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import numpy as np
import pandas as pd
import re
from pathlib import Path
def read_and_preprocess_spreadsheet():
# path = list(Path.cwd().glob('Rori_Framework_v1.xlsx'))
DATA_DIR = Path(__file__).parent.parent / "mathtext_fastapi" / "data" / "Rori_Framework_v1.xlsx"
script_df = pd.read_excel(DATA_DIR, engine='openpyxl')
script_df.columns = script_df.columns[:2].tolist() + script_df.columns[2:11].astype(int).astype(str).tolist() + script_df.columns[11:].tolist()
script_df.fillna('', inplace=True)
return script_df
def build_horizontal_transitions_by_row(direction, skill_code, row):
match_arr = []
sideways_transitions = []
second_match = match+1
if direction == 'left':
second_match = match-1
for i in range(9):
# Grade column
current_grade = i+1
if row[current_grade].lower().strip() == 'x':
match_arr.append(i)
for match in match_arr:
if match_arr[-1] != match:
sideways_transitions.append([
direction,
f"{skill_code}_G{match}",
f"{skill_code}_G{second_match}"
])
return sideways_transitions
def extract_skill_code(skill):
pattern = r'[A-Z][0-9]\.\d+\.\d+'
result = re.search(pattern, skill)
return result.group()
def build_horizontal_transitions(script_df):
horizontal_transitions = []
for index, row in script_df.iterrows():
skill_code = extract_skill_code(row['Knowledge or Skill'])
rightward_matches = []
for i in range(9):
# Grade column
current_grade = i+1
if row[current_grade].lower().strip() == 'x':
rightward_matches.append(i)
for match in rightward_matches:
if rightward_matches[-1] != match:
horizontal_transitions.append([
"right",
f"{skill_code}_G{match}",
f"{skill_code}_G{match+1}"
])
leftward_matches = []
for i in reversed(range(9)):
current_grade = i
if row[current_grade].lower().strip() == 'x':
leftward_matches.append(i)
for match in leftward_matches:
if leftward_matches[0] != match:
horizontal_transitions.append([
"left",
f"{skill_code}_G{match}",
f"{skill_code}_G{match-1}"
])
return horizontal_transitions
def gather_all_vertical_matches(script_df):
all_matches = []
columns = ['1', '2', '3', '4', '5', '6', '7', '8', '9']
for column in columns:
for index, value in script_df[column].iteritems():
row_num = index + 1
if value == 'x':
# Extract skill code
skill_code = extract_skill_code(
script_df['Knowledge or Skill'][row_num-1]
)
all_matches.append([skill_code, column])
return all_matches
def build_vertical_transitions(script_df):
vertical_transitions = []
all_matches = gather_all_vertical_matches(script_df)
# Downward
for index, match in enumerate(all_matches):
skill = match[0]
row_num = match[1]
if all_matches[-1] != match:
vertical_transitions.append([
"down",
f"{skill}_G{row_num}",
f"{all_matches[index+1][0]}_G{row_num}"
])
# Upward
for index, match in reversed(list(enumerate(all_matches))):
skill = match[0]
row_num = match[1]
if all_matches[0] != match:
vertical_transitions.append([
"up",
f"{skill}_G{row_num}",
f"{all_matches[index-1][0]}_G{row_num}"
])
return vertical_transitions
def build_all_states(all_transitions):
all_states = []
for transition in all_transitions:
for index, state in enumerate(transition):
if index == 0:
continue
if state not in all_states:
all_states.append(state)
return all_states
def build_curriculum_logic():
script_df = read_and_preprocess_spreadsheet()
horizontal_transitions = build_horizontal_transitions(script_df)
vertical_transitions = build_vertical_transitions(script_df)
all_transitions = horizontal_transitions + vertical_transitions
all_states = build_all_states(all_transitions)
return all_states, all_transitions
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