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 ..metrics import bias, r2, rmse, si
from .base_plotting import DefaultStaticPlotting
from .colors import default_colors
[docs]
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
[docs]
def validation_scatter(
axs: Axes,
x: np.ndarray,
y: np.ndarray,
xlabel: str,
ylabel: str,
title: str,
cmap: str = "rainbow",
) -> 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.
cmap : str, optional
Colormap to use for the scatter plot. Default is "rainbow".
"""
x2, y2, z = density_scatter(x, y)
# plot
axs.scatter(x2, y2, c=z, s=5, cmap=cmap)
# 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
)
label = "\n".join(
(
r"BIAS = %.2f" % (bias(x2, y2),),
r"SI = %.2f" % (si(x2, y2),),
r"RMSE = %.2f" % (rmse(x2, y2),),
r"R² = %.2f" % (r2(x2, y2),),
)
)
axs.text(
0.05,
0.95,
label,
transform=axs.transAxes,
fontsize=9,
verticalalignment="top",
bbox=props,
)
[docs]
def plot_scatters_in_triangle(
dataframes: List[pd.DataFrame],
data_colors: Optional[List[str]] = None,
**kwargs,
) -> Tuple[Figure, np.ndarray]:
"""
Plot scatter plots of the dataframes with axes in a triangle arrangement.
Parameters
----------
dataframes : List[pd.DataFrame]
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
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
-------
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)
# 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