[JTH] copy binary files option available and scatter plot generalized for datamining techniques

bluemath_tk/core/decorators.py

@@ -216,7 +216,14 @@ def wrapper(
             raise ValueError("Number of iterations must be integer and > 0")
         if not isinstance(normalize_data, bool):
             raise TypeError("Normalize data must be a boolean")
-        return func(self, data, directional_variables, num_iteration)
+        return func(
+            self,
+            data,
+            directional_variables,
+            custom_scale_factor,
+            num_iteration,
+            normalize_data,
+        )
 
     return wrapper
 

bluemath_tk/core/operations.py

@@ -65,9 +65,10 @@ def normalize(
     ...     }
     ... )
     >>> normalized_data, scale_factor = normalize(data=df)
+
     >>> import numpy as np
     >>> import xarray as xr
-    >>> from bluemath_tk.core.data import normalize
+    >>> from bluemath_tk.core.operations import normalize
     >>> ds = xr.Dataset(
     ...     {
     ...         "Hs": (("time",), np.random.rand(1000) * 7),
@@ -85,6 +86,7 @@ def normalize(
         vars_to_normalize = list(data.data_vars)
     else:
         raise TypeError("Data must be a pandas DataFrame or an xarray Dataset")
+
     normalized_data = data.copy()  # Copy data to avoid bad memory replacements
     scale_factor = (
         custom_scale_factor.copy()
@@ -122,6 +124,7 @@ def normalize(
         normalized_data[data_var] = (normalized_data[data_var] - data_var_min) / (
             data_var_max - data_var_min
         )
+
     return normalized_data, scale_factor
 
 
@@ -173,6 +176,7 @@ def denormalize(
     ...     "Dir": [0, 360],
     ... }
     >>> denormalized_data = denormalize(normalized_data=df, scale_factor=scale_factor)
+
     >>> import numpy as np
     >>> import xarray as xr
     >>> from bluemath_tk.core.operations import denormalize
@@ -204,6 +208,7 @@ def denormalize(
             data[data_var] * (scale_factor[data_var][1] - scale_factor[data_var][0])
             + scale_factor[data_var][0]
         )
+
     return data
 
 
@@ -263,6 +268,7 @@ def standarize(
             },
             coords=data.coords,
         )
+
     return standarized_data, scaler
 
 
@@ -308,6 +314,7 @@ def destandarize(
             },
             coords=standarized_data.coords,
         )
+
     return data
 
 

bluemath_tk/core/plotting/scatter.py

@@ -0,0 +1,83 @@
+from typing import List, Tuple
+
+import numpy as np
+import pandas as pd
+from matplotlib.axes import Axes
+from matplotlib.figure import Figure
+
+from .base_plotting import DefaultStaticPlotting
+from .colors import default_colors
+
+
+def plot_scatters_in_triangle(
+    dataframes: List[pd.DataFrame],
+    data_colors: List[str] = default_colors,
+    **kwargs,
+) -> Tuple[Figure, Axes]:
+    """
+    Plot a scatter plot of the dataframes with axes in a triangle.
+
+    Parameters
+    ----------
+    dataframes : List[pd.DataFrame]
+        List of dataframes to plot.
+    data_colors : List[str], optional
+        List of colors for the dataframes.
+    **kwargs : dict, optional
+        Keyword arguments for the scatter plot. Will be passed to the
+        DefaultStaticPlotting.plot_scatter method, which is the same
+        as the one in matplotlib.pyplot.scatter.
+        For example, to change the marker size, you can use:
+        ``plot_scatters_in_triangle(dataframes, s=10)``
+
+    Returns
+    -------
+    fig : Figure
+        Figure object.
+    axes : Axes
+        Axes object.
+    """
+
+    # Get the number and names of variables from the first dataframe
+    variables_names = list(dataframes[0].columns)
+    num_variables = len(variables_names)
+
+    # Check variables names are in all dataframes
+    for df in dataframes:
+        if not all(v in df.columns for v in variables_names):
+            raise ValueError(
+                f"Variables {variables_names} are not in dataframe {df.columns}."
+            )
+
+    # Create figure and axes
+    default_static_plot = DefaultStaticPlotting()
+    fig, axes = default_static_plot.get_subplots(
+        nrows=num_variables - 1,
+        ncols=num_variables - 1,
+        sharex=False,
+        sharey=False,
+    )
+    if isinstance(axes, Axes):
+        axes = np.array([[axes]])
+
+    for c1, v1 in enumerate(variables_names[1:]):
+        for c2, v2 in enumerate(variables_names[:-1]):
+            for idf, df in enumerate(dataframes):
+                default_static_plot.plot_scatter(
+                    ax=axes[c2, c1],
+                    x=df[v1],
+                    y=df[v2],
+                    c=data_colors[idf],
+                    alpha=0.6,
+                    **kwargs,
+                )
+            if c1 == c2:
+                axes[c2, c1].set_xlabel(variables_names[c1 + 1])
+                axes[c2, c1].set_ylabel(variables_names[c2])
+            elif c1 > c2:
+                axes[c2, c1].xaxis.set_ticklabels([])
+                axes[c2, c1].yaxis.set_ticklabels([])
+            else:
+                fig.delaxes(axes[c2, c1])
+
+    return fig, axes

bluemath_tk/datamining/_base_datamining.py

@@ -4,24 +4,18 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
-from matplotlib import pyplot as plt
 from matplotlib.axes import Axes
+from matplotlib.figure import Figure
 
 from ..core.models import BlueMathModel
 from ..core.plotting.base_plotting import DefaultStaticPlotting
+from ..core.plotting.scatter import plot_scatters_in_triangle
 
 
 class BaseSampling(BlueMathModel):
     """
     Base class for all sampling BlueMath models.
     This class provides the basic structure for all sampling models.
-
-    Methods
-    -------
-    generate : pd.DataFrame
-        Generates samples.
-    plot_generated_data : Tuple[plt.figure, plt.axes]
-        Plots the generated data on a scatter plot matrix.
     """
 
     @abstractmethod
@@ -52,7 +46,7 @@ def plot_generated_data(
         self,
         data_color: str = "blue",
         **kwargs,
-    ) -> Tuple[plt.figure, plt.axes]:
+    ) -> Tuple[Figure, Axes]:
         """
         Plots the generated data on a scatter plot matrix.
 
@@ -65,9 +59,9 @@ def plot_generated_data(
 
         Returns
         -------
-        plt.figure
+        Figure
             The figure object containing the plot.
-        plt.axes
+        Axes
             Array of axes objects for the subplots.
 
         Raises
@@ -76,40 +70,14 @@ def plot_generated_data(
             If the data is empty.
         """
 
-        if not self.data.empty:
-            variables_names = list(self.data.columns)
-            num_variables = len(variables_names)
-        else:
+        if self.data.empty:
             raise ValueError("Data must be a non-empty DataFrame with columns to plot.")
 
-        # Create figure and axes
-        default_static_plot = DefaultStaticPlotting()
-        fig, axes = default_static_plot.get_subplots(
-            nrows=num_variables - 1,
-            ncols=num_variables - 1,
-            sharex=False,
-            sharey=False,
+        fig, axes = plot_scatters_in_triangle(
+            dataframes=[self.data],
+            data_colors=[data_color],
+            **kwargs,
         )
-        if isinstance(axes, Axes):
-            axes = np.array([[axes]])
-
-        for c1, v1 in enumerate(variables_names[1:]):
-            for c2, v2 in enumerate(variables_names[:-1]):
-                default_static_plot.plot_scatter(
-                    ax=axes[c2, c1],
-                    x=self.data[v1],
-                    y=self.data[v2],
-                    c=data_color,
-                    **kwargs,
-                )
-                if c1 == c2:
-                    axes[c2, c1].set_xlabel(variables_names[c1 + 1])
-                    axes[c2, c1].set_ylabel(variables_names[c2])
-                elif c1 > c2:
-                    axes[c2, c1].xaxis.set_ticklabels([])
-                    axes[c2, c1].yaxis.set_ticklabels([])
-                else:
-                    fig.delaxes(axes[c2, c1])
 
         return fig, axes
 
@@ -162,7 +130,7 @@ def fit(
             self.custom_scale_factor = custom_scale_factor.copy()
         else:
             self.logger.info(
-                "Normalization is disabled. Using default scale factor (0, 1) for all fitting variables."
+                "Normalization is disabled. Set normalize_data to True to enable normalization."
             )
             self.custom_scale_factor = {
                 fitting_variable: (0, 1) for fitting_variable in self.fitting_variables
@@ -198,7 +166,7 @@ def plot_selected_centroids(
         centroids_color: str = "red",
         plot_text: bool = False,
         **kwargs,
-    ) -> Tuple[plt.figure, plt.axes]:
+    ) -> Tuple[Figure, Axes]:
         """
         Plots data and selected centroids on a scatter plot matrix.
 
@@ -215,9 +183,9 @@ def plot_selected_centroids(
 
         Returns
         -------
-        plt.figure
+        Figure
             The figure object containing the plot.
-        plt.axes
+        Axes
             Array of axes objects for the subplots.
 
         Raises
@@ -231,59 +199,27 @@ def plot_selected_centroids(
             and list(self.data.columns) != []
         ):
             variables_names = list(self.data.columns)
-            num_variables = len(variables_names)
         else:
             raise ValueError(
                 "Data and centroids must have the same number of columns > 0."
             )
 
-        # Create figure and axes
-        default_static_plot = DefaultStaticPlotting()
-        fig, axes = default_static_plot.get_subplots(
-            nrows=num_variables - 1,
-            ncols=num_variables - 1,
-            sharex=False,
-            sharey=False,
+        fig, axes = plot_scatters_in_triangle(
+            dataframes=[self.data, self.centroids],
+            data_colors=[data_color, centroids_color],
+            **kwargs,
         )
-        if isinstance(axes, Axes):
-            axes = np.array([[axes]])
-
-        for c1, v1 in enumerate(variables_names[1:]):
-            for c2, v2 in enumerate(variables_names[:-1]):
-                default_static_plot.plot_scatter(
-                    ax=axes[c2, c1],
-                    x=self.data[v1],
-                    y=self.data[v2],
-                    c=data_color,
-                    alpha=0.6,
-                    **kwargs,
-                )
-                if self.centroids is not None:
-                    default_static_plot.plot_scatter(
-                        ax=axes[c2, c1],
-                        x=self.centroids[v1],
-                        y=self.centroids[v2],
-                        c=centroids_color,
-                        alpha=0.9,
-                        **kwargs,
-                    )
-                    if plot_text:
-                        for i in range(self.centroids.shape[0]):
-                            axes[c2, c1].text(
-                                self.centroids[v1][i],
-                                self.centroids[v2][i],
-                                str(i + 1),
-                                fontsize=12,
-                                fontweight="bold",
-                            )
-                if c1 == c2:
-                    axes[c2, c1].set_xlabel(variables_names[c1 + 1])
-                    axes[c2, c1].set_ylabel(variables_names[c2])
-                elif c1 > c2:
-                    axes[c2, c1].xaxis.set_ticklabels([])
-                    axes[c2, c1].yaxis.set_ticklabels([])
-                else:
-                    fig.delaxes(axes[c2, c1])
+        if plot_text:
+            for c1, v1 in enumerate(variables_names[1:]):
+                for c2, v2 in enumerate(variables_names[:-1]):
+                    for i in range(self.centroids.shape[0]):
+                        axes[c2, c1].text(
+                            self.centroids[v1][i],
+                            self.centroids[v2][i],
+                            str(i + 1),
+                            fontsize=12,
+                            fontweight="bold",
+                        )
 
         return fig, axes
 
@@ -292,7 +228,7 @@ def plot_data_as_clusters(
         data: pd.DataFrame,
         nearest_centroids: np.ndarray,
         **kwargs,
-    ) -> Tuple[plt.figure, plt.axes]:
+    ) -> Tuple[Figure, Axes]:
         """
         Plots data as nearest clusters.
 
@@ -307,9 +243,9 @@ def plot_data_as_clusters(
 
         Returns
         -------
-        plt.figure
+        Figure
             The figure object containing the plot.
-        plt.axes
+        Axes
             The axes object for the plot.
         """