Refactoring, split into more files. Add more personalisation
This commit is contained in:
Noe Brucy
+180
-73
@@ -17,8 +17,9 @@ from matplotlib.collections import PatchCollection
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from functools import partial
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from numpy.polynomial.polynomial import polyfit
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from scipy.ndimage.filters import gaussian_filter1d
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from scipy import optimize
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from postprocessor import *
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from comparator import *
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P.rcParams["image.cmap"] = "plasma"
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@@ -95,7 +96,7 @@ class Plotter(Aggregator, BaseProcessor):
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)
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# Get postprocesor objets for each run
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self.pp_runs = self.comp.pp_runs
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self.pp = self.comp.pp
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# Define log prefix
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self.log_id = "[plot {}] ".format(self.pp_params.out.tag)
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@@ -128,7 +129,7 @@ class Plotter(Aggregator, BaseProcessor):
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for run in self.runs:
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for i, num in enumerate(self.nums[run]):
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plot_filename = self._find_filename(name_full, run, num)
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save = tables.open_file(self.pp_runs[run][num].filename)
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save = tables.open_file(self.pp[run][num].filename)
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try:
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self._plot_rule(
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rule,
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@@ -182,38 +183,36 @@ class Plotter(Aggregator, BaseProcessor):
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else:
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self._log("Plot {} is already done, skipping...".format(plot_filename))
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def _find_filename(self, name_full, run=None, num=None):
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if not self.pp_params.out.tag == "":
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tag_name = "_" + self.pp_params.out.tag
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else:
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tag_name = ""
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def _find_filename(self, name_full, run=None, num=None, fmt=None):
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if not run is None and not num is None:
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return (
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self.path_out
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+ "/"
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+ run
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+ "/"
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+ name_full
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+ tag_name
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+ "_"
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+ format(num, "05")
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+ self.pp_params.plot.out_ext
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)
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elif not run is None:
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return (
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self.path_out
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+ "/"
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+ run
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+ "/"
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+ name_full
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+ tag_name
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+ self.pp_params.plot.out_ext
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)
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else:
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return (
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self.path_out + "/" + name_full + tag_name + self.pp_params.plot.out_ext
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)
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tag_name = self.pp_params.out.tag
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if fmt is None and self.pp_params.out.fmt == "":
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if not self.pp_params.out.tag == "":
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tag_name = "_" + tag_name
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if not run is None and not num is None:
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fmt = "{out}/{run}/{name}{tag}_{run}_{num:05}_{ext}"
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elif not run is None:
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fmt = "{out}/{run}/{name}{tag}_{run}{ext}"
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else:
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fmt = "{out}/{name}{tag}{ext}"
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elif fmt is None:
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fmt = self.pp_params.out.fmt
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nml = None
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if not run is None:
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nml = self.comp.namelist[run]
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return fmt.format(
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run=run,
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name=name_full,
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tag=tag_name,
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num=num,
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nml=nml,
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out=self.path_out,
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ext=self.pp_params.out.ext,
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)
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def _label_run(self, run, node, label, nml_key):
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def get_label_nml(nml_key):
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@@ -226,7 +225,7 @@ class Plotter(Aggregator, BaseProcessor):
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if prop_name in self.value_convert:
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prop_value_str = self.value_convert[prop_name](prop_value)
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elif type(prop_value) in [int, float]:
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prop_value_str = "${:.6g}$".format(prop_value)
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prop_value_str = convert_exp(prop_value, digits=5)
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else:
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prop_value_str = str(prop_value)
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return r"{} = {}".format(prop_label, prop_value_str)
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@@ -443,7 +442,7 @@ class Plotter(Aggregator, BaseProcessor):
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**kwargs
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):
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node = self.save.get_node("/hist/" + name + "_" + ax_los)
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node = self.save.get_node("/hist/" + name)
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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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@@ -493,10 +492,15 @@ class Plotter(Aggregator, BaseProcessor):
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yunit=None,
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xunit_coeff=1.0,
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yunit_coeff=1.0,
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linearfit=False,
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fit=None,
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fitlabel=None,
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smooth=0,
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sigma_err=2.0,
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nml_key=None,
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runs=None,
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yerr_kind="std",
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colors=None,
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nml_color=None,
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**kwargs
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):
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@@ -514,60 +518,146 @@ class Plotter(Aggregator, BaseProcessor):
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P.ylabel(ylabel)
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P.grid()
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yerr = None
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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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mask = np.isfinite(x) & 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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P.plot(x, y, "*", **kwargs)
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(base_line,) = P.plot(x, y, "*", **kwargs)
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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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P.errorbar(x, y, yerr=yerr, fmt="*", **kwargs)
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else:
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yerr = None
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if runs is None:
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runs = self.runs
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for run in runs:
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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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if yerr_kind == "std":
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yerr = node_y.std.read() * yunit_old.express(yunit) * sigma_err
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mask = np.isfinite(x) & np.isfinite(y) & np.isfinite(yerr)
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x, y, yerr = x[mask], y[mask], yerr[mask]
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if smooth > 0:
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y = gaussian_filter1d(y, sigma=smooth)
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base_line, _, _ = P.errorbar(x, y, yerr=yerr, label=label, **kwargs)
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elif yerr_kind in ["min_max", "95per"]:
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if yerr_kind == "min_max":
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yerr_min = node_y.min.read() * yunit_old.express(yunit)
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yerr_max = node_y.max.read() * yunit_old.express(yunit)
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elif yerr_kind == "95per":
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yerr_min = node_y.q025.read() * yunit_old.express(yunit)
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yerr_max = node_y.q975.read() * yunit_old.express(yunit)
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yerr = yerr_max - yerr_min
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mask = (
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np.isfinite(x)
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& np.isfinite(y)
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& np.isfinite(yerr_min)
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& np.isfinite(yerr_max)
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)
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x, y, yerr, yerr_min, yerr_max = (
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x[mask],
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y[mask],
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yerr[mask],
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yerr_min[mask],
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yerr_max[mask],
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)
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base_line, _, _ = P.errorbar(
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x, y, yerr=[y - yerr_min, yerr_max - y], label=label, **kwargs
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)
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else:
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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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(base_line,) = P.plot(x, y, "*", **kwargs)
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else:
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if runs is None:
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runs = self.runs
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for i, run in enumerate(runs):
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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(x) & 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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P.plot(x, y, label=label_run, **kwargs)
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if colors is None:
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(base_line,) = P.plot(x, y, label=label_run, **kwargs)
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else:
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if nml_color is None:
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color = colors[i % len(colors)]
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(base_line,) = P.plot(x, y, label=label_run, **kwargs)
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else:
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nml = self.comp.get_nml(nml_color, run)
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color = colors[nml]
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(base_line,) = P.plot(x, y, label=label_run, color=color, **kwargs)
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P.legend()
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if linearfit:
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self._overlay_linearfit(x, y, yerr)
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if not fit is None:
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self._overlay_fit(
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x,
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y,
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yerr,
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kind=fit,
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ls="--",
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lw=1.5,
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color=base_line.get_color(),
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label=fitlabel,
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)
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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, _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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def _overlay_fit(self, x, y, yerr=None, kind="linear", label=None, **kwargs):
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if kind == "linear":
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if yerr is None:
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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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)
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else:
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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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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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if label is None:
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label = r"Linear fit with slope ${:.3g}$ and $R^2 = {:.3f}$".format(
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a, rho
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)
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else:
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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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self._log(
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"Linear fit y = {} x + {} with residual {}".format(a, b, residual)
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)
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if label is None:
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label = r"Linear fit with slope ${:.3g}$".format(a)
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P.plot(x, a * x + b, label=label, **kwargs)
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elif kind == "power_law":
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if yerr is None:
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(a, b, rho, _map_rule, stderr) = linregress(np.log10(x), np.log10(y))
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self._log(
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"Power law fit y = x^({}) * 10^({}) 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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fitfunc = lambda p, x: p[0] + p[1] * x
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errfunc = lambda p, x, y, err: (y - fitfunc(p, x)) / err
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pinit = [1.0, -1.0]
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out = optimize.leastsq(
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errfunc,
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pinit,
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args=(np.log10(x), np.log10(y), yerr / y),
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full_output=1,
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)
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c = out[0]
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b, a = c[0], c[1]
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residual = errfunc(c, np.log10(x), np.log10(y), yerr / y)
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self._log(
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"Power law fit y = x^({}) * 10^({}) with residual {}".format(
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a, b, residual
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)
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)
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if label is None:
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label = r"Power-law fit with index {:.1f}".format(a)
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P.plot(x, (10 ** b) * x ** a, label=label, **kwargs)
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def overlay_kennicutt(self, n0, step):
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P.grid(False)
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@@ -650,6 +740,12 @@ class Plotter(Aggregator, BaseProcessor):
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"Toomre Q parameter for a Keplerian disk",
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dependencies=["Q"],
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),
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"rho_pdf": PlotRule(
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self,
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partial(self._plot_hist, "rho_pdf"),
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"$\rho$-PDF",
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dependencies=["rho_pdf"],
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),
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}
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averageables = ["coldens", "rho", "T", "Q"]
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@@ -767,6 +863,17 @@ 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_power": 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_power",
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xunit=cst.Myr,
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),
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kind="series",
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dependencies=["turb_power"],
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),
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"sigma": PlotRule(
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self,
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partial(
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Block a user