rm conf.scaling.dimensional: no longer perform auto-scaling

The implementation to automatically make values dimensional was a little
clunky and caused complexity in the code preventing further refactoring.
This option is disabled for now. The function performing the
dimensionalization is still available to be called in user scripts.

Author: amorison

stagpy/_step.py

@@ -471,8 +471,7 @@ class _Rprofs:
 
     :class:`_Rprofs` implements the getitem mechanism.  Keys are profile names
     defined in :data:`stagpy.phyvars.RPROF[_EXTRA]`.  Items are
-    :class:`stagpy.datatypes.Rprof` instances.  Note that
-    profiles are automatically scaled if conf.scaling.dimensional is True.
+    :class:`stagpy.datatypes.Rprof` instances.
 
     Attributes:
         step: the :class:`Step` owning the :class:`_Rprofs` instance
@@ -518,8 +517,6 @@ def __getitem__(self, name: str) -> Rprof:
             meta = rpf.meta
         else:
             raise error.UnknownRprofVarError(name)
-        rprof, _ = step.sdat.scale(rprof, meta.dim)
-        rad, _ = step.sdat.scale(rad, "m")
 
         return Rprof(rprof, rad, meta)
 

stagpy/commands.py

@@ -9,8 +9,6 @@
 from shutil import get_terminal_size
 from textwrap import TextWrapper, indent
 
-import pandas
-
 from . import __version__, phyvars, stagyydata
 from ._helpers import baredoc
 from .config import CONFIG_LOCAL, Config
@@ -55,24 +53,6 @@ def info_cmd() -> None:
         else:
             print()
         series = step.timeinfo.loc[list(conf.info.output)]
-        if conf.scaling.dimensional:
-            series = series.copy()
-            dimensions = []
-            for var, val in series.iteritems():
-                meta = phyvars.TIME.get(var)
-                dim = meta.dim if meta is not None else "1"
-                if dim == "1":
-                    dimensions.append("")
-                else:
-                    series[var], dim = sdat.scale(val, dim)
-                    dimensions.append(dim)
-            series = pandas.concat(
-                [
-                    series,
-                    pandas.Series(data=dimensions, index=series.index, name="dim"),
-                ],
-                axis=1,
-            )  # type: ignore
         print(indent(series.to_string(header=False), "  "))
         print()
 

stagpy/config.py

@@ -78,7 +78,6 @@ class Scaling(Section):
 
     yearins: float = entry(val=3.154e7, in_cli=False, doc="year in seconds")
     ttransit: float = entry(val=1.78e15, in_cli=False, doc="transit time in My")
-    dimensional: bool = switch_opt(False, None, "use dimensional units")
     time_in_y: bool = switch_opt(True, None, "dimensional time is in year")
     vel_in_cmpy: bool = switch_opt(True, None, "dimensional velocity is in cm/year")
     factors: Dict[str, str] = entry(

stagpy/field.py

@@ -180,9 +180,7 @@ def plot_scalar(
         step: a :class:`~stagpy._step.Step` of a StagyyData instance.
         var: the scalar field name.
         field: if not None, it is plotted instead of step.fields[var].  This is
-            useful to plot a masked or rescaled array.  Note that if
-            conf.scaling.dimensional is True, this field will be scaled
-            accordingly.
+            useful to plot a masked or rescaled array.
         axis: the :class:`matplotlib.axes.Axes` object where the field should
             be plotted.  If set to None, a new figure with one subplot is
             created.
@@ -216,8 +214,6 @@ def plot_scalar(
     if conf.field.shift:
         fld = np.roll(fld, conf.field.shift, axis=0)
 
-    fld, unit = step.sdat.scale(fld, meta.dim)
-
     if axis is None:
         fig, axis = plt.subplots(ncols=1)
     else:
@@ -250,11 +246,7 @@ def plot_scalar(
     if conf.field.colorbar:
         cax = make_axes_locatable(axis).append_axes("right", size="3%", pad=0.15)
         cbar = plt.colorbar(surf, cax=cax)
-        cbar.set_label(
-            meta.description
-            + (" pert." if conf.field.perturbation else "")
-            + (f" ({unit})" if unit else "")
-        )
+        cbar.set_label(meta.description + (" pert." if conf.field.perturbation else ""))
     if step.geom.spherical or conf.plot.ratio is None:
         axis.set_aspect("equal")
         axis.set_axis_off()
@@ -283,9 +275,7 @@ def plot_iso(
         step: a :class:`~stagpy._step.Step` of a StagyyData instance.
         var: the scalar field name.
         field: if not None, it is plotted instead of step.fields[var].  This is
-            useful to plot a masked or rescaled array.  Note that if
-            conf.scaling.dimensional is True, this field will be scaled
-            accordingly.
+            useful to plot a masked or rescaled array.
         extra: options that will be passed on to
             :func:`matplotlib.axes.Axes.contour`.
     """
@@ -352,8 +342,7 @@ def _findminmax(
     for step in sdat.walk.filter(snap=True):
         for var in sovs:
             if var in step.fields:
-                field = step.fields[var]
-                vals, _ = sdat.scale(field.values, field.meta.dim)
+                vals = step.fields[var].values
                 if var in minmax:
                     minmax[var] = (
                         min(minmax[var][0], np.nanmin(vals)),
@@ -401,10 +390,10 @@ def cmd() -> None:
                     elif valid_field_var(var[1] + "1"):
                         plot_vec(axis, step, var[1], conf=conf)
             if conf.field.timelabel:
-                time, unit = sdat.scale(step.timeinfo["t"], "s")
+                time = step.timeinfo["t"]
                 time = _helpers.scilabel(time)
                 axes[0, 0].text(
-                    0.02, 1.02, f"$t={time}$ {unit}", transform=axes[0, 0].transAxes
+                    0.02, 1.02, f"$t={time}$", transform=axes[0, 0].transAxes
                 )
             oname = "_".join(chain.from_iterable(vfig))
             plt.tight_layout(w_pad=3)

stagpy/rprof.py

@@ -31,7 +31,6 @@ def plot_rprofs(
     if conf is None:
         conf = Config.default_()
     stepstr = rprofs.stepstr
-    sdat = rprofs.step.sdat
 
     for vfig in names:
         fig, axes = plt.subplots(
@@ -48,7 +47,7 @@ def plot_rprofs(
                 rad = rpf.rad
                 meta = rpf.meta
                 if conf.rprof.depth:
-                    rad = sdat.scale(rprofs.bounds[1], "m")[0] - rad
+                    rad = rprofs.bounds[1] - rad
                 axes[iplt].plot(rprof, rad, conf.rprof.style, label=meta.description)
                 if xlabel is None:
                     xlabel = meta.kind
@@ -57,8 +56,6 @@ def plot_rprofs(
             if ivar == 0:
                 xlabel = meta.description
             if xlabel:
-                _, unit = sdat.scale(1, meta.dim)
-                xlabel += f" ({unit})" if unit else ""
                 axes[iplt].set_xlabel(xlabel)
             if vplt[0][:3] == "eta":  # list of log variables
                 axes[iplt].set_xscale("log")
@@ -68,8 +65,6 @@ def plot_rprofs(
         if conf.rprof.depth:
             axes[0].invert_yaxis()
         ylabel = "Depth" if conf.rprof.depth else "Radius"
-        _, unit = sdat.scale(1, "m")
-        ylabel += f" ({unit})" if unit else ""
         axes[0].set_ylabel(ylabel)
         _helpers.saveplot(fig, fname + stepstr)
 
@@ -84,14 +79,11 @@ def plot_grid(step: Step) -> None:
             instance.
     """
     drprof = step.rprofs["dr"]
-    _, unit = step.sdat.scale(1, "m")
-    if unit:
-        unit = f" ({unit})"
     fig, (ax1, ax2) = plt.subplots(2, sharex=True)
     ax1.plot(drprof.rad, "-ko")
-    ax1.set_ylabel("$r$" + unit)
+    ax1.set_ylabel("$r$")
     ax2.plot(drprof.values, "-ko")
-    ax2.set_ylabel("$dr$" + unit)
+    ax2.set_ylabel("$dr$")
     ax2.set_xlim([-0.5, len(drprof.rad) - 0.5])
     ax2.set_xlabel("Cell number")
     _helpers.saveplot(fig, "grid", step.istep)

stagpy/stagyydata.py

@@ -142,8 +142,7 @@ class _Tseries:
 
     :class:`_Tseries` implements the getitem mechanism.  Keys are series names
     defined in :data:`stagpy.phyvars.TIME[_EXTRA]`.  Items are
-    :class:`stagpy.datatypes.Tseries` instances.  Note that series are
-    automatically scaled if conf.scaling.dimensional is True.
+    :class:`stagpy.datatypes.Tseries` instances.
 
     Attributes:
         sdat: the :class:`StagyyData` instance owning the :class:`_Tseries`
@@ -194,8 +193,6 @@ def __getitem__(self, name: str) -> Tseries:
             meta = tseries.meta
         else:
             raise error.UnknownTimeVarError(name)
-        series, _ = self.sdat.scale(series, meta.dim)
-        time, _ = self.sdat.scale(time, "s")
         return Tseries(series, time, meta)
 
     def tslice(
@@ -818,15 +815,8 @@ def scale(
             unit: the dimension of data as defined in phyvars.
         Return:
             scaled quantity and unit string.
-        Other Parameters:
-            conf.scaling.dimensional: if set to False (default), the factor is
-                always 1.
         """
-        if (
-            self.par.get("switches", "dimensional_units", True)
-            or not conf.scaling.dimensional
-            or unit == "1"
-        ):
+        if self.par.get("switches", "dimensional_units", True) or unit == "1":
             return data, ""
         scaling = phyvars.SCALES[unit](self.scales)
         factor = conf.scaling.factors.get(unit, " ")

stagpy/time_series.py

@@ -79,9 +79,6 @@ def plot_time_series(
             if ivar == 0:
                 ylabel = tseries.meta.description
             if ylabel:
-                _, unit = sdat.scale(1, tseries.meta.dim)
-                if unit:
-                    ylabel += f" ({unit})"
                 axes[iplt].set_ylabel(ylabel)
             if vplt[0][:3] == "eta":  # list of log variables
                 axes[iplt].set_yscale("log")
@@ -91,10 +88,7 @@ def plot_time_series(
             axes[iplt].tick_params()
             for time_mark in time_marks:
                 axes[iplt].axvline(time_mark, color="black", linestyle="--")
-        _, unit = sdat.scale(1, "s")
-        if unit:
-            unit = f" ({unit})"
-        axes[-1].set_xlabel("Time" + unit)
+        axes[-1].set_xlabel("Time")
         axes[-1].set_xlim(tstart, tend)
         axes[-1].tick_params()
         _helpers.saveplot(fig, "_".join(fname))