/
profiles.py
1109 lines (989 loc) · 43.2 KB
/
profiles.py
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"""
Profile classes, to deal with generating and obtaining profiles
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
import numpy as np
from yt.fields.derived_field import DerivedField
from yt.frontends.ytdata.utilities import \
save_as_dataset
from yt.funcs import \
get_output_filename, \
ensure_list, \
iterable, \
issue_deprecation_warning
from yt.units.yt_array import \
array_like_field, \
YTQuantity
from yt.units.unit_object import Unit
from yt.data_objects.field_data import YTFieldData
from yt.utilities.exceptions import \
YTIllDefinedProfile
from yt.utilities.lib.misc_utilities import \
new_bin_profile1d, \
new_bin_profile2d, \
new_bin_profile3d
from yt.utilities.parallel_tools.parallel_analysis_interface import \
ParallelAnalysisInterface, parallel_objects
from yt.utilities.lib.particle_mesh_operations import \
CICDeposit_2, \
NGPDeposit_2
def _sanitize_min_max_units(amin, amax, finfo, registry):
# returns a copy of amin and amax, converted to finfo's output units
umin = getattr(amin, 'units', None)
umax = getattr(amax, 'units', None)
if umin is None:
umin = Unit(finfo.output_units, registry=registry)
rmin = YTQuantity(amin, umin)
else:
rmin = amin.in_units(finfo.output_units)
if umax is None:
umax = Unit(finfo.output_units, registry=registry)
rmax = YTQuantity(amax, umax)
else:
rmax = amax.in_units(finfo.output_units)
return rmin, rmax
def preserve_source_parameters(func):
def save_state(*args, **kwargs):
# Temporarily replace the 'field_parameters' for a
# grid with the 'field_parameters' for the data source
prof = args[0]
source = args[1]
if hasattr(source, 'field_parameters'):
old_params = source.field_parameters
source.field_parameters = prof._data_source.field_parameters
tr = func(*args, **kwargs)
source.field_parameters = old_params
else:
tr = func(*args, **kwargs)
return tr
return save_state
class ProfileFieldAccumulator(object):
def __init__(self, n_fields, size):
shape = size + (n_fields,)
self.values = np.zeros(shape, dtype="float64")
self.mvalues = np.zeros(shape, dtype="float64")
self.qvalues = np.zeros(shape, dtype="float64")
self.used = np.zeros(size, dtype='bool')
self.weight_values = np.zeros(size, dtype="float64")
class ProfileND(ParallelAnalysisInterface):
"""The profile object class"""
def __init__(self, data_source, weight_field = None):
self.data_source = data_source
self.ds = data_source.ds
self.field_map = {}
self.field_info = {}
self.field_data = YTFieldData()
if weight_field is not None:
self.standard_deviation = YTFieldData()
weight_field = self.data_source._determine_fields(weight_field)[0]
else:
self.standard_deviation = None
self.weight_field = weight_field
self.field_units = {}
ParallelAnalysisInterface.__init__(self, comm=data_source.comm)
@property
def variance(self):
issue_deprecation_warning("""
profile.variance incorrectly returns the profile standard deviation and has
been deprecated, use profile.standard_deviation instead."""
)
return self.standard_deviation
def add_fields(self, fields):
"""Add fields to profile
Parameters
----------
fields : list of field names
A list of fields to create profile histograms for
"""
fields = self.data_source._determine_fields(fields)
for f in fields:
self.field_info[f] = self.data_source.ds.field_info[f]
temp_storage = ProfileFieldAccumulator(len(fields), self.size)
citer = self.data_source.chunks([], "io")
for chunk in parallel_objects(citer):
self._bin_chunk(chunk, fields, temp_storage)
self._finalize_storage(fields, temp_storage)
def set_field_unit(self, field, new_unit):
"""Sets a new unit for the requested field
Parameters
----------
field : string or field tuple
The name of the field that is to be changed.
new_unit : string or Unit object
The name of the new unit.
"""
if field in self.field_units:
self.field_units[field] = \
Unit(new_unit, registry=self.ds.unit_registry)
else:
fd = self.field_map[field]
if fd in self.field_units:
self.field_units[fd] = \
Unit(new_unit, registry=self.ds.unit_registry)
else:
raise KeyError("%s not in profile!" % (field))
def _finalize_storage(self, fields, temp_storage):
# We use our main comm here
# This also will fill _field_data
for i, field in enumerate(fields):
# q values are returned as q * weight but we want just q
temp_storage.qvalues[..., i][temp_storage.used] /= \
temp_storage.weight_values[temp_storage.used]
# get the profile data from all procs
all_store = {self.comm.rank: temp_storage}
all_store = self.comm.par_combine_object(all_store,
"join", datatype="dict")
all_val = np.zeros_like(temp_storage.values)
all_mean = np.zeros_like(temp_storage.mvalues)
all_std = np.zeros_like(temp_storage.qvalues)
all_weight = np.zeros_like(temp_storage.weight_values)
all_used = np.zeros_like(temp_storage.used, dtype="bool")
# Combine the weighted mean and standard deviation from each processor.
# For two samples with total weight, mean, and standard deviation
# given by w, m, and s, their combined mean and standard deviation are:
# m12 = (m1 * w1 + m2 * w2) / (w1 + w2)
# s12 = (m1 * (s1**2 + (m1 - m12)**2) +
# m2 * (s2**2 + (m2 - m12)**2)) / (w1 + w2)
# Here, the mvalues are m and the qvalues are s**2.
for p in sorted(all_store.keys()):
all_used += all_store[p].used
old_mean = all_mean.copy()
old_weight = all_weight.copy()
all_weight[all_store[p].used] += \
all_store[p].weight_values[all_store[p].used]
for i, field in enumerate(fields):
all_val[..., i][all_store[p].used] += \
all_store[p].values[..., i][all_store[p].used]
all_mean[..., i][all_store[p].used] = \
(all_mean[..., i] * old_weight +
all_store[p].mvalues[..., i] *
all_store[p].weight_values)[all_store[p].used] / \
all_weight[all_store[p].used]
all_std[..., i][all_store[p].used] = \
(old_weight * (all_std[..., i] +
(old_mean[..., i] - all_mean[..., i])**2) +
all_store[p].weight_values *
(all_store[p].qvalues[..., i] +
(all_store[p].mvalues[..., i] -
all_mean[..., i])**2))[all_store[p].used] / \
all_weight[all_store[p].used]
all_std = np.sqrt(all_std)
del all_store
self.used = all_used
blank = ~all_used
self.weight = all_weight
self.weight[blank] = 0.0
for i, field in enumerate(fields):
if self.weight_field is None:
self.field_data[field] = \
array_like_field(self.data_source,
all_val[...,i], field)
else:
self.field_data[field] = \
array_like_field(self.data_source,
all_mean[...,i], field)
self.standard_deviation[field] = \
array_like_field(self.data_source,
all_std[...,i], field)
self.standard_deviation[field][blank] = 0.0
self.field_data[field][blank] = 0.0
self.field_units[field] = self.field_data[field].units
if isinstance(field, tuple):
self.field_map[field[1]] = field
else:
self.field_map[field] = field
def _bin_chunk(self, chunk, fields, storage):
raise NotImplementedError
def _filter(self, bin_fields):
# cut_points is set to be everything initially, but
# we also want to apply a filtering based on min/max
filter = np.ones(bin_fields[0].shape, dtype='bool')
for (mi, ma), data in zip(self.bounds, bin_fields):
filter &= (data > mi)
filter &= (data < ma)
return filter, [data[filter] for data in bin_fields]
def _get_data(self, chunk, fields):
# We are using chunks now, which will manage the field parameters and
# the like.
bin_fields = [chunk[bf] for bf in self.bin_fields]
# We want to make sure that our fields are within the bounds of the
# binning
filter, bin_fields = self._filter(bin_fields)
if not np.any(filter): return None
arr = np.zeros((bin_fields[0].size, len(fields)), dtype="float64")
for i, field in enumerate(fields):
units = chunk.ds.field_info[field].output_units
arr[:,i] = chunk[field][filter].in_units(units)
if self.weight_field is not None:
units = chunk.ds.field_info[self.weight_field].output_units
weight_data = chunk[self.weight_field].in_units(units)
else:
weight_data = np.ones(filter.size, dtype="float64")
weight_data = weight_data[filter]
# So that we can pass these into
return arr, weight_data, bin_fields
def __getitem__(self, field):
fname = self.field_map.get(field, None)
if fname is None:
if isinstance(field, tuple):
fname = self.field_map.get(field[1], None)
elif isinstance(field, DerivedField):
fname = self.field_map.get(field.name[1], None)
if fname is None:
raise KeyError(field)
else:
if getattr(self, 'fractional', False):
return self.field_data[fname]
else:
return self.field_data[fname].in_units(self.field_units[fname])
def items(self):
return [(k,self[k]) for k in self.field_data.keys()]
def keys(self):
return self.field_data.keys()
def __iter__(self):
return sorted(self.items())
def _get_bins(self, mi, ma, n, take_log):
if take_log:
ret = np.logspace(np.log10(mi), np.log10(ma), n+1)
# at this point ret[0] and ret[-1] are not exactly equal to
# mi and ma due to round-off error. Let's force them to be
# mi and ma exactly to avoid incorrectly discarding cells near
# the edges. See Issue #1300.
ret[0], ret[-1] = mi, ma
return ret
else:
return np.linspace(mi, ma, n+1)
def save_as_dataset(self, filename=None):
r"""Export a profile to a reloadable yt dataset.
This function will take a profile and output a dataset
containing all relevant fields. The resulting dataset
can be reloaded as a yt dataset.
Parameters
----------
filename : str, optional
The name of the file to be written. If None, the name
will be a combination of the original dataset plus
the type of object, e.g., Profile1D.
Returns
-------
filename : str
The name of the file that has been created.
Examples
--------
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> ad = ds.all_data()
>>> profile = yt.create_profile(ad, ["density", "temperature"],
... "cell_mass", weight_field=None,
... n_bins=(128, 128))
>>> fn = profile.save_as_dataset()
>>> prof_ds = yt.load(fn)
>>> print (prof_ds.data["cell_mass"])
(128, 128)
>>> print (prof_ds.data["x"].shape) # x bins as 1D array
(128,)
>>> print (prof_ds.data["density"]) # x bins as 2D array
(128, 128)
>>> p = yt.PhasePlot(prof_ds.data, "density", "temperature",
... "cell_mass", weight_field=None)
>>> p.save()
"""
keyword = "%s_%s" % (str(self.ds), self.__class__.__name__)
filename = get_output_filename(filename, keyword, ".h5")
args = ("field", "log")
extra_attrs = {"data_type": "yt_profile",
"profile_dimensions": self.size,
"weight_field": self.weight_field,
"fractional": self.fractional}
data = {}
data.update(self.field_data)
data["weight"] = self.weight
data["used"] = self.used.astype("float64")
dimensionality = 0
bin_data = []
for ax in "xyz":
if hasattr(self, ax):
dimensionality += 1
data[ax] = getattr(self, ax)
bin_data.append(data[ax])
bin_field_name = "%s_bins" % ax
data[bin_field_name] = getattr(self, bin_field_name)
extra_attrs["%s_range" % ax] = self.ds.arr([data[bin_field_name][0],
data[bin_field_name][-1]])
for arg in args:
key = "%s_%s" % (ax, arg)
extra_attrs[key] = getattr(self, key)
bin_fields = np.meshgrid(*bin_data)
for i, ax in enumerate("xyz"[:dimensionality]):
data[getattr(self, "%s_field" % ax)] = bin_fields[i]
extra_attrs["dimensionality"] = dimensionality
ftypes = dict([(field, "data") for field in data])
save_as_dataset(self.ds, filename, data, field_types=ftypes,
extra_attrs=extra_attrs)
return filename
class ProfileNDFromDataset(ProfileND):
"""
An ND profile object loaded from a ytdata dataset.
"""
def __init__(self, ds):
ProfileND.__init__(self, ds.data, ds.parameters["weight_field"])
self.fractional = ds.parameters["fractional"]
exclude_fields = ["used", "weight"]
for ax in "xyz"[:ds.dimensionality]:
setattr(self, ax, ds.data[ax])
setattr(self, "%s_bins" % ax, ds.data["%s_bins" % ax])
field_name = tuple(ds.parameters["%s_field" % ax])
setattr(self, "%s_field" % ax, field_name)
self.field_info[field_name] = ds.field_info[field_name]
setattr(self, "%s_log" % ax, ds.parameters["%s_log" % ax])
exclude_fields.extend([ax, "%s_bins" % ax,
ds.parameters["%s_field" % ax][1]])
self.weight = ds.data["weight"]
self.used = ds.data["used"].d.astype(bool)
profile_fields = [f for f in ds.field_list
if f[1] not in exclude_fields]
for field in profile_fields:
self.field_map[field[1]] = field
self.field_data[field] = ds.data[field]
self.field_info[field] = ds.field_info[field]
self.field_units[field] = ds.data[field].units
class Profile1D(ProfileND):
"""An object that represents a 1D profile.
Parameters
----------
data_source : AMD3DData object
The data object to be profiled
x_field : string field name
The field to profile as a function of
x_n : integer
The number of bins along the x direction.
x_min : float
The minimum value of the x profile field. If supplied without units,
assumed to be in the output units for x_field.
x_max : float
The maximum value of the x profile field. If supplied without units,
assumed to be in the output units for x_field.
x_log : boolean
Controls whether or not the bins for the x field are evenly
spaced in linear (False) or log (True) space.
weight_field : string field name
The field to weight the profiled fields by.
"""
def __init__(self, data_source, x_field, x_n, x_min, x_max, x_log,
weight_field = None):
super(Profile1D, self).__init__(data_source, weight_field)
self.x_field = data_source._determine_fields(x_field)[0]
self.field_info[self.x_field] = \
self.data_source.ds.field_info[self.x_field]
self.x_log = x_log
x_min, x_max = _sanitize_min_max_units(
x_min, x_max, self.field_info[self.x_field], self.ds.unit_registry)
self.x_bins = array_like_field(data_source,
self._get_bins(x_min, x_max, x_n, x_log),
self.x_field)
self.size = (self.x_bins.size - 1,)
self.bin_fields = (self.x_field,)
self.x = 0.5*(self.x_bins[1:]+self.x_bins[:-1])
def _bin_chunk(self, chunk, fields, storage):
rv = self._get_data(chunk, fields)
if rv is None: return
fdata, wdata, (bf_x,) = rv
bf_x.convert_to_units(self.field_info[self.x_field].output_units)
bin_ind = np.digitize(bf_x, self.x_bins) - 1
new_bin_profile1d(bin_ind, wdata, fdata,
storage.weight_values, storage.values,
storage.mvalues, storage.qvalues,
storage.used)
# We've binned it!
def set_x_unit(self, new_unit):
"""Sets a new unit for the x field
parameters
----------
new_unit : string or Unit object
The name of the new unit.
"""
self.x_bins.convert_to_units(new_unit)
self.x = 0.5*(self.x_bins[1:]+self.x_bins[:-1])
@property
def bounds(self):
return ((self.x_bins[0], self.x_bins[-1]),)
def plot(self):
r"""
This returns a :class:~yt.visualization.profile_plotter.ProfilePlot
with the fields that have been added to this object.
"""
from yt.visualization.profile_plotter import ProfilePlot
return ProfilePlot.from_profiles(self)
class Profile1DFromDataset(ProfileNDFromDataset, Profile1D):
"""
A 1D profile object loaded from a ytdata dataset.
"""
def __init(self, ds):
ProfileNDFromDataset.__init__(self, ds)
class Profile2D(ProfileND):
"""An object that represents a 2D profile.
Parameters
----------
data_source : AMD3DData object
The data object to be profiled
x_field : string field name
The field to profile as a function of along the x axis.
x_n : integer
The number of bins along the x direction.
x_min : float
The minimum value of the x profile field. If supplied without units,
assumed to be in the output units for x_field.
x_max : float
The maximum value of the x profile field. If supplied without units,
assumed to be in the output units for x_field.
x_log : boolean
Controls whether or not the bins for the x field are evenly
spaced in linear (False) or log (True) space.
y_field : string field name
The field to profile as a function of along the y axis
y_n : integer
The number of bins along the y direction.
y_min : float
The minimum value of the y profile field. If supplied without units,
assumed to be in the output units for y_field.
y_max : float
The maximum value of the y profile field. If supplied without units,
assumed to be in the output units for y_field.
y_log : boolean
Controls whether or not the bins for the y field are evenly
spaced in linear (False) or log (True) space.
weight_field : string field name
The field to weight the profiled fields by.
"""
def __init__(self, data_source,
x_field, x_n, x_min, x_max, x_log,
y_field, y_n, y_min, y_max, y_log,
weight_field = None):
super(Profile2D, self).__init__(data_source, weight_field)
# X
self.x_field = data_source._determine_fields(x_field)[0]
self.x_log = x_log
self.field_info[self.x_field] = \
self.data_source.ds.field_info[self.x_field]
x_min, x_max = _sanitize_min_max_units(
x_min, x_max, self.field_info[self.x_field], self.ds.unit_registry)
self.x_bins = array_like_field(data_source,
self._get_bins(x_min, x_max, x_n, x_log),
self.x_field)
# Y
self.y_field = data_source._determine_fields(y_field)[0]
self.y_log = y_log
self.field_info[self.y_field] = \
self.data_source.ds.field_info[self.y_field]
y_min, y_max = _sanitize_min_max_units(
y_min, y_max, self.field_info[self.y_field], self.ds.unit_registry)
self.y_bins = array_like_field(data_source,
self._get_bins(y_min, y_max, y_n, y_log),
self.y_field)
self.size = (self.x_bins.size - 1, self.y_bins.size - 1)
self.bin_fields = (self.x_field, self.y_field)
self.x = 0.5*(self.x_bins[1:]+self.x_bins[:-1])
self.y = 0.5*(self.y_bins[1:]+self.y_bins[:-1])
def _bin_chunk(self, chunk, fields, storage):
rv = self._get_data(chunk, fields)
if rv is None: return
fdata, wdata, (bf_x, bf_y) = rv
bf_x.convert_to_units(self.field_info[self.x_field].output_units)
bin_ind_x = np.digitize(bf_x, self.x_bins) - 1
bf_y.convert_to_units(self.field_info[self.y_field].output_units)
bin_ind_y = np.digitize(bf_y, self.y_bins) - 1
new_bin_profile2d(bin_ind_x, bin_ind_y, wdata, fdata,
storage.weight_values, storage.values,
storage.mvalues, storage.qvalues,
storage.used)
# We've binned it!
def set_x_unit(self, new_unit):
"""Sets a new unit for the x field
parameters
----------
new_unit : string or Unit object
The name of the new unit.
"""
self.x_bins.convert_to_units(new_unit)
self.x = 0.5*(self.x_bins[1:]+self.x_bins[:-1])
def set_y_unit(self, new_unit):
"""Sets a new unit for the y field
parameters
----------
new_unit : string or Unit object
The name of the new unit.
"""
self.y_bins.convert_to_units(new_unit)
self.y = 0.5*(self.y_bins[1:]+self.y_bins[:-1])
@property
def bounds(self):
return ((self.x_bins[0], self.x_bins[-1]),
(self.y_bins[0], self.y_bins[-1]))
def plot(self):
r"""
This returns a :class:~yt.visualization.profile_plotter.PhasePlot with
the fields that have been added to this object.
"""
from yt.visualization.profile_plotter import PhasePlot
return PhasePlot.from_profile(self)
class Profile2DFromDataset(ProfileNDFromDataset, Profile2D):
"""
A 2D profile object loaded from a ytdata dataset.
"""
def __init(self, ds):
ProfileNDFromDataset.__init__(self, ds)
class ParticleProfile(Profile2D):
"""An object that represents a *deposited* 2D profile. This is like a
Profile2D, except that it is intended for particle data. Instead of just
binning the particles, the added fields will be deposited onto the mesh
using either the nearest-grid-point or cloud-in-cell interpolation kernels.
Parameters
----------
data_source : AMD3DData object
The data object to be profiled
x_field : string field name
The field to profile as a function of along the x axis.
x_n : integer
The number of bins along the x direction.
x_min : float
The minimum value of the x profile field. If supplied without units,
assumed to be in the output units for x_field.
x_max : float
The maximum value of the x profile field. If supplied without units,
assumed to be in the output units for x_field.
y_field : string field name
The field to profile as a function of along the y axis
y_n : integer
The number of bins along the y direction.
y_min : float
The minimum value of the y profile field. If supplied without units,
assumed to be in the output units for y_field.
y_max : float
The maximum value of the y profile field. If supplied without units,
assumed to be in the output units for y_field.
weight_field : string field name
The field to use for weighting. Default is None.
deposition : string, optional
The interpolation kernal to be used for
deposition. Valid choices:
"ngp" : nearest grid point interpolation
"cic" : cloud-in-cell interpolation
"""
accumulation = False
fractional = False
def __init__(self, data_source,
x_field, x_n, x_min, x_max,
y_field, y_n, y_min, y_max,
weight_field=None, deposition="ngp"):
x_field = data_source._determine_fields(x_field)[0]
y_field = data_source._determine_fields(y_field)[0]
# set the log parameters to False (since that doesn't make much sense
# for deposited data) and also turn off the weight field.
super(ParticleProfile, self).__init__(data_source,
x_field,
x_n, x_min, x_max, False,
y_field,
y_n, y_min, y_max, False,
weight_field=weight_field)
self.LeftEdge = [self.x_bins[0], self.y_bins[0]]
self.dx = (self.x_bins[-1] - self.x_bins[0]) / x_n
self.dy = (self.y_bins[-1] - self.y_bins[0]) / y_n
self.CellSize = [self.dx, self.dy]
self.CellVolume = np.product(self.CellSize)
self.GridDimensions = np.array([x_n, y_n], dtype=np.int32)
self.known_styles = ["ngp", "cic"]
if deposition not in self.known_styles:
raise NotImplementedError(deposition)
self.deposition = deposition
# Either stick the particle field in the nearest bin,
# or spread it out using the 2D CIC deposition function
def _bin_chunk(self, chunk, fields, storage):
rv = self._get_data(chunk, fields)
if rv is None: return
fdata, wdata, (bf_x, bf_y) = rv
# make sure everything has the same units before deposition.
# the units will be scaled to the correct values later.
LE = np.array([self.LeftEdge[0].in_units(bf_x.units),
self.LeftEdge[1].in_units(bf_y.units)])
cell_size = np.array([self.CellSize[0].in_units(bf_x.units),
self.CellSize[1].in_units(bf_y.units)])
for fi, field in enumerate(fields):
Np = fdata[:, fi].size
if self.deposition == "ngp":
func = NGPDeposit_2
elif self.deposition == 'cic':
func = CICDeposit_2
if self.weight_field is None:
deposit_vals = fdata[:, fi]
else:
deposit_vals = wdata*fdata[:, fi]
func(bf_x, bf_y, deposit_vals, Np,
storage.values[:, :, fi],
LE,
self.GridDimensions,
cell_size)
locs = storage.values[:, :, fi] != 0.0
storage.used[locs] = True
if self.weight_field is not None:
func(bf_x, bf_y, wdata, Np,
storage.weight_values,
LE,
self.GridDimensions,
cell_size)
else:
storage.weight_values[locs] = 1.0
storage.mvalues[locs, fi] = storage.values[locs, fi] \
/ storage.weight_values[locs]
# We've binned it!
class Profile3D(ProfileND):
"""An object that represents a 2D profile.
Parameters
----------
data_source : AMD3DData object
The data object to be profiled
x_field : string field name
The field to profile as a function of along the x axis.
x_n : integer
The number of bins along the x direction.
x_min : float
The minimum value of the x profile field. If supplied without units,
assumed to be in the output units for x_field.
x_max : float
The maximum value of the x profile field. If supplied without units,
assumed to be in the output units for x_field.
x_log : boolean
Controls whether or not the bins for the x field are evenly
spaced in linear (False) or log (True) space.
y_field : string field name
The field to profile as a function of along the y axis
y_n : integer
The number of bins along the y direction.
y_min : float
The minimum value of the y profile field. If supplied without units,
assumed to be in the output units for y_field.
y_max : float
The maximum value of the y profile field. If supplied without units,
assumed to be in the output units for y_field.
y_log : boolean
Controls whether or not the bins for the y field are evenly
spaced in linear (False) or log (True) space.
z_field : string field name
The field to profile as a function of along the z axis
z_n : integer
The number of bins along the z direction.
z_min : float
The minimum value of the z profile field. If supplied without units,
assumed to be in the output units for z_field.
z_max : float
The maximum value of thee z profile field. If supplied without units,
assumed to be in the output units for z_field.
z_log : boolean
Controls whether or not the bins for the z field are evenly
spaced in linear (False) or log (True) space.
weight_field : string field name
The field to weight the profiled fields by.
"""
def __init__(self, data_source,
x_field, x_n, x_min, x_max, x_log,
y_field, y_n, y_min, y_max, y_log,
z_field, z_n, z_min, z_max, z_log,
weight_field = None):
super(Profile3D, self).__init__(data_source, weight_field)
# X
self.x_field = data_source._determine_fields(x_field)[0]
self.x_log = x_log
self.field_info[self.x_field] = \
self.data_source.ds.field_info[self.x_field]
x_min, x_max = _sanitize_min_max_units(
x_min, x_max, self.field_info[self.x_field], self.ds.unit_registry)
self.x_bins = array_like_field(data_source,
self._get_bins(x_min, x_max, x_n, x_log),
self.x_field)
# Y
self.y_field = data_source._determine_fields(y_field)[0]
self.y_log = y_log
self.field_info[self.y_field] = \
self.data_source.ds.field_info[self.y_field]
y_min, y_max = _sanitize_min_max_units(
y_min, y_max, self.field_info[self.y_field], self.ds.unit_registry)
self.y_bins = array_like_field(data_source,
self._get_bins(y_min, y_max, y_n, y_log),
self.y_field)
# Z
self.z_field = data_source._determine_fields(z_field)[0]
self.z_log = z_log
self.field_info[self.z_field] = \
self.data_source.ds.field_info[self.z_field]
z_min, z_max = _sanitize_min_max_units(
z_min, z_max, self.field_info[self.z_field], self.ds.unit_registry)
self.z_bins = array_like_field(data_source,
self._get_bins(z_min, z_max, z_n, z_log),
self.z_field)
self.size = (self.x_bins.size - 1,
self.y_bins.size - 1,
self.z_bins.size - 1)
self.bin_fields = (self.x_field, self.y_field, self.z_field)
self.x = 0.5*(self.x_bins[1:]+self.x_bins[:-1])
self.y = 0.5*(self.y_bins[1:]+self.y_bins[:-1])
self.z = 0.5*(self.z_bins[1:]+self.z_bins[:-1])
def _bin_chunk(self, chunk, fields, storage):
rv = self._get_data(chunk, fields)
if rv is None: return
fdata, wdata, (bf_x, bf_y, bf_z) = rv
bf_x.convert_to_units(self.field_info[self.x_field].output_units)
bin_ind_x = np.digitize(bf_x, self.x_bins) - 1
bf_y.convert_to_units(self.field_info[self.y_field].output_units)
bin_ind_y = np.digitize(bf_y, self.y_bins) - 1
bf_z.convert_to_units(self.field_info[self.z_field].output_units)
bin_ind_z = np.digitize(bf_z, self.z_bins) - 1
new_bin_profile3d(bin_ind_x, bin_ind_y, bin_ind_z, wdata, fdata,
storage.weight_values, storage.values,
storage.mvalues, storage.qvalues,
storage.used)
# We've binned it!
@property
def bounds(self):
return ((self.x_bins[0], self.x_bins[-1]),
(self.y_bins[0], self.y_bins[-1]),
(self.z_bins[0], self.z_bins[-1]))
def set_x_unit(self, new_unit):
"""Sets a new unit for the x field
parameters
----------
new_unit : string or Unit object
The name of the new unit.
"""
self.x_bins.convert_to_units(new_unit)
self.x = 0.5*(self.x_bins[1:]+self.x_bins[:-1])
def set_y_unit(self, new_unit):
"""Sets a new unit for the y field
parameters
----------
new_unit : string or Unit object
The name of the new unit.
"""
self.y_bins.convert_to_units(new_unit)
self.y = 0.5*(self.y_bins[1:]+self.y_bins[:-1])
def set_z_unit(self, new_unit):
"""Sets a new unit for the z field
parameters
----------
new_unit : string or Unit object
The name of the new unit.
"""
self.z_bins.convert_to_units(new_unit)
self.z = 0.5*(self.z_bins[1:]+self.z_bins[:-1])
class Profile3DFromDataset(ProfileNDFromDataset, Profile3D):
"""
A 2D profile object loaded from a ytdata dataset.
"""
def __init(self, ds):
ProfileNDFromDataset.__init__(self, ds)
def sanitize_field_tuple_keys(input_dict, data_source):
if input_dict is not None:
dummy = {}
for item in input_dict:
dummy[data_source._determine_fields(item)[0]] = input_dict[item]
return dummy
else:
return input_dict
def create_profile(data_source, bin_fields, fields, n_bins=64,
extrema=None, logs=None, units=None,
weight_field="cell_mass",
accumulation=False, fractional=False,
deposition='ngp'):
r"""
Create a 1, 2, or 3D profile object.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument.
Parameters
----------
data_source : YTSelectionContainer Object
The data object to be profiled.
bin_fields : list of strings
List of the binning fields for profiling.
fields : list of strings
The fields to be profiled.
n_bins : int or list of ints
The number of bins in each dimension. If None, 64 bins for
each bin are used for each bin field.
Default: 64.
extrema : dict of min, max tuples
Minimum and maximum values of the bin_fields for the profiles.
The keys correspond to the field names. Defaults to the extrema
of the bin_fields of the dataset. If a units dict is provided, extrema
are understood to be in the units specified in the dictionary.
logs : dict of boolean values
Whether or not to log the bin_fields for the profiles.
The keys correspond to the field names. Defaults to the take_log
attribute of the field.
units : dict of strings
The units of the fields in the profiles, including the bin_fields.
weight_field : str or tuple field identifier
The weight field for computing weighted average for the profile
values. If None, the profile values are sums of the data in
each bin.
accumulation : bool or list of bools
If True, the profile values for a bin n are the cumulative sum of
all the values from bin 0 to n. If -True, the sum is reversed so
that the value for bin n is the cumulative sum from bin N (total bins)
to n. If the profile is 2D or 3D, a list of values can be given to
control the summation in each dimension independently.
Default: False.
fractional : If True the profile values are divided by the sum of all
the profile data such that the profile represents a probability
distribution function.
deposition : Controls the type of deposition used for ParticlePhasePlots.
Valid choices are 'ngp' and 'cic'. Default is 'ngp'. This parameter is
ignored the if the input fields are not of particle type.
Examples
--------
Create a 1d profile. Access bin field from profile.x and field
data from profile[<field_name>].
>>> ds = load("DD0046/DD0046")
>>> ad = ds.all_data()
>>> profile = create_profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
"""
bin_fields = data_source._determine_fields(bin_fields)
fields = ensure_list(fields)
is_pfield = [data_source.ds._get_field_info(f).particle_type
for f in bin_fields + fields]
wf = None
if weight_field is not None:
wf = data_source.ds._get_field_info(weight_field)
is_pfield.append(wf.particle_type)
wf = wf.name
if any(is_pfield) and not all(is_pfield):
raise YTIllDefinedProfile(
bin_fields, data_source._determine_fields(fields), wf, is_pfield)
elif len(bin_fields) == 1:
cls = Profile1D
elif len(bin_fields) == 2 and all(is_pfield):
# log bin_fields set to False for Particle Profiles.
# doesn't make much sense for CIC deposition.
# accumulation and fractional set to False as well.
logs = {bin_fields[0]: False, bin_fields[1]: False}
accumulation = False
fractional = False
cls = ParticleProfile
elif len(bin_fields) == 2:
cls = Profile2D