import re
import pandas as pd
from typing import Union
def extract_table_by_name_from_energyplus_results_html(html_file_path: str, table_name: str) -> Union[pd.DataFrame, str, None]:
"""
Reads an EnergyPlus results HTML file, finds the table immediately following the
specified table name (enclosed within <b> tags), and extracts it as a pandas DataFrame.
Args:
html_file_path (str): The path to the EnergyPlus results HTML file.
table_name (str): The exact text name of the table as it appears in the
EnergyPlus HTML file (e.g., 'Zone Sensible Cooling').
The function will search for this name enclosed within <b> tags.
Returns:
pandas.DataFrame or str or None: The extracted table as a DataFrame,
an error message (str) if the table is not found,
or None if some unexpected error occurs.
"""
table_label = f'<b>{table_name}</b>'
try:
with open(html_file_path, 'r', encoding='utf-8') as f:
html_content = f.read()
except FileNotFoundError:
return "Error: HTML file not found at the specified path."
# Find the position after the specified table name (within <b> tags)
start_index = html_content.find(table_label)
if start_index == -1:
return f"Error: Table with name '{table_name}' not found in the HTML."
start_index += len(table_label) # Move past the label
start_index = html_content.find('<table', start_index)
if start_index == -1:
return f"Error: Table not found immediately after '{table_name}'."
# Find the end of the table
end_index = html_content.find('</table>', start_index)
if end_index == -1:
return "Error: Closing '</table>' tag not found."
table_html = html_content[start_index:end_index + len('</table>')]
# Extract table rows
rows = re.findall(r'<tr.*?>(.*?)</tr>', table_html, re.DOTALL)
if not rows:
return "Error: No rows found in the table."
data = []
headers = []
for i, row_html in enumerate(rows):
# Extract cells, handling both th (header) and td (data)
cells = re.findall(r'<(th|td).*?>(.*?)</\1>', row_html, re.DOTALL)
row_data = [cell[1].strip() for cell in cells]
if i == 0:
headers = row_data
elif row_data:
data.append(row_data)
if not data:
return "Error: No data found in the table."
df = pd.DataFrame(data, columns=headers if headers else None)
return df
