diff --git a/.gitignore b/.gitignore index 08a5a6e..c89374f 100644 --- a/.gitignore +++ b/.gitignore @@ -13,6 +13,9 @@ googlemaps-api-key.txt bin cachedir + +sample_annotator/cache/ + examples/outputs temp/ diff --git a/make-gold-cache.Makefile b/make-gold-cache.Makefile index f72d051..68d7bf0 100644 --- a/make-gold-cache.Makefile +++ b/make-gold-cache.Makefile @@ -13,6 +13,12 @@ local/gold-studies.tsv: downloads/goldData.xlsx --sheet-name Study \ --output-file $@ +local/gold-biosamples.tsv: downloads/goldData.xlsx + poetry run python sample_annotator/file_utils/xlsx_to_tsv.py \ + --excel-file $< \ + --sheet-name Study \ + --output-file $@ + local/gold-study-ids.txt: local/gold-studies.tsv # without the grep filter, this introduces some noise (non-id rows) tail -n +2 $< | cut -f 1 | sort | grep 'Gs' > $@ @@ -49,4 +55,26 @@ load-gold-biosamples-into-mongo: local/gold-study-ids-subset.txt # --biosample-output-file local/gold-biosamples-only.json \ # --project-output-file local/gold-projects-only.json \ # --remove-contacts \ -# --remove-nulls \ No newline at end of file +# --remove-nulls + +local/gold-emp500-study-id.txt: + echo "Gs0154244" > $@ + sleep 1 + +local/gold-emp500-cache.json: local/gold-emp500-study-id.txt + # ~ 3 seconds/uncached study + # GOLD has ~ 63k studies + # < 2 days to fetch all studies ? + poetry run python sample_annotator/clients/gold_client.py \ + --verbose \ + fetch-studies \ + --output-format json \ + --output $@ \ + --include-biosamples \ + --authentication-file config/gold-key.txt \ + $< + +local/gold-emp500-cache.tsv: local/gold-emp500-cache.json + poetry run python sample_annotator/parse_biosample_metadata_from_gold_study.py \ + --input-file $< \ + --output-file $@ diff --git a/poetry.lock b/poetry.lock index 2acf9a0..30a2fe4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,38 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. + +[[package]] +name = "affine" +version = "2.4.0" +description = "Matrices describing affine transformation of the plane" +optional = false +python-versions = ">=3.7" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" +files = [ + {file = "affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92"}, + {file = "affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea"}, +] + +[package.extras] +dev = ["coveralls", "flake8", "pydocstyle"] +test = ["pytest (>=4.6)", "pytest-cov"] + +[[package]] +name = "airium" +version = "0.2.6" +description = "Easy and quick html builder with natural syntax correspondence (python->html). No templates needed. Serves pure pythonic library with no dependencies." +optional = false +python-versions = "*" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" +files = [ + {file = "airium-0.2.6-py3-none-any.whl", hash = "sha256:50af5cf491e084f27909e29a93550b4170e587cde01334d58c6249644ee8c6c2"}, + {file = "airium-0.2.6.tar.gz", hash = "sha256:ccab36b798b6cce3d0c5074e52ce8059f6e82991caae4985f42cadfad80b1de4"}, +] + +[package.extras] +dev = ["pytest (>=6.2,<7.0)", "pytest-cov (>=3.0,<4.0)", "pytest-mock (>=3.6,<4.0)"] +parse = ["beautifulsoup4 (>=4.10.0,<5.0)", "requests (>=2.12.0,<3)"] [[package]] name = "alabaster" @@ -6,6 +40,8 @@ version = "1.0.0" description = "A light, configurable Sphinx theme" optional = false python-versions = ">=3.10" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b"}, {file = "alabaster-1.0.0.tar.gz", hash = "sha256:c00dca57bca26fa62a6d7d0a9fcce65f3e026e9bfe33e9c538fd3fbb2144fd9e"}, @@ -17,6 +53,8 @@ version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" +groups = ["main", "dev"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, @@ -28,16 +66,33 @@ version = "4.9.3" description = "ANTLR 4.9.3 runtime for Python 3.7" optional = false python-versions = "*" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"}, ] +[[package]] +name = "appdirs" +version = "1.4.4" +description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." +optional = false +python-versions = "*" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" +files = [ + {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"}, + {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"}, +] + [[package]] name = "arrow" version = "1.3.0" description = "Better dates & times for Python" optional = false python-versions = ">=3.8" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"}, {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"}, @@ -53,13 +108,15 @@ test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock [[package]] name = "attrs" -version = "24.3.0" +version = "25.1.0" description = "Classes Without Boilerplate" optional = false python-versions = ">=3.8" +groups = ["main", "dev"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ - {file = "attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308"}, - {file = "attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff"}, + {file = "attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a"}, + {file = "attrs-25.1.0.tar.gz", hash = "sha256:1c97078a80c814273a76b2a298a932eb681c87415c11dee0a6921de7f1b02c3e"}, ] [package.extras] @@ -72,13 +129,15 @@ tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] [[package]] name = "autopep8" -version = "2.3.1" +version = "2.3.2" description = "A tool that automatically formats Python code to conform to the PEP 8 style guide" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" +groups = ["dev"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ - {file = "autopep8-2.3.1-py2.py3-none-any.whl", hash = "sha256:a203fe0fcad7939987422140ab17a930f684763bf7335bdb6709991dd7ef6c2d"}, - {file = "autopep8-2.3.1.tar.gz", hash = "sha256:8d6c87eba648fdcfc83e29b788910b8643171c395d9c4bcf115ece035b9c9dda"}, + {file = "autopep8-2.3.2-py2.py3-none-any.whl", hash = "sha256:ce8ad498672c845a0c3de2629c15b635ec2b05ef8177a6e7c91c74f3e9b51128"}, + {file = "autopep8-2.3.2.tar.gz", hash = "sha256:89440a4f969197b69a995e4ce0661b031f455a9f776d2c5ba3dbd83466931758"}, ] [package.dependencies] @@ -87,31 +146,49 @@ tomli = {version = "*", markers = "python_version < \"3.11\""} [[package]] name = "babel" -version = "2.16.0" +version = "2.17.0" description = "Internationalization utilities" optional = false python-versions = ">=3.8" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ - {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"}, - {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"}, + {file = "babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2"}, + {file = "babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d"}, ] [package.extras] -dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"] +dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"] + +[[package]] +name = "bcp47" +version = "0.1.0" +description = "Language tags made easy" +optional = false +python-versions = "*" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" +files = [ + {file = "bcp47-0.1.0-py3-none-any.whl", hash = "sha256:a567b7fe881c900916a9db62ac8f792370c7ddf2664e601b74eeed35d8758aa5"}, + {file = "bcp47-0.1.0.tar.gz", hash = "sha256:37cc7a03fd696149ebfe7de785c5bad473f911eddac8415cf479584049cf03d5"}, +] [[package]] name = "beautifulsoup4" -version = "4.12.3" +version = "4.13.3" description = "Screen-scraping library" optional = false -python-versions = ">=3.6.0" +python-versions = ">=3.7.0" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ - {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"}, - {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"}, + {file = "beautifulsoup4-4.13.3-py3-none-any.whl", hash = "sha256:99045d7d3f08f91f0d656bc9b7efbae189426cd913d830294a15eefa0ea4df16"}, + {file = "beautifulsoup4-4.13.3.tar.gz", hash = "sha256:1bd32405dacc920b42b83ba01644747ed77456a65760e285fbc47633ceddaf8b"}, ] [package.dependencies] soupsieve = ">1.2" +typing-extensions = ">=4.0.0" [package.extras] cchardet = ["cchardet"] @@ -122,13 +199,15 @@ lxml = ["lxml"] [[package]] name = "bioregistry" -version = "0.11.29" +version = "0.11.35" description = "Integrated registry of biological databases and nomenclatures" optional = false python-versions = ">=3.9" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ - {file = "bioregistry-0.11.29-py3-none-any.whl", hash = "sha256:f4d8b9130297b505c186a51aa5451a355f29e9fef25fd358441b6c95bb5606e4"}, - {file = "bioregistry-0.11.29.tar.gz", hash = "sha256:6d6b1c9943bb80eea4fee699d1f9f86b13a260151468db53e70c557b9a0538ef"}, + {file = "bioregistry-0.11.35-py3-none-any.whl", hash = "sha256:64713a6595190175d506f2053b1599707a8cf266a2ba44cff3c2e29b71063f90"}, + {file = "bioregistry-0.11.35.tar.gz", hash = "sha256:f3c57638c193161674f3569312dd6c3a3f99ace862a5c8e0c7986c06e30815d0"}, ] [package.dependencies] @@ -147,6 +226,7 @@ docs = ["autodoc_pydantic", "sphinx (>=8)", "sphinx-click", "sphinx-rtd-theme (> export = ["ndex2", "pyyaml", "rdflib", "rdflib-jsonld"] gha = ["more_itertools"] health = ["click_default_group", "jinja2", "pandas", "pyyaml", "tabulate"] +paper-ranking = ["indra", "more_itertools", "pandas", "scikit-learn", "tabulate"] tests = ["coverage", "httpx", "indra", "more_itertools", "pytest", "scikit-learn"] web = ["a2wsgi", "bootstrap-flask (<=2.0.0)", "curies[fastapi]", "fastapi", "flask (<2.2.4)", "markdown", "pyyaml", "rdflib", "rdflib-endpoint", "rdflib-jsonld", "uvicorn", "werkzeug (<2.3.0)"] @@ -156,6 +236,8 @@ version = "24.10.0" description = "The uncompromising code formatter." optional = false python-versions = ">=3.9" +groups = ["dev"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "black-24.10.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e6668650ea4b685440857138e5fe40cde4d652633b1bdffc62933d0db4ed9812"}, {file = "black-24.10.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1c536fcf674217e87b8cc3657b81809d3c085d7bf3ef262ead700da345bfa6ea"}, @@ -198,24 +280,56 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] name = "cachetools" -version = "5.5.0" +version = "5.5.1" description = "Extensible memoizing collections and decorators" optional = false python-versions = ">=3.7" +groups = ["dev"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ - 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{file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"}, - {file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"}, + {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"}, + {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"}, ] [[package]] @@ -224,6 +338,8 @@ version = "0.2.1" description = "rdflib collections flattening graph" optional = false python-versions = "*" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "CFGraph-0.2.1.tar.gz", hash = "sha256:b57fe7044a10b8ff65aa3a8a8ddc7d4cd77bf511b42e57289cd52cbc29f8fe74"}, ] @@ -237,6 +353,8 @@ version = "5.2.0" description = "Universal encoding detector for Python 3" optional = false python-versions = ">=3.7" +groups = ["main", "dev"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970"}, {file = "chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7"}, @@ -248,6 +366,8 @@ version = "3.4.1" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." optional = false python-versions = ">=3.7" +groups = ["main", "dev"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "charset_normalizer-3.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:91b36a978b5ae0ee86c394f5a54d6ef44db1de0815eb43de826d41d21e4af3de"}, {file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7461baadb4dc00fd9e0acbe254e3d7d2112e7f92ced2adc96e54ef6501c5f176"}, @@ -343,12 +463,39 @@ files = [ {file = "charset_normalizer-3.4.1.tar.gz", hash = "sha256:44251f18cd68a75b56585dd00dae26183e102cd5e0f9f1466e6df5da2ed64ea3"}, ] +[[package]] +name = "class-resolver" +version = "0.5.4" +description = "Lookup and instantiate classes with style." +optional = false +python-versions = ">=3.9" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" +files = [ + {file = 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index 0000000..e22e8f8 --- /dev/null +++ b/sample_annotator/alaska_air_nlcds.py @@ -0,0 +1,111 @@ +import click +import pandas as pd +import rasterio +from pyproj import Transformer + +# get Alaska land coverage files from https://s3-us-west-2.amazonaws.com/mrlc/NLCD_2016_Land_Cover_AK_20200724.zip +# they are in the ERDAS IMAGINE (.img) raster dataset format +# other states have newer files in GeoTIFF format + +# the "Marina Nieto-Caballero air study proposed local scale" file comes from +# https://docs.google.com/spreadsheets/d/1QpS2ZWFDGqn_NV6YcHqI58i8bS5SjN0CxIRCeUYoxuA/edit?gid=2029591052#gid=2029591052 + +# NLCD Land Cover Classification Dictionary +NLCD_CLASSES = { + 11: "Open Water", + 12: "Perennial Ice/Snow", + 21: "Developed, Open Space", + 22: "Developed, Low Intensity", + 23: "Developed, Medium Intensity", + 24: "Developed, High Intensity", + 31: "Barren Land", + 41: "Deciduous Forest", + 42: "Evergreen Forest", + 43: "Mixed Forest", + 51: "Dwarf Scrub", + 52: "Shrub/Scrub", + 71: "Grassland/Herbaceous", + 72: "Sedge/Herbaceous", + 73: "Lichens", + 74: "Moss", + 81: "Pasture/Hay", + 82: "Cultivated Crops", + 90: "Woody Wetlands", + 95: "Emergent Herbaceous Wetlands" +} + + +def classify_land_cover(nlcd_file: str, tsv_file: str, output_file: str) -> None: + """ + Classifies land cover types for coordinates in a TSV file using an NLCD raster. + + :param nlcd_file: Path to the NLCD raster (.img) file. + :param tsv_file: Path to the TSV file containing coordinates. + :param output_file: Path to save the output TSV file with classification results. + """ + try: + # Read input TSV file + df = pd.read_csv(tsv_file, sep='\t') + + # Open the NLCD raster + with rasterio.open(nlcd_file) as src: + transformer = Transformer.from_crs("EPSG:4326", src.crs, always_xy=True) + + # Prepare results + results = [] + + for _, row in df.iterrows(): + lon, lat = row['lon val'], row['lat val'] + sample_name = row['sample name'] + + # Transform coordinates to raster CRS + x, y = transformer.transform(lon, lat) + + # Extract NLCD land cover class + try: + land_cover_value = next(src.sample([(x, y)]))[0] + land_cover_description = NLCD_CLASSES.get(land_cover_value, "Unknown Class") + except StopIteration: + land_cover_value = None + land_cover_description = "No Data" + + # Store results + results.append([sample_name, land_cover_value, land_cover_description]) + + # Create DataFrame + results_df = pd.DataFrame(results, columns=['Sample Name', 'NLCD Code', 'NLCD Description']) + + # Save to TSV + results_df.to_csv(output_file, sep='\t', index=False) + + click.echo(f"Land cover classification completed. Results saved to '{output_file}'.") + + except Exception as e: + click.echo(f"Error: {e}", err=True) + + +@click.command() +@click.option('--nlcd-file', + type=click.Path(exists=True), + required=True, + default="../downloads/NLCD_2016_Land_Cover_AK_20200724.img", + help="Path to NLCD .img file.") +@click.option('--tsv-file', + type=click.Path(exists=True), + required=True, + default="../downloads/Marina Nieto-Caballero air study proposed local scale - merged and curated (1).tsv", + help="Path to input TSV file with coordinates.") +@click.option('--output-file', + type=click.Path(), + required=True, + default="../local/nlcd_land_cover_results.tsv", + help="Path to save the output TSV file.") +def main(nlcd_file: str, tsv_file: str, output_file: str) -> None: + """ + CLI tool for classifying land cover using an NLCD raster and a TSV file with coordinates. + """ + classify_land_cover(nlcd_file, tsv_file, output_file) + + +if __name__ == "__main__": + main() diff --git a/sample_annotator/alaska_air_osm.py b/sample_annotator/alaska_air_osm.py new file mode 100644 index 0000000..6c52da6 --- /dev/null +++ b/sample_annotator/alaska_air_osm.py @@ -0,0 +1,111 @@ +import click +import osmnx as ox +import pandas as pd +import time +from typing import List, Dict, Any + +# Default file paths +default_tsv_file = "../downloads/Marina Nieto-Caballero air study proposed local scale - merged and curated (1).tsv" +default_output_file = "../local/osm_all_features_results.tsv" + +# Threshold for flagging uncommon features +uncommon_threshold = 0.2 + + +def fetch_osm_features(lat: float, lon: float, dist: int = 1000) -> pd.DataFrame: + """ + Fetch OpenStreetMap (OSM) features near a specified latitude and longitude. + + :param lat: Latitude of the location. + :param lon: Longitude of the location. + :param dist: Search radius in meters (default is 1000). + :return: DataFrame containing the fetched OSM features. + """ + tags = { + 'aeroway': True, 'amenity': True, 'barrier': True, 'boundary': True, 'building': True, + 'emergency': True, 'geological': True, 'highway': True, 'historic': True, 'landuse': True, + 'leisure': True, 'man_made': True, 'military': True, 'natural': True, 'place': True, + 'surface': True, 'tourism': True, 'water': True, 'waterway': True, 'wetland': True + } + return ox.features_from_point((lat, lon), tags=tags, dist=dist) + + +def process_osm_data(tsv_file: str, output_file: str) -> None: + """ + Processes a TSV file with coordinates and fetches OSM features for each location. + + :param tsv_file: Path to the input TSV file. + :param output_file: Path to save the results. + """ + df = pd.read_csv(tsv_file, sep='\t') + results: List[Dict[str, Any]] = [] + + for idx, row in df.iterrows(): + lat, lon = row['lat val'], row['lon val'] + sample_name = row['sample name'] + + click.echo(f"Processing {idx + 1}/{len(df)}: Sample '{sample_name}' at ({lat}, {lon})") + + try: + features = fetch_osm_features(lat, lon) + if features.empty: + feature_info = "No features found" + else: + feature_info = [ + ", ".join( + f"{k}: {v}" for k, v in feature.items() + if pd.notnull(v) and k != 'geometry' and ( + k in ['name', 'name:en', 'old_name', 'old_name:en'] or ( + not k.startswith('name:') and not k.startswith('old_name:') + ) + ) + ) + for _, feature in features.iterrows() + ] + feature_info = feature_info if feature_info else ['No English name found'] + + for info in feature_info: + results.append({ + 'sample_name': sample_name, + 'latitude': lat, + 'longitude': lon, + 'feature_info': info + }) + except Exception as e: + click.echo(f"Error fetching data for Sample '{sample_name}': {e}", err=True) + results.append({ + 'sample_name': sample_name, + 'latitude': lat, + 'longitude': lon, + 'feature_info': f"Error fetching data: {e}" + }) + + time.sleep(1) # 1-second delay between requests + + results_df = pd.DataFrame(results) + + # Identify and flag uncommon features across samples + feature_counts = results_df['feature_info'].value_counts() + total_samples = len(df) + dynamic_column_name = f"lt_{int(uncommon_threshold * 100)}_pct_of_samples" + results_df[dynamic_column_name] = results_df['feature_info'].apply( + lambda x: True if feature_counts[x] / total_samples < uncommon_threshold else False + ) + + # Save to TSV + results_df.to_csv(output_file, sep='\t', index=False) + click.echo(f"OSM feature extraction completed. Results saved to '{output_file}'.") + + +@click.command() +@click.option('--tsv-file', type=click.Path(exists=True), default=default_tsv_file, help="Path to input TSV file.") +@click.option('--output-file', type=click.Path(), default=default_output_file, help="Path to save the output TSV file.") +def main(tsv_file: str, output_file: str) -> None: + """ + CLI tool for extracting OpenStreetMap features based on provided coordinates in a TSV file. + """ + process_osm_data(tsv_file, output_file) + + +if __name__ == "__main__": + main() diff --git a/sample_annotator/parse_biosample_metadata_from_gold_study.py b/sample_annotator/parse_biosample_metadata_from_gold_study.py new file mode 100644 index 0000000..9a61cb8 --- /dev/null +++ b/sample_annotator/parse_biosample_metadata_from_gold_study.py @@ -0,0 +1,127 @@ +import json +import csv +from typing import Dict, List, Any +import click +from oaklib import get_adapter +from oaklib.datamodels.vocabulary import IS_A + + +@click.command() +@click.option("--input-file", "-i", default="gold-cache.json", help="Path to the input JSON file.") +@click.option("--output-file", "-o", default="biosamples.tsv", help="Path to the output TSV file.") +def main(input_file: str, output_file: str) -> None: + """ + This script processes a JSON file containing biosample metadata and + outputs a TSV file with one row per biosample and its associated projects. + """ + + adapter = get_adapter("sqlite:obo:envo") # slowest part?! + + biome_id = "ENVO:00000428" # Biome + environmental_material_id = "ENVO:00010483" # Environmental material + abp = "ENVO:01000813" # astronomical body part + + # Get all subclasses (descendants) of 'biome' + biome_subclasses = adapter.descendants(biome_id, reflexive=False, predicates=[IS_A]) + biome_subclasses = list(biome_subclasses) # Convert to a list for easier handling + + # Get all subclasses (descendants) of 'environmental material' + environmental_material_subclasses = adapter.descendants(environmental_material_id, reflexive=False, + predicates=[IS_A]) + environmental_material_subclasses = list(environmental_material_subclasses) # Convert to a list + + abp_subclasses = adapter.descendants(abp, reflexive=False, predicates=[IS_A]) + abp_subclasses = list(abp_subclasses) # Convert to a list + + non_biome_non_em_abps = list(set(abp_subclasses) - set(biome_subclasses) - set(environmental_material_subclasses)) + + # Load the JSON data + with open(input_file, "r") as f: + data: List[Dict[str, Any]] = json.load(f) + + # Open the output TSV file + with open(output_file, "w", newline="") as f: + # Create a CSV writer with tab delimiter + writer = csv.writer(f, delimiter="\t") + + # Write the header row + writer.writerow([ + "studyGoldId", "biosampleGoldId", "biosampleName", "description", + "ecosystemPathId", "ecosystem", "ecosystemCategory", "ecosystemType", "ecosystemSubtype", + "specificEcosystem", + "envoBroadScaleId", "envoBroadScaleLabel", "bad_ebs", + "envoLocalScaleId", "envoLocalScaleLabel", "bad_els", + "envoMediumId", "envoMediumLabel", "bad_em", + "mixsPackage", + "ncbiTaxId", "ncbiTaxName", + "modDate", + "ncbiBioProjectAccession", "ncbiBioSampleAccession", + "projectGoldId", "sraExperimentIds" + ]) + + # Iterate over the biosamples in the JSON data + for item in data: + study_gold_id: str = item["studyGoldId"] + for biosample in item["biosamples"]: + # Extract the required fields from the biosample data + biosample_gold_id: str = biosample["biosampleGoldId"] + biosample_name: str = biosample["biosampleName"] + description: str = biosample["description"] + + ecosystem_path_id: int = biosample["ecosystemPathId"] + ecosystem: str = biosample["ecosystem"] + ecosystem_category: str = biosample["ecosystemCategory"] + ecosystem_type: str = biosample["ecosystemType"] + ecosystem_subtype: str = biosample["ecosystemSubtype"] + specific_ecosystem: str = biosample["specificEcosystem"] + + envo_broad_scale_id: str = biosample["envoBroadScale"]["id"].replace("_", ":") + envo_broad_scale_label: str = biosample["envoBroadScale"]["label"] + bad_ebs = envo_broad_scale_id not in biome_subclasses + envo_local_scale_id: str = biosample["envoLocalScale"]["id"].replace("_", ":") + envo_local_scale_label: str = biosample["envoLocalScale"]["label"] + bad_els = envo_local_scale_id not in non_biome_non_em_abps + envo_medium_id: str = biosample["envoMedium"]["id"].replace("_", ":") + envo_medium_label: str = biosample["envoMedium"]["label"] + bad_em = envo_medium_id not in environmental_material_subclasses + mixs_package: str = biosample["mixsPackage"] + ncbi_tax_id: int = biosample["ncbiTaxId"] + ncbi_tax_name: str = biosample["ncbiTaxName"] + mod_date: str = biosample["modDate"] + + ncbi_bio_project_accessions = [] + ncbi_bio_sample_accessions = [] + project_gold_ids = [] + sra_experiment_ids = [] + + for project in biosample["projects"]: + ncbi_bio_project_accessions.append(project["ncbiBioProjectAccession"]) + ncbi_bio_sample_accessions.append(project["ncbiBioSampleAccession"]) + project_gold_ids.append(project["projectGoldId"]) + sra_experiment_ids.extend(project["sraExperimentIds"]) + + # Join the lists into strings with a delimiter (e.g., semicolon) + ncbi_bio_project_accessions_str = ";".join(sorted(list(set(ncbi_bio_project_accessions)))) + ncbi_bio_sample_accessions_str = ";".join(sorted(list(set(ncbi_bio_sample_accessions)))) + project_gold_ids_str = ";".join(sorted(list(set(project_gold_ids)))) + sra_experiment_ids_str = ";".join(sorted(list(set(sra_experiment_ids)))) + + writer.writerow([ + study_gold_id, biosample_gold_id, biosample_name, description, + ecosystem_path_id, ecosystem, ecosystem_category, ecosystem_type, ecosystem_subtype, + specific_ecosystem, + envo_broad_scale_id, envo_broad_scale_label, bad_ebs, + envo_local_scale_id, envo_local_scale_label, bad_els, + envo_medium_id, envo_medium_label, bad_em, + mixs_package, + ncbi_tax_id, ncbi_tax_name, + mod_date, + ncbi_bio_project_accessions_str, # Write the concatenated string + ncbi_bio_sample_accessions_str, # Write the concatenated string + project_gold_ids_str, # Write the concatenated string + sra_experiment_ids_str, # Write the concatenated string + ]) + + +if __name__ == "__main__": + main()