 'astrometric_n_good_obs_al' / label for column 27 TFORM27 = '26J ' / format for column 27 TDIM27 = '(1,26) ' / dimensions for column 27 TUCD27 = 'meta.number' TTYPE28 = 'astrometric_n_good_obs_ac' / label for column 28 TFORM28 = '26J ' / format for column 28 TDIM28 = '(1,26) ' / dimensions for column 28 TUCD28 = 'meta.number' TTYPE29 = 'astrometric_n_bad_obs_al' / label for column 29 TFORM29 = '26J ' / format for column 29 TDIM29 = '(1,26) ' / dimensions for column 29 TUCD29 = 'meta.number' TTYPE30 = 'astrometric_n_bad_obs_ac' / label for column 30 TFORM30 = '26J ' / format for column 30 TDIM30 = '(1,26) ' / dimensions for column 30 TUCD30 = 'meta.number' TTYPE31 = 'astrometric_delta_q' / label for column 31 TFORM31 = '26E ' / format for column 31 TDIM31 = '(1,26) ' / dimensions for column 31 TUCD31 = 'stat.value' TTYPE32 = 'astrometric_excess_noise' / label for column 32 TFORM32 = '26D ' / format for column 32 TDIM32 = '(1,26) ' / dimensions for column 32 TUNIT32 = 'Angle[mas]' / units for column 32 TUCD32 = 'stat.value' TTYPE33 = 'astrometric_excess_noise_sig' / label for column 33 TFORM33 = '26D ' / format for column 33 TDIM33 = '(1,26) ' / dimensions for column 33 TUCD33 = 'stat.value' TTYPE34 = 'astrometric_primary_flag' / label for column 34 TFORM34 = '26L ' / format for column 34 TDIM34 = '(1,26) ' / dimensions for column 34 TUCD34 = 'meta.code' TTYPE35 = 'astrometric_relegation_factor' / label for column 35 TFORM35 = '26E ' / format for column 35 TDIM35 = '(1,26) ' / dimensions for column 35 TUCD35 = 'arith.factor' TTYPE36 = 'astrometric_weight_al' / label for column 36 TFORM36 = '26E ' / format for column 36 TDIM36 = '(1,26) ' / dimensions for column 36 TUNIT36 = 'Angle[mas^-2]' / units for column 36 TUCD36 = 'stat.weight;stat.mean' TTYPE37 = 'astrometric_weight_ac' / label for column 37 TFORM37 = '26E ' / format for column 37 TDIM37 = '(1,26) ' / dimensions for column 37 TUNIT37 = 'Angle[mas^-2]' / units for column 37 TUCD37 = 'stat.weight;stat.mean' TTYPE38 = 'astrometric_priors_used' / label for column 38 TFORM38 = '26J ' / format for column 38 TDIM38 = '(1,26) ' / dimensions for column 38 TTYPE39 = 'matched_observations' / label for column 39 TFORM39 = '26I ' / format for column 39 TDIM39 = '(1,26) ' / dimensions for column 39 TUCD39 = 'meta.number' TTYPE40 = 'duplicated_source' / label for column 40 TFORM40 = '26L ' / format for column 40 TDIM40 = '(1,26) ' / dimensions for column 40 TTYPE41 = 'scan_direction_strength_k1' / label for column 41 TFORM41 = '26E ' / format for column 41 TDIM41 = '(1,26) ' / dimensions for column 41 TTYPE42 = 'scan_direction_strength_k2' / label for column 42 TFORM42 = '26E ' / format for column 42 TDIM42 = '(1,26) ' / dimensions for column 42 TTYPE43 = 'scan_direction_strength_k3' / label for column 43 TFORM43 = '26E ' / format for column 43 TDIM43 = '(1,26) ' / dimensions for column 43 TTYPE44 = 'scan_direction_strength_k4' / label for column 44 TFORM44 = '26E ' / format for column 44 TDIM44 = '(1,26) ' / dimensions for column 44 TTYPE45 = 'scan_direction_mean_k1' / label for column 45 TFORM45 = '26E ' / format for column 45 TDIM45 = '(1,26) ' / dimensions for column 45 TUNIT45 = 'Angle[deg]' / units for column 45 TTYPE46 = 'scan_direction_mean_k2' / label for column 46 TFORM46 = '26E ' / format for column 46 TDIM46 = '(1,26) ' / dimensions for column 46 TUNIT46 = 'Angle[deg]' / units for column 46 TTYPE47 = 'scan_direction_mean_k3' / label for column 47 TFORM47 = '26E ' / format for column 47 TDIM47 = '(1,26) ' / dimensions for column 47 TUNIT47 = 'Angle[deg]' / units for column 47 TTYPE48 = 'scan_direction_mean_k4' / label for column 48 TFORM48 = '26E ' / format for column 48 TDIM48 = '(1,26) ' / dimensions for column 48 TUNIT48 = 'Angle[deg]' / units for column 48 TTYPE49 = 'phot_g_n_obs' / label for column 49 TFORM49 = '26J ' / format for column 49 TDIM49 = '(1,26) ' / dimensions for column 49 TUCD49 = 'meta.number' TTYPE50 = 'phot_g_mean_flux' / label for column 50 TFORM50 = '26D ' / format for column 50 TDIM50 = '(1,26) ' / dimensions for column 50 TUNIT50 = 'Flux[e-/s]' / units for column 50 TCOMM50 = 'Mean flux in the G-band.' TUCD50 = 'phot.flux;stat.mean;em.opt' TTYPE51 = 'phot_g_mean_flux_error' / label for column 51 TFORM51 = '26D ' / format for column 51 TDIM51 = '(1,26) ' / dimensions for column 51 TUNIT51 = 'Flux[e-/s]' / units for column 51 TCOMM51 = 'Error on the mean flux in the G-band.' TUCD51 = 'stat.error;phot.flux;stat.mean;em.opt' TTYPE52 = 'phot_g_mean_mag' / label for column 52 TFORM52 = '26D ' / format for column 52 TDIM52 = '(1,26) ' / dimensions for column 52 TUNIT52 = 'Magnitude[mag]' / units for column 52 TUCD52 = 'phot.mag;stat.mean;em.opt' TTYPE53 = 'phot_variable_flag' / label for column 53 TFORM53 = '338A ' / format for column 53 TDIM53 = '(13,26) ' / dimensions for column 53 TUNIT53 = 'Dimensionless[see description]' / units for column 53 TUCD53 = 'meta.code;src.var' TTYPE54 = 'l ' / label for column 54 TFORM54 = '26D ' / format for column 54 TDIM54 = '(1,26) ' / dimensions for column 54 TUNIT54 = 'Angle[deg]' / units for column 54 TUCD54 = 'pos.galactic.lon' TTYPE55 = 'b ' / label for column 55 TFORM55 = '26D ' / format for column 55 TDIM55 = '(1,26) ' / dimensions for column 55 TUNIT55 = 'Angle[deg]' / units for column 55 TUCD55 = 'pos.galactic.lat' TTYPE56 = 'ecl_lon ' / label for column 56 TFORM56 = '26D ' / format for column 56 TDIM56 = '(1,26) ' / dimensions for column 56 TUNIT56 = 'Angle[deg]' / units for column 56 TUCD56 = 'pos.ecliptic.lon' TTYPE57 = 'ecl_lat ' / label for column 57 TFORM57 = '26D ' / format for column 57 TDIM57 = '(1,26) ' / dimensions for column 57 TUNIT57 = 'Angle[deg]' / units for column 57 TUCD57 = 'pos.ecliptic.lat' TTYPE58 = 'tmass_oid' / label for column 58 TFORM58 = '26K ' / format for column 58 TDIM58 = '(1,26) ' / dimensions for column 58 TUCD58 = 'meta.id;meta.main' TTYPE59 = 'original_ext_source_id' / label for column 59 TFORM59 = '442A ' / format for column 59 TDIM59 = '(17,26) ' / dimensions for column 59 TCOMM59 = 'The unique source identifier in the original External catalogue.' TUCD59 = 'meta.id.cross' TTYPE60 = 'angular_distance' / label for column 60 TFORM60 = '26D ' / format for column 60 TDIM60 = '(1,26) ' / dimensions for column 60 TUNIT60 = 'Angle[arcsec]' / units for column 60 TUCD60 = 'pos.angDistance' TTYPE61 = 'number_of_neighbours' / label for column 61 TFORM61 = '26J ' / format for column 61 TDIM61 = '(1,26) ' / dimensions for column 61 TUCD61 = 'meta.number' TTYPE62 = 'number_of_mates' / label for column 62 TFORM62 = '26I ' / format for column 62 TDIM62 = '(1,26) ' / dimensions for column 62 TUCD62 = 'meta.number' TTYPE63 = 'best_neighbour_multiplicity' / label for column 63 TFORM63 = '26I ' / format for column 63 TDIM63 = '(1,26) ' / dimensions for column 63 TUCD63 = 'meta.number' TTYPE64 = 'proper_motion_flag' / label for column 64 TFORM64 = '26I ' / format for column 64 TDIM64 = '(1,26) ' / dimensions for column 64 TUCD64 = 'meta.code.status' TTYPE65 = 'ph_qual ' / label for column 65 TFORM65 = '78A ' / format for column 65 TDIM65 = '(3,26) ' / dimensions for column 65 TUCD65 = 'meta.code.qual;phot' TTYPE66 = 'designation' / label for column 66 TFORM66 = '442A ' / format for column 66 TDIM66 = '(17,26) ' / dimensions for column 66 TUCD66 = 'meta.id.cross' TTYPE67 = 'err_maj ' / label for column 67 TFORM67 = '26E ' / format for column 67 TDIM67 = '(1,26) ' / dimensions for column 67 TUNIT67 = 'Angle[arcsec]' / units for column 67 TCOMM67 = 'Semi-major axis length of the one sigma position uncertainty ellipse'TUCD67 = 'stat.error' TTYPE68 = 'err_min ' / label for column 68 TFORM68 = '26E ' / format for column 68 TDIM68 = '(1,26) ' / dimensions for column 68 TUNIT68 = 'Angle[arcsec]' / units for column 68 TCOMM68 = 'Semi-minor axis length of the one sigma position uncertainty ellipse'TUCD68 = 'stat.error' TTYPE69 = 'err_ang ' / label for column 69 TFORM69 = '26J ' / format for column 69 TDIM69 = '(1,26) ' / dimensions for column 69 TUNIT69 = 'Angle[deg]' / units for column 69 TUCD69 = 'position angle' TTYPE70 = 'j_m ' / label for column 70 TFORM70 = '26E ' / format for column 70 TDIM70 = '(1,26) ' / dimensions for column 70 TUNIT70 = 'Magnitude[mag]' / units for column 70 TUCD70 = 'phot.mag;em.IR.J' TTYPE71 = 'j_msigcom' / label for column 71 TFORM71 = '26E ' / format for column 71 TDIM71 = '(1,26) ' / dimensions for column 71 TUNIT71 = 'Magnitude[mag]' / units for column 71 TUCD71 = 'stat.error;phot.mag;em.IR.J' TTYPE72 = 'h_m ' / label for column 72 TFORM72 = '26E ' / format for column 72 TDIM72 = '(1,26) ' / dimensions for column 72 TUNIT72 = 'Magnitude[mag]' / units for column 72 TUCD72 = 'phot.mag;em.IR.H' TTYPE73 = 'h_msigcom' / label for column 73 TFORM73 = '26E ' / format for column 73 TDIM73 = '(1,26) ' / dimensions for column 73 TUNIT73 = 'Magnitude[mag]' / units for column 73 TUCD73 = 'stat.error;phot.mag;em.IR.H' TTYPE74 = 'ks_m ' / label for column 74 TFORM74 = '26E ' / format for column 74 TDIM74 = '(1,26) ' / dimensions for column 74 TUNIT74 = 'Magnitude[mag]' / units for column 74 TUCD74 = 'phot.mag;em.IR.K' TTYPE75 = 'ks_msigcom' / label for column 75 TFORM75 = '26E ' / format for column 75 TDIM75 = '(1,26) ' / dimensions for column 75 TUNIT75 = 'Magnitude[mag]' / units for column 75 TUCD75 = 'stat.error;phot.mag;em.IR.K' TTYPE76 = 'ext_key ' / label for column 76 TFORM76 = '26K ' / format for column 76 TDIM76 = '(1,26) ' / dimensions for column 76 TUCD76 = 'meta.id.cross' TNULL76 = -9223372036854775808 / blank value TTYPE77 = 'j_date ' / label for column 77 TFORM77 = '26D ' / format for column 77 TDIM77 = '(1,26) ' / dimensions for column 77 TUNIT77 = 'Time[Julian Date (day)]' / units for column 77 TUCD77 = 'time.epoch' DATE-HDU= '2017-03-22T11:37:38' / Date of HDU creation (UTC) STILVERS= '3.1 ' / Version of STIL software STILCLAS= 'uk.ac.starlink.votable.ColFitsPlusTableWriter' / Author class in STILEND ²²²²²²²²²²²²²²²²²²²²²²²²²²!ÿÿˆ”€!ÿÿ 0S€!ÿþà!ÿþЀ!ÿÿ¨€!ÿþÀ€!ÿûø€!ÿþh!ÿþˆŒ€!ÿþÐ!ÿþ˜Ž!ÿÿ¸–!ÿÿ¸–€!ÿûð‰€!ÿþ°#UUHá#UUPí€#UU#UUŽ&àù€&Àô€&(ô#UUÐ,€#UU(º#UUH¸€#UU8Ì€3w”¶‚–O=µA¸ÝH(×+Ö¶ó7î!úO£! oä!b3Q(«µÞ*ä-è”)W/sÕ Z=Þ £Ð â\3<Ÿ^)|b!þ‘ 5)ôÇiá40 §%L,*ƒ’@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|@Ÿ|?“õnN#§9?š{c ñW¦?£ $ÔÛ÷?†4ôÿÐ"-? ±$Çx½:?’ñFûº'Ð?¶6±xÓƒ?z>Ÿ›?«g(Ê"è·?k§ÁYn?£Â™©ß?‘»§uzû?•ø·q.B?±ÿqß,G??¬qEîSoú?Ÿa7PT¤?ƒTqxC…‹?¨EÜwÜé?¡ÚÊsÀ?¦ÒçÿŽ M?”u¤Ô×@ñ?œYŸM:?©„ïtå7E?³. 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deleted file mode 100644 index c147754e0f..0000000000 --- a/packages/vaex-core/vaex/test/files/gaia-small-fits-basic.fits +++ /dev/null @@ -1,22 +0,0 @@ -SIMPLE = T / Standard FITS format BITPIX = 8 / Character data NAXIS = 0 / No image, just extensions EXTEND = T / There are standard extensions COMMENT Dummy header; see following table extension END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / 8-bit bytes NAXIS = 2 / 2-dimensional table NAXIS1 = 436 / width of table in bytes NAXIS2 = 26 / number of rows in table PCOUNT = 0 / size of special data area GCOUNT = 1 / one data group TFIELDS = 77 / number of columns EXTNAME = 'gaia-small.vot' / table name TTYPE1 = 'solution_id' / label for column 1 TFORM1 = 'K ' / format for column 1 TUCD1 = 'meta.id;meta.version' TTYPE2 = 'source_id' / label for column 2 TFORM2 = 'K ' / format for column 2 TUCD2 = 'meta.id;meta.main' TTYPE3 = 'random_index' / label for column 3 TFORM3 = 'K ' / format for column 3 TUCD3 = 'meta.code' TTYPE4 = 'ref_epoch' / label for column 4 TFORM4 = 'D ' / format for column 4 TUNIT4 = 'Time[Julian Years]' / units for column 4 TUCD4 = 'meta.ref;time.epoch' TTYPE5 = 'ra ' / label for column 5 TFORM5 = 'D ' / format for column 5 TUNIT5 = 'Angle[deg]' / units for column 5 TCOMM5 = 'J2000 right ascension with respect to the ICRS' TUCD5 = 'pos.eq.ra;meta.main' TUTYP5 = 'Char.SpatialAxis.Coverage.Location.Coord.Position2D.Value2.C1' TTYPE6 = 'ra_error' / label for column 6 TFORM6 = 'D ' / format for column 6 TUNIT6 = 'Angle[mas]' / units for column 6 TUCD6 = 'stat.error;pos.eq.ra' TTYPE7 = 'dec ' / label for column 7 TFORM7 = 'D ' / format for column 7 TUNIT7 = 'Angle[deg]' / units for column 7 TCOMM7 = 'J2000 declination with respect to the ICRS' TUCD7 = 'pos.eq.dec;meta.main' TUTYP7 = 'Char.SpatialAxis.Coverage.Location.Coord.Position2D.Value2.C2' TTYPE8 = 'dec_error' / label for column 8 TFORM8 = 'D ' / format for column 8 TUNIT8 = 'Angle[mas]' / units for column 8 TUCD8 = 'stat.error;pos.eq.dec' TTYPE9 = 'parallax' / label for column 9 TFORM9 = 'D ' / format for column 9 TUNIT9 = 'Angle[mas]' / units for column 9 TUCD9 = 'pos.parallax' TTYPE10 = 'parallax_error' / label for column 10 TFORM10 = 'D ' / format for column 10 TUNIT10 = 'Angle[mas]' / units for column 10 TUCD10 = 'stat.error;pos.parallax' TTYPE11 = 'pmra ' / label for column 11 TFORM11 = 'D ' / format for column 11 TUNIT11 = 'Angular Velocity[mas/year]' / units for column 11 TUCD11 = 'pos.pm;pos.eq.ra' TTYPE12 = 'pmra_error' / label for column 12 TFORM12 = 'D ' / format for column 12 TUNIT12 = 'Angular Velocity[mas/year]' / units for column 12 TUCD12 = 'stat.error;pos.pm;pos.eq.ra' TTYPE13 = 'pmdec ' / label for column 13 TFORM13 = 'D ' / format for column 13 TUNIT13 = 'Angular Velocity[mas/year]' / units for column 13 TUCD13 = 'pos.pm;pos.eq.dec' TTYPE14 = 'pmdec_error' / label for column 14 TFORM14 = 'D ' / format for column 14 TUNIT14 = 'Angular Velocity[mas/year]' / units for column 14 TUCD14 = 'stat.error;pos.pm;pos.eq.dec' TTYPE15 = 'ra_dec_corr' / label for column 15 TFORM15 = 'E ' / format for column 15 TUNIT15 = 'Dimensionless[see description]' / units for column 15 TUCD15 = 'stat.correlation' TTYPE16 = 'ra_parallax_corr' / label for column 16 TFORM16 = 'E ' / format for column 16 TUNIT16 = 'Dimensionless[see description]' / units for column 16 TUCD16 = 'stat.correlation' TTYPE17 = 'ra_pmra_corr' / label for column 17 TFORM17 = 'E ' / format for column 17 TUNIT17 = 'Dimensionless[see description]' / units for column 17 TUCD17 = 'stat.correlation' TTYPE18 = 'ra_pmdec_corr' / label for column 18 TFORM18 = 'E ' / format for column 18 TUNIT18 = 'Dimensionless[see description]' / units for column 18 TUCD18 = 'stat.correlation' TTYPE19 = 'dec_parallax_corr' / label for column 19 TFORM19 = 'E ' / format for column 19 TUNIT19 = 'Dimensionless[see description]' / units for column 19 TUCD19 = 'stat.correlation' TTYPE20 = 'dec_pmra_corr' / label for column 20 TFORM20 = 'E ' / format for column 20 TUNIT20 = 'Dimensionless[see description]' / units for column 20 TUCD20 = 'stat.correlation' TTYPE21 = 'dec_pmdec_corr' / label for column 21 TFORM21 = 'E ' / format for column 21 TUNIT21 = 'Dimensionless[see description]' / units for column 21 TUCD21 = 'stat.correlation' TTYPE22 = 'parallax_pmra_corr' / label for column 22 TFORM22 = 'E ' / format for column 22 TUNIT22 = 'Dimensionless[see description]' / units for column 22 TUCD22 = 'stat.correlation' TTYPE23 = 'parallax_pmdec_corr' / label for column 23 TFORM23 = 'E ' / format for column 23 TUNIT23 = 'Dimensionless[see description]' / units for column 23 TUCD23 = 'stat.correlation' TTYPE24 = 'pmra_pmdec_corr' / label for column 24 TFORM24 = 'E ' / format for column 24 TUNIT24 = 'Dimensionless[see description]' / units for column 24 TUCD24 = 'stat.correlation' TTYPE25 = 'astrometric_n_obs_al' / label for column 25 TFORM25 = 'J ' / format for column 25 TUCD25 = 'meta.number' TTYPE26 = 'astrometric_n_obs_ac' / label for column 26 TFORM26 = 'J ' / format for column 26 TUCD26 = 'meta.number' TTYPE27 = 'astrometric_n_good_obs_al' / label for column 27 TFORM27 = 'J ' / format for column 27 TUCD27 = 'meta.number' TTYPE28 = 'astrometric_n_good_obs_ac' / label for column 28 TFORM28 = 'J ' / format for column 28 TUCD28 = 'meta.number' TTYPE29 = 'astrometric_n_bad_obs_al' / label for column 29 TFORM29 = 'J ' / format for column 29 TUCD29 = 'meta.number' TTYPE30 = 'astrometric_n_bad_obs_ac' / label for column 30 TFORM30 = 'J ' / format for column 30 TUCD30 = 'meta.number' TTYPE31 = 'astrometric_delta_q' / label for column 31 TFORM31 = 'E ' / format for column 31 TUCD31 = 'stat.value' TTYPE32 = 'astrometric_excess_noise' / label for column 32 TFORM32 = 'D ' / format for column 32 TUNIT32 = 'Angle[mas]' / units for column 32 TUCD32 = 'stat.value' TTYPE33 = 'astrometric_excess_noise_sig' / label for column 33 TFORM33 = 'D ' / format for column 33 TUCD33 = 'stat.value' TTYPE34 = 'astrometric_primary_flag' / label for column 34 TFORM34 = 'L ' / format for column 34 TUCD34 = 'meta.code' TTYPE35 = 'astrometric_relegation_factor' / label for column 35 TFORM35 = 'E ' / format for column 35 TUCD35 = 'arith.factor' TTYPE36 = 'astrometric_weight_al' / label for column 36 TFORM36 = 'E ' / format for column 36 TUNIT36 = 'Angle[mas^-2]' / units for column 36 TUCD36 = 'stat.weight;stat.mean' TTYPE37 = 'astrometric_weight_ac' / label for column 37 TFORM37 = 'E ' / format for column 37 TUNIT37 = 'Angle[mas^-2]' / units for column 37 TUCD37 = 'stat.weight;stat.mean' TTYPE38 = 'astrometric_priors_used' / label for column 38 TFORM38 = 'J ' / format for column 38 TTYPE39 = 'matched_observations' / label for column 39 TFORM39 = 'I ' / format for column 39 TUCD39 = 'meta.number' TTYPE40 = 'duplicated_source' / label for column 40 TFORM40 = 'L ' / format for column 40 TTYPE41 = 'scan_direction_strength_k1' / label for column 41 TFORM41 = 'E ' / format for column 41 TTYPE42 = 'scan_direction_strength_k2' / label for column 42 TFORM42 = 'E ' / format for column 42 TTYPE43 = 'scan_direction_strength_k3' / label for column 43 TFORM43 = 'E ' / format for column 43 TTYPE44 = 'scan_direction_strength_k4' / label for column 44 TFORM44 = 'E ' / format for column 44 TTYPE45 = 'scan_direction_mean_k1' / label for column 45 TFORM45 = 'E ' / format for column 45 TUNIT45 = 'Angle[deg]' / units for column 45 TTYPE46 = 'scan_direction_mean_k2' / label for column 46 TFORM46 = 'E ' / format for column 46 TUNIT46 = 'Angle[deg]' / units for column 46 TTYPE47 = 'scan_direction_mean_k3' / label for column 47 TFORM47 = 'E ' / format for column 47 TUNIT47 = 'Angle[deg]' / units for column 47 TTYPE48 = 'scan_direction_mean_k4' / label for column 48 TFORM48 = 'E ' / format for column 48 TUNIT48 = 'Angle[deg]' / units for column 48 TTYPE49 = 'phot_g_n_obs' / label for column 49 TFORM49 = 'J ' / format for column 49 TUCD49 = 'meta.number' TTYPE50 = 'phot_g_mean_flux' / label for column 50 TFORM50 = 'D ' / format for column 50 TUNIT50 = 'Flux[e-/s]' / units for column 50 TCOMM50 = 'Mean flux in the G-band.' TUCD50 = 'phot.flux;stat.mean;em.opt' TTYPE51 = 'phot_g_mean_flux_error' / label for column 51 TFORM51 = 'D ' / format for column 51 TUNIT51 = 'Flux[e-/s]' / units for column 51 TCOMM51 = 'Error on the mean flux in the G-band.' TUCD51 = 'stat.error;phot.flux;stat.mean;em.opt' TTYPE52 = 'phot_g_mean_mag' / label for column 52 TFORM52 = 'D ' / format for column 52 TUNIT52 = 'Magnitude[mag]' / units for column 52 TUCD52 = 'phot.mag;stat.mean;em.opt' TTYPE53 = 'phot_variable_flag' / label for column 53 TFORM53 = '13A ' / format for column 53 TUNIT53 = 'Dimensionless[see description]' / units for column 53 TUCD53 = 'meta.code;src.var' TTYPE54 = 'l ' / label for column 54 TFORM54 = 'D ' / format for column 54 TUNIT54 = 'Angle[deg]' / units for column 54 TUCD54 = 'pos.galactic.lon' TTYPE55 = 'b ' / label for column 55 TFORM55 = 'D ' / format for column 55 TUNIT55 = 'Angle[deg]' / units for column 55 TUCD55 = 'pos.galactic.lat' TTYPE56 = 'ecl_lon ' / label for column 56 TFORM56 = 'D ' / format for column 56 TUNIT56 = 'Angle[deg]' / units for column 56 TUCD56 = 'pos.ecliptic.lon' TTYPE57 = 'ecl_lat ' / label for column 57 TFORM57 = 'D ' / format for column 57 TUNIT57 = 'Angle[deg]' / units for column 57 TUCD57 = 'pos.ecliptic.lat' TTYPE58 = 'tmass_oid' / label for column 58 TFORM58 = 'K ' / format for column 58 TUCD58 = 'meta.id;meta.main' TTYPE59 = 'original_ext_source_id' / label for column 59 TFORM59 = '17A ' / format for column 59 TCOMM59 = 'The unique source identifier in the original External catalogue.' TUCD59 = 'meta.id.cross' TTYPE60 = 'angular_distance' / label for column 60 TFORM60 = 'D ' / format for column 60 TUNIT60 = 'Angle[arcsec]' / units for column 60 TUCD60 = 'pos.angDistance' TTYPE61 = 'number_of_neighbours' / label for column 61 TFORM61 = 'J ' / format for column 61 TUCD61 = 'meta.number' TTYPE62 = 'number_of_mates' / label for column 62 TFORM62 = 'I ' / format for column 62 TUCD62 = 'meta.number' TTYPE63 = 'best_neighbour_multiplicity' / label for column 63 TFORM63 = 'I ' / format for column 63 TUCD63 = 'meta.number' TTYPE64 = 'proper_motion_flag' / label for column 64 TFORM64 = 'I ' / format for column 64 TUCD64 = 'meta.code.status' TTYPE65 = 'ph_qual ' / label for column 65 TFORM65 = '3A ' / format for column 65 TUCD65 = 'meta.code.qual;phot' TTYPE66 = 'designation' / label for column 66 TFORM66 = '17A ' / format for column 66 TUCD66 = 'meta.id.cross' TTYPE67 = 'err_maj ' / label for column 67 TFORM67 = 'E ' / format for column 67 TUNIT67 = 'Angle[arcsec]' / units for column 67 TCOMM67 = 'Semi-major axis length of the one sigma position uncertainty ellipse'TUCD67 = 'stat.error' TTYPE68 = 'err_min ' / label for column 68 TFORM68 = 'E ' / format for column 68 TUNIT68 = 'Angle[arcsec]' / units for column 68 TCOMM68 = 'Semi-minor axis length of the one sigma position uncertainty ellipse'TUCD68 = 'stat.error' TTYPE69 = 'err_ang ' / label for column 69 TFORM69 = 'J ' / format for column 69 TUNIT69 = 'Angle[deg]' / units for column 69 TUCD69 = 'position angle' TTYPE70 = 'j_m ' / label for column 70 TFORM70 = 'E ' / format for column 70 TUNIT70 = 'Magnitude[mag]' / units for column 70 TUCD70 = 'phot.mag;em.IR.J' TTYPE71 = 'j_msigcom' / label for column 71 TFORM71 = 'E ' / format for column 71 TUNIT71 = 'Magnitude[mag]' / units for column 71 TUCD71 = 'stat.error;phot.mag;em.IR.J' TTYPE72 = 'h_m ' / label for column 72 TFORM72 = 'E ' / format for column 72 TUNIT72 = 'Magnitude[mag]' / units for column 72 TUCD72 = 'phot.mag;em.IR.H' TTYPE73 = 'h_msigcom' / label for column 73 TFORM73 = 'E ' / format for column 73 TUNIT73 = 'Magnitude[mag]' / units for column 73 TUCD73 = 'stat.error;phot.mag;em.IR.H' TTYPE74 = 'ks_m ' / label for column 74 TFORM74 = 'E ' / format for column 74 TUNIT74 = 'Magnitude[mag]' / units for column 74 TUCD74 = 'phot.mag;em.IR.K' TTYPE75 = 'ks_msigcom' / label for column 75 TFORM75 = 'E ' / format for column 75 TUNIT75 = 'Magnitude[mag]' / units for column 75 TUCD75 = 'stat.error;phot.mag;em.IR.K' TTYPE76 = 'ext_key ' / label for column 76 TFORM76 = 'K ' / format for column 76 TNULL76 = -9223372036854775808 / blank value for column 76 TUCD76 = 'meta.id.cross' TTYPE77 = 'j_date ' / label for column 77 TFORM77 = 'D ' / format for column 77 TUNIT77 = 'Time[Julian Date (day)]' / units for column 77 TUCD77 = 'time.epoch' DATE-HDU= '2017-03-22T11:36:51' / Date of HDU creation (UTC) STILVERS= '3.1 ' / Version of STIL software STILCLAS= 'uk.ac.starlink.fits.FitsTableWriter' / Author class in STIL software END 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COMMENT The VOTable describes the metadata of the table contained COMMENT in the following BINTABLE extension. COMMENT Such a BINTABLE extension can be used on its own as a perfectly COMMENT good table, but the information from this HDU may provide some COMMENT useful additional metadata. COMMENT There is one following BINTABLE. NTABLE = 1 / Number of following BINTABLE HDUs END - - - - - - -SELECT * - - -FROM gaiadr1.gaia_source -JOIN gaiadr1.tmass_best_neighbour -ON gaiadr1.gaia_source.source_id = gaiadr1.tmass_best_neighbour.source_id -JOIN gaiadr1.tmass_original_valid -ON gaiadr1.tmass_best_neighbour.tmass_oid = gaiadr1.tmass_original_valid.tmass_oid - -WHERE - -gaiadr1.gaia_source.ra < 0.1 and -gaiadr1.gaia_source.dec < 0.1 and -gaiadr1.gaia_source.ra > -0.1 and -gaiadr1.gaia_source.dec > -0.1 - - -If you use public Gaia DR1 data in your paper, please take note of our guide on how to acknowledge and cite Gaia DR1: http://gaia.esac.esa.int/documentation/GDR1/Miscellaneous/sec_credit_and_citation_instructions.html - - - - -1490181948554O - - - -All Gaia data processed by the Data Processing and Analysis Consortium -comes tagged with a solution identifier. This is a numeric field -attached to each table row that can be used to unequivocally identify -the version of all the subsystems that where used in the generation of -the data as well as the input data used. It is mainly for internal DPAC -use but is included in the published data releases to enable end users -to examine the provenance of processed data products. To decode a given -solution ID visit - - - -Unique identifier of the Gaia source, the attribute corresponds to -GaiaSource.sourceId - - - -Random index which can be used to select smaller subsets of the data -that are still representative. The column contains a random permutation -of the numbers from 0 to N-1, where N is the number of rows. - -The random index can be useful for validation (testing on 10 different -random subsets), visualization (displaying 1% of the data), and -statistical exploration of the data, without the need to download all -the data. - - - -Reference epoch to which the astrometic source parameters are referred, -expressed as a Julian Year in TCB. - - -J2000 right ascension with respect to the ICRS - - -Standard error \sigma_{\alpha *} \equiv \sigma_\alpha\cos\delta of the -right ascension of the source in ICRS at the reference epoch refEpoch. - - -J2000 declination with respect to the ICRS - - -Standard error \sigma_\delta of the declination of the source in ICRS at -the reference epoch refEpoch - - -Absolute barycentric stellar parallax \varpi of the soure at the -reference epoch refEpoch - - -Standard error \sigma_\varpi of the stellar parallax at the reference -epoch refEpoch - - -Proper motion in right ascension \mu_{\alpha *} of the source in ICRS at -the reference epoch refEpoch. This is the projection of the proper -motion vector in the direction of increasing right ascension. - - -Standard error \sigma_{\mu\alpha *} of the proper motion vector in right -ascension at the reference epoch refEpoch - - -Proper motion in declination \mu_\delta of the source at the reference -epoch refEpoch. This is the projection of the proper motion vector in -the direction of increasing declination. - - -Standard error \sigma_{\mu\delta} of the proper motion in declination at -the reference epoch refEpoch - - -Correlation between right ascension and declination, in dimensionless -units [-1:+1] - - -Correlation between right ascension and parallax, in dimensionless units -[-1:+1] - - -Correlation between right ascension and proper motion in right -ascension, in dimensionless units [-1:+1] - - -Correlation between right ascension and proper motion in declination, in -dimensionless units [-1:+1] - - -Correlation between declination and parallax, in dimensionless units -[-1:+1] - - -Correlation between declination and proper motion in right ascension, in -dimensionless units [-1:+1] - - -Correlation between declination and proper motion in declination, in -dimensionless units [-1:+1] - - -Correlation between parallax and proper motion in right ascension, in -dimensionless units [-1:+1] - - -Correlation between parallax and proper motion in declination, in -dimensionless units [-1:+1] - - -Correlation between proper motion in right ascension and proper motion -in declination, in dimensionless units [-1:+1] - - -Total number of AL observations (= CCD transits) used in the astrometric -solution of the source, independent of their weight. Note that some -observations may be strongly downweighted (see astrometricNBadObsAl). - - - -Total number of AC observations (= CCD transits) used in the astrometric -solution of the source, independent of their weight. Note that some -observations may be strongly downweighted (see astrometricNBadObsAc). -Nearly all sources having G <13 will have AC observations from 2d -windows, while fainter than that limit only \sim1% of stars (the -so–called ‘calibration faint stars’) are assigned 2d windows resulting -in AC observations. - - - -Number of AL observations (= CCD transits) that were not strongly -downweighted in the astrometric solution of the source. Strongly -downweighted observations (with downweighting factor w<0.2) are instead -counted in astrometricNBadObsAl. The sum of astrometricNGoodObsAl and -astrometricNBadObsAl equals astrometricNObsAl, the total number of AL -observations used in the astrometric solution of the source. - - - -Number of AC observations (= CCD transits) that were not strongly -downweighted in the astrometric solution of the source. Strongly -downweighted observations (with downweighting factor w<0.2) are instead -counted in astrometricNBadObsAc. The sum of astrometricNGoodObsAc and -astrometricNBadObsAc equals astrometricNObsAc, the total number of AC -observations used in the astrometric solution of the source. - - - -Number of AL observations (= CCD transits) that were strongly -downweighted in the astrometric solution of the source, and therefore -contributed little to the determination of the astrometric parameters. -An observation is considered to be strongly downweighted if its -downweighting factor w<0.2, which means that the absolute value of the -astrometric residual exceeds 4.83 times the total uncertainty of the -observation, calculated as the quadratic sum of the centroiding -uncertainty, excess source noise, and excess attitude noise. - - - -Number of AC observations (= CCD transits) that were strongly -downweighted in the astrometric solution of the source, and therefore -contributed little to the determination of the astrometric parameters. -An observation is considered to be strongly downweighted if its -downweighting factor w<0.2, which means that the absolute value of the -astrometric residual exceeds 4.83 times the total uncertainty of the -observation, calculated as the quadratic sum of the centroiding -uncertainty, excess source noise, and excess attitude noise. - - - -In the TGAS solution \tt astrometricDeltaQ (\Delta Q) indicates the -discrepancy between the Hipparcos proper motion and the TGAS proper -motion. A large value of \tt deltaQ could indicate non-linear motion -(e.g. in a binary). - -The precise definition is - -\Delta Q = -\begin{bmatrix} -\Delta\mu_{\alpha *} & \Delta\mu_{\delta} -\end{bmatrix} -\left(\vec{C}_\text{pm,\,T}+\vec{C}_\text{pm,\,H}\right)^{-1} -\begin{bmatrix} -\Delta\mu_{\alpha *} \\ \Delta\mu_{\delta} -\end{bmatrix} - -where \Delta\mu_{\alpha *} = \mu_{\alpha *,\rm T}-\mu_{\alpha *,\rm H}, -\Delta\mu_{\delta} = \mu_{\delta,\rm T}-\mu_{\delta,\rm H}, with T and H -indicating values from the Gaia DR1 (TGAS) solution and Hipparcos -catalogue. \vec{C}_\text{pm,\,T} and \vec{C}_\text{pm,\,H} are the -corresponding 2\times 2 covariance matrices. - -In order to compute \Delta Q the two sets of proper motions must use the -same reference frame and the same reference epoch. Thus, the proper -motion components as given in the Hipparcos catalogue were rotated to -the Gaia DR1 reference frame, and then propagated to the Gaia reference -epoch. - - -This is the excess noise \epsilon_i of the source. It measures the -disagreement, expressed as an angle, between the observations of a -source and the best-fitting standard astrometric model (using five -astrometric parameters). The assumed observational noise in each -observation is quadratically increased by \epsilon_i in order to -statistically match the residuals in the astrometric solution. A value -of 0 signifies that the source is astrometrically well-behaved, i.e.  -that the residuals of the fit statistically agree with the assumed -observational noise. A positive value signifies that the residuals are -statistically larger than expected. - -The significance of \epsilon_i is given by \tt -astrometricExcessNoiseSig (D). If D\le 2 then \epsilon_i is probably not -significant, and the source may be astrometrically well-behaved even if -\epsilon_i is large. - -The excess noise \epsilon_i may absorb all kinds of modelling errors -that are not accounted for by the observational noise (image centroiding -error) or the excess attitude noise. Such modelling errors include LSF -and PSF calibration errors, geometric instrument calibration errors, and -part of the high-frequency attitude noise. These modelling errors are -particularly important in the early data releases, but should decrease -as the astrometric modelling of the instrument and attitude improves -over the years. - -Additionally, sources that deviate from the standard five-parameter -astrometric model (e.g. unresolved binaries, exoplanet systems, etc.) -may have positive \epsilon_i. Given the many other possible -contributions to the excess noise, the user must study the empirical -distributions of \epsilon_i and D to make sensible cutoffs before -filtering out sources for their particular application. - -In Gaia DR1, the excess source noise has the same interpretation as -described above for both the primary (TGAS) and secondary data sets. It -measures the disagreement between the five-parameter model and the -observations, augmented by the different priors used. Thus, in TGAS the -excess noise may be increased if the proper motion seen during the 14 -months of Gaia observations are not in agreement with the proper motion -inferred from the Tycho-2/Gaia comparison. In the secondary solution the -excess noise may be increased if the Gaia observations indicate a proper -motion or parallax several times larger than the prior uncertainty. - -The excess source noise is further explained in Sects. 3.6 and 5.1.2 of -Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -A dimensionless measure (D) of the significance of the calculated \tt -astrometricExcessNoise (\epsilon_i). A value D>2 indicates that the -given \epsilon_i is probably significant. - -For good fits in the limit of a large number of observations, D should -be zero in half of the cases and approximately follow the positive half -of a normal distribution with zero mean and unit standard deviation for -the other half. Consequently, D is expected to be greater than 2 for -only a few percent of the sources with well-behaved astrometric -solutions. - -In the early data releases \epsilon_i will however include instrument -and attitude modelling errors that are statistically significant and -could result in large values of \epsilon_i and D. The user must study -the empirical distributions of these statistics and make sensible -cutoffs before filtering out sources for their particular application. - -The excess noise significance is further explained in Sect. 5.1.2 of -Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Flag indicating if this source was used as a primary source (\tt true) -or secondary source (\tt false). Only primary sources contribute to the -estimation of attitude, calibration, and global parameters. The -estimation of source parameters is otherwise done in exactly the same -way for primary and secondary sources. - - -Relegation factor of the source calculated as per Eq. (118) in Lindegren -et al. (2012) used for the primary selection process. - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Mean astrometric weight of the source in the AL direction. - -The mean astrometric weight of the source is calculated as per Eq. (119) -in Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Mean astrometric weight of the source in the AC direction - -The mean astrometric weight of the source is calculated as per Eq. (119) -in Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Type of prior used in the astrometric solution: - -- 0: No prior used - -- 1: Galaxy Bayesian Prior for parallax and proper motion - -- 2: Galaxy Bayesian Prior for parallax and proper motion relaxed by - factor 10 - -- 3: Hipparcos prior for position - -- 4: Hipparcos prior for position and proper motion - -- 5: Tycho2 prior for position - -- 6: Quasar prior for proper motion - -The Galaxy Bayesian Prior is defined in , where it is denoted -\sigma_{\varpi,F90} (for the parallax) and -\sigma_{\mu,F90}={\cal R}\sigma_{\varpi,F90}, with {\cal -R}=10 yr^{-1} (for proper motion). The Galaxy Bayesian Prior relaxed by -a factor 10 is 10\sigma_{\varpi,F90} and 10\sigma_{\mu,F90}, -respectively. - -For Gaia DR1 the only types of priors used are 2 (for the secondary data -set), 3 (for the Hipparcos subset of the primary data set), or 5 (for -the non-Hipparcos subset of the primary data set). Type 6 was used for -internal calibration purposes and alignment of the reference frame, but -the corresponding astrometric results are in general not published. - - - -This field indicates the number of observations (detection transits) -that have been matched to a given source during the last internal -crossmatch revision. - - - -During data processing, this source happened to been duplicated and one -source only has been kept. This may indicate observational, -cross-matching or processing problems, or stellar multiplicity, and -probable astrometric or photometric problems in all cases. In DR1, for -close doubles with separations below some 2 arcsec, truncated windows -have not been processed, neither in astrometry and photometry. The -transmitted window is centred on the brighter part of the acquired -window, so the brighter component has a better chance to be selected, -even when processing the fainter transit. If more than two images are -contained in a window, the result of the image parameter determination -is un-predictable in the sense that it might refer to either (or -neither) image, and no consistency is assured. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -Number of observations (CCD transits) that contributed to the G mean -flux and mean flux error. - - - -Mean flux in the G-band. - - -Error on the mean flux in the G-band. - - -Mean magnitude in the G band. This is computed from the G-band mean flux -applying the magnitude zero-point in the Vega scale. - - -Flag indicating if variability was identified in the photometric G band: - -- source not processed and/or exported to catalogue - -- Source not identified as variable - -- source identified and processed as variable, see tables - PhotVariableSummary, PhotVariableTimeSeriesGfov, - PhotVariableTimeSeriesGfovStatisticalParameters, and Cepheid or - Rrlyrae for more details. - -Note that for this data release only a small subset of (variable) -sources was processed and/or exported, so for many (known) variable -sources this flag is set to “NOT AVAILABLEâ€. No “CONSTANT†sources were -exported either. - - -Galactic Longitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Galactic Latitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Ecliptic Longitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Ecliptic Latitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Incremental unique numeric identifier (increasing with declination). -This is the only field which was not in the original 2MASS catalogue, -but was added for cross-match purposes. - - - -The unique source identifier in the original External catalogue. - - -Angular distance between a Gaia source and its nearest neighbour in the -External Catalogue - - -Number of sources in the External Catalogue which match the Gaia source -within position errors. -The identifiers of all the neighbours can be found in the Neighbourhood -table. - - - -Number of other Gaia sources that have as best-neighbour the same -External Catalogue source. -In case there are no other Gaia sources with the same best-neighbour in -the external catalogue, the number of mates is equal to zero. -Given the Gaia high angular resolution, it will happen that what appears -as a single object in an external catalogue will be resolved by Gaia and -as such will be the best-match of more than one Gaia object. - - - -The best-match to a Gaia source in an external catalogue is the source -in the external catalogue that has the highest probability to be the -best-match. -As the probability is based on positional and density properties, it -could happen that there is more than one source in the external -catalogue with the same probability. -Even if a single best-match is always chosen, this field tells the user -if there were more “best†neighbours. Those neighbours can be found in -the Neighbourhood table. - - - -This flag is set to 0 if Gaia proper motions were not available and were -thus not used in the XMatch. -This flag is set to 1 if Gaia proper motions were available and were -thus used in the XMatch (for the First Gaia release the TGASS -sub-sample. - - - -Photometric quality flag. Three character flag, one character per band -[JHKs], that provides a summary of the net quality of the default -photometry in each band. -The value for ph_qual is set for a band according to the precedence of -the table below. For example, a source that is tested and meets the -conditions for category “X†is not tested for subsequent qualities. - -- “X†- There is a detection at this location, but no valid brightness - estimate can be extracted using any algorithm. Default magnitude is - null. - -- “U†- Upper limit on magnitude. Source is not detected in this band, - or it is detected, but not resolved in a consistent fashion with - other bands. A value of ph_qual=“U†does not necessarily mean that - there is no flux detected in this band at the location. - -- “F†- This category includes sources where a reliable estimate of - the photometric error could not be determined. The uncertainties - reported for these sources in [jhk]_msigcom are flags and have - numeric values >8.0. - -- “E†- This category includes detections where the goodness-of-fit - quality of the profile-fit photometry was very poor, or detections - where psf fit photometry did not converge and an aperture magnitude - is reported, or detections where the number of frames was too small - in relation to the number of frames in which a detection was - geometrically possible. - -- “A†- Detections in any brightness regime where valid measurements - were made with [jhk]_snr>10 AND [jhk]_cmsig<0.10857. - -- “B†- Detections in any brightness regime where valid measurements - were made with [jhk]_snr>7 AND [jhk]_cmsig<0.15510. - -- “C†- Detections in any brightness regime where valid measurements - were made with [jhk]_snr>5 AND [jhk]_cmsig<0.21714. - -- “D†- Detections in any brightness regime where valid measurements - were made with no [jhk]_snr or [jhk]_cmsig requirement. - - -Sexagesimal, equatorial position-based source name in the form: -hmmssss+ddmmsss[ABC...] The prefix “2MASS J†in not explicitely listed -in the designation. - - -Semi-major axis length of the one sigma position uncertainty ellipse - - -Semi-minor axis length of the one sigma position uncertainty ellipse - - -Position angle on the sky of the semi-major axis of the position -uncertainty ellipse (East of North) - - - -Default J-band magnitude. -In case of a source not detected in the J-band, it is an upper limit and -the corresponding total photometric uncertainty is NULL. -In case of a source detected in the J-band and with no useful brightness -estimate, it is set to NULL. - - -Total photometric uncertainty for the default J-band magnitude. This -column is NULL if the default magnitude is a 95% confidence upper limit -(i.e. the source is not detected, or inconsistently deblended in the -J-band). - - -Default H-band magnitude. -In case of a source not detected in the H-band, it is an upper limit and -the corresponding total photometric uncertainty is NULL. -In case of a source detected in the H-band and with no useful brightness -estimate, it is set to NULL. - - -Total photometric uncertainty for the default H-band magnitude. This -column is NULL if the default magnitude is a 95% confidence upper limit -(i.e. the source is not detected, or inconsistently deblended in the -H-band). - - -Default Ks-band magnitude. -In case of a source not detected in the Ks-band, it is an upper limit -and the corresponding total photometric uncertainty is NULL. -In case of a source detected in the Ks-band and with no useful -brightness estimate, it is set to NULL. - - -Total photometric uncertainty for the default Ks-band magnitude. This -column is NULL if the default magnitude is a 95% confidence upper limit -(i.e. the source is not detected, or inconsistently deblended in the -Ks-band. - - -Unique identification number of the record in the XSC (2MASS Extended -Source Catalogue) that corresponds to this point source. Can be NULL if -no corresponding extended source is present in the XSC. - - - -The Julian Date of the source measurement accurate to +30 seconds. This -value is extrapolated from the start time of the Survey scan using the -difference between the declinations of the source and the first row in -the Tile divided by the scanning rate of the telescope (approximately -5sec). The scanning rate of the two 2MASS telescopes was slightly -different because of the need to optimize the dithering of images on the -arrays. - - -
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-
XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / 8-bit bytes NAXIS = 2 / 2-dimensional table NAXIS1 = 436 / width of table in bytes NAXIS2 = 26 / number of rows in table PCOUNT = 0 / size of special data area GCOUNT = 1 / one data group TFIELDS = 77 / number of columns EXTNAME = 'gaia-small.vot' / table name TTYPE1 = 'solution_id' / label for column 1 TFORM1 = 'K ' / format for column 1 TUCD1 = 'meta.id;meta.version' TTYPE2 = 'source_id' / label for column 2 TFORM2 = 'K ' / format for column 2 TUCD2 = 'meta.id;meta.main' TTYPE3 = 'random_index' / label for column 3 TFORM3 = 'K ' / format for column 3 TUCD3 = 'meta.code' TTYPE4 = 'ref_epoch' / label for column 4 TFORM4 = 'D ' / format for column 4 TUNIT4 = 'Time[Julian Years]' / units for column 4 TUCD4 = 'meta.ref;time.epoch' TTYPE5 = 'ra ' / label for column 5 TFORM5 = 'D ' / format for column 5 TUNIT5 = 'Angle[deg]' / units for column 5 TCOMM5 = 'J2000 right ascension with respect to the ICRS' TUCD5 = 'pos.eq.ra;meta.main' TUTYP5 = 'Char.SpatialAxis.Coverage.Location.Coord.Position2D.Value2.C1' TTYPE6 = 'ra_error' / label for column 6 TFORM6 = 'D ' / format for column 6 TUNIT6 = 'Angle[mas]' / units for column 6 TUCD6 = 'stat.error;pos.eq.ra' TTYPE7 = 'dec ' / label for column 7 TFORM7 = 'D ' / format for column 7 TUNIT7 = 'Angle[deg]' / units for column 7 TCOMM7 = 'J2000 declination with respect to the ICRS' TUCD7 = 'pos.eq.dec;meta.main' TUTYP7 = 'Char.SpatialAxis.Coverage.Location.Coord.Position2D.Value2.C2' TTYPE8 = 'dec_error' / label for column 8 TFORM8 = 'D ' / format for column 8 TUNIT8 = 'Angle[mas]' / units for column 8 TUCD8 = 'stat.error;pos.eq.dec' TTYPE9 = 'parallax' / label for column 9 TFORM9 = 'D ' / format for column 9 TUNIT9 = 'Angle[mas]' / units for column 9 TUCD9 = 'pos.parallax' TTYPE10 = 'parallax_error' / label for column 10 TFORM10 = 'D ' / format for column 10 TUNIT10 = 'Angle[mas]' / units for column 10 TUCD10 = 'stat.error;pos.parallax' TTYPE11 = 'pmra ' / label for column 11 TFORM11 = 'D ' / format for column 11 TUNIT11 = 'Angular Velocity[mas/year]' / units for column 11 TUCD11 = 'pos.pm;pos.eq.ra' TTYPE12 = 'pmra_error' / label for column 12 TFORM12 = 'D ' / format for column 12 TUNIT12 = 'Angular Velocity[mas/year]' / units for column 12 TUCD12 = 'stat.error;pos.pm;pos.eq.ra' TTYPE13 = 'pmdec ' / label for column 13 TFORM13 = 'D ' / format for column 13 TUNIT13 = 'Angular Velocity[mas/year]' / units for column 13 TUCD13 = 'pos.pm;pos.eq.dec' TTYPE14 = 'pmdec_error' / label for column 14 TFORM14 = 'D ' / format for column 14 TUNIT14 = 'Angular Velocity[mas/year]' / units for column 14 TUCD14 = 'stat.error;pos.pm;pos.eq.dec' TTYPE15 = 'ra_dec_corr' / label for column 15 TFORM15 = 'E ' / format for column 15 TUNIT15 = 'Dimensionless[see description]' / units for column 15 TUCD15 = 'stat.correlation' TTYPE16 = 'ra_parallax_corr' / label for column 16 TFORM16 = 'E ' / format for column 16 TUNIT16 = 'Dimensionless[see description]' / units for column 16 TUCD16 = 'stat.correlation' TTYPE17 = 'ra_pmra_corr' / label for column 17 TFORM17 = 'E ' / format for column 17 TUNIT17 = 'Dimensionless[see description]' / units for column 17 TUCD17 = 'stat.correlation' TTYPE18 = 'ra_pmdec_corr' / label for column 18 TFORM18 = 'E ' / format for column 18 TUNIT18 = 'Dimensionless[see description]' / units for column 18 TUCD18 = 'stat.correlation' TTYPE19 = 'dec_parallax_corr' / label for column 19 TFORM19 = 'E ' / format for column 19 TUNIT19 = 'Dimensionless[see description]' / units for column 19 TUCD19 = 'stat.correlation' TTYPE20 = 'dec_pmra_corr' / label for column 20 TFORM20 = 'E ' / format for column 20 TUNIT20 = 'Dimensionless[see description]' / units for column 20 TUCD20 = 'stat.correlation' TTYPE21 = 'dec_pmdec_corr' / label for column 21 TFORM21 = 'E ' / format for column 21 TUNIT21 = 'Dimensionless[see description]' / units for column 21 TUCD21 = 'stat.correlation' TTYPE22 = 'parallax_pmra_corr' / label for column 22 TFORM22 = 'E ' / format for column 22 TUNIT22 = 'Dimensionless[see description]' / units for column 22 TUCD22 = 'stat.correlation' TTYPE23 = 'parallax_pmdec_corr' / label for column 23 TFORM23 = 'E ' / format for column 23 TUNIT23 = 'Dimensionless[see description]' / units for column 23 TUCD23 = 'stat.correlation' TTYPE24 = 'pmra_pmdec_corr' / label for column 24 TFORM24 = 'E ' / format for column 24 TUNIT24 = 'Dimensionless[see description]' / units for column 24 TUCD24 = 'stat.correlation' TTYPE25 = 'astrometric_n_obs_al' / label for column 25 TFORM25 = 'J ' / format for column 25 TUCD25 = 'meta.number' TTYPE26 = 'astrometric_n_obs_ac' / label for column 26 TFORM26 = 'J ' / format for column 26 TUCD26 = 'meta.number' TTYPE27 = 'astrometric_n_good_obs_al' / label for column 27 TFORM27 = 'J ' / format for column 27 TUCD27 = 'meta.number' TTYPE28 = 'astrometric_n_good_obs_ac' / label for column 28 TFORM28 = 'J ' / format for column 28 TUCD28 = 'meta.number' TTYPE29 = 'astrometric_n_bad_obs_al' / label for column 29 TFORM29 = 'J ' / format for column 29 TUCD29 = 'meta.number' TTYPE30 = 'astrometric_n_bad_obs_ac' / label for column 30 TFORM30 = 'J ' / format for column 30 TUCD30 = 'meta.number' TTYPE31 = 'astrometric_delta_q' / label for column 31 TFORM31 = 'E ' / format for column 31 TUCD31 = 'stat.value' TTYPE32 = 'astrometric_excess_noise' / label for column 32 TFORM32 = 'D ' / format for column 32 TUNIT32 = 'Angle[mas]' / units for column 32 TUCD32 = 'stat.value' TTYPE33 = 'astrometric_excess_noise_sig' / label for column 33 TFORM33 = 'D ' / format for column 33 TUCD33 = 'stat.value' TTYPE34 = 'astrometric_primary_flag' / label for column 34 TFORM34 = 'L ' / format for column 34 TUCD34 = 'meta.code' TTYPE35 = 'astrometric_relegation_factor' / label for column 35 TFORM35 = 'E ' / format for column 35 TUCD35 = 'arith.factor' TTYPE36 = 'astrometric_weight_al' / label for column 36 TFORM36 = 'E ' / format for column 36 TUNIT36 = 'Angle[mas^-2]' / units for column 36 TUCD36 = 'stat.weight;stat.mean' TTYPE37 = 'astrometric_weight_ac' / label for column 37 TFORM37 = 'E ' / format for column 37 TUNIT37 = 'Angle[mas^-2]' / units for column 37 TUCD37 = 'stat.weight;stat.mean' TTYPE38 = 'astrometric_priors_used' / label for column 38 TFORM38 = 'J ' / format for column 38 TTYPE39 = 'matched_observations' / label for column 39 TFORM39 = 'I ' / format for column 39 TUCD39 = 'meta.number' TTYPE40 = 'duplicated_source' / label for column 40 TFORM40 = 'L ' / format for column 40 TTYPE41 = 'scan_direction_strength_k1' / label for column 41 TFORM41 = 'E ' / format for column 41 TTYPE42 = 'scan_direction_strength_k2' / label for column 42 TFORM42 = 'E ' / format for column 42 TTYPE43 = 'scan_direction_strength_k3' / label for column 43 TFORM43 = 'E ' / format for column 43 TTYPE44 = 'scan_direction_strength_k4' / label for column 44 TFORM44 = 'E ' / format for column 44 TTYPE45 = 'scan_direction_mean_k1' / label for column 45 TFORM45 = 'E ' / format for column 45 TUNIT45 = 'Angle[deg]' / units for column 45 TTYPE46 = 'scan_direction_mean_k2' / label for column 46 TFORM46 = 'E ' / format for column 46 TUNIT46 = 'Angle[deg]' / units for column 46 TTYPE47 = 'scan_direction_mean_k3' / label for column 47 TFORM47 = 'E ' / format for column 47 TUNIT47 = 'Angle[deg]' / units for column 47 TTYPE48 = 'scan_direction_mean_k4' / label for column 48 TFORM48 = 'E ' / format for column 48 TUNIT48 = 'Angle[deg]' / units for column 48 TTYPE49 = 'phot_g_n_obs' / label for column 49 TFORM49 = 'J ' / format for column 49 TUCD49 = 'meta.number' TTYPE50 = 'phot_g_mean_flux' / label for column 50 TFORM50 = 'D ' / format for column 50 TUNIT50 = 'Flux[e-/s]' / units for column 50 TCOMM50 = 'Mean flux in the G-band.' TUCD50 = 'phot.flux;stat.mean;em.opt' TTYPE51 = 'phot_g_mean_flux_error' / label for column 51 TFORM51 = 'D ' / format for column 51 TUNIT51 = 'Flux[e-/s]' / units for column 51 TCOMM51 = 'Error on the mean flux in the G-band.' TUCD51 = 'stat.error;phot.flux;stat.mean;em.opt' TTYPE52 = 'phot_g_mean_mag' / label for column 52 TFORM52 = 'D ' / format for column 52 TUNIT52 = 'Magnitude[mag]' / units for column 52 TUCD52 = 'phot.mag;stat.mean;em.opt' TTYPE53 = 'phot_variable_flag' / label for column 53 TFORM53 = '13A ' / format for column 53 TUNIT53 = 'Dimensionless[see description]' / units for column 53 TUCD53 = 'meta.code;src.var' TTYPE54 = 'l ' / label for column 54 TFORM54 = 'D ' / format for column 54 TUNIT54 = 'Angle[deg]' / units for column 54 TUCD54 = 'pos.galactic.lon' TTYPE55 = 'b ' / label for column 55 TFORM55 = 'D ' / format for column 55 TUNIT55 = 'Angle[deg]' / units for column 55 TUCD55 = 'pos.galactic.lat' TTYPE56 = 'ecl_lon ' / label for column 56 TFORM56 = 'D ' / format for column 56 TUNIT56 = 'Angle[deg]' / units for column 56 TUCD56 = 'pos.ecliptic.lon' TTYPE57 = 'ecl_lat ' / label for column 57 TFORM57 = 'D ' / format for column 57 TUNIT57 = 'Angle[deg]' / units for column 57 TUCD57 = 'pos.ecliptic.lat' TTYPE58 = 'tmass_oid' / label for column 58 TFORM58 = 'K ' / format for column 58 TUCD58 = 'meta.id;meta.main' TTYPE59 = 'original_ext_source_id' / label for column 59 TFORM59 = '17A ' / format for column 59 TCOMM59 = 'The unique source identifier in the original External catalogue.' TUCD59 = 'meta.id.cross' TTYPE60 = 'angular_distance' / label for column 60 TFORM60 = 'D ' / format for column 60 TUNIT60 = 'Angle[arcsec]' / units for column 60 TUCD60 = 'pos.angDistance' TTYPE61 = 'number_of_neighbours' / label for column 61 TFORM61 = 'J ' / format for column 61 TUCD61 = 'meta.number' TTYPE62 = 'number_of_mates' / label for column 62 TFORM62 = 'I ' / format for column 62 TUCD62 = 'meta.number' TTYPE63 = 'best_neighbour_multiplicity' / label for column 63 TFORM63 = 'I ' / format for column 63 TUCD63 = 'meta.number' TTYPE64 = 'proper_motion_flag' / label for column 64 TFORM64 = 'I ' / format for column 64 TUCD64 = 'meta.code.status' TTYPE65 = 'ph_qual ' / label for column 65 TFORM65 = '3A ' / format for column 65 TUCD65 = 'meta.code.qual;phot' TTYPE66 = 'designation' / label for column 66 TFORM66 = '17A ' / format for column 66 TUCD66 = 'meta.id.cross' TTYPE67 = 'err_maj ' / label for column 67 TFORM67 = 'E ' / format for column 67 TUNIT67 = 'Angle[arcsec]' / units for column 67 TCOMM67 = 'Semi-major axis length of the one sigma position uncertainty ellipse'TUCD67 = 'stat.error' TTYPE68 = 'err_min ' / label for column 68 TFORM68 = 'E ' / format for column 68 TUNIT68 = 'Angle[arcsec]' / units for column 68 TCOMM68 = 'Semi-minor axis length of the one sigma position uncertainty ellipse'TUCD68 = 'stat.error' TTYPE69 = 'err_ang ' / label for column 69 TFORM69 = 'J ' / format for column 69 TUNIT69 = 'Angle[deg]' / units for column 69 TUCD69 = 'position angle' TTYPE70 = 'j_m ' / label for column 70 TFORM70 = 'E ' / format for column 70 TUNIT70 = 'Magnitude[mag]' / units for column 70 TUCD70 = 'phot.mag;em.IR.J' TTYPE71 = 'j_msigcom' / label for column 71 TFORM71 = 'E ' / format for column 71 TUNIT71 = 'Magnitude[mag]' / units for column 71 TUCD71 = 'stat.error;phot.mag;em.IR.J' TTYPE72 = 'h_m ' / label for column 72 TFORM72 = 'E ' / format for column 72 TUNIT72 = 'Magnitude[mag]' / units for column 72 TUCD72 = 'phot.mag;em.IR.H' TTYPE73 = 'h_msigcom' / label for column 73 TFORM73 = 'E ' / format for column 73 TUNIT73 = 'Magnitude[mag]' / units for column 73 TUCD73 = 'stat.error;phot.mag;em.IR.H' TTYPE74 = 'ks_m ' / label for column 74 TFORM74 = 'E ' / format for column 74 TUNIT74 = 'Magnitude[mag]' / units for column 74 TUCD74 = 'phot.mag;em.IR.K' TTYPE75 = 'ks_msigcom' / label for column 75 TFORM75 = 'E ' / format for column 75 TUNIT75 = 'Magnitude[mag]' / units for column 75 TUCD75 = 'stat.error;phot.mag;em.IR.K' TTYPE76 = 'ext_key ' / label for column 76 TFORM76 = 'K ' / format for column 76 TNULL76 = -9223372036854775808 / blank value for column 76 TUCD76 = 'meta.id.cross' TTYPE77 = 'j_date ' / label for column 77 TFORM77 = 'D ' / format for column 77 TUNIT77 = 'Time[Julian Date (day)]' / units for column 77 TUCD77 = 'time.epoch' DATE-HDU= '2017-03-22T11:37:11' / Date of HDU creation (UTC) STILVERS= '3.1 ' / Version of STIL software STILCLAS= 'uk.ac.starlink.votable.FitsPlusTableWriter' / Author class in STIL soEND 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This is a numeric field -attached to each table row that can be used to unequivocally identify -the version of all the subsystems that where used in the generation of -the data as well as the input data used. It is mainly for internal DPAC -use but is included in the published data releases to enable end users -to examine the provenance of processed data products. To decode a given -solution ID visit - - - -Unique identifier of the Gaia source, the attribute corresponds to -GaiaSource.sourceId - - - -Random index which can be used to select smaller subsets of the data -that are still representative. The column contains a random permutation -of the numbers from 0 to N-1, where N is the number of rows. - -The random index can be useful for validation (testing on 10 different -random subsets), visualization (displaying 1% of the data), and -statistical exploration of the data, without the need to download all -the data. - - - -Reference epoch to which the astrometic source parameters are referred, -expressed as a Julian Year in TCB. - - -J2000 right ascension with respect to the ICRS - - -Standard error \sigma_{\alpha *} \equiv \sigma_\alpha\cos\delta of the -right ascension of the source in ICRS at the reference epoch refEpoch. - - -J2000 declination with respect to the ICRS - - -Standard error \sigma_\delta of the declination of the source in ICRS at -the reference epoch refEpoch - - -Absolute barycentric stellar parallax \varpi of the soure at the -reference epoch refEpoch - - -Standard error \sigma_\varpi of the stellar parallax at the reference -epoch refEpoch - - -Proper motion in right ascension \mu_{\alpha *} of the source in ICRS at -the reference epoch refEpoch. This is the projection of the proper -motion vector in the direction of increasing right ascension. - - -Standard error \sigma_{\mu\alpha *} of the proper motion vector in right -ascension at the reference epoch refEpoch - - -Proper motion in declination \mu_\delta of the source at the reference -epoch refEpoch. This is the projection of the proper motion vector in -the direction of increasing declination. - - -Standard error \sigma_{\mu\delta} of the proper motion in declination at -the reference epoch refEpoch - - -Correlation between right ascension and declination, in dimensionless -units [-1:+1] - - -Correlation between right ascension and parallax, in dimensionless units -[-1:+1] - - -Correlation between right ascension and proper motion in right -ascension, in dimensionless units [-1:+1] - - -Correlation between right ascension and proper motion in declination, in -dimensionless units [-1:+1] - - -Correlation between declination and parallax, in dimensionless units -[-1:+1] - - -Correlation between declination and proper motion in right ascension, in -dimensionless units [-1:+1] - - -Correlation between declination and proper motion in declination, in -dimensionless units [-1:+1] - - -Correlation between parallax and proper motion in right ascension, in -dimensionless units [-1:+1] - - -Correlation between parallax and proper motion in declination, in -dimensionless units [-1:+1] - - -Correlation between proper motion in right ascension and proper motion -in declination, in dimensionless units [-1:+1] - - -Total number of AL observations (= CCD transits) used in the astrometric -solution of the source, independent of their weight. Note that some -observations may be strongly downweighted (see astrometricNBadObsAl). - - - -Total number of AC observations (= CCD transits) used in the astrometric -solution of the source, independent of their weight. Note that some -observations may be strongly downweighted (see astrometricNBadObsAc). -Nearly all sources having G <13 will have AC observations from 2d -windows, while fainter than that limit only \sim1% of stars (the -so–called ‘calibration faint stars’) are assigned 2d windows resulting -in AC observations. - - - -Number of AL observations (= CCD transits) that were not strongly -downweighted in the astrometric solution of the source. Strongly -downweighted observations (with downweighting factor w<0.2) are instead -counted in astrometricNBadObsAl. The sum of astrometricNGoodObsAl and -astrometricNBadObsAl equals astrometricNObsAl, the total number of AL -observations used in the astrometric solution of the source. - - - -Number of AC observations (= CCD transits) that were not strongly -downweighted in the astrometric solution of the source. Strongly -downweighted observations (with downweighting factor w<0.2) are instead -counted in astrometricNBadObsAc. The sum of astrometricNGoodObsAc and -astrometricNBadObsAc equals astrometricNObsAc, the total number of AC -observations used in the astrometric solution of the source. - - - -Number of AL observations (= CCD transits) that were strongly -downweighted in the astrometric solution of the source, and therefore -contributed little to the determination of the astrometric parameters. -An observation is considered to be strongly downweighted if its -downweighting factor w<0.2, which means that the absolute value of the -astrometric residual exceeds 4.83 times the total uncertainty of the -observation, calculated as the quadratic sum of the centroiding -uncertainty, excess source noise, and excess attitude noise. - - - -Number of AC observations (= CCD transits) that were strongly -downweighted in the astrometric solution of the source, and therefore -contributed little to the determination of the astrometric parameters. -An observation is considered to be strongly downweighted if its -downweighting factor w<0.2, which means that the absolute value of the -astrometric residual exceeds 4.83 times the total uncertainty of the -observation, calculated as the quadratic sum of the centroiding -uncertainty, excess source noise, and excess attitude noise. - - - -In the TGAS solution \tt astrometricDeltaQ (\Delta Q) indicates the -discrepancy between the Hipparcos proper motion and the TGAS proper -motion. A large value of \tt deltaQ could indicate non-linear motion -(e.g. in a binary). - -The precise definition is - -\Delta Q = -\begin{bmatrix} -\Delta\mu_{\alpha *} & \Delta\mu_{\delta} -\end{bmatrix} -\left(\vec{C}_\text{pm,\,T}+\vec{C}_\text{pm,\,H}\right)^{-1} -\begin{bmatrix} -\Delta\mu_{\alpha *} \\ \Delta\mu_{\delta} -\end{bmatrix} - -where \Delta\mu_{\alpha *} = \mu_{\alpha *,\rm T}-\mu_{\alpha *,\rm H}, -\Delta\mu_{\delta} = \mu_{\delta,\rm T}-\mu_{\delta,\rm H}, with T and H -indicating values from the Gaia DR1 (TGAS) solution and Hipparcos -catalogue. \vec{C}_\text{pm,\,T} and \vec{C}_\text{pm,\,H} are the -corresponding 2\times 2 covariance matrices. - -In order to compute \Delta Q the two sets of proper motions must use the -same reference frame and the same reference epoch. Thus, the proper -motion components as given in the Hipparcos catalogue were rotated to -the Gaia DR1 reference frame, and then propagated to the Gaia reference -epoch. - - -This is the excess noise \epsilon_i of the source. It measures the -disagreement, expressed as an angle, between the observations of a -source and the best-fitting standard astrometric model (using five -astrometric parameters). The assumed observational noise in each -observation is quadratically increased by \epsilon_i in order to -statistically match the residuals in the astrometric solution. A value -of 0 signifies that the source is astrometrically well-behaved, i.e.  -that the residuals of the fit statistically agree with the assumed -observational noise. A positive value signifies that the residuals are -statistically larger than expected. - -The significance of \epsilon_i is given by \tt -astrometricExcessNoiseSig (D). If D\le 2 then \epsilon_i is probably not -significant, and the source may be astrometrically well-behaved even if -\epsilon_i is large. - -The excess noise \epsilon_i may absorb all kinds of modelling errors -that are not accounted for by the observational noise (image centroiding -error) or the excess attitude noise. Such modelling errors include LSF -and PSF calibration errors, geometric instrument calibration errors, and -part of the high-frequency attitude noise. These modelling errors are -particularly important in the early data releases, but should decrease -as the astrometric modelling of the instrument and attitude improves -over the years. - -Additionally, sources that deviate from the standard five-parameter -astrometric model (e.g. unresolved binaries, exoplanet systems, etc.) -may have positive \epsilon_i. Given the many other possible -contributions to the excess noise, the user must study the empirical -distributions of \epsilon_i and D to make sensible cutoffs before -filtering out sources for their particular application. - -In Gaia DR1, the excess source noise has the same interpretation as -described above for both the primary (TGAS) and secondary data sets. It -measures the disagreement between the five-parameter model and the -observations, augmented by the different priors used. Thus, in TGAS the -excess noise may be increased if the proper motion seen during the 14 -months of Gaia observations are not in agreement with the proper motion -inferred from the Tycho-2/Gaia comparison. In the secondary solution the -excess noise may be increased if the Gaia observations indicate a proper -motion or parallax several times larger than the prior uncertainty. - -The excess source noise is further explained in Sects. 3.6 and 5.1.2 of -Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -A dimensionless measure (D) of the significance of the calculated \tt -astrometricExcessNoise (\epsilon_i). A value D>2 indicates that the -given \epsilon_i is probably significant. - -For good fits in the limit of a large number of observations, D should -be zero in half of the cases and approximately follow the positive half -of a normal distribution with zero mean and unit standard deviation for -the other half. Consequently, D is expected to be greater than 2 for -only a few percent of the sources with well-behaved astrometric -solutions. - -In the early data releases \epsilon_i will however include instrument -and attitude modelling errors that are statistically significant and -could result in large values of \epsilon_i and D. The user must study -the empirical distributions of these statistics and make sensible -cutoffs before filtering out sources for their particular application. - -The excess noise significance is further explained in Sect. 5.1.2 of -Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Flag indicating if this source was used as a primary source (\tt true) -or secondary source (\tt false). Only primary sources contribute to the -estimation of attitude, calibration, and global parameters. The -estimation of source parameters is otherwise done in exactly the same -way for primary and secondary sources. - - -Relegation factor of the source calculated as per Eq. (118) in Lindegren -et al. (2012) used for the primary selection process. - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Mean astrometric weight of the source in the AL direction. - -The mean astrometric weight of the source is calculated as per Eq. (119) -in Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Mean astrometric weight of the source in the AC direction - -The mean astrometric weight of the source is calculated as per Eq. (119) -in Lindegren et al. (2012). - -Lindegren, L., U. Lammers, D. Hobbs, O’MullaneW., U. Bastian, and J. -Hernandez. 2012. “The Astrometric Core Solution for the Gaia Mission. -Overview of Models, Algorithms, and Software Implementation.†Astronomy -and Astrophysics 538 (February). - - -Type of prior used in the astrometric solution: - -- 0: No prior used - -- 1: Galaxy Bayesian Prior for parallax and proper motion - -- 2: Galaxy Bayesian Prior for parallax and proper motion relaxed by - factor 10 - -- 3: Hipparcos prior for position - -- 4: Hipparcos prior for position and proper motion - -- 5: Tycho2 prior for position - -- 6: Quasar prior for proper motion - -The Galaxy Bayesian Prior is defined in , where it is denoted -\sigma_{\varpi,F90} (for the parallax) and -\sigma_{\mu,F90}={\cal R}\sigma_{\varpi,F90}, with {\cal -R}=10 yr^{-1} (for proper motion). The Galaxy Bayesian Prior relaxed by -a factor 10 is 10\sigma_{\varpi,F90} and 10\sigma_{\mu,F90}, -respectively. - -For Gaia DR1 the only types of priors used are 2 (for the secondary data -set), 3 (for the Hipparcos subset of the primary data set), or 5 (for -the non-Hipparcos subset of the primary data set). Type 6 was used for -internal calibration purposes and alignment of the reference frame, but -the corresponding astrometric results are in general not published. - - - -This field indicates the number of observations (detection transits) -that have been matched to a given source during the last internal -crossmatch revision. - - - -During data processing, this source happened to been duplicated and one -source only has been kept. This may indicate observational, -cross-matching or processing problems, or stellar multiplicity, and -probable astrometric or photometric problems in all cases. In DR1, for -close doubles with separations below some 2 arcsec, truncated windows -have not been processed, neither in astrometry and photometry. The -transmitted window is centred on the brighter part of the acquired -window, so the brighter component has a better chance to be selected, -even when processing the fainter transit. If more than two images are -contained in a window, the result of the image parameter determination -is un-predictable in the sense that it might refer to either (or -neither) image, and no consistency is assured. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionStrength[k-1] (k=1,2,3,4) is the absolute value of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -The scanDirectionStrength is a number between 0 and 1, where 0 means -that the scan directions are well spread out in different directions, -while 1 means that they are concentrated in a single direction (given by -scanAngleMean). - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionStrength[0] is the degree of -concentration when the sense of direction is taken into account, while -scanDirectionStrength[1] is the degree of concentration without regard -to the sense of direction. A large value of scanDirectionStrength[3] -indicates that the scans are concentrated in two nearly orthogonal -directions. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -The scanDirectionStrength and scanDirectionMean quantify the -distribution of AL scan directions across the source. -scanDirectionMean[k-1] (k=1,2,3,4) is 1/k times the argument of the -trigonometric moments m_k=\langle\exp(ik\theta)\rangle, where \theta is -the position angle of the scan and the mean value is taken over the -nObs[0] AL observations contributing to the astrometric parameters of -the source. \theta is defined in the usual astronomical sense: \theta=0 -when the FoV is moving towards local North, and \theta=90^\circ towards -local East. - -scanDirectionMean[k-1] is an angle between -180^\circ/k and -+180^\circ/k, giving the mean position angle of the scans at order k. - -The different orders k are statistics of the scan directions modulo -360^\circ/k. For example, at first order (k=1), \theta=10^\circ and -\theta=190^\circ count as different directions, but at second order -(k=2) they are the same. Thus, scanDirectionMean[0] is the mean -direction when the sense of direction is taken into account, while -scanDirectionMean[1] is the mean direction without regard to the sense -of the direction. For example, scanDirectionMean[0] = 0 means that the -scans preferentially go towards North, while scanDirectionMean[1] = 0 -means that they preferentially go in the North-South direction, and -scanDirectionMean[4] = 0 that they preferentially go either in the -North-South or in the East-West direction. - - -Number of observations (CCD transits) that contributed to the G mean -flux and mean flux error. - - - -Mean flux in the G-band. - - -Error on the mean flux in the G-band. - - -Mean magnitude in the G band. This is computed from the G-band mean flux -applying the magnitude zero-point in the Vega scale. - - -Flag indicating if variability was identified in the photometric G band: - -- source not processed and/or exported to catalogue - -- Source not identified as variable - -- source identified and processed as variable, see tables - PhotVariableSummary, PhotVariableTimeSeriesGfov, - PhotVariableTimeSeriesGfovStatisticalParameters, and Cepheid or - Rrlyrae for more details. - -Note that for this data release only a small subset of (variable) -sources was processed and/or exported, so for many (known) variable -sources this flag is set to “NOT AVAILABLEâ€. No “CONSTANT†sources were -exported either. - - -Galactic Longitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Galactic Latitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Ecliptic Longitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Ecliptic Latitude of the object at reference epoch refEpoch, see ESA, -1997, ’The Hipparcos and Tycho Catalogues’, ESA SP-1200, Volume 1, -Section 1.5.3, for the conversion details. - - -Incremental unique numeric identifier (increasing with declination). -This is the only field which was not in the original 2MASS catalogue, -but was added for cross-match purposes. - - - -The unique source identifier in the original External catalogue. - - -Angular distance between a Gaia source and its nearest neighbour in the -External Catalogue - - -Number of sources in the External Catalogue which match the Gaia source -within position errors. -The identifiers of all the neighbours can be found in the Neighbourhood -table. - - - -Number of other Gaia sources that have as best-neighbour the same -External Catalogue source. -In case there are no other Gaia sources with the same best-neighbour in -the external catalogue, the number of mates is equal to zero. -Given the Gaia high angular resolution, it will happen that what appears -as a single object in an external catalogue will be resolved by Gaia and -as such will be the best-match of more than one Gaia object. - - - -The best-match to a Gaia source in an external catalogue is the source -in the external catalogue that has the highest probability to be the -best-match. -As the probability is based on positional and density properties, it -could happen that there is more than one source in the external -catalogue with the same probability. -Even if a single best-match is always chosen, this field tells the user -if there were more “best†neighbours. Those neighbours can be found in -the Neighbourhood table. - - - -This flag is set to 0 if Gaia proper motions were not available and were -thus not used in the XMatch. -This flag is set to 1 if Gaia proper motions were available and were -thus used in the XMatch (for the First Gaia release the TGASS -sub-sample. - - - -Photometric quality flag. Three character flag, one character per band -[JHKs], that provides a summary of the net quality of the default -photometry in each band. -The value for ph_qual is set for a band according to the precedence of -the table below. For example, a source that is tested and meets the -conditions for category “X†is not tested for subsequent qualities. - -- “X†- There is a detection at this location, but no valid brightness - estimate can be extracted using any algorithm. Default magnitude is - null. - -- “U†- Upper limit on magnitude. Source is not detected in this band, - or it is detected, but not resolved in a consistent fashion with - other bands. A value of ph_qual=“U†does not necessarily mean that - there is no flux detected in this band at the location. - -- “F†- This category includes sources where a reliable estimate of - the photometric error could not be determined. The uncertainties - reported for these sources in [jhk]_msigcom are flags and have - numeric values >8.0. - -- “E†- This category includes detections where the goodness-of-fit - quality of the profile-fit photometry was very poor, or detections - where psf fit photometry did not converge and an aperture magnitude - is reported, or detections where the number of frames was too small - in relation to the number of frames in which a detection was - geometrically possible. - -- “A†- Detections in any brightness regime where valid measurements - were made with [jhk]_snr>10 AND [jhk]_cmsig<0.10857. - -- “B†- Detections in any brightness regime where valid measurements - were made with [jhk]_snr>7 AND [jhk]_cmsig<0.15510. - -- “C†- Detections in any brightness regime where valid measurements - were made with [jhk]_snr>5 AND [jhk]_cmsig<0.21714. - -- “D†- Detections in any brightness regime where valid measurements - were made with no [jhk]_snr or [jhk]_cmsig requirement. - - -Sexagesimal, equatorial position-based source name in the form: -hmmssss+ddmmsss[ABC...] The prefix “2MASS J†in not explicitely listed -in the designation. - - -Semi-major axis length of the one sigma position uncertainty ellipse - - -Semi-minor axis length of the one sigma position uncertainty ellipse - - -Position angle on the sky of the semi-major axis of the position -uncertainty ellipse (East of North) - - - -Default J-band magnitude. -In case of a source not detected in the J-band, it is an upper limit and -the corresponding total photometric uncertainty is NULL. -In case of a source detected in the J-band and with no useful brightness -estimate, it is set to NULL. - - -Total photometric uncertainty for the default J-band magnitude. This -column is NULL if the default magnitude is a 95% confidence upper limit -(i.e. the source is not detected, or inconsistently deblended in the -J-band). - - -Default H-band magnitude. -In case of a source not detected in the H-band, it is an upper limit and -the corresponding total photometric uncertainty is NULL. -In case of a source detected in the H-band and with no useful brightness -estimate, it is set to NULL. - - -Total photometric uncertainty for the default H-band magnitude. This -column is NULL if the default magnitude is a 95% confidence upper limit -(i.e. the source is not detected, or inconsistently deblended in the -H-band). - - -Default Ks-band magnitude. -In case of a source not detected in the Ks-band, it is an upper limit -and the corresponding total photometric uncertainty is NULL. -In case of a source detected in the Ks-band and with no useful -brightness estimate, it is set to NULL. - - -Total photometric uncertainty for the default Ks-band magnitude. This -column is NULL if the default magnitude is a 95% confidence upper limit -(i.e. the source is not detected, or inconsistently deblended in the -Ks-band. - - -Unique identification number of the record in the XSC (2MASS Extended -Source Catalogue) that corresponds to this point source. Can be NULL if -no corresponding extended source is present in the XSC. - - - -The Julian Date of the source measurement accurate to +30 seconds. This -value is extrapolated from the start time of the Survey scan using the -difference between the declinations of the source and the first row in -the Tile divided by the scanning rate of the telescope (approximately -5sec). The scanning rate of the two 2MASS telescopes was slightly -different because of the need to optimize the dithering of images on the -arrays. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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