chore: use time_line instead of timeline

Author: Mrglglglglgl

docs/analysis_modules.md

@@ -21,7 +21,7 @@ Line plots are particularly good for visualizing sequences that are ordered or s
 
 They are often used to compare trends across categories, show the impact of events on performance, and visualize changes over time-like sequences.
 
-Note: While this module can handle datetime values on the x-axis, the **timeline** plot module has additional features that make working with datetimes easier, such as easily resampling the data to alternate time frames.
+Note: While this module can handle datetime values on the x-axis, the **plots.time_line** plot module has additional features that make working with datetimes easier, such as easily resampling the data to alternate time frames.
 
 </div>
 

pyretailscience/plots/line.py

@@ -8,7 +8,7 @@
 The sequences used in this module can include values like "days since an event" (e.g., -2, -1, 0, 1, 2) or "months
 since a competitor store opened." **This module is not intended for use with actual datetime values**. If a datetime
 or datetime-like column is passed as **`x_col`**, a warning will be triggered, suggesting the use of the
-**`timeline`** module.
+**`plots.time_line`** module.
 
 ### Core Features
 
@@ -28,7 +28,7 @@
 ### Limitations and Handling of Temporal Data
 
 - **Limited Handling of Temporal Data**: This module can plot simple time-based sequences, such as "days since an event," but it cannot manipulate or directly handle datetime or date-like columns. It is not optimized for actual datetime values.
-If a datetime column is passed or more complex temporal plotting is needed, a warning will suggest using the **`timeline`** module, which is specifically designed for working with temporal data and performing time-based manipulation.
+If a datetime column is passed or more complex temporal plotting is needed, a warning will suggest using the **`plots.time_line`** module, which is specifically designed for working with temporal data and performing time-based manipulation.
 - **Pre-Aggregated Data Required**: The module does not perform any data aggregation, so all data must be pre-aggregated before being passed in for plotting.
 
 """
@@ -77,14 +77,14 @@ def plot(
     """
     if x_col is not None and pd.api.types.is_datetime64_any_dtype(df[x_col]):
         warnings.warn(
-            f"The column '{x_col}' is datetime-like. Consider using the 'plots.timeline' module for time-based plots.",
+            f"The column '{x_col}' is datetime-like. Consider using the 'plots.time_line' module for time-based plots.",
             UserWarning,
             stacklevel=2,
         )
 
     elif x_col is None and pd.api.types.is_datetime64_any_dtype(df.index):
         warnings.warn(
-            "The DataFrame index is datetime-like. Consider using the 'plots.timeline' module for time-based plots.",
+            "The DataFrame index is datetime-like. Consider using the 'plots.time_line' module for time-based plots.",
             UserWarning,
             stacklevel=2,
         )

tests/plots/test_line.py

@@ -96,7 +96,7 @@ def test_plot_warns_if_xcol_is_datetime(sample_dataframe, mocker):
     )
 
     warnings.warn.assert_called_once_with(
-        "The column 'x' is datetime-like. Consider using the 'plots.timeline' module for time-based plots.",
+        "The column 'x' is datetime-like. Consider using the 'plots.time_line' module for time-based plots.",
         UserWarning,
         stacklevel=2,
     )
@@ -265,7 +265,7 @@ def test_plot_with_datetime_index_warns(sample_dataframe, mocker):
 
     # Assert that the warning about datetime-like index was raised
     warnings.warn.assert_called_once_with(
-        "The DataFrame index is datetime-like. Consider using the 'plots.timeline' module for time-based plots.",
+        "The DataFrame index is datetime-like. Consider using the 'plots.time_line' module for time-based plots.",
         UserWarning,
         stacklevel=2,
     )