Noe Brucy
854 lines
28 KiB
Python
854 lines
28 KiB
Python
# coding: utf-8
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import pandas as pd
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from baseprocessor import *
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mass_func = lambda dset: dset["rho"] * dset["dx"] ** 3 # Mass function
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vol_func = lambda dset: dset["dx"] ** 3 # Volume function
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getter_T = lambda dset: dset["P"] / dset["rho"] # Temperature
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class PostProcessor(HDF5Container):
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"""
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This class enable to compute and save derived quantities from the raw output
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"""
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# Axes information
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_ax_nb = {"x": 0, "y": 1, "z": 2} # Number of each axes
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_axes_h = {"x": "y", "y": "x", "z": "x"} # Associated horizontal axe
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_axes_v = {"x": "z", "y": "z", "z": "y"} # Associated vertical axe
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G = 1.0 # Gravitational constant
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cells_loaded = False
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def __init__(self, path=None, num=None, path_out=None, pp_params=None, tag=None):
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"""
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Creates the basic structures needed for the outputs
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"""
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super(PostProcessor, self).__init__(path, path_out, pp_params, tag)
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# Open outfile
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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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self.filename = (
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self.path_out + "/postproc_" + tag_name + format(num, "05") + ".h5"
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)
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self.cells_filename = (
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self.path_out + "/cells_" + tag_name + format(num, "05") + ".h5"
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)
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if not os.path.exists(self.path_out):
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os.makedirs(self.path_out)
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self.open()
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# Ramses Output
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self.path = path
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self.run = os.path.basename(path)
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self.num = num
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self._ro = pymses.RamsesOutput(
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path,
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num,
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order=self.pp_params.pymses.order,
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verbose=self.pp_params.pymses.verbose,
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)
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self._amr = self._ro.amr_source(self.pp_params.pymses.variables)
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self.info = self._ro.info.copy()
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# Density operator
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self._rho_op = ScalarOperator(
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lambda dset: dset["rho"], self._ro.info["unit_density"]
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)
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# Density ray tracer
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if self.pp_params.pymses.fft:
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self._rt = splatting.SplatterProcessor(
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self._amr, self._ro.info, self._rho_op
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)
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else:
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self._rt = raytracing.RayTracer(self._amr, self._ro.info, self._rho_op)
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# Set the extend of the image
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self._radius = 0.5 / self.pp_params.pymses.zoom
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self._lbox = self.info["boxlen"]
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center = self.pp_params.pymses.center
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im_extent = [
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(-self._radius + center[0]),
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(self._radius + center[0]),
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(-self._radius + center[1]),
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(self._radius + center[1]),
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]
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# Get time
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time = self._ro.info["time"] # time in codeunits
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# Set post processing attributes
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self.save.root._v_attrs.dir = os.path.dirname(path)
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self.save.root._v_attrs.run = os.path.basename(path)
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self.save.root._v_attrs.num = num
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self.save.root._v_attrs.lbox = self._lbox
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self.save.root._v_attrs.unit_length = self.info["unit_length"]
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self.save.root._v_attrs.time = time
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if not "/maps" in self.save:
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self.save.create_group("/", "maps", "2D maps")
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self.save.root.maps._v_attrs.center = center
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self.save.root.maps._v_attrs.radius = self._radius
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self.save.root.maps._v_attrs.im_extent = im_extent
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# Initialize cameras
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self._cam = {}
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for ax_los in self._ax_nb: # los = line of sight
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ax_h = self._axes_h[ax_los]
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ax_v = self._axes_v[ax_los]
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self._cam[ax_los] = Camera(
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center=self.pp_params.pymses.center,
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line_of_sight_axis=ax_los,
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region_size=[2.0 * self._radius, 2.0 * self._radius],
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distance=self._radius,
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far_cut_depth=self._radius,
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up_vector=ax_v,
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map_max_size=self.pp_params.pymses.map_size,
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)
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self.close()
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self.log_id = "[{}, {}] ".format(self.run, self.num)
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self.def_rules()
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def load_cells(self):
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"""
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Load all cells from the source file in the memory.
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Cells will be accessible trough self.cells
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(/!\ Long and memory heavy)
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"""
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if not self.cells_loaded:
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if os.path.exists(self.cells_filename):
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cells_hdf5 = tables.open_file(self.cells_filename, mode="r")
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try:
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node = cells_hdf5.get_node("/cells")
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self.cells = {}
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for key in node._v_children:
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self.cells[key] = cells_hdf5.get_node("/cells/" + key).read()
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finally:
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cells_hdf5.close()
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else:
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cell_source = CellsToPoints(self._amr)
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cells_pymses = cell_source.flatten()
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self.cells = {}
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for key in cells_pymses.fields:
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self.cells[key] = cells_pymses[key]
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self.cells["dx"] = cells_pymses.get_sizes()
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self.cells["pos"] = cells_pymses.points
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if self.pp_params.process.save_cells:
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cells_hdf5 = tables.open_file(self.cells_filename, mode="w")
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try:
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for key in self.cells:
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cells_hdf5.create_array(
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"/cells", key, self.cells[key], "", createparents=True
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)
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finally:
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cells_hdf5.close()
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self.cells_loaded = True
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def unload_cells(self):
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"""
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Free space in the memory by telling the garbage collectors that
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self.cells is not needed
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"""
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if self.cells_loaded:
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del self.cells
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self.cells_loaded = False
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def _slice(self, getter, ax_los="z", z=0, unit=cst.none):
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"""
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Slice process function.
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Return a slice of the source box.
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Parameters
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----------
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getter : callable
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A callable that extract the wanted data from a pymses dataset
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ax_los : string
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The axis perpendicular to the slice plane
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z : float
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Coordinate of the slice on the ax_los axis
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unit : cst.Unit
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Unit of the resulting dataset
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Returns
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-------
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A numpy array containing the slice
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"""
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op = ScalarOperator(getter, unit)
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datamap = slicing.SliceMap(self._amr, self._cam[ax_los], op, z=z)
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return datamap.map.T
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def _ax_avg(
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self, getter, ax_los, unit=cst.none, mass_weighted=True, surf_qty=False
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):
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"""
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Map of the average of a quantity (given by getter) along an axis (ax_los)
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Return 2D array
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"""
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if mass_weighted:
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def num(cells):
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value = getter(cells)
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mass = mass_func(cells)
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# Transpose (.T) is for vectorial values
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return (mass * value.T).T
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op = FractionOperator(num, mass_function, unit)
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else:
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op = ScalarOperator(getter, unit)
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if pp_params.pymses.fft:
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rt = splatting.SplatterProcessor(self._amr, self._ro.info, op)
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else:
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rt = raytracing.RayTracer(self._amr, self._ro.info, op)
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datamap = rt.process(self._cam[ax_los], surf_qty=surf_qty)
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return datamap.map.T
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def _get_axis(self, axis):
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if isinstance(axis, str):
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axis = self._ax_nb[axis]
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self.load_cells()
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return np.sort(np.unique(self.cells["pos"][:, axis]))
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def _plane_avg_uniform(self, getter, axis, unit=cst.none, mass_weighted=False):
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"""
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Profile of the average of a quantity (given by getter) perpendicular to an axis
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WARNING : This version only works on an uniform grid, need of a box version for AMR
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"""
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self.load_cells()
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if isinstance(axis, str):
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axis = self._ax_nb[axis]
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axis_data = self.cells["pos"][:, axis]
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value = getter(self.cells)
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df = pd.DataFrame({"axis": axis_data})
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if mass_weighted:
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mass = mass_func(self.cells)
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tot_mass = np.sum(mass)
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df["value"] = value * mass / tot_mass
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else:
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df["value"] = value
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if self.pp_params.process.unload_cells:
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self.unload_cells()
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df.sort_values("axis", inplace=True)
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return df.groupby("axis").mean().values[:, 0]
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def _vol_avg(self, getter, mass_weighted=True):
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self.load_cells()
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value = getter(self.cells)
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if mass_weighted:
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mass = mass_func(self.cells)
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# Transpose (.T) is for vectorial values
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data = np.sum((mass * value.T).T, axis=0) / np.sum(mass)
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else:
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data = np.sum(value, axis=0)
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if self.pp_params.process.unload_cells:
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self.unload_cells()
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return data
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def _vol_pdf(self, getter, bins=100, logbins=False, weight_func=vol_func):
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self.load_cells()
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data = getter(self.cells)
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if logbins:
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data = np.log10(data)
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weights = weight_func(self.cells)
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if self.pp_params.process.unload_cells:
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self.unload_cells()
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values, edges = np.histogram(data, bins, weights=weights)
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centers = 0.5 * (edges[1:] + edges[:-1])
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return (np.stack([values, centers]), {"logbins": logbins})
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def _mwa_sigma(self, axes=["x", "y", "z"]):
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mw_speed = self.save.get_node("/globals/mwa_speed").read()
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if axes == ["x", "y", "z"]:
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def getter(dset):
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return np.sum((dset["vel"] - mw_speed) ** 2, axis=1)
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else:
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def getter(dset):
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sigma_squared = 0.0
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for ax in axes:
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ax_nb = self._ax_nb[ax]
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sigma_sq_ax = (dset["vel"][:, ax_nb] - mw_speed[ax_nb]) ** 2
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sigma_squared = sigma_squared + sigma_sq_ax
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return sigma_squared
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return np.sqrt(self._vol_avg(getter, mass_weighted=True))
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def _coldens(self, ax_los):
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datamap = self._rt.process(self._cam[ax_los], surf_qty=True)
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return datamap.map.T
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def _rho(self, ax_los, z=0.0):
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datamap_rho = slicing.SliceMap(self._amr, self._cam[ax_los], self._rho_op, z=z)
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return (datamap_rho.map).T
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def _speed_h(self, ax_los, z=0.0):
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vh_op = ScalarOperator(
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lambda dset: dset["vel"][:, self._ax_nb[self._axes_h[ax_los]]],
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self._ro.info["unit_velocity"],
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)
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dmap_vh = slicing.SliceMap(self._amr, self._cam[ax_los], vh_op, z=z).map.T
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return dmap_vh
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def _speed_v(self, ax_los, z=0.0):
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vv_op = ScalarOperator(
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lambda dset: dset["vel"][:, self._ax_nb[self._axes_v[ax_los]]],
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self._ro.info["unit_velocity"],
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)
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dmap_vv = slicing.SliceMap(self._amr, self._cam[ax_los], vv_op, z=z).map.T
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return dmap_vv
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def _temperature(self, ax_los, z=0.0):
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P_op = ScalarOperator(lambda dset: dset["P"], self._ro.info["unit_pressure"])
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dmap_P = (slicing.SliceMap(self._amr, self._cam[ax_los], P_op, z=z)).map.T
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dmap_rho = self.save.get_node("/maps/rho_{}".format(ax_los)).read()
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return dmap_P / dmap_rho
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def _levels(self, ax_los):
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self._amr.set_read_levelmax(self.pp_params.pymses.levelmax)
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level_op = MaxLevelOperator()
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rt_level = raytracing.RayTracer(self._amr, self._ro.info, level_op)
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datamap = rt_level.process(self._cam[ax_los], surf_qty=True)
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return datamap.map.T
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def _jeans(self, ax_los):
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dmap_T = self.save.get_node("/maps/T_" + ax_los).read()
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dmap_rho = self.save.get_node("/maps/rho_" + ax_los).read()
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dmap_jeans = np.sqrt(np.pi * dmap_T / dmap_rho)
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return dmap_jeans
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def _jeans_ratio(self, ax_los):
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dmap_jeans = self.save.get_node("/maps/jeans_" + ax_los).read()
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dmap_levels = self.save.get_node("/maps/levels_" + ax_los).read()
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dmap_jeans_ratio = dmap_jeans * 2 ** (dmap_levels)
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return dmap_jeans_ratio
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def _toomreQ_disk(self, ax_los):
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"""
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Compute the Toomre Q parameter in a Keplerian disk
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"""
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# Operator to compute the angular speed times rho
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def omega_rho_func(dset):
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pos = dset.get_cell_centers()
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pos = pos - (self.pp_params.disk.pos_star / self._lbox)
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xx = pos[:, :, 0]
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yy = pos[:, :, 1]
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rc = np.sqrt(xx ** 2 + yy ** 2) # cylindrical radius
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vx = dset["vel"][:, :, 0]
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vy = dset["vel"][:, :, 1]
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omega_rho = 1.0 / rc ** 2
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omega_rho = omega_rho * dset["rho"]
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vyx = vy * xx
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vxy = vx * yy
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omega_rho = omega_rho * (vyx - vxy)
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return omega_rho
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# Operator to compute the angular speed
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omega_op = FractionOperator(
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omega_rho_func, lambda dset: dset["rho"], 1.0 / self._ro.info["unit_time"]
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)
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# Operator to compute the sound speed
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cs_op = FractionOperator(
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lambda dset: dset["P"],
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lambda dset: dset["rho"],
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self._ro.info["unit_velocity"],
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)
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# Ray tracer for the angular speed
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rt_omega = raytracing.RayTracer(self._amr, self._ro.info, omega_op)
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# Ray tracer for the sound speed
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if self.pp_params.pymses.fft:
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rt_cs = splatting.SplatterProcessor(
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self._amr, self._ro.info, cs_op, surf_qty=False
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)
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else:
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rt_cs = raytracing.RayTracer(self._amr, self._ro.info, cs_op)
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dmap_omega = rt_omega.process(self._cam[ax_los])
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dmap_cs = rt_cs.process(self._cam[ax_los])
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dmap_col = self.save.root.maps.coldens_z.read()
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map_Q = (
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(self._lbox * dmap_cs.map.T)
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* dmap_omega.map.T
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/ (np.pi * self.G * dmap_col)
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)
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return map_Q
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def _radial_bins(self, _):
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"""
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Computes radial bins (for disk)
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"""
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pos_star = self.pp_params.disk.pos_star
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im_extent = self.save.root.maps._v_attrs.im_extent
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# radius of the corner of the box plus a margin
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rad_of_box = (
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np.sqrt(
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(im_extent[1] - pos_star[0]) ** 2 + (im_extent[3] - pos_star[1]) ** 2
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)
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+ 0.1
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)
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bin_in = self.pp_params.disk.bin_in
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bin_out = self.pp_params.disk.bin_out
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nb_bin = self.pp_params.disk.nb_bin
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# radial bins
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if self.pp_params.disk.binning == "log":
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lrad_in = np.log10(bin_in)
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lrad_ext = np.log10(bin_out)
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rad_bins = np.logspace(lrad_in, lrad_ext, num=nb_bin)
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elif self.pp_params.disk.binning == "lin":
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rad_bins = np.linspace(bin_in, bin_out, num=nb_bin)
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# Add boundaries
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rad_bins = np.concatenate(([0.0], rad_bins, [rad_of_box]))
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return rad_bins
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def _rr(self, _):
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"""
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Computes the radius from the center
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"""
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im_extent = self.save.root.maps._v_attrs.im_extent
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map_size = self.pp_params.pymses.map_size
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pos_star = self.pp_params.disk.pos_star
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x = np.linspace(im_extent[0], im_extent[1], map_size)
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y = np.linspace(im_extent[2], im_extent[3], map_size)
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xx, yy = np.meshgrid(x, y)
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rr = np.sqrt((xx - pos_star[0]) ** 2 + (yy - pos_star[1]) ** 2)
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return rr
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def _bins_on_map(self, ax_los):
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rad_bins = self.save.get_node("/radial/radial_bins_" + ax_los).read()
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rr = self.save.get_node("/maps/rr_" + ax_los).read()
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# Find appropriate bin for each coordinate set
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bins = np.zeros(rr.shape, dtype=int)
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for r in rad_bins[1:]:
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bins = bins + (rr >= r).astype(int)
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return bins
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def _rad_avg(self, name, ax_los):
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radial_bins = self.save.get_node("/radial/radial_bins_" + ax_los).read()
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bins_on_map = self.save.get_node("/maps/bins_on_map_" + ax_los).read()
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dmap = self.save.get_node("/maps/" + name + "_" + ax_los).read()
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# mean of all the cells in the bin
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mean_bin = np.zeros(len(radial_bins) - 1)
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for j in range(len(radial_bins) - 1):
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|
mean_bin[j] = np.mean(dmap[bins_on_map == j])
|
|
return mean_bin
|
|
|
|
def _rad_avg_map(self, name, ax_los):
|
|
|
|
radial_bins = self.save.get_node("/radial/radial_bins_" + ax_los).read()
|
|
bins_on_map = self.save.get_node("/maps/bins_on_map_" + ax_los).read()
|
|
rr = self.save.get_node("/maps/rr_" + ax_los).read()
|
|
mean_bin = self.save.get_node("/radial/rad_avg_" + name + "_" + ax_los).read()
|
|
|
|
# Add value for border
|
|
mean_bin = np.concatenate(([mean_bin[0]], mean_bin))
|
|
|
|
rr_flat = rr.flatten()
|
|
bins_on_map_flat = bins_on_map.flatten()
|
|
|
|
# Compute the map azimuthally averaged
|
|
# use linear interpolation to improve accuracy
|
|
avg_flat = (radial_bins[bins_on_map_flat + 1] - rr_flat) * mean_bin[
|
|
bins_on_map_flat
|
|
]
|
|
avg_flat = (
|
|
avg_flat
|
|
+ (rr_flat - radial_bins[bins_on_map_flat]) * mean_bin[bins_on_map_flat + 1]
|
|
)
|
|
avg_flat = avg_flat / (
|
|
radial_bins[bins_on_map_flat + 1] - radial_bins[bins_on_map_flat]
|
|
)
|
|
avg_map = np.reshape(avg_flat, rr.shape)
|
|
|
|
return avg_map
|
|
|
|
def _fluct_map(self, name, ax_los):
|
|
|
|
dmap = self.save.get_node("/maps/" + name + "_" + ax_los).read()
|
|
avg_map = self.save.get_node("/maps/avg_map_" + name + "_" + ax_los).read()
|
|
|
|
return dmap / avg_map
|
|
|
|
def _rad_fluct_pdf(self, name, ax_los):
|
|
fluct_map = self.save.get_node("/maps/fluct_" + name + "_" + ax_los).read()
|
|
rr = self.save.get_node("/maps/rr_" + ax_los).read()
|
|
|
|
mask_pdf = (rr > self.pp_params.disk.rmin_pdf) & (
|
|
rr < self.pp_params.disk.rmax_pdf
|
|
)
|
|
|
|
nb_cells = np.sum(mask_pdf.flatten())
|
|
values, edges = np.histogram(
|
|
np.log10(fluct_map[mask_pdf].flatten()),
|
|
self.pp_params.pdf.nb_bin,
|
|
weights=np.ones(nb_cells) / nb_cells,
|
|
)
|
|
centers = 0.5 * (edges[1:] + edges[:-1])
|
|
return np.stack([values, centers])
|
|
|
|
def _fit_pdf(self, name, ax_los):
|
|
pdf = self.save.get_node("/hist/pdf_" + name + "_" + ax_los)
|
|
values, centers = pdf.read()
|
|
mask_fit = (
|
|
(centers > self.pp_params.pdf.xmin_fit)
|
|
& (centers < self.pp_params.pdf.xmax_fit)
|
|
& (values > 0)
|
|
)
|
|
(slope, origin, correlation, _, stderr) = linregress(
|
|
centers[mask_fit], np.log10(values[mask_fit])
|
|
)
|
|
|
|
pdf.attrs.slope = slope
|
|
pdf.attrs.origin = origin
|
|
pdf.attrs.correlation = correlation
|
|
pdf.attrs.stderr = stderr
|
|
pdf.attrs.var = np.var
|
|
return True
|
|
|
|
def _clumps(self):
|
|
name = self.path_out + "/" + self.tag + "_" + str(self.num).zfill(5)
|
|
hop_save = name + "_hop" + "_prop_struct.save"
|
|
|
|
me.make_clump_hop(
|
|
self.path,
|
|
self.num,
|
|
name + "_hop",
|
|
self.pp_params.hop.rho_thres,
|
|
self.pp_params.hop.lvl_thres,
|
|
[0.5, 0.5, 0.5],
|
|
1,
|
|
path_out=path_out + "/",
|
|
path_hop="./",
|
|
force=True,
|
|
gcomp=False,
|
|
)
|
|
hop_save = me.clump_properties(
|
|
name + "_hop", path, num, path_out=path_out + "/", gcomp=False
|
|
)
|
|
f = open(path_out + "/" + hop_save)
|
|
hop_data = pickle.load(f)
|
|
f.close()
|
|
return hop_data
|
|
|
|
def _sinks(self):
|
|
csv_name = (
|
|
self.path
|
|
+ "/output_"
|
|
+ str(self.num).zfill(5)
|
|
+ "/sink_"
|
|
+ str(self.num).zfill(5)
|
|
+ ".csv"
|
|
)
|
|
sinks = np.loadtxt(csv_name, delimiter=",")
|
|
header = [
|
|
"Id",
|
|
"M",
|
|
"dmf",
|
|
"x",
|
|
"y",
|
|
"z",
|
|
"vx",
|
|
"vy",
|
|
"vz",
|
|
"rot_period",
|
|
"lx",
|
|
"ly",
|
|
"lz",
|
|
"acc_rate",
|
|
"acc_lum",
|
|
"age",
|
|
"int_lum",
|
|
"Teff",
|
|
]
|
|
if len(sinks) == 0:
|
|
sinks = np.zeros(len(header))
|
|
|
|
sinks_dict = {}
|
|
for key, a in zip(header, sinks):
|
|
sinks_dict[key] = a
|
|
|
|
return sinks_dict
|
|
|
|
def def_rules(self):
|
|
|
|
self.rules = {
|
|
# Base rules
|
|
"coldens": Rule(
|
|
self,
|
|
self._coldens,
|
|
"Column density",
|
|
"/maps",
|
|
unit=self.info["unit_density"] * self.info["unit_length"],
|
|
),
|
|
"rho": Rule(
|
|
self,
|
|
self._rho,
|
|
"Density slice",
|
|
"/maps",
|
|
unit=self.info["unit_density"],
|
|
),
|
|
"speed_h": Rule(
|
|
self,
|
|
self._speed_h,
|
|
"Horizontal speed slice wrt the line of sight",
|
|
"/maps",
|
|
unit=self.info["unit_velocity"],
|
|
),
|
|
"speed_v": Rule(
|
|
self,
|
|
self._speed_v,
|
|
"Vertical speed slice wrt the line of sight",
|
|
"/maps",
|
|
unit=self.info["unit_velocity"],
|
|
),
|
|
"T": Rule(
|
|
self,
|
|
self._temperature,
|
|
"Temperature slice",
|
|
"/maps",
|
|
dependencies=["rho"],
|
|
unit=self.info["unit_temperature"],
|
|
),
|
|
"levels": Rule(
|
|
self, self._levels, "Max level within line of sight", "/maps"
|
|
),
|
|
"jeans": Rule(
|
|
self,
|
|
self._jeans,
|
|
"Jeans lenght slice",
|
|
"/maps",
|
|
dependencies=["rho", "T"],
|
|
),
|
|
"jeans_ratio": Rule(
|
|
self,
|
|
self._jeans_ratio,
|
|
"Jeans' lenght divided by the max resolution",
|
|
"/maps",
|
|
dependencies=["jeans", "levels"],
|
|
),
|
|
"Q": Rule(
|
|
self,
|
|
self._toomreQ_disk,
|
|
"Toomre Q parameter for a Keplerian disk",
|
|
"/maps",
|
|
dependencies=["coldens"],
|
|
is_valid=lambda _: self.pp_params.disk.on,
|
|
),
|
|
"sinks": Rule(
|
|
self,
|
|
self._sinks,
|
|
group="/datasets",
|
|
unit={
|
|
"Id": cst.none,
|
|
"M": cst.Msun,
|
|
"dmf": cst.Msun,
|
|
"x": "",
|
|
"y": "",
|
|
"z": "",
|
|
"vx": "",
|
|
"vy": "",
|
|
"vz": "",
|
|
"rot_period": "[y]",
|
|
"lx": "|l|",
|
|
"ly": "|l|",
|
|
"lz": "|l|",
|
|
"acc_rate": "[Msol/y]",
|
|
"acc_lum": "[Lsol]",
|
|
"age": cst.year,
|
|
"int_lum": "[Lsol]",
|
|
"Teff": cst.K,
|
|
},
|
|
),
|
|
"clumps": Rule(self, self._clumps, group="/datasets"),
|
|
# Helpers
|
|
"radial_bins": Rule(self, self._radial_bins, "Radial bins", "/radial"),
|
|
"rr": Rule(self, self._rr, "Coordinate map", "/maps"),
|
|
"bins_on_map": Rule(
|
|
self,
|
|
self._bins_on_map,
|
|
"Convert map coordinates to bins",
|
|
"/maps",
|
|
dependencies=["radial_bins", "rr"],
|
|
),
|
|
# PDF
|
|
"rho_pdf": Rule(
|
|
self,
|
|
partial(self._vol_pdf, partial(simple_getter, "rho"), logbins=True),
|
|
"Global rho-PDF",
|
|
"/hist",
|
|
unit=self.info["unit_density"],
|
|
),
|
|
"T_pdf": Rule(
|
|
self,
|
|
partial(self._vol_pdf, getter_T, logbins=True),
|
|
"Global T-PDF",
|
|
"/hist",
|
|
unit=self.info["unit_temperature"],
|
|
),
|
|
"P_pdf": Rule(
|
|
self,
|
|
partial(self._vol_pdf, getter_T, logbins=True),
|
|
"Global P-PDF",
|
|
"/hist",
|
|
unit=self.info["unit_pressure"],
|
|
),
|
|
# Profiles
|
|
"axis": Rule(
|
|
self,
|
|
partial(self._get_axis),
|
|
"Axis",
|
|
"/profile",
|
|
unit=self.info["unit_length"],
|
|
),
|
|
"rho_prof": Rule(
|
|
self,
|
|
partial(self._plane_avg_uniform, partial(simple_getter, "rho")),
|
|
"Rho profile",
|
|
"/profile",
|
|
unit=self.info["unit_density"],
|
|
dependencies=["axis"],
|
|
),
|
|
# globals
|
|
"time_num": Rule(
|
|
self,
|
|
lambda: self.info["time"],
|
|
"Time",
|
|
"/globals",
|
|
unit=self.info["unit_time"],
|
|
),
|
|
"mwa_speed": Rule(
|
|
self,
|
|
partial(self._vol_avg, partial(simple_getter, "vel")),
|
|
"Mass weighted speed average",
|
|
"/globals",
|
|
unit=self.info["unit_velocity"],
|
|
),
|
|
"mwa_sigma": Rule(
|
|
self,
|
|
self._mwa_sigma,
|
|
"Mass weighted speed average",
|
|
"/globals",
|
|
dependencies={"mwa_speed": None},
|
|
unit=self.info["unit_velocity"],
|
|
),
|
|
}
|
|
|
|
# Average and other
|
|
averageables = ["coldens", "rho", "T", "Q"]
|
|
for name in averageables:
|
|
self.rules["rad_avg_" + name] = Rule(
|
|
self,
|
|
partial(self._rad_avg, name),
|
|
"Azimuthal average of {}".format(name),
|
|
"/radial",
|
|
dependencies=["radial_bins", "bins_on_map", name],
|
|
)
|
|
|
|
self.rules["avg_map_" + name] = Rule(
|
|
self,
|
|
partial(self._rad_avg_map, name),
|
|
"Interpolated map of azimuthal average of {}".format(name),
|
|
"/maps",
|
|
dependencies=["radial_bins", "bins_on_map", "rr", "rad_avg_" + name],
|
|
)
|
|
self.rules["fluct_" + name] = Rule(
|
|
self,
|
|
partial(self._fluct_map, name),
|
|
"Fluctuation wrt to average of {}".format(name),
|
|
"/maps",
|
|
dependencies=[name, "avg_map_" + name],
|
|
)
|
|
self.rules["pdf_" + name] = Rule(
|
|
self,
|
|
partial(self._rad_fluct_pdf, name),
|
|
"Probability density function of {} fluctuations".format(name),
|
|
"/hist",
|
|
dependencies=["rr", "fluct_" + name],
|
|
)
|
|
|
|
self.rules["fit_pdf_" + name] = Rule(
|
|
self,
|
|
partial(self._fit_pdf, name),
|
|
"Fit the PDF of {} fluctuations".format(name),
|
|
"/hist",
|
|
dependencies=["pdf_" + name],
|
|
)
|
|
|
|
self._gen_rule_transform("fluct_coldens", np.max, "max", group="/globals")
|
|
|
|
super(PostProcessor, self).def_rules()
|
|
|
|
|
|
def get_time(path, num):
|
|
"""
|
|
Return the time of the output (code units)
|
|
|
|
Parameters
|
|
----------
|
|
num output number
|
|
path_out path of the pipeline output
|
|
|
|
Returns
|
|
-------
|
|
time the time of the output (code units)
|
|
"""
|
|
try:
|
|
f = open(
|
|
path
|
|
+ "/output_"
|
|
+ str(num).zfill(5)
|
|
+ "/info_"
|
|
+ str(num).zfill(5)
|
|
+ ".txt"
|
|
)
|
|
for line in f:
|
|
ls = line.split()
|
|
if len(ls) > 1 and ls[0] == "time":
|
|
time = float(ls[2])
|
|
break
|
|
f.close()
|
|
return time
|
|
except IOError as e:
|
|
print(e)
|
|
return np.nan
|