def _fill_checked_value(self, is_complete, value, decimal=1):
#資料若存在data_complete_list裡面時，出現nan就寫0 (是不是只有日數才合理)
if isinstance(value, Decimal):
value = float(value)
if np.isnan(value):
value = '0'
if not is_complete:
if isinstance(value, float):
return self.incomplete_symbol + get_round_str(value, decimal)
else:
return self.incomplete_symbol + str(value)
if isinstance(value, float):
return get_round_str(value, decimal)
return value
def _fill_checked_value(self, is_complete, value, decimal=1):
#資料若存在data_complete_list裡面時，出現nan就寫0 (是不是只有日數才合理)
if isinstance(value, Decimal):
value = float(value)
if np.isnan(value):
value = '0'
if not is_complete:
if isinstance(value, float):
return self.incomplete_symbol + get_round_str(value, decimal)
else:
return self.incomplete_symbol + str(value)
if isinstance(value, float):
return get_round_str(value, decimal)
return value
def get_maximum(self):
values_without_nan = self.filter_nan_data(self.data)
# index, max_value = max(enumerate(values), key=operator.itemgetter(1))
if not values_without_nan:
return '0'
max_value = np.nanmax(values_without_nan)
return get_round_str(max_value, 1)
def get_maximum(self):
values_without_nan = self.filter_nan_data(self.data)
# index, max_value = max(enumerate(values), key=operator.itemgetter(1))
if not values_without_nan:
return '0'
max_value = np.nanmax(values_without_nan)
return get_round_str(max_value, 1)
def get_sum_of_data(self, decimal=1):
values_without_nan = self.filter_nan_data(self.data)
if not values_without_nan:
return '0'
sum_of_data = np.nansum(values_without_nan)
if isinstance(sum_of_data, float):
return get_round_str(sum_of_data, decimal)
return sum_of_data
def get_sum_of_data(self, decimal=1):
values_without_nan = self.filter_nan_data(self.data)
if not values_without_nan:
return '0'
sum_of_data = np.nansum(values_without_nan)
if isinstance(sum_of_data, float):
return get_round_str(sum_of_data, decimal)
return sum_of_data
def get_minimum(self):
values_without_nan = self.filter_nan_data(self.data)
if not values_without_nan:
return '0'
min_value = np.nanmin(values_without_nan)
return get_round_str(min_value, 1)
def get_average_of_data(self):
values_without_nan = self.filter_nan_data(self.data)
if not values_without_nan:
return '0'
average_of_data = np.nanmean(values_without_nan)
return get_round_str(average_of_data, 1)
def get_minimum(self):
values_without_nan = self.filter_nan_data(self.data)
if not values_without_nan:
return '0'
min_value = np.nanmin(values_without_nan)
return get_round_str(min_value, 1)
def get_average_of_data(self):
values_without_nan = self.filter_nan_data(self.data)
if not values_without_nan:
return '0'
average_of_data = np.nanmean(values_without_nan)
return get_round_str(average_of_data, 1)
