#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import pandas as pd
from settings import *
from utilities.initialise_log import save_log
class check_general_settings():
def __init__(self):
if training_dataset or map_generation:
if os.path.isdir(path_train):
save_path = path_train + '/check_user_input.log'
else:
save_path = os.path.dirname(path_train) + '/check_user_input.log'
elif prediction_dataset:
if os.path.isdir(path_pred):
save_path = path_pred + '/check_user_input.log'
else:
save_path = os.path.dirname(path_pred) + '/check_user_input.log'
else:
save_path = '/check_user_input.log'
if os.path.exists(save_path):
os.remove(save_path)
self.logger = save_log(save_path)
self.logger.info("Start checking user input")
self.error = False
self.set_up_dic()
self.check_bools()
self.check_list()
self.check_int()
self.check_int_float()
self.check_string()
self.check_path()
self.check_bb()
self.check_if_right_params_are_set()
self.check_extension()
self.check_path_extension_geosummary()
for handler in self.logger.handlers:
handler.close()
self.logger.removeHandler(handler)
def check_if_right_params_are_set(self):
if training_dataset is None and prediction_dataset is None and map_generation is None:
self.logger.error('Specify a purpose of the run! Set either training_dataset, prediction_dataset and/or map_generation')
self.error = True
if None in [crs, no_value, random_seed, resolution]:
self.logger.error('Set the general settings crs, no_value, random_seed and resolution!')
self.error = True
if training_dataset:
if train_from_scratch is None and train_delete is None:
self.logger.error('Speciy whether you want to generate training dataset from scratch or add/remove feature(s)')
self.error = True
else:
if None in [preprocessing, data_summary_path, key_to_include_path, path_train, path_landslide_database, ID, landslide_database_x, landslide_database_y, path_nonls_locations, num_nonls, nonls_database_x, nonls_database_y]:
self.logger.error('Speciy all necessary parameters for training dataset generation!')
self.error = True
if prediction_dataset:
if pred_from_scratch is None and pred_delete is None:
self.logger.error('Speciy whether you want to generate prediction dataset from scratch or add/remove feature(s)')
self.error = True
else:
if None in [data_summary_path, key_to_include_path, bounding_box, path_pred]:
self.logger.error('Speciy all necessary parameters for prediction dataset generation!')
self.error = True
if map_generation:
if None in [path_ml, size, not_included_pred_data, not_included_train_data, num_trees, criterion, depth, model_to_save, model_to_load, model_database_dir, parallel]:
self.logger.error('Speciy all necessary parameters for map generation!')
self.error = True
def set_up_dic(self):
self.dic = {}
self.dic['bool'] = [training_dataset, train_from_scratch, train_delete, prediction_dataset, pred_from_scratch, pred_delete, map_generation, parallel]
self.dic['path'] = [path_ml, data_summary_path, key_to_include_path, path_train, path_landslide_database, path_nonls_locations, path_pred, model_database_dir]
self.dic['str'] = [crs, ID, landslide_database_x, landslide_database_y, nonls_database_x, nonls_database_y, criterion, model_to_save, model_to_load]
self.dic['int'] = [resolution, random_seed, num_nonls, num_trees, depth]
self.dic['int_float'] = [size, no_value]
self.dic['list'] = [bounding_box, not_included_pred_data, not_included_train_data]
self.dic_steps = {}
self.dic_steps['general'] = []
self.dic_steps['run_purpose'] = [training_dataset, prediction_dataset, map_generation]
def check_extension(self):
for path in [data_summary_path, key_to_include_path, path_landslide_database, path_train]:
if path is not None:
if '.' in path:
if len(path.split('.')) != 2:
self.logger.error(path + ': Paths must not contain full stops!')
self.error = True
else:
if path.split('.')[1] != 'csv':
self.logger.error(path + ': wrong file format! Needs to be csv')
self.error = True
else:
self.logger.warning(path + ': Directory is given. Generic file name will be used')
for path in [path_pred, path_nonls_locations]:
if path is not None:
if '.' in path:
if len(path.split('.')) != 2:
self.logger.error(path + ': Paths must not contain full stops!')
self.error = True
else:
if path.split('.')[1] != 'nc':
self.logger.error(path + ': wrong file format! Needs to be nc')
self.error = True
else:
self.logger.warning(path + ': Directory is given. Generic file name will be used')
def check_bools(self):
self.logger.info("Start checking bools")
for key in self.dic['bool']:
if key is not None:
if type(key) is not bool:
self.logger.info(key + ': not a bool')
self.error = True
def check_list(self):
self.logger.info("Start checking list")
for key in self.dic['list']:
if key is not None:
if type(key) is not list:
self.logger.info(key + ': not a list')
self.error = True
def check_int(self):
self.logger.info("Start checking integers")
for key in self.dic['int']:
if key is not None:
if type(key) is not int:
self.logger.info(key + ': not an integer')
self.error = True
def check_int_float(self):
self.logger.info("Start checking integers and floats")
for key in self.dic['int_float']:
if key is not None:
if type(key) is not int and type(key) is not float:
self.logger.info(key + ': not an integer or float')
self.error = True
def check_string(self):
self.logger.info("Start checking strings")
for key in self.dic['str']:
if key is not None:
if type(key) is not str:
self.logger.info(key + ': not a string')
self.error = True
def check_path(self):
self.logger.info("Start checking paths")
for key in self.dic['path']:
if key is not None:
self.logger.info(key)
if type(key) is not str:
self.logger.info(key + ': path is not a string')
self.error = True
else:
if key == path_train and training_dataset is True:
pass
elif key == path_pred and prediction_dataset is True:
pass
else:
if not os.path.exists(key):
self.logger.error(key + ': path could not be found!')
self.error = True
def check_bb(self):
if bounding_box is not None:
if bounding_box[1] >= bounding_box[0]:
self.logger.error('Careful! South coordinate north of north coordinate!')
self.error = True
if bounding_box[2] >= bounding_box[3]:
if (((bounding_box[2] < 0 and bounding_box[2] > -10) and (bounding_box[3] > 0 and bounding_box[3] < 10))
or ((bounding_box[2] > 0 and bounding_box[2] > 170) and (bounding_box[3] < 0 and bounding_box[3] < -170))):
self.logger.warning('Careful! Please check east and west coordinates!')
else:
self.logger.error('Careful! West coordinate east of east coordinate!')
def check_path_extension_geosummary(self):
self.logger.info('Start checking paths in geospatial data summary')
if data_summary_path is not None and key_to_include_path is not None:
if os.path.exists(data_summary_path) and os.path.exists(key_to_include_path):
if data_summary_path.split('.')[1] != 'csv' and key_to_include_path.split('.')[1] != 'csv':
summary = pd.read_csv(data_summary_path)
keys_to_include = pd.read_csv(key_to_include_path)
for key in list(keys_to_include['keys_to_include']):
idx = list(summary['keys']).index(key)
if summary.at[idx, 'path'].split('.')[1] not in ['nc', 'tif', 'tiff']:
self.logger.error(key + ': Wrong file format!')
self.error = True
if not os.path.exists(summary.at[idx, 'path']):
self.logger.error(key + ': File cannot be found!')
self.error = True