221 lines
12 KiB
Python
221 lines
12 KiB
Python
self.conn.commit()
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return True
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def process_csv_with_teacher_mapping(self, csv_file):
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"""Process CSV with teacher-subject mapping based on positional order"""
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if not os.path.exists(csv_file):
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return False
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with open(csv_file, 'r', encoding='utf-8') as file:
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reader = csv.reader(file)
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rows = list(reader)
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# Identify header row - look for the row containing "ФИО" (full name) or similar indicators
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header_idx = None
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for i, row in enumerate(rows):
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for cell in row:
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if "ФИО" in str(cell) or "фио" in str(cell).lower() or "Ф.И.О." in str(cell) or "ф.и.о." in str(cell):
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header_idx = i
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break
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if header_idx is not None:
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break
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if header_idx is None:
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# Check if this file contains class and name columns that identify it as a student data file
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# Even if the header doesn't contain ФИО, we might still be able to identify student data
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has_class_indicators = any(
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any(indicator in str(cell).lower() for cell in row for indicator in ['класс', 'class'])
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for row in rows[:min(len(rows), 10)] # Check first 10 rows
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)
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has_name_indicators = any(
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any(indicator in str(cell).lower() for cell in row for indicator in ['имя', 'name', 'фамилия', 'surname'])
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for row in rows[:min(len(rows), 10)] # Check first 10 rows
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)
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if has_class_indicators and has_name_indicators:
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# Try to find the header row by looking for class and name indicators
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for i, row in enumerate(rows):
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if any(indicator in str(cell).lower() for cell in row for indicator in ['класс', 'class']) and \
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any(indicator in str(cell).lower() for cell in row for indicator in ['имя', 'name', 'фамилия', 'surname']):
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header_idx = i
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break
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if header_idx is None:
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print(f"Skipping {csv_file} - does not appear to be student data with ФИО/class columns")
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return False
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# Find teacher-subject mappings in the first 0-15 rows before the header
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teacher_subject_map = {}
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# Build a mapping of subject names in the header row
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header_row = rows[header_idx]
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header_subjects = {}
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for col_idx, subject_name in enumerate(header_row):
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subject_name = str(subject_name).strip()
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if (subject_name and
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subject_name.lower() not in ['ф.и.о.', 'фио', 'класс', 'номер', 'сортировка', 'шкафчика', 'локера'] and
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subject_name.strip() != "" and
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"ф.и.о" not in subject_name.lower() and
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"сортировка" not in subject_name.lower() and
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"номер" not in subject_name.lower() and
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"№" not in subject_name):
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header_subjects[col_idx] = subject_name # Map column index to subject name
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# Process rows before the header to find teacher names and map them to subjects
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for i in range(min(15, header_idx)): # Check first 15 rows before header
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current_row = rows[i]
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# Process all cells in the row to find teacher names and their adjacent context
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for j, cell_value in enumerate(current_row):
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cell_str = str(cell_value).strip()
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# Check if this cell is a likely teacher name
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if self._is_likely_teacher_name(cell_str):
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# Look for context on the left (department) and right (subject)
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left_context = ""
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right_context = ""
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# Get left neighbor (department)
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if j > 0 and j-1 < len(current_row):
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left_context = str(current_row[j-1]).strip()
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# Get right neighbor (subject)
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if j < len(current_row) - 1:
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right_context = str(current_row[j+1]).strip()
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# Try to determine the subject based on adjacency
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matched_subject = None
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# First priority: right neighbor if it matches a subject in the header
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if right_context and j+1 in header_subjects:
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matched_subject = header_subjects[j+1]
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# Second priority: use left context if it semantically relates to a teacher
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elif left_context and any(keyword in left_context.lower() for keyword in ['учитель', 'teacher', 'кафедра', 'department']):
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# If left context indicates a department, look for subject to the right of teacher
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if j+1 in header_subjects:
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matched_subject = header_subjects[j+1]
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# If no subject to the right, try to map by position
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elif j in header_subjects:
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matched_subject = header_subjects[j]
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# Third priority: try to map by position
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elif j in header_subjects:
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matched_subject = header_subjects[j]
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# Only add if we don't have a better teacher name for this subject yet
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if matched_subject and (matched_subject not in teacher_subject_map or
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'Default Teacher for' in teacher_subject_map.get(matched_subject, '')):
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teacher_subject_map[matched_subject] = cell_str
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# If the cell contains multiple names (separated by newlines), process each separately
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elif '\n' in cell_str or '\\n' in cell_str:
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cell_parts = [part.strip() for part in cell_str.replace('\\n', '\n').split('\n') if part.strip()]
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for part in cell_parts:
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if self._is_likely_teacher_name(part):
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# Look for context on the left (department) and right (subject)
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left_context = ""
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right_context = ""
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# Get left neighbor (department)
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if j > 0 and j-1 < len(current_row):
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left_context = str(current_row[j-1]).strip()
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# Get right neighbor (subject)
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if j < len(current_row) - 1:
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right_context = str(current_row[j+1]).strip()
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# Try to determine the subject based on adjacency
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matched_subject = None
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# First priority: right neighbor if it matches a subject in the header
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if right_context and j+1 in header_subjects:
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matched_subject = header_subjects[j+1]
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# Second priority: use left context if it semantically relates to a teacher
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elif left_context and any(keyword in left_context.lower() for keyword in ['учитель', 'teacher', 'кафедра', 'department']):
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# If left context indicates a department, look for subject to the right of teacher
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if j+1 in header_subjects:
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matched_subject = header_subjects[j+1]
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# If no subject to the right, try to map by position
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elif j in header_subjects:
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matched_subject = header_subjects[j]
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# Third priority: try to map by position
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elif j in header_subjects:
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matched_subject = header_subjects[j]
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# Only add if we don't have a better teacher name for this subject yet
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if matched_subject and (matched_subject not in teacher_subject_map or
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'Default Teacher for' in teacher_subject_map.get(matched_subject, '')):
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teacher_subject_map[matched_subject] = part
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# Additional validation: Remove any teacher-subject mappings that seem incorrect
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validated_teacher_subject_map = {}
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for subject, teacher in teacher_subject_map.items():
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# Only add to validated map if teacher name passes all checks
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if self._is_likely_teacher_name(teacher):
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validated_teacher_subject_map[subject] = teacher
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else:
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print(f"Warning: Invalid teacher name '{teacher}' detected for subject '{subject}', skipping...")
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teacher_subject_map = validated_teacher_subject_map
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# Additional teacher-subject mapping: scan the data rows for teacher names paired with subjects
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# In many CSV files, teacher names appear in the same rows as subject data
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for i in range(header_idx + 1, min(len(rows), header_idx + 50)): # Check first 50 data rows
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current_row = rows[i]
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for j, cell_value in enumerate(current_row):
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cell_str = str(cell_value).strip()
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# If cell contains a likely teacher name and corresponds to a subject column
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if self._is_likely_teacher_name(cell_str) and j in header_subjects:
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subject_name = header_subjects[j]
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# Only add if we don't have a better teacher name for this subject yet
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if (subject_name not in teacher_subject_map or
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'Default Teacher for' in teacher_subject_map.get(subject_name, '')):
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teacher_subject_map[subject_name] = cell_str
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# Process each student row
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for student_row in rows[header_idx + 1:]:
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# Determine the structure dynamically based on the header
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class_col_idx = None
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name_col_idx = None
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# Find the index of the class column (usually called "Класс")
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for idx, header in enumerate(header_row):
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if "Класс" in str(header) or "класс" in str(header) or "Class" in str(header) or "class" in str(header):
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class_col_idx = idx
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break
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# Find the index of the name column (usually called "ФИО")
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for idx, header in enumerate(header_row):
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if "ФИО" in str(header) or "ф.и.о." in str(header).lower() or "name" in str(header).lower():
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name_col_idx = idx
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break
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# If we couldn't find the columns properly, skip this row
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if class_col_idx is None or name_col_idx is None:
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continue
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# Check if this row has valid data in the expected columns
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if (len(student_row) > max(class_col_idx, name_col_idx) and
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student_row[class_col_idx].strip() and # class name exists
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student_row[name_col_idx].strip() and # student name exists
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self._is_valid_student_record_by_cols(student_row, class_col_idx, name_col_idx)):
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name = student_row[name_col_idx].strip() # Name column
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class_name = student_row[class_col_idx].strip() # Class column
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# Insert student into the database
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self.cursor.execute(
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"INSERT OR IGNORE INTO students (class_name, full_name) VALUES (?, ?)",
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(class_name, name)
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)
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# Get the student_id for this student
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self.cursor.execute("SELECT student_id FROM students WHERE full_name = ? AND class_name = ?", (name, class_name))
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student_id_result = self.cursor.fetchone()
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if student_id_result is None:
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continue
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student_id = student_id_result[0]
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# Process schedule data for this student
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