Used pypdf to create extract_specification_document_tables in parsing.py. Should allow easier indexing of the various file types in future. Will need to adapt for files other than .MCA and look at formalising into a local database.

pyproject.toml

@@ -7,7 +7,10 @@ authors = [
 ]
 requires-python = ">=3.14"
 dependencies = [
-    "requests (>=2.34.2,<3.0.0)"
+    "requests (>=2.34.2,<3.0.0)",
+    "pypdf (>=6.12.0,<7.0.0)",
+    "pandas (>=3.0.3,<4.0.0)",
+    "pandas-stubs (>=3.0.0.260204,<4.0.0.0)"
 ]
 
 

src/national_rail_timetable/__main__.py

@@ -1,4 +1,6 @@
 from national_rail_timetable.nr_requests import fetch_nr_token, fetch_nr_timetable_files
+from national_rail_timetable.parsing import extract_specification_document_tables
 
-print(fetch_nr_token())
-print(fetch_nr_timetable_files())
+# print(fetch_nr_token())
+# print(fetch_nr_timetable_files())
+print(extract_specification_document_tables())

src/national_rail_timetable/parsing.py

@@ -0,0 +1,116 @@
+"""
+Parsing tools for the ZipFile returned by nr_requests.
+Aimed primarily towards producing a reduced sqlite database.
+"""
+# pyright: reportAny=false
+# pyright: reportUnknownVariableType=false
+# pyright: reportUnknownArgumentType=false
+# pyright: reportUnknownMemberType=false
+
+# Imports
+from itertools import pairwise
+from pathlib import Path
+import pandas as pd
+import numpy as np
+from pypdf import PageObject, PdfReader
+
+
+# Init.
+SPECIFICATION_TABLE_LOCATIONS = {
+    "MCA_HD": (15, 0),
+    "MCA_BS": (16, 0),
+    "MCA_BX": (17, 0),
+    "MCA_LO": (17, 1),
+    "MCA_LI": (18, 0),
+    "MCA_CR": (18, 1),
+    "MCA_LT": (19, 0),
+    "MCA_AA": (21, 0),
+    "MCA_TI": (22, 0),
+    "MCA_TA": (22, 1),
+    "MCA_TD": (23, 0),
+    "MCA_ZZ": (23, 1),
+}
+
+
+# Functions
+#  TODO: Implement better header check and row breaks to ingest .ALF and others.
+def extract_specification_document_tables(
+    path: Path | None = None,  # pyright: ignore[reportRedeclaration]
+) -> dict[str, pd.DataFrame]:
+    path: Path = (
+        path
+        if path is not None
+        else Path(__file__).parents[2] / "data/Timetable Specification.pdf"
+    )
+    if not path.exists() or path.is_dir():
+        raise FileNotFoundError(
+            "The path given does not exist or is a folder. "
+            + "Please supply the path to the RSP's Timetable Specification document. "
+            + "Or move it to the path expected. "
+            + "The file can be found at https://www.rspaccreditation.org/publicDocumentation.php#RSPS5xxx. "
+            + "Please select RSPS5046 pertaining to timetable data. "
+            + f'Path tried: "{path}"'
+        )
+    reader = PdfReader(path)
+
+    tables: dict[str, pd.DataFrame] = {}
+    for key, (page_number, table_number) in SPECIFICATION_TABLE_LOCATIONS.items():
+        page: PageObject = reader.pages[page_number]
+
+        text = page.extract_text(extraction_mode="layout")
+        text = text.split("Field description")[table_number + 1]
+        text = text[len(text.split("\n")[0]) + 1 :]
+        text = text.split("\n\n")[0]
+
+        lines = text.split("\n")
+        max_line_length = max([len(line) for line in lines])
+        characters = np.array(
+            [
+                [
+                    character
+                    for character in (line + " " * max_line_length)[:max_line_length]
+                ]
+                for line in lines
+            ],
+            dtype="U1",
+        )
+
+        column_breaks = np.sum(np.equal(characters, " "), axis=0) == characters.shape[0]
+        column_breaks = column_breaks[2:] & column_breaks[1:-1] & column_breaks[:-2]
+        column_breaks = [True, *column_breaks, False, False]
+        column_breaks = np.diff(np.array(column_breaks, dtype=np.int8)) == 1
+
+        row_breaks = (
+            characters[:, np.arange(characters.shape[1])[column_breaks][0] - 1] != " "
+        )
+
+        records: list[list[str]] = []
+        for rl, ru in pairwise([*np.arange(characters.shape[0])[row_breaks], None]):
+            records.append([])
+            for cl, cu in pairwise(
+                [0, *np.arange(characters.shape[1])[column_breaks], None]
+            ):
+                records[-1].append(
+                    " ".join(
+                        "".join(
+                            [
+                                character
+                                for character in np.ravel(characters[rl:ru, cl:cu])
+                            ]
+                        )
+                        .strip()
+                        .split()
+                    )
+                )
+        tables[key] = pd.DataFrame(
+            records,
+            columns=[
+                "Field",
+                "Field Description",
+                "Length",
+                "Position",
+                "Notes",
+            ],
+        )
+
+    return tables