Add troubleshooting section to readme file

tests/test_timesfm.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 from datetime import datetime, timedelta
 
 import numpy as np
@@ -22,10 +21,10 @@ import pytest
 import timesfm
 
 
-def create_sample_dataframe(
-    start_date: datetime, end_date: datetime, freq: str = "D"
-) -> pd.DataFrame:
-    """
+def create_sample_dataframe(start_date: datetime,
+                            end_date: datetime,
+                            freq: str = "D") -> pd.DataFrame:
+  """
     Create a sample DataFrame with time series data.
 
     Args:
@@ -36,10 +35,10 @@ def create_sample_dataframe(
     Returns:
         pd.DataFrame: DataFrame with columns 'unique_id', 'ds', and 'ts'.
     """
-    date_range = pd.date_range(start=start_date, end=end_date, freq=freq)
-    ts_data = np.random.randn(len(date_range))
-    df = pd.DataFrame({"unique_id": "ts-1", "ds": date_range, "ts": ts_data})
-    return df
+  date_range = pd.date_range(start=start_date, end=end_date, freq=freq)
+  ts_data = np.random.randn(len(date_range))
+  df = pd.DataFrame({"unique_id": "ts-1", "ds": date_range, "ts": ts_data})
+  return df
 
 
 @pytest.mark.parametrize("context_length", [128, 256, 512])
@@ -50,42 +49,41 @@ def test_timesfm_forecast_on_df(
     prediction_length: int,
     freq: str,
 ) -> None:
-    model = timesfm.TimesFm(
-        context_len=context_length,
-        horizon_len=prediction_length,
-        input_patch_len=32,
-        output_patch_len=128,
-        num_layers=20,
-        model_dims=1280,
-        backend="cpu",
-    )
-    model.load_from_checkpoint(repo_id="google/timesfm-1.0-200m")
+  model = timesfm.TimesFm(
+      context_len=context_length,
+      horizon_len=prediction_length,
+      input_patch_len=32,
+      output_patch_len=128,
+      num_layers=20,
+      model_dims=1280,
+      backend="cpu",
+  )
+  model.load_from_checkpoint(repo_id="google/timesfm-1.0-200m")
 
-    end_date = datetime.now()
-    start_date = end_date - timedelta(days=context_length)
-    input_df = create_sample_dataframe(start_date, end_date, freq)
+  end_date = datetime.now()
+  start_date = end_date - timedelta(days=context_length)
+  input_df = create_sample_dataframe(start_date, end_date, freq)
 
-    forecast_df = model.forecast_on_df(
-        inputs=input_df,
-        freq=freq,
-        value_name="ts",
-        num_jobs=-1,
-    )
+  forecast_df = model.forecast_on_df(
+      inputs=input_df,
+      freq=freq,
+      value_name="ts",
+      num_jobs=-1,
+  )
 
-    assert (
-        len(forecast_df) == prediction_length
-    ), f"Expected forecast length of {prediction_length}, but got {len(forecast_df)}"
-    assert (
-        "timesfm" in forecast_df.columns
-    ), "Forecast DataFrame should contain 'timesfm' column"
+  assert (
+      len(forecast_df) == prediction_length
+  ), f"Expected forecast length of {prediction_length}, but got {len(forecast_df)}"
+  assert ("timesfm" in forecast_df.columns
+         ), "Forecast DataFrame should contain 'timesfm' column"
 
-    last_input_date = input_df["ds"].max()
-    first_forecast_date = forecast_df["ds"].min()
-    expected_first_forecast_date = last_input_date + pd.Timedelta(1, unit=freq)
-    assert (
-        first_forecast_date == expected_first_forecast_date
-    ), f"Forecast should start from {expected_first_forecast_date}, but starts from {first_forecast_date}"
+  last_input_date = input_df["ds"].max()
+  first_forecast_date = forecast_df["ds"].min()
+  expected_first_forecast_date = last_input_date + pd.Timedelta(1, unit=freq)
+  assert (
+      first_forecast_date == expected_first_forecast_date
+  ), f"Forecast should start from {expected_first_forecast_date}, but starts from {first_forecast_date}"
 
-    print(
-        f"Successful forecast with context_length={context_length}, prediction_length={prediction_length}, freq={freq}"
-    )
+  print(
+      f"Successful forecast with context_length={context_length}, prediction_length={prediction_length}, freq={freq}"
+  )