feat: enhance rate calculation to use minimum rates across all determinant keys

- Merge base_rate and calculate_period_rates functions into a single calculate_rates function
- Modify rate calculation to use minimum rates from all determinant keys instead of first key only
- Extract report generation into a separate function for better code organization
- Retain all original logging functionality
- Add input validation and proper error handling

Author: Alex870521

AeroViz/dataProcess/Optical/_absorption.py

@@ -5,9 +5,11 @@ def _absCoe(df, instru, specified_band: list):
 
     band_AE33 = np.array([370, 470, 520, 590, 660, 880, 950])
     band_BC1054 = np.array([370, 430, 470, 525, 565, 590, 660, 700, 880, 950])
+    band_MA350 = np.array([375, 470, 528, 625, 880])
 
     MAE_AE33 = np.array([18.47, 14.54, 13.14, 11.58, 10.35, 7.77, 7.19]) * 1e-3
     MAE_BC1054 = np.array([18.48, 15.90, 14.55, 13.02, 12.10, 11.59, 10.36, 9.77, 7.77, 7.20]) * 1e-3
+    MAE_MA350 = np.array([24.069, 19.070, 17.028, 14.091, 10.120]) * 1e-3
 
     band = band_AE33 if instru == 'AE33' else band_BC1054
     MAE = MAE_AE33 if instru == 'AE33' else MAE_BC1054

AeroViz/plot/templates/__init__.py

@@ -1,6 +1,6 @@
 from .ammonium_rich import ammonium_rich
 from .contour import *
-from .corr_matrix import corr_matrix
+from .corr_matrix import corr_matrix, cross_corr_matrix
 from .diurnal_pattern import *
 from .koschmieder import *
 from .metal_heatmap import metal_heatmaps, process_data_with_two_df

AeroViz/plot/templates/corr_matrix.py

@@ -9,18 +9,20 @@
 
 from AeroViz.plot.utils import *
 
-__all__ = ['corr_matrix']
+__all__ = ['corr_matrix', 'cross_corr_matrix']
 
 
 @set_figure
 def corr_matrix(data: pd.DataFrame,
                 cmap: str = "RdBu",
                 ax: Axes | None = None,
+                items_order: list = None,  # 新增參數用於指定順序
                 **kwargs
                 ) -> tuple[Figure, Axes]:
     fig, ax = plt.subplots(**kwargs.get('fig_kws', {})) if ax is None else (ax.get_figure(), ax)
 
     _corr = data.corr()
+    breakpoint()
     corr = pd.melt(_corr.reset_index(), id_vars='index')
     corr.columns = ['x', 'y', 'value']
 
@@ -94,8 +96,172 @@ def value_to_color(val):
         label='p < 0.05'
     )
 
-    ax.legend(handles=[point2], labels=['p < 0.05'], bbox_to_anchor=(0.05, 1, 0.1, 0.05))
+    ax.legend(handles=[point2], labels=['p < 0.05'], bbox_to_anchor=(0.02, 1, 0.05, 0.05))
 
     plt.show()
 
     return fig, ax
+
+
+@set_figure(figsize=(6, 6))
+def cross_corr_matrix(data1: pd.DataFrame,
+                      data2: pd.DataFrame,
+                      cmap: str = "RdBu",
+                      ax: Axes | None = None,
+                      items_order: list = None,  # 新增參數用於指定順序
+                      **kwargs
+                      ) -> tuple[Figure, Axes]:
+    """
+    Create a correlation matrix between two different DataFrames.
+
+    Parameters:
+    -----------
+    data1 : pd.DataFrame
+        First DataFrame
+    data2 : pd.DataFrame
+        Second DataFrame
+    cmap : str, optional
+        Color map for the correlation matrix
+    ax : Axes, optional
+        Matplotlib axes to plot on
+    items_order : list, optional
+        List specifying the order of items to display
+    **kwargs : dict
+        Additional keyword arguments
+    """
+    if ax is None:
+        fig_kws = kwargs.get('fig_kws', {})
+        default_figsize = fig_kws.get('figsize', (8, 8))
+        fig = plt.figure(figsize=default_figsize)
+        ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
+    else:
+        fig = ax.get_figure()
+
+    # 如果沒有指定順序，使用原始列名順序
+    if items_order is None:
+        x_labels = list(data1.columns)
+        y_labels = list(data2.columns)
+    else:
+        # 使用指定順序，但只包含實際存在於數據中的列
+        x_labels = [item for item in items_order if item in data1.columns]
+        y_labels = [item for item in items_order if item in data2.columns]
+
+    # Calculate cross-correlation between the two DataFrames
+    correlations = []
+    p_values_list = []
+
+    for col1 in x_labels:  # 使用指定順序的列名
+        for col2 in y_labels:
+            try:
+                mask = ~(np.isnan(data1[col1]) | np.isnan(data2[col2]))
+                if mask.sum() > 2:
+                    corr, p_val = pearsonr(data1[col1][mask], data2[col2][mask])
+                else:
+                    corr, p_val = np.nan, np.nan
+            except Exception as e:
+                print(f"Error calculating correlation for {col1} and {col2}: {str(e)}")
+                corr, p_val = np.nan, np.nan
+
+            correlations.append({
+                'x': col1,
+                'y': col2,
+                'value': corr
+            })
+            if p_val is not None and p_val < 0.05:
+                p_values_list.append({
+                    'x': col1,
+                    'y': col2,
+                    'value': p_val
+                })
+
+    corr = pd.DataFrame(correlations)
+    p_values = pd.DataFrame(p_values_list)
+
+    # Create mapping using the specified order
+    x_to_num = {label: i for i, label in enumerate(x_labels)}
+    y_to_num = {label: i for i, label in enumerate(y_labels)}
+
+    # 調整標籤顯示
+    ax.set_xticks([x_to_num[v] for v in x_labels])
+    ax.set_xticklabels(x_labels, rotation=45, ha='right')
+    ax.set_yticks([y_to_num[v] for v in y_labels])
+    ax.set_yticklabels(y_labels)
+
+    ax.grid(False, 'major')
+    ax.grid(True, 'minor')
+    ax.set_xticks([t + 0.5 for t in ax.get_xticks()], minor=True)
+    ax.set_yticks([t + 0.5 for t in ax.get_yticks()], minor=True)
+
+    ax.set_xlim([-0.5, max([v for v in x_to_num.values()]) + 0.5])
+    ax.set_ylim([-0.5, max([v for v in y_to_num.values()]) + 0.5])
+
+    # Color mapping
+    n_colors = 256
+    palette = sns.color_palette(cmap, n_colors=n_colors)
+    color_min, color_max = [-1, 1]
+
+    def value_to_color(val):
+        if pd.isna(val):
+            return (1, 1, 1)
+        val_position = float((val - color_min)) / (color_max - color_min)
+        val_position = np.clip(val_position, 0, 1)
+        ind = int(val_position * (n_colors - 1))
+        return palette[ind]
+
+    # Plot correlation squares
+    x_coords = corr['x'].map(x_to_num)
+    y_coords = corr['y'].map(y_to_num)
+    sizes = corr['value'].abs().fillna(0) * 70
+    colors = [value_to_color(val) for val in corr['value']]
+
+    point = ax.scatter(
+        x=x_coords,
+        y=y_coords,
+        s=sizes,
+        c=colors,
+        marker='s',
+        label='$R^{2}$'
+    )
+
+    # 調整顏色軸的位置和大小
+    cax = fig.add_axes([0.91, 0.1, 0.02, 0.8])
+    axes_image = plt.cm.ScalarMappable(cmap=colormaps[cmap])
+    cbar = plt.colorbar(mappable=axes_image, cax=cax, label=r'$R^{2}$')
+    cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])
+    cbar.set_ticklabels(np.linspace(-1, 1, 5))
+
+    # Plot significance markers
+    if not p_values.empty:
+        point2 = ax.scatter(
+            x=p_values['x'].map(x_to_num),
+            y=p_values['y'].map(y_to_num),
+            s=10,
+            marker='*',
+            color='k',
+            label='p < 0.05'
+        )
+        ax.legend(handles=[point2], labels=['p < 0.05'],
+                  bbox_to_anchor=(0.005, 1.04), loc='upper left')
+
+    # Add labels
+    ax.set_xlabel('NZ', labelpad=10)
+    ax.set_ylabel('FS', labelpad=10)
+
+    plt.show()
+
+    return fig, ax
+
+
+if __name__ == '__main__':
+    import pandas as pd
+    from pandas import to_numeric
+
+    df_NZ = pd.read_csv('/Users/chanchihyu/Desktop/NZ_minion_202402-202411.csv', parse_dates=True, index_col=0)
+    df_FS = pd.read_csv('/Users/chanchihyu/Desktop/FS_minion_202402-202411.csv', parse_dates=True, index_col=0)
+
+    items = ['Ext', 'Sca', 'Abs', 'PNC', 'PSC', 'PVC', 'SO2', 'NO', 'NOx', 'NO2', 'CO', 'O3', 'THC', 'NMHC', 'CH4',
+             'PM10', 'PM2.5', 'WS', 'AT', 'RH',
+             'OC', 'EC', 'Na+', 'NH4+', 'NO3-', 'SO42-', 'Al', 'Si', 'Ca', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Cu', 'Zn']
+    df_NZ = df_NZ.apply(to_numeric, errors='coerce')
+
+    corr_matrix(df_NZ[items], items_order=items)

AeroViz/plot/templates/metal_heatmap.py

@@ -117,11 +117,11 @@ def normalize_and_split(df, df2):
     return df, df2
 
 
-@set_figure(figsize=(12, 3), fs=6)
+@set_figure(figsize=(6, 3), fs=8, fw='normal')
 def metal_heatmaps(df,
                    process=True,
-                   major_freq='24h',
-                   minor_freq='12h',
+                   major_freq='10d',
+                   minor_freq='1d',
                    cmap='jet',
                    ax: Axes | None = None,
                    **kwargs
@@ -131,7 +131,7 @@ def metal_heatmaps(df,
 
     fig, ax = plt.subplots(**kwargs.get('fig_kws', {})) if ax is None else (ax.get_figure(), ax)
 
-    sns.heatmap(df.T, vmin=None, vmax=3, cmap=cmap, xticklabels=False, yticklabels=True,
+    sns.heatmap(df.T, vmin=None, vmax=3, cmap=cmap, xticklabels=True, yticklabels=True,
                 cbar_kws={'label': 'Z score', "pad": 0.02})
     ax.grid(color='gray', linestyle='-', linewidth=0.3)
 
@@ -142,14 +142,23 @@ def metal_heatmaps(df,
     # Set the major and minor ticks
     ax.set_xticks(ticks=[df.index.get_loc(t) for t in major_tick])
     ax.set_xticks(ticks=[df.index.get_loc(t) for t in minor_tick], minor=True)
-    ax.set_xticklabels(major_tick.strftime('%F'))
+    ax.set_xticklabels(major_tick.strftime('%F'), rotation=0)
     ax.tick_params(axis='y', rotation=0)
 
     ax.set(xlabel='',
-           ylabel='',
+           ylabel='Trace metals',
            title=kwargs.get('title', None)
            )
 
+    if kwargs.get('savefig'):
+        plt.savefig(kwargs.get('savefig'), dpi=600)
+
     plt.show()
 
     return fig, ax
+
+
+if __name__ == '__main__':
+    fig, ax = plt.subplots(1, 1, figsize=(6, 6))
+    plt.title('text', font={'weight': 'bold'})
+    plt.show()