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-version = "0.2.13" -description = "Measures the displayed width of unicode strings in a terminal" -optional = false -python-versions = "*" -files = [ - {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, - {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, -] - [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "a9b8928e119e14cc24e5430d42232017967d38972fe7a432d9b946ac8e9ade01" +content-hash = "394d4b5ff99d4930d2ba23948704b7c816374af8f5c809e08371460309df38d9" diff --git a/pyproject.toml b/pyproject.toml index 06de095..0f9d8f4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ fuzzywuzzy = "^0.18.0" openpyxl = "^3.1.2" python-levenshtein = "^0.25.0" pyyaml = "^6.0.1" -ipython = "^8.22.2" +dataio = {git = "git@github.com:dsih-artpark/dataio.git"} [build-system] diff --git a/src/epipipeline/standardise/dengue/karnataka.py b/src/epipipeline/standardise/dengue/karnataka.py index 70bba3a..1348043 100644 --- a/src/epipipeline/standardise/dengue/karnataka.py +++ b/src/epipipeline/standardise/dengue/karnataka.py @@ -15,7 +15,6 @@ import requests import yaml - pd.set_option('future.no_silent_downcasting', True) @@ -135,7 +134,7 @@ def standardise_ka_linelist_v1(preprocessed_data_dict, regionIDs_dict, missing_cols = list(missing_cols) df = pd.concat(standardised_data_dict.values(), ignore_index=True) - df = df.drop(columns=missing_cols, errors='ignore') + # df = df.drop(columns=missing_cols, errors='ignore') if version == "v2": df["metadata.primaryDate"] = df["event.symptomOnsetDate"].fillna(df["event.test.sampleCollectionDate"]).fillna(df["event.test.resultDate"]) # noqa: E501 diff --git a/src/epipipeline/standardise/gisdata/__init__.py b/src/epipipeline/standardise/gisdata/__init__.py index 2e1e2fb..9a2d9b8 100644 --- a/src/epipipeline/standardise/gisdata/__init__.py +++ b/src/epipipeline/standardise/gisdata/__init__.py @@ -129,7 +129,9 @@ def fuzzy_matching(string, choices, threshold): # what does process.extractOne do? best_match = process.extractOne(str(string), choices) - if best_match[1] > threshold: + if best_match is None: + return None + elif best_match[1] > threshold: return best_match[0] else: return None