Fix errors and bugs in the given code, without changing its intended functionality. ONLY return the fixed code and nothing else. Enclose the entire code in a single code block.



from darts.dataprocessing.transformers import Scaler
from darts.datasets import AirPassengersDataset
from darts.models import KalmanForecaster
from darts.utils.timeseries_generation import datetime_attribute_timeseries
from darts import TimeSeries
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

# Define the train-test split ratio
train_ratio = 0.8

# Sample data for demonstration purposes
filtered_refgps = filtered_refgps # dont change this, already have one

# Determine the number of training samples
num_samples = len(filtered_refgps)
train_size = int(num_samples * train_ratio)

# Split the data into train and test sets
train_data = filtered_refgps[:train_size]

# Convert to pandas DataFrame to use TimeSeries.from_dataframe
train_df = pd.DataFrame(train_data, columns=['value'])

# Convert to TimeSeries
train_series = TimeSeries.from_dataframe(train_df, value_cols='value')

# Initialize KalmanForecaster
model = KalmanForecaster(dim_x=12)

# Initial training
model.fit(train_series)

preds = []

# Define a function to update the model with new data
def update_model_with_new_data(new_data):
    pred = model.predict(1)
    preds.append(pred)

    # Append needs to respect the time index, so create a time index for new data
    last_time = train_series.end_time()
    new_series = new_data.with_columns_renamed(None, 'value').with_static_covariates(train_series.static_covariates)
    ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-67-aa2a3d753227> in <cell line: 54>()
     55     print(value)
     56     new_data = TimeSeries.from_dataframe(pd.DataFrame([value], columns=['value']), fill_missing_dates=True, freq=train_series.freq)
---> 57     train_series = update_model_with_new_data(new_data)
     58     model.fit(train_series)
     59 

1 frames
/usr/local/lib/python3.10/dist-packages/darts/timeseries.py in with_columns_renamed(self, col_names, col_names_new)
   4288 
   4289         raise_if_not(
-> 4290             all([(x in self.columns.to_list()) for x in col_names]),
   4291             "Some column names in col_names don't exist in the time series.",
   4292             logger,

TypeError: 'NoneType' object is not iterable
    new_series = new_series.add_datetime_attribute('time')
    if len(new_series) > 1:
        new_series = new_series.drop_columns('value').with_static_covariates(train_series.static_covariates)
    
    updated_series = train_series.append(new_series)
    return updated_series

test_data = filtered_refgps[train_size:]

for value in test_data:
    print(value)
    new_data = TimeSeries.from_dataframe(pd.DataFrame([value], columns=['value']), fill_missing_dates=True, freq=train_series.freq)
    train_series = update_model_with_new_data(new_data)
    model.fit(train_series)

# Flatten predictions for scoring
flattened_preds = np.array([pred.values()[0][0] for pred in preds])

# Calculate metrics
mse = mean_squared_error(test_data, flattened_preds)
rmse = np.sqrt(mse)
mae = mean_absolute_error(test_data, flattened_preds)
r2 = r2_score(test_data, flattened_preds)

print(f'MSE: {mse}, RMSE: {rmse}, MAE: {mae}, R2: {r2}')