Possible to call rule directly
This commit is contained in:
Noe Brucy
+97
-19
@@ -55,6 +55,7 @@ class Plotter(Aggregator, BaseProcessor):
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label_convert = {
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"turb_rms": "$f_{rms}$",
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"beta": "$\\beta$",
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"beta_cool": "$\\beta_{c}$",
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"dens0": "$n_0$",
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"sfr_avg_window": "window",
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"comp_frac": "$\\zeta$",
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@@ -286,6 +287,7 @@ class Plotter(Aggregator, BaseProcessor):
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title=None,
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nml_key=None,
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put_time=True,
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time_unit=cst.Myr,
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cmap="plasma",
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norm="log",
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autoscale=True,
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@@ -330,7 +332,9 @@ class Plotter(Aggregator, BaseProcessor):
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if put_time:
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time = self.save.root._v_attrs.time * self.comp.info["unit_time"]
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time_str = "time = {:.6g} Myr".format(time.express(cst.Myr))
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time_str = "time = {:.6g} {}".format(
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time.express(time_unit), time_unit.latex
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)
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if len(title) > 0:
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title = title + " | " + time_str
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else:
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@@ -423,19 +427,51 @@ class Plotter(Aggregator, BaseProcessor):
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if not label is None:
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P.ylabel(label)
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def _plot_hist(self, name, ax_los="z", label=None, ylog=False):
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def _plot_hist(
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self,
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name,
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ax_los,
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run,
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label=None,
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unit=None,
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unit_coeff=1.0,
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title=None,
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nml_key=None,
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put_time=True,
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time_unit=cst.Myr,
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ylog=False,
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**kwargs
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):
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pdf = self.save.get_node("/hist/" + name + "_" + ax_los)
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values, centers = pdf.read()
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node = self.save.get_node("/hist/" + name + "_" + ax_los)
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label, unit_old, unit = self._ax_label_unit(node, label, unit, unit_coeff)
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values, centers = node.read() * unit_old.express(unit)
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width = centers[1] - centers[0]
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P.bar(centers, values, width, log=ylog)
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P.bar(centers, values, width, log=ylog, **kwargs)
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P.grid()
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title = self._label_run(run, node, title, nml_key)
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if put_time:
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time = self.save.root._v_attrs.time * self.comp.info["unit_time"]
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time_str = "time = {:.6g} {}".format(
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time.express(time_unit), time_unit.latex
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)
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if len(title) > 0:
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title = title + " | " + time_str
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else:
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title = time_str
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P.title(title)
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if not label is None:
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P.xlabel(label)
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if "/hist/fit_" + name + "_" + ax_los in self.save:
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slope = pdf.attrs.slope
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origin = pdf.attrs.origin
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slope = node.attrs.slope
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origin = node.attrs.origin
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P.plot(
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centers,
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10 ** (slope * centers + origin),
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@@ -481,6 +517,8 @@ class Plotter(Aggregator, BaseProcessor):
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if node_y._v_attrs.CLASS == "ARRAY":
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x = node_x.read() * xunit_old.express(xunit)
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y = node_y.read() * yunit_old.express(yunit)
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mask = np.isfinite(y)
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x, y = x[mask], y[mask]
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if smooth > 0:
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y = gaussian_filter1d(y, sigma=smooth)
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yerr = None
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@@ -488,6 +526,8 @@ class Plotter(Aggregator, BaseProcessor):
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elif "mean" in node_y:
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x = node_x.read() * xunit_old.express(xunit)
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y = node_y.mean.read() * yunit_old.express(yunit)
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mask = np.isfinite(y)
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x, y = x[mask], y[mask]
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if smooth > 0:
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y = gaussian_filter1d(y, sigma=smooth)
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yerr = node_y.std.read() * yunit_old.express(yunit)
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@@ -500,6 +540,8 @@ class Plotter(Aggregator, BaseProcessor):
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x_run, y_run = node_x[run], node_y[run]
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x = x_run.read() * xunit_old.express(xunit)
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y = y_run.read() * yunit_old.express(yunit)
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mask = np.isfinite(y)
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x, y = x[mask], y[mask]
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if smooth > 0:
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y = gaussian_filter1d(y, sigma=smooth)
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label_run = self._label_run(run, y_run, label, nml_key)
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@@ -507,16 +549,25 @@ class Plotter(Aggregator, BaseProcessor):
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P.legend()
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if linearfit:
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_overlay_linearfit(x, y, yerr)
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self._overlay_linearfit(x, y, yerr)
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def _overlay_linearfit(x, y, yerr=None):
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def _overlay_linearfit(self, x, y, yerr=None, fit_order=1):
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if yerr is None:
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(a, b, rho, _, stderr) = linregress(x, y)
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(a, b, rho, _map_rule, stderr) = linregress(x, y)
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self._log(
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"Linear fit y = {} x + {} with R^2 = {} and error is {}".format(
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a, b, rho, stderr
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)
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)
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else:
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c = polyfit(x, y, 1, w=[1.0 / ty for ty in yerr], full=False)
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fit = polyfit(x, y, 1, w=[1.0 / ty for ty in yerr], full=True)
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c = fit[0]
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residual = fit[1][0][0]
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b, a = c[0], c[1]
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P.plot(x, a * y + b, "--", linewidth=1.5)
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self._log(
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"Linear fit y = {} x + {} with residual {}".format(a, b, residual)
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)
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P.plot(x, a * x + b, "--", linewidth=1.5)
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def overlay_kennicutt(self, n0, step):
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P.grid(False)
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@@ -638,7 +689,7 @@ class Plotter(Aggregator, BaseProcessor):
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label="{}/avg({})".format(name, name),
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),
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"Probability density function of {} fluctuations".format(name),
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dependencies=["pdf_" + name],
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dependencies=["fit_pdf_" + name],
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)
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self.rules.update(
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@@ -647,12 +698,14 @@ class Plotter(Aggregator, BaseProcessor):
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self,
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partial(
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self._plot,
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"/comp/beta",
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"/comp/avg_pdf_slope_coldens",
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linearfit=True,
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"/comp/nml_cloud_params/beta_cool",
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"/comp/avg_time_pdf_slope_coldens",
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),
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kind="comp",
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dependencies=["beta", "avg_pdf_slope_coldens"],
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dependencies={
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"nml": "cloud_params/beta_cool",
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"avg_time_pdf_slope_coldens": None,
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},
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),
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"sink_mass": PlotRule(
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self,
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@@ -703,6 +756,17 @@ class Plotter(Aggregator, BaseProcessor):
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kind="series",
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dependencies=["rms_from_log"],
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),
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"turb_energy": PlotRule(
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self,
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partial(
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self._plot,
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"/series/rms_from_log/time",
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"/series/rms_from_log/turb_energy",
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xunit=cst.Myr,
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),
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kind="series",
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dependencies=["rms_from_log"],
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),
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"sigma": PlotRule(
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self,
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partial(
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@@ -713,15 +777,29 @@ class Plotter(Aggregator, BaseProcessor):
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xunit=cst.Myr,
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yunit=cst.km_s,
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),
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kind="comp",
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kind="series",
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dependencies=["time_sigma"],
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),
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"max_fluct_coldens": PlotRule(
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self,
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partial(
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self._plot,
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"/series/time",
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"/series/time_max_fluct_coldens_z",
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ylabel="$\\max(\Sigma/\overline{\Sigma})$",
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xunit=cst.Myr,
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),
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kind="series",
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dependencies={"time_max_fluct_coldens": "z"},
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),
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"plot": PlotRule(
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self, lambda arg, **kwargs: self._plot(*arg, **kwargs), kind="comp"
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),
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}
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)
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super(Plotter, self).def_rules()
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class InteractiveGUI:
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"""
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