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- import json
- def get_closest_col(x, current_cols):
- """Trouve la colonne dont l'X est le plus proche de la valeur."""
- return min(current_cols, key=lambda c: abs(x - c[0]))
- def transform_to_clean_markdown(data):
- current_cols = []
- output_lines = []
-
- for entry in data:
- points = entry.get("points", [])
- if not points:
- continue
-
- # 1. Détection et formatage des En-têtes (Cxxxx)
- headers_in_row = [(p[0], p[2]) for p in points if str(p[2]).startswith('C')]
-
- if headers_in_row:
- # On ajoute un séparateur si un tableau existait déjà avant
- if output_lines:
- output_lines.append("\n---\n")
-
- current_cols = headers_in_row
- col_names = [c[1] for c in current_cols]
- # Header propre sans coordonnées
- output_lines.append(f"| Code | {' | '.join(col_names)} |")
- output_lines.append(f"| :--- | {' | '.join([':---'] * len(col_names))} |")
- continue
- # 2. Détection et alignement des Données (Rxxxx)
- row_label_pt = next((p for p in points if str(p[2]).startswith('R')), None)
-
- if row_label_pt and current_cols:
- row_label = row_label_pt[2]
- # Initialisation de la ligne avec des "0"
- row_dict = {c[1]: "0" for c in current_cols}
-
- for p in points:
- x_val, _, text = p
- if text == row_label:
- continue
-
- # Placement précis via coordonnée X
- best_col = get_closest_col(x_val, current_cols)
- row_dict[best_col[1]] = str(text).strip()
-
- # Construction de la ligne finale
- ordered_values = [row_dict[c[1]] for c in current_cols]
- output_lines.append(f"| **{row_label}** | {' | '.join(ordered_values)} |")
-
- # Note : On ignore volontairement le bloc "else" (INFO) pour nettoyer le bruit
-
- return "\n".join(output_lines)
- #print(transform_to_clean_markdown(json))
|
