feat: Add plot_params to allow custom figsize (#68)

pytrendy/detect_trends.py

@@ -8,7 +8,7 @@
 from .io.plot_pytrendy import plot_pytrendy
 from .io.results_pytrendy import PyTrendyResults
 
-def detect_trends(df:pd.DataFrame, date_col:str, value_col: str, plot=True, method_params:dict=None) -> PyTrendyResults:
+def detect_trends(df:pd.DataFrame, date_col:str, value_col: str, plot=True, method_params:dict=None, plot_params:dict=None) -> PyTrendyResults:
     """
     This is the main function that runs trend detection end-to-end.
 
@@ -38,9 +38,12 @@ def detect_trends(df:pd.DataFrame, date_col:str, value_col: str, plot=True, meth
             Defaults to `True`.
         method_params (dict, optional):
             Optional parameters to customize detection heuristics. Supported keys:
-
             - **is_abrupt_padded** (`bool`): Whether to pad abrupt transitions between segments. Defaults to `False`.
             - **abrupt_padding** (`int`): Number of days to pad around abrupt transitions. Only referenced when `is_abrupt_padded` is `True`. Defaults to `28`.
+        plot_params (dict, optional):
+            Dictionary of plotting parameters to pass to `plot_pytrendy`. Supported keys:
+            - **figsize** (`tuple`): The figure size for the plot. Defaults to (20,5)
+
 
     Returns:
         PyTrendyResults:
@@ -66,7 +69,7 @@ def detect_trends(df:pd.DataFrame, date_col:str, value_col: str, plot=True, meth
     segments = get_segments(df)
     segments = refine_segments(df, value_col, segments, method_params)
     segments = analyse_segments(df, value_col, segments)
-    if plot: plot_pytrendy(df, value_col, segments)
+    if plot: plot_pytrendy(df, value_col, segments, plot_params=plot_params)
 
     results = PyTrendyResults(segments)
     return results

pytrendy/io/plot_pytrendy.py

@@ -5,7 +5,7 @@
 import matplotlib.dates as mdates
 import matplotlib.patches as mpatches
 
-def plot_pytrendy(df: pd.DataFrame, value_col: str, segments_enhanced: list[dict], suppress_show: bool = False) -> plt.Figure:
+def plot_pytrendy(df: pd.DataFrame, value_col: str, segments_enhanced: list[dict], suppress_show: bool = False, plot_params: dict = None) -> plt.Figure:
     """
     Visualizes detected trend segments over the original time series signal.
 
@@ -21,6 +21,8 @@ def plot_pytrendy(df: pd.DataFrame, value_col: str, segments_enhanced: list[dict
             List of segment dictionaries containing keys like `'start'`, `'end'`, `'direction'`, `'trend_class'`, and `'change_rank'`.
         suppress_show (bool, optional):
             If True, suppresses the automatic display of the plot with plt.show(). Defaults to False.
+        plot_params (dict, optional):
+            Dictionary of plotting parameters. Currently supports 'figsize' (tuple). Defaults to None.
 
     Returns:
         matplotlib.figure.Figure:
@@ -35,7 +37,10 @@ def plot_pytrendy(df: pd.DataFrame, value_col: str, segments_enhanced: list[dict
         'Noise': 'lightgray',
     }
 
-    fig, ax = plt.subplots(figsize=(20, 5))
+    plot_params = plot_params or {}
+    figsize = plot_params.get("figsize", (20, 5))
+
+    fig, ax = plt.subplots(figsize=figsize)
 
     # Plot the value line
     ax.plot(df.index, df[value_col], color='black', lw=1)

tests/tests_plotting/custom/test_custom_plot_params.py

@@ -0,0 +1,35 @@
+import pytest
+import pandas as pd
+import pytrendy as pt
+from pytrendy.io.plot_pytrendy import plot_pytrendy
+
+class TestCustomPlotParams:
+    """Test custom plot parameters for plot visualization."""
+
+    def _prepare_and_plot(self, df, value_col, segments, **kwargs):
+        """Helper to prepare dataframe and create plot."""
+        df['date'] = pd.to_datetime(df['date'])
+        df = df.set_index('date')[[value_col]]
+        return plot_pytrendy(df, value_col, segments, suppress_show=True, **kwargs)
+
+    @pytest.mark.core
+    @pytest.mark.plot
+    @pytest.mark.mpl_image_compare(baseline_dir='./', filename='test_custom_plot_params_figsize.png', style='default', remove_text=True)
+    def test_custom_plot_params_figsize(self):
+        """Test custom figsize in plot parameters."""
+        df = pt.load_data('series_synthetic')
+        results = pt.detect_trends(
+            df,
+            date_col='date',
+            value_col='gradual',
+            plot=False,
+            method_params=dict(is_abrupt_padded=False)
+        )
+
+        plot_params = {
+            'figsize': (16, 8),
+            'title': "Custom Plot Title"
+        }
+
+        fig = self._prepare_and_plot(df, 'gradual', results.segments, plot_params=plot_params)
+        return fig