from create_new_formularios import get_sorted_date_keys from pathlib import Path from datetime import datetime from create_new_usuarios import get_macros_from_string import statistics def query_formularios(data, query_list, debug=False, file_name=None): if file_name is not None: debug = True print(f"***************** file_name: {file_name} *****************") # Get date keys date_keys = get_sorted_date_keys(data) if debug: print(f"\n\n\ndate_keys: {date_keys}") # List of date keys that match the query date_keys_that_match = [] # No data value no_data_value = "| no data" # Get all the keys in the query_list queries_list = [] for query in query_list: queries_list.append(query) if debug: print("queries_list:") for query in queries_list: for key in query.keys(): print(f"\tkey: {key}", end=" --> ") for second_key in query[key].keys(): print(f"{key}: {query[key][second_key]}", end=", ") print("") # For each date key get all the keys for date_key in date_keys: if debug: print(f"\n * date_key: {date_key}") # match is a boolean that will be true if the key is in query_dict match = False if debug: print(f"\tinitial match value: {match}") # Get all the keys in the data data_keys = data[date_key].keys() if debug: print(f"\tkeys: {data_keys}") # Find for each key if it is in query_dict for query in queries_list: # Get the query key query_key = list(query.keys())[0] # Get the query operator and value query_operator = query[query_key]['operator'] is_operator_for_numbers = query_operator == '>' or query_operator == '<' or query_operator == '>=' or query_operator == '<=' query_value = query[query_key]['value'] type_of_query_value = type(query_value) is_query_value_string = type_of_query_value == str is_query_value_number = type_of_query_value == int or type_of_query_value == float # Check if the query key is in the data if query_key in data_keys: # Get the data value data_value = data[date_key][query_key] if isinstance(data_value, str): data_value = data_value.lower() data_value = data_value.replace('á', 'a') data_value = data_value.replace('é', 'e') data_value = data_value.replace('í', 'i') data_value = data_value.replace('ó', 'o') data_value = data_value.replace('ú', 'u') type_of_data_value = type(data_value) is_data_value_string = type_of_data_value == str is_data_value_number = type_of_data_value == int or type_of_data_value == float is_data_value_and_query_value_number = is_data_value_number and is_query_value_number is_data_value_and_query_value_string = is_data_value_string and is_query_value_string is_data_value_or_query_value_number = is_data_value_number or is_query_value_number is_data_value_or_query_value_string = is_data_value_string or is_query_value_string if debug: print(f"\t\tchecking \"{query_key}\" in data, query operator: \"{query_operator}\", query value: \"{query_value}\", data value: \"{data_value}\"") # Check if the data value matches the query value if query_operator == '==': if query_value == data_value: match = True if debug: print(f"\t\t\t\"{query_value}\" is equal to \"{data_value}\", match: {match}") else: match = False if debug: print(f"\t\t\t\"{query_value}\" is NOT equal to \"{data_value}\", match: {match}") break # continue elif query_operator == '!=': if query_value != data_value: match = True if debug: print(f"\t\t\t\"{query_value}\" is NOT equal to \"{data_value}\", match: {match}") else: match = False if debug: print(f"\t\t\t\"{query_value}\" is NOT equal to \"{data_value}\", match: {match}") break # continue elif is_query_value_number and is_data_value_number and query_operator == '>': if data_value > query_value: match = True if debug: print(f"\t\t\t\"{query_value}\" is greater than \"{data_value}\", match: {match}") else: match = False if debug: print(f"\t\t\t\"{query_value}\" is NOT greater than \"{data_value}\", match: {match}") break # continue elif is_query_value_number and is_data_value_number and query_operator == '<': if data_value < query_value: match = True if debug: print(f"\t\t\t\"{query_value}\" is less than \"{data_value}\", match: {match}") else: match = False if debug: print(f"\t\t\t\"{query_value}\" is NOT less than \"{data_value}\", match: {match}") break # continue elif is_query_value_number and is_data_value_number and query_operator == '>=': if data_value >= query_value: match = True if debug: print(f"\t\t\t\"{query_value}\" is greater than or equal to \"{data_value}\", match: {match}") else: match = False if debug: print(f"\t\t\t\"{query_value}\" is NOT greater than or equal to \"{data_value}\", match: {match}") break # continue elif is_query_value_number and is_data_value_number and query_operator == '<=': if data_value <= query_value: match = True if debug: print(f"\t\t\t\"{query_value}\" is less than or equal to \"{data_value}\", match: {match}") else: match = False if debug: print(f"\t\t\t\"{query_value}\" is NOT less than or equal to \"{data_value}\", match: {match}") break # continue elif is_query_value_string and is_data_value_string and query_operator == 'in' or query_operator == 'contains': if query_value in data_value or no_data_value in data_value: match = True if debug: print(f"\t\t\t\"{query_value}\" is in \"{data_value}\", match: {match}") else: match = False if debug: print(f"\t\t\t\"{query_value}\" is NOT in \"{data_value}\", match: {match}") break # continue elif is_query_value_string and is_data_value_string and (query_operator == 'NOT in' or query_operator == 'NOT contains'): if query_value not in data_value or no_data_value in data_value: match = True if debug: print(f"\t\t\t\"{query_value}\" is NOT in \"{data_value}\", match: {match}") else: match = False if debug: print(f"\t\t\t\"{query_value}\" is NOT in \"{data_value}\", match: {match}") break # continue elif query_operator == 'is null': if data_value is None: match = True if debug: print(f"\t\t\t\"{query_value}\" is null, match: {match}") else: match = False if debug: print(f"\t\t\t\"{query_value}\" is NOT null, match: {match}") break # continue elif query_operator == 'is NOT null': if data_value is not None: match = True if debug: print(f"\t\t\t\"{query_value}\" is NOT null, match: {match}") else: match = False if debug: print(f"\t\t\t\"{query_value}\" is NOT null, match: {match}") break # continue elif is_operator_for_numbers and is_data_value_or_query_value_string: if is_data_value_string and is_query_value_string: match = False if debug: print(f"\t\t\toperator \"{query_operator}\" NOT supported, because data value is string and query value is string, match: {match}") break elif is_data_value_string and is_query_value_number: match = False if debug: print(f"\t\t\toperator \"{query_operator}\" NOT supported, because data value is string, match: {match}") break else: match = False if debug: print(f"\t\t\toperator \"{query_operator}\" NOT supported, because query value is number, match: {match}") break else: match = False if debug: print(f"\t\t\toperator \"{query_operator}\" NOT supported, match: {match}") break # continue # If the match is true, add the date_key to the list if match: if debug: print(f"\t***** {query_key} matches, adding date_key: {date_key} *****") date_keys_that_match.append(date_key) if debug: print("\t dates that match:") for date_key in date_keys_that_match: print(f"\t\t{date_key}") return date_keys_that_match def string_date_list_to_date_list(string_date_list): date_list = [] for string_date in string_date_list: date_list.append(datetime.strptime(string_date, '%Y-%m-%d')) return date_list def date_to_string(date): string_date = date.strftime('%Y-%m-%d') string_date = string_date.replace('-01', '-1') string_date = string_date.replace('-02', '-2') string_date = string_date.replace('-03', '-3') string_date = string_date.replace('-04', '-4') string_date = string_date.replace('-05', '-5') string_date = string_date.replace('-06', '-6') string_date = string_date.replace('-07', '-7') string_date = string_date.replace('-08', '-8') string_date = string_date.replace('-09', '-9') return string_date def get_days_between_dates(date1, date2): if isinstance(date1, str): date1 = datetime.strptime(date1, '%Y-%m-%d') if isinstance(date2, str): date2 = datetime.strptime(date2, '%Y-%m-%d') return (date1 - date2).days def query_usuarios(data, query_list, limit_days=8, debug=False): # Get date keys date_keys = get_sorted_date_keys(data) if debug: print(f"\tdate_keys: {date_keys}") # Format date_keys to date objects date_keys = string_date_list_to_date_list(date_keys) # Format query_list to date objects if debug: print(f"\tquery_list: {query_list}") query_list = string_date_list_to_date_list(query_list) # Create empty list to store the date_keys that match date_keys_that_match = [] # Create empty list to store the macros differences date macros_differences_dates = [] # Iterate for each query_list date for query_date in query_list: # Iterate for each date_key for date_key in date_keys: # Get the days between the query_date and the date_key days_between = get_days_between_dates(query_date, date_key) if days_between <= limit_days and days_between > 0: if debug: print(f"\tdays between form data {date_to_string(query_date)} and macros change data {date_to_string(date_key)}: {days_between}") # Add the date_key to the list and break the loop, because is first mach so is match with less days between dates date_keys_that_match.append(date_to_string(date_key)) # Add the date_key to the macros_differences_dates list macros_differences_dates.append(date_to_string(query_date)) break return date_keys_that_match, macros_differences_dates def get_macros_differences(data, dates_list): macros_differences_list = [] for date in dates_list: macros_differences_list.append(data[date]['diferencia_macros']) return macros_differences_list def get_min_max_mean_mode_macros_differences(macros_differences_list): # Create list for each macro train_day_protein_list = [] train_day_carbs_list = [] train_day_fat_list = [] intratrain_protein_list = [] intratrain_carbs_list = [] rest_day_protein_list = [] rest_day_carbs_list = [] rest_day_fat_list = [] # Iterate over the macros differences list for macros_difference in macros_differences_list: # Get the macros difference as a list of integers macros_difference_int_list = get_macros_from_string(macros_difference) # Append the macros difference to the list train_day_protein_list.append(macros_difference_int_list[0]) train_day_carbs_list.append(macros_difference_int_list[1]) train_day_fat_list.append(macros_difference_int_list[2]) intratrain_protein_list.append(macros_difference_int_list[3]) intratrain_carbs_list.append(macros_difference_int_list[4]) rest_day_protein_list.append(macros_difference_int_list[5]) rest_day_carbs_list.append(macros_difference_int_list[6]) rest_day_fat_list.append(macros_difference_int_list[7]) # Get the min, max, mean and mode of the macros differences min_train_day_protein = min(train_day_protein_list) max_train_day_protein = max(train_day_protein_list) mean_train_day_protein = sum(train_day_protein_list) / len(train_day_protein_list) mode_train_day_protein = statistics.mode(train_day_protein_list) train_day_protein_std = (min_train_day_protein, max_train_day_protein, mean_train_day_protein, mode_train_day_protein) min_train_day_carbs = min(train_day_carbs_list) max_train_day_carbs = max(train_day_carbs_list) mean_train_day_carbs = sum(train_day_carbs_list) / len(train_day_carbs_list) mode_train_day_carbs = statistics.mode(train_day_carbs_list) train_day_carbs_std = (min_train_day_carbs, max_train_day_carbs, mean_train_day_carbs, mode_train_day_carbs) min_train_day_fat = min(train_day_fat_list) max_train_day_fat = max(train_day_fat_list) mean_train_day_fat = sum(train_day_fat_list) / len(train_day_fat_list) mode_train_day_fat = statistics.mode(train_day_fat_list) train_day_fat_std = (min_train_day_fat, max_train_day_fat, mean_train_day_fat, mode_train_day_fat) min_intratrain_protein = min(intratrain_protein_list) max_intratrain_protein = max(intratrain_protein_list) mean_intratrain_protein = sum(intratrain_protein_list) / len(intratrain_protein_list) mode_intratrain_protein = statistics.mode(intratrain_protein_list) intratrain_protein_std = (min_intratrain_protein, max_intratrain_protein, mean_intratrain_protein, mode_intratrain_protein) min_intratrain_carbs = min(intratrain_carbs_list) max_intratrain_carbs = max(intratrain_carbs_list) mean_intratrain_carbs = sum(intratrain_carbs_list) / len(intratrain_carbs_list) mode_intratrain_carbs = statistics.mode(intratrain_carbs_list) intratrain_carbs_std = (min_intratrain_carbs, max_intratrain_carbs, mean_intratrain_carbs, mode_intratrain_carbs) min_rest_day_protein = min(rest_day_protein_list) max_rest_day_protein = max(rest_day_protein_list) mean_rest_day_protein = sum(rest_day_protein_list) / len(rest_day_protein_list) mode_rest_day_protein = statistics.mode(rest_day_protein_list) rest_day_protein_std = (min_rest_day_protein, max_rest_day_protein, mean_rest_day_protein, mode_rest_day_protein) min_rest_day_carbs = min(rest_day_carbs_list) max_rest_day_carbs = max(rest_day_carbs_list) mean_rest_day_carbs = sum(rest_day_carbs_list) / len(rest_day_carbs_list) mode_rest_day_carbs = statistics.mode(rest_day_carbs_list) rest_day_carbs_std = (min_rest_day_carbs, max_rest_day_carbs, mean_rest_day_carbs, mode_rest_day_carbs) min_rest_day_fat = min(rest_day_fat_list) max_rest_day_fat = max(rest_day_fat_list) mean_rest_day_fat = sum(rest_day_fat_list) / len(rest_day_fat_list) mode_rest_day_fat = statistics.mode(rest_day_fat_list) rest_day_fat_std = (min_rest_day_fat, max_rest_day_fat, mean_rest_day_fat, mode_rest_day_fat) return train_day_protein_std, train_day_carbs_std, train_day_fat_std, intratrain_protein_std, intratrain_carbs_std, rest_day_protein_std, rest_day_carbs_std, rest_day_fat_std def clustering_esfuerzo_dieta_response(response, debug=False): # Options: # No entiendo la calculadora, quiero menús tipo, cárgame 4|I: 2 # No costó nada|A: 1504 # Costó demasiado, súbeme macros|D: 28 # Costó, pero me adapto a nuevos ajustes|C: 331 # Iba a coger menús tipo, pero al final por precio no|D: 13 # Costó demasiado, bájame macros|D: 42 # No entiendo la calculadora, quiero menús tipo, cárgame 2|I: 3 # # Clustering: # 0 (No data): # No entiendo la calculadora, quiero menús tipo, cárgame 4|I: 2 | No data # Iba a coger menús tipo, pero al final por precio no|D: 13 | No data # No entiendo la calculadora, quiero menús tipo, cárgame 2|I: 3 | No data # 1 (costó subir macros): # Costó demasiado, súbeme macros|D: 28 | costo subir macros # 2 (costó bajar macros): # Costó demasiado, bájame macros|D: 42 | costo bajar macros # 3 (costó y me adapto a nuevos ajustes): # Costó, pero me adapto a nuevos ajustes|C: 331 | costo y me adapto a nuevos ajustes # 4 (no costó): # No costó nada|A: 1504 | no costo if " | No data".lower() in response.lower() or 'no data'.lower() in response.lower(): if debug: print(f"\t\t{response} -> no data") return 'no data' elif " | costo subir macros".lower() in response.lower() or 'costo subir macros'.lower() in response.lower(): if debug: print(f"\t\t{response} -> costo subir macros") return 'costo subir macros' elif " | costo bajar macros".lower() in response.lower() or 'costo bajar macros'.lower() in response.lower(): if debug: print(f"\t\t{response} -> costo bajar macros") return 'costo bajar macros' elif " | costo y me adapto a nuevos ajustes".lower() in response.lower() or 'costo y me adapto a nuevos ajustes'.lower() in response.lower(): if debug: print(f"\t\t{response} -> costo y me adapto a nuevos ajustes") return 'costo y me adapto a nuevos ajustes' elif " | no costo".lower() in response.lower() or 'no costo'.lower() in response.lower(): if debug: print(f"\t\t{response} -> no costo") return 'no costo' else: if debug: print(f"\t\t{response} -> no data") return 'no data' def clustering_objetivo_response(response, debug=False): # Options: # definición (nada cambia)|A: 1031 # empezamos a definir (cambia)|C: 92 # perder peso (nada cambia)|A: 21 # volumen (nada cambia)|A: 688 # empezamos a coger volumen (cambia)|C: 78 # empezamos a coger volumen, sobre todo tren inferior (cambia)|C: 7 # empezamos a coger volumen, en todo el cuerpo (cambia)|C: 6 # # Clustering: # 0 (definición): # definición (nada cambia)|A: 1031 | definición # empezamos a definir (cambia)|C: 92 | definición # perder peso (nada cambia)|A: 21 | definición # 1 (volumen): # volumen (nada cambia)|A: 688 | volumen # empezamos a coger volumen (cambia)|C: 78 | volumen # empezamos a coger volumen, sobre todo tren inferior (cambia)|C: 7 | volumen # empezamos a coger volumen, en todo el cuerpo (cambia)|C: 6 | volumen if " | definicion".lower() in response.lower() or 'definicion'.lower() in response.lower(): if debug: print(f"\t\t{response} -> definicion") return 'definicion' elif " | volumen".lower() in response.lower() or 'volumen'.lower() in response.lower(): if debug: print(f"\t\t{response} -> volumen") return 'volumen' else: if debug: print(f"\t\t{response} -> no data") return 'no data' def clustering_entrenamiento_response(response, debug=False): # Options: # Lo hice perfecto|A|10: 838 # He fallado algunos días, pero sí|B|5: 98 # Lesión importante: 16 # Lo hice prácticamente perfecto|A|8: 416 # Pequeña lesión: 63 # No hice nada, mantenemos la rutina un mes más|I|0: 64 # Alárgame la rutina una semana más|I|6: 32 # # Clustering: # 0 (bien): # Lo hice perfecto|A|10: 838 | bien # He fallado algunos días, pero sí|B|5: 98 | bien # Lo hice prácticamente perfecto|A|8: 416 | bien # 1 (mal): # Lesión importante: 16 | mal # Pequeña lesión: 63 | mal # No hice nada, mantenemos la rutina un mes más|I|0: 64 | mal # Alárgame la rutina una semana más|I|6: 32 | mal if " | bien".lower() in response.lower() or 'bien'.lower() in response.lower(): if debug: print(f"\t\t{response} -> bien") return 'bien' elif " | mal".lower() in response.lower() or 'mal'.lower() in response.lower(): if debug: print(f"\t\t{response} -> mal") return 'mal' else: if debug: print(f"\t\t{response} -> no data") return 'no data' def clustering_cumplimiento_dieta_response(response, debug=False): # Options: # al 70%|B|6: 564 # regular, me cuesta llegar|C|5: 57 # Nada, mantén mis macros|I|0: 123 # casi perfecta|A|9: 610 # regular, me salto la dieta|C|4: 6 # Perfecta|A|10: 563 # # Clustering: # 0 (bien): # al 70%|B|6: 564 | bien # casi perfecta|A|9: 610 | bien # Perfecta|A|10: 563 | bien # 1 (regular): # regular, me cuesta llegar|C|5: 57 | regular # regular, me salto la dieta|C|4: 6 | regular # 2 (mal): # Nada, mantén mis macros|I|0: 123 | mal if " | bien".lower() in response.lower() or 'bien'.lower() in response.lower(): if debug: print(f"\t\t{response} -> bien") return 'bien' elif " | regular".lower() in response.lower() or 'regular'.lower() in response.lower(): if debug: print(f"\t\t{response} -> regular") return 'regular' elif "nada" in response.lower() or 'mal'.lower() in response.lower(): if debug: print(f"\t\t{response} -> mal") return 'mal' else: if debug: print(f"\t\t{response} -> no data") return 'no data' def clustering_compromiso_response(response, debug=False): # Options: # Bueno, pero mejorable|B|7: 604 # Mal, pero a partir de ahora voy a por todas|C|0: 319 # Mal, demasiado exigente|D|0: 15 # Máximo|A|10: 985 # # Clustering: # 0 (bueno): # Bueno, pero mejorable|B|7: 604 | bueno # Máximo|A|10: 985 | bueno # 1 (mal): # Mal, pero a partir de ahora voy a por todas|C|0: 319 | mal # Mal, demasiado exigente|D|0: 15 | mal if " | bueno".lower() in response.lower() or 'bueno'.lower() in response.lower(): if debug: print(f"\t\t{response} -> bueno") return 'bueno' elif " | mal".lower() in response.lower() or 'mal'.lower() in response.lower(): if debug: print(f"\t\t{response} -> mal") return 'mal' else: if debug: print(f"\t\t{response} -> no data") return 'no data' def clustering_diferencia_peso_response(diff, debug=False): diff_min = None diff_max = None if diff <= -5.0: if debug: print(f"\t\t-10 <= {diff} <= -5") diff_min = -10 diff_max = -5 elif diff <= -4.5: if debug: print(f"\t\t-5 <= {diff} <= -4.5") diff_min = -5 diff_max = -4.5 elif diff <= -4.0: if debug: print(f"\t\t-4.5 <= {diff} <= -4.0") diff_min = -4.5 diff_max = -4.0 elif diff <= -3.5: if debug: print(f"\t\t-4.0 <= {diff} <= -3.5") diff_min = -4.0 diff_max = -3.5 elif diff <= -3.0: if debug: print(f"\t\t-3.5 <= {diff} <= -3.0") diff_min = -3.5 diff_max = -3.0 elif diff <= -2.5: if debug: print(f"\t\t-3.0 <= {diff} <= -2.5") diff_min = -3.0 diff_max = -2.5 elif diff <= -2.0: if debug: print(f"\t\t-2.5 <= {diff} <= -2.0") diff_min = -2.5 diff_max = -2.0 elif diff <= -1.5: if debug: print(f"\t\t-2.0 <= {diff} <= -1.5") diff_min = -2.0 diff_max = -1.5 elif diff <= -1.0: if debug: print(f"\t\t-1.5 <= {diff} <= -1.0") diff_min = -1.5 diff_max = -1.0 elif diff <= -0.5: if debug: print(f"\t\t-1.0 <= {diff} <= -0.5") diff_min = -1.0 diff_max = -0.5 elif diff <= 0.0: if debug: print(f"\t\t-0.5 <= {diff} <= 0.0") diff_min = -0.5 diff_max = 0.0 elif diff <= 0.5: if debug: print(f"\t\t0.0 <= {diff} <= 0.5") diff_min = 0.0 diff_max = 0.5 elif diff <= 1.0: if debug: print(f"\t\t0.5 <= {diff} <= 1.0") diff_min = 0.5 diff_max = 1.0 elif diff <= 1.5: if debug: print(f"\t\t1.0 <= {diff} <= 1.5") diff_min = 1.0 diff_max = 1.5 elif diff <= 2.0: if debug: print(f"\t\t1.5 <= {diff} <= 2.0") diff_min = 1.5 diff_max = 2.0 elif diff <= 2.5: if debug: print(f"\t\t2.0 <= {diff} <= 2.5") diff_min = 2.0 diff_max = 2.5 elif diff <= 3.0: if debug: print(f"\t\t2.5 <= {diff} <= 3.0") diff_min = 2.5 diff_max = 3.0 elif diff <= 3.5: if debug: print(f"\t\t3.0 <= {diff} <= 3.5") diff_min = 3.0 diff_max = 3.5 elif diff <= 4.0: if debug: print(f"\t\t3.5 <= {diff} <= 4.0") diff_min = 3.5 diff_max = 4.0 elif diff <= 4.5: if debug: print(f"\t\t4.0 <= {diff} <= 4.5") diff_min = 4.0 diff_max = 4.5 elif diff <= 5.0: if debug: print(f"\t\t4.5 <= {diff} <= 5.0") diff_min = 4.5 diff_max = 5.0 else: if debug: print(f"\t\t{diff} -> no data") diff_min = None diff_max = None return diff_min, diff_max def dieta_response(response_esfuerzo, response_cumplimiento, debug=False): # esfuerzo dieta: # 0 (No data): # No entiendo la calculadora, quiero menús tipo, cárgame 4|I: 2 # Iba a coger menús tipo, pero al final por precio no|D: 13 # No entiendo la calculadora, quiero menús tipo, cárgame 2|I: 3 # 1 (costó subir macros): # Costó demasiado, súbeme macros|D: 28 # 2 (costó bajar macros): # Costó demasiado, bájame macros|D: 42 # 3 (costó y me adapto a nuevos ajustes): # Costó, pero me adapto a nuevos ajustes|C: 331 # 4 (no costó): # No costó nada|A: 1504 # compromiso dieta: # 0 (bien): # al 70%|B|6: 564 # casi ©|A|9: 610 # Perfecta|A|10: 563 # 1 (regular): # regular, me cuesta llegar|C|5: 57 # regular, me salto la dieta|C|4: 6 # 2 (mal): # Nada, mantén mis macros|I|0: 123 esfuerzo_dieta_cluster = clustering_esfuerzo_dieta_response(response_esfuerzo, debug) cumplimiento_dieta_cluster = clustering_cumplimiento_dieta_response(response_cumplimiento, debug) if esfuerzo_dieta_cluster == 0: dieta_bien = cumplimiento_dieta_cluster == 0 dieta_regular = cumplimiento_dieta_cluster == 1 dieta_mal = cumplimiento_dieta_cluster == 2 else: dieta_bien = esfuerzo_dieta_cluster == 4 and cumplimiento_dieta_cluster == 0 dieta_regular = esfuerzo_dieta_cluster == 3 and cumplimiento_dieta_cluster == 1 dieta_mal = (esfuerzo_dieta_cluster == 2 or esfuerzo_dieta_cluster == 1) and cumplimiento_dieta_cluster == 2 if dieta_bien: return 0 elif dieta_regular: return 1 elif dieta_mal: return 2 else: return 3 def make_query(cluster_esfuerzo_dieta, cluster_objetivo, cluster_entrenamiento, cluster_cumplimiento_dieta, cluster_compromiso, diff_peso_min, diff_peso_max, basic_query=False): if not basic_query: query = [ { 'esfuerzoParaCumplirDieta': { 'operator': 'in', 'value': cluster_esfuerzo_dieta, } }, { 'objetivo': { 'operator': 'in', 'value': cluster_objetivo, } }, { 'cumplimientoEntrenamiento': { 'operator': 'in', 'value': cluster_entrenamiento, } }, { 'cumplimientoDieta': { 'operator': 'in', 'value': cluster_cumplimiento_dieta, } }, { 'compromiso': { 'operator': 'in', 'value': cluster_compromiso, } }, { 'diferencia_peso': { 'operator': '<=', 'value': diff_peso_max, } }, { 'diferencia_peso': { 'operator': '>=', 'value': diff_peso_min, } } ] else: query = [ { 'objetivo': { 'operator': 'in', 'value': cluster_objetivo, } }, { 'cumplimientoEntrenamiento': { 'operator': 'in', 'value': cluster_entrenamiento, } }, { 'cumplimientoDieta': { 'operator': 'in', 'value': cluster_cumplimiento_dieta, } }, ] if cluster_esfuerzo_dieta.lower() == 'costo subir macros'.lower() or cluster_esfuerzo_dieta.lower() == 'costo bajar macros'.lower(): # Remove diferencia peso query.pop(6) query.pop(5) # Remove compromiso query.pop(4) # Remove cumplimiento dieta query.pop(3) # Remove cumplimiento entrenamiento query.pop(2) # Remove objetivo query.pop(1) elif cluster_esfuerzo_dieta.lower() == 'no data'.lower(): # Remove esfuerzo dieta query.pop(0) return query