Initial commit - Selenium automation framework

tests/regression/exchange/idas/bidding/excel_reader.py

@@ -0,0 +1,167 @@
+from pathlib import Path
+import re
+import zipfile
+from xml.etree import ElementTree as ET
+
+
+MAIN_NS = {"a": "http://schemas.openxmlformats.org/spreadsheetml/2006/main"}
+REL_NS = {"r": "http://schemas.openxmlformats.org/officeDocument/2006/relationships"}
+PACKAGE_REL_NS = {"pr": "http://schemas.openxmlformats.org/package/2006/relationships"}
+
+
+def load_single_bid_market_matrix(excel_path, trading_market, sheet_name="Sheet1", excel_layout=None):
+
+    excel_file = Path(excel_path)
+    if not excel_file.exists():
+        raise FileNotFoundError(f"Excel input file not found: {excel_file}")
+
+    layout = excel_layout or {}
+
+    with zipfile.ZipFile(excel_file) as workbook_zip:
+        shared_strings = _load_shared_strings(workbook_zip)
+        sheet_xml_path = _resolve_sheet_xml_path(workbook_zip, sheet_name)
+        sheet_root = ET.fromstring(workbook_zip.read(sheet_xml_path))
+        cell_map = _load_cell_map(sheet_root, shared_strings)
+
+    start_column = layout.get("column_start", "B")
+    market_price_rows = layout.get("market_price_rows", {})
+    common_data_start_row = int(layout.get("common_data_start_row", 6))
+    price_row = int(market_price_rows.get(trading_market, 2))
+    _, start_col = _split_cell_reference(f"{start_column}1")
+    max_row, max_col = _max_used_extent(cell_map, price_row, start_col)
+
+    matrix = []
+    for row_index in [price_row, *range(common_data_start_row, max_row + 1)]:
+        row_values = []
+        for col_index in range(start_col, max_col + 1):
+            row_values.append(cell_map.get((row_index, col_index), ""))
+        matrix.append(row_values)
+
+    matrix = _trim_trailing_empty_rows_and_columns(matrix)
+    if not matrix or not matrix[0]:
+        raise ValueError(f"No bidding data found in {excel_file} for market {trading_market}.")
+
+    tsv = "\n".join("\t".join(value for value in row) for row in matrix)
+    return {
+        "matrix": matrix,
+        "tsv": tsv,
+        "row_count": len(matrix),
+        "column_count": len(matrix[0]),
+        "first_value": matrix[0][0],
+    }
+
+
+def _load_shared_strings(workbook_zip):
+
+    try:
+        root = ET.fromstring(workbook_zip.read("xl/sharedStrings.xml"))
+    except KeyError:
+        return []
+
+    values = []
+    for item in root.findall("a:si", MAIN_NS):
+        text_parts = [node.text or "" for node in item.findall(".//a:t", MAIN_NS)]
+        values.append("".join(text_parts))
+    return values
+
+
+def _resolve_sheet_xml_path(workbook_zip, sheet_name):
+
+    workbook_root = ET.fromstring(workbook_zip.read("xl/workbook.xml"))
+    relationships_root = ET.fromstring(workbook_zip.read("xl/_rels/workbook.xml.rels"))
+
+    relationship_map = {
+        rel.attrib["Id"]: rel.attrib["Target"]
+        for rel in relationships_root.findall("pr:Relationship", PACKAGE_REL_NS)
+    }
+
+    for sheet in workbook_root.findall("a:sheets/a:sheet", {**MAIN_NS, **REL_NS}):
+        if sheet.attrib.get("name") != sheet_name:
+            continue
+
+        relationship_id = sheet.attrib.get(f"{{{REL_NS['r']}}}id")
+        target = relationship_map[relationship_id]
+        target = target.lstrip("/")
+        if not target.startswith("xl/"):
+            target = f"xl/{target}"
+        return target
+
+    raise KeyError(f"Sheet '{sheet_name}' not found in workbook.")
+
+
+def _load_cell_map(sheet_root, shared_strings):
+
+    cell_map = {}
+
+    for row in sheet_root.findall(".//a:sheetData/a:row", MAIN_NS):
+        for cell in row.findall("a:c", MAIN_NS):
+            reference = cell.attrib.get("r", "")
+            row_index, col_index = _split_cell_reference(reference)
+            cell_map[(row_index, col_index)] = _cell_value(cell, shared_strings)
+
+    return cell_map
+
+
+def _cell_value(cell, shared_strings):
+
+    cell_type = cell.attrib.get("t")
+
+    if cell_type == "inlineStr":
+        text_nodes = cell.findall(".//a:t", MAIN_NS)
+        return "".join(node.text or "" for node in text_nodes)
+
+    value_node = cell.find("a:v", MAIN_NS)
+    if value_node is None or value_node.text is None:
+        return ""
+
+    raw_value = value_node.text
+    if cell_type == "s":
+        return shared_strings[int(raw_value)]
+
+    return raw_value
+
+
+def _split_cell_reference(reference):
+
+    match = re.match(r"([A-Z]+)(\d+)", reference)
+    if not match:
+        raise ValueError(f"Unsupported cell reference: {reference}")
+
+    column_letters, row_number = match.groups()
+    return int(row_number), _column_letters_to_number(column_letters)
+
+
+def _column_letters_to_number(column_letters):
+
+    result = 0
+    for character in column_letters:
+        result = result * 26 + (ord(character) - ord("A") + 1)
+    return result
+
+
+def _max_used_extent(cell_map, start_row, start_col):
+
+    used_rows = [row for row, col in cell_map if row >= start_row and col >= start_col and cell_map[(row, col)] != ""]
+    used_cols = [col for row, col in cell_map if row >= start_row and col >= start_col and cell_map[(row, col)] != ""]
+
+    if not used_rows or not used_cols:
+        raise ValueError("No populated cells were found in the requested data area.")
+
+    return max(used_rows), max(used_cols)
+
+
+def _trim_trailing_empty_rows_and_columns(matrix):
+
+    while matrix and all(value == "" for value in matrix[-1]):
+        matrix.pop()
+
+    if not matrix:
+        return matrix
+
+    while matrix and matrix[0]:
+        if any(row[-1] != "" for row in matrix):
+            break
+        for row in matrix:
+            row.pop()
+
+    return matrix