✔ Test if dtypes are as expected in produced Parquet files

check_dtypes.py

@@ -1,87 +0,0 @@
-import re
-import warnings
-import os
-
-with warnings.catch_warnings():
-    # Filter warnings on WSL
-    if "Microsoft" in os.uname().release:
-        warnings.simplefilter("ignore")
-    import pandas as pd
-
-
-mods_info = pd.read_parquet("mods_info_df.parquet")
-page_info = pd.read_parquet("page_info_df.parquet")
-alto_info = pd.read_parquet("alto_info_df.parquet")
-
-# Check
-EXPECTED_TYPES = {
-
-        # mods_info
-
-        r"mets_file": ("object", ["str"]),
-        r"titleInfo_title": ("object", ["str"]),
-        r"titleInfo_subTitle": ("object", ["str", "NoneType"]),
-        r"titleInfo_partName": ("object", ["str", "NoneType"]),
-        r"identifier-.*": ("object", ["str", "NoneType"]),
-        r"location_.*": ("object", ["str", "NoneType"]),
-        r"name\d+_.*roleTerm": ("object", ["ndarray", "NoneType"]),
-        r"name\d+_.*": ("object", ["str", "NoneType"]),
-        r"relatedItem-.*_recordInfo_recordIdentifier": ("object", ["str", "NoneType"]),
-        r"typeOfResource": ("object", ["str", "NoneType"]),
-        r"accessCondition-.*": ("object", ["str", "NoneType"]),
-        r"originInfo-.*": ("object", ["str", "NoneType"]),
-
-        r".*-count": ("Int64", None),
-
-        r"genre-.*": ("object", ["ndarray", "NoneType"]),
-        r"subject-.*": ("object", ["ndarray", "NoneType"]),
-        r"language_.*Term": ("object", ["ndarray", "NoneType"]),
-        r"classification-.*": ("object", ["ndarray", "NoneType"]),
-
-        # page_info
-
-        r"fileGrp_.*_file_FLocat_href": ("object", ["str", "NoneType"]),
-        r"structMap-LOGICAL_TYPE_.*": ("boolean", None),
-
-        # alto_info
-
-        r"Description_.*": ("object", ["str", "NoneType"]),
-        r"Layout_Page_ID": ("object", ["str", "NoneType"]),
-        r"Layout_Page_PHYSICAL_(IMG|IMAGE)_NR": ("object", ["str", "NoneType"]),
-        r"Layout_Page_PROCESSING": ("object", ["str", "NoneType"]),
-        r"Layout_Page_QUALITY": ("object", ["str", "NoneType"]),
-        r"Layout_Page_//alto:String/@WC-.*": ("Float64", None),
-        r"alto_xmlns": ("object", ["str", "NoneType"]),
-
-        r"Layout_Page_(WIDTH|HEIGHT)": ("Int64", None),
-}
-
-def expected_types(c):
-    for r, types in EXPECTED_TYPES.items():
-        if re.fullmatch(r, c):
-            edt = types[0]
-            einner_types = types[1]
-            if einner_types:
-                einner_types = set(einner_types)
-            return edt, einner_types
-    return None, None
-
-def check_types(df):
-    for c in df.columns:
-        dt = df.dtypes[c]
-        edt, einner_types = expected_types(c)
-
-        if edt is None:
-            print(f"No expected dtype known for column {c} (got {dt})")
-        elif dt != edt:
-            print(f"Unexpected dtype {dt} for column {c} (expected {edt})")
-
-        if edt == "object":
-            inner_types = set(type(v).__name__ for v in df[c])
-            if any(it not in einner_types for it in inner_types):
-                print(f"Unexpected inner types {inner_types} for column {c} (expected {einner_types})")
-
-check_types(mods_info)
-check_types(page_info)
-check_types(alto_info)
-

src/mods4pandas/alto4pandas.py

@@ -138,7 +138,7 @@ def walk(m):
 @click.argument('alto_files', type=click.Path(exists=True), required=True, nargs=-1)
 @click.option('--output', '-o', 'output_file', type=click.Path(), help='Output Parquet file',
               default='alto_info_df.parquet', show_default=True)
-def process(alto_files: List[str], output_file: str):
+def process_command(alto_files: List[str], output_file: str):
     """
     A tool to convert the ALTO metadata in INPUT to a pandas DataFrame.
 
@@ -151,6 +151,9 @@ def process(alto_files: List[str], output_file: str):
     - and a CSV file with all conversion warnings.
     """
 
+    process(alto_files, output_file)
+
+def process(alto_files: List[str], output_file: str):
     # Extend file list if directories are given
     alto_files_real = []
     for m in alto_files:

src/mods4pandas/mods4pandas.py

@@ -382,7 +382,7 @@ def pages_to_dict(mets, raise_errors=True) -> List[Dict]:
 @click.option('--output', '-o', 'output_file', type=click.Path(), help='Output Parquet file',
               default='mods_info_df.parquet', show_default=True)
 @click.option('--output-page-info', type=click.Path(), help='Output page info Parquet file')
-def process(mets_files: list[str], output_file: str, output_page_info: str):
+def process_command(mets_files: list[str], output_file: str, output_page_info: str):
     """
     A tool to convert the MODS metadata in INPUT to a pandas DataFrame.
 
@@ -393,7 +393,9 @@ def process(mets_files: list[str], output_file: str, output_page_info: str):
 
     Per-page information (e.g. structure information) can be output to a separate Parquet file.
     """
+    process(mets_files, output_file, output_page_info)
 
+def process(mets_files: list[str], output_file: str, output_page_info: str):
     # Extend file list if directories are given
     mets_files_real: list[str] = []
     for m in mets_files:
@@ -476,7 +478,7 @@ def main():
     for prefix, uri in ns.items():
         ET.register_namespace(prefix, uri)
 
-    process()
+    process_command()
 
 
 if __name__ == '__main__':

src/mods4pandas/tests/test_alto.py

@@ -1,9 +1,13 @@
+from pathlib import Path
+import re
 from lxml import etree as ET
+import pandas as pd
 
 
-from mods4pandas.alto4pandas import alto_to_dict
+from mods4pandas.alto4pandas import alto_to_dict, process
 from mods4pandas.lib import flatten
 
+TESTS_DATA_DIR = Path(__file__).parent / "data"
 
 def dict_fromstring(x):
    return flatten(alto_to_dict(ET.fromstring(x)))
@@ -79,3 +83,50 @@ def test_String_TAGREF_counts():
     """)
     assert d['Layout_Page_//alto:String[@TAGREFS]-count'] == 3
     assert d['Layout_Page_String-count'] == 4
+
+
+def test_dtypes(tmp_path):
+    alto_dir = (TESTS_DATA_DIR / "alto").absolute().as_posix()
+    alto_info_df_parquet = (tmp_path / "test_dtypes_alto_info.parquet").as_posix()
+    process([alto_dir], alto_info_df_parquet)
+    alto_info_df = pd.read_parquet(alto_info_df_parquet)
+
+    EXPECTED_TYPES = {
+        r"Description_.*": ("object", ["str", "NoneType"]),
+        r"Layout_Page_ID": ("object", ["str", "NoneType"]),
+        r"Layout_Page_PHYSICAL_(IMG|IMAGE)_NR": ("object", ["str", "NoneType"]),
+        r"Layout_Page_PROCESSING": ("object", ["str", "NoneType"]),
+        r"Layout_Page_QUALITY": ("object", ["str", "NoneType"]),
+        r"Layout_Page_//alto:String/@WC-.*": ("Float64", None),
+        r".*-count": ("Int64", None),
+        r"alto_xmlns": ("object", ["str", "NoneType"]),
+
+        r"Layout_Page_(WIDTH|HEIGHT)": ("Int64", None),
+    }
+    def expected_types(c):
+        """Return the expected types for column c."""
+        for r, types in EXPECTED_TYPES.items():
+            if re.fullmatch(r, c):
+                edt = types[0]
+                einner_types = types[1]
+                if einner_types:
+                    einner_types = set(einner_types)
+                return edt, einner_types
+        return None, None
+
+    def check_types(df):
+        """Check the types of the DataFrame df."""
+        for c in df.columns:
+            dt = df.dtypes[c]
+            edt, einner_types = expected_types(c)
+            print(c, dt, edt)
+
+            assert edt is not None, f"No expected dtype known for column {c} (got {dt})"
+            assert dt == edt, f"Unexpected dtype {dt} for column {c} (expected {edt})"
+
+            if edt == "object":
+                inner_types = set(type(v).__name__ for v in df[c])
+                assert all(it in einner_types for it in inner_types), \
+                    f"Unexpected inner types {inner_types} for column {c} (expected {einner_types})"
+
+    check_types(alto_info_df)

src/mods4pandas/tests/test_mods4pandas.py

@@ -1,10 +1,14 @@
+from pathlib import Path
+import re
 from lxml import etree as ET
+import pandas as pd
 import pytest
 
 
-from mods4pandas.mods4pandas import mods_to_dict
+from mods4pandas.mods4pandas import mods_to_dict, process
 from mods4pandas.lib import flatten
 
+TESTS_DATA_DIR = Path(__file__).parent / "data"
 
 def dict_fromstring(x):
     """Helper function to parse a MODS XML string to a flattened dict"""
@@ -151,3 +155,68 @@ def test_relatedItem():
     """)
 
     assert d['relatedItem-original_recordInfo_recordIdentifier-dnb-ppn'] == '1236513355'
+
+def test_dtypes(tmp_path):
+    mets_files = [p.absolute().as_posix() for p in (TESTS_DATA_DIR / "mets-mods").glob("*.xml")]
+    mods_info_df_parquet = (tmp_path / "test_dtypes_mods_info.parquet").as_posix()
+    page_info_df_parquet = (tmp_path / "test_dtypes_page_info.parquet").as_posix()
+    process(mets_files, mods_info_df_parquet, page_info_df_parquet)
+    mods_info_df = pd.read_parquet(mods_info_df_parquet)
+    page_info_df = pd.read_parquet(page_info_df_parquet)
+
+    EXPECTED_TYPES = {
+        # mods_info
+
+        r"mets_file": ("object", ["str"]),
+        r"titleInfo_title": ("object", ["str"]),
+        r"titleInfo_subTitle": ("object", ["str", "NoneType"]),
+        r"titleInfo_partName": ("object", ["str", "NoneType"]),
+        r"identifier-.*": ("object", ["str", "NoneType"]),
+        r"location_.*": ("object", ["str", "NoneType"]),
+        r"name\d+_.*roleTerm": ("object", ["ndarray", "NoneType"]),
+        r"name\d+_.*": ("object", ["str", "NoneType"]),
+        r"relatedItem-.*_recordInfo_recordIdentifier": ("object", ["str", "NoneType"]),
+        r"typeOfResource": ("object", ["str", "NoneType"]),
+        r"accessCondition-.*": ("object", ["str", "NoneType"]),
+        r"originInfo-.*": ("object", ["str", "NoneType"]),
+
+        r".*-count": ("Int64", None),
+
+        r"genre-.*": ("object", ["ndarray", "NoneType"]),
+        r"subject-.*": ("object", ["ndarray", "NoneType"]),
+        r"language_.*Term": ("object", ["ndarray", "NoneType"]),
+        r"classification-.*": ("object", ["ndarray", "NoneType"]),
+
+        # page_info
+
+        r"fileGrp_.*_file_FLocat_href": ("object", ["str", "NoneType"]),
+        r"structMap-LOGICAL_TYPE_.*": ("boolean", None),
+    }
+    def expected_types(c):
+        """Return the expected types for column c."""
+        for r, types in EXPECTED_TYPES.items():
+            if re.fullmatch(r, c):
+                edt = types[0]
+                einner_types = types[1]
+                if einner_types:
+                    einner_types = set(einner_types)
+                return edt, einner_types
+        return None, None
+
+    def check_types(df):
+        """Check the types of the DataFrame df."""
+        for c in df.columns:
+            dt = df.dtypes[c]
+            edt, einner_types = expected_types(c)
+            print(c, dt, edt)
+
+            assert edt is not None, f"No expected dtype known for column {c} (got {dt})"
+            assert dt == edt, f"Unexpected dtype {dt} for column {c} (expected {edt})"
+
+            if edt == "object":
+                inner_types = set(type(v).__name__ for v in df[c])
+                assert all(it in einner_types for it in inner_types), \
+                    f"Unexpected inner types {inner_types} for column {c} (expected {einner_types})"
+
+    check_types(mods_info_df)
+    check_types(page_info_df)