Merge pull request #109 from GeoOcean/107-add-calvalwaves-tool

107 add calvalwaves tool

Author: tausiaj

.github/workflows/python-tests.yml

@@ -48,6 +48,6 @@ jobs:
           source /usr/share/miniconda/etc/profile.d/conda.sh
           conda activate bluemath-tk
           python -m unittest discover tests/datamining/
-          python -m unittest discover tests/downloaders/
+          python -m unittest discover tests/distributions/
           python -m unittest discover tests/interpolation/
           python -m unittest discover tests/wrappers/

bluemath_tk/core/decorators.py

@@ -437,3 +437,59 @@ def wrapper(
         )
 
     return wrapper
+
+
+def validate_data_calval(func):
+    """
+    Decorator to validate data in CalVal class fit method.
+
+    Parameters
+    ----------
+    func : callable
+        The function to be decorated
+
+    Returns
+    -------
+    callable
+        The decorated function
+    """
+
+    @functools.wraps(func)
+    def wrapper(
+        self,
+        data: pd.DataFrame,
+        data_longitude: float,
+        data_latitude: float,
+        data_to_calibrate: pd.DataFrame,
+        max_time_diff: int = 2,
+    ):
+        if not isinstance(data, pd.DataFrame):
+            raise TypeError("Data must be a pandas DataFrame")
+        if not isinstance(data_longitude, float):
+            raise TypeError("Longitude must be a float")
+        data_longitude = data_longitude % 360
+        if not isinstance(data_latitude, float):
+            raise TypeError("Latitude must be a float")
+        if not isinstance(data_to_calibrate, pd.DataFrame):
+            raise TypeError("Data to calibrate must be a pandas DataFrame")
+        if "LONGITUDE" not in data_to_calibrate.columns:
+            raise ValueError(
+                "Data to calibrate must contain a column named 'LONGITUDE'"
+            )
+        if "LATITUDE" not in data_to_calibrate.columns:
+            raise ValueError("Data to calibrate must contain a column named 'LATITUDE'")
+        if "Hs_CAL" not in data_to_calibrate.columns:
+            raise ValueError("Data to calibrate must contain a column named 'Hs_CAL'")
+        if not isinstance(max_time_diff, int) or max_time_diff <= 0:
+            raise ValueError("Maximum time difference must be an integer and > 0")
+
+        return func(
+            self,
+            data,
+            data_longitude,
+            data_latitude,
+            data_to_calibrate,
+            max_time_diff,
+        )
+
+    return wrapper

bluemath_tk/core/plotting/scatter.py

@@ -1,43 +1,236 @@
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 import numpy as np
 import pandas as pd
 from matplotlib.axes import Axes
 from matplotlib.figure import Figure
+from scipy.stats import gaussian_kde, probplot
+from sklearn.metrics import mean_squared_error
 
 from .base_plotting import DefaultStaticPlotting
 from .colors import default_colors
 
 
+def rmse(pred: np.ndarray, tar: np.ndarray) -> float:
+    """
+    Calculate the Root Mean Square Error between predicted and target values.
+
+    Parameters
+    ----------
+    pred : np.ndarray
+        Array of predicted values.
+    tar : np.ndarray
+        Array of target/actual values.
+
+    Returns
+    -------
+    float
+        The Root Mean Square Error value.
+    """
+
+    if len(pred) != len(tar):
+        raise ValueError("pred and tar must have the same length")
+
+    return np.sqrt(((pred - tar) ** 2).mean())
+
+
+def bias(pred: np.ndarray, tar: np.ndarray) -> float:
+    """
+    Calculate the bias between predicted and target values.
+
+    Parameters
+    ----------
+    pred : np.ndarray
+        Array of predicted values.
+    tar : np.ndarray
+        Array of target/actual values.
+
+    Returns
+    -------
+    float
+        The bias value (mean difference between predictions and targets).
+    """
+
+    if len(pred) != len(tar):
+        raise ValueError("pred and tar must have the same length")
+
+    return sum(pred - tar) / len(pred)
+
+
+def si(pred: np.ndarray, tar: np.ndarray) -> float:
+    """
+    Calculate the Scatter Index between predicted and target values.
+
+    Parameters
+    ----------
+    pred : np.ndarray
+        Array of predicted values.
+    tar : np.ndarray
+        Array of target/actual values.
+
+    Returns
+    -------
+    float
+        The Scatter Index value.
+    """
+
+    if len(pred) != len(tar):
+        raise ValueError("pred and tar must have the same length")
+
+    pred_mean = pred.mean()
+    tar_mean = tar.mean()
+
+    return np.sqrt(sum(((pred - pred_mean) - (tar - tar_mean)) ** 2) / (sum(tar**2)))
+
+
+def density_scatter(
+    x: np.ndarray, y: np.ndarray
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Compute a density scatter for two arrays using gaussian KDE.
+
+    Parameters
+    ----------
+    x : np.ndarray
+        X values for the scatter plot.
+    y : np.ndarray
+        Y values for the scatter plot.
+
+    Returns
+    -------
+    Tuple[np.ndarray, np.ndarray, np.ndarray]
+        A tuple containing:
+        - Sorted x values
+        - Sorted y values
+        - Density values corresponding to each point
+    """
+
+    if len(x) != len(y):
+        raise ValueError("x and y must have the same length")
+
+    xy = np.vstack([x, y])
+    z = gaussian_kde(xy)(xy)
+    idx = z.argsort()
+    x1, y1, z = x[idx], y[idx], z[idx]
+
+    return x1, y1, z
+
+
+def validation_scatter(
+    axs: Axes,
+    x: np.ndarray,
+    y: np.ndarray,
+    xlabel: str,
+    ylabel: str,
+    title: str,
+) -> None:
+    """
+    Plot a density scatter and Q-Q plot for validation.
+
+    Parameters
+    ----------
+    axs : Axes
+        Matplotlib axes to plot on.
+    x : np.ndarray
+        X values for the scatter plot.
+    y : np.ndarray
+        Y values for the scatter plot.
+    xlabel : str
+        Label for the X-axis.
+    ylabel : str
+        Label for the Y-axis.
+    title : str
+        Title for the plot.
+    """
+
+    x2, y2, z = density_scatter(x, y)
+
+    # plot
+    axs.scatter(x2, y2, c=z, s=5, cmap="rainbow")
+
+    # labels
+    axs.set_xlabel(xlabel)
+    axs.set_ylabel(ylabel)
+    axs.set_title(title)
+
+    # axis limits
+    maxt = np.ceil(max(max(x) + 0.1, max(y) + 0.1))
+    axs.set_xlim(0, maxt)
+    axs.set_ylim(0, maxt)
+    axs.plot([0, maxt], [0, maxt], "-r")
+    axs.set_xticks(np.linspace(0, maxt, 5))
+    axs.set_yticks(np.linspace(0, maxt, 5))
+    axs.set_aspect("equal")
+
+    # qq-plot
+    xq = probplot(x, dist="norm")
+    yq = probplot(y, dist="norm")
+    axs.plot(xq[0][1], yq[0][1], "o", markersize=0.5, color="k", label="Q-Q plot")
+
+    # diagnostic errors
+    props = dict(
+        boxstyle="round", facecolor="w", edgecolor="grey", linewidth=0.8, alpha=0.5
+    )
+    mse = mean_squared_error(x2, y2)
+    rmse_e = rmse(x2, y2)
+    BIAS = bias(x2, y2)
+    SI = si(x2, y2)
+    label = "\n".join(
+        (
+            r"RMSE = %.2f" % (rmse_e,),
+            r"mse =  %.2f" % (mse,),
+            r"BIAS = %.2f" % (BIAS,),
+            R"SI = %.2f" % (SI,),
+        )
+    )
+    axs.text(
+        0.05,
+        0.95,
+        label,
+        transform=axs.transAxes,
+        fontsize=9,
+        verticalalignment="top",
+        bbox=props,
+    )
+
+
 def plot_scatters_in_triangle(
     dataframes: List[pd.DataFrame],
-    data_colors: List[str] = default_colors,
+    data_colors: Optional[List[str]] = None,
     **kwargs,
-) -> Tuple[Figure, Axes]:
+) -> Tuple[Figure, np.ndarray]:
     """
-    Plot a scatter plot of the dataframes with axes in a triangle.
+    Plot scatter plots of the dataframes with axes in a triangle arrangement.
 
     Parameters
     ----------
     dataframes : List[pd.DataFrame]
-        List of dataframes to plot.
-    data_colors : List[str], optional
-        List of colors for the dataframes.
+        List of dataframes to plot. Each dataframe should contain the same columns.
+    data_colors : Optional[List[str]], optional
+        List of colors for the dataframes. If None, uses default_colors.
     **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)``
+        Additional keyword arguments for the scatter plot. These will be passed to
+        matplotlib.pyplot.scatter. Common parameters include:
+        - s : float, marker size
+        - alpha : float, transparency
+        - marker : str, marker style
 
     Returns
     -------
-    fig : Figure
-        Figure object.
-    axes : Axes
-        Axes object.
+    Tuple[Figure, np.ndarray]
+        A tuple containing:
+        - Figure object
+        - 2D array of Axes objects
+
+    Raises
+    ------
+    ValueError
+        If the variables in the first dataframe are not present in all other dataframes.
     """
 
+    if data_colors is None:
+        data_colors = default_colors
+
     # Get the number and names of variables from the first dataframe
     variables_names = list(dataframes[0].columns)
     num_variables = len(variables_names)