Add timeseries plots to general_utils.py

unseen/general_utils.py

@@ -1,6 +1,9 @@
 """General utility functions."""
 
 import argparse
+from matplotlib.ticker import AutoMinorLocator
+import matplotlib.pyplot as plt
+import numpy as np
 import xclim
 
 
@@ -69,3 +72,160 @@ def convert_units(da, target_units):
             raise e
 
     return da
+
+
+def plot_timeseries_scatter(
+    da,
+    da_obs=None,
+    ax=None,
+    title=None,
+    label=None,
+    obs_label=None,
+    units=None,
+    time_dim="time",
+    outfile=None,
+):
+    """Timeseries scatter plot of ensemble and observed data.
+
+    Parameters
+    ----------
+    da : xarray.DataArray
+        Stacked ensemble data
+    da_obs : xarray.DataArray, optional
+        Observed data
+    ax : matplotlib.axes.Axes, optional
+        Axis to plot on, if None, a new figure is created
+    title : str, optional
+        Title of the plot
+    label : str, optional
+        Label for ensemble data
+    obs_label : str, optional
+        Label for observed data
+    units : str, optional
+        Units of the data. If None, the units attribute of da is used.
+    time_dim : str, optional
+        Name of the time dimension in da and da_obs
+    outfile : str, optional
+        Filename to save the plot
+
+    Returns
+    -------
+    ax : matplotlib.axes.Axes
+        Axis object
+    """
+    if units is None:
+        if "units" in da.attrs:
+            units = da.attrs["units"]
+        else:
+            units = ""
+
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize=(10, 4))
+    if title is not None:
+        ax.set_title(title, loc="left")
+
+    # Plot ensemble data
+    ax.scatter(da[time_dim], da, s=3, c="lightskyblue", label=label)
+    # Plot observed data
+    if da_obs is not None:
+        ax.scatter(
+            da_obs[time_dim],
+            da_obs,
+            s=20,
+            c="k",
+            marker="x",
+            label=obs_label,
+        )
+
+    ax.set_ylabel(units)
+    ax.set_xmargin(1e-2)
+    ax.xaxis.set_minor_locator(AutoMinorLocator())
+    ax.yaxis.set_minor_locator(AutoMinorLocator())
+    ax.legend()
+
+    if outfile:
+        plt.tight_layout()
+        plt.savefig(outfile, dpi=200, bbox_inches="tight")
+    return ax
+
+
+def plot_timeseries_box_plot(
+    da,
+    da_obs=None,
+    ax=None,
+    title=None,
+    label=None,
+    obs_label=None,
+    units=None,
+    time_dim="time",
+    outfile=None,
+):
+    """Timeseries box and whisker plot of ensemble and observed data.
+
+    Parameters
+    ----------
+    da : xarray.DataArray
+        Stacked ensemble data (see Notes about the time dimension)
+    da_obs : xarray.DataArray, optional
+        Observed data
+    ax : matplotlib.axes.Axes, optional
+        Axis to plot on, if None, a new figure is created
+    title : str, optional
+        Title of the plot
+    label : str, optional
+        Label for ensemble data
+    obs_label : str, optional
+        Label for observed data
+    units : str, optional
+        Units of the data. If None, the units attribute of da is used.
+    time_dim : str, optional
+        Name of the time dimension in da and da_obs
+    outfile : str, optional
+        Filename to save the plot
+
+    Returns
+    -------
+    ax : matplotlib.axes.Axes
+        Axis object
+
+    Notes
+    -----
+    Ensure all time dimensions are set to the correct frequency before calling this function.
+
+    Examples
+    --------
+    - Input ensemble data grouped by year:
+        da = da_orig.copy()
+        da.coords["time"] = da.time.dt.year
+        da["init_date"] = da.init_date.dt.year
+        da = da.stack({"sample": ["ensemble", "init_date", "lead_time"]})
+        plot_timeseries_box_plot(da, time_dim="time")
+    """
+    if units is None:
+        if "units" in da.attrs:
+            units = da.attrs["units"]
+        else:
+            units = ""
+    # Group model data
+    da_grps = [da.isel(sample=v) for k, v in da.groupby(da[time_dim]).groups.items()]
+
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize=(10, 4))
+    if title is not None:
+        ax.set_title(title, loc="left")
+
+    # Plot model box and whiskers for each unique time
+    ax.boxplot(da_grps, positions=np.unique(da[time_dim]), manage_ticks=False)
+
+    # Plot observed data as blue crosses
+    ax.scatter(da_obs[time_dim], da_obs, s=30, c="b", marker="x", label=obs_label)
+    ax.set_ylabel(units)
+    ax.set_xmargin(1e-2)
+    ax.xaxis.set_minor_locator(AutoMinorLocator())
+    ax.yaxis.set_minor_locator(AutoMinorLocator())
+    ax.legend()
+
+    if outfile:
+        plt.tight_layout()
+        plt.savefig(outfile, dpi=200, bbox_inches="tight")
+    return ax