Merge pull request #179 from ArtesiaWater/dev

New Major release 0.10.0

LICENSE

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2020 O.E. Ebbens, D.A. Brakenhoff, R. Calje
+Copyright (c) 2020 O.N. Ebbens, D.A. Brakenhoff, R. Calje
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

hydropandas/__init__.py

@@ -15,6 +15,7 @@
     read_fews,
     read_imod,
     read_knmi,
+    read_lizard,
     read_menyanthes,
     read_modflow,
     read_pastastore,

hydropandas/extensions/gwobs.py

@@ -298,19 +298,21 @@ def get_zvec(x, y, gwf=None, ds=None):
 
             cid = nlmod.dims.xy_to_icell2d((x, y), ds)
             sel = ds.sel(icell2d=cid)
-            zvec = np.concatenate(([sel["top"].data], sel["botm"].data))
+            zvec = np.concatenate(([sel["top"].values], sel["botm"].values))
             mask = np.isnan(zvec)
             idx = np.where(~mask, np.arange(mask.size), 0)
             np.maximum.accumulate(idx, axis=0, out=idx)
             zvec[mask] = zvec[idx[mask]]
         else:
             sel = ds.sel(x=x, y=y, method="nearest")
-            first_notna = np.nonzero(np.isfinite(np.atleast_1d(sel["top"].data)))[0][0]
-            if sel["top"].data.shape == tuple():
-                top = np.atleast_1d(sel["top"].data)
+            first_notna = np.nonzero(np.isfinite(np.atleast_1d(sel["top"].values)))[0][
+                0
+            ]
+            if sel["top"].values.shape == tuple():
+                top = np.atleast_1d(sel["top"].values)
             else:
-                top = np.atleast_1d(sel["top"].data[[first_notna]])
-            zvec = np.concatenate([top, sel["botm"].data])
+                top = np.atleast_1d(sel["top"].values[[first_notna]])
+            zvec = np.concatenate([top, sel["botm"].values])
             mask = np.isnan(zvec)
             idx = np.where(~mask, np.arange(mask.size), 0)
             np.maximum.accumulate(idx, axis=0, out=idx)

hydropandas/extensions/plots.py

@@ -4,7 +4,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.gridspec import GridSpec
+from tqdm.auto import tqdm
 
+from ..observation import GroundwaterObs
 from . import accessor
 
 logger = logging.getLogger(__name__)
@@ -644,6 +646,59 @@ def section_plot(
 
         return fig, axes
 
+    def series_per_group(self, plot_column, by=None, savefig=True, outputdir="."):
+        """Plot time series per group.
+
+        The default groupby is based on identical x, y coordinates, so plots unique
+        time series per location.
+
+        Parameters
+        ----------
+        plot_column : str
+            name of column containing time series data
+        by : (list of) str or (list of) array-like
+            groupby parameters, default is None which sets groupby to
+            columns ["x", "y"].
+        savefig : bool, optional
+            save figures, by default True
+        outputdir : str, optional
+            path to output directory, by default the current directory (".")
+        """
+        if by is None:
+            by = ["x", "y"]
+        gr = self._obj.groupby(by=by)
+        for _, group in tqdm(gr, desc="Plotting series per group", total=len(gr)):
+            f, ax = plt.subplots(1, 1, figsize=(10, 3))
+            for name, row in group.iterrows():
+                if isinstance(row.obs, GroundwaterObs):
+                    lbl = (
+                        f"{name} (NAP{row['screen_top']:+.1f}"
+                        f"-{row['screen_bottom']:+.1f}m)"
+                    )
+                else:
+                    lbl = f"{name}"
+                ax.plot(
+                    row.obs.index,
+                    row.obs[plot_column],
+                    label=lbl,
+                )
+            ax.legend(
+                loc=(0, 1),
+                frameon=False,
+                ncol=min(group.index.size, 3),
+                fontsize="x-small",
+            )
+            ax.set_ylabel(row["unit"])
+            ax.grid(True)
+            f.tight_layout()
+            if savefig:
+                if isinstance(row.obs, GroundwaterObs):
+                    name = name.split("-")[0]
+                f.savefig(
+                    os.path.join(outputdir, f"{name}.png"), bbox_inches="tight", dpi=150
+                )
+                plt.close(f)
+
 
 @accessor.register_obs_accessor("plots")
 class ObsPlots:

hydropandas/io/fews.py

@@ -395,11 +395,13 @@ class of the observations, e.g. GroundwaterObs or WaterlvlObs
                 [d + " " + t for d, t in zip(date, time)], errors="coerce"
             )
             ts = pd.DataFrame(events, index=index)
-            ts.loc[:, "value"] = ts.loc[:, "value"].astype(float)
 
-            if remove_nan and (not ts.empty):
-                ts.dropna(subset=["value"], inplace=True)
-                header["unit"] = "m NAP"
+            if not ts.empty:
+                ts["value"] = ts["value"].astype(float)
+
+                if remove_nan:
+                    ts.dropna(subset=["value"], inplace=True)
+                    header["unit"] = "m NAP"
 
             o, header = _obs_from_meta(ts, header, translate_dic, ObsClass)
             if locationIds is not None: